The development of a probabilistic approach to forecast coastal change

USGS Publications Warehouse

Lentz, Erika E.; Hapke, Cheryl J.; Rosati, Julie D.; Wang, Ping; Roberts, Tiffany M.

2011-01-01

This study demonstrates the applicability of a Bayesian probabilistic model as an effective tool in predicting post-storm beach changes along sandy coastlines. Volume change and net shoreline movement are modeled for two study sites at Fire Island, New York in response to two extratropical storms in 2007 and 2009. Both study areas include modified areas adjacent to unmodified areas in morphologically different segments of coast. Predicted outcomes are evaluated against observed changes to test model accuracy and uncertainty along 163 cross-shore transects. Results show strong agreement in the cross validation of predictions vs. observations, with 70-82% accuracies reported. Although no consistent spatial pattern in inaccurate predictions could be determined, the highest prediction uncertainties appeared in locations that had been recently replenished. Further testing and model refinement are needed; however, these initial results show that Bayesian networks have the potential to serve as important decision-support tools in forecasting coastal change.

Cetacean range and climate in the eastern North Atlantic: future predictions and implications for conservation.

PubMed

Lambert, Emily; Pierce, Graham J; Hall, Karen; Brereton, Tom; Dunn, Timothy E; Wall, Dave; Jepson, Paul D; Deaville, Rob; MacLeod, Colin D

2014-06-01

There is increasing evidence that the distributions of a large number of species are shifting with global climate change as they track changing surface temperatures that define their thermal niche. Modelling efforts to predict species distributions under future climates have increased with concern about the overall impact of these distribution shifts on species ecology, and especially where barriers to dispersal exist. Here we apply a bio-climatic envelope modelling technique to investigate the impacts of climate change on the geographic range of ten cetacean species in the eastern North Atlantic and to assess how such modelling can be used to inform conservation and management. The modelling process integrates elements of a species' habitat and thermal niche, and employs "hindcasting" of historical distribution changes in order to verify the accuracy of the modelled relationship between temperature and species range. If this ability is not verified, there is a risk that inappropriate or inaccurate models will be used to make future predictions of species distributions. Of the ten species investigated, we found that while the models for nine could successfully explain current spatial distribution, only four had a good ability to predict distribution changes over time in response to changes in water temperature. Applied to future climate scenarios, the four species-specific models with good predictive abilities indicated range expansion in one species and range contraction in three others, including the potential loss of up to 80% of suitable white-beaked dolphin habitat. Model predictions allow identification of affected areas and the likely time-scales over which impacts will occur. Thus, this work provides important information on both our ability to predict how individual species will respond to future climate change and the applicability of predictive distribution models as a tool to help construct viable conservation and management strategies. © 2014 John Wiley & Sons Ltd.

Perception of Risk and Terrorism-Related Behavior Change: Dual Influences of Probabilistic Reasoning and Reality Testing.

PubMed

Denovan, Andrew; Dagnall, Neil; Drinkwater, Kenneth; Parker, Andrew; Clough, Peter

2017-01-01

The present study assessed the degree to which probabilistic reasoning performance and thinking style influenced perception of risk and self-reported levels of terrorism-related behavior change. A sample of 263 respondents, recruited via convenience sampling, completed a series of measures comprising probabilistic reasoning tasks (perception of randomness, base rate, probability, and conjunction fallacy), the Reality Testing subscale of the Inventory of Personality Organization (IPO-RT), the Domain-Specific Risk-Taking Scale, and a terrorism-related behavior change scale. Structural equation modeling examined three progressive models. Firstly, the Independence Model assumed that probabilistic reasoning, perception of risk and reality testing independently predicted terrorism-related behavior change. Secondly, the Mediation Model supposed that probabilistic reasoning and reality testing correlated, and indirectly predicted terrorism-related behavior change through perception of risk. Lastly, the Dual-Influence Model proposed that probabilistic reasoning indirectly predicted terrorism-related behavior change via perception of risk, independent of reality testing. Results indicated that performance on probabilistic reasoning tasks most strongly predicted perception of risk, and preference for an intuitive thinking style (measured by the IPO-RT) best explained terrorism-related behavior change. The combination of perception of risk with probabilistic reasoning ability in the Dual-Influence Model enhanced the predictive power of the analytical-rational route, with conjunction fallacy having a significant indirect effect on terrorism-related behavior change via perception of risk. The Dual-Influence Model possessed superior fit and reported similar predictive relations between intuitive-experiential and analytical-rational routes and terrorism-related behavior change. The discussion critically examines these findings in relation to dual-processing frameworks. This includes considering the limitations of current operationalisations and recommendations for future research that align outcomes and subsequent work more closely to specific dual-process models.

Perception of Risk and Terrorism-Related Behavior Change: Dual Influences of Probabilistic Reasoning and Reality Testing

PubMed Central

Denovan, Andrew; Dagnall, Neil; Drinkwater, Kenneth; Parker, Andrew; Clough, Peter

2017-01-01

The present study assessed the degree to which probabilistic reasoning performance and thinking style influenced perception of risk and self-reported levels of terrorism-related behavior change. A sample of 263 respondents, recruited via convenience sampling, completed a series of measures comprising probabilistic reasoning tasks (perception of randomness, base rate, probability, and conjunction fallacy), the Reality Testing subscale of the Inventory of Personality Organization (IPO-RT), the Domain-Specific Risk-Taking Scale, and a terrorism-related behavior change scale. Structural equation modeling examined three progressive models. Firstly, the Independence Model assumed that probabilistic reasoning, perception of risk and reality testing independently predicted terrorism-related behavior change. Secondly, the Mediation Model supposed that probabilistic reasoning and reality testing correlated, and indirectly predicted terrorism-related behavior change through perception of risk. Lastly, the Dual-Influence Model proposed that probabilistic reasoning indirectly predicted terrorism-related behavior change via perception of risk, independent of reality testing. Results indicated that performance on probabilistic reasoning tasks most strongly predicted perception of risk, and preference for an intuitive thinking style (measured by the IPO-RT) best explained terrorism-related behavior change. The combination of perception of risk with probabilistic reasoning ability in the Dual-Influence Model enhanced the predictive power of the analytical-rational route, with conjunction fallacy having a significant indirect effect on terrorism-related behavior change via perception of risk. The Dual-Influence Model possessed superior fit and reported similar predictive relations between intuitive-experiential and analytical-rational routes and terrorism-related behavior change. The discussion critically examines these findings in relation to dual-processing frameworks. This includes considering the limitations of current operationalisations and recommendations for future research that align outcomes and subsequent work more closely to specific dual-process models. PMID:29062288

Modeled changes of cerebellar activity in mutant mice are predictive of their learning impairments

NASA Astrophysics Data System (ADS)

Badura, Aleksandra; Clopath, Claudia; Schonewille, Martijn; de Zeeuw, Chris I.

2016-11-01

Translating neuronal activity to measurable behavioral changes has been a long-standing goal of systems neuroscience. Recently, we have developed a model of phase-reversal learning of the vestibulo-ocular reflex, a well-established, cerebellar-dependent task. The model, comprising both the cerebellar cortex and vestibular nuclei, reproduces behavioral data and accounts for the changes in neural activity during learning in wild type mice. Here, we used our model to predict Purkinje cell spiking as well as behavior before and after learning of five different lines of mutant mice with distinct cell-specific alterations of the cerebellar cortical circuitry. We tested these predictions by obtaining electrophysiological data depicting changes in neuronal spiking. We show that our data is largely consistent with the model predictions for simple spike modulation of Purkinje cells and concomitant behavioral learning in four of the mutants. In addition, our model accurately predicts a shift in simple spike activity in a mutant mouse with a brainstem specific mutation. This combination of electrophysiological and computational techniques opens a possibility of predicting behavioral impairments from neural activity.

Modeled changes of cerebellar activity in mutant mice are predictive of their learning impairments

PubMed Central

Badura, Aleksandra; Clopath, Claudia; Schonewille, Martijn; De Zeeuw, Chris I.

2016-01-01

Translating neuronal activity to measurable behavioral changes has been a long-standing goal of systems neuroscience. Recently, we have developed a model of phase-reversal learning of the vestibulo-ocular reflex, a well-established, cerebellar-dependent task. The model, comprising both the cerebellar cortex and vestibular nuclei, reproduces behavioral data and accounts for the changes in neural activity during learning in wild type mice. Here, we used our model to predict Purkinje cell spiking as well as behavior before and after learning of five different lines of mutant mice with distinct cell-specific alterations of the cerebellar cortical circuitry. We tested these predictions by obtaining electrophysiological data depicting changes in neuronal spiking. We show that our data is largely consistent with the model predictions for simple spike modulation of Purkinje cells and concomitant behavioral learning in four of the mutants. In addition, our model accurately predicts a shift in simple spike activity in a mutant mouse with a brainstem specific mutation. This combination of electrophysiological and computational techniques opens a possibility of predicting behavioral impairments from neural activity. PMID:27805050

Predicting body temperature and activity of adult Polyommatus icarus using neural network models under current and projected climate scenarios.

PubMed

Howe, P D; Bryant, S R; Shreeve, T G

2007-10-01

We use field observations in two geographic regions within the British Isles and regression and neural network models to examine the relationship between microhabitat use, thoracic temperatures and activity in a widespread lycaenid butterfly, Polyommatus icarus. We also make predictions for future activity under climate change scenarios. Individuals from a univoltine northern population initiated flight with significantly lower thoracic temperatures than individuals from a bivoltine southern population. Activity is dependent on body temperature and neural network models of body temperature are better at predicting body temperature than generalized linear models. Neural network models of activity with a sole input of predicted body temperature (using weather and microclimate variables) are good predictors of observed activity and were better predictors than generalized linear models. By modelling activity under climate change scenarios for 2080 we predict differences in activity in relation to both regional differences of climate change and differing body temperature requirements for activity in different populations. Under average conditions for low-emission scenarios there will be little change in the activity of individuals from central-southern Britain and a reduction in northwest Scotland from 2003 activity levels. Under high-emission scenarios, flight-dependent activity in northwest Scotland will increase the greatest, despite smaller predicted increases in temperature and decreases in cloud cover. We suggest that neural network models are an effective way of predicting future activity in changing climates for microhabitat-specialist butterflies and that regional differences in the thermoregulatory response of populations will have profound effects on how they respond to climate change.

An Application to the Prediction of LOD Change Based on General Regression Neural Network

NASA Astrophysics Data System (ADS)

Zhang, X. H.; Wang, Q. J.; Zhu, J. J.; Zhang, H.

2011-07-01

Traditional prediction of the LOD (length of day) change was based on linear models, such as the least square model and the autoregressive technique, etc. Due to the complex non-linear features of the LOD variation, the performances of the linear model predictors are not fully satisfactory. This paper applies a non-linear neural network - general regression neural network (GRNN) model to forecast the LOD change, and the results are analyzed and compared with those obtained with the back propagation neural network and other models. The comparison shows that the performance of the GRNN model in the prediction of the LOD change is efficient and feasible.

Product component genealogy modeling and field-failure prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

King, Caleb; Hong, Yili; Meeker, William Q.

Many industrial products consist of multiple components that are necessary for system operation. There is an abundance of literature on modeling the lifetime of such components through competing risks models. During the life-cycle of a product, it is common for there to be incremental design changes to improve reliability, to reduce costs, or due to changes in availability of certain part numbers. These changes can affect product reliability but are often ignored in system lifetime modeling. By incorporating this information about changes in part numbers over time (information that is readily available in most production databases), better accuracy can bemore » achieved in predicting time to failure, thus yielding more accurate field-failure predictions. This paper presents methods for estimating parameters and predictions for this generational model and a comparison with existing methods through the use of simulation. Our results indicate that the generational model has important practical advantages and outperforms the existing methods in predicting field failures.« less

Product component genealogy modeling and field-failure prediction

DOE PAGES

King, Caleb; Hong, Yili; Meeker, William Q.

2016-04-13

Many industrial products consist of multiple components that are necessary for system operation. There is an abundance of literature on modeling the lifetime of such components through competing risks models. During the life-cycle of a product, it is common for there to be incremental design changes to improve reliability, to reduce costs, or due to changes in availability of certain part numbers. These changes can affect product reliability but are often ignored in system lifetime modeling. By incorporating this information about changes in part numbers over time (information that is readily available in most production databases), better accuracy can bemore » achieved in predicting time to failure, thus yielding more accurate field-failure predictions. This paper presents methods for estimating parameters and predictions for this generational model and a comparison with existing methods through the use of simulation. Our results indicate that the generational model has important practical advantages and outperforms the existing methods in predicting field failures.« less

Predictions of future ephemeral springtime waterbird stopover habitat availability under global change

USGS Publications Warehouse

Uden, Daniel R.; Allen, Craig R.; Bishop, Andrew A.; Grosse, Roger; Jorgensen, Christopher F.; LaGrange, Theodore G.; Stutheit, Randy G.; Vrtiska, Mark P.

2015-01-01

In the present period of rapid, worldwide change in climate and landuse (i.e., global change), successful biodiversity conservation warrants proactive management responses, especially for long-distance migratory species. However, the development and implementation of management strategies can be impeded by high levels of uncertainty and low levels of control over potentially impactful future events and their effects. Scenario planning and modeling are useful tools for expanding perspectives and informing decisions under these conditions. We coupled scenario planning and statistical modeling to explain and predict playa wetland inundation (i.e., presence/absence of water) and ponded area (i.e., extent of water) in the Rainwater Basin, an anthropogenically altered landscape that provides critical stopover habitat for migratory waterbirds. Inundation and ponded area models for total wetlands, those embedded in rowcrop fields, and those not embedded in rowcrop fields were trained and tested with wetland ponding data from 2004 and 2006–2009, and then used to make additional predictions under two alternative climate change scenarios for the year 2050, yielding a total of six predictive models and 18 prediction sets. Model performance ranged from moderate to good, with inundation models outperforming ponded area models, and models for non-rowcrop-embedded wetlands outperforming models for total wetlands and rowcrop-embedded wetlands. Model predictions indicate that if the temperature and precipitation changes assumed under our climate change scenarios occur, wetland stopover habitat availability in the Rainwater Basin could decrease in the future. The results of this and similar studies could be aggregated to increase knowledge about the potential spatial and temporal distributions of future stopover habitat along migration corridors, and to develop and prioritize multi-scale management actions aimed at mitigating the detrimental effects of global change on migratory waterbird populations.

Predictive accuracy of a ground-water model--Lessons from a postaudit

USGS Publications Warehouse

Konikow, Leonard F.

1986-01-01

Hydrogeologic studies commonly include the development, calibration, and application of a deterministic simulation model. To help assess the value of using such models to make predictions, a postaudit was conducted on a previously studied area in the Salt River and lower Santa Cruz River basins in central Arizona. A deterministic, distributed-parameter model of the ground-water system in these alluvial basins was calibrated by Anderson (1968) using about 40 years of data (1923–64). The calibrated model was then used to predict future water-level changes during the next 10 years (1965–74). Examination of actual water-level changes in 77 wells from 1965–74 indicates a poor correlation between observed and predicted water-level changes. The differences have a mean of 73 ft that is, predicted declines consistently exceeded those observed and a standard deviation of 47 ft. The bias in the predicted water-level change can be accounted for by the large error in the assumed total pumpage during the prediction period. However, the spatial distribution of errors in predicted water-level change does not correlate with the spatial distribution of errors in pumpage. Consequently, the lack of precision probably is not related only to errors in assumed pumpage, but may indicate the presence of other sources of error in the model, such as the two-dimensional representation of a three-dimensional problem or the lack of consideration of land-subsidence processes. This type of postaudit is a valuable method of verifying a model, and an evaluation of predictive errors can provide an increased understanding of the system and aid in assessing the value of undertaking development of a revised model.

Coupling centennial-scale shoreline change to sea-level rise and coastal morphology in the Gulf of Mexico using a Bayesian network

USGS Publications Warehouse

Plant, Nathaniel G.

2016-01-01

Predictions of coastal evolution driven by episodic and persistent processes associated with storms and relative sea-level rise (SLR) are required to test our understanding, evaluate our predictive capability, and to provide guidance for coastal management decisions. Previous work demonstrated that the spatial variability of long-term shoreline change can be predicted using observed SLR rates, tide range, wave height, coastal slope, and a characterization of the geomorphic setting. The shoreline is not suf- ficient to indicate which processes are important in causing shoreline change, such as overwash that depends on coastal dune elevations. Predicting dune height is intrinsically important to assess future storm vulnerability. Here, we enhance shoreline-change predictions by including dune height as a vari- able in a statistical modeling approach. Dune height can also be used as an input variable, but it does not improve the shoreline-change prediction skill. Dune-height input does help to reduce prediction uncer- tainty. That is, by including dune height, the prediction is more precise but not more accurate. Comparing hindcast evaluations, better predictive skill was found when predicting dune height (0.8) compared with shoreline change (0.6). The skill depends on the level of detail of the model and we identify an optimized model that has high skill and minimal overfitting. The predictive model can be implemented with a range of forecast scenarios, and we illustrate the impacts of a higher future sea-level. This scenario shows that the shoreline change becomes increasingly erosional and more uncertain. Predicted dune heights are lower and the dune height uncertainty decreases.

Dispersal and extrapolation on the accuracy of temporal predictions from distribution models for the Darwin's frog.

PubMed

Uribe-Rivera, David E; Soto-Azat, Claudio; Valenzuela-Sánchez, Andrés; Bizama, Gustavo; Simonetti, Javier A; Pliscoff, Patricio

2017-07-01

Climate change is a major threat to biodiversity; the development of models that reliably predict its effects on species distributions is a priority for conservation biogeography. Two of the main issues for accurate temporal predictions from Species Distribution Models (SDM) are model extrapolation and unrealistic dispersal scenarios. We assessed the consequences of these issues on the accuracy of climate-driven SDM predictions for the dispersal-limited Darwin's frog Rhinoderma darwinii in South America. We calibrated models using historical data (1950-1975) and projected them across 40 yr to predict distribution under current climatic conditions, assessing predictive accuracy through the area under the ROC curve (AUC) and True Skill Statistics (TSS), contrasting binary model predictions against temporal-independent validation data set (i.e., current presences/absences). To assess the effects of incorporating dispersal processes we compared the predictive accuracy of dispersal constrained models with no dispersal limited SDMs; and to assess the effects of model extrapolation on the predictive accuracy of SDMs, we compared this between extrapolated and no extrapolated areas. The incorporation of dispersal processes enhanced predictive accuracy, mainly due to a decrease in the false presence rate of model predictions, which is consistent with discrimination of suitable but inaccessible habitat. This also had consequences on range size changes over time, which is the most used proxy for extinction risk from climate change. The area of current climatic conditions that was absent in the baseline conditions (i.e., extrapolated areas) represents 39% of the study area, leading to a significant decrease in predictive accuracy of model predictions for those areas. Our results highlight (1) incorporating dispersal processes can improve predictive accuracy of temporal transference of SDMs and reduce uncertainties of extinction risk assessments from global change; (2) as geographical areas subjected to novel climates are expected to arise, they must be reported as they show less accurate predictions under future climate scenarios. Consequently, environmental extrapolation and dispersal processes should be explicitly incorporated to report and reduce uncertainties in temporal predictions of SDMs, respectively. Doing so, we expect to improve the reliability of the information we provide for conservation decision makers under future climate change scenarios. © 2017 by the Ecological Society of America.

Landscape genetics as a tool for conservation planning: predicting the effects of landscape change on gene flow.

PubMed

van Strien, Maarten J; Keller, Daniela; Holderegger, Rolf; Ghazoul, Jaboury; Kienast, Felix; Bolliger, Janine

2014-03-01

For conservation managers, it is important to know whether landscape changes lead to increasing or decreasing gene flow. Although the discipline of landscape genetics assesses the influence of landscape elements on gene flow, no studies have yet used landscape-genetic models to predict gene flow resulting from landscape change. A species that has already been severely affected by landscape change is the large marsh grasshopper (Stethophyma grossum), which inhabits moist areas in fragmented agricultural landscapes in Switzerland. From transects drawn between all population pairs within maximum dispersal distance (< 3 km), we calculated several measures of landscape composition as well as some measures of habitat configuration. Additionally, a complete sampling of all populations in our study area allowed incorporating measures of population topology. These measures together with the landscape metrics formed the predictor variables in linear models with gene flow as response variable (F(ST) and mean pairwise assignment probability). With a modified leave-one-out cross-validation approach, we selected the model with the highest predictive accuracy. With this model, we predicted gene flow under several landscape-change scenarios, which simulated construction, rezoning or restoration projects, and the establishment of a new population. For some landscape-change scenarios, significant increase or decrease in gene flow was predicted, while for others little change was forecast. Furthermore, we found that the measures of population topology strongly increase model fit in landscape genetic analysis. This study demonstrates the use of predictive landscape-genetic models in conservation and landscape planning.

Livestock Helminths in a Changing Climate: Approaches and Restrictions to Meaningful Predictions

PubMed Central

Fox, Naomi J.; Marion, Glenn; Davidson, Ross S.; White, Piran C. L.; Hutchings, Michael R.

2012-01-01

Simple Summary Parasitic helminths represent one of the most pervasive challenges to livestock, and their intensity and distribution will be influenced by climate change. There is a need for long-term predictions to identify potential risks and highlight opportunities for control. We explore the approaches to modelling future helminth risk to livestock under climate change. One of the limitations to model creation is the lack of purpose driven data collection. We also conclude that models need to include a broad view of the livestock system to generate meaningful predictions. Abstract Climate change is a driving force for livestock parasite risk. This is especially true for helminths including the nematodes Haemonchus contortus, Teladorsagia circumcincta, Nematodirus battus, and the trematode Fasciola hepatica, since survival and development of free-living stages is chiefly affected by temperature and moisture. The paucity of long term predictions of helminth risk under climate change has driven us to explore optimal modelling approaches and identify current bottlenecks to generating meaningful predictions. We classify approaches as correlative or mechanistic, exploring their strengths and limitations. Climate is one aspect of a complex system and, at the farm level, husbandry has a dominant influence on helminth transmission. Continuing environmental change will necessitate the adoption of mitigation and adaptation strategies in husbandry. Long term predictive models need to have the architecture to incorporate these changes. Ultimately, an optimal modelling approach is likely to combine mechanistic processes and physiological thresholds with correlative bioclimatic modelling, incorporating changes in livestock husbandry and disease control. Irrespective of approach, the principal limitation to parasite predictions is the availability of active surveillance data and empirical data on physiological responses to climate variables. By combining improved empirical data and refined models with a broad view of the livestock system, robust projections of helminth risk can be developed. PMID:26486780

Misleading prioritizations from modelling range shifts under climate change

USGS Publications Warehouse

Sofaer, Helen R.; Jarnevich, Catherine S.; Flather, Curtis H.

2018-01-01

AimConservation planning requires the prioritization of a subset of taxa and geographical locations to focus monitoring and management efforts. Integration of the threats and opportunities posed by climate change often relies on predictions from species distribution models, particularly for assessments of vulnerability or invasion risk for multiple taxa. We evaluated whether species distribution models could reliably rank changes in species range size under climate and land use change.LocationConterminous U.S.A.Time period1977–2014.Major taxa studiedPasserine birds.MethodsWe estimated ensembles of species distribution models based on historical North American Breeding Bird Survey occurrences for 190 songbirds, and generated predictions to recent years given c. 35 years of observed land use and climate change. We evaluated model predictions using standard metrics of discrimination performance and a more detailed assessment of the ability of models to rank species vulnerability to climate change based on predicted range loss, range gain, and overall change in range size.ResultsSpecies distribution models yielded unreliable and misleading assessments of relative vulnerability to climate and land use change. Models could not accurately predict range expansion or contraction, and therefore failed to anticipate patterns of range change among species. These failures occurred despite excellent overall discrimination ability and transferability to the validation time period, which reflected strong performance at the majority of locations that were either always or never occupied by each species.Main conclusionsModels failed for the questions and at the locations of greatest interest to conservation and management. This highlights potential pitfalls of multi-taxa impact assessments under global change; in our case, models provided misleading rankings of the most impacted species, and spatial information about range changes was not credible. As modelling methods and frameworks continue to be refined, performance assessments and validation efforts should focus on the measures of risk and vulnerability useful for decision-making.

Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures

PubMed Central

Ngendahimana, David K.; Fagerholm, Cara L.; Sun, Jiayang; Bruckman, Laura S.

2017-01-01

Accelerated weathering exposures were performed on poly(ethylene-terephthalate) (PET) films. Longitudinal multi-level predictive models as a function of PET grades and exposure types were developed for the change in yellowness index (YI) and haze (%). Exposures with similar change in YI were modeled using a linear fixed-effects modeling approach. Due to the complex nature of haze formation, measurement uncertainty, and the differences in the samples’ responses, the change in haze (%) depended on individual samples’ responses and a linear mixed-effects modeling approach was used. When compared to fixed-effects models, the addition of random effects in the haze formation models significantly increased the variance explained. For both modeling approaches, diagnostic plots confirmed independence and homogeneity with normally distributed residual errors. Predictive R2 values for true prediction error and predictive power of the models demonstrated that the models were not subject to over-fitting. These models enable prediction under pre-defined exposure conditions for a given exposure time (or photo-dosage in case of UV light exposure). PET degradation under cyclic exposures combining UV light and condensing humidity is caused by photolytic and hydrolytic mechanisms causing yellowing and haze formation. Quantitative knowledge of these degradation pathways enable cross-correlation of these lab-based exposures with real-world conditions for service life prediction. PMID:28498875

Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter-basin water transfer project.

PubMed

Zeng, Qinghui; Liu, Yi; Zhao, Hongtao; Sun, Mingdong; Li, Xuyong

2017-04-01

Inter-basin water transfer projects might cause complex hydro-chemical and biological variation in the receiving aquatic ecosystems. Whether machine learning models can be used to predict changes in phytoplankton community composition caused by water transfer projects have rarely been studied. In the present study, we used machine learning models to predict the total algal cell densities and changes in phytoplankton community composition in Miyun reservoir caused by the middle route of the South-to-North Water Transfer Project (SNWTP). The model performances of four machine learning models, including regression trees (RT), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) were evaluated and the best model was selected for further prediction. The results showed that the predictive accuracies (Pearson's correlation coefficient) of the models were RF (0.974), ANN (0.951), SVM (0.860), and RT (0.817) in the training step and RF (0.806), ANN (0.734), SVM (0.730), and RT (0.692) in the testing step. Therefore, the RF model was the best method for estimating total algal cell densities. Furthermore, the predicted accuracies of the RF model for dominant phytoplankton phyla (Cyanophyta, Chlorophyta, and Bacillariophyta) in Miyun reservoir ranged from 0.824 to 0.869 in the testing step. The predicted proportions with water transfer of the different phytoplankton phyla ranged from -8.88% to 9.93%, and the predicted dominant phyla with water transfer in each season remained unchanged compared to the phytoplankton succession without water transfer. The results of the present study provide a useful tool for predicting the changes in phytoplankton community caused by water transfer. The method is transferrable to other locations via establishment of models with relevant data to a particular area. Our findings help better understanding the possible changes in aquatic ecosystems influenced by inter-basin water transfer. Copyright © 2017 Elsevier Ltd. All rights reserved.

A linear regression model for predicting PNW estuarine temperatures in a changing climate

EPA Science Inventory

Pacific Northwest coastal regions, estuaries, and associated ecosystems are vulnerable to the potential effects of climate change, especially to changes in nearshore water temperature. While predictive climate models simulate future air temperatures, no such projections exist for...

Radicals and Reservoirs in the GMI Chemistry and Transport Model: Comparison to Measurements

NASA Technical Reports Server (NTRS)

Douglas, Anne R.; Stolarski, Richard S.; Strahan, Susan E.; Connell, Peter S.

2004-01-01

The most important use of atmospheric chemistry and transport models is to predict the future composition of the atmosphere. The amounts of gases like chlorofluorcarbons, methyl bromide, nitrous oxide and methane are changing and the stratospheric ozone layer will change because these gases are changing. Methyl bromide, nitrous oxide and methane all have natural sources, and also change because of human activity. Chlorofluorcarbons are man-made gases; these are known to decrease stratospheric ozone and future production is banned. They are long-lived gases, and many decades will pass before they are insignificant in the atmosphere. The models are used to predict changes in ozone and other gases; this is a straightforward application. The models must be also tested using observations for the present day atmosphere. This is a challenging task, because the model contains more than 50 species and more than 150 chemical reactions. Data from satellites, ground stations, aircraft and balloons are used to evaluate the model. Different models that are used in international assessments produce different results; in the most recent assessment some predict that ozone will return to 1980 levels by 2025 and others predict that this will not happen until 2050. Since all the parts of the models are conceptually the same, there must be differences in implementation that produce these differences, This work takes a single model, two different sets of winds and temperatures, and repeats the same prediction for the future. Here we compare the results for these two simulations with many observations. The purpose is to identify differences in the model results for the present atmosphere that will lead to different predictions. This sort of controlled comparison will reduce uncertainty in the predictions for stratospheric ozone.

Eco-hydrologic model cascades: Simulating land use and climate change impacts on hydrology, hydraulics and habitats for fish and macroinvertebrates.

PubMed

Guse, Björn; Kail, Jochem; Radinger, Johannes; Schröder, Maria; Kiesel, Jens; Hering, Daniel; Wolter, Christian; Fohrer, Nicola

2015-11-15

Climate and land use changes affect the hydro- and biosphere at different spatial scales. These changes alter hydrological processes at the catchment scale, which impact hydrodynamics and habitat conditions for biota at the river reach scale. In order to investigate the impact of large-scale changes on biota, a cascade of models at different scales is required. Using scenario simulations, the impact of climate and land use change can be compared along the model cascade. Such a cascade of consecutively coupled models was applied in this study. Discharge and water quality are predicted with a hydrological model at the catchment scale. The hydraulic flow conditions are predicted by hydrodynamic models. The habitat suitability under these hydraulic and water quality conditions is assessed based on habitat models for fish and macroinvertebrates. This modelling cascade was applied to predict and compare the impacts of climate- and land use changes at different scales to finally assess their effects on fish and macroinvertebrates. Model simulations revealed that magnitude and direction of change differed along the modelling cascade. Whilst the hydrological model predicted a relevant decrease of discharge due to climate change, the hydraulic conditions changed less. Generally, the habitat suitability for fish decreased but this was strongly species-specific and suitability even increased for some species. In contrast to climate change, the effect of land use change on discharge was negligible. However, land use change had a stronger impact on the modelled nitrate concentrations affecting the abundances of macroinvertebrates. The scenario simulations for the two organism groups illustrated that direction and intensity of changes in habitat suitability are highly species-dependent. Thus, a joined model analysis of different organism groups combined with the results of hydrological and hydrodynamic models is recommended to assess the impact of climate and land use changes on river ecosystems. Copyright © 2015 Elsevier B.V. All rights reserved.

The transparency, reliability and utility of tropical rainforest land-use and land-cover change models.

PubMed

Rosa, Isabel M D; Ahmed, Sadia E; Ewers, Robert M

2014-06-01

Land-use and land-cover (LULC) change is one of the largest drivers of biodiversity loss and carbon emissions globally. We use the tropical rainforests of the Amazon, the Congo basin and South-East Asia as a case study to investigate spatial predictive models of LULC change. Current predictions differ in their modelling approaches, are highly variable and often poorly validated. We carried out a quantitative review of 48 modelling methodologies, considering model spatio-temporal scales, inputs, calibration and validation methods. In addition, we requested model outputs from each of the models reviewed and carried out a quantitative assessment of model performance for tropical LULC predictions in the Brazilian Amazon. We highlight existing shortfalls in the discipline and uncover three key points that need addressing to improve the transparency, reliability and utility of tropical LULC change models: (1) a lack of openness with regard to describing and making available the model inputs and model code; (2) the difficulties of conducting appropriate model validations; and (3) the difficulty that users of tropical LULC models face in obtaining the model predictions to help inform their own analyses and policy decisions. We further draw comparisons between tropical LULC change models in the tropics and the modelling approaches and paradigms in other disciplines, and suggest that recent changes in the climate change and species distribution modelling communities may provide a pathway that tropical LULC change modellers may emulate to further improve the discipline. Climate change models have exerted considerable influence over public perceptions of climate change and now impact policy decisions at all political levels. We suggest that tropical LULC change models have an equally high potential to influence public opinion and impact the development of land-use policies based on plausible future scenarios, but, to do that reliably may require further improvements in the discipline. © 2014 John Wiley & Sons Ltd.

Prospective evaluation of a Bayesian model to predict organizational change.

PubMed

Molfenter, Todd; Gustafson, Dave; Kilo, Chuck; Bhattacharya, Abhik; Olsson, Jesper

2005-01-01

This research examines a subjective Bayesian model's ability to predict organizational change outcomes and sustainability of those outcomes for project teams participating in a multi-organizational improvement collaborative.

A multi-model framework for simulating wildlife population response to land-use and climate change

USGS Publications Warehouse

McRae, B.H.; Schumaker, N.H.; McKane, R.B.; Busing, R.T.; Solomon, A.M.; Burdick, C.A.

2008-01-01

Reliable assessments of how human activities will affect wildlife populations are essential for making scientifically defensible resource management decisions. A principle challenge of predicting effects of proposed management, development, or conservation actions is the need to incorporate multiple biotic and abiotic factors, including land-use and climate change, that interact to affect wildlife habitat and populations through time. Here we demonstrate how models of land-use, climate change, and other dynamic factors can be integrated into a coherent framework for predicting wildlife population trends. Our framework starts with land-use and climate change models developed for a region of interest. Vegetation changes through time under alternative future scenarios are predicted using an individual-based plant community model. These predictions are combined with spatially explicit animal habitat models to map changes in the distribution and quality of wildlife habitat expected under the various scenarios. Animal population responses to habitat changes and other factors are then projected using a flexible, individual-based animal population model. As an example application, we simulated animal population trends under three future land-use scenarios and four climate change scenarios in the Cascade Range of western Oregon. We chose two birds with contrasting habitat preferences for our simulations: winter wrens (Troglodytes troglodytes), which are most abundant in mature conifer forests, and song sparrows (Melospiza melodia), which prefer more open, shrubby habitats. We used climate and land-use predictions from previously published studies, as well as previously published predictions of vegetation responses using FORCLIM, an individual-based forest dynamics simulator. Vegetation predictions were integrated with other factors in PATCH, a spatially explicit, individual-based animal population simulator. Through incorporating effects of landscape history and limited dispersal, our framework predicted population changes that typically exceeded those expected based on changes in mean habitat suitability alone. Although land-use had greater impacts on habitat quality than did climate change in our simulations, we found that small changes in vital rates resulting from climate change or other stressors can have large consequences for population trajectories. The ability to integrate bottom-up demographic processes like these with top-down constraints imposed by climate and land-use in a dynamic modeling environment is a key advantage of our approach. The resulting framework should allow researchers to synthesize existing empirical evidence, and to explore complex interactions that are difficult or impossible to capture through piecemeal modeling approaches. ?? 2008 Elsevier B.V.

Climate change in grasslands, shrublands, and deserts of the interior American West: a review and needs assessment

Treesearch

Deborah M. Finch

2012-01-01

Recent research and species distribution modeling predict large changes in the distributions of species and vegetation types in the western interior of the United States in response to climate change. This volume reviews existing climate models that predict species and vegetation changes in the western United States, and it synthesizes knowledge about climate change...

A test of the vulnerability model: temperament and temperament change as predictors of future mental disorders - the TRAILS study.

PubMed

Laceulle, Odilia M; Ormel, Johan; Vollebergh, Wilma A M; van Aken, Marcel A G; Nederhof, Esther

2014-03-01

This study aimed to test the vulnerability model of the relationship between temperament and mental disorders using a large sample of adolescents from the TRacking Adolescents Individual Lives' Survey (TRAILS). The vulnerability model argues that particular temperaments can place individuals at risk for the development of mental health problems. Importantly, the model may imply that not only baseline temperament predicts mental health problems prospectively, but additionally, that changes in temperament predict corresponding changes in risk for mental health problems. Data were used from 1195 TRAILS participants. Adolescent temperament was assessed both at age 11 and at age 16. Onset of mental disorders between age 16 and 19 was assessed at age 19, by means of the World Health Organization Composite International Diagnostic Interview (WHO CIDI). Results showed that temperament at age 11 predicted future mental disorders, thereby providing support for the vulnerability model. Moreover, temperament change predicted future mental disorders above and beyond the effect of basal temperament. For example, an increase in frustration increased the risk of mental disorders proportionally. This study confirms, and extends, the vulnerability model. Consequences of both temperament and temperament change were general (e.g., changes in frustration predicted both internalizing and externalizing disorders) as well as dimension specific (e.g., changes in fear predicted internalizing but not externalizing disorders). These findings confirm previous studies, which showed that mental disorders have both unique and shared underlying temperamental risk factors. © 2013 The Authors. Journal of Child Psychology and Psychiatry © 2013 Association for Child and Adolescent Mental Health.

Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble

PubMed Central

Jiang, Mingkai; Felzer, Benjamin S.; Sahagian, Dork

2016-01-01

Characterizing precipitation seasonality and variability in the face of future uncertainty is important for a well-informed climate change adaptation strategy. Using the Colwell index of predictability and monthly normalized precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensembles, this study identifies spatial hotspots of changes in precipitation predictability in the United States under various climate scenarios. Over the historic period (1950–2005), the recurrent pattern of precipitation is highly predictable in the East and along the coastal Northwest, and is less so in the arid Southwest. Comparing the future (2040–2095) to the historic period, larger changes in precipitation predictability are observed under Representative Concentration Pathways (RCP) 8.5 than those under RCP 4.5. Finally, there are region-specific hotspots of future changes in precipitation predictability, and these hotspots often coincide with regions of little projected change in total precipitation, with exceptions along the wetter East and parts of the drier central West. Therefore, decision-makers are advised to not rely on future total precipitation as an indicator of water resources. Changes in precipitation predictability and the subsequent changes on seasonality and variability are equally, if not more, important factors to be included in future regional environmental assessment. PMID:27425819

Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble.

PubMed

Jiang, Mingkai; Felzer, Benjamin S; Sahagian, Dork

2016-07-18

Characterizing precipitation seasonality and variability in the face of future uncertainty is important for a well-informed climate change adaptation strategy. Using the Colwell index of predictability and monthly normalized precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensembles, this study identifies spatial hotspots of changes in precipitation predictability in the United States under various climate scenarios. Over the historic period (1950-2005), the recurrent pattern of precipitation is highly predictable in the East and along the coastal Northwest, and is less so in the arid Southwest. Comparing the future (2040-2095) to the historic period, larger changes in precipitation predictability are observed under Representative Concentration Pathways (RCP) 8.5 than those under RCP 4.5. Finally, there are region-specific hotspots of future changes in precipitation predictability, and these hotspots often coincide with regions of little projected change in total precipitation, with exceptions along the wetter East and parts of the drier central West. Therefore, decision-makers are advised to not rely on future total precipitation as an indicator of water resources. Changes in precipitation predictability and the subsequent changes on seasonality and variability are equally, if not more, important factors to be included in future regional environmental assessment.

Water and salt balance modelling to predict the effects of land-use changes in forested catchments. 1. Small catchment water balance model

NASA Astrophysics Data System (ADS)

Sivapalan, Murugesu; Ruprecht, John K.; Viney, Neil R.

1996-03-01

A long-term water balance model has been developed to predict the hydrological effects of land-use change (especially forest clearing) in small experimental catchments in the south-west of Western Australia. This small catchment model has been used as the building block for the development of a large catchment-scale model, and has also formed the basis for a coupled water and salt balance model, developed to predict the changes in stream salinity resulting from land-use and climate change. The application of the coupled salt and water balance model to predict stream salinities in two small experimental catchments, and the application of the large catchment-scale model to predict changes in water yield in a medium-sized catchment that is being mined for bauxite, are presented in Parts 2 and 3, respectively, of this series of papers.The small catchment model has been designed as a simple, robust, conceptually based model of the basic daily water balance fluxes in forested catchments. The responses of the catchment to rainfall and pan evaporation are conceptualized in terms of three interdependent subsurface stores A, B and F. Store A depicts a near-stream perched aquifer system; B represents a deeper, permanent groundwater system; and F is an intermediate, unsaturated infiltration store. The responses of these stores are characterized by a set of constitutive relations which involves a number of conceptual parameters. These parameters are estimated by calibration by comparing observed and predicted runoff. The model has performed very well in simulations carried out on Salmon and Wights, two small experimental catchments in the Collie River basin in south-west Western Australia. The results from the application of the model to these small catchments are presented in this paper.

Rainfall and its seasonality over the Amazon in the 21st century as assessed by the coupled models for the IPCC AR4

NASA Astrophysics Data System (ADS)

Li, Wenhong; Fu, Rong; Dickinson, Robert E.

2006-01-01

The global climate models for the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) predict very different changes of rainfall over the Amazon under the SRES A1B scenario for global climate change. Five of the eleven models predict an increase of annual rainfall, three models predict a decrease of rainfall, and the other three models predict no significant changes in the Amazon rainfall. We have further examined two models. The UKMO-HadCM3 model predicts an El Niño-like sea surface temperature (SST) change and warming in the northern tropical Atlantic which appear to enhance atmospheric subsidence and consequently reduce clouds over the Amazon. The resultant increase of surface solar absorption causes a stronger surface sensible heat flux and thus reduces relative humidity of the surface air. These changes decrease the rate and length of wet season rainfall and surface latent heat flux. This decreased wet season rainfall leads to drier soil during the subsequent dry season, which in turn can delay the transition from the dry to wet season. GISS-ER predicts a weaker SST warming in the western Pacific and the southern tropical Atlantic which increases moisture transport and hence rainfall in the Amazon. In the southern Amazon and Nordeste where the strongest rainfall increase occurs, the resultant higher soil moisture supports a higher surface latent heat flux during the dry and transition season and leads to an earlier wet season onset.

A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis

NASA Astrophysics Data System (ADS)

Wang, Jun; Wang, Yang; Zeng, Hui

2016-01-01

A key issue to address in synthesizing spatial data with variable-support in spatial analysis and modeling is the change-of-support problem. We present an approach for solving the change-of-support and variable-support data fusion problems. This approach is based on geostatistical inverse modeling that explicitly accounts for differences in spatial support. The inverse model is applied here to produce both the best predictions of a target support and prediction uncertainties, based on one or more measurements, while honoring measurements. Spatial data covering large geographic areas often exhibit spatial nonstationarity and can lead to computational challenge due to the large data size. We developed a local-window geostatistical inverse modeling approach to accommodate these issues of spatial nonstationarity and alleviate computational burden. We conducted experiments using synthetic and real-world raster data. Synthetic data were generated and aggregated to multiple supports and downscaled back to the original support to analyze the accuracy of spatial predictions and the correctness of prediction uncertainties. Similar experiments were conducted for real-world raster data. Real-world data with variable-support were statistically fused to produce single-support predictions and associated uncertainties. The modeling results demonstrate that geostatistical inverse modeling can produce accurate predictions and associated prediction uncertainties. It is shown that the local-window geostatistical inverse modeling approach suggested offers a practical way to solve the well-known change-of-support problem and variable-support data fusion problem in spatial analysis and modeling.

Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling.

PubMed

Feng, Xiaohui; Uriarte, María; González, Grizelle; Reed, Sasha; Thompson, Jill; Zimmerman, Jess K; Murphy, Lora

2018-01-01

Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here, we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species-specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured interannual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including aboveground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model-data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate. © 2017 John Wiley & Sons Ltd.

Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling

USGS Publications Warehouse

Feng, Xiaohui; Uriarte, María; González, Grizelle; Reed, Sasha C.; Thompson, Jill; Zimmerman, Jess K.; Murphy, Lora

2018-01-01

Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species-specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured inter-annual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including above-ground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model-data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate.

A Physiologically Based Pharmacokinetic Model for Pregnant Women to Predict the Pharmacokinetics of Drugs Metabolized Via Several Enzymatic Pathways.

PubMed

Dallmann, André; Ince, Ibrahim; Coboeken, Katrin; Eissing, Thomas; Hempel, Georg

2017-09-18

Physiologically based pharmacokinetic modeling is considered a valuable tool for predicting pharmacokinetic changes in pregnancy to subsequently guide in-vivo pharmacokinetic trials in pregnant women. The objective of this study was to extend and verify a previously developed physiologically based pharmacokinetic model for pregnant women for the prediction of pharmacokinetics of drugs metabolized via several cytochrome P450 enzymes. Quantitative information on gestation-specific changes in enzyme activity available in the literature was incorporated in a pregnancy physiologically based pharmacokinetic model and the pharmacokinetics of eight drugs metabolized via one or multiple cytochrome P450 enzymes was predicted. The tested drugs were caffeine, midazolam, nifedipine, metoprolol, ondansetron, granisetron, diazepam, and metronidazole. Pharmacokinetic predictions were evaluated by comparison with in-vivo pharmacokinetic data obtained from the literature. The pregnancy physiologically based pharmacokinetic model successfully predicted the pharmacokinetics of all tested drugs. The observed pregnancy-induced pharmacokinetic changes were qualitatively and quantitatively reasonably well predicted for all drugs. Ninety-seven percent of the mean plasma concentrations predicted in pregnant women fell within a twofold error range and 63% within a 1.25-fold error range. For all drugs, the predicted area under the concentration-time curve was within a 1.25-fold error range. The presented pregnancy physiologically based pharmacokinetic model can quantitatively predict the pharmacokinetics of drugs that are metabolized via one or multiple cytochrome P450 enzymes by integrating prior knowledge of the pregnancy-related effect on these enzymes. This pregnancy physiologically based pharmacokinetic model may thus be used to identify potential exposure changes in pregnant women a priori and to eventually support informed decision making when clinical trials are designed in this special population.

Livestock Helminths in a Changing Climate: Approaches and Restrictions to Meaningful Predictions.

PubMed

Fox, Naomi J; Marion, Glenn; Davidson, Ross S; White, Piran C L; Hutchings, Michael R

2012-03-06

Climate change is a driving force for livestock parasite risk. This is especially true for helminths including the nematodes Haemonchus contortus, Teladorsagia circumcincta, Nematodirus battus, and the trematode Fasciola hepatica, since survival and development of free-living stages is chiefly affected by temperature and moisture. The paucity of long term predictions of helminth risk under climate change has driven us to explore optimal modelling approaches and identify current bottlenecks to generating meaningful predictions. We classify approaches as correlative or mechanistic, exploring their strengths and limitations. Climate is one aspect of a complex system and, at the farm level, husbandry has a dominant influence on helminth transmission. Continuing environmental change will necessitate the adoption of mitigation and adaptation strategies in husbandry. Long term predictive models need to have the architecture to incorporate these changes. Ultimately, an optimal modelling approach is likely to combine mechanistic processes and physiological thresholds with correlative bioclimatic modelling, incorporating changes in livestock husbandry and disease control. Irrespective of approach, the principal limitation to parasite predictions is the availability of active surveillance data and empirical data on physiological responses to climate variables. By combining improved empirical data and refined models with a broad view of the livestock system, robust projections of helminth risk can be developed.

Stages of Change or Changes of Stage? Predicting Transitions in Transtheoretical Model Stages in Relation to Healthy Food Choice

ERIC Educational Resources Information Center

Armitage, Christopher J.; Sheeran, Paschal; Conner, Mark; Arden, Madelynne A.

2004-01-01

Relatively little research has examined factors that account for transitions between transtheoretical model (TTM) stages of change. The present study (N=787) used sociodemographic, TTM, and theory of planned behavior (TPB) variables, as well as theory-driven interventions to predict changes in stage. Longitudinal analyses revealed that…

Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM) III: Scenario analysis

USGS Publications Warehouse

Huisman, J.A.; Breuer, L.; Bormann, H.; Bronstert, A.; Croke, B.F.W.; Frede, H.-G.; Graff, T.; Hubrechts, L.; Jakeman, A.J.; Kite, G.; Lanini, J.; Leavesley, G.; Lettenmaier, D.P.; Lindstrom, G.; Seibert, J.; Sivapalan, M.; Viney, N.R.; Willems, P.

2009-01-01

An ensemble of 10 hydrological models was applied to the same set of land use change scenarios. There was general agreement about the direction of changes in the mean annual discharge and 90% discharge percentile predicted by the ensemble members, although a considerable range in the magnitude of predictions for the scenarios and catchments under consideration was obvious. Differences in the magnitude of the increase were attributed to the different mean annual actual evapotranspiration rates for each land use type. The ensemble of model runs was further analyzed with deterministic and probabilistic ensemble methods. The deterministic ensemble method based on a trimmed mean resulted in a single somewhat more reliable scenario prediction. The probabilistic reliability ensemble averaging (REA) method allowed a quantification of the model structure uncertainty in the scenario predictions. It was concluded that the use of a model ensemble has greatly increased our confidence in the reliability of the model predictions. ?? 2008 Elsevier Ltd.

Microbial models with data-driven parameters predict stronger soil carbon responses to climate change.

PubMed

Hararuk, Oleksandra; Smith, Matthew J; Luo, Yiqi

2015-06-01

Long-term carbon (C) cycle feedbacks to climate depend on the future dynamics of soil organic carbon (SOC). Current models show low predictive accuracy at simulating contemporary SOC pools, which can be improved through parameter estimation. However, major uncertainty remains in global soil responses to climate change, particularly uncertainty in how the activity of soil microbial communities will respond. To date, the role of microbes in SOC dynamics has been implicitly described by decay rate constants in most conventional global carbon cycle models. Explicitly including microbial biomass dynamics into C cycle model formulations has shown potential to improve model predictive performance when assessed against global SOC databases. This study aimed to data-constrained parameters of two soil microbial models, evaluate the improvements in performance of those calibrated models in predicting contemporary carbon stocks, and compare the SOC responses to climate change and their uncertainties between microbial and conventional models. Microbial models with calibrated parameters explained 51% of variability in the observed total SOC, whereas a calibrated conventional model explained 41%. The microbial models, when forced with climate and soil carbon input predictions from the 5th Coupled Model Intercomparison Project (CMIP5), produced stronger soil C responses to 95 years of climate change than any of the 11 CMIP5 models. The calibrated microbial models predicted between 8% (2-pool model) and 11% (4-pool model) soil C losses compared with CMIP5 model projections which ranged from a 7% loss to a 22.6% gain. Lastly, we observed unrealistic oscillatory SOC dynamics in the 2-pool microbial model. The 4-pool model also produced oscillations, but they were less prominent and could be avoided, depending on the parameter values. © 2014 John Wiley & Sons Ltd.

A model integrating longshore and cross-shore processes for predicting long-term shoreline response to climate change

USGS Publications Warehouse

Vitousek, Sean; Barnard, Patrick; Limber, Patrick W.; Erikson, Li; Cole, Blake

2017-01-01

We present a shoreline change model for coastal hazard assessment and management planning. The model, CoSMoS-COAST (Coastal One-line Assimilated Simulation Tool), is a transect-based, one-line model that predicts short-term and long-term shoreline response to climate change in the 21st century. The proposed model represents a novel, modular synthesis of process-based models of coastline evolution due to longshore and cross-shore transport by waves and sea-level rise. Additionally, the model uses an extended Kalman filter for data assimilation of historical shoreline positions to improve estimates of model parameters and thereby improve confidence in long-term predictions. We apply CoSMoS-COAST to simulate sandy shoreline evolution along 500 km of coastline in Southern California, which hosts complex mixtures of beach settings variably backed by dunes, bluffs, cliffs, estuaries, river mouths, and urban infrastructure, providing applicability of the model to virtually any coastal setting. Aided by data assimilation, the model is able to reproduce the observed signal of seasonal shoreline change for the hindcast period of 1995-2010, showing excellent agreement between modeled and observed beach states. The skill of the model during the hindcast period improves confidence in the model's predictive capability when applied to the forecast period (2010-2100) driven by GCM-projected wave and sea-level conditions. Predictions of shoreline change with limited human intervention indicate that 31% to 67% of Southern California beaches may become completely eroded by 2100 under sea-level rise scenarios of 0.93 to 2.0 m.

A model integrating longshore and cross-shore processes for predicting long-term shoreline response to climate change

NASA Astrophysics Data System (ADS)

Vitousek, Sean; Barnard, Patrick L.; Limber, Patrick; Erikson, Li; Cole, Blake

2017-04-01

We present a shoreline change model for coastal hazard assessment and management planning. The model, CoSMoS-COAST (Coastal One-line Assimilated Simulation Tool), is a transect-based, one-line model that predicts short-term and long-term shoreline response to climate change in the 21st century. The proposed model represents a novel, modular synthesis of process-based models of coastline evolution due to longshore and cross-shore transport by waves and sea level rise. Additionally, the model uses an extended Kalman filter for data assimilation of historical shoreline positions to improve estimates of model parameters and thereby improve confidence in long-term predictions. We apply CoSMoS-COAST to simulate sandy shoreline evolution along 500 km of coastline in Southern California, which hosts complex mixtures of beach settings variably backed by dunes, bluffs, cliffs, estuaries, river mouths, and urban infrastructure, providing applicability of the model to virtually any coastal setting. Aided by data assimilation, the model is able to reproduce the observed signal of seasonal shoreline change for the hindcast period of 1995-2010, showing excellent agreement between modeled and observed beach states. The skill of the model during the hindcast period improves confidence in the model's predictive capability when applied to the forecast period (2010-2100) driven by GCM-projected wave and sea level conditions. Predictions of shoreline change with limited human intervention indicate that 31% to 67% of Southern California beaches may become completely eroded by 2100 under sea level rise scenarios of 0.93 to 2.0 m.

Modelling climate change responses in tropical forests: similar productivity estimates across five models, but different mechanisms and responses

NASA Astrophysics Data System (ADS)

Rowland, L.; Harper, A.; Christoffersen, B. O.; Galbraith, D. R.; Imbuzeiro, H. M. A.; Powell, T. L.; Doughty, C.; Levine, N. M.; Malhi, Y.; Saleska, S. R.; Moorcroft, P. R.; Meir, P.; Williams, M.

2014-11-01

Accurately predicting the response of Amazonia to climate change is important for predicting changes across the globe. However, changes in multiple climatic factors simultaneously may result in complex non-linear responses, which are difficult to predict using vegetation models. Using leaf and canopy scale observations, this study evaluated the capability of five vegetation models (CLM3.5, ED2, JULES, SiB3, and SPA) to simulate the responses of canopy and leaf scale productivity to changes in temperature and drought in an Amazonian forest. The models did not agree as to whether gross primary productivity (GPP) was more sensitive to changes in temperature or precipitation. There was greater model-data consistency in the response of net ecosystem exchange to changes in temperature, than in the response to temperature of leaf area index (LAI), net photosynthesis (An) and stomatal conductance (gs). Modelled canopy scale fluxes are calculated by scaling leaf scale fluxes to LAI, and therefore in this study similarities in modelled ecosystem scale responses to drought and temperature were the result of inconsistent leaf scale and LAI responses among models. Across the models, the response of An to temperature was more closely linked to stomatal behaviour than biochemical processes. Consequently all the models predicted that GPP would be higher if tropical forests were 5 °C colder, closer to the model optima for gs. There was however no model consistency in the response of the An-gs relationship when temperature changes and drought were introduced simultaneously. The inconsistencies in the An-gs relationships amongst models were caused by to non-linear model responses induced by simultaneous drought and temperature change. To improve the reliability of simulations of the response of Amazonian rainforest to climate change the mechanistic underpinnings of vegetation models need more complete validation to improve accuracy and consistency in the scaling of processes from leaf to canopy.

Modelling ecological systems in a changing world

PubMed Central

Evans, Matthew R.

2012-01-01

The world is changing at an unprecedented rate. In such a situation, we need to understand the nature of the change and to make predictions about the way in which it might affect systems of interest; often we may also wish to understand what might be done to mitigate the predicted effects. In ecology, we usually make such predictions (or forecasts) by making use of mathematical models that describe the system and projecting them into the future, under changed conditions. Approaches emphasizing the desirability of simple models with analytical tractability and those that use assumed causal relationships derived statistically from data currently dominate ecological modelling. Although such models are excellent at describing the way in which a system has behaved, they are poor at predicting its future state, especially in novel conditions. In order to address questions about the impact of environmental change, and to understand what, if any, action might be taken to ameliorate it, ecologists need to develop the ability to project models into novel, future conditions. This will require the development of models based on understanding the processes that result in a system behaving the way it does, rather than relying on a description of the system, as a whole, remaining valid indefinitely. PMID:22144381

Climate change likely to reduce orchid bee abundance even in climatic suitable sites.

PubMed

Faleiro, Frederico Valtuille; Nemésio, André; Loyola, Rafael

2018-06-01

Studies have tested whether model predictions based on species' occurrence can predict the spatial pattern of population abundance. The relationship between predicted environmental suitability and population abundance varies in shape, strength and predictive power. However, little attention has been paid to the congruence in predictions of different models fed with occurrence or abundance data, in particular when comparing metrics of climate change impact. Here, we used the ecological niche modeling fit with presence-absence and abundance data of orchid bees to predict the effect of climate change on species and assembly level distribution patterns. In addition, we assessed whether predictions of presence-absence models can be used as a proxy to abundance patterns. We obtained georeferenced abundance data of orchid bees (Hymenoptera: Apidae: Euglossina) in the Brazilian Atlantic Forest. Sampling method consisted in attracting male orchid bees to baits of at least five different aromatic compounds and collecting the individuals with entomological nets or bait traps. We limited abundance data to those obtained by similar standard sampling protocol to avoid bias in abundance estimation. We used boosted regression trees to model ecological niches and project them into six climate models and two Representative Concentration Pathways. We found that models based on species occurrences worked as a proxy for changes in population abundance when the output of the models were continuous; results were very different when outputs were discretized to binary predictions. We found an overall trend of diminishing abundance in the future, but a clear retention of climatically suitable sites too. Furthermore, geographic distance to gained climatic suitable areas can be very short, although it embraces great variation. Changes in species richness and turnover would be concentrated in western and southern Atlantic Forest. Our findings offer support to the ongoing debate of suitability-abundance models and can be used to support spatial conservation prioritization schemes and species triage in Atlantic Forest. © 2018 John Wiley & Sons Ltd.

Arctic shrubification mediates the impacts of warming climate on changes to tundra vegetation

NASA Astrophysics Data System (ADS)

Mod, Heidi K.; Luoto, Miska

2016-12-01

Climate change has been observed to expand distributions of woody plants in many areas of arctic and alpine environments—a phenomenon called shrubification. New spatial arrangements of shrubs cause further changes in vegetation via changing dynamics of biotic interactions. However, the mediating influence of shrubification is rarely acknowledged in predictions of tundra vegetation change. Here, we examine possible warming-induced landscape-level vegetation changes in a high-latitude environment using species distribution modelling (SDM), specifically concentrating on the impacts of shrubification on ambient vegetation. First, we produced estimates of current shrub and tree cover and forecasts of their expansion under climate change scenarios to be incorporated to SDMs of 116 vascular plants. Second, the predictions of vegetation change based on the models including only abiotic predictors and the models including abiotic, shrub and tree predictors were compared in a representative test area. Based on our model predictions, abundance of woody plants will expand, thus decreasing predicted species richness, amplifying species turnover and increasing the local extinction risk for ambient vegetation. However, the spatial variation demonstrated in our predictions highlights that tundra vegetation can be expected to show a wide variety of different responses to the combined effects of warming and shrubification, depending on the original plant species pool and environmental conditions. We conclude that realistic forecasts of the future require acknowledging the role of shrubification in warming-induced tundra vegetation change.

Prediction of body lipid change in pregnancy and lactation.

PubMed

Friggens, N C; Ingvartsen, K L; Emmans, G C

2004-04-01

A simple method to predict the genetically driven pattern of body lipid change through pregnancy and lactation in dairy cattle is proposed. The rationale and evidence for genetically driven body lipid change have their basis in evolutionary considerations and in the homeorhetic changes in lipid metabolism through the reproductive cycle. The inputs required to predict body lipid change are body lipid mass at calving (kg) and the date of conception (days in milk). Body lipid mass can be derived from body condition score and live weight. A key assumption is that there is a linear rate of change of the rate of body lipid change (dL/dt) between calving and a genetically determined time in lactation (T') at which a particular level of body lipid (L') is sought. A second assumption is that there is a linear rate of change of the rate of body lipid change (dL/dt) between T' and the next calving. The resulting model was evaluated using 2 sets of data. The first was from Holstein cows with 3 different levels of body fatness at calving. The second was from Jersey cows in first, second, and third parity. The model was found to reproduce the observed patterns of change in body lipid reserves through lactation in both data sets. The average error of prediction was low, less than the variation normally associated with the recording of condition score, and was similar for the 2 data sets. When the model was applied using the initially suggested parameter values derived from the literature the average error of prediction was 0.185 units of condition score (+/- 0.086 SD). After minor adjustments to the parameter values, the average error of prediction was 0.118 units of condition score (+/- 0.070 SD). The assumptions on which the model is based were sufficient to predict the changes in body lipid of both Holstein and Jersey cows under different nutritional conditions and parities. Thus, the model presented here shows that it is possible to predict genetically driven curves of body lipid change through lactation in a simple way that requires few parameters and inputs that can be derived in practice. It is expected that prediction of the cow's energy requirements can be substantially improved, particularly in early lactation, by incorporating a genetically driven body energy mobilization.

Predicting effects of environmental change on a migratory herbivore

USGS Publications Warehouse

Stillman, R A; Wood, K A; Gilkerson, Whelan; Elkinton, E; Black, J. M.; Ward, David H.; Petrie, M.

2015-01-01

Changes in climate, food abundance and disturbance from humans threaten the ability of species to successfully use stopover sites and migrate between non-breeding and breeding areas. To devise successful conservation strategies for migratory species we need to be able to predict how such changes will affect both individuals and populations. Such predictions should ideally be process-based, focusing on the mechanisms through which changes alter individual physiological state and behavior. In this study we use a process-based model to evaluate how Black Brant (Branta bernicla nigricans) foraging on common eelgrass (Zostera marina) at a stopover site (Humboldt Bay, USA), may be affected by changes in sea level, food abundance and disturbance. The model is individual-based, with empirically based parameters, and incorporates the immigration of birds into the site, tidal changes in availability of eelgrass, seasonal and depth-related changes in eelgrass biomass, foraging behavior and energetics of the birds, and their mass-dependent decisions to emigrate. The model is validated by comparing predictions to observations across a range of system properties including the time birds spent foraging, probability of birds emigrating, mean stopover duration, peak bird numbers, rates of mass gain and distribution of birds within the site: all 11 predictions were within 35% of the observed value, and 8 within 20%. The model predicted that the eelgrass within the site could potentially support up to five times as many birds as currently use the site. Future predictions indicated that the rate of mass gain and mean stopover duration were relatively insensitive to sea level rise over the next 100 years, primarily because eelgrass habitat could redistribute shoreward into intertidal mudflats within the site to compensate for higher sea levels. In contrast, the rate of mass gain and mean stopover duration were sensitive to changes in total eelgrass biomass and the percentage of time for which birds were disturbed. We discuss the consequences of these predictions for Black Brant conservation. A wide range of migratory species responses are expected in response to environmental change. Process-based models are potential tools to predict such responses and understand the mechanisms which underpin them.

Modelling the effects of climate change on the distribution and production of marine fishes: accounting for trophic interactions in a dynamic bioclimate envelope model.

PubMed

Fernandes, Jose A; Cheung, William W L; Jennings, Simon; Butenschön, Momme; de Mora, Lee; Frölicher, Thomas L; Barange, Manuel; Grant, Alastair

2013-08-01

Climate change has already altered the distribution of marine fishes. Future predictions of fish distributions and catches based on bioclimate envelope models are available, but to date they have not considered interspecific interactions. We address this by combining the species-based Dynamic Bioclimate Envelope Model (DBEM) with a size-based trophic model. The new approach provides spatially and temporally resolved predictions of changes in species' size, abundance and catch potential that account for the effects of ecological interactions. Predicted latitudinal shifts are, on average, reduced by 20% when species interactions are incorporated, compared to DBEM predictions, with pelagic species showing the greatest reductions. Goodness-of-fit of biomass data from fish stock assessments in the North Atlantic between 1991 and 2003 is improved slightly by including species interactions. The differences between predictions from the two models may be relatively modest because, at the North Atlantic basin scale, (i) predators and competitors may respond to climate change together; (ii) existing parameterization of the DBEM might implicitly incorporate trophic interactions; and/or (iii) trophic interactions might not be the main driver of responses to climate. Future analyses using ecologically explicit models and data will improve understanding of the effects of inter-specific interactions on responses to climate change, and better inform managers about plausible ecological and fishery consequences of a changing environment. © 2013 John Wiley & Sons Ltd.

Predicting future coexistence in a North American ant community

PubMed Central

Bewick, Sharon; Stuble, Katharine L; Lessard, Jean-Phillipe; Dunn, Robert R; Adler, Frederick R; Sanders, Nathan J

2014-01-01

Global climate change will remodel ecological communities worldwide. However, as a consequence of biotic interactions, communities may respond to climate change in idiosyncratic ways. This makes predictive models that incorporate biotic interactions necessary. We show how such models can be constructed based on empirical studies in combination with predictions or assumptions regarding the abiotic consequences of climate change. Specifically, we consider a well-studied ant community in North America. First, we use historical data to parameterize a basic model for species coexistence. Using this model, we determine the importance of various factors, including thermal niches, food discovery rates, and food removal rates, to historical species coexistence. We then extend the model to predict how the community will restructure in response to several climate-related changes, such as increased temperature, shifts in species phenology, and altered resource availability. Interestingly, our mechanistic model suggests that increased temperature and shifts in species phenology can have contrasting effects. Nevertheless, for almost all scenarios considered, we find that the most subordinate ant species suffers most as a result of climate change. More generally, our analysis shows that community composition can respond to climate warming in nonintuitive ways. For example, in the context of a community, it is not necessarily the most heat-sensitive species that are most at risk. Our results demonstrate how models that account for niche partitioning and interspecific trade-offs among species can be used to predict the likely idiosyncratic responses of local communities to climate change. PMID:24963378

Evaluation of methodology for detecting/predicting migration of forest species

Treesearch

Dale S. Solomon; William B. Leak

1996-01-01

Available methods for analyzing migration of forest species are evaluated, including simulation models, remeasured plots, resurveys, pollen/vegetation analysis, and age/distance trends. Simulation models have provided some of the most drastic estimates of species changes due to predicted changes in global climate. However, these models require additional testing...

Tropical and Extratropical Cyclone Damages under Climate Change

NASA Astrophysics Data System (ADS)

Ranson, M.; Kousky, C.; Ruth, M.; Jantarasami, L.; Crimmins, A.; Tarquinio, L.

2014-12-01

This paper provides the first quantitative synthesis of the rapidly growing literature on future tropical and extratropical cyclone losses under climate change. We estimate a probability distribution for the predicted impact of changes in global surface air temperatures on future storm damages, using an ensemble of 296 estimates of the temperature-damage relationship from twenty studies. Our analysis produces three main empirical results. First, we find strong but not conclusive support for the hypothesis that climate change will cause damages from tropical cyclones and wind storms to increase, with most models (84 and 92 percent, respectively) predicting higher future storm damages due to climate change. Second, there is substantial variation in projected changes in losses across regions. Potential changes in damages are greatest in the North Atlantic basin, where the multi-model average predicts that a 2.5°C increase in global surface air temperature would cause hurricane damages to increase by 62 percent. The ensemble predictions for Western North Pacific tropical cyclones and European wind storms (extratropical cyclones) are approximately one third of that magnitude. Finally, our analysis shows that existing models of storm damages under climate change generate a wide range of predictions, ranging from moderate decreases to very large increases in losses.

Population activity statistics dissect subthreshold and spiking variability in V1.

PubMed

Bányai, Mihály; Koman, Zsombor; Orbán, Gergő

2017-07-01

Response variability, as measured by fluctuating responses upon repeated performance of trials, is a major component of neural responses, and its characterization is key to interpret high dimensional population recordings. Response variability and covariability display predictable changes upon changes in stimulus and cognitive or behavioral state, providing an opportunity to test the predictive power of models of neural variability. Still, there is little agreement on which model to use as a building block for population-level analyses, and models of variability are often treated as a subject of choice. We investigate two competing models, the doubly stochastic Poisson (DSP) model assuming stochasticity at spike generation, and the rectified Gaussian (RG) model tracing variability back to membrane potential variance, to analyze stimulus-dependent modulation of both single-neuron and pairwise response statistics. Using a pair of model neurons, we demonstrate that the two models predict similar single-cell statistics. However, DSP and RG models have contradicting predictions on the joint statistics of spiking responses. To test the models against data, we build a population model to simulate stimulus change-related modulations in pairwise response statistics. We use single-unit data from the primary visual cortex (V1) of monkeys to show that while model predictions for variance are qualitatively similar to experimental data, only the RG model's predictions are compatible with joint statistics. These results suggest that models using Poisson-like variability might fail to capture important properties of response statistics. We argue that membrane potential-level modeling of stochasticity provides an efficient strategy to model correlations. NEW & NOTEWORTHY Neural variability and covariability are puzzling aspects of cortical computations. For efficient decoding and prediction, models of information encoding in neural populations hinge on an appropriate model of variability. Our work shows that stimulus-dependent changes in pairwise but not in single-cell statistics can differentiate between two widely used models of neuronal variability. Contrasting model predictions with neuronal data provides hints on the noise sources in spiking and provides constraints on statistical models of population activity. Copyright © 2017 the American Physiological Society.

The effect of changes in space shuttle parameters on the NASA/MSFC multilayer diffusion model predictions of surface HCl concentrations

NASA Technical Reports Server (NTRS)

Glasser, M. E.; Rundel, R. D.

1978-01-01

A method for formulating these changes into the model input parameters using a preprocessor program run on a programed data processor was implemented. The results indicate that any changes in the input parameters are small enough to be negligible in comparison to meteorological inputs and the limitations of the model and that such changes will not substantially increase the number of meteorological cases for which the model will predict surface hydrogen chloride concentrations exceeding public safety levels.

Predicting hydrological response to forest changes by simple statistical models: the selection of the best indicator of forest changes with a hydrological perspective

NASA Astrophysics Data System (ADS)

Ning, D.; Zhang, M.; Ren, S.; Hou, Y.; Yu, L.; Meng, Z.

2017-01-01

Forest plays an important role in hydrological cycle, and forest changes will inevitably affect runoff across multiple spatial scales. The selection of a suitable indicator for forest changes is essential for predicting forest-related hydrological response. This study used the Meijiang River, one of the headwaters of the Poyang Lake as an example to identify the best indicator of forest changes for predicting forest change-induced hydrological responses. Correlation analysis was conducted first to detect the relationships between monthly runoff and its predictive variables including antecedent monthly precipitation and indicators for forest changes (forest coverage, vegetation indices including EVI, NDVI, and NDWI), and by use of the identified predictive variables that were most correlated with monthly runoff, multiple linear regression models were then developed. The model with best performance identified in this study included two independent variables -antecedent monthly precipitation and NDWI. It indicates that NDWI is the best indicator of forest change in hydrological prediction while forest coverage, the most commonly used indicator of forest change is insignificantly related to monthly runoff. This highlights the use of vegetation index such as NDWI to indicate forest changes in hydrological studies. This study will provide us with an efficient way to quantify the hydrological impact of large-scale forest changes in the Meijiang River watershed, which is crucial for downstream water resource management and ecological protection in the Poyang Lake basin.

Predicting ecological responses in a changing ocean: the effects of future climate uncertainty.

PubMed

Freer, Jennifer J; Partridge, Julian C; Tarling, Geraint A; Collins, Martin A; Genner, Martin J

2018-01-01

Predicting how species will respond to climate change is a growing field in marine ecology, yet knowledge of how to incorporate the uncertainty from future climate data into these predictions remains a significant challenge. To help overcome it, this review separates climate uncertainty into its three components (scenario uncertainty, model uncertainty, and internal model variability) and identifies four criteria that constitute a thorough interpretation of an ecological response to climate change in relation to these parts (awareness, access, incorporation, communication). Through a literature review, the extent to which the marine ecology community has addressed these criteria in their predictions was assessed. Despite a high awareness of climate uncertainty, articles favoured the most severe emission scenario, and only a subset of climate models were used as input into ecological analyses. In the case of sea surface temperature, these models can have projections unrepresentative against a larger ensemble mean. Moreover, 91% of studies failed to incorporate the internal variability of a climate model into results. We explored the influence that the choice of emission scenario, climate model, and model realisation can have when predicting the future distribution of the pelagic fish, Electrona antarctica . Future distributions were highly influenced by the choice of climate model, and in some cases, internal variability was important in determining the direction and severity of the distribution change. Increased clarity and availability of processed climate data would facilitate more comprehensive explorations of climate uncertainty, and increase in the quality and standard of marine prediction studies.

Coupling of Markov chains and cellular automata spatial models to predict land cover changes (case study: upper Ci Leungsi catchment area)

NASA Astrophysics Data System (ADS)

Marko, K.; Zulkarnain, F.; Kusratmoko, E.

2016-11-01

Land cover changes particular in urban catchment area has been rapidly occur. Land cover changes occur as a result of increasing demand for built-up area. Various kinds of environmental and hydrological problems e.g. floods and urban heat island can happen if the changes are uncontrolled. This study aims to predict land cover changes using coupling of Markov chains and cellular automata. One of the most rapid land cover changes is occurs at upper Ci Leungsi catchment area that located near Bekasi City and Jakarta Metropolitan Area. Markov chains has a good ability to predict the probability of change statistically while cellular automata believed as a powerful method in reading the spatial patterns of change. Temporal land cover data was obtained by remote sensing satellite imageries. In addition, this study also used multi-criteria analysis to determine which driving factor that could stimulate the changes such as proximity, elevation, and slope. Coupling of these two methods could give better prediction model rather than just using it separately. The prediction model was validated using existing 2015 land cover data and shown a satisfactory kappa coefficient. The most significant increasing land cover is built-up area from 24% to 53%.

National Scale Prediction of Soil Carbon Sequestration under Scenarios of Climate Change

NASA Astrophysics Data System (ADS)

Izaurralde, R. C.; Thomson, A. M.; Potter, S. R.; Atwood, J. D.; Williams, J. R.

2006-12-01

Carbon sequestration in agricultural soils is gaining momentum as a tool to mitigate the rate of increase of atmospheric CO2. Researchers from the Pacific Northwest National Laboratory, Texas A&M University, and USDA-NRCS used the EPIC model to develop national-scale predictions of soil carbon sequestration with adoption of no till (NT) under scenarios of climate change. In its current form, the EPIC model simulates soil C changes resulting from heterotrophic respiration and wind / water erosion. Representative modeling units were created to capture the climate, soil, and management variability at the 8-digit hydrologic unit (USGS classification) watershed scale. The soils selected represented at least 70% of the variability within each watershed. This resulted in 7,540 representative modeling units for 1,412 watersheds. Each watershed was assigned a major crop system: corn, soybean, spring wheat, winter wheat, cotton, hay, alfalfa, corn-soybean rotation or wheat-fallow rotation based on information from the National Resource Inventory. Each representative farm was simulated with conventional tillage and no tillage, and with and without irrigation. Climate change scenarios for two future periods (2015-2045 and 2045-2075) were selected from GCM model runs using the IPCC SRES scenarios of A2 and B2 from the UK Hadley Center (HadCM3) and US DOE PCM (PCM) models. Changes in mean and standard deviation of monthly temperature and precipitation were extracted from gridded files and applied to baseline climate (1960-1990) for each of the 1,412 modeled watersheds. Modeled crop yields were validated against historical USDA NASS county yields (1960-1990). The HadCM3 model predicted the most severe changes in climate parameters. Overall, there would be little difference between the A2 and B2 scenarios. Carbon offsets were calculated as the difference in soil C change between conventional and no till. Overall, C offsets during the first 30-y period (513 Tg C) are predicted to be 36% higher than those predicted during the second period. The climate projections of the PCM model had more positive impact on soil C sequestration than those predicted with the HadCM3 model.

Thermal niche estimators and the capability of poor dispersal species to cope with climate change

NASA Astrophysics Data System (ADS)

Sánchez-Fernández, David; Rizzo, Valeria; Cieslak, Alexandra; Faille, Arnaud; Fresneda, Javier; Ribera, Ignacio

2016-03-01

For management strategies in the context of global warming, accurate predictions of species response are mandatory. However, to date most predictions are based on niche (bioclimatic) models that usually overlook biotic interactions, behavioral adjustments or adaptive evolution, and assume that species can disperse freely without constraints. The deep subterranean environment minimises these uncertainties, as it is simple, homogeneous and with constant environmental conditions. It is thus an ideal model system to study the effect of global change in species with poor dispersal capabilities. We assess the potential fate of a lineage of troglobitic beetles under global change predictions using different approaches to estimate their thermal niche: bioclimatic models, rates of thermal niche change estimated from a molecular phylogeny, and data from physiological studies. Using bioclimatic models, at most 60% of the species were predicted to have suitable conditions in 2080. Considering the rates of thermal niche change did not improve this prediction. However, physiological data suggest that subterranean species have a broad thermal tolerance, allowing them to stand temperatures never experienced through their evolutionary history. These results stress the need of experimental approaches to assess the capability of poor dispersal species to cope with temperatures outside those they currently experience.

Development and corroboration of a bioenergetics model for northern pikeminnow (Ptychocheilus oregonensis) feeding on juvenile salmonids in the Columbia River

USGS Publications Warehouse

Petersen, J.H.; Ward, D.L.

1999-01-01

A bioenergetics model was developed and corroborated for northern pikeminnow Ptychocheilus oregonensis, an important predator on juvenile salmonids in the Pacific Northwest. Predictions of modeled predation rate on salmonids were compared with field data from three areas of John Day Reservoir (Columbia River). To make bioenergetics model estimates of predation rate, three methods were used to approximate the change in mass of average predators during 30-d growth periods: observed change in mass between the first and the second month, predicted change in mass calculated with seasonal growth rates, and predicted change in mass based on an annual growth model. For all reservoir areas combined, bioenergetics model predictions of predation on salmon were 19% lower than field estimates based on observed masses, 45% lower than estimates based on seasonal growth rates, and 15% lower than estimates based on the annual growth model. For each growth approach, the largest differences in field-versus-model predation occurred at the midreservoir area (-84% to -67% difference). Model predictions of the rate of predation on salmonids were examined for sensitivity to parameter variation, swimming speed, sampling bias caused by gear selectivity, and asymmetric size distributions of predators. The specific daily growth rate of northern pikeminnow predicted by the model was highest in July and October and decreased during August. The bioenergetics model for northern pikeminnow performed well compared with models for other fish species that have been tested with field data. This model should be a useful tool for evaluating management actions such as predator removal, examining the influence of temperature on predation rates, and exploring interactions between predators in the Columbia River basin.

Modeling strength loss in wood by chemical composition. Part I, An individual component model for southern pine

Treesearch

J. E. Winandy; P. K. Lebow

2001-01-01

In this study, we develop models for predicting loss in bending strength of clear, straight-grained pine from changes in chemical composition. Although significant work needs to be done before truly universal predictive models are developed, a quantitative fundamental relationship between changes in chemical composition and strength loss for pine was demonstrated. In...

[Prediction method of rural landscape pattern evolution based on life cycle: a case study of Jinjing Town, Hunan Province, China].

PubMed

Ji, Xiang; Liu, Li-Ming; Li, Hong-Qing

2014-11-01

Taking Jinjing Town in Dongting Lake area as a case, this paper analyzed the evolution of rural landscape patterns by means of life cycle theory, simulated the evolution cycle curve, and calculated its evolution period, then combining CA-Markov model, a complete prediction model was built based on the rule of rural landscape change. The results showed that rural settlement and paddy landscapes of Jinjing Town would change most in 2020, with the rural settlement landscape increased to 1194.01 hm2 and paddy landscape greatly reduced to 3090.24 hm2. The quantitative and spatial prediction accuracies of the model were up to 99.3% and 96.4%, respectively, being more explicit than single CA-Markov model. The prediction model of rural landscape patterns change proposed in this paper would be helpful for rural landscape planning in future.

Developing a tuberculosis transmission model that accounts for changes in population health.

PubMed

Oxlade, Olivia; Schwartzman, Kevin; Benedetti, Andrea; Pai, Madhukar; Heymann, Jody; Menzies, Dick

2011-01-01

Simulation models are useful in policy planning for tuberculosis (TB) control. To accurately assess interventions, important modifiers of the epidemic should be accounted for in evaluative models. Improvements in population health were associated with the declining TB epidemic in the pre-antibiotic era and may be relevant today. The objective of this study was to develop and validate a TB transmission model that accounted for changes in population health. We developed a deterministic TB transmission model, using reported data from the pre-antibiotic era in England. Change in adjusted life expectancy, used as a proxy for general health, was used to determine the rate of change of key epidemiological parameters. Predicted outcomes included risk of TB infection and TB mortality. The model was validated in the setting of the Netherlands and then applied to modern Peru. The model, developed in the setting of England, predicted TB trends in the Netherlands very accurately. The R(2) value for correlation between observed and predicted data was 0.97 and 0.95 for TB infection and mortality, respectively. In Peru, the predicted decline in incidence prior to the expansion of "Directly Observed Treatment Short Course" (The DOTS strategy) was 3.7% per year (observed = 3.9% per year). After DOTS expansion, the predicted decline was very similar to the observed decline of 5.8% per year. We successfully developed and validated a TB model, which uses a proxy for population health to estimate changes in key epidemiology parameters. Population health contributed significantly to improvement in TB outcomes observed in Peru. Changing population health should be incorporated into evaluative models for global TB control.

Sensorimotor synchronization with tempo-changing auditory sequences: Modeling temporal adaptation and anticipation.

PubMed

van der Steen, M C Marieke; Jacoby, Nori; Fairhurst, Merle T; Keller, Peter E

2015-11-11

The current study investigated the human ability to synchronize movements with event sequences containing continuous tempo changes. This capacity is evident, for example, in ensemble musicians who maintain precise interpersonal coordination while modulating the performance tempo for expressive purposes. Here we tested an ADaptation and Anticipation Model (ADAM) that was developed to account for such behavior by combining error correction processes (adaptation) with a predictive temporal extrapolation process (anticipation). While previous computational models of synchronization incorporate error correction, they do not account for prediction during tempo-changing behavior. The fit between behavioral data and computer simulations based on four versions of ADAM was assessed. These versions included a model with adaptation only, one in which adaptation and anticipation act in combination (error correction is applied on the basis of predicted tempo changes), and two models in which adaptation and anticipation were linked in a joint module that corrects for predicted discrepancies between the outcomes of adaptive and anticipatory processes. The behavioral experiment required participants to tap their finger in time with three auditory pacing sequences containing tempo changes that differed in the rate of change and the number of turning points. Behavioral results indicated that sensorimotor synchronization accuracy and precision, while generally high, decreased with increases in the rate of tempo change and number of turning points. Simulations and model-based parameter estimates showed that adaptation mechanisms alone could not fully explain the observed precision of sensorimotor synchronization. Including anticipation in the model increased the precision of simulated sensorimotor synchronization and improved the fit of model to behavioral data, especially when adaptation and anticipation mechanisms were linked via a joint module based on the notion of joint internal models. Overall results suggest that adaptation and anticipation mechanisms both play an important role during sensorimotor synchronization with tempo-changing sequences. This article is part of a Special Issue entitled SI: Prediction and Attention. Copyright © 2015 Elsevier B.V. All rights reserved.

Probabilistic prediction of barrier-island response to hurricanes

USGS Publications Warehouse

Plant, Nathaniel G.; Stockdon, Hilary F.

2012-01-01

Prediction of barrier-island response to hurricane attack is important for assessing the vulnerability of communities, infrastructure, habitat, and recreational assets to the impacts of storm surge, waves, and erosion. We have demonstrated that a conceptual model intended to make qualitative predictions of the type of beach response to storms (e.g., beach erosion, dune erosion, dune overwash, inundation) can be reformulated in a Bayesian network to make quantitative predictions of the morphologic response. In an application of this approach at Santa Rosa Island, FL, predicted dune-crest elevation changes in response to Hurricane Ivan explained about 20% to 30% of the observed variance. An extended Bayesian network based on the original conceptual model, which included dune elevations, storm surge, and swash, but with the addition of beach and dune widths as input variables, showed improved skill compared to the original model, explaining 70% of dune elevation change variance and about 60% of dune and shoreline position change variance. This probabilistic approach accurately represented prediction uncertainty (measured with the log likelihood ratio), and it outperformed the baseline prediction (i.e., the prior distribution based on the observations). Finally, sensitivity studies demonstrated that degrading the resolution of the Bayesian network or removing data from the calibration process reduced the skill of the predictions by 30% to 40%. The reduction in skill did not change conclusions regarding the relative importance of the input variables, and the extended model's skill always outperformed the original model.

Stock price change rate prediction by utilizing social network activities.

PubMed

Deng, Shangkun; Mitsubuchi, Takashi; Sakurai, Akito

2014-01-01

Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques.

Stock Price Change Rate Prediction by Utilizing Social Network Activities

PubMed Central

Mitsubuchi, Takashi; Sakurai, Akito

2014-01-01

Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques. PMID:24790586

Indigenous Waters: Applying the SWAT Hydrological Model to the Lumbee River Watershed

NASA Astrophysics Data System (ADS)

Painter, J.; Singh, N.; Martin, K. L.; Vose, J. M.; Wear, D. N.; Emanuel, R. E.

2016-12-01

Hydrological modeling can reveal insight about how rainfall becomes streamflow in a watershed comprising heterogeneous soils, terrain and land cover. Modeling can also help disentangle predicted impacts of climate and land use change on hydrological processes. We applied a hydrological model to the Lumbee River watershed, also known as the Lumber River Watershed, in the coastal plain of North Carolina (USA) to better understand how streamflow may be impacted by predicted climate and land use change in the mid-21st century. The Lumbee River flows through a predominantly Native American community, which may be affected by changing water resources during this period. The long-term goal of our project is to predict the effects of climate and land use change on the Lumbee River watershed and on the Native community that relies upon the river. We applied the Soil & Water Assessment Tool for ArcGIS (ArcSWAT), which was calibrated to historical climate and USGS streamflow data during the late 20th century, and we determined frequency distributions for key model parameters that best predicted streamflow during this time period. After calibrating and validating the model during the historical period, we identified land use and climate projections to represent a range of future conditions in the watershed. Specifically, we selected downscaled climate forcing data from four general circulation models running the RCP8.5 scenario. We also selected land use projections from a cornerstone scenario of the USDA Forest Service's Southern Forest Futures Project. This presentation reports on our methods for propagating parameter and climatic uncertainty through model predictions, and it reports on spatial patterns of land use change predicted by the cornerstone scenario.

A comparison between the clinical significance and growth mixture modelling early change methods at predicting negative outcomes.

PubMed

Flood, Nicola; Page, Andrew; Hooke, Geoff

2018-05-03

Routine outcome monitoring benefits treatment by identifying potential no change and deterioration. The present study compared two methods of identifying early change and their ability to predict negative outcomes on self-report symptom and wellbeing measures. 1467 voluntary day patients participated in a 10-day group Cognitive Behaviour Therapy (CBT) program and completed the symptom and wellbeing measures daily. Early change, as defined by (a) the clinical significance method and (b) longitudinal modelling, was compared on each measure. Early change, as defined by the simpler clinical significance method, was superior at predicting negative outcomes than longitudinal modelling. The longitudinal modelling method failed to detect a group of deteriorated patients, and agreement between the early change methods and the final unchanged outcome was higher for the clinical significance method. Therapists could use the clinical significance early change method during treatment to alert them of patients at risk for negative outcomes, which in turn could allow therapists to prevent those negative outcomes from occurring.

Using Regional Climate Projections to Guide Grassland Community Restoration in the Face of Climate Change

PubMed Central

Kane, Kristin; Debinski, Diane M.; Anderson, Chris; Scasta, John D.; Engle, David M.; Miller, James R.

2017-01-01

Grassland loss has been extensive worldwide, endangering the associated biodiversity and human well-being that are both dependent on these ecosystems. Ecologists have developed approaches to restore grassland communities and many have been successful, particularly where soils are rich, precipitation is abundant, and seeds of native plant species can be obtained. However, climate change adds a new filter needed in planning grassland restoration efforts. Potential responses of species to future climate conditions must also be considered in planning for long-term resilience. We demonstrate this methodology using a site-specific model and a maximum entropy approach to predict changes in habitat suitability for 33 grassland plant species in the tallgrass prairie region of the U.S. using the Intergovernmental Panel on Climate Change scenarios A1B and A2. The A1B scenario predicts an increase in temperature from 1.4 to 6.4°C, whereas the A2 scenario predicts temperature increases from 2 to 5.4°C and much greater CO2 emissions than the A1B scenario. Both scenarios predict these changes to occur by the year 2100. Model projections for 2040 under the A1B scenario predict that all but three modeled species will lose ~90% of their suitable habitat. Then by 2080, all species except for one will lose ~90% of their suitable habitat. Models run using the A2 scenario predict declines in habitat for just four species by 2040, but models predict that by 2080, habitat suitability will decline for all species. The A2 scenario appears based on our results to be the less severe climate change scenario for our species. Our results demonstrate that many common species, including grasses, forbs, and shrubs, are sensitive to climate change. Thus, grassland restoration alternatives should be evaluated based upon the long-term viability in the context of climate change projections and risk of plant species loss. PMID:28536591

Life history trade-off moderates model predictions of diversity loss from climate change.

PubMed

Moor, Helen

2017-01-01

Climate change can trigger species range shifts, local extinctions and changes in diversity. Species interactions and dispersal capacity are important mediators of community responses to climate change. The interaction between multispecies competition and variation in dispersal capacity has recently been shown to exacerbate the effects of climate change on diversity and to increase predictions of extinction risk dramatically. Dispersal capacity, however, is part of a species' overall ecological strategy and are likely to trade off with other aspects of its life history that influence population growth and persistence. In plants, a well-known example is the trade-off between seed mass and seed number. The presence of such a trade-off might buffer the diversity loss predicted by models with random but neutral (i.e. not impacting fitness otherwise) differences in dispersal capacity. Using a trait-based metacommunity model along a warming climatic gradient the effect of three different dispersal scenarios on model predictions of diversity change were compared. Adding random variation in species dispersal capacity caused extinctions by the introduction of strong fitness differences due an inherent property of the dispersal kernel. Simulations including a fitness-equalising trade-off based on empirical relationships between seed mass (here affecting dispersal distance, establishment probability, and seedling biomass) and seed number (fecundity) maintained higher initial species diversity and predicted lower extinction risk and diversity loss during climate change than simulations with variable dispersal capacity. Large seeded species persisted during climate change, but developed lags behind their climate niche that may cause extinction debts. Small seeded species were more extinction-prone during climate change but tracked their niches through dispersal and colonisation, despite competitive resistance from residents. Life history trade-offs involved in coexistence mechanisms may increase community resilience to future climate change and are useful guides for model development.

Population response to climate change: linear vs. non-linear modeling approaches.

PubMed

Ellis, Alicia M; Post, Eric

2004-03-31

Research on the ecological consequences of global climate change has elicited a growing interest in the use of time series analysis to investigate population dynamics in a changing climate. Here, we compare linear and non-linear models describing the contribution of climate to the density fluctuations of the population of wolves on Isle Royale, Michigan from 1959 to 1999. The non-linear self excitatory threshold autoregressive (SETAR) model revealed that, due to differences in the strength and nature of density dependence, relatively small and large populations may be differentially affected by future changes in climate. Both linear and non-linear models predict a decrease in the population of wolves with predicted changes in climate. Because specific predictions differed between linear and non-linear models, our study highlights the importance of using non-linear methods that allow the detection of non-linearity in the strength and nature of density dependence. Failure to adopt a non-linear approach to modelling population response to climate change, either exclusively or in addition to linear approaches, may compromise efforts to quantify ecological consequences of future warming.

Estimating thermal performance curves from repeated field observations

USGS Publications Warehouse

Childress, Evan; Letcher, Benjamin H.

2017-01-01

Estimating thermal performance of organisms is critical for understanding population distributions and dynamics and predicting responses to climate change. Typically, performance curves are estimated using laboratory studies to isolate temperature effects, but other abiotic and biotic factors influence temperature-performance relationships in nature reducing these models' predictive ability. We present a model for estimating thermal performance curves from repeated field observations that includes environmental and individual variation. We fit the model in a Bayesian framework using MCMC sampling, which allowed for estimation of unobserved latent growth while propagating uncertainty. Fitting the model to simulated data varying in sampling design and parameter values demonstrated that the parameter estimates were accurate, precise, and unbiased. Fitting the model to individual growth data from wild trout revealed high out-of-sample predictive ability relative to laboratory-derived models, which produced more biased predictions for field performance. The field-based estimates of thermal maxima were lower than those based on laboratory studies. Under warming temperature scenarios, field-derived performance models predicted stronger declines in body size than laboratory-derived models, suggesting that laboratory-based models may underestimate climate change effects. The presented model estimates true, realized field performance, avoiding assumptions required for applying laboratory-based models to field performance, which should improve estimates of performance under climate change and advance thermal ecology.

Hot limpets: predicting body temperature in a conductance-mediated thermal system.

PubMed

Denny, Mark W; Harley, Christopher D G

2006-07-01

Living at the interface between the marine and terrestrial environments, intertidal organisms may serve as a bellwether for environmental change and a test of our ability to predict its biological consequences. However, current models do not allow us to predict the body temperature of intertidal organisms whose heat budgets are strongly affected by conduction to and from the substratum. Here, we propose a simple heat-budget model of one such animal, the limpet Lottia gigantea, and test the model against measurements made in the field. Working solely from easily measured physical and meteorological inputs, the model predicts the daily maximal body temperatures of live limpets within a fraction of a degree, suggesting that it may be a useful tool for exploring the thermal biology of limpets and for predicting effects of climate change. The model can easily be adapted to predict the temperatures of chitons, acorn barnacles, keyhole limpets, and encrusting animals and plants.

Assessment of the Ability of Contemporary Climate Models to Assess Adequately the Risk of Possible Regional Anomalies and Trends

NASA Astrophysics Data System (ADS)

Mokhov, I. I.

2018-04-01

The results describing the ability of contemporary global and regional climate models not only to assess the risk of general trends of changes but also to predict qualitatively new regional effects are presented. In particular, model simulations predicted spatially inhomogeneous changes in the wind and wave conditions in the Arctic basins, which have been confirmed in recent years. According to satellite and reanalysis data, a qualitative transition to the regime predicted by model simulations occurred about a decade ago.

Unraveling past impacts of climate change and land management on historic peatland development using proxy-based reconstruction, monitoring data and process modeling.

PubMed

Heinemeyer, Andreas; Swindles, Graeme T

2018-05-08

Peatlands represent globally significant soil carbon stores that have been accumulating for millennia under water-logged conditions. However, deepening water-table depths (WTD) from climate change or human-induced drainage could stimulate decomposition resulting in peatlands turning from carbon sinks to carbon sources. Contemporary WTD ranges of testate amoebae (TA) are commonly used to predict past WTD in peatlands using quantitative transfer function models. Here we present, for the first time, a study comparing TA-based WTD reconstructions to instrumentally monitored WTD and hydrological model predictions using the MILLENNIA peatland model to examine past peatland responses to climate change and land management. Although there was very good agreement between monitored and modeled WTD, TA-reconstructed water table was consistently deeper. Predictions from a larger European TA transfer function data set were wetter, but the overall directional fit to observed WTD was better for a TA transfer function based on data from northern England. We applied a regression-based offset correction to the reconstructed WTD for the validation period (1931-2010). We then predicted WTD using available climate records as MILLENNIA model input and compared the offset-corrected TA reconstruction to MILLENNIA WTD predictions over an extended period (1750-1931) with available climate reconstructions. Although the comparison revealed striking similarities in predicted overall WTD patterns, particularly for a recent drier period (1965-1995), there were clear periods when TA-based WTD predictions underestimated (i.e. drier during 1830-1930) and overestimated (i.e. wetter during 1760-1830) past WTD compared to MILLENNIA model predictions. Importantly, simulated grouse moor management scenarios may explain the drier TA WTD predictions, resulting in considerable model predicted carbon losses and reduced methane emissions, mainly due to drainage. This study demonstrates the value of a site-specific and combined data-model validation step toward using TA-derived moisture conditions to understand past climate-driven peatland development and carbon budgets alongside modeling likely management impacts. © 2018 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Ensemble ecosystem modeling for predicting ecosystem response to predator reintroduction.

PubMed

Baker, Christopher M; Gordon, Ascelin; Bode, Michael

2017-04-01

Introducing a new or extirpated species to an ecosystem is risky, and managers need quantitative methods that can predict the consequences for the recipient ecosystem. Proponents of keystone predator reintroductions commonly argue that the presence of the predator will restore ecosystem function, but this has not always been the case, and mathematical modeling has an important role to play in predicting how reintroductions will likely play out. We devised an ensemble modeling method that integrates species interaction networks and dynamic community simulations and used it to describe the range of plausible consequences of 2 keystone-predator reintroductions: wolves (Canis lupus) to Yellowstone National Park and dingoes (Canis dingo) to a national park in Australia. Although previous methods for predicting ecosystem responses to such interventions focused on predicting changes around a given equilibrium, we used Lotka-Volterra equations to predict changing abundances through time. We applied our method to interaction networks for wolves in Yellowstone National Park and for dingoes in Australia. Our model replicated the observed dynamics in Yellowstone National Park and produced a larger range of potential outcomes for the dingo network. However, we also found that changes in small vertebrates or invertebrates gave a good indication about the potential future state of the system. Our method allowed us to predict when the systems were far from equilibrium. Our results showed that the method can also be used to predict which species may increase or decrease following a reintroduction and can identify species that are important to monitor (i.e., species whose changes in abundance give extra insight into broad changes in the system). Ensemble ecosystem modeling can also be applied to assess the ecosystem-wide implications of other types of interventions including assisted migration, biocontrol, and invasive species eradication. © 2016 Society for Conservation Biology.

Pollen dispersal slows geographical range shift and accelerates ecological niche shift under climate change

PubMed Central

Aguilée, Robin; Raoul, Gaël; Rousset, François; Ronce, Ophélie

2016-01-01

Species may survive climate change by migrating to track favorable climates and/or adapting to different climates. Several quantitative genetics models predict that species escaping extinction will change their geographical distribution while keeping the same ecological niche. We introduce pollen dispersal in these models, which affects gene flow but not directly colonization. We show that plant populations may escape extinction because of both spatial range and ecological niche shifts. Exact analytical formulas predict that increasing pollen dispersal distance slows the expected spatial range shift and accelerates the ecological niche shift. There is an optimal distance of pollen dispersal, which maximizes the sustainable rate of climate change. These conclusions hold in simulations relaxing several strong assumptions of our analytical model. Our results imply that, for plants with long distance of pollen dispersal, models assuming niche conservatism may not accurately predict their future distribution under climate change. PMID:27621443

Pollen dispersal slows geographical range shift and accelerates ecological niche shift under climate change.

PubMed

Aguilée, Robin; Raoul, Gaël; Rousset, François; Ronce, Ophélie

2016-09-27

Species may survive climate change by migrating to track favorable climates and/or adapting to different climates. Several quantitative genetics models predict that species escaping extinction will change their geographical distribution while keeping the same ecological niche. We introduce pollen dispersal in these models, which affects gene flow but not directly colonization. We show that plant populations may escape extinction because of both spatial range and ecological niche shifts. Exact analytical formulas predict that increasing pollen dispersal distance slows the expected spatial range shift and accelerates the ecological niche shift. There is an optimal distance of pollen dispersal, which maximizes the sustainable rate of climate change. These conclusions hold in simulations relaxing several strong assumptions of our analytical model. Our results imply that, for plants with long distance of pollen dispersal, models assuming niche conservatism may not accurately predict their future distribution under climate change.

Experimental evaluation of a recursive model identification technique for type 1 diabetes.

PubMed

Finan, Daniel A; Doyle, Francis J; Palerm, Cesar C; Bevier, Wendy C; Zisser, Howard C; Jovanovic, Lois; Seborg, Dale E

2009-09-01

A model-based controller for an artificial beta cell requires an accurate model of the glucose-insulin dynamics in type 1 diabetes subjects. To ensure the robustness of the controller for changing conditions (e.g., changes in insulin sensitivity due to illnesses, changes in exercise habits, or changes in stress levels), the model should be able to adapt to the new conditions by means of a recursive parameter estimation technique. Such an adaptive strategy will ensure that the most accurate model is used for the current conditions, and thus the most accurate model predictions are used in model-based control calculations. In a retrospective analysis, empirical dynamic autoregressive exogenous input (ARX) models were identified from glucose-insulin data for nine type 1 diabetes subjects in ambulatory conditions. Data sets consisted of continuous (5-minute) glucose concentration measurements obtained from a continuous glucose monitor, basal insulin infusion rates and times and amounts of insulin boluses obtained from the subjects' insulin pumps, and subject-reported estimates of the times and carbohydrate content of meals. Two identification techniques were investigated: nonrecursive, or batch methods, and recursive methods. Batch models were identified from a set of training data, whereas recursively identified models were updated at each sampling instant. Both types of models were used to make predictions of new test data. For the purpose of comparison, model predictions were compared to zero-order hold (ZOH) predictions, which were made by simply holding the current glucose value constant for p steps into the future, where p is the prediction horizon. Thus, the ZOH predictions are model free and provide a base case for the prediction metrics used to quantify the accuracy of the model predictions. In theory, recursive identification techniques are needed only when there are changing conditions in the subject that require model adaptation. Thus, the identification and validation techniques were performed with both "normal" data and data collected during conditions of reduced insulin sensitivity. The latter were achieved by having the subjects self-administer a medication, prednisone, for 3 consecutive days. The recursive models were allowed to adapt to this condition of reduced insulin sensitivity, while the batch models were only identified from normal data. Data from nine type 1 diabetes subjects in ambulatory conditions were analyzed; six of these subjects also participated in the prednisone portion of the study. For normal test data, the batch ARX models produced 30-, 45-, and 60-minute-ahead predictions that had average root mean square error (RMSE) values of 26, 34, and 40 mg/dl, respectively. For test data characterized by reduced insulin sensitivity, the batch ARX models produced 30-, 60-, and 90-minute-ahead predictions with average RMSE values of 27, 46, and 59 mg/dl, respectively; the recursive ARX models demonstrated similar performance with corresponding values of 27, 45, and 61 mg/dl, respectively. The identified ARX models (batch and recursive) produced more accurate predictions than the model-free ZOH predictions, but only marginally. For test data characterized by reduced insulin sensitivity, RMSE values for the predictions of the batch ARX models were 9, 5, and 5% more accurate than the ZOH predictions for prediction horizons of 30, 60, and 90 minutes, respectively. In terms of RMSE values, the 30-, 60-, and 90-minute predictions of the recursive models were more accurate than the ZOH predictions, by 10, 5, and 2%, respectively. In this experimental study, the recursively identified ARX models resulted in predictions of test data that were similar, but not superior, to the batch models. Even for the test data characteristic of reduced insulin sensitivity, the batch and recursive models demonstrated similar prediction accuracy. The predictions of the identified ARX models were only marginally more accurate than the model-free ZOH predictions. Given the simplicity of the ARX models and the computational ease with which they are identified, however, even modest improvements may justify the use of these models in a model-based controller for an artificial beta cell. 2009 Diabetes Technology Society.

PREDICTING CLIMATE-INDUCED RANGE SHIFTS: MODEL DIFFERENCES AND MODEL RELIABILITY

EPA Science Inventory

Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common ...

Examining the last few decades of global hydroclimate for evidence of anthropogenic change amidst natural variability

NASA Astrophysics Data System (ADS)

Seager, R.; Naik, N.; Ting, M.; Kushnir, Y.; Kelley, C. P.

2011-12-01

Climate models robustly predict that the deep tropics and mid-latitude-to-subpolar regions will moisten, and the subtropical dry zones both dry and expand, as a consequence of global warming driven by rising greenhouse gases. The models also predict that this transition to a more extreme climatological mean global hydroclimate should already be underway. Given the importance of these predictions it is an imperative that the climate science community assess whether there is evidence within the observational record that they are correct. This task is made difficult by the tremendous natural variability of the hydrological cycle on seasonal to multidecadal timescales. Here we will use instrumental observations, reanalyses, sea surface temperature forced atmosphere models and coupled model simulations, and a variety of methodologies, to attempt to separate global radiatively-forced hydroclimate change from ongoing natural variability. The results will be applied to explain trends and recent events in key regions such as Mexico, the United States and the Mediterranean. It is concluded that the signal of anthropogenic change is small compared to the amplitude of natural variability but that it is a discernible contributor. Globally the evidence reveals that radiatively-forced hydroclimate change is occurring with an amplitude and spatial pattern largely consistent with the predictions by IPCC AR4 models of hydroclimate change to date. However it will also be shown that the radiatively-forced component does not in and of itself provide a useful prediction of near term hydroclimate change because for many regions the amplitude of natural decadal variability is as large or larger. Useful predictions need to account for how natural variability may evolve as well as forced change.

Observed and Projected Changes to the Precipitation Annual Cycle

DOE PAGES

Marvel, Kate; Biasutti, Michela; Bonfils, Celine; ...

2017-06-08

Anthropogenic climate change is predicted to cause spatial and temporal shifts in precipitation patterns. These may be apparent in changes to the annual cycle of zonal mean precipitation P. Trends in the amplitude and phase of the P annual cycle in two long-term, global satellite datasets are broadly similar. Model-derived fingerprints of externally forced changes to the amplitude and phase of the P seasonal cycle, combined with these observations, enable a formal detection and attribution analysis. Observed amplitude changes are inconsistent with model estimates of internal variability but not attributable to the model-predicted response to external forcing. This mismatch betweenmore » observed and predicted amplitude changes is consistent with the sustained La Niña–like conditions that characterize the recent slowdown in the rise of the global mean temperature. However, observed changes to the annual cycle phase do not seem to be driven by this recent hiatus. Furthermore these changes are consistent with model estimates of forced changes, are inconsistent (in one observational dataset) with estimates of internal variability, and may suggest the emergence of an externally forced signal.« less

Elucidating Inherent Uncertainties in Data Assimilation for Predictions Incorporating Non-stationary Processes - Focus on Predictive Phenology

NASA Astrophysics Data System (ADS)

Lowman, L.; Barros, A. P.

2017-12-01

Data assimilation (DA) is the widely accepted procedure for estimating parameters within predictive models because of the adaptability and uncertainty quantification offered by Bayesian methods. DA applications in phenology modeling offer critical insights into how extreme weather or changes in climate impact the vegetation life cycle. Changes in leaf onset and senescence, root phenology, and intermittent leaf shedding imply large changes in the surface radiative, water, and carbon budgets at multiple scales. Models of leaf phenology require concurrent atmospheric and soil conditions to determine how biophysical plant properties respond to changes in temperature, light and water demand. Presently, climatological records for fraction of photosynthetically active radiation (FPAR) and leaf area index (LAI), the modelled states indicative of plant phenology, are not available. Further, DA models are typically trained on short periods of record (e.g. less than 10 years). Using limited records with a DA framework imposes non-stationarity on estimated parameters and the resulting predicted model states. This talk discusses how uncertainty introduced by the inherent non-stationarity of the modeled processes propagates through a land-surface hydrology model coupled to a predictive phenology model. How water demand is accounted for in the upscaling of DA model inputs and analysis period serves as a key source of uncertainty in the FPAR and LAI predictions. Parameters estimated from different DA effectively calibrate a plant water-use strategy within the land-surface hydrology model. For example, when extreme droughts are included in the DA period, the plants are trained to uptake water, transpire, and assimilate carbon under favorable conditions and quickly shut down at the onset of water stress.

Dynamic-landscape metapopulation models predict complex response of wildlife populations to climate and landscape change

Treesearch

Thomas W. Bonnot; Frank R. Thompson; Joshua J. Millspaugh

2017-01-01

The increasing need to predict how climate change will impact wildlife species has exposed limitations in how well current approaches model important biological processes at scales at which those processes interact with climate. We used a comprehensive approach that combined recent advances in landscape and population modeling into dynamic-landscape metapopulation...

Genetically informed ecological niche models improve climate change predictions.

PubMed

Ikeda, Dana H; Max, Tamara L; Allan, Gerard J; Lau, Matthew K; Shuster, Stephen M; Whitham, Thomas G

2017-01-01

We examined the hypothesis that ecological niche models (ENMs) more accurately predict species distributions when they incorporate information on population genetic structure, and concomitantly, local adaptation. Local adaptation is common in species that span a range of environmental gradients (e.g., soils and climate). Moreover, common garden studies have demonstrated a covariance between neutral markers and functional traits associated with a species' ability to adapt to environmental change. We therefore predicted that genetically distinct populations would respond differently to climate change, resulting in predicted distributions with little overlap. To test whether genetic information improves our ability to predict a species' niche space, we created genetically informed ecological niche models (gENMs) using Populus fremontii (Salicaceae), a widespread tree species in which prior common garden experiments demonstrate strong evidence for local adaptation. Four major findings emerged: (i) gENMs predicted population occurrences with up to 12-fold greater accuracy than models without genetic information; (ii) tests of niche similarity revealed that three ecotypes, identified on the basis of neutral genetic markers and locally adapted populations, are associated with differences in climate; (iii) our forecasts indicate that ongoing climate change will likely shift these ecotypes further apart in geographic space, resulting in greater niche divergence; (iv) ecotypes that currently exhibit the largest geographic distribution and niche breadth appear to be buffered the most from climate change. As diverse agents of selection shape genetic variability and structure within species, we argue that gENMs will lead to more accurate predictions of species distributions under climate change. © 2016 John Wiley & Sons Ltd.

Modeling responses of large-river fish populations to global climate change through downscaling and incorporation of predictive uncertainty

USGS Publications Warehouse

Wildhaber, Mark L.; Wikle, Christopher K.; Anderson, Christopher J.; Franz, Kristie J.; Moran, Edward H.; Dey, Rima; Mader, Helmut; Kraml, Julia

2012-01-01

Climate change operates over a broad range of spatial and temporal scales. Understanding its effects on ecosystems requires multi-scale models. For understanding effects on fish populations of riverine ecosystems, climate predicted by coarse-resolution Global Climate Models must be downscaled to Regional Climate Models to watersheds to river hydrology to population response. An additional challenge is quantifying sources of uncertainty given the highly nonlinear nature of interactions between climate variables and community level processes. We present a modeling approach for understanding and accomodating uncertainty by applying multi-scale climate models and a hierarchical Bayesian modeling framework to Midwest fish population dynamics and by linking models for system components together by formal rules of probability. The proposed hierarchical modeling approach will account for sources of uncertainty in forecasts of community or population response. The goal is to evaluate the potential distributional changes in an ecological system, given distributional changes implied by a series of linked climate and system models under various emissions/use scenarios. This understanding will aid evaluation of management options for coping with global climate change. In our initial analyses, we found that predicted pallid sturgeon population responses were dependent on the climate scenario considered.

Sensitivity of two dispersion models (AERMOD and ISCST3) to input parameters for a rural ground-level area source.

PubMed

Faulkner, William B; Shaw, Bryan W; Grosch, Tom

2008-10-01

As of December 2006, the American Meteorological Society/U.S. Environmental Protection Agency (EPA) Regulatory Model with Plume Rise Model Enhancements (AERMOD-PRIME; hereafter AERMOD) replaced the Industrial Source Complex Short Term Version 3 (ISCST3) as the EPA-preferred regulatory model. The change from ISCST3 to AERMOD will affect Prevention of Significant Deterioration (PSD) increment consumption as well as permit compliance in states where regulatory agencies limit property line concentrations using modeling analysis. Because of differences in model formulation and the treatment of terrain features, one cannot predict a priori whether ISCST3 or AERMOD will predict higher or lower pollutant concentrations downwind of a source. The objectives of this paper were to determine the sensitivity of AERMOD to various inputs and compare the highest downwind concentrations from a ground-level area source (GLAS) predicted by AERMOD to those predicted by ISCST3. Concentrations predicted using ISCST3 were sensitive to changes in wind speed, temperature, solar radiation (as it affects stability class), and mixing heights below 160 m. Surface roughness also affected downwind concentrations predicted by ISCST3. AERMOD was sensitive to changes in albedo, surface roughness, wind speed, temperature, and cloud cover. Bowen ratio did not affect the results from AERMOD. These results demonstrate AERMOD's sensitivity to small changes in wind speed and surface roughness. When AERMOD is used to determine property line concentrations, small changes in these variables may affect the distance within which concentration limits are exceeded by several hundred meters.

Climate change, species distribution models, and physiological performance metrics: predicting when biogeographic models are likely to fail.

PubMed

Woodin, Sarah A; Hilbish, Thomas J; Helmuth, Brian; Jones, Sierra J; Wethey, David S

2013-09-01

Modeling the biogeographic consequences of climate change requires confidence in model predictions under novel conditions. However, models often fail when extended to new locales, and such instances have been used as evidence of a change in physiological tolerance, that is, a fundamental niche shift. We explore an alternative explanation and propose a method for predicting the likelihood of failure based on physiological performance curves and environmental variance in the original and new environments. We define the transient event margin (TEM) as the gap between energetic performance failure, defined as CTmax, and the upper lethal limit, defined as LTmax. If TEM is large relative to environmental fluctuations, models will likely fail in new locales. If TEM is small relative to environmental fluctuations, models are likely to be robust for new locales, even when mechanism is unknown. Using temperature, we predict when biogeographic models are likely to fail and illustrate this with a case study. We suggest that failure is predictable from an understanding of how climate drives nonlethal physiological responses, but for many species such data have not been collected. Successful biogeographic forecasting thus depends on understanding when the mechanisms limiting distribution of a species will differ among geographic regions, or at different times, resulting in realized niche shifts. TEM allows prediction of the likelihood of such model failure.

Contemporary Drought and Future Effects of Climate Change on the Endangered Blunt-Nosed Leopard Lizard, Gambelia sila.

PubMed

Westphal, Michael F; Stewart, Joseph A E; Tennant, Erin N; Butterfield, H Scott; Sinervo, Barry

2016-01-01

Extreme weather events can provide unique opportunities for testing models that predict the effect of climate change. Droughts of increasing severity have been predicted under numerous models, thus contemporary droughts may allow us to test these models prior to the onset of the more extreme effects predicted with a changing climate. In the third year of an ongoing severe drought, surveys failed to detect neonate endangered blunt-nosed leopard lizards in a subset of previously surveyed populations where we expected to see them. By conducting surveys at a large number of sites across the range of the species over a short time span, we were able to establish a strong positive correlation between winter precipitation and the presence of neonate leopard lizards over geographic space. Our results are consistent with those of numerous longitudinal studies and are in accordance with predictive climate change models. We suggest that scientists can take immediate advantage of droughts while they are still in progress to test patterns of occurrence in other drought-sensitive species and thus provide for more robust models of climate change effects on biodiversity.

Negative impacts of climate change on cereal yields: statistical evidence from France

NASA Astrophysics Data System (ADS)

Gammans, Matthew; Mérel, Pierre; Ortiz-Bobea, Ariel

2017-05-01

In several world regions, climate change is predicted to negatively affect crop productivity. The recent statistical yield literature emphasizes the importance of flexibly accounting for the distribution of growing-season temperature to better represent the effects of warming on crop yields. We estimate a flexible statistical yield model using a long panel from France to investigate the impacts of temperature and precipitation changes on wheat and barley yields. Winter varieties appear sensitive to extreme cold after planting. All yields respond negatively to an increase in spring-summer temperatures and are a decreasing function of precipitation about historical precipitation levels. Crop yields are predicted to be negatively affected by climate change under a wide range of climate models and emissions scenarios. Under warming scenario RCP8.5 and holding growing areas and technology constant, our model ensemble predicts a 21.0% decline in winter wheat yield, a 17.3% decline in winter barley yield, and a 33.6% decline in spring barley yield by the end of the century. Uncertainty from climate projections dominates uncertainty from the statistical model. Finally, our model predicts that continuing technology trends would counterbalance most of the effects of climate change.

High skill in low-frequency climate response through fluctuation dissipation theorems despite structural instability.

PubMed

Majda, Andrew J; Abramov, Rafail; Gershgorin, Boris

2010-01-12

Climate change science focuses on predicting the coarse-grained, planetary-scale, longtime changes in the climate system due to either changes in external forcing or internal variability, such as the impact of increased carbon dioxide. The predictions of climate change science are carried out through comprehensive, computational atmospheric, and oceanic simulation models, which necessarily parameterize physical features such as clouds, sea ice cover, etc. Recently, it has been suggested that there is irreducible imprecision in such climate models that manifests itself as structural instability in climate statistics and which can significantly hamper the skill of computer models for climate change. A systematic approach to deal with this irreducible imprecision is advocated through algorithms based on the Fluctuation Dissipation Theorem (FDT). There are important practical and computational advantages for climate change science when a skillful FDT algorithm is established. The FDT response operator can be utilized directly for multiple climate change scenarios, multiple changes in forcing, and other parameters, such as damping and inverse modelling directly without the need of running the complex climate model in each individual case. The high skill of FDT in predicting climate change, despite structural instability, is developed in an unambiguous fashion using mathematical theory as guidelines in three different test models: a generic class of analytical models mimicking the dynamical core of the computer climate models, reduced stochastic models for low-frequency variability, and models with a significant new type of irreducible imprecision involving many fast, unstable modes.

Mitigation of climate change impacts on raptors by behavioural adaptation: ecological buffering mechanisms

NASA Astrophysics Data System (ADS)

Wichmann, Matthias C.; Groeneveld, Jürgen; Jeltsch, Florian; Grimm, Volker

2005-07-01

The predicted climate change causes deep concerns on the effects of increasing temperatures and changing precipitation patterns on species viability and, in turn, on biodiversity. Models of Population Viability Analysis (PVA) provide a powerful tool to assess the risk of species extinction. However, most PVA models do not take into account the potential effects of behavioural adaptations. Organisms might adapt to new environmental situations and thereby mitigate negative effects of climate change. To demonstrate such mitigation effects, we use an existing PVA model describing a population of the tawny eagle ( Aquila rapax) in the southern Kalahari. This model does not include behavioural adaptations. We develop a new model by assuming that the birds enlarge their average territory size to compensate for lower amounts of precipitation. Here, we found the predicted increase in risk of extinction due to climate change to be much lower than in the original model. However, this "buffering" of climate change by behavioural adaptation is not very effective in coping with increasing interannual variances. We refer to further examples of ecological "buffering mechanisms" from the literature and argue that possible buffering mechanisms should be given due consideration when the effects of climate change on biodiversity are to be predicted.

e-Cow: an animal model that predicts herbage intake, milk yield and live weight change in dairy cows grazing temperate pastures, with and without supplementary feeding.

PubMed

Baudracco, J; Lopez-Villalobos, N; Holmes, C W; Comeron, E A; Macdonald, K A; Barry, T N; Friggens, N C

2012-06-01

This animal simulation model, named e-Cow, represents a single dairy cow at grazing. The model integrates algorithms from three previously published models: a model that predicts herbage dry matter (DM) intake by grazing dairy cows, a mammary gland model that predicts potential milk yield and a body lipid model that predicts genetically driven live weight (LW) and body condition score (BCS). Both nutritional and genetic drives are accounted for in the prediction of energy intake and its partitioning. The main inputs are herbage allowance (HA; kg DM offered/cow per day), metabolisable energy and NDF concentrations in herbage and supplements, supplements offered (kg DM/cow per day), type of pasture (ryegrass or lucerne), days in milk, days pregnant, lactation number, BCS and LW at calving, breed or strain of cow and genetic merit, that is, potential yields of milk, fat and protein. Separate equations are used to predict herbage intake, depending on the cutting heights at which HA is expressed. The e-Cow model is written in Visual Basic programming language within Microsoft Excel®. The model predicts whole-lactation performance of dairy cows on a daily basis, and the main outputs are the daily and annual DM intake, milk yield and changes in BCS and LW. In the e-Cow model, neither herbage DM intake nor milk yield or LW change are needed as inputs; instead, they are predicted by the e-Cow model. The e-Cow model was validated against experimental data for Holstein-Friesian cows with both North American (NA) and New Zealand (NZ) genetics grazing ryegrass-based pastures, with or without supplementary feeding and for three complete lactations, divided into weekly periods. The model was able to predict animal performance with satisfactory accuracy, with concordance correlation coefficients of 0.81, 0.76 and 0.62 for herbage DM intake, milk yield and LW change, respectively. Simulations performed with the model showed that it is sensitive to genotype by feeding environment interactions. The e-Cow model tended to overestimate the milk yield of NA genotype cows at low milk yields, while it underestimated the milk yield of NZ genotype cows at high milk yields. The approach used to define the potential milk yield of the cow and equations used to predict herbage DM intake make the model applicable for predictions in countries with temperate pastures.

Temperature variability is a key component in accurately forecasting the effects of climate change on pest phenology.

PubMed

Merrill, Scott C; Peairs, Frank B

2017-02-01

Models describing the effects of climate change on arthropod pest ecology are needed to help mitigate and adapt to forthcoming changes. Challenges arise because climate data are at resolutions that do not readily synchronize with arthropod biology. Here we explain how multiple sources of climate and weather data can be synthesized to quantify the effects of climate change on pest phenology. Predictions of phenological events differ substantially between models that incorporate scale-appropriate temperature variability and models that do not. As an illustrative example, we predicted adult emergence of a pest of sunflower, the sunflower stem weevil Cylindrocopturus adspersus (LeConte). Predictions of the timing of phenological events differed by an average of 11 days between models with different temperature variability inputs. Moreover, as temperature variability increases, developmental rates accelerate. Our work details a phenological modeling approach intended to help develop tools to plan for and mitigate the effects of climate change. Results show that selection of scale-appropriate temperature data is of more importance than selecting a climate change emission scenario. Predictions derived without appropriate temperature variability inputs will likely result in substantial phenological event miscalculations. Additionally, results suggest that increased temperature instability will lead to accelerated pest development. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Predicting Bone Mechanical State During Recovery After Long-Duration Skeletal Unloading Using QCT and Finite Element Modeling

NASA Technical Reports Server (NTRS)

Chang, Katarina L.; Pennline, James A.

2013-01-01

During long-duration missions at the International Space Station, astronauts experience weightlessness leading to skeletal unloading. Unloading causes a lack of a mechanical stimulus that triggers bone cellular units to remove mass from the skeleton. A mathematical system of the cellular dynamics predicts theoretical changes to volume fractions and ash fraction in response to temporal variations in skeletal loading. No current model uses image technology to gather information about a skeletal site s initial properties to calculate bone remodeling changes and then to compare predicted bone strengths with the initial strength. The goal of this study is to use quantitative computed tomography (QCT) in conjunction with a computational model of the bone remodeling process to establish initial bone properties to predict changes in bone mechanics during bone loss and recovery with finite element (FE) modeling. Input parameters for the remodeling model include bone volume fraction and ash fraction, which are both computed from the QCT images. A non-destructive approach to measure ash fraction is also derived. Voxel-based finite element models (FEM) created from QCTs provide initial evaluation of bone strength. Bone volume fraction and ash fraction outputs from the computational model predict changes to the elastic modulus of bone via a two-parameter equation. The modulus captures the effect of bone remodeling and functions as the key to evaluate of changes in strength. Application of this time-dependent modulus to FEMs and composite beam theory enables an assessment of bone mechanics during recovery. Prediction of bone strength is not only important for astronauts, but is also pertinent to millions of patients with osteoporosis and low bone density.

WEPPCAT: An Online tool for assessing and managing the potential impacts of climate change on sediment loading to streams using the Water Erosion Prediction Project (WEPP) Model

EPA Science Inventory

WEPPCAT is an on-line tool that provides a flexible capability for creating user-determined climate change scenarios for assessing the potential impacts of climate change on sediment loading to streams using the USDA’s Water Erosion Prediction Project (WEPP) Model. In combination...

Modeling and predicting vegetation response of western USA grasslands, shrublands, and deserts to climate change (Chapter 1)

Treesearch

Megan M. Friggens; Marcus V. Warwell; Jeanne C. Chambers; Stanley G. Kitchen

2012-01-01

Experimental research and species distribution modeling predict large changes in the distributions of species and vegetation types in the Interior West due to climate change. Species’ responses will depend not only on their physiological tolerances but also on their phenology, establishment properties, biotic interactions, and capacity to evolve and migrate. Because...

A Resource-Based Modelling Framework to Assess Habitat Suitability for Steppe Birds in Semiarid Mediterranean Agricultural Systems

PubMed Central

Cardador, Laura; De Cáceres, Miquel; Bota, Gerard; Giralt, David; Casas, Fabián; Arroyo, Beatriz; Mougeot, François; Cantero-Martínez, Carlos; Moncunill, Judit; Butler, Simon J.; Brotons, Lluís

2014-01-01

European agriculture is undergoing widespread changes that are likely to have profound impacts on farmland biodiversity. The development of tools that allow an assessment of the potential biodiversity effects of different land-use alternatives before changes occur is fundamental to guiding management decisions. In this study, we develop a resource-based model framework to estimate habitat suitability for target species, according to simple information on species’ key resource requirements (diet, foraging habitat and nesting site), and examine whether it can be used to link land-use and local species’ distribution. We take as a study case four steppe bird species in a lowland area of the north-eastern Iberian Peninsula. We also compare the performance of our resource-based approach to that obtained through habitat-based models relating species’ occurrence and land-cover variables. Further, we use our resource-based approach to predict the effects that change in farming systems can have on farmland bird habitat suitability and compare these predictions with those obtained using the habitat-based models. Habitat suitability estimates generated by our resource-based models performed similarly (and better for one study species) than habitat based-models when predicting current species distribution. Moderate prediction success was achieved for three out of four species considered by resource-based models and for two of four by habitat-based models. Although, there is potential for improving the performance of resource-based models, they provide a structure for using available knowledge of the functional links between agricultural practices, provision of key resources and the response of organisms to predict potential effects of changing land-uses in a variety of context or the impacts of changes such as altered management practices that are not easily incorporated into habitat-based models. PMID:24667825

Modeling rate sensitivity of exercise transient responses to limb motion.

PubMed

Yamashiro, Stanley M; Kato, Takahide

2014-10-01

Transient responses of ventilation (V̇e) to limb motion can exhibit predictive characteristics. In response to a change in limb motion, a rapid change in V̇e is commonly observed with characteristics different than during a change in workload. This rapid change has been attributed to a feed-forward or adaptive response. Rate sensitivity was explored as a specific hypothesis to explain predictive V̇e responses to limb motion. A simple model assuming an additive feed-forward summation of V̇e proportional to the rate of change of limb motion was studied. This model was able to successfully account for the adaptive phase correction observed during human sinusoidal changes in limb motion. Adaptation of rate sensitivity might also explain the reduction of the fast component of V̇e responses previously reported following sudden exercise termination. Adaptation of the fast component of V̇e response could occur by reduction of rate sensitivity. Rate sensitivity of limb motion was predicted by the model to reduce the phase delay between limb motion and V̇e response without changing the steady-state response to exercise load. In this way, V̇e can respond more quickly to an exercise change without interfering with overall feedback control. The asymmetry between responses to an incremental and decremental ramp change in exercise can also be accounted for by the proposed model. Rate sensitivity leads to predicted behavior, which resembles responses observed in exercise tied to expiratory reserve volume. Copyright © 2014 the American Physiological Society.

Assessing conservation relevance of organism-environment relations using predicted changes in response variables

USGS Publications Warehouse

Gutzwiller, Kevin J.; Barrow, Wylie C.; White, Joseph D.; Johnson-Randall, Lori; Cade, Brian S.; Zygo, Lisa M.

2010-01-01

1. Organism–environment models are used widely in conservation. The degree to which they are useful for informing conservation decisions – the conservation relevance of these relations – is important because lack of relevance may lead to misapplication of scarce conservation resources or failure to resolve important conservation dilemmas. Even when models perform well based on model fit and predictive ability, conservation relevance of associations may not be clear without also knowing the magnitude and variability of predicted changes in response variables. 2. We introduce a method for evaluating the conservation relevance of organism–environment relations that employs confidence intervals for predicted changes in response variables. The confidence intervals are compared to a preselected magnitude of change that marks a threshold (trigger) for conservation action. To demonstrate the approach, we used a case study from the Chihuahuan Desert involving relations between avian richness and broad-scale patterns of shrubland. We considered relations for three winters and two spatial extents (1- and 2-km-radius areas) and compared predicted changes in richness to three thresholds (10%, 20% and 30% change). For each threshold, we examined 48 relations. 3. The method identified seven, four and zero conservation-relevant changes in mean richness for the 10%, 20% and 30% thresholds respectively. These changes were associated with major (20%) changes in shrubland cover, mean patch size, the coefficient of variation for patch size, or edge density but not with major changes in shrubland patch density. The relative rarity of conservation-relevant changes indicated that, overall, the relations had little practical value for informing conservation decisions about avian richness. 4. The approach we illustrate is appropriate for various response and predictor variables measured at any temporal or spatial scale. The method is broadly applicable across ecological environments, conservation objectives, types of statistical predictive models and levels of biological organization. By focusing on magnitudes of change that have practical significance, and by using the span of confidence intervals to incorporate uncertainty of predicted changes, the method can be used to help improve the effectiveness of conservation efforts.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marvel, Kate; Biasutti, Michela; Bonfils, Celine

Anthropogenic climate change is predicted to cause spatial and temporal shifts in precipitation patterns. These may be apparent in changes to the annual cycle of zonal mean precipitation P. Trends in the amplitude and phase of the P annual cycle in two long-term, global satellite datasets are broadly similar. Model-derived fingerprints of externally forced changes to the amplitude and phase of the P seasonal cycle, combined with these observations, enable a formal detection and attribution analysis. Observed amplitude changes are inconsistent with model estimates of internal variability but not attributable to the model-predicted response to external forcing. This mismatch betweenmore » observed and predicted amplitude changes is consistent with the sustained La Niña–like conditions that characterize the recent slowdown in the rise of the global mean temperature. However, observed changes to the annual cycle phase do not seem to be driven by this recent hiatus. Furthermore these changes are consistent with model estimates of forced changes, are inconsistent (in one observational dataset) with estimates of internal variability, and may suggest the emergence of an externally forced signal.« less

Climate change and maize yield in Iowa

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Hong; Twine, Tracy E.; Girvetz, Evan

Climate is changing across the world, including the major maize-growing state of Iowa in the USA. To maintain crop yields, farmers will need a suite of adaptation strategies, and choice of strategy will depend on how the local to regional climate is expected to change. Here we predict how maize yield might change through the 21 st century as compared with late 20 th century yields across Iowa, USA, a region representing ideal climate and soils for maize production that contributes substantially to the global maize economy. To account for climate model uncertainty, we drive a dynamic ecosystem model withmore » output from six climate models and two future climate forcing scenarios. Despite a wide range in the predicted amount of warming and change to summer precipitation, all simulations predict a decrease in maize yields from late 20 th century to middle and late 21 st century ranging from 15% to 50%. Linear regression of all models predicts a 6% state-averaged yield decrease for every 1°C increase in warm season average air temperature. When the influence of moisture stress on crop growth is removed from the model, yield decreases either remain the same or are reduced, depending on predicted changes in warm season precipitation. Lastly, our results suggest that even if maize were to receive all the water it needed, under the strongest climate forcing scenario yields will decline by 10-20% by the end of the 21 st century.« less

Climate change and maize yield in Iowa

DOE PAGES

Xu, Hong; Twine, Tracy E.; Girvetz, Evan

2016-05-24

Climate is changing across the world, including the major maize-growing state of Iowa in the USA. To maintain crop yields, farmers will need a suite of adaptation strategies, and choice of strategy will depend on how the local to regional climate is expected to change. Here we predict how maize yield might change through the 21 st century as compared with late 20 th century yields across Iowa, USA, a region representing ideal climate and soils for maize production that contributes substantially to the global maize economy. To account for climate model uncertainty, we drive a dynamic ecosystem model withmore » output from six climate models and two future climate forcing scenarios. Despite a wide range in the predicted amount of warming and change to summer precipitation, all simulations predict a decrease in maize yields from late 20 th century to middle and late 21 st century ranging from 15% to 50%. Linear regression of all models predicts a 6% state-averaged yield decrease for every 1°C increase in warm season average air temperature. When the influence of moisture stress on crop growth is removed from the model, yield decreases either remain the same or are reduced, depending on predicted changes in warm season precipitation. Lastly, our results suggest that even if maize were to receive all the water it needed, under the strongest climate forcing scenario yields will decline by 10-20% by the end of the 21 st century.« less

Enhancing seasonal climate prediction capacity for the Pacific countries

NASA Astrophysics Data System (ADS)

Kuleshov, Y.; Jones, D.; Hendon, H.; Charles, A.; Cottrill, A.; Lim, E.-P.; Langford, S.; de Wit, R.; Shelton, K.

2012-04-01

Seasonal and inter-annual climate variability is a major factor in determining the vulnerability of many Pacific Island Countries to climate change and there is need to improve weekly to seasonal range climate prediction capabilities beyond what is currently available from statistical models. In the seasonal climate prediction project under the Australian Government's Pacific Adaptation Strategy Assistance Program (PASAP), we describe a comprehensive project to strengthen the climate prediction capacities in National Meteorological Services in 14 Pacific Island Countries and East Timor. The intent is particularly to reduce the vulnerability of current services to a changing climate, and improve the overall level of information available assist with managing climate variability. Statistical models cannot account for aspects of climate variability and change that are not represented in the historical record. In contrast, dynamical physics-based models implicitly include the effects of a changing climate whatever its character or cause and can predict outcomes not seen previously. The transition from a statistical to a dynamical prediction system provides more valuable and applicable climate information to a wide range of climate sensitive sectors throughout the countries of the Pacific region. In this project, we have developed seasonal climate outlooks which are based upon the current dynamical model POAMA (Predictive Ocean-Atmosphere Model for Australia) seasonal forecast system. At present, meteorological services of the Pacific Island Countries largely employ statistical models for seasonal outlooks. Outcomes of the PASAP project enhanced capabilities of the Pacific Island Countries in seasonal prediction providing National Meteorological Services with an additional tool to analyse meteorological variables such as sea surface temperatures, air temperature, pressure and rainfall using POAMA outputs and prepare more accurate seasonal climate outlooks.

End-of-Discharge and End-of-Life Prediction in Lithium-Ion Batteries with Electrochemistry-Based Aging Models

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Kulkarni, Chetan S.

2016-01-01

As batteries become increasingly prevalent in complex systems such as aircraft and electric cars, monitoring and predicting battery state of charge and state of health becomes critical. In order to accurately predict the remaining battery power to support system operations for informed operational decision-making, age-dependent changes in dynamics must be accounted for. Using an electrochemistry-based model, we investigate how key parameters of the battery change as aging occurs, and develop models to describe aging through these key parameters. Using these models, we demonstrate how we can (i) accurately predict end-of-discharge for aged batteries, and (ii) predict the end-of-life of a battery as a function of anticipated usage. The approach is validated through an experimental set of randomized discharge profiles.

Prediction of moisture variation during composting process: A comparison of mathematical models.

PubMed

Wang, Yongjiang; Ai, Ping; Cao, Hongliang; Liu, Zhigang

2015-10-01

This study was carried out to develop and compare three models for simulating the moisture content during composting. Model 1 described changes in water content using mass balance, while Model 2 introduced a liquid-gas transferred water term. Model 3 predicted changes in moisture content without complex degradation kinetics. Average deviations for Model 1-3 were 8.909, 7.422 and 5.374 kg m(-3) while standard deviations were 10.299, 8.374 and 6.095, respectively. The results showed that Model 1 is complex and involves more state variables, but can be used to reveal the effect of humidity on moisture content. Model 2 tested the hypothesis of liquid-gas transfer and was shown to be capable of predicting moisture content during composting. Model 3 could predict water content well without considering degradation kinetics. Copyright © 2015 Elsevier Ltd. All rights reserved.

Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts.

PubMed

Bokhorst, Stef; Pedersen, Stine Højlund; Brucker, Ludovic; Anisimov, Oleg; Bjerke, Jarle W; Brown, Ross D; Ehrich, Dorothee; Essery, Richard L H; Heilig, Achim; Ingvander, Susanne; Johansson, Cecilia; Johansson, Margareta; Jónsdóttir, Ingibjörg Svala; Inga, Niila; Luojus, Kari; Macelloni, Giovanni; Mariash, Heather; McLennan, Donald; Rosqvist, Gunhild Ninis; Sato, Atsushi; Savela, Hannele; Schneebeli, Martin; Sokolov, Aleksandr; Sokratov, Sergey A; Terzago, Silvia; Vikhamar-Schuler, Dagrun; Williamson, Scott; Qiu, Yubao; Callaghan, Terry V

2016-09-01

Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region.

Changing Arctic Snow Cover: A Review of Recent Developments and Assessment of Future Needs for Observations, Modelling, and Impacts

NASA Technical Reports Server (NTRS)

Bokhorst, Stef; Pedersen, Stine Hojlund; Brucker, Ludovic; Anisimov, Oleg; Bjerke, Jarle W.; Brown, Ross D.; Ehrich, Dorothee; Essery, Richard L. H.; Heilig, Achim; Ingvander, Susanne;

Initial CGE Model Results Summary Exogenous and Endogenous Variables Tests

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edwards, Brian Keith; Boero, Riccardo; Rivera, Michael Kelly

The following discussion presents initial results of tests of the most recent version of the National Infrastructure Simulation and Analysis Center Dynamic Computable General Equilibrium (CGE) model developed by Los Alamos National Laboratory (LANL). The intent of this is to test and assess the model’s behavioral properties. The test evaluated whether the predicted impacts are reasonable from a qualitative perspective. This issue is whether the predicted change, be it an increase or decrease in other model variables, is consistent with prior economic intuition and expectations about the predicted change. One of the purposes of this effort is to determine whethermore » model changes are needed in order to improve its behavior qualitatively and quantitatively.« less

Thermal niche estimators and the capability of poor dispersal species to cope with climate change

PubMed Central

Sánchez-Fernández, David; Rizzo, Valeria; Cieslak, Alexandra; Faille, Arnaud; Fresneda, Javier; Ribera, Ignacio

2016-01-01

For management strategies in the context of global warming, accurate predictions of species response are mandatory. However, to date most predictions are based on niche (bioclimatic) models that usually overlook biotic interactions, behavioral adjustments or adaptive evolution, and assume that species can disperse freely without constraints. The deep subterranean environment minimises these uncertainties, as it is simple, homogeneous and with constant environmental conditions. It is thus an ideal model system to study the effect of global change in species with poor dispersal capabilities. We assess the potential fate of a lineage of troglobitic beetles under global change predictions using different approaches to estimate their thermal niche: bioclimatic models, rates of thermal niche change estimated from a molecular phylogeny, and data from physiological studies. Using bioclimatic models, at most 60% of the species were predicted to have suitable conditions in 2080. Considering the rates of thermal niche change did not improve this prediction. However, physiological data suggest that subterranean species have a broad thermal tolerance, allowing them to stand temperatures never experienced through their evolutionary history. These results stress the need of experimental approaches to assess the capability of poor dispersal species to cope with temperatures outside those they currently experience. PMID:26983802

Developing and Testing a Model to Predict Outcomes of Organizational Change

PubMed Central

Gustafson, David H; Sainfort, François; Eichler, Mary; Adams, Laura; Bisognano, Maureen; Steudel, Harold

2003-01-01

Objective To test the effectiveness of a Bayesian model employing subjective probability estimates for predicting success and failure of health care improvement projects. Data Sources Experts' subjective assessment data for model development and independent retrospective data on 221 healthcare improvement projects in the United States, Canada, and the Netherlands collected between 1996 and 2000 for validation. Methods A panel of theoretical and practical experts and literature in organizational change were used to identify factors predicting the outcome of improvement efforts. A Bayesian model was developed to estimate probability of successful change using subjective estimates of likelihood ratios and prior odds elicited from the panel of experts. A subsequent retrospective empirical analysis of change efforts in 198 health care organizations was performed to validate the model. Logistic regression and ROC analysis were used to evaluate the model's performance using three alternative definitions of success. Data Collection For the model development, experts' subjective assessments were elicited using an integrative group process. For the validation study, a staff person intimately involved in each improvement project responded to a written survey asking questions about model factors and project outcomes. Results Logistic regression chi-square statistics and areas under the ROC curve demonstrated a high level of model performance in predicting success. Chi-square statistics were significant at the 0.001 level and areas under the ROC curve were greater than 0.84. Conclusions A subjective Bayesian model was effective in predicting the outcome of actual improvement projects. Additional prospective evaluations as well as testing the impact of this model as an intervention are warranted. PMID:12785571

Depression and Delinquency Covariation in an Accelerated Longitudinal Sample of Adolescents

PubMed Central

Kofler, Michael J.; McCart, Michael R.; Zajac, Kristyn; Ruggiero, Kenneth J.; Saunders, Benjamin E.; Kilpatrick, Dean G.

2015-01-01

Objectives The current study tested opposing predictions stemming from the failure and acting out theories of depression-delinquency covariation. Methods Participants included a nationwide longitudinal sample of adolescents (N = 3,604) ages 12 to 17. Competing models were tested using cohort-sequential latent growth curve modeling to determine whether depressive symptoms at age 12 (baseline) predicted concurrent and age-related changes in delinquent behavior, whether the opposite pattern was apparent (delinquency predicting depression), and whether initial levels of depression predict changes in delinquency significantly better than vice versa. Results Early depressive symptoms predicted age-related changes in delinquent behavior significantly better than early delinquency predicted changes in depressive symptoms. In addition, the impact of gender on age-related changes in delinquent symptoms was mediated by gender differences in depressive symptom changes, indicating that depressive symptoms are a particularly salient risk factor for delinquent behavior in girls. Conclusion Early depressive symptoms represent a significant risk factor for later delinquent behavior – especially for girls – and appear to be a better predictor of later delinquency than early delinquency is of later depression. These findings provide support for the acting out theory and contradict failure theory predictions. PMID:21787049

Regression Simulation Model. Appendix X. Users Manual,

DTIC Science & Technology

1981-03-01

change as the prediction equations become refined. Whereas no notice will be provided when the changes are made, the programs will be modified such that...NATIONAL BUREAU Of STANDARDS 1963 A ___,_ __ _ __ _ . APPENDIX X ( R4/ EGRESSION IMULATION ’jDEL. Ape’A ’) 7 USERS MANUA submitted to The Great River...regression analysis and to establish a prediction equation (model). The prediction equation contains the partial regression coefficients (B-weights) which

Assessing effects of variation in global climate data sets on spatial predictions from climate envelope models

USGS Publications Warehouse

Romañach, Stephanie; Watling, James I.; Fletcher, Robert J.; Speroterra, Carolina; Bucklin, David N.; Brandt, Laura A.; Pearlstine, Leonard G.; Escribano, Yesenia; Mazzotti, Frank J.

2014-01-01

Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role of uncertainty in future conditions on model predictions, models also may be sensitive to the initial conditions on which they are trained. Although climate envelope models are usually trained using data on contemporary climate, we lack systematic comparisons of model performance and predictions across alternative climate data sets available for model training. Here, we seek to fill that gap by comparing variability in predictions between two contemporary climate data sets to variability in spatial predictions among three alternative projections of future climate. Overall, correlations between monthly temperature and precipitation variables were very high for both contemporary and future data. Model performance varied across algorithms, but not between two alternative contemporary climate data sets. Spatial predictions varied more among alternative general-circulation models describing future climate conditions than between contemporary climate data sets. However, we did find that climate envelope models with low Cohen's kappa scores made more discrepant spatial predictions between climate data sets for the contemporary period than did models with high Cohen's kappa scores. We suggest conservation planners evaluate multiple performance metrics and be aware of the importance of differences in initial conditions for spatial predictions from climate envelope models.

Temperature dependence of the hydrated electron's excited-state relaxation. I. Simulation predictions of resonance Raman and pump-probe transient absorption spectra of cavity and non-cavity models

NASA Astrophysics Data System (ADS)

Zho, Chen-Chen; Farr, Erik P.; Glover, William J.; Schwartz, Benjamin J.

2017-08-01

We use one-electron non-adiabatic mixed quantum/classical simulations to explore the temperature dependence of both the ground-state structure and the excited-state relaxation dynamics of the hydrated electron. We compare the results for both the traditional cavity picture and a more recent non-cavity model of the hydrated electron and make definite predictions for distinguishing between the different possible structural models in future experiments. We find that the traditional cavity model shows no temperature-dependent change in structure at constant density, leading to a predicted resonance Raman spectrum that is essentially temperature-independent. In contrast, the non-cavity model predicts a blue-shift in the hydrated electron's resonance Raman O-H stretch with increasing temperature. The lack of a temperature-dependent ground-state structural change of the cavity model also leads to a prediction of little change with temperature of both the excited-state lifetime and hot ground-state cooling time of the hydrated electron following photoexcitation. This is in sharp contrast to the predictions of the non-cavity model, where both the excited-state lifetime and hot ground-state cooling time are expected to decrease significantly with increasing temperature. These simulation-based predictions should be directly testable by the results of future time-resolved photoelectron spectroscopy experiments. Finally, the temperature-dependent differences in predicted excited-state lifetime and hot ground-state cooling time of the two models also lead to different predicted pump-probe transient absorption spectroscopy of the hydrated electron as a function of temperature. We perform such experiments and describe them in Paper II [E. P. Farr et al., J. Chem. Phys. 147, 074504 (2017)], and find changes in the excited-state lifetime and hot ground-state cooling time with temperature that match well with the predictions of the non-cavity model. In particular, the experiments reveal stimulated emission from the excited state with an amplitude and lifetime that decreases with increasing temperature, a result in contrast to the lack of stimulated emission predicted by the cavity model but in good agreement with the non-cavity model. Overall, until ab initio calculations describing the non-adiabatic excited-state dynamics of an excess electron with hundreds of water molecules at a variety of temperatures become computationally feasible, the simulations presented here provide a definitive route for connecting the predictions of cavity and non-cavity models of the hydrated electron with future experiments.

Predicting habitat suitability and geographic distribution of anchovy (Engraulis ringens) due to climate change in the coastal areas off Chile

NASA Astrophysics Data System (ADS)

Silva, Claudio; Andrade, Isabel; Yáñez, Eleuterio; Hormazabal, Samuel; Barbieri, María Ángela; Aranis, Antonio; Böhm, Gabriela

2016-08-01

The effects of climate change on ocean conditions will have impacts on fish stocks, primarily through physiological and behavioural effects, such as changes in growth, reproduction, mortality and distribution. Habitat and distribution predictions for marine fishery species under climate change scenarios are important for understanding the overall impacts of such global changes on the human society and on the ecosystem. In this study, we examine the impacts of climate change on anchovy fisheries off Chile using predicted changes in global models according to the National Centre for Atmospheric Research (NCAR) Community Climate System Model 3.0 (CCSM3) and IPCC high future CO2 emission scenario A2, habitat suitability index (HSI) models and satellite-based sea surface temperature (SST) and chlorophyll-a (Chl-a) estimates from high-resolution regional models for the simulation period 2015-2065. Predictions of SST from global climate models were regionalised using the Delta statistical downscaling technique. Predictions of chlorophyll-a were developed using historical Chl-a and SST (2003-2013) satellite data and applying a harmonic model. The results show an increase in SST of up to 2.5 °C by 2055 in the north and central-south area for an A2 scenario. The habitat suitability index model was developed using historical (2001-2011) monthly fisheries and environmental data. The catch per unit effort (CPUE) was used as an abundance index in developing the HSI models and was calculated as the total catch (ton) by hold capacity (m3) in a 10‧ × 10‧ fishing grid square of anchovy, integrated over one month of fishing activity. The environmental data included the distance to coast (DC), thermal (SST) and food availability (Chl-a) conditions. The HSI modelling consists of estimating SI curves based on available evidence regarding the optimum range of environmental conditions for anchovy and estimating an integrated HSI using the Arithmetic Mean Model (AMM) method. The results of this work show that the model has produced robust estimates of habitat suitability and geographic distribution off Chile and has been especially effective in capturing the spatial and temporal variability of CPUE. Using IDRISI geographical information system (GIS), these HSI models simulated monthly changes in the habitat suitability (i.e., relative abundance) and distribution of anchovy off Chile forced by changes in the regionalised SST and Chl-a as projected by the NCAR model under the A2 emission scenario. The simulations predicted a moderate negative change of 17% and 13% for the north and central-south areas, respectively, in the habitat suitability (i.e., potential relative abundance) of anchovy by 2055.

Predicting the natural flow regime: Models for assessing hydrological alteration in streams

USGS Publications Warehouse

Carlisle, D.M.; Falcone, J.; Wolock, D.M.; Meador, M.R.; Norris, R.H.

2009-01-01

Understanding the extent to which natural streamflow characteristics have been altered is an important consideration for ecological assessments of streams. Assessing hydrologic condition requires that we quantify the attributes of the flow regime that would be expected in the absence of anthropogenic modifications. The objective of this study was to evaluate whether selected streamflow characteristics could be predicted at regional and national scales using geospatial data. Long-term, gaged river basins distributed throughout the contiguous US that had streamflow characteristics representing least disturbed or near pristine conditions were identified. Thirteen metrics of the magnitude, frequency, duration, timing and rate of change of streamflow were calculated using a 20-50 year period of record for each site. We used random forests (RF), a robust statistical modelling approach, to develop models that predicted the value for each streamflow metric using natural watershed characteristics. We compared the performance (i.e. bias and precision) of national- and regional-scale predictive models to that of models based on landscape classifications, including major river basins, ecoregions and hydrologic landscape regions (HLR). For all hydrologic metrics, landscape stratification models produced estimates that were less biased and more precise than a null model that accounted for no natural variability. Predictive models at the national and regional scale performed equally well, and substantially improved predictions of all hydrologic metrics relative to landscape stratification models. Prediction error rates ranged from 15 to 40%, but were 25% for most metrics. We selected three gaged, non-reference sites to illustrate how predictive models could be used to assess hydrologic condition. These examples show how the models accurately estimate predisturbance conditions and are sensitive to changes in streamflow variability associated with long-term land-use change. We also demonstrate how the models can be applied to predict expected natural flow characteristics at ungaged sites. ?? 2009 John Wiley & Sons, Ltd.

Predicting future changes in Muskegon River Watershed game fish distributions under future land cover alteration and climate change scenarios

USGS Publications Warehouse

Steen, Paul J.; Wiley, Michael J.; Schaeffer, Jeffrey S.

2010-01-01

Future alterations in land cover and climate are likely to cause substantial changes in the ranges of fish species. Predictive distribution models are an important tool for assessing the probability that these changes will cause increases or decreases in or the extirpation of species. Classification tree models that predict the probability of game fish presence were applied to the streams of the Muskegon River watershed, Michigan. The models were used to study three potential future scenarios: (1) land cover change only, (2) land cover change and a 3°C increase in air temperature by 2100, and (3) land cover change and a 5°C increase in air temperature by 2100. The analysis indicated that the expected change in air temperature and subsequent change in water temperatures would result in the decline of coldwater fish in the Muskegon watershed by the end of the 21st century while cool- and warmwater species would significantly increase their ranges. The greatest decline detected was a 90% reduction in the probability that brook trout Salvelinus fontinalis would occur in Bigelow Creek. The greatest increase was a 276% increase in the probability that northern pike Esox lucius would occur in the Middle Branch River. Changes in land cover are expected to cause large changes in a few fish species, such as walleye Sander vitreus and Chinook salmon Oncorhynchus tshawytscha, but not to drive major changes in species composition. Managers can alter stream environmental conditions to maximize the probability that species will reside in particular stream reaches through application of the classification tree models. Such models represent a good way to predict future changes, as they give quantitative estimates of the n-dimensional niches for particular species.

Eco-genetic modeling of contemporary life-history evolution.

PubMed

Dunlop, Erin S; Heino, Mikko; Dieckmann, Ulf

2009-10-01

We present eco-genetic modeling as a flexible tool for exploring the course and rates of multi-trait life-history evolution in natural populations. We build on existing modeling approaches by combining features that facilitate studying the ecological and evolutionary dynamics of realistically structured populations. In particular, the joint consideration of age and size structure enables the analysis of phenotypically plastic populations with more than a single growth trajectory, and ecological feedback is readily included in the form of density dependence and frequency dependence. Stochasticity and life-history trade-offs can also be implemented. Critically, eco-genetic models permit the incorporation of salient genetic detail such as a population's genetic variances and covariances and the corresponding heritabilities, as well as the probabilistic inheritance and phenotypic expression of quantitative traits. These inclusions are crucial for predicting rates of evolutionary change on both contemporary and longer timescales. An eco-genetic model can be tightly coupled with empirical data and therefore may have considerable practical relevance, in terms of generating testable predictions and evaluating alternative management measures. To illustrate the utility of these models, we present as an example an eco-genetic model used to study harvest-induced evolution of multiple traits in Atlantic cod. The predictions of our model (most notably that harvesting induces a genetic reduction in age and size at maturation, an increase or decrease in growth capacity depending on the minimum-length limit, and an increase in reproductive investment) are corroborated by patterns observed in wild populations. The predicted genetic changes occur together with plastic changes that could phenotypically mask the former. Importantly, our analysis predicts that evolutionary changes show little signs of reversal following a harvest moratorium. This illustrates how predictions offered by eco-genetic models can enable and guide evolutionarily sustainable resource management.

Thermal and hydrologic responses to climate change predict marked alterations in boreal stream invertebrate assemblages.

PubMed

Mustonen, Kaisa-Riikka; Mykrä, Heikki; Marttila, Hannu; Sarremejane, Romain; Veijalainen, Noora; Sippel, Kalle; Muotka, Timo; Hawkins, Charles P

2018-06-01

Air temperature at the northernmost latitudes is predicted to increase steeply and precipitation to become more variable by the end of the 21st century, resulting in altered thermal and hydrological regimes. We applied five climate scenarios to predict the future (2070-2100) benthic macroinvertebrate assemblages at 239 near-pristine sites across Finland (ca. 1200 km latitudinal span). We used a multitaxon distribution model with air temperature and modeled daily flow as predictors. As expected, projected air temperature increased the most in northernmost Finland. Predicted taxonomic richness also increased the most in northern Finland, congruent with the predicted northwards shift of many species' distributions. Compositional changes were predicted to be high even without changes in richness, suggesting that species replacement may be the main mechanism causing climate-induced changes in macroinvertebrate assemblages. Northern streams were predicted to lose much of the seasonality of their flow regimes, causing potentially marked changes in stream benthic assemblages. Sites with the highest loss of seasonality were predicted to support future assemblages that deviate most in compositional similarity from the present-day assemblages. Macroinvertebrate assemblages were also predicted to change more in headwaters than in larger streams, as headwaters were particularly sensitive to changes in flow patterns. Our results emphasize the importance of focusing protection and mitigation on headwater streams with high-flow seasonality because of their vulnerability to climate change. © 2018 John Wiley & Sons Ltd.

Data-Conditioned Distributions of Groundwater Recharge Under Climate Change Scenarios

NASA Astrophysics Data System (ADS)

McLaughlin, D.; Ng, G. C.; Entekhabi, D.; Scanlon, B.

2008-12-01

Groundwater recharge is likely to be impacted by climate change, with changes in precipitation amounts altering moisture availability and changes in temperature affecting evaporative demand. This could have major implications for sustainable aquifer pumping rates and contaminant transport into groundwater reservoirs in the future, thus making predictions of recharge under climate change very important. Unfortunately, in dry environments where groundwater resources are often most critical, low recharge rates are difficult to resolve due to high sensitivity to modeling and input errors. Some recent studies on climate change and groundwater have considered recharge using a suite of general circulation model (GCM) weather predictions, an obvious and key source of uncertainty. This work extends beyond those efforts by also accounting for uncertainty in other land-surface model inputs in a probabilistic manner. Recharge predictions are made using a range of GCM projections for a rain-fed cotton site in the semi-arid Southern High Plains region of Texas. Results showed that model simulations using a range of unconstrained literature-based parameter values produce highly uncertain and often misleading recharge rates. Thus, distributional recharge predictions are found using soil and vegetation parameters conditioned on current unsaturated zone soil moisture and chloride concentration observations; assimilation of observations is carried out with an ensemble importance sampling method. Our findings show that the predicted distribution shapes can differ for the various GCM conditions considered, underscoring the importance of probabilistic analysis over deterministic simulations. The recharge predictions indicate that the temporal distribution (over seasons and rain events) of climate change will be particularly critical for groundwater impacts. Overall, changes in recharge amounts and intensity were often more pronounced than changes in annual precipitation and temperature, thus suggesting high susceptibility of groundwater systems to future climate change. Our approach provides a probabilistic sensitivity analysis of recharge under potential climate changes, which will be critical for future management of water resources.

Airport Noise Prediction Model -- MOD 7

DOT National Transportation Integrated Search

1978-07-01

The MOD 7 Airport Noise Prediction Model is fully operational. The language used is Fortran, and it has been run on several different computer systems. Its capabilities include prediction of noise levels for single parameter changes, for multiple cha...

Measuring and predicting prostate cancer related quality of life changes using EPIC for clinical practice.

PubMed

Chipman, Jonathan J; Sanda, Martin G; Dunn, Rodney L; Wei, John T; Litwin, Mark S; Crociani, Catrina M; Regan, Meredith M; Chang, Peter

2014-03-01

We expanded the clinical usefulness of EPIC-CP (Expanded Prostate Cancer Index Composite for Clinical Practice) by evaluating its responsiveness to health related quality of life changes, defining the minimally important differences for an individual patient change in each domain and applying it to a sexual outcome prediction model. In 1,201 subjects from a previously described multicenter longitudinal cohort we modeled the EPIC-CP domain scores of each treatment group before treatment, and at short-term and long-term followup. We considered a posttreatment domain score change from pretreatment of 0.5 SD or greater clinically significant and p ≤ 0.01 statistically significant. We determined the domain minimally important differences using the pooled 0.5 SD of the 2, 6, 12 and 24-month posttreatment changes from pretreatment values. We then recalibrated an EPIC-CP based nomogram model predicting 2-year post-prostatectomy functional erection from that developed using EPIC-26. For each health related quality of life domain EPIC-CP was sensitive to similar posttreatment health related quality of life changes with time, as was observed using EPIC-26. The EPIC-CP minimally important differences in changes in the urinary incontinence, urinary irritation/obstruction, bowel, sexual and vitality/hormonal domains were 1.0, 1.3, 1.2, 1.6 and 1.0, respectively. The EPIC-CP based sexual prediction model performed well (AUC 0.76). It showed robust agreement with its EPIC-26 based counterpart with 10% or less predicted probability differences between models in 95% of individuals and a mean ± SD difference of 0.0 ± 0.05 across all individuals. EPIC-CP is responsive to health related quality of life changes during convalescence and it can be used to predict 2-year post-prostatectomy sexual outcomes. It can facilitate shared medical decision making and patient centered care. Copyright © 2014 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Predicting climate-induced range shifts: model differences and model reliability.

Treesearch

Joshua J. Lawler; Denis White; Ronald P. Neilson; Andrew R. Blaustein

2006-01-01

Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common data set, we investigated the potential implications of alternative modeling approaches for...

Fire spread in chaparral – a comparison of laboratory data and model predictions in burning live fuels

Treesearch

David R. Weise; Eunmo Koo; Xiangyang Zhou; Shankar Mahalingam; Frédéric Morandini; Jacques-Henri Balbi

2016-01-01

Fire behaviour data from 240 laboratory fires in high-density live chaparral fuel beds were compared with model predictions. Logistic regression was used to develop a model to predict fire spread success in the fuel beds and linear regression was used to predict rate of spread. Predictions from the Rothermel equation and three proposed changes as well as two physically...

Psychosocial work environment factors and weight change: a prospective study among Danish health care workers.

PubMed

Gram Quist, Helle; Christensen, Ulla; Christensen, Karl Bang; Aust, Birgit; Borg, Vilhelm; Bjorner, Jakob B

2013-01-17

Lifestyle variables may serve as important intermediate factors between psychosocial work environment and health outcomes. Previous studies, focussing on work stress models have shown mixed and weak results in relation to weight change. This study aims to investigate psychosocial factors outside the classical work stress models as potential predictors of change in body mass index (BMI) in a population of health care workers. A cohort study, with three years follow-up, was conducted among Danish health care workers (3982 women and 152 men). Logistic regression analyses examined change in BMI (more than +/- 2 kg/m(2)) as predicted by baseline psychosocial work factors (work pace, workload, quality of leadership, influence at work, meaning of work, predictability, commitment, role clarity, and role conflicts) and five covariates (age, cohabitation, physical work demands, type of work position and seniority). Among women, high role conflicts predicted weight gain, while high role clarity predicted both weight gain and weight loss. Living alone also predicted weight gain among women, while older age decreased the odds of weight gain. High leadership quality predicted weight loss among men. Associations were generally weak, with the exception of quality of leadership, age, and cohabitation. This study of a single occupational group suggested a few new risk factors for weight change outside the traditional work stress models.

Impact of climate change on mercury concentrations and deposition in the eastern United States.

PubMed

Megaritis, Athanasios G; Murphy, Benjamin N; Racherla, Pavan N; Adams, Peter J; Pandis, Spyros N

2014-07-15

The global-regional climate-air pollution modeling system (GRE-CAPS) was applied over the eastern United States to study the impact of climate change on the concentration and deposition of atmospheric mercury. Summer and winter periods (300 days for each) were simulated, and the present-day model predictions (2000s) were compared to the future ones (2050s) assuming constant emissions. Climate change affects Hg(2+) concentrations in both periods. On average, atmospheric Hg(2+) levels are predicted to increase in the future by 3% in summer and 5% in winter respectively due to enhanced oxidation of Hg(0) under higher temperatures. The predicted concentration change of Hg(2+) was found to vary significantly in space due to regional-scale changes in precipitation, ranging from -30% to 30% during summer and -20% to 40% during winter. Particulate mercury, Hg(p) has a similar spatial response to climate change as Hg(2+), while Hg(0) levels are not predicted to change significantly. In both periods, the response of mercury deposition to climate change varies spatially with an average predicted increase of 6% during summer and 4% during winter. During summer, deposition increases are predicted mostly in the western parts of the domain while mercury deposition is predicted to decrease in the Northeast and also in many areas in the Midwest and Southeast. During winter mercury deposition is predicted to change from -30% to 50% mainly due to the changes in rainfall and the corresponding changes in wet deposition. Copyright © 2014 Elsevier B.V. All rights reserved.

Do causal concentration-response functions exist? A critical review of associational and causal relations between fine particulate matter and mortality.

PubMed

Cox, Louis Anthony Tony

2017-08-01

Concentration-response (C-R) functions relating concentrations of pollutants in ambient air to mortality risks or other adverse health effects provide the basis for many public health risk assessments, benefits estimates for clean air regulations, and recommendations for revisions to existing air quality standards. The assumption that C-R functions relating levels of exposure and levels of response estimated from historical data usefully predict how future changes in concentrations would change risks has seldom been carefully tested. This paper critically reviews literature on C-R functions for fine particulate matter (PM2.5) and mortality risks. We find that most of them describe historical associations rather than valid causal models for predicting effects of interventions that change concentrations. The few papers that explicitly attempt to model causality rely on unverified modeling assumptions, casting doubt on their predictions about effects of interventions. A large literature on modern causal inference algorithms for observational data has been little used in C-R modeling. Applying these methods to publicly available data from Boston and the South Coast Air Quality Management District around Los Angeles shows that C-R functions estimated for one do not hold for the other. Changes in month-specific PM2.5 concentrations from one year to the next do not help to predict corresponding changes in average elderly mortality rates in either location. Thus, the assumption that estimated C-R relations predict effects of pollution-reducing interventions may not be true. Better causal modeling methods are needed to better predict how reducing air pollution would affect public health.

[Effects of sampling plot number on tree species distribution prediction under climate change].

PubMed

Liang, Yu; He, Hong-Shi; Wu, Zhi-Wei; Li, Xiao-Na; Luo, Xu

2013-05-01

Based on the neutral landscapes under different degrees of landscape fragmentation, this paper studied the effects of sampling plot number on the prediction of tree species distribution at landscape scale under climate change. The tree species distribution was predicted by the coupled modeling approach which linked an ecosystem process model with a forest landscape model, and three contingent scenarios and one reference scenario of sampling plot numbers were assumed. The differences between the three scenarios and the reference scenario under different degrees of landscape fragmentation were tested. The results indicated that the effects of sampling plot number on the prediction of tree species distribution depended on the tree species life history attributes. For the generalist species, the prediction of their distribution at landscape scale needed more plots. Except for the extreme specialist, landscape fragmentation degree also affected the effects of sampling plot number on the prediction. With the increase of simulation period, the effects of sampling plot number on the prediction of tree species distribution at landscape scale could be changed. For generalist species, more plots are needed for the long-term simulation.

Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other's Actions by Humans.

PubMed

Ikegami, Tsuyoshi; Ganesh, Gowrishankar

2017-01-01

The question of how humans predict outcomes of observed motor actions by others is a fundamental problem in cognitive and social neuroscience. Previous theoretical studies have suggested that the brain uses parts of the forward model (used to estimate sensory outcomes of self-generated actions) to predict outcomes of observed actions. However, this hypothesis has remained controversial due to the lack of direct experimental evidence. To address this issue, we analyzed the behavior of darts experts in an understanding learning paradigm and utilized computational modeling to examine how outcome prediction of observed actions affected the participants' ability to estimate their own actions. We recruited darts experts because sports experts are known to have an accurate outcome estimation of their own actions as well as prediction of actions observed in others. We first show that learning to predict the outcomes of observed dart throws deteriorates an expert's abilities to both produce his own darts actions and estimate the outcome of his own throws (or self-estimation). Next, we introduce a state-space model to explain the trial-by-trial changes in the darts performance and self-estimation through our experiment. The model-based analysis reveals that the change in an expert's self-estimation is explained only by considering a change in the individual's forward model, showing that an improvement in an expert's ability to predict outcomes of observed actions affects the individual's forward model. These results suggest that parts of the same forward model are utilized in humans to both estimate outcomes of self-generated actions and predict outcomes of observed actions.

IPMP 2013 - A comprehensive data analysis tool for predictive microbiology

USDA-ARS?s Scientific Manuscript database

Predictive microbiology is an area of applied research in food science that uses mathematical models to predict the changes in the population of pathogenic or spoilage microorganisms in foods undergoing complex environmental changes during processing, transportation, distribution, and storage. It f...

Probabilistic Forecasting of Coastal Morphodynamic Storm Response at Fire Island, New York

NASA Astrophysics Data System (ADS)

Wilson, K.; Adams, P. N.; Hapke, C. J.; Lentz, E. E.; Brenner, O.

2013-12-01

Site-specific probabilistic models of shoreline change are useful because they are derived from direct observations so that local factors, which greatly influence coastal response, are inherently considered by the model. Fire Island, a 50-km barrier island off Long Island, New York, is periodically subject to large storms, whose waves and storm surge dramatically alter beach morphology. Nor'Ida, which impacted the Fire Island coast in 2009, was one of the larger storms to occur in the early 2000s. In this study, we improve upon a Bayesian Network (BN) model informed with historical data to predict shoreline change from Nor'Ida. We present two BN models, referred to as 'original' model (BNo) and 'revised' model (BNr), designed to predict the most probable magnitude of net shoreline movement (NSM), as measured at 934 cross-shore transects, spanning 46 km. Both are informed with observational data (wave impact hours, shoreline and dune toe change rates, pre-storm beach width, and measured NSM) organized within five nodes, but the revised model contains a sixth node to represent the distribution of material added during an April 2009 nourishment project. We evaluate model success by examining the percentage of transects on which the model chooses the correct (observed) bin value of NSM. Comparisons of observed to model-predicted NSM show BNr has slightly higher predictive success over the total study area and significantly higher success at nourished locations. The BNo, which neglects anthropogenic modification history, correctly predicted the most probable NSM in 66.6% of transects, with ambiguous prediction at 12.7% of the locations. BNr, which incorporates anthropogenic modification history, resulted in 69.4% predictive accuracy and 13.9% ambiguity. However, across nourished transects, BNr reported 72.9% predictive success, while BNo reported 61.5% success. Further, at nourished transects, BNr reported higher ambiguity of 23.5% compared to 9.9% in BNo. These results demonstrate that BNr recognizes that nourished transects may behave differently from the expectation derived from historical data and therefore is more 'cautious' in its predictions at these locations. In contrast, BNo is more confident, but less accurate, demonstrating the risk of ignoring the influences of anthropogenic modification in a probabilistic model. Over the entire study region, both models produced greatest predictive accuracy for low retreat observations (BNo: 77.6%; BNr: 76.0%) and least success at predicting low advance observations, although BNr shows considerable improvement over BNo (39.4% vs. 28.6%, respectively). BNr also was significantly more accurate at predicting observations of no shoreline change (BNo: 56.2%; BNr: 68.93%). Both models were accurate for 60% of high advance observations, and reported high predictive success for high retreat observations (BNo: 69.1%; BNr: 67.6%), the scenario of greatest concern to coastal managers.

Can air temperature be used to project influences of climate change on stream temperature?

USGS Publications Warehouse

Arismendi, Ivan; Safeeq, Mohammad; Dunham, Jason B.; Johnson, Sherri L.

2014-01-01

Worldwide, lack of data on stream temperature has motivated the use of regression-based statistical models to predict stream temperatures based on more widely available data on air temperatures. Such models have been widely applied to project responses of stream temperatures under climate change, but the performance of these models has not been fully evaluated. To address this knowledge gap, we examined the performance of two widely used linear and nonlinear regression models that predict stream temperatures based on air temperatures. We evaluated model performance and temporal stability of model parameters in a suite of regulated and unregulated streams with 11–44 years of stream temperature data. Although such models may have validity when predicting stream temperatures within the span of time that corresponds to the data used to develop them, model predictions did not transfer well to other time periods. Validation of model predictions of most recent stream temperatures, based on air temperature–stream temperature relationships from previous time periods often showed poor performance when compared with observed stream temperatures. Overall, model predictions were less robust in regulated streams and they frequently failed in detecting the coldest and warmest temperatures within all sites. In many cases, the magnitude of errors in these predictions falls within a range that equals or exceeds the magnitude of future projections of climate-related changes in stream temperatures reported for the region we studied (between 0.5 and 3.0 °C by 2080). The limited ability of regression-based statistical models to accurately project stream temperatures over time likely stems from the fact that underlying processes at play, namely the heat budgets of air and water, are distinctive in each medium and vary among localities and through time.

Impacts of climate change and renewable energy development on habitat of an endemic squirrel, Xerospermophilus mohavensis, in the Mojave Desert, USA

USGS Publications Warehouse

Inman, Richard D.; Esque, Todd C.; Nussear, Kenneth E.; Leitner, Philip; Matocq, Marjorie D.; Weisberg, Peter J.; Dilts, Thomas E.

2016-01-01

Predicting changes in species distributions under a changing climate is becoming widespread with the use of species distribution models (SDMs). The resulting predictions of future potential habitat can be cast in light of planned land use changes, such as urban expansion and energy development to identify areas with potential conflict. However, SDMs rarely incorporate an understanding of dispersal capacity, and therefore assume unlimited dispersal in potential range shifts under uncertain climate futures. We use SDMs to predict future distributions of the Mojave ground squirrel, Xerospermophilus mohavensis Merriam, and incorporate partial dispersal models informed by field data on juvenile dispersal to assess projected impact of climate change and energy development on future distributions of X. mohavensis. Our models predict loss of extant habitat, but also concurrent gains of new habitat under two scenarios of future climate change. Under the B1 emissions scenario- a storyline describing a convergent world with emphasis on curbing greenhouse gas emissions- our models predicted losses of up to 64% of extant habitat by 2080, while under the increased greenhouse gas emissions of the A2 scenario, we suggest losses of 56%. New potential habitat may become available to X. mohavensis, thereby offsetting as much as 6330 km2 (50%) of the current habitat lost. Habitat lost due to planned energy development was marginal compared to habitat lost from changing climates, but disproportionately affected current habitat. Future areas of overlap in potential habitat between the two climate change scenarios are identified and discussed in context of proposed energy development.

Managing distribution changes in time series prediction

NASA Astrophysics Data System (ADS)

Matias, J. M.; Gonzalez-Manteiga, W.; Taboada, J.; Ordonez, C.

2006-07-01

When a problem is modeled statistically, a single distribution model is usually postulated that is assumed to be valid for the entire space. Nonetheless, this practice may be somewhat unrealistic in certain application areas, in which the conditions of the process that generates the data may change; as far as we are aware, however, no techniques have been developed to tackle this problem.This article proposes a technique for modeling and predicting this change in time series with a view to improving estimates and predictions. The technique is applied, among other models, to the hypernormal distribution recently proposed. When tested on real data from a range of stock market indices the technique produces better results that when a single distribution model is assumed to be valid for the entire period of time studied.Moreover, when a global model is postulated, it is highly recommended to select the hypernormal distribution parameter in the same likelihood maximization process.

Dynamic evaluation of CMAQ part I: Separating the effects of ...

EPA Pesticide Factsheets

A dynamic evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.0.1 was conducted to evaluate the model's ability to predict changes in ozone levels between 2002 and 2005, a time period characterized by emission reductions associated with the EPA's Nitrogen Oxides State Implementation Plan as well as significant reductions in mobile source emissions. Model results for the summers of 2002 and 2005 were compared to simulations from a previous version of CMAQ to assess the impact of model updates on predicted pollutant response. Changes to the model treatment of emissions, meteorology and chemistry had substantial impacts on the simulated ozone concentrations. While the median bias for high summertime ozone decreased in both years compared to previous simulations, the observed decrease in ozone from 2002 to 2005 in the eastern US continued to be underestimated by the model. Additional “cross” simulations were used to decompose the model predicted change in ozone into the change due to emissions, the change due to meteorology and any remaining change not explained individually by these two components. The decomposition showed that the emission controls led to a decrease in modeled high summertime ozone close to twice as large as the decrease attributable to changes in meteorology alone. Quantifying the impact of retrospective emission controls by removing the impacts of meteorology during the control period can be a valuable approac

The predicted influence of climate change on lesser prairie-chicken reproductive parameters

USGS Publications Warehouse

Grisham, Blake A.; Boal, Clint W.; Haukos, David A.; Davis, D.; Boydston, Kathy K.; Dixon, Charles; Heck, Willard R.

2013-01-01

The Southern High Plains is anticipated to experience significant changes in temperature and precipitation due to climate change. These changes may influence the lesser prairie-chicken (Tympanuchus pallidicinctus) in positive or negative ways. We assessed the potential changes in clutch size, incubation start date, and nest survival for lesser prairie-chickens for the years 2050 and 2080 based on modeled predictions of climate change and reproductive data for lesser prairie-chickens from 2001-2011 on the Southern High Plains of Texas and New Mexico. We developed 9 a priori models to assess the relationship between reproductive parameters and biologically relevant weather conditions. We selected weather variable(s) with the most model support and then obtained future predicted values from climatewizard.org. We conducted 1,000 simulations using each reproductive parameter's linear equation obtained from regression calculations, and the future predicted value for each weather variable to predict future reproductive parameter values for lesser prairie-chickens. There was a high degree of model uncertainty for each reproductive value. Winter temperature had the greatest effect size for all three parameters, suggesting a negative relationship between above-average winter temperature and reproductive output. The above-average winter temperatures are correlated to La Nina events, which negatively affect lesser prairie-chickens through resulting drought conditions. By 2050 and 2080, nest survival was predicted to be below levels considered viable for population persistence; however, our assessment did not consider annual survival of adults, chick survival, or the positive benefit of habitat management and conservation, which may ultimately offset the potentially negative effect of drought on nest survival.

The predicted influence of climate change on lesser prairie-chicken reproductive parameters.

PubMed

Grisham, Blake A; Boal, Clint W; Haukos, David A; Davis, Dawn M; Boydston, Kathy K; Dixon, Charles; Heck, Willard R

2013-01-01

The Southern High Plains is anticipated to experience significant changes in temperature and precipitation due to climate change. These changes may influence the lesser prairie-chicken (Tympanuchus pallidicinctus) in positive or negative ways. We assessed the potential changes in clutch size, incubation start date, and nest survival for lesser prairie-chickens for the years 2050 and 2080 based on modeled predictions of climate change and reproductive data for lesser prairie-chickens from 2001-2011 on the Southern High Plains of Texas and New Mexico. We developed 9 a priori models to assess the relationship between reproductive parameters and biologically relevant weather conditions. We selected weather variable(s) with the most model support and then obtained future predicted values from climatewizard.org. We conducted 1,000 simulations using each reproductive parameter's linear equation obtained from regression calculations, and the future predicted value for each weather variable to predict future reproductive parameter values for lesser prairie-chickens. There was a high degree of model uncertainty for each reproductive value. Winter temperature had the greatest effect size for all three parameters, suggesting a negative relationship between above-average winter temperature and reproductive output. The above-average winter temperatures are correlated to La Niña events, which negatively affect lesser prairie-chickens through resulting drought conditions. By 2050 and 2080, nest survival was predicted to be below levels considered viable for population persistence; however, our assessment did not consider annual survival of adults, chick survival, or the positive benefit of habitat management and conservation, which may ultimately offset the potentially negative effect of drought on nest survival.

Moving From Static to Dynamic Models of the Onset of Mental Disorder: A Review.

PubMed

Nelson, Barnaby; McGorry, Patrick D; Wichers, Marieke; Wigman, Johanna T W; Hartmann, Jessica A

2017-05-01

In recent years, there has been increased focus on subthreshold stages of mental disorders, with attempts to model and predict which individuals will progress to full-threshold disorder. Given this research attention and the clinical significance of the issue, this article analyzes the assumptions of the theoretical models in the field. Psychiatric research into predicting the onset of mental disorder has shown an overreliance on one-off sampling of cross-sectional data (ie, a snapshot of clinical state and other risk markers) and may benefit from taking dynamic changes into account in predictive modeling. Cross-disciplinary approaches to complex system structures and changes, such as dynamical systems theory, network theory, instability mechanisms, chaos theory, and catastrophe theory, offer potent models that can be applied to the emergence (or decline) of psychopathology, including psychosis prediction, as well as to transdiagnostic emergence of symptoms. Psychiatric research may benefit from approaching psychopathology as a system rather than as a category, identifying dynamics of system change (eg, abrupt vs gradual psychosis onset), and determining the factors to which these systems are most sensitive (eg, interpersonal dynamics and neurochemical change) and the individual variability in system architecture and change. These goals can be advanced by testing hypotheses that emerge from cross-disciplinary models of complex systems. Future studies require repeated longitudinal assessment of relevant variables through either (or a combination of) micro-level (momentary and day-to-day) and macro-level (month and year) assessments. Ecological momentary assessment is a data collection technique appropriate for micro-level assessment. Relevant statistical approaches are joint modeling and time series analysis, including metric-based and model-based methods that draw on the mathematical principles of dynamical systems. This next generation of prediction studies may more accurately model the dynamic nature of psychopathology and system change as well as have treatment implications, such as introducing a means of identifying critical periods of risk for mental state deterioration.

Modelling climate change responses in tropical forests: similar productivity estimates across five models, but different mechanisms and responses

NASA Astrophysics Data System (ADS)

Rowland, L.; Harper, A.; Christoffersen, B. O.; Galbraith, D. R.; Imbuzeiro, H. M. A.; Powell, T. L.; Doughty, C.; Levine, N. M.; Malhi, Y.; Saleska, S. R.; Moorcroft, P. R.; Meir, P.; Williams, M.

2015-04-01

Accurately predicting the response of Amazonia to climate change is important for predicting climate change across the globe. Changes in multiple climatic factors simultaneously result in complex non-linear ecosystem responses, which are difficult to predict using vegetation models. Using leaf- and canopy-scale observations, this study evaluated the capability of five vegetation models (Community Land Model version 3.5 coupled to the Dynamic Global Vegetation model - CLM3.5-DGVM; Ecosystem Demography model version 2 - ED2; the Joint UK Land Environment Simulator version 2.1 - JULES; Simple Biosphere model version 3 - SiB3; and the soil-plant-atmosphere model - SPA) to simulate the responses of leaf- and canopy-scale productivity to changes in temperature and drought in an Amazonian forest. The models did not agree as to whether gross primary productivity (GPP) was more sensitive to changes in temperature or precipitation, but all the models were consistent with the prediction that GPP would be higher if tropical forests were 5 °C cooler than current ambient temperatures. There was greater model-data consistency in the response of net ecosystem exchange (NEE) to changes in temperature than in the response to temperature by net photosynthesis (An), stomatal conductance (gs) and leaf area index (LAI). Modelled canopy-scale fluxes are calculated by scaling leaf-scale fluxes using LAI. At the leaf-scale, the models did not agree on the temperature or magnitude of the optimum points of An, Vcmax or gs, and model variation in these parameters was compensated for by variations in the absolute magnitude of simulated LAI and how it altered with temperature. Across the models, there was, however, consistency in two leaf-scale responses: (1) change in An with temperature was more closely linked to stomatal behaviour than biochemical processes; and (2) intrinsic water use efficiency (IWUE) increased with temperature, especially when combined with drought. These results suggest that even up to fairly extreme temperature increases from ambient levels (+6 °C), simulated photosynthesis becomes increasingly sensitive to gs and remains less sensitive to biochemical changes. To improve the reliability of simulations of the response of Amazonian rainforest to climate change, the mechanistic underpinnings of vegetation models need to be validated at both leaf- and canopy-scales to improve accuracy and consistency in the quantification of processes within and across an ecosystem.

Adaptation of clinical prediction models for application in local settings.

PubMed

Kappen, Teus H; Vergouwe, Yvonne; van Klei, Wilton A; van Wolfswinkel, Leo; Kalkman, Cor J; Moons, Karel G M

2012-01-01

When planning to use a validated prediction model in new patients, adequate performance is not guaranteed. For example, changes in clinical practice over time or a different case mix than the original validation population may result in inaccurate risk predictions. To demonstrate how clinical information can direct updating a prediction model and development of a strategy for handling missing predictor values in clinical practice. A previously derived and validated prediction model for postoperative nausea and vomiting was updated using a data set of 1847 patients. The update consisted of 1) changing the definition of an existing predictor, 2) reestimating the regression coefficient of a predictor, and 3) adding a new predictor to the model. The updated model was then validated in a new series of 3822 patients. Furthermore, several imputation models were considered to handle real-time missing values, so that possible missing predictor values could be anticipated during actual model use. Differences in clinical practice between our local population and the original derivation population guided the update strategy of the prediction model. The predictive accuracy of the updated model was better (c statistic, 0.68; calibration slope, 1.0) than the original model (c statistic, 0.62; calibration slope, 0.57). Inclusion of logistical variables in the imputation models, besides observed patient characteristics, contributed to a strategy to deal with missing predictor values at the time of risk calculation. Extensive knowledge of local, clinical processes provides crucial information to guide the process of adapting a prediction model to new clinical practices.

STRUM: structure-based prediction of protein stability changes upon single-point mutation.

PubMed

Quan, Lijun; Lv, Qiang; Zhang, Yang

2016-10-01

Mutations in human genome are mainly through single nucleotide polymorphism, some of which can affect stability and function of proteins, causing human diseases. Several methods have been proposed to predict the effect of mutations on protein stability; but most require features from experimental structure. Given the fast progress in protein structure prediction, this work explores the possibility to improve the mutation-induced stability change prediction using low-resolution structure modeling. We developed a new method (STRUM) for predicting stability change caused by single-point mutations. Starting from wild-type sequences, 3D models are constructed by the iterative threading assembly refinement (I-TASSER) simulations, where physics- and knowledge-based energy functions are derived on the I-TASSER models and used to train STRUM models through gradient boosting regression. STRUM was assessed by 5-fold cross validation on 3421 experimentally determined mutations from 150 proteins. The Pearson correlation coefficient (PCC) between predicted and measured changes of Gibbs free-energy gap, ΔΔG, upon mutation reaches 0.79 with a root-mean-square error 1.2 kcal/mol in the mutation-based cross-validations. The PCC reduces if separating training and test mutations from non-homologous proteins, which reflects inherent correlations in the current mutation sample. Nevertheless, the results significantly outperform other state-of-the-art methods, including those built on experimental protein structures. Detailed analyses show that the most sensitive features in STRUM are the physics-based energy terms on I-TASSER models and the conservation scores from multiple-threading template alignments. However, the ΔΔG prediction accuracy has only a marginal dependence on the accuracy of protein structure models as long as the global fold is correct. These data demonstrate the feasibility to use low-resolution structure modeling for high-accuracy stability change prediction upon point mutations. http://zhanglab.ccmb.med.umich.edu/STRUM/ CONTACT: qiang@suda.edu.cn and zhng@umich.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

STRUM: structure-based prediction of protein stability changes upon single-point mutation

PubMed Central

Quan, Lijun; Lv, Qiang; Zhang, Yang

2016-01-01

Motivation: Mutations in human genome are mainly through single nucleotide polymorphism, some of which can affect stability and function of proteins, causing human diseases. Several methods have been proposed to predict the effect of mutations on protein stability; but most require features from experimental structure. Given the fast progress in protein structure prediction, this work explores the possibility to improve the mutation-induced stability change prediction using low-resolution structure modeling. Results: We developed a new method (STRUM) for predicting stability change caused by single-point mutations. Starting from wild-type sequences, 3D models are constructed by the iterative threading assembly refinement (I-TASSER) simulations, where physics- and knowledge-based energy functions are derived on the I-TASSER models and used to train STRUM models through gradient boosting regression. STRUM was assessed by 5-fold cross validation on 3421 experimentally determined mutations from 150 proteins. The Pearson correlation coefficient (PCC) between predicted and measured changes of Gibbs free-energy gap, ΔΔG, upon mutation reaches 0.79 with a root-mean-square error 1.2 kcal/mol in the mutation-based cross-validations. The PCC reduces if separating training and test mutations from non-homologous proteins, which reflects inherent correlations in the current mutation sample. Nevertheless, the results significantly outperform other state-of-the-art methods, including those built on experimental protein structures. Detailed analyses show that the most sensitive features in STRUM are the physics-based energy terms on I-TASSER models and the conservation scores from multiple-threading template alignments. However, the ΔΔG prediction accuracy has only a marginal dependence on the accuracy of protein structure models as long as the global fold is correct. These data demonstrate the feasibility to use low-resolution structure modeling for high-accuracy stability change prediction upon point mutations. Availability and Implementation: http://zhanglab.ccmb.med.umich.edu/STRUM/ Contact: qiang@suda.edu.cn and zhng@umich.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27318206

Predicting the effect of climate change on African trypanosomiasis: integrating epidemiology with parasite and vector biology

PubMed Central

Moore, Sean; Shrestha, Sourya; Tomlinson, Kyle W.; Vuong, Holly

2012-01-01

Climate warming over the next century is expected to have a large impact on the interactions between pathogens and their animal and human hosts. Vector-borne diseases are particularly sensitive to warming because temperature changes can alter vector development rates, shift their geographical distribution and alter transmission dynamics. For this reason, African trypanosomiasis (sleeping sickness), a vector-borne disease of humans and animals, was recently identified as one of the 12 infectious diseases likely to spread owing to climate change. We combine a variety of direct effects of temperature on vector ecology, vector biology and vector–parasite interactions via a disease transmission model and extrapolate the potential compounding effects of projected warming on the epidemiology of African trypanosomiasis. The model predicts that epidemics can occur when mean temperatures are between 20.7°C and 26.1°C. Our model does not predict a large-range expansion, but rather a large shift of up to 60 per cent in the geographical extent of the range. The model also predicts that 46–77 million additional people may be at risk of exposure by 2090. Future research could expand our analysis to include other environmental factors that influence tsetse populations and disease transmission such as humidity, as well as changes to human, livestock and wildlife distributions. The modelling approach presented here provides a framework for using the climate-sensitive aspects of vector and pathogen biology to predict changes in disease prevalence and risk owing to climate change. PMID:22072451

Stochastic or statistic? Comparing flow duration curve models in ungauged basins and changing climates

NASA Astrophysics Data System (ADS)

Müller, M. F.; Thompson, S. E.

2015-09-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drives of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by a strong wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are strongly favored over statistical models.

Comparing statistical and process-based flow duration curve models in ungauged basins and changing rain regimes

NASA Astrophysics Data System (ADS)

Müller, M. F.; Thompson, S. E.

2016-02-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drivers of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by frequent wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are favored over statistical models.

Life history trade-off moderates model predictions of diversity loss from climate change

PubMed Central

2017-01-01

Climate change can trigger species range shifts, local extinctions and changes in diversity. Species interactions and dispersal capacity are important mediators of community responses to climate change. The interaction between multispecies competition and variation in dispersal capacity has recently been shown to exacerbate the effects of climate change on diversity and to increase predictions of extinction risk dramatically. Dispersal capacity, however, is part of a species’ overall ecological strategy and are likely to trade off with other aspects of its life history that influence population growth and persistence. In plants, a well-known example is the trade-off between seed mass and seed number. The presence of such a trade-off might buffer the diversity loss predicted by models with random but neutral (i.e. not impacting fitness otherwise) differences in dispersal capacity. Using a trait-based metacommunity model along a warming climatic gradient the effect of three different dispersal scenarios on model predictions of diversity change were compared. Adding random variation in species dispersal capacity caused extinctions by the introduction of strong fitness differences due an inherent property of the dispersal kernel. Simulations including a fitness-equalising trade-off based on empirical relationships between seed mass (here affecting dispersal distance, establishment probability, and seedling biomass) and seed number (fecundity) maintained higher initial species diversity and predicted lower extinction risk and diversity loss during climate change than simulations with variable dispersal capacity. Large seeded species persisted during climate change, but developed lags behind their climate niche that may cause extinction debts. Small seeded species were more extinction-prone during climate change but tracked their niches through dispersal and colonisation, despite competitive resistance from residents. Life history trade-offs involved in coexistence mechanisms may increase community resilience to future climate change and are useful guides for model development. PMID:28520770

Impact of Future Climate on Radial Growth of Four Major Boreal Tree Species in the Eastern Canadian Boreal Forest

PubMed Central

Huang, Jian-Guo; Bergeron, Yves; Berninger, Frank; Zhai, Lihong; Tardif, Jacques C.; Denneler, Bernhard

2013-01-01

Immediate phenotypic variation and the lagged effect of evolutionary adaptation to climate change appear to be two key processes in tree responses to climate warming. This study examines these components in two types of growth models for predicting the 2010–2099 diameter growth change of four major boreal species Betula papyrifera, Pinus banksiana, Picea mariana, and Populus tremuloides along a broad latitudinal gradient in eastern Canada under future climate projections. Climate-growth response models for 34 stands over nine latitudes were calibrated and cross-validated. An adaptive response model (A-model), in which the climate-growth relationship varies over time, and a fixed response model (F-model), in which the relationship is constant over time, were constructed to predict future growth. For the former, we examined how future growth of stands in northern latitudes could be forecasted using growth-climate equations derived from stands currently growing in southern latitudes assuming that current climate in southern locations provide an analogue for future conditions in the north. For the latter, we tested if future growth of stands would be maximally predicted using the growth-climate equation obtained from the given local stand assuming a lagged response to climate due to genetic constraints. Both models predicted a large growth increase in northern stands due to more benign temperatures, whereas there was a minimal growth change in southern stands due to potentially warm-temperature induced drought-stress. The A-model demonstrates a changing environment whereas the F-model highlights a constant growth response to future warming. As time elapses we can predict a gradual transition between a response to climate associated with the current conditions (F-model) to a more adapted response to future climate (A-model). Our modeling approach provides a template to predict tree growth response to climate warming at mid-high latitudes of the Northern Hemisphere. PMID:23468879

Impact of future climate on radial growth of four major boreal tree species in the Eastern Canadian boreal forest.

PubMed

Huang, Jian-Guo; Bergeron, Yves; Berninger, Frank; Zhai, Lihong; Tardif, Jacques C; Denneler, Bernhard

2013-01-01

Immediate phenotypic variation and the lagged effect of evolutionary adaptation to climate change appear to be two key processes in tree responses to climate warming. This study examines these components in two types of growth models for predicting the 2010-2099 diameter growth change of four major boreal species Betula papyrifera, Pinus banksiana, Picea mariana, and Populus tremuloides along a broad latitudinal gradient in eastern Canada under future climate projections. Climate-growth response models for 34 stands over nine latitudes were calibrated and cross-validated. An adaptive response model (A-model), in which the climate-growth relationship varies over time, and a fixed response model (F-model), in which the relationship is constant over time, were constructed to predict future growth. For the former, we examined how future growth of stands in northern latitudes could be forecasted using growth-climate equations derived from stands currently growing in southern latitudes assuming that current climate in southern locations provide an analogue for future conditions in the north. For the latter, we tested if future growth of stands would be maximally predicted using the growth-climate equation obtained from the given local stand assuming a lagged response to climate due to genetic constraints. Both models predicted a large growth increase in northern stands due to more benign temperatures, whereas there was a minimal growth change in southern stands due to potentially warm-temperature induced drought-stress. The A-model demonstrates a changing environment whereas the F-model highlights a constant growth response to future warming. As time elapses we can predict a gradual transition between a response to climate associated with the current conditions (F-model) to a more adapted response to future climate (A-model). Our modeling approach provides a template to predict tree growth response to climate warming at mid-high latitudes of the Northern Hemisphere.

Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

Treesearch

R. Quinn Thomas; Evan B. Brooks; Annika L. Jersild; Eric J. Ward; Randolph H. Wynne; Timothy J. Albaugh; Heather Dinon-Aldridge; Harold E. Burkhart; Jean-Christophe Domec; Timothy R. Fox; Carlos A. Gonzalez-Benecke; Timothy A. Martin; Asko Noormets; David A. Sampson; Robert O. Teskey

2017-01-01

Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model–data fusion, allows the use of...

Predicting non-stationary algal dynamics following changes in hydrometeorological conditions using data assimilation techniques

NASA Astrophysics Data System (ADS)

Kim, S.; Seo, D. J.

2017-12-01

When water temperature (TW) increases due to changes in hydrometeorological conditions, the overall ecological conditions change in the aquatic system. The changes can be harmful to human health and potentially fatal to fish habitat. Therefore, it is important to assess the impacts of thermal disturbances on in-stream processes of water quality variables and be able to predict effectiveness of possible actions that may be taken for water quality protection. For skillful prediction of in-stream water quality processes, it is necessary for the watershed water quality models to be able to reflect such changes. Most of the currently available models, however, assume static parameters for the biophysiochemical processes and hence are not able to capture nonstationaries seen in water quality observations. In this work, we assess the performance of the Hydrological Simulation Program-Fortran (HSPF) in predicting algal dynamics following TW increase. The study area is located in the Republic of Korea where waterway change due to weir construction and drought concurrently occurred around 2012. In this work we use data assimilation (DA) techniques to update model parameters as well as the initial condition of selected state variables for in-stream processes relevant to algal growth. For assessment of model performance and characterization of temporal variability, various goodness-of-fit measures and wavelet analysis are used.

Modeling Climate Change in the Absence of Climate Change Data. Editorial Comment

NASA Technical Reports Server (NTRS)

Skiles, J. W.

1995-01-01

Practitioners of climate change prediction base many of their future climate scenarios on General Circulation Models (GCM's), each model with differing assumptions and parameter requirements. For representing the atmosphere, GCM's typically contain equations for calculating motion of particles, thermodynamics and radiation, and continuity of water vapor. Hydrology and heat balance are usually included for continents, and sea ice and heat balance are included for oceans. The current issue of this journal contains a paper by Van Blarcum et al. (1995) that predicts runoff from nine high-latitude rivers under a doubled CO2 atmosphere. The paper is important since river flow is an indicator variable for climate change. The authors show that precipitation will increase under the imposed perturbations and that owing to higher temperatures earlier in the year that cause the snow pack to melt sooner, runoff will also increase. They base their simulations on output from a GCM coupled with an interesting water routing scheme they have devised. Climate change models have been linked to other models to predict deforestation.

A dynamic model for predicting growth in zinc-deficient stunted infants given supplemental zinc.

PubMed

Wastney, Meryl E; McDonald, Christine M; King, Janet C

2018-05-01

Zinc deficiency limits infant growth and increases susceptibility to infections, which further compromises growth. Zinc supplementation improves the growth of zinc-deficient stunted infants, but the amount, frequency, and duration of zinc supplementation required to restore growth in an individual child is unknown. A dynamic model of zinc metabolism that predicts changes in weight and length of zinc-deficient, stunted infants with dietary zinc would be useful to define effective zinc supplementation regimens. The aims of this study were to develop a dynamic model for zinc metabolism in stunted, zinc-deficient infants and to use that model to predict the growth response when those infants are given zinc supplements. A model of zinc metabolism was developed using data on zinc kinetics, tissue zinc, and growth requirements for healthy 9-mo-old infants. The kinetic model was converted to a dynamic model by replacing the rate constants for zinc absorption and excretion with functions for these processes that change with zinc intake. Predictions of the dynamic model, parameterized for zinc-deficient, stunted infants, were compared with the results of 5 published zinc intervention trials. The model was then used to predict the results for zinc supplementation regimes that varied in the amount, frequency, and duration of zinc dosing. Model predictions agreed with published changes in plasma zinc after zinc supplementation. Predictions of weight and length agreed with 2 studies, but overpredicted values from a third study in which other nutrient deficiencies may have been growth limiting; the model predicted that zinc absorption was impaired in that study. The model suggests that frequent, smaller doses (5-10 mg Zn/d) are more effective for increasing growth in stunted, zinc-deficient 9-mo-old infants than are larger, less-frequent doses. The dose amount affects the duration of dosing necessary to restore and maintain plasma zinc concentration and growth.

Predicted Hematologic and Plasma Volume Responses Following Rapid Ascent to Progressive Altitudes

DTIC Science & Technology

2014-06-01

of these changes, and define baseline demographics and physiologic descriptors that are important in predicting these changes. The overall impact of... physiologic descriptors that are important in predicting these changes. Using general linear mixed models and a comprehensive relational database...accomplished using a comprehensive relational database containing individual ascent profiles, demographics, and physiologic subject descriptors as well as

Climate model diversity in the Northern Hemisphere Polar vortex response to climate change.

NASA Astrophysics Data System (ADS)

Simpson, I.; Seager, R.; Hitchcock, P.; Cohen, N.

2017-12-01

Global climate models vary widely in their predictions of the future of the Northern Hemisphere stratospheric polar vortex, with some showing a significant strengthening of the vortex, some showing a significant weakening and others displaying a response that is not outside of the range expected from internal variability alone. This inter-model spread in stratospheric predictions may account for some inter-model spread in tropospheric predictions with important implications for the storm tracks and regional climate change, particularly for the North Atlantic sector. Here, our current state of understanding of this model spread and its tropospheric impacts will be reviewed. Previous studies have proposed relationships between a models polar vortex response to climate change and its present day vortex climatology while others have demonstrated links between a models polar vortex response and changing wave activity coming up from the troposphere below under a warming climate. The extent to which these mechanisms can account for the spread in polar vortex changes exhibited by the Coupled Model Intercomparison Project, phase 5 models will be assessed. In addition, preliminary results from a series of idealized experiments with the Community Atmosphere Model will be presented. In these experiments, nudging of the stratospheric zonal mean state has been imposed to mimic the inter-model spread in the polar vortex response to climate change so that the downward influence of the spread in zonal mean stratospheric responses on the tropospheric circulation can be assessed within one model.

Food-web models predict species abundances in response to habitat change.

PubMed

Gotelli, Nicholas J; Ellison, Aaron M

2006-10-01

Plant and animal population sizes inevitably change following habitat loss, but the mechanisms underlying these changes are poorly understood. We experimentally altered habitat volume and eliminated top trophic levels of the food web of invertebrates that inhabit rain-filled leaves of the carnivorous pitcher plant Sarracenia purpurea. Path models that incorporated food-web structure better predicted population sizes of food-web constituents than did simple keystone species models, models that included only autecological responses to habitat volume, or models including both food-web structure and habitat volume. These results provide the first experimental confirmation that trophic structure can determine species abundances in the face of habitat loss.

Tampa Bay Water Clarity Model (TBWCM): As a Predictive Tool

EPA Science Inventory

The Tampa Bay Water Clarity Model was developed as a predictive tool for estimating the impact of changing nutrient loads on water clarity as measured by secchi depth. The model combines a physical mixing model with an irradiance model and nutrient cycling model. A 10 segment bi...

Experimental characterization and numerical modeling of tissue electrical conductivity during pulsed electric fields for irreversible electroporation treatment planning.

PubMed

Neal, Robert E; Garcia, Paulo A; Robertson, John L; Davalos, Rafael V

2012-04-01

Irreversible electroporation is a new technique to kill cells in targeted tissue, such as tumors, through a nonthermal mechanism using electric pulses to irrecoverably disrupt the cell membrane. Treatment effects relate to the tissue electric field distribution, which can be predicted with numerical modeling for therapy planning. Pulse effects will change the cell and tissue properties through thermal and electroporation (EP)-based processes. This investigation characterizes these changes by measuring the electrical conductivity and temperature of ex vivo renal porcine tissue within a single pulse and for a 200 pulse protocol. These changes are incorporated into an equivalent circuit model for cells and tissue with a variable EP-based resistance, providing a potential method to estimate conductivity as a function of electric field and pulse length for other tissues. Finally, a numerical model using a human kidney volumetric mesh evaluated how treatment predictions vary when EP- and temperature-based electrical conductivity changes are incorporated. We conclude that significant changes in predicted outcomes will occur when the experimental results are applied to the numerical model, where the direction and degree of change varies with the electric field considered.

Replacing the nucleus pulposus of the intervertebral disk: prediction of suitable properties of a replacement material using finite element analysis.

PubMed

Meakin, J R

2001-03-01

An axisymmetric finite element model of a human lumbar disk was developed to investigate the properties required of an implant to replace the nucleus pulposus. In the intact disk, the nucleus was modeled as a fluid, and the annulus as an elastic solid. The Young's modulus of the annulus was determined empirically by matching model predictions to experimental results. The model was checked for sensitivity to the input parameter values and found to give reasonable behavior. The model predicted that removal of the nucleus would change the response of the annulus to compression. This prediction was consistent with experimental results, thus validating the model. Implants to fill the cavity produced by nucleus removal were modeled as elastic solids. The Poisson's ratio was fixed at 0.49, and the Young's modulus was varied from 0.5 to 100 MPa. Two sizes of implant were considered: full size (filling the cavity) and small size (smaller than the cavity). The model predicted that a full size implant would reverse the changes to annulus behavior, but a smaller implant would not. By comparing the stress distribution in the annulus, the ideal Young's modulus was predicted to be approximately 3 MPa. These predictions have implications for current nucleus implant designs. Copyright 2001 Kluwer Academic Publishers

Predictive value of the transtheoretical model to smoking cessation in hospitalized patients with cardiovascular disease.

PubMed

Chouinard, Maud-Christine; Robichaud-Ekstrand, Sylvie

2007-02-01

Several authors have questioned the transtheoretical model. Determining the predictive value of each cognitive-behavioural element within this model could explain the multiple successes reported in smoking cessation programmes. The purpose of this study was to predict point-prevalent smoking abstinence at 2 and 6 months, using the constructs of the transtheoretical model, when applied to a pooled sample of individuals who were hospitalized for a cardiovascular event. The study follows a predictive correlation design. Recently hospitalized patients (n=168) with cardiovascular disease were pooled from a randomized, controlled trial. Independent variables of the predictive transtheoretical model comprise stages and processes of change, pros and cons to quit smoking (decisional balance), self-efficacy, and social support. These were evaluated at baseline, 2 and 6 months. Compared to smokers, individuals who abstained from smoking at 2 and 6 months were more confident at baseline to remain non-smokers, perceived less pros and cons to continue smoking, utilized less consciousness raising and self-re-evaluation experiential processes of change, and received more positive reinforcement from their social network with regard to their smoke-free behaviour. Self-efficacy and stages of change at baseline were predictive of smoking abstinence after 6 months. Other variables found to be predictive of smoking abstinence at 6 months were an increase in self-efficacy; an increase in positive social support behaviour and a decrease of the pros within the decisional balance. The results partially support the predictive value of the transtheoretical model constructs in smoking cessation for cardiovascular disease patients.

Decadal Recruitment and Mortality of Ponderosa pine Predicted for the 21st Century Under five Downscaled Climate Change Scenarios

NASA Astrophysics Data System (ADS)

Ironside, K. E.; Cole, K. L.; Eischeid, J. K.; Garfin, G. M.; Shaw, J. D.; Cobb, N. S.

2008-12-01

Ponderosa pine (Pinus ponderosa var. scopulorum) is the dominant conifer in higher elevation regions of the southwestern United States. Because this species is so prominent, southwestern montane ecosystems will be significantly altered if this species is strongly affected by future climate changes. These changes could be highly challenging for land management agencies. In order to model the consequences of future climates, 20th Century recruitment events and mortality for ponderosa pine were characterized using measures of seasonal water balance (precipitation - potential evapotranspiration). These relationships, assuming they will remain unchanged, were then used to predict 21st Century changes in ponderosa pine occurrence in the southwest. Twenty-one AR4 IPCC General Circulation Model (GCM) A1B simulation results were ranked on their ability to simulate the later 20th Century (1950-2000 AD) precipitation seasonality, spatial patterns, and quantity in the western United States. Among the top ranked GCMs, five were selected for downscaling to a 4 km grid that represented a range in predictions in terms of changes in water balance. Predicted decadal changes in southwestern ponderosa pine for the 21st Century for these five climate change scenarios were calculated using a multiple quadratic logistic regression model. Similar models of other western tree species (Pinus edulis, Yucca brevifolia) predicted severe contractions, especially in the southern half of their ranges. However, the results for Ponderosa pine suggested future expansions throughout its range to both higher and lower elevations, as well as very significant expansions northward.

How does spatial variability of climate affect catchment streamflow predictions?

EPA Science Inventory

Spatial variability of climate can negatively affect catchment streamflow predictions if it is not explicitly accounted for in hydrologic models. In this paper, we examine the changes in streamflow predictability when a hydrologic model is run with spatially variable (distribute...

Sea Level Affecting Marshes Model (SLAMM) ‐ New functionality for predicting changes in distribution of submerged aquatic vegetation in response to sea level rise

USGS Publications Warehouse

Lee II, Henry; Reusser, Deborah A.; Frazier, Melanie R; McCoy, Lee M; Clinton, Patrick J.; Clough, Jonathan S.

2014-01-01

The “Sea‐Level Affecting Marshes Model” (SLAMM) is a moderate resolution model used to predict the effects of sea level rise on marsh habitats (Craft et al. 2009). SLAMM has been used extensively on both the west coast (e.g., Glick et al., 2007) and east coast (e.g., Geselbracht et al., 2011) of the United States to evaluate potential changes in the distribution and extent of tidal marsh habitats. However, a limitation of the current version of SLAMM, (Version 6.2) is that it lacks the ability to model distribution changes in seagrass habitat resulting from sea level rise. Because of the ecological importance of SAV habitats, U.S. EPA, USGS, and USDA partnered with Warren Pinnacle Consulting to enhance the SLAMM modeling software to include new functionality in order to predict changes in Zostera marina distribution within Pacific Northwest estuaries in response to sea level rise. Specifically, the objective was to develop a SAV model that used generally available GIS data and parameters that were predictive and that could be customized for other estuaries that have GIS layers of existing SAV distribution. This report describes the procedure used to develop the SAV model for the Yaquina Bay Estuary, Oregon, appends a statistical script based on the open source R software to generate a similar SAV model for other estuaries that have data layers of existing SAV, and describes how to incorporate the model coefficients from the site‐specific SAV model into SLAMM to predict the effects of sea level rise on Zostera marina distributions. To demonstrate the applicability of the R tools, we utilize them to develop model coefficients for Willapa Bay, Washington using site‐specific SAV data.

A Bayesian network approach to predicting nest presence of thefederally-threatened piping plover (Charadrius melodus) using barrier island features

USGS Publications Warehouse

Gieder, Katherina D.; Karpanty, Sarah M.; Fraser, James D.; Catlin, Daniel H.; Gutierrez, Benjamin T.; Plant, Nathaniel G.; Turecek, Aaron M.; Thieler, E. Robert

2014-01-01

Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modelling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model’s dataset. We found that model predictions were more successful when the range of physical conditions included in model development was varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modelling impacts of sea-level rise- or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.

Valuing river characteristics using combined site choice and participation travel cost models.

PubMed

Johnstone, C; Markandya, A

2006-08-01

This paper presents new welfare measures for marginal changes in river quality in selected English rivers. The river quality indicators used include chemical, biological and habitat-level attributes. Economic values for recreational use of three types of river-upland, lowland and chalk-are presented. A survey of anglers was carried out and using these data, two travel cost models were estimated, one to predict the numbers of trips and the other to predict angling site choice. These models were then linked to estimate the welfare associated with marginal changes in river quality using the participation levels as estimated in the trip prediction model. The model results showed that higher flow rates, biological quality and nutrient pollution levels affect site choice and influence the likelihood of a fishing trip. Consumer surplus values per trip for a 10% change in river attributes range from pound 0.04 to pound 3.93 ( pound 2001) depending on the attribute.

The anchor-based minimal important change, based on receiver operating characteristic analysis or predictive modeling, may need to be adjusted for the proportion of improved patients.

PubMed

Terluin, Berend; Eekhout, Iris; Terwee, Caroline B

2017-03-01

Patients have their individual minimal important changes (iMICs) as their personal benchmarks to determine whether a perceived health-related quality of life (HRQOL) change constitutes a (minimally) important change for them. We denote the mean iMIC in a group of patients as the "genuine MIC" (gMIC). The aims of this paper are (1) to examine the relationship between the gMIC and the anchor-based minimal important change (MIC), determined by receiver operating characteristic analysis or by predictive modeling; (2) to examine the impact of the proportion of improved patients on these MICs; and (3) to explore the possibility to adjust the MIC for the influence of the proportion of improved patients. Multiple simulations of patient samples involved in anchor-based MIC studies with different characteristics of HRQOL (change) scores and distributions of iMICs. In addition, a real data set is analyzed for illustration. The receiver operating characteristic-based and predictive modeling MICs equal the gMIC when the proportion of improved patients equals 0.5. The MIC is estimated higher than the gMIC when the proportion improved is greater than 0.5, and the MIC is estimated lower than the gMIC when the proportion improved is less than 0.5. Using an equation including the predictive modeling MIC, the log-odds of improvement, the standard deviation of the HRQOL change score, and the correlation between the HRQOL change score and the anchor results in an adjusted MIC reflecting the gMIC irrespective of the proportion of improved patients. Adjusting the predictive modeling MIC for the proportion of improved patients assures that the adjusted MIC reflects the gMIC. We assumed normal distributions and global perceived change scores that were independent on the follow-up score. Additionally, floor and ceiling effects were not taken into account. Copyright © 2017 Elsevier Inc. All rights reserved.

A dynamic eco-evolutionary model predicts slow response of alpine plants to climate warming.

PubMed

Cotto, Olivier; Wessely, Johannes; Georges, Damien; Klonner, Günther; Schmid, Max; Dullinger, Stefan; Thuiller, Wilfried; Guillaume, Frédéric

2017-05-05

Withstanding extinction while facing rapid climate change depends on a species' ability to track its ecological niche or to evolve a new one. Current methods that predict climate-driven species' range shifts use ecological modelling without eco-evolutionary dynamics. Here we present an eco-evolutionary forecasting framework that combines niche modelling with individual-based demographic and genetic simulations. Applying our approach to four endemic perennial plant species of the Austrian Alps, we show that accounting for eco-evolutionary dynamics when predicting species' responses to climate change is crucial. Perennial species persist in unsuitable habitats longer than predicted by niche modelling, causing delayed range losses; however, their evolutionary responses are constrained because long-lived adults produce increasingly maladapted offspring. Decreasing population size due to maladaptation occurs faster than the contraction of the species range, especially for the most abundant species. Monitoring of species' local abundance rather than their range may likely better inform on species' extinction risks under climate change.

Evaluation of model predictions of the ecological effects of 4-nonylphenol -- before and after model refinement

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hanratty, M.P.; Liber, K.

1994-12-31

The Littoral Ecosystem Risk Assessment Model (LERAM) is a bioenergetic ecosystem effects model. It links single species toxicity data to a bioenergetic model of the trophic structure of an ecosystem in order to simulate community and ecosystem level effects of chemical stressors. LERAM was used in 1992 to simulate the ecological effects of diflubenzuron. When compared to the results from a littoral enclosure study, the model exaggerated the cascading of effects through the trophic levels of the littoral ecosystem. It was hypothesized that this could be corrected by making minor changes in the representation of the littoral food web. Twomore » refinements of the model were therefore performed: (1) the plankton and macroinvertebrate model populations [eg., predatory Copepoda, herbivorous Insecta, green phytoplankton, etc.] were changed to better represent the habitat and feeding preferences of the endemic taxa; and (2) the method for modeling the microbial degradation of detritus (and the resulting nutrient remineralization) was changed from simulating bacterial populations to simulating bacterial function. Model predictions of the ecological effects of 4-nonylphenol were made before and after these refinements. Both sets of predictions were then compared to the results from a littoral enclosure study of the ecological effects of 4-nonylphenol. The changes in the LERAM predictions were then used to determine the success of the refinements, to guide. future research, and to further define LERAM`s domain of application.« less

Thermal regimes of Rocky Mountain lakes warm with climate change

PubMed Central

Roberts, James J.

2017-01-01

Anthropogenic climate change is causing a wide range of stresses in aquatic ecosystems, primarily through warming thermal conditions. Lakes, in response to these changes, are experiencing increases in both summer temperatures and ice-free days. We used continuous records of lake surface temperature and air temperature to create statistical models of daily mean lake surface temperature to assess thermal changes in mountain lakes. These models were combined with downscaled climate projections to predict future thermal conditions for 27 high-elevation lakes in the southern Rocky Mountains. The models predict a 0.25°C·decade-1 increase in mean annual lake surface temperature through the 2080s, which is greater than warming rates of streams in this region. Most striking is that on average, ice-free days are predicted to increase by 5.9 days ·decade-1, and summer mean lake surface temperature is predicted to increase by 0.47°C·decade-1. Both could profoundly alter the length of the growing season and potentially change the structure and function of mountain lake ecosystems. These results highlight the changes expected of mountain lakes and stress the importance of incorporating climate-related adaptive strategies in the development of resource management plans. PMID:28683083

Thermal regimes of Rocky Mountain lakes warm with climate change

USGS Publications Warehouse

Roberts, James J.; Fausch, Kurt D.; Schmidt, Travis S.; Walters, David M.

2017-01-01

Anthropogenic climate change is causing a wide range of stresses in aquatic ecosystems, primarily through warming thermal conditions. Lakes, in response to these changes, are experiencing increases in both summer temperatures and ice-free days. We used continuous records of lake surface temperature and air temperature to create statistical models of daily mean lake surface temperature to assess thermal changes in mountain lakes. These models were combined with downscaled climate projections to predict future thermal conditions for 27 high-elevation lakes in the southern Rocky Mountains. The models predict a 0.25°C·decade-1increase in mean annual lake surface temperature through the 2080s, which is greater than warming rates of streams in this region. Most striking is that on average, ice-free days are predicted to increase by 5.9 days ·decade-1, and summer mean lake surface temperature is predicted to increase by 0.47°C·decade-1. Both could profoundly alter the length of the growing season and potentially change the structure and function of mountain lake ecosystems. These results highlight the changes expected of mountain lakes and stress the importance of incorporating climate-related adaptive strategies in the development of resource management plans.

Thermal regimes of Rocky Mountain lakes warm with climate change.

PubMed

Roberts, James J; Fausch, Kurt D; Schmidt, Travis S; Walters, David M

2017-01-01

Anthropogenic climate change is causing a wide range of stresses in aquatic ecosystems, primarily through warming thermal conditions. Lakes, in response to these changes, are experiencing increases in both summer temperatures and ice-free days. We used continuous records of lake surface temperature and air temperature to create statistical models of daily mean lake surface temperature to assess thermal changes in mountain lakes. These models were combined with downscaled climate projections to predict future thermal conditions for 27 high-elevation lakes in the southern Rocky Mountains. The models predict a 0.25°C·decade-1 increase in mean annual lake surface temperature through the 2080s, which is greater than warming rates of streams in this region. Most striking is that on average, ice-free days are predicted to increase by 5.9 days ·decade-1, and summer mean lake surface temperature is predicted to increase by 0.47°C·decade-1. Both could profoundly alter the length of the growing season and potentially change the structure and function of mountain lake ecosystems. These results highlight the changes expected of mountain lakes and stress the importance of incorporating climate-related adaptive strategies in the development of resource management plans.

Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other’s Actions by Humans

PubMed Central

Ganesh, Gowrishankar

2017-01-01

Abstract The question of how humans predict outcomes of observed motor actions by others is a fundamental problem in cognitive and social neuroscience. Previous theoretical studies have suggested that the brain uses parts of the forward model (used to estimate sensory outcomes of self-generated actions) to predict outcomes of observed actions. However, this hypothesis has remained controversial due to the lack of direct experimental evidence. To address this issue, we analyzed the behavior of darts experts in an understanding learning paradigm and utilized computational modeling to examine how outcome prediction of observed actions affected the participants’ ability to estimate their own actions. We recruited darts experts because sports experts are known to have an accurate outcome estimation of their own actions as well as prediction of actions observed in others. We first show that learning to predict the outcomes of observed dart throws deteriorates an expert’s abilities to both produce his own darts actions and estimate the outcome of his own throws (or self-estimation). Next, we introduce a state-space model to explain the trial-by-trial changes in the darts performance and self-estimation through our experiment. The model-based analysis reveals that the change in an expert’s self-estimation is explained only by considering a change in the individual’s forward model, showing that an improvement in an expert’s ability to predict outcomes of observed actions affects the individual’s forward model. These results suggest that parts of the same forward model are utilized in humans to both estimate outcomes of self-generated actions and predict outcomes of observed actions. PMID:29340300

Predicting the Responses of Soil Nitrite-Oxidizers to Multi-Factorial Global Change: A Trait-Based Approach

DOE PAGES

Le Roux, Xavier; Bouskill, Nicholas J.; Niboyet, Audrey; ...

2016-05-17

Soil microbial diversity is huge and a few grams of soil contain more bacterial taxa than there are bird species on Earth. This high diversity often makes predicting the responses of soil bacteria to environmental change intractable and restricts our capacity to predict the responses of soil functions to global change. Here, using a long-term field experiment in a California grassland, we studied the main and interactive effects of three global change factors (increased atmospheric CO 2 concentration, precipitation and nitrogen addition, and all their factorial combinations, based on global change scenarios for central California) on the potential activity, abundancemore » and dominant taxa of soil nitrite-oxidizing bacteria (NOB). Using a trait-based model, we then tested whether categorizing NOB into a few functional groups unified by physiological traits enables understanding and predicting how soil NOB respond to global environmental change. Contrasted responses to global change treatments were observed between three main NOB functional types. In particular, putatively mixotrophic Nitrobacter, rare under most treatments, became dominant under the 'High CO 2 +Nitrogen+Precipitation' treatment. The mechanistic trait-based model, which simulated ecological niches of NOB types consistent with previous ecophysiological reports, helped predicting the observed effects of global change on NOB and elucidating the underlying biotic and abiotic controls. Our results are a starting point for representing the overwhelming diversity of soil bacteria by a few functional types that can be incorporated into models of terrestrial ecosystems and biogeochemical processes.« less

Predicting the Responses of Soil Nitrite-Oxidizers to Multi-Factorial Global Change: A Trait-Based Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Le Roux, Xavier; Bouskill, Nicholas J.; Niboyet, Audrey

Soil microbial diversity is huge and a few grams of soil contain more bacterial taxa than there are bird species on Earth. This high diversity often makes predicting the responses of soil bacteria to environmental change intractable and restricts our capacity to predict the responses of soil functions to global change. Here, using a long-term field experiment in a California grassland, we studied the main and interactive effects of three global change factors (increased atmospheric CO 2 concentration, precipitation and nitrogen addition, and all their factorial combinations, based on global change scenarios for central California) on the potential activity, abundancemore » and dominant taxa of soil nitrite-oxidizing bacteria (NOB). Using a trait-based model, we then tested whether categorizing NOB into a few functional groups unified by physiological traits enables understanding and predicting how soil NOB respond to global environmental change. Contrasted responses to global change treatments were observed between three main NOB functional types. In particular, putatively mixotrophic Nitrobacter, rare under most treatments, became dominant under the 'High CO 2 +Nitrogen+Precipitation' treatment. The mechanistic trait-based model, which simulated ecological niches of NOB types consistent with previous ecophysiological reports, helped predicting the observed effects of global change on NOB and elucidating the underlying biotic and abiotic controls. Our results are a starting point for representing the overwhelming diversity of soil bacteria by a few functional types that can be incorporated into models of terrestrial ecosystems and biogeochemical processes.« less

Personalized long-term prediction of cognitive function: Using sequential assessments to improve model performance.

PubMed

Chi, Chih-Lin; Zeng, Wenjun; Oh, Wonsuk; Borson, Soo; Lenskaia, Tatiana; Shen, Xinpeng; Tonellato, Peter J

2017-12-01

Prediction of onset and progression of cognitive decline and dementia is important both for understanding the underlying disease processes and for planning health care for populations at risk. Predictors identified in research studies are typically accessed at one point in time. In this manuscript, we argue that an accurate model for predicting cognitive status over relatively long periods requires inclusion of time-varying components that are sequentially assessed at multiple time points (e.g., in multiple follow-up visits). We developed a pilot model to test the feasibility of using either estimated or observed risk factors to predict cognitive status. We developed two models, the first using a sequential estimation of risk factors originally obtained from 8 years prior, then improved by optimization. This model can predict how cognition will change over relatively long time periods. The second model uses observed rather than estimated time-varying risk factors and, as expected, results in better prediction. This model can predict when newly observed data are acquired in a follow-up visit. Performances of both models that are evaluated in10-fold cross-validation and various patient subgroups show supporting evidence for these pilot models. Each model consists of multiple base prediction units (BPUs), which were trained using the same set of data. The difference in usage and function between the two models is the source of input data: either estimated or observed data. In the next step of model refinement, we plan to integrate the two types of data together to flexibly predict dementia status and changes over time, when some time-varying predictors are measured only once and others are measured repeatedly. Computationally, both data provide upper and lower bounds for predictive performance. Copyright © 2017 Elsevier Inc. All rights reserved.

Crop status evaluations and yield predictions

NASA Technical Reports Server (NTRS)

Haun, J. R.

1976-01-01

One phase of the large area crop inventory project is presented. Wheat yield models based on the input of environmental variables potentially obtainable through the use of space remote sensing were developed and demonstrated. By the use of a unique method for visually qualifying daily plant development and subsequent multifactor computer analyses, it was possible to develop practical models for predicting crop development and yield. Development of wheat yield prediction models was based on the discovery that morphological changes in plants are detected and quantified on a daily basis, and that this change during a portion of the season was proportional to yield.

Predicting the Effect of Changing Precipitation Extremes and Land Cover Change on Urban Water Quality

NASA Astrophysics Data System (ADS)

SUN, N.; Yearsley, J. R.; Lettenmaier, D. P.

2013-12-01

Recent research shows that precipitation extremes in many of the largest U.S. urban areas have increased over the last 60 years. These changes have important implications for stormwater runoff and water quality, which in urban areas are dominated by the most extreme precipitation events. We assess the potential implications of changes in extreme precipitation and changing land cover in urban and urbanizing watersheds at the regional scale using a combination of hydrology and water quality models. Specifically, we describe the integration of a spatially distributed hydrological model - the Distributed Hydrology Soil Vegetation Model (DHSVM), the urban water quality model in EPA's Storm Water Management Model (SWMM), the semi-Lagrangian stream temperature model RBM10, and dynamical and statistical downscaling methods applied to global climate predictions. Key output water quality parameters include total suspended solids (TSS), toal nitrogen, total phosphorous, fecal coliform bacteria and stream temperature. We have evaluated the performance of the modeling system in the highly urbanized Mercer Creek watershed in the rapidly growing Bellevue urban area in WA, USA. The results suggest that the model is able to (1) produce reasonable streamflow predictions at fine temporal and spatial scales; (2) provide spatially distributed water temperature predictions that mostly agree with observations throughout a complex stream network, and characterize impacts of climate, landscape, near-stream vegetation change on stream temperature at local and regional scales; and (3) capture plausibly the response of water quality constituents to varying magnitude of precipitation events in urban environments. Next we will extend the scope of the study from the Mercer Creek watershed to include the entire Puget Sound Basin, WA, USA.

Fast and Slow Precipitation Responses to Individual Climate Forcers: A PDRMIP Multimodel Study

NASA Technical Reports Server (NTRS)

Samset, B. H.; Myhre, G.; Forster, P.M.; Hodnebrog, O.; Andrews, T.; Faluvegi, G.; Flaschner, D.; Kasoar, M.; Kharin, V.; Kirkevag, A.;

The Predicted Influence of Climate Change on Lesser Prairie-Chicken Reproductive Parameters

PubMed Central

Grisham, Blake A.; Boal, Clint W.; Haukos, David A.; Davis, Dawn M.; Boydston, Kathy K.; Dixon, Charles; Heck, Willard R.

2013-01-01

The Southern High Plains is anticipated to experience significant changes in temperature and precipitation due to climate change. These changes may influence the lesser prairie-chicken (Tympanuchus pallidicinctus) in positive or negative ways. We assessed the potential changes in clutch size, incubation start date, and nest survival for lesser prairie-chickens for the years 2050 and 2080 based on modeled predictions of climate change and reproductive data for lesser prairie-chickens from 2001–2011 on the Southern High Plains of Texas and New Mexico. We developed 9 a priori models to assess the relationship between reproductive parameters and biologically relevant weather conditions. We selected weather variable(s) with the most model support and then obtained future predicted values from climatewizard.org. We conducted 1,000 simulations using each reproductive parameter’s linear equation obtained from regression calculations, and the future predicted value for each weather variable to predict future reproductive parameter values for lesser prairie-chickens. There was a high degree of model uncertainty for each reproductive value. Winter temperature had the greatest effect size for all three parameters, suggesting a negative relationship between above-average winter temperature and reproductive output. The above-average winter temperatures are correlated to La Niña events, which negatively affect lesser prairie-chickens through resulting drought conditions. By 2050 and 2080, nest survival was predicted to be below levels considered viable for population persistence; however, our assessment did not consider annual survival of adults, chick survival, or the positive benefit of habitat management and conservation, which may ultimately offset the potentially negative effect of drought on nest survival. PMID:23874549

COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease.

PubMed

Zhu, Fan; Panwar, Bharat; Dodge, Hiroko H; Li, Hongdong; Hampstead, Benjamin M; Albin, Roger L; Paulson, Henry L; Guan, Yuanfang

2016-10-05

We present COMPASS, a COmputational Model to Predict the development of Alzheimer's diSease Spectrum, to model Alzheimer's disease (AD) progression. This was the best-performing method in recent crowdsourcing benchmark study, DREAM Alzheimer's Disease Big Data challenge to predict changes in Mini-Mental State Examination (MMSE) scores over 24-months using standardized data. In the present study, we conducted three additional analyses beyond the DREAM challenge question to improve the clinical contribution of our approach, including: (1) adding pre-validated baseline cognitive composite scores of ADNI-MEM and ADNI-EF, (2) identifying subjects with significant declines in MMSE scores, and (3) incorporating SNPs of top 10 genes connected to APOE identified from functional-relationship network. For (1) above, we significantly improved predictive accuracy, especially for the Mild Cognitive Impairment (MCI) group. For (2), we achieved an area under ROC of 0.814 in predicting significant MMSE decline: our model has 100% precision at 5% recall, and 91% accuracy at 10% recall. For (3), "genetic only" model has Pearson's correlation of 0.15 to predict progression in the MCI group. Even though addition of this limited genetic model to COMPASS did not improve prediction of progression of MCI group, the predictive ability of SNP information extended beyond well-known APOE allele.

A multilevel modeling approach to examining the implementation-effectiveness relationship of a behavior change intervention for health care professional trainees.

PubMed

Tomasone, Jennifer R; Sweet, Shane N; McReynolds, Stuart; Martin Ginis, Kathleen A

2017-09-01

Changing Minds, Changing Lives, a seminar-mediated behavior change intervention, aims to enhance health care professionals' (HCPs') social cognitions for discussing leisure-time physical activity (LTPA) with patients with physical disabilities. This study examines which seminar implementation variables (presenter characteristics, delivery components) predict effectiveness using multilevel modeling. HCP trainees (n = 564) attended 24 seminars and completed Theory of Planned Behavior-based measures for discussing LTPA at pre-, post-, 1-month post-, and 6-months post-seminar. Implementation variables were extracted from presenter-completed questionnaires/checklists. Seminars presented by a HCP predicted positive changes in all cognitions pre-post but negative changes in attitudes and perceived behavioral control (PBC) over follow-up (ps < .05). The number of seminars the presenter had delivered predicted negative changes in attitudes and PBC during follow-up (ps < .001). Inclusion of audiovisual components predicted positive changes in attitudes pre-post (p < .001). Presenter characteristics may be "key ingredients" to educational interventions for HCPs; however, future studies should examine additional implementation variables.

Evaluating models of climate and forest vegetation

NASA Technical Reports Server (NTRS)

Clark, James S.

1992-01-01

Understanding how the biosphere may respond to increasing trace gas concentrations in the atmosphere requires models that contain vegetation responses to regional climate. Most of the processes ecologists study in forests, including trophic interactions, nutrient cycling, and disturbance regimes, and vital components of the world economy, such as forest products and agriculture, will be influenced in potentially unexpected ways by changing climate. These vegetation changes affect climate in the following ways: changing C, N, and S pools; trace gases; albedo; and water balance. The complexity of the indirect interactions among variables that depend on climate, together with the range of different space/time scales that best describe these processes, make the problems of modeling and prediction enormously difficult. These problems of predicting vegetation response to climate warming and potential ways of testing model predictions are the subjects of this chapter.

Improving the forecast for biodiversity under climate change.

PubMed

Urban, M C; Bocedi, G; Hendry, A P; Mihoub, J-B; Pe'er, G; Singer, A; Bridle, J R; Crozier, L G; De Meester, L; Godsoe, W; Gonzalez, A; Hellmann, J J; Holt, R D; Huth, A; Johst, K; Krug, C B; Leadley, P W; Palmer, S C F; Pantel, J H; Schmitz, A; Zollner, P A; Travis, J M J

2016-09-09

New biological models are incorporating the realistic processes underlying biological responses to climate change and other human-caused disturbances. However, these more realistic models require detailed information, which is lacking for most species on Earth. Current monitoring efforts mainly document changes in biodiversity, rather than collecting the mechanistic data needed to predict future changes. We describe and prioritize the biological information needed to inform more realistic projections of species' responses to climate change. We also highlight how trait-based approaches and adaptive modeling can leverage sparse data to make broader predictions. We outline a global effort to collect the data necessary to better understand, anticipate, and reduce the damaging effects of climate change on biodiversity. Copyright © 2016, American Association for the Advancement of Science.

Plant water potential improves prediction of empirical stomatal models.

PubMed

Anderegg, William R L; Wolf, Adam; Arango-Velez, Adriana; Choat, Brendan; Chmura, Daniel J; Jansen, Steven; Kolb, Thomas; Li, Shan; Meinzer, Frederick; Pita, Pilar; Resco de Dios, Víctor; Sperry, John S; Wolfe, Brett T; Pacala, Stephen

2017-01-01

Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

A way forward for fire-caused tree mortality prediction: Modeling a physiological consequence of fire

Treesearch

Kathleen L. Kavanaugh; Matthew B. Dickinson; Anthony S. Bova

2010-01-01

Current operational methods for predicting tree mortality from fire injury are regression-based models that only indirectly consider underlying causes and, thus, have limited generality. A better understanding of the physiological consequences of tree heating and injury are needed to develop biophysical process models that can make predictions under changing or novel...

Association between objective and subjective binge eating and psychopathology during a psychological treatment trial for bulimic symptoms.

PubMed

Goldschmidt, Andrea B; Accurso, Erin C; Crosby, Ross D; Cao, Li; Ellison, Jo; Smith, Tracey L; Klein, Marjorie H; Mitchell, James E; Crow, Scott J; Wonderlich, Stephen A; Peterson, Carol B

2016-12-01

Although loss of control (LOC) while eating is a core construct of bulimia nervosa (BN), questions remain regarding its validity and prognostic significance independent of overeating. We examined trajectories of objective and subjective binge eating (OBE and SBE, respectively; i.e., LOC eating episodes involving an objectively or subjectively large amount of food) among adults participating in psychological treatments for BN-spectrum disorders (n = 80). We also explored whether changes in the frequency of these eating episodes differentially predicted changes in eating-related and general psychopathology and, conversely, whether changes in eating-related and general psychopathology predicted differential changes in the frequency of these eating episodes. Linear mixed models with repeated measures revealed that OBE decreased twice as rapidly as SBE throughout treatment and 4-month follow-up. Generalized linear models revealed that baseline to end-of-treatment reductions in SBE frequency predicted baseline to 4-month follow-up changes in eating-related psychopathology, depression, and anxiety, while changes in OBE frequency were not predictive of psychopathology at 4-month follow-up. Zero-inflation models indicated that baseline to end-of-treatment changes in eating-related psychopathology and depression symptoms predicted baseline to 4-month follow-up changes in OBE frequency, while changes in anxiety and self-esteem did not. Baseline to end-of-treatment changes in eating-related psychopathology, self-esteem, and anxiety predicted baseline to 4-month follow-up changes in SBE frequency, while baseline to end-of-treatment changes in depression did not. Based on these findings, LOC accompanied by objective overeating may reflect distress at having consumed an objectively large amount of food, whereas LOC accompanied by subjective overeating may reflect more generalized distress related to one's eating- and mood-related psychopathology. BN treatments should comprehensively target LOC eating and related psychopathology, particularly in the context of subjectively large episodes, to improve global outcomes. Copyright © 2016. Published by Elsevier Ltd.

Accurate and dynamic predictive model for better prediction in medicine and healthcare.

PubMed

Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

2018-05-01

Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

A data-driven model for constraint of present-day glacial isostatic adjustment in North America

NASA Astrophysics Data System (ADS)

Simon, K. M.; Riva, R. E. M.; Kleinherenbrink, M.; Tangdamrongsub, N.

2017-09-01

Geodetic measurements of vertical land motion and gravity change are incorporated into an a priori model of present-day glacial isostatic adjustment (GIA) in North America via least-squares adjustment. The result is an updated GIA model wherein the final predicted signal is informed by both observational data, and prior knowledge (or intuition) of GIA inferred from models. The data-driven method allows calculation of the uncertainties of predicted GIA fields, and thus offers a significant advantage over predictions from purely forward GIA models. In order to assess the influence each dataset has on the final GIA prediction, the vertical land motion and GRACE-measured gravity data are incorporated into the model first independently (i.e., one dataset only), then simultaneously. The relative weighting of the datasets and the prior input is iteratively determined by variance component estimation in order to achieve the most statistically appropriate fit to the data. The best-fit model is obtained when both datasets are inverted and gives respective RMS misfits to the GPS and GRACE data of 1.3 mm/yr and 0.8 mm/yr equivalent water layer change. Non-GIA signals (e.g., hydrology) are removed from the datasets prior to inversion. The post-fit residuals between the model predictions and the vertical motion and gravity datasets, however, suggest particular regions where significant non-GIA signals may still be present in the data, including unmodeled hydrological changes in the central Prairies west of Lake Winnipeg. Outside of these regions of misfit, the posterior uncertainty of the predicted model provides a measure of the formal uncertainty associated with the GIA process; results indicate that this quantity is sensitive to the uncertainty and spatial distribution of the input data as well as that of the prior model information. In the study area, the predicted uncertainty of the present-day GIA signal ranges from ∼0.2-1.2 mm/yr for rates of vertical land motion, and from ∼3-4 mm/yr of equivalent water layer change for gravity variations.

Benchmarking novel approaches for modelling species range dynamics

PubMed Central

Zurell, Damaris; Thuiller, Wilfried; Pagel, Jörn; Cabral, Juliano S; Münkemüller, Tamara; Gravel, Dominique; Dullinger, Stefan; Normand, Signe; Schiffers, Katja H.; Moore, Kara A.; Zimmermann, Niklaus E.

2016-01-01

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species’ range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species’ response to climate change but also emphasise several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species. PMID:26872305

Benchmarking novel approaches for modelling species range dynamics.

PubMed

Zurell, Damaris; Thuiller, Wilfried; Pagel, Jörn; Cabral, Juliano S; Münkemüller, Tamara; Gravel, Dominique; Dullinger, Stefan; Normand, Signe; Schiffers, Katja H; Moore, Kara A; Zimmermann, Niklaus E

2016-08-01

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species' range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species' response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species. © 2016 John Wiley & Sons Ltd.

Food-Web Models Predict Species Abundances in Response to Habitat Change

PubMed Central

Gotelli, Nicholas J; Ellison, Aaron M

2006-01-01

Plant and animal population sizes inevitably change following habitat loss, but the mechanisms underlying these changes are poorly understood. We experimentally altered habitat volume and eliminated top trophic levels of the food web of invertebrates that inhabit rain-filled leaves of the carnivorous pitcher plant Sarracenia purpurea. Path models that incorporated food-web structure better predicted population sizes of food-web constituents than did simple keystone species models, models that included only autecological responses to habitat volume, or models including both food-web structure and habitat volume. These results provide the first experimental confirmation that trophic structure can determine species abundances in the face of habitat loss. PMID:17002518

Sensitivity of river fishes to climate change: The role of hydrological stressors on habitat range shifts.

PubMed

Segurado, Pedro; Branco, Paulo; Jauch, Eduardo; Neves, Ramiro; Ferreira, M Teresa

2016-08-15

Climate change will predictably change hydrological patterns and processes at the catchment scale, with impacts on habitat conditions for fish. The main goal of this study is to assess how shifts in fish habitat favourability under climate change scenarios are affected by hydrological stressors. The interplay between climate and hydrological stressors has important implications in river management under climate change because management actions to control hydrological parameters are more feasible than controlling climate. This study was carried out in the Tamega catchment of the Douro basin. A set of hydrological stressor variables were generated through a process-based modelling based on current climate data (2008-2014) and also considering a high-end future climate change scenario. The resulting parameters, along with climatic and site-descriptor variables were used as explanatory variables in empirical habitat models for nine fish species using boosted regression trees. Models were calibrated for the whole Douro basin using 254 fish sampling sites and predictions under future climate change scenarios were made for the Tamega catchment. Results show that models using climatic variables but not hydrological stressors produce more stringent predictions of future favourability, predicting more distribution contractions or stronger range shifts. The use of hydrological stressors strongly influences projections of habitat favourability shifts; the integration of these stressors in the models thinned shifts in range due to climate change. Hydrological stressors were retained in the models for most species and had a high importance, demonstrating that it is important to integrate hydrology in studies of impacts of climate change on freshwater fishes. This is a relevant result because it means that management actions to control hydrological parameters in rivers will have an impact on the effects of climate change and may potentially be helpful to mitigate its negative effects on fish populations and assemblages. Copyright © 2016 Elsevier B.V. All rights reserved.

The influence of coarse-scale environmental features on current and predicted future distributions of narrow-range endemic crayfish populations

USGS Publications Warehouse

Dyer, Joseph J.; Brewer, Shannon K.; Worthington, Thomas A.; Bergey, Elizabeth A.

2013-01-01

1.A major limitation to effective management of narrow-range crayfish populations is the paucity of information on the spatial distribution of crayfish species and a general understanding of the interacting environmental variables that drive current and future potential distributional patterns. 2.Maximum Entropy Species Distribution Modeling Software (MaxEnt) was used to predict the current and future potential distributions of four endemic crayfish species in the Ouachita Mountains. Current distributions were modelled using climate, geology, soils, land use, landform and flow variables thought to be important to lotic crayfish. Potential changes in the distribution were forecast by using models trained on current conditions and projecting onto the landscape predicted under climate-change scenarios. 3.The modelled distribution of the four species closely resembled the perceived distribution of each species but also predicted populations in streams and catchments where they had not previously been collected. Soils, elevation and winter precipitation and temperature most strongly related to current distributions and represented 6587% of the predictive power of the models. Model accuracy was high for all models, and model predictions of new populations were verified through additional field sampling. 4.Current models created using two spatial resolutions (1 and 4.5km2) showed that fine-resolution data more accurately represented current distributions. For three of the four species, the 1-km2 resolution models resulted in more conservative predictions. However, the modelled distributional extent of Orconectes leptogonopodus was similar regardless of data resolution. Field validations indicated 1-km2 resolution models were more accurate than 4.5-km2 resolution models. 5.Future projected (4.5-km2 resolution models) model distributions indicated three of the four endemic species would have truncated ranges with low occurrence probabilities under the low-emission scenario, whereas two of four species would be severely restricted in range under moderatehigh emissions. Discrepancies in the two emission scenarios probably relate to the exclusion of behavioural adaptations from species-distribution models. 6.These model predictions illustrate possible impacts of climate change on narrow-range endemic crayfish populations. The predictions do not account for biotic interactions, migration, local habitat conditions or species adaptation. However, we identified the constraining landscape features acting on these populations that provide a framework for addressing habitat needs at a fine scale and developing targeted and systematic monitoring programmes.

Numerical formulation for the prediction of solid/liquid change of a binary alloy

NASA Technical Reports Server (NTRS)

Schneider, G. E.; Tiwari, S. N.

1990-01-01

A computational model is presented for the prediction of solid/liquid phase change energy transport including the influence of free convection fluid flow in the liquid phase region. The computational model considers the velocity components of all non-liquid phase change material control volumes to be zero but fully solves the coupled mass-momentum problem within the liquid region. The thermal energy model includes the entire domain and uses an enthalpy like model and a recently developed method for handling the phase change interface nonlinearity. Convergence studies are performed and comparisons made with experimental data for two different problem specifications. The convergence studies indicate that grid independence was achieved and the comparison with experimental data indicates excellent quantitative prediction of the melt fraction evolution. Qualitative data is also provided in the form of velocity vector diagrams and isotherm plots for selected times in the evolution of both problems. The computational costs incurred are quite low by comparison with previous efforts on solving these problems.

ISO Mid-Infrared Spectra of Reflection Nebulae

NASA Technical Reports Server (NTRS)

Werner, M.; Uchida, K.; Sellgren, K.; Houdashelt, M.

1999-01-01

Our goal is to test predictions of models attributing the IEFs to polycyclic aromatic hydrocarbons (PAHs). Interstellar models predict PAHs change from singly ionized to neutral as the UV intensity, Go, decreases.

A synopsis of climate change effects on groundwater recharge

NASA Astrophysics Data System (ADS)

Smerdon, Brian D.

2017-12-01

Six review articles published between 2011 and 2016 on groundwater and climate change are briefly summarized. This synopsis focuses on aspects related to predicting changes to groundwater recharge conditions, with several common conclusions between the review articles being noted. The uncertainty of distribution and trend in future precipitation from General Circulation Models (GCMs) results in varying predictions of recharge, so much so that modelling studies are often not able to predict the magnitude and direction (increase or decrease) of future recharge conditions. Evolution of modelling approaches has led to the use of multiple GCMs and hydrologic models to create an envelope of future conditions that reflects the probability distribution. The choice of hydrologic model structure and complexity, and the choice of emissions scenario, has been investigated and somewhat resolved; however, recharge results remain sensitive to downscaling methods. To overcome uncertainty and provide practical use in water management, the research community indicates that modelling at a mesoscale, somewhere between watersheds and continents, is likely ideal. Improvements are also suggested for incorporating groundwater processes within GCMs.

Evaluation of relative response factor methodology for demonstrating attainment of ozone in Houston, Texas.

PubMed

Vizuete, William; Biton, Leiran; Jeffries, Harvey E; Couzo, Evan

2010-07-01

In 2007, the U.S. Environmental Protection Agency (EPA) released guidance on demonstrating attainment of the federal ozone (O3) standard. This guidance recommended a change in the use of air quality model (AQM) predictions from an absolute to a relative way. This was accomplished by using a ratio, and not the absolute difference of AQM O3 predictions from a historical year to an attainment year. This ratio of O3 concentrations, labeled the relative response factor (RRF), is multiplied by an average of observed concentrations at every monitor. In this analysis, whether the methodology used to calculate RRFs is severing the source-receptor relationship for a given monitor was investigated. Model predictions were generated with a regulatory AQM system used to support the 2004 Houston-Galveston-Brazoria State Implementation Plan. Following the procedures in the EPA guidance, an attainment demonstration was completed using regulatory AQM predictions and measurements from the Houston ground-monitoring network. Results show that the model predictions used for the RRF calculation were often based on model conditions that were geographically remote from observations and counter to wind flow. Many of the monitors used the same model predictions for an RRF, even if that O3 plume did not impact it. The RRF methodology resulted in severing the true source-receptor relationship for a monitor. This analysis also showed that model performance could influence RRF values, and values at monitoring sites appear to be sensitive to model bias. Results indicate an inverse linear correlation of RRFs with model bias at each monitor (R2 = 0.47), resulting in a change in future O3 design values up to 5 parts per billion (ppb). These results suggest that the application of RRF methodology in Houston, TX, should be changed from using all model predictions above 85 ppb to a method that removes any predictions that are not relevant to the observed source-receptor relationship.

Potential impacts of climate change on the winter distribution of Afro-Palaearctic migrant passerines

PubMed Central

Barbet-Massin, Morgane; Walther, Bruno A.; Thuiller, Wilfried; Rahbek, Carsten; Jiguet, Frédéric

2009-01-01

We modelled the present and future sub-Saharan winter distributions of 64 trans-Saharan migrant passerines to predict the potential impacts of climate change. These predictions used the recent ensemble modelling developments and the latest IPCC climatic simulations to account for possible methodological uncertainties. Results suggest that 37 species would face a range reduction by 2100 (16 of these by more than 50%); however, the median range size variation is −13 per cent (from −97 to +980%) under a full dispersal hypothesis. Range centroids were predicted to shift by 500±373 km. Predicted changes in range size and location were spatially structured, with species that winter in southern and eastern Africa facing larger range contractions and shifts. Predicted changes in regional species richness for these long-distance migrants are increases just south of the Sahara and on the Arabian Peninsula and major decreases in southern and eastern Africa. PMID:19324660

Behavioral Change Theories Can Inform the Prediction of Young Adults' Adoption of a Plant-Based Diet

ERIC Educational Resources Information Center

Wyker, Brett A.; Davison, Kirsten K.

2010-01-01

Objective: Drawing on the Theory of Planned Behavior (TPB) and the Transtheoretical Model (TTM), this study (1) examines links between stages of change for following a plant-based diet (PBD) and consuming more fruits and vegetables (FV); (2) tests an integrated theoretical model predicting intention to follow a PBD; and (3) identifies associated…

Morphodynamic data assimilation used to understand changing coasts

USGS Publications Warehouse

Plant, Nathaniel G.; Long, Joseph W.

2015-01-01

Morphodynamic data assimilation blends observations with model predictions and comes in many forms, including linear regression, Kalman filter, brute-force parameter estimation, variational assimilation, and Bayesian analysis. Importantly, data assimilation can be used to identify sources of prediction errors that lead to improved fundamental understanding. Overall, models incorporating data assimilation yield better information to the people who must make decisions impacting safety and wellbeing in coastal regions that experience hazards due to storms, sea-level rise, and erosion. We present examples of data assimilation associated with morphologic change. We conclude that enough morphodynamic predictive capability is available now to be useful to people, and that we will increase our understanding and the level of detail of our predictions through assimilation of observations and numerical-statistical models.

Collaborative Proposal: Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic System Model (RASM)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maslowski, Wieslaw

This project aims to develop, apply and evaluate a regional Arctic System model (RASM) for enhanced decadal predictions. Its overarching goal is to advance understanding of the past and present states of arctic climate and to facilitate improvements in seasonal to decadal predictions. In particular, it will focus on variability and long-term change of energy and freshwater flows through the arctic climate system. The project will also address modes of natural climate variability as well as extreme and rapid climate change in a region of the Earth that is: (i) a key indicator of the state of global climate throughmore » polar amplification and (ii) which is undergoing environmental transitions not seen in instrumental records. RASM will readily allow the addition of other earth system components, such as ecosystem or biochemistry models, thus allowing it to facilitate studies of climate impacts (e.g., droughts and fires) and of ecosystem adaptations to these impacts. As such, RASM is expected to become a foundation for more complete Arctic System models and part of a model hierarchy important for improving climate modeling and predictions.« less

Behavioral change theories can inform the prediction of young adults' adoption of a plant-based diet.

PubMed

Wyker, Brett A; Davison, Kirsten K

2010-01-01

Drawing on the Theory of Planned Behavior (TPB) and the Transtheoretical Model (TTM), this study (1) examines links between stages of change for following a plant-based diet (PBD) and consuming more fruits and vegetables (FV); (2) tests an integrated theoretical model predicting intention to follow a PBD; and (3) identifies associated salient beliefs. Cross-sectional. Large public university in the northeastern United States. 204 college students. TPB and TTM constructs were assessed using validated scales. Outcome, normative, and control beliefs were measured using open-ended questions. The overlap between stages of change for FV consumption and adopting a PBD was assessed using Spearman rank correlation analysis and cross-tab comparisons. The proposed model predicting adoption of a PBD was tested using structural equation modeling (SEM). Salient beliefs were coded using automatic response coding software. No association was found between stages of change for FV consumption and following a PBD. Results from SEM analyses provided support for the proposed model predicting intention to follow a PBD. Gender differences in salient beliefs for following a PBD were found. Results demonstrate the potential for effective theory-driven and stage-tailored public health interventions to promote PBDs. Copyright 2010 Society for Nutrition Education. Published by Elsevier Inc. All rights reserved.

Population dynamics can be more important than physiological limits for determining range shifts under climate change.

PubMed

Fordham, Damien A; Mellin, Camille; Russell, Bayden D; Akçakaya, Reşit H; Bradshaw, Corey J A; Aiello-Lammens, Matthew E; Caley, Julian M; Connell, Sean D; Mayfield, Stephen; Shepherd, Scoresby A; Brook, Barry W

2013-10-01

Evidence is accumulating that species' responses to climate changes are best predicted by modelling the interaction of physiological limits, biotic processes and the effects of dispersal-limitation. Using commercially harvested blacklip (Haliotis rubra) and greenlip abalone (Haliotis laevigata) as case studies, we determine the relative importance of accounting for interactions among physiology, metapopulation dynamics and exploitation in predictions of range (geographical occupancy) and abundance (spatially explicit density) under various climate change scenarios. Traditional correlative ecological niche models (ENM) predict that climate change will benefit the commercial exploitation of abalone by promoting increased abundances without any reduction in range size. However, models that account simultaneously for demographic processes and physiological responses to climate-related factors result in future (and present) estimates of area of occupancy (AOO) and abundance that differ from those generated by ENMs alone. Range expansion and population growth are unlikely for blacklip abalone because of important interactions between climate-dependent mortality and metapopulation processes; in contrast, greenlip abalone should increase in abundance despite a contraction in AOO. The strongly non-linear relationship between abalone population size and AOO has important ramifications for the use of ENM predictions that rely on metrics describing change in habitat area as proxies for extinction risk. These results show that predicting species' responses to climate change often require physiological information to understand climatic range determinants, and a metapopulation model that can make full use of this data to more realistically account for processes such as local extirpation, demographic rescue, source-sink dynamics and dispersal-limitation. © 2013 John Wiley & Sons Ltd.

Development of a Probabilistic Decision-Support Model to Forecast Coastal Resilience

NASA Astrophysics Data System (ADS)

Wilson, K.; Safak, I.; Brenner, O.; Lentz, E. E.; Hapke, C. J.

2016-02-01

Site-specific forecasts of coastal change are a valuable management tool in preparing for and assessing storm-driven impacts in coastal areas. More specifically, understanding the likelihood of storm impacts, recovery following events, and the alongshore variability of both is central in evaluating vulnerability and resiliency of barrier islands. We introduce a probabilistic modeling framework that integrates hydrodynamic, anthropogenic, and morphologic components of the barrier system to evaluate coastal change at Fire Island, New York. The model is structured on a Bayesian network (BN), which utilizes observations to learn statistical relationships between system variables. In addition to predictive ability, probabilistic models convey the level of confidence associated with a prediction, an important consideration for coastal managers. Our model predicts the likelihood of morphologic change on the upper beach based on several decades of beach monitoring data. A coupled hydrodynamic BN combines probabilistic and deterministic modeling approaches; by querying nearly two decades of nested-grid wave simulations that account for both distant swells and local seas, we produce scenarios of event and seasonal wave climates. The wave scenarios of total water level - a sum of run up, surge and tide - and anthropogenic modification are the primary drivers of morphologic change in our model structure. Preliminary results show the hydrodynamic BN is able to reproduce time series of total water levels, a critical validation process before generating scenarios, and forecasts of geomorphic change over three month intervals are up to 70% accurate. Predictions of storm-induced change and recovery are linked to evaluate zones of persistent vulnerability or resilience and will help managers target restoration efforts, identify areas most vulnerable to habitat degradation, and highlight resilient zones that may best support relocation of critical infrastructure.

Impacts of past and future climate change on wind energy resources in the United States

NASA Astrophysics Data System (ADS)

McCaa, J. R.; Wood, A.; Eichelberger, S.; Westrick, K.

2009-12-01

The links between climate change and trends in wind energy resources have important potential implications for the wind energy industry, and have received significant attention in recent studies. We have conducted two studies that provide insights into the potential for climate change to affect future wind power production. In one experiment, we projected changes in power capacity for a hypothetical wind farm located near Kennewick, Washington, due to greenhouse gas-induced climate change, estimated using a set of regional climate model simulations. Our results show that the annual wind farm power capacity is projected to decrease 1.3% by 2050. In a wider study focusing on wind speed instead of power, we analyzed projected changes in wind speed from 14 different climate simulations that were performed in support of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Our results show that the predicted ensemble mean changes in annual mean wind speeds are expected to be modest. However, seasonal changes and changes predicted by individual models are large enough to affect the profitability of existing and future wind projects. The majority of the model simulations reveal that near-surface wind speed values are expected to shift poleward in response to the IPCC A2 emission scenario, particularly during the winter season. In the United States, most models agree that the mean annual wind speed values will increase in a region extending from the Great Lakes southward across the Midwest and into Texas. Decreased values, though, are predicted across most of the western United States. However, these predicted changes have a strong seasonal dependence, with wind speed increases over most of the United States during the winter and decreases over the northern United States during the summer.

Ontogenetic loss of phenotypic plasticity of age at metamorphosis in tadpoles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hensley, F.R.

1993-12-01

Amphibian larvae exhibit phenotypic plasticity in size at metamorphosis and duration of the larval period. I used Pseudacris crucifer tadpoles to test two models for predicting tadpole age and size at metamorphosis under changing environmental conditions. The Wilbur-Collins model states that metamorphosis is initiated as a function of a tadpole's size and relative growth rate, and predicts that changes in growth rate throughout the larval period affect age and size at metamorphosis. An alternative model, the fixed-rate model, states that age at metamorphosis is fixed early in larval life, and subsequent changes in growth rate will have no effect onmore » the length of the larval period. My results confirm that food supplies affect both age and size at metamorphosis, but developmental rates became fixed at approximately Gosner (1960) stages 35-37. Neither model completely predicted these results. I suggest that the generally accepted Wilbur-Collins model is improved by incorporating a point of fixed developmental timing. Growth trajectories predicted from this modified model fit the results of this study better than trajectories based on either of the original models. The results of this study suggests a constraint that limits the simultaneous optimization of age and size at metamorphosis. 32 refs., 5 figs., 1 tab.« less

Shifts in the climate space of temperate cyprinid fishes due to climate change are coupled with altered body sizes and growth rates.

PubMed

Ruiz-Navarro, Ana; Gillingham, Phillipa K; Britton, J Robert

2016-09-01

Predictions of species responses to climate change often focus on distribution shifts, although responses can also include shifts in body sizes and population demographics. Here, shifts in the distributional ranges ('climate space'), body sizes (as maximum theoretical body sizes, L∞) and growth rates (as rate at which L∞ is reached, K) were predicted for five fishes of the Cyprinidae family in a temperate region over eight climate change projections. Great Britain was the model area, and the model species were Rutilus rutilus, Leuciscus leuciscus, Squalius cephalus, Gobio gobio and Abramis brama. Ensemble models predicted that the species' climate spaces would shift in all modelled projections, with the most drastic changes occurring under high emissions; all range centroids shifted in a north-westerly direction. Predicted climate space expanded for R. rutilus and A. brama, contracted for S. cephalus, and for L. leuciscus and G. gobio, expanded under low-emission scenarios but contracted under high emissions, suggesting the presence of some climate-distribution thresholds. For R. rutilus, A. brama, S. cephalus and G. gobio, shifts in their climate space were coupled with predicted shifts to significantly smaller maximum body sizes and/or faster growth rates, aligning strongly to aspects of temperature-body size theory. These predicted shifts in L∞ and K had considerable consequences for size-at-age per species, suggesting substantial alterations in population age structures and abundances. Thus, when predicting climate change outcomes for species, outputs that couple shifts in climate space with altered body sizes and growth rates provide considerable insights into the population and community consequences, especially for species that cannot easily track their thermal niches. © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

[Potential distribution of Panax ginseng and its predicted responses to climate change.

PubMed

Zhao, Ze Fang; Wei, Hai Yan; Guo, Yan Long; Gu, Wei

2016-11-18

This study utilized Panax ginseng as the research object. Based on BioMod2 platform, with species presence data and 22 climatic variables, the potential geographic distribution of P. ginseng under the current conditions in northeast China was simulated with ten species distribution model. And then with the receiver-operating characteristic curve (ROC) as weights, we build an ensemble model, which integrated the results of 10 models, using the ensemble model, the future distributions of P. ginseng were also projected for the periods 2050s and 2070s under the climate change scenarios of RCP 8.5, RCP 6, RCP 4.5 and RCP 2.6 emission scenarios described in the Special Report on Emissions Scenarios (SRES) of IPCC (Intergovernmental Panel on Climate Change). The results showed that for the entire region of study area, under the present climatic conditions, 10.4% of the areas were identified as suitable habitats, which were mainly located in northeast Changbai Mountains area and the southeastern region of the Xiaoxing'an Mountains. The model simulations indicated that the suitable habitats would have a relatively significant change under the different climate change scenarios, and generally the range of suitable habitats would be a certain degree of decrease. Meanwhile, the goodness-of-fit, predicted ranges, and weights of explanatory variables was various for each model. And according to the goodness-of-fit, Maxent had the highest model performance, and GAM, RF and ANN were followed, while SRE had the lowest prediction accuracy. In this study we established an ensemble model, which could improve the accuracy of the existing species distribution models, and optimization of species distribution prediction results.

The effects of climate change on instream nitrogen transport in the contiguous United States

NASA Astrophysics Data System (ADS)

Alam, M. J.; Goodall, J. L.

2011-12-01

Excessive nitrogen loading has caused significant environmental impacts such as eutrophication and hypoxia in waterbodies around the world. Nitrogen loading is largely dependent on nonpoint source pollution and nitrogen transport from nonpoint source pollution is greatly impacted by climate conditions. For example, increased precipitation leads to more runoff and a higher nitrogen yield. However, higher temperatures also impact nitrogen transport in that higher temperatures increase denitrification and therefore reduce nitrogen yield. The purpose of this research is to quantify potential changes in nitrogen yield for the contiguous United States under predicted climate change scenarios, specifically changes in precipitation and air temperature. The analysis was performed for high (A2) and low (B1) emission scenarios and for the year 2030, 2050 and 2090. We used 11 different IPCC (The Intergovernmental Panel on Climate Change) models predicted precipitation and temperature estimates to capture uncertainty. The SPARROW model was calibrated using historical nitrogen loading data and used to predict nitrogen yields for future climate conditions. We held nitrogen source data constant in order to isolate the impact of predicted precipitation and temperature changes for each model scenario. Preliminary results suggest an overall decrease in nitrogen yield if climate change impacts are considered in isolation. For the A2 scenario, the model results indicated an overall incremental nitrogen yield decrease of 2-17% by the year 2030, 4-26% by the year 2050, and 11-45% by the year 2090. The B1 emission scenario also indicated an incremental yield decrease, but at lesser amounts of 2-18%, 5-21% and 10-38% by the years 2030, 2050, and 2090, respectively. This decrease is mainly due to higher predicted temperatures that result in increased denitrification rates.

Social Identity in People with Multiple Sclerosis: An Examination of Family Identity and Mood.

PubMed

Barker, Alex B; Lincoln, Nadina B; Hunt, Nigel; dasNair, Roshan

2018-01-01

Mood disorders are highly prevalent in people with multiple sclerosis (MS). MS causes changes to a person's sense of self. The Social Identity Model of Identity Change posits that group membership can have a positive effect on mood during identity change. The family is a social group implicated in adjustment to MS. The objectives of this study were to investigate whether family identity can predict mood in people with MS and to test whether this prediction was mediated by social support and connectedness to others. This cross-sectional survey of 195 participants comprised measures of family identity, family social support, connectedness to others, and mood. Family identity predicted mood both directly and indirectly through parallel mediators of family social support and connectedness to others. Family identity predicted mood as posited by the Social Identity Model of Identity Change. Involving the family in adjustment to MS could reduce low mood.

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post Contacts Change Log Events Calendar Numerical Forecast Systems NCEP Model Analysis and Guidance Page [< Modeling Center NOAA Center for Weather and Climate Prediction (NCWCP) 5830 University Research Court

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post missed NDAS cycles since 1 Apr 1995 Log of NAM model code changes Log of NAM model test runs Problems and Prediction (NCWCP) 5830 University Research Court College Park, MD 20740 Page Author: EMC Webmaster Page

Insensitivity of evapotranspiration to seasonal rainfall distribution directs climate change impacts at water yield

NASA Astrophysics Data System (ADS)

Montaldo, N.; Oren, R.

2017-12-01

Over the past century, climate change is affecting precipitation regimes across the world. In the Mediterranean regions there is a persistent trend of precipitation and runoff decreases, generating a desertification process. Given the past winter precipitation shifts, the impacts on evapotranspiration (ET) need to be carefully evaluated, and the compelling question is what will be the impact of future climate change scenarios (predicting changes of precipitation and vapor pressure deficit, VPD) on evapotranspiration and water yield? Looking for the key elements of the climate change that are impacting annual ET, we investigate main climate conditions (e.g. precipitation and VPD) and basin physiographic properties contributing to annual ET. We propose a simplified model for annual ET predictions that accounts for the strong meteo seasonality typical of Mediterranean climates, using the steady state assumption of the basin water balance at mean annual scale. We investigate the Sardinia case study because the position of the island of Sardinia in the center of the western Mediterranean Sea basin and its low urbanization and human activity make Sardinia a perfect reference laboratory for Mediterranean hydrologic studies. Sardinian runoff decreased drastically over the 1975-2010 period, with mean yearly runoff reduced by more than 40% compared to the previous 1922-1974 period, and most yearly runoff in the Sardinian basins (70% on average) is produced by winter precipitation due to the seasonality typical of the Mediterranean climate regime. The use of our proposed model allows to predict future ET and water yield using future climate scenarios. We use the future climate scenarios predicted by Global climate models (GCM) in the Fifth Assessment report of the Intergovernmental Panel on Climate Change (IPCC), and we select most reliable models testing the past GCM predictions with historical data. Contrasting shifts of precipitation (both positive and negative) are predicted in the future scenarios by GCMs but these changes will produce significant changes (level of significance > 90%) only in runoff and not in ET. Surprisingly, we show that ET is insensitive to intra-annual rainfall distribution changes, and is insensitive to VPD scenario changes.

Including the dynamic relationship between climatic variables and leaf area index in a hydrological model to improve streamflow prediction under a changing climate

NASA Astrophysics Data System (ADS)

Tesemma, Z. K.; Wei, Y.; Peel, M. C.; Western, A. W.

2015-06-01

Anthropogenic climate change is projected to enrich the atmosphere with carbon dioxide, change vegetation dynamics and influence the availability of water at the catchment scale. This study combines a nonlinear model for estimating changes in leaf area index (LAI) due to climatic fluctuations with the variable infiltration capacity (VIC) hydrological model to improve catchment streamflow prediction under a changing climate. The combined model was applied to 13 gauged sub-catchments with different land cover types (crop, pasture and tree) in the Goulburn-Broken catchment, Australia, for the "Millennium Drought" (1997-2009) relative to the period 1983-1995, and for two future periods (2021-2050 and 2071-2100) and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP8.5) which were compared with the baseline historical period of 1981-2010. This region was projected to be warmer and mostly drier in the future as predicted by 38 Coupled Model Intercomparison Project Phase 5 (CMIP5) runs from 15 global climate models (GCMs) and for two emission scenarios. The results showed that during the Millennium Drought there was about a 29.7-66.3 % reduction in mean annual runoff due to reduced precipitation and increased temperature. When drought-induced changes in LAI were included, smaller reductions in mean annual runoff of between 29.3 and 61.4 % were predicted. The proportional increase in runoff due to modeling LAI was 1.3-10.2 % relative to not including LAI. For projected climate change under the RCP4.5 emission scenario, ignoring the LAI response to changing climate could lead to a further reduction in mean annual runoff of between 2.3 and 27.7 % in the near-term (2021-2050) and 2.3 to 23.1 % later in the century (2071-2100) relative to modeling the dynamic response of LAI to precipitation and temperature changes. Similar results (near-term 2.5-25.9 % and end of century 2.6-24.2 %) were found for climate change under the RCP8.5 emission scenario. Incorporating climate-induced changes in LAI in the VIC model reduced the projected declines in streamflow and confirms the importance of including the effects of changes in LAI in future projections of streamflow.

Detailed predictions of climate induced changes in the thermal and flow regimes in mountain streams of the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Santiago, José M.; Muñoz-Mas, Rafael; García de Jalón, Diego; Solana, Joaquín; Alonso, Carlos; Martínez-Capel, Francisco; Ribalaygua, Jaime; Pórtoles, Javier; Monjo, Robert

2016-04-01

Streamflow and temperature regimes are well-known to influence on the availability of suitable physical habitat for instream biological communities. General Circulation Models (GCMs) have predicted significant changes in timing and geographic distribution of precipitation and atmospheric temperature for the ongoing century. However, differences in these predictions may arise when focusing on different spatial and temporal scales. Therefore, to perform substantiated mitigation and management actions detailed scales are necessary to adequately forecast the consequent thermal and flow regimes. Regional predictions are relatively abundant but detailed ones, both spatially and temporally, are still scarce. The present study aimed at predicting the effects of climate change on the thermal and flow regime in the Iberian Peninsula, refining the resolution of previous studies. For this purpose, the study encompassed 28 sites at eight different mountain rivers and streams in the central part of the Iberian Peninsula (Spain). The daily flow was modelled using different daily, monthly and quarterly lags of the historical precipitation and temperature time series. These precipitation-runoff models were developed by means of M5 model trees. On the other hand water temperature was modelled at similar time scale by means of nonlinear regression from dedicated site-specific data. The developed models were used to simulate the temperature and flow regime under two Representative Concentration Pathway (RCPs) climate change scenarios (RCP 4.5 and RCP 8.5) until the end of the present century by considering nine different GCMs, which were pertinently downscaled. The precipitation-runoff models achieved high accuracy (NSE>0.7), especially in regards of the low flows of the historical series. Results concomitantly forecasted flow reductions between 7 and 17 % (RCP4.5) and between 8 and 49% (RCP8.5) of the annual average in the most cases, being variable the magnitude and timing at each site. The largest predicted changes will occur in summer and the complete depletion of some river segments was forecasted. Winter was the only season predicted flows to remain mostly unaffected by climate change. Mean annual stream temperature was predicted to experience heavy increases, especially during the second half of the century, varying from 0.3 to 1.6°C (RCP4.5), and 0.8 to 4.0°C (RCP8.5). Annual maximum and minimum average temperature increases were predicted to be between 0.1 and 1.5°C (RCP4.5) and between 0.2 and 3.0°C (RCP8.5), and between 0.4 and 1.8°C (RCP4.5) and between 1.1 and 4.5°C (RCP8.5), respectively. The most important increases were predicted to occur in summer while winter will experience the lesser ones. Geology attributable differences on thermal regime were observed between rivers. These results suggested the exacerbation of the principal characteristics of the Mediterranean climate-induced flow regimes with increased summer water temperatures and reduced low flows. Consequently, the synergistic effects of these climate induced changes may significantly impacts instream communities. Predictions of this study will be useful for designing habitat managing strategies for climate change adaptation at the local level. The revealed particularities reinforce the convenience of refining local predictions to design effective management policies.

Comparison of time series models for predicting campylobacteriosis risk in New Zealand.

PubMed

Al-Sakkaf, A; Jones, G

2014-05-01

Predicting campylobacteriosis cases is a matter of considerable concern in New Zealand, after the number of the notified cases was the highest among the developed countries in 2006. Thus, there is a need to develop a model or a tool to predict accurately the number of campylobacteriosis cases as the Microbial Risk Assessment Model used to predict the number of campylobacteriosis cases failed to predict accurately the number of actual cases. We explore the appropriateness of classical time series modelling approaches for predicting campylobacteriosis. Finding the most appropriate time series model for New Zealand data has additional practical considerations given a possible structural change, that is, a specific and sudden change in response to the implemented interventions. A univariate methodological approach was used to predict monthly disease cases using New Zealand surveillance data of campylobacteriosis incidence from 1998 to 2009. The data from the years 1998 to 2008 were used to model the time series with the year 2009 held out of the data set for model validation. The best two models were then fitted to the full 1998-2009 data and used to predict for each month of 2010. The Holt-Winters (multiplicative) and ARIMA (additive) intervention models were considered the best models for predicting campylobacteriosis in New Zealand. It was noticed that the prediction by an additive ARIMA with intervention was slightly better than the prediction by a Holt-Winter multiplicative method for the annual total in year 2010, the former predicting only 23 cases less than the actual reported cases. It is confirmed that classical time series techniques such as ARIMA with intervention and Holt-Winters can provide a good prediction performance for campylobacteriosis risk in New Zealand. The results reported by this study are useful to the New Zealand Health and Safety Authority's efforts in addressing the problem of the campylobacteriosis epidemic. © 2013 Blackwell Verlag GmbH.

Describing and Predicting Developmental Profiles of Externalizing Problems from Childhood to Adulthood

PubMed Central

Petersen, Isaac T.; Bates, John E.; Dodge, Kenneth A.; Lansford, Jennifer E.; Pettit, Gregory S.

2014-01-01

This longitudinal study considers externalizing behavior problems from ages 5 to 27 (N = 585). Externalizing problem ratings by mothers, fathers, teachers, peers, and self-report were modeled with growth curves. Risk and protective factors across many different domains and time frames were included as predictors of the trajectories. A major contribution of the study is in demonstrating how heterotypic continuity and changing measures can be handled in modeling changes in externalizing behavior over long developmental periods. On average, externalizing problems decreased from early childhood to preadolescence, increased during adolescence, and decreased from late adolescence to adulthood. There was strong nonlinear continuity in externalizing problems over time. Family process, peer process, stress, and individual characteristics predicted externalizing problems beyond the strong continuity of externalizing problems. The model accounted for 70% of the variability in the development of externalizing problems. The model’s predicted values showed moderate sensitivity and specificity in prediction of arrests, illegal drug use, and drunk driving. Overall, the study showed that by using changing, developmentally-relevant measures and simultaneously taking into account numerous characteristics of children and their living situations, research can model lengthy spans of development and improve predictions of the development of later, severe externalizing problems. PMID:25166430

Modeling streamflow in a snow-dominated forest watershed using the Water Erosion Prediction Project (WEPP) model

USDA-ARS?s Scientific Manuscript database

The Water Erosion Prediction Project (WEPP) model was originally developed for hillslope and small watershed applications. The model simulates complex interactive processes influencing erosion, such as surface runoff, soil-water changes, vegetation growth and senescence, and snow accumulation and me...

Hydrological model parameterization using NDVI values to account for the effects of land-cover change on the rainfall-runoff response

USDA-ARS?s Scientific Manuscript database

Classic rainfall-runoff models usually use historical data to estimate model parameters and mean values of parameters are considered for predictions. However, due to climate changes and human effects, the parameters of model change temporally. To overcome this problem, Normalized Difference Vegetati...

Modeling Menstrual Cycle Length and Variability at the Approach of Menopause Using Hierarchical Change Point Models

PubMed Central

Huang, Xiaobi; Elliott, Michael R.; Harlow, Siobán D.

2013-01-01

SUMMARY As women approach menopause, the patterns of their menstrual cycle lengths change. To study these changes, we need to jointly model both the mean and variability of cycle length. Our proposed model incorporates separate mean and variance change points for each woman and a hierarchical model to link them together, along with regression components to include predictors of menopausal onset such as age at menarche and parity. Additional complexity arises from the fact that the calendar data have substantial missingness due to hormone use, surgery, and failure to report. We integrate multiple imputation and time-to event modeling in a Bayesian estimation framework to deal with different forms of the missingness. Posterior predictive model checks are applied to evaluate the model fit. Our method successfully models patterns of women’s menstrual cycle trajectories throughout their late reproductive life and identifies change points for mean and variability of segment length, providing insight into the menopausal process. More generally, our model points the way toward increasing use of joint mean-variance models to predict health outcomes and better understand disease processes. PMID:24729638

Comparing and combining process-based crop models and statistical models with some implications for climate change

NASA Astrophysics Data System (ADS)

Roberts, Michael J.; Braun, Noah O.; Sinclair, Thomas R.; Lobell, David B.; Schlenker, Wolfram

2017-09-01

We compare predictions of a simple process-based crop model (Soltani and Sinclair 2012), a simple statistical model (Schlenker and Roberts 2009), and a combination of both models to actual maize yields on a large, representative sample of farmer-managed fields in the Corn Belt region of the United States. After statistical post-model calibration, the process model (Simple Simulation Model, or SSM) predicts actual outcomes slightly better than the statistical model, but the combined model performs significantly better than either model. The SSM, statistical model and combined model all show similar relationships with precipitation, while the SSM better accounts for temporal patterns of precipitation, vapor pressure deficit and solar radiation. The statistical and combined models show a more negative impact associated with extreme heat for which the process model does not account. Due to the extreme heat effect, predicted impacts under uniform climate change scenarios are considerably more severe for the statistical and combined models than for the process-based model.

Separating direct and indirect effects of global change: a population dynamic modeling approach using readily available field data.

PubMed

Farrer, Emily C; Ashton, Isabel W; Knape, Jonas; Suding, Katharine N

2014-04-01

Two sources of complexity make predicting plant community response to global change particularly challenging. First, realistic global change scenarios involve multiple drivers of environmental change that can interact with one another to produce non-additive effects. Second, in addition to these direct effects, global change drivers can indirectly affect plants by modifying species interactions. In order to tackle both of these challenges, we propose a novel population modeling approach, requiring only measurements of abundance and climate over time. To demonstrate the applicability of this approach, we model population dynamics of eight abundant plant species in a multifactorial global change experiment in alpine tundra where we manipulated nitrogen, precipitation, and temperature over 7 years. We test whether indirect and interactive effects are important to population dynamics and whether explicitly incorporating species interactions can change predictions when models are forecast under future climate change scenarios. For three of the eight species, population dynamics were best explained by direct effect models, for one species neither direct nor indirect effects were important, and for the other four species indirect effects mattered. Overall, global change had negative effects on species population growth, although species responded to different global change drivers, and single-factor effects were slightly more common than interactive direct effects. When the fitted population dynamic models were extrapolated under changing climatic conditions to the end of the century, forecasts of community dynamics and diversity loss were largely similar using direct effect models that do not explicitly incorporate species interactions or best-fit models; however, inclusion of species interactions was important in refining the predictions for two of the species. The modeling approach proposed here is a powerful way of analyzing readily available datasets which should be added to our toolbox to tease apart complex drivers of global change. © 2013 John Wiley & Sons Ltd.

Skilful Seasonal Predictions of Summer European Rainfall

NASA Astrophysics Data System (ADS)

Dunstone, Nick; Smith, Doug; Scaife, Adam; Hermanson, Leon; Fereday, David; O'Reilly, Chris; Stirling, Alison; Eade, Rosie; Gordon, Margaret; MacLachlan, Craig; Woollings, Tim; Sheen, Katy; Belcher, Stephen

2018-04-01

Year-to-year variability in Northern European summer rainfall has profound societal and economic impacts; however, current seasonal forecast systems show no significant forecast skill. Here we show that skillful predictions are possible (r 0.5, p < 0.001) using the latest high-resolution Met Office near-term prediction system over 1960-2017. The model predictions capture both low-frequency changes (e.g., wet summers 2007-2012) and some of the large individual events (e.g., dry summer 1976). Skill is linked to predictable North Atlantic sea surface temperature variability changing the supply of water vapor into Northern Europe and so modulating convective rainfall. However, dynamical circulation variability is not well predicted in general—although some interannual skill is found. Due to the weak amplitude of the forced model signal (likely caused by missing or weak model responses), very large ensembles (>80 members) are required for skillful predictions. This work is promising for the development of European summer rainfall climate services.

Model-Based Analysis of Cell Cycle Responses to Dynamically Changing Environments

PubMed Central

Seaton, Daniel D; Krishnan, J

2016-01-01

Cell cycle progression is carefully coordinated with a cell’s intra- and extracellular environment. While some pathways have been identified that communicate information from the environment to the cell cycle, a systematic understanding of how this information is dynamically processed is lacking. We address this by performing dynamic sensitivity analysis of three mathematical models of the cell cycle in Saccharomyces cerevisiae. We demonstrate that these models make broadly consistent qualitative predictions about cell cycle progression under dynamically changing conditions. For example, it is shown that the models predict anticorrelated changes in cell size and cell cycle duration under different environments independently of the growth rate. This prediction is validated by comparison to available literature data. Other consistent patterns emerge, such as widespread nonmonotonic changes in cell size down generations in response to parameter changes. We extend our analysis by investigating glucose signalling to the cell cycle, showing that known regulation of Cln3 translation and Cln1,2 transcription by glucose is sufficient to explain the experimentally observed changes in cell cycle dynamics at different glucose concentrations. Together, these results provide a framework for understanding the complex responses the cell cycle is capable of producing in response to dynamic environments. PMID:26741131

Estimating Changes in Runoff and Carbon Exports From Northern Canadian Catchments Under a 2X CO2 Atmosphere

NASA Astrophysics Data System (ADS)

Clair, T. A.; Ehrman, J. M.

2006-12-01

The doubling of atmospheric CO2 on temperature and precipitation will change annual runoff and dissolved organic carbon (DOC) export patterns in northern Canada. Because of the physical size and the range of climatic changes of northern Canada, we found it necessary to model potential changes in river water and carbon exports in the region using a neural network approach. We developed a model for hydrology and one for DOC using as inputs, monthly General Circulation Model temperature and precipitation predictions, historical hydrology and dissolved organic carbon values, as well as catchment size and slope. Mining Environment Canada's historical hydrology and water chemistry databases allowed us to identify 20 sites suitable for our analysis. The site results were summarized within the Canadian Terrestrial Ecozone classification system. Our results show spring melts occurring one month sooner in all northern ecozones except for the Hudson Bay Plains zone, with changes in melt intensity occurring in most regions. The DOC model predicts that exports from catchments will increase by between 10 and 20% depending on the ecozone. Generally, we predict that major changes in both hydrology and carbon cycling should be expected in northern Canadian ecosystems in a warmer planet.

Attributing spatial and temporal changes in soil C in the UK to environmental drivers

NASA Astrophysics Data System (ADS)

Thomas, Amy; Cosby, Bernard; Quin, Sam; Henrys, Pete; Robinson, David; Emmett, Bridget

2015-04-01

The largest terrestrial pool of carbon is found in soils. Understanding how soil C responds to drivers of change (land use and management, atmospheric deposition, climate change) and how these responses are modified by inherent soil properties is crucial if we are to manage soils more sustainably in the future. Here we attempt to attribute spatial and temporal changes in UK soil C to environmental drivers using data from the UK Countryside Survey (CS), a national soil survey across England, Scotland and Wales repeated in 1978, 1998 and 2007. A mixed model approach was used to model soil C concentration (g C kg-1) and density (t C ha-1) and their absolute changes for the time periods 1978-1998, 1998-2007 and 1978-2007 across the CS sites using a variety of explanatory variables: soil (parent material, pH, moisture, Olsen-P, Shannon Diversity Index); atmospheric deposition (nitrogen and sulphur); climate (growing degree days and rain); and land use (aggregate vegetation class). Spatially, prediction of soil C concentration was good; soil moisture, pH, vegetation class and dominant grain size were all significant predictors. Field capacity also appeared to be important; however this data was only collected for a fraction of sites. N% was also strongly related to soil C concentration and density, as would be expected due to coupling of C and N in soil OM pools. Although N may drive soil C through impact on plant productivity, this cannot be separated from correlated C and N losses with OM decomposition, and hence N was not included as a driver for modelling. Predictive power for C density is not as strong as for concentration, which may reflect nonlinear relationships not represented by the modelling approach. Temporally, change in soil C is more difficult to explain, and model predictive power was lower. Change in soil pH was important in explaining change in C concentration and density, along with change in atmospheric deposition; decrease in deposition and associated soil acidity (increase in pH) was associated with a decrease in soil C concentration and density. Change in soil moisture or rainfall was also important. Inherent soil and site properties such as soil texture, vegetation class and parent material appeared to contribute most to the prediction of soil C change through modulation of the relationship between change in soil C and change in pH. Including anthropogenic and natural drivers in models of soil C stocks and changes in the UK enables assessment of the relative importance of each across the UK CS sites, however interactions among the drivers are more difficult to disentangle. Given the statistical significance of a number of drivers and soil variables in predicting soil C stocks and changes in the UK, it is important that these continue to be measured to allow better model development and more reliable predictions of future soil C conditions.

Thermal history regulates methylbutenol basal emission rate in Pinus ponderosa.

PubMed

Gray, Dennis W; Goldstein, Allen H; Lerdau, Manuel T

2006-07-01

Methylbutenol (MBO) is a 5-carbon alcohol that is emitted by many pines in western North America, which may have important impacts on the tropospheric chemistry of this region. In this study, we document seasonal changes in basal MBO emission rates and test several models predicting these changes based on thermal history. These models represent extensions of the ISO G93 model that add a correction factor C(basal), allowing MBO basal emission rates to change as a function of thermal history. These models also allow the calculation of a new emission parameter E(standard30), which represents the inherent capacity of a plant to produce MBO, independent of current or past environmental conditions. Most single-component models exhibited large departures in early and late season, and predicted day-to-day changes in basal emission rate with temporal offsets of up to 3 d relative to measured basal emission rates. Adding a second variable describing thermal history at a longer time scale improved early and late season model performance while retaining the day-to-day performance of the parent single-component model. Out of the models tested, the T(amb),T(max7) model exhibited the best combination of day-to-day and seasonal predictions of basal MBO emission rates.

Piecewise multivariate modelling of sequential metabolic profiling data.

PubMed

Rantalainen, Mattias; Cloarec, Olivier; Ebbels, Timothy M D; Lundstedt, Torbjörn; Nicholson, Jeremy K; Holmes, Elaine; Trygg, Johan

2008-02-19

Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

Experimentally validated mathematical model of analyte uptake by permeation passive samplers.

PubMed

Salim, F; Ioannidis, M; Górecki, T

2017-11-15

A mathematical model describing the sampling process in a permeation-based passive sampler was developed and evaluated numerically. The model was applied to the Waterloo Membrane Sampler (WMS), which employs a polydimethylsiloxane (PDMS) membrane as a permeation barrier, and an adsorbent as a receiving phase. Samplers of this kind are used for sampling volatile organic compounds (VOC) from air and soil gas. The model predicts the spatio-temporal variation of sorbed and free analyte concentrations within the sampler components (membrane, sorbent bed and dead volume), from which the uptake rate throughout the sampling process can be determined. A gradual decline in the uptake rate during the sampling process is predicted, which is more pronounced when sampling higher concentrations. Decline of the uptake rate can be attributed to diminishing analyte concentration gradient within the membrane, which results from resistance to mass transfer and the development of analyte concentration gradients within the sorbent bed. The effects of changing the sampler component dimensions on the rate of this decline in the uptake rate can be predicted from the model. Performance of the model was evaluated experimentally for sampling of toluene vapors under controlled conditions. The model predictions proved close to the experimental values. The model provides a valuable tool to predict changes in the uptake rate during sampling, to assign suitable exposure times at different analyte concentration levels, and to optimize the dimensions of the sampler in a manner that minimizes these changes during the sampling period.

Complexity of cardiac signals for predicting changes in alpha-waves after stress in patients undergoing cardiac catheterization

NASA Astrophysics Data System (ADS)

Chiu, Hung-Chih; Lin, Yen-Hung; Lo, Men-Tzung; Tang, Sung-Chun; Wang, Tzung-Dau; Lu, Hung-Chun; Ho, Yi-Lwun; Ma, Hsi-Pin; Peng, Chung-Kang

2015-08-01

The hierarchical interaction between electrical signals of the brain and heart is not fully understood. We hypothesized that the complexity of cardiac electrical activity can be used to predict changes in encephalic electricity after stress. Most methods for analyzing the interaction between the heart rate variability (HRV) and electroencephalography (EEG) require a computation-intensive mathematical model. To overcome these limitations and increase the predictive accuracy of human relaxing states, we developed a method to test our hypothesis. In addition to routine linear analysis, multiscale entropy and detrended fluctuation analysis of the HRV were used to quantify nonstationary and nonlinear dynamic changes in the heart rate time series. Short-time Fourier transform was applied to quantify the power of EEG. The clinical, HRV, and EEG parameters of postcatheterization EEG alpha waves were analyzed using change-score analysis and generalized additive models. In conclusion, the complexity of cardiac electrical signals can be used to predict EEG changes after stress.

Complexity of cardiac signals for predicting changes in alpha-waves after stress in patients undergoing cardiac catheterization

PubMed Central

Chiu, Hung-Chih; Lin, Yen-Hung; Lo, Men-Tzung; Tang, Sung-Chun; Wang, Tzung-Dau; Lu, Hung-Chun; Ho, Yi-Lwun; Ma, Hsi-Pin; Peng, Chung-Kang

2015-01-01

The hierarchical interaction between electrical signals of the brain and heart is not fully understood. We hypothesized that the complexity of cardiac electrical activity can be used to predict changes in encephalic electricity after stress. Most methods for analyzing the interaction between the heart rate variability (HRV) and electroencephalography (EEG) require a computation-intensive mathematical model. To overcome these limitations and increase the predictive accuracy of human relaxing states, we developed a method to test our hypothesis. In addition to routine linear analysis, multiscale entropy and detrended fluctuation analysis of the HRV were used to quantify nonstationary and nonlinear dynamic changes in the heart rate time series. Short-time Fourier transform was applied to quantify the power of EEG. The clinical, HRV, and EEG parameters of postcatheterization EEG alpha waves were analyzed using change-score analysis and generalized additive models. In conclusion, the complexity of cardiac electrical signals can be used to predict EEG changes after stress. PMID:26286628

Indirect Effects of Environmental Change in Resource Competition Models.

PubMed

Kleinhesselink, Andrew R; Adler, Peter B

2015-12-01

Anthropogenic environmental change can affect species directly by altering physiological rates or indirectly by changing competitive outcomes. The unknown strength of competition-mediated indirect effects makes it difficult to predict species abundances in the face of ongoing environmental change. Theory developed with phenomenological competition models shows that indirect effects are weak when coexistence is strongly stabilized, but these models lack a mechanistic link between environmental change and species performance. To extend existing theory, we examined the relationship between coexistence and indirect effects in mechanistic resource competition models. We defined environmental change as a change in resource supply points and quantified the resulting competition-mediated indirect effects on species abundances. We found that the magnitude of indirect effects increases in proportion to niche overlap. However, indirect effects also depend on differences in how competitors respond to the change in resource supply, an insight hidden in nonmechanistic models. Our analysis demonstrates the value of using niche overlap to predict the strength of indirect effects and clarifies the types of indirect effects that global change can have on competing species.

Multi-nutrient, multi-group model of present and future oceanic phytoplankton communities

NASA Astrophysics Data System (ADS)

Litchman, E.; Klausmeier, C. A.; Miller, J. R.; Schofield, O. M.; Falkowski, P. G.

2006-11-01

Phytoplankton community composition profoundly affects patterns of nutrient cycling and the dynamics of marine food webs; therefore predicting present and future phytoplankton community structure is crucial to understand how ocean ecosystems respond to physical forcing and nutrient limitations. We develop a mechanistic model of phytoplankton communities that includes multiple taxonomic groups (diatoms, coccolithophores and prasinophytes), nutrients (nitrate, ammonium, phosphate, silicate and iron), light, and a generalist zooplankton grazer. Each taxonomic group was parameterized based on an extensive literature survey. We test the model at two contrasting sites in the modern ocean, the North Atlantic (North Atlantic Bloom Experiment, NABE) and subarctic North Pacific (ocean station Papa, OSP). The model successfully predicts general patterns of community composition and succession at both sites: In the North Atlantic, the model predicts a spring diatom bloom, followed by coccolithophore and prasinophyte blooms later in the season. In the North Pacific, the model reproduces the low chlorophyll community dominated by prasinophytes and coccolithophores, with low total biomass variability and high nutrient concentrations throughout the year. Sensitivity analysis revealed that the identity of the most sensitive parameters and the range of acceptable parameters differed between the two sites. We then use the model to predict community reorganization under different global change scenarios: a later onset and extended duration of stratification, with shallower mixed layer depths due to increased greenhouse gas concentrations; increase in deep water nitrogen; decrease in deep water phosphorus and increase or decrease in iron concentration. To estimate uncertainty in our predictions, we used a Monte Carlo sampling of the parameter space where future scenarios were run using parameter combinations that produced acceptable modern day outcomes and the robustness of the predictions was determined. Change in the onset and duration of stratification altered the timing and the magnitude of the spring diatom bloom in the North Atlantic and increased total phytoplankton and zooplankton biomass in the North Pacific. Changes in nutrient concentrations in some cases changed dominance patterns of major groups, as well as total chlorophyll and zooplankton biomass. Based on these scenarios, our model suggests that global environmental change will inevitably alter phytoplankton community structure and potentially impact global biogeochemical cycles.

Growth response of conifers in Adirondack plantations to changing environment: Model approaches based on stem-analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pan, Y.

1993-01-01

Based on model approaches, three conifer species, red pine, Norway spruce and Scots pine grown in plantations at Pack Demonstration Forest, in the southeastern Adirondack mountains of New York, were chosen to study growth response to different environmental changes, including silvicultural treatments and changes in climate and chemical environment. Detailed stem analysis data provided a basis for constructing tree growth models. These models were organized into three groups: morphological, dynamic and predictive. The morphological model was designed to evaluate relationship between tree attributes and interactive influences of intrinsic and extrinsic factors on the annual increments. Three types of morphological patternsmore » have been characterized: space-time patterns of whole-stem rings, intrinsic wood deposition pattern along the tree-stem, and bolewood allocation ratio patterns along the tree-stem. The dynamic model reflects the growth process as a system which responds to extrinsic signal inputs, including fertilization pulses, spacing effects and climatic disturbance, as well as intrinsic feedback. Growth signals indicative of climatic effects were used to construct growth-climate models using both multivariate analysis and Kalman filter methods. The predictive model utilized GCMs and growth-climate relationships to forecast tree growth responses in relation to future scenarios of CO[sub 2]-induced climate change. Prediction results indicate that different conifer species have individualistic growth response to future climatic change and suggest possible changes in future growth and distribution of naturally occurring conifers in this region.« less

Self-Regulation and Recall: Growth Curve Modeling of Intervention Outcomes for Older Adults

PubMed Central

West, Robin L.; Hastings, Erin C.

2013-01-01

Memory training has often been supported as a potential means to improve performance for older adults. Less often studied are the characteristics of trainees that benefit most from training. Using a self-regulatory perspective, the current project examined a latent growth curve model to predict training-related gains for middle-aged and older adult trainees from individual differences (e.g., education), information processing skills (strategy use) and self-regulatory factors such as self-efficacy, control, and active engagement in training. For name recall, a model including strategy usage and strategy change as predictors of memory gain, along with self-efficacy and self-efficacy change, showed comparable fit to a more parsimonious model including only self-efficacy variables as predictors. The best fit to the text recall data was a model focusing on self-efficacy change as the main predictor of memory change, and that model showed significantly better fit than a model also including strategy usage variables as predictors. In these models, overall performance was significantly predicted by age and memory self-efficacy, and subsequent training-related gains in performance were best predicted directly by change in self-efficacy (text recall), or indirectly through the impact of active engagement and self-efficacy on gains (name recall). These results underscore the benefits of targeting self-regulatory factors in intervention programs designed to improve memory skills. PMID:21604891

Self-regulation and recall: growth curve modeling of intervention outcomes for older adults.

PubMed

West, Robin L; Hastings, Erin C

2011-12-01

Memory training has often been supported as a potential means to improve performance for older adults. Less often studied are the characteristics of trainees that benefit most from training. Using a self-regulatory perspective, the current project examined a latent growth curve model to predict training-related gains for middle-aged and older adult trainees from individual differences (e.g., education), information processing skills (strategy use) and self-regulatory factors such as self-efficacy, control, and active engagement in training. For name recall, a model including strategy usage and strategy change as predictors of memory gain, along with self-efficacy and self-efficacy change, showed comparable fit to a more parsimonious model including only self-efficacy variables as predictors. The best fit to the text recall data was a model focusing on self-efficacy change as the main predictor of memory change, and that model showed significantly better fit than a model also including strategy usage variables as predictors. In these models, overall performance was significantly predicted by age and memory self-efficacy, and subsequent training-related gains in performance were best predicted directly by change in self-efficacy (text recall), or indirectly through the impact of active engagement and self-efficacy on gains (name recall). These results underscore the benefits of targeting self-regulatory factors in intervention programs designed to improve memory skills.

Cross-cultural adjustment to the United States: the role of contextualized extraversion change

PubMed Central

Liu, Mengqiao; Huang, Jason L.

2015-01-01

Personality traits can predict how well-sojourners and expatriates adjust to new cultures, but the adjustment process remains largely unexamined. Based on recent findings that reveal personality traits predict as well as respond to life events and experiences, this research focuses on within-person change in contextualized extraversion and its predictive validity for cross-cultural adjustment in international students who newly arrived in US colleges. We proposed that the initial level as well as the rate of change in school extraversion (i.e., contextualized extraversion that reflects behavioral tendency in school settings) will predict cross-cultural adjustment, withdrawal cognitions, and school satisfaction. Latent growth modeling of three-wave longitudinal surveys of 215 new international students (54% female, Mage = 24 years) revealed that the initial level of school extraversion significantly predicted cross-cultural adjustment, (lower) withdrawal cognitions, and satisfaction, while the rate of change (increase) in school extraversion predicted cross-cultural adjustment and (lower) withdrawal cognitions. We further modeled global extraversion and cross-cultural motivation as antecedents and explored within-person change in school extraversion as a proximal factor that affects adjustment outcomes. The findings highlight the malleability of contextualized personality, and more importantly, the importance of understanding within-person change in contextualized personality in a cross-cultural adjustment context. The study points to more research that explicate the process of personality change in other contexts. PMID:26579033

Cross-cultural adjustment to the United States: the role of contextualized extraversion change.

PubMed

Liu, Mengqiao; Huang, Jason L

2015-01-01

Personality traits can predict how well-sojourners and expatriates adjust to new cultures, but the adjustment process remains largely unexamined. Based on recent findings that reveal personality traits predict as well as respond to life events and experiences, this research focuses on within-person change in contextualized extraversion and its predictive validity for cross-cultural adjustment in international students who newly arrived in US colleges. We proposed that the initial level as well as the rate of change in school extraversion (i.e., contextualized extraversion that reflects behavioral tendency in school settings) will predict cross-cultural adjustment, withdrawal cognitions, and school satisfaction. Latent growth modeling of three-wave longitudinal surveys of 215 new international students (54% female, M age = 24 years) revealed that the initial level of school extraversion significantly predicted cross-cultural adjustment, (lower) withdrawal cognitions, and satisfaction, while the rate of change (increase) in school extraversion predicted cross-cultural adjustment and (lower) withdrawal cognitions. We further modeled global extraversion and cross-cultural motivation as antecedents and explored within-person change in school extraversion as a proximal factor that affects adjustment outcomes. The findings highlight the malleability of contextualized personality, and more importantly, the importance of understanding within-person change in contextualized personality in a cross-cultural adjustment context. The study points to more research that explicate the process of personality change in other contexts.

Income gains predict cognitive functioning longitudinally throughout later childhood in poor children.

PubMed

Raffington, Laurel; Prindle, John J; Shing, Yee Lee

2018-04-26

Alleviating disadvantage in low-income environments predicts higher cognitive abilities during early childhood. It is less established whether family income continues to predict cognitive growth in later childhood or whether there may even be bidirectional dynamics. Notably, living in poverty may moderate income-cognition dynamics. In this study, we investigated longitudinal dynamics over 7 waves of data collection from 1,168 children between the ages of 4.6 and 12 years, 226 (19%) of whom lived in poverty in at least 1 wave, as part of the NICHD Study of Early Child Care and Youth Development. Two sets of dual change-score models evaluated, first, whether a score predicted change from that wave to the next and, second, whether change from 1 wave to the next predicted the following score. As previous comparisons have documented, poor children had substantially lower average starting points and cognitive growth slopes through later childhood. The first set of models showed that income scores did not predict cognitive change. In reverse, child cognitive scores positively predicted income change. We speculated that parents may reduce their work investment, thus reducing income gains, when their children fall behind. Second, income changes continued to positively predict higher cognitive scores at the following wave for poor children only, which suggests that income gains and losses continue to be a leading indicator in time of poor children's cognitive performance in later childhood. This study underlined the need to look at changes in income, allow for poverty moderation, and explore bidirectional income-cognition dynamics in middle childhood. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Simulating polar bear energetics during a seasonal fast using a mechanistic model.

PubMed

Mathewson, Paul D; Porter, Warren P

2013-01-01

In this study we tested the ability of a mechanistic model (Niche Mapper™) to accurately model adult, non-denning polar bear (Ursus maritimus) energetics while fasting during the ice-free season in the western Hudson Bay. The model uses a steady state heat balance approach, which calculates the metabolic rate that will allow an animal to maintain its core temperature in its particular microclimate conditions. Predicted weight loss for a 120 day fast typical of the 1990s was comparable to empirical studies of the population, and the model was able to reach a heat balance at the target metabolic rate for the entire fast, supporting use of the model to explore the impacts of climate change on polar bears. Niche Mapper predicted that all but the poorest condition bears would survive a 120 day fast under current climate conditions. When the fast extended to 180 days, Niche Mapper predicted mortality of up to 18% for males. Our results illustrate how environmental conditions, variation in animal properties, and thermoregulation processes may impact survival during extended fasts because polar bears were predicted to require additional energetic expenditure for thermoregulation during a 180 day fast. A uniform 3°C temperature increase reduced male mortality during a 180 day fast from 18% to 15%. Niche Mapper explicitly links an animal's energetics to environmental conditions and thus can be a valuable tool to help inform predictions of climate-related population changes. Since Niche Mapper is a generic model, it can make energetic predictions for other species threatened by climate change.

Simulating Polar Bear Energetics during a Seasonal Fast Using a Mechanistic Model

PubMed Central

Mathewson, Paul D.; Porter, Warren P.

2013-01-01

In this study we tested the ability of a mechanistic model (Niche Mapper™) to accurately model adult, non-denning polar bear (Ursus maritimus) energetics while fasting during the ice-free season in the western Hudson Bay. The model uses a steady state heat balance approach, which calculates the metabolic rate that will allow an animal to maintain its core temperature in its particular microclimate conditions. Predicted weight loss for a 120 day fast typical of the 1990s was comparable to empirical studies of the population, and the model was able to reach a heat balance at the target metabolic rate for the entire fast, supporting use of the model to explore the impacts of climate change on polar bears. Niche Mapper predicted that all but the poorest condition bears would survive a 120 day fast under current climate conditions. When the fast extended to 180 days, Niche Mapper predicted mortality of up to 18% for males. Our results illustrate how environmental conditions, variation in animal properties, and thermoregulation processes may impact survival during extended fasts because polar bears were predicted to require additional energetic expenditure for thermoregulation during a 180 day fast. A uniform 3°C temperature increase reduced male mortality during a 180 day fast from 18% to 15%. Niche Mapper explicitly links an animal’s energetics to environmental conditions and thus can be a valuable tool to help inform predictions of climate-related population changes. Since Niche Mapper is a generic model, it can make energetic predictions for other species threatened by climate change. PMID:24019883

A new paradigm for predicting zonal-mean climate and climate change

NASA Astrophysics Data System (ADS)

Armour, K.; Roe, G.; Donohoe, A.; Siler, N.; Markle, B. R.; Liu, X.; Feldl, N.; Battisti, D. S.; Frierson, D. M.

2016-12-01

How will the pole-to-equator temperature gradient, or large-scale patterns of precipitation, change under global warming? Answering such questions typically involves numerical simulations with comprehensive general circulation models (GCMs) that represent the complexities of climate forcing, radiative feedbacks, and atmosphere and ocean dynamics. Yet, our understanding of these predictions hinges on our ability to explain them through the lens of simple models and physical theories. Here we present evidence that zonal-mean climate, and its changes, can be understood in terms of a moist energy balance model that represents atmospheric heat transport as a simple diffusion of latent and sensible heat (as a down-gradient transport of moist static energy, with a diffusivity coefficient that is nearly constant with latitude). We show that the theoretical underpinnings of this model derive from the principle of maximum entropy production; that its predictions are empirically supported by atmospheric reanalyses; and that it successfully predicts the behavior of a hierarchy of climate models - from a gray radiation aquaplanet moist GCM, to comprehensive GCMs participating in CMIP5. As an example of the power of this paradigm, we show that, given only patterns of local radiative feedbacks and climate forcing, the moist energy balance model accurately predicts the evolution of zonal-mean temperature and atmospheric heat transport as simulated by the CMIP5 ensemble. These results suggest that, despite all of its dynamical complexity, the atmosphere essentially responds to energy imbalances by simply diffusing latent and sensible heat down-gradient; this principle appears to explain zonal-mean climate and its changes under global warming.

A predictive pilot model for STOL aircraft landing

NASA Technical Reports Server (NTRS)

Kleinman, D. L.; Killingsworth, W. R.

1974-01-01

An optimal control approach has been used to model pilot performance during STOL flare and landing. The model is used to predict pilot landing performance for three STOL configurations, each having a different level of automatic control augmentation. Model predictions are compared with flight simulator data. It is concluded that the model can be effective design tool for studying analytically the effects of display modifications, different stability augmentation systems, and proposed changes in the landing area geometry.

Climate-Induced Boreal Forest Change: Predictions versus Current Observations

NASA Technical Reports Server (NTRS)

Soja, Amber J.; Tchebakova, Nadezda M.; French, Nancy H. F.; Flannigan, Michael D.; Shugart, Herman H.; Stocks, Brian J.; Sukhinin, Anatoly I.; Parfenova, E. I.; Chapin, F. Stuart, III; Stackhouse, Paul W., Jr.

2007-01-01

For about three decades, there have been many predictions of the potential ecological response in boreal regions to the currently warmer conditions. In essence, a widespread, naturally occurring experiment has been conducted over time. In this paper, we describe previously modeled predictions of ecological change in boreal Alaska, Canada and Russia, and then we investigate potential evidence of current climate-induced change. For instance, ecological models have suggested that warming will induce the northern and upslope migration of the treeline and an alteration in the current mosaic structure of boreal forests. We present evidence of the migration of keystone ecosystems in the upland and lowland treeline of mountainous regions across southern Siberia. Ecological models have also predicted a moisture-stress-related dieback in white spruce trees in Alaska, and current investigations show that as temperatures increase, white spruce tree growth is declining. Additionally, it was suggested that increases in infestation and wildfire disturbance would be catalysts that precipitate the alteration of the current mosaic forest composition. In Siberia, five of the last seven years have resulted in extreme fire seasons, and extreme fire years have also been more frequent in both Alaska and Canada. In addition, Alaska has experienced extreme and geographically expansive multi-year outbreaks of the spruce beetle, which had been previously limited by the cold, moist environment. We suggest that there is substantial evidence throughout the circumboreal region to conclude that the biosphere within the boreal terrestrial environment has already responded to the transient effects of climate change. Additionally, temperature increases and warming-induced change are progressing faster than had been predicted in some regions, suggesting a potential non-linear rapid response to changes in climate, as opposed to the predicted slow linear response to climate change.

Contrasted demographic responses facing future climate change in Southern Ocean seabirds.

PubMed

Barbraud, Christophe; Rivalan, Philippe; Inchausti, Pablo; Nevoux, Marie; Rolland, Virginie; Weimerskirch, Henri

2011-01-01

1. Recent climate change has affected a wide range of species, but predicting population responses to projected climate change using population dynamics theory and models remains challenging, and very few attempts have been made. The Southern Ocean sea surface temperature and sea ice extent are projected to warm and shrink as concentrations of atmospheric greenhouse gases increase, and several top predator species are affected by fluctuations in these oceanographic variables. 2. We compared and projected the population responses of three seabird species living in sub-tropical, sub-Antarctic and Antarctic biomes to predicted climate change over the next 50 years. Using stochastic population models we combined long-term demographic datasets and projections of sea surface temperature and sea ice extent for three different IPCC emission scenarios (from most to least severe: A1B, A2, B1) from general circulation models of Earth's climate. 3. We found that climate mostly affected the probability to breed successfully, and in one case adult survival. Interestingly, frequent nonlinear relationships in demographic responses to climate were detected. Models forced by future predicted climatic change provided contrasted population responses depending on the species considered. The northernmost distributed species was predicted to be little affected by a future warming of the Southern Ocean, whereas steep declines were projected for the more southerly distributed species due to sea surface temperature warming and decrease in sea ice extent. For the most southerly distributed species, the A1B and B1 emission scenarios were respectively the most and less damaging. For the two other species, population responses were similar for all emission scenarios. 4. This is among the first attempts to study the demographic responses for several populations with contrasted environmental conditions, which illustrates that investigating the effects of climate change on core population dynamics is feasible for different populations using a common methodological framework. Our approach was limited to single populations and have neglected population settlement in new favourable habitats or changes in inter-specific relations as a potential response to future climate change. Predictions may be enhanced by merging demographic population models and climatic envelope models. © 2010 The Authors. Journal compilation © 2010 British Ecological Society.

Fish habitat regression under water scarcity scenarios in the Douro River basin

NASA Astrophysics Data System (ADS)

Segurado, Pedro; Jauch, Eduardo; Neves, Ramiro; Ferreira, Teresa

2015-04-01

Climate change will predictably alter hydrological patterns and processes at the catchment scale, with impacts on habitat conditions for fish. The main goals of this study are to identify the stream reaches that will undergo more pronounced flow reduction under different climate change scenarios and to assess which fish species will be more affected by the consequent regression of suitable habitats. The interplay between changes in flow and temperature and the presence of transversal artificial obstacles (dams and weirs) is analysed. The results will contribute to river management and impact mitigation actions under climate change. This study was carried out in the Tâmega catchment of the Douro basin. A set of 29 Hydrological, climatic, and hydrogeomorphological variables were modelled using a water modelling system (MOHID), based on meteorological data recorded monthly between 2008 and 2014. The same variables were modelled considering future climate change scenarios. The resulting variables were used in empirical habitat models of a set of key species (brown trout Salmo trutta fario, barbell Barbus bocagei, and nase Pseudochondrostoma duriense) using boosted regression trees. The stream segments between tributaries were used as spatial sampling units. Models were developed for the whole Douro basin using 401 fish sampling sites, although the modelled probabilities of species occurrence for each stream segment were predicted only for the Tâmega catchment. These probabilities of occurrence were used to classify stream segments into suitable and unsuitable habitat for each fish species, considering the future climate change scenario. The stream reaches that were predicted to undergo longer flow interruptions were identified and crossed with the resulting predictive maps of habitat suitability to compute the total area of habitat loss per species. Among the target species, the brown trout was predicted to be the most sensitive to habitat regression due to the interplay of flow reduction, increase of temperature and transversal barriers. This species is therefore a good indicator of climate change impacts in rivers and therefore we recommend using this species as a target of monitoring programs to be implemented in the context of climate change adaptation strategies.

Model of land cover change prediction in West Java using cellular automata-Markov chain (CA-MC)

NASA Astrophysics Data System (ADS)

Virtriana, Riantini; Sumarto, Irawan; Deliar, Albertus; Pasaribu, Udjianna S.; Taufik, Moh.

2015-04-01

Land is a fundamental factor that closely related to economic growth and supports the needs of human life. Land-use activity is a major issue and challenge for country planners. The cause of change in land use type activity may be due to socio economic development or due to changes in the environment or may be due to both. In an effort to understand the phenomenon of land cover changes, can be approached through land cover change modelling. Based on the facts and data contained, West Java has a high economic activity that will have an impact on land cover change. CA-MC is a model that used to determine the statistical change probabilistic for each of land cover type from land cover data at different time periods. CA-MC is able to provide the output of land cover type that should occurred. Results from a CA-MC modelling in predicting land cover changes showed an accuracy rate of 95.42%.

Bayesian prediction of future ice sheet volume using local approximation Markov chain Monte Carlo methods

NASA Astrophysics Data System (ADS)

Davis, A. D.; Heimbach, P.; Marzouk, Y.

2017-12-01

We develop a Bayesian inverse modeling framework for predicting future ice sheet volume with associated formal uncertainty estimates. Marine ice sheets are drained by fast-flowing ice streams, which we simulate using a flowline model. Flowline models depend on geometric parameters (e.g., basal topography), parameterized physical processes (e.g., calving laws and basal sliding), and climate parameters (e.g., surface mass balance), most of which are unknown or uncertain. Given observations of ice surface velocity and thickness, we define a Bayesian posterior distribution over static parameters, such as basal topography. We also define a parameterized distribution over variable parameters, such as future surface mass balance, which we assume are not informed by the data. Hyperparameters are used to represent climate change scenarios, and sampling their distributions mimics internal variation. For example, a warming climate corresponds to increasing mean surface mass balance but an individual sample may have periods of increasing or decreasing surface mass balance. We characterize the predictive distribution of ice volume by evaluating the flowline model given samples from the posterior distribution and the distribution over variable parameters. Finally, we determine the effect of climate change on future ice sheet volume by investigating how changing the hyperparameters affects the predictive distribution. We use state-of-the-art Bayesian computation to address computational feasibility. Characterizing the posterior distribution (using Markov chain Monte Carlo), sampling the full range of variable parameters and evaluating the predictive model is prohibitively expensive. Furthermore, the required resolution of the inferred basal topography may be very high, which is often challenging for sampling methods. Instead, we leverage regularity in the predictive distribution to build a computationally cheaper surrogate over the low dimensional quantity of interest (future ice sheet volume). Continual surrogate refinement guarantees asymptotic sampling from the predictive distribution. Directly characterizing the predictive distribution in this way allows us to assess the ice sheet's sensitivity to climate variability and change.

Time Course of Visual Extrapolation Accuracy

DTIC Science & Technology

1995-09-01

The pond and duckweed problem: Three experiments on the misperception of exponential growth . Acta Psychologica 43, 239-251. Wiener, E.L., 1962...random variation in tracker velocity. Both models predicted changes in hit and false alarm rates well, except in a condition where response asymmetries...systematic velocity error in tracking, only random variation in tracker velocity. Both models predicted changes in hit and false alarm rates well

Modelling soil erosion in rainfed vineyards of northeast of Spain under climate change: effects of increasing rainfall intensity

NASA Astrophysics Data System (ADS)

Concepción Ramos, Maria

2017-04-01

This aim of the research was to analyse the effect of rainfall distribution and intensity on soil erosion in vines cultivated in the Mediterranean under the projected climate change scenario. The simulations were done at plot scale using the WEPP model. Climatic data for the period 1996-2014 were obtained from a meteorological station located 6km far from the plot. Soil characteristics such as texture, organic matter content, water retention capacity and infiltration were analysed. Runoff and soil losses were measured at four locations within the plot during 4 years and used to calibrate and validate the model. According to evidences recorded in the area, changes of rainfall intensities of 10 and 20% were considered for different rainfall distributions. The simulations were extended to the predicted changes for 2030, 2050 and 2070 based on the HadGEM2-CC under the Representative Concentration Pathways (RCPs) 8.5 scenario. WEPP model provided a suitable prediction of the seasonal runoff and erosion as simulated relatively well the runoff and erosion of the most important events although some deficiencies were found for those events that produced low runoff. The simulation confirmed the contribution of the extreme events to annual erosion rates in 70%, on average. The model responded to changes in precipitation predicted under a climate change scenario with a decrease of runoff and erosion, and with higher erosion rates for an increase in rainfall intensity. A 10% increase may imply erosion rates up to 22% greater for the scenario 2030, and despite the predicted decrease in precipitation for the scenario 2050, soil losses may be up to 40% greater than at present for some rainfall distributions and intensity rainfall increases of 20%. These findings show the need of considering rainfall intensity as one of the main driven factors when soil erosion rates under climate change are predicted. Keywords: extreme events, rainfall distribution, runoff, soil losses, wines, WEPP.

Simulation of Climate Change Impacts on Wheat-Fallow Cropping Systems

USDA-ARS?s Scientific Manuscript database

Agricultural system simulation models are predictive tools for assessing climate change impacts on crop production. In this study, RZWQM2 that contains the DSSAT 4.0-CERES model was evaluated for simulating climate change impacts on wheat growth. The model was calibrated and validated using data fro...

Ecological niche modeling for a cultivated plant species: a case study on taro (Colocasia esculenta) in Hawaii.

PubMed

Kodis, Mali'o; Galante, Peter; Sterling, Eleanor J; Blair, Mary E

2018-04-26

Under the threat of ongoing and projected climate change, communities in the Pacific Islands face challenges of adapting culture and lifestyle to accommodate a changing landscape. Few models can effectively predict how biocultural livelihoods might be impacted. Here, we examine how environmental and anthropogenic factors influence an ecological niche model (ENM) for the realized niche of cultivated taro (Colocasia esculenta) in Hawaii. We created and tuned two sets of ENMs: one using only environmental variables, and one using both environmental and cultural characteristics of Hawaii. These models were projected under two different Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCPs) for 2070. Models were selected and evaluated using average omission rate and area under the receiver operating characteristic curve (AUC). We compared optimal model predictions by comparing the percentage of taro plots predicted present and measured ENM overlap using Schoener's D statistic. The model including only environmental variables consisted of 19 Worldclim bioclimatic variables, in addition to slope, altitude, distance to perennial streams, soil evaporation, and soil moisture. The optimal model with environmental variables plus anthropogenic features also included a road density variable (which we assumed as a proxy for urbanization) and a variable indicating agricultural lands of importance to the state of Hawaii. The model including anthropogenic features performed better than the environment-only model based on omission rate, AUC, and review of spatial projections. The two models also differed in spatial projections for taro under anticipated future climate change. Our results demonstrate how ENMs including anthropogenic features can predict which areas might be best suited to plant cultivated species in the future, and how these areas could change under various climate projections. These predictions might inform biocultural conservation priorities and initiatives. In addition, we discuss the incongruences that arise when traditional ENM theory is applied to species whose distribution has been significantly impacted by human intervention, particularly at a fine scale relevant to biocultural conservation initiatives. © 2018 by the Ecological Society of America.

Approaches to predicting potential impacts of climate change on forest disease: an example with Armillaria root disease

Treesearch

Ned B. Klopfenstein; Mee-Sook Kim; John W. Hanna; Bryce A. Richardson; John E. Lundquist

2009-01-01

Predicting climate change influences on forest diseases will foster forest management practices that minimize adverse impacts of diseases. Precise locations of accurately identified pathogens and hosts must be documented and spatially referenced to determine which climatic factors influence species distribution. With this information, bioclimatic models can predict the...

Response of hydrology to climate change in the southern Appalachian mountains using Bayesian inference

Treesearch

Wei Wu; James S. Clark; James M. Vose

2012-01-01

Predicting long-term consequences of climate change on hydrologic processes has been limited due to the needs to accommodate the uncertainties in hydrological measurements for calibration, and to account for the uncertainties in the models that would ingest those calibrations and uncertainties in climate predictions as basis for hydrological predictions. We implemented...

Priming effect and microbial diversity in ecosystem functioning and response to global change: a modeling approach using the SYMPHONY model.

PubMed

Perveen, Nazia; Barot, Sébastien; Alvarez, Gaël; Klumpp, Katja; Martin, Raphael; Rapaport, Alain; Herfurth, Damien; Louault, Frédérique; Fontaine, Sébastien

2014-04-01

Integration of the priming effect (PE) in ecosystem models is crucial to better predict the consequences of global change on ecosystem carbon (C) dynamics and its feedbacks on climate. Over the last decade, many attempts have been made to model PE in soil. However, PE has not yet been incorporated into any ecosystem models. Here, we build plant/soil models to explore how PE and microbial diversity influence soil/plant interactions and ecosystem C and nitrogen (N) dynamics in response to global change (elevated CO2 and atmospheric N depositions). Our results show that plant persistence, soil organic matter (SOM) accumulation, and low N leaching in undisturbed ecosystems relies on a fine adjustment of microbial N mineralization to plant N uptake. This adjustment can be modeled in the SYMPHONY model by considering the destruction of SOM through PE, and the interactions between two microbial functional groups: SOM decomposers and SOM builders. After estimation of parameters, SYMPHONY provided realistic predictions on forage production, soil C storage and N leaching for a permanent grassland. Consistent with recent observations, SYMPHONY predicted a CO2 -induced modification of soil microbial communities leading to an intensification of SOM mineralization and a decrease in the soil C stock. SYMPHONY also indicated that atmospheric N deposition may promote SOM accumulation via changes in the structure and metabolic activities of microbial communities. Collectively, these results suggest that the PE and functional role of microbial diversity may be incorporated in ecosystem models with a few additional parameters, improving accuracy of predictions. © 2013 John Wiley & Sons Ltd.

Testing the predictive power of the transtheoretical model of behavior change applied to dietary fat intake

PubMed Central

Wright, Julie A.; Velicer, Wayne F.; Prochaska, James O.

2009-01-01

This study evaluated how well predictions from the transtheoretical model (TTM) generalized from smoking to diet. Longitudinal data were used from a randomized control trial on reducing dietary fat consumption in adults (n =1207) recruited from primary care practices. Predictive power was evaluated by making a priori predictions of the magnitude of change expected in the TTM constructs of temptation, pros and cons, and 10 processes of change when an individual transitions between the stages of change. Generalizability was evaluated by testing predictions based on smoking data. Three sets of predictions were made for each stage: Precontemplation (PC), Contemplation (C) and Preparation (PR) based on stage transition categories of no progress, progress and regression determined by stage at baseline versus stage at the 12-month follow-up. Univariate analysis of variance between stage transition groups was used to calculate the effect size [omega squared (ω2)]. For diet predictions based on diet data, there was a high degree of confirmation: 92%, 95% and 92% for PC, C and PR, respectively. For diet predictions based on smoking data, 77%, 79% and 85% were confirmed, respectively, suggesting a moderate degree of generalizability. This study revised effect size estimates for future theory testing on the TTM applied to dietary fat. PMID:18400785

Incorporating population viability models into species status assessment and listing decisions under the U.S. Endangered Species Act

USGS Publications Warehouse

McGowan, Conor P.; Allan, Nathan; Servoss, Jeff; Hedwall, Shaula J.; Wooldridge, Brian

2017-01-01

Assessment of a species' status is a key part of management decision making for endangered and threatened species under the U.S. Endangered Species Act. Predicting the future state of the species is an essential part of species status assessment, and projection models can play an important role in developing predictions. We built a stochastic simulation model that incorporated parametric and environmental uncertainty to predict the probable future status of the Sonoran desert tortoise in the southwestern United States and North Central Mexico. Sonoran desert tortoise was a Candidate species for listing under the Endangered Species Act, and decision makers wanted to use model predictions in their decision making process. The model accounted for future habitat loss and possible effects of climate change induced droughts to predict future population growth rates, abundances, and quasi-extinction probabilities. Our model predicts that the population will likely decline over the next few decades, but there is very low probability of quasi-extinction less than 75 years into the future. Increases in drought frequency and intensity may increase extinction risk for the species. Our model helped decision makers predict and characterize uncertainty about the future status of the species in their listing decision. We incorporated complex ecological processes (e.g., climate change effects on tortoises) in transparent and explicit ways tailored to support decision making processes related to endangered species.

Comparative Study of Shrinkage and Non-Shrinkage Model of Food Drying

NASA Astrophysics Data System (ADS)

Shahari, N.; Jamil, N.; Rasmani, KA.

2016-08-01

A single phase heat and mass model has always been used to represent the moisture and temperature distribution during the drying of food. Several effects of the drying process, such as physical and structural changes, have been considered in order to increase understanding of the movement of water and temperature. However, the comparison between the heat and mass equation with and without structural change (in terms of shrinkage), which can affect the accuracy of the prediction model, has been little investigated. In this paper, two mathematical models to describe the heat and mass transfer in food, with and without the assumption of structural change, were analysed. The equations were solved using the finite difference method. The converted coordinate system was introduced within the numerical computations for the shrinkage model. The result shows that the temperature with shrinkage predicts a higher temperature at a specific time compared to that of the non-shrinkage model. Furthermore, the predicted moisture content decreased faster at a specific time when the shrinkage effect was included in the model.

Modeling the Earth system in the Mission to Planet Earth era

NASA Technical Reports Server (NTRS)

Unninayar, Sushel; Bergman, Kenneth H.

1993-01-01

A broad overview is made of global earth system modeling in the Mission to Planet Earth (MTPE) era for the multidisciplinary audience encompassed by the Global Change Research Program (GCRP). Time scales of global system fluctuation and change are described in Section 2. Section 3 provides a rubric for modeling the global earth system, as presently understood. The ability of models to predict the future state of the global earth system and the extent to which their predictions are reliable are covered in Sections 4 and 5. The 'engineering' use of global system models (and predictions) is covered in Section 6. Section 7 covers aspects of an increasing need for improved transform algorithms and better methods to assimilate this information into global models. Future monitoring and data requirements are detailed in Section 8. Section 9 covers the NASA-initiated concept 'Mission to Planet Earth,' which employs space and ground based measurement systems to provide the scientific basis for understanding global change. Section 10 concludes this review with general remarks concerning the state of global system modeling and observing technology and the need for future research.

Validation of catchment models for predicting land-use and climate change impacts. 2. Case study for a Mediterranean catchment

NASA Astrophysics Data System (ADS)

Parkin, G.; O'Donnell, G.; Ewen, J.; Bathurst, J. C.; O'Connell, P. E.; Lavabre, J.

1996-02-01

Validation methods commonly used to test catchment models are not capable of demonstrating a model's fitness for making predictions for catchments where the catchment response is not known (including hypothetical catchments, and future conditions of existing catchments which are subject to land-use or climate change). This paper describes the first use of a new method of validation (Ewen and Parkin, 1996. J. Hydrol., 175: 583-594) designed to address these types of application; the method involves making 'blind' predictions of selected hydrological responses which are considered important for a particular application. SHETRAN (a physically based, distributed catchment modelling system) is tested on a small Mediterranean catchment. The test involves quantification of the uncertainty in four predicted features of the catchment response (continuous hydrograph, peak discharge rates, monthly runoff, and total runoff), and comparison of observations with the predicted ranges for these features. The results of this test are considered encouraging.

Stream Discharge and Evapotranspiration Responses to Climate Change and Their Associated Uncertainties in a Large Semi-Arid Basin

NASA Astrophysics Data System (ADS)

Bassam, S.; Ren, J.

2017-12-01

Predicting future water availability in watersheds is very important for proper water resources management, especially in semi-arid regions with scarce water resources. Hydrological models have been considered as powerful tools in predicting future hydrological conditions in watershed systems in the past two decades. Streamflow and evapotranspiration are the two important components in watershed water balance estimation as the former is the most commonly-used indicator of the overall water budget estimation, and the latter is the second biggest component of water budget (biggest outflow from the system). One of the main concerns in watershed scale hydrological modeling is the uncertainties associated with model prediction, which could arise from errors in model parameters and input meteorological data, or errors in model representation of the physics of hydrological processes. Understanding and quantifying these uncertainties are vital to water resources managers for proper decision making based on model predictions. In this study, we evaluated the impacts of different climate change scenarios on the future stream discharge and evapotranspiration, and their associated uncertainties, throughout a large semi-arid basin using a stochastically-calibrated, physically-based, semi-distributed hydrological model. The results of this study could provide valuable insights in applying hydrological models in large scale watersheds, understanding the associated sensitivity and uncertainties in model parameters, and estimating the corresponding impacts on interested hydrological process variables under different climate change scenarios.

Predicting extinctions as a result of climate change

Treesearch

Mark W. Schwartz; Louis R. Iverson; Anantha M. Prasad; Stephen N. Matthews; Raymond J. O' Connor; Raymond J. O' Connor

2006-01-01

Widespread extinction is a predicted ecological consequence of global warming. Extinction risk under climate change scenarios is a function of distribution breadth. Focusing on trees and birds of the eastern United States, we used joint climate and environment models to examine fit and climate change vulnerability as a function of distribution breadth. We found that...

Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States

NASA Astrophysics Data System (ADS)

Luke, Adam; Vrugt, Jasper A.; AghaKouchak, Amir; Matthew, Richard; Sanders, Brett F.

2017-07-01

Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies on predictions of out-of-sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split-sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log-Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference.

Modeling and Predicting the Stress Relaxation of Composites with Short and Randomly Oriented Fibers

PubMed Central

Obaid, Numaira; Sain, Mohini

2017-01-01

The addition of short fibers has been experimentally observed to slow the stress relaxation of viscoelastic polymers, producing a change in the relaxation time constant. Our recent study attributed this effect of fibers on stress relaxation behavior to the interfacial shear stress transfer at the fiber-matrix interface. This model explained the effect of fiber addition on stress relaxation without the need to postulate structural changes at the interface. In our previous study, we developed an analytical model for the effect of fully aligned short fibers, and the model predictions were successfully compared to finite element simulations. However, in most industrial applications of short-fiber composites, fibers are not aligned, and hence it is necessary to examine the time dependence of viscoelastic polymers containing randomly oriented short fibers. In this study, we propose an analytical model to predict the stress relaxation behavior of short-fiber composites where the fibers are randomly oriented. The model predictions were compared to results obtained from Monte Carlo finite element simulations, and good agreement between the two was observed. The analytical model provides an excellent tool to accurately predict the stress relaxation behavior of randomly oriented short-fiber composites. PMID:29053601

Modelling the multidimensional niche by linking functional traits to competitive performance

PubMed Central

Maynard, Daniel S.; Leonard, Kenneth E.; Drake, John M.; Hall, David W.; Crowther, Thomas W.; Bradford, Mark A.

2015-01-01

Linking competitive outcomes to environmental conditions is necessary for understanding species' distributions and responses to environmental change. Despite this importance, generalizable approaches for predicting competitive outcomes across abiotic gradients are lacking, driven largely by the highly complex and context-dependent nature of biotic interactions. Here, we present and empirically test a novel niche model that uses functional traits to model the niche space of organisms and predict competitive outcomes of co-occurring populations across multiple resource gradients. The model makes no assumptions about the underlying mode of competition and instead applies to those settings where relative competitive ability across environments correlates with a quantifiable performance metric. To test the model, a series of controlled microcosm experiments were conducted using genetically related strains of a widespread microbe. The model identified trait microevolution and performance differences among strains, with the predicted competitive ability of each organism mapped across a two-dimensional carbon and nitrogen resource space. Areas of coexistence and competitive dominance between strains were identified, and the predicted competitive outcomes were validated in approximately 95% of the pairings. By linking trait variation to competitive ability, our work demonstrates a generalizable approach for predicting and modelling competitive outcomes across changing environmental contexts. PMID:26136444

Effects of Climate Change and Fisheries Bycatch on Shy Albatross (Thalassarche cauta) in Southern Australia

PubMed Central

2015-01-01

The impacts of climate change on marine species are often compounded by other stressors that make direct attribution and prediction difficult. Shy albatrosses (Thalassarche cauta) breeding on Albatross Island, Tasmania, show an unusually restricted foraging range, allowing easier discrimination between the influence of non-climate stressors (fisheries bycatch) and environmental variation. Local environmental conditions (rainfall, air temperature, and sea-surface height, an indicator of upwelling) during the vulnerable chick-rearing stage, have been correlated with breeding success of shy albatrosses. We use an age-, stage- and sex-structured population model to explore potential relationships between local environmental factors and albatross breeding success while accounting for fisheries bycatch by trawl and longline fisheries. The model uses time-series of observed breeding population counts, breeding success, adult and juvenile survival rates and a bycatch mortality observation for trawl fishing to estimate fisheries catchability, environmental influence, natural mortality rate, density dependence, and productivity. Observed at-sea distributions for adult and juvenile birds were coupled with reported fishing effort to estimate vulnerability to incidental bycatch. The inclusion of rainfall, temperature and sea-surface height as explanatory variables for annual chick mortality rate was statistically significant. Global climate models predict little change in future local average rainfall, however, increases are forecast in both temperatures and upwelling, which are predicted to have detrimental and beneficial effects, respectively, on breeding success. The model shows that mitigation of at least 50% of present bycatch is required to offset losses due to future temperature changes, even if upwelling increases substantially. Our results highlight the benefits of using an integrated modeling approach, which uses available demographic as well as environmental data within a single estimation framework, to provide future predictions. Such predictions inform the development of management options in the face of climate change. PMID:26057739

Effects of Climate Change and Fisheries Bycatch on Shy Albatross (Thalassarche cauta) in Southern Australia.

PubMed

Thomson, Robin B; Alderman, Rachael L; Tuck, Geoffrey N; Hobday, Alistair J

2015-01-01

The impacts of climate change on marine species are often compounded by other stressors that make direct attribution and prediction difficult. Shy albatrosses (Thalassarche cauta) breeding on Albatross Island, Tasmania, show an unusually restricted foraging range, allowing easier discrimination between the influence of non-climate stressors (fisheries bycatch) and environmental variation. Local environmental conditions (rainfall, air temperature, and sea-surface height, an indicator of upwelling) during the vulnerable chick-rearing stage, have been correlated with breeding success of shy albatrosses. We use an age-, stage- and sex-structured population model to explore potential relationships between local environmental factors and albatross breeding success while accounting for fisheries bycatch by trawl and longline fisheries. The model uses time-series of observed breeding population counts, breeding success, adult and juvenile survival rates and a bycatch mortality observation for trawl fishing to estimate fisheries catchability, environmental influence, natural mortality rate, density dependence, and productivity. Observed at-sea distributions for adult and juvenile birds were coupled with reported fishing effort to estimate vulnerability to incidental bycatch. The inclusion of rainfall, temperature and sea-surface height as explanatory variables for annual chick mortality rate was statistically significant. Global climate models predict little change in future local average rainfall, however, increases are forecast in both temperatures and upwelling, which are predicted to have detrimental and beneficial effects, respectively, on breeding success. The model shows that mitigation of at least 50% of present bycatch is required to offset losses due to future temperature changes, even if upwelling increases substantially. Our results highlight the benefits of using an integrated modeling approach, which uses available demographic as well as environmental data within a single estimation framework, to provide future predictions. Such predictions inform the development of management options in the face of climate change.

Monitoring risk-adjusted outcomes in congenital heart surgery: does the appropriateness of a risk model change with time?

PubMed

Tsang, Victor T; Brown, Katherine L; Synnergren, Mats Johanssen; Kang, Nicholas; de Leval, Marc R; Gallivan, Steve; Utley, Martin

2009-02-01

Risk adjustment of outcomes in pediatric congenital heart surgery is challenging due to the great diversity in diagnoses and procedures. We have previously shown that variable life-adjusted display (VLAD) charts provide an effective graphic display of risk-adjusted outcomes in this specialty. A question arises as to whether the risk model used remains appropriate over time. We used a recently developed graphic technique to evaluate the performance of an existing risk model among those patients at a single center during 2000 to 2003 originally used in model development. We then compared the distribution of predicted risk among these patients with that among patients in 2004 to 2006. Finally, we constructed a VLAD chart of risk-adjusted outcomes for the latter period. Among 1083 patients between April 2000 and March 2003, the risk model performed well at predicted risks above 3%, underestimated mortality at 2% to 3% predicted risk, and overestimated mortality below 2% predicted risk. There was little difference in the distribution of predicted risk among these patients and among 903 patients between June 2004 and October 2006. Outcomes for the more recent period were appreciably better than those expected according to the risk model. This finding cannot be explained by any apparent bias in the risk model combined with changes in case-mix. Risk models can, and hopefully do, become out of date. There is scope for complacency in the risk-adjusted audit if the risk model used is not regularly recalibrated to reflect changing standards and expectations.

Climate-based models for pulsed resources improve predictability of consumer population dynamics: outbreaks of house mice in forest ecosystems.

PubMed

Holland, E Penelope; James, Alex; Ruscoe, Wendy A; Pech, Roger P; Byrom, Andrea E

2015-01-01

Accurate predictions of the timing and magnitude of consumer responses to episodic seeding events (masts) are important for understanding ecosystem dynamics and for managing outbreaks of invasive species generated by masts. While models relating consumer populations to resource fluctuations have been developed successfully for a range of natural and modified ecosystems, a critical gap that needs addressing is better prediction of resource pulses. A recent model used change in summer temperature from one year to the next (ΔT) for predicting masts for forest and grassland plants in New Zealand. We extend this climate-based method in the framework of a model for consumer-resource dynamics to predict invasive house mouse (Mus musculus) outbreaks in forest ecosystems. Compared with previous mast models based on absolute temperature, the ΔT method for predicting masts resulted in an improved model for mouse population dynamics. There was also a threshold effect of ΔT on the likelihood of an outbreak occurring. The improved climate-based method for predicting resource pulses and consumer responses provides a straightforward rule of thumb for determining, with one year's advance warning, whether management intervention might be required in invaded ecosystems. The approach could be applied to consumer-resource systems worldwide where climatic variables are used to model the size and duration of resource pulses, and may have particular relevance for ecosystems where global change scenarios predict increased variability in climatic events.

Hybrid experimental/analytical models of structural dynamics - Creation and use for predictions

NASA Technical Reports Server (NTRS)

Balmes, Etienne

1993-01-01

An original complete methodology for the construction of predictive models of damped structural vibrations is introduced. A consistent definition of normal and complex modes is given which leads to an original method to accurately identify non-proportionally damped normal mode models. A new method to create predictive hybrid experimental/analytical models of damped structures is introduced, and the ability of hybrid models to predict the response to system configuration changes is discussed. Finally a critical review of the overall methodology is made by application to the case of the MIT/SERC interferometer testbed.

The Neural Correlates of Hierarchical Predictions for Perceptual Decisions.

PubMed

Weilnhammer, Veith A; Stuke, Heiner; Sterzer, Philipp; Schmack, Katharina

2018-05-23

Sensory information is inherently noisy, sparse, and ambiguous. In contrast, visual experience is usually clear, detailed, and stable. Bayesian theories of perception resolve this discrepancy by assuming that prior knowledge about the causes underlying sensory stimulation actively shapes perceptual decisions. The CNS is believed to entertain a generative model aligned to dynamic changes in the hierarchical states of our volatile sensory environment. Here, we used model-based fMRI to study the neural correlates of the dynamic updating of hierarchically structured predictions in male and female human observers. We devised a crossmodal associative learning task with covertly interspersed ambiguous trials in which participants engaged in hierarchical learning based on changing contingencies between auditory cues and visual targets. By inverting a Bayesian model of perceptual inference, we estimated individual hierarchical predictions, which significantly biased perceptual decisions under ambiguity. Although "high-level" predictions about the cue-target contingency correlated with activity in supramodal regions such as orbitofrontal cortex and hippocampus, dynamic "low-level" predictions about the conditional target probabilities were associated with activity in retinotopic visual cortex. Our results suggest that our CNS updates distinct representations of hierarchical predictions that continuously affect perceptual decisions in a dynamically changing environment. SIGNIFICANCE STATEMENT Bayesian theories posit that our brain entertains a generative model to provide hierarchical predictions regarding the causes of sensory information. Here, we use behavioral modeling and fMRI to study the neural underpinnings of such hierarchical predictions. We show that "high-level" predictions about the strength of dynamic cue-target contingencies during crossmodal associative learning correlate with activity in orbitofrontal cortex and the hippocampus, whereas "low-level" conditional target probabilities were reflected in retinotopic visual cortex. Our findings empirically corroborate theorizations on the role of hierarchical predictions in visual perception and contribute substantially to a longstanding debate on the link between sensory predictions and orbitofrontal or hippocampal activity. Our work fundamentally advances the mechanistic understanding of perceptual inference in the human brain. Copyright © 2018 the authors 0270-6474/18/385008-14$15.00/0.

A changing climate: impacts on human exposures to O3 using an integrated modeling methodology

EPA Science Inventory

Predicting the impacts of changing climate on human exposure to air pollution requires future scenarios that account for changes in ambient pollutant concentrations, population sizes and distributions, and housing stocks. An integrated methodology to model changes in human exposu...

Assessment of climate change impact on yield of major crops in the Banas River Basin, India.

PubMed

Dubey, Swatantra Kumar; Sharma, Devesh

2018-09-01

Crop growth models like AquaCrop are useful in understanding the impact of climate change on crop production considering the various projections from global circulation models and regional climate models. The present study aims to assess the climate change impact on yield of major crops in the Banas River Basin i.e., wheat, barley and maize. Banas basin is part of the semi-arid region of Rajasthan state in India. AquaCrop model is used to calculate the yield of all the three crops for a historical period of 30years (1981-2010) and then compared with observed yield data. Root Mean Square Error (RMSE) values are calculated to assess the model accuracy in prediction of yield. Further, the calibrated model is used to predict the possible impacts of climate change and CO 2 concentration on crop yield using CORDEX-SA climate projections of three driving climate models (CNRM-CM5, CCSM4 and MPI-ESM-LR) for two different scenarios (RCP4.5 and RCP8.5) for the future period 2021-2050. RMSE values of simulated yield with respect to observed yield of wheat, barley and maize are 11.99, 16.15 and 19.13, respectively. It is predicted that crop yield of all three crops will increase under the climate change conditions for future period (2021-2050). Copyright © 2018 Elsevier B.V. All rights reserved.

Using Multitheory Model of Health Behavior Change to Predict Adequate Sleep Behavior.

PubMed

Knowlden, Adam P; Sharma, Manoj; Nahar, Vinayak K

The purpose of this article was to use the multitheory model of health behavior change in predicting adequate sleep behavior in college students. A valid and reliable survey was administered in a cross-sectional design (n = 151). For initiation of adequate sleep behavior, the construct of behavioral confidence (P < .001) was found to be significant and accounted for 24.4% of the variance. For sustenance of adequate sleep behavior, changes in social environment (P < .02), emotional transformation (P < .001), and practice for change (P < .001) were significant and accounted for 34.2% of the variance.

Modeling Clinical Outcomes in Prostate Cancer: Application and Validation of the Discrete Event Simulation Approach.

PubMed

Pan, Feng; Reifsnider, Odette; Zheng, Ying; Proskorovsky, Irina; Li, Tracy; He, Jianming; Sorensen, Sonja V

2018-04-01

Treatment landscape in prostate cancer has changed dramatically with the emergence of new medicines in the past few years. The traditional survival partition model (SPM) cannot accurately predict long-term clinical outcomes because it is limited by its ability to capture the key consequences associated with this changing treatment paradigm. The objective of this study was to introduce and validate a discrete-event simulation (DES) model for prostate cancer. A DES model was developed to simulate overall survival (OS) and other clinical outcomes based on patient characteristics, treatment received, and disease progression history. We tested and validated this model with clinical trial data from the abiraterone acetate phase III trial (COU-AA-302). The model was constructed with interim data (55% death) and validated with the final data (96% death). Predicted OS values were also compared with those from the SPM. The DES model's predicted time to chemotherapy and OS are highly consistent with the final observed data. The model accurately predicts the OS hazard ratio from the final data cut (predicted: 0.74; 95% confidence interval [CI] 0.64-0.85 and final actual: 0.74; 95% CI 0.6-0.88). The log-rank test to compare the observed and predicted OS curves indicated no statistically significant difference between observed and predicted curves. However, the predictions from the SPM based on interim data deviated significantly from the final data. Our study showed that a DES model with properly developed risk equations presents considerable improvements to the more traditional SPM in flexibility and predictive accuracy of long-term outcomes. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Alpine glacial relict species losing out to climate change: The case of the fragmented mountain hare population (Lepus timidus) in the Alps.

PubMed

Rehnus, Maik; Bollmann, Kurt; Schmatz, Dirk R; Hackländer, Klaus; Braunisch, Veronika

2018-03-13

Alpine and Arctic species are considered to be particularly vulnerable to climate change, which is expected to cause habitat loss, fragmentation and-ultimately-extinction of cold-adapted species. However, the impact of climate change on glacial relict populations is not well understood, and specific recommendations for adaptive conservation management are lacking. We focused on the mountain hare (Lepus timidus) as a model species and modelled species distribution in combination with patch and landscape-based connectivity metrics. They were derived from graph-theory models to quantify changes in species distribution and to estimate the current and future importance of habitat patches for overall population connectivity. Models were calibrated based on 1,046 locations of species presence distributed across three biogeographic regions in the Swiss Alps and extrapolated according to two IPCC scenarios of climate change (RCP 4.5 & 8.5), each represented by three downscaled global climate models. The models predicted an average habitat loss of 35% (22%-55%) by 2100, mainly due to an increase in temperature during the reproductive season. An increase in habitat fragmentation was reflected in a 43% decrease in patch size, a 17% increase in the number of habitat patches and a 34% increase in inter-patch distance. However, the predicted changes in habitat availability and connectivity varied considerably between biogeographic regions: Whereas the greatest habitat losses with an increase in inter-patch distance were predicted at the southern and northern edges of the species' Alpine distribution, the greatest increase in patch number and decrease in patch size is expected in the central Swiss Alps. Finally, both the number of isolated habitat patches and the number of patches crucial for maintaining the habitat network increased under the different variants of climate change. Focusing conservation action on the central Swiss Alps may help mitigate the predicted effects of climate change on population connectivity. © 2018 John Wiley & Sons Ltd.

An experimental and theoretical evaluation of increased thermal diffusivity phase change devices

NASA Technical Reports Server (NTRS)

White, S. P.; Golden, J. O.; Stermole, F. J.

1972-01-01

This study was to experimentally evaluate and mathematically model the performance of phase change thermal control devices containing high thermal conductivity metal matrices. Three aluminum honeycomb filters were evaluated at five different heat flux levels using n-oct-adecane as the test material. The system was mathematically modeled by approximating the partial differential equations with a three-dimensional implicit alternating direction technique. The mathematical model predicts the system quite well. All of the phase change times are predicted. The heating of solid phase is predicted exactly while there is some variation between theoretical and experimental results in the liquid phase. This variation in the liquid phase could be accounted for by the fact that there are some heat losses in the cell and there could be some convection in the experimental system.

Predicting the genetic consequences of future climate change: The power of coupling spatial demography, the coalescent, and historical landscape changes.

PubMed

Brown, Jason L; Weber, Jennifer J; Alvarado-Serrano, Diego F; Hickerson, Michael J; Franks, Steven J; Carnaval, Ana C

2016-01-01

Climate change is a widely accepted threat to biodiversity. Species distribution models (SDMs) are used to forecast whether and how species distributions may track these changes. Yet, SDMs generally fail to account for genetic and demographic processes, limiting population-level inferences. We still do not understand how predicted environmental shifts will impact the spatial distribution of genetic diversity within taxa. We propose a novel method that predicts spatially explicit genetic and demographic landscapes of populations under future climatic conditions. We use carefully parameterized SDMs as estimates of the spatial distribution of suitable habitats and landscape dispersal permeability under present-day, past, and future conditions. We use empirical genetic data and approximate Bayesian computation to estimate unknown demographic parameters. Finally, we employ these parameters to simulate realistic and complex models of responses to future environmental shifts. We contrast parameterized models under current and future landscapes to quantify the expected magnitude of change. We implement this framework on neutral genetic data available from Penstemon deustus. Our results predict that future climate change will result in geographically widespread declines in genetic diversity in this species. The extent of reduction will heavily depend on the continuity of population networks and deme sizes. To our knowledge, this is the first study to provide spatially explicit predictions of within-species genetic diversity using climatic, demographic, and genetic data. Our approach accounts for climatic, geographic, and biological complexity. This framework is promising for understanding evolutionary consequences of climate change, and guiding conservation planning. © 2016 Botanical Society of America.

Model uncertainties do not affect observed patterns of species richness in the Amazon.

PubMed

Sales, Lilian Patrícia; Neves, Olívia Viana; De Marco, Paulo; Loyola, Rafael

2017-01-01

Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale-patterns of species richness and species vulnerability to climate change-are affected by the inputs used to model and project species distribution. We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors. The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses. From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species.

The importance of considering shifts in seasonal changes in discharges when predicting future phosphorus loads in streams

USGS Publications Warehouse

LaBeau, Meredith B.; Mayer, Alex S.; Griffis, Veronica; Watkins, David Jr.; Robertson, Dale M.; Gyawali, Rabi

2015-01-01

In this work, we hypothesize that phosphorus (P) concentrations in streams vary seasonally and with streamflow and that it is important to incorporate this variation when predicting changes in P loading associated with climate change. Our study area includes 14 watersheds with a range of land uses throughout the U.S. Great Lakes Basin. We develop annual seasonal load-discharge regression models for each watershed and apply these models with simulated discharges generated for future climate scenarios to simulate future P loading patterns for two periods: 2046–2065 and 2081–2100. We utilize output from the Coupled Model Intercomparison Project phase 3 downscaled climate change projections that are input into the Large Basin Runoff Model to generate future discharge scenarios, which are in turn used as inputs to the seasonal P load regression models. In almost all cases, the seasonal load-discharge models match observed loads better than the annual models. Results using the seasonal models show that the concurrence of nonlinearity in the load-discharge model and changes in high discharges in the spring months leads to the most significant changes in P loading for selected tributaries under future climate projections. These results emphasize the importance of using seasonal models to understand the effects of future climate change on nutrient loads.

Statistical Approaches for Spatiotemporal Prediction of Low Flows

NASA Astrophysics Data System (ADS)

Fangmann, A.; Haberlandt, U.

2017-12-01

An adequate assessment of regional climate change impacts on streamflow requires the integration of various sources of information and modeling approaches. This study proposes simple statistical tools for inclusion into model ensembles, which are fast and straightforward in their application, yet able to yield accurate streamflow predictions in time and space. Target variables for all approaches are annual low flow indices derived from a data set of 51 records of average daily discharge for northwestern Germany. The models require input of climatic data in the form of meteorological drought indices, derived from observed daily climatic variables, averaged over the streamflow gauges' catchments areas. Four different modeling approaches are analyzed. Basis for all pose multiple linear regression models that estimate low flows as a function of a set of meteorological indices and/or physiographic and climatic catchment descriptors. For the first method, individual regression models are fitted at each station, predicting annual low flow values from a set of annual meteorological indices, which are subsequently regionalized using a set of catchment characteristics. The second method combines temporal and spatial prediction within a single panel data regression model, allowing estimation of annual low flow values from input of both annual meteorological indices and catchment descriptors. The third and fourth methods represent non-stationary low flow frequency analyses and require fitting of regional distribution functions. Method three is subject to a spatiotemporal prediction of an index value, method four to estimation of L-moments that adapt the regional frequency distribution to the at-site conditions. The results show that method two outperforms successive prediction in time and space. Method three also shows a high performance in the near future period, but since it relies on a stationary distribution, its application for prediction of far future changes may be problematic. Spatiotemporal prediction of L-moments appeared highly uncertain for higher-order moments resulting in unrealistic future low flow values. All in all, the results promote an inclusion of simple statistical methods in climate change impact assessment.

A fiber-based constitutive model predicts changes in amount and organization of matrix proteins with development and disease in the mouse aorta

PubMed Central

Cheng, Jeffrey K.; Stoilov, Ivan; Mecham, Robert P.

2013-01-01

Decreased elastin in mice (Eln+/−) yields a functioning vascular system with elevated blood pressure and increased arterial stiffness that is morphologically distinct from wild-type mice (WT). Yet, function is retained enough that there is no appreciable effect on life span and some mechanical properties are maintained constant. It is not understood how the mouse modifies the normal developmental process to produce a functioning vascular system despite a deficiency in elastin. To quantify changes in mechanical properties, we have applied a fiber-based constitutive model to mechanical data from the ascending aorta during postnatal development of WT and Eln+/− mice. Results indicate that the fiber-based constitutive model is capable of distinguishing elastin amounts and identifying trends during development. We observe an increase in predicted circumferential stress contribution from elastin with age, which correlates with increased elastin amounts from protein quantification data. The model also predicts changes in the unloaded collagen fiber orientation with age, which must be verified in future work. In Eln+/− mice, elastin amounts are decreased at each age, along with the predicted circumferential stress contribution of elastin. Collagen amounts in Eln+/− aorta are comparable to WT, but the predicted circumferential stress contribution of collagen is increased. This may be due to altered organization or structure of the collagen fibers. Relating quantifiable changes in arterial mechanics with changes in extracellular matrix (ECM) protein amounts will help in understanding developmental remodeling and in producing treatments for human diseases affecting ECM proteins. PMID:22790326

Revisiting concepts of thermal physiology: Predicting responses of mammals to climate change.

PubMed

Mitchell, Duncan; Snelling, Edward P; Hetem, Robyn S; Maloney, Shane K; Strauss, Willem Maartin; Fuller, Andrea

2018-02-26

The accuracy of predictive models (also known as mechanistic or causal models) of animal responses to climate change depends on properly incorporating the principles of heat transfer and thermoregulation into those models. Regrettably, proper incorporation of these principles is not always evident. We have revisited the relevant principles of thermal physiology and analysed how they have been applied in predictive models of large mammals, which are particularly vulnerable, to climate change. We considered dry heat exchange, evaporative heat transfer, the thermoneutral zone and homeothermy, and we examined the roles of size and shape in the thermal physiology of large mammals. We report on the following misconceptions in influential predictive models: underestimation of the role of radiant heat transfer, misassignment of the role and misunderstanding of the sustainability of evaporative cooling, misinterpretation of the thermoneutral zone as a zone of thermal tolerance or as a zone of sustainable energetics, confusion of upper critical temperature and critical thermal maximum, overestimation of the metabolic energy cost of evaporative cooling, failure to appreciate that the current advantages of size and shape will become disadvantageous as climate change advances, misassumptions about skin temperature and, lastly, misconceptions about the relationship between body core temperature and its variability with body mass in large mammals. Not all misconceptions invalidate the models, but we believe that preventing inappropriate assumptions from propagating will improve model accuracy, especially as models progress beyond their current typically static format to include genetic and epigenetic adaptation that can result in phenotypic plasticity. © 2018 The Authors. Journal of Animal Ecology © 2018 British Ecological Society.

Simulation and prediction the impact of climate change into water resources in Bengawan Solo watershed based on CCAM (Conformal Cubic Atmospheric Model) data

NASA Astrophysics Data System (ADS)

Sipayung, Sinta B.; Nurlatifah, Amalia; Siswanto, Bambang

2018-05-01

Bengawan Solo Watershed is one of the largest watersheds in Indonesia. This watershed flows in many areas both in Central Java and East Java. Therefore, the water resources condition greatly affects many people. This research will be conducted on prediction of climate change effect on water resources condition in terms of rainfall conditions in Bengawan Solo River Basin. The goal of this research is to know and predict the climate change impact on water resources based on CCAM (Conformal Cubic Atmosphere Model) with downscaling baseline (historical) model data from 1949 to 2005 and RCP 4.5 from 2006 to 2069. The modeling data was validated with in-situ data (measurement data). To analyse the water availability condition in Bengawan Solo Watershed, the simulation of river flow and water balance condition were done in Bengawan Solo River. Simulation of river flow and water balance conditions were done with ArcSWAT model using climate data from CCAM, DEM SRTM 90 meter, soil type, and land use data. The results of this simulation indicate there is (i) The CCAM data itself after validation has a pretty good result when compared to the insitu data. Based on CCAM simulation results, it is predicted that in 2040-2069 rainfall in Bengawan Solo River Basin will decrease, to a maximum of only about 1 mm when compared to 1971-2000. (ii) The CCAM rainfall prediction itself shows that rainfall in Bengawan Solo River basin will decline until 2069 although the decline itself is not significant and tends to be negligible (rainfall is considered unchanged) (iii) Both in the DJF and JJA seasons, precipitation is predicted to decline as well despite the significant decline. (iv) The river flow simulation show that the water resources in Bengawan Solo River did not change significantly. This event occurred because the rainfall also did not change greatly and close to 0 mm/month.

Fostering marginalized youths' political participation: longitudinal roles of parental political socialization and youth sociopolitical development.

PubMed

Diemer, Matthew A

2012-09-01

This study examines the roles of parental political socialization and the moral commitment to change social inequalities in predicting marginalized youths' (defined here as lower-SES youth of color) political participation. These issues are examined by applying structural equation modeling to a longitudinal panel of youth. Because tests of measurement invariance suggested racial/ethnic heterogeneity, the structural model was fit separately for three racial/ethnic groups. For each group, parental political socialization: discussion predicted youths' commitment to produce social change and for two groups, longitudinally predicted political participation. This study contributes to the literature by examining civic/political participation among disparate racial/ethnic groups, addresses an open scholarly question (whether youths' commitment to create social change predicts their "traditional" participation), and emphasizes parents' role in fostering marginalized youths' civic and political participation.

Elevated carbon dioxide is predicted to promote coexistence among competing species in a trait-based model

DOE PAGES

Ali, Ashehad A.; Medlyn, Belinda E.; Aubier, Thomas G.; ...

2015-10-06

Differential species responses to atmospheric CO 2 concentration (C a) could lead to quantitative changes in competition among species and community composition, with flow-on effects for ecosystem function. However, there has been little theoretical analysis of how elevated C a (eC a) will affect plant competition, or how composition of plant communities might change. Such theoretical analysis is needed for developing testable hypotheses to frame experimental research. Here, we investigated theoretically how plant competition might change under eC a by implementing two alternative competition theories, resource use theory and resource capture theory, in a plant carbon and nitrogen cycling model.more » The model makes several novel predictions for the impact of eC a on plant community composition. Using resource use theory, the model predicts that eC a is unlikely to change species dominance in competition, but is likely to increase coexistence among species. Using resource capture theory, the model predicts that eC a may increase community evenness. Collectively, both theories suggest that eC a will favor coexistence and hence that species diversity should increase with eC a. Our theoretical analysis leads to a novel hypothesis for the impact of eC a on plant community composition. In this study, the hypothesis has potential to help guide the design and interpretation of eC a experiments.« less

A post audit of a model-designed ground water extraction system.

PubMed

Andersen, Peter F; Lu, Silong

2003-01-01

Model post audits test the predictive capabilities of ground water models and shed light on their practical limitations. In the work presented here, ground water model predictions were used to design an extraction/treatment/injection system at a military ammunition facility and then were re-evaluated using site-specific water-level data collected approximately one year after system startup. The water-level data indicated that performance specifications for the design, i.e., containment, had been achieved over the required area, but that predicted water-level changes were greater than observed, particularly in the deeper zones of the aquifer. Probable model error was investigated by determining the changes that were required to obtain an improved match to observed water-level changes. This analysis suggests that the originally estimated hydraulic properties were in error by a factor of two to five. These errors may have resulted from attributing less importance to data from deeper zones of the aquifer and from applying pumping test results to a volume of material that was larger than the volume affected by the pumping test. To determine the importance of these errors to the predictions of interest, the models were used to simulate the capture zones resulting from the originally estimated and updated parameter values. The study suggests that, despite the model error, the ground water model contributed positively to the design of the remediation system.

A clinician's perspective on memory reconsolidation as the primary basis for psychotherapeutic change in posttraumatic stress disorder (PTSD).

PubMed

Kimbrel, Nathan A; Meyer, Eric C; Beckham, Jean C

2015-01-01

Lane et al.'s proposal that psychotherapeutic change comes about through memory reconsolidation is compelling; however, the model would be strengthened by the inclusion of predictions regarding additional factors that might influence treatment response, predictions for improving outcomes for non-responsive patients, and a discussion of how the proposed model might explain individual differences in vulnerability for mental health problems.

Misspecification in Latent Change Score Models: Consequences for Parameter Estimation, Model Evaluation, and Predicting Change.

PubMed

Clark, D Angus; Nuttall, Amy K; Bowles, Ryan P

2018-01-01

Latent change score models (LCS) are conceptually powerful tools for analyzing longitudinal data (McArdle & Hamagami, 2001). However, applications of these models typically include constraints on key parameters over time. Although practically useful, strict invariance over time in these parameters is unlikely in real data. This study investigates the robustness of LCS when invariance over time is incorrectly imposed on key change-related parameters. Monte Carlo simulation methods were used to explore the impact of misspecification on parameter estimation, predicted trajectories of change, and model fit in the dual change score model, the foundational LCS. When constraints were incorrectly applied, several parameters, most notably the slope (i.e., constant change) factor mean and autoproportion coefficient, were severely and consistently biased, as were regression paths to the slope factor when external predictors of change were included. Standard fit indices indicated that the misspecified models fit well, partly because mean level trajectories over time were accurately captured. Loosening constraint improved the accuracy of parameter estimates, but estimates were more unstable, and models frequently failed to converge. Results suggest that potentially common sources of misspecification in LCS can produce distorted impressions of developmental processes, and that identifying and rectifying the situation is a challenge.

Using modelling to predict impacts of sea level rise and increased turbidity on seagrass distributions in estuarine embayments

NASA Astrophysics Data System (ADS)

Davis, Tom R.; Harasti, David; Smith, Stephen D. A.; Kelaher, Brendan P.

2016-11-01

Climate change induced sea level rise will affect shallow estuarine habitats, which are already under threat from multiple anthropogenic stressors. Here, we present the results of modelling to predict potential impacts of climate change associated processes on seagrass distributions. We use a novel application of relative environmental suitability (RES) modelling to examine relationships between variables of physiological importance to seagrasses (light availability, wave exposure, and current flow) and seagrass distributions within 5 estuarine embayments. Models were constructed separately for Posidonia australis and Zostera muelleri subsp. capricorni using seagrass data from Port Stephens estuary, New South Wales, Australia. Subsequent testing of models used independent datasets from four other estuarine embayments (Wallis Lake, Lake Illawarra, Merimbula Lake, and Pambula Lake) distributed along 570 km of the east Australian coast. Relative environmental suitability models provided adequate predictions for seagrass distributions within Port Stephens and the other estuarine embayments, indicating that they may have broad regional application. Under the predictions of RES models, both sea level rise and increased turbidity are predicted to cause substantial seagrass losses in deeper estuarine areas, resulting in a net shoreward movement of seagrass beds. Seagrass species distribution models developed in this study provide a valuable tool to predict future shifts in estuarine seagrass distributions, allowing identification of areas for protection, monitoring and rehabilitation.

An ecophysiological perspective on likely giant panda habitat responses to climate change.

PubMed

Zhang, Yuke; Mathewson, Paul D; Zhang, Qiongyue; Porter, Warren P; Ran, Jianghong

2018-04-01

Threatened and endangered species are more vulnerable to climate change due to small population and specific geographical distribution. Therefore, identifying and incorporating the biological processes underlying a species' adaptation to its environment are important for determining whether they can persist in situ. Correlative models are widely used to predict species' distribution changes, but generally fail to capture the buffering capacity of organisms. Giant pandas (Ailuropoda melanoleuca) live in topographically complex mountains and are known to avoid heat stress. Although many studies have found that climate change will lead to severe habitat loss and threaten previous conservation efforts, the mechanisms underlying panda's responses to climate change have not been explored. Here, we present a case study in Daxiangling Mountains, one of the six Mountain Systems that giant panda distributes. We used a mechanistic model, Niche Mapper, to explore what are likely panda habitat response to climate change taking physiological, behavioral and ecological responses into account, through which we map panda's climatic suitable activity area (SAA) for the first time. We combined SAA with bamboo forest distribution to yield highly suitable habitat (HSH) and seasonal suitable habitat (SSH), and their temporal dynamics under climate change were predicted. In general, SAA in the hottest month (July) would reduce 11.7%-52.2% by 2070, which is more moderate than predicted bamboo habitat loss (45.6%-86.9%). Limited by the availability of bamboo and forest, panda's suitable habitat loss increases, and only 15.5%-68.8% of current HSH would remain in 2070. Our method of mechanistic modeling can help to distinguish whether habitat loss is caused by thermal environmental deterioration or food loss under climate change. Furthermore, mechanistic models can produce robust predictions by incorporating ecophysiological feedbacks and minimizing extrapolation into novel environments. We suggest that a mechanistic approach should be incorporated into distribution predictions and conservation planning. © 2017 John Wiley & Sons Ltd.

Baseline Hemodynamics and Response to Contrast Media During Diagnostic Cardiac Catheterization Predict Adverse Events in Heart Failure Patients.

PubMed

Denardo, Scott J; Vock, David M; Schmalfuss, Carsten M; Young, Gregory D; Tcheng, James E; O'Connor, Christopher M

2016-07-01

Contrast media administered during cardiac catheterization can affect hemodynamic variables. However, little is documented about the effects of contrast on hemodynamics in heart failure patients or the prognostic value of baseline and changes in hemodynamics for predicting subsequent adverse events. In this prospective study of 150 heart failure patients, we measured hemodynamics at baseline and after administration of iodixanol or iopamidol contrast. One-year Kaplan-Meier estimates of adverse event-free survival (death, heart failure hospitalization, and rehospitalization) were generated, grouping patients by baseline measures of pulmonary capillary wedge pressure (PCWP) and cardiac index (CI), and by changes in those measures after contrast administration. We used Cox proportional hazards modeling to assess sequentially adding baseline PCWP and change in CI to 5 validated risk models (Seattle Heart Failure Score, ESCAPE [Evaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization Effectiveness], CHARM [Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity], CORONA [Controlled Rosuvastatin Multinational Trial in Heart Failure], and MAGGIC [Meta-Analysis Global Group in Chronic Heart Failure]). Median contrast volume was 109 mL. Both contrast media caused similarly small but statistically significant changes in most hemodynamic variables. There were 39 adverse events (26.0%). Adverse event rates increased using the composite metric of baseline PCWP and change in CI (P<0.01); elevated baseline PCWP and decreased CI after contrast correlated with the poorest prognosis. Adding both baseline PCWP and change in CI to the 5 risk models universally improved their predictive value (P≤0.02). In heart failure patients, the administration of contrast causes small but significant changes in hemodynamics. Calculating baseline PCWP with change in CI after contrast predicts adverse events and increases the predictive value of existing models. Patients with elevated baseline PCWP and decreased CI after contrast merit greatest concern. © 2016 American Heart Association, Inc.

Early changes in socioeconomic status do not predict changes in body mass in the first decade of life.

PubMed

Starkey, Leighann; Revenson, Tracey A

2015-04-01

Many studies link childhood socioeconomic status (SES) to body mass index (BMI), but few account for the impact of socioeconomic mobility throughout the lifespan. This study aims to investigate the impact of socioeconomic mobility on changes in BMI in childhood. Analyses tested whether [1] socioeconomic status influences BMI, [2] changes in socioeconomic status impact changes in BMI, and [3] timing of socioeconomic status mobility impacts BMI. Secondary data spanning birth to age 9 were analyzed. SES and BMI were investigated with gender, birth weight, maternal race/ethnicity, and maternal nativity as covariates. Autoregressive structural equation modeling and latent growth modeling were used. Socioeconomic status in the first year of life predicted body mass index. Child covariates were consistently associated with body mass index. Rate of change in socioeconomic status did not predict change in body mass index. The findings suggest that early socioeconomic status may most influence body mass in later childhood.

Timing and Prediction of Climate Change and Hydrological Impacts: Periodicity in Natural Variations

EPA Science Inventory

Hydrological impacts from climate change are of principal interest to water resource policy-makers and practicing engineers, and predictive climatic models have been extensively investigated to quantify the impacts. In palaeoclmatic investigations, climate proxy evidence has une...

Phylogeny predicts future habitat shifts due to climate change.

PubMed

Kuntner, Matjaž; Năpăruş, Magdalena; Li, Daiqin; Coddington, Jonathan A

2014-01-01

Taxa may respond differently to climatic changes, depending on phylogenetic or ecological effects, but studies that discern among these alternatives are scarce. Here, we use two species pairs from globally distributed spider clades, each pair representing two lifestyles (generalist, specialist) to test the relative importance of phylogeny versus ecology in predicted responses to climate change. We used a recent phylogenetic hypothesis for nephilid spiders to select four species from two genera (Nephilingis and Nephilengys) that match the above criteria, are fully allopatric but combined occupy all subtropical-tropical regions. Based on their records, we modeled each species niche spaces and predicted their ecological shifts 20, 40, 60, and 80 years into the future using customized GIS tools and projected climatic changes. Phylogeny better predicts the species current ecological preferences than do lifestyles. By 2080 all species face dramatic reductions in suitable habitat (54.8-77.1%) and adapt by moving towards higher altitudes and latitudes, although at different tempos. Phylogeny and life style explain simulated habitat shifts in altitude, but phylogeny is the sole best predictor of latitudinal shifts. Models incorporating phylogenetic relatedness are an important additional tool to predict accurately biotic responses to global change.

A hybrid deep neural network and physically based distributed model for river stage prediction

NASA Astrophysics Data System (ADS)

hitokoto, Masayuki; sakuraba, Masaaki

2016-04-01

We developed the real-time river stage prediction model, using the hybrid deep neural network and physically based distributed model. As the basic model, 4 layer feed-forward artificial neural network (ANN) was used. As a network training method, the deep learning technique was applied. To optimize the network weight, the stochastic gradient descent method based on the back propagation method was used. As a pre-training method, the denoising autoencoder was used. Input of the ANN model is hourly change of water level and hourly rainfall, output data is water level of downstream station. In general, the desirable input of the ANN has strong correlation with the output. In conceptual hydrological model such as tank model and storage-function model, river discharge is governed by the catchment storage. Therefore, the change of the catchment storage, downstream discharge subtracted from rainfall, can be the potent input candidate of the ANN model instead of rainfall. From this point of view, the hybrid deep neural network and physically based distributed model was developed. The prediction procedure of the hybrid model is as follows; first, downstream discharge was calculated by the distributed model, and then estimates the hourly change of catchment storage form rainfall and calculated discharge as the input of the ANN model, and finally the ANN model was calculated. In the training phase, hourly change of catchment storage can be calculated by the observed rainfall and discharge data. The developed model was applied to the one catchment of the OOYODO River, one of the first-grade river in Japan. The modeled catchment is 695 square km. For the training data, 5 water level gauging station and 14 rain-gauge station in the catchment was used. The training floods, superior 24 events, were selected during the period of 2005-2014. Prediction was made up to 6 hours, and 6 models were developed for each prediction time. To set the proper learning parameters and network architecture of the ANN model, sensitivity analysis was done by the case study approach. The prediction result was evaluated by the superior 4 flood events by the leave-one-out cross validation. The prediction result of the basic 4 layer ANN was better than the conventional 3 layer ANN model. However, the result did not reproduce well the biggest flood event, supposedly because the lack of the sufficient high-water level flood event in the training data. The result of the hybrid model outperforms the basic ANN model and distributed model, especially improved the performance of the basic ANN model in the biggest flood event.

The impact of fishing-induced mortality on the evolution of alternative life-history tactics in brook charr

PubMed Central

Thériault, Véronique; Dunlop, Erin S; Dieckmann, Ulf; Bernatchez, Louis; Dodson, Julian J

2008-01-01

Although contemporary trends indicative of evolutionary change have been detected in the life-history traits of exploited populations, it is not known to what extent fishing influences the evolution of alternative life-history tactics in migratory species such as salmonids. Here, we build a model to predict the evolution of anadromy and residency in an exploited population of brook charr, Salvelinus fontinalis. Our model allows for both phenotypic plasticity and genetic change in the age and size at migration by including migration reaction norms. Using this model, we predict that fishing of anadromous individuals over the course of 100 years causes evolution in the migration reaction norm, resulting in a decrease in average probabilities of migration with increasing harvest rate. Moreover, we show that differences in natural mortalities in freshwater greatly influence the magnitude and rate of evolutionary change. The fishing-induced changes in migration predicted by our model alter population abundances and reproductive output and should be accounted for in the sustainable management of salmonids. PMID:25567640

Prediction of the dollar to the ruble rate. A system-theoretic approach

NASA Astrophysics Data System (ADS)

Borodachev, Sergey M.

2017-07-01

Proposed a simple state-space model of dollar rate formation based on changes in oil prices and some mechanisms of money transfer between monetary and stock markets. Comparison of predictions by means of input-output model and state-space model is made. It concludes that with proper use of statistical data (Kalman filter) the second approach provides more adequate predictions of the dollar rate.

The Role of Cognitive, Metacognitive, and Motivational Variables in Conceptual Change: Preservice Early Childhood Teachers' Conceptual Understanding of the Cause of Lunar Phases

ERIC Educational Resources Information Center

Sackes, Mesut

2010-01-01

This study seeks to explore and describe the role of cognitive, metacognitive, and motivational variables in conceptual change. More specifically, the purposes of the study were (1) to investigate the predictive ability of a learning model that was developed based on the intentional conceptual change perspective in predicting change in conceptual…

Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya.

PubMed

Sewe, Maquins Odhiambo; Tozan, Yesim; Ahlm, Clas; Rocklöv, Joacim

2017-06-01

Malaria surveillance data provide opportunity to develop forecasting models. Seasonal variability in environmental factors correlate with malaria transmission, thus the identification of transmission patterns is useful in developing prediction models. However, with changing seasonal transmission patterns, either due to interventions or shifting weather seasons, traditional modelling approaches may not yield adequate predictive skill. Two statistical models,a general additive model (GAM) and GAMBOOST model with boosted regression were contrasted by assessing their predictive accuracy in forecasting malaria admissions at lead times of one to three months. Monthly admission data for children under five years with confirmed malaria at the Siaya district hospital in Western Kenya for the period 2003 to 2013 were used together with satellite derived data on rainfall, average temperature and evapotranspiration(ET). There was a total of 8,476 confirmed malaria admissions. The peak of malaria season changed and malaria admissions reduced overtime. The GAMBOOST model at 1-month lead time had the highest predictive skill during both the training and test periods and thus can be utilized in a malaria early warning system.

Behavior problems and placement change in a national child welfare sample: a prospective study.

PubMed

Aarons, Gregory A; James, Sigrid; Monn, Amy R; Raghavan, Ramesh; Wells, Rebecca S; Leslie, Laurel K

2010-01-01

There is ongoing debate regarding the impact of youth behavior problems on placement change in child welfare compared to the impact of placement change on behavior problems. Existing studies provide support for both perspectives. The purpose of this study was to prospectively examine the relations of behavior problems and placement change in a nationally representative sample of youths in the National Survey of Child and Adolescent Well-Being. The sample consisted of 500 youths in the child welfare system with out-of-home placements over the course of the National Survey of Child and Adolescent Well-Being study. We used a prospective cross-lag design and path analysis to examine reciprocal effects of behavior problems and placement change, testing an overall model and models examining effects of age and gender. In the overall model, out of a total of eight path coefficients, behavior problems significantly predicted placement changes for three paths and placement change predicted behavior problems for one path. Internalizing and externalizing behavior problems at baseline predicted placement change between baseline and 18 months. Behavior problems at an older age and externalizing behavior at 18 months appear to confer an increased risk of placement change. Of note, among female subjects, placement changes later in the study predicted subsequent internalizing and externalizing behavior problems. In keeping with recommendations from a number of professional bodies, we suggest that initial and ongoing screening for internalizing and externalizing behavior problems be instituted as part of standard practice for youths entering or transitioning in the child welfare system.

The Roles of Dispersal, Fecundity, and Predation in the Population Persistence of an Oak (Quercus engelmannii) under Global Change

PubMed Central

Conlisk, Erin; Lawson, Dawn; Syphard, Alexandra D.; Franklin, Janet; Flint, Lorraine; Flint, Alan; Regan, Helen M.

2012-01-01

A species’ response to climate change depends on the interaction of biotic and abiotic factors that define future habitat suitability and species’ ability to migrate or adapt. The interactive effects of processes such as fire, dispersal, and predation have not been thoroughly addressed in the climate change literature. Our objective was to examine how life history traits, short-term global change perturbations, and long-term climate change interact to affect the likely persistence of an oak species - Quercus engelmannii (Engelmann oak). Specifically, we combined dynamic species distribution models, which predict suitable habitat, with stochastic, stage-based metapopulation models, which project population trajectories, to evaluate the effects of three global change factors – climate change, land use change, and altered fire frequency – emphasizing the roles of dispersal and seed predation. Our model predicted dramatic reduction in Q. engelmannii abundance, especially under drier climates and increased fire frequency. When masting lowers seed predation rates, decreased masting frequency leads to large abundance decreases. Current rates of dispersal are not likely to prevent these effects, although increased dispersal could mitigate population declines. The results suggest that habitat suitability predictions by themselves may under-estimate the impact of climate change for other species and locations. PMID:22623955

The origins of computer weather prediction and climate modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lynch, Peter

2008-03-20

Numerical simulation of an ever-increasing range of geophysical phenomena is adding enormously to our understanding of complex processes in the Earth system. The consequences for mankind of ongoing climate change will be far-reaching. Earth System Models are capable of replicating climate regimes of past millennia and are the best means we have of predicting the future of our climate. The basic ideas of numerical forecasting and climate modeling were developed about a century ago, long before the first electronic computer was constructed. There were several major practical obstacles to be overcome before numerical prediction could be put into practice. Amore » fuller understanding of atmospheric dynamics allowed the development of simplified systems of equations; regular radiosonde observations of the free atmosphere and, later, satellite data, provided the initial conditions; stable finite difference schemes were developed; and powerful electronic computers provided a practical means of carrying out the prodigious calculations required to predict the changes in the weather. Progress in weather forecasting and in climate modeling over the past 50 years has been dramatic. In this presentation, we will trace the history of computer forecasting through the ENIAC integrations to the present day. The useful range of deterministic prediction is increasing by about one day each decade, and our understanding of climate change is growing rapidly as Earth System Models of ever-increasing sophistication are developed.« less

The origins of computer weather prediction and climate modeling

NASA Astrophysics Data System (ADS)

Lynch, Peter

2008-03-01

Numerical simulation of an ever-increasing range of geophysical phenomena is adding enormously to our understanding of complex processes in the Earth system. The consequences for mankind of ongoing climate change will be far-reaching. Earth System Models are capable of replicating climate regimes of past millennia and are the best means we have of predicting the future of our climate. The basic ideas of numerical forecasting and climate modeling were developed about a century ago, long before the first electronic computer was constructed. There were several major practical obstacles to be overcome before numerical prediction could be put into practice. A fuller understanding of atmospheric dynamics allowed the development of simplified systems of equations; regular radiosonde observations of the free atmosphere and, later, satellite data, provided the initial conditions; stable finite difference schemes were developed; and powerful electronic computers provided a practical means of carrying out the prodigious calculations required to predict the changes in the weather. Progress in weather forecasting and in climate modeling over the past 50 years has been dramatic. In this presentation, we will trace the history of computer forecasting through the ENIAC integrations to the present day. The useful range of deterministic prediction is increasing by about one day each decade, and our understanding of climate change is growing rapidly as Earth System Models of ever-increasing sophistication are developed.

Seizure prediction in hippocampal and neocortical epilepsy using a model-based approach

PubMed Central

Aarabi, Ardalan; He, Bin

2014-01-01

Objectives The aim of this study is to develop a model based seizure prediction method. Methods A neural mass model was used to simulate the macro-scale dynamics of intracranial EEG data. The model was composed of pyramidal cells, excitatory and inhibitory interneurons described through state equations. Twelve model’s parameters were estimated by fitting the model to the power spectral density of intracranial EEG signals and then integrated based on information obtained by investigating changes in the parameters prior to seizures. Twenty-one patients with medically intractable hippocampal and neocortical focal epilepsy were studied. Results Tuned to obtain maximum sensitivity, an average sensitivity of 87.07% and 92.6% with an average false prediction rate of 0.2 and 0.15/h were achieved using maximum seizure occurrence periods of 30 and 50 min and a minimum seizure prediction horizon of 10 s, respectively. Under maximum specificity conditions, the system sensitivity decreased to 82.9% and 90.05% and the false prediction rates were reduced to 0.16 and 0.12/h using maximum seizure occurrence periods of 30 and 50 min, respectively. Conclusions The spatio-temporal changes in the parameters demonstrated patient-specific preictal signatures that could be used for seizure prediction. Significance The present findings suggest that the model-based approach may aid prediction of seizures. PMID:24374087

Evaluation and prediction of long-term environmental effects of nonmetallic materials, second phase

NASA Technical Reports Server (NTRS)

1983-01-01

Changes in the functional properties of a number of nonmetallic materials were evaluated experimentally as a function of simulated space environments and to use such data to develop models for accelerated test methods useful for predicting such behavioral changes. The effects of changed particle irradiations on candidate space materials are evaluated.

A simplified approach to predict performance degradation of a solid oxide fuel cell anode

NASA Astrophysics Data System (ADS)

Khan, Muhammad Zubair; Mehran, Muhammad Taqi; Song, Rak-Hyun; Lee, Jong-Won; Lee, Seung-Bok; Lim, Tak-Hyoung

2018-07-01

The agglomeration of nickel (Ni) particles in a Ni-cermet anode is a significant degradation phenomenon for solid oxide fuel cells (SOFCs). This work aims to predict the performance degradation of SOFCs due to Ni grain growth by using a simplified approach. Accelerated aging of Ni-scandia stabilized zirconia (SSZ) as an SOFC anode is carried out at 900 °C and subsequent microstructural evolution is investigated every 100 h up to 1000 h using scanning electron microscopy (SEM). The resulting morphological changes are quantified using a two-dimensional image analysis technique that yields the particle size, phase proportion, and triple phase boundary (TPB) point distribution. The electrochemical properties of an anode-supported SOFC are characterized using electrochemical impedance spectroscopy (EIS). The changes of particle size and TPB length in the anode as a function of time are in excellent agreement with the power-law coarsening model. This model is further combined with an electrochemical model to predict the changes in the anode polarization resistance. The predicted polarization resistances are in good agreement with the experimentally obtained values. This model for prediction of anode lifetime provides deep insight into the time-dependent Ni agglomeration behavior and its impact on the electrochemical performance degradation of the SOFC anode.

Projections of Future Summertime Ozone over the U.S.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pfister, G. G.; Walters, Stacy; Lamarque, J. F.

This study uses a regional fully coupled chemistry-transport model to assess changes in surface ozone over the summertime U.S. between present and a 2050 future time period at high spatial resolution (12 km grid spacing) under the SRES A2 climate and RCP8.5 anthropogenic pre-cursor emission scenario. The impact of predicted changes in climate and global background ozone is estimated to increase surface ozone over most of the U.S; the 5th - 95th percentile range for daily 8-hour maximum surface ozone increases from 31-79 ppbV to 30-87 ppbV between the present and future time periods. The analysis of a set ofmore » meteorological drivers suggests that these mostly will add to increasing ozone, but the set of simulations conducted does not allow to separate this effect from that through enhanced global background ozone. Statistically the most robust positive feedbacks are through increased temperature, biogenic emissions and solar radiation. Stringent emission controls can counteract these feedbacks and if considered, we estimate large reductions in surface ozone with the 5th-95th percentile reduced to 27-55 ppbV. A comparison of the high-resolution projections to global model projections shows that even though the global model is biased high in surface ozone compared to the regional model and compared to observations, both the global and the regional model predict similar changes in ozone between the present and future time periods. However, on smaller spatial scales, the regional predictions show more pronounced changes between urban and rural regimes that cannot be resolved at the coarse resolution of global model. In addition, the sign of the changes in overall ozone mixing ratios can be different between the global and the regional predictions in certain regions, such as the Western U.S. This study confirms the key role of emission control strategies in future air quality predictions and demonstrates the need for considering degradation of air quality with future climate change in emission policy making. It also illustrates the need for high resolution modeling when the objective is to address regional and local air quality or establish links to human health and society.« less

Predictive Modeling of Rice Yellow Stem Borer Population Dynamics under Climate Change Scenarios in Indramayu

NASA Astrophysics Data System (ADS)

Nurhayati, E.; Koesmaryono, Y.; Impron

2017-03-01

Rice Yellow Stem Borer (YSB) is one of the major insect pests in rice plants that has high attack intensity in rice production center areas, especially in West Java. This pest is consider as holometabola insects that causes rice damage in the vegetative phase (deadheart) as well as generative phase (whitehead). Climatic factor is one of the environmental factors influence the pattern of dynamics population. The purpose of this study was to develop a predictive modeling of YSB pest dynamics population under climate change scenarios (2016-2035 period) using Dymex Model in Indramayu area, West Java. YSB modeling required two main components, namely climate parameters and YSB development lower threshold of temperature (To) to describe YSB life cycle in every phase. Calibration and validation test of models showed the coefficient of determination (R2) between the predicted results and observations of the study area were 0.74 and 0.88 respectively, which was able to illustrate the development, mortality, transfer of individuals from one stage to the next life also fecundity and YSB reproduction. On baseline climate condition, there was a tendency of population abundance peak (outbreak) occured when a change of rainfall intensity in the rainy season transition to dry season or the opposite conditions was happen. In both of application of climate change scenarios, the model outputs were generated well and able to predict the pattern of YSB population dynamics with a the increasing trend of specific population numbers, generation numbers per season and also shifting pattern of populations abundance peak in the future climatic conditions. These results can be adopted as a tool to predict outbreak and to give early warning to control YSB pest more effectively.

Impacts of Climate Change on Native Landcover: Seeking Future Climatic Refuges

PubMed Central

Mangabeira Albernaz, Ana Luisa

2016-01-01

Climate change is a driver for diverse impacts on global biodiversity. We investigated its impacts on native landcover distribution in South America, seeking to predict its effect as a new force driving habitat loss and population isolation. Moreover, we mapped potential future climatic refuges, which are likely to be key areas for biodiversity conservation under climate change scenarios. Climatically similar native landcovers were aggregated using a decision tree, generating a reclassified landcover map, from which 25% of the map’s coverage was randomly selected to fuel distribution models. We selected the best geographical distribution models among twelve techniques, validating the predicted distribution for current climate with the landcover map and used the best technique to predict the future distribution. All landcover categories showed changes in area and displacement of the latitudinal/longitudinal centroid. Closed vegetation was the only landcover type predicted to expand its distributional range. The range contractions predicted for other categories were intense, even suggesting extirpation of the sparse vegetation category. The landcover refuges under future climate change represent a small proportion of the South American area and they are disproportionately represented and unevenly distributed, predominantly occupying five of 26 South American countries. The predicted changes, regardless of their direction and intensity, can put biodiversity at risk because they are expected to occur in the near future in terms of the temporal scales of ecological and evolutionary processes. Recognition of the threat of climate change allows more efficient conservation actions. PMID:27618445

Impacts of Climate Change on Native Landcover: Seeking Future Climatic Refuges.

PubMed

Zanin, Marina; Mangabeira Albernaz, Ana Luisa

2016-01-01

Climate change is a driver for diverse impacts on global biodiversity. We investigated its impacts on native landcover distribution in South America, seeking to predict its effect as a new force driving habitat loss and population isolation. Moreover, we mapped potential future climatic refuges, which are likely to be key areas for biodiversity conservation under climate change scenarios. Climatically similar native landcovers were aggregated using a decision tree, generating a reclassified landcover map, from which 25% of the map's coverage was randomly selected to fuel distribution models. We selected the best geographical distribution models among twelve techniques, validating the predicted distribution for current climate with the landcover map and used the best technique to predict the future distribution. All landcover categories showed changes in area and displacement of the latitudinal/longitudinal centroid. Closed vegetation was the only landcover type predicted to expand its distributional range. The range contractions predicted for other categories were intense, even suggesting extirpation of the sparse vegetation category. The landcover refuges under future climate change represent a small proportion of the South American area and they are disproportionately represented and unevenly distributed, predominantly occupying five of 26 South American countries. The predicted changes, regardless of their direction and intensity, can put biodiversity at risk because they are expected to occur in the near future in terms of the temporal scales of ecological and evolutionary processes. Recognition of the threat of climate change allows more efficient conservation actions.

Neural response to pictorial health warning labels can predict smoking behavioral change

PubMed Central

Riddle, Philip J.; Newman-Norlund, Roger D.; Baer, Jessica; Thrasher, James F.

2016-01-01

In order to improve our understanding of how pictorial health warning labels (HWLs) influence smoking behavior, we examined whether brain activity helps to explain smoking behavior above and beyond self-reported effectiveness of HWLs. We measured the neural response in the ventromedial prefrontal cortex (vmPFC) and the amygdala while adult smokers viewed HWLs. Two weeks later, participants’ self-reported smoking behavior and biomarkers of smoking behavior were reassessed. We compared multiple models predicting change in self-reported smoking behavior (cigarettes per day [CPD]) and change in a biomarkers of smoke exposure (expired carbon monoxide [CO]). Brain activity in the vmPFC and amygdala not only predicted changes in CO, but also accounted for outcome variance above and beyond self-report data. Neural data were most useful in predicting behavioral change as quantified by the objective biomarker (CO). This pattern of activity was significantly modulated by individuals’ intention to quit. The finding that both cognitive (vmPFC) and affective (amygdala) brain areas contributed to these models supports the idea that smokers respond to HWLs in a cognitive-affective manner. Based on our findings, researchers may wish to consider using neural data from both cognitive and affective networks when attempting to predict behavioral change in certain populations (e.g. cigarette smokers). PMID:27405615

Neural response to pictorial health warning labels can predict smoking behavioral change.

PubMed

Riddle, Philip J; Newman-Norlund, Roger D; Baer, Jessica; Thrasher, James F

2016-11-01

In order to improve our understanding of how pictorial health warning labels (HWLs) influence smoking behavior, we examined whether brain activity helps to explain smoking behavior above and beyond self-reported effectiveness of HWLs. We measured the neural response in the ventromedial prefrontal cortex (vmPFC) and the amygdala while adult smokers viewed HWLs. Two weeks later, participants' self-reported smoking behavior and biomarkers of smoking behavior were reassessed. We compared multiple models predicting change in self-reported smoking behavior (cigarettes per day [CPD]) and change in a biomarkers of smoke exposure (expired carbon monoxide [CO]). Brain activity in the vmPFC and amygdala not only predicted changes in CO, but also accounted for outcome variance above and beyond self-report data. Neural data were most useful in predicting behavioral change as quantified by the objective biomarker (CO). This pattern of activity was significantly modulated by individuals' intention to quit. The finding that both cognitive (vmPFC) and affective (amygdala) brain areas contributed to these models supports the idea that smokers respond to HWLs in a cognitive-affective manner. Based on our findings, researchers may wish to consider using neural data from both cognitive and affective networks when attempting to predict behavioral change in certain populations (e.g. cigarette smokers). © The Author (2016). Published by Oxford University Press.

Evaluating simplistic methods to understand current distributions and forecast distribution changes under climate change scenarios: An example with coypu (Myocastor coypus)

USGS Publications Warehouse

Jarnevich, Catherine S.; Young, Nicholas E; Sheffels, Trevor R.; Carter, Jacoby; Systma, Mark D.; Talbert, Colin

2017-01-01

Invasive species provide a unique opportunity to evaluate factors controlling biogeographic distributions; we can consider introduction success as an experiment testing suitability of environmental conditions. Predicting potential distributions of spreading species is not easy, and forecasting potential distributions with changing climate is even more difficult. Using the globally invasive coypu (Myocastor coypus [Molina, 1782]), we evaluate and compare the utility of a simplistic ecophysiological based model and a correlative model to predict current and future distribution. The ecophysiological model was based on winter temperature relationships with nutria survival. We developed correlative statistical models using the Software for Assisted Habitat Modeling and biologically relevant climate data with a global extent. We applied the ecophysiological based model to several global circulation model (GCM) predictions for mid-century. We used global coypu introduction data to evaluate these models and to explore a hypothesized physiological limitation, finding general agreement with known coypu distribution locally and globally and support for an upper thermal tolerance threshold. Global circulation model based model results showed variability in coypu predicted distribution among GCMs, but had general agreement of increasing suitable area in the USA. Our methods highlighted the dynamic nature of the edges of the coypu distribution due to climate non-equilibrium, and uncertainty associated with forecasting future distributions. Areas deemed suitable habitat, especially those on the edge of the current known range, could be used for early detection of the spread of coypu populations for management purposes. Combining approaches can be beneficial to predicting potential distributions of invasive species now and in the future and in exploring hypotheses of factors controlling distributions.

Uncertainty in Model Predictions of Vibrio Vulnificus Response to Climate Variability and Change: A Chesapeake Bay Case Study

NASA Technical Reports Server (NTRS)

Urquhart, Erin A.; Zaitchik, Benjamin F.; Waugh, Darryn W.; Guikema, Seth D.; Del Castillo, Carlos E.

2014-01-01

The effect that climate change and variability will have on waterborne bacteria is a topic of increasing concern for coastal ecosystems, including the Chesapeake Bay. Surface water temperature trends in the Bay indicate a warming pattern of roughly 0.3-0.4 C per decade over the past 30 years. It is unclear what impact future warming will have on pathogens currently found in the Bay, including Vibrio spp. Using historical environmental data, combined with three different statistical models of Vibrio vulnificus probability, we explore the relationship between environmental change and predicted Vibrio vulnificus presence in the upper Chesapeake Bay. We find that the predicted response of V. vulnificus probability to high temperatures in the Bay differs systematically between models of differing structure. As existing publicly available datasets are inadequate to determine which model structure is most appropriate, the impact of climatic change on the probability of V. vulnificus presence in the Chesapeake Bay remains uncertain. This result points to the challenge of characterizing climate sensitivity of ecological systems in which data are sparse and only statistical models of ecological sensitivity exist.

Douglas-fir plantations in Europe: a retrospective test of assisted migration to address climate change.

PubMed

Isaac-Renton, Miriam G; Roberts, David R; Hamann, Andreas; Spiecker, Heinrich

2014-08-01

We evaluate genetic test plantations of North American Douglas-fir provenances in Europe to quantify how tree populations respond when subjected to climate regime shifts, and we examined whether bioclimate envelope models developed for North America to guide assisted migration under climate change can retrospectively predict the success of these provenance transfers to Europe. The meta-analysis is based on long-term growth data of 2800 provenances transferred to 120 European test sites. The model was generally well suited to predict the best performing provenances along north-south gradients in Western Europe, but failed to predict superior performance of coastal North American populations under continental climate conditions in Eastern Europe. However, model projections appear appropriate when considering additional information regarding adaptation of Douglas-fir provenances to withstand frost and drought, even though the model partially fails in a validation against growth traits alone. We conclude by applying the partially validated model to climate change scenarios for Europe, demonstrating that climate trends observed over the last three decades warrant changes to current use of Douglas-fir provenances in plantation forestry throughout Western and Central Europe. © 2014 John Wiley & Sons Ltd.

Experimental characterization of intrapulse tissue conductivity changes for electroporation.

PubMed

Neal, Robert E; Garcia, Paulo A; Robertson, John L; Davalos, Rafael V

2011-01-01

Cells exposed to short electric pulses experience a change in their transmembrane potential, which can lead to increased membrane permeability of the cell. When the energy of the pulses surpasses a threshold, the cell dies in a non-thermal manner known as irreversible electroporation (IRE). IRE has shown promise in the focal ablation of pathologic tissues. Its non-thermal mechanism spares sensitive structures and facilitates rapid lesion resolution. IRE effects depend on the electric field distribution, which can be predicted with numerical modeling. When the cells become permeabilized, the bulk tissue properties change, affecting this distribution. For IRE to become a reliable and successful treatment of diseased tissues, robust predictive treatment planning methods must be developed. It is vital to understand the changes in tissue properties undergoing the electric pulses to improve numerical models and predict treatment volumes. We report on the experimental characterization of these changes for kidney tissue. Tissue samples were pulsed between plate electrodes while intrapulse voltage and current data were measured to determine the conductivity of the tissue during the pulse. Conductivity was then established as a function of the electric field to which the tissue is exposed. This conductivity curve was used in a numerical model to demonstrate the impact of accounting for these changes when modeling electric field distributions to develop treatment plans.

Assessment of the climate change impacts on fecal coliform contamination in a tidal estuarine system.

PubMed

Liu, Wen-Cheng; Chan, Wen-Ting

2015-12-01

Climate change is one of the key factors affecting the future microbiological water quality in rivers and tidal estuaries. A coupled 3D hydrodynamic and fecal coliform transport model was developed and applied to the Danshuei River estuarine system for predicting the influences of climate change on microbiological water quality. The hydrodynamic and fecal coliform model was validated using observational salinity and fecal coliform distributions. According to the analyses of the statistical error, predictions of the salinity and the fecal coliform concentration from the model simulation quantitatively agreed with the observed data. The validated model was then applied to predict the fecal coliform contamination as a result of climate change, including the change of freshwater discharge and the sea level rise. We found that the reduction of freshwater discharge under climate change scenarios resulted in an increase in the fecal coliform concentration. The sea level rise would decrease fecal coliform distributions because both the water level and the water volume increased. A reduction in freshwater discharge has a negative impact on the fecal coliform concentration, whereas a rising sea level has a positive influence on the fecal coliform contamination. An appropriate strategy for the effective microbiological management in tidal estuaries is required to reveal the persistent trends of climate in the future.

Modelling Complexity: Making Sense of Leadership Issues in 14-19 Education

ERIC Educational Resources Information Center

Briggs, Ann R. J.

2008-01-01

Modelling of statistical data is a well established analytical strategy. Statistical data can be modelled to represent, and thereby predict, the forces acting upon a structure or system. For the rapidly changing systems in the world of education, modelling enables the researcher to understand, to predict and to enable decisions to be based upon…

Rates of Change in Naturalistic Psychotherapy: Contrasting Dose-Effect and Good-Enough Level Models of Change

ERIC Educational Resources Information Center

Baldwin, Scott A.; Berkeljon, Arjan; Atkins, David C.; Olsen, Joseph A.; Nielsen, Stevan L.

2009-01-01

Most research on the dose-effect model of change has combined data across patients who vary in their total dose of treatment and has implicitly assumed that the rate of change during therapy is constant across doses. In contrast, the good-enough level model predicts that rate of change will be related to total dose of therapy. In this study, the…

Increasing Potential Risk of a Global Aquatic Invader in Europe in Contrast to Other Continents under Future Climate Change

PubMed Central

Liu, Xuan; Guo, Zhongwei; Ke, Zunwei; Wang, Supen; Li, Yiming

2011-01-01

Background Anthropogenically-induced climate change can alter the current climatic habitat of non-native species and can have complex effects on potentially invasive species. Predictions of the potential distributions of invasive species under climate change will provide critical information for future conservation and management strategies. Aquatic ecosystems are particularly vulnerable to invasive species and climate change, but the effect of climate change on invasive species distributions has been rather neglected, especially for notorious global invaders. Methodology/Principal Findings We used ecological niche models (ENMs) to assess the risks and opportunities that climate change presents for the red swamp crayfish (Procambarus clarkii), which is a worldwide aquatic invasive species. Linking the factors of climate, topography, habitat and human influence, we developed predictive models incorporating both native and non-native distribution data of the crayfish to identify present areas of potential distribution and project the effects of future climate change based on a consensus-forecast approach combining the CCCMA and HADCM3 climate models under two emission scenarios (A2a and B2a) by 2050. The minimum temperature from the coldest month, the human footprint and precipitation of the driest quarter contributed most to the species distribution models. Under both the A2a and B2a scenarios, P. clarkii shifted to higher latitudes in continents of both the northern and southern hemispheres. However, the effect of climate change varied considerately among continents with an expanding potential in Europe and contracting changes in others. Conclusions/Significance Our findings are the first to predict the impact of climate change on the future distribution of a globally invasive aquatic species. We confirmed the complexities of the likely effects of climate change on the potential distribution of globally invasive species, and it is extremely important to develop wide-ranging and effective control measures according to predicted geographical shifts and changes. PMID:21479188

Challenges in predicting climate change impacts on pome fruit phenology

NASA Astrophysics Data System (ADS)

Darbyshire, Rebecca; Webb, Leanne; Goodwin, Ian; Barlow, E. W. R.

2014-08-01

Climate projection data were applied to two commonly used pome fruit flowering models to investigate potential differences in predicted full bloom timing. The two methods, fixed thermal time and sequential chill-growth, produced different results for seven apple and pear varieties at two Australian locations. The fixed thermal time model predicted incremental advancement of full bloom, while results were mixed from the sequential chill-growth model. To further investigate how the sequential chill-growth model reacts under climate perturbed conditions, four simulations were created to represent a wider range of species physiological requirements. These were applied to five Australian locations covering varied climates. Lengthening of the chill period and contraction of the growth period was common to most results. The relative dominance of the chill or growth component tended to predict whether full bloom advanced, remained similar or was delayed with climate warming. The simplistic structure of the fixed thermal time model and the exclusion of winter chill conditions in this method indicate it is unlikely to be suitable for projection analyses. The sequential chill-growth model includes greater complexity; however, reservations in using this model for impact analyses remain. The results demonstrate that appropriate representation of physiological processes is essential to adequately predict changes to full bloom under climate perturbed conditions with greater model development needed.

Predictive modeling of surimi cake shelf life at different storage temperatures

NASA Astrophysics Data System (ADS)

Wang, Yatong; Hou, Yanhua; Wang, Quanfu; Cui, Bingqing; Zhang, Xiangyu; Li, Xuepeng; Li, Yujin; Liu, Yuanping

2017-04-01

The Arrhenius model of the shelf life prediction which based on the TBARS index was established in this study. The results showed that the significant changed of AV, POV, COV and TBARS with temperature increased, and the reaction rate constants k was obtained by the first order reaction kinetics model. Then the secondary model fitting was based on the Arrhenius equation. There was the optimal fitting accuracy of TBARS in the first and the secondary model fitting (R2≥0.95). The verification test indicated that the relative error between the shelf life model prediction value and actual value was within ±10%, suggesting the model could predict the shelf life of surimi cake.

Insulin resistance change and antiretroviral therapy exposure in HIV-infected and uninfected Rwandan women: a longitudinal analysis.

PubMed

Mutimura, Eugene; Hoover, Donald R; Shi, Qiuhu; Dusingize, Jean Claude; Sinayobye, Jean D'Amour; Cohen, Mardge; Anastos, Kathryn

2015-01-01

We longitudinally assessed predictors of insulin resistance (IR) change among HIV-uninfected and HIV-infected (ART-initiators and ART-non-initiators) Rwandan women. HIV-infected (HIV+) and uninfected (HIV-) women provided demographic and clinical measures: age, body mass index (BMI) in Kg/(height in meters)2, Fat-Mass (FMI) and Fat-Free-Mass (FFMI) index, fasting serum glucose and insulin. Homeostasis Model Assessment (HOMA) was calculated to estimate IR change over time in log10 transformed HOMA measured at study enrollment or prior to ART initiation in 3 groups: HIV- (n = 194), HIV+ ART-non-initiators (n=95) and HIV+ ART-initiators (n=371). ANCOVA linear regression models of change in log10-HOMA were fit with all models included the first log10 HOMA as a predictor. Mean±SD log10-HOMA was -0.18±0.39 at the 1st and -0.21±0.41 at the 2nd measure, with mean change of 0.03±0.44. In the final model (all women) BMI at 1st HOMA measure (0.014; 95% CI=0.006-0.021 per kg/m2; p<0.001) and change in BMI from 1st to 2nd measure (0.024; 95% CI=0.013-0.035 per kg/m2; p<0.001) predicted HOMA change. When restricted to subjects with FMI measures, FMI at 1st HOMA measure (0.020; 95% CI=0.010-0.030 per kg/m2; p<0.001) and change in FMI from 1st to 2nd measure (0.032; 95% CI=0.020-0.043 per kg/m2; p<0.0001) predicted change in HOMA. While ART use did not predict change in log10-HOMA, untreated HIV+ women had a significant decline in IR over time. Use or duration of AZT, d4T and EFV was not associated with HOMA change in HIV+ women. Baseline BMI and change in BMI, and in particular fat mass and change in fat mass predicted insulin resistance change over ~3 years in HIV-infected and uninfected Rwandan women. Exposure to specific ART (d4T, AZT, EFV) did not predict insulin resistance change in ART-treated HIV-infected Rwandan women.

The accuracy of matrix population model projections for coniferous trees in the Sierra Nevada, California

USGS Publications Warehouse

van Mantgem, P.J.; Stephenson, N.L.

2005-01-01

1 We assess the use of simple, size-based matrix population models for projecting population trends for six coniferous tree species in the Sierra Nevada, California. We used demographic data from 16 673 trees in 15 permanent plots to create 17 separate time-invariant, density-independent population projection models, and determined differences between trends projected from initial surveys with a 5-year interval and observed data during two subsequent 5-year time steps. 2 We detected departures from the assumptions of the matrix modelling approach in terms of strong growth autocorrelations. We also found evidence of observation errors for measurements of tree growth and, to a more limited degree, recruitment. Loglinear analysis provided evidence of significant temporal variation in demographic rates for only two of the 17 populations. 3 Total population sizes were strongly predicted by model projections, although population dynamics were dominated by carryover from the previous 5-year time step (i.e. there were few cases of recruitment or death). Fractional changes to overall population sizes were less well predicted. Compared with a null model and a simple demographic model lacking size structure, matrix model projections were better able to predict total population sizes, although the differences were not statistically significant. Matrix model projections were also able to predict short-term rates of survival, growth and recruitment. Mortality frequencies were not well predicted. 4 Our results suggest that simple size-structured models can accurately project future short-term changes for some tree populations. However, not all populations were well predicted and these simple models would probably become more inaccurate over longer projection intervals. The predictive ability of these models would also be limited by disturbance or other events that destabilize demographic rates. ?? 2005 British Ecological Society.

Prediction of climate change in Brunei Darussalam using statistical downscaling model

NASA Astrophysics Data System (ADS)

Hasan, Dk. Siti Nurul Ain binti Pg. Ali; Ratnayake, Uditha; Shams, Shahriar; Nayan, Zuliana Binti Hj; Rahman, Ena Kartina Abdul

2017-06-01

Climate is changing and evidence suggests that the impact of climate change would influence our everyday lives, including agriculture, built environment, energy management, food security and water resources. Brunei Darussalam located within the heart of Borneo will be affected both in terms of precipitation and temperature. Therefore, it is crucial to comprehend and assess how important climate indicators like temperature and precipitation are expected to vary in the future in order to minimise its impact. This study assesses the application of a statistical downscaling model (SDSM) for downscaling General Circulation Model (GCM) results for maximum and minimum temperatures along with precipitation in Brunei Darussalam. It investigates future climate changes based on numerous scenarios using Hadley Centre Coupled Model, version 3 (HadCM3), Canadian Earth System Model (CanESM2) and third-generation Coupled Global Climate Model (CGCM3) outputs. The SDSM outputs were improved with the implementation of bias correction and also using a monthly sub-model instead of an annual sub-model. The outcomes of this assessment show that monthly sub-model performed better than the annual sub-model. This study indicates a satisfactory applicability for generation of maximum temperatures, minimum temperatures and precipitation for future periods of 2017-2046 and 2047-2076. All considered models and the scenarios were consistent in predicting increasing trend of maximum temperature, increasing trend of minimum temperature and decreasing trend of precipitations. Maximum overall trend of Tmax was also observed for CanESM2 with Representative Concentration Pathways (RCP) 8.5 scenario. The increasing trend is 0.014 °C per year. Accordingly, by 2076, the highest prediction of average maximum temperatures is that it will increase by 1.4 °C. The same model predicts an increasing trend of Tmin of 0.004 °C per year, while the highest trend is seen under CGCM3-A2 scenario which is 0.009 °C per year. The highest change predicted for the Tmin is therefore 0.9 °C by 2076. The precipitation showed a maximum trend of decrease of 12.7 mm year. It is also seen in the output using CanESM2 data that precipitation will be more chaotic with some reaching 4800 mm per year and also producing low rainfall about 1800 mm per year. All GCMs considered are consistent in predicting it is very likely that Brunei is expected to experience more warming as well as less frequent precipitation events but with a possibility of intensified and drastically high rainfalls in the future.

Predicted impacts of climate change on malaria transmission in West Africa

NASA Astrophysics Data System (ADS)

Yamana, T. K.; Eltahir, E. A. B.

2014-12-01

Increases in temperature and changes in precipitation due to climate change are expected to alter the spatial distribution of malaria transmission. This is especially true in West Africa, where malaria prevalence follows the current north-south gradients in temperature and precipitation. We assess the skill of GCMs at simulating past and present climate in West Africa in order to select the most credible climate predictions for the periods 2030-2060 and 2070-2100. We then use the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS), a mechanistic model of malaria transmission, to translate the predicted changes in climate into predicted changes availability of mosquito breeding sites, mosquito populations, and malaria prevalence. We investigate the role of acquired immunity in determining a population's response to changes in exposure to the malaria parasite.

Prediction of lung density changes after radiotherapy by cone beam computed tomography response markers and pre-treatment factors for non-small cell lung cancer patients.

PubMed

Bernchou, Uffe; Hansen, Olfred; Schytte, Tine; Bertelsen, Anders; Hope, Andrew; Moseley, Douglas; Brink, Carsten

2015-10-01

This study investigates the ability of pre-treatment factors and response markers extracted from standard cone-beam computed tomography (CBCT) images to predict the lung density changes induced by radiotherapy for non-small cell lung cancer (NSCLC) patients. Density changes in follow-up computed tomography scans were evaluated for 135 NSCLC patients treated with radiotherapy. Early response markers were obtained by analysing changes in lung density in CBCT images acquired during the treatment course. The ability of pre-treatment factors and CBCT markers to predict lung density changes induced by radiotherapy was investigated. Age and CBCT markers extracted at 10th, 20th, and 30th treatment fraction significantly predicted lung density changes in a multivariable analysis, and a set of response models based on these parameters were established. The correlation coefficient for the models was 0.35, 0.35, and 0.39, when based on the markers obtained at the 10th, 20th, and 30th fraction, respectively. The study indicates that younger patients without lung tissue reactions early into their treatment course may have minimal radiation induced lung density increase at follow-up. Further investigations are needed to examine the ability of the models to identify patients with low risk of symptomatic toxicity. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Predicting changes in volcanic activity through modelling magma ascent rate.

NASA Astrophysics Data System (ADS)

Thomas, Mark; Neuberg, Jurgen

2013-04-01

It is a simple fact that changes in volcanic activity happen and in retrospect they are easy to spot, the dissimilar eruption dynamics between an effusive and explosive event are not hard to miss. However to be able to predict such changes is a much more complicated process. To cause altering styles of activity we know that some part or combination of parts within the system must vary with time, as if there is no physical change within the system, why would the change in eruptive activity occur? What is unknown is which parts or how big a change is needed. We present the results of a suite of conduit flow models that aim to answer these questions by assessing the influence of individual model parameters such as the dissolved water content or magma temperature. By altering these variables in a systematic manner we measure the effect of the changes by observing the modelled ascent rate. We use the ascent rate as we believe it is a very important indicator that can control the style of eruptive activity. In particular, we found that the sensitivity of the ascent rate to small changes in model parameters surprising. Linking these changes to observable monitoring data in a way that these data could be used as a predictive tool is the ultimate goal of this work. We will show that changes in ascent rate can be estimated by a particular type of seismicity. Low frequency seismicity, thought to be caused by the brittle failure of melt is often linked with the movement of magma within a conduit. We show that acceleration in the rate of low frequency seismicity can correspond to an increase in the rate of magma movement and be used as an indicator for potential changes in eruptive activity.

The Rate of Seasonal Changes in Temperature Alters Acclimation of Performance under Climate Change.

PubMed

Nilsson-Örtman, Viktor; Johansson, Frank

2017-12-01

How the ability to acclimate will impact individual performance and ecological interactions under climate change remains poorly understood. Theory predicts that the benefit an organism can gain from acclimating depends on the rate at which temperatures change relative to the time it takes to induce beneficial acclimation. Here, we present a conceptual model showing how slower seasonal changes under climate change can alter species' relative performance when they differ in acclimation rate and magnitude. To test predictions from theory, we performed a microcosm experiment where we reared a mid- and a high-latitude damselfly species alone or together under the rapid seasonality currently experienced at 62°N and the slower seasonality predicted for this latitude under climate change and measured larval growth and survival. To separate acclimation effects from fixed thermal responses, we simulated growth trajectories based on species' growth rates at constant temperatures and quantified how much and how fast species needed to acclimate to match the observed growth trajectories. Consistent with our predictions, the results showed that the midlatitude species had a greater capacity for acclimation than the high-latitude species. Furthermore, since acclimation occurred at a slower rate than seasonal temperature changes, the midlatitude species had a small growth advantage over the high-latitude species under the current seasonality but a greater growth advantage under the slower seasonality predicted for this latitude under climate change. In addition, the two species did not differ in survival under the current seasonality, but the midlatitude species had higher survival under the predicted climate change scenario, possibly because rates of cannibalism were lower when smaller heterospecifics were present. These findings highlight the need to incorporate acclimation rates in ecological models.

Modeling the Climatic Consequences of Geoengineering

NASA Astrophysics Data System (ADS)

Somerville, R. C.

2005-12-01

The last half-century has seen the development of physically comprehensive computer models of the climate system. These models are the primary tool for making predictions of climate change due to human activities, such as emitting greenhouse gases into the atmosphere. Because scientific understanding of the climate system is incomplete, however, any climate model will necessarily have imperfections. The inevitable uncertainties associated with these models have sometimes been cited as reasons for not taking action to reduce such emissions. Climate models could certainly be employed to predict the results of various attempts at geoengineering, but many questions would arise. For example, in considering proposals to increase the planetary reflectivity by brightening parts of the land surface or by orbiting mirrors, can models be used to bound the results and to warm of unintended consequences? How could confidence limits be placed on such model results? How can climate changes due to proposed geoengineering be distinguished from natural variability? There are historical parallels on smaller scales, in which models have been employed to predict the results of attempts to alter the weather, such as the use of cloud seeding for precipitation enhancement, hail suppression and hurricane modification. However, there are also many lessons to be learned from the recent record of using models to simulate the effects of the great unintended geoengineering experiment involving greenhouse gases, now in progress. In this major research effort, the same types of questions have been studied at length. The best modern models have demonstrated an impressive ability to predict some aspects of climate change. A large body of evidence has already accumulated through comparing model predictions to many observed aspects of recent climate change, ranging from increases in ocean heat content to changes in atmospheric water vapor to reductions in glacier extent. The preponderance of expert opinion is that this evidence is now sufficient to establish the human cause of much recent climate change. Nevertheless, no model can provide detailed and fully trustworthy answers to every possible question of interest. As an example, how will the climatology of Atlantic hurricanes change as the greenhouse effect becomes stronger? Can models reliably forecast changes in the length of the hurricane season or changes in the geographical regions affected by hurricanes? The answer is no, or at least, not yet. Additionally, climate models are not based entirely on first principles, such as Newtonian physics. Instead, they have been developed primarily to simulate the present climate and relatively small departures from it. To achieve this goal, a certain amount of empiricism has been built into the models. The result has sometimes been to increase the apparent realism of models at the cost of limiting their generality. Thus, the available climate models may well be less capable of simulating a geoengineering experiment that might lead to a radically different climate. New model development may be required for this new application. The challenge is to distinguish between what models can and cannot do well. It would be irresponsible and unethical, either to undertake geoengineering projects without modeling their consequences, or to place blind faith in the models. To decide how best to model a proposed geoengineering technique requires a deep understanding of the strengths and weaknesses of climate models. The history of modeling successes and failures is a valuable guide to the wise interpretation of model results.

Potential of satellite-derived ecosystem functional attributes to anticipate species range shifts

NASA Astrophysics Data System (ADS)

Alcaraz-Segura, Domingo; Lomba, Angela; Sousa-Silva, Rita; Nieto-Lugilde, Diego; Alves, Paulo; Georges, Damien; Vicente, Joana R.; Honrado, João P.

2017-05-01

In a world facing rapid environmental changes, anticipating their impacts on biodiversity is of utmost relevance. Remotely-sensed Ecosystem Functional Attributes (EFAs) are promising predictors for Species Distribution Models (SDMs) by offering an early and integrative response of vegetation performance to environmental drivers. Species of high conservation concern would benefit the most from a better ability to anticipate changes in habitat suitability. Here we illustrate how yearly projections from SDMs based on EFAs could reveal short-term changes in potential habitat suitability, anticipating mid-term shifts predicted by climate-change-scenario models. We fitted two sets of SDMs for 41 plant species of conservation concern in the Iberian Peninsula: one calibrated with climate variables for baseline conditions and projected under two climate-change-scenarios (future conditions); and the other calibrated with EFAs for 2001 and projected annually from 2001 to 2013. Range shifts predicted by climate-based models for future conditions were compared to the 2001-2013 trends from EFAs-based models. Projections of EFAs-based models estimated changes (mostly contractions) in habitat suitability that anticipated, for the majority (up to 64%) of species, the mid-term shifts projected by traditional climate-change-scenario forecasting, and showed greater agreement with the business-as-usual scenario than with the sustainable-development one. This study shows how satellite-derived EFAs can be used as meaningful essential biodiversity variables in SDMs to provide early-warnings of range shifts and predictions of short-term fluctuations in suitable conditions for multiple species.

A new framework for climate sensitivity and prediction: a modelling perspective

NASA Astrophysics Data System (ADS)

Ragone, Francesco; Lucarini, Valerio; Lunkeit, Frank

2016-03-01

The sensitivity of climate models to increasing CO2 concentration and the climate response at decadal time-scales are still major factors of uncertainty for the assessment of the long and short term effects of anthropogenic climate change. While the relative slow progress on these issues is partly due to the inherent inaccuracies of numerical climate models, this also hints at the need for stronger theoretical foundations to the problem of studying climate sensitivity and performing climate change predictions with numerical models. Here we demonstrate that it is possible to use Ruelle's response theory to predict the impact of an arbitrary CO2 forcing scenario on the global surface temperature of a general circulation model. Response theory puts the concept of climate sensitivity on firm theoretical grounds, and addresses rigorously the problem of predictability at different time-scales. Conceptually, these results show that performing climate change experiments with general circulation models is a well defined problem from a physical and mathematical point of view. Practically, these results show that considering one single CO2 forcing scenario is enough to construct operators able to predict the response of climatic observables to any other CO2 forcing scenario, without the need to perform additional numerical simulations. We also introduce a general relationship between climate sensitivity and climate response at different time scales, thus providing an explicit definition of the inertia of the system at different time scales. This technique allows also for studying systematically, for a large variety of forcing scenarios, the time horizon at which the climate change signal (in an ensemble sense) becomes statistically significant. While what we report here refers to the linear response, the general theory allows for treating nonlinear effects as well. These results pave the way for redesigning and interpreting climate change experiments from a radically new perspective.

Dependence regulation in newlywed couples: A prospective examination.

PubMed

Derrick, Jaye L; Leonard, Kenneth E; Homish, Gregory G

2012-12-01

According to the Risk Regulation Model (Murray, S. L., Holmes, J. G., & Collins, N. L. (2006). Optimizing assurance: The risk regulation system in relationships. Psychological Bulletin, 132 , 641-666), people need to trust in their partner's regard before they risk interdependence. The current study prospectively examines the association between perceived regard and levels of dependence in newlywed couples over nine years of marriage. Analyses demonstrate that changes in perceived regard predict levels of dependence, changes in dependence do not predict perceived regard, and alternative explanations cannot account for these effects. Further, changes in perceived regard prospectively predict divorce, and levels of dependence mediate this association. Results are discussed in terms of the dependence regulation component of the Risk Regulation Model.

A new spatial multiple discrete-continuous modeling approach to land use change analysis.

DOT National Transportation Integrated Search

2013-09-01

This report formulates a multiple discrete-continuous probit (MDCP) land-use model within a : spatially explicit economic structural framework for land-use change decisions. The spatial : MDCP model is capable of predicting both the type and intensit...

Reconciling NOx emissions reductions and ozone trends in ...

EPA Pesticide Factsheets

Dynamic evaluation seeks to assess the ability of photochemical models to replicate changes in air quality as emissions and other conditions change. When a model fails to replicate an observed change, a key challenge is to discern whether the discrepancy is caused by errors in meteorological simulations, errors in emission magnitudes and changes, or inaccurate responses of simulated pollutant concentrations to emission changes. In this study, the Community Multiscale Air Quality (CMAQ) model is applied to simulate the ozone (O3) change after the NOx SIP Call and mobile emission controls substantially reduced nitrogen oxides (NOx) emissions in the eastern U.S. from 2002 to 2006. For both modeled and observed O3, changes in episode average daily maximal 8-h O3 were highly correlated (R2 = 0.89) with changes in the 95th percentile, although the magnitudes of reductions increased nonlinearly at high percentile O3 concentrations. Observed downward changes in mean NOx (−11.6 to −2.5 ppb) and 8-h O3 (−10.4 to −4.7 ppb) concentrations in metropolitan areas in the NOx SIP Call region were under-predicted by 31%–64% and 26%–66%, respectively. The under-predicted O3 improvements in the NOx SIP Call region could not be explained by adjusting for temperature biases in the meteorological input, or by considering uncertainties in the chemical reaction rate constants. However, the under-prediction in O3 improvements could be alleviated by 5%–31% by constraining NO

The Coherent Flame Model for Turbulent Chemical Reactions

DTIC Science & Technology

1977-01-01

numerical integration of the resulting differential equations. The model predicts the flame length and superficial comparison with experiments suggest a...value for the single universal constant. The theory correctly predicts the change of flame length with changes in stoich- iometric ratio for the...indicate the X will be some where between 0.1 and 0.5. Figure 13 is presented to show the effect of equivalence ratio, , on the flame length when the

Symbiont diversity may help coral reefs survive moderate climate change.

PubMed

Baskett, Marissa L; Gaines, Steven D; Nisbet, Roger M

2009-01-01

Given climate change, thermal stress-related mass coral-bleaching events present one of the greatest anthropogenic threats to coral reefs. While corals and their symbiotic algae may respond to future temperatures through genetic adaptation and shifts in community compositions, the climate may change too rapidly for coral response. To test this potential for response, here we develop a model of coral and symbiont ecological dynamics and symbiont evolutionary dynamics. Model results without variation in symbiont thermal tolerance predict coral reef collapse within decades under multiple future climate scenarios, consistent with previous threshold-based predictions. However, model results with genetic or community-level variation in symbiont thermal tolerance can predict coral reef persistence into the next century, provided low enough greenhouse gas emissions occur. Therefore, the level of greenhouse gas emissions will have a significant effect on the future of coral reefs, and accounting for biodiversity and biological dynamics is vital to estimating the size of this effect.

Potential effects of climate change on members of the Palaeotropical pitcher plant family Nepenthaceae.

PubMed

Gray, Laura K; Clarke, Charles; Wint, G R William; Moran, Jonathan A

2017-01-01

Anthropogenic climate change is predicted to have profound effects on species distributions over the coming decades. In this paper, we used maximum entropy modelling (Maxent) to estimate the effects of projected changes in climate on extent of climatically-suitable habitat for two Nepenthes pitcher plant species in Borneo. The model results predicted an increase in area of climatically-suitable habitat for the lowland species Nepenthes rafflesiana by 2100; in contrast, the highland species Nepenthes tentaculata was predicted to undergo significant loss of climatically-suitable habitat over the same period. Based on the results of the models, we recommend that research be undertaken into practical mitigation strategies, as approximately two-thirds of Nepenthes are restricted to montane habitats. Highland species with narrow elevational ranges will be at particularly high risk, and investigation into possible mitigation strategies should be focused on them.

Potential effects of climate change on members of the Palaeotropical pitcher plant family Nepenthaceae

PubMed Central

Gray, Laura K.; Clarke, Charles; Wint, G. R. William

2017-01-01

Anthropogenic climate change is predicted to have profound effects on species distributions over the coming decades. In this paper, we used maximum entropy modelling (Maxent) to estimate the effects of projected changes in climate on extent of climatically-suitable habitat for two Nepenthes pitcher plant species in Borneo. The model results predicted an increase in area of climatically-suitable habitat for the lowland species Nepenthes rafflesiana by 2100; in contrast, the highland species Nepenthes tentaculata was predicted to undergo significant loss of climatically-suitable habitat over the same period. Based on the results of the models, we recommend that research be undertaken into practical mitigation strategies, as approximately two-thirds of Nepenthes are restricted to montane habitats. Highland species with narrow elevational ranges will be at particularly high risk, and investigation into possible mitigation strategies should be focused on them. PMID:28817596

Structural changes and out-of-sample prediction of realized range-based variance in the stock market

NASA Astrophysics Data System (ADS)

Gong, Xu; Lin, Boqiang

2018-03-01

This paper aims to examine the effects of structural changes on forecasting the realized range-based variance in the stock market. Considering structural changes in variance in the stock market, we develop the HAR-RRV-SC model on the basis of the HAR-RRV model. Subsequently, the HAR-RRV and HAR-RRV-SC models are used to forecast the realized range-based variance of S&P 500 Index. We find that there are many structural changes in variance in the U.S. stock market, and the period after the financial crisis contains more structural change points than the period before the financial crisis. The out-of-sample results show that the HAR-RRV-SC model significantly outperforms the HAR-BV model when they are employed to forecast the 1-day, 1-week, and 1-month realized range-based variances, which means that structural changes can improve out-of-sample prediction of realized range-based variance. The out-of-sample results remain robust across the alternative rolling fixed-window, the alternative threshold value in ICSS algorithm, and the alternative benchmark models. More importantly, we believe that considering structural changes can help improve the out-of-sample performances of most of other existing HAR-RRV-type models in addition to the models used in this paper.

Forecasting climate change impacts on plant populations over large spatial extents

DOE PAGES

Tredennick, Andrew T.; Hooten, Mevin B.; Aldridge, Cameron L.; ...

2016-10-24

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. Here, we overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates inmore » the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Finally, our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.« less

Forecasting climate change impacts on plant populations over large spatial extents

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tredennick, Andrew T.; Hooten, Mevin B.; Aldridge, Cameron L.

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. Here, we overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates inmore » the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Finally, our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.« less

Forecasting climate change impacts on plant populations over large spatial extents

USGS Publications Warehouse

Tredennick, Andrew T.; Hooten, Mevin B.; Aldridge, Cameron L.; Homer, Collin G.; Kleinhesselink, Andrew R.; Adler, Peter B.

2016-01-01

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. We overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates in the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.

Characterizing and predicting species distributions across environments and scales: Argentine ant occurrences in the eye of the beholder

USGS Publications Warehouse

Menke, S.B.; Holway, D.A.; Fisher, R.N.; Jetz, W.

2009-01-01

Aim: Species distribution models (SDMs) or, more specifically, ecological niche models (ENMs) are a useful and rapidly proliferating tool in ecology and global change biology. ENMs attempt to capture associations between a species and its environment and are often used to draw biological inferences, to predict potential occurrences in unoccupied regions and to forecast future distributions under environmental change. The accuracy of ENMs, however, hinges critically on the quality of occurrence data. ENMs often use haphazardly collected data rather than data collected across the full spectrum of existing environmental conditions. Moreover, it remains unclear how processes affecting ENM predictions operate at different spatial scales. The scale (i.e. grain size) of analysis may be dictated more by the sampling regime than by biologically meaningful processes. The aim of our study is to jointly quantify how issues relating to region and scale affect ENM predictions using an economically important and ecologically damaging invasive species, the Argentine ant (Linepithema humile). Location: California, USA. Methods: We analysed the relationship between sampling sufficiency, regional differences in environmental parameter space and cell size of analysis and resampling environmental layers using two independently collected sets of presence/absence data. Differences in variable importance were determined using model averaging and logistic regression. Model accuracy was measured with area under the curve (AUC) and Cohen's kappa. Results: We first demonstrate that insufficient sampling of environmental parameter space can cause large errors in predicted distributions and biological interpretation. Models performed best when they were parametrized with data that sufficiently sampled environmental parameter space. Second, we show that altering the spatial grain of analysis changes the relative importance of different environmental variables. These changes apparently result from how environmental constraints and the sampling distributions of environmental variables change with spatial grain. Conclusions: These findings have clear relevance for biological inference. Taken together, our results illustrate potentially general limitations for ENMs, especially when such models are used to predict species occurrences in novel environments. We offer basic methodological and conceptual guidelines for appropriate sampling and scale matching. ?? 2009 The Authors Journal compilation ?? 2009 Blackwell Publishing.

Predicting the distributions of predator (snow leopard) and prey (blue sheep) under climate change in the Himalaya.

PubMed

Aryal, Achyut; Shrestha, Uttam Babu; Ji, Weihong; Ale, Som B; Shrestha, Sujata; Ingty, Tenzing; Maraseni, Tek; Cockfield, Geoff; Raubenheimer, David

2016-06-01

Future climate change is likely to affect distributions of species, disrupt biotic interactions, and cause spatial incongruity of predator-prey habitats. Understanding the impacts of future climate change on species distribution will help in the formulation of conservation policies to reduce the risks of future biodiversity losses. Using a species distribution modeling approach by MaxEnt, we modeled current and future distributions of snow leopard (Panthera uncia) and its common prey, blue sheep (Pseudois nayaur), and observed the changes in niche overlap in the Nepal Himalaya. Annual mean temperature is the major climatic factor responsible for the snow leopard and blue sheep distributions in the energy-deficient environments of high altitudes. Currently, about 15.32% and 15.93% area of the Nepal Himalaya are suitable for snow leopard and blue sheep habitats, respectively. The bioclimatic models show that the current suitable habitats of both snow leopard and blue sheep will be reduced under future climate change. The predicted suitable habitat of the snow leopard is decreased when blue sheep habitats is incorporated in the model. Our climate-only model shows that only 11.64% (17,190 km(2)) area of Nepal is suitable for the snow leopard under current climate and the suitable habitat reduces to 5,435 km(2) (reduced by 24.02%) after incorporating the predicted distribution of blue sheep. The predicted distribution of snow leopard reduces by 14.57% in 2030 and by 21.57% in 2050 when the predicted distribution of blue sheep is included as compared to 1.98% reduction in 2030 and 3.80% reduction in 2050 based on the climate-only model. It is predicted that future climate may alter the predator-prey spatial interaction inducing a lower degree of overlap and a higher degree of mismatch between snow leopard and blue sheep niches. This suggests increased energetic costs of finding preferred prey for snow leopards - a species already facing energetic constraints due to the limited dietary resources in its alpine habitat. Our findings provide valuable information for extension of protected areas in future.

Stress testing hydrologic models using bottom-up climate change assessment

NASA Astrophysics Data System (ADS)

Stephens, C.; Johnson, F.; Marshall, L. A.

2017-12-01

Bottom-up climate change assessment is a promising approach for understanding the vulnerability of a system to potential future changes. The technique has been utilised successfully in risk-based assessments of future flood severity and infrastructure vulnerability. We find that it is also an ideal tool for assessing hydrologic model performance in a changing climate. In this study, we applied bottom-up climate change to compare the performance of two different hydrologic models (an event-based and a continuous model) under increasingly severe climate change scenarios. This allowed us to diagnose likely sources of future prediction error in the two models. The climate change scenarios were based on projections for southern Australia, which indicate drier average conditions with increased extreme rainfall intensities. We found that the key weakness in using the event-based model to simulate drier future scenarios was the model's inability to dynamically account for changing antecedent conditions. This led to increased variability in model performance relative to the continuous model, which automatically accounts for the wetness of a catchment through dynamic simulation of water storages. When considering more intense future rainfall events, representation of antecedent conditions became less important than assumptions around (non)linearity in catchment response. The linear continuous model we applied may underestimate flood risk in a future climate with greater extreme rainfall intensity. In contrast with the recommendations of previous studies, this indicates that continuous simulation is not necessarily the key to robust flood modelling under climate change. By applying bottom-up climate change assessment, we were able to understand systematic changes in relative model performance under changing conditions and deduce likely sources of prediction error in the two models.

US forest response to projected climate-related stress: a tolerance perspective.

PubMed

Liénard, Jean; Harrison, John; Strigul, Nikolay

2016-08-01

Although it is widely recognized that climate change will require a major spatial reorganization of forests, our ability to predict exactly how and where forest characteristics and distributions will change has been rather limited. Current efforts to predict future distribution of forested ecosystems as a function of climate include species distribution models (for fine-scale predictions) and potential vegetation climate envelope models (for coarse-grained, large-scale predictions). Here, we develop and apply an intermediate approach wherein we use stand-level tolerances of environmental stressors to understand forest distributions and vulnerabilities to anticipated climate change. In contrast to other existing models, this approach can be applied at a continental scale while maintaining a direct link to ecologically relevant, climate-related stressors. We first demonstrate that shade, drought, and waterlogging tolerances of forest stands are strongly correlated with climate and edaphic conditions in the conterminous United States. This discovery allows the development of a tolerance distribution model (TDM), a novel quantitative tool to assess landscape level impacts of climate change. We then focus on evaluating the implications of the drought TDM. Using an ensemble of 17 climate change models to drive this TDM, we estimate that 18% of US ecosystems are vulnerable to drought-related stress over the coming century. Vulnerable areas include mostly the Midwest United States and Northeast United States, as well as high-elevation areas of the Rocky Mountains. We also infer stress incurred by shifting climate should create an opening for the establishment of forest types not currently seen in the conterminous United States. © 2016 John Wiley & Sons Ltd.

Predicting potential ranges of primary malaria vectors and malaria in northern South America based on projected changes in climate, land cover and human population.

PubMed

Alimi, Temitope O; Fuller, Douglas O; Qualls, Whitney A; Herrera, Socrates V; Arevalo-Herrera, Myriam; Quinones, Martha L; Lacerda, Marcus V G; Beier, John C

2015-08-20

Changes in land use and land cover (LULC) as well as climate are likely to affect the geographic distribution of malaria vectors and parasites in the coming decades. At present, malaria transmission is concentrated mainly in the Amazon basin where extensive agriculture, mining, and logging activities have resulted in changes to local and regional hydrology, massive loss of forest cover, and increased contact between malaria vectors and hosts. Employing presence-only records, bioclimatic, topographic, hydrologic, LULC and human population data, we modeled the distribution of malaria and two of its dominant vectors, Anopheles darlingi, and Anopheles nuneztovari s.l. in northern South America using the species distribution modeling platform Maxent. Results from our land change modeling indicate that about 70,000 km(2) of forest land would be lost by 2050 and 78,000 km(2) by 2070 compared to 2010. The Maxent model predicted zones of relatively high habitat suitability for malaria and the vectors mainly within the Amazon and along coastlines. While areas with malaria are expected to decrease in line with current downward trends, both vectors are predicted to experience range expansions in the future. Elevation, annual precipitation and temperature were influential in all models both current and future. Human population mostly affected An. darlingi distribution while LULC changes influenced An. nuneztovari s.l. distribution. As the region tackles the challenge of malaria elimination, investigations such as this could be useful for planning and management purposes and aid in predicting and addressing potential impediments to elimination.

Landscape models of brook trout abundance and distribution in lotic habitat with field validation

USGS Publications Warehouse

McKenna, James E.; Johnson, James H.

2011-01-01

Brook trout Salvelinus fontinalis are native fish in decline owing to environmental changes. Predictions of their potential distribution and a better understanding of their relationship to habitat conditions would enhance the management and conservation of this valuable species. We used over 7,800 brook trout observations throughout New York State and georeferenced, multiscale landscape condition data to develop four regionally specific artificial neural network models to predict brook trout abundance in rivers and streams. Land cover data provided a general signature of human activity, but other habitat variables were resistant to anthropogenic changes (i.e., changing on a geological time scale). The resulting models predict the potential for any stream to support brook trout. The models were validated by holding 20% of the data out as a test set and by comparison with additional field collections from a variety of habitat types. The models performed well, explaining more than 90% of data variability. Errors were often associated with small spatial displacements of predicted values. When compared with the additional field collections (39 sites), 92% of the predictions were off by only a single class from the field-observed abundances. Among “least-disturbed” field collection sites, all predictions were correct or off by a single abundance class, except for one where brown trout Salmo trutta were present. Other degrading factors were evident at most sites where brook trout were absent or less abundant than predicted. The most important habitat variables included landscape slope, stream and drainage network sizes, water temperature, and extent of forest cover. Predicted brook trout abundances were applied to all New York streams, providing a synoptic map of the distribution of brook trout habitat potential. These fish models set benchmarks of best potential for streams to support brook trout under broad-scale human influences and can assist with planning and identification of protection or rehabilitation sites.

Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

NASA Technical Reports Server (NTRS)

Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

2016-01-01

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

Measuring motivation using the transtheoretical (stages of change) model: A follow-up study of people who failed an online hearing screening.

PubMed

Ingo, Elisabeth; Brännström, K Jonas; Andersson, Gerhard; Lunner, Thomas; Laplante-Lévesque, Ariane

2016-07-01

Acceptance and readiness to seek professional help have shown to be important factors for favourable audiological rehabilitation outcomes. Theories from health psychology such as the transtheoretical (stages-of-change) model could help understand behavioural change in people with hearing impairment. In recent studies, the University of Rhode Island change assessment (URICA) has been found to have good predictive validity. In a previous study, 224 Swedish adults who had failed an online hearing screening completed URICA and two other measures of stages of change. This follow-up aimed to: (1) determine prevalence of help-seeking at a hearing clinic and hearing aid uptake, and (2) explore the predictive validity of the stages of change measures by a follow-up on the 224 participants who had failed a hearing screening 18 months previously. A total of 122 people (54%) completed the follow-up online questionnaire, including the three measures and questions regarding experience with hearing help-seeking and hearing aid uptake. Since failing the online hearing screening, 61% of participants had sought help. A good predictive validity for a one-item measure of stages of change was reported. The Staging algorithm was the stages of change measure with the best ability to predict help-seeking 18 months later.

Predicting landscape vegetation dynamics using state-and-transition simulation models

Treesearch

Colin J. Daniel; Leonardo Frid

2012-01-01

This paper outlines how state-and-transition simulation models (STSMs) can be used to project changes in vegetation over time across a landscape. STSMs are stochastic, empirical simulation models that use an adapted Markov chain approach to predict how vegetation will transition between states over time, typically in response to interactions between succession,...

Spatial models reveal the microclimatic buffering capacity of old-growth forests

Treesearch

Sarah J. K. Frey; Adam S. Hadley; Sherri L. Johnson; Mark Schulze; Julia A. Jones; Matthew. G. Betts

2016-01-01

Climate change is predicted to cause widespread declines in biodiversity, but these predictions are derived from coarse-resolution climate models applied at global scales. Such models lack the capacity to incorporate microclimate variability, which is critical to biodiversity microrefugia. In forested montane regions, microclimate is thought to be influenced by...

Investigation of an alternative generic model for predicting pharmacokinetic changes during physiological stress.

PubMed

Peng, Henry T; Edginton, Andrea N; Cheung, Bob

2013-10-01

Physiologically based pharmacokinetic models were developed using MATLAB Simulink® and PK-Sim®. We compared the capability and usefulness of these two models by simulating pharmacokinetic changes of midazolam under exercise and heat stress to verify the usefulness of MATLAB Simulink® as a generic PBPK modeling software. Although both models show good agreement with experimental data obtained under resting condition, their predictions of pharmacokinetics changes are less accurate in the stressful conditions. However, MATLAB Simulink® may be more flexible to include physiologically based processes such as oral absorption and simulate various stress parameters such as stress intensity, duration and timing of drug administration to improve model performance. Further work will be conducted to modify algorithms in our generic model developed using MATLAB Simulink® and to investigate pharmacokinetics under other physiological stress such as trauma. © The Author(s) 2013.

Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy

NASA Astrophysics Data System (ADS)

Kruse Christensen, Nikolaj; Ferre, Ty Paul A.; Fiandaca, Gianluca; Christensen, Steen

2017-03-01

We present a workflow for efficient construction and calibration of large-scale groundwater models that includes the integration of airborne electromagnetic (AEM) data and hydrological data. In the first step, the AEM data are inverted to form a 3-D geophysical model. In the second step, the 3-D geophysical model is translated, using a spatially dependent petrophysical relationship, to form a 3-D hydraulic conductivity distribution. The geophysical models and the hydrological data are used to estimate spatially distributed petrophysical shape factors. The shape factors primarily work as translators between resistivity and hydraulic conductivity, but they can also compensate for structural defects in the geophysical model. The method is demonstrated for a synthetic case study with sharp transitions among various types of deposits. Besides demonstrating the methodology, we demonstrate the importance of using geophysical regularization constraints that conform well to the depositional environment. This is done by inverting the AEM data using either smoothness (smooth) constraints or minimum gradient support (sharp) constraints, where the use of sharp constraints conforms best to the environment. The dependency on AEM data quality is also tested by inverting the geophysical model using data corrupted with four different levels of background noise. Subsequently, the geophysical models are used to construct competing groundwater models for which the shape factors are calibrated. The performance of each groundwater model is tested with respect to four types of prediction that are beyond the calibration base: a pumping well's recharge area and groundwater age, respectively, are predicted by applying the same stress as for the hydrologic model calibration; and head and stream discharge are predicted for a different stress situation. As expected, in this case the predictive capability of a groundwater model is better when it is based on a sharp geophysical model instead of a smoothness constraint. This is true for predictions of recharge area, head change, and stream discharge, while we find no improvement for prediction of groundwater age. Furthermore, we show that the model prediction accuracy improves with AEM data quality for predictions of recharge area, head change, and stream discharge, while there appears to be no accuracy improvement for the prediction of groundwater age.

Development of a Land Use Mapping and Monitoring Protocol for the High Plains Region: A Multitemporal Remote Sensing Application

NASA Technical Reports Server (NTRS)

Price, Kevin P.; Nellis, M. Duane

1996-01-01

The purpose of this project was to develop a practical protocol that employs multitemporal remotely sensed imagery, integrated with environmental parameters to model and monitor agricultural and natural resources in the High Plains Region of the United States. The value of this project would be extended throughout the region via workshops targeted at carefully selected audiences and designed to transfer remote sensing technology and the methods and applications developed. Implementation of such a protocol using remotely sensed satellite imagery is critical for addressing many issues of regional importance, including: (1) Prediction of rural land use/land cover (LULC) categories within a region; (2) Use of rural LULC maps for successive years to monitor change; (3) Crop types derived from LULC maps as important inputs to water consumption models; (4) Early prediction of crop yields; (5) Multi-date maps of crop types to monitor patterns related to crop change; (6) Knowledge of crop types to monitor condition and improve prediction of crop yield; (7) More precise models of crop types and conditions to improve agricultural economic forecasts; (8;) Prediction of biomass for estimating vegetation production, soil protection from erosion forces, nonpoint source pollution, wildlife habitat quality and other related factors; (9) Crop type and condition information to more accurately predict production of biogeochemicals such as CO2, CH4, and other greenhouse gases that are inputs to global climate models; (10) Provide information regarding limiting factors (i.e., economic constraints of pumping, fertilizing, etc.) used in conjunction with other factors, such as changes in climate for predicting changes in rural LULC; (11) Accurate prediction of rural LULC used to assess the effectiveness of government programs such as the U.S. Soil Conservation Service (SCS) Conservation Reserve Program; and (12) Prediction of water demand based on rural LULC that can be related to rates of draw-down of underground water supplies.

Climate change impact on seaweed meadow distribution in the North Atlantic rocky intertidal

PubMed Central

Jueterbock, Alexander; Tyberghein, Lennert; Verbruggen, Heroen; Coyer, James A; Olsen, Jeanine L; Hoarau, Galice

2013-01-01

The North-Atlantic has warmed faster than all other ocean basins and climate change scenarios predict sea surface temperature isotherms to shift up to 600 km northwards by the end of the 21st century. The pole-ward shift has already begun for many temperate seaweed species that are important intertidal foundation species. We asked the question: Where will climate change have the greatest impact on three foundational, macroalgal species that occur along North-Atlantic shores: Fucus serratus, Fucus vesiculosus, and Ascophyllum nodosum? To predict distributional changes of these key species under three IPCC (Intergovernmental Panel on Climate Change) climate change scenarios (A2, A1B, and B1) over the coming two centuries, we generated Ecological Niche Models with the program MAXENT. Model predictions suggest that these three species will shift northwards as an assemblage or “unit” and that phytogeographic changes will be most pronounced in the southern Arctic and the southern temperate provinces. Our models predict that Arctic shores in Canada, Greenland, and Spitsbergen will become suitable for all three species by 2100. Shores south of 45° North will become unsuitable for at least two of the three focal species on both the Northwest- and Northeast-Atlantic coasts by 2200. If these foundational species are unable to adapt to the rising temperatures, they will lose their centers of genetic diversity and their loss will trigger an unpredictable shift in the North-Atlantic intertidal ecosystem. PMID:23762521

Molecular Structure-Based Large-Scale Prediction of Chemical-Induced Gene Expression Changes.

PubMed

Liu, Ruifeng; AbdulHameed, Mohamed Diwan M; Wallqvist, Anders

2017-09-25

The quantitative structure-activity relationship (QSAR) approach has been used to model a wide range of chemical-induced biological responses. However, it had not been utilized to model chemical-induced genomewide gene expression changes until very recently, owing to the complexity of training and evaluating a very large number of models. To address this issue, we examined the performance of a variable nearest neighbor (v-NN) method that uses information on near neighbors conforming to the principle that similar structures have similar activities. Using a data set of gene expression signatures of 13 150 compounds derived from cell-based measurements in the NIH Library of Integrated Network-based Cellular Signatures program, we were able to make predictions for 62% of the compounds in a 10-fold cross validation test, with a correlation coefficient of 0.61 between the predicted and experimentally derived signatures-a reproducibility rivaling that of high-throughput gene expression measurements. To evaluate the utility of the predicted gene expression signatures, we compared the predicted and experimentally derived signatures in their ability to identify drugs known to cause specific liver, kidney, and heart injuries. Overall, the predicted and experimentally derived signatures had similar receiver operating characteristics, whose areas under the curve ranged from 0.71 to 0.77 and 0.70 to 0.73, respectively, across the three organ injury models. However, detailed analyses of enrichment curves indicate that signatures predicted from multiple near neighbors outperformed those derived from experiments, suggesting that averaging information from near neighbors may help improve the signal from gene expression measurements. Our results demonstrate that the v-NN method can serve as a practical approach for modeling large-scale, genomewide, chemical-induced, gene expression changes.

Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations

USGS Publications Warehouse

Gerber, Brian D.; Kendall, William L.; Hooten, Mevin B.; Dubovsky, James A.; Drewien, Roderick C.

2015-01-01

Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment.Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression.Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring–summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect.Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are likely not unique to cranes. The alteration of hydrological patterns and water levels by drought may impact many migratory, wetland nesting birds in the Rocky Mountains and beyond.Generalizable predictive models (trained by out-of-sample fit and based on ecological hypotheses) are needed by conservation and management decision-makers. Statistical regularization improves predictions and provides a general framework for fitting models with a large number of predictors, even those with collinearity, to simultaneously identify an optimal predictive model while conducting rigorous Bayesian model selection. Our framework is important for understanding population dynamics under a changing climate and has direct applications for making harvest and habitat management decisions.

Species-specific ecological niche modelling predicts different range contractions for Lutzomyia intermedia and a related vector of Leishmania braziliensis following climate change in South America.

PubMed

McIntyre, Shannon; Rangel, Elizabeth F; Ready, Paul D; Carvalho, Bruno M

2017-03-24

Before 1996 the phlebotomine sand fly Lutzomyia neivai was usually treated as a synonym of the morphologically similar Lutzomyia intermedia, which has long been considered a vector of Leishmania braziliensis, the causative agent of much cutaneous leishmaniasis in South America. This report investigates the likely range changes of both sand fly species in response to a stabilisation climate change scenario (RCP4.5) and a high greenhouse gas emissions one (RCP8.5). Ecological niche modelling was used to identify areas of South America with climates currently suitable for each species, and then the future distributions of these climates were predicted based on climate change scenarios. Compared with the previous ecological niche model of L. intermedia (sensu lato) produced using the GARP algorithm in 2003, the current investigation modelled the two species separately, making use of verified presence records and additional records after 2001. Also, the new ensemble approach employed ecological niche modelling algorithms (including Maximum Entropy, Random Forests and Support Vector Machines) that have been widely adopted since 2003 and perform better than GARP, as well as using a more recent climate change model (HadGEM2) considered to have better performance at higher resolution than the earlier one (HadCM2). Lutzomyia intermedia was shown to be the more tropical of the two species, with its climatic niche defined by higher annual mean temperatures and lower temperature seasonality, in contrast to the more subtropical L. neivai. These different latitudinal ranges explain the two species' predicted responses to climate change by 2050, with L. intermedia mostly contracting its range (except perhaps in northeast Brazil) and L. neivai mostly shifting its range southwards in Brazil and Argentina. This contradicts the findings of the 2003 report, which predicted more range expansion. The different findings can be explained by the improved data sets and modelling methods. Our findings indicate that climate change will not always lead to range expansion of disease vectors such as sand flies. Ecological niche models should be species specific, carefully selected and combined in an ensemble approach.

Insights on the impact of systematic model errors on data assimilation performance in changing catchments

NASA Astrophysics Data System (ADS)

Pathiraja, S.; Anghileri, D.; Burlando, P.; Sharma, A.; Marshall, L.; Moradkhani, H.

2018-03-01

The global prevalence of rapid and extensive land use change necessitates hydrologic modelling methodologies capable of handling non-stationarity. This is particularly true in the context of Hydrologic Forecasting using Data Assimilation. Data Assimilation has been shown to dramatically improve forecast skill in hydrologic and meteorological applications, although such improvements are conditional on using bias-free observations and model simulations. A hydrologic model calibrated to a particular set of land cover conditions has the potential to produce biased simulations when the catchment is disturbed. This paper sheds new light on the impacts of bias or systematic errors in hydrologic data assimilation, in the context of forecasting in catchments with changing land surface conditions and a model calibrated to pre-change conditions. We posit that in such cases, the impact of systematic model errors on assimilation or forecast quality is dependent on the inherent prediction uncertainty that persists even in pre-change conditions. Through experiments on a range of catchments, we develop a conceptual relationship between total prediction uncertainty and the impacts of land cover changes on the hydrologic regime to demonstrate how forecast quality is affected when using state estimation Data Assimilation with no modifications to account for land cover changes. This work shows that systematic model errors as a result of changing or changed catchment conditions do not always necessitate adjustments to the modelling or assimilation methodology, for instance through re-calibration of the hydrologic model, time varying model parameters or revised offline/online bias estimation.

Modeling potential hydrochemical responses to climate change and rising CO2 at the Hubbard Brook Experimental Forest using a dynamic biogeochemical model (PnET-BGC)

Treesearch

Afshin Pourmokhtarian; Charles T. Driscoll; John L. Campbell; Katharine Hayhoe

2012-01-01

Dynamic hydrochemical models are useful tools for understanding and predicting the interactive effects of climate change, atmospheric CO2, and atmospheric deposition on the hydrology and water quality of forested watersheds. We used the biogeochemical model, PnET-BGC, to evaluate the effects of potential future changes in temperature,...

Family nonuniversal Z' models with protected flavor-changing interactions

NASA Astrophysics Data System (ADS)

Celis, Alejandro; Fuentes-Martín, Javier; Jung, Martin; Serôdio, Hugo

2015-07-01

We define a new class of Z' models with neutral flavor-changing interactions at tree level in the down-quark sector. They are related in an exact way to elements of the quark mixing matrix due to an underlying flavored U(1)' gauge symmetry, rendering these models particularly predictive. The same symmetry implies lepton-flavor nonuniversal couplings, fully determined by the gauge structure of the model. Our models allow us to address presently observed deviations from the standard model and specific correlations among the new physics contributions to the Wilson coefficients C9,10' ℓ can be tested in b →s ℓ+ℓ- transitions. We furthermore predict lepton-universality violations in Z' decays, testable at the LHC.

Computational model of in vivo human energy metabolism during semi-starvation and re-feeding

PubMed Central

Hall, Kevin D.

2008-01-01

Changes of body weight and composition are the result of complex interactions among metabolic fluxes contributing to macronutrient balances. To better understand these interactions, a mathematical model was constructed that used the measured dietary macronutrient intake during semi-starvation and re-feeding as model inputs and computed whole-body energy expenditure, de novo lipogenesis, gluconeogenesis, as well as turnover and oxidation of carbohydrate, fat and protein. Published in vivo human data provided the basis for the model components which were integrated by fitting a few unknown parameters to the classic Minnesota human starvation experiment. The model simulated the measured body weight and fat mass changes during semi-starvation and re-feeding and predicted the unmeasured metabolic fluxes underlying the body composition changes. The resting metabolic rate matched the experimental measurements and required a model of adaptive thermogenesis. Re-feeding caused an elevation of de novo lipogenesis which, along with increased fat intake, resulted in a rapid repletion and overshoot of body fat. By continuing the computer simulation with the pre-starvation diet and physical activity, the original body weight and composition was eventually restored, but body fat mass was predicted to take more than one additional year to return to within 5% of its original value. The model was validated by simulating a recently published short-term caloric restriction experiment without changing the model parameters. The predicted changes of body weight, fat mass, resting metabolic rate, and nitrogen balance matched the experimental measurements thereby providing support for the validity of the model. PMID:16449298

Young Women’s Dynamic Family Size Preferences in the Context of Transitioning Fertility

PubMed Central

Yeatman, Sara; Sennott, Christie; Culpepper, Steven

2013-01-01

Dynamic theories of family size preferences posit that they are not a fixed and stable goal but rather are akin to a moving target that changes within individuals over time. Nonetheless, in high-fertility contexts, changes in family size preferences tend to be attributed to low construct validity and measurement error instead of genuine revisions in preferences. To address the appropriateness of this incongruity, the present study examines evidence for the sequential model of fertility among a sample of young Malawian women living in a context of transitioning fertility. Using eight waves of closely spaced data and fixed-effects models, we find that these women frequently change their reported family size preferences and that these changes are often associated with changes in their relationship and reproductive circumstances. The predictability of change gives credence to the argument that ideal family size is a meaningful construct, even in this higher-fertility setting. Changes are not equally predictable across all women, however, and gamma regression results demonstrate that women for whom reproduction is a more distant goal change their fertility preferences in less-predictable ways. PMID:23619999

Young women's dynamic family size preferences in the context of transitioning fertility.

PubMed

Yeatman, Sara; Sennott, Christie; Culpepper, Steven

2013-10-01

Dynamic theories of family size preferences posit that they are not a fixed and stable goal but rather are akin to a moving target that changes within individuals over time. Nonetheless, in high-fertility contexts, changes in family size preferences tend to be attributed to low construct validity and measurement error instead of genuine revisions in preferences. To address the appropriateness of this incongruity, the present study examines evidence for the sequential model of fertility among a sample of young Malawian women living in a context of transitioning fertility. Using eight waves of closely spaced data and fixed-effects models, we find that these women frequently change their reported family size preferences and that these changes are often associated with changes in their relationship and reproductive circumstances. The predictability of change gives credence to the argument that ideal family size is a meaningful construct, even in this higher-fertility setting. Changes are not equally predictable across all women, however, and gamma regression results demonstrate that women for whom reproduction is a more distant goal change their fertility preferences in less-predictable ways.

Preference, resistance to change, and the cumulative decision model.

PubMed

Grace, Randolph C

2018-01-01

According to behavioral momentum theory (Nevin & Grace, 2000a), preference in concurrent chains and resistance to change in multiple schedules are independent measures of a common construct representing reinforcement history. Here I review the original studies on preference and resistance to change in which reinforcement variables were manipulated parametrically, conducted by Nevin, Grace and colleagues between 1997 and 2002, as well as more recent research. The cumulative decision model proposed by Grace and colleagues for concurrent chains is shown to provide a good account of both preference and resistance to change, and is able to predict the increased sensitivity to reinforcer rate and magnitude observed with constant-duration components. Residuals from fits of the cumulative decision model to preference and resistance to change data were positively correlated, supporting the prediction of behavioral momentum theory. Although some questions remain, the learning process assumed by the cumulative decision model, in which outcomes are compared against a criterion that represents the average outcome value in the current context, may provide a plausible model for the acquisition of differential resistance to change. © 2018 Society for the Experimental Analysis of Behavior.

A growing degree-day model for determination of Fasciola hepatica infection risk in New Zealand with future predictions using climate change models.

PubMed

Haydock, L A J; Pomroy, W E; Stevenson, M A; Lawrence, K E

2016-09-15

Infections of ruminants with Fasciola hepatica are considered to be of regional importance within New Zealand but there is very little recent information on its prevalence or severity other than anecdotal reports. Generally they are considered to be of secondary importance compared to gastrointestinal nematode infections. Utilizing data from Virtual Climate Stations (n=11491) distributed on a 5km grid around New Zealand a growing degree-day model was used to describe the risk of infection with liver fluke from 1972 to 2012 and then to apply the predictions to estimate the risk of fluke infections within New Zealand for the years 2040 and 2090. The growing degree-day model was validated against the most recent survey of infection within New Zealand in 1984. A strong positive linear relationship for 1984 between F. hepatica prevalence in lambs and infection risk (p<0.001; R 2 =0.71) was found indicating the model was effective for New Zealand. A linear regression for risk values from 14 regions in New Zealand for 1972-2012 did not show any discernible change in risk of infection over this time period (p>0.05). Post-hoc comparisons indicate the risk in Westland was found to be substantially higher (p<0.05) than all other regions with Northland ranked second highest. Notable predicted changes in F. hepatica infection risk in 2040 and 2090 were detected although they did vary between different climate change scenarios. The highest average percentage changes in infection risk were found in regions with low initial risk values such as Canterbury and Otago; in these regions 2090 infection risk is expected to rise by an average of 186% and 184%, respectively. Despite the already high levels of infection risk in Westland, values are expected to rise by a further 76% by 2090. The model does show some areas with little change with Taranaki predicted to experience only very minor increases in infection risk with average 2040 and 2090 predicted changes of 0% and 29%, respectively. Overall, these results suggest the significance of F. hepatica in New Zealand farming systems is probably underestimated and that this risk will generally increase with global warming following climate change. Copyright © 2016 Elsevier B.V. All rights reserved.

Biotic responses buffer warming-induced soil organic carbon loss in Arctic tundra.

PubMed

Liang, Junyi; Xia, Jiangyang; Shi, Zheng; Jiang, Lifen; Ma, Shuang; Lu, Xingjie; Mauritz, Marguerite; Natali, Susan M; Pegoraro, Elaine; Penton, C Ryan; Plaza, César; Salmon, Verity G; Celis, Gerardo; Cole, James R; Konstantinidis, Konstantinos T; Tiedje, James M; Zhou, Jizhong; Schuur, Edward A G; Luo, Yiqi

2018-05-26

Climate warming can result in both abiotic (e.g., permafrost thaw) and biotic (e.g., microbial functional genes) changes in Arctic tundra. Recent research has incorporated dynamic permafrost thaw in Earth system models (ESMs) and indicates that Arctic tundra could be a significant future carbon (C) source due to the enhanced decomposition of thawed deep soil C. However, warming-induced biotic changes may influence biologically related parameters and the consequent projections in ESMs. How model parameters associated with biotic responses will change under warming and to what extent these changes affect projected C budgets have not been carefully examined. In this study, we synthesized six data sets over five years from a soil warming experiment at the Eight Mile Lake, Alaska, into the Terrestrial ECOsystem (TECO) model with a probabilistic inversion approach. The TECO model used multiple soil layers to track dynamics of thawed soil under different treatments. Our results show that warming increased light use efficiency of vegetation photosynthesis but decreased baseline (i.e., environment-corrected) turnover rates of SOC in both the fast and slow pools in comparison with those under control. Moreover, the parameter changes generally amplified over time, suggesting processes of gradual physiological acclimation and functional gene shifts of both plants and microbes. The TECO model predicted that field warming from 2009 to 2013 resulted in cumulative C losses of 224 or 87 g m -2 , respectively, without or with changes in those parameters. Thus, warming-induced parameter changes reduced predicted soil C loss by 61%. Our study suggests that it is critical to incorporate biotic changes in ESMs to improve the model performance in predicting C dynamics in permafrost regions. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Tracing the influence of land-use change on water quality and coral reefs using a Bayesian model.

PubMed

Brown, Christopher J; Jupiter, Stacy D; Albert, Simon; Klein, Carissa J; Mangubhai, Sangeeta; Maina, Joseph M; Mumby, Peter; Olley, Jon; Stewart-Koster, Ben; Tulloch, Vivitskaia; Wenger, Amelia

2017-07-06

Coastal ecosystems can be degraded by poor water quality. Tracing the causes of poor water quality back to land-use change is necessary to target catchment management for coastal zone management. However, existing models for tracing the sources of pollution require extensive data-sets which are not available for many of the world's coral reef regions that may have severe water quality issues. Here we develop a hierarchical Bayesian model that uses freely available satellite data to infer the connection between land-uses in catchments and water clarity in coastal oceans. We apply the model to estimate the influence of land-use change on water clarity in Fiji. We tested the model's predictions against underwater surveys, finding that predictions of poor water quality are consistent with observations of high siltation and low coverage of sediment-sensitive coral genera. The model thus provides a means to link land-use change to declines in coastal water quality.

Feasibility of dynamic risk prediction for hepatocellular carcinoma development in patients with chronic hepatitis B.

PubMed

Jeon, Mi Young; Lee, Hye Won; Kim, Seung Up; Kim, Beom Kyung; Park, Jun Yong; Kim, Do Young; Han, Kwang-Hyub; Ahn, Sang Hoon

2018-04-01

Several risk prediction models for hepatocellular carcinoma (HCC) development are available. We explored whether the use of risk prediction models can dynamically predict HCC development at different time points in chronic hepatitis B (CHB) patients. Between 2006 and 2014, 1397 CHB patients were recruited. All patients underwent serial transient elastography at intervals of >6 months. The median age of this study population (931 males and 466 females) was 49.0 years. The median CU-HCC, REACH-B, LSM-HCC and mREACH-B score at enrolment were 4.0, 9.0, 10.0 and 8.0 respectively. During the follow-up period (median, 68.0 months), 87 (6.2%) patients developed HCC. All risk prediction models were successful in predicting HCC development at both the first liver stiffness (LS) measurement (hazard ratio [HR] = 1.067-1.467 in the subgroup without antiviral therapy [AVT] and 1.096-1.458 in the subgroup with AVT) and second LS measurement (HR = 1.125-1.448 in the subgroup without AVT and 1.087-1.249 in the subgroup with AVT). In contrast, neither the absolute nor percentage change in the scores from the risk prediction models predicted HCC development (all P > .05). The mREACH-B score performed similarly or significantly better than did the other scores (AUROCs at 5 years, 0.694-0.862 vs 0.537-0.875). Dynamic prediction of HCC development at different time points was achieved using four risk prediction models, but not using the changes in the absolute and percentage values between two time points. The mREACH-B score was the most appropriate prediction model of HCC development among four prediction models. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Comparison of propofol pharmacokinetic and pharmacodynamic models for awake craniotomy: A prospective observational study.

PubMed

Soehle, Martin; Wolf, Christina F; Priston, Melanie J; Neuloh, Georg; Bien, Christian G; Hoeft, Andreas; Ellerkmann, Richard K

2015-08-01

Anaesthesia for awake craniotomy aims for an unconscious patient at the beginning and end of surgery but a rapidly awakening and responsive patient during the awake period. Therefore, an accurate pharmacokinetic/pharmacodynamic (PK/PD) model for propofol is required to tailor depth of anaesthesia. To compare the predictive performances of the Marsh and the Schnider PK/PD models during awake craniotomy. A prospective observational study. Single university hospital from February 2009 to May 2010. Twelve patients undergoing elective awake craniotomy for resection of brain tumour or epileptogenic areas. Arterial blood samples were drawn at intervals and the propofol plasma concentration was determined. The prediction error, bias [median prediction error (MDPE)] and inaccuracy [median absolute prediction error (MDAPE)] of the Marsh and the Schnider models were calculated. The secondary endpoint was the prediction probability PK, by which changes in the propofol effect-site concentration (as derived from simultaneous PK/PD modelling) predicted changes in anaesthetic depth (measured by the bispectral index). The Marsh model was associated with a significantly (P = 0.05) higher inaccuracy (MDAPE 28.9 ± 12.0%) than the Schnider model (MDAPE 21.5 ± 7.7%) and tended to reach a higher bias (MDPE Marsh -11.7 ± 14.3%, MDPE Schnider -5.4 ± 20.7%, P = 0.09). MDAPE was outside of accepted limits in six (Marsh model) and two (Schnider model) of 12 patients. The prediction probability was comparable between the Marsh (PK 0.798 ± 0.056) and the Schnider model (PK 0.787 ± 0.055), but after adjusting the models to each individual patient, the Schnider model achieved significantly higher prediction probabilities (PK 0.807 ± 0.056, P = 0.05). When using the 'asleep-awake-asleep' anaesthetic technique during awake craniotomy, we advocate using the PK/PD model proposed by Schnider. Due to considerable interindividual variation, additional monitoring of anaesthetic depth is recommended. ClinicalTrials.gov identifier: NCT 01128465.

Moving on from rigid plant stoichiometry: Optimal canopy nitrogen allocation within a novel land surface model

NASA Astrophysics Data System (ADS)

Caldararu, S.; Kern, M.; Engel, J.; Zaehle, S.

2016-12-01

Despite recent advances in global vegetation models, we still lack the capacity to predict observed vegetation responses to experimental environmental changes such as elevated CO2, increased temperature or nutrient additions. In particular for elevated CO2 (FACE) experiments, studies have shown that this is related in part to the models' inability to represent plastic changes in nutrient use and biomass allocation. We present a newly developed vegetation model which aims to overcome these problems by including optimality processes to describe nitrogen (N) and carbon allocation within the plant. We represent nitrogen allocation to the canopy and within the canopy between photosynthetic components as an optimal processes which aims to maximize net primary production (NPP) of the plant. We also represent biomass investment into aboveground and belowground components (root nitrogen uptake , biological N fixation) as an optimal process that maximizes plant growth by considering plant carbon and nutrient demands as well as acquisition costs. The model can now represent plastic changes in canopy N content and chlorophyll and Rubisco concentrations as well as in belowground allocation both on seasonal and inter-annual time scales. Specifically, we show that under elevated CO2 conditions, the model predicts a lower optimal leaf N concentration, which, combined with a redistribution of leaf N between the Rubisco and chlorophyll components, leads to a continued NPP response under high CO2, where models with a fixed canopy stoichiometry would predicts a quick onset of N limitation. In general, our model aims to include physiologically-based plant processes and avoid arbitrarily imposed parameters and thresholds in order to improve our predictive capability of vegetation responses under changing environmental conditions.

Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

NASA Astrophysics Data System (ADS)

Quinn Thomas, R.; Brooks, Evan B.; Jersild, Annika L.; Ward, Eric J.; Wynne, Randolph H.; Albaugh, Timothy J.; Dinon-Aldridge, Heather; Burkhart, Harold E.; Domec, Jean-Christophe; Fox, Thomas R.; Gonzalez-Benecke, Carlos A.; Martin, Timothy A.; Noormets, Asko; Sampson, David A.; Teskey, Robert O.

2017-07-01

Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model-data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO2) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6 × 105 km2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO2, decreased precipitation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.

The fate of threatened coastal dune habitats in Italy under climate change scenarios.

PubMed

Prisco, Irene; Carboni, Marta; Acosta, Alicia T R

2013-01-01

Coastal dunes worldwide harbor threatened habitats characterized by high diversity in terms of plant communities. In Italy, recent assessments have highlighted the insufficient state of conservation of these habitats as defined by the EU Habitats Directive. The effects of predicted climate change could have dramatic consequences for coastal environments in the near future. An assessment of the efficacy of protection measures under climate change is thus a priority. Here, we have developed environmental envelope models for the most widespread dune habitats in Italy, following two complementary approaches: an "indirect" plant-species-based one and a simple "direct" one. We analyzed how habitats distribution will be altered under the effects of two climate change scenarios and evaluated if the current Italian network of protected areas will be effective in the future after distribution shifts. While modeling dune habitats with the "direct" approach was unsatisfactory, "indirect" models had a good predictive performance, highlighting the importance of using species' responses to climate change for modeling these habitats. The results showed that habitats closer to the sea may even increase their geographical distribution in the near future. The transition dune habitat is projected to remain stable, although mobile and fixed dune habitats are projected to lose most of their actual geographical distribution, the latter being more sensitive to climate change effects. Gap analysis highlighted that the habitats' distribution is currently adequately covered by protected areas, achieving the conservation target. However, according to predictions, protection level for mobile and fixed dune habitats is predicted to drop drastically under the climate change scenarios which we examined. Our results provide useful insights for setting management priorities and better addressing conservation efforts to preserve these threatened habitats in future.

The Fate of Threatened Coastal Dune Habitats in Italy under Climate Change Scenarios

PubMed Central

Prisco, Irene; Carboni, Marta; Acosta, Alicia T. R.

2013-01-01

Coastal dunes worldwide harbor threatened habitats characterized by high diversity in terms of plant communities. In Italy, recent assessments have highlighted the insufficient state of conservation of these habitats as defined by the EU Habitats Directive. The effects of predicted climate change could have dramatic consequences for coastal environments in the near future. An assessment of the efficacy of protection measures under climate change is thus a priority. Here, we have developed environmental envelope models for the most widespread dune habitats in Italy, following two complementary approaches: an “indirect” plant-species-based one and a simple “direct” one. We analyzed how habitats distribution will be altered under the effects of two climate change scenarios and evaluated if the current Italian network of protected areas will be effective in the future after distribution shifts. While modeling dune habitats with the “direct” approach was unsatisfactory, “indirect” models had a good predictive performance, highlighting the importance of using species’ responses to climate change for modeling these habitats. The results showed that habitats closer to the sea may even increase their geographical distribution in the near future. The transition dune habitat is projected to remain stable, although mobile and fixed dune habitats are projected to lose most of their actual geographical distribution, the latter being more sensitive to climate change effects. Gap analysis highlighted that the habitats’ distribution is currently adequately covered by protected areas, achieving the conservation target. However, according to predictions, protection level for mobile and fixed dune habitats is predicted to drop drastically under the climate change scenarios which we examined. Our results provide useful insights for setting management priorities and better addressing conservation efforts to preserve these threatened habitats in future. PMID:23874787

The dynamics of bedrock channel adjustment: Modeling the influence of sediment supply, weathering, and lithology on channel cross-sectional and longitudinal shape

NASA Astrophysics Data System (ADS)

Wobus, C.; Tucker, G.; Anderson, R.; Kean, J.; Small, E.; Hancock, G.

2007-12-01

The cross-sectional form of a natural river channel controls the capacity of the system to carry water off a landscape, to convey sediment derived from hillslopes, and to erode its bed and banks. Numerical models that describe the response of a landscape to changes in climate or tectonics therefore require formulations that can accommodate changes in channel cross-sectional geometry through time. We have developed a 2D numerical model that computes the formation of a channel in a cohesive, detachment-limited substrate subject to steady, unidirectional flow. Boundary shear stress is calculated using a simple approximation of the flow field in which log-velocity profiles are assumed to apply along vectors that are perpendicular to the local boundary surface. The resulting model predictions for the velocity structure, peak boundary shear stress, and equilibrium channel shape compare well with the predictions of a more sophisticated but more computationally demanding ray-isovel model. For example, the mean velocities computed by the two models are consistent to within ~3%, and the predicted peak shear stress is consistent to within ~7%. The efficiency of our model makes it suitable for calculations of long-term morphologic change both in single cross-sections and in series of cross-sections arrayed downstream. For a uniform substrate, the model predicts a strong tendency toward a fixed width-to-depth ratio, regardless of gradient or discharge. The model predicts power-law relationships between width and discharge with an exponent near 2/5, and between width and gradient with an exponent near -1/5. Recent enhancements to the model include the addition of sediment, which increases the width-to-depth ratio at steady state by favoring erosion of the channel walls relative to the channel bed (the "cover effect"). Inclusion of a probability density function of discharges with a simple parameterization of weathering along channel banks leads to the formation of model strath terraces. Downstream changes in substrate erodibility or tectonic uplift rate lead to step-function changes in channel width, consistent with empirical observations. Finally, explicit inclusion of bedload transport allows channel width, gradient, and the pattern of sediment flux to evolve dynamically, allowing us to explore the response of bedrock channels to both spatial patterns of rock uplift, and temporal variations in sediment input.

Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM). I: Model intercomparison with current land use

USGS Publications Warehouse

Breuer, L.; Huisman, J.A.; Willems, P.; Bormann, H.; Bronstert, A.; Croke, B.F.W.; Frede, H.-G.; Graff, T.; Hubrechts, L.; Jakeman, A.J.; Kite, G.; Lanini, J.; Leavesley, G.; Lettenmaier, D.P.; Lindstrom, G.; Seibert, J.; Sivapalan, M.; Viney, N.R.

2009-01-01

This paper introduces the project on 'Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM)' that aims at investigating the envelope of predictions on changes in hydrological fluxes due to land use change. As part of a series of four papers, this paper outlines the motivation and setup of LUCHEM, and presents a model intercomparison for the present-day simulation results. Such an intercomparison provides a valuable basis to investigate the effects of different model structures on model predictions and paves the ground for the analysis of the performance of multi-model ensembles and the reliability of the scenario predictions in companion papers. In this study, we applied a set of 10 lumped, semi-lumped and fully distributed hydrological models that have been previously used in land use change studies to the low mountainous Dill catchment, Germany. Substantial differences in model performance were observed with Nash-Sutcliffe efficiencies ranging from 0.53 to 0.92. Differences in model performance were attributed to (1) model input data, (2) model calibration and (3) the physical basis of the models. The models were applied with two sets of input data: an original and a homogenized data set. This homogenization of precipitation, temperature and leaf area index was performed to reduce the variation between the models. Homogenization improved the comparability of model simulations and resulted in a reduced average bias, although some variation in model data input remained. The effect of the physical differences between models on the long-term water balance was mainly attributed to differences in how models represent evapotranspiration. Semi-lumped and lumped conceptual models slightly outperformed the fully distributed and physically based models. This was attributed to the automatic model calibration typically used for this type of models. Overall, however, we conclude that there was no superior model if several measures of model performance are considered and that all models are suitable to participate in further multi-model ensemble set-ups and land use change scenario investigations. ?? 2008 Elsevier Ltd. All rights reserved.

Modelling obesity trends in Australia: unravelling the past and predicting the future.

PubMed

Hayes, A J; Lung, T W C; Bauman, A; Howard, K

2017-01-01

Modelling is increasingly being used to predict the epidemiology of obesity progression and its consequences. The aims of this study were: (a) to present and validate a model for prediction of obesity among Australian adults and (b) to use the model to project the prevalence of obesity and severe obesity by 2025. Individual level simulation combined with survey estimation techniques to model changing population body mass index (BMI) distribution over time. The model input population was derived from a nationally representative survey in 1995, representing over 12 million adults. Simulations were run for 30 years. The model was validated retrospectively and then used to predict obesity and severe obesity by 2025 among different aged cohorts and at a whole population level. The changing BMI distribution over time was well predicted by the model and projected prevalence of weight status groups agreed with population level data in 2008, 2012 and 2014.The model predicts more growth in obesity among younger than older adult cohorts. Projections at a whole population level, were that healthy weight will decline, overweight will remain steady, but obesity and severe obesity prevalence will continue to increase beyond 2016. Adult obesity prevalence was projected to increase from 19% in 1995 to 35% by 2025. Severe obesity (BMI>35), which was only around 5% in 1995, was projected to be 13% by 2025, two to three times the 1995 levels. The projected rise in obesity severe obesity will have more substantial cost and healthcare system implications than in previous decades. Having a robust epidemiological model is key to predicting these long-term costs and health outcomes into the future.

Can beaches survive climate change?

USGS Publications Warehouse

Vitousek, Sean; Barnard, Patrick L.; Limber, Patrick W.

2017-01-01

Anthropogenic climate change is driving sea level rise, leading to numerous impacts on the coastal zone, such as increased coastal flooding, beach erosion, cliff failure, saltwater intrusion in aquifers, and groundwater inundation. Many beaches around the world are currently experiencing chronic erosion as a result of gradual, present-day rates of sea level rise (about 3 mm/year) and human-driven restrictions in sand supply (e.g., harbor dredging and river damming). Accelerated sea level rise threatens to worsen coastal erosion and challenge the very existence of natural beaches throughout the world. Understanding and predicting the rates of sea level rise and coastal erosion depends on integrating data on natural systems with computer simulations. Although many computer modeling approaches are available to simulate shoreline change, few are capable of making reliable long-term predictions needed for full adaption or to enhance resilience. Recent advancements have allowed convincing decadal to centennial-scale predictions of shoreline evolution. For example, along 500 km of the Southern California coast, a new model featuring data assimilation predicts that up to 67% of beaches may completely erode by 2100 without large-scale human interventions. In spite of recent advancements, coastal evolution models must continue to improve in their theoretical framework, quantification of accuracy and uncertainty, computational efficiency, predictive capability, and integration with observed data, in order to meet the scientific and engineering challenges produced by a changing climate.

Model uncertainties do not affect observed patterns of species richness in the Amazon

PubMed Central

Sales, Lilian Patrícia; Neves, Olívia Viana; De Marco, Paulo

2017-01-01

Background Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale—patterns of species richness and species vulnerability to climate change—are affected by the inputs used to model and project species distribution. Methods We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors. Results The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses. Conclusions From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species. PMID:29023503

Predicting impacts of climate change on medicinal asclepiads of Pakistan using Maxent modeling

NASA Astrophysics Data System (ADS)

Khanum, Rizwana; Mumtaz, A. S.; Kumar, Sunil

2013-05-01

Maximum entropy (Maxent) modeling was used to predict the potential climatic niches of three medicinally important Asclepiad species: Pentatropis spiralis, Tylophora hirsuta, and Vincetoxicum arnottianum. All three species are members of the Asclepiad plant family, yet they differ in ecological requirements, biogeographic importance, and conservation value. Occurrence data were collected from herbarium specimens held in major herbaria of Pakistan and two years (2010 and 2011) of field surveys. The Maxent model performed better than random for the three species with an average test AUC value of 0.74 for P. spiralis, 0.84 for V. arnottianum, and 0.59 for T. hirsuta. Under the future climate change scenario, the Maxent model predicted habitat gains for P. spiralis in southern Punjab and Balochistan, and loss of habitat in south-eastern Sindh. Vincetoxicum arnottianum as well as T. hirsuta would gain habitat in upper Peaks of northern parts of Pakistan. T. hirsuta is predicted to lose most of the habitats in northern Punjab and in parches from lower peaks of Galliat, Zhob, Qalat etc. The predictive modeling approach presented here may be applied to other rare Asclepiad species, especially those under constant extinction threat.

Reservoir creep and induced seismicity: inferences from geomechanical modeling of gas depletion in the Groningen field

NASA Astrophysics Data System (ADS)

van Wees, Jan-Diederik; Osinga, Sander; Van Thienen-Visser, Karin; Fokker, Peter A.

2018-03-01

The Groningen gas field in the Netherlands experienced an immediate reduction in seismic events in the year following a massive cut in production. This reduction is inconsistent with existing models of seismicity predictions adopting compaction strains as proxy, since reservoir creep would then result in a more gradual reduction of seismic events after a production stop. We argue that the discontinuity in seismic response relates to a physical discontinuity in stress loading rate on faults upon the arrest of pressure change. The stresses originate from a combination of the direct poroelastic effect through the pressure changes and the delayed effect of ongoing compaction after cessation of reservoir production. Both mechanisms need to be taken into account. To this end, we employed finite-element models in a workflow that couples Kelvin-Chain reservoir creep with a semi-analytical approach for the solution of slip and seismic moment from the predicted stress change. For ratios of final creep and elastic compaction up to 5, the model predicts that the cumulative seismic moment evolution after a production stop is subject to a very moderate increase, 2-10 times less than the values predicted by the alternative approaches using reservoir compaction strain as proxy. This is in agreement with the low seismicity in the central area of the Groningen field immediately after reduction in production. The geomechanical model findings support scope for mitigating induced seismicity through adjusting rates of pressure change by cutting down production.

Factors related to reduction in the consumption of fast food: application of the theory-based approaches.

PubMed

Zeinab, Jalambadani; Gholamreza, Garmaroudi; Mehdi, Yaseri; Mahmood, Tavousi; Korush, Jafarian

2017-09-21

The Trans-Theoretical model (TTM) and Theory of Planned Behaviour (TPB) may be promising models for understanding and predicting reduction in the consumption of fast food. The aim of this study was to examine the applicability of the Trans-Theoretical model (TTM) and the additional predictive role of the subjective norms and perceived behavioural control in predicting reduction consumption of fast food in obese Iranian adolescent girls. A cross sectional study design was conducted among twelve randomly selected schools in Sabzevar, Iran from 2015 to 2017. Four hundred eighty five randomly selected students consented to participate in the study. Hierarchical regression models used to predict the role of important variables that can influence the reduction in the consumption of fast food among students. using SPSS version 22. Variables Perceived behavioural control (r=0.58, P<0.001), Subjective norms (r=0.51, P<0.001), self-efficacy (r=0.49, P<0.001), decisional balance (pros) (r=0.29, P<0.001), decisional balance (cons) (r=0.25, P<0.001), stage of change (r=0.38, P<0.001), were significantly and positively correlated while experiential processes of change (r=0.08, P=0.135) and behavioural processes of change (r=0.09, P=0.145), were not significant. The study demonstrated that the TTM (except the experiential and behavioural processes of change) focusing on the perceived behavioural control and subjective norms are useful models for reduction in the consumption of fast food.

Regional climate change predictions from the Goddard Institute for Space Studies high resolution GCM

NASA Technical Reports Server (NTRS)

Crane, Robert G.; Hewitson, Bruce

1990-01-01

Model simulations of global climate change are seen as an essential component of any program aimed at understanding human impact on the global environment. A major weakness of current general circulation models (GCMs), however, is their inability to predict reliably the regional consequences of a global scale change, and it is these regional scale predictions that are necessary for studies of human/environmental response. This research is directed toward the development of a methodology for the validation of the synoptic scale climatology of GCMs. This is developed with regard to the Goddard Institute for Space Studies (GISS) GCM Model 2, with the specific objective of using the synoptic circulation form a doubles CO2 simulation to estimate regional climate change over North America, south of Hudson Bay. This progress report is specifically concerned with validating the synoptic climatology of the GISS GCM, and developing the transfer function to derive grid-point temperatures from the synoptic circulation. Principal Components Analysis is used to characterize the primary modes of the spatial and temporal variability in the observed and simulated climate, and the model validation is based on correlations between component loadings, and power spectral analysis of the component scores. The results show that the high resolution GISS model does an excellent job of simulating the synoptic circulation over the U.S., and that grid-point temperatures can be predicted with reasonable accuracy from the circulation patterns.

Accelerated Changes in Cortical Thickness Measurements with Age in Military Service Members with Traumatic Brain Injury.

PubMed

Savjani, Ricky R; Taylor, Brian A; Acion, Laura; Wilde, Elisabeth A; Jorge, Ricardo E

2017-11-15

Finding objective and quantifiable imaging markers of mild traumatic brain injury (TBI) has proven challenging, especially in the military population. Changes in cortical thickness after injury have been reported in animals and in humans, but it is unclear how these alterations manifest in the chronic phase, and it is difficult to characterize accurately with imaging. We used cortical thickness measures derived from Advanced Normalization Tools (ANTs) to predict a continuous demographic variable: age. We trained four different regression models (linear regression, support vector regression, Gaussian process regression, and random forests) to predict age from healthy control brains from publicly available datasets (n = 762). We then used these models to predict brain age in military Service Members with TBI (n = 92) and military Service Members without TBI (n = 34). Our results show that all four models overpredicted age in Service Members with TBI, and the predicted age difference was significantly greater compared with military controls. These data extend previous civilian findings and show that cortical thickness measures may reveal an association of accelerated changes over time with military TBI.

Computational Modeling of Space Physiology for Informing Spaceflight Countermeasure Design and Predictions of Efficacy

NASA Technical Reports Server (NTRS)

Lewandowski, B. E.; DeWitt, J. K.; Gallo, C. A.; Gilkey, K. M.; Godfrey, A. P.; Humphreys, B. T.; Jagodnik, K. M.; Kassemi, M.; Myers, J. G.; Nelson, E. S.;

Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity.

PubMed

Passini, Elisa; Britton, Oliver J; Lu, Hua Rong; Rohrbacher, Jutta; Hermans, An N; Gallacher, David J; Greig, Robert J H; Bueno-Orovio, Alfonso; Rodriguez, Blanca

2017-01-01

Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC 50 /Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca 2+ -transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca 2+ /late Na + currents and Na + /Ca 2+ -exchanger, reduced Na + /K + -pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density (fast/late Na + and Ca 2+ currents) exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca 2+ -transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.

Plant distributions in the southwestern United States; a scenario assessment of the modern-day and future distribution ranges of 166 Species

USGS Publications Warehouse

Thomas, Kathryn A.; Guertin, Patricia P.; Gass, Leila

2012-01-01

The authors developed spatial models of the predicted modern-day suitable habitat (SH) of 166 dominant and indicator plant species of the southwestern United States (herein referred to as the Southwest) and then conducted a coarse assessment of potential future changes in the distribution of their suitable habitat under three climate-change scenarios for two time periods. We used Maxent-based spatial modeling to predict the modern-day and future scenarios of SH for each species in an over 342-million-acre area encompassing all or parts of six states in the Southwest--Arizona, California, Colorado, Nevada, New Mexico, and Utah. Modern-day SH models were predicted by our using 26 annual and monthly average temperature and precipitation variables, averaged for the years 1971-2000. Future SH models were predicted for each species by our using six climate models based on application of the average of 16 General Circulation Models to Intergovernmental Panel on Climate Change emission scenarios B1, A1B, and A2 for two time periods, 2040 to 2069 and 2070 and 2100, referred to respectively as the 2050 and 2100 time periods. The assessment examined each species' vulnerability to loss of modern-day SH under future climate scenarios, potential to gain SH under future climate scenarios, and each species' estimated risk as a function of both vulnerability and potential gains. All 166 species were predicted to lose modern-day SH in the future climate change scenarios. In the 2050 time period, nearly 30 percent of the species lost 75 percent or more of their modern-day suitable habitat, 21 species gained more new SH than their modern-day SH, and 30 species gained less new SH than 25 percent of their modern-day SH. In the 2100 time period, nearly half of the species lost 75 percent or more of their modern-day SH, 28 species gained more new SH than their modern-day SH, and 34 gained less new SH than 25 percent of their modern-day SH. Using nine risk categories we found only two species were in the least risk category, while 20 species were in the highest risk category. The assessment showed that species respond independently to predicted climate change, suggesting that current plant assemblages may disassemble under predicted climate change scenarios. This report presents the results for each species in tables (Appendix A) and maps (14 for each species) in Appendix B.

Predicting Age-Appropriate Pharmacokinetics of Six Volatile Organic Compounds in the Rat Utilizing Physiologically Based Pharmacokinetic Modeling

EPA Science Inventory

The capability of physiologically based pharmacokinetic models to incorporate age-appropriate physiological and chemical-specific parameters was utilized to predict changes in internal dosimetry for six volatile organic compounds (VOCs) across different ages of rats.

When is an Inhibitory Synapse Effective?

NASA Astrophysics Data System (ADS)

Qian, Ning; Sejnowski, Terrence J.

1990-10-01

Interactions between excitatory and inhibitory synaptic inputs on dendrites determine the level of activity in neurons. Models based on the cable equation predict that silent shunting inhibition can strongly veto the effect of an excitatory input. The cable model assumes that ionic concentrations do not change during the electrical activity, which may not be a valid assumption, especially for small structures such as dendritic spines. We present here an analysis and computer simulations to show that for large Cl^- conductance changes, the more general Nernst-Planck electrodiffusion model predicts that shunting inhibition on spines should be much less effective than that predicted by the cable model. This is a consequence of the large changes in the intracellular ionic concentration of Cl^- that can occur in small structures, which would alter the reversal potential and reduce the driving force for Cl^-. Shunting inhibition should therefore not be effective on spines, but it could be significantly more effective on the dendritic shaft at the base of the spine. In contrast to shunting inhibition, hyperpolarizing synaptic inhibition mediated by K^+ currents can be very effective in reducing the excitatory synaptic potentials on the same spine if the excitatory conductance change is less than 10 nS. We predict that if the inhibitory synapses found on cortical spines are to be effective, then they should be mediated by K^+ through GABA_B receptors.

A Multimodel Ensemble Analysis of Global Changes in Plant Water Use Efficiency and Primary Productivity in the 21st Century

NASA Astrophysics Data System (ADS)

Bernardes, S.

2017-12-01

Outputs from coupled carbon-climate models show considerable variability in atmospheric and land fields over the 21st century, including changes in temperature and in the spatiotemporal distribution and quantity of precipitation over the planet. Reductions in water availability due to decreased precipitation and increased water demand by the atmosphere may reduce carbon uptake by critical ecosystems. Conversely, increases in atmospheric carbon dioxide have the potential to offset reductions in productivity. This work focuses on predicted responses of plants to environmental changes and on how plants will adjust their water use efficiency (WUE, plant production per water loss by evapotranspiration) in the 21st century. Predicted changes in WUE were investigated using an ensemble of Earth System Models from the Coupled Model Intercomparison Project 5 (CMIP5), flux tower data and products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Scenarios for climate futures used two representative concentration pathways, including carbon concentration peak in 2040 (RCP4.5) and rising emissions throughout the 21st century (RCP8.5). Model results included the periods 2006-2009 (predicted) and 1850-2005 (reference). IPCC SREX regions were used to compare modeled, flux and satellite data and to address the significant intermodel variability observed for the CMIP5 ensemble (larger variability for RCP8.5, higher intermodel agreement in Southeast Asia, lower intermodel agreement in arid areas). An evaluation of model skill at the regional level supported model selection and the spatiotemporal analysis of changes in WUE. Departures of projected conditions in relation to historical values are presented for both concentration pathways at global, regional levels, including latitudinal distributions. High model sensitivity to different concentration pathways and increase in GPP and WUE was observed for most of the planet (increases consistently higher for RCP8.5). Higher latitudes in the northern hemisphere (boreal region) are predicted to experience higher increases in GPP and WUE, with WUE usually following GPP in changes. Models point to decreases in productivity and WUE mostly in the tropics, affecting tropical forests in the Amazon and in Central America.

Assessing uncertainty in mechanistic models

Treesearch

Edwin J. Green; David W. MacFarlane; Harry T. Valentine

2000-01-01

Concern over potential global change has led to increased interest in the use of mechanistic models for predicting forest growth. The rationale for this interest is that empirical models may be of limited usefulness if environmental conditions change. Intuitively, we expect that mechanistic models, grounded as far as possible in an understanding of the biology of tree...

Using Models to Teach about Climate Change: A look at NGSS Expectations and Teacher Perceptions

NASA Astrophysics Data System (ADS)

Yarker, M. B.; Stanier, C. O.; Forbes, C.; Park, S.

2013-12-01

The Next Generation Science Standards have been updated from the previous version of the standards with some much needed emphasis on topics in climate and climate change. In particular, the standards have focused on K-12 students learning about science models, which is extremely important when discussing climate change. The NGSS suggest that students be able to 1) develop and use science models (not just use them to explain a concept) because this is how scientists actually use models during the scientific process; and 2) understand systems and system models across all science concepts and all age levels because it leads to further understanding about a more complex natural system (like climate change). To summarize, the NGSS expects that K-12 students should develop and use system models across disciplines and age groups in a way that is similar to how scientists use them in practice, which is to make predictions about unanswered questions. Research indicates that students who learn about science content using an approach that aligns more authentically with the way real science inquiry is done have a better understanding of the content, better understanding of the nature of science, improved critical thinking skills, and improved problem solving skills. Research also indicates that most teachers are aware of this method to teach science content, but sometimes have trouble implementing it into the classroom effectively for many reasons. If accepted, this presentation will share an approach to incorporate modeling into the classroom effectively as well as report the results from a study that qualitatively look at three teacher's perspectives on using models in the classroom while teaching units about climate change, in order to identify how/why teachers struggle to teach about models involved in content related to climate change. Preliminary results indicate that the teachers in this study view models as an effective way to explain a concept to their students, but none of them mention or discuss the predictive power of models. Although models are a useful way to explain a complex phenomenon concisely, arguably the most important role science models play in scientific inquiry is their ability to allow scientists to make prediction, especially when it comes to climate change. Since all three teachers overlooked the predictive power of models, it indicates that that they do not have a firm understanding of the role science models play in making scientific predictions. In conclusion, there is discrepancy between what the NGSS indicate students should be learning about modeling and what teachers are prepared to teach. In order to better prepare teachers to meet the demands required of them, they need to be better educated about models, what they are, what they do, and how scientists use them. By preparing teachers to teach K-12 students about the role models play in climate research, we can build a more knowledgeable society that is better prepared to make informed decisions on how to deal with issues in our changing climate.

[Prediction of 137Cs behaviour in the soil-plant system in the territory of Semipalatinsk test site].

PubMed

Spiridonov, S I; Mukusheva, M K; Gontarenko, I A; Fesenko, S V; Baranov, S A

2005-01-01

A mathematical model of 137Cs behaviour in the soil-plant system is presented. The model has been parameterized for the area adjacent to the testing area Ground Zero of the Semipalatinsk Test Site. The model describes the main processes responsible for the changes in 137Cs content in the soil solution and, thereby, dynamics of the radionuclide uptake by vegetation. The results are taken from predictive and retrospective calculations that reflect the dynamics of 137Cs distribution by species in soil after nuclear explosions. The importance of factors governing 137Cs accumulation in plants within the STS area is assessed. The analysis of sensitivity of the output model variable to changes in its parameters revealed that the key soil properties significantly influence the results of prediction of 137Cs content in plants.

Predicted changes in advanced turboprop noise with shaft angle of attack

NASA Technical Reports Server (NTRS)

Padula, S. L.; Block, P. J. W.

1984-01-01

Advanced turboprop blade designs and new propeller installation schemes motivated an effort to include unsteady loading effects in existing propeller noise prediction computer programs. The present work validates the prediction capability while studing the effects of shaft inclination on the radiated sound field. Classical methods of propeller performance analysis supply the time-dependent blade loading needed to calculate noise. Polar plots of the sound pressure level (SPL) of the first four harmonics and overall SPL are indicative of the change in directivity pattern as a function of propeller angle of attack. Noise predictions are compared with newly available wind tunnel data and the accuracy and applicability of the prediction method are discussed. It is concluded that unsteady blade loading caused by inclining the propeller with respect to the flow changes the directionality and the intensity of the radiated noise. These changes are well modeled by the present quasi-steady prediction method.

Predictions for snow cover, glaciers and runoff in a changing climate

USDA-ARS?s Scientific Manuscript database

The problem of evaluating the hydrological effects of climate change has opened a new field of applications for snowmelt runoff models. The Snowmelt Runoff Model (SRM) has been used to evaluate climate change effects on basins in North America, the Swiss Alps, and the Himalayas. Snow covered area ...

Predictive models for fish assemblages in eastern USA streams: implications for assessing biodiversity

USGS Publications Warehouse

Meador, Michael R.; Carlisle, Daren M.

2009-01-01

Management and conservation of aquatic systems require the ability to assess biological conditions and identify changes in biodiversity. Predictive models for fish assemblages were constructed to assess biological condition and changes in biodiversity for streams sampled in the eastern United States as part of the U.S. Geological Survey's National Water Quality Assessment Program. Separate predictive models were developed for northern and southern regions. Reference sites were designated using land cover and local professional judgment. Taxonomic completeness was quantified based on the ratio of the number of observed native fish species expected to occur to the number of expected native fish species. Models for both regions accurately predicted fish species composition at reference sites with relatively high precision and low bias. In general, species that occurred less frequently than expected (decreasers) tended to prefer riffle areas and larger substrates, such as gravel and cobble, whereas increaser species (occurring more frequently than expected) tended to prefer pools, backwater areas, and vegetated and sand substrates. In the north, the percentage of species identified as increasers and the percentage identified as decreasers were equal, whereas in the south nearly two-thirds of the species examined were identified as decreasers. Predictive models of fish species can provide a standardized indicator for consistent assessments of biological condition at varying spatial scales and critical information for an improved understanding of fish species that are potentially at risk of loss with changing water quality conditions.

Time-varying parameter models for catchments with land use change: the importance of model structure

NASA Astrophysics Data System (ADS)

Pathiraja, Sahani; Anghileri, Daniela; Burlando, Paolo; Sharma, Ashish; Marshall, Lucy; Moradkhani, Hamid

2018-05-01

Rapid population and economic growth in Southeast Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modeling methodologies capable of handling changing land use conditions are therefore becoming ever more important and are receiving increasing attention from hydrologists. A recently developed data-assimilation-based framework that allows model parameters to vary through time in response to signals of change in observations is considered for a medium-sized catchment (2880 km2) in northern Vietnam experiencing substantial but gradual land cover change. We investigate the efficacy of the method as well as the importance of the chosen model structure in ensuring the success of a time-varying parameter method. The method was used with two lumped daily conceptual models (HBV and HyMOD) that gave good-quality streamflow predictions during pre-change conditions. Although both time-varying parameter models gave improved streamflow predictions under changed conditions compared to the time-invariant parameter model, persistent biases for low flows were apparent in the HyMOD case. It was found that HyMOD was not suited to representing the modified baseflow conditions, resulting in extreme and unrealistic time-varying parameter estimates. This work shows that the chosen model can be critical for ensuring the time-varying parameter framework successfully models streamflow under changing land cover conditions. It can also be used to determine whether land cover changes (and not just meteorological factors) contribute to the observed hydrologic changes in retrospective studies where the lack of a paired control catchment precludes such an assessment.

Models of vegetation change for landscape planning: a comparison of FETM, LANDSUM, SIMPPLLE, and VDDT

Treesearch

T. M. Barrett

2001-01-01

Landscape assessment and planning often depend on the ability to predict change of vegetation. This report compares four modeling systems (FETM, LANDSUM, SIMPPLLE, and VDDT) that can be used to understand changes resulting from succession, natural disturbance, and management activities. The four models may be useful for regional or local assessments in National Forest...

Analysis of a High-Lift Multi-Element Airfoil using a Navier-Stokes Code

NASA Technical Reports Server (NTRS)

Whitlock, Mark E.

1995-01-01

A thin-layer Navier-Stokes code, CFL3D, was utilized to compute the flow over a high-lift multi-element airfoil. This study was conducted to improve the prediction of high-lift flowfields using various turbulence models and improved glidding techniques. An overset Chimera grid system is used to model the three element airfoil geometry. The effects of wind tunnel wall modeling, changes to the grid density and distribution, and embedded grids are discussed. Computed pressure and lift coefficients using Spalart-Allmaras, Baldwin-Barth, and Menter's kappa-omega - Shear Stress Transport (SST) turbulence models are compared with experimental data. The ability of CFL3D to predict the effects on lift coefficient due to changes in Reynolds number changes is also discussed.

Bayesian modeling approach for characterizing groundwater arsenic contamination in the Mekong River basin.

PubMed

Cha, YoonKyung; Kim, Young Mo; Choi, Jae-Woo; Sthiannopkao, Suthipong; Cho, Kyung Hwa

2016-01-01

In the Mekong River basin, groundwater from tube-wells is a major drinking water source. However, arsenic (As) contamination in groundwater resources has become a critical issue in the watershed. In this study, As species such as total As (AsTOT), As(III), and As(V), were monitored across the watershed to investigate their characteristics and inter-relationships with water quality parameters, including pH and redox potential (Eh). The data illustrated a dramatic change in the relationship between AsTOT and Eh over a specific Eh range, suggesting the importance of Eh in predicting AsTOT. Thus, a Bayesian change-point model was developed to predict AsTOT concentrations based on Eh and pH, to determine changes in the AsTOT-Eh relationship. The model captured the Eh change-point (∼-100±15mV), which was compatible with the data. Importantly, the inclusion of this change-point in the model resulted in improved model fit and prediction accuracy; AsTOT concentrations were strongly negatively related to Eh values higher than the change-point. The process underlying this relationship was subsequently posited to be the reductive dissolution of mineral oxides and As release. Overall, AsTOT showed a weak positive relationship with Eh at a lower range, similar to those commonly observed in the Mekong River basin delta. It is expected that these results would serve as a guide for establishing public health strategies in the Mekong River Basin. Copyright © 2015 Elsevier Ltd. All rights reserved.

Toward Process-resolving Synthesis and Prediction of Arctic Climate Change Using the Regional Arctic System Model

NASA Astrophysics Data System (ADS)

Maslowski, W.

2017-12-01

The Regional Arctic System Model (RASM) has been developed to better understand the operation of Arctic System at process scale and to improve prediction of its change at a spectrum of time scales. RASM is a pan-Arctic, fully coupled ice-ocean-atmosphere-land model with marine biogeochemistry extension to the ocean and sea ice models. The main goal of our research is to advance a system-level understanding of critical processes and feedbacks in the Arctic and their links with the Earth System. The secondary, an equally important objective, is to identify model needs for new or additional observations to better understand such processes and to help constrain models. Finally, RASM has been used to produce sea ice forecasts for September 2016 and 2017, in contribution to the Sea Ice Outlook of the Sea Ice Prediction Network. Future RASM forecasts, are likely to include increased resolution for model components and ecosystem predictions. Such research is in direct support of the US environmental assessment and prediction needs, including those of the U.S. Navy, Department of Defense, and the recent IARPC Arctic Research Plan 2017-2021. In addition to an overview of RASM technical details, selected model results are presented from a hierarchy of climate models together with available observations in the region to better understand potential oceanic contributions to polar amplification. RASM simulations are analyzed to evaluate model skill in representing seasonal climatology as well as interannual and multi-decadal climate variability and predictions. Selected physical processes and resulting feedbacks are discussed to emphasize the need for fully coupled climate model simulations, high model resolution and sensitivity of simulated sea ice states to scale dependent model parameterizations controlling ice dynamics, thermodynamics and coupling with the atmosphere and ocean.

Modelling the influence of predicted future climate change on the risk of wind damage within New Zealand's planted forests.

PubMed

Moore, John R; Watt, Michael S

2015-08-01

Wind is the major abiotic disturbance in New Zealand's planted forests, but little is known about how the risk of wind damage may be affected by future climate change. We linked a mechanistic wind damage model (ForestGALES) to an empirical growth model for radiata pine (Pinus radiata D. Don) and a process-based growth model (cenw) to predict the risk of wind damage under different future emissions scenarios and assumptions about the future wind climate. The cenw model was used to estimate site productivity for constant CO2 concentration at 1990 values and for assumed increases in CO2 concentration from current values to those expected during 2040 and 2090 under the B1 (low), A1B (mid-range) and A2 (high) emission scenarios. Stand development was modelled for different levels of site productivity, contrasting silvicultural regimes and sites across New Zealand. The risk of wind damage was predicted for each regime and emission scenario combination using the ForestGALES model. The sensitivity to changes in the intensity of the future wind climate was also examined. Results showed that increased tree growth rates under the different emissions scenarios had the greatest impact on the risk of wind damage. The increase in risk was greatest for stands growing at high stand density under the A2 emissions scenario with increased CO2 concentration. The increased productivity under this scenario resulted in increased tree height, without a corresponding increase in diameter, leading to more slender trees that were predicted to be at greater risk from wind damage. The risk of wind damage was further increased by the modest increases in the extreme wind climate that are predicted to occur. These results have implications for the development of silvicultural regimes that are resilient to climate change and also indicate that future productivity gains may be offset by greater losses from disturbances. © 2015 John Wiley & Sons Ltd.

CEDAR-GEM Challenge for Systematic Assessment of Ionosphere/Thermosphere Models in Predicting TEC During the 2006 December Storm Event

NASA Astrophysics Data System (ADS)

Shim, J. S.; Rastätter, L.; Kuznetsova, M.; Bilitza, D.; Codrescu, M.; Coster, A. J.; Emery, B. A.; Fedrizzi, M.; Förster, M.; Fuller-Rowell, T. J.; Gardner, L. C.; Goncharenko, L.; Huba, J.; McDonald, S. E.; Mannucci, A. J.; Namgaladze, A. A.; Pi, X.; Prokhorov, B. E.; Ridley, A. J.; Scherliess, L.; Schunk, R. W.; Sojka, J. J.; Zhu, L.

2017-10-01

In order to assess current modeling capability of reproducing storm impacts on total electron content (TEC), we considered quantities such as TEC, TEC changes compared to quiet time values, and the maximum value of the TEC and TEC changes during a storm. We compared the quantities obtained from ionospheric models against ground-based GPS TEC measurements during the 2006 AGU storm event (14-15 December 2006) in the selected eight longitude sectors. We used 15 simulations obtained from eight ionospheric models, including empirical, physics-based, coupled ionosphere-thermosphere, and data assimilation models. To quantitatively evaluate performance of the models in TEC prediction during the storm, we calculated skill scores such as RMS error, Normalized RMS error (NRMSE), ratio of the modeled to observed maximum increase (Yield), and the difference between the modeled peak time and observed peak time. Furthermore, to investigate latitudinal dependence of the performance of the models, the skill scores were calculated for five latitude regions. Our study shows that RMSE of TEC and TEC changes of the model simulations range from about 3 TECU (total electron content unit, 1 TECU = 1016 el m-2) (in high latitudes) to about 13 TECU (in low latitudes), which is larger than latitudinal average GPS TEC error of about 2 TECU. Most model simulations predict TEC better than TEC changes in terms of NRMSE and the difference in peak time, while the opposite holds true in terms of Yield. Model performance strongly depends on the quantities considered, the type of metrics used, and the latitude considered.

Model-based analysis of N-glycosylation in Chinese hamster ovary cells

PubMed Central

Krambeck, Frederick J.; Bennun, Sandra V.; Betenbaugh, Michael J.

2017-01-01

The Chinese hamster ovary (CHO) cell is the gold standard for manufacturing of glycosylated recombinant proteins for production of biotherapeutics. The similarity of its glycosylation patterns to the human versions enable the products of this cell line favorable pharmacokinetic properties and lower likelihood of causing immunogenic responses. Because glycan structures are the product of the concerted action of intracellular enzymes, it is difficult to predict a priori how the effects of genetic manipulations alter glycan structures of cells and therapeutic properties. For that reason, quantitative models able to predict glycosylation have emerged as promising tools to deal with the complexity of glycosylation processing. For example, an earlier version of the same model used in this study was used by others to successfully predict changes in enzyme activities that could produce a desired change in glycan structure. In this study we utilize an updated version of this model to provide a comprehensive analysis of N-glycosylation in ten Chinese hamster ovary (CHO) cell lines that include a wild type parent and nine mutants of CHO, through interpretation of previously published mass spectrometry data. The updated N-glycosylation mathematical model contains up to 50,605 glycan structures. Adjusting the enzyme activities in this model to match N-glycan mass spectra produces detailed predictions of the glycosylation process, enzyme activity profiles and complete glycosylation profiles of each of the cell lines. These profiles are consistent with biochemical and genetic data reported previously. The model-based results also predict glycosylation features of the cell lines not previously published, indicating more complex changes in glycosylation enzyme activities than just those resulting directly from gene mutations. The model predicts that the CHO cell lines possess regulatory mechanisms that allow them to adjust glycosylation enzyme activities to mitigate side effects of the primary loss or gain of glycosylation function known to exist in these mutant cell lines. Quantitative models of CHO cell glycosylation have the potential for predicting how glycoengineering manipulations might affect glycoform distributions to improve the therapeutic performance of glycoprotein products. PMID:28486471

Can phenological models predict tree phenology accurately under climate change conditions?

NASA Astrophysics Data System (ADS)

Chuine, Isabelle; Bonhomme, Marc; Legave, Jean Michel; García de Cortázar-Atauri, Inaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

2014-05-01

The onset of the growing season of trees has been globally earlier by 2.3 days/decade during the last 50 years because of global warming and this trend is predicted to continue according to climate forecast. The effect of temperature on plant phenology is however not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud dormancy, and on the other hand higher temperatures are necessary to promote bud cells growth afterwards. Increasing phenological changes in temperate woody species have strong impacts on forest trees distribution and productivity, as well as crops cultivation areas. Accurate predictions of trees phenology are therefore a prerequisite to understand and foresee the impacts of climate change on forests and agrosystems. Different process-based models have been developed in the last two decades to predict the date of budburst or flowering of woody species. They are two main families: (1) one-phase models which consider only the ecodormancy phase and make the assumption that endodormancy is always broken before adequate climatic conditions for cell growth occur; and (2) two-phase models which consider both the endodormancy and ecodormancy phases and predict a date of dormancy break which varies from year to year. So far, one-phase models have been able to predict accurately tree bud break and flowering under historical climate. However, because they do not consider what happens prior to ecodormancy, and especially the possible negative effect of winter temperature warming on dormancy break, it seems unlikely that they can provide accurate predictions in future climate conditions. It is indeed well known that a lack of low temperature results in abnormal pattern of bud break and development in temperate fruit trees. An accurate modelling of the dormancy break date has thus become a major issue in phenology modelling. Two-phases phenological models predict that global warming should delay or compromise dormancy break at the species equatorward range limits leading to a delay or even impossibility to flower or set new leaves. These models are classically parameterized with flowering or budburst dates only, with no information on the dormancy break date because this information is very scarce. We evaluated the efficiency of a set of process-based phenological models to accurately predict the dormancy break dates of four fruit trees. Our results show that models calibrated solely with flowering or budburst dates do not accurately predict the dormancy break date. Providing dormancy break date for the model parameterization results in much more accurate simulation of this latter, with however a higher error than that on flowering or bud break dates. But most importantly, we show also that models not calibrated with dormancy break dates can generate significant differences in forecasted flowering or bud break dates when using climate scenarios. Our results claim for the urgent need of massive measurements of dormancy break dates in forest and fruit trees to yield more robust projections of phenological changes in a near future.

Modelling of land use change in Indramayu District, West Java Province

NASA Astrophysics Data System (ADS)

Handayani, L. D. W.; Tejaningrum, M. A.; Damrah, F.

2017-01-01

Indramayu District into a strategic area for a stopover and overseas from East Java area because Indramayu District passed the north coast main lane, which is the first as the economic lifeblood of the Java Island. Indramayu District is part of mainstream economic Java pathways so that physical development of the area and population density as well as community activities grew by leaps and bounds. Growth acceleration raised the level of land use change. Land use change and population activities in coastal area would reduce the carrying capacity and impact on environmental quality. This research aim to analyse landuse change of years 2000 and 2011 in Indramayu District. Using this land use change map, we can predict the condition of landuse change of year 2022 in Indramayu District. Cellular Automata Markov (Markov CA) Method is used to create a spatial model of land use changes. The results of this study are predictive of land use in 2022 and the suitability with Spatial Plan (RTRW). A settlement increase predicted to continue in the future the designation of the land according to the spatial plan should be maintained.

Validation of catchment models for predicting land-use and climate change impacts. 1. Method

NASA Astrophysics Data System (ADS)

Ewen, J.; Parkin, G.

1996-02-01

Computer simulation models are increasingly being proposed as tools capable of giving water resource managers accurate predictions of the impact of changes in land-use and climate. Previous validation testing of catchment models is reviewed, and it is concluded that the methods used do not clearly test a model's fitness for such a purpose. A new generally applicable method is proposed. This involves the direct testing of fitness for purpose, uses established scientific techniques, and may be implemented within a quality assured programme of work. The new method is applied in Part 2 of this study (Parkin et al., J. Hydrol., 175:595-613, 1996).

Active inference, communication and hermeneutics☆

PubMed Central

Friston, Karl J.; Frith, Christopher D.

2015-01-01

Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others – during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions – both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then – in principle – they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. PMID:25957007

Active inference, communication and hermeneutics.

PubMed

Friston, Karl J; Frith, Christopher D

2015-07-01

Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others--during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions--both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then--in principle--they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Ecological Niche Modelling Predicts Southward Expansion of Lutzomyia (Nyssomyia) flaviscutellata (Diptera: Psychodidae: Phlebotominae), Vector of Leishmania (Leishmania) amazonensis in South America, under Climate Change.

PubMed

Carvalho, Bruno M; Rangel, Elizabeth F; Ready, Paul D; Vale, Mariana M

2015-01-01

Vector borne diseases are susceptible to climate change because distributions and densities of many vectors are climate driven. The Amazon region is endemic for cutaneous leishmaniasis and is predicted to be severely impacted by climate change. Recent records suggest that the distributions of Lutzomyia (Nyssomyia) flaviscutellata and the parasite it transmits, Leishmania (Leishmania) amazonensis, are expanding southward, possibly due to climate change, and sometimes associated with new human infection cases. We define the vector's climatic niche and explore future projections under climate change scenarios. Vector occurrence records were compiled from the literature, museum collections and Brazilian Health Departments. Six bioclimatic variables were used as predictors in six ecological niche model algorithms (BIOCLIM, DOMAIN, MaxEnt, GARP, logistic regression and Random Forest). Projections for 2050 used 17 general circulation models in two greenhouse gas representative concentration pathways: "stabilization" and "high increase". Ensemble models and consensus maps were produced by overlapping binary predictions. Final model outputs showed good performance and significance. The use of species absence data substantially improved model performance. Currently, L. flaviscutellata is widely distributed in the Amazon region, with records in the Atlantic Forest and savannah regions of Central Brazil. Future projections indicate expansion of the climatically suitable area for the vector in both scenarios, towards higher latitudes and elevations. L. flaviscutellata is likely to find increasingly suitable conditions for its expansion into areas where human population size and density are much larger than they are in its current locations. If environmental conditions change as predicted, the range of the vector is likely to expand to southeastern and central-southern Brazil, eastern Paraguay and further into the Amazonian areas of Bolivia, Peru, Ecuador, Colombia and Venezuela. These areas will only become endemic for L. amazonensis, however, if they have competent reservoir hosts and transmission dynamics matching those in the Amazon region.

Ecological Niche Modelling Predicts Southward Expansion of Lutzomyia (Nyssomyia) flaviscutellata (Diptera: Psychodidae: Phlebotominae), Vector of Leishmania (Leishmania) amazonensis in South America, under Climate Change

PubMed Central

Carvalho, Bruno M.; Ready, Paul D.

2015-01-01

Vector borne diseases are susceptible to climate change because distributions and densities of many vectors are climate driven. The Amazon region is endemic for cutaneous leishmaniasis and is predicted to be severely impacted by climate change. Recent records suggest that the distributions of Lutzomyia (Nyssomyia) flaviscutellata and the parasite it transmits, Leishmania (Leishmania) amazonensis, are expanding southward, possibly due to climate change, and sometimes associated with new human infection cases. We define the vector’s climatic niche and explore future projections under climate change scenarios. Vector occurrence records were compiled from the literature, museum collections and Brazilian Health Departments. Six bioclimatic variables were used as predictors in six ecological niche model algorithms (BIOCLIM, DOMAIN, MaxEnt, GARP, logistic regression and Random Forest). Projections for 2050 used 17 general circulation models in two greenhouse gas representative concentration pathways: “stabilization” and “high increase”. Ensemble models and consensus maps were produced by overlapping binary predictions. Final model outputs showed good performance and significance. The use of species absence data substantially improved model performance. Currently, L. flaviscutellata is widely distributed in the Amazon region, with records in the Atlantic Forest and savannah regions of Central Brazil. Future projections indicate expansion of the climatically suitable area for the vector in both scenarios, towards higher latitudes and elevations. L. flaviscutellata is likely to find increasingly suitable conditions for its expansion into areas where human population size and density are much larger than they are in its current locations. If environmental conditions change as predicted, the range of the vector is likely to expand to southeastern and central-southern Brazil, eastern Paraguay and further into the Amazonian areas of Bolivia, Peru, Ecuador, Colombia and Venezuela. These areas will only become endemic for L. amazonensis, however, if they have competent reservoir hosts and transmission dynamics matching those in the Amazon region. PMID:26619186

Correlates of motivation to change in pathological gamblers completing cognitive-behavioral group therapy.

PubMed

Gómez-Peña, Mónica; Penelo, Eva; Granero, Roser; Fernández-Aranda, Fernando; Alvarez-Moya, Eva; Santamaría, Juan José; Moragas, Laura; Neus Aymamí, Maria; Gunnard, Katarina; Menchón, José M; Jimenez-Murcia, Susana

2012-07-01

The present study analyzes the association between the motivation to change and the cognitive-behavioral group intervention, in terms of dropouts and relapses, in a sample of male pathological gamblers. The specific objectives were as follows: (a) to estimate the predictive value of baseline University of Rhode Island Change Assessment scale (URICA) scores (i.e., at the start of the study) as regards the risk of relapse and dropout during treatment and (b) to assess the incremental predictive ability of URICA scores, as regards the mean change produced in the clinical status of patients between the start and finish of treatment. The relationship between the URICA and the response to treatment was analyzed by means of a pre-post design applied to a sample of 191 patients who were consecutively receiving cognitive-behavioral group therapy. The statistical analysis included logistic regression models and hierarchical multiple linear regression models. The discriminative ability of the models including the four URICA scores regarding the likelihood of relapse and dropout was acceptable (area under the receiver operating haracteristic curve: .73 and .71, respectively). No significant predictive ability was found as regards the differences between baseline and posttreatment scores (changes in R(2) below 5% in the multiple regression models). The availability of useful measures of motivation to change would enable treatment outcomes to be optimized through the application of specific therapeutic interventions. © 2012 Wiley Periodicals, Inc.

Monitoring and Predicting Land-use Changes and the Hydrology of the Urbanized Paochiao Watershed in Taiwan Using Remote Sensing Data, Urban Growth Models and a Hydrological Model.

PubMed

Lin, Yu-Pin; Lin, Yun-Bin; Wang, Yen-Tan; Hong, Nien-Ming

2008-02-04

Monitoring and simulating urban sprawl and its effects on land-use patterns andhydrological processes in urbanized watersheds are essential in land-use and waterresourceplanning and management. This study applies a novel framework to the urbangrowth model Slope, Land use, Excluded land, Urban extent, Transportation, andHillshading (SLEUTH) and land-use change with the Conversion of Land use and itsEffects (CLUE-s) model using historical SPOT images to predict urban sprawl in thePaochiao watershed in Taipei County, Taiwan. The historical and predicted land-use datawas input into Patch Analyst to obtain landscape metrics. This data was also input to theGeneralized Watershed Loading Function (GWLF) model to analyze the effects of futureurban sprawl on the land-use patterns and watershed hydrology. The landscape metrics ofthe historical SPOT images show that land-use patterns changed between 1990-2000. TheSLEUTH model accurately simulated historical land-use patterns and urban sprawl in thePaochiao watershed, and simulated future clustered land-use patterns (2001-2025). TheCLUE-s model also simulated land-use patterns for the same period and yielded historical trends in the metrics of land-use patterns. The land-use patterns predicted by the SLEUTHand CLUE-s models show the significant impact urban sprawl will have on land-usepatterns in the Paochiao watershed. The historical and predicted land-use patterns in thewatershed tended to fragment, had regular shapes and interspersion patterns, but wererelatively less isolated in 2001-2025 and less interspersed from 2005-2025 compared withland-use pattern in 1990. During the study, the variability and magnitude of hydrologicalcomponents based on the historical and predicted land-use patterns were cumulativelyaffected by urban sprawl in the watershed; specifically, surface runoff increasedsignificantly by 22.0% and baseflow decreased by 18.0% during 1990-2025. The proposedapproach is an effective means of enhancing land-use monitoring and management ofurbanized watersheds.

Current and Future Patterns of Global Marine Mammal Biodiversity

PubMed Central

Kaschner, Kristin; Tittensor, Derek P.; Ready, Jonathan; Gerrodette, Tim; Worm, Boris

2011-01-01

Quantifying the spatial distribution of taxa is an important prerequisite for the preservation of biodiversity, and can provide a baseline against which to measure the impacts of climate change. Here we analyse patterns of marine mammal species richness based on predictions of global distributional ranges for 115 species, including all extant pinnipeds and cetaceans. We used an environmental suitability model specifically designed to address the paucity of distributional data for many marine mammal species. We generated richness patterns by overlaying predicted distributions for all species; these were then validated against sightings data from dedicated long-term surveys in the Eastern Tropical Pacific, the Northeast Atlantic and the Southern Ocean. Model outputs correlated well with empirically observed patterns of biodiversity in all three survey regions. Marine mammal richness was predicted to be highest in temperate waters of both hemispheres with distinct hotspots around New Zealand, Japan, Baja California, the Galapagos Islands, the Southeast Pacific, and the Southern Ocean. We then applied our model to explore potential changes in biodiversity under future perturbations of environmental conditions. Forward projections of biodiversity using an intermediate Intergovernmental Panel for Climate Change (IPCC) temperature scenario predicted that projected ocean warming and changes in sea ice cover until 2050 may have moderate effects on the spatial patterns of marine mammal richness. Increases in cetacean richness were predicted above 40° latitude in both hemispheres, while decreases in both pinniped and cetacean richness were expected at lower latitudes. Our results show how species distribution models can be applied to explore broad patterns of marine biodiversity worldwide for taxa for which limited distributional data are available. PMID:21625431

Performance of a process-based hydrodynamic model in predicting shoreline change

NASA Astrophysics Data System (ADS)

Safak, I.; Warner, J. C.; List, J. H.

2012-12-01

Shoreline change is controlled by a complex combination of processes that include waves, currents, sediment characteristics and availability, geologic framework, human interventions, and sea level rise. A comprehensive data set of shoreline position (14 shorelines between 1978-2002) along the continuous and relatively non-interrupted North Carolina Coast from Oregon Inlet to Cape Hatteras (65 km) reveals a spatial pattern of alternating erosion and accretion, with an erosional average shoreline change rate of -1.6 m/yr and up to -8 m/yr in some locations. This data set gives a unique opportunity to study long-term shoreline change in an area hit by frequent storm events while relatively uninfluenced by human interventions and the effects of tidal inlets. Accurate predictions of long-term shoreline change may require a model that accurately resolves surf zone processes and sediment transport patterns. Conventional methods for predicting shoreline change such as one-line models and regression of shoreline positions have been designed for computational efficiency. These methods, however, not only have several underlying restrictions (validity for small angle of wave approach, assuming bottom contours and shoreline to be parallel, depth of closure, etc.) but also their empirical estimates of sediment transport rates in the surf zone have been shown to vary greatly from the calculations of process-based hydrodynamic models. We focus on hind-casting long-term shoreline change using components of the process-based, three-dimensional coupled-ocean-atmosphere-wave-sediment transport modeling system (COAWST). COAWST is forced with historical predictions of atmospheric and oceanographic data from public-domain global models. Through a method of coupled concurrent grid-refinement approach in COAWST, the finest grid with resolution of O(10 m) that covers the surf zone along the section of interest is forced at its spatial boundaries with waves and currents computed on the grids that cover the U.S. East Coast with resolutions as low as O(1 km). The computed patterns of the gradients of surf-zone integrated longshore sediment transport rates are compared with the observed shoreline change.

Are We Predicting the Actual or Apparent Distribution of Temperate Marine Fishes?

PubMed Central

Monk, Jacquomo; Ierodiaconou, Daniel; Harvey, Euan; Rattray, Alex; Versace, Vincent L.

2012-01-01

Planning for resilience is the focus of many marine conservation programs and initiatives. These efforts aim to inform conservation strategies for marine regions to ensure they have inbuilt capacity to retain biological diversity and ecological function in the face of global environmental change – particularly changes in climate and resource exploitation. In the absence of direct biological and ecological information for many marine species, scientists are increasingly using spatially-explicit, predictive-modeling approaches. Through the improved access to multibeam sonar and underwater video technology these models provide spatial predictions of the most suitable regions for an organism at resolutions previously not possible. However, sensible-looking, well-performing models can provide very different predictions of distribution depending on which occurrence dataset is used. To examine this, we construct species distribution models for nine temperate marine sedentary fishes for a 25.7 km2 study region off the coast of southeastern Australia. We use generalized linear model (GLM), generalized additive model (GAM) and maximum entropy (MAXENT) to build models based on co-located occurrence datasets derived from two underwater video methods (i.e. baited and towed video) and fine-scale multibeam sonar based seafloor habitat variables. Overall, this study found that the choice of modeling approach did not considerably influence the prediction of distributions based on the same occurrence dataset. However, greater dissimilarity between model predictions was observed across the nine fish taxa when the two occurrence datasets were compared (relative to models based on the same dataset). Based on these results it is difficult to draw any general trends in regards to which video method provides more reliable occurrence datasets. Nonetheless, we suggest predictions reflecting the species apparent distribution (i.e. a combination of species distribution and the probability of detecting it). Consequently, we also encourage researchers and marine managers to carefully interpret model predictions. PMID:22536325

Predicting Deforestation Patterns in Loreto, Peru from 2000-2010 Using a Nested GLM Approach

NASA Astrophysics Data System (ADS)

Vijay, V.; Jenkins, C.; Finer, M.; Pimm, S.

2013-12-01

Loreto is the largest province in Peru, covering about 370,000 km2. Because of its remote location in the Amazonian rainforest, it is also one of the most sparsely populated. Though a majority of the region remains covered by forest, deforestation is being driven by human encroachment through industrial activities and the spread of colonization and agriculture. The importance of accurate predictive modeling of deforestation has spawned an extensive body of literature on the topic. We present a nested GLM approach based on predictions of deforestation from 2000-2010 and using variables representing the expected drivers of deforestation. Models were constructed using 2000 to 2005 changes and tested against data for 2005 to 2010. The most complex model, which included transportation variables (roads and navigable rivers), spatial contagion processes, population centers and industrial activities, performed better in predicting the 2005 to 2010 changes (75.8% accurate) than did a simpler model using only transportation variables (69.2% accurate). Finally we contrast the GLM approach with a more complex spatially articulated model.

The Theory of Planned Behavior within the Stages of the Transtheoretical Model: Latent Structural Modeling of Stage-Specific Prediction Patterns in Physical Activity

ERIC Educational Resources Information Center

Lippke, Sonia; Nigg, Claudio R.; Maddock, Jay E.

2007-01-01

This is the first study to test whether the stages of change of the transtheoretical model are qualitatively different through exploring discontinuity patterns in theory of planned behavior (TPB) variables using latent multigroup structural equation modeling (MSEM) with AMOS. Discontinuity patterns in terms of latent means and prediction patterns…

Geometric Metamorphosis

PubMed Central

Niethammer, Marc; Hart, Gabriel L.; Pace, Danielle F.; Vespa, Paul M.; Irimia, Andrei; Van Horn, John D.; Aylward, Stephen R.

2013-01-01

Standard image registration methods do not account for changes in image appearance. Hence, metamorphosis approaches have been developed which jointly estimate a space deformation and a change in image appearance to construct a spatio-temporal trajectory smoothly transforming a source to a target image. For standard metamorphosis, geometric changes are not explicitly modeled. We propose a geometric metamorphosis formulation, which explains changes in image appearance by a global deformation, a deformation of a geometric model, and an image composition model. This work is motivated by the clinical challenge of predicting the long-term effects of traumatic brain injuries based on time-series images. This work is also applicable to the quantification of tumor progression (e.g., estimating its infiltrating and displacing components) and predicting chronic blood perfusion changes after stroke. We demonstrate the utility of the method using simulated data as well as scans from a clinical traumatic brain injury patient. PMID:21995083

Predicting Age-appropriate Pharmacokinetics of Six Volatile Organic Compounds in the Rat Utilizing Physiologically-based Pharmacokinetic Modeling (T)

EPA Science Inventory

The capability of physiologically-based pharmacokinetic (PBPK) models to incorporate ageappropriate physiological and chemical-specific parameters was utilized in this study to predict changes in internal dosimetry for six volatile organic compounds (VOCs) across different ages o...

Computational modelling of the piglet brain to simulate near-infrared spectroscopy and magnetic resonance spectroscopy data collected during oxygen deprivation.

PubMed

Moroz, Tracy; Banaji, Murad; Robertson, Nicola J; Cooper, Chris E; Tachtsidis, Ilias

2012-07-07

We describe a computational model to simulate measurements from near-infrared spectroscopy (NIRS) and magnetic resonance spectroscopy (MRS) in the piglet brain. Piglets are often subjected to anoxic, hypoxic and ischaemic insults, as experimental models for human neonates. The model aims to help interpret measurements and increase understanding of physiological processes occurring during such insults. It is an extension of a previous model of circulation and mitochondrial metabolism. This was developed to predict NIRS measurements in the brains of healthy adults i.e. concentration changes of oxyhaemoglobin and deoxyhaemoglobin and redox state changes of cytochrome c oxidase (CCO). We altered and enhanced the model to apply to the anaesthetized piglet brain. It now includes metabolites measured by (31)P-MRS, namely phosphocreatine, inorganic phosphate and adenosine triphosphate (ATP). It also includes simple descriptions of glycolysis, lactate dynamics and the tricarboxylic acid (TCA) cycle. The model is described, and its simulations compared with existing measurements from piglets during anoxia. The NIRS and MRS measurements are predicted well, although this requires a reduction in blood pressure autoregulation. Predictions of the cerebral metabolic rate of oxygen consumption (CMRO(2)) and lactate concentration, which were not measured, are given. Finally, the model is used to investigate hypotheses regarding changes in CCO redox state during anoxia.

A hybrid intelligent method for three-dimensional short-term prediction of dissolved oxygen content in aquaculture.

PubMed

Chen, Yingyi; Yu, Huihui; Cheng, Yanjun; Cheng, Qianqian; Li, Daoliang

2018-01-01

A precise predictive model is important for obtaining a clear understanding of the changes in dissolved oxygen content in crab ponds. Highly accurate interval forecasting of dissolved oxygen content is fundamental to reduce risk, and three-dimensional prediction can provide more accurate results and overall guidance. In this study, a hybrid three-dimensional (3D) dissolved oxygen content prediction model based on a radial basis function (RBF) neural network, K-means and subtractive clustering was developed and named the subtractive clustering (SC)-K-means-RBF model. In this modeling process, K-means and subtractive clustering methods were employed to enhance the hyperparameters required in the RBF neural network model. The comparison of the predicted results of different traditional models validated the effectiveness and accuracy of the proposed hybrid SC-K-means-RBF model for three-dimensional prediction of dissolved oxygen content. Consequently, the proposed model can effectively display the three-dimensional distribution of dissolved oxygen content and serve as a guide for feeding and future studies.

Prediction of movement intention using connectivity within motor-related network: An electrocorticography study.

PubMed

Kang, Byeong Keun; Kim, June Sic; Ryun, Seokyun; Chung, Chun Kee

2018-01-01

Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user's intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention.

Improved NASA-ANOPP Noise Prediction Computer Code for Advanced Subsonic Propulsion Systems

NASA Technical Reports Server (NTRS)

Kontos, K. B.; Janardan, B. A.; Gliebe, P. R.

1996-01-01

Recent experience using ANOPP to predict turbofan engine flyover noise suggests that it over-predicts overall EPNL by a significant amount. An improvement in this prediction method is desired for system optimization and assessment studies of advanced UHB engines. An assessment of the ANOPP fan inlet, fan exhaust, jet, combustor, and turbine noise prediction methods is made using static engine component noise data from the CF6-8OC2, E(3), and QCSEE turbofan engines. It is shown that the ANOPP prediction results are generally higher than the measured GE data, and that the inlet noise prediction method (Heidmann method) is the most significant source of this overprediction. Fan noise spectral comparisons show that improvements to the fan tone, broadband, and combination tone noise models are required to yield results that more closely simulate the GE data. Suggested changes that yield improved fan noise predictions but preserve the Heidmann model structure are identified and described. These changes are based on the sets of engine data mentioned, as well as some CFM56 engine data that was used to expand the combination tone noise database. It should be noted that the recommended changes are based on an analysis of engines that are limited to single stage fans with design tip relative Mach numbers greater than one.

Predicting CD4 count changes among patients on antiretroviral treatment: Application of data mining techniques.

PubMed

Kebede, Mihiretu; Zegeye, Desalegn Tigabu; Zeleke, Berihun Megabiaw

2017-12-01

To monitor the progress of therapy and disease progression, periodic CD4 counts are required throughout the course of HIV/AIDS care and support. The demand for CD4 count measurement is increasing as ART programs expand over the last decade. This study aimed to predict CD4 count changes and to identify the predictors of CD4 count changes among patients on ART. A cross-sectional study was conducted at the University of Gondar Hospital from 3,104 adult patients on ART with CD4 counts measured at least twice (baseline and most recent). Data were retrieved from the HIV care clinic electronic database and patients` charts. Descriptive data were analyzed by SPSS version 20. Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was followed to undertake the study. WEKA version 3.8 was used to conduct a predictive data mining. Before building the predictive data mining models, information gain values and correlation-based Feature Selection methods were used for attribute selection. Variables were ranked according to their relevance based on their information gain values. J48, Neural Network, and Random Forest algorithms were experimented to assess model accuracies. The median duration of ART was 191.5 weeks. The mean CD4 count change was 243 (SD 191.14) cells per microliter. Overall, 2427 (78.2%) patients had their CD4 counts increased by at least 100 cells per microliter, while 4% had a decline from the baseline CD4 value. Baseline variables including age, educational status, CD8 count, ART regimen, and hemoglobin levels predicted CD4 count changes with predictive accuracies of J48, Neural Network, and Random Forest being 87.1%, 83.5%, and 99.8%, respectively. Random Forest algorithm had a superior performance accuracy level than both J48 and Artificial Neural Network. The precision, sensitivity and recall values of Random Forest were also more than 99%. Nearly accurate prediction results were obtained using Random Forest algorithm. This algorithm could be used in a low-resource setting to build a web-based prediction model for CD4 count changes. Copyright © 2017 Elsevier B.V. All rights reserved.

Stretch-dependent changes in surface profiles of the human crystalline lens during accommodation: A finite element study

PubMed Central

Pour, Hooman Mohammad; Kanapathipillai, Sangarapillai; Zarrabi, Khosrow; Manns, Fabrice; Ho, Arthur

2015-01-01

Background A nonlinear isotropic finite element (FE) model of a 29 year old human crystalline lens was constructed to study the effects of various geometrical parameters on lens accommodation. Methods The model simulates dis-accommodation by stretching of the lens and predicts the change in the lens capsule, cortex and nucleus surface profiles at select states of stretching/accommodation. Multiple regression analysis (MRA) is used to develop a stretch-dependent mathematical model relating the lens sagittal height to the radial position of the lens surface as a function of dis-accommodative stretch. A load analysis is performed to compare the FE results to empirical results from lens stretcher studies. Using the predicted geometrical changes, the optical response of the whole eye during accommodation was analysed by ray-tracing. Results Aspects of lens shape change relative to stretch were evaluated including change in diameter (d), central thickness (T) and accommodation (A). Maximum accommodation achieved was 10.29 D. From the MRA, the stretch-dependent mathematical model of the lens shape related lens curvatures as a function of lens ciliary stretch well (maximum mean-square residual error 2.5×10−3 µm, p<0.001). The results are compared with those from in vitro studies. Conclusions The FE and ray-tracing predictions are consistent with EVAS studies in terms of load and power change versus change in thickness. The mathematical stretch-dependent model of accommodation presented may have utility in investigating lens behaviour at states other than the relaxed or fully-accommodated states. PMID:25727940

Mapping, Bayesian Geostatistical Analysis and Spatial Prediction of Lymphatic Filariasis Prevalence in Africa

PubMed Central

Slater, Hannah; Michael, Edwin

2013-01-01

There is increasing interest to control or eradicate the major neglected tropical diseases. Accurate modelling of the geographic distributions of parasitic infections will be crucial to this endeavour. We used 664 community level infection prevalence data collated from the published literature in conjunction with eight environmental variables, altitude and population density, and a multivariate Bayesian generalized linear spatial model that allows explicit accounting for spatial autocorrelation and incorporation of uncertainty in input data and model parameters, to construct the first spatially-explicit map describing LF prevalence distribution in Africa. We also ran the best-fit model against predictions made by the HADCM3 and CCCMA climate models for 2050 to predict the likely distributions of LF under future climate and population changes. We show that LF prevalence is strongly influenced by spatial autocorrelation between locations but is only weakly associated with environmental covariates. Infection prevalence, however, is found to be related to variations in population density. All associations with key environmental/demographic variables appear to be complex and non-linear. LF prevalence is predicted to be highly heterogenous across Africa, with high prevalences (>20%) estimated to occur primarily along coastal West and East Africa, and lowest prevalences predicted for the central part of the continent. Error maps, however, indicate a need for further surveys to overcome problems with data scarcity in the latter and other regions. Analysis of future changes in prevalence indicates that population growth rather than climate change per se will represent the dominant factor in the predicted increase/decrease and spread of LF on the continent. We indicate that these results could play an important role in aiding the development of strategies that are best able to achieve the goals of parasite elimination locally and globally in a manner that may also account for the effects of future climate change on parasitic infection. PMID:23951194

Predicting human developmental toxicity of pharmaceuticals using human embryonic stem cells and metabolomics

DOE Office of Scientific and Technical Information (OSTI.GOV)

West, Paul R., E-mail: pwest@stemina.co; Weir, April M.; Smith, Alan M.

2010-08-15

Teratogens, substances that may cause fetal abnormalities during development, are responsible for a significant number of birth defects. Animal models used to predict teratogenicity often do not faithfully correlate to human response. Here, we seek to develop a more predictive developmental toxicity model based on an in vitro method that utilizes both human embryonic stem (hES) cells and metabolomics to discover biomarkers of developmental toxicity. We developed a method where hES cells were dosed with several drugs of known teratogenicity then LC-MS analysis was performed to measure changes in abundance levels of small molecules in response to drug dosing. Statisticalmore » analysis was employed to select for specific mass features that can provide a prediction of the developmental toxicity of a substance. These molecules can serve as biomarkers of developmental toxicity, leading to better prediction of teratogenicity. In particular, our work shows a correlation between teratogenicity and changes of greater than 10% in the ratio of arginine to asymmetric dimethylarginine levels. In addition, this study resulted in the establishment of a predictive model based on the most informative mass features. This model was subsequently tested for its predictive accuracy in two blinded studies using eight drugs of known teratogenicity, where it correctly predicted the teratogenicity for seven of the eight drugs. Thus, our initial data shows that this platform is a robust alternative to animal and other in vitro models for the prediction of the developmental toxicity of chemicals that may also provide invaluable information about the underlying biochemical pathways.« less

Effects of social contact and zygosity on 21-y weight change in male twins.

PubMed

McCaffery, Jeanne M; Franz, Carol E; Jacobson, Kristen; Leahey, Tricia M; Xian, Hong; Wing, Rena R; Lyons, Michael J; Kremen, William S

2011-08-01

Recent evidence indicates that social contact is related to similarities in weight gain over time. However, no studies have examined this effect in a twin design, in which genetic and other environmental effects can also be estimated. We determined whether the frequency of social contact is associated with similarity in weight change from young adulthood (mean age: 20 y) to middle age (mean age: 41 y) in twins and quantified the percentage of variance in weight change attributable to social contact, genetic factors, and other environmental influences. Participants were 1966 monozygotic and 1529 dizygotic male twin pairs from the Vietnam-Era Twin Registry. Regression models tested whether frequency of social contact and zygosity predicted twin pair similarity in body mass index (BMI) change and weight change. Twin modeling was used to partition the percentage variance attributable to social contact, genetic, and other environmental effects. Twins gained an average of 3.99 BMI units, or 13.23 kg (29.11 lb), over 21 y. In regression models, both zygosity (P < 0.001) and degree of social contact (P < 0.02) significantly predicted twin pair similarity in BMI change. In twin modeling, social contact between twins contributed 16% of the variance in BMI change (P < 0.001), whereas genetic factors contributed 42%, with no effect of additional shared environmental factors (1%). Similar results were obtained for weight change. Frequency of social contact significantly predicted twin pair similarity in BMI and weight change over 21 y, independent of zygosity and other shared environmental influences.

National Centers for Environmental Prediction

Science.gov Websites

Organization Search Enter text Search Navigation Bar End Cap Search EMC Go Branches Global Climate and Weather Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post Configuration Collaborators Documentation and Code FAQ Operational Change Log Parallel Experiment Change Log

National Centers for Environmental Prediction

Science.gov Websites

Organization Search Enter text Search Navigation Bar End Cap Search EMC Go Branches Global Climate and Weather Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post Collaborators Documentation and Code FAQ Operational Change Log Parallel Experiment Change Log Contacts

Forecasting the effects of land use scenarios on farmland birds reveal a potential mitigation of climate change impacts.

PubMed

Princé, Karine; Lorrillière, Romain; Barbet-Massin, Morgane; Léger, François; Jiguet, Frédéric

2015-01-01

Climate and land use changes are key drivers of current biodiversity trends, but interactions between these drivers are poorly modeled, even though they could amplify or mitigate negative impacts of climate change. Here, we attempt to predict the impacts of different agricultural change scenarios on common breeding birds within farmland included in the potential future climatic suitable areas for these species. We used the Special Report on Emissions Scenarios (SRES) to integrate likely changes in species climatic suitability, based on species distribution models, and changes in area of farmland, based on the IMAGE model, inside future climatic suitable areas. We also developed six farmland cover scenarios, based on expert opinion, which cover a wide spectrum of potential changes in livestock farming and cropping patterns by 2050. We ran generalized linear mixed models to calibrate the effects of farmland cover and climate change on bird specific abundance within 386 small agricultural regions. We used model outputs to predict potential changes in bird populations on the basis of predicted changes in regional farmland cover, in area of farmland and in species climatic suitability. We then examined the species sensitivity according to their habitat requirements. A scenario based on extensification of agricultural systems (i.e., low-intensity agriculture) showed the greatest potential to reduce reverse current declines in breeding birds. To meet ecological requirements of a larger number of species, agricultural policies accounting for regional disparities and landscape structure appear more efficient than global policies uniformly implemented at national scale. Interestingly, we also found evidence that farmland cover changes can mitigate the negative effect of climate change. Here, we confirm that there is a potential for countering negative effects of climate change by adaptive management of landscape. We argue that such studies will help inform sustainable agricultural policies for the future.

Updating Known Distribution Models for Forecasting Climate Change Impact on Endangered Species

PubMed Central

Muñoz, Antonio-Román; Márquez, Ana Luz; Real, Raimundo

2013-01-01

To plan endangered species conservation and to design adequate management programmes, it is necessary to predict their distributional response to climate change, especially under the current situation of rapid change. However, these predictions are customarily done by relating de novo the distribution of the species with climatic conditions with no regard of previously available knowledge about the factors affecting the species distribution. We propose to take advantage of known species distribution models, but proceeding to update them with the variables yielded by climatic models before projecting them to the future. To exemplify our proposal, the availability of suitable habitat across Spain for the endangered Bonelli's Eagle (Aquila fasciata) was modelled by updating a pre-existing model based on current climate and topography to a combination of different general circulation models and Special Report on Emissions Scenarios. Our results suggested that the main threat for this endangered species would not be climate change, since all forecasting models show that its distribution will be maintained and increased in mainland Spain for all the XXI century. We remark on the importance of linking conservation biology with distribution modelling by updating existing models, frequently available for endangered species, considering all the known factors conditioning the species' distribution, instead of building new models that are based on climate change variables only. PMID:23840330

Updating known distribution models for forecasting climate change impact on endangered species.

PubMed

Muñoz, Antonio-Román; Márquez, Ana Luz; Real, Raimundo

2013-01-01

To plan endangered species conservation and to design adequate management programmes, it is necessary to predict their distributional response to climate change, especially under the current situation of rapid change. However, these predictions are customarily done by relating de novo the distribution of the species with climatic conditions with no regard of previously available knowledge about the factors affecting the species distribution. We propose to take advantage of known species distribution models, but proceeding to update them with the variables yielded by climatic models before projecting them to the future. To exemplify our proposal, the availability of suitable habitat across Spain for the endangered Bonelli's Eagle (Aquila fasciata) was modelled by updating a pre-existing model based on current climate and topography to a combination of different general circulation models and Special Report on Emissions Scenarios. Our results suggested that the main threat for this endangered species would not be climate change, since all forecasting models show that its distribution will be maintained and increased in mainland Spain for all the XXI century. We remark on the importance of linking conservation biology with distribution modelling by updating existing models, frequently available for endangered species, considering all the known factors conditioning the species' distribution, instead of building new models that are based on climate change variables only.

Can integrative catchment management mitigate future water quality issues caused by climate change and socio-economic development?

NASA Astrophysics Data System (ADS)

Honti, Mark; Schuwirth, Nele; Rieckermann, Jörg; Stamm, Christian

2017-03-01

The design and evaluation of solutions for integrated surface water quality management requires an integrated modelling approach. Integrated models have to be comprehensive enough to cover the aspects relevant for management decisions, allowing for mapping of larger-scale processes such as climate change to the regional and local contexts. Besides this, models have to be sufficiently simple and fast to apply proper methods of uncertainty analysis, covering model structure deficits and error propagation through the chain of sub-models. Here, we present a new integrated catchment model satisfying both conditions. The conceptual iWaQa model was developed to support the integrated management of small streams. It can be used to predict traditional water quality parameters, such as nutrients and a wide set of organic micropollutants (plant and material protection products), by considering all major pollutant pathways in urban and agricultural environments. Due to its simplicity, the model allows for a full, propagative analysis of predictive uncertainty, including certain structural and input errors. The usefulness of the model is demonstrated by predicting future surface water quality in a small catchment with mixed land use in the Swiss Plateau. We consider climate change, population growth or decline, socio-economic development, and the implementation of management strategies to tackle urban and agricultural point and non-point sources of pollution. Our results indicate that input and model structure uncertainties are the most influential factors for certain water quality parameters. In these cases model uncertainty is already high for present conditions. Nevertheless, accounting for today's uncertainty makes management fairly robust to the foreseen range of potential changes in the next decades. The assessment of total predictive uncertainty allows for selecting management strategies that show small sensitivity to poorly known boundary conditions. The identification of important sources of uncertainty helps to guide future monitoring efforts and pinpoints key indicators, whose evolution should be closely followed to adapt management. The possible impact of climate change is clearly demonstrated by water quality substantially changing depending on single climate model chains. However, when all climate trajectories are combined, the human land use and management decisions have a larger influence on water quality against a time horizon of 2050 in the study.

Gauging climate change effects at local scales: weather-based indices to monitor insect harassment in caribou.

PubMed

Witter, Leslie A; Johnson, Chris J; Croft, Bruno; Gunn, Anne; Poirier, Lisa M

2012-09-01

Climate change is occurring at an accelerated rate in the Arctic. Insect harassment may be an important link between increased summer temperature and reduced body condition in caribou and reindeer (both Rangifer tarandus). To examine the effects of climate change at a scale relevant to Rangifer herds, we developed monitoring indices using weather to predict activity of parasitic insects across the central Arctic. During 2007-2009, we recorded weather conditions and used carbon dioxide baited traps to monitor activity of mosquitoes (Culicidae), black flies (Simuliidae), and oestrid flies (Oestridae) on the post-calving and summer range of the Bathurst barren-ground caribou (Rangifer tarandus groenlandicus) herd in Northwest Territories and Nunavut, Canada. We developed statistical models representing hypotheses about effects of weather, habitat, location, and temporal variables on insect activity. We used multinomial logistic regression to model mosquito and black fly activity, and logistic regression to model oestrid fly presence. We used information theory to select models to predict activity levels of insects. Using historical weather data, we used hindcasting to develop a chronology of insect activity on the Bathurst range from 1957 to 2008. Oestrid presence and mosquito and black fly activity levels were explained by temperature. Wind speed, light intensity, barometric pressure, relative humidity, vegetation, topography, location, time of day, and growing degree-days also affected mosquito and black fly levels. High predictive ability of all models justified the use of weather to index insect activity. Retrospective analyses indicated conditions favoring mosquito activity declined since the late 1950s, while predicted black fly and oestrid activity increased. Our indices can be used as monitoring tools to gauge potential changes in insect harassment due to climate change at scales relevant to caribou herds.

Dietary and exercise change following acute cardiac syndrome onset: A latent class growth modelling analysis.

PubMed

Bennett, Paul; Gruszczynska, Ewa; Marke, Victoria

2016-10-01

The present study aim determine sub-group trajectories of change on measures of diet and exercise following acute coronary syndrome. 150 participants were assessed in hospital, 1 month and 6 months subsequently on measures including physical activity, diet, illness beliefs, coping and mood. Change trajectories were measured using latent class growth modelling. Multinomial logistic regression was used to predict class membership. These analyses revealed changes in exercise were confined to a sub-group of participants already reporting relatively high exercise levels; those eating less healthily evidenced modest dietary improvements. Coping, gender, depression and perceived control predicted group membership to a modest degree. © The Author(s) 2015.

Decadal climate prediction (project GCEP).

PubMed

Haines, Keith; Hermanson, Leon; Liu, Chunlei; Putt, Debbie; Sutton, Rowan; Iwi, Alan; Smith, Doug

2009-03-13

Decadal prediction uses climate models forced by changing greenhouse gases, as in the International Panel for Climate Change, but unlike longer range predictions they also require initialization with observations of the current climate. In particular, the upper-ocean heat content and circulation have a critical influence. Decadal prediction is still in its infancy and there is an urgent need to understand the important processes that determine predictability on these timescales. We have taken the first Hadley Centre Decadal Prediction System (DePreSys) and implemented it on several NERC institute compute clusters in order to study a wider range of initial condition impacts on decadal forecasting, eventually including the state of the land and cryosphere. The eScience methods are used to manage submission and output from the many ensemble model runs required to assess predictive skill. Early results suggest initial condition skill may extend for several years, even over land areas, but this depends sensitively on the definition used to measure skill, and alternatives are presented. The Grid for Coupled Ensemble Prediction (GCEP) system will allow the UK academic community to contribute to international experiments being planned to explore decadal climate predictability.

Persistent Organic Pollutants in Norwegian Men from 1979 to 2007: Intraindividual Changes, Age–Period–Cohort Effects, and Model Predictions

PubMed Central

Breivik, Knut; Fuskevåg, Ole-Martin; Nieboer, Evert; Odland, Jon Øyvind; Sandanger, Torkjel Manning

2013-01-01

Background: Longitudinal monitoring studies of persistent organic pollutants (POPs) in human populations are important to better understand changes with time and age, and for future predictions. Objectives: We sought to describe serum POP time trends on an individual level, investigate age–period–cohort effects, and compare predicted polychlorinated biphenyl (PCB) concentrations to measured values. Methods: Serum was sampled in 1979, 1986, 1994, 2001, and 2007 from a cohort of 53 men in Northern Norway and analyzed for 41 POPs. Time period, age, and birth cohort effects were assessed by graphical analyses and mixed-effect models. We derived the predicted concentrations of four PCBs for each sampling year using the CoZMoMAN model. Results: The median decreases in summed serum POP concentrations (lipid-adjusted) in 1986, 1994, 2001, and 2007 relative to 1979 were –22%, –52%, –54%, and –68%, respectively. We observed substantial declines in all POP groups with the exception of chlordanes. Time period (reflected by sampling year) was the strongest descriptor of changes in PCB-153 concentrations. Predicted PCB-153 concentrations were consistent with measured concentrations in the study population. Conclusions: Our results suggest substantial intraindividual declines in serum concentrations of legacy POPs from 1979 to 2007 in men from Northern Norway. These changes are consistent with reduced environmental exposure during these 30 years and highlight the relation between historic emissions and POP concentrations measured in humans. Observed data and interpretations are supported by estimates from the CoZMoMAN emission-based model. A longitudinal decrease in concentrations with age was evident for all birth cohorts. Overall, our findings support the relevance of age–period–cohort effects to human biomonitoring of environmental contaminants. Citation: Nøst TH, Breivik K, Fuskevåg OM, Nieboer E, Odland JØ, Sandanger TM. 2013. Persistent organic pollutants in Norwegian men from 1979 to 2007: intraindividual changes, age–period–cohort effects, and model predictions. Environ Health Perspect 121:1292–1298; http://dx.doi.org/10.1289/ehp.1206317 PMID:24007675

Fluorescence Competition Assay Measurements of Free Energy Changes for RNA Pseudoknots†

PubMed Central

2009-01-01

RNA pseudoknots have important functions, and thermodynamic stability is a key to predicting pseudoknots in RNA sequences and to understanding their functions. Traditional methods, such as UV melting and differential scanning calorimetry, for measuring RNA thermodynamics are restricted to temperature ranges around the melting temperature for a pseudoknot. Here, we report RNA pseudoknot free energy changes at 37 °C measured by fluorescence competition assays. Sequence-dependent studies for the loop 1−stem 2 region reveal (1) the individual nearest-neighbor hydrogen bonding (INN-HB) model provides a reasonable estimate for the free energy change when a Watson−Crick base pair in stem 2 is changed, (2) the loop entropy can be estimated by a statistical polymer model, although some penalty for certain loop sequences is necessary, and (3) tertiary interactions can significantly stabilize pseudoknots and extending the length of stem 2 may alter tertiary interactions such that the INN-HB model does not predict the net effect of adding a base pair. The results can inform writing of algorithms for predicting and/or designing RNA secondary structures. PMID:19921809

The impact of climatological model biases in the North Atlantic jet on predicted future circulation change

NASA Astrophysics Data System (ADS)

Simpson, I.

2015-12-01

A long standing bias among global climate models (GCMs) is their incorrect representation of the wintertime circulation of the North Atlantic region. Specifically models tend to exhibit a North Atlantic jet (and associated storm track) that is too zonal, extending across central Europe, when it should tilt northward toward Scandinavia. GCM's consistently predict substantial changes in the large scale circulation in this region, consisting of a localized anti-cyclonic circulation, centered over the Mediterranean and accompanied by increased aridity there and increased storminess over Northern Europe.Here, we present preliminary results from experiments that are designed to address the question of what the impact of the climatological circulation biases might be on this predicted future response. Climate change experiments will be compared in two versions of the Community Earth System Model: the first is a free running version of the model, as typically used in climate prediction; the second is a bias corrected version of the model in which a seasonally varying cycle of bias correction tendencies are applied to the wind and temperature fields. These bias correction tendencies are designed to account for deficiencies in the fast parameterized processes, with an aim to push the model toward a more realistic climatology.While these experiments come with the caveat that they assume the bias correction tendencies will remain constant with time, they allow for an initial assessment, through controlled experiments, of the impact that biases in the climatological circulation can have on future predictions in this region. They will also motivate future work that can make use of the bias correction tendencies to understand the underlying physical processes responsible for the incorrect tilt of the jet.

Predicting future spatial distribution of SOC across entire France

NASA Astrophysics Data System (ADS)

Meersmans, Jeroen; Van Rompaey, Anton; Quine, Tim; Martin, Manuel; Pagé, Christian; Arrouays, Dominique

2013-04-01

Soil organic carbon (SOC) is widely recognized as a key factor controlling soil quality and as a crucial and active component of the global C-cycle. Hence, there exists a growing interest in monitoring and modeling the spatial and temporal behavior of this pool. So far, a large attempt has been made to map SOC at national scales for current and/or past situations. Despite some coarse predictions, detailed spatial SOC predictions for the future are still lacking. In this study we aim to predict future spatial evolution of SOC driven by climate and land use change for France up to the year 2100. Therefore, we combined 1) an existing model, predicting SOC as a function of soil type, climate, land use and management (Meersmans et al 2012), with 2) eight different IPCC spatial explicit climate change predictions (conducted by CERFACS) and 3) Land use change scenario predictions. We created business-as-usual land use change scenarios by extrapolating observed trends and calibrating logistic regression models, incorporating a large set of physical and socio-economic factors, at the regional level in combination with a multi-objective land allocation (MOLA) procedure. The resultant detailed projections of future SOC evolution across all regions of France, allow us to identify regions that are most likely to be characterized by a significant gain or loss of SOC and the degree to which land use decisions/outcomes control the scale of loss and gain. Therefore, this methodology and resulting maps can be considered as powerful tools to aid decision making concerning appropriate soil management, in order to enlarge SOC storage possibilities and reduce soil related CO2 fluxes.

Healthy work revisited: do changes in time strain predict well-being?

PubMed

Moen, Phyllis; Kelly, Erin L; Lam, Jack

2013-04-01

Building on Karasek and Theorell (R. Karasek & T. Theorell, 1990, Healthy work: Stress, productivity, and the reconstruction of working life, New York, NY: Basic Books), we theorized and tested the relationship between time strain (work-time demands and control) and seven self-reported health outcomes. We drew on survey data from 550 employees fielded before and 6 months after the implementation of an organizational intervention, the results only work environment (ROWE) in a white-collar organization. Cross-sectional (wave 1) models showed psychological time demands and time control measures were related to health outcomes in expected directions. The ROWE intervention did not predict changes in psychological time demands by wave 2, but did predict increased time control (a sense of time adequacy and schedule control). Statistical models revealed increases in psychological time demands and time adequacy predicted changes in positive (energy, mastery, psychological well-being, self-assessed health) and negative (emotional exhaustion, somatic symptoms, psychological distress) outcomes in expected directions, net of job and home demands and covariates. This study demonstrates the value of including time strain in investigations of the health effects of job conditions. Results encourage longitudinal models of change in psychological time demands as well as time control, along with the development and testing of interventions aimed at reducing time strain in different populations of workers.

The effect of Earth's oblateness on the seismic moment estimation from satellite gravimetry

NASA Astrophysics Data System (ADS)

Dai, Chunli; Guo, Junyi; Shang, Kun; Shum, C. K.; Wang, Rongjiang

2018-05-01

Over the last decade, satellite gravimetry, as a new class of geodetic sensors, has been increasingly studied for its use in improving source model inversion for large undersea earthquakes. When these satellite-observed gravity change data are used to estimate source parameters such as seismic moment, the forward modelling of earthquake seismic deformation is crucial because imperfect modelling could lead to errors in the resolved source parameters. Here, we discuss several modelling issues and focus on one modelling deficiency resulting from the upward continuation of gravity change considering the Earth's oblateness, which is ignored in contemporary studies. For the low degree (degree 60) time-variable gravity solutions from Gravity Recovery and Climate Experiment mission data, the model-predicted gravity change would be overestimated by 9 per cent for the 2011 Tohoku earthquake, and about 6 per cent for the 2010 Maule earthquake. For high degree gravity solutions, the model-predicted gravity change at degree 240 would be overestimated by 30 per cent for the 2011 Tohoku earthquake, resulting in the seismic moment to be systematically underestimated by 30 per cent.

Competitive and demographic leverage points of community shifts under climate warming

PubMed Central

Sorte, Cascade J. B.; White, J. Wilson

2013-01-01

Accelerating rates of climate change and a paucity of whole-community studies of climate impacts limit our ability to forecast shifts in ecosystem structure and dynamics, particularly because climate change can lead to idiosyncratic responses via both demographic effects and altered species interactions. We used a multispecies model to predict which processes and species' responses are likely to drive shifts in the composition of a space-limited benthic marine community. Our model was parametrized from experimental manipulations of the community. Model simulations indicated shifts in species dominance patterns as temperatures increase, with projected shifts in composition primarily owing to the temperature dependence of growth, mortality and competition for three critical species. By contrast, warming impacts on two other species (rendering them weaker competitors for space) and recruitment rates of all species were of lesser importance in determining projected community changes. Our analysis reveals the importance of temperature-dependent competitive interactions for predicting effects of changing climate on such communities. Furthermore, by identifying processes and species that could disproportionately leverage shifts in community composition, our results contribute to a mechanistic understanding of climate change impacts, thereby allowing more insightful predictions of future biodiversity patterns. PMID:23658199

Potential impacts of climate change on birds and trees of the eastern United States: newest climate scenarios and species abundance modelling techniques

Treesearch

L.R. Iverson; A.M. Prasad; S.N. Matthews; M.P. Peters

2007-01-01

Climate change is affecting an increasing number of species the world over, and evidence is mounting that these changes will continue to accelerate. There have been many studies that use a modelling approach to predict the effects of future climatic change on ecological systems, including by us (Iverson et al. 1999, Matthews et al. 2004); this modelling approach uses a...

Implications of between-isolate variation for climate change impact modelling of Haemonchus contortus populations.

PubMed

Rose Vineer, H; Steiner, J; Knapp-Lawitzke, F; Bull, K; von Son-de Fernex, E; Bosco, A; Hertzberg, H; Demeler, J; Rinaldi, L; Morrison, A A; Skuce, P; Bartley, D J; Morgan, E R

2016-10-15

The impact of climate change on parasites and parasitic diseases is a growing concern and numerous empirical and mechanistic models have been developed to predict climate-driven spatial and temporal changes in the distribution of parasites and disease risk. Variation in parasite phenotype and life-history traits between isolates could undermine the application of such models at broad spatial scales. Seasonal variation in the transmission of the haematophagous gastrointestinal nematode Haemonchus contortus, one of the most pathogenic helminth species infecting sheep and goats worldwide, is primarily determined by the impact of environmental conditions on the free-living stages. To evaluate variability in the development success and mortality of the free-living stages of H. contortus and the impact of this variability on future climate impact modelling, three isolates of diverse origin were cultured at a range of temperatures between 15°C and 37°C to determine their development success compared with simulations using the GLOWORM-FL H. contortus model. No significant difference was observed in the developmental success of the three isolates of H. contortus tested, nor between isolates and model simulations. However, development success of all isolates at 37°C was lower than predicted by the model, suggesting the potential for overestimation of transmission risk at higher temperatures, such as those predicted under some scenarios of climate change. Recommendations are made for future climate impact modelling of gastrointestinal nematodes. Copyright © 2016 Elsevier B.V. All rights reserved.

Model-based learning and the contribution of the orbitofrontal cortex to the model-free world

PubMed Central

McDannald, Michael A.; Takahashi, Yuji K.; Lopatina, Nina; Pietras, Brad W.; Jones, Josh L.; Schoenbaum, Geoffrey

2012-01-01

Learning is proposed to occur when there is a discrepancy between reward prediction and reward receipt. At least two separate systems are thought to exist: one in which predictions are proposed to be based on model-free or cached values; and another in which predictions are model-based. A basic neural circuit for model-free reinforcement learning has already been described. In the model-free circuit the ventral striatum (VS) is thought to supply a common-currency reward prediction to midbrain dopamine neurons that compute prediction errors and drive learning. In a model-based system, predictions can include more information about an expected reward, such as its sensory attributes or current, unique value. This detailed prediction allows for both behavioral flexibility and learning driven by changes in sensory features of rewards alone. Recent evidence from animal learning and human imaging suggests that, in addition to model-free information, the VS also signals model-based information. Further, there is evidence that the orbitofrontal cortex (OFC) signals model-based information. Here we review these data and suggest that the OFC provides model-based information to this traditional model-free circuitry and offer possibilities as to how this interaction might occur. PMID:22487030

Incorporating uncertainty in predictive species distribution modelling.

PubMed

Beale, Colin M; Lennon, Jack J

2012-01-19

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

Taking dietary habits into account: A computational method for modeling food choices that goes beyond price

PubMed Central

Jones-Smith, Jessica C.; Igusa, Takeru

2017-01-01

Computational models have gained popularity as a predictive tool for assessing proposed policy changes affecting dietary choice. Specifically, they have been used for modeling dietary changes in response to economic interventions, such as price and income changes. Herein, we present a novel addition to this type of model by incorporating habitual behaviors that drive individuals to maintain or conform to prior eating patterns. We examine our method in a simulated case study of food choice behaviors of low-income adults in the US. We use data from several national datasets, including the National Health and Nutrition Examination Survey (NHANES), the US Bureau of Labor Statistics and the USDA, to parameterize our model and develop predictive capabilities in 1) quantifying the influence of prior diet preferences when food budgets are increased and 2) simulating the income elasticities of demand for four food categories. Food budgets can increase because of greater affordability (due to food aid and other nutritional assistance programs), or because of higher income. Our model predictions indicate that low-income adults consume unhealthy diets when they have highly constrained budgets, but that even after budget constraints are relaxed, these unhealthy eating behaviors are maintained. Specifically, diets in this population, before and after changes in food budgets, are characterized by relatively low consumption of fruits and vegetables and high consumption of fat. The model results for income elasticities also show almost no change in consumption of fruit and fat in response to changes in income, which is in agreement with data from the World Bank’s International Comparison Program (ICP). Hence, the proposed method can be used in assessing the influences of habitual dietary patterns on the effectiveness of food policies. PMID:28542615

Taking dietary habits into account: A computational method for modeling food choices that goes beyond price.

PubMed

Beheshti, Rahmatollah; Jones-Smith, Jessica C; Igusa, Takeru

2017-01-01

Computational models have gained popularity as a predictive tool for assessing proposed policy changes affecting dietary choice. Specifically, they have been used for modeling dietary changes in response to economic interventions, such as price and income changes. Herein, we present a novel addition to this type of model by incorporating habitual behaviors that drive individuals to maintain or conform to prior eating patterns. We examine our method in a simulated case study of food choice behaviors of low-income adults in the US. We use data from several national datasets, including the National Health and Nutrition Examination Survey (NHANES), the US Bureau of Labor Statistics and the USDA, to parameterize our model and develop predictive capabilities in 1) quantifying the influence of prior diet preferences when food budgets are increased and 2) simulating the income elasticities of demand for four food categories. Food budgets can increase because of greater affordability (due to food aid and other nutritional assistance programs), or because of higher income. Our model predictions indicate that low-income adults consume unhealthy diets when they have highly constrained budgets, but that even after budget constraints are relaxed, these unhealthy eating behaviors are maintained. Specifically, diets in this population, before and after changes in food budgets, are characterized by relatively low consumption of fruits and vegetables and high consumption of fat. The model results for income elasticities also show almost no change in consumption of fruit and fat in response to changes in income, which is in agreement with data from the World Bank's International Comparison Program (ICP). Hence, the proposed method can be used in assessing the influences of habitual dietary patterns on the effectiveness of food policies.

Is the future already here? The impact of climate change on the distribution of the eastern coral snake (Micrurus fulvius).

PubMed

Archis, Jennifer N; Akcali, Christopher; Stuart, Bryan L; Kikuchi, David; Chunco, Amanda J

2018-01-01

Anthropogenic climate change is a significant global driver of species distribution change. Although many species have undergone range expansion at their poleward limits, data on several taxonomic groups are still lacking. A common method for studying range shifts is using species distribution models to evaluate current, and predict future, distributions. Notably, many sources of 'current' climate data used in species distribution modeling use the years 1950-2000 to calculate climatic averages. However, this does not account for recent (post 2000) climate change. This study examines the influence of climate change on the eastern coral snake ( Micrurus fulvius ). Specifically, we: (1) identified the current range and suitable environment of M. fulvius in the Southeastern United States, (2) investigated the potential impacts of climate change on the distribution of M. fulvius , and (3) evaluated the utility of future models in predicting recent (2001-2015) records. We used the species distribution modeling program Maxent and compared both current (1950-2000) and future (2050) climate conditions. Future climate models showed a shift in the distribution of suitable habitat across a significant portion of the range; however, results also suggest that much of the Southeastern United States will be outside the range of current conditions, suggesting that there may be no-analog environments in the future. Most strikingly, future models were more effective than the current models at predicting recent records, suggesting that range shifts may already be occurring. These results have implications for both M. fulvius and its Batesian mimics. More broadly, we recommend future Maxent studies consider using future climate data along with current data to better estimate the current distribution.

Ensembles-based predictions of climate change impacts on bioclimatic zones in Northeast Asia

NASA Astrophysics Data System (ADS)

Choi, Y.; Jeon, S. W.; Lim, C. H.; Ryu, J.

2017-12-01

Biodiversity is rapidly declining globally and efforts are needed to mitigate this continually increasing loss of species. Clustering of areas with similar habitats can be used to prioritize protected areas and distribute resources for the conservation of species, selection of representative sample areas for research, and evaluation of impacts due to environmental changes. In this study, Northeast Asia (NEA) was classified into 14 bioclimatic zones using statistical techniques, which are correlation analysis and principal component analysis (PCA), and the iterative self-organizing data analysis technique algorithm (ISODATA). Based on these bioclimatic classification, we predicted shift of bioclimatic zones due to climate change. The input variables include the current climatic data (1960-1990) and the future climatic data of the HadGEM2-AO model (RCP 4.5(2050, 2070) and 8.5(2050, 2070)) provided by WorldClim. Using these data, multi-modeling methods including maximum likelihood classification, random forest, and species distribution modelling have been used to project the impact of climate change on the spatial distribution of bioclimatic zones within NEA. The results of various models were compared and analyzed by overlapping each result. As the result, significant changes in bioclimatic conditions can be expected throughout the NEA by 2050s and 2070s. The overall zones moved upward and some zones were predicted to disappear. This analysis provides the basis for understanding potential impacts of climate change on biodiversity and ecosystem. Also, this could be used more effectively to support decision making on climate change adaptation.

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Singh, Vijay P.; Xia, Youlong

2018-03-01

Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large-scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high-quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

Improving predictions of carbon fluxes in the tropics undre climatic changes using ED2

NASA Astrophysics Data System (ADS)

Feng, X.; Uriarte, M.

2016-12-01

Tropical forests play a critical role in the exchange of carbon between land and atmosphere, highlighting the urgency of understanding the effects of climate change on these ecosystems. The most optimistic predictions of climate models indicate that global mean temperatures will increase by up to 2 0C with some tropical regions experiencing extreme heat. Drought and heat-induced tree mortality will accelerate the release of carbon to the atmosphere creating a positive feedback that greatly exacerbates global warming. Thus, under a warmer and drier climate, tropical forests may become net sources, rather than sinks, of carbon. Earth system models have not reached a consensus on the magnitude and direction of climate change impacts on tropical forests, calling into question the reliability of their predictions. Thus, there is an immediate need to improve the representation of tropical forests in earth system models to make robust predictions. The goal of our study is to quantify the responses of tropical forests to climate variability and improve the predictive capacity of terrestrial ecosystem models. We have collected species-specific physiological and functional trait data from 144 tree species in a Puerto Rican rainforest to parameterize the Ecosystem Demography model (ED2). The large amount of data generated by this research will lead to better validation and lowering the uncertainty in future model predictions. To best represent the forest landscape in ED2, all the trees have been assigned to three plant functional types (PFTs): early, mid, and late successional species. Trait data for each PFT were synthesized in a Bayesian meta-analytical model and posterior distributions of traits were used to parameterize the ED2 model. Model predictions show that biomass production of late successional PFT (118.89 ton/ha) was consistently higher than mid (71.33 ton/ha) and early (13.21 ton/ha) PFTs. However, mid successional PFT had the highest contributions to NPP for the modeled period. Tropical forest biomass reduces by 30% under future drought scenario turning the tropics into carbon sources. Ensemble runs were conducted to construct error estimates around model forecasts, to compare modeled and observed aboveground biomass, and to identify which processes and tree species need further study.

Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thomas, R. Quinn; Brooks, Evan B.; Jersild, Annika L.

Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model–data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions,more » DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO 2) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6 × 10 5 km 2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO 2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO 2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO 2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO 2, decreased precipitation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.« less

Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

DOE PAGES

Thomas, R. Quinn; Brooks, Evan B.; Jersild, Annika L.; ...

2017-07-26

Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model–data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions,more » DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO 2) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6 × 10 5 km 2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO 2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO 2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO 2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO 2, decreased precipitation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.« less

Kinetic modeling of growth and lipid body induction in Chlorella pyrenoidosa under heterotrophic conditions.

PubMed

Sachdeva, Neha; Kumar, G Dinesh; Gupta, Ravi Prakash; Mathur, Anshu Shankar; Manikandan, B; Basu, Biswajit; Tuli, Deepak Kumar

2016-10-01

The aim of the present work was to develop a mathematical model to describe the biomass and (total) lipid productivity of Chlorella pyrenoidosa NCIM 2738 under heterotrophic conditions. Biomass growth rate was predicted by Droop's cell quota model, while changes observed in cell quota (utilization) under carbon excess conditions were used for the modeling and predicting the lipid accumulation rate. The model was simulated under non-limiting (excess) carbon and limiting nitrate concentration and validated with experimental data for the culture grown in batch (flask) mode under different nitrate concentrations. The present model incorporated two modes (growth and stressed) for the prediction of endogenous lipid synthesis/induction and aimed to predict the effect and response of the microalgae under nutrient starvation (stressed) conditions. MATLAB and Genetic Algorithm were employed for the prediction and validation of the model parameters. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting Hopelessness and Psychological Distress: The Role of Perfectionism and Coping

ERIC Educational Resources Information Center

O'Connor, Rory C.; O'Connor, Daryl B.

2003-01-01

This study investigated an integrative model involving the relationship between perfectionism (P. L. Hewitt & G. L. Flett, 1991) and coping (C. S. Carver, A F. Scheier. & J. K. Weintraub, 1989) to predict changes in hopelessness and general psychological distress among college students. Results indicated that changes in psychological well-being…

Outgassing and dimensional changes of polymer matrix composites in space

NASA Technical Reports Server (NTRS)

Tennyson, R. C.; Matthews, R.

1993-01-01

A thermal-vacuum outgassing model and test protocol for predicting outgassing times and dimensional changes for polymer matrix composites is described. Experimental results derived from a 'control' sample are used to provide the basis for analytical predictions to compare with the outgassing response of Long Duration Exposure Facility (LDEF) flight samples.

LANDIS PRO: a landscape model that predicts forest composition and structure changes at regional scales

Treesearch

Wen J. Wang; Hong S. He; Jacob S. Fraser; Frank R. Thompson; Stephen R. Shifley; Martin A. Spetich

2014-01-01

LANDIS PRO predicts forest composition and structure changes incorporating species-, stand-, and landscape-scales processes at regional scales. Species-scale processes include tree growth, establishment, and mortality. Stand-scale processes contain density- and size-related resource competition that regulates self-thinning and seedling establishment. Landscapescale...

On-Line Learning and the Implications for School Design

ERIC Educational Resources Information Center

Stack, Greg

2011-01-01

"Disrupting Class," published in 2008, is the story of how disruptive innovation, innovation that changes the business model organizations, will fundamentally change the American school system. The book's most startling prediction is that half of all high school classes will be on-line by 2019. In considering these predictions, the author began to…

The Use of Statistical Downscaling to Project Regional Climate Changes as they Relate to Future Energy Production

NASA Astrophysics Data System (ADS)

Werth, D. W.; O'Steen, L.; Chen, K.; Altinakar, M. S.; Garrett, A.; Aleman, S.; Ramalingam, V.

2010-12-01

Global climate change has the potential for profound impacts on society, and poses significant challenges to government and industry in the areas of energy security and sustainability. Given that the ability to exploit energy resources often depends on the climate, the possibility of climate change means we cannot simply assume that the untapped potential of today will still exist in the future. Predictions of future climate are generally based on global climate models (GCMs) which, due to computational limitations, are run at spatial resolutions of hundreds of kilometers. While the results from these models can predict climatic trends averaged over large spatial and temporal scales, their ability to describe the effects of atmospheric phenomena that affect weather on regional to local scales is inadequate. We propose the use of several optimized statistical downscaling techniques that can infer climate change at the local scale from coarse resolution GCM predictions, and apply the results to assess future sustainability for two sources of energy production dependent on adequate water resources: nuclear power (through the dissipation of waste heat from cooling towers, ponds, etc.) and hydroelectric power. All methods will be trained with 20th century data, and applied to data from the years 2040-2049 to get the local-scale changes. Models of cooling tower operation and hydropower potential will then use the downscaled data to predict the possible changes in energy production, and the implications of climate change on plant siting, design, and contribution to the future energy grid can then be examined.

Time dependent neural network models for detecting changes of state in complex processes: applications in earth sciences and astronomy.

PubMed

Valdés, Julio J; Bonham-Carter, Graeme

2006-03-01

A computational intelligence approach is used to explore the problem of detecting internal state changes in time dependent processes; described by heterogeneous, multivariate time series with imprecise data and missing values. Such processes are approximated by collections of time dependent non-linear autoregressive models represented by a special kind of neuro-fuzzy neural network. Grid and high throughput computing model mining procedures based on neuro-fuzzy networks and genetic algorithms, generate: (i) collections of models composed of sets of time lag terms from the time series, and (ii) prediction functions represented by neuro-fuzzy networks. The composition of the models and their prediction capabilities, allows the identification of changes in the internal structure of the process. These changes are associated with the alternation of steady and transient states, zones with abnormal behavior, instability, and other situations. This approach is general, and its sensitivity for detecting subtle changes of state is revealed by simulation experiments. Its potential in the study of complex processes in earth sciences and astrophysics is illustrated with applications using paleoclimate and solar data.

Assessing spatiotemporal changes in forest carbon turnover times in observational data and models

NASA Astrophysics Data System (ADS)

Yu, K.; Smith, W. K.; Trugman, A. T.; van Mantgem, P.; Peng, C.; Condit, R.; Anderegg, W.

2017-12-01

Forests influence global carbon and water cycles, biophysical land-atmosphere feedbacks, and atmospheric composition. The capacity of forests to sequester atmospheric CO2 in a changing climate depends not only on the response of carbon uptake (i.e., gross primary productivity) but also on the simultaneous change in carbon residence time. However, changes in carbon residence with climate change are uncertain, impacting the accuracy of predictions of future terrestrial carbon cycle dynamics. Here, we use long-term forest inventory data representative of tropical, temperate, and boreal forests; satellite-based estimates of net primary productivity and vegetation carbon stock; and six models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to investigate spatiotemporal trends in carbon residence time and its relation to climate. Forest inventory and satellite-based estimates of carbon residence time show a pervasive decreasing trend across global forests. In contrast, the CMIP5 models diverge in predicting historical and future trends in carbon residence time. Divergence across CMIP5 models indicate carbon turnover times are not well constrained by observations, which likely contributes to large variability in future carbon cycle projections.

Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation.

PubMed

Zhang, Fan; Liu, Runsheng; Zheng, Jie

2016-12-23

Linking computational models of signaling pathways to predicted cellular responses such as gene expression regulation is a major challenge in computational systems biology. In this work, we present Sig2GRN, a Cytoscape plugin that is able to simulate time-course gene expression data given the user-defined external stimuli to the signaling pathways. A generalized logical model is used in modeling the upstream signaling pathways. Then a Boolean model and a thermodynamics-based model are employed to predict the downstream changes in gene expression based on the simulated dynamics of transcription factors in signaling pathways. Our empirical case studies show that the simulation of Sig2GRN can predict changes in gene expression patterns induced by DNA damage signals and drug treatments. As a software tool for modeling cellular dynamics, Sig2GRN can facilitate studies in systems biology by hypotheses generation and wet-lab experimental design. http://histone.scse.ntu.edu.sg/Sig2GRN/.

Temporal scale dependent interactions between multiple environmental disturbances in microcosm ecosystems.

PubMed

Garnier, Aurélie; Pennekamp, Frank; Lemoine, Mélissa; Petchey, Owen L

2017-12-01

Global environmental change has negative impacts on ecological systems, impacting the stable provision of functions, goods, and services. Whereas effects of individual environmental changes (e.g. temperature change or change in resource availability) are reasonably well understood, we lack information about if and how multiple changes interact. We examined interactions among four types of environmental disturbance (temperature, nutrient ratio, carbon enrichment, and light) in a fully factorial design using a microbial aquatic ecosystem and observed responses of dissolved oxygen saturation at three temporal scales (resistance, resilience, and return time). We tested whether multiple disturbances combine in a dominant, additive, or interactive fashion, and compared the predictability of dissolved oxygen across scales. Carbon enrichment and shading reduced oxygen concentration in the short term (i.e. resistance); although no other effects or interactions were statistically significant, resistance decreased as the number of disturbances increased. In the medium term, only enrichment accelerated recovery, but none of the other effects (including interactions) were significant. In the long term, enrichment and shading lengthened return times, and we found significant two-way synergistic interactions between disturbances. The best performing model (dominant, additive, or interactive) depended on the temporal scale of response. In the short term (i.e. for resistance), the dominance model predicted resistance of dissolved oxygen best, due to a large effect of carbon enrichment, whereas none of the models could predict the medium term (i.e. resilience). The long-term response was best predicted by models including interactions among disturbances. Our results indicate the importance of accounting for the temporal scale of responses when researching the effects of environmental disturbances on ecosystems. © 2017 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Increased Drought Impacts on Temperate Rainforests from Southern South America: Results of a Process-Based, Dynamic Forest Model

PubMed Central

Gutiérrez, Alvaro G.; Armesto, Juan J.; Díaz, M. Francisca; Huth, Andreas

2014-01-01

Increased droughts due to regional shifts in temperature and rainfall regimes are likely to affect forests in temperate regions in the coming decades. To assess their consequences for forest dynamics, we need predictive tools that couple hydrologic processes, soil moisture dynamics and plant productivity. Here, we developed and tested a dynamic forest model that predicts the hydrologic balance of North Patagonian rainforests on Chiloé Island, in temperate South America (42°S). The model incorporates the dynamic linkages between changing rainfall regimes, soil moisture and individual tree growth. Declining rainfall, as predicted for the study area, should mean up to 50% less summer rain by year 2100. We analysed forest responses to increased drought using the model proposed focusing on changes in evapotranspiration, soil moisture and forest structure (above-ground biomass and basal area). We compared the responses of a young stand (YS, ca. 60 years-old) and an old-growth forest (OG, >500 years-old) in the same area. Based on detailed field measurements of water fluxes, the model provides a reliable account of the hydrologic balance of these evergreen, broad-leaved rainforests. We found higher evapotranspiration in OG than YS under current climate. Increasing drought predicted for this century can reduce evapotranspiration by 15% in the OG compared to current values. Drier climate will alter forest structure, leading to decreases in above ground biomass by 27% of the current value in OG. The model presented here can be used to assess the potential impacts of climate change on forest hydrology and other threats of global change on future forests such as fragmentation, introduction of exotic tree species, and changes in fire regimes. Our study expands the applicability of forest dynamics models in remote and hitherto overlooked regions of the world, such as southern temperate rainforests. PMID:25068869

Increased drought impacts on temperate rainforests from southern South America: results of a process-based, dynamic forest model.

PubMed

Gutiérrez, Alvaro G; Armesto, Juan J; Díaz, M Francisca; Huth, Andreas

2014-01-01

Increased droughts due to regional shifts in temperature and rainfall regimes are likely to affect forests in temperate regions in the coming decades. To assess their consequences for forest dynamics, we need predictive tools that couple hydrologic processes, soil moisture dynamics and plant productivity. Here, we developed and tested a dynamic forest model that predicts the hydrologic balance of North Patagonian rainforests on Chiloé Island, in temperate South America (42°S). The model incorporates the dynamic linkages between changing rainfall regimes, soil moisture and individual tree growth. Declining rainfall, as predicted for the study area, should mean up to 50% less summer rain by year 2100. We analysed forest responses to increased drought using the model proposed focusing on changes in evapotranspiration, soil moisture and forest structure (above-ground biomass and basal area). We compared the responses of a young stand (YS, ca. 60 years-old) and an old-growth forest (OG, >500 years-old) in the same area. Based on detailed field measurements of water fluxes, the model provides a reliable account of the hydrologic balance of these evergreen, broad-leaved rainforests. We found higher evapotranspiration in OG than YS under current climate. Increasing drought predicted for this century can reduce evapotranspiration by 15% in the OG compared to current values. Drier climate will alter forest structure, leading to decreases in above ground biomass by 27% of the current value in OG. The model presented here can be used to assess the potential impacts of climate change on forest hydrology and other threats of global change on future forests such as fragmentation, introduction of exotic tree species, and changes in fire regimes. Our study expands the applicability of forest dynamics models in remote and hitherto overlooked regions of the world, such as southern temperate rainforests.

Modeling and predicting abstract concept or idea introduction and propagation through geopolitical groups

NASA Astrophysics Data System (ADS)

Jaenisch, Holger M.; Handley, James W.; Hicklen, Michael L.

2007-04-01

This paper describes a novel capability for modeling known idea propagation transformations and predicting responses to new ideas from geopolitical groups. Ideas are captured using semantic words that are text based and bear cognitive definitions. We demonstrate a unique algorithm for converting these into analytical predictive equations. Using the illustrative idea of "proposing a gasoline price increase of 1 per gallon from 2" and its changing perceived impact throughout 5 demographic groups, we identify 13 cost of living Diplomatic, Information, Military, and Economic (DIME) features common across all 5 demographic groups. This enables the modeling and monitoring of Political, Military, Economic, Social, Information, and Infrastructure (PMESII) effects of each group to this idea and how their "perception" of this proposal changes. Our algorithm and results are summarized in this paper.

Genomic signals of selection predict climate-driven population declines in a migratory bird.

PubMed

Bay, Rachael A; Harrigan, Ryan J; Underwood, Vinh Le; Gibbs, H Lisle; Smith, Thomas B; Ruegg, Kristen

2018-01-05

The ongoing loss of biodiversity caused by rapid climatic shifts requires accurate models for predicting species' responses. Despite evidence that evolutionary adaptation could mitigate climate change impacts, evolution is rarely integrated into predictive models. Integrating population genomics and environmental data, we identified genomic variation associated with climate across the breeding range of the migratory songbird, yellow warbler ( Setophaga petechia ). Populations requiring the greatest shifts in allele frequencies to keep pace with future climate change have experienced the largest population declines, suggesting that failure to adapt may have already negatively affected populations. Broadly, our study suggests that the integration of genomic adaptation can increase the accuracy of future species distribution models and ultimately guide more effective mitigation efforts. Copyright © 2018, American Association for the Advancement of Science.

Documentation of the Benson Diesel Engine Simulation Program

NASA Technical Reports Server (NTRS)

Vangerpen, Jon

1988-01-01

This report documents the Benson Diesel Engine Simulation Program and explains how it can be used to predict the performance of diesel engines. The program was obtained from the Garrett Turbine Engine Company but has been extensively modified since. The program is a thermodynamic simulation of the diesel engine cycle which uses a single zone combustion model. It can be used to predict the effect of changes in engine design and operating parameters such as valve timing, speed and boost pressure. The most significan change made to this program is the addition of a more detailed heat transfer model to predict metal part temperatures. This report contains a description of the sub-models used in the Benson program, a description of the input parameters and sample program runs.

Some considerations on the use of ecological models to predict species' geographic distributions

USGS Publications Warehouse

Peterjohn, B.G.

2001-01-01

Peterson (2001) used Genetic Algorithm for Rule-set Prediction (GARP) models to predict distribution patterns from Breeding Bird Survey (BBS) data. Evaluations of these models should consider inherent limitations of BBS data: (1) BBS methods may not sample species and habitats equally; (2) using BBS data for both model development and testing may overlook poor fit of some models; and (3) BBS data may not provide the desired spatial resolution or capture temporal changes in species distributions. The predictive value of GARP models requires additional study, especially comparisons with distribution patterns from independent data sets. When employed at appropriate temporal and geographic scales, GARP models show considerable promise for conservation biology applications but provide limited inferences concerning processes responsible for the observed patterns.

Review of Nearshore Morphologic Prediction

NASA Astrophysics Data System (ADS)

Plant, N. G.; Dalyander, S.; Long, J.

2014-12-01

The evolution of the world's erodible coastlines will determine the balance between the benefits and costs associated with human and ecological utilization of shores, beaches, dunes, barrier islands, wetlands, and estuaries. So, we would like to predict coastal evolution to guide management and planning of human and ecological response to coastal changes. After decades of research investment in data collection, theoretical and statistical analysis, and model development we have a number of empirical, statistical, and deterministic models that can predict the evolution of the shoreline, beaches, dunes, and wetlands over time scales of hours to decades, and even predict the evolution of geologic strata over the course of millennia. Comparisons of predictions to data have demonstrated that these models can have meaningful predictive skill. But these comparisons also highlight the deficiencies in fundamental understanding, formulations, or data that are responsible for prediction errors and uncertainty. Here, we review a subset of predictive models of the nearshore to illustrate tradeoffs in complexity, predictive skill, and sensitivity to input data and parameterization errors. We identify where future improvement in prediction skill will result from improved theoretical understanding, and data collection, and model-data assimilation.

An eco-evolutionary IBM improves predictions of future geneticconnectivity for American Pikas (Ochotona princeps) in Crater Lake National Park, Oregon

EPA Science Inventory

In the face of rapid, contemporary climate change, conservationbiologists are relying heavily on species distribution models (SDMs)to predict shifting occupancy and distribution patterns in responseto future conditions. These models are critical tools for assessingvulnerability t...

PREDICTIVE SIMULATION MODELING FOR ANTIANDROGEN IMPACTS ON RODENT PROSTATE

EPA Science Inventory

Predictive simulation modeling for antiandrogen impacts on rodent prostate
HA Barton1, RW Setzer1, LK Potter1,2
1US EPA, ORD, NHEERL, ETD, PKB, Research Triangle Park, NC and 2Curriculum in Toxicology, UNC, Chapel Hill, NC

Changes in rodent prostate weight and functi...

Density-dependent microbial turnover improves soil carbon model predictions of long-term litter manipulations

NASA Astrophysics Data System (ADS)

Georgiou, Katerina; Abramoff, Rose; Harte, John; Riley, William; Torn, Margaret

2017-04-01

Climatic, atmospheric, and land-use changes all have the potential to alter soil microbial activity via abiotic effects on soil or mediated by changes in plant inputs. Recently, many promising microbial models of soil organic carbon (SOC) decomposition have been proposed to advance understanding and prediction of climate and carbon (C) feedbacks. Most of these models, however, exhibit unrealistic oscillatory behavior and SOC insensitivity to long-term changes in C inputs. Here we diagnose the sources of instability in four models that span the range of complexity of these recent microbial models, by sequentially adding complexity to a simple model to include microbial physiology, a mineral sorption isotherm, and enzyme dynamics. We propose a formulation that introduces density-dependence of microbial turnover, which acts to limit population sizes and reduce oscillations. We compare these models to results from 24 long-term C-input field manipulations, including the Detritus Input and Removal Treatment (DIRT) experiments, to show that there are clear metrics that can be used to distinguish and validate the inherent dynamics of each model structure. We find that widely used first-order models and microbial models without density-dependence cannot readily capture the range of long-term responses observed across the DIRT experiments as a direct consequence of their model structures. The proposed formulation improves predictions of long-term C-input changes, and implies greater SOC storage associated with CO2-fertilization-driven increases in C inputs over the coming century compared to common microbial models. Finally, we discuss our findings in the context of improving microbial model behavior for inclusion in Earth System Models.