The interactional foundations of MaxEnt: Open questions

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

Harré, Michael S., E-mail: michael.harre@sydney.edu.au

One of the simplest and potentially most useful techniques to be developed in the 20{sup th} century, a century noted for an ever more mathematically sophisticated formulation of the sciences, is that of maximising the entropy of a system in order to generate a descriptive, stochastic model of that system in closed form, often abbreviated to MaxEnt. The extension of MaxEnt to systems beyond the physics from which it originated is hampered by the fact that the microscopic physical interactions that are not justified or justifiable within the MaxEnt framework need to be falsifiably evaluated in each new field ofmore » application. It is not obvious that such justification exists for many systems in which the interactions are not directly based on physics. For example what is the justification for the use of MaxEnt in biology, climate modelling or economics? Is it simply a useful heuristic or is there some deeper connection with the foundations of some systems? Without further critical examination of the microscopic foundations that give rise to the success of the MaxEnt principle it is difficult to motivate the use of such techniques in other fields except through theoretically an practically unsatisfying analogical arguments. This article briefly presents the basis of MaxEnt principles as originally introduced in statistical mechanics in the Jaynes form, the Tsallis form and the Rényi form. Several different applications are introduced including that of ecological diversity where maximising the different diversity measures is equivalent to maximising different entropic functionals.« less

Inverting Monotonic Nonlinearities by Entropy Maximization

PubMed Central

López-de-Ipiña Pena, Karmele; Caiafa, Cesar F.

2016-01-01

This paper proposes a new method for blind inversion of a monotonic nonlinear map applied to a sum of random variables. Such kinds of mixtures of random variables are found in source separation and Wiener system inversion problems, for example. The importance of our proposed method is based on the fact that it permits to decouple the estimation of the nonlinear part (nonlinear compensation) from the estimation of the linear one (source separation matrix or deconvolution filter), which can be solved by applying any convenient linear algorithm. Our new nonlinear compensation algorithm, the MaxEnt algorithm, generalizes the idea of Gaussianization of the observation by maximizing its entropy instead. We developed two versions of our algorithm based either in a polynomial or a neural network parameterization of the nonlinear function. We provide a sufficient condition on the nonlinear function and the probability distribution that gives a guarantee for the MaxEnt method to succeed compensating the distortion. Through an extensive set of simulations, MaxEnt is compared with existing algorithms for blind approximation of nonlinear maps. Experiments show that MaxEnt is able to successfully compensate monotonic distortions outperforming other methods in terms of the obtained Signal to Noise Ratio in many important cases, for example when the number of variables in a mixture is small. Besides its ability for compensating nonlinearities, MaxEnt is very robust, i.e. showing small variability in the results. PMID:27780261

Inverting Monotonic Nonlinearities by Entropy Maximization.

PubMed

Solé-Casals, Jordi; López-de-Ipiña Pena, Karmele; Caiafa, Cesar F

2016-01-01

This paper proposes a new method for blind inversion of a monotonic nonlinear map applied to a sum of random variables. Such kinds of mixtures of random variables are found in source separation and Wiener system inversion problems, for example. The importance of our proposed method is based on the fact that it permits to decouple the estimation of the nonlinear part (nonlinear compensation) from the estimation of the linear one (source separation matrix or deconvolution filter), which can be solved by applying any convenient linear algorithm. Our new nonlinear compensation algorithm, the MaxEnt algorithm, generalizes the idea of Gaussianization of the observation by maximizing its entropy instead. We developed two versions of our algorithm based either in a polynomial or a neural network parameterization of the nonlinear function. We provide a sufficient condition on the nonlinear function and the probability distribution that gives a guarantee for the MaxEnt method to succeed compensating the distortion. Through an extensive set of simulations, MaxEnt is compared with existing algorithms for blind approximation of nonlinear maps. Experiments show that MaxEnt is able to successfully compensate monotonic distortions outperforming other methods in terms of the obtained Signal to Noise Ratio in many important cases, for example when the number of variables in a mixture is small. Besides its ability for compensating nonlinearities, MaxEnt is very robust, i.e. showing small variability in the results.

Maximum entropy estimation of a Benzene contaminated plume using ecotoxicological assays.

PubMed

Wahyudi, Agung; Bartzke, Mariana; Küster, Eberhard; Bogaert, Patrick

2013-01-01

Ecotoxicological bioassays, e.g. based on Danio rerio teratogenicity (DarT) or the acute luminescence inhibition with Vibrio fischeri, could potentially lead to significant benefits for detecting on site contaminations on qualitative or semi-quantitative bases. The aim was to use the observed effects of two ecotoxicological assays for estimating the extent of a Benzene groundwater contamination plume. We used a Maximum Entropy (MaxEnt) method to rebuild a bivariate probability table that links the observed toxicity from the bioassays with Benzene concentrations. Compared with direct mapping of the contamination plume as obtained from groundwater samples, the MaxEnt concentration map exhibits on average slightly higher concentrations though the global pattern is close to it. This suggest MaxEnt is a valuable method to build a relationship between quantitative data, e.g. contaminant concentrations, and more qualitative or indirect measurements, in a spatial mapping framework, which is especially useful when clear quantitative relation is not at hand. Copyright © 2012 Elsevier Ltd. All rights reserved.

Implications of climate change for bird conservation in the southwestern U.S

Treesearch

Megan M. Friggens; Deborah M. Finch

2015-01-01

Future expected changes in climate and human activity threaten many riparian habitats, particularly in the southwestern U.S. Using Maximum Entropy (MaxEnt3.3.3) modeling, we characterized habitat relationships and generated spatial predictions of habitat suitability for the Lucy’s warbler (Oreothlypis luciae), the Southwestern willow flycatcher (Empidonax...

Predicting the spatiotemporal distributions of marine fish species utilizing earth system data in a maximum entropy modeling framework

NASA Astrophysics Data System (ADS)

Wang, L.; Kerr, L. A.; Bridger, E.

2016-12-01

Changes in species distributions have been widely associated with climate change. Understanding how ocean conditions influence marine fish distributions is critical for elucidating the role of climate in ecosystem change and forecasting how fish may be distributed in the future. Species distribution models (SDMs) can enable estimation of the likelihood of encountering species in space or time as a function of environmental conditions. Traditional SDMs are applied to scientific-survey data that include both presences and absences. Maximum entropy (MaxEnt) models are promising tools as they can be applied to presence-only data, such as those collected from fisheries or citizen science programs. We used MaxEnt to relate the occurrence records of marine fish species (e.g. Atlantic herring, Atlantic mackerel, and butterfish) from NOAA Northeast Fisheries Observer Program to environmental conditions. Environmental variables from earth system data, such as sea surface temperature (SST), sea bottom temperature (SBT), Chlorophyll-a, bathymetry, North Atlantic oscillation (NAO), and Atlantic multidecadal oscillation (AMO), were matched with species occurrence for MaxEnt modeling the fish distributions in Northeast Shelf area. We developed habitat suitability maps for these species, and assessed the relative influence of environmental factors on their distributions. Overall, SST and Chlorophyll-a had greatest influence on their monthly distributions, with bathymetry and SBT having moderate influence and climate indices (NAO and AMO) having little influence. Across months, Atlantic herring distribution was most related to SST 10th percentile, and Atlantic mackerel and butterfish distributions were most related to previous month SST. The fish distributions were most affected by previous month Chlorophyll-a in summer months, which may indirectly indicate the accumulative impact of primary productivity. Results highlighted the importance of spatial and temporal scales when using SDMs to investigate the habitat suitability and distributions of a focal species. MaxEnt models have the potential to provide hindcasts of where species might have been in the past in relation to historical environmental conditions, nowcasts in relation to current conditions, or forecasts of future species distributions.

Predicting Changes in Macrophyte Community Structure from Functional Traits in a Freshwater Lake: A Test of Maximum Entropy Model

PubMed Central

Fu, Hui; Zhong, Jiayou; Yuan, Guixiang; Guo, Chunjing; Lou, Qian; Zhang, Wei; Xu, Jun; Ni, Leyi; Xie, Ping; Cao, Te

2015-01-01

Trait-based approaches have been widely applied to investigate how community dynamics respond to environmental gradients. In this study, we applied a series of maximum entropy (maxent) models incorporating functional traits to unravel the processes governing macrophyte community structure along water depth gradient in a freshwater lake. We sampled 42 plots and 1513 individual plants, and measured 16 functional traits and abundance of 17 macrophyte species. Study results showed that maxent model can be highly robust (99.8%) in predicting the species relative abundance of macrophytes with observed community-weighted mean (CWM) traits as the constraints, while relative low (about 30%) with CWM traits fitted from water depth gradient as the constraints. The measured traits showed notably distinct importance in predicting species abundances, with lowest for perennial growth form and highest for leaf dry mass content. For tuber and leaf nitrogen content, there were significant shifts in their effects on species relative abundance from positive in shallow water to negative in deep water. This result suggests that macrophyte species with tuber organ and greater leaf nitrogen content would become more abundant in shallow water, but would become less abundant in deep water. Our study highlights how functional traits distributed across gradients provide a robust path towards predictive community ecology. PMID:26167856

Comparing Habitat Suitability and Connectivity Modeling Methods for Conserving Pronghorn Migrations

PubMed Central

Poor, Erin E.; Loucks, Colby; Jakes, Andrew; Urban, Dean L.

2012-01-01

Terrestrial long-distance migrations are declining globally: in North America, nearly 75% have been lost. Yet there has been limited research comparing habitat suitability and connectivity models to identify migration corridors across increasingly fragmented landscapes. Here we use pronghorn (Antilocapra americana) migrations in prairie habitat to compare two types of models that identify habitat suitability: maximum entropy (Maxent) and expert-based (Analytic Hierarchy Process). We used distance to wells, distance to water, NDVI, land cover, distance to roads, terrain shape and fence presence to parameterize the models. We then used the output of these models as cost surfaces to compare two common connectivity models, least-cost modeling (LCM) and circuit theory. Using pronghorn movement data from spring and fall migrations, we identified potential migration corridors by combining each habitat suitability model with each connectivity model. The best performing model combination was Maxent with LCM corridors across both seasons. Maxent out-performed expert-based habitat suitability models for both spring and fall migrations. However, expert-based corridors can perform relatively well and are a cost-effective alternative if species location data are unavailable. Corridors created using LCM out-performed circuit theory, as measured by the number of pronghorn GPS locations present within the corridors. We suggest the use of a tiered approach using different corridor widths for prioritizing conservation and mitigation actions, such as fence removal or conservation easements. PMID:23166656

Comparing habitat suitability and connectivity modeling methods for conserving pronghorn migrations.

PubMed

Poor, Erin E; Loucks, Colby; Jakes, Andrew; Urban, Dean L

2012-01-01

Terrestrial long-distance migrations are declining globally: in North America, nearly 75% have been lost. Yet there has been limited research comparing habitat suitability and connectivity models to identify migration corridors across increasingly fragmented landscapes. Here we use pronghorn (Antilocapra americana) migrations in prairie habitat to compare two types of models that identify habitat suitability: maximum entropy (Maxent) and expert-based (Analytic Hierarchy Process). We used distance to wells, distance to water, NDVI, land cover, distance to roads, terrain shape and fence presence to parameterize the models. We then used the output of these models as cost surfaces to compare two common connectivity models, least-cost modeling (LCM) and circuit theory. Using pronghorn movement data from spring and fall migrations, we identified potential migration corridors by combining each habitat suitability model with each connectivity model. The best performing model combination was Maxent with LCM corridors across both seasons. Maxent out-performed expert-based habitat suitability models for both spring and fall migrations. However, expert-based corridors can perform relatively well and are a cost-effective alternative if species location data are unavailable. Corridors created using LCM out-performed circuit theory, as measured by the number of pronghorn GPS locations present within the corridors. We suggest the use of a tiered approach using different corridor widths for prioritizing conservation and mitigation actions, such as fence removal or conservation easements.

Multitemporal Modelling of Socio-Economic Wildfire Drivers in Central Spain between the 1980s and the 2000s: Comparing Generalized Linear Models to Machine Learning Algorithms

PubMed Central

Vilar, Lara; Gómez, Israel; Martínez-Vega, Javier; Echavarría, Pilar; Riaño, David; Martín, M. Pilar

2016-01-01

The socio-economic factors are of key importance during all phases of wildfire management that include prevention, suppression and restoration. However, modeling these factors, at the proper spatial and temporal scale to understand fire regimes is still challenging. This study analyses socio-economic drivers of wildfire occurrence in central Spain. This site represents a good example of how human activities play a key role over wildfires in the European Mediterranean basin. Generalized Linear Models (GLM) and machine learning Maximum Entropy models (Maxent) predicted wildfire occurrence in the 1980s and also in the 2000s to identify changes between each period in the socio-economic drivers affecting wildfire occurrence. GLM base their estimation on wildfire presence-absence observations whereas Maxent on wildfire presence-only. According to indicators like sensitivity or commission error Maxent outperformed GLM in both periods. It achieved a sensitivity of 38.9% and a commission error of 43.9% for the 1980s, and 67.3% and 17.9% for the 2000s. Instead, GLM obtained 23.33, 64.97, 9.41 and 18.34%, respectively. However GLM performed steadier than Maxent in terms of the overall fit. Both models explained wildfires from predictors such as population density and Wildland Urban Interface (WUI), but differed in their relative contribution. As a result of the urban sprawl and an abandonment of rural areas, predictors like WUI and distance to roads increased their contribution to both models in the 2000s, whereas Forest-Grassland Interface (FGI) influence decreased. This study demonstrates that human component can be modelled with a spatio-temporal dimension to integrate it into wildfire risk assessment. PMID:27557113

Multitemporal Modelling of Socio-Economic Wildfire Drivers in Central Spain between the 1980s and the 2000s: Comparing Generalized Linear Models to Machine Learning Algorithms.

PubMed

Vilar, Lara; Gómez, Israel; Martínez-Vega, Javier; Echavarría, Pilar; Riaño, David; Martín, M Pilar

2016-01-01

The socio-economic factors are of key importance during all phases of wildfire management that include prevention, suppression and restoration. However, modeling these factors, at the proper spatial and temporal scale to understand fire regimes is still challenging. This study analyses socio-economic drivers of wildfire occurrence in central Spain. This site represents a good example of how human activities play a key role over wildfires in the European Mediterranean basin. Generalized Linear Models (GLM) and machine learning Maximum Entropy models (Maxent) predicted wildfire occurrence in the 1980s and also in the 2000s to identify changes between each period in the socio-economic drivers affecting wildfire occurrence. GLM base their estimation on wildfire presence-absence observations whereas Maxent on wildfire presence-only. According to indicators like sensitivity or commission error Maxent outperformed GLM in both periods. It achieved a sensitivity of 38.9% and a commission error of 43.9% for the 1980s, and 67.3% and 17.9% for the 2000s. Instead, GLM obtained 23.33, 64.97, 9.41 and 18.34%, respectively. However GLM performed steadier than Maxent in terms of the overall fit. Both models explained wildfires from predictors such as population density and Wildland Urban Interface (WUI), but differed in their relative contribution. As a result of the urban sprawl and an abandonment of rural areas, predictors like WUI and distance to roads increased their contribution to both models in the 2000s, whereas Forest-Grassland Interface (FGI) influence decreased. This study demonstrates that human component can be modelled with a spatio-temporal dimension to integrate it into wildfire risk assessment.

MaxEnt-Based Ecological Theory: A Template for Integrated Catchment Theory

NASA Astrophysics Data System (ADS)

Harte, J.

2017-12-01

The maximum information entropy procedure (MaxEnt) is both a powerful tool for inferring least-biased probability distributions from limited data and a framework for the construction of complex systems theory. The maximum entropy theory of ecology (METE) describes remarkably well widely observed patterns in the distribution, abundance and energetics of individuals and taxa in relatively static ecosystems. An extension to ecosystems undergoing change in response to disturbance or natural succession (DynaMETE) is in progress. I describe the structure of both the static and the dynamic theory and show a range of comparisons with census data. I then propose a generalization of the MaxEnt approach that could provide a framework for a predictive theory of both static and dynamic, fully-coupled, eco-socio-hydrological catchment systems.

Predicting the distribution of the Asian tapir in Peninsular Malaysia using maximum entropy modeling.

PubMed

Clements, Gopalasamy Reuben; Rayan, D Mark; Aziz, Sheema Abdul; Kawanishi, Kae; Traeholt, Carl; Magintan, David; Yazi, Muhammad Fadlli Abdul; Tingley, Reid

2012-12-01

In 2008, the IUCN threat status of the Asian tapir (Tapirus indicus) was reclassified from 'vulnerable' to 'endangered'. The latest distribution map from the IUCN Red List suggests that the tapirs' native range is becoming increasingly fragmented in Peninsular Malaysia, but distribution data collected by local researchers suggest a more extensive geographical range. Here, we compile a database of 1261 tapir occurrence records within Peninsular Malaysia, and demonstrate that this species, indeed, has a much broader geographical range than the IUCN range map suggests. However, extreme spatial and temporal bias in these records limits their utility for conservation planning. Therefore, we used maximum entropy (MaxEnt) modeling to elucidate the potential extent of the Asian tapir's occurrence in Peninsular Malaysia while accounting for bias in existing distribution data. Our MaxEnt model predicted that the Asian tapir has a wider geographic range than our fine-scale data and the IUCN range map both suggest. Approximately 37% of Peninsular Malaysia contains potentially suitable tapir habitats. Our results justify a revision to the Asian tapir's extent of occurrence in the IUCN Red List. Furthermore, our modeling demonstrated that selectively logged forests encompass 45% of potentially suitable tapir habitats, underscoring the importance of these habitats for the conservation of this species in Peninsular Malaysia. © 2012 Wiley Publishing Asia Pty Ltd, ISZS and IOZ/CAS.

Global potential distribution of Drosophila suzukii (Diptera, Drosophilidae)

PubMed Central

dos Santos, Luana A.; Mendes, Mayara F.; Krüger, Alexandra P.; Blauth, Monica L.; Gottschalk, Marco S.

2017-01-01

Drosophila suzukii (Matsumura) is a species native to Western Asia that is able to pierce intact fruit during egg laying, causing it to be considered a fruit crop pest in many countries. Drosophila suzukii have a rapid expansion worldwide; occurrences were recorded in North America and Europe in 2008, and South America in 2013. Due to this rapid expansion, we modeled the potential distribution of this species using the Maximum Entropy Modeling (MaxEnt) algorithm and the Genetic Algorithm for Ruleset Production (GARP) using 407 sites with known occurrences worldwide and 11 predictor variables. After 1000 replicates, the value of the average area under the curve (AUC) of the model predictions with 1000 replicates was 0.97 for MaxEnt and 0.87 for GARP, indicating that both models had optimal performances. The environmental variables that most influenced the prediction of the MaxEnt model were the annual mean temperature, the maximum temperature of the warmest month, the mean temperature of the coldest quarter and the annual precipitation. The models indicated high environmental suitability, mainly in temperate and subtropical areas in the continents of Asia, Europe and North and South America, where the species has already been recorded. The potential for further invasions of the African and Australian continents is predicted due to the environmental suitability of these areas for this species. PMID:28323903

Ensemble forecasting of potential habitat for three invasive fishes

USGS Publications Warehouse

Poulos, Helen M.; Chernoff, Barry; Fuller, Pam L.; Butman, David

2012-01-01

Aquatic invasive species pose major ecological and economic threats to aquatic ecosystems worldwide via displacement, predation, or hybridization with native species and the alteration of aquatic habitats and hydrologic cycles. Modeling the habitat suitability of alien aquatic species through spatially explicit mapping is an increasingly important risk assessment tool. Habitat modeling also facilitates identification of key environmental variables influencing invasive species distributions. We compared four modeling methods to predict the potential continental United States distributions of northern snakehead Channa argus (Cantor, 1842), round goby Neogobius melanostomus (Pallas, 1814), and silver carp Hypophthalmichthys molitrix (Valenciennes, 1844) using maximum entropy (Maxent), the genetic algorithm for rule set production (GARP), DOMAIN, and support vector machines (SVM). We used inventory records from the USGS Nonindigenous Aquatic Species Database and a geographic information system of 20 climatic and environmental variables to generate individual and ensemble distribution maps for each species. The ensemble maps from our study performed as well as or better than all of the individual models except Maxent. The ensemble and Maxent models produced significantly higher accuracy individual maps than GARP, one-class SVMs, or DOMAIN. The key environmental predictor variables in the individual models were consistent with the tolerances of each species. Results from this study provide insights into which locations and environmental conditions may promote the future spread of invasive fish in the US.

A Statistical Model for Multilingual Entity Detection and Tracking

DTIC Science & Technology

2004-01-01

tomatic Content Extraction ( ACE ) evaluation achieved top-tier results in all three evaluation languages. 1 Introduction Detecting entities, whether named...of com- bining the detected mentions into groups of references to the same object. The work presented here is motivated by the ACE eval- uation...Entropy (MaxEnt henceforth) (Berger et al., 1996) and Robust Risk Minimization (RRM henceforth) 1For a description of the ACE program see http

Geospatial modeling approach to monument construction using Michigan from A.D. 1000-1600 as a case study.

PubMed

Howey, Meghan C L; Palace, Michael W; McMichael, Crystal H

2016-07-05

Building monuments was one way that past societies reconfigured their landscapes in response to shifting social and ecological factors. Understanding the connections between those factors and monument construction is critical, especially when multiple types of monuments were constructed across the same landscape. Geospatial technologies enable past cultural activities and environmental variables to be examined together at large scales. Many geospatial modeling approaches, however, are not designed for presence-only (occurrence) data, which can be limiting given that many archaeological site records are presence only. We use maximum entropy modeling (MaxEnt), which works with presence-only data, to predict the distribution of monuments across large landscapes, and we analyze MaxEnt output to quantify the contributions of spatioenvironmental variables to predicted distributions. We apply our approach to co-occurring Late Precontact (ca. A.D. 1000-1600) monuments in Michigan: (i) mounds and (ii) earthwork enclosures. Many of these features have been destroyed by modern development, and therefore, we conducted archival research to develop our monument occurrence database. We modeled each monument type separately using the same input variables. Analyzing variable contribution to MaxEnt output, we show that mound and enclosure landscape suitability was driven by contrasting variables. Proximity to inland lakes was key to mound placement, and proximity to rivers was key to sacred enclosures. This juxtaposition suggests that mounds met local needs for resource procurement success, whereas enclosures filled broader regional needs for intergroup exchange and shared ritual. Our study shows how MaxEnt can be used to develop sophisticated models of past cultural processes, including monument building, with imperfect, limited, presence-only data.

Habitat selection of Tragulus napu and Tragulus javanicus using MaxEnt analysis

NASA Astrophysics Data System (ADS)

Taher, Taherah Mohd; Lihan, Tukimat; Mustapha, Muzzneena Ahmad; Nor, Shukor Mohd

2018-04-01

Large areas are converted into commercial land use such as agriculture and urban as a result from the increasing economic and population demand. This situation is largely affecting wildlife and its habitat. Malaysia as one of the largest oil palm-producing countries, should take precaution into conserving its forest and wildlife diversity. Although big mammal such as elephant and tiger are significant for wildlife diversity, medium and small mammals also contribute to the biological richness in Malaysia. This study aims to predict suitable habitat of medium mammal, Tragulus napu and Tragulus javanicus in the study area and identify its habitat characteristics. The method applied in this study uses maximum entropy (MaxEnt) modeling which utilized species distribution data and selected environmental variables to alienate potential habitat in the study area. The characteristic of the habitat was identified from the result of MaxEnt analysis. This method of habitat modeling shows different extent of predicted suitable habitat in the study area of both species in which Tragulus napu has a limited distribution compared to Tragulus javanicus. However, some characteristics are similar in both habitats. The knowledge on species habitat characteristics is important to predict wildlife habitat in order to make best decision on land use management and conservation.

Entropic Inference

NASA Astrophysics Data System (ADS)

Caticha, Ariel

2011-03-01

In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEnt and Bayes' rule, and therefore unifies the two themes of these workshops—the Maximum Entropy and the Bayesian methods—into a single general inference scheme.

Predicting the potential environmental suitability for Theileria orientalis transmission in New Zealand cattle using maximum entropy niche modelling.

PubMed

Lawrence, K E; Summers, S R; Heath, A C G; McFadden, A M J; Pulford, D J; Pomroy, W E

2016-07-15

The tick-borne haemoparasite Theileria orientalis is the most important infectious cause of anaemia in New Zealand cattle. Since 2012 a previously unrecorded type, T. orientalis type 2 (Ikeda), has been associated with disease outbreaks of anaemia, lethargy, jaundice and deaths on over 1000 New Zealand cattle farms, with most of the affected farms found in the upper North Island. The aim of this study was to model the relative environmental suitability for T. orientalis transmission throughout New Zealand, to predict the proportion of cattle farms potentially suitable for active T. orientalis infection by region, island and the whole of New Zealand and to estimate the average relative environmental suitability per farm by region, island and the whole of New Zealand. The relative environmental suitability for T. orientalis transmission was estimated using the Maxent (maximum entropy) modelling program. The Maxent model predicted that 99% of North Island cattle farms (n=36,257), 64% South Island cattle farms (n=15,542) and 89% of New Zealand cattle farms overall (n=51,799) could potentially be suitable for T. orientalis transmission. The average relative environmental suitability of T. orientalis transmission at the farm level was 0.34 in the North Island, 0.02 in the South Island and 0.24 overall. The study showed that the potential spatial distribution of T. orientalis environmental suitability was much greater than presumed in the early part of the Theileria associated bovine anaemia (TABA) epidemic. Maximum entropy offers a computer efficient method of modelling the probability of habitat suitability for an arthropod vectored disease. This model could help estimate the boundaries of the endemically stable and endemically unstable areas for T. orientalis transmission within New Zealand and be of considerable value in informing practitioner and farmer biosecurity decisions in these respective areas. Copyright © 2016 Elsevier B.V. All rights reserved.

Maximum entropy models as a tool for building precise neural controls.

PubMed

Savin, Cristina; Tkačik, Gašper

2017-10-01

Neural responses are highly structured, with population activity restricted to a small subset of the astronomical range of possible activity patterns. Characterizing these statistical regularities is important for understanding circuit computation, but challenging in practice. Here we review recent approaches based on the maximum entropy principle used for quantifying collective behavior in neural activity. We highlight recent models that capture population-level statistics of neural data, yielding insights into the organization of the neural code and its biological substrate. Furthermore, the MaxEnt framework provides a general recipe for constructing surrogate ensembles that preserve aspects of the data, but are otherwise maximally unstructured. This idea can be used to generate a hierarchy of controls against which rigorous statistical tests are possible. Copyright © 2017 Elsevier Ltd. All rights reserved.

Climate controls the distribution of a widespread invasive species: Implications for future range expansion

USGS Publications Warehouse

McDowell, W.G.; Benson, A.J.; Byers, J.E.

2014-01-01

1. Two dominant drivers of species distributions are climate and habitat, both of which are changing rapidly. Understanding the relative importance of variables that can control distributions is critical, especially for invasive species that may spread rapidly and have strong effects on ecosystems. 2. Here, we examine the relative importance of climate and habitat variables in controlling the distribution of the widespread invasive freshwater clam Corbicula fluminea, and we model its future distribution under a suite of climate scenarios using logistic regression and maximum entropy modelling (MaxEnt). 3. Logistic regression identified climate variables as more important than habitat variables in controlling Corbicula distribution. MaxEnt modelling predicted Corbicula's range expansion westward and northward to occupy half of the contiguous United States. By 2080, Corbicula's potential range will expand 25–32%, with more than half of the continental United States being climatically suitable. 4. Our combination of multiple approaches has revealed the importance of climate over habitat in controlling Corbicula's distribution and validates the climate-only MaxEnt model, which can readily examine the consequences of future climate projections. 5. Given the strong influence of climate variables on Corbicula's distribution, as well as Corbicula's ability to disperse quickly and over long distances, Corbicula is poised to expand into New England and the northern Midwest of the United States. Thus, the direct effects of climate change will probably be compounded by the addition of Corbicula and its own influences on ecosystem function.

Radar studies of the atmosphere using spatial and frequency diversity

NASA Astrophysics Data System (ADS)

Yu, Tian-You

This work provides results from a thorough investigation of atmospheric radar imaging including theory, numerical simulations, observational verification, and applications. The theory is generalized to include the existing imaging techniques of coherent radar imaging (CRI) and range imaging (RIM), which are shown to be special cases of three-dimensional imaging (3D Imaging). Mathematically, the problem of atmospheric radar imaging is posed as an inverse problem. In this study, the Fourier, Capon, and maximum entropy (MaxEnt) methods are proposed to solve the inverse problem. After the introduction of the theory, numerical simulations are used to test, validate, and exercise these techniques. Statistical comparisons of the three methods of atmospheric radar imaging are presented for various signal-to-noise ratio (SNR), receiver configuration, and frequency sampling. The MaxEnt method is shown to generally possess the best performance for low SNR. The performance of the Capon method approaches the performance of the MaxEnt method for high SNR. In limited cases, the Capon method actually outperforms the MaxEnt method. The Fourier method generally tends to distort the model structure due to its limited resolution. Experimental justification of CRI and RIM is accomplished using the Middle and Upper (MU) Atmosphere Radar in Japan and the SOUnding SYstem (SOUSY) in Germany, respectively. A special application of CRI to the observation of polar mesosphere summer echoes (PMSE) is used to show direct evidence of wave steepening and possibly explain gravity wave variations associated with PMSE.

Estimation of Wild Fire Risk Area based on Climate and Maximum Entropy in Korean Peninsular

NASA Astrophysics Data System (ADS)

Kim, T.; Lim, C. H.; Song, C.; Lee, W. K.

2015-12-01

The number of forest fires and accompanying human injuries and physical damages has been increased by frequent drought. In this study, forest fire danger zone of Korea is estimated to predict and prepare for future forest fire hazard regions. The MaxEnt (Maximum Entropy) model is used to estimate the forest fire hazard region which estimates the probability distribution of the status. The MaxEnt model is primarily for the analysis of species distribution, but its applicability for various natural disasters is getting recognition. The detailed forest fire occurrence data collected by the MODIS for past 5 years (2010-2014) is used as occurrence data for the model. Also meteorology, topography, vegetation data are used as environmental variable. In particular, various meteorological variables are used to check impact of climate such as annual average temperature, annual precipitation, precipitation of dry season, annual effective humidity, effective humidity of dry season, aridity index. Consequently, the result was valid based on the AUC(Area Under the Curve) value (= 0.805) which is used to predict accuracy in the MaxEnt model. Also predicted forest fire locations were practically corresponded with the actual forest fire distribution map. Meteorological variables such as effective humidity showed the greatest contribution, and topography variables such as TWI (Topographic Wetness Index) and slope also contributed on the forest fire. As a result, the east coast and the south part of Korea peninsula were predicted to have high risk on the forest fire. In contrast, high-altitude mountain area and the west coast appeared to be safe with the forest fire. The result of this study is similar with former studies, which indicates high risks of forest fire in accessible area and reflects climatic characteristics of east and south part in dry season. To sum up, we estimated the forest fire hazard zone with existing forest fire locations and environment variables and had meaningful result with artificial and natural effect. It is expected to predict future forest fire risk with future climate variables as the climate changes.

An in situ USAXS-SAXS-WAXS study of precipitate size distribution evolution in a model Ni-based alloy.

PubMed

Andrews, Ross N; Serio, Joseph; Muralidharan, Govindarajan; Ilavsky, Jan

2017-06-01

Intermetallic γ' precipitates typically strengthen nickel-based superalloys. The shape, size and spatial distribution of strengthening precipitates critically influence alloy strength, while their temporal evolution characteristics determine the high-temperature alloy stability. Combined ultra-small-, small- and wide-angle X-ray scattering (USAXS-SAXS-WAXS) analysis can be used to evaluate the temporal evolution of an alloy's precipitate size distribution (PSD) and phase structure during in situ heat treatment. Analysis of PSDs from USAXS-SAXS data employs either least-squares fitting of a preordained PSD model or a maximum entropy (MaxEnt) approach, the latter avoiding a priori definition of a functional form of the PSD. However, strong low- q scattering from grain boundaries and/or structure factor effects inhibit MaxEnt analysis of typical alloys. This work describes the extension of Bayesian-MaxEnt analysis methods to data exhibiting structure factor effects and low- q power law slopes and demonstrates their use in an in situ study of precipitate size evolution during heat treatment of a model Ni-Al-Si alloy.

An in situ USAXS–SAXS–WAXS study of precipitate size distribution evolution in a model Ni-based alloy

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

Andrews, Ross N.; Serio, Joseph A.; Muralidharan, Govindarajan

Intermetallic γ' precipitates typically strengthen nickel-based superalloys. The shape, size and spatial distribution of strengthening precipitates critically influence alloy strength, while their temporal evolution characteristics determine the high-temperature alloy stability. Combined ultra-small-, small- and wide-angle X-ray scattering (USAXS–SAXS–WAXS) analysis can be used to evaluate the temporal evolution of an alloy's precipitate size distribution (PSD) and phase structure duringin situheat treatment. Analysis of PSDs from USAXS–SAXS data employs either least-squares fitting of a preordained PSD model or a maximum entropy (MaxEnt) approach, the latter avoidinga prioridefinition of a functional form of the PSD. However, strong low-qscattering from grain boundaries and/or structuremore » factor effects inhibit MaxEnt analysis of typical alloys. Lastly, this work describes the extension of Bayesian–MaxEnt analysis methods to data exhibiting structure factor effects and low-qpower law slopes and demonstrates their use in anin situstudy of precipitate size evolution during heat treatment of a model Ni–Al–Si alloy.« less

An in situ USAXS–SAXS–WAXS study of precipitate size distribution evolution in a model Ni-based alloy1

PubMed Central

Andrews, Ross N.; Serio, Joseph; Muralidharan, Govindarajan; Ilavsky, Jan

2017-01-01

Intermetallic γ′ precipitates typically strengthen nickel-based superalloys. The shape, size and spatial distribution of strengthening precipitates critically influence alloy strength, while their temporal evolution characteristics determine the high-temperature alloy stability. Combined ultra-small-, small- and wide-angle X-ray scattering (USAXS–SAXS–WAXS) analysis can be used to evaluate the temporal evolution of an alloy’s precipitate size distribution (PSD) and phase structure during in situ heat treatment. Analysis of PSDs from USAXS–SAXS data employs either least-squares fitting of a preordained PSD model or a maximum entropy (MaxEnt) approach, the latter avoiding a priori definition of a functional form of the PSD. However, strong low-q scattering from grain boundaries and/or structure factor effects inhibit MaxEnt analysis of typical alloys. This work describes the extension of Bayesian–MaxEnt analysis methods to data exhibiting structure factor effects and low-q power law slopes and demonstrates their use in an in situ study of precipitate size evolution during heat treatment of a model Ni–Al–Si alloy. PMID:28656039

An in situ USAXS–SAXS–WAXS study of precipitate size distribution evolution in a model Ni-based alloy

DOE PAGES

Andrews, Ross N.; Serio, Joseph A.; Muralidharan, Govindarajan; ...

2017-05-30

Intermetallic γ' precipitates typically strengthen nickel-based superalloys. The shape, size and spatial distribution of strengthening precipitates critically influence alloy strength, while their temporal evolution characteristics determine the high-temperature alloy stability. Combined ultra-small-, small- and wide-angle X-ray scattering (USAXS–SAXS–WAXS) analysis can be used to evaluate the temporal evolution of an alloy's precipitate size distribution (PSD) and phase structure duringin situheat treatment. Analysis of PSDs from USAXS–SAXS data employs either least-squares fitting of a preordained PSD model or a maximum entropy (MaxEnt) approach, the latter avoidinga prioridefinition of a functional form of the PSD. However, strong low-qscattering from grain boundaries and/or structuremore » factor effects inhibit MaxEnt analysis of typical alloys. Lastly, this work describes the extension of Bayesian–MaxEnt analysis methods to data exhibiting structure factor effects and low-qpower law slopes and demonstrates their use in anin situstudy of precipitate size evolution during heat treatment of a model Ni–Al–Si alloy.« less

A habitat overlap analysis derived from maxent for tamarisk and the south-western willow flycatcher

NASA Astrophysics Data System (ADS)

York, Patricia; Evangelista, Paul; Kumar, Sunil; Graham, James; Flather, Curtis; Stohlgren, Thomas

2011-06-01

Biologic control of the introduced and invasive, woody plant tamarisk ( Tamarix spp, saltcedar) in south-western states is controversial because it affects habitat of the federally endangered South-western Willow Flycatcher ( Empidonax traillii extimus). These songbirds sometimes nest in tamarisk where floodplain-level invasion replaces native habitats. Biologic control, with the saltcedar leaf beetle ( Diorhabda elongate), began along the Virgin River, Utah, in 2006, enhancing the need for comprehensive understanding of the tamarisk-flycatcher relationship. We used maximum entropy (Maxent) modeling to separately quantify the current extent of dense tamarisk habitat (>50% cover) and the potential extent of habitat available for E. traillii extimus within the studied watersheds. We used transformations of 2008 Landsat Thematic Mapper images and a digital elevation model as environmental input variables. Maxent models performed well for the flycatcher and tamarisk with Area Under the ROC Curve (AUC) values of 0.960 and 0.982, respectively. Classification of thresholds and comparison of the two Maxent outputs indicated moderate spatial overlap between predicted suitable habitat for E. traillii extimus and predicted locations with dense tamarisk stands, where flycatcher habitat will potentially change flycatcher habitats. Dense tamarisk habitat comprised 500 km2 within the study area, of which 11.4% was also modeled as potential habitat for E. traillii extimus. Potential habitat modeled for the flycatcher constituted 190 km2, of which 30.7% also contained dense tamarisk habitat. Results showed that both native vegetation and dense tamarisk habitats exist in the study area and that most tamarisk infestations do not contain characteristics that satisfy the habitat requirements of E. traillii extimus. Based on this study, effective biologic control of Tamarix spp. may, in the short term, reduce suitable habitat available to E. traillii extimus, but also has the potential in the long term to increase suitable habitat if appropriate mixes of native woody vegetation replace tamarisk in biocontrol areas.

Accelerated echo planar J-resolved spectroscopic imaging in prostate cancer: a pilot validation of non-linear reconstruction using total variation and maximum entropy.

PubMed

Nagarajan, Rajakumar; Iqbal, Zohaib; Burns, Brian; Wilson, Neil E; Sarma, Manoj K; Margolis, Daniel A; Reiter, Robert E; Raman, Steven S; Thomas, M Albert

2015-11-01

The overlap of metabolites is a major limitation in one-dimensional (1D) spectral-based single-voxel MRS and multivoxel-based MRSI. By combining echo planar spectroscopic imaging (EPSI) with a two-dimensional (2D) J-resolved spectroscopic (JPRESS) sequence, 2D spectra can be recorded in multiple locations in a single slice of prostate using four-dimensional (4D) echo planar J-resolved spectroscopic imaging (EP-JRESI). The goal of the present work was to validate two different non-linear reconstruction methods independently using compressed sensing-based 4D EP-JRESI in prostate cancer (PCa): maximum entropy (MaxEnt) and total variation (TV). Twenty-two patients with PCa with a mean age of 63.8 years (range, 46-79 years) were investigated in this study. A 4D non-uniformly undersampled (NUS) EP-JRESI sequence was implemented on a Siemens 3-T MRI scanner. The NUS data were reconstructed using two non-linear reconstruction methods, namely MaxEnt and TV. Using both TV and MaxEnt reconstruction methods, the following observations were made in cancerous compared with non-cancerous locations: (i) higher mean (choline + creatine)/citrate metabolite ratios; (ii) increased levels of (choline + creatine)/spermine and (choline + creatine)/myo-inositol; and (iii) decreased levels of (choline + creatine)/(glutamine + glutamate). We have shown that it is possible to accelerate the 4D EP-JRESI sequence by four times and that the data can be reliably reconstructed using the TV and MaxEnt methods. The total acquisition duration was less than 13 min and we were able to detect and quantify several metabolites. Copyright © 2015 John Wiley & Sons, Ltd.

The use of remotely sensed environmental data in the study of asthma disease

NASA Astrophysics Data System (ADS)

Ayres-Sampaio, D.; Teodoro, A. C.; Freitas, A.; Sillero, N.

2012-09-01

Despite the growing use of Remote Sensing (RS) data in epidemiological studies, several diseases, including asthma, have not been studied yet using RS potentialities. Asthma is a chronic inflammatory disorder of the airway that affects people of all ages throughout the world. The expression of this disease can be influenced by some environmental factors such as allergens, air pollution or climate conditions. In this study, we modeled the distribution of asthma in each season, using Maximum entropy (Maxent) model and presence data obtained from a national database with asthma public hospitals admissions in Mainland Portugal, with discharges between years 2003 and 2008. We considered data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to retrieve estimates of near-surface air temperature and relative humidity. Land-use regression (LUR) models were developed to produce estimates of three pollutants: PM10, NO2, and CO. Moreover, MODIS Normalized Difference Vegetation Index (NDVI) was also used in the construction of Maxent models. All Maxent models predicted similar suitable areas and obtained acceptable area under the curve (AUC) values (~0.75) of the ROC plot. Our results show a strong relationship between asthma presence and NO2, suggesting that asthmatic people living in urban areas with high traffic volume have an increased risk of suffering asthma attacks. Furthermore, there is evidence of the effect of PM10, CO, and RH (during the Summer) in asthma expression. RS data have a great potential but also presents limitations that should be addressed to allow studying more complex diseases.

Elements of the cognitive universe

NASA Astrophysics Data System (ADS)

Topsøe, Flemming

2017-06-01

"The least biased inference, taking available information into account, is the one with maximum entropy". So we are taught by Jaynes. The many followers from a broad spectrum of the natural and social sciences point to the wisdom of this principle, the maximum entropy principle, MaxEnt. But "entropy" need not be tied only to classical entropy and thus to probabilistic thinking. In fact, the arguments found in Jaynes' writings and elsewhere can, as we shall attempt to demonstrate, profitably be revisited, elaborated and transformed to apply in a much more general abstract setting. The approach is based on game theoretical thinking. Philosophical considerations dealing with notions of cognition - basically truth and belief - lie behind. Quantitative elements are introduced via a concept of description effort. An interpretation of Tsallis Entropy is indicated.

Mapping distribution of Rastrelliger kanagurta in the exclusive economic zone (EEZ) of Malaysia using maximum entropy modeling approach

NASA Astrophysics Data System (ADS)

Yusop, Syazwani Mohd; Mustapha, Muzzneena Ahmad

2018-04-01

The coupling of fishing locations for R. kanagurta obtained from SEAFDEC and multi-sensor satellite imageries of oceanographic variables; sea surface temperature (SST), sea surface height (SSH) and chl-a concentration (chl-a) were utilized to evaluate the performance of maximum entropy (MaxEnt) models for R. kanagurta fishing ground for prediction. Besides, this study was conducted to identify the relative percentage contribution of each environmental variable considered in order to describe the effects of the oceanographic factors on the species distribution in the study area. The potential fishing grounds during intermonsoon periods; April and October 2008-2009 were simulated separately and covered the near-coast of Kelantan, Terengganu, Pahang and Johor. The oceanographic conditions differed between regions by the inherent seasonal variability. The seasonal and spatial extents of potential fishing grounds were largely explained by chl-a concentration (0.21-0.99 mg/m3 in April and 0.28-1.00 mg/m3 in October), SSH (77.37-85.90 cm in April and 107.60-108.97 cm in October) and SST (30.43-33.70 °C in April and 30.48-30.97 °C in October). The constructed models were applicable and therefore they were suitable for predicting the potential fishing zones of R. kanagurta in EEZ. The results from this study revealed MaxEnt's potential for predicting the spatial distribution of R. kanagurta and highlighted the use of multispectral satellite images for describing the seasonal potential fishing grounds.

Mapping the climatic suitable habitat of oriental arborvitae (Platycladus orientalis) for introduction and cultivation at a global scale.

PubMed

Li, Guoqing; Du, Sheng; Wen, Zhongming

2016-07-21

Oriental arborvitae (Platycladus orientalis) is an important afforestation and ornamental tree species, which is native in eastern Asian. Therefore, a global suitable habitat map for oriental arborvitae is urgently needed for global promotion and cultivation. Here, the potential habitat and climatic requirements of oriental arborvitae at global scale were simulated using herbariums data and 13 thermal-moisture variables as input data for maximum entropy model (MaxEnt). The simulation performance of MaxEnt is evaluated by ten-fold cross-validation and a jackknife procedure. Results show that the potential habitat and climate envelop of oriental arborvitae can be successfully simulated by MaxEnt at global scale, with a mean test AUC value of 0.93 and mean training AUC value of 0.95. Thermal factors play more important roles than moisture factors in controlling the distribution boundary of oriental arborvitae's potential ranges. There are about 50 countries suitable for introduction and cultivation of oriental arborvitae with an area of 2.0 × 10(7) km(2), which occupied 13.8% of land area on the earth. This unique study will provide valuable information and insights needed to identify new regions with climatically suitable habitats for cultivation and introduction of oriental arborvitae around the world.

Mapping the climatic suitable habitat of oriental arborvitae (Platycladus orientalis) for introduction and cultivation at a global scale

PubMed Central

Li, Guoqing; Du, Sheng; Wen, Zhongming

2016-01-01

Oriental arborvitae (Platycladus orientalis) is an important afforestation and ornamental tree species, which is native in eastern Asian. Therefore, a global suitable habitat map for oriental arborvitae is urgently needed for global promotion and cultivation. Here, the potential habitat and climatic requirements of oriental arborvitae at global scale were simulated using herbariums data and 13 thermal-moisture variables as input data for maximum entropy model (MaxEnt). The simulation performance of MaxEnt is evaluated by ten-fold cross-validation and a jackknife procedure. Results show that the potential habitat and climate envelop of oriental arborvitae can be successfully simulated by MaxEnt at global scale, with a mean test AUC value of 0.93 and mean training AUC value of 0.95. Thermal factors play more important roles than moisture factors in controlling the distribution boundary of oriental arborvitae’s potential ranges. There are about 50 countries suitable for introduction and cultivation of oriental arborvitae with an area of 2.0 × 107 km2, which occupied 13.8% of land area on the earth. This unique study will provide valuable information and insights needed to identify new regions with climatically suitable habitats for cultivation and introduction of oriental arborvitae around the world. PMID:27443221

Convex foundations for generalized MaxEnt models

NASA Astrophysics Data System (ADS)

Frongillo, Rafael; Reid, Mark D.

2014-12-01

We present an approach to maximum entropy models that highlights the convex geometry and duality of generalized exponential families (GEFs) and their connection to Bregman divergences. Using our framework, we are able to resolve a puzzling aspect of the bijection of Banerjee and coauthors between classical exponential families and what they call regular Bregman divergences. Their regularity condition rules out all but Bregman divergences generated from log-convex generators. We recover their bijection and show that a much broader class of divergences correspond to GEFs via two key observations: 1) Like classical exponential families, GEFs have a "cumulant" C whose subdifferential contains the mean: Eo˜pθ[φ(o)]∈∂C(θ) ; 2) Generalized relative entropy is a C-Bregman divergence between parameters: DF(pθ,pθ')= D C(θ,θ') , where DF becomes the KL divergence for F = -H. We also show that every incomplete market with cost function C can be expressed as a complete market, where the prices are constrained to be a GEF with cumulant C. This provides an entirely new interpretation of prediction markets, relating their design back to the principle of maximum entropy.

Modeling and mapping the current and future distribution of Pseudomonas syringae pv. actinidiae under climate change in China.

PubMed

Wang, Rulin; Li, Qing; He, Shisong; Liu, Yuan; Wang, Mingtian; Jiang, Gan

2018-01-01

Bacterial canker of kiwifruit caused by Pseudomonas syringae pv. actinidiae (Psa) is a major threat to the kiwifruit industry throughout the world and accounts for substantial economic losses in China. The aim of the present study was to test and explore the possibility of using MaxEnt (maximum entropy models) to predict and analyze the future large-scale distribution of Psa in China. Based on the current environmental factors, three future climate scenarios, which were suggested by the fifth IPCC report, and the current distribution sites of Psa, MaxEnt combined with ArcGIS was applied to predict the potential suitable areas and the changing trend of Psa in China. The jackknife test and correlation analysis were used to choose dominant climatic factors. The receiver operating characteristic curve (ROC) drawn by MaxEnt was used to evaluate the accuracy of the simulation. The results showed that under current climatic conditions, the area from latitude 25° to 36°N and from longitude 101° to 122°E is the primary potential suitable area of Psa in China. The highly suitable area (with suitability between 66 and 100) was mainly concentrated in Northeast Sichuan, South Shaanxi, most of Chongqing, West Hubei and Southwest Gansu and occupied 4.94% of land in China. Under different future emission scenarios, both the areas and the centers of the suitable areas all showed differences compared with the current situation. Four climatic variables, i.e., maximum April temperature (19%), mean temperature of the coldest quarter (14%), precipitation in May (11.5%) and minimum temperature in October (10.8%), had the largest impact on the distribution of Psa. The MaxEnt model is potentially useful for forecasting the future adaptive distribution of Psa under climate change, and it provides important guidance for comprehensive management.

Predicting the spatial and temporal distributions of marine fish species utilizing earth system data in a MaxEnt model framework

NASA Astrophysics Data System (ADS)

Wang, L.; Kerr, L. A.; Bridger, E.

2016-02-01

Changes in species distributions have been widely associated with climate change. Understanding how ocean temperatures influence species distributions is critical for elucidating the role of climate in ecosystem change as well as for forecasting how species may be distributed in the future. As such, species distribution modeling (SDM) is increasingly useful in marine ecosystems research, as it can enable estimation of the likelihood of encountering marine fish in space or time as a function of a set of environmental and ecosystem conditions. Many traditional SDM approaches are applied to species data collected through standardized methods that include both presence and absence records, but are incapable of using presence-only data, such as those collected from fisheries or through citizen science programs. Maximum entropy (MaxEnt) models provide promising tools as they can predict species distributions from incomplete information (presence-only data). We developed a MaxEnt framework to relate the occurrence records of several marine fish species (e.g. Atlantic herring, Atlantic mackerel, and butterfish) to environmental conditions. Environmental variables derived from remote sensing, such as monthly average sea surface temperature (SST), are matched with fish species data, and model results indicate the relative occurrence rate of the species as a function of the environmental variables. The results can be used to provide hindcasts of where species might have been in the past in relation to historical environmental conditions, nowcasts in relation to current conditions, and forecasts of future species distributions. In this presentation, we will assess the relative influence of several environmental factors on marine fish species distributions, and evaluate the effects of data coverage on these presence-only models. We will also discuss how the information from species distribution forecasts can support climate adaptation planning in marine fisheries.

Potential habitat distribution for the freshwater diatom Didymosphenia geminata in the continental US

USGS Publications Warehouse

Kumar, S.; Spaulding, S.A.; Stohlgren, T.J.; Hermann, K.A.; Schmidt, T.S.; Bahls, L.L.

2009-01-01

The diatom Didymosphenia geminata is a single-celled alga found in lakes, streams, and rivers. Nuisance blooms of D geminata affect the diversity, abundance, and productivity of other aquatic organisms. Because D geminata can be transported by humans on waders and other gear, accurate spatial prediction of habitat suitability is urgently needed for early detection and rapid response, as well as for evaluation of monitoring and control programs. We compared four modeling methods to predict D geminata's habitat distribution; two methods use presence-absence data (logistic regression and classification and regression tree [CART]), and two involve presence data (maximum entropy model [Maxent] and genetic algorithm for rule-set production [GARP]). Using these methods, we evaluated spatially explicit, bioclimatic and environmental variables as predictors of diatom distribution. The Maxent model provided the most accurate predictions, followed by logistic regression, CART, and GARP. The most suitable habitats were predicted to occur in the western US, in relatively cool sites, and at high elevations with a high base-flow index. The results provide insights into the factors that affect the distribution of D geminata and a spatial basis for the prediction of nuisance blooms. ?? The Ecological Society of America.

The Brandeis Dice Problem and Statistical Mechanics

NASA Astrophysics Data System (ADS)

van Enk, Steven J.

2014-11-01

Jaynes invented the Brandeis Dice Problem as a simple illustration of the MaxEnt (Maximum Entropy) procedure that he had demonstrated to work so well in Statistical Mechanics. I construct here two alternative solutions to his toy problem. One, like Jaynes' solution, uses MaxEnt and yields an analog of the canonical ensemble, but at a different level of description. The other uses Bayesian updating and yields an analog of the micro-canonical ensemble. Both, unlike Jaynes' solution, yield error bars, whose operational merits I discuss. These two alternative solutions are not equivalent for the original Brandeis Dice Problem, but become so in what must, therefore, count as the analog of the thermodynamic limit, M-sided dice with M → ∞. Whereas the mathematical analogies between the dice problem and Stat Mech are quite close, there are physical properties that the former lacks but that are crucial to the workings of the latter. Stat Mech is more than just MaxEnt.

Modeling potential habitats for alien species Dreissena polymorpha in continental USA

USGS Publications Warehouse

Mingyang, Li; Yunwei, Ju; Kumar, Sunil; Stohlgren, Thomas J.

2008-01-01

The effective measure to minimize the damage of invasive species is to block the potential invasive species to enter into suitable areas. 1864 occurrence points with GPS coordinates and 34 environmental variables from Daymet datasets were gathered, and 4 modeling methods, i.e., Logistic Regression (LR), Classification and Regression Trees (CART), Genetic Algorithm for Rule-Set Prediction (GARP), and maximum entropy method (Maxent), were introduced to generate potential geographic distributions for invasive species Dreissena polymorpha in Continental USA. Then 3 statistical criteria of the area under the Receiver Operating Characteristic curve (AUC), Pearson correlation (COR) and Kappa value were calculated to evaluate the performance of the models, followed by analyses on major contribution variables. Results showed that in terms of the 3 statistical criteria, the prediction results of the 4 ecological niche models were either excellent or outstanding, in which Maxent outperformed the others in 3 aspects of predicting current distribution habitats, selecting major contribution factors, and quantifying the influence of environmental variables on habitats. Distance to water, elevation, frequency of precipitation and solar radiation were 4 environmental forcing factors. The method suggested in the paper can have some reference meaning for modeling habitats of alien species in China and provide a direction to prevent Mytilopsis sallei on the Chinese coast line.

Comparing Distribution of Harbour Porpoises (Phocoena phocoena) Derived from Satellite Telemetry and Passive Acoustic Monitoring

PubMed Central

Rigét, Frank F.; Kyhn, Line A.; Sveegaard, Signe; Dietz, Rune; Tougaard, Jakob; Carlström, Julia A. K.; Carlén, Ida; Koblitz, Jens C.; Teilmann, Jonas

2016-01-01

Cetacean monitoring is essential in determining the status of a population. Different monitoring methods should reflect the real trends in abundance and patterns in distribution, and results should therefore ideally be independent of the selected method. Here, we compare two independent methods of describing harbour porpoise (Phocoena phocoena) relative distribution pattern in the western Baltic Sea. Satellite locations from 13 tagged harbour porpoises were used to build a Maximum Entropy (MaxEnt) model of suitable habitats. The data set was subsampled to one location every second day, which were sufficient to make reliable models over the summer (Jun-Aug) and autumn (Sep-Nov) seasons. The modelled results were compared to harbour porpoise acoustic activity obtained from 36 static acoustic monitoring stations (C-PODs) covering the same area. The C-POD data was expressed as the percentage of porpoise positive days/hours (the number of days/hours per day with porpoise detections) by season. The MaxEnt model and C-POD data showed a significant linear relationship with a strong decline in porpoise occurrence from west to east. This study shows that two very different methods provide comparable information on relative distribution patterns of harbour porpoises even in a low density area. PMID:27463509

Predicting the geographical distribution of two invasive termite species from occurrence data.

PubMed

Tonini, Francesco; Divino, Fabio; Lasinio, Giovanna Jona; Hochmair, Hartwig H; Scheffrahn, Rudolf H

2014-10-01

Predicting the potential habitat of species under both current and future climate change scenarios is crucial for monitoring invasive species and understanding a species' response to different environmental conditions. Frequently, the only data available on a species is the location of its occurrence (presence-only data). Using occurrence records only, two models were used to predict the geographical distribution of two destructive invasive termite species, Coptotermes gestroi (Wasmann) and Coptotermes formosanus Shiraki. The first model uses a Bayesian linear logistic regression approach adjusted for presence-only data while the second one is the widely used maximum entropy approach (Maxent). Results show that the predicted distributions of both C. gestroi and C. formosanus are strongly linked to urban development. The impact of future scenarios such as climate warming and population growth on the biotic distribution of both termite species was also assessed. Future climate warming seems to affect their projected probability of presence to a lesser extent than population growth. The Bayesian logistic approach outperformed Maxent consistently in all models according to evaluation criteria such as model sensitivity and ecological realism. The importance of further studies for an explicit treatment of residual spatial autocorrelation and a more comprehensive comparison between both statistical approaches is suggested.

Statistical theory on the analytical form of cloud particle size distributions

NASA Astrophysics Data System (ADS)

Wu, Wei; McFarquhar, Greg

2017-11-01

Several analytical forms of cloud particle size distributions (PSDs) have been used in numerical modeling and remote sensing retrieval studies of clouds and precipitation, including exponential, gamma, lognormal, and Weibull distributions. However, there is no satisfying physical explanation as to why certain distribution forms preferentially occur instead of others. Theoretically, the analytical form of a PSD can be derived by directly solving the general dynamic equation, but no analytical solutions have been found yet. Instead of using a process level approach, the use of the principle of maximum entropy (MaxEnt) for determining the analytical form of PSDs from the perspective of system is examined here. Here, the issue of variability under coordinate transformations that arises using the Gibbs/Shannon definition of entropy is identified, and the use of the concept of relative entropy to avoid these problems is discussed. Focusing on cloud physics, the four-parameter generalized gamma distribution is proposed as the analytical form of a PSD using the principle of maximum (relative) entropy with assumptions on power law relations between state variables, scale invariance and a further constraint on the expectation of one state variable (e.g. bulk water mass). DOE ASR.

Multi-Scale Approach for Predicting Fish Species Distributions across Coral Reef Seascapes

PubMed Central

Pittman, Simon J.; Brown, Kerry A.

2011-01-01

Two of the major limitations to effective management of coral reef ecosystems are a lack of information on the spatial distribution of marine species and a paucity of data on the interacting environmental variables that drive distributional patterns. Advances in marine remote sensing, together with the novel integration of landscape ecology and advanced niche modelling techniques provide an unprecedented opportunity to reliably model and map marine species distributions across many kilometres of coral reef ecosystems. We developed a multi-scale approach using three-dimensional seafloor morphology and across-shelf location to predict spatial distributions for five common Caribbean fish species. Seascape topography was quantified from high resolution bathymetry at five spatial scales (5–300 m radii) surrounding fish survey sites. Model performance and map accuracy was assessed for two high performing machine-learning algorithms: Boosted Regression Trees (BRT) and Maximum Entropy Species Distribution Modelling (MaxEnt). The three most important predictors were geographical location across the shelf, followed by a measure of topographic complexity. Predictor contribution differed among species, yet rarely changed across spatial scales. BRT provided ‘outstanding’ model predictions (AUC = >0.9) for three of five fish species. MaxEnt provided ‘outstanding’ model predictions for two of five species, with the remaining three models considered ‘excellent’ (AUC = 0.8–0.9). In contrast, MaxEnt spatial predictions were markedly more accurate (92% map accuracy) than BRT (68% map accuracy). We demonstrate that reliable spatial predictions for a range of key fish species can be achieved by modelling the interaction between the geographical location across the shelf and the topographic heterogeneity of seafloor structure. This multi-scale, analytic approach is an important new cost-effective tool to accurately delineate essential fish habitat and support conservation prioritization in marine protected area design, zoning in marine spatial planning, and ecosystem-based fisheries management. PMID:21637787

Multi-scale approach for predicting fish species distributions across coral reef seascapes.

PubMed

Pittman, Simon J; Brown, Kerry A

2011-01-01

Two of the major limitations to effective management of coral reef ecosystems are a lack of information on the spatial distribution of marine species and a paucity of data on the interacting environmental variables that drive distributional patterns. Advances in marine remote sensing, together with the novel integration of landscape ecology and advanced niche modelling techniques provide an unprecedented opportunity to reliably model and map marine species distributions across many kilometres of coral reef ecosystems. We developed a multi-scale approach using three-dimensional seafloor morphology and across-shelf location to predict spatial distributions for five common Caribbean fish species. Seascape topography was quantified from high resolution bathymetry at five spatial scales (5-300 m radii) surrounding fish survey sites. Model performance and map accuracy was assessed for two high performing machine-learning algorithms: Boosted Regression Trees (BRT) and Maximum Entropy Species Distribution Modelling (MaxEnt). The three most important predictors were geographical location across the shelf, followed by a measure of topographic complexity. Predictor contribution differed among species, yet rarely changed across spatial scales. BRT provided 'outstanding' model predictions (AUC = >0.9) for three of five fish species. MaxEnt provided 'outstanding' model predictions for two of five species, with the remaining three models considered 'excellent' (AUC = 0.8-0.9). In contrast, MaxEnt spatial predictions were markedly more accurate (92% map accuracy) than BRT (68% map accuracy). We demonstrate that reliable spatial predictions for a range of key fish species can be achieved by modelling the interaction between the geographical location across the shelf and the topographic heterogeneity of seafloor structure. This multi-scale, analytic approach is an important new cost-effective tool to accurately delineate essential fish habitat and support conservation prioritization in marine protected area design, zoning in marine spatial planning, and ecosystem-based fisheries management.

Predicting species richness and distribution ranges of centipedes at the northern edge of Europe

NASA Astrophysics Data System (ADS)

Georgopoulou, Elisavet; Djursvoll, Per; Simaiakis, Stylianos M.

2016-07-01

In recent decades, interest in understanding species distributions and exploring processes that shape species diversity has increased, leading to the development of advanced methods for the exploitation of occurrence data for analytical and ecological purposes. Here, with the use of georeferenced centipede data, we explore the importance and contribution of bioclimatic variables and land cover, and predict distribution ranges and potential hotspots in Norway. We used a maximum entropy analysis (Maxent) to model species' distributions, aiming at exploring centres of distribution, latitudinal spans and northern range boundaries of centipedes in Norway. The performance of all Maxent models was better than random with average test area under the curve (AUC) values above 0.893 and True Skill Statistic (TSS) values above 0.593. Our results showed a highly significant latitudinal gradient of increased species richness in southern grid-cells. Mean temperatures of warmest and coldest quarters explained much of the potential distribution of species. Predictive modelling analyses revealed that south-eastern Norway and the Atlantic coast in the west (inclusive of the major fjord system of Sognefjord), are local biodiversity hotspots with regard to high predictive species co-occurrence. We conclude that our predicted northward shifts of centipedes' distributions in Norway are likely a result of post-glacial recolonization patterns, species' ecological requirements and dispersal abilities.

Deterministic physical systems under uncertain initial conditions: the case of maximum entropy applied to projectile motion

NASA Astrophysics Data System (ADS)

Montecinos, Alejandra; Davis, Sergio; Peralta, Joaquín

2018-07-01

The kinematics and dynamics of deterministic physical systems have been a foundation of our understanding of the world since Galileo and Newton. For real systems, however, uncertainty is largely present via external forces such as friction or lack of precise knowledge about the initial conditions of the system. In this work we focus on the latter case and describe the use of inference methodologies in solving the statistical properties of classical systems subject to uncertain initial conditions. In particular we describe the application of the formalism of maximum entropy (MaxEnt) inference to the problem of projectile motion, given information about the average horizontal range over many realizations. By using MaxEnt we can invert the problem and use the provided information on the average range to reduce the original uncertainty in the initial conditions. Also, additional insight into the initial condition's probabilities, and the projectile path distribution itself, can be achieved based on the value of the average horizontal range. The wide applicability of this procedure, as well as its ease of use, reveals a useful tool with which to revisit a large number of physics problems, from classrooms to frontier research.

Spatial analysis techniques applied to uranium prospecting in Chihuahua State, Mexico

NASA Astrophysics Data System (ADS)

Hinojosa de la Garza, Octavio R.; Montero Cabrera, María Elena; Sanín, Luz H.; Reyes Cortés, Manuel; Martínez Meyer, Enrique

2014-07-01

To estimate the distribution of uranium minerals in Chihuahua, the advanced statistical model "Maximun Entropy Method" (MaxEnt) was applied. A distinguishing feature of this method is that it can fit more complex models in case of small datasets (x and y data), as is the location of uranium ores in the State of Chihuahua. For georeferencing uranium ores, a database from the United States Geological Survey and workgroup of experts in Mexico was used. The main contribution of this paper is the proposal of maximum entropy techniques to obtain the mineral's potential distribution. For this model were used 24 environmental layers like topography, gravimetry, climate (worldclim), soil properties and others that were useful to project the uranium's distribution across the study area. For the validation of the places predicted by the model, comparisons were done with other research of the Mexican Service of Geological Survey, with direct exploration of specific areas and by talks with former exploration workers of the enterprise "Uranio de Mexico". Results. New uranium areas predicted by the model were validated, finding some relationship between the model predictions and geological faults. Conclusions. Modeling by spatial analysis provides additional information to the energy and mineral resources sectors.

Maximum entropy and equations of state for random cellular structures

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

Rivier, N.

Random, space-filling cellular structures (biological tissues, metallurgical grain aggregates, foams, etc.) are investigated. Maximum entropy inference under a few constraints yields structural equations of state, relating the size of cells to their topological shape. These relations are known empirically as Lewis's law in Botany, or Desch's relation in Metallurgy. Here, the functional form of the constraints is now known as a priori, and one takes advantage of this arbitrariness to increase the entropy further. The resulting structural equations of state are independent of priors, they are measurable experimentally and constitute therefore a direct test for the applicability of MaxEnt inferencemore » (given that the structure is in statistical equilibrium, a fact which can be tested by another simple relation (Aboav's law)). 23 refs., 2 figs., 1 tab.« less

Investigating the Trade-Off Between Power Generation and Environmental Impact of Tidal-Turbine Arrays Using Array Layout Optimisation and Habitat Sustainability Modelling.

NASA Astrophysics Data System (ADS)

du Feu, R. J.; Funke, S. W.; Kramer, S. C.; Hill, J.; Piggott, M. D.

2016-12-01

The installation of tidal turbines into the ocean will inevitably affect the environment around them. However, due to the relative infancy of this sector the extent and severity of such effects is unknown. The layout of an array of turbines is an important factor in determining not only the array's final yield but also how it will influence regional hydrodynamics. This in turn could affect, for example, sediment transportation or habitat suitability. The two potentially competing objectives of extracting energy from the tidal current, and of limiting any environmental impact consequent to influencing that current, are investigated here. This relationship is posed as a multi-objective optimisation problem. OpenTidalFarm, an array layout optimisation tool, and MaxEnt, habitat sustainability modelling software, are used to evaluate scenarios off the coast of the UK. MaxEnt is used to estimate the likelihood of finding a species in a given location based upon environmental input data and presence data of the species. Environmental features which are known to impact habitat, specifically those affected by the presence of an array, such as bed shear stress, are chosen as inputs. MaxEnt then uses a maximum-entropy modelling approach to estimate population distribution across the modelled area. OpenTidalFarm is used to maximise the power generated by an array, or multiple arrays, through adjusting the position and number of turbines within them. It uses a 2D shallow water model with turbine arrays represented as adjustable friction fields. It has the capability to also optimise for user created functionals that can be expressed mathematically. This work uses two functionals; power extracted by the array, and the suitability of habitat as predicted by MaxEnt. A gradient-based local optimisation is used to adjust the array layout at each iteration. This work presents arrays that are optimised for both yield and the viability of habitat for chosen species. In each scenario studied, a range of array formations is found expressing varying preferences for either functional. Further analyses then allow for the identification of trade-offs between the two key societal objectives of energy production and conservation. This in turn produces information valuable to stakeholders and policymakers when making decisions on array design.

[Regionalization study of Dioscorea nipponica in Jilin province based on MaxEnt and ArcGIS].

PubMed

Wang, Zhe; Li, Bo; Xiao, Jing-Lei; Jiang, Da-Cheng

2017-11-01

At the urgent practical issue of resource protection and artificial cultivation area selection of Dioscorea nipponica, the dominant environmental factors affecting the distribution of D. nipponicain Jilin province were selected by field investigation and using the maximum information entropy model and geographic information technology. MaxEnt model study found that the standard deviation of seasonal variation of temperature, precipitation in October and other six environmental factors on the growth of D. nipponica are the greatest impacting factors. The range of suitability for the growth of D. nipponica was 4.612 08×10-6-0.544 31, and the regionalization study was divided into four parts: high fitness area, middle fitness area, low fitness area and unfavorable area. The high fitness area is concentrated in the central and southern areas of Jilin Province, using ArcGIS statistical environment factors in the appropriate area of the numerical situation. The results showed that the regionalization study of D. nipponica was basically the same as the actual situation. It is clear that the natural environment suitable for the growth of D. nipponica is also the basis for the protection of the resources and the selection of cultivated area. Copyright© by the Chinese Pharmaceutical Association.

MaxEnt, second variation, and generalized statistics

NASA Astrophysics Data System (ADS)

Plastino, A.; Rocca, M. C.

2015-10-01

There are two kinds of Tsallis-probability distributions: heavy tail ones and compact support distributions. We show here, by appeal to functional analysis' tools, that for lower bound Hamiltonians, the second variation's analysis of the entropic functional guarantees that the heavy tail q-distribution constitutes a maximum of Tsallis' entropy. On the other hand, in the compact support instance, a case by case analysis is necessary in order to tackle the issue.

Bounding species distribution models

USGS Publications Warehouse

Stohlgren, T.J.; Jarnevich, C.S.; Esaias, W.E.; Morisette, J.T.

2011-01-01

Species distribution models are increasing in popularity for mapping suitable habitat for species of management concern. Many investigators now recognize that extrapolations of these models with geographic information systems (GIS) might be sensitive to the environmental bounds of the data used in their development, yet there is no recommended best practice for "clamping" model extrapolations. We relied on two commonly used modeling approaches: classification and regression tree (CART) and maximum entropy (Maxent) models, and we tested a simple alteration of the model extrapolations, bounding extrapolations to the maximum and minimum values of primary environmental predictors, to provide a more realistic map of suitable habitat of hybridized Africanized honey bees in the southwestern United States. Findings suggest that multiple models of bounding, and the most conservative bounding of species distribution models, like those presented here, should probably replace the unbounded or loosely bounded techniques currently used. ?? 2011 Current Zoology.

Bounding Species Distribution Models

NASA Technical Reports Server (NTRS)

Stohlgren, Thomas J.; Jarnevich, Cahterine S.; Morisette, Jeffrey T.; Esaias, Wayne E.

2011-01-01

Species distribution models are increasing in popularity for mapping suitable habitat for species of management concern. Many investigators now recognize that extrapolations of these models with geographic information systems (GIS) might be sensitive to the environmental bounds of the data used in their development, yet there is no recommended best practice for "clamping" model extrapolations. We relied on two commonly used modeling approaches: classification and regression tree (CART) and maximum entropy (Maxent) models, and we tested a simple alteration of the model extrapolations, bounding extrapolations to the maximum and minimum values of primary environmental predictors, to provide a more realistic map of suitable habitat of hybridized Africanized honey bees in the southwestern United States. Findings suggest that multiple models of bounding, and the most conservative bounding of species distribution models, like those presented here, should probably replace the unbounded or loosely bounded techniques currently used [Current Zoology 57 (5): 642-647, 2011].

Information and Entropy

NASA Astrophysics Data System (ADS)

Caticha, Ariel

2007-11-01

What is information? Is it physical? We argue that in a Bayesian theory the notion of information must be defined in terms of its effects on the beliefs of rational agents. Information is whatever constrains rational beliefs and therefore it is the force that induces us to change our minds. This problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), which is designed for updating from arbitrary priors given information in the form of arbitrary constraints, includes as special cases both MaxEnt (which allows arbitrary constraints) and Bayes' rule (which allows arbitrary priors). Thus, ME unifies the two themes of these workshops—the Maximum Entropy and the Bayesian methods—into a single general inference scheme that allows us to handle problems that lie beyond the reach of either of the two methods separately. I conclude with a couple of simple illustrative examples.

How climate change might influence the potential distribution of weed, bushmint (Hyptis suaveolens)?

PubMed

Padalia, Hitendra; Srivastava, Vivek; Kushwaha, S P S

2015-04-01

Invasive species and climate change are considered as the most serious global environmental threats. In this study, we investigated the influence of projected global climate change on the potential distribution of one of the world's most successful invader weed, bushmint (Hyptis suaveolens (L.) Poit.). We used spatial data on 20 environmental variables at a grid resolution of 5 km, and 564 presence records of bushmint from its native and introduced range. The climatic profiles of the native and invaded sites were analyzed in a multi-variate space in order to examine the differences in the position of climatic niches. Maximum Entropy (MaxEnt) model was used to predict the potential distribution of bushmint using presence records from entire range (invaded and native) along with 14 eco-physiologically relevant predictor variables. Subsequently, the trained MaxEnt model was fed with Hadley Centre Coupled Model (HadCM3) climate projections to predict potential distribution of bushmint by the year 2050 under A2a and B2a emission scenarios. MaxEnt predictions were very accurate with an Area Under Curve (AUC) value of 0.95. The results of Principal Component Analysis (PCA) indicated that climatic niche of bushmint on the invaded sites is not entirely similar to its climatic niche in the native range. A vast area spread between 34 ° 02' north and 28 ° 18' south latitudes in tropics was predicted climatically suitable for bushmint. West and middle Africa, tropical southeast Asia, and northern Australia were predicted at high invasion risk. Study indicates enlargement, retreat, or shift across bushmint's invasion range under the influence of climate change. Globally, bushmint's potential distribution might shrink in future with more shrinkage for A2a scenario than B2a. The study outcome has immense potential for undertaking effective preventive/control measures and long-term management strategies for regions/countries, which are at higher risk of bushmint's invasion.

Challenges in identifying sites climatically matched to the native ranges of animal invaders.

PubMed

Rodda, Gordon H; Jarnevich, Catherine S; Reed, Robert N

2011-02-09

Species distribution models are often used to characterize a species' native range climate, so as to identify sites elsewhere in the world that may be climatically similar and therefore at risk of invasion by the species. This endeavor provoked intense public controversy over recent attempts to model areas at risk of invasion by the Indian Python (Python molurus). We evaluated a number of MaxEnt models on this species to assess MaxEnt's utility for vertebrate climate matching. Overall, we found MaxEnt models to be very sensitive to modeling choices and selection of input localities and background regions. As used, MaxEnt invoked minimal protections against data dredging, multi-collinearity of explanatory axes, and overfitting. As used, MaxEnt endeavored to identify a single ideal climate, whereas different climatic considerations may determine range boundaries in different parts of the native range. MaxEnt was extremely sensitive to both the choice of background locations for the python, and to selection of presence points: inclusion of just four erroneous localities was responsible for Pyron et al.'s conclusion that no additional portions of the U.S. mainland were at risk of python invasion. When used with default settings, MaxEnt overfit the realized climate space, identifying models with about 60 parameters, about five times the number of parameters justifiable when optimized on the basis of Akaike's Information Criterion. When used with default settings, MaxEnt may not be an appropriate vehicle for identifying all sites at risk of colonization. Model instability and dearth of protections against overfitting, multi-collinearity, and data dredging may combine with a failure to distinguish fundamental from realized climate envelopes to produce models of limited utility. A priori identification of biologically realistic model structure, combined with computational protections against these statistical problems, may produce more robust models of invasion risk.

Challenges in Identifying Sites Climatically Matched to the Native Ranges of Animal Invaders

PubMed Central

Rodda, Gordon H.; Jarnevich, Catherine S.; Reed, Robert N.

2011-01-01

Background Species distribution models are often used to characterize a species' native range climate, so as to identify sites elsewhere in the world that may be climatically similar and therefore at risk of invasion by the species. This endeavor provoked intense public controversy over recent attempts to model areas at risk of invasion by the Indian Python (Python molurus). We evaluated a number of MaxEnt models on this species to assess MaxEnt's utility for vertebrate climate matching. Methodology/Principal Findings Overall, we found MaxEnt models to be very sensitive to modeling choices and selection of input localities and background regions. As used, MaxEnt invoked minimal protections against data dredging, multi-collinearity of explanatory axes, and overfitting. As used, MaxEnt endeavored to identify a single ideal climate, whereas different climatic considerations may determine range boundaries in different parts of the native range. MaxEnt was extremely sensitive to both the choice of background locations for the python, and to selection of presence points: inclusion of just four erroneous localities was responsible for Pyron et al.'s conclusion that no additional portions of the U.S. mainland were at risk of python invasion. When used with default settings, MaxEnt overfit the realized climate space, identifying models with about 60 parameters, about five times the number of parameters justifiable when optimized on the basis of Akaike's Information Criterion. Conclusions/Significance When used with default settings, MaxEnt may not be an appropriate vehicle for identifying all sites at risk of colonization. Model instability and dearth of protections against overfitting, multi-collinearity, and data dredging may combine with a failure to distinguish fundamental from realized climate envelopes to produce models of limited utility. A priori identification of biologically realistic model structure, combined with computational protections against these statistical problems, may produce more robust models of invasion risk. PMID:21347411

Challenges in identifying sites climatically matched to the native ranges of animal invaders

USGS Publications Warehouse

Rodda, G.H.; Jarnevich, C.S.; Reed, R.N.

2011-01-01

Background: Species distribution models are often used to characterize a species' native range climate, so as to identify sites elsewhere in the world that may be climatically similar and therefore at risk of invasion by the species. This endeavor provoked intense public controversy over recent attempts to model areas at risk of invasion by the Indian Python (Python molurus). We evaluated a number of MaxEnt models on this species to assess MaxEnt's utility for vertebrate climate matching. Methodology/Principal Findings: Overall, we found MaxEnt models to be very sensitive to modeling choices and selection of input localities and background regions. As used, MaxEnt invoked minimal protections against data dredging, multi-collinearity of explanatory axes, and overfitting. As used, MaxEnt endeavored to identify a single ideal climate, whereas different climatic considerations may determine range boundaries in different parts of the native range. MaxEnt was extremely sensitive to both the choice of background locations for the python, and to selection of presence points: inclusion of just four erroneous localities was responsible for Pyron et al.'s conclusion that no additional portions of the U.S. mainland were at risk of python invasion. When used with default settings, MaxEnt overfit the realized climate space, identifying models with about 60 parameters, about five times the number of parameters justifiable when optimized on the basis of Akaike's Information Criterion. Conclusions/Significance: When used with default settings, MaxEnt may not be an appropriate vehicle for identifying all sites at risk of colonization. Model instability and dearth of protections against overfitting, multi-collinearity, and data dredging may combine with a failure to distinguish fundamental from realized climate envelopes to produce models of limited utility. A priori identification of biologically realistic model structure, combined with computational protections against these statistical problems, may produce more robust models of invasion risk.

Modeling Malaria Vector Distribution under Climate Change Scenarios in Kenya

NASA Astrophysics Data System (ADS)

Ngaina, J. N.

2017-12-01

Projecting the distribution of malaria vectors under climate change is essential for planning integrated vector control strategies for sustaining elimination and preventing reintroduction of malaria. However, in Kenya, little knowledge exists on the possible effects of climate change on malaria vectors. Here we assess the potential impact of future climate change on locally dominant Anopheles vectors including Anopheles gambiae, Anopheles arabiensis, Anopheles merus, Anopheles funestus, Anopheles pharoensis and Anopheles nili. Environmental data (Climate, Land cover and elevation) and primary empirical geo-located species-presence data were identified. The principle of maximum entropy (Maxent) was used to model the species' potential distribution area under paleoclimate, current and future climates. The Maxent model was highly accurate with a statistically significant AUC value. Simulation-based estimates suggest that the environmentally suitable area (ESA) for Anopheles gambiae, An. arabiensis, An. funestus and An. pharoensis would increase under all two scenarios for mid-century (2016-2045), but decrease for end century (2071-2100). An increase in ESA of An. Funestus was estimated under medium stabilizing (RCP4.5) and very heavy (RCP8.5) emission scenarios for mid-century. Our findings can be applied in various ways such as the identification of additional localities where Anopheles malaria vectors may already exist, but has not yet been detected and the recognition of localities where it is likely to spread to. Moreover, it will help guide future sampling location decisions, help with the planning of vector control suites nationally and encourage broader research inquiry into vector species niche modeling

Possible dynamical explanations for Paltridge's principle of maximum entropy production

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

Virgo, Nathaniel, E-mail: nathanielvirgo@gmail.com; Ikegami, Takashi, E-mail: nathanielvirgo@gmail.com

2014-12-05

Throughout the history of non-equilibrium thermodynamics a number of theories have been proposed in which complex, far from equilibrium flow systems are hypothesised to reach a steady state that maximises some quantity. Perhaps the most celebrated is Paltridge's principle of maximum entropy production for the horizontal heat flux in Earth's atmosphere, for which there is some empirical support. There have been a number of attempts to derive such a principle from maximum entropy considerations. However, we currently lack a more mechanistic explanation of how any particular system might self-organise into a state that maximises some quantity. This is in contrastmore » to equilibrium thermodynamics, in which models such as the Ising model have been a great help in understanding the relationship between the predictions of MaxEnt and the dynamics of physical systems. In this paper we show that, unlike in the equilibrium case, Paltridge-type maximisation in non-equilibrium systems cannot be achieved by a simple dynamical feedback mechanism. Nevertheless, we propose several possible mechanisms by which maximisation could occur. Showing that these occur in any real system is a task for future work. The possibilities presented here may not be the only ones. We hope that by presenting them we can provoke further discussion about the possible dynamical mechanisms behind extremum principles for non-equilibrium systems, and their relationship to predictions obtained through MaxEnt.« less

MaxEnt analysis of a water distribution network in Canberra, ACT, Australia

NASA Astrophysics Data System (ADS)

Waldrip, Steven H.; Niven, Robert K.; Abel, Markus; Schlegel, Michael; Noack, Bernd R.

2015-01-01

A maximum entropy (MaxEnt) method is developed to infer the state of a pipe flow network, for situations in which there is insufficient information to form a closed equation set. This approach substantially extends existing deterministic methods for the analysis of engineered flow networks (e.g. Newton's method or the Hardy Cross scheme). The network is represented as an undirected graph structure, in which the uncertainty is represented by a continuous relative entropy on the space of internal and external flow rates. The head losses (potential differences) on the network are treated as dependent variables, using specified pipe-flow resistance functions. The entropy is maximised subject to "observable" constraints on the mean values of certain flow rates and/or potential differences, and also "physical" constraints arising from the frictional properties of each pipe and from Kirchhoff's nodal and loop laws. A numerical method is developed in Matlab for solution of the integral equation system, based on multidimensional quadrature. Several nonlinear resistance functions (e.g. power-law and Colebrook) are investigated, necessitating numerical solution of the implicit Lagrangian by a double iteration scheme. The method is applied to a 1123-node, 1140-pipe water distribution network for the suburb of Torrens in the Australian Capital Territory, Australia, using network data supplied by water authority ACTEW Corporation Limited. A number of different assumptions are explored, including various network geometric representations, prior probabilities and constraint settings, yielding useful predictions of network demand and performance. We also propose this methodology be used in conjunction with in-flow monitoring systems, to obtain better inferences of user consumption without large investments in monitoring equipment and maintenance.

Monitoring potential geographical distribution of four wild bird species in China

NASA Astrophysics Data System (ADS)

Dai, S.; Feng, D.; Xu, B.

2015-12-01

The outbreak of highly pathogenic avian influenza (HPAI) of the H5N1 subtype in wild birds and poultry have caught worldwide attention. To explore the association between wild bird migration and avian influenza virus transmission, we monitored potential geographical distribution of four wild bird species that might carry the avian influenza viruses in China. They are Bar-headed geese (Anser indicus), Ruddy Shelduck (Tadorna ferruginea), Whooper Swan (Cygnus cygnus) and Black-headed Gull (Larus ridibundus). They served as major reservoir of the avian influenza viruses. We used bird watching records with the precise latitude/longitude coordinates from January 2002 to August 2014, and environmental variables with a pixel resolution of 5 km × 5 km from 2002 to 2014. The study utilized maximum entropy (MaxEnt) model based on ecological niche model approaches, and got the following results: 1) MaxEnt model have good discriminatory ability with the area under the curve (AUC) of the receiver operating curve (ROC) of 0.86-0.97; 2) The four wild bird species were estimated to concentrate in the North China Plain, the middle and lower region of the Yangtze River, Qinghai Lake, Tianshan Mountain and Tarim Basin, part of Tibet Plateau, and Hengduan Mountains; 3) Radiation and the minimum temperature were found to provide the most significant information. Our findings will help to understand the spread of avian influenza viruses by wild bird migration in China, which benefits for effective monitoring strategies and prevention measures.

Modeling impacts of climate change on the potential distribution of the carcinogenic liver fluke, Opisthorchis viverrini, in Thailand.

PubMed

Suwannatrai, A; Pratumchart, K; Suwannatrai, K; Thinkhamrop, K; Chaiyos, J; Kim, C S; Suwanweerakamtorn, R; Boonmars, T; Wongsaroj, T; Sripa, B

2017-01-01

Global climate change is now regarded as imposing a significant threat of enhancing transmission of parasitic diseases. Maximum entropy species distribution modeling (MaxEnt) was used to explore how projected climate change could affect the potential distribution of the carcinogenic liver fluke, Opisthorchis viverrini, in Thailand. A range of climate variables was used: the Hadley Global Environment Model 2-Earth System (HadGEM2-ES) climate change model and also the IPCC scenarios A2a for 2050 and 2070. Occurrence data from surveys conducted in 2009 and 2014 were obtained from the Department of Disease Control, Ministry of Public Health, Thailand. The MaxEnt model performed better than random for O. viverrini with training AUC values greater than 0.8 under current and future climatic conditions. The current distribution of O. viverrini is significantly affected by precipitation and minimum temperature. According to current conditions, parts of Thailand climatically suitable for O. viverrini are mostly in the northeast and north, but the parasite is largely absent from southern Thailand. Under future climate change scenarios, the distribution of O. viverrini in 2050 should be significantly affected by precipitation, maximum temperature, and mean temperature of the wettest quarter, whereas in 2070, significant factors are likely to be precipitation during the coldest quarter, maximum, and minimum temperatures. Maps of predicted future distribution revealed a drastic decrease in presence of O. viverrini in the northeast region. The information gained from this study should be a useful reference for implementing long-term prevention and control strategies for O. viverrini in Thailand.

Ensemble habitat mapping of invasive plant species

USGS Publications Warehouse

Stohlgren, T.J.; Ma, P.; Kumar, S.; Rocca, M.; Morisette, J.T.; Jarnevich, C.S.; Benson, N.

2010-01-01

Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species-environment matching models for risk analysis. ?? 2010 Society for Risk Analysis.

Parameter Estimation as a Problem in Statistical Thermodynamics.

PubMed

Earle, Keith A; Schneider, David J

2011-03-14

In this work, we explore the connections between parameter fitting and statistical thermodynamics using the maxent principle of Jaynes as a starting point. In particular, we show how signal averaging may be described by a suitable one particle partition function, modified for the case of a variable number of particles. These modifications lead to an entropy that is extensive in the number of measurements in the average. Systematic error may be interpreted as a departure from ideal gas behavior. In addition, we show how to combine measurements from different experiments in an unbiased way in order to maximize the entropy of simultaneous parameter fitting. We suggest that fit parameters may be interpreted as generalized coordinates and the forces conjugate to them may be derived from the system partition function. From this perspective, the parameter fitting problem may be interpreted as a process where the system (spectrum) does work against internal stresses (non-optimum model parameters) to achieve a state of minimum free energy/maximum entropy. Finally, we show how the distribution function allows us to define a geometry on parameter space, building on previous work[1, 2]. This geometry has implications for error estimation and we outline a program for incorporating these geometrical insights into an automated parameter fitting algorithm.

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

MODIS imagery improves pest risk assessment: A case study of wheat stem sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA

USGS Publications Warehouse

Lestina, Jordan; Cook, Maxwell; Kumar, Sunil; Morisette, Jeffrey T.; Ode, Paul J.; Peirs, Frank

2016-01-01

Wheat stem sawfly (Cephus cinctus Norton, Hymenoptera: Cephidae) has long been a significant insect pest of spring, and more recently, winter wheat in the northern Great Plains. Wheat stem sawfly was first observed infesting winter wheat in Colorado in 2010 and, subsequently, has spread rapidly throughout wheat production regions of the state. Here, we used maximum entropy modeling (MaxEnt) to generate habitat suitability maps in order to predict the risk of crop damage as this species spreads throughout the winter wheat-growing regions of Colorado. We identified environmental variables that influence the current distribution of wheat stem sawfly in the state and evaluated whether remotely sensed variables improved model performance. We used presence localities of C. cinctus and climatic, topographic, soils, and normalized difference vegetation index and enhanced vegetation index data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery as environmental variables. All models had high performance in that they were successful in predicting suitable habitat for C. cinctus in its current distribution in eastern Colorado. The enhanced vegetation index for the month of April improved model performance and was identified as a top contributor to MaxEnt model. Soil clay percent at 0–5 cm, temperature seasonality, and precipitation seasonality were also associated with C. cinctus distribution in Colorado. The improved model performance resulting from integrating vegetation indices in our study demonstrates the ability of remote sensing technologies to enhance species distribution modeling. These risk maps generated can assist managers in planning control measures for current infestations and assess the future risk of C. cinctus establishment in currently uninfested regions.

Uncertainty of future projections of species distributions in mountainous regions.

PubMed

Tang, Ying; Winkler, Julie A; Viña, Andrés; Liu, Jianguo; Zhang, Yuanbin; Zhang, Xiaofeng; Li, Xiaohong; Wang, Fang; Zhang, Jindong; Zhao, Zhiqiang

2018-01-01

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.

Uncertainty of future projections of species distributions in mountainous regions

PubMed Central

Tang, Ying; Viña, Andrés; Liu, Jianguo; Zhang, Yuanbin; Zhang, Xiaofeng; Li, Xiaohong; Wang, Fang; Zhang, Jindong; Zhao, Zhiqiang

2018-01-01

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution. PMID:29320501

Development of the first georeferenced map of Rhipicephalus (Boophilus) spp. in Mexico from 1970 to date and prediction of its spatial distribution.

PubMed

Alcala-Canto, Yazmin; Figueroa-Castillo, Juan Antonio; Ibarra-Velarde, Froylán; Vera-Montenegro, Yolanda; Cervantes-Valencia, María Eugenia; Salem, Abdelfattah Z M; Cuéllar-Ordaz, Jorge Alfredo

2018-05-07

The tick genus Ripicephalus (Boophilus), particularly R. microplus, is one of the most important ectoparasites that affects livestock health and considered an epidemiological risk because it causes significant economic losses due, mainly, to restrictions in the export of infested animals to several countries. Its spatial distribution has been tied to environmental factors, mainly warm temperatures and high relative humidity. In this work, we integrated a dataset consisting of 5843 records of Rhipicephalus spp., in Mexico covering close to 50 years to know which environmental variables mostly influence this ticks' distribution. Occurrences were georeferenced using the software DIVA-GIS and the potential current distribution was modelled using the maximum entropy method (Maxent). The algorithm generated a map of high predictive capability (Area under the curve = 0.942), providing the various contribution and permutation importance of the tested variables. Precipitation seasonality, particularly in March, and isothermality were found to be the most significant climate variables in determining the probability of spatial distribution of Rhipicephalus spp. in Mexico (15.7%, 36.0% and 11.1%, respectively). Our findings demonstrate that Rhipicephalus has colonized Mexico widely, including areas characterized by different types of climate. We conclude that the Maxent distribution model using Rhipicephalus records and a set of environmental variables can predict the extent of the tick range in this country, information that should support the development of integrated control strategies.

Using the Maxent program for species distribution modelling to assess invasion risk

USGS Publications Warehouse

Jarnevich, Catherine S.; Young, Nicholas E.; Venette, R.C

2015-01-01

MAXENT is a software package used to relate known species occurrences to information describing the environment, such as climate, topography, anthropogenic features or soil data, and forecast the presence or absence of a species at unsampled locations. This particular method is one of the most popular species distribution modelling techniques because of its consistent strong predictive performance and its ease to implement. This chapter discusses the decisions and techniques needed to prepare a correlative climate matching model for the native range of an invasive alien species and use this model to predict the potential distribution of this species in a potentially invaded range (i.e. a novel environment) by using MAXENT for the Burmese python (Python molurus bivittatus) as a case study. The chapter discusses and demonstrates the challenges that are associated with this approach and examines the inherent limitations that come with using MAXENT to forecast distributions of invasive alien species.

Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions

USGS Publications Warehouse

Royle, J. Andrew; Chandler, Richard B.; Yackulic, Charles; Nichols, James D.

2012-01-01

1. Understanding the factors affecting species occurrence is a pre-eminent focus of applied ecological research. However, direct information about species occurrence is lacking for many species. Instead, researchers sometimes have to rely on so-called presence-only data (i.e. when no direct information about absences is available), which often results from opportunistic, unstructured sampling. MAXENT is a widely used software program designed to model and map species distribution using presence-only data. 2. We provide a critical review of MAXENT as applied to species distribution modelling and discuss how it can lead to inferential errors. A chief concern is that MAXENT produces a number of poorly defined indices that are not directly related to the actual parameter of interest – the probability of occurrence (ψ). This focus on an index was motivated by the belief that it is not possible to estimate ψ from presence-only data; however, we demonstrate that ψ is identifiable using conventional likelihood methods under the assumptions of random sampling and constant probability of species detection. 3. The model is implemented in a convenient r package which we use to apply the model to simulated data and data from the North American Breeding Bird Survey. We demonstrate that MAXENT produces extreme under-predictions when compared to estimates produced by logistic regression which uses the full (presence/absence) data set. We note that MAXENT predictions are extremely sensitive to specification of the background prevalence, which is not objectively estimated using the MAXENT method. 4. As with MAXENT, formal model-based inference requires a random sample of presence locations. Many presence-only data sets, such as those based on museum records and herbarium collections, may not satisfy this assumption. However, when sampling is random, we believe that inference should be based on formal methods that facilitate inference about interpretable ecological quantities instead of vaguely defined indices.

Potential distribution of Mexican primates: modeling the ecological niche with the maximum entropy algorithm.

PubMed

Vidal-García, Francisca; Serio-Silva, Juan Carlos

2011-07-01

We developed a potential distribution model for the tropical rain forest species of primates of southern Mexico: the black howler monkey (Alouatta pigra), the mantled howler monkey (Alouatta palliata), and the spider monkey (Ateles geoffroyi). To do so, we applied the maximum entropy algorithm from the ecological niche modeling program MaxEnt. For each species, we used occurrence records from scientific collections, and published and unpublished sources, and we also used the 19 environmental coverage variables related to precipitation and temperature from WorldClim to develop the models. The predicted distribution of A. pigra was strongly associated with the mean temperature of the warmest quarter (23.6%), whereas the potential distributions of A. palliata and A. geoffroyi were strongly associated with precipitation during the coldest quarter (52.2 and 34.3% respectively). The potential distribution of A. geoffroyi is broader than that of the Alouatta spp. The areas with the greatest probability of presence of A. pigra and A. palliata are strongly associated with riparian vegetation, whereas the presence of A. geoffroyi is more strongly associated with the presence of rain forest. Our most significant contribution is the identification of areas with a high probability of the presence of these primate species, which is information that can be applied to planning future studies and then establishing criteria for the creation of areas to primate conservation in Mexico.

Impact of climate change on vector transmission of Trypanosoma cruzi (Chagas, 1909) in North America.

PubMed

Carmona-Castro, O; Moo-Llanes, D A; Ramsey, J M

2018-03-01

Climate change can influence the geographical range of the ecological niche of pathogens by altering biotic interactions with vectors and reservoirs. The distributions of 20 epidemiologically important triatomine species in North America were modelled, comparing the genetic algorithm for rule-set prediction (GARP) and maximum entropy (MaxEnt), with or without topographical variables. Potential shifts in transmission niche for Trypanosoma cruzi (Trypanosomatida: Trypanosomatidae) (Chagas, 1909) were analysed for 2050 and 2070 in Representative Concentration Pathway (RCP) 4.5 and RCP 8.5. There were no significant quantitative range differences between the GARP and MaxEnt models, but GARP models best represented known distributions for most species [partial-receiver operating characteristic (ROC) > 1]; elevation was an important variable contributing to the ecological niche model (ENM). There was little difference between niche breadth projections for RCP 4.5 and RCP 8.5; the majority of species shifted significantly in both periods. Those species with the greatest current distribution range are expected to have the greatest shifts. Positional changes in the centroid, although reduced for most species, were associated with latitude. A significant increase or decrease in mean niche elevation is expected principally for Neotropical 1 species. The impact of climate change will be specific to each species, its biogeographical region and its latitude. North American triatomines with the greatest current distribution ranges (Nearctic 2 and Nearctic/Neotropical) will have the greatest future distribution shifts. Significant shifts (increases or decreases) in mean elevation over time are projected principally for the Neotropical species with the broadest current distributions. Changes in the vector exposure threat to the human population were significant for both future periods, with a 1.48% increase for urban populations and a 1.76% increase for rural populations in 2050. © 2017 The Royal Entomological Society.

Mapping Global Potential Risk of Mango Sudden Decline Disease Caused by Ceratocystis fimbriata.

PubMed

Galdino, Tarcísio Visintin da Silva; Kumar, Sunil; Oliveira, Leonardo S S; Alfenas, Acelino C; Neven, Lisa G; Al-Sadi, Abdullah M; Picanço, Marcelo C

2016-01-01

The Mango Sudden Decline (MSD), also referred to as Mango Wilt, is an important disease of mango in Brazil, Oman and Pakistan. This fungus is mainly disseminated by the mango bark beetle, Hypocryphalus mangiferae (Stebbing), by infected plant material, and the infested soils where it is able to survive for long periods. The best way to avoid losses due to MSD is to prevent its establishment in mango production areas. Our objectives in this study were to: (1) predict the global potential distribution of MSD, (2) identify the mango growing areas that are under potential risk of MSD establishment, and (3) identify climatic factors associated with MSD distribution. Occurrence records were collected from Brazil, Oman and Pakistan where the disease is currently known to occur in mango. We used the correlative maximum entropy based model (MaxEnt) algorithm to assess the global potential distribution of MSD. The MaxEnt model predicted suitable areas in countries where the disease does not already occur in mango, but where mango is grown. Among these areas are the largest mango producers in the world including India, China, Thailand, Indonesia, and Mexico. The mean annual temperature, precipitation of coldest quarter, precipitation seasonality, and precipitation of driest month variables contributed most to the potential distribution of MSD disease. The mango bark beetle vector is known to occur beyond the locations where MSD currently exists and where the model predicted suitable areas, thus showing a high likelihood for disease establishment in areas predicted by our model. Our study is the first to map the potential risk of MSD establishment on a global scale. This information can be used in designing strategies to prevent introduction and establishment of MSD disease, and in preparation of efficient pest risk assessments and monitoring programs.

Mapping Global Potential Risk of Mango Sudden Decline Disease Caused by Ceratocystis fimbriata

PubMed Central

Oliveira, Leonardo S. S.; Alfenas, Acelino C.; Neven, Lisa G.; Al-Sadi, Abdullah M.

2016-01-01

The Mango Sudden Decline (MSD), also referred to as Mango Wilt, is an important disease of mango in Brazil, Oman and Pakistan. This fungus is mainly disseminated by the mango bark beetle, Hypocryphalus mangiferae (Stebbing), by infected plant material, and the infested soils where it is able to survive for long periods. The best way to avoid losses due to MSD is to prevent its establishment in mango production areas. Our objectives in this study were to: (1) predict the global potential distribution of MSD, (2) identify the mango growing areas that are under potential risk of MSD establishment, and (3) identify climatic factors associated with MSD distribution. Occurrence records were collected from Brazil, Oman and Pakistan where the disease is currently known to occur in mango. We used the correlative maximum entropy based model (MaxEnt) algorithm to assess the global potential distribution of MSD. The MaxEnt model predicted suitable areas in countries where the disease does not already occur in mango, but where mango is grown. Among these areas are the largest mango producers in the world including India, China, Thailand, Indonesia, and Mexico. The mean annual temperature, precipitation of coldest quarter, precipitation seasonality, and precipitation of driest month variables contributed most to the potential distribution of MSD disease. The mango bark beetle vector is known to occur beyond the locations where MSD currently exists and where the model predicted suitable areas, thus showing a high likelihood for disease establishment in areas predicted by our model. Our study is the first to map the potential risk of MSD establishment on a global scale. This information can be used in designing strategies to prevent introduction and establishment of MSD disease, and in preparation of efficient pest risk assessments and monitoring programs. PMID:27415625

Environmental and socio-economic risk modelling for Chagas disease in Bolivia.

PubMed

Mischler, Paula; Kearney, Michael; McCarroll, Jennifer C; Scholte, Ronaldo G C; Vounatsou, Penelope; Malone, John B

2012-09-01

Accurately defining disease distributions and calculating disease risk is an important step in the control and prevention of diseases. Geographical information systems (GIS) and remote sensing technologies, with maximum entropy (Maxent) ecological niche modelling computer software, were used to create predictive risk maps for Chagas disease in Bolivia. Prevalence rates were calculated from 2007 to 2009 household infection survey data for Bolivia, while environmental data were compiled from the Worldclim database and MODIS satellite imagery. Socio-economic data were obtained from the Bolivian National Institute of Statistics. Disease models identified altitudes at 500-3,500 m above the mean sea level (MSL), low annual precipitation (45-250 mm), and higher diurnal range of temperature (10-19 °C; peak 16 °C) as compatible with the biological requirements of the insect vectors. Socio-economic analyses demonstrated the importance of improved housing materials and water source. Home adobe wall materials and having to fetch drinking water from rivers or wells without pump were found to be highly related to distribution of the disease by the receiver operator characteristic (ROC) area under the curve (AUC) (0.69 AUC, 0.67 AUC and 0.62 AUC, respectively), while areas with hardwood floors demonstrated a direct negative relationship (-0.71 AUC). This study demonstrates that Maxent modelling can be used in disease prevalence and incidence studies to provide governmental agencies with an easily learned, understandable method to define areas as either high, moderate or low risk for the disease. This information may be used in resource planning, targeting and implementation. However, access to high-resolution, sub-municipality socio-economic data (e.g. census tracts) would facilitate elucidation of the relative influence of poverty-related factors on regional disease dynamics.

Using NASA Earth Observing Satellites and Statistical Model Analysis to Monitor Vegetation and Habitat Rehabilitation in Southwest Virginia's Reclaimed Mine Lands

NASA Astrophysics Data System (ADS)

Tate, Z.; Dusenge, D.; Elliot, T. S.; Hafashimana, P.; Medley, S.; Porter, R. P.; Rajappan, R.; Rodriguez, P.; Spangler, J.; Swaminathan, R. S.; VanGundy, R. D.

2014-12-01

The majority of the population in southwest Virginia depends economically on coal mining. In 2011, coal mining generated $2,000,000 in tax revenue to Wise County alone. However, surface mining completely removes land cover and leaves the land exposed to erosion. The destruction of the forest cover directly impacts local species, as some are displaced and others perish in the mining process. Even though surface mining has a negative impact on the environment, land reclamation efforts are in place to either restore mined areas to their natural vegetated state or to transform these areas for economic purposes. This project aimed to monitor the progress of land reclamation and the effect on the return of local species. By incorporating NASA Earth observations, such as Landsat 8 Operational Land Imager (OLI) and Landsat 5 Thematic Mapper (TM), re-vegetation process in reclamation sites was estimated through a Time series analysis using the Normalized Difference Vegetation Index (NDVI). A continuous source of cloud free images was accomplished by utilizing the Spatial and Temporal Adaptive Reflectance Fusion Model (STAR-FM). This model developed synthetic Landsat imagery by integrating the high-frequency temporal information from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and high-resolution spatial information from Landsat sensors In addition, the Maximum Entropy Modeling (MaxENT), an eco-niche model was used to estimate the adaptation of animal species to the newly formed habitats. By combining factors such as land type, precipitation from Tropical Rainfall Measuring Mission (TRMM), and slope from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the MaxENT model produced a statistical analysis on the probability of species habitat. Altogether, the project compiled the ecological information which can be used to identify suitable habitats for local species in reclaimed mined areas.

a Maximum Entropy Model of the Bearded Capuchin Monkey Habitat Incorporating Topography and Spectral Unmixing Analysis

NASA Astrophysics Data System (ADS)

Howard, A. M.; Bernardes, S.; Nibbelink, N.; Biondi, L.; Presotto, A.; Fragaszy, D. M.; Madden, M.

2012-07-01

Movement patterns of bearded capuchin monkeys (Cebus (Sapajus) libidinosus) in northeastern Brazil are likely impacted by environmental features such as elevation, vegetation density, or vegetation type. Habitat preferences of these monkeys provide insights regarding the impact of environmental features on species ecology and the degree to which they incorporate these features in movement decisions. In order to evaluate environmental features influencing movement patterns and predict areas suitable for movement, we employed a maximum entropy modelling approach, using observation points along capuchin monkey daily routes as species presence points. We combined these presence points with spatial data on important environmental features from remotely sensed data on land cover and topography. A spectral mixing analysis procedure was used to generate fraction images that represent green vegetation, shade and soil of the study area. A Landsat Thematic Mapper scene of the area of study was geometrically and atmospherically corrected and used as input in a Minimum Noise Fraction (MNF) procedure and a linear spectral unmixing approach was used to generate the fraction images. These fraction images and elevation were the environmental layer inputs for our logistic MaxEnt model of capuchin movement. Our models' predictive power (test AUC) was 0.775. Areas of high elevation (>450 m) showed low probabilities of presence, and percent green vegetation was the greatest overall contributor to model AUC. This work has implications for predicting daily movement patterns of capuchins in our field site, as suitability values from our model may relate to habitat preference and facility of movement.

A new species of Desmopachria Babington (Coleoptera: Dytiscidae) from Cuba with a prediction of its geographic distribution and notes on other Cuban species of the genus.

PubMed

Megna, Yoandri S; Sánchez-Fernández, David

2014-01-10

A new species, Desmopachria andreae sp. n. is described from Cuba. Diagnostic characters including illustrations of male genitalia are provided and illustrated for the five species of the genus occurring on the island. For these five species both a simple key to adults and maps of their known distribution in Cuba are also provided. Using a Maximun Entropy method (MaxEnt), a distribution model was developed for D. andreae sp.n. Based on the model's predictions, this species has a higher probability of occurring in high altitude forests (above 1000 m a.s.l.), characterised by relatively low temperatures especially during the hottest and wettest seasons, specifically, the mountainous areas of the Macizo de Guamuhaya (Central Cuba), Sierra Maestra (S Cuba) and Nipe-Sagua-Baracoa (NE Cuba). In some of these areas the species has not yet been recorded, and should be searched for in future field surveys.

Predicting the potential distribution of main malaria vectors Anopheles stephensi, An. culicifacies s.l. and An. fluviatilis s.l. in Iran based on maximum entropy model.

PubMed

Pakdad, Kamran; Hanafi-Bojd, Ahmad Ali; Vatandoost, Hassan; Sedaghat, Mohammad Mehdi; Raeisi, Ahmad; Moghaddam, Abdolreza Salahi; Foroushani, Abbas Rahimi

2017-05-01

Malaria is considered as a major public health problem in southern areas of Iran. The goal of this study was to predict best ecological niches of three main malaria vectors of Iran: Anopheles stephensi, Anopheles culicifacies s.l. and Anopheles fluviatilis s.l. A databank was created which included all published data about Anopheles species of Iran from 1961 to 2015. The suitable environmental niches for the three above mentioned Anopheles species were predicted using maximum entropy model (MaxEnt). AUC (area under Roc curve) values were 0.943, 0.974 and 0.956 for An. stephensi, An. culicifacies s.l. and An. fluviatilis s.l respectively, which are considered as high potential power of model in the prediction of species niches. The biggest bioclimatic contributor for An. stephensi and An. fluviatilis s.l. was bio 15 (precipitation seasonality), 25.5% and 36.1% respectively, followed by bio 1 (annual mean temperature), 20.8% for An. stephensi and bio 4 (temperature seasonality) with 49.4% contribution for An. culicifacies s.l. This is the first step in the mapping of the country's malaria vectors. Hence, future weather situation can change the dispersal maps of Anopheles. Iran is under elimination phase of malaria, so that such spatio-temporal studies are essential and could provide guideline for decision makers for IVM strategies in problematic areas. Copyright © 2017 Elsevier B.V. All rights reserved.

Using a MaxEnt Classifier for the Automatic Content Scoring of Free-Text Responses

NASA Astrophysics Data System (ADS)

Sukkarieh, Jana Z.

2011-03-01

Criticisms against multiple-choice item assessments in the USA have prompted researchers and organizations to move towards constructed-response (free-text) items. Constructed-response (CR) items pose many challenges to the education community—one of which is that they are expensive to score by humans. At the same time, there has been widespread movement towards computer-based assessment and hence, assessment organizations are competing to develop automatic content scoring engines for such items types—which we view as a textual entailment task. This paper describes how MaxEnt Modeling is used to help solve the task. MaxEnt has been used in many natural language tasks but this is the first application of the MaxEnt approach to textual entailment and automatic content scoring.

Mapping the potential distribution of the invasive Red Shiner, Cyprinella lutrensis (Teleostei: Cyprinidae) across waterways of the conterminous United States

USGS Publications Warehouse

Poulos, Helen M.; Chernoff, Barry; Fuller, Pam L.; Butman, David

2012-01-01

Predicting the future spread of non-native aquatic species continues to be a high priority for natural resource managers striving to maintain biodiversity and ecosystem function. Modeling the potential distributions of alien aquatic species through spatially explicit mapping is an increasingly important tool for risk assessment and prediction. Habitat modeling also facilitates the identification of key environmental variables influencing species distributions. We modeled the potential distribution of an aggressive invasive minnow, the red shiner (Cyprinella lutrensis), in waterways of the conterminous United States using maximum entropy (Maxent). We used inventory records from the USGS Nonindigenous Aquatic Species Database, native records for C. lutrensis from museum collections, and a geographic information system of 20 raster climatic and environmental variables to produce a map of potential red shiner habitat. Summer climatic variables were the most important environmental predictors of C. lutrensis distribution, which was consistent with the high temperature tolerance of this species. Results from this study provide insights into the locations and environmental conditions in the US that are susceptible to red shiner invasion.

Identifying areas of deforestation risk for REDD+ using a species modeling tool

PubMed Central

Riveros, Juan Carlos; Forrest, Jessica L

2014-01-01

Background To implement the REDD+ mechanism (Reducing Emissions for Deforestation and Forest Degradation, countries need to prioritize areas to combat future deforestation CO2 emissions, identify the drivers of deforestation around which to develop mitigation actions, and quantify and value carbon for financial mechanisms. Each comes with its own methodological challenges, and existing approaches and tools to do so can be costly to implement or require considerable technical knowledge and skill. Here, we present an approach utilizing a machine learning technique known as Maximum Entropy Modeling (Maxent) to identify areas at high deforestation risk in the study area in Madre de Dios, Peru under a business-as-usual scenario in which historic deforestation rates continue. We link deforestation risk area to carbon density values to estimate future carbon emissions. We quantified area deforested and carbon emissions between 2000 and 2009 as the basis of the scenario. Results We observed over 80,000 ha of forest cover lost from 2000-2009 (0.21% annual loss), representing over 39 million Mg CO2. The rate increased rapidly following the enhancement of the Inter Oceanic Highway in 2005. Accessibility and distance to previous deforestation were strong predictors of deforestation risk, while land use designation was less important. The model performed consistently well (AUC > 0.9), significantly better than random when we compared predicted deforestation risk to observed. If past deforestation rates continue, we estimate that 132,865 ha of forest could be lost by the year 2020, representing over 55 million Mg CO2. Conclusions Maxent provided a reliable method for identifying areas at high risk of deforestation and the major explanatory variables that could draw attention for mitigation action planning under REDD+. The tool is accessible, replicable and easy to use; all necessary for producing good risk estimates and adapt models after potential landscape change. We propose this approach for developing countries planning to meet requirements under REDD+. PMID:25489336

Identifying areas of deforestation risk for REDD+ using a species modeling tool.

PubMed

Aguilar-Amuchastegui, Naikoa; Riveros, Juan Carlos; Forrest, Jessica L

2014-01-01

To implement the REDD+ mechanism (Reducing Emissions for Deforestation and Forest Degradation, countries need to prioritize areas to combat future deforestation CO2 emissions, identify the drivers of deforestation around which to develop mitigation actions, and quantify and value carbon for financial mechanisms. Each comes with its own methodological challenges, and existing approaches and tools to do so can be costly to implement or require considerable technical knowledge and skill. Here, we present an approach utilizing a machine learning technique known as Maximum Entropy Modeling (Maxent) to identify areas at high deforestation risk in the study area in Madre de Dios, Peru under a business-as-usual scenario in which historic deforestation rates continue. We link deforestation risk area to carbon density values to estimate future carbon emissions. We quantified area deforested and carbon emissions between 2000 and 2009 as the basis of the scenario. We observed over 80,000 ha of forest cover lost from 2000-2009 (0.21% annual loss), representing over 39 million Mg CO2. The rate increased rapidly following the enhancement of the Inter Oceanic Highway in 2005. Accessibility and distance to previous deforestation were strong predictors of deforestation risk, while land use designation was less important. The model performed consistently well (AUC > 0.9), significantly better than random when we compared predicted deforestation risk to observed. If past deforestation rates continue, we estimate that 132,865 ha of forest could be lost by the year 2020, representing over 55 million Mg CO2. Maxent provided a reliable method for identifying areas at high risk of deforestation and the major explanatory variables that could draw attention for mitigation action planning under REDD+. The tool is accessible, replicable and easy to use; all necessary for producing good risk estimates and adapt models after potential landscape change. We propose this approach for developing countries planning to meet requirements under REDD+.

Deep-sea benthic megafaunal habitat suitability modelling: A global-scale maximum entropy model for xenophyophores

NASA Astrophysics Data System (ADS)

Ashford, Oliver S.; Davies, Andrew J.; Jones, Daniel O. B.

2014-12-01

Xenophyophores are a group of exclusively deep-sea agglutinating rhizarian protozoans, at least some of which are foraminifera. They are an important constituent of the deep-sea megafauna that are sometimes found in sufficient abundance to act as a significant source of habitat structure for meiofaunal and macrofaunal organisms. This study utilised maximum entropy modelling (Maxent) and a high-resolution environmental database to explore the environmental factors controlling the presence of Xenophyophorea and two frequently sampled xenophyophore species that are taxonomically stable: Syringammina fragilissima and Stannophyllum zonarium. These factors were also used to predict the global distribution of each taxon. Areas of high habitat suitability for xenophyophores were highlighted throughout the world's oceans, including in a large number of areas yet to be suitably sampled, but the Northeast and Southeast Atlantic Ocean, Gulf of Mexico and Caribbean Sea, the Red Sea and deep-water regions of the Malay Archipelago represented particular hotspots. The two species investigated showed more specific habitat requirements when compared to the model encompassing all xenophyophore records, perhaps in part due to the smaller number and relatively more clustered nature of the presence records available for modelling at present. The environmental variables depth, oxygen parameters, nitrate concentration, carbon-chemistry parameters and temperature were of greatest importance in determining xenophyophore distributions, but, somewhat surprisingly, hydrodynamic parameters were consistently shown to have low importance, possibly due to the paucity of well-resolved global hydrodynamic datasets. The results of this study (and others of a similar type) have the potential to guide further sample collection, environmental policy, and spatial planning of marine protected areas and industrial activities that impact the seafloor, particularly those that overlap with aggregations of these conspicuously large single-celled eukaryotes.

[Influence of road on breeding habitat of Nipponia nippon based on MaxEnt model].

PubMed

Zhang, Hui; Gao, Ji Xi; Ma, Meng Xiao; Shao, Fang Ze; Wang, Qiao; Li, Guang Yu; Qiu, Jie; Zhou, Ke Xin

2017-04-18

Quantitative study on effects of roads on suitable breeding habitats of wildlife is one of topics that need in-depth research in road ecology. Crested ibis (Nipponia nippon), a first class nationally protected bird species, is the species of interest in this research. Using the Maximum Entropy Models (MaxEnt) in the Species Distribution Model (SDM) toolbox of ArcGIS, autocorrelation of environmental variables were analyzed and environmental variables with r>0.8 were removed. Ten environmental variables were chosen as impact factors for the breeding habitat of crested ibis, including mean temperature of coldest quarter, landscape type, normalized difference vegetation index(NDVI), slope, aspect, distance to waters, distance to paddy field, distance to high-grade roads (expressway, national way, provincial way), and distance to low-grade roads (country road). By analyzing the contribution rate of each environmental variable, the results showed that the mean temperature of coldest quarter, landscape type, distance to paddy field, and distance to high-grade roads were the main factors determining breeding habitat of crested ibis. The suitable distribution of crested ibis' nesting area was under the following scenarios: variable road present (scenario1), high-grade road absent (scenario2), and low-grade road absent (scenario 3). The results showed that the presence of roads affected suitable nesting areas of crested ibis with high-grade roads showing a larger influence than low-grade roads. The presence of high-grade roads and low-grade roads decreased the suitable nesting areas of crested ibis by 66.23 and 35.69 km 2 , respectively. The crested ibis preferred to nest in areas distant from high-grade roads, with an average road avoidance distance of 1500 m. This study was of great significance for formulating management measures to protect crested ibis and provide a reference for quantitative assessment on impacts of engineering and construction projects on wildlife.

Little pigeons can carry great messages: potential distribution and ecology of Uranotaenia (Pseudoficalbia) unguiculata Edwards, 1913 (Diptera: Culicidae), a lesser-known mosquito species from the Western Palaearctic.

PubMed

Filatov, Serhii

2017-10-10

Uranotaenia unguiculata is a Palaearctic mosquito species with poorly known distribution and ecology. This study is aimed at filling the gap in our understanding of the species potential distribution and its environmental requirements through a species distribution modelling (SDM) exercise. Furthermore, aspects of the mosquito ecology that may be relevant to the epidemiology of certain zoonotic vector-borne diseases in Europe are discussed. A maximum entropy (Maxent) modelling approach has been applied to predict the potential distribution of Ur. unguiculata in the Western Palaearctic. Along with the high accuracy and predictive power, the model reflects well the known species distribution and predicts as highly suitable some areas where the occurrence of the species is hitherto unknown. To our knowledge, the potential distribution of a mosquito species from the genus Uranotaenia is modelled for the first time. Provided that Ur. unguiculata is a widely-distributed species, and some pathogens of zoonotic concern have been detected in this mosquito on several occasions, the question regarding its host associations and possible epidemiological role warrants further investigation.

Host-to-host variation of ecological interactions in polymicrobial infections

NASA Astrophysics Data System (ADS)

Mukherjee, Sayak; Weimer, Kristin E.; Seok, Sang-Cheol; Ray, Will C.; Jayaprakash, C.; Vieland, Veronica J.; Swords, W. Edward; Das, Jayajit

2015-02-01

Host-to-host variability with respect to interactions between microorganisms and multicellular hosts are commonly observed in infection and in homeostasis. However, the majority of mechanistic models used to analyze host-microorganism relationships, as well as most of the ecological theories proposed to explain coevolution of hosts and microbes, are based on averages across a host population. By assuming that observed variations are random and independent, these models overlook the role of differences between hosts. Here, we analyze mechanisms underlying host-to-host variations of bacterial infection kinetics, using the well characterized experimental infection model of polymicrobial otitis media (OM) in chinchillas, in combination with population dynamic models and a maximum entropy (MaxEnt) based inference scheme. We find that the nature of the interactions between bacterial species critically regulates host-to-host variations in these interactions. Surprisingly, seemingly unrelated phenomena, such as the efficiency of individual bacterial species in utilizing nutrients for growth, and the microbe-specific host immune response, can become interdependent in a host population. The latter finding suggests a potential mechanism that could lead to selection of specific strains of bacterial species during the coevolution of the host immune response and the bacterial species.

Understanding the dynamics in distribution of invasive alien plant species under predicted climate change in Western Himalaya

PubMed Central

Chitale, Vishwas; Rijal, Srijana Joshi; Bisht, Neha; Shrestha, Bharat Babu

2018-01-01

Invasive alien plant species (IAPS) can pose severe threats to biodiversity and stability of native ecosystems, therefore, predicting the distribution of the IAPS plays a crucial role in effective planning and management of ecosystems. In the present study, we use Maximum Entropy (MaxEnt) modelling approach to predict the potential of distribution of eleven IAPS under future climatic conditions under RCP 2.6 and RCP 8.5 in part of Kailash sacred landscape region in Western Himalaya. Based on the model predictions, distribution of most of these invasive plants is expected to expand under future climatic scenarios, which might pose a serious threat to the native ecosystems through competition for resources in the study area. Native scrublands and subtropical needle-leaved forests will be the most affected ecosystems by the expansion of these IAPS. The present study is first of its kind in the Kailash Sacred Landscape in the field of invasive plants and the predictions of potential distribution under future climatic conditions from our study could help decision makers in planning and managing these forest ecosystems effectively. PMID:29664961

Understanding the dynamics in distribution of invasive alien plant species under predicted climate change in Western Himalaya.

PubMed

Thapa, Sunil; Chitale, Vishwas; Rijal, Srijana Joshi; Bisht, Neha; Shrestha, Bharat Babu

2018-01-01

Invasive alien plant species (IAPS) can pose severe threats to biodiversity and stability of native ecosystems, therefore, predicting the distribution of the IAPS plays a crucial role in effective planning and management of ecosystems. In the present study, we use Maximum Entropy (MaxEnt) modelling approach to predict the potential of distribution of eleven IAPS under future climatic conditions under RCP 2.6 and RCP 8.5 in part of Kailash sacred landscape region in Western Himalaya. Based on the model predictions, distribution of most of these invasive plants is expected to expand under future climatic scenarios, which might pose a serious threat to the native ecosystems through competition for resources in the study area. Native scrublands and subtropical needle-leaved forests will be the most affected ecosystems by the expansion of these IAPS. The present study is first of its kind in the Kailash Sacred Landscape in the field of invasive plants and the predictions of potential distribution under future climatic conditions from our study could help decision makers in planning and managing these forest ecosystems effectively.

Potential distribution dataset of honeybees in Indian Ocean Islands: Case study of Zanzibar Island.

PubMed

Mwalusepo, Sizah; Muli, Eliud; Nkoba, Kiatoko; Nguku, Everlyn; Kilonzo, Joseph; Abdel-Rahman, Elfatih M; Landmann, Tobias; Fakih, Asha; Raina, Suresh

2017-10-01

Honeybees ( Apis mellifera ) are principal insect pollinators, whose worldwide distribution and abundance is known to largely depend on climatic conditions. However, the presence records dataset on potential distribution of honeybees in Indian Ocean Islands remain less documented. Presence records in shape format and probability of occurrence of honeybees with different temperature change scenarios is provided in this article across Zanzibar Island. Maximum entropy (Maxent) package was used to analyse the potential distribution of honeybees. The dataset provides information on the current and future distribution of the honey bees in Zanzibar Island. The dataset is of great importance for improving stakeholders understanding of the role of temperature change on the spatial distribution of honeybees.

Improving national-scale invasion maps: Tamarisk in the western United States

USGS Publications Warehouse

Jarnevich, C.S.; Evangelista, P.; Stohlgren, T.J.; Morisette, J.

2011-01-01

New invasions, better field data, and novel spatial-modeling techniques often drive the need to revisit previous maps and models of invasive species. Such is the case with the at least 10 species of Tamarix, which are invading riparian systems in the western United States and expanding their range throughout North America. In 2006, we developed a National Tamarisk Map by using a compilation of presence and absence locations with remotely sensed data and statistical modeling techniques. Since the publication of that work, our database of Tamarix distributions has grown significantly. Using the updated database of species occurrence, new predictor variables, and the maximum entropy (Maxent) model, we have revised our potential Tamarix distribution map for the western United States. Distance-to-water was the strongest predictor in the model (58.1%), while mean temperature of the warmest quarter was the second best predictor (18.4%). Model validation, averaged from 25 model iterations, indicated that our analysis had strong predictive performance (AUC = 0.93) and that the extent of Tamarix distributions is much greater than previously thought. The southwestern United States had the greatest suitable habitat, and this result differed from the 2006 model. Our work highlights the utility of iterative modeling for invasive species habitat modeling as new information becomes available. ?? 2011.

A Bayesian perspective on Markovian dynamics and the fluctuation theorem

NASA Astrophysics Data System (ADS)

Virgo, Nathaniel

2013-08-01

One of E. T. Jaynes' most important achievements was to derive statistical mechanics from the maximum entropy (MaxEnt) method. I re-examine a relatively new result in statistical mechanics, the Evans-Searles fluctuation theorem, from a MaxEnt perspective. This is done in the belief that interpreting such results in Bayesian terms will lead to new advances in statistical physics. The version of the fluctuation theorem that I will discuss applies to discrete, stochastic systems that begin in a non-equilibrium state and relax toward equilibrium. I will show that for such systems the fluctuation theorem can be seen as a consequence of the fact that the equilibrium distribution must obey the property of detailed balance. Although the principle of detailed balance applies only to equilibrium ensembles, it puts constraints on the form of non-equilibrium trajectories. This will be made clear by taking a novel kind of Bayesian perspective, in which the equilibrium distribution is seen as a prior over the system's set of possible trajectories. Non-equilibrium ensembles are calculated from this prior using Bayes' theorem, with the initial conditions playing the role of the data. I will also comment on the implications of this perspective for the question of how to derive the second law.

Using species distribution models to optimize vector control in the framework of the tsetse eradication campaign in Senegal.

PubMed

Dicko, Ahmadou H; Lancelot, Renaud; Seck, Momar T; Guerrini, Laure; Sall, Baba; Lo, Mbargou; Vreysen, Marc J B; Lefrançois, Thierry; Fonta, William M; Peck, Steven L; Bouyer, Jérémy

2014-07-15

Tsetse flies are vectors of human and animal trypanosomoses in sub-Saharan Africa and are the target of the Pan African Tsetse and Trypanosomiasis Eradication Campaign (PATTEC). Glossina palpalis gambiensis (Diptera: Glossinidae) is a riverine species that is still present as an isolated metapopulation in the Niayes area of Senegal. It is targeted by a national eradication campaign combining a population reduction phase based on insecticide-treated targets (ITTs) and cattle and an eradication phase based on the sterile insect technique. In this study, we used species distribution models to optimize control operations. We compared the probability of the presence of G. p. gambiensis and habitat suitability using a regularized logistic regression and Maxent, respectively. Both models performed well, with an area under the curve of 0.89 and 0.92, respectively. Only the Maxent model predicted an expert-based classification of landscapes correctly. Maxent predictions were therefore used throughout the eradication campaign in the Niayes to make control operations more efficient in terms of deployment of ITTs, release density of sterile males, and location of monitoring traps used to assess program progress. We discuss how the models' results informed about the particular ecology of tsetse in the target area. Maxent predictions allowed optimizing efficiency and cost within our project, and might be useful for other tsetse control campaigns in the framework of the PATTEC and, more generally, other vector or insect pest control programs.

Presence-only Species Distribution Modeling for King Mackerel (Scomberomorus cavalla) and its 31 Prey Species in the Gulf of Mexico

NASA Astrophysics Data System (ADS)

Cai, X.; Simons, J.; Carollo, C.; Sterba-Boatwright, B.; Sadovski, A.

2016-02-01

Ecosystem based fisheries management has been broadly recognized throughout the world as a way to achieve better conservation. Therefore, there is a strong need for mapping of multi-species interactions or spatial distributions. Species distribution models are widely applied since information regarding the presence of species is usually only available for limited locations due to the high cost of fisheries surveys. Instead of regular presence and absence records, a large proportion of the fisheries survey data have only presence records. This makes the modeling problem one of one-class classification (presence only), which is much more complex than the regular two-class classification (presence/absence). In this study, four different presence-only species distribution algorithms (Bioclim, Domain, Mahal and Maxent) were applied using 13 environmental parameters (e.g., depth, DO, bottom types) as predictors to model the distribution of king mackerel (Scomberomorus cavalla) and its 31 prey species in the Gulf of Mexico (a total of 13625 georeferenced presence records from OBIS and GBIF were used). Five-fold cross validations were applied for each of the 128 (4 algorithms × 32 species) models. Area under curve (AUC) and correlation coefficient (R) were used to evaluate the model performances. The AUC of the models based on these four algorithms were 0.83±0.14, 0.77±0.16, 0.94±0.06 and 0.94±0.06, respectively; while R for the models were 0.47±0.27, 0.43±0.24, 0.27±0.16 and 0.76±0.16, respectively. Post hoc with Tukey's test showed that AUC for the Maxent-based models were significantly (p<0.05) higher than those for Bioclim and Domain based models, but insignificantly different from those for Mahal-based models (p=0.955); while R for the Maxent-based models were significantly higher than those for all the other three types of models (p<0.05). Thus, we concluded that the Maxent-based models had the best performance. High AUC and R also indicated that Maxent-based models could provide robust and reliable results to model target species distributions, and they can be further used to model the king mackerel food web in the Gulf of Mexico to help managers better manage related fisheries resources.

[Forest lighting fire forecasting for Daxing'anling Mountains based on MAXENT model].

PubMed

Sun, Yu; Shi, Ming-Chang; Peng, Huan; Zhu, Pei-Lin; Liu, Si-Lin; Wu, Shi-Lei; He, Cheng; Chen, Feng

2014-04-01

Daxing'anling Mountains is one of the areas with the highest occurrence of forest lighting fire in Heilongjiang Province, and developing a lightning fire forecast model to accurately predict the forest fires in this area is of importance. Based on the data of forest lightning fires and environment variables, the MAXENT model was used to predict the lightning fire in Daxing' anling region. Firstly, we studied the collinear diagnostic of each environment variable, evaluated the importance of the environmental variables using training gain and the Jackknife method, and then evaluated the prediction accuracy of the MAXENT model using the max Kappa value and the AUC value. The results showed that the variance inflation factor (VIF) values of lightning energy and neutralized charge were 5.012 and 6.230, respectively. They were collinear with the other variables, so the model could not be used for training. Daily rainfall, the number of cloud-to-ground lightning, and current intensity of cloud-to-ground lightning were the three most important factors affecting the lightning fires in the forest, while the daily average wind speed and the slope was of less importance. With the increase of the proportion of test data, the max Kappa and AUC values were increased. The max Kappa values were above 0.75 and the average value was 0.772, while all of the AUC values were above 0.5 and the average value was 0. 859. With a moderate level of prediction accuracy being achieved, the MAXENT model could be used to predict forest lightning fire in Daxing'anling Mountains.

[Production regionalization study of Chinese angelica based on MaxEnt model].

PubMed

Yan, Hui; Zhang, Xiao-Bo; Zhu, Shou-Dong; Qian, Da-Wei; Guo, Lan-Ping; Huang, Lu-Qi; Duan, Jin-Ao

2016-09-01

The distribution information of Chinese angelica was collected by interview investigation and field survey, and 43 related environmental factors were collected, some kinds of functional chemical constituents of Angelica sinensis were analyzed. Integrated climate, topography and other related ecological factors, the habitat suitability study was conducted based on Arc geographic information system(ArcGIS),and maximum entropy model. Application of R language to establish the relationship between the effective component of Chinese angelica and enviromental factors model, using ArcGIS software space to carry out space calculation method for the quality regionalization of Chinese angelica. The results showed that 4 major ecological factors had obvious influence on ecology suitability distributions of Chinese angelica, including altitude, soil sub category, May precipitation and the warmest month of the highest temperature, et al. It is suitable for the living habits of the Chinese angelica, cold and humid climate, which is suitable for the deep area of the soil. In addition, the ecological suitability regionalization based on the effect of Chinese angelica also provides a new suitable distribution area other than the traditional distribution area, which provides a scientific basis for the reasonable introduction of Chinese angelica. Copyright© by the Chinese Pharmaceutical Association.

Factors affecting seasonal habitat use, and predicted range of two tropical deer in Indonesian rainforest

NASA Astrophysics Data System (ADS)

Rahman, Dede Aulia; Gonzalez, Georges; Haryono, Mohammad; Muhtarom, Aom; Firdaus, Asep Yayus; Aulagnier, Stéphane

2017-07-01

There is an urgent recognized need for conservation of tropical forest deer. In order to identify some environmental factors affecting conservation, we analyzed the seasonal habitat use of two Indonesian deer species, Axis kuhlii in Bawean Island and Muntiacus muntjak in south-western Java Island, in response to several physical, climatic, biological, and anthropogenic variables. Camera trapping was performed in different habitat types during both wet and dry season to record these elusive species. The highest number of photographs was recorded in secondary forest and during the dry season for both Bawean deer and red muntjac. In models, anthropogenic and climatic variables were the main predictors of habitat use. Distances to cultivated area and to settlement were the most important for A. kuhlii in the dry season. Distances to cultivated area and annual rainfall were significant for M. muntjak in both seasons. Then we modelled their predictive range using Maximum entropy modelling (Maxent). We concluded that forest landscape is the fundamental scale for deer management, and that secondary forests are potentially important landscape elements for deer conservation. Important areas for conservation were identified accounting of habitat transformation in both study areas.

Using species distribution models to optimize vector control in the framework of the tsetse eradication campaign in Senegal

PubMed Central

Dicko, Ahmadou H.; Lancelot, Renaud; Seck, Momar T.; Guerrini, Laure; Sall, Baba; Lo, Mbargou; Vreysen, Marc J. B.; Lefrançois, Thierry; Fonta, William M.; Peck, Steven L.; Bouyer, Jérémy

2014-01-01

Tsetse flies are vectors of human and animal trypanosomoses in sub-Saharan Africa and are the target of the Pan African Tsetse and Trypanosomiasis Eradication Campaign (PATTEC). Glossina palpalis gambiensis (Diptera: Glossinidae) is a riverine species that is still present as an isolated metapopulation in the Niayes area of Senegal. It is targeted by a national eradication campaign combining a population reduction phase based on insecticide-treated targets (ITTs) and cattle and an eradication phase based on the sterile insect technique. In this study, we used species distribution models to optimize control operations. We compared the probability of the presence of G. p. gambiensis and habitat suitability using a regularized logistic regression and Maxent, respectively. Both models performed well, with an area under the curve of 0.89 and 0.92, respectively. Only the Maxent model predicted an expert-based classification of landscapes correctly. Maxent predictions were therefore used throughout the eradication campaign in the Niayes to make control operations more efficient in terms of deployment of ITTs, release density of sterile males, and location of monitoring traps used to assess program progress. We discuss how the models’ results informed about the particular ecology of tsetse in the target area. Maxent predictions allowed optimizing efficiency and cost within our project, and might be useful for other tsetse control campaigns in the framework of the PATTEC and, more generally, other vector or insect pest control programs. PMID:24982143

Lowland tapir distribution and habitat loss in South America.

PubMed

Cordeiro, Jose Luis Passos; Fragoso, José M V; Crawshaw, Danielle; Oliveira, Luiz Flamarion B

2016-01-01

The development of species distribution models (SDMs) can help conservation efforts by generating potential distributions and identifying areas of high environmental suitability for protection. Our study presents a distribution and habitat map for lowland tapir in South America. We also describe the potential habitat suitability of various geographical regions and habitat loss, inside and outside of protected areas network. Two different SDM approaches, MAXENT and ENFA, produced relative different Habitat Suitability Maps for the lowland tapir. While MAXENT was efficient at identifying areas as suitable or unsuitable, it was less efficient (when compared to the results by ENFA) at identifying the gradient of habitat suitability. MAXENT is a more multifaceted technique that establishes more complex relationships between dependent and independent variables. Our results demonstrate that for at least one species, the lowland tapir, the use of a simple consensual approach (average of ENFA and MAXENT models outputs) better reflected its current distribution patterns. The Brazilian ecoregions have the highest habitat loss for the tapir. Cerrado and Atlantic Forest account for nearly half (48.19%) of the total area lost. The Amazon region contains the largest area under protection, and the most extensive remaining habitat for the tapir, but also showed high levels of habitat loss outside protected areas, which increases the importance of support for proper management.

Lowland tapir distribution and habitat loss in South America

PubMed Central

Fragoso, José M.V.; Crawshaw, Danielle; Oliveira, Luiz Flamarion B.

2016-01-01

The development of species distribution models (SDMs) can help conservation efforts by generating potential distributions and identifying areas of high environmental suitability for protection. Our study presents a distribution and habitat map for lowland tapir in South America. We also describe the potential habitat suitability of various geographical regions and habitat loss, inside and outside of protected areas network. Two different SDM approaches, MAXENT and ENFA, produced relative different Habitat Suitability Maps for the lowland tapir. While MAXENT was efficient at identifying areas as suitable or unsuitable, it was less efficient (when compared to the results by ENFA) at identifying the gradient of habitat suitability. MAXENT is a more multifaceted technique that establishes more complex relationships between dependent and independent variables. Our results demonstrate that for at least one species, the lowland tapir, the use of a simple consensual approach (average of ENFA and MAXENT models outputs) better reflected its current distribution patterns. The Brazilian ecoregions have the highest habitat loss for the tapir. Cerrado and Atlantic Forest account for nearly half (48.19%) of the total area lost. The Amazon region contains the largest area under protection, and the most extensive remaining habitat for the tapir, but also showed high levels of habitat loss outside protected areas, which increases the importance of support for proper management. PMID:27672509

Spatial analysis of plague in California: niche modeling predictions of the current distribution and potential response to climate change

PubMed Central

Holt, Ashley C; Salkeld, Daniel J; Fritz, Curtis L; Tucker, James R; Gong, Peng

2009-01-01

Background Plague, caused by the bacterium Yersinia pestis, is a public and wildlife health concern in California and the western United States. This study explores the spatial characteristics of positive plague samples in California and tests Maxent, a machine-learning method that can be used to develop niche-based models from presence-only data, for mapping the potential distribution of plague foci. Maxent models were constructed using geocoded seroprevalence data from surveillance of California ground squirrels (Spermophilus beecheyi) as case points and Worldclim bioclimatic data as predictor variables, and compared and validated using area under the receiver operating curve (AUC) statistics. Additionally, model results were compared to locations of positive and negative coyote (Canis latrans) samples, in order to determine the correlation between Maxent model predictions and areas of plague risk as determined via wild carnivore surveillance. Results Models of plague activity in California ground squirrels, based on recent climate conditions, accurately identified case locations (AUC of 0.913 to 0.948) and were significantly correlated with coyote samples. The final models were used to identify potential plague risk areas based on an ensemble of six future climate scenarios. These models suggest that by 2050, climate conditions may reduce plague risk in the southern parts of California and increase risk along the northern coast and Sierras. Conclusion Because different modeling approaches can yield substantially different results, care should be taken when interpreting future model predictions. Nonetheless, niche modeling can be a useful tool for exploring and mapping the potential response of plague activity to climate change. The final models in this study were used to identify potential plague risk areas based on an ensemble of six future climate scenarios, which can help public managers decide where to allocate surveillance resources. In addition, Maxent model results were significantly correlated with coyote samples, indicating that carnivore surveillance programs will continue to be important for tracking the response of plague to future climate conditions. PMID:19558717

Environmental variables and definitive host distribution: a habitat suitability modelling for endohelminth parasites in the marine realm

PubMed Central

Kuhn, Thomas; Cunze, Sarah; Kochmann, Judith; Klimpel, Sven

2016-01-01

Marine nematodes of the genus Anisakis are common parasites of a wide range of aquatic organisms. Public interest is primarily based on their importance as zoonotic agents of the human Anisakiasis, a severe infection of the gastro-intestinal tract as result of consuming live larvae in insufficiently cooked fish dishes. The diverse nature of external impacts unequally influencing larval and adult stages of marine endohelminth parasites requires the consideration of both abiotic and biotic factors. Whereas abiotic factors are generally more relevant for early life stages and might also be linked to intermediate hosts, definitive hosts are indispensable for a parasite’s reproduction. In order to better understand the uneven occurrence of parasites in fish species, we here use the maximum entropy approach (Maxent) to model the habitat suitability for nine Anisakis species accounting for abiotic parameters as well as biotic data (definitive hosts). The modelled habitat suitability reflects the observed distribution quite well for all Anisakis species, however, in some cases, habitat suitability exceeded the known geographical distribution, suggesting a wider distribution than presently recorded. We suggest that integrative modelling combining abiotic and biotic parameters is a valid approach for habitat suitability assessments of Anisakis, and potentially other marine parasite species. PMID:27507328

Climate Change-Induced Range Expansion of a Subterranean Rodent: Implications for Rangeland Management in Qinghai-Tibetan Plateau

PubMed Central

Su, Junhu; Aryal, Achyut; Nan, Zhibiao; Ji, Weihong

2015-01-01

Disturbances, both human-induced and natural, may re-shape ecosystems by influencing their composition, structure, and functional processes. Plateau zokor (Eospalax baileyi) is a typical subterranean rodent endemic to Qinghai-Tibetan Plateau (QTP), which are considered ecosystem engineers influencing the alpine ecosystem function. It is also regarded as a pest aggravating the degradation of overgrazed grassland and subject to regular control in QTP since 1950s. Climate change has been predicted in this region but little research exists exploring its impact on such subterranean rodent populations. Using plateau zokor as a model, through maximum entropy niche-based modeling (Maxent) and sustainable habitat models, we investigate zokor habitat dynamics driven by the future climate scenarios. Our models project that zokor suitable habitat will increase by 6.25% in 2050 in QTP. The predication indicated more threats in terms of grassland degradation as zokor suitable habitat will increase in 2050. Distribution of zokors will shift much more in their southern range with lower elevation compare to northern range with higher elevation. The estimated distance of shift ranges from 1 km to 94 km from current distribution. Grassland management should take into account such predictions in order to design mitigation measures to prevent further grassland degradation in QTP under climate change scenarios. PMID:26406891

Environmental variables and definitive host distribution: a habitat suitability modelling for endohelminth parasites in the marine realm

NASA Astrophysics Data System (ADS)

Kuhn, Thomas; Cunze, Sarah; Kochmann, Judith; Klimpel, Sven

2016-08-01

Marine nematodes of the genus Anisakis are common parasites of a wide range of aquatic organisms. Public interest is primarily based on their importance as zoonotic agents of the human Anisakiasis, a severe infection of the gastro-intestinal tract as result of consuming live larvae in insufficiently cooked fish dishes. The diverse nature of external impacts unequally influencing larval and adult stages of marine endohelminth parasites requires the consideration of both abiotic and biotic factors. Whereas abiotic factors are generally more relevant for early life stages and might also be linked to intermediate hosts, definitive hosts are indispensable for a parasite’s reproduction. In order to better understand the uneven occurrence of parasites in fish species, we here use the maximum entropy approach (Maxent) to model the habitat suitability for nine Anisakis species accounting for abiotic parameters as well as biotic data (definitive hosts). The modelled habitat suitability reflects the observed distribution quite well for all Anisakis species, however, in some cases, habitat suitability exceeded the known geographical distribution, suggesting a wider distribution than presently recorded. We suggest that integrative modelling combining abiotic and biotic parameters is a valid approach for habitat suitability assessments of Anisakis, and potentially other marine parasite species.

Seasonal Variation in the Spatial Distribution of Basking Sharks (Cetorhinus maximus) in the Lower Bay of Fundy, Canada

PubMed Central

Siders, Zachary A.; Westgate, Andrew J.; Johnston, David W.; Murison, Laurie D.; Koopman, Heather N.

2013-01-01

The local distribution of basking sharks in the Bay of Fundy (BoF) is unknown despite frequent occurrences in the area from May to November. Defining this species’ spatial habitat use is critical for accurately assessing its Special Concern conservation status in Atlantic Canada. We developed maximum entropy distribution models for the lower BoF and the northeast Gulf of Maine (GoM) to describe spatiotemporal variation in habitat use of basking sharks. Under the Maxent framework, we assessed model responses and distribution shifts in relation to known migratory behavior and local prey dynamics. We used 10 years (2002-2011) of basking shark surface sightings from July-October acquired during boat-based surveys in relation to chlorophyll-a concentration, sea surface temperature, bathymetric features, and distance to seafloor contours to assess habitat suitability. Maximum entropy estimations were selected based on AICc criterion and used to predict habitat utilizing three model-fitting routines as well as converted to binary suitable/non-suitable habitat using the maximum sensitivity and specificity threshold. All models predicted habitat better than random (AUC values >0.796). From July-September, a majority of habitat was in the BoF, in waters >100 m deep, and in the Grand Manan Basin. In October, a majority of the habitat shifted southward into the GoM and to areas >200 m deep. Model responses suggest that suitable habitat from July - October is dependent on a mix of distance to the 0, 100, 150, and 200 m contours but in some models on sea surface temperature (July) and chlorophyll-a (August and September). Our results reveal temporally dynamic habitat use of basking sharks within the BoF and GoM. The relative importance of predictor variables suggests that prey dynamics constrained the species distribution in the BoF. Also, suitable habitat shifted minimally from July-September providing opportunities to conserve the species during peak abundance in the region. PMID:24324747

Global Assessment of Seasonal Potential Distribution of Mediterranean Fruit Fly, Ceratitis capitata (Diptera: Tephritidae)

PubMed Central

Szyniszewska, Anna M.; Tatem, Andrew J.

2014-01-01

The Mediterranean fruit fly (Medfly) is one of the world's most economically damaging pests. It displays highly seasonal population dynamics, and the environmental conditions suitable for its abundance are not constant throughout the year in most places. An extensive literature search was performed to obtain the most comprehensive data on the historical and contemporary spatio-temporal occurrence of the pest globally. The database constructed contained 2328 unique geo-located entries on Medfly detection sites from 43 countries and nearly 500 unique localities, as well as information on hosts, life stages and capture method. Of these, 125 localities had information on the month when Medfly was recorded and these data were complemented by additional material found in comprehensive databases available online. Records from 1980 until present were used for medfly environmental niche modeling. Maximum Entropy Algorithm (MaxEnt) and a set of seasonally varying environmental covariates were used to predict the fundamental niche of the Medfly on a global scale. Three seasonal maps were also produced: January-April, May-August and September-December. Models performed significantly better than random achieving high accuracy scores, indicating a good discrimination of suitable versus unsuitable areas for the presence of the species. PMID:25375649

Ecological suitability modeling for anthrax in the Kruger National Park, South Africa.

PubMed

Steenkamp, Pieter Johan; van Heerden, Henriette; van Schalkwyk, Ockert Louis

2018-01-01

The spores of the soil-borne bacterium, Bacillus anthracis, which causes anthrax are highly resistant to adverse environmental conditions. Under ideal conditions, anthrax spores can survive for many years in the soil. Anthrax is known to be endemic in the northern part of Kruger National Park (KNP) in South Africa (SA), with occasional epidemics spreading southward. The aim of this study was to identify and map areas that are ecologically suitable for the harboring of B. anthracis spores within the KNP. Anthrax surveillance data and selected environmental variables were used as inputs to the maximum entropy (Maxent) species distribution modeling method. Anthrax positive carcasses from 1988-2011 in KNP (n = 597) and a total of 40 environmental variables were used to predict and evaluate their relative contribution to suitability for anthrax occurrence in KNP. The environmental variables that contributed the most to the occurrence of anthrax were soil type, normalized difference vegetation index (NDVI) and precipitation. Apart from the endemic Pafuri region, several other areas within KNP were classified as ecologically suitable. The outputs of this study could guide future surveillance efforts to focus on predicted suitable areas for anthrax, since the KNP currently uses passive surveillance to detect anthrax outbreaks.

Spatial modelling of disease using data- and knowledge-driven approaches.

PubMed

Stevens, Kim B; Pfeiffer, Dirk U

2011-09-01

The purpose of spatial modelling in animal and public health is three-fold: describing existing spatial patterns of risk, attempting to understand the biological mechanisms that lead to disease occurrence and predicting what will happen in the medium to long-term future (temporal prediction) or in different geographical areas (spatial prediction). Traditional methods for temporal and spatial predictions include general and generalized linear models (GLM), generalized additive models (GAM) and Bayesian estimation methods. However, such models require both disease presence and absence data which are not always easy to obtain. Novel spatial modelling methods such as maximum entropy (MAXENT) and the genetic algorithm for rule set production (GARP) require only disease presence data and have been used extensively in the fields of ecology and conservation, to model species distribution and habitat suitability. Other methods, such as multicriteria decision analysis (MCDA), use knowledge of the causal factors of disease occurrence to identify areas potentially suitable for disease. In addition to their less restrictive data requirements, some of these novel methods have been shown to outperform traditional statistical methods in predictive ability (Elith et al., 2006). This review paper provides details of some of these novel methods for mapping disease distribution, highlights their advantages and limitations, and identifies studies which have used the methods to model various aspects of disease distribution. Copyright © 2011. Published by Elsevier Ltd.

Environmental suitability for Lutzomyia (Nyssomyia) whitmani (Diptera: Psychodidae: Phlebotominae) and the occurrence of American cutaneous leishmaniasis in Brazil.

PubMed

da Costa, Simone Miranda; Cordeiro, José Luís Passos; Rangel, Elizabeth Ferreira

2018-03-07

Leishmaniasis represents an important public health problem in Brazil. The continuous process of urbanization and expansion of human activities in forest areas impacts natural habitats, modifying the ecology of some species of Leishmania, as well as its vectors and reservoirs and, consequently, changes the epidemiological pattern that contributes to the expansion of American cutaneous leishmaniasis in Brazil. Here, we discuss Lutzomyia (Nyssomyia) whitmani, the main vector of ACL, transmitting two dermotropic Leishmania species including Leishmania (Viannia) braziliensis and Leishmania (V.) shawi. We used the maximum entropy niche modelling approach (MaxEnt) to evaluate the environmental suitability of L. (N.) whitmani and the transmission of ACL in Brazil, in addition to designing models for a future scenario of climate change. MaxEnt was used under the "auto-features" mode and the default settings, with 100-fold repetition (bootstrap). The logistic output was used with higher values in the habitat suitability map, representing more favourable conditions for the occurrence of L. (N.) whitmani and human cases of ACL. Two models were developed: the Lutzomyia (N.) whitmani model (LWM) and the American cutaneous leishmaniasis model (ACLM). LWM identified the species "preferential habitat" included regions with moderate annual precipitation (AP) between 1000-1600 mm, intermediate vegetation density (NDVI) values, mean temperature of the coldest quarter (MTCQ), between 15-21 °C, and annual mean temperature (AMT), between 19-24 °C. ACLM indicates that ACL is strongly associated with areas of intermediate density vegetation, areas with AP between 800-1200 mm, MTCQ above 16 °C and AMT below 23 °C. The models generated for L. (N.) whitmani and ACL indicated a satisfactory predictive capacity. Future projections of LWM indicate an expansion of climatic suitability for L. (N.) whitmani for the northern and southern regions of Brazil. Future projections of ACL indicate the ongoing process of disease expansion in the face of the predicted climatic changes and reinforce the broad geographical expanse of this disease in Brazil. The models were able to identify that a continuous process of environmental degradation favours the establishment of L. (N.) whitmani and the occurrence of ACL by a strong association of the vector(s) and ACL to areas of intermediate vegetation cover density.

[Modeling of species distribution using topography and remote sensing data, with vascular plants of the Tukuringra Range low mountain belt (Zeya state Nature Reserve, Amur Region) as a case study].

PubMed

Dudov, S V

2016-01-01

On the basis of maximum entropy method embedded in MaxEnt software, the cartographic models are designed for spatial distribution of 63 species of vascular plants inhabiting low mountain belt of the Tukuringra Range. Initial data for modeling were actual points of a species occurrence, data on remote sensing (multispectral space snapshots by Landsat), and a digital topographic model. It is found out that the structure of factors contributing to the model is related to species ecological amplitude. The distribution of stenotopic species is determined, mainly, by the topography, which thermal and humidity conditions of habitats are associated with. To the models for eurytopic species, variables formed on the basis of remote sensing contribute significantly, those variables encompassing the parameters of the soil-vegetable cover. In course of the obtained models analyzing, three principal groups of species are revealed that have similar distribution pattern. Species of the first group are restricted in their distribution by the slopes of the. River Zeya and River Giluy gorges. Species of the second group are associated with the southern macroslope of the range and with southern slopes of large rivers' valleys. The third group incorporates those species that are distributed over the whole territory under study.

Modelling the Species Distribution of Flat-Headed Cats (Prionailurus planiceps), an Endangered South-East Asian Small Felid

PubMed Central

Hearn, Andrew J.; Hesse, Deike; Mohamed, Azlan; Traeholdt, Carl; Cheyne, Susan M.; Sunarto, Sunarto; Jayasilan, Mohd-Azlan; Ross, Joanna; Shapiro, Aurélie C.; Sebastian, Anthony; Dech, Stefan; Breitenmoser, Christine; Sanderson, Jim; Duckworth, J. W.; Hofer, Heribert

2010-01-01

Background The flat-headed cat (Prionailurus planiceps) is one of the world's least known, highly threatened felids with a distribution restricted to tropical lowland rainforests in Peninsular Thailand/Malaysia, Borneo and Sumatra. Throughout its geographic range large-scale anthropogenic transformation processes, including the pollution of fresh-water river systems and landscape fragmentation, raise concerns regarding its conservation status. Despite an increasing number of camera-trapping field surveys for carnivores in South-East Asia during the past two decades, few of these studies recorded the flat-headed cat. Methodology/Principal Findings In this study, we designed a predictive species distribution model using the Maximum Entropy (MaxEnt) algorithm to reassess the potential current distribution and conservation status of the flat-headed cat. Eighty-eight independent species occurrence records were gathered from field surveys, literature records, and museum collections. These current and historical records were analysed in relation to bioclimatic variables (WorldClim), altitude (SRTM) and minimum distance to larger water resources (Digital Chart of the World). Distance to water was identified as the key predictor for the occurrence of flat-headed cats (>50% explanation). In addition, we used different land cover maps (GLC2000, GlobCover and SarVision LLC for Borneo), information on protected areas and regional human population density data to extract suitable habitats from the potential distribution predicted by the MaxEnt model. Between 54% and 68% of suitable habitat has already been converted to unsuitable land cover types (e.g. croplands, plantations), and only between 10% and 20% of suitable land cover is categorised as fully protected according to the IUCN criteria. The remaining habitats are highly fragmented and only a few larger forest patches remain. Conclusion/Significance Based on our findings, we recommend that future conservation efforts for the flat-headed cat should focus on the identified remaining key localities and be implemented through a continuous dialogue between local stakeholders, conservationists and scientists to ensure its long-term survival. The flat-headed cat can serve as a flagship species for the protection of several other endangered species associated with the threatened tropical lowland forests and surface fresh-water sources in this region. PMID:20305809

Modelling the species distribution of flat-headed cats (Prionailurus planiceps), an endangered South-East Asian small felid.

PubMed

Wilting, Andreas; Cord, Anna; Hearn, Andrew J; Hesse, Deike; Mohamed, Azlan; Traeholdt, Carl; Cheyne, Susan M; Sunarto, Sunarto; Jayasilan, Mohd-Azlan; Ross, Joanna; Shapiro, Aurélie C; Sebastian, Anthony; Dech, Stefan; Breitenmoser, Christine; Sanderson, Jim; Duckworth, J W; Hofer, Heribert

2010-03-17

The flat-headed cat (Prionailurus planiceps) is one of the world's least known, highly threatened felids with a distribution restricted to tropical lowland rainforests in Peninsular Thailand/Malaysia, Borneo and Sumatra. Throughout its geographic range large-scale anthropogenic transformation processes, including the pollution of fresh-water river systems and landscape fragmentation, raise concerns regarding its conservation status. Despite an increasing number of camera-trapping field surveys for carnivores in South-East Asia during the past two decades, few of these studies recorded the flat-headed cat. In this study, we designed a predictive species distribution model using the Maximum Entropy (MaxEnt) algorithm to reassess the potential current distribution and conservation status of the flat-headed cat. Eighty-eight independent species occurrence records were gathered from field surveys, literature records, and museum collections. These current and historical records were analysed in relation to bioclimatic variables (WorldClim), altitude (SRTM) and minimum distance to larger water resources (Digital Chart of the World). Distance to water was identified as the key predictor for the occurrence of flat-headed cats (>50% explanation). In addition, we used different land cover maps (GLC2000, GlobCover and SarVision LLC for Borneo), information on protected areas and regional human population density data to extract suitable habitats from the potential distribution predicted by the MaxEnt model. Between 54% and 68% of suitable habitat has already been converted to unsuitable land cover types (e.g. croplands, plantations), and only between 10% and 20% of suitable land cover is categorised as fully protected according to the IUCN criteria. The remaining habitats are highly fragmented and only a few larger forest patches remain. Based on our findings, we recommend that future conservation efforts for the flat-headed cat should focus on the identified remaining key localities and be implemented through a continuous dialogue between local stakeholders, conservationists and scientists to ensure its long-term survival. The flat-headed cat can serve as a flagship species for the protection of several other endangered species associated with the threatened tropical lowland forests and surface fresh-water sources in this region.

The impact of global warming on the range distribution of different climatic groups of Aspidoscelis costata costata.

PubMed

Güizado-Rodríguez, Martha Anahí; Ballesteros-Barrera, Claudia; Casas-Andreu, Gustavo; Barradas-Miranda, Victor Luis; Téllez-Valdés, Oswaldo; Salgado-Ugarte, Isaías Hazarmabeth

2012-12-01

The ectothermic nature of reptiles makes them especially sensitive to global warming. Although climate change and its implications are a frequent topic of detailed studies, most of these studies are carried out without making a distinction between populations. Here we present the first study of an Aspidoscelis species that evaluates the effects of global warming on its distribution using ecological niche modeling. The aims of our study were (1) to understand whether predicted warmer climatic conditions affect the geographic potential distribution of different climatic groups of Aspidoscelis costata costata and (2) to identify potential altitudinal changes of these groups under global warming. We used the maximum entropy species distribution model (MaxEnt) to project the potential distributions expected for the years 2020, 2050, and 2080 under a single simulated climatic scenario. Our analysis suggests that some climatic groups of Aspidoscelis costata costata will exhibit reductions and in others expansions in their distribution, with potential upward shifts toward higher elevation in response to climate warming. Different climatic groups were revealed in our analysis that subsequently showed heterogeneous responses to climatic change illustrating the complex nature of species geographic responses to environmental change and the importance of modeling climatic or geographic groups and/or populations instead of the entire species' range treated as a homogeneous entity.

Ice-age survival of Atlantic cod: agreement between palaeoecology models and genetics

PubMed Central

Bigg, Grant R; Cunningham, Clifford W; Ottersen, Geir; Pogson, Grant H; Wadley, Martin R; Williamson, Phillip

2007-01-01

Scant scientific attention has been given to the abundance and distribution of marine biota in the face of the lower sea level, and steeper latitudinal gradient in climate, during the ice-age conditions that have dominated the past million years. Here we examine the glacial persistence of Atlantic cod (Gadus morhua) populations using two ecological-niche-models (ENM) and the first broad synthesis of multi-locus gene sequence data for this species. One ENM uses a maximum entropy approach (Maxent); the other is a new ENM for Atlantic cod, using ecophysiological parameters based on observed reproductive events rather than adult distribution. Both the ENMs were tested for present-day conditions, then used to hindcast ranges at the last glacial maximum (LGM) ca 21 kyr ago, employing climate model data. Although the LGM range of Atlantic cod was much smaller, and fragmented, both the ENMs agreed that populations should have been able to persist in suitable habitat on both sides of the Atlantic. The genetic results showed a degree of trans-Atlantic divergence consistent with genealogically continuous populations on both sides of the North Atlantic since long before the LGM, confirming the ENM results. In contrast, both the ENMs and the genetic data suggest that the Greenland G. morhua population post-dates the LGM. PMID:17999951

Modeling suitable habitat of invasive red lionfish Pterois volitans (Linnaeus, 1758) in North and South America’s coastal waters

USGS Publications Warehouse

Evangelista, Paul H.; Young, Nicholas E.; Schofield, Pamela J.; Jarnevich, Catherine S.

2016-01-01

We used two common correlative species-distribution models to predict suitable habitat of invasive red lionfish Pterois volitans (Linnaeus, 1758) in the western Atlantic and eastern Pacific Oceans. The Generalized Linear Model (GLM) and the Maximum Entropy (Maxent) model were applied using the Software for Assisted Habitat Modeling. We compared models developed using native occurrences, using non-native occurrences, and using both native and non-native occurrences. Models were trained using occurrence data collected before 2010 and evaluated with occurrence data collected from the invaded range during or after 2010. We considered a total of 22 marine environmental variables. Models built with non-native only or both native and non-native occurrence data outperformed those that used only native occurrences. Evaluation metrics based on the independent test data were highest for models that used both native and non-native occurrences. Bathymetry was the strongest environmental predictor for all models and showed increasing suitability as ocean floor depth decreased, with salinity ranking the second strongest predictor for models that used native and both native and non-native occurrences, indicating low habitat suitability for salinities <30. Our model results also suggest that red lionfish could continue to invade southern latitudes in the western Atlantic Ocean and may establish localized populations in the eastern Pacific Ocean. We reiterate the importance in the choice of the training data source (native, non-native, or native/non-native) used to develop correlative species distribution models for invasive species.

The Hyper-Envelope Modeling Interface (HEMI): A Novel Approach Illustrated Through Predicting Tamarisk (Tamarix spp.) Habitat in the Western USA

USGS Publications Warehouse

Graham, Jim; Young, Nick; Jarnevich, Catherine S.; Newman, Greg; Evangelista, Paul; Stohlgren, Thomas J.

2013-01-01

Habitat suitability maps are commonly created by modeling a species’ environmental niche from occurrences and environmental characteristics. Here, we introduce the hyper-envelope modeling interface (HEMI), providing a new method for creating habitat suitability models using Bezier surfaces to model a species niche in environmental space. HEMI allows modeled surfaces to be visualized and edited in environmental space based on expert knowledge and does not require absence points for model development. The modeled surfaces require relatively few parameters compared to similar modeling approaches and may produce models that better match ecological niche theory. As a case study, we modeled the invasive species tamarisk (Tamarix spp.) in the western USA. We compare results from HEMI with those from existing similar modeling approaches (including BioClim, BioMapper, and Maxent). We used synthetic surfaces to create visualizations of the various models in environmental space and used modified area under the curve (AUC) statistic and akaike information criterion (AIC) as measures of model performance. We show that HEMI produced slightly better AUC values, except for Maxent and better AIC values overall. HEMI created a model with only ten parameters while Maxent produced a model with over 100 and BioClim used only eight. Additionally, HEMI allowed visualization and editing of the model in environmental space to develop alternative potential habitat scenarios. The use of Bezier surfaces can provide simple models that match our expectations of biological niche models and, at least in some cases, out-perform more complex approaches.

The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models.

PubMed

Syfert, Mindy M; Smith, Matthew J; Coomes, David A

2013-01-01

Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as "feature types" in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.

Assessment of multi-wildfire occurrence data for machine learning based risk modelling

NASA Astrophysics Data System (ADS)

Lim, C. H.; Kim, M.; Kim, S. J.; Yoo, S.; Lee, W. K.

2017-12-01

The occurrence of East Asian wildfires is mainly caused by human-activities, but the extreme drought increased due to the climate change caused wildfires and they spread to large-scale fires. Accurate occurrence location data is required for modelling wildfire probability and risk. In South Korea, occurrence data surveyed through KFS (Korea Forest Service) and MODIS (MODerate-resolution Imaging Spectroradiometer) satellite-based active fire data can be utilized. In this study, two sorts of wildfire occurrence data were applied to select suitable occurrence data for machine learning based wildfire risk modelling. MaxEnt (Maximum Entropy) model based on machine learning is used for wildfire risk modelling, and two types of occurrence data and socio-economic and climate-environment data are applied to modelling. In the results with KFS survey based data, the low relationship was shown with climate-environmental factors, and the uncertainty of coordinate information appeared. The MODIS-based active fire data were found outside the forests, and there were a lot of spots that did not match the actual wildfires. In order to utilize MODIS-based active fire data, it was necessary to extract forest area and utilize only high-confidence level data. In KFS data, it was necessary to separate the analysis according to the damage scale to improve the modelling accuracy. Ultimately, it is considered to be the best way to simulate the wildfire risk by constructing more accurate information by combining two sorts of wildfire occurrence data.

Predicting pre-Columbian anthropogenic soils in Amazonia

PubMed Central

McMichael, C. H.; Palace, M. W.; Bush, M. B.; Braswell, B.; Hagen, S.; Neves, E. G.; Silman, M. R.; Tamanaha, E. K.; Czarnecki, C.

2014-01-01

The extent and intensity of pre-Columbian impacts on lowland Amazonia have remained uncertain and controversial. Various indicators can be used to gauge the impact of pre-Columbian societies, but the formation of nutrient-enriched terra preta soils has been widely accepted as an indication of long-term settlement and site fidelity. Using known and newly discovered terra preta sites and maximum entropy algorithms (Maxent), we determined the influence of regional environmental conditions on the likelihood that terra pretas would have been formed at any given location in lowland Amazonia. Terra pretas were most frequently found in central and eastern Amazonia along the lower courses of the major Amazonian rivers. Terrain, hydrologic and soil characteristics were more important predictors of terra preta distributions than climatic conditions. Our modelling efforts indicated that terra pretas are likely to be found throughout ca 154 063 km2 or 3.2% of the forest. We also predict that terra preta formation was limited in most of western Amazonia. Model results suggested that the distribution of terra preta was highly predictable based on environmental parameters. We provided targets for future archaeological surveys under the vast forest canopy and also highlighted how few of the long-term forest inventory sites in Amazonia are able to capture the effects of historical disturbance. PMID:24403329

Predicting pre-Columbian anthropogenic soils in Amazonia.

PubMed

McMichael, C H; Palace, M W; Bush, M B; Braswell, B; Hagen, S; Neves, E G; Silman, M R; Tamanaha, E K; Czarnecki, C

2014-02-22

The extent and intensity of pre-Columbian impacts on lowland Amazonia have remained uncertain and controversial. Various indicators can be used to gauge the impact of pre-Columbian societies, but the formation of nutrient-enriched terra preta soils has been widely accepted as an indication of long-term settlement and site fidelity. Using known and newly discovered terra preta sites and maximum entropy algorithms (Maxent), we determined the influence of regional environmental conditions on the likelihood that terra pretas would have been formed at any given location in lowland Amazonia. Terra pretas were most frequently found in central and eastern Amazonia along the lower courses of the major Amazonian rivers. Terrain, hydrologic and soil characteristics were more important predictors of terra preta distributions than climatic conditions. Our modelling efforts indicated that terra pretas are likely to be found throughout ca 154 063 km(2) or 3.2% of the forest. We also predict that terra preta formation was limited in most of western Amazonia. Model results suggested that the distribution of terra preta was highly predictable based on environmental parameters. We provided targets for future archaeological surveys under the vast forest canopy and also highlighted how few of the long-term forest inventory sites in Amazonia are able to capture the effects of historical disturbance.

Ecological suitability modeling for anthrax in the Kruger National Park, South Africa

PubMed Central

Steenkamp, Pieter Johan; van Schalkwyk, Ockert Louis

2018-01-01

The spores of the soil-borne bacterium, Bacillus anthracis, which causes anthrax are highly resistant to adverse environmental conditions. Under ideal conditions, anthrax spores can survive for many years in the soil. Anthrax is known to be endemic in the northern part of Kruger National Park (KNP) in South Africa (SA), with occasional epidemics spreading southward. The aim of this study was to identify and map areas that are ecologically suitable for the harboring of B. anthracis spores within the KNP. Anthrax surveillance data and selected environmental variables were used as inputs to the maximum entropy (Maxent) species distribution modeling method. Anthrax positive carcasses from 1988–2011 in KNP (n = 597) and a total of 40 environmental variables were used to predict and evaluate their relative contribution to suitability for anthrax occurrence in KNP. The environmental variables that contributed the most to the occurrence of anthrax were soil type, normalized difference vegetation index (NDVI) and precipitation. Apart from the endemic Pafuri region, several other areas within KNP were classified as ecologically suitable. The outputs of this study could guide future surveillance efforts to focus on predicted suitable areas for anthrax, since the KNP currently uses passive surveillance to detect anthrax outbreaks. PMID:29377918

Refining climate change projections for organisms with low dispersal abilities: a case study of the Caspian whip snake.

PubMed

Sahlean, Tiberiu C; Gherghel, Iulian; Papeş, Monica; Strugariu, Alexandru; Zamfirescu, Ştefan R

2014-01-01

Climate warming is one of the most important threats to biodiversity. Ectothermic organisms such as amphibians and reptiles are especially vulnerable as climatic conditions affect them directly. Ecological niche models (ENMs) are increasingly popular in ecological studies, but several drawbacks exist, including the limited ability to account for the dispersal potential of the species. In this study, we use ENMs to explore the impact of global climate change on the Caspian whip snake (Dolichophis caspius) as model for organisms with low dispersal abilities and to quantify dispersal to novel areas using GIS techniques. Models generated using Maxent 3.3.3 k and GARP for current distribution were projected on future climatic scenarios. A cost-distance analysis was run in ArcGIS 10 using geomorphological features, ecological conditions, and human footprint as "costs" to dispersal of the species to obtain a Maximum Dispersal Range (MDR) estimate. All models developed were statistically significant (P<0.05) and recovered the currently known distribution of D. caspius. Models projected on future climatic conditions using Maxent predicted a doubling of suitable climatic area, while GARP predicted a more conservative expansion. Both models agreed on an expansion of suitable area northwards, with minor decreases at the southern distribution limit. The MDR area calculated using the Maxent model represented a third of the total area of the projected model. The MDR based on GARP models recovered only about 20% of the total area of the projected model. Thus, incorporating measures of species' dispersal abilities greatly reduced estimated area of potential future distributions.

Landscape epidemiology in urban environments: The example of rodent-borne Trypanosoma in Niamey, Niger.

PubMed

Rossi, Jean-Pierre; Kadaouré, Ibrahima; Godefroid, Martin; Dobigny, Gauthier

2017-10-05

Trypanosomes are protozoan parasites found worldwide, infecting humans and animals. In the past decade, the number of reports on atypical human cases due to Trypanosoma lewisi or T. lewisi-like has increased urging to investigate the multiple factors driving the disease dynamics, particularly in cities where rodents and humans co-exist at high densities. In the present survey, we used a species distribution model, Maxent, to assess the spatial pattern of Trypanosoma-positive rodents in the city of Niamey. The explanatory variables were landscape metrics describing urban landscape composition and physiognomy computed from 8 land-cover classes. We computed the metrics around each data location using a set of circular buffers of increasing radii (20m, 40m, 60m, 80m and 100m). For each spatial resolution, we determined the optimal combination of feature class and regularization multipliers by fitting Maxent with the full dataset. Since our dataset was small (114 occurrences) we expected an important uncertainty associated to data partitioning into calibration and evaluation datasets. We thus performed 350 independent model runs with a training dataset representing a random subset of 80% of the occurrences and the optimal Maxent parameters. Each model yielded a map of habitat suitability over Niamey, which was transformed into a binary map implementing a threshold maximizing the sensitivity and the specificity. The resulting binary maps were combined to display the proportion of models that indicated a good environmental suitability for Trypanosoma-positive rodents. Maxent performed better with landscape metrics derived from buffers of 80m. Habitat suitability for Trypanosoma-positive rodents exhibited large patches linked to urban features such as patch richness and the proportion of landscape covered by concrete or tarred areas. Such inferences could be helpful in assessing areas at risk, setting of monitoring programs, public and medical staff awareness or even vaccination campaigns. Copyright © 2017 Elsevier B.V. All rights reserved.

Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation.

PubMed

Armean, Irina M; Lilley, Kathryn S; Trotter, Matthew W B; Pilkington, Nicholas C V; Holden, Sean B

2018-06-01

Protein-protein interactions (PPI) play a crucial role in our understanding of protein function and biological processes. The standardization and recording of experimental findings is increasingly stored in ontologies, with the Gene Ontology (GO) being one of the most successful projects. Several PPI evaluation algorithms have been based on the application of probabilistic frameworks or machine learning algorithms to GO properties. Here, we introduce a new training set design and machine learning based approach that combines dependent heterogeneous protein annotations from the entire ontology to evaluate putative co-complex protein interactions determined by empirical studies. PPI annotations are built combinatorically using corresponding GO terms and InterPro annotation. We use a S.cerevisiae high-confidence complex dataset as a positive training set. A series of classifiers based on Maximum Entropy and support vector machines (SVMs), each with a composite counterpart algorithm, are trained on a series of training sets. These achieve a high performance area under the ROC curve of ≤0.97, outperforming go2ppi-a previously established prediction tool for protein-protein interactions (PPI) based on Gene Ontology (GO) annotations. https://github.com/ima23/maxent-ppi. sbh11@cl.cam.ac.uk. Supplementary data are available at Bioinformatics online.

Impact of climate and host availability on future distribution of Colorado potato beetle.

PubMed

Wang, Cong; Hawthorne, David; Qin, Yujia; Pan, Xubin; Li, Zhihong; Zhu, Shuifang

2017-07-03

Colorado Potato Beetle (CPB) is a devastating invasive pest of potato both in its native North America and now across Eurasia. It also damages eggplant, tomato and feeds on several wild species in the Solanaceae, such as S. eleagnifolium and S. rostratum Dunal (SR). Since first categorized as a pest in 1864, CPB has spread rapidly across North America, Europe and Asia. In light of its invasiveness and economic importance, it is necessary to study how climate change and host availability may alter the distribution of the CPB. Maximum Entropy (MaxEnt) models were used to anticipate global range expansion as influenced by environmental conditions, and by the possibility of cooperative invasion of CPB and its wild host SR. The results indicate that both CPB and SR can occupy warm areas of North America, South Africa, Europe, China, and Australia. Future climate conditions may promote CPB expansion into northern regions and SR into the circumpolar latitudes. The existing range and continued spread of SR may also assist the global expansion of CPB. Future management of this pest should consider the impacts of global climate change and host availability on its potential global distribution.

Confronting species distribution model predictions with species functional traits.

PubMed

Wittmann, Marion E; Barnes, Matthew A; Jerde, Christopher L; Jones, Lisa A; Lodge, David M

2016-02-01

Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development or validation rarely considers functional components of species traits or other types of biological data. We implemented a species distribution model (Maxent) to predict global climate habitat suitability for Grass Carp (Ctenopharyngodon idella). We then tested the relationship between the degree of climate habitat suitability predicted by Maxent and the individual growth rates of both wild (N = 17) and stocked (N = 51) Grass Carp populations using correlation analysis. The Grass Carp Maxent model accurately reflected the global occurrence data (AUC = 0.904). Observations of Grass Carp growth rate covered six continents and ranged from 0.19 to 20.1 g day(-1). Species distribution model predictions were correlated (r = 0.5, 95% CI (0.03, 0.79)) with observed growth rates for wild Grass Carp populations but were not correlated (r = -0.26, 95% CI (-0.5, 0.012)) with stocked populations. Further, a review of the literature indicates that the few studies for other species that have previously assessed the relationship between the degree of predicted climate habitat suitability and species functional traits have also discovered significant relationships. Thus, species distribution models may provide inferences beyond just where a species may occur, providing a useful tool to understand the linkage between species distributions and underlying biological mechanisms.

SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses.

PubMed

Brown, Jason L; Bennett, Joseph R; French, Connor M

2017-01-01

SDMtoolbox 2.0 is a software package for spatial studies of ecology, evolution, and genetics. The release of SDMtoolbox 2.0 allows researchers to use the most current ArcGIS software and MaxEnt software, and reduces the amount of time that would be spent developing common solutions. The central aim of this software is to automate complicated and repetitive spatial analyses in an intuitive graphical user interface. One core tenant facilitates careful parameterization of species distribution models (SDMs) to maximize each model's discriminatory ability and minimize overfitting. This includes carefully processing of occurrence data, environmental data, and model parameterization. This program directly interfaces with MaxEnt, one of the most powerful and widely used species distribution modeling software programs, although SDMtoolbox 2.0 is not limited to species distribution modeling or restricted to modeling in MaxEnt. Many of the SDM pre- and post-processing tools have 'universal' analogs for use with any modeling software. The current version contains a total of 79 scripts that harness the power of ArcGIS for macroecology, landscape genetics, and evolutionary studies. For example, these tools allow for biodiversity quantification (such as species richness or corrected weighted endemism), generation of least-cost paths and corridors among shared haplotypes, assessment of the significance of spatial randomizations, and enforcement of dispersal limitations of SDMs projected into future climates-to only name a few functions contained in SDMtoolbox 2.0. Lastly, dozens of generalized tools exists for batch processing and conversion of GIS data types or formats, which are broadly useful to any ArcMap user.

Potential distribution of the invasive freshwater dinoflagellate Ceratium furcoides (Levander) Langhans (Dinophyta) in South America.

PubMed

Meichtry de Zaburlín, Norma; Vogler, Roberto E; Molina, María J; Llano, Víctor M

2016-04-01

Dinoflagellates of the genus Ceratium are predominantly found in marine environments, with a few species in inland waters. Over the last decades, the freshwater species Ceratium hirundinella and Ceratium furcoides have colonized and invaded several South American basins. The purpose of this study was to create a distribution model for the invasive dinoflagellate C. furcoides in South America in order to further investigate the basins at potential risk, as well as the environmental conditions that influence its expansion. This species is known to develop blooms due to its mobility, resistance to sedimentation, and optimized use of resources. Although nontoxic, blooms of the species cause many problems to both the natural ecosystems and water users. Potential distribution was predicted by using a maximum entropy algorithm (MaxEnt). Model was run with 101 occurrences obtained from the scientific literature, and climatic, hydrological and topographic variables. The developed model had a very good performance for the study area. The most susceptible areas identified were mainly concentrated in the basins between southeastern Brazil and northeastern Argentina. Besides already affected regions, new potentially suitable areas were identified in temperate regions of South America. The information generated here will be useful for authorities responsible for water and watershed management to monitor the spread of this species and address problems related to its establishment in new environments. © 2015 Phycological Society of America.

Predicting impacts of climate change on habitat connectivity of Kalopanax septemlobus in South Korea

NASA Astrophysics Data System (ADS)

Kang, Wanmo; Minor, Emily S.; Lee, Dowon; Park, Chan-Ryul

2016-02-01

Understanding the drivers of habitat distribution patterns and assessing habitat connectivity are crucial for conservation in the face of climate change. In this study, we examined a sparsely distributed tree species, Kalopanax septemlobus (Araliaceae), which has been heavily disturbed by human use in temperate forests of South Korea. We used maximum entropy distribution modeling (MaxEnt) to identify the climatic and topographic factors driving the distribution of the species. Then, we constructed habitat models under current and projected climate conditions for the year 2050 and evaluated changes in the extent and connectivity of the K. septemlobus habitat. Annual mean temperature and terrain slope were the two most important predictors of species distribution. Our models predicted the range shift of K. septemlobus toward higher elevations under medium-low and high emissions scenarios for 2050, with dramatic reductions in suitable habitat (51% and 85%, respectively). In addition, connectivity analysis indicated that climate change is expected to reduce future levels of habitat connectivity. Even under the Representative Construction Pathway (RCP) 4.5 medium-low warming scenario, the projected climate conditions will decrease habitat connectivity by 78%. Overall, suitable habitats for K. septemlobus populations will likely become more isolated depending on the severity of global warming. The approach presented here can be used to efficiently assess species and habitat vulnerability to climate change.

Assessing the Application of a Geographic Presence-Only Model for Land Suitability Mapping

PubMed Central

Heumann, Benjamin W.; Walsh, Stephen J.; McDaniel, Phillip M.

2011-01-01

Recent advances in ecological modeling have focused on novel methods for characterizing the environment that use presence-only data and machine-learning algorithms to predict the likelihood of species occurrence. These novel methods may have great potential for land suitability applications in the developing world where detailed land cover information is often unavailable or incomplete. This paper assesses the adaptation and application of the presence-only geographic species distribution model, MaxEnt, for agricultural crop suitability mapping in a rural Thailand where lowland paddy rice and upland field crops predominant. To assess this modeling approach, three independent crop presence datasets were used including a social-demographic survey of farm households, a remote sensing classification of land use/land cover, and ground control points, used for geodetic and thematic reference that vary in their geographic distribution and sample size. Disparate environmental data were integrated to characterize environmental settings across Nang Rong District, a region of approximately 1,300 sq. km in size. Results indicate that the MaxEnt model is capable of modeling crop suitability for upland and lowland crops, including rice varieties, although model results varied between datasets due to the high sensitivity of the model to the distribution of observed crop locations in geographic and environmental space. Accuracy assessments indicate that model outcomes were influenced by the sample size and the distribution of sample points in geographic and environmental space. The need for further research into accuracy assessments of presence-only models lacking true absence data is discussed. We conclude that the Maxent model can provide good estimates of crop suitability, but many areas need to be carefully scrutinized including geographic distribution of input data and assessment methods to ensure realistic modeling results. PMID:21860606

Modeling the Distribution of African Savanna Elephants in Kruger National Park: AN Application of Multi-Scale GLOBELAND30 Data

NASA Astrophysics Data System (ADS)

Xu, W.; Hays, B.; Fayrer-Hosken, R.; Presotto, A.

2016-06-01

The ability of remote sensing to represent ecologically relevant features at multiple spatial scales makes it a powerful tool for studying wildlife distributions. Species of varying sizes perceive and interact with their environment at differing scales; therefore, it is important to consider the role of spatial resolution of remotely sensed data in the creation of distribution models. The release of the Globeland30 land cover classification in 2014, with its 30 m resolution, presents the opportunity to do precisely that. We created a series of Maximum Entropy distribution models for African savanna elephants (Loxodonta africana) using Globeland30 data analyzed at varying resolutions. We compared these with similarly re-sampled models created from the European Space Agency's Global Land Cover Map (Globcover). These data, in combination with GIS layers of topography and distance to roads, human activity, and water, as well as elephant GPS collar data, were used with MaxEnt software to produce the final distribution models. The AUC (Area Under the Curve) scores indicated that the models created from 600 m data performed better than other spatial resolutions and that the Globeland30 models generally performed better than the Globcover models. Additionally, elevation and distance to rivers seemed to be the most important variables in our models. Our results demonstrate that Globeland30 is a valid alternative to the well-established Globcover for creating wildlife distribution models. It may even be superior for applications which require higher spatial resolution and less nuanced classifications.

Shifting distributions of adult Atlantic sturgeon amidst post-industrialization and future impacts in the Delaware River: a maximum entropy approach.

PubMed

Breece, Matthew W; Oliver, Matthew J; Cimino, Megan A; Fox, Dewayne A

2013-01-01

Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) experienced severe declines due to habitat destruction and overfishing beginning in the late 19(th) century. Subsequent to the boom and bust period of exploitation, there has been minimal fishing pressure and improving habitats. However, lack of recovery led to the 2012 listing of Atlantic sturgeon under the Endangered Species Act. Although habitats may be improving, the availability of high quality spawning habitat, essential for the survival and development of eggs and larvae may still be a limiting factor in the recovery of Atlantic sturgeon. To estimate adult Atlantic sturgeon spatial distributions during riverine occupancy in the Delaware River, we utilized a maximum entropy (MaxEnt) approach along with passive biotelemetry during the likely spawning season. We found that substrate composition and distance from the salt front significantly influenced the locations of adult Atlantic sturgeon in the Delaware River. To broaden the scope of this study we projected our model onto four scenarios depicting varying locations of the salt front in the Delaware River: the contemporary location of the salt front during the likely spawning season, the location of the salt front during the historic fishery in the late 19(th) century, an estimated shift in the salt front by the year 2100 due to climate change, and an extreme drought scenario, similar to that which occurred in the 1960's. The movement of the salt front upstream as a result of dredging and climate change likely eliminated historic spawning habitats and currently threatens areas where Atlantic sturgeon spawning may be taking place. Identifying where suitable spawning substrate and water chemistry intersect with the likely occurrence of adult Atlantic sturgeon in the Delaware River highlights essential spawning habitats, enhancing recovery prospects for this imperiled species.

Coral reef habitat response to climate change scenarios.

PubMed

Freeman, Lauren A; Kleypas, Joan A; Miller, Arthur J

2013-01-01

Coral reef ecosystems are threatened by both climate change and direct anthropogenic stress. Climate change will alter the physico-chemical environment that reefs currently occupy, leaving only limited regions that are conducive to reef habitation. Identifying these regions early may aid conservation efforts and inform decisions to transplant particular coral species or groups. Here a species distribution model (Maxent) is used to describe habitat suitable for coral reef growth. Two climate change scenarios (RCP4.5, RCP8.5) from the National Center for Atmospheric Research's Community Earth System Model were used with Maxent to determine environmental suitability for corals (order Scleractinia). Environmental input variables best at representing the limits of suitable reef growth regions were isolated using a principal component analysis. Climate-driven changes in suitable habitat depend strongly on the unique region of reefs used to train Maxent. Increased global habitat loss was predicted in both climate projections through the 21(st) century. A maximum habitat loss of 43% by 2100 was predicted in RCP4.5 and 82% in RCP8.5. When the model is trained solely with environmental data from the Caribbean/Atlantic, 83% of global habitat was lost by 2100 for RCP4.5 and 88% was lost for RCP8.5. Similarly, global runs trained only with Pacific Ocean reefs estimated that 60% of suitable habitat would be lost by 2100 in RCP4.5 and 90% in RCP8.5. When Maxent was trained solely with Indian Ocean reefs, suitable habitat worldwide increased by 38% in RCP4.5 by 2100 and 28% in RCP8.5 by 2050. Global habitat loss by 2100 was just 10% for RCP8.5. This projection suggests that shallow tropical sites in the Indian Ocean basin experience conditions today that are most similar to future projections of worldwide conditions. Indian Ocean reefs may thus be ideal candidate regions from which to select the best strands of coral for potential re-seeding efforts.

Mapping current and potential distribution of non-native Prosopis juliflora in the Afar region of Ethiopia

USGS Publications Warehouse

Wakie, Tewodros; Evangelista, Paul H.; Jarnevich, Catherine S.; Laituri, Melinda

2014-01-01

We used correlative models with species occurrence points, Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, and topo-climatic predictors to map the current distribution and potential habitat of invasive Prosopis juliflora in Afar, Ethiopia. Time-series of MODIS Enhanced Vegetation Indices (EVI) and Normalized Difference Vegetation Indices (NDVI) with 250 m2 spatial resolution were selected as remote sensing predictors for mapping distributions, while WorldClim bioclimatic products and generated topographic variables from the Shuttle Radar Topography Mission product (SRTM) were used to predict potential infestations. We ran Maxent models using non-correlated variables and the 143 species-occurrence points. Maxent generated probability surfaces were converted into binary maps using the 10-percentile logistic threshold values. Performances of models were evaluated using area under the receiver-operating characteristic (ROC) curve (AUC). Our results indicate that the extent of P. juliflora invasion is approximately 3,605 km2 in the Afar region (AUC = 0.94), while the potential habitat for future infestations is 5,024 km2 (AUC = 0.95). Our analyses demonstrate that time-series of MODIS vegetation indices and species occurrence points can be used with Maxent modeling software to map the current distribution of P. juliflora, while topo-climatic variables are good predictors of potential habitat in Ethiopia. Our results can quantify current and future infestations, and inform management and policy decisions for containing P. juliflora. Our methods can also be replicated for managing invasive species in other East African countries.

Habitat suitability and movement corridors of grey wolf (Canis lupus) in Northern Pakistan.

PubMed

Kabir, Muhammad; Hameed, Shoaib; Ali, Hussain; Bosso, Luciano; Din, Jaffar Ud; Bischof, Richard; Redpath, Steve; Nawaz, Muhammad Ali

2017-01-01

Habitat suitability models are useful to understand species distribution and to guide management and conservation strategies. The grey wolf (Canis lupus) has been extirpated from most of its historic range in Pakistan primarily due to its impact on livestock and livelihoods. We used non-invasive survey data from camera traps and genetic sampling to develop a habitat suitability model for C. lupus in northern Pakistan and to explore the extent of connectivity among populations. We detected suitable habitat of grey wolf using a maximum entropy approach (Maxent ver. 3.4.0) and identified suitable movement corridors using the Circuitscape 4.0 tool. Our model showed high levels of predictive performances, as seen from the values of area under curve (0.971±0.002) and true skill statistics (0.886±0.021). The main predictors for habitat suitability for C. lupus were distances to road, mean temperature of the wettest quarter and distance to river. The model predicted ca. 23,129 km2 of suitable areas for wolf in Pakistan, with much of suitable habitat in remote and inaccessible areas that appeared to be well connected through vulnerable movement corridors. These movement corridors suggest that potentially the wolf range can expand in Pakistan's Northern Areas. However, managing protected areas with stringent restrictions is challenging in northern Pakistan, in part due to heavy dependence of people on natural resources. The habitat suitability map provided by this study can inform future management strategies by helping authorities to identify key conservation areas.

Potential Stream Density in Mid-Atlantic U.S. Watersheds

PubMed Central

Elmore, Andrew J.; Julian, Jason P.; Guinn, Steven M.; Fitzpatrick, Matthew C.

2013-01-01

Stream network density exerts a strong influence on ecohydrologic processes in watersheds, yet existing stream maps fail to capture most headwater streams and therefore underestimate stream density. Furthermore, discrepancies between mapped and actual stream length vary between watersheds, confounding efforts to understand the impacts of land use on stream ecosystems. Here we report on research that predicts stream presence from coupled field observations of headwater stream channels and terrain variables that were calculated both locally and as an average across the watershed upstream of any location on the landscape. Our approach used maximum entropy modeling (MaxEnt), a robust method commonly implemented to model species distributions that requires information only on the presence of the entity of interest. In validation, the method correctly predicts the presence of 86% of all 10-m stream segments and errors are low (<1%) for catchments larger than 10 ha. We apply this model to the entire Potomac River watershed (37,800 km2) and several adjacent watersheds to map stream density and compare our results with the National Hydrography Dataset (NHD). We find that NHD underestimates stream density by up to 250%, with errors being greatest in the densely urbanized cities of Washington, DC and Baltimore, MD and in regions where the NHD has never been updated from its original, coarse-grain mapping. This work is the most ambitious attempt yet to map stream networks over a large region and will have lasting implications for modeling and conservation efforts. PMID:24023704

Modeling the Habitat Retreat of the Rediscovered Endemic Hawaiian Moth Omiodes continuatalis Wallengren (Lepidoptera: Crambidae)

PubMed Central

Vorsino, Adam E.; King, Cynthia B.; Haines, William P.; Rubinoff, Daniel

2013-01-01

Survey data over the last 100 years indicate that populations of the endemic Hawaiian leafroller moth, Omiodes continuatalis (Wallengren) (Lepidoptera: Crambidae), have declined, and the species is extirpated from large portions of its original range. Declines have been attributed largely to the invasion of non-native parasitoid species into Hawaiian ecosystems. To quantify changes in O. continuatalis distribution, we applied the maximum entropy modeling approach using Maxent. The model referenced historical (1892–1967) and current (2004–2008) survey data, to create predictive habitat suitability maps which illustrate the probability of occurrence of O. continuatalis based on historical data as contrasted with recent survey results. Probability of occurrence is predicted based on the association of biotic (vegetation) and abiotic (proxy of precipitation, proxy of temperature, elevation) environmental factors with 141 recent and historic survey locations, 38 of which O. continuatalis were collected from. Models built from the historical and recent surveys suggest habitat suitable for O. continuatalis has changed significantly over time, decreasing both in quantity and quality. We reference these data to examine the potential effects of non-native parasitoids as a factor in changing habitat suitability and range contraction for O. continuatalis. Synthesis and applications: Our results suggest that the range of O. continuatalis, an endemic Hawaiian species of conservation concern, has shrunk as its environment has degraded. Although few range shifts have been previously demonstrated in insects, such contractions caused by pressure from introduced species may be important factors in insect extinctions. PMID:23300954

Towards the dynamic prediction of wildfire danger. Modeling temporal scenarios of fire-occurrence in Northeast Spain

NASA Astrophysics Data System (ADS)

Martín, Yago; Rodrigues, Marcos

2017-04-01

Up to date models of human-caused ignition probability have commonly been developed from a static or structural point of view, regardless of the time cycles that drive human behavior or environmental conditions. However, human drivers mostly have a temporal dimension, and fuel conditions are subjected to temporal changes as well, which is why a historical/temporal perspective is often required. Previous studies in the region suggest that human driving factors of wildfires have undergone significant shifts in inter-annual occurrence probability models, thus varying over time. On the other hand, an increasing role of environmental conditions has also been reported. This research comprehensively analyzes the intra-annual dimension of fire occurrence and fire-triggering factors using NW Spain as a test area, moving one-step forward towards achieving more accurate predictions, to ultimately develop dynamic predictive models. To this end, several intra-annual presence-only models have been calibrated, exploring seasonal variations of environmental conditions and short-term cycles of human activity (working- vs non-working days). Models were developed from accurately geolocated fire data in the 2008-2012 period, and GIS and remote sensing (MOD1A2 and MOD16) information . Specifically, 8 occurrence data subsets (scenarios) were constructed by splitting fire records into 4 seasons (winter, spring, summer and autumn) then separating each season into 2 new categories (working and non-working days). This allows analyzing the temporal variation of socioeconomic (urban- and agricultural-interfaces, transport and road networks, and human settlements) and environmental (fuel conditions) factors associated with occurrence. Models were calibrated applying the Maximum Entropy algorithm (MaxEnt). The MaxEnt algorithm was selected as it is the most widespread approach to deal with presence-only data, as may be the case of fire occurrence. The dependent variable for each scenario was created on a conceptual framework which assumed that there were no true cases of fire absence. Model accuracy was assessed using a cross-validation k-fold procedure, whereas variable importance was addressed using a jacknife approach combined with AUC estimation. Results reported model performances around 0.8 AUC in all temporal scenarios. In addition, large variability was observed in the contribution of explanatory factors, with accessibility variables and fuel conditions as key factors along models. Overall, we believe our approach is reliable enough to derive dynamic predictions of human-caused fire occurrence probability. To our knowledge, this is the first attempt to combine presence-only models based on XY located fire data, with remote sensing information and intra-annual scenarios also including cycles of human activity.

Lagrange constraint neural network for audio varying BSS

NASA Astrophysics Data System (ADS)

Szu, Harold H.; Hsu, Charles C.

2002-03-01

Lagrange Constraint Neural Network (LCNN) is a statistical-mechanical ab-initio model without assuming the artificial neural network (ANN) model at all but derived it from the first principle of Hamilton and Lagrange Methodology: H(S,A)= f(S)- (lambda) C(s,A(x,t)) that incorporates measurement constraint C(S,A(x,t))= (lambda) ([A]S-X)+((lambda) 0-1)((Sigma) isi -1) using the vector Lagrange multiplier-(lambda) and a- priori Shannon Entropy f(S) = -(Sigma) i si log si as the Contrast function of unknown number of independent sources si. Szu et al. have first solved in 1997 the general Blind Source Separation (BSS) problem for spatial-temporal varying mixing matrix for the real world remote sensing where a large pixel footprint implies the mixing matrix [A(x,t)] necessarily fill with diurnal and seasonal variations. Because the ground truth is difficult to be ascertained in the remote sensing, we have thus illustrated in this paper, each step of the LCNN algorithm for the simulated spatial-temporal varying BSS in speech, music audio mixing. We review and compare LCNN with other popular a-posteriori Maximum Entropy methodologies defined by ANN weight matrix-[W] sigmoid-(sigma) post processing H(Y=(sigma) ([W]X)) by Bell-Sejnowski, Amari and Oja (BSAO) called Independent Component Analysis (ICA). Both are mirror symmetric of the MaxEnt methodologies and work for a constant unknown mixing matrix [A], but the major difference is whether the ensemble average is taken at neighborhood pixel data X's in BASO or at the a priori sources S variables in LCNN that dictates which method works for spatial-temporal varying [A(x,t)] that would not allow the neighborhood pixel average. We expected the success of sharper de-mixing by the LCNN method in terms of a controlled ground truth experiment in the simulation of variant mixture of two music of similar Kurtosis (15 seconds composed of Saint-Saens Swan and Rachmaninov cello concerto).

Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.

PubMed

Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J

2010-12-01

Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies conservation planning. Journal compilation © 2010 Society for Conservation Biology. No claim to original US government works.

SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses

PubMed Central

Bennett, Joseph R.; French, Connor M.

2017-01-01

SDMtoolbox 2.0 is a software package for spatial studies of ecology, evolution, and genetics. The release of SDMtoolbox 2.0 allows researchers to use the most current ArcGIS software and MaxEnt software, and reduces the amount of time that would be spent developing common solutions. The central aim of this software is to automate complicated and repetitive spatial analyses in an intuitive graphical user interface. One core tenant facilitates careful parameterization of species distribution models (SDMs) to maximize each model’s discriminatory ability and minimize overfitting. This includes carefully processing of occurrence data, environmental data, and model parameterization. This program directly interfaces with MaxEnt, one of the most powerful and widely used species distribution modeling software programs, although SDMtoolbox 2.0 is not limited to species distribution modeling or restricted to modeling in MaxEnt. Many of the SDM pre- and post-processing tools have ‘universal’ analogs for use with any modeling software. The current version contains a total of 79 scripts that harness the power of ArcGIS for macroecology, landscape genetics, and evolutionary studies. For example, these tools allow for biodiversity quantification (such as species richness or corrected weighted endemism), generation of least-cost paths and corridors among shared haplotypes, assessment of the significance of spatial randomizations, and enforcement of dispersal limitations of SDMs projected into future climates—to only name a few functions contained in SDMtoolbox 2.0. Lastly, dozens of generalized tools exists for batch processing and conversion of GIS data types or formats, which are broadly useful to any ArcMap user. PMID:29230356

Development and field validation of a regional, management-scale habitat model: A koala Phascolarctos cinereus case study.

PubMed

Law, Bradley; Caccamo, Gabriele; Roe, Paul; Truskinger, Anthony; Brassil, Traecey; Gonsalves, Leroy; McConville, Anna; Stanton, Matthew

2017-09-01

Species distribution models have great potential to efficiently guide management for threatened species, especially for those that are rare or cryptic. We used MaxEnt to develop a regional-scale model for the koala Phascolarctos cinereus at a resolution (250 m) that could be used to guide management. To ensure the model was fit for purpose, we placed emphasis on validating the model using independently-collected field data. We reduced substantial spatial clustering of records in coastal urban areas using a 2-km spatial filter and by modeling separately two subregions separated by the 500-m elevational contour. A bias file was prepared that accounted for variable survey effort. Frequency of wildfire, soil type, floristics and elevation had the highest relative contribution to the model, while a number of other variables made minor contributions. The model was effective in discriminating different habitat suitability classes when compared with koala records not used in modeling. We validated the MaxEnt model at 65 ground-truth sites using independent data on koala occupancy (acoustic sampling) and habitat quality (browse tree availability). Koala bellows ( n  = 276) were analyzed in an occupancy modeling framework, while site habitat quality was indexed based on browse trees. Field validation demonstrated a linear increase in koala occupancy with higher modeled habitat suitability at ground-truth sites. Similarly, a site habitat quality index at ground-truth sites was correlated positively with modeled habitat suitability. The MaxEnt model provided a better fit to estimated koala occupancy than the site-based habitat quality index, probably because many variables were considered simultaneously by the model rather than just browse species. The positive relationship of the model with both site occupancy and habitat quality indicates that the model is fit for application at relevant management scales. Field-validated models of similar resolution would assist in guiding management of conservation-dependent species.

Restricted cross-scale habitat selection by American beavers.

PubMed

Francis, Robert A; Taylor, Jimmy D; Dibble, Eric; Strickland, Bronson; Petro, Vanessa M; Easterwood, Christine; Wang, Guiming

2017-12-01

Animal habitat selection, among other ecological phenomena, is spatially scale dependent. Habitat selection by American beavers Castor canadensis (hereafter, beaver) has been studied at singular spatial scales, but to date no research addresses multi-scale selection. Our objectives were to determine if beaver habitat selection was specialized to semiaquatic habitats and if variables explaining habitat selection are consistent between landscape and fine spatial scales. We built maximum entropy (MaxEnt) models to relate landscape-scale presence-only data to landscape variables, and used generalized linear mixed models to evaluate fine spatial scale habitat selection using global positioning system (GPS) relocation data. Explanatory variables between the landscape and fine spatial scale were compared for consistency. Our findings suggested that beaver habitat selection at coarse (study area) and fine (within home range) scales was congruent, and was influenced by increasing amounts of woody wetland edge density and shrub edge density, and decreasing amounts of open water edge density. Habitat suitability at the landscape scale also increased with decreasing amounts of grass frequency. As territorial, central-place foragers, beavers likely trade-off open water edge density (i.e., smaller non-forested wetlands or lodges closer to banks) for defense and shorter distances to forage and obtain construction material. Woody plants along edges and expanses of open water for predator avoidance may limit beaver fitness and subsequently determine beaver habitat selection.

Restricted cross-scale habitat selection by American beavers

PubMed Central

Taylor, Jimmy D; Dibble, Eric; Strickland, Bronson; Petro, Vanessa M; Easterwood, Christine; Wang, Guiming

2017-01-01

Abstract Animal habitat selection, among other ecological phenomena, is spatially scale dependent. Habitat selection by American beavers Castor canadensis (hereafter, beaver) has been studied at singular spatial scales, but to date no research addresses multi-scale selection. Our objectives were to determine if beaver habitat selection was specialized to semiaquatic habitats and if variables explaining habitat selection are consistent between landscape and fine spatial scales. We built maximum entropy (MaxEnt) models to relate landscape-scale presence-only data to landscape variables, and used generalized linear mixed models to evaluate fine spatial scale habitat selection using global positioning system (GPS) relocation data. Explanatory variables between the landscape and fine spatial scale were compared for consistency. Our findings suggested that beaver habitat selection at coarse (study area) and fine (within home range) scales was congruent, and was influenced by increasing amounts of woody wetland edge density and shrub edge density, and decreasing amounts of open water edge density. Habitat suitability at the landscape scale also increased with decreasing amounts of grass frequency. As territorial, central-place foragers, beavers likely trade-off open water edge density (i.e., smaller non-forested wetlands or lodges closer to banks) for defense and shorter distances to forage and obtain construction material. Woody plants along edges and expanses of open water for predator avoidance may limit beaver fitness and subsequently determine beaver habitat selection. PMID:29492032

The Invasive Species Forecasting System (ISFS): An iRODS-Based, Cloud-Enabled Decision Support System for Invasive Species Habitat Suitability Modeling

NASA Technical Reports Server (NTRS)

Gill, Roger; Schnase, John L.

2012-01-01

The Invasive Species Forecasting System (ISFS) is an online decision support system that allows users to load point occurrence field sample data for a plant species of interest and quickly generate habitat suitability maps for geographic regions of interest, such as a national park, monument, forest, or refuge. Target customers for ISFS are natural resource managers and decision makers who have a need for scientifically valid, model- based predictions of the habitat suitability of plant species of management concern. In a joint project involving NASA and the Maryland Department of Natural Resources, ISFS has been used to model the potential distribution of Wavyleaf Basketgrass in Maryland's Chesapeake Bay Watershed. Maximum entropy techniques are used to generate predictive maps using predictor datasets derived from remotely sensed data and climate simulation outputs. The workflow to run a model is implemented in an iRODS microservice using a custom ISFS file driver that clips and re-projects data to geographic regions of interest, then shells out to perform MaxEnt processing on the input data. When the model completes, all output files and maps from the model run are registered in iRODS and made accessible to the user. The ISFS user interface is a web browser that uses the iRODS PHP client to interact with the ISFS/iRODS- server. ISFS is designed to reside in a VMware virtual machine running SLES 11 and iRODS 3.0. The ISFS virtual machine is hosted in a VMware vSphere private cloud infrastructure to deliver the online service.

Intraspecific variation buffers projected climate change impacts on Pinus contorta

PubMed Central

Oney, Brian; Reineking, Björn; O'Neill, Gregory; Kreyling, Juergen

2013-01-01

Species distribution modeling (SDM) is an important tool to assess the impact of global environmental change. Many species exhibit ecologically relevant intraspecific variation, and few studies have analyzed its relevance for SDM. Here, we compared three SDM techniques for the highly variable species Pinus contorta. First, applying a conventional SDM approach, we used MaxEnt to model the subject as a single species (species model), based on presence–absence observations. Second, we used MaxEnt to model each of the three most prevalent subspecies independently and combined their projected distributions (subspecies model). Finally, we used a universal growth transfer function (UTF), an approach to incorporate intraspecific variation utilizing provenance trial tree growth data. Different model approaches performed similarly when predicting current distributions. MaxEnt model discrimination was greater (AUC – species model: 0.94, subspecies model: 0.95, UTF: 0.89), but the UTF was better calibrated (slope and bias – species model: 1.31 and −0.58, subspecies model: 1.44 and −0.43, UTF: 1.01 and 0.04, respectively). Contrastingly, for future climatic conditions, projections of lodgepole pine habitat suitability diverged. In particular, when the species' intraspecific variability was acknowledged, the species was projected to better tolerate climatic change as related to suitable habitat without migration (subspecies model: 26% habitat loss or UTF: 24% habitat loss vs. species model: 60% habitat loss), and given unlimited migration may increase amount of suitable habitat (subspecies model: 8% habitat gain or UTF: 12% habitat gain vs. species model: 51% habitat loss) in the climatic period 2070–2100 (SRES A2 scenario, HADCM3). We conclude that models derived from within-species data produce different and better projections, and coincide with ecological theory. Furthermore, we conclude that intraspecific variation may buffer against adverse effects of climate change. A key future research challenge lies in assessing the extent to which species can utilize intraspecific variation under rapid environmental change. PMID:23467191

[Study on ecological suitability of Gardenia jasminoides based on ArcGIS and Maxent model].

PubMed

Miao, Qi; Yuan, Yuan-Jian; Luo, Guang-Ming; Wei, Chun-Hua; Rao, Ya-Qi; Gong, Yu-Hong; Zhang, Lan; Shao, Jian; Dong, Yan-Kai

2016-09-01

The application of ArcGIS and Maxent modelto analyze the ecological suitability of Gardenia jasminoides.Taking 85 batches of Gardenia as the basis of analysis, the selection of ecological factors for the growth of Gardenia. The results showed that the average precipitation in April, the average precipitation in November and the average precipitation in August were the most important factors affecting the growth of Gardenia. The relative concentration of Gardenia suitable growth region,north to the south of Shaanxi province, south of Henan, central Anhui, south to the north of Hainan province, west to central Sichuan province, east of Zhejiang coastal area, northeast of Taiwan. Copyright© by the Chinese Pharmaceutical Association.

Impact Assessment of Mikania Micrantha on Land Cover and Maxent Modeling to Predict its Potential Invasion Sites

NASA Astrophysics Data System (ADS)

Baidar, T.; Shrestha, A. B.; Ranjit, R.; Adhikari, R.; Ghimire, S.; Shrestha, N.

2017-05-01

Mikania micrantha is one of the major invasive alien plant species in tropical moist forest regions of Asia including Nepal. Recently, this weed is spreading at an alarming rate in Chitwan National Park (CNP) and threatening biodiversity. This paper aims to assess the impacts of Mikania micrantha on different land cover and to predict potential invasion sites in CNP using Maxent model. Primary data for this were presence point coordinates and perceived Mikania micrantha cover collected through systematic random sampling technique. Rapideye image, Shuttle Radar Topographic Mission data and bioclimatic variables were acquired as secondary data. Mikania micrantha distribution maps were prepared by overlaying the presence points on image classified by object based image analysis. The overall accuracy of classification was 90 % with Kappa coefficient 0.848. A table depicting the number of sample points in each land cover with respective Mikania micrantha coverage was extracted from the distribution maps to show the impact. The riverine forest was found to be the most affected land cover with 85.98 % presence points and sal forest was found to be very less affected with only 17.02 % presence points. Maxent modeling predicted the areas near the river valley as the potential invasion sites with statistically significant Area Under the Receiver Operating Curve (AUC) value of 0.969. Maximum temperature of warmest month and annual precipitation were identified as the predictor variables that contribute the most to Mikania micrantha's potential distribution.

Shifts in the ecological niche of Lutzomyia peruensis under climate change scenarios in Peru.

PubMed

Moo-Llanes, D A; Arque-Chunga, W; Carmona-Castro, O; Yañez-Arenas, C; Yañez-Trujillano, H H; Cheverría-Pacheco, L; Baak-Baak, C M; Cáceres, A G

2017-06-01

The Peruvian Andes presents a climate suitable for many species of sandfly that are known vectors of leishmaniasis or bartonellosis, including Lutzomyia peruensis (Diptera: Psychodidae), among others. In the present study, occurrences data for Lu. peruensis were compiled from several items in the scientific literature from Peru published between 1927 and 2015. Based on these data, ecological niche models were constructed to predict spatial distributions using three algorithms [Support vector machine (SVM), the Genetic Algorithm for Rule-set Prediction (GARP) and Maximum Entropy (MaxEnt)]. In addition, the environmental requirements of Lu. peruensis and three niche characteristics were modelled in the context of future climate change scenarios: (a) potential changes in niche breadth; (b) shifts in the direction and magnitude of niche centroids, and (c) shifts in elevation range. The model identified areas that included environments suitable for Lu. peruensis in most regions of Peru (45.77%) and an average altitude of 3289 m a.s.l. Under climate change scenarios, a decrease in the distribution areas of Lu. peruensis was observed for all representative concentration pathways. However, the centroid of the species' ecological niche showed a northwest direction in all climate change scenarios. The information generated in this study may help health authorities responsible for the supervision of strategies to control leishmaniasis to coordinate, plan and implement appropriate strategies for each area of risk, taking into account the geographic distribution and potential dispersal of Lu. peruensis. © 2017 The Royal Entomological Society.

Predictive habitat modelling of humpback (Megaptera novaeangliae) and Antarctic minke (Balaenoptera bonaerensis) whales in the Southern Ocean as a planning tool for seismic surveys

NASA Astrophysics Data System (ADS)

Bombosch, Annette; Zitterbart, Daniel P.; Van Opzeeland, Ilse; Frickenhaus, Stephan; Burkhardt, Elke; Wisz, Mary S.; Boebel, Olaf

2014-09-01

Seismic surveys are frequently a matter of concern regarding their potentially negative impacts on marine mammals. In the Southern Ocean, which provides a critical habitat for several endangered cetacean species, seismic research activities are undertaken at a circumpolar scale. In order to minimize impacts of these surveys, pre-cruise planning requires detailed, spatio-temporally resolved knowledge on the likelihood of encountering these species in the survey area. In this publication we present predictive habitat modelling as a potential tool to support decisions for survey planning. We associated opportunistic sightings (2005-2011) of humpback (Megaptera novaeangliae, N=93) and Antarctic minke whales (Balaenoptera bonaerensis, N=139) with a range of static and dynamic environmental variables. A maximum entropy algorithm (Maxent) was used to develop habitat models and to calculate daily basinwide/circumpolar prediction maps to evaluate how species-specific habitat conditions evolved throughout the spring and summer months. For both species, prediction maps revealed considerable changes in habitat suitability throughout the season. Suitable humpback whale habitat occurred predominantly in ice-free areas, expanding southwards with the retreating sea ice edge, whereas suitable Antarctic minke whale habitat was consistently predicted within sea ice covered areas. Daily, large-scale prediction maps provide a valuable tool to design layout and timing of seismic surveys as they allow the identification and consideration of potential spatio-temporal hotspots to minimize potential impacts of seismic surveys on Antarctic cetacean species.

The effect of climate change on the distribution of a tropical zoanthid (Palythoa caribaeorum) and its ecological implications.

PubMed

Durante, Leonardo M; Cruz, Igor C S; Lotufo, Tito M C

2018-01-01

Palythoa caribaeorum is a zoanthid often dominant in shallow rocky environments along the west coast of the Atlantic Ocean, from the tropics to the subtropics. This species has high environmental tolerance and is a good space competitor in reef environments. Considering current and future scenarios in the global climate regime, this study aimed to model and analyze the distribution of P. caribaeorum , generating maps of potential distribution for the present and the year 2100. The distribution was modeled using maximum entropy (Maxent) based on 327 occurrence sites retrieved from the literature. Calcite concentration, maximum chlorophyll- a concentration, salinity, pH, and temperature range yielded a model with the smallest Akaike information criterion (2649.8), and were used in the present and future distribution model. Data from the HadGEM2-ES climate model were used to generate the projections for the year 2100. The present distribution of P. caribaeorum shows that parts of the Brazilian coast, Caribbean Sea, and Florida are suitable regions for the species, as they are characterized by high salinity and pH and small temperature variation. An expansion of the species' distribution was forecast northward under mild climate scenarios, while a decrease of suitable areas was forecast in the south. In the climate scenario with the most intense changes, P. caribaeorum would lose one-half of its suitable habitats, including the northernmost and southernmost areas of its distribution. The Caribbean Sea and northeastern Brazil, as well as other places under the influence of coastal upwellings, may serve as potential havens for this species.

The effect of climate change on the distribution of a tropical zoanthid (Palythoa caribaeorum) and its ecological implications

PubMed Central

Cruz, Igor C.S.

2018-01-01

Palythoa caribaeorum is a zoanthid often dominant in shallow rocky environments along the west coast of the Atlantic Ocean, from the tropics to the subtropics. This species has high environmental tolerance and is a good space competitor in reef environments. Considering current and future scenarios in the global climate regime, this study aimed to model and analyze the distribution of P. caribaeorum, generating maps of potential distribution for the present and the year 2100. The distribution was modeled using maximum entropy (Maxent) based on 327 occurrence sites retrieved from the literature. Calcite concentration, maximum chlorophyll-a concentration, salinity, pH, and temperature range yielded a model with the smallest Akaike information criterion (2649.8), and were used in the present and future distribution model. Data from the HadGEM2-ES climate model were used to generate the projections for the year 2100. The present distribution of P. caribaeorum shows that parts of the Brazilian coast, Caribbean Sea, and Florida are suitable regions for the species, as they are characterized by high salinity and pH and small temperature variation. An expansion of the species’ distribution was forecast northward under mild climate scenarios, while a decrease of suitable areas was forecast in the south. In the climate scenario with the most intense changes, P. caribaeorum would lose one-half of its suitable habitats, including the northernmost and southernmost areas of its distribution. The Caribbean Sea and northeastern Brazil, as well as other places under the influence of coastal upwellings, may serve as potential havens for this species. PMID:29785350

Integrating species distributional, conservation planning, and individual based population models: A case study in critical habitat evaluation for the Northern Spotted Owl

EPA Science Inventory

Background / Question / Methods As part of the ongoing northern spotted owl recovery planning effort, we evaluated a series of alternative potential critical habitat scenarios using a species-distribution model (MaxEnt), a conservation-planning model (Zonation), and an individua...

Modeling of the ecological niches of the anopheles spp in Ecuador by the use of geo-informatic tools.

PubMed

Padilla, Oswaldo; Rosas, Pablo; Moreno, Wilson; Toulkeridis, Theofilos

2017-06-01

Ecuador in the northwestern edge of South America is struggling by vector-borne diseases with an endemic-epidemic behavior leading to an enormous public health problem. Malaria, which has a cyclicality in its dynamics, is closely related to climatic, ecological and socio-economic phenomena. The main objective of this research has been to compare three different prediction species models, the so-called Maxent, logistic regression and multi criteria evaluation with fuzzy logic, in order to determine the model which best describes the ecological niche of the Anopheles spp species, which transmits malaria within Ecuador. After performing a detailed data collection and data processing, we applied the mentioned models and validated them with a statistical analysis in order to discover that the Maxent model has been the model that best defines the distribution of Anopheles spp within the territory. The determined sites, which are of high strategic value and important for the increasing national development, will now be able to initiate preventive countermeasures based on this study. Copyright © 2016 Elsevier Ltd. All rights reserved.

Integrating distributional, spatial prioritization, and individual-based models to evaluate potential critical habitat networks: A case study using the Northern Spotted Owl

EPA Science Inventory

As part of the northern spotted owl recovery planning effort, we evaluated a series of alternative critical habitat scenarios using a species-distribution model (MaxEnt), a conservation-planning model (Zonation), and an individual-based population model (HexSim). With this suite ...

Climatic-Induced Shifts in the Distribution of Teak ( Tectona grandis) in Tropical Asia: Implications for Forest Management and Planning

NASA Astrophysics Data System (ADS)

Deb, Jiban Chandra; Phinn, Stuart; Butt, Nathalie; McAlpine, Clive A.

2017-09-01

Modelling the future suitable climate space for tree species has become a widely used tool for forest management planning under global climate change. Teak ( Tectona grandis) is one of the most valuable tropical hardwood species in the international timber market, and natural teak forests are distributed from India through Myanmar, Laos and Thailand. The extents of teak forests are shrinking due to deforestation and the local impacts of global climate change. However, the direct impacts of climate changes on the continental-scale distributions of native and non-native teak have not been examined. In this study, we developed a species distribution model for teak across its entire native distribution in tropical Asia, and its non-native distribution in Bangladesh. We used presence-only records of trees and twelve environmental variables that were most representative for current teak distributions in South and Southeast Asia. MaxEnt (maximum entropy) models were used to model the distributions of teak under current and future climate scenarios. We found that land use/land cover change and elevation were the two most important variables explaining the current and future distributions of native and non-native teak in tropical Asia. Changes in annual precipitation, precipitation seasonality and annual mean actual evapotranspiration may result in shifts in the distributions of teak across tropical Asia. We discuss the implications for the conservation of critical teak habitats, forest management planning, and risks of biological invasion that may occur due to its cultivation in non-native ranges.

Habitat suitability and movement corridors of grey wolf (Canis lupus) in Northern Pakistan

PubMed Central

Kabir, Muhammad; Hameed, Shoaib; Ali, Hussain; Bosso, Luciano; Din, Jaffar Ud; Bischof, Richard; Redpath, Steve

2017-01-01

Habitat suitability models are useful to understand species distribution and to guide management and conservation strategies. The grey wolf (Canis lupus) has been extirpated from most of its historic range in Pakistan primarily due to its impact on livestock and livelihoods. We used non-invasive survey data from camera traps and genetic sampling to develop a habitat suitability model for C. lupus in northern Pakistan and to explore the extent of connectivity among populations. We detected suitable habitat of grey wolf using a maximum entropy approach (Maxent ver. 3.4.0) and identified suitable movement corridors using the Circuitscape 4.0 tool. Our model showed high levels of predictive performances, as seen from the values of area under curve (0.971±0.002) and true skill statistics (0.886±0.021). The main predictors for habitat suitability for C. lupus were distances to road, mean temperature of the wettest quarter and distance to river. The model predicted ca. 23,129 km2 of suitable areas for wolf in Pakistan, with much of suitable habitat in remote and inaccessible areas that appeared to be well connected through vulnerable movement corridors. These movement corridors suggest that potentially the wolf range can expand in Pakistan’s Northern Areas. However, managing protected areas with stringent restrictions is challenging in northern Pakistan, in part due to heavy dependence of people on natural resources. The habitat suitability map provided by this study can inform future management strategies by helping authorities to identify key conservation areas. PMID:29121089

Automatic Identification of Critical Follow-Up Recommendation Sentences in Radiology Reports

PubMed Central

Yetisgen-Yildiz, Meliha; Gunn, Martin L.; Xia, Fei; Payne, Thomas H.

2011-01-01

Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. When recommendations are not systematically identified and promptly communicated to referrers, poor patient outcomes can result. Using information technology can improve communication and improve patient safety. In this paper, we describe a text processing approach that uses natural language processing (NLP) and supervised text classification methods to automatically identify critical recommendation sentences in radiology reports. To increase the classification performance we enhanced the simple unigram token representation approach with lexical, semantic, knowledge-base, and structural features. We tested different combinations of those features with the Maximum Entropy (MaxEnt) classification algorithm. Classifiers were trained and tested with a gold standard corpus annotated by a domain expert. We applied 5-fold cross validation and our best performing classifier achieved 95.60% precision, 79.82% recall, 87.0% F-score, and 99.59% classification accuracy in identifying the critical recommendation sentences in radiology reports. PMID:22195225

Automatic identification of critical follow-up recommendation sentences in radiology reports.

PubMed

Yetisgen-Yildiz, Meliha; Gunn, Martin L; Xia, Fei; Payne, Thomas H

2011-01-01

Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. When recommendations are not systematically identified and promptly communicated to referrers, poor patient outcomes can result. Using information technology can improve communication and improve patient safety. In this paper, we describe a text processing approach that uses natural language processing (NLP) and supervised text classification methods to automatically identify critical recommendation sentences in radiology reports. To increase the classification performance we enhanced the simple unigram token representation approach with lexical, semantic, knowledge-base, and structural features. We tested different combinations of those features with the Maximum Entropy (MaxEnt) classification algorithm. Classifiers were trained and tested with a gold standard corpus annotated by a domain expert. We applied 5-fold cross validation and our best performing classifier achieved 95.60% precision, 79.82% recall, 87.0% F-score, and 99.59% classification accuracy in identifying the critical recommendation sentences in radiology reports.

High-recall protein entity recognition using a dictionary

PubMed Central

Kou, Zhenzhen; Cohen, William W.; Murphy, Robert F.

2010-01-01

Protein name extraction is an important step in mining biological literature. We describe two new methods for this task: semiCRFs and dictionary HMMs. SemiCRFs are a recently-proposed extension to conditional random fields that enables more effective use of dictionary information as features. Dictionary HMMs are a technique in which a dictionary is converted to a large HMM that recognizes phrases from the dictionary, as well as variations of these phrases. Standard training methods for HMMs can be used to learn which variants should be recognized. We compared the performance of our new approaches to that of Maximum Entropy (Max-Ent) and normal CRFs on three datasets, and improvement was obtained for all four methods over the best published results for two of the datasets. CRFs and semiCRFs achieved the highest overall performance according to the widely-used F-measure, while the dictionary HMMs performed the best at finding entities that actually appear in the dictionary—the measure of most interest in our intended application. PMID:15961466

Presence-only modeling using MAXENT: when can we trust the inferences?

USGS Publications Warehouse

Yackulic, Charles B.; Chandler, Richard; Zipkin, Elise F.; Royle, J. Andrew; Nichols, James D.; Grant, Evan H. Campbell; Veran, Sophie

2013-01-01

5. We conclude with a series of recommendations foremost that researchers analyse data in a presence–absence framework whenever possible, because fewer assumptions are required and inferences can be made about clearly defined parameters such as occurrence probability.

Deriving the species richness distribution of Geotrupinae (Coleoptera: Scarabaeoidea) in Mexico from the overlap of individual model predictions.

PubMed

Trotta-Moreu, Nuria; Lobo, Jorge M

2010-02-01

Predictions from individual distribution models for Mexican Geotrupinae species were overlaid to obtain a total species richness map for this group. A database (GEOMEX) that compiles available information from the literature and from several entomological collections was used. A Maximum Entropy method (MaxEnt) was applied to estimate the distribution of each species, taking into account 19 climatic variables as predictors. For each species, suitability values ranging from 0 to 100 were calculated for each grid cell on the map, and 21 different thresholds were used to convert these continuous suitability values into binary ones (presence-absence). By summing all of the individual binary maps, we generated a species richness prediction for each of the considered thresholds. The number of species and faunal composition thus predicted for each Mexican state were subsequently compared with those observed in a preselected set of well-surveyed states. Our results indicate that the sum of individual predictions tends to overestimate species richness but that the selection of an appropriate threshold can reduce this bias. Even under the most optimistic prediction threshold, the mean species richness error is 61% of the observed species richness, with commission errors being significantly more common than omission errors (71 +/- 29 versus 18 +/- 10%). The estimated distribution of Geotrupinae species richness in Mexico in discussed, although our conclusions are preliminary and contingent on the scarce and probably biased available data.

Species distribution model transferability and model grain size - finer may not always be better.

PubMed

Manzoor, Syed Amir; Griffiths, Geoffrey; Lukac, Martin

2018-05-08

Species distribution models have been used to predict the distribution of invasive species for conservation planning. Understanding spatial transferability of niche predictions is critical to promote species-habitat conservation and forecasting areas vulnerable to invasion. Grain size of predictor variables is an important factor affecting the accuracy and transferability of species distribution models. Choice of grain size is often dependent on the type of predictor variables used and the selection of predictors sometimes rely on data availability. This study employed the MAXENT species distribution model to investigate the effect of the grain size on model transferability for an invasive plant species. We modelled the distribution of Rhododendron ponticum in Wales, U.K. and tested model performance and transferability by varying grain size (50 m, 300 m, and 1 km). MAXENT-based models are sensitive to grain size and selection of variables. We found that over-reliance on the commonly used bioclimatic variables may lead to less accurate models as it often compromises the finer grain size of biophysical variables which may be more important determinants of species distribution at small spatial scales. Model accuracy is likely to increase with decreasing grain size. However, successful model transferability may require optimization of model grain size.

[Ecological suitability regionalization for Gastrodia elata in Zhaotong based on Maxent and ArcGIS].

PubMed

Shi, Zi-Wei; Ma, Cong-Ji; Kang, Chuan-Zhi; Wang, Li; Zhang, Zhi-Hui; Chen, Jun-Fei; Zhang, Xiao-Bo; Liu, Da-Hui

2016-09-01

In this paper, the potential distribution information and ecological suitability regionalization for Gastrodia elata in Zhaotong were studied based on the climate, terrain, soil and vegetation factors analysis by Maxent and ArcGIS. The results showed that the highly potential distribution (suitability index>0.6) mainly located in Zhaotong, Yunnan province（Zhenxiong,Yiliang and Daguan county, with an area of 2 872 km²）, and Bijie, Guizhou province （Hezhang,Bijie,Weining county, 1 251 km²）. The AUC of ROC curve was above 0.99, indicating that the predictive results with the Maxent model were highly precise. The main ecological factors determining the potential distribution were the altitude, average rainfall in November, average rainfall in October, vegetation types, average rainfall in March, average rainfall in April,soil types,isothermal characteristic and average rainfall in June. The environmental variables in the highly potential areas were determined as altitude around 1 450-2 200 m,annual average temperature around 18.0-20.4 ℃,annual average precipitation around 900 mm,yellow soil or yellow brown soil,and acid sandy loam or slightly acidic sandy loam.The results will provide valuable references for plantation regionalization and the siting for imitation wild planting of G. elata in Zhaotong. Copyright© by the Chinese Pharmaceutical Association.

Climate change effects on the geographic distribution of specialist tree species of the Brazilian tropical dry forests.

PubMed

Rodrigues, P M S; Silva, J O; Eisenlohr, P V; Schaefer, C E G R

2015-08-01

The aim of this study was to evaluate the ecological niche models (ENMs) for three specialist trees (Anadenanthera colubrina, Aspidosperma pyrifolium and Myracrodruon urundeuva) in seasonally dry tropical forests (SDTFs) in Brazil, considering present and future pessimist scenarios (2080) of climate change. These three species exhibit typical deciduousness and are widely distributed by SDTF in South America, being important in studies of the historical and evolutionary processes experienced by this ecosystem. The modeling of the potential geographic distribution of species was done by the method of maximum entropy (Maxent).We verified a general expansion of suitable areas for occurrence of the three species in future (c.a., 18%), although there was reduction of areas with high environmental suitability in Caatinga region. Precipitation of wettest quarter and temperature seasonality were the predictor variables that most contributed to our models. Climatic changes can provide more severe and longer dry season with increasing temperature and tree mortality in tropics. On this scenario, areas currently occupied by rainforest and savannas could become more suitable for occurrence of the SDTF specialist trees, whereas regions occupied by Caatinga could not support the future level of unsustainable (e.g., aridity). Long-term multidisciplinary studies are necessary to make reliable predictions of the plant's adaptation strategies and responses to climate changes in dry forest at community level. Based on the high deforestation rate, endemism and threat, public policies to minimize the effects of climate change on the biodiversity found within SDTFs must be undertaken rapidly.

Land Suitability Modeling using a Geographic Socio-Environmental Niche-Based Approach: A Case Study from Northeastern Thailand

PubMed Central

Heumann, Benjamin W.; Walsh, Stephen J.; Verdery, Ashton M.; McDaniel, Phillip M.; Rindfuss, Ronald R.

2012-01-01

Understanding the pattern-process relations of land use/land cover change is an important area of research that provides key insights into human-environment interactions. The suitability or likelihood of occurrence of land use such as agricultural crop types across a human-managed landscape is a central consideration. Recent advances in niche-based, geographic species distribution modeling (SDM) offer a novel approach to understanding land suitability and land use decisions. SDM links species presence-location data with geospatial information and uses machine learning algorithms to develop non-linear and discontinuous species-environment relationships. Here, we apply the MaxEnt (Maximum Entropy) model for land suitability modeling by adapting niche theory to a human-managed landscape. In this article, we use data from an agricultural district in Northeastern Thailand as a case study for examining the relationships between the natural, built, and social environments and the likelihood of crop choice for the commonly grown crops that occur in the Nang Rong District – cassava, heavy rice, and jasmine rice, as well as an emerging crop, fruit trees. Our results indicate that while the natural environment (e.g., elevation and soils) is often the dominant factor in crop likelihood, the likelihood is also influenced by household characteristics, such as household assets and conditions of the neighborhood or built environment. Furthermore, the shape of the land use-environment curves illustrates the non-continuous and non-linear nature of these relationships. This approach demonstrates a novel method of understanding non-linear relationships between land and people. The article concludes with a proposed method for integrating the niche-based rules of land use allocation into a dynamic land use model that can address both allocation and quantity of agricultural crops. PMID:24187378

Climate and pH predict the potential range of the invasive apple snail (Pomacea insularum) in the southeastern United States.

PubMed

Byers, James E; McDowell, William G; Dodd, Shelley R; Haynie, Rebecca S; Pintor, Lauren M; Wilde, Susan B

2013-01-01

Predicting the potential range of invasive species is essential for risk assessment, monitoring, and management, and it can also inform us about a species' overall potential invasiveness. However, modeling the distribution of invasive species that have not reached their equilibrium distribution can be problematic for many predictive approaches. We apply the modeling approach of maximum entropy (MaxEnt) that is effective with incomplete, presence-only datasets to predict the distribution of the invasive island apple snail, Pomacea insularum. This freshwater snail is native to South America and has been spreading in the USA over the last decade from its initial introductions in Texas and Florida. It has now been documented throughout eight southeastern states. The snail's extensive consumption of aquatic vegetation and ability to accumulate and transmit algal toxins through the food web heighten concerns about its spread. Our model shows that under current climate conditions the snail should remain mostly confined to the coastal plain of the southeastern USA where it is limited by minimum temperature in the coldest month and precipitation in the warmest quarter. Furthermore, low pH waters (pH <5.5) are detrimental to the snail's survival and persistence. Of particular note are low-pH blackwater swamps, especially Okefenokee Swamp in southern Georgia (with a pH below 4 in many areas), which are predicted to preclude the snail's establishment even though many of these areas are well matched climatically. Our results elucidate the factors that affect the regional distribution of P. insularum, while simultaneously presenting a spatial basis for the prediction of its future spread. Furthermore, the model for this species exemplifies that combining climatic and habitat variables is a powerful way to model distributions of invasive species.

Using remote sensing, ecological niche modeling, and Geographic Information Systems for Rift Valley fever risk assessment in the United States

NASA Astrophysics Data System (ADS)

Tedrow, Christine Atkins

The primary goal in this study was to explore remote sensing, ecological niche modeling, and Geographic Information Systems (GIS) as aids in predicting candidate Rift Valley fever (RVF) competent vector abundance and distribution in Virginia, and as means of estimating where risk of establishment in mosquitoes and risk of transmission to human populations would be greatest in Virginia. A second goal in this study was to determine whether the remotely-sensed Normalized Difference Vegetation Index (NDVI) can be used as a proxy variable of local conditions for the development of mosquitoes to predict mosquito species distribution and abundance in Virginia. As part of this study, a mosquito surveillance database was compiled to archive the historical patterns of mosquito species abundance in Virginia. In addition, linkages between mosquito density and local environmental and climatic patterns were spatially and temporally examined. The present study affirms the potential role of remote sensing imagery for species distribution prediction, and it demonstrates that ecological niche modeling is a valuable predictive tool to analyze the distributions of populations. The MaxEnt ecological niche modeling program was used to model predicted ranges for potential RVF competent vectors in Virginia. The MaxEnt model was shown to be robust, and the candidate RVF competent vector predicted distribution map is presented. The Normalized Difference Vegetation Index (NDVI) was found to be the most useful environmental-climatic variable to predict mosquito species distribution and abundance in Virginia. However, these results indicate that a more robust prediction is obtained by including other environmental-climatic factors correlated to mosquito densities (e.g., temperature, precipitation, elevation) with NDVI. The present study demonstrates that remote sensing and GIS can be used with ecological niche and risk modeling methods to estimate risk of virus establishment in mosquitoes and transmission to humans. Maps delineating the geographic areas in Virginia with highest risk for RVF establishment in mosquito populations and RVF disease transmission to human populations were generated in a GIS using human, domestic animal, and white-tailed deer population estimates and the MaxEnt potential RVF competent vector species distribution prediction. The candidate RVF competent vector predicted distribution and RVF risk maps presented in this study can help vector control agencies and public health officials focus Rift Valley fever surveillance efforts in geographic areas with large co-located populations of potential RVF competent vectors and human, domestic animal, and wildlife hosts. Keywords. Rift Valley fever, risk assessment, Ecological Niche Modeling, MaxEnt, Geographic Information System, remote sensing, Pearson's Product-Moment Correlation Coefficient, vectors, mosquito distribution, mosquito density, mosquito surveillance, United States, Virginia, domestic animals, white-tailed deer, ArcGIS

Shifting Distributions of Adult Atlantic Sturgeon Amidst Post-Industrialization and Future Impacts in the Delaware River: a Maximum Entropy Approach

PubMed Central

Breece, Matthew W.; Oliver, Matthew J.; Cimino, Megan A.; Fox, Dewayne A.

2013-01-01

Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) experienced severe declines due to habitat destruction and overfishing beginning in the late 19th century. Subsequent to the boom and bust period of exploitation, there has been minimal fishing pressure and improving habitats. However, lack of recovery led to the 2012 listing of Atlantic sturgeon under the Endangered Species Act. Although habitats may be improving, the availability of high quality spawning habitat, essential for the survival and development of eggs and larvae may still be a limiting factor in the recovery of Atlantic sturgeon. To estimate adult Atlantic sturgeon spatial distributions during riverine occupancy in the Delaware River, we utilized a maximum entropy (MaxEnt) approach along with passive biotelemetry during the likely spawning season. We found that substrate composition and distance from the salt front significantly influenced the locations of adult Atlantic sturgeon in the Delaware River. To broaden the scope of this study we projected our model onto four scenarios depicting varying locations of the salt front in the Delaware River: the contemporary location of the salt front during the likely spawning season, the location of the salt front during the historic fishery in the late 19th century, an estimated shift in the salt front by the year 2100 due to climate change, and an extreme drought scenario, similar to that which occurred in the 1960’s. The movement of the salt front upstream as a result of dredging and climate change likely eliminated historic spawning habitats and currently threatens areas where Atlantic sturgeon spawning may be taking place. Identifying where suitable spawning substrate and water chemistry intersect with the likely occurrence of adult Atlantic sturgeon in the Delaware River highlights essential spawning habitats, enhancing recovery prospects for this imperiled species. PMID:24260570

Habitat preference of Viminella flagellum (Alcyonacea: Ellisellidae) in relation to bathymetric variables in southeastern Sardinian waters

NASA Astrophysics Data System (ADS)

Giusti, M.; Bo, M.; Angiolillo, M.; Cannas, R.; Cau, A.; Follesa, M. C.; Canese, S.

2017-04-01

The whip-like gorgonian Viminella flagellum (Alcyonacea, Ellisellidae) has an Atlantic-Mediterranean distribution. In Italian seas, the species is a common component of deep-sea rocky environments from the north-western Mediterranean area to the Strait of Sicily. V. flagellum grows in deep habitats, sometimes outlining the environment with dense forests. This work describes its habitat in an area in the southeastern Sardinian waters (central-western Mediterranean Sea), where a dense forest of the species was found. The specimens were filmed and photographed between 120 and 260 m depth with a Remotely Operated Vehicle (ROV). High-resolution multibeam echosounder (MBES) bathymetry data of the area were acquired and morphometric parameters were derived. These parameters were assumed to be relevant for the distribution of the whip-like gorgonians and were used in an Ecological Niche Factor Analysis (ENFA) to explore the niche and habitat preference of the target species. In order to identify which Eco-geographical variables are useful to predict coral distribution, we used the Maximum Entropy model (MaxEnt). Our results gave a first description of the habitat preference of V. flagellum. Specimens were found in a distinctive habitat, with respect to the overall features of the entire studied area which was characterised by a marked slope in a simple rocky seabed system, within a water depth range of 125-150 m.

Green sturgeon distribution in the Pacific Ocean estimated from modeled oceanographic features and migration behavior.

PubMed

Huff, David D; Lindley, Steven T; Wells, Brian K; Chai, Fei

2012-01-01

The green sturgeon (Acipenser medirostris), which is found in the eastern Pacific Ocean from Baja California to the Bering Sea, tends to be highly migratory, moving long distances among estuaries, spawning rivers, and distant coastal regions. Factors that determine the oceanic distribution of green sturgeon are unclear, but broad-scale physical conditions interacting with migration behavior may play an important role. We estimated the distribution of green sturgeon by modeling species-environment relationships using oceanographic and migration behavior covariates with maximum entropy modeling (MaxEnt) of species geographic distributions. The primary concentration of green sturgeon was estimated from approximately 41-51.5° N latitude in the coastal waters of Washington, Oregon, and Vancouver Island and in the vicinity of San Francisco and Monterey Bays from 36-37° N latitude. Unsuitably cold water temperatures in the far north and energetic efficiencies associated with prevailing water currents may provide the best explanation for the range-wide marine distribution of green sturgeon. Independent trawl records, fisheries observer records, and tagging studies corroborated our findings. However, our model also delineated patchily distributed habitat south of Monterey Bay, though there are few records of green sturgeon from this region. Green sturgeon are likely influenced by countervailing pressures governing their dispersal. They are behaviorally directed to revisit natal freshwater spawning rivers and persistent overwintering grounds in coastal marine habitats, yet they are likely physiologically bounded by abiotic and biotic environmental features. Impacts of human activities on green sturgeon or their habitat in coastal waters, such as bottom-disturbing trawl fisheries, may be minimized through marine spatial planning that makes use of high-quality species distribution information.

[Potential distribution and geographic characteristics of wild populations of Vanilla planifolia (Orchidaceae) Oaxaca, Mexico].

PubMed

Hernandez-Ruiz, Jesús; Herrera-Cabrera, B Edgar; Delgado-Alvarado, Adriana; Salazar-Rojas, Víctor M; Bustamante-Gonzalez, Ángel; Campos-Contreras, Jorge E; Ramírez-Juarez, Javier

2016-03-01

Wild specimens of Vanilla planifolia represent a vital part of this resource primary gene pool, and some plants have only been reported in Oaxaca, Mexico. For this reason, we studied its geographical distribution within the state, to locate and describe the ecological characteristics of the areas where they have been found, in order to identify potential areas of establishment. The method comprised four stages: 1) the creation of a database with herbarium records, 2) the construction of the potential distribution based on historical herbarium records for the species, using the model of maximum entropy (MaxEnt) and 22 bioclimatic variables as predictors; 3) an in situ systematic search of individuals, based on herbarium records and areas of potential distribution in 24 municipalities, to determine the habitat current situation and distribution; 4) the description of the environmental factors of potential ecological niches generated by MaxEnt. A review of herbarium collections revealed a total of 18 records of V. planifolia between 1939 and 1998. The systematic search located 28 plants distributed in 12 sites in 95 364 Km(2). The most important variables that determined the model of vanilla potential distribution were: precipitation in the rainy season (61.9 %), soil moisture regime (23.4 %) and precipitation during the four months of highest rainfall (8.1 %). The species potential habitat was found to be distributed in four zones: wet tropics of the Gulf of Mexico, humid temperate, humid tropical, and humid temperate in the Pacific. Precipitation oscillated within the annual ranges of 2 500 to 4 000 mm, with summer rains, and winter precipitation as 5 to 10 % of the total. The moisture regime and predominating climate were udic type I (330 to 365 days of moisture) and hot humid (Am/A(C) m). The plants were located at altitudes of 200 to 1 190 masl, on rough hillsides that generally make up the foothills of mountain systems, with altitudes of 1 300 to 2 500 masl. In natural conditions, distribution of the species is not limited to high evergreen forests, since it was also found in mountain mesophyll and tropical evergreen forests. The location of new specimens of V. planifolia in its wild condition reduces the potential distribution area by 66 %. This area is fragmented into three geographically separated areas. Habitat reduction was due to the increased number of located plants that define the environmental conditions into a more accurate level. Conservation actions can thus be designed and implemented, focusing on more specific areas within the state of Oaxaca, Mexico.

Species Distribution Modelling: Contrasting presence-only models with plot abundance data.

PubMed

Gomes, Vitor H F; IJff, Stéphanie D; Raes, Niels; Amaral, Iêda Leão; Salomão, Rafael P; de Souza Coelho, Luiz; de Almeida Matos, Francisca Dionízia; Castilho, Carolina V; de Andrade Lima Filho, Diogenes; López, Dairon Cárdenas; Guevara, Juan Ernesto; Magnusson, William E; Phillips, Oliver L; Wittmann, Florian; de Jesus Veiga Carim, Marcelo; Martins, Maria Pires; Irume, Mariana Victória; Sabatier, Daniel; Molino, Jean-François; Bánki, Olaf S; da Silva Guimarães, José Renan; Pitman, Nigel C A; Piedade, Maria Teresa Fernandez; Mendoza, Abel Monteagudo; Luize, Bruno Garcia; Venticinque, Eduardo Martins; de Leão Novo, Evlyn Márcia Moraes; Vargas, Percy Núñez; Silva, Thiago Sanna Freire; Manzatto, Angelo Gilberto; Terborgh, John; Reis, Neidiane Farias Costa; Montero, Juan Carlos; Casula, Katia Regina; Marimon, Beatriz S; Marimon, Ben-Hur; Coronado, Euridice N Honorio; Feldpausch, Ted R; Duque, Alvaro; Zartman, Charles Eugene; Arboleda, Nicolás Castaño; Killeen, Timothy J; Mostacedo, Bonifacio; Vasquez, Rodolfo; Schöngart, Jochen; Assis, Rafael L; Medeiros, Marcelo Brilhante; Simon, Marcelo Fragomeni; Andrade, Ana; Laurance, William F; Camargo, José Luís; Demarchi, Layon O; Laurance, Susan G W; de Sousa Farias, Emanuelle; Nascimento, Henrique Eduardo Mendonça; Revilla, Juan David Cardenas; Quaresma, Adriano; Costa, Flavia R C; Vieira, Ima Célia Guimarães; Cintra, Bruno Barçante Ladvocat; Castellanos, Hernán; Brienen, Roel; Stevenson, Pablo R; Feitosa, Yuri; Duivenvoorden, Joost F; Aymard C, Gerardo A; Mogollón, Hugo F; Targhetta, Natalia; Comiskey, James A; Vicentini, Alberto; Lopes, Aline; Damasco, Gabriel; Dávila, Nállarett; García-Villacorta, Roosevelt; Levis, Carolina; Schietti, Juliana; Souza, Priscila; Emilio, Thaise; Alonso, Alfonso; Neill, David; Dallmeier, Francisco; Ferreira, Leandro Valle; Araujo-Murakami, Alejandro; Praia, Daniel; do Amaral, Dário Dantas; Carvalho, Fernanda Antunes; de Souza, Fernanda Coelho; Feeley, Kenneth; Arroyo, Luzmila; Pansonato, Marcelo Petratti; Gribel, Rogerio; Villa, Boris; Licona, Juan Carlos; Fine, Paul V A; Cerón, Carlos; Baraloto, Chris; Jimenez, Eliana M; Stropp, Juliana; Engel, Julien; Silveira, Marcos; Mora, Maria Cristina Peñuela; Petronelli, Pascal; Maas, Paul; Thomas-Caesar, Raquel; Henkel, Terry W; Daly, Doug; Paredes, Marcos Ríos; Baker, Tim R; Fuentes, Alfredo; Peres, Carlos A; Chave, Jerome; Pena, Jose Luis Marcelo; Dexter, Kyle G; Silman, Miles R; Jørgensen, Peter Møller; Pennington, Toby; Di Fiore, Anthony; Valverde, Fernando Cornejo; Phillips, Juan Fernando; Rivas-Torres, Gonzalo; von Hildebrand, Patricio; van Andel, Tinde R; Ruschel, Ademir R; Prieto, Adriana; Rudas, Agustín; Hoffman, Bruce; Vela, César I A; Barbosa, Edelcilio Marques; Zent, Egleé L; Gonzales, George Pepe Gallardo; Doza, Hilda Paulette Dávila; de Andrade Miranda, Ires Paula; Guillaumet, Jean-Louis; Pinto, Linder Felipe Mozombite; de Matos Bonates, Luiz Carlos; Silva, Natalino; Gómez, Ricardo Zárate; Zent, Stanford; Gonzales, Therany; Vos, Vincent A; Malhi, Yadvinder; Oliveira, Alexandre A; Cano, Angela; Albuquerque, Bianca Weiss; Vriesendorp, Corine; Correa, Diego Felipe; Torre, Emilio Vilanova; van der Heijden, Geertje; Ramirez-Angulo, Hirma; Ramos, José Ferreira; Young, Kenneth R; Rocha, Maira; Nascimento, Marcelo Trindade; Medina, Maria Natalia Umaña; Tirado, Milton; Wang, Ophelia; Sierra, Rodrigo; Torres-Lezama, Armando; Mendoza, Casimiro; Ferreira, Cid; Baider, Cláudia; Villarroel, Daniel; Balslev, Henrik; Mesones, Italo; Giraldo, Ligia Estela Urrego; Casas, Luisa Fernanda; Reategui, Manuel Augusto Ahuite; Linares-Palomino, Reynaldo; Zagt, Roderick; Cárdenas, Sasha; Farfan-Rios, William; Sampaio, Adeilza Felipe; Pauletto, Daniela; Sandoval, Elvis H Valderrama; Arevalo, Freddy Ramirez; Huamantupa-Chuquimaco, Isau; Garcia-Cabrera, Karina; Hernandez, Lionel; Gamarra, Luis Valenzuela; Alexiades, Miguel N; Pansini, Susamar; Cuenca, Walter Palacios; Milliken, William; Ricardo, Joana; Lopez-Gonzalez, Gabriela; Pos, Edwin; Ter Steege, Hans

2018-01-17

Species distribution models (SDMs) are widely used in ecology and conservation. Presence-only SDMs such as MaxEnt frequently use natural history collections (NHCs) as occurrence data, given their huge numbers and accessibility. NHCs are often spatially biased which may generate inaccuracies in SDMs. Here, we test how the distribution of NHCs and MaxEnt predictions relates to a spatial abundance model, based on a large plot dataset for Amazonian tree species, using inverse distance weighting (IDW). We also propose a new pipeline to deal with inconsistencies in NHCs and to limit the area of occupancy of the species. We found a significant but weak positive relationship between the distribution of NHCs and IDW for 66% of the species. The relationship between SDMs and IDW was also significant but weakly positive for 95% of the species, and sensitivity for both analyses was high. Furthermore, the pipeline removed half of the NHCs records. Presence-only SDM applications should consider this limitation, especially for large biodiversity assessments projects, when they are automatically generated without subsequent checking. Our pipeline provides a conservative estimate of a species' area of occupancy, within an area slightly larger than its extent of occurrence, compatible to e.g. IUCN red list assessments.

Transferability of species distribution models for the detection of an invasive alien bryophyte using imaging spectroscopy data

NASA Astrophysics Data System (ADS)

Skowronek, Sandra; Van De Kerchove, Ruben; Rombouts, Bjorn; Aerts, Raf; Ewald, Michael; Warrie, Jens; Schiefer, Felix; Garzon-Lopez, Carol; Hattab, Tarek; Honnay, Olivier; Lenoir, Jonathan; Rocchini, Duccio; Schmidtlein, Sebastian; Somers, Ben; Feilhauer, Hannes

2018-06-01

Remote sensing is a promising tool for detecting invasive alien plant species. Mapping and monitoring those species requires accurate detection. So far, most studies relied on models that are locally calibrated and validated against available field data. Consequently, detecting invasive alien species at new study areas requires the acquisition of additional field data which can be expensive and time-consuming. Model transfer might thus provide a viable alternative. Here, we mapped the distribution of the invasive alien bryophyte Campylopus introflexus to i) assess the feasibility of spatially transferring locally calibrated models for species detection between four different heathland areas in Germany and Belgium and ii) test the potential of combining calibration data from different sites in one species distribution model (SDM). In a first step, four different SDMs were locally calibrated and validated by combining field data and airborne imaging spectroscopy data with a spatial resolution ranging from 1.8 m to 4 m and a spectral resolution of about 10 nm (244 bands). A one-class classifier, Maxent, which is based on the comparison of probability densities, was used to generate all SDMs. In a second step, each model was transferred to the three other study areas and the performance of the models for predicting C. introflexus occurrences was assessed. Finally, models combining calibration data from three study areas were built and tested on the remaining fourth site. In this step, different combinations of Maxent modelling parameters were tested. For the local models, the area under the curve for a test dataset (test AUC) was between 0.57-0.78, while the test AUC for the single transfer models ranged between 0.45-0.89. For the combined models the test AUC was between 0.54-0.9. The success of transferring models calibrated in one site to another site highly depended on the respective study site; the combined models provided higher test AUC values than the locally calibrated models for three out of four study sites. Furthermore, we also demonstrated the importance of optimizing the Maxent modelling parameters. Overall, our results indicate the potential of a combined model to map C. introflexus without the need for new calibration data.

Challenges of predicting the potential distribution of a slow-spreading invader: a habitat suitability map for an invasive riparian tree

USGS Publications Warehouse

Jarnevich, Catherine S.; Reynolds, Lindsay V.

2011-01-01

Understanding the potential spread of invasive species is essential for land managers to prevent their establishment and restore impacted habitat. Habitat suitability modeling provides a tool for researchers and managers to understand the potential extent of invasive species spread. Our goal was to use habitat suitability modeling to map potential habitat of the riparian plant invader, Russian olive (Elaeagnus angustifolia). Russian olive has invaded riparian habitat across North America and is continuing to expand its range. We compiled 11 disparate datasets for Russian olive presence locations (n = 1,051 points and 139 polygons) in the western US and used Maximum entropy (Maxent) modeling to develop two habitat suitability maps for Russian olive in the western United States: one with coarse-scale water data and one with fine-scale water data. Our models were able to accurately predict current suitable Russian olive habitat (Coarse model: training AUC = 0.938, test AUC = 0.907; Fine model: training AUC = 0.923, test AUC = 0.885). Distance to water was the most important predictor for Russian olive presence in our coarse-scale water model, but it was only the fifth most important variable in the fine-scale model, suggesting that when water bodies are considered on a fine scale, Russian olive does not necessarily rely on water. Our model predicted that Russian olive has suitable habitat further west from its current distribution, expanding into the west coast and central North America. Our methodology proves useful for identifying potential future areas of invasion. Model results may be influenced by locations of cultivated individuals and sampling bias. Further study is needed to examine the potential for Russian olive to invade beyond its current range. Habitat suitability modeling provides an essential tool for enhancing our understanding of invasive species spread.

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

Assessing the global risk of establishment of Codling moth (Cydia pomonella) using CLIMEX and MaxEnt niche models

USDA-ARS?s Scientific Manuscript database

Accurate assessment of insect pest establishment risk is needed by national plant protection organizations to negotiate international trade of horticultural commodities that can potentially carry the pests and result in inadvertent introductions in the importing countries. We used mechanistic and co...

Modeling potential distribution of Oligoryzomys longicaudatus, the Andes virus (Genus: Hantavirus) reservoir, in Argentina.

PubMed

Andreo, Verónica; Glass, Gregory; Shields, Timothy; Provensal, Cecilia; Polop, Jaime

2011-09-01

We constructed a model to predict the potential distribution of Oligoryzomys longicaudatus, the reservoir of Andes virus (Genus: Hantavirus), in Argentina. We developed an extensive database of occurrence records from published studies and our own surveys and compared two methods to model the probability of O. longicaudatus presence; logistic regression and MaxEnt algorithm. The environmental variables used were tree, grass and bare soil cover from MODIS imagery and, altitude and 19 bioclimatic variables from WorldClim database. The models performances were evaluated and compared both by threshold dependent and independent measures. The best models included tree and grass cover, mean diurnal temperature range, and precipitation of the warmest and coldest seasons. The potential distribution maps for O. longicaudatus predicted the highest occurrence probabilities along the Andes range, from 32°S and narrowing southwards. They also predicted high probabilities for the south-central area of Argentina, reaching the Atlantic coast. The Hantavirus Pulmonary Syndrome cases coincided with mean occurrence probabilities of 95 and 77% for logistic and MaxEnt models, respectively. HPS transmission zones in Argentine Patagonia matched the areas with the highest probability of presence. Therefore, colilargos presence probability may provide an approximate risk of transmission and act as an early tool to guide control and prevention plans.

Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence.

PubMed

Mi, Chunrong; Huettmann, Falk; Guo, Yumin; Han, Xuesong; Wen, Lijia

2017-01-01

Species distribution models (SDMs) have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane ( Grus monacha , n  = 33), White-naped Crane ( Grus vipio , n  = 40), and Black-necked Crane ( Grus nigricollis , n  = 75) in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model), Random Forest, CART (Classification and Regression Tree) and Maxent (Maximum Entropy Models). In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC) and true skill statistic (TSS)) were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial) ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid assessments and decisions for efficient conservation.

Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence

PubMed Central

Mi, Chunrong; Huettmann, Falk; Han, Xuesong; Wen, Lijia

2017-01-01

Species distribution models (SDMs) have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane (Grus monacha, n = 33), White-naped Crane (Grus vipio, n = 40), and Black-necked Crane (Grus nigricollis, n = 75) in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model), Random Forest, CART (Classification and Regression Tree) and Maxent (Maximum Entropy Models). In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC) and true skill statistic (TSS)) were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial) ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid assessments and decisions for efficient conservation. PMID:28097060

Quest for secondary μSR signals for Fe3O4 using MaxEnt: a Verwey phase transition study.

NASA Astrophysics Data System (ADS)

Boekema, C.; Colebaugh, A.; Lee, A.-L.; Lin, I.; Cabot, A.; Morante, C.

Most muon-spin rotation (μSR) time series for magnetite (Fe3O4) have been interpreted in terms of one μSR frequency signal. Its Fourier transform appears to confirm this internal magnetic field. Yet many time series show a beat pattern, strongly suggesting a second signal with a close-by frequency. We are searching for secondary signals in zero-field Fe3O4 μ SR data using Maximum Entropy, a recently developed technique more sensitive than curve fitting and/or Fourier transformation. There is also another dilemma namely: the upper signal found for Fe3O4 has a local magnetic field larger than the maximum allowable vectorial sum of external and internal contributions. However, the (non)occurrence of secondary signals may shed light on the nature of the Verwey phase transition and its precursors in the Fe3O4 Mott-Wigner glass between Tv (123 K) and twice Tv (247 K). Research supported by LANL-DOE, SETI-NASA, SJSU & AFC.

Gene movement and genetic association with regional climate gradients in California valley oak (Quercus lobata Née) in the face of climate change

USGS Publications Warehouse

Sork, Victoria L.; Davis, Frank W.; Westfall, Robert; Flint, Alan L.; Ikegami, Makihiko; Wang, Hongfang; Grivet, Delphine

2010-01-01

Rapid climate change jeopardizes tree populations by shifting current climate zones. To avoid extinction, tree populations must tolerate, adapt, or migrate. Here we investigate geographic patterns of genetic variation in valley oak, Quercus lobata N??e, to assess how underlying genetic structure of populations might influence this species' ability to survive climate change. First, to understand how genetic lineages shape spatial genetic patterns, we examine historical patterns of colonization. Second, we examine the correlation between multivariate nuclear genetic variation and climatic variation. Third, to illustrate how geographic genetic variation could interact with regional patterns of 21st Century climate change, we produce region-specific bioclimatic distributions of valley oak using Maximum Entropy (MAXENT) models based on downscaled historical (1971-2000) and future (2070-2100) climate grids. Future climatologies are based on a moderate-high (A2) carbon emission scenario and two different global climate models. Chloroplast markers indicate historical range-wide connectivity via colonization, especially in the north. Multivariate nuclear genotypes show a strong association with climate variation that provides opportunity for local adaptation to the conditions within their climatic envelope. Comparison of regional current and projected patterns of climate suitability indicates that valley oaks grow in distinctly different climate conditions in different parts of their range. Our models predict widely different regional outcomes from local displacement of a few kilometres to hundreds of kilometres. We conclude that the relative importance of migration, adaptation, and tolerance are likely to vary widely for populations among regions, and that late 21st Century conditions could lead to regional extinctions. ?? 2010 Blackwell Publishing Ltd.

Identifying Pelagic Habitat Hotspots of Neon Flying Squid in the Temperate Waters of the Central North Pacific

PubMed Central

Alabia, Irene D.; Saitoh, Sei-Ichi; Mugo, Robinson; Igarashi, Hiromichi; Ishikawa, Yoichi; Usui, Norihisa; Kamachi, Masafumi; Awaji, Toshiyuki; Seito, Masaki

2015-01-01

We identified the pelagic habitat hotspots of the neon flying squid (Ommastrephes bartramii) in the central North Pacific from May to July and characterized the spatial patterns of squid aggregations in relation to oceanographic features such as mesoscale oceanic eddies and the Transition Zone Chlorophyll-a Front (TZCF). The data used for the habitat model construction and analyses were squid fishery information, remotely-sensed and numerical model-derived environmental data from May to July 1999–2010. Squid habitat hotspots were deduced from the monthly Maximum Entropy (MaxEnt) models and were identified as regions of persistent high suitable habitat across the 12-year period. The distribution of predicted squid habitat hotspots in central North Pacific revealed interesting spatial and temporal patterns likely linked with the presence and dynamics of oceanographic features in squid’s putative foraging grounds from late spring to summer. From May to June, the inferred patches of squid habitat hotspots developed within the Kuroshio-Oyashio transition zone (KOTZ; 37–40°N) and further expanded north towards the subarctic frontal zone (SAFZ; 40–44°N) in July. The squid habitat hotspots within the KOTZ and areas west of the dateline (160°W-180°) were likely influenced and associated with the highly dynamic and transient oceanic eddies and could possibly account for lower squid suitable habitat persistence obtained from these regions. However, predicted squid habitat hotspots located in regions east of the dateline (180°-160°W) from June to July, showed predominantly higher squid habitat persistence presumably due to their proximity to the mean position of the seasonally-shifting TZCF and consequent utilization of the highly productive waters of the SAFZ. PMID:26571118

Identifying Pelagic Habitat Hotspots of Neon Flying Squid in the Temperate Waters of the Central North Pacific.

PubMed

Alabia, Irene D; Saitoh, Sei-Ichi; Mugo, Robinson; Igarashi, Hiromichi; Ishikawa, Yoichi; Usui, Norihisa; Kamachi, Masafumi; Awaji, Toshiyuki; Seito, Masaki

2015-01-01

We identified the pelagic habitat hotspots of the neon flying squid (Ommastrephes bartramii) in the central North Pacific from May to July and characterized the spatial patterns of squid aggregations in relation to oceanographic features such as mesoscale oceanic eddies and the Transition Zone Chlorophyll-a Front (TZCF). The data used for the habitat model construction and analyses were squid fishery information, remotely-sensed and numerical model-derived environmental data from May to July 1999-2010. Squid habitat hotspots were deduced from the monthly Maximum Entropy (MaxEnt) models and were identified as regions of persistent high suitable habitat across the 12-year period. The distribution of predicted squid habitat hotspots in central North Pacific revealed interesting spatial and temporal patterns likely linked with the presence and dynamics of oceanographic features in squid's putative foraging grounds from late spring to summer. From May to June, the inferred patches of squid habitat hotspots developed within the Kuroshio-Oyashio transition zone (KOTZ; 37-40°N) and further expanded north towards the subarctic frontal zone (SAFZ; 40-44°N) in July. The squid habitat hotspots within the KOTZ and areas west of the dateline (160°W-180°) were likely influenced and associated with the highly dynamic and transient oceanic eddies and could possibly account for lower squid suitable habitat persistence obtained from these regions. However, predicted squid habitat hotspots located in regions east of the dateline (180°-160°W) from June to July, showed predominantly higher squid habitat persistence presumably due to their proximity to the mean position of the seasonally-shifting TZCF and consequent utilization of the highly productive waters of the SAFZ.

Unveiling the conservation biogeography of a data-deficient endangered bird species under climate change.

PubMed

Hu, Junhua; Liu, Yang

2014-01-01

It remains a challenge to identify the geographical patterns and underlying environmental associations of species with unique ecological niches and distinct behaviors. This in turn hinders our understanding of the ecology as well as effective conservation management of threatened species. The white-eared night heron (Gorsachius magnificus) is a non-migratory nocturnal bird species that has a patchy distribution in the mountainous forests of East Asia. It is currently categorized as "Endangered" on the IUCN Red List, primarily due to its restricted range and fragmented habitat. To improve our knowledge of the biogeography and conservation of this species, we modeled the geographical pattern of its suitable habitat and evaluated the potential impacts of climate change using ecological niche modeling with a maximum entropy approach implemented in Maxent. Our results indicated that the amount of suitable habitat in all of East Asia was about 130 000 km(2), which can be spatially subdivided into several mountain ranges in southern and southwestern China and northern Vietnam. The extent of suitable habitat range may shrink by more than 35% under a predicted changing climate when assuming the most pessimistic condition of dispersal, while some more suitable habitat would be available if the heron could disperse unrestrainedly. The significant future changes in habitat suitability suggested for Gorsachius magnificus urge caution in any downgrading of Red List status that may be considered. Our results also discern potentially suitable areas for future survey efforts on new populations. Overall, this study demonstrates that ecological niche modeling offers an important tool for evaluating the habitat suitability and potential impacts of climate change on an enigmatic and endangered species based on limited presence data.

Gene movement and genetic association with regional climate gradients in California valley oak (Quercus lobata Née) in the face of climate change.

PubMed

Sork, Victoria L; Davis, Frank W; Westfall, Robert; Flint, Alan; Ikegami, Makihiko; Wang, Hongfang; Grivet, Delphine

2010-09-01

Rapid climate change jeopardizes tree populations by shifting current climate zones. To avoid extinction, tree populations must tolerate, adapt, or migrate. Here we investigate geographic patterns of genetic variation in valley oak, Quercus lobata Née, to assess how underlying genetic structure of populations might influence this species' ability to survive climate change. First, to understand how genetic lineages shape spatial genetic patterns, we examine historical patterns of colonization. Second, we examine the correlation between multivariate nuclear genetic variation and climatic variation. Third, to illustrate how geographic genetic variation could interact with regional patterns of 21st Century climate change, we produce region-specific bioclimatic distributions of valley oak using Maximum Entropy (MAXENT) models based on downscaled historical (1971-2000) and future (2070-2100) climate grids. Future climatologies are based on a moderate-high (A2) carbon emission scenario and two different global climate models. Chloroplast markers indicate historical range-wide connectivity via colonization, especially in the north. Multivariate nuclear genotypes show a strong association with climate variation that provides opportunity for local adaptation to the conditions within their climatic envelope. Comparison of regional current and projected patterns of climate suitability indicates that valley oaks grow in distinctly different climate conditions in different parts of their range. Our models predict widely different regional outcomes from local displacement of a few kilometres to hundreds of kilometres. We conclude that the relative importance of migration, adaptation, and tolerance are likely to vary widely for populations among regions, and that late 21st Century conditions could lead to regional extinctions.

Projected loss of a salamander diversity hotspot as a consequence of projected global climate change.

PubMed

Milanovich, Joseph R; Peterman, William E; Nibbelink, Nathan P; Maerz, John C

2010-08-16

Significant shifts in climate are considered a threat to plants and animals with significant physiological limitations and limited dispersal abilities. The southern Appalachian Mountains are a global hotspot for plethodontid salamander diversity. Plethodontids are lungless ectotherms, so their ecology is strongly governed by temperature and precipitation. Many plethodontid species in southern Appalachia exist in high elevation habitats that may be at or near their thermal maxima, and may also have limited dispersal abilities across warmer valley bottoms. We used a maximum-entropy approach (program Maxent) to model the suitable climatic habitat of 41 plethodontid salamander species inhabiting the Appalachian Highlands region (33 individual species and eight species included within two species complexes). We evaluated the relative change in suitable climatic habitat for these species in the Appalachian Highlands from the current climate to the years 2020, 2050, and 2080, using both the HADCM3 and the CGCM3 models, each under low and high CO(2) scenarios, and using two-model thresholds levels (relative suitability thresholds for determining suitable/unsuitable range), for a total of 8 scenarios per species. While models differed slightly, every scenario projected significant declines in suitable habitat within the Appalachian Highlands as early as 2020. Species with more southern ranges and with smaller ranges had larger projected habitat loss. Despite significant differences in projected precipitation changes to the region, projections did not differ significantly between global circulation models. CO(2) emissions scenario and model threshold had small effects on projected habitat loss by 2020, but did not affect longer-term projections. Results of this study indicate that choice of model threshold and CO(2) emissions scenario affect short-term projected shifts in climatic distributions of species; however, these factors and choice of global circulation model have relatively small affects on what is significant projected loss of habitat for many salamander species that currently occupy the Appalachian Highlands.

Delineating Ecological Boundaries of Hanuman Langur Species Complex in Peninsular India Using MaxEnt Modeling Approach

PubMed Central

Chetan, Nag; Praveen, Karanth K.; Vasudeva, Gururaja Kotambylu

2014-01-01

Hanuman langur is one of the widely distributed and extensively studied non-human diurnal primates in India. Until recently it was believed to be a single species - Semnopithecus entellus. Recent molecular and morphological studies suggest that the Hanuman langurs consists of at least three species S. entellus, S. hypoleucos and S. priam. Furthermore, morphological studies suggested that both S. hypoleucos and S. priam have at least three subspecies in each. We explored the use of ecological niche modeling (ENM) to confirm the validity of these seven taxa and an additional taxon S. johnii belonging to the same genus. MaxEnt modeling tool was used with 19 bioclimatic, 12 vegetation and 6 hydrological environmental layers. We reduced total environmental variables to 14 layers after testing for collinearity and an independent test for model prediction was done using ENMTools. A total of 196 non-overlapping data points from primary and secondary sources were used as inputs for ENM. Results showed eight distinct ecological boundaries, corroborating the eight taxa mentioned above thereby confirming validity of these eight taxa. The study, for the first time provided ecological variables that determined the ecological requirements and distribution of members of the Hanuman langur species complex in the Indian peninsula. PMID:24498377

Delineating ecological boundaries of Hanuman langur species complex in peninsular India using MaxEnt modeling approach.

PubMed

Nag, Chetan; Chetan, Nag; Karanth, K Praveen; Praveen, Karanth K; Gururaja, Kotambylu Vasudeva; Vasudeva, Gururaja Kotambylu

2014-01-01

Hanuman langur is one of the widely distributed and extensively studied non-human diurnal primates in India. Until recently it was believed to be a single species - Semnopithecus entellus. Recent molecular and morphological studies suggest that the Hanuman langurs consists of at least three species S. entellus, S. hypoleucos and S. priam. Furthermore, morphological studies suggested that both S. hypoleucos and S. priam have at least three subspecies in each. We explored the use of ecological niche modeling (ENM) to confirm the validity of these seven taxa and an additional taxon S. johnii belonging to the same genus. MaxEnt modeling tool was used with 19 bioclimatic, 12 vegetation and 6 hydrological environmental layers. We reduced total environmental variables to 14 layers after testing for collinearity and an independent test for model prediction was done using ENMTools. A total of 196 non-overlapping data points from primary and secondary sources were used as inputs for ENM. Results showed eight distinct ecological boundaries, corroborating the eight taxa mentioned above thereby confirming validity of these eight taxa. The study, for the first time provided ecological variables that determined the ecological requirements and distribution of members of the Hanuman langur species complex in the Indian peninsula.

Multi-temporal analysis of forest fire risk driven by environmental and socio-economic change in the Republic of Korea

NASA Astrophysics Data System (ADS)

Kim, S. J.; Lim, C. H.; Kim, G. S.; Lee, W. K.

2017-12-01

Analysis of forest fire risk is important in disaster risk reduction (DRR) since it provides a way to manage forest fires. Climate and socio-economic factors are important in the cause of forest fires, and the role of the socio-economic factors in prevention and preparedness of forest fires is increasing. As most of the forest fires in the Republic of Korea are highly related to human activities, both environmental factors and socio-economic factors were considered into the analysis of forest fire risk. In this study, the Maximum Entropy (MaxEnt) model was used to predict the potential geographical distribution and probability of forest fire occurrence spatially and temporally from 1980s to the 2010s in the Republic of Korea by multi-temporal analysis and analyze the relationship between forest fires and the factors. As a result of the risk analysis, there was an overall increasing trend in forest fire risk from the 1980s to the 2000s, and socio-economic factors were highly correlated with the occurrence of forest fires. The study demonstrates that the socio-economic factors considered as human activities can increase the occurrence of forest fires. The result implies that managing human activities are significant to prevent forest fire occurrence. In addition, timely forest fire prevention and control is necessary as drought index such as Standardized Precipitation Index (SPI) also affected forest fires.

Habitat suitability of patch types: a case study of the Yosemite toad

USGS Publications Warehouse

Liang, Christina T.; Stohlgren, Thomas J.

2011-01-01

Understanding patch variability is crucial in understanding the spatial population structure of wildlife species, especially for rare or threatened species. We used a well-tested maximum entropy species distribution model (Maxent) to map the Yosemite toad (Anaxyrus (= Bufo) canorus) in the Sierra Nevada mountains of California. Twenty-six environmental variables were included in the model representing climate, topography, land cover type, and disturbance factors (e.g., distances to agricultural lands, fire perimeters, and timber harvest areas) throughout the historic range of the toad. We then took a novel approach to the study of spatially structured populations by applying the species-environmental matching model separately for 49 consistently occupied sites of the Yosemite toad compared to 27 intermittently occupied sites. We found that the distribution of the entire population was highly predictable (AUC = 0.95±0.03 SD), and associated with low slopes, specific vegetation types (wet meadow, alpine-dwarf shrub, montane chaparral, red fir, and subalpine conifer), and warm temperatures. The consistently occupied sites were also associated with these same factors, and they were also highly predictable (AUC = 0.95±0.05 SD). However, the intermittently occupied sites were associated with distance to fire perimeter, a slightly different response to vegetation types, distance to timber harvests, and a much broader set of aspect classes (AUC = 0.90±0.11 SD). We conclude that many studies of species distributions may benefit by modeling spatially structured populations separately. Modeling and monitoring consistently-occupied sites may provide a realistic snapshot of current species-environment relationships, important climatic and topographic patterns associated with species persistence patterns, and an understanding of the plasticity of the species to respond to varying climate regimes across its range. Meanwhile, modeling and monitoring of widely dispersing individuals and intermittently occupied sites may uncover environmental thresholds and human-related threats to population persistence.

Implications of Climate Change for Bird Conservation in the Southwestern U.S. under Three Alternative Futures

PubMed Central

Friggens, Megan M.; Finch, Deborah M.

2015-01-01

Future expected changes in climate and human activity threaten many riparian habitats, particularly in the southwestern U.S. Using Maximum Entropy (MaxEnt3.3.3) modeling, we characterized habitat relationships and generated spatial predictions of habitat suitability for the Lucy’s warbler (Oreothlypis luciae), the Southwestern willow flycatcher (Empidonax traillii extimus) and the Western yellow-billed cuckoo (Coccyzus americanus). Our goal was to provide site- and species-specific information that can be used by managers to identify areas for habitat conservation and/or restoration along the Rio Grande in New Mexico. We created models of suitable habitat for each species based on collection and survey samples and climate, biophysical, and vegetation data. We projected habitat suitability under future climates by applying these models to conditions generated from three climate models for 2030, 2060 and 2090. By comparing current and future distributions, we identified how habitats are likely to change as a result of changing climate and the consequences of those changes for these bird species. We also examined whether land ownership of high value sites shifts under changing climate conditions. Habitat suitability models performed well. Biophysical characteristics were more important that climate conditions for predicting habitat suitability with distance to water being the single most important predictor. Climate, though less important, was still influential and led to declines of suitable habitat of more than 60% by 2090. For all species, suitable habitat tended to shrink over time within the study area leaving a few core areas of high importance. Overall, climate changes will increase habitat fragmentation and reduce breeding habitat patch size. The best strategy for conserving bird species within the Rio Grande will include measures to maintain and restore critical habitat refugia. This study provides an example of a presence-only habitat model that can be used to inform the management of species at intermediate scales. PMID:26700871

Implications of Climate Change for Bird Conservation in the Southwestern U.S. under Three Alternative Futures.

PubMed

Friggens, Megan M; Finch, Deborah M

2015-01-01

Future expected changes in climate and human activity threaten many riparian habitats, particularly in the southwestern U.S. Using Maximum Entropy (MaxEnt3.3.3) modeling, we characterized habitat relationships and generated spatial predictions of habitat suitability for the Lucy's warbler (Oreothlypis luciae), the Southwestern willow flycatcher (Empidonax traillii extimus) and the Western yellow-billed cuckoo (Coccyzus americanus). Our goal was to provide site- and species-specific information that can be used by managers to identify areas for habitat conservation and/or restoration along the Rio Grande in New Mexico. We created models of suitable habitat for each species based on collection and survey samples and climate, biophysical, and vegetation data. We projected habitat suitability under future climates by applying these models to conditions generated from three climate models for 2030, 2060 and 2090. By comparing current and future distributions, we identified how habitats are likely to change as a result of changing climate and the consequences of those changes for these bird species. We also examined whether land ownership of high value sites shifts under changing climate conditions. Habitat suitability models performed well. Biophysical characteristics were more important that climate conditions for predicting habitat suitability with distance to water being the single most important predictor. Climate, though less important, was still influential and led to declines of suitable habitat of more than 60% by 2090. For all species, suitable habitat tended to shrink over time within the study area leaving a few core areas of high importance. Overall, climate changes will increase habitat fragmentation and reduce breeding habitat patch size. The best strategy for conserving bird species within the Rio Grande will include measures to maintain and restore critical habitat refugia. This study provides an example of a presence-only habitat model that can be used to inform the management of species at intermediate scales.

Ecological niche modeling of sympatric krill predators around Marguerite Bay, Western Antarctic Peninsula

NASA Astrophysics Data System (ADS)

Friedlaender, Ari S.; Johnston, David W.; Fraser, William R.; Burns, Jennifer; Halpin, Patrick N.; Costa, Daniel P.

2011-07-01

Adélie penguins ( Pygoscelis adeliae), carabeater seals ( Lobodon carcinophagus), humpback ( Megaptera novaeangliae), and minke whales ( Balaenoptera bonaernsis) are found in the waters surrounding the Western Antarctic Peninsula. Each species relies primarily on Antarctic krill ( Euphausia superba) and has physiological constraints and foraging behaviors that dictate their ecological niches. Understanding the degree of ecological overlap between sympatric krill predators is critical to understanding and predicting the impacts on climate-driven changes to the Antarctic marine ecosystem. To explore ecological relationships amongst sympatric krill predators, we developed ecological niche models using a maximum entropy modeling approach (Maxent) that allows the integration of data collected by a variety of means (e.g. satellite-based locations and visual observations). We created spatially explicit probability distributions for the four krill predators in fall 2001 and 2002 in conjunction with a suite of environmental variables. We find areas within Marguerite Bay with high krill predator occurrence rates or biological hot spots. We find the modeled ecological niches for Adélie penguins and crabeater seals may be affected by their physiological needs to haul-out on substrate. Thus, their distributions may be less dictated by proximity to prey and more so by physical features that over time provide adequate access to prey. Humpback and minke whales, being fully marine and having greater energetic demands, occupy ecological niches more directly proximate to prey. We also find evidence to suggest that the amount of overlap between modeled niches is relatively low, even for species with similar energetic requirements. In a rapidly changing and variable environment, our modeling work shows little indication that krill predators maintain similar ecological niches across years around Marguerite Bay. Given the amount of variability in the marine environment around the Antarctic Peninsula and how this affects the local abundance of prey, there may be consequences for krill predators with historically little niche overlap to increase the potential for interspecific competition for shared prey resources.

The impact of climate change on the distribution of two threatened Dipterocarp trees.

PubMed

Deb, Jiban C; Phinn, Stuart; Butt, Nathalie; McAlpine, Clive A

2017-04-01

Two ecologically and economically important, and threatened Dipterocarp trees Sal ( Shorea robusta ) and Garjan ( Dipterocarpus turbinatus ) form mono-specific canopies in dry deciduous, moist deciduous, evergreen, and semievergreen forests across South Asia and continental parts of Southeast Asia. They provide valuable timber and play an important role in the economy of many Asian countries. However, both Dipterocarp trees are threatened by continuing forest clearing, habitat alteration, and global climate change. While climatic regimes in the Asian tropics are changing, research on climate change-driven shifts in the distribution of tropical Asian trees is limited. We applied a bioclimatic modeling approach to these two Dipterocarp trees Sal and Garjan. We used presence-only records for the tree species, five bioclimatic variables, and selected two climatic scenarios (RCP4.5: an optimistic scenario and RCP8.5: a pessimistic scenario) and three global climate models (GCMs) to encompass the full range of variation in the models. We modeled climate space suitability for both species, projected to 2070, using a climate envelope modeling tool "MaxEnt" (the maximum entropy algorithm). Annual precipitation was the key bioclimatic variable in all GCMs for explaining the current and future distributions of Sal and Garjan (Sal: 49.97 ± 1.33; Garjan: 37.63 ± 1.19). Our models predict that suitable climate space for Sal will decline by 24% and 34% (the mean of the three GCMs) by 2070 under RCP4.5 and RCP8.5, respectively. In contrast, the consequences of imminent climate change appear less severe for Garjan, with a decline of 17% and 27% under RCP4.5 and RCP8.5, respectively. The findings of this study can be used to set conservation guidelines for Sal and Garjan by identifying vulnerable habitats in the region. In addition, the natural habitats of Sal and Garjan can be categorized as low to high risk under changing climates where artificial regeneration should be undertaken for forest restoration.

Mapping global potential risk of establishment of Rhagoletis pomonella (Diptera: Tephritidae) using MaxEnt and CLIMEX niche models

USDA-ARS?s Scientific Manuscript database

The apple maggot, Rhagoletis pomonella (Walsh) (Diptera: Tephritidae), is a major quarantine pest of apples (Malus domestica Borkhausen) in the United States. Apple maggot is found only in North America and negatively impacts the apple industry in the western U.S. by reducing grower access to export...

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

PubMed Central

Weiss, Brandi A.; Dardick, William

2015-01-01

This article introduces an entropy-based measure of data–model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data–model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data–model fit to assess how well logistic regression models classify cases into observed categories. PMID:29795897

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression.

PubMed

Weiss, Brandi A; Dardick, William

2016-12-01

This article introduces an entropy-based measure of data-model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data-model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data-model fit to assess how well logistic regression models classify cases into observed categories.

Modelling the meteorological forest fire niche in heterogeneous pyrologic conditions.

PubMed

De Angelis, Antonella; Ricotta, Carlo; Conedera, Marco; Pezzatti, Gianni Boris

2015-01-01

Fire regimes are strongly related to weather conditions that directly and indirectly influence fire ignition and propagation. Identifying the most important meteorological fire drivers is thus fundamental for daily fire risk forecasting. In this context, several fire weather indices have been developed focussing mainly on fire-related local weather conditions and fuel characteristics. The specificity of the conditions for which fire danger indices are developed makes its direct transfer and applicability problematic in different areas or with other fuel types. In this paper we used the low-to-intermediate fire-prone region of Canton Ticino as a case study to develop a new daily fire danger index by implementing a niche modelling approach (Maxent). In order to identify the most suitable weather conditions for fires, different combinations of input variables were tested (meteorological variables, existing fire danger indices or a combination of both). Our findings demonstrate that such combinations of input variables increase the predictive power of the resulting index and surprisingly even using meteorological variables only allows similar or better performances than using the complex Canadian Fire Weather Index (FWI). Furthermore, the niche modelling approach based on Maxent resulted in slightly improved model performance and in a reduced number of selected variables with respect to the classical logistic approach. Factors influencing final model robustness were the number of fire events considered and the specificity of the meteorological conditions leading to fire ignition.

Mapping risk of plague in Qinghai-Tibetan Plateau, China.

PubMed

Qian, Quan; Zhao, Jian; Fang, Liqun; Zhou, Hang; Zhang, Wenyi; Wei, Lan; Yang, Hong; Yin, Wenwu; Cao, Wuchun; Li, Qun

2014-07-10

Qinghai-Tibetan Plateau of China is known to be the plague endemic region where marmot (Marmota himalayana) is the primary host. Human plague cases are relatively low incidence but high mortality, which presents unique surveillance and public health challenges, because early detection through surveillance may not always be feasible and infrequent clinical cases may be misdiagnosed. Based on plague surveillance data and environmental variables, Maxent was applied to model the presence probability of plague host. 75% occurrence points were randomly selected for training model, and the rest 25% points were used for model test and validation. Maxent model performance was measured as test gain and test AUC. The optimal probability cut-off value was chosen by maximizing training sensitivity and specificity simultaneously. We used field surveillance data in an ecological niche modeling (ENM) framework to depict spatial distribution of natural foci of plague in Qinghai-Tibetan Plateau. Most human-inhabited areas at risk of exposure to enzootic plague are distributed in the east and south of the Plateau. Elevation, temperature of land surface and normalized difference vegetation index play a large part in determining the distribution of the enzootic plague. This study provided a more detailed view of spatial pattern of enzootic plague and human-inhabited areas at risk of plague. The maps could help public health authorities decide where to perform plague surveillance and take preventive measures in Qinghai-Tibetan Plateau.

Modelling the Meteorological Forest Fire Niche in Heterogeneous Pyrologic Conditions

PubMed Central

De Angelis, Antonella; Ricotta, Carlo; Conedera, Marco; Pezzatti, Gianni Boris

2015-01-01

Fire regimes are strongly related to weather conditions that directly and indirectly influence fire ignition and propagation. Identifying the most important meteorological fire drivers is thus fundamental for daily fire risk forecasting. In this context, several fire weather indices have been developed focussing mainly on fire-related local weather conditions and fuel characteristics. The specificity of the conditions for which fire danger indices are developed makes its direct transfer and applicability problematic in different areas or with other fuel types. In this paper we used the low-to-intermediate fire-prone region of Canton Ticino as a case study to develop a new daily fire danger index by implementing a niche modelling approach (Maxent). In order to identify the most suitable weather conditions for fires, different combinations of input variables were tested (meteorological variables, existing fire danger indices or a combination of both). Our findings demonstrate that such combinations of input variables increase the predictive power of the resulting index and surprisingly even using meteorological variables only allows similar or better performances than using the complex Canadian Fire Weather Index (FWI). Furthermore, the niche modelling approach based on Maxent resulted in slightly improved model performance and in a reduced number of selected variables with respect to the classical logistic approach. Factors influencing final model robustness were the number of fire events considered and the specificity of the meteorological conditions leading to fire ignition. PMID:25679957

Predicting the distribution pattern of small carnivores in response to environmental factors in the Western Ghats.

PubMed

Kalle, Riddhika; Ramesh, Tharmalingam; Qureshi, Qamar; Sankar, Kalyanasundaram

2013-01-01

Due to their secretive habits, predicting the pattern of spatial distribution of small carnivores has been typically challenging, yet for conservation management it is essential to understand the association between this group of animals and environmental factors. We applied maximum entropy modeling (MaxEnt) to build distribution models and identify environmental predictors including bioclimatic variables, forest and land cover type, topography, vegetation index and anthropogenic variables for six small carnivore species in Mudumalai Tiger Reserve. Species occurrence records were collated from camera-traps and vehicle transects during the years 2010 and 2011. We used the average training gain from forty model runs for each species to select the best set of predictors. The area under the curve (AUC) of the receiver operating characteristic plot (ROC) ranged from 0.81 to 0.93 for the training data and 0.72 to 0.87 for the test data. In habitat models for F. chaus, P. hermaphroditus, and H. smithii "distance to village" and precipitation of the warmest quarter emerged as some of the most important variables. "Distance to village" and aspect were important for V. indica while "distance to village" and precipitation of the coldest quarter were significant for H. vitticollis. "Distance to village", precipitation of the warmest quarter and land cover were influential variables in the distribution of H. edwardsii. The map of predicted probabilities of occurrence showed potentially suitable habitats accounting for 46 km(2) of the reserve for F. chaus, 62 km(2) for V. indica, 30 km(2) for P. hermaphroditus, 63 km(2) for H. vitticollis, 45 km(2) for H. smithii and 28 km(2) for H. edwardsii. Habitat heterogeneity driven by the east-west climatic gradient was correlated with the spatial distribution of small carnivores. This study exemplifies the usefulness of modeling small carnivore distribution to prioritize and direct conservation planning for habitat specialists in southern India.

Predicting the Distribution Pattern of Small Carnivores in Response to Environmental Factors in the Western Ghats

PubMed Central

Kalle, Riddhika; Ramesh, Tharmalingam; Qureshi, Qamar; Sankar, Kalyanasundaram

2013-01-01

Due to their secretive habits, predicting the pattern of spatial distribution of small carnivores has been typically challenging, yet for conservation management it is essential to understand the association between this group of animals and environmental factors. We applied maximum entropy modeling (MaxEnt) to build distribution models and identify environmental predictors including bioclimatic variables, forest and land cover type, topography, vegetation index and anthropogenic variables for six small carnivore species in Mudumalai Tiger Reserve. Species occurrence records were collated from camera-traps and vehicle transects during the years 2010 and 2011. We used the average training gain from forty model runs for each species to select the best set of predictors. The area under the curve (AUC) of the receiver operating characteristic plot (ROC) ranged from 0.81 to 0.93 for the training data and 0.72 to 0.87 for the test data. In habitat models for F. chaus, P. hermaphroditus, and H. smithii “distance to village” and precipitation of the warmest quarter emerged as some of the most important variables. “Distance to village” and aspect were important for V. indica while “distance to village” and precipitation of the coldest quarter were significant for H. vitticollis. “Distance to village”, precipitation of the warmest quarter and land cover were influential variables in the distribution of H. edwardsii. The map of predicted probabilities of occurrence showed potentially suitable habitats accounting for 46 km2 of the reserve for F. chaus, 62 km2 for V. indica, 30 km2 for P. hermaphroditus, 63 km2 for H. vitticollis, 45 km2 for H. smithii and 28 km2 for H. edwardsii. Habitat heterogeneity driven by the east-west climatic gradient was correlated with the spatial distribution of small carnivores. This study exemplifies the usefulness of modeling small carnivore distribution to prioritize and direct conservation planning for habitat specialists in southern India. PMID:24244470

[Study on ecological suitability regionalization of Corni Fructus based on Maxent and ArcGIS model].

PubMed

Zhang, Fei; Chen, Sui-Qing; Wang, Li-Li; Zhang, Tao; Zhang, Xiao-Bo; Zhu, Shou-Dong

2017-08-01

Through planting regionalization the scientific basis for planting area of high-quality medicinal materials was predicted. Through interview investigation and field survey, the distribution information of Corni Fructus in China was collected,and 89 sampling point from 14 producing areas were collected. Climate and topography of Corni Fructus were analyzed, the ecological adaptability of study was conducted based on ArcGIS and Maxent. Different suitability grade at potential areas and regionalization map were formulated. There are nine ecological factors affecting the growth of Corni Fructus, for example precipitation in November and March and vegetation type. The results showed that the most suitable habitats are Henan, Shaanxi, Zhejiang, Chongqing, Hubei, Sichuan, Anhui, Hunan and Shandong province. Using the spatial analysis method,the study not only illustrates the most suitable for the surroundings of Corni Fructus,but also provides a scientific reference for wild resource tending, introduction and cultivation, and artificial planting base and directing production layout. Copyright© by the Chinese Pharmaceutical Association.

Social Values for Ecosystem Services, version 3.0 (SolVES 3.0): documentation and user manual

USGS Publications Warehouse

Sherrouse, Ben C.; Semmens, Darius J.

2015-01-01

The geographic information system (GIS) tool, Social Values for Ecosystem Services (SolVES), was developed to incorporate quantified and spatially explicit measures of social values into ecosystem service assessments. SolVES 3.0 continues to extend the functionality of SolVES, which was designed to assess, map, and quantify the social values of ecosystem services. Social values—the perceived, nonmarket values the public ascribes to ecosystem services, particularly cultural services, such as aesthetics and recreation—can be evaluated for various stakeholder groups. These groups are distinguishable by their attitudes and preferences regarding public uses, such as motorized recreation and logging. As with previous versions, SolVES 3.0 derives a quantitative 10-point, social-values metric—the value index—from a combination of spatial and nonspatial responses to public value and preference surveys. The tool also calculates metrics characterizing the underlying environment, such as average distance to water and dominant landcover. SolVES 3.0 is integrated with Maxent maximum entropy modeling software to generate more complete social-value maps and offer robust statistical models describing the relationship between the value index and explanatory environmental variables. A model’s goodness of fit to a primary study area and its potential performance in transferring social values to similar areas using value-transfer methodology can be evaluated. SolVES 3.0 provides an improved public-domain tool for decision makers and researchers to evaluate the social values of ecosystem services and to facilitate discussions among diverse stakeholders regarding the tradeoffs among ecosystem services in a variety of physical and social contexts ranging from forest and rangeland to coastal and marine.

[Cultural regionalization for Coptis chinensis based on 3S technology platform Ⅰ. Study on growth suitability for Coptis chinensis based on ecological factors analysis by Maxent and ArcGIS model].

PubMed

Liu, Xin; Yang, Yan-Fang; Song, Hong-Ping; Zhang, Xiao-Bo; Huang, Lu-Qi; Wu, He-Zhen

2016-09-01

At the urgent request of Coptis chinensis planting,growth suitability as assessment indicators for C. chinensis cultivation was proposed and analyzed in this paper , based on chemical quality determination and ecological fators analysis by Maxent and ArcGIS model. Its potential distribution areas at differernt suitability grade and regionalization map were formulated based on statistical theory and growth suitability theory. The results showed that the most suitable habitats is some parts of Chongqing and Hubei province, such as Shizhu, Lichuan, Wulong, Wuxi, Enshi. There are seven ecological factor is the main ecological factors affect the growth of Coptidis Rhizoma, including altitude, precipitation in February and September and the rise of precipitation and altitude is conducive to the accumulation of total alkaloid content in C. chinensis. Therefore, The results of the study not only illustrates the most suitable for the surroundings of Coptidis Rhizoma, also helpful to further research and practice of cultivation regionalization, wild resource monitoring and large-scale cultivation of traditional Chinese medicine plants. Copyright© by the Chinese Pharmaceutical Association.

Modeling Loop Entropy

PubMed Central

Chirikjian, Gregory S.

2011-01-01

Proteins fold from a highly disordered state into a highly ordered one. Traditionally, the folding problem has been stated as one of predicting ‘the’ tertiary structure from sequential information. However, new evidence suggests that the ensemble of unfolded forms may not be as disordered as once believed, and that the native form of many proteins may not be described by a single conformation, but rather an ensemble of its own. Quantifying the relative disorder in the folded and unfolded ensembles as an entropy difference may therefore shed light on the folding process. One issue that clouds discussions of ‘entropy’ is that many different kinds of entropy can be defined: entropy associated with overall translational and rotational Brownian motion, configurational entropy, vibrational entropy, conformational entropy computed in internal or Cartesian coordinates (which can even be different from each other), conformational entropy computed on a lattice; each of the above with different solvation and solvent models; thermodynamic entropy measured experimentally, etc. The focus of this work is the conformational entropy of coil/loop regions in proteins. New mathematical modeling tools for the approximation of changes in conformational entropy during transition from unfolded to folded ensembles are introduced. In particular, models for computing lower and upper bounds on entropy for polymer models of polypeptide coils both with and without end constraints are presented. The methods reviewed here include kinematics (the mathematics of rigid-body motions), classical statistical mechanics and information theory. PMID:21187223

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

ERIC Educational Resources Information Center

Weiss, Brandi A.; Dardick, William

2016-01-01

This article introduces an entropy-based measure of data-model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify…

Estimating the melting point, entropy of fusion, and enthalpy of ...

EPA Pesticide Factsheets

The entropies of fusion, enthalies of fusion, and melting points of organic compounds can be estimated through three models developed using the SPARC (SPARC Performs Automated Reasoning in Chemistry) platform. The entropy of fusion is modeled through a combination of interaction terms and physical descriptors. The enthalpy of fusion is modeled as a function of the entropy of fusion, boiling point, and fexibility of the molecule. The melting point model is the enthlapy of fusion divided by the entropy of fusion. These models were developed in part to improve SPARC's vapor pressure and solubility models. These models have been tested on 904 unique compounds. The entropy model has a RMS of 12.5 J mol-1K-1. The enthalpy model has a RMS of 4.87 kJ mol-1. The melting point model has a RMS of 54.4°C. Published in the journal, SAR and QSAR in Environmental Research

Estimating the melting point, entropy of fusion, and enthalpy of fusion of organic compounds via SPARC.

PubMed

Whiteside, T S; Hilal, S H; Brenner, A; Carreira, L A

2016-08-01

The entropy of fusion, enthalpy of fusion, and melting point of organic compounds can be estimated through three models developed using the SPARC (SPARC Performs Automated Reasoning in Chemistry) platform. The entropy of fusion is modelled through a combination of interaction terms and physical descriptors. The enthalpy of fusion is modelled as a function of the entropy of fusion, boiling point, and flexibility of the molecule. The melting point model is the enthalpy of fusion divided by the entropy of fusion. These models were developed in part to improve SPARC's vapour pressure and solubility models. These models have been tested on 904 unique compounds. The entropy model has a RMS of 12.5 J mol(-1) K(-1). The enthalpy model has a RMS of 4.87 kJ mol(-1). The melting point model has a RMS of 54.4°C.

Crowd macro state detection using entropy model

NASA Astrophysics Data System (ADS)

Zhao, Ying; Yuan, Mengqi; Su, Guofeng; Chen, Tao

2015-08-01

In the crowd security research area a primary concern is to identify the macro state of crowd behaviors to prevent disasters and to supervise the crowd behaviors. The entropy is used to describe the macro state of a self-organization system in physics. The entropy change indicates the system macro state change. This paper provides a method to construct crowd behavior microstates and the corresponded probability distribution using the individuals' velocity information (magnitude and direction). Then an entropy model was built up to describe the crowd behavior macro state. Simulation experiments and video detection experiments were conducted. It was verified that in the disordered state, the crowd behavior entropy is close to the theoretical maximum entropy; while in ordered state, the entropy is much lower than half of the theoretical maximum entropy. The crowd behavior macro state sudden change leads to the entropy change. The proposed entropy model is more applicable than the order parameter model in crowd behavior detection. By recognizing the entropy mutation, it is possible to detect the crowd behavior macro state automatically by utilizing cameras. Results will provide data support on crowd emergency prevention and on emergency manual intervention.

Design of an Agent-Based Model to Examine Population-Environment Interactions in Nang Rong District, Thailand.

PubMed

Walsh, Stephen J; Malanson, George P; Entwisle, Barbara; Rindfuss, Ronald R; Mucha, Peter J; Heumann, Benjamin W; McDaniel, Philip M; Frizzelle, Brian G; Verdery, Ashton M; Williams, Nathalie; Xiaozheng, Yao; Ding, Deng

2013-05-01

The design of an Agent-Based Model (ABM) is described that integrates Social and Land Use Modules to examine population-environment interactions in a former agricultural frontier in Northeastern Thailand. The ABM is used to assess household income and wealth derived from agricultural production of lowland, rain-fed paddy rice and upland field crops in Nang Rong District as well as remittances returned to the household from family migrants who are engaged in off-farm employment in urban destinations. The ABM is supported by a longitudinal social survey of nearly 10,000 households, a deep satellite image time-series of land use change trajectories, multi-thematic social and ecological data organized within a GIS, and a suite of software modules that integrate data derived from an agricultural cropping system model (DSSAT - Decision Support for Agrotechnology Transfer) and a land suitability model (MAXENT - Maximum Entropy), in addition to multi-dimensional demographic survey data of individuals and households. The primary modules of the ABM are the Initialization Module, Migration Module, Assets Module, Land Suitability Module, Crop Yield Module, Fertilizer Module, and the Land Use Change Decision Module. The architecture of the ABM is described relative to module function and connectivity through uni-directional or bi-directional links. In general, the Social Modules simulate changes in human population and social networks, as well as changes in population migration and household assets, whereas the Land Use Modules simulate changes in land use types, land suitability, and crop yields. We emphasize the description of the Land Use Modules - the algorithms and interactions between the modules are described relative to the project goals of assessing household income and wealth relative to shifts in land use patterns, household demographics, population migration, social networks, and agricultural activities that collectively occur within a marginalized environment that is subjected to a suite of endogenous and exogenous dynamics.

Design of an Agent-Based Model to Examine Population-Environment Interactions in Nang Rong District, Thailand

PubMed Central

Walsh, Stephen J.; Malanson, George P.; Entwisle, Barbara; Rindfuss, Ronald R.; Mucha, Peter J.; Heumann, Benjamin W.; McDaniel, Philip M.; Frizzelle, Brian G.; Verdery, Ashton M.; Williams, Nathalie; Xiaozheng, Yao; Ding, Deng

2013-01-01

The design of an Agent-Based Model (ABM) is described that integrates Social and Land Use Modules to examine population-environment interactions in a former agricultural frontier in Northeastern Thailand. The ABM is used to assess household income and wealth derived from agricultural production of lowland, rain-fed paddy rice and upland field crops in Nang Rong District as well as remittances returned to the household from family migrants who are engaged in off-farm employment in urban destinations. The ABM is supported by a longitudinal social survey of nearly 10,000 households, a deep satellite image time-series of land use change trajectories, multi-thematic social and ecological data organized within a GIS, and a suite of software modules that integrate data derived from an agricultural cropping system model (DSSAT – Decision Support for Agrotechnology Transfer) and a land suitability model (MAXENT – Maximum Entropy), in addition to multi-dimensional demographic survey data of individuals and households. The primary modules of the ABM are the Initialization Module, Migration Module, Assets Module, Land Suitability Module, Crop Yield Module, Fertilizer Module, and the Land Use Change Decision Module. The architecture of the ABM is described relative to module function and connectivity through uni-directional or bi-directional links. In general, the Social Modules simulate changes in human population and social networks, as well as changes in population migration and household assets, whereas the Land Use Modules simulate changes in land use types, land suitability, and crop yields. We emphasize the description of the Land Use Modules – the algorithms and interactions between the modules are described relative to the project goals of assessing household income and wealth relative to shifts in land use patterns, household demographics, population migration, social networks, and agricultural activities that collectively occur within a marginalized environment that is subjected to a suite of endogenous and exogenous dynamics. PMID:24277975

Social values for ecosystem services (SolVES): Documentation and user manual, version 2.0

USGS Publications Warehouse

Sherrouse, Benson C.; Semmens, Darius J.

2012-01-01

In response to the need for incorporating quantified and spatially explicit measures of social values into ecosystem services assessments, the Rocky Mountain Geographic Science Center (RMGSC), in collaboration with Colorado State University, developed a geographic information system (GIS) application, Social Values for Ecosystem Services (SolVES). With version 2.0 (SolVES 2.0), RMGSC has improved and extended the functionality of SolVES, which was designed to assess, map, and quantify the perceived social values of ecosystem services. Social values such as aesthetics, biodiversity, and recreation can be evaluated for various stakeholder groups as distinguished by their attitudes and preferences regarding public uses, such as motorized recreation and logging. As with the previous version, SolVES 2.0 derives a quantitative, 10-point, social-values metric, the Value Index, from a combination of spatial and nonspatial responses to public attitude and preference surveys and calculates metrics characterizing the underlying environment, such as average distance to water and dominant landcover. Additionally, SolVES 2.0 integrates Maxent maximum entropy modeling software to generate more complete social value maps and to produce robust statistical models describing the relationship between the social values maps and explanatory environmental variables. The performance of these models can be evaluated for a primary study area, as well as for similar areas where primary survey data are not available but where social value mapping could potentially be completed using value-transfer methodology. SolVES 2.0 also introduces the flexibility for users to define their own social values and public uses, model any number and type of environmental variable, and modify the spatial resolution of analysis. With these enhancements, SolVES 2.0 provides an improved public domain tool for decisionmakers and researchers to evaluate the social values of ecosystem services and to facilitate discussions among diverse stakeholders regarding the tradeoffs among different ecosystem services in a variety of physical and social contexts ranging from forest and rangeland to coastal and marine.

Formulating the shear stress distribution in circular open channels based on the Renyi entropy

NASA Astrophysics Data System (ADS)

Khozani, Zohreh Sheikh; Bonakdari, Hossein

2018-01-01

The principle of maximum entropy is employed to derive the shear stress distribution by maximizing the Renyi entropy subject to some constraints and by assuming that dimensionless shear stress is a random variable. A Renyi entropy-based equation can be used to model the shear stress distribution along the entire wetted perimeter of circular channels and circular channels with flat beds and deposited sediments. A wide range of experimental results for 12 hydraulic conditions with different Froude numbers (0.375 to 1.71) and flow depths (20.3 to 201.5 mm) were used to validate the derived shear stress distribution. For circular channels, model performance enhanced with increasing flow depth (mean relative error (RE) of 0.0414) and only deteriorated slightly at the greatest flow depth (RE of 0.0573). For circular channels with flat beds, the Renyi entropy model predicted the shear stress distribution well at lower sediment depth. The Renyi entropy model results were also compared with Shannon entropy model results. Both models performed well for circular channels, but for circular channels with flat beds the Renyi entropy model displayed superior performance in estimating the shear stress distribution. The Renyi entropy model was highly precise and predicted the shear stress distribution in a circular channel with RE of 0.0480 and in a circular channel with a flat bed with RE of 0.0488.

An Equation for Moist Entropy in a Precipitating and Icy Atmosphere

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Simpson, Joanne; Zeng, Xiping

2003-01-01

Moist entropy is nearly conserved in adiabatic motion. It is redistributed rather than created by moist convection. Thus moist entropy and its equation, as a healthy direction, can be used to construct analytical and numerical models for the interaction between tropical convective clouds and large-scale circulations. Hence, an accurate equation of moist entropy is needed for the analysis and modeling of atmospheric convective clouds. On the basis of the consistency between the energy and the entropy equations, a complete equation of moist entropy is derived from the energy equation. The equation expresses explicitly the internal and external sources of moist entropy, including those in relation to the microphysics of clouds and precipitation. In addition, an accurate formula for the surface flux of moist entropy from the underlying surface into the air above is derived. Because moist entropy deals "easily" with the transition among three water phases, it will be used as a prognostic variable in the next generation of cloud-resolving models (e. g. a global cloud-resolving model) for low computational noise. Its equation that is derived in this paper is accurate and complete, providing a theoretical basis for using moist entropy as a prognostic variable in the long-term modeling of clouds and large-scale circulations.

From hybrid swarms to swarms of hybrids

USGS Publications Warehouse

Stohlgren, Thomas J.; Szalanski, Allen L; Gaskin, John F.; Young, Nicholas E.; West, Amanda; Jarnevich, Catherine S.; Tripodi, Amber

2014-01-01

Science has shown that the introgression or hybridization of modern humans (Homo sapiens) with Neanderthals up to 40,000 YBP may have led to the swarm of modern humans on earth. However, there is little doubt that modern trade and transportation in support of the humans has continued to introduce additional species, genotypes, and hybrids to every country on the globe. We assessed the utility of species distributions modeling of genotypes to assess the risk of current and future invaders. We evaluated 93 locations of the genus Tamarix for which genetic data were available. Maxent models of habitat suitability showed that the hybrid, T. ramosissima x T. chinensis, was slightly greater than the parent taxa (AUCs > 0.83). General linear models of Africanized honey bees, a hybrid cross of Tanzanian Apis mellifera scutellata and a variety of European honey bee including A. m. ligustica, showed that the Africanized bees (AUC = 0.81) may be displacing European honey bees (AUC > 0.76) over large areas of the southwestern U.S. More important, Maxent modeling of sub-populations (A1 and A26 mitotypes based on mDNA) could be accurately modeled (AUC > 0.9), and they responded differently to environmental drivers. This suggests that rapid evolutionary change may be underway in the Africanized bees, allowing the bees to spread into new areas and extending their total range. Protecting native species and ecosystems may benefit from risk maps of harmful invasive species, hybrids, and genotypes.

Nonadditive entropy maximization is inconsistent with Bayesian updating

NASA Astrophysics Data System (ADS)

Pressé, Steve

2014-11-01

The maximum entropy method—used to infer probabilistic models from data—is a special case of Bayes's model inference prescription which, in turn, is grounded in basic propositional logic. By contrast to the maximum entropy method, the compatibility of nonadditive entropy maximization with Bayes's model inference prescription has never been established. Here we demonstrate that nonadditive entropy maximization is incompatible with Bayesian updating and discuss the immediate implications of this finding. We focus our attention on special cases as illustrations.

Nonadditive entropy maximization is inconsistent with Bayesian updating.

PubMed

Pressé, Steve

2014-11-01

The maximum entropy method-used to infer probabilistic models from data-is a special case of Bayes's model inference prescription which, in turn, is grounded in basic propositional logic. By contrast to the maximum entropy method, the compatibility of nonadditive entropy maximization with Bayes's model inference prescription has never been established. Here we demonstrate that nonadditive entropy maximization is incompatible with Bayesian updating and discuss the immediate implications of this finding. We focus our attention on special cases as illustrations.

The relative contribution of climatic, edaphic, and biotic drivers to risk of tree mortality from drought

NASA Astrophysics Data System (ADS)

March, R. G.; Moore, G. W.; Edgar, C. B.; Lawing, A. M.; Washington-Allen, R. A.

2015-12-01

In recorded history, the 2011 Texas Drought was comparable in severity only to a drought that occurred 300 years ago. By mid-September, 88% of the state experienced 'exceptional' conditions, with the rest experiencing 'extreme' or 'severe' drought. By recent estimates, the 2011 Texas Drought killed 6.2% of all the state's trees, at a rate nearly 9 times greater than average. The vast spatial scale and relatively uniform intensity of this drought has provided an opportunity to examine the comparative interactions among forest types, terrain, and edaphic factors across major climate gradients which in 2011 were subjected to extreme drought conditions that ultimately caused massive tree mortality. We used maximum entropy modeling (Maxent) to rank environmental landscape factors with the potential to drive drought-related tree mortality and test the assumption that the relative importance of these factors are scale-dependent. Occurrence data of dead trees were collected during the summer of 2012 from 599 field plots distributed across Texas with 30% used for model evaluation. Bioclimatic variables, ecoregions, soils characteristics, and topographic variables were modeled with drought-killed tree occurrence. Their relative contribution to the model was seen as their relative importance in driving mortality. To test determinants at a more local scale, we examined Landsat 7 scenes in East and West Texas with moderate-resolution data for the same variables above with the exception of climate. All models were significantly better than random in binomial tests of omission and receiver operating characteristic analyses. The modeled spatial distribution of probability of occurrence showed high probability of mortality in the east-central oak woodlands and the mixed pine-hardwood forest region in northeast Texas. Both regional and local models were dominated by biotic factors (ecoregion and forest type, respectively). Forest density and precipitation of driest month also contributed highly to the regional model. The local models gave more importance to available water storage at root zone and hillshade. Understanding how environmental factors drive drought-related mortality can help predict vulnerable landscapes and aid in preparing for future drought events.

On entropy, financial markets and minority games

NASA Astrophysics Data System (ADS)

Zapart, Christopher A.

2009-04-01

The paper builds upon an earlier statistical analysis of financial time series with Shannon information entropy, published in [L. Molgedey, W. Ebeling, Local order, entropy and predictability of financial time series, European Physical Journal B-Condensed Matter and Complex Systems 15/4 (2000) 733-737]. A novel generic procedure is proposed for making multistep-ahead predictions of time series by building a statistical model of entropy. The approach is first demonstrated on the chaotic Mackey-Glass time series and later applied to Japanese Yen/US dollar intraday currency data. The paper also reinterprets Minority Games [E. Moro, The minority game: An introductory guide, Advances in Condensed Matter and Statistical Physics (2004)] within the context of physical entropy, and uses models derived from minority game theory as a tool for measuring the entropy of a model in response to time series. This entropy conditional upon a model is subsequently used in place of information-theoretic entropy in the proposed multistep prediction algorithm.

The performance evaluation model of mining project founded on the weight optimization entropy value method

NASA Astrophysics Data System (ADS)

Mao, Chao; Chen, Shou

2017-01-01

According to the traditional entropy value method still have low evaluation accuracy when evaluating the performance of mining projects, a performance evaluation model of mineral project founded on improved entropy is proposed. First establish a new weight assignment model founded on compatible matrix analysis of analytic hierarchy process (AHP) and entropy value method, when the compatibility matrix analysis to achieve consistency requirements, if it has differences between subjective weights and objective weights, moderately adjust both proportions, then on this basis, the fuzzy evaluation matrix for performance evaluation. The simulation experiments show that, compared with traditional entropy and compatible matrix analysis method, the proposed performance evaluation model of mining project based on improved entropy value method has higher accuracy assessment.

Analysis of interacting entropy-corrected holographic and new agegraphic dark energies

NASA Astrophysics Data System (ADS)

Ranjit, Chayan; Debnath, Ujjal

In the present work, we assume the flat FRW model of the universe is filled with dark matter and dark energy where they are interacting. For dark energy model, we consider the entropy-corrected HDE (ECHDE) model and the entropy-corrected NADE (ECNADE). For entropy-corrected models, we assume logarithmic correction and power law correction. For ECHDE model, length scale L is assumed to be Hubble horizon and future event horizon. The ωde-ωde‧ analysis for our different horizons are discussed.

Entropy, complexity, and Markov diagrams for random walk cancer models.

PubMed

Newton, Paul K; Mason, Jeremy; Hurt, Brian; Bethel, Kelly; Bazhenova, Lyudmila; Nieva, Jorge; Kuhn, Peter

2014-12-19

The notion of entropy is used to compare the complexity associated with 12 common cancers based on metastatic tumor distribution autopsy data. We characterize power-law distributions, entropy, and Kullback-Liebler divergence associated with each primary cancer as compared with data for all cancer types aggregated. We then correlate entropy values with other measures of complexity associated with Markov chain dynamical systems models of progression. The Markov transition matrix associated with each cancer is associated with a directed graph model where nodes are anatomical locations where a metastatic tumor could develop, and edge weightings are transition probabilities of progression from site to site. The steady-state distribution corresponds to the autopsy data distribution. Entropy correlates well with the overall complexity of the reduced directed graph structure for each cancer and with a measure of systemic interconnectedness of the graph, called graph conductance. The models suggest that grouping cancers according to their entropy values, with skin, breast, kidney, and lung cancers being prototypical high entropy cancers, stomach, uterine, pancreatic and ovarian being mid-level entropy cancers, and colorectal, cervical, bladder, and prostate cancers being prototypical low entropy cancers, provides a potentially useful framework for viewing metastatic cancer in terms of predictability, complexity, and metastatic potential.

Entropy, complexity, and Markov diagrams for random walk cancer models

NASA Astrophysics Data System (ADS)

Newton, Paul K.; Mason, Jeremy; Hurt, Brian; Bethel, Kelly; Bazhenova, Lyudmila; Nieva, Jorge; Kuhn, Peter

2014-12-01

The notion of entropy is used to compare the complexity associated with 12 common cancers based on metastatic tumor distribution autopsy data. We characterize power-law distributions, entropy, and Kullback-Liebler divergence associated with each primary cancer as compared with data for all cancer types aggregated. We then correlate entropy values with other measures of complexity associated with Markov chain dynamical systems models of progression. The Markov transition matrix associated with each cancer is associated with a directed graph model where nodes are anatomical locations where a metastatic tumor could develop, and edge weightings are transition probabilities of progression from site to site. The steady-state distribution corresponds to the autopsy data distribution. Entropy correlates well with the overall complexity of the reduced directed graph structure for each cancer and with a measure of systemic interconnectedness of the graph, called graph conductance. The models suggest that grouping cancers according to their entropy values, with skin, breast, kidney, and lung cancers being prototypical high entropy cancers, stomach, uterine, pancreatic and ovarian being mid-level entropy cancers, and colorectal, cervical, bladder, and prostate cancers being prototypical low entropy cancers, provides a potentially useful framework for viewing metastatic cancer in terms of predictability, complexity, and metastatic potential.

[Cultural regionalization for Notopterygium incisum based on 3S technology platform. I. Evaluation for growth suitability for N. incisum based on ecological factors analysis by Maxent and ArcGIS model].

PubMed

Sun, Hong-bing; Sun, Hui; Jiang, Shun-yuan; Zhou, Yi; Cao, Wen-long; Ji, Ming-chang; Zhy, Wen-tao; Yan, Han-jing

2015-03-01

Growth suitability as assessment indicators for medicinal plants cultivation was proposed based on chemical quality determination and ecological factors analysis by Maxent and ArcGIS model. Notopterygium incisum, an endangered Chinese medicinal plant, was analyzed as a case, its potential distribution areas at different suitability grade and regionalization map were formulated based on growth suitability theory. The results showed that the most suitable habitats is Sichuan province, and more than 60% of the most suitable areawas located in the western Sichuan such as Aba and Ganzi prefectures for N. incisum. The results indicated that habitat altitude, average air temperature in September, and vegetation types were the dominant factors contributing to the grade of plant growth, precipitation and slope were the major factors contributing to notopterol accumulation in its underground parts, while isoimperatorin in its underground parts was negatively corelated with precipitation and slope of its habitat. However, slope as a factor influencing chemical components seemed to be a pseudo corelationship. Therefore, there were distinguishing differences between growth suitability and quality suitability for medicinal plants, which was helpful to further research and practice of cultivation regionalization, wild resource monitoring and large-scale cultivation of traditional Chinese medicine plants.

Uncertainties have a meaning: Information entropy as a quality measure for 3-D geological models

NASA Astrophysics Data System (ADS)

Wellmann, J. Florian; Regenauer-Lieb, Klaus

2012-03-01

Analyzing, visualizing and communicating uncertainties are important issues as geological models can never be fully determined. To date, there exists no general approach to quantify uncertainties in geological modeling. We propose here to use information entropy as an objective measure to compare and evaluate model and observational results. Information entropy was introduced in the 50s and defines a scalar value at every location in the model for predictability. We show that this method not only provides a quantitative insight into model uncertainties but, due to the underlying concept of information entropy, can be related to questions of data integration (i.e. how is the model quality interconnected with the used input data) and model evolution (i.e. does new data - or a changed geological hypothesis - optimize the model). In other words information entropy is a powerful measure to be used for data assimilation and inversion. As a first test of feasibility, we present the application of the new method to the visualization of uncertainties in geological models, here understood as structural representations of the subsurface. Applying the concept of information entropy on a suite of simulated models, we can clearly identify (a) uncertain regions within the model, even for complex geometries; (b) the overall uncertainty of a geological unit, which is, for example, of great relevance in any type of resource estimation; (c) a mean entropy for the whole model, important to track model changes with one overall measure. These results cannot easily be obtained with existing standard methods. The results suggest that information entropy is a powerful method to visualize uncertainties in geological models, and to classify the indefiniteness of single units and the mean entropy of a model quantitatively. Due to the relationship of this measure to the missing information, we expect the method to have a great potential in many types of geoscientific data assimilation problems — beyond pure visualization.

Selecting a Conservation Surrogate Species for Small Fragmented Habitats Using Ecological Niche Modelling

PubMed Central

Nekaris, K. Anne-Isola; Arnell, Andrew P.; Svensson, Magdalena S.

2015-01-01

Simple Summary Large “charismatic” animals (with widespread popular appeal) are often used as flagship species to raise awareness for conservation. Deforestation and forest fragmentation are among the main threats to biodiversity, and in many places such species are disappearing. In this paper we aim to find a suitable species among the less charismatic animal species left in the fragmented forests of South-western Sri Lanka. We selected ten candidates, using a questionnaire survey along with computer modelling of their distributions. The red slender loris and the fishing cat came out as finalists as they were both appealing to local people, and fulfilled selected ecological criteria. Abstract Flagship species are traditionally large, charismatic animals used to rally conservation efforts. Accepted flagship definitions suggest they need only fulfil a strategic role, unlike umbrella species that are used to shelter cohabitant taxa. The criteria used to select both flagship and umbrella species may not stand up in the face of dramatic forest loss, where remaining fragments may only contain species that do not suit either set of criteria. The Cinderella species concept covers aesthetically pleasing and overlooked species that fulfil the criteria of flagships or umbrellas. Such species are also more likely to occur in fragmented habitats. We tested Cinderella criteria on mammals in the fragmented forests of the Sri Lankan Wet Zone. We selected taxa that fulfilled both strategic and ecological roles. We created a shortlist of ten species, and from a survey of local perceptions highlighted two finalists. We tested these for umbrella characteristics against the original shortlist, utilizing Maximum Entropy (MaxEnt) modelling, and analysed distribution overlap using ArcGIS. The criteria highlighted Loris tardigradus tardigradus and Prionailurus viverrinus as finalists, with the former having highest flagship potential. We suggest Cinderella species can be effective conservation surrogates especially in habitats where traditional flagship species have been extirpated. PMID:26479135

Contemporary Remotely Sensed Data Products Refine Invasive Plants Risk Mapping in Data Poor Regions.

PubMed

Truong, Tuyet T A; Hardy, Giles E St J; Andrew, Margaret E

2017-01-01

Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam's lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species.

Contemporary Remotely Sensed Data Products Refine Invasive Plants Risk Mapping in Data Poor Regions

PubMed Central

Truong, Tuyet T. A.; Hardy, Giles E. St. J.; Andrew, Margaret E.

2017-01-01

Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam’s lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species. PMID:28555147

Entropy, complexity, and Markov diagrams for random walk cancer models

PubMed Central

Newton, Paul K.; Mason, Jeremy; Hurt, Brian; Bethel, Kelly; Bazhenova, Lyudmila; Nieva, Jorge; Kuhn, Peter

2014-01-01

The notion of entropy is used to compare the complexity associated with 12 common cancers based on metastatic tumor distribution autopsy data. We characterize power-law distributions, entropy, and Kullback-Liebler divergence associated with each primary cancer as compared with data for all cancer types aggregated. We then correlate entropy values with other measures of complexity associated with Markov chain dynamical systems models of progression. The Markov transition matrix associated with each cancer is associated with a directed graph model where nodes are anatomical locations where a metastatic tumor could develop, and edge weightings are transition probabilities of progression from site to site. The steady-state distribution corresponds to the autopsy data distribution. Entropy correlates well with the overall complexity of the reduced directed graph structure for each cancer and with a measure of systemic interconnectedness of the graph, called graph conductance. The models suggest that grouping cancers according to their entropy values, with skin, breast, kidney, and lung cancers being prototypical high entropy cancers, stomach, uterine, pancreatic and ovarian being mid-level entropy cancers, and colorectal, cervical, bladder, and prostate cancers being prototypical low entropy cancers, provides a potentially useful framework for viewing metastatic cancer in terms of predictability, complexity, and metastatic potential. PMID:25523357

Using entropy measures to characterize human locomotion.

PubMed

Leverick, Graham; Szturm, Tony; Wu, Christine Q

2014-12-01

Entropy measures have been widely used to quantify the complexity of theoretical and experimental dynamical systems. In this paper, the value of using entropy measures to characterize human locomotion is demonstrated based on their construct validity, predictive validity in a simple model of human walking and convergent validity in an experimental study. Results show that four of the five considered entropy measures increase meaningfully with the increased probability of falling in a simple passive bipedal walker model. The same four entropy measures also experienced statistically significant increases in response to increasing age and gait impairment caused by cognitive interference in an experimental study. Of the considered entropy measures, the proposed quantized dynamical entropy (QDE) and quantization-based approximation of sample entropy (QASE) offered the best combination of sensitivity to changes in gait dynamics and computational efficiency. Based on these results, entropy appears to be a viable candidate for assessing the stability of human locomotion.

Present and future ecological niche modeling of garter snake species from the Trans-Mexican Volcanic Belt

PubMed Central

García-Vázquez, Uri; D’Addario, Maristella

2018-01-01

Land use and climate change are affecting the abundance and distribution of species. The Trans-Mexican Volcanic Belt (TMVB) is a very diverse region due to geological history, geographic position, and climate. It is also one of the most disturbed regions in Mexico. Reptiles are particularly sensitive to environmental changes due to their low dispersal capacity and thermal ecology. In this study, we define the important environmental variables (considering climate, topography, and land use) and potential distribution (present and future) of the five Thamnophis species present in TMVB. To do so, we used the maximum entropy modeling software (MAXENT). First, we modeled to select the most important variables to explain the distribution of each species, then we modeled again using only the most important variables and projected these models to the future considering a middle-moderate climate change scenario (rcp45), and land use and vegetation variables for the year 2050 (generated according to land use changes that occurred between years 2002 and 2011). Arid vegetation had an important negative effect on habitat suitability for all species, and minimum temperature of the coldest month was important for four of the five species. Thamnophis cyrtopsis was the species with the lowest tolerance to minimum temperatures. The maximum temperature of the warmest month was important for T. scalaris and T. cyrtopsis. Low percentages of agriculture were positive for T. eques and T. melanogaster but, at higher values, agriculture had a negative effect on habitat suitability for both species. Elevation was the most important variable to explain T. eques and T. melanogaster potential distribution while distance to Abies forests was the most important variable for T. scalaris and T. scaliger. All species had a high proportion of their potential distribution in the TMVB. However, according to our models, all Thamnophis species will experience reductions in their potential distribution in this region. T. scalaris will suffer the biggest reduction because this species is limited by high temperatures and will not be able to shift its distribution upward, as it is already present in the highest elevations of the TMVB. PMID:29666767

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.

High Resolution Habitat Suitability Modelling For Restricted-Range Hawaiian Alpine Arthropod Species

NASA Astrophysics Data System (ADS)

Stephenson, N. M.

2016-12-01

Mapping potentially suitable habitat is critical for effective species conservation and management but can be challenging in areas exhibiting complex heterogeneity. An approach that combines non-intrusive spatial data collection techniques and field data can lead to a better understanding of landscapes and species distributions. Nysius wekiuicola, commonly known as the wēkiu bug, is the most studied arthropod species endemic to the Maunakea summit in Hawai`i, yet details about its geographic distribution and habitat use remain poorly understood. To predict the geographic distribution of N. wekiuicola, MaxEnt habitat suitability models were generated from a diverse set of input variables, including fifteen years of species occurrence data, high resolution digital elevation models, surface mineralogy maps derived from hyperspectral remote sensing, and climate data. Model results indicate that elevation (78.2 percent), and the presence of nanocrystalline hematite surface minerals (13.7 percent) had the highest influence, with lesser contributions from aspect, slope, and other surface mineral classes. Climatic variables were not included in the final analysis due to auto-correlation and coarse spatial resolution. Biotic factors relating to predation and competition also likely dictate wēkiu bug capture patterns and influence our results. The wēkiu bug range and habitat suitability models generated as a result of this study will be directly incorporated into management and restoration goals for the summit region and can also be adapted for other arthropod species present, leading to a more holistic understanding of metacommunity dynamics. Key words: Microhabitat, Structure from Motion, Lidar, MaxEnt, Habitat Suitability

Projected Regression Methods for Inverting Fredholm Integrals: Formalism and Application to Analytical Continuation

NASA Astrophysics Data System (ADS)

Arsenault, Louis-Francois; Neuberg, Richard; Hannah, Lauren A.; Millis, Andrew J.

We present a machine learning-based statistical regression approach to the inversion of Fredholm integrals of the first kind by studying an important example for the quantum materials community, the analytical continuation problem of quantum many-body physics. It involves reconstructing the frequency dependence of physical excitation spectra from data obtained at specific points in the complex frequency plane. The approach provides a natural regularization in cases where the inverse of the Fredholm kernel is ill-conditioned and yields robust error metrics. The stability of the forward problem permits the construction of a large database of input-output pairs. Machine learning methods applied to this database generate approximate solutions which are projected onto the subspace of functions satisfying relevant constraints. We show that for low input noise the method performs as well or better than Maximum Entropy (MaxEnt) under standard error metrics, and is substantially more robust to noise. We expect the methodology to be similarly effective for any problem involving a formally ill-conditioned inversion, provided that the forward problem can be efficiently solved. AJM was supported by the Office of Science of the U.S. Department of Energy under Subcontract No. 3F-3138 and LFA by the Columbia Univeristy IDS-ROADS project, UR009033-05 which also provided part support to RN and LH.

Application of Maxent Multivariate Analysis to Define Climate-Change Effects on Species Distributions and Changes

DTIC Science & Technology

2014-09-01

approaches. Ecological Modelling Volume 200, Issues 1–2, 10, pp 1–19. Buhlmann, Kurt A ., Thomas S.B. Akre , John B. Iverson, Deno Karapatakis, Russell A ...statistical multivariate analysis to define the current and projected future range probability for species of interest to Army land managers. A software...15 Figure 4. RCW omission rate and predicted area as a function of the cumulative threshold

Mathematical and information-geometrical entropy for phenomenological Fourier and non-Fourier heat conduction

NASA Astrophysics Data System (ADS)

Li, Shu-Nan; Cao, Bing-Yang

2017-09-01

The second law of thermodynamics governs the direction of heat transport, which provides the foundational definition of thermodynamic Clausius entropy. The definitions of entropy are further generalized for the phenomenological heat transport models in the frameworks of classical irreversible thermodynamics and extended irreversible thermodynamics (EIT). In this work, entropic functions from mathematics are combined with phenomenological heat conduction models and connected to several information-geometrical conceptions. The long-time behaviors of these mathematical entropies exhibit a wide diversity and physical pictures in phenomenological heat conductions, including the tendency to thermal equilibrium, and exponential decay of nonequilibrium and asymptotics, which build a bridge between the macroscopic and microscopic modelings. In contrast with the EIT entropies, the mathematical entropies expressed in terms of the internal energy function can avoid singularity paired with nonpositive local absolute temperature caused by non-Fourier heat conduction models.

Derivation of Hunt equation for suspension distribution using Shannon entropy theory

NASA Astrophysics Data System (ADS)

Kundu, Snehasis

2017-12-01

In this study, the Hunt equation for computing suspension concentration in sediment-laden flows is derived using Shannon entropy theory. Considering the inverse of the void ratio as a random variable and using principle of maximum entropy, probability density function and cumulative distribution function of suspension concentration is derived. A new and more general cumulative distribution function for the flow domain is proposed which includes several specific other models of CDF reported in literature. This general form of cumulative distribution function also helps to derive the Rouse equation. The entropy based approach helps to estimate model parameters using suspension data of sediment concentration which shows the advantage of using entropy theory. Finally model parameters in the entropy based model are also expressed as functions of the Rouse number to establish a link between the parameters of the deterministic and probabilistic approaches.

A new assessment method for urbanization environmental impact: urban environment entropy model and its application.

PubMed

Ouyang, Tingping; Fu, Shuqing; Zhu, Zhaoyu; Kuang, Yaoqiu; Huang, Ningsheng; Wu, Zhifeng

2008-11-01

The thermodynamic law is one of the most widely used scientific principles. The comparability between the environmental impact of urbanization and the thermodynamic entropy was systematically analyzed. Consequently, the concept "Urban Environment Entropy" was brought forward and the "Urban Environment Entropy" model was established for urbanization environmental impact assessment in this study. The model was then utilized in a case study for the assessment of river water quality in the Pearl River Delta Economic Zone. The results indicated that the assessing results of the model are consistent to that of the equalized synthetic pollution index method. Therefore, it can be concluded that the Urban Environment Entropy model has high reliability and can be applied widely in urbanization environmental assessment research using many different environmental parameters.

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.

Two aspects of black hole entropy in Lanczos-Lovelock models of gravity

NASA Astrophysics Data System (ADS)

Kolekar, Sanved; Kothawala, Dawood; Padmanabhan, T.

2012-03-01

We consider two specific approaches to evaluate the black hole entropy which are known to produce correct results in the case of Einstein’s theory and generalize them to Lanczos-Lovelock models. In the first approach (which could be called extrinsic), we use a procedure motivated by earlier work by Pretorius, Vollick, and Israel, and by Oppenheim, and evaluate the entropy of a configuration of densely packed gravitating shells on the verge of forming a black hole in Lanczos-Lovelock theories of gravity. We find that this matter entropy is not equal to (it is less than) Wald entropy, except in the case of Einstein theory, where they are equal. The matter entropy is proportional to the Wald entropy if we consider a specific mth-order Lanczos-Lovelock model, with the proportionality constant depending on the spacetime dimensions D and the order m of the Lanczos-Lovelock theory as (D-2m)/(D-2). Since the proportionality constant depends on m, the proportionality between matter entropy and Wald entropy breaks down when we consider a sum of Lanczos-Lovelock actions involving different m. In the second approach (which could be called intrinsic), we generalize a procedure, previously introduced by Padmanabhan in the context of general relativity, to study off-shell entropy of a class of metrics with horizon using a path integral method. We consider the Euclidean action of Lanczos-Lovelock models for a class of metrics off shell and interpret it as a partition function. We show that in the case of spherically symmetric metrics, one can interpret the Euclidean action as the free energy and read off both the entropy and energy of a black hole spacetime. Surprisingly enough, this leads to exactly the Wald entropy and the energy of the spacetime in Lanczos-Lovelock models obtained by other methods. We comment on possible implications of the result.

Subgrid-scale Condensation Modeling for Entropy-based Large Eddy Simulations of Clouds

NASA Astrophysics Data System (ADS)

Kaul, C. M.; Schneider, T.; Pressel, K. G.; Tan, Z.

2015-12-01

An entropy- and total water-based formulation of LES thermodynamics, such as that used by the recently developed code PyCLES, is advantageous from physical and numerical perspectives. However, existing closures for subgrid-scale thermodynamic fluctuations assume more traditional choices for prognostic thermodynamic variables, such as liquid potential temperature, and are not directly applicable to entropy-based modeling. Since entropy and total water are generally nonlinearly related to diagnosed quantities like temperature and condensate amounts, neglecting their small-scale variability can lead to bias in simulation results. Here we present the development of a subgrid-scale condensation model suitable for use with entropy-based thermodynamic formulations.

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.

Modeling Information Content Via Dirichlet-Multinomial Regression Analysis.

PubMed

Ferrari, Alberto

2017-01-01

Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions. Here a method to perform inference on entropy in such conditions is proposed. Building on results coming from studies in the field of Bayesian entropy estimation, a symmetric Dirichlet-multinomial regression model, able to deal efficiently with the issue of mean entropy estimation, is formulated. Through a simulation study the model is shown to outperform linear modeling in a vast range of scenarios and to have promising statistical properties. As a practical example, the method is applied to a data set coming from a real experiment on animal communication.

Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains

NASA Astrophysics Data System (ADS)

Cofré, Rodrigo; Maldonado, Cesar

2018-01-01

We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. We review large deviations techniques useful in this context to describe properties of accuracy and convergence in terms of sampling size. We use these results to study the statistical fluctuation of correlations, distinguishability and irreversibility of maximum entropy Markov chains. We illustrate these applications using simple examples where the large deviation rate function is explicitly obtained for maximum entropy models of relevance in this field.

Fit-for-purpose: species distribution model performance depends on evaluation criteria - Dutch Hoverflies as a case study.

PubMed

Aguirre-Gutiérrez, Jesús; Carvalheiro, Luísa G; Polce, Chiara; van Loon, E Emiel; Raes, Niels; Reemer, Menno; Biesmeijer, Jacobus C

2013-01-01

Understanding species distributions and the factors limiting them is an important topic in ecology and conservation, including in nature reserve selection and predicting climate change impacts. While Species Distribution Models (SDM) are the main tool used for these purposes, choosing the best SDM algorithm is not straightforward as these are plentiful and can be applied in many different ways. SDM are used mainly to gain insight in 1) overall species distributions, 2) their past-present-future probability of occurrence and/or 3) to understand their ecological niche limits (also referred to as ecological niche modelling). The fact that these three aims may require different models and outputs is, however, rarely considered and has not been evaluated consistently. Here we use data from a systematically sampled set of species occurrences to specifically test the performance of Species Distribution Models across several commonly used algorithms. Species range in distribution patterns from rare to common and from local to widespread. We compare overall model fit (representing species distribution), the accuracy of the predictions at multiple spatial scales, and the consistency in selection of environmental correlations all across multiple modelling runs. As expected, the choice of modelling algorithm determines model outcome. However, model quality depends not only on the algorithm, but also on the measure of model fit used and the scale at which it is used. Although model fit was higher for the consensus approach and Maxent, Maxent and GAM models were more consistent in estimating local occurrence, while RF and GBM showed higher consistency in environmental variables selection. Model outcomes diverged more for narrowly distributed species than for widespread species. We suggest that matching study aims with modelling approach is essential in Species Distribution Models, and provide suggestions how to do this for different modelling aims and species' data characteristics (i.e. sample size, spatial distribution).

Entanglement entropy production in gravitational collapse: covariant regularization and solvable models

NASA Astrophysics Data System (ADS)

Bianchi, Eugenio; De Lorenzo, Tommaso; Smerlak, Matteo

2015-06-01

We study the dynamics of vacuum entanglement in the process of gravitational collapse and subsequent black hole evaporation. In the first part of the paper, we introduce a covariant regularization of entanglement entropy tailored to curved spacetimes; this regularization allows us to propose precise definitions for the concepts of black hole "exterior entropy" and "radiation entropy." For a Vaidya model of collapse we find results consistent with the standard thermodynamic properties of Hawking radiation. In the second part of the paper, we compute the vacuum entanglement entropy of various spherically-symmetric spacetimes of interest, including the nonsingular black hole model of Bardeen, Hayward, Frolov and Rovelli-Vidotto and the "black hole fireworks" model of Haggard-Rovelli. We discuss specifically the role of event and trapping horizons in connection with the behavior of the radiation entropy at future null infinity. We observe in particular that ( i) in the presence of an event horizon the radiation entropy diverges at the end of the evaporation process, ( ii) in models of nonsingular evaporation (with a trapped region but no event horizon) the generalized second law holds only at early times and is violated in the "purifying" phase, ( iii) at late times the radiation entropy can become negative (i.e. the radiation can be less correlated than the vacuum) before going back to zero leading to an up-down-up behavior for the Page curve of a unitarily evaporating black hole.

Entropy-based financial asset pricing.

PubMed

Ormos, Mihály; Zibriczky, Dávid

2014-01-01

We investigate entropy as a financial risk measure. Entropy explains the equity premium of securities and portfolios in a simpler way and, at the same time, with higher explanatory power than the beta parameter of the capital asset pricing model. For asset pricing we define the continuous entropy as an alternative measure of risk. Our results show that entropy decreases in the function of the number of securities involved in a portfolio in a similar way to the standard deviation, and that efficient portfolios are situated on a hyperbola in the expected return-entropy system. For empirical investigation we use daily returns of 150 randomly selected securities for a period of 27 years. Our regression results show that entropy has a higher explanatory power for the expected return than the capital asset pricing model beta. Furthermore we show the time varying behavior of the beta along with entropy.

Entropy-Based Financial Asset Pricing

PubMed Central

Ormos, Mihály; Zibriczky, Dávid

2014-01-01

We investigate entropy as a financial risk measure. Entropy explains the equity premium of securities and portfolios in a simpler way and, at the same time, with higher explanatory power than the beta parameter of the capital asset pricing model. For asset pricing we define the continuous entropy as an alternative measure of risk. Our results show that entropy decreases in the function of the number of securities involved in a portfolio in a similar way to the standard deviation, and that efficient portfolios are situated on a hyperbola in the expected return – entropy system. For empirical investigation we use daily returns of 150 randomly selected securities for a period of 27 years. Our regression results show that entropy has a higher explanatory power for the expected return than the capital asset pricing model beta. Furthermore we show the time varying behavior of the beta along with entropy. PMID:25545668

Entropy Generation Across Earth's Bow Shock

NASA Technical Reports Server (NTRS)

Parks, George K.; McCarthy, Michael; Fu, Suiyan; Lee E. s; Cao, Jinbin; Goldstein, Melvyn L.; Canu, Patrick; Dandouras, Iannis S.; Reme, Henri; Fazakerley, Andrew;

Fine-scale predictive mapping of Cold Water Coral species in the Cap de Creus Canyon (NW Mediterranean): first insights

NASA Astrophysics Data System (ADS)

Lo Iacono, Claudio; Gonzalez-Villanueva, Rita; Gori, Andrea; Orejas, Covadonga; Gili, Josep Maria

2013-04-01

Cold-water corals (CWC) are azooxanthellate species which develop in a complex environment ruled out by a delicate interplay between geological, biological and oceanographic conditions.High impact deep-sea bottom trawling activities are seriously compromising the health and state of conservation of CWC habitats. It has been recently discovered that submarine canyons can act as hosting areas for benthic communities dominated by CWCs. Favorable environmental conditions along the canyons coupled with the rough seafloor morphology can foster their development and facilitate their preservation from the trawling threat. The aim of this study is to statistically predict the distribution of three CWC species (Madrepora oculata, Lophelia pertusa, Dendrophyllia cornigera) in the Cap de Creus Canyon (NW Mediterranean) based on high-resolution swath-bathymetry data (pixel resolution: 5m) and video observations from the submersible JAGO (IFM-GEOMAR). Species distribution models have been constructed with a Maximum Entropy approach (MaxEnt model) using the presence data from video imagery and layers derived from multibeam bathymetry such as slope, geomorphologic category, rugosity, aspect (orientation of the pixel respect to the North) and backscatter. For the three species the predictive model performance is outstanding, with the area under the curve (AUC) from the sensitivity-specificity approach of 0.98 for M. oculata and D. cornigera and of 0.99 for L. pertusa. The most relevant variables responsible for the CWC distribution are the slope and aspect for M. oculata and L. pertusa, and rugosity and aspect for D. cornigera. According to the models, CWC species are most likely to be found on the medium to steep rough walls of the southern flank of the Cap de Creus Canyon and almost exclusively along the regions facing the North and the North-West, from where strong organic sediment-rich currents flow. Results are coherent with previous observations and quantitative studies performed in the area. Insights coming out from the application of geo-spatial statistical models could represent the basis for the development of a scientifically-based approach in the planning and management of Marine Protected Areas.

Expected Shannon Entropy and Shannon Differentiation between Subpopulations for Neutral Genes under the Finite Island Model.

PubMed

Chao, Anne; Jost, Lou; Hsieh, T C; Ma, K H; Sherwin, William B; Rollins, Lee Ann

2015-01-01

Shannon entropy H and related measures are increasingly used in molecular ecology and population genetics because (1) unlike measures based on heterozygosity or allele number, these measures weigh alleles in proportion to their population fraction, thus capturing a previously-ignored aspect of allele frequency distributions that may be important in many applications; (2) these measures connect directly to the rich predictive mathematics of information theory; (3) Shannon entropy is completely additive and has an explicitly hierarchical nature; and (4) Shannon entropy-based differentiation measures obey strong monotonicity properties that heterozygosity-based measures lack. We derive simple new expressions for the expected values of the Shannon entropy of the equilibrium allele distribution at a neutral locus in a single isolated population under two models of mutation: the infinite allele model and the stepwise mutation model. Surprisingly, this complex stochastic system for each model has an entropy expressable as a simple combination of well-known mathematical functions. Moreover, entropy- and heterozygosity-based measures for each model are linked by simple relationships that are shown by simulations to be approximately valid even far from equilibrium. We also identify a bridge between the two models of mutation. We apply our approach to subdivided populations which follow the finite island model, obtaining the Shannon entropy of the equilibrium allele distributions of the subpopulations and of the total population. We also derive the expected mutual information and normalized mutual information ("Shannon differentiation") between subpopulations at equilibrium, and identify the model parameters that determine them. We apply our measures to data from the common starling (Sturnus vulgaris) in Australia. Our measures provide a test for neutrality that is robust to violations of equilibrium assumptions, as verified on real world data from starlings.

Backward transfer entropy: Informational measure for detecting hidden Markov models and its interpretations in thermodynamics, gambling and causality

PubMed Central

Ito, Sosuke

2016-01-01

The transfer entropy is a well-established measure of information flow, which quantifies directed influence between two stochastic time series and has been shown to be useful in a variety fields of science. Here we introduce the transfer entropy of the backward time series called the backward transfer entropy, and show that the backward transfer entropy quantifies how far it is from dynamics to a hidden Markov model. Furthermore, we discuss physical interpretations of the backward transfer entropy in completely different settings of thermodynamics for information processing and the gambling with side information. In both settings of thermodynamics and the gambling, the backward transfer entropy characterizes a possible loss of some benefit, where the conventional transfer entropy characterizes a possible benefit. Our result implies the deep connection between thermodynamics and the gambling in the presence of information flow, and that the backward transfer entropy would be useful as a novel measure of information flow in nonequilibrium thermodynamics, biochemical sciences, economics and statistics. PMID:27833120

Backward transfer entropy: Informational measure for detecting hidden Markov models and its interpretations in thermodynamics, gambling and causality

NASA Astrophysics Data System (ADS)

Ito, Sosuke

2016-11-01

The transfer entropy is a well-established measure of information flow, which quantifies directed influence between two stochastic time series and has been shown to be useful in a variety fields of science. Here we introduce the transfer entropy of the backward time series called the backward transfer entropy, and show that the backward transfer entropy quantifies how far it is from dynamics to a hidden Markov model. Furthermore, we discuss physical interpretations of the backward transfer entropy in completely different settings of thermodynamics for information processing and the gambling with side information. In both settings of thermodynamics and the gambling, the backward transfer entropy characterizes a possible loss of some benefit, where the conventional transfer entropy characterizes a possible benefit. Our result implies the deep connection between thermodynamics and the gambling in the presence of information flow, and that the backward transfer entropy would be useful as a novel measure of information flow in nonequilibrium thermodynamics, biochemical sciences, economics and statistics.

Global sensitivity analysis for fuzzy inputs based on the decomposition of fuzzy output entropy

NASA Astrophysics Data System (ADS)

Shi, Yan; Lu, Zhenzhou; Zhou, Yicheng

2018-06-01

To analyse the component of fuzzy output entropy, a decomposition method of fuzzy output entropy is first presented. After the decomposition of fuzzy output entropy, the total fuzzy output entropy can be expressed as the sum of the component fuzzy entropy contributed by fuzzy inputs. Based on the decomposition of fuzzy output entropy, a new global sensitivity analysis model is established for measuring the effects of uncertainties of fuzzy inputs on the output. The global sensitivity analysis model can not only tell the importance of fuzzy inputs but also simultaneously reflect the structural composition of the response function to a certain degree. Several examples illustrate the validity of the proposed global sensitivity analysis, which is a significant reference in engineering design and optimization of structural systems.

Aedes albopictus and Aedes japonicus - two invasive mosquito species with different temperature niches in Europe.

PubMed

Cunze, Sarah; Koch, Lisa K; Kochmann, Judith; Klimpel, Sven

2016-11-04

Aedes albopictus and Ae. japonicus are two of the most widespread invasive mosquito species that have recently become established in western Europe. Both species are associated with the transmission of a number of serious diseases and are projected to continue their spread in Europe. In the present study, we modelled the habitat suitability for both species under current and future climatic conditions by means of an Ensemble forecasting approach. We additionally compared the modelled MAXENT niches of Ae. albopictus and Ae. japonicus regarding temperature and precipitation requirements. Both species were modelled to find suitable habitat conditions in distinct areas within Europe: Ae. albopictus within the Mediterranean regions in southern Europe, Ae. japonicus within the more temperate regions of central Europe. Only in few regions, suitable habitat conditions were projected to overlap for both species. Whereas Ae. albopictus is projected to be generally promoted by climate change in Europe, the area modelled to be climatically suitable for Ae. japonicus is projected to decrease under climate change. This projection of range reduction under climate change relies on the assumption that Ae. japonicus is not able to adapt to warmer climatic conditions. The modelled MAXENT temperature niches of Ae. japonicus were found to be narrower with an optimum at lower temperatures compared to the niches of Ae. albopictus. Species distribution models identifying areas with high habitat suitability can help improving monitoring programmes for invasive species currently in place. However, as mosquito species are known to be able to adapt to new environmental conditions within the invasion range quickly, niche evolution of invasive mosquito species should be closely followed upon in future studies.

Random versus maximum entropy models of neural population activity

NASA Astrophysics Data System (ADS)

Ferrari, Ulisse; Obuchi, Tomoyuki; Mora, Thierry

2017-04-01

The principle of maximum entropy provides a useful method for inferring statistical mechanics models from observations in correlated systems, and is widely used in a variety of fields where accurate data are available. While the assumptions underlying maximum entropy are intuitive and appealing, its adequacy for describing complex empirical data has been little studied in comparison to alternative approaches. Here, data from the collective spiking activity of retinal neurons is reanalyzed. The accuracy of the maximum entropy distribution constrained by mean firing rates and pairwise correlations is compared to a random ensemble of distributions constrained by the same observables. For most of the tested networks, maximum entropy approximates the true distribution better than the typical or mean distribution from that ensemble. This advantage improves with population size, with groups as small as eight being almost always better described by maximum entropy. Failure of maximum entropy to outperform random models is found to be associated with strong correlations in the population.

Generalized entropy formalism and a new holographic dark energy model

NASA Astrophysics Data System (ADS)

Sayahian Jahromi, A.; Moosavi, S. A.; Moradpour, H.; Morais Graça, J. P.; Lobo, I. P.; Salako, I. G.; Jawad, A.

2018-05-01

Recently, the Rényi and Tsallis generalized entropies have extensively been used in order to study various cosmological and gravitational setups. Here, using a special type of generalized entropy, a generalization of both the Rényi and Tsallis entropy, together with holographic principle, we build a new model for holographic dark energy. Thereinafter, considering a flat FRW universe, filled by a pressureless component and the new obtained dark energy model, the evolution of cosmos has been investigated showing satisfactory results and behavior. In our model, the Hubble horizon plays the role of IR cutoff, and there is no mutual interaction between the cosmos components. Our results indicate that the generalized entropy formalism may open a new window to become more familiar with the nature of spacetime and its properties.

Spatio-Temporal Distribution of Vector-Host Contact (VHC) Ratios and Ecological Niche Modeling of the West Nile Virus Mosquito Vector, Culex quinquefasciatus, in the City of New Orleans, LA, USA

PubMed Central

Michaels, Sarah R.; Riegel, Claudia; Pereira, Roberto M.; Zipperer, Wayne; Lockaby, B. Graeme; Koehler, Philip G.

2017-01-01

The consistent sporadic transmission of West Nile Virus (WNV) in the city of New Orleans justifies the need for distribution risk maps highlighting human risk of mosquito bites. We modeled the influence of biophysical and socioeconomic metrics on the spatio-temporal distributions of presence/vector-host contact (VHC) ratios of WNV vector, Culex quinquefasciatus, within their flight range. Biophysical and socioeconomic data were extracted within 5-km buffer radii around sampling localities of gravid female Culex quinquefasciatus. The spatio-temporal correlations between VHC data and 33 variables, including climate, land use-land cover (LULC), socioeconomic, and land surface terrain were analyzed using stepwise linear regression models (RM). Using MaxEnt, we developed a distribution model using the correlated predicting variables. Only 12 factors showed significant correlations with spatial distribution of VHC ratios (R2 = 81.62, p < 0.01). Non-forested wetland (NFWL), tree density (TD) and residential-urban (RU) settings demonstrated the strongest relationship. The VHC ratios showed monthly environmental resilience in terms of number and type of influential factors. The highest prediction power of RU and other urban and built up land (OUBL), was demonstrated during May–August. This association was positively correlated with the onset of the mosquito WNV infection rate during June. These findings were confirmed by the Jackknife analysis in MaxEnt and independently collected field validation points. The spatial and temporal correlations of VHC ratios and their response to the predicting variables are discussed. PMID:28786934

Spatio-Temporal Distribution of Vector-Host Contact (VHC) Ratios and Ecological Niche Modeling of the West Nile Virus Mosquito Vector, Culex quinquefasciatus, in the City of New Orleans, LA, USA.

PubMed

Sallam, Mohamed F; Michaels, Sarah R; Riegel, Claudia; Pereira, Roberto M; Zipperer, Wayne; Lockaby, B Graeme; Koehler, Philip G

2017-08-08

The consistent sporadic transmission of West Nile Virus (WNV) in the city of New Orleans justifies the need for distribution risk maps highlighting human risk of mosquito bites. We modeled the influence of biophysical and socioeconomic metrics on the spatio-temporal distributions of presence/vector-host contact (VHC) ratios of WNV vector, Culex quinquefasciatus , within their flight range . Biophysical and socioeconomic data were extracted within 5-km buffer radii around sampling localities of gravid female Culex quinquefasciatus . The spatio-temporal correlations between VHC data and 33 variables, including climate, land use-land cover (LULC), socioeconomic, and land surface terrain were analyzed using stepwise linear regression models (RM). Using MaxEnt, we developed a distribution model using the correlated predicting variables. Only 12 factors showed significant correlations with spatial distribution of VHC ratios ( R ² = 81.62, p < 0.01). Non-forested wetland (NFWL), tree density (TD) and residential-urban (RU) settings demonstrated the strongest relationship. The VHC ratios showed monthly environmental resilience in terms of number and type of influential factors. The highest prediction power of RU and other urban and built up land (OUBL), was demonstrated during May-August. This association was positively correlated with the onset of the mosquito WNV infection rate during June. These findings were confirmed by the Jackknife analysis in MaxEnt and independently collected field validation points. The spatial and temporal correlations of VHC ratios and their response to the predicting variables are discussed.

Scaling of the entropy budget with surface temperature in radiative-convective equilibrium

NASA Astrophysics Data System (ADS)

Singh, Martin S.; O'Gorman, Paul A.

2016-09-01

The entropy budget of the atmosphere is examined in simulations of radiative-convective equilibrium with a cloud-system resolving model over a wide range of surface temperatures from 281 to 311 K. Irreversible phase changes and the diffusion of water vapor account for more than half of the irreversible entropy production within the atmosphere, even in the coldest simulation. As the surface temperature is increased, the atmospheric radiative cooling rate increases, driving a greater entropy sink that must be matched by greater irreversible entropy production. The entropy production resulting from irreversible moist processes increases at a similar fractional rate as the entropy sink and at a lower rate than that implied by Clausius-Clapeyron scaling. This allows the entropy production from frictional drag on hydrometeors and on the atmospheric flow to also increase with warming, in contrast to recent results for simulations with global climate models in which the work output decreases with warming. A set of approximate scaling relations is introduced for the terms in the entropy budget as the surface temperature is varied, and many of the terms are found to scale with the mean surface precipitation rate. The entropy budget provides some insight into changes in frictional dissipation in response to warming or changes in model resolution, but it is argued that frictional dissipation is not closely linked to other measures of convective vigor.

Dynamical entropy via entropy of non-random matrices: application to stability and complexity in modelling ecosystems.

PubMed

Chakrabarti, C G; Ghosh, Koyel

2013-10-01

In the present paper we have first introduced a measure of dynamical entropy of an ecosystem on the basis of the dynamical model of the system. The dynamical entropy which depends on the eigenvalues of the community matrix of the system leads to a consistent measure of complexity of the ecosystem to characterize the dynamical behaviours such as the stability, instability and periodicity around the stationary states of the system. We have illustrated the theory with some model ecosystems. Copyright © 2013 Elsevier Inc. All rights reserved.

Entropy Based Genetic Association Tests and Gene-Gene Interaction Tests

PubMed Central

de Andrade, Mariza; Wang, Xin

2011-01-01

In the past few years, several entropy-based tests have been proposed for testing either single SNP association or gene-gene interaction. These tests are mainly based on Shannon entropy and have higher statistical power when compared to standard χ2 tests. In this paper, we extend some of these tests using a more generalized entropy definition, Rényi entropy, where Shannon entropy is a special case of order 1. The order λ (>0) of Rényi entropy weights the events (genotype/haplotype) according to their probabilities (frequencies). Higher λ places more emphasis on higher probability events while smaller λ (close to 0) tends to assign weights more equally. Thus, by properly choosing the λ, one can potentially increase the power of the tests or the p-value level of significance. We conducted simulation as well as real data analyses to assess the impact of the order λ and the performance of these generalized tests. The results showed that for dominant model the order 2 test was more powerful and for multiplicative model the order 1 or 2 had similar power. The analyses indicate that the choice of λ depends on the underlying genetic model and Shannon entropy is not necessarily the most powerful entropy measure for constructing genetic association or interaction tests. PMID:23089811

New classes of Lorenz curves by maximizing Tsallis entropy under mean and Gini equality and inequality constraints

NASA Astrophysics Data System (ADS)

Preda, Vasile; Dedu, Silvia; Gheorghe, Carmen

2015-10-01

In this paper, by using the entropy maximization principle with Tsallis entropy, new distribution families for modeling the income distribution are derived. Also, new classes of Lorenz curves are obtained by applying the entropy maximization principle with Tsallis entropy, under mean and Gini index equality and inequality constraints.

Entanglement entropy and entanglement spectrum of the Kitaev model.

PubMed

Yao, Hong; Qi, Xiao-Liang

2010-08-20

In this letter, we obtain an exact formula for the entanglement entropy of the ground state and all excited states of the Kitaev model. Remarkably, the entanglement entropy can be expressed in a simple separable form S = SG+SF, with SF the entanglement entropy of a free Majorana fermion system and SG that of a Z2 gauge field. The Z2 gauge field part contributes to the universal "topological entanglement entropy" of the ground state while the fermion part is responsible for the nonlocal entanglement carried by the Z2 vortices (visons) in the non-Abelian phase. Our result also enables the calculation of the entire entanglement spectrum and the more general Renyi entropy of the Kitaev model. Based on our results we propose a new quantity to characterize topologically ordered states--the capacity of entanglement, which can distinguish the st ates with and without topologically protected gapless entanglement spectrum.

Two-phase thermodynamic model for computing entropies of liquids reanalyzed

NASA Astrophysics Data System (ADS)

Sun, Tao; Xian, Jiawei; Zhang, Huai; Zhang, Zhigang; Zhang, Yigang

2017-11-01

The two-phase thermodynamic (2PT) model [S.-T. Lin et al., J. Chem. Phys. 119, 11792-11805 (2003)] provides a promising paradigm to efficiently determine the ionic entropies of liquids from molecular dynamics. In this model, the vibrational density of states (VDoS) of a liquid is decomposed into a diffusive gas-like component and a vibrational solid-like component. By treating the diffusive component as hard sphere (HS) gas and the vibrational component as harmonic oscillators, the ionic entropy of the liquid is determined. Here we examine three issues crucial for practical implementations of the 2PT model: (i) the mismatch between the VDoS of the liquid system and that of the HS gas; (ii) the excess entropy of the HS gas; (iii) the partition of the gas-like and solid-like components. Some of these issues have not been addressed before, yet they profoundly change the entropy predicted from the model. Based on these findings, a revised 2PT formalism is proposed and successfully tested in systems with Lennard-Jones potentials as well as many-atom potentials of liquid metals. Aside from being capable of performing quick entropy estimations for a wide range of systems, the formalism also supports fine-tuning to accurately determine entropies at specific thermal states.

Finite entanglement entropy and spectral dimension in quantum gravity

NASA Astrophysics Data System (ADS)

Arzano, Michele; Calcagni, Gianluca

2017-12-01

What are the conditions on a field theoretic model leading to a finite entanglement entropy density? We prove two very general results: (1) Ultraviolet finiteness of a theory does not guarantee finiteness of the entropy density; (2) If the spectral dimension of the spatial boundary across which the entropy is calculated is non-negative at all scales, then the entanglement entropy cannot be finite. These conclusions, which we verify in several examples, negatively affect all quantum-gravity models, since their spectral dimension is always positive. Possible ways out are considered, including abandoning the definition of the entanglement entropy in terms of the boundary return probability or admitting an analytic continuation (not a regularization) of the usual definition. In the second case, one can get a finite entanglement entropy density in multi-fractional theories and causal dynamical triangulations.

Weighted fractional permutation entropy and fractional sample entropy for nonlinear Potts financial dynamics

NASA Astrophysics Data System (ADS)

Xu, Kaixuan; Wang, Jun

2017-02-01

In this paper, recently introduced permutation entropy and sample entropy are further developed to the fractional cases, weighted fractional permutation entropy (WFPE) and fractional sample entropy (FSE). The fractional order generalization of information entropy is utilized in the above two complexity approaches, to detect the statistical characteristics of fractional order information in complex systems. The effectiveness analysis of proposed methods on the synthetic data and the real-world data reveals that tuning the fractional order allows a high sensitivity and more accurate characterization to the signal evolution, which is useful in describing the dynamics of complex systems. Moreover, the numerical research on nonlinear complexity behaviors is compared between the returns series of Potts financial model and the actual stock markets. And the empirical results confirm the feasibility of the proposed model.

Universal Features of Left-Right Entanglement Entropy.

PubMed

Das, Diptarka; Datta, Shouvik

2015-09-25

We show the presence of universal features in the entanglement entropy of regularized boundary states for (1+1)D conformal field theories on a circle when the reduced density matrix is obtained by tracing over right- or left-moving modes. We derive a general formula for the left-right entanglement entropy in terms of the central charge and the modular S matrix of the theory. When the state is chosen to be an Ishibashi state, this measure of entanglement is shown to precisely reproduce the spatial entanglement entropy of a (2+1)D topological quantum field theory. We explicitly evaluate the left-right entanglement entropies for the Ising model, the tricritical Ising model and the su[over ^](2)_{k} Wess-Zumino-Witten model as examples.

The Primordial Entropy of Jupiter

NASA Astrophysics Data System (ADS)

Cumming, Andrew; Helled, Ravit; Venturini, Julia

2018-04-01

The formation history of giant planets determines their primordial structure and consequent evolution. We simulate various formation paths of Jupiter to determine its primordial entropy, and find that a common outcome is for proto-Jupiter to have non-convective regions in its interior. We use planet formation models to calculate how the entropy and post-formation luminosity depend on model properties such as the solid accretion rate and opacity, and show that the gas accretion rate and its time evolution play a key role in determining the entropy profile. The predicted luminosity of Jupiter shortly after formation varies by a factor of 2-3 for different choices of model parameters. We find that entropy gradients inside Jupiter persist for ˜10 Myr after formation. We suggest that these gradients should be considered together with heavy-element composition gradients when modeling Jupiter's evolution and internal structure.

The primordial entropy of Jupiter

NASA Astrophysics Data System (ADS)

Cumming, Andrew; Helled, Ravit; Venturini, Julia

2018-07-01

The formation history of giant planets determines their primordial structure and consequent evolution. We simulate various formation paths of Jupiter to determine its primordial entropy, and find that a common outcome is for proto-Jupiter to have non-convective regions in its interior. We use planet formation models to calculate how the entropy and post-formation luminosity depend on model properties such as the solid accretion rate and opacity, and show that the gas accretion rate and its time evolution play a key role in determining the entropy profile. The predicted luminosity of Jupiter shortly after formation varies by a factor of 2-3 for different choices of model parameters. We find that entropy gradients inside Jupiter persist for ˜10 Myr after formation. We suggest that these gradients should be considered together with heavy-element composition gradients when modelling Jupiter's evolution and internal structure.

Wavelet entropy of BOLD time series: An application to Rolandic epilepsy.

PubMed

Gupta, Lalit; Jansen, Jacobus F A; Hofman, Paul A M; Besseling, René M H; de Louw, Anton J A; Aldenkamp, Albert P; Backes, Walter H

2017-12-01

To assess the wavelet entropy for the characterization of intrinsic aberrant temporal irregularities in the time series of resting-state blood-oxygen-level-dependent (BOLD) signal fluctuations. Further, to evaluate the temporal irregularities (disorder/order) on a voxel-by-voxel basis in the brains of children with Rolandic epilepsy. The BOLD time series was decomposed using the discrete wavelet transform and the wavelet entropy was calculated. Using a model time series consisting of multiple harmonics and nonstationary components, the wavelet entropy was compared with Shannon and spectral (Fourier-based) entropy. As an application, the wavelet entropy in 22 children with Rolandic epilepsy was compared to 22 age-matched healthy controls. The images were obtained by performing resting-state functional magnetic resonance imaging (fMRI) using a 3T system, an 8-element receive-only head coil, and an echo planar imaging pulse sequence ( T2*-weighted). The wavelet entropy was also compared to spectral entropy, regional homogeneity, and Shannon entropy. Wavelet entropy was found to identify the nonstationary components of the model time series. In Rolandic epilepsy patients, a significantly elevated wavelet entropy was observed relative to controls for the whole cerebrum (P = 0.03). Spectral entropy (P = 0.41), regional homogeneity (P = 0.52), and Shannon entropy (P = 0.32) did not reveal significant differences. The wavelet entropy measure appeared more sensitive to detect abnormalities in cerebral fluctuations represented by nonstationary effects in the BOLD time series than more conventional measures. This effect was observed in the model time series as well as in Rolandic epilepsy. These observations suggest that the brains of children with Rolandic epilepsy exhibit stronger nonstationary temporal signal fluctuations than controls. 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1728-1737. © 2017 International Society for Magnetic Resonance in Medicine.

Conservation Priorities in a Biodiversity Hotspot: Analysis of Narrow Endemic Plant Species in New Caledonia

PubMed Central

Wulff, Adrien S.; Hollingsworth, Peter M.; Ahrends, Antje; Jaffré, Tanguy; Veillon, Jean-Marie; L’Huillier, Laurent; Fogliani, Bruno

2013-01-01

New Caledonia is a global biodiversity hotspot facing extreme environmental degradation. Given the urgent need for conservation prioritisation, we have made a first-pass quantitative assessment of the distribution of Narrow Endemic Species (NES) in the flora to identify species and sites that are potentially important for conservation action. We assessed the distributional status of all angiosperm and gymnosperm species using data from taxonomic descriptions and herbarium samples. We characterised species as being NES if they occurred in 3 or fewer locations. In total, 635 of the 2930 assessed species were classed as NES, of which only 150 have been subjected to the IUCN conservation assessment. As the distributional patterns of un-assessed species from one or two locations correspond well with assessed species which have been classified as Critically Endangered or Endangered respectively, we suggest that our distributional data can be used to prioritise species for IUCN assessment. We also used the distributional data to produce a map of “Hotspots of Plant Narrow Endemism” (HPNE). Combined, we used these data to evaluate the coincidence of NES with mining activities (a major source of threat on New Caledonia) and also areas of conservation protection. This is to identify species and locations in most urgent need of further conservation assessment and subsequent action. Finally, we grouped the NES based on the environments they occurred in and modelled the habitat distribution of these groups with a Maximum Entropy Species Distribution Model (MaxEnt). The NES were separable into three different groups based primarily on geological differences. The distribution of the habitat types for each group coincide partially with the HPNE described above and also indicates some areas which have high habitat suitability but few recorded NES. Some of these areas may represent under-sampled hotspots of narrow endemism and are priorities for further field work. PMID:24058470

Human-climate interaction during the Early Upper Paleolithic: testing the hypothesis of an adaptive shift between the Proto-Aurignacian and the Early Aurignacian.

PubMed

Banks, William E; d'Errico, Francesco; Zilhão, João

2013-01-01

The Aurignacian technocomplex comprises a succession of culturally distinct phases. Between its first two subdivisions, the Proto-Aurignacian and the Early Aurignacian, we see a shift from single to separate reduction sequences for blade and bladelet production, the appearance of split-based antler points, and a number of other changes in stone tool typology and technology as well as in symbolic material culture. Bayesian modeling of available (14)C determinations, conducted within the framework of this study, indicates that these material culture changes are coincident with abrupt and marked climatic changes. The Proto-Aurignacian occurs during an interval (ca. 41.5-39.9 k cal BP) of relative climatic amelioration, Greenland Interstadials (GI) 10 and 9, punctuated by a short cold stadial. The Early Aurignacian (ca. 39.8-37.9 k cal BP) predominantly falls within the climatic phase known as Heinrich Stadial (HS) 4, and its end overlaps with the beginning of GI 8, the former being predominantly characterized by cold and dry conditions across the European continent. We use eco-cultural niche modeling to quantitatively evaluate whether these shifts in material culture are correlated with environmental variability and, if so, whether the ecological niches exploited by human populations shifted accordingly. We employ genetic algorithm (GARP) and maximum entropy (Maxent) techniques to estimate the ecological niches exploited by humans (i.e., eco-cultural niches) during these two phases of the Aurignacian. Partial receiver operating characteristic analyses are used to evaluate niche variability between the two phases. Results indicate that the changes in material culture between the Proto-Aurignacian and the Early Aurignacian are associated with an expansion of the ecological niche. These shifts in both the eco-cultural niche and material culture are interpreted to represent an adaptive response to the relative deterioration of environmental conditions at the onset of HS4. Copyright © 2012 Elsevier Ltd. All rights reserved.

Using Streamflow and Stream Temperature to Assess the Potential Responses of Freshwater Fish to Climate Change

NASA Astrophysics Data System (ADS)

VanCompernolle, M.; Ficklin, D. L.; Knouft, J.

2017-12-01

Streamflow and stream temperature are key variables influencing growth, reproduction, and mortality of freshwater fish. Climate-induced changes in these variables are expected to alter the structure and function of aquatic ecosystems. Using Maxent, a species distribution model (SDM) based on the principal of maximum entropy, we predicted potential distributional responses of 100 fish species in the Mobile River Basin (MRB) to changes in climate based on contemporary and future streamflow and stream temperature estimates. Geologic, topographic, and landcover data were also included in each SDM to determine the contribution of these physical variables in defining areas of suitable habitat for each species. Using an ensemble of Global Climate Model (GCM) projections under a high emissions scenario, predicted distributions for each species across the MRB were produced for both a historical time period, 1975-1994, and a future time period, 2060-2079, and changes in total area and the percent change in historical suitable habitat for each species were calculated. Results indicate that flow (28%), temperature (29%), and geology (29%), on average, contribute evenly to determining areas of suitable habitat for fish species in the MRB, with landcover and slope playing more limited roles. Temperature contributed slightly more predictive ability to SDMs (31%) for the 77 species experiencing overall declines in areas of suitable habitat, but only 21% for the 23 species gaining habitat across all GCMs. Species are expected to lose between 15-24% of their historical suitable habitat, with threatened and endangered species losing 22-30% and those endemic to the MRB losing 19-28%. Sculpins (Cottidae) are expected to lose the largest amount of historical habitat (up to 84%), while pygmy sunfish (Elassomatidae) are expected to lose less than 1% of historical habitat. Understanding which species may be at risk of habitat loss under future projections of climate change can help fisheries managers better prepare for potential alterations in species composition not only within the MRB, but other watersheds throughout the world.

Evaluating Current and Future Rangeland Health in the Great Basin Ecoregion Using NASA Earth Observing Systems

NASA Astrophysics Data System (ADS)

Essoudry, E.; Wilson, K.; Ely, J.; Patadia, N.; Zajic, B.; Torres-Perez, J. L.; Schmidt, C.

2014-12-01

The Great Basin ecoregion in the western United States represents one of the last large expanses of wild lands in the nation and is currently facing significant challenges due to human impacts, drought, invasive species encroachment such as cheatgrass, and climate change. Rangelands in the Great Basin are of important ecological and economic significance for the United States; however, 40% of public rangelands fail to meet required health standards set by the Bureau of Land Management (BLM). This project provided a set of assessment tools for researchers and land managers that integrate remotely-sensed and in situ datasets to quantify and mitigate threats to public lands in the Great Basin ecoregion. The study area, which accounts for 20% of the total Great Basin ecoregion, was analyzed using 30 m resolution data from Landsat 8. Present conditions were evaluated from vegetation indices, landscape features, hydrological processes, and atmospheric conditions derived from the remotely-sensed data and validated with available in situ ground survey data, provided by the BLM. Rangeland health metrics were developed and landscape change drivers were identified. Subsequently, projected climate conditions derived from the Coupled Model Intercomparison Project (CMIP5) were used to forecast the impact of changing climatic conditions within the study area according to the RCP4.5 and RCP8.5 projections. These forecasted conditions were used in the Maximum Entropy Model (MaxEnt) to predict areas at risk for rangeland degradation on 30 year intervals for 2040, 2070, and 2100. Finally, vegetation health risk maps were provided to the project partners to aid in future land management decisions in the Great Basin ecoregion. These tools provide a low cost solution to assess landscape conditions, provide partners with a metric to identify potential problematic areas, and mitigate serious threats to the ecosystems.

Estimating the melting point, entropy of fusion, and enthalpy of fusion of organic compounds via SPARC

EPA Science Inventory

The entropies of fusion, enthalies of fusion, and melting points of organic compounds can be estimated through three models developed using the SPARC (SPARC Performs Automated Reasoning in Chemistry) platform. The entropy of fusion is modeled through a combination of interaction ...

Maximum Tsallis entropy with generalized Gini and Gini mean difference indices constraints

NASA Astrophysics Data System (ADS)

Khosravi Tanak, A.; Mohtashami Borzadaran, G. R.; Ahmadi, J.

2017-04-01

Using the maximum entropy principle with Tsallis entropy, some distribution families for modeling income distribution are obtained. By considering income inequality measures, maximum Tsallis entropy distributions under the constraint on generalized Gini and Gini mean difference indices are derived. It is shown that the Tsallis entropy maximizers with the considered constraints belong to generalized Pareto family.

Modelling the spreading rate of controlled communicable epidemics through an entropy-based thermodynamic model

NASA Astrophysics Data System (ADS)

Wang, WenBin; Wu, ZiNiu; Wang, ChunFeng; Hu, RuiFeng

2013-11-01

A model based on a thermodynamic approach is proposed for predicting the dynamics of communicable epidemics assumed to be governed by controlling efforts of multiple scales so that an entropy is associated with the system. All the epidemic details are factored into a single and time-dependent coefficient, the functional form of this coefficient is found through four constraints, including notably the existence of an inflexion point and a maximum. The model is solved to give a log-normal distribution for the spread rate, for which a Shannon entropy can be defined. The only parameter, that characterizes the width of the distribution function, is uniquely determined through maximizing the rate of entropy production. This entropy-based thermodynamic (EBT) model predicts the number of hospitalized cases with a reasonable accuracy for SARS in the year 2003. This EBT model can be of use for potential epidemics such as avian influenza and H7N9 in China.

Maximum entropy modeling risk of anthrax in the Republic of Kazakhstan.

PubMed

Abdrakhmanov, S K; Mukhanbetkaliyev, Y Y; Korennoy, F I; Sultanov, A A; Kadyrov, A S; Kushubaev, D B; Bakishev, T G

2017-09-01

The objective of this study was to zone the territory of the Republic of Kazakhstan (RK) into risk categories according to the probability of anthrax emergence in farm animals as stipulated by the re-activation of preserved natural foci. We used historical data on anthrax morbidity in farm animals during the period 1933 - 2014, collected by the veterinary service of the RK. The database covers the entire territory of the RK and contains 4058 anthrax outbreaks tied to 1798 unique locations. Considering the strongly pronounced natural focality of anthrax, we employed environmental niche modeling (Maxent) to reveal patterns in the outbreaks' linkages to specific combinations of environmental factors. The set of bioclimatic factors BIOCLIM, derived from remote sensing data, the altitude above sea level, the land cover type, the maximum green vegetation fraction (MGVF) and the soil type were examined as explanatory variables. The model demonstrated good predictive ability, while the MGVF, the bioclimatic variables reflecting precipitation level and humidity, and the soil type were found to contribute most significantly to the model. A continuous probability surface was obtained that reflects the suitability of the study area for the emergence of anthrax outbreaks. The surface was turned into a categorical risk map by averaging the probabilities within the administrative divisions at the 2nd level and putting them into four categories of risk, namely: low, medium, high and very high risk zones, where very high risk refers to more than 50% suitability to the disease re-emergence and low risk refers to less than 10% suitability. The map indicated increased risk of anthrax re-emergence in the districts along the northern, eastern and south-eastern borders of the country. It was recommended that the national veterinary service uses the risk map for the development of contra-epizootic measures aimed at the prevention of anthrax re-emergence in historically affected regions of the RK. The map can also be considered when developing large-scale construction projects in the areas comprising preserved soil foci of anthrax. Copyright © 2017 Elsevier B.V. All rights reserved.

Species Distribution Modeling between Abies koreana and Abies nephrolepis According to the RCP Scenarios in South Korea

NASA Astrophysics Data System (ADS)

Lee, S. G.; Kim, I. S.; Lee, W. K.; Kwon, H. J.; Byeon, J. G.; Yun, J. E.

2016-12-01

Vulnerable plant that includes species in crisis of extinction is shown by environment, competition between various species. The climate is one of the main factor that affect to the plant distribution. The most essential particular to make species distribution model is distribution data, and secondly environmental factors. 179 taxon plant classified according to the distribution, it consist of characteristic and regional distribution criteria. In case of climate data, 1960-1990 period made by World Clim Data is applied which has 0.86㎢ spatial resolution. It separates temperature and precipitation factor. To predict potential distribution, Maxent(Maximum Entropy Model) is applied that is widely known as suitable model in case of presence distributional data only. Among the target species, Abies koreana and Abies nephrolepis have no clearly key to identify, so their differences of distribution and environmental fator information could act useful. In order to know the distinction according to the classifying species, Abies koreana and Abies nephrolepis are typically selected. Abies koreana distributes at high mountain in Southern part of Korean Peninsula, otherwise Abies nephrolepis is at high mountain in from Middle latitude(over the 37°) in South Korea. These species has been the center of controversy recently, because the classification key of these species is not scientifically clear yet. In this perspective these species predicted potential distribution depend on whether these are same species or not. In the result of considering these species are same, entire predicted distribution area is wider, especially Jiri-san mountain(latitude : 35°) which is the highest latitude of the Abies koreana distributed point. On the other side, result of considering different species is shown that Abies koreana could climatically survive near by Soerak-san mountain(latitude : 37°), but Abies nephrolepis could not live in Halla-san mountan(33°) in Jeju-island which is the most southern island in Korean Peniesula. These results could supply a different view of the unclearly classified species related to the environmental factor, besides it has significantly meaning to consider both habitat environment and horizontal distribution from the past to the future.

Is substrate composition a suitable predictor for deep-water coral occurrence on fine scales?

NASA Astrophysics Data System (ADS)

Bennecke, Swaantje; Metaxas, Anna

2017-06-01

Species distribution modelling can be applied to identify potentially suitable habitat for species with largely unknown distributions, such as many deep-water corals. Important variables influencing species occurrence in the deep sea, e.g. substrate composition, are often not included in these modelling approaches because high-resolution data are unavailable. We investigated the relationship between substrate composition and the occurrence of the two deep-water octocoral species Primnoa resedaeformis and Paragorgia arborea, which require hard substrate for attachment. On a scale of 10s of metres, we analysed images of the seafloor taken at two locations inside the Northeast Channel Coral Conservation Area in the Northwest Atlantic. We interpolated substrate composition over the sampling areas and determined the contribution of substrate classes, depth and slope to describe habitat suitability using maximum entropy modelling (Maxent). Substrate composition was similar at both sites - dominated by pebbles in a matrix of sand (>80%) with low percentages of suitable substrate for coral occurrence. Coral abundance was low at site 1 (0.9 colonies of P. resedaeformis per 100 m2) and high at site 2 (63 colonies of P. resedaeformis per 100 m2) indicating that substrate alone is not sufficient to explain varying patterns in coral occurrence. Spatial interpolations of substrate classes revealed the difficulty to accurately resolve sparsely distributed boulders (3-5% of substrate). Boulders were by far the most important variable in the habitat suitability model (HSM) for P. resedaeformis at site 1, indicating the fundamental influence of a substrate class that is the least abundant. At site 2, HSMs identified cobbles and sand/pebble as the most important variables for habitat suitability. However, substrate classes were correlated making it difficult to determine the influence of individual variables. To provide accurate information on habitat suitability for the two coral species, substrate composition needs to be quantified so that small fractions (<20% contribution of certain substrate class) of suitable substrate are resolved. While the collection and analysis of high-resolution data is costly and spatially limited, the required resolution is unlikely to be achieved in coarse-scale interpolations of substrate data.

Effects of global changes on the climatic niche of the tick Ixodes ricinus inferred by species distribution modelling.

PubMed

Porretta, Daniele; Mastrantonio, Valentina; Amendolia, Sara; Gaiarsa, Stefano; Epis, Sara; Genchi, Claudio; Bandi, Claudio; Otranto, Domenico; Urbanelli, Sandra

2013-09-19

Global climate change can seriously impact on the epidemiological dynamics of vector-borne diseases. In this study we investigated how future climatic changes could affect the climatic niche of Ixodes ricinus (Acari, Ixodida), among the most important vectors of pathogens of medical and veterinary concern in Europe. Species Distribution Modelling (SDM) was used to reconstruct the climatic niche of I. ricinus, and to project it into the future conditions for 2050 and 2080, under two scenarios: a continuous human demographic growth and a severe increase of gas emissions (scenario A2), and a scenario that proposes lower human demographic growth than A2, and a more sustainable gas emissions (scenario B2). Models were reconstructed using the algorithm of "maximum entropy", as implemented in the software Maxent 3.3.3e; 4,544 occurrence points and 15 bioclimatic variables were used. In both scenarios an increase of climatic niche of about two times greater than the current area was predicted as well as a higher climatic suitability under the scenario B2 than A2. Such an increase occurred both in a latitudinal and longitudinal way, including northern Eurasian regions (e.g. Sweden and Russia), that were previously unsuitable for the species. Our models are congruent with the predictions of range expansion already observed in I. ricinus at a regional scale and provide a qualitative and quantitative assessment of the future climatically suitable areas for I. ricinus at a continental scale. Although the use of SDM at a higher resolution should be integrated by a more refined analysis of further abiotic and biotic data, the results presented here suggest that under future climatic scenarios most of the current distribution area of I. ricinus could remain suitable and significantly increase at a continental geographic scale. Therefore disease outbreaks of pathogens transmitted by this tick species could emerge in previous non-endemic geographic areas. Further studies will implement and refine present data toward a better understanding of the risk represented by I. ricinus to human health.

Topological entanglement entropy of fracton stabilizer codes

NASA Astrophysics Data System (ADS)

Ma, Han; Schmitz, A. T.; Parameswaran, S. A.; Hermele, Michael; Nandkishore, Rahul M.

2018-03-01

Entanglement entropy provides a powerful characterization of two-dimensional gapped topological phases of quantum matter, intimately tied to their description by topological quantum field theories (TQFTs). Fracton topological orders are three-dimensional gapped topologically ordered states of matter that lack a TQFT description. We show that three-dimensional fracton phases are nevertheless characterized, at least partially, by universal structure in the entanglement entropy of their ground-state wave functions. We explicitly compute the entanglement entropy for two archetypal fracton models, the "X-cube model" and "Haah's code," and demonstrate the existence of a nonlocal contribution that scales linearly in subsystem size. We show via Schrieffer-Wolff transformations that this piece of the entanglement entropy of fracton models is robust against arbitrary local perturbations of the Hamiltonian. Finally, we argue that these results may be extended to characterize localization-protected fracton topological order in excited states of disordered fracton models.

Natural entropy production in an inflationary model for a polarized vacuum

NASA Astrophysics Data System (ADS)

Berman, Marcelo Samuel; Som, Murari M.

2007-08-01

Though entropy production is forbidden in standard FRW Cosmology, Berman and Som presented a simple inflationary model where entropy production by bulk viscosity, during standard inflation without ad hoc pressure terms can be accommodated with Robertson Walker’s metric, so the requirement that the early Universe be anisotropic is not essential in order to have entropy growth during inflationary phase, as we show. Entropy also grows due to shear viscosity, for the anisotropic case. The intrinsically inflationary metric that we propose can be thought of as defining a polarized vacuum, and leads directly to the desired effects without the need of introducing extra pressure terms.

Entropy of measurement and erasure: Szilard's membrane model revisited

NASA Astrophysics Data System (ADS)

Leff, Harvey S.; Rex, Andrew F.

1994-11-01

It is widely believed that measurement is accompanied by irreversible entropy increase. This conventional wisdom is based in part on Szilard's 1929 study of entropy decrease in a thermodynamic system by intelligent intervention (i.e., a Maxwell's demon) and Brillouin's association of entropy with information. Bennett subsequently argued that information acquisition is not necessarily irreversible, but information erasure must be dissipative (Landauer's principle). Inspired by the ensuing debate, we revisit the membrane model introduced by Szilard and find that it can illustrate and clarify (1) reversible measurement, (2) information storage, (3) decoupling of the memory from the system being measured, and (4) entropy increase associated with memory erasure and resetting.

Quantile based Tsallis entropy in residual lifetime

NASA Astrophysics Data System (ADS)

Khammar, A. H.; Jahanshahi, S. M. A.

2018-02-01

Tsallis entropy is a generalization of type α of the Shannon entropy, that is a nonadditive entropy unlike the Shannon entropy. Shannon entropy may be negative for some distributions, but Tsallis entropy can always be made nonnegative by choosing appropriate value of α. In this paper, we derive the quantile form of this nonadditive's entropy function in the residual lifetime, namely the residual quantile Tsallis entropy (RQTE) and get the bounds for it, depending on the Renyi's residual quantile entropy. Also, we obtain relationship between RQTE and concept of proportional hazards model in the quantile setup. Based on the new measure, we propose a stochastic order and aging classes, and study its properties. Finally, we prove characterizations theorems for some well known lifetime distributions. It is shown that RQTE uniquely determines the parent distribution unlike the residual Tsallis entropy.

Entanglement sum rules.

PubMed

Swingle, Brian

2013-09-06

We compute the entanglement entropy of a wide class of models that may be characterized as describing matter coupled to gauge fields. Our principle result is an entanglement sum rule that states that the entropy of the full system is the sum of the entropies of the two components. In the context of the models we consider, this result applies to the full entropy, but more generally it is a statement about the additivity of universal terms in the entropy. Our proof simultaneously extends and simplifies previous arguments, with extensions including new models at zero temperature as well as the ability to treat finite temperature crossovers. We emphasize that while the additivity is an exact statement, each term in the sum may still be difficult to compute. Our results apply to a wide variety of phases including Fermi liquids, spin liquids, and some non-Fermi liquid metals. For example, we prove that our model of an interacting Fermi liquid has exactly the log violation of the area law for entanglement entropy predicted by the Widom formula in agreement with earlier arguments.

An entropy-assisted musculoskeletal shoulder model.

PubMed

Xu, Xu; Lin, Jia-Hua; McGorry, Raymond W

2017-04-01

Optimization combined with a musculoskeletal shoulder model has been used to estimate mechanical loading of musculoskeletal elements around the shoulder. Traditionally, the objective function is to minimize the summation of the total activities of the muscles with forces, moments, and stability constraints. Such an objective function, however, tends to neglect the antagonist muscle co-contraction. In this study, an objective function including an entropy term is proposed to address muscle co-contractions. A musculoskeletal shoulder model is developed to apply the proposed objective function. To find the optimal weight for the entropy term, an experiment was conducted. In the experiment, participants generated various 3-D shoulder moments in six shoulder postures. The surface EMG of 8 shoulder muscles was measured and compared with the predicted muscle activities based on the proposed objective function using Bhattacharyya distance and concordance ratio under different weight of the entropy term. The results show that a small weight of the entropy term can improve the predictability of the model in terms of muscle activities. Such a result suggests that the concept of entropy could be helpful for further understanding the mechanism of muscle co-contractions as well as developing a shoulder biomechanical model with greater validity. Copyright © 2017 Elsevier Ltd. All rights reserved.

An entropy model to measure heterogeneity of pedestrian crowds using self-propelled agents

NASA Astrophysics Data System (ADS)

Rangel-Huerta, A.; Ballinas-Hernández, A. L.; Muñoz-Meléndez, A.

2017-05-01

An entropy model to characterize the heterogeneity of a pedestrian crowd in a counter-flow corridor is presented. Pedestrians are modeled as self-propelled autonomous agents that are able to perform maneuvers to avoid collisions based on a set of simple rules of perception and action. An observer can determine a probability distribution function of the displayed behavior of pedestrians based only on external information. Three types of pedestrian are modeled, relaxed, standard and hurried pedestrians depending on their preferences of turn and non-turn when walking. Thus, using these types of pedestrians two crowds can be simulated: homogeneous and heterogeneous crowds. Heterogeneity is measured in this research based on the entropy in function of time. For that, the entropy of a homogeneous crowd comprising standard pedestrians is used as reference. A number of simulations to measure entropy of pedestrian crowds were conducted by varying different combinations of types of pedestrians, initial simulation conditions of macroscopic flow, as well as density of the crowd. Results from these simulations show that our entropy model is sensitive enough to capture the effect of both the initial simulation conditions about the spatial distribution of pedestrians in a corridor, and the composition of a crowd. Also, a relevant finding is that entropy in function of density presents a phase transition in the critical region.

Generalized Information Theory Meets Human Cognition: Introducing a Unified Framework to Model Uncertainty and Information Search.

PubMed

Crupi, Vincenzo; Nelson, Jonathan D; Meder, Björn; Cevolani, Gustavo; Tentori, Katya

2018-06-17

Searching for information is critical in many situations. In medicine, for instance, careful choice of a diagnostic test can help narrow down the range of plausible diseases that the patient might have. In a probabilistic framework, test selection is often modeled by assuming that people's goal is to reduce uncertainty about possible states of the world. In cognitive science, psychology, and medical decision making, Shannon entropy is the most prominent and most widely used model to formalize probabilistic uncertainty and the reduction thereof. However, a variety of alternative entropy metrics (Hartley, Quadratic, Tsallis, Rényi, and more) are popular in the social and the natural sciences, computer science, and philosophy of science. Particular entropy measures have been predominant in particular research areas, and it is often an open issue whether these divergences emerge from different theoretical and practical goals or are merely due to historical accident. Cutting across disciplinary boundaries, we show that several entropy and entropy reduction measures arise as special cases in a unified formalism, the Sharma-Mittal framework. Using mathematical results, computer simulations, and analyses of published behavioral data, we discuss four key questions: How do various entropy models relate to each other? What insights can be obtained by considering diverse entropy models within a unified framework? What is the psychological plausibility of different entropy models? What new questions and insights for research on human information acquisition follow? Our work provides several new pathways for theoretical and empirical research, reconciling apparently conflicting approaches and empirical findings within a comprehensive and unified information-theoretic formalism. Copyright © 2018 Cognitive Science Society, Inc.

Configurational entropy of polar glass formers and the effect of electric field on glass transition

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

Matyushov, Dmitry V., E-mail: dmitrym@asu.edu

2016-07-21

A model of low-temperature polar liquids is constructed that accounts for the configurational heat capacity, entropy, and the effect of a strong electric field on the glass transition. The model is based on the Padé-truncated perturbation expansions of the liquid state theory. Depending on parameters, it accommodates an ideal glass transition of vanishing configurational entropy and its avoidance, with a square-root divergent enumeration function at the point of its termination. A composite density-temperature parameter ρ{sup γ}/T, often used to represent combined pressure and temperature data, follows from the model. The theory is in good agreement with the experimental data formore » excess (over the crystal state) thermodynamics of molecular glass formers. We suggest that the Kauzmann entropy crisis might be a signature of vanishing configurational entropy of a subset of degrees of freedom, multipolar rotations in our model. This scenario has observable consequences: (i) a dynamical crossover of the relaxation time and (ii) the fragility index defined by the ratio of the excess heat capacity and excess entropy at the glass transition. The Kauzmann temperature of vanishing configurational entropy and the corresponding glass transition temperature shift upward when the electric field is applied. The temperature shift scales quadratically with the field strength.« less

A Discrete Constraint for Entropy Conservation and Sound Waves in Cloud-Resolving Modeling

NASA Technical Reports Server (NTRS)

Zeng, Xi-Ping; Tao, Wei-Kuo; Simpson, Joanne

2003-01-01

Ideal cloud-resolving models contain little-accumulative errors. When their domain is so large that synoptic large-scale circulations are accommodated, they can be used for the simulation of the interaction between convective clouds and the large-scale circulations. This paper sets up a framework for the models, using moist entropy as a prognostic variable and employing conservative numerical schemes. The models possess no accumulative errors of thermodynamic variables when they comply with a discrete constraint on entropy conservation and sound waves. Alternatively speaking, the discrete constraint is related to the correct representation of the large-scale convergence and advection of moist entropy. Since air density is involved in entropy conservation and sound waves, the challenge is how to compute sound waves efficiently under the constraint. To address the challenge, a compensation method is introduced on the basis of a reference isothermal atmosphere whose governing equations are solved analytically. Stability analysis and numerical experiments show that the method allows the models to integrate efficiently with a large time step.

Thermodynamics of mixing water with dimethyl sulfoxide, as seen from computer simulations.

PubMed

Idrissi, Abdenacer; Marekha, Bogdan; Barj, Mohamed; Jedlovszky, Pál

2014-07-24

The Helmholtz free energy, energy, and entropy of mixing of eight different models of dimethyl sulfoxide (DMSO) with four widely used water models are calculated at 298 K over the entire composition range by means of thermodynamic integration along a suitably chosen thermodynamic path, and compared with experimental data. All 32 model combinations considered are able to reproduce the experimental values rather well, within RT (free energy and energy) and R (entropy) at any composition, and quite often the deviation from the experimental data is even smaller, being in the order of the uncertainty of the calculated free energy or energy, and entropy values of 0.1 kJ/mol and 0.1 J/(mol K), respectively. On the other hand, none of the model combinations considered can accurately reproduce all three experimental functions simultaneously. Furthermore, the fact that the entropy of mixing changes sign with increasing DMSO mole fraction is only reproduced by a handful of model pairs. Model combinations that (i) give the best reproduction of the experimental free energy, while still reasonably well reproducing the experimental energy and entropy of mixing, and (ii) that give the best reproduction of the experimental energy and entropy, while still reasonably well reproducing the experimental free energy of mixing, are identified.

Integrating local pastoral knowledge, participatory mapping, and species distribution modeling for risk assessment of invasive rubber vine (Cryptostegia grandiflora) in Ethiopia’s Afar region

USGS Publications Warehouse

Luizza, Matthew; Wakie, Tewodros; Evangelista, Paul; Jarnevich, Catherine S.

2016-01-01

The threats posed by invasive plants span ecosystems and economies worldwide. Local knowledge of biological invasions has proven beneficial for invasive species research, but to date no work has integrated this knowledge with species distribution modeling for invasion risk assessments. In this study, we integrated pastoral knowledge with Maxent modeling to assess the suitable habitat and potential impacts of invasive Cryptostegia grandiflora Robx. Ex R.Br. (rubber vine) in Ethiopia’s Afar region. We conducted focus groups with seven villages across the Amibara and Awash-Fentale districts. Pastoral knowledge revealed the growing threat of rubber vine, which to date has received limited attention in Ethiopia, and whose presence in Afar was previously unknown to our team. Rubber vine occurrence points were collected in the field with pastoralists and processed in Maxent with MODIS-derived vegetation indices, topographic data, and anthropogenic variables. We tested model fit using a jackknife procedure and validated the final model with an independent occurrence data set collected through participatory mapping activities with pastoralists. A Multivariate Environmental Similarity Surface analysis revealed areas with novel environmental conditions for future targeted surveys. Model performance was evaluated using area under the receiver-operating characteristic curve (AUC) and showed good fit across the jackknife models (average AUC = 0.80) and the final model (test AUC = 0.96). Our results reveal the growing threat rubber vine poses to Afar, with suitable habitat extending downstream of its current known location in the middle Awash River basin. Local pastoral knowledge provided important context for its rapid expansion due to acute changes in seasonality and habitat alteration, in addition to threats posed to numerous endemic tree species that provide critical provisioning ecosystem services. This work demonstrates the utility of integrating local ecological knowledge with species distribution modeling for early detection and targeted surveying of recently established invasive species.

Projecting environmental suitable areas for malaria transmission in China under climate change scenarios.

PubMed

Hundessa, Samuel; Li, Shanshan; Liu, De Li; Guo, Jinpeng; Guo, Yuming; Zhang, Wenyi; Williams, Gail

2018-04-01

The proportion of imported malaria cases in China has increased over recent years, and has presented challenges for the malaria elimination program in China. However, little is known about the geographic distribution and environmental suitability for malaria transmission under projected climate change scenarios. Using the MaxEnt model based on malaria presence-only records, we produced environmental suitability maps and examined the relative contribution of topographic, demographic, and environmental risk factors for P. vivax and P. falciparum malaria in China. The MaxEnt model estimated that environmental suitability areas (ESAs) for malaria cover the central, south, southwest, east and northern regions, with a slightly wider range of ESAs extending to the northeast region for P. falciparum. There was spatial agreement between the location of imported cases and area environmentally suitable for malaria transmission. The ESAs of P. vivax and P. falciparum are projected to increase in some parts of southwest, south, central, north and northeast regions in the 2030s, 2050s, and 2080s, by a greater amount for P. falciparum under the RCP8.5 scenario. Temperature and NDVI values were the most influential in defining the ESAs for P. vivax, and temperature and precipitation the most influential for P. falciparum malaria. This study estimated that the ESA for malaria transmission in China will increase with climate change and highlights the potential establishment of further local transmission. This model should be used to support malaria control by targeting areas where interventions on malaria transmission need to be enhanced. Copyright © 2017 Elsevier Inc. All rights reserved.

Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.

PubMed

Redding, David W; Lucas, Tim C D; Blackburn, Tim M; Jones, Kate E

2017-01-01

Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species' ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT), to a spatial Bayesian SDM method (fitted using R-INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1-3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10-12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account for spatial autocorrelation in an SDM context and, by taking account of random effects, produce outputs that can better elucidate the role of covariates in predicting species occurrence. Given that it is often unclear what the drivers are behind data clumping in an empirical occurrence dataset, or indeed how geographically restricted these data are, spatially-explicit Bayesian SDMs may be the better choice when modelling the spatial distribution of target species.

Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy.

PubMed

Cao, Yuzhen; Cai, Lihui; Wang, Jiang; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

2015-08-01

In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.

Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy

NASA Astrophysics Data System (ADS)

Cao, Yuzhen; Cai, Lihui; Wang, Jiang; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

2015-08-01

In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.

Dynamic approximate entropy electroanatomic maps detect rotors in a simulated atrial fibrillation model.

PubMed

Ugarte, Juan P; Orozco-Duque, Andrés; Tobón, Catalina; Kremen, Vaclav; Novak, Daniel; Saiz, Javier; Oesterlein, Tobias; Schmitt, Clauss; Luik, Armin; Bustamante, John

2014-01-01

There is evidence that rotors could be drivers that maintain atrial fibrillation. Complex fractionated atrial electrograms have been located in rotor tip areas. However, the concept of electrogram fractionation, defined using time intervals, is still controversial as a tool for locating target sites for ablation. We hypothesize that the fractionation phenomenon is better described using non-linear dynamic measures, such as approximate entropy, and that this tool could be used for locating the rotor tip. The aim of this work has been to determine the relationship between approximate entropy and fractionated electrograms, and to develop a new tool for rotor mapping based on fractionation levels. Two episodes of chronic atrial fibrillation were simulated in a 3D human atrial model, in which rotors were observed. Dynamic approximate entropy maps were calculated using unipolar electrogram signals generated over the whole surface of the 3D atrial model. In addition, we optimized the approximate entropy calculation using two real multi-center databases of fractionated electrogram signals, labeled in 4 levels of fractionation. We found that the values of approximate entropy and the levels of fractionation are positively correlated. This allows the dynamic approximate entropy maps to localize the tips from stable and meandering rotors. Furthermore, we assessed the optimized approximate entropy using bipolar electrograms generated over a vicinity enclosing a rotor, achieving rotor detection. Our results suggest that high approximate entropy values are able to detect a high level of fractionation and to locate rotor tips in simulated atrial fibrillation episodes. We suggest that dynamic approximate entropy maps could become a tool for atrial fibrillation rotor mapping.

Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.

PubMed

Fourcade, Yoan; Engler, Jan O; Rödder, Dennis; Secondi, Jean

2014-01-01

MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one "virtual" derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.

Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias

PubMed Central

Fourcade, Yoan; Engler, Jan O.; Rödder, Dennis; Secondi, Jean

2014-01-01

MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one “virtual” derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases. PMID:24818607

Climate change may alter breeding ground distributions of eastern migratory monarchs (Danaus plexippus) via range expansion of Asclepias host plants.

PubMed

Lemoine, Nathan P

2015-01-01

Climate change can profoundly alter species' distributions due to changes in temperature, precipitation, or seasonality. Migratory monarch butterflies (Danaus plexippus) may be particularly susceptible to climate-driven changes in host plant abundance or reduced overwintering habitat. For example, climate change may significantly reduce the availability of overwintering habitat by restricting the amount of area with suitable microclimate conditions. However, potential effects of climate change on monarch northward migrations remain largely unknown, particularly with respect to their milkweed (Asclepias spp.) host plants. Given that monarchs largely depend on the genus Asclepias as larval host plants, the effects of climate change on monarch northward migrations will most likely be mediated by climate change effects on Asclepias. Here, I used MaxEnt species distribution modeling to assess potential changes in Asclepias and monarch distributions under moderate and severe climate change scenarios. First, Asclepias distributions were projected to extend northward throughout much of Canada despite considerable variability in the environmental drivers of each individual species. Second, Asclepias distributions were an important predictor of current monarch distributions, indicating that monarchs may be constrained as much by the availability of Asclepias host plants as environmental variables per se. Accordingly, modeling future distributions of monarchs, and indeed any tightly coupled plant-insect system, should incorporate the effects of climate change on host plant distributions. Finally, MaxEnt predictions of Asclepias and monarch distributions were remarkably consistent among general circulation models. Nearly all models predicted that the current monarch summer breeding range will become slightly less suitable for Asclepias and monarchs in the future. Asclepias, and consequently monarchs, should therefore undergo expanded northern range limits in summer months while encountering reduced habitat suitability throughout the northern migration.

Climate Change May Alter Breeding Ground Distributions of Eastern Migratory Monarchs (Danaus plexippus) via Range Expansion of Asclepias Host Plants

PubMed Central

Lemoine, Nathan P.

2015-01-01

Climate change can profoundly alter species’ distributions due to changes in temperature, precipitation, or seasonality. Migratory monarch butterflies (Danaus plexippus) may be particularly susceptible to climate-driven changes in host plant abundance or reduced overwintering habitat. For example, climate change may significantly reduce the availability of overwintering habitat by restricting the amount of area with suitable microclimate conditions. However, potential effects of climate change on monarch northward migrations remain largely unknown, particularly with respect to their milkweed (Asclepias spp.) host plants. Given that monarchs largely depend on the genus Asclepias as larval host plants, the effects of climate change on monarch northward migrations will most likely be mediated by climate change effects on Asclepias. Here, I used MaxEnt species distribution modeling to assess potential changes in Asclepias and monarch distributions under moderate and severe climate change scenarios. First, Asclepias distributions were projected to extend northward throughout much of Canada despite considerable variability in the environmental drivers of each individual species. Second, Asclepias distributions were an important predictor of current monarch distributions, indicating that monarchs may be constrained as much by the availability of Asclepias host plants as environmental variables per se. Accordingly, modeling future distributions of monarchs, and indeed any tightly coupled plant-insect system, should incorporate the effects of climate change on host plant distributions. Finally, MaxEnt predictions of Asclepias and monarch distributions were remarkably consistent among general circulation models. Nearly all models predicted that the current monarch summer breeding range will become slightly less suitable for Asclepias and monarchs in the future. Asclepias, and consequently monarchs, should therefore undergo expanded northern range limits in summer months while encountering reduced habitat suitability throughout the northern migration. PMID:25705876

Assessing the potential for establishment of western cherry fruit fly using ecological niche modeling.

PubMed

Kumar, Sunil; Neven, Lisa G; Yee, Wee L

2014-06-01

Sweet cherries, Prunus avium (L.) L., grown in the western United States are exported to many countries around the world. Some of these countries have enforced strict quarantine rules and trade restrictions owing to concerns about the potential establishment and subsequent spread of western cherry fruit fly, Rhagoletis indifferens Curran (Diptera: Tephritidae), a major quarantine pest of sweet cherry. We used 1) niche models (CLIMEX and MaxEnt) to map the climatic suitability, 2) North Carolina State University-Animal and Plant Health Inspection Service Plant Pest Forecasting System to examine chilling requirement, and 3) host distribution and availability to assess the potential for establishment of R. indifferens in areas of western North America where it currently does not exist and eight current or potential fresh sweet cherry markets: Colombia, India, Indonesia, Malaysia, Taiwan, Thailand, Venezuela, and Vietnam. Results from niche models conformed well to the current distribution of R. indifferens in western North America. MaxEnt and CLIMEX models had high performance and predicted climatic suitability in some of the countries (e.g., Andean range in Colombia and Venezuela, northern and northeastern India, central Taiwan, and parts of Vietnam). However, our results showed no potential for establishment of R. indifferens in Colombia, Indonesia, Malaysia, Taiwan, Thailand, Venezuela, and Vietnam when the optimal chilling requirement to break diapause (minimum temperature < or = 3 degree C for at least 15 wk) was used as the criterion for whether establishment can occur. Furthermore, these countries have no host plant species available for R. indifferens. Our results can be used to make scientifically informed international trade decisions and negotiations by policy makers.

Long memory and volatility clustering: Is the empirical evidence consistent across stock markets?

NASA Astrophysics Data System (ADS)

Bentes, Sónia R.; Menezes, Rui; Mendes, Diana A.

2008-06-01

Long memory and volatility clustering are two stylized facts frequently related to financial markets. Traditionally, these phenomena have been studied based on conditionally heteroscedastic models like ARCH, GARCH, IGARCH and FIGARCH, inter alia. One advantage of these models is their ability to capture nonlinear dynamics. Another interesting manner to study the volatility phenomenon is by using measures based on the concept of entropy. In this paper we investigate the long memory and volatility clustering for the SP 500, NASDAQ 100 and Stoxx 50 indexes in order to compare the US and European Markets. Additionally, we compare the results from conditionally heteroscedastic models with those from the entropy measures. In the latter, we examine Shannon entropy, Renyi entropy and Tsallis entropy. The results corroborate the previous evidence of nonlinear dynamics in the time series considered.

Psychological Entropy: A Framework for Understanding Uncertainty-Related Anxiety

ERIC Educational Resources Information Center

Hirsh, Jacob B.; Mar, Raymond A.; Peterson, Jordan B.

2012-01-01

Entropy, a concept derived from thermodynamics and information theory, describes the amount of uncertainty and disorder within a system. Self-organizing systems engage in a continual dialogue with the environment and must adapt themselves to changing circumstances to keep internal entropy at a manageable level. We propose the entropy model of…

A new one-dimensional radiative equilibrium model for investigating atmospheric radiation entropy flux.

PubMed

Wu, Wei; Liu, Yangang

2010-05-12

A new one-dimensional radiative equilibrium model is built to analytically evaluate the vertical profile of the Earth's atmospheric radiation entropy flux under the assumption that atmospheric longwave radiation emission behaves as a greybody and shortwave radiation as a diluted blackbody. Results show that both the atmospheric shortwave and net longwave radiation entropy fluxes increase with altitude, and the latter is about one order in magnitude greater than the former. The vertical profile of the atmospheric net radiation entropy flux follows approximately that of the atmospheric net longwave radiation entropy flux. Sensitivity study further reveals that a 'darker' atmosphere with a larger overall atmospheric longwave optical depth exhibits a smaller net radiation entropy flux at all altitudes, suggesting an intrinsic connection between the atmospheric net radiation entropy flux and the overall atmospheric longwave optical depth. These results indicate that the overall strength of the atmospheric irreversible processes at all altitudes as determined by the corresponding atmospheric net entropy flux is closely related to the amount of greenhouse gases in the atmosphere.

Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system.

PubMed

Min, Jianliang; Wang, Ping; Hu, Jianfeng

2017-01-01

Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1-2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver.

Large Eddy Simulation of Entropy Generation in a Turbulent Mixing Layer

NASA Astrophysics Data System (ADS)

Sheikhi, Reza H.; Safari, Mehdi; Hadi, Fatemeh

2013-11-01

Entropy transport equation is considered in large eddy simulation (LES) of turbulent flows. The irreversible entropy generation in this equation provides a more general description of subgrid scale (SGS) dissipation due to heat conduction, mass diffusion and viscosity effects. A new methodology is developed, termed the entropy filtered density function (En-FDF), to account for all individual entropy generation effects in turbulent flows. The En-FDF represents the joint probability density function of entropy, frequency, velocity and scalar fields within the SGS. An exact transport equation is developed for the En-FDF, which is modeled by a system of stochastic differential equations, incorporating the second law of thermodynamics. The modeled En-FDF transport equation is solved by a Lagrangian Monte Carlo method. The methodology is employed to simulate a turbulent mixing layer involving transport of passive scalars and entropy. Various modes of entropy generation are obtained from the En-FDF and analyzed. Predictions are assessed against data generated by direct numerical simulation (DNS). The En-FDF predictions are in good agreements with the DNS data.

New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network.

PubMed

Jiang, Quansheng; Shen, Yehu; Li, Hua; Xu, Fengyu

2018-01-24

Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery.

Hydrostatic Chandra X-ray analysis of SPT-selected galaxy clusters - I. Evolution of profiles and core properties

NASA Astrophysics Data System (ADS)

Sanders, J. S.; Fabian, A. C.; Russell, H. R.; Walker, S. A.

2018-02-01

We analyse Chandra X-ray Observatory observations of a set of galaxy clusters selected by the South Pole Telescope using a new publicly available forward-modelling projection code, MBPROJ2, assuming hydrostatic equilibrium. By fitting a power law plus constant entropy model we find no evidence for a central entropy floor in the lowest entropy systems. A model of the underlying central entropy distribution shows a narrow peak close to zero entropy which accounts for 60 per cent of the systems, and a second broader peak around 130 keV cm2. We look for evolution over the 0.28-1.2 redshift range of the sample in density, pressure, entropy and cooling time at 0.015R500 and at 10 kpc radius. By modelling the evolution of the central quantities with a simple model, we find no evidence for a non-zero slope with redshift. In addition, a non-parametric sliding median shows no significant change. The fraction of cool-core clusters with central cooling times below 2 Gyr is consistent above and below z = 0.6 (˜30-40 per cent). Both by comparing the median thermodynamic profiles, centrally biased towards cool cores, in two redshift bins, and by modelling the evolution of the unbiased average profile as a function of redshift, we find no significant evolution beyond self-similar scaling in any of our examined quantities. Our average modelled radial density, entropy and cooling-time profiles appear as power laws with breaks around 0.2R500. The dispersion in these quantities rises inwards of this radius to around 0.4 dex, although some of this scatter can be fitted by a bimodal model.

Transfer Entropy as a Log-Likelihood Ratio

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Bossomaier, Terry

2012-09-01

Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.

Transfer entropy as a log-likelihood ratio.

PubMed

Barnett, Lionel; Bossomaier, Terry

2012-09-28

Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.

Entropy generation across Earth's collisionless bow shock.

PubMed

Parks, G K; Lee, E; McCarthy, M; Goldstein, M; Fu, S Y; Cao, J B; Canu, P; Lin, N; Wilber, M; Dandouras, I; Réme, H; Fazakerley, A

2012-02-10

Earth's bow shock is a collisionless shock wave but entropy has never been directly measured across it. The plasma experiments on Cluster and Double Star measure 3D plasma distributions upstream and downstream of the bow shock allowing calculation of Boltzmann's entropy function H and his famous H theorem, dH/dt≤0. The collisionless Boltzmann (Vlasov) equation predicts that the total entropy does not change if the distribution function across the shock becomes nonthermal, but it allows changes in the entropy density. Here, we present the first direct measurements of entropy density changes across Earth's bow shock and show that the results generally support the model of the Vlasov analysis. These observations are a starting point for a more sophisticated analysis that includes 3D computer modeling of collisionless shocks with input from observed particles, waves, and turbulences.

15N backbone dynamics of the S-peptide from ribonuclease A in its free and S-protein bound forms: toward a site-specific analysis of entropy changes upon folding.

PubMed Central

Alexandrescu, A. T.; Rathgeb-Szabo, K.; Rumpel, K.; Jahnke, W.; Schulthess, T.; Kammerer, R. A.

1998-01-01

Backbone 15N relaxation parameters (R1, R2, 1H-15N NOE) have been measured for a 22-residue recombinant variant of the S-peptide in its free and S-protein bound forms. NMR relaxation data were analyzed using the "model-free" approach (Lipari & Szabo, 1982). Order parameters obtained from "model-free" simulations were used to calculate 1H-15N bond vector entropies using a recently described method (Yang & Kay, 1996), in which the form of the probability density function for bond vector fluctuations is derived from a diffusion-in-a-cone motional model. The average change in 1H-15N bond vector entropies for residues T3-S15, which become ordered upon binding of the S-peptide to the S-protein, is -12.6+/-1.4 J/mol.residue.K. 15N relaxation data suggest a gradient of decreasing entropy values moving from the termini toward the center of the free peptide. The difference between the entropies of the terminal and central residues is about -12 J/mol residue K, a value comparable to that of the average entropy change per residue upon complex formation. Similar entropy gradients are evident in NMR relaxation studies of other denatured proteins. Taken together, these observations suggest denatured proteins may contain entropic contributions from non-local interactions. Consequently, calculations that model the entropy of a residue in a denatured protein as that of a residue in a di- or tri-peptide, might over-estimate the magnitude of entropy changes upon folding. PMID:9521116

Parsimonious Charge Deconvolution for Native Mass Spectrometry

PubMed Central

2018-01-01

Charge deconvolution infers the mass from mass over charge (m/z) measurements in electrospray ionization mass spectra. When applied over a wide input m/z or broad target mass range, charge-deconvolution algorithms can produce artifacts, such as false masses at one-half or one-third of the correct mass. Indeed, a maximum entropy term in the objective function of MaxEnt, the most commonly used charge deconvolution algorithm, favors a deconvolved spectrum with many peaks over one with fewer peaks. Here we describe a new “parsimonious” charge deconvolution algorithm that produces fewer artifacts. The algorithm is especially well-suited to high-resolution native mass spectrometry of intact glycoproteins and protein complexes. Deconvolution of native mass spectra poses special challenges due to salt and small molecule adducts, multimers, wide mass ranges, and fewer and lower charge states. We demonstrate the performance of the new deconvolution algorithm on a range of samples. On the heavily glycosylated plasma properdin glycoprotein, the new algorithm could deconvolve monomer and dimer simultaneously and, when focused on the m/z range of the monomer, gave accurate and interpretable masses for glycoforms that had previously been analyzed manually using m/z peaks rather than deconvolved masses. On therapeutic antibodies, the new algorithm facilitated the analysis of extensions, truncations, and Fab glycosylation. The algorithm facilitates the use of native mass spectrometry for the qualitative and quantitative analysis of protein and protein assemblies. PMID:29376659

Entanglement entropy at infinite-randomness fixed points in higher dimensions.

PubMed

Lin, Yu-Cheng; Iglói, Ferenc; Rieger, Heiko

2007-10-05

The entanglement entropy of the two-dimensional random transverse Ising model is studied with a numerical implementation of the strong-disorder renormalization group. The asymptotic behavior of the entropy per surface area diverges at, and only at, the quantum phase transition that is governed by an infinite-randomness fixed point. Here we identify a double-logarithmic multiplicative correction to the area law for the entanglement entropy. This contrasts with the pure area law valid at the infinite-randomness fixed point in the diluted transverse Ising model in higher dimensions.

Entropic manifestations of topological order in three dimensions

NASA Astrophysics Data System (ADS)

Bullivant, Alex; Pachos, Jiannis K.

2016-03-01

We evaluate the entanglement entropy of exactly solvable Hamiltonians corresponding to general families of three-dimensional topological models. We show that the modification to the entropic area law due to three-dimensional topological properties is richer than the two-dimensional case. In addition to the reduction of the entropy caused by a nonzero vacuum expectation value of contractible loop operators, a topological invariant emerges that increases the entropy if the model consists of nontrivially braiding anyons. As a result the three-dimensional topological entanglement entropy provides only partial information about the two entropic topological invariants.

Binding stability of peptides on major histocompatibility complex class I proteins: role of entropy and dynamics.

PubMed

Gul, Ahmet; Erman, Burak

2018-01-16

Prediction of peptide binding on specific human leukocyte antigens (HLA) has long been studied with successful results. We herein describe the effects of entropy and dynamics by investigating the binding stabilities of 10 nanopeptides on various HLA Class I alleles using a theoretical model based on molecular dynamics simulations. The fluctuational entropies of the peptides are estimated over a temperature range of 310-460 K. The estimated entropies correlate well with experimental binding affinities of the peptides: peptides that have higher binding affinities have lower entropies compared to non-binders, which have significantly larger entropies. The computation of the entropies is based on a simple model that requires short molecular dynamics trajectories and allows for approximate but rapid determination. The paper draws attention to the long neglected dynamic aspects of peptide binding, and provides a fast computation scheme that allows for rapid scanning of large numbers of peptides on selected HLA antigens, which may be useful in defining the right peptides for personal immunotherapy.

Neuronal Entropy-Rate Feature of Entopeduncular Nucleus in Rat Model of Parkinson's Disease.

PubMed

Darbin, Olivier; Jin, Xingxing; Von Wrangel, Christof; Schwabe, Kerstin; Nambu, Atsushi; Naritoku, Dean K; Krauss, Joachim K; Alam, Mesbah

2016-03-01

The function of the nigro-striatal pathway on neuronal entropy in the basal ganglia (BG) output nucleus, i.e. the entopeduncular nucleus (EPN) was investigated in the unilaterally 6-hyroxydopamine (6-OHDA)-lesioned rat model of Parkinson's disease (PD). In both control subjects and subjects with 6-OHDA lesion of dopamine (DA) the nigro-striatal pathway, a histological hallmark for parkinsonism, neuronal entropy in EPN was maximal in neurons with firing rates ranging between 15 and 25 Hz. In 6-OHDA lesioned rats, neuronal entropy in the EPN was specifically higher in neurons with firing rates above 25 Hz. Our data establishes that the nigro-striatal pathway controls neuronal entropy in motor circuitry and that the parkinsonian condition is associated with abnormal relationship between firing rate and neuronal entropy in BG output nuclei. The neuronal firing rates and entropy relationship provide putative relevant electrophysiological information to investigate the sensory-motor processing in normal condition and conditions such as movement disorders.

Binding stability of peptides on major histocompatibility complex class I proteins: role of entropy and dynamics

NASA Astrophysics Data System (ADS)

Gul, Ahmet; Erman, Burak

2018-03-01

Prediction of peptide binding on specific human leukocyte antigens (HLA) has long been studied with successful results. We herein describe the effects of entropy and dynamics by investigating the binding stabilities of 10 nanopeptides on various HLA Class I alleles using a theoretical model based on molecular dynamics simulations. The fluctuational entropies of the peptides are estimated over a temperature range of 310-460 K. The estimated entropies correlate well with experimental binding affinities of the peptides: peptides that have higher binding affinities have lower entropies compared to non-binders, which have significantly larger entropies. The computation of the entropies is based on a simple model that requires short molecular dynamics trajectories and allows for approximate but rapid determination. The paper draws attention to the long neglected dynamic aspects of peptide binding, and provides a fast computation scheme that allows for rapid scanning of large numbers of peptides on selected HLA antigens, which may be useful in defining the right peptides for personal immunotherapy.

Maximum entropy production: Can it be used to constrain conceptual hydrological models?

Treesearch

M.C. Westhoff; E. Zehe

2013-01-01

In recent years, optimality principles have been proposed to constrain hydrological models. The principle of maximum entropy production (MEP) is one of the proposed principles and is subject of this study. It states that a steady state system is organized in such a way that entropy production is maximized. Although successful applications have been reported in...

Double symbolic joint entropy in nonlinear dynamic complexity analysis

NASA Astrophysics Data System (ADS)

Yao, Wenpo; Wang, Jun

2017-07-01

Symbolizations, the base of symbolic dynamic analysis, are classified as global static and local dynamic approaches which are combined by joint entropy in our works for nonlinear dynamic complexity analysis. Two global static methods, symbolic transformations of Wessel N. symbolic entropy and base-scale entropy, and two local ones, namely symbolizations of permutation and differential entropy, constitute four double symbolic joint entropies that have accurate complexity detections in chaotic models, logistic and Henon map series. In nonlinear dynamical analysis of different kinds of heart rate variability, heartbeats of healthy young have higher complexity than those of the healthy elderly, and congestive heart failure (CHF) patients are lowest in heartbeats' joint entropy values. Each individual symbolic entropy is improved by double symbolic joint entropy among which the combination of base-scale and differential symbolizations have best complexity analysis. Test results prove that double symbolic joint entropy is feasible in nonlinear dynamic complexity analysis.

Block entropy and quantum phase transition in the anisotropic Kondo necklace model

NASA Astrophysics Data System (ADS)

Mendoza-Arenas, J. J.; Franco, R.; Silva-Valencia, J.

2010-06-01

We study the von Neumann block entropy in the Kondo necklace model for different anisotropies η in the XY interaction between conduction spins using the density matrix renormalization group method. It was found that the block entropy presents a maximum for each η considered, and, comparing it with the results of the quantum criticality of the model based on the behavior of the energy gap, we observe that the maximum block entropy occurs at the quantum critical point between an antiferromagnetic and a Kondo singlet state, so this measure of entanglement is useful for giving information about where a quantum phase transition occurs in this model. We observe that the block entropy also presents a maximum at the quantum critical points that are obtained when an anisotropy Δ is included in the Kondo exchange between localized and conduction spins; when Δ diminishes for a fixed value of η, the critical point increases, favoring the antiferromagnetic phase.

Dynamic Approximate Entropy Electroanatomic Maps Detect Rotors in a Simulated Atrial Fibrillation Model

PubMed Central

Ugarte, Juan P.; Orozco-Duque, Andrés; Tobón, Catalina; Kremen, Vaclav; Novak, Daniel; Saiz, Javier; Oesterlein, Tobias; Schmitt, Clauss; Luik, Armin; Bustamante, John

2014-01-01

There is evidence that rotors could be drivers that maintain atrial fibrillation. Complex fractionated atrial electrograms have been located in rotor tip areas. However, the concept of electrogram fractionation, defined using time intervals, is still controversial as a tool for locating target sites for ablation. We hypothesize that the fractionation phenomenon is better described using non-linear dynamic measures, such as approximate entropy, and that this tool could be used for locating the rotor tip. The aim of this work has been to determine the relationship between approximate entropy and fractionated electrograms, and to develop a new tool for rotor mapping based on fractionation levels. Two episodes of chronic atrial fibrillation were simulated in a 3D human atrial model, in which rotors were observed. Dynamic approximate entropy maps were calculated using unipolar electrogram signals generated over the whole surface of the 3D atrial model. In addition, we optimized the approximate entropy calculation using two real multi-center databases of fractionated electrogram signals, labeled in 4 levels of fractionation. We found that the values of approximate entropy and the levels of fractionation are positively correlated. This allows the dynamic approximate entropy maps to localize the tips from stable and meandering rotors. Furthermore, we assessed the optimized approximate entropy using bipolar electrograms generated over a vicinity enclosing a rotor, achieving rotor detection. Our results suggest that high approximate entropy values are able to detect a high level of fractionation and to locate rotor tips in simulated atrial fibrillation episodes. We suggest that dynamic approximate entropy maps could become a tool for atrial fibrillation rotor mapping. PMID:25489858

Criteria for predicting the formation of single-phase high-entropy alloys

DOE PAGES

Troparevsky, M Claudia; Morris, James R..; Kent, Paul R.; ...

2015-03-15

High entropy alloys constitute a new class of materials whose very existence poses fundamental questions. Originally thought to be stabilized by the large entropy of mixing, these alloys have attracted attention due to their potential applications, yet no model capable of robustly predicting which combinations of elements will form a single-phase currently exists. Here we propose a model that, through the use of high-throughput computation of the enthalpies of formation of binary compounds, is able to confirm all known high-entropy alloys while rejecting similar alloys that are known to form multiple phases. Despite the increasing entropy, our model predicts thatmore » the number of potential single-phase multicomponent alloys decreases with an increasing number of components: out of more than two million possible 7-component alloys considered, fewer than twenty single-phase alloys are likely.« less

Predicting a roadkill hotspots based on spatial distribution of Korean water deer (Hydropotes inermis argyropus) using Maxent model in South Korea Expressway : In Case of Cheongju-Sangju Expressway

NASA Astrophysics Data System (ADS)

Park, Hyomin; Lee, Sangdon

2016-04-01

Road construction has direct and indirect effects on ecosystems. Especially wildlife-vehicle conflicts (roadkills) caused by roads are a considerable threat for population of many species. This study aims to identify the effects of topographic characteristics and spatial distribution of Korean water deer (Hydropotes inermis). Korean water deer is indigenous and native species in Korea that listed LC (least concern) by IUCN redlist categories. Korean water deer population is growing every year occupying for most of roadkills (>70%) in Korean express highway. In order to predict a distribution of the Korean water deer, we selected factors that most affected water deer's habitat. Major habitats of waterdeer are known as agricultural area, forest area and water. Based on this result, eight factors were selected (land cover map, vegetation map, age class of forest, diameter class of tree, population, slope of study site, elevation of study site, distance of river), and made a thematic map by using GIS program (ESRI, Arc GIS 10.3.1 ver.). To analyze the affected factors of waterdeer distribution, GPS data and thematic map of study area were entered into Maxent model (Maxent 3.3.3.k.). Results of analysis were verified by the AUC (Area Unit Curve) of ROC (Receiver Operating Characteristic). The ROC curve used the sensitivity and specificity as a reference for determining the prediction efficiency of the model and AUC area of ROC curve was higher prediction efficiency closer to '1.' Selecting factors that affected the distribution of waterdeer were land cover map, diameter class of tree and elevation of study site. The value of AUC was 0.623. To predict the water deer's roadkills hot spot on Cheongju-Sangju Expressway, the thematic map was prepared based on GPS data of roadkill spots. As a result, the topographic factors that affected waterdeer roadkill were land cover map, actual vegetation map and age class of forest and the value of AUC was 0.854. Through this study, we could identify the site and hot spots that water deer frequently expected to use based on quantitative data on the spatial and topographic factors. Therefore, we can suggest ways to minimize roadkills by selecting the hot spots and by suggesting construction of eco-corridors. This study will significantly enhance human-wildlife conflicts by identifying key habitat areas for wild mammals.

Rényi information flow in the Ising model with single-spin dynamics.

PubMed

Deng, Zehui; Wu, Jinshan; Guo, Wenan

2014-12-01

The n-index Rényi mutual information and transfer entropies for the two-dimensional kinetic Ising model with arbitrary single-spin dynamics in the thermodynamic limit are derived as functions of ensemble averages of observables and spin-flip probabilities. Cluster Monte Carlo algorithms with different dynamics from the single-spin dynamics are thus applicable to estimate the transfer entropies. By means of Monte Carlo simulations with the Wolff algorithm, we calculate the information flows in the Ising model with the Metropolis dynamics and the Glauber dynamics, respectively. We find that not only the global Rényi transfer entropy, but also the pairwise Rényi transfer entropy, peaks in the disorder phase.

Steepest entropy ascent quantum thermodynamic model of electron and phonon transport

NASA Astrophysics Data System (ADS)

Li, Guanchen; von Spakovsky, Michael R.; Hin, Celine

2018-01-01

An advanced nonequilibrium thermodynamic model for electron and phonon transport is formulated based on the steepest-entropy-ascent quantum thermodynamics framework. This framework, based on the principle of steepest entropy ascent (or the equivalent maximum entropy production principle), inherently satisfies the laws of thermodynamics and mechanics and is applicable at all temporal and spatial scales even in the far-from-equilibrium realm. Specifically, the model is proven to recover the Boltzmann transport equations in the near-equilibrium limit and the two-temperature model of electron-phonon coupling when no dispersion is assumed. The heat and mass transport at a temperature discontinuity across a homogeneous interface where the dispersion and coupling of electron and phonon transport are both considered are then modeled. Local nonequilibrium system evolution and nonquasiequilibrium interactions are predicted and the results discussed.

Free Energy, Enthalpy and Entropy from Implicit Solvent End-Point Simulations.

PubMed

Fogolari, Federico; Corazza, Alessandra; Esposito, Gennaro

2018-01-01

Free energy is the key quantity to describe the thermodynamics of biological systems. In this perspective we consider the calculation of free energy, enthalpy and entropy from end-point molecular dynamics simulations. Since the enthalpy may be calculated as the ensemble average over equilibrated simulation snapshots the difficulties related to free energy calculation are ultimately related to the calculation of the entropy of the system and in particular of the solvent entropy. In the last two decades implicit solvent models have been used to circumvent the problem and to take into account solvent entropy implicitly in the solvation terms. More recently outstanding advancement in both implicit solvent models and in entropy calculations are making the goal of free energy estimation from end-point simulations more feasible than ever before. We review briefly the basic theory and discuss the advancements in light of practical applications.

Studies on entanglement entropy for Hubbard model with hole-doping and external magnetic field [rapid communication

NASA Astrophysics Data System (ADS)

Yao, K. L.; Li, Y. C.; Sun, X. Z.; Liu, Q. M.; Qin, Y.; Fu, H. H.; Gao, G. Y.

2005-10-01

By using the density matrix renormalization group (DMRG) method for the one-dimensional (1D) Hubbard model, we have studied the von Neumann entropy of a quantum system, which describes the entanglement of the system block and the rest of the chain. It is found that there is a close relation between the entanglement entropy and properties of the system. The hole-doping can alter the charge charge and spin spin interactions, resulting in charge polarization along the chain. By comparing the results before and after the doping, we find that doping favors increase of the von Neumann entropy and thus also favors the exchange of information along the chain. Furthermore, we calculated the spin and entropy distribution in external magnetic filed. It is confirmed that both the charge charge and the spin spin interactions affect the exchange of information along the chain, making the entanglement entropy redistribute.

Exact analytical thermodynamic expressions for a Brownian heat engine

NASA Astrophysics Data System (ADS)

Taye, Mesfin Asfaw

2015-09-01

The nonequilibrium thermodynamics feature of a Brownian motor operating between two different heat baths is explored as a function of time t . Using the Gibbs entropy and Schnakenberg microscopic stochastic approach, we find exact closed form expressions for the free energy, the rate of entropy production, and the rate of entropy flow from the system to the outside. We show that when the system is out of equilibrium, it constantly produces entropy and at the same time extracts entropy out of the system. Its entropy production and extraction rates decrease in time and saturate to a constant value. In the long time limit, the rate of entropy production balances the rate of entropy extraction, and at equilibrium both entropy production and extraction rates become zero. Furthermore, via the present model, many thermodynamic theories can be checked.

Exact analytical thermodynamic expressions for a Brownian heat engine.

PubMed

Taye, Mesfin Asfaw

2015-09-01

The nonequilibrium thermodynamics feature of a Brownian motor operating between two different heat baths is explored as a function of time t. Using the Gibbs entropy and Schnakenberg microscopic stochastic approach, we find exact closed form expressions for the free energy, the rate of entropy production, and the rate of entropy flow from the system to the outside. We show that when the system is out of equilibrium, it constantly produces entropy and at the same time extracts entropy out of the system. Its entropy production and extraction rates decrease in time and saturate to a constant value. In the long time limit, the rate of entropy production balances the rate of entropy extraction, and at equilibrium both entropy production and extraction rates become zero. Furthermore, via the present model, many thermodynamic theories can be checked.

Irreversible entropy model for damage diagnosis in resistors

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

Cuadras, Angel, E-mail: angel.cuadras@upc.edu; Crisóstomo, Javier; Ovejas, Victoria J.

2015-10-28

We propose a method to characterize electrical resistor damage based on entropy measurements. Irreversible entropy and the rate at which it is generated are more convenient parameters than resistance for describing damage because they are essentially positive in virtue of the second law of thermodynamics, whereas resistance may increase or decrease depending on the degradation mechanism. Commercial resistors were tested in order to characterize the damage induced by power surges. Resistors were biased with constant and pulsed voltage signals, leading to power dissipation in the range of 4–8 W, which is well above the 0.25 W nominal power to initiate failure. Entropymore » was inferred from the added power and temperature evolution. A model is proposed to understand the relationship among resistance, entropy, and damage. The power surge dissipates into heat (Joule effect) and damages the resistor. The results show a correlation between entropy generation rate and resistor failure. We conclude that damage can be conveniently assessed from irreversible entropy generation. Our results for resistors can be easily extrapolated to other systems or machines that can be modeled based on their resistance.« less

Entropy Measurement for Biometric Verification Systems.

PubMed

Lim, Meng-Hui; Yuen, Pong C

2016-05-01

Biometric verification systems are designed to accept multiple similar biometric measurements per user due to inherent intrauser variations in the biometric data. This is important to preserve reasonable acceptance rate of genuine queries and the overall feasibility of the recognition system. However, such acceptance of multiple similar measurements decreases the imposter's difficulty of obtaining a system-acceptable measurement, thus resulting in a degraded security level. This deteriorated security needs to be measurable to provide truthful security assurance to the users. Entropy is a standard measure of security. However, the entropy formula is applicable only when there is a single acceptable possibility. In this paper, we develop an entropy-measuring model for biometric systems that accepts multiple similar measurements per user. Based on the idea of guessing entropy, the proposed model quantifies biometric system security in terms of adversarial guessing effort for two practical attacks. Excellent agreement between analytic and experimental simulation-based measurement results on a synthetic and a benchmark face dataset justify the correctness of our model and thus the feasibility of the proposed entropy-measuring approach.

Optimizing an estuarine water quality monitoring program through an entropy-based hierarchical spatiotemporal Bayesian framework

NASA Astrophysics Data System (ADS)

Alameddine, Ibrahim; Karmakar, Subhankar; Qian, Song S.; Paerl, Hans W.; Reckhow, Kenneth H.

2013-10-01

The total maximum daily load program aims to monitor more than 40,000 standard violations in around 20,000 impaired water bodies across the United States. Given resource limitations, future monitoring efforts have to be hedged against the uncertainties in the monitored system, while taking into account existing knowledge. In that respect, we have developed a hierarchical spatiotemporal Bayesian model that can be used to optimize an existing monitoring network by retaining stations that provide the maximum amount of information, while identifying locations that would benefit from the addition of new stations. The model assumes the water quality parameters are adequately described by a joint matrix normal distribution. The adopted approach allows for a reduction in redundancies, while emphasizing information richness rather than data richness. The developed approach incorporates the concept of entropy to account for the associated uncertainties. Three different entropy-based criteria are adopted: total system entropy, chlorophyll-a standard violation entropy, and dissolved oxygen standard violation entropy. A multiple attribute decision making framework is adopted to integrate the competing design criteria and to generate a single optimal design. The approach is implemented on the water quality monitoring system of the Neuse River Estuary in North Carolina, USA. The model results indicate that the high priority monitoring areas identified by the total system entropy and the dissolved oxygen violation entropy criteria are largely coincident. The monitoring design based on the chlorophyll-a standard violation entropy proved to be less informative, given the low probabilities of violating the water quality standard in the estuary.

Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system

PubMed Central

Min, Jianliang; Wang, Ping

2017-01-01

Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1–2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver. PMID:29220351

Hydration entropy change from the hard sphere model.

PubMed

Graziano, Giuseppe; Lee, Byungkook

2002-12-10

The gas to liquid transfer entropy change for a pure non-polar liquid can be calculated quite accurately using a hard sphere model that obeys the Carnahan-Starling equation of state. The same procedure fails to produce a reasonable value for hydrogen bonding liquids such as water, methanol and ethanol. However, the size of the molecules increases when the hydrogen bonds are turned off to produce the hard sphere system and the volume packing density rises. We show here that the hard sphere system that has this increased packing density reproduces the experimental transfer entropy values rather well. The gas to water transfer entropy values for small non-polar hydrocarbons is also not reproduced by a hard sphere model, whether one uses the normal (2.8 A diameter) or the increased (3.2 A) size for water. At least part of the reason that the hard sphere model with 2.8 A size water produces too small entropy change is that the size of water is too small for a system without hydrogen bonds. The reason that the 3.2 A model also produces too small entropy values is that this is an overly crowded system and that the free volume introduced in the system by the addition of a solute molecule produces too much of a relief to this crowding. A hard sphere model, in which the free volume increase is limited by requiring that the average surface-to-surface distance between the solute and water molecules is the same as that between the increased-size water molecules, does approximately reproduce the experimental hydration entropy values. Copyright 2002 Elsevier Science B.V.

Entanglement entropy in critical phenomena and analog models of quantum gravity

NASA Astrophysics Data System (ADS)

Fursaev, Dmitri V.

2006-06-01

A general geometrical structure of the entanglement entropy for spatial partition of a relativistic QFT system is established by using methods of the effective gravity action and the spectral geometry. A special attention is payed to the subleading terms in the entropy in different dimensions and to behavior in different states. It is conjectured, on the base of relation between the entropy and the action, that in a fundamental theory the ground state entanglement entropy per unit area equals 1/(4GN), where GN is the Newton constant in the low-energy gravity sector of the theory. The conjecture opens a new avenue in analogue gravity models. For instance, in higher-dimensional condensed matter systems, which near a critical point are described by relativistic QFT’s, the entanglement entropy density defines an effective gravitational coupling. By studying the properties of this constant one can get new insights in quantum gravity phenomena, such as the universality of the low-energy physics, the renormalization group behavior of GN, the statistical meaning of the Bekenstein-Hawking entropy.

Entanglement of a quantum field with a dispersive medium.

PubMed

Klich, Israel

2012-08-10

In this Letter we study the entanglement of a quantum radiation field interacting with a dielectric medium. In particular, we describe the quantum mixed state of a field interacting with a dielectric through plasma and Drude models and show that these generate very different entanglement behavior, as manifested in the entanglement entropy of the field. We also present a formula for a "Casimir" entanglement entropy, i.e., the distance dependence of the field entropy. Finally, we study a toy model of the interaction between two plates. In this model, the field entanglement entropy is divergent; however, as in the Casimir effect, its distance-dependent part is finite, and the field matter entanglement is reduced when the objects are far.

Predicting habitat suitability for rare plants at local spatial scales using a species distribution model.

PubMed

Gogol-Prokurat, Melanie

2011-01-01

If species distribution models (SDMs) can rank habitat suitability at a local scale, they may be a valuable conservation planning tool for rare, patchily distributed species. This study assessed the ability of Maxent, an SDM reported to be appropriate for modeling rare species, to rank habitat suitability at a local scale for four edaphic endemic rare plants of gabbroic soils in El Dorado County, California, and examined the effects of grain size, spatial extent, and fine-grain environmental predictors on local-scale model accuracy. Models were developed using species occurrence data mapped on public lands and were evaluated using an independent data set of presence and absence locations on surrounding lands, mimicking a typical conservation-planning scenario that prioritizes potential habitat on unsurveyed lands surrounding known occurrences. Maxent produced models that were successful at discriminating between suitable and unsuitable habitat at the local scale for all four species, and predicted habitat suitability values were proportional to likelihood of occurrence or population abundance for three of four species. Unfortunately, models with the best discrimination (i.e., AUC) were not always the most useful for ranking habitat suitability. The use of independent test data showed metrics that were valuable for evaluating which variables and model choices (e.g., grain, extent) to use in guiding habitat prioritization for conservation of these species. A goodness-of-fit test was used to determine whether habitat suitability values ranked habitat suitability on a continuous scale. If they did not, a minimum acceptable error predicted area criterion was used to determine the threshold for classifying habitat as suitable or unsuitable. I found a trade-off between model extent and the use of fine-grain environmental variables: goodness of fit was improved at larger extents, and fine-grain environmental variables improved local-scale accuracy, but fine-grain variables were not available at large extents. No single model met all habitat prioritization criteria, and the best models were overlaid to identify consensus areas of high suitability. Although the four species modeled here co-occur and are treated together for conservation planning, model accuracy and predicted suitable areas varied among species.

Modeling the Overalternating Bias with an Asymmetric Entropy Measure

PubMed Central

Gronchi, Giorgio; Raglianti, Marco; Noventa, Stefano; Lazzeri, Alessandro; Guazzini, Andrea

2016-01-01

Psychological research has found that human perception of randomness is biased. In particular, people consistently show the overalternating bias: they rate binary sequences of symbols (such as Heads and Tails in coin flipping) with an excess of alternation as more random than prescribed by the normative criteria of Shannon's entropy. Within data mining for medical applications, Marcellin proposed an asymmetric measure of entropy that can be ideal to account for such bias and to quantify subjective randomness. We fitted Marcellin's entropy and Renyi's entropy (a generalized form of uncertainty measure comprising many different kinds of entropies) to experimental data found in the literature with the Differential Evolution algorithm. We observed a better fit for Marcellin's entropy compared to Renyi's entropy. The fitted asymmetric entropy measure also showed good predictive properties when applied to different datasets of randomness-related tasks. We concluded that Marcellin's entropy can be a parsimonious and effective measure of subjective randomness that can be useful in psychological research about randomness perception. PMID:27458418

Advances in modeling trait-based plant community assembly.

PubMed

Laughlin, Daniel C; Laughlin, David E

2013-10-01

In this review, we examine two new trait-based models of community assembly that predict the relative abundance of species from a regional species pool. The models use fundamentally different mathematical approaches and the predictions can differ considerably. Maxent obtains the most even probability distribution subject to community-weighted mean trait constraints. Traitspace predicts low probabilities for any species whose trait distribution does not pass through the environmental filter. Neither model maximizes functional diversity because of the emphasis on environmental filtering over limiting similarity. Traitspace can test for the effects of limiting similarity by explicitly incorporating intraspecific trait variation. The range of solutions in both models could be used to define the range of natural variability of community composition in restoration projects. Copyright © 2013 Elsevier Ltd. All rights reserved.

Combining dispersal, landscape connectivity and habitat suitability to assess climate-induced changes in the distribution of Cunningham's skink, Egernia cunninghami.

PubMed

Ofori, Benjamin Y; Stow, Adam J; Baumgartner, John B; Beaumont, Linda J

2017-01-01

The ability of species to track their climate niche is dependent on their dispersal potential and the connectivity of the landscape matrix linking current and future suitable habitat. However, studies modeling climate-driven range shifts rarely address the movement of species across landscapes realistically, often assuming "unlimited" or "no" dispersal. Here, we incorporate dispersal rate and landscape connectivity with a species distribution model (Maxent) to assess the extent to which the Cunningham's skink (Egernia cunninghami) may be capable of tracking spatial shifts in suitable habitat as climate changes. Our model was projected onto four contrasting, but equally plausible, scenarios describing futures that are (relative to now) hot/wet, warm/dry, hot/with similar precipitation and warm/wet, at six time horizons with decadal intervals (2020-2070) and at two spatial resolutions: 1 km and 250 m. The size of suitable habitat was projected to decline 23-63% at 1 km and 26-64% at 250 m, by 2070. Combining Maxent output with the dispersal rate of the species and connectivity of the intervening landscape matrix showed that most current populations in regions projected to become unsuitable in the medium to long term, will be unable to shift the distance necessary to reach suitable habitat. In particular, numerous populations currently inhabiting the trailing edge of the species' range are highly unlikely to be able to disperse fast enough to track climate change. Unless these populations are capable of adaptation they are likely to be extirpated. We note, however, that the core of the species distribution remains suitable across the broad spectrum of climate scenarios considered. Our findings highlight challenges faced by philopatric species and the importance of adaptation for the persistence of peripheral populations under climate change.

Species diversity of sand flies and ecological niche model of Phlebotomus papatasi in central Iran.

PubMed

Abedi-Astaneh, Fatemeh; Akhavan, Amir Ahmad; Shirzadi, Mohammd Reza; Rassi, Yavar; Yaghoobi-Ershadi, Mohammad Reza; Hanafi-Bojd, Ahmad Ali; Akbarzadeh, Kamran; Nafar-Shalamzari, Reza; Parsi, Sohbat; Abbasi, Ali; Raufi, Hedayatollah

2015-09-01

Cutaneous leishmaniasis (CL) is the most important vector-borne disease in Iran. Qom Province is a very important area in the case of CL transmission, because of high traffic population from other parts of the country, or even other countries, as well as existence of confirmed foci of the disease. The aim of this study was to determine the ecology of sand flies in two different climates of this province and model the distribution of the main vector. Sand flies were collected monthly during April 2013-April 2014, at 22 urban/rural collection sites. Site selection was constrained by the geographical distribution of CL cases in recent years. Shannon-Weiner and Evenness indices were used to compare diversity in two studied climates. ArcGIS and MaxEnt were used to map and predict the appropriate ecological niches for sand flies. Totally, 5389 sand flies were collected and 12 species were identified. The most abundant species were Sergentomyia sintoni, P. papatasi, P. sergenti s.l. and Phlebotomus alexandri. Two peaks of activity were found in May and August in lowlands; while in mountainous areas they were observed in June and September. Species diversity in mountainous areas was found to be higher than in lowlands. The environmental variable with the highest gain in MaxEnt model was the monthly mean of (max temp-min temp). A big part of the lowland areas provides good ecological niches for P. papatasi and therefore higher transmission potential. These findings can be used in stratification of potential for CL transmission in Qom province. Copyright © 2015 Elsevier B.V. All rights reserved.

Bayesian or Laplacien inference, entropy and information theory and information geometry in data and signal processing

NASA Astrophysics Data System (ADS)

Mohammad-Djafari, Ali

2015-01-01

The main object of this tutorial article is first to review the main inference tools using Bayesian approach, Entropy, Information theory and their corresponding geometries. This review is focused mainly on the ways these tools have been used in data, signal and image processing. After a short introduction of the different quantities related to the Bayes rule, the entropy and the Maximum Entropy Principle (MEP), relative entropy and the Kullback-Leibler divergence, Fisher information, we will study their use in different fields of data and signal processing such as: entropy in source separation, Fisher information in model order selection, different Maximum Entropy based methods in time series spectral estimation and finally, general linear inverse problems.

The third law of thermodynamics and the fractional entropies

NASA Astrophysics Data System (ADS)

Baris Bagci, G.

2016-08-01

We consider the fractal calculus based Ubriaco and Machado entropies and investigate whether they conform to the third law of thermodynamics. The Ubriaco entropy satisfies the third law of thermodynamics in the interval 0 < q ≤ 1 exactly where it is also thermodynamically stable. The Machado entropy, on the other hand, yields diverging inverse temperature in the region 0 < q ≤ 1, albeit with non-vanishing negative entropy values. Therefore, despite the divergent inverse temperature behavior, the Machado entropy fails the third law of thermodynamics. We also show that the aforementioned results are also supported by the one-dimensional Ising model with no external field.

When can preheating affect the CMB?

NASA Astrophysics Data System (ADS)

Tsujikawa, Shinji; Bassett, Bruce A.

2002-05-01

We discuss the principles governing the selection of inflationary models for which preheating can affect the CMB. This is a (fairly small) subset of those models which have nonnegligible entropy/isocurvature perturbations on large scales during inflation. We study new models which belong to this class-two-field inflation with negative nonminimal coupling and hybrid/double/supernatural inflation models where the tachyonic growth of entropy perturbations can lead to the variation of the curvature perturbation, /R, on super-Hubble scales. Finally, we present evidence against recent claims for the variation of /R in the absence of substantial super-Hubble entropy perturbations.

Predictive uncertainty in auditory sequence processing

PubMed Central

Hansen, Niels Chr.; Pearce, Marcus T.

2014-01-01

Previous studies of auditory expectation have focused on the expectedness perceived by listeners retrospectively in response to events. In contrast, this research examines predictive uncertainty—a property of listeners' prospective state of expectation prior to the onset of an event. We examine the information-theoretic concept of Shannon entropy as a model of predictive uncertainty in music cognition. This is motivated by the Statistical Learning Hypothesis, which proposes that schematic expectations reflect probabilistic relationships between sensory events learned implicitly through exposure. Using probability estimates from an unsupervised, variable-order Markov model, 12 melodic contexts high in entropy and 12 melodic contexts low in entropy were selected from two musical repertoires differing in structural complexity (simple and complex). Musicians and non-musicians listened to the stimuli and provided explicit judgments of perceived uncertainty (explicit uncertainty). We also examined an indirect measure of uncertainty computed as the entropy of expectedness distributions obtained using a classical probe-tone paradigm where listeners rated the perceived expectedness of the final note in a melodic sequence (inferred uncertainty). Finally, we simulate listeners' perception of expectedness and uncertainty using computational models of auditory expectation. A detailed model comparison indicates which model parameters maximize fit to the data and how they compare to existing models in the literature. The results show that listeners experience greater uncertainty in high-entropy musical contexts than low-entropy contexts. This effect is particularly apparent for inferred uncertainty and is stronger in musicians than non-musicians. Consistent with the Statistical Learning Hypothesis, the results suggest that increased domain-relevant training is associated with an increasingly accurate cognitive model of probabilistic structure in music. PMID:25295018

New Insights into the Fractional Order Diffusion Equation Using Entropy and Kurtosis.

PubMed

Ingo, Carson; Magin, Richard L; Parrish, Todd B

2014-11-01

Fractional order derivative operators offer a concise description to model multi-scale, heterogeneous and non-local systems. Specifically, in magnetic resonance imaging, there has been recent work to apply fractional order derivatives to model the non-Gaussian diffusion signal, which is ubiquitous in the movement of water protons within biological tissue. To provide a new perspective for establishing the utility of fractional order models, we apply entropy for the case of anomalous diffusion governed by a fractional order diffusion equation generalized in space and in time. This fractional order representation, in the form of the Mittag-Leffler function, gives an entropy minimum for the integer case of Gaussian diffusion and greater values of spectral entropy for non-integer values of the space and time derivatives. Furthermore, we consider kurtosis, defined as the normalized fourth moment, as another probabilistic description of the fractional time derivative. Finally, we demonstrate the implementation of anomalous diffusion, entropy and kurtosis measurements in diffusion weighted magnetic resonance imaging in the brain of a chronic ischemic stroke patient.

Information-Theoretic Uncertainty of SCFG-Modeled Folding Space of The Non-coding RNA

PubMed Central

Manzourolajdad, Amirhossein; Wang, Yingfeng; Shaw, Timothy I.; Malmberg, Russell L.

2012-01-01

RNA secondary structure ensembles define probability distributions for alternative equilibrium secondary structures of an RNA sequence. Shannon’s Entropy is a measure for the amount of diversity present in any ensemble. In this work, Shannon’s entropy of the SCFG ensemble on an RNA sequence is derived and implemented in polynomial time for both structurally ambiguous and unambiguous grammars. Micro RNA sequences generally have low folding entropy, as previously discovered. Surprisingly, signs of significantly high folding entropy were observed in certain ncRNA families. More effective models coupled with targeted randomization tests can lead to a better insight into folding features of these families. PMID:23160142

Entropy model of dissipative structure on corporate social responsibility

NASA Astrophysics Data System (ADS)

Li, Zuozhi; Jiang, Jie

2017-06-01

Enterprise is prompted to fulfill the social responsibility requirement by the internal and external environment. In this complex system, some studies suggest that firms have an orderly or chaotic entropy exchange behavior. Based on the theory of dissipative structure, this paper constructs the entropy index system of corporate social responsibility(CSR) and explores the dissipative structure of CSR through Brusselator model criterion. Picking up listed companies of the equipment manufacturing, the research shows that CSR has positive incentive to negative entropy and promotes the stability of dissipative structure. In short, the dissipative structure of CSR has a positive impact on the interests of stakeholders and corporate social images.

Renyi Entropies in Particle Cascades

NASA Astrophysics Data System (ADS)

Bialas, A.; Czyz, W.; Ostruszka, A.

2003-01-01

Renyi entropies for particle distributions following from the general cascade models are discussed. The p-model and the β distribution introduced in earlier studies of cascades are discussed in some detail. Some phenomenological consequences are pointed out.

Pressure transfer function of a JT15D nozzle due to acoustic and convected entropy fluctuations

NASA Astrophysics Data System (ADS)

Miles, J. H.

An acoustic transmission matrix analysis of sound propagation in a variable area duct with and without flow is extended to include convected entropy fluctuations. The boundary conditions used in the analysis are a transfer function relating entropy and pressure at the nozzle inlet and the nozzle exit impedance. The nozzle pressure transfer function calculated is compared with JT15D turbofan engine nozzle data. The one dimensional theory for sound propagation in a variable area nozzle with flow but without convected entropy is good at the low engine speeds where the nozzle exit Mach number is low (M=0.2) and the duct exit impedance model is good. The effect of convected entropy appears to be so negligible that it is obscured by the inaccuracy of the nozzle exit impedance model, the lack of information on the magnitude of the convected entropy and its phase relationship with the pressure, and the scatter in the data. An improved duct exit impedance model is required at the higher engine speeds where the nozzle exit Mach number is high (M=0.56) and at low frequencies (below 120 Hz).

Generation of skeletal mechanism by means of projected entropy participation indices

NASA Astrophysics Data System (ADS)

Paolucci, Samuel; Valorani, Mauro; Ciottoli, Pietro Paolo; Galassi, Riccardo Malpica

2017-11-01

When the dynamics of reactive systems develop very-slow and very-fast time scales separated by a range of active time scales, with gaps in the fast/active and slow/active time scales, then it is possible to achieve multi-scale adaptive model reduction along-with the integration of the ODEs using the G-Scheme. The scheme assumes that the dynamics is decomposed into active, slow, fast, and invariant subspaces. We derive expressions that establish a direct link between time scales and entropy production by using estimates provided by the G-Scheme. To calculate the contribution to entropy production, we resort to a standard model of a constant pressure, adiabatic, batch reactor, where the mixture temperature of the reactants is initially set above the auto-ignition temperature. Numerical experiments show that the contribution to entropy production of the fast subspace is of the same magnitude as the error threshold chosen for the identification of the decomposition of the tangent space, and the contribution of the slow subspace is generally much smaller than that of the active subspace. The information on entropy production associated with reactions within each subspace is used to define an entropy participation index that is subsequently utilized for model reduction.

Psychoacoustic entropy theory and its implications for performance practice

NASA Astrophysics Data System (ADS)

Strohman, Gregory J.

This dissertation attempts to motivate, derive and imply potential uses for a generalized perceptual theory of musical harmony called psychoacoustic entropy theory. This theory treats the human auditory system as a physical system which takes acoustic measurements. As a result, the human auditory system is subject to all the appropriate uncertainties and limitations of other physical measurement systems. This is the theoretic basis for defining psychoacoustic entropy. Psychoacoustic entropy is a numerical quantity which indexes the degree to which the human auditory system perceives instantaneous disorder within a sound pressure wave. Chapter one explains the importance of harmonic analysis as a tool for performance practice. It also outlines the critical limitations for many of the most influential historical approaches to modeling harmonic stability, particularly when compared to available scientific research in psychoacoustics. Rather than analyze a musical excerpt, psychoacoustic entropy is calculated directly from sound pressure waves themselves. This frames psychoacoustic entropy theory in the most general possible terms as a theory of musical harmony, enabling it to be invoked for any perceivable sound. Chapter two provides and examines many widely accepted mathematical models of the acoustics and psychoacoustics of these sound pressure waves. Chapter three introduces entropy as a precise way of measuring perceived uncertainty in sound pressure waves. Entropy is used, in combination with the acoustic and psychoacoustic models introduced in chapter two, to motivate the mathematical formulation of psychoacoustic entropy theory. Chapter four shows how to use psychoacoustic entropy theory to analyze the certain types of musical harmonies, while chapter five applies the analytical tools developed in chapter four to two short musical excerpts to influence their interpretation. Almost every form of harmonic analysis invokes some degree of mathematical reasoning. However, the limited scope of most harmonic systems used for Western common practice music greatly simplifies the necessary level of mathematical detail. Psychoacoustic entropy theory requires a greater deal of mathematical complexity due to its sheer scope as a generalized theory of musical harmony. Fortunately, under specific assumptions the theory can take on vastly simpler forms. Psychoacoustic entropy theory appears to be highly compatible with the latest scientific research in psychoacoustics. However, the theory itself should be regarded as a hypothesis and this dissertation an experiment in progress. The evaluation of psychoacoustic entropy theory as a scientific theory of human sonic perception must wait for more rigorous future research.

EEG entropy measures in anesthesia

PubMed Central

Liang, Zhenhu; Wang, Yinghua; Sun, Xue; Li, Duan; Voss, Logan J.; Sleigh, Jamie W.; Hagihira, Satoshi; Li, Xiaoli

2015-01-01

Highlights: ► Twelve entropy indices were systematically compared in monitoring depth of anesthesia and detecting burst suppression.► Renyi permutation entropy performed best in tracking EEG changes associated with different anesthesia states.► Approximate Entropy and Sample Entropy performed best in detecting burst suppression. Objective: Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs' effect is lacking. In this study, we compare the capability of 12 entropy indices for monitoring depth of anesthesia (DoA) and detecting the burst suppression pattern (BSP), in anesthesia induced by GABAergic agents. Methods: Twelve indices were investigated, namely Response Entropy (RE) and State entropy (SE), three wavelet entropy (WE) measures [Shannon WE (SWE), Tsallis WE (TWE), and Renyi WE (RWE)], Hilbert-Huang spectral entropy (HHSE), approximate entropy (ApEn), sample entropy (SampEn), Fuzzy entropy, and three permutation entropy (PE) measures [Shannon PE (SPE), Tsallis PE (TPE) and Renyi PE (RPE)]. Two EEG data sets from sevoflurane-induced and isoflurane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, pharmacokinetic/pharmacodynamic (PK/PD) modeling and prediction probability (Pk) analysis were applied. The multifractal detrended fluctuation analysis (MDFA) as a non-entropy measure was compared. Results: All the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline variability, higher coefficient of determination (R2) and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an advantage in computation efficiency compared with MDFA. Conclusion: Each entropy index has its advantages and disadvantages in estimating DoA. Overall, it is suggested that the RPE index was a superior measure. Investigating the advantages and disadvantages of these entropy indices could help improve current clinical indices for monitoring DoA. PMID:25741277

Quelques applications de la decomposition de l'entropie en psychologie et pedagogie (Some Applications of the Resolution of Entropy in Psychology and Pedagogy)

ERIC Educational Resources Information Center

Gillet, Louis

1971-01-01

Psychological and educational measurement is carried out according to the type of model used and data collected. The H entropy which shows the dispersion of the data can be divided into intragroup and intergroup entropy. Choice of colors, sociometrical choice, and the communications are three situations where this resolution can be applied. (MF)

The Rényi entropy H2 as a rigorous, measurable lower bound for the entropy of the interaction region in multi-particle production processes

NASA Astrophysics Data System (ADS)

Bialas, A.; Czyz, W.; Zalewski, K.

2006-10-01

A model-independent lower bound on the entropy S of the multi-particle system produced in high energy collisions, provided by the measurable Rényi entropy H2, is shown to be very effective. Estimates show that the ratio H2/S remains close to one half for all realistic values of the parameters.

Refined generalized multiscale entropy analysis for physiological signals

NASA Astrophysics Data System (ADS)

Liu, Yunxiao; Lin, Youfang; Wang, Jing; Shang, Pengjian

2018-01-01

Multiscale entropy analysis has become a prevalent complexity measurement and been successfully applied in various fields. However, it only takes into account the information of mean values (first moment) in coarse-graining procedure. Then generalized multiscale entropy (MSEn) considering higher moments to coarse-grain a time series was proposed and MSEσ2 has been implemented. However, the MSEσ2 sometimes may yield an imprecise estimation of entropy or undefined entropy, and reduce statistical reliability of sample entropy estimation as scale factor increases. For this purpose, we developed the refined model, RMSEσ2, to improve MSEσ2. Simulations on both white noise and 1 / f noise show that RMSEσ2 provides higher entropy reliability and reduces the occurrence of undefined entropy, especially suitable for short time series. Besides, we discuss the effect on RMSEσ2 analysis from outliers, data loss and other concepts in signal processing. We apply the proposed model to evaluate the complexity of heartbeat interval time series derived from healthy young and elderly subjects, patients with congestive heart failure and patients with atrial fibrillation respectively, compared to several popular complexity metrics. The results demonstrate that RMSEσ2 measured complexity (a) decreases with aging and diseases, and (b) gives significant discrimination between different physiological/pathological states, which may facilitate clinical application.

Structure of the entanglement entropy of (3+1)-dimensional gapped phases of matter

NASA Astrophysics Data System (ADS)

Zheng, Yunqin; He, Huan; Bradlyn, Barry; Cano, Jennifer; Neupert, Titus; Bernevig, B. Andrei

2018-05-01

We study the entanglement entropy of gapped phases of matter in three spatial dimensions. We focus in particular on size-independent contributions to the entropy across entanglement surfaces of arbitrary topologies. We show that for low energy fixed-point theories, the constant part of the entanglement entropy across any surface can be reduced to a linear combination of the entropies across a sphere and a torus. We first derive our results using strong sub-additivity inequalities along with assumptions about the entanglement entropy of fixed-point models, and identify the topological contribution by considering the renormalization group flow; in this way we give an explicit definition of topological entanglement entropy Stopo in (3+1)D, which sharpens previous results. We illustrate our results using several concrete examples and independent calculations, and show adding "twist" terms to the Lagrangian can change Stopo in (3+1)D. For the generalized Walker-Wang models, we find that the ground state degeneracy on a 3-torus is given by exp(-3 Stopo[T2] ) in terms of the topological entanglement entropy across a 2-torus. We conjecture that a similar relationship holds for Abelian theories in (d +1 ) dimensional spacetime, with the ground state degeneracy on the d -torus given by exp(-d Stopo[Td -1] ) .

Multiscale Shannon's Entropy Modeling of Orientation and Distance in Steel Fiber Micro-Tomography Data.

PubMed

Chiverton, John P; Ige, Olubisi; Barnett, Stephanie J; Parry, Tony

2017-11-01

This paper is concerned with the modeling and analysis of the orientation and distance between steel fibers in X-ray micro-tomography data. The advantage of combining both orientation and separation in a model is that it helps provide a detailed understanding of how the steel fibers are arranged, which is easy to compare. The developed models are designed to summarize the randomness of the orientation distribution of the steel fibers both locally and across an entire volume based on multiscale entropy. Theoretical modeling, simulation, and application to real imaging data are shown here. The theoretical modeling of multiscale entropy for orientation includes a proof showing the final form of the multiscale taken over a linear range of scales. A series of image processing operations are also included to overcome interslice connectivity issues to help derive the statistical descriptions of the orientation distributions of the steel fibers. The results demonstrate that multiscale entropy provides unique insights into both simulated and real imaging data of steel fiber reinforced concrete.

Bivariate Rainfall and Runoff Analysis Using Shannon Entropy Theory

NASA Astrophysics Data System (ADS)

Rahimi, A.; Zhang, L.

2012-12-01

Rainfall-Runoff analysis is the key component for many hydrological and hydraulic designs in which the dependence of rainfall and runoff needs to be studied. It is known that the convenient bivariate distribution are often unable to model the rainfall-runoff variables due to that they either have constraints on the range of the dependence or fixed form for the marginal distributions. Thus, this paper presents an approach to derive the entropy-based joint rainfall-runoff distribution using Shannon entropy theory. The distribution derived can model the full range of dependence and allow different specified marginals. The modeling and estimation can be proceeded as: (i) univariate analysis of marginal distributions which includes two steps, (a) using the nonparametric statistics approach to detect modes and underlying probability density, and (b) fitting the appropriate parametric probability density functions; (ii) define the constraints based on the univariate analysis and the dependence structure; (iii) derive and validate the entropy-based joint distribution. As to validate the method, the rainfall-runoff data are collected from the small agricultural experimental watersheds located in semi-arid region near Riesel (Waco), Texas, maintained by the USDA. The results of unviariate analysis show that the rainfall variables follow the gamma distribution, whereas the runoff variables have mixed structure and follow the mixed-gamma distribution. With this information, the entropy-based joint distribution is derived using the first moments, the first moments of logarithm transformed rainfall and runoff, and the covariance between rainfall and runoff. The results of entropy-based joint distribution indicate: (1) the joint distribution derived successfully preserves the dependence between rainfall and runoff, and (2) the K-S goodness of fit statistical tests confirm the marginal distributions re-derived reveal the underlying univariate probability densities which further assure that the entropy-based joint rainfall-runoff distribution are satisfactorily derived. Overall, the study shows the Shannon entropy theory can be satisfactorily applied to model the dependence between rainfall and runoff. The study also shows that the entropy-based joint distribution is an appropriate approach to capture the dependence structure that cannot be captured by the convenient bivariate joint distributions. Joint Rainfall-Runoff Entropy Based PDF, and Corresponding Marginal PDF and Histogram for W12 Watershed The K-S Test Result and RMSE on Univariate Distributions Derived from the Maximum Entropy Based Joint Probability Distribution;

Climatic and anthropogenic drivers of northern Amazon fires during the 2015-2016 El Niño event.

PubMed

Fonseca, Marisa G; Anderson, Liana O; Arai, Egidio; Shimabukuro, Yosio E; Xaud, Haron A M; Xaud, Maristela R; Madani, Nima; Wagner, Fabien H; Aragão, Luiz E O C

2017-12-01

The strong El Niño Southern Oscillation (ENSO) event that occurred in 2015-2016 caused extreme drought in the northern Brazilian Amazon, especially in the state of Roraima, increasing fire occurrence. Here we map the extent of precipitation and fire anomalies and quantify the effects of climatic and anthropogenic drivers on fire occurrence during the 2015-2016 dry season (from December 2015 to March 2016) in the state of Roraima. To achieve these objectives we first estimated the spatial pattern of precipitation anomalies, based on long-term data from the TRMM (Tropical Rainfall Measuring Mission), and the fire anomaly, based on MODIS (Moderate Resolution Imaging Spectroradiometer) active fire detections during the referred period. Then, we integrated climatic and anthropogenic drivers in a Maximum Entropy (MaxEnt) model to quantify fire probability, assessing (1) the model accuracy during the 2015-2016 and the 2016-2017 dry seasons; (2) the relative importance of each predictor variable on the model predictive performance; and (3) the response curves, showing how each environmental variable affects the fire probability. Approximately 59% (132,900 km 2 ) of the study area was exposed to precipitation anomalies ≤-1 standard deviation (SD) in January and ~48% (~106,800 km 2 ) in March. About 38% (86,200 km 2 ) of the study area experienced fire anomalies ≥1 SD in at least one month between December 2015 and March 2016. The distance to roads and the direct ENSO effect on fire occurrence were the two most influential variables on model predictive performance. Despite the improvement of governmental actions of fire prevention and firefighting in Roraima since the last intense ENSO event (1997-1998), we show that fire still gets out of control in the state during extreme drought events. Our results indicate that if no prevention actions are undertaken, future road network expansion and a climate-induced increase in water stress will amplify fire occurrence in the northern Amazon, even in its humid dense forests. As an additional outcome of our analysis, we conclude that the model and the data we used may help to guide on-the-ground fire-prevention actions and firefighting planning and therefore minimize fire-related ecosystems degradation, economic losses and carbon emissions in Roraima. © 2017 by the Ecological Society of America.

Maxent modeling for predicting the potential geographical distribution of two peony species under climate change.

PubMed

Zhang, Keliang; Yao, Linjun; Meng, Jiasong; Tao, Jun

2018-09-01

Paeonia (Paeoniaceae), an economically important plant genus, includes many popular ornamentals and medicinal plant species used in traditional Chinese medicine. Little is known about the properties of the habitat distribution and the important eco-environmental factors shaping the suitability. Based on high-resolution environmental data for current and future climate scenarios, we modeled the present and future suitable habitat for P. delavayi and P. rockii by Maxent, evaluated the importance of environmental factors in shaping their distribution, and identified distribution shifts under climate change scenarios. The results showed that the moderate and high suitable areas for P. delavayi and P. rockii encompassed ca. 4.46×10 5 km 2 and 1.89×10 5 km 2 , respectively. Temperature seasonality and isothermality were identified as the most critical factors shaping P. delavayi distribution, and UVB-4 and annual precipitation were identified as the most critical for shaping P. rockii distribution. Under the scenario with a low concentration of greenhouse gas emissions (RCP2.6), the range of both species increased as global warming intensified; however, under the scenario with higher concentrations of emissions (RCP8.5), the suitable habitat range of P. delavayi decreased while P. rockii increased. Overall, our prediction showed that a shift in distribution of suitable habitat to higher elevations would gradually become more significant. The information gained from this study should provide a useful reference for implementing long-term conservation and management strategies for these species. Copyright © 2018. Published by Elsevier B.V.

Predicting the impacts of climate change on the potential distribution of major native non-food bioenergy plants in China.

PubMed

Wang, Wenguo; Tang, Xiaoyu; Zhu, Qili; Pan, Ke; Hu, Qichun; He, Mingxiong; Li, Jiatang

2014-01-01

Planting non-food bioenergy crops on marginal lands is an alternative bioenergy development solution in China. Native non-food bioenergy plants are also considered to be a wise choice to reduce the threat of invasive plants. In this study, the impacts of climate change (a consensus of IPCC scenarios A2a for 2080) on the potential distribution of nine non-food bioenergy plants native to China (viz., Pistacia chinensis, Cornus wilsoniana, Xanthoceras sorbifolia, Vernicia fordii, Sapium sebiferum, Miscanthus sinensis, M. floridulus, M. sacchariflorus and Arundo donax) were analyzed using a MaxEnt species distribution model. The suitable habitats of the nine non-food plants were distributed in the regions east of the Mongolian Plateau and the Tibetan Plateau, where the arable land is primarily used for food production. Thus, the large-scale cultivation of those plants for energy production will have to rely on the marginal lands. The variables of "precipitation of the warmest quarter" and "annual mean temperature" were the most important bioclimatic variables for most of the nine plants according to the MaxEnt modeling results. Global warming in coming decades may result in a decrease in the extent of suitable habitat in the tropics but will have little effect on the total distribution area of each plant. The results indicated that it will be possible to grow these plants on marginal lands within these areas in the future. This work should be beneficial for the domestication and cultivation of those bioenergy plants and should facilitate land-use planning for bioenergy crops in China.

Schistosoma japonicum transmission risk maps at present and under climate change in mainland China.

PubMed

Zhu, Gengping; Fan, Jingyu; Peterson, A Townsend

2017-10-01

The South-to-North Water Diversion (SNWD) project is designed to channel fresh water from the Yangtze River north to more industrialized parts of China. An important question is whether future climate change and dispersal via the SNWD may synergistically favor a northward expansion of species involved in hosting and transmitting schistosomiasis in China, specifically the intermediate host, Oncomelania hupensis. In this study, climate spaces occupied by the four subspecies of O. hupensis (O. h. hupensis, O. h. robertsoni, O. h. guangxiensis and O. h. tangi) were estimated, and niche conservatism tested among each pair of subspecies. Fine-tuned Maxent (fMaxent) and ensemble models were used to anticipate potential distributions of O. hupensis under future climate change scenarios. We were largely unable to reject the null hypothesis that climatic niches are conserved among the four subspecies, so factors other than climate appear to account for the divergence of O. hupensis populations across mainland China. Both model approaches indicated increased suitability and range expansion in O. h. hupensis in the future; an eastward and northward shift in O. h. robertsioni and O. h. guangxiensis, respectively; and relative distributional stability in O. h. gangi. The southern parts of the Central Route of SNWD will coincide with suitable areas for O. h. hupensis in 2050-2060; its suitable areas will also expand northward along the southern parts of the Eastern Route by 2080-2090. Our results call for rigorous monitoring and surveillance of schistosomiasis along the southern Central Route and Eastern Route of the SNWD in a future, warmer China.

Essential equivalence of the general equation for the nonequilibrium reversible-irreversible coupling (GENERIC) and steepest-entropy-ascent models of dissipation for nonequilibrium thermodynamics.

PubMed

Montefusco, Alberto; Consonni, Francesco; Beretta, Gian Paolo

2015-04-01

By reformulating the steepest-entropy-ascent (SEA) dynamical model for nonequilibrium thermodynamics in the mathematical language of differential geometry, we compare it with the primitive formulation of the general equation for the nonequilibrium reversible-irreversible coupling (GENERIC) model and discuss the main technical differences of the two approaches. In both dynamical models the description of dissipation is of the "entropy-gradient" type. SEA focuses only on the dissipative, i.e., entropy generating, component of the time evolution, chooses a sub-Riemannian metric tensor as dissipative structure, and uses the local entropy density field as potential. GENERIC emphasizes the coupling between the dissipative and nondissipative components of the time evolution, chooses two compatible degenerate structures (Poisson and degenerate co-Riemannian), and uses the global energy and entropy functionals as potentials. As an illustration, we rewrite the known GENERIC formulation of the Boltzmann equation in terms of the square root of the distribution function adopted by the SEA formulation. We then provide a formal proof that in more general frameworks, whenever all degeneracies in the GENERIC framework are related to conservation laws, the SEA and GENERIC models of the dissipative component of the dynamics are essentially interchangeable, provided of course they assume the same kinematics. As part of the discussion, we note that equipping the dissipative structure of GENERIC with the Leibniz identity makes it automatically SEA on metric leaves.

Shannon information entropy in heavy-ion collisions

NASA Astrophysics Data System (ADS)

Ma, Chun-Wang; Ma, Yu-Gang

2018-03-01

The general idea of information entropy provided by C.E. Shannon "hangs over everything we do" and can be applied to a great variety of problems once the connection between a distribution and the quantities of interest is found. The Shannon information entropy essentially quantify the information of a quantity with its specific distribution, for which the information entropy based methods have been deeply developed in many scientific areas including physics. The dynamical properties of heavy-ion collisions (HICs) process make it difficult and complex to study the nuclear matter and its evolution, for which Shannon information entropy theory can provide new methods and observables to understand the physical phenomena both theoretically and experimentally. To better understand the processes of HICs, the main characteristics of typical models, including the quantum molecular dynamics models, thermodynamics models, and statistical models, etc., are briefly introduced. The typical applications of Shannon information theory in HICs are collected, which cover the chaotic behavior in branching process of hadron collisions, the liquid-gas phase transition in HICs, and the isobaric difference scaling phenomenon for intermediate mass fragments produced in HICs of neutron-rich systems. Even though the present applications in heavy-ion collision physics are still relatively simple, it would shed light on key questions we are seeking for. It is suggested to further develop the information entropy methods in nuclear reactions models, as well as to develop new analysis methods to study the properties of nuclear matters in HICs, especially the evolution of dynamics system.

Moisture sorption isotherms and thermodynamic properties of mexican mennonite-style cheese.

PubMed

Martinez-Monteagudo, Sergio I; Salais-Fierro, Fabiola

2014-10-01

Moisture adsorption isotherms of fresh and ripened Mexican Mennonite-style cheese were investigated using the static gravimetric method at 4, 8, and 12 °C in a water activity range (aw) of 0.08-0.96. These isotherms were modeled using GAB, BET, Oswin and Halsey equations through weighed non-linear regression. All isotherms were sigmoid in shape, showing a type II BET isotherm, and the data were best described by GAB model. GAB model coefficients revealed that water adsorption by cheese matrix is a multilayer process characterized by molecules that are strongly bound in the monolayer and molecules that are slightly structured in a multilayer. Using the GAB model, it was possible to estimate thermodynamic functions (net isosteric heat, differential entropy, integral enthalpy and entropy, and enthalpy-entropy compensation) as function of moisture content. For both samples, the isosteric heat and differential entropy decreased with moisture content in exponential fashion. The integral enthalpy gradually decreased with increasing moisture content after reached a maximum value, while the integral entropy decreased with increasing moisture content after reached a minimum value. A linear compensation was found between integral enthalpy and entropy suggesting enthalpy controlled adsorption. Determination of moisture content and aw relationship yields to important information of controlling the ripening, drying and storage operations as well as understanding of the water state within a cheese matrix.

The Conditional Entropy Power Inequality for Bosonic Quantum Systems

NASA Astrophysics Data System (ADS)

De Palma, Giacomo; Trevisan, Dario

2018-06-01

We prove the conditional Entropy Power Inequality for Gaussian quantum systems. This fundamental inequality determines the minimum quantum conditional von Neumann entropy of the output of the beam-splitter or of the squeezing among all the input states where the two inputs are conditionally independent given the memory and have given quantum conditional entropies. We also prove that, for any couple of values of the quantum conditional entropies of the two inputs, the minimum of the quantum conditional entropy of the output given by the conditional Entropy Power Inequality is asymptotically achieved by a suitable sequence of quantum Gaussian input states. Our proof of the conditional Entropy Power Inequality is based on a new Stam inequality for the quantum conditional Fisher information and on the determination of the universal asymptotic behaviour of the quantum conditional entropy under the heat semigroup evolution. The beam-splitter and the squeezing are the central elements of quantum optics, and can model the attenuation, the amplification and the noise of electromagnetic signals. This conditional Entropy Power Inequality will have a strong impact in quantum information and quantum cryptography. Among its many possible applications there is the proof of a new uncertainty relation for the conditional Wehrl entropy.

The Conditional Entropy Power Inequality for Bosonic Quantum Systems

NASA Astrophysics Data System (ADS)

De Palma, Giacomo; Trevisan, Dario

2018-01-01

We prove the conditional Entropy Power Inequality for Gaussian quantum systems. This fundamental inequality determines the minimum quantum conditional von Neumann entropy of the output of the beam-splitter or of the squeezing among all the input states where the two inputs are conditionally independent given the memory and have given quantum conditional entropies. We also prove that, for any couple of values of the quantum conditional entropies of the two inputs, the minimum of the quantum conditional entropy of the output given by the conditional Entropy Power Inequality is asymptotically achieved by a suitable sequence of quantum Gaussian input states. Our proof of the conditional Entropy Power Inequality is based on a new Stam inequality for the quantum conditional Fisher information and on the determination of the universal asymptotic behaviour of the quantum conditional entropy under the heat semigroup evolution. The beam-splitter and the squeezing are the central elements of quantum optics, and can model the attenuation, the amplification and the noise of electromagnetic signals. This conditional Entropy Power Inequality will have a strong impact in quantum information and quantum cryptography. Among its many possible applications there is the proof of a new uncertainty relation for the conditional Wehrl entropy.

A pairwise maximum entropy model accurately describes resting-state human brain networks

PubMed Central

Watanabe, Takamitsu; Hirose, Satoshi; Wada, Hiroyuki; Imai, Yoshio; Machida, Toru; Shirouzu, Ichiro; Konishi, Seiki; Miyashita, Yasushi; Masuda, Naoki

2013-01-01

The resting-state human brain networks underlie fundamental cognitive functions and consist of complex interactions among brain regions. However, the level of complexity of the resting-state networks has not been quantified, which has prevented comprehensive descriptions of the brain activity as an integrative system. Here, we address this issue by demonstrating that a pairwise maximum entropy model, which takes into account region-specific activity rates and pairwise interactions, can be robustly and accurately fitted to resting-state human brain activities obtained by functional magnetic resonance imaging. Furthermore, to validate the approximation of the resting-state networks by the pairwise maximum entropy model, we show that the functional interactions estimated by the pairwise maximum entropy model reflect anatomical connexions more accurately than the conventional functional connectivity method. These findings indicate that a relatively simple statistical model not only captures the structure of the resting-state networks but also provides a possible method to derive physiological information about various large-scale brain networks. PMID:23340410

Entropy corrected holographic dark energy models in modified gravity

NASA Astrophysics Data System (ADS)

Jawad, Abdul; Azhar, Nadeem; Rani, Shamaila

We consider the power law and the entropy corrected holographic dark energy (HDE) models with Hubble horizon in the dynamical Chern-Simons modified gravity. We explore various cosmological parameters and planes in this framework. The Hubble parameter lies within the consistent range at the present and later epoch for both entropy corrected models. The deceleration parameter explains the accelerated expansion of the universe. The equation of state (EoS) parameter corresponds to quintessence and cold dark matter (ΛCDM) limit. The ωΛ-ωΛ‧ approaches to ΛCDM limit and freezing region in both entropy corrected models. The statefinder parameters are consistent with ΛCDM limit and dark energy (DE) models. The generalized second law of thermodynamics remain valid in all cases of interacting parameter. It is interesting to mention here that our results of Hubble, EoS parameter and ωΛ-ωΛ‧ plane show consistency with the present observations like Planck, WP, BAO, H0, SNLS and nine-year WMAP.

Gravitational baryogenesis in running vacuum models

NASA Astrophysics Data System (ADS)

Oikonomou, V. K.; Pan, Supriya; Nunes, Rafael C.

2017-08-01

We study the gravitational baryogenesis mechanism for generating baryon asymmetry in the context of running vacuum models. Regardless of whether these models can produce a viable cosmological evolution, we demonstrate that they produce a nonzero baryon-to-entropy ratio even if the universe is filled with conformal matter. This is a sound difference between the running vacuum gravitational baryogenesis and the Einstein-Hilbert one, since in the latter case, the predicted baryon-to-entropy ratio is zero. We consider two well known and most used running vacuum models and show that the resulting baryon-to-entropy ratio is compatible with the observational data. Moreover, we also show that the mechanism of gravitational baryogenesis may constrain the running vacuum models.

DEM interpolation weight calculation modulus based on maximum entropy

NASA Astrophysics Data System (ADS)

Chen, Tian-wei; Yang, Xia

2015-12-01

There is negative-weight in traditional interpolation of gridding DEM, in the article, the principle of Maximum Entropy is utilized to analyze the model system which depends on modulus of space weight. Negative-weight problem of the DEM interpolation is researched via building Maximum Entropy model, and adding nonnegative, first and second order's Moment constraints, the negative-weight problem is solved. The correctness and accuracy of the method was validated with genetic algorithm in matlab program. The method is compared with the method of Yang Chizhong interpolation and quadratic program. Comparison shows that the volume and scaling of Maximum Entropy's weight is fit to relations of space and the accuracy is superior to the latter two.

Entropy Generation in Regenerative Systems

NASA Technical Reports Server (NTRS)

Kittel, Peter

1995-01-01

Heat exchange to the oscillating flows in regenerative coolers generates entropy. These flows are characterized by oscillating mass flows and oscillating temperatures. Heat is transferred between the flow and heat exchangers and regenerators. In the former case, there is a steady temperature difference between the flow and the heat exchangers. In the latter case, there is no mean temperature difference. In this paper a mathematical model of the entropy generated is developed for both cases. Estimates of the entropy generated by this process are given for oscillating flows in heat exchangers and in regenerators. The practical significance of this entropy is also discussed.

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

Liu, Maoyuan; Besford, Quinn Alexander; Mulvaney, Thomas

The entropy of hydrophobic solvation has been explained as the result of ordered solvation structures, of hydrogen bonds, of the small size of the water molecule, of dispersion forces, and of solvent density fluctuations. We report a new approach to the calculation of the entropy of hydrophobic solvation, along with tests of and comparisons to several other methods. The methods are assessed in the light of the available thermodynamic and spectroscopic information on the effects of temperature on hydrophobic solvation. Five model hydrophobes in SPC/E water give benchmark solvation entropies via Widom’s test-particle insertion method, and other methods and modelsmore » are tested against these particle-insertion results. Entropies associated with distributions of tetrahedral order, of electric field, and of solvent dipole orientations are examined. We find these contributions are small compared to the benchmark particle-insertion entropy. Competitive with or better than other theories in accuracy, but with no free parameters, is the new estimate of the entropy contributed by correlations between dipole moments. Dipole correlations account for most of the hydrophobic solvation entropy for all models studied and capture the distinctive temperature dependence seen in thermodynamic and spectroscopic experiments. Entropies based on pair and many-body correlations in number density approach the correct magnitudes but fail to describe temperature and size dependences, respectively. Hydrogen-bond definitions and free energies that best reproduce entropies from simulations are reported, but it is difficult to choose one hydrogen bond model that fits a variety of experiments. The use of information theory, scaled-particle theory, and related methods is discussed briefly. Our results provide a test of the Frank-Evans hypothesis that the negative solvation entropy is due to structured water near the solute, complement the spectroscopic detection of that solvation structure by identifying the structural feature responsible for the entropy change, and point to a possible explanation for the observed dependence on length scale. Our key results are that the hydrophobic effect, i.e. the signature, temperature-dependent, solvation entropy of nonpolar molecules in water, is largely due to a dispersion force arising from correlations between rotating permanent dipole moments, that the strength of this force depends on the Kirkwood g-factor, and that the strength of this force may be obtained exactly without simulation.« less

Is the hypothesis about a low entropy initial state of the Universe necessary for explaining the arrow of time?

NASA Astrophysics Data System (ADS)

Goldstein, Sheldon; Tumulka, Roderich; Zanghı, Nino

2016-07-01

According to statistical mechanics, microstates of an isolated physical system (say, a gas in a box) at time t0 in a given macrostate of less-than-maximal entropy typically evolve in such a way that the entropy at time t increases with |t -t0| in both time directions. In order to account for the observed entropy increase in only one time direction, the thermodynamic arrow of time, one usually appeals to the hypothesis that the initial state of the Universe was one of very low entropy. In certain recent models of cosmology, however, no hypothesis about the initial state of the Universe is invoked. We discuss how the emergence of a thermodynamic arrow of time in such models can nevertheless be compatible with the above-mentioned consequence of statistical mechanics, appearances to the contrary notwithstanding.

Entropy-aware projected Landweber reconstruction for quantized block compressive sensing of aerial imagery

NASA Astrophysics Data System (ADS)

Liu, Hao; Li, Kangda; Wang, Bing; Tang, Hainie; Gong, Xiaohui

2017-01-01

A quantized block compressive sensing (QBCS) framework, which incorporates the universal measurement, quantization/inverse quantization, entropy coder/decoder, and iterative projected Landweber reconstruction, is summarized. Under the QBCS framework, this paper presents an improved reconstruction algorithm for aerial imagery, QBCS, with entropy-aware projected Landweber (QBCS-EPL), which leverages the full-image sparse transform without Wiener filter and an entropy-aware thresholding model for wavelet-domain image denoising. Through analyzing the functional relation between the soft-thresholding factors and entropy-based bitrates for different quantization methods, the proposed model can effectively remove wavelet-domain noise of bivariate shrinkage and achieve better image reconstruction quality. For the overall performance of QBCS reconstruction, experimental results demonstrate that the proposed QBCS-EPL algorithm significantly outperforms several existing algorithms. With the experiment-driven methodology, the QBCS-EPL algorithm can obtain better reconstruction quality at a relatively moderate computational cost, which makes it more desirable for aerial imagery applications.

MoNbTaV Medium-Entropy Alloy

DOE PAGES

Yao, Hongwei; Qiao, Jun -Wei; Gao, Michael; ...

2016-05-19

Guided by CALPHAD (Calculation of Phase Diagrams) modeling, the refractory medium-entropy alloy MoNbTaV was synthesized by vacuum arc melting under a high-purity argon atmosphere. A body-centered cubic solid solution phase was experimentally confirmed in the as-cast ingot using X-ray diffraction and scanning electron microscopy. The measured lattice parameter of the alloy (3.208 Å) obeys the rule of mixtures (ROM), but the Vickers microhardness (4.95 GPa) and the yield strength (1.5 GPa) are about 4.5 and 4.6 times those estimated from the ROM, respectively. Using a simple model on solid solution strengthening predicts a yield strength of approximately 1.5 GPa. Inmore » conclusion, thermodynamic analysis shows that the total entropy of the alloy is more than three times the configurational entropy at room temperature, and the entropy of mixing exhibits a small negative departure from ideal mixing.« less

Entropic criterion for model selection

NASA Astrophysics Data System (ADS)

Tseng, Chih-Yuan

2006-10-01

Model or variable selection is usually achieved through ranking models according to the increasing order of preference. One of methods is applying Kullback-Leibler distance or relative entropy as a selection criterion. Yet that will raise two questions, why use this criterion and are there any other criteria. Besides, conventional approaches require a reference prior, which is usually difficult to get. Following the logic of inductive inference proposed by Caticha [Relative entropy and inductive inference, in: G. Erickson, Y. Zhai (Eds.), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, AIP Conference Proceedings, vol. 707, 2004 (available from arXiv.org/abs/physics/0311093)], we show relative entropy to be a unique criterion, which requires no prior information and can be applied to different fields. We examine this criterion by considering a physical problem, simple fluids, and results are promising.

Universality of entropy scaling in one dimensional gapless models.

PubMed

Korepin, V E

2004-03-05

We consider critical models in one dimension. We study the ground state in the thermodynamic limit (infinite lattice). We are interested in an entropy of a subsystem. We calculate the entropy of a part of the ground state from a space interval (0,x). At zero temperature it describes the entanglement of the part of the ground state from this interval with the rest of the ground state. We obtain an explicit formula for the entropy of the subsystem at any temperature. At zero temperature our formula reproduces a logarithmic formula, discovered by Vidal, Latorre, Rico, and Kitaev for spin chains. We prove our formula by means of conformal field theory and the second law of thermodynamics. Our formula is universal. We illustrate it for a Bose gas with a delta interaction and for the Hubbard model.

Magnetization and isothermal magnetic entropy change of a mixed spin-1 and spin-2 Heisenberg superlattice

NASA Astrophysics Data System (ADS)

Xu, Ping; Du, An

2017-09-01

A superlattice composed of spin-1 and spin-2 with ABAB … structure was described with Heisenberg model. The magnetizations and magnetic entropy changes under different magnetic fields were calculated by the Green's function method. The magnetization compensation phenomenon could be observed by altering the intralayer exchange interactions and the single-ion anisotropies of spins. Along with the temperature increasing, the system in the absence of magnetization compensation shows normal magnetic entropy change and displays a peak near the critical temperature, and yet the system with magnetization compensation shows normal magnetic entropy change near the compensation temperature but inverse magnetic entropy change near the critical temperature. Finally, we illustrated the reasons of different behaviors of magnetic entropy change by analyzing the contributions of two sublattices to the total magnetic entropy change.

Entropy and econophysics

NASA Astrophysics Data System (ADS)

Rosser, J. Barkley

2016-12-01

Entropy is a central concept of statistical mechanics, which is the main branch of physics that underlies econophysics, the application of physics concepts to understand economic phenomena. It enters into econophysics both in an ontological way through the Second Law of Thermodynamics as this drives the world economy from its ecological foundations as solar energy passes through food chains in dissipative process of entropy rising and production fundamentally involving the replacement of lower entropy energy states with higher entropy ones. In contrast the mathematics of entropy as appearing in information theory becomes the basis for modeling financial market dynamics as well as income and wealth distribution dynamics. It also provides the basis for an alternative view of stochastic price equilibria in economics, as well providing a crucial link between econophysics and sociophysics, keeping in mind the essential unity of the various concepts of entropy.

A diameter-sensitive flow entropy method for reliability consideration in water distribution system design

NASA Astrophysics Data System (ADS)

Liu, Haixing; Savić, Dragan; Kapelan, Zoran; Zhao, Ming; Yuan, Yixing; Zhao, Hongbin

2014-07-01

Flow entropy is a measure of uniformity of pipe flows in water distribution systems. By maximizing flow entropy one can identify reliable layouts or connectivity in networks. In order to overcome the disadvantage of the common definition of flow entropy that does not consider the impact of pipe diameter on reliability, an extended definition of flow entropy, termed as diameter-sensitive flow entropy, is proposed. This new methodology is then assessed by using other reliability methods, including Monte Carlo Simulation, a pipe failure probability model, and a surrogate measure (resilience index) integrated with water demand and pipe failure uncertainty. The reliability assessment is based on a sample of WDS designs derived from an optimization process for each of the two benchmark networks. Correlation analysis is used to evaluate quantitatively the relationship between entropy and reliability. To ensure reliability, a comparative analysis between the flow entropy and the new method is conducted. The results demonstrate that the diameter-sensitive flow entropy shows consistently much stronger correlation with the three reliability measures than simple flow entropy. Therefore, the new flow entropy method can be taken as a better surrogate measure for reliability and could be potentially integrated into the optimal design problem of WDSs. Sensitivity analysis results show that the velocity parameters used in the new flow entropy has no significant impact on the relationship between diameter-sensitive flow entropy and reliability.

Entropy of dynamical social networks

NASA Astrophysics Data System (ADS)

Zhao, Kun; Karsai, Marton; Bianconi, Ginestra

2012-02-01

Dynamical social networks are evolving rapidly and are highly adaptive. Characterizing the information encoded in social networks is essential to gain insight into the structure, evolution, adaptability and dynamics. Recently entropy measures have been used to quantify the information in email correspondence, static networks and mobility patterns. Nevertheless, we still lack methods to quantify the information encoded in time-varying dynamical social networks. In this talk we present a model to quantify the entropy of dynamical social networks and use this model to analyze the data of phone-call communication. We show evidence that the entropy of the phone-call interaction network changes according to circadian rhythms. Moreover we show that social networks are extremely adaptive and are modified by the use of technologies such as mobile phone communication. Indeed the statistics of duration of phone-call is described by a Weibull distribution and is significantly different from the distribution of duration of face-to-face interactions in a conference. Finally we investigate how much the entropy of dynamical social networks changes in realistic models of phone-call or face-to face interactions characterizing in this way different type human social behavior.

Application of a Real-Time, Calculable Limiting Form of the Renyi Entropy for Molecular Imaging of Tumors

PubMed Central

Marsh, J. N.; Wallace, K. D.; McCarthy, J. E.; Wickerhauser, M. V.; Maurizi, B. N.; Lanza, G. M.; Wickline, S. A.; Hughes, M. S.

2011-01-01

Previously, we reported new methods for ultrasound signal characterization using entropy, Hf; a generalized entropy, the Renyi entropy, If(r); and a limiting form of Renyi entropy suitable for real-time calculation, If,∞. All of these quantities demonstrated significantly more sensitivity to subtle changes in scattering architecture than energy-based methods in certain settings. In this study, the real-time calculable limit of the Renyi entropy, If,∞, is applied for the imaging of angiogenic murine neovasculature in a breast cancer xenograft using a targeted contrast agent. It is shown that this approach may be used to detect reliably the accumulation of targeted nanoparticles at five minutes post-injection in this in vivo model. PMID:20679020

Entanglement entropy and entanglement spectrum of triplet topological superconductors.

PubMed

Oliveira, T P; Ribeiro, P; Sacramento, P D

2014-10-22

We analyze the entanglement entropy properties of a 2D p-wave superconductor with Rashba spin-orbit coupling, which displays a rich phase-space that supports non-trivial topological phases, as the chemical potential and the Zeeman term are varied. We show that the entanglement entropy and its derivatives clearly signal the topological transitions and we find numerical evidence that for this model the derivative with respect to the magnetization provides a sensible signature of each topological phase. Following the area law for the entanglement entropy, we systematically analyze the contributions that are proportional to or independent of the perimeter of the system, as a function of the Hamiltonian coupling constants and the geometry of the finite subsystem. For this model, we show that even though the topological entanglement entropy vanishes, it signals the topological phase transitions in a finite system. We also observe a relationship between a topological contribution to the entanglement entropy in a half-cylinder geometry and the number of edge states, and that the entanglement spectrum has robust modes associated with each edge state, as in other topological systems.

Fine structure of the entanglement entropy in the O(2) model.

PubMed

Yang, Li-Ping; Liu, Yuzhi; Zou, Haiyuan; Xie, Z Y; Meurice, Y

2016-01-01

We compare two calculations of the particle density in the superfluid phase of the O(2) model with a chemical potential μ in 1+1 dimensions. The first relies on exact blocking formulas from the Tensor Renormalization Group (TRG) formulation of the transfer matrix. The second is a worm algorithm. We show that the particle number distributions obtained with the two methods agree well. We use the TRG method to calculate the thermal entropy and the entanglement entropy. We describe the particle density, the two entropies and the topology of the world lines as we increase μ to go across the superfluid phase between the first two Mott insulating phases. For a sufficiently large temporal size, this process reveals an interesting fine structure: the average particle number and the winding number of most of the world lines in the Euclidean time direction increase by one unit at a time. At each step, the thermal entropy develops a peak and the entanglement entropy increases until we reach half-filling and then decreases in a way that approximately mirrors the ascent. This suggests an approximate fermionic picture.

Entanglement entropy from tensor network states for stabilizer codes

NASA Astrophysics Data System (ADS)

He, Huan; Zheng, Yunqin; Bernevig, B. Andrei; Regnault, Nicolas

2018-03-01

In this paper, we present the construction of tensor network states (TNS) for some of the degenerate ground states of three-dimensional (3D) stabilizer codes. We then use the TNS formalism to obtain the entanglement spectrum and entropy of these ground states for some special cuts. In particular, we work out examples of the 3D toric code, the X-cube model, and the Haah code. The latter two models belong to the category of "fracton" models proposed recently, while the first one belongs to the conventional topological phases. We mention the cases for which the entanglement entropy and spectrum can be calculated exactly: For these, the constructed TNS is a singular value decomposition (SVD) of the ground states with respect to particular entanglement cuts. Apart from the area law, the entanglement entropies also have constant and linear corrections for the fracton models, while the entanglement entropies for the toric code models only have constant corrections. For the cuts we consider, the entanglement spectra of these three models are completely flat. We also conjecture that the negative linear correction to the area law is a signature of extensive ground-state degeneracy. Moreover, the transfer matrices of these TNSs can be constructed. We show that the transfer matrices are projectors whose eigenvalues are either 1 or 0. The number of nonzero eigenvalues is tightly related to the ground-state degeneracy.

Application of Bayesian Maximum Entropy Filter in parameter calibration of groundwater flow model in PingTung Plain

NASA Astrophysics Data System (ADS)

Cheung, Shao-Yong; Lee, Chieh-Han; Yu, Hwa-Lung

2017-04-01

Due to the limited hydrogeological observation data and high levels of uncertainty within, parameter estimation of the groundwater model has been an important issue. There are many methods of parameter estimation, for example, Kalman filter provides a real-time calibration of parameters through measurement of groundwater monitoring wells, related methods such as Extended Kalman Filter and Ensemble Kalman Filter are widely applied in groundwater research. However, Kalman Filter method is limited to linearity. This study propose a novel method, Bayesian Maximum Entropy Filtering, which provides a method that can considers the uncertainty of data in parameter estimation. With this two methods, we can estimate parameter by given hard data (certain) and soft data (uncertain) in the same time. In this study, we use Python and QGIS in groundwater model (MODFLOW) and development of Extended Kalman Filter and Bayesian Maximum Entropy Filtering in Python in parameter estimation. This method may provide a conventional filtering method and also consider the uncertainty of data. This study was conducted through numerical model experiment to explore, combine Bayesian maximum entropy filter and a hypothesis for the architecture of MODFLOW groundwater model numerical estimation. Through the virtual observation wells to simulate and observe the groundwater model periodically. The result showed that considering the uncertainty of data, the Bayesian maximum entropy filter will provide an ideal result of real-time parameters estimation.

Entropy and Galilean Invariance of Lattice Boltzmann Theories

NASA Astrophysics Data System (ADS)

Chikatamarla, Shyam S.; Karlin, Iliya V.

2006-11-01

A theory of lattice Boltzmann (LB) models for hydrodynamic simulation is developed upon a novel relation between entropy construction and roots of Hermite polynomials. A systematic procedure is described for constructing numerically stable and complete Galilean invariant LB models. The stability of the new LB models is illustrated with a shock tube simulation.

Identification of priority conservation areas and potential corridors for jaguars in the Caatinga biome, Brazil.

PubMed

Morato, Ronaldo Gonçalves; Ferraz, Katia Maria Paschoaletto Micchi de Barros; de Paula, Rogério Cunha; de Campos, Cláudia Bueno

2014-01-01

The jaguar, Panthera onca, is a top predator with the extant population found within the Brazilian Caatinga biome now known to be on the brink of extinction. Designing new conservation units and potential corridors are therefore crucial for the long-term survival of the species within the Caatinga biome. Thus, our aims were: 1) to recognize suitable areas for jaguar occurrence, 2) to delineate areas for jaguar conservation (PJCUs), 3) to design corridors among priority areas, and 4) to prioritize PJCUs. A total of 62 points records of jaguar occurrence and 10 potential predictors were analyzed in a GIS environment. A predictive distributional map was obtained using Species Distribution Modeling (SDM) as performed by the Maximum Entropy (Maxent) algorithm. Areas equal to or higher than the median suitability value of 0.595 were selected as of high suitability for jaguar occurrence and named as Priority Jaguar Conservation Units (PJCU). Ten PJCUs with sizes varying from 23.6 km2 to 4,311.0 km2 were identified. Afterwards, we combined the response curve, as generated by SDM, and expert opinions to create a permeability matrix and to identify least cost corridors and buffer zones between each PJCU pair. Connectivity corridors and buffer zone for jaguar movement included an area of 8.884,26 km2 and the total corridor length is about 160.94 km. Prioritizing criteria indicated the PJCU representing c.a. 68.61% of the total PJCU area (PJCU # 1) as of high priority for conservation and connectivity with others PJCUs (PJCUs # 4, 5 and 7) desirable for the long term survival of the species. In conclusion, by using the jaguar as a focal species and combining SDM and expert opinion we were able to create a valid framework for practical conservation actions at the Caatinga biome. The same approach could be used for the conservation of other carnivores.

Identification of Priority Conservation Areas and Potential Corridors for Jaguars in the Caatinga Biome, Brazil

PubMed Central

Morato, Ronaldo Gonçalves; Ferraz, Katia Maria Paschoaletto Micchi de Barros; de Paula, Rogério Cunha; de Campos, Cláudia Bueno

2014-01-01

The jaguar, Panthera onca, is a top predator with the extant population found within the Brazilian Caatinga biome now known to be on the brink of extinction. Designing new conservation units and potential corridors are therefore crucial for the long-term survival of the species within the Caatinga biome. Thus, our aims were: 1) to recognize suitable areas for jaguar occurrence, 2) to delineate areas for jaguar conservation (PJCUs), 3) to design corridors among priority areas, and 4) to prioritize PJCUs. A total of 62 points records of jaguar occurrence and 10 potential predictors were analyzed in a GIS environment. A predictive distributional map was obtained using Species Distribution Modeling (SDM) as performed by the Maximum Entropy (Maxent) algorithm. Areas equal to or higher than the median suitability value of 0.595 were selected as of high suitability for jaguar occurrence and named as Priority Jaguar Conservation Units (PJCU). Ten PJCUs with sizes varying from 23.6 km2 to 4,311.0 km2 were identified. Afterwards, we combined the response curve, as generated by SDM, and expert opinions to create a permeability matrix and to identify least cost corridors and buffer zones between each PJCU pair. Connectivity corridors and buffer zone for jaguar movement included an area of 8.884,26 km2 and the total corridor length is about 160.94 km. Prioritizing criteria indicated the PJCU representing c.a. 68.61% of the total PJCU area (PJCU # 1) as of high priority for conservation and connectivity with others PJCUs (PJCUs # 4, 5 and 7) desirable for the long term survival of the species. In conclusion, by using the jaguar as a focal species and combining SDM and expert opinion we were able to create a valid framework for practical conservation actions at the Caatinga biome. The same approach could be used for the conservation of other carnivores. PMID:24709817

Evaluating the role of fronts in habitat overlaps between cold and warm water species in the western North Pacific: A proof of concept

NASA Astrophysics Data System (ADS)

Mugo, Robinson M.; Saitoh, Sei-Ichi; Takahashi, Fumihiro; Nihira, Akira; Kuroyama, Tadaaki

2014-09-01

Cold- and warm-water species' fishing grounds show a spatial synchrony around fronts in the western North Pacific (WNP). However, it is not yet clear whether a front (thermal, salinity or chlorophyll) acts as an absolute barrier to fish migration on either side or its structure allows interaction of species with different physiological requirements. Our objective was to assess potential areas of overlap between cold- and warm-water species using probabilities of presence derived from fishery datasets and remotely sensed environment data in the Kuroshio-Oyashio region in the WNP. Fishery data comprised skipjack tuna (Katsuwonus pelamis) fishing locations and proxy presences (derived from fishing night light images) for neon flying squid (Ommastrephes bartrami) and Pacific saury (Cololabis saira). Monthly (August-November) satellite remotely sensed sea-surface temperature, chlorophyll-a and sea-surface height anomaly images were used as environment data. Maximum entropy (MaxEnt) models were used to determine probabilities of presence (PoP) for each set of fishery and environment data for the area 35-45°N and 140-160°E. Maps of both sets of PoPs were compared and areas of overlap identified using a combined probability map. Results indicated that areas of spatial overlap existed among the species habitats, which gradually widened from September to November. The reasons for these overlaps include the presence of strong thermal/ocean-color gradients between cold Oyashio and warm Kuroshio waters, and also the presence of the sub-arctic front. Due to the high abundance of food along frontal zones, the species use the fronts as foraging grounds while confining within physiologically tolerable waters on either side of the front. The interaction zone around the front points to areas that might be accessible to both species for foraging, which suggests intense prey-predator interaction zones.

Entropy and cosmology.

NASA Astrophysics Data System (ADS)

Zucker, M. H.

This paper is a critical analysis and reassessment of entropic functioning as it applies to the question of whether the ultimate fate of the universe will be determined in the future to be "open" (expanding forever to expire in a big chill), "closed" (collapsing to a big crunch), or "flat" (balanced forever between the two). The second law of thermodynamics declares that entropy can only increase and that this principle extends, inevitably, to the universe as a whole. This paper takes the position that this extension is an unwarranted projection based neither on experience nonfact - an extrapolation that ignores the powerful effect of a gravitational force acting within a closed system. Since it was originally presented by Clausius, the thermodynamic concept of entropy has been redefined in terms of "order" and "disorder" - order being equated with a low degree of entropy and disorder with a high degree. This revised terminology more subjective than precise, has generated considerable confusion in cosmology in several critical instances. For example - the chaotic fireball of the big bang, interpreted by Stephen Hawking as a state of disorder (high entropy), is infinitely hot and, thermally, represents zero entropy (order). Hawking, apparently focusing on the disorderly "chaotic" aspect, equated it with a high degree of entropy - overlooking the fact that the universe is a thermodynamic system and that the key factor in evaluating the big-bang phenomenon is the infinitely high temperature at the early universe, which can only be equated with zero entropy. This analysis resolves this confusion and reestablishes entropy as a cosmological function integrally linked to temperature. The paper goes on to show that, while all subsystems contained within the universe require external sources of energization to have their temperatures raised, this requirement does not apply to the universe as a whole. The universe is the only system that, by itself can raise its own temperature and thus, by itself; reverse entropy. The vast encompassing gravitational forces that the universe has at its disposal, forces that dominate the phase of contraction, provide the compacting, compressive mechanism that regenerates heat in an expanded, cooled universe and decreases entropy. And this phenomenon takes place without diminishing or depleting the finite amount of mass/energy with which the universe began. The fact that the universe can reverse the entropic process leads to possibilities previously ignored when assessing which of the three models (open, closed, of flat) most probably represents the future of the universe. After analyzing the models, the conclusion reached here is that the open model is only an expanded version of the closed model and therefore is not open, and the closed model will never collapse to a big crunch and, therefore, is not closed. Which leaves a modified model, oscillating forever between limited phases of expansion and contraction (a universe in "dynamic equilibrium") as the only feasible choice.

The Holographic Entropy Cone

DOE PAGES

Bao, Ning; Nezami, Sepehr; Ooguri, Hirosi; ...

2015-09-21

We initiate a systematic enumeration and classification of entropy inequalities satisfied by the Ryu-Takayanagi formula for conformal field theory states with smooth holographic dual geometries. For 2, 3, and 4 regions, we prove that the strong subadditivity and the monogamy of mutual information give the complete set of inequalities. This is in contrast to the situation for generic quantum systems, where a complete set of entropy inequalities is not known for 4 or more regions. We also find an infinite new family of inequalities applicable to 5 or more regions. The set of all holographic entropy inequalities bounds the phasemore » space of Ryu-Takayanagi entropies, defining the holographic entropy cone. We characterize this entropy cone by reducing geometries to minimal graph models that encode the possible cutting and gluing relations of minimal surfaces. We find that, for a fixed number of regions, there are only finitely many independent entropy inequalities. To establish new holographic entropy inequalities, we introduce a combinatorial proof technique that may also be of independent interest in Riemannian geometry and graph theory.« less

Free Energy in Introductory Physics

NASA Astrophysics Data System (ADS)

Prentis, Jeffrey J.; Obsniuk, Michael J.

2016-02-01

Energy and entropy are two of the most important concepts in science. For all natural processes where a system exchanges energy with its environment, the energy of the system tends to decrease and the entropy of the system tends to increase. Free energy is the special concept that specifies how to balance the opposing tendencies to minimize energy and maximize entropy. There are many pedagogical articles on energy and entropy. Here we present a simple model to illustrate the concept of free energy and the principle of minimum free energy.

Effects of the Stark Shift on the Evolution of the Field Entropy and Entanglement in the Two-Photon Jaynes-Cummings Model

NASA Technical Reports Server (NTRS)

Fang, Mao Fa

1996-01-01

The evolution of the field entropy in the two-photon JCM in the presence of the Stark shift is investigated, and the effects of the dynamic Stark shift on the evolution of the field entropy and entanglement between the atom and field, are examined. The results show that the dynamic Stark shift plays an important role in the evolution of the field entropy in two-photon processes.

Exact computation of the maximum-entropy potential of spiking neural-network models.

PubMed

Cofré, R; Cessac, B

2014-05-01

Understanding how stimuli and synaptic connectivity influence the statistics of spike patterns in neural networks is a central question in computational neuroscience. The maximum-entropy approach has been successfully used to characterize the statistical response of simultaneously recorded spiking neurons responding to stimuli. However, in spite of good performance in terms of prediction, the fitting parameters do not explain the underlying mechanistic causes of the observed correlations. On the other hand, mathematical models of spiking neurons (neuromimetic models) provide a probabilistic mapping between the stimulus, network architecture, and spike patterns in terms of conditional probabilities. In this paper we build an exact analytical mapping between neuromimetic and maximum-entropy models.

Entropy production in a Glauber–Ising irreversible model with dynamical competition

NASA Astrophysics Data System (ADS)

Barbosa, Oscar A.; Tomé, Tânia

2018-06-01

An out of equilibrium Glauber–Ising model, evolving in accordance with an irreversible and stochastic Markovian dynamics, is analyzed in order to improve our comprehension concerning critical behavior and phase transitions in nonequilibrium systems. Therefore, a lattice model ruled by the competition between two Glauber dynamics acting on interlaced square lattices is proposed. Previous results have shown how the entropy production provides information about irreversibility and criticality. Mean-field approximations and Monte Carlo simulations were used in the analysis. The results obtained here show a continuous phase transition, reflected in the entropy production as a logarithmic divergence of its derivative, which suggests a shared universality class with the irreversible models invariant under the symmetry operations of the Ising model.

Quantifying ‘Causality’ in Complex Systems: Understanding Transfer Entropy

PubMed Central

Abdul Razak, Fatimah; Jensen, Henrik Jeldtoft

2014-01-01

‘Causal’ direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of ‘causal’ direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets. PMID:24955766

Local entanglement entropy of fermions as a marker of quantum phase transition in the one-dimensional Hubbard model

NASA Astrophysics Data System (ADS)

Cha, Min-Chul; Chung, Myung-Hoon

2018-05-01

We study quantum phase transition of interacting fermions by measuring the local entanglement entropy in the one-dimensional Hubbard model. The reduced density matrices for blocks of a few sites are constructed from the ground state wave function in infinite systems by adopting the matrix product state representation where time-evolving block decimations are performed to obtain the lowest energy states. The local entanglement entropy, constructed from the reduced density matrices, as a function of the chemical potential shows clear signatures of the Mott transition. The value of the central charge, numerically determined from the universal properties of the local entanglement entropy, confirms that the transition is caused by the suppression of the charge degrees of freedom.

Implications of climate change on the distribution of the tick vector Ixodes scapularis and risk for Lyme disease in the Texas-Mexico transboundary region.

PubMed

Feria-Arroyo, Teresa P; Castro-Arellano, Ivan; Gordillo-Perez, Guadalupe; Cavazos, Ana L; Vargas-Sandoval, Margarita; Grover, Abha; Torres, Javier; Medina, Raul F; de León, Adalberto A Pérez; Esteve-Gassent, Maria D

2014-04-25

Disease risk maps are important tools that help ascertain the likelihood of exposure to specific infectious agents. Understanding how climate change may affect the suitability of habitats for ticks will improve the accuracy of risk maps of tick-borne pathogen transmission in humans and domestic animal populations. Lyme disease (LD) is the most prevalent arthropod borne disease in the US and Europe. The bacterium Borrelia burgdorferi causes LD and it is transmitted to humans and other mammalian hosts through the bite of infected Ixodes ticks. LD risk maps in the transboundary region between the U.S. and Mexico are lacking. Moreover, none of the published studies that evaluated the effect of climate change in the spatial and temporal distribution of I. scapularis have focused on this region. The area of study included Texas and a portion of northeast Mexico. This area is referred herein as the Texas-Mexico transboundary region. Tick samples were obtained from various vertebrate hosts in the region under study. Ticks identified as I. scapularis were processed to obtain DNA and to determine if they were infected with B. burgdorferi using PCR. A maximum entropy approach (MAXENT) was used to forecast the present and future (2050) distribution of B. burgdorferi-infected I. scapularis in the Texas-Mexico transboundary region by correlating geographic data with climatic variables. Of the 1235 tick samples collected, 109 were identified as I. scapularis. Infection with B. burgdorferi was detected in 45% of the I. scapularis ticks collected. The model presented here indicates a wide distribution for I. scapularis, with higher probability of occurrence along the Gulf of Mexico coast. Results of the modeling approach applied predict that habitat suitable for the distribution of I. scapularis in the Texas-Mexico transboundary region will remain relatively stable until 2050. The Texas-Mexico transboundary region appears to be part of a continuum in the pathogenic landscape of LD. Forecasting based on climate trends provides a tool to adapt strategies in the near future to mitigate the impact of LD related to its distribution and risk for transmission to human populations in the Mexico-US transboundary region.

An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming

USGS Publications Warehouse

Sherrouse, Benson C.; Semmens, Darius J.; Clement, Jessica M.

2014-01-01

Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders. With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, social-value information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems.

The increase of the functional entropy of the human brain with age.

PubMed

Yao, Y; Lu, W L; Xu, B; Li, C B; Lin, C P; Waxman, D; Feng, J F

2013-10-09

We use entropy to characterize intrinsic ageing properties of the human brain. Analysis of fMRI data from a large dataset of individuals, using resting state BOLD signals, demonstrated that a functional entropy associated with brain activity increases with age. During an average lifespan, the entropy, which was calculated from a population of individuals, increased by approximately 0.1 bits, due to correlations in BOLD activity becoming more widely distributed. We attribute this to the number of excitatory neurons and the excitatory conductance decreasing with age. Incorporating these properties into a computational model leads to quantitatively similar results to the fMRI data. Our dataset involved males and females and we found significant differences between them. The entropy of males at birth was lower than that of females. However, the entropies of the two sexes increase at different rates, and intersect at approximately 50 years; after this age, males have a larger entropy.

The Increase of the Functional Entropy of the Human Brain with Age

PubMed Central

Yao, Y.; Lu, W. L.; Xu, B.; Li, C. B.; Lin, C. P.; Waxman, D.; Feng, J. F.

2013-01-01

We use entropy to characterize intrinsic ageing properties of the human brain. Analysis of fMRI data from a large dataset of individuals, using resting state BOLD signals, demonstrated that a functional entropy associated with brain activity increases with age. During an average lifespan, the entropy, which was calculated from a population of individuals, increased by approximately 0.1 bits, due to correlations in BOLD activity becoming more widely distributed. We attribute this to the number of excitatory neurons and the excitatory conductance decreasing with age. Incorporating these properties into a computational model leads to quantitatively similar results to the fMRI data. Our dataset involved males and females and we found significant differences between them. The entropy of males at birth was lower than that of females. However, the entropies of the two sexes increase at different rates, and intersect at approximately 50 years; after this age, males have a larger entropy. PMID:24103922

Double strand breaks may be a missing link between entropy and aging.

PubMed

Lenart, Peter; Bienertová-Vašků, Julie

2016-07-01

It has been previously suggested that an increase in entropy production leads to aging. However, the mechanisms linking increased entropy production in living mass to aging are currently unclear. Even though entropy cannot be easily associated with any specific molecular damage, the increase of entropy in structural mass may be connected with heat stress, which is known to generate double strand breaks. Double strand breaks, which are in turn known to play an important role in process of aging, are thus connected to both aging and an increase of entropy. In view of these associations, we propose a new model where the increase of entropy leads to the formation of double strand breaks, resulting in an aging phenotype. This not only offers a new perspective on aging research and facilitates experimental validation, but could also serve as a useful explanatory tool. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A Critical Look at Entropy-Based Gene-Gene Interaction Measures.

PubMed

Lee, Woojoo; Sjölander, Arvid; Pawitan, Yudi

2016-07-01

Several entropy-based measures for detecting gene-gene interaction have been proposed recently. It has been argued that the entropy-based measures are preferred because entropy can better capture the nonlinear relationships between genotypes and traits, so they can be useful to detect gene-gene interactions for complex diseases. These suggested measures look reasonable at intuitive level, but so far there has been no detailed characterization of the interactions captured by them. Here we study analytically the properties of some entropy-based measures for detecting gene-gene interactions in detail. The relationship between interactions captured by the entropy-based measures and those of logistic regression models is clarified. In general we find that the entropy-based measures can suffer from a lack of specificity in terms of target parameters, i.e., they can detect uninteresting signals as interactions. Numerical studies are carried out to confirm theoretical findings. © 2016 WILEY PERIODICALS, INC.

Universality in volume-law entanglement of scrambled pure quantum states.

PubMed

Nakagawa, Yuya O; Watanabe, Masataka; Fujita, Hiroyuki; Sugiura, Sho

2018-04-24

A pure quantum state can fully describe thermal equilibrium as long as one focuses on local observables. The thermodynamic entropy can also be recovered as the entanglement entropy of small subsystems. When the size of the subsystem increases, however, quantum correlations break the correspondence and mandate a correction to this simple volume law. The elucidation of the size dependence of the entanglement entropy is thus essentially important in linking quantum physics with thermodynamics. Here we derive an analytic formula of the entanglement entropy for a class of pure states called cTPQ states representing equilibrium. We numerically find that our formula applies universally to any sufficiently scrambled pure state representing thermal equilibrium, i.e., energy eigenstates of non-integrable models and states after quantum quenches. Our formula is exploited as diagnostics for chaotic systems; it can distinguish integrable models from non-integrable models and many-body localization phases from chaotic phases.

Monotonic entropy growth for a nonlinear model of random exchanges.

PubMed

Apenko, S M

2013-02-01

We present a proof of the monotonic entropy growth for a nonlinear discrete-time model of a random market. This model, based on binary collisions, also may be viewed as a particular case of Ulam's redistribution of energy problem. We represent each step of this dynamics as a combination of two processes. The first one is a linear energy-conserving evolution of the two-particle distribution, for which the entropy growth can be easily verified. The original nonlinear process is actually a result of a specific "coarse graining" of this linear evolution, when after the collision one variable is integrated away. This coarse graining is of the same type as the real space renormalization group transformation and leads to an additional entropy growth. The combination of these two factors produces the required result which is obtained only by means of information theory inequalities.

Monotonic entropy growth for a nonlinear model of random exchanges

NASA Astrophysics Data System (ADS)

Apenko, S. M.

2013-02-01

We present a proof of the monotonic entropy growth for a nonlinear discrete-time model of a random market. This model, based on binary collisions, also may be viewed as a particular case of Ulam's redistribution of energy problem. We represent each step of this dynamics as a combination of two processes. The first one is a linear energy-conserving evolution of the two-particle distribution, for which the entropy growth can be easily verified. The original nonlinear process is actually a result of a specific “coarse graining” of this linear evolution, when after the collision one variable is integrated away. This coarse graining is of the same type as the real space renormalization group transformation and leads to an additional entropy growth. The combination of these two factors produces the required result which is obtained only by means of information theory inequalities.

Modeling sediment concentration in debris flow by Tsallis entropy

NASA Astrophysics Data System (ADS)

Singh, Vijay P.; Cui, Huijuan

2015-02-01

Debris flow is a natural hazard that occurs in landscapes having high slopes, such as mountainous areas. It can be so powerful that it destroys whatever comes in its way, that is, it can kill people and animals; decimate roads, bridges, railway tracks, homes and other property; and fill reservoirs. Owing to its frequent occurrence, it is receiving considerable attention these days. Of fundamental importance in debris flow modeling is the determination of concentration of debris (or sediment) in the flow. The usual approach to determining debris flow concentration is either empirical or hydraulic. Both approaches are deterministic and therefore say nothing about the uncertainty associated with the sediment concentration in the flow. This paper proposes to model debris flow concentration using the Tsallis entropy theory. Verification of the entropy-based distribution of debris flow concentration using the data and equations reported in the literature shows that the Tsallis entropy-proposed model is capable of mimicking the field observed concentration and has potential for practical application.

Mathematical model for thermal and entropy analysis of thermal solar collectors by using Maxwell nanofluids with slip conditions, thermal radiation and variable thermal conductivity

NASA Astrophysics Data System (ADS)

Aziz, Asim; Jamshed, Wasim; Aziz, Taha

2018-04-01

In the present research a simplified mathematical model for the solar thermal collectors is considered in the form of non-uniform unsteady stretching surface. The non-Newtonian Maxwell nanofluid model is utilized for the working fluid along with slip and convective boundary conditions and comprehensive analysis of entropy generation in the system is also observed. The effect of thermal radiation and variable thermal conductivity are also included in the present model. The mathematical formulation is carried out through a boundary layer approach and the numerical computations are carried out for Cu-water and TiO2-water nanofluids. Results are presented for the velocity, temperature and entropy generation profiles, skin friction coefficient and Nusselt number. The discussion is concluded on the effect of various governing parameters on the motion, temperature variation, entropy generation, velocity gradient and the rate of heat transfer at the boundary.

Information Measures for Multisensor Systems

DTIC Science & Technology

2013-12-11

permuted to generate spectra that were non- physical but preserved the entropy of the source spectra. Another 1000 spectra were constructed to mimic co...Research Laboratory (NRL) has yielded probabilistic models for spectral data that enable the computation of information measures such as entropy and...22308 Chemical sensing Information theory Spectral data Information entropy Information divergence Mass spectrometry Infrared spectroscopy Multisensor

How will climate change affect the potential distribution of Eurasian Tree Sparrows Passer montanus in North America?

USGS Publications Warehouse

Graham, Jim; Jarnevich, Catherine; Young, Nick; Newman, Greg; Stohlgren, Thomas

2011-01-01

Habitat suitability models have been used to predict the present and future potential distribution of a variety of species. Eurasian tree sparrows Passer montanus, native to Eurasia, have established populations in other parts of the world. In North America, their current distribution is limited to a relatively small region around its original introduction to St. Louis, Missouri. We combined data from the Global Biodiversity Information Facility with current and future climate data to create habitat suitability models using Maxent for this species. Under projected climate change scenarios, our models show that the distribution and range of the Eurasian tree sparrow could increase as far as the Pacific Northwest and Newfoundland. This is potentially important information for prioritizing the management and control of this non-native species.

Entanglement entropy in a boundary impurity model.

PubMed

Levine, G C

2004-12-31

Boundary impurities are known to dramatically alter certain bulk properties of (1+1)-dimensional strongly correlated systems. The entanglement entropy of a zero temperature Luttinger liquid bisected by a single impurity is computed using a novel finite size scaling or bosonization scheme. For a Luttinger liquid of length 2L and UV cutoff epsilon, the boundary impurity correction (deltaSimp) to the logarithmic entanglement entropy (Sent proportional, variant lnL/epsilon scales as deltaSimp approximately yrlnL/epsilon, where yr is the renormalized backscattering coupling constant. In this way, the entanglement entropy within a region is related to scattering through the region's boundary. In the repulsive case (g<1), deltaSimp diverges (negatively) suggesting that the entropy vanishes. Our results are consistent with the recent conjecture that entanglement entropy decreases irreversibly along renormalization group flow.

Quantitative characterization of brazing performance for Sn-plated silver alloy fillers

NASA Astrophysics Data System (ADS)

Wang, Xingxing; Peng, Jin; Cui, Datian

2017-12-01

Two types of AgCuZnSn fillers were prepared based on BAg50CuZn and BAg34CuZnSn alloy through a combinative process of electroplating and thermal diffusion. The models of wetting entropy and joint strength entropy of AgCuZnSn filler metals were established. The wetting entropy of the Sn-plated silver brazing alloys are lower than the traditional fillers, and its joint strength entropy value is slightly higher than the latter. The wetting entropy value of the Sn-plated brazing alloys and traditional filler metal are similar to the change trend of the wetting area. The trend of the joint strength entropy value with those fillers are consisted with the tensile strength of the stainless steel joints with the increase of Sn content.

Application of a real-time, calculable limiting form of the Renyi entropy for molecular imaging of tumors.

PubMed

Marsh, Jon N; Wallace, Kirk D; McCarthy, John E; Wickerhauser, Mladen V; Maurizi, Brian N; Lanza, Gregory M; Wickline, Samuel A; Hughes, Michael S

2010-08-01

Previously, we reported new methods for ultrasound signal characterization using entropy, H(f); a generalized entropy, the Renyi entropy, I(f)(r); and a limiting form of Renyi entropy suitable for real-time calculation, I(f),(infinity). All of these quantities demonstrated significantly more sensitivity to subtle changes in scattering architecture than energy-based methods in certain settings. In this study, the real-time calculable limit of the Renyi entropy, I(f),(infinity), is applied for the imaging of angiogenic murine neovasculature in a breast cancer xenograft using a targeted contrast agent. It is shown that this approach may be used to reliably detect the accumulation of targeted nanoparticles at five minutes post-injection in this in vivo model.

Entropy Transfer between Residue Pairs and Allostery in Proteins: Quantifying Allosteric Communication in Ubiquitin.

PubMed

Hacisuleyman, Aysima; Erman, Burak

2017-01-01

It has recently been proposed by Gunasakaran et al. that allostery may be an intrinsic property of all proteins. Here, we develop a computational method that can determine and quantify allosteric activity in any given protein. Based on Schreiber's transfer entropy formulation, our approach leads to an information transfer landscape for the protein that shows the presence of entropy sinks and sources and explains how pairs of residues communicate with each other using entropy transfer. The model can identify the residues that drive the fluctuations of others. We apply the model to Ubiquitin, whose allosteric activity has not been emphasized until recently, and show that there are indeed systematic pathways of entropy and information transfer between residues that correlate well with the activities of the protein. We use 600 nanosecond molecular dynamics trajectories for Ubiquitin and its complex with human polymerase iota and evaluate entropy transfer between all pairs of residues of Ubiquitin and quantify the binding susceptibility changes upon complex formation. We explain the complex formation propensities of Ubiquitin in terms of entropy transfer. Important residues taking part in allosteric communication in Ubiquitin predicted by our approach are in agreement with results of NMR relaxation dispersion experiments. Finally, we show that time delayed correlation of fluctuations of two interacting residues possesses an intrinsic causality that tells which residue controls the interaction and which one is controlled. Our work shows that time delayed correlations, entropy transfer and causality are the required new concepts for explaining allosteric communication in proteins.

Mixing times towards demographic equilibrium in insect populations with temperature variable age structures.

PubMed

Damos, Petros

2015-08-01

In this study, we use entropy related mixing rate modules to measure the effects of temperature on insect population stability and demographic breakdown. The uncertainty in the age of the mother of a randomly chosen newborn, and how it is moved after a finite act of time steps, is modeled using a stochastic transformation of the Leslie matrix. Age classes are represented as a cycle graph and its transitions towards the stable age distribution are brought forth as an exact Markov chain. The dynamics of divergence, from a non equilibrium state towards equilibrium, are evaluated using the Kolmogorov-Sinai entropy. Moreover, Kullback-Leibler distance is applied as information-theoretic measure to estimate exact mixing times of age transitions probabilities towards equilibrium. Using empirically data, we show that on the initial conditions and simulated projection's trough time, that population entropy can effectively be applied to detect demographic variability towards equilibrium under different temperature conditions. Changes in entropy are correlated with the fluctuations of the insect population decay rates (i.e. demographic stability towards equilibrium). Moreover, shorter mixing times are directly linked to lower entropy rates and vice versa. This may be linked to the properties of the insect model system, which in contrast to warm blooded animals has the ability to greatly change its metabolic and demographic rates. Moreover, population entropy and the related distance measures that are applied, provide a means to measure these rates. The current results and model projections provide clear biological evidence why dynamic population entropy may be useful to measure population stability. Copyright © 2015 Elsevier Inc. All rights reserved.

Measurement of entanglement entropy in the two-dimensional Potts model using wavelet analysis.

PubMed

Tomita, Yusuke

2018-05-01

A method is introduced to measure the entanglement entropy using a wavelet analysis. Using this method, the two-dimensional Haar wavelet transform of a configuration of Fortuin-Kasteleyn (FK) clusters is performed. The configuration represents a direct snapshot of spin-spin correlations since spin degrees of freedom are traced out in FK representation. A snapshot of FK clusters loses image information at each coarse-graining process by the wavelet transform. It is shown that the loss of image information measures the entanglement entropy in the Potts model.

First-principles calculation of entropy for liquid metals.

PubMed

Desjarlais, Michael P

2013-12-01

We demonstrate the accurate calculation of entropies and free energies for a variety of liquid metals using an extension of the two-phase thermodynamic (2PT) model based on a decomposition of the velocity autocorrelation function into gas-like (hard sphere) and solid-like (harmonic) subsystems. The hard sphere model for the gas-like component is shown to give systematically high entropies for liquid metals as a direct result of the unphysical Lorentzian high-frequency tail. Using a memory function framework we derive a generally applicable velocity autocorrelation and frequency spectrum for the diffusive component which recovers the low-frequency (long-time) behavior of the hard sphere model while providing for realistic short-time coherence and high-frequency tails to the spectrum. This approach provides a significant increase in the accuracy of the calculated entropies for liquid metals and is compared to ambient pressure data for liquid sodium, aluminum, gallium, tin, and iron. The use of this method for the determination of melt boundaries is demonstrated with a calculation of the high-pressure bcc melt boundary for sodium. With the significantly improved accuracy available with the memory function treatment for softer interatomic potentials, the 2PT model for entropy calculations should find broader application in high energy density science, warm dense matter, planetary science, geophysics, and material science.

First-principles calculation of entropy for liquid metals

NASA Astrophysics Data System (ADS)

Desjarlais, Michael P.

2013-12-01

We demonstrate the accurate calculation of entropies and free energies for a variety of liquid metals using an extension of the two-phase thermodynamic (2PT) model based on a decomposition of the velocity autocorrelation function into gas-like (hard sphere) and solid-like (harmonic) subsystems. The hard sphere model for the gas-like component is shown to give systematically high entropies for liquid metals as a direct result of the unphysical Lorentzian high-frequency tail. Using a memory function framework we derive a generally applicable velocity autocorrelation and frequency spectrum for the diffusive component which recovers the low-frequency (long-time) behavior of the hard sphere model while providing for realistic short-time coherence and high-frequency tails to the spectrum. This approach provides a significant increase in the accuracy of the calculated entropies for liquid metals and is compared to ambient pressure data for liquid sodium, aluminum, gallium, tin, and iron. The use of this method for the determination of melt boundaries is demonstrated with a calculation of the high-pressure bcc melt boundary for sodium. With the significantly improved accuracy available with the memory function treatment for softer interatomic potentials, the 2PT model for entropy calculations should find broader application in high energy density science, warm dense matter, planetary science, geophysics, and material science.

Entropy-Based Search Algorithm for Experimental Design

NASA Astrophysics Data System (ADS)

Malakar, N. K.; Knuth, K. H.

2011-03-01

The scientific method relies on the iterated processes of inference and inquiry. The inference phase consists of selecting the most probable models based on the available data; whereas the inquiry phase consists of using what is known about the models to select the most relevant experiment. Optimizing inquiry involves searching the parameterized space of experiments to select the experiment that promises, on average, to be maximally informative. In the case where it is important to learn about each of the model parameters, the relevance of an experiment is quantified by Shannon entropy of the distribution of experimental outcomes predicted by a probable set of models. If the set of potential experiments is described by many parameters, we must search this high-dimensional entropy space. Brute force search methods will be slow and computationally expensive. We present an entropy-based search algorithm, called nested entropy sampling, to select the most informative experiment for efficient experimental design. This algorithm is inspired by Skilling's nested sampling algorithm used in inference and borrows the concept of a rising threshold while a set of experiment samples are maintained. We demonstrate that this algorithm not only selects highly relevant experiments, but also is more efficient than brute force search. Such entropic search techniques promise to greatly benefit autonomous experimental design.

RNA Thermodynamic Structural Entropy

PubMed Central

Garcia-Martin, Juan Antonio; Clote, Peter

2015-01-01

Conformational entropy for atomic-level, three dimensional biomolecules is known experimentally to play an important role in protein-ligand discrimination, yet reliable computation of entropy remains a difficult problem. Here we describe the first two accurate and efficient algorithms to compute the conformational entropy for RNA secondary structures, with respect to the Turner energy model, where free energy parameters are determined from UV absorption experiments. An algorithm to compute the derivational entropy for RNA secondary structures had previously been introduced, using stochastic context free grammars (SCFGs). However, the numerical value of derivational entropy depends heavily on the chosen context free grammar and on the training set used to estimate rule probabilities. Using data from the Rfam database, we determine that both of our thermodynamic methods, which agree in numerical value, are substantially faster than the SCFG method. Thermodynamic structural entropy is much smaller than derivational entropy, and the correlation between length-normalized thermodynamic entropy and derivational entropy is moderately weak to poor. In applications, we plot the structural entropy as a function of temperature for known thermoswitches, such as the repression of heat shock gene expression (ROSE) element, we determine that the correlation between hammerhead ribozyme cleavage activity and total free energy is improved by including an additional free energy term arising from conformational entropy, and we plot the structural entropy of windows of the HIV-1 genome. Our software RNAentropy can compute structural entropy for any user-specified temperature, and supports both the Turner’99 and Turner’04 energy parameters. It follows that RNAentropy is state-of-the-art software to compute RNA secondary structure conformational entropy. Source code is available at https://github.com/clotelab/RNAentropy/; a full web server is available at http://bioinformatics.bc.edu/clotelab/RNAentropy, including source code and ancillary programs. PMID:26555444

RNA Thermodynamic Structural Entropy.

PubMed

Garcia-Martin, Juan Antonio; Clote, Peter

2015-01-01

Conformational entropy for atomic-level, three dimensional biomolecules is known experimentally to play an important role in protein-ligand discrimination, yet reliable computation of entropy remains a difficult problem. Here we describe the first two accurate and efficient algorithms to compute the conformational entropy for RNA secondary structures, with respect to the Turner energy model, where free energy parameters are determined from UV absorption experiments. An algorithm to compute the derivational entropy for RNA secondary structures had previously been introduced, using stochastic context free grammars (SCFGs). However, the numerical value of derivational entropy depends heavily on the chosen context free grammar and on the training set used to estimate rule probabilities. Using data from the Rfam database, we determine that both of our thermodynamic methods, which agree in numerical value, are substantially faster than the SCFG method. Thermodynamic structural entropy is much smaller than derivational entropy, and the correlation between length-normalized thermodynamic entropy and derivational entropy is moderately weak to poor. In applications, we plot the structural entropy as a function of temperature for known thermoswitches, such as the repression of heat shock gene expression (ROSE) element, we determine that the correlation between hammerhead ribozyme cleavage activity and total free energy is improved by including an additional free energy term arising from conformational entropy, and we plot the structural entropy of windows of the HIV-1 genome. Our software RNAentropy can compute structural entropy for any user-specified temperature, and supports both the Turner'99 and Turner'04 energy parameters. It follows that RNAentropy is state-of-the-art software to compute RNA secondary structure conformational entropy. Source code is available at https://github.com/clotelab/RNAentropy/; a full web server is available at http://bioinformatics.bc.edu/clotelab/RNAentropy, including source code and ancillary programs.

Computing the Entropy of Kerr-Newman Black Hole Without Brick Walls Method

NASA Astrophysics Data System (ADS)

Zhang, Li-Chun; Wu, Yue-Qin; Li, Huai-Fan; Ren, Zhao

By using the entanglement entropy method, the statistical entropy of the Bose and Fermi fields in a thin film is calculated and the Bekenstein-Hawking entropy of Kerr-Newman black hole is obtained. Here, the Bose and Fermi fields are entangled with the quantum states in Kerr-Newman black hole and are outside of the horizon. The divergence of brick-wall model is avoided without any cutoff by the new equation of state density obtained with the generalized uncertainty principle. The calculation implies that the high density quantum states near the event horizon are strongly correlated with the quantum states in black hole. The black hole entropy is a quantum effect. It is an intrinsic characteristic of space-time. The ultraviolet cutoff in the brick-wall model is unreasonable. The generalized uncertainty principle should be considered in the high energy quantum field near the event horizon. From the calculation, the constant λ introduced in the generalized uncertainty principle is related to polar angle θ in an axisymmetric space-time.

Free energy and entropy of a dipolar liquid by computer simulations

NASA Astrophysics Data System (ADS)

Palomar, Ricardo; Sesé, Gemma

2018-02-01

Thermodynamic properties for a system composed of dipolar molecules are computed. Free energy is evaluated by means of the thermodynamic integration technique, and it is also estimated by using a perturbation theory approach, in which every molecule is modeled as a hard sphere within a square well, with an electric dipole at its center. The hard sphere diameter, the range and depth of the well, and the dipole moment have been calculated from properties easily obtained in molecular dynamics simulations. Connection between entropy and dynamical properties is explored in the liquid and supercooled states by using instantaneous normal mode calculations. A model is proposed in order to analyze translation and rotation contributions to entropy separately. Both contributions decrease upon cooling, and a logarithmic correlation between excess entropy associated with translation and the corresponding proportion of imaginary frequency modes is encountered. Rosenfeld scaling law between reduced diffusion and excess entropy is tested, and the origin of its failure at low temperatures is investigated.

Habitat Heterogeneity Variably Influences Habitat Selection by Wild Herbivores in a Semi-Arid Tropical Savanna Ecosystem

PubMed Central

Muposhi, Victor K.; Gandiwa, Edson; Chemura, Abel; Bartels, Paul; Makuza, Stanley M.; Madiri, Tinaapi H.

2016-01-01

An understanding of the habitat selection patterns by wild herbivores is critical for adaptive management, particularly towards ecosystem management and wildlife conservation in semi arid savanna ecosystems. We tested the following predictions: (i) surface water availability, habitat quality and human presence have a strong influence on the spatial distribution of wild herbivores in the dry season, (ii) habitat suitability for large herbivores would be higher compared to medium-sized herbivores in the dry season, and (iii) spatial extent of suitable habitats for wild herbivores will be different between years, i.e., 2006 and 2010, in Matetsi Safari Area, Zimbabwe. MaxEnt modeling was done to determine the habitat suitability of large herbivores and medium-sized herbivores. MaxEnt modeling of habitat suitability for large herbivores using the environmental variables was successful for the selected species in 2006 and 2010, except for elephant (Loxodonta africana) for the year 2010. Overall, large herbivores probability of occurrence was mostly influenced by distance from rivers. Distance from roads influenced much of the variability in the probability of occurrence of medium-sized herbivores. The overall predicted area for large and medium-sized herbivores was not different. Large herbivores may not necessarily utilize larger habitat patches over medium-sized herbivores due to the habitat homogenizing effect of water provisioning. Effect of surface water availability, proximity to riverine ecosystems and roads on habitat suitability of large and medium-sized herbivores in the dry season was highly variable thus could change from one year to another. We recommend adaptive management initiatives aimed at ensuring dynamic water supply in protected areas through temporal closure and or opening of water points to promote heterogeneity of wildlife habitats. PMID:27680673

Rat-bites of an epidemic proportion in Peshawar vale; a GIS based approach in risk assessment.

PubMed

Fatima, Syeda Hira; Zaidi, Farrah; Adnan, Muhammad; Ali, Asad; Jamal, Qaiser; Khisroon, Muhammad

2018-03-19

Contemporary studies demonstrate that rodent bites do not occur frequently. However, a huge number of cases were reported from Peshawar vale, Pakistan during 2016. Two species, the local black rat Rattus rattus (Linnaeus, 1758) and the invasive brown rat Rattus norvegicus (Berkenhout, 1769) might be the suspected cause. Several studies indicated the invasion of brown rats into Pakistan presumably via port city of Karachi. In this study, we modeled geospatial distribution of rodent bites for risk assessment in the region. Bite cases reported to tertiary care lady reading hospital were monitored from January 1 to August 31, 2016. Among 1747 cases, statistically informative data (n = 1295) was used for analyses. MaxEnt algorithm was employed for geospatial modeling, taking into account various environmental variables (temperature, precipitation, humidity, and elevation) and anthropogenic factors (human population density, distance from roads, distance from water channels, and land use/land cover). MaxEnt results revealed that urban slums (84.5%) are at highest risk followed by croplands (10.9%) and shrublands (2.7%). Anthropogenic factors affecting incidence of rodent bites included host density (contribution: 34.7), distance from water channels (3.2), land use/land cover (2.8), and distance from roads (2). Most of the cases occurred within a radius of 0.3 km from roads and 5 km from water channels. Rodent bite incidence is currently at its peak in Peshawar vale. Factors significantly affecting rodents' bite activity and their distribution and dispersal include urbanization, distance from roads, and water channels. Further studies are needed to determine the impact of invasion by brown rat on bite incidence.

Predicting the Impacts of Climate Change on the Potential Distribution of Major Native Non-Food Bioenergy Plants in China

PubMed Central

Wang, Wenguo; Tang, Xiaoyu; Zhu, Qili; Pan, Ke; Hu, Qichun; He, Mingxiong; Li, Jiatang

2014-01-01

Planting non-food bioenergy crops on marginal lands is an alternative bioenergy development solution in China. Native non-food bioenergy plants are also considered to be a wise choice to reduce the threat of invasive plants. In this study, the impacts of climate change (a consensus of IPCC scenarios A2a for 2080) on the potential distribution of nine non-food bioenergy plants native to China (viz., Pistacia chinensis, Cornus wilsoniana, Xanthoceras sorbifolia, Vernicia fordii, Sapium sebiferum, Miscanthus sinensis, M. floridulus, M. sacchariflorus and Arundo donax) were analyzed using a MaxEnt species distribution model. The suitable habitats of the nine non-food plants were distributed in the regions east of the Mongolian Plateau and the Tibetan Plateau, where the arable land is primarily used for food production. Thus, the large-scale cultivation of those plants for energy production will have to rely on the marginal lands. The variables of “precipitation of the warmest quarter” and “annual mean temperature” were the most important bioclimatic variables for most of the nine plants according to the MaxEnt modeling results. Global warming in coming decades may result in a decrease in the extent of suitable habitat in the tropics but will have little effect on the total distribution area of each plant. The results indicated that it will be possible to grow these plants on marginal lands within these areas in the future. This work should be beneficial for the domestication and cultivation of those bioenergy plants and should facilitate land-use planning for bioenergy crops in China. PMID:25365425

Schistosoma japonicum transmission risk maps at present and under climate change in mainland China

PubMed Central

Fan, Jingyu; Peterson, A. Townsend

2017-01-01

Background The South-to-North Water Diversion (SNWD) project is designed to channel fresh water from the Yangtze River north to more industrialized parts of China. An important question is whether future climate change and dispersal via the SNWD may synergistically favor a northward expansion of species involved in hosting and transmitting schistosomiasis in China, specifically the intermediate host, Oncomelania hupensis. Methodology/ Principal findings In this study, climate spaces occupied by the four subspecies of O. hupensis (O. h. hupensis, O. h. robertsoni, O. h. guangxiensis and O. h. tangi) were estimated, and niche conservatism tested among each pair of subspecies. Fine-tuned Maxent (fMaxent) and ensemble models were used to anticipate potential distributions of O. hupensis under future climate change scenarios. We were largely unable to reject the null hypothesis that climatic niches are conserved among the four subspecies, so factors other than climate appear to account for the divergence of O. hupensis populations across mainland China. Both model approaches indicated increased suitability and range expansion in O. h. hupensis in the future; an eastward and northward shift in O. h. robertsioni and O. h. guangxiensis, respectively; and relative distributional stability in O. h. gangi. Conclusions/Significance The southern parts of the Central Route of SNWD will coincide with suitable areas for O. h. hupensis in 2050–2060; its suitable areas will also expand northward along the southern parts of the Eastern Route by 2080–2090. Our results call for rigorous monitoring and surveillance of schistosomiasis along the southern Central Route and Eastern Route of the SNWD in a future, warmer China. PMID:29040273

Inferring Markov chains: Bayesian estimation, model comparison, entropy rate, and out-of-class modeling.

PubMed

Strelioff, Christopher C; Crutchfield, James P; Hübler, Alfred W

2007-07-01

Markov chains are a natural and well understood tool for describing one-dimensional patterns in time or space. We show how to infer kth order Markov chains, for arbitrary k , from finite data by applying Bayesian methods to both parameter estimation and model-order selection. Extending existing results for multinomial models of discrete data, we connect inference to statistical mechanics through information-theoretic (type theory) techniques. We establish a direct relationship between Bayesian evidence and the partition function which allows for straightforward calculation of the expectation and variance of the conditional relative entropy and the source entropy rate. Finally, we introduce a method that uses finite data-size scaling with model-order comparison to infer the structure of out-of-class processes.

Calculating the Entropy of Solid and Liquid Metals, Based on Acoustic Data

NASA Astrophysics Data System (ADS)

Tekuchev, V. V.; Kalinkin, D. P.; Ivanova, I. V.

2018-05-01

The entropies of iron, cobalt, rhodium, and platinum are studied for the first time, based on acoustic data and using the Debye theory and rigid-sphere model, from 298 K up to the boiling point. A formula for the melting entropy of metals is validated. Good agreement between the research results and the literature data is obtained.

Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine

NASA Astrophysics Data System (ADS)

Jia, Rui-Sheng; Sun, Hong-Mei; Peng, Yan-Jun; Liang, Yong-Quan; Lu, Xin-Ming

2017-07-01

Microseismic monitoring is an effective means for providing early warning of rock or coal dynamical disasters, and its first step is microseismic event detection, although low SNR microseismic signals often cannot effectively be detected by routine methods. To solve this problem, this paper presents permutation entropy and a support vector machine to detect low SNR microseismic events. First, an extraction method of signal features based on multi-scale permutation entropy is proposed by studying the influence of the scale factor on the signal permutation entropy. Second, the detection model of low SNR microseismic events based on the least squares support vector machine is built by performing a multi-scale permutation entropy calculation for the collected vibration signals, constructing a feature vector set of signals. Finally, a comparative analysis of the microseismic events and noise signals in the experiment proves that the different characteristics of the two can be fully expressed by using multi-scale permutation entropy. The detection model of microseismic events combined with the support vector machine, which has the features of high classification accuracy and fast real-time algorithms, can meet the requirements of online, real-time extractions of microseismic events.

The progression of the entropy of a five dimensional psychotherapeutic system

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

Badalamenti, A.F.; Langs, R.J.

This paper presents a study of the deterministic and stochastic behavior of the entropy of a 5-dimensional, 2400 state, system across each of six psychotherapeutic sessions. The growth of entropy was found to be logarithmic in each session. The stochastic behavior of a moving 600 second estimator of entropy revealed a Box-Jenkins model of type (1,1,0) - that is, the first difference of the entropy series was first order autoregressive or prior state sensitive. In addition, the patient and therapist entropy series exhibited no significant cross correlation across lags of -300 to +300 seconds. Yet all such pairs of seriesmore » exhibited high coherency past the frequency of .06 (on a full range of 0 to .5). Furthermore, all the patients and therapists were attracted to a geometric center of mass in 5-dimensional space which was different from the geometric center of the region where the system lived. The process significance of the findings and the relationship between the deterministic and stochastic results are discussed. The paper is then placed in the broader context of our efforts to provide new and meaningful quantitative dimensions and mathematical models to psychotherapy research. 59 refs.« less

The Evolution of Gas Giant Entropy During Formation by Runaway Accretion

NASA Astrophysics Data System (ADS)

Berardo, David; Cumming, Andrew; Marleau, Gabriel-Dominique

2017-01-01

We calculate the evolution of gas giant planets during the runaway gas accretion phase of formation, to understand how the luminosity of young giant planets depends on the accretion conditions. We construct steady-state envelope models, and run time-dependent simulations of accreting planets with the code Modules for Experiments in Stellar Astrophysics. We show that the evolution of the internal entropy depends on the contrast between the internal adiabat and the entropy of the accreted material, parametrized by the shock temperature T 0 and pressure P 0. At low temperatures ({T}0≲ 300-1000 {{K}}, depending on model parameters), the accreted material has a lower entropy than the interior. The convection zone extends to the surface and can drive a high luminosity, leading to rapid cooling and cold starts. For higher temperatures, the accreted material has a higher entropy than the interior, giving a radiative zone that stalls cooling. For {T}0≳ 2000 {{K}}, the surface-interior entropy contrast cannot be accommodated by the radiative envelope, and the accreted matter accumulates with high entropy, forming a hot start. The final state of the planet depends on the shock temperature, accretion rate, and starting entropy at the onset of runaway accretion. Cold starts with L≲ 5× {10}-6 {L}⊙ require low accretion rates and starting entropy, and the temperature of the accreting material needs to be maintained close to the nebula temperature. If instead the temperature is near the value required to radiate the accretion luminosity, 4π {R}2σ {T}04˜ ({GM}\\dot{M}/R), as suggested by previous work on radiative shocks in the context of star formation, gas giant planets form in a hot start with L˜ {10}-4 {L}⊙ .

Measuring Renyi entanglement entropy in quantum Monte Carlo simulations.

PubMed

Hastings, Matthew B; González, Iván; Kallin, Ann B; Melko, Roger G

2010-04-16

We develop a quantum Monte Carlo procedure, in the valence bond basis, to measure the Renyi entanglement entropy of a many-body ground state as the expectation value of a unitary Swap operator acting on two copies of the system. An improved estimator involving the ratio of Swap operators for different subregions enables convergence of the entropy in a simulation time polynomial in the system size. We demonstrate convergence of the Renyi entropy to exact results for a Heisenberg chain. Finally, we calculate the scaling of the Renyi entropy in the two-dimensional Heisenberg model and confirm that the Néel ground state obeys the expected area law for systems up to linear size L=32.

Casimir self-entropy of a spherical electromagnetic δ -function shell

NASA Astrophysics Data System (ADS)

Milton, Kimball A.; Kalauni, Pushpa; Parashar, Prachi; Li, Yang

2017-10-01

In this paper we continue our program of computing Casimir self-entropies of idealized electrical bodies. Here we consider an electromagnetic δ -function sphere ("semitransparent sphere") whose electric susceptibility has a transverse polarization with arbitrary strength. Dispersion is incorporated by a plasma-like model. In the strong-coupling limit, a perfectly conducting spherical shell is realized. We compute the entropy for both low and high temperatures. The transverse electric self-entropy is negative as expected, but the transverse magnetic self-entropy requires ultraviolet and infrared renormalization (subtraction), and, surprisingly, is only positive for sufficiently strong coupling. Results are robust under different regularization schemes. These rather surprising findings require further investigation.

The equivalence of minimum entropy production and maximum thermal efficiency in endoreversible heat engines.

PubMed

Haseli, Y

2016-05-01

The objective of this study is to investigate the thermal efficiency and power production of typical models of endoreversible heat engines at the regime of minimum entropy generation rate. The study considers the Curzon-Ahlborn engine, the Novikov's engine, and the Carnot vapor cycle. The operational regimes at maximum thermal efficiency, maximum power output and minimum entropy production rate are compared for each of these engines. The results reveal that in an endoreversible heat engine, a reduction in entropy production corresponds to an increase in thermal efficiency. The three criteria of minimum entropy production, the maximum thermal efficiency, and the maximum power may become equivalent at the condition of fixed heat input.

Production of Entanglement Entropy by Decoherence

NASA Astrophysics Data System (ADS)

Merkli, M.; Berman, G. P.; Sayre, R. T.; Wang, X.; Nesterov, A. I.

We examine the dynamics of entanglement entropy of all parts in an open system consisting of a two-level dimer interacting with an environment of oscillators. The dimer-environment interaction is almost energy conserving. We find the precise link between decoherence and production of entanglement entropy. We show that not all environment oscillators carry significant entanglement entropy and we identify the oscillator frequency regions which contribute to the production of entanglement entropy. For energy conserving dimer-environment interactions the models are explicitly solvable and our results hold for all dimer-environment coupling strengths. We carry out a mathematically rigorous perturbation theory around the energy conserving situation in the presence of small non-energy conserving interactions.

Entanglement Entropy of Black Holes.

PubMed

Solodukhin, Sergey N

2011-01-01

The entanglement entropy is a fundamental quantity, which characterizes the correlations between sub-systems in a larger quantum-mechanical system. For two sub-systems separated by a surface the entanglement entropy is proportional to the area of the surface and depends on the UV cutoff, which regulates the short-distance correlations. The geometrical nature of entanglement-entropy calculation is particularly intriguing when applied to black holes when the entangling surface is the black-hole horizon. I review a variety of aspects of this calculation: the useful mathematical tools such as the geometry of spaces with conical singularities and the heat kernel method, the UV divergences in the entropy and their renormalization, the logarithmic terms in the entanglement entropy in four and six dimensions and their relation to the conformal anomalies. The focus in the review is on the systematic use of the conical singularity method. The relations to other known approaches such as 't Hooft's brick-wall model and the Euclidean path integral in the optical metric are discussed in detail. The puzzling behavior of the entanglement entropy due to fields, which non-minimally couple to gravity, is emphasized. The holographic description of the entanglement entropy of the blackhole horizon is illustrated on the two- and four-dimensional examples. Finally, I examine the possibility to interpret the Bekenstein-Hawking entropy entirely as the entanglement entropy.

Entanglement Entropy of Black Holes

NASA Astrophysics Data System (ADS)

Solodukhin, Sergey N.

2011-10-01

The entanglement entropy is a fundamental quantity, which characterizes the correlations between sub-systems in a larger quantum-mechanical system. For two sub-systems separated by a surface the entanglement entropy is proportional to the area of the surface and depends on the UV cutoff, which regulates the short-distance correlations. The geometrical nature of entanglement-entropy calculation is particularly intriguing when applied to black holes when the entangling surface is the black-hole horizon. I review a variety of aspects of this calculation: the useful mathematical tools such as the geometry of spaces with conical singularities and the heat kernel method, the UV divergences in the entropy and their renormalization, the logarithmic terms in the entanglement entropy in four and six dimensions and their relation to the conformal anomalies. The focus in the review is on the systematic use of the conical singularity method. The relations to other known approaches such as 't Hooft's brick-wall model and the Euclidean path integral in the optical metric are discussed in detail. The puzzling behavior of the entanglement entropy due to fields, which non-minimally couple to gravity, is emphasized. The holographic description of the entanglement entropy of the blackhole horizon is illustrated on the two- and four-dimensional examples. Finally, I examine the possibility to interpret the Bekenstein-Hawking entropy entirely as the entanglement entropy.

Connectivity in the human brain dissociates entropy and complexity of auditory inputs☆

PubMed Central

Nastase, Samuel A.; Iacovella, Vittorio; Davis, Ben; Hasson, Uri

2015-01-01

Complex systems are described according to two central dimensions: (a) the randomness of their output, quantified via entropy; and (b) their complexity, which reflects the organization of a system's generators. Whereas some approaches hold that complexity can be reduced to uncertainty or entropy, an axiom of complexity science is that signals with very high or very low entropy are generated by relatively non-complex systems, while complex systems typically generate outputs with entropy peaking between these two extremes. In understanding their environment, individuals would benefit from coding for both input entropy and complexity; entropy indexes uncertainty and can inform probabilistic coding strategies, whereas complexity reflects a concise and abstract representation of the underlying environmental configuration, which can serve independent purposes, e.g., as a template for generalization and rapid comparisons between environments. Using functional neuroimaging, we demonstrate that, in response to passively processed auditory inputs, functional integration patterns in the human brain track both the entropy and complexity of the auditory signal. Connectivity between several brain regions scaled monotonically with input entropy, suggesting sensitivity to uncertainty, whereas connectivity between other regions tracked entropy in a convex manner consistent with sensitivity to input complexity. These findings suggest that the human brain simultaneously tracks the uncertainty of sensory data and effectively models their environmental generators. PMID:25536493

Entropy generation method to quantify thermal comfort.

PubMed

Boregowda, S C; Tiwari, S N; Chaturvedi, S K

2001-12-01

The present paper presents a thermodynamic approach to assess the quality of human-thermal environment interaction and quantify thermal comfort. The approach involves development of entropy generation term by applying second law of thermodynamics to the combined human-environment system. The entropy generation term combines both human thermal physiological responses and thermal environmental variables to provide an objective measure of thermal comfort. The original concepts and definitions form the basis for establishing the mathematical relationship between thermal comfort and entropy generation term. As a result of logic and deterministic approach, an Objective Thermal Comfort Index (OTCI) is defined and established as a function of entropy generation. In order to verify the entropy-based thermal comfort model, human thermal physiological responses due to changes in ambient conditions are simulated using a well established and validated human thermal model developed at the Institute of Environmental Research of Kansas State University (KSU). The finite element based KSU human thermal computer model is being utilized as a "Computational Environmental Chamber" to conduct series of simulations to examine the human thermal responses to different environmental conditions. The output from the simulation, which include human thermal responses and input data consisting of environmental conditions are fed into the thermal comfort model. Continuous monitoring of thermal comfort in comfortable and extreme environmental conditions is demonstrated. The Objective Thermal Comfort values obtained from the entropy-based model are validated against regression based Predicted Mean Vote (PMV) values. Using the corresponding air temperatures and vapor pressures that were used in the computer simulation in the regression equation generates the PMV values. The preliminary results indicate that the OTCI and PMV values correlate well under ideal conditions. However, an experimental study is needed in the future to fully establish the validity of the OTCI formula and the model. One of the practical applications of this index is that could it be integrated in thermal control systems to develop human-centered environmental control systems for potential use in aircraft, mass transit vehicles, intelligent building systems, and space vehicles.

Entropy generation method to quantify thermal comfort

NASA Technical Reports Server (NTRS)

Boregowda, S. C.; Tiwari, S. N.; Chaturvedi, S. K.

2001-01-01

The present paper presents a thermodynamic approach to assess the quality of human-thermal environment interaction and quantify thermal comfort. The approach involves development of entropy generation term by applying second law of thermodynamics to the combined human-environment system. The entropy generation term combines both human thermal physiological responses and thermal environmental variables to provide an objective measure of thermal comfort. The original concepts and definitions form the basis for establishing the mathematical relationship between thermal comfort and entropy generation term. As a result of logic and deterministic approach, an Objective Thermal Comfort Index (OTCI) is defined and established as a function of entropy generation. In order to verify the entropy-based thermal comfort model, human thermal physiological responses due to changes in ambient conditions are simulated using a well established and validated human thermal model developed at the Institute of Environmental Research of Kansas State University (KSU). The finite element based KSU human thermal computer model is being utilized as a "Computational Environmental Chamber" to conduct series of simulations to examine the human thermal responses to different environmental conditions. The output from the simulation, which include human thermal responses and input data consisting of environmental conditions are fed into the thermal comfort model. Continuous monitoring of thermal comfort in comfortable and extreme environmental conditions is demonstrated. The Objective Thermal Comfort values obtained from the entropy-based model are validated against regression based Predicted Mean Vote (PMV) values. Using the corresponding air temperatures and vapor pressures that were used in the computer simulation in the regression equation generates the PMV values. The preliminary results indicate that the OTCI and PMV values correlate well under ideal conditions. However, an experimental study is needed in the future to fully establish the validity of the OTCI formula and the model. One of the practical applications of this index is that could it be integrated in thermal control systems to develop human-centered environmental control systems for potential use in aircraft, mass transit vehicles, intelligent building systems, and space vehicles.

Safety Assessment of Dangerous Goods Transport Enterprise Based on the Relative Entropy Aggregation in Group Decision Making Model

PubMed Central

Wu, Jun; Li, Chengbing; Huo, Yueying

2014-01-01

Safety of dangerous goods transport is directly related to the operation safety of dangerous goods transport enterprise. Aiming at the problem of the high accident rate and large harm in dangerous goods logistics transportation, this paper took the group decision making problem based on integration and coordination thought into a multiagent multiobjective group decision making problem; a secondary decision model was established and applied to the safety assessment of dangerous goods transport enterprise. First of all, we used dynamic multivalue background and entropy theory building the first level multiobjective decision model. Secondly, experts were to empower according to the principle of clustering analysis, and combining with the relative entropy theory to establish a secondary rally optimization model based on relative entropy in group decision making, and discuss the solution of the model. Then, after investigation and analysis, we establish the dangerous goods transport enterprise safety evaluation index system. Finally, case analysis to five dangerous goods transport enterprises in the Inner Mongolia Autonomous Region validates the feasibility and effectiveness of this model for dangerous goods transport enterprise recognition, which provides vital decision making basis for recognizing the dangerous goods transport enterprises. PMID:25477954

Safety assessment of dangerous goods transport enterprise based on the relative entropy aggregation in group decision making model.

PubMed

Wu, Jun; Li, Chengbing; Huo, Yueying

2014-01-01

Safety of dangerous goods transport is directly related to the operation safety of dangerous goods transport enterprise. Aiming at the problem of the high accident rate and large harm in dangerous goods logistics transportation, this paper took the group decision making problem based on integration and coordination thought into a multiagent multiobjective group decision making problem; a secondary decision model was established and applied to the safety assessment of dangerous goods transport enterprise. First of all, we used dynamic multivalue background and entropy theory building the first level multiobjective decision model. Secondly, experts were to empower according to the principle of clustering analysis, and combining with the relative entropy theory to establish a secondary rally optimization model based on relative entropy in group decision making, and discuss the solution of the model. Then, after investigation and analysis, we establish the dangerous goods transport enterprise safety evaluation index system. Finally, case analysis to five dangerous goods transport enterprises in the Inner Mongolia Autonomous Region validates the feasibility and effectiveness of this model for dangerous goods transport enterprise recognition, which provides vital decision making basis for recognizing the dangerous goods transport enterprises.

Effect of Entropy Generation on Wear Mechanics and System Reliability

NASA Astrophysics Data System (ADS)

Gidwani, Akshay; James, Siddanth; Jagtap, Sagar; Karthikeyan, Ram; Vincent, S.

2018-04-01

Wear is an irreversible phenomenon. Processes such as mutual sliding and rolling between materials involve entropy generation. These processes are monotonic with respect to time. The concept of entropy generation is further quantified using Degradation Entropy Generation theorem formulated by Michael D. Bryant. The sliding-wear model can be extrapolated to different instances in order to further provide a potential analysis of machine prognostics as well as system and process reliability for various processes besides even mere mechanical processes. In other words, using the concept of ‘entropy generation’ and wear, one can quantify the reliability of a system with respect to time using a thermodynamic variable, which is the basis of this paper. Thus in the present investigation, a unique attempt has been made to establish correlation between entropy-wear-reliability which can be useful technique in preventive maintenance.

Entropy production in a photovoltaic cell

NASA Astrophysics Data System (ADS)

Ansari, Mohammad H.

2017-05-01

We evaluate entropy production in a photovoltaic cell that is modeled by four electronic levels resonantly coupled to thermally populated field modes at different temperatures. We use a formalism recently proposed, the so-called multiple parallel worlds, to consistently address the nonlinearity of entropy in terms of density matrix. Our result shows that entropy production is the difference between two flows: a semiclassical flow that linearly depends on occupational probabilities, and another flow that depends nonlinearly on quantum coherence and has no semiclassical analog. We show that entropy production in the cells depends on environmentally induced decoherence time and energy detuning. We characterize regimes where reversal flow of information takes place from a cold to hot bath. Interestingly, we identify a lower bound on entropy production, which sets limitations on the statistics of dissipated heat in the cells.

The Smoothed Dirichlet Distribution: Understanding Cross-Entropy Ranking in Information Retrieval

DTIC Science & Technology

2006-07-01

reflect those of the spon- sor. viii ABSTRACT Unigram Language modeling is a successful probabilistic framework for Information Retrieval (IR) that uses...the Relevance model (RM), a state-of-the-art model for IR in the language modeling framework that uses the same cross-entropy as its ranking function...In addition, the SD based classifier provides more flexibility than RM in modeling documents owing to a consistent generative framework . We

A two-phase copula entropy-based multiobjective optimization approach to hydrometeorological gauge network design

NASA Astrophysics Data System (ADS)

Xu, Pengcheng; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi; Liu, Jiufu; Zou, Ying; He, Ruimin

2017-12-01

Hydrometeorological data are needed for obtaining point and areal mean, quantifying the spatial variability of hydrometeorological variables, and calibration and verification of hydrometeorological models. Hydrometeorological networks are utilized to collect such data. Since data collection is expensive, it is essential to design an optimal network based on the minimal number of hydrometeorological stations in order to reduce costs. This study proposes a two-phase copula entropy- based multiobjective optimization approach that includes: (1) copula entropy-based directional information transfer (CDIT) for clustering the potential hydrometeorological gauges into several groups, and (2) multiobjective method for selecting the optimal combination of gauges for regionalized groups. Although entropy theory has been employed for network design before, the joint histogram method used for mutual information estimation has several limitations. The copula entropy-based mutual information (MI) estimation method is shown to be more effective for quantifying the uncertainty of redundant information than the joint histogram (JH) method. The effectiveness of this approach is verified by applying to one type of hydrometeorological gauge network, with the use of three model evaluation measures, including Nash-Sutcliffe Coefficient (NSC), arithmetic mean of the negative copula entropy (MNCE), and MNCE/NSC. Results indicate that the two-phase copula entropy-based multiobjective technique is capable of evaluating the performance of regional hydrometeorological networks and can enable decision makers to develop strategies for water resources management.

Instantons and entanglement entropy

NASA Astrophysics Data System (ADS)

Bhattacharyya, Arpan; Hung, Ling-Yan; Melby-Thompson, Charles M.

2017-10-01

We would like to put the area law — believed to be obeyed by entanglement entropies in the ground state of a local field theory — to scrutiny in the presence of nonperturbative effects. We study instanton corrections to entanglement entropy in various models whose instanton contributions are well understood, including U(1) gauge theory in 2+1 dimensions and false vacuum decay in ϕ 4 theory, and we demonstrate that the area law is indeed obeyed in these models. We also perform numerical computations for toy wavefunctions mimicking the theta vacuum of the (1+1)-dimensional Schwinger model. Our results indicate that such superpositions exhibit no more violation of the area law than the logarithmic behavior of a single Fermi surface.

Maximum-entropy description of animal movement.

PubMed

Fleming, Chris H; Subaşı, Yiğit; Calabrese, Justin M

2015-03-01

We introduce a class of maximum-entropy states that naturally includes within it all of the major continuous-time stochastic processes that have been applied to animal movement, including Brownian motion, Ornstein-Uhlenbeck motion, integrated Ornstein-Uhlenbeck motion, a recently discovered hybrid of the previous models, and a new model that describes central-place foraging. We are also able to predict a further hierarchy of new models that will emerge as data quality improves to better resolve the underlying continuity of animal movement. Finally, we also show that Langevin equations must obey a fluctuation-dissipation theorem to generate processes that fall from this class of maximum-entropy distributions when the constraints are purely kinematic.

Entropy, matter, and cosmology.

PubMed

Prigogine, I; Géhéniau, J

1986-09-01

The role of irreversible processes corresponding to creation of matter in general relativity is investigated. The use of Landau-Lifshitz pseudotensors together with conformal (Minkowski) coordinates suggests that this creation took place in the early universe at the stage of the variation of the conformal factor. The entropy production in this creation process is calculated. It is shown that these dissipative processes lead to the possibility of cosmological models that start from empty conditions and gradually build up matter and entropy. Gravitational entropy takes a simple meaning as associated to the entropy that is necessary to produce matter. This leads to an extension of the third law of thermodynamics, as now the zero point of entropy becomes the space-time structure out of which matter is generated. The theory can be put into a convenient form using a supplementary "C" field in Einstein's field equations. The role of the C field is to express the coupling between gravitation and matter leading to irreversible entropy production.

Gradient Dynamics and Entropy Production Maximization

NASA Astrophysics Data System (ADS)

Janečka, Adam; Pavelka, Michal

2018-01-01

We compare two methods for modeling dissipative processes, namely gradient dynamics and entropy production maximization. Both methods require similar physical inputs-how energy (or entropy) is stored and how it is dissipated. Gradient dynamics describes irreversible evolution by means of dissipation potential and entropy, it automatically satisfies Onsager reciprocal relations as well as their nonlinear generalization (Maxwell-Onsager relations), and it has statistical interpretation. Entropy production maximization is based on knowledge of free energy (or another thermodynamic potential) and entropy production. It also leads to the linear Onsager reciprocal relations and it has proven successful in thermodynamics of complex materials. Both methods are thermodynamically sound as they ensure approach to equilibrium, and we compare them and discuss their advantages and shortcomings. In particular, conditions under which the two approaches coincide and are capable of providing the same constitutive relations are identified. Besides, a commonly used but not often mentioned step in the entropy production maximization is pinpointed and the condition of incompressibility is incorporated into gradient dynamics.

RELATIONSHIP BETWEEN ENTROPY OF SPIKE TIMING AND FIRING RATE IN ENTOPEDUNCULAR NUCLEUS NEURONS IN ANESTHETIZED RATS: FUNCTION OF THE NIGRO-STRIATAL PATHWAY

PubMed Central

Darbin, Olivier; Jin, Xingxing; von Wrangel, Christof; Schwabe, Kerstin; Nambu, Atsushi; Naritoku, Dean K; Krauss, Joachim K.; Alam, Mesbah

2016-01-01

The function of the nigro-striatal pathway on neuronal entropy in the basal ganglia (BG) output nucleus (entopeduncular nucleus, EPN) was investigated in the unilaterally 6-hyroxydopamine (6-OHDA)-lesioned rat model of Parkinson’s disease (PD). In both control subjects and subjects with 6-OHDA lesion of the nigro-striatal pathway, a histological hallmark for parkinsonism, neuronal entropy in EPN was maximal in neurons with firing rates ranging between 15Hz and 25 Hz. In 6-OHDA lesioned rats, neuronal entropy in the EPN was specifically higher in neurons with firing rates above 25Hz. Our data establishes that nigro-striatal pathway controls neuronal entropy in motor circuitry and that the parkinsonian condition is associated with abnormal relationship between firing rate and neuronal entropy in BG output nuclei. The neuronal firing rates and entropy relationship provide putative relevant electrophysiological information to investigate the sensory-motor processing in normal condition and conditions with movement disorders. PMID:26711712

Entropy Transfer between Residue Pairs and Allostery in Proteins: Quantifying Allosteric Communication in Ubiquitin

PubMed Central

2017-01-01

It has recently been proposed by Gunasakaran et al. that allostery may be an intrinsic property of all proteins. Here, we develop a computational method that can determine and quantify allosteric activity in any given protein. Based on Schreiber's transfer entropy formulation, our approach leads to an information transfer landscape for the protein that shows the presence of entropy sinks and sources and explains how pairs of residues communicate with each other using entropy transfer. The model can identify the residues that drive the fluctuations of others. We apply the model to Ubiquitin, whose allosteric activity has not been emphasized until recently, and show that there are indeed systematic pathways of entropy and information transfer between residues that correlate well with the activities of the protein. We use 600 nanosecond molecular dynamics trajectories for Ubiquitin and its complex with human polymerase iota and evaluate entropy transfer between all pairs of residues of Ubiquitin and quantify the binding susceptibility changes upon complex formation. We explain the complex formation propensities of Ubiquitin in terms of entropy transfer. Important residues taking part in allosteric communication in Ubiquitin predicted by our approach are in agreement with results of NMR relaxation dispersion experiments. Finally, we show that time delayed correlation of fluctuations of two interacting residues possesses an intrinsic causality that tells which residue controls the interaction and which one is controlled. Our work shows that time delayed correlations, entropy transfer and causality are the required new concepts for explaining allosteric communication in proteins. PMID:28095404

Exact valence bond entanglement entropy and probability distribution in the XXX spin chain and the potts model.

PubMed

Jacobsen, J L; Saleur, H

2008-02-29

We determine exactly the probability distribution of the number N_(c) of valence bonds connecting a subsystem of length L>1 to the rest of the system in the ground state of the XXX antiferromagnetic spin chain. This provides, in particular, the asymptotic behavior of the valence-bond entanglement entropy S_(VB)=N_(c)ln2=4ln2/pi(2)lnL disproving a recent conjecture that this should be related with the von Neumann entropy, and thus equal to 1/3lnL. Our results generalize to the Q-state Potts model.

Visualizing the Entropy Change of a Thermal Reservoir

ERIC Educational Resources Information Center

Langbeheim, Elon; Safran, Samuel A.; Yerushalmi, Edit

2014-01-01

When a system exchanges energy with a constant-temperature environment, the entropy of the surroundings changes. A lattice model of a fluid thermal reservoir can provide a visualization of the microscopic changes that occur in the surroundings upon energy transfer from the system. This model can be used to clarify the consistency of phenomena such…

Exploiting Acoustic and Syntactic Features for Automatic Prosody Labeling in a Maximum Entropy Framework

PubMed Central

Sridhar, Vivek Kumar Rangarajan; Bangalore, Srinivas; Narayanan, Shrikanth S.

2009-01-01

In this paper, we describe a maximum entropy-based automatic prosody labeling framework that exploits both language and speech information. We apply the proposed framework to both prominence and phrase structure detection within the Tones and Break Indices (ToBI) annotation scheme. Our framework utilizes novel syntactic features in the form of supertags and a quantized acoustic–prosodic feature representation that is similar to linear parameterizations of the prosodic contour. The proposed model is trained discriminatively and is robust in the selection of appropriate features for the task of prosody detection. The proposed maximum entropy acoustic–syntactic model achieves pitch accent and boundary tone detection accuracies of 86.0% and 93.1% on the Boston University Radio News corpus, and, 79.8% and 90.3% on the Boston Directions corpus. The phrase structure detection through prosodic break index labeling provides accuracies of 84% and 87% on the two corpora, respectively. The reported results are significantly better than previously reported results and demonstrate the strength of maximum entropy model in jointly modeling simple lexical, syntactic, and acoustic features for automatic prosody labeling. PMID:19603083

On the limits of probabilistic forecasting in nonlinear time series analysis II: Differential entropy.

PubMed

Amigó, José M; Hirata, Yoshito; Aihara, Kazuyuki

2017-08-01

In a previous paper, the authors studied the limits of probabilistic prediction in nonlinear time series analysis in a perfect model scenario, i.e., in the ideal case that the uncertainty of an otherwise deterministic model is due to only the finite precision of the observations. The model consisted of the symbolic dynamics of a measure-preserving transformation with respect to a finite partition of the state space, and the quality of the predictions was measured by the so-called ignorance score, which is a conditional entropy. In practice, though, partitions are dispensed with by considering numerical and experimental data to be continuous, which prompts us to trade off in this paper the Shannon entropy for the differential entropy. Despite technical differences, we show that the core of the previous results also hold in this extended scenario for sufficiently high precision. The corresponding imperfect model scenario will be revisited too because it is relevant for the applications. The theoretical part and its application to probabilistic forecasting are illustrated with numerical simulations and a new prediction algorithm.

Effect of age on the performance of bispectral and entropy indices during sevoflurane pediatric anesthesia: a pharmacometric study.

PubMed

Sciusco, Alberto; Standing, Joseph F; Sheng, Yucheng; Raimondo, Pasquale; Cinnella, Gilda; Dambrosio, Michele

2017-04-01

Bispectral index (BIS) and entropy monitors have been proposed for use in children, but research has not supported their validity for infants. However, effective monitoring of young children may be even more important than for adults, to aid appropriate anesthetic dosing and reduce the chance of adverse consequences. This prospective study aimed to investigate the relationships between age and the predictive performance of BIS and entropy monitors in measuring the anesthetic drug effects within a pediatric surgery setting. We concurrently recorded BIS and entropy (SE/RE) in 48 children aged 1 month-12 years, undergoing general anesthesia with sevoflurane and fentanyl. Nonlinear mixed effects modeling was used to characterize the concentration-response relationship independently between the three monitor indicators with sevoflurane. The model's goodness-of-fit was assessed by prediction-corrected visual predictive checks. Model fit with age was evaluated using absolute conditional individual weighted residuals (|CIWRES|). The ability of BIS and entropy monitors to describe the effect of anesthesia was compared with prediction probabilities (P K ) in different age groups. Intraoperative and awakening values were compared in the age groups. The correlation between BIS and entropy was also calculated. |CIWRES| vs age showed an increasing trend in the model's accuracy for all three indicators. P K probabilities were similar for all three indicators within each age group, though lower in infants. The linear correlations between BIS and entropy in different age groups were lower for infants. Infants also tended to have lower values during surgery and at awakening than older children, while toddlers had higher values. Performance of both monitors improves as age increases. Our results suggest a need for the development of new monitor algorithms or calibration to better account for the age-specific EEG dynamics of younger patients. © 2017 John Wiley & Sons Ltd.

Maxent Harmonic Grammars and Phonetic Duration

ERIC Educational Resources Information Center

Lefkowitz, Lee Michael

2017-01-01

Research in phonetics has established the grammatical status of gradient phonetic patterns in language, suggesting that there is a component of the grammar that governs systematic relationships between discrete phonological representations and gradiently continuous acoustic or articulatory phonetic representations. This dissertation joins several…

Novel Topic Authorship Attribution

DTIC Science & Technology

2011-03-01

by fairness and public welfare concerns: plagiarism detection and identifying authors in a criminal investigation or intelligence setting. The...MEGAM format which makes this MaxEnt classifier a natural choice. MEGAM is publicly available for download [25] and has no restrictions for academic use

1/ f noise from the laws of thermodynamics for finite-size fluctuations.

PubMed

Chamberlin, Ralph V; Nasir, Derek M

2014-07-01

Computer simulations of the Ising model exhibit white noise if thermal fluctuations are governed by Boltzmann's factor alone; whereas we find that the same model exhibits 1/f noise if Boltzmann's factor is extended to include local alignment entropy to all orders. We show that this nonlinear correction maintains maximum entropy during equilibrium fluctuations. Indeed, as with the usual way to resolve Gibbs' paradox that avoids entropy reduction during reversible processes, the correction yields the statistics of indistinguishable particles. The correction also ensures conservation of energy if an instantaneous contribution from local entropy is included. Thus, a common mechanism for 1/f noise comes from assuming that finite-size fluctuations strictly obey the laws of thermodynamics, even in small parts of a large system. Empirical evidence for the model comes from its ability to match the measured temperature dependence of the spectral-density exponents in several metals and to show non-Gaussian fluctuations characteristic of nanoscale systems.

Experiments and Model for Serration Statistics in Low-Entropy, Medium-Entropy, and High-Entropy Alloys

DOE PAGES

Carroll, Robert; Lee, Chi; Tsai, Che-Wei; ...

2015-11-23

In this study, high-entropy alloys (HEAs) are new alloys that contain five or more elements in roughly-equal proportion. We present new experiments and theory on the deformation behavior of HEAs under slow stretching (straining), and observe differences, compared to conventional alloys with fewer elements. For a specific range of temperatures and strain-rates, HEAs deform in a jerky way, with sudden slips that make it difficult to precisely control the deformation. An analytic model explains these slips as avalanches of slipping weak spots and predicts the observed slip statistics, stress-strain curves, and their dependence on temperature, strain-rate, and material composition. Themore » ratio of the weak spots’ healing rate to the strain-rate is the main tuning parameter, reminiscent of the Portevin- LeChatellier effect and time-temperature superposition in polymers. Our model predictions agree with the experimental results. The proposed widely-applicable deformation mechanism is useful for deformation control and alloy design.« less

Entropy growth in emotional online dialogues

NASA Astrophysics Data System (ADS)

Sienkiewicz, J.; Skowron, M.; Paltoglou, G.; Hołyst, Janusz A.

2013-02-01

We analyze emotionally annotated massive data from IRC (Internet Relay Chat) and model the dialogues between its participants by assuming that the driving force for the discussion is the entropy growth of emotional probability distribution.

[Application of entropy-weight TOPSIS model in synthetical quality evaluation of Angelica sinensis growing in Gansu Province].

PubMed

Gu, Zhi-rong; Wang, Ya-li; Sun, Yu-jing; Dind, Jun-xia

2014-09-01

To investigate the establishment and application methods of entropy-weight TOPSIS model in synthetical quality evaluation of traditional Chinese medicine with Angelica sinensis growing in Gansu Province as an example. The contents of ferulic acid, 3-butylphthalide, Z-butylidenephthalide, Z-ligustilide, linolic acid, volatile oil, and ethanol soluble extractive were used as an evaluation index set. The weights of each evaluation index were determined by information entropy method. The entropyweight TOPSIS model was established to synthetically evaluate the quality of Angelica sinensis growing in Gansu Province by Euclid closeness degree. The results based on established model were in line with the daodi meaning and the knowledge of clinical experience. The established model was simple in calculation, objective, reliable, and can be applied to synthetical quality evaluation of traditional Chinese medicine.

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

Chen, Pisin; Hsin, Po-Shen; Niu, Yuezhen, E-mail: pisinchen@phys.ntu.edu.tw, E-mail: r01222031@ntu.edu.tw, E-mail: yuezhenniu@gmail.com

We investigate the entropy evolution in the early universe by computing the change of the entanglement entropy in Freedmann-Robertson-Walker quantum cosmology in the presence of particle horizon. The matter is modeled by a Chaplygin gas so as to provide a smooth interpolation between inflationary and radiation epochs, rendering the evolution of entropy from early time to late time trackable. We found that soon after the onset of the inflation, the total entanglement entropy rapidly decreases to a minimum. It then rises monotonically in the remainder of the inflation epoch as well as the radiation epoch. Our result is in qualitativemore » agreement with the area law of Ryu and Takayanagi including the logarithmic correction. We comment on the possible implication of our finding to the cosmological entropy problem.« less

Entropy maximization under the constraints on the generalized Gini index and its application in modeling income distributions

NASA Astrophysics Data System (ADS)

Khosravi Tanak, A.; Mohtashami Borzadaran, G. R.; Ahmadi, J.

2015-11-01

In economics and social sciences, the inequality measures such as Gini index, Pietra index etc., are commonly used to measure the statistical dispersion. There is a generalization of Gini index which includes it as special case. In this paper, we use principle of maximum entropy to approximate the model of income distribution with a given mean and generalized Gini index. Many distributions have been used as descriptive models for the distribution of income. The most widely known of these models are the generalized beta of second kind and its subclass distributions. The obtained maximum entropy distributions are fitted to the US family total money income in 2009, 2011 and 2013 and their relative performances with respect to generalized beta of second kind family are compared.