The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models

NASA Astrophysics Data System (ADS)

Koch, Julian; Cüneyd Demirel, Mehmet; Stisen, Simon

2018-05-01

The process of model evaluation is not only an integral part of model development and calibration but also of paramount importance when communicating modelling results to the scientific community and stakeholders. The modelling community has a large and well-tested toolbox of metrics to evaluate temporal model performance. In contrast, spatial performance evaluation does not correspond to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study makes a contribution towards advancing spatial-pattern-oriented model calibration by rigorously testing a multiple-component performance metric. The promoted SPAtial EFficiency (SPAEF) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multiple-component approach is found to be advantageous in order to achieve the complex task of comparing spatial patterns. SPAEF, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are applied in a spatial-pattern-oriented model calibration of a catchment model in Denmark. Results suggest the importance of multiple-component metrics because stand-alone metrics tend to fail to provide holistic pattern information. The three SPAEF components are found to be independent, which allows them to complement each other in a meaningful way. In order to optimally exploit spatial observations made available by remote sensing platforms, this study suggests applying bias insensitive metrics which further allow for a comparison of variables which are related but may differ in unit. This study applies SPAEF in the hydrological context using the mesoscale Hydrologic Model (mHM; version 5.8), but we see great potential across disciplines related to spatially distributed earth system modelling.

Utilizing remote sensing of thematic mapper data to improve our understanding of estuarine processes and their influence on the productivity of estuarine-dependent fisheries

NASA Technical Reports Server (NTRS)

Browder, Joan A.; May, L. Nelson, Jr.; Rosenthal, Alan; Baumann, Robert H.; Gosselink, James G.

1987-01-01

A stochastic spatial computer model addressing coastal resource problems in Lousiana is being refined and validated using thematic mapper (TM) imagery. The TM images of brackish marsh sites were processed and data were tabulated on spatial parameters from TM images of the salt marsh sites. The Fisheries Image Processing Systems (FIPS) was used to analyze the TM scene. Activities were concentrated on improving the structure of the model and developing a structure and methodology for calibrating the model with spatial-pattern data from the TM imagery.

Predicting wildfire occurrence distribution with spatial point process models and its uncertainty assessment: a case study in the Lake Tahoe Basin, USA

Treesearch

Jian Yang; Peter J. Weisberg; Thomas E. Dilts; E. Louise Loudermilk; Robert M. Scheller; Alison Stanton; Carl Skinner

2015-01-01

Strategic fire and fuel management planning benefits from detailed understanding of how wildfire occurrences are distributed spatially under current climate, and from predictive models of future wildfire occurrence given climate change scenarios. In this study, we fitted historical wildfire occurrence data from 1986 to 2009 to a suite of spatial point process (SPP)...

Review of forest landscape models: types, methods, development and applications

Treesearch

Weimin Xi; Robert N. Coulson; Andrew G. Birt; Zong-Bo Shang; John D. Waldron; Charles W. Lafon; David M. Cairns; Maria D. Tchakerian; Kier D. Klepzig

2009-01-01

Forest landscape models simulate forest change through time using spatially referenced data across a broad spatial scale (i.e. landscape scale) generally larger than a single forest stand. Spatial interactions between forest stands are a key component of such models. These models can incorporate other spatio-temporal processes such as...

A Study on the Effects of Spatial Scale on Snow Process in Hyper-Resolution Hydrological Modelling over Mountainous Areas

NASA Astrophysics Data System (ADS)

Garousi Nejad, I.; He, S.; Tang, Q.; Ogden, F. L.; Steinke, R. C.; Frazier, N.; Tarboton, D. G.; Ohara, N.; Lin, H.

2017-12-01

Spatial scale is one of the main considerations in hydrological modeling of snowmelt in mountainous areas. The size of model elements controls the degree to which variability can be explicitly represented versus what needs to be parameterized using effective properties such as averages or other subgrid variability parameterizations that may degrade the quality of model simulations. For snowmelt modeling terrain parameters such as slope, aspect, vegetation and elevation play an important role in the timing and quantity of snowmelt that serves as an input to hydrologic runoff generation processes. In general, higher resolution enhances the accuracy of the simulation since fine meshes represent and preserve the spatial variability of atmospheric and surface characteristics better than coarse resolution. However, this increases computational cost and there may be a scale beyond which the model response does not improve due to diminishing sensitivity to variability and irreducible uncertainty associated with the spatial interpolation of inputs. This paper examines the influence of spatial resolution on the snowmelt process using simulations of and data from the Animas River watershed, an alpine mountainous area in Colorado, USA, using an unstructured distributed physically based hydrological model developed for a parallel computing environment, ADHydro. Five spatial resolutions (30 m, 100 m, 250 m, 500 m, and 1 km) were used to investigate the variations in hydrologic response. This study demonstrated the importance of choosing the appropriate spatial scale in the implementation of ADHydro to obtain a balance between representing spatial variability and the computational cost. According to the results, variation in the input variables and parameters due to using different spatial resolution resulted in changes in the obtained hydrological variables, especially snowmelt, both at the basin-scale and distributed across the model mesh.

Towards more accurate isoscapes encouraging results from wine, water and marijuana data/model and model/model comparisons.

NASA Astrophysics Data System (ADS)

West, J. B.; Ehleringer, J. R.; Cerling, T.

2006-12-01

Understanding how the biosphere responds to change it at the heart of biogeochemistry, ecology, and other Earth sciences. The dramatic increase in human population and technological capacity over the past 200 years or so has resulted in numerous, simultaneous changes to biosphere structure and function. This, then, has lead to increased urgency in the scientific community to try to understand how systems have already responded to these changes, and how they might do so in the future. Since all biospheric processes exhibit some patchiness or patterns over space, as well as time, we believe that understanding the dynamic interactions between natural systems and human technological manipulations can be improved if these systems are studied in an explicitly spatial context. We present here results of some of our efforts to model the spatial variation in the stable isotope ratios (δ2H and δ18O) of plants over large spatial extents, and how these spatial model predictions compare to spatially explicit data. Stable isotopes trace and record ecological processes and as such, if modeled correctly over Earth's surface allow us insights into changes in biosphere states and processes across spatial scales. The data-model comparisons show good agreement, in spite of the remaining uncertainties (e.g., plant source water isotopic composition). For example, inter-annual changes in climate are recorded in wine stable isotope ratios. Also, a much simpler model of leaf water enrichment driven with spatially continuous global rasters of precipitation and climate normals largely agrees with complex GCM modeling that includes leaf water δ18O. Our results suggest that modeling plant stable isotope ratios across large spatial extents may be done with reasonable accuracy, including over time. These spatial maps, or isoscapes, can now be utilized to help understand spatially distributed data, as well as to help guide future studies designed to understand ecological change across landscapes.

The Challenges to Coupling Dynamic Geospatial Models

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

Goldstein, N

2006-06-23

Many applications of modeling spatial dynamic systems focus on a single system and a single process, ignoring the geographic and systemic context of the processes being modeled. A solution to this problem is the coupled modeling of spatial dynamic systems. Coupled modeling is challenging for both technical reasons, as well as conceptual reasons. This paper explores the benefits and challenges to coupling or linking spatial dynamic models, from loose coupling, where information transfer between models is done by hand, to tight coupling, where two (or more) models are merged as one. To illustrate the challenges, a coupled model of Urbanizationmore » and Wildfire Risk is presented. This model, called Vesta, was applied to the Santa Barbara, California region (using real geospatial data), where Urbanization and Wildfires occur and recur, respectively. The preliminary results of the model coupling illustrate that coupled modeling can lead to insight into the consequences of processes acting on their own.« less

NONSTATIONARY SPATIAL MODELING OF ENVIRONMENTAL DATA USING A PROCESS CONVOLUTION APPROACH

EPA Science Inventory

Traditional approaches to modeling spatial processes involve the specification of the covariance structure of the field. Although such methods are straightforward to understand and effective in some situations, there are often problems in incorporating non-stationarity and in ma...

Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting.

PubMed

Yu, Wenxi; Liu, Yang; Ma, Zongwei; Bi, Jun

2017-08-01

Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM 2.5 is a promising way to fill the areas that are not covered by ground PM 2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM 2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM 2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R 2  = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM 2.5 estimates.

Analysis of the dependence of extreme rainfalls

NASA Astrophysics Data System (ADS)

Padoan, Simone; Ancey, Christophe; Parlange, Marc

2010-05-01

The aim of spatial analysis is to quantitatively describe the behavior of environmental phenomena such as precipitation levels, wind speed or daily temperatures. A number of generic approaches to spatial modeling have been developed[1], but these are not necessarily ideal for handling extremal aspects given their focus on mean process levels. The areal modelling of the extremes of a natural process observed at points in space is important in environmental statistics; for example, understanding extremal spatial rainfall is crucial in flood protection. In light of recent concerns over climate change, the use of robust mathematical and statistical methods for such analyses has grown in importance. Multivariate extreme value models and the class of maxstable processes [2] have a similar asymptotic motivation to the univariate Generalized Extreme Value (GEV) distribution , but providing a general approach to modeling extreme processes incorporating temporal or spatial dependence. Statistical methods for max-stable processes and data analyses of practical problems are discussed by [3] and [4]. This work illustrates methods to the statistical modelling of spatial extremes and gives examples of their use by means of a real extremal data analysis of Switzerland precipitation levels. [1] Cressie, N. A. C. (1993). Statistics for Spatial Data. Wiley, New York. [2] de Haan, L and Ferreria A. (2006). Extreme Value Theory An Introduction. Springer, USA. [3] Padoan, S. A., Ribatet, M and Sisson, S. A. (2009). Likelihood-Based Inference for Max-Stable Processes. Journal of the American Statistical Association, Theory & Methods. In press. [4] Davison, A. C. and Gholamrezaee, M. (2009), Geostatistics of extremes. Journal of the Royal Statistical Society, Series B. To appear.

Testing the generality of the zoom-lens model: Evidence for visual-pathway specific effects of attended-region size on perception.

PubMed

Goodhew, Stephanie C; Lawrence, Rebecca K; Edwards, Mark

2017-05-01

There are volumes of information available to process in visual scenes. Visual spatial attention is a critically important selection mechanism that prevents these volumes from overwhelming our visual system's limited-capacity processing resources. We were interested in understanding the effect of the size of the attended area on visual perception. The prevailing model of attended-region size across cognition, perception, and neuroscience is the zoom-lens model. This model stipulates that the magnitude of perceptual processing enhancement is inversely related to the size of the attended region, such that a narrow attended-region facilitates greater perceptual enhancement than a wider region. Yet visual processing is subserved by two major visual pathways (magnocellular and parvocellular) that operate with a degree of independence in early visual processing and encode contrasting visual information. Historically, testing of the zoom-lens has used measures of spatial acuity ideally suited to parvocellular processing. This, therefore, raises questions about the generality of the zoom-lens model to different aspects of visual perception. We found that while a narrow attended-region facilitated spatial acuity and the perception of high spatial frequency targets, it had no impact on either temporal acuity or the perception of low spatial frequency targets. This pattern also held up when targets were not presented centrally. This supports the notion that visual attended-region size has dissociable effects on magnocellular versus parvocellular mediated visual processing.

Cognitive Process Modeling of Spatial Ability: The Assembling Objects Task

ERIC Educational Resources Information Center

Ivie, Jennifer L.; Embretson, Susan E.

2010-01-01

Spatial ability tasks appear on many intelligence and aptitude tests. Although the construct validity of spatial ability tests has often been studied through traditional correlational methods, such as factor analysis, less is known about the cognitive processes involved in solving test items. This study examines the cognitive processes involved in…

High-Dimensional Bayesian Geostatistics

PubMed Central

Banerjee, Sudipto

2017-01-01

With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarchical spatiotemporal process models have become widely deployed statistical tools for researchers to better understand the complex nature of spatial and temporal variability. However, fitting hierarchical spatiotemporal models often involves expensive matrix computations with complexity increasing in cubic order for the number of spatial locations and temporal points. This renders such models unfeasible for large data sets. This article offers a focused review of two methods for constructing well-defined highly scalable spatiotemporal stochastic processes. Both these processes can be used as “priors” for spatiotemporal random fields. The first approach constructs a low-rank process operating on a lower-dimensional subspace. The second approach constructs a Nearest-Neighbor Gaussian Process (NNGP) that ensures sparse precision matrices for its finite realizations. Both processes can be exploited as a scalable prior embedded within a rich hierarchical modeling framework to deliver full Bayesian inference. These approaches can be described as model-based solutions for big spatiotemporal datasets. The models ensure that the algorithmic complexity has ~ n floating point operations (flops), where n the number of spatial locations (per iteration). We compare these methods and provide some insight into their methodological underpinnings. PMID:29391920

High-Dimensional Bayesian Geostatistics.

PubMed

Banerjee, Sudipto

2017-06-01

With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarchical spatiotemporal process models have become widely deployed statistical tools for researchers to better understand the complex nature of spatial and temporal variability. However, fitting hierarchical spatiotemporal models often involves expensive matrix computations with complexity increasing in cubic order for the number of spatial locations and temporal points. This renders such models unfeasible for large data sets. This article offers a focused review of two methods for constructing well-defined highly scalable spatiotemporal stochastic processes. Both these processes can be used as "priors" for spatiotemporal random fields. The first approach constructs a low-rank process operating on a lower-dimensional subspace. The second approach constructs a Nearest-Neighbor Gaussian Process (NNGP) that ensures sparse precision matrices for its finite realizations. Both processes can be exploited as a scalable prior embedded within a rich hierarchical modeling framework to deliver full Bayesian inference. These approaches can be described as model-based solutions for big spatiotemporal datasets. The models ensure that the algorithmic complexity has ~ n floating point operations (flops), where n the number of spatial locations (per iteration). We compare these methods and provide some insight into their methodological underpinnings.

A Neurobehavioral Model of Flexible Spatial Language Behaviors

ERIC Educational Resources Information Center

Lipinski, John; Schneegans, Sebastian; Sandamirskaya, Yulia; Spencer, John P.; Schoner, Gregor

2012-01-01

We propose a neural dynamic model that specifies how low-level visual processes can be integrated with higher level cognition to achieve flexible spatial language behaviors. This model uses real-word visual input that is linked to relational spatial descriptions through a neural mechanism for reference frame transformations. We demonstrate that…

Spatially distributed modelling of pesticide leaching at European scale with the PyCatch modelling framework

NASA Astrophysics Data System (ADS)

Schmitz, Oliver; van der Perk, Marcel; Karssenberg, Derek; Häring, Tim; Jene, Bernhard

2017-04-01

The modelling of pesticide transport through the soil and estimating its leaching to groundwater is essential for an appropriate environmental risk assessment. Pesticide leaching models commonly used in regulatory processes often lack the capability of providing a comprehensive spatial view, as they are implemented as non-spatial point models or only use a few combinations of representative soils to simulate specific plots. Furthermore, their handling of spatial input and output data and interaction with available Geographical Information Systems tools is limited. Therefore, executing several scenarios simulating and assessing the potential leaching on national or continental scale at high resolution is rather inefficient and prohibits the straightforward identification of areas prone to leaching. We present a new pesticide leaching model component of the PyCatch framework developed in PCRaster Python, an environmental modelling framework tailored to the development of spatio-temporal models (http://www.pcraster.eu). To ensure a feasible computational runtime of large scale models, we implemented an elementary field capacity approach to model soil water. Currently implemented processes are evapotranspiration, advection, dispersion, sorption, degradation and metabolite transformation. Not yet implemented relevant additional processes such as surface runoff, snowmelt, erosion or other lateral flows can be integrated with components already implemented in PyCatch. A preliminary version of the model executes a 20-year simulation of soil water processes for Germany (20 soil layers, 1 km2 spatial resolution, and daily timestep) within half a day using a single CPU. A comparison of the soil moisture and outflow obtained from the PCRaster implementation and PELMO, a commonly used pesticide leaching model, resulted in an R2 of 0.98 for the FOCUS Hamburg scenario. We will further discuss the validation of the pesticide transport processes and show case studies applied to European countries.

A necessarily complex model to explain the biogeography of the amphibians and reptiles of Madagascar.

PubMed

Brown, Jason L; Cameron, Alison; Yoder, Anne D; Vences, Miguel

2014-10-09

Pattern and process are inextricably linked in biogeographic analyses, though we can observe pattern, we must infer process. Inferences of process are often based on ad hoc comparisons using a single spatial predictor. Here, we present an alternative approach that uses mixed-spatial models to measure the predictive potential of combinations of hypotheses. Biodiversity patterns are estimated from 8,362 occurrence records from 745 species of Malagasy amphibians and reptiles. By incorporating 18 spatially explicit predictions of 12 major biogeographic hypotheses, we show that mixed models greatly improve our ability to explain the observed biodiversity patterns. We conclude that patterns are influenced by a combination of diversification processes rather than by a single predominant mechanism. A 'one-size-fits-all' model does not exist. By developing a novel method for examining and synthesizing spatial parameters such as species richness, endemism and community similarity, we demonstrate the potential of these analyses for understanding the diversification history of Madagascar's biota.

Effects of Topography-based Subgrid Structures on Land Surface Modeling

NASA Astrophysics Data System (ADS)

Tesfa, T. K.; Ruby, L.; Brunke, M.; Thornton, P. E.; Zeng, X.; Ghan, S. J.

2017-12-01

Topography has major control on land surface processes through its influence on atmospheric forcing, soil and vegetation properties, network topology and drainage area. Consequently, accurate climate and land surface simulations in mountainous regions cannot be achieved without considering the effects of topographic spatial heterogeneity. To test a computationally less expensive hyper-resolution land surface modeling approach, we developed topography-based landunits within a hierarchical subgrid spatial structure to improve representation of land surface processes in the ACME Land Model (ALM) with minimal increase in computational demand, while improving the ability to capture the spatial heterogeneity of atmospheric forcing and land cover influenced by topography. This study focuses on evaluation of the impacts of the new spatial structures on modeling land surface processes. As a first step, we compare ALM simulations with and without subgrid topography and driven by grid cell mean atmospheric forcing to isolate the impacts of the subgrid topography on the simulated land surface states and fluxes. Recognizing that subgrid topography also has important effects on atmospheric processes that control temperature, radiation, and precipitation, methods are being developed to downscale atmospheric forcings. Hence in the second step, the impacts of the subgrid topographic structure on land surface modeling will be evaluated by including spatial downscaling of the atmospheric forcings. Preliminary results on the atmospheric downscaling and the effects of the new spatial structures on the ALM simulations will be presented.

Predictive computation of genomic logic processing functions in embryonic development

PubMed Central

Peter, Isabelle S.; Faure, Emmanuel; Davidson, Eric H.

2012-01-01

Gene regulatory networks (GRNs) control the dynamic spatial patterns of regulatory gene expression in development. Thus, in principle, GRN models may provide system-level, causal explanations of developmental process. To test this assertion, we have transformed a relatively well-established GRN model into a predictive, dynamic Boolean computational model. This Boolean model computes spatial and temporal gene expression according to the regulatory logic and gene interactions specified in a GRN model for embryonic development in the sea urchin. Additional information input into the model included the progressive embryonic geometry and gene expression kinetics. The resulting model predicted gene expression patterns for a large number of individual regulatory genes each hour up to gastrulation (30 h) in four different spatial domains of the embryo. Direct comparison with experimental observations showed that the model predictively computed these patterns with remarkable spatial and temporal accuracy. In addition, we used this model to carry out in silico perturbations of regulatory functions and of embryonic spatial organization. The model computationally reproduced the altered developmental functions observed experimentally. Two major conclusions are that the starting GRN model contains sufficiently complete regulatory information to permit explanation of a complex developmental process of gene expression solely in terms of genomic regulatory code, and that the Boolean model provides a tool with which to test in silico regulatory circuitry and developmental perturbations. PMID:22927416

Nonparametric Bayesian Segmentation of a Multivariate Inhomogeneous Space-Time Poisson Process.

PubMed

Ding, Mingtao; He, Lihan; Dunson, David; Carin, Lawrence

2012-12-01

A nonparametric Bayesian model is proposed for segmenting time-evolving multivariate spatial point process data. An inhomogeneous Poisson process is assumed, with a logistic stick-breaking process (LSBP) used to encourage piecewise-constant spatial Poisson intensities. The LSBP explicitly favors spatially contiguous segments, and infers the number of segments based on the observed data. The temporal dynamics of the segmentation and of the Poisson intensities are modeled with exponential correlation in time, implemented in the form of a first-order autoregressive model for uniformly sampled discrete data, and via a Gaussian process with an exponential kernel for general temporal sampling. We consider and compare two different inference techniques: a Markov chain Monte Carlo sampler, which has relatively high computational complexity; and an approximate and efficient variational Bayesian analysis. The model is demonstrated with a simulated example and a real example of space-time crime events in Cincinnati, Ohio, USA.

Different modelling approaches to evaluate nitrogen transport and turnover at the watershed scale

NASA Astrophysics Data System (ADS)

Epelde, Ane Miren; Antiguedad, Iñaki; Brito, David; Jauch, Eduardo; Neves, Ramiro; Garneau, Cyril; Sauvage, Sabine; Sánchez-Pérez, José Miguel

2016-08-01

This study presents the simulation of hydrological processes and nutrient transport and turnover processes using two integrated numerical models: Soil and Water Assessment Tool (SWAT) (Arnold et al., 1998), an empirical and semi-distributed numerical model; and Modelo Hidrodinâmico (MOHID) (Neves, 1985), a physics-based and fully distributed numerical model. This work shows that both models reproduce satisfactorily water and nitrate exportation at the watershed scale at annual and daily basis, MOHID providing slightly better results. At the watershed scale, both SWAT and MOHID simulated similarly and satisfactorily the denitrification amount. However, as MOHID numerical model was the only one able to reproduce adequately the spatial variation of the soil hydrological conditions and water table level fluctuation, it proved to be the only model able of reproducing the spatial variation of the nutrient cycling processes that are dependent to the soil hydrological conditions such as the denitrification process. This evidences the strength of the fully distributed and physics-based models to simulate the spatial variability of nutrient cycling processes that are dependent to the hydrological conditions of the soils.

Multiphysics Modeling and Simulations of Mil A46100 Armor-Grade Martensitic Steel Gas Metal Arc Welding Process

NASA Astrophysics Data System (ADS)

Grujicic, M.; Ramaswami, S.; Snipes, J. S.; Yen, C.-F.; Cheeseman, B. A.; Montgomery, J. S.

2013-10-01

A multiphysics computational model has been developed for the conventional Gas Metal Arc Welding (GMAW) joining process and used to analyze butt-welding of MIL A46100, a prototypical high-hardness armor martensitic steel. The model consists of five distinct modules, each covering a specific aspect of the GMAW process, i.e., (a) dynamics of welding-gun behavior; (b) heat transfer from the electric arc and mass transfer from the electrode to the weld; (c) development of thermal and mechanical fields during the GMAW process; (d) the associated evolution and spatial distribution of the material microstructure throughout the weld region; and (e) the final spatial distribution of the as-welded material properties. To make the newly developed GMAW process model applicable to MIL A46100, the basic physical-metallurgy concepts and principles for this material have to be investigated and properly accounted for/modeled. The newly developed GMAW process model enables establishment of the relationship between the GMAW process parameters (e.g., open circuit voltage, welding current, electrode diameter, electrode-tip/weld distance, filler-metal feed speed, and gun travel speed), workpiece material chemistry, and the spatial distribution of as-welded material microstructure and properties. The predictions of the present GMAW model pertaining to the spatial distribution of the material microstructure and properties within the MIL A46100 weld region are found to be consistent with general expectations and prior observations.

Part 3 Specialized aspects of GIS and spatial analysis . Garage band science and dynamic spatial models

NASA Astrophysics Data System (ADS)

Box, Paul W.

GIS and spatial analysis is suited mainly for static pictures of the landscape, but many of the processes that need exploring are dynamic in nature. Dynamic processes can be complex when put in a spatial context; our ability to study such processes will probably come with advances in understanding complex systems in general. Cellular automata and agent-based models are two prime candidates for exploring complex spatial systems, but are difficult to implement. Innovative tools that help build complex simulations will create larger user communities, who will probably find novel solutions for understanding complexity. A significant source for such innovations is likely to be from the collective efforts of hobbyists and part-time programmers, who have been dubbed ``garage-band scientists'' in the popular press.

Spatial perspectives in state-and-transition models: A missing link to land management?

USDA-ARS?s Scientific Manuscript database

Conceptual models of alternative states and thresholds are based largely on observations of ecosystem processes at a few points in space. Because the distribution of alternative states in spatially-structured ecosystems is the result of variations in pattern-process interactions at different scales,...

Surrogate modelling for the prediction of spatial fields based on simultaneous dimensionality reduction of high-dimensional input/output spaces.

PubMed

Crevillén-García, D

2018-04-01

Time-consuming numerical simulators for solving groundwater flow and dissolution models of physico-chemical processes in deep aquifers normally require some of the model inputs to be defined in high-dimensional spaces in order to return realistic results. Sometimes, the outputs of interest are spatial fields leading to high-dimensional output spaces. Although Gaussian process emulation has been satisfactorily used for computing faithful and inexpensive approximations of complex simulators, these have been mostly applied to problems defined in low-dimensional input spaces. In this paper, we propose a method for simultaneously reducing the dimensionality of very high-dimensional input and output spaces in Gaussian process emulators for stochastic partial differential equation models while retaining the qualitative features of the original models. This allows us to build a surrogate model for the prediction of spatial fields in such time-consuming simulators. We apply the methodology to a model of convection and dissolution processes occurring during carbon capture and storage.

Development and evaluation of spatial point process models for epidermal nerve fibers.

PubMed

Olsbo, Viktor; Myllymäki, Mari; Waller, Lance A; Särkkä, Aila

2013-06-01

We propose two spatial point process models for the spatial structure of epidermal nerve fibers (ENFs) across human skin. The models derive from two point processes, Φb and Φe, describing the locations of the base and end points of the fibers. Each point of Φe (the end point process) is connected to a unique point in Φb (the base point process). In the first model, both Φe and Φb are Poisson processes, yielding a null model of uniform coverage of the skin by end points and general baseline results and reference values for moments of key physiologic indicators. The second model provides a mechanistic model to generate end points for each base, and we model the branching structure more directly by defining Φe as a cluster process conditioned on the realization of Φb as its parent points. In both cases, we derive distributional properties for observable quantities of direct interest to neurologists such as the number of fibers per base, and the direction and range of fibers on the skin. We contrast both models by fitting them to data from skin blister biopsy images of ENFs and provide inference regarding physiological properties of ENFs. Copyright © 2013 Elsevier Inc. All rights reserved.

Quantitative evaluation of the risk induced by dominant geomorphological processes on different land uses, based on GIS spatial analysis models

NASA Astrophysics Data System (ADS)

Ştefan, Bilaşco; Sanda, Roşca; Ioan, Fodorean; Iuliu, Vescan; Sorin, Filip; Dănuţ, Petrea

2017-12-01

Maramureş Land is mostly characterized by agricultural and forestry land use due to its specific configuration of topography and its specific pedoclimatic conditions. Taking into consideration the trend of the last century from the perspective of land management, a decrease in the surface of agricultural lands to the advantage of built-up and grass lands, as well as an accelerated decrease in the forest cover due to uncontrolled and irrational forest exploitation, has become obvious. The field analysis performed on the territory of Maramureş Land has highlighted a high frequency of two geomorphologic processes — landslides and soil erosion — which have a major negative impact on land use due to their rate of occurrence. The main aim of the present study is the GIS modeling of the two geomorphologic processes, determining a state of vulnerability (the USLE model for soil erosion and a quantitative model based on the morphometric characteristics of the territory, derived from the HG. 447/2003) and their integration in a complex model of cumulated vulnerability identification. The modeling of the risk exposure was performed using a quantitative approach based on models and equations of spatial analysis, which were developed with modeled raster data structures and primary vector data, through a matrix highlighting the correspondence between vulnerability and land use classes. The quantitative analysis of the risk was performed by taking into consideration the exposure classes as modeled databases and the land price as a primary alphanumeric database using spatial analysis techniques for each class by means of the attribute table. The spatial results highlight the territories with a high risk to present geomorphologic processes that have a high degree of occurrence and represent a useful tool in the process of spatial planning.

Quantitative evaluation of the risk induced by dominant geomorphological processes on different land uses, based on GIS spatial analysis models

NASA Astrophysics Data System (ADS)

Ştefan, Bilaşco; Sanda, Roşca; Ioan, Fodorean; Iuliu, Vescan; Sorin, Filip; Dănuţ, Petrea

2018-06-01

Maramureş Land is mostly characterized by agricultural and forestry land use due to its specific configuration of topography and its specific pedoclimatic conditions. Taking into consideration the trend of the last century from the perspective of land management, a decrease in the surface of agricultural lands to the advantage of built-up and grass lands, as well as an accelerated decrease in the forest cover due to uncontrolled and irrational forest exploitation, has become obvious. The field analysis performed on the territory of Maramureş Land has highlighted a high frequency of two geomorphologic processes — landslides and soil erosion — which have a major negative impact on land use due to their rate of occurrence. The main aim of the present study is the GIS modeling of the two geomorphologic processes, determining a state of vulnerability (the USLE model for soil erosion and a quantitative model based on the morphometric characteristics of the territory, derived from the HG. 447/2003) and their integration in a complex model of cumulated vulnerability identification. The modeling of the risk exposure was performed using a quantitative approach based on models and equations of spatial analysis, which were developed with modeled raster data structures and primary vector data, through a matrix highlighting the correspondence between vulnerability and land use classes. The quantitative analysis of the risk was performed by taking into consideration the exposure classes as modeled databases and the land price as a primary alphanumeric database using spatial analysis techniques for each class by means of the attribute table. The spatial results highlight the territories with a high risk to present geomorphologic processes that have a high degree of occurrence and represent a useful tool in the process of spatial planning.

Benefits of incorporating spatial organisation of catchments for a semi-distributed hydrological model

NASA Astrophysics Data System (ADS)

Schumann, Andreas; Oppel, Henning

2017-04-01

To represent the hydrological behaviour of catchments a model should reproduce/reflect the hydrologically most relevant catchment characteristics. These are heterogeneously distributed within a watershed but often interrelated and subject of a certain spatial organisation. Since common models are mostly based on fundamental assumptions about hydrological processes, the reduction of variance of catchment properties as well as the incorporation of the spatial organisation of the catchment is desirable. We have developed a method that combines the idea of the width-function used for determination of the geomorphologic unit hydrograph with information about soil or topography. With this method we are able to assess the spatial organisation of selected catchment characteristics. An algorithm was developed that structures a watershed into sub-basins and other spatial units to minimise its heterogeneity. The outcomes of this algorithm are used for the spatial setup of a semi-distributed model. Since the spatial organisation of a catchment is not bound to a single characteristic, we have to embed information of multiple catchment properties. For this purpose we applied a fuzzy-based method to combine the spatial setup for multiple single characteristics into a union, optimal spatial differentiation. Utilizing this method, we are able to propose a spatial structure for a semi-distributed hydrological model, comprising the definition of sub-basins and a zonal classification within each sub-basin. Besides the improved spatial structuring, the performed analysis ameliorates modelling in another way. The spatial variability of catchment characteristics, which is considered by a minimum of heterogeneity in the zones, can be considered in a parameter constrained calibration scheme in a case study both options were used to explore the benefits of incorporating the spatial organisation and derived parameter constraints for the parametrisation of a HBV-96 model. We use two benchmark model setups (lumped and semi-distributed by common approaches) to address the benefits for different time and spatial scales. Moreover, the benefits for calibration effort, model performance in validation periods and process extrapolation are shown.

Tigers on trails: occupancy modeling for cluster sampling.

PubMed

Hines, J E; Nichols, J D; Royle, J A; MacKenzie, D I; Gopalaswamy, A M; Kumar, N Samba; Karanth, K U

2010-07-01

Occupancy modeling focuses on inference about the distribution of organisms over space, using temporal or spatial replication to allow inference about the detection process. Inference based on spatial replication strictly requires that replicates be selected randomly and with replacement, but the importance of these design requirements is not well understood. This paper focuses on an increasingly popular sampling design based on spatial replicates that are not selected randomly and that are expected to exhibit Markovian dependence. We develop two new occupancy models for data collected under this sort of design, one based on an underlying Markov model for spatial dependence and the other based on a trap response model with Markovian detections. We then simulated data under the model for Markovian spatial dependence and fit the data to standard occupancy models and to the two new models. Bias of occupancy estimates was substantial for the standard models, smaller for the new trap response model, and negligible for the new spatial process model. We also fit these models to data from a large-scale tiger occupancy survey recently conducted in Karnataka State, southwestern India. In addition to providing evidence of a positive relationship between tiger occupancy and habitat, model selection statistics and estimates strongly supported the use of the model with Markovian spatial dependence. This new model provides another tool for the decomposition of the detection process, which is sometimes needed for proper estimation and which may also permit interesting biological inferences. In addition to designs employing spatial replication, we note the likely existence of temporal Markovian dependence in many designs using temporal replication. The models developed here will be useful either directly, or with minor extensions, for these designs as well. We believe that these new models represent important additions to the suite of modeling tools now available for occupancy estimation in conservation monitoring. More generally, this work represents a contribution to the topic of cluster sampling for situations in which there is a need for specific modeling (e.g., reflecting dependence) for the distribution of the variable(s) of interest among subunits.

A hierarchical model for estimating the spatial distribution and abundance of animals detected by continuous-time recorders

USGS Publications Warehouse

Dorazio, Robert; Karanth, K. Ullas

2017-01-01

MotivationSeveral spatial capture-recapture (SCR) models have been developed to estimate animal abundance by analyzing the detections of individuals in a spatial array of traps. Most of these models do not use the actual dates and times of detection, even though this information is readily available when using continuous-time recorders, such as microphones or motion-activated cameras. Instead most SCR models either partition the period of trap operation into a set of subjectively chosen discrete intervals and ignore multiple detections of the same individual within each interval, or they simply use the frequency of detections during the period of trap operation and ignore the observed times of detection. Both practices make inefficient use of potentially important information in the data.Model and data analysisWe developed a hierarchical SCR model to estimate the spatial distribution and abundance of animals detected with continuous-time recorders. Our model includes two kinds of point processes: a spatial process to specify the distribution of latent activity centers of individuals within the region of sampling and a temporal process to specify temporal patterns in the detections of individuals. We illustrated this SCR model by analyzing spatial and temporal patterns evident in the camera-trap detections of tigers living in and around the Nagarahole Tiger Reserve in India. We also conducted a simulation study to examine the performance of our model when analyzing data sets of greater complexity than the tiger data.BenefitsOur approach provides three important benefits: First, it exploits all of the information in SCR data obtained using continuous-time recorders. Second, it is sufficiently versatile to allow the effects of both space use and behavior of animals to be specified as functions of covariates that vary over space and time. Third, it allows both the spatial distribution and abundance of individuals to be estimated, effectively providing a species distribution model, even in cases where spatial covariates of abundance are unknown or unavailable. We illustrated these benefits in the analysis of our data, which allowed us to quantify differences between nocturnal and diurnal activities of tigers and to estimate their spatial distribution and abundance across the study area. Our continuous-time SCR model allows an analyst to specify many of the ecological processes thought to be involved in the distribution, movement, and behavior of animals detected in a spatial trapping array of continuous-time recorders. We plan to extend this model to estimate the population dynamics of animals detected during multiple years of SCR surveys.

A Neurobehavioral Model of Flexible Spatial Language Behaviors

PubMed Central

Lipinski, John; Schneegans, Sebastian; Sandamirskaya, Yulia; Spencer, John P.; Schöner, Gregor

2012-01-01

We propose a neural dynamic model that specifies how low-level visual processes can be integrated with higher level cognition to achieve flexible spatial language behaviors. This model uses real-word visual input that is linked to relational spatial descriptions through a neural mechanism for reference frame transformations. We demonstrate that the system can extract spatial relations from visual scenes, select items based on relational spatial descriptions, and perform reference object selection in a single unified architecture. We further show that the performance of the system is consistent with behavioral data in humans by simulating results from 2 independent empirical studies, 1 spatial term rating task and 1 study of reference object selection behavior. The architecture we present thereby achieves a high degree of task flexibility under realistic stimulus conditions. At the same time, it also provides a detailed neural grounding for complex behavioral and cognitive processes. PMID:21517224

Optimization of Gas Metal Arc Welding (GMAW) Process for Maximum Ballistic Limit in MIL A46100 Steel Welded All-Metal Armor

NASA Astrophysics Data System (ADS)

Grujicic, M.; Ramaswami, S.; Snipes, J. S.; Yavari, R.; Yen, C.-F.; Cheeseman, B. A.

2015-01-01

Our recently developed multi-physics computational model for the conventional gas metal arc welding (GMAW) joining process has been upgraded with respect to its predictive capabilities regarding the process optimization for the attainment of maximum ballistic limit within the weld. The original model consists of six modules, each dedicated to handling a specific aspect of the GMAW process, i.e., (a) electro-dynamics of the welding gun; (b) radiation-/convection-controlled heat transfer from the electric arc to the workpiece and mass transfer from the filler metal consumable electrode to the weld; (c) prediction of the temporal evolution and the spatial distribution of thermal and mechanical fields within the weld region during the GMAW joining process; (d) the resulting temporal evolution and spatial distribution of the material microstructure throughout the weld region; (e) spatial distribution of the as-welded material mechanical properties; and (f) spatial distribution of the material ballistic limit. In the present work, the model is upgraded through the introduction of the seventh module in recognition of the fact that identification of the optimum GMAW process parameters relative to the attainment of the maximum ballistic limit within the weld region entails the use of advanced optimization and statistical sensitivity analysis methods and tools. The upgraded GMAW process model is next applied to the case of butt welding of MIL A46100 (a prototypical high-hardness armor-grade martensitic steel) workpieces using filler metal electrodes made of the same material. The predictions of the upgraded GMAW process model pertaining to the spatial distribution of the material microstructure and ballistic limit-controlling mechanical properties within the MIL A46100 butt weld are found to be consistent with general expectations and prior observations.

Computational models of spatial updating in peri-saccadic perception

PubMed Central

Hamker, Fred H.; Zirnsak, Marc; Ziesche, Arnold; Lappe, Markus

2011-01-01

Perceptual phenomena that occur around the time of a saccade, such as peri-saccadic mislocalization or saccadic suppression of displacement, have often been linked to mechanisms of spatial stability. These phenomena are usually regarded as errors in processes of trans-saccadic spatial transformations and they provide important tools to study these processes. However, a true understanding of the underlying brain processes that participate in the preparation for a saccade and in the transfer of information across it requires a closer, more quantitative approach that links different perceptual phenomena with each other and with the functional requirements of ensuring spatial stability. We review a number of computational models of peri-saccadic spatial perception that provide steps in that direction. Although most models are concerned with only specific phenomena, some generalization and interconnection between them can be obtained from a comparison. Our analysis shows how different perceptual effects can coherently be brought together and linked back to neuronal mechanisms on the way to explaining vision across saccades. PMID:21242143

Modelling and simulation techniques for membrane biology.

PubMed

Burrage, Kevin; Hancock, John; Leier, André; Nicolau, Dan V

2007-07-01

One of the most important aspects of Computational Cell Biology is the understanding of the complicated dynamical processes that take place on plasma membranes. These processes are often so complicated that purely temporal models cannot always adequately capture the dynamics. On the other hand, spatial models can have large computational overheads. In this article, we review some of these issues with respect to chemistry, membrane microdomains and anomalous diffusion and discuss how to select appropriate modelling and simulation paradigms based on some or all the following aspects: discrete, continuous, stochastic, delayed and complex spatial processes.

Generating daily high spatial land surface temperatures by combining ASTER and MODIS land surface temperature products for environmental process monitoring.

PubMed

Wu, Mingquan; Li, Hua; Huang, Wenjiang; Niu, Zheng; Wang, Changyao

2015-08-01

There is a shortage of daily high spatial land surface temperature (LST) data for use in high spatial and temporal resolution environmental process monitoring. To address this shortage, this work used the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and the Spatial and Temporal Data Fusion Approach (STDFA) to estimate high spatial and temporal resolution LST by combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST and Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. The actual ASTER LST products were used to evaluate the precision of the combined LST images using the correlation analysis method. This method was tested and validated in study areas located in Gansu Province, China. The results show that all the models can generate daily synthetic LST image with a high correlation coefficient (r) of 0.92 between the synthetic image and the actual ASTER LST observations. The ESTARFM has the best performance, followed by the STDFA and the STARFM. Those models had better performance in desert areas than in cropland. The STDFA had better noise immunity than the other two models.

Design, development, and application of LANDIS-II, a spatial landscape simulation model with flexible temporal and spatial resolution

Treesearch

Robert M. Scheller; James B. Domingo; Brian R. Sturtevant; Jeremy S. Williams; Arnold Rudy; Eric J. Gustafson; David J. Mladenoff

2007-01-01

We introduce LANDIS-II, a landscape model designed to simulate forest succession and disturbances. LANDIS-II builds upon and preserves the functionality of previous LANDIS forest landscape simulation models. LANDIS-II is distinguished by the inclusion of variable time steps for different ecological processes; our use of a rigorous development and testing process used...

Automating an integrated spatial data-mining model for landfill site selection

NASA Astrophysics Data System (ADS)

Abujayyab, Sohaib K. M.; Ahamad, Mohd Sanusi S.; Yahya, Ahmad Shukri; Ahmad, Siti Zubaidah; Aziz, Hamidi Abdul

2017-10-01

An integrated programming environment represents a robust approach to building a valid model for landfill site selection. One of the main challenges in the integrated model is the complicated processing and modelling due to the programming stages and several limitations. An automation process helps avoid the limitations and improve the interoperability between integrated programming environments. This work targets the automation of a spatial data-mining model for landfill site selection by integrating between spatial programming environment (Python-ArcGIS) and non-spatial environment (MATLAB). The model was constructed using neural networks and is divided into nine stages distributed between Matlab and Python-ArcGIS. A case study was taken from the north part of Peninsular Malaysia. 22 criteria were selected to utilise as input data and to build the training and testing datasets. The outcomes show a high-performance accuracy percentage of 98.2% in the testing dataset using 10-fold cross validation. The automated spatial data mining model provides a solid platform for decision makers to performing landfill site selection and planning operations on a regional scale.

Estimating Function Approaches for Spatial Point Processes

NASA Astrophysics Data System (ADS)

Deng, Chong

Spatial point pattern data consist of locations of events that are often of interest in biological and ecological studies. Such data are commonly viewed as a realization from a stochastic process called spatial point process. To fit a parametric spatial point process model to such data, likelihood-based methods have been widely studied. However, while maximum likelihood estimation is often too computationally intensive for Cox and cluster processes, pairwise likelihood methods such as composite likelihood, Palm likelihood usually suffer from the loss of information due to the ignorance of correlation among pairs. For many types of correlated data other than spatial point processes, when likelihood-based approaches are not desirable, estimating functions have been widely used for model fitting. In this dissertation, we explore the estimating function approaches for fitting spatial point process models. These approaches, which are based on the asymptotic optimal estimating function theories, can be used to incorporate the correlation among data and yield more efficient estimators. We conducted a series of studies to demonstrate that these estmating function approaches are good alternatives to balance the trade-off between computation complexity and estimating efficiency. First, we propose a new estimating procedure that improves the efficiency of pairwise composite likelihood method in estimating clustering parameters. Our approach combines estimating functions derived from pairwise composite likeli-hood estimation and estimating functions that account for correlations among the pairwise contributions. Our method can be used to fit a variety of parametric spatial point process models and can yield more efficient estimators for the clustering parameters than pairwise composite likelihood estimation. We demonstrate its efficacy through a simulation study and an application to the longleaf pine data. Second, we further explore the quasi-likelihood approach on fitting second-order intensity function of spatial point processes. However, the original second-order quasi-likelihood is barely feasible due to the intense computation and high memory requirement needed to solve a large linear system. Motivated by the existence of geometric regular patterns in the stationary point processes, we find a lower dimension representation of the optimal weight function and propose a reduced second-order quasi-likelihood approach. Through a simulation study, we show that the proposed method not only demonstrates superior performance in fitting the clustering parameter but also merits in the relaxation of the constraint of the tuning parameter, H. Third, we studied the quasi-likelihood type estimating funciton that is optimal in a certain class of first-order estimating functions for estimating the regression parameter in spatial point process models. Then, by using a novel spectral representation, we construct an implementation that is computationally much more efficient and can be applied to more general setup than the original quasi-likelihood method.

Spatio-temporal models of mental processes from fMRI.

PubMed

Janoos, Firdaus; Machiraju, Raghu; Singh, Shantanu; Morocz, Istvan Ákos

2011-07-15

Understanding the highly complex, spatially distributed and temporally organized phenomena entailed by mental processes using functional MRI is an important research problem in cognitive and clinical neuroscience. Conventional analysis methods focus on the spatial dimension of the data discarding the information about brain function contained in the temporal dimension. This paper presents a fully spatio-temporal multivariate analysis method using a state-space model (SSM) for brain function that yields not only spatial maps of activity but also its temporal structure along with spatially varying estimates of the hemodynamic response. Efficient algorithms for estimating the parameters along with quantitative validations are given. A novel low-dimensional feature-space for representing the data, based on a formal definition of functional similarity, is derived. Quantitative validation of the model and the estimation algorithms is provided with a simulation study. Using a real fMRI study for mental arithmetic, the ability of this neurophysiologically inspired model to represent the spatio-temporal information corresponding to mental processes is demonstrated. Moreover, by comparing the models across multiple subjects, natural patterns in mental processes organized according to different mental abilities are revealed. Copyright © 2011 Elsevier Inc. All rights reserved.

Representing spatial and temporal complexity in ecohydrological models: a meta-analysis focusing on groundwater - surface water interactions

NASA Astrophysics Data System (ADS)

McDonald, Karlie; Mika, Sarah; Kolbe, Tamara; Abbott, Ben; Ciocca, Francesco; Marruedo, Amaia; Hannah, David; Schmidt, Christian; Fleckenstein, Jan; Karuse, Stefan

2016-04-01

Sub-surface hydrologic processes are highly dynamic, varying spatially and temporally with strong links to the geomorphology and hydrogeologic properties of an area. This spatial and temporal complexity is a critical regulator of biogeochemical and ecological processes within the interface groundwater - surface water (GW-SW) ecohydrological interface and adjacent ecosystems. Many GW-SW models have attempted to capture this spatial and temporal complexity with varying degrees of success. The incorporation of spatial and temporal complexity within GW-SW model configuration is important to investigate interactions with transient storage and subsurface geology, infiltration and recharge, and mass balance of exchange fluxes at the GW-SW ecohydrological interface. Additionally, characterising spatial and temporal complexity in GW-SW models is essential to derive predictions using realistic environmental conditions. In this paper we conduct a systematic Web of Science meta-analysis of conceptual, hydrodynamic, and reactive and heat transport models of the GW-SW ecohydrological interface since 2004 to explore how these models handled spatial and temporal complexity. The freshwater - groundwater ecohydrological interface was the most commonly represented in publications between 2004 and 2014 with 91% of papers followed by marine 6% and estuarine systems with 3% of papers. Of the GW-SW models published since 2004, the 52% have focused on hydrodynamic processes and <15% covered more than one process (e.g. heat and reactive transport). Within the hydrodynamic subset, 25% of models focused on a vertical depth of <5m. The primary scientific and technological limitations of incorporating spatial and temporal variability into GW-SW models are identified as the inclusion of woody debris, carbon sources, subsurface geological structures and bioclogging into model parameterization. The technological limitations influence the types of models applied, such as hydrostatic coupled models and fully intrinsic saturated and unsaturated models, and the assumptions or simplifications scientists apply to investigate the GW-SW ecohydrological interface. We investigated the type of modelling approaches applied across different scales (site, reach, catchment, nested catchments) and assessed the simplifications in environmental conditions and complexity that are commonly made in model configuration. Understanding the theoretical concepts that underpin these current modelling approaches is critical for scientists to develop measures to derive predictions from realistic environmental conditions at management relevant scales and establish best-practice modelling approaches for improving the scientific understanding and management of the GW-SW interface. Additionally, the assessment of current modelling approaches informs our proposed framework for the progress of GW-SW models in the future. The framework presented aims to increase future scientific, technological and management integration and the identification of research priorities to allow spatial and temporal complexity to be better incorporated into GW-SW models.

A Computational Model of Spatial Visualization Capacity

ERIC Educational Resources Information Center

Lyon, Don R.; Gunzelmann, Glenn; Gluck, Kevin A.

2008-01-01

Visualizing spatial material is a cornerstone of human problem solving, but human visualization capacity is sharply limited. To investigate the sources of this limit, we developed a new task to measure visualization accuracy for verbally-described spatial paths (similar to street directions), and implemented a computational process model to…

Gaussian Process Regression Model in Spatial Logistic Regression

NASA Astrophysics Data System (ADS)

Sofro, A.; Oktaviarina, A.

2018-01-01

Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.

Spatial heterogeneity of leaf area index across scales from simulation and remote sensing

NASA Astrophysics Data System (ADS)

Reichenau, Tim G.; Korres, Wolfgang; Montzka, Carsten; Schneider, Karl

2016-04-01

Leaf area index (LAI, single sided leaf area per ground area) influences mass and energy exchange of vegetated surfaces. Therefore LAI is an input variable for many land surface schemes of coupled large scale models, which do not simulate LAI. Since these models typically run on rather coarse resolution grids, LAI is often inferred from coarse resolution remote sensing. However, especially in agriculturally used areas, a grid cell of these products often covers more than a single land-use. In that case, the given LAI does not apply to any single land-use. Therefore, the overall spatial heterogeneity in these datasets differs from that on resolutions high enough to distinguish areas with differing land-use. Detailed process-based plant growth models simulate LAI for separate plant functional types or specific species. However, limited availability of observations causes reduced spatial heterogeneity of model input data (soil, weather, land-use). Since LAI is strongly heterogeneous in space and time and since processes depend on LAI in a nonlinear way, a correct representation of LAI spatial heterogeneity is also desirable on coarse resolutions. The current study assesses this issue by comparing the spatial heterogeneity of LAI from remote sensing (RapidEye) and process-based simulations (DANUBIA simulation system) across scales. Spatial heterogeneity is assessed by analyzing LAI frequency distributions (spatial variability) and semivariograms (spatial structure). Test case is the arable land in the fertile loess plain of the Rur catchment near the Germany-Netherlands border.

[Temporal and spatial heterogeneity analysis of optimal value of sensitive parameters in ecological process model: The BIOME-BGC model as an example.

PubMed

Li, Yi Zhe; Zhang, Ting Long; Liu, Qiu Yu; Li, Ying

2018-01-01

The ecological process models are powerful tools for studying terrestrial ecosystem water and carbon cycle at present. However, there are many parameters for these models, and weather the reasonable values of these parameters were taken, have important impact on the models simulation results. In the past, the sensitivity and the optimization of model parameters were analyzed and discussed in many researches. But the temporal and spatial heterogeneity of the optimal parameters is less concerned. In this paper, the BIOME-BGC model was used as an example. In the evergreen broad-leaved forest, deciduous broad-leaved forest and C3 grassland, the sensitive parameters of the model were selected by constructing the sensitivity judgment index with two experimental sites selected under each vegetation type. The objective function was constructed by using the simulated annealing algorithm combined with the flux data to obtain the monthly optimal values of the sensitive parameters at each site. Then we constructed the temporal heterogeneity judgment index, the spatial heterogeneity judgment index and the temporal and spatial heterogeneity judgment index to quantitatively analyze the temporal and spatial heterogeneity of the optimal values of the model sensitive parameters. The results showed that the sensitivity of BIOME-BGC model parameters was different under different vegetation types, but the selected sensitive parameters were mostly consistent. The optimal values of the sensitive parameters of BIOME-BGC model mostly presented time-space heterogeneity to different degrees which varied with vegetation types. The sensitive parameters related to vegetation physiology and ecology had relatively little temporal and spatial heterogeneity while those related to environment and phenology had generally larger temporal and spatial heterogeneity. In addition, the temporal heterogeneity of the optimal values of the model sensitive parameters showed a significant linear correlation with the spatial heterogeneity under the three vegetation types. According to the temporal and spatial heterogeneity of the optimal values, the parameters of the BIOME-BGC model could be classified in order to adopt different parameter strategies in practical application. The conclusion could help to deeply understand the parameters and the optimal values of the ecological process models, and provide a way or reference for obtaining the reasonable values of parameters in models application.

Effect of spatial organisation behaviour on upscaling the overland flow formation in an arable land

NASA Astrophysics Data System (ADS)

Silasari, Rasmiaditya; Blöschl, Günter

2014-05-01

Overland flow during rainfall events on arable land is important to investigate as it affects the land erosion process and water quality in the river. The formation of overland flow may happen through different ways (i.e. Hortonian overland flow, saturation excess overland flow) which is influenced by the surface and subsurface soil characteristics (i.e. land cover, soil infiltration rate). As the soil characteristics vary throughout the entire catchment, it will form distinct spatial patterns with organised or random behaviour. During the upscaling of hydrological processes from plot to catchment scale, this behaviour will become substantial since organised patterns will result in higher spatial connectivity and thus higher conductivity. However, very few of the existing studies explicitly address this effect of spatial organisations of the patterns in upscaling the hydrological processes to the catchment scale. This study will assess the upscaling of overland flow formation with concerns of spatial organisation behaviour of the patterns by application of direct field observations under natural conditions using video camera and soil moisture sensors and investigation of the underlying processes using a physical-based hydrology model. The study area is a Hydrological Open Air Laboratory (HOAL) located at Petzenkirchen, Lower Austria. It is a 64 ha catchment with land use consisting of arable land (87%), forest (6%), pasture (5%) and paved surfaces (2%). A video camera is installed 7m above the ground on a weather station mast in the middle of the arable land to monitor the overland flow patterns during rainfall events in a 2m x 6m plot scale. Soil moisture sensors with continuous measurement at different depth (5, 10, 20 and 50cm) are installed at points where the field is monitored by the camera. The patterns of overland flow formation and subsurface flow state at the plot scale will be generated using a coupled surface-subsurface flow physical-based hydrology model. The observation data will be assimilated into the model to verify the corresponding processes between surface and subsurface flow during the rainfall events. The patterns of conductivity then will be analyzed at catchment scale using the spatial stochastic analysis based on the classification of soil characteristics of the entire catchment. These patterns of conductivity then will be applied in the model at catchment scale to see how the organisational behaviour can affect the spatial connectivity of the hydrological processes and the results of the catchment response. A detailed modelling of the underlying processes in the physical-based model will allow us to see the direct effect of the spatial connectivity to the occurring surface and subsurface flow. This will improve the analysis of the effect of spatial organisations of the patterns in upscaling the hydrological processes from plot to catchment scale.

A New Methodology of Spatial Cross-Correlation Analysis

PubMed Central

Chen, Yanguang

2015-01-01

Spatial correlation modeling comprises both spatial autocorrelation and spatial cross-correlation processes. The spatial autocorrelation theory has been well-developed. It is necessary to advance the method of spatial cross-correlation analysis to supplement the autocorrelation analysis. This paper presents a set of models and analytical procedures for spatial cross-correlation analysis. By analogy with Moran’s index newly expressed in a spatial quadratic form, a theoretical framework is derived for geographical cross-correlation modeling. First, two sets of spatial cross-correlation coefficients are defined, including a global spatial cross-correlation coefficient and local spatial cross-correlation coefficients. Second, a pair of scatterplots of spatial cross-correlation is proposed, and the plots can be used to visually reveal the causality behind spatial systems. Based on the global cross-correlation coefficient, Pearson’s correlation coefficient can be decomposed into two parts: direct correlation (partial correlation) and indirect correlation (spatial cross-correlation). As an example, the methodology is applied to the relationships between China’s urbanization and economic development to illustrate how to model spatial cross-correlation phenomena. This study is an introduction to developing the theory of spatial cross-correlation, and future geographical spatial analysis might benefit from these models and indexes. PMID:25993120

A new methodology of spatial cross-correlation analysis.

PubMed

Chen, Yanguang

2015-01-01

Spatial correlation modeling comprises both spatial autocorrelation and spatial cross-correlation processes. The spatial autocorrelation theory has been well-developed. It is necessary to advance the method of spatial cross-correlation analysis to supplement the autocorrelation analysis. This paper presents a set of models and analytical procedures for spatial cross-correlation analysis. By analogy with Moran's index newly expressed in a spatial quadratic form, a theoretical framework is derived for geographical cross-correlation modeling. First, two sets of spatial cross-correlation coefficients are defined, including a global spatial cross-correlation coefficient and local spatial cross-correlation coefficients. Second, a pair of scatterplots of spatial cross-correlation is proposed, and the plots can be used to visually reveal the causality behind spatial systems. Based on the global cross-correlation coefficient, Pearson's correlation coefficient can be decomposed into two parts: direct correlation (partial correlation) and indirect correlation (spatial cross-correlation). As an example, the methodology is applied to the relationships between China's urbanization and economic development to illustrate how to model spatial cross-correlation phenomena. This study is an introduction to developing the theory of spatial cross-correlation, and future geographical spatial analysis might benefit from these models and indexes.

A spatial error model with continuous random effects and an application to growth convergence

NASA Astrophysics Data System (ADS)

Laurini, Márcio Poletti

2017-10-01

We propose a spatial error model with continuous random effects based on Matérn covariance functions and apply this model for the analysis of income convergence processes (β -convergence). The use of a model with continuous random effects permits a clearer visualization and interpretation of the spatial dependency patterns, avoids the problems of defining neighborhoods in spatial econometrics models, and allows projecting the spatial effects for every possible location in the continuous space, circumventing the existing aggregations in discrete lattice representations. We apply this model approach to analyze the economic growth of Brazilian municipalities between 1991 and 2010 using unconditional and conditional formulations and a spatiotemporal model of convergence. The results indicate that the estimated spatial random effects are consistent with the existence of income convergence clubs for Brazilian municipalities in this period.

ML-Space: Hybrid Spatial Gillespie and Particle Simulation of Multi-Level Rule-Based Models in Cell Biology.

PubMed

Bittig, Arne T; Uhrmacher, Adelinde M

2017-01-01

Spatio-temporal dynamics of cellular processes can be simulated at different levels of detail, from (deterministic) partial differential equations via the spatial Stochastic Simulation algorithm to tracking Brownian trajectories of individual particles. We present a spatial simulation approach for multi-level rule-based models, which includes dynamically hierarchically nested cellular compartments and entities. Our approach ML-Space combines discrete compartmental dynamics, stochastic spatial approaches in discrete space, and particles moving in continuous space. The rule-based specification language of ML-Space supports concise and compact descriptions of models and to adapt the spatial resolution of models easily.

Computational models of human vision with applications

NASA Technical Reports Server (NTRS)

Wandell, B. A.

1985-01-01

Perceptual problems in aeronautics were studied. The mechanism by which color constancy is achieved in human vision was examined. A computable algorithm was developed to model the arrangement of retinal cones in spatial vision. The spatial frequency spectra are similar to the spectra of actual cone mosaics. The Hartley transform as a tool of image processing was evaluated and it is suggested that it could be used in signal processing applications, GR image processing.

Estimating Prediction Uncertainty from Geographical Information System Raster Processing: A User's Manual for the Raster Error Propagation Tool (REPTool)

USGS Publications Warehouse

Gurdak, Jason J.; Qi, Sharon L.; Geisler, Michael L.

2009-01-01

The U.S. Geological Survey Raster Error Propagation Tool (REPTool) is a custom tool for use with the Environmental System Research Institute (ESRI) ArcGIS Desktop application to estimate error propagation and prediction uncertainty in raster processing operations and geospatial modeling. REPTool is designed to introduce concepts of error and uncertainty in geospatial data and modeling and provide users of ArcGIS Desktop a geoprocessing tool and methodology to consider how error affects geospatial model output. Similar to other geoprocessing tools available in ArcGIS Desktop, REPTool can be run from a dialog window, from the ArcMap command line, or from a Python script. REPTool consists of public-domain, Python-based packages that implement Latin Hypercube Sampling within a probabilistic framework to track error propagation in geospatial models and quantitatively estimate the uncertainty of the model output. Users may specify error for each input raster or model coefficient represented in the geospatial model. The error for the input rasters may be specified as either spatially invariant or spatially variable across the spatial domain. Users may specify model output as a distribution of uncertainty for each raster cell. REPTool uses the Relative Variance Contribution method to quantify the relative error contribution from the two primary components in the geospatial model - errors in the model input data and coefficients of the model variables. REPTool is appropriate for many types of geospatial processing operations, modeling applications, and related research questions, including applications that consider spatially invariant or spatially variable error in geospatial data.

Approximate spatial reasoning

NASA Technical Reports Server (NTRS)

Dutta, Soumitra

1988-01-01

A model for approximate spatial reasoning using fuzzy logic to represent the uncertainty in the environment is presented. Algorithms are developed which can be used to reason about spatial information expressed in the form of approximate linguistic descriptions similar to the kind of spatial information processed by humans. Particular attention is given to static spatial reasoning.

Two stochastic models useful in petroleum exploration

NASA Technical Reports Server (NTRS)

Kaufman, G. M.; Bradley, P. G.

1972-01-01

A model of the petroleum exploration process that tests empirically the hypothesis that at an early stage in the exploration of a basin, the process behaves like sampling without replacement is proposed along with a model of the spatial distribution of petroleum reserviors that conforms to observed facts. In developing the model of discovery, the following topics are discussed: probabilitistic proportionality, likelihood function, and maximum likelihood estimation. In addition, the spatial model is described, which is defined as a stochastic process generating values of a sequence or random variables in a way that simulates the frequency distribution of areal extent, the geographic location, and shape of oil deposits

Using the spatial filtering process to evaluate the nonbreeding range of Rusty Blackbird Euphagus carolinus

Treesearch

Paul Hamel; Esra Ozdenrol

2008-01-01

During the nonbreeding period, Rusty Blackbird (Euphagus carolinus) occurs predominantly in forested wetland habitats in the southeastern U.S. We used spatial filtering of Christmas Bird Count data to identify areas within the nonbreeding range where the species occurs at higher than expected probability. Spatial filtering is an epidemiological modeling process...

Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.

PubMed

Shi, Junxing; Wen, Haiguang; Zhang, Yizhen; Han, Kuan; Liu, Zhongming

2018-05-01

The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN better predicted cortical responses to natural movie stimuli than the CNN, at all visual areas, especially those along the dorsal stream. As a fully observable model of visual processing, the RNN also revealed a cortical hierarchy of temporal receptive window, dynamics of process memory, and spatiotemporal representations. These results support the hypothesis of process memory, and demonstrate the potential of using the RNN for in-depth computational understanding of dynamic natural vision. © 2018 Wiley Periodicals, Inc.

Testing the dual-pathway model for auditory processing in human cortex.

PubMed

Zündorf, Ida C; Lewald, Jörg; Karnath, Hans-Otto

2016-01-01

Analogous to the visual system, auditory information has been proposed to be processed in two largely segregated streams: an anteroventral ("what") pathway mainly subserving sound identification and a posterodorsal ("where") stream mainly subserving sound localization. Despite the popularity of this assumption, the degree of separation of spatial and non-spatial auditory information processing in cortex is still under discussion. In the present study, a statistical approach was implemented to investigate potential behavioral dissociations for spatial and non-spatial auditory processing in stroke patients, and voxel-wise lesion analyses were used to uncover their neural correlates. The results generally provided support for anatomically and functionally segregated auditory networks. However, some degree of anatomo-functional overlap between "what" and "where" aspects of processing was found in the superior pars opercularis of right inferior frontal gyrus (Brodmann area 44), suggesting the potential existence of a shared target area of both auditory streams in this region. Moreover, beyond the typically defined posterodorsal stream (i.e., posterior superior temporal gyrus, inferior parietal lobule, and superior frontal sulcus), occipital lesions were found to be associated with sound localization deficits. These results, indicating anatomically and functionally complex cortical networks for spatial and non-spatial auditory processing, are roughly consistent with the dual-pathway model of auditory processing in its original form, but argue for the need to refine and extend this widely accepted hypothesis. Copyright © 2015 Elsevier Inc. All rights reserved.

Ballistic-Failure Mechanisms in Gas Metal Arc Welds of Mil A46100 Armor-Grade Steel: A Computational Investigation

NASA Astrophysics Data System (ADS)

Grujicic, M.; Snipes, J. S.; Galgalikar, R.; Ramaswami, S.; Yavari, R.; Yen, C.-F.; Cheeseman, B. A.

2014-09-01

In our recent work, a multi-physics computational model for the conventional gas metal arc welding (GMAW) joining process was introduced. The model is of a modular type and comprises five modules, each designed to handle a specific aspect of the GMAW process, i.e.: (i) electro-dynamics of the welding-gun; (ii) radiation-/convection-controlled heat transfer from the electric-arc to the workpiece and mass transfer from the filler-metal consumable electrode to the weld; (iii) prediction of the temporal evolution and the spatial distribution of thermal and mechanical fields within the weld region during the GMAW joining process; (iv) the resulting temporal evolution and spatial distribution of the material microstructure throughout the weld region; and (v) spatial distribution of the as-welded material mechanical properties. In the present work, the GMAW process model has been upgraded with respect to its predictive capabilities regarding the spatial distribution of the mechanical properties controlling the ballistic-limit (i.e., penetration-resistance) of the weld. The model is upgraded through the introduction of the sixth module in the present work in recognition of the fact that in thick steel GMAW weldments, the overall ballistic performance of the armor may become controlled by the (often inferior) ballistic limits of its weld (fusion and heat-affected) zones. To demonstrate the utility of the upgraded GMAW process model, it is next applied to the case of butt-welding of a prototypical high-hardness armor-grade martensitic steel, MIL A46100. The model predictions concerning the spatial distribution of the material microstructure and ballistic-limit-controlling mechanical properties within the MIL A46100 butt-weld are found to be consistent with prior observations and general expectations.

On the Nexus of the Spatial Dynamics of Global Urbanization and the Age of the City

PubMed Central

Scheuer, Sebastian; Haase, Dagmar; Volk, Martin

2016-01-01

A number of concepts exist regarding how urbanization can be described as a process. Understanding this process that affects billions of people and its future development in a spatial manner is imperative to address related issues such as human quality of life. In the focus of spatially explicit studies on urbanization is typically a city, a particular urban region, an agglomeration. However, gaps remain in spatially explicit global models. This paper addresses that issue by examining the spatial dynamics of urban areas over time, for a full coverage of the world. The presented model identifies past, present and potential future hotspots of urbanization as a function of an urban area's spatial variation and age, whose relation could be depicted both as a proxy and as a path of urban development. PMID:27490199

On the Nexus of the Spatial Dynamics of Global Urbanization and the Age of the City.

PubMed

Scheuer, Sebastian; Haase, Dagmar; Volk, Martin

2016-01-01

A number of concepts exist regarding how urbanization can be described as a process. Understanding this process that affects billions of people and its future development in a spatial manner is imperative to address related issues such as human quality of life. In the focus of spatially explicit studies on urbanization is typically a city, a particular urban region, an agglomeration. However, gaps remain in spatially explicit global models. This paper addresses that issue by examining the spatial dynamics of urban areas over time, for a full coverage of the world. The presented model identifies past, present and potential future hotspots of urbanization as a function of an urban area's spatial variation and age, whose relation could be depicted both as a proxy and as a path of urban development.

Fuzzy Similarity and Fuzzy Inclusion Measures in Polyline Matching: A Case Study of Potential Streams Identification for Archaeological Modelling in GIS

NASA Astrophysics Data System (ADS)

Ďuračiová, Renata; Rášová, Alexandra; Lieskovský, Tibor

2017-12-01

When combining spatial data from various sources, it is often important to determine similarity or identity of spatial objects. Besides the differences in geometry, representations of spatial objects are inevitably more or less uncertain. Fuzzy set theory can be used to address both modelling of the spatial objects uncertainty and determining the identity, similarity, and inclusion of two sets as fuzzy identity, fuzzy similarity, and fuzzy inclusion. In this paper, we propose to use fuzzy measures to determine the similarity or identity of two uncertain spatial object representations in geographic information systems. Labelling the spatial objects by the degree of their similarity or inclusion measure makes the process of their identification more efficient. It reduces the need for a manual control. This leads to a more simple process of spatial datasets update from external data sources. We use this approach to get an accurate and correct representation of historical streams, which is derived from contemporary digital elevation model, i.e. we identify the segments that are similar to the streams depicted on historical maps.

Restricted spatial regression in practice: Geostatistical models, confounding, and robustness under model misspecification

USGS Publications Warehouse

Hanks, Ephraim M.; Schliep, Erin M.; Hooten, Mevin B.; Hoeting, Jennifer A.

2015-01-01

In spatial generalized linear mixed models (SGLMMs), covariates that are spatially smooth are often collinear with spatially smooth random effects. This phenomenon is known as spatial confounding and has been studied primarily in the case where the spatial support of the process being studied is discrete (e.g., areal spatial data). In this case, the most common approach suggested is restricted spatial regression (RSR) in which the spatial random effects are constrained to be orthogonal to the fixed effects. We consider spatial confounding and RSR in the geostatistical (continuous spatial support) setting. We show that RSR provides computational benefits relative to the confounded SGLMM, but that Bayesian credible intervals under RSR can be inappropriately narrow under model misspecification. We propose a posterior predictive approach to alleviating this potential problem and discuss the appropriateness of RSR in a variety of situations. We illustrate RSR and SGLMM approaches through simulation studies and an analysis of malaria frequencies in The Gambia, Africa.

Validating modelled variable surface saturation in the riparian zone with thermal infrared images

NASA Astrophysics Data System (ADS)

Glaser, Barbara; Klaus, Julian; Frei, Sven; Frentress, Jay; Pfister, Laurent; Hopp, Luisa

2015-04-01

Variable contributing areas and hydrological connectivity have become prominent new concepts for hydrologic process understanding in recent years. The dynamic connectivity within the hillslope-riparian-stream (HRS) system is known to have a first order control on discharge generation and especially the riparian zone functions as runoff buffering or producing zone. However, despite their importance, the highly dynamic processes of contraction and extension of saturation within the riparian zone and its impact on runoff generation still remain not fully understood. In this study, we analysed the potential of a distributed, fully coupled and physically based model (HydroGeoSphere) to represent the spatial and temporal water flux dynamics of a forested headwater HRS system (6 ha) in western Luxembourg. The model was set up and parameterised under consideration of experimentally-derived knowledge of catchment structure and was run for a period of four years (October 2010 to August 2014). For model evaluation, we especially focused on the temporally varying spatial patterns of surface saturation. We used ground-based thermal infrared (TIR) imagery to map surface saturation with a high spatial and temporal resolution and collected 20 panoramic snapshots of the riparian zone (ca. 10 by 20 m) under different hydrologic conditions. These TIR panoramas were used in addition to several classical discharge and soil moisture time series for a spatially-distributed model validation. In a manual calibration process we optimised model parameters (e.g. porosity, saturated hydraulic conductivity, evaporation depth) to achieve a better agreement between observed and modelled discharges and soil moistures. The subsequent validation of surface saturation patterns by a visual comparison of processed TIR panoramas and corresponding model output panoramas revealed an overall good accordance for all but one region that was always too dry in the model. However, quantitative comparisons of modelled and observed saturated pixel percentages and of their modelled and measured relationships to concurrent discharges revealed remarkable similarities. During the calibration process we observed that surface saturation patterns were mostly affected by changing the soil properties of the topsoil in the riparian zone, but that the discharge behaviour did not change substantially at the same time. This effect of various spatial patterns occurring concomitant to a nearly unchanged integrated response demonstrates the importance of spatially distributed validation data. Our study clearly benefited from using different kinds of data - spatially integrated and distributed, temporally continuous and discrete - for the model evaluation procedure.

Comparing spatial diversification and meta-population models in the Indo-Australian Archipelago

PubMed Central

Chalmandrier, Loïc; Albouy, Camille; Descombes, Patrice; Sandel, Brody; Faurby, Soren; Svenning, Jens-Christian; Zimmermann, Niklaus E.

2018-01-01

Reconstructing the processes that have shaped the emergence of biodiversity gradients is critical to understand the dynamics of diversification of life on Earth. Islands have traditionally been used as model systems to unravel the processes shaping biological diversity. MacArthur and Wilson's island biogeographic model predicts diversity to be based on dynamic interactions between colonization and extinction rates, while treating islands themselves as geologically static entities. The current spatial configuration of islands should influence meta-population dynamics, but long-term geological changes within archipelagos are also expected to have shaped island biodiversity, in part by driving diversification. Here, we compare two mechanistic models providing inferences on species richness at a biogeographic scale: a mechanistic spatial-temporal model of species diversification and a spatial meta-population model. While the meta-population model operates over a static landscape, the diversification model is driven by changes in the size and spatial configuration of islands through time. We compare the inferences of both models to floristic diversity patterns among land patches of the Indo-Australian Archipelago. Simulation results from the diversification model better matched observed diversity than a meta-population model constrained only by the contemporary landscape. The diversification model suggests that the dynamic re-positioning of islands promoting land disconnection and reconnection induced an accumulation of particularly high species diversity on Borneo, which is central within the island network. By contrast, the meta-population model predicts a higher diversity on the mainlands, which is less compatible with empirical data. Our analyses highlight that, by comparing models with contrasting assumptions, we can pinpoint the processes that are most compatible with extant biodiversity patterns. PMID:29657753

Comparing spatial diversification and meta-population models in the Indo-Australian Archipelago.

PubMed

Chalmandrier, Loïc; Albouy, Camille; Descombes, Patrice; Sandel, Brody; Faurby, Soren; Svenning, Jens-Christian; Zimmermann, Niklaus E; Pellissier, Loïc

2018-03-01

Reconstructing the processes that have shaped the emergence of biodiversity gradients is critical to understand the dynamics of diversification of life on Earth. Islands have traditionally been used as model systems to unravel the processes shaping biological diversity. MacArthur and Wilson's island biogeographic model predicts diversity to be based on dynamic interactions between colonization and extinction rates, while treating islands themselves as geologically static entities. The current spatial configuration of islands should influence meta-population dynamics, but long-term geological changes within archipelagos are also expected to have shaped island biodiversity, in part by driving diversification. Here, we compare two mechanistic models providing inferences on species richness at a biogeographic scale: a mechanistic spatial-temporal model of species diversification and a spatial meta-population model. While the meta-population model operates over a static landscape, the diversification model is driven by changes in the size and spatial configuration of islands through time. We compare the inferences of both models to floristic diversity patterns among land patches of the Indo-Australian Archipelago. Simulation results from the diversification model better matched observed diversity than a meta-population model constrained only by the contemporary landscape. The diversification model suggests that the dynamic re-positioning of islands promoting land disconnection and reconnection induced an accumulation of particularly high species diversity on Borneo, which is central within the island network. By contrast, the meta-population model predicts a higher diversity on the mainlands, which is less compatible with empirical data. Our analyses highlight that, by comparing models with contrasting assumptions, we can pinpoint the processes that are most compatible with extant biodiversity patterns.

Spatial dependence of extreme rainfall

NASA Astrophysics Data System (ADS)

Radi, Noor Fadhilah Ahmad; Zakaria, Roslinazairimah; Satari, Siti Zanariah; Azman, Muhammad Az-zuhri

2017-05-01

This study aims to model the spatial extreme daily rainfall process using the max-stable model. The max-stable model is used to capture the dependence structure of spatial properties of extreme rainfall. Three models from max-stable are considered namely Smith, Schlather and Brown-Resnick models. The methods are applied on 12 selected rainfall stations in Kelantan, Malaysia. Most of the extreme rainfall data occur during wet season from October to December of 1971 to 2012. This period is chosen to assure the available data is enough to satisfy the assumption of stationarity. The dependence parameters including the range and smoothness, are estimated using composite likelihood approach. Then, the bootstrap approach is applied to generate synthetic extreme rainfall data for all models using the estimated dependence parameters. The goodness of fit between the observed extreme rainfall and the synthetic data is assessed using the composite likelihood information criterion (CLIC). Results show that Schlather model is the best followed by Brown-Resnick and Smith models based on the smallest CLIC's value. Thus, the max-stable model is suitable to be used to model extreme rainfall in Kelantan. The study on spatial dependence in extreme rainfall modelling is important to reduce the uncertainties of the point estimates for the tail index. If the spatial dependency is estimated individually, the uncertainties will be large. Furthermore, in the case of joint return level is of interest, taking into accounts the spatial dependence properties will improve the estimation process.

Monitoring tropical vegetation succession with LANDSAT data

NASA Technical Reports Server (NTRS)

Robinson, V. B. (Principal Investigator)

1983-01-01

The shadowing problem, which is endemic to the use of LANDSAT in tropical areas, and the ability to model changes over space and through time are problems to be addressed when monitoring tropical vegetation succession. Application of a trend surface analysis model to major land cover classes in a mountainous region of the Phillipines shows that the spatial modeling of radiance values can provide a useful approach to tropical rain forest succession monitoring. Results indicate shadowing effects may be due primarily to local variations in the spectral responses. These variations can be compensated for through the decomposition of the spatial variation in both elevation and MSS data. Using the model to estimate both elevation and spectral terrain surface as a posteriori inputs in the classification process leads to improved classification accuracy for vegetation of cover of this type. Spatial patterns depicted by the MSS data reflect the measurement of responses to spatial processes acting at several scales.

High resolution climate scenarios for snowmelt modelling in small alpine catchments

NASA Astrophysics Data System (ADS)

Schirmer, M.; Peleg, N.; Burlando, P.; Jonas, T.

2017-12-01

Snow in the Alps is affected by climate change with regard to duration, timing and amount. This has implications with respect to important societal issues as drinking water supply or hydropower generation. In Switzerland, the latter received a lot of attention following the political decision to phase out of nuclear electricity production. An increasing number of authorization requests for small hydropower plants located in small alpine catchments was observed in the recent years. This situation generates ecological conflicts, while the expected climate change poses a threat to water availability thus putting at risk investments in such hydropower plants. Reliable high-resolution climate scenarios are thus required, which account for small-scale processes to achieve realistic predictions of snowmelt runoff and its variability in small alpine catchments. We therefore used a novel model chain by coupling a stochastic 2-dimensional weather generator (AWE-GEN-2d) with a state-of-the-art energy balance snow cover model (FSM). AWE-GEN-2d was applied to generate ensembles of climate variables at very fine temporal and spatial resolution, thus providing all climatic input variables required for the energy balance modelling. The land-surface model FSM was used to describe spatially variable snow cover accumulation and melt processes. The FSM was refined to allow applications at very high spatial resolution by specifically accounting for small-scale processes, such as a subgrid-parametrization of snow covered area or an improved representation of forest-snow processes. For the present study, the model chain was tested for current climate conditions using extensive observational dataset of different spatial and temporal coverage. Small-scale spatial processes such as elevation gradients or aspect differences in the snow distribution were evaluated using airborne LiDAR data. 40-year of monitoring data for snow water equivalent, snowmelt and snow-covered area for entire Switzerland was used to verify snow distribution patterns at coarser spatial and temporal scale. The ability of the model chain to reproduce current climate conditions in small alpine catchments makes this model combination an outstanding candidate to produce high resolution climate scenarios of snowmelt in small alpine catchments.

Design and implementation of spatial knowledge grid for integrated spatial analysis

NASA Astrophysics Data System (ADS)

Liu, Xiangnan; Guan, Li; Wang, Ping

2006-10-01

Supported by spatial information grid(SIG), the spatial knowledge grid (SKG) for integrated spatial analysis utilizes the middleware technology in constructing the spatial information grid computation environment and spatial information service system, develops spatial entity oriented spatial data organization technology, carries out the profound computation of the spatial structure and spatial process pattern on the basis of Grid GIS infrastructure, spatial data grid and spatial information grid (specialized definition). At the same time, it realizes the complex spatial pattern expression and the spatial function process simulation by taking the spatial intelligent agent as the core to establish space initiative computation. Moreover through the establishment of virtual geographical environment with man-machine interactivity and blending, complex spatial modeling, network cooperation work and spatial community decision knowledge driven are achieved. The framework of SKG is discussed systematically in this paper. Its implement flow and the key technology with examples of overlay analysis are proposed as well.

Towards a voxel-based geographic automata for the simulation of geospatial processes

NASA Astrophysics Data System (ADS)

Jjumba, Anthony; Dragićević, Suzana

2016-07-01

Many geographic processes evolve in a three dimensional space and time continuum. However, when they are represented with the aid of geographic information systems (GIS) or geosimulation models they are modelled in a framework of two-dimensional space with an added temporal component. The objective of this study is to propose the design and implementation of voxel-based automata as a methodological approach for representing spatial processes evolving in the four-dimensional (4D) space-time domain. Similar to geographic automata models which are developed to capture and forecast geospatial processes that change in a two-dimensional spatial framework using cells (raster geospatial data), voxel automata rely on the automata theory and use three-dimensional volumetric units (voxels). Transition rules have been developed to represent various spatial processes which range from the movement of an object in 3D to the diffusion of airborne particles and landslide simulation. In addition, the proposed 4D models demonstrate that complex processes can be readily reproduced from simple transition functions without complex methodological approaches. The voxel-based automata approach provides a unique basis to model geospatial processes in 4D for the purpose of improving representation, analysis and understanding their spatiotemporal dynamics. This study contributes to the advancement of the concepts and framework of 4D GIS.

The Canadian Hydrological Model (CHM): A multi-scale, variable-complexity hydrological model for cold regions

NASA Astrophysics Data System (ADS)

Marsh, C.; Pomeroy, J. W.; Wheater, H. S.

2016-12-01

There is a need for hydrological land surface schemes that can link to atmospheric models, provide hydrological prediction at multiple scales and guide the development of multiple objective water predictive systems. Distributed raster-based models suffer from an overrepresentation of topography, leading to wasted computational effort that increases uncertainty due to greater numbers of parameters and initial conditions. The Canadian Hydrological Model (CHM) is a modular, multiphysics, spatially distributed modelling framework designed for representing hydrological processes, including those that operate in cold-regions. Unstructured meshes permit variable spatial resolution, allowing coarse resolutions at low spatial variability and fine resolutions as required. Model uncertainty is reduced by lessening the necessary computational elements relative to high-resolution rasters. CHM uses a novel multi-objective approach for unstructured triangular mesh generation that fulfills hydrologically important constraints (e.g., basin boundaries, water bodies, soil classification, land cover, elevation, and slope/aspect). This provides an efficient spatial representation of parameters and initial conditions, as well as well-formed and well-graded triangles that are suitable for numerical discretization. CHM uses high-quality open source libraries and high performance computing paradigms to provide a framework that allows for integrating current state-of-the-art process algorithms. The impact of changes to model structure, including individual algorithms, parameters, initial conditions, driving meteorology, and spatial/temporal discretization can be easily tested. Initial testing of CHM compared spatial scales and model complexity for a spring melt period at a sub-arctic mountain basin. The meshing algorithm reduced the total number of computational elements and preserved the spatial heterogeneity of predictions.

Application of spatial Poisson process models to air mass thunderstorm rainfall

NASA Technical Reports Server (NTRS)

Eagleson, P. S.; Fennessy, N. M.; Wang, Qinliang; Rodriguez-Iturbe, I.

1987-01-01

Eight years of summer storm rainfall observations from 93 stations in and around the 154 sq km Walnut Gulch catchment of the Agricultural Research Service, U.S. Department of Agriculture, in Arizona are processed to yield the total station depths of 428 storms. Statistical analysis of these random fields yields the first two moments, the spatial correlation and variance functions, and the spatial distribution of total rainfall for each storm. The absolute and relative worth of three Poisson models are evaluated by comparing their prediction of the spatial distribution of storm rainfall with observations from the second half of the sample. The effect of interstorm parameter variation is examined.

Spatially explicit land-use and land-cover scenarios for the Great Plains of the United States

USGS Publications Warehouse

Sohl, Terry L.; Sleeter, Benjamin M.; Sayler, Kristi L.; Bouchard, Michelle A.; Reker, Ryan R.; Bennett, Stacie L.; Sleeter, Rachel R.; Kanengieter, Ronald L.; Zhu, Zhi-Liang

2012-01-01

The Great Plains of the United States has undergone extensive land-use and land-cover change in the past 150 years, with much of the once vast native grasslands and wetlands converted to agricultural crops, and much of the unbroken prairie now heavily grazed. Future land-use change in the region could have dramatic impacts on ecological resources and processes. A scenario-based modeling framework is needed to support the analysis of potential land-use change in an uncertain future, and to mitigate potentially negative future impacts on ecosystem processes. We developed a scenario-based modeling framework to analyze potential future land-use change in the Great Plains. A unique scenario construction process, using an integrated modeling framework, historical data, workshops, and expert knowledge, was used to develop quantitative demand for future land-use change for four IPCC scenarios at the ecoregion level. The FORE-SCE model ingested the scenario information and produced spatially explicit land-use maps for the region at relatively fine spatial and thematic resolutions. Spatial modeling of the four scenarios provided spatial patterns of land-use change consistent with underlying assumptions and processes associated with each scenario. Economically oriented scenarios were characterized by significant loss of natural land covers and expansion of agricultural and urban land uses. Environmentally oriented scenarios experienced modest declines in natural land covers to slight increases. Model results were assessed for quantity and allocation disagreement between each scenario pair. In conjunction with the U.S. Geological Survey's Biological Carbon Sequestration project, the scenario-based modeling framework used for the Great Plains is now being applied to the entire United States.

A computational model of spatial visualization capacity.

PubMed

Lyon, Don R; Gunzelmann, Glenn; Gluck, Kevin A

2008-09-01

Visualizing spatial material is a cornerstone of human problem solving, but human visualization capacity is sharply limited. To investigate the sources of this limit, we developed a new task to measure visualization accuracy for verbally-described spatial paths (similar to street directions), and implemented a computational process model to perform it. In this model, developed within the Adaptive Control of Thought-Rational (ACT-R) architecture, visualization capacity is limited by three mechanisms. Two of these (associative interference and decay) are longstanding characteristics of ACT-R's declarative memory. A third (spatial interference) is a new mechanism motivated by spatial proximity effects in our data. We tested the model in two experiments, one with parameter-value fitting, and a replication without further fitting. Correspondence between model and data was close in both experiments, suggesting that the model may be useful for understanding why visualizing new, complex spatial material is so difficult.

Implications of the spatial dynamics of fire spread for the bistability of savanna and forest.

PubMed

Schertzer, E; Staver, A C; Levin, S A

2015-01-01

The role of fire in expanding the global distribution of savanna is well recognized. Empirical observations and modeling suggest that fire spread has a threshold response to fuel-layer continuity, which sets up a positive feedback that maintains savanna-forest bistability. However, modeling has so far failed to examine fire spread as a spatial process that interacts with vegetation. Here, we use simple, well-supported assumptions about fire spread as an infection process and its effects on trees to ask whether spatial dynamics qualitatively change the potential for savanna-forest bistability. We show that the spatial effects of fire spread are the fundamental reason that bistability is possible: because fire spread is an infection process, it exhibits a threshold response to fuel continuity followed by a rapid increase in fire size. Other ecological processes affecting fire spread may also contribute including temporal variability in demography or fire spread. Finally, including the potential for spatial aggregation increases the potential both for savanna-forest bistability and for savanna and forest to coexist in a landscape mosaic.

Secondary dispersal driven by overland flow in drylands: Review and mechanistic model development.

PubMed

Thompson, Sally E; Assouline, Shmuel; Chen, Li; Trahktenbrot, Ana; Svoray, Tal; Katul, Gabriel G

2014-01-01

Seed dispersal alters gene flow, reproduction, migration and ultimately spatial organization of dryland ecosystems. Because many seeds in drylands lack adaptations for long-distance dispersal, seed transport by secondary processes such as tumbling in the wind or mobilization in overland flow plays a dominant role in determining where seeds ultimately germinate. Here, recent developments in modeling runoff generation in spatially complex dryland ecosystems are reviewed with the aim of proposing improvements to mechanistic modeling of seed dispersal processes. The objective is to develop a physically-based yet operational framework for determining seed dispersal due to surface runoff, a process that has gained recent experimental attention. A Buoyant OBject Coupled Eulerian - Lagrangian Closure model (BOB-CELC) is proposed to represent seed movement in shallow surface flows. The BOB-CELC is then employed to investigate the sensitivity of seed transport to landscape and storm properties and to the spatial configuration of vegetation patches interspersed within bare earth. The potential to simplify seed transport outcomes by considering the limiting behavior of multiple runoff events is briefly considered, as is the potential for developing highly mechanistic, spatially explicit models that link seed transport, vegetation structure and water movement across multiple generations of dryland plants.

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks

PubMed Central

Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Oliva, Aude

2017-01-01

Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100 ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250 ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative model of how scene size representations may arise in the brain, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and suggest that deep neural networks are a promising framework to investigate how spatial layout representations emerge in the human brain. PMID:27039703

Internal Catchment Process Simulation in a Snow-Dominated Basin: Performance Evaluation with Spatiotemporally Variable Runoff Generation and Groundwater Dynamics

NASA Astrophysics Data System (ADS)

Kuras, P. K.; Weiler, M.; Alila, Y.; Spittlehouse, D.; Winkler, R.

2006-12-01

Hydrologic models have been increasingly used in forest hydrology to overcome the limitations of paired watershed experiments, where vegetative recovery and natural variability obscure the inferences and conclusions that can be drawn from such studies. Models, however, are also plagued by uncertainty stemming from a limited understanding of hydrological processes in forested catchments and parameter equifinality is a common concern. This has created the necessity to improve our understanding of how hydrological systems work, through the development of hydrological measures, analyses and models that address the question: are we getting the right answers for the right reasons? Hence, physically-based, spatially-distributed hydrologic models should be validated with high-quality experimental data describing multiple concurrent internal catchment processes under a range of hydrologic regimes. The distributed hydrology soil vegetation model (DHSVM) frequently used in forest management applications is an example of a process-based model used to address the aforementioned circumstances, and this study takes a novel approach at collectively examining the ability of a pre-calibrated model application to realistically simulate outlet flows along with the spatial-temporal variation of internal catchment processes including: continuous groundwater dynamics at 9 locations, stream and road network flow at 67 locations for six individual days throughout the freshet, and pre-melt season snow distribution. Model efficiency was improved over prior evaluations due to continuous efforts in improving the quality of meteorological data in the watershed. Road and stream network flows were very well simulated for a range of hydrological conditions, and the spatial distribution of the pre-melt season snowpack was in general agreement with observed values. The model was effective in simulating the spatial variability of subsurface flow generation, except at locations where strong stream-groundwater interactions existed, as the model is not capable of simulating such processes and subsurface flows always drain to the stream network. The model has proven overall to be quite capable in realistically simulating internal catchment processes in the watershed, which creates more confidence in future model applications exploring the effects of various forest management scenarios on the watershed's hydrological processes.

A multistream model of visual word recognition.

PubMed

Allen, Philip A; Smith, Albert F; Lien, Mei-Ching; Kaut, Kevin P; Canfield, Angie

2009-02-01

Four experiments are reported that test a multistream model of visual word recognition, which associates letter-level and word-level processing channels with three known visual processing streams isolated in macaque monkeys: the magno-dominated (MD) stream, the interblob-dominated (ID) stream, and the blob-dominated (BD) stream (Van Essen & Anderson, 1995). We show that mixing the color of adjacent letters of words does not result in facilitation of response times or error rates when the spatial-frequency pattern of a whole word is familiar. However, facilitation does occur when the spatial-frequency pattern of a whole word is not familiar. This pattern of results is not due to different luminance levels across the different-colored stimuli and the background because isoluminant displays were used. Also, the mixed-case, mixed-hue facilitation occurred when different display distances were used (Experiments 2 and 3), so this suggests that image normalization can adjust independently of object size differences. Finally, we show that this effect persists in both spaced and unspaced conditions (Experiment 4)--suggesting that inappropriate letter grouping by hue cannot account for these results. These data support a model of visual word recognition in which lower spatial frequencies are processed first in the more rapid MD stream. The slower ID and BD streams may process some lower spatial frequency information in addition to processing higher spatial frequency information, but these channels tend to lose the processing race to recognition unless the letter string is unfamiliar to the MD stream--as with mixed-case presentation.

Explaining variation in tropical plant community composition: influence of environmental and spatial data quality.

PubMed

Jones, Mirkka M; Tuomisto, Hanna; Borcard, Daniel; Legendre, Pierre; Clark, David B; Olivas, Paulo C

2008-03-01

The degree to which variation in plant community composition (beta-diversity) is predictable from environmental variation, relative to other spatial processes, is of considerable current interest. We addressed this question in Costa Rican rain forest pteridophytes (1,045 plots, 127 species). We also tested the effect of data quality on the results, which has largely been overlooked in earlier studies. To do so, we compared two alternative spatial models [polynomial vs. principal coordinates of neighbour matrices (PCNM)] and ten alternative environmental models (all available environmental variables vs. four subsets, and including their polynomials vs. not). Of the environmental data types, soil chemistry contributed most to explaining pteridophyte community variation, followed in decreasing order of contribution by topography, soil type and forest structure. Environmentally explained variation increased moderately when polynomials of the environmental variables were included. Spatially explained variation increased substantially when the multi-scale PCNM spatial model was used instead of the traditional, broad-scale polynomial spatial model. The best model combination (PCNM spatial model and full environmental model including polynomials) explained 32% of pteridophyte community variation, after correcting for the number of sampling sites and explanatory variables. Overall evidence for environmental control of beta-diversity was strong, and the main floristic gradients detected were correlated with environmental variation at all scales encompassed by the study (c. 100-2,000 m). Depending on model choice, however, total explained variation differed more than fourfold, and the apparent relative importance of space and environment could be reversed. Therefore, we advocate a broader recognition of the impacts that data quality has on analysis results. A general understanding of the relative contributions of spatial and environmental processes to species distributions and beta-diversity requires that methodological artefacts are separated from real ecological differences.

Spatial Selection and Local Adaptation Jointly Shape Life-History Evolution during Range Expansion.

PubMed

Van Petegem, Katrien H P; Boeye, Jeroen; Stoks, Robby; Bonte, Dries

2016-11-01

In the context of climate change and species invasions, range shifts increasingly gain attention because the rates at which they occur in the Anthropocene induce rapid changes in biological assemblages. During range shifts, species experience multiple selection pressures. For poleward expansions in particular, it is difficult to interpret observed evolutionary dynamics because of the joint action of evolutionary processes related to spatial selection and to adaptation toward local climatic conditions. To disentangle the effects of these two processes, we integrated stochastic modeling and data from a common garden experiment, using the spider mite Tetranychus urticae as a model species. By linking the empirical data with those derived form a highly parameterized individual-based model, we infer that both spatial selection and local adaptation contributed to the observed latitudinal life-history divergence. Spatial selection best described variation in dispersal behavior, while variation in development was best explained by adaptation to the local climate. Divergence in life-history traits in species shifting poleward could consequently be jointly determined by contemporary evolutionary dynamics resulting from adaptation to the environmental gradient and from spatial selection. The integration of modeling with common garden experiments provides a powerful tool to study the contribution of these evolutionary processes on life-history evolution during range expansion.

Application of GIS Rapid Mapping Technology in Disaster Monitoring

NASA Astrophysics Data System (ADS)

Wang, Z.; Tu, J.; Liu, G.; Zhao, Q.

2018-04-01

With the rapid development of GIS and RS technology, especially in recent years, GIS technology and its software functions have been increasingly mature and enhanced. And with the rapid development of mathematical statistical tools for spatial modeling and simulation, has promoted the widespread application and popularization of quantization in the field of geology. Based on the investigation of field disaster and the construction of spatial database, this paper uses remote sensing image, DEM and GIS technology to obtain the data information of disaster vulnerability analysis, and makes use of the information model to carry out disaster risk assessment mapping.Using ArcGIS software and its spatial data modeling method, the basic data information of the disaster risk mapping process was acquired and processed, and the spatial data simulation tool was used to map the disaster rapidly.

Temporal and Spatial Variation in Peatland Carbon Cycling and Implications for Interpreting Responses of an Ecosystem-Scale Warming Experiment

DOE PAGES

Griffiths, Natalie A.; Hanson, Paul J.; Ricciuto, Daniel M.; ...

2017-11-22

Here, we are conducting a large-scale, long-term climate change response experiment in an ombrotrophic peat bog in Minnesota to evaluate the effects of warming and elevated CO 2 on ecosystem processes using empirical and modeling approaches. To better frame future assessments of peatland responses to climate change, we characterized and compared spatial vs. temporal variation in measured C cycle processes and their environmental drivers. We also conducted a sensitivity analysis of a peatland C model to identify how variation in ecosystem parameters contributes to model prediction uncertainty. High spatial variability in C cycle processes resulted in the inability to determinemore » if the bog was a C source or sink, as the 95% confidence interval ranged from a source of 50 g C m –2 yr –1 to a sink of 67 g C m –2 yr –1. Model sensitivity analysis also identified that spatial variation in tree and shrub photosynthesis, allocation characteristics, and maintenance respiration all contributed to large variations in the pretreatment estimates of net C balance. Variation in ecosystem processes can be more thoroughly characterized if more measurements are collected for parameters that are highly variable over space and time, and especially if those measurements encompass environmental gradients that may be driving the spatial and temporal variation (e.g., hummock vs. hollow microtopographies, and wet vs. dry years). Together, the coupled modeling and empirical approaches indicate that variability in C cycle processes and their drivers must be taken into account when interpreting the significance of experimental warming and elevated CO 2 treatments.« less

Temporal and Spatial Variation in Peatland Carbon Cycling and Implications for Interpreting Responses of an Ecosystem-Scale Warming Experiment

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

Griffiths, Natalie A.; Hanson, Paul J.; Ricciuto, Daniel M.

Here, we are conducting a large-scale, long-term climate change response experiment in an ombrotrophic peat bog in Minnesota to evaluate the effects of warming and elevated CO 2 on ecosystem processes using empirical and modeling approaches. To better frame future assessments of peatland responses to climate change, we characterized and compared spatial vs. temporal variation in measured C cycle processes and their environmental drivers. We also conducted a sensitivity analysis of a peatland C model to identify how variation in ecosystem parameters contributes to model prediction uncertainty. High spatial variability in C cycle processes resulted in the inability to determinemore » if the bog was a C source or sink, as the 95% confidence interval ranged from a source of 50 g C m –2 yr –1 to a sink of 67 g C m –2 yr –1. Model sensitivity analysis also identified that spatial variation in tree and shrub photosynthesis, allocation characteristics, and maintenance respiration all contributed to large variations in the pretreatment estimates of net C balance. Variation in ecosystem processes can be more thoroughly characterized if more measurements are collected for parameters that are highly variable over space and time, and especially if those measurements encompass environmental gradients that may be driving the spatial and temporal variation (e.g., hummock vs. hollow microtopographies, and wet vs. dry years). Together, the coupled modeling and empirical approaches indicate that variability in C cycle processes and their drivers must be taken into account when interpreting the significance of experimental warming and elevated CO 2 treatments.« less

Correction of mid-spatial-frequency errors by smoothing in spin motion for CCOS

NASA Astrophysics Data System (ADS)

Zhang, Yizhong; Wei, Chaoyang; Shao, Jianda; Xu, Xueke; Liu, Shijie; Hu, Chen; Zhang, Haichao; Gu, Haojin

2015-08-01

Smoothing is a convenient and efficient way to correct mid-spatial-frequency errors. Quantifying the smoothing effect allows improvements in efficiency for finishing precision optics. A series experiments in spin motion are performed to study the smoothing effects about correcting mid-spatial-frequency errors. Some of them use a same pitch tool at different spinning speed, and others at a same spinning speed with different tools. Introduced and improved Shu's model to describe and compare the smoothing efficiency with different spinning speed and different tools. From the experimental results, the mid-spatial-frequency errors on the initial surface were nearly smoothed out after the process in spin motion and the number of smoothing times can be estimated by the model before the process. Meanwhile this method was also applied to smooth the aspherical component, which has an obvious mid-spatial-frequency error after Magnetorheological Finishing processing. As a result, a high precision aspheric optical component was obtained with PV=0.1λ and RMS=0.01λ.

Data integration for inference about spatial processes: A model-based approach to test and account for data inconsistency

PubMed Central

Pedrini, Paolo; Bragalanti, Natalia; Groff, Claudio

2017-01-01

Recently-developed methods that integrate multiple data sources arising from the same ecological processes have typically utilized structured data from well-defined sampling protocols (e.g., capture-recapture and telemetry). Despite this new methodological focus, the value of opportunistic data for improving inference about spatial ecological processes is unclear and, perhaps more importantly, no procedures are available to formally test whether parameter estimates are consistent across data sources and whether they are suitable for integration. Using data collected on the reintroduced brown bear population in the Italian Alps, a population of conservation importance, we combined data from three sources: traditional spatial capture-recapture data, telemetry data, and opportunistic data. We developed a fully integrated spatial capture-recapture (SCR) model that included a model-based test for data consistency to first compare model estimates using different combinations of data, and then, by acknowledging data-type differences, evaluate parameter consistency. We demonstrate that opportunistic data lend itself naturally to integration within the SCR framework and highlight the value of opportunistic data for improving inference about space use and population size. This is particularly relevant in studies of rare or elusive species, where the number of spatial encounters is usually small and where additional observations are of high value. In addition, our results highlight the importance of testing and accounting for inconsistencies in spatial information from structured and unstructured data so as to avoid the risk of spurious or averaged estimates of space use and consequently, of population size. Our work supports the use of a single modeling framework to combine spatially-referenced data while also accounting for parameter consistency. PMID:28973034

Land surface hydrology parameterization for atmospheric general circulation models including subgrid scale spatial variability

NASA Technical Reports Server (NTRS)

Entekhabi, D.; Eagleson, P. S.

1989-01-01

Parameterizations are developed for the representation of subgrid hydrologic processes in atmospheric general circulation models. Reasonable a priori probability density functions of the spatial variability of soil moisture and of precipitation are introduced. These are used in conjunction with the deterministic equations describing basic soil moisture physics to derive expressions for the hydrologic processes that include subgrid scale variation in parameters. The major model sensitivities to soil type and to climatic forcing are explored.

Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences

PubMed Central

Jung, Minju; Hwang, Jungsik; Tani, Jun

2015-01-01

It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most biological and computational modeling studies have mainly focused on the spatial domain and do not discuss temporal domain processing of the visual cortex. Several studies on the visual cortex and other brain areas associated with motor control support that the brain also uses its hierarchical structure as a processing mechanism for temporal information. Based on the success of previous computational models using spatial hierarchy and temporal hierarchy observed in the brain, the current report introduces a novel neural network model for the recognition of dynamic visual image patterns based solely on the learning of exemplars. This model is characterized by the application of both spatial and temporal constraints on local neural activities, resulting in the self-organization of a spatio-temporal hierarchy necessary for the recognition of complex dynamic visual image patterns. The evaluation with the Weizmann dataset in recognition of a set of prototypical human movement patterns showed that the proposed model is significantly robust in recognizing dynamically occluded visual patterns compared to other baseline models. Furthermore, an evaluation test for the recognition of concatenated sequences of those prototypical movement patterns indicated that the model is endowed with a remarkable capability for the contextual recognition of long-range dynamic visual image patterns. PMID:26147887

Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences.

PubMed

Jung, Minju; Hwang, Jungsik; Tani, Jun

2015-01-01

It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most biological and computational modeling studies have mainly focused on the spatial domain and do not discuss temporal domain processing of the visual cortex. Several studies on the visual cortex and other brain areas associated with motor control support that the brain also uses its hierarchical structure as a processing mechanism for temporal information. Based on the success of previous computational models using spatial hierarchy and temporal hierarchy observed in the brain, the current report introduces a novel neural network model for the recognition of dynamic visual image patterns based solely on the learning of exemplars. This model is characterized by the application of both spatial and temporal constraints on local neural activities, resulting in the self-organization of a spatio-temporal hierarchy necessary for the recognition of complex dynamic visual image patterns. The evaluation with the Weizmann dataset in recognition of a set of prototypical human movement patterns showed that the proposed model is significantly robust in recognizing dynamically occluded visual patterns compared to other baseline models. Furthermore, an evaluation test for the recognition of concatenated sequences of those prototypical movement patterns indicated that the model is endowed with a remarkable capability for the contextual recognition of long-range dynamic visual image patterns.

Using a spatially-distributed hydrologic biogeochemistry model to study the spatial variation of carbon processes in a Critical Zone Observatory

NASA Astrophysics Data System (ADS)

Shi, Y.; Eissenstat, D. M.; Davis, K. J.; He, Y.

2015-12-01

Forest carbon processes are affected by soil moisture, soil temperature and solar radiation. Most of the current biogeochemical models are 1-D and represent one point in space. Therefore they can neither resolve topographically driven hill-slope soil moisture patterns, nor simulate the nonlinear effects of soil moisture on carbon processes. A spatially-distributed biogeochemistry model, Flux-PIHM-BGC, has been developed by coupling the Biome-BGC (BBGC) model with a coupled physically-based land surface hydrologic model, Flux-PIHM. Flux-PIHM incorporates a land-surface scheme (adapted from the Noah land surface model) into the Penn State Integrated Hydrologic Model (PIHM). Because PIHM is capable of simulating lateral water flow and deep groundwater, Flux-PIHM is able to represent the link between groundwater and the surface energy balance, as well as the land surface heterogeneities caused by topography. Flux-PIHM-BGC model was tested at the Susquehanna/Shale Hills critical zone observatory (SSHCZO). The abundant observations at the SSHCZO, including eddy covariance fluxes, soil moisture, groundwater level, sap flux, stream discharge, litterfall, leaf area index, aboveground carbon stock, and soil carbon efflux, provided an ideal test bed for the coupled model. Model results show that when uniform solar radiation is used, vegetation carbon and soil carbon are positively correlated with soil moisture in space, which agrees with the observations within the watershed. When topographically-driven solar radiation is used, however, the wetter valley floor becomes radiation limited, and produces less vegetation and soil carbon than the drier hillslope due to the assumption that canopy height is uniform in the watershed. This contradicts with the observations, and suggests that a tree height model with dynamic allocation model are needed to reproduce the spatial variation of carbon processes within a watershed.

Spatial-temporal modeling of malware propagation in networks.

PubMed

Chen, Zesheng; Ji, Chuanyi

2005-09-01

Network security is an important task of network management. One threat to network security is malware (malicious software) propagation. One type of malware is called topological scanning that spreads based on topology information. The focus of this work is on modeling the spread of topological malwares, which is important for understanding their potential damages, and for developing countermeasures to protect the network infrastructure. Our model is motivated by probabilistic graphs, which have been widely investigated in machine learning. We first use a graphical representation to abstract the propagation of malwares that employ different scanning methods. We then use a spatial-temporal random process to describe the statistical dependence of malware propagation in arbitrary topologies. As the spatial dependence is particularly difficult to characterize, the problem becomes how to use simple (i.e., biased) models to approximate the spatially dependent process. In particular, we propose the independent model and the Markov model as simple approximations. We conduct both theoretical analysis and extensive simulations on large networks using both real measurements and synthesized topologies to test the performance of the proposed models. Our results show that the independent model can capture temporal dependence and detailed topology information and, thus, outperforms the previous models, whereas the Markov model incorporates a certain spatial dependence and, thus, achieves a greater accuracy in characterizing both transient and equilibrium behaviors of malware propagation.

Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology.

PubMed

Renner, Ian W; Warton, David I

2013-03-01

Modeling the spatial distribution of a species is a fundamental problem in ecology. A number of modeling methods have been developed, an extremely popular one being MAXENT, a maximum entropy modeling approach. In this article, we show that MAXENT is equivalent to a Poisson regression model and hence is related to a Poisson point process model, differing only in the intercept term, which is scale-dependent in MAXENT. We illustrate a number of improvements to MAXENT that follow from these relations. In particular, a point process model approach facilitates methods for choosing the appropriate spatial resolution, assessing model adequacy, and choosing the LASSO penalty parameter, all currently unavailable to MAXENT. The equivalence result represents a significant step in the unification of the species distribution modeling literature. Copyright © 2013, The International Biometric Society.

Efficient statistical mapping of avian count data

USGS Publications Warehouse

Royle, J. Andrew; Wikle, C.K.

2005-01-01

We develop a spatial modeling framework for count data that is efficient to implement in high-dimensional prediction problems. We consider spectral parameterizations for the spatially varying mean of a Poisson model. The spectral parameterization of the spatial process is very computationally efficient, enabling effective estimation and prediction in large problems using Markov chain Monte Carlo techniques. We apply this model to creating avian relative abundance maps from North American Breeding Bird Survey (BBS) data. Variation in the ability of observers to count birds is modeled as spatially independent noise, resulting in over-dispersion relative to the Poisson assumption. This approach represents an improvement over existing approaches used for spatial modeling of BBS data which are either inefficient for continental scale modeling and prediction or fail to accommodate important distributional features of count data thus leading to inaccurate accounting of prediction uncertainty.

Cometary atmospheres: Modeling the spatial distribution of observed neutral radicals

NASA Technical Reports Server (NTRS)

Combi, M. R.

1985-01-01

Progress on modeling the spatial distributions of cometary radicals is described. The Monte Carlo particle-trajectory model was generalized to include the full time dependencies of initial comet expansion velocities, nucleus vaporization rates, photochemical lifetimes and photon emission rates which enter the problem through the comet's changing heliocentric distance and velocity. The effect of multiple collisions in the transition zone from collisional coupling to true free flow were also included. Currently available observations of the spatial distributions of the neutral radicals, as well as the latest available photochemical data were re-evaluated. Preliminary exploratory model results testing the effects of various processes on observable spatial distributions are also discussed.

High-dynamic-range scene compression in humans

NASA Astrophysics Data System (ADS)

McCann, John J.

2006-02-01

Single pixel dynamic-range compression alters a particular input value to a unique output value - a look-up table. It is used in chemical and most digital photographic systems having S-shaped transforms to render high-range scenes onto low-range media. Post-receptor neural processing is spatial, as shown by the physiological experiments of Dowling, Barlow, Kuffler, and Hubel & Wiesel. Human vision does not render a particular receptor-quanta catch as a unique response. Instead, because of spatial processing, the response to a particular quanta catch can be any color. Visual response is scene dependent. Stockham proposed an approach to model human range compression using low-spatial frequency filters. Campbell, Ginsberg, Wilson, Watson, Daly and many others have developed spatial-frequency channel models. This paper describes experiments measuring the properties of desirable spatial-frequency filters for a variety of scenes. Given the radiances of each pixel in the scene and the observed appearances of objects in the image, one can calculate the visual mask for that individual image. Here, visual mask is the spatial pattern of changes made by the visual system in processing the input image. It is the spatial signature of human vision. Low-dynamic range images with many white areas need no spatial filtering. High-dynamic-range images with many blacks, or deep shadows, require strong spatial filtering. Sun on the right and shade on the left requires directional filters. These experiments show that variable scene- scenedependent filters are necessary to mimic human vision. Although spatial-frequency filters can model human dependent appearances, the problem still remains that an analysis of the scene is still needed to calculate the scene-dependent strengths of each of the filters for each frequency.

Rockfall hazard analysis using LiDAR and spatial modeling

NASA Astrophysics Data System (ADS)

Lan, Hengxing; Martin, C. Derek; Zhou, Chenghu; Lim, Chang Ho

2010-05-01

Rockfalls have been significant geohazards along the Canadian Class 1 Railways (CN Rail and CP Rail) since their construction in the late 1800s. These rockfalls cause damage to infrastructure, interruption of business, and environmental impacts, and their occurrence varies both spatially and temporally. The proactive management of these rockfall hazards requires enabling technologies. This paper discusses a hazard assessment strategy for rockfalls along a section of a Canadian railway using LiDAR and spatial modeling. LiDAR provides accurate topographical information of the source area of rockfalls and along their paths. Spatial modeling was conducted using Rockfall Analyst, a three dimensional extension to GIS, to determine the characteristics of the rockfalls in terms of travel distance, velocity and energy. Historical rockfall records were used to calibrate the physical characteristics of the rockfall processes. The results based on a high-resolution digital elevation model from a LiDAR dataset were compared with those based on a coarse digital elevation model. A comprehensive methodology for rockfall hazard assessment is proposed which takes into account the characteristics of source areas, the physical processes of rockfalls and the spatial attribution of their frequency and energy.

Interaction of dissolution, sorption and biodegradation on transport of BTEX in a saturated groundwater system: Numerical modeling and spatial moment analysis

NASA Astrophysics Data System (ADS)

Valsala, Renu; Govindarajan, Suresh Kumar

2018-06-01

Interaction of various physical, chemical and biological transport processes plays an important role in deciding the fate and migration of contaminants in groundwater systems. In this study, a numerical investigation on the interaction of various transport processes of BTEX in a saturated groundwater system is carried out. In addition, the multi-component dissolution from a residual BTEX source under unsteady flow conditions is incorporated in the modeling framework. The model considers Benzene, Toluene, Ethyl Benzene and Xylene dissolving from the residual BTEX source zone to undergo sorption and aerobic biodegradation within the groundwater aquifer. Spatial concentration profiles of dissolved BTEX components under the interaction of various sorption and biodegradation conditions have been studied. Subsequently, a spatial moment analysis is carried out to analyze the effect of interaction of various transport processes on the total dissolved mass and the mobility of dissolved BTEX components. Results from the present numerical study suggest that the interaction of dissolution, sorption and biodegradation significantly influence the spatial distribution of dissolved BTEX components within the saturated groundwater system. Mobility of dissolved BTEX components is also found to be affected by the interaction of these transport processes.

A novel computational model to probe visual search deficits during motor performance

PubMed Central

Singh, Tarkeshwar; Fridriksson, Julius; Perry, Christopher M.; Tryon, Sarah C.; Ross, Angela; Fritz, Stacy

2016-01-01

Successful execution of many motor skills relies on well-organized visual search (voluntary eye movements that actively scan the environment for task-relevant information). Although impairments of visual search that result from brain injuries are linked to diminished motor performance, the neural processes that guide visual search within this context remain largely unknown. The first objective of this study was to examine how visual search in healthy adults and stroke survivors is used to guide hand movements during the Trail Making Test (TMT), a neuropsychological task that is a strong predictor of visuomotor and cognitive deficits. Our second objective was to develop a novel computational model to investigate combinatorial interactions between three underlying processes of visual search (spatial planning, working memory, and peripheral visual processing). We predicted that stroke survivors would exhibit deficits in integrating the three underlying processes, resulting in deteriorated overall task performance. We found that normal TMT performance is associated with patterns of visual search that primarily rely on spatial planning and/or working memory (but not peripheral visual processing). Our computational model suggested that abnormal TMT performance following stroke is associated with impairments of visual search that are characterized by deficits integrating spatial planning and working memory. This innovative methodology provides a novel framework for studying how the neural processes underlying visual search interact combinatorially to guide motor performance. NEW & NOTEWORTHY Visual search has traditionally been studied in cognitive and perceptual paradigms, but little is known about how it contributes to visuomotor performance. We have developed a novel computational model to examine how three underlying processes of visual search (spatial planning, working memory, and peripheral visual processing) contribute to visual search during a visuomotor task. We show that deficits integrating spatial planning and working memory underlie abnormal performance in stroke survivors with frontoparietal damage. PMID:27733596

A novel spatial performance metric for robust pattern optimization of distributed hydrological models

NASA Astrophysics Data System (ADS)

Stisen, S.; Demirel, C.; Koch, J.

2017-12-01

Evaluation of performance is an integral part of model development and calibration as well as it is of paramount importance when communicating modelling results to stakeholders and the scientific community. There exists a comprehensive and well tested toolbox of metrics to assess temporal model performance in the hydrological modelling community. On the contrary, the experience to evaluate spatial performance is not corresponding to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study aims at making a contribution towards advancing spatial pattern oriented model evaluation for distributed hydrological models. This is achieved by introducing a novel spatial performance metric which provides robust pattern performance during model calibration. The promoted SPAtial EFficiency (spaef) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multi-component approach is necessary in order to adequately compare spatial patterns. spaef, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are tested in a spatial pattern oriented model calibration of a catchment model in Denmark. The calibration is constrained by a remote sensing based spatial pattern of evapotranspiration and discharge timeseries at two stations. Our results stress that stand-alone metrics tend to fail to provide holistic pattern information to the optimizer which underlines the importance of multi-component metrics. The three spaef components are independent which allows them to complement each other in a meaningful way. This study promotes the use of bias insensitive metrics which allow comparing variables which are related but may differ in unit in order to optimally exploit spatial observations made available by remote sensing platforms. We see great potential of spaef across environmental disciplines dealing with spatially distributed modelling.

Improving evapotranspiration processes in distrubing hydrological models using Remote Sensing derived ET products.

NASA Astrophysics Data System (ADS)

Abitew, T. A.; van Griensven, A.; Bauwens, W.

2015-12-01

Evapotranspiration is the main process in hydrology (on average around 60%), though has not received as much attention in the evaluation and calibration of hydrological models. In this study, Remote Sensing (RS) derived Evapotranspiration (ET) is used to improve the spatially distributed processes of ET of SWAT model application in the upper Mara basin (Kenya) and the Blue Nile basin (Ethiopia). The RS derived ET data is obtained from recently compiled global datasets (continuously monthly data at 1 km resolution from MOD16NBI,SSEBop,ALEXI,CMRSET models) and from regionally applied Energy Balance Models (for several cloud free days). The RS-RT data is used in different forms: Method 1) to evaluate spatially distributed evapotransiration model resultsMethod 2) to calibrate the evotranspiration processes in hydrological modelMethod 3) to bias-correct the evapotranpiration in hydrological model during simulation after changing the SWAT codesAn inter-comparison of the RS-ET products shows that at present there is a significant bias, but at the same time an agreement on the spatial variability of ET. The ensemble mean of different ET products seems the most realistic estimation and was further used in this study.The results show that:Method 1) the spatially mapped evapotranspiration of hydrological models shows clear differences when compared to RS derived evapotranspiration (low correlations). Especially evapotranspiration in forested areas is strongly underestimated compared to other land covers.Method 2) Calibration allows to improve the correlations between the RS and hydrological model results to some extent.Method 3) Bias-corrections are efficient in producing (sesonal or annual) evapotranspiration maps from hydrological models which are very similar to the patterns obtained from RS data.Though the bias-correction is very efficient, it is advised to improve the model results by better representing the ET processes by improved plant/crop computations, improved agricultural management practices or by providing improved meteorological data.

Extended experience benefits spatial mental model development with route but not survey descriptions.

PubMed

Brunyé, Tad T; Taylor, Holly A

2008-02-01

Spatial descriptions symbolically represent environmental information through language and are written in two primary perspectives: survey, analogous to viewing a map, and route, analogous to navigation. Readers of survey or route descriptions form abstracted perspective flexible representations of the described environment, or spatial mental models. The present two experiments investigated the maintenance of perspective in spatial mental models as a function of description perspective and experience (operationalized through repetition), and as reflected in self-paced reading times. Experiment 1 involved studying survey and route descriptions either once or three times, then completing map drawing and true/false statement verification. Results demonstrated that spatial mental models are readily formed with survey descriptions, but require relatively more experience with route descriptions; further, some limited evidence suggests perspective dependence in spatial mental models, even following extended experience. Experiment 2 measured self-paced reading during three successive description presentations. Average reading times over the three presentations reduced more for survey relative to route descriptions, and there was no evidence for perspective specificity in resulting spatial mental models. This supports Experiment 1 findings demonstrating the relatively time-consuming nature of acquiring spatial mental models from route, but not survey descriptions. Results are discussed with regard to developmental, discourse processing, and spatial mental model theory.

Neural dynamics of motion processing and speed discrimination.

PubMed

Chey, J; Grossberg, S; Mingolla, E

1998-09-01

A neural network model of visual motion perception and speed discrimination is presented. The model shows how a distributed population code of speed tuning, that realizes a size-speed correlation, can be derived from the simplest mechanisms whereby activations of multiple spatially short-range filters of different size are transformed into speed-turned cell responses. These mechanisms use transient cell responses to moving stimuli, output thresholds that covary with filter size, and competition. These mechanisms are proposed to occur in the V1-->MT cortical processing stream. The model reproduces empirically derived speed discrimination curves and simulates data showing how visual speed perception and discrimination can be affected by stimulus contrast, duration, dot density and spatial frequency. Model motion mechanisms are analogous to mechanisms that have been used to model 3-D form and figure-ground perception. The model forms the front end of a larger motion processing system that has been used to simulate how global motion capture occurs, and how spatial attention is drawn to moving forms. It provides a computational foundation for an emerging neural theory of 3-D form and motion perception.

Hierarchical spatial models of abundance and occurrence from imperfect survey data

USGS Publications Warehouse

Royle, J. Andrew; Kery, M.; Gautier, R.; Schmid, Hans

2007-01-01

Many estimation and inference problems arising from large-scale animal surveys are focused on developing an understanding of patterns in abundance or occurrence of a species based on spatially referenced count data. One fundamental challenge, then, is that it is generally not feasible to completely enumerate ('census') all individuals present in each sample unit. This observation bias may consist of several components, including spatial coverage bias (not all individuals in the Population are exposed to sampling) and detection bias (exposed individuals may go undetected). Thus, observations are biased for the state variable (abundance, occupancy) that is the object of inference. Moreover, data are often sparse for most observation locations, requiring consideration of methods for spatially aggregating or otherwise combining sparse data among sample units. The development of methods that unify spatial statistical models with models accommodating non-detection is necessary to resolve important spatial inference problems based on animal survey data. In this paper, we develop a novel hierarchical spatial model for estimation of abundance and occurrence from survey data wherein detection is imperfect. Our application is focused on spatial inference problems in the Swiss Survey of Common Breeding Birds. The observation model for the survey data is specified conditional on the unknown quadrat population size, N(s). We augment the observation model with a spatial process model for N(s), describing the spatial variation in abundance of the species. The model includes explicit sources of variation in habitat structure (forest, elevation) and latent variation in the form of a correlated spatial process. This provides a model-based framework for combining the spatially referenced samples while at the same time yielding a unified treatment of estimation problems involving both abundance and occurrence. We provide a Bayesian framework for analysis and prediction based on the integrated likelihood, and we use the model to obtain estimates of abundance and occurrence maps for the European Jay (Garrulus glandarius), a widespread, elusive, forest bird. The naive national abundance estimate ignoring imperfect detection and incomplete quadrat coverage was 77 766 territories. Accounting for imperfect detection added approximately 18 000 territories, and adjusting for coverage bias added another 131 000 territories to yield a fully corrected estimate of the national total of about 227 000 territories. This is approximately three times as high as previous estimates that assume every territory is detected in each quadrat.

EVALUATING HYDROLOGICAL RESPONSE TO ...

EPA Pesticide Factsheets

Studies of future management and policy options based on different assumptions provide a mechanism to examine possible outcomes and especially their likely benefits or consequences. Planning and assessment in land and water resource management are evolving toward complex, spatially explicit regional assessments. These problems have to be addressed with distributed models that can compute runoff and erosion at different spatial and temporal scales. The extensive data requirements and the difficult task of building input parameter files, however, have long been an obstacle to the timely and cost-effective use of such complex models by resource managers. The U.S. EPA Landscape Ecology Branch in collaboration with the USDA-ARS Southwest Watershed Research Center has developed a geographic information system (GIS) tool to facilitate this process. A GIS provides the framework within which spatially distributed data are collected and used to prepare model input files, and model results are evaluated. The Automated Geospatial Watershed Assessment (AGWA) tool uses widely available standardized spatial datasets that can be obtained via the internet at no cost to the user. The data are used to develop input parameter files for KINEROS2 and SWAT, two watershed runoff and erosion simulation models that operate at different spatial and temporal scales. AGWA automates the process of transforming digital data into simulation model results and provides a visualization tool

Using a spatially-distributed hydrologic biogeochemistry model to study the spatial variation of carbon processes in a Critical Zone Observatory

NASA Astrophysics Data System (ADS)

Shi, Y.; Eissenstat, D. M.; Davis, K. J.; He, Y.

2016-12-01

Forest carbon processes are affected by, among other factors, soil moisture, soil temperature, soil nutrients and solar radiation. Most of the current biogeochemical models are 1-D and represent one point in space. Therefore, they cannot resolve the topographically driven hill-slope land surface heterogeneity or the spatial pattern of nutrient availability. A spatially distributed forest ecosystem model, Flux-PIHM-BGC, has been developed by coupling a 1-D mechanistic biogeochemical model Biome-BGC (BBGC) with a spatially distributed land surface hydrologic model, Flux-PIHM. Flux-PIHM is a coupled physically based model, which incorporates a land-surface scheme into the Penn State Integrated Hydrologic Model (PIHM). The land surface scheme is adapted from the Noah land surface model. Flux-PIHM is able to represent the link between groundwater and the surface energy balance, as well as the land surface heterogeneities caused by topography. In the coupled Flux-PIHM-BGC model, each Flux-PIHM model grid couples a 1-D BBGC model, while soil nitrogen is transported among model grids via subsurface water flow. In each grid, Flux-PIHM provides BBGC with soil moisture, soil temperature, and solar radiation information, while BBGC provides Flux-PIHM with leaf area index. The coupled Flux-PIHM-BGC model has been implemented at the Susquehanna/Shale Hills critical zone observatory (SSHCZO). Model results suggest that the vegetation and soil carbon distribution is primarily constrained by nitorgen availability (affected by nitorgen transport via topographically driven subsurface flow), and also constrained by solar radiation and root zone soil moisture. The predicted vegetation and soil carbon distribution generally agrees with the macro pattern observed within the watershed. The coupled ecosystem-hydrologic model provides an important tool to study the impact of topography on watershed carbon processes, as well as the impact of climate change on water resources.

Predictor variable resolution governs modeled soil types

USDA-ARS?s Scientific Manuscript database

Soil mapping identifies different soil types by compressing a unique suite of spatial patterns and processes across multiple spatial scales. It can be quite difficult to quantify spatial patterns of soil properties with remotely sensed predictor variables. More specifically, matching the right scale...

Using IBMs to Investigate Spatially-dependent Processes in Landscape Genetics Theory

EPA Science Inventory

Much of landscape and conservation genetics theory has been derived using non-spatialmathematical models. Here, we use a mechanistic, spatially-explicit, eco-evolutionary IBM to examine the utility of this theoretical framework in landscapes with spatial structure. Our analysis...

Virtual Embryo: Cell-Agent Based Modeling of Developmental Processes and Toxicities (CSS BOSC)

EPA Science Inventory

Spatial regulation of cellular dynamics is fundamental to morphological development. As such, chemical disruption of spatial dynamics is a determinant of developmental toxicity. Incorporating spatial dynamics into AOPs for developmental toxicity is desired but constrained by the ...

Swarm Intelligence for Urban Dynamics Modelling

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

Ghnemat, Rawan; Bertelle, Cyrille; Duchamp, Gerard H. E.

2009-04-16

In this paper, we propose swarm intelligence algorithms to deal with dynamical and spatial organization emergence. The goal is to model and simulate the developement of spatial centers using multi-criteria. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. We propose an extension of the ant nest building algorithm with multi-center and adaptive process. Typically, this model is suitable to analyse and simulate urban dynamics like gentrification or the dynamics of the cultural equipment in urban area.

Integrated Spatial Models of Non Native Plant Invasion, Fire Risk, and Wildlife Habitat to Support Conservation of Military and Adjacent Lands in the Arid Southwest

DTIC Science & Technology

2015-12-01

FINAL REPORT Integrated spatial models of non-native plant invasion, fire risk, and wildlife habitat to support conservation of military and...as reflecting the official policy or position of the Department of Defense. Reference herein to any specific commercial product, process, or service...2. REPORT TYPE Final 3. DATES COVERED (From - To) 26/4/2010 – 25/10/2015 4. TITLE AND SUBTITLE Integrated Spatial Models of Non-Native Plant

Swarm Intelligence for Urban Dynamics Modelling

NASA Astrophysics Data System (ADS)

Ghnemat, Rawan; Bertelle, Cyrille; Duchamp, Gérard H. E.

2009-04-01

In this paper, we propose swarm intelligence algorithms to deal with dynamical and spatial organization emergence. The goal is to model and simulate the developement of spatial centers using multi-criteria. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. We propose an extension of the ant nest building algorithm with multi-center and adaptive process. Typically, this model is suitable to analyse and simulate urban dynamics like gentrification or the dynamics of the cultural equipment in urban area.

A spatial scaling relationship for soil moisture in a semiarid landscape, using spatial scaling relationships for pedology

NASA Astrophysics Data System (ADS)

Willgoose, G. R.; Chen, M.; Cohen, S.; Saco, P. M.; Hancock, G. R.

2013-12-01

In humid areas it is generally considered that soil moisture scales spatially according to the wetness index of the landscape. This scaling arises from lateral flow downslope of ground water within the soil zone. However, in semi-arid and drier regions, this lateral flow is small and fluxes are dominated by vertical flows driven by infiltration and evapotranspiration. Thus, in the absence of runon processes, soil moisture at a location is more driven by local factors such as soil and vegetation properties at that location rather than upstream processes draining to that point. The 'apparent' spatial randomness of soil and vegetation properties generally suggests that soil moisture for semi-arid regions is spatially random. In this presentation a new analysis of neutron probe data during summer from the Tarrawarra site near Melbourne, Australia shows persistent spatial organisation of soil moisture over several years. This suggests a link between permanent features of the catchment (e.g. soil properties) and soil moisture distribution, even though the spatial pattern of soil moisture during the 4 summers monitored appears spatially random. This and other data establishes a prima facie case that soil variations drive spatial variation in soil moisture. Accordingly, we used a previously published spatial scaling relationship for soil properties derived using the mARM pedogenesis model to simulate the spatial variation of soil grading. This soil grading distribution was used in the Rosetta pedotransfer model to derive a spatial distribution of soil functional properties (e.g. saturated hydraulic conductivity, porosity). These functional properties were then input into the HYDRUS-1D soil moisture model and soil moisture simulated for 3 years at daily resolution. The HYDRUS model used had previously been calibrated to field observed soil moisture data at our SASMAS field site. The scaling behaviour of soil moisture derived from this modelling will be discussed and compared with observed data from our SASMAS field sites.

An Empirical Bayes Approach to Spatial Analysis

NASA Technical Reports Server (NTRS)

Morris, C. N.; Kostal, H.

1983-01-01

Multi-channel LANDSAT data are collected in several passes over agricultural areas during the growing season. How empirical Bayes modeling can be used to develop crop identification and discrimination techniques that account for spatial correlation in such data is considered. The approach models the unobservable parameters and the data separately, hoping to take advantage of the fact that the bulk of spatial correlation lies in the parameter process. The problem is then framed in terms of estimating posterior probabilities of crop types for each spatial area. Some empirical Bayes spatial estimation methods are used to estimate the logits of these probabilities.

Auditory spatial processing in Alzheimer’s disease

PubMed Central

Golden, Hannah L.; Nicholas, Jennifer M.; Yong, Keir X. X.; Downey, Laura E.; Schott, Jonathan M.; Mummery, Catherine J.; Crutch, Sebastian J.

2015-01-01

The location and motion of sounds in space are important cues for encoding the auditory world. Spatial processing is a core component of auditory scene analysis, a cognitively demanding function that is vulnerable in Alzheimer’s disease. Here we designed a novel neuropsychological battery based on a virtual space paradigm to assess auditory spatial processing in patient cohorts with clinically typical Alzheimer’s disease (n = 20) and its major variant syndrome, posterior cortical atrophy (n = 12) in relation to healthy older controls (n = 26). We assessed three dimensions of auditory spatial function: externalized versus non-externalized sound discrimination, moving versus stationary sound discrimination and stationary auditory spatial position discrimination, together with non-spatial auditory and visual spatial control tasks. Neuroanatomical correlates of auditory spatial processing were assessed using voxel-based morphometry. Relative to healthy older controls, both patient groups exhibited impairments in detection of auditory motion, and stationary sound position discrimination. The posterior cortical atrophy group showed greater impairment for auditory motion processing and the processing of a non-spatial control complex auditory property (timbre) than the typical Alzheimer’s disease group. Voxel-based morphometry in the patient cohort revealed grey matter correlates of auditory motion detection and spatial position discrimination in right inferior parietal cortex and precuneus, respectively. These findings delineate auditory spatial processing deficits in typical and posterior Alzheimer’s disease phenotypes that are related to posterior cortical regions involved in both syndromic variants and modulated by the syndromic profile of brain degeneration. Auditory spatial deficits contribute to impaired spatial awareness in Alzheimer’s disease and may constitute a novel perceptual model for probing brain network disintegration across the Alzheimer’s disease syndromic spectrum. PMID:25468732

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks.

PubMed

Martin Cichy, Radoslaw; Khosla, Aditya; Pantazis, Dimitrios; Oliva, Aude

2017-06-01

Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative model of how scene size representations may arise in the brain, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and suggest that deep neural networks are a promising framework to investigate how spatial layout representations emerge in the human brain. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Nonparametric Bayesian models for a spatial covariance.

PubMed

Reich, Brian J; Fuentes, Montserrat

2012-01-01

A crucial step in the analysis of spatial data is to estimate the spatial correlation function that determines the relationship between a spatial process at two locations. The standard approach to selecting the appropriate correlation function is to use prior knowledge or exploratory analysis, such as a variogram analysis, to select the correct parametric correlation function. Rather that selecting a particular parametric correlation function, we treat the covariance function as an unknown function to be estimated from the data. We propose a flexible prior for the correlation function to provide robustness to the choice of correlation function. We specify the prior for the correlation function using spectral methods and the Dirichlet process prior, which is a common prior for an unknown distribution function. Our model does not require Gaussian data or spatial locations on a regular grid. The approach is demonstrated using a simulation study as well as an analysis of California air pollution data.

A three-dimensional point process model for the spatial distribution of disease occurrence in relation to an exposure source.

PubMed

Grell, Kathrine; Diggle, Peter J; Frederiksen, Kirsten; Schüz, Joachim; Cardis, Elisabeth; Andersen, Per K

2015-10-15

We study methods for how to include the spatial distribution of tumours when investigating the relation between brain tumours and the exposure from radio frequency electromagnetic fields caused by mobile phone use. Our suggested point process model is adapted from studies investigating spatial aggregation of a disease around a source of potential hazard in environmental epidemiology, where now the source is the preferred ear of each phone user. In this context, the spatial distribution is a distribution over a sample of patients rather than over multiple disease cases within one geographical area. We show how the distance relation between tumour and phone can be modelled nonparametrically and, with various parametric functions, how covariates can be included in the model and how to test for the effect of distance. To illustrate the models, we apply them to a subset of the data from the Interphone Study, a large multinational case-control study on the association between brain tumours and mobile phone use. Copyright © 2015 John Wiley & Sons, Ltd.

Scaling Effects of Cr(VI) Reduction Kinetics. The Role of Geochemical Heterogeneity

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

Wang, Li; Li, Li

2015-10-22

The natural subsurface is highly heterogeneous with minerals distributed in different spatial patterns. Fundamental understanding of how mineral spatial distribution patterns regulate sorption process is important for predicting the transport and fate of chemicals. Existing studies about the sorption was carried out in well-mixed batch reactors or uniformly packed columns, with few data available on the effects of spatial heterogeneities. As a result, there is a lack of data and understanding on how spatial heterogeneities control sorption processes. In this project, we aim to understand and develop modeling capabilities to predict the sorption of Cr(VI), an omnipresent contaminant in naturalmore » systems due to its natural occurrence and industrial utilization. We systematically examine the role of spatial patterns of illite, a common clay, in determining the extent of transport limitation and scaling effects associated with Cr(VI) sorption capacity and kinetics using column experiments and reactive transport modeling. Our results showed that the sorbed mass and rates can differ by an order of magnitude due to of the illite spatial heterogeneities and transport limitation. With constraints from data, we also developed the capabilities of modeling Cr(VI) in heterogeneous media. The developed model is then utilized to understand the general principles that govern the relationship between sorption and connectivity, a key measure of the spatial pattern characteristics. This correlation can be used to estimate Cr(VI) sorption characteristics in heterogeneous porous media. Insights gained here bridge gaps between laboratory and field application in hydrogeology and geochemical field, and advance predictive understanding of reactive transport processes in the natural heterogeneous subsurface. We believe that these findings will be of interest to a large number of environmental geochemists and engineers, hydrogeologists, and those interested in contaminant fate and transport, water quality and water composition, and natural attenuation processes in natural systems.« less

Simulation of spatial and temporal properties of aftershocks by means of the fiber bundle model

NASA Astrophysics Data System (ADS)

Monterrubio-Velasco, Marisol; Zúñiga, F. R.; Márquez-Ramírez, Victor Hugo; Figueroa-Soto, Angel

2017-11-01

The rupture processes of any heterogeneous material constitute a complex physical problem. Earthquake aftershocks show temporal and spatial behaviors which are consequence of the heterogeneous stress distribution and multiple rupturing following the main shock. This process is difficult to model deterministically due to the number of parameters and physical conditions, which are largely unknown. In order to shed light on the minimum requirements for the generation of aftershock clusters, in this study, we perform a simulation of the main features of such a complex process by means of a fiber bundle (FB) type model. The FB model has been widely used to analyze the fracture process in heterogeneous materials. It is a simple but powerful tool that allows modeling the main characteristics of a medium such as the brittle shallow crust of the earth. In this work, we incorporate spatial properties, such as the Coulomb stress change pattern, which help simulate observed characteristics of aftershock sequences. In particular, we introduce a parameter ( P) that controls the probability of spatial distribution of initial loads. Also, we use a "conservation" parameter ( π), which accounts for the load dissipation of the system, and demonstrate its influence on the simulated spatio-temporal patterns. Based on numerical results, we find that P has to be in the range 0.06 < P < 0.30, whilst π needs to be limited by a very narrow range ( 0.60 < π < 0.66) in order to reproduce aftershocks pattern characteristics which resemble those of observed sequences. This means that the system requires a small difference in the spatial distribution of initial stress, and a very particular fraction of load transfer in order to generate realistic aftershocks.

Exploring the Spatial and Temporal Organization of a Cell’s Proteome

PubMed Central

Beck, Martin; Topf, Maya; Frazier, Zachary; Tjong, Harianto; Xu, Min; Zhang, Shihua; Alber, Frank

2013-01-01

To increase our current understanding of cellular processes, such as cell signaling and division, knowledge is needed about the spatial and temporal organization of the proteome at different organizational levels. These levels cover a wide range of length and time scales: from the atomic structures of macromolecules for inferring their molecular function, to the quantitative description of their abundance, and distribution in the cell. Emerging new experimental technologies are greatly increasing the availability of such spatial information on the molecular organization in living cells. This review addresses three fields that have significantly contributed to our understanding of the proteome’s spatial and temporal organization: first, methods for the structure determination of individual macromolecular assemblies, specifically the fitting of atomic structures into density maps generated from electron microscopy techniques; second, research that visualizes the spatial distributions of these complexes within the cellular context using cryo electron tomography techniques combined with computational image processing; and third, methods for the spatial modeling of the dynamic organization of the proteome, specifically those methods for simulating reaction and diffusion of proteins and complexes in crowded intracellular fluids. The long-term goal is to integrate the varied data about a proteome’s organization into a spatially explicit, predictive model of cellular processes. PMID:21094684

Spatial-Temporal Intelligence: Original Thinking Processes of Gifted Inventors.

ERIC Educational Resources Information Center

Cooper, Eileen E.

2000-01-01

This psychological phenomenological research analyzed cognition of 7 adult inventors and proposes a theory of original, creative thinking. Spatial intelligence is reviewed. Results provide 7 findings, including cognitive, motivational, affective, and psychokinesthetic factors. Spatial-temporal intelligence is theorized as an abstract model of…

Improving Junior High School Students' Spatial Reasoning Ability through Model Eliciting Activities with Cabri 3D

ERIC Educational Resources Information Center

Hartatiana; Darhim; Nurlaelah, Elah

2018-01-01

One of students' abilities which can facilitate them to understand geometric concepts is spatial reasoning ability. Spatial reasoning ability can be defined as an ability involving someone's cognitive processing to present and manipulate spatial figures, relationship, and figure formations. This research aims to find out significant difference on…

Spatial Construction Skills of Chimpanzees ("Pan Troglodytes") and Young Human Children ("Homo Sapiens Sapiens")

ERIC Educational Resources Information Center

Poti, Patrizia; Hayashi, Misato; Matsuzawa, Tetsuro

2009-01-01

Spatial construction tasks are basic tests of visual-spatial processing. Two studies have assessed spatial construction skills in chimpanzees (Pan troglodytes) and young children (Homo sapiens sapiens) with a block modelling task. Study 1a subjects were three young chimpanzees and five adult chimpanzees. Study 1b subjects were 30 human children…

Cognitive components of a mathematical processing network in 9-year-old children.

PubMed

Szűcs, Dénes; Devine, Amy; Soltesz, Fruzsina; Nobes, Alison; Gabriel, Florence

2014-07-01

We determined how various cognitive abilities, including several measures of a proposed domain-specific number sense, relate to mathematical competence in nearly 100 9-year-old children with normal reading skill. Results are consistent with an extended number processing network and suggest that important processing nodes of this network are phonological processing, verbal knowledge, visuo-spatial short-term and working memory, spatial ability and general executive functioning. The model was highly specific to predicting arithmetic performance. There were no strong relations between mathematical achievement and verbal short-term and working memory, sustained attention, response inhibition, finger knowledge and symbolic number comparison performance. Non-verbal intelligence measures were also non-significant predictors when added to our model. Number sense variables were non-significant predictors in the model and they were also non-significant predictors when entered into regression analysis with only a single visuo-spatial WM measure. Number sense variables were predicted by sustained attention. Results support a network theory of mathematical competence in primary school children and falsify the importance of a proposed modular 'number sense'. We suggest an 'executive memory function centric' model of mathematical processing. Mapping a complex processing network requires that studies consider the complex predictor space of mathematics rather than just focusing on a single or a few explanatory factors.

Cognitive components of a mathematical processing network in 9-year-old children

PubMed Central

Szűcs, Dénes; Devine, Amy; Soltesz, Fruzsina; Nobes, Alison; Gabriel, Florence

2014-01-01

We determined how various cognitive abilities, including several measures of a proposed domain-specific number sense, relate to mathematical competence in nearly 100 9-year-old children with normal reading skill. Results are consistent with an extended number processing network and suggest that important processing nodes of this network are phonological processing, verbal knowledge, visuo-spatial short-term and working memory, spatial ability and general executive functioning. The model was highly specific to predicting arithmetic performance. There were no strong relations between mathematical achievement and verbal short-term and working memory, sustained attention, response inhibition, finger knowledge and symbolic number comparison performance. Non-verbal intelligence measures were also non-significant predictors when added to our model. Number sense variables were non-significant predictors in the model and they were also non-significant predictors when entered into regression analysis with only a single visuo-spatial WM measure. Number sense variables were predicted by sustained attention. Results support a network theory of mathematical competence in primary school children and falsify the importance of a proposed modular ‘number sense’. We suggest an ‘executive memory function centric’ model of mathematical processing. Mapping a complex processing network requires that studies consider the complex predictor space of mathematics rather than just focusing on a single or a few explanatory factors. PMID:25089322

LAPSUS: soil erosion - landscape evolution model

NASA Astrophysics Data System (ADS)

van Gorp, Wouter; Temme, Arnaud; Schoorl, Jeroen

2015-04-01

LAPSUS is a soil erosion - landscape evolution model which is capable of simulating landscape evolution of a gridded DEM by using multiple water, mass movement and human driven processes on multiple temporal and spatial scales. It is able to deal with a variety of human landscape interventions such as landuse management and tillage and it can model their interactions with natural processes. The complex spatially explicit feedbacks the model simulates demonstrate the importance of spatial interaction of human activity and erosion deposition patterns. In addition LAPSUS can model shallow landsliding, slope collapse, creep, solifluction, biological and frost weathering, fluvial behaviour. Furthermore, an algorithm to deal with natural depressions has been added and event-based modelling with an improved infiltration description and dust deposition has been pursued. LAPSUS has been used for case studies in many parts of the world and is continuously developing and expanding. it is now available for third-party and educational use. It has a comprehensive user interface and it is accompanied by a manual and exercises. The LAPSUS model is highly suitable to quantify and understand catchment-scale erosion processes. More information and a download link is available on www.lapsusmodel.nl.

High-resolution modeling of a marine ecosystem using the FRESCO hydroecological model

NASA Astrophysics Data System (ADS)

Zalesny, V. B.; Tamsalu, R.

2009-02-01

The FRESCO (Finnish Russian Estonian Cooperation) mathematical model describing a marine hydroecosystem is presented. The methodology of the numerical solution is based on the method of multicomponent splitting into physical and biological processes, spatial coordinates, etc. The model is used for the reproduction of physical and biological processes proceeding in the Baltic Sea. Numerical experiments are performed with different spatial resolutions for four marine basins that are enclosed into one another: the Baltic Sea, the Gulf of Finland, the Tallinn-Helsinki water area, and Tallinn Bay. Physical processes are described by the equations of nonhydrostatic dynamics, including the k-ω parametrization of turbulence. Biological processes are described by the three-dimensional equations of an aquatic ecosystem with the use of a size-dependent parametrization of biochemical reactions. The main goal of this study is to illustrate the efficiency of the developed numerical technique and to demonstrate the importance of a high spatial resolution for water basins that have complex bottom topography, such as the Baltic Sea. Detailed information about the atmospheric forcing, bottom topography, and coastline is very important for the description of coastal dynamics and specific features of a marine ecosystem. Experiments show that the spatial inhomogeneity of hydroecosystem fields is caused by the combined effect of upwelling, turbulent mixing, surface-wave breaking, and temperature variations, which affect biochemical reactions.

SPATIAL FOREST SOIL PROPERTIES FOR ECOLOGICAL MODELING IN THE WESTERN OREGON CASCADES

EPA Science Inventory

The ultimate objective of this work is to provide a spatially distributed database of soil properties to serve as inputs to model ecological processes in western forests at the landscape scale. The Central Western Oregon Cascades are rich in biodiversity and they are a fascinati...

Modeling Spatial and Temporal Aspects of Visual Backward Masking

ERIC Educational Resources Information Center

Hermens, Frouke; Luksys, Gediminas; Gerstner, Wulfram; Herzog, Michael H.; Ernst, Udo

2008-01-01

Visual backward masking is a versatile tool for understanding principles and limitations of visual information processing in the human brain. However, the mechanisms underlying masking are still poorly understood. In the current contribution, the authors show that a structurally simple mathematical model can explain many spatial and temporal…

Application of Characterization, Modeling, and Analytics Towards Understanding Process Structure Linkages in Metallic 3D Printing (Postprint)

DTIC Science & Technology

2017-08-01

of metallic additive manufacturing processes and show that combining experimental data with modelling and advanced data processing and analytics...manufacturing processes and show that combining experimental data with modelling and advanced data processing and analytics methods will accelerate that...geometries, we develop a methodology that couples experimental data and modelling to convert the scan paths into spatially resolved local thermal histories

A Process Model for the Comprehension of Organic Chemistry Notation

ERIC Educational Resources Information Center

Havanki, Katherine L.

2012-01-01

This dissertation examines the cognitive processes individuals use when reading organic chemistry equations and factors that affect these processes, namely, visual complexity of chemical equations and participant characteristics (expertise, spatial ability, and working memory capacity). A six stage process model for the comprehension of organic…

What Do They Have in Common? Drivers of Streamflow Spatial Correlation and Prediction of Flow Regimes in Ungauged Locations

NASA Astrophysics Data System (ADS)

Betterle, A.; Radny, D.; Schirmer, M.; Botter, G.

2017-12-01

The spatial correlation of daily streamflows represents a statistical index encapsulating the similarity between hydrographs at two arbitrary catchment outlets. In this work, a process-based analytical framework is utilized to investigate the hydrological drivers of streamflow spatial correlation through an extensive application to 78 pairs of stream gauges belonging to 13 unregulated catchments in the eastern United States. The analysis provides insight on how the observed heterogeneity of the physical processes that control flow dynamics ultimately affect streamflow correlation and spatial patterns of flow regimes. Despite the variability of recession properties across the study catchments, the impact of heterogeneous drainage rates on the streamflow spatial correlation is overwhelmed by the spatial variability of frequency and intensity of effective rainfall events. Overall, model performances are satisfactory, with root mean square errors between modeled and observed streamflow spatial correlation below 10% in most cases. We also propose a method for estimating streamflow correlation in the absence of discharge data, which proves useful to predict streamflow regimes in ungauged areas. The method consists in setting a minimum threshold on the modeled flow correlation to individuate hydrologically similar sites. Catchment outlets that are most correlated (ρ>0.9) are found to be characterized by analogous streamflow distributions across a broad range of flow regimes.

Real-time distribution of pelagic fish: combining hydroacoustics, GIS and spatial modelling at a fine spatial scale.

PubMed

Muška, Milan; Tušer, Michal; Frouzová, Jaroslava; Mrkvička, Tomáš; Ricard, Daniel; Seďa, Jaromír; Morelli, Federico; Kubečka, Jan

2018-03-29

Understanding spatial distribution of organisms in heterogeneous environment remains one of the chief issues in ecology. Spatial organization of freshwater fish was investigated predominantly on large-scale, neglecting important local conditions and ecological processes. However, small-scale processes are of an essential importance for individual habitat preferences and hence structuring trophic cascades and species coexistence. In this work, we analysed the real-time spatial distribution of pelagic freshwater fish in the Římov Reservoir (Czechia) observed by hydroacoustics in relation to important environmental predictors during 48 hours at 3-h interval. Effect of diurnal cycle was revealed of highest significance in all spatial models with inverse trends between fish distribution and predictors in day and night in general. Our findings highlighted daytime pelagic fish distribution as highly aggregated, with general fish preferences for central, deep and highly illuminated areas, whereas nighttime distribution was more disperse and fish preferred nearshore steep sloped areas with higher depth. This turnover suggests prominent movements of significant part of fish assemblage between pelagic and nearshore areas on a diel basis. In conclusion, hydroacoustics, GIS and spatial modelling proved as valuable tool for predicting local fish distribution and elucidate its drivers, which has far reaching implications for understanding freshwater ecosystem functioning.

Forest forming process and dynamic vegetation models under global change

Treesearch

A. Shvidenko; E. Gustafson

2009-01-01

The paper analyzes mathematical models that are used to project the dynamics of forest ecosystems on different spatial and temporal scales. Landscape disturbance and succession models (LDSMs) are of a particular interest for studying the forest forming process in Northern Eurasia. They have a solid empirical background and are able to model ecological processes under...

Modeling Agricultural Watersheds with the Soil and Water Assessment Tool (SWAT): Calibration and Validation with a Novel Procedure for Spatially Explicit HRUs.

PubMed

Teshager, Awoke Dagnew; Gassman, Philip W; Secchi, Silvia; Schoof, Justin T; Misgna, Girmaye

2016-04-01

Applications of the Soil and Water Assessment Tool (SWAT) model typically involve delineation of a watershed into subwatersheds/subbasins that are then further subdivided into hydrologic response units (HRUs) which are homogeneous areas of aggregated soil, landuse, and slope and are the smallest modeling units used within the model. In a given standard SWAT application, multiple potential HRUs (farm fields) in a subbasin are usually aggregated into a single HRU feature. In other words, the standard version of the model combines multiple potential HRUs (farm fields) with the same landuse/landcover, soil, and slope, but located at different places of a subbasin (spatially non-unique), and considers them as one HRU. In this study, ArcGIS pre-processing procedures were developed to spatially define a one-to-one match between farm fields and HRUs (spatially unique HRUs) within a subbasin prior to SWAT simulations to facilitate input processing, input/output mapping, and further analysis at the individual farm field level. Model input data such as landuse/landcover (LULC), soil, crop rotation, and other management data were processed through these HRUs. The SWAT model was then calibrated/validated for Raccoon River watershed in Iowa for 2002-2010 and Big Creek River watershed in Illinois for 2000-2003. SWAT was able to replicate annual, monthly, and daily streamflow, as well as sediment, nitrate and mineral phosphorous within recommended accuracy in most cases. The one-to-one match between farm fields and HRUs created and used in this study is a first step in performing LULC change, climate change impact, and other analyses in a more spatially explicit manner.

Modeling Agricultural Watersheds with the Soil and Water Assessment Tool (SWAT): Calibration and Validation with a Novel Procedure for Spatially Explicit HRUs

NASA Astrophysics Data System (ADS)

Teshager, Awoke Dagnew; Gassman, Philip W.; Secchi, Silvia; Schoof, Justin T.; Misgna, Girmaye

2016-04-01

Applications of the Soil and Water Assessment Tool (SWAT) model typically involve delineation of a watershed into subwatersheds/subbasins that are then further subdivided into hydrologic response units (HRUs) which are homogeneous areas of aggregated soil, landuse, and slope and are the smallest modeling units used within the model. In a given standard SWAT application, multiple potential HRUs (farm fields) in a subbasin are usually aggregated into a single HRU feature. In other words, the standard version of the model combines multiple potential HRUs (farm fields) with the same landuse/landcover, soil, and slope, but located at different places of a subbasin (spatially non-unique), and considers them as one HRU. In this study, ArcGIS pre-processing procedures were developed to spatially define a one-to-one match between farm fields and HRUs (spatially unique HRUs) within a subbasin prior to SWAT simulations to facilitate input processing, input/output mapping, and further analysis at the individual farm field level. Model input data such as landuse/landcover (LULC), soil, crop rotation, and other management data were processed through these HRUs. The SWAT model was then calibrated/validated for Raccoon River watershed in Iowa for 2002-2010 and Big Creek River watershed in Illinois for 2000-2003. SWAT was able to replicate annual, monthly, and daily streamflow, as well as sediment, nitrate and mineral phosphorous within recommended accuracy in most cases. The one-to-one match between farm fields and HRUs created and used in this study is a first step in performing LULC change, climate change impact, and other analyses in a more spatially explicit manner.

Applications of genetic data to improve management and conservation of river fishes and their habitats

USGS Publications Warehouse

Scribner, Kim T.; Lowe, Winsor H.; Landguth, Erin L.; Luikart, Gordon; Infante, Dana M.; Whelan, Gary; Muhlfeld, Clint C.

2015-01-01

Environmental variation and landscape features affect ecological processes in fluvial systems; however, assessing effects at management-relevant temporal and spatial scales is challenging. Genetic data can be used with landscape models and traditional ecological assessment data to identify biodiversity hotspots, predict ecosystem responses to anthropogenic effects, and detect impairments to underlying processes. We show that by combining taxonomic, demographic, and genetic data of species in complex riverscapes, managers can better understand the spatial and temporal scales over which environmental processes and disturbance influence biodiversity. We describe how population genetic models using empirical or simulated genetic data quantify effects of environmental processes affecting species diversity and distribution. Our summary shows that aquatic assessment initiatives that use standardized data sets to direct management actions can benefit from integration of genetic data to improve the predictability of disturbance–response relationships of river fishes and their habitats over a broad range of spatial and temporal scales.

Optimal estimator model for human spatial orientation

NASA Technical Reports Server (NTRS)

Borah, J.; Young, L. R.; Curry, R. E.

1979-01-01

A model is being developed to predict pilot dynamic spatial orientation in response to multisensory stimuli. Motion stimuli are first processed by dynamic models of the visual, vestibular, tactile, and proprioceptive sensors. Central nervous system function is then modeled as a steady-state Kalman filter which blends information from the various sensors to form an estimate of spatial orientation. Where necessary, this linear central estimator has been augmented with nonlinear elements to reflect more accurately some highly nonlinear human response characteristics. Computer implementation of the model has shown agreement with several important qualitative characteristics of human spatial orientation, and it is felt that with further modification and additional experimental data the model can be improved and extended. Possible means are described for extending the model to better represent the active pilot with varying skill and work load levels.

Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests

NASA Astrophysics Data System (ADS)

Wheeler, David C.; Waller, Lance A.

2009-03-01

In this paper, we compare and contrast a Bayesian spatially varying coefficient process (SVCP) model with a geographically weighted regression (GWR) model for the estimation of the potentially spatially varying regression effects of alcohol outlets and illegal drug activity on violent crime in Houston, Texas. In addition, we focus on the inherent coefficient shrinkage properties of the Bayesian SVCP model as a way to address increased coefficient variance that follows from collinearity in GWR models. We outline the advantages of the Bayesian model in terms of reducing inflated coefficient variance, enhanced model flexibility, and more formal measuring of model uncertainty for prediction. We find spatially varying effects for alcohol outlets and drug violations, but the amount of variation depends on the type of model used. For the Bayesian model, this variation is controllable through the amount of prior influence placed on the variance of the coefficients. For example, the spatial pattern of coefficients is similar for the GWR and Bayesian models when a relatively large prior variance is used in the Bayesian model.

Social priming of hemispatial neglect affects spatial coding: Evidence from the Simon task.

PubMed

Arend, Isabel; Aisenberg, Daniela; Henik, Avishai

2016-10-01

In the Simon effect (SE), choice reactions are fast if the location of the stimulus and the response correspond when stimulus location is task-irrelevant; therefore, the SE reflects the automatic processing of space. Priming of social concepts was found to affect automatic processing in the Stroop effect. We investigated whether spatial coding measured by the SE can be affected by the observer's mental state. We used two social priming manipulations of impairments: one involving spatial processing - hemispatial neglect (HN) and another involving color perception - achromatopsia (ACHM). In two experiments the SE was reduced in the "neglected" visual field (VF) under the HN, but not under the ACHM manipulation. Our results show that spatial coding is sensitive to spatial representations that are not derived from task-relevant parameters, but from the observer's cognitive state. These findings dispute stimulus-response interference models grounded on the idea of the automaticity of spatial processing. Copyright © 2016. Published by Elsevier Inc.

RockFall analyst: A GIS extension for three-dimensional and spatially distributed rockfall hazard modeling

NASA Astrophysics Data System (ADS)

Lan, Hengxing; Derek Martin, C.; Lim, C. H.

2007-02-01

Geographic information system (GIS) modeling is used in combination with three-dimensional (3D) rockfall process modeling to assess rockfall hazards. A GIS extension, RockFall Analyst (RA), which is capable of effectively handling large amounts of geospatial information relative to rockfall behaviors, has been developed in ArcGIS using ArcObjects and C#. The 3D rockfall model considers dynamic processes on a cell plane basis. It uses inputs of distributed parameters in terms of raster and polygon features created in GIS. Two major components are included in RA: particle-based rockfall process modeling and geostatistics-based rockfall raster modeling. Rockfall process simulation results, 3D rockfall trajectories and their velocity features either for point seeders or polyline seeders are stored in 3D shape files. Distributed raster modeling, based on 3D rockfall trajectories and a spatial geostatistical technique, represents the distribution of spatial frequency, the flying and/or bouncing height, and the kinetic energy of falling rocks. A distribution of rockfall hazard can be created by taking these rockfall characteristics into account. A barrier analysis tool is also provided in RA to aid barrier design. An application of these modeling techniques to a case study is provided. The RA has been tested in ArcGIS 8.2, 8.3, 9.0 and 9.1.

A spatially explicit hydro-ecological modeling framework (BEPS-TerrainLab V2.0): Model description and test in a boreal ecosystem in Eastern North America

NASA Astrophysics Data System (ADS)

Govind, Ajit; Chen, Jing Ming; Margolis, Hank; Ju, Weimin; Sonnentag, Oliver; Giasson, Marc-André

2009-04-01

SummaryA spatially explicit, process-based hydro-ecological model, BEPS-TerrainLab V2.0, was developed to improve the representation of ecophysiological, hydro-ecological and biogeochemical processes of boreal ecosystems in a tightly coupled manner. Several processes unique to boreal ecosystems were implemented including the sub-surface lateral water fluxes, stratification of vegetation into distinct layers for explicit ecophysiological representation, inclusion of novel spatial upscaling strategies and biogeochemical processes. To account for preferential water fluxes common in humid boreal ecosystems, a novel scheme was introduced based on laboratory analyses. Leaf-scale ecophysiological processes were upscaled to canopy-scale by explicitly considering leaf physiological conditions as affected by light and water stress. The modified model was tested with 2 years of continuous measurements taken at the Eastern Old Black Spruce Site of the Fluxnet-Canada Research Network located in a humid boreal watershed in eastern Canada. Comparison of the simulated and measured ET, water-table depth (WTD), volumetric soil water content (VSWC) and gross primary productivity (GPP) revealed that BEPS-TerrainLab V2.0 simulates hydro-ecological processes with reasonable accuracy. The model was able to explain 83% of the ET, 92% of the GPP variability and 72% of the WTD dynamics. The model suggests that in humid ecosystems such as eastern North American boreal watersheds, topographically driven sub-surface baseflow is the main mechanism of soil water partitioning which significantly affects the local-scale hydrological conditions.

Top-down attentional control in spatially coincident stimuli enhances activity in both task-relevant and task-irrelevant regions of cortex

PubMed Central

Erickson, Kirk I.; Prakash, Ruchika Shaurya; Kim, Jennifer S.; Sutton, Bradley P.; Colcombe, Stanley J.; Kramer, Arthur F.

2010-01-01

Models of selective attention predict that focused attention to spatially contiguous stimuli may result in enhanced activity in areas of cortex specialized for processing task-relevant and task-irrelevant information. We examined this hypothesis by localizing color-sensitive areas (CSA) and word and letter sensitive areas of cortex and then examining modulation of these regions during performance of a modified version of the Stroop task in which target and distractors are spatially coincident. We report that only the incongruent condition with the highest cognitive demand showed increased activity in CSA relative to other conditions, indicating an attentional enhancement in target processing areas. We also found an enhancement of activity in one region sensitive to word/letter processing during the most cognitively demanding incongruent condition indicating greater processing of the distractor dimension. Correlations with performance revealed that top-down modulation during the task was critical for effective filtering of irrelevant information in conflict conditions. These results support predictions made by models of selective attention and suggest an important mechanism of top-down attentional control in spatially contiguous stimuli. PMID:18804123

Application of Climate Assessment Tool (CAT) to estimate climate variability impacts on nutrient loading from local watersheds

Treesearch

Ying Ouyang; Prem B. Parajuli; Gary Feng; Theodor D. Leininger; Yongshan Wan; Padmanava Dash

2018-01-01

A vast amount of future climate scenario datasets, created by climate models such as general circulation models (GCMs), have been used in conjunction with watershed models to project future climate variability impact on hydrological processes and water quality. However, these low spatial-temporal resolution datasets are often difficult to downscale spatially and...

Integration of a three-dimensional process-based hydrological model into the Object Modeling System

USDA-ARS?s Scientific Manuscript database

The integration of a spatial process model into an environmental modelling framework can enhance the model’s capabilities. We present the integration of the GEOtop model into the Object Modeling System (OMS) version 3.0 and illustrate its application in a small watershed. GEOtop is a physically base...

Geophysics in INSPIRE

NASA Astrophysics Data System (ADS)

Sőrés, László

2013-04-01

INSPIRE is a European directive to harmonize spatial data in Europe. Its' aim is to establish a transparent, multidisciplinary network of environmental information by using international standards and OGC web services. Spatial data themes defined in the annex of the directive cover 34 domains that are closely bundled to environment and spatial information. According to the INSPIRE roadmap all data providers must setup discovery, viewing and download services and restructure data stores to provide spatial data as defined by the underlying specifications by 2014 December 1. More than 3000 institutions are going to be involved in the progress. During the data specification process geophysics as an inevitable source of geo information was introduced to Annex II Geology. Within the Geology theme Geophysics is divided into core and extended model. The core model contains specifications for legally binding data provisioning and is going to be part of the Implementation Rules of the INSPIRE directives. To minimize the work load of obligatory data transformations the scope of the core model is very limited and simple. It covers the most essential geophysical feature types that are relevant in economic and environmental context. To fully support the use cases identified by the stake holders the extended model was developed. It contains a wide range of spatial object types for geophysical measurements, processed and interpreted results, and wrapper classes to help data providers in using the Observation and Measurements (O&M) standard for geophysical data exchange. Instead of introducing the traditional concept of "geophysical methods" at a high structural level the data model classifies measurements and geophysical models based on their spatial characteristics. Measurements are classified as geophysical station (point), geophysical profile (curve) and geophysical swath (surface). Generic classes for processing results and interpretation models are curve model (1D), surface model (2D), and solid model (3D). Both measurements and models are derived from O&M sampling features that may be linked to sampling procedures and observation results. Geophysical products are output of complex procedures and can precisely be described as chains of consecutive O&M observations. For describing geophysical processes and results the data model both supports the use of OGC standard XML encoding (SensorML, SWE, GML) and traditional industry standards (SPS, UKOOA, SEG formats). To control the scope of the model and to harmonize terminology an initial set of extendable code lists was developed. The attempt to create a hierarchical SKOS vocabulary of terms for geophysical methods, resource types, processes, properties and technical parameters was partly based on the work done in the eContentPlus GEOMIND project. The result is far from being complete, and the work must be continued in the future.

Disentangling working memory processes during spatial span assessment: a modeling analysis of preferred eye movement strategies.

PubMed

Patt, Virginie M; Thomas, Michael L; Minassian, Arpi; Geyer, Mark A; Brown, Gregory G; Perry, William

2014-01-01

The neurocognitive processes involved during classic spatial working memory (SWM) assessment were investigated by examining naturally preferred eye movement strategies. Cognitively healthy adult volunteers were tested in a computerized version of the Corsi Block-Tapping Task--a spatial span task requiring the short term maintenance of a series of locations presented in a specific order--coupled with eye tracking. Modeling analysis was developed to characterize eye-tracking patterns across all task phases, including encoding, retention, and recall. Results revealed a natural preference for local gaze maintenance during both encoding and retention, with fewer than 40% fixated targets. These findings contrasted with the stimulus retracing pattern expected during recall as a result of task demands, with 80% fixated targets. Along with participants' self-reported strategies of mentally "making shapes," these results suggest the involvement of covert attention shifts and higher order cognitive Gestalt processes during spatial span tasks, challenging instrument validity as a single measure of SWM storage capacity.

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

A flexible cure rate model for spatially correlated survival data based on generalized extreme value distribution and Gaussian process priors.

PubMed

Li, Dan; Wang, Xia; Dey, Dipak K

2016-09-01

Our present work proposes a new survival model in a Bayesian context to analyze right-censored survival data for populations with a surviving fraction, assuming that the log failure time follows a generalized extreme value distribution. Many applications require a more flexible modeling of covariate information than a simple linear or parametric form for all covariate effects. It is also necessary to include the spatial variation in the model, since it is sometimes unexplained by the covariates considered in the analysis. Therefore, the nonlinear covariate effects and the spatial effects are incorporated into the systematic component of our model. Gaussian processes (GPs) provide a natural framework for modeling potentially nonlinear relationship and have recently become extremely powerful in nonlinear regression. Our proposed model adopts a semiparametric Bayesian approach by imposing a GP prior on the nonlinear structure of continuous covariate. With the consideration of data availability and computational complexity, the conditionally autoregressive distribution is placed on the region-specific frailties to handle spatial correlation. The flexibility and gains of our proposed model are illustrated through analyses of simulated data examples as well as a dataset involving a colon cancer clinical trial from the state of Iowa. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Utility of computer simulations in landscape genetics

Treesearch

Bryan K. Epperson; Brad H. McRae; Kim Scribner; Samuel A. Cushman; Michael S. Rosenberg; Marie-Josee Fortin; Patrick M. A. James; Melanie Murphy; Stephanie Manel; Pierre Legendre; Mark R. T. Dale

2010-01-01

Population genetics theory is primarily based on mathematical models in which spatial complexity and temporal variability are largely ignored. In contrast, the field of landscape genetics expressly focuses on how population genetic processes are affected by complex spatial and temporal environmental heterogeneity. It is spatially explicit and relates patterns to...

Detection and recognition of simple spatial forms

NASA Technical Reports Server (NTRS)

Watson, A. B.

1983-01-01

A model of human visual sensitivity to spatial patterns is constructed. The model predicts the visibility and discriminability of arbitrary two-dimensional monochrome images. The image is analyzed by a large array of linear feature sensors, which differ in spatial frequency, phase, orientation, and position in the visual field. All sensors have one octave frequency bandwidths, and increase in size linearly with eccentricity. Sensor responses are processed by an ideal Bayesian classifier, subject to uncertainty. The performance of the model is compared to that of the human observer in detecting and discriminating some simple images.

Modeling the isotopic evolution of snowpack and snowmelt: Testing a spatially distributed parsimonious approach.

PubMed

Ala-Aho, Pertti; Tetzlaff, Doerthe; McNamara, James P; Laudon, Hjalmar; Kormos, Patrick; Soulsby, Chris

2017-07-01

Use of stable water isotopes has become increasingly popular in quantifying water flow paths and travel times in hydrological systems using tracer-aided modeling. In snow-influenced catchments, snowmelt produces a traceable isotopic signal, which differs from original snowfall isotopic composition because of isotopic fractionation in the snowpack. These fractionation processes in snow are relatively well understood, but representing their spatiotemporal variability in tracer-aided studies remains a challenge. We present a novel, parsimonious modeling method to account for the snowpack isotope fractionation and estimate isotope ratios in snowmelt water in a fully spatially distributed manner. Our model introduces two calibration parameters that alone account for the isotopic fractionation caused by sublimation from interception and ground snow storage, and snowmelt fractionation progressively enriching the snowmelt runoff. The isotope routines are linked to a generic process-based snow interception-accumulation-melt model facilitating simulation of spatially distributed snowmelt runoff. We use a synthetic modeling experiment to demonstrate the functionality of the model algorithms in different landscape locations and under different canopy characteristics. We also provide a proof-of-concept model test and successfully reproduce isotopic ratios in snowmelt runoff sampled with snowmelt lysimeters in two long-term experimental catchment with contrasting winter conditions. To our knowledge, the method is the first such tool to allow estimation of the spatially distributed nature of isotopic fractionation in snowpacks and the resulting isotope ratios in snowmelt runoff. The method can thus provide a useful tool for tracer-aided modeling to better understand the integrated nature of flow, mixing, and transport processes in snow-influenced catchments.

Local topography shapes fine-scale spatial genetic structure in the Arkansas Valley evening primrose, Oenothera harringtonii (Onagraceae).

PubMed

Rhodes, Matthew K; Fant, Jeremie B; Skogen, Krissa A

2014-01-01

Identifying factors that shape the spatial distribution of genetic variation is crucial to understanding many population- and landscape-level processes. In this study, we explore fine-scale spatial genetic structure in Oenothera harringtonii (Onagraceae), an insect-pollinated, gravity-dispersed herb endemic to the grasslands of south-central and southeastern Colorado, USA. We genotyped 315 individuals with 11 microsatellite markers and utilized a combination of spatial autocorrelation analyses and landscape genetic models to relate life history traits and landscape features to dispersal processes. Spatial genetic structure was consistent with theoretical expectations of isolation by distance, but this pattern was weak (Sp = 0.00374). Anisotropic analyses indicated that spatial genetic structure was markedly directional, in this case consistent with increased dispersal along prominent slopes. Landscape genetic models subsequently confirmed that spatial genetic variation was significantly influenced by local topographic heterogeneity, specifically that geographic distance, elevation and aspect were important predictors of spatial genetic structure. Among these variables, geographic distance was ~68% more important than elevation in describing spatial genetic variation, and elevation was ~42% more important than aspect after removing the effect of geographic distance. From these results, we infer a mechanism of hydrochorous seed dispersal along major drainages aided by seasonal monsoon rains. Our findings suggest that landscape features may shape microevolutionary processes at much finer spatial scales than typically considered, and stress the importance of considering how particular dispersal vectors are influenced by their environmental context. © The American Genetic Association 2014. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

GIS-based analysis and modelling with empirical and remotely-sensed data on coastline advance and retreat

NASA Astrophysics Data System (ADS)

Ahmad, Sajid Rashid

With the understanding that far more research remains to be done on the development and use of innovative and functional geospatial techniques and procedures to investigate coastline changes this thesis focussed on the integration of remote sensing, geographical information systems (GIS) and modelling techniques to provide meaningful insights on the spatial and temporal dynamics of coastline changes. One of the unique strengths of this research was the parameterization of the GIS with long-term empirical and remote sensing data. Annual empirical data from 1941--2007 were analyzed by the GIS, and then modelled with statistical techniques. Data were also extracted from Landsat TM and ETM+ images. The band ratio method was used to extract the coastlines. Topographic maps were also used to extract digital map data. All data incorporated into ArcGIS 9.2 were analyzed with various modules, including Spatial Analyst, 3D Analyst, and Triangulated Irregular Networks. The Digital Shoreline Analysis System was used to analyze and predict rates of coastline change. GIS results showed the spatial locations along the coast that will either advance or retreat over time. The linear regression results highlighted temporal changes which are likely to occur along the coastline. Box-Jenkins modelling procedures were utilized to determine statistical models which best described the time series (1941--2007) of coastline change data. After several iterations and goodness-of-fit tests, second-order spatial cyclic autoregressive models, first-order autoregressive models and autoregressive moving average models were identified as being appropriate for describing the deterministic and random processes operating in Guyana's coastal system. The models highlighted not only cyclical patterns in advance and retreat of the coastline, but also the existence of short and long-term memory processes. Long-term memory processes could be associated with mudshoal propagation and stabilization while short-term memory processes were indicative of transitory hydrodynamic and other processes. An innovative framework for a spatio-temporal information-based system (STIBS) was developed. STIBS incorporated diverse datasets within a GIS, dynamic computer-based simulation models, and a spatial information query and graphical subsystem. Tests of the STIBS proved that it could be used to simulate and visualize temporal variability in shifting morphological states of the coastline.

Death wins against life in a spatially extended model of the caspase-3/8 feedback loop.

PubMed

Daub, M; Waldherr, S; Allgöwer, F; Scheurich, P; Schneider, G

2012-01-01

Apoptosis is an important physiological process which enables organisms to remove unwanted or damaged cells. A mathematical model of the extrinsic pro-apoptotic signaling pathway has been introduced by Eissing et al. (2007) and a bistable behavior with a stable death state and a stable life state of the reaction system has been established. In this paper, we consider a spatial extension of the extrinsic pro-apoptotic signaling pathway incorporating diffusion terms and make a model-based, numerical analysis of the apoptotic switch in the spatial dimension. For the parameter regimes under consideration it turns out that for this model diffusion homogenizes rapidly the concentrations which afterward are governed by the original reaction system. The activation of effector-caspase 3 depends on the space averaged initial concentration of pro-caspase 8 and pro-caspase 3 at the beginning of the process. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

The coalescent process in models with selection and recombination.

PubMed

Hudson, R R; Kaplan, N L

1988-11-01

The statistical properties of the process describing the genealogical history of a random sample of genes at a selectively neutral locus which is linked to a locus at which natural selection operates are investigated. It is found that the equations describing this process are simple modifications of the equations describing the process assuming that the two loci are completely linked. Thus, the statistical properties of the genealogical process for a random sample at a neutral locus linked to a locus with selection follow from the results obtained for the selected locus. Sequence data from the alcohol dehydrogenase (Adh) region of Drosophila melanogaster are examined and compared to predictions based on the theory. It is found that the spatial distribution of nucleotide differences between Fast and Slow alleles of Adh is very similar to the spatial distribution predicted if balancing selection operates to maintain the allozyme variation at the Adh locus. The spatial distribution of nucleotide differences between different Slow alleles of Adh do not match the predictions of this simple model very well.

A simple model for factory distribution: Historical effect in an industry city

NASA Astrophysics Data System (ADS)

Uehara, Takashi; Sato, Kazunori; Morita, Satoru; Maeda, Yasunobu; Yoshimura, Jin; Tainaka, Kei-ichi

2016-02-01

The construction and discontinuance processes of factories are complicated problems in sociology. We focus on the spatial and temporal changes of factories at Hamamatsu city in Japan. Real data indicate that the clumping degree of factories decreases as the density of factory increases. To represent the spatial and temporal changes of factories, we apply "contact process" which is one of cellular automata. This model roughly explains the dynamics of factory distribution. We also find "historical effect" in spatial distribution. Namely, the recent factories have been dispersed due to the past distribution during the period of economic bubble. This effect may be related to heavy shock in Japanese stock market.

Temporal consistency of spatial pattern in growth of the mussel, Mytilus edulis: Implications for predictive modelling

NASA Astrophysics Data System (ADS)

Bergström, Per; Lindegarth, Susanne; Lindegarth, Mats

2013-10-01

Human pressures on coastal seas are increasing and methods for sustainable management, including spatial planning and mitigative actions, are therefore needed. In coastal areas worldwide, the development of mussel farming as an economically and ecologically sustainable industry requires geographic information on the growth and potential production capacity. In practice this means that coherent maps of temporally stable spatial patterns of growth need to be available in the planning process and that maps need to be based on mechanistic or empirical models. Therefore, as a first step towards development of models of growth, we assessed empirically the fundamental requirement that there are temporally consistent spatial patterns of growth in the blue mussel, Mytilus edulis. Using a pilot study we designed and dimensioned a transplant experiment, where the spatial consistency in the growth of mussels was evaluated at two resolutions. We found strong temporal and scale-dependent spatial variability in growth but patterns suggested that spatial patterns were uncoupled between growth of shell and that of soft tissue. Spatial patterns of shell growth were complex and largely inconsistent among years. Importantly, however, the growth of soft tissue was qualitatively consistent among years at the scale of km. The results suggest that processes affecting the whole coastal area cause substantial differences in growth of soft tissue among years but that factors varying at the scale of km create strong and persistent spatial patterns of growth, with a potential doubling of productivity by identifying the most suitable locations. We conclude that the observed spatial consistency provides a basis for further development of predictive modelling and mapping of soft tissue growth in these coastal areas. Potential causes of observed patterns, consequences for mussel-farming as a tool for mitigating eutrophication, aspects of precision of modelling and sampling of mussel growth as well as ecological functions in general are discussed.

REMOTE SENSING AND SPATIALLY EXPLICIT LANDSCAPE-BASED NITROGEN MODELING METHODS DEVELOPMENT IN THE NEUSE RIVER BASIN, NC

EPA Science Inventory

The objective of this research was to model and map the spatial patterns of excess nitrogen (N) sources across the landscape within the Neuse River Basin (NRB) of North
Carolina. The process included an initial land cover characterization effort to map landscape "patches" at ...

Classifying and comparing spatial models of fire dynamics

Treesearch

Geoffrey J. Cary; Robert E. Keane; Mike D. Flannigan

2007-01-01

Wildland fire is a significant disturbance in many ecosystems worldwide and the interaction of fire with climate and vegetation over long time spans has major effects on vegetation dynamics, ecosystem carbon budgets, and patterns of biodiversity. Landscape-Fire-Succession Models (LFSMs) that simulate the linked processes of fire and vegetation development in a spatial...

Integrated landscape/hydrologic modeling tool for semiarid watersheds

Treesearch

Mariano Hernandez; Scott N. Miller

2000-01-01

An integrated hydrologic modeling/watershed assessment tool is being developed to aid in determining the susceptibility of semiarid landscapes to natural and human-induced changes across a range of scales. Watershed processes are by definition spatially distributed and are highly variable through time, and this approach is designed to account for their spatial and...

Strategy Generalization across Orientation Tasks: Testing a Computational Cognitive Model

ERIC Educational Resources Information Center

Gunzelmann, Glenn

2008-01-01

Humans use their spatial information processing abilities flexibly to facilitate problem solving and decision making in a variety of tasks. This article explores the question of whether a general strategy can be adapted for performing two different spatial orientation tasks by testing the predictions of a computational cognitive model. Human…

Integrating Spatial Components into FIA Models of Forest Resources: Some Technical Aspects

Treesearch

Pat Terletzky; Tracey Frescino

2005-01-01

We examined two software packages to determine their feasibility of implementing spatially explicit, forest resource models that integrate Forest Inventory and Analysis data (FIA). ARCINFO and Interactive Data Language (IDL) were examined for their input requirements, speed of processing, storage requirements, and flexibility of implementing. Implementations of two...

Spatio-Temporal Data Analysis at Scale Using Models Based on Gaussian Processes

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

Stein, Michael

Gaussian processes are the most commonly used statistical model for spatial and spatio-temporal processes that vary continuously. They are broadly applicable in the physical sciences and engineering and are also frequently used to approximate the output of complex computer models, deterministic or stochastic. We undertook research related to theory, computation, and applications of Gaussian processes as well as some work on estimating extremes of distributions for which a Gaussian process assumption might be inappropriate. Our theoretical contributions include the development of new classes of spatial-temporal covariance functions with desirable properties and new results showing that certain covariance models lead tomore » predictions with undesirable properties. To understand how Gaussian process models behave when applied to deterministic computer models, we derived what we believe to be the first significant results on the large sample properties of estimators of parameters of Gaussian processes when the actual process is a simple deterministic function. Finally, we investigated some theoretical issues related to maxima of observations with varying upper bounds and found that, depending on the circumstances, standard large sample results for maxima may or may not hold. Our computational innovations include methods for analyzing large spatial datasets when observations fall on a partially observed grid and methods for estimating parameters of a Gaussian process model from observations taken by a polar-orbiting satellite. In our application of Gaussian process models to deterministic computer experiments, we carried out some matrix computations that would have been infeasible using even extended precision arithmetic by focusing on special cases in which all elements of the matrices under study are rational and using exact arithmetic. The applications we studied include total column ozone as measured from a polar-orbiting satellite, sea surface temperatures over the Pacific Ocean, and annual temperature extremes at a site in New York City. In each of these applications, our theoretical and computational innovations were directly motivated by the challenges posed by analyzing these and similar types of data.« less

Effective 3-D surface modeling for geographic information systems

NASA Astrophysics Data System (ADS)

Yüksek, K.; Alparslan, M.; Mendi, E.

2013-11-01

In this work, we propose a dynamic, flexible and interactive urban digital terrain platform (DTP) with spatial data and query processing capabilities of Geographic Information Systems (GIS), multimedia database functionality and graphical modeling infrastructure. A new data element, called Geo-Node, which stores image, spatial data and 3-D CAD objects is developed using an efficient data structure. The system effectively handles data transfer of Geo-Nodes between main memory and secondary storage with an optimized Directional Replacement Policy (DRP) based buffer management scheme. Polyhedron structures are used in Digital Surface Modeling (DSM) and smoothing process is performed by interpolation. The experimental results show that our framework achieves high performance and works effectively with urban scenes independent from the amount of spatial data and image size. The proposed platform may contribute to the development of various applications such as Web GIS systems based on 3-D graphics standards (e.g. X3-D and VRML) and services which integrate multi-dimensional spatial information and satellite/aerial imagery.

Effective 3-D surface modeling for geographic information systems

NASA Astrophysics Data System (ADS)

Yüksek, K.; Alparslan, M.; Mendi, E.

2016-01-01

In this work, we propose a dynamic, flexible and interactive urban digital terrain platform with spatial data and query processing capabilities of geographic information systems, multimedia database functionality and graphical modeling infrastructure. A new data element, called Geo-Node, which stores image, spatial data and 3-D CAD objects is developed using an efficient data structure. The system effectively handles data transfer of Geo-Nodes between main memory and secondary storage with an optimized directional replacement policy (DRP) based buffer management scheme. Polyhedron structures are used in digital surface modeling and smoothing process is performed by interpolation. The experimental results show that our framework achieves high performance and works effectively with urban scenes independent from the amount of spatial data and image size. The proposed platform may contribute to the development of various applications such as Web GIS systems based on 3-D graphics standards (e.g., X3-D and VRML) and services which integrate multi-dimensional spatial information and satellite/aerial imagery.

Spatial Modeling of Iron Transformations Within Artificial Soil Aggregates

NASA Astrophysics Data System (ADS)

Kausch, M.; Meile, C.; Pallud, C.

2008-12-01

Structured soils exhibit significant variations in transport characteristics at the aggregate scale. Preferential flow occurs through macropores while predominantly diffusive exchange takes place in intra-aggregate micropores. Such environments characterized by mass transfer limitations are conducive to the formation of small-scale chemical gradients and promote strong spatial variation in processes controlling the fate of redox-sensitive elements such as Fe. In this study, we present a reactive transport model used to spatially resolve iron bioreductive processes occurring within a spherical aggregate at the interface between advective and diffusive domains. The model is derived from current conceptual models of iron(hydr)oxide (HFO) transformations and constrained by literature and experimental data. Data were obtained from flow-through experiments on artificial soil aggregates inoculated with Shewanella putrefaciens strain CN32, and include the temporal evolution of the bulk solution composition, as well as spatial information on the final solid phase distribution within aggregates. With all iron initially in the form of ferrihydrite, spatially heterogeneous formation of goethite/lepidocrocite, magnetite and siderite was observed during the course of the experiments. These transformations were reproduced by the model, which ascribes a central role to divalent iron as a driver of HFO transformations and master variable in the rate laws of the considered reaction network. The predicted dissolved iron breakthrough curves also match the experimental ones closely. Thus, the computed chemical concentration fields help identify factors governing the observed trends in the solid phase distribution patterns inside the aggregate. Building on a mechanistic description of transformation reactions, fluid flow and solute transport, the model was able to describe the observations and hence illustrates the importance of small-scale gradients and dynamics of bioreductive processes for assessing bulk iron cycling. As HFOs are ubiquitous in soils, such process-level understanding of aggregate-scale iron dynamics has broad implications for the prediction of the subsurface fate of nutrients and contaminants that interact strongly with HFO surfaces.

Parallelization of a spatial random field characterization process using the Method of Anchored Distributions and the HTCondor high throughput computing system

NASA Astrophysics Data System (ADS)

Osorio-Murillo, C. A.; Over, M. W.; Frystacky, H.; Ames, D. P.; Rubin, Y.

2013-12-01

A new software application called MAD# has been coupled with the HTCondor high throughput computing system to aid scientists and educators with the characterization of spatial random fields and enable understanding the spatial distribution of parameters used in hydrogeologic and related modeling. MAD# is an open source desktop software application used to characterize spatial random fields using direct and indirect information through Bayesian inverse modeling technique called the Method of Anchored Distributions (MAD). MAD relates indirect information with a target spatial random field via a forward simulation model. MAD# executes inverse process running the forward model multiple times to transfer information from indirect information to the target variable. MAD# uses two parallelization profiles according to computational resources available: one computer with multiple cores and multiple computers - multiple cores through HTCondor. HTCondor is a system that manages a cluster of desktop computers for submits serial or parallel jobs using scheduling policies, resources monitoring, job queuing mechanism. This poster will show how MAD# reduces the time execution of the characterization of random fields using these two parallel approaches in different case studies. A test of the approach was conducted using 1D problem with 400 cells to characterize saturated conductivity, residual water content, and shape parameters of the Mualem-van Genuchten model in four materials via the HYDRUS model. The number of simulations evaluated in the inversion was 10 million. Using the one computer approach (eight cores) were evaluated 100,000 simulations in 12 hours (10 million - 1200 hours approximately). In the evaluation on HTCondor, 32 desktop computers (132 cores) were used, with a processing time of 60 hours non-continuous in five days. HTCondor reduced the processing time for uncertainty characterization by a factor of 20 (1200 hours reduced to 60 hours.)

Can Process Understanding Help Elucidate The Structure Of The Critical Zone? Comparing Process-Based Soil Formation Models With Digital Soil Mapping.

NASA Astrophysics Data System (ADS)

Vanwalleghem, T.; Román, A.; Peña, A.; Laguna, A.; Giráldez, J. V.

2017-12-01

There is a need for better understanding the processes influencing soil formation and the resulting distribution of soil properties in the critical zone. Soil properties can exhibit strong spatial variation, even at the small catchment scale. Especially soil carbon pools in semi-arid, mountainous areas are highly uncertain because bulk density and stoniness are very heterogeneous and rarely measured explicitly. In this study, we explore the spatial variability in key soil properties (soil carbon stocks, stoniness, bulk density and soil depth) as a function of processes shaping the critical zone (weathering, erosion, soil water fluxes and vegetation patterns). We also compare the potential of traditional digital soil mapping versus a mechanistic soil formation model (MILESD) for predicting these key soil properties. Soil core samples were collected from 67 locations at 6 depths. Total soil organic carbon stocks were 4.38 kg m-2. Solar radiation proved to be the key variable controlling soil carbon distribution. Stone content was mostly controlled by slope, indicating the importance of erosion. Spatial distribution of bulk density was found to be highly random. Finally, total carbon stocks were predicted using a random forest model whose main covariates were solar radiation and NDVI. The model predicts carbon stocks that are double as high on north versus south-facing slopes. However, validation showed that these covariates only explained 25% of the variation in the dataset. Apparently, present-day landscape and vegetation properties are not sufficient to fully explain variability in the soil carbon stocks in this complex terrain under natural vegetation. This is attributed to a high spatial variability in bulk density and stoniness, key variables controlling carbon stocks. Similar results were obtained with the mechanistic soil formation model MILESD, suggesting that more complex models might be needed to further explore this high spatial variability.

Longitudinal patterns and response lengths of algae in riverine ecosystems: A model analysis emphasising benthic-pelagic interactions.

PubMed

Jäger, Christoph G; Borchardt, Dietrich

2018-04-07

In riverine ecosystems primary production is principally possible in two habitats: in the benthic layer by sessile algae and in the surface water by planktonic algae being transported downstream. The relevance of these two habitats generally changes along the rivers' continuum. However, analyses of the interaction of algae in these two habitats and their controlling factors in riverine ecosystems are, so far, very rare. We use a simplified advection-diffusion model system combined with ecological process kinetics to analyse the interaction of benthic and planktonic algae and nutrients along idealised streams and rivers at regional to large scales. Because many of the underlying processes affecting algal dynamics are influenced by depth, we focus particularly on the impact of river depth on this interaction. At constant environmental conditions all state variables approach stable spatial equilibria along the river, independent of the boundary conditions at the upstream end. Because our model is very robust against changes of turbulent diffusion and stream velocity, these spatial equilibria can be analysed by a simplified ordinary differential equation (ode) version of our model. This model variant reveals that at shallower river depths, phytoplankton can exist only when it is subsidised by detaching benthic algae, and in turn, at deeper river depths, benthic algae can exist only in low biomasses which are subsidised by sinking planktonic algae. We generalise the spatial dynamics of the model system using different conditions at the upstream end of the model, which mimic various natural or anthropogenic factors (pristine source, dam, inflow of a waste water treatment plant, and dilution from e.g. a tributary) and analyse how these scenarios influence different aspects of the longitudinal spatial dynamics of the full spatial model: the relation of spatial equilibrium to spatial maximum, the distance to the spatial maximum, and the response length. Generally, our results imply that shallow systems recover within significantly shorter distances from spatially distinct disturbances when compared to deep systems, independent of the type of disturbance. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Sampling design optimisation for rainfall prediction using a non-stationary geostatistical model

NASA Astrophysics Data System (ADS)

Wadoux, Alexandre M. J.-C.; Brus, Dick J.; Rico-Ramirez, Miguel A.; Heuvelink, Gerard B. M.

2017-09-01

The accuracy of spatial predictions of rainfall by merging rain-gauge and radar data is partly determined by the sampling design of the rain-gauge network. Optimising the locations of the rain-gauges may increase the accuracy of the predictions. Existing spatial sampling design optimisation methods are based on minimisation of the spatially averaged prediction error variance under the assumption of intrinsic stationarity. Over the past years, substantial progress has been made to deal with non-stationary spatial processes in kriging. Various well-documented geostatistical models relax the assumption of stationarity in the mean, while recent studies show the importance of considering non-stationarity in the variance for environmental processes occurring in complex landscapes. We optimised the sampling locations of rain-gauges using an extension of the Kriging with External Drift (KED) model for prediction of rainfall fields. The model incorporates both non-stationarity in the mean and in the variance, which are modelled as functions of external covariates such as radar imagery, distance to radar station and radar beam blockage. Spatial predictions are made repeatedly over time, each time recalibrating the model. The space-time averaged KED variance was minimised by Spatial Simulated Annealing (SSA). The methodology was tested using a case study predicting daily rainfall in the north of England for a one-year period. Results show that (i) the proposed non-stationary variance model outperforms the stationary variance model, and (ii) a small but significant decrease of the rainfall prediction error variance is obtained with the optimised rain-gauge network. In particular, it pays off to place rain-gauges at locations where the radar imagery is inaccurate, while keeping the distribution over the study area sufficiently uniform.

Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate.

PubMed

Beer, Christian; Reichstein, Markus; Tomelleri, Enrico; Ciais, Philippe; Jung, Martin; Carvalhais, Nuno; Rödenbeck, Christian; Arain, M Altaf; Baldocchi, Dennis; Bonan, Gordon B; Bondeau, Alberte; Cescatti, Alessandro; Lasslop, Gitta; Lindroth, Anders; Lomas, Mark; Luyssaert, Sebastiaan; Margolis, Hank; Oleson, Keith W; Roupsard, Olivier; Veenendaal, Elmar; Viovy, Nicolas; Williams, Christopher; Woodward, F Ian; Papale, Dario

2010-08-13

Terrestrial gross primary production (GPP) is the largest global CO(2) flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 +/- 8 petagrams of carbon per year (Pg C year(-1)) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate predictions exhibit a large between-model variation of GPP's latitudinal patterns and show higher spatial correlations between GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate-carbon cycle process models.

Spatial and Temporal Soil Moisture Behavior in a Headwater Watershed of the Mantiqueira Range, Minas Gerais, Brazil

USDA-ARS?s Scientific Manuscript database

The characterization of temporal and spatial variability of soil moisture is highly relevant in watersheds for understanding the many hydrological and erosion processes, to better model the processes and apply them to conservation planning. The goal of this study was to map soil moisture of the surf...

Tangible Landscape: Cognitively Grasping the Flow of Water

NASA Astrophysics Data System (ADS)

Harmon, B. A.; Petrasova, A.; Petras, V.; Mitasova, H.; Meentemeyer, R. K.

2016-06-01

Complex spatial forms like topography can be challenging to understand, much less intentionally shape, given the heavy cognitive load of visualizing and manipulating 3D form. Spatiotemporal processes like the flow of water over a landscape are even more challenging to understand and intentionally direct as they are dependent upon their context and require the simulation of forces like gravity and momentum. This cognitive work can be offloaded onto computers through 3D geospatial modeling, analysis, and simulation. Interacting with computers, however, can also be challenging, often requiring training and highly abstract thinking. Tangible computing - an emerging paradigm of human-computer interaction in which data is physically manifested so that users can feel it and directly manipulate it - aims to offload this added cognitive work onto the body. We have designed Tangible Landscape, a tangible interface powered by an open source geographic information system (GRASS GIS), so that users can naturally shape topography and interact with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about scientific phenomena in a rapid, iterative process. Conceptually Tangible Landscape couples a malleable physical model with a digital model of a landscape through a continuous cycle of 3D scanning, geospatial modeling, and projection. We ran a flow modeling experiment to test whether tangible interfaces like this can effectively enhance spatial performance by offloading cognitive processes onto computers and our bodies. We used hydrological simulations and statistics to quantitatively assess spatial performance. We found that Tangible Landscape enhanced 3D spatial performance and helped users understand water flow.

An observational assessment of the influence of mesoscale and submesoscale heterogeneity on ocean biogeochemical reactions

NASA Astrophysics Data System (ADS)

Martin, Adrian P.; Lévy, Marina; van Gennip, Simon; Pardo, Silvia; Srokosz, Meric; Allen, John; Painter, Stuart C.; Pidcock, Roz

2015-09-01

Numerous observations demonstrate that considerable spatial variability exists in components of the marine planktonic ecosystem at the mesoscale and submesoscale (100 km-1 km). The causes and consequences of physical processes at these scales ("eddy advection") influencing biogeochemistry have received much attention. Less studied, the nonlinear nature of most ecological and biogeochemical interactions means that such spatial variability has consequences for regional estimates of processes including primary production and grazing, independent of the physical processes. This effect has been termed "eddy reactions." Models remain our most powerful tools for extrapolating hypotheses for biogeochemistry to global scales and to permit future projections. The spatial resolution of most climate and global biogeochemical models means that processes at the mesoscale and submesoscale are poorly resolved. Modeling work has previously suggested that the neglected eddy reactions may be almost as large as the mean field estimates in some cases. This study seeks to quantify the relative size of eddy and mean reactions observationally, using in situ and satellite data. For primary production, grazing, and zooplankton mortality the eddy reactions are between 7% and 15% of the mean reactions. These should be regarded as preliminary estimates to encourage further observational estimates and not taken as a justification for ignoring eddy reactions. Compared to modeling estimates, there are inconsistencies in the relative magnitude of eddy reactions and in correlations which are a major control on their magnitude. One possibility is that models exhibit much stronger spatial correlations than are found in reality, effectively amplifying the magnitude of eddy reactions.

Evaluating a process-based model for use in streambank stabilization and stream restoration: insights on the bank stability and toe erosion model (BSTEM)

USDA-ARS?s Scientific Manuscript database

Streambank retreat is a complex cyclical process involving subaerial processes, fluvial erosion, seepage erosion, and geotechnical failures and is driven by several soil properties that themselves are temporally and spatially variable. Therefore, it can be extremely challenging to predict and model ...

Information Processing and Human Abilities

ERIC Educational Resources Information Center

Kirby, John R.; Das, J. P.

1978-01-01

The simultaneous and successive processing model of cognitive abilities was compared to a traditional primary mental abilities model. Simultaneous processing was found to be primarily related to spatial ability; and to a lesser extent, to memory and inductive reasoning. Subjects were 104 fourth-grade urban males. (Author/GD C)

Soil Erosion as a stochastic process

NASA Astrophysics Data System (ADS)

Casper, Markus C.

2015-04-01

The main tools to provide estimations concerning risk and amount of erosion are different types of soil erosion models: on the one hand, there are empirically based model concepts on the other hand there are more physically based or process based models. However, both types of models have substantial weak points. All empirical model concepts are only capable of providing rough estimates over larger temporal and spatial scales, they do not account for many driving factors that are in the scope of scenario related analysis. In addition, the physically based models contain important empirical parts and hence, the demand for universality and transferability is not given. As a common feature, we find, that all models rely on parameters and input variables, which are to certain, extend spatially and temporally averaged. A central question is whether the apparent heterogeneity of soil properties or the random nature of driving forces needs to be better considered in our modelling concepts. Traditionally, researchers have attempted to remove spatial and temporal variability through homogenization. However, homogenization has been achieved through physical manipulation of the system, or by statistical averaging procedures. The price for obtaining this homogenized (average) model concepts of soils and soil related processes has often been a failure to recognize the profound importance of heterogeneity in many of the properties and processes that we study. Especially soil infiltrability and the resistance (also called "critical shear stress" or "critical stream power") are the most important empirical factors of physically based erosion models. The erosion resistance is theoretically a substrate specific parameter, but in reality, the threshold where soil erosion begins is determined experimentally. The soil infiltrability is often calculated with empirical relationships (e.g. based on grain size distribution). Consequently, to better fit reality, this value needs to be corrected experimentally. To overcome this disadvantage of our actual models, soil erosion models are needed that are able to use stochastic directly variables and parameter distributions. There are only some minor approaches in this direction. The most advanced is the model "STOSEM" proposed by Sidorchuk in 2005. In this model, only a small part of the soil erosion processes is described, the aggregate detachment and the aggregate transport by flowing water. The concept is highly simplified, for example, many parameters are temporally invariant. Nevertheless, the main problem is that our existing measurements and experiments are not geared to provide stochastic parameters (e.g. as probability density functions); in the best case they deliver a statistical validation of the mean values. Again, we get effective parameters, spatially and temporally averaged. There is an urgent need for laboratory and field experiments on overland flow structure, raindrop effects and erosion rate, which deliver information on spatial and temporal structure of soil and surface properties and processes.

Modeling structural change in spatial system dynamics: A Daisyworld example.

PubMed

Neuwirth, C; Peck, A; Simonović, S P

2015-03-01

System dynamics (SD) is an effective approach for helping reveal the temporal behavior of complex systems. Although there have been recent developments in expanding SD to include systems' spatial dependencies, most applications have been restricted to the simulation of diffusion processes; this is especially true for models on structural change (e.g. LULC modeling). To address this shortcoming, a Python program is proposed to tightly couple SD software to a Geographic Information System (GIS). The approach provides the required capacities for handling bidirectional and synchronized interactions of operations between SD and GIS. In order to illustrate the concept and the techniques proposed for simulating structural changes, a fictitious environment called Daisyworld has been recreated in a spatial system dynamics (SSD) environment. The comparison of spatial and non-spatial simulations emphasizes the importance of considering spatio-temporal feedbacks. Finally, practical applications of structural change models in agriculture and disaster management are proposed.

The spatial structure of a nonlinear receptive field.

PubMed

Schwartz, Gregory W; Okawa, Haruhisa; Dunn, Felice A; Morgan, Josh L; Kerschensteiner, Daniel; Wong, Rachel O; Rieke, Fred

2012-11-01

Understanding a sensory system implies the ability to predict responses to a variety of inputs from a common model. In the retina, this includes predicting how the integration of signals across visual space shapes the outputs of retinal ganglion cells. Existing models of this process generalize poorly to predict responses to new stimuli. This failure arises in part from properties of the ganglion cell response that are not well captured by standard receptive-field mapping techniques: nonlinear spatial integration and fine-scale heterogeneities in spatial sampling. Here we characterize a ganglion cell's spatial receptive field using a mechanistic model based on measurements of the physiological properties and connectivity of only the primary excitatory circuitry of the retina. The resulting simplified circuit model successfully predicts ganglion-cell responses to a variety of spatial patterns and thus provides a direct correspondence between circuit connectivity and retinal output.

Measuring directional urban spatial interaction in China: A migration perspective

PubMed Central

Li, Fangzhou; Feng, Zhiming; Li, Peng; You, Zhen

2017-01-01

The study of urban spatial interaction is closely linked to that of economic geography, urban planning, regional development, and so on. Currently, this topic is generating a great deal of interest among researchers who are striving to find accurate ways to measure urban spatial interaction. Classical spatial interaction models lack theoretical guidance and require complicated parameter-adjusting processes. The radiation model, however, as proposed by Simini et al. with rigorous formula derivation, can simulate directional urban spatial interaction. We applied the radiation model in China to simulate the directional migration number among 337 nationwide research units, comprising 4 municipalities and 333 prefecture-level cities. We then analyzed the overall situation in Chinese cities, the interaction intensity hierarchy, and the prime urban agglomerations from the perspective of migration. This was done to ascertain China’s urban spatial interaction and regional development from 2000 to 2010 to reveal ground realities. PMID:28141853

Measuring directional urban spatial interaction in China: A migration perspective.

PubMed

Li, Fangzhou; Feng, Zhiming; Li, Peng; You, Zhen

2017-01-01

The study of urban spatial interaction is closely linked to that of economic geography, urban planning, regional development, and so on. Currently, this topic is generating a great deal of interest among researchers who are striving to find accurate ways to measure urban spatial interaction. Classical spatial interaction models lack theoretical guidance and require complicated parameter-adjusting processes. The radiation model, however, as proposed by Simini et al. with rigorous formula derivation, can simulate directional urban spatial interaction. We applied the radiation model in China to simulate the directional migration number among 337 nationwide research units, comprising 4 municipalities and 333 prefecture-level cities. We then analyzed the overall situation in Chinese cities, the interaction intensity hierarchy, and the prime urban agglomerations from the perspective of migration. This was done to ascertain China's urban spatial interaction and regional development from 2000 to 2010 to reveal ground realities.

Spatial extremes modeling applied to extreme precipitation data in the state of Paraná

NASA Astrophysics Data System (ADS)

Olinda, R. A.; Blanchet, J.; dos Santos, C. A. C.; Ozaki, V. A.; Ribeiro, P. J., Jr.

2014-11-01

Most of the mathematical models developed for rare events are based on probabilistic models for extremes. Although the tools for statistical modeling of univariate and multivariate extremes are well developed, the extension of these tools to model spatial extremes includes an area of very active research nowadays. A natural approach to such a modeling is the theory of extreme spatial and the max-stable process, characterized by the extension of infinite dimensions of multivariate extreme value theory, and making it possible then to incorporate the existing correlation functions in geostatistics and therefore verify the extremal dependence by means of the extreme coefficient and the Madogram. This work describes the application of such processes in modeling the spatial maximum dependence of maximum monthly rainfall from the state of Paraná, based on historical series observed in weather stations. The proposed models consider the Euclidean space and a transformation referred to as space weather, which may explain the presence of directional effects resulting from synoptic weather patterns. This method is based on the theorem proposed for de Haan and on the models of Smith and Schlather. The isotropic and anisotropic behavior of these models is also verified via Monte Carlo simulation. Estimates are made through pairwise likelihood maximum and the models are compared using the Takeuchi Information Criterion. By modeling the dependence of spatial maxima, applied to maximum monthly rainfall data from the state of Paraná, it was possible to identify directional effects resulting from meteorological phenomena, which, in turn, are important for proper management of risks and environmental disasters in countries with its economy heavily dependent on agribusiness.

Modeling the Land Use/Cover Change in an Arid Region Oasis City Constrained by Water Resource and Environmental Policy Change using Cellular Automata Model

NASA Astrophysics Data System (ADS)

Hu, X.; Li, X.; Lu, L.

2017-12-01

Land use/cover change (LUCC) is an important subject in the research of global environmental change and sustainable development, while spatial simulation on land use/cover change is one of the key content of LUCC and is also difficult due to the complexity of the system. The cellular automata (CA) model had an irreplaceable role in simulating of land use/cover change process due to the powerful spatial computing power. However, the majority of current CA land use/cover models were binary-state model that could not provide more general information about the overall spatial pattern of land use/cover change. Here, a multi-state logistic-regression-based Markov cellular automata (MLRMCA) model and a multi-state artificial-neural-network-based Markov cellular automata (MANNMCA) model were developed and were used to simulate complex land use/cover evolutionary process in an arid region oasis city constrained by water resource and environmental policy change, the Zhangye city during the period of 1990-2010. The results indicated that the MANNMCA model was superior to MLRMCA model in simulated accuracy. These indicated that by combining the artificial neural network with CA could more effectively capture the complex relationships between the land use/cover change and a set of spatial variables. Although the MLRMCA model were also some advantages, the MANNMCA model was more appropriate for simulating complex land use/cover dynamics. The two proposed models were effective and reliable, and could reflect the spatial evolution of regional land use/cover changes. These have also potential implications for the impact assessment of water resources, ecological restoration, and the sustainable urban development in arid areas.

Is attention based on spatial contextual memory preferentially guided by low spatial frequency signals?

PubMed

Patai, Eva Zita; Buckley, Alice; Nobre, Anna Christina

2013-01-01

A popular model of visual perception states that coarse information (carried by low spatial frequencies) along the dorsal stream is rapidly transmitted to prefrontal and medial temporal areas, activating contextual information from memory, which can in turn constrain detailed input carried by high spatial frequencies arriving at a slower rate along the ventral visual stream, thus facilitating the processing of ambiguous visual stimuli. We were interested in testing whether this model contributes to memory-guided orienting of attention. In particular, we asked whether global, low-spatial frequency (LSF) inputs play a dominant role in triggering contextual memories in order to facilitate the processing of the upcoming target stimulus. We explored this question over four experiments. The first experiment replicated the LSF advantage reported in perceptual discrimination tasks by showing that participants were faster and more accurate at matching a low spatial frequency version of a scene, compared to a high spatial frequency version, to its original counterpart in a forced-choice task. The subsequent three experiments tested the relative contributions of low versus high spatial frequencies during memory-guided covert spatial attention orienting tasks. Replicating the effects of memory-guided attention, pre-exposure to scenes associated with specific spatial memories for target locations (memory cues) led to higher perceptual discrimination and faster response times to identify targets embedded in the scenes. However, either high or low spatial frequency cues were equally effective; LSF signals did not selectively or preferentially contribute to the memory-driven attention benefits to performance. Our results challenge a generalized model that LSFs activate contextual memories, which in turn bias attention and facilitate perception.

Is Attention Based on Spatial Contextual Memory Preferentially Guided by Low Spatial Frequency Signals?

PubMed Central

Patai, Eva Zita; Buckley, Alice; Nobre, Anna Christina

2013-01-01

A popular model of visual perception states that coarse information (carried by low spatial frequencies) along the dorsal stream is rapidly transmitted to prefrontal and medial temporal areas, activating contextual information from memory, which can in turn constrain detailed input carried by high spatial frequencies arriving at a slower rate along the ventral visual stream, thus facilitating the processing of ambiguous visual stimuli. We were interested in testing whether this model contributes to memory-guided orienting of attention. In particular, we asked whether global, low-spatial frequency (LSF) inputs play a dominant role in triggering contextual memories in order to facilitate the processing of the upcoming target stimulus. We explored this question over four experiments. The first experiment replicated the LSF advantage reported in perceptual discrimination tasks by showing that participants were faster and more accurate at matching a low spatial frequency version of a scene, compared to a high spatial frequency version, to its original counterpart in a forced-choice task. The subsequent three experiments tested the relative contributions of low versus high spatial frequencies during memory-guided covert spatial attention orienting tasks. Replicating the effects of memory-guided attention, pre-exposure to scenes associated with specific spatial memories for target locations (memory cues) led to higher perceptual discrimination and faster response times to identify targets embedded in the scenes. However, either high or low spatial frequency cues were equally effective; LSF signals did not selectively or preferentially contribute to the memory-driven attention benefits to performance. Our results challenge a generalized model that LSFs activate contextual memories, which in turn bias attention and facilitate perception. PMID:23776509

Digital hydrologic networks supporting applications related to spatially referenced regression modeling

USGS Publications Warehouse

Brakebill, John W.; Wolock, David M.; Terziotti, Silvia

2011-01-01

Digital hydrologic networks depicting surface-water pathways and their associated drainage catchments provide a key component to hydrologic analysis and modeling. Collectively, they form common spatial units that can be used to frame the descriptions of aquatic and watershed processes. In addition, they provide the ability to simulate and route the movement of water and associated constituents throughout the landscape. Digital hydrologic networks have evolved from derivatives of mapping products to detailed, interconnected, spatially referenced networks of water pathways, drainage areas, and stream and watershed characteristics. These properties are important because they enhance the ability to spatially evaluate factors that affect the sources and transport of water-quality constituents at various scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW), a process-based ⁄ statistical model, relies on a digital hydrologic network in order to establish relations between quantities of monitored contaminant flux, contaminant sources, and the associated physical characteristics affecting contaminant transport. Digital hydrologic networks modified from the River Reach File (RF1) and National Hydrography Dataset (NHD) geospatial datasets provided frameworks for SPARROW in six regions of the conterminous United States. In addition, characteristics of the modified RF1 were used to update estimates of mean-annual streamflow. This produced more current flow estimates for use in SPARROW modeling.

Impaired spatial processing in a mouse model of fragile X syndrome.

PubMed

Ghilan, Mohamed; Bettio, Luis E B; Noonan, Athena; Brocardo, Patricia S; Gil-Mohapel, Joana; Christie, Brian R

2018-05-17

Fragile X syndrome (FXS) is the most common form of inherited intellectual impairment. The Fmr1 -/y mouse model has been previously shown to have deficits in context discrimination tasks but not in the elevated plus-maze. To further characterize this FXS mouse model and determine whether hippocampal-mediated behaviours are affected in these mice, dentate gyrus (DG)-dependent spatial processing and Cornu ammonis 1 (CA1)-dependent temporal order discrimination tasks were evaluated. In agreement with previous findings of long-term potentiation deficits in the DG of this transgenic model of FXS, the results reported here demonstrate that Fmr1 -/y mice perform poorly in the DG-dependent metric change spatial processing task. However, Fmr1 -/y mice did not present deficits in the CA1-dependent temporal order discrimination task, and were able to remember the order in which objects were presented to them to the same extent as their wild-type littermate controls. These data suggest that the previously reported subregional-specific differences in hippocampal synaptic plasticity observed in the Fmr1 -/y mouse model may manifest as selective behavioural deficits in hippocampal-dependent tasks. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

What spatial scales are believable for climate model projections of sea surface temperature?

NASA Astrophysics Data System (ADS)

Kwiatkowski, Lester; Halloran, Paul R.; Mumby, Peter J.; Stephenson, David B.

2014-09-01

Earth system models (ESMs) provide high resolution simulations of variables such as sea surface temperature (SST) that are often used in off-line biological impact models. Coral reef modellers have used such model outputs extensively to project both regional and global changes to coral growth and bleaching frequency. We assess model skill at capturing sub-regional climatologies and patterns of historical warming. This study uses an established wavelet-based spatial comparison technique to assess the skill of the coupled model intercomparison project phase 5 models to capture spatial SST patterns in coral regions. We show that models typically have medium to high skill at capturing climatological spatial patterns of SSTs within key coral regions, with model skill typically improving at larger spatial scales (≥4°). However models have much lower skill at modelling historical warming patters and are shown to often perform no better than chance at regional scales (e.g. Southeast Asian) and worse than chance at finer scales (<8°). Our findings suggest that output from current generation ESMs is not yet suitable for making sub-regional projections of change in coral bleaching frequency and other marine processes linked to SST warming.

Spatial evolutionary epidemiology of spreading epidemics

PubMed Central

2016-01-01

Most spatial models of host–parasite interactions either neglect the possibility of pathogen evolution or consider that this process is slow enough for epidemiological dynamics to reach an equilibrium on a fast timescale. Here, we propose a novel approach to jointly model the epidemiological and evolutionary dynamics of spatially structured host and pathogen populations. Starting from a multi-strain epidemiological model, we use a combination of spatial moment equations and quantitative genetics to analyse the dynamics of mean transmission and virulence in the population. A key insight of our approach is that, even in the absence of long-term evolutionary consequences, spatial structure can affect the short-term evolution of pathogens because of the build-up of spatial differentiation in mean virulence. We show that spatial differentiation is driven by a balance between epidemiological and genetic effects, and this quantity is related to the effect of kin competition discussed in previous studies of parasite evolution in spatially structured host populations. Our analysis can be used to understand and predict the transient evolutionary dynamics of pathogens and the emergence of spatial patterns of phenotypic variation. PMID:27798295

Spatial evolutionary epidemiology of spreading epidemics.

PubMed

Lion, S; Gandon, S

2016-10-26

Most spatial models of host-parasite interactions either neglect the possibility of pathogen evolution or consider that this process is slow enough for epidemiological dynamics to reach an equilibrium on a fast timescale. Here, we propose a novel approach to jointly model the epidemiological and evolutionary dynamics of spatially structured host and pathogen populations. Starting from a multi-strain epidemiological model, we use a combination of spatial moment equations and quantitative genetics to analyse the dynamics of mean transmission and virulence in the population. A key insight of our approach is that, even in the absence of long-term evolutionary consequences, spatial structure can affect the short-term evolution of pathogens because of the build-up of spatial differentiation in mean virulence. We show that spatial differentiation is driven by a balance between epidemiological and genetic effects, and this quantity is related to the effect of kin competition discussed in previous studies of parasite evolution in spatially structured host populations. Our analysis can be used to understand and predict the transient evolutionary dynamics of pathogens and the emergence of spatial patterns of phenotypic variation. © 2016 The Author(s).

Enhanced long-term and impaired short-term spatial memory in GluA1 AMPA receptor subunit knockout mice: evidence for a dual-process memory model.

PubMed

Sanderson, David J; Good, Mark A; Skelton, Kathryn; Sprengel, Rolf; Seeburg, Peter H; Rawlins, J Nicholas P; Bannerman, David M

2009-06-01

The GluA1 AMPA receptor subunit is a key mediator of hippocampal synaptic plasticity and is especially important for a rapidly-induced, short-lasting form of potentiation. GluA1 gene deletion impairs hippocampus-dependent, spatial working memory, but spares hippocampus-dependent spatial reference memory. These findings may reflect the necessity of GluA1-dependent synaptic plasticity for short-term memory of recently visited places, but not for the ability to form long-term associations between a particular spatial location and an outcome. This hypothesis is in concordance with the theory that short-term and long-term memory depend on dissociable psychological processes. In this study we tested GluA1-/- mice on both short-term and long-term spatial memory using a simple novelty preference task. Mice were given a series of repeated exposures to a particular spatial location (the arm of a Y-maze) before their preference for a novel spatial location (the unvisited arm of the maze) over the familiar spatial location was assessed. GluA1-/- mice were impaired if the interval between the trials was short (1 min), but showed enhanced spatial memory if the interval between the trials was long (24 h). This enhancement was caused by the interval between the exposure trials rather than the interval prior to the test, thus demonstrating enhanced learning and not simply enhanced performance or expression of memory. This seemingly paradoxical enhancement of hippocampus-dependent spatial learning may be caused by GluA1 gene deletion reducing the detrimental effects of short-term memory on subsequent long-term learning. Thus, these results support a dual-process model of memory in which short-term and long-term memory are separate and sometimes competitive processes.

Topologically Consistent Models for Efficient Big Geo-Spatio Data Distribution

NASA Astrophysics Data System (ADS)

Jahn, M. W.; Bradley, P. E.; Doori, M. Al; Breunig, M.

2017-10-01

Geo-spatio-temporal topology models are likely to become a key concept to check the consistency of 3D (spatial space) and 4D (spatial + temporal space) models for emerging GIS applications such as subsurface reservoir modelling or the simulation of energy and water supply of mega or smart cities. Furthermore, the data management for complex models consisting of big geo-spatial data is a challenge for GIS and geo-database research. General challenges, concepts, and techniques of big geo-spatial data management are presented. In this paper we introduce a sound mathematical approach for a topologically consistent geo-spatio-temporal model based on the concept of the incidence graph. We redesign DB4GeO, our service-based geo-spatio-temporal database architecture, on the way to the parallel management of massive geo-spatial data. Approaches for a new geo-spatio-temporal and object model of DB4GeO meeting the requirements of big geo-spatial data are discussed in detail. Finally, a conclusion and outlook on our future research are given on the way to support the processing of geo-analytics and -simulations in a parallel and distributed system environment.

Working Memory and Strategy Use Contribute to Gender Differences in Spatial Ability

ERIC Educational Resources Information Center

Wang, Lu; Carr, Martha

2014-01-01

In this review, a new model that is grounded in information-processing theory is proposed to account for gender differences in spatial ability. The proposed model assumes that the relative strength of working memory, as expressed by the ratio of visuospatial working memory to verbal working memory, influences the type of strategies used on spatial…

Median Filter Noise Reduction of Image and Backpropagation Neural Network Model for Cervical Cancer Classification

NASA Astrophysics Data System (ADS)

Wutsqa, D. U.; Marwah, M.

2017-06-01

In this paper, we consider spatial operation median filter to reduce the noise in the cervical images yielded by colposcopy tool. The backpropagation neural network (BPNN) model is applied to the colposcopy images to classify cervical cancer. The classification process requires an image extraction by using a gray level co-occurrence matrix (GLCM) method to obtain image features that are used as inputs of BPNN model. The advantage of noise reduction is evaluated by comparing the performances of BPNN models with and without spatial operation median filter. The experimental result shows that the spatial operation median filter can improve the accuracy of the BPNN model for cervical cancer classification.

Soil nutrient-landscape relationships in a lowland tropical rainforest in Panama

USGS Publications Warehouse

Barthold, F.K.; Stallard, R.F.; Elsenbeer, H.

2008-01-01

Soils play a crucial role in biogeochemical cycles as spatially distributed sources and sinks of nutrients. Any spatial patterns depend on soil forming processes, our understanding of which is still limited, especially in regards to tropical rainforests. The objective of our study was to investigate the effects of landscape properties, with an emphasis on the geometry of the land surface, on the spatial heterogeneity of soil chemical properties, and to test the suitability of soil-landscape modeling as an appropriate technique to predict the spatial variability of exchangeable K and Mg in a humid tropical forest in Panama. We used a design-based, stratified sampling scheme to collect soil samples at 108 sites on Barro Colorado Island, Panama. Stratifying variables are lithology, vegetation and topography. Topographic variables were generated from high-resolution digital elevation models with a grid size of 5 m. We took samples from five depths down to 1 m, and analyzed for total and exchangeable K and Mg. We used simple explorative data analysis techniques to elucidate the importance of lithology for soil total and exchangeable K and Mg. Classification and Regression Trees (CART) were adopted to investigate importance of topography, lithology and vegetation for the spatial distribution of exchangeable K and Mg and with the intention to develop models that regionalize the point observations using digital terrain data as explanatory variables. Our results suggest that topography and vegetation do not control the spatial distribution of the selected soil chemical properties at a landscape scale and lithology is important to some degree. Exchangeable K is distributed equally across the study area indicating that other than landscape processes, e.g. biogeochemical processes, are responsible for its spatial distribution. Lithology contributes to the spatial variation of exchangeable Mg but controlling variables could not be detected. The spatial variation of soil total K and Mg is mainly influenced by lithology. ?? 2007 Elsevier B.V. All rights reserved.

Heteroskedasticity as a leading indicator of desertification in spatially explicit data.

PubMed

Seekell, David A; Dakos, Vasilis

2015-06-01

Regime shifts are abrupt transitions between alternate ecosystem states including desertification in arid regions due to drought or overgrazing. Regime shifts may be preceded by statistical anomalies such as increased autocorrelation, indicating declining resilience and warning of an impending shift. Tests for conditional heteroskedasticity, a type of clustered variance, have proven powerful leading indicators for regime shifts in time series data, but an analogous indicator for spatial data has not been evaluated. A spatial analog for conditional heteroskedasticity might be especially useful in arid environments where spatial interactions are critical in structuring ecosystem pattern and process. We tested the efficacy of a test for spatial heteroskedasticity as a leading indicator of regime shifts with simulated data from spatially extended vegetation models with regular and scale-free patterning. These models simulate shifts from extensive vegetative cover to bare, desert-like conditions. The magnitude of spatial heteroskedasticity increased consistently as the modeled systems approached a regime shift from vegetated to desert state. Relative spatial autocorrelation, spatial heteroskedasticity increased earlier and more consistently. We conclude that tests for spatial heteroskedasticity can contribute to the growing toolbox of early warning indicators for regime shifts analyzed with spatially explicit data.

Improving the spatial representation of soil properties and hydrology using topographically derived initialization processes in the SWAT model

USDA-ARS?s Scientific Manuscript database

Topography exerts critical controls on many hydrologic, geomorphologic, and environmental biophysical processes. Unfortunately many watershed modeling systems use topography only to define basin boundaries and stream channels and do not explicitly account for the topographic controls on processes su...

Quantitative imaging of carbon dimer precursor for nanomaterial synthesis in the carbon arc

DOE PAGES

Vekselman, V.; Khrabry, A.; Kaganovich, I.; ...

2018-02-06

Delineating the dominant processes responsible for nanomaterial synthesis in a plasma environment requires measurements of the precursor species contributing to the growth of nanostructures. Here, we performed comprehensive measurements of spatial and temporal profiles of carbon dimers (C 2) in sub-atmospheric-pressure carbon arc by laser-induced fluorescence. Measured spatial profiles of C 2 coincide with the growth region of carbon nanotubes (Fang et al 2016 Carbon 107 273–80) and vary depending on the arc operation mode, which is determined by the discharge current and the ablation rate of the graphite anode. The C 2 density profile exhibits large spatial and timemore » variations due to motion of the arc core. A comparison of the experimental data with the 2D simulation results of self-consistent arc modeling shows good agreement. The model predicts well the main processes determining spatial profiles of carbon dimers (C 2).« less

Quantitative imaging of carbon dimer precursor for nanomaterial synthesis in the carbon arc

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

Vekselman, V.; Khrabry, A.; Kaganovich, I.

Delineating the dominant processes responsible for nanomaterial synthesis in a plasma environment requires measurements of the precursor species contributing to the growth of nanostructures. Here, we performed comprehensive measurements of spatial and temporal profiles of carbon dimers (C 2) in sub-atmospheric-pressure carbon arc by laser-induced fluorescence. Measured spatial profiles of C 2 coincide with the growth region of carbon nanotubes (Fang et al 2016 Carbon 107 273–80) and vary depending on the arc operation mode, which is determined by the discharge current and the ablation rate of the graphite anode. The C 2 density profile exhibits large spatial and timemore » variations due to motion of the arc core. A comparison of the experimental data with the 2D simulation results of self-consistent arc modeling shows good agreement. The model predicts well the main processes determining spatial profiles of carbon dimers (C 2).« less

Visual Processing of Object Velocity and Acceleration

DTIC Science & Technology

1991-12-13

more recently, Dr. Grzywacz’s applications of filtering models to the psychophysics of speed discrimination; 3) the McKee-Welch studies on the...population of spatio-temporally oriented filters to encode velocity. Dr. Grzywacz has attempted to reconcile his model with a variety of psychophysical...by many authors.23 In these models , the image is tectors have different sizes and spatial positions, but they all spatially and temporally filtered

Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

PubMed Central

Martinet, Louis-Emmanuel; Sheynikhovich, Denis; Benchenane, Karim; Arleo, Angelo

2011-01-01

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates. PMID:21625569

Modeling fixation locations using spatial point processes.

PubMed

Barthelmé, Simon; Trukenbrod, Hans; Engbert, Ralf; Wichmann, Felix

2013-10-01

Whenever eye movements are measured, a central part of the analysis has to do with where subjects fixate and why they fixated where they fixated. To a first approximation, a set of fixations can be viewed as a set of points in space; this implies that fixations are spatial data and that the analysis of fixation locations can be beneficially thought of as a spatial statistics problem. We argue that thinking of fixation locations as arising from point processes is a very fruitful framework for eye-movement data, helping turn qualitative questions into quantitative ones. We provide a tutorial introduction to some of the main ideas of the field of spatial statistics, focusing especially on spatial Poisson processes. We show how point processes help relate image properties to fixation locations. In particular we show how point processes naturally express the idea that image features' predictability for fixations may vary from one image to another. We review other methods of analysis used in the literature, show how they relate to point process theory, and argue that thinking in terms of point processes substantially extends the range of analyses that can be performed and clarify their interpretation.

Neural network for processing both spatial and temporal data with time based back-propagation

NASA Technical Reports Server (NTRS)

Villarreal, James A. (Inventor); Shelton, Robert O. (Inventor)

1993-01-01

Neural networks are computing systems modeled after the paradigm of the biological brain. For years, researchers using various forms of neural networks have attempted to model the brain's information processing and decision-making capabilities. Neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to the processing of temporal data has been severely restricted. The invention introduces a novel technique which adds the dimension of time to the well known back-propagation neural network algorithm. In the space-time neural network disclosed herein, the synaptic weights between two artificial neurons (processing elements) are replaced with an adaptable-adjustable filter. Instead of a single synaptic weight, the invention provides a plurality of weights representing not only association, but also temporal dependencies. In this case, the synaptic weights are the coefficients to the adaptable digital filters. Novelty is believed to lie in the disclosure of a processing element and a network of the processing elements which are capable of processing temporal as well as spacial data.

Research into the influence of spatial variability and scale on the parameterization of hydrological processes

NASA Technical Reports Server (NTRS)

Wood, Eric F.

1993-01-01

The objectives of the research were as follows: (1) Extend the Representative Elementary Area (RE) concept, first proposed and developed in Wood et al, (1988), to the water balance fluxes of the interstorm period (redistribution, evapotranspiration and baseflow) necessary for the analysis of long-term water balance processes. (2) Derive spatially averaged water balance model equations for spatially variable soil, topography and vegetation, over A RANGE OF CLIMATES. This is a necessary step in our goal to derive consistent hydrologic results up to GCM grid scales necessary for global climate modeling. (3) Apply the above macroscale water balance equations with remotely sensed data and begin to explore the feasibility of parameterizing the water balance constitutive equations at GCM grid scale.

Theoretical aspect of suitable spatial boundary condition specified for adjoint model on limited area

NASA Astrophysics Data System (ADS)

Wang, Yuan; Wu, Rongsheng

2001-12-01

Theoretical argumentation for so-called suitable spatial condition is conducted by the aid of homotopy framework to demonstrate that the proposed boundary condition does guarantee that the over-specification boundary condition resulting from an adjoint model on a limited-area is no longer an issue, and yet preserve its well-poseness and optimal character in the boundary setting. The ill-poseness of over-specified spatial boundary condition is in a sense, inevitable from an adjoint model since data assimilation processes have to adapt prescribed observations that used to be over-specified at the spatial boundaries of the modeling domain. In the view of pragmatic implement, the theoretical framework of our proposed condition for spatial boundaries indeed can be reduced to the hybrid formulation of nudging filter, radiation condition taking account of ambient forcing, together with Dirichlet kind of compatible boundary condition to the observations prescribed in data assimilation procedure. All of these treatments, no doubt, are very familiar to mesoscale modelers.

Analysis and Research on Spatial Data Storage Model Based on Cloud Computing Platform

NASA Astrophysics Data System (ADS)

Hu, Yong

2017-12-01

In this paper, the data processing and storage characteristics of cloud computing are analyzed and studied. On this basis, a cloud computing data storage model based on BP neural network is proposed. In this data storage model, it can carry out the choice of server cluster according to the different attributes of the data, so as to complete the spatial data storage model with load balancing function, and have certain feasibility and application advantages.

D GIS for Flood Modelling in River Valleys

NASA Astrophysics Data System (ADS)

Tymkow, P.; Karpina, M.; Borkowski, A.

2016-06-01

The objective of this study is implementation of system architecture for collecting and analysing data as well as visualizing results for hydrodynamic modelling of flood flows in river valleys using remote sensing methods, tree-dimensional geometry of spatial objects and GPU multithread processing. The proposed solution includes: spatial data acquisition segment, data processing and transformation, mathematical modelling of flow phenomena and results visualization. Data acquisition segment was based on aerial laser scanning supplemented by images in visible range. Vector data creation was based on automatic and semiautomatic algorithms of DTM and 3D spatial features modelling. Algorithms for buildings and vegetation geometry modelling were proposed or adopted from literature. The implementation of the framework was designed as modular software using open specifications and partially reusing open source projects. The database structure for gathering and sharing vector data, including flood modelling results, was created using PostgreSQL. For the internal structure of feature classes of spatial objects in a database, the CityGML standard was used. For the hydrodynamic modelling the solutions of Navier-Stokes equations in two-dimensional version was implemented. Visualization of geospatial data and flow model results was transferred to the client side application. This gave the independence from server hardware platform. A real-world case in Poland, which is a part of Widawa River valley near Wroclaw city, was selected to demonstrate the applicability of proposed system.

Process-based modeling of temperature and water profiles in the seedling recruitment zone: Part I. Model validation

USDA-ARS?s Scientific Manuscript database

Process-based modeling provides detailed spatial and temporal information of the soil environment in the shallow seedling recruitment zone across field topography where measurements of soil temperature and water may not sufficiently describe the zone. Hourly temperature and water profiles within the...

3-D vision and figure-ground separation by visual cortex.

PubMed

Grossberg, S

1994-01-01

A neural network theory of three-dimensional (3-D) vision, called FACADE theory, is described. The theory proposes a solution of the classical figure-ground problem for biological vision. It does so by suggesting how boundary representations and surface representations are formed within a boundary contour system (BCS) and a feature contour system (FCS). The BCS and FCS interact reciprocally to form 3-D boundary and surface representations that are mutually consistent. Their interactions generate 3-D percepts wherein occluding and occluded object parts are separated, completed, and grouped. The theory clarifies how preattentive processes of 3-D perception and figure-ground separation interact reciprocally with attentive processes of spatial localization, object recognition, and visual search. A new theory of stereopsis is proposed that predicts how cells sensitive to multiple spatial frequencies, disparities, and orientations are combined by context-sensitive filtering, competition, and cooperation to form coherent BCS boundary segmentations. Several factors contribute to figure-ground pop-out, including: boundary contrast between spatially contiguous boundaries, whether due to scenic differences in luminance, color, spatial frequency, or disparity; partially ordered interactions from larger spatial scales and disparities to smaller scales and disparities; and surface filling-in restricted to regions surrounded by a connected boundary. Phenomena such as 3-D pop-out from a 2-D picture, Da Vinci stereopsis, 3-D neon color spreading, completion of partially occluded objects, and figure-ground reversals are analyzed. The BCS and FCS subsystems model aspects of how the two parvocellular cortical processing streams that join the lateral geniculate nucleus to prestriate cortical area V4 interact to generate a multiplexed representation of Form-And-Color-And-DEpth, or FACADE, within area V4. Area V4 is suggested to support figure-ground separation and to interact with cortical mechanisms of spatial attention, attentive object learning, and visual search. Adaptive resonance theory (ART) mechanisms model aspects of how prestriate visual cortex interacts reciprocally with a visual object recognition system in inferotemporal (IT) cortex for purposes of attentive object learning and categorization. Object attention mechanisms of the What cortical processing stream through IT cortex are distinguished from spatial attention mechanisms of the Where cortical processing stream through parietal cortex. Parvocellular BCS and FCS signals interact with the model What stream. Parvocellular FCS and magnocellular motion BCS signals interact with the model Where stream.(ABSTRACT TRUNCATED AT 400 WORDS)

A spatial haplotype copying model with applications to genotype imputation.

PubMed

Yang, Wen-Yun; Hormozdiari, Farhad; Eskin, Eleazar; Pasaniuc, Bogdan

2015-05-01

Ever since its introduction, the haplotype copy model has proven to be one of the most successful approaches for modeling genetic variation in human populations, with applications ranging from ancestry inference to genotype phasing and imputation. Motivated by coalescent theory, this approach assumes that any chromosome (haplotype) can be modeled as a mosaic of segments copied from a set of chromosomes sampled from the same population. At the core of the model is the assumption that any chromosome from the sample is equally likely to contribute a priori to the copying process. Motivated by recent works that model genetic variation in a geographic continuum, we propose a new spatial-aware haplotype copy model that jointly models geography and the haplotype copying process. We extend hidden Markov models of haplotype diversity such that at any given location, haplotypes that are closest in the genetic-geographic continuum map are a priori more likely to contribute to the copying process than distant ones. Through simulations starting from the 1000 Genomes data, we show that our model achieves superior accuracy in genotype imputation over the standard spatial-unaware haplotype copy model. In addition, we show the utility of our model in selecting a small personalized reference panel for imputation that leads to both improved accuracy as well as to a lower computational runtime than the standard approach. Finally, we show our proposed model can be used to localize individuals on the genetic-geographical map on the basis of their genotype data.

Methods for landslide susceptibility modelling in Lower Austria

NASA Astrophysics Data System (ADS)

Bell, Rainer; Petschko, Helene; Glade, Thomas; Leopold, Philip; Heiss, Gerhard; Proske, Herwig; Granica, Klaus; Schweigl, Joachim; Pomaroli, Gilbert

2010-05-01

Landslide susceptibility modelling and implementation of the resulting maps is still a challenge for geoscientists, spatial and infrastructure planners. Particularly on a regional scale landslide processes and their dynamics are poorly understood. Furthermore, the availability of appropriate spatial data in high resolution is often a limiting factor for modelling high quality landslide susceptibility maps for large study areas. However, these maps form an important basis for preventive spatial planning measures. Thus, new methods have to be developed, especially focussing on the implementation of final maps into spatial planning processes. The main objective of the project "MoNOE" (Method development for landslide susceptibility modelling in Lower Austria) is to design a method for landslide susceptibility modelling for a large study area (about 10.200 km²) and to produce landslide susceptibility maps which are finally implemented in the spatial planning strategies of the Federal state of Lower Austria. The project focuses primarily on the landslide types fall and slide. To enable susceptibility modelling, landslide inventories for the respective landslide types must be compiled and relevant data has to be gathered, prepared and homogenized. Based on this data new methods must be developed to tackle the needs of the spatial planning strategies. Considerable efforts will also be spent on the validation of the resulting maps for each landslide type. A great challenge will be the combination of the susceptibility maps for slides and falls in just one single susceptibility map (which is requested by the government) and the definition of the final visualisation. Since numerous landslides have been favoured or even triggered by human impact, the human influence on landslides will also have to be investigated. Furthermore possibilities to integrate respective findings in regional susceptibility modelling will be explored. According to these objectives the project is structured in four work packages namely data preparation and homogenization (WP1), susceptibility modelling and validation (WP2), integrative susceptibility assessment (WP3) and human impact (WP4). The expected results are a landslide inventory map covering all endangered parts of the Federal state of Lower Austria, a land cover map of Lower Austria with high spatial resolution, processed spatial input data and an optimized integrative susceptibility map visualized at a scale of 1:25.000. The structure of the research project, research strategies as well as first results will be presented at the conference. The project is funded by the Federal state government of Lower Austria.

Spatial occupancy models for large data sets

USGS Publications Warehouse

Johnson, Devin S.; Conn, Paul B.; Hooten, Mevin B.; Ray, Justina C.; Pond, Bruce A.

2013-01-01

Since its development, occupancy modeling has become a popular and useful tool for ecologists wishing to learn about the dynamics of species occurrence over time and space. Such models require presence–absence data to be collected at spatially indexed survey units. However, only recently have researchers recognized the need to correct for spatially induced overdisperison by explicitly accounting for spatial autocorrelation in occupancy probability. Previous efforts to incorporate such autocorrelation have largely focused on logit-normal formulations for occupancy, with spatial autocorrelation induced by a random effect within a hierarchical modeling framework. Although useful, computational time generally limits such an approach to relatively small data sets, and there are often problems with algorithm instability, yielding unsatisfactory results. Further, recent research has revealed a hidden form of multicollinearity in such applications, which may lead to parameter bias if not explicitly addressed. Combining several techniques, we present a unifying hierarchical spatial occupancy model specification that is particularly effective over large spatial extents. This approach employs a probit mixture framework for occupancy and can easily accommodate a reduced-dimensional spatial process to resolve issues with multicollinearity and spatial confounding while improving algorithm convergence. Using open-source software, we demonstrate this new model specification using a case study involving occupancy of caribou (Rangifer tarandus) over a set of 1080 survey units spanning a large contiguous region (108 000 km2) in northern Ontario, Canada. Overall, the combination of a more efficient specification and open-source software allows for a facile and stable implementation of spatial occupancy models for large data sets.

Have Sex Differences in Spatial Ability Evolved from Male Competition for Mating and Female Concern for Survival?

ERIC Educational Resources Information Center

Ecuyer-Dab, Isabelle; Robert, Michele

2004-01-01

Drawing on the theoretical and empirical foundations of two evolutionary models, we argue that, among humans and other mammals, a twofold selection process would parsimoniously account for sex-linked advantages in spatial contexts. In males, a superiority for both solving navigation-related spatial problems and understanding physical principles…

An Investigation of the Visual and Tactile Spatial Reasoning Abilities of the Hearing Impaired/Deaf Student.

ERIC Educational Resources Information Center

Hauptman, Anna R.

Two experiments involving 42 students from the Model Secondary School for the Deaf investigated both the visual and tactile components in the processing of spatial information. Test measures used were the Figures Rotations Test, Group Embedded Figures Test, and Tactile Rotations Test. The study suggested that spatial reasoning is a determining…

A neuroanatomical model of space-based and object-centered processing in spatial neglect.

PubMed

Pedrazzini, Elena; Schnider, Armin; Ptak, Radek

2017-11-01

Visual attention can be deployed in space-based or object-centered reference frames. Right-hemisphere damage may lead to distinct deficits of space- or object-based processing, and such dissociations are thought to underlie the heterogeneous nature of spatial neglect. Previous studies have suggested that object-centered processing deficits (such as in copying, reading or line bisection) result from damage to retro-rolandic regions while impaired spatial exploration reflects damage to more anterior regions. However, this evidence is based on small samples and heterogeneous tasks. Here, we tested a theoretical model of neglect that takes in account the space- and object-based processing and relates them to neuroanatomical predictors. One hundred and one right-hemisphere-damaged patients were examined with classic neuropsychological tests and structural brain imaging. Relations between neglect measures and damage to the temporal-parietal junction, intraparietal cortex, insula and middle frontal gyrus were examined with two structural equation models by assuming that object-centered processing (involved in line bisection and single-word reading) and space-based processing (involved in cancelation tasks) either represented a unique latent variable or two distinct variables. Of these two models the latter had better explanatory power. Damage to the intraparietal sulcus was a significant predictor of object-centered, but not space-based processing, while damage to the temporal-parietal junction predicted space-based, but not object-centered processing. Space-based processing and object-centered processing were strongly intercorrelated, indicating that they rely on similar, albeit partly dissociated processes. These findings indicate that object-centered and space-based deficits in neglect are partly independent and result from superior parietal and inferior parietal damage, respectively.

Spatial elements of mortality risk in old-growth forests

USGS Publications Warehouse

Das, Adrian; Battles, John; van Mantgem, Phillip J.; Stephenson, Nathan L.

2008-01-01

For many species of long-lived organisms, such as trees, survival appears to be the most critical vital rate affecting population persistence. However, methods commonly used to quantify tree death, such as relating tree mortality risk solely to diameter growth, almost certainly do not account for important spatial processes. Our goal in this study was to detect and, if present, to quantify the relevance of such processes. For this purpose, we examined purely spatial aspects of mortality for four species, Abies concolor, Abies magnifica, Calocedrus decurrens, and Pinus lambertiana, in an old-growth conifer forest in the Sierra Nevada of California, USA. The analysis was performed using data from nine fully mapped long-term monitoring plots.In three cases, the results unequivocally supported the inclusion of spatial information in models used to predict mortality. For Abies concolor, our results suggested that growth rate may not always adequately capture increased mortality risk due to competition. We also found evidence of a facilitative effect for this species, with mortality risk decreasing with proximity to conspecific neighbors. For Pinus lambertiana, mortality risk increased with density of conspecific neighbors, in keeping with a mechanism of increased pathogen or insect pressure (i.e., a Janzen-Connell type effect). Finally, we found that models estimating risk of being crushed were strongly improved by the inclusion of a simple index of spatial proximity.Not only did spatial indices improve models, those improvements were relevant for mortality prediction. For P. lambertiana, spatial factors were important for estimation of mortality risk regardless of growth rate. For A. concolor, although most of the population fell within spatial conditions in which mortality risk was well described by growth, trees that died occurred outside those conditions in a disproportionate fashion. Furthermore, as stands of A. concolor become increasingly dense, such spatial factors are likely to become increasingly important. In general, models that fail to account for spatial pattern are at risk of failure as conditions change.

The effect of spatial organization of targets and distractors on the capacity to selectively memorize objects in visual short-term memory.

PubMed

Abbes, Aymen Ben; Gavault, Emmanuelle; Ripoll, Thierry

2014-01-01

We conducted a series of experiments to explore how the spatial configuration of objects influences the selection and the processing of these objects in a visual short-term memory task. We designed a new experiment in which participants had to memorize 4 targets presented among 4 distractors. Targets were cued during the presentation of distractor objects. Their locations varied according to 4 spatial configurations. From the first to the last configuration, the distance between targets' locations was progressively increased. The results revealed a high capacity to select and memorize targets embedded among distractors even when targets were extremely distant from each other. This capacity is discussed in relation to the unitary conception of attention, models of split attention, and the competitive interaction model. Finally, we propose that the spatial dispersion of objects has different effects on attentional allocation and processing stages. Thus, when targets are extremely distant from each other, attentional allocation becomes more difficult while processing becomes easier. This finding implicates that these 2 aspects of attention need to be more clearly distinguished in future research.

Retinex at 50: color theory and spatial algorithms, a review

NASA Astrophysics Data System (ADS)

McCann, John J.

2017-05-01

Retinex Imaging shares two distinct elements: first, a model of human color vision; second, a spatial-imaging algorithm for making better reproductions. Edwin Land's 1964 Retinex Color Theory began as a model of human color vision of real complex scenes. He designed many experiments, such as Color Mondrians, to understand why retinal cone quanta catch fails to predict color constancy. Land's Retinex model used three spatial channels (L, M, S) that calculated three independent sets of monochromatic lightnesses. Land and McCann's lightness model used spatial comparisons followed by spatial integration across the scene. The parameters of their model were derived from extensive observer data. This work was the beginning of the second Retinex element, namely, using models of spatial vision to guide image reproduction algorithms. Today, there are many different Retinex algorithms. This special section, "Retinex at 50," describes a wide variety of them, along with their different goals, and ground truths used to measure their success. This paper reviews (and provides links to) the original Retinex experiments and image-processing implementations. Observer matches (measuring appearances) have extended our understanding of how human spatial vision works. This paper describes a collection very challenging datasets, accumulated by Land and McCann, for testing algorithms that predict appearance.

Local accumulation times for spatial difference in morphogen concentration

NASA Astrophysics Data System (ADS)

Wen, Xiaoqing; Yin, Hongwei

During development of multicellular organisms, spatial patterns of cells and tissue organizations rely on the action of morphogens, which are signaling molecules and act as dose-dependent regulators of gene expression and cellular differentiation. Since some experimental evidences have indicated that the spatial difference in morphogen concentration regulates cellular proliferation rather than this concentration profile in developing tissues, we propose spatially discrete models to describe this difference for a synthesis-diffusion-degradation process of morphogen in infinite and finite development fields, respectively. For both of models, we respectively derive analytical expressions of local accumulation times, which are required to form the steady state of the spatial difference in morphogen concentration. Our results show that the local accumulation times for the spatial difference in morphogen concentrations are different from the ones for morphogen concentration profiles.

AHP-based spatial analysis of water quality impact assessment due to change in vehicular traffic caused by highway broadening in Sikkim Himalaya

NASA Astrophysics Data System (ADS)

Banerjee, Polash; Ghose, Mrinal Kanti; Pradhan, Ratika

2018-05-01

Spatial analysis of water quality impact assessment of highway projects in mountainous areas remains largely unexplored. A methodology is presented here for Spatial Water Quality Impact Assessment (SWQIA) due to highway-broadening-induced vehicular traffic change in the East district of Sikkim. Pollution load of the highway runoff was estimated using an Average Annual Daily Traffic-Based Empirical model in combination with mass balance model to predict pollution in the rivers within the study area. Spatial interpolation and overlay analysis were used for impact mapping. Analytic Hierarchy Process-Based Water Quality Status Index was used to prepare a composite impact map. Model validation criteria, cross-validation criteria, and spatial explicit sensitivity analysis show that the SWQIA model is robust. The study shows that vehicular traffic is a significant contributor to water pollution in the study area. The model is catering specifically to impact analysis of the concerned project. It can be an aid for decision support system for the project stakeholders. The applicability of SWQIA model needs to be explored and validated in the context of a larger set of water quality parameters and project scenarios at a greater spatial scale.

Principles of Temporal Processing Across the Cortical Hierarchy.

PubMed

Himberger, Kevin D; Chien, Hsiang-Yun; Honey, Christopher J

2018-05-02

The world is richly structured on multiple spatiotemporal scales. In order to represent spatial structure, many machine-learning models repeat a set of basic operations at each layer of a hierarchical architecture. These iterated spatial operations - including pooling, normalization and pattern completion - enable these systems to recognize and predict spatial structure, while robust to changes in the spatial scale, contrast and noisiness of the input signal. Because our brains also process temporal information that is rich and occurs across multiple time scales, might the brain employ an analogous set of operations for temporal information processing? Here we define a candidate set of temporal operations, and we review evidence that they are implemented in the mammalian cerebral cortex in a hierarchical manner. We conclude that multiple consecutive stages of cortical processing can be understood to perform temporal pooling, temporal normalization and temporal pattern completion. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Spatial price dynamics: From complex network perspective

NASA Astrophysics Data System (ADS)

Li, Y. L.; Bi, J. T.; Sun, H. J.

2008-10-01

The spatial price problem means that if the supply price plus the transportation cost is less than the demand price, there exists a trade. Thus, after an amount of exchange, the demand price will decrease. This process is continuous until an equilibrium state is obtained. However, how the trade network structure affects this process has received little attention. In this paper, we give a evolving model to describe the levels of spatial price on different complex network structures. The simulation results show that the network with shorter path length is sensitive to the variation of prices.

Computer-Controlled Cylindrical Polishing Process for Large X-Ray Mirror Mandrels

NASA Technical Reports Server (NTRS)

Khan, Gufran S.; Gubarev, Mikhail; Speegle, Chet; Ramsey, Brian

2010-01-01

We are developing high-energy grazing incidence shell optics for hard-x-ray telescopes. The resolution of a mirror shells depends on the quality of cylindrical mandrel from which they are being replicated. Mid-spatial-frequency axial figure error is a dominant contributor in the error budget of the mandrel. This paper presents our efforts to develop a deterministic cylindrical polishing process in order to keep the mid-spatial-frequency axial figure errors to a minimum. Simulation software is developed to model the residual surface figure errors of a mandrel due to the polishing process parameters and the tools used, as well as to compute the optical performance of the optics. The study carried out using the developed software was focused on establishing a relationship between the polishing process parameters and the mid-spatial-frequency error generation. The process parameters modeled are the speeds of the lap and the mandrel, the tool s influence function, the contour path (dwell) of the tools, their shape and the distribution of the tools on the polishing lap. Using the inputs from the mathematical model, a mandrel having conical approximated Wolter-1 geometry, has been polished on a newly developed computer-controlled cylindrical polishing machine. The preliminary results of a series of polishing experiments demonstrate a qualitative agreement with the developed model. We report our first experimental results and discuss plans for further improvements in the polishing process. The ability to simulate the polishing process is critical to optimize the polishing process, improve the mandrel quality and significantly reduce the cost of mandrel production

Controls on the spatial variability of key soil properties: comparing field data with a mechanistic soilscape evolution model

NASA Astrophysics Data System (ADS)

Vanwalleghem, T.; Román, A.; Giraldez, J. V.

2016-12-01

There is a need for better understanding the processes influencing soil formation and the resulting distribution of soil properties. Soil properties can exhibit strong spatial variation, even at the small catchment scale. Especially soil carbon pools in semi-arid, mountainous areas are highly uncertain because bulk density and stoniness are very heterogeneous and rarely measured explicitly. In this study, we explore the spatial variability in key soil properties (soil carbon stocks, stoniness, bulk density and soil depth) as a function of processes shaping the critical zone (weathering, erosion, soil water fluxes and vegetation patterns). We also compare the potential of a geostatistical versus a mechanistic soil formation model (MILESD) for predicting these key soil properties. Soil core samples were collected from 67 locations at 6 depths. Total soil organic carbon stocks were 4.38 kg m-2. Solar radiation proved to be the key variable controlling soil carbon distribution. Stone content was mostly controlled by slope, indicating the importance of erosion. Spatial distribution of bulk density was found to be highly random. Finally, total carbon stocks were predicted using a random forest model whose main covariates were solar radiation and NDVI. The model predicts carbon stocks that are double as high on north versus south-facing slopes. However, validation showed that these covariates only explained 25% of the variation in the dataset. Apparently, present-day landscape and vegetation properties are not sufficient to fully explain variability in the soil carbon stocks in this complex terrain under natural vegetation. This is attributed to a high spatial variability in bulk density and stoniness, key variables controlling carbon stocks. Similar results were obtained with the mechanistic soil formation model MILESD, suggesting that more complex models might be needed to further explore this high spatial variability.

Spatial autocorrelation among automated geocoding errors and its effects on testing for disease clustering

PubMed Central

Li, Jie; Fang, Xiangming

2010-01-01

Automated geocoding of patient addresses is an important data assimilation component of many spatial epidemiologic studies. Inevitably, the geocoding process results in positional errors. Positional errors incurred by automated geocoding tend to reduce the power of tests for disease clustering and otherwise affect spatial analytic methods. However, there are reasons to believe that the errors may often be positively spatially correlated and that this may mitigate their deleterious effects on spatial analyses. In this article, we demonstrate explicitly that the positional errors associated with automated geocoding of a dataset of more than 6000 addresses in Carroll County, Iowa are spatially autocorrelated. Furthermore, through two simulation studies of disease processes, including one in which the disease process is overlain upon the Carroll County addresses, we show that spatial autocorrelation among geocoding errors maintains the power of two tests for disease clustering at a level higher than that which would occur if the errors were independent. Implications of these results for cluster detection, privacy protection, and measurement-error modeling of geographic health data are discussed. PMID:20087879

On the Limiting Markov Process of Energy Exchanges in a Rarely Interacting Ball-Piston Gas

NASA Astrophysics Data System (ADS)

Bálint, Péter; Gilbert, Thomas; Nándori, Péter; Szász, Domokos; Tóth, Imre Péter

2017-02-01

We analyse the process of energy exchanges generated by the elastic collisions between a point-particle, confined to a two-dimensional cell with convex boundaries, and a `piston', i.e. a line-segment, which moves back and forth along a one-dimensional interval partially intersecting the cell. This model can be considered as the elementary building block of a spatially extended high-dimensional billiard modeling heat transport in a class of hybrid materials exhibiting the kinetics of gases and spatial structure of solids. Using heuristic arguments and numerical analysis, we argue that, in a regime of rare interactions, the billiard process converges to a Markov jump process for the energy exchanges and obtain the expression of its generator.

The effects of seed dispersal on the simulation of long-term forest landscape change

Treesearch

Hong S. He; David J. Mladenoff

1999-01-01

The study of forest landscape change requires an understanding of the complex interactions of both spatial and temporal factors. Traditionally, forest gap models have been used to simulate change on small and independent plots. While gap models are useful in examining forest ecological dynamics across temporal scales, large, spatial processes, such as seed dispersal,...

Designing Spatial Visualisation Tasks for Middle School Students with a 3D Modelling Software: An Instrumental Approach

ERIC Educational Resources Information Center

Turgut, Melih; Uygan, Candas

2015-01-01

In this work, certain task designs to enhance middle school students' spatial visualisation ability, in the context of an instrumental approach, have been developed. 3D modelling software, SketchUp®, was used. In the design process, software tools were focused on and, thereafter, the aim was to interpret the instrumental genesis and spatial…

Spatial Modeling and Uncertainty Assessment of Fine Scale Surface Processes Based on Coarse Terrain Elevation Data

NASA Astrophysics Data System (ADS)

Rasera, L. G.; Mariethoz, G.; Lane, S. N.

2017-12-01

Frequent acquisition of high-resolution digital elevation models (HR-DEMs) over large areas is expensive and difficult. Satellite-derived low-resolution digital elevation models (LR-DEMs) provide extensive coverage of Earth's surface but at coarser spatial and temporal resolutions. Although useful for large scale problems, LR-DEMs are not suitable for modeling hydrologic and geomorphic processes at scales smaller than their spatial resolution. In this work, we present a multiple-point geostatistical approach for downscaling a target LR-DEM based on available high-resolution training data and recurrent high-resolution remote sensing images. The method aims at generating several equiprobable HR-DEMs conditioned to a given target LR-DEM by borrowing small scale topographic patterns from an analogue containing data at both coarse and fine scales. An application of the methodology is demonstrated by using an ensemble of simulated HR-DEMs as input to a flow-routing algorithm. The proposed framework enables a probabilistic assessment of the spatial structures generated by natural phenomena operating at scales finer than the available terrain elevation measurements. A case study in the Swiss Alps is provided to illustrate the methodology.

Fully coupled approach to modeling shallow water flow, sediment transport, and bed evolution in rivers

NASA Astrophysics Data System (ADS)

Li, Shuangcai; Duffy, Christopher J.

2011-03-01

Our ability to predict complex environmental fluid flow and transport hinges on accurate and efficient simulations of multiple physical phenomenon operating simultaneously over a wide range of spatial and temporal scales, including overbank floods, coastal storm surge events, drying and wetting bed conditions, and simultaneous bed form evolution. This research implements a fully coupled strategy for solving shallow water hydrodynamics, sediment transport, and morphological bed evolution in rivers and floodplains (PIHM_Hydro) and applies the model to field and laboratory experiments that cover a wide range of spatial and temporal scales. The model uses a standard upwind finite volume method and Roe's approximate Riemann solver for unstructured grids. A multidimensional linear reconstruction and slope limiter are implemented, achieving second-order spatial accuracy. Model efficiency and stability are treated using an explicit-implicit method for temporal discretization with operator splitting. Laboratory-and field-scale experiments were compiled where coupled processes across a range of scales were observed and where higher-order spatial and temporal accuracy might be needed for accurate and efficient solutions. These experiments demonstrate the ability of the fully coupled strategy in capturing dynamics of field-scale flood waves and small-scale drying-wetting processes.

Space-time modeling of soil moisture

NASA Astrophysics Data System (ADS)

Chen, Zijuan; Mohanty, Binayak P.; Rodriguez-Iturbe, Ignacio

2017-11-01

A physically derived space-time mathematical representation of the soil moisture field is carried out via the soil moisture balance equation driven by stochastic rainfall forcing. The model incorporates spatial diffusion and in its original version, it is shown to be unable to reproduce the relative fast decay in the spatial correlation functions observed in empirical data. This decay resulting from variations in local topography as well as in local soil and vegetation conditions is well reproduced via a jitter process acting multiplicatively over the space-time soil moisture field. The jitter is a multiplicative noise acting on the soil moisture dynamics with the objective to deflate its correlation structure at small spatial scales which are not embedded in the probabilistic structure of the rainfall process that drives the dynamics. These scales of order of several meters to several hundred meters are of great importance in ecohydrologic dynamics. Properties of space-time correlation functions and spectral densities of the model with jitter are explored analytically, and the influence of the jitter parameters, reflecting variabilities of soil moisture at different spatial and temporal scales, is investigated. A case study fitting the derived model to a soil moisture dataset is presented in detail.

Temporal associations for spatial events: the role of the dentate gyrus.

PubMed

Morris, Andrea M; Curtis, Brian J; Churchwell, John C; Maasberg, David W; Kesner, Raymond P

2013-11-01

Previous research suggests that the dorsal dentate gyrus (DG) hippocampal subregion mediates spatial processing functions. However, a novel role for the DG in temporal processing for spatial information has begun to emerge based on the development of a computational model of neurogenesis. According to this model, adult born granule cells in the DG contribute to a temporal associative integration process for events presented closer in time. Currently, there is a paucity of behavioral evidence to support the temporal integration theory. Therefore, we developed a novel behavioral paradigm to investigate the role of the dDG in temporal integration for proximal and distal spatial events. Male Long-Evans rats were randomly assigned to a control group or to receive bilateral intracranial infusions of colchicine into the dDG. Following recovery from surgery, each rat was tested on a cued-recall of sequence paradigm. In this task, animals were allowed to explore identical objects placed in designated spatial locations on a cheeseboard maze across 2 days (e.g., Day 1: A and B; Day 2: C and D). One week later, animals were given a brief cue (A or C) followed by a preference test between spatial location B and D. Control animals had a significant preference for the spatial location previously paired with the cue (the temporal associate) whereas dDG lesioned animals failed to show a preference. These findings suggest that selective colchicine-induced dDG lesions are capable of disrupting the formation of temporal associations between spatial events presented close in time. Copyright © 2013 Elsevier B.V. All rights reserved.

Dynamic interactions between visual working memory and saccade target selection

PubMed Central

Schneegans, Sebastian; Spencer, John P.; Schöner, Gregor; Hwang, Seongmin; Hollingworth, Andrew

2014-01-01

Recent psychophysical experiments have shown that working memory for visual surface features interacts with saccadic motor planning, even in tasks where the saccade target is unambiguously specified by spatial cues. Specifically, a match between a memorized color and the color of either the designated target or a distractor stimulus influences saccade target selection, saccade amplitudes, and latencies in a systematic fashion. To elucidate these effects, we present a dynamic neural field model in combination with new experimental data. The model captures the neural processes underlying visual perception, working memory, and saccade planning relevant to the psychophysical experiment. It consists of a low-level visual sensory representation that interacts with two separate pathways: a spatial pathway implementing spatial attention and saccade generation, and a surface feature pathway implementing color working memory and feature attention. Due to bidirectional coupling between visual working memory and feature attention in the model, the working memory content can indirectly exert an effect on perceptual processing in the low-level sensory representation. This in turn biases saccadic movement planning in the spatial pathway, allowing the model to quantitatively reproduce the observed interaction effects. The continuous coupling between representations in the model also implies that modulation should be bidirectional, and model simulations provide specific predictions for complementary effects of saccade target selection on visual working memory. These predictions were empirically confirmed in a new experiment: Memory for a sample color was biased toward the color of a task-irrelevant saccade target object, demonstrating the bidirectional coupling between visual working memory and perceptual processing. PMID:25228628

Thematic and spatial resolutions affect model-based predictions of tree species distribution.

PubMed

Liang, Yu; He, Hong S; Fraser, Jacob S; Wu, ZhiWei

2013-01-01

Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance). We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity) had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types) and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution.

Thematic and Spatial Resolutions Affect Model-Based Predictions of Tree Species Distribution

PubMed Central

Liang, Yu; He, Hong S.; Fraser, Jacob S.; Wu, ZhiWei

2013-01-01

Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance). We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity) had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types) and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution. PMID:23861828

Global patterns and climate drivers of water-use efficiency in terrestrial ecosystems deduced from satellite-based datasets and carbon cycle models

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

Sun, Yan; Piao, Shilong; Huang, Mengtian

Our aim is to investigate how ecosystem water-use efficiency (WUE) varies spatially under different climate conditions, and how spatial variations in WUE differ from those of transpiration-based water-use efficiency (WUE t) and transpiration-based inherent water-use efficiency (IWUE t). LocationGlobal terrestrial ecosystems. We investigated spatial patterns of WUE using two datasets of gross primary productivity (GPP) and evapotranspiration (ET) and four biosphere model estimates of GPP and ET. Spatial relationships between WUE and climate variables were further explored through regression analyses. Global WUE estimated by two satellite-based datasets is 1.9 ± 0.1 and 1.8 ± 0.6g C m -2mm -1 lowermore » than the simulations from four process-based models (2.0 ± 0.3g C m -2mm -1) but comparable within the uncertainty of both approaches. In both satellite-based datasets and process models, precipitation is more strongly associated with spatial gradients of WUE for temperate and tropical regions, but temperature dominates north of 50 degrees N. WUE also increases with increasing solar radiation at high latitudes. The values of WUE from datasets and process-based models are systematically higher in wet regions (with higher GPP) than in dry regions. WUE t shows a lower precipitation sensitivity than WUE, which is contrary to leaf- and plant-level observations. IWUE t, the product of WUE t and water vapour deficit, is found to be rather conservative with spatially increasing precipitation, in agreement with leaf- and plant-level measurements. In conclusion, WUE, WUE t and IWUE t produce different spatial relationships with climate variables. In dry ecosystems, water losses from evaporation from bare soil, uncorrelated with productivity, tend to make WUE lower than in wetter regions. Yet canopy conductance is intrinsically efficient in those ecosystems and maintains a higher IWUEt. This suggests that the responses of each component flux of evapotranspiration should be analysed separately when investigating regional gradients in WUE, its temporal variability and its trends.« less

Global patterns and climate drivers of water-use efficiency in terrestrial ecosystems deduced from satellite-based datasets and carbon cycle models

DOE PAGES

Sun, Yan; Piao, Shilong; Huang, Mengtian; ...

2015-12-23

Our aim is to investigate how ecosystem water-use efficiency (WUE) varies spatially under different climate conditions, and how spatial variations in WUE differ from those of transpiration-based water-use efficiency (WUE t) and transpiration-based inherent water-use efficiency (IWUE t). LocationGlobal terrestrial ecosystems. We investigated spatial patterns of WUE using two datasets of gross primary productivity (GPP) and evapotranspiration (ET) and four biosphere model estimates of GPP and ET. Spatial relationships between WUE and climate variables were further explored through regression analyses. Global WUE estimated by two satellite-based datasets is 1.9 ± 0.1 and 1.8 ± 0.6g C m -2mm -1 lowermore » than the simulations from four process-based models (2.0 ± 0.3g C m -2mm -1) but comparable within the uncertainty of both approaches. In both satellite-based datasets and process models, precipitation is more strongly associated with spatial gradients of WUE for temperate and tropical regions, but temperature dominates north of 50 degrees N. WUE also increases with increasing solar radiation at high latitudes. The values of WUE from datasets and process-based models are systematically higher in wet regions (with higher GPP) than in dry regions. WUE t shows a lower precipitation sensitivity than WUE, which is contrary to leaf- and plant-level observations. IWUE t, the product of WUE t and water vapour deficit, is found to be rather conservative with spatially increasing precipitation, in agreement with leaf- and plant-level measurements. In conclusion, WUE, WUE t and IWUE t produce different spatial relationships with climate variables. In dry ecosystems, water losses from evaporation from bare soil, uncorrelated with productivity, tend to make WUE lower than in wetter regions. Yet canopy conductance is intrinsically efficient in those ecosystems and maintains a higher IWUEt. This suggests that the responses of each component flux of evapotranspiration should be analysed separately when investigating regional gradients in WUE, its temporal variability and its trends.« less

geophylobuilder 1.0: an arcgis extension for creating 'geophylogenies'.

PubMed

Kidd, David M; Liu, Xianhua

2008-01-01

Evolution is inherently a spatiotemporal process; however, despite this, phylogenetic and geographical data and models remain largely isolated from one another. Geographical information systems provide a ready-made spatial modelling, analysis and dissemination environment within which phylogenetic models can be explicitly linked with their associated spatial data and subsequently integrated with other georeferenced data sets describing the biotic and abiotic environment. geophylobuilder 1.0 is an extension for the arcgis geographical information system that builds a 'geophylogenetic' data model from a phylogenetic tree and associated geographical data. Geophylogenetic database objects can subsequently be queried, spatially analysed and visualized in both 2D and 3D within a geographical information systems. © 2007 The Authors.

Nonrandom community assembly and high temporal turnover promote regional coexistence in tropics but not temperate zone.

PubMed

Freestone, Amy L; Inouye, Brian D

2015-01-01

A persistent challenge for ecologists is understanding the ecological mechanisms that maintain global patterns of biodiversity, particularly the latitudinal diversity gradient of peak species richness in the tropics. Spatial and temporal variation in community composition contribute to these patterns of biodiversity, but how this variation and its underlying processes change across latitude remains unresolved. Using a model system of sessile marine invertebrates across 25 degrees of latitude, from the temperate zone to the tropics, we tested the prediction that spatial and temporal patterns of taxonomic richness and composition, and the community assembly processes underlying these patterns, will differ across latitude. Specifically, we predicted that high beta diversity (spatial variation in composition) and high temporal turnover contribute to the high species richness of the tropics. Using a standardized experimental approach that controls for several confounding factors that hinder interpretation of prior studies, we present results that support our predictions. In the temperate zone, communities were more similar across spatial scales from centimeters to tens of kilometers and temporal scales up to one year than at lower latitudes. Since the patterns at northern latitudes were congruent with a null model, stochastic assembly processes are implicated. In contrast, the communities in the tropics were a dynamic spatial and temporal mosaic, with low similarity even across small spatial scales and high temporal turnover at both local and regional scales. Unlike the temperate zone, deterministic community assembly processes such as predation likely contributed to the high beta diversity in the tropics. Our results suggest that community assembly processes and temporal dynamics vary across latitude and help structure and maintain latitudinal patterns of diversity.

Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings.

PubMed

Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P; Nair, Vimala D

2017-09-11

Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (K ex ) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.

Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance

USGS Publications Warehouse

Wilson, T.L.; Odei, J.B.; Hooten, M.B.; Edwards, T.C.

2010-01-01

Conservationists routinely use species distribution models to plan conservation, restoration and development actions, while ecologists use them to infer process from pattern. These models tend to work well for common or easily observable species, but are of limited utility for rare and cryptic species. This may be because honest accounting of known observation bias and spatial autocorrelation are rarely included, thereby limiting statistical inference of resulting distribution maps. We specified and implemented a spatially explicit Bayesian hierarchical model for a cryptic mammal species (pygmy rabbit Brachylagus idahoensis). Our approach used two levels of indirect sign that are naturally hierarchical (burrows and faecal pellets) to build a model that allows for inference on regression coefficients as well as spatially explicit model parameters. We also produced maps of rabbit distribution (occupied burrows) and relative abundance (number of burrows expected to be occupied by pygmy rabbits). The model demonstrated statistically rigorous spatial prediction by including spatial autocorrelation and measurement uncertainty. We demonstrated flexibility of our modelling framework by depicting probabilistic distribution predictions using different assumptions of pygmy rabbit habitat requirements. Spatial representations of the variance of posterior predictive distributions were obtained to evaluate heterogeneity in model fit across the spatial domain. Leave-one-out cross-validation was conducted to evaluate the overall model fit. Synthesis and applications. Our method draws on the strengths of previous work, thereby bridging and extending two active areas of ecological research: species distribution models and multi-state occupancy modelling. Our framework can be extended to encompass both larger extents and other species for which direct estimation of abundance is difficult. ?? 2010 The Authors. Journal compilation ?? 2010 British Ecological Society.

An Augmented Two-Layer Model Captures Nonlinear Analog Spatial Integration Effects in Pyramidal Neuron Dendrites.

PubMed

Jadi, Monika P; Behabadi, Bardia F; Poleg-Polsky, Alon; Schiller, Jackie; Mel, Bartlett W

2014-05-01

In pursuit of the goal to understand and eventually reproduce the diverse functions of the brain, a key challenge lies in reverse engineering the peculiar biology-based "technology" that underlies the brain's remarkable ability to process and store information. The basic building block of the nervous system is the nerve cell, or "neuron," yet after more than 100 years of neurophysiological study and 60 years of modeling, the information processing functions of individual neurons, and the parameters that allow them to engage in so many different types of computation (sensory, motor, mnemonic, executive, etc.) remain poorly understood. In this paper, we review both historical and recent findings that have led to our current understanding of the analog spatial processing capabilities of dendrites, the major input structures of neurons, with a focus on the principal cell type of the neocortex and hippocampus, the pyramidal neuron (PN). We encapsulate our current understanding of PN dendritic integration in an abstract layered model whose spatially sensitive branch-subunits compute multidimensional sigmoidal functions. Unlike the 1-D sigmoids found in conventional neural network models, multidimensional sigmoids allow the cell to implement a rich spectrum of nonlinear modulation effects directly within their dendritic trees.

Hedonic approaches based on spatial econometrics and spatial statistics: application to evaluation of project benefits

NASA Astrophysics Data System (ADS)

Tsutsumi, Morito; Seya, Hajime

2009-12-01

This study discusses the theoretical foundation of the application of spatial hedonic approaches—the hedonic approach employing spatial econometrics or/and spatial statistics—to benefits evaluation. The study highlights the limitations of the spatial econometrics approach since it uses a spatial weight matrix that is not employed by the spatial statistics approach. Further, the study presents empirical analyses by applying the Spatial Autoregressive Error Model (SAEM), which is based on the spatial econometrics approach, and the Spatial Process Model (SPM), which is based on the spatial statistics approach. SPMs are conducted based on both isotropy and anisotropy and applied to different mesh sizes. The empirical analysis reveals that the estimated benefits are quite different, especially between isotropic and anisotropic SPM and between isotropic SPM and SAEM; the estimated benefits are similar for SAEM and anisotropic SPM. The study demonstrates that the mesh size does not affect the estimated amount of benefits. Finally, the study provides a confidence interval for the estimated benefits and raises an issue with regard to benefit evaluation.

A Spatially Continuous Model of Carbohydrate Digestion and Transport Processes in the Colon

PubMed Central

Moorthy, Arun S.; Brooks, Stephen P. J.; Kalmokoff, Martin; Eberl, Hermann J.

2015-01-01

A spatially continuous mathematical model of transport processes, anaerobic digestion and microbial complexity as would be expected in the human colon is presented. The model is a system of first-order partial differential equations with context determined number of dependent variables, and stiff, non-linear source terms. Numerical simulation of the model is used to elucidate information about the colon-microbiota complex. It is found that the composition of materials on outflow of the model does not well-describe the composition of material in other model locations, and inferences using outflow data varies according to model reactor representation. Additionally, increased microbial complexity allows the total microbial community to withstand major system perturbations in diet and community structure. However, distribution of strains and functional groups within the microbial community can be modified depending on perturbation length and microbial kinetic parameters. Preliminary model extensions and potential investigative opportunities using the computational model are discussed. PMID:26680208

The complex roles of space and environment in structuring functional, taxonomic and phylogenetic beta diversity of frogs in the Atlantic Forest

PubMed Central

Luiz, Amom Mendes; Sawaya, Ricardo J.

2018-01-01

Ecological communities are complex entities that can be maintained and structured by niche-based processes such as environmental conditions, and spatial processes such as dispersal. Thus, diversity patterns may be shaped simultaneously at different spatial scales by very distinct processes. Herein we assess whether and how functional, taxonomic, and phylogenetic beta diversities of frog tadpoles are explained by environmental and/or spatial predictors. We implemented a distance–based redundancy analysis to explore variation in components of beta diversity explained by pure environmental and pure spatial predictors, as well as their interactions, at both fine and broad spatial scales. Our results indicated important but complex roles of spatial and environmental predictors in structuring phylogenetic, taxonomic and functional beta diversities. The pure fine-scales spatial fraction was more important in structuring all beta diversity components, especially to functional and taxonomical spatial turnover. Environmental variables such as canopy cover and vegetation structure were important predictors of all components, but especially to functional and taxonomic beta diversity. We emphasize that distinct factors related to environment and space are affecting distinct components of beta diversity in different ways. Although weaker, phylogenetic beta diversity, which is structured more on biogeographical scales, and thus can be represented by spatially structured processes, was more related to broad spatial processes than other components. However, selected fine-scale spatial predictors denoted negative autocorrelation, which may be revealing the existence of differences in unmeasured habitat variables among samples. Although overall important, local environmental-based processes explained better functional and taxonomic beta diversity, as these diversity components carry an important ecological value. We highlight the importance of assessing different components of diversity patterns at different scales by spatially explicit models in order to improve our understanding of community structure and help to unravel the complex nature of biodiversity. PMID:29672575

Flexible hydrological modeling - Disaggregation from lumped catchment scale to higher spatial resolutions

NASA Astrophysics Data System (ADS)

Tran, Quoc Quan; Willems, Patrick; Pannemans, Bart; Blanckaert, Joris; Pereira, Fernando; Nossent, Jiri; Cauwenberghs, Kris; Vansteenkiste, Thomas

2015-04-01

Based on an international literature review on model structures of existing rainfall-runoff and hydrological models, a generalized model structure is proposed. It consists of different types of meteorological components, storage components, splitting components and routing components. They can be spatially organized in a lumped way, or on a grid, spatially interlinked by source-to-sink or grid-to-grid (cell-to-cell) routing. The grid size of the model can be chosen depending on the application. The user can select/change the spatial resolution depending on the needs and/or the evaluation of the accuracy of the model results, or use different spatial resolutions in parallel for different applications. Major research questions addressed during the study are: How can we assure consistent results of the model at any spatial detail? How can we avoid strong or sudden changes in model parameters and corresponding simulation results, when one moves from one level of spatial detail to another? How can we limit the problem of overparameterization/equifinality when we move from the lumped model to the spatially distributed model? The proposed approach is a step-wise one, where first the lumped conceptual model is calibrated using a systematic, data-based approach, followed by a disaggregation step where the lumped parameters are disaggregated based on spatial catchment characteristics (topography, land use, soil characteristics). In this way, disaggregation can be done down to any spatial scale, and consistently among scales. Only few additional calibration parameters are introduced to scale the absolute spatial differences in model parameters, but keeping the relative differences as obtained from the spatial catchment characteristics. After calibration of the spatial model, the accuracies of the lumped and spatial models were compared for peak, low and cumulative runoff total and sub-flows (at downstream and internal gauging stations). For the distributed models, additional validation on spatial results was done for the groundwater head values at observation wells. To ensure that the lumped model can produce results as accurate as the spatially distributed models or close regardless to the number of parameters and implemented physical processes, it was checked whether the structure of the lumped models had to be adjusted. The concept has been implemented in a PCRaster - Python platform and tested for two Belgian case studies (catchments of the rivers Dijle and Grote Nete). So far, use is made of existing model structures (NAM, PDM, VHM and HBV). Acknowledgement: These results were obtained within the scope of research activities for the Flemish Environment Agency (VMM) - division Operational Water Management on "Next Generation hydrological modeling", in cooperation with IMDC consultants, and for Flanders Hydraulics Research (Waterbouwkundig Laboratorium) on "Effect of climate change on the hydrological regime of navigable watercourses in Belgium".

Digital Hydrologic Networks Supporting Applications Related to Spatially Referenced Regression Modeling

USGS Publications Warehouse

Brakebill, J.W.; Wolock, D.M.; Terziotti, S.E.

2011-01-01

Digital hydrologic networks depicting surface-water pathways and their associated drainage catchments provide a key component to hydrologic analysis and modeling. Collectively, they form common spatial units that can be used to frame the descriptions of aquatic and watershed processes. In addition, they provide the ability to simulate and route the movement of water and associated constituents throughout the landscape. Digital hydrologic networks have evolved from derivatives of mapping products to detailed, interconnected, spatially referenced networks of water pathways, drainage areas, and stream and watershed characteristics. These properties are important because they enhance the ability to spatially evaluate factors that affect the sources and transport of water-quality constituents at various scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW), a process-based/statistical model, relies on a digital hydrologic network in order to establish relations between quantities of monitored contaminant flux, contaminant sources, and the associated physical characteristics affecting contaminant transport. Digital hydrologic networks modified from the River Reach File (RF1) and National Hydrography Dataset (NHD) geospatial datasets provided frameworks for SPARROW in six regions of the conterminous United States. In addition, characteristics of the modified RF1 were used to update estimates of mean-annual streamflow. This produced more current flow estimates for use in SPARROW modeling. ?? 2011 American Water Resources Association. This article is a U.S. Government work and is in the public domain in the USA.

Signal detection evidence for limited capacity in visual search

PubMed Central

Fencsik, David E.; Flusberg, Stephen J.; Horowitz, Todd S.; Wolfe, Jeremy M.

2014-01-01

The nature of capacity limits (if any) in visual search has been a topic of controversy for decades. In 30 years of work, researchers have attempted to distinguish between two broad classes of visual search models. Attention-limited models have proposed two stages of perceptual processing: an unlimited-capacity preattentive stage, and a limited-capacity selective attention stage. Conversely, noise-limited models have proposed a single, unlimited-capacity perceptual processing stage, with decision processes influenced only by stochastic noise. Here, we use signal detection methods to test a strong prediction of attention-limited models. In standard attention-limited models, performance of some searches (feature searches) should only be limited by a preattentive stage. Other search tasks (e.g., spatial configuration search for a “2” among “5”s) should be additionally limited by an attentional bottleneck. We equated average accuracies for a feature and a spatial configuration search over set sizes of 1–8 for briefly presented stimuli. The strong prediction of attention-limited models is that, given overall equivalence in performance, accuracy should be better on the spatial configuration search than on the feature search for set size 1, and worse for set size 8. We confirm this crossover interaction and show that it is problematic for at least one class of one-stage decision models. PMID:21901574

Separating spatial search and efficiency rates as components of predation risk

PubMed Central

DeCesare, Nicholas J.

2012-01-01

Predation risk is an important driver of ecosystems, and local spatial variation in risk can have population-level consequences by affecting multiple components of the predation process. I use resource selection and proportional hazard time-to-event modelling to assess the spatial drivers of two key components of risk—the search rate (i.e. aggregative response) and predation efficiency rate (i.e. functional response)—imposed by wolves (Canis lupus) in a multi-prey system. In my study area, both components of risk increased according to topographic variation, but anthropogenic features affected only the search rate. Predicted models of the cumulative hazard, or risk of a kill, underlying wolf search paths validated well with broad-scale variation in kill rates, suggesting that spatial hazard models provide a means of scaling up from local heterogeneity in predation risk to population-level dynamics in predator–prey systems. Additionally, I estimated an integrated model of relative spatial predation risk as the product of the search and efficiency rates, combining the distinct contributions of spatial heterogeneity to each component of risk. PMID:22977145

Spatial patterns of modern period human-caused fire occurrence in the Missouri Ozark Highlands

Treesearch

Jian Yang; Hong S. Healy; Stephen R. Shifley; Eric J. Gustafson

2007-01-01

The spatial pattern of forest fire locations is important in the study of the dynamics of fire disturbance. In this article we used a spatial point process modeling approach to quantitatively study the effects of land cover, topography, roads, municipalities, ownership, and population density on fire occurrence reported between 1970 and 2002 in the Missouri Ozark...

Empirical modeling of spatial and temporal variation in warm season nocturnal air temperatures in two North Idaho mountain ranges, USA

Treesearch

Zachery A. Holden; Michael A. Crimmins; Samuel A. Cushman; Jeremy S. Littell

2010-01-01

Accurate, fine spatial resolution predictions of surface air temperatures are critical for understanding many hydrologic and ecological processes. This study examines the spatial and temporal variability in nocturnal air temperatures across a mountainous region of Northern Idaho. Principal components analysis (PCA) was applied to a network of 70 Hobo temperature...

Spatial modeling of cell signaling networks.

PubMed

Cowan, Ann E; Moraru, Ion I; Schaff, James C; Slepchenko, Boris M; Loew, Leslie M

2012-01-01

The shape of a cell, the sizes of subcellular compartments, and the spatial distribution of molecules within the cytoplasm can all control how molecules interact to produce a cellular behavior. This chapter describes how these spatial features can be included in mechanistic mathematical models of cell signaling. The Virtual Cell computational modeling and simulation software is used to illustrate the considerations required to build a spatial model. An explanation of how to appropriately choose between physical formulations that implicitly or explicitly account for cell geometry and between deterministic versus stochastic formulations for molecular dynamics is provided, along with a discussion of their respective strengths and weaknesses. As a first step toward constructing a spatial model, the geometry needs to be specified and associated with the molecules, reactions, and membrane flux processes of the network. Initial conditions, diffusion coefficients, velocities, and boundary conditions complete the specifications required to define the mathematics of the model. The numerical methods used to solve reaction-diffusion problems both deterministically and stochastically are then described and some guidance is provided in how to set up and run simulations. A study of cAMP signaling in neurons ends the chapter, providing an example of the insights that can be gained in interpreting experimental results through the application of spatial modeling. Copyright © 2012 Elsevier Inc. All rights reserved.

A Note on Spatial Averaging and Shear Stresses Within Urban Canopies

NASA Astrophysics Data System (ADS)

Xie, Zheng-Tong; Fuka, Vladimir

2018-04-01

One-dimensional urban models embedded in mesoscale numerical models may place several grid points within the urban canopy. This requires an accurate parametrization for shear stresses (i.e. vertical momentum fluxes) including the dispersive stress and momentum sinks at these points. We used a case study with a packing density of 33% and checked rigorously the vertical variation of spatially-averaged total shear stress, which can be used in a one-dimensional column urban model. We found that the intrinsic spatial average, in which the volume or area of the solid parts are not included in the average process, yield greater time-spatial average of total stress within the canopy and a more evident abrupt change at the top of the buildings than the comprehensive spatial average, in which the volume or area of the solid parts are included in the average.

Land Use Change on Household Farms in the Ecuadorian Amazon: Design and Implementation of an Agent-Based Model.

PubMed

Mena, Carlos F; Walsh, Stephen J; Frizzelle, Brian G; Xiaozheng, Yao; Malanson, George P

2011-01-01

This paper describes the design and implementation of an Agent-Based Model (ABM) used to simulate land use change on household farms in the Northern Ecuadorian Amazon (NEA). The ABM simulates decision-making processes at the household level that is examined through a longitudinal, socio-economic and demographic survey that was conducted in 1990 and 1999. Geographic Information Systems (GIS) are used to establish spatial relationships between farms and their environment, while classified Landsat Thematic Mapper (TM) imagery is used to set initial land use/land cover conditions for the spatial simulation, assess from-to land use/land cover change patterns, and describe trajectories of land use change at the farm and landscape levels. Results from prior studies in the NEA provide insights into the key social and ecological variables, describe human behavioral functions, and examine population-environment interactions that are linked to deforestation and agricultural extensification, population migration, and demographic change. Within the architecture of the model, agents are classified as active or passive. The model comprises four modules, i.e., initialization, demography, agriculture, and migration that operate individually, but are linked through key household processes. The main outputs of the model include a spatially-explicit representation of the land use/land cover on survey and non-survey farms and at the landscape level for each annual time-step, as well as simulated socio-economic and demographic characteristics of households and communities. The work describes the design and implementation of the model and how population-environment interactions can be addressed in a frontier setting. The paper contributes to land change science by examining important pattern-process relations, advocating a spatial modeling approach that is capable of synthesizing fundamental relationships at the farm level, and links people and environment in complex ways.

Spatial Moran models, II: cancer initiation in spatially structured tissue

PubMed Central

Foo, J; Leder, K

2016-01-01

We study the accumulation and spread of advantageous mutations in a spatial stochastic model of cancer initiation on a lattice. The parameters of this general model can be tuned to study a variety of cancer types and genetic progression pathways. This investigation contributes to an understanding of how the selective advantage of cancer cells together with the rates of mutations driving cancer, impact the process and timing of carcinogenesis. These results can be used to give insights into tumor heterogeneity and the “cancer field effect,” the observation that a malignancy is often surrounded by cells that have undergone premalignant transformation. PMID:26126947

Modeling of non-uniform spatial arrangement of fibers in a ceramic matrix composite

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

Yang, S.; Tewari, A.; Gokhale, A.M.

In the unidirectional fiber reinforced composites, the spatial agreement of fibers is often non-uniform. These non-uniformities are linked to the processing conditions, and they affect the properties of the composite. In this contribution, a recently developed digital image analysis technique is used to quantify the non-uniform spatial arrangement of Nicalon fibers in a ceramic matrix composite (CMC). These quantitative data are utilized to develop a six parameter computer simulated microstructure model that is statistically equivalent to the non-uniform microstructure of the CMC. The simulated microstructure can be utilized as a RVE for the micro-mechanical modeling studies.

Accounting for Landscape Heterogeneity Improves Spatial Predictions of Tree Vulnerability to Drought

NASA Astrophysics Data System (ADS)

Schwantes, A. M.; Parolari, A.; Swenson, J. J.; Johnson, D. M.; Domec, J. C.; Jackson, R. B.; Pelak, N. F., III; Porporato, A. M.

2017-12-01

Globally, as climate change continues, forest vulnerability to droughts and heatwaves is increasing, but vulnerability differs regionally and locally depending on landscape position. However, most models used in forecasting forest responses to heatwaves and droughts do not incorporate relevant spatial processes. To improve predictions of spatial tree vulnerability, we employed a non-linear stochastic model of soil moisture dynamics across a landscape, accounting for spatial differences in aspect, topography, and soils. Our unique approach integrated plant hydraulics and landscape processes, incorporating effects from lateral redistribution of water using a topographic index and radiation and temperature differences attributable to aspect. Across a watershed in central Texas we modeled dynamic water stress for a dominant tree species, Juniperus ashei. We compared our results to a detailed spatial dataset of drought-impacted areas (>25% canopy loss) derived from remote sensing during the severe 2011 drought. We then projected future dynamic water stress through the 21st century using climate projections from 10 global climate models under two scenarios, and compared models with and without landscape heterogeneity. Within this watershed, 42% of J. ashei dominated systems were impacted by the 2011 drought. Modeled dynamic water stress tracked these spatial patterns of observed drought-impacted areas. Total accuracy increased from 59%, when accounting only for soil variability, to 73% when including lateral redistribution of water and radiation and temperature effects. Dynamic water stress was projected to increase through the 21st century, with only minimal buffering from the landscape. During the hotter and more severe droughts projected in the 21st century, up to 90% of the watershed crossed a dynamic water stress threshold associated with canopy loss in 2011. Favorable microsites may exist across a landscape where trees can persist; however, if future droughts are too severe, the buffering capacity of a heterogenous landscape could be overwhelmed. Incorporating spatial data will improve projections of future tree water stress and identification of potential resilient refugia.

Inverse modelling of fluvial sediment connectivity identifies characteristics and spatial distribution of sediment sources in a large river network.

NASA Astrophysics Data System (ADS)

Schmitt, R. J. P.; Bizzi, S.; Kondolf, G. M.; Rubin, Z.; Castelletti, A.

2016-12-01

Field and laboratory evidence indicates that the spatial distribution of transport in both alluvial and bedrock rivers is an adaptation to sediment supply. Sediment supply, in turn, depends on spatial distribution and properties (e.g., grain sizes and supply rates) of individual sediment sources. Analyzing the distribution of transport capacity in a river network could hence clarify the spatial distribution and properties of sediment sources. Yet, challenges include a) identifying magnitude and spatial distribution of transport capacity for each of multiple grain sizes being simultaneously transported, and b) estimating source grain sizes and supply rates, both at network scales. Herein, we approach the problem of identifying the spatial distribution of sediment sources and the resulting network sediment fluxes in a major, poorly monitored tributary (80,000 km2) of the Mekong. Therefore, we apply the CASCADE modeling framework (Schmitt et al. (2016)). CASCADE calculates transport capacities and sediment fluxes for multiple grainsizes on the network scale based on remotely-sensed morphology and modelled hydrology. CASCADE is run in an inverse Monte Carlo approach for 7500 random initializations of source grain sizes. In all runs, supply of each source is inferred from the minimum downstream transport capacity for the source grain size. Results for each realization are compared to sparse available sedimentary records. Only 1 % of initializations reproduced the sedimentary record. Results for these realizations revealed a spatial pattern in source supply rates, grain sizes, and network sediment fluxes that correlated well with map-derived patterns in lithology and river-morphology. Hence, we propose that observable river hydro-morphology contains information on upstream source properties that can be back-calculated using an inverse modeling approach. Such an approach could be coupled to more detailed models of hillslope processes in future to derive integrated models of hillslope production and fluvial transport processes, which is particularly useful to identify sediment provenance in poorly monitored river basins.

Spatial Self-Organization of Vegetation Subject to Climatic Stress-Insights from a System Dynamics-Individual-Based Hybrid Model.

PubMed

Vincenot, Christian E; Carteni, Fabrizio; Mazzoleni, Stefano; Rietkerk, Max; Giannino, Francesco

2016-01-01

In simulation models of populations or communities, individual plants have often been obfuscated in favor of aggregated vegetation. This simplification comes with a loss of biological detail and a smoothing out of the demographic noise engendered by stochastic individual-scale processes and heterogeneities, which is significant among others when studying the viability of small populations facing challenging fluctuating environmental conditions. This consideration has motivated the development of precise plant-centered models. The accuracy gained in the representation of plant biology has then, however, often been balanced by the disappearance in models of important plant-soil interactions (esp. water dynamics) due to the inability of most individual-based frameworks to simulate complex continuous processes. In this study, we used a hybrid modeling approach, namely integrated System Dynamics (SD)-Individual-based (IB), to illustrate the importance of individual plant dynamics to explain spatial self-organization of vegetation in arid environments. We analyzed the behavior of this model under different parameter sets either related to individual plant properties (such as seed dispersal distance and reproductive age) or the environment (such as intensity and yearly distribution of precipitation events). While the results of this work confirmed the prevailing theory on vegetation patterning, they also revealed the importance therein of plant-level processes that cannot be rendered by reaction-diffusion models. Initial spatial distribution of plants, reproductive age, and average seed dispersal distance, by impacting patch size and vegetation aggregation, affected pattern formation and population survival under climatic variations. Besides, changes in precipitation regime altered the demographic structure and spatial organization of vegetation patches by affecting plants differentially depending on their age and biomass. Water availability influenced non-linearly total biomass density. Remarkably, lower precipitation resulted in lower mean plant age yet higher mean individual biomass. Moreover, seasonal variations in rainfall greater than a threshold (here, ±0.45 mm from the 1.3 mm baseline) decreased mean total biomass and generated limit cycles, which, in the case of large variations, were preceded by chaotic demographic and spatial behavior. In some cases, peculiar spatial patterns (e.g., rings) were also engendered. On a technical note, the shortcomings of the present model and the benefit of hybrid modeling for virtual investigations in plant science are discussed.

Spatial Self-Organization of Vegetation Subject to Climatic Stress—Insights from a System Dynamics—Individual-Based Hybrid Model

PubMed Central

Vincenot, Christian E.; Carteni, Fabrizio; Mazzoleni, Stefano; Rietkerk, Max; Giannino, Francesco

2016-01-01

In simulation models of populations or communities, individual plants have often been obfuscated in favor of aggregated vegetation. This simplification comes with a loss of biological detail and a smoothing out of the demographic noise engendered by stochastic individual-scale processes and heterogeneities, which is significant among others when studying the viability of small populations facing challenging fluctuating environmental conditions. This consideration has motivated the development of precise plant-centered models. The accuracy gained in the representation of plant biology has then, however, often been balanced by the disappearance in models of important plant-soil interactions (esp. water dynamics) due to the inability of most individual-based frameworks to simulate complex continuous processes. In this study, we used a hybrid modeling approach, namely integrated System Dynamics (SD)—Individual-based (IB), to illustrate the importance of individual plant dynamics to explain spatial self-organization of vegetation in arid environments. We analyzed the behavior of this model under different parameter sets either related to individual plant properties (such as seed dispersal distance and reproductive age) or the environment (such as intensity and yearly distribution of precipitation events). While the results of this work confirmed the prevailing theory on vegetation patterning, they also revealed the importance therein of plant-level processes that cannot be rendered by reaction-diffusion models. Initial spatial distribution of plants, reproductive age, and average seed dispersal distance, by impacting patch size and vegetation aggregation, affected pattern formation and population survival under climatic variations. Besides, changes in precipitation regime altered the demographic structure and spatial organization of vegetation patches by affecting plants differentially depending on their age and biomass. Water availability influenced non-linearly total biomass density. Remarkably, lower precipitation resulted in lower mean plant age yet higher mean individual biomass. Moreover, seasonal variations in rainfall greater than a threshold (here, ±0.45 mm from the 1.3 mm baseline) decreased mean total biomass and generated limit cycles, which, in the case of large variations, were preceded by chaotic demographic and spatial behavior. In some cases, peculiar spatial patterns (e.g., rings) were also engendered. On a technical note, the shortcomings of the present model and the benefit of hybrid modeling for virtual investigations in plant science are discussed. PMID:27252707

Research on the spatial analysis method of seismic hazard for island

NASA Astrophysics Data System (ADS)

Jia, Jing; Jiang, Jitong; Zheng, Qiuhong; Gao, Huiying

2017-05-01

Seismic hazard analysis(SHA) is a key component of earthquake disaster prevention field for island engineering, whose result could provide parameters for seismic design microscopically and also is the requisite work for the island conservation planning’s earthquake and comprehensive disaster prevention planning macroscopically, in the exploitation and construction process of both inhabited and uninhabited islands. The existing seismic hazard analysis methods are compared in their application, and their application and limitation for island is analysed. Then a specialized spatial analysis method of seismic hazard for island (SAMSHI) is given to support the further related work of earthquake disaster prevention planning, based on spatial analysis tools in GIS and fuzzy comprehensive evaluation model. The basic spatial database of SAMSHI includes faults data, historical earthquake record data, geological data and Bouguer gravity anomalies data, which are the data sources for the 11 indices of the fuzzy comprehensive evaluation model, and these indices are calculated by the spatial analysis model constructed in ArcGIS’s Model Builder platform.

Ecotoxicology and spatial modeling in population dynamics: an illustration with brown trout.

PubMed

Chaumot, Arnaud; Charles, Sandrine; Flammarion, Patrick; Auger, Pierre

2003-05-01

We developed a multiregion matrix population model to explore how the demography of a hypothetical brown trout population living in a river network varies in response to different spatial scenarios of cadmium contamination. Age structure, spatial distribution, and demographic and migration processes are taken into account in the model. Chronic or acute cadmium concentrations affect the demographic parameters at the scale of the river range. The outputs of the model constitute population-level end points (the asymptotic population growth rate, the stable age structure, and the asymptotic spatial distribution) that allow comparing the different spatial scenarios of contamination regarding the demographic response at the scale of the whole river network. An analysis of the sensitivity of these end points to lower order parameters enables us to link the local effects of cadmium to the global demographic behavior of the brown trout population. Such a link is of broad interest in the point of view of ecotoxicological management.

Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology.

PubMed

VoPham, Trang; Hart, Jaime E; Laden, Francine; Chiang, Yao-Yi

2018-04-17

Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environmental epidemiology, exposure modeling is a commonly used approach to conduct exposure assessment to determine the distribution of exposures in study populations. geoAI technologies provide important advantages for exposure modeling in environmental epidemiology, including the ability to incorporate large amounts of big spatial and temporal data in a variety of formats; computational efficiency; flexibility in algorithms and workflows to accommodate relevant characteristics of spatial (environmental) processes including spatial nonstationarity; and scalability to model other environmental exposures across different geographic areas. The objectives of this commentary are to provide an overview of key concepts surrounding the evolving and interdisciplinary field of geoAI including spatial data science, machine learning, deep learning, and data mining; recent geoAI applications in research; and potential future directions for geoAI in environmental epidemiology.

A Microsaccadic Account of Attentional Capture and Inhibition of Return in Posner Cueing

PubMed Central

Tian, Xiaoguang; Yoshida, Masatoshi; Hafed, Ziad M.

2016-01-01

Microsaccades exhibit systematic oscillations in direction after spatial cueing, and these oscillations correlate with facilitatory and inhibitory changes in behavioral performance in the same tasks. However, independent of cueing, facilitatory and inhibitory changes in visual sensitivity also arise pre-microsaccadically. Given such pre-microsaccadic modulation, an imperative question to ask becomes: how much of task performance in spatial cueing may be attributable to these peri-movement changes in visual sensitivity? To investigate this question, we adopted a theoretical approach. We developed a minimalist model in which: (1) microsaccades are repetitively generated using a rise-to-threshold mechanism, and (2) pre-microsaccadic target onset is associated with direction-dependent modulation of visual sensitivity, as found experimentally. We asked whether such a model alone is sufficient to account for performance dynamics in spatial cueing. Our model not only explained fine-scale microsaccade frequency and direction modulations after spatial cueing, but it also generated classic facilitatory (i.e., attentional capture) and inhibitory [i.e., inhibition of return (IOR)] effects of the cue on behavioral performance. According to the model, cues reflexively reset the oculomotor system, which unmasks oscillatory processes underlying microsaccade generation; once these oscillatory processes are unmasked, “attentional capture” and “IOR” become direct outcomes of pre-microsaccadic enhancement or suppression, respectively. Interestingly, our model predicted that facilitatory and inhibitory effects on behavior should appear as a function of target onset relative to microsaccades even without prior cues. We experimentally validated this prediction for both saccadic and manual responses. We also established a potential causal mechanism for the microsaccadic oscillatory processes hypothesized by our model. We used retinal-image stabilization to experimentally control instantaneous foveal motor error during the presentation of peripheral cues, and we found that post-cue microsaccadic oscillations were severely disrupted. This suggests that microsaccades in spatial cueing tasks reflect active oculomotor correction of foveal motor error, rather than presumed oscillatory covert attentional processes. Taken together, our results demonstrate that peri-microsaccadic changes in vision can go a long way in accounting for some classic behavioral phenomena. PMID:27013991

Subgrid spatial variability of soil hydraulic functions for hydrological modelling

NASA Astrophysics Data System (ADS)

Kreye, Phillip; Meon, Günter

2016-07-01

State-of-the-art hydrological applications require a process-based, spatially distributed hydrological model. Runoff characteristics are demanded to be well reproduced by the model. Despite that, the model should be able to describe the processes at a subcatchment scale in a physically credible way. The objective of this study is to present a robust procedure to generate various sets of parameterisations of soil hydraulic functions for the description of soil heterogeneity on a subgrid scale. Relations between Rosetta-generated values of saturated hydraulic conductivity (Ks) and van Genuchten's parameters of soil hydraulic functions were statistically analysed. An universal function that is valid for the complete bandwidth of Ks values could not be found. After concentrating on natural texture classes, strong correlations were identified for all parameters. The obtained regression results were used to parameterise sets of hydraulic functions for each soil class. The methodology presented in this study is applicable on a wide range of spatial scales and does not need input data from field studies. The developments were implemented into a hydrological modelling system.

Demography-based adaptive network model reproduces the spatial organization of human linguistic groups

NASA Astrophysics Data System (ADS)

Capitán, José A.; Manrubia, Susanna

2015-12-01

The distribution of human linguistic groups presents a number of interesting and nontrivial patterns. The distributions of the number of speakers per language and the area each group covers follow log-normal distributions, while population and area fulfill an allometric relationship. The topology of networks of spatial contacts between different linguistic groups has been recently characterized, showing atypical properties of the degree distribution and clustering, among others. Human demography, spatial conflicts, and the construction of networks of contacts between linguistic groups are mutually dependent processes. Here we introduce an adaptive network model that takes all of them into account and successfully reproduces, using only four model parameters, not only those features of linguistic groups already described in the literature, but also correlations between demographic and topological properties uncovered in this work. Besides their relevance when modeling and understanding processes related to human biogeography, our adaptive network model admits a number of generalizations that broaden its scope and make it suitable to represent interactions between agents based on population dynamics and competition for space.

Demography-based adaptive network model reproduces the spatial organization of human linguistic groups.

PubMed

Capitán, José A; Manrubia, Susanna

2015-12-01

The distribution of human linguistic groups presents a number of interesting and nontrivial patterns. The distributions of the number of speakers per language and the area each group covers follow log-normal distributions, while population and area fulfill an allometric relationship. The topology of networks of spatial contacts between different linguistic groups has been recently characterized, showing atypical properties of the degree distribution and clustering, among others. Human demography, spatial conflicts, and the construction of networks of contacts between linguistic groups are mutually dependent processes. Here we introduce an adaptive network model that takes all of them into account and successfully reproduces, using only four model parameters, not only those features of linguistic groups already described in the literature, but also correlations between demographic and topological properties uncovered in this work. Besides their relevance when modeling and understanding processes related to human biogeography, our adaptive network model admits a number of generalizations that broaden its scope and make it suitable to represent interactions between agents based on population dynamics and competition for space.

Contrasting patterns of fine-scale herb layer species composition in temperate forests

NASA Astrophysics Data System (ADS)

Chudomelová, Markéta; Zelený, David; Li, Ching-Feng

2017-04-01

Although being well described at the landscape level, patterns in species composition of forest herb layer are rarely studied at smaller scales. Here, we examined fine-scale environmental determinants and spatial structures of herb layer communities in thermophilous oak- and hornbeam dominated forests of the south-eastern part of the Czech Republic. Species composition of herb layer vegetation and environmental variables were recorded within a fixed grid of 2 × 2 m subplots regularly distributed within 1-ha quadrate plots in three forest stands. For each site, environmental models best explaining species composition were constructed using constrained ordination analysis. Spatial eigenvector mapping was used to model and account for spatial structures in community variation. Mean Ellenberg indicator values calculated for each subplot were used for ecological interpretation of spatially structured residual variation. The amount of variation explained by environmental and spatial models as well as the selection of variables with the best explanatory power differed among sites. As an important environmental factor, relative elevation was common to all three sites, while pH and canopy openness were shared by two sites. Both environmental and community variation was mostly coarse-scaled, as was the spatially structured portion of residual variation. When corrected for bias due to spatial autocorrelation, those environmental factors with already weak explanatory power lost their significance. Only a weak evidence of possibly omitted environmental predictor was found for autocorrelated residuals of site models using mean Ellenberg indicator values. Community structure was determined by different factors at different sites. The relative importance of environmental filtering vs. spatial processes was also site specific, implying that results of fine-scale studies tend to be shaped by local conditions. Contrary to expectations based on other studies, overall dominance of spatial processes at fine scale has not been detected. Ecologists should keep this in mind when making generalizations about community dynamics.

Conceptual model of sediment processes in the upper Yuba River watershed, Sierra Nevada, CA

USGS Publications Warehouse

Curtis, J.A.; Flint, L.E.; Alpers, Charles N.; Yarnell, S.M.

2005-01-01

This study examines the development of a conceptual model of sediment processes in the upper Yuba River watershed; and we hypothesize how components of the conceptual model may be spatially distributed using a geographical information system (GIS). The conceptual model illustrates key processes controlling sediment dynamics in the upper Yuba River watershed and was tested and revised using field measurements, aerial photography, and low elevation videography. Field reconnaissance included mass wasting and channel storage inventories, assessment of annual channel change in upland tributaries, and evaluation of the relative importance of sediment sources and transport processes. Hillslope erosion rates throughout the study area are relatively low when compared to more rapidly eroding landscapes such as the Pacific Northwest and notable hillslope sediment sources include highly erodible andesitic mudflows, serpentinized ultramafics, and unvegetated hydraulic mine pits. Mass wasting dominates surface erosion on the hillslopes; however, erosion of stored channel sediment is the primary contributor to annual sediment yield. We used GIS to spatially distribute the components of the conceptual model and created hillslope erosion potential and channel storage models. The GIS models exemplify the conceptual model in that landscapes with low potential evapotranspiration, sparse vegetation, steep slopes, erodible geology and soils, and high road densities display the greatest hillslope erosion potential and channel storage increases with increasing stream order. In-channel storage in upland tributaries impacted by hydraulic mining is an exception. Reworking of stored hydraulic mining sediment in low-order tributaries continues to elevate upper Yuba River sediment yields. Finally, we propose that spatially distributing the components of a conceptual model in a GIS framework provides a guide for developing more detailed sediment budgets or numerical models making it an inexpensive way to develop a roadmap for understanding sediment dynamics at a watershed scale.

Harnessing Big Data to Represent 30-meter Spatial Heterogeneity in Earth System Models

NASA Astrophysics Data System (ADS)

Chaney, N.; Shevliakova, E.; Malyshev, S.; Van Huijgevoort, M.; Milly, C.; Sulman, B. N.

2016-12-01

Terrestrial land surface processes play a critical role in the Earth system; they have a profound impact on the global climate, food and energy production, freshwater resources, and biodiversity. One of the most fascinating yet challenging aspects of characterizing terrestrial ecosystems is their field-scale (˜30 m) spatial heterogeneity. It has been observed repeatedly that the water, energy, and biogeochemical cycles at multiple temporal and spatial scales have deep ties to an ecosystem's spatial structure. Current Earth system models largely disregard this important relationship leading to an inadequate representation of ecosystem dynamics. In this presentation, we will show how existing global environmental datasets can be harnessed to explicitly represent field-scale spatial heterogeneity in Earth system models. For each macroscale grid cell, these environmental data are clustered according to their field-scale soil and topographic attributes to define unique sub-grid tiles. The state-of-the-art Geophysical Fluid Dynamics Laboratory (GFDL) land model is then used to simulate these tiles and their spatial interactions via the exchange of water, energy, and nutrients along explicit topographic gradients. Using historical simulations over the contiguous United States, we will show how a robust representation of field-scale spatial heterogeneity impacts modeled ecosystem dynamics including the water, energy, and biogeochemical cycles as well as vegetation composition and distribution.

Species sorting and patch dynamics in harlequin metacommunities affect the relative importance of environment and space.

PubMed

Leibold, Mathew A; Loeuille, Nicolas

2015-12-01

Metacommunity theory indicates that variation in local community structure can be partitioned into components including those related to local environmental conditions vs. spatial effects and that these can be quantified using statistical methods based on variation partitioning. It has been hypothesized that joint associations of community composition with environment and space could be due to patch dynamics involving colonization-extinction processes in environmentally heterogeneous landscapes but this has yet to be theoretically shown. We develop a two-patch, type-two, species competition model in such a "harlequin" landscape (where different patches have different environments) to evaluate how composition is related to environmental and spatial effects as a function of background extinction rate. Using spatially implicit analytical models, we find that the environmental association of community composition declines with extinction rate as expected. Using spatially explicit simulation models, we further find that there is an increase in the spatial structure with extinction due to spatial patterning into clusters that are not related to environmental conditions but that this increase is limited. Natural metacommunities often show both environment and spatial determination even under conditions of relatively high isolation and these could be more easily explained by our model than alternative metacommunity models.

Space evolution model and empirical analysis of an urban public transport network

NASA Astrophysics Data System (ADS)

Sui, Yi; Shao, Feng-jing; Sun, Ren-cheng; Li, Shu-jing

2012-07-01

This study explores the space evolution of an urban public transport network, using empirical evidence and a simulation model validated on that data. Public transport patterns primarily depend on traffic spatial-distribution, demands of passengers and expected utility of investors. Evolution is an iterative process of satisfying the needs of passengers and investors based on a given traffic spatial-distribution. The temporal change of urban public transport network is evaluated both using topological measures and spatial ones. The simulation model is validated using empirical data from nine big cities in China. Statistical analyses on topological and spatial attributes suggest that an evolution network with traffic demands characterized by power-law numerical values which distribute in a mode of concentric circles tallies well with these nine cities.

The impact of design-based modeling instruction on seventh graders' spatial abilities and model-based argumentation

NASA Astrophysics Data System (ADS)

McConnell, William J.

Due to the call of current science education reform for the integration of engineering practices within science classrooms, design-based instruction is receiving much attention in science education literature. Although some aspect of modeling is often included in well-known design-based instructional methods, it is not always a primary focus. The purpose of this study was to better understand how design-based instruction with an emphasis on scientific modeling might impact students' spatial abilities and their model-based argumentation abilities. In the following mixed-method multiple case study, seven seventh grade students attending a secular private school in the Mid-Atlantic region of the United States underwent an instructional intervention involving design-based instruction, modeling and argumentation. Through the course of a lesson involving students in exploring the interrelatedness of the environment and an animal's form and function, students created and used multiple forms of expressed models to assist them in model-based scientific argument. Pre/post data were collected through the use of The Purdue Spatial Visualization Test: Rotation, the Mental Rotation Test and interviews. Other data included a spatial activities survey, student artifacts in the form of models, notes, exit tickets, and video recordings of students throughout the intervention. Spatial abilities tests were analyzed using descriptive statistics while students' arguments were analyzed using the Instrument for the Analysis of Scientific Curricular Arguments and a behavior protocol. Models were analyzed using content analysis and interviews and all other data were coded and analyzed for emergent themes. Findings in the area of spatial abilities included increases in spatial reasoning for six out of seven participants, and an immense difference in the spatial challenges encountered by students when using CAD software instead of paper drawings to create models. Students perceived 3D printed models to better assist them in scientific argumentation over paper drawing models. In fact, when given a choice, students rarely used paper drawing to assist in argument. There was also a difference in model utility between the two different model types. Participants explicitly used 3D printed models to complete gestural modeling, while participants rarely looked at 2D models when involved in gestural modeling. This study's findings added to current theory dealing with the varied spatial challenges involved in different modes of expressed models. This study found that depth, symmetry and the manipulation of perspectives are typically spatial challenges students will attend to using CAD while they will typically ignore them when drawing using paper and pencil. This study also revealed a major difference in model-based argument in a design-based instruction context as opposed to model-based argument in a typical science classroom context. In the context of design-based instruction, data revealed that design process is an important part of model-based argument. Due to the importance of design process in model-based argumentation in this context, trusted methods of argument analysis, like the coding system of the IASCA, was found lacking in many respects. Limitations and recommendations for further research were also presented.

OpenFLUID: an open-source software environment for modelling fluxes in landscapes

NASA Astrophysics Data System (ADS)

Fabre, Jean-Christophe; Rabotin, Michaël; Crevoisier, David; Libres, Aline; Dagès, Cécile; Moussa, Roger; Lagacherie, Philippe; Raclot, Damien; Voltz, Marc

2013-04-01

Integrative landscape functioning has become a common concept in environmental management. Landscapes are complex systems where many processes interact in time and space. In agro-ecosystems, these processes are mainly physical processes, including hydrological-processes, biological processes and human activities. Modelling such systems requires an interdisciplinary approach, coupling models coming from different disciplines, developed by different teams. In order to support collaborative works, involving many models coupled in time and space for integrative simulations, an open software modelling platform is a relevant answer. OpenFLUID is an open source software platform for modelling landscape functioning, mainly focused on spatial fluxes. It provides an advanced object-oriented architecture allowing to i) couple models developed de novo or from existing source code, and which are dynamically plugged to the platform, ii) represent landscapes as hierarchical graphs, taking into account multi-scale, spatial heterogeneities and landscape objects connectivity, iii) run and explore simulations in many ways : using the OpenFLUID software interfaces for users (command line interface, graphical user interface), or using external applications such as GNU R through the provided ROpenFLUID package. OpenFLUID is developed in C++ and relies on open source libraries only (Boost, libXML2, GLib/GTK, OGR/GDAL, …). For modelers and developers, OpenFLUID provides a dedicated environment for model development, which is based on an open source toolchain, including the Eclipse editor, the GCC compiler and the CMake build system. OpenFLUID is distributed under the GPLv3 open source license, with a special exception allowing to plug existing models licensed under any license. It is clearly in the spirit of sharing knowledge and favouring collaboration in a community of modelers. OpenFLUID has been involved in many research applications, such as modelling of hydrological network transfer, diagnosis and prediction of water quality taking into account human activities, study of the effect of spatial organization on hydrological fluxes, modelling of surface-subsurface water exchanges, … At LISAH research unit, OpenFLUID is the supporting development platform of the MHYDAS model, which is a distributed model for agrosystems (Moussa et al., 2002, Hydrological Processes, 16, 393-412). OpenFLUID web site : http://www.openfluid-project.org

Effects of input uncertainty on cross-scale crop modeling

NASA Astrophysics Data System (ADS)

Waha, Katharina; Huth, Neil; Carberry, Peter

2014-05-01

The quality of data on climate, soils and agricultural management in the tropics is in general low or data is scarce leading to uncertainty in process-based modeling of cropping systems. Process-based crop models are common tools for simulating crop yields and crop production in climate change impact studies, studies on mitigation and adaptation options or food security studies. Crop modelers are concerned about input data accuracy as this, together with an adequate representation of plant physiology processes and choice of model parameters, are the key factors for a reliable simulation. For example, assuming an error in measurements of air temperature, radiation and precipitation of ± 0.2°C, ± 2 % and ± 3 % respectively, Fodor & Kovacs (2005) estimate that this translates into an uncertainty of 5-7 % in yield and biomass simulations. In our study we seek to answer the following questions: (1) are there important uncertainties in the spatial variability of simulated crop yields on the grid-cell level displayed on maps, (2) are there important uncertainties in the temporal variability of simulated crop yields on the aggregated, national level displayed in time-series, and (3) how does the accuracy of different soil, climate and management information influence the simulated crop yields in two crop models designed for use at different spatial scales? The study will help to determine whether more detailed information improves the simulations and to advise model users on the uncertainty related to input data. We analyse the performance of the point-scale crop model APSIM (Keating et al., 2003) and the global scale crop model LPJmL (Bondeau et al., 2007) with different climate information (monthly and daily) and soil conditions (global soil map and African soil map) under different agricultural management (uniform and variable sowing dates) for the low-input maize-growing areas in Burkina Faso/West Africa. We test the models' response to different levels of input data from very little to very detailed information, and compare the models' abilities to represent the spatial variability and temporal variability in crop yields. We display the uncertainty in crop yield simulations from different input data and crop models in Taylor diagrams which are a graphical summary of the similarity between simulations and observations (Taylor, 2001). The observed spatial variability can be represented well from both models (R=0.6-0.8) but APSIM predicts higher spatial variability than LPJmL due to its sensitivity to soil parameters. Simulations with the same crop model, climate and sowing dates have similar statistics and therefore similar skill to reproduce the observed spatial variability. Soil data is less important for the skill of a crop model to reproduce the observed spatial variability. However, the uncertainty in simulated spatial variability from the two crop models is larger than from input data settings and APSIM is more sensitive to input data then LPJmL. Even with a detailed, point-scale crop model and detailed input data it is difficult to capture the complexity and diversity in maize cropping systems.

Individual-Based Model of Microbial Life on Hydrated Rough Soil Surfaces

PubMed Central

Kim, Minsu; Or, Dani

2016-01-01

Microbial life in soil is perceived as one of the most interesting ecological systems, with microbial communities exhibiting remarkable adaptability to vast dynamic environmental conditions. At the same time, it is a notoriously challenging system to understand due to its complexity including physical, chemical, and biological factors in synchrony. This study presents a spatially-resolved model of microbial dynamics on idealised rough soil surfaces represented as patches with different (roughness) properties that preserve the salient hydration physics of real surfaces. Cell level microbial interactions are considered within an individual-based formulation including dispersion and various forms of trophic dependencies (competition, mutualism). The model provides new insights into mechanisms affecting microbial community dynamics and gives rise to spontaneous formation of microbial community spatial patterns. The framework is capable of representing many interacting species and provides diversity metrics reflecting surface conditions and their evolution over time. A key feature of the model is its spatial scalability that permits representation of microbial processes from cell-level (micro-metric scales) to soil representative volumes at sub-metre scales. Several illustrative examples of microbial trophic interactions and population dynamics highlight the potential of the proposed modelling framework to quantitatively study soil microbial processes. The model is highly applicable in a wide range spanning from quantifying spatial organisation of multiple species under various hydration conditions to predicting microbial diversity residing in different soils. PMID:26807803

Anatomical and spatial matching in imitation: Evidence from left and right brain-damaged patients.

PubMed

Mengotti, Paola; Ripamonti, Enrico; Pesavento, Valentina; Rumiati, Raffaella Ida

2015-12-01

Imitation is a sensorimotor process whereby the visual information present in the model's movement has to be coupled with the activation of the motor system in the observer. This also implies that greater the similarity between the seen and the produced movement, the easier it will be to execute the movement, a process also known as ideomotor compatibility. Two components can influence the degree of similarity between two movements: the anatomical and the spatial component. The anatomical component is present when the model and imitator move the same body part (e.g., the right hand) while the spatial component is present when the movement of the model and that of the imitator occur at the same spatial position. Imitation can be achieved by relying on both components, but typically the model's and imitator's movements are matched either anatomically or spatially. The aim of this study was to ascertain the contribution of the left and right hemisphere to the imitation accomplished either with anatomical or spatial matching (or with both). Patients with unilateral left and right brain damage performed an ideomotor task and a gesture imitation task. Lesions in the left and right hemispheres gave rise to different performance deficits. Patients with lesions in the left hemisphere showed impaired imitation when anatomical matching was required, and patients with lesions in the right hemisphere showed impaired imitation when spatial matching was required. Lesion analysis further revealed a differential involvement of left and right hemispheric regions, such as the parietal opercula, in supporting imitation in the ideomotor task. Similarly, gesture imitation seemed to rely on different regions in the left and right hemisphere, such as parietal regions in the left hemisphere and premotor, somatosensory and subcortical regions in the right hemisphere. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of system dynamics and participatory spatial group model building in animal health: A case study of East Coast Fever interventions in Lundazi and Monze districts of Zambia

PubMed Central

Skjerve, Eystein; Rich, Magda; Rich, Karl M.

2017-01-01

East Coast Fever (ECF) is the most economically important production disease among traditional beef cattle farmers in Zambia. Despite the disease control efforts by the government, donors, and farmers, ECF cases are increasing. Why does ECF oscillate over time? Can alternative approaches such as systems thinking contribute solutions to the complex ECF problem, avoid unintended consequences, and achieve sustainable results? To answer these research questions and inform the design and implementation of ECF interventions, we qualitatively investigated the influence of dynamic socio-economic, cultural, and ecological factors. We used system dynamics modelling to specify these dynamics qualitatively, and an innovative participatory framework called spatial group model building (SGMB). SGMB uses participatory geographical information system (GIS) concepts and techniques to capture the role of spatial phenomenon in the context of complex systems, allowing stakeholders to identify spatial phenomenon directly on physical maps and integrate such information in model development. Our SGMB process convened focus groups of beef value chain stakeholders in two distinct production systems. The focus groups helped to jointly construct a series of interrelated system dynamics models that described ECF in a broader systems context. Thus, a complementary objective of this study was to demonstrate the applicability of system dynamics modelling and SGMB in animal health. The SGMB process revealed policy leverage points in the beef cattle value chain that could be targeted to improve ECF control. For example, policies that develop sustainable and stable cattle markets and improve household income availability may have positive feedback effects on investment in animal health. The results obtained from a SGMB process also demonstrated that a “one-size-fits-all” approach may not be equally effective in policing ECF in different agro-ecological zones due to the complex interactions of socio-ecological context with important, and often ignored, spatial patterns. PMID:29244862

Application of system dynamics and participatory spatial group model building in animal health: A case study of East Coast Fever interventions in Lundazi and Monze districts of Zambia.

PubMed

Mumba, Chisoni; Skjerve, Eystein; Rich, Magda; Rich, Karl M

2017-01-01

East Coast Fever (ECF) is the most economically important production disease among traditional beef cattle farmers in Zambia. Despite the disease control efforts by the government, donors, and farmers, ECF cases are increasing. Why does ECF oscillate over time? Can alternative approaches such as systems thinking contribute solutions to the complex ECF problem, avoid unintended consequences, and achieve sustainable results? To answer these research questions and inform the design and implementation of ECF interventions, we qualitatively investigated the influence of dynamic socio-economic, cultural, and ecological factors. We used system dynamics modelling to specify these dynamics qualitatively, and an innovative participatory framework called spatial group model building (SGMB). SGMB uses participatory geographical information system (GIS) concepts and techniques to capture the role of spatial phenomenon in the context of complex systems, allowing stakeholders to identify spatial phenomenon directly on physical maps and integrate such information in model development. Our SGMB process convened focus groups of beef value chain stakeholders in two distinct production systems. The focus groups helped to jointly construct a series of interrelated system dynamics models that described ECF in a broader systems context. Thus, a complementary objective of this study was to demonstrate the applicability of system dynamics modelling and SGMB in animal health. The SGMB process revealed policy leverage points in the beef cattle value chain that could be targeted to improve ECF control. For example, policies that develop sustainable and stable cattle markets and improve household income availability may have positive feedback effects on investment in animal health. The results obtained from a SGMB process also demonstrated that a "one-size-fits-all" approach may not be equally effective in policing ECF in different agro-ecological zones due to the complex interactions of socio-ecological context with important, and often ignored, spatial patterns.

Single Plant Root System Modeling under Soil Moisture Variation

NASA Astrophysics Data System (ADS)

Yabusaki, S.; Fang, Y.; Chen, X.; Scheibe, T. D.

2016-12-01

A prognostic Virtual Plant-Atmosphere-Soil System (vPASS) model is being developed that integrates comprehensively detailed mechanistic single plant modeling with microbial, atmospheric, and soil system processes in its immediate environment. Three broad areas of process module development are targeted: Incorporating models for root growth and function, rhizosphere interactions with bacteria and other organisms, litter decomposition and soil respiration into established porous media flow and reactive transport models Incorporating root/shoot transport, growth, photosynthesis and carbon allocation process models into an integrated plant physiology model Incorporating transpiration, Volatile Organic Compounds (VOC) emission, particulate deposition and local atmospheric processes into a coupled plant/atmosphere model. The integrated plant ecosystem simulation capability is being developed as open source process modules and associated interfaces under a modeling framework. The initial focus addresses the coupling of root growth, vascular transport system, and soil under drought scenarios. Two types of root water uptake modeling approaches are tested: continuous root distribution and constitutive root system architecture. The continuous root distribution models are based on spatially averaged root development process parameters, which are relatively straightforward to accommodate in the continuum soil flow and reactive transport module. Conversely, the constitutive root system architecture models use root growth rates, root growth direction, and root branching to evolve explicit root geometries. The branching topologies require more complex data structures and additional input parameters. Preliminary results are presented for root model development and the vascular response to temporal and spatial variations in soil conditions.

Spatial Intensity Duration Frequency Relationships Using Hierarchical Bayesian Analysis for Urban Areas

NASA Astrophysics Data System (ADS)

Rupa, Chandra; Mujumdar, Pradeep

2016-04-01

In urban areas, quantification of extreme precipitation is important in the design of storm water drains and other infrastructure. Intensity Duration Frequency (IDF) relationships are generally used to obtain design return level for a given duration and return period. Due to lack of availability of extreme precipitation data for sufficiently large number of years, estimating the probability of extreme events is difficult. Typically, a single station data is used to obtain the design return levels for various durations and return periods, which are used in the design of urban infrastructure for the entire city. In an urban setting, the spatial variation of precipitation can be high; the precipitation amounts and patterns often vary within short distances of less than 5 km. Therefore it is crucial to study the uncertainties in the spatial variation of return levels for various durations. In this work, the extreme precipitation is modeled spatially using the Bayesian hierarchical analysis and the spatial variation of return levels is studied. The analysis is carried out with Block Maxima approach for defining the extreme precipitation, using Generalized Extreme Value (GEV) distribution for Bangalore city, Karnataka state, India. Daily data for nineteen stations in and around Bangalore city is considered in the study. The analysis is carried out for summer maxima (March - May), monsoon maxima (June - September) and the annual maxima rainfall. In the hierarchical analysis, the statistical model is specified in three layers. The data layer models the block maxima, pooling the extreme precipitation from all the stations. In the process layer, the latent spatial process characterized by geographical and climatological covariates (lat-lon, elevation, mean temperature etc.) which drives the extreme precipitation is modeled and in the prior level, the prior distributions that govern the latent process are modeled. Markov Chain Monte Carlo (MCMC) algorithm (Metropolis Hastings algorithm within a Gibbs sampler) is used to obtain the samples of parameters from the posterior distribution of parameters. The spatial maps of return levels for specified return periods, along with the associated uncertainties, are obtained for the summer, monsoon and annual maxima rainfall. Considering various covariates, the best fit model is selected using Deviance Information Criteria. It is observed that the geographical covariates outweigh the climatological covariates for the monsoon maxima rainfall (latitude and longitude). The best covariates for summer maxima and annual maxima rainfall are mean summer precipitation and mean monsoon precipitation respectively, including elevation for both the cases. The scale invariance theory, which states that statistical properties of a process observed at various scales are governed by the same relationship, is used to disaggregate the daily rainfall to hourly scales. The spatial maps of the scale are obtained for the study area. The spatial maps of IDF relationships thus generated are useful in storm water designs, adequacy analysis and identifying the vulnerable flooding areas.

The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

NASA Astrophysics Data System (ADS)

Szymanowski, Mariusz; Kryza, Maciej

2017-02-01

Our study examines the role of auxiliary variables in the process of spatial modelling and mapping of climatological elements, with air temperature in Poland used as an example. The multivariable algorithms are the most frequently applied for spatialization of air temperature, and their results in many studies are proved to be better in comparison to those obtained by various one-dimensional techniques. In most of the previous studies, two main strategies were used to perform multidimensional spatial interpolation of air temperature. First, it was accepted that all variables significantly correlated with air temperature should be incorporated into the model. Second, it was assumed that the more spatial variation of air temperature was deterministically explained, the better was the quality of spatial interpolation. The main goal of the paper was to examine both above-mentioned assumptions. The analysis was performed using data from 250 meteorological stations and for 69 air temperature cases aggregated on different levels: from daily means to 10-year annual mean. Two cases were considered for detailed analysis. The set of potential auxiliary variables covered 11 environmental predictors of air temperature. Another purpose of the study was to compare the results of interpolation given by various multivariable methods using the same set of explanatory variables. Two regression models: multiple linear (MLR) and geographically weighted (GWR) method, as well as their extensions to the regression-kriging form, MLRK and GWRK, respectively, were examined. Stepwise regression was used to select variables for the individual models and the cross-validation method was used to validate the results with a special attention paid to statistically significant improvement of the model using the mean absolute error (MAE) criterion. The main results of this study led to rejection of both assumptions considered. Usually, including more than two or three of the most significantly correlated auxiliary variables does not improve the quality of the spatial model. The effects of introduction of certain variables into the model were not climatologically justified and were seen on maps as unexpected and undesired artefacts. The results confirm, in accordance with previous studies, that in the case of air temperature distribution, the spatial process is non-stationary; thus, the local GWR model performs better than the global MLR if they are specified using the same set of auxiliary variables. If only GWR residuals are autocorrelated, the geographically weighted regression-kriging (GWRK) model seems to be optimal for air temperature spatial interpolation.

Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification.

PubMed

Liu, Da; Li, Jianxun

2016-12-16

Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.

Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning

PubMed Central

Officer, Rick; Clarke, Maurice; Reid, David G.; Brophy, Deirdre

2017-01-01

Boosted Regression Trees. Excellent for data-poor spatial management but hard to use Marine resource managers and scientists often advocate spatial approaches to manage data-poor species. Existing spatial prediction and management techniques are either insufficiently robust, struggle with sparse input data, or make suboptimal use of multiple explanatory variables. Boosted Regression Trees feature excellent performance and are well suited to modelling the distribution of data-limited species, but are extremely complicated and time-consuming to learn and use, hindering access for a wide potential user base and therefore limiting uptake and usage. BRTs automated and simplified for accessible general use with rich feature set We have built a software suite in R which integrates pre-existing functions with new tailor-made functions to automate the processing and predictive mapping of species abundance data: by automating and greatly simplifying Boosted Regression Tree spatial modelling, the gbm.auto R package suite makes this powerful statistical modelling technique more accessible to potential users in the ecological and modelling communities. The package and its documentation allow the user to generate maps of predicted abundance, visualise the representativeness of those abundance maps and to plot the relative influence of explanatory variables and their relationship to the response variables. Databases of the processed model objects and a report explaining all the steps taken within the model are also generated. The package includes a previously unavailable Decision Support Tool which combines estimated escapement biomass (the percentage of an exploited population which must be retained each year to conserve it) with the predicted abundance maps to generate maps showing the location and size of habitat that should be protected to conserve the target stocks (candidate MPAs), based on stakeholder priorities, such as the minimisation of fishing effort displacement. Gbm.auto for management in various settings By bridging the gap between advanced statistical methods for species distribution modelling and conservation science, management and policy, these tools can allow improved spatial abundance predictions, and therefore better management, decision-making, and conservation. Although this package was built to support spatial management of a data-limited marine elasmobranch fishery, it should be equally applicable to spatial abundance modelling, area protection, and stakeholder engagement in various scenarios. PMID:29216310

Lattice Three-Species Models of the Spatial Spread of Rabies among FOXES

NASA Astrophysics Data System (ADS)

Benyoussef, A.; Boccara, N.; Chakib, H.; Ez-Zahraouy, H.

Lattice models describing the spatial spread of rabies among foxes are studied. In these models, the fox population is divided into three-species: susceptible (S), infected or incubating (I), and infectious or rabid (R). They are based on the fact that susceptible and incubating foxes are territorial while rabid foxes have lost their sense of direction and move erratically. Two different models are investigated: a one-dimensional coupled-map lattice model, and a two-dimensional automata network model. Both models take into account the short-range character of the infection process and the diffusive motion of rabid foxes. Numerical simulations show how the spatial distribution of rabies, and the speed of propagation of the epizootic front depend upon the carrying capacity of the environment and diffusion of rabid foxes out of their territory.

Visualisierungen im Lehr-Lern-Process (Visualizations in the Process of Teaching and Learning).

ERIC Educational Resources Information Center

Schnotz, Wolfgang; Zink, Thomas; Pfeiffer, Michael

1996-01-01

Discusses the role of visualization of information in learning. Theorizes that the comprehension of visualizations is a process of structure mapping between a visuo-spatial configuration and a mental model. Tests the model and finds differences in the use of text and picture information to answer different kinds of text questions. (DSK)

A spatial emergy model for Alachua County, Florida

NASA Astrophysics Data System (ADS)

Lambert, James David

A spatial model of the distribution of energy flows and storages in Alachua County, Florida, was created and used to analyze spatial patterns of energy transformation hierarchy in relation to spatial patterns of human settlement. Emergy, the available energy of one kind previously required directly or indirectly to make a product or service, was used as a measure of the quality of the different forms of energy flows and storages. Emergy provides a common unit of measure for comparing the productive contributions of natural processes with those of economic and social processes---it is an alternative to using money for measuring value. A geographic information system was used to create a spatial model and make maps that show the distribution and magnitude of different types of energy and emergy flows and storages occurring in one-hectare land units. Energy transformities were used to convert individual energy flows and storages into emergy units. Maps of transformities were created that reveal a clear spatial pattern of energy transformation hierarchy. The maps display patterns of widely-dispersed areas with lower transformity energy flows and storages, and smaller, centrally-located areas with higher transformities. Energy signature graphs and spatial unit transformities were used to characterize and compare the types and amounts of energy being consumed and stored according to land use classification, planning unit, and neighborhood categories. Emergy ratio maps and spatial unit ratios were created by dividing the values for specific emergy flows or storages by the values for other emergy flows or storages. Spatial context analysis was used to analyze the spatial distribution patterns of mean and maximum values for emergy flows and storages. The modeling method developed for this study is general and applicable to all types of landscapes and could be applied at any scale. An advantage of this general approach is that the results of other studies using this method will be directly comparable with the results of this study. The results and conclusions of this study reinforce the hypothesis that an urban landscape will develop a predictable spatial pattern that can be described in terms of a universal energy transformation hierarchy.

The stochastic runoff-runon process: Extending its analysis to a finite hillslope

NASA Astrophysics Data System (ADS)

Jones, O. D.; Lane, P. N. J.; Sheridan, G. J.

2016-10-01

The stochastic runoff-runon process models the volume of infiltration excess runoff from a hillslope via the overland flow path. Spatial variability is represented in the model by the spatial distribution of rainfall and infiltration, and their ;correlation scale;, that is, the scale at which the spatial correlation of rainfall and infiltration become negligible. Notably, the process can produce runoff even when the mean rainfall rate is less than the mean infiltration rate, and it displays a gradual increase in net runoff as the rainfall rate increases. In this paper we present a number of contributions to the analysis of the stochastic runoff-runon process. Firstly we illustrate the suitability of the process by fitting it to experimental data. Next we extend previous asymptotic analyses to include the cases where the mean rainfall rate equals or exceeds the mean infiltration rate, and then use Monte Carlo simulation to explore the range of parameters for which the asymptotic limit gives a good approximation on finite hillslopes. Finally we use this to obtain an equation for the mean net runoff, consistent with our asymptotic results but providing an excellent approximation for finite hillslopes. Our function uses a single parameter to capture spatial variability, and varying this parameter gives us a family of curves which interpolate between known upper and lower bounds for the mean net runoff.

Effects of spatial variability and scale on areal -average evapotranspiration

NASA Technical Reports Server (NTRS)

Famiglietti, J. S.; Wood, Eric F.

1993-01-01

This paper explores the effect of spatial variability and scale on areally-averaged evapotranspiration. A spatially-distributed water and energy balance model is employed to determine the effect of explicit patterns of model parameters and atmospheric forcing on modeled areally-averaged evapotranspiration over a range of increasing spatial scales. The analysis is performed from the local scale to the catchment scale. The study area is King's Creek catchment, an 11.7 sq km watershed located on the native tallgrass prairie of Kansas. The dominant controls on the scaling behavior of catchment-average evapotranspiration are investigated by simulation, as is the existence of a threshold scale for evapotranspiration modeling, with implications for explicit versus statistical representation of important process controls. It appears that some of our findings are fairly general, and will therefore provide a framework for understanding the scaling behavior of areally-averaged evapotranspiration at the catchment and larger scales.

Patterns and scaling properties of surface soil moisture in an agricultural landscape: An ecohydrological modeling study

NASA Astrophysics Data System (ADS)

Korres, W.; Reichenau, T. G.; Schneider, K.

2013-08-01

Soil moisture is a key variable in hydrology, meteorology and agriculture. Soil moisture, and surface soil moisture in particular, is highly variable in space and time. Its spatial and temporal patterns in agricultural landscapes are affected by multiple natural (precipitation, soil, topography, etc.) and agro-economic (soil management, fertilization, etc.) factors, making it difficult to identify unequivocal cause and effect relationships between soil moisture and its driving variables. The goal of this study is to characterize and analyze the spatial and temporal patterns of surface soil moisture (top 20 cm) in an intensively used agricultural landscape (1100 km2 northern part of the Rur catchment, Western Germany) and to determine the dominant factors and underlying processes controlling these patterns. A second goal is to analyze the scaling behavior of surface soil moisture patterns in order to investigate how spatial scale affects spatial patterns. To achieve these goals, a dynamically coupled, process-based and spatially distributed ecohydrological model was used to analyze the key processes as well as their interactions and feedbacks. The model was validated for two growing seasons for the three main crops in the investigation area: Winter wheat, sugar beet, and maize. This yielded RMSE values for surface soil moisture between 1.8 and 7.8 vol.% and average RMSE values for all three crops of 0.27 kg m-2 for total aboveground biomass and 0.93 for green LAI. Large deviations of measured and modeled soil moisture can be explained by a change of the infiltration properties towards the end of the growing season, especially in maize fields. The validated model was used to generate daily surface soil moisture maps, serving as a basis for an autocorrelation analysis of spatial patterns and scale. Outside of the growing season, surface soil moisture patterns at all spatial scales depend mainly upon soil properties. Within the main growing season, larger scale patterns that are induced by soil properties are superimposed by the small scale land use pattern and the resulting small scale variability of evapotranspiration. However, this influence decreases at larger spatial scales. Most precipitation events cause temporarily higher surface soil moisture autocorrelation lengths at all spatial scales for a short time even beyond the autocorrelation lengths induced by soil properties. The relation of daily spatial variance to the spatial scale of the analysis fits a power law scaling function, with negative values of the scaling exponent, indicating a decrease in spatial variability with increasing spatial resolution. High evapotranspiration rates cause an increase in the small scale soil moisture variability, thus leading to large negative values of the scaling exponent. Utilizing a multiple regression analysis, we found that 53% of the variance of the scaling exponent can be explained by a combination of an independent LAI parameter and the antecedent precipitation.

The consequence of spatial visual processing dysfunction caused by traumatic brain injury (TBI).

PubMed

Padula, William V; Capo-Aponte, Jose E; Padula, William V; Singman, Eric L; Jenness, Jonathan

2017-01-01

A bi-modal visual processing model is supported by research to affect dysfunction following a traumatic brain injury (TBI). TBI causes dysfunction of visual processing affecting binocularity, spatial orientation, posture and balance. Research demonstrates that prescription of prisms influence the plasticity between spatial visual processing and motor-sensory systems improving visual processing and reducing symptoms following a TBI. The rationale demonstrates that visual processing underlies the functional aspects of binocularity, balance and posture. The bi-modal visual process maintains plasticity for efficiency. Compromise causes Post Trauma Vision Syndrome (PTVS) and Visual Midline Shift Syndrome (VMSS). Rehabilitation through use of lenses, prisms and sectoral occlusion has inter-professional implications in rehabilitation affecting the plasticity of the bi-modal visual process, thereby improving binocularity, spatial orientation, posture and balance Main outcomes: This review provides an opportunity to create a new perspective of the consequences of TBI on visual processing and the symptoms that are often caused by trauma. It also serves to provide a perspective of visual processing dysfunction that has potential for developing new approaches of rehabilitation. Understanding vision as a bi-modal process facilitates a new perspective of visual processing and the potentials for rehabilitation following a concussion, brain injury or other neurological events.

Robust Kriged Kalman Filtering

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

Baingana, Brian; Dall'Anese, Emiliano; Mateos, Gonzalo

2015-11-11

Although the kriged Kalman filter (KKF) has well-documented merits for prediction of spatial-temporal processes, its performance degrades in the presence of outliers due to anomalous events, or measurement equipment failures. This paper proposes a robust KKF model that explicitly accounts for presence of measurement outliers. Exploiting outlier sparsity, a novel l1-regularized estimator that jointly predicts the spatial-temporal process at unmonitored locations, while identifying measurement outliers is put forth. Numerical tests are conducted on a synthetic Internet protocol (IP) network, and real transformer load data. Test results corroborate the effectiveness of the novel estimator in joint spatial prediction and outlier identification.

GPP in Loblolly Pine: A Monthly Comparison of Empirical and Process Models

Treesearch

Christopher Gough; John Seiler; Kurt Johnsen; David Arthur Sampson

2002-01-01

Monthly and yearly gross primary productivity (GPP) estimates derived from an empirical and two process based models (3PG and BIOMASS) were compared. Spatial and temporal variation in foliar gas photosynthesis was examined and used to develop GPP prediction models for fertilized nine-year-old loblolly pine (Pinus taeda) stands located in the North...

Evaluating crown fire rate of spread predictions from physics-based models

Treesearch

C. M. Hoffman; J. Ziegler; J. Canfield; R. R. Linn; W. Mell; C. H. Sieg; F. Pimont

2015-01-01

Modeling the behavior of crown fires is challenging due to the complex set of coupled processes that drive the characteristics of a spreading wildfire and the large range of spatial and temporal scales over which these processes occur. Detailed physics-based modeling approaches such as FIRETEC and the Wildland Urban Interface Fire Dynamics Simulator (WFDS) simulate...

Catchment hydro-biogeochemical response to forest harvest intensity and spatial pattern

EPA Science Inventory

We apply a new model, Visualizing Ecosystems for Land Management Assessment (VELMA), to Watershed 10 (WS10) in the H.J. Andrews Experimental Forest to simulate the effects of harvest intensity and spatial pattern on catchment hydrological and biogeochemical processes. Specificall...

Sampling design for spatially distributed hydrogeologic and environmental processes

USGS Publications Warehouse

Christakos, G.; Olea, R.A.

1992-01-01

A methodology for the design of sampling networks over space is proposed. The methodology is based on spatial random field representations of nonhomogeneous natural processes, and on optimal spatial estimation techniques. One of the most important results of random field theory for physical sciences is its rationalization of correlations in spatial variability of natural processes. This correlation is extremely important both for interpreting spatially distributed observations and for predictive performance. The extent of site sampling and the types of data to be collected will depend on the relationship of subsurface variability to predictive uncertainty. While hypothesis formulation and initial identification of spatial variability characteristics are based on scientific understanding (such as knowledge of the physics of the underlying phenomena, geological interpretations, intuition and experience), the support offered by field data is statistically modelled. This model is not limited by the geometric nature of sampling and covers a wide range in subsurface uncertainties. A factorization scheme of the sampling error variance is derived, which possesses certain atttactive properties allowing significant savings in computations. By means of this scheme, a practical sampling design procedure providing suitable indices of the sampling error variance is established. These indices can be used by way of multiobjective decision criteria to obtain the best sampling strategy. Neither the actual implementation of the in-situ sampling nor the solution of the large spatial estimation systems of equations are necessary. The required values of the accuracy parameters involved in the network design are derived using reference charts (readily available for various combinations of data configurations and spatial variability parameters) and certain simple yet accurate analytical formulas. Insight is gained by applying the proposed sampling procedure to realistic examples related to sampling problems in two dimensions. ?? 1992.

Calibration of a distributed hydrologic model for six European catchments using remote sensing data

NASA Astrophysics Data System (ADS)

Stisen, S.; Demirel, M. C.; Mendiguren González, G.; Kumar, R.; Rakovec, O.; Samaniego, L. E.

2017-12-01

While observed streamflow has been the single reference for most conventional hydrologic model calibration exercises, the availability of spatially distributed remote sensing observations provide new possibilities for multi-variable calibration assessing both spatial and temporal variability of different hydrologic processes. In this study, we first identify the key transfer parameters of the mesoscale Hydrologic Model (mHM) controlling both the discharge and the spatial distribution of actual evapotranspiration (AET) across six central European catchments (Elbe, Main, Meuse, Moselle, Neckar and Vienne). These catchments are selected based on their limited topographical and climatic variability which enables to evaluate the effect of spatial parameterization on the simulated evapotranspiration patterns. We develop a European scale remote sensing based actual evapotranspiration dataset at a 1 km grid scale driven primarily by land surface temperature observations from MODIS using the TSEB approach. Using the observed AET maps we analyze the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mHM model. This model allows calibrating one-basin-at-a-time or all-basins-together using its unique structure and multi-parameter regionalization approach. Results will indicate any tradeoffs between spatial pattern and discharge simulation during model calibration and through validation against independent internal discharge locations. Moreover, added value on internal water balances will be analyzed.

Non-equilibrium Quasi-Chemical Nucleation Model

NASA Astrophysics Data System (ADS)

Gorbachev, Yuriy E.

2018-04-01

Quasi-chemical model, which is widely used for nucleation description, is revised on the basis of recent results in studying of non-equilibrium effects in reacting gas mixtures (Kolesnichenko and Gorbachev in Appl Math Model 34:3778-3790, 2010; Shock Waves 23:635-648, 2013; Shock Waves 27:333-374, 2017). Non-equilibrium effects in chemical reactions are caused by the chemical reactions themselves and therefore these contributions should be taken into account in the corresponding expressions for reaction rates. Corrections to quasi-equilibrium reaction rates are of two types: (a) spatially homogeneous (caused by physical-chemical processes) and (b) spatially inhomogeneous (caused by gas expansion/compression processes and proportional to the velocity divergency). Both of these processes play an important role during the nucleation and are included into the proposed model. The method developed for solving the generalized Boltzmann equation for chemically reactive gases is applied for solving the set of equations of the revised quasi-chemical model. It is shown that non-equilibrium processes lead to essential deviation of the quasi-stationary distribution and therefore the nucleation rate from its traditional form.

Formulating Spatially Varying Performance in the Statistical Fusion Framework

PubMed Central

Landman, Bennett A.

2012-01-01

To date, label fusion methods have primarily relied either on global (e.g. STAPLE, globally weighted vote) or voxelwise (e.g. locally weighted vote) performance models. Optimality of the statistical fusion framework hinges upon the validity of the stochastic model of how a rater errs (i.e., the labeling process model). Hitherto, approaches have tended to focus on the extremes of potential models. Herein, we propose an extension to the STAPLE approach to seamlessly account for spatially varying performance by extending the performance level parameters to account for a smooth, voxelwise performance level field that is unique to each rater. This approach, Spatial STAPLE, provides significant improvements over state-of-the-art label fusion algorithms in both simulated and empirical data sets. PMID:22438513

Proximity to natural amenities: A seemingly unrelated hedonic regression model with spatial durbin and spatial error processes

Treesearch

German M. Izon; Michael S. Hand; Daniel W. Mccollum; Jennifer A. Thacher; Robert P. Berrens

2016-01-01

The existing literature suggests that the presence of natural amenities, such as open spaces, can be highly valued and affect economic decisions about where people live and work. This article contributes to previous research by testing this hypothesis using a unique micro-level data set and by examining spatial variations in income levels and housing prices in the...

Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas - a review

NASA Astrophysics Data System (ADS)

Cristiano, Elena; ten Veldhuis, Marie-claire; van de Giesen, Nick

2017-07-01

In urban areas, hydrological processes are characterized by high variability in space and time, making them sensitive to small-scale temporal and spatial rainfall variability. In the last decades new instruments, techniques, and methods have been developed to capture rainfall and hydrological processes at high resolution. Weather radars have been introduced to estimate high spatial and temporal rainfall variability. At the same time, new models have been proposed to reproduce hydrological response, based on small-scale representation of urban catchment spatial variability. Despite these efforts, interactions between rainfall variability, catchment heterogeneity, and hydrological response remain poorly understood. This paper presents a review of our current understanding of hydrological processes in urban environments as reported in the literature, focusing on their spatial and temporal variability aspects. We review recent findings on the effects of rainfall variability on hydrological response and identify gaps where knowledge needs to be further developed to improve our understanding of and capability to predict urban hydrological response.

Stochastic simulation of spatially correlated geo-processes

USGS Publications Warehouse

Christakos, G.

1987-01-01

In this study, developments in the theory of stochastic simulation are discussed. The unifying element is the notion of Radon projection in Euclidean spaces. This notion provides a natural way of reconstructing the real process from a corresponding process observable on a reduced dimensionality space, where analysis is theoretically easier and computationally tractable. Within this framework, the concept of space transformation is defined and several of its properties, which are of significant importance within the context of spatially correlated processes, are explored. The turning bands operator is shown to follow from this. This strengthens considerably the theoretical background of the geostatistical method of simulation, and some new results are obtained in both the space and frequency domains. The inverse problem is solved generally and the applicability of the method is extended to anisotropic as well as integrated processes. Some ill-posed problems of the inverse operator are discussed. Effects of the measurement error and impulses at origin are examined. Important features of the simulated process as described by geomechanical laws, the morphology of the deposit, etc., may be incorporated in the analysis. The simulation may become a model-dependent procedure and this, in turn, may provide numerical solutions to spatial-temporal geologic models. Because the spatial simu??lation may be technically reduced to unidimensional simulations, various techniques of generating one-dimensional realizations are reviewed. To link theory and practice, an example is computed in detail. ?? 1987 International Association for Mathematical Geology.

A stochastic-geometric model of soil variation in Pleistocene patterned ground

NASA Astrophysics Data System (ADS)

Lark, Murray; Meerschman, Eef; Van Meirvenne, Marc

2013-04-01

In this paper we examine the spatial variability of soil in parent material with complex spatial structure which arises from complex non-linear geomorphic processes. We show that this variability can be better-modelled by a stochastic-geometric model than by a standard Gaussian random field. The benefits of the new model are seen in the reproduction of features of the target variable which influence processes like water movement and pollutant dispersal. Complex non-linear processes in the soil give rise to properties with non-Gaussian distributions. Even under a transformation to approximate marginal normality, such variables may have a more complex spatial structure than the Gaussian random field model of geostatistics can accommodate. In particular the extent to which extreme values of the variable are connected in spatially coherent regions may be misrepresented. As a result, for example, geostatistical simulation generally fails to reproduce the pathways for preferential flow in an environment where coarse infill of former fluvial channels or coarse alluvium of braided streams creates pathways for rapid movement of water. Multiple point geostatistics has been developed to deal with this problem. Multiple point methods proceed by sampling from a set of training images which can be assumed to reproduce the non-Gaussian behaviour of the target variable. The challenge is to identify appropriate sources of such images. In this paper we consider a mode of soil variation in which the soil varies continuously, exhibiting short-range lateral trends induced by local effects of the factors of soil formation which vary across the region of interest in an unpredictable way. The trends in soil variation are therefore only apparent locally, and the soil variation at regional scale appears random. We propose a stochastic-geometric model for this mode of soil variation called the Continuous Local Trend (CLT) model. We consider a case study of soil formed in relict patterned ground with pronounced lateral textural variations arising from the presence of infilled ice-wedges of Pleistocene origin. We show how knowledge of the pedogenetic processes in this environment, along with some simple descriptive statistics, can be used to select and fit a CLT model for the apparent electrical conductivity (ECa) of the soil. We use the model to simulate realizations of the CLT process, and compare these with realizations of a fitted Gaussian random field. We show how statistics that summarize the spatial coherence of regions with small values of ECa, which are expected to have coarse texture and so larger saturated hydraulic conductivity, are better reproduced by the CLT model than by the Gaussian random field. This suggests that the CLT model could be used to generate an unlimited supply of training images to allow multiple point geostatistical simulation or prediction of this or similar variables.

Functional resilience of microbial ecosystems in soil: How important is a spatial analysis?

NASA Astrophysics Data System (ADS)

König, Sara; Banitz, Thomas; Centler, Florian; Frank, Karin; Thullner, Martin

2015-04-01

Microbial life in soil is exposed to fluctuating environmental conditions influencing the performance of microbially mediated ecosystem services such as biodegradation of contaminants. However, as this environment is typically very heterogeneous, spatial aspects can be expected to play a major role for the ability to recover from a stress event. To determine key processes for functional resilience, simple scenarios with varying stress intensities were simulated within a microbial simulation model and the biodegradation rate in the recovery phase monitored. Parameters including microbial growth and dispersal rates were varied over a typical range to consider microorganisms with varying properties. Besides an aggregated temporal monitoring, the explicit observation of the spatio-temporal dynamics proved essential to understand the recovery process. For a mechanistic understanding of the model system, scenarios were also simulated with selected processes being switched-off. Results of the mechanistic and the spatial view show that the key factors for functional recovery with respect to biodegradation after a simple stress event depend on the location of the observed habitats. The limiting factors near unstressed areas are spatial processes - the mobility of the bacteria as well as substrate diffusion - the longer the distance to the unstressed region the more important becomes the process growth. Furthermore, recovery depends on the stress intensity - after a low stress event the spatial configuration has no influence on the key factors for functional resilience. To confirm these results, we repeated the stress scenarios but this time including an additional dispersal network representing a fungal network in soil. The system benefits from an increased spatial performance due to the higher mobility of the degrading microorganisms. However, this effect appears only in scenarios where the spatial distribution of the stressed area plays a role. With these simulations we show that spatial aspects play a main role for recovering after a severe stress event in a highly heterogeneous environment such as soil, and thus the relevance of the exact distribution of the stressed area. In consequence a spatial-mechanistic view is necessary for examining the functional resilience as the aggregated temporal view alone could not have led to these conclusions. Further research should explore the importance of a spatial view for quantifying the recovery of the ecosystem service also after more complex stress regimes.

Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States

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

Li, Zhengpeng; Liu, Shuguang; Tan, Zhengxi

2014-04-01

Accurately quantifying the spatial and temporal variability of net primary production (NPP) for croplands is essential to understand regional cropland carbon dynamics. We compared three NPP estimates for croplands in the Midwestern United States: inventory-based estimates using crop yield data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS); estimates from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product; and estimates from the General Ensemble biogeochemical Modeling System (GEMS) process-based model. The three methods estimated mean NPP in the range of 469–687 g C m -2 yr -1 and total NPP in the range of 318–490more » Tg C yr -1 for croplands in the Midwest in 2007 and 2008. The NPP estimates from crop yield data and the GEMS model showed the mean NPP for croplands was over 650 g C m -2 yr -1 while the MODIS NPP product estimated the mean NPP was less than 500 g C m -2 yr -1. MODIS NPP also showed very different spatial variability of the cropland NPP from the other two methods. We found these differences were mainly caused by the difference in the land cover data and the crop specific information used in the methods. Our study demonstrated that the detailed mapping of the temporal and spatial change of crop species is critical for estimating the spatial and temporal variability of cropland NPP. Finally, we suggest that high resolution land cover data with species–specific crop information should be used in satellite-based and process-based models to improve carbon estimates for croplands.« less

Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States

USGS Publications Warehouse

Li, Zhengpeng; Liu, Shuguang; Tan, Zhengxi; Bliss, Norman B.; Young, Claudia J.; West, Tristram O.; Ogle, Stephen M.

2014-01-01

Accurately quantifying the spatial and temporal variability of net primary production (NPP) for croplands is essential to understand regional cropland carbon dynamics. We compared three NPP estimates for croplands in the Midwestern United States: inventory-based estimates using crop yield data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS); estimates from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product; and estimates from the General Ensemble biogeochemical Modeling System (GEMS) process-based model. The three methods estimated mean NPP in the range of 469–687 g C m−2 yr−1and total NPP in the range of 318–490 Tg C yr−1 for croplands in the Midwest in 2007 and 2008. The NPP estimates from crop yield data and the GEMS model showed the mean NPP for croplands was over 650 g C m−2 yr−1 while the MODIS NPP product estimated the mean NPP was less than 500 g C m−2 yr−1. MODIS NPP also showed very different spatial variability of the cropland NPP from the other two methods. We found these differences were mainly caused by the difference in the land cover data and the crop specific information used in the methods. Our study demonstrated that the detailed mapping of the temporal and spatial change of crop species is critical for estimating the spatial and temporal variability of cropland NPP. We suggest that high resolution land cover data with species–specific crop information should be used in satellite-based and process-based models to improve carbon estimates for croplands.

[Urbanization expanding process and its spatial characteristics in changping district of Beijing, China].

PubMed

Zhang, Feng; Zhang, Xinshi

2005-06-01

Urbanization is a massive and unplanned experiment that already affects large acreages worldwide, and has profound social and ecological consequences for both urban and rural residents. Therefore, to comprehensively quantify the urbanization process and to investigate its ecological consequences become the central issues in urban ecological studies. Combining urbanization expanding index with landscape metrics, this paper quantified the urbanization expanding process and the urbanization spatial characteristics in Changping District of Beijing. The results showed that there were three main urbanization models, i. e., urban fringe belt-expending model, main transportation routes line-expending model, and satellite city panel-expending model. The urbanization expansion index showed that urbanization mainly took place during the period from 1989 to 1996, and the urban landscape metrics indicated that there were urban patches isolated expanding and new urban patches emerged from 1989 to 1996, mainly amalgamated expanding from 1996 to 2001 in urban fringe belt-expanding region. In transportation routes line-expanding region, the urban patches isolated expanding, amalgamated expanding and new urban patches emerging took place simultaneously, and mainly urban patches amalgamated expanding during the former period, and new urban patches constantly turning up around satellite city during the latter in satellite city panel-expanding region. This study showed that urbanization expansion integrating landscape metrics might reveal the urbanization expanding process and its spatial characteristics, and would be a good example for the application of landscape metrics.

Generalized estimators of avian abundance from count survey data

USGS Publications Warehouse

Royle, J. Andrew

2004-01-01

I consider modeling avian abundance from spatially referenced bird count data collected according to common protocols such as capture?recapture, multiple observer, removal sampling and simple point counts. Small sample sizes and large numbers of parameters have motivated many analyses that disregard the spatial indexing of the data, and thus do not provide an adequate treatment of spatial structure. I describe a general framework for modeling spatially replicated data that regards local abundance as a random process, motivated by the view that the set of spatially referenced local populations (at the sample locations) constitute a metapopulation. Under this view, attention can be focused on developing a model for the variation in local abundance independent of the sampling protocol being considered. The metapopulation model structure, when combined with the data generating model, define a simple hierarchical model that can be analyzed using conventional methods. The proposed modeling framework is completely general in the sense that broad classes of metapopulation models may be considered, site level covariates on detection and abundance may be considered, and estimates of abundance and related quantities may be obtained for sample locations, groups of locations, unsampled locations. Two brief examples are given, the first involving simple point counts, and the second based on temporary removal counts. Extension of these models to open systems is briefly discussed.

Predictive spatial modeling of narcotic crop growth patterns

USGS Publications Warehouse

Waltz, Frederick A.; Moore, D.G.

1986-01-01

Spatial models for predicting the geographic distribution of marijuana crops have been developed and are being evaluated for use in law enforcement programs. The models are based on growing condition preferences and on psychological inferences regarding grower behavior. Experiences of local law officials were used to derive the initial model, which was updated and improved as data from crop finds were archived and statistically analyzed. The predictive models are changed as crop locations are moved in response to the pressures of law enforcement. The models use spatial data in a raster geographic information system. The spatial data are derived from the U.S. Geological Survey's US GeoData, standard 7.5-minute topographic quadrangle maps, interpretations of aerial photographs, and thematic maps. Updating of cultural patterns, canopy closure, and other dynamic features is conducted through interpretation of aerial photographs registered to the 7.5-minute quadrangle base. The model is used to numerically weight various data layers that have been processed using spread functions, edge definition, and categorization. The building of the spatial data base, model development, model application, product generation, and use are collectively referred to as the Area Reduction Program (ARP). The goal of ARP is to provide law enforcement officials with tactical maps that show the most likely locations for narcotic crops.

A Formal Investigation of Human Spatial Control Skills: Mathematical Formalization, Skill Development, and Skill Assessment

NASA Astrophysics Data System (ADS)

Li, Bin

Spatial control behaviors account for a large proportion of human everyday activities from normal daily tasks, such as reaching for objects, to specialized tasks, such as driving, surgery, or operating equipment. These behaviors involve intensive interactions within internal processes (i.e. cognitive, perceptual, and motor control) and with the physical world. This dissertation builds on a concept of interaction pattern and a hierarchical functional model. Interaction pattern represents a type of behavior synergy that humans coordinates cognitive, perceptual, and motor control processes. It contributes to the construction of the hierarchical functional model that delineates humans spatial control behaviors as the coordination of three functional subsystems: planning, guidance, and tracking/pursuit. This dissertation formalizes and validates these two theories and extends them for the investigation of human spatial control skills encompassing development and assessment. Specifically, this dissertation first presents an overview of studies in human spatial control skills encompassing definition, characteristic, development, and assessment, to provide theoretical evidence for the concept of interaction pattern and the hierarchical functional model. The following, the human experiments for collecting motion and gaze data and techniques to register and classify gaze data, are described. This dissertation then elaborates and mathematically formalizes the hierarchical functional model and the concept of interaction pattern. These theories then enables the construction of a succinct simulation model that can reproduce a variety of human performance with a minimal set of hypotheses. This validates the hierarchical functional model as a normative framework for interpreting human spatial control behaviors. The dissertation then investigates human skill development and captures the emergence of interaction pattern. The final part of the dissertation applies the hierarchical functional model for skill assessment and introduces techniques to capture interaction patterns both from the top down using their geometric features and from the bottom up using their dynamical characteristics. The validity and generality of the skill assessment is illustrated using two the remote-control flight and laparoscopic surgical training experiments.

Spatial memory in foraging games.

PubMed

Kerster, Bryan E; Rhodes, Theo; Kello, Christopher T

2016-03-01

Foraging and foraging-like processes are found in spatial navigation, memory, visual search, and many other search functions in human cognition and behavior. Foraging is commonly theorized using either random or correlated movements based on Lévy walks, or a series of decisions to remain or leave proximal areas known as "patches". Neither class of model makes use of spatial memory, but search performance may be enhanced when information about searched and unsearched locations is encoded. A video game was developed to test the role of human spatial memory in a canonical foraging task. Analyses of search trajectories from over 2000 human players yielded evidence that foraging movements were inherently clustered, and that clustering was facilitated by spatial memory cues and influenced by memory for spatial locations of targets found. A simple foraging model is presented in which spatial memory is used to integrate aspects of Lévy-based and patch-based foraging theories to perform a kind of area-restricted search, and thereby enhance performance as search unfolds. Using only two free parameters, the model accounts for a variety of findings that individually support competing theories, but together they argue for the integration of spatial memory into theories of foraging. Copyright © 2015 Elsevier B.V. All rights reserved.

When Content Matters: The Role of Processing Code in Tactile Display Design.

PubMed

Ferris, Thomas K; Sarter, Nadine

2010-01-01

The distribution of tasks and stimuli across multiple modalities has been proposed as a means to support multitasking in data-rich environments. Recently, the tactile channel and, more specifically, communication via the use of tactile/haptic icons have received considerable interest. Past research has examined primarily the impact of concurrent task modality on the effectiveness of tactile information presentation. However, it is not well known to what extent the interpretation of iconic tactile patterns is affected by another attribute of information: the information processing codes of concurrent tasks. In two driving simulation studies (n = 25 for each), participants decoded icons composed of either spatial or nonspatial patterns of vibrations (engaging spatial and nonspatial processing code resources, respectively) while concurrently interpreting spatial or nonspatial visual task stimuli. As predicted by Multiple Resource Theory, performance was significantly worse (approximately 5-10 percent worse) when the tactile icons and visual tasks engaged the same processing code, with the overall worst performance in the spatial-spatial task pairing. The findings from these studies contribute to an improved understanding of information processing and can serve as input to multidimensional quantitative models of timesharing performance. From an applied perspective, the results suggest that competition for processing code resources warrants consideration, alongside other factors such as the naturalness of signal-message mapping, when designing iconic tactile displays. Nonspatially encoded tactile icons may be preferable in environments which already rely heavily on spatial processing, such as car cockpits.

The overlap between false belief and spatial reorientation in the temporo-parietal junction: The role of input modality and task.

PubMed

Özdem, Ceylan; Brass, Marcel; Van der Cruyssen, Laurens; Van Overwalle, Frank

2017-04-01

Neuroimaging research has demonstrated that the temporo-parietal junction (TPJ) is activated when unexpected stimuli appear in spatial reorientation tasks as well as during thinking about the beliefs of other people triggered by verbal scenarios. While the role of potential common component processes subserved by the TPJ has been extensively studied to explain this common activation, the potential confounding role of input modality (spatial vs. verbal) has been largely ignored. To investigate the role of input modality apart from task processes, we developed a novel spatial false belief task based on moving shapes. We explored the overlap in TPJ activation across this novel task and traditional tasks of spatial reorientation (Posner) and verbal belief (False Belief vs. Photo stories). The results show substantial overlap across the same spatial input modality (both reorientation and false belief) as well as across the common task process (verbal and spatial belief), but no triple overlap. This suggests the potential for an overarching function of the TPJ, with some degree of specialization in different subregions due to modality, function and connectivity. The results are discussed with respect to recent theoretical models of the TPJ.

The sense and non-sense of plot-scale, catchment-scale, continental-scale and global-scale hydrological modelling

NASA Astrophysics Data System (ADS)

Bronstert, Axel; Heistermann, Maik; Francke, Till

2017-04-01

Hydrological models aim at quantifying the hydrological cycle and its constituent processes for particular conditions, sites or periods in time. Such models have been developed for a large range of spatial and temporal scales. One must be aware that the question which is the appropriate scale to be applied depends on the overall question under study. Therefore, it is not advisable to give a general applicable guideline on what is "the best" scale for a model. This statement is even more relevant for coupled hydrological, ecological and atmospheric models. Although a general statement about the most appropriate modelling scale is not recommendable, it is worth to have a look on what are the advantages and the shortcomings of micro-, meso- and macro-scale approaches. Such an appraisal is of increasing importance, since increasingly (very) large / global scale approaches and models are under operation and therefore the question arises how far and for what purposes such methods may yield scientifically sound results. It is important to understand that in most hydrological (and ecological, atmospheric and other) studies process scale, measurement scale, and modelling scale differ from each other. In some cases, the differences between theses scales can be of different orders of magnitude (example: runoff formation, measurement and modelling). These differences are a major source of uncertainty in description and modelling of hydrological, ecological and atmospheric processes. Let us now summarize our viewpoint of the strengths (+) and weaknesses (-) of hydrological models of different scales: Micro scale (e.g. extent of a plot, field or hillslope): (+) enables process research, based on controlled experiments (e.g. infiltration; root water uptake; chemical matter transport); (+) data of state conditions (e.g. soil parameter, vegetation properties) and boundary fluxes (e.g. rainfall or evapotranspiration) are directly measurable and reproducible; (+) equations based on first principals, partly pde-type, are available for several processes (but not for all), because measurement and modelling scale are compatible (-) the spatial model domain are hardly representative for larger spatial entities, including regions for which water resources management decisions are to be taken; straightforward upsizing is also limited by data availability and computational requirements. Meso scale (e.g. extent of a small to large catchment or region): (+) the spatial extent of the model domain has approximately the same extent as the regions for which water resources management decisions are to be taken. I.e., such models enable water resources quantification at the scale of most water management decisions; (+) data of some state conditions (e.g. vegetation cover, topography, river network and cross sections) are available; (+) data of some boundary fluxes (in particular surface runoff / channel flow) are directly measurable with mostly sufficient certainty; (+) equations, partly based on simple water budgeting, partly variants of pde-type equations, are available for most hydrological processes. This enables the construction of meso-scale distributed models reflecting the spatial heterogeneity of regions/landscapes; (-) process scale, measurement scale, and modelling scale differ from each other for a number of processes, e.g., such as runoff generation; (-) the process formulation (usually derived from micro-scale studies) cannot directly be transferred to the modelling domain. Upscaling procedures for this purpose are not readily and generally available. Macro scale (e.g. extent of a continent up to global): (+) the spatial extent of the model may cover the whole Earth. This enables an attractive global display of model results; (+) model results might be technically interchangeable or at least comparable with results from other global models, such as global climate models; (-) process scale, measurement scale, and modelling scale differ heavily from each other for all hydrological and associated processes; (-) the model domain and its results are not representative regions for which water resources management decisions are to be taken. (-) both state condition and boundary flux data are hardly available for the whole model domain. Water management data and discharge data from remote regions are particular incomplete / unavailable for this scale. This undermines the model's verifiability; (-) since process formulation and resulting modelling reliability at this scale is very limited, such models can hardly show any explanatory skills or prognostic power; (-) since both the entire model domain and the spatial sub-units cover large areas, model results represent values averaged over at least the spatial sub-unit's extent. In many cases, the applied time scale implies a long-term averaging in time, too. We emphasize the importance to be aware of the above mentioned strengths and weaknesses of those scale-specific models. (Many of the) results of the current global model studies do not reflect such limitations. In particular, we consider the averaging over large model entities in space and/or time inadequate. Many hydrological processes are of a non-linear nature, including threshold-type behaviour. Such features cannot be reflected by such large scale entities. The model results therefore can be of little or no use for water resources decisions and/or even misleading for public debates or decision making. Some rather newly developed sustainability concepts, e.g. "Planetary Boundaries" in which humanity may "continue to develop and thrive for generations to come" are based on such global-scale approaches and models. However, many of the major problems regarding sustainability on Earth, e.g. water scarcity, do not exhibit on a global but on a regional scale. While on a global scale water might look like being available in sufficient quantity and quality, there are many regions where water problems already have very harmful or even devastating effects. Therefore, it is the challenge to derive models and observation programmes for regional scales. In case a global display is desired future efforts should be directed towards the development of a global picture based on a mosaic of regional sound assessments, rather than "zooming into" the results of large-scale simulations. Still, a key question remains to be discussed, i.e. for which purpose models at this (global) scale can be used.

Feller processes: the next generation in modeling. Brownian motion, Lévy processes and beyond.

PubMed

Böttcher, Björn

2010-12-03

We present a simple construction method for Feller processes and a framework for the generation of sample paths of Feller processes. The construction is based on state space dependent mixing of Lévy processes. Brownian Motion is one of the most frequently used continuous time Markov processes in applications. In recent years also Lévy processes, of which Brownian Motion is a special case, have become increasingly popular. Lévy processes are spatially homogeneous, but empirical data often suggest the use of spatially inhomogeneous processes. Thus it seems necessary to go to the next level of generalization: Feller processes. These include Lévy processes and in particular brownian motion as special cases but allow spatial inhomogeneities. Many properties of Feller processes are known, but proving the very existence is, in general, very technical. Moreover, an applicable framework for the generation of sample paths of a Feller process was missing. We explain, with practitioners in mind, how to overcome both of these obstacles. In particular our simulation technique allows to apply Monte Carlo methods to Feller processes.

Feller Processes: The Next Generation in Modeling. Brownian Motion, Lévy Processes and Beyond

PubMed Central

Böttcher, Björn

2010-01-01

We present a simple construction method for Feller processes and a framework for the generation of sample paths of Feller processes. The construction is based on state space dependent mixing of Lévy processes. Brownian Motion is one of the most frequently used continuous time Markov processes in applications. In recent years also Lévy processes, of which Brownian Motion is a special case, have become increasingly popular. Lévy processes are spatially homogeneous, but empirical data often suggest the use of spatially inhomogeneous processes. Thus it seems necessary to go to the next level of generalization: Feller processes. These include Lévy processes and in particular Brownian motion as special cases but allow spatial inhomogeneities. Many properties of Feller processes are known, but proving the very existence is, in general, very technical. Moreover, an applicable framework for the generation of sample paths of a Feller process was missing. We explain, with practitioners in mind, how to overcome both of these obstacles. In particular our simulation technique allows to apply Monte Carlo methods to Feller processes. PMID:21151931

The effect of spatial organization of targets and distractors on the capacity to selectively memorize objects in visual short-term memory

PubMed Central

Abbes, Aymen Ben; Gavault, Emmanuelle; Ripoll, Thierry

2014-01-01

We conducted a series of experiments to explore how the spatial configuration of objects influences the selection and the processing of these objects in a visual short-term memory task. We designed a new experiment in which participants had to memorize 4 targets presented among 4 distractors. Targets were cued during the presentation of distractor objects. Their locations varied according to 4 spatial configurations. From the first to the last configuration, the distance between targets’ locations was progressively increased. The results revealed a high capacity to select and memorize targets embedded among distractors even when targets were extremely distant from each other. This capacity is discussed in relation to the unitary conception of attention, models of split attention, and the competitive interaction model. Finally, we propose that the spatial dispersion of objects has different effects on attentional allocation and processing stages. Thus, when targets are extremely distant from each other, attentional allocation becomes more difficult while processing becomes easier. This finding implicates that these 2 aspects of attention need to be more clearly distinguished in future research. PMID:25339978

Designing a data portal for synthesis modeling

NASA Astrophysics Data System (ADS)

Holmes, M. A.

2006-12-01

Processing of field and model data in multi-disciplinary integrated science studies is a vital part of synthesis modeling. Collection and storage techniques for field data vary greatly between the participating scientific disciplines due to the nature of the data being collected, whether it be in situ, remotely sensed, or recorded by automated data logging equipment. Spreadsheets, personal databases, text files and binary files are used in the initial storage and processing of the raw data. In order to be useful to scientists, engineers and modelers the data need to be stored in a format that is easily identifiable, accessible and transparent to a variety of computing environments. The Model Operations and Synthesis (MOAS) database and associated web portal were created to provide such capabilities. The industry standard relational database is comprised of spatial and temporal data tables, shape files and supporting metadata accessible over the network, through a menu driven web-based portal or spatially accessible through ArcSDE connections from the user's local GIS desktop software. A separate server provides public access to spatial data and model output in the form of attributed shape files through an ArcIMS web-based graphical user interface.

Pine invasions in treeless environments: dispersal overruns microsite heterogeneity.

PubMed

Pauchard, Aníbal; Escudero, Adrián; García, Rafael A; de la Cruz, Marcelino; Langdon, Bárbara; Cavieres, Lohengrin A; Esquivel, Jocelyn

2016-01-01

Understanding biological invasions patterns and mechanisms is highly needed for forecasting and managing these processes and their negative impacts. At small scales, ecological processes driving plant invasions are expected to produce a spatially explicit pattern driven by propagule pressure and local ground heterogeneity. Our aim was to determine the interplay between the intensity of seed rain, using distance to a mature plantation as a proxy, and microsite heterogeneity in the spreading of Pinus contorta in the treeless Patagonian steppe. Three one-hectare plots were located under different degrees of P. contorta invasion (Coyhaique Alto, 45° 30'S and 71° 42'W). We fitted three types of inhomogeneous Poisson models to each pine plot in an attempt for describing the observed pattern as accurately as possible: the "dispersal" models, "local ground heterogeneity" models, and "combined" models, using both types of covariates. To include the temporal axis in the invasion process, we analyzed both the pattern of young and old recruits and also of all recruits together. As hypothesized, the spatial patterns of recruited pines showed coarse scale heterogeneity. Early pine invasion spatial patterns in our Patagonian steppe site is not different from expectations of inhomogeneous Poisson processes taking into consideration a linear and negative dependency of pine recruit intensity on the distance to afforestations. Models including ground-cover predictors were able to describe the point pattern process only in a couple of cases but never better than dispersal models. This finding concurs with the idea that early invasions depend more on seed pressure than on the biotic and abiotic relationships seed and seedlings establish at the microsite scale. Our results show that without a timely and active management, P. contorta will invade the Patagonian steppe independently of the local ground-cover conditions.

Spatial modelling of evapotranspiration in the Luquillo experimental forest of Puerto Rico using remotely-sensed data.

Treesearch

Wei Wu; Charles A.S. Hall; Frederick N. Scatena; Lindi J. Quackenbush

2006-01-01

Summary Actual evapotranspiration (aET) and related processes in tropical forests can explain 70% of the lateral global energy transport through latent heat, and therefore are very important in the redistribution of water on the Earth’s surface [Mauser, M., Scha¨dlich, S., 1998. Modelling the spatial distribution of evapotranspiration on different scales using remote...

Crime Pattern Analysis: A Spatial Frequent Pattern Mining Approach

DTIC Science & Technology

2012-05-10

econometrics. A companion to theoretical econometrics, pages 310-330, 1988. [5] L. Anselin, J. Cohen, D. Cook, W. Gorr, and G. Tita . Spatial analyses...52] G. Mohler, M. Short, P. Brantingham, F. Schoenberg, and G. Tita . Self-exciting point process modeling of crime. Journal of the American...Systems, 9:462, 2010. [69] M. Short, P. Brantingham, A. Bertozzi, and G. Tita . Dissipation and displacement of hotspots in reaction-diffusion models

Modelling the development and arrangement of the primary vascular structure in plants.

PubMed

Cartenì, Fabrizio; Giannino, Francesco; Schweingruber, Fritz Hans; Mazzoleni, Stefano

2014-09-01

The process of vascular development in plants results in the formation of a specific array of bundles that run throughout the plant in a characteristic spatial arrangement. Although much is known about the genes involved in the specification of procambium, phloem and xylem, the dynamic processes and interactions that define the development of the radial arrangement of such tissues remain elusive. This study presents a spatially explicit reaction-diffusion model defining a set of logical and functional rules to simulate the differentiation of procambium, phloem and xylem and their spatial patterns, starting from a homogeneous group of undifferentiated cells. Simulation results showed that the model is capable of reproducing most vascular patterns observed in plants, from primitive and simple structures made up of a single strand of vascular bundles (protostele), to more complex and evolved structures, with separated vascular bundles arranged in an ordered pattern within the plant section (e.g. eustele). The results presented demonstrate, as a proof of concept, that a common genetic-molecular machinery can be the basis of different spatial patterns of plant vascular development. Moreover, the model has the potential to become a useful tool to test different hypotheses of genetic and molecular interactions involved in the specification of vascular tissues.

Modeling Spatial Dependence of Rainfall Extremes Across Multiple Durations

NASA Astrophysics Data System (ADS)

Le, Phuong Dong; Leonard, Michael; Westra, Seth

2018-03-01

Determining the probability of a flood event in a catchment given that another flood has occurred in a nearby catchment is useful in the design of infrastructure such as road networks that have multiple river crossings. These conditional flood probabilities can be estimated by calculating conditional probabilities of extreme rainfall and then transforming rainfall to runoff through a hydrologic model. Each catchment's hydrological response times are unlikely to be the same, so in order to estimate these conditional probabilities one must consider the dependence of extreme rainfall both across space and across critical storm durations. To represent these types of dependence, this study proposes a new approach for combining extreme rainfall across different durations within a spatial extreme value model using max-stable process theory. This is achieved in a stepwise manner. The first step defines a set of common parameters for the marginal distributions across multiple durations. The parameters are then spatially interpolated to develop a spatial field. Storm-level dependence is represented through the max-stable process for rainfall extremes across different durations. The dependence model shows a reasonable fit between the observed pairwise extremal coefficients and the theoretical pairwise extremal coefficient function across all durations. The study demonstrates how the approach can be applied to develop conditional maps of the return period and return level across different durations.

Visual attention: The past 25 years

PubMed Central

Carrasco, Marisa

2012-01-01

This review focuses on covert attention and how it alters early vision. I explain why attention is considered a selective process, the constructs of covert attention, spatial endogenous and exogenous attention, and feature-based attention. I explain how in the last 25 years research on attention has characterized the effects of covert attention on spatial filters and how attention influences the selection of stimuli of interest. This review includes the effects of spatial attention on discriminability and appearance in tasks mediated by contrast sensitivity and spatial resolution; the effects of feature-based attention on basic visual processes, and a comparison of the effects of spatial and feature-based attention. The emphasis of this review is on psychophysical studies, but relevant electrophysiological and neuroimaging studies and models regarding how and where neuronal responses are modulated are also discussed. PMID:21549742

Visual attention: the past 25 years.

PubMed

Carrasco, Marisa

2011-07-01

This review focuses on covert attention and how it alters early vision. I explain why attention is considered a selective process, the constructs of covert attention, spatial endogenous and exogenous attention, and feature-based attention. I explain how in the last 25 years research on attention has characterized the effects of covert attention on spatial filters and how attention influences the selection of stimuli of interest. This review includes the effects of spatial attention on discriminability and appearance in tasks mediated by contrast sensitivity and spatial resolution; the effects of feature-based attention on basic visual processes, and a comparison of the effects of spatial and feature-based attention. The emphasis of this review is on psychophysical studies, but relevant electrophysiological and neuroimaging studies and models regarding how and where neuronal responses are modulated are also discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Moving forward socio-economically focused models of deforestation.

PubMed

Dezécache, Camille; Salles, Jean-Michel; Vieilledent, Ghislain; Hérault, Bruno

2017-09-01

Whilst high-resolution spatial variables contribute to a good fit of spatially explicit deforestation models, socio-economic processes are often beyond the scope of these models. Such a low level of interest in the socio-economic dimension of deforestation limits the relevancy of these models for decision-making and may be the cause of their failure to accurately predict observed deforestation trends in the medium term. This study aims to propose a flexible methodology for taking into account multiple drivers of deforestation in tropical forested areas, where the intensity of deforestation is explicitly predicted based on socio-economic variables. By coupling a model of deforestation location based on spatial environmental variables with several sub-models of deforestation intensity based on socio-economic variables, we were able to create a map of predicted deforestation over the period 2001-2014 in French Guiana. This map was compared to a reference map for accuracy assessment, not only at the pixel scale but also over cells ranging from 1 to approximately 600 sq. km. Highly significant relationships were explicitly established between deforestation intensity and several socio-economic variables: population growth, the amount of agricultural subsidies, gold and wood production. Such a precise characterization of socio-economic processes allows to avoid overestimation biases in high deforestation areas, suggesting a better integration of socio-economic processes in the models. Whilst considering deforestation as a purely geographical process contributes to the creation of conservative models unable to effectively assess changes in the socio-economic and political contexts influencing deforestation trends, this explicit characterization of the socio-economic dimension of deforestation is critical for the creation of deforestation scenarios in REDD+ projects. © 2017 John Wiley & Sons Ltd.

A set of simple cell processes is sufficient to model spiral cleavage.

PubMed

Brun-Usan, Miguel; Marín-Riera, Miquel; Grande, Cristina; Truchado-Garcia, Marta; Salazar-Ciudad, Isaac

2017-01-01

During cleavage, different cellular processes cause the zygote to become partitioned into a set of cells with a specific spatial arrangement. These processes include the orientation of cell division according to: an animal-vegetal gradient; the main axis (Hertwig's rule) of the cell; and the contact areas between cells or the perpendicularity between consecutive cell divisions (Sachs' rule). Cell adhesion and cortical rotation have also been proposed to be involved in spiral cleavage. We use a computational model of cell and tissue biomechanics to account for the different existing hypotheses about how the specific spatial arrangement of cells in spiral cleavage arises during development. Cell polarization by an animal-vegetal gradient, a bias to perpendicularity between consecutive cell divisions (Sachs' rule), cortical rotation and cell adhesion, when combined, reproduce the spiral cleavage, whereas other combinations of processes cannot. Specifically, cortical rotation is necessary at the 8-cell stage to direct all micromeres in the same direction. By varying the relative strength of these processes, we reproduce the spatial arrangement of cells in the blastulae of seven different invertebrate species. © 2017. Published by The Company of Biologists Ltd.

The impact of resource dependence of the mechanisms of life on the spatial population dynamics of an in silico microbial community

NASA Astrophysics Data System (ADS)

Daly, Aisling J.; Baetens, Jan M.; De Baets, Bernard

2016-12-01

Biodiversity has a critical impact on ecosystem functionality and stability, and thus the current biodiversity crisis has motivated many studies of the mechanisms that sustain biodiversity, a notable example being non-transitive or cyclic competition. We therefore extend existing microscopic models of communities with cyclic competition by incorporating resource dependence in demographic processes, characteristics of natural systems often oversimplified or overlooked by modellers. The spatially explicit nature of our individual-based model of three interacting species results in the formation of stable spatial structures, which have significant effects on community functioning, in agreement with experimental observations of pattern formation in microbial communities.

Regional assessments of the Nation's water quality—Improved understanding of stream nutrient sources through enhanced modeling capabilities

USGS Publications Warehouse

Preston, Stephen D.; Alexander, Richard B.; Woodside, Michael D.

2011-01-01

The U.S. Geological Survey (USGS) recently completed assessments of stream nutrients in six major regions extending over much of the conterminous United States. SPARROW (SPAtially Referenced Regressions On Watershed attributes) models were developed for each region to explain spatial patterns in monitored stream nutrient loads in relation to human activities and natural resources and processes. The model information, reported by stream reach and catchment, provides contrasting views of the spatial patterns of nutrient source contributions, including those from urban (wastewater effluent and diffuse runoff from developed land), agricultural (farm fertilizers and animal manure), and specific background sources (atmospheric nitrogen deposition, soil phosphorus, forest nitrogen fixation, and channel erosion).

Spatial Heterogeneity in the Effects of Immigration and Diversity on Neighborhood Homicide Rates

PubMed Central

Graif, Corina; Sampson, Robert J.

2010-01-01

This paper examines the connection of immigration and diversity to homicide by advancing a recently developed approach to modeling spatial dynamics—geographically weighted regression. In contrast to traditional global averaging, we argue on substantive grounds that neighborhood characteristics vary in their effects across neighborhood space, a process of “spatial heterogeneity.” Much like treatment-effect heterogeneity and distinct from spatial spillover, our analysis finds considerable evidence that neighborhood characteristics in Chicago vary significantly in predicting homicide, in some cases showing countervailing effects depending on spatial location. In general, however, immigrant concentration is either unrelated or inversely related to homicide, whereas language diversity is consistently linked to lower homicide. The results shed new light on the immigration-homicide nexus and suggest the pitfalls of global averaging models that hide the reality of a highly diversified and spatially stratified metropolis. PMID:20671811

A hybrid silicon membrane spatial light modulator for optical information processing

NASA Technical Reports Server (NTRS)

Pape, D. R.; Hornbeck, L. J.

1984-01-01

A new two dimensional, fast, analog, electrically addressable, silicon based membrane spatial light modulator (SLM) was developed for optical information processing applications. Coherent light reflected from the mirror elements is phase modulated producing an optical Fourier transform of an analog signal input to the device. The DMD architecture and operating parameters related to this application are presented. A model is developed that describes the optical Fourier transform properties of the DMD.

Proceedings of the Lake Wilderness Attention Conference Held at Seattle Washington, 22-24 September 1980.

DTIC Science & Technology

1981-07-10

Pohlmann, L. D. Some models of observer behavior in two-channel auditory signal detection. Perception and Psychophy- sics, 1973, 14, 101-109. Spelke...spatial), and processing modalities ( auditory versus visual input, vocal versus manual response). If validated, this configuration has both theoretical...conclusion that auditory and visual processes will compete, as will spatial and verbal (albeit to a lesser extent than auditory - auditory , visual-visual

GéoSAS: A modular and interoperable Open Source Spatial Data Infrastructure for research

NASA Astrophysics Data System (ADS)

Bera, R.; Squividant, H.; Le Henaff, G.; Pichelin, P.; Ruiz, L.; Launay, J.; Vanhouteghem, J.; Aurousseau, P.; Cudennec, C.

2015-05-01

To-date, the commonest way to deal with geographical information and processes still appears to consume local resources, i.e. locally stored data processed on a local desktop or server. The maturity and subsequent growing use of OGC standards to exchange data on the World Wide Web, enhanced in Europe by the INSPIRE Directive, is bound to change the way people (and among them research scientists, especially in environmental sciences) make use of, and manage, spatial data. A clever use of OGC standards can help scientists to better store, share and use data, in particular for modelling. We propose a framework for online processing by making an intensive use of OGC standards. We illustrate it using the Spatial Data Infrastructure (SDI) GéoSAS which is the SDI set up for researchers' needs in our department. It is based on the existing open source, modular and interoperable Spatial Data Architecture geOrchestra.

Adding Spatially Correlated Noise to a Median Ionosphere

NASA Astrophysics Data System (ADS)

Holmes, J. M.; Egert, A. R.; Dao, E. V.; Colman, J. J.; Parris, R. T.

2017-12-01

We describe a process for adding spatially correlated noise to a background ionospheric model, in this case the International Reference Ionosphere (IRI). Monthly median models do a good job describing bulk features of the ionosphere in a median sense. It is well known that the ionosphere almost never actually looks like its median. For the purposes of constructing an Operational System Simulation Experiment, it may be desirable to construct an ionosphere more similar to a particular instant, hour, or day than to the monthly median. We will examine selected data from the Global Ionosphere Radio Observatory (GIRO) database and estimate the amount of variance captured by the IRI model. We will then examine spatial and temporal correlations within the residuals. This analysis will be used to construct a temporal-spatial gridded ionosphere that represents a particular instantiation of those statistics.

Extinction threshold for spatial forest dynamics with height structure.

PubMed

Garcia-Domingo, Josep L; Saldaña, Joan

2011-05-07

We present a pair-approximation model for spatial forest dynamics defined on a regular lattice. The model assumes three possible states for a lattice site: empty (gap site), occupied by an immature tree, and occupied by a mature tree, and considers three nonlinearities in the dynamics associated to the processes of light interference, gap expansion, and recruitment. We obtain an expression of the basic reproduction number R(0) which, in contrast to the one obtained under the mean-field approach, uses information about the spatial arrangement of individuals close to extinction. Moreover, we analyze the corresponding survival-extinction transition of the forest and the spatial correlations among gaps, immature and mature trees close to this critical point. Predictions of the pair-approximation model are compared with those of a cellular automaton. Copyright © 2011 Elsevier Ltd. All rights reserved.

Forest landscape models, a tool for understanding the effect of the large-scale and long-term landscape processes

Treesearch

Hong S. He; Robert E. Keane; Louis R. Iverson

2008-01-01

Forest landscape models have become important tools for understanding large-scale and long-term landscape (spatial) processes such as climate change, fire, windthrow, seed dispersal, insect outbreak, disease propagation, forest harvest, and fuel treatment, because controlled field experiments designed to study the effects of these processes are often not possible (...

A Fuzzy Cognitive Model of aeolian instability across the South Texas Sandsheet

NASA Astrophysics Data System (ADS)

Houser, C.; Bishop, M. P.; Barrineau, C. P.

2014-12-01

Characterization of aeolian systems is complicated by rapidly changing surface-process regimes, spatio-temporal scale dependencies, and subjective interpretation of imagery and spatial data. This paper describes the development and application of analytical reasoning to quantify instability of an aeolian environment using scale-dependent information coupled with conceptual knowledge of process and feedback mechanisms. Specifically, a simple Fuzzy Cognitive Model (FCM) for aeolian landscape instability was developed that represents conceptual knowledge of key biophysical processes and feedbacks. Model inputs include satellite-derived surface biophysical and geomorphometric parameters. FCMs are a knowledge-based Artificial Intelligence (AI) technique that merges fuzzy logic and neural computing in which knowledge or concepts are structured as a web of relationships that is similar to both human reasoning and the human decision-making process. Given simple process-form relationships, the analytical reasoning model is able to map the influence of land management practices and the geomorphology of the inherited surface on aeolian instability within the South Texas Sandsheet. Results suggest that FCMs can be used to formalize process-form relationships and information integration analogous to human cognition with future iterations accounting for the spatial interactions and temporal lags across the sand sheets.

Modelling dendritic ecological networks in space: anintegrated network perspective

USGS Publications Warehouse

Peterson, Erin E.; Ver Hoef, Jay M.; Isaak, Dan J.; Falke, Jeffrey A.; Fortin, Marie-Josée; Jordon, Chris E.; McNyset, Kristina; Monestiez, Pascal; Ruesch, Aaron S.; Sengupta, Aritra; Som, Nicholas; Steel, E. Ashley; Theobald, David M.; Torgersen, Christian E.; Wenger, Seth J.

2013-01-01

the context of stream ecology. Within this context, we summarise the key innovations of a new family of spatial statistical models that describe spatial relationships in DENs. Finally, we discuss how different network analyses may be combined to address more complex and novel research questions. While our main focus is streams, the taxonomy of network analyses is also relevant anywhere spatial patterns in both network and 2-D space can be used to explore the influence of multi-scale processes on biota and their habitat (e.g. plant morphology and pest infestation, or preferential migration along stream or road corridors).

Addressing spatial scales and new mechanisms in climate impact ecosystem modeling

NASA Astrophysics Data System (ADS)

Poulter, B.; Joetzjer, E.; Renwick, K.; Ogunkoya, G.; Emmett, K.

2015-12-01

Climate change impacts on vegetation distributions are typically addressed using either an empirical approach, such as a species distribution model (SDM), or with process-based methods, for example, dynamic global vegetation models (DGVMs). Each approach has its own benefits and disadvantages. For example, an SDM is constrained by data and few parameters, but does not include adaptation or acclimation processes or other ecosystem feedbacks that may act to mitigate or enhance climate effects. Alternatively, a DGVM model includes many mechanisms relating plant growth and disturbance to climate, but simulations are costly to perform at high-spatial resolution and there remains large uncertainty on a variety of fundamental physical processes. To address these issues, here, we present two DGVM-based case studies where i) high-resolution (1 km) simulations are being performed for vegetation in the Greater Yellowstone Ecosystem using a biogeochemical, forest gap model, LPJ-GUESS, and ii) where new mechanisms for simulating tropical tree-mortality are being introduced. High-resolution DGVM model simulations require not only computing and reorganizing code but also a consideration of scaling issues on vegetation dynamics and stochasticity and also on disturbance and migration. New mechanisms for simulating forest mortality must consider hydraulic limitations and carbon reserves and their interactions on source-sink dynamics and in controlling water potentials. Improving DGVM approaches by addressing spatial scale challenges and integrating new approaches for estimating forest mortality will provide new insights more relevant for land management and possibly reduce uncertainty by physical processes more directly comparable to experimental and observational evidence.

Spatial uncertainty analysis: Propagation of interpolation errors in spatially distributed models

USGS Publications Warehouse

Phillips, D.L.; Marks, D.G.

1996-01-01

In simulation modelling, it is desirable to quantify model uncertainties and provide not only point estimates for output variables but confidence intervals as well. Spatially distributed physical and ecological process models are becoming widely used, with runs being made over a grid of points that represent the landscape. This requires input values at each grid point, which often have to be interpolated from irregularly scattered measurement sites, e.g., weather stations. Interpolation introduces spatially varying errors which propagate through the model We extended established uncertainty analysis methods to a spatial domain for quantifying spatial patterns of input variable interpolation errors and how they propagate through a model to affect the uncertainty of the model output. We applied this to a model of potential evapotranspiration (PET) as a demonstration. We modelled PET for three time periods in 1990 as a function of temperature, humidity, and wind on a 10-km grid across the U.S. portion of the Columbia River Basin. Temperature, humidity, and wind speed were interpolated using kriging from 700- 1000 supporting data points. Kriging standard deviations (SD) were used to quantify the spatially varying interpolation uncertainties. For each of 5693 grid points, 100 Monte Carlo simulations were done, using the kriged values of temperature, humidity, and wind, plus random error terms determined by the kriging SDs and the correlations of interpolation errors among the three variables. For the spring season example, kriging SDs averaged 2.6??C for temperature, 8.7% for relative humidity, and 0.38 m s-1 for wind. The resultant PET estimates had coefficients of variation (CVs) ranging from 14% to 27% for the 10-km grid cells. Maps of PET means and CVs showed the spatial patterns of PET with a measure of its uncertainty due to interpolation of the input variables. This methodology should be applicable to a variety of spatially distributed models using interpolated inputs.

The spatial structure of chronic morbidity: evidence from UK census returns.

PubMed

Dutey-Magni, Peter F; Moon, Graham

2016-08-24

Disease prevalence models have been widely used to estimate health, lifestyle and disability characteristics for small geographical units when other data are not available. Yet, knowledge is often lacking about how to make informed decisions around the specification of such models, especially regarding spatial assumptions placed on their covariance structure. This paper is concerned with understanding processes of spatial dependency in unexplained variation in chronic morbidity. 2011 UK census data on limiting long-term illness (LLTI) is used to look at the spatial structure in chronic morbidity across England and Wales. The variance and spatial clustering of the odds of LLTI across local authority districts (LADs) and middle layer super output areas are measured across 40 demographic cross-classifications. A series of adjacency matrices based on distance, contiguity and migration flows are tested to examine the spatial structure in LLTI. Odds are then modelled using a logistic mixed model to examine the association with district-level covariates and their predictive power. The odds of chronic illness are more dispersed than local age characteristics, mortality, hospitalisation rates and chance alone would suggest. Of all adjacency matrices, the three-nearest neighbour method is identified as the best fitting. Migration flows can also be used to construct spatial weights matrices which uncover non-negligible autocorrelation. Once the most important characteristics observable at the LAD-level are taken into account, substantial spatial autocorrelation remains which can be modelled explicitly to improve disease prevalence predictions. Systematic investigation of spatial structures and dependency is important to develop model-based estimation tools in chronic disease mapping. Spatial structures reflecting migration interactions are easy to develop and capture autocorrelation in LLTI. Patterns of spatial dependency in the geographical distribution of LLTI are not comparable across ethnic groups. Ethnic stratification of local health information is needed and there is potential to further address complexity in prevalence models by improving access to disaggregated data.

Spatially distributed model calibration of flood inundation guided by consequences such as loss of property

NASA Astrophysics Data System (ADS)

Pappenberger, F.; Beven, K. J.; Frodsham, K.; Matgen, P.

2005-12-01

Flood inundation models play an increasingly important role in assessing flood risk. The growth of 2D inundation models that are intimately related to raster maps of floodplains is occurring at the same time as an increase in the availability of 2D remote data (e.g. SAR images and aerial photographs), against which model performancee can be evaluated. This requires new techniques to be explored in order to evaluate model performance in two dimensional space. In this paper we present a fuzzified pattern matching algorithm which compares favorably to a set of traditional measures. However, we further argue that model calibration has to go beyond the comparison of physical properties and should demonstrate how a weighting towards consequences, such as loss of property, can enhance model focus and prediction. Indeed, it will be necessary to abandon a fully spatial comparison in many scenarios to concentrate the model calibration exercise on specific points such as hospitals, police stations or emergency response centers. It can be shown that such point evaluations lead to significantly different flood hazard maps due to the averaging effect of a spatial performance measure. A strategy to balance the different needs (accuracy at certain spatial points and acceptable spatial performance) has to be based in a public and political decision making process.

A simple distributed sediment delivery approach for rural catchments

NASA Astrophysics Data System (ADS)

Reid, Lucas; Scherer, Ulrike

2014-05-01

The transfer of sediments from source areas to surface waters is a complex process. In process based erosion models sediment input is thus quantified by representing all relevant sub processes such as detachment, transport and deposition of sediment particles along the flow path to the river. A successful application of these models requires, however, a large amount of spatially highly resolved data on physical catchment characteristics, which is only available for a few, well examined small catchments. For the lack of appropriate models, the empirical Universal Soil Loss Equation (USLE) is widely applied to quantify the sediment production in meso to large scale basins. As the USLE provides long-term mean soil loss rates, it is often combined with spatially lumped models to estimate the sediment delivery ratio (SDR). In these models, the SDR is related to data on morphological characteristics of the catchment such as average local relief, drainage density, proportion of depressions or soil texture. Some approaches include the relative distance between sediment source areas and the river channels. However, several studies showed that spatially lumped parameters describing the morphological characteristics are only of limited value to represent the factors of influence on sediment transport at the catchment scale. Sediment delivery is controlled by the location of the sediment source areas in the catchment and the morphology along the flow path to the surface water bodies. This complex interaction of spatially varied physiographic characteristics cannot be adequately represented by lumped morphological parameters. The objective of this study is to develop a simple but spatially distributed approach to quantify the sediment delivery ratio by considering the characteristics of the flow paths in a catchment. We selected a small catchment located in in an intensively cultivated loess region in Southwest Germany as study area for the development of the SDR approach. The flow pathways were extracted in a geographic information system. Then the sediment delivery ratio for each source area was determined using an empirical approach considering the slope, morphology and land use properties along the flow path. As a benchmark for the calibration of the model parameters we used results of a detailed process based erosion model available for the study area. Afterwards the approach was tested in larger catchments located in the same loess region.

Climatological Downscaling and Evaluation of AGRMET Precipitation Analyses Over the Continental U.S.

NASA Astrophysics Data System (ADS)

Garcia, M.; Peters-Lidard, C. D.; Eylander, J. B.; Daly, C.; Tian, Y.; Zeng, J.

2007-05-01

The spatially distributed application of a land surface model (LSM) over a region of interest requires the application of similarly distributed precipitation fields that can be derived from various sources, including surface gauge networks, surface-based radar, and orbital platforms. The spatial variability of precipitation influences the spatial organization of soil temperature and moisture states and, consequently, the spatial variability of land- atmosphere fluxes. The accuracy of spatially-distributed precipitation fields can contribute significantly to the uncertainty of model-based hydrological states and fluxes at the land surface. Collaborations between the Air Force Weather Agency (AFWA), NASA, and Oregon State University have led to improvements in the processing of meteorological forcing inputs for the NASA-GSFC Land Information System (LIS; Kumar et al. 2006), a sophisticated framework for LSM operation and model coupling experiments. Efforts at AFWA toward the production of surface hydrometeorological products are currently in transition from the legacy Agricultural Meteorology modeling system (AGRMET) to use of the LIS framework and procedures. Recent enhancements to meteorological input processing for application to land surface models in LIS include the assimilation of climate-based information for the spatial interpolation and downscaling of precipitation fields. Climatological information included in the LIS-based downscaling procedure for North America is provided by a monthly high-resolution PRISM (Daly et al. 1994, 2002; Daly 2006) dataset based on a 30-year analysis period. The combination of these sources and methods attempts to address the strengths and weaknesses of available legacy products, objective interpolation methods, and the PRISM knowledge-based methodology. All of these efforts are oriented on an operational need for timely estimation of spatial precipitation fields at adequate spatial resolution for customer dissemination and near-real-time simulations in regions of interest. This work focuses on value added to the AGRMET precipitation product by the inclusion of high-quality climatological information on a monthly time scale. The AGRMET method uses microwave-based satellite precipitation estimates from various polar-orbiting platforms (NOAA POES and DMSP), infrared-based estimates from geostationary platforms (GOES, METEOSAT, etc.), related cloud analysis products, and surface gauge observations in a complex and hierarchical blending process. Results from processing of the legacy AGRMET precipitation products over the U.S. using LIS-based methods for downscaling, both with and without climatological factors, are evaluated against high-resolution monthly analyses using the PRISM knowledge- based method (Daly et al. 2002). It is demonstrated that the incorporation of climatological information in a downscaling procedure can significantly enhance the accuracy, and potential utility, of AFWA precipitation products for military and civilian customer applications.

Systems Modeling at Multiple Levels of Regulation: Linking Systems and Genetic Networks to Spatially Explicit Plant Populations

PubMed Central

Kitchen, James L.; Allaby, Robin G.

2013-01-01

Selection and adaptation of individuals to their underlying environments are highly dynamical processes, encompassing interactions between the individual and its seasonally changing environment, synergistic or antagonistic interactions between individuals and interactions amongst the regulatory genes within the individual. Plants are useful organisms to study within systems modeling because their sedentary nature simplifies interactions between individuals and the environment, and many important plant processes such as germination or flowering are dependent on annual cycles which can be disrupted by climate behavior. Sedentism makes plants relevant candidates for spatially explicit modeling that is tied in with dynamical environments. We propose that in order to fully understand the complexities behind plant adaptation, a system that couples aspects from systems biology with population and landscape genetics is required. A suitable system could be represented by spatially explicit individual-based models where the virtual individuals are located within time-variable heterogeneous environments and contain mutable regulatory gene networks. These networks could directly interact with the environment, and should provide a useful approach to studying plant adaptation. PMID:27137364

The underlying processes of a soil mite metacommunity on a small scale.

PubMed

Dong, Chengxu; Gao, Meixiang; Guo, Chuanwei; Lin, Lin; Wu, Donghui; Zhang, Limin

2017-01-01

Metacommunity theory provides an understanding of how ecological processes regulate local community assemblies. However, few field studies have evaluated the underlying mechanisms of a metacommunity on a small scale through revealing the relative roles of spatial and environmental filtering in structuring local community composition. Based on a spatially explicit sampling design in 2012 and 2013, this study aims to evaluate the underlying processes of a soil mite metacommunity on a small spatial scale (50 m) in a temperate deciduous forest located at the Maoershan Ecosystem Research Station, Northeast China. Moran's eigenvector maps (MEMs) were used to model independent spatial variables. The relative importance of spatial (including trend variables, i.e., geographical coordinates, and broad- and fine-scale spatial variables) and environmental factors in driving the soil mite metacommunity was determined by variation partitioning. Mantel and partial Mantel tests and a redundancy analysis (RDA) were also used to identify the relative contributions of spatial and environmental variables. The results of variation partitioning suggested that the relatively large and significant variance was a result of spatial variables (including broad- and fine-scale spatial variables and trend), indicating the importance of dispersal limitation and autocorrelation processes. The significant contribution of environmental variables was detected in 2012 based on a partial Mantel test, and soil moisture and soil organic matter were especially important for the soil mite metacommunity composition in both years. The study suggested that the soil mite metacommunity was primarily regulated by dispersal limitation due to broad-scale and neutral biotic processes at a fine-scale and that environmental filtering might be of subordinate importance. In conclusion, a combination of metacommunity perspectives between neutral and species sorting theories was suggested to be important in the observed structure of the soil mite metacommunity at the studied small scale.

The underlying processes of a soil mite metacommunity on a small scale

PubMed Central

Guo, Chuanwei; Lin, Lin; Wu, Donghui; Zhang, Limin

2017-01-01

Metacommunity theory provides an understanding of how ecological processes regulate local community assemblies. However, few field studies have evaluated the underlying mechanisms of a metacommunity on a small scale through revealing the relative roles of spatial and environmental filtering in structuring local community composition. Based on a spatially explicit sampling design in 2012 and 2013, this study aims to evaluate the underlying processes of a soil mite metacommunity on a small spatial scale (50 m) in a temperate deciduous forest located at the Maoershan Ecosystem Research Station, Northeast China. Moran’s eigenvector maps (MEMs) were used to model independent spatial variables. The relative importance of spatial (including trend variables, i.e., geographical coordinates, and broad- and fine-scale spatial variables) and environmental factors in driving the soil mite metacommunity was determined by variation partitioning. Mantel and partial Mantel tests and a redundancy analysis (RDA) were also used to identify the relative contributions of spatial and environmental variables. The results of variation partitioning suggested that the relatively large and significant variance was a result of spatial variables (including broad- and fine-scale spatial variables and trend), indicating the importance of dispersal limitation and autocorrelation processes. The significant contribution of environmental variables was detected in 2012 based on a partial Mantel test, and soil moisture and soil organic matter were especially important for the soil mite metacommunity composition in both years. The study suggested that the soil mite metacommunity was primarily regulated by dispersal limitation due to broad-scale and neutral biotic processes at a fine-scale and that environmental filtering might be of subordinate importance. In conclusion, a combination of metacommunity perspectives between neutral and species sorting theories was suggested to be important in the observed structure of the soil mite metacommunity at the studied small scale. PMID:28481906

Rapid Response Tools and Datasets for Post-fire Hydrological Modeling

NASA Astrophysics Data System (ADS)

Miller, Mary Ellen; MacDonald, Lee H.; Billmire, Michael; Elliot, William J.; Robichaud, Pete R.

2016-04-01

Rapid response is critical following natural disasters. Flooding, erosion, and debris flows are a major threat to life, property and municipal water supplies after moderate and high severity wildfires. The problem is that mitigation measures must be rapidly implemented if they are to be effective, but they are expensive and cannot be applied everywhere. Fires, runoff, and erosion risks also are highly heterogeneous in space, so there is an urgent need for a rapid, spatially-explicit assessment. Past post-fire modeling efforts have usually relied on lumped, conceptual models because of the lack of readily available, spatially-explicit data layers on the key controls of topography, vegetation type, climate, and soil characteristics. The purpose of this project is to develop a set of spatially-explicit data layers for use in process-based models such as WEPP, and to make these data layers freely available. The resulting interactive online modeling database (http://geodjango.mtri.org/geowepp/) is now operational and publically available for 17 western states in the USA. After a fire, users only need to upload a soil burn severity map, and this is combined with the pre-existing data layers to generate the model inputs needed for spatially explicit models such as GeoWEPP (Renschler, 2003). The development of this online database has allowed us to predict post-fire erosion and various remediation scenarios in just 1-7 days for six fires ranging in size from 4-540 km2. These initial successes have stimulated efforts to further improve the spatial extent and amount of data, and add functionality to support the USGS debris flow model, batch processing for Disturbed WEPP (Elliot et al., 2004) and ERMiT (Robichaud et al., 2007), and to support erosion modeling for other land uses, such as agriculture or mining. The design and techniques used to create the database and the modeling interface are readily repeatable for any area or country that has the necessary topography, climate, soil, and land cover datasets.

The Michelin red guide of the brain: role of dopamine in goal-oriented navigation.

PubMed

Retailleau, Aude; Boraud, Thomas

2014-01-01

Spatial learning has been recognized over the years to be under the control of the hippocampus and related temporal lobe structures. Hippocampal damage often causes severe impairments in the ability to learn and remember a location in space defined by distal visual cues. Such cognitive disabilities are found in Parkinsonian patients. We recently investigated the role of dopamine in navigation in the 6-Hydroxy-dopamine (6-OHDA) rat, a model of Parkinson's disease (PD) commonly used to investigate the pathophysiology of dopamine depletion (Retailleau et al., 2013). We demonstrated that dopamine (DA) is essential to spatial learning as its depletion results in spatial impairments. Our results showed that the behavioral effect of DA depletion is correlated with modification of the neural encoding of spatial features and decision making processes in hippocampus. However, the origin of these alterations in the neural processing of the spatial information needs to be clarified. It could result from a local effect: dopamine depletion disturbs directly the processing of relevant spatial information at hippocampal level. Alternatively, it could result from a more distributed network effect: dopamine depletion elsewhere in the brain (entorhinal cortex, striatum, etc.) modifies the way hippocampus processes spatial information. Recent experimental evidence in rodents, demonstrated indeed, that other brain areas are involved in the acquisition of spatial information. Amongst these, the cortex-basal ganglia (BG) loop is known to be involved in reinforcement learning and has been identified as an important contributor to spatial learning. In particular, it has been shown that altered activity of the BG striatal complex can impair the ability to perform spatial learning tasks. The present review provides a glimpse of the findings obtained over the past decade that support a dialog between these two structures during spatial learning under DA control.

An Augmented Two-Layer Model Captures Nonlinear Analog Spatial Integration Effects in Pyramidal Neuron Dendrites

PubMed Central

JADI, MONIKA P.; BEHABADI, BARDIA F.; POLEG-POLSKY, ALON; SCHILLER, JACKIE; MEL, BARTLETT W.

2014-01-01

In pursuit of the goal to understand and eventually reproduce the diverse functions of the brain, a key challenge lies in reverse engineering the peculiar biology-based “technology” that underlies the brain’s remarkable ability to process and store information. The basic building block of the nervous system is the nerve cell, or “neuron,” yet after more than 100 years of neurophysiological study and 60 years of modeling, the information processing functions of individual neurons, and the parameters that allow them to engage in so many different types of computation (sensory, motor, mnemonic, executive, etc.) remain poorly understood. In this paper, we review both historical and recent findings that have led to our current understanding of the analog spatial processing capabilities of dendrites, the major input structures of neurons, with a focus on the principal cell type of the neocortex and hippocampus, the pyramidal neuron (PN). We encapsulate our current understanding of PN dendritic integration in an abstract layered model whose spatially sensitive branch-subunits compute multidimensional sigmoidal functions. Unlike the 1-D sigmoids found in conventional neural network models, multidimensional sigmoids allow the cell to implement a rich spectrum of nonlinear modulation effects directly within their dendritic trees. PMID:25554708

Process-based upscaling of surface-atmosphere exchange

NASA Astrophysics Data System (ADS)

Keenan, T. F.; Prentice, I. C.; Canadell, J.; Williams, C. A.; Wang, H.; Raupach, M. R.; Collatz, G. J.; Davis, T.; Stocker, B.; Evans, B. J.

2015-12-01

Empirical upscaling techniques such as machine learning and data-mining have proven invaluable tools for the global scaling of disparate observations of surface-atmosphere exchange, but are not based on a theoretical understanding of the key processes involved. This makes spatial and temporal extrapolation outside of the training domain difficult at best. There is therefore a clear need for the incorporation of knowledge of ecosystem function, in combination with the strength of data mining. Here, we present such an approach. We describe a novel diagnostic process-based model of global photosynthesis and ecosystem respiration, which is directly informed by a variety of global datasets relevant to ecosystem state and function. We use the model framework to estimate global carbon cycling both spatially and temporally, with a specific focus on the mechanisms responsible for long-term change. Our results show the importance of incorporating process knowledge into upscaling approaches, and highlight the effect of key processes on the terrestrial carbon cycle.

Greenland Ice Sheet seasonal and spatial mass variability from model simulations and GRACE (2003-2012)

NASA Astrophysics Data System (ADS)

Alexander, Patrick M.; Tedesco, Marco; Schlegel, Nicole-Jeanne; Luthcke, Scott B.; Fettweis, Xavier; Larour, Eric

2016-06-01

Improving the ability of regional climate models (RCMs) and ice sheet models (ISMs) to simulate spatiotemporal variations in the mass of the Greenland Ice Sheet (GrIS) is crucial for prediction of future sea level rise. While several studies have examined recent trends in GrIS mass loss, studies focusing on mass variations at sub-annual and sub-basin-wide scales are still lacking. At these scales, processes responsible for mass change are less well understood and modeled, and could potentially play an important role in future GrIS mass change. Here, we examine spatiotemporal variations in mass over the GrIS derived from the Gravity Recovery and Climate Experiment (GRACE) satellites for the January 2003-December 2012 period using a "mascon" approach, with a nominal spatial resolution of 100 km, and a temporal resolution of 10 days. We compare GRACE-estimated mass variations against those simulated by the Modèle Atmosphérique Régionale (MAR) RCM and the Ice Sheet System Model (ISSM). In order to properly compare spatial and temporal variations in GrIS mass from GRACE with model outputs, we find it necessary to spatially and temporally filter model results to reproduce leakage of mass inherent in the GRACE solution. Both modeled and satellite-derived results point to a decline (of -178.9 ± 4.4 and -239.4 ± 7.7 Gt yr-1 respectively) in GrIS mass over the period examined, but the models appear to underestimate the rate of mass loss, especially in areas below 2000 m in elevation, where the majority of recent GrIS mass loss is occurring. On an ice-sheet-wide scale, the timing of the modeled seasonal cycle of cumulative mass (driven by summer mass loss) agrees with the GRACE-derived seasonal cycle, within limits of uncertainty from the GRACE solution. However, on sub-ice-sheet-wide scales, some areas exhibit significant differences in the timing of peaks in the annual cycle of mass change. At these scales, model biases, or processes not accounted for by models related to ice dynamics or hydrology, may lead to the observed differences. This highlights the need for further evaluation of modeled processes at regional and seasonal scales, and further study of ice sheet processes not accounted for, such as the role of subglacial hydrology in variations in glacial flow.

Pair and triplet approximation of a spatial lattice population model with multiscale dispersal using Markov chains for estimating spatial autocorrelation.

PubMed

Hiebeler, David E; Millett, Nicholas E

2011-06-21

We investigate a spatial lattice model of a population employing dispersal to nearest and second-nearest neighbors, as well as long-distance dispersal across the landscape. The model is studied via stochastic spatial simulations, ordinary pair approximation, and triplet approximation. The latter method, which uses the probabilities of state configurations of contiguous blocks of three sites as its state variables, is demonstrated to be greatly superior to pair approximations for estimating spatial correlation information at various scales. Correlations between pairs of sites separated by arbitrary distances are estimated by constructing spatial Markov processes using the information from both approximations. These correlations demonstrate why pair approximation misses basic qualitative features of the model, such as decreasing population density as a large proportion of offspring are dropped on second-nearest neighbors, and why triplet approximation is able to include them. Analytical and numerical results show that, excluding long-distance dispersal, the initial growth rate of an invading population is maximized and the equilibrium population density is also roughly maximized when the population spreads its offspring evenly over nearest and second-nearest neighboring sites. Copyright © 2011 Elsevier Ltd. All rights reserved.

Cortical Transformation of Spatial Processing for Solving the Cocktail Party Problem: A Computational Model123

PubMed Central

Dong, Junzi; Colburn, H. Steven

2016-01-01

In multisource, “cocktail party” sound environments, human and animal auditory systems can use spatial cues to effectively separate and follow one source of sound over competing sources. While mechanisms to extract spatial cues such as interaural time differences (ITDs) are well understood in precortical areas, how such information is reused and transformed in higher cortical regions to represent segregated sound sources is not clear. We present a computational model describing a hypothesized neural network that spans spatial cue detection areas and the cortex. This network is based on recent physiological findings that cortical neurons selectively encode target stimuli in the presence of competing maskers based on source locations (Maddox et al., 2012). We demonstrate that key features of cortical responses can be generated by the model network, which exploits spatial interactions between inputs via lateral inhibition, enabling the spatial separation of target and interfering sources while allowing monitoring of a broader acoustic space when there is no competition. We present the model network along with testable experimental paradigms as a starting point for understanding the transformation and organization of spatial information from midbrain to cortex. This network is then extended to suggest engineering solutions that may be useful for hearing-assistive devices in solving the cocktail party problem. PMID:26866056

Cortical Transformation of Spatial Processing for Solving the Cocktail Party Problem: A Computational Model(1,2,3).

PubMed

Dong, Junzi; Colburn, H Steven; Sen, Kamal

2016-01-01

In multisource, "cocktail party" sound environments, human and animal auditory systems can use spatial cues to effectively separate and follow one source of sound over competing sources. While mechanisms to extract spatial cues such as interaural time differences (ITDs) are well understood in precortical areas, how such information is reused and transformed in higher cortical regions to represent segregated sound sources is not clear. We present a computational model describing a hypothesized neural network that spans spatial cue detection areas and the cortex. This network is based on recent physiological findings that cortical neurons selectively encode target stimuli in the presence of competing maskers based on source locations (Maddox et al., 2012). We demonstrate that key features of cortical responses can be generated by the model network, which exploits spatial interactions between inputs via lateral inhibition, enabling the spatial separation of target and interfering sources while allowing monitoring of a broader acoustic space when there is no competition. We present the model network along with testable experimental paradigms as a starting point for understanding the transformation and organization of spatial information from midbrain to cortex. This network is then extended to suggest engineering solutions that may be useful for hearing-assistive devices in solving the cocktail party problem.

Enhanced Long-Term and Impaired Short-Term Spatial Memory in GluA1 AMPA Receptor Subunit Knockout Mice: Evidence for a Dual-Process Memory Model

ERIC Educational Resources Information Center

Sanderson, David J.; Good, Mark A.; Skelton, Kathryn; Sprengel, Rolf; Seeburg, Peter H.; Rawlins, J. Nicholas P.; Bannerman, David M.

2009-01-01

The GluA1 AMPA receptor subunit is a key mediator of hippocampal synaptic plasticity and is especially important for a rapidly-induced, short-lasting form of potentiation. GluA1 gene deletion impairs hippocampus-dependent, spatial working memory, but spares hippocampus-dependent spatial reference memory. These findings may reflect the necessity of…

Integrating Remote Sensing Information Into A Distributed Hydrological Model for Improving Water Budget Predictions in Large-scale Basins through Data Assimilation.

PubMed

Qin, Changbo; Jia, Yangwen; Su, Z; Zhou, Zuhao; Qiu, Yaqin; Suhui, Shen

2008-07-29

This paper investigates whether remote sensing evapotranspiration estimates can be integrated by means of data assimilation into a distributed hydrological model for improving the predictions of spatial water distribution over a large river basin with an area of 317,800 km2. A series of available MODIS satellite images over the Haihe River basin in China are used for the year 2005. Evapotranspiration is retrieved from these 1×1 km resolution images using the SEBS (Surface Energy Balance System) algorithm. The physically-based distributed model WEP-L (Water and Energy transfer Process in Large river basins) is used to compute the water balance of the Haihe River basin in the same year. Comparison between model-derived and remote sensing retrieval basin-averaged evapotranspiration estimates shows a good piecewise linear relationship, but their spatial distribution within the Haihe basin is different. The remote sensing derived evapotranspiration shows variability at finer scales. An extended Kalman filter (EKF) data assimilation algorithm, suitable for non-linear problems, is used. Assimilation results indicate that remote sensing observations have a potentially important role in providing spatial information to the assimilation system for the spatially optical hydrological parameterization of the model. This is especially important for large basins, such as the Haihe River basin in this study. Combining and integrating the capabilities of and information from model simulation and remote sensing techniques may provide the best spatial and temporal characteristics for hydrological states/fluxes, and would be both appealing and necessary for improving our knowledge of fundamental hydrological processes and for addressing important water resource management problems.

Integrating Remote Sensing Information Into A Distributed Hydrological Model for Improving Water Budget Predictions in Large-scale Basins through Data Assimilation

PubMed Central

Qin, Changbo; Jia, Yangwen; Su, Z.(Bob); Zhou, Zuhao; Qiu, Yaqin; Suhui, Shen

2008-01-01

This paper investigates whether remote sensing evapotranspiration estimates can be integrated by means of data assimilation into a distributed hydrological model for improving the predictions of spatial water distribution over a large river basin with an area of 317,800 km2. A series of available MODIS satellite images over the Haihe River basin in China are used for the year 2005. Evapotranspiration is retrieved from these 1×1 km resolution images using the SEBS (Surface Energy Balance System) algorithm. The physically-based distributed model WEP-L (Water and Energy transfer Process in Large river basins) is used to compute the water balance of the Haihe River basin in the same year. Comparison between model-derived and remote sensing retrieval basin-averaged evapotranspiration estimates shows a good piecewise linear relationship, but their spatial distribution within the Haihe basin is different. The remote sensing derived evapotranspiration shows variability at finer scales. An extended Kalman filter (EKF) data assimilation algorithm, suitable for non-linear problems, is used. Assimilation results indicate that remote sensing observations have a potentially important role in providing spatial information to the assimilation system for the spatially optical hydrological parameterization of the model. This is especially important for large basins, such as the Haihe River basin in this study. Combining and integrating the capabilities of and information from model simulation and remote sensing techniques may provide the best spatial and temporal characteristics for hydrological states/fluxes, and would be both appealing and necessary for improving our knowledge of fundamental hydrological processes and for addressing important water resource management problems. PMID:27879946

Prismatic Adaptation Induces Plastic Changes onto Spatial and Temporal Domains in Near and Far Space.

PubMed

Patané, Ivan; Farnè, Alessandro; Frassinetti, Francesca

2016-01-01

A large literature has documented interactions between space and time suggesting that the two experiential domains may share a common format in a generalized magnitude system (ATOM theory). To further explore this hypothesis, here we measured the extent to which time and space are sensitive to the same sensorimotor plasticity processes, as induced by classical prismatic adaptation procedures (PA). We also exanimated whether spatial-attention shifts on time and space processing, produced through PA, extend to stimuli presented beyond the immediate near space. Results indicated that PA affected both temporal and spatial representations not only in the near space (i.e., the region within which the adaptation occurred), but also in the far space. In addition, both rightward and leftward PA directions caused opposite and symmetrical modulations on time processing, whereas only leftward PA biased space processing rightward. We discuss these findings within the ATOM framework and models that account for PA effects on space and time processing. We propose that the differential and asymmetrical effects following PA may suggest that temporal and spatial representations are not perfectly aligned.

Exogenous spatial attention decreases audiovisual integration.

PubMed

Van der Stoep, N; Van der Stigchel, S; Nijboer, T C W

2015-02-01

Multisensory integration (MSI) and spatial attention are both mechanisms through which the processing of sensory information can be facilitated. Studies on the interaction between spatial attention and MSI have mainly focused on the interaction between endogenous spatial attention and MSI. Most of these studies have shown that endogenously attending a multisensory target enhances MSI. It is currently unclear, however, whether and how exogenous spatial attention and MSI interact. In the current study, we investigated the interaction between these two important bottom-up processes in two experiments. In Experiment 1 the target location was task-relevant, and in Experiment 2 the target location was task-irrelevant. Valid or invalid exogenous auditory cues were presented before the onset of unimodal auditory, unimodal visual, and audiovisual targets. We observed reliable cueing effects and multisensory response enhancement in both experiments. To examine whether audiovisual integration was influenced by exogenous spatial attention, the amount of race model violation was compared between exogenously attended and unattended targets. In both Experiment 1 and Experiment 2, a decrease in MSI was observed when audiovisual targets were exogenously attended, compared to when they were not. The interaction between exogenous attention and MSI was less pronounced in Experiment 2. Therefore, our results indicate that exogenous attention diminishes MSI when spatial orienting is relevant. The results are discussed in terms of models of multisensory integration and attention.

Spatial probability models of fire in the desert grasslands of the southwestern USA

USDA-ARS?s Scientific Manuscript database

Fire is an important driver of ecological processes in semiarid environments; however, the role of fire in desert grasslands of the Southwestern US is controversial and the regional fire distribution is largely unknown. We characterized the spatial distribution of fire in the desert grassland region...

ASSESSING THE RISK ASSOCIATED WITH MERCURY: USING REVA'S WEBTOOL TO COMPARE DATA, ASSUMPTIONS, AND MODELS

EPA Science Inventory

The problem of assessing risk from mercury across the nation is extremely complex involving integration of I) our understanding of the methylation process in ecosystems, 2) the identification and spatial distribution of sensitive populations, and 3) the spatial pattern of mercury...

Are Automatic Imitation and Spatial Compatibility Mediated by Different Processes?

ERIC Educational Resources Information Center

Cooper, Richard P.; Catmur, Caroline; Heyes, Cecilia

2013-01-01

Automatic imitation or "imitative compatibility" is thought to be mediated by the mirror neuron system and to be a laboratory model of the motor mimicry that occurs spontaneously in naturalistic social interaction. Imitative compatibility and spatial compatibility effects are known to depend on different stimulus dimensions--body…

Multi-scale soil salinity mapping and monitoring with proximal and remote sensing

USDA-ARS?s Scientific Manuscript database

This talk is part of a technical short course on “Soil mapping and process modelling at diverse scales”. In the talk, guidelines, special considerations, protocols, and strengths and limitations are presented for characterizing spatial and temporal variation in soil salinity at several spatial scale...

A simple enrichment correction factor for improving erosion estimation by rare earth oxide tracers

USDA-ARS?s Scientific Manuscript database

Spatially distributed soil erosion data are needed to better understanding soil erosion processes and validating distributed erosion models. Rare earth element (REE) oxides were used to generate spatial erosion data. However, a general concern on the accuracy of the technique arose due to selective ...

Progress on Discrete Fracture Network models with implications on the predictions of permeability and flow channeling structure

NASA Astrophysics Data System (ADS)

Darcel, C.; Davy, P.; Le Goc, R.; Maillot, J.; Selroos, J. O.

2017-12-01

We present progress on Discrete Fracture Network (DFN) flow modeling, including realistic advanced DFN spatial structures and local fracture transmissivity properties, through an application to the Forsmark site in Sweden. DFN models are a framework to combine fracture datasets from different sources and scales and to interpolate them in combining statistical distributions and stereological relations. The resulting DFN upscaling function - size density distribution - is a model component key to extrapolating fracture size densities between data gaps, from borehole core up to site scale. Another important feature of DFN models lays in the spatial correlations between fractures, with still unevaluated consequences on flow predictions. Indeed, although common Poisson (i.e. spatially random) models are widely used, they do not reflect these geological evidences for more complex structures. To model them, we define a DFN growth process from kinematic rules for nucleation, growth and stopping conditions. It mimics in a simplified way the geological fracturing processes and produces DFN characteristics -both upscaling function and spatial correlations- fully consistent with field observations. DFN structures are first compared for constant transmissivities. Flow simulations for the kinematic and equivalent Poisson DFN models show striking differences: with the kinematic DFN, connectivity and permeability are significantly smaller, down to a difference of one order of magnitude, and flow is much more channelized. Further flow analyses are performed with more realistic transmissivity distribution conditions (sealed parts, relations to fracture sizes, orientations and in-situ stress field). The relative importance of the overall DFN structure in the final flow predictions is discussed.

Numerical Study of the Role of Microphysical Latent Heating and Surface Heat Fluxes in a Severe Precipitation Event in the Warm Sector over Southern China

NASA Astrophysics Data System (ADS)

Yin, Jin-Fang; Wang, Dong-Hai; Liang, Zhao-Ming; Liu, Chong-Jian; Zhai, Guo-Qing; Wang, Hong

2018-02-01

Simulations of the severe precipitation event that occurred in the warm sector over southern China on 08 May 2014 are conducted using the Advanced Weather Research and Forecasting (WRF-ARWv3.5.1) model to investigate the roles of microphysical latent heating and surface heat fluxes during the severe precipitation processes. At first, observations from surface rain gauges and ground-based weather radars are used to evaluate the model outputs. Results show that the spatial distribution of 24-h accumulated precipitation is well reproduced, and the temporal and spatial distributions of the simulated radar reflectivity agree well with the observations. Then, several sensitive simulations are performed with the identical model configurations, except for different options in microphysical latent heating and surface heat fluxes. From the results, one of the significant findings is that the latent heating from warm rain microphysical processes heats the atmosphere in the initial phase of the precipitation and thus convective systems start by self-triggering and self-organizing, despite the fact that the environmental conditions are not favorable to the occurrence of precipitation event at the initial phase. In the case of the severe precipitation event over the warm sector, both warm and ice microphysical processes are active with the ice microphysics processes activated almost two hours later. According to the sensitive results, there is a very weak precipitation without heavy rainfall belt when microphysical latent heating is turned off. In terms of this precipitation event, the warm microphysics processes play significant roles on precipitation intensity, while the ice microphysics processes have effects on the spatial distribution of precipitation. Both surface sensible and latent heating have effects on the precipitation intensity and spatial distribution. By comparison, the surface sensible heating has a strong influence on the spatial distribution of precipitation, and the surface latent heating has only a slight impact on the precipitation intensity. The results indicate that microphysical latent heating might be an important factor for severe precipitation forecast in the warm sector over southern China. Surface sensible heating can have considerable influence on the precipitation spatial distribution and should not be neglected in the case of weak large-scale conditions with abundant water vapor in the warm sector.

Alternative spatial configurations to reflect landscape structure in a hydrological model: SUMMA applications to the Reynolds Creek Watershed and the Columbia River Basin

NASA Astrophysics Data System (ADS)

Nijssen, Bart; Clark, Martyn; Mizukami, Naoki; Chegwidden, Oriana

2016-04-01

Most existing hydrological models use a fixed representation of landscape structure. For example, high-resolution, spatially-distributed models may use grid cells that exchange moisture through the saturated subsurface or may divide the landscape into hydrologic response units that only exchange moisture through surface channels. Alternatively, many regional models represent the landscape through coarse elements that do not model any moisture exchange between these model elements. These spatial organizations are often represented at a low-level in the model code and its data structures, which makes it difficult to evaluate different landscape representations using the same hydrological model. Instead, such experimentation requires the use of multiple, different hydrological models, which in turn complicates the analysis, because differences in model outcomes are no longer constrained by differing spatial representations. This inflexibility in the representation of landscape structure also limits a model's capability for scaling local processes to regional outcomes. In this study, we used the Structure for Unifying Multiple Modeling Alternatives (SUMMA) to evaluate different model spatial configurations to represent landscape structure and to evaluate scaling behavior. SUMMA can represent the moisture exchange between arbitrarily shaped landscape elements in a number of different ways, while using the same model parameterizations for vertical fluxes. This allows us to isolate the effects of changes in landscape representations on modeled hydrological fluxes and states. We examine the effects of spatial configuration in Reynolds Creek, Idaho, USA, which is a research watershed with gaged areas from 1-20 km2. We then use the same modeling system to evaluate scaling behavior in simulated hydrological fluxes in the Columbia River Basin, Pacific Northwest, USA. This basin drains more than 500,000 km2 and includes the Reynolds Creek Watershed.

The SNARC effect is not a unitary phenomenon.

PubMed

Basso Moro, Sara; Dell'Acqua, Roberto; Cutini, Simone

2018-04-01

Models of the spatial-numerical association of response codes (SNARC) effect-faster responses to small numbers using left effectors, and the converse for large numbers-diverge substantially in localizing the root cause of this effect along the numbers' processing chain. One class of models ascribes the cause of the SNARC effect to the inherently spatial nature of the semantic representation of numerical magnitude. A different class of models ascribes the effect's cause to the processing dynamics taking place during response selection. To disentangle these opposing views, we devised a paradigm combining magnitude comparison and stimulus-response switching in order to monitor modulations of the SNARC effect while concurrently tapping both semantic and response-related processing stages. We observed that the SNARC effect varied nonlinearly as a function of both manipulated factors, a result that can hardly be reconciled with a unitary cause of the SNARC effect.

ANIMAL MODELS OF COGNITIVE DEVELOPMENT IN NEUROTOXICITY

EPA Science Inventory

The thesis of this chapter has been that spatial delayed alternation versus position discrimination learning can serve as a valuable rodent model of cognitive development in neurotoxicology. his model captures dual process conceptualizations of memory in human neuropsychology and...

Modeling Best Management Practices (BMPs) with HSPF

EPA Science Inventory

The Hydrological Simulation Program-Fortran (HSPF) is a semi-distributed watershed model, which simulates hydrology and water quality processes at user-specified spatial and temporal scales. Although HSPF is a comprehensive and highly flexible model, a number of investigators not...

Spatial analysis of instream nitrogen loads and factors controlling nitrogen delivery to streams in the southeastern United States using spatially referenced regression on watershed attributes (SPARROW) and regional classification frameworks

USGS Publications Warehouse

Hoos, A.B.; McMahon, G.

2009-01-01

Understanding how nitrogen transport across the landscape varies with landscape characteristics is important for developing sound nitrogen management policies. We used a spatially referenced regression analysis (SPARROW) to examine landscape characteristics influencing delivery of nitrogen from sources in a watershed to stream channels. Modelled landscape delivery ratio varies widely (by a factor of 4) among watersheds in the southeastern United States - higher in the western part (Tennessee, Alabama, and Mississippi) than in the eastern part, and the average value for the region is lower compared to other parts of the nation. When we model landscape delivery ratio as a continuous function of local-scale landscape characteristics, we estimate a spatial pattern that varies as a function of soil and climate characteristics but exhibits spatial structure in residuals (observed load minus predicted load). The spatial pattern of modelled landscape delivery ratio and the spatial pattern of residuals coincide spatially with Level III ecoregions and also with hydrologic landscape regions. Subsequent incorporation into the model of these frameworks as regional scale variables improves estimation of landscape delivery ratio, evidenced by reduced spatial bias in residuals, and suggests that cross-scale processes affect nitrogen attenuation on the landscape. The model-fitted coefficient values are logically consistent with the hypothesis that broad-scale classifications of hydrologic response help to explain differential rates of nitrogen attenuation, controlling for local-scale landscape characteristics. Negative model coefficients for hydrologic landscape regions where the primary flow path is shallow ground water suggest that a lower fraction of nitrogen mass will be delivered to streams; this relation is reversed for regions where the primary flow path is overland flow.

Spatial analysis of instream nitrogen loads and factors controlling nitrogen delivery to streams in the southeastern United States using spatially referenced regression on watershed attributes (SPARROW) and regional classification frameworks

USGS Publications Warehouse

Hoos, Anne B.; McMahon, Gerard

2009-01-01

Understanding how nitrogen transport across the landscape varies with landscape characteristics is important for developing sound nitrogen management policies. We used a spatially referenced regression analysis (SPARROW) to examine landscape characteristics influencing delivery of nitrogen from sources in a watershed to stream channels. Modelled landscape delivery ratio varies widely (by a factor of 4) among watersheds in the southeastern United States—higher in the western part (Tennessee, Alabama, and Mississippi) than in the eastern part, and the average value for the region is lower compared to other parts of the nation. When we model landscape delivery ratio as a continuous function of local-scale landscape characteristics, we estimate a spatial pattern that varies as a function of soil and climate characteristics but exhibits spatial structure in residuals (observed load minus predicted load). The spatial pattern of modelled landscape delivery ratio and the spatial pattern of residuals coincide spatially with Level III ecoregions and also with hydrologic landscape regions. Subsequent incorporation into the model of these frameworks as regional scale variables improves estimation of landscape delivery ratio, evidenced by reduced spatial bias in residuals, and suggests that cross-scale processes affect nitrogen attenuation on the landscape. The model-fitted coefficient values are logically consistent with the hypothesis that broad-scale classifications of hydrologic response help to explain differential rates of nitrogen attenuation, controlling for local-scale landscape characteristics. Negative model coefficients for hydrologic landscape regions where the primary flow path is shallow ground water suggest that a lower fraction of nitrogen mass will be delivered to streams; this relation is reversed for regions where the primary flow path is overland flow.

Excellent approach to modeling urban expansion by fuzzy cellular automata: agent base model

NASA Astrophysics Data System (ADS)

Khajavigodellou, Yousef; Alesheikh, Ali A.; Mohammed, Abdulrazak A. S.; Chapi, Kamran

2014-09-01

Recently, the interaction between humans and their environment is the one of important challenges in the world. Landuse/ cover change (LUCC) is a complex process that includes actors and factors at different social and spatial levels. The complexity and dynamics of urban systems make the applicable practice of urban modeling very difficult. With the increased computational power and the greater availability of spatial data, micro-simulation such as the agent based and cellular automata simulation methods, has been developed by geographers, planners, and scholars, and it has shown great potential for representing and simulating the complexity of the dynamic processes involved in urban growth and land use change. This paper presents Fuzzy Cellular Automata in Geospatial Information System and remote Sensing to simulated and predicted urban expansion pattern. These FCA-based dynamic spatial urban models provide an improved ability to forecast and assess future urban growth and to create planning scenarios, allowing us to explore the potential impacts of simulations that correspond to urban planning and management policies. A fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on Land use change is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. The model integrates an ABM (agent-based model) and FCA (Fuzzy Cellular Automata) to investigate a complex decision-making process and future urban dynamic processes. Based on this model rapid development and green land protection under the influences of the behaviors and decision modes of regional authority agents, real estate developer agents, resident agents and non- resident agents and their interactions have been applied to predict the future development patterns of the Erbil metropolitan region.

Does spatial variation in environmental conditions affect recruitment? A study using a 3-D model of Peruvian anchovy

NASA Astrophysics Data System (ADS)

Xu, Yi; Rose, Kenneth A.; Chai, Fei; Chavez, Francisco P.; Ayón, Patricia

2015-11-01

We used a 3-dimensional individual-based model (3-D IBM) of Peruvian anchovy to examine how spatial variation in environmental conditions affects larval and juvenile growth and survival, and recruitment. Temperature, velocity, and phytoplankton and zooplankton concentrations generated from a coupled hydrodynamic Nutrients-Phytoplankton-Zooplankton-Detritus (NPZD) model, mapped to a three dimensional rectangular grid, were used to simulate anchovy populations. The IBM simulated individuals as they progressed from eggs to recruitment at 10 cm. Eggs and yolk-sac larvae were followed hourly through the processes of development, mortality, and movement (advection), and larvae and juveniles were followed daily through the processes of growth, mortality, and movement (advection plus behavior). A bioenergetics model was used to grow larvae and juveniles. The NPZD model provided prey fields which influence both food consumption rate as well as behavior mediated movement with individuals going to grids cells having optimal growth conditions. We compared predicted recruitment for monthly cohorts for 1990 through 2004 between the full 3-D IBM and a point (0-D) model that used spatially-averaged environmental conditions. The 3-D and 0-D versions generated similar interannual patterns in monthly recruitment for 1991-2004, with the 3-D results yielding consistently higher survivorship. Both versions successfully captured the very poor recruitment during the 1997-1998 El Niño event. Higher recruitment in the 3-D simulations was due to higher survival during the larval stage resulting from individuals searching for more favorable temperatures that lead to faster growth rates. The strong effect of temperature was because both model versions provided saturating food conditions for larval and juvenile anchovies. We conclude with a discussion of how explicit treatment of spatial variation affected simulated recruitment, other examples of fisheries modeling analyses that have used a similar approach to assess the influence of spatial variation, and areas for further model development.

New tools for linking human and earth system models: The Toolbox for Human-Earth System Interaction & Scaling (THESIS)

NASA Astrophysics Data System (ADS)

O'Neill, B. C.; Kauffman, B.; Lawrence, P.

2016-12-01

Integrated analysis of questions regarding land, water, and energy resources often requires integration of models of different types. One type of integration is between human and earth system models, since both societal and physical processes influence these resources. For example, human processes such as changes in population, economic conditions, and policies govern the demand for land, water and energy, while the interactions of these resources with physical systems determine their availability and environmental consequences. We have begun to develop and use a toolkit for linking human and earth system models called the Toolbox for Human-Earth System Integration and Scaling (THESIS). THESIS consists of models and software tools to translate, scale, and synthesize information from and between human system models and earth system models (ESMs), with initial application to linking the NCAR integrated assessment model, iPETS, with the NCAR earth system model, CESM. Initial development is focused on urban areas and agriculture, sectors that are both explicitly represented in both CESM and iPETS. Tools are being made available to the community as they are completed (see https://www2.cgd.ucar.edu/sections/tss/iam/THESIS_tools). We discuss four general types of functions that THESIS tools serve (Spatial Distribution, Spatial Properties, Consistency, and Outcome Evaluation). Tools are designed to be modular and can be combined in order to carry out more complex analyses. We illustrate their application to both the exposure of population to climate extremes and to the evaluation of climate impacts on the agriculture sector. For example, projecting exposure to climate extremes involves use of THESIS tools for spatial population, spatial urban land cover, the characteristics of both, and a tool to bring urban climate information together with spatial population information. Development of THESIS tools is continuing and open to the research community.

Simulating CRN derived erosion rates in a transient Andean catchment using the TTLEM model

NASA Astrophysics Data System (ADS)

Campforts, Benjamin; Vanacker, Veerle; Herman, Frédéric; Schwanghart, Wolfgang; Tenrorio Poma, Gustavo; Govers, Gerard

2017-04-01

Assessing the impact of mountain building and erosion on the earth surface is key to reconstruct and predict terrestrial landscape evolution. Landscape evolution models (LEMs) are an essential tool in this research effort as they allow to integrate our growing understanding of physical processes governing erosion and transport of mass across the surface. The recent development of several LEMs opens up new areas of research in landscape evolution. Here, we want to seize this opportunity by answering a fundamental research question: does a model designed to simulate landscape evolution over geological timescales allows to simulate spatially varying erosion rates at a millennial timescale? We selected the highly transient Paute catchment in the Southeastern Ecuadorian Andes as a study area. We found that our model (TTLEM) is capable to better explain the spatial patterns of ca. 30 Cosmogenic Radio Nuclide (CRN) derived catchment wide erosion rates in comparison to a classical, statistical approach. Thus, the use of process-based landscape evolution models may not only be of great help to understand long-term landscape evolution but also in understanding spatial and temporal variations in sediment fluxes at the millennial time scale.

Spatiotemporal integration for tactile localization during arm movements: a probabilistic approach.

PubMed

Maij, Femke; Wing, Alan M; Medendorp, W Pieter

2013-12-01

It has been shown that people make systematic errors in the localization of a brief tactile stimulus that is delivered to the index finger while they are making an arm movement. Here we modeled these spatial errors with a probabilistic approach, assuming that they follow from temporal uncertainty about the occurrence of the stimulus. In the model, this temporal uncertainty converts into a spatial likelihood about the external stimulus location, depending on arm velocity. We tested the prediction of the model that the localization errors depend on arm velocity. Participants (n = 8) were instructed to localize a tactile stimulus that was presented to their index finger while they were making either slow- or fast-targeted arm movements. Our results confirm the model's prediction that participants make larger localization errors when making faster arm movements. The model, which was used to fit the errors for both slow and fast arm movements simultaneously, accounted very well for all the characteristics of these data with temporal uncertainty in stimulus processing as the only free parameter. We conclude that spatial errors in dynamic tactile perception stem from the temporal precision with which tactile inputs are processed.

Intelligent Context-Aware and Adaptive Interface for Mobile LBS

PubMed Central

Liu, Yanhong

2015-01-01

Context-aware user interface plays an important role in many human-computer Interaction tasks of location based services. Although spatial models for context-aware systems have been studied extensively, how to locate specific spatial information for users is still not well resolved, which is important in the mobile environment where location based services users are impeded by device limitations. Better context-aware human-computer interaction models of mobile location based services are needed not just to predict performance outcomes, such as whether people will be able to find the information needed to complete a human-computer interaction task, but to understand human processes that interact in spatial query, which will in turn inform the detailed design of better user interfaces in mobile location based services. In this study, a context-aware adaptive model for mobile location based services interface is proposed, which contains three major sections: purpose, adjustment, and adaptation. Based on this model we try to describe the process of user operation and interface adaptation clearly through the dynamic interaction between users and the interface. Then we show how the model applies users' demands in a complicated environment and suggested the feasibility by the experimental results. PMID:26457077

Calibration of a Distributed Hydrological Model using Remote Sensing Evapotranspiration data in the Semi-Arid Punjab Region of Pakista

NASA Astrophysics Data System (ADS)

Becker, R.; Usman, M.

2017-12-01

A SWAT (Soil Water Assessment Tool) model is applied in the semi-arid Punjab region in Pakistan. The physically based hydrological model is set up to simulate hydrological processes and water resources demands under future land use, climate change and irrigation management scenarios. In order to successfully run the model, detailed focus is laid on the calibration procedure of the model. The study deals with the following calibration issues:i. lack of reliable calibration/validation data, ii. difficulty to accurately model a highly managed system with a physically based hydrological model and iii. use of alternative and spatially distributed data sets for model calibration. In our study area field observations are rare and the entirely human controlled irrigation system renders central calibration parameters (e.g. runoff/curve number) unsuitable, as it can't be assumed that they represent the natural behavior of the hydrological system. From evapotranspiration (ET) however principal hydrological processes can still be inferred. Usman et al. (2015) derived satellite based monthly ET data for our study area based on SEBAL (Surface Energy Balance Algorithm) and created a reliable ET data set which we use in this study to calibrate our SWAT model. The initial SWAT model performance is evaluated with respect to the SEBAL results using correlation coefficients, RMSE, Nash-Sutcliffe efficiencies and mean differences. Particular focus is laid on the spatial patters, investigating the potential of a spatially differentiated parameterization instead of just using spatially uniform calibration data. A sensitivity analysis reveals the most sensitive parameters with respect to changes in ET, which are then selected for the calibration process.Using the SEBAL-ET product we calibrate the SWAT model for the time period 2005-2006 using a dynamically dimensioned global search algorithm to minimize RMSE. The model improvement after the calibration procedure is finally evaluated based on the previously chosen evaluation criteria for the time period 2007-2008. The study reveals the sensitivity of SWAT model parameters to changes in ET in a semi-arid and human controlled system and the potential of calibrating those parameters using satellite derived ET data.

Modeling disturbance and succession in forest landscapes using LANDIS: introduction

Treesearch

Brian R. Sturtevant; Eric J. Gustafson; Hong S. He

2004-01-01

Modeling forest landscape change is challenging because it involves the interaction of a variety of factors and processes, such as climate, succession, disturbance, and management. These processes occur at various spatial and temporal scales, and the interactions can be complex on heterogeneous landscapes. Because controlled field experiments designed to investigate...

Deterministic ripple-spreading model for complex networks.

PubMed

Hu, Xiao-Bing; Wang, Ming; Leeson, Mark S; Hines, Evor L; Di Paolo, Ezequiel

2011-04-01

This paper proposes a deterministic complex network model, which is inspired by the natural ripple-spreading phenomenon. The motivations and main advantages of the model are the following: (i) The establishment of many real-world networks is a dynamic process, where it is often observed that the influence of a few local events spreads out through nodes, and then largely determines the final network topology. Obviously, this dynamic process involves many spatial and temporal factors. By simulating the natural ripple-spreading process, this paper reports a very natural way to set up a spatial and temporal model for such complex networks. (ii) Existing relevant network models are all stochastic models, i.e., with a given input, they cannot output a unique topology. Differently, the proposed ripple-spreading model can uniquely determine the final network topology, and at the same time, the stochastic feature of complex networks is captured by randomly initializing ripple-spreading related parameters. (iii) The proposed model can use an easily manageable number of ripple-spreading related parameters to precisely describe a network topology, which is more memory efficient when compared with traditional adjacency matrix or similar memory-expensive data structures. (iv) The ripple-spreading model has a very good potential for both extensions and applications.

Wolf Creek Research Basin Cold REgion Process Studies - 1992-2003

NASA Astrophysics Data System (ADS)

Janowicz, R.; Hedstrom, N.; Pomeroy, J.; Granger, R.; Carey, S.

2004-12-01

The development of hydrological models in northern regions are complicated by cold region processes. Sparse vegetation influences snowpack accumulation, redistribution and melt, frozen ground effects infiltration and runoff and cold soils in the summer effect evapotranspiration rates. Situated in the upper Yukon River watershed, the 195 km2 Wolf Creek Research Basin was instrumented in 1992 to calibrate hydrologic flow models, and has since evolved into a comprehensive study of cold region processes and linkages, contributing significantly to hydrological and climate change modelling. Studies include those of precipitation distribution, snowpack accumulation and redistribution, energy balance, snowmelt infiltration, and water balance. Studies of the spatial variability of hydrometeorological data demonstrate the importance of physical parameters on their distribution and control on runoff processes. Many studies have also identified the complex interaction of several of the physical parameters, including topography, vegetation and frozen ground (seasonal or permafrost) as important. They also show that there is a fundamental, underlying spatial structure to the watershed that must be adequately represented in parameterization schemes for scaling and watershed modelling. The specific results of numerous studies are presented.

Assessment of the Suitability of High Resolution Numerical Weather Model Outputs for Hydrological Modelling in Mountainous Cold Regions

NASA Astrophysics Data System (ADS)

Rasouli, K.; Pomeroy, J. W.; Hayashi, M.; Fang, X.; Gutmann, E. D.; Li, Y.

2017-12-01

The hydrology of mountainous cold regions has a large spatial variability that is driven both by climate variability and near-surface process variability associated with complex terrain and patterns of vegetation, soils, and hydrogeology. There is a need to downscale large-scale atmospheric circulations towards the fine scales that cold regions hydrological processes operate at to assess their spatial variability in complex terrain and quantify uncertainties by comparison to field observations. In this research, three high resolution numerical weather prediction models, namely, the Intermediate Complexity Atmosphere Research (ICAR), Weather Research and Forecasting (WRF), and Global Environmental Multiscale (GEM) models are used to represent spatial and temporal patterns of atmospheric conditions appropriate for hydrological modelling. An area covering high mountains and foothills of the Canadian Rockies was selected to assess and compare high resolution ICAR (1 km × 1 km), WRF (4 km × 4 km), and GEM (2.5 km × 2.5 km) model outputs with station-based meteorological measurements. ICAR with very low computational cost was run with different initial and boundary conditions and with finer spatial resolution, which allowed an assessment of modelling uncertainty and scaling that was difficult with WRF. Results show that ICAR, when compared with WRF and GEM, performs very well in precipitation and air temperature modelling in the Canadian Rockies, while all three models show a fair performance in simulating wind and humidity fields. Representation of local-scale atmospheric dynamics leading to realistic fields of temperature and precipitation by ICAR, WRF, and GEM makes these models suitable for high resolution cold regions hydrological predictions in complex terrain, which is a key factor in estimating water security in western Canada.

Causal modelling applied to the risk assessment of a wastewater discharge.

PubMed

Paul, Warren L; Rokahr, Pat A; Webb, Jeff M; Rees, Gavin N; Clune, Tim S

2016-03-01

Bayesian networks (BNs), or causal Bayesian networks, have become quite popular in ecological risk assessment and natural resource management because of their utility as a communication and decision-support tool. Since their development in the field of artificial intelligence in the 1980s, however, Bayesian networks have evolved and merged with structural equation modelling (SEM). Unlike BNs, which are constrained to encode causal knowledge in conditional probability tables, SEMs encode this knowledge in structural equations, which is thought to be a more natural language for expressing causal information. This merger has clarified the causal content of SEMs and generalised the method such that it can now be performed using standard statistical techniques. As it was with BNs, the utility of this new generation of SEM in ecological risk assessment will need to be demonstrated with examples to foster an understanding and acceptance of the method. Here, we applied SEM to the risk assessment of a wastewater discharge to a stream, with a particular focus on the process of translating a causal diagram (conceptual model) into a statistical model which might then be used in the decision-making and evaluation stages of the risk assessment. The process of building and testing a spatial causal model is demonstrated using data from a spatial sampling design, and the implications of the resulting model are discussed in terms of the risk assessment. It is argued that a spatiotemporal causal model would have greater external validity than the spatial model, enabling broader generalisations to be made regarding the impact of a discharge, and greater value as a tool for evaluating the effects of potential treatment plant upgrades. Suggestions are made on how the causal model could be augmented to include temporal as well as spatial information, including suggestions for appropriate statistical models and analyses.

Doubly stochastic Poisson process models for precipitation at fine time-scales

NASA Astrophysics Data System (ADS)

Ramesh, Nadarajah I.; Onof, Christian; Xie, Dichao

2012-09-01

This paper considers a class of stochastic point process models, based on doubly stochastic Poisson processes, in the modelling of rainfall. We examine the application of this class of models, a neglected alternative to the widely-known Poisson cluster models, in the analysis of fine time-scale rainfall intensity. These models are mainly used to analyse tipping-bucket raingauge data from a single site but an extension to multiple sites is illustrated which reveals the potential of this class of models to study the temporal and spatial variability of precipitation at fine time-scales.

Spatial Structure of Seagrass Suggests That Size-Dependent Plant Traits Have a Strong Influence on the Distribution and Maintenance of Tropical Multispecies Meadows

PubMed Central

Ooi, Jillian L. S.; Van Niel, Kimberly P.; Kendrick, Gary A.; Holmes, Karen W.

2014-01-01

Background Seagrass species in the tropics occur in multispecies meadows. How these meadows are maintained through species co-existence and what their ecological drivers may be has been an overarching question in seagrass biogeography. In this study, we quantify the spatial structure of four co-existing species and infer potential ecological processes from these structures. Methods and Results Species presence/absence data were collected using underwater towed and dropped video cameras in Pulau Tinggi, Malaysia. The geostatistical method, utilizing semivariograms, was used to describe the spatial structure of Halophila spp, Halodule uninervis, Syringodium isoetifolium and Cymodocea serrulata. Species had spatial patterns that were oriented in the along-shore and across-shore directions, nested with larger species in meadow interiors, and consisted of multiple structures that indicate the influence of 2–3 underlying processes. The Linear Model of Coregionalization (LMC) was used to estimate the amount of variance contributing to the presence of a species at specific spatial scales. These distances were <2.5 m (micro-scale), 2.5–50 m (fine-scale) and >50 m (broad-scale) in the along-shore; and <2.5 m (micro-scale), 2.5–140 m (fine-scale) and >140 m (broad-scale) in the across-shore. The LMC suggests that smaller species (Halophila spp and H. uninervis) were most influenced by broad-scale processes such as hydrodynamics and water depth whereas large, localised species (S. isoetifolium and C. serrulata) were more influenced by finer-scale processes such as sediment burial, seagrass colonization and growth, and physical disturbance. Conclusion In this study, we provide evidence that spatial structure is distinct even when species occur in well-mixed multispecies meadows, and we suggest that size-dependent plant traits have a strong influence on the distribution and maintenance of tropical marine plant communities. This study offers a contrast from previous spatial models of seagrasses which have largely focused on monospecific temperate meadows. PMID:24497978

Spatial structure of seagrass suggests that size-dependent plant traits have a strong influence on the distribution and maintenance of tropical multispecies meadows.

PubMed

Ooi, Jillian L S; Van Niel, Kimberly P; Kendrick, Gary A; Holmes, Karen W

2014-01-01

Seagrass species in the tropics occur in multispecies meadows. How these meadows are maintained through species co-existence and what their ecological drivers may be has been an overarching question in seagrass biogeography. In this study, we quantify the spatial structure of four co-existing species and infer potential ecological processes from these structures. Species presence/absence data were collected using underwater towed and dropped video cameras in Pulau Tinggi, Malaysia. The geostatistical method, utilizing semivariograms, was used to describe the spatial structure of Halophila spp, Halodule uninervis, Syringodium isoetifolium and Cymodocea serrulata. Species had spatial patterns that were oriented in the along-shore and across-shore directions, nested with larger species in meadow interiors, and consisted of multiple structures that indicate the influence of 2-3 underlying processes. The Linear Model of Coregionalization (LMC) was used to estimate the amount of variance contributing to the presence of a species at specific spatial scales. These distances were <2.5 m (micro-scale), 2.5-50 m (fine-scale) and >50 m (broad-scale) in the along-shore; and <2.5 m (micro-scale), 2.5-140 m (fine-scale) and >140 m (broad-scale) in the across-shore. The LMC suggests that smaller species (Halophila spp and H. uninervis) were most influenced by broad-scale processes such as hydrodynamics and water depth whereas large, localised species (S. isoetifolium and C. serrulata) were more influenced by finer-scale processes such as sediment burial, seagrass colonization and growth, and physical disturbance. In this study, we provide evidence that spatial structure is distinct even when species occur in well-mixed multispecies meadows, and we suggest that size-dependent plant traits have a strong influence on the distribution and maintenance of tropical marine plant communities. This study offers a contrast from previous spatial models of seagrasses which have largely focused on monospecific temperate meadows.

A space-time multiscale modelling of Earth's gravity field variations

NASA Astrophysics Data System (ADS)

Wang, Shuo; Panet, Isabelle; Ramillien, Guillaume; Guilloux, Frédéric

2017-04-01

The mass distribution within the Earth varies over a wide range of spatial and temporal scales, generating variations in the Earth's gravity field in space and time. These variations are monitored by satellites as the GRACE mission, with a 400 km spatial resolution and 10 days to 1 month temporal resolution. They are expressed in the form of gravity field models, often with a fixed spatial or temporal resolution. The analysis of these models allows us to study the mass transfers within the Earth system. Here, we have developed space-time multi-scale models of the gravity field, in order to optimize the estimation of gravity signals resulting from local processes at different spatial and temporal scales, and to adapt the time resolution of the model to its spatial resolution according to the satellites sampling. For that, we first build a 4D wavelet family combining spatial Poisson wavelets with temporal Haar wavelets. Then, we set-up a regularized inversion of inter-satellites gravity potential differences in a bayesian framework, to estimate the model parameters. To build the prior, we develop a spectral analysis, localized in time and space, of geophysical models of mass transport and associated gravity variations. Finally, we test our approach to the reconstruction of space-time variations of the gravity field due to hydrology. We first consider a global distribution of observations along the orbit, from a simplified synthetic hydrology signal comprising only annual variations at large spatial scales. Then, we consider a regional distribution of observations in Africa, and a larger number of spatial and temporal scales. We test the influence of an imperfect prior and discuss our results.

Priming processes in the Simon task: more evidence from the lexical decision task for a third route in the Simon effect.

PubMed

Metzker, Manja; Dreisbach, Gesine

2011-06-01

Recently, it was proposed that the Simon effect would result not only from two interfering processes, as classical dual-route models assume, but from three processes. It was argued that priming from the spatial code to the nonspatial code might facilitate the identification of the nonspatial stimulus feature in congruent Simon trials. In the present study, the authors provide evidence that the identification of the nonspatial information can be facilitated by the activation of an associated spatial code. In three experiments, participants first associated centrally presented animal and fruit pictures with spatial responses. Subsequently, participants decided whether laterally presented letter strings were words (animal, fruit, or other words) or nonwords; stimulus position could be congruent or incongruent to the associated spatial code. As hypothesized, animal and fruit words were identified faster at congruent than at incongruent stimulus positions from the association phase. The authors conclude that the activation of the spatial code spreads to the nonspatial code, resulting in facilitated stimulus identification in congruent trials. These results speak to the assumption of a third process involved in the Simon task.

A mathematical model for foreign body reactions in 2D.

PubMed

Su, Jianzhong; Gonzales, Humberto Perez; Todorov, Michail; Kojouharov, Hristo; Tang, Liping

2011-02-01

The foreign body reactions are commonly referred to the network of immune and inflammatory reactions of human or animals to foreign objects placed in tissues. They are basic biological processes, and are also highly relevant to bioengineering applications in implants, as fibrotic tissue formations surrounding medical implants have been found to substantially reduce the effectiveness of devices. Despite of intensive research on determining the mechanisms governing such complex responses, few mechanistic mathematical models have been developed to study such foreign body reactions. This study focuses on a kinetics-based predictive tool in order to analyze outcomes of multiple interactive complex reactions of various cells/proteins and biochemical processes and to understand transient behavior during the entire period (up to several months). A computational model in two spatial dimensions is constructed to investigate the time dynamics as well as spatial variation of foreign body reaction kinetics. The simulation results have been consistent with experimental data and the model can facilitate quantitative insights for study of foreign body reaction process in general.

Land-use evaluation for sustainable construction in a protected area: A case of Sara mountain national park.

PubMed

Ristić, Vladica; Maksin, Marija; Nenković-Riznić, Marina; Basarić, Jelena

2018-01-15

The process of making decisions on sustainable development and construction begins in spatial and urban planning when defining the suitability of using land for sustainable construction in a protected area (PA) and its immediate and regional surroundings. The aim of this research is to propose and assess a model for evaluating land-use suitability for sustainable construction in a PA and its surroundings. The methodological approach of Multi-Criteria Decision Analysis was used in the formation of this model and adapted for the research; it was combined with the adapted Analytical hierarchy process and the Delphi process, and supported by a geographical information system (GIS) within the framework of ESRI ArcGIS software - Spatial analyst. The model is applied to the case study of Sara mountain National Park in Kosovo. The result of the model is a "map of integrated assessment of land-use suitability for sustainable construction in a PA for the natural factor". Copyright © 2017 Elsevier Ltd. All rights reserved.

Spatially explicit simulation of hydrologically controlled carbon and nitrogen cycles and associated feedback mechanisms in a boreal ecosystem

NASA Astrophysics Data System (ADS)

Govind, Ajit; Chen, Jing Ming; Ju, Weimin

2009-06-01

Ecosystem models that simulate biogeochemical processes usually ignore hydrological controls that govern them. It is quite possible that topographically driven water fluxes significantly influence the spatial distribution of C sources and sinks because of their large contribution to the local water balance. To investigate this, we simulated biogeochemical processes along with the associated feedback mechanisms in a boreal ecosystem using a spatially explicit hydroecological model, boreal ecosystem productivity simulator (BEPS)-TerrainLab V2.0, that has a tight coupling of ecophysiological, hydrological, and biogeochemical processes. First, the simulated dynamics of snowpack, soil temperature, net ecosystem productivity (NEP), and total ecosystem respiration (TER) were validated with high-frequency measurements for 2 years. The model was able to explain 80% of the variability in NEP and 84% of the variability in TER. Further, we investigated the influence of topographically driven subsurface base flow on soil C and N cycling and on the spatiotemporal patterns of C sources and sinks using three hydrological modeling scenarios that differed in hydrological conceptualizations. In general, the scenarios that had nonexplicit hydrological representation overestimated NEP, as opposed to the scenario that had an explicit (realistic) representation. The key processes controlling the NEP differences were attributed to the combined effects of variations in photosynthesis (due to changes in stomatal conductance and nitrogen (N) availability), heterotrophic respiration, and autotrophic respiration, all of which occur simultaneously affecting NEP. Feedback relationships were also found to exacerbate the differences. We identified six types of NEP differences (biases), of which the most commonly found was due to an underestimation of the existing C sources, highlighting the vulnerability of regional-scale ecosystem models that ignore hydrological processes.

The model of drugs distribution dynamics in biological tissue

NASA Astrophysics Data System (ADS)

Ginevskij, D. A.; Izhevskij, P. V.; Sheino, I. N.

2017-09-01

The dose distribution by Neutron Capture Therapy follows the distribution of 10B in the tissue. The modern models of pharmacokinetics of drugs describe the processes occurring in conditioned "chambers" (blood-organ-tumor), but fail to describe the spatial distribution of the drug in the tumor and in normal tissue. The mathematical model of the spatial distribution dynamics of drugs in the tissue, depending on the concentration of the drug in the blood, was developed. The modeling method is the representation of the biological structure in the form of a randomly inhomogeneous medium in which the 10B distribution occurs. The parameters of the model, which cannot be determined rigorously in the experiment, are taken as the quantities subject to the laws of the unconnected random processes. The estimates of 10B distribution preparations in the tumor and healthy tissue, inside/outside the cells, are obtained.

Improving simulated spatial distribution of productivity and biomass in Amazon forests using the ACME land model

NASA Astrophysics Data System (ADS)

Yang, X.; Thornton, P. E.; Ricciuto, D. M.; Shi, X.; Xu, M.; Hoffman, F. M.; Norby, R. J.

2017-12-01

Tropical forests play a crucial role in the global carbon cycle, accounting for one third of the global NPP and containing about 25% of global vegetation biomass and soil carbon. This is particularly true for tropical forests in the Amazon region, as it comprises approximately 50% of the world's tropical forests. It is therefore important for us to understand and represent the processes that determine the fluxes and storage of carbon in these forests. In this study, we show that the implementation of phosphorus (P) cycle and P limitation in the ACME Land Model (ALM) improves simulated spatial pattern of NPP. The P-enabled ALM is able to capture the west-to-east gradient of productivity, consistent with field observations. We also show that by improving the representation of mortality processes, ALM is able to reproduce the observed spatial pattern of above ground biomass across the Amazon region.

Mammogram registration using the Cauchy-Navier spline

NASA Astrophysics Data System (ADS)

Wirth, Michael A.; Choi, Christopher

2001-07-01

The process of comparative analysis involves inspecting mammograms for characteristic signs of potential cancer by comparing various analogous mammograms. Factors such as the deformable behavior of the breast, changes in breast positioning, and the amount/geometry of compression may contribute to spatial differences between corresponding structures in corresponding mammograms, thereby significantly complicating comparative analysis. Mammogram registration is a process whereby spatial differences between mammograms can be reduced. Presented in this paper is a nonrigid approach to matching corresponding mammograms based on a physical registration model. Many of the earliest approaches to mammogram registration used spatial transformations which were innately rigid or affine in nature. More recently algorithms have incorporated radial basis functions such as the Thin-Plate Spline to match mammograms. The approach presented here focuses on the use of the Cauchy-Navier Spline, a deformable registration model which offers approximate nonrigid registration. The utility of the Cauchy-Navier Spline is illustrated by matching both temporal and bilateral mammograms.

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.

Geospatial Data Stream Processing in Python Using FOSS4G Components

NASA Astrophysics Data System (ADS)

McFerren, G.; van Zyl, T.

2016-06-01

One viewpoint of current and future IT systems holds that there is an increase in the scale and velocity at which data are acquired and analysed from heterogeneous, dynamic sources. In the earth observation and geoinformatics domains, this process is driven by the increase in number and types of devices that report location and the proliferation of assorted sensors, from satellite constellations to oceanic buoy arrays. Much of these data will be encountered as self-contained messages on data streams - continuous, infinite flows of data. Spatial analytics over data streams concerns the search for spatial and spatio-temporal relationships within and amongst data "on the move". In spatial databases, queries can assess a store of data to unpack spatial relationships; this is not the case on streams, where spatial relationships need to be established with the incomplete data available. Methods for spatially-based indexing, filtering, joining and transforming of streaming data need to be established and implemented in software components. This article describes the usage patterns and performance metrics of a number of well known FOSS4G Python software libraries within the data stream processing paradigm. In particular, we consider the RTree library for spatial indexing, the Shapely library for geometric processing and transformation and the PyProj library for projection and geodesic calculations over streams of geospatial data. We introduce a message oriented Python-based geospatial data streaming framework called Swordfish, which provides data stream processing primitives, functions, transports and a common data model for describing messages, based on the Open Geospatial Consortium Observations and Measurements (O&M) and Unidata Common Data Model (CDM) standards. We illustrate how the geospatial software components are integrated with the Swordfish framework. Furthermore, we describe the tight temporal constraints under which geospatial functionality can be invoked when processing high velocity, potentially infinite geospatial data streams. The article discusses the performance of these libraries under simulated streaming loads (size, complexity and volume of messages) and how they can be deployed and utilised with Swordfish under real load scenarios, illustrated by a set of Vessel Automatic Identification System (AIS) use cases. We conclude that the described software libraries are able to perform adequately under geospatial data stream processing scenarios - many real application use cases will be handled sufficiently by the software.

Canopies to Continents: What spatial scales are needed to represent landcover distributions in earth system models?

NASA Astrophysics Data System (ADS)

Guenther, A. B.; Duhl, T.

2011-12-01

Increasing computational resources have enabled a steady improvement in the spatial resolution used for earth system models. Land surface models and landcover distributions have kept ahead by providing higher spatial resolution than typically used in these models. Satellite observations have played a major role in providing high resolution landcover distributions over large regions or the entire earth surface but ground observations are needed to calibrate these data and provide accurate inputs for models. As our ability to resolve individual landscape components improves, it is important to consider what scale is sufficient for providing inputs to earth system models. The required spatial scale is dependent on the processes being represented and the scientific questions being addressed. This presentation will describe the development a contiguous U.S. landcover database using high resolution imagery (1 to 1000 meters) and surface observations of species composition and other landcover characteristics. The database includes plant functional types and species composition and is suitable for driving land surface models (CLM and MEGAN) that predict land surface exchange of carbon, water, energy and biogenic reactive gases (e.g., isoprene, sesquiterpenes, and NO). We investigate the sensitivity of model results to landcover distributions with spatial scales ranging over six orders of magnitude (1 meter to 1000000 meters). The implications for predictions of regional climate and air quality will be discussed along with recommendations for regional and global earth system modeling.

A Behavioral Model of Landscape Change in the Amazon Basin: The Colonist Case

NASA Technical Reports Server (NTRS)

Walker, R. A.; Drzyzga, S. A.; Li, Y. L.; Wi, J. G.; Caldas, M.; Arima, E.; Vergara, D.

2004-01-01

This paper presents the prototype of a predictive model capable of describing both magnitudes of deforestation and its spatial articulation into patterns of forest fragmentation. In a departure from other landscape models, it establishes an explicit behavioral foundation for algorithm development, predicated on notions of the peasant economy and on household production theory. It takes a 'bottom-up' approach, generating the process of land-cover change occurring at lot level together with the geography of a transportation system to describe regional landscape change. In other words, it translates the decentralized decisions of individual households into a collective, spatial impact. In so doing, the model unites the richness of survey research on farm households with the analytical rigor of spatial analysis enabled by geographic information systems (GIs). The paper describes earlier efforts at spatial modeling, provides a critique of the so-called spatially explicit model, and elaborates a behavioral foundation by considering farm practices of colonists in the Amazon basin. It then uses, insight from the behavioral statement to motivate a GIs-based model architecture. The model is implemented for a long-standing colonization frontier in the eastern sector of the basin, along the Trans-Amazon Highway in the State of Para, Brazil. Results are subjected to both sensitivity analysis and error assessment, and suggestions are made about how the model could be improved.

On extreme events for non-spatial and spatial branching Brownian motions

NASA Astrophysics Data System (ADS)

Avan, Jean; Grosjean, Nicolas; Huillet, Thierry

2015-04-01

We study the impact of having a non-spatial branching mechanism with infinite variance on some parameters (height, width and first hitting time) of an underlying Bienaymé-Galton-Watson branching process. Aiming at providing a comparative study of the spread of an epidemics whose dynamics is given by the modulus of a branching Brownian motion (BBM) we then consider spatial branching processes in dimension d, not necessarily integer. The underlying branching mechanism is either a binary branching model or one presenting infinite variance. In particular we evaluate the chance p(x) of being hit if the epidemics started away at distance x. We compute the large x tail probabilities of this event, both when the branching mechanism is regular and when it exhibits very large fluctuations.

USING ECO-EVOLUTIONARY INDIVIDUAL-BASED MODELS TO INVESTIGATE SPATIALLY-DEPENDENT PROCESSES IN CONSERVATION GENETICS

EPA Science Inventory

Eco-evolutionary population simulation models are powerful new forecasting tools for exploring management strategies for climate change and other dynamic disturbance regimes. Additionally, eco-evo individual-based models (IBMs) are useful for investigating theoretical feedbacks ...

Spatial self-organization in hybrid models of multicellular adhesion

NASA Astrophysics Data System (ADS)

Bonforti, Adriano; Duran-Nebreda, Salva; Montañez, Raúl; Solé, Ricard

2016-10-01

Spatial self-organization emerges in distributed systems exhibiting local interactions when nonlinearities and the appropriate propagation of signals are at work. These kinds of phenomena can be modeled with different frameworks, typically cellular automata or reaction-diffusion systems. A different class of dynamical processes involves the correlated movement of agents over space, which can be mediated through chemotactic movement or minimization of cell-cell interaction energy. A classic example of the latter is given by the formation of spatially segregated assemblies when cells display differential adhesion. Here, we consider a new class of dynamical models, involving cell adhesion among two stochastically exchangeable cell states as a minimal model capable of exhibiting well-defined, ordered spatial patterns. Our results suggest that a whole space of pattern-forming rules is hosted by the combination of physical differential adhesion and the value of probabilities modulating cell phenotypic switching, showing that Turing-like patterns can be obtained without resorting to reaction-diffusion processes. If the model is expanded allowing cells to proliferate and die in an environment where diffusible nutrient and toxic waste are at play, different phases are observed, characterized by regularly spaced patterns. The analysis of the parameter space reveals that certain phases reach higher population levels than other modes of organization. A detailed exploration of the mean-field theory is also presented. Finally, we let populations of cells with different adhesion matrices compete for reproduction, showing that, in our model, structural organization can improve the fitness of a given cell population. The implications of these results for ecological and evolutionary models of pattern formation and the emergence of multicellularity are outlined.

Spatial transferability of habitat suitability models of Nephrops norvegicus among fished areas in the Northeast Atlantic: sufficiently stable for marine resource conservation?

PubMed

Lauria, Valentina; Power, Anne Marie; Lordan, Colm; Weetman, Adrian; Johnson, Mark P

2015-01-01

Knowledge of the spatial distribution and habitat associations of species in relation to the environment is essential for their management and conservation. Habitat suitability models are useful in quantifying species-environment relationships and predicting species distribution patterns. Little is known, however, about the stability and performance of habitat suitability models when projected into new areas (spatial transferability) and how this can inform resource management. The aims of this study were to model habitat suitability of Norway lobster (Nephrops norvegicus) in five fished areas of the Northeast Atlantic (Aran ground, Irish Sea, Celtic Sea, Scotland Inshore and Fladen ground), and to test for spatial transferability of habitat models among multiple regions. Nephrops burrow density was modelled using generalised additive models (GAMs) with predictors selected from four environmental variables (depth, slope, sediment and rugosity). Models were evaluated and tested for spatial transferability among areas. The optimum models (lowest AICc) for different areas always included depth and sediment as predictors. Burrow densities were generally greater at depth and in finer sediments, but relationships for individual areas were sometimes more complex. Aside from an inclusion of depth and sediment, the optimum models differed between fished areas. When it came to tests of spatial transferability, however, most of the models were able to predict Nephrops density in other areas. Furthermore, transferability was not dependent on use of the optimum models since competing models were also able to achieve a similar level of transferability to new areas. A degree of decoupling between model 'fitting' performance and spatial transferability supports the use of simpler models when extrapolating habitat suitability maps to different areas. Differences in the form and performance of models from different areas may supply further information on the processes shaping species' distributions. Spatial transferability of habitat models can be used to support fishery management when the information is scarce but caution needs to be applied when making inference and a multi-area transferability analysis is preferable to bilateral comparisons between areas.

Spatially explicit models for inference about density in unmarked or partially marked populations

USGS Publications Warehouse

Chandler, Richard B.; Royle, J. Andrew

2013-01-01

Recently developed spatial capture–recapture (SCR) models represent a major advance over traditional capture–recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area. Furthermore, unlike nonspatial CR methods, SCR models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers and sample locations. Although the utility of SCR methods is gaining recognition, the requirement that all individuals can be uniquely identified excludes their use in many contexts. In this paper, we develop models for situations in which individual recognition is not possible, thereby allowing SCR concepts to be applied in studies of unmarked or partially marked populations. The data required for our model are spatially referenced counts made on one or more sample occasions at a collection of closely spaced sample units such that individuals can be encountered at multiple locations. Our approach includes a spatial point process for the animal activity centers and uses the spatial correlation in counts as information about the number and location of the activity centers. Camera-traps, hair snares, track plates, sound recordings, and even point counts can yield spatially correlated count data, and thus our model is widely applicable. A simulation study demonstrated that while the posterior mean exhibits frequentist bias on the order of 5–10% in small samples, the posterior mode is an accurate point estimator as long as adequate spatial correlation is present. Marking a subset of the population substantially increases posterior precision and is recommended whenever possible. We applied our model to avian point count data collected on an unmarked population of the northern parula (Parula americana) and obtained a density estimate (posterior mode) of 0.38 (95% CI: 0.19–1.64) birds/ha. Our paper challenges sampling and analytical conventions in ecology by demonstrating that neither spatial independence nor individual recognition is needed to estimate population density—rather, spatial dependence can be informative about individual distribution and density.

A MODELING AND SIMULATION LANGUAGE FOR BIOLOGICAL CELLS WITH COUPLED MECHANICAL AND CHEMICAL PROCESSES

PubMed Central

Somogyi, Endre; Glazier, James A.

2017-01-01

Biological cells are the prototypical example of active matter. Cells sense and respond to mechanical, chemical and electrical environmental stimuli with a range of behaviors, including dynamic changes in morphology and mechanical properties, chemical uptake and secretion, cell differentiation, proliferation, death, and migration. Modeling and simulation of such dynamic phenomena poses a number of computational challenges. A modeling language describing cellular dynamics must naturally represent complex intra and extra-cellular spatial structures and coupled mechanical, chemical and electrical processes. Domain experts will find a modeling language most useful when it is based on concepts, terms and principles native to the problem domain. A compiler must then be able to generate an executable model from this physically motivated description. Finally, an executable model must efficiently calculate the time evolution of such dynamic and inhomogeneous phenomena. We present a spatial hybrid systems modeling language, compiler and mesh-free Lagrangian based simulation engine which will enable domain experts to define models using natural, biologically motivated constructs and to simulate time evolution of coupled cellular, mechanical and chemical processes acting on a time varying number of cells and their environment. PMID:29303160

A MODELING AND SIMULATION LANGUAGE FOR BIOLOGICAL CELLS WITH COUPLED MECHANICAL AND CHEMICAL PROCESSES.

PubMed

Somogyi, Endre; Glazier, James A

2017-04-01

Biological cells are the prototypical example of active matter. Cells sense and respond to mechanical, chemical and electrical environmental stimuli with a range of behaviors, including dynamic changes in morphology and mechanical properties, chemical uptake and secretion, cell differentiation, proliferation, death, and migration. Modeling and simulation of such dynamic phenomena poses a number of computational challenges. A modeling language describing cellular dynamics must naturally represent complex intra and extra-cellular spatial structures and coupled mechanical, chemical and electrical processes. Domain experts will find a modeling language most useful when it is based on concepts, terms and principles native to the problem domain. A compiler must then be able to generate an executable model from this physically motivated description. Finally, an executable model must efficiently calculate the time evolution of such dynamic and inhomogeneous phenomena. We present a spatial hybrid systems modeling language, compiler and mesh-free Lagrangian based simulation engine which will enable domain experts to define models using natural, biologically motivated constructs and to simulate time evolution of coupled cellular, mechanical and chemical processes acting on a time varying number of cells and their environment.

Can Retinal Ganglion Cell Dipoles Seed Iso-Orientation Domains in the Visual Cortex?

PubMed Central

Schottdorf, Manuel; Eglen, Stephen J.; Wolf, Fred; Keil, Wolfgang

2014-01-01

It has been argued that the emergence of roughly periodic orientation preference maps (OPMs) in the primary visual cortex (V1) of carnivores and primates can be explained by a so-called statistical connectivity model. This model assumes that input to V1 neurons is dominated by feed-forward projections originating from a small set of retinal ganglion cells (RGCs). The typical spacing between adjacent cortical orientation columns preferring the same orientation then arises via Moiré-Interference between hexagonal ON/OFF RGC mosaics. While this Moiré-Interference critically depends on long-range hexagonal order within the RGC mosaics, a recent statistical analysis of RGC receptive field positions found no evidence for such long-range positional order. Hexagonal order may be only one of several ways to obtain spatially repetitive OPMs in the statistical connectivity model. Here, we investigate a more general requirement on the spatial structure of RGC mosaics that can seed the emergence of spatially repetitive cortical OPMs, namely that angular correlations between so-called RGC dipoles exhibit a spatial structure similar to that of OPM autocorrelation functions. Both in cat beta cell mosaics as well as primate parasol receptive field mosaics we find that RGC dipole angles are spatially uncorrelated. To help assess the level of these correlations, we introduce a novel point process that generates mosaics with realistic nearest neighbor statistics and a tunable degree of spatial correlations of dipole angles. Using this process, we show that given the size of available data sets, the presence of even weak angular correlations in the data is very unlikely. We conclude that the layout of ON/OFF ganglion cell mosaics lacks the spatial structure necessary to seed iso-orientation domains in the primary visual cortex. PMID:24475081

Mapping and modeling the urban landscape in Bangkok, Thailand: Physical-spectral-spatial relations of population-environmental interactions

NASA Astrophysics Data System (ADS)

Shao, Yang

This research focuses on the application of remote sensing, geographic information systems, statistical modeling, and spatial analysis to examine the dynamics of urban land cover, urban structure, and population-environment interactions in Bangkok, Thailand, with an emphasis on rural-to-urban migration from rural Nang Rong District, Northeast Thailand to the primate city of Bangkok. The dissertation consists of four main sections: (1) development of remote sensing image classification and change-detection methods for characterizing imperviousness for Bangkok, Thailand from 1993-2002; (2) development of 3-D urban mapping methods, using high spatial resolution IKONOS satellite images, to assess high-rises and other urban structures; (3) assessment of urban spatial structure from 2-D and 3-D perspectives; and (4) an analysis of the spatial clustering of migrants from Nang Rong District in Bangkok and the neighborhood environments of migrants' locations. Techniques are developed to improve the accuracy of the neural network classification approach for the analysis of remote sensing data, with an emphasis on the spectral unmixing problem. The 3-D building heights are derived using the shadow information on the high-resolution IKONOS image. The results from the 2-D and 3-D mapping are further examined to assess urban structure and urban feature identification. This research contributes to image processing of remotely-sensed images and urban studies. The rural-urban migration process and migrants' settlement patterns are examined using spatial statistics, GIS, and remote sensing perspectives. The results show that migrants' spatial clustering in urban space is associated with the source village and a number of socio-demographic variables. In addition, the migrants' neighborhood environments in urban setting are modeled using a set of geographic and socio-demographic variables, and the results are scale-dependent.

Can retinal ganglion cell dipoles seed iso-orientation domains in the visual cortex?

PubMed

Schottdorf, Manuel; Eglen, Stephen J; Wolf, Fred; Keil, Wolfgang

2014-01-01

It has been argued that the emergence of roughly periodic orientation preference maps (OPMs) in the primary visual cortex (V1) of carnivores and primates can be explained by a so-called statistical connectivity model. This model assumes that input to V1 neurons is dominated by feed-forward projections originating from a small set of retinal ganglion cells (RGCs). The typical spacing between adjacent cortical orientation columns preferring the same orientation then arises via Moiré-Interference between hexagonal ON/OFF RGC mosaics. While this Moiré-Interference critically depends on long-range hexagonal order within the RGC mosaics, a recent statistical analysis of RGC receptive field positions found no evidence for such long-range positional order. Hexagonal order may be only one of several ways to obtain spatially repetitive OPMs in the statistical connectivity model. Here, we investigate a more general requirement on the spatial structure of RGC mosaics that can seed the emergence of spatially repetitive cortical OPMs, namely that angular correlations between so-called RGC dipoles exhibit a spatial structure similar to that of OPM autocorrelation functions. Both in cat beta cell mosaics as well as primate parasol receptive field mosaics we find that RGC dipole angles are spatially uncorrelated. To help assess the level of these correlations, we introduce a novel point process that generates mosaics with realistic nearest neighbor statistics and a tunable degree of spatial correlations of dipole angles. Using this process, we show that given the size of available data sets, the presence of even weak angular correlations in the data is very unlikely. We conclude that the layout of ON/OFF ganglion cell mosaics lacks the spatial structure necessary to seed iso-orientation domains in the primary visual cortex.

Abiotic and biotic controls of spatial pattern at alpine treeline

USGS Publications Warehouse

Malanson, George P.; Xiao, Ningchuan; Alftine, K.J.; Bekker, Mathew; Butler, David R.; Brown, Daniel G.; Cairns, David M.; Fagre, Daniel; Walsh, Stephen J.

2000-01-01

At alpine treeline, trees and krummholz forms affect the environment in ways that increase their growth and reproduction. We assess the way in which these positive feedbacks combine in spatial patterns to alter the environment in the neighborhood of existing plants. The research is significant because areas of alpine tundra are susceptible to encroachment by woody species as climate changes. Moreover, understanding the general processes of plant invasion is important. The importance of spatial pattern has been recognized, but the spatial pattern of positive feedbacks per se has not been explored in depth. We present a linked set of models of vegetation change at an alpine forest-tundra ecotone. Our aim is to create models that are as simple as possible in order to test specific hypotheses. We present results from a model of the resource averaging hypothesis and the positive feedback switch hypothesis of treelines. We compare the patterns generated by the models to patterns observed in fine scale remotely sensed data.

Modelling temporal and spatial dynamics of benthic fauna in North-West-European shelf seas

NASA Astrophysics Data System (ADS)

Lessin, Gennadi; Bruggeman, Jorn; Artioli, Yuri; Butenschön, Momme; Blackford, Jerry

2017-04-01

Benthic zones of shallow shelf seas receive high amounts of organic material. Physical processes such as resuspension, as well as complex transformations mediated by diverse faunal and microbial communities, define fate of this material, which can be returned to the water column, reworked within sediments or ultimately buried. In recent years, numerical models of various complexity and serving different goals have been developed and applied in order to better understand and predict dynamics of benthic processes. ERSEM includes explicit parameterisations of several groups of benthic biota, which makes it particularly applicable for studies of benthic biodiversity, biological interactions within sediments and benthic-pelagic coupling. To assess model skill in reproducing temporal (inter-annual and seasonal) dynamics of major benthic macrofaunal groups, 1D model simulation results were compared with data from the Western Channel Observatory (WCO) benthic survey. The benthic model was forced with organic matter deposition rates inferred from observed phytoplankton abundance and model parameters were subsequently recalibrated. Based on model results and WCO data comparison, deposit-feeders exert clear seasonal variability, while for suspension-feeders inter-annual variability is more pronounced. Spatial distribution of benthic fauna was investigated using results of a full-scale NEMO-ERSEM hindcast simulation of the North-West European Shelf Seas area, covering the period of 1981-2014. Results suggest close relationship between spatial distribution of biomass of benthic faunal functional groups in relation to bathymetry, hydrodynamic conditions and organic matter supply. Our work highlights that it is feasible to construct, implement and validate models that explicitly include functional groups of benthic macrofauna. Moreover, the modelling approach delivers detailed information on benthic biogeochemistry and food-web at spatial and temporal scales that are unavailable through other sources but highly relevant to marine management, planning and policy.

A parallel implementation of an off-lattice individual-based model of multicellular populations

NASA Astrophysics Data System (ADS)

Harvey, Daniel G.; Fletcher, Alexander G.; Osborne, James M.; Pitt-Francis, Joe

2015-07-01

As computational models of multicellular populations include ever more detailed descriptions of biophysical and biochemical processes, the computational cost of simulating such models limits their ability to generate novel scientific hypotheses and testable predictions. While developments in microchip technology continue to increase the power of individual processors, parallel computing offers an immediate increase in available processing power. To make full use of parallel computing technology, it is necessary to develop specialised algorithms. To this end, we present a parallel algorithm for a class of off-lattice individual-based models of multicellular populations. The algorithm divides the spatial domain between computing processes and comprises communication routines that ensure the model is correctly simulated on multiple processors. The parallel algorithm is shown to accurately reproduce the results of a deterministic simulation performed using a pre-existing serial implementation. We test the scaling of computation time, memory use and load balancing as more processes are used to simulate a cell population of fixed size. We find approximate linear scaling of both speed-up and memory consumption on up to 32 processor cores. Dynamic load balancing is shown to provide speed-up for non-regular spatial distributions of cells in the case of a growing population.

Land Use Change on Household Farms in the Ecuadorian Amazon: Design and Implementation of an Agent-Based Model

PubMed Central

Mena, Carlos F.; Walsh, Stephen J.; Frizzelle, Brian G.; Xiaozheng, Yao; Malanson, George P.

2010-01-01

This paper describes the design and implementation of an Agent-Based Model (ABM) used to simulate land use change on household farms in the Northern Ecuadorian Amazon (NEA). The ABM simulates decision-making processes at the household level that is examined through a longitudinal, socio-economic and demographic survey that was conducted in 1990 and 1999. Geographic Information Systems (GIS) are used to establish spatial relationships between farms and their environment, while classified Landsat Thematic Mapper (TM) imagery is used to set initial land use/land cover conditions for the spatial simulation, assess from-to land use/land cover change patterns, and describe trajectories of land use change at the farm and landscape levels. Results from prior studies in the NEA provide insights into the key social and ecological variables, describe human behavioral functions, and examine population-environment interactions that are linked to deforestation and agricultural extensification, population migration, and demographic change. Within the architecture of the model, agents are classified as active or passive. The model comprises four modules, i.e., initialization, demography, agriculture, and migration that operate individually, but are linked through key household processes. The main outputs of the model include a spatially-explicit representation of the land use/land cover on survey and non-survey farms and at the landscape level for each annual time-step, as well as simulated socio-economic and demographic characteristics of households and communities. The work describes the design and implementation of the model and how population-environment interactions can be addressed in a frontier setting. The paper contributes to land change science by examining important pattern-process relations, advocating a spatial modeling approach that is capable of synthesizing fundamental relationships at the farm level, and links people and environment in complex ways. PMID:24436501

Object size determines the spatial spread of visual time

PubMed Central

McGraw, Paul V.; Roach, Neil W.; Whitaker, David

2016-01-01

A key question for temporal processing research is how the nervous system extracts event duration, despite a notable lack of neural structures dedicated to duration encoding. This is in stark contrast with the orderly arrangement of neurons tasked with spatial processing. In this study, we examine the linkage between the spatial and temporal domains. We use sensory adaptation techniques to generate after-effects where perceived duration is either compressed or expanded in the opposite direction to the adapting stimulus' duration. Our results indicate that these after-effects are broadly tuned, extending over an area approximately five times the size of the stimulus. This region is directly related to the size of the adapting stimulus—the larger the adapting stimulus the greater the spatial spread of the after-effect. We construct a simple model to test predictions based on overlapping adapted versus non-adapted neuronal populations and show that our effects cannot be explained by any single, fixed-scale neural filtering. Rather, our effects are best explained by a self-scaled mechanism underpinned by duration selective neurons that also pool spatial information across earlier stages of visual processing. PMID:27466452

Comparing apples with apples: Using spatially distributed time series of monitoring data for model evaluation

NASA Astrophysics Data System (ADS)

Solazzo, E.; Galmarini, S.

2015-07-01

A more sensible use of monitoring data for the evaluation and development of regional-scale atmospheric models is proposed. The motivation stems from observing current practices in this realm where the quality of monitoring data is seldom questioned and model-to-data deviation is uniquely attributed to model deficiency. Efforts are spent to quantify the uncertainty intrinsic to the measurement process, but aspects connected to model evaluation and development have recently emerged that remain obscure, such as the spatial representativeness and the homogeneity of signals subjects of our investigation. By using time series of hourly records of ozone for a whole year (2006) collected by the European AirBase network the area of representativeness is firstly analysed showing, for similar class of stations (urban, suburban, rural), large heterogeneity and high sensitivity to the density of the network and to the noise of the signal, suggesting the mere station classification to be not a suitable candidate to help select the pool of stations used in model evaluation. Therefore a novel, more robust technique is developed based on the spatial properties of the associativity of the spectral components of the ozone time series, in an attempt to determine the level of homogeneity. The spatial structure of the associativity among stations is informative of the spatial representativeness of that specific component and automatically tells about spatial anisotropy. Time series of ozone data from North American networks have also been analysed to support the methodology. We find that the low energy components (especially the intra-day signal) suffer from a too strong influence of country-level network set-up in Europe, and different networks in North America, showing spatial heterogeneity exactly at the administrative border that separates countries in Europe and at areas separating different networks in North America. For model evaluation purposes these elements should be treated as purely stochastic and discarded, while retaining the portion of the signal useful to the evaluation process. Trans-boundary discontinuity of the intra-day signal along with cross-network grouping has been found to be predominant. Skills of fifteen regional chemical-transport modelling systems have been assessed in light of this result, finding an improved accuracy of up to 5% when the intra-day signal is removed with respect to the case where all components are analysed.

Spatial variation in mandibular bone elastic modulus and its effect on structural bending stiffness: A test case using the Taï Forest monkeys.

PubMed

Le, Kim N; Marsik, Matthew; Daegling, David J; Duque, Ana; McGraw, William Scott

2017-03-01

We investigated how heterogeneity in material stiffness affects structural stiffness in the cercopithecid mandibular cortical bone. We assessed (1) whether this effect changes the interpretation of interspecific structural stiffness variation across four primate species, (2) whether the heterogeneity is random, and (3) whether heterogeneity mitigates bending stress in the jaw associated with food processing. The sample consisted of Taï Forest, Cote d'Ivoire, monkeys: Cercocebus atys, Piliocolobus badius, Colobus polykomos, and Cercopithecus diana. Vickers indentation hardness samples estimated elastic moduli throughout the cortical bone area of each coronal section of postcanine corpus. For each section, we calculated maximum area moment of inertia, I max (structural mechanical property), under three models of material heterogeneity, as well as spatial autocorrelation statistics (Moran's I, I MORAN ). When the model considered material stiffness variation and spatial patterning, I max decreased and individual ranks based on structural stiffness changed. Rank changes were not significant across models. All specimens showed positive (nonrandom) spatial autocorrelation. Differences in I MORAN were not significant among species, and there were no discernable patterns of autocorrelation within species. Across species, significant local I MORAN was often attributed to proximity of low moduli in the alveolar process and high moduli in the basal process. While our sample did not demonstrate species differences in the degree of spatial autocorrelation of elastic moduli, there may be mechanical effects of heterogeneity (relative strength and rigidity) that do distinguish at the species or subfamilial level (i.e., colobines vs. cercopithecines). The potential connections of heterogeneity to diet and/or taxonomy remain to be discovered. © 2016 Wiley Periodicals, Inc.

EVALUATING EFFECTS OF LOW QUALITY HABITATS ON REGIONAL GROWTH IN PEOMYCUS LEUCOPUS: INSIGHTS FROM FIELD-PARAMETERIZED SPATIAL MATRIX MODELS.

EPA Science Inventory

Due to complex population dynamics and source-sink metapopulation processes, animal fitness sometimes varies across landscapes in ways that cannot be deduced from simple density patterns. In this study, we examine spatial patterns in fitness using a combination of intensive fiel...

Forest Ecosystem Analysis Using a GIS

Treesearch

S.G. McNulty; W.T. Swank

1996-01-01

Forest ecosystem studies have expanded spatially in recent years to address large scale environmental issues. We are using a geographic information system (GIS) to understand and integrate forest processes at landscape to regional spatial scales. This paper presents three diverse research studies using a GIS. First, we used a GIS to develop a landscape scale model to...

Where and Why There? Spatial Thinking with Geographic Information Systems

ERIC Educational Resources Information Center

Milson, Andrew J.; Curtis, Mary D.

2009-01-01

The authors developed and implemented a project for high school geography students that modeled the processes in a site selection analysis using Geographic Information Systems (GIS). They sought to explore how spatial thinking could be fostered by using the MyWorld GIS software that was designed specifically for educational uses. The task posed…

Remaking Memories: Reconsolidation Updates Positively Motivated Spatial Memory in Rats

ERIC Educational Resources Information Center

Jones, Bethany; Bukoski, Elizabeth; Nadel, Lynn; Fellous, Jean-Marc

2012-01-01

There is strong evidence that reactivation of a memory returns it to a labile state, initiating a restabilization process termed reconsolidation, which allows for updating of the memory. In this study we investigated reactivation-dependent updating using a new positively motivated spatial task in rodents that was designed specifically to model a…

Spatiotemporal dynamics of landscape pattern and hydrologic process in watershed systems

NASA Astrophysics Data System (ADS)

Randhir, Timothy O.; Tsvetkova, Olga

2011-06-01

SummaryLand use change is influenced by spatial and temporal factors that interact with watershed resources. Modeling these changes is critical to evaluate emerging land use patterns and to predict variation in water quantity and quality. The objective of this study is to model the nature and emergence of spatial patterns in land use and water resource impacts using a spatially explicit and dynamic landscape simulation. Temporal changes are predicted using a probabilistic Markovian process and spatial interaction through cellular automation. The MCMC (Monte Carlo Markov Chain) analysis with cellular automation is linked to hydrologic equations to simulate landscape patterns and processes. The spatiotemporal watershed dynamics (SWD) model is applied to a subwatershed in the Blackstone River watershed of Massachusetts to predict potential land use changes and expected runoff and sediment loading. Changes in watershed land use and water resources are evaluated over 100 years at a yearly time step. Results show high potential for rapid urbanization that could result in lowering of groundwater recharge and increased storm water peaks. The watershed faces potential decreases in agricultural and forest area that affect open space and pervious cover of the watershed system. Water quality deteriorated due to increased runoff which can also impact stream morphology. While overland erosion decreased, instream erosion increased from increased runoff from urban areas. Use of urban best management practices (BMPs) in sensitive locations, preventive strategies, and long-term conservation planning will be useful in sustaining the watershed system.

Development of spatial density maps based on geoprocessing web services: application to tuberculosis incidence in Barcelona, Spain.

PubMed

Dominkovics, Pau; Granell, Carlos; Pérez-Navarro, Antoni; Casals, Martí; Orcau, Angels; Caylà, Joan A

2011-11-29

Health professionals and authorities strive to cope with heterogeneous data, services, and statistical models to support decision making on public health. Sophisticated analysis and distributed processing capabilities over geocoded epidemiological data are seen as driving factors to speed up control and decision making in these health risk situations. In this context, recent Web technologies and standards-based web services deployed on geospatial information infrastructures have rapidly become an efficient way to access, share, process, and visualize geocoded health-related information. Data used on this study is based on Tuberculosis (TB) cases registered in Barcelona city during 2009. Residential addresses are geocoded and loaded into a spatial database that acts as a backend database. The web-based application architecture and geoprocessing web services are designed according to the Representational State Transfer (REST) principles. These web processing services produce spatial density maps against the backend database. The results are focused on the use of the proposed web-based application to the analysis of TB cases in Barcelona. The application produces spatial density maps to ease the monitoring and decision making process by health professionals. We also include a discussion of how spatial density maps may be useful for health practitioners in such contexts. In this paper, we developed web-based client application and a set of geoprocessing web services to support specific health-spatial requirements. Spatial density maps of TB incidence were generated to help health professionals in analysis and decision-making tasks. The combined use of geographic information tools, map viewers, and geoprocessing services leads to interesting possibilities in handling health data in a spatial manner. In particular, the use of spatial density maps has been effective to identify the most affected areas and its spatial impact. This study is an attempt to demonstrate how web processing services together with web-based mapping capabilities suit the needs of health practitioners in epidemiological analysis scenarios.

Development of spatial density maps based on geoprocessing web services: application to tuberculosis incidence in Barcelona, Spain

PubMed Central

2011-01-01

Background Health professionals and authorities strive to cope with heterogeneous data, services, and statistical models to support decision making on public health. Sophisticated analysis and distributed processing capabilities over geocoded epidemiological data are seen as driving factors to speed up control and decision making in these health risk situations. In this context, recent Web technologies and standards-based web services deployed on geospatial information infrastructures have rapidly become an efficient way to access, share, process, and visualize geocoded health-related information. Methods Data used on this study is based on Tuberculosis (TB) cases registered in Barcelona city during 2009. Residential addresses are geocoded and loaded into a spatial database that acts as a backend database. The web-based application architecture and geoprocessing web services are designed according to the Representational State Transfer (REST) principles. These web processing services produce spatial density maps against the backend database. Results The results are focused on the use of the proposed web-based application to the analysis of TB cases in Barcelona. The application produces spatial density maps to ease the monitoring and decision making process by health professionals. We also include a discussion of how spatial density maps may be useful for health practitioners in such contexts. Conclusions In this paper, we developed web-based client application and a set of geoprocessing web services to support specific health-spatial requirements. Spatial density maps of TB incidence were generated to help health professionals in analysis and decision-making tasks. The combined use of geographic information tools, map viewers, and geoprocessing services leads to interesting possibilities in handling health data in a spatial manner. In particular, the use of spatial density maps has been effective to identify the most affected areas and its spatial impact. This study is an attempt to demonstrate how web processing services together with web-based mapping capabilities suit the needs of health practitioners in epidemiological analysis scenarios. PMID:22126392

Investigating the impact of spatial priors on the performance of model-based IVUS elastography

PubMed Central

Richards, M S; Doyley, M M

2012-01-01

This paper describes methods that provide pre-requisite information for computing circumferential stress in modulus elastograms recovered from vascular tissue—information that could help cardiologists detect life-threatening plaques and predict their propensity to rupture. The modulus recovery process is an ill-posed problem; therefore additional information is needed to provide useful elastograms. In this work, prior geometrical information was used to impose hard or soft constraints on the reconstruction process. We conducted simulation and phantom studies to evaluate and compare modulus elastograms computed with soft and hard constraints versus those computed without any prior information. The results revealed that (1) the contrast-to-noise ratio of modulus elastograms achieved using the soft prior and hard prior reconstruction methods exceeded those computed without any prior information; (2) the soft prior and hard prior reconstruction methods could tolerate up to 8 % measurement noise; and (3) the performance of soft and hard prior modulus elastogram degraded when incomplete spatial priors were employed. This work demonstrates that including spatial priors in the reconstruction process should improve the performance of model-based elastography, and the soft prior approach should enhance the robustness of the reconstruction process to errors in the geometrical information. PMID:22037648

Strategy generalization across orientation tasks: testing a computational cognitive model.

PubMed

Gunzelmann, Glenn

2008-07-08

Humans use their spatial information processing abilities flexibly to facilitate problem solving and decision making in a variety of tasks. This article explores the question of whether a general strategy can be adapted for performing two different spatial orientation tasks by testing the predictions of a computational cognitive model. Human performance was measured on an orientation task requiring participants to identify the location of a target either on a map (find-on-map) or within an egocentric view of a space (find-in-scene). A general strategy instantiated in a computational cognitive model of the find-on-map task, based on the results from Gunzelmann and Anderson (2006), was adapted to perform both tasks and used to generate performance predictions for a new study. The qualitative fit of the model to the human data supports the view that participants were able to tailor a general strategy to the requirements of particular spatial tasks. The quantitative differences between the predictions of the model and the performance of human participants in the new experiment expose individual differences in sample populations. The model provides a means of accounting for those differences and a framework for understanding how human spatial abilities are applied to naturalistic spatial tasks that involve reasoning with maps. 2008 Cognitive Science Society, Inc.

Computationally efficient video restoration for Nyquist sampled imaging sensors combining an affine-motion-based temporal Kalman filter and adaptive Wiener filter.

PubMed

Rucci, Michael; Hardie, Russell C; Barnard, Kenneth J

2014-05-01

In this paper, we present a computationally efficient video restoration algorithm to address both blur and noise for a Nyquist sampled imaging system. The proposed method utilizes a temporal Kalman filter followed by a correlation-model based spatial adaptive Wiener filter (AWF). The Kalman filter employs an affine background motion model and novel process-noise variance estimate. We also propose and demonstrate a new multidelay temporal Kalman filter designed to more robustly treat local motion. The AWF is a spatial operation that performs deconvolution and adapts to the spatially varying residual noise left in the Kalman filter stage. In image areas where the temporal Kalman filter is able to provide significant noise reduction, the AWF can be aggressive in its deconvolution. In other areas, where less noise reduction is achieved with the Kalman filter, the AWF balances the deconvolution with spatial noise reduction. In this way, the Kalman filter and AWF work together effectively, but without the computational burden of full joint spatiotemporal processing. We also propose a novel hybrid system that combines a temporal Kalman filter and BM3D processing. To illustrate the efficacy of the proposed methods, we test the algorithms on both simulated imagery and video collected with a visible camera.

Role of natural analogs in performance assessment of nuclear waste repositories

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

Sagar, B.; Wittmeyer, G.W.

1995-09-01

Mathematical models of the flow of water and transport of radionuclides in porous media will be used to assess the ability of deep geologic repositories to safely contain nuclear waste. These models must, in some sense, be validated to ensure that they adequately describe the physical processes occurring within the repository and its geologic setting. Inasmuch as the spatial and temporal scales over which these models must be applied in performance assessment are very large, validation of these models against laboratory and small-scale field experiments may be considered inadequate. Natural analogs may provide validation data that are representative of physico-chemicalmore » processes that occur over spatial and temporal scales as large or larger than those relevant to repository design. The authors discuss the manner in which natural analog data may be used to increase confidence in performance assessment models and conclude that, while these data may be suitable for testing the basic laws governing flow and transport, there is insufficient control of boundary and initial conditions and forcing functions to permit quantitative validation of complex, spatially distributed flow and transport models. The authors also express their opinion that, for collecting adequate data from natural analogs, resources will have to be devoted to them that are much larger than are devoted to them at present.« less

Three-dimensional modelling of slope stability using the Local Factor of Safety concept

NASA Astrophysics Data System (ADS)

Moradi, Shirin; Huisman, Sander; Beck, Martin; Vereecken, Harry; Class, Holger

2017-04-01

Slope stability is governed by coupled hydrological and mechanical processes. The slope stability depends on the effective stress, which in turn depends on the weight of the soil and the matrix potential. Therefore, changes in water content and matrix potential associated with infiltration will affect slope stability. Most available models describing these coupled hydro-mechanical processes either rely on a one- or two-dimensional representation of hydrological and mechanical properties and processes, which obviously is a strong simplification in many applications. Therefore, the aim of this work is to develop a three-dimensional hydro-mechanical model that is able to capture the effect of spatial and temporal variability of both mechanical and hydrological parameters on slope stability. For this, we rely on DuMux, which is a free and open-source simulator for flow and transport processes in porous media that facilitates coupling of different model approaches and offers flexibility for model development. We use the Richards equation to model unsaturated water flow. The simulated water content and matrix potential distribution is used to calculate the effective stress. We only consider linear elasticity and solve for statically admissible fields of stress and displacement without invoking failure or the redistribution of post-failure stress or displacement. The Local Factor of Safety concept is used to evaluate slope stability in order to overcome some of the main limitations of commonly used methods based on limit equilibrium considerations. In a first step, we compared our model implementation with a 2D benchmark model that was implemented in COMSOL Multiphysics. In a second step, we present in-silico experiments with the newly developed 3D model to show the effect of slope morphology, spatial variability in hydraulic and mechanical material properties, and spatially variable soil depth on simulated slope stability. It is expected that this improved physically-based three-dimensional hydro-mechanical model is able to provide more reliable slope instability predictions in more complex situations.

Spatial Release From Masking in Simulated Cochlear Implant Users With and Without Access to Low-Frequency Acoustic Hearing

PubMed Central

Dietz, Mathias; Hohmann, Volker; Jürgens, Tim

2015-01-01

For normal-hearing listeners, speech intelligibility improves if speech and noise are spatially separated. While this spatial release from masking has already been quantified in normal-hearing listeners in many studies, it is less clear how spatial release from masking changes in cochlear implant listeners with and without access to low-frequency acoustic hearing. Spatial release from masking depends on differences in access to speech cues due to hearing status and hearing device. To investigate the influence of these factors on speech intelligibility, the present study measured speech reception thresholds in spatially separated speech and noise for 10 different listener types. A vocoder was used to simulate cochlear implant processing and low-frequency filtering was used to simulate residual low-frequency hearing. These forms of processing were combined to simulate cochlear implant listening, listening based on low-frequency residual hearing, and combinations thereof. Simulated cochlear implant users with additional low-frequency acoustic hearing showed better speech intelligibility in noise than simulated cochlear implant users without acoustic hearing and had access to more spatial speech cues (e.g., higher binaural squelch). Cochlear implant listener types showed higher spatial release from masking with bilateral access to low-frequency acoustic hearing than without. A binaural speech intelligibility model with normal binaural processing showed overall good agreement with measured speech reception thresholds, spatial release from masking, and spatial speech cues. This indicates that differences in speech cues available to listener types are sufficient to explain the changes of spatial release from masking across these simulated listener types. PMID:26721918

ERP correlates of anticipatory attention: spatial and non-spatial specificity and relation to subsequent selective attention.

PubMed

Dale, Corby L; Simpson, Gregory V; Foxe, John J; Luks, Tracy L; Worden, Michael S

2008-06-01

Brain-based models of visual attention hypothesize that attention-related benefits afforded to imperative stimuli occur via enhancement of neural activity associated with relevant spatial and non-spatial features. When relevant information is available in advance of a stimulus, anticipatory deployment processes are likely to facilitate allocation of attention to stimulus properties prior to its arrival. The current study recorded EEG from humans during a centrally-cued covert attention task. Cues indicated relevance of left or right visual field locations for an upcoming motion or orientation discrimination. During a 1 s delay between cue and S2, multiple attention-related events occurred at frontal, parietal and occipital electrode sites. Differences in anticipatory activity associated with the non-spatial task properties were found late in the delay, while spatially-specific modulation of activity occurred during both early and late periods and continued during S2 processing. The magnitude of anticipatory activity preceding the S2 at frontal scalp sites (and not occipital) was predictive of the magnitude of subsequent selective attention effects on the S2 event-related potentials observed at occipital electrodes. Results support the existence of multiple anticipatory attention-related processes, some with differing specificity for spatial and non-spatial task properties, and the hypothesis that levels of activity in anterior areas are important for effective control of subsequent S2 selective attention.

View-invariant object category learning, recognition, and search: how spatial and object attention are coordinated using surface-based attentional shrouds.

PubMed

Fazl, Arash; Grossberg, Stephen; Mingolla, Ennio

2009-02-01

How does the brain learn to recognize an object from multiple viewpoints while scanning a scene with eye movements? How does the brain avoid the problem of erroneously classifying parts of different objects together? How are attention and eye movements intelligently coordinated to facilitate object learning? A neural model provides a unified mechanistic explanation of how spatial and object attention work together to search a scene and learn what is in it. The ARTSCAN model predicts how an object's surface representation generates a form-fitting distribution of spatial attention, or "attentional shroud". All surface representations dynamically compete for spatial attention to form a shroud. The winning shroud persists during active scanning of the object. The shroud maintains sustained activity of an emerging view-invariant category representation while multiple view-specific category representations are learned and are linked through associative learning to the view-invariant object category. The shroud also helps to restrict scanning eye movements to salient features on the attended object. Object attention plays a role in controlling and stabilizing the learning of view-specific object categories. Spatial attention hereby coordinates the deployment of object attention during object category learning. Shroud collapse releases a reset signal that inhibits the active view-invariant category in the What cortical processing stream. Then a new shroud, corresponding to a different object, forms in the Where cortical processing stream, and search using attention shifts and eye movements continues to learn new objects throughout a scene. The model mechanistically clarifies basic properties of attention shifts (engage, move, disengage) and inhibition of return. It simulates human reaction time data about object-based spatial attention shifts, and learns with 98.1% accuracy and a compression of 430 on a letter database whose letters vary in size, position, and orientation. The model provides a powerful framework for unifying many data about spatial and object attention, and their interactions during perception, cognition, and action.

Improving data management and dissemination in web based information systems by semantic enrichment of descriptive data aspects

NASA Astrophysics Data System (ADS)

Gebhardt, Steffen; Wehrmann, Thilo; Klinger, Verena; Schettler, Ingo; Huth, Juliane; Künzer, Claudia; Dech, Stefan

2010-10-01

The German-Vietnamese water-related information system for the Mekong Delta (WISDOM) project supports business processes in Integrated Water Resources Management in Vietnam. Multiple disciplines bring together earth and ground based observation themes, such as environmental monitoring, water management, demographics, economy, information technology, and infrastructural systems. This paper introduces the components of the web-based WISDOM system including data, logic and presentation tier. It focuses on the data models upon which the database management system is built, including techniques for tagging or linking metadata with the stored information. The model also uses ordered groupings of spatial, thematic and temporal reference objects to semantically tag datasets to enable fast data retrieval, such as finding all data in a specific administrative unit belonging to a specific theme. A spatial database extension is employed by the PostgreSQL database. This object-oriented database was chosen over a relational database to tag spatial objects to tabular data, improving the retrieval of census and observational data at regional, provincial, and local areas. While the spatial database hinders processing raster data, a "work-around" was built into WISDOM to permit efficient management of both raster and vector data. The data model also incorporates styling aspects of the spatial datasets through styled layer descriptions (SLD) and web mapping service (WMS) layer specifications, allowing retrieval of rendered maps. Metadata elements of the spatial data are based on the ISO19115 standard. XML structured information of the SLD and metadata are stored in an XML database. The data models and the data management system are robust for managing the large quantity of spatial objects, sensor observations, census and document data. The operational WISDOM information system prototype contains modules for data management, automatic data integration, and web services for data retrieval, analysis, and distribution. The graphical user interfaces facilitate metadata cataloguing, data warehousing, web sensor data analysis and thematic mapping.

Agent-based modeling of malaria vectors: the importance of spatial simulation.

PubMed

Bomblies, Arne

2014-07-03

The modeling of malaria vector mosquito populations yields great insight into drivers of malaria transmission at the village scale. Simulation of individual mosquitoes as "agents" in a distributed, dynamic model domain may be greatly beneficial for simulation of spatial relationships of vectors and hosts. In this study, an agent-based model is used to simulate the life cycle and movement of individual malaria vector mosquitoes in a Niger Sahel village, with individual simulated mosquitoes interacting with their physical environment as well as humans. Various processes that are known to be epidemiologically important, such as the dependence of parity on flight distance between developmental habitat and blood meal hosts and therefore spatial relationships of pools and houses, are readily simulated using this modeling paradigm. Impacts of perturbations can be evaluated on the basis of vectorial capacity, because the interactions between individuals that make up the population- scale metric vectorial capacity can be easily tracked for simulated mosquitoes and human blood meal hosts, without the need to estimate vectorial capacity parameters. As expected, model results show pronounced impacts of pool source reduction from larvicide application and draining, but with varying degrees of impact depending on the spatial relationship between pools and human habitation. Results highlight the importance of spatially-explicit simulation that can model individuals such as in an agent-based model. The impacts of perturbations on village scale malaria transmission depend on spatial locations of individual mosquitoes, as well as the tracking of relevant life cycle events and characteristics of individual mosquitoes. This study demonstrates advantages of using an agent-based approach for village-scale mosquito simulation to address questions in which spatial relationships are known to be important.

Forecasting the effects of land-use and climate change on wildlife communities and habitats in the lower Mississippi Valley

USGS Publications Warehouse

Faulkner, Stephen P.

2010-01-01

Landscape patterns and processes reflect both natural ecosystem attributes and the policy and management decisions of individual Federal, State, county, and private organizations. Land-use regulation, water management, and habitat conservation and restoration efforts increasingly rely on landscape-level approaches that incorporate scientific information into the decision-making process. Since management actions are implemented to affect future conditions, decision-support models are necessary to forecast potential future conditions resulting from these decisions. Spatially explicit modeling approaches enable testing of different scenarios and help evaluate potential outcomes of management actions in conjunction with natural processes such as climate change. The ability to forecast the effects of changing land use and climate is critically important to land and resource managers since their work is inherently site specific, yet conservation strategies and practices are expressed at higher spatial and temporal scales that must be considered in the decisionmaking process.

Validating a spatially distributed hydrological model with soil morphology data

NASA Astrophysics Data System (ADS)

Doppler, T.; Honti, M.; Zihlmann, U.; Weisskopf, P.; Stamm, C.

2014-09-01

Spatially distributed models are popular tools in hydrology claimed to be useful to support management decisions. Despite the high spatial resolution of the computed variables, calibration and validation is often carried out only on discharge time series at specific locations due to the lack of spatially distributed reference data. Because of this restriction, the predictive power of these models, with regard to predicted spatial patterns, can usually not be judged. An example of spatial predictions in hydrology is the prediction of saturated areas in agricultural catchments. These areas can be important source areas for inputs of agrochemicals to the stream. We set up a spatially distributed model to predict saturated areas in a 1.2 km2 catchment in Switzerland with moderate topography and artificial drainage. We translated soil morphological data available from soil maps into an estimate of the duration of soil saturation in the soil horizons. This resulted in a data set with high spatial coverage on which the model predictions were validated. In general, these saturation estimates corresponded well to the measured groundwater levels. We worked with a model that would be applicable for management decisions because of its fast calculation speed and rather low data requirements. We simultaneously calibrated the model to observed groundwater levels and discharge. The model was able to reproduce the general hydrological behavior of the catchment in terms of discharge and absolute groundwater levels. However, the the groundwater level predictions were not accurate enough to be used for the prediction of saturated areas. Groundwater level dynamics were not adequately reproduced and the predicted spatial saturation patterns did not correspond to those estimated from the soil map. Our results indicate that an accurate prediction of the groundwater level dynamics of the shallow groundwater in our catchment that is subject to artificial drainage would require a model that better represents processes at the boundary between the unsaturated and the saturated zone. However, data needed for such a more detailed model are not generally available. This severely hampers the practical use of such models despite their usefulness for scientific purposes.

Spatial interpolation schemes of daily precipitation for hydrologic modeling

USGS Publications Warehouse

Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.

2012-01-01

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

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

Kirtley, John R., E-mail: jkirtley@stanford.edu; Rosenberg, Aaron J.; Palmstrom, Johanna C.

Superconducting QUantum Interference Device (SQUID) microscopy has excellent magnetic field sensitivity, but suffers from modest spatial resolution when compared with other scanning probes. This spatial resolution is determined by both the size of the field sensitive area and the spacing between this area and the sample surface. In this paper we describe scanning SQUID susceptometers that achieve sub-micron spatial resolution while retaining a white noise floor flux sensitivity of ≈2μΦ{sub 0}/Hz{sup 1/2}. This high spatial resolution is accomplished by deep sub-micron feature sizes, well shielded pickup loops fabricated using a planarized process, and a deep etch step that minimizes themore » spacing between the sample surface and the SQUID pickup loop. We describe the design, modeling, fabrication, and testing of these sensors. Although sub-micron spatial resolution has been achieved previously in scanning SQUID sensors, our sensors not only achieve high spatial resolution but also have integrated modulation coils for flux feedback, integrated field coils for susceptibility measurements, and batch processing. They are therefore a generally applicable tool for imaging sample magnetization, currents, and susceptibilities with higher spatial resolution than previous susceptometers.« less

Spatial sensitivity of inorganic carbon to model setup: North Sea and Baltic Sea with ECOSMO

NASA Astrophysics Data System (ADS)

Castano Primo, Rocio; Schrum, Corinna; Daewel, Ute

2015-04-01

In ocean biogeochemical models it is critical to capture the key processes adequately so they do not only reproduce the observations but that those processes are reproduced correctly. One key issue is the choice of parameters, which in most cases are estimates with large uncertainties. This can be the product of actual lack of detailed knowledge of the process, or the manner the processes are implemented, more or less complex. In addition, the model sensitivity is not necessarily homogenous across the spatial domain modelled, which adds another layer of complexity to biogeochemical modelling. In the particular case of the inorganic carbon cycle, there are several sets of carbonate constants that can be chosen. The calculated air-sea CO2 flux is largely dependent on the parametrization chosen. In addition, the different parametrizations all the underlying processes that in some way impact the carbon cycle beyond the carbonate dissociation and fluxes give results that can be significantly different. Examples of these processes are phytoplankton growth rates or remineralization rates. Despite their geographical proximity, the North and Baltic Seas exhibit very different dynamics. The North Sea receives important inflows of Atlantic waters, while the Baltic Sea is an almost enclosed system, with very little exchange from the North Sea. Wind, tides, and freshwater supply act very differently, but dominantly structure the ecosystem dynamics on spatial and temporal scales. The biological community is also different. Cyanobacteria, which are important due to their ability to fix atmospheric nitrogen, and they are only present in the Baltic Sea. These differentiating features have a strong impact in the biogeochemical cycles and ultimately shape the variations in the carbonate chemistry. Here the ECOSMO model was employed on the North Sea and Baltic Sea. The model is set so both are modelled at the same time, instead of having them run separately. ECOSMO is a 3-D coupled physical-biogeochemical model, which resolves the cycles of nitrogen, phosphorus and silicate. It includes 3 functional groups of phytoplankton and 2 groups of zooplankton. In addition, an inorganic carbon module has been incorporated and coupled. Alkalinity and DIC are chosen as prognostic variables, from which pH, pCO2 and air-sea CO2 flux are calculated. The model is run with different sets of carbonate dissociation parameters, air-sea flux parametrizations, phytoplankton growth and remineralization rates. The sensitivity of the inorganic carbon variables will be assessed, both in the whole model domain and the North and Baltic Sea independently. We search for the critical parameters that have a larger impact, whether such impact is spatially dependent and the effect on the validation of the carbonate module.

OpenMP parallelization of a gridded SWAT (SWATG)

NASA Astrophysics Data System (ADS)

Zhang, Ying; Hou, Jinliang; Cao, Yongpan; Gu, Juan; Huang, Chunlin

2017-12-01

Large-scale, long-term and high spatial resolution simulation is a common issue in environmental modeling. A Gridded Hydrologic Response Unit (HRU)-based Soil and Water Assessment Tool (SWATG) that integrates grid modeling scheme with different spatial representations also presents such problems. The time-consuming problem affects applications of very high resolution large-scale watershed modeling. The OpenMP (Open Multi-Processing) parallel application interface is integrated with SWATG (called SWATGP) to accelerate grid modeling based on the HRU level. Such parallel implementation takes better advantage of the computational power of a shared memory computer system. We conducted two experiments at multiple temporal and spatial scales of hydrological modeling using SWATG and SWATGP on a high-end server. At 500-m resolution, SWATGP was found to be up to nine times faster than SWATG in modeling over a roughly 2000 km2 watershed with 1 CPU and a 15 thread configuration. The study results demonstrate that parallel models save considerable time relative to traditional sequential simulation runs. Parallel computations of environmental models are beneficial for model applications, especially at large spatial and temporal scales and at high resolutions. The proposed SWATGP model is thus a promising tool for large-scale and high-resolution water resources research and management in addition to offering data fusion and model coupling ability.

Speciation in the Derrida-Higgs model with finite genomes and spatial populations

NASA Astrophysics Data System (ADS)

de Aguiar, Marcus A. M.

2017-02-01

The speciation model proposed by Derrida and Higgs demonstrated that a sexually reproducing population can split into different species in the absence of natural selection or any type of geographic isolation, provided that mating is assortative and the number of genes involved in the process is infinite. Here we revisit this model and simulate it for finite genomes, focusing on the question of how many genes it actually takes to trigger neutral sympatric speciation. We find that, for typical parameters used in the original model, it takes the order of 105 genes. We compare the results with a similar spatially explicit model where about 100 genes suffice for speciation. We show that when the number of genes is small the species that emerge are strongly segregated in space. For a larger number of genes, on the other hand, the spatial structure of the population is less important and the species distribution overlap considerably.

The biogeodynamics of microbial landscapes

NASA Astrophysics Data System (ADS)

Battin, T. J.; Hödl, I.; Bertuzzo, E.; Mari, L.; Suweis, S. S.; Rinaldo, A.

2011-12-01

Spatial configuration is fundamental in defining the structural and functional properties of biological systems. Biofilms, surface-attached and matrix-enclosed microorganisms, are a striking example of spatial organisation. Coupled biotic and abiotic processes shape the spatial organisation across scales of the landscapes formed by these benthic biofilms in streams and rivers. Experimenting with such biofilms in streams, we found that, depending on the streambed topography and the related hydrodynamic microenvironment, biofilm landscapes form increasingly diverging spatial patterns as they grow. Strikingly, however, cluster size distributions tend to converge even in contrasting hydrodynamic microenvironments. To reproduce the observed cluster size distributions we used a continuous, size-structured population model. The model accounts for the formation, growth, erosion and merging of biofilm clusters. Our results suggest not only that hydrodynamic forcing induce the diverging patterning of the microbial landscape, but also that microorganisms have developed strategies to equally exploit spatial resources independently of the physical structure of the microenvironment where they live.

Quantitative modeling of soil genesis processes

NASA Technical Reports Server (NTRS)

Levine, E. R.; Knox, R. G.; Kerber, A. G.

1992-01-01

For fine spatial scale simulation, a model is being developed to predict changes in properties over short-, meso-, and long-term time scales within horizons of a given soil profile. Processes that control these changes can be grouped into five major process clusters: (1) abiotic chemical reactions; (2) activities of organisms; (3) energy balance and water phase transitions; (4) hydrologic flows; and (5) particle redistribution. Landscape modeling of soil development is possible using digitized soil maps associated with quantitative soil attribute data in a geographic information system (GIS) framework to which simulation models are applied.

Mesocell study area snow distributions for the Cold Land Processes Experiment (CLPX)

Treesearch

Glen E. Liston; Christopher A. Hiemstra; Kelly Elder; Donald W. Cline

2008-01-01

The Cold Land Processes Experiment (CLPX) had a goal of describing snow-related features over a wide range of spatial and temporal scales. This required linking disparate snow tools and datasets into one coherent, integrated package. Simulating realistic high-resolution snow distributions and features requires a snow-evolution modeling system (SnowModel) that can...

Parallel processing of general and specific threat during early stages of perception

PubMed Central

2016-01-01

Differential processing of threat can consummate as early as 100 ms post-stimulus. Moreover, early perception not only differentiates threat from non-threat stimuli but also distinguishes among discrete threat subtypes (e.g. fear, disgust and anger). Combining spatial-frequency-filtered images of fear, disgust and neutral scenes with high-density event-related potentials and intracranial source estimation, we investigated the neural underpinnings of general and specific threat processing in early stages of perception. Conveyed in low spatial frequencies, fear and disgust images evoked convergent visual responses with similarly enhanced N1 potentials and dorsal visual (middle temporal gyrus) cortical activity (relative to neutral cues; peaking at 156 ms). Nevertheless, conveyed in high spatial frequencies, fear and disgust elicited divergent visual responses, with fear enhancing and disgust suppressing P1 potentials and ventral visual (occipital fusiform) cortical activity (peaking at 121 ms). Therefore, general and specific threat processing operates in parallel in early perception, with the ventral visual pathway engaged in specific processing of discrete threats and the dorsal visual pathway in general threat processing. Furthermore, selectively tuned to distinctive spatial-frequency channels and visual pathways, these parallel processes underpin dimensional and categorical threat characterization, promoting efficient threat response. These findings thus lend support to hybrid models of emotion. PMID:26412811

A study on spatial decision support systems for HIV/AIDS prevention based on COM GIS technology

NASA Astrophysics Data System (ADS)

Yang, Kun; Luo, Huasong; Peng, Shungyun; Xu, Quanli

2007-06-01

Based on the deeply analysis of the current status and the existing problems of GIS technology applications in Epidemiology, this paper has proposed the method and process for establishing the spatial decision support systems of AIDS epidemic prevention by integrating the COM GIS, Spatial Database, GPS, Remote Sensing, and Communication technologies, as well as ASP and ActiveX software development technologies. One of the most important issues for constructing the spatial decision support systems of AIDS epidemic prevention is how to integrate the AIDS spreading models with GIS. The capabilities of GIS applications in the AIDS epidemic prevention have been described here in this paper firstly. Then some mature epidemic spreading models have also been discussed for extracting the computation parameters. Furthermore, a technical schema has been proposed for integrating the AIDS spreading models with GIS and relevant geospatial technologies, in which the GIS and model running platforms share a common spatial database and the computing results can be spatially visualized on Desktop or Web GIS clients. Finally, a complete solution for establishing the decision support systems of AIDS epidemic prevention has been offered in this paper based on the model integrating methods and ESRI COM GIS software packages. The general decision support systems are composed of data acquisition sub-systems, network communication sub-systems, model integrating sub-systems, AIDS epidemic information spatial database sub-systems, AIDS epidemic information querying and statistical analysis sub-systems, AIDS epidemic dynamic surveillance sub-systems, AIDS epidemic information spatial analysis and decision support sub-systems, as well as AIDS epidemic information publishing sub-systems based on Web GIS.

Implementation of marine spatial planning in shellfish aquaculture management: modeling studies in a Norwegian fjord.

PubMed

Filgueira, Ramon; Grant, Jon; Strand, Øivind

2014-06-01

Shellfish carrying capacity is determined by the interaction of a cultured species with its ecosystem, which is strongly influenced by hydrodynamics. Water circulation controls the exchange of matter between farms and the adjacent areas, which in turn establishes the nutrient supply that supports phytoplankton populations. The complexity of water circulation makes necessary the use of hydrodynamic models with detailed spatial resolution in carrying capacity estimations. This detailed spatial resolution also allows for the study of processes that depend on specific spatial arrangements, e.g., the most suitable location to place farms, which is crucial for marine spatial planning, and consequently for decision support systems. In the present study, a fully spatial physical-biogeochemical model has been combined with scenario building and optimization techniques as a proof of concept of the use of ecosystem modeling as an objective tool to inform marine spatial planning. The object of this exercise was to generate objective knowledge based on an ecosystem approach to establish new mussel aquaculture areas in a Norwegian fjord. Scenario building was used to determine the best location of a pump that can be used to bring nutrient-rich deep waters to the euphotic layer, increasing primary production, and consequently, carrying capacity for mussel cultivation. In addition, an optimization tool, parameter estimation (PEST), was applied to the optimal location and mussel standing stock biomass that maximize production, according to a preestablished carrying capacity criterion. Optimization tools allow us to make rational and transparent decisions to solve a well-defined question, decisions that are essential for policy makers. The outcomes of combining ecosystem models with scenario building and optimization facilitate planning based on an ecosystem approach, highlighting the capabilities of ecosystem modeling as a tool for marine spatial planning.

The Analytical Limits of Modeling Short Diffusion Timescales

NASA Astrophysics Data System (ADS)

Bradshaw, R. W.; Kent, A. J.

2016-12-01

Chemical and isotopic zoning in minerals is widely used to constrain the timescales of magmatic processes such as magma mixing and crystal residence, etc. via diffusion modeling. Forward modeling of diffusion relies on fitting diffusion profiles to measured compositional gradients. However, an individual measurement is essentially an average composition for a segment of the gradient defined by the spatial resolution of the analysis. Thus there is the potential for the analytical spatial resolution to limit the timescales that can be determined for an element of given diffusivity, particularly where the scale of the gradient approaches that of the measurement. Here we use a probabilistic modeling approach to investigate the effect of analytical spatial resolution on estimated timescales from diffusion modeling. Our method investigates how accurately the age of a synthetic diffusion profile can be obtained by modeling an "unknown" profile derived from discrete sampling of the synthetic compositional gradient at a given spatial resolution. We also include the effects of analytical uncertainty and the position of measurements relative to the diffusion gradient. We apply this method to the spatial resolutions of common microanalytical techniques (LA-ICP-MS, SIMS, EMP, NanoSIMS). Our results confirm that for a given diffusivity, higher spatial resolution gives access to shorter timescales, and that each analytical spacing has a minimum timescale, below which it overestimates the timescale. For example, for Ba diffusion in plagioclase at 750 °C timescales are accurate (within 20%) above 10, 100, 2,600, and 71,000 years at 0.3, 1, 5, and 25 mm spatial resolution, respectively. For Sr diffusion in plagioclase at 750 °C, timescales are accurate above 0.02, 0.2, 4, and 120 years at the same spatial resolutions. Our results highlight the importance of selecting appropriate analytical techniques to estimate accurate diffusion-based timescales.

Sensitivity of snowpack storage to precipitation and temperature using spatial and temporal analog models

NASA Astrophysics Data System (ADS)

Luce, Charles H.; Lopez-Burgos, Viviana; Holden, Zachary

2014-12-01

Empirical sensitivity analyses are important for evaluation of the effects of a changing climate on water resources and ecosystems. Although mechanistic models are commonly applied for evaluation of climate effects for snowmelt, empirical relationships provide a first-order validation of the various postulates required for their implementation. Previous studies of empirical sensitivity for April 1 snow water equivalent (SWE) in the western United States were developed by regressing interannual variations in SWE to winter precipitation and temperature. This offers a temporal analog for climate change, positing that a warmer future looks like warmer years. Spatial analogs are used to hypothesize that a warmer future may look like warmer places, and are frequently applied alternatives for complex processes, or states/metrics that show little interannual variability (e.g., forest cover). We contrast spatial and temporal analogs for sensitivity of April 1 SWE and the mean residence time of snow (SRT) using data from 524 Snowpack Telemetry (SNOTEL) stations across the western U.S. We built relatively strong models using spatial analogs to relate temperature and precipitation climatology to snowpack climatology (April 1 SWE, R2=0.87, and SRT, R2=0.81). Although the poorest temporal analog relationships were in areas showing the highest sensitivity to warming, spatial analog models showed consistent performance throughout the range of temperature and precipitation. Generally, slopes from the spatial relationships showed greater thermal sensitivity than the temporal analogs, and high elevation stations showed greater vulnerability using a spatial analog than shown in previous modeling and sensitivity studies. The spatial analog models provide a simple perspective to evaluate potential futures and may be useful in further evaluation of snowpack with warming.

Methods used to parameterize the spatially-explicit components of a state-and-transition simulation model

USGS Publications Warehouse

Sleeter, Rachel; Acevedo, William; Soulard, Christopher E.; Sleeter, Benjamin M.

2015-01-01

Spatially-explicit state-and-transition simulation models of land use and land cover (LULC) increase our ability to assess regional landscape characteristics and associated carbon dynamics across multiple scenarios. By characterizing appropriate spatial attributes such as forest age and land-use distribution, a state-and-transition model can more effectively simulate the pattern and spread of LULC changes. This manuscript describes the methods and input parameters of the Land Use and Carbon Scenario Simulator (LUCAS), a customized state-and-transition simulation model utilized to assess the relative impacts of LULC on carbon stocks for the conterminous U.S. The methods and input parameters are spatially explicit and describe initial conditions (strata, state classes and forest age), spatial multipliers, and carbon stock density. Initial conditions were derived from harmonization of multi-temporal data characterizing changes in land use as well as land cover. Harmonization combines numerous national-level datasets through a cell-based data fusion process to generate maps of primary LULC categories. Forest age was parameterized using data from the North American Carbon Program and spatially-explicit maps showing the locations of past disturbances (i.e. wildfire and harvest). Spatial multipliers were developed to spatially constrain the location of future LULC transitions. Based on distance-decay theory, maps were generated to guide the placement of changes related to forest harvest, agricultural intensification/extensification, and urbanization. We analyze the spatially-explicit input parameters with a sensitivity analysis, by showing how LUCAS responds to variations in the model input. This manuscript uses Mediterranean California as a regional subset to highlight local to regional aspects of land change, which demonstrates the utility of LUCAS at many scales and applications.

Mapping Thermal Habitat of Ectotherms Based on Behavioral Thermoregulation in a Controlled Thermal Environment

NASA Astrophysics Data System (ADS)

Fei, T.; Skidmore, A.; Liu, Y.

2012-07-01

Thermal environment is especially important to ectotherm because a lot of physiological functions rely on the body temperature such as thermoregulation. The so-called behavioural thermoregulation function made use of the heterogeneity of the thermal properties within an individual's habitat to sustain the animal's physiological processes. This function links the spatial utilization and distribution of individual ectotherm with the thermal properties of habitat (thermal habitat). In this study we modelled the relationship between the two by a spatial explicit model that simulates the movements of a lizard in a controlled environment. The model incorporates a lizard's transient body temperatures with a cellular automaton algorithm as a way to link the physiology knowledge of the animal with the spatial utilization of its microhabitat. On a larger spatial scale, 'thermal roughness' of the habitat was defined and used to predict the habitat occupancy of the target species. The results showed the habitat occupancy can be modelled by the cellular automaton based algorithm at a smaller scale, and can be modelled by the thermal roughness index at a larger scale.

Scalable Topic Modeling: Online Learning, Diagnostics, and Recommendation

DTIC Science & Technology

2017-03-01

Chinese restaurant processes. Journal of Machine Learning Research, 12:2461–2488, 2011. 15. L. Hannah, D. Blei and W. Powell. Dirichlet process mixtures of...34. S. Ghosh, A. Ungureanu, E. Sudderth, and D. Blei. A Spatial distance dependent Chinese restaurant process for image segmentation. In Neural

Exploring new topography-based subgrid spatial structures for improving land surface modeling

DOE PAGES

Tesfa, Teklu K.; Leung, Lai-Yung Ruby

2017-02-22

Topography plays an important role in land surface processes through its influence on atmospheric forcing, soil and vegetation properties, and river network topology and drainage area. Land surface models with a spatial structure that captures spatial heterogeneity, which is directly affected by topography, may improve the representation of land surface processes. Previous studies found that land surface modeling, using subbasins instead of structured grids as computational units, improves the scalability of simulated runoff and streamflow processes. In this study, new land surface spatial structures are explored by further dividing subbasins into subgrid structures based on topographic properties, including surface elevation,more » slope and aspect. Two methods (local and global) of watershed discretization are applied to derive two types of subgrid structures (geo-located and non-geo-located) over the topographically diverse Columbia River basin in the northwestern United States. In the global method, a fixed elevation classification scheme is used to discretize subbasins. The local method utilizes concepts of hypsometric analysis to discretize each subbasin, using different elevation ranges that also naturally account for slope variations. The relative merits of the two methods and subgrid structures are investigated for their ability to capture topographic heterogeneity and the implications of this on representations of atmospheric forcing and land cover spatial patterns. Results showed that the local method reduces the standard deviation (SD) of subgrid surface elevation in the study domain by 17 to 19 % compared to the global method, highlighting the relative advantages of the local method for capturing subgrid topographic variations. The comparison between the two types of subgrid structures showed that the non-geo-located subgrid structures are more consistent across different area threshold values than the geo-located subgrid structures. Altogether the local method and non-geo-located subgrid structures effectively and robustly capture topographic, climatic and vegetation variability, which is important for land surface modeling.« less

Exploring new topography-based subgrid spatial structures for improving land surface modeling

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

Tesfa, Teklu K.; Leung, Lai-Yung Ruby

Topography plays an important role in land surface processes through its influence on atmospheric forcing, soil and vegetation properties, and river network topology and drainage area. Land surface models with a spatial structure that captures spatial heterogeneity, which is directly affected by topography, may improve the representation of land surface processes. Previous studies found that land surface modeling, using subbasins instead of structured grids as computational units, improves the scalability of simulated runoff and streamflow processes. In this study, new land surface spatial structures are explored by further dividing subbasins into subgrid structures based on topographic properties, including surface elevation,more » slope and aspect. Two methods (local and global) of watershed discretization are applied to derive two types of subgrid structures (geo-located and non-geo-located) over the topographically diverse Columbia River basin in the northwestern United States. In the global method, a fixed elevation classification scheme is used to discretize subbasins. The local method utilizes concepts of hypsometric analysis to discretize each subbasin, using different elevation ranges that also naturally account for slope variations. The relative merits of the two methods and subgrid structures are investigated for their ability to capture topographic heterogeneity and the implications of this on representations of atmospheric forcing and land cover spatial patterns. Results showed that the local method reduces the standard deviation (SD) of subgrid surface elevation in the study domain by 17 to 19 % compared to the global method, highlighting the relative advantages of the local method for capturing subgrid topographic variations. The comparison between the two types of subgrid structures showed that the non-geo-located subgrid structures are more consistent across different area threshold values than the geo-located subgrid structures. Altogether the local method and non-geo-located subgrid structures effectively and robustly capture topographic, climatic and vegetation variability, which is important for land surface modeling.« less

Optimization and universality of Brownian search in a basic model of quenched heterogeneous media

NASA Astrophysics Data System (ADS)

Godec, Aljaž; Metzler, Ralf

2015-05-01

The kinetics of a variety of transport-controlled processes can be reduced to the problem of determining the mean time needed to arrive at a given location for the first time, the so-called mean first-passage time (MFPT) problem. The occurrence of occasional large jumps or intermittent patterns combining various types of motion are known to outperform the standard random walk with respect to the MFPT, by reducing oversampling of space. Here we show that a regular but spatially heterogeneous random walk can significantly and universally enhance the search in any spatial dimension. In a generic minimal model we consider a spherically symmetric system comprising two concentric regions with piecewise constant diffusivity. The MFPT is analyzed under the constraint of conserved average dynamics, that is, the spatially averaged diffusivity is kept constant. Our analytical calculations and extensive numerical simulations demonstrate the existence of an optimal heterogeneity minimizing the MFPT to the target. We prove that the MFPT for a random walk is completely dominated by what we term direct trajectories towards the target and reveal a remarkable universality of the spatially heterogeneous search with respect to target size and system dimensionality. In contrast to intermittent strategies, which are most profitable in low spatial dimensions, the spatially inhomogeneous search performs best in higher dimensions. Discussing our results alongside recent experiments on single-particle tracking in living cells, we argue that the observed spatial heterogeneity may be beneficial for cellular signaling processes.

Estimation of Fractional Plant Lifeform Cover Using Landsat and Airborne LiDAR/hyperspectral Data

NASA Astrophysics Data System (ADS)

Parra, A. S.; Xu, Q.; Dilts, T.; Weisberg, P.; Greenberg, J. A.

2017-12-01

Land-cover change has generally been understood as the result of local, landscape or regional-scale processes with most studies focusing on case-study landscapes or smaller regions. However, as we observe similar types of land-cover change occurring across different biomes worldwide, it becomes clear that global-scale processes such as climate change and CO2 fertilization, in interaction with local influences, are underlying drivers in land-cover change patterns. Prior studies on global land-cover change may not have had a suitable spatial, temporal and thematic resolution for allowing the identification of such patterns. Furthermore, the lack of globally consistent spatial data products also constitutes a limiting factor in evaluating both proximate and ultimate causes of land-cover change. In this study, we derived a global model for broadleaf tree, needleleaf tree, shrub, herbaceous, and "other" fractional cover using Landsat imagery. Combined LiDAR/hyperspectral data sets were used for calibration and validation of the Landsat-derived products. Spatially explicit uncertainties were also created as part of the data products. Our results highlight the potential for large-scale studies that model local and global influences on land-cover transition types and rates at fine thematic, spatial, and temporal resolutions. These spatial data products are relevant for identifying patterns in land-cover change due to underlying global-scale processes and can provide valuable insights into climatic and land-use factors determining vegetation distributions.

Marked point process for modelling seismic activity (case study in Sumatra and Java)

NASA Astrophysics Data System (ADS)

Pratiwi, Hasih; Sulistya Rini, Lia; Wayan Mangku, I.

2018-05-01

Earthquake is a natural phenomenon that is random, irregular in space and time. Until now the forecast of earthquake occurrence at a location is still difficult to be estimated so that the development of earthquake forecast methodology is still carried out both from seismology aspect and stochastic aspect. To explain the random nature phenomena, both in space and time, a point process approach can be used. There are two types of point processes: temporal point process and spatial point process. The temporal point process relates to events observed over time as a sequence of time, whereas the spatial point process describes the location of objects in two or three dimensional spaces. The points on the point process can be labelled with additional information called marks. A marked point process can be considered as a pair (x, m) where x is the point of location and m is the mark attached to the point of that location. This study aims to model marked point process indexed by time on earthquake data in Sumatra Island and Java Island. This model can be used to analyse seismic activity through its intensity function by considering the history process up to time before t. Based on data obtained from U.S. Geological Survey from 1973 to 2017 with magnitude threshold 5, we obtained maximum likelihood estimate for parameters of the intensity function. The estimation of model parameters shows that the seismic activity in Sumatra Island is greater than Java Island.

Stochastic Downscaling of Digital Elevation Models

NASA Astrophysics Data System (ADS)

Rasera, Luiz Gustavo; Mariethoz, Gregoire; Lane, Stuart N.

2016-04-01

High-resolution digital elevation models (HR-DEMs) are extremely important for the understanding of small-scale geomorphic processes in Alpine environments. In the last decade, remote sensing techniques have experienced a major technological evolution, enabling fast and precise acquisition of HR-DEMs. However, sensors designed to measure elevation data still feature different spatial resolution and coverage capabilities. Terrestrial altimetry allows the acquisition of HR-DEMs with centimeter to millimeter-level precision, but only within small spatial extents and often with dead ground problems. Conversely, satellite radiometric sensors are able to gather elevation measurements over large areas but with limited spatial resolution. In the present study, we propose an algorithm to downscale low-resolution satellite-based DEMs using topographic patterns extracted from HR-DEMs derived for example from ground-based and airborne altimetry. The method consists of a multiple-point geostatistical simulation technique able to generate high-resolution elevation data from low-resolution digital elevation models (LR-DEMs). Initially, two collocated DEMs with different spatial resolutions serve as an input to construct a database of topographic patterns, which is also used to infer the statistical relationships between the two scales. High-resolution elevation patterns are then retrieved from the database to downscale a LR-DEM through a stochastic simulation process. The output of the simulations are multiple equally probable DEMs with higher spatial resolution that also depict the large-scale geomorphic structures present in the original LR-DEM. As these multiple models reflect the uncertainty related to the downscaling, they can be employed to quantify the uncertainty of phenomena that are dependent on fine topography, such as catchment hydrological processes. The proposed methodology is illustrated for a case study in the Swiss Alps. A swissALTI3D HR-DEM (with 5 m resolution) and a SRTM-derived LR-DEM from the Western Alps are used to downscale a SRTM-based LR-DEM from the eastern part of the Alps. The results show that the method is capable of generating multiple high-resolution synthetic DEMs that reproduce the spatial structure and statistics of the original DEM.

Better models are more effectively connected models

NASA Astrophysics Data System (ADS)

Nunes, João Pedro; Bielders, Charles; Darboux, Frederic; Fiener, Peter; Finger, David; Turnbull-Lloyd, Laura; Wainwright, John

2016-04-01

The concept of hydrologic and geomorphologic connectivity describes the processes and pathways which link sources (e.g. rainfall, snow and ice melt, springs, eroded areas and barren lands) to accumulation areas (e.g. foot slopes, streams, aquifers, reservoirs), and the spatial variations thereof. There are many examples of hydrological and sediment connectivity on a watershed scale; in consequence, a process-based understanding of connectivity is crucial to help managers understand their systems and adopt adequate measures for flood prevention, pollution mitigation and soil protection, among others. Modelling is often used as a tool to understand and predict fluxes within a catchment by complementing observations with model results. Catchment models should therefore be able to reproduce the linkages, and thus the connectivity of water and sediment fluxes within the systems under simulation. In modelling, a high level of spatial and temporal detail is desirable to ensure taking into account a maximum number of components, which then enables connectivity to emerge from the simulated structures and functions. However, computational constraints and, in many cases, lack of data prevent the representation of all relevant processes and spatial/temporal variability in most models. In most cases, therefore, the level of detail selected for modelling is too coarse to represent the system in a way in which connectivity can emerge; a problem which can be circumvented by representing fine-scale structures and processes within coarser scale models using a variety of approaches. This poster focuses on the results of ongoing discussions on modelling connectivity held during several workshops within COST Action Connecteur. It assesses the current state of the art of incorporating the concept of connectivity in hydrological and sediment models, as well as the attitudes of modellers towards this issue. The discussion will focus on the different approaches through which connectivity can be represented in models: either by allowing it to emerge from model behaviour or by parameterizing it inside model structures; and on the appropriate scale at which processes should be represented explicitly or implicitly. It will also explore how modellers themselves approach connectivity through the results of a community survey. Finally, it will present the outline of an international modelling exercise aimed at assessing how different modelling concepts can capture connectivity in real catchments.

GeoProMT: A Collaborative Platform Supporting Natural Hazards Project Management From Assessment to Resilience

NASA Astrophysics Data System (ADS)

Renschler, C.; Sheridan, M. F.; Patra, A. K.

2008-05-01

The impact and consequences of extreme geophysical events (hurricanes, floods, wildfires, volcanic flows, mudflows, etc.) on properties and processes should be continuously assessed by a well-coordinated interdisciplinary research and outreach approach addressing risk assessment and resilience. Communication between various involved disciplines and stakeholders is the key to a successful implementation of an integrated risk management plan. These issues become apparent at the level of decision support tools for extreme events/disaster management in natural and managed environments. The Geospatial Project Management Tool (GeoProMT) is a collaborative platform for research and training to document and communicate the fundamental steps in transforming information for extreme events at various scales for analysis and management. GeoProMT is an internet-based interface for the management of shared geo-spatial and multi-temporal information such as measurements, remotely sensed images, and other GIS data. This tool enhances collaborative research activities and the ability to assimilate data from diverse sources by integrating information management. This facilitates a better understanding of natural processes and enhances the integrated assessment of resilience against both the slow and fast onset of hazard risks. Fundamental to understanding and communicating complex natural processes are: (a) representation of spatiotemporal variability, extremes, and uncertainty of environmental properties and processes in the digital domain, (b) transformation of their spatiotemporal representation across scales (e.g. interpolation, aggregation, disaggregation.) during data processing and modeling in the digital domain, and designing and developing tools for (c) geo-spatial data management, and (d) geo-spatial process modeling and effective implementation, and (e) supporting decision- and policy-making in natural resources and hazard management at various spatial and temporal scales of interest. GeoProMT is useful for researchers, practitioners, and decision-makers, because it provides an integrated environmental system assessment and data management approach that considers the spatial and temporal scales and variability in natural processes. Particularly in the occurrence or onset of extreme events it can utilize the latest data sources that are available at variable scales, combine them with existing information, and update assessment products such as risk and vulnerability assessment maps. Because integrated geo-spatial assessment requires careful consideration of all the steps in utilizing data, modeling and decision-making formats, each step in the sequence must be assessed in terms of how information is being scaled. At the process scale various geophysical models (e.g. TITAN, LAHARZ, or many other examples) are appropriate for incorporation in the tool. Some examples that illustrate our approach include: 1) coastal parishes impacted by Hurricane Rita (Southwestern Louisiana), 2) a watershed affected by extreme rainfall induced debris-flows (Madison County, Virginia; Panabaj, Guatemala; Casita, Nicaragua), and 3) the potential for pyroclastic flows to threaten a city (Tungurahua, Ecuador). This research was supported by the National Science Foundation.

SALMOD: A population model for salmonids: user's manual. Version W3

USGS Publications Warehouse

Bartholow, John; Heasley, John; Laake, Jeff; Sandelin, Jeff; Coughlan, Beth A.K.; Moos, Alan

2002-01-01

SALMOD is a computer model that simulates the dynamics of freshwater salmonid populations, both anadromous and resident. The conceptual model was developed in a workshop setting (Williamson et al. 1993) using fish experts concerned with Trinity River chinook restoration. The model builds on the foundation laid by similar models (see Cheslak and Jacobson 1990). The model’s premise that that egg and fish mortality are directly related to spatially and temporally variable micro- and macrohabitat limitations, which themselves are related to the timing and amount of streamflow and other meteorological variables. Habitat quality and capacity are characterized by the hydraulic and thermal properties of individual mesohabitats, which we use as spatial “computation units” in the model. The model tracks a population of spatially distinct cohorts that originate as gees and grow from one life stage to another as a function of local water temperature. Individual cohorts either remain in the computational unit in which they emerged or move, in whole or in part, to nearby units (see McCormick et al. 1998). Model processes include spawning (with red superimposition and incubation losses), growth (including egg maturation), mortality, and movement (freshet-induced, habitat-induced, and seasonal). Model processes are implemented such that the user (modeler) has the ability to more-or-less program the model on the fly to create the dynamics thought to animate the population. SALMOD then tabulates the various causes of mortality and the whereabouts of fish.

Egocentric-updating during navigation facilitates episodic memory retrieval.

PubMed

Gomez, Alice; Rousset, Stéphane; Baciu, Monica

2009-11-01

Influential models suggest that spatial processing is essential for episodic memory [O'Keefe, J., & Nadel, L. (1978). The hippocampus as a cognitive map. London: Oxford University Press]. However, although several types of spatial relations exist, such as allocentric (i.e. object-to-object relations), egocentric (i.e. static object-to-self relations) or egocentric updated on navigation information (i.e. self-to-environment relations in a dynamic way), usually only allocentric representations are described as potentially subserving episodic memory [Nadel, L., & Moscovitch, M. (1998). Hippocampal contributions to cortical plasticity. Neuropharmacology, 37(4-5), 431-439]. This study proposes to confront the allocentric representation hypothesis with an egocentric updated with self-motion representation hypothesis. In the present study, we explored retrieval performance in relation to these two types of spatial processing levels during learning. Episodic remembering has been assessed through Remember responses in a recall and in a recognition task, combined with a "Remember-Know-Guess" paradigm [Gardiner, J. M. (2001). Episodic memory and autonoetic consciousness: A first-person approach. Philosophical Transactions of the Royal Society B: Biological Sciences, 356(1413), 1351-1361] to assess the autonoetic level of responses. Our results show that retrieval performance was significantly higher when encoding was performed in the egocentric-updated condition. Although egocentric updated with self-motion and allocentric representations are not mutually exclusive, these results suggest that egocentric updating processing facilitates remember responses more than allocentric processing. The results are discussed according to Burgess and colleagues' model of episodic memory [Burgess, N., Becker, S., King, J. A., & O'Keefe, J. (2001). Memory for events and their spatial context: models and experiments. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 356(1413), 1493-1503].

A statistical model of extreme storm rainfall

NASA Astrophysics Data System (ADS)

Smith, James A.; Karr, Alan F.

1990-02-01

A model of storm rainfall is developed for the central Appalachian region of the United States. The model represents the temporal occurrence of major storms and, for a given storm, the spatial distribution of storm rainfall. Spatial inhomogeneities of storm rainfall and temporal inhomogeneities of the storm occurrence process are explicitly represented. The model is used for estimating recurrence intervals of extreme storms. The parameter estimation procedure developed for the model is based on the substitution principle (method of moments) and requires data from a network of rain gages. The model is applied to a 5000 mi2 (12,950 km2) region in the Valley and Ridge Province of Virginia and West Virginia.

The basis function approach for modeling autocorrelation in ecological data

USGS Publications Warehouse

Hefley, Trevor J.; Broms, Kristin M.; Brost, Brian M.; Buderman, Frances E.; Kay, Shannon L.; Scharf, Henry; Tipton, John; Williams, Perry J.; Hooten, Mevin B.

2017-01-01

Analyzing ecological data often requires modeling the autocorrelation created by spatial and temporal processes. Many seemingly disparate statistical methods used to account for autocorrelation can be expressed as regression models that include basis functions. Basis functions also enable ecologists to modify a wide range of existing ecological models in order to account for autocorrelation, which can improve inference and predictive accuracy. Furthermore, understanding the properties of basis functions is essential for evaluating the fit of spatial or time-series models, detecting a hidden form of collinearity, and analyzing large data sets. We present important concepts and properties related to basis functions and illustrate several tools and techniques ecologists can use when modeling autocorrelation in ecological data.

Comparisons of aerosol optical depth provided by seviri satellite observations and CAMx air quality modelling

NASA Astrophysics Data System (ADS)

Fernandes, A.; Riffler, M.; Ferreira, J.; Wunderle, S.; Borrego, C.; Tchepel, O.

2015-04-01

Satellite data provide high spatial coverage and characterization of atmospheric components for vertical column. Additionally, the use of air pollution modelling in combination with satellite data opens the challenging perspective to analyse the contribution of different pollution sources and transport processes. The main objective of this work is to study the AOD over Portugal using satellite observations in combination with air pollution modelling. For this purpose, satellite data provided by Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) on-board the geostationary Meteosat-9 satellite on AOD at 550 nm and modelling results from the Chemical Transport Model (CAMx - Comprehensive Air quality Model) were analysed. The study period was May 2011 and the aim was to analyse the spatial variations of AOD over Portugal. In this study, a multi-temporal technique to retrieve AOD over land from SEVIRI was used. The proposed method takes advantage of SEVIRI's high temporal resolution of 15 minutes and high spatial resolution. CAMx provides the size distribution of each aerosol constituent among a number of fixed size sections. For post processing, CAMx output species per size bin have been grouped into total particulate sulphate (PSO4), total primary and secondary organic aerosols (POA + SOA), total primary elemental carbon (PEC) and primary inert material per size bin (CRST1 to CRST_4) to be used in AOD quantification. The AOD was calculated by integration of aerosol extinction coefficient (Qext) on the vertical column. The results were analysed in terms of temporal and spatial variations. The analysis points out that the implemented methodology provides a good spatial agreement between modelling results and satellite observation for dust outbreak studied (10th -17th of May 2011). A correlation coefficient of r=0.79 was found between the two datasets. This work provides relevant background to start the integration of these two different types of the data in order to improve air pollution assessment.

Social and spatial processes associated with childhood diarrheal disease in Matlab, Bangladesh.

PubMed

Perez-Heydrich, Carolina; Furgurson, Jill M; Giebultowicz, Sophia; Winston, Jennifer J; Yunus, Mohammad; Streatfield, Peter Kim; Emch, Michael

2013-01-01

We develop novel methods for conceptualizing geographic space and social networks to evaluate their respective and combined contributions to childhood diarrheal incidence. After defining maternal networks according to direct familial linkages between females, and road networks using satellite imagery of the study area, we use a spatial econometrics model to evaluate the significance of correlation terms relating childhood diarrheal incidence to the incidence observed within respective networks. Disease was significantly clustered within road networks across time, but only inconsistently correlated within maternal networks. These methods could be widely applied to systems in which both social and spatial processes jointly influence health outcomes. Copyright © 2012 Elsevier Ltd. All rights reserved.

Effective connectivity in the neural network underlying coarse-to-fine categorization of visual scenes. A dynamic causal modeling study.

PubMed

Kauffmann, Louise; Chauvin, Alan; Pichat, Cédric; Peyrin, Carole

2015-10-01

According to current models of visual perception scenes are processed in terms of spatial frequencies following a predominantly coarse-to-fine processing sequence. Low spatial frequencies (LSF) reach high-order areas rapidly in order to activate plausible interpretations of the visual input. This triggers top-down facilitation that guides subsequent processing of high spatial frequencies (HSF) in lower-level areas such as the inferotemporal and occipital cortices. However, dynamic interactions underlying top-down influences on the occipital cortex have never been systematically investigated. The present fMRI study aimed to further explore the neural bases and effective connectivity underlying coarse-to-fine processing of scenes, particularly the role of the occipital cortex. We used sequences of six filtered scenes as stimuli depicting coarse-to-fine or fine-to-coarse processing of scenes. Participants performed a categorization task on these stimuli (indoor vs. outdoor). Firstly, we showed that coarse-to-fine (compared to fine-to-coarse) sequences elicited stronger activation in the inferior frontal gyrus (in the orbitofrontal cortex), the inferotemporal cortex (in the fusiform and parahippocampal gyri), and the occipital cortex (in the cuneus). Dynamic causal modeling (DCM) was then used to infer effective connectivity between these regions. DCM results revealed that coarse-to-fine processing resulted in increased connectivity from the occipital cortex to the inferior frontal gyrus and from the inferior frontal gyrus to the inferotemporal cortex. Critically, we also observed an increase in connectivity strength from the inferior frontal gyrus to the occipital cortex, suggesting that top-down influences from frontal areas may guide processing of incoming signals. The present results support current models of visual perception and refine them by emphasizing the role of the occipital cortex as a cortical site for feedback projections in the neural network underlying coarse-to-fine processing of scenes. Copyright © 2015 Elsevier Inc. All rights reserved.

An improved neutral landscape model for recreating real landscapes and generating landscape series for spatial ecological simulations.

PubMed

van Strien, Maarten J; Slager, Cornelis T J; de Vries, Bauke; Grêt-Regamey, Adrienne

2016-06-01

Many studies have assessed the effect of landscape patterns on spatial ecological processes by simulating these processes in computer-generated landscapes with varying composition and configuration. To generate such landscapes, various neutral landscape models have been developed. However, the limited set of landscape-level pattern variables included in these models is often inadequate to generate landscapes that reflect real landscapes. In order to achieve more flexibility and variability in the generated landscapes patterns, a more complete set of class- and patch-level pattern variables should be implemented in these models. These enhancements have been implemented in Landscape Generator (LG), which is a software that uses optimization algorithms to generate landscapes that match user-defined target values. Developed for participatory spatial planning at small scale, we enhanced the usability of LG and demonstrated how it can be used for larger scale ecological studies. First, we used LG to recreate landscape patterns from a real landscape (i.e., a mountainous region in Switzerland). Second, we generated landscape series with incrementally changing pattern variables, which could be used in ecological simulation studies. We found that LG was able to recreate landscape patterns that approximate those of real landscapes. Furthermore, we successfully generated landscape series that would not have been possible with traditional neutral landscape models. LG is a promising novel approach for generating neutral landscapes and enables testing of new hypotheses regarding the influence of landscape patterns on ecological processes. LG is freely available online.

Total probabilities of ensemble runoff forecasts

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

2017-04-01

Ensemble forecasting has a long history from meteorological modelling, as an indication of the uncertainty of the forecasts. However, it is necessary to calibrate and post-process the ensembles as the they often exhibit both bias and dispersion errors. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters varying in space and time, while giving a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, which makes it unsuitable for our purpose. Our post-processing method of the ensembles is developed in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu), where we are making forecasts for whole Europe, and based on observations from around 700 catchments. As the target is flood forecasting, we are also more interested in improving the forecast skill for high-flows rather than in a good prediction of the entire flow regime. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different meteorological forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to estimate the total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but we are adding a spatial penalty in the calibration process to force a spatial correlation of the parameters. The penalty takes distance, stream-connectivity and size of the catchment areas into account. This can in some cases have a slight negative impact on the calibration error, but avoids large differences between parameters of nearby locations, whether stream connected or not. The spatial calibration also makes it easier to interpolate the post-processing parameters to uncalibrated locations. We also look into different methods for handling the non-normal distributions of runoff data and the effect of different data transformations on forecasts skills in general and for floods in particular. Berrocal, V. J., Raftery, A. E. and Gneiting, T.: Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts, Mon. Weather Rev., 135(4), 1386-1402, doi:10.1175/MWR3341.1, 2007. Engeland, K. and Steinsland, I.: Probabilistic postprocessing models for flow forecasts for a system of catchments and several lead times, Water Resour. Res., 50(1), 182-197, doi:10.1002/2012WR012757, 2014. Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T.: Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation, Mon. Weather Rev., 133(5), 1098-1118, doi:10.1175/MWR2904.1, 2005. Hemri, S., Fundel, F. and Zappa, M.: Simultaneous calibration of ensemble river flow predictions over an entire range of lead times, Water Resour. Res., 49(10), 6744-6755, doi:10.1002/wrcr.20542, 2013. Raftery, A. E., Gneiting, T., Balabdaoui, F. and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev., 133(5), 1155-1174, doi:10.1175/MWR2906.1, 2005.

Uncovering stability mechanisms in microbial ecosystems - combining microcosm experiments, computational modelling and ecological theory in a multidisciplinary approach

NASA Astrophysics Data System (ADS)

Worrich, Anja; König, Sara; Banitz, Thomas; Centler, Florian; Frank, Karin; Kästner, Matthias; Miltner, Anja; Thullner, Martin; Wick, Lukas

2015-04-01

Although bacterial degraders in soil are commonly exposed to fluctuating environmental conditions, the functional performance of the biodegradation processes can often be maintained by resistance and resilience mechanisms. However, there is still a gap in the mechanistic understanding of key factors contributing to the stability of such an ecosystem service. Therefore we developed an integrated approach combining microcosm experiments, simulation models and ecological theory to directly make use of the strengths of these disciplines. In a continuous interplay process, data, hypotheses, and central questions are exchanged between disciplines to initiate new experiments and models to ultimately identify buffer mechanisms and factors providing functional stability. We focus on drying and rewetting-cycles in soil ecosystems, which are a major abiotic driver for bacterial activity. Functional recovery of the system was found to depend on different spatial processes in the computational model. In particular, bacterial motility is a prerequisite for biodegradation if either bacteria or substrate are heterogeneously distributed. Hence, laboratory experiments focussing on bacterial dispersal processes were conducted and confirmed this finding also for functional resistance. Obtained results will be incorporated into the model in the next step. Overall, the combination of computational modelling and laboratory experiments identified spatial processes as the main driving force for functional stability in the considered system, and has proved a powerful methodological approach.

Multi-Scale Modeling to Improve Single-Molecule, Single-Cell Experiments

NASA Astrophysics Data System (ADS)

Munsky, Brian; Shepherd, Douglas

2014-03-01

Single-cell, single-molecule experiments are producing an unprecedented amount of data to capture the dynamics of biological systems. When integrated with computational models, observations of spatial, temporal and stochastic fluctuations can yield powerful quantitative insight. We concentrate on experiments that localize and count individual molecules of mRNA. These high precision experiments have large imaging and computational processing costs, and we explore how improved computational analyses can dramatically reduce overall data requirements. In particular, we show how analyses of spatial, temporal and stochastic fluctuations can significantly enhance parameter estimation results for small, noisy data sets. We also show how full probability distribution analyses can constrain parameters with far less data than bulk analyses or statistical moment closures. Finally, we discuss how a systematic modeling progression from simple to more complex analyses can reduce total computational costs by orders of magnitude. We illustrate our approach using single-molecule, spatial mRNA measurements of Interleukin 1-alpha mRNA induction in human THP1 cells following stimulation. Our approach could improve the effectiveness of single-molecule gene regulation analyses for many other process.

Ontology patterns for complex topographic feature yypes

USGS Publications Warehouse

Varanka, Dalia E.

2011-01-01

Complex feature types are defined as integrated relations between basic features for a shared meaning or concept. The shared semantic concept is difficult to define in commonly used geographic information systems (GIS) and remote sensing technologies. The role of spatial relations between complex feature parts was recognized in early GIS literature, but had limited representation in the feature or coverage data models of GIS. Spatial relations are more explicitly specified in semantic technology. In this paper, semantics for topographic feature ontology design patterns (ODP) are developed as data models for the representation of complex features. In the context of topographic processes, component assemblages are supported by resource systems and are found on local landscapes. The topographic ontology is organized across six thematic modules that can account for basic feature types, resource systems, and landscape types. Types of complex feature attributes include location, generative processes and physical description. Node/edge networks model standard spatial relations and relations specific to topographic science to represent complex features. To demonstrate these concepts, data from The National Map of the U. S. Geological Survey was converted and assembled into ODP.

Do the anterior and lateral thalamic nuclei make distinct contributions to spatial representation and memory?

PubMed

Clark, Benjamin J; Harvey, Ryan E

2016-09-01

The anterior and lateral thalamus has long been considered to play an important role in spatial and mnemonic cognitive functions; however, it remains unclear whether each region makes a unique contribution to spatial information processing. We begin by reviewing evidence from anatomical studies and electrophysiological recordings which suggest that at least one of the functions of the anterior thalamus is to guide spatial orientation in relation to a global or distal spatial framework, while the lateral thalamus serves to guide behavior in relation to a local or proximal framework. We conclude by reviewing experimental work using targeted manipulations (lesion or neuronal silencing) of thalamic nuclei during spatial behavior and single-unit recordings from neuronal representations of space. Our summary of this literature suggests that although the evidence strongly supports a working model of spatial information processing involving the anterior thalamus, research regarding the role of the lateral thalamus is limited and requires further attention. We therefore identify a number of major gaps in this research and suggest avenues of future study that could potentially solidify our understanding of the relative roles of anterior and lateral thalamic regions in spatial representation and memory. Copyright © 2016 Elsevier Inc. All rights reserved.

A Flexible Modeling Framework For Hydraulic and Water Quality Performance Assessment of Stormwater Green Infrastructure

EPA Science Inventory

A flexible framework has been created for modeling multi-dimensional hydrological and water quality processes within stormwater green infrastructures (GIs). The framework models a GI system using a set of blocks (spatial features) and connectors (interfaces) representing differen...

Sensory neural pathways revisited to unravel the temporal dynamics of the Simon effect: A model-based cognitive neuroscience approach.

PubMed

Salzer, Yael; de Hollander, Gilles; Forstmann, Birte U

2017-06-01

The Simon task is one of the most prominent interference tasks and has been extensively studied in experimental psychology and cognitive neuroscience. Despite years of research, the underlying mechanism driving the phenomenon and its temporal dynamics are still disputed. Within the framework of the review, we adopt a model-based cognitive neuroscience approach. We first go over key findings in the literature of the Simon task, discuss competing qualitative cognitive theories and the difficulty of testing them empirically. We then introduce sequential sampling models, a particular class of mathematical cognitive process models. Finally, we argue that the brain architecture accountable for the processing of spatial ('where') and non-spatial ('what') information, could constrain these models. We conclude that there is a clear need to bridge neural and behavioral measures, and that mathematical cognitive models may facilitate the construction of this bridge and work towards revealing the underlying mechanisms of the Simon effect. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multivariate Non-Symmetric Stochastic Models for Spatial Dependence Models

NASA Astrophysics Data System (ADS)

Haslauer, C. P.; Bárdossy, A.

2017-12-01

A copula based multivariate framework allows more flexibility to describe different kind of dependences than what is possible using models relying on the confining assumption of symmetric Gaussian models: different quantiles can be modelled with a different degree of dependence; it will be demonstrated how this can be expected given process understanding. maximum likelihood based multivariate quantitative parameter estimation yields stable and reliable results; not only improved results in cross-validation based measures of uncertainty are obtained but also a more realistic spatial structure of uncertainty compared to second order models of dependence; as much information as is available is included in the parameter estimation: incorporation of censored measurements (e.g., below detection limit, or ones that are above the sensitive range of the measurement device) yield to more realistic spatial models; the proportion of true zeros can be jointly estimated with and distinguished from censored measurements which allow estimates about the age of a contaminant in the system; secondary information (categorical and on the rational scale) has been used to improve the estimation of the primary variable; These copula based multivariate statistical techniques are demonstrated based on hydraulic conductivity observations at the Borden (Canada) site, the MADE site (USA), and a large regional groundwater quality data-set in south-west Germany. Fields of spatially distributed K were simulated with identical marginal simulation, identical second order spatial moments, yet substantially differing solute transport characteristics when numerical tracer tests were performed. A statistical methodology is shown that allows the delineation of a boundary layer separating homogenous parts of a spatial data-set. The effects of this boundary layer (macro structure) and the spatial dependence of K (micro structure) on solute transport behaviour is shown.

The role of spatial attention in visual word processing

NASA Technical Reports Server (NTRS)

Mccann, Robert S.; Folk, Charles L.; Johnston, James C.

1992-01-01

Subjects made lexical decisions on a target letter string presented above or below fixation. In Experiments 1 and 2, target location was cued 100 ms in advance of target onset. Responses were faster on validly than on invalidly cued trials. In Experiment 3, the target was sometimes accompanied by irrelevant stimuli on the other side of fixation; in such cases, responses were slowed (a spatial filtering effect). Both cuing and filtering effects on response time were additive with effects of word frequency and lexical status (words vs. nonwords). These findings are difficult to reconcile with claims that spatial attention is less involved in processing familiar words than in unfamiliar words and nonwords. The results can be reconciled with a late-selection locus of spatial attention only with difficulty, but are easily explained by early-selection models.

Empirical Research on Spatial Diffusion Process of Knowledge Spillovers

NASA Astrophysics Data System (ADS)

Jin, Xuehui

2018-02-01

Firstly, this paper gave a brief review of the core issues of previous studies on spatial distribution of knowledge spillovers. That laid the theoretical foundation for further research. Secondly, this paper roughly described the diffusion process of solar patents in Bejing-Tianjin-Hebei and the Pearl River Delta regions by means of correlation analysis based on patent information of the application date and address of patentee. After that, this paper introduced the variables of spatial distance, knowledge absorptive capacity, knowledge gap and pollution control and built the empirical model of patent, and then collecting data to test them. The results showed that knowledge absorptive capacity was the most significant factor than the other three, followed by the knowledge gap. The influence of spatial distance on knowledge spillovers was limited and the most weak influence factor was pollution control.

Fragility of foot process morphology in kidney podocytes arises from chaotic spatial propagation of cytoskeletal instability

PubMed Central

Deerinck, Thomas J.; Chen, Yibang; He, John C.; Ellisman, Mark H.; Iyengar, Ravi

2017-01-01

Kidney podocytes’ function depends on fingerlike projections (foot processes) that interdigitate with those from neighboring cells to form the glomerular filtration barrier. The integrity of the barrier depends on spatial control of dynamics of actin cytoskeleton in the foot processes. We determined how imbalances in regulation of actin cytoskeletal dynamics could result in pathological morphology. We obtained 3-D electron microscopy images of podocytes and used quantitative features to build dynamical models to investigate how regulation of actin dynamics within foot processes controls local morphology. We find that imbalances in regulation of actin bundling lead to chaotic spatial patterns that could impair the foot process morphology. Simulation results are consistent with experimental observations for cytoskeletal reconfiguration through dysregulated RhoA or Rac1, and they predict compensatory mechanisms for biochemical stability. We conclude that podocyte morphology, optimized for filtration, is intrinsically fragile, whereby local transient biochemical imbalances may lead to permanent morphological changes associated with pathophysiology. PMID:28301477