Computation of the integrated flow of particles between polygons

Bouvier, Annie A.; Kiêu, Kiên; Adamczyk, Katarzyna; Monod, Hervé

doi:10.1016/j.envsoft.2008.11.006

Cited by 23 publications

(17 citation statements)

References 15 publications

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“…From eqn , the probability of a propagule dispersing from patch i to patch j is computed by performing the integration of the dispersal function g (·) between pairs of points that belong to the areas A i and A j of patches i and j , respectively (Bouvier et al. ):

{italicμ}_{i j} = \frac{\underset{A_{i}}{false\int} \int_{A_{j}} g ((∥∥, z - z^{'})) d z d z^{'}}{A_{i}}

…”

Section: Model and Methodsmentioning

confidence: 99%

Crop pathogen emergence and evolution in agro‐ecological landscapes

Papaïx

Burdon

Zhan

et al. 2015

Evolutionary Applications

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Remnant areas hosting natural vegetation in agricultural landscapes can impact the disease epidemiology and evolutionary dynamics of crop pathogens. However, the potential consequences for crop diseases of the composition, the spatial configuration and the persistence time of the agro-ecological interface – the area where crops and remnant vegetation are in contact – have been poorly studied. Here, we develop a demographic–genetic simulation model to study how the spatial and temporal distribution of remnant wild vegetation patches embedded in an agricultural landscape can drive the emergence of a crop pathogen and its subsequent specialization on the crop host. We found that landscape structures that promoted larger pathogen populations on the wild host facilitated the emergence of a crop pathogen, but such landscape structures also reduced the potential for the pathogen population to adapt to the crop. In addition, the evolutionary trajectory of the pathogen population was determined by interactions between the factors describing the landscape structure and those describing the pathogen life histories. Our study contributes to a better understanding of how the shift of land-use patterns in agricultural landscapes might influence crop diseases to provide predictive tools to evaluate management practices.

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{italicμ}_{i j} = \frac{\underset{A_{i}}{false\int} \int_{A_{j}} g ((∥∥, z - z^{'})) d z d z^{'}}{A_{i}}

…”

Section: Model and Methodsmentioning

confidence: 99%

Crop pathogen emergence and evolution in agro‐ecological landscapes

Papaïx

Burdon

Zhan

et al. 2015

Evolutionary Applications

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“…To avoid border effects, was considered as a torus in the calculations of dispersal rates and habitat aggregation (see after). The dispersal rates in Equation (2) were computed using the CaliFloPP algorithm [36] on the patches.…”

Section: Model and Methodsmentioning

confidence: 99%

Dynamics of Adaptation in Spatially Heterogeneous Metapopulations

et al. 2013

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The selection pressure experienced by organisms often varies across the species range. It is hence crucial to characterise the link between environmental spatial heterogeneity and the adaptive dynamics of species or populations. We address this issue by studying the phenotypic evolution of a spatial metapopulation using an adaptive dynamics approach. The singular strategy is found to be the mean of the optimal phenotypes in each habitat with larger weights for habitats present in large and well connected patches. The presence of spatial clusters of habitats in the metapopulation is found to facilitate specialisation and to increase both the level of adaptation and the evolutionary speed of the population when dispersal is limited. By showing that spatial structures are crucial in determining the specialisation level and the evolutionary speed of a population, our results give insight into the influence of spatial heterogeneity on the niche breadth of species.

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“…In the future version of the package, computation time should be reduced, without loss of numerical precision or the introduction of numerical errors, by (i) implementing more efficient algorithms for polygon rasterization, (ii) improving the storage of matrices, (iii) improving the calculation of the convolution; in particular, our use of FFT should be compared with CaliFloPP, which is a software for computing flows of particules between pairs of polygons, (iv) implementing the polygon rasterization, the matrix storage, and the convolution into a faster low‐level programming language (e.g., C++), which would additionally lead to reduce the dependence of our package on other R packages (e.g., fftwtools, raster), and (v) using suitable parallel computing strategies on CPU or GPU…”

Section: Discussionmentioning

confidence: 99%

“…Available tools based on exposure‐hazard models generally include either a spatial component or a toxicokinetic–toxicodynamic (TK‐TD) component, but not both . Examples of space‐oriented tools are the Risktrace software, which allows spatially explicit exposure assessments; TrajStat, which is a GIS‐based software that uses various trajectory statistical analysis methods to identify potential sources from long‐term air pollution measurement data; Spatstat, which is an R package for analyzing spatial point patterns and that can be applied to carry out risk analyses; Sparr, which allows analyses of spatial relative risk using fixed and adaptive kernel density estimation with R; CALIFLOPP, which allows the calculation of flows of particles between polygons . Concerning exposure‐hazard models including a TK–TD component, the packages GUTS and HTTK propose TK or TK–TD models for the analysis, understanding, and simulation of sublethal effects of toxic compounds …”

Section: Introductionmentioning

confidence: 99%

A Spatio‐Temporal Exposure‐Hazard Model for Assessing Biological Risk and Impact

et al. 2017

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We developed a simulation model for quantifying the spatio-temporal distribution of contaminants (e.g., xenobiotics) and assessing the risk of exposed populations at the landscape level. The model is a spatio-temporal exposure-hazard model based on (i) tools of stochastic geometry (marked polygon and point processes) for structuring the landscape and describing the exposed individuals, (ii) a dispersal kernel describing the dissemination of contaminants from polygon sources, and (iii) an (eco)toxicological equation describing the toxicokinetics and dynamics of contaminants in affected individuals. The model was implemented in the briskaR package (biological risk assessment with R) of the R software. This article presents the model background, the use of the package in an illustrative example, namely, the effect of genetically modified maize pollen on nontarget Lepidoptera, and typical comparisons of landscape configurations that can be carried out with our model (different configurations lead to different mortality rates in the treated example). In real case studies, parameters and parametric functions encountered in the model will have to be precisely specified to obtain realistic measures of risk and impact and accurate comparisons of landscape configurations. Our modeling framework could be applied to study other risks related to agriculture, for instance, pathogen spread in crops or livestock, and could be adapted to cope with other hazards such as toxic emissions from industrial areas having health effects on surrounding populations. Moreover, the R package has the potential to help risk managers in running quantitative risk assessments and testing management strategies.

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Computation of the integrated flow of particles between polygons

Cited by 23 publications

References 15 publications

Crop pathogen emergence and evolution in agro‐ecological landscapes

Crop pathogen emergence and evolution in agro‐ecological landscapes

Dynamics of Adaptation in Spatially Heterogeneous Metapopulations

A Spatio‐Temporal Exposure‐Hazard Model for Assessing Biological Risk and Impact

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