Resource‐explicit interactions in spatial population models

1. Continuous-space population models can yield significantly different results from their panmictic counterparts when assessing evolutionary, ecological, or population-genetic processes. However, the computational burden of spatial models is typically much greater than that of panmictic models due to the overhead of determining which individuals interact with one another and how strongly they interact. While these calculations are necessary to model local competition that regulates the population density, they can lead to prohibitively long runtimes. 2. Here, we present a novel modeling method in which the resources available to a population are abstractly represented as an additional layer of the simulation. Instead of interacting directly with one another, individuals interact indirectly via this resource layer. 3. We find that this method closely matches other spatial models, yet can dramatically increase the speed of the model, allowing the simulation of much larger populations. 4. In addition to improved runtimes, models structured in this manner exhibit other desirable characteristics, including more explicit control over population density near the edge of the simulated area, and an efficient route for modeling complex heterogeneous landscapes.

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Publication Details

Journal
Methods in Ecology and Evolution
Published
2024-10-18
DOI
https://doi.org/10.1111/2041-210x.14432
Citations
4
Primary Topic
Animal Ecology and Behavior Studies
Type
article
Field-Weighted Citation Impact
1.84

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article

Resource‐explicit interactions in spatial population models

Samuel E. Champer, Jackson Champer, B. Haller, Philipp W. Messer et al.
4 citations
Methods in Ecology and Evolution
Animal Ecology and Behavior Studies
1.84
article

Resource‐explicit interactions in spatial population models

Samuel E. Champer, Jackson Champer, B. Haller, Philipp W. Messer, Bryan Chae
article en
4 citations

Abstract

1. Continuous-space population models can yield significantly different results from their panmictic counterparts when assessing evolutionary, ecological, or population-genetic processes. However, the computational burden of spatial models is typically much greater than that of panmictic models due to the overhead of determining which individuals interact with one another and how strongly they interact. While these calculations are necessary to model local competition that regulates the population density, they can lead to prohibitively long runtimes. 2. Here, we present a novel modeling method in which the resources available to a population are abstractly represented as an additional layer of the simulation. Instead of interacting directly with one another, individuals interact indirectly via this resource layer. 3. We find that this method closely matches other spatial models, yet can dramatically increase the speed of the model, allowing the simulation of much larger populations. 4. In addition to improved runtimes, models structured in this manner exhibit other desirable characteristics, including more explicit control over population density near the edge of the simulated area, and an efficient route for modeling complex heterogeneous landscapes.

Methods in Ecology and EvolutionVol. 15(12)
Peking University (CN), Cornell University (US), Center for Life Sciences (CN)
National Institutes of Health
Openalex Percentile: Top 14%
Animal Ecology and Behavior Studies
1.84
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Resource‐explicit interactions in spatial population models — Samuel E. Champer, Jackson Champer, et al. · Methods in Ecology and Evolution (2024) | TGRS Research Map | TGRS