ArticleMethods in ecology and evolution2024
Resource-explicit interactions in spatial population models.
Article in Methods in ecology and evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- A simulation-based deep learning framework for spatially explicit malaria modeling of CRISPR suppression gene drive mosquitoes.iScience · 2026Article
- BSocial Tool Deciphers Highly Functional Plant Growth-Promoting Bacterial Consortia.Microbial biotechnology · 2025Article
- Predicting the invasiveness of threshold-dependent gene drives.bioRxiv : the preprint server for biology · 2025Article
- Population Genetics Meets Ecology: A Guide to Individual-Based Simulations in Continuous Landscapes.Ecology and evolution · 2025Article
- Resource-explicit interactions in spatial population models.Methods in ecology and evolution · 2024Article
- Performance characteristics allow for confinement of a CRISPR toxin-antidote gene drive for population suppression in a reaction-diffusion model.Proceedings. Biological sciences · 2024Article
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5 authors.
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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.
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