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Resource-explicit interactions in spatial population models.
Champer, Samuel E; Chae, Bryan; Haller, Benjamin C; Champer, Jackson; Messer, Philipp W.
Afiliação
  • Champer SE; Department of Computational Biology, Cornell University, Ithaca, NY 14853.
  • Chae B; Department of Computational Biology, Cornell University, Ithaca, NY 14853.
  • Haller BC; Department of Computational Biology, Cornell University, Ithaca, NY 14853.
  • Champer J; Center for Bioinformatics, School of Life Sciences, Center for Life Sciences, Peking University, Beijing, China 100871.
  • Messer PW; Department of Computational Biology, Cornell University, Ithaca, NY 14853.
bioRxiv ; 2024 Jan 15.
Article em En | MEDLINE | ID: mdl-38293045
ABSTRACT
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. Though these calculations are necessary to model local competition that regulates the population density, they can lead to prohibitively long runtimes. 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. 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. Additionally, models structured in this manner exhibit other desirable characteristics, including more realistic spatial dynamics near the edge of the simulated area, and an efficient route for modeling more complex heterogeneous landscapes.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: BioRxiv Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: BioRxiv Ano de publicação: 2024 Tipo de documento: Article