Population Genetics Meets Ecology: A Guide to Individual‐Based Simulations in Continuous Landscapes

Individual-based simulation has become an increasingly crucial tool for many fields of population biology. However, continuous geography is important to many applications, and implementing realistic and stable simulations in continuous space presents a variety of difficulties, from modeling choices to computational efficiency. This paper aims to be a practical guide to spatial simulation, helping researchers to implement individual-based simulations and avoid common pitfalls. To do this, we delve into mechanisms of mating, reproduction, density-dependent feedback, and dispersal, all of which may vary across the landscape, discuss how these affect population dynamics, and describe how to parameterize simulations in convenient ways (for instance, to achieve a desired population density). We also demonstrate how to implement these models using the current version of the individual-based simulator, SLiM. We additionally discuss natural selection-in particular, how genetic variation can affect demographic processes. Finally, we provide four short vignettes: simulations of pikas that shift their range up a mountain as temperatures rise; mosquitoes that live in rivers as juveniles and experience seasonally changing habitat; cane toads that expand across Australia, reaching 120 million individuals; and monarch butterflies whose populations are regulated by an explicitly modeled resource (milkweed).

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Publication Details

Journal
Ecology and Evolution
Published
2025-04-01
DOI
https://doi.org/10.1002/ece3.71098
Citations
8
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
14.71

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article

Population Genetics Meets Ecology: A Guide to Individual‐Based Simulations in Continuous Landscapes

Benjamin C. Haller, Andrew D. Kern, Samuel E. Champer, Sohini Ramachandran et al.
8 citations
Ecology and Evolution
Species Distribution and Climate Change
14.71
article

Population Genetics Meets Ecology: A Guide to Individual‐Based Simulations in Continuous Landscapes

Benjamin C. Haller, Andrew D. Kern, Samuel E. Champer, Sohini Ramachandran, Peter L. Ralph, Elizabeth T. Chevy, Jiseon Min, Silas Tittes, Victoria Caudill, Chris C. R. Smith, Philipp W. Messer, Clara T. Rehmann
article en
8 citations

Abstract

Individual-based simulation has become an increasingly crucial tool for many fields of population biology. However, continuous geography is important to many applications, and implementing realistic and stable simulations in continuous space presents a variety of difficulties, from modeling choices to computational efficiency. This paper aims to be a practical guide to spatial simulation, helping researchers to implement individual-based simulations and avoid common pitfalls. To do this, we delve into mechanisms of mating, reproduction, density-dependent feedback, and dispersal, all of which may vary across the landscape, discuss how these affect population dynamics, and describe how to parameterize simulations in convenient ways (for instance, to achieve a desired population density). We also demonstrate how to implement these models using the current version of the individual-based simulator, SLiM. We additionally discuss natural selection-in particular, how genetic variation can affect demographic processes. Finally, we provide four short vignettes: simulations of pikas that shift their range up a mountain as temperatures rise; mosquitoes that live in rivers as juveniles and experience seasonally changing habitat; cane toads that expand across Australia, reaching 120 million individuals; and monarch butterflies whose populations are regulated by an explicitly modeled resource (milkweed).

Ecology and EvolutionVol. 15(4)
University of Oregon (US), Cornell University (US), Brown University (US)
National Institutes of Health, National Human Genome Research Institute, National Institute of General Medical Sciences
Life in Land
Openalex Percentile: Top 1%
Species Distribution and Climate Change
14.71
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