Relationships between migration rates and landscape resistance assessed using individual-based simulations

Authors

  • E. L. LANDGUTH,

    1. University of Montana, Mathematics Building, Missoula, MT 59812, USA
    2. USDA Forest Service, Rocky Mountain Research Station, 800 E Beckwith, Missoula, MT 59801, USA
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  • S. A. CUSHMAN,

    1. USDA Forest Service, Rocky Mountain Research Station, 800 E Beckwith, Missoula, MT 59801, USA
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  • M.A. MURPHY,

    1. Colorado State University, Biology Department, Fort Collins, CO 80523-1878, USA
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  • G. LUIKART

    1. Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto (CIBIO-UP), Campus Agrário de Vairão, 4485-661 Vairão, Portugal
    2. University of Montana, Division of Biological Sciences, Missoula, MT 59812, USA
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Erin L. Landguth, Fax: (406) 243-2674; E-mail: erin.landguth@umconnect.umt.edu

Abstract

Linking landscape effects on gene flow to processes such as dispersal and mating is essential to provide a conceptual foundation for landscape genetics. It is particularly important to determine how classical population genetic models relate to recent individual-based landscape genetic models when assessing individual movement and its influence on population genetic structure. We used classical Wright–Fisher models and spatially explicit, individual-based, landscape genetic models to simulate gene flow via dispersal and mating in a series of landscapes representing two patches of habitat separated by a barrier. We developed a mathematical formula that predicts the relationship between barrier strength (i.e., permeability) and the migration rate (m) across the barrier, thereby linking spatially explicit landscape genetics to classical population genetics theory. We then assessed the reliability of the function by obtaining population genetics parameters (m, FST) using simulations for both spatially explicit and Wright–Fisher simulation models for a range of gene flow rates. Next, we show that relaxing some of the assumptions of the Wright–Fisher model can substantially change population substructure (i.e., FST). For example, isolation by distance among individuals on each side of a barrier maintains an FST of ∼0.20 regardless of migration rate across the barrier, whereas panmixia on each side of the barrier results in an FST that changes with m as predicted by classical population genetics theory. We suggest that individual-based, spatially explicit modelling provides a general framework to investigate how interactions between movement and landscape resistance drive population genetic patterns and connectivity across complex landscapes.

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