Abstract
Dispersal can impact population dynamics and geographic variation, and thus, genetic approaches that can establish which landscape factors influence population connectivity have ecological and evolutionary importance. Mixed models that account for the error structure of pairwise datasets are increasingly used to compare models relating genetic differentiation to pairwise measures of landscape resistance. A model selection framework based on information criteria metrics or explained variance may help disentangle the ecological and landscape factors influencing genetic structure, yet there are currently no consensus for the best protocols. Here, we develop landscape‐directed simulations and test a series of replicates that emulate independent empirical datasets of two species with different life history characteristics (greater sage‐grouse; eastern foxsnake). We determined that in our simulated scenarios, AIC and BIC were the best model selection indices and that marginal R 2 values were biased toward more complex models. The model coefficients for landscape variables generally reflected the underlying dispersal model with confidence intervals that did not overlap with zero across the entire model set. When we controlled for geographic distance, variables not in the underlying dispersal models (i.e., nontrue) typically overlapped zero. Our study helps establish methods for using linear mixed models to identify the features underlying patterns of dispersal across a variety of landscapes.
Highlights
Identifying the natural and anthropogenic landscape features that promote or impede dispersal provides ecological context for understanding how populations are structured across a landscape (Manel, Schwartz, Luikart, & Taberlet, 2003)
Dispersal is critical to local population dynamics (Vance, 1984), and when it results in gene flow, it is essential to maintaining genetic diversity (Epps et al, 2005)
As a result of the importance of dispersal, the last decade has seen a proliferation of quantitative methods that combine landscape modeling with genetic data to test hypotheses regarding the relative influence of landscape factors on spatial genetic structure (Balkenhol, Waits, & Dezzani, 2009; Manel et al, 2003; Storfer et al, 2007)
Summary
Identifying the natural and anthropogenic landscape features that promote or impede dispersal provides ecological context for understanding how populations are structured across a landscape (Manel, Schwartz, Luikart, & Taberlet, 2003). The best approach for quantifying model fit and comparing models that represent different hypotheses is far from clear This lack of clarity largely stems from the nonindependent error structure within pairwise datasets that preclude standard information theoretic model selection approaches. Mantel’s test are largely limited to a maximum of three matrices (i.e., two independent variables) with a propensity for inflated error rates (Guillot & Rousset, 2013), and nonindependence issues for MRDM make model selection indices prone to bias (Goldberg & Waits, 2010; Van Strien, Keller, & Holderegger, 2012) This limits the use of these approaches for disentangling the ecological complexity surrounding spatial genetic structure. We provide genetic simulation scripts that can be used to test population- based spatial genetic approaches
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