Developing approaches for linear mixed modeling in landscape genetics through landscape-directed dispersal simulations

Ecology and Evolution
By: , and 

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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 R2 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.

Publication type Article
Publication Subtype Journal Article
Title Developing approaches for linear mixed modeling in landscape genetics through landscape-directed dispersal simulations
Series title Ecology and Evolution
DOI 10.1002/ece3.2825
Volume 7
Issue 11
Year Published 2017
Language English
Publisher Wiley
Contributing office(s) Forest and Rangeland Ecosystem Science Center
Description 11 p.
First page 3751
Last page 3761
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