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dc.contributor.authorRow, Jeffrey R.
dc.contributor.authorKnick, Steven T.
dc.contributor.authorOyler-McCance, Sara J.
dc.contributor.authorLougheed, Stephen C.
dc.contributor.authorFedy, Bradley C. 13:48:56 (GMT) 13:48:56 (GMT)
dc.description.abstractDispersal 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.en
dc.description.sponsorshipEndangered Species Recovery Fund (WWF, Environment Canada, Ontario Ministry of Natural Resources)en
dc.description.sponsorshipUS Bureau of Land Managementen
dc.description.sponsorshipUS Geological Surveyen
dc.description.sponsorshipWyoming Game and Fish Departmenten
dc.rightsAttribution 4.0 International*
dc.subjectfox snakeen
dc.subjectmixed modelsen
dc.subjectmaximum-likelihood population-effects modelsen
dc.subjectmodel selectionen
dc.subjectspatial genetic simulationsen
dc.subject.lcshsage grouseen
dc.titleDeveloping approaches for linear mixed modeling in landscape genetics through landscape-directed dispersal simulationsen
dcterms.bibliographicCitationRow, J. R., Knick, S. T., Oyler-McCance, S. J., Lougheed, S. C., & Fedy, B. C. (2017). Developing approaches for linear mixed modeling in landscape genetics through landscape-directed dispersal simulations. Ecology and Evolution, 7(11), 3751–3761.
uws.contributor.affiliation1Faculty of Environmenten
uws.contributor.affiliation2School of Environment, Resources and Sustainabilityen

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Attribution 4.0 International
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