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Developing approaches for linear mixed modeling in landscape genetics through landscape‐directed dispersal simulations
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 increas...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5468135/ https://www.ncbi.nlm.nih.gov/pubmed/28616172 http://dx.doi.org/10.1002/ece3.2825 |
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author | Row, Jeffrey R. Knick, Steven T. Oyler‐McCance, Sara J. Lougheed, Stephen C. Fedy, Bradley C. |
author_facet | Row, Jeffrey R. Knick, Steven T. Oyler‐McCance, Sara J. Lougheed, Stephen C. Fedy, Bradley C. |
author_sort | Row, Jeffrey R. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5468135 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-54681352017-06-14 Developing approaches for linear mixed modeling in landscape genetics through landscape‐directed dispersal simulations Row, Jeffrey R. Knick, Steven T. Oyler‐McCance, Sara J. Lougheed, Stephen C. Fedy, Bradley C. Ecol Evol Original Research 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. John Wiley and Sons Inc. 2017-04-18 /pmc/articles/PMC5468135/ /pubmed/28616172 http://dx.doi.org/10.1002/ece3.2825 Text en © 2017 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Research Row, Jeffrey R. Knick, Steven T. Oyler‐McCance, Sara J. Lougheed, Stephen C. Fedy, Bradley C. Developing approaches for linear mixed modeling in landscape genetics through landscape‐directed dispersal simulations |
title | Developing approaches for linear mixed modeling in landscape genetics through landscape‐directed dispersal simulations |
title_full | Developing approaches for linear mixed modeling in landscape genetics through landscape‐directed dispersal simulations |
title_fullStr | Developing approaches for linear mixed modeling in landscape genetics through landscape‐directed dispersal simulations |
title_full_unstemmed | Developing approaches for linear mixed modeling in landscape genetics through landscape‐directed dispersal simulations |
title_short | Developing approaches for linear mixed modeling in landscape genetics through landscape‐directed dispersal simulations |
title_sort | developing approaches for linear mixed modeling in landscape genetics through landscape‐directed dispersal simulations |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5468135/ https://www.ncbi.nlm.nih.gov/pubmed/28616172 http://dx.doi.org/10.1002/ece3.2825 |
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