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Comparison of Bayesian Clustering and Edge Detection Methods for Inferring Boundaries in Landscape Genetics

Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three...

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Detalles Bibliográficos
Autores principales: Safner, Toni, Miller, Mark P., McRae, Brad H., Fortin, Marie-Josée, Manel, Stéphanie
Formato: Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3083678/
https://www.ncbi.nlm.nih.gov/pubmed/21541031
http://dx.doi.org/10.3390/ijms12020865
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author Safner, Toni
Miller, Mark P.
McRae, Brad H.
Fortin, Marie-Josée
Manel, Stéphanie
author_facet Safner, Toni
Miller, Mark P.
McRae, Brad H.
Fortin, Marie-Josée
Manel, Stéphanie
author_sort Safner, Toni
collection PubMed
description Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods’ effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance.
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spelling pubmed-30836782011-05-03 Comparison of Bayesian Clustering and Edge Detection Methods for Inferring Boundaries in Landscape Genetics Safner, Toni Miller, Mark P. McRae, Brad H. Fortin, Marie-Josée Manel, Stéphanie Int J Mol Sci Article Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods’ effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance. Molecular Diversity Preservation International (MDPI) 2011-01-25 /pmc/articles/PMC3083678/ /pubmed/21541031 http://dx.doi.org/10.3390/ijms12020865 Text en © 2011 by the authors; licensee MDPI, Basel, Switzerland. http://creativecommons.org/licenses/by/3.0 This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Safner, Toni
Miller, Mark P.
McRae, Brad H.
Fortin, Marie-Josée
Manel, Stéphanie
Comparison of Bayesian Clustering and Edge Detection Methods for Inferring Boundaries in Landscape Genetics
title Comparison of Bayesian Clustering and Edge Detection Methods for Inferring Boundaries in Landscape Genetics
title_full Comparison of Bayesian Clustering and Edge Detection Methods for Inferring Boundaries in Landscape Genetics
title_fullStr Comparison of Bayesian Clustering and Edge Detection Methods for Inferring Boundaries in Landscape Genetics
title_full_unstemmed Comparison of Bayesian Clustering and Edge Detection Methods for Inferring Boundaries in Landscape Genetics
title_short Comparison of Bayesian Clustering and Edge Detection Methods for Inferring Boundaries in Landscape Genetics
title_sort comparison of bayesian clustering and edge detection methods for inferring boundaries in landscape genetics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3083678/
https://www.ncbi.nlm.nih.gov/pubmed/21541031
http://dx.doi.org/10.3390/ijms12020865
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