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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...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2011
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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. |
format | Text |
id | pubmed-3083678 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
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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