Safner2011865

Référence

Safner, T., Miller, M.P., McRae, B.H., Fortin, M.-J., Manel, S. (2011) Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics. International Journal of Molecular Sciences, 12(2):865-889. (Scopus )

Résumé

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. © 2011 by the authors; licensee MDPI, Basel, Switzerland.

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@ARTICLE { Safner2011865,
    AUTHOR = { Safner, T. and Miller, M.P. and McRae, B.H. and Fortin, M.-J. and Manel, S. },
    TITLE = { Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics },
    JOURNAL = { International Journal of Molecular Sciences },
    YEAR = { 2011 },
    VOLUME = { 12 },
    NUMBER = { 2 },
    PAGES = { 865-889 },
    NOTE = { cited By 65 },
    ABSTRACT = { 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. © 2011 by the authors; licensee MDPI, Basel, Switzerland. },
    AFFILIATION = { Laboratory of Alpine Ecology, Equipe Population Genomics and Biodiversity, UMR CNRS 5553, University Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France; Department of Plant Breeding, Genetics and Biometrics, Faculty of Agriculture, University of Zagreb, Svetosimunska 25, 10000 Zagreb, Croatia; Department of Biology, Utah State University, 5305 Old Main Hill, Logan, UT 84321, United States; Nature Conservancy, 1917 1st Ave, Seattle, WA 98101, United States; Department of Ecology and Evolutionary Biology, University of Toronto, Ontario, M6R 2R8, Canada; Laboratory of Population Environment Development, UMR 151 UP/IRD, University Aix-Marseille I, 3 place Victor Hugo, 13331 Marseille Cedex 03, France; U.S. Geological Survey Forest and Rangeland Ecosystem Science Center, 3200 SW Jefferson Way, Corvallis, OR 97331, United States },
    AUTHOR_KEYWORDS = { Edge detection methods; Genetic boundaries; Landscape genetics; Spatial Bayesian clustering },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.3390/ijms12020865 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-79952275743&doi=10.3390%2fijms12020865&partnerID=40&md5=ff2801afe4f9e98044db296a908397f1 },
}

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