Segelbacher2010375

Référence

Segelbacher, G., Cushman, S.A., Epperson, B.K., Fortin, M.-J., Francois, O., Hardy, O.J., Holderegger, R., Taberlet, P., Waits, L.P., Manel, S. (2010) Applications of landscape genetics in conservation biology: Concepts and challenges. Conservation Genetics, 11(2):375-385. (Scopus )

Résumé

Landscape genetics plays an increasingly important role in the management and conservation of species. Here, we highlight some of the opportunities and challenges in using landscape genetic approaches in conservation biology. We first discuss challenges related to sampling design and introduce several recent methodological developments in landscape genetics (analyses based on pairwise relatedness, the application of Bayesian methods, inference from landscape resistance and a shift from population-based to individual-based analyses). We then show how simulations can foster the field of landscape genetics and, finally, elaborate on technical developments in sequencing techniques that will dramatically improve our ability to study genetic variation in wild species, opening up new and unprecedented avenues for genetic analysis in conservation biology. © 2010 Springer Science+Business Media B.V.

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@ARTICLE { Segelbacher2010375,
    AUTHOR = { Segelbacher, G. and Cushman, S.A. and Epperson, B.K. and Fortin, M.-J. and Francois, O. and Hardy, O.J. and Holderegger, R. and Taberlet, P. and Waits, L.P. and Manel, S. },
    TITLE = { Applications of landscape genetics in conservation biology: Concepts and challenges },
    JOURNAL = { Conservation Genetics },
    YEAR = { 2010 },
    VOLUME = { 11 },
    NUMBER = { 2 },
    PAGES = { 375-385 },
    NOTE = { cited By 162 },
    ABSTRACT = { Landscape genetics plays an increasingly important role in the management and conservation of species. Here, we highlight some of the opportunities and challenges in using landscape genetic approaches in conservation biology. We first discuss challenges related to sampling design and introduce several recent methodological developments in landscape genetics (analyses based on pairwise relatedness, the application of Bayesian methods, inference from landscape resistance and a shift from population-based to individual-based analyses). We then show how simulations can foster the field of landscape genetics and, finally, elaborate on technical developments in sequencing techniques that will dramatically improve our ability to study genetic variation in wild species, opening up new and unprecedented avenues for genetic analysis in conservation biology. © 2010 Springer Science+Business Media B.V. },
    AFFILIATION = { Department of Wildlife Ecology and Management, University Freiburg, Tennenbacher Str. 4, 79106 Freiburg, Germany; Rocky Mountain Research Station, US Forest Service, Missoula, MT, United States; Michigan State University, East Lansing, MI 48824, United States; Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON M5S 3G5, Canada; Laboratoire TIMC, Group of Mathematical Biology, UMR CNRS 5525, Université Joseph Fourier, 38706 La Tronche, France; Evolutionary Biology and Ecology, CP 160/12, Faculté des Sciences, Université Libre de Bruxelles, 50 Av. F. Roosevelt, 1050 Brussels, Belgium; WSL Swiss Federal Research Institute, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland; Laboratoire d' Ecologie Alpine, UMR CNRS 5553, Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 09, France; Fish and Wildlife Resources, University of Idaho, Moscow, ID 83844-1136, United States; Laboratoire Population Environnement Développement, UMR 151 UP/IRD, Université de Provence, 3 Place Victor Hugo, 13331 Marseille Cedex 03, France },
    AUTHOR_KEYWORDS = { Bayesian analysis; Conservation biology; Genotyping; Isolation by distance; Landscape resistance; Sampling design; Simulation modelling },
    DOCUMENT_TYPE = { Review },
    DOI = { 10.1007/s10592-009-0044-5 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952096687&doi=10.1007%2fs10592-009-0044-5&partnerID=40&md5=f7ca2f7816f6aa5bf78cd73da172bfa4 },
}

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