Spear20103576

Référence

Spear, S.F., Balkenhol, N., Fortin, M.-J., McRae, B.H., Scribner, K. (2010) Use of resistance surfaces for landscape genetic studies: Considerations for parameterization and analysis. Molecular Ecology, 19(17):3576-3591. (Scopus )

Résumé

Measures of genetic structure among individuals or populations collected at different spatial locations across a landscape are commonly used as surrogate measures of functional (i.e. demographic or genetic) connectivity. In order to understand how landscape characteristics influence functional connectivity, resistance surfaces are typically created in a raster GIS environment. These resistance surfaces represent hypothesized relationships between landscape features and gene flow, and are based on underlying biological functions such as relative abundance or movement probabilities in different land cover types. The biggest challenge for calculating resistance surfaces is assignment of resistance values to different landscape features. Here, we first identify study objectives that are consistent with the use of resistance surfaces and critically review the various approaches that have been used to parameterize resistance surfaces and select optimal models in landscape genetics. We then discuss the biological assumptions and considerations that influence analyses using resistance surfaces, such as the relationship between gene flow and dispersal, how habitat suitability may influence animal movement, and how resistance surfaces can be translated into estimates of functional landscape connectivity. Finally, we outline novel approaches for creating optimal resistance surfaces using either simulation or computational methods, as well as alternatives to resistance surfaces (e.g. network and buffered paths). These approaches have the potential to improve landscape genetic analyses, but they also create new challenges. We conclude that no single way of using resistance surfaces is appropriate for every situation. We suggest that researchers carefully consider objectives, important biological assumptions and available parameterization and validation techniques when planning landscape genetic studies. © 2010 Blackwell Publishing Ltd.

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@ARTICLE { Spear20103576,
    AUTHOR = { Spear, S.F. and Balkenhol, N. and Fortin, M.-J. and McRae, B.H. and Scribner, K. },
    TITLE = { Use of resistance surfaces for landscape genetic studies: Considerations for parameterization and analysis },
    JOURNAL = { Molecular Ecology },
    YEAR = { 2010 },
    VOLUME = { 19 },
    NUMBER = { 17 },
    PAGES = { 3576-3591 },
    NOTE = { cited By 249 },
    ABSTRACT = { Measures of genetic structure among individuals or populations collected at different spatial locations across a landscape are commonly used as surrogate measures of functional (i.e. demographic or genetic) connectivity. In order to understand how landscape characteristics influence functional connectivity, resistance surfaces are typically created in a raster GIS environment. These resistance surfaces represent hypothesized relationships between landscape features and gene flow, and are based on underlying biological functions such as relative abundance or movement probabilities in different land cover types. The biggest challenge for calculating resistance surfaces is assignment of resistance values to different landscape features. Here, we first identify study objectives that are consistent with the use of resistance surfaces and critically review the various approaches that have been used to parameterize resistance surfaces and select optimal models in landscape genetics. We then discuss the biological assumptions and considerations that influence analyses using resistance surfaces, such as the relationship between gene flow and dispersal, how habitat suitability may influence animal movement, and how resistance surfaces can be translated into estimates of functional landscape connectivity. Finally, we outline novel approaches for creating optimal resistance surfaces using either simulation or computational methods, as well as alternatives to resistance surfaces (e.g. network and buffered paths). These approaches have the potential to improve landscape genetic analyses, but they also create new challenges. We conclude that no single way of using resistance surfaces is appropriate for every situation. We suggest that researchers carefully consider objectives, important biological assumptions and available parameterization and validation techniques when planning landscape genetic studies. © 2010 Blackwell Publishing Ltd. },
    AFFILIATION = { Indigo Snake Initiative, 579 Highway 441 South, Clayton, GA 30525, United States; Department of Fish and Wildlife Resources, University of Idaho, Moscow, ID 83844, United States; Leibniz-Institute for Zoo and Wildlife Research, Evolutionary Genetics, Alfred-Kowalke-Strasse 17, D-10252 Berlin, Germany; Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON M5S 3G5, Canada; Nature Conservancy, 1917 1st Avenue, Seattle, WA 98101, United States; Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, United States },
    AUTHOR_KEYWORDS = { circuit theory; landscape genetics; least-cost path; resistance surface },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1111/j.1365-294X.2010.04657.x },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-77956082819&doi=10.1111%2fj.1365-294X.2010.04657.x&partnerID=40&md5=0203d4d8dbd7619d109b346cc7ccae0d },
}

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