Data from: Multicollinearity in spatial genetics: separating the wheat from the chaff using commonality analyses

Jerome G. Prunier, Marc Colyn, Kim F. Nimon, Xavier Legendre & Marie Christine Flamand
Direct gradient analyses in spatial genetics provide unique opportunities to describe the inherent complexity of genetic variation in wildlife species and are the object of many methodological developments. However, multicollinearity among explanatory variables are a systemic issue in multivariate regression analyses and are likely to cause serious difficulties in properly interpreting results of direct gradient analyses, with the risk of erroneous conclusions, misdirected research and inefficient or counter-productive conservation measures. Using simulated datasets along with...

Registration Year

  • 2014
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Resource Types

  • Dataset
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Affiliations

  • The University of Texas at Tyler
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  • Universit√© Catholique de Louvain
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  • French National Centre for Scientific Research
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  • National Museum of Natural History
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