What is it about?
Spatial genetics is a promising but young discipline, lacking efficient statistical tools to deal with a permanent issue: non-independence among spatial predictors. We showed how commonality analyses can help identify the true drivers of genetic differentiation, thus avoiding spurious conclusions stemming from collinearity issues.
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Why is it important?
As a detailed variance partitioning procedure, commonality analyses allow disentangling the relative contribution of each predictor, identifying the amount of variance uniquely accounted for by each predictor and revealing suppression situations, where collinearity among predictors is responsible for spurious effects, that may otherwise have gone unnoticed.
Perspectives
Commonality analysis is an outstanding tool in spatial genetics, allowing to identify the possible drivers of genetic differentiation while avoiding spurious conclusions that may have otherwise gone unnoticed. A must have in the spatial genetic toolbox.
Dr Jérôme G. Prunier
Station d'Ecologie Expérimentale du CNRS à Moulis
Read the Original
This page is a summary of: Multicollinearity in spatial genetics: separating the wheat from the chaff using commonality analyses, Molecular Ecology, January 2015, Wiley,
DOI: 10.1111/mec.13029.
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