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

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 linear and logistic regressions on distance matrices, we illustrate how commonality analysis (CA), a detailed variance partitioning procedure that was recently introduced in the field of ecology, can be used to deal with non-independence among spatial predictors. By decomposing model fit indices into unique and common (or shared) variance components, CA allows identifying the location and magnitude of multicolline...