Data from: Regression commonality analyses on hierarchical genetic distances.
Prunier, Jérôme G.; Colyn, Marc; Legendre, Xavier; Flamand, Marie-Christine (2016), Data from: Regression commonality analyses on hierarchical genetic distances., Dryad, Dataset, https://doi.org/10.5061/dryad.t5m22
Landscape genetics is emerging as an important way of supporting decision-making in landscape management, in response to the deterioration of matrix permeability due to habitat loss and fragmentation. In line with unremitting methodological developments in landscape genetics, a new analytical procedure was recently proposed as a way of evaluating the effects of landscape gradients on genetic structures. This procedure is based on the computation of inter-individual hierarchical genetic distances (HGD), a metric of genetic differentiation taking into account the hierarchical structure in populations as inferred from clustering algorithms. HGD can be used as dependent variables in multivariate regressions to assess the effects of various landscape predictors on spatial patterns of genetic differentiation. However, multicollinearity may obscure the interpretation of multivariate regressions. We illustrate how regression commonality analyses (CA), a detailed variance partitioning procedure that can be used to deal with multicollinearity issues, can thoroughly improve our understanding of landscape connectivity when HGD are used as a dependent variable, with the red deer (Cervus elaphus) as an empirical example. Using logistic regression commonality analyses on HGD, we showed that semi-natural open areas, transportation infrastructures and, to a lesser extent, urban areas and rivers, were associated with an increase in hierarchical genetic differentiation in red deer. Regressions based on HGD provided detailed results that could not have been obtained with regressions based on standard genetic distances, with notably additional insights as to the possible influence of linear features such as roads and highways on landscape connectivity. Furthermore, CA helped identify synergistic associations among variables as well as suppressors, thus resolving inconsistencies among hierarchical levels and revealing spurious correlations that may have gone unnoticed in the course of classical regression analyses. We thus recommend the use of regression commonality analysis on hierarchical genetic distances as a promising statistical tool for landscape geneticists.