Table 1.
Sampling sites included in the micro-geographic study area with their labels (Pop ID), provenances (Municipality), sample sizes (N), altitude (E), geographic coordinates (Lat: latitude; Long: longitude) and values of annual mean temperature (T) and precipitation (P).
Table 2.
Sampling sites included in the macro-geographic investigation with their labels (Pop ID), provenances (Country), sample sizes (N), altitude (E), geographic coordinates (Lat: latitude; Long: longitude) and values of annual mean temperature (bio01) and precipitation (bio12).
Table 3.
Outlier detection using BayeScan results at the macro-geographic scale: populations assigned according their geographic position (All populations), according to all STRUCTURE clusters (all-clusters).
Table 4.
Summary of significant regression models according to the FDR (False Discovery Rate) method [66].
Figure 1.
Bayesian cluster analysis using STRUCTURE [51].
Log likelihood value (Ln(Pr(X|K)) of Pritchard plot is shown for micro and macro-geographic scales(A). Macro-geographic populations clustering according to the Bayesian method implemented in STRUCTURE (B). The population dot colours represent the cluster that includes the majority of individuals within populations. The species distribution range is in green (created using Q-GIS based on description from [25]).