Table 1 Results of the multivariate analyses performed with the pairwise approach

Species	Genetic distance		Environmental factors	r	β	U	C
*C. elaphus*	BCD R² = 0.016*		Null raster (R)	0.120	0.105*	0.0087	0.0058
			Artificial areas (R)	0.059	0.019*	0.0003	0.0031
			Coniferous forests (C)	0.072	0.022*	0.0004	0.0048
			Primary roads (R; k = 1000)	0.034	0.008*	0.0001	0.0011
			Rivers (R; k = 10)	0.014	0.004*	0.0000	0.0002
	a _R R² = 0.013*		Null raster (R)	0.111	0.103*	0.0095	0.0027
			Artificial areas (R)	0.051	0.021*	0.0004	0.0022
			Primary roads (R; k = 1000)	0.028	0.004*	0.0000	0.0008
	LKC R² = 0.015*		Null raster (R)	− 0.120	− 0.117*	0.0108	0.0037
			Agricultural areas (R)	− 0.054	− 0.005*	0.0000	0.0029
			Coniferous forests (C)	− 0.053	− 0.004*	0.0000	0.0028
			Motorways (R; k = 1000)	− 0.009	− 0.004*	0.0000	0.0001
			Railways (R; k = 10)	− 0.013	− 0.000	0.0000	0.0002
*S. scrofa*	BCD R² = 0.024*		Null raster (R)	0.093	0.105*	0.0007	0.0080
			Elevation (C)	− 0.030	− 0.040*	0.0001	0.0008
			Elevation (R)	0.116	0.012*	0.0000	0.0135
			Agricultural areas (R)	− 0.115	− 0.092*	0.0036	0.0096
			Artificial areas (R)	0.039	0.019*	0.0002	0.0013
	a _R R² = 0.012*		Null raster (R)	0.096	0.029*	0.0002	0.0089
			Elevation (R)	0.065	0.076*	0.0013	0.0029
			Agricultural areas (R)	0.061	0.027*	0.0004	0.0033
			Coniferous forests (C)	0.037	0.046*	0.0005	0.0009
			Motorways (R; k = 1000)	0.026	0.018*	0.0003	0.0003
	LKC R² = 0.011*	0 negative	Null raster (R)	− 0.102	− 0.085*	0.0018	0.0086
			Elevation (R)	− 0.049	− 0.021*	0.0001	0.0023
			Artificial areas (R)	− 0.013	− 0.011*	0.0001	0.0001
			Coniferous forests (C)	− 0.044	− 0.017*	0.0001	0.0018
			Railways (R; k = 10)	− 0.007	− 0.002	0.0000	0.0000

For each species and each genetic distance, the table provides the results of a MRDM (multiple regressions on distance matrices) analysis and additional parameters derived from CA (commonality analysis) after having successively removed identified suppressors. For each environmental factor, are provided: Pearson’s correlation coefficient (r), β weights (β), as well as unique and common contributions (U, C) of environmental distances to the variance in the dependent variable. MRDM-CAs were each time performed between one genetic distances matrix and several matrices of environmental distances. (*) refers to significant determination coefficients R² or β-values (p-values < 0.05 after Benjamini–Hochberg correction), “C”/“R” indicate if the considered environmental raster was respectively treated as a conductance or resistance factor, and k corresponds to the parameter used to transform the initial raster file (see the text for further details)

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