Landscape genetics
We optimized five single resistance layers using two genetic distances. For the two runs from each optimization process that converged on the same ranking among predictors compared using AICc, we averaged the parameters optimized between the two runs. Elevation was identified as the best model using AICc, followed by roads as the second-highest supported model, regardless of the genetic distance used (Table 1). Elevation also received the highest support as the top-ranked single-surface model across all bootstrap iterations (Table S11). For the elevation model, optimized resistance was positively correlated with elevation, dramatically increasing at low elevation and reaching a plateau at ~1,500 m a.s.l. Although resistance values were also positively associated with roughness, resistance only significantly increased at high roughness (Fig. S6). In terms of road layer optimization, the category of highways displayed the highest resistance, thus acting as barriers to gene flow, whereas the category of main roads exhibited the lowest resistance compared to the category of patches, thus denoting corridors for P. bengalensis (Fig. S7).
After optimization using all possible combinations of resistance layers, we generated 21 models for each genetic distance. Model comparisons using AICc did not converge on the same results for the DNei and DPCA distances. Elevation+roads was identified as the best multi-surface model using DPCA distance, whereas elevation+roughness was the best-supported model using DNei (Table S12). Considering the small delta AICc (< 2) and comparable marginalR 2 of the second-best model, we consider the performance of the elevation+roughness+roads model as good as the top model using DNei. The bootstrap analysis using DNei supports the elevation+roughness model as being the best, followed by the elevation+roads model. In contrast, bootstrap analysis using DPCA distance indicated the elevation model as the best, followed by the elevation+roads model (Table S13). Finally, model comparison using RCM also generated inconsistent results between genetic distances. In this case, the elevation+roughness+roads model was superior to all alternative models using DNei, whereas the Landuse+human density model was selected as the best using DPCA (Fig. S8). Overall, elevation proved to be the most significant single variable, regardless of the genetic distance used. Models including elevation and roads exhibited the highest support according to AICc, and the second highest support based on bootstrap analysis, using both types of genetic distance.