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.