Drivers of adaptive genetic variation
To evaluate the relative contributions of demographic history,
environment, and geography to shaping the genetic variation ofScutellaria in Taiwan, partial redundancy analysis (pRDA) was
performed. First, the geographic coordinates were converted into dbMEMs,
and the first three axes were retained as proxies of geographic
structure. Second, PCA was performed using the allele frequencies among
populations, and PC1 and PC2 were retained as proxies of demographic
history. Variation partitioning was carried out using the allele
frequencies of each population as responses and either of the groups of
predictors, including (1) geographic structure, (2) demographic history,
and (3) the eight environmental factors (Fig. 3C) we selected by VIF as
explanatory variables, while conditioning the remaining predictors. The
variation explained by the full model and each testable portion was
assessed and tested for significance. Significant predictors were
selected by forward selection. The pRDA analyses, dbMEM, and forward
selection were conducted using the R package vegan (Oksanen et al.,
2007).