Estimating the effect of environment on among population expression trait differentiation
To further address H1 & H2 at the per-transcript level, we identified climatic drivers of adaptive differentiation for eachQ ST category (Fig. 2) using redundancy analysis (RDA) as implemented in the vegan v.2.5.6 package in R (Oksanen et al ., 2013). Following Menon et al . (2021), we classified the environmental variables obtained from ClimateNA into drought- and freeze-associated variables, as these two axes are likely the primary selective agents across the hybrid zone populations. To reduce the dimensionality of the environmental dataset, we performed principal component analysis (PCA) separately for the drought- and freeze-associated variables and retained PC axes that explained at least 90% of the variance. We used the population-level estimates of gene expression as the response matrix with the drought-associated PCs, freeze-associated PCs, and geography as the predictor matrices. Geography was represented by scaled and centered estimates of latitude and longitude. For each Q ST category, we evaluated the effect of the full model that included all three predictors, as well as the marginal effects of terms when the full model was significant (p < 0.05).