Landscape genomics
We used generalized dissimilarity modelling (GDM) to visualise the relationship between allele frequency and climate (Ferrier et al., 2007). GDM is a statistical method that predicts spatial patterns of allelic turnover across geographic regions due to climate by generating an I-spline turnover plot for each tested predictor and uses percent deviance explained as a measure of model fit. GDM analyses was run using the gdm package v 1.3.7 in R (Manion et al., 2018), considering a genotypic matrix (pairwise F ST) and a pairwise climate matrix that includes geographic coordinates. GDM was applied on all the putatively adaptive SNPs identified by the EAA as significant. For each variable, the SNP with highest value of deviance explained was selected for plotting and mapping of predicted allelic turnover to test our landscape sorting of adaptive alleles hypothesis.
Following the GDM transformation of the climate variables for each SNP, we performed PCA on the extracted values using R to generate three principal components. The three PCs are then converted into a RGB raster grid (R = PC1, G = PC2 and B = PC3) using custom R rode. The RGB layers were displayed using QGIS V3.16 (QGI.S.org, 2021) overlaying the distribution of jarrah. The RGB colour palette assigned to each of the raster layers will display the allelic turnover in the geographic space, where similar colours correspond to similar predicted patterns of adaptive genetic variability. To test the hypothesis of additive variation, we ran GDM analyses on groups of SNPs related to specific GO terms for each of the five climates and visualised how the allelic turnover within the GO term was related to that climate. To compare importance of GO terms, we added deviance of SNP groups together to create an ‘additive score’. The HierFSTAT package (Goudet, 2005) in R was used to create population pairwise F STmatrices with the SNPs from top GO terms for each climate variable. Overall, this model addresses genetic variation that is related to climate variables, discriminating this variation from geographic distances (Fitzpatrick & Keller, 2015). The GDM spline plots show the association between predicted ecological distances and genetic dissimilarities; the y ‐axis on the spline plots is therefore labelled as partial genetic distance, as it describes a portion of genetic distance, and the height of each spline indicates the magnitude of genomic turnover of a SNP along the climate gradient.