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.