Annotation and gene ontology
Full annotation results for SNPs associated with each variable are given in Supporting Information (Table S2, tabs 6–10). Of the 2,336 unique candidate SNPs associated with the climate variables, 1,440 SNPs were linked to functionally annotated genes (Blast -score > 60), which represents 10.6% of the total candidates set (13,534). TMAX delivered the highest amount of linked functionally annotated genes (474), followed by PWQ(312), TSEAS (237), PMA (214) and TMIN (203). Most of these candidate SNPs were linked to functionally annotated genes (Table 2) . For example, JAR00198, associated with both TSEAS and TMIN, was located in a trans-cinnamate 4-monooxygenase (TCMO) gene; JAR00662, associated with TSEAS, was found in a UPF0496 protein gene; two SNPS associated with TMAX, JAR00038 and JAR00207 were found on transcription repressor MYB6 and transcription factor MYB44 genes respectively. For PMA, JAR02395 was located in a peroxidase 72 gene; and for PWQ, JAR00273 was located in a 10 kDa chaperonin gene.
Gene ontology enrichment analysis explored how groups of annotated SNPs relate to similar functions (Table S2, tabs 11-15). Several enriched GO terms in the biological process category are highlighted (Table 3): a GO term associated with response to light stimulus (GO:0009416) was found with the SNPs related to TSEAS. Genes associated with this GO term are linked to cellular response processes (in terms of components movement, enzyme production, and secretion and protein expression) from abiotic stimulus, specifically electromagnetic radiation and light. A GO term related to karrikin stimulus was found associated with TMIN (GO:0080167). As for PMA and PWQ, GO terms with high counts of SNPs were found for each variable (GO:0044763 and GO:1901566, respectively) as well as a term related to UV response (GO:0009411) associated with PMA.
Landscape Modelling
The SNPs associated with enriched GO terms (Table 3) were used in a GDM analysis to measure allelic turnover across climatic gradients (Figure 4). The patterns of allelic turnover varied by climatic variable: overall, GDM showed small to moderate response, in terms of deviance explained. The GO term associated with PWQ explained more deviance on the SNPs group (n = 21 ; 21.22%, Figure 4e) than any other climate variable association using GO-groups of SNPs, followed by GO terms associated with TMIN (n =15 ; 14.27%, Figure 4c). TSEAS, TMAX and PMAshowed a similar deviance for allelic turnover composition (<5% for each group of SNPs). A specific SNP associated with PWQ , JAR00476, explained the highest deviance (35.5%) of all the GO terms groups of SNPs used for the GDM modelling. We also applied a GDM analysis to all individual SNPs associated with the 5 climatic variables (Figure S4), and the SNP that explained the highest deviance for each variable was selected to display spatial patterns of allelic turnover (Figure 5): TSEAS – JAR00269 (39.2%); TMAX – JAR11943 (25.5%); TMIN – JAR01172 (16.8%); PMA – JAR10596 (21.9%) and PWQ – JAR06621 (36.9%). The SNP associated with TSEAS (JAR00269) explained more deviance than any other in the whole dataset across the 5 climate variables, followed by a SNP associated with PWQ (JAR06621). There is rapid turnover noticeable for the three temperature variables from the coastal to eastern populations in the north of the range, and more gradual turnover from the northern populations to the southern populations (Figure 5a, b, c). But even among the three temperature variables, there are major differences in adaptive patterns. For instance, while TSEAS and TMAX display a similar rapid turnover from the coastal to eastern populations in the north of the range, and fairly gradual turnover from the northern populations to the southern populations, TMIN follows the same trend in the northern region, but a rapid turnover is present between the coastal and inland populations in the south region. In contrast, the precipitation variables showed rapid turnover in the southern or central parts of the distribution, and more gradual turnover in the northern distribution (Figure 5d, e). In southern areas, PWQ shows a rapid turnover between coastal and inland southern populations, while PMA shows a more gradual pattern in this region.