Data from: Landscape genomics provides evidence of climate-associated genetic variation in Mexican populations of Quercus rugosa Nee

Local adaptation is a critical evolutionary process that allows plants to
grow better in their local compared to nonnative habitat and results in
species-wide geographic patterns of adaptive genetic variation. For forest
tree species with a long generation time, this spatial genetic
heterogeneity can shape the ability of trees to respond to rapid climate
change. Here, we identify genomic variation that may confer local
environmental adaptations and then predict the extent of adaptive mismatch
under future climate as a tool for forest restoration or management of the
widely distributed high-elevation oak species Quercus rugosa in Mexico.
Using genotyping-by-sequencing, we identified 5354 single-nucleotide
polymorphisms (SNPs) genotyped from 103 individuals across 17 sites in the
Trans-Mexican Volcanic Belt, and, after controlling for neutral genetic
structure, we detected 74 FST-outlier SNPs and 97 SNPs associated with
climate variation. Then, we deployed a nonlinear multivariate model,
Gradient Forest (GF), to map turnover in allele frequencies along
environmental gradients and predict areas most sensitive to climate
change. We found that spatial patterns of genetic variation were most
strongly associated with precipitation seasonality and geographical
distance. We identified regions of contemporary genetic and climatic
similarities, and predicted regions where future populations of Q. rugosa
might be at risk due to high expected rate of climate change. Our findings
provide preliminary details for future management strategies of Q. rugosa
in Mexico and also illustrate how a landscape genomic approach can provide
a useful tool for conservation and resource management strategies.