Data from: Association of genetic and climatic variability in giant sequoia, Sequoiadendron giganteum, reveals signatures of local adaptation along moisture-related gradients

Uncovering the genetic basis of local adaptation is a major goal of
evolutionary biology and conservation science alike. In an era of climate
change, an understanding of how environmental factors shape adaptive
diversity is crucial to predicting species response and directing
management. Here, we investigate patterns of genomic variation in giant
sequoia, an iconic and ecologically important tree species, using
1364 bi-allelic single nucleotide polymorphisms (SNPs). We use
an FST outlier test and two genotype-environment
association methods, latent factor mixed models (LFMM) and redundancy
analysis (RDA), to detect complex signatures of local adaptation. Results
indicate 79 genomic regions of potential adaptive importance, with limited
overlap between the detection methods. Of the 58 loci detected by LFMM, 51
showed strong correlations to a precipitation driven composite variable
and seven to a temperature-related variable. RDA revealed 24 outlier loci
with association to climate variables, all of which showed strongest
relationship to summer precipitation. Nine candidate loci were indicated
by two methods. After correcting for geographic distance, RDA models using
climate predictors accounted for 49% of the explained variance and showed
significant correlations between SNPs and climatic factors. Here, we
present evidence of local adaptation in giant sequoia along gradients of
precipitation and provide a first step towards identifying genomic regions
of adaptive significance. The results of this study will provide
information to guide management strategies that seek to maximize adaptive
potential in the face of climate change.