Scale-dependent signatures of local adaptation in a foundation tree species

Understanding local adaptation is critical for conservation management under rapidly changing environmental conditions. Local adaptation inferred from genotype-environment associations may show different genomic patterns depending on the spatial scale of sampling, due to differences in the slope of environmental gradients and the level of gene flow. We compared signatures of local adaptation across the genome of mountain ash (Eucalyptus regnans) at two spatial scales: A species-wide data set and a topographically-complex subregional data set. We genotyped 367 individual trees at over 3700 single-nucleotide polymorphisms (SNPs), quantified patterns of spatial genetic structure among populations, and used two analytical methods to identify loci associated with at least one of three environmental variables at each spatial scale. Together, the analyses identified 549 potentially adaptive SNPs at the subregion scale, and 435 SNPs at the range-wide scale. A total of 39 genic or near-genic SNPs, associated with 28 genes, were identified at both spatial scales, although no SNP was identified by both methods at both scales. We observed that nongenic regions had significantly higher homozygote excess than genic regions, possibly due to selective elimination of inbred genotypes during stand development. Our results suggest that strong environmental selection occurs in mountain ash, and that the identification of putatively adaptive loci can differ substantially depending on the spatial scale of analyses. We also highlight the importance of multiple adaptive genetic architectures for understanding patterns of local adaptation across large heterogenous landscapes, with comparison of putatively adaptive loci among spatial scales providing crucial insights into the process of adaptation.

Automated summary

Understanding local adaptation is crucial for conservation, and this study on mountain ash trees shows that the identification of putatively adaptive loci can vary significantly depending on the spatial scale of analysis. Strong environmental selection was observed, with different genomic patterns of local adaptation at different spatial scales.