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Dryad

Data from: Landscape features along migratory routes influence adaptive genomic variation in anadromous steelhead (Oncorhynchus mykiss)

Data files

Oct 18, 2017 version files 57.33 MB
Oct 18, 2017 version files 57.35 MB

Abstract

Organisms typically show evidence of adaptation to features within their local environment. However, many species undergo long-distance dispersal or migration across larger geographical regions that consist of highly heterogeneous habitats. Therefore, selection may influence adaptive genetic variation associated with landscape features at residing sites and along migration routes in migratory species. We tested for genomic adaptation to landscape features at natal spawning sites and along migration paths to the ocean of anadromous steelhead trout (Oncorhynchus mykiss) in the Columbia River Basin. Results from multivariate ordination, gene-environment association, and outlier analyses using 24,526 single-nucleotide polymorphisms (SNPs) provided evidence that adaptive allele frequencies were more commonly associated with landscape features along migration paths than features at natal sites (91.2% versus 8.8% of adaptive loci, respectively). Among the 45 landscape variables tested, migration distance to the ocean and mean annual precipitation along migration paths were significantly associated with adaptive genetic variation in three distinct genetic clusters. Additionally, variables such as minimum migration water temperature and mean migration slope were significant only in inland stocks of steelhead that migrate up to 1200 km farther than those near the coast, indicating regional differences in migratory selective pressures. This study provides novel approaches for investigating migratory corridors and some of the first evidence that environment along migration paths can lead to substantial divergent selection. Consequently, our approach to understand genetic adaptation to migration conditions can be applied to other migratory species when migration or dispersal paths are generally known.