Environmental adaptation in Chinook salmon (Oncorhynchus tshawytscha) throughout their North American range

Abstract

Landscape genomics is a rapidly growing field with recent advances in both genotyping efficiency and statistical analyses that provide insight towards local adaptation of populations under varying environmental and selective pressure. Chinook salmon (Oncorhynchus tshawytscha) are a broadly distributed Pacific salmon species, occupying a diversity of habitats throughout the northeastern Pacific with pronounced variation in environmental and climate features but little is understood regarding local adaptation in this species. We used a multivariate method, redundancy analysis (RDA), to identify polygenic correlations between 19,703 SNP loci and a suite of environmental variables in 46 collections of Chinook salmon (1956 total individuals) distributed throughout much of its North American range. Models in RDA were conducted on both rangewide and regional scales by hierarchical partitioning of the populations into three distinct genetic lineages. Our results indicate that between 5.8 and 21.8% of genomic variation can be accounted for by environmental features, and 566 putatively adaptive loci were identified as targets of environmental adaptation. The most influential drivers of adaptive divergence included precipitation in the driest quarter of the year (Rangewide and North Coastal Lineage, anova P = 0.002 and 0.01, respectively), precipitation in the wettest quarter of the year (Interior Columbia River Stream-Type Lineage, anova P = 0.03), variation in mean diurnal range in temperature (South Coastal Lineage, ANOVA P = 0.005), and migration distance (Rangewide, anova P = 0.001). Our results indicate that environmental features are strong drivers of adaptive genomic divergence in this species, and provide a foundation to investigate how Chinook salmon might respond to global environmental change.

Climate variables explain neutral and adaptive variation within salmonid metapopulations: the importance of replication in landscape genetics

“…; Hecht et al . ). However, our ability to test for patterns of polygenic adaptation was limited in this study, and future work would benefit from a greater number of genetic markers and testing approaches specific to discovering polygenetic adaptation.…”

Genomics and telemetry suggest a role for migration harshness in determining overwintering habitat choice, but not gene flow, in anadromous Arctic Char

“…Other studies have identified SNP markers linked to migration timing in both Chinook salmon (Brieuc, Ono, Drinan, & Naish, ) and steelhead trout ( O. mykiss ; Hess, Zendt, Matala, & Narum, ). Finally, Hecht, Matala, Hess, and Narum () linked variation in RAD markers with 24 environmental correlates using redundancy analysis in Chinook salmon, finding that freshwater migration distance was the environmental variable explaining most of the genomic variation. This last result parallels our own observations suggesting the importance of migratory harshness and is also corroborated by many classic studies demonstrating the importance of migratory difficulty as a selective factor driving adaptation in morphological, physiological and life history traits (Bernatchez & Dodson, ; Crossin et al., ; Eliason et al., ; Schaffer & Elson, ).…”

On the maintenance of genetic variation and adaptation to environmental change: considerations from population genomics in fishes

“…In contrast, fewer fish studies have used a landscape genomic framework to detecting selection by finding statistical associations between local allele frequencies and environmental variables and those have mainly been performed on salmonids (Zueva et al , ; Hecht et al , ; Hand et al , ). For instance, Hecht et al () used RADseq to genotype nearly 2000 Chinook salmon Oncorhynchus tshawytscha (Walbaum 1792) from 46 localities at about 20 000 SNPs in order to document environmental adaptation throughout the North American range of the species, from California to Alaska. They applied a multivariate landscape genomic technique (redundancy analysis, RDA), to identify correlations between genetic markers and 24 environmental and climatic traits.…”