Torbjørn Forseth*, Bjørn T. Barlaup, Bengt Finstad, Peder Fiske, Harald Gjøsæter, Morten Falkegård, Atle Hindar, Tor Atle Mo, Audun H. Rikardsen, Eva B. Thorstad, Leif Asbjørn Vøllestad, and Vidar Wennevik Norwegian Institute for Nature Research, P.O. Box 5685 Sluppen, Trondheim N-7485, Norway Uni Research Environment, P.O. Box 7810, Bergen N-5020, Norway Institute of Marine Research, P.O. Box 1870 Nordnes, Bergen N-5817, Norway Norwegian Institute for Water Research, Gaustadalléen 21, Oslo N-0349, Norway Norwegian Veterinary Institute, P.O. Box 750 Sentrum, Oslo N-0106, Norway UiT The Arctic University of Norway, Tromsø N-9037, Norway Department of Biosciences, University of Oslo, Centre for Ecological and Evolutionary Synthesis, P.O. Box 1066, Blindern, Oslo N-0316, Norway *Corresponding author: tel: þ47 926 43437; e-mail: torbjorn.forseth@nina.no.

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The major threats to Atlantic salmon in Norway

Genetic population assignment used to inform wildlife management and conservation efforts requires panels of highly informative genetic markers and sensitive assignment tests. We explored the utility of machine-learning algorithms (random forest, regularized random forest and guided regularized random forest) compared with FST ranking for selection of single nucleotide polymorphisms (SNP) for fine-scale population assignment. We applied these methods to an unpublished SNP data set for Atlantic salmon (Salmo salar) and a published SNP data set for Alaskan Chinook salmon (Oncorhynchus tshawytscha). In each species, we identified the minimum panel size required to obtain a self-assignment accuracy of at least 90% using each method to create panels of 50-700 markers Panels of SNPs identified using random forest-based methods performed up to 7.8 and 11.2 percentage points better than FST-selected panels of similar size for the Atlantic salmon and Chinook salmon data, respectively. Self-assignment accuracy ≥90% was obtained with panels of 670 and 384 SNPs for each data set, respectively, a level of accuracy never reached for these species using FST-selected panels. Our results demonstrate a role for machine-learning approaches in marker selection across large genomic data sets to improve assignment for management and conservation of exploited populations.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/eva.12524

Applications of random forest feature selection for fine-scale genetic population assignment

Understanding the mechanisms by which populations adapt to their environments is a fundamental aim in biology. However, it remains challenging to identify the genetic basis of traits, provide evidence of genetic changes and quantify phenotypic responses. Age at maturity in Atlantic salmon represents an ideal trait to study contemporary adaptive evolution as it has been associated with a single locus in the vgll3 region and has also strongly changed in recent decades. Here, we provide an empirical example of contemporary adaptive evolution of a large-effect locus driving contrasting sex-specific evolutionary responses at the phenotypic level. We identified an 18% decrease in the vgll3 allele associated with late maturity in a large and diverse salmon population over 36 years, induced by sex-specific selection during sea migration. Those genetic changes resulted in a significant evolutionary response only in males, due to sex-specific dominance patterns and vgll3 allelic effects. The vgll3 allelic and dominance effects differed greatly in a second population and were likely to generate different selection and evolutionary patterns. Our study highlights the importance of knowledge of genetic architecture to better understand fitness trait evolution and phenotypic diversity. It also emphasizes the potential role of adaptive evolution in the trend towards earlier maturation observed in numerous Atlantic salmon populations worldwide. Age at maturity in Atlantic salmon has been associated with a single locus with sex-specific effects. Here, the authors show rapid evolution towards early maturity in males of a large salmon population induced by sex-specific selection during sea migration.

Rapid sex-specific evolution of age at maturity is shaped by genetic architecture in Atlantic salmon.

We used over 154 000 scale samples collected from salmon fisheries in the large River Teno system over a 40-year period to quantify life history diversity and long-term trends. We identified 120 di...

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Life history variation across four decades in a diverse population complex of Atlantic salmon in a large subarctic river

Addressing biocomplexity in fisheries management is a challenge requiring an ability to differentiate among distinct populations contributing to fisheries. We produced extensive genetic baseline data involving 36 sampling locations and 33 microsatellite markers, which allowed characterization of the genetic structure and diversity in a large Atlantic salmon (Salmo salar) population complex of the River Teno system, northernmost Europe. Altogether, we identified 28 hierarchically structured and genetically distinct population segments (global FST = 0.065) corresponding exceptionally well with their geographical locations. An assessment of factors affecting the stock identification accuracy indicated that the identification success is largely defined by the interaction of genetic divergence and the baseline sample sizes. The choice between the two statistical methods tested for performance in genetic stock identification, ONCOR and cBAYES, was not critical, albeit the latter demonstrated slightly higher ide...

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Ian Russel

Papers

Working group on North Atlantic salmon (WGNAS)