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Data from: Environmental extremes drive population structure at the northern range limit of Atlantic salmon in North America
Sylvester, Emma V.A.; Beiko, Robert G.; Bentzen, Paul; Paterson, Ian; Horne, John B.; Watson, Beth; Lehnert, Sarah; Duffy, Steven; Clément, Marie; Robertson, Martha J.; Bradbury, Ian R.; Sylvester, Emma V. A. — 2018-08-23 Conservation of exploited species requires an understanding of both genetic diversity and the dominant structuring forces, particularly near range limits, where climatic variation can drive rapid expansions or contractions of geographic range. Here, we examine population structure and landscape associations in Atlantic salmon (Salmo salar) across a heterogeneous landscape near the northern range limit in Labrador, Canada. Analysis of two amplicon-based data sets containing 101 microsatellites and 376 single nucleotide polymorphisms (SNPs) from 35 locations revealed clear differentiation between populations spawning in rivers flowing into a large marine embayment (Lake Melville) compared to coastal populations. The mechanisms influencing the differentiation of embayment populations were investigated using both multivariate and machine-learning landscape genetic approaches. We identified temperature as the strongest correlate with genetic structure, particularly warm temperature extremes and wider annual temperature ranges. The genomic basis of this divergence was further explored using a subset of locations (n=17) and a 220K SNP array. SNPs associated with spatial structuring and temperature mapped to a diverse set of genes and molecular pathways, including regulation of gene expression, immune response, and cell development and differentiation. The results spanning molecular marker types and both novel and established methods clearly show climate-associated, fine-scale population structure across an environmental gradient in Atlantic salmon near its range limit in North America, highlighting valuable approaches for predicting population responses to climate change and managing species sustainability.
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Data from: Estimating the relative fitness of escaped farmed salmon offspring in the wild and modeling the consequences of invasion for wild populations
Sylvester, Emma V.A.; Wringe, Brendan F.; Duffy, Steven J.; Hamilton, Lorraine C.; Fleming, Ian A.; Castellani, Marco; Bentzen, Paul; Bradbury, Ian R.; Sylvester, Emma V. A. — 2018-11-27 Throughout their native range, wild Atlantic salmon populations are threatened by hybridization and introgression with escapees from net-pen salmon aquaculture. Although domestic-wild hybrid offspring have shown reduced fitness in lab and field experiments, consequential impacts on population abundance and genetic integrity remain difficult to predict in the field, in part because the strength of selection against domestic offspring is often unknown and context-dependent. Here we follow a single large escape event of farmed Atlantic salmon in southern Newfoundland and monitor changes in the in-river proportions of hybrids and feral individuals over time using genetically-based hybrid identification. Over a three-year period following the escape, the overall proportion of wild parr increased consistently (total wild proportion of 71.6%, 75.1%, 87.5% each year, respectively), with subsequent declines in feral (genetically pure farmed individuals originating from escaped, farmed adults) and hybrid parr. We quantify the strength of selection against parr of aquaculture ancestry and explore the genetic and demographic consequences for populations in the region. Within-cohort changes in the relative proportions of feral and F1 parr suggest reduced relative survival compared to wild individuals over the first (0.15 and 0.81 for feral and F1, respectively), and second years of life (0.26, 0.83). These relative survivorship estimates were used to inform an individual-based salmon eco-genetic model to project changes in adult abundance and overall allele frequency across three invasion scenarios ranging from short-term to long-term invasion and three relative survival scenarios. Modeling results indicate that total population abundance and time to recovery were greatly affected by relative survivorship and predict significant declines in wild population abundance under continued large escape events and calculated survivorship. Overall this work demonstrates the importance of estimating the strength of selection against domestic offspring in the wild to predict the long-term impact of farmed salmon escape events on wild populations.
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Data from: Applications of random forest feature selection for fine-scale genetic population assignment
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Sylvester, Emma V.A.; Bentzen, Paul; Bradbury, Ian R.; Clément, Marie; Pearce, Jon; Horne, John; Beiko, Robert G.; Sylvester, Emma V. A. — 2017-07-27 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 dataset 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 dataset, 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 datasets to improve assignment for management and conservation of exploited populations. https://creativecommons.org/publicdomain/zero/1.0/

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(Author: Sylvester, Emma V. A.)

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