Population structure and genomic evidence for local adaptation to freshwater and marine environments in anadromous Arctic Char ( <i>Salvelinus alpinus</i> ) throughout Nunavik, Québec, Canada
Bibliographic record
Abstract
Abstract Distinguishing neutral and adaptive genetic variation is one of the main challenges in investigating processes shaping population structure in the wild, and landscape genomics can help identify signatures of adaptation to contrasting environments. Arctic Char ( Salvelinus alpinus ) is an anadromous salmonid and the most harvested fish species by Inuit people, particularly so in Nunavik (Canada), one of the most recently deglaciated region in the world. Unlike most other anadromous salmonids, Arctic Char occupy coastal habitats near their overwintering rivers during their marine phase. The main objective of this study was to document the putative neutral and adaptive genomic variation of anadromous Arctic Char populations in Nunavik (Québec, Canada) and bordering regions. A second objective was to interpret our results in the context of fisheries management in Nunavik. We used genotyping-by-sequencing (GBS) to genotype 18,112 filtered single nucleotide polymorphisms (SNPs) for 650 individuals sampled in 23 locations (average sample size per location = 28) along >2,000 km of coastline. Our results reveal a hierarchical genetic structure, whereby neighboring hydrographic systems harbour distinct populations grouping within major oceanographic basins, namely the Hudson Bay, Hudson Strait, Ungava Bay and Labrador Sea. We found genetic diversity and differentiation to be consistent with both the expected post-glacial recolonization history and patterns of isolation-by-distance reflecting contemporary gene flow. Furthermore, using three gene-environment association (GEA) methods we found genomic evidence for local adaptation to freshwater and marine environmental components, especially in relation to sea-surface and air temperatures during summer, as well as salinity. Our results support fisheries management at a regional level, and other implications on hatchery projects and adaptation to climate change are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".