Genomic Basis and Climate Change Vulnerability of Migration Timing in Atlantic Salmon ( <i>Salmo salar</i> )
Bibliographic record
Abstract
ABSTRACT With global environmental change, mismatches between seasonal movements of species and environmental conditions are increasingly impacting survival and persistence. Atlantic salmon ( Salmo salar ) perform long‐distance marine migrations culminating in a return to natal rivers, the timing of which varies among and within populations. Global declines of salmon raise the possibility that phenological mismatches could be a contributing factor; however, the underlying genetic architecture of run timing remains poorly understood. Here, we use a 220 K SNP Array to examine the association of genetic variation with run timing at a population level for 11 North American rivers. We also ask what the potential vulnerability of run timing is to future climate change by estimating trait‐specific genomic offsets, i.e., predicted shifts in allele frequencies at loci associated with run timing under projected climate change, yielding relative estimates for each population. Detected associations suggest a polygenic basis for run timing, including a large structural variant and maturation‐associated genes previously characterised in Atlantic salmon ( six6 , vgll3 ), and ppfia2 , a migration‐timing gene conserved across vertebrates. Genomic offsets associated with climate change impacts for run timing were highest in more northern populations, suggesting potential maladaptation in future migrations. By describing the genetic architecture of run timing in North American Atlantic salmon and possible impacts of climate change on the persistence of life‐history strategies, results from this study contribute towards a better understanding of this complex life‐history trait to inform future conservation management.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".