Landscape structure and climatic variation determine Atlantic salmon genetic connectivity in the Northwest Atlantic
Bibliographic record
Abstract
The identification of landscape or climatic correlates of genetic connectivity can reveal the processes that regulate spatial diversity and inform the management and conservation of exploited or endangered species. Increasingly, the role of local adaptation in regulating spatial structure is gaining recognition, though disentangling its influence from that of dispersal and genetic drift remains challenging. We examined genetic connectivity using 15 microsatellite loci in 3546 Atlantic salmon (Salmo salar) from 47 locations in Newfoundland and Labrador. Cluster analysis revealed regional associations, but examples of unexpectedly high divergence over small spatial scales (<50 km) or low divergence over large scales (>500 km) were common with little evidence of isolation by distance (R2= 0.04). We evaluated the hypothesis that spatial structure is less influenced by straying and more by adaptation and (or) drift using sample-associated data on 22 landscape and climate variables and both stepwise multiple regression and redundancy analysis. Landscape variables explained ∼40% of the variation in spatial structure with freshwater habitat area as the largest determinant. Linkages were also observed with water chemistry and winter severity, supporting hypotheses of adaptive divergence. Overall, the results suggest Atlantic salmon populations are structured by extensive genetic drift and low rates of effective straying and demonstrate how a landscape genetics approach can inform wildlife management and conservation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".