Determinants of hierarchical genetic structure in Atlantic salmon populations: environmental factors vs. anthropogenic influences
Bibliographic record
Abstract
Disentangling the effects of natural environmental features and anthropogenic factors on the genetic structure of endangered populations is an important challenge for conservation biology. Here, we investigated the combined influences of major environmental features and stocking with non-native fish on the genetic structure and local adaptation of Atlantic salmon (Salmo salar) populations. We used 17 microsatellite loci to genotype 975 individuals originating from 34 French rivers. Bayesian analyses revealed a hierarchical genetic structure into five geographically distinct clusters. Coastal distance, geological substrate and river length were strong predictors of population structure. Gene flow was higher among rivers with similar geologies, suggesting local adaptation to geological substrate. The effect of river length was mainly owing to one highly differentiated population that has the farthest spawning grounds off the river mouth (up to 900km) and the largest fish, suggesting local adaptation to river length. We detected high levels of admixture in stocked populations but also in neighbouring ones, implying large-scale impacts of stocking through dispersal of non-native individuals. However, we found relatively few admixed individuals suggesting a lower fitness of stocked fish and/or some reproductive isolation between wild and stocked individuals. When excluding stocked populations, genetic structure increased as did its correlation with environmental factors. This study overall indicates that geological substrate and river length are major environmental factors influencing gene flow and potential local adaptation among Atlantic salmon populations but that stocking with non-native individuals may ultimately disrupt these natural patterns of gene flow among locally adapted populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".