Impact of supplementation on deleterious mutation distribution in an exploited salmonid
Bibliographic record
Abstract
Deleterious mutations have important implications for the evolutionary trajectories of populations. While several studies recently investigated the dynamics of deleterious mutations in wild populations, no study has yet explored the fate of deleterious mutations in a context of populations managed by supplementation. Here, based on a dataset of nine wild and 15 supplemented Lake Trout populations genotyped at 4,982 single nucleotide polymorphisms (SNP)s by means of genotype by sequencing (GBS), we explored the effect of supplementation on the frequency of putatively deleterious variants. Three main findings are consequential for the management of fish populations. First, an increase in neutral genetic diversity in stocked populations compared with unstocked ones was observed. Second, putatively deleterious mutations were more likely to be found in unstocked than in stocked populations, suggesting a lower efficiency to purge deleterious mutations in unstocked lakes. Third, a population currently used as a major source for supplementation is characterized by several fixed putatively deleterious alleles. Therefore, other source populations with lower abundance of putatively deleterious mutations should be favored as sources of supplementation. We discuss management implications of our results, especially pertaining to the joint identification of neutral and deleterious mutations that could help refining the choice of source and sink populations for supplementation in order to maximize their evolutionary potential and to limit their mutation load.
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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".