Individual genotypes of 1 416 brook trout genotyped at 14 779 high-quality SNPs for studying local adaptation and maladaptation in small populations
Bibliographic record
Abstract
Investigating the relative importance of neutral versus selective processes governing the accumulation of genetic variants is a key goal in both evolutionary and conservation biology. This is particularly true in the context of small populations, where genetic drift can counteract the effect of selection. Using Brook Charr (Salvelinus fontinalis) from Québec, Canada as a case study, we investigated the importance of demographic versus selective processes governing the accumulation of both adaptive and maladaptive mutations in closed versus open and connected populations to assess gene flow effect. This was achieved by using 14 779 high-quality filtered SNPs genotyped among 1 416 fish representing 50 populations from three life history types: lacustrine (closed populations), riverine and anadromous (connected populations). Using the Provean algorithm, we observed a considerable accumulation of putative deleterious mutations across populations. The absence of correlation between the occurrence of putatively beneficial or deleterious mutations and local recombination rate supports the hypothesis that genetic drift might be the main driver of the accumulation of such variants. However, despite a lower genetic diversity observed in lacustrine than in riverine or anadromous populations, lacustrine populations do not exhibit more deleterious mutations than the two other history types, suggesting that the negative effect of genetic drift in lacustrine populations may be mitigated by that of relaxed purifying selection. Moreover, we also identified genomic regions associated with anadromy, as well as an overrepresentation of transposable elements associated with variation in environmental variables, thus supporting the importance of transposable elements in adaptation.
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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 | 0.001 | 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 | 0.001 | 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) | 0.002 | 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".