Adaptive and maladaptive genetic diversity in small populations; insights from the Brook Charr ( <i>Salvelinus fontinalis)</i> case study
Bibliographic record
Abstract
Abstract Investigating the relative importance of neutral versus selective processes governing the accumulation of genetic variants is a key goal in evolutionary biology. This is particularly true in the context of small populations, where genetic drift can counteract the effect of selection. In this study, we investigated the accumulation of putatively beneficial and harmful variations using 7,950 high-quality filtered SNPs among 36 lacustrine, seven riverine and seven anadromous Brook Charr ( Salvelinus fontinalis ) populations (n = 1,193) from Québec, Canada. Using the Provean algorithm, we observed an accumulation of deleterious mutations that tend to be more prevalent in isolated lacustrine and riverine populations than the more connected anadromous populations. In addition, the absence of correlation between the occurrence of putative beneficial nor deleterious mutations and local recombination rate supports the hypothesis that genetic drift might be the main driver of the accumulation of such variants. Despite the effect of pronounced genetic drift and limited gene flow in non-anadromous populations, several loci representing biological functions of potential adaptive significance were associated with environmental variables, and particularly with temperature. We also identified genomic regions associated with anadromy. We also observed 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 | 0.001 |
| 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".