Life’s a ditch: demographic history and environmental factors shape fine-scale local adaptation within small populations of brook trout
Bibliographic record
Abstract
Many studies have investigated the loss of adaptive potential in endangered or exploited species experiencing recent population declines. Less research has studied adaptive genomic variation in small populations known to have persisted for long periods, despite the unique contribution that such populations provide for determining mechanisms underlying population persistence in evolutionary and conservation modeling. Small populations of Brook trout ( Salvelinus fontinalis) have persisted in Cape Race, Newfoundland for >12 000 years. We used genotyping-by-sequencing data to investigate the demographic history and adaptive genomic variation of 26 populations with effective population sizes ( N e ) ranging from 11 to 442, and to explore mechanisms underlying long-term persistence. We show that all populations experienced demographic declines following postglacial colonization but have remained in their current, small N e state for thousands of years. We also reveal greater homozygosity in adaptive alleles within the smallest populations and found signatures of adaptive divergence in small populations relating to several abiotic and biotic factors. Our study illustrates how the demographic history of a species can influence the adaptive dynamics of small populations persisting over long time periods.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".