Size reductions and genomic changes within two generations in wild walleye populations: associated with harvest?
Bibliographic record
Abstract
Abstract The extent and rate of harvest‐induced genetic changes in natural populations may impact population productivity, recovery, and persistence. While there is substantial evidence for phenotypic changes in harvested fishes, knowledge of genetic change in the wild remains limited, as phenotypic and genetic data are seldom considered in tandem, and the number of generations needed for genetic changes to occur is not well understood. We quantified changes in size‐at‐age, sex‐specific changes in body size, and genomic metrics in three harvested walleye ( Sander vitreus ) populations and a fourth reference population with low harvest levels over a 15‐year period in Mistassini Lake, Quebec. We also collected Indigenous knowledge (IK) surrounding concerns about these populations over time. Using ~9,000 SNPs, genomic metrics included changes in population structure, neutral genomic diversity, effective population size, and signatures of selection. Indigenous knowledge revealed overall reductions in body size and number of fish caught. Smaller body size, a small reduction in size‐at‐age, nascent changes to population structure (population differentiation within one river and homogenization between two others), and signatures of selection between historical and contemporary samples reflected coupled phenotypic and genomic change in the three harvested populations in both sexes, while no change occurred in the reference population. Sex‐specific analyses revealed differences in both body size and genomic metrics but were inconclusive about whether one sex was disproportionately affected. Although alternative explanations cannot be ruled out, our collective results are consistent with the hypothesis that genetic changes associated with harvesting may arise within 1–2.5 generations in long‐lived wild fishes. This study thus demonstrates the need to investigate concerns about harvest‐induced evolution quickly once they have been raised.
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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 | 0.001 | 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) | 0.000 | 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".