Genetic architecture of age at maturity can generate divergent and disruptive harvest-induced evolution
Bibliographic record
Abstract
Life-history traits are generally assumed to be inherited quantitatively. Fishing that targets large, old individuals is expected to decrease age at maturity. In Atlantic salmon (Salmo salar), it has recently been discovered that sea age at maturity is under strong control by a single locus with sexually dimorphic expression of heterozygotes, which makes it less intuitive to predict how life histories respond to selective fishing. We explore evolutionary responses to fishing in Atlantic salmon, using eco-evolutionary simulations with two alternative scenarios for the genetic architecture of age at maturity: (i) control by multiple loci with additive effects and (ii) control by one locus with sexually dimorphic expression. We show that multi-locus control leads to unidirectional evolution towards earlier maturation, whereas single-locus control causes largely divergent and disruptive evolution of age at maturity without a clear phenotypic trend but a wide range of alternative evolutionary trajectories and greater trait variability within trajectories. Our results indicate that the range of evolutionary responses to selective fishing can be wider than previously thought and that a lack of phenotypic trend need not imply that evolution has not occurred. These findings underscore the role of genetic architecture of life-history traits in understanding how human-induced selection can shape target populations.This article is part of the themed issue 'Human influences on evolution, and the ecological and societal consequences'.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".