Consequences of sexual selection for fisheries‐induced evolution: an exploratory analysis
Bibliographic record
Abstract
Reproductive behaviour and mating system complexity may influence fisheries-induced evolution. Mate choice and intrasexual competition might favour late-, large-maturing genotypes in contrast to the selection imposed by many fisheries. Here, we simulate changes to the mean and variance in body size of Atlantic cod (Gadus morhua) concomitant with increased fishing intensity. Comparing selection differentials (S) for length under the assumptions that size does and does not affect reproductive success, we find that the strength of selection for smaller body size associated with increased fishing pressure depends on: (i) the initial variance in body size; (ii) changes to the variance in size with increasing fishing intensity; and (iii) the influence of size on reproductive success. If the initial variability in length is sufficiently high and its coefficient of variation (CV) increases with fishing intensity, the predicted evolutionary shift towards smaller size generated by fishing is less than that expected under the assumption that reproductive success is independent of size. However, if size influences reproduction and if the CV in body size declines as fishing pressure increases, a trend that may be characteristic of many intensively exploited populations, the strength of selection for smaller size is predicted to be comparatively rapid. We conclude that fisheries-induced evolution can be influenced by changes to the mean and variance of traits under sexual selection, and that the benefits of maintaining broad phenotypic variability in traits such as body size may be greater than previously thought.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".