Darwinian fishery science: lessons from the Atlantic silverside (<i>Menidia menidia</i>)
Bibliographic record
Abstract
The potential of fishing mortality to cause rapid evolutionary changes in life history has received relatively little attention. By focusing only on ecological responses, standard fisheries theory and practice implicitly assume either that genetic influences on life history in the wild are negligible or that natural selection and adaptation is a slow process that can be effectively ignored. Lack of contrary evidence has allowed these assumptions to persist. Drawing upon >25 years of research on the Atlantic silverside (Menidia menidia), we show that adaptive genetic variation in many traits is finely tuned to natural variation in climate. Much of this variation is caused by a gradient in size-selective winter mortality and involves two- to threefold changes in physiological traits that influence population productivity. Many other species are now known to display similar patterns. Harvest experiments show that these traits can evolve rapidly in response to size-selective fishing. Hence, the pool of genotypes that code for life history traits is a highly dynamic property of populations. We argue that the lessons from Menidia are applicable to many exploited species where similar observations would be difficult to obtain and advocate greater use of species models to address fundamental questions in fishery science.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".