Fishing-induced evolution and changing reproductive ecology of fish: the evolution of steepness
Bibliographic record
Abstract
Fishing can induce evolutionary changes in individual life history traits, leading to fish that mature smaller and younger and with larger gonads, so that they reproduce more intensely. The steepness of a stock–recruitment relationship is commonly defined as the fraction of recruitment of an unfished population obtained when the spawning stock biomass is 20% of its unfished level. We use a model of harvest-induced evolutionary change to understand how the steepness of the stock–recruitment relationship changes due to fishing. If the true spawning stock biomass is known, the stock–recruitment relationship changes little under fishing-induced evolution and there is little concern for fisheries management. When management is based on a total biomass – recruitment relationship, recruitment may be underestimated, which is also of little concern from a sustainability perspective. However, when the number of spawners – recruitment relationship is used to forecast recruitment, management practice that ignores the evolution of steepness may overestimate recruitment and therefore recommend catches that exceed safe biological limits. Using outdated maturity ogives underestimates spawning stock biomass, which results in steeper and higher stock–recruitment relationships as life histories evolve. Although of little concern for sustainability, this may pose challenges for practical fisheries management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".