Saving large fish through harvest slots outperforms the classical minimum‐length limit when the aim is to achieve multiple harvest and catch‐related fisheries objectives
Bibliographic record
Abstract
Abstract We address the problem of optimal size‐selective exploitation in an age‐structured fish population model by systematically examining how density and size dependency in growth, mortality and fecundity affect optimal harvesting patterns when judged against a set of fisheries objectives. The study offers five key insights. First, while minimum‐length limits often maximize the biomass yield, exploitation using harvest slots (i.e. regulations that protect both immature and very large individuals) can generate within 95% of maximum yield; harvest slots also generally maximize the number of fish that are harvested. Second, density dependence in growth and size‐dependent mortality predict more liberal optimal size limits than those derived under assumptions of no density and size dependence. Third, strong density dependence in growth maximizes the catch of trophy fish only when modest harvest is introduced; the same holds for numbers harvested, when the stock–recruitment function follows the Ricker type. Fourth, the inclusion of size‐dependent maternal effects on fecundity or egg viability has only limited effects on optimal size limits, unless the increase in fecundity with mass (“hyperallometry”) is very large. However, large hyperallometry in fecundity shifts the optimal size limit for biomass yield from the traditional minimum‐length limit to a harvest slot. Fifth, harvest slots generally provide the best compromises among multiple objectives. We conclude that harvest slots, or more generally dome‐shaped selectivity to harvest, can outperform the standard minimum‐length selectivity. The exact configuration of optimal size limits crucially depends on objectives, local fishing pressure, the stock–recruitment function, and the density and size dependency of growth, mortality and fecundity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".