Can catch share fisheries better track management targets?
Bibliographic record
Abstract
Abstract Fisheries management based on catch shares – divisions of annual fleet‐wide quotas among individuals or groups – has been strongly supported for their economic benefits, but biological consequences have not been rigorously quantified. We used a global meta‐analysis of 345 stocks to assess whether fisheries under catch shares were more likely to track management targets set for sustainable harvest than fisheries managed only by fleet‐wide quota caps or effort controls. We examined three ratios: catch‐to‐quota, current exploitation rate to target exploitation rate and current biomass to target biomass. For each, we calculated the mean response, variation around the target and the frequency of undesirable outcomes with respect to these targets. Regional effects were stronger than any other explanatory variable we examined. After accounting for region, we found the effects of catch shares primarily on catch‐to‐quota ratios: these ratios were less variable over time than in other fisheries. Over‐exploitation occurred in only 9% of stocks under catch shares compared to 13% of stocks under fleet‐wide quota caps. Additionally, over‐exploitation occurred in 41% of stocks under effort controls, suggesting a substantial benefit of quota caps alone. In contrast, there was no evidence for a response in the biomass of exploited populations because of either fleet‐wide quota caps or individual catch shares. Thus, for many fisheries, management controls improve under catch shares in terms of reduced variation in catch around quota targets, but ecological benefits in terms of increased biomass may not be realized by catch shares alone.
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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 | 0.035 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".