Estimates of Shark at-vessel, Post-release Mortality, and Retention Ban Effects on Stopping Overfishing
Bibliographic record
Abstract
Sharks are among the most threatened groups of exploited fishes, comprising common bycatch across many fisheries. Management efforts intended to safeguard threatened species have increasingly focused on retention bans to reduce bycatch mortality. However, the population effects of such measures remain unevaluated across species. We combined available data from 160 studies providing estimates of at-vessel or post-release mortality for 147 taxa caught by different fishing gears to create random-forest regression models and predict mortality rates for 341 shark species incidentally captured by longlines or gillnets. Smaller-bodied species inhabiting shallow waters were more likely to suffer at-vessel mortality compared to their deep-water counterparts, which were more likely to suffer post-release mortality. We then use results for longlines to simulate the effect of retention bans in reducing fishing mortality to sustainable levels. Our metric consists of the ratio between the proportion of each species’ population caught and discarded (PMAX) under a retention ban divided by the fishing mortality (F) predicted to achieve maximum sustainable yield (FMSY). Our calculations show that a retention ban yielded an average ~three-fold higher PMAX compared to FMSY, with 18% of the species having PMAX/FMSY < 2, 72.3% having 2 < PMAX/FMSY < 5, and 9.7% having PMAX/FMSY > 5. For threatened species, median PMAX/FMSY = 2.28 and non-threatened ones had median PMAX/FMSY = 2.77. Our study shows that retention prohibitions could reduce shark mortality, but must be combined with additional measures to stop overfishing, especially for low-productivity species.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".