Conservation successes and challenges for wide-ranging sharks and rays
Bibliographic record
Abstract
Overfishing is the most significant threat facing sharks and rays. Given the growth in consumption of seafood, combined with the compounding effects of habitat loss, climate change, and pollution, there is a need to identify recovery paths, particularly in poorly managed and poorly monitored fisheries. Here, we document conservation through fisheries management success for 11 coastal sharks in US waters by comparing population trends through a Bayesian state-space model before and after the implementation of the 1993 Fisheries Management Plan for Sharks. We took advantage of the spatial and temporal gradients in fishing exposure and fisheries management in the Western Atlantic to analyze the effect on the Red List status of all 26 wide-ranging coastal sharks and rays. We show that extinction risk was greater where fishing pressure was higher, but this was offset by the strength of management engagement (indicated by strength of National and Regional Plan of Action for sharks and rays). The regional Red List Index (which tracks changes in extinction risk through time) declined in all regions until the 1980s but then improved in the North and Central Atlantic such that the average extinction risk is currently half that in the Southwest. Many sharks and rays are wide ranging, and successful fisheries management in one country can be undone by poorly regulated or unregulated fishing elsewhere. Our study underscores that well-enforced, science-based management of carefully monitored fisheries can achieve conservation success, even for slow-growing 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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".