Why have global shark and ray landings declined: improved management or overfishing?
Bibliographic record
Abstract
Abstract Global chondrichthyan (shark, ray, skate and chimaera) landings, reported to the United Nations Food and Agriculture Organization (FAO), peaked in 2003 and in the decade since have declined by almost 20%. In the FAO's 2012 ‘State of the World's Fisheries and Aquaculture’ report, the authors ‘hoped’ the reductions in landings were partially due to management implementation rather than population decline. Here, we tested their hypothesis. Post‐peak chondrichthyan landings trajectories from 126 countries were modelled against seven indirect and direct fishing pressure measures and eleven measures of fisheries management performance, while accounting for ecosystem attributes. We found the recent improvement in international or national fisheries management was not yet strong enough to account for the recent decline in chondrichthyan landings. Instead, the landings declines were more closely related to fishing pressure and ecosystem attribute measures. Countries with the greatest declines had high human coastal population sizes or high shark and ray meat exports such as Pakistan, Sri Lanka and Thailand. While important progress had been made, country‐level fisheries management measures did not yet have the strength or coverage to halt overfishing and avert population declines of chondrichthyans. Increased implementation of legally binding operational fisheries management and species‐specific reporting is urgently required to avoid declines and ensure fisheries sustainability and food security.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".