Sea cucumber fisheries: global analysis of stocks, management measures and drivers of overfishing
Bibliographic record
Abstract
Abstract Worldwide, most sea cucumber fisheries are ineffectively managed, leading to declining stocks and potentially eroding the resilience of fisheries. We analyse trends in catches, fishery status, fishing participation and regulatory measures among 77 sea cucumber fisheries through data from recent fishery reports and fishery managers. Critical gaps in fisheries biology knowledge of even commonly targeted species undermine the expected success of management strategies. Most tropical fisheries are small‐scale, older and typified by numerous (>8) species, whereas temperate fisheries are often emerging, mono‐specific and industrialized. Fisher participation data indicated about 3 million sea cucumber fishers worldwide. Fisher participation rates were significantly related to the average annual yield. permanova analysis showed that over‐exploited and depleted fisheries employed different sets of measures than fisheries with healthier stocks, and a non‐metric multidimensional scaling ordination illustrated that a broad set of regulatory measures typified sustainable fisheries. SIMPER and regression tree analyses identified that the dissimilarity was most related to enforcement capacity, number of species harvested, fleet (vessel) controls, limited entry controls and rotational closures. The national Human Development Index was significantly lower in countries with over‐exploited and depleted fisheries. Where possible, managers should limit the number of fishers and vessel size and establish short lists of permissible commercial species in multispecies fisheries. Our findings emphasize an imperative to support the enforcement capacity in low‐income countries, in which risk of biodiversity loss is exceptionally high. Solutions for greater resilience of sea cucumber stocks must be embedded within those for poverty reduction and alternative livelihood options.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".