Contrasting Global Trends in Marine Fishery Status Obtained from Catches and from Stock Assessments
Bibliographic record
Abstract
There are differences in perception of the status of fisheries around the world that may partly stem from how data on trends in catches over time have been used. On the basis of catch trends, it has been suggested that about 70% of all stocks are overexploited due to unsustainable harvesting and 30% of all stocks have collapsed to <10% of unfished levels. Catch trends also suggest that over time an increasing number of stocks will be overexploited and collapsed. We evaluated how use of catch data affects assessment of fisheries stock status. We analyzed simulated random catch data with no trend. We examined well-studied stocks classified as collapsed on the basis of catch data to determine whether these stocks actually were collapsed. We also used stock assessments to compare stock status derived from catch data with status derived from biomass data. Status of stocks derived from catch trends was almost identical to what one would expect if catches were randomly generated with no trend. Most classifications of collapse assigned on the basis of catch data were due to taxonomic reclassification, regulatory changes in fisheries, and market changes. In our comparison of biomass data with catch trends, catch trends overestimated the percentage of overexploited and collapsed stocks. Although our biomass data were primarily from industrial fisheries in developed countries, the status of these stocks estimated from catch data was similar to the status of stocks in the rest of the world estimated from catch data. We conclude that at present 28-33% of all stocks are overexploited and 7-13% of all stocks are collapsed. Additionally, the proportion of fished stocks that are overexploited or collapsed has been fairly stable in recent years.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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".