Assessing global marine fishery status with a revised dynamic catch-based method and stock-assessment reference points
Bibliographic record
Abstract
Abstract Anderson, S. C., Branch, T. A., Ricard, D., and Lotze, H. K. 2012. Assessing global marine fishery status with a revised dynamic catch-based method and stock-assessment reference points. – ICES Journal of Marine Science, 69: . The assessment of fishery status is essential for management, yet fishery-independent estimates of abundance are lacking for most fisheries. Methods exist to infer fishery status from catches, but the most commonly used method is biased towards classifying fisheries as overexploited or collapsed through time and does not account for still-developing fisheries. We introduce a revised method that overcomes these deficiencies by smoothing catch series iteratively, declaring fisheries developing within three years of peak catch, and calibrating thresholds to biological reference points. Compared with status obtained from stock-assessment reference points for 210 stocks, our approach provides a more realistic assessment than the original method, but cannot be perfect because catches are influenced by factors other than biomass. Applied to FAO catches, our method suggests in 2006 32% of global fisheries were developing, 27% fully exploited, 25% overexploited, and 16% collapsed or closed. Although less dire than previous assessments, this still indicates substantial numbers of overexploited stocks. Probably because median exploitation rate decreased since 1992, our catch-based results do not reflect recent stabilization of assessed-stock biomass. Whether this outlook also applies to unassessed stocks can only be revealed with increased or more representative collection of biomass- and exploitation-rate trends.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".