Area‐based management of blue water fisheries: Current knowledge and research needs
Bibliographic record
Abstract
Abstract The pelagic fisheries beyond the continental shelves are currently managed with a range of tools largely based on regulating effort or target catch. These tools comprise both static and dynamic area‐based approaches to include gear limitations, closed areas and bycatch limits. There are increasing calls for additional area‐based interventions, particularly expansion of marine protected areas, with many now advocating closing 30% of the oceans to fishing. In this paper, we review the objectives, methods and successes of area‐based management of blue water fisheries across objectives related to food production and environmental, social and economic impacts. We also consider the methods used to evaluate the performance of area‐based regulations and provide a summary of the relative quality of evidence from alternative evaluation approaches. We found that few area‐based approaches have been rigorously evaluated, and that it is often difficult to obtain requisite observational data to define a counterfactual to infer any causal effect for such evaluation. Management agencies have been relatively successful at maintaining important commercial species at or near their target abundance, but success at meeting ecological or social goals is less clear. The high mobility of both target and bycatch species generally reduces the effectiveness of area‐based management, and shifting distributions due to climate change suggest that adaptive rather than static approaches will be preferred. We prioritize research and management actions that would make area‐based management more effective.
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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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".