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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".