A typology of fisheries management tools: using experience to catalyse greater success
Bibliographic record
Abstract
Abstract Fisheries provide nutrition and livelihoods for coastal populations, but many fisheries are fully or over‐exploited and we lack an approach for analysing which factors affect management tool performance. We conducted a literature review of 390 studies to assess how fisheries characteristics affected management tool performance across both small‐scale and large‐scale fisheries. We defined success as increased or maintained abundance or biomass, reductions in fishing mortality or improvements in population status. Because the literature only covered a narrow set of biological factors, we also conducted an expert elicitation to create a typology of broader fishery characteristics, enabling conditions and design considerations that affect performance. The literature suggested that the most commonly used management tool in a region was often the most successful, although the scale of success varied. Management tools were more often deemed successful when used in combination, particularly pairings of tools that controlled fishing mortality or effort with spatial management. Examples of successful combinations were the use of catch limits with quotas and limited entry, and marine protected areas with effort restrictions. The most common factors associated with inadequate biological performance were ‘structural’ issues, including poor design or implementation. The expert‐derived typologies revealed strong local leadership, high community involvement and governance capacity as common factors of success across management tool categories (i.e. input, output and technical measures), but the degree of importance varied. Our results are designed to inform selection of appropriate management tools based on empirical data and experience to increase the likelihood of successful fisheries management.
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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 | 0.029 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".