Report card on ecosystem‐based fisheries management in tuna regional fisheries management organizations
Bibliographic record
Abstract
Abstract International instruments of fisheries governance have set the core principles for the management of highly migratory fishes. We evaluated the progress of tuna Regional Fisheries Management Organizations (tRFMOs) in implementing the ecological component of ecosystem‐based fisheries management (EBFM). We first developed a best case tRFMO for EBFM implementation. Second, we developed criteria to evaluate progress in applying EBFM against this best case tRFMO. We assessed progress of the following four ecological components: target species, bycatch species, ecosystem properties and trophic relationships, and habitats. We found that many of the elements necessary for an operational EBFM are already present, yet they have been implemented in an ad hoc way, without a long‐term vision and a formalized plan. Overall, tRFMOs have made considerable progress monitoring the impacts of fisheries on target species, moderate progress for bycatch species, and little progress for ecosystem properties and trophic relationships and habitats. The tRFMOs appear to be halfway towards implementing the ecological component of EBFM, yet it is clear that the “low‐hanging fruit” has been plucked and the more difficult, but surmountable, issues remain, notably the sustainable management of bycatch. All tRFMOs share the same challenge of developing a formal mechanism to better integrate ecosystem science and advice into management decisions. We hope to further discussion across the tRFMOs to inform the development of operational EBFM plans.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".