Explicitly incorporating ecosystem-based fisheries management into management strategy evaluation, with a focus on small pelagics
Bibliographic record
Abstract
The need to transition from traditional single-species fisheries management approaches towards ecosystem-based fisheries management (EBFM), or an ecosystem approach to fisheries, is widely recognised. EBFM is particularly important when considering management actions for economically valuable fisheries for small pelagic forage fish, given their key ecological role. Management strategy evaluation (MSE) is an effective approach to advance the quantitative implementation of EBFM by enabling stakeholders to explore trade-offs among competing ecosystem-related objectives. This paper puts forward six different approaches to advance EBFM with MSE explicity, by taking advantage of data and research already available and by guiding future research. These approaches can be grouped into those which (i) involve the operating model and/or link directly to the operating model while potentially providing additional performance metrics to evaluate ecosystem objectives, (ii) can be incorporated into the performance metrics, and (iii) involve the harvest control rule of the management procedure. This review demonstrates that immediate steps can be taken to implement EBFM targeted at quantitative tactical management, even without a complex, data-rich ecosystem operating model.
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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 | 0.028 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".