Implementing the precautionary approach into fisheries management: Biomass reference points and uncertainty buffers
Bibliographic record
Abstract
Abstract The precautionary approach to fisheries management advocates for risk‐averse management strategies that include biological reference points and account for scientific uncertainty (i.e. process, model and observation uncertainty). In this regard, two approaches have been recommended: (a) biomass reference points to safeguard against low stock biomass, and (b) uncertainty buffers that reduce the catch limit as a function of the scientific uncertainty. This study compares the effectiveness of these two precautionary approaches in recovering over‐exploited fish stocks. We evaluate the performance of more than 80 harvest control rules (HCRs) within a stochastic management strategy evaluation ( MSE ) framework for three stocks with contrasting life‐history parameters and under various levels of scientific uncertainty. The results show that both approaches reduce the risk of overfishing at the expense of expected yield. This risk‐yield trade‐off strongly depends on the HCRs, life‐history parameters of the species, as well as the level of the scientific uncertainty. Nevertheless, some combinations of biomass threshold and limit reference points as well as uncertainty buffers lead to a more favourable risk‐yield trade‐off than other rules. This study elucidates the multiple factors affecting the effectiveness of management strategies and highlights key features of HCRs for precautionary 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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".