Trade‐offs for data‐limited fisheries when using harvest strategies based on catch‐only models
Bibliographic record
Abstract
Abstract Many of the world's fisheries are unassessed, with little information about population status or risk of overfishing. Unassessed fisheries are particularly predominant in developing countries and in small‐scale fisheries, where they are important for food security. Several catch‐only methods based on time series of fishery catch and commonly available life‐history traits have been developed to estimate stock status (defined as biomass relative to biomass at maximum sustainable yield: B/BMSY). While their stock status performance has been extensively studied, performance of catch‐only models as a management tool is unknown. We evaluated the extent to which a superensemble of three prominent catch‐only models can provide a reliable basis for fisheries management and how performance compares across management strategies that control catch or fishing effort. We used a management strategy evaluation framework to determine whether a superensemble of catch‐only models can reliably inform harvest control rules (HCRs). Across five simulated fish life histories and two harvest‐dynamic types, catch‐only models and HCR combinations reduced the risk of overfishing and increased the proportion of stocks above BMSY compared to business as usual, though often resulted in poor yields. Precautionary HCRs based on fishing effort were robust and insensitive to error in catch‐only models, while catch‐based HCRs caused high probabilities of overfishing and more overfished populations. Catch‐only methods tended to overestimate B/BMSY for our simulated data sets. The catch‐only superensemble combined with precautionary effort‐based HCRs could be part of a stepping stone approach for managing some data‐limited stocks while working towards more data‐moderate assessment methods.
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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 | 0.039 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".