Performance of stock assessments for mixed-population fisheries: the illustrative case of Atlantic bluefin tuna
Bibliographic record
Abstract
Abstract Accounting for movement and mixing in stock assessment is important for managing sustainable fisheries, particularly for highly migratory species. However, many fisheries management approaches continue to use single-stock, single-area models to assess mixed-population stocks that are known to have complex movement dynamics. We evaluated a single-stock, single-area stock assessment model’s performance on fishery pseudodata generated using a spatially complex operating model that incorporates movement and mixing of simulated Atlantic bluefin tuna-like populations. Structural model misspecification produced positively biased perceptions of size and productivity of the smaller western population, based on supplement by the larger eastern population, and negatively biased perceptions of the size and productivity of the eastern population due to net movement of fish out of the eastern stock area. This bias could lead to unintended overexploitation of the smaller western population and potential for foregone yield of the larger eastern population. Our findings provide a greater understanding of the effects of movement and mixing on single-stock, single-area model-based management approaches and emphasize the importance of explicitly considering these dynamics in ensuring the sustainability of highly migratory species like Atlantic bluefin tuna.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".