Comparing alternative harvest strategies to address robustness to recruitment variability and uncertainty: implications for Alaska sablefish tested with management strategy evaluation
Bibliographic record
Abstract
Developing robust fisheries management strategies for exploited fish stocks is imperative amid rapid ecosystem changes. In Alaska, sablefish ( Anoplopoma fimbria) have recently experienced several large recruitment events, resulting in rapid population growth and a concomitant increase in catch of small, low value fish. Current management may not ensure long-term economic stability nor maintain the age structure diversity necessary for population resilience. Using a management strategy evaluation (MSE) framework, we assessed alternative management strategies under random, regime-like, and recruitment-failure scenarios. Strategies that substantially reduced fishing mortality improved stock size and age diversity. Catch stability constraint strategies provided minimal long-term benefits and increased risk during recruitment collapses, although they slightly accelerated population recovery times. Harvest caps maintained higher population sizes, promoted moderate, consistent catches, and modestly expanded population age structure. However, no strategy prevented population declines under prolonged recruitment failure. Results underscore the importance of refining harvest control rules to better balance catch and population stability.
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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 | 0.031 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".