When does hypoxia affect management performance of a fishery? A management strategy evaluation of Dungeness crab (<i>Metacarcinus magister</i>) fisheries in Hood Canal, Washington, USA
Bibliographic record
Abstract
Management strategies for fisheries typically do not account for environmental stressors, such as hypoxia (dissolved oxygen < 2 mg·L−1). Hypoxia can lead to shoaling of organisms into normoxic habitats, enhancing catchability, which could reduce the performance of fishery management strategies. Here, we conducted a management strategy evaluation of Dungeness crab (Metacarcinus magister) fisheries in Hood Canal, Washington, a seasonally hypoxic fjord in Puget Sound. Specifically, we asked whether the current management strategy was robust to hypoxia-induced catchability changes under alternative scenarios of illegal take, incidental capture mortality, and reproductive limitation. We find that the management strategy performed well to changes in catchability when illegal and incidental fishing mortality was low and fishing did not lead to reproductive limitation. However, the performance eroded markedly (reduced long-term catch and (or) population and higher catch variation) under the alternative scenarios. These findings underscore the benefit of applying an ecosystem approach to fisheries management because it identifies potential risks to management strategies in systems subject to environmental change.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".