Optimal dynamic spatial closures can improve fishery yield and reduce fishing-induced habitat damage
Bibliographic record
Abstract
Bottom-towed fishing gears produce significant amounts of seafood globally but can result in seafloor habitat damage. Spatial closures provide an important option for mitigating benthic impacts, but their performance as a fisheries management policy depends on numerous factors, including how fish respond to habitat quality changes. Spatial fisheries management has largely focused on marine protected areas with static locations, overlooking dynamic spatial closures that change through time. To investigate the performance of dynamic closures, we develop a spatial fishery model with fishing-induced habitat damage, where habitat quality can affect both fish productivity and movement. We find that dynamic spatial closures often achieve greater harvest and habitat protection than fixed marine protected areas or conventional nonspatial maximum sustainable yield management, especially under strong habitat–stock interactions. Determining optimal dynamic spatial closures may require considerable information, but we find that simple policies of fixed-schedule rotating closures also perform well. Dynamic spatial closures have received less attention as fisheries management tools, and our results demonstrate their potential value for addressing both harvest and habitat impacts from fishing.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".