Impact of a large-scale area closure on patterns of fishing disturbance and the consequences for benthic communities
Bibliographic record
Abstract
Abstract Seasonal area closures of fisheries are primarily used to reduce fishing mortality on target species. In the absence of effort controls, fishing vessels displaced from a closed area will impact fish populations and the environment elsewhere. Based on the observed response of the North Sea beam trawl fleet to the closure of the “cod box” and an existing size-based model of the impacts of beam trawling, we predict the effects of seasonal area closures on benthic communities in the central North Sea. We suggest that repeated seasonal area closures would lead to a slightly more homogeneous distribution of annual trawling activity, although the distribution would remain patchy rather than random. The increased homogeneity, coupled with the displacement of trawling activity to previously unfished areas, is predicted to have slightly greater cumulative impacts on total benthic invertebrate production and lead to localized reductions in benthic biomass for several years. To ensure the effective integration of fisheries and environmental management, the wider consequences of fishery management actions should be considered a priori. Thus, when seasonal closures increase the homogeneity of overall disturbance or lead to the redistribution of trawling activity to environmentally sensitive or previously unfished areas, then effort reductions or permanent area closures should be considered as a management option. The latter would lead to a single but permanent redistribution of fishing disturbance, with lower cumulative impacts on benthic communities in the long run.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".