A size-based model of the impacts of bottom trawling on benthic community structure
Bibliographic record
Abstract
Bottom trawling causes widespread disturbance to the sediments in shallow-shelf seas. The resultant mortality of benthic fauna is strongly size dependent. We empirically demonstrate that beam trawling frequency in the central North Sea had a greater effect on fauna size distribution in a soft sediment benthic community than variables such as sediment particle size and water depth. Accordingly, we simulated the impacts of trawling disturbance on benthos using a model consisting of 37 organism size classes between 1 μg and 140 g wet weight. The model produced a productionbiomass versus size relationship consistent with published studies and allowed us to predict the impacts of trawling frequency on benthos size distributions. Outputs were consistent with empirical data; however, at high yet realistic trawling frequencies, the model predicted an extirpation of most macrofauna. Empirical data show that macrofauna persist in many heavily trawled regions; therefore, we suggest that trawling by real fisheries is sufficiently heterogeneous to provide spatial refuges less impacted by trawling. If correct, our analyses suggest that fishery management measures that do not reduce total effort but do lead to effort displacement and spatial homogenization (e.g., temporarily closed areas) may have adverse effects on the systemic persistence of intermediate- and large-sized macrofauna.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".