Complementarity and sensitivity of benthic state indicators to bottom‐trawl fishing disturbance
Bibliographic record
Abstract
Many indicators have been developed to assess the state of benthic communities and identify seabed habitats most at risk from bottom trawling disturbance. However, the large variety of indicators and their development and application under specific geographic areas and management contexts has made it difficult to evaluate their wider utility. We compared the complementarity/uniqueness, sensitivity, and selectivity of 18 benthic indicators to pressure of bottom trawling. Seventeen common datasets with broad regional representation covering a range of pressure gradients from bottom trawling disturbance (n = 14), eutrophication (n = 1), marine pollution (n = 1), and oxygen depletion (n = 1) were used for the comparison. The outcomes of most indicators were correlated to a certain extent with response to bottom trawling disturbance, and two complementary groups of indicators were identified: diversity-based and biological trait-based indicators. Trait-based indicators that quantify the changes in relative abundance of sensitive taxa were most effective in identifying benthic community change in response to bottom trawling disturbance. None of the indicators responded to the trawling pressure gradient in all datasets, and some showed a response that were opposed to the theoretical expectation for some gradients. Indicators that showed clear responses to bottom trawling disturbance also showed clear responses in at least one other pressure gradient, suggesting those indicators are not pressure specific. These results emphasize the importance of selecting several indicators, at least one from each group (diversity and trait-based), to capture the broader signals of change in benthic communities due to bottom trawling activities. Our systematic approach offers the basis from which scientific advisors and/or managers can select suitable combinations of indicators to arrive at a sensitive and comprehensive benthic status assessment.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".