What happens when we stop bottom trawling
Bibliographic record
Abstract
Based on evidence of conservation successes obtained in marine protected areas, one of the main options to mitigate impact and restore biodiversity that have been damaged by bottom trawling (BT) fishing is to ban the practice in certain areas. Since resources are being increasingly invested in the creation and management of those areas, assessments of their effectiveness are essential for guiding future conservation planning. This is an original study that aims at understanding what happens to marine biota after an area is closed to BT, and which environmental characteristics of the area might influence the ecological outcomes. We synthesized evidence from peer reviewed studies and grey literature. The areas where BT have been excluded range between 0.6 km2 and 10,000 km2. More than one third of them (37%) have less then 20 km2, while only 15% more than 1,000 km2. Approximately 45% of the areas have been established for conservation purposes, while 30% are responses to severely depleted fishing stocks, and 8% are measures to address conflicts between different fisheries. 88% of the areas assessed are located in the N hemisphere, and 81% are in the temperate zone. One third of the areas present biogenic sediment. We have found that more than 50% of the studies compared areas where bottom trawling had ceased to areas where this fishing continued, with no temporal component, while only 15% of the assessments used the more robust BACI (before-after-control-impact) design. Most of the assessments evaluated parameters in epifauna (47%), followed by fish (35%) and infauna (9%). Preliminary results show that 87% of the parameters assessed in the studies presented positive (52%) or neutral (35%) outcomes, indicating that recovery after bottom trawling is possible and happens in a variety of contexts and environments. However, changes can take a long time, and in some cases may be elusive. We expect that the development and expansion of our study will help elucidate some of the possible reasons for different outcomes.
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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 | 0.014 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".