What Do We Really Know About The Ecological Impacts Of Bottom Trawling?
Bibliographic record
Abstract
Given the array of concerns about bottom trawling – and the many attempts to provide reassurance about its sustainability – it is surprising and worrying how little we know about the effects of these fisheries on ocean spaces and species. Our study uncovers critical gaps in our understanding of the global impact of bottom trawling on marine ecosystems, highlighting the challenges these knowledge deficiencies pose for effective environmental, social, and economic management. Our extensive literature survey reveals significant skews in global understanding of the ecological impacts of bottom trawling, in four respects. First, just 164 of the 4000 bottom trawling related papers and reports we screened focused on the gears’ ecological impacts (~4%). Second, relevant assessments have been biased toward the global north (90% of studies), especially the North Atlantic Ocean (48%) and the Mediterranean (21%), despite the majority of BT pressure occurring in the global south. Third, most papers focused on impacts to marine organisms (85%) rather than habitats (30%), with some overlap. Fourth, invertebrates (particularly epifauna) received more attention than vertebrates (56 and 38%, respectively), whereas studies of habitats were more evenly distributed across biogenic and non-biogenic substrates. In all primary research that we surveyed, under one-third of the studies explored impacts of bottom trawling on fish. However, our deep dive into FAO reports (n=2852 studies reviewed and 200 retained), complemented by primary literature (n=36 papers) for data-deficient regions, has so far documented at least 3053 fish species caught by bottom trawlers in national waters (four times previous estimates), spanning 1050 genera, 313 families, 72 orders, and 5 classes. The real number will be greater, as studies predominantly documented medium to large-bodied species (mean max length = 68 ± 3 cm), some studies mentioned unrecorded species, and key bottom trawling regions were underrepresented in FAO reports. About one-tenth of species caught by bottom trawls are assessed as threatened on the IUCN Red List, and 51% as Least Concern. However, 38% were Data Deficient or had not yet been assessed. Our research underscores the pressing need to address the limitations and imbalances in our understanding of the ecological impacts of bottom trawls. These biases and gaps in our understanding means we are allowing bottom trawling without adequate information on the challenges it is posing – placing the burden of proof on ocean ecosystems and ocean dependent people. We advocate for a more representative – and thus holistic – analysis of bottom trawling that encompasses a wider range of regions and taxa. Full understanding is vital to determining the role of bottom trawling in a sustainable ocean future. After all, we cannot manage what we do not know.
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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 | 0.020 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".