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Record W4408824628 · doi:10.5194/oos2025-824

What happens when we stop bottom trawling

2025· preprint· en· W4408824628 on OpenAlexaff
Joana Dutilh De Capitani, Amanda C. J. Vincent, Sarah J. Foster

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsTrawlingBottom trawlingTop-down and bottom-up designFisheryComputer scienceFishingBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.061
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0050.007
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.033
GPT teacher head0.286
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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