A global history of bottom trawling and disturbance to continental shelf systems
Bibliographic record
Abstract
The bottom trawling industry, which provides roughly one quarter of global seafood landings, has a great environmental cost. By dragging nets and other collection devices over the seabed, trawling degrades and impacts seabed habitats, catches a range of non-target species, and has potentially significant implications for our climate via disturbance of sedimentary carbon stocks. Despite this, we have a relatively poor understanding of how, when, and where, intense historical trawling impacts occurred on continental shelf seas. In this presentation, we describe our work to reconstruct the long, lost history of bottom trawling, from its growth in the North Sea during the 18th century, to its global spread and industrialisation in the 1950s. We make use of sources such as national archives, government records, historical accounts, and popular media, to estimate the size and fishing power of early trawling fleets during the 19th and early 20th century. We combine this with descriptions of expanding trawling grounds and past trawling gears to map seabed impacts backwards in time. Latterly, we describe the expansion of bottom trawling post-1950 using trawling landings and effort data collated by the Sea Around Us Project (University British Columbia). We do this by allocating fleet data to fishing ports around the world, and subsequently modelling fishing effort and behaviour across the continental shelves for the period ca. 1950 to 2010. In doing so, we aim to provide a first synthesised global history of bottom trawling, as well as estimates of shelf seabed areas impacted by these activities. The results of this study are expected to shed new night light on the scale and extent of seabed disturbance caused by the bottom trawling industry, while emphasising the importance of historical context for addressing and managing contemporary marine conservation and today’s climate challenges.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".