DEVELOPING BOTTOM TRAWLS TO IMPROVE SIZE AND SPECIES SELECTIVITY IN NORTH ATLANTIC FISHERIES
Bibliographic record
Abstract
Bottom trawling is an important and principal capture technique in groundfish fisheries, providing a high proportion of fisheries production to the Northern Atlantic. However, bottom trawling is associated with the bycatch of undersized fish and unwanted species, a large concern from a marine ecosystem perspective. Thus, developing gear designs to improve trawl selectivity (reduce the capture of non-target species and juveniles of target species) is necessary. This thesis has focused on developing trawl designs for an emerging redfish (Sebastes spp.) fishery in Canada and understanding the groundgear selectivity of an Icelandic commercial bottom trawl. Firstly, I developed a shaking codend by attaching an elliptical-shaped canvas at the posterior of a T90 codend (codend mesh rotated 90˚ in the transversal direction) to reduce the capture of undersized redfish in the catch. The results showed that the shaking codend had a higher amplitude ratio, period, and total acceleration and captured less redfish < 22 cm than the T90 codend without canvas. Secondly, I developed a semi-pelagic trawl to capture redfish using the French rigging technique. Semi-pelagic trawls are effective at capturing redfish off the seabed and potentially reduce bycatch of unwanted species. Next, I quantified the length-dependent escape of fish under a commercial bottom trawl in Iceland. The results showed length-dependent escape for roundfish, where more small fish escaped under the groundgear than did large fish, compared with flatfish, whose escape varied among species. Finally, I quantified fish behavior at the mouth of a bottom trawl. Small roundfish (Atlantic cod (Gadus morhua) < 20 cm and haddock (Melanogrammus aeglefinus) < 11 cm) tend to escape under the trawl at the center area of the groundgear, while larger individuals with greater swimming capacity seek escape openings under the fishing line at the wing areas. For flatfish and monkfish, the results varied. These length-dependent behaviors are related to fish response behavior, escape behavior, size, and likely swimming capacity. The findings of this thesis can have potential implications for the development of the emerging redfish fishery in Canada and for developing groundgear to improve bottom trawl selectivity in North Atlantic fisheries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".