Can fishing gear protect non-target fish? Design and evaluation of bycatch reduction technology for commercial fisheries
Bibliographic record
Abstract
The impacts of commercial fishing extend far beyond direct effects on targeted species. As much as 40% of global marine catch is attributable to bycatch, or the capture of non-target organisms which occurs during fishing. The amount of bycatch in a fishery is determined in part by the selectivity of the industry’s fishing gear, and bycatch mitigation often focuses on improving the selectivity of these gears. This thesis explores bycatch mitigation through the design and evaluation of bycatch reduction devices (BRDs), or fishing gear modifications aimed specifically at reducing non-target catch while maintaining the catch of target species. I examine BRDs using a three-pronged assessment, which tests a modified gear’s effects on non-target catch, on target catch, and on practicality for use in commercial fisheries (all relative to unmodified gear). I first perform a global-scale meta-analysis on technologies designed to protect elasmobranchs (sharks and rays) from longline fisheries. I show that most technologies are broadly ineffective at reducing elasmobranch bycatch, and that many studies fail to adequately assess novel BRDs across all three dimensions of gear performance. The remainder of my thesis focuses on the research and development of BRDs for a British Columbia fishery which employs trapping gear to capture spot prawns (Pandalus platyceros). Using data from fishery-independent surveys, I show that these traps catch rockfish (Sebastes spp.) as bycatch, a multi-species genus which is depleted due to overfishing and which suffers high discard mortality due to barotrauma incurred during the fishing process. I demonstrate that a novel underwater camera system can be used to study prawn traps in situ, and use insights from this analysis to inform the design of BRDs for prawn traps. I conclude my thesis with an assessment of BRDs of my own design, using both catch data and in situ observations conducted using my underwater camera apparatus. Overall, this thesis demonstrates the challenges in designing effective BRDs, and provides a framework for assessment that can be used as a template in future studies of fishing gear design.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".