Commercial fishing gear loss in Canada's Pacific Ocean: answering the why, where, and how with a mixed methods, transdisciplinary approach
Bibliographic record
Abstract
Derelict fishing gear comprises a large portion of the world’s marine plastic pollution, causing damage to marine habitats, wildlife, and fishing industries globally. To mitigate these issues, managers and marine stakeholders must understand the reasons for, and areas of, fishing gear loss specific to their region. Additionally, regional case studies are important to add to the global literature on derelict gear research. I conducted a global review of reasons for commercial gear loss, and used the findings to design a commercial fisher questionnaire in Canada’s Pacific region as a case study. I carried out these dockside and on-line questionnaires to record commercial fishers’ experiences with lost gear. Additionally, I used a species distribution model approach to identify variables associated with presence of derelict gear. Lost gear presence data for the model came from both the questionnaire and existing data for the region, and results from the previous literature review and questionnaire informed which environmental and fishing variables to include. I then used results from the model to predict areas with high probability of derelict gear occurrence. The global review highlighted that the most common reasons for gear loss were interactions with other fishing vessels and their gear, marine weather, and snagging on submerged features. Questionnaire results with 29 fishers indicated that snagging gear on rough substrate was the most important reason for loss across all gear categories, and that Hecate Strait, Clayoquot Sound, and the Strait of Georgia were prevalent areas of gear loss. Through the questionnaire, fishers indicated various ways to reduce gear loss including: using high quality gear that is well maintained, knowledge sharing amongst the fleet, preventing overcrowding in fishing areas, and keeping static and active gear types away from each other. The species distribution model approach indicated that bathymetry, fishing effort, and wind were the most important variables in derelict gear occurrence and predicted the highest probability of gear loss in similar areas as the survey. These results can support removal efforts and management decisions to mitigate issues caused by derelict gear by increasing the scientific understanding of the topic in Canada’s Pacific region.
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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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".