Three decades of observer records reveal ongoing risks of marine mammal depredation and entanglement in Canada’s Atlantic fisheries
Bibliographic record
Abstract
Abstract Marine mammals adapt foraging strategies in response to human activities and environmental change, often resulting in conflicts with fisheries. Whale-fishery interactions, including depredation and entanglement, pose significant challenges for both marine conservation and fisheries management. This study presents the first comprehensive, multi-fishery analysis of marine mammal behaviour and gear-associated incidents using at-sea observer (ASO) records collected between 1990 and 2023 across Canada’s North Atlantic. The final dataset comprised 5184 marine mammal sightings reported by ASOs across multiple fisheries. The aims were to: (1) assess the severity of incident records, ranging from close approaches, depredation, injury, entanglement, and death; (2) calculate the minimum frequency of sightings and incidents by gear type and target species; and (3) identify trends in depredation incidents across species and fisheries. Results reveal widespread depredation behaviour by deep-diving northern bottlenose (Hyperoodon ampullatus) and sperm whales (Physeter macrocephalus), particularly in Greenland halibut (Reinhardtius hippoglossoides) fisheries. Gillnets and bottom trawls were responsible for most entanglement incidents, with small odontocetes like harbour porpoises (Phocoena phocoena) experiencing relatively higher rates of severe entanglement. Entanglement rates for endangered northern bottlenose whales exceeded limits considered sustainable under current recovery objectives. Minimum annual incident rates exceeded 3 observed events per year for long-finned pilot whales (Globicephala melas) and 1.8 for harbour porpoises, across all reported sightings. Risks varied across regions, gear types, and species. These findings highlight the need for fishery-specific mitigation, spatial management, and expanded observer coverage to reduce harmful interactions and promote long-term coexistence between marine mammals and fishing operations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".