Population level impacts of gillnet entanglement mortality on three alcid species in British Columbia, Canada
Bibliographic record
Abstract
Abstract Incidental mortality via entanglement in non-selective gillnets is a known conservation issue for marine birds globally, and specifically, in the productive marine waters of British Columbia, Canada. Three alcid species are particularly susceptible to gillnet bycatch (common murres Uria aalge, marbled murrelets Brachyramphus marmoratus (listed as “Threatened” under Canada’s Species at Risk Act) and rhinoceros auklets Cerorhinca monocerata), with estimates of mortality in commercial salmon net fisheries ranging from hundreds to thousands of individuals annually. Despite the risk posed by gillnets, the population-level impacts of mortality due to entanglement have not been estimated. Therefore, we wanted to (1) understand how varying levels of gillnet bycatch may impact population growth and persistence and (2) estimate the population size needed to withstand recent estimates of entanglement mortality. We used a simulation-based approach of matrix projection models to estimate the impact of gillnet bycatch on population growth and probability of extinction within 25 years. We found that the common murre population was the most vulnerable with current rates of gillnet bycatch producing a high probability of extinction. The population size needed to withstand current estimates of gillnet bycatch was estimated at over an order of magnitude higher than the current population size, indicating bycatch mortality in Canadian waters is taking common murres breeding in the USA. Extinction risk for marbled murrelets was estimated at ∼1% in 25 years given current estimates of gillnet bycatch, contributing to other anthropogenic threats such as loss and fragmentation of nesting habitat. Rhinoceros auklets had very low extinction risk due to the large population size compared to estimates of bycatch. This study highlights the species-specific differences in the impact of bycatch on these alcid populations and the importance of moving away from gillnets toward more selective fishing methods to reduce mortality of vulnerable seabird populations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".