Using fisheries risk assessment to inform precautionary and collaborative management in a declining coho salmon fishery
Bibliographic record
Abstract
The conservation and management of Pacific salmon in Canada faces an uncertain future. While fisheries policies, like Canada's Wild Salmon Policy, increasingly emphasize conservation, salmon continue to decline due to cumulative pressures of climate change, habitat loss, and overfishing, requiring precautionary management. We quantified population dynamics for 52 coho salmon populations along the North and Central Coast of British Columbia from 1980 to 2020 to determine population status, assess risks from mixed-stock fisheries spanning U.S., Canadian, and Indigenous jurisdictions, and implemented forward simulations of alternative productivity and harvest scenarios to inform collaborative management. Since 2017, we found productivity shifts associated with marine heatwaves led to a 37% decline in coho abundance and 40%–69% of populations falling below biological reference points. Although long-term coho recovery depended on future productivity trends, reduced harvest across U.S. and Canadian fisheries can improve short-term recoveries. While rebuilding coho will depend largely on whether productivity improves, harvest management remains one of the few tools available to provide a safe operating space for coho populations and their fisheries to adapt to ongoing ecosystem changes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".