Uncertainties in population dynamics and outcomes of regulations in sockeye salmon (Oncorhynchus nerka) fisheries: implications for management
Bibliographic record
Abstract
Fisheries managers usually have multiple options available but are often unclear on how to choose among them owing to uncertainties in biological and management components of fisheries systems. We evaluated the performance of current and possible future assessment and management practices for sockeye salmon ( Oncorhynchus nerka ) in British Columbia and Alaska by using a computer model that included major biological and management components and their associated uncertainties (interannual variability in recruitment, age-at-maturity, and sex ratio, as well as uncertainty in observations of spawner abundances, forecasts of recruitment, and outcomes from implementing management regulations). One option for management practices that we evaluated was designed to make the forecasting model more realistic by accounting for long-term trends in age-at-maturity. A second option was designed to reduce deviations between management targets and actual or “realized” harvest levels. We found that compared with practices that ignore those sources of uncertainty, the second option produced annual catches that were higher, on average, and less variable over time while maintaining recruitment above critical conservation levels. Contrary to our expectations, the first modification did not result in comparable benefits. Our results demonstrate the value of using simulation models to evaluate potential modifications to Pacific salmon management practices.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".