Evaluation of methods for spawner–recruit analysis in mixed-stock Pacific salmon fisheries
Bibliographic record
Abstract
Salmon populations harvested in mixed-stock fisheries can exhibit genotypic, behavioral, and life history diversity that can lead to heterogeneity in population productivity and size. Methods to quantify this heterogeneity among populations in mixed-stock fisheries are not well-established but are critical to assessing harvest–biodiversity trade-offs when setting harvest policies. We developed an integrated, age-structured, state-space model that allows for more complete use of available data and sharing of information than simpler methods. We compared a suite of state-space models of varying structural complexity to simpler regression-based approaches and, as an example case, fitted them to data from 13 Chinook salmon (Oncorhynchus tshawytscha) populations in the Kuskokwim drainage in western Alaska. We found biological and policy conclusions were largely consistent among state-space models but differed strongly from regression-based approaches. Simulation trials illustrated our state-space models were largely unbiased with respect to spawner–recruit parameters, abundance states, and derived biological reference points, whereas the regression-based approaches showed substantial bias. These findings suggest our state-space model shows promise for informing harvest policy evaluations of harvest–biodiversity trade-offs in mixed-stock salmon fisheries.
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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 | 0.051 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".