Incorporating demographic information into spawner–recruit analyses alters biological reference point estimates for a western Alaska salmon population
Bibliographic record
Abstract
Changes over time in age, sex, and length-at-age of returning Pacific salmon have been widely observed, suggesting concurrent declines in per capita reproductive output. Thus, assessment models assuming stationary reproductive output may inaccurately estimate biological reference points that inform harvest policies. We extended age-structured state-space spawner–recruit models to accommodate demographic time trends and fishery selectivity to investigate temporal changes in reference points using Kuskokwim River Chinook salmon (Oncorhynchus tshawytscha). We illustrate that observed demographic changes have likely reduced per capita reproductive output in an additive manner, for example, models including changes in both length-at-age and age composition showed larger declines than models incorporating only one time trend. Translated into biological reference points using a yield-per-recruit algorithm, we found escapement needed for maximum sustained catch has likely increased over time, but the magnitude further depended on size-selective harvest (i.e., larger increases for reference points based on larger mesh gillnets). Compared to traditional salmon assessments, our approach that acknowledges demographic time trends allows more complete use of available data and facilitates evaluating trade-offs among gear-specific harvest policies.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".