Impact of time-varying productivity on estimated stock–recruitment parameters and biological reference points
Bibliographic record
Abstract
Models with time-varying parameters are increasingly being considered in the assessment of fish stocks, but their reliability when used to derive biological reference points or benchmarks has not been thoroughly evaluated. Here, we evaluated stock–recruitment models with and without time-varying productivity in a simulation framework for sockeye salmon (Oncorhynchus nerka) under different scenarios of productivity and exploitation. Ignoring trends in productivity led to overestimates of productivity and underestimates of capacity when both exploitation rates and productivity declined over time, resulting in an underestimation on average of benchmarks of biological status. Despite being less biased, time-varying models had relatively poor fit based on AICc and BIC model selection criteria. Our simulation results were compared with empirical analyses of 12 Fraser River sockeye salmon stocks in British Columbia, Canada. Although benchmarks were less biased when based on time-varying models, underlying true benchmarks based on spawner abundances at maximum sustainable yield, SMSY, trend downwards when productivity declines, which may not be aligned with conservation objectives. We conclude with best practices when adapting biological benchmarks to time-varying productivity.
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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 | 0.016 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".