Recommendations for estimating and detecting time-varying spawner-recruit dynamics in fish populations
Bibliographic record
Abstract
Models that account for time-varying dynamics are increasingly used in population assessments in recognition of changing biological and environmental conditions. We performed a systematic simulation analysis based on a semelparous life history to evaluate the performance of various Ricker spawner-recruit models including stationary, random-walk, and regime shift models, that offer various interpretations of time-varying dynamics. Estimation models that allowed parameters to vary following random-walks tended to perform equally well or outperform regime shift and stationary models. However these results were not consistent across all scenarios examined. We also evaluated the performance of model selection criteria commonly used to identify time-varying processes. Both likelihood based model selection criteria (AICc and BIC) and cross-validation methods (LFO) were found to be unreliable, with a few exceptions. Changes in productivity were more identifiable than changes in capacity or both parameters, which were often indiscernible from stationary dynamics. The results were sensitive to the magnitude of parameter change and extent of residual variability (unexplained error), with greater changes and lower error being easier to accurately estimate and select. Given this context dependence for the accuracy of parameter estimates with time-varying models, and unreliable nature of selection criteria, we recommend that analysts conduct case-specific simulation-evaluations when model choices may have important and divergent management implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.305 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.011 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".