Separating recruitment and mortality time lags for a delay-difference production model
Bibliographic record
Abstract
Many fishery production models implicitly incorporate a single time lag for both recruitment and mortality despite the fact that in populations of breeding adults, deaths occur yearly while the entry of new adults comes from juveniles born potentially many years prior to adulthood. Models that do not account for this difference in timing will overestimate abundance for a decreasing stock and underestimate increases during a recovery period. We investigated the effect of incorporating unequal recruitment and mortality time lags into depletion-based stock reduction analysis (DB-SRA), a stock assessment method for data-poor species. Using both simulated data and catch series of Pacific rockfish (Sebastes spp.), we found that for declining stocks with no mortality delay and a recruitment time lag equal to age-at-maturity, estimated overfishing limits were up to 40% lower than those from the model with both time lags equal to age-at-maturity. Deviation between the two models’ predictions increases with age-at-maturity and natural mortality rate, suggesting that time lag separation is most important for long-lived species. We propose a correction factor for net production models that eliminates stock overestimation due to implicitly equal time lags.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".