The influence of maternal effects in larval survival on fishery harvest reference points for two life-history patterns
Bibliographic record
Abstract
Increased larval viability with increased spawner age (i.e., maternal effects) have been observed in Atlantic cod (Gadus morhua) and Pacific rockfish (Sebastes spp.) stocks. Analytical results from a Beverton–Holt recruitment model indicate density-independent maternal effects affected the relative stock productivity and fishing rate reference points. We simulated populations based on Pacific cod (Gadus macrocephalus) and Pacific ocean perch (Sebastes alutus) to explore how estimates of reference points Fmsy and Fcrash are affected by maternal effects and potential interactions with life-history pattern, recruitment autocorrelation, and exploitation rate. Estimates of Fmsy and Fcrash were made from populations with maternal effects using either total larvae (proportional to eggs) or viable larvae (incorporating the maternal effect). Maternal effects have the largest impact upon estimated population productivity at high fishing rates. Estimates of Fmsy and Fcrash for cod were also affected by autocorrelated recruitment variability because of their reduced longevity compared with Pacific ocean perch. These results suggest the importance of evaluating the influence of maternal effects on estimated stock productivity on a case-by-case basis, particularly for depleted stocks composed of relatively young spawners.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".