Maternal age, fecundity, egg quality, and recruitment: linking stock structure to recruitment using an age-structured Ricker model
Bibliographic record
Abstract
Understanding the process of recruitment is fundamental to fisheries biology and management. However, recruitment in natural populations is highly variable and rarely well described by classical stock–recruitment relationships (SRRs). Recent analyses suggest that the age composition of the spawning biomass may play an important role in the mismatch between SRRs and data. Here we develop a generalization of the Ricker SRR that incorporates age structure by allowing mortality and fecundity rates to depend on maternal age. We provide a flexible SRR with biologically interpretable parameters that can be estimated from existing fisheries time series and use a Bayesian framework that enables parameters to be informed by experimental data. We apply our method to the Icelandic population of Atlantic cod ( Gadus morhua ) and show models that include age structure effects outperform the classical Ricker SRR that ignores age structure. Our results indicate a strong effect of spawning stock age structure on recruitment dynamics in this population. Our approach provides a biologically interpretable and immediately applicable method for investigating the consequences for spawning stock age structure on recruitment.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".