Reconsidering the impossible — linking environmental drivers to growth, mortality, and recruitment of fish
Bibliographic record
Abstract
After a century of research into the drivers of early life (EL) growth and mortality, fisheries science has acquired limited capacity to predict future recruitment. A meta-analysis of stock assessment time series revealed that it may be difficult to identify stock– or environmental–recruitment drivers given limited variability in spawner biomass, recruitment, and survivorship in most populations. In nearly 50% of the stocks, there was limited information at low spawner biomass, limiting the reliability of fits to stock–recruitment models. Furthermore, variations in survivorship in 50% of year-classes resulted in less than a 2.5-fold change in recruitment. Simulations of three scenarios of change in EL growth and mortality rates demonstrated that they must covary positively to reproduce variations in survivorship consistent with observations. The potentially limited reliability of stock–recruitment relationships to predict year-class strength in many stocks and the low variability in survivorship in a large proportion of year-classes has important implications for the development of projections of stock productivity used in scientific advice. Furthermore, if a positive growth–mortality relationship underlies variations in survivorship, new research approaches are required to understand the trophic relationships that govern the dynamics of early life stages of fish and patterns of recruitment variability.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".