A fine-scale multi-step approach to understand fish recruitment variability
Bibliographic record
Abstract
Recruitment is one of the dominant processes regulating fish population productivity. It is, however, notoriously difficult to predict, as it is the result of a complex multi-step process. Various fine-scale drivers might act on the pathway from adult population characteristics to spawning behaviour and egg production, and then to recruitment. Here, we provide a holistic analysis of the Northwest Atlantic mackerel recruitment process from 1982 to 2017 and exemplify why broad-scale recruitment-environment relationships could become unstable over time. Various demographic and environmental drivers had a synergetic effect on recruitment, but larval survival through a spatio-temporal match with prey was shown to be the key process. Recruitment was also mediated by maternal effects and a parent-offspring fitness trade-off due to the different feeding regimes of adults and larvae. A mismatch curtails the effects of high larval prey densities, so that despite the abundance of food in recent years, recruitment was relatively low and the pre-existing relationship with overall prey abundance broke down. Our results reaffirm major recruitment hypotheses and demonstrate the importance of fine-scale processes along the recruitment pathway, helping to improve recruitment predictions and potentially fisheries management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".