Oceanographic drivers of offspring abundance may increase or decrease reproductive variance in a temperate marine fish
Bibliographic record
Abstract
In species that reproduce into uncertain environments, the relationship between mean reproductive success (the abundance of new recruits) and the variance in reproductive success (whether adults contribute disproportionally more offspring) may not be straightforward because of stochastic environmental processes that create high variance in reproductive success among adults. In this study, we investigated the relationships between oceanography, reproductive success and reproductive variance in the black rockfish, Sebastes melanops, a long-lived temperate reef fish with pelagic larvae. We quantified black rockfish recruitment, genetic diversity and growth rates from otolith microstructure over 5 years (2005-2009) during which oceanographic conditions differed. We used cross-correlations to determine windows of time during which oceanographic variables were significantly correlated with the resulting abundance or genetic diversity of recruits. We found that warmer ocean temperatures were positively correlated with the abundance of recruits, as well as the effective number of breeders. In contrast, the strength of coastal upwelling during settlement was positively correlated with the annual abundance of new recruits, but was negatively correlated with the effective number of breeders. Larval growth rates were explained substantially more by temperature than by upwelling and suggested that temperature affected survival through growth, while upwelling affected survival through transport. Our results indicated that cold ocean temperatures and intense upwelling caused sweepstakes-like processes to operate on black rockfish populations, despite high abundances of recruits. We propose that a decoupling of the mean and variance in reproductive success may be characteristic of organisms that reproduce into uncertain environments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".