Maternal and environmental influences on egg size and juvenile life‐history traits in Pacific salmon
Bibliographic record
Abstract
Life-history traits such as fecundity and offspring size are shaped by investment trade-offs faced by mothers and mediated by environmental conditions. We use a 21-year time series for three populations of wild sockeye salmon (Oncorhynchus nerka) to test predictions for such trade-offs and responses to conditions faced by females during migration, and offspring during incubation. In years when their 1100 km upstream migration was challenged by high water discharges, females that reached spawning streams had invested less in gonads by producing smaller but not fewer eggs. These smaller eggs produced lighter juveniles, and this effect was further amplified in years when the incubation water was warm. This latter result suggests that there should be selection for larger eggs to compensate in populations that consistently experience warm incubation temperatures. A comparison among 16 populations, with matching migration and rearing environments but different incubation environments (i.e., separate spawning streams), confirmed this prediction; smaller females produced larger eggs for their size in warmer creeks. Taken together, these results reveal how maternal phenotype and environmental conditions can shape patterns of reproductive investment and consequently juvenile fitness-related traits within and among populations.
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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 | 0.000 | 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".