Adaptive Maternal Investment in the Wild? Links between Maternal Growth Trajectory and Offspring Size, Growth, and Survival in Contrasting Environments
Bibliographic record
Abstract
Life-history theory predicts that investment per offspring should correlate negatively with the quality of the environment that offspring are anticipated to encounter; parents may use their own experience as juveniles to predict this environment and may modulate offspring traits, such as growth capacity and initial size. We manipulated nutrient levels in the juvenile habitat of wild Atlantic salmon (Salmo salar) to investigate the hypothesis that the egg size that maximizes juvenile growth and survival depends on environmental quality. We also tested whether offspring traits were related to parental growth trajectory. Mothers that grew fast when young produced more offspring and smaller offspring than mothers that grew slowly to reach the same size. Despite their size disadvantage, offspring of faster-growing mothers grew faster than those of slower-growing mothers in all environments, counter to the expectation that they would be competitively disadvantaged. However, they had lower relative survival in environments where the density of older predatory/competitor fish was relatively high. These links between maternal (but not paternal) growth trajectory and offspring survival rate were independent of egg size, underscoring that mothers may be adjusting egg traits other than size to suit the environment their offspring are anticipated to face.
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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".