The Adaptive Value of Stress‐Induced Phenotypes: Effects of Maternally Derived Corticosterone on Sex‐Biased Investment, Cost of Reproduction, and Maternal Fitness
Bibliographic record
Abstract
The question of why maternal stress influences offspring phenotype is of significant interest to evolutionary physiologists. Although embryonic exposure to maternally derived glucocorticoids (i.e., corticosterone) generally reduces offspring quality, effects may adaptively match maternal quality with offspring demand. We present results from an interannual field experiment in European starlings (Sturnus vulgaris) designed explicitly to examine the fitness consequences of exposing offspring to maternally derived stress hormones. We combined a manipulation of yolk corticosterone (yolk injections) with a manipulation of maternal chick-rearing ability (feather clipping of mothers) to quantify the adaptive value of corticosterone-induced offspring phenotypes in relation to maternal quality. We then examined how corticosterone-induced "matching" within this current reproductive attempt affected future fecundity and maternal survival. First, our results provide support that low-quality mothers transferring elevated corticosterone to eggs invest in daughters as predicted by sex allocation theory. Second, corticosterone-mediated sex-biased investment resulted in rapid male-biased mortality resulting in brood reduction, which provided a better match between maternal quality and brood demand. Third, corticosterone-mediated matching reduced investment in current reproduction for low-quality mothers, resulting in fitness gains through increased survival and future fecundity. Results indicate that the transfer of stress hormones to eggs by low-quality mothers can be adaptive since corticosterone-mediated sex-biased investment matches the quality of a mother to offspring demand, ultimately increasing maternal fitness. Our results also indicate that the branding of the proximate effects of maternal glucocorticoids on offspring as negative ignores the possibility that short-term phenotypic changes may actually increase maternal fitness.
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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 | 0.000 |
| 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".