Error management theory and the adaptive significance of transgenerational maternal‐stress effects on offspring phenotype
Bibliographic record
Abstract
It is well established that circulating maternal stress hormones (glucocorticoids, GCs) can alter offspring phenotype. There is also a growing body of empirical work, within ecology and evolution, indicating that maternal GCs link the environment experienced by the mother during gestation with changes in offspring phenotype. These changes are considered to be adaptive if the maternal environment matches the offspring's environment and maladaptive if it does not. While these ideas are conceptually sound, we lack a testable framework that can be used to investigate the fitness costs and benefits of altered offspring phenotypes across relevant future environments. We present error management theory as the foundation for a framework that can be used to assess the adaptive potential of maternal stress hormones on offspring phenotype across relevant postnatal scenarios. To encourage rigorous testing of our framework, we provide field-testable hypotheses regarding the potential adaptive role of maternal stress across a diverse array of taxa and life histories, as well as suggestions regarding how our framework might provide insight into past, present, and future research. This perspective provides an informed lens through which to design and interpret experiments on the effects of maternal stress, provides a framework for predicting and testing variation in maternal stress across and within taxa, and also highlights how rapid environmental change that induces maternal stress may lead to evolutionary traps.
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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 | 0.005 | 0.012 |
| 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 | 0.006 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".