Determining the adaptive potential of maternal stress
Bibliographic record
Abstract
Ecological and medical researchers are investing great effort to determine the role of Maternally-Derived Stress (MDS) as an inducer of phenotypic plasticity in offspring. Many researchers have interpreted phenotypic responses as unavoidable negative outcomes (e.g., small birth weight, high anxiety); however, a biased underestimate of the adaptive potential of MDS-induced effects is possible if they are not viewed within an ecologically relevant or a life-history optimization framework. We review the ecological and environmental drivers of MDS, how MDS signals are transferred to offspring, and what responses MDS induces. Results from four free-living vertebrate systems reveals that although MDS induces seemingly negative investment trade-offs in offspring, these phenotypic adjustments can be adaptive if they better match the offspring to future environments; however, responses can prove maladaptive if they unreliably predict (i.e., are mismatched to) future environments. Furthermore, MDS-induced adjustments that may prove maladaptive for individual offspring can still prove adaptive to mothers by reducing current reproductive investment, and benefitting lifetime reproductive success. We suggest that to properly determine the adaptive potential of MDS, researchers must take a broader integrated life-history perspective, appreciate both the immediate and longer term environmental context, and examine lifetime offspring and 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".