Maternal adversity and ecological stressors in natural populations: the role of stress axis programming in individuals, with implications for populations and communities
Bibliographic record
Abstract
Summary Biomedical researchers have long appreciated that maternal stressors can induce preparative and adaptive programming in offspring via exposure to maternal Glucocorticoids ( GC s). However, few ecologists are aware of the capacity for maternal GC exposure to translate ecological and environmental stressors into preparative and adaptive programmed offspring responses in free‐living systems. We review a growing body of experimental work indicating that circulating maternal GC s link ecological stressors with adaptive programming of the stress axis. Throughout, we emphasise that natural and human‐induced ecological stressors play a fundamental role in programming the capacity of individuals, populations and communities to respond to both predictable and unpredictable ecological change via translating maternal adversity into responsive programming of the vertebrate stress axis. To encourage rigorous testing of this paradigm in a broad range of ecological systems, we introduce the principal extrinsic stressors with a recognised potential to alter maternal circulating GC levels. We then review from the biomedical literature regarding the underlying physiological and epigenetic mechanisms of stress‐induced programming of individual phenotypes to predict how variation in ecological stressors can produce individual variation in stress axis management. To appreciate the potential evolutionary inertia (i.e. adaptive value) of maternally programmed individual variation, we review key recent studies in free‐living systems that test its adaptive function, and then discuss how variation in stress‐axis programming may scale up to influence populations and ecological communities. Given the huge potential of this field, it is encouraging that ecologists are beginning to examine how and why maternal GC s translate ecological and environmental stressors into preparative stress axis programming in free‐living systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".