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Maternal adversity and ecological stressors in natural populations: the role of stress axis programming in individuals, with implications for populations and communities

2012· article· en· W2148650828 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFunctional Ecology · 2012
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsUniversity of TorontoUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaConnaught Fund
KeywordsStressorBiologyEcologyOffspringAdaptive responseNeurosciencePregnancyGenetics

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.307
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it