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Record W2116595332 · doi:10.1111/1365-2435.12009

Life history and the ecology of stress: how do glucocorticoid hormones influence life‐history variation in animals?

2012· article· en· W2116595332 on OpenAlexafffund
Erica J. Crespi, Tony D. Williams, Tim S. Jessop, Brendan Delehanty

Bibliographic record

VenueFunctional Ecology · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsThe Scarborough HospitalUniversity of TorontoSimon Fraser University
FundersDivision of Integrative Organismal SystemsNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyLife history theoryLife historyVariation (astronomy)TraitVertebrateEcologyEvolutionary biologyPhenotypic plasticityReproductionZoologyGenetics

Abstract

fetched live from OpenAlex

Summary Glucocorticoids hormones ( GC s) are intuitively important for mediation of age‐dependent vertebrate life‐history transitions through their effects on ontogeny alongside underpinning variation in life‐history traits and trade‐offs in vertebrates. These concepts largely derive from the ability of GC s to alter energy allocation, physiology and behaviour that influences key life‐history traits involving age‐specific life‐history transitions, reproduction and survival. Studies across vertebrates have shown that the neuroendocrine stress axis plays a role in the developmental processes that lead up to age‐specific early life‐history transitions. While environmental sensitivity of the stress axis allows for it to modulate the timing of these transitions within species, little is known as to how variation in stress axis function has been adapted to produce interspecific variation in the timing of life‐history transitions. Our assessment of the literature confirms that of previous reviews that there is only equivocal evidence for correlative or direct functional relationships between GC s and variation in reproduction and survival. We conclude that the relationships between GC s and life‐history traits are complex and general patterns cannot be easily discerned with current research approaches and experimental designs. We identify several future research directions including: (i) integration of proximate and ultimate measures, including longitudinal studies that measure effects of GC s on more than one life‐history trait or in multiple environmental contexts, to test explicit hypotheses about how GC s and life‐history variation are related and (ii) the measurement of additional factors that modulate the effects of GC s on life‐history traits (e.g. GC receptors and binding protein levels) to better infer neurendocrine stress axis actions. Conceptual models of HPA /I axis actions, such as allostatic load and reactive scope, to some extent explicitly predict the role of GC s in a life‐history context, but are descriptive in nature. We propose that GC effects on life‐history transitions, survival probabilities and fecundity can be modelled in existing quantitative demographic frameworks to improve our understanding of how GC variation influences life‐history evolution and GC ‐mediated effects on population dynamics

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.191
Teacher spread0.167 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations416
Published2012
Admission routes2
Has abstractyes

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