Life history and the ecology of stress: how do glucocorticoid hormones influence life‐history variation in animals?
Bibliographic record
Abstract
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
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".