Biophysical models unravel associations between glucocorticoids and thermoregulatory costs across avian species
Bibliographic record
Abstract
Abstract Glucocorticoids (GCs) are metabolic hormones that regulate physiological and behavioural responses to environmental change and mediate homeostasis maintenance in vertebrates. Despite the assumption that GCs covary with energy metabolism, we yet lack a mechanistic understanding of how environmental factors such as temperature modulate GC variation through their effect on organismal energy balance. In particular, the mechanisms linking temperature‐dependent metabolic rate and GCs at broad spatial scales and across species remain poorly investigated. Here we used biophysical models to calculate thermoregulatory costs (i.e. the amount of heat required to keep body temperature in homeostasis) of free‐living birds as a function of environmental conditions, body size, shape and insulating layer of feathers. We then investigated the link between ambient temperature, cost of thermoregulation and baseline plasma GC concentrations in a comparative study including GC data from 94 bird species from HormoneBase. We found a significant, positive association between thermoregulatory costs and baseline GC concentrations. Interestingly, models including thermoregulatory costs better explained GC variation when compared to those including ambient temperature as a predictor variable. This result suggests that body size and shape fundamentally modulate energy requirements for thermoregulation and thereby GC concentrations in the wild. By providing a mechanistic description of the link between ambient temperature and thermoregulatory costs, biophysical models provide a tool to predict the impact of environmental conditions on energy metabolism. Our work demonstrates that differences in thermoregulation modulate variation in GC concentrations across a broad climatic gradient. A free Plain Language Summary can be found within the Supporting Information of this article.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".