Social hierarchy reveals thermoregulatory trade-offs in response to repeated stressors
Bibliographic record
Abstract
Coping with stressors can require substantial energetic investment, and when resources are limited, such investment can preclude simultaneous expenditure on other biological processes. Among endotherms, energetic demands of thermoregulation can be immense, yet our understanding of whether a stress response is sufficient to induce changes in thermoregulatory investment is limited. Using the black-capped chickadee as a model species, we tested a hypothesis that stress-induced changes in surface temperature, a well-documented phenomenon across vertebrates, stem from trade-offs between thermoregulation and stress responsiveness. Because social subordination is known to constrain access to resources in this species, we predicted that surface temperature and dry heat loss of social subordinates, but not social dominants, would fall under stress exposure at low ambient temperatures (“Ta”), and rise under stress exposure at high Ta, thus permitting a reduction in expenditure toward thermoregulation. To test our predictions, we exposed four social groups of chickadees to repeated stressors and control conditions across a Ta gradient (ndays/treatment/group=30), whilst remotely monitoring social interactions and surface temperatures. Supporting our hypothesis, we show that: 1) social subordinates (n=12), who fed less than social dominants and alone experienced stress-induced mass-loss, displayed significantly larger changes in surface temperature following stress exposure than social dominants (n=8), and 2) stress-induced changes in surface temperature significantly increased heat conservation at low Tas and heat dissipation at high Tas among social subordinates alone. These results suggest that chickadees adjust their thermoregulatory strategies under stress when resources are limited by ecologically relevant processes.
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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 | 0.000 | 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 | 0.001 |
| 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".