Consequences of sex‐specific sociability for thermoregulation in male vervet monkeys during winter
Bibliographic record
Abstract
Abstract While most primates are tropical animals, a number of species experience markedly cold winters. In a high latitude arid environment, wild female vervet monkeys (Chlorocebus pygerythrus) that are socially integrated experience reduced cold stress. Here, we ask whether sociability is similarly salient for male vervet monkeys, who reside in non‐natal groups as adults and who must, therefore, develop social relationships on arrival. We use body temperature and social data from 15 free‐ranging male vervet monkeys to determine whether the number of grooming partners is as important for males during winter and whether the length of residency is positively associated with body temperature. We also assess whether larger body size and higher dominance rank mitigate the need for social partnerships. Like females, male vervets respond to lower 24 h ambient temperatures and winter's progression by decreasing minimum and mean 24 h body temperatures and by regulating their temperatures less precisely. Male rank had no effect, while larger body size was associated primarily with reduced temperature fluctuations. Males with more social partners sustained higher minimum and mean body temperatures but, unexpectedly, regulated their temperatures less tightly. Further analysis revealed that higher minimum and mean temperatures were best accounted for by the number of female partners, while increased temperature fluctuation was driven by the number of male partners. As winter and the mating season overlap, we interpret this as indicating that a need to sustain male associations incurs physiological stress that is reflected as a thermoregulatory cost. Lastly, we show that longer residency is associated with higher minimum body temperatures and lower temperature fluctuations independently of social affiliation.
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 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 | 0.000 |
| 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 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".