Mother–offspring conflict and body temperature regulation during gestation and lactation in a wild primate
Bibliographic record
Abstract
Abstract The physiological performance of a mother during reproduction represents a trade‐off between continued investment in her current offspring, and the mother's own survival and ability to invest in future offspring. Here, we used core body temperature ( T b ) patterns to examine the degree to which maternal body temperatures support the infant during periods of gestation and lactation. We implanted 30 wild vervet monkeys ( Chlorocebus pygerythrus ) with miniature data loggers to obtain continuous measurements of core T b during periods of typical (i.e. non‐drought periods) and limited (i.e. drought period) resource availability. We tracked maternal T b profiles across the gestation and lactation periods, associated with 23 births, and compared those with T b profiles of non‐reproductive females. This allowed us to examine the flexibility in maternal body temperatures and test whether limited resource availability shifts priority away from offspring investment and towards self‐maintenance. Vervet monkeys demonstrated the predicted pattern of gestational hypothermia and improved homeothermy in the gestation period during typical conditions, consistent with the maintenance of a thermal gradient to facilitate heat loss from the foetus. During periods of limited resource availability (i.e. drought), mothers were less homeothermic and more hyperthermic during the gestation period. Vervet monkeys showed no evidence of lactational hyperthermia during typical conditions. During the drought, lactating mothers demonstrated hyperthermia and increased variability in body temperature, consistent with the increased metabolic demands and water requirements for milk production required to support growing infants. Although a mother's degree of homeothermy during gestation and lactation was unrelated to her infant's chance of survival to weaning, mothers did show flexibility in the degree to which they prioritized the maintenance of a thermal environment that supports their infant's development. Together, our findings demonstrate that flexibility in a mother's investment in thermoregulation during gestation and lactation may reflect a bet‐hedging trade‐off between self‐maintenance and offspring investment. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".