Alloparenting is associated with reduced maternal lactation effort and faster weaning in wild chimpanzees
Bibliographic record
Abstract
Alloparenting, when individuals other than the mother assist with infant care, can vary between and within populations and has potential fitness costs and benefits for individuals involved. We investigated the effects of alloparenting on the speed with which infants were weaned, a potential component of maternal fitness because of how it can affect inter-birth intervals, in wild chimpanzees (Pan troglodytes schweinfurthii) at Ngogo, Uganda. We also provide, to our knowledge, the first description of alloparenting in this population and present a novel measure of the contribution of milk to infant diets through faecal stable nitrogen isotopes (δ15N). Using 42 mother–infant pairs, we tested associations of two alloparenting dimensions, natal attraction (interest in infants) and infant handling (holding, carrying), to the proportion of time mothers spent feeding and to maternal lactation effort (mean nursing rates and mother–infant δ15N differences). Neither natal attraction nor infant handling was significantly associated with feeding time. Infant handling was inversely associated with both measures of lactation effort, although natal attraction showed no association. Alloparenting may benefit mothers by enabling females to invest in their next offspring sooner through accelerated weaning. Our findings emphasize the significance of alloparenting as a flexible component of female reproductive strategies in some species.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".