Investigating infant feeding development in wild chimpanzees using stable isotopes of naturally shed hair
Bibliographic record
Abstract
Abstract Measuring the relative contributions of milk and non‐milk foods in the diets of primate infants is difficult from observations. Stable carbon (δ 13 C) and nitrogen (δ 15 N) isotopes in hair can be used to physiologically track infant feeding through development, but few wild studies have done so, likely due to the difficulty in collecting hair non‐invasively. We assessed infant feeding at different ages in wild chimpanzees ( Pan troglodytes ) at Ngogo, Uganda using δ 13 C and δ 15 N of keratin in 164 naturally shed hairs from 29 infants (61 hairs), 6 juveniles (7 hairs), 28 mothers (67 hairs) and 14 adult males (29 hairs). Hairs were collected when they stuck to feces during defecation or from the ground after chimpanzees groomed or rested. We could not distinguish between the hairs of infants and mothers using strand length and diameter. Infants 1–2 years old were most enriched in 13 C and 15 N and showed means of 1.1‰ in δ 13 C and 2.1‰ in δ 15 N above their mothers. Infants at 2 years had hair δ 13 C values like those of their mothers, which suggests infants began relying more heavily on plants around this age. While mother‐infant δ 13 C and δ 15 N differences generally decreased with offspring age, as is expected when infants rely increasingly more on independent foraging through development, milk seemed to remain an important dietary component for infants older than 2.5 years, as evidenced by continuing elevated δ 15 N. We showed that stable carbon and nitrogen isotopes in naturally shed hairs can feasibly detect trophic level differences between chimpanzee infants and mothers. Since it can mitigate some of the limitations associated with behavioral and fecal stable isotope data, the use of hair stable isotopes is a useful, non‐invasive tool for assessing infant feeding development in wild primates.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".