Exploring the contribution and significance of animal protein in the diet of bonobos by stable isotope ratio analysis of hair
Bibliographic record
Abstract
In primates, age, sex, and social status can strongly influence access to food resources. In Pan, these criteria are assumed to influence access to vertebrate meat. However, the significance of meat in terms of its role in the nutrition of Pan is still debated. Here we present a study using stable carbon and nitrogen isotope ratios in hair samples from habituated, wild bonobos (Pan paniscus) to explore these issues. Over a period of 5 mo hair samples were collected from fresh bonobo nests at LuiKotale, Democratic Republic of Congo. Hair samples were assigned to known individuals and were of sufficient length to allow the evaluation of isotopic variation over several months. Samples of plant foods and sympatric fauna were also analyzed. The δ(13)C and δ(15)N results of the bonobo hair were remarkably homogeneous over time and for the group as a whole. There are no differences in diet between the sexes. Within the group of males, however, there was a positive correlation between dominance status and δ(15)N. The isotopic data indicate that the contribution of fauna to bonobo diet is marginal and that plant food is the dietary protein source. In only some cases did elevated δ(15)N hair values correlate with observed faunivory and not correspond to the δ(15)N measured in the dominant plant foods. Given the large variation in hunting and meat eating of Pan across the African continent, the detection of seasonal changes in faunivory by elevated δ(15)N values in sectioned ape hair is a promising approach.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".