Identification of energy consumption and nutritional stress by isotopic and elemental analysis of urine in bonobos ( <i>Pan paniscus</i> )
Bibliographic record
Abstract
A mounting body of evidence suggests that changes in energetic conditions like prolonged starvation can be monitored using stable isotope ratios of tissues such as bone, muscle, hair, and blood. However, it is unclear if urinary stable isotope ratios reflect a variation in energetic condition, especially if these changes in energetic condition are accompanied by shifts in dietary composition. In a feeding experiment conducted on captive bonobos (Pan paniscus), we monitored urinary δ(13)C, δ(15)N, total C (carbon), total N (nitrogen), and C/N ratios and compared these results with glucocorticoid levels under gradually changing energy availability and dietary composition. Measurements of daily collected urine samples over a period of 31 days showed that while shifts in urinary isotope signatures of δ(13)C and δ(15)N as well as total C were best explained by changes in energy consumption, urinary total N excretion as well as the C/N ratios matched the variation in dietary composition. Furthermore, when correcting for fluctuations in dietary composition, the isotope signatures of δ(13)C and δ(15)N as well as total C correlated with urinary glucocorticoid levels; however, the urinary total N and the C/N ratio did not. These results indicate for the first time that it is possible to non-invasively explore specific longitudinal records on animal energetic conditions and dietary compositions with urinary stable isotope ratios and elemental compositions, and this research provides a strong foundation for investigating how ecological factors and social dynamics affect feeding habits in wild animal populations such as primates.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".