Exploring the Utility of the Gut Microbiome as a Longitudinal Health Monitoring Tool in Sanctuary Chimpanzees (<i>Pan troglodytes</i>)
Bibliographic record
Abstract
The primary goal of captive primate management is to ensure optimal health and welfare of the animals in our care. Given that the gut microbiome interacts closely with host metabolism, immunity, and even cognition, it represents a potentially powerful tool for identifying subtle changes in health status across a range of body systems simultaneously. However, thus far, it has not been widely tested or implemented as a monitoring tool. In this study, we used longitudinal microbiome sampling of newly arrived chimpanzees at Chimp Haven to explore the feasibility of using the gut microbiome as a health and welfare biomarker in a sanctuary environment. We also tested the hypothesis that a transition to a new living environment, and integration into new social groupings, would result in temporal changes in chimpanzee gut microbiome composition. The collection of longitudinal microbiome data at Chimp Haven was feasible, and it revealed temporal shifts that were unique to each individual and, in some cases, correlated to other known impacts on health and behavior. We found limited evidence for microbial change over time after arrival at Chimp Haven that was consistent across individuals. In contrast, social group and enclosure, and to a lesser extent, age and sex, were associated with differences in gut microbiome composition. Microbiome composition was also associated with overall health status categories. However, many of the effects we detected were most apparent when using longitudinal data, as opposed to single time point samples. Additionally, we found important effects of technical factors, specifically outdoor temperature and time to collection, on our data. Overall, we demonstrate that the gut microbiome has the potential to be effectively deployed as a tool for health and environmental monitoring in a population of sanctuary chimpanzees, but the design must be carefully considered. We encourage other institutions to apply these approaches and integrate health and physiology data to build on the utility of gut microbiome analysis for ensuring the welfare of captive primates in a range of contexts.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".