Fecal microbiomes of non‐human primates in Western Uganda reveal species‐specific communities largely resistant to habitat perturbation
Bibliographic record
Abstract
Primate gastrointestinal microbial communities are becoming increasingly appreciated for their relevance to comparative medicine and conservation, but the factors that structure primate "microbiomes" remain controversial. This study examined a community of primates in Kibale National Park, Uganda, to assess the relative importance of host species and location in structuring gastrointestinal microbiomes. Fecal samples were collected from primates in intact forest and from primates in highly disturbed forest fragments. People and livestock living nearby were also included, as was a geographically distant population of related red colobus in Kenya. A culture-free microbial community fingerprinting technique was used to analyze fecal microbiomes from 124 individual red colobus (Procolobus rufomitratus), 100 individual black-and-white colobus (Colobus guereza), 111 individual red-tailed guenons (Cercopithecus ascanius), 578 human volunteers, and 364 domestic animals, including cattle (Bos indicus and B. indicus × B. taurus crosses), goats (Caprus hircus), sheep (Ovis aries), and pigs (Sus scrofa). Microbiomes sorted strongly by host species, and forest fragmentation did not alter this pattern. Microbiomes of Kenyan red colobus sorted distinctly from microbiomes of Ugandan red colobus, but microbiomes from these two red colobus populations clustered more closely with each other than with any other species. Microbiomes from red colobus and black-and-white colobus were more differentiated than would be predicted by the phylogenetic relatedness of these two species, perhaps reflecting heretofore underappreciated differences in digestive physiology between the species. Within Kibale, social group membership influenced intra-specific variation among microbiomes. However, intra-specific variation was higher among primates in forest fragments than among primates in intact forest, perhaps reflecting the physical separation of fragments. These results suggest that, in this system, species-specific processes such as gastrointestinal physiology strongly structure microbial communities, and that primate microbiomes are relatively resistant to perturbation, even across large geographic distances or in the face of habitat disturbance.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".