Small to modest impact of social group on the gut microbiome of wild Costa Rican capuchins in a seasonal forest
Bibliographic record
Abstract
The horizontal transmission of pathogenic and beneficial microbes has implications for health and development of socially living animals. Social group is repeatedly implicated as an important predictor of gut microbiome structure among primates, with individuals in neighboring social groups exhibiting distinct microbiomes. Here we examine whether group membership is a predictor of gut microbiome structure and diversity across three groups of white-faced capuchins (Cebus capucinus imitator) inhabiting a seasonal Costa Rican forest. We collected 62 fecal samples from 18 adult females during four sampling bouts. Sampling bouts spanned the dry-to-wet-to-dry seasonal transitions. To investigate gut microbial composition, we sequenced the V4 region of the 16S rRNA gene. We used the DADA2 pipeline to assign amplicon sequence variants and the RDP database to classify taxa. Our findings are: 1) gut microbiomes of capuchins clustered by social group in the late dry season, but this pattern was less evident in other sampling bouts; 2) social group was a significant variable in a PERMANOVA test of beta diversity, but it accounted for less variation than season; 3) social group was not an important predictor of abundance for the ten most abundant microbial taxa in capuchins; 4) when examining log2-fold abundances of microbes between social groups, there were significant differences in some pairwise comparisons. While this is suggestive of group-wide differences, individual variation may have a strong impact and should be assessed in future studies. Overall, we found a minor impact of social group membership on the gut microbiota of wild white-faced capuchins. Future research including home range overlap and resource use, as well as fine-scale investigation of individual variation, will further elucidate patterns of socially structured microbes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".