Seasonality of the gut microbiota of free-ranging white-faced capuchins in a tropical dry forest
Bibliographic record
Abstract
Research on the gut microbiota of free-ranging mammals is offering new insights into dietary ecology. However, for free-ranging primates, little information is available for how microbiomes are influenced by ecological variation through time. Primates inhabiting seasonal tropical dry forests undergo seasonally specific decreases in food abundance and water availability, which have been linked to adverse health effects. Throughout the course of a seasonal transition in 2014, we collected fecal samples from three social groups of free-ranging white-faced capuchin monkeys (Cebus capucinus imitator) in Sector Santa Rosa, Área de Conservación Guanacaste, Costa Rica. 16S rRNA sequencing data reveal that unlike other primates, the white-faced capuchin monkey gut is dominated by Bifidobacterium and Streptococcus. Linear mixed effects models indicate that abundances of these genera are associated with fluctuating availability and consumption of fruit and arthropods, whereas beta diversity clusters by rainfall season. Whole shotgun metagenomics revealed that the capuchin gut is dominated by carbohydrate-binding modules associated with digestion of plant polysaccharides and chitin, matching seasonal dietary patterns. We conclude that rainfall and diet are associated with the diversity, composition, and function of the capuchin gut microbiome. Additionally, microbial fluctuations are likely contributing to nutrient uptake and the health of wild primate populations.
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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 | 0.000 | 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) | 0.001 | 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".