Fecal proteomics of wild capuchins reveals impacts of season, diet, age, and, sex on gut physiology
Bibliographic record
Abstract
Abstract Understanding how the physiology of free-ranging mammals is impacted by environmental stressors is a major focus of ecological research. However, the constraints of non-invasive sampling pose serious challenges to the acquisition of physiological data from most species of primates. As a result, little is known about how the gut responds to ecological stimuli at the cellular level in wild populations. Recent research has demonstrated that proteomics could fill this knowledge gap by sequencing and quantifying proteins directly from primate feces. In order to ascertain how the gut of free-ranging white-faced capuchin monkeys ( C. imitator ) is influenced by environmental heterogeneity, diet, age, and sex, we sequenced 45 fecal proteomes from 24 individuals from the Sector Santa Rosa population in Costa Rica, using liquid chromatography-tandem mass spectrometry with label-free quantification. Fecal proteins assigned to C. imitator were strongly localized to gut tissues and functionally enriched for digestive and immune functions. We identified 41 capuchin candidate proteins linked to seasonality, age, sex, and diet. We also quantified abundances of dietary fruit, dietary insects, helminth gut parasites, and gut microbes. Our results demonstrate the viability of using quantitative fecal proteomics in free-ranging populations of mammals to integrate host physiology, diet, and microbial ecology through non-invasive means.
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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 | 0.001 | 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".