The faecal metabolome of black howler monkeys (<i>Alouatta pigra</i>) varies in response to seasonal dietary changes
Bibliographic record
Abstract
Mammals rely on the metabolic functions of their gut microbiota to meet their energetic needs and digest potentially toxic components in their diet. The gut microbiome plastically responds to shifts in host diet and may buffer variation in energy and nutrient availability. However, it is unclear how seasonal differences in the gut microbiome influence microbial metabolism and nutrients available to hosts. In this study, we examine seasonal variation in the gut metabolome of black howler monkeys (Alouatta pigra) to determine whether those variations are associated with differences in gut microbiome composition and nutrient intake, and if plasticity in the gut microbiome buffers shortfalls in energy or nutrient intake. We integrated data on the metabolome of 81 faecal samples from 16 individuals collected across three distinct seasons with gut microbiome, nutrient intake and plant metabolite consumption data from the same period. Faecal metabolite profiles differed significantly between seasons and were strongly associated with changes in plant metabolite consumption. However, microbial community composition and faecal metabolite composition were not strongly associated. Additionally, the connectivity and stability of faecal metabolome networks varied seasonally, with network connectivity being highest during the dry, fruit-dominated season when black howler monkey diets were calorically and nutritionally constrained. Network stability was highest during the dry, leaf-dominated season when most nutrients were being consumed at intermediate rates. Our results suggest that the gut microbiome buffers seasonal variation in dietary intake, and that the buffering effect is most limited when host diet becomes calorically or nutritionally restricted.
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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".