Host phylogeny and functional traits differentiate gut microbiomes in a diverse natural community of small mammals
Bibliographic record
Abstract
Differences in the bacterial communities inhabiting mammalian gut microbiomes tend to reflect the phylogenetic relatedness of their hosts, a pattern dubbed phylosymbiosis. Although most research on this pattern has compared the gut microbiomes of host species across biomes, understanding the evolutionary and ecological processes that generate phylosymbiosis requires comparisons across phylogenetic scales and under similar ecological conditions. We analysed the gut microbiomes of 14 sympatric small mammal species in a semi-arid African savanna, hypothesizing that there would be a strong phylosymbiotic pattern associated with differences in their body sizes and diets. Consistent with phylosymbiosis, microbiome dissimilarity increased with phylogenetic distance among hosts, ranging from congeneric sets of mice and hares that did not differ significantly in microbiome composition to species from different taxonomic orders that had almost no gut bacteria in common. While phylosymbiosis was detected among just the 11 species of rodents, it was substantially weaker at this scale than in comparisons involving all 14 species together. In contrast, microbiome diversity and composition were generally more strongly correlated with body size, dietary breadth, and dietary overlap in comparisons restricted to rodents than in those including all lineages. The starkest divides in microbiome composition thus reflected the broad evolutionary divergence of hosts, regardless of body size or diet, while subtler microbiome differences reflected variation in ecologically important traits of closely related hosts. Strong phylosymbiotic patterns arose deep in the phylogeny, and ecological filters that promote functional differentiation of cooccurring host species may disrupt or obscure this pattern near the tips.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".