Spatiotemporal variation in the gut microbiomes of co‐occurring wild rodent species
Bibliographic record
Abstract
Abstract Mammalian gut microbiomes differ within and among hosts. Hosts that occupy a broad range of environments may exhibit greater spatiotemporal variation in their microbiome than those constrained as specialists to narrower subsets of resources or habitats. This can occur if widespread host encounter a variety of ecological conditions that act to diversify their gut microbiomes and/or if generalized host species tend to form large populations that promote sharing and maintenance of diverse microbes. We studied spatiotemporal variation in the gut microbiomes of three co‐occurring rodent species across an environmental gradient in a Kenyan savanna. We hypothesized: (1) the taxonomic, phylogenetic, and functional compositions of gut microbiomes as predicted using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) differ significantly among host species; (2) microbiome richness increases with population size for all host species; and (3) host species exhibit different levels of seasonal change in their gut microbiomes, reflecting different sensitivities to the environment. We evaluated changes in gut microbiome composition according to host species identity, site, and host population size using three years of capture–mark–recapture data and 351 microbiome samples. Host species differed significantly in microbiome composition, though the two species with more specialized diets and higher demographic sensitivities showed only slightly greater microbiome variability than those of a widespread dietary generalist. Total microbiome richness increased significantly with host population size for all species, but only one of the more specialized species also exhibited greater individual‐level microbiome richness in large populations. Across co‐occurring rodent species with diverse diets and life histories, large host population sizes were associated both with greater population‐level microbiome richness (sampling effects) and turnover in the relative abundance of bacterial taxa (environmental effects), but there was no consistent pattern for individual‐level richness (individual specialization). Together, our results show that maintenance of large host populations contributes to the maintenance of gut microbiome diversity in wild mammals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".