Patterns and determinants of the global herbivorous mycobiome
Bibliographic record
Abstract
Abstract In spite of their indispensable role in host nutrition, the anaerobic gut fungal (AGF) component of the herbivorous gut microbiome remains poorly characterized. To examine global patterns and determinants of AGF diversity, we generated and analyzed an amplicon dataset from 661 fecal samples from 34 animal species, 9 families, and 6 continents. We identified 56 novel genera, greatly expanding AGF diversity beyond current estimates. Both stochastic (homogenizing dispersal and drift) and deterministic (homogenizing selection) processes played an integral role in shaping AGF communities, with a higher level of stochasticity observed in foregut fermenters. Community structure analysis revealed a distinct pattern of phylosymbiosis, where host-associated (animal species, family, and gut type), rather than ecological (domestication status and biogeography) factors predominantly shaped the community. Hindgut fermenters exhibited stronger and more specific fungal-host associations, compared to broader mostly non-host specific associations in foregut fermenters. Transcriptomics-enabled phylogenomic and molecular clock analyses of 52 strains from 14 genera indicated that most genera with preferences for hindgut hosts evolved earlier (44–58 Mya), while those with preferences for foregut hosts evolved more recently (22–32 Mya). This pattern is in agreement with the sole dependence of herbivores on hindgut fermentation past the Cretaceous-Paleogene (K-Pg) extinction event through the Paleocene and Eocene, and the later rapid evolution of animals employing foregut fermentation strategy during the early Miocene. Only a few AGF genera deviated from this pattern of co-evolutionary phylosymbiosis, by exhibiting preferences suggestive of post-evolutionary environmental filtering. Our results greatly expand the documented scope of AGF diversity and provide an ecologically and evolutionary-grounded model to explain the observed patterns of AGF diversity in extant animal hosts.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".