Eukaryotic composition across seasons and social groups in the gut microbiota of wild baboons
Bibliographic record
Abstract
BACKGROUND: Animals coexist with complex microbiota, including bacteria, viruses, and eukaryotes (e.g., fungi, protists, and helminths). While high-throughput sequencing is commonly used to characterize bacterial communities in animal microbiota, these methods are less often applied to gut eukaryotic composition. Here we used shotgun metagenomic sequencing to characterize eukaryotic diversity in the microbiomes of wild baboons and tested the degree to which eukaryotic community composition was predicted by host social group membership, sex, age, sequencing depth, and season of sample collection. RESULTS: We analyzed a total of 75 fecal samples collected in 2012 and 2014 from 73 wild baboons in the Amboseli ecosystem in Kenya. DNA from these samples was subjected to shotgun metagenomic sequencing, revealing members of the kingdoms Protista, Chromista, and Fungi in 90.7%, 46.7%, and 20.3% of all samples, respectively (percentages indicate the percent of samples in which each kingdom was observed). Social group membership explained 11.2% of the global diversity in gut eukaryotic species composition, but we did not detect statistically significant effects of season, host age, or host sex. Across samples, the most prevalent protists were Entamoeba coli (74.66% of samples), Enteromonas hominis (53.33% of samples), and Blastocystis subtype 3 (38.66% of samples), while the most prevalent fungi included Pichia manshurica (14.66% of samples), and Ogataea naganishii (6.66% of samples). CONCLUSIONS: Protista, Chromista, and Fungi are common members of the gut microbiome of wild baboons. More work on eukaryotic members of primate gut microbiota is important for primate health monitoring and management strategies.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".