Temporal variation of the microbiome is dependent on body region in a wild mammal (Tamiasciurus hudsonicus)
Bibliographic record
Abstract
Microbial communities are increasingly being recognized as important to host health in wild mammals, but how these communities are characterized can have important consequences on the results of these studies. Previous research has explored temporal variation in microbial communities in humans and lab mammals, but few have investigated how microbiomes fluctuate in wild populations and none have examined the temporal dynamics of these fluctuations in different body regions on a wild mammal. Using Illumina MiSeq sequencing of the V3-V4 16S rRNA gene regions, we characterized the buccal and gut microbiomes of wild North American red squirrels, Tamiasciurus hudsonicus, to measure changes in these two microbiomes over short (<2 weeks), medium (2-4 weeks) and long (>1 month) term sampling periods. While we observed short and medium temporal stability in the buccal microbiome, the gut microbiome varied between medium and long-term sampling periods. There was no evidence of intra-individual correlations between buccal and gut microbiome change, suggesting that temporal stability is dependent on the body region and factors affecting microbial stability may be specific to body sites. From these findings, we urge researchers to be cautious in interpreting results from single temporal sampling periods when quantifying characteristic microbiomes in wild mammals.
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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 | 0.000 | 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".