Stochasticity Highlights the Development of Both the Gastrointestinal and Upper-Respiratory-Tract Microbiomes of Neonatal Dairy Calves in Early Life
Bibliographic record
Abstract
The microbiome of dairy calves undergoes extensive change due to various forces during the first weeks of life. Importantly, diseases such as bovine respiratory disease (BRD) and calf diarrhea can have profound impacts on the early-life microbiome. Therefore, a longitudinal, repeated-measures pilot study was designed to characterize the establishment of nasal and fecal microbiomes of dairy calves, assess the governing forces of microbial assembly, and evaluate how disease states impact these microbial ecologies. Dairy calves (n = 19) were clinically evaluated for gastrointestinal and respiratory disease across three weeks beginning at age ≤ seven days old. Fecal (n = 57) and nasal (n = 57) microbial samples were taken for paired-end 16S rRNA gene amplicon sequencing. Taxonomy and diversity analyses were used to characterize early-life nasal and fecal microbiomes. Stochasticity and determinism were measured using normalized stochasticity testing (NST) and Dirichlet multinomial model (DMM). All analyses were tested for statistical significance. Clinical diarrhea was observed in 11 of the 19 calves. Clinical BRD was not independently observed among the cohort; however, two calves presented clinical signs of both BRD and diarrhea. Taxonomic analysis revealed that fecal samples were highlighted by Bacteroidaceae (40%; relative abundance), Ruminococcaceae (13%), and Lachnospiraceae (10%), with changes in diversity (Kruskal–Wallis; p < 0.05) and composition (PERMANOVA; p < 0.05). Clinical diarrhea reduced diversity in the fecal microbiome but did not impact composition. Nasal samples featured Moraxellaceae (49%), Mycoplasmataceae (16%), and Pasteurellaceae (3%). While no diversity changes were seen in nasal samples, compositional changes were observed (p < 0.05). NST metrics (Kruskal–Wallis; p > 0.01) and DMM (PERMANOVA; p < 0.01) revealed that stochastic, neutral theory-based assembly dynamics govern early-life microbial composition and that distinct microbial populations drive community composition in healthy and diarrheic calves.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".