Patterns of community assembly in the developing chicken microbiome reveal rapid primary succession
Bibliographic record
Abstract
The fine-scale temporal dynamics of the chicken gut microbiome are unexplored, but thought to be critical for chicken health and productivity. Here, we monitored the fecal microbiome of healthy chickens on days 1-7, 10, 14, 21, 28, and 35 after hatching, and performed 16S rRNA amplicon sequencing in order to obtain a high-resolution census of the fecal microbiome over time. In the period studied, the fecal microbiomes of the developing chickens showed a linear-log increase in community richness and consistent shifts in community composition. Three successional stages were detected: the first stage was dominated by vertically transmitted or rapidly colonizing taxa including Streptococcus and Escherichia/Shigella; in the second stage beginning on day 4, these taxa were displaced by rapid-growing taxa including Lachnospiraceae and Ruminococcus-like species variants; and in the third stage, starting on day 10, slow-growing, specialist taxa including Candidatus Arthrobacter and Romboutsia were detected. The patterns of displacement and the previously reported ecological characteristics of many of the dominant taxa observed suggest that resource competition plays an important role in regulating successional dynamics in the developing chicken gut. We propose that the boundaries between successional stages (3-4 and 14-21 days after hatching) may be optimal times for microbiome interventions.
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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 | 0.000 |
| 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".