Covariation of the gut microbiome with diet in Aves
Bibliographic record
Abstract
Abstract Background:Research over the past few decades has revealed a vital role for the gut microbiome in the health of various animals including birds. Multiple factors can influence the gut microbiome. Opportunistic feeding and multiple other environment factors can influence the results, and bias the conclusions, when wild animals are used to study the influence of phylogeny and diet on their gut microbiomes. Therefore, to study this question in this study, we collected fecal samples from 43 species of Aves at one time to avoid influences such as geography, weather, and season. Results:Approaches based on both 16S rRNA gene sequencing (135 samples) and whole metagenome shotgun sequencing (17 samples) were used. Our data show that diets containing native starch will increase the abundance of Lactobacillus in gut microbiome, while those containing plant-derived fiber will mainly enrich the levels of Clostridium. Greater numbers of Fusobacteria and Proteobacteria are detected in carnivorous birds, while in birds fed a commercial corn-soybean basal diet, a stronger inner-connected microbial community containing Clostridia and Bacteroidia was enriched. Furthermore, a microbial functional analysis based on the metagenomic sequences showed that the function of microbes was adapted to different food types to achieve the most beneficial state for the hosts. Conclusions:The covariation of diet and gut microbiome identified in our study demonstrates modulation of the gut microbiome by dietary diversity and expands our knowledge of diet-microbiome-host interactions in birds.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".