Captive animal gut microbiota is populated with microorganisms that are relevant to the digestion of host dominant diet
Bibliographic record
Abstract
Diet contributes to changes in animal gut microbiomes, which in turn impacts host fitness and immune system development. Drastic changes in diet are often inevitable when animals adjust to captive lifestyles in facilities and could influence host health status by shifting gut microbiome composition. In this study, we evaluated the effects of the dominant diet component on the variation of gut microbiota biodiversity of captive herbivores, omnivores and carnivore species. We performed 16S rRNA gene amplicon sequencing analysis using QIIME2 and R. Alpha diversity differed significantly (Faith’s phylogenetic distance p = 0.04; Pielou’s evenness p = 0.01) among herbivore groups primarily consuming fruits versus general plant materials, but not observed for the same diet grouping in omnivores. Captive carnivores with invertebrate-dominant diets had higher phylogenetic diversity (p = 0.005) compared to those primarily consuming mammals and birds. The most prevalent and abundant microbial taxa in the gut microbiota vary with the general diet types (carnivore, herbivore, and omnivore). Additionally, among the dominant food-type consumed, the key microbial taxa that significantly differ in abundance likely play functional roles in host digestion. These insights to bacterial biodiversity within captive species, ranging from herbivorous to carnivorous species, can potentially aid conservation management practices that aim to improve animal health and wellbeing in captivity.
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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) | 0.001 | 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) | 0.002 | 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".