The gut microbiome as an indicator of habitat disturbance in a Critically Endangered lemur
Bibliographic record
Abstract
BACKGROUND: Habitat disturbance affects the biology and health of animals globally. Understanding the factors that contribute to the differential responses of animals to habitat disturbance is critical for conservation. The gut microbiota represents a potential pathway through which host responses to habitat disturbance might be mediated. However, a lack of quantitative environmental data in many gut microbiome (GM) studies of wild animals limits our ability to pinpoint mechanisms through which habitat disturbance affects the GM. Here, we examine the impact of anthropogenic habitat disturbance on the diet and GM of the Critically Endangered black-and-white ruffed lemur (Varecia variegata editorum). We collected fecal samples and behavioral data from Varecia occupying habitats qualitatively categorized as primary forest, moderately disturbed forest, and heavily disturbed forest. RESULTS: Varecia diet and GM composition differed substantially across sites. Dietary richness predicted GM richness across sites, and overall GM composition was strongly correlated to diet composition. Additionally, the consumption of three specific food items positively correlated to the relative abundances of five microbial strains and one microbial genus across sites. However, diet did not explain all of the GM variation in our dataset, and differences in the GM were detected that were not correlated with diet, as measured. CONCLUSIONS: Our data suggest that diet is an important influence on the Varecia GM across habitats and thus could be leveraged in novel conservation efforts in the future. However, other factors such as contact with humans should also be accounted for. Overall, we demonstrate that quantitative data describing host habitats must be paired with GM data to better target the specific mechanisms through which environmental change affects the GM.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".