Environment and host species shape the skin microbiome of captive neotropical bats
Bibliographic record
Abstract
BACKGROUND: A wide range of microorganisms inhabit animal skin. This microbial community (microbiome) plays an important role in host defense against pathogens and disease. Bats (Chiroptera: Mammalia) are an ecologically and evolutionarily diversified group with a relatively unexplored skin microbiome. The bat skin microbiome could play a role in disease resistance, for example, to white nose syndrome (WNS), an infection which has been devastating North American bat populations. However, fundamental knowledge of the bat skin microbiome is needed before understanding its role in health and disease resistance. Captive neotropical frugivorous bats Artibeus jamaicensis and Carollia perspicillataprovide a simple controlled system in which to characterize the factors shaping the bat microbiome. Here, we aimed to determine the relative importance of habitat and host species on the bat skin microbiome. METHODS: We performed high-throughput 16S rRNA gene sequencing of the skin microbiome of two different bat species living in captivity in two different habitats. In the first habitat, A. jamaicensis and C. perspicillata lived together, while the second habitat contained only A. jamaicensis. RESULTS: We found that both habitat and host species shape the composition and diversity of the skin microbiome, with habitat having the strongest influence. Cohabitating A. jamaicensis and C. perspicillata shared more similar skin microbiomes than members of the same species (A. jamaicensis) across two habitats. DISCUSSION: These results suggest that in captivity, the skin microbial community is homogenised by the shared environments and individual proximities of bats living together in the same habitat, at the expense of the innate host species factors. The predominant influence of habitat suggests that environmental microorganisms or pathogens might colonize bat skin. We also propose that bat populations could differ in pathogen susceptibility depending on their immediate environment and habitat.
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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".