DNA metabarcoding analyses reveal fine-scale microbiome structures on Western Canadian bat wings
Bibliographic record
Abstract
ABSTRACT Healthy wings are vital for the survival and reproduction of bats, and wing microbiome is a key component of bat wing health. However, relatively little is known about the wing microbiome of bats in western Canada where the white nose syndrome has become an increasing threat. Here, we used DNA metabarcoding to investigate the bacterial and fungal communities on the wings of three bat species: the big brown bat ( Eptesicus fuscus ), the Yuma myotis ( Myotis yumanensis ), and the little brown myotis ( M. lucifugus ) from four field sites in Lillooet, British Columbia, Canada. The bacterial 16S rRNA metabarcoding revealed a total of 4,167 amplicon sequence variants (ASVs) belonging to 27 phyla, 639 genera, and 533 known and 2,423 unknown species. The wing bacteria were dominated by phyla Proteobacteria, Firmicutes, Bacteroides, and Actinobacteria, and the most common genera were Delftia , Bordetella , Sphingomonas , Phyllobacterium , Bradyrhizobium , Pseudomonas , and Corynebacterium . The fungal internal transcribed spacer (ITS) metabarcoding revealed a total of 11,722 ASVs belonging to 16 phyla, 806 genera, and 1,420 known and 10,302 unknown species. The wing fungi were dominated by phyla Ascomycota, Basidiomycota, and Motierellomycota, and the most common genera were Cladosporium , Aspergillus , and Mycosphaerella . Principal coordinates analysis showed that both bat species and field sites contributed variably to the diversity and distribution of bacterial and fungal communities on bat wings. Interestingly, both positive and negative correlations were found in their relative abundances among several groups of microbial taxa. We discuss the implications of our results for bat health, including the management of P. destructans infection and white-nose syndrome spread. IMPORTANCE Microbiomes play important roles in host health. White-nose syndrome (WNS), a fungal infection of bat wings and muzzles, has threatened bat populations across North America since 2006. Recent research suggest that the skin microbiome of bats may play a significant role in bat's susceptibility to WNS. However, relatively little is known about the skin microbiome composition and function in bats in Western Canada, a region with a high diversity of bats, but WNS has yet to be a major issue. Here, we revealed high bacterial and fungal diversities on the skin of three common bat species in Lillooet, British Columbia, including several highly prevalent microbial species that have been rarely reported in other regions. Our analyses showed fine-scale structures of bat wing microbiome based on local sites and bat species. The knowledge obtained from WNS-naïve bat populations in this study may help develop mitigation and management strategies against WNS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".