Mycobiome of the Bat White Nose Syndrome (WNS) Affected Caves and Mines reveals High Diversity of Fungi and Local Adaptation by the Fungal Pathogen Pseudogymnoascus (Geomyces) destructans
Bibliographic record
Abstract
The investigations of the bat White Nose Syndrome (WNS) have yet to provide answers as to how the causative fungus Pseudogymnoascus (Geomyces) destructans (Pd) first appeared in the Northeast and how a single clone has spread rapidly in the US and Canada. We aimed to catalogue Pd and all other fungi (mycobiome) by the culture-dependent (CD) and culture-independent (CI) methods in four Mines and two Caves from the epicenter of WNS zoonotic. Six hundred sixty-five fungal isolates were obtained by CD method including the live recovery of Pd. Seven hundred three nucleotide sequences that met the definition of operational taxonomic units (OTUs) were recovered by CI methods. Most OTUs belonged to unidentified clones deposited in the databases as environmental nucleic acid sequences (ENAS). The core mycobiome of WNS affected sites comprised of 46 species of fungi from 31 genera recovered in culture, and 17 fungal genera and 31 ENAS identified from clone libraries. Fungi such as Arthroderma spp., Geomyces spp., Kernia spp., Mortierella spp., Penicillium spp., and Verticillium spp. were predominant in culture while Ganoderma spp., Geomyces spp., Mortierella spp., Penicillium spp. and Trichosporon spp. were abundant is clone libraries. Alpha diversity analyses from CI data revealed that fungal community structure was highly diverse. However, the true species diversity remains undetermined due to under sampling. The frequent recovery of Pd indicated that the pathogen has adapted to WNS-afflicted habitats. Further, this study supports the hypothesis that Pd is an introduced species. These findings underscore the need for integrated WNS control measures that target both bats and the fungal pathogen.
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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 | 0.000 | 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".