Population Genetic Investigation of the White-Nose Syndrome pathogen, Pseudogymonascus destructans, in North America
Bibliographic record
Abstract
Fungal infections of animals have become an increasingly important global issue. White-Nose Syndrome is an ongoing fungal epizootic of North American hibernating bats, caused by epidermal infections of the fungus, Pseudogymnoascus destructans. Infections emerged early in 2006 in New York State and have since spread to 35 US States and seven Canadian Provinces, with rates of mortality exceeding 90% in some bat colonies. As an emerging outbreak in North America, the transmission of P. destructans is assumed to occur in a radial fashion outwards from the point of origin. In addition, the factors that may influence P. destructans transmission have been postulated, but not tested before. Lastly, as reproduction is assumed to be strictly clonal in North America, invasive populations should have low genetic diversity, and may even accumulate deleterious mutations over time. The aim of my PhD research is to test these assumptions regarding the spread, evolution, and adaptation of P. destructans using combination of genotyping methods. My results showed how P. destructans isolates have shifted in terms of phenotypes and physiological capabilities since being introduced. In addition, I describe patterns of connectivity across the landscape, which are more consist with the level of anthropogenic activity than variation in climate. The mutations common to all invasive strains of P. destructans are associated with adaptations that have occurred since being introduced from Europe, some with relevant metabolic functions that fit their pathogenic lifestyle. Together, my results revealed significant phenotypic and genotypic changes during the spread of P. destructans in North America. The factors identified here that influence the phenotypic and genotypic changes should help developing better management strategies against the White-Nose Syndrome 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 | 0.001 |
| 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) | 0.000 | 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".