Tracking the prevalence of a fungal pathogen, <i>Batrachochytrium dendrobatidis</i> (chytrid fungus), using environmental DNA
Bibliographic record
Abstract
Abstract Chytridiomycosis, a primary disease driving widespread and unprecedented amphibian declines, is caused by the fungal pathogen Batrachochytrium dendrobatidis (Bd). Tracking Bd through space and time requires monitoring protocols that efficiently and reliably assess pathogen prevalence and intensity, which in turn requires an understanding of environment–pathogen dynamics. Environmental DNA (eDNA) was used to track Bd prevalence and intensity in 95 waterbodies in southern Ontario, Canada, and assess zoospore counts relative to biotic, abiotic, and geographic factors. Bd was also monitored on a semi‐weekly basis in 10 waterbodies to better understand patterns of temporal variability. Bd showed variable prevalence, with 47% and 29% of waterbodies having zoospores detected in May and July, respectively. Patterns of prevalence were markedly variable both within and across waterbodies, indicating high spatio‐temporal heterogeneity. Bd prevalence was not related to environmental factors, geographic variables, or amphibian species richness, but intensity was negatively related to estimated canopy cover. In intensively sampled waterbodies, Bd counts were highly variable through time, with some sites switching from detection to non‐detection (and vice versa) across 2‐week intervals. We conclude that eDNA can be a useful tool for monitoring Bd zoospores in wetlands but emphasize the need for additional research into environmental and methodological factors affecting zoospore detection and abundance before this method should be widely adopted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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 teacher head, 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".