Distribution of the disease pathogen <i>Batrachochytrium dendrobatidis</i> in non‐epidemic amphibian communities of western Canada
Bibliographic record
Abstract
Chytridiomycosis is an emerging infectious disease of amphibians caused by the waterborne pathogen Batrachochytrium dendrobatidis (Bd) and is responsible for the recent decline of species worldwide. Elucidating patterns in disease prevalence has proved challenging, as small‐scale studies to date have provided conflicting results. We present data on the prevalence of Bd (the proportion of individuals with Bd) collected from amphibians sampled throughout British Columbia. We had two different datasets: our original dataset includes 1129 individuals sampled in 103 sites representing 14 species. A second dataset has 839 individuals sampled in 19 sites representing 10 species. We use a Bayesian state‐space occupancy modelling approach to estimate the probability that an individual is Bd+ as a function of individual and site characteristics. Cross‐validation techniques and the original dataset were used to find the best model: this model includes species, life stage, and geographic location. Our results suggest that Bd prevalence is not strongly related to seasonality, latitude or site type. Within a species, Bd prevalence depended on life stage; the watershed in which a site occurs may also usefully predict prevalence. Overall observed infection prevalence was ∼16%. Our best model accurately assigns Bd status to an individual ∼ 42% of the time. Taking advantage of the Bayesian framework, we ran an analysis with the second dataset using estimates from the original model as prior values. We present posterior density distributions for those sites and species with narrow credible intervals, and show that sites tend to be either highly likely or highly unlikely to have individuals with Bd, while individuals of some stages of some species have an intermediate likelihood of being Bd‐positive. The Bayesian model using informed priors had increased accuracy rates in assigning Bd status to both individuals and groups of individuals.
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 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 | 0.000 | 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) | 0.000 | 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 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".