Data from: Spatiotemporal heterogeneity decouples infection parameters of amphibian chytridiomycosis
Bibliographic record
Abstract
Emerging infectious diseases are responsible for declines in wildlife populations around the globe. Mass mortality events associated with emerging infectious diseases are often associated with high number of infected individuals (prevalence) and high pathogen loads within individuals (intensity). At the landscape scale spatial and temporal variation in environmental conditions can alter the relationship between these infection parameters and blur the overall picture of disease dynamics. 2. Quantitative estimates of how infection parameters covary with environmental heterogeneity at the landscape scale are scarce. Predicting rates of pathogen transmission and identifying wild populations at risk of disease epidemics requires that we elucidate the factors that shape, and potentially decouple, the link between pathogen prevalence and intensity of infection over complex ecological landscapes. 3. Using a network of 41 populations of the amphibian host Rana pipiens in Ontario, Canada, we present the spatial and temporal heterogeneity in pathogen prevalence and intensity of infection of the chytrid fungus Batrachochytrium dendrobatidis (Bd), across a 3-year period. We then quantify how covariation between both infection parameters measured during late summer, are modified by previously experienced spatiotemporal environmental heterogeneity across 14 repeat sampled populations. 4. Late summer Bd infection parameters are governed, at least in part, by different environmental factors operating during separate host life history events. Our results provide evidence for a relationship between Bd prevalence and thermal regimes prior to host breeding at the site level, and a relationship between intensity of infection and aquatic conditions (precipitation, hydroshed size and river density) throughout host breeding period at the site level. This demonstrates that microclimatic variation within temporal windows, can drive divergent patterns of pathogen dynamics within and across years, by effecting changes in host behaviour which interfere with the pathogen’s ability to infect and re-infect hosts. 5. A clearer understanding of the role that spatiotemporal heterogeneity has upon infection parameters will provide valuable insights into host-pathogen epidemiology, as well as more fundamental aspects of the ecology and evolution of interspecific interactions.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".