Impacts of sequential and simultaneous coinfection by major amphibian pathogens on disease outcomes
Bibliographic record
Abstract
Abstract In natural environments, hosts frequently experience infections from multiple pathogenic species or strains, significantly influencing disease dynamics. Despite shared susceptible hosts and overlapping distributions, the impacts of coinfections by the two most threatening global amphibian pathogens, Batrachochytrium dendrobatidis (Bd) and Ranavirus (Rv), remain largely understudied. This study offers new insights into how simultaneous and sequential exposures to Bd and Rv influence disease outcomes in an amphibian host under controlled experimental conditions. Our findings reveal that the sequence and timing of pathogen exposure can lead to contrasting outcomes. Animals previously exposed to Rv displayed the highest mortality following Bd infection, whereas simultaneous exposure to both pathogens resulted in higher survival than single infections. These findings suggest that priority effects, driven by differences in the timing and order of pathogen exposure, can exacerbate disease severity in amphibian populations, particularly in communities with persistent, sublethal Rv infections. This study highlights the critical role of pathogen interactions in shaping disease dynamics and emphasizes the importance of integrating coinfections into wildlife disease management strategies to mitigate biodiversity crises in amphibians and beyond.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".