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Using a Bayesian network to clarify areas requiring research in a host-pathogen system.

ABSTRACT

Bayesian network analyses can integrate complex relationships to examine a range of hypotheses and identify areas that lack associated empirical studies, to prioritise future research. We examined complex relationships in host and pathogen biology to examine disease-driven decline by the amphibian chytrid fungus, Batrachochytrium dendrobatidis (Bd), a pathogen that is reducing amphibian biodiversity globally. We constructed a Bayesian network consisting of a range of behavioural, genetic, physiological, and environmental variables that influence disease, and used them to predict host population trends (the variable 'Population trend' which could be declining or stable). The behaviour of the nodes (the way in which the variables probabilistically responded to changes in states of the parents, which are the nodes or variables that directly influenced them in the graphical model) in our model was consistent with published results, suggesting that the construction of our model reflected the complex relationships characteristic of host-pathogen interactions. We varied the impacts of specific variables in the model, to reveal factors with the most influence on host population trend. Changes to climatic conditions alone did not strongly influence the probability of population decline, suggesting that epidemics in this system do not occur solely because of climate, but instead interacted with other factors such as the capacity of the frog immune system to suppress disease. The effect of the adaptive immune system and disease reservoirs were important to the population trend, but there was little empirical information available for model construction: we suggest research in these areas will aid understanding of chytridiomycosis-induced declines. We include the input of our full model as a base that can be used to understand other systems, and we demonstrate that such analyses are useful tools for reviewing existing literature and identifying links poorly supported by evidence, and for understanding complexities in emerging infectious disease systems. This article is protected by copyright. All rights reserved.