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Title: Using a Bayesian network to clarify areas requiring research in a host-pathogen system
Contributor(s): Mengersen, K (author); Alford, R A (author); Schwarzkopf, L (author); Bower, D S  (author)
Publication Date: 2017-12
Early Online Version: 2017-05-02
DOI: 10.1111/cobi.12950
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Abstract: Bayesian network analyses can be used to interactively change the strength of effect of variables in a model to explore complex relationships in new ways. In doing so, they allow one to identify influential nodes that are not well studied empirically so that future research can be prioritized. We identified relationships in host and pathogen biology to examine disease-driven declines of amphibians associated with amphibian chytrid fungus (Batrachochytrium dendrobatidis). We constructed a Bayesian network consisting of behavioral, genetic, physiological, and environmental variables that influence disease and used them to predict host population trends. We varied the impacts of specific variables in the model to reveal factors with the most influence on host population trend. The behavior 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) was consistent with published results. The frog population had a 49% probability of decline when all states were set at their original values, and this probability increased when body temperatures were cold, the immune system was not suppressing infection, and the ambient environment was conducive to growth of B. dendrobatidis. These findings suggest the construction of our model reflected the complex relationships characteristic of host-pathogen interactions. Changes to climatic variables alone did not strongly influence the probability of population decline, which suggests that climate interacts with other factors such as the capacity of the frog immune system to suppress disease. Changes to the adaptive immune system and disease reservoirs had a large effect on the population trend, but there was little empirical information available for model construction. Our model inputs can be used as a base to examine other systems, and our results show that such analyses are useful tools for reviewing existing literature, identifying links poorly supported by evidence, and understanding complexities in emerging infectious-disease systems.
Publication Type: Journal Article
Grant Details: ARC/DP130101635
Source of Publication: Conservation Biology, 31(6), p. 1373-1382
Publisher: Wiley-Blackwell Publishing, Inc
Place of Publication: United States of America
ISSN: 0888-8892
Field of Research (FOR): 050103 Invasive Species Ecology
050202 Conservation and Biodiversity
Socio-Economic Outcome Codes: 960807 Fresh, Ground and Surface Water Flora, Fauna and Biodiversity
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Environmental and Rural Science

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