One of the fundamental challenges in contemporary disease ecology involves understanding infection dynamics within complex communities composed of multiple hosts and multiple pathogens. Hosts in nature are exposed to a 'cocktail' of different pathogens, therefore a central question concerns how interactions between co-occurring pathogens affect disease severity and pathogen transmission in host communities. Most research to date has been focused at a single level, examining either how multiple infections influence individual host pathology or using population surveys to identify correlations in pathogen co-occurrence within a host population. This focus on single scales (i.e., within-host vs. between- host) neglects a critically important question - namely, how do pathogen interactions within hosts 'scale up' to influence between- host processes, such as transmission and disease dynamics? The primary goal of this project is to understand how interactions among three virulent pathogens at different scales of biological complexity, including within hosts, between species, and among communities, combine to influence disease dynamics in amphibians, a group of globally threatened vertebrates. This project combines cross-sectional field surveys of wetland communities with controlled laboratory and mesocosm experiments to determine (1) how amphibian pathogens co-vary in occurrence and intensity across multiple spatial scales (individual hosts, host species, wetland communities), (2) the individual and combined effects of each pathogen on host pathology and pathogen infection success, and (3) the net effects of variation in host and pathogen community structure for pathogen transmission and host-pathogen dynamics. A stochastic, simulation-based modeling framework, uniquely focused on individual hosts, will be used to interpret experimental results and link field distributions of pathogens with underlying mechanisms. This project focuses on three pathogens that have been widely implicated in causing amphibian pathology: the chytrid fungus Batrachochytrium dendrobatidis, the trematode Ribeiroia ondatrae, and the viral genus Ranavirus.
Results from this work will build toward a more mechanistic understanding of how changes in complex ecological communities - including both hosts and pathogens - interact to influence disease risk, laying a foundation to develop effective tools for forecasting epidemics and improving our understanding of disease emergence in both humans and wildlife. A broader understanding of the conditions in which pathogens interact to affect disease patterns has immediate relevance for public health, conservation, and wildlife management.
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