Testing community ecology theory as a framework for predicting co-infection: host resource ratios and viral pathogens

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A vast array of microbes (microscopic organisms), such as fungi, bacteria, and viruses, live inside every animal and plant on Earth. These microbial communities influence the growth, reproduction, and death rates of their hosts. Not all microbes are detrimental; the effects of microbes span the full range from causing deadly disease to being essential for host health. Despite the importance of microbial diversity to host well-being, we have little ability to predict the diversity and composition of microbial communities within hosts. This project will address this knowledge gap by testing whether a mathematical model developed originally for plants and algae ('resource-ratio theory') can be used to understand what determines the diversity and composition of the microbes that inhabit plant hosts. The data from these experiments will be used to understand when the predictions of the resource-ratio model apply to microbial communities and how it can be modified to make better predictions. A deeper understanding of the controls on microbial diversity will lead to more accurate predictions about microbes within hosts, which may speed advances in medicine and agriculture.The researchers will grow plants in conditions that vary in the supply of critical soil nutrients, and they will manipulate and measure the abundance and diversity of a group of plant viruses that cause one of the most widespread and devastating diseases of cereal crops worldwide (barley and cereal yellow dwarf viruses; B/CYDV's). Five B/CYD virus species will be used to test the resource-ratio model in a series of growth-chamber and field experiments. In each experiment, they will create a wide range of conditions experienced by the viruses by growing host plants under different resource (nitrogen and phosphorus) supply rates. The researchers will measure viral abundance (titer) within individuals, using quantitative Polymerase Chain Reaction (qPCR) for each virus grown alone and in mixture. They will use viral abundance measurements through time to estimate virus population growth rates. The resource-ratio model will be tested with lab experiments, in order to predict viral diversity in each host. The resource-ratio model will also be modified to include elements of the virus and host biology that could cause deviations from the classic theory, such as host defences against infection by microbes and microbial effects on host nutrient content. To test the relevance of this model to more complex natural communities, the researchers will collect data on B/CYDV titers in field experiments replicated at a local field site and in 30 grasslands spanning 5 continents.