| dc.description.abstract | Plant viruses interact with arthropod vectors in complex ecosystems consisting of many different components. Non-vector insect herbivores and plant-mutualistic bacteria are often present in these systems, and may have significant impacts on pathogens and vectors. Conversely, plant pathogens and vectors may significantly affect herbivores and plant mutualists. Here, we show evidence of reciprocal interactions between a plant virus, a vector herbivore, a non-vector herbivore, and a species of soil-dwelling rhizobia bacteria.
In Chapter 1 we provide an overview of the different community components that may influence plant pathogens, and summarize the current literature on this topic. In Chapter 2, we show results from a study demonstrating reciprocal interactions between a non-vector herbivore (Sitona lineatus) and a plant virus, pea enation mosaic virus (PEMV). Virus-infected pea plants (Pisum sativum) are attractive to S. lineatus, and feeding by S. lineatus increases the titer of PEMV in infected plants. Moreover, feeding by S. lineatus on pea hosts influences the host selection behavior of the Acyrthosiphon pisum, the main vector of PEMV. In Chapter 3, we demonstrate that S. lineatus can also influence inter-host spread of PEMV. We observed higher incidence of virus in experimental mesocosms occupied by S. lineatus, and discovered that A. pisum is competitively displaced by S. lineatus in a way that increases the inoculation efficiency of PEMV. In Chapter 4, we examine how the mutualistic relationship between P. sativum and a nitrogen-fixing bacterium, Rhizobium leguminosarum biovar. viciae, influences A. pisum and PEMV. Inoculation with R. leguminosarum results in decreases in aphid population and a subsequent reduction in PEMV incidence, while soil sterilization results in a boost in aphid populations and reductions in yield. These results indicate that the traditional host-vector-pathogen model may be an oversimplification of complex pathosystems. Factoring in other members of the community may provide more robust predictions of pathogen prevalence and spread. | en_US |
