Pathogens play an important role for many host organisms, ranging from population regulation to species invasion, which in turn, have applications for issues such as disease control, pest control and biodiversity. However, much of our empirical understanding of host-pathogen ecology and evolution is derived from scenarios where the host is infected by a single pathogen or parasite.
Molecular techniques have revealed that many infections in insect hosts are caused by several pathogen genotypes which differ phenotypically in their interaction with the host. This raises the question of how competing genotypes co-exist in the field. The simplest assumption would be that competition between genotypes within a host is a race to gain the greatest share of resources (host tissues), as in the tragedy of the commons. However, this would act to reduce genetic diversity rather than to maintain it.
In this talk we aim to build upon recent developments in the literature by conducting single infection bioassays to obtain data on within-host growth and fitness parameters for phenotypically different and similar strains of nucleopolyhedroviruses in the Lepdipoteran host Spodoptera exigua. Using these data, a simple mechanistic mathematical model (a coupled system of differential equations) is derived and fitted. Predictions from this model agree with empirical findings, such as increased virus doses leading to decreased virus yields.
Extending the single infection model, we assume that in mixed infections viruses compete for host resources, but otherwise act independently. The outcome of these simple assumptions leads to multiple infections producing some non-intuitive outcomes. For example, we predict that multiple infections can lead to significantly increased or decreased virus yield when compared to single infections. This resource mediated competitive synergistic or antagonistic relationship depends crucially on the composition of the multiple infections and the time-lag between infections, which potentially has great importance for understanding pathogen coexistence and for biocontrol design.