Spatial pattern and process in plant-pathogen interactions

An individual host's likelihood of acquiring an infectious disease depends, in large part, on the location of the host relative to sources of infection, proximity to other hosts, and occupation of specific microhabitats that confer increased susceptibility. Spatial organization of the host and pathogen populations then may be critical in determining patterns of disease occurrence and dynamics. We discuss three fundamental problems associated with understanding the interactions of plants, pathogens, and spatial structure: (1) how one characterizes spatial patterns, (2) how we determine spatial dynamic processes from a given spatial pattern, and (3) how we then simulate the spatial dynamics presumed to be dominating a given host-pathogen interaction. To demonstrate methods for the characterization of spatial pattern, we analyzed spatial maps of a Silene latifolia population infected with the anther smut Microbotryum violaceum, using join-count statistics, continuous spatial autocorrelation, and Mantel's test. Different statistical techniques provided different interpretations of the same data set, indicating the value of using multiple methods. We described spatial structure in the plant population, the pathogen population (factoring out the plant population structure), and spatial cross correlation between two variables (plant gender and disease status). Each of these tests provides information on how the disease may be spreading in the population. We are not very optimistic about the prospect of determining underlying disease-spread processes purely from analysis of spatial pattern. Most spatial patterns can potentially be generated by a number of biological processes (e.g., ''true'' vs. ''apparent'' contagion), and it is not possible to distinguish between hypotheses without additional information about the system. A difficulty in modeling spatial processes is simulation of populations with given spatial patterns. Simulated spatially structured populations are useful for predicting disease dynamics in a spatial context. We present a number of techniques for creating these spatially structured populations, including a method we developed for modeling the effect of vector behavior on the spread of a plant virus.