Use of SADIE statistics to study spatial dynamics of plant disease epidemics

Abstract

Using a previously developed stochastic simulation model for plant disease epidemics, the relationship of the SADIE aggregation statistic Ia with initial epidemic conditions, spore dispersal distance, sampling quadrat size and other spatial statistics was investigated. Most variation in Ia was attributable to the initial spatial pattern of infected plants and sampling quadrat size. The importance of initial spatial pattern on SADIE clustering indices (for patches and gaps) was also demonstrated using a number of selected data sets. Correlation of Ia with clustering indices was close to 1·0. Epidemics arising from the regular and random initial patterns resulted in the smallest and greatest Ia values, respectively, at sampling times after disease spread had occurred. Furthermore, the variability in Ia between simulation runs also varied greatly with initial patterns, being lowest and greatest for the clumped and random initial patterns, respectively. Ia increased initially and then decreased with increasing incidence, especially for the clumped and random initial patterns. Overall, the effect of median spore dispersal distance on Ia was very small, especially for the random initial pattern. The correlation between Ia and intraclass correlation was generally small and varied greatly between initial patterns. However, there was a high positive correlation between Ia and a parameter describing the rate of decline of autocorrelation over spatial lags, indicating that Ia, clustering indices and autocorrelations measure some common properties of patterns.