Modeling Aceria tosichella biotype distribution over geographic space and time.

The wheat curl mite, Aceria tosichella Keifer, one of the most destructive arthropod pests of bread wheat worldwide, inflicts significant annual reductions in grain yields. Moreover, A. tosichella is the only vector for several economically important wheat viruses in the Americas, Australia and Europe. To date, mite-resistant wheat genotypes have proven to be one of the most effective methods of controlling the A. tosichella-virus complex. Thus, it is important to elucidate A. tosichella population genetic structure, in order to better predict improved mite and virus management. Two genetically distinct A. tosichella lineages occur as pests of wheat in Australia, Europe, North America, South America and the Middle East. These lineages are known as type 1 and type 2 in Australia and North America and in Europe and South America as MT-8 and MT-1, respectively. Type 1 and type 2 mites in Australia and North America are delineated by internal transcribed spacer 1 region (ITS1) and cytochrome oxidase I region (COI) sequence differences. In North America, two A. tosichella genotypes known as biotypes are recognized by their response to the Cmc3 mite resistance gene in wheat. Aceria tosichella biotype 1 is susceptible to Cmc3 and biotype 2 is virulent to Cmc3. In this study, ITS1 and COI sequence differences in 25 different populations of A. tosichella of known biotype 1 or biotype 2 composition were characterized for ITS1 and COI sequence differences and used to model spatio-temporal dynamics based on biotype prevalence. Results showed that the proportion of biotype 1 and 2 varies both spatially and temporally. Greater ranges of cropland and grassland within 5000m of the sample site, as well as higher mean monthly precipitation during the month prior to sampling appear to reduce the probability of occurrence of biotype 1 and increase the probability of occurrence of biotype 2. The results suggest that spatio-temporal modeling can effectively improve A. tosichella management. Continual integration of additional current and future precipitation and ground cover data into the existing model will further improve the accuracy of predicting the occurrence of A. tosichella in annual wheat crops, allowing producers to make informed decisions about the selection of varieties with different A. tosichella resistance genes.