The geographical distribution of yellow dwarf virus serotypes and their aphid vectors in Australia

This article reviews and analyzes the literature on Yellow dwarf viruses (YDVs) in Australia, examining the range of environmental and climatic factors that explain the observed geographical distribution of the virus and its vectors. BYDV-PAV, vectored mainly by the aphid Rhopalosiphum padi, is the most prevalent YDV species in wheat and grasslands across all states, except Queensland. BYDV-RMV, vectored mainly by Rhopalosiphum maidis, dominates in Queensland grasslands, with very low incidence in wheat. Queensland experiences higher rainfall and warmer temperatures than southern Australia. Across Australia disease incidence in wheat is generally low (around 10 %) and varies from year to year, with the highest incidence found on occasion in Western Australia (up to 52 %) and the lowest in Queensland (<1 %). Across Australia there is a much higher virus incidence and more variation in YDV species present in grasslands than in wheat, although in general BYDV-PAV still dominates. An overview of the differences between the YDV species in terms of symptoms, impacts, frequency, transmission rates and geographical distribution is necessary to appreciate the implications of virus spread across Australia, as well as the risks from the interaction of YDV with more recently introduced wheat pathogens. This overview is set in the context of a changing climate, with a discussion of the possible implications of anthropogenic climate change for future epidemics. For example, increasing temperatures in the future may result in more rapid transmission of the virus in the cooler months than at present, with implications for winter crops such as wheat, where YDV currently does most damage. Also, there is potential for the spread of BYDV-RMV further south, as changes in climatic conditions alter both the transmission potential of the virus as well as the vectoring potential by the aphids R. padi and R. maidis. Finally, critical knowledge gaps are identified, highlighting a need for ongoing seasonal monitoring of the virus and vectors to support the use of simulation models to predict the incidence of YDVs in near real-time.