Thu, Aug 05, 2021:On Demand
Background/Question/Methods
Emerging plant diseases are a global threat to the health and productivity of plants in both native and managed ecosystems. Yet, an overall lack of tools that can accurately predict when and where to expect diseases in real-time (regularly updated) has hindered efforts to rapidly detect and control invading plant pathogens. We are customizing our new spatial modeling tool known as DDRP (Degree-Days, Risk, and Phenological event mapping) to produce forecasts of infection severity and establishment risk for invasive plant pathogens in the conterminous United States (CONUS). The tool uses high-resolution gridded climate data (temperature, dew point, and precipitation) and a three-hour time step to model temperature-development rates and climate stress accumulation over a growing season in regions as large as CONUS. We used the tool on a destructive fungal pathogen, Calonectria pseudonaviculata, that has caused widespread declines of native boxwood forests in Eurasia, and substantial economic losses for the horticultural industry in Europe and the United States. Analyses of published data on the effects of temperature and leaf wetness period were used to parameterize the infection risk model. We generated climate suitability models using semi-mechanistic (CLIMEX) and correlative (e.g., Maxent) approaches to help calibrate climate stress parameters for the establishment risk model.
Results/Conclusions Predictions of infection risk were concordant with those of the site-based version of the model which depends upon hourly data, and were validated by post-hoc evaluation of numerous outbreak reports. Risk of outbreaks and establishment of C. pseudonaviculata were high throughout eastern CONUS with the exception of the northern-most (coldest) areas, as well as in coastal areas of western states (i.e. California, Oregon, and Washington). This finding suggests that several areas beyond where boxwood blight already occurs (primarily in the Mid-Atlantic and Southeast) are vulnerable to invasion by the boxwood blight pathogen. Model outputs can help identify areas to concentrate surveillance resources and efforts for rapid detection, and improve the timing of blight control with fungicide applications. Additionally, outputs produced over multiple seasons and years can provide insight into the potential impacts of climate change on disease outbreaks and the pathogen’s distribution. Our modeling tool can be readily modified to predict risk of infection and establishment for other plant pathogens in CONUS, and to produce maps for other parts of the world in which necessary input climate datasets are available.
Results/Conclusions Predictions of infection risk were concordant with those of the site-based version of the model which depends upon hourly data, and were validated by post-hoc evaluation of numerous outbreak reports. Risk of outbreaks and establishment of C. pseudonaviculata were high throughout eastern CONUS with the exception of the northern-most (coldest) areas, as well as in coastal areas of western states (i.e. California, Oregon, and Washington). This finding suggests that several areas beyond where boxwood blight already occurs (primarily in the Mid-Atlantic and Southeast) are vulnerable to invasion by the boxwood blight pathogen. Model outputs can help identify areas to concentrate surveillance resources and efforts for rapid detection, and improve the timing of blight control with fungicide applications. Additionally, outputs produced over multiple seasons and years can provide insight into the potential impacts of climate change on disease outbreaks and the pathogen’s distribution. Our modeling tool can be readily modified to predict risk of infection and establishment for other plant pathogens in CONUS, and to produce maps for other parts of the world in which necessary input climate datasets are available.