Timing matters: remotely sensed vegetation greenness can predict insect vector migration and therefore outbreaks of curly top disease

Due to climate change, outbreaks of insect-vectored plant viruses have become increasingly unpredictable. In-depth insights into region-level spatio-temporal dynamics of insect vector migration can be used to forecast plant virus outbreaks in agricultural landscapes; yet, it is often poorly understood. To explore this, we examined the incidence of beet curly top virus (BCTV) in 2,196 tomato fields from 2013 to 2022. In America, the beet leafhopper (Circulifer tenellus) is the exclusive vector of BCTV. We examined factors associated with BCTV incidence and spring migration of the beet leafhopper from non-agricultural overwintering areas. We conducted an experimental study to demonstrate beet leafhopper dispersal in response to greenness of plants, and spring migration time was estimated using a model based on vegetation greenness. We found a negative correlation between vegetation greenness and spring migration probability from the overwintering areas. Furthermore, BCTV incidence was significantly associated with spring migration time rather than environmental conditions per se. Specifically, severe BCTV outbreaks in California in 2013 and 2021 were accurately predicted by the model based on early beet leafhopper spring migration. Our results provide experimental and field-based support that early spring migration of the insect vector is the primary factor contributing to BCTV outbreaks. Additionally, the predictive model for spring migration time was implemented into a web-based mapping system, serving as a decision support tool for management purposes. This article describes an experimental and analytical framework of considerable relevance to region-wide forecasting and modeling of insect-vectored diseases of concern to crops, livestock, and humans.