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.