Abstract

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.

中文翻译：

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时机很重要：遥感植被绿度可以预测昆虫媒介的迁移，从而预测卷顶病的爆发

摘要

由于气候变化，昆虫传播的植物病毒的爆发变得越来越难以预测。深入了解昆虫媒介迁移的区域级时空动态，可用于预测农业景观中植物病毒的爆发；然而，人们常常对它知之甚少。为了探究这一点，我们调查了 2013 年至 2022 年 2,196 个番茄田中甜菜卷顶病毒(BCTV)的发病情况。在美国，甜菜叶蝉 ( Circulifer tenellus ) 是 BCTV 的唯一载体。我们研究了与 BCTV 发病率和甜菜叶蝉从非农业越冬地区春季迁徙相关的因素。我们进行了一项实验研究，以证明甜菜叶蝉的扩散对植物绿色的响应，并使用基于植被绿色的模型估算了春季迁徙时间。我们发现植被绿度与春季从越冬地区迁徙的概率之间存在负相关。此外，BCTV 发病率与春季迁徙时间显着相关，而非环境条件本身。具体而言，基于早期甜菜叶蝉春季迁徙的模型准确预测了 2013 年和 2021 年加利福尼亚州严重的 BCTV 爆发。我们的研究结果提供了实验和实地支持，表明昆虫媒介的早春迁徙是导致 BCTV 爆发的主要因素。此外，春季迁徙时间的预测模型被实施到基于网络的地图系统中，作为管理目的的决策支持工具。本文描述了一个与作物、牲畜和人类关注的昆虫媒介疾病的区域范围预测和建模密切相关的实验和分析框架。