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

IF 4.3 1区农林科学 Q1 ENTOMOLOGY Journal of Pest Science Pub Date : 2024-03-19 DOI:10.1007/s10340-024-01771-4

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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.

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时机很重要：遥感植被绿度可预测虫媒迁徙，进而预测卷心菜病的爆发

摘要由于气候变化，由昆虫传播的植物病毒爆发变得越来越难以预测。深入了解区域一级昆虫媒介迁移的时空动态可用于预测农业景观中植物病毒的暴发，然而，人们对这一问题往往知之甚少。为了探讨这一问题，我们研究了 2013 年至 2022 年期间 2196 块番茄田的甜菜卷曲顶端病毒（BCTV）发病率。在美国，甜菜叶蝉（Circulifer tenellus）是 BCTV 的唯一传播媒介。我们研究了与 BCTV 发生率和甜菜叶蝉从非农业越冬区春季迁徙有关的因素。我们进行了一项实验研究，以证明甜菜叶蝉的扩散与植物的绿度有关，并利用基于植被绿度的模型估算了春季迁徙时间。我们发现植被绿度与从越冬区春季迁徙的概率呈负相关。此外，BCTV发病率与春季迁徙时间而非环境条件本身有显著关联。具体而言，根据甜菜叶蝉春季迁徙的早期时间，模型准确预测了加利福尼亚州在2013年和2021年爆发的严重BCTV疫情。我们的研究结果提供了实验和实地支持，即昆虫媒介的早春迁徙是导致 BCTV 爆发的主要因素。此外，春季迁飞时间预测模型还被应用到一个基于网络的制图系统中，作为管理决策支持工具。本文介绍了一个实验和分析框架，该框架对作物、牲畜和人类关注的昆虫传播疾病的全区域预测和建模具有重要意义。

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来源期刊

Journal of Pest Science 生物-昆虫学

CiteScore

10.40

自引率

8.30%

发文量

114

审稿时长

6-12 weeks

期刊介绍： Journal of Pest Science publishes high-quality papers on all aspects of pest science in agriculture, horticulture (including viticulture), forestry, urban pests, and stored products research, including health and safety issues. Journal of Pest Science reports on advances in control of pests and animal vectors of diseases, the biology, ethology and ecology of pests and their antagonists, and the use of other beneficial organisms in pest control. The journal covers all noxious or damaging groups of animals, including arthropods, nematodes, molluscs, and vertebrates. Journal of Pest Science devotes special attention to emerging and innovative pest control strategies, including the side effects of such approaches on non-target organisms, for example natural enemies and pollinators, and the implementation of these strategies in integrated pest management. Journal of Pest Science also publishes papers on the management of agro- and forest ecosystems where this is relevant to pest control. Papers on important methodological developments relevant for pest control will be considered as well.