气候变化领域集成服务门户(CCPortal): Timing matters: remotely sensed vegetation greenness can predict insect vector migration and therefore outbreaks of curly top disease

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DOI	10.1007/s10340-024-01771-4
	Timing matters: remotely sensed vegetation greenness can predict insect vector migration and therefore outbreaks of curly top disease
	Lee, Hyoseok; Wintermantel, William M.; Trumble, John T.; Nansen, Christian
发表日期	2024
ISSN	1612-4758
EISSN	1612-4766
英文摘要	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.
英文关键词	Enhanced vegetation index; Flight behavior; Migration mapping; Satellite imagery
语种	英语
WOS研究方向	Entomology
WOS类目	Entomology
WOS记录号	WOS:001187477400002
来源期刊	JOURNAL OF PEST SCIENCE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/289062
作者单位	University of California System; University of California Davis; United States Department of Agriculture (USDA); University of California System; University of California Riverside; United States Department of Agriculture (USDA)
推荐引用方式 GB/T 7714	Lee, Hyoseok,Wintermantel, William M.,Trumble, John T.,et al. Timing matters: remotely sensed vegetation greenness can predict insect vector migration and therefore outbreaks of curly top disease[J],2024.
APA	Lee, Hyoseok,Wintermantel, William M.,Trumble, John T.,&Nansen, Christian.(2024).Timing matters: remotely sensed vegetation greenness can predict insect vector migration and therefore outbreaks of curly top disease.JOURNAL OF PEST SCIENCE.
MLA	Lee, Hyoseok,et al."Timing matters: remotely sensed vegetation greenness can predict insect vector migration and therefore outbreaks of curly top disease".JOURNAL OF PEST SCIENCE (2024).