Epidemiology and Modelling Group, Department of Plant Sciences, University of Cambridge, Cambridge, CB2 3EA, UK.
Atmospheric Dispersion and Air Quality (ADAQ), Met Office, Exeter, EX1 3PB, UK.
Nat Plants. 2017 Oct;3(10):780-786. doi: 10.1038/s41477-017-0017-5. Epub 2017 Sep 25.
Infectious crop diseases spreading over large agricultural areas pose a threat to food security. Aggressive strains of the obligate pathogenic fungus Puccinia graminis f.sp. tritici (Pgt), causing the crop disease wheat stem rust, have been detected in East Africa and the Middle East, where they lead to substantial economic losses and threaten livelihoods of farmers. The majority of commercially grown wheat cultivars worldwide are susceptible to these emerging strains, which pose a risk to global wheat production, because the fungal spores transmitting the disease can be wind-dispersed over regions and even continents . Targeted surveillance and control requires knowledge about airborne dispersal of pathogens, but the complex nature of long-distance dispersal poses significant challenges for quantitative research . We combine international field surveys, global meteorological data, a Lagrangian dispersion model and high-performance computational resources to simulate a set of disease outbreak scenarios, tracing billions of stochastic trajectories of fungal spores over dynamically changing host and environmental landscapes for more than a decade. This provides the first quantitative assessment of spore transmission frequencies and amounts amongst all wheat producing countries in Southern/East Africa, the Middle East and Central/South Asia. We identify zones of high air-borne connectivity that geographically correspond with previously postulated wheat rust epidemiological zones (characterized by endemic disease and free movement of inoculum) , and regions with genetic similarities in related pathogen populations . We quantify the circumstances (routes, timing, outbreak sizes) under which virulent pathogen strains such as 'Ug99' pose a threat from long-distance dispersal out of East Africa to the large wheat producing areas in Pakistan and India. Long-term mean spore dispersal trends (predominant direction, frequencies, amounts) are summarized for all countries in the domain (Supplementary Data). Our mechanistic modelling framework can be applied to other geographic areas, adapted for other pathogens and used to provide risk assessments in real-time .
具有侵染性的农作物病害在大面积的农业区域蔓延，对粮食安全构成了威胁。在东非和中东，已检测到引起小麦茎锈病的专性病原真菌禾柄锈菌（Puccinia graminis f.sp. tritici，Pgt）的强毒株系，这些强毒株系导致了巨大的经济损失，并威胁到农民的生计。目前，全球范围内大多数商业化种植的小麦品种均易受这些新兴株系的影响，这对全球小麦生产构成了威胁，因为传播疾病的真菌孢子可以在区域甚至大陆间随风扩散。有针对性的监测和控制需要了解病原体的空气传播，但长距离扩散的复杂性质给定量研究带来了重大挑战。我们结合了国际实地调查、全球气象数据、拉格朗日扩散模型和高性能计算资源，模拟了一组疾病爆发场景，在动态变化的宿主和环境景观中追踪了数十亿个真菌孢子的随机轨迹，时间跨度超过 10 年。这首次对南非/东非、中东和中亚/南亚所有小麦生产国的孢子传播频率和数量进行了定量评估。我们确定了高空气连通性的区域，这些区域在地理位置上与先前推测的小麦锈病流行病学区域（以地方性疾病和接种体自由流动为特征）以及与相关病原体种群具有遗传相似性的区域相对应。我们量化了在某些情况下（传播途径、时间、爆发规模），诸如“Ug99”等毒力强的病原菌株系可能通过长距离扩散从东非传播到巴基斯坦和印度的大型小麦产区，从而构成威胁。我们总结了该区域内所有国家的长期平均孢子扩散趋势（主要方向、频率、数量）（补充数据）。我们的机械建模框架可应用于其他地理区域，适应其他病原体，并可用于实时提供风险评估。
