1Forage Seed and Cereal Research Unit, U.S. Department of Agriculture Agricultural Research Service, Corvallis, OR 97331.
2Department of Statistics, Oregon State University, Corvallis, OR 97331.
Phytopathology. 2019 Aug;109(8):1392-1403. doi: 10.1094/PHYTO-12-18-0483-R. Epub 2019 Jul 1.
Dispersal is a fundamental aspect of epidemic development at multiple spatial scales, including those that extend beyond the borders of individual fields and to the landscape level. In this research, we used the powdery mildew of the hop pathosystem (caused by ) to formulate a model of pathogen dispersal during spring (May to June) and early summer (June to July) at the intermediate scale between synoptic weather systems and microclimate (mesoscale) based on a census of commercial hop yards during 2014 to 2017 in a production region in western Oregon. This pathosystem is characterized by a low level of overwintering of the pathogen as a result of absence of the ascigerious stage of the fungus and consequent annual cycles of localized survival via bud perennation and pathogen spread by windborne dispersal. An individual hop yard was considered a node in the model, whose disease status in a given month was expressed as a nonlinear function of disease incidence in the preceding month, susceptibility to two races of the fungus, and disease spread from other nodes as influenced by their disease incidence, area, distance away, and wind run and direction in the preceding month. Parameters were estimated by maximum likelihood over all 4 years but were allowed to vary for time transition periods from May to June and from June to July. The model accounted for 34 to 90% of the observed variation in disease incidence at the field level, depending on the year and season. Network graphs and analyses suggest that dispersal was dominated by relatively localized dispersal events (<2 km) among the network of fields, being mostly restricted to the same or adjacent farms. When formed, predicted disease attributable to dispersal from other hop yards (edges) associated with longer distance dispersal was more frequent in the June to July time transition. Edges with a high probability of disease transmission were formed in instances where yards were in close proximity or where disease incidence was relatively high in large hop yards, as moderated by wind run. The modeling approach provides a flexible and generalizable framework for understanding and predicting pathogen dispersal at the regional level as well as the implications of network connectivity on epidemic development.
扩散是多个空间尺度上流行病发展的一个基本方面，包括超越单个领域边界并扩展到景观尺度的那些方面。在这项研究中，我们使用啤酒花病害系统的白粉病（由 引起），根据 2014 年至 2017 年在俄勒冈州西部一个生产地区对商业啤酒花田进行的普查，在天气系统和小气候（中尺度）之间的中间尺度上，制定了一个在春季（5 月至 6 月）和初夏（6 月至 7 月）期间病原体扩散的模型。该病害系统的特点是由于真菌的有性阶段缺失，病原体越冬水平低，因此通过芽的多年生和由风传播的病原体传播来实现局部生存的年度循环。单个啤酒花田被视为模型中的一个节点，其在给定月份的疾病状况由前一个月的疾病发病率、对真菌两个菌系的易感性以及其他节点的疾病传播表达为一个非线性函数，这些传播受到它们的疾病发病率、面积、距离以及在前一个月的风向和风速的影响。参数通过所有 4 年的最大似然法进行估计，但允许在从 5 月到 6 月以及从 6 月到 7 月的时间过渡期间发生变化。该模型解释了田间水平疾病发病率的 34%至 90%的观测变化，具体取决于年份和季节。网络图形和分析表明，扩散主要由网络中各个田间相对本地化的扩散事件（<2 公里）主导，主要限于同一或相邻的农场。当由其他啤酒花田（边）引起的、与远距离扩散相关的、可归因于扩散的预测疾病形成时，在 6 月至 7 月的时间过渡中更为频繁。在场地靠近或在大啤酒花田中有相对高的疾病发病率的情况下，边形成的可能性更大，这由风向风速来调节。该建模方法为理解和预测区域水平上的病原体扩散以及网络连接性对流行病发展的影响提供了一个灵活和可推广的框架。
