Prediction of spread and regional development of hop powdery mildew: A network analysis

Authors: Gent, David - Dave; BHATTACHARYYA, SHARMODEEP - Oregon State University; RUIZ, TREVOR - Oregon State University

Submitted to: Phytopathology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/28/2019
Publication Date: 7/1/2019
Citation: Gent, D.H., Bhattacharyya, S., Ruiz, T. 2019. Prediction of spread and regional development of hop powdery mildew: A network analysis. Phytopathology. 109(8):1392-1403. https://doi.org/10.1094/PHYTO-12-18-0483-R.
DOI: https://doi.org/10.1094/PHYTO-12-18-0483-R

Interpretive Summary: Epidemics develop because pathogens disperse. In this research, we used the powdery mildew of hop disease system to formulate a statistical model of pathogen dispersal during spring and early summer to predict the development and spread of the fungus regionally among farms. An individual hop yard was considered a point in the model, and its disease status in a given month was influenced by its disease level in the preceding month, susceptibility to two races of the fungus, and disease spread from other fields. The impact of other fields was influenced by their level of disease, area, distance away, and the wind run and direction in the preceding time period. Graphs of disease spread indicated that dispersal was dominated by relatively localized dispersal events (less than 2 km) among the network of fields, being mostly restricted to the same or adjacent farms. Links between fields associated with longer distance dispersal were more frequent in early summer than spring. Situations where a high probability of disease transmission were predicted occurred when fields were in close proximity or where disease levels were 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. It also provides targets for improving disease management.

Technical Abstract: 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. We used the powdery mildew of hop pathosystem to formulate a model of pathogen dispersal during spring and early summer at the 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 due to the 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 levels 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 time period. Parameters were estimated by maximum likelihood over all four years, but allowed to vary for time transition periods from May to June and 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 (less than 2 km) among the network of fields, being mostly restricted to the same or adjacent farms. When formed, edges associated with longer distance dispersal were more frequent in the later 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.