- Journal Article
Plant disease emergences have dramatically increased recently as a result of global changes, especially with respect to trade, host genetic uniformity, and climate change. A better understanding of the conditions and processes determining epidemic outbreaks caused by the emergence of a new pathogen, or pathogen strain, is needed to develop strategies and inform decisions to manage emerging diseases. A polyetic process-based model is developed to analyse conditions of disease emergence. This model simulates polycyclic epidemics during successive growing seasons, the yield losses they cause, and the pathogen survival between growing seasons. This framework considers one immigrant strain coming in a single event into a system where a resident strain is already established. Outcomes are formulated as probability of emergence, time to emergence, and yield loss, resulting from deterministic and stochastic simulations. An analytical solution to determine a threshold for emergence is also derived. Analyses focus on the effects of two fitness parameters on emergence: the relative rate of reproduction (epidemic speed), and the relative rate of mortality (decay of population between seasons). Analyses revealed that stochasticity is a critical feature of disease emergence. The simulations suggest that: (a) emergence may require a series of independent immigration events before a successful invasion takes place; (b) an explosion in the population size of the new pathogen (or strain) may be preceded by many successive growing seasons of cryptic presence following an immigration event; and (c) survival between growing seasons is as important as reproduction during the growing season in determining disease emergence. © 2020 British Society for Plant Pathology Show more
Journal / seriesPlant Pathology
Pages / Article No.
Subjectglobal change; pandemic; pathogen fitness; pathogen survival; polyetic epidemics; process-based model
Organisational unit03516 - McDonald, Bruce / McDonald, Bruce
161453 - Epidemiology of major wheat diseases: using eco-evolutionary models to learn from epidemic and genomic data (SNF)
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