Mon, Aug 15, 2022: 5:00 PM-6:30 PM
ESA Exhibit Hall
Background/Question/MethodsOne central issue in coffee-leaf rust (Hemileia vastatrix) epidemiology is to understand what determines the intensity and the timing of yearly infections in coffee plantations. However, most experimental studies report infection as an average at the plot level, obscuring the role of potentially key factors like rust dispersion or the planting pattern. Dynamical modeling can shed light on how these factors may be interrelated and affect the epidemic dynamics. Here, we first review the rust epidemic patterns of different sites, which reveal large variability in the duration and magnitude of the different epidemiologic phases. We then present a spatially explicit model where the host population is subdivided into discrete patches linked through spore dispersion, modeled with simple diffusion. With this model, we study the role of planting arrangement, dispersion intensity, plant-level dynamics and degree of the initial infection on the spatially averaged maximum peak of infection and its timing.
Results/ConclusionsOur results suggest that the epidemic timeline can be divided into two phases: a time lag and a growth phase per se. The model shows that the spore diffusion rate modifies the maximum average tree infection (MATI) and the time to MATI, either by diminishing the time-overlap between individual plant infections or by preventing some plants from reaching their maximum peak during the epidemic. Planting arrangements also modify the plot epidemic dynamics, which can have multiple peaks, or a rather smooth pattern in plots with otherwise similar conditions. Additionally, the length of the time lag is qualitatively affected by differences in the initial tree and plot infections. Finally, one of the model parameters, the infected leaf-fall rate (ɣ) at the plant level, dramatically changes the MATI. These results highlight the importance of explicitly considering the spatial aspects of coffee agroecosystems when measuring and managing rust infection, and provide guidelines to reduce rust dispersion rate, for example by removing and bagging away infected leaves without increasing human mediated spore dispersal between plants.
Results/ConclusionsOur results suggest that the epidemic timeline can be divided into two phases: a time lag and a growth phase per se. The model shows that the spore diffusion rate modifies the maximum average tree infection (MATI) and the time to MATI, either by diminishing the time-overlap between individual plant infections or by preventing some plants from reaching their maximum peak during the epidemic. Planting arrangements also modify the plot epidemic dynamics, which can have multiple peaks, or a rather smooth pattern in plots with otherwise similar conditions. Additionally, the length of the time lag is qualitatively affected by differences in the initial tree and plot infections. Finally, one of the model parameters, the infected leaf-fall rate (ɣ) at the plant level, dramatically changes the MATI. These results highlight the importance of explicitly considering the spatial aspects of coffee agroecosystems when measuring and managing rust infection, and provide guidelines to reduce rust dispersion rate, for example by removing and bagging away infected leaves without increasing human mediated spore dispersal between plants.