Predicting the risk of cucurbit downy mildew in the eastern United States using an integrated aerobiological model

Abstract

Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75%, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73%. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good performance in predicting the risk of cucurbit downy mildew outbreak in the eastern United States.

A general trait‐based modelling framework for revealing patterns of airborne fungal dispersal threats to agriculture and native flora

“…different fungal classes in terms of their morphological traits). Previous studies on modelling LAD have focused on specific species or on certain biological processes (Pan et al, 2006;Savage et al, 2010;Meyer et al, 2017a;Neufeld et al, 2018;Visser et al, 2019;Oneto et al, 2020). In contrast, our study is generic and focuses on demonstrating how technology can inform biosecurity assessment by simply configuring and parameterizing a well-validated dispersion model.…”

“…Many of the studies using such simulation models focus on specific species (e.g. Pan et al ., 2006; Meyer et al ., 2017a; Neufeld et al ., 2018) or on certain biological processes of spores underlying atmospheric dispersal (e.g. Savage et al ., 2010; Oneto et al ., 2020).…”

Bioaerosol field measurements: Challenges and perspectives in outdoor studies

“…When the deposition of airborne pathogen propagules depends on washout by rainfall, collection of rain is a very useful means to monitor pathogen arrival. Rainfall collection has been deployed successfully to monitor arrival of airborne spores of soybean rust (Isard et al 2011) and of downy mildew of cucurbits (Neufeld et al 2018) leading to significant financial gains for the farmer and environmental benefits because of informed decisions that allow reduced pesticide applications (Isard et al 2011).…”

Global benefits and domestic costs of a cooperative surveillance strategy to control transboundary crop pathogens

“…Other methods rely on the identification of certain recurrent network patterns, where the disease spread is the fastest (Chadès et al, 2011). Concerning surveillance, relevant nodes correspond to those that may allow early disease detection if systematically monitored (Holme, 2017; Neufeld et al, 2018; Sutrave et al, 2012).…”