Identification of meteorological predictors of Fusarium graminearum ascospore release using correlation and causality analyses
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Fusarium head blight (FHB), caused by the plant pathogen Fusarium graminearum, is a significant threat to small grains production worldwide. Additional knowledge is required to clarify the influence of meteorological conditions on the release of ascospores of F. graminearum. Here, a new application of causality analysis is used to determine how meteorological conditions cause ascospore release. Two types of causality analyses, convergent cross mapping and multivariate state space forecasting, were applied to field measurements of airborne ascospores of F. graminearum over two years. Convergent cross mapping identified relative humidity, solar radiation, wind speed, and air temperature as predictors of ascospore release. Multivariate state space forecasting identified solar radiation and relative humidity as effective predictors of ascospore release. Increased concentration of ascospores in the atmosphere primarily occurred during periods of high relative humidity, low solar radiation, and low wind speed. Results from this study may assist producers in managing FHB in small grains by narrowing the timing and application of fungicides around major ascospore release intervals predicted by meteorological conditions.
KeywordsFungus Fusarium head blight Bioaerosol Causality analysis Convergent cross mapping Disease management Fusarium graminearum Multivariate forecasting
This research was supported by the National Science Foundation (NSF) under Grant Numbers DGE-0966125 (IGERT: MultiScale Transport in Environmental and Physiological System (MultiSTEPS)) and CMMI-1150456 (Integrating Geometric, Probabilistic, and Topological Methods for Phase Space Transport in Dynamical Systems). A portion of this work was also supported by a grant through the Virginia Small Grains Board (449281, Improving the Management of FHB through an Increased Understanding of how the Pathogen Releases its Spores). The authors thank Dr. Aaron J. Prussin, II for his input on this project. The authors thank the Virginia Tech Laboratory for Interdisciplinary Statistical Analysis for assistance with statistical methods and interpretation.
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