Forensic epidemiology: New sensor-based plant pathogen detection: Where to look for evidence in a 300-acre crop
F. NUTTER (1). (1) Iowa State University, Ames, IA, U.S.A.
The detection of invasive biotic plant pathogens, deliberate or otherwise, remains a key challenge in forensics epidemiology. The integration of remote sensing, Global Positioning Systems (GPS), and Geographic Information Systems (GIS) technologies, coupled with geostatistical analyses, can provide valid, science-based evidence concerning the presence and geospatial, distribution of invasive plant pathogens. Using the soybean rust pathosystem as a model, we have successfully extracted pathogen-specific spatial and temporal patterns from aerial, satellite, and ground-based sensors that can be used to detect, and accurately differentiate soybean rust, from other soybean diseases (with close to 100% accuracy). Using a GIS script that we developed called Gradient Finder, pathogen-specific spatial and temporal patterns can now be used to detect, identify, and map within-field anomalies caused by plant diseases. Using this approach, soybean rust disease foci can now be easily distinguished from disease patches caused by sudden death syndrome. Geospatial analyses can then be used to determine if spatial patterns are indicative of a natural or a deliberate introduction (i.e., a crime scene). The integration of remote sensing, GPS, and GIS technologies can also be used to deliver precise GPS coordinates as to where investigators on the ground should obtain pathogen isolates (and other evidence) for genetic analyses concerning the population structure of pathogen isolates.
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