Different pathogens utilize different methods of dispersal, with wind and water being the most common, and the dispersal mechanism will help to determine the shape of the dispersal and disease gradients. Other methods of dispersal, such as insects, farm equipment and humans or other animals also play a role. Dispersal of a pathogen is essential to the development of an epidemic. Therefore modeling propagule dispersal from an inoculum source is an important first step for calculating the spread of the disease from a focus and for determining the number of propagules that have the potential to travel long distances and initiate new epidemics.
Geagea et al. (1999) examined short distance dispersal of rust spores that were dry-dispersed and dispersed by rain-splash. Using different combinations of drop diameter and fall height above the plant, they concluded that the number of spores released increased as the diameter of the drops and the fall height above the plant increased. In most cases after the initial three drops the number of spores decreased as the number of drops increased. They also found that the number of spores deposited on a horizontal surface decreased with increasing distance from the inoculum source.
For long distance dispersal analysis, one of the difficulties is to quantify and obtain samples at long distances, since it is possible that confounding of the data may occur due to interference from different foci. Therefore, it is important to have a thorough understanding of a pathogen’s life cycle, as well as understanding the most important mechanisms of dispersal. Finally, for good predictions of long distance dispersal events, the source of inoculum needs to be readily quantified.
If you would like more experience with R in the context of ecology and epidemiology, see our other exercises for the study of disease progress over time (Sparks, et al. (2008), disease forecasting (Esker, et al. (2008), and analysis of spatial pattern (Sparks, et al. (2008). For other plant disease epidemiology exercises see Francl and Neher (1997) and for general exercises in ecology and epidemiology see Donovan and Weldon (2002).
This document was prepared as part of a course in the Ecology and Epidemiology of Plant Pathogens at Kansas State University. Esker and Sparks were the lead writers of the document, Garrett was faculty adviser, and student contributors appear in alphabetical order. We appreciate the very helpful comments of P. Garfinkel, PHI reviewers, and members of the KSU course. It’s also a pleasure to acknowledge support by the U.S. National Science Foundation under Grants DEB-0130692, DEB-0516046, EF-0525712 (as part of the joint NSF-NIH Ecology of Infectious Disease program) and DBI-0630726, by the Ecological Genomics Initiative of Kansas through NSF Grant No. EPS-0236913 with matching funds from the Kansas Technology Enterprise Corporation, by the Office of Science (Program in Ecosystem Research), U.S. Department of Energy, Grant No. DE-FG02-04ER63892, by the U.S. Agency for International Development for the Sustainable Agriculture and Natural Resources Management Collaborative Research Support Program (SANREM CRSP) under terms of Cooperative Agreement Award No. EPP-A-00-04-00013-00 to the Office of International Research and Development at Virginia Tech and for the Integrated Pest Management CRSP, by USDA grant 2002-34103-11746, and by the NSF Long Term Ecological Research Program at Konza Prairie. This is Kansas State Experiment Station Contribution No. 07-314-J.
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