Spatial patterns can reveal useful information about plant diseases. With new, more powerful computers datasets can be larger, and the analysis of spatial pattern can take on new dimensions.
The topics covered in this document barely scratch the surface of the analyses that R is capable of conducting. R has several well developed modules that allow for more advanced geostatistical analyses than have been illustrated in this document. Packages such as geoR, and spdep are available among many others that extend R's capabilities to provide analysis of spatial data. Be sure to read any documentation before using any of the packages and understand the analyses completely to make sure they are applicable to your data.
If you would like more experience with R in the context of epidemiology, see our other exercises for the study of plant disease progress over time (Sparks et al. 2008), dispersal (Esker et al. 2007), and disease forecasting (Esker et al., 2008). For other plant disease epidemiology exercises see Francl and Neher (1997) and for general exercises in ecology and evolutionary biology see Donovan and Weldon (2002).
Acknowledgments
This document was prepared as part of a course in the Ecology and Epidemiology of Plant Pathogens at Kansas State University. Sparks and Esker 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, S. Pethybridge, 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. 08-99-J.
Next, suggested quiz and study questions.
