|Van Den Bosch, F.|
Submitted to: Workshop Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 3/15/2009
Publication Date: 6/1/2009
Citation: Gottwald, T.R., Irey, M., Van Den Bosch, F. 2009. Estimating the spatial distribution of a plant disease epidemic from a sample. Workshop Proceedings. Interpretive Summary: When new plant diseases are discovered that are impacting crops, one of the first things that is needed is a method to survey and sample for the disease, in order to determine its prevalence and distribution and decide on appropriate control or eradication measures. Unfortunately, very often little to no information is know about the disease and the way it is distributed in affected fields or orchards. In this paper we present a method that can estimate the characteristics of how the disease is distributed in the field from a single observation. This information can them be used to develop a sampling method that can be used by researchers and regulatory agencies to find the disease in the region and to enable them to make policy/regulatory decisions quickly.
Technical Abstract: Sampling is of central importance in plant pathology. It facilitates our understanding of how epidemics develop in space and time and can also be used to inform disease management decisions. Making inferences from a sample is necessary because we rarely have the resources to conduct a complete census of the population we are interested in i.e. we cannot assess the disease status of every plant in a field or of every field in a region. In this paper we describe a method to estimate the spatial distribution of a plant disease which accounts for the spatial structure of the host distribution. Additionally, we also account for the distance-dependent mechanisms by which real epidemics develop. We first describe the method and then apply the method to the distribution of an economically significant disease of citrus, Huanglongbing. This is used as an example only and the method can be applied to any pathogen which exhibits distance-dependent patterns of spread. The method captures the main aggregates of disease distribution well.