Location: Subtropical Plant Pathology ResearchTitle: A generic risk-based surveying method for invading plant pathogens
|PARNELL, STEPHEN - Rothamsted Research|
|VAN DEN BOSCH, FRANK - Rothamsted Research|
Submitted to: Ecological Applications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/5/2013
Publication Date: 6/1/2014
Citation: Parnell, S., Gottwald, T.R., Riley, T.D., van den Bosch, F. 2014. A generic risk-based surveying method for invading plant pathogens. Ecological Applications. 24(4):779-790. https://doi.org/10.1890/13-0704.1.
Interpretive Summary: The rise in international trade and travel has led to a rapid increase in the number of invading plant pests with often severe environmental and economic repercussions for agriculture. The best control strategy is early detection so that control measures can be implemented. Unfortunately, early detection at very low incidence before the pest has spread generally requires extensive sampling efforts. Very little research effort has focused on the design of sampling plans to detect initial outbreaks. Thus, development of early detection survey methods will save costly sampling resources and increase the likelihood of success in control programs. We introduce a generic risk-based survey method to maximize the number of outbreak sites detected in a sampling program across broad crop areas. We use a number of biological characteristics of the pest, crop, and environment to estimate risk of introduction and establishment. We look at how fast the pest increases to estimate if an outbreak will occur and what size (how much area it will probably cover). We tested this survey method against the invasive disease, citrus huanglongbing, in Florida. The results suggest that risk-based sampling/survey method can be effective even in situations where little information on a pest is available. The sampling program was able to maximize the number of outbreak sites detected. This methodology has been deployed to regulatory agencies to determine sampling plans for invading plant pests for early detection and superior control.
Technical Abstract: The rise in international trade and travel has led to a rapid increase in the number of invading plant pests with often severe environmental and economic repercussions for plant communities. To control an invading pest it is crucial that new outbreak sites are quickly detected in a landscape so that control measures can be implemented. To achieve a level of detection sufficiently high for control to be successful generally requires extensive sampling efforts. Surprisingly little research has focused on the design of sampling plans for outbreak detection and consequently in practice many sampling programs are ad-hoc and not well informed by available information. Any improvement will save costly sampling resources and increase the likelihood of success in the control program. We introduce a generic risk-based method to maximize the number of outbreak sites detected in a sampling program in a heterogeneous landscape. Risk at each location in a landscape is determined by the product of the basic reproductive number (R0), as a measure of outbreak size, and the probability of occurrence of an outbreak (P). We show how risk can be used to weight a random sample so that the highest risk locations have the highest probability of selection. The method is demonstrated and tested using a high-quality spatially and temporally resolved dataset on a current invading citrus pest in Florida, Huanglongbing (HLB). Since often little information is available on an invading pest we contrast the scenario where little is known with the scenario where a considerable amount is known. The results suggest that risk-based sampling can be effective even in situations where little population dynamic information on a pest is available. Synthesis and applications. The control of invading plant pests requires effective sampling programs to be in place. We introduce a risk-based method for the design of sampling programs to maximize the number of outbreak sites detected. We thus provide a rational methodology to be adopted by regulatory agencies to determine sampling plans for invading plant pests.