A generic risk-based surveying method for invading plant pathogens

Authors: PARNELL, S - Rothamsted Research; Gottwald, Timothy; Riley, Timothy; VAN DEN BOSCH, F - Rothamsted Research

Submitted to: Ecological Applications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/5/2013
Publication Date: 5/1/2014
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.

Interpretive Summary: Invasive plant pathogens are increasing with international trade and travel with damaging environmental and economic consequences. Recent examples include tree diseases such as Sudden Oak Death in the Western US and Ash Dieback in Europe. To control an invading pathogen it is crucial that newly infected sites are quickly detected so that measures can be implemented to control the epidemic. However, since resources are often limited, not all locations can be inspected and locations must be prioritised for survey. Existing approaches to achieve this are often rely on detailed data collection which is difficult and time consuming, especially when new arrivals are unanticipated. Consequently regulatory sampling responses are often ad-hoc and developed without complete information leading to the sub-optimal deployment of expensive sampling resources. We have constructed a flexible risk-based sampling method which is pest generic and enables available information to be utilised to develop informed sampling programs for virtually any biologically relevant plant pest. By targeting risk we can develop sampling schemes that identify high-impact locations which can be subsequently treated in order to reduce pest spread in the landscape. In this paper we demonstrate and test the method using a dataset on citrus Huanglongbing disease (HLB) in Florida. We show that even when available information is relatively minimal, the method has strong predictive value and can result in highly effective targeted surveying plans. The method is especially useful for regulatory agencies and therefore especially useful for USDA APHIS and state agencies when faced with new pest intoroductions.

Technical Abstract: Invasive plant pathogens are increasing with international trade and travel with damaging environmental and economic consequences. Recent examples include tree diseases such as Sudden Oak Death in the Western US and Ash Dieback in Europe. To control an invading pathogen it is crucial that newly infected sites are quickly detected so that measures can be implemented to control the epidemic. However, since sampling resources are often limited, not all locations can be inspected and locations must be prioritised for surveying. Existing approaches to achieve this are often species-specific and rely on detailed data collection and parameterisation, which is difficult, especially when new arrivals are unanticipated. Consequently regulatory sampling responses are often ad-hoc and developed without due consideration of epidemiology, leading to the sub-optimal deployment of expensive sampling resources. We introduce a flexible risk-based sampling method which is pathogen generic and enables available information to be utilised to develop epidemiologically informed sampling programs for virtually any biologically relevant plant pathogen. By targeting risk we aim to inform sampling schemes that identify high-impact locations which can be subsequently treated in order to reduce inoculum in the landscape. This ‘damage limitation’ is often the initial management objective following the first discovery of a new invader. Risk at each location is determined by the product of the basic reproductive number (R0), as a measure of local epidemic size, and the probability of infection (P). We illustrate how the risk-estimates can be used to prioritise a survey by weighting a random sample so that the highest risk locations have the highest probability of selection. We demonstrate and test the method using a high-quality spatially and temporally resolved dataset on Huanglongbing disease (HLB) in Florida. We show that even when available epidemiological information is relatively minimal, the method has strong predictive value and can result in highly effective targeted surveying plans.