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Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

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Stanaway, M.A., Mengersen, K.L. and Reeves, R. (2011) Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions. Environmental and Ecological Statistics, 18 (3). pp. 569-591.

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Article Link: http://dx.doi.org/10.1007/s10651-010-0152-x

Organisation URL: http://deedi.qld.gov.au/
Publisher URL: http://www.springerlink.com/


Early detection surveillance programs aim to find invasions of exotic plant pests and diseases before they are too widespread to eradicate. However, the value of these programs can be difficult to justify when no positive detections are made. To demonstrate the value of pest absence information provided by these programs, we use a hierarchical Bayesian framework to model estimates of incursion extent with and without surveillance. A model for the latent invasion process provides the baseline against which surveillance data are assessed. Ecological knowledge and pest management criteria are introduced into the model using informative priors for invasion parameters. Observation models assimilate information from spatio-temporal presence/absence data to accommodate imperfect detection and generate posterior estimates of pest extent. When applied to an early detection program operating in Queensland, Australia, the framework demonstrates that this typical surveillance regime provides a modest reduction in the estimate that a surveyed district is infested. More importantly, the model suggests that early detection surveillance programs can provide a dramatic reduction in the putative area of incursion and therefore offer a substantial benefit to incursion management. By mapping spatial estimates of the point probability of infestation, the model identifies where future surveillance resources can be most effectively deployed.

Item Type:Article
Corporate Creators:Department of Employment, Economic Development and Innovation (DEEDI), Biosecurity Queensland, Queensland University of Technology
Business groups:Biosecurity Queensland
Additional Information:© Springer Science+Business Media.
Keywords:Invasive species; risk analysis; quarantine; non-indigenous species, detectability.
Subjects:Science > Invasive Species > Modelling
Plant pests and diseases > Pest control and treatment of diseases. Plant protection > Inspection. Quarantine
Science > Statistics > Simulation modelling
Live Archive:15 Nov 2011 23:06
Last Modified:03 Sep 2021 16:49

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