Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions |
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Authors: | Mark A Stanaway Kerrie L Mengersen Robert Reeves |
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Institution: | (1) Cooperative Research Centre for National Plant Biosecurity, Queensland University of Technology, Brisbane, Australia; |
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Abstract: | 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. |
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