Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions


Autoria(s): Stanaway, Mark; Mengersen, Kerrie; Reeves, Robert
Data(s)

2011

Resumo

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.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/43264/

Publicador

Springer New York LLC

Relação

http://eprints.qut.edu.au/43264/1/43264.pdf

DOI:10.1007/s10651-010-0152-x

Stanaway, Mark, Mengersen, Kerrie, & Reeves, Robert (2011) Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions. Environmental and Ecological Statistics, 18(3), pp. 569-591.

Direitos

Springer

The final publication is available at link.springer.com

Fonte

Faculty of Science and Technology; School of Mathematical Sciences; Science & Engineering Faculty; Mathematical Sciences

Palavras-Chave #010000 MATHEMATICAL SCIENCES #050000 ENVIRONMENTAL SCIENCES #060000 BIOLOGICAL SCIENCES #Invasive species, Risk Analysis, Quarantine, Non-indigenous species, Detectability
Tipo

Journal Article