QUT ePrints

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

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.

View at publisher

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.

Impact and interest:

1 citations in Scopus
Search Google Scholar™
2 citations in Web of Science®

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

77 since deposited on 13 Jul 2011
66 in the past twelve months

Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 43264
Item Type: Journal Article
Keywords: Invasive species, Risk Analysis, Quarantine, Non-indigenous species, Detectability
DOI: 10.1007/s10651-010-0152-x
ISSN: 1352-8505
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000)
Australian and New Zealand Standard Research Classification > ENVIRONMENTAL SCIENCES (050000)
Australian and New Zealand Standard Research Classification > BIOLOGICAL SCIENCES (060000)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Past > Schools > Mathematical Sciences
Copyright Owner: Springer
Copyright Statement: The final publication is available at link.springer.com
Deposited On: 13 Jul 2011 23:11
Last Modified: 23 Apr 2013 15:17

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page