Predicting island biosecurity risk from introduced fauna using Bayesian Belief Networks

Lohr, Cheryl, Wenger, Amelia, Woodberry, Owen, Pressey, Robert L. and Morris, Keith (2017) Predicting island biosecurity risk from introduced fauna using Bayesian Belief Networks. Science of the Total Environment, 601-602 1173-1181. doi:10.1016/j.scitotenv.2017.05.281

Author Lohr, Cheryl
Wenger, Amelia
Woodberry, Owen
Pressey, Robert L.
Morris, Keith
Title Predicting island biosecurity risk from introduced fauna using Bayesian Belief Networks
Journal name Science of the Total Environment   Check publisher's open access policy
ISSN 1879-1026
Publication date 2017-12-01
Sub-type Article (original research)
DOI 10.1016/j.scitotenv.2017.05.281
Open Access Status Not yet assessed
Volume 601-602
Start page 1173
End page 1181
Total pages 9
Place of publication Amsterdam, Netherlands
Publisher Elsevier BV
Language eng
Subject 2305 Environmental Engineering
2304 Environmental Chemistry
2311 Waste Management and Disposal
2310 Pollution
Abstract Around the globe, islands are the last refuge for many threatened and endemic species. Islands are frequently also important sites for recreation, cultural activities, and industrial development, all of which facilitate the establishment of invasive species. Surveillance is employed on islands to detect the establishment of invasive species after their arrival, leading to decisions about follow-up actions. Unless surveillance is prioritised according to risk of establishment of invasives, it may be infeasible to implement efficiently over large tracts of publicly accessible land, especially in data-deficient areas. The key biosecurity problem for many regions is one of prioritizing sites for surveillance activities and identifying invasive species most likely to disperse to, and establish, and proliferate on those sites. We created a series of Bayesian Belief Networks (BBNs), linked by Java computing code and the freely available GeNIe application to automate the creation and computation of species- and site-specific biosecurity BBNs. The BBNs require data on island attributes, recreational or industrial visitor load, infrastructure, habitat availability, and animal behaviour and dispersal via swimming, flying, human movement, land bridges, or flood plumes. We used this biosecurity BBN to estimate the risk of 11 invasive faunal species arriving and establishing on 600 islands along the Pilbara coastline, Western Australia. Sensitivity analyses were conducted to identify nodes within the BBNs that required refined data inputs. Propagule pressure was the node with the greatest influence over the number of arrivals. Other nodes such as the number of visitors to islands and swimming capabilities of invasive animals greatly influenced the model results. Across the 11 species studied, our models predicted one arrival per 300 visitors. The biosecurity BBN can be used to identify the islands at highest risk from establishment of invasive species within any archipelago/s, and the invasive species most likely to establish on each island.
Keyword Biosecurity
Invasive species
Risk assessment
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
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School of Earth and Environmental Sciences
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