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. pp. 1173-1181.

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Abstract

Around the globe, islands are the last refuge formany 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 BeliefNetworks (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 analyseswere 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 fromestablishment of invasive specieswithin any archipelago/s, and the invasive species most likely to establish on each island.

Item ID: 50407
Item Type: Article (Research - C1)
ISSN: 1879-1026
Keywords: biosecurity, island, invasive species, dispersal, risk assessment, surveillance
Funders: Gorgon Barrow Island Net Conservation Benefits Fund
Date Deposited: 20 Sep 2017 08:40
FoR Codes: 06 BIOLOGICAL SCIENCES > 0602 Ecology > 060205 Marine and Estuarine Ecology (incl Marine Ichthyology) @ 100%
SEO Codes: 96 ENVIRONMENT > 9604 Control of Pests, Diseases and Exotic Species > 960407 Control of Pests, Diseases and Exotic Species in Marine Environments @ 100%
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