Predicting invasive fungal pathogens using invasive pest assemblages: Testing model predictions in a virtual world

Paini, Dean R., Bianchi, Felix J.J.A., Northfield, Tobin D., and De Barro, Paul J. (2011) Predicting invasive fungal pathogens using invasive pest assemblages: Testing model predictions in a virtual world. PLoS ONE, 6 (10). e25695. e25695-e25695.

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Predicting future species invasions presents significant challenges to researchers and government agencies. Simply considering the vast number of potential species that could invade an area can be insurmountable. One method, recently suggested, which can analyse large datasets of invasive species simultaneously is that of a self organising map (SOM), a form of artificial neural network which can rank species by establishment likelihood. We used this method to analyse the worldwide distribution of 486 fungal pathogens and then validated the method by creating a virtual world of invasive species in which to test the SOM. This novel validation method allowed us to test SOM's ability to rank those species that can establish above those that can't. Overall, we found the SOM highly effective, having on average, a 96–98% success rate (depending on the virtual world parameters). We also found that regions with fewer species present (i.e. 1–10 species) were more difficult for the SOM to generate an accurately ranked list, with success rates varying from 100% correct down to 0% correct. However, we were able to combine the numbers of species present in a region with clustering patterns in the SOM, to further refine confidence in lists generated from these sparsely populated regions. We then used the results from the virtual world to determine confidences for lists generated from the fungal pathogen dataset. Specifically, for lists generated for Australia and its states and territories, the reliability scores were between 84–98%. We conclude that a SOM analysis is a reliable method for analysing a large dataset of potential invasive species and could be used by biosecurity agencies around the world resulting in a better overall assessment of invasion risk.

Item ID: 28463
Item Type: Article (Research - C1)
ISSN: 1932-6203
Additional Information:

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funders: Cooperative Research Corporation for National Plant Biosecurity
Projects and Grants: CRC 10001
Date Deposited: 15 Aug 2013 02:25
FoR Codes: 05 ENVIRONMENTAL SCIENCES > 0501 Ecological Applications > 050103 Invasive Species Ecology @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970105 Expanding Knowledge in the Environmental Sciences @ 100%
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