Predicting Invasive Fungal Pathogens Using Invasive Pest Assemblages: Testing Model Predictions in a Virtual World

Dean R Paini,Tobin D Northfield,Felix J J A Bianchi,Paul J De Barro,Chaoyang Xue

doi:10.1371/journal.pone.0025695

Abstract

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.

Highlights

While invasive species cause significant environmental and economic damage worldwide [1,2,3], predicting which of the hundreds or thousands of potentially invasive species are most likely to invade a region presents a significant challenge
self organising map (SOM) analysis of fungal pathogen dataset Establishment likelihood lists of the top 100 absent species were generated for all states and territories of Australia, as well as Australia as a whole (Tables S1, S2, S3, S4, S5, S6, S7, and S8)
The negative binomial distribution generated from the 20% scenario did not explain the fungal pathogen data set as well as the distribution generated from the 50% scenario (D AIC = 1972.51), suggesting that the 50% scenario was a better fit to the fungal pathogen data than the data from the 20% scenario (Figure 1)

Summary

Introduction

While invasive species cause significant environmental and economic damage worldwide [1,2,3], predicting which of the hundreds or thousands of potentially invasive species are most likely to invade a region presents a significant challenge Those tasked with the responsibility of preventing biological invasions are often required to prioritise across often extensive lists of potential invaders as part of resource allocating activities. Many biosecurity agencies around the world utilise consultative processes aimed at eliciting expert opinion from researchers, government officers, and industry stakeholders These experts are often asked to assess and prioritise across a large number of potential invasive species, and while their experience and knowledge may be extensive, it is unlikely to extend to all species under consideration. This elicitation process can be susceptible to framing, context dependence and motivational bias which can lead to flawed prioritisations, poor decision making and misallocation of usually limited mitigation resources [11]

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Conclusion