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Summary1. The many thousands of potential invasive species pose one of the greatest threats to global biodiversity world-wide. In this study we propose that assemblages of well-known global invasive pest species, irrespective of whether they arise by anthropogenic means, are non-random species groupings that contain hidden predictive information. Such information can assist the identification and prioritization of species that have the potential to pose an invasive threat in regions where they are not normally found. 2. Data comprising the presence and absence of 844 insect pest species recorded over 459 geographical regions world-wide were analysed using a self-organizing map (SOM), a well-known artificial neural network algorithm. The SOM analysis classified the high dimensional data into two-dimensional space such that geographical areas that had similar pest species assemblages were organized as neighbours on a map or grid. 3. The SOM analysis allowed each species to be ranked in terms of its risk of invasion in each area based on the strength of its association with the assemblage that was characteristic for each geographical region. A risk map for example species was produced to illustrate how such a map can be compared with the species' actual distribution and used with other information, such as the species' biotic characteristics and interactions with the abiotic environment, to improve pest risk assessments further. 4. Synthesis and applications . This study presents a new approach to the identification of potentially high-risk invasive pest species based on the hypothesis that global insect pest assemblages are non-random species groupings that can be subjected to traditional community analysis. A well-known data mining and knowledge discovery method for high dimensional data, SOM, was used to determine pest species assemblages for global regions. Species were ranked according to their potential for establishment based on their strength of association with the species assemblage that characterizes a particular region. Such an analysis can then be used to support additional risk assessment of potential invasive species, giving invasive species researchers, conservation managers, quarantine and biosecurity scientists a means for prioritizing species as candidates for further research.

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