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For greater preparedness, pest risk assessors are required to prioritise long lists of pest species with potential to establish and cause significant impact in an endangered area. Such prioritization is often qualitative, subjective, and sometimes biased, relying mostly on expert and stakeholder consultation. In recent years, cluster based analyses have been used to investigate regional pest species assemblages or pest profiles to indicate the risk of new organism establishment. Such an approach is based on the premise that the cooccurrence of well-known global invasive pest species in a region is not random, and that the pest species profile or assemblage integrates complex functional relationships that are difficult to tease apart. In other words, the assemblage can help identify and prioritise species that pose a threat in a target region. A computational intelligence method called a Kohonen self-organizing map (SOM), a type of artificial neural network, was the first clustering method applied to analyse assemblages of invasive pests. The SOM is a well known dimension reduction and visualization method especially useful for high dimensional data that more conventional clustering methods may not analyse suitably. Like all clustering algorithms, the SOM can give details of clusters that identify regions with similar pest assemblages, possible donor and recipient regions. More important, however SOM connection weights that result from the analysis can be used to rank the strength of association of each species within each regional assemblage. Species with high weights that are not already established in the target region are identified as high risk. However, the SOM analysis is only the first step in a process to assess risk to be used alongside or incorporated within other measures. Here we illustrate the application of SOM analyses in a range of contexts in invasive species risk assessment, and discuss other clustering methods such as k-means, hierarchical clustering and the incorporation of the SOM analysis into criteria based approaches to assess pest risk.
Prioritization of introduction pathways is seen as an important component of the management of biological invasions. We address whether established alien plants, mammals, freshwater fish and terrestrial invertebrates with known ecological impacts are associated with particular introduction pathways (release, escape, contaminant, stowaway, corridor and unaided). We used the information from the European alien species database DAISIE (www.europe-aliens.org) supplemented by the EASIN catalogue (European Alien Species Information Network), and expert knowledge. Plants introduced by the pathways release, corridor and unaided were disproportionately more likely to have ecological impacts than those introduced as contaminants. In contrast, impacts were not associated with particular introduction pathways for invertebrates, mammals or fish. Thus, while for plants management strategies should be targeted towards the appropriate pathways, for animals, management should focus on reducing the total number of taxa introduced, targeting those pathways responsible for high numbers of introductions. However, regardless of taxonomic group, having multiple introduction pathways increases the likelihood of the species having an ecological impact. This may simply reflect that species introduced by multiple pathways have high propagule pressure and so have a high probability of establishment. Clearly, patterns of invasion are determined by many interacting factors and management strategies should reflect this complexity.