Refine
Document Type
- Article (2)
Language
- English (2)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Keywords
The trade in plants for planting is a major pathway for the introduction and further spread of alien plants, pests and diseases. Information about the structure of plant trade networks is not generally available, but it is valuable for better assessing the potential risks associated with the trade in live plants and the development of prevention and mitigation measures and policy. The discovery of two larvae of Anoplophora chinensis (citrus longhorn beetle – CLB) in 2009, at a nursery importing Acer palmatum from China in one of the major Dutch tree nursery areas, has resulted in the creation of a detailed dataset on the intra- European Union trade in its potential hosts. This study describes European imports of the primary host of A. chinensis, Acer spp., into the Netherlands (1998-2012) and the effects of the finding in a tree nursery area. In addition, shipments of Acer spp. from 138 producers in the nursery area in 2009 were analysed in a one-off analysis of intra-EU trade. The volume of Acer spp. imports from Asia was stable early during the studied period, and declined to 5% of the initial imports after a period of interceptions, illustrating the effect of regulations. The number of notifications of A. chinensis infestations in imported consignments of Acer spp. increased sharply in the years up to 2007, then declined as imports also reduced. Although plants were shipped to destinations throughout Europe, each producer shipped plants only to few destinations in few countries. Most of the plants were shipped to nurseries in EU countries. These patterns could make it easier to target these high risk destinations for control measures. The lack of transaction records makes it difficult to trace the destination of plants. More systematic electronic record keeping by traders and growers and the data being collated in a database that can be made available to regulatory authorities, together with further studies of plant trade data using network approaches, would be beneficial for improving trace-back and trace-forward and provide better safeguards for plant health and quality.
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.