Worner, Susan P. and Gevrey, Muriel and Eschen, René and Kenis, Marc and Paini, Dean and Singh, Sunil K. and Suiter, Karl and Watts, Michael J. (2013) Prioritizing the risk of plant pests by clustering methods; self-organising maps, k-means and hierarchical clustering. NeoBiota, 18 . pp. 83-102. ISSN 1619-0033
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Abstract
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.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > Q Science (General) S Agriculture > S Agriculture (General) |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Biological and Chemical Sciences |
| Depositing User: | Fulori Nainoca - Waqairagata |
| Date Deposited: | 17 Oct 2016 03:51 |
| Last Modified: | 17 Oct 2016 03:55 |
| URI: | https://repository.usp.ac.fj/id/eprint/9403 |
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