A3 Vertaisarvioitu kirjan tai muun kokoomateoksen osa
Detecting and Interpreting Patterns within Regional Pest Species Assemblages using Self-organizing Maps and other Clustering Methods
Tekijät: Worner S, Eschen R, Kenis M, Paini D, Saikkonen K, Suiter K, Singh S, Vanninen I, Watts M
Toimittaja: Venette, RC
Julkaisuvuosi: 2015
Kokoomateoksen nimi: Pest risk modelling and mapping for invasive alien species
Tietokannassa oleva lehden nimi: PEST RISK MODELLING AND MAPPING FOR INVASIVE ALIEN SPECIES
Lehden akronyymi: CABI INVASIVE SER
Vuosikerta: 7
Aloitussivu: 97
Lopetussivu: 114
Sivujen määrä: 18
ISBN: 978-1-78064-394-6
DOI: https://doi.org/10.1079/9781780643946.0097
Tiivistelmä
This chapter highlights quantitative methods designed to identify and rank exotic species with potential risk to cause economic and/or environmental harm if they establish in a new area. Until now, pest risk assessments have tended to be qualitative and reactive instead of quantitative and proactive. Here, a computational-intelligence technique called a self-organizing map (SOM) is described that can be used to analyse regional profiles or assemblages of pest species to determine their potential for establishment in new regions. In addition to the SOM, two other useful clustering or classification algorithms, k-means and hierarchical analysis, are also demonstrated to provide a quantitative framework to the risk assessment process. The examples described for each method illustrate how a pest risk analyst can identify, from a large list of potential hazards, which species present the most risk to target areas. Furthermore, examples are given of how such analyses may indicate donor and recipient regions for pest invasion and can highlight previously unknown or ignored threats for further investigation. Finally, cautions are provided and limitations of SOMs and other clustering methods applied to the area of pest risk assessment are discussed.
This chapter highlights quantitative methods designed to identify and rank exotic species with potential risk to cause economic and/or environmental harm if they establish in a new area. Until now, pest risk assessments have tended to be qualitative and reactive instead of quantitative and proactive. Here, a computational-intelligence technique called a self-organizing map (SOM) is described that can be used to analyse regional profiles or assemblages of pest species to determine their potential for establishment in new regions. In addition to the SOM, two other useful clustering or classification algorithms, k-means and hierarchical analysis, are also demonstrated to provide a quantitative framework to the risk assessment process. The examples described for each method illustrate how a pest risk analyst can identify, from a large list of potential hazards, which species present the most risk to target areas. Furthermore, examples are given of how such analyses may indicate donor and recipient regions for pest invasion and can highlight previously unknown or ignored threats for further investigation. Finally, cautions are provided and limitations of SOMs and other clustering methods applied to the area of pest risk assessment are discussed.
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