Risk assessment models for invasive species: uncertainty in rankings from multi-criteria analysis
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- 作者：Kurt K. Benke ; Jackie L. Steel and John E. Weiss
- 关键词：Invasive species ; Monte Carlo simulation ; Multi ; criteria decision analysis ; Uncertainty analysis ; Weed risk assessment
- 刊名：Biological Invasions
- 出版时间：January 2011
- 出版年：2011
- 期刊代码：14_13873547
- 类别：med
- 卷：13
- 期：1
- 页码：239-253
- 数据来源：sp
摘要
Uncertainty analysis is described in the context of risk assessment for invasive plant species, where assessment criteria can be weighted using a weight-assignment methodology based on multi-criteria decision analysis (MCDA). A description is given of the essential elements of the Victorian Weed Risk Assessment (VWRA) model that ranks weed species according to scores determined from the synthesis of expert opinion and published literature. The VWRA model uses MCDA to produce a priority ranking of risk for pest plant species by compiling complex data into components with similar themes, arranging these components into the appropriate hierarchical order and then assigning criterion weights to each component. The aim of the study was to investigate the uncertainty and statistical significance in the ranking of the invasive species produced by the model. The methodology used for the uncertainty analysis is described and employed in the evaluation of the two categories of interest, represented by the statistical factors of impact and invasiveness. The criteria contributing to the uncertainty in the predicted ranking were found to be mainly in the impact category, rather than the invasiveness category, and related to agricultural factors such as vector status, reductions in yield quantity and increasing harvest cost.