The European spruce bark beetle ‘Ips typographus L.’ is the most serious disturbance agent for European forests. The complex interactions of many influencing factors need to be integrated into a model-based decision-support system to reduce the potential loss of forests. This paper compares two methodological approaches for spatially-explicit prediction of the predisposition for bark beetle infestations. The fuzzy analytic hierarchy process and the Bayesian belief networks were used in combination with a geographical information system to manage uncertainties. Using available data resources, the two approaches were evaluated to produce robust results for forest practitioners and to support measures to minimize the spread of bark beetles. The findings revealed that nearly 32% of the sites investigated in a case study were moderately-high or high risk categories. It is concluded that BBN is more efficient. Both methods can easily be used to analyze environmental problems involving complex interactions among various criteria.

欧洲云杉小蠹虫“ Ips的typographusL.' 是欧洲森林最严重的干扰因子。许多影响因素的复杂相互作用需要整合到基于模型的决策支持系统中，以减少森林的潜在损失。本文比较了两种方法学方法，用于对树皮甲虫感染的易感性进行空间显式预测。模糊层次分析过程和贝叶斯信念网络与地理信息系统结合使用来管理不确定性。使用可用的数据资源，对这两种方法进行了评估，以便为森林从业者产生可靠的结果，并支持采取措施最大限度地减少树皮甲虫的传播。调查结果显示，在案例研究中调查的站点中有近 32% 属于中高或高风险类别。结论是BBN更有效。这两种方法都可以很容易地用于分析涉及各种标准之间复杂相互作用的环境问题。