Nonnative insects and pathogens pose major threats to forest ecosystems worldwide, greatly diminishing the ecosystem services trees provide. Given the high global diversity of arthropod and microbial species, their often unknown biological features or even identities, and their ease of accidental transport, there is an urgent need to better forecast the most likely species to cause damage. Several risk assessment approaches have been proposed or implemented to guide preventative measures. However, the underlying assumptions of each approach have rarely been explicitly identified or critically evaluated. We propose that evaluating the implicit assumptions, optimal usages, and advantages and limitations of each approach could help improve their combined utility. We consider four general categories: using prior pest status in native and previously invaded regions; evaluating statistical patterns of traits and gene sequences associated with a high impact; sentinel and other plantings to expose trees to insects and pathogens in native, nonnative, or experimental settings; and laboratory assays using detached plant parts or seedlings under controlled conditions. We evaluate how and under what conditions the assumptions of each approach are best met and propose methods for integrating multiple approaches to improve our forecasting ability and prevent losses from invasive pests.

中文翻译：

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预测入侵森林昆虫和病原体损害的方法：交叉评估

外来昆虫和病原体对全球森林生态系统构成重大威胁，大大削弱了树木提供的生态系统服务。鉴于节肢动物和微生物物种在全球范围内的高度多样性、它们通常不为人知的生物学特征甚至身份，以及它们易于意外运输，迫切需要更好地预测最有可能造成损害的物种。已经提出或实施了几种风险评估方法来指导预防措施。然而，每种方法的基本假设很少被明确识别或严格评估。我们建议评估每种方法的隐含假设、最佳用法以及优点和局限性可以帮助提高它们的综合效用。我们考虑四大类：使用本地和先前入侵地区的先前有害生物状况；评估与高影响相关的性状和基因序列的统计模式；哨兵和其他植物使树木在本地、非本地或实验环境中暴露于昆虫和病原体；在受控条件下使用分离的植物部分或幼苗进行实验室化验。我们评估了每种方法的假设如何以及在什么条件下得到最好的满足，并提出了整合多种方法的方法，以提高我们的预测能力并防止入侵性害虫造成的损失。在受控条件下使用分离的植物部分或幼苗进行实验室化验。我们评估了每种方法的假设如何以及在什么条件下得到最好的满足，并提出了整合多种方法的方法，以提高我们的预测能力并防止入侵性害虫造成的损失。在受控条件下使用分离的植物部分或幼苗进行实验室化验。我们评估了每种方法的假设如何以及在什么条件下得到最好的满足，并提出了整合多种方法的方法，以提高我们的预测能力并防止入侵性害虫造成的损失。