Suppr超能文献

生物防治中使用的风险分析框架及新型贝叶斯网络工具的介绍。

Risk Analysis Frameworks Used in Biological Control and Introduction of a Novel Bayesian Network Tool.

机构信息

New Zealand Crown Research Institutes, New Zealand Forest Research Institute (Scion), Rotorua, 3046, New Zealand.

Better Border Biosecurity (B3), New Zealand.

出版信息

Risk Anal. 2022 Jun;42(6):1255-1276. doi: 10.1111/risa.13812. Epub 2021 Aug 30.

Abstract

Classical biological control, the introduction of natural enemies to new environments to control unwanted pests or weeds, is, despite numerous successful examples, associated with rising concerns about unwanted environmental impacts such as population decline of nontarget species. Recognition of these biosafety risks is globally increasing, and prerelease assessments of biological control agents (BCAs) have become more rigorous in many countries. We review the current approaches to risk assessment for BCAs as used in Australasia, Europe, and North America. Traditionally, these assessments focus on providing assurance about the specificity of a proposed BCA, generally via a list of suitable versus nonsuitable hosts determined through laboratory specificity tests (i.e., by determining the BCA's physiological host range). The outcome of interactions of proposed agents in the natural environment can differ from laboratory-based predictions. Potential nontarget host testing may be incomplete, additional ecological barriers under field conditions may limit encounters between BCA and nontargets or reduce attack levels, and BCAs could disperse to habitats beyond those used by the target species and adversely affect nontarget species. We advocate for the adoption of more comprehensive, ecologically-based, probabilistic risk assessment approaches to BCA introductions. An example is provided using a Bayesian network that can integrate information on probabilities and uncertainties of a BCA to spread and establish in new habitats, interact with nontarget species in these habitats, and eventually negatively impact the populations of these nontarget species. Our new model, Biocontrol Adverse Impact Probability Assessment, aims to be incorporated into a structured decision-making framework to support national regulatory authorities.

摘要

经典生物防治,即将自然天敌引入新环境以控制有害生物或杂草,尽管有许多成功的例子,但也伴随着越来越多的人们对非目标物种数量减少等不良环境影响的担忧。这些生物安全风险的认识在全球范围内正在增加,许多国家对生物防治剂(BCA)的投放前评估变得更加严格。我们回顾了澳大拉西亚、欧洲和北美的生物防治剂风险评估的当前方法。传统上,这些评估侧重于通过通过实验室特异性测试确定的合适与不合适宿主的清单,为拟议的 BCA 的特异性提供保证(即,通过确定 BCA 的生理宿主范围)。在自然环境中提出的生物防治剂的相互作用的结果可能与基于实验室的预测不同。潜在的非目标宿主测试可能不完整,在野外条件下可能会增加额外的生态障碍,限制 BCA 与非目标生物相遇或减少攻击水平,并且 BCA 可能扩散到目标物种以外的栖息地,并对非目标物种造成不利影响。我们提倡采用更全面、基于生态的、概率风险评估方法来引入 BCA。我们提供了一个使用贝叶斯网络的示例,该网络可以整合有关 BCA 在新栖息地中传播和建立、与这些栖息地中的非目标物种相互作用以及最终对这些非目标物种的种群产生负面影响的概率和不确定性的信息。我们的新模型,生物防治不良影响概率评估,旨在被纳入一个结构化决策框架,以支持国家监管机构。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验