Plant Pathology Department, University of Florida, Gainesville, Florida 32611, USA; email:
Institute for Sustainable Food Systems, University of Florida, Gainesville, Florida 32611, USA.
Annu Rev Phytopathol. 2018 Aug 25;56:559-580. doi: 10.1146/annurev-phyto-080516-035326. Epub 2018 Jul 6.
Plant pathology must address a number of challenges, most of which are characterized by complexity. Network analysis offers useful tools for addressing complex systems and an opportunity for synthesis within plant pathology and between it and relevant disciplines such as in the social sciences. We discuss applications of network analysis, which ultimately may be integrated together into more synthetic analyses of how to optimize plant disease management systems. The analysis of microbiome networks and tripartite phytobiome networks of host-vector-pathogen interactions offers promise for identifying biocontrol strategies and anticipating disease emergence. Linking epidemic network analysis with social network analysis will support strategies for sustainable agricultural development and for scaling up solutions for disease management. Statistical tools for evaluating networks, such as Bayesian network analysis and exponential random graph models, have been underused in plant pathology and are promising for informing strategies. We conclude with research priorities for network analysis applications in plant pathology.
植物病理学必须应对许多挑战，其中大多数都具有复杂性的特点。网络分析为解决复杂系统提供了有用的工具，也为植物病理学内部以及与相关学科（如社会科学）之间的综合提供了机会。我们讨论了网络分析的应用，最终这些应用可能会整合到如何优化植物病害管理系统的更综合的分析中。微生物组网络和寄主-介体-病原体相互作用的三分体植物生物群网络分析为识别生物防治策略和预测疾病发生提供了希望。将传染病网络分析与社会网络分析联系起来，将支持可持续农业发展的战略，并为疾病管理的解决方案提供支持。在植物病理学中，贝叶斯网络分析和指数随机图模型等用于评估网络的统计工具尚未得到充分利用，但它们为制定策略提供了很大的潜力。我们最后总结了植物病理学中网络分析应用的研究重点。
