The role of passive surveillance and citizen science in plant health.
The role of passive surveillance and citizen science in plant health.
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DOI：
10.1186/s43170-020-00016-5
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发表时间：
2020
影响因子：
--
通讯作者：
Parnell S
中科院分区：
文献类型：
--
作者：
Brown N;Pérez-Sierra A;Crow P;Parnell S
早期发现植物病虫害对任何根除或控制方案的成功至关重要，但用于监测的资源往往有限。然而，植物卫生当局可以利用监测健康不良迹象的个人和利益攸关方团体的观察结果。公开的数据最常与公民科学团体有关，但这些团体只是更广泛的专业代理人、土地使用者和所有者网络的一部分，他们都可以通过“被动监视”为显著增加监视工作做出贡献。这些特别报告代表了个人的偶然观察，当发现害虫和疾病时，他们可能不一定在寻找害虫和疾病的迹象。被动监测为支持国家和国际监测计划提供了重要的观测结果，在入境点和植物贸易之外的更广泛的景观中发现潜在的未知问题。本文旨在描述各种形式的被动监视，确定可应用于这些“混乱”的非结构化数据的分析方法，并指出如何建立和维护新程序。案例研究讨论了两个树木健康项目从英国（TreeAlert和Observatree），以说明现有的被动监测方案的挑战和成功。在分析被动监测报告时，重要的是要了解观察员发现和报告每个工厂健康问题的概率，这将取决于症状的独特性和观察员的经验。还必须评估报告的代表性，以及报告在某些地方是否更经常出现。从大型数据集预测物种分布的方法越来越多，但需要更多的工作来了解这些方法如何应用于罕见事件，如新引入。一般监测的一个解决方案是建立和维护一个树木健康志愿者网络，但这需要在培训、反馈和参与方面进行大量投资，以保持动力。已经有许多被动监视方案的工作实例，解释所产生的数据集的一套备选方案正在迅速增加。
The early detection of plant pests and diseases is vital to the success of any eradication or control programme, but the resources for surveillance are often limited. Plant health authorities can however make use of observations from individuals and stakeholder groups who are monitoring for signs of ill health. Volunteered data is most often discussed in relation to citizen science groups, however these groups are only part of a wider network of professional agents, land-users and owners who can all contribute to significantly increase surveillance efforts through “passive surveillance”. These ad-hoc reports represent chance observations by individuals who may not necessarily be looking for signs of pests and diseases when they are discovered. Passive surveillance contributes vital observations in support of national and international surveillance programs, detecting potentially unknown issues in the wider landscape, beyond points of entry and the plant trade. This review sets out to describe various forms of passive surveillance, identify analytical methods that can be applied to these “messy” unstructured data, and indicate how new programs can be established and maintained. Case studies discuss two tree health projects from Great Britain (TreeAlert and Observatree) to illustrate the challenges and successes of existing passive surveillance programmes. When analysing passive surveillance reports it is important to understand the observers’ probability to detect and report each plant health issue, which will vary depending on how distinctive the symptoms are and the experience of the observer. It is also vital to assess how representative the reports are and whether they occur more frequently in certain locations. Methods are increasingly available to predict species distributions from large datasets, but more work is needed to understand how these apply to rare events such as new introductions. One solution for general surveillance is to develop and maintain a network of tree health volunteers, but this requires a large investment in training, feedback and engagement to maintain motivation. There are already many working examples of passive surveillance programmes and the suite of options to interpret the resulting datasets is growing rapidly.