Expanding general surveillance can improve invasive species detection to support eradication. Traditionally, citizens report observations to government agencies and mobile-phone-based tools provide incremental submission and processing efficiencies. However, citizen-reported data have high false positive rates and diagnostics laboratories are not resourced to process large observation volumes. We demonstrate ‘Find-A-Pest’ a partnership model whereby citizens, including Māori groups, and industry representatives both contribute observations and undertake identifications. We combine a mobile-phone-based app, database, and content management system with data linked to iNaturalist NZ. We present data from a 3.5-month case study assessing the effectiveness at delivering improved general surveillance outcomes. Installed by 497 users, there were 471 observations of 176 taxa submitted by 74 individuals. In combination, citizen and industry identifiers processed 99% of observations with only 1% (5 submissions) forwarded to Biosecurity New Zealand. Citizens’ identifications were comprehensive and rapid: 79.4% of submitted observations were identified by citizens with 57.3% and 95.4% of these processed within an hour or day, respectively. Citizen identifications were correct 95.5% of the time. Many observations (56.1%) were of high-priority species profiled in app fact sheets. Find-A-Pest demonstrates that general surveillance partnerships can effectively distribute identification effort, thereby reducing false positive loads within government diagnostics laboratories. Find-A-pest was stable, robust, and endorsed as fit for purpose by users. Achieving biosecurity outcomes, such as early detection to facilitate eradication, will require a much larger-scale participation in Find-A-Pest. We suggest applying behaviour change theory to expand participation across diverse groups in future.

中文翻译：

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通过将公民既作为观察者又作为识别者，扩大对入侵物种的一般监视

扩大常规监测可以改善入侵物种的发现，以支持根除。传统上，公民向政府机构报告观察结果，而基于移动电话的工具可提高提交和处理效率。但是，公民报告的数据具有很高的假阳性率，并且诊断实验室没有资源来处理大量观察数据。我们展示了“寻找害虫”的伙伴关系模型，公民（包括毛利人团体）和行业代表都可以进行观察并进行识别。我们将基于手机的应用程序，数据库和内容管理系统与链接到iNaturalist NZ的数据结合在一起。我们提供了一个为期3.5个月的案例研究得出的数据，这些数据评估了改善常规监测结果的有效性。由497位用户安装，74个人提交了176个分类单元的471个观测值。结合起来，公民和行业标识处理了99％的观察结果，只有1％（5个提交）被转发给了新西兰生物安全局。公民的身份识别是全面而迅速的：公民识别的提交意见中有79.4％被公民识别，其中一个小时或一天之内分别处理了57.3％和95.4％。公民身份正确率高达95.5％。许多观察结果（56.1％）是在应用情况说明书中描述的高优先级物种。Find-A-Pest表明，一般的监视合作伙伴关系可以有效地分配识别工作，从而减少政府诊断实验室中的误报负荷。Find-A-Pest稳定，健壮，并得到用户认可。实现生物安全成果，例如早期检测以促进根除，将需要大规模参与Find-A-Pest。我们建议将来应用行为改变理论来扩大跨不同群体的参与。