LAUSR

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.