The effectiveness of citizen surveillance for detecting exotic vertebrates

Citizen observations of the natural world are increasing in detail, growing in volume and increasingly being shared on web-based platforms for the purpose of sharing information and/or the crowd-sourcing of species identification. From a biosecurity perspective, such citizen data streams are important as they are responsible for the majority of post-border reports and most detections of exotic pest species of concern. The sharing of sightings amongst what are effectively communities of practice is a key driver of having the sighting of an exotic pest species recognized and reported. Whilst it is clear that the eyes, ears, cameras, and microphones of citizens are a major component of biosecurity surveillance, it is unclear what level of surveillance this provides in the prospective sense. As an example, what confidence does citizen science provide about “proof of absence” for exotic pests of concern? The taxonomy of surveillance used within the field of biosecurity would classify such citizen activities as contributing to “general surveillance,” for which non-detections are typically not recorded and methods of quantitative analysis are still under development. We argue that while not recorded, there is considerable information about citizens activities that routinely underpins peoples mental inference about the level of surveillance provided by citizen activities. Furthermore, we show that it is possible to make such inference from general surveillance transparent by describing and characterizing the activities that potentially generate sightings in a way that is amenable to quantitative analysis. In the context of evaluating surveillance provided by citizens for incursions of exotic vertebrates, we provide examples of citizen observations providing early warning and hence preventing the establishment of species from a range of animal groups. Historically, analysis of the power of general surveillance has been restricted to being conceptual, based on qualitative arguments. We provide this, but also provide a quantitative model framework and provide examples of how different forms of general surveillance data may be analyzed, particularly in supporting inference of eradication/extinction.