Combes, M.; Brown, N.; Thompson, R. N.; Mastin, A.; Crow, P.; Parnell, S.

Invasive plant pests and pathogens cause significant environmental and economic damage. Visual inspection remains a central tenet of plant health surveys, but its sensitivity (probability of correctly identifying the presence of a pest) and specificity (probability of correctly identifying the absence of a pest) is usually ignored. These parameters facilitate calculation of surveillance metrics which are critical for effective contingency planning and outbreak management. To address this, twenty-three citizen scientist surveyors assessed up to 175 oak trees for three symptoms of acute oak decline. The same trees were also assessed by an expert who has monitored these trees annually for over a decade. The sensitivity and specificity of surveyors was calculated using the expert data as the gold-standard. The utility of a workflow utilising Bayesian modelling was then examined using simulated data to estimate these parameters in the absence of a rarely available gold-standard dataset. There was large variation in sensitivity and specificity between surveyors and symptoms, although the sensitivity was positively related to the number of symptoms on a tree. By leveraging surveyor observations of two symptoms from a minimum of 80 trees on two sites, with knowledge of whether a site has higher (~0.6) or lower (~0.3) true disease prevalence we show that sensitivity and specificity can be estimated without gold-standard data. We highlight that sensitivity and specificity will depend on the symptoms of a pest or disease, the individual surveyor, and the survey protocol. This has consequences for how surveys are designed to detect and monitor outbreaks, as well as the interpretation of survey data that is used to inform outbreak management.