Using 'sentinel' plants to improve early detection of invasive plant pathogens.

Infectious diseases of plants present an ongoing and increasing threat to international biosecurity, with wide-ranging implications. An important challenge in plant disease management is achieving early detection of invading pathogens, which requires effective surveillance through the implementation of appropriate monitoring programmes. However, when monitoring relies on visual inspection as a means of detection, surveillance is often hindered by a long incubation period (delay from infection to symptom onset) during which plants may be infectious but not displaying visible symptoms. 'Sentinel' plants–alternative susceptible host species that display visible symptoms of infection more rapidly–could be introduced to at-risk populations and included in monitoring programmes to act as early warning beacons for infection. However, while sentinel hosts exhibit faster disease progression and so allow pathogens to be detected earlier, this often comes at a cost: faster disease progression typically promotes earlier onward transmission. Here, we construct a computational model of pathogen transmission to explore this trade-off and investigate how including sentinel plants in monitoring programmes could facilitate earlier detection of invasive plant pathogens. Using Xylella fastidiosa infection in Olea europaea (European olive) as a current high profile case study, for which Catharanthus roseus (Madagascan periwinkle) is a candidate sentinel host, we apply a Bayesian optimisation algorithm to determine the optimal number of sentinel hosts to introduce for a given sampling effort, as well as the optimal division of limited surveillance resources between crop and sentinel plants. Our results demonstrate that including sentinel plants in monitoring programmes can reduce the expected prevalence of infection upon outbreak detection substantially, increasing the feasibility of local outbreak containment. Author summary: Plant diseases affect the environment and the economy negatively, with implications for biodiversity and food security. Fast detection of invading pathogens is essential to prevent widespread transmission. This is challenging, however, because many plant diseases have a long presymptomatic period (delay from initial infection to symptom onset) in globally important hosts. During this presymptomatic period, infection may spread undetected to other plants. While the presymptomatic period can be long, plant diseases often affect multiple host species, with different epidemiological characteristics. This provides an opportunity for planting sentinel plants, which are alternative host species that display visible symptoms of infection quickly, as early warning beacons for infection. In this research article, we use mathematical modelling to explore the potential for sentinel plants to aid plant disease monitoring programmes. We show that, for a high-profile plant pathogen (Xylella fastidiosa, which is currently devastating olive groves in southern Europe), the use of sentinel plants allows new outbreaks to be identified quickly, reducing the prevalence of infection when outbreaks are detected. Model simulations also indicate that our results apply more generally. Sentinel plants have the potential to assist fast detection of a wide range of invading plant pathogens. [ABSTRACT FROM AUTHOR]