The value of volunteer surveillance for the early detection of biological invaders.

Early detection of invaders requires finding small numbers of individuals across large landscapes. It has been argued that the only feasible way to achieve the sampling effort needed for early detection of an invader is to involve volunteer groups (citizen scientists, passive surveyors, etc.). A key concern is that volunteers may have a considerable false-positive and false-negative rate. The question then becomes whether verification of a report from a volunteer is worth the effort. This question is the topic of this paper. Since we are interested in early detection we calculate the Z% upper limit of the one sided confidence interval of the incidence (fraction infected) and use the term maximum expected plausible incidence for this. We compare the maximum plausible incidence when the expert samples on their own, qE∼, and the maximum plausible incidence when the expert only verifies cases reported by the volunteer surveyor to be infected, qV∼. The maximum plausible incidences qE∼ and qV∼. are related as, qV∼=θfp1-θfnqE∼ where θfp and θfn are the false positive and false negative rate of the volunteer surveyor, respectively. We also show that the optimal monitoring programme consists of verifying only the cases reported by the volunteer surveyor if, TXTN<θfp1-θfn, where TN is the time needed for a sample taken by the expert and TX is the time needed for an expert to verify a case reported by a volunteer surveyor. Our results can be used to calculate the maximum plausible incidence of a plant disease based on reports of passive surveyors that have been verified by experts and data from experts sampling on their own. The results can also be used in the development phase of a surveillance project to assess whether including passive surveyor reports is useful in the early detection of exotic invaders.

Epidemiologically-based strategies for the detection of emerging plant pathogens.

Emerging pests and pathogens of plants are a major threat to natural and managed ecosystems worldwide. Whilst it is well accepted that surveillance activities are key to both the early detection of new incursions and the ability to identify pest-free areas, the performance of these activities must be evaluated to ensure they are fit for purpose. This requires consideration of the number of potential hosts inspected or tested as well as the epidemiology of the pathogen and the detection method used. In the case of plant pathogens, one particular concern is whether the visual inspection of plant hosts for signs of disease is able to detect the presence of these pathogens at low prevalences, given that it takes time for these symptoms to develop. One such pathogen is the ST53 strain of the vector-borne bacterial pathogen Xylella fastidiosa in olive hosts, which was first identified in southern Italy in 2013. Additionally, X. fastidiosa ST53 in olive has a rapid rate of spread, which could also have important implications for surveillance. In the current study, we evaluate how well visual surveillance would be expected to perform for this pathogen and investigate whether molecular testing of either tree hosts or insect vectors offer feasible alternatives. Our results identify the main constraints to each of these strategies and can be used to inform and improve both current and future surveillance activities.

The potential for soybean to diversify the production of plant-based protein in the UK.

Soybean (Glycine max) offers an important source of plant-based protein. Currently much of Europe's soybean is imported, but there are strong economic and agronomic arguments for boosting local production. Soybean is grown in central and eastern Europe but is less favoured in the North due to climate. We conducted field trials across three seasons and two sites in the UK to test the viability of early-maturing soybean varieties and used the data from these trials to calibrate and validate the Rothamsted Landscape Model. Once validated, the model was used to predict the probability soybean would mature and the associated yield for 26 sites across the UK based on weather data under current, near-future (2041-60) and far-future (2081-2100) climate. Two representative concentration pathways, a midrange mitigation scenario (RCP4.5) and a high emission scenario (RCP8.5) were also explored. Our analysis revealed that under current climate early maturing varieties will mature in the south of the UK, but the probability of failure increases with latitude. Of the 26 sites considered, only at one did soybean mature for every realisation. Predicted expected yields ranged between 1.39 t ha-1 and 1.95 t ha-1 across sites. Under climate change these varieties are likely to mature as far north as southern Scotland. With greater levels of CO2, yield is predicted to increase by as much as 0.5 t ha-1 at some sites in the far future, but this is tempered by other effects of climate change meaning that for most sites no meaningful increase in yield is expected. We conclude that soybean is likely to be a viable crop in the UK and for similar climates at similar latitudes in Northern Europe in the future but that for yields to be economically attractive for local markets, varieties must be chosen to align with the growing season.

Sampling for disease absence-deriving informed monitoring from epidemic traits.

Monitoring for disease requires subsets of the host population to be sampled and tested for the pathogen. If all the samples return healthy, what are the chances the disease was present but missed? In this paper, we developed a statistical approach to solve this problem considering the fundamental property of infectious diseases: their growing incidence in the host population. The model gives an estimate of the incidence probability density as a function of the sampling effort, and can be reversed to derive adequate monitoring patterns ensuring a given maximum incidence in the population. We then present an approximation of this model, providing a simple rule of thumb for practitioners. The approximation is shown to be accurate for a sample size larger than 20, and we demonstrate its use by applying it to three plant pathogens: citrus canker, bacterial blight and grey mould.