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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.

Monitoring networks are improved by additional sensors. Optimal configurations of sensors give better representations of the process of interest, maximising its exploration while minimising the need for costly infrastructure. By modelling the monitored process, we can identify gaps in its representation, i.e. uncertain predictions, where additional sensors should be located. Here, with data collected from the Rothamsted Insect Survey network, we train an artificial neural network to predict the seasonal aphid arrival from environmental variables. We focus on estimating prediction uncertainty across the UK to guide the addition of a sensor to the network. We first illustrate how to estimate uncertainty in neural networks, hence making them relevant for model-based monitoring network optimisation. Then we highlight critical areas of agricultural importance where additional traps would improve decision support and crop protection in the UK. Possible applications include most ecological monitoring and surveillance activities, but also the weather or pollution monitoring.

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., Graphical abstract Unlabelled Image, Highlights • Data on novel soybean varieties was used to calibrate and validate the Rothamsted Landscape Model. • Simulations were run for 26 UK sites using current and future climate scenarios. • Under current climate early-maturing varieties matured in the south of the UK. • Under climate change soybean is predicted to mature as far north as Scotland. • No meaningful increases in yield are predicted under climate change.

International audience

International audience; Sustainable pest control strategies hinge on the knowledge of movement ecology within the agricultural landscape where contrasted habitat qualities intermingle, thereby influencing arthropod dispersal. Hence, habitat manipulation in space and time can be a lever for action to control pests with regard to landscape compositional constraints. In this study, we examined the role of grassland arrangements in field colonisation by soil-dwelling pests within a dynamic agricultural landscape, and discussed the implications for pest management with a focus on wireworms. For this purpose, we proposed a framework combining (i) a spatially explicit and mechanistic model describing the pest population dynamics in both aerial and soil compartments involved along its life cycle, and (ii) spatiotemporal representations of various landscape contexts. We addressed the role of grassland in plot history, in plot neighbourhood, or in both history and neighbourhood. Our results show that species with a short life cycle are more responsive to changes in land use, and that the neighbourhood effect strongly relies on dispersal mechanisms (random vs directed movements). We also highlight how the arrangement of grassy landscape elements in space and time can mitigate crop infestation by soil-dwelling pests, thereby emphasizing the relevance of managing grassland regimes. Once informed by critical pest life traits, our approach opens avenue for the exploration of the spatiotemporal land use manipulation meant for pest management. Future research consists in the exploration of suppressive patterns in simplified but realistic agricultural landscapes, generated under agronomic constraints at the farm or landscape scales.

Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which we may estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence does not change between monitoring rounds, resulting in an underestimation of the disease incidence. In this paper, we develop an incidence estimation model accounting for epidemic growth with monitoring rounds that sample varying incidence. We also show how to accommodate the asymptomatic period that is the characteristic of most diseases. For practical use, we produce an approximation of the model, which is subsequently shown to be accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations. The estimation model is made available as an online application. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

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.

A good knowledge about species traits variability in relation to their environment is the cornerstone of landscape-oriented species management studies. One way to infer this relationship is to compare species signatures in space and time from field data with spatially explicit population dynamics models outputs. However, the inference robustness relies on the available field data, and thus on the quality of the underlying sampling strategy. Field sampling is constrained by several factors, such as the number of landscape replicates, possible number of temporal sessions and number of sample locations, that need to be accounted for prior to field sampling. We set and illustrate a heuristic method to answer the question of optimal sampling conditioned by these landscape-induced constraints. First we studied a real agricultural landscape to determine its mean properties in terms of configuration and composition. The real landscape properties were used as constraints in a landscape model to generate a collection of landscapes with similar properties. On the other hand, we formulated population dynamics models (hereafter noted Process Models (PM)) carrying competing hypotheses about two ecological processes—population growth and dispersal—in relation to spatial covariates for Pterostichus melanarius, a carabid species involved in pest regulation. We simulated these spatially explicit models and extracted their sampling-dependent signatures, i.e. metrics computed on different population samples. We defined a sampling design quality as its ability to capture the contrasts between the PM signatures, summarised by the performance of a classification procedure. The most relevant sampling design was selected on the basis of classification performance and in situ feasibility. Finally we explored the effects of the a priori ecological hypotheses quality on classification performances, through a sensitivity analysis of the PM parameters. While some improvements remain to be achieved before being fully operational for landscape ecologists, our framework contributes to bringing closer sampling theory and its application on the field. It endorses the use of landscape modelling to design sampling prior to field experiment to bring out the best from sampled data.

Individual ability to survive and reproduce is driven by the foraging for various kinds of resources. In agricultural landscapes, accounting for the distributions of the resources foraged by a pest can help in assessing whether or not its population may damage crops. Such outcome results from landscape-dependent processes occurring at several biological scales. We present a reaction–advection–diffusion population dynamics model accounting for the foraging process as a driver of an insect pest redistribution in space. It is based on three individual-scale components: resource perception, energy supply management, as well as their optimal interaction determining the optimal foraging strategies. These elements depend on the distribution of two competing resources (namely, feeding and laying sites) affecting (resp. positively or negatively) the energy supply of an insect pest. Here, resource distribution affects the population dynamics through (1) directed motions towards needed resources in response to their perception, and (2) a metabolic currency materialised by a limited laying ability and an additional death by starvation. Building on its multiscale and mechanistically enhanced sensitivity to landscape modifications, we applied this calibrated tool to a theoretical landscape planning problem. Eventually, the model appears as an efficient tool to achieve pest control, used to propose landscape arrangements more resilient to pest outbreaks.

International audience; Modern agriculture suffers from its dependence on chemical inputs and subsequent impacts on health and environment. Alternatively, protecting crops against pests can be achieved through the reinforcement of regulation ecological services. Our work propounds a data-driven methodological framework to derive relevant agricultural landscape rearrangements enhancing populations of beneficial organisms regulating pests.Building on spatialised entomological and geographic data, we developed a parsimonious reaction–diffusion model describing the population dynamics of beneficial organisms. Parameter estimation was carried out in a Bayesian framework accounting for uncertainty in the measurement.Thousands of agricultural landscapes were generated under agronomic specifications dealt with as constraint satisfaction problems. Population dynamics was simulated on each landscape with the fitted reaction-diffusion model mentioned above, and two metrics of abundances allowed the assessment of the regulation performance of the landscape spatial arrangements. One metric is a mean field performance criterion assessing the regulation performance from the landscape composition only, the other is a spatial performance metric assessing the performance resulting from the whole landscape spatial configuration. The former is computed with a non-spatialised form of the population dynamics model, the latter results from the reaction-diffusion model of the population dynamics. Comparing these metrics enabled to quantify the impact of spatial arrangements, hence allowing arrangements proposals. This framework was applied to the case study of a ground beetle species involved in the biological regulation of weeds. The arrangement proposals abides by the productive agronomic constraint that is the landscape composition, while they allow for significant habitat quality enhancement (or deterioration) for the beneficial organism (or a pest). Minor adaptations of our integrated data-driven approach would suit numerous situations ranging from the provision of enhanced ecosystem services to land management for conservation.

International audience; Resource distribution, through its effects on individual foraging and survival, drives population dynamics across the landscape. In an agricultural context, resource distribution is therefore a key information in assessing whether or not a pest population may invade and persist in a given environment. Addressing this issue by means of numerical exploration requires a population model with a sound dependence on the landscape. In this paper, we demonstrate that this dependence is effectively secured by a multi-scale description of the population. We derived a reaction-diffusion population model accounting for two individual-scale processes determining resource utilisation: (1) resource perception as a determinant of mobility and (2) energy supply management as a determinant of survival. In this model, the distribution of two competing resources (feeding and laying sites) affects the spatial population dynamics of a dipteran pest through a heterogeneous dispersion of the individuals and a metabolic currency. We conducted a global sensitivity analysis to evaluate the impact of both individual-scale processes on the population dynamics. This exploration demonstrated the biological relevance of the model according to field observations and theoretical expectations. Our key finding is that resource perception and energy supply management appear as significant as the demographic component regarding the resulting dynamics of the pest. Building on its acute multi-scale landscape dependence, this model may be particularly useful for investigating the putative relationships between agricultural landscape features and pest outbreaks