Identifying Lookouts for Epidemio-Surveillance: Application to the Emergence of Xylella fastidiosa in France

Ten Challenges to Understanding and Managing the Insect-Transmitted, Xylem-Limited Bacterial Pathogen Xylella fastidiosa

An unprecedented plant health emergency in olives has been registered over the last decade in Italy, arguably more severe than what occurred repeatedly in grapes in the United States in the last 140 years. These emergencies are epidemics caused by a stealthy pathogen, the xylem-limited, insect-transmitted bacterium Xylella fastidiosa. Although these epidemics spurred research that answered many questions about the biology and management of this pathogen, many gaps in knowledge remain. For this review, we set out to represent both the U.S. and European perspectives on the most pressing challenges that need to be addressed. These are presented in 10 sections that we hope will stimulate discussion and interdisciplinary research. We reviewed intrinsic problems that arise from the fastidious growth of X. fastidiosa, the lack of specificity for insect transmission, and the economic and social importance of perennial mature woody plant hosts. Epidemiological models and predictions of pathogen establishment and disease expansion, vital for preparedness, are based on very limited data. Most of the current knowledge has been gathered from a few pathosystems, whereas several hundred remain to be studied, probably including those that will become the center of the next epidemic. Unfortunately, aspects of a particular pathosystem are not always transferable to others. We recommend diversification of research topics of both fundamental and applied nature addressing multiple pathosystems. Increasing preparedness through knowledge acquisition is the best strategy to anticipate and manage diseases caused by this pathogen, described as "the most dangerous plant bacterium known worldwide."

Computing Geographical Networks Generated by Air-Mass Movement

As air masses move within the troposphere, they transport a multitude of components including gases and particles such as pollen and microorganisms. These movements generate atmospheric highways that connect geographic areas at distant, local, and global scales that particles can ride depending on their aerodynamic properties and their reaction to environmental conditions. In this article we present an approach and an accompanying web application called tropolink for measuring the extent to which distant locations are potentially connected by air-mass movement. This approach is based on the computation of trajectories of air masses with the HYSPLIT atmospheric transport and dispersion model, and on the computation of connection frequencies, called connectivities, in the purpose of building trajectory-based geographical networks. It is illustrated for different spatial and temporal scales with three case studies related to plant epidemiology. The web application that we designed allows the user to easily perform intensive computation and mobilize massive archived gridded meteorological data to build weighted directed networks. The analysis of such networks allowed us for example, to describe the potential of invasion of a migratory pest beyond its actual distribution. Our approach could also be used to compute geographical networks generated by air-mass movement for diverse application domains, for example, to assess long-term risk of spread from persistent or recurrent sources of pollutants, including wildfire smoke.

Forecasting Pathogen Dynamics with Bayesian Model-Averaging: Application to Xylella fastidiosa

Forecasting invasive-pathogen dynamics is paramount to anticipate eradication and containment strategies. Such predictions can be obtained using a model grounded on partial differential equations (PDE; often exploited to model invasions) and fitted to surveillance data. This framework allows the construction of phenomenological but concise models relying on mechanistic hypotheses and real observations. However, it may lead to models with overly rigid behavior and possible data-model mismatches. Hence, to avoid drawing a forecast grounded on a single PDE-based model that would be prone to errors, we propose to apply Bayesian model averaging (BMA), which allows us to account for both parameter and model uncertainties. Thus, we propose a set of different competing PDE-based models for representing the pathogen dynamics, we use an adaptive multiple importance sampling algorithm (AMIS) to estimate parameters of each competing model from surveillance data in a mechanistic-statistical framework, we evaluate the posterior probabilities of models by comparing different approaches proposed in the literature, and we apply BMA to draw posterior distributions of parameters and a posterior forecast of the pathogen dynamics. This approach is applied to predict the extent of Xylella fastidiosa in South Corsica, France, a phytopathogenic bacterium detected in situ in Europe less than 10 years ago (Italy 2013, France 2015). Separating data into training and validation sets, we show that the BMA forecast outperforms competing forecast approaches.

Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation

Plant pathology has developed a wide range of concepts and tools for improving plant disease management, including models for understanding and responding to new risks from climate change. Most of these tools can be improved using new advances in artificial intelligence (AI), such as machine learning to integrate massive data sets in predictive models. There is the potential to develop automated analyses of risk that alert decision-makers, from farm managers to national plant protection organizations, to the likely need for action and provide decision support for targeting responses. We review machine-learning applications in plant pathology and synthesize ideas for the next steps to make the most of these tools in digital agriculture. Global projects, such as the proposed global surveillance system for plant disease, will be strengthened by the integration of the wide range of new data, including data from tools like remote sensors, that are used to evaluate the risk ofplant disease. There is exciting potential for the use of AI to strengthen global capacity building as well, from image analysis for disease diagnostics and associated management recommendations on farmers' phones to future training methodologies for plant pathologists that are customized in real-time for management needs in response to the current risks. International cooperation in integrating data and models will help develop the most effective responses to new challenges from climate change.

Cattle transport network predicts endemic and epidemic foot-and-mouth disease risk on farms in Turkey

The structure of contact networks affects the likelihood of disease spread at the population scale and the risk of infection at any given node. Though this has been well characterized for both theoretical and empirical networks for the spread of epidemics on completely susceptible networks, the long-term impact of network structure on risk of infection with an endemic pathogen, where nodes can be infected more than once, has been less well characterized. Here, we analyze detailed records of the transportation of cattle among farms in Turkey to characterize the global and local attributes of the directed—weighted shipments network between 2007-2012. We then study the correlations between network properties and the likelihood of infection with, or exposure to, foot-and-mouth disease (FMD) over the same time period using recorded outbreaks. The shipments network shows a complex combination of features (local and global) that have not been previously reported in other networks of shipments; i.e. small-worldness, scale-freeness, modular structure, among others. We find that nodes that were either infected or at high risk of infection with FMD (within one link from an infected farm) had disproportionately higher degree, were more central (eigenvector centrality and coreness), and were more likely to be net recipients of shipments compared to those that were always more than 2 links away from an infected farm. High in-degree (i.e. many shipments received) was the best univariate predictor of infection. Low in-coreness (i.e. peripheral nodes) was the best univariate predictor of nodes always more than 2 links away from an infected farm. These results are robust across the three different serotypes of FMD observed in Turkey and during periods of low-endemic prevalence and high-prevalence outbreaks.

Contact network epidemiology has been extensively used in the context of infectious diseases, primarily focusing on epidemic diseases. In this paper we use detailed recorded data about cattle exchange between farms in Turkey from 2007 to 2012, to build, analyze and characterize the directed-weighted complex network of shipments of cattle. Additionally, using outbreaks data about recorded cases of foot-and-mouth disease (FMD) in Turkey, we assess the correlation between the “farm’s” position in the network (importance) and the risk of being infected with FMD, which has been endemic in Turkey for a long time. We find some network measures that are more likely to identify high-risk and low-risk farms (in-degree and in-coreness, respectively) when proposing strategies for surveillance or containment of an infectious disease.

Field and Landscape Risk Factors Impacting Flavescence Dorée Infection: Insights from Spatial Bayesian Modeling in the Bordeaux Vineyards

Flavescence dorée (FD) is a quarantine disease threatening European vineyards. Its management is based on mandatory insecticide treatments and the uprooting of infected plants identified during annual surveys. Field surveys are currently not optimized because the drivers affecting FD spread in vineyard landscapes remain poorly understood. We collated a georeferenced dataset of FD detection, collected from 34,581 vineyard plots over 5 years in the South West France wine region. Spatial models fitted with integrated nested Laplace approximation were used to identify local and landscape factors affecting FD detection and infection. Our analysis highlights the importance of sampling period on FD detection and of local practices and landscape context on FD infection. At field scale, altitude and cultivar choice were the main factors affecting FD infection. In particular, the odds ratio of FD infection in fields planted with the susceptible Cabernet Sauvignon, Cabernet Franc, or Muscadelle varieties were approximately twice those in fields planted with the less susceptible Merlot. Field infection was also affected by the field's immediate surroundings (within a circle with a radius of 150 to 200 m), corresponding to landscapes of 7 to 12 ha. In particular, the probability of FD infection increased with the proportions of forest and urban land and with the proportion of susceptible cultivars, demonstrating that the cultivar composition impacts FD epidemiology at landscape scale. The satisfactory predictive performance of the model for identifying districts with a prevalence of FD detection >10% of the fields suggests that it could be used to target areas in which future surveys would be most valuable.

Xylella fastidiosa Infection Reshapes Microbial Composition and Network Associations in the Xylem of Almond Trees

Xylella fastidiosa represents a major threat to important crops worldwide including almond, citrus, grapevine, and olives. Nowadays, there are no efficient control measures for X. fastidiosa, and the use of preventive measures and host resistance represent the most practical disease management strategies. Research on vessel-associated microorganisms is gaining special interest as an innate natural defense of plants to cope against infection by xylem-inhabiting pathogens. The objective of this research has been to characterize, by next-generation sequencing (NGS) analysis, the microbial communities residing in the xylem sap of almond trees affected by almond leaf scorch disease (ALSD) in a recent X. fastidiosa outbreak occurring in Alicante province, Spain. We also determined community composition changes and network associations occurring between xylem-inhabiting microbial communities and X. fastidiosa. For that, a total of 91 trees with or without ALSD symptoms were selected from a total of eight representative orchards located in five municipalities within the X. fastidiosa-demarcated area. X. fastidiosa infection in each tree was verified by quantitative polymerase chain reaction (qPCR) analysis, with 54% of the trees being tested X. fastidiosa-positive. Globally, Xylella (27.4%), Sphingomonas (13.9%), and Hymenobacter (12.7%) were the most abundant bacterial genera, whereas Diplodia (30.18%), a member of the family Didymellaceae (10.7%), and Aureobasidium (9.9%) were the most predominant fungal taxa. Furthermore, principal coordinate analysis (PCoA) of Bray–Curtis and weighted UniFrac distances differentiated almond xylem bacterial communities mainly according to X. fastidiosa infection, in contrast to fungal community structure that was not closely related to the presence of the pathogen. Similar results were obtained when X. fastidiosa reads were removed from the bacterial data set although the effect was less pronounced. Co-occurrence network analysis revealed negative associations among four amplicon sequence variants (ASVs) assigned to X. fastidiosa with different bacterial ASVs belonging to 1174-901-12, Abditibacterium, Sphingomonas, Methylobacterium–Methylorubrum, Modestobacter, Xylophilus, and a non-identified member of the family Solirubrobacteraceae. Determination of the close-fitting associations between xylem-inhabiting microorganisms and X. fastidiosa may help to reveal specific microbial players associated with the suppression of ALSD under high X. fastidiosa inoculum pressure. These identified microorganisms would be good candidates to be tested in planta, to produce almond plants more resilient to X. fastidiosa infection when inoculated by endotherapy, contributing to suppress ALSD.

Tracking the outbreak: an optimized sequential adaptive strategy for Xylella fastidiosa delimiting surveys

The EU plant health legislation enforces the implementation of intensive surveillance programs for quarantine pests. After an outbreak, surveys are implemented to delimit the extent of the infested zone and to manage disease control. Surveillance in agricultural and natural environments can be enhanced by increasing the survey efforts. Budget constraints often limit inspection and sampling intensities, thus making it necessary to adapt and optimize surveillance strategies. A sequential adaptive delimiting survey involving a three-phase and a two-phase design with increasing spatial resolution was developed and implemented for the Xylella fastidiosa demarcated area in Alicante, Spain. Inspection and sampling intensities were optimized using simulation-based methods. Sampling intensity thresholds were evaluated by quantifying their effect on the estimation of X. fastidiosa incidence. This strategy made it possible to sequence inspection and sampling taking into account increasing spatial resolutions, and to adapt the inspection and sampling intensities according to the information obtained in the previous, coarser, spatial resolution. The proposed strategy was able to efficiently delimit the extent of Xylella fastidiosa, while improving on the efficiency and maintaining the efficacy of the official survey campaign. From a methodological perspective, our approach provides new insights into alternative delimiting designs and new reference sampling intensity values.

Emerging strains of watermelon mosaic virus in Southeastern France: model-based estimation of the dates and places of introduction

Where and when alien organisms are successfully introduced are central questions to elucidate biotic and abiotic conditions favorable to the introduction, establishment and spread of invasive species. We propose a modelling framework to analyze multiple introductions by several invasive genotypes or genetic variants, in competition with a resident population, when observations provide knowledge on the relative proportions of each variant at some dates and places. This framework is based on a mechanistic-statistical model coupling a reaction–diffusion model with a probabilistic observation model. We apply it to a spatio-temporal dataset reporting the relative proportions of five genetic variants of watermelon mosaic virus (WMV, genus Potyvirus, family Potyviridae) in infections of commercial cucurbit fields. Despite the parsimonious nature of the model, it succeeds in fitting the data well and provides an estimation of the dates and places of successful introduction of each emerging variant as well as a reconstruction of the dynamics of each variant since its introduction.

Optimal invasive species surveillance in the real world: practical advances from research

When alien species make incursions into novel environments, early detection through surveillance is critical to minimizing their impacts and preserving the possibility of timely eradication. However, incipient populations can be difficult to detect, and usually, there are limited resources for surveillance or other response activities. Modern optimization techniques enable surveillance planning that accounts for the biology and expected behavior of an invasive species while exploring multiple scenarios to identify the most cost-effective options. Nevertheless, most optimization models omit some real-world limitations faced by practitioners during multi-day surveillance campaigns, such as daily working time constraints, the time and cost to access survey sites and personnel work schedules. Consequently, surveillance managers must rely on their own judgments to handle these logistical details, and default to their experience during implementation. This is sensible, but their decisions may fail to address all relevant factors and may not be cost-effective. A better planning strategy is to determine optimal routing to survey sites while accounting for common daily logistical constraints. Adding site access and other logistical constraints imposes restrictions on the scope and extent of the surveillance effort, yielding costlier but more realistic expectations of the surveillance outcomes than in a theoretical planning case.

Eco-Epidemiological Uncertainties of Emerging Plant Diseases: The Challenge of Predicting Xylella fastidiosa Dynamics in Novel Environments

In order to prevent and control the emergence of biosecurity threats such as vector-borne diseases of plants, it is vital to understand drivers of entry, establishment, and spatiotemporal spread, as well as the form, timing, and effectiveness of disease management strategies. An inherent challenge for policy in combatting emerging disease is the uncertainty associated with intervention planning in areas not yet affected, based on models and data from current outbreaks. Following the recent high-profile emergence of the bacterium Xylella fastidiosa in a number of European countries, we review the most pertinent epidemiological uncertainties concerning the dynamics of this bacterium in novel environments. To reduce the considerable ecological and socio-economic impacts of these outbreaks, eco-epidemiological research in a broader range of environmental conditions needs to be conducted and used to inform policy to enhance disease risk assessment, and support successful policy-making decisions. By characterizing infection pathways, we can highlight the uncertainties that surround our knowledge of this disease, drawing attention to how these are amplified when trying to predict and manage outbreaks in currently unaffected locations. To help guide future research and decision-making processes, we invited experts in different fields of plant pathology to identify data to prioritize when developing pest risk assessments. Our analysis revealed that epidemiological uncertainty is mainly driven by the large variety of hosts, vectors, and bacterial strains, leading to a range of different epidemiological characteristics further magnified by novel environmental conditions. These results offer new insights on how eco-epidemiological analyses can enhance understanding of plant disease spread and support management recommendations.[Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

The role of passive surveillance and citizen science in plant health

The early detection of plant pests and diseases is vital to the success of any eradication or control programme, but the resources for surveillance are often limited. Plant health authorities can however make use of observations from individuals and stakeholder groups who are monitoring for signs of ill health. Volunteered data is most often discussed in relation to citizen science groups, however these groups are only part of a wider network of professional agents, land-users and owners who can all contribute to significantly increase surveillance efforts through "passive surveillance". These ad-hoc reports represent chance observations by individuals who may not necessarily be looking for signs of pests and diseases when they are discovered. Passive surveillance contributes vital observations in support of national and international surveillance programs, detecting potentially unknown issues in the wider landscape, beyond points of entry and the plant trade. This review sets out to describe various forms of passive surveillance, identify analytical methods that can be applied to these "messy" unstructured data, and indicate how new programs can be established and maintained. Case studies discuss two tree health projects from Great Britain (TreeAlert and Observatree) to illustrate the challenges and successes of existing passive surveillance programmes. When analysing passive surveillance reports it is important to understand the observers' probability to detect and report each plant health issue, which will vary depending on how distinctive the symptoms are and the experience of the observer. It is also vital to assess how representative the reports are and whether they occur more frequently in certain locations. Methods are increasingly available to predict species distributions from large datasets, but more work is needed to understand how these apply to rare events such as new introductions. One solution for general surveillance is to develop and maintain a network of tree health volunteers, but this requires a large investment in training, feedback and engagement to maintain motivation. There are already many working examples of passive surveillance programmes and the suite of options to interpret the resulting datasets is growing rapidly.

Optimising risk-based surveillance for early detection of invasive plant pathogens

Emerging infectious diseases (EIDs) of plants continue to devastate ecosystems and livelihoods worldwide. Effective management requires surveillance to detect epidemics at an early stage. However, despite the increasing use of risk-based surveillance programs in plant health, it remains unclear how best to target surveillance resources to achieve this. We combine a spatially explicit model of pathogen entry and spread with a statistical model of detection and use a stochastic optimisation routine to identify which arrangement of surveillance sites maximises the probability of detecting an invading epidemic. Our approach reveals that it is not always optimal to target the highest-risk sites and that the optimal strategy differs depending on not only patterns of pathogen entry and spread but also the choice of detection method. That is, we find that spatial correlation in risk can make it suboptimal to focus solely on the highest-risk sites, meaning that it is best to avoid ‘putting all your eggs in one basket’. However, this depends on an interplay with other factors, such as the sensitivity of available detection methods. Using the economically important arboreal disease huanglongbing (HLB), we demonstrate how our approach leads to a significant performance gain and cost saving in comparison with conventional methods to targeted surveillance.

Emerging infectious diseases of plants continue to devastate ecosystems and livelihoods worldwide. By linking a mathematical model of pest spread with a computational optimisation routine, this study identifies where to look for invasive pests if we wish to detect them at an early stage; this method improves upon conventional methods of risk-based surveillance and is robust to model misspecification.

Semi-automated identification of biological control agent using artificial intelligence

The accurate identification of biological control agents is necessary for monitoring and preventing contamination in integrated pest management (IPM); however, this is difficult for non-taxonomists to achieve in the field. Many machine learning techniques have been developed for multiple applications (e.g., identification of biological organisms). Some phytoseiids are biological control agents for small pests, such as Neoseiulus barkeri Hughes. To identify a precise biological control agent, a boosting machine learning classification, namely eXtreme Gradient Boosting (XGBoost), was introduced in this study for the semi-automated identification of phytoseiid mites. XGBoost analyses were based on 22 quantitative morphological features among 512 specimens of N. barkeri and related phytoseiid species. These features were extracted manually from photomicrograph of mites and included dorsal and ventrianal shield lengths, setal lengths, and length and width of spermatheca. The results revealed 100% accuracy rating, and seta j4 achieved significant discrimination among specimens. The present study provides a path through which skills and experiences can be transferred between experts and non-experts. This can serve as a foundation for future studies on the automated identification of biological control agents for IPM.

Global Cropland Connectivity: A Risk Factor for Invasion and Saturation by Emerging Pathogens and Pests

The geographic pattern of cropland is an important risk factor for invasion and saturation by crop-specific pathogens and arthropods. Understanding cropland networks supports smart pest sampling and mitigation strategies. We evaluate global networks of cropland connectivity for key vegetatively propagated crops (banana and plantain, cassava, potato, sweet potato, and yam) important for food security in the tropics. For each crop, potential movement between geographic location pairs was evaluated using a gravity model, with associated uncertainty quantification. The highly linked hub and bridge locations in cropland connectivity risk maps are likely priorities for surveillance and management, and for tracing intraregion movement of pathogens and pests. Important locations are identified beyond those locations that simply have high crop density. Cropland connectivity risk maps provide a new risk component for integration with other factors-such as climatic suitability, genetic resistance, and global trade routes-to inform pest risk assessment and mitigation.

Spatial Bayesian Modeling Applied to the Surveys of Xylella fastidiosa in Alicante (Spain) and Apulia (Italy)

The plant-pathogenic bacterium Xylella fastidiosa was first reported in Europe in 2013, in the province of Lecce, Italy, where extensive areas were affected by the olive quick decline syndrome, caused by the subsp. pauca. In Alicante, Spain, almond leaf scorch, caused by X. fastidiosa subsp. multiplex, was detected in 2017. The effects of climatic and spatial factors on the geographic distribution of X. fastidiosa in these two infested regions in Europe were studied. The presence/absence data of X. fastidiosa in the official surveys were analyzed using Bayesian hierarchical models through the integrated nested Laplace approximation (INLA) methodology. Climatic covariates were obtained from the WorldClim v.2 database. A categorical variable was also included according to Purcell's minimum winter temperature thresholds for the risk of occurrence of Pierce's disease of grapevine, caused by X. fastidiosa subsp. fastidiosa. In Alicante, data were presented aggregated on a 1 km grid (lattice data), where the spatial effect was included in the model through a conditional autoregressive structure. In Lecce, data were observed at continuous locations occurring within a defined spatial domain (geostatistical data). Therefore, the spatial effect was included via the stochastic partial differential equation approach. In Alicante, the pathogen was detected in all four of Purcell's categories, illustrating the environmental plasticity of the subsp. multiplex. Here, none of the climatic covariates were retained in the selected model. Only two of Purcell's categories were represented in Lecce. The mean diurnal range (bio2) and the mean temperature of the wettest quarter (bio8) were retained in the selected model, with a negative relationship with the presence of the pathogen. However, this may be due to the heterogeneous sampling distribution having a confounding effect with the climatic covariates. In both regions, the spatial structure had a strong influence on the models, but not the climatic covariates. Therefore, pathogen distribution was largely defined by the spatial relationship between geographic locations. This substantial contribution of the spatial effect in the models might indicate that the current extent of X. fastidiosa in the study regions had arisen from a single focus or from several foci, which have been coalesced.

From Nucleotides to Satellite Imagery: Approaches to Identify and Manage the Invasive Pathogen Xylella fastidiosa and Its Insect Vectors in Europe

Biological invasions represent some of the most severe threats to local communities and ecosystems. Among invasive species, the vector-borne pathogen Xylella fastidiosa is responsible for a wide variety of plant diseases and has profound environmental, social and economic impacts. Once restricted to the Americas, it has recently invaded Europe, where multiple dramatic outbreaks have highlighted critical challenges for its management. Here, we review the most recent advances on the identification, distribution and management of X. fastidiosa and its insect vectors in Europe through genetic and spatial ecology methodologies. We underline the most important theoretical and technological gaps that remain to be bridged. Challenges and future research directions are discussed in the light of improving our understanding of this invasive species, its vectors and host–pathogen interactions. We highlight the need of including different, complimentary outlooks in integrated frameworks to substantially improve our knowledge on invasive processes and optimize resources allocation. We provide an overview of genetic, spatial ecology and integrated approaches that will aid successful and sustainable management of one of the most dangerous threats to European agriculture and ecosystems.

Assessing the multi-pathway threat from an invasive agricultural pest: Tuta absoluta in Asia

Modern food systems facilitate rapid dispersal of pests and pathogens through multiple pathways. The complexity of spread dynamics and data inadequacy make it challenging to model the phenomenon and also to prepare for emerging invasions. We present a generic framework to study the spatio-temporal spread of invasive species as a multi-scale propagation process over a time-varying network accounting for climate, biology, seasonal production, trade and demographic information. Machine learning techniques are used in a novel manner to capture model variability and analyse parameter sensitivity. We applied the framework to understand the spread of a devastating pest of tomato, Tuta absoluta, in South and Southeast Asia, a region at the frontier of its current range. Analysis with respect to historical invasion records suggests that even with modest self-mediated spread capabilities, the pest can quickly expand its range through domestic city-to-city vegetable trade. Our models forecast that within 5-7 years, Tuta absoluta will invade all major vegetable growing areas of mainland Southeast Asia assuming unmitigated spread. Monitoring high-consumption areas can help in early detection, and targeted interventions at major production areas can effectively reduce the rate of spread.

Revisiting the Concept of Host Range of Plant Pathogens

Strategies to manage plant disease-from use of resistant varieties to crop rotation, elimination of reservoirs, landscape planning, surveillance, quarantine, risk modeling, and anticipation of disease emergences-all rely on knowledge of pathogen host range. However, awareness of the multitude of factors that influence the outcome of plant-microorganism interactions, the spatial and temporal dynamics of these factors, and the diversity of any given pathogen makes it increasingly challenging to define simple, all-purpose rules to circumscribe the host range of a pathogen. For bacteria, fungi, oomycetes, and viruses, we illustrate that host range is often an overlapping continuum-more so than the separation of discrete pathotypes-and that host jumps are common. By setting the mechanisms of plant-pathogen interactions into the scales of contemporary land use and Earth history, we propose a framework to assess the frontiers of host range for practical applications and research on pathogen evolution.

Dating and localizing an invasion from post-introduction data and a coupled reaction-diffusion-absorption model

Invasion of new territories by alien organisms is of primary concern for environmental and health agencies and has been a core topic in mathematical modeling, in particular in the intents of reconstructing the past dynamics of the alien organisms and predicting their future spatial extents. Partial differential equations offer a rich and flexible modeling framework that has been applied to a large number of invasions. In this article, we are specifically interested in dating and localizing the introduction that led to an invasion using mathematical modeling, post-introduction data and an adequate statistical inference procedure. We adopt a mechanistic-statistical approach grounded on a coupled reaction-diffusion-absorption model representing the dynamics of an organism in an heterogeneous domain with respect to growth. Initial conditions (including the date and site of the introduction) and model parameters related to diffusion, reproduction and mortality are jointly estimated in the Bayesian framework by using an adaptive importance sampling algorithm. This framework is applied to the invasion of Xylella fastidiosa, a phytopathogenic bacterium detected in South Corsica in 2015, France.

Xylella fastidiosa: climate suitability of European continent

The bacterium Xylella fastidiosa (Xf) is a plant endophyte native to the Americas that causes diseases in many crops of economic importance (grapevine, Citrus, Olive trees etc). Xf has been recently detected in several regions outside of its native range including Europe where little is known about its potential geographical expansion. We collected data documenting the native and invaded ranges of the Xf subspecies fastidiosa, pauca and multiplex and fitted bioclimatic species distribution models (SDMs) to assess the potential climate suitability of European continent for those pathogens. According to model predictions, the currently reported distribution of Xf in Europe is small compared to the large extent of climatically suitable areas. The regions at high risk encompass the Mediterranean coastal areas of Spain, Greece, Italy and France, the Atlantic coastal areas of France, Portugal and Spain as well as the southwestern regions of Spain and lowlands in southern Italy. The extent of predicted climatically suitable conditions for the different subspecies are contrasted. The subspecies multiplex, and to a certain extent the subspecies fastidiosa, represent a threat to most of Europe while the climatically suitable areas for the subspecies pauca are mostly limited to the Mediterranean basin. These results provide crucial information for the design of a spatially informed European-scale integrated management strategy, including early detection surveys in plants and insect vectors and quarantine measures.

A lattice model to manage the vector and the infection of the Xylella fastidiosa on olive trees

Since October 2013 a new devastating plant disease, known as Olive Quick Decline Syndrome, has been killing most of the olive trees distributed in Apulia, South Italy. Xylella fastidiosa pauca ST53 is the plant pathogenic bacterium responsible for the disease, and the adult Meadow Spittlebug, Philaenus spumarius (L.) (Hemiptera Aphrophoridae), is its main vector. This study proposes a lattice model for the pathogen invasion of olive orchard aimed at identifying an appropriate strategy for arresting the infection, built on the management of the vector throughout its entire life cycle. In our model the olive orchard is depicted as a simple square lattice with olive trees and herbaceous vegetation distributed on the lattice sites in order to mimic the typical structure of an olive orchard; adult vectors are represented by particles moving on the lattice according to rules dictated by the interplay between vector and vegetation life cycles or phenology; the transmission process of the bacterium is regulated by a stochastic Susceptible, Infected and Removed model. On this baseline model, we build-up a proper Integrated Pest Management strategy based on tailoring, timing, and tuning of available control actions. We demonstrate that it is possible to reverse the hitherto unstoppable Xylella fastidiosa pauca ST53 invasion, by a rational vector and transmission control strategy.

Update of the Scientific Opinion on the risks to plant health posed by Xylella fastidiosa in the EU territory

EFSA was asked to update the 2015 EFSA risk assessment on Xylella fastidiosa for the territory of the EU. In particular, EFSA was asked to focus on potential establishment, short- and long-range spread, the length of the asymptomatic period, the impact of X. fastidiosa and an update on risk reduction options. EFSA was asked to take into account the different subspecies and Sequence Types of X. fastidiosa. This was attempted throughout the scientific opinion but several issues with data availability meant that this could only be partially achieved. Models for risk of establishment showed most of the EU territory may be potentially suitable for X. fastidiosa although southern EU is most at risk. Differences in estimated areas of potential establishment were evident among X. fastidiosa subspecies, particularly X. fastidiosa subsp. multiplex which demonstrated areas of potential establishment further north in the EU. The model of establishment could be used to develop targeted surveys by Member States. The asymptomatic period of X. fastidiosa varied significantly for different host and pathogen subspecies combinations, for example from a median of approximately 1 month in ornamental plants and up to 10 months in olive, for pauca. This variable and long asymptomatic period is a considerable limitation to successful detection and control, particularly where surveillance is based on visual inspection. Modelling suggested that local eradication (e.g. within orchards) is possible, providing sampling intensity is sufficient for early detection and effective control measures are implemented swiftly (e.g. within 30 days). Modelling of long-range spread (e.g. regional scale) demonstrated the important role of long-range dispersal and the need to better understand this. Reducing buffer zone width in both containment and eradication scenarios increased the area infected. Intensive surveillance for early detection, and consequent plant removal, of new outbreaks is crucial for both successful eradication and containment at the regional scale, in addition to effective vector control. The assessment of impacts indicated that almond and Citrus spp. were at lower impact on yield compared to olive. Although the lowest impact was estimated for grapevine, and the highest for olive, this was based on several assumptions including that the assessment considered only Philaenus spumarius as a vector. If other xylem-feeding insects act as vectors the impact could be different. Since the Scientific Opinion published in 2015, there are still no risk reduction options that can remove the bacterium from the plant in open field conditions. Short- and long-range spread modelling showed that an early detection and rapid application of phytosanitary measures, consisting among others of plant removal and vector control, are essential to prevent further spread of the pathogen to new areas. Further data collection will allow a reduction in uncertainty and facilitate more tailored and effective control given the intraspecific diversity of X. fastidiosa and wide host range.

Addressing the New Global Threat of Xylella fastidiosa

Xylella fastidiosa is one of the most important threats to plant health worldwide. This bacterial pathogen has a long history, causing disease in the Americas on a range of agricultural crops and trees, with severe economic repercussions particularly on grapevine and citrus. In Europe, X. fastidiosa was detected for the first time in 2013 in association with a severe disease affecting olive trees in southern Italy. Subsequent mandatory surveys throughout Europe led to discoveries in France and Spain in various host species and environments. Detection of additional introductions of X. fastidiosa continue to be reported from Europe, for example from northern Italy in late 2018. These events are leading to a sea change in research, monitoring and management efforts as exemplified by the articles in this Focus Issue . X. fastidiosa is part of complex pathosystems together with hosts and vectors. Although certain X. fastidiosa subspecies and environments have been well studied, particularly those that pertain to established disease in North and South America, this represents only a fraction of the existing genetic, epidemiological, and ecological diversity. This Focus Issue highlights some of the key challenges that must be overcome to address this new global threat, recent advances in understanding the pathosystem, and steps toward improved disease control. It brings together the broad research themes needed to address the global threat of X. fastidiosa, encompassing topics from host susceptibility and resistance, genome sequencing, detection methods, transmission by vectors, epidemiological drivers, chemical and biological control, to public databases and social sciences. Open communication and collaboration among scientists, stakeholders, and the general public from different parts of the world will pave the path to novel ideas to understand and combat this pathogen.