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

A Decade after the Outbreak of Xylella fastidiosa subsp. pauca in Apulia (Southern Italy): Methodical Literature Analysis of Research Strategies

In 2013, an outbreak of Xylella fastidiosa (Xf) was identified for the first time in Europe, in the extreme south of Italy (Apulia, Salento territory). The locally identified subspecies pauca turned out to be lethal for olive trees, starting an unprecedented phytosanitary emergency for one of the most iconic cultivations of the Mediterranean area. Xf pauca (Xfp) is responsible for a severe disease, the olive quick decline syndrome (OQDS), spreading epidemically and with dramatic impact on the agriculture, the landscape, the tourism and the cultural heritage of this region. The bacterium, transmitted by insects that feed on xylem sap, causes rapid wilting in olive trees due to biofilm formation, which obstructs the plant xylematic vessels. The aim of this review is to perform a thorough analysis that offers a general overview of the published work, from 2013 to December 2023, related to the Xfp outbreak in Apulia. This latter hereto has killed millions of olive trees and left a ghostly landscape with more than 8000 square kilometers of infected territory, that is 40% of the region. The majority of the research efforts made to date to combat Xfp in olive plants are listed in the present review, starting with the early attempts to identify the bacterium, the investigations to pinpoint and possibly control the vector, the assessment of specific diagnostic techniques and the pioneered therapeutic approaches. Interestingly, according to the general set criteria for the preliminary examination of the accessible scientific literature related to the Xfp outbreak on Apulian olive trees, fewer than 300 papers can be found over the last decade. Most of them essentially emphasize the importance of developing diagnostic tools that can identify the disease early, even when infected plants are still asymptomatic, in order to reduce the risk of infection for the surrounding plants. On the other hand, in the published work, the diagnostic focus (57%) overwhelmingly encompasses all other possible investigation goals such as vectors, impacts and possible treatments. Notably, between 2013 and 2023, only 6.3% of the literature reports addressing the topic of Xfp in Apulia were concerned with the application of specific treatments against the bacterium. Among them, those reporting field trials on infected plants, including simple pruning indications, were further limited (6%).

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."

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.

Prevalence and Distribution of Phony Peach Disease (Caused by Xylella fastidiosa) in the United States

Peach is an important specialty fruit crop in the United States, and phony peach disease (PPD), caused by Xylella fastidiosa subsp. multiplex, has been a major cause of yield loss since it was first observed in 1885. Under a federal eradication program, surveys of PPD were conducted from 1929 to 1972, when the program was terminated. No surveys have been conducted in approximately 50 years; therefore, the current prevalence of PPD in the United States is unknown, especially in the Southeast, where damage was previously most severe. To ascertain the status of PPD, we surveyed orchards in Alabama, Florida, Georgia, and South Carolina from June to August 2020 and, except for South Carolina and northern Georgia, PPD was prevalent. Trees in 17 orchards were subjected to confirmation of X. fastidiosa using the AmplifyRP XRT+ for X. fastidiosa to corroborate our visual assessments; based on these tests, PPD incidence in the orchards ranged from 0 to 30.5%. Ancillary written surveys of relative PPD presence and prevalence were sent to fruit pathologists from universities in 20 states where PPD was historically reported. Only 35.0% of respondents reported that PPD either currently or recently occurred in their state and, of these, three reported PPD to be of significant concern. The results of the physical and written surveys indicate that PPD remains prevalent mainly in the southeastern region of the United States but, in other states where previously reported, it is either not present or has very low prevalence when compared with historical accounts of the disease.

Disentangling the Factors Affecting the Dynamic of Pseudocercospora fijiensis: Quantification of Weather, Fungicide, and Landscape Effects

Quantifying the effect of landscape composition on disease dynamics remains challenging because it depends on many factors. In this study, we used a hybrid process-based/statistical modeling approach to separate the effect of the landscape composition on the epidemiology of banana leaf streak disease (BLSD) from weather and fungicide effects. We parameterized our model with a 5-year dataset, including weekly measures of BLSD on 83 plots in Martinique. After estimating the intrinsic growth parameters of the stage evolution of the disease (SED), we evaluated the dynamic effect of five fungicides. Then, we added the intra- and inter-annual effect on disease dynamics using a generalized linear model. Finally, the whole model was used to assess the annual effect of the landscape on the SED for 11 plots. We evaluated the significance of the landscape composition (proportions of landscape elements in 200-, 500-, 800-, 1,000-m-radius buffer zones) on the landscape effect evaluated with the model. The percentage of hedgerows in a 200-m-radius buffer zone was negatively correlated to the landscape effect, i.e., it acted as a constraint against BLSD spreading and development. The proportion of managed-banana-plants in a 1,000-m-radius buffer zone was negatively correlated to the landscape effect, probably due to a mass effect of fungicide treatments. Inversely, the proportions of forest and the proportion of unmanaged-banana-plants, both in 1,000-m-radius buffer zones, were positively correlated with the landscape effect. Our study provides a holistic approach of the role biotic and abiotic factors play on the dynamics of BLSD.

A cool climate perspective on grapevine breeding: climate change and sustainability are driving forces for changing varieties in a traditional market

A multitude of diverse breeding goals need to be combined in a new cultivar, which always forces to compromise. The biggest challenge grapevine breeders face is the extraordinarily complex trait of wine quality, which is the all-pervasive and most debated characteristic. Since the 1920s, Germany runs continuous grapevine breeding programmes. This continuity was the key to success and lead to various new cultivars on the market, so called PIWIs. Initially, introduced pests and diseases such as phylloxera, powdery and downy mildew were the driving forces for breeding. However, preconceptions about the wine quality of new resistant selections impeded the market introduction. These preconceptions are still echoing today and may be the reason in large parts of the viticultural community for: (1) ignoring substantial breeding progress, and (2) sticking to successful markets of well-known varietal wines or blends (e.g. Chardonnay, Cabernet Sauvignon, Riesling). New is the need to improve viticulture´s sustainability and to adapt to changing environmental conditions. Climate change with its extreme weather will impose the need for a change in cultivars in many wine growing regions. Therefore, a paradigm shift is knocking on the door: new varieties (PIWIs) versus traditional varieties for climate adapted and sustainable viticulture. However, it will be slow process and viticulture is politically well advised to pave the way to variety innovation. In contrast to the widely available PIWIs, competitive cultivars created by means of new breeding technologies (NBT, e.g. through CRISPR/Cas) are still decades from introduction to the market.

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.

Modeling the Spatial Distribution of Xylella fastidiosa: A Nonstationary Approach with Dispersal Barriers

Spatial species distribution models often assume isotropy and stationarity, implying that spatial dependence is direction-invariant and uniform throughout the study area. However, these assumptions are violated when dispersal barriers are present. Despite this, the issue of nonstationarity has been little explored in the context of plant health. The objective of this study was to evaluate the influence of barriers in the distribution of Xylella fastidiosa in the demarcated area in Alicante, Spain. Occurrence data from 2018 were analyzed through spatial Bayesian hierarchical models. The stationary model, illustrating a scenario without control interventions or geographical features, was compared with three nonstationary models: a model with mountains as physical barriers, and two models with a continuous and discontinuous perimeter barrier representing hypothetical control interventions. In the stationary model, the posterior mean of the spatial range, as the distance where two observations are uncorrelated, was 4,030 m 95% credible interval (2,907 to 5,564). This distance can be used to define the buffer zone in the demarcated area. The predicted probability of X. fastidiosa presence in the area outside the barrier was 0.46 with the stationary model, whereas it was reduced to 0.29 and 0.36 with the continuous and discontinuous barrier models, respectively. Differences between the discontinuous and continuous barrier models showed that breaks, where no control interventions were implemented, resulted in a higher predicted probability of X. fastidiosa presence in the areas with low sampling intensity. These results may help authorities prioritize the areas for surveillance and disease control.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Continuous Pest Surveillance and Monitoring Constitute a Tool for Sustainable Agriculture: Case of Xylella fastidiosa in Morocco

Climate and trade changes are reshaping the cartographic distribution of lethal pervasive pathogens. Among serious emerging challenges is Xylella fastidiosa (Xf), a xylem-limited phytopathogenic bacterium that produces losses and damages to numerous crops of high economic and agronomic importance. Lately, this grave quarantine pathogen has expended its distribution by arriving to several European countries and infecting both wild and cultivated plants, and no cure has been identified so far. Countries without current outbreaks like Morocco, need to monitor theirs crops frequently because detecting diseases in the early stages may reduce the huge losses caused by Xf. For that purpose, inspections were managed in different regions in Morocco from March 2020 to July 2021 to assess the presence of Xf in several growing areas of vulnerable economic crops (i.e., almond, citrus and olive). To extend the likelihood of detection, hosts have been inspected and sampled randomly over different environments including symptomatic and asymptomatic plants. Each sample was screened for the existence of Xf by using the DAS-ELISA commercial kit, while, further analyses were carried out for doubtful samples, by PCR. Results of both tests did not show any positive sample in the investigated areas. This finding is an update on the Xf situation in Morocco and confirms that this country is still a free territory from this bacterium, at least in the monitored regions.

Linking Climate Variables to Large-Scale Spatial Pattern and Risk of Citrus Huanglongbing: A Hierarchical Bayesian Modeling Approach

Huanglongbing (HLB) is one of the most important diseases affecting citriculture in the world. Knowledge of climatic factors linked to HLB risk at large spatial scales is limited. We gathered HLB presence and absence data from official surveys conducted in the state of Minas Gerais, Brazil, over 13 years. The total count of orange and mandarin orchards, and mean orchard area, normalized to a spatial grid of 60 cells (55 × 55 km), were derived from the same database. Monthly climate normals (1984 to 2013) of rainfall, mean temperature, and wind speed split into rainy (September to April) and dry (May to August) seasons (annual summary was retained) were obtained for each grid cell. Two hierarchical Bayesian modeling approaches were evaluated, both based on the integrated nested Laplace approximation method. The first, the climate covariates model (CC model), used orchard, climate, and the spatial effect as covariates. The second, principal components (PC model), used the first three components from a principal component analysis of all variables and the spatial effect as covariates. Both models showed an inverse relationship between posterior prevalence and grid cell mean temperature during the dry season. Annual wind speed, as well as annual and rainy season rainfall, contributed to HLB risk in the CC and PC models, respectively. A partial influence of neighboring regions on HLB risk was observed. The results should assist policymakers in defining regions at HLB risk and guide monitoring strategies to mitigate further spread of HLB in the state of Minas Gerais.

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.