# A practitioner's framework for reorienting plant health surveillance toward agroecological outcomes

Cindy E. Morris, Sylvie Malembic-Maher, Claire Nédellec, Sylvie Dallot, Marc Bardin, Cecile Desbiez, Nicolas Sauvion, Alexandra Schoeny, Eric Verdin, Samuel Soubeyrand

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Cindy E. Morris, Sylvie Malembic-Maher, Claire Nédellec, Sylvie Dallot, Marc Bardin, et al. A practitioner's framework for reorienting plant health surveillance toward agroecological outcomes. 2026. hal-05496003

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# A practitioner's framework for reorienting plant health surveillance toward agroecological outcomes

Sylvie Malembic-Maher¹, Claire Nédellec², Sylvie Dallot³, Marc Bardin⁴, Cecile Desbiez⁴, Nicolas Sauvion³, Alexandra Schoeny⁴, Eric Verdin⁴, Samuel Soubeyrand⁵, Cindy E. Morris⁴

¹Univ. Bordeaux, INRAE, Biologie du Fruit et Pathologie, F-33140, Villenave d'Ornon, France

²Université Paris-Saclay, INRAE, MalAGE, F-78352 Jouy-en-Josas, France

³PHIM, Univ Montpellier, INRAE, CIRAD, IRD, Institut Agro, Montpellier, France

⁴INRAE, Pathologie Végétale, F-84140, Montfavet, France

⁵INRAE, BioSP, F-84914 Avignon, France

**Corresponding authors:** cindy.morris@inrae.fr, sylvie.malembic-maher@inrae.fr

**Keywords:** Plant health management, epidemiological surveillance, pathosystem, disease complex, indicators, biotic and abiotic stress, prophylaxis, radar chart, grapevine Flavescence dorée, *Prunus* decline

## Abstract

Epidemiological surveillance (ES) for crop production is the systematic process of collecting, analyzing and interpreting plant health data with the purpose of providing information for decision-making concerning practices that can avoid, mitigate or cure disease. At present, converging social and environmental concerns are increasing pressure on farmers and land managers to reduce or even phase out synthetic pesticides, creating a pressing need to better align ES with low-input, ecological-based crop production practices. Here we propose a framework for conceiving and prioritizing indicators for ES based on their potential to contribute specifically to management strategies that are curative or eradicative, prophylactic or that are based on agroecological principles. The framework organizes indicators along eight spatiotemporal axes and across different types of observations (explicit and collateral manifestations of pathogens and diseases, and predisposing factors), and it characterizes their contribution to each of the three management practices. Based on synthesis of the literature and expert knowledge for several diseases and syndromes in perennial and annual crops as illustrative case studies, we show how current epidemiological knowledge can inspire upgraded surveillance strategies that better support agroecology, and we point to future trends where additional research and tools are needed. This framework is novel because it goes beyond recent calls for modernizing ES that generally emphasize technological innovations, yet remain largely focused on observing pests and diseases without clear explanation of the associated management decisions nor the type of agricultural production system (conventional, organic, etc.) they target.

## Introduction

Surveillance systems for plant health provide information that orients decisions concerning agronomic, horticultural or sylvicultural practices for avoiding, mitigating, curing or eradicating stresses that are deleterious for plant health. Converging social and environmental concerns are putting increasing pressure on farmers and land managers to reduce, or even phase out, the use of synthetic pesticides for crop protection (Jacquet et al., 2022). Assuring plant health while decreasing the use of pesticides will require enhanced vigilance that is timely enough to allow for responses to risky situations and compatible with ecological management practices. Reinforcing and redesigning plant health surveillance systems – hereafter referred to as epidemiological surveillance (ES) – will require the
development and use of relevant, accurate and tractable indicators (Soubeyrand et al., 2024). Furthermore, upgraded surveillance systems will need to account for the growing awareness that environmental biodiversity - within and beyond cultivated lands - can contribute to human, livestock and plant diseases as reservoirs of pathogens (Banerjee, Van Der Heijden, 2023; Bardin et al., 2018; Ben Moussa et al., 2022; Morris et al., 2023), and reservoirs of herbivorous pests, disease vectors or their natural enemies (Gabriele-Rivet et al., 2017; Thurman et al., 2024; Trivellone et al., 2021; Yang et al., 2025). This awareness opens opportunities to develop robust surveillance indicators to assess links between different sectors of health management (human, veterinary, crops, forest, soils, water, wildlife, natural environments, etc.), thereby facilitating joint surveillance efforts and a shared understanding of ecological processes (Morris et al., 2022).

Currently, most indicators of plant health risks concern relatively short-term time scales and short-distance spatial scales. These include the state of abiotic conditions; the detection of the pest, the pathogen or its vector; and the presence of symptoms mostly during the season of production of the crop of interest and in local buffer zones (Abboud et al., 2019; Bousset et al., 2015; Brown et al., 2020; Michel et al., 2016; Rimbaud et al., 2019). The nature of these indicators often confines the decision process to the most efficient management practices in the short term, such as pesticides and culling in particular (Chen et al., 2020). Even when they help to limit pesticide use by targeting actual needs rather than following routine schedules, these indicators remain insufficient to guide strategies that account for the broader ecological context of crop production and the urgent need for long-term, systemic mitigation approaches for crop health. More specifically, effective prevention of pests and diseases within a given growing season requires information about the nearby or regional presence of pests, pathogens or vectors, and/or on the likelihood of weather conditions conducive to outbreaks. For longer-term prevention, particularly that based on the reinforcement of ecological processes and ecosystem services (referred to broadly here as agroecology), there is a critical need for indicators derived from historical trends or predictive models. Whereas these latter indicators could be derived from experience in implementing curative and short-term prevention methods, there is nevertheless an important need to promote the conception and deployment of novel indicators that can be better leveraged to accompany ecological crop management practices.

Despite the central role of indicators in epidemiological surveillance, their relevance to agroecology remains largely neglected. Here we propose a framework for conceiving and prioritizing indicators in terms of their potential to be leveraged for agroecology. Although this approach is based on standard principles of plant disease epidemiology, we go beyond typical epidemiological analyses to clearly describe the extent to which indicators foster prophylactic and agroecological management practices while considering crop production as part of heterogeneous landscapes and ecosystems that are dynamic in space and time. This yields an operational tool to design and implement ES practices compatible with the goals of any given agricultural system. Here we focus on its application in the transition toward agroecology and its utility to pinpoint priority research questions to fill knowledge gaps.

As this approach is intended as a forward-looking guide toward, we present the indicators in the form of a compass. We formalize this compass using radar charts that depict eight categories of indicators, which characterize ES in terms of their spatial and temporal dimensions and the type of phenomena observed. A main objective of delineating axes of indicators is to highlight their relationship to the decisions made for managing plant health. In particular, we specify the extent to which each category of indicators can be leveraged for prophylaxis and agroecological practices, i.e., its potential contribution to these practices. On each axis, the radar-chart scores aggregate the intrinsic performance of the corresponding indicator together with their potential contribution to prophylaxis or ecology-based plant health management. We select radar charts as a suitable representation of this typology because they facilitate visual comparisons between systems (i.e., pathosystems or other complexes of stress on plants) while intuitively conveying graduated dimensions. They also make it straightforward to visualize the expected scope for progress along each axis in the near or more distant future.

To illustrate the approach, we apply the radar charts to two specific cases i) the vector-borne Flavescence dorée phytoplasma of grapevine (FDp) and ii) a complex of biotic and abiotic stresses associated to decline of *Prunus* spp. These two cases provide contrasting examples of quantity and comprehensiveness of knowledge and availability of resources for implementing surveillance. In addition, we provide a spreadsheet for elaborating radar charts for other pathosystems, illustrated using tomato powdery mildew (Suppl. Tab. 1) and arthropod-borne viruses in annual Mediterranean horticultural crops (Suppl. Tab. 2).
## 93 Delineation of eight categories of ES indicators

To facilitate charting different approaches to ES, we defined eight categories (called axes) of indicators for ES (Tab.1) that specify how different types of observations are collected in space and time as described below. The objective of the delineation is to provide a fixed framework in which to position the most comprehensive range of observations that can be imagined relative to plant health.

**Direct (axis 1):** indicators representing validated, explicit observations of disease symptoms (caused by a biological agent) associated with a crop in the field or in the greenhouse. The observations can be subsequently validated by diagnostics that confirm the presence of the pathogen. These indicators are based on visualization of symptoms (human eye or photos), signs of the pathogen (fungal sporulation, for example) and/or molecular or serological detection of the pathogen (genes or full genome sequences, antigens). This axis corresponds to what could be considered as current classical disease diagnostics. This axis does not include the explicit observation of vectors of pathogens. They are considered in axes 3 and 5.

**Specific (axis 2):** indicators whose predictive value or correlation with disease risk for the targeted pathosystem or disease complex has been demonstrated. They include the physical conditions of the cropped field (e.g., temperature, relative humidity, soil type and moisture), crop genotype and phenological stage, crop age, agronomic practices (e.g., fertilization, tilling, rotation, use of pesticides or biocontrol), as well as measures of biodiversity (e.g., richness/diversity of natural enemies or semi-natural habitat cover) or the intensity of deployment of agroecological practices (e.g., proportion of area with flower strips/hedgerows, crop and cover-crop diversification). The age of the plot of land or cropping site can also be considered as a longer-term indicator as a function of the available data on its predictive value.

**Nearby (axis 3):** indicators describing the environment surrounding the cropped field, from the field edge out to the distance over which the pathogen can realistically disperse within the epidemiological cycle of the disease. They capture variables related to land use, associated flora and fauna, human and animal activities (e.g., industrial, urban, agronomic), and their degree of connectivity with the crop via air, water, and movement of humans and animals. Examples include characteristics of the landscape (i.e., composition of vegetation, land use type such as urban, pastures, commercial zones or other; weeds, wild plants, feral or abandoned fields of the crop), value of the land (this is particularly important for vineyards), pesticides use in the surrounding area, local soil properties (especially those affecting drainage and flood risks), proximity to the ground water table and other hydrological aspects, as well as expected trends for these characteristics in the environment surrounding the field. The presence of vectors can be included here, especially when already integrated into existing surveillance programs. However, vectors can also be captured under indirect observations (axis 5). This axis can include detection of the pathogen in reservoirs, but it is not intended to cover occurrences of the disease of the crop of interest. These occurrences are covered in axis 4.

**Recent (axis 4):** indicators representing previous, recent and relevant events such as occurrences of the disease in the crop or in the region near the crop. Recent occurrences are often reported in surveillance bulletins issued by agricultural extension/advisory services (e.g., France's Chambers of Agriculture) or by agencies of ministries of agriculture. The scale of space is similar to that of axis 3 but the scale of time can extend more into the recent past than for axis 3. Axis 4 mobilizes Direct indicators of axis 1. For instance, recent indicators can include molecular or serological diagnostics implemented in nearby regions".

**Indirect (axis 5):** indicators capturing disease-related signals other than symptoms or explicit detection of the pathogen. These include variables that describe disease-related odors (detected with dogs or electronic noses), infection-specific hyperspectral signatures, microbiome-associated traits, observation of insect vectors or their indirect detection using presence-detection sensors such as acoustic sensors and imaging devices. The scales of space and time are similar to those of axis 1 in the sense that the indicators correspond to observations in a crop during a growing season. Nevertheless, they can be early warning indicators when they manifest before symptom expression.
139 **Opportunistic (axis 6):** indicators arising from the exploration of the wide range of readily accessible data sources, such as smart sensors, social networks, citizen science initiatives, digital maps and remotely sensed imagery.
140 These data can be mined without a priori assumptions about their mechanistic relevance to disease processes.
141 yielding indicators that are empirically associated with disease risk and can therefore serve as proxies. An example
142 of such serendipitous indicators in other types of environmental surveillance is the "Waffle House index", which
143 uses the post-hurricane operational status of a widespread restaurant chain as a proxy for local disruption and
144 short-term disaster recovery capacity, despite having no direct mechanistic link to the hazard itself (Dobie et al.,
145 2019).
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147 **Far (axis 7):** explicit and collateral indicators described in axes 1 and 5, respectively, but considered at a spatial scale extending to regions beyond those considered in axis 3. They include both the information collected in distant regions and the processes that link them with the local production area of interest. They consider present and recent historical events whose emanations or consequences could have contemporary impact. Pertinent variables and resources include worldwide distributions of pathogens and vectors, habitat suitability maps, air mass movement, transport data, etc. as illustrated by Radici and colleagues, for example (Radici et al., 2022).
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153 **Future (axis 8):** indicators representing explicit, collateral and pre-disposing phenomena that are future predictions from projections or extrapolations of historical information at a time scale beyond the other axes. This can include predictions of future conditions for climate, land use and habitable zones for species of plants, pathogens and vectors. These indicators can be used, for example, to make long-term disease risk maps or to address the question of where the crops in the future will be best adapted for future plantings in light of predicted changes in climates, land use and biomes.
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159 Guidelines for assessing how each axis can support agroecological management of plant health

160 To create a tool to reorient ES toward agroecological outcomes, we propose a two-step procedure to score how strongly each axis contribute to this objective. The procedure explicitly links the information produced by ES indicators to management decisions they can inform.
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163 **Step 1: theoretical potential.** We first define a 0-10 scale that captures the theoretic potential of each indicator axis to enable actionable options for prophylaxis and agroecology (Fig. 1). As mentioned above, curative actions typically rely on here-and-now diagnostics and primarily pesticide applications or culling. In contrast, prevention and agroecological practices require information from larger windows of time and must consider cropped fields within landscape and regional contexts.
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168 **Step 2: operational feasibility.** We then weight this theoretical potential using expert opinion to reflect the reality of current knowledge, practices and technologies (i.e., the feasibility of implementing the indicators and using the obtained information in decision-making).
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171 To assess feasibility, experts should consider three attributes of intrinsic performance: (i) indicator accuracy, (ii) ease of data acquisition, and (iii) availability of the workforce and infrastructure to collect data (human observers, robotics, satellites, etc.). Data acquisition can be more or less labor-intensive depending on the indicator. For example, certain molecular tests might require time-consuming preparation of samples or expensive materials, whereas direct visual observation of symptoms or ambient conditions is rapid and can use variables extracted from open databases (e.g., land use, meteorology) in near real time. Workflow and material availability also vary among pathosystems and disease complexes. For diseases that are recognized as objects of quarantine, there are often more dedicated labor resources and widely available, validated detection methods than for non-regulated diseases. Where human resources are limited, automated acquisition with robots (Liu et al., 2023) or satellites (Mikaberidze et al., 2025), as well as citizen-generated data (Brown et al., 2020), can partly compensate for this deficiency, provided that access and costs are not prohibitive. In addition to labor availability, the accuracy of an indicator affects its practical use. This can be dependent on the inherent ambiguity of the indicator concerning the state of health of a plant (such as symptoms or molecular profiles of associated microorganisms), the level of competence of the person who collects and interprets the information for the indicator, and the ease by which data relative to a given indicator can be stored, curated or corrected. Finally, based on expert scores for each axis, values are
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assigned to the radar chart to represent current capabilities, and - where relevant - anticipated progress under ongoing research and likely - or hopeful - trends (Figs. 3 and 4).

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Charting indicators, example 1: Flavescence dorée (FD)

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The Flavescence dorée phytoplasma (FDp) is associated with a severe disease of grapevine (Boudon-Padieu, 2002). The main insect vector is the invasive North American grapevine leafhopper *Scaphoideus titanus* Ball, 1932 (Hemiptera: Cicadellidae), although research is revealing other insects capable of transmitting the phytoplasma in the surrounding semi-natural or wild habitats as well as within vineyards, and ultimately to grapevine (Gonella *et al.*, 2024a). Detected in the 1950's in South-West France, FD spread to most of the vineyards of Southern Europe in the following decades (Jeger *et al.*, 2016). Due to the severity of the symptoms and the epidemic spread of the disease, the associated phytoplasma was classified as a quarantine organism under EU and national regulations, with regional surveillance carried out by accredited local plant protection organizations and winegrower associations. When the disease is detected, mandatory measures consist of the removal of infected plants and insecticide treatments against the vector in defined perimeters. Despite the implementation of management, FD is now present in 14 European countries and responsible for average yield losses estimated at around 4 % (Nougadère *et al.*, 2025). In France, nearly 50 % of vineyard area is treated with insecticides against S. titanus (data from the French Ministry of Agriculture). We use this disease as a detailed case study for the eight-axis framework (Fig. 2) because its quarantine status and economic importance have resulted in extensive knowledge of its etiology and epidemiology.

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Status and future trends of indicators

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Direct (axis 1). At present, official surveys rely on visual detection of grapevine yellows symptoms, which are not specific to FD, followed by targeted molecular detection of FDp (Eppo, 2016; Pelletier et al., 2009) performed by accredited laboratories. FD surveillance is supervised by regional plant protection services and is carried out by trained practitioners from diverse organizations depending on the region including plant protection services, agricultural chambers, winegrower associations or the winegrowers themselves. Regional differences in organization result in variations in the intensity and effectiveness of the annual monitoring: from less than 10 % to 100 % of the vineyards (Malembic-Maher et al., 2023). In France, the data generated by surveillance and monitoring are compiled into annual reports detailing the distribution of the disease at regional scales with a consolidated synthesis produced at the national scale. Observation data entry tools and web applications for the identification and geolocalization of infected plants are insufficiently developed (Ephytia Di@gnoView or Agiir)<fnref n="1" />. Data format is poorly harmonized between regions, making it difficult to centralize and further analyze the collected data. Characterization of the genotype of FDp, in addition to its detection in samples, also contributes to decisions about management practices. Indeed, FDp genetic variants can pose different epidemiological risks depending on their origin (i.e., from epidemics in vineyards vs. wild reservoirs of plants other than Vitis spp.) and on their ability to be transmitted by the grapevine leafhopper. They are identified and traced in European vineyards by sequencing a single house-keeping gene (map gene) (Arnaud et al., 2007; Malembic-Maher et al., 2020) and their epidemic potential can be estimated using sequence variation in variable membrane protein (Vmp) loci (Malembic-Maher et al., 2020; Rossi et al., 2019), opening new means for risk analyses.

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Within the next five years, this axis will be deployed more intensively. New European rules will likely lead to the harmonization of surveillance intensity and quality in FDp eradication zones<fnref n="2" />. However, in containment zones<fnref n="3" />

<footnote n="1">¹ http://ephytia.inra.fr/fr/P/151/Di_gno and http://ephytia.inra.fr/fr/P/128/Agiir (accessed 30 Jan 2026)</footnote>

<footnote n="2">² Regulation (EU) 2016/2031 of the European Parliament of the Council of 26 October 2016 on protective measures against pests of plants, amending Regulations (EU) No 228/2013, (EU) No 652/2014 and (EU) No 1143/2014 of the European Parliament and of the Council and repealing Council Directives 69/464/EEC, 74/647/EEC, 93/85/EEC, 98/57/EC, 2000/29/EC, 2006/91/EC and 2007/33/EC. OJ L 317, 23.11.2016, p. 4–104. http://data.europa.eu/eli/reg/2016/2031/oj</footnote>

<footnote n="3">³ Commission Implementing Regulation (EU) 2022/1630 of 21 September 2022 establishing measures for the containment of Grapevine flavescence dorée phytoplasma within certain demarcated areas. OJ L 245, 22.9.2022, p. 27–44. http://data.europa.eu/eli/reg_impl/2022/1630/oj</footnote>
225 (Commission Implementing Regulation EU 2022/1630), surveillance improvement strongly depends on the financial means of local winegrower organizations and their ability to organize collectively. Overall, the recent emergence of FDp in new European regions (Czech Republic, Romania, Montenegro, Val de Loire and Champagne in France), combined with the progress in surveillance and with the social demand on reducing pesticides, should lead to an increase in monitoring intensity and effectiveness (Malembic-Maher et al., 2023).
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230 In the near future, more precise molecular-based detection methods will be implemented. Genotyping of FDp based on *map* and *vmp* genes could soon be recognized as official EPPO methods and should be implemented at the European scale for pertinent management decisions (Loiseau *et al.*, 2024). For easier FDp detection in the field, real-time LAMP (Loop-mediated isothermal amplification) has been validated (Kogovšek *et al.*, 2017) but still need expensive equipment. A first RPA (recombinase polymerase amplification) field kit has also been developed by a private company (AmplifyRP® XRT for Phytoplasma vitis FD-kit, Agdia, Elkhart, IN, USA) but its performance has not been validated (Matić *et al.*, 2022). Detection and genotyping of FDp at the genome scale by high-throughput sequencing is also under development (Salar *et al.*, 2024). The efficiency of symptom detection can also be enhanced by deep learning-based analyses of images from cameras embarked on cropping machines or drones. This is currently at the research stage (Boulent *et al.*, 2020) with the goal to improve the specificity of the detection. To improve data management and curation, web applications combined with data warehouses for geolocalized data entry and data analysis are at the research and experimental stage (Bimonte *et al.*, 2024). Such data can be used more efficiently in modeling studies of disease dynamics at the plot level and should lead to the development of decision support tools for prioritizing monitoring and management at the local scale (Adrakey *et al.*, 2023) if the data are stored, easily accessed and can be appropriately curated.
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245 Beyond the coming 5 years, the ease of detecting symptoms will likely be improved via remote sensing and more powerful algorithms to handle the variability and complexity of data (Gatou et al., 2024). In parallel, "mobile labs" or "toolbox kits" will facilitate the molecular detection of FDp by field managers and winegrowers themselves (Thielert et al., 2024). If technology development trends continue, managers and technicians will be connected to user-friendly web applications and decision support tools for real-time monitoring and management at the field scale. Collected data will likely feed directly into regional and national geo-referenced databases structured in data warehouses that will be used as decision support tools for prioritizing monitoring and as information tools for the winegrowers (Bimonte et al., 2024). In spite of significant technological advances, several factors limit the utility of direct detection of the pathogen and symptoms as a means for managing FDp grapevine yellows. Firstly, there is a latency period of at least one year between infection and symptom expression resulting in a delay between disease dispersal and detection (Boudon-Padieu, 2002). Furthermore, rootstocks and feral grapevines that are non-symptomatic carriers of the phytoplasma (Caudwell et al., 1994; Eveillard et al., 2016) are not covered by surveillance efforts and can act as reservoirs of FDp. This should be compensated by indirect detection methods (see axis 5).
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259 *Specific (axis 2)*. One of the main factors specifically influencing the risk of FD is the inherent susceptibility of grapevine cultivars to the disease. This has been well-characterized for French and Italian cultivars in experimental and field conditions (Adrakey et al., 2022; Eveillard et al., 2016; Ripamonti et al., 2020), and the tolerance of rootstocks as asymptomatic carriers of the phytoplasma has also been demonstrated (Eveillard et al., 2016). Environmental factors or practices such as the effect of altitude, season, plant density, vineyard age, rogueing, and insecticides on disease dynamics have been evaluated based on field observations and modeling in Bordeaux vineyards (Adrakey et al., 2022; Lessio et al., 2014) and the optimum temperature for phytoplasma development has been determined (Salar et al., 2013). Specific practices such as the rogueing of canes or the use of specific plant defense stimulators are suspected to favor the partial recovery of plants (Maggi et al., 2017; Miliordos et al., 2017; Romanazzi et al., 2013) but require further investigations. In contrast, data are lacking on the influence of ambient weather conditions (temperature, humidity, winds) and on stresses such as heat and drought. Environmental factors and practices that influence the behavior, survival and transmission efficiency of leafhopper vectors, such as cultivar, insecticide use and seasonal temperatures, are also taken into account in modeling and forecasting disease risk (Gonella et al., 2024a; Lessio, Alma, 2021). With changing cultural practices, it is suspected
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that organic agriculture could favor FD risk due to a lack of efficiency of the permitted insecticides (Prazaru et al., 2023; Tacoli et al., 2017) even though this has not yet been clearly demonstrated at the landscape scale by comparison of disease rates in conventional and organic vineyards (Adrakey et al. 2022). Although it is relatively easy to obtain environmental data used for the indicators described here, information on practices is much more difficult to compile. Their validity as relevant variables has been established mostly at field scale or for specific areas, such as the Bordeaux vineyards (Adrakey et al., 2022). Finally, there are also voids of data on natural regulation by parasitoids and predators (Bocca et al., 2023) and influence of plant and insect microbiomes, on numerous aspects of the biology of the leafhopper, and on phytoplasma transmission and multiplication in the plant (Chuche, Thiéry, 2014).

Within the next five years, the validity of the indicators described above will likely be extended to other regions than Bordeaux where the impact of FD is important, including Savoy and Burgundy in France (Riley et al., 2023). Tools to support decision-making are also likely to emerge from ongoing work to develop or improve indirect detection indicators from hyperspectral signals and sniffer dogs, for example, to support farmers in adjusting their practices during the production season rather than focusing on solely end of the season measures, such as roguing (Adrakey et al., 2023). For managing leafhopper vectors, risk models accounting for insect temperature sensitivities are being improved by considering microclimatic variability at the local scale. Also, there will be data available on the effect of vibrational mating disruption that could influence vector population dynamics (Thiéry et al., 2023). Beyond the next five years, these indicators could become more informative as research advances on how climate and global changes affect the epidemiology of FD. For example, risk mapping and predictions of leafhopper vector dynamics could incorporate the effects of extreme events and shifts in mean temperatures, which can alter the phenology and physiology of both the plant and the insect vector. New knowledge in this regard could be coupled to growing, open databases on grower practices and land use surrounding vineyards.

Nearby (axis 3). There are numerous local phenomena occurring within a production season that are indicators of risk of FD. For example, land use and agronomic practices in the near environment can influence the probability of grapevine yellows. In the Bordeaux region, the probability of FD infection increases with the proportion of forests and urban land and with the proportion of susceptible cultivars within 150 – 200 m of a given vineyard and slightly with the proportion of plots under organic practices at the landscape scale (Adrakey et al., 2022). Overall, the model developed by Adrakey and colleagues could satisfactorily predict the location of districts with a prevalence of FD detection > 10 %. The property value of vineyards in specific areas is also an indicator of the probability of FD infection as winegrowers with more valuable property tend to better respect compliance with regulatory measures for insect control thereby reducing disease pressure in the neighboring district (Ay, Gozlan, 2020).

FD is disseminated by an insect vector with the majority of winged adults moving less than 30 m and some up to 300 m at least (Lessio et al., 2014). Heavy-tailed dispersal kernels best fit the spread of FD in the Bordeaux region with about 50% of new infection occurring within 10.5 m of the source plant (Adrakey et al., 2023). Feral grapevines surrounding grapevine plots, that are not surveyed and not treated with insecticides, can act as reservoirs of the vector and of the phytoplasma with movements of the vector to cultivated plots (Eveillard et al., 2016; Lessio et al., 2014; Ripamonti et al., 2020). In addition to feral grapevines, wild alder and clematis plants have been identified as reservoirs of FDp with occasional transfer of specific genetic variants to adjacent grapevines achieved by other vectors species than S. titanus feeding on these plants (Malembic-Maher et al., 2020). Whereas these latter two plant species are mentioned as a possible risk factor by European risk evaluations (Tramontini et al., 2020), the presence of non-cultivated grapevine has been included as a risk factor by European regulation and plant protection services (cf. Commission implementing regulation (EU) 2019/2072 annex VIII<fnref n="4" />).

Within the next five years, spatial statistical and mechanistic approaches for disease distribution will be extended to vineyards in other regions than Bordeaux, including Burgundy, Savoy and Champagne in France (Riley et al., 2023). In addition, spatial analyses will benefit from complete and updated data – including infection rate, plot

<footnote n="4">⁴ Commission Implementing Regulation (EU) 2019/2072 of 28 November 2019 establishing uniform conditions for the implementation of Regulation (EU) 2016/2031 of the European Parliament and the Council, as regards protective measures against pests of plants, and repealing Commission Regulation (EC) No 690/2008 and amending Commission Implementing Regulation (EU) 2018/2019. Annex VIII, https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=celex:32019R2072 (accessed 30 Jan 2026)</footnote>
characteristics and landscape composition - provided by winegrower organizations and stored in a centralized platform for use by researchers and technicians as already implemented for grapevine trunk diseases (Etienne et al. 2025)<fnref n="5" />. Tools for cartographic visualization of data, will be available as R Shiny applications, for example, and will facilitate the creation of risk maps. Advances in the localization of feral grapevines and other plant species that are reservoirs will contribute to a more efficient surveillance (Jarausch et al., 2025). Furthermore, the list of wild plant reservoirs and alternative vectors in the environment of grapevine plots will be enriched through ongoing research (Auriol et al., 2023; Rigamonti et al., 2023). This should allow for evaluating the importance of reservoirs such as hazelnut, ailanthus and salix, observed in some regions (Casati et al., 2017; Kogej Zwitter et al., 2023). Some maps of abandoned vineyards and feral grapevines will be accessible from ongoing work on remote sensing and citizen science efforts involving winegrowers and the general public as it is the case in Bordeaux vineyards.

Beyond the coming five years, advances in remote sensing - using sensors mounted on field machinery or drones could enable highly detailed characterization of both cultivated and non-cultivated landscape features (e.g., slopes, hedges, forest hedges, urban areas) and of potential reservoir plant species (e.g., feral grapevine, alders, clematis) (Chang et al., 2025; Karahan et al., 2025). Surveillance of environmental reservoirs will be improved by advances in molecular identification of leafhopper vectors (e.g., barcoding, eDNA). The census of favorable landscape features could be enhanced with information from citizen participatory monitoring. These comprehensive data for landscape features and human activities (e.g., parks, public property, gardens, urban zones) could be stored in a centralized and open-access platform. Data on winegrower practices (e.g., cultivars, insecticide use, organic farming) could also be centralized but without open access to insure privacy of business practices. Methods of surveillance could deploy a wide range of tools including hyperspectral sensors and sniffer dogs combined with mobile molecular labs to foster the systematic detection of the phytoplasma in asymptomatic reservoir plants in the environments and in particular via barcoding techniques deployed on possible alternative vectors. Localization of leafhopper vectors could be enhanced by deploying attractive traps in suspected reservoirs equipped with automated image-capturing and insect recognition technologies.

**Recent (axis 4).** Due to the quarantine status of FDp, data about historical occurrence of the disease is relatively abundant. At the regional scale, the occurrence and distribution of the disease in France is annually recorded and publicly available<fnref n="6" />. Five to ten years of observation data are generally available upon request with specific conventions. However, as already mentioned for axis 1, these data may suffer from lack of quality and precision. Disease occurrence and distribution at the European scale is regularly updated in EPPO reports and maps<fnref n="7" />, in EFSA publications (Jeger et al., 2016; Tramontini et al., 2020) and in national surveillance bulletins<fnref n="8" />. Efforts to harmonize the past decade of data on FDp occurrence, in ongoing research projects, should improve the quality and availability of historical data within the next five years. Over the longer term, data collected at the scale of municipalities could be stored in freely accessible databases and used in accurate risk prediction models to facilitate disease risk early predictions.

**Indirect (axis 5).** There are options for indicators of collateral phenomena for FD. Metabolomic approaches showed that infection with FDp induces important biochemical changes in grapevine primary and secondary metabolites (Prezelj et al., 2016; Teixeira et al., 2023). It is reasonable to speculate that these changes alter spectral properties (Carli et al., 2025). Indeed, few studies showed that FDp-infected leaves could be efficiently detected by multispectral sensors after and before the onset of symptoms (Carli et al., 2025; Imran et al., 2023). These preliminary studies were conducted at leaf level only, on few plants and cultivars and the specificity to FD-infection (from other biotic or abiotic stress) has not yet been evaluated. However, Thierlert and colleagues (Thielert et al., 2024) recently demonstrated a good ability to distinguish leaves infected by FDp from leaves infected by another phytoplasma associated with the Bois Noir disease. Another indirect indicator of FD is the presence of leafhopper vectors. Direct visual monitoring of *S. titanus* or with yellow sticky tape traps is routine in many regions (Tramontini et al., 2020). In some cases it is assisted by image analysis of traps developed by private companies (Gonella et

<footnote n="5">⁵ https://plateforme-esv.fr/thematiques/GTdeperissement_de_la_vigne (accessed 30 Jan 2026)</footnote>

<footnote n="6">⁶ For example, see https://draaf.auvergne-rhone-alpes.agriculture.gouv.fr/carte-interactive-r1624.html or https://www.gdon-bordeaux.fr/documentations/ (accessed 30 Jan 2026)</footnote>

<footnote n="7">⁷ https://gd.eppo.int/taxon/PHYP64/distribution (accessed 30 Jan 2026)</footnote>

<footnote n="8">⁸ https://plateforme-esv.fr/bulletins_et_points_sur_VSI (accessed 30 Jan 2026)</footnote>
al., 2024a), however it is not readily accessible to all winegrowers because of its high cost. Real-time LAMP is at the research stage for identifying FDp in vectors (Matić et al., 2022). In the near future, multispectral detection of FD is likely to be improved (Jarausch et al., 2023). In addition, new methods of indirect detection of vector-borne plant pathogens in plants and vectors based on odors sensed by dogs are being explored (Gottwald et al., 2020). If costs of insect vector detection by connected traps are reduced, they could be more widely implemented in the near future. Another means of insect vector monitoring could also involve the detection of specific vibratory signals produced by S. *titanus* females (Mazzoni et al., 2009). In a few years, management decisions could likely include data on the additional vector species that are being discovered (Auriol et al., 2023). Advances in the molecular detection of specific FDp receptor(s) in insect guts (Canuto et al., 2023) could also contribute to enhancing the specificity of vector detection. In the longer term, improvements in the above-mentioned technologies will enhance their efficiency and availability. For example, wide spread use of dogs for detecting disease is being accompanied by increasingly rigorous validation methods and a greater availability of trained dogs (Gottwald et al., 2020; Juge et al., 2022). Canine detection could be complemented by sensors with nanomaterials detecting volatile organic compounds specific to FD (Gan et al., 2023) or sensors to detect specific sounds emitted by stressed plants (Khait et al., 2023). The state of plant health might also be reflected in the structure and composition of the endogenous microflora of grapevines that could be detected via either chemical emissions or metagenomic profiling. Development of sensitive and specific molecular tools for detection of FDp in insects, as has been achieved for other vector-borne plant diseases (Farigoule et al., 2022), will also enhance the efficiency of the indicators in this axis. Training dogs to detect contaminated vectors might also be within the realm of possibilities. Furthermore, advances in light detection and ranging (LiDAR) sensors, which use laser pulses to measure distance and detect moving targets, could facilitate tracking of insect flight (Li et al., 2020).

**Opportunistic (axis 6).** Currently, there are no serendipitous indicators of FD that have been identified across spatial or temporal scales. Yet, advances in genomics and environmental monitoring may soon create new opportunities. For example, mining gene and genome sequences from grapevine pathogens — within platforms aimed at characterizing the global grapevine virome (Hily et al., 2019) — could be extended to detect FDp transcripts. However, such approaches may face delays between sample collection and data availability. Their usefulness will grow as repositories of environmental samples and sequencing data expand in scale and scope. Increasing open-access datasets and global monitoring efforts will improve the chances of detecting FDp in unexpected sources: stomach contents of vector predators (e.g., bats, birds, spiders) (Jedlicka et al., 2017), insect biorepositories (Trivellone et al., 2021), or S. *titanus* caught fortuitously by traps designed for other species (Luquet et al., 2023). These indirect sources, if systematically explored, could provide a broad and low-cost surveillance network. Environmental DNA and RNA metabarcoding of vineyard soils, foliage, or water are promising, non-invasive detection methods (Mandal, Das, 2025), but their effectiveness in real-world epidemic forecasting has yet to be demonstrated. When combined with multi-omic approaches — including metagenomics, metabolomics, and transcriptomics — and contextualized with environmental variables, these techniques are expected to enhance our understanding of microbial functions and ecological roles in communities. Likewise, the automated mining of such complex datasets, coupled with machine learning, remains largely conceptual, yet it offers the potential to identify early-warning indicators and support the development of predictive models for epidemic outbreaks. Participatory surveillance and grapevine microbiome analyses may also reveal early signals, adding locally grounded knowledge to more formal systems.

**Far (axis 7).** Environmental conditions, weather events and the occurrence of disease or vectors at medium and long distances from a given vineyard can provide information about disease risk if there are analytical means to associate spatially distant situations. The cartography of vineyards with FD across many of the production regions, as described for axis 4 provides the data for such analyses. The medium-distance transport of vectors via the leaves that often remain on pruning machines has been suspected (Tramontini et al., 2020) and experimentally demonstrated (Chavarri Padilla A., personal communication). Analysis is in progress to assess the connectivity of FD among the parcels of a same winegrower due to movement of vehicles and machinery belonging to the same owner. Disease distribution observations also suggest medium-distance transport of infected insects by winds, southward through the Rhone valley or northward in Catalonia (Rahola et al., 1997) and from Slovenia to Austria (Steffek et al., 2007). The capacity of long-distance dissemination of S. *titanus* by winds (i.e., several dozen kilometers or even more) remains unknown. However, some studies have documented migration patterns of other
leafhoppers species that appear to be associated with atmospheric trajectories (Wu et al., 2019). In addition, long-distance transport of the vector or of the phytoplasma through the exchange of grapevine material has been documented by population genetics approaches on *S. titanus* and by tracing material exchange (Jeger et al., 2016; Papura et al., 2009). In Europe, the monitoring of nursery stock and mother plants, including their immediate vicinity, has been mandatorily implemented to avoid movement of contaminated materials (Commission implementing regulation (EU) 2019/2072 annex VIII, cited above). However, the rootstock mother plants are not monitored because they remain asymptomatic, and therefore canes must undergo hot water treatment to eliminate the phytoplasma (Caudwell et al., 1997).

The utility of medium and long-distance indicators will depend on advances in connectivity analyses that integrate global mapped data on vineyards, their health status, the presence of insect vectors and alternative reservoirs, while also considering both natural dispersal pathways (e.g., wind currents) and human-mediated factors such as road networks, insecticide use, and the sharing of machinery. Further improvement beyond five years could be attained by reinforcing the data needed to understand the potential for long-distance dissemination of *S. titanus* by winds or machinery. This could involve mark-capture experiments. Modeling of the movement of machinery between and within vineyards could be achieved with additional data. Potential wind-driven dissemination trajectories of insects can be predicted using existing atmospheric data (Wang et al., 2026) and dedicated tools such as Tropolink that infer air mass movements to identify the most probable pathways of aerial transport (Richard et al., 2023). With the sequencing of the first complete genome of FDp (Debonneville et al., 2022), quick progress should be made in molecular epidemiology and population genetics in order to reconstitute medium to long-distance dissemination pathways through European vineyards, as it was recently investigated for the quarantine vector-borne bacteria 'Candidatus Liberibacter asiaticus' infecting citrus worldwide (Pruvost et al., 2024). The risk maps that could be created with such analyses would be highly informative. However, they would likely lack some data on the movement of plant materials that are considered confidential for commercial reasons.

More broadly, integrating diverse textual and data sources is becoming essential to anticipate threats like FD (Soubeyrand et al., 2024). Today, relevant information remains scattered and fragmented across scientific articles, grey literature, and non-interoperable databases — each covering only partial aspects such as insect vectors, genotypes, plant hosts, or transmission routes (Malembic-Maher et al., 2020). Recent advances in text mining (i.e., Natural Language Processing, Information Extraction, and Knowledge Representation) offer the ability to automate the extraction and structuring of this dispersed knowledge into coherent, searchable frameworks. Such systems could support targeted surveillance, track disease spread, and accelerate hypothesis generation by linking genomic, ecological, geographic, and citizen science data. While still emerging in plant health, initiatives such as France's Plant Health Epidemiological Surveillance Platform show promise (Duperier et al., 2023). Comparable strategies have already proven effective in human health — for instance, the CORD-19 platform enabled large-scale structuring of COVID-19 literature (Casey et al., 2023; Michel et al., 2020; Reddy et al., 2021). However, to be fully operational for plant systems, several challenges remain — including data quality, interoperability, and coordination across disciplines — all requiring sustained investment and collaboration.

**Future (axis 8).** Coupling historical data on FD epidemics and projection of future climate and global change scenarios raises the possibility of long-term forecasting of disease risk. The history of disease emergence and spread in Europe over the last 70 years is well described at the scale of the winegrowing regions (Boudon-Padieu, 2002; Jeger et al., 2016; Malembic-Maher et al., 2020; Tramontini et al., 2020). For the vector, its North American origin and dispersal are well-documented (Chuche, Thiéry, 2014; Papura et al., 2012), but there is uncertainty about the date of the first event of emergence in Europe (Chuche, Thiéry, 2014). At the scale of municipalities and individual vineyards, precise data on dispersal is not easily available for dates before 10 years ago. Based on various management options, Lessio and colleagues (Lessio et al., 2015) forecasted disease dynamics at the scale of individual vineyards over a 70-year period. Furthermore, modeling approaches have been developed to evaluate the future dispersal of the vector and of the disease in Europe with climate projections for the next ten years (Jeger et al., 2016).

Within the next five years, modeling efforts will contribute to improving the efficiency of the indicators of this axis. For example, very recent modeling work evaluated the disease dynamics at plot scale over ten years in three grapevine plots in Bordeaux according to rogueing and then linked the predictions to management and surveillance
options (Adrakey et al., 2023). These models are being adapted so that they will be relevant to other regions such as Champagne and Cahors. Beyond five years, text mining could contribute to progress in the utility of this axe. Building on recent advances in automated information extraction, concrete applications can already be envisioned in plant health and for FD in particular (Soubeyrand et al., 2024). For instance, historical texts could be mined for early mentions of *S. titanus* in fauna inventories, offering new insights into the timeline of its introduction in Europe and France. Likewise, analyzing archival reports from plant protection services could refine our understanding of the first documented cases of FD, which remain poorly dated in some regions. With more precise timelines for the initial occurrences of *S. titanus* and FD, combined with detailed knowledge of grapevine distribution and population genetics, predictions of future disease and vector dynamics could be significantly improved by integrating climate projections and management practices. However, a major source of uncertainty remains the stability of vineyard distribution in light of the current trend of vineyard area reduction in southern regions and widespread vineyard abandonment. Additionally, potential changes in the quarantine status introduce further unpredictability as they would strongly influence growers' practices. Finally, inherent uncertainties in climate projections are likely to complicate accurate risk forecasting.

## Surveillance strategies to optimize the production of actionable information for the transition to agroecological management of FD

The summaries of the state of the art and projected trends presented above support our vision that indicators that provide information about indirect, far, opportunistic and future phenomena relative to FD will make the most significant contributions, now and in the future, towards vineyard practices that sustain plant health via prevention and agroecology (Fig. 2). Whereas this opinion is influenced by the definition of the potential service capacity that we have attributed to each axis of indicators (Fig. 1), it is nevertheless supported by current and projected developments in technology, databases and modeling. In the following paragraphs we present the justification of this perspective.

Direct indicators of axis 1 have been widely developed for FD monitoring at the European scale (Tramontini et al., 2020). However, they suffer from the time lag between epidemic dynamics and detection, and are failing to identify asymptomatic reservoirs. They can facilitate disease control only if they are combined with rigorous efforts to uproot infected plants, and eliminate the vector by insecticide treatments, for which negative impacts are well-characterized. As they provide limited information that would be actionable for prevention and ecological practices, they were scored at 2 on the radar scale. In the future, it can be assumed that symptom-based surveillance will be improved due to substantial resources, better organization and technological developments in detection and traceability. This progress will facilitate earlier intervention and mitigate the negative impacts of management based on eradication. Evaluating the virulence of the phytoplasmas by genotyping also increase the utility of these indicators in decision-making. However, they will remain within a curative framework and will not exceed 2 on the radar scale.

The Specific, Nearby and Recent indicators from axes 2, 3 and 4 provide useful information for local risk assessment, which helps to calibrate the intensity of surveillance and to develop adaptive management of FD in order to modulate the number of insecticide treatments, their timing and the refinement of management areas (Shea et al., 2002). In recent years, indicators from these axes have been increasingly used in vineyards, mainly where the disease has become endemic and cannot be eradicated (Malembic-Maher et al., 2023; Tramontini et al., 2020), leading us to attribute scores of 2 and 3 on the radar chart. In the near future, their efficient development as described above and their combined implementation through risk maps could contribute to better anticipation and avoidance of disease in specific areas, increasing their scores to 4 and 5. The monitoring should be spatially optimized and combined with preventive actions like the management of non-symptomatic wild *Vitis* reservoirs and the disruption of the vector's movements. In the future, if the longer-term predictive power of specific indicators can be improved, their contribution to agricultural transitions could be increased because of the ease and non-invasiveness of measurement. They could be used for optimizing the efficiency of methods that are alternative to insecticides that are being developed in order to reduce the populations of the vector (Gonella et al., 2024a). They could also be used to optimize the local deployment of less susceptible cultivars to FD and other grapevine diseases (Zaffaroni et al., 2025). Indicators concerning Nearby phenomena also have a good potential in the future if they benefit from databases on land use and grower practices and from diagnostics based on increasingly easy detection
of FD in landscapes, vectors and suspected potential vectors. These developments correspond to a range of modern technologies including drones, other remote sensing techniques and mobile laboratories. The resulting data will foster earlier preventive actions to suppress local reservoirs and will likely reduce the need for pesticides. The increasing availability of information on curated, open-access databases, the greater spatial coverage of the available data and the user friendliness of risk mapping tools will increase the amount of time available for implementation of plant health management strategies toward eventual agro-ecological methods, ultimately leading to propositions for modifications of land use and practices that could have important prophylactic impacts. However, such transitions will probably be difficult to implement without addressing and overcoming the socio-economic implications. They could be facilitated by participatory initiatives called "living lab", including stakeholders and the complexity of issues at the territorial level (Zavratnik et al., 2019). Such initiatives have started to be implemented in Bordeaux vineyards for a better management of FD (Malembic-Maher et al., 2023). For the Recent axis, its ability to support disease prevention strategies will largely depend on the predictive power of the models it relies on. Historical records of disease outbreaks may, in some cases, offer a stronger statistical basis for predicting current outbreaks than do local indicators (such as weather conditions or crop status), because they directly reflect past pathogen dynamics. However, this predictive relationship may weaken when extended to other cultivars, regions, or environmental conditions, which can significantly alter host-pathogen interactions. Furthermore, while the link between past and current outbreaks may remain relevant for the next five to ten years, it could be increasingly undermined by the rising frequency of extreme weather events.

Indirect indicators can help to steer the management of FD to agroecological practices at the local scale, especially if they are combined with indicators from axes 2, 3 and 4. At present, efforts are mainly focused on the monitoring of insect vector populations as described by Tramontini and colleagues (Tramontini et al., 2020). This supports the decision to modulate insecticide treatments in vineyards by reducing their number when the populations are low. Axis 5 is currently scored at 2 on the chart. In the near future, efforts will be devoted to indirect detection of the phytoplasma presence in plants in order to overcome the issues related to the cryptic phase of the disease and to silent reservoir *Vitis* plants whose influence on epidemic dynamics is well-characterized (Adrakey et al., 2023; Lessio et al., 2015). Although indirect methods will offer great potential for detection in individual hosts, it is not yet known if they are applicable for larger scale monitoring and there are uncertainties about their effect on FD dynamics as they were evaluated for other vector-borne diseases (Mastin et al., 2022). This could be especially true if these indicators are combined with alternative methods to control the leafhopper vector that could be less efficient than synthetic insecticides (Bocca et al., 2023; Gonella et al., 2024b). In the future, the contribution of this axis has the potential to increase due to: (i) technological developments of the methods, (ii) the ability of preventive or agroecological practices to rapidly influence the state of these indicators, and (iii) the feasibility of making repeated observations to assess the effectiveness of plant health management strategies leading us to attribute a score of 9. This is particularly promising for indicators based on odors, spectral profiles, and the presence of the insect vectors, which could be monitored with non-destructive techniques that facilitate repeated measurements. In contrast, monitoring of physiological indicators – whether facilitated or not by low-destructive technologies - is unlikely to provide information that is specific to FD alone nor early enough to fully support long-term agroecological practices, thereby limiting their overall usefulness for decision-making. Likewise, in spite of advances in early detection of FDp-carrying insect vectors, timely eradication currently relies on use of pesticides.

Indicators in the Far axis have highly beneficial impacts on agroecology because they can help to prevent the entry of epidemic strains in FD-free areas through medium to long-distance transport of infected plant material or vectors. Due to their importance, such indicators have already been promoted through the European regulations on the traceability of grapevine material, the surveillance of nurseries and mother stocks and the mandatory hot water treatments of plant material originating from contaminated areas, scoring the axis at 3. However, as entry events by long-distance transport are shown to be less frequent than short distance dispersal (Jeger et al., 2016), their impact will be poorer in areas where FD is endemic. The contribution of Far indicators to sustaining agroecological practices also depends on: (i) the time interval between the occurrence of a distant indicator event and its eventual impact on plant health in a given vineyard, and (ii) the predictive power of the association between distant and proximal phenomena. For example, in the future, understanding the dissemination pathways of FD through farm machinery could support the development of effective methods to disrupt transmission and limit disease spread. Prophylactic practices are already being promoted in some vineyards by raising awareness among winegrowers to
remove the leaves from their machinery before moving it. Similarly, the timing for releasing predatory insects or deploying traps or nets could be informed by studies identifying, when and from where, winds could transport insect vectors to vineyards. The strength of this axis in promoting the agroecological management of wine production in face of FD will depend on the accessibility and timeliness of databases about distant phenomena, on research improving the predictive accuracy of risk maps, and on the tools for regularly updating these maps. The development of local indicators from axes 2 to 5 will greatly contribute to increase the accuracy of risk-maps in the areas that are sources of inoculum in order to prevent long-distance dispersal of the disease

Although no current initiatives focus on developing opportunistic indicators for FD, such indicators have the potential to underpin long-term anticipatory management measures. They might be derived from routine monitoring of molecular databases to detect shifts in frequencies of genetic markers linked to the most aggressive genotypes of FDp. Research on virulence-associated traits may inform the development of scanning criteria to include genotypes not yet linked to disease but carrying predictive pathogenic markers, using approaches analogous to those applied in anticipating zoonotic virus emergence (Olival et al., 2017; Pandit et al., 2022). In parallel, initiatives to develop such indicators would greatly increase the potential service capacity of the current molecular monitoring conducted by diagnostic services. The systematic monitoring of multispectral signals from sensors mounted on the machines regularly passing in the vineyards could also be very promising.

Indicators of Future phenomena offer the greatest leverage for advancing the agroecological transition of vineyards because they create the longest planning horizon, enabling major changes, such as landscape redesign or diversification (Rusch, 2025), deployment of less susceptible grapevine varieties, relocation of vineyards - especially mother plots or nurseries - in areas with lower infection risk. Obviously, the effectiveness of such indicators will depend on accurate modeling, simulations, and risk mapping to predict future phenomena, thereby guiding sustainable crop management and landscape adjustments to mitigate FD. Stakeholders will need support to embrace these transformative shifts and to invest in agroecological practices based on forward-looking scenarios, co-design and participatory approaches (Rusch, 2025). Public policy incentives may also prove crucial. Therefore, further research is essential to develop reliable indicators and to secure backing from both stakeholders and policymakers.

591

## Charting indicators, example 2: Leveraging shared data to build cross-risk indicators for *Prunus* spp. plant health.

The case of FD illustrates how disease quarantine status of a high-value crop such as grapevine mobilizes substantial resources for developing and deploying a wide range of surveillance indicators. In other perennial crops, including stone fruit trees, major pathogens such as the plum pox virus (the causal agent of sharka disease) and 'Candidatus Phytoplasma prunorum' (responsible for European stone fruit yellows, ESFY) can generate comparably severe economic losses. However, they are classified as regulated non-quarantine pests (RNQP) in the EU (EU 2019/2072, annex IV) and do not typically benefit from the same resources as quarantine pests. Other RNQP pathogens generally have less quantitative impact on the fruit sector, although they can cause significant losses sporadically (e.g., ilarviruses such as the prune dwarf virus (PDV) and the prunus necrotic ringspot virus (PNRSV), and bacteria such as *Pseudomonas syringae* pathovars and *Xanthomonas arboricola* pv. *pruni*). Among other various causes of declines, diebacks and cankers of *Prunus* spp., only three pathogens (i.e., the bacterium *Xylella fastidiosa*, the cherry rasp leaf virus (CRLV) and the fungus *Apiosporina morbosa* are currently classified as quarantine pests, but neither the fungus nor the virus are present in the EU (EPPO Global Database<fnref n="9" />). Taken together, these pathosystems require continuous decision-making to maintain crop health and secure adequate yields. Doing so requires monitoring a broad set of stress factors – not just a single disease. This is especially true in perennial crops, where stresses and infection accumulate over the crop's lifespan (Serrie et al., 2024), amplifying the consequences of adverse conditions experienced in any given year. At the same time, breeding for resistance to multiple, co-occurring stresses remains particularly challenging. For fruit trees in particular - that require decades

<footnote n="9">⁹ https://gd.eppo.int (accessed 01/23/2026)</footnote>
of work to obtain new varieties – it has been proposed that generic traits reflecting innate defense capacity and conferring resilience to diverse stresses should be targeted (Serrie et al., 2024). This highlights the value of indicators that are shared across complexes of stress, or that at least can be derived from a common set of observations, thereby streamlining and unifying the surveillance effort for the crop. Uniting a complex of stress factors also allows us to pool literature on *Prunus* health, which is markedly less rich than that for FD. Hence, our analysis is more succinct than for the previous case study.

Here we apply the logic of the eight axes of indicators to the case of orchards of *Prunus* spp. stone fruit trees (apricot, peach, cherry, plum, nectarine and almond) for a group of common stresses that can lead to the decline of trees (roots, trunk, stems and branches). Risks to the longevity of *Prunus* spp. are a pertinent concern because, once the trees begin to yield fruit at the age of about 3 years, they can remain viable and productive for up to 15 years (peach), to about 25-30 years (almond, cherry, nectarine) and beyond 40-50 years or more (apricot and plum). All stone fruit *Prunus* spp. are deciduous and monoecious with perfect flowers but they vary in the timing of their phenology and the need for cold. The lifespan and productivity of the trees can be reduced due to weakening of the stems, branches, trunks and roots from the effect of either a single microbial pathogen or the combined effects of multiple pathogens, extreme temperatures and drought. To protect the health of *Prunus* crops, a panoply of pesticides are used against an ensemble of pathogens, pathogen-vectors and insect feeders. The extent of pesticide use in conventional *Prunus* production systems is revealed in recent reports of residues on fruit. Cherries and peaches have been ranked among the top 5 fruits from conventional production in terms of the frequency of pesticide residues (>90% of fruits with at least 1 pesticide residue)<fnref n="10" />. Peaches from Turkish production contained residues of 40 different pesticides, including 12 that have not been approved in the European Union (Keklik et al., 2025). Likewise, high frequencies of pesticide residues on peaches have been observed in other markets across the Mediterranean basin (Malhat et al., 2025). High frequencies of pesticide residues have also been reported for nectarines (Keklik et al., 2025) and concern is mounting about pesticide residues on apricots across China (Yang et al., 2022).

Diverse microorganisms are associated with the weakening, and the eventual decline and death of *Prunus* spp. in Europe (Tab. 2). These include soil-borne pathogens that attack roots and root collars, fungi that are frequently found as endophytes but can eventually cause death and decline via their capacity to degrade lignin and cellulose, bacteria that can invade the vascular system, and viruses and viroids that interfere with normal physiological processes such as photosynthesis, nutrient transport, and hormonal signaling (Pallas et al., 2012). These microorganisms, and especially viruses, cause discoloration, deformation and reduction in leaf surface area, necrosis at the grafting point, stem pitting, stunted growth and poor canopy development that weaken the tree over time, leading to decline. Tree death can occur when associated with other pathogens, illustrating the interest in anticipating and avoiding these predisposing factors. Furthermore, the accumulation of mixed infections – of viroids, viruses and phytoplasmas in particular – can lead to antagonistic or synergistic effects (Hadidi et al., 2011), thereby reducing the predictive power of surveillance that is oriented on single pathogens.

Abiotic environmental conditions can also directly degrade tree health or exacerbate the weakening of *Prunus* trees caused by pathogens. The most notable of these are water stress and freezing temperatures (Agusti-Brisach et al., 2020; Krugner, Backus, 2014; Vigouroux, 1991; Vigouroux, Bussi, 1994; Villalobos-González et al., 2024). *Prunus* spp. have shown remarkable adaptation to a wide range of environmental conditions over the nearly 7500 years of their exploitation and domestication (Rodríguez-Robles et al., 2024). However, depending on the history of local efforts for propagation of trees in this genus, different genetic lines within species of *Prunus* can have strong local adaptations and hence poor adaptive potential to changing environmental conditions (Dai et al., 2024). Additional predisposing factors include agronomic practices such as fertilization, that alters plant physiology, and pruning that creates wounds for penetration of pathogens. Production of many of the *Prunus* spp. involves grafting on rootstocks that can help overcome some of the biotic and abiotic stresses (Uğur et al., 2023), but also creates a wound that can be susceptible to frost damage depending on the height of the graft (Lamichhane et al., 2014).

<footnote n="10">¹⁰ https://www.generations-futures.fr/actualites/residus-pesticides-classement/#:~:text=le%20top%205%20des%20fruits,et%20cl%C3%A9mentines%2Fmandarines%20(87.2%25) (accessed 5 Feb 2026)</footnote>
657 **Status and future of surveillance indicators for Prunus spp. and prospects for health management**

658 To support sustainable agroecological production of *Prunus* spp., future surveillance of tree health could rely on
659 indicators derived from measurements of metabolic parameters and ecophysiological traits, profiles of the
660 endophytic and rhizosphere microbial communities, and predisposing abiotic conditions while also accounting for
661 the vicissitudes of these abiotic conditions as Earth's climate changes. Our vision of the contribution of the axes of
662 surveillance in this regard – at present, in the near future and farther beyond – is depicted in Fig. 3 and explained
663 in detail below.

664 We assert that surveillance could be sustainably optimized and could leverage agroecological practices for *Prunus* spp. by capitalizing on knowledge about the ensemble of predisposing factors for decline and death. Although each of the individual diseases of *Prunus* spp. have specific predisposing factors, there is a common set of conditions that seem to have overriding importance for *Prunus* health, viz., temperature extremes, soil type, water availability, and nematode damage to roots. Indeed, some of these environmental factors can directly cause damage to plant health. However, here we are considering their per-disposing effect on decline. Chilling and frost lead to developmental alterations, physical damage and expression of cascades of genes related to stress responses (Alisoltani *et al.*, 2019). Soil type and drainage capacity are associated with root asphyxiation (Vigouroux *et al.*, 2006) and they exacerbate the vulnerability to frost damage (Vigouroux, Bussi, 1994). The soil's water potential can alter susceptibility to fungal pathogens (Miller *et al.*, 2023). The likelihood for water stress and high temperature extremes are also highly predictive of the risk of emergence of *X.fastidiosa* (Martinetti, Soubeyrand, 2019). Severity of ESFY symptoms on *Prunus* is linked to stresses such as drought and frost that weaken the plant and increase phytoplasma activity, leading potentially to tree decline (Marcone *et al.*, 2010). In addition, feeding by nematodes on roots of stone fruit trees, as well as apple, pecan and walnut trees, can damage and weaken roots and make them more vulnerable to attack by soil-borne plant pathogens (Askary *et al.*, 2012). Furthermore, nematodes are vectors of various viruses of *Prunus* spp. (e.g., ToRSV, CRLV and CLRV) (Martelli, Uyemoto, 2011). The current state of accessibility of data of these pre-disposing factors led us to attribute a score of 2 on the Specific axis for their current contribution to plant health management. However, the potential for progress in monitoring these pre-disposing factors led us to estimate that the score could rise to 6 after about 5 years and, when coupled to climate modeling, could contribute to a score of 9 on the Future axis as described below.

684 Monitoring abiotic conditions is standard practice of regional weather services, chambers of agriculture and farmers
685 themselves. Such information can be combined with data from maps of soil properties and forecasts of drought
686 conditions, as well as precise frost alerts that are becoming increasingly available. Knowledge of the link between
687 abiotic conditions and *Prunus* decline and death are giving way to the development of avoidance practices. For
688 example, elevating the height of grafts in apricot to up to 120 cm from the ground level has led to considerable
689 reduction in frost damage to the graft wound (Prunier *et al.*, 1999) and can reduce the burden of frost protection
690 interventions during mild frosts. In this case, the adaptive agricultural practice depends on forecasting regional
691 tendencies over a scale of time corresponding to the creation and plantation of grafted trees. Likewise, regional
692 maps of soil type could guide the localization of new orchards or soil preparation for the establishment of new
693 orchards or for replanting. Nematode infestation could be assessed with techniques that are increasingly automated
694 and adapted to a wide range of nematode species, including techniques based on molecular, hyperspectral and
695 image analyses (Pun *et al.*, 2024). There is a huge potential for nematodes as indicators of risks for plant health
696 and of the underlying ecological phenomena that could be leveraged for management. Indicators seen as promising
697 include taxonomic characteristics of nematode assemblages and functional characteristics related to feeding
698 behavior, metabolic rates and the balance of r- and K-strategists, for example (Ferris, Benavides, 2024). This could
699 help point out the relative importance of herbivores - that feed on roots - compared to predatory carnivorous
700 nematodes and could provide means to evaluate responses of nematode assemblages to soil management
701 Overall, the data concerning these predisposing factors is very generic, with pertinence not only for
702 *Prunus* spp., but also for a wide range of perennial crops. Regional governments or agricultural services could
703 support their agriculture by providing updated, wide scale data about soil properties, forecasts of temperature
704 extremes, and nematode population characteristics in particular. The utility of these Specific indicators (axis 2) to
705 foster agroecological practices could be improved via modeling approaches that combine variables to maximize
706 predictive power, adapted to the crop of interest. By projecting on a longer time scale than the present production
season, the composite indicators would be part of the Future axis (axis 8) to promote avoidance and adaptation of management practices for orchards in the future or even contribute to long term planning of orchard re-localization.

Indirect indicators (axis 5) of *Prunus* spp. health can also foster the implementation of agroecological practices and boost the current score of 2 to a score of 9. According to Serrie and colleagues (Serrie *et al.*, 2024; Serrie *et al.*, 2025), metabolic parameters and ecophysiological traits such as photosynthetic activity, evapotranspiration rates, water use efficiency, canopy temperature, etc. are operational measurements of resilience and thereby provide information about plant health. Currently, orchard water stress is commonly inferred from infrared thermometry, which can reflect transpiration rates (Mccauley *et al.*, 2024), and from measurements of key physiological variables such as stomatal conductance, leaf water potential, canopy temperature (Velazquez-Chavez *et al.*, 2024). These techniques have broad application to crops in general and therefore the equipment needed to make these measurements will likely become increasingly accessible. The information they provide will contribute to alleviating water stress either by well-timed irrigation or by agricultural practices that foster moisture retention of soils or breeding of varieties that are less sensitive to water stress. The power of Indirect indicators of *Prunus* health could also be bolstered in the future via odor detection with dogs as described in the section about FD.

Likewise, information from the microbiome, obtained through metagenomics, will contribute to the utility of Indirect indicators for managing *Prunus* health. High-throughput sequencing methods offer the possibility of obtaining rather complete profiles of bacterial, viral and fungal community compositions from individual samples (Roman-Reyna, Crandall, 2024). Firstly, this generic nature of this information opens the possibility of optimizing surveillance efforts via a shared labor base for the required bioinformatics analyses and shared standard practices (Roman-Reyna, Crandall, 2024). It could contribute to describing the diversity of soil microflora where *Agrobacterium tumefasciens* – a bacterial pathogen and the causal agent of crown gall - is prevalent, could help to reveal the nature of endophytic communities where fungi such as the Botryosphaeriaceae can be latent pathogens (Slippers, Wingfield, 2007) and could be part of the post-entry quarantine surveillance proposed for viruses (Bag *et al.*, 2015). Furthermore, a common set of samples and analytical results could lead to indicators of microbial profiles linked to plant health. As an example, the microbial profiles linked to the resistance of the invasive *Prunus serotina* to fungal pathogens are being investigated (Baranowska *et al.*, 2023). Such profiles could be associated with predisposing factors and the likelihood of resilience responses in face of disease pressure to develop robust indicators. With the development of readily accessible indicators of the state of the microbiome, research could be pursued on management of the microbiome via plant nutrition, grafting, inoculating with consortia of biocontrol organisms, etc.

As argued above, explicit detection of pathogens or visible symptoms within an orchard (axis 1) offers limited advantage for implementing truly preventive or agroecologically-based strategies: by the time infection is detected, management options are often already constrained. Yet, identifying which diseases occur across regions – and which are newly emerging – remains essential for risk assessment, preparedness, and targeted action. The key challenge for sustainable, agroecological production of *Prunus* spp. (and of crops more broadly) is therefore not simply to improve diagnostic tools, but to deploy them in ways that actively support a transition away from pesticide dependence. Firstly, they can be deployed to assure that nursery stocks are free from regulated organisms, thereby avoiding the initial arrival of a pathogen. In the EU, plants for planting into orchards should be sold free of the quarantine and regulated non quarantine viruses (Annex II and Annex IV of EU regulation 2019/2072). The EPPO standard PM 3/76 provides guidelines for the inspection of places of production and molecular detection tools such as real-time PCR targeting nucleic acid sequences are available for all viruses cited in the EU regulation (Beaver-Kanuya, Harper, 2019; Diaz-Lara *et al.*, 2020; Tang *et al.*, 2014). Similar consideration for the initial microbiological status of trees at the time of planting could be applied to other vascular pathogens of *Prunus* spp., if indeed the microflora of the tree at planting is more important than the local environment as the source of a pathogen. In the case of *P. syringae*, for example, the existence of multiple environmental reservoirs within apricot orchard raises the question of which sources contribute most to contamination over a tree's lifespan (Parisi *et al.*, 2019). Likewise, the initial microbial status of trees at planting might not be sufficient to protect them from vector-borne pathogens such as *X. fastidiosa*.

Here we also noted that the Nearby, Far and Opportunistic axes have little potential for supporting the transition toward agroecological production practices in face of the complexity of stresses that contribute to *Prunus* decline. These axes could, nevertheless, contribute to managing *Prunus* health in the face of stress factors taken individually
especially as indicators of reservoirs of pathogens or vectors (Nearby axis) or processes that disperse pathogens or vectors (Far axis) as illustrated in the case of FD. However, in the management of *Prunus* decline, our expert opinion is that axes describing the conditions of the orchards themselves have the greatest potential to serve agroecology.

Overall, we argue that for crops where major investments in diagnostics, surveillance capacity, and research funding are limited, the most effective surveillance strategy is to prioritize the collection and analysis of shared data streams that can inform multiple diseases and syndromes affecting the crop. For *Prunus* spp., the Specific, Indirect and Future indicators described above are likely to be the most useful for facilitating prophylaxis and agroecology-based plant health management, especially as their long-term predictive performance improves (Fig.3). In contrast, indicators based on recent events (e.g., previous occurrences of decline) or on characteristics of nearby or distant fields (e.g., feral or wild *Prunus* plants, insect populations, local pesticide use) may prove less informative, because their contribution to the multifactorial decline syndrome remains poorly resolved.

## 769 Discussion

Optimizing the surveillance of plant health has recently received increasing attention, especially in light of the proliferation of new technologies for collecting, storing and analyzing data (Jarrad et al., 2015; Mastin et al., 2020; Parnell et al., 2017). An overriding goal of modernizing plant health surveillance declared in many of these treatises is to reduce uncertainties in the estimation of risk under the premise that early detection and risk estimation is essential for effective control. However, such calls for modernization seldom specify which management practices can be informed by early detection and risk estimation, apart from those concerning eradication or restrictions on the commercial movement of plant material. Furthermore, these perspectives on future trends in surveillance of plant health focus almost solely on direct detection of disease or pathogens/pests or their inference via models. Even a recent One Health perspective on plant biosecurity set the goals of surveillance and policies only in terms of pathogen/pest-free zones without consideration of agronomic practices or environmental factors that contribute to plant health (Alawneh et al., 2025) - factors that are nevertheless interconnected in One Health initiatives (Morris et al., 2024). Overall, most treatises on the importance of plant health surveillance systems claim that “effective management requires surveillance to detect epidemics at an early stage” (Mastin et al., 2020) without explaining the management options that can be leveraged from surveillance data nor the relevant mode of production of the agri-food system (conventional, intensive, sustainable, regenerative, organic, agroecological, etc.).

The key contribution of the approach presented here for designing surveillance is its clear articulation of how surveillance indicators inform plant health management decisions. As such, it can be used to not only conceive optimal surveillance strategies based on the available tools, resources and labor but also to accompany stakeholders and practitioners toward explicitly supporting a given mode of plant production. Here we have illustrated this by focusing on the transition toward management practices that limit synthetic inputs and favor agroecology. Our approach can also serve as a tool for teaching the principles of epidemiology and for training staff of services that are responsible for implementing disease surveillance. The tables in the supplemental documents (Supplemental Tables S1 and S2) will guide users to reflect on the potential of each axis of indicators and associated management options. By deploying these tables to elaborate radar charts, users might realize, as we did, that the precise scope of each axis could be defined differently, but that establishing fixed definitions is key to a clarifying where progress can be made in surveillance. Finally, our framework can also point out knowledge gaps to be filled by research to develop the range of potential indicators described by Soubeyrand and colleagues (Soubeyrand et al., 2024). These could include those that represent profiles of future pests and new favorable habitats that arise with changes in climate, agronomic practices and land use; anomalies that occur in a range of relevant databases; or genetic indicators of pathogenic potentials in metagenomes from various substrates that come into contact with plant production systems, for example.

Our approach reinforces the genuine broad scope of the term 'surveillance'. In the context of plant production, epidemiological surveillance is the systematic process of collecting data related to plant health and analyzing and interpreting them to assess risks for disease outbreaks. The purpose of this process is to provide information for decision-making concerning practices of plant production that can avoid, mitigate or cure disease. We feel that the
current sense of surveillance has become rather focused on explicit observations (symptoms or the pathogen/pest themselves in near or real-time and space) for which the range of management options is relatively limited. Our work promotes the notion that surveillance should include data that serve the full range of decisions that can be made about managing plant health. Indeed, several of the indicators that we propose here (pre-disposing conditions or collateral phenomena in particular) are observed by practitioners themselves or provided by weather services. However, monitoring the ensemble of types of indicators described here could be part of official surveillance services to: (i) facilitate access to more comprehensive information that fosters a greater range of management options, and (ii) capitalize on an economy of scale for multipurpose indicators. Implementation of this approach will directly affect the work of staff of surveillance services in that the scope of observations and data to consider is broadened. It also opens opportunities for new professions where, for examples, experts in characterization of microbiomes or soil microbial communities could find broad applications of their competence in surveillance for plant health and in validation of the success of plant health management practices.

Once surveillance indicators are clearly linked to management practices, it becomes evident that surveillance itself can be redesigned using backward planning. In other words, the indicators could be chosen to support the intended production system goal. We can use the example of the transition toward organic agriculture to illustrate how surveillance could be designed to support this. Aside from the administrative and regulatory obligations, the general universal goals of organic agriculture are restoring soil organic matter and tending to overall ecosystem management to promote the establishment of ecosystem services that reduce or eliminate the need for external inputs (Sarkar et al., 2021). Organic farmers deploy practices such as use of green and farm manures, organic compost, rotation and recovery of crop residues to boost soil organic matter. There are dozens of indicators of success of these practices toward the goals of organic agriculture that assess soil traits (e.g., structure and mineralization rate, water retention capacity, microbial biomass and community structure), crop quality parameters (e.g., content of vitamins, micronutrients, protein, bioactive compounds, antioxidants) and yield (Sarkar et al., 2021). First, testing manures, composts and plant residues for the presence of plant pathogens (Nearby axis) can help prevent contamination of soils. Second, organic practices can influence plant health and may therefore serve as indicators of disease risk (Specific axis). Use of organic amendments influences soil properties such as formation of soil aggregates and the amount of organic carbon (Schrama et al., 2018). Their use can also be correlated with reduced risk of soil borne diseases (Mehta et al., 2014). In addition, aspects of plant physiology associated with organic practices could be indicators of disease tolerance. For example, several studies show consistently higher levels of phenolics, flavonoids, vitamin C, carotenoids or antioxidant activity in organic vs. conventional crops (Hallmann, 2012; Średnicka-Tober et al., 2020). Phenolics in particular enhance stress resilience (biotic and abiotic) and nutritional value (Xu, Wang, 2025). Organic agricultural practices can also contribute to enhancing biodiversity. Enhanced diversity of vegetation in areas surrounding cropped fields (Nearby axis) – and, more broadly, increased landscape heterogeneity through higher crop diversity and the presence of semi-natural habitats - can foster populations of predators that regulate insect pests or vectors of plant pathogens (Perrot et al., 2023). Indicators of future trends (Future axis) are also important to consider for organic agriculture. The transition of farms toward organic agriculture generally requires several years so as to respect the regulatory conversion period (European_Union, 2018) and might take over a decade to attain the yields typical of conventional agriculture (Schrama et al., 2018). Therefore, long-term goals of organic agriculture for soil microbiology, local biodiversity, and crop performance, for example, are just as dependent on models that predict the impact of climate change as are the goals of plant health for durability of crop resistance to disease and avoiding the emergence of new pathogenic populations. Overall, this example illustrates the potential to ally the goals of plant health surveillance with those of agroecological transition. Rendering these goals mutually compatible and mutually informative could generate economies of scale for surveillance and help steer research toward the key knowledge gaps that determine where the two agendas converge, and where they may conflict.

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**Acknowledgements** The conception and realization of this paper was supported by funding from the French National Research Agency (ANR) for the BEYOND project (contract # 20-PCPA-0002).

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<table><caption>Table 1. The scope of eight axes of indicators for surveillance of the status and risks for plant health. The axes represent different combinations of i) spatial scales of where observations are made concerning the target crop and disease, ii) temporal scales of when the observations are made relative to the epidemiological cycle of the disease and iii) the types of observations that can be made or data that can be used to assess the state of plant health and the risk for disease. For the “Future” axe, the indicators are not observed *sensu stricto* in nature. Rather, they are predictions about the future for the locality of the crop</caption><thead><tr><th rowspan="2"></th><th colspan="3">Space</th><th colspan="3">Time</th><th colspan="3">Observation</th></tr><tr><th>crop</th><th>nearby</th><th>distant</th><th>past</th><th>present</th><th>future</th><th>Explicit:</th><th>Collateral</th><th>Pre-disposing:</th></tr><tr><th>Axe</th><th></th><th></th><th></th><th></th><th></th><th></th><th>Symptoms, Causal agent</th><th>Disease-associated chemicals or organisms</th><th>Environmental conditions</th></tr></thead><tbody><tr><th>Direct</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Specific</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Nearby</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Recent</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Indirect</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Opportunistic</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Far</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Future</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<table><caption>**Table 2.** Biotic factors that weaken trunks, branches and roots of *Prunus* spp in Europe and contribute to shortened life span</caption><tbody><tr><td><strong>Soil-borne pathogens and pests</strong></td><td></td></tr><tr><td>Armillaria spp.</td><td>(Devkota, Hammerschmidt, 2019)</td></tr><tr><td>Phytophthora spp.</td><td>(Kouyeas, 1977)</td></tr><tr><td>Agrobacterium tumefasciens</td><td>(Rubio-Cabetas et al., 2001)</td></tr><tr><td>nematodes</td><td>(Clavero-Camacho et al., 2024)</td></tr><tr><td><strong>Endophytic fungi that degrade cellulose or lignin</strong></td><td></td></tr><tr><td>Cytospora spp.</td><td>(Spetik et al., 2024)</td></tr><tr><td>Eutypa spp., numerous Botryosphaeriaceae</td><td>(Slippers, Wingfield, 2007)</td></tr><tr><td><strong>Bacteria that invade the vascular system</strong></td><td></td></tr><tr><td>Pseudomonas syringae</td><td>(Parisi et al., 2019; Ruinelli et al., 2019)</td></tr><tr><td>Xylella fastidiosa</td><td>(Matsumoto et al., 2023)</td></tr><tr><td>numerous 'Candidatus Phytoplasma' species alone or in combination</td><td>(Abu Alloush et al., 2023; Hadidi et al., 2011; Molnar et al., 2024)</td></tr><tr><td><strong>Viruses and viroids that alter development and metabolism</strong></td><td>(examples)</td></tr><tr><td>Ilarvirus PDV (plum dwarf virus, PDV); Ilarvirus PNRSV (prunus necrotic ringspot virus, PNRSV)</td><td>(Pallas, García, 2011; Pallas et al., 2012; Uyemoto, Scott, 1992)</td></tr><tr><td>Nepovirus avii (cherry leaf roll virus (CLRV); Nepovirus lycopersici (tomato ringspot virus, ToRSV)</td><td>(Hadidi et al., 2011)</td></tr><tr><td>Potyvirus plumpoxi (plum pox virus, PPV)</td><td>(Rimbaud et al., 2015)</td></tr></tbody></table>
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<figure><img src="image_2.png" /></figure>

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Figure 1. The scope of actionable plant health management options that are serviced by each axis of surveillance indicators.

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<figure><img src="image_3.png" /><figcaption>Figure 2. Radar chart of the contribution of surveillance indicators to the management strategies of the Flavescence dorée phytoplasma of grapevine. Impact is charted for the current state of the art (black line), for what is feasible within about 5 years (red line) and for what we can project for the future beyond the next 5 years (yellow line). The background color of the chart indicates the extent to which the use of the indicators in the axe can move management practices away from curative methods toward prophylactic approaches based on disease avoidance or to the agroecologization of plant production as described in Fig. 1.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 3. Radar chart of the contribution of surveillance indicators to management strategies for decline of roots, trunk, stems and branches of cultivated *Prunus* spp. The scale for charting and the color codes of the figure are described in Fig. 2.</figcaption></figure>
889
890 Supplemental Table 1. Scoring table for radar charts. Example of powdery mildew of tomato under French greenhouse conditions
891

892
893 Supplemental Table 2. Scoring table for radar charts. Example of non quarantine-regulated arthropod-borne viruses in annual Mediterranean horticultural crops in France.
894

895
896
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