Miguel Montes-Borrego's research while affiliated with Spanish National Research Council and other places

The bacterium Xylella fastidiosa (Xf) is a plant pathogen that can block the flow of water and nutrients through the xylem. Xf symptoms may be confounded with generic water stress responses. Here, we assessed changes in biochemical, biophysical and photosynthetic traits, inferred using biophysical models, in Xf-affected almond orchards under rainfed and irrigated conditions on the Island of Majorca (Balearic Islands, Spain). Recent research has demonstrated the early detection of Xf-infections by monitoring spectral changes associated with pigments, canopy structural traits, fluorescence emission and transpiration. Nevertheless, there is still a need to make further progress in monitoring physiological processes (e.g., photosynthesis rate) to be able to efficiently detect when Xf-infection causes subtle spectral changes in photosynthesis. This paper explores the ability of parsimonious machine learning (ML) algorithms to detect Xf-infected trees operationally, when considering a proxy of photosynthetic capacity, namely the maximum carboxylation rate (V cmax), along with carbon-based constituents (CBC, including lignin), and leaf biochemical traits and tree-crown temperature (T c) as an indicator of transpiration rates. The ML framework proposed here reduced the uncertainties associated with the extraction of reflectance spectra and temperature from individual tree crowns using high-resolution hyper-spectral and thermal images. We showed that the relative importance of V cmax and leaf biochemical constituents (e.g., CBC) in the ML model for the detection of Xf at early stages of development were intrinsically associated with the water and nutritional conditions of almond trees. Overall, the functional traits that were most consistently altered by Xf-infection were V cmax , pigments, CBC, and T c , and, particularly in rainfed-trees, anthocyanins, and T c. The parsimonious ML model for Xf detection yielded accuracies exceeding 90% (kappa = 0.80). This study brings progress in the development of an operational ML framework for the detection of Xf outbreaks based on plant traits related to photosynthetic capacity, plant biochemistry and structural decay parameters.

Among the agricultural practices promoted by the Common Agricultural Policy to increase soil functions, the use of cover crops is a recommended tool to improve the sustainability of Mediterranean woody crops such as olive orchards. However, there is a broad range of cover crop typologies in relation to its implementation, control and species composition. In that sense, the influence of different plant species on soil quality indicators in olive orchards remains unknown yet. This study describes the effects of four treatments based on the implementation of different ground covers (CC-GRA: sown cover crop with gramineous, CC-MIX: sown cover crop with a mixture of species and CC-NAT: cover crop with spontaneous vegetation) and conventional tillage (TILL) on soil erosion, soil physicochemical and biological properties after 8 years of cover crop establishment. Our results demonstrated that the presence of a temporary cover crop (CC), compared to a soil under tillage (TILL), can reduce soil losses and maintain good soil physicochemical properties and modify greatly the structure and diversity of soil bacterial communities and its functioning. The presence of a homogeneous CC of gramineous (Lolium rigidum or Lolilum multiflorum) (CC-GR) for 8 years increased the functional properties of the soil as compared to TILL; although the most relevant change was a modification on the bacterial community composition that was clearly different from the rest of treatments. On the other hand, the use of a mixture of plant species (CC-MIX) as a CC for only two years although did not modify greatly the structure and diversity of soil bacterial communities compared to the TILL soil, induced significant changes on the functional properties of the soil and reverted those properties to a level similar to that of an undisturbed soil that had maintained a natural cover of spontaneous vegetation for decades (CC-NAT).

A R T I C L E I N F O Keywords: Hyperspectral Thermal Epidemic spread model Radiative transfer model SWIR domain Xylella fastidiosa Nitrogen Machine learning A B S T R A C T The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf-infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf (n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400-850 nm) and shortwave infrared regions (SWIR, 950-1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (T c), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDF a), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR (n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64-65% and kappa = 0.26-31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution.

Among the agricultural practices promoted by the Common Agricultural Policy to increase soil functions, the use of cover crops is a recommended tool to improve the sustainability of Mediter-ranean woody crops such as olive orchards. However, there is a broad range of cover crop ty-pologies in relation to its implementation, control and species composition. In that sense, the in-fluence of different plant species on soil quality indicators in olive orchards remains unknown yet. This study describes the effects of four treatments based on the implementation of different ground covers (CC-NAT, CC-GRA and CC-MIX) and conventional tillage (TILL) on soil erosion, soil physicochemical and biological properties, and soil microbial communities after 8 years of cover crop establishment. Our results have demonstrated that the presence of a temporary cover crop (CC), compared to a soil under tillage (TILL), can reduce soil losses and maintain good soil physicochemical properties and modify greatly the structure and diversity of soil bacterial com-munities and its functioning. The presence of a homogeneous CC of gramineous (Lolium rigidum or Lolilum multiflorum) (CC-GR) for 8 years significantly increased the functional properties of the soil as compared to TILL; although the most significant change was a modification on the bacte-rial community composition that was clearly different from the rest of treatments. On the other hand, the use of a mixture of plant species (CC-MIX) as a CC for only two years although did not modify greatly the structure and diversity of soil bacterial communities compared to the TILL soil, induced significant changes on the functional properties of the soil, and reverted those properties to a level similar to that of an undisturbed soil that had maintained a natural cover of spontaneous vegetation for decades (CC-NAT).

Vascular pathogens are the causal agents of main diseases threatening the health and growth of olive crops worldwide. The use of endophytic microorganisms represents a challenging and promising strategy for management of vascular diseases in olive. Although current research has been focused on analyzing the structure and diversity of the endophytic microbial communities inhabiting the olive xylem, the characterization of this ecological niche has been overlooked and to date remain unexplored, despite that the characterization of the xylem sap composition is essential to unravel the nutritional requirements of xylem-limited microorganisms. In this study, branches from plantlets and adult olive trees of cultivars Picual and Arbequina were selected to characterize the chemical and microbial composition of olive xylem sap extracted using a Scholander pressure chamber. Metabolome and ionome analyses of xylem sap were performed by proton nuclear magnetic resonance (NMR) spectroscopy-based and by inductively coupled plasma with optical emission spectroscopy (ICP-OES), respectively. Olive xylem sap metabolites included a higher relative percentage of sugars (54.35%), followed by alcohols (28.85%), amino acids (8.01%), organic acids (7.68%), and osmolytes (1.12%). Within each of these groups, the main metabolites in the olive xylem sap were mannitol, ethanol, glutamine, acetic acid, and trigonelline, whereas K and Cl− were the main element and inorganic anion, respectively. Metabolomic profile varied when comparing olive plant age and genotype. The levels of glucose, fructose, sucrose and mannitol, choline, B and PO43− were significantly higher in adult trees than in plantlets for both olive genotypes, whereas NO3− and Rb content showed the opposite behavior. On the other hand, levels of aspartic acid, phenylalanine, and Na were significantly higher in ‘Picual’ than in ‘Arbequina’, whereas Fe showed the opposite behavior, but only for adult trees. Microbiome composition identified Firmicutes (67%), Proteobacteria (22%) and Actinobacteriota (11%) as the main phyla, while at the genus level Anoxybacillus (52%), Cutibacterium (7%), Massilia (6%), and Pseudomonas (3%) were the most representative. Both non-supervised hierarchical clustering analysis and supervised PLS-DA analysis differentiated xylem sap chemical and microbial composition first, according to the age of the plant and then by the olive genotype. PLS-DA analysis revealed that B, ethanol, Fe, fructose, glucose, mannitol, sucrose, and Sr, and Anoxybacillus, Cutibacterium, and Bradyrhizobium were the most significant chemical compounds and bacterial genera, respectively, in the discrimination of adult olive trees and plantlets. Knowledge of the chemical composition of xylem sap will lead to a better understanding of the complex nutritional requirements of olive xylem-inhabiting microorganisms, including vascular pathogens and their potential antagonists, and may allow the better design of artificial growing media to improve the culturing of the olive microbiome.

Vascular pathogens are the causal agents of main diseases threatening the health and growth of olive crops worldwide. The use of endophytic microorganisms represents a challenging and promising strategy for management of vascular diseases in olive. Although current research has been focused on analyzing the structure and diversity of the endophytic microbial communities inhabiting the olive xylem, the characterization of this ecological niche has been overlooked and to date remain unexplored, despite that the characterization of the xylem sap composition is essential to unravel the nutritional requirements of xylem-limited microorganisms. In this study, branches from plantlets and adult olive trees of cultivars ‘Picual’ and ‘Arbequina' were selected to characterize the chemical composition of olive xylem sap extracted using a Scholander pressure chamber. Metabolome and ionome analyses of xylem sap were performed by proton nuclear magnetic resonance (NMR) spectroscopy-based and by inductively coupled plasma with optical emission spectroscopy (ICP-OES), respectively. Olive xylem sap metabolites included a higher relative percentage of sugars (54.35%), followed by alcohols (28.85%), amino acids (8.01%), organic acids (7.68%) and osmolytes (1.12%). Within each of these groups, the main metabolites in the olive xylem sap were mannitol, ethanol, glutamine, acetate and trigonelline, whereas K and Cl- were the main element and inorganic anion, respectively. Metabolomic profile varied when comparing olive plant age and genotype. The levels of glucose, fructose, sucrose and mannitol, choline, B and PO43 were significantly higher in adult trees than in plantlets for both olive genotypes, whereas NO3- and Rb content showed the opposite behavior. On the other hand, levels of aspartate, phenylalanine and Na were significantly higher in ‘Picual’ than in ‘Arbequina’ whereas Fe showed the opposite behavior but only for adult trees. Non-supervised hierarchical clustering analysis separated xylem sap composition firstly according to the plant age and then by the olive cultivar. Supervised PLS-DA analysis revealed that B, ethanol, Fe, Fructose, glucose, mannitol, sucrose and Sr were the most significative compounds discriminating adult trees from plantlets, whereas asparagine, aspartate, glutamate and phenylalanine or aspartate, arginine, ethanol and Sr were the most contributory compounds in the discrimination of both olive genotypes for adult trees or plantlets, respectively. Knowledge of the chemical composition of xylem sap will lead to a better understanding of the complex nutritional requirements of olive xylem-inhabiting microorganisms, including its vascular pathogens, and would allow the design of artificial growing media to improve culturing the olive microbiome.

Disease outbreaks caused by introduced Phytophthora species have been increasing in British forests and woodlands in recent years. A better knowledge of the Phytophthora communities already present in the UK is of great importance when developing management and mitigation strategies for these diseases. To do this, soils were sampled in “disturbed” sites, meaning sites frequently visited by the public, with recent and new plantings or soil disturbances versus more “natural” forest and woodland sites with little disturbance or management. Phytophthora diversity was assessed using high-throughput Illumina sequencing targeting the widely accepted barcoding Internal Transcribed Spacer 1 (ITS1) region of rRNA and comparing it with the mitochondrial cytochrome c oxidase I (COI) gene. Isolation of Phytophthora was run in parallel. Nothophytophthora spp. and Phytophthora spp. were detected in 79 and 41 of the 132 locations of the 14 studied sites when using ITS or COI, respectively. A total of 20 Phytophthora amplicon sequence variants (ASVs) were assigned to known Phytophthora species from eight clades (1a, 2, 2b, 3a, 5, 6b, 7a, 8b, 8c, 8d, 10a, and 10b) and 12 ASVs from six clades (1a, 2c, 3a, 3b, 6b, 7a, 8b, 8c, and 8d) when using ITS or COI, respectively. Only at two locations were the results in agreement for ITS, COI, and isolation. Additionally, 21 and 17 unknown Phytophthora phylotypes were detected using the ITS and COI, respectively. Several Phytophthora spp. within clades 7 and 8, including very important forest pathogens such as P. austrocedri and P. ramorum, were identified and found more frequently at “disturbed” sites. Additionally, eight ASVs identified as Nothophytophthora spp. were detected representing the first report of species within this new genus in Britain. Only three species not known to be present in Britain (P. castaneae, P. capsici, and P. fallax) were detected with the ITS primers and not with COI. To confirm the presence of these or any potential new Phytophthora species, sites should be re-sampled for confirmation. Additionally, there is a need to confirm if these species are a threat to British trees and try to establish any eradication measures required to mitigate Phytophthora spread in Britain.

Identification of sequence types (ST) of Xylella fastidiosa based on direct MultiLocus Sequence Typing (MLST) of plant DNA samples is partly efficient. In order to improve the sensitivity of X. fastidiosa identification, we developed a direct nested-MLST assay on plant extracted DNA. This method was performed based on a largely used scheme targeting seven housekeeping gene (HKG) loci (cysG, gltT, holC, leuA, malF, nuoL, petC). Samples analyzed included 49 plant species and two insect species (Philaenus spumarius, Neophilaenus campestris) that were collected in 2017 (106 plant samples in France), in 2018 (162 plant samples in France, 40 plant samples and 26 insect samples in Spain), and in 2019 (30 plant samples in Spain). With the nested approach, a significant higher number of samples were amplified. The threshold was improved by 100 to 1000 times compared to conventional PCR. Using nested-MLST assay, plants that were not yet considered hosts tested positive and revealed novel alleles in France, whereas for Spanish samples it was possible to assign the subspecies or ST to samples considered as new hosts in Europe. Direct typing by nested-MLST from plant material has an increased sensitivity and may be useful for epidemiological purposes.

Verticillium wilt, caused by the defoliating pathotype of Verticillium dahliae, is the most devastating soil-borne fungal disease of olive trees, and leads to low yields and high rates of tree mortality in highly susceptible cultivars. The disease is widely distributed throughout the Mediterranean olive-growing region and is one of the major limiting factors of olive oil production. Other than effects on crop yield, little is known about the effect of the disease on the content of volatile compounds and phenolics that are produced during the oil extraction process and determine virgin olive oil (VOO) quality and commercial value. Here, we aim to study the effect of Verticillium wilt of the olive tree on the content of phenolic and volatile compounds related to the sensory properties of VOO. Results showed that synthesis of six and five straight-chain carbon volatile compounds were higher and lower, respectively, in oils extracted from infected trees. Pathogen infection affected volatile compounds known to be contributors to VOO aroma: average content of one of the main positive contributors to VOO aroma, (E)-hex-2-enal, was 38% higher in oils extracted from infected trees, whereas average content of the main unpleasant volatile compound, pent-1-en-3-one, was almost 50% lower. In contrast, there was a clear effect of pathogen infection on the content of compounds responsible for VOO taste, where average content of the main bitterness contributor, oleuropein aglycone, was 18% lower in oil extracted from infected plants, and content of oleocanthal, the main contributor to pungency, was 26% lower. We believe this is the first evidence of the effect of Verticillium wilt infection of olive trees on volatile compounds and phenolics that are responsible of the aroma, taste, and commercial value of VOO.