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Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits.

ABSTRACT: 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 short-wave 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.

SUBMITTER: Camino C

PROVIDER: S-EPMC8169955 | biostudies-literature |

REPOSITORIES: biostudies-literature

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

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Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits.

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