IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-021-26335-3.html
   My bibliography  Save this article

Divergent abiotic spectral pathways unravel pathogen stress signals across species

Author

Listed:
  • P. J. Zarco-Tejada

    (University of Melbourne
    Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC))

  • T. Poblete

    (University of Melbourne)

  • C. Camino

    (European Commission, Joint Research Centre (JRC))

  • V. Gonzalez-Dugo

    (Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC))

  • R. Calderon

    (School of Integrative Plant Science, Cornell AgriTech, Cornell University)

  • A. Hornero

    (Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC)
    Swansea University)

  • R. Hernandez-Clemente

    (Swansea University)

  • M. Román-Écija

    (Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC))

  • M. P. Velasco-Amo

    (Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC))

  • B. B. Landa

    (Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC))

  • P. S. A. Beck

    (European Commission, Joint Research Centre (JRC))

  • M. Saponari

    (CNR, Istituto per la Protezione Sostenibile delle Piante)

  • D. Boscia

    (CNR, Istituto per la Protezione Sostenibile delle Piante)

  • J. A. Navas-Cortes

    (Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC))

Abstract

Plant pathogens pose increasing threats to global food security, causing yield losses that exceed 30% in food-deficit regions. Xylella fastidiosa (Xf) represents the major transboundary plant pest and one of the world’s most damaging pathogens in terms of socioeconomic impact. Spectral screening methods are critical to detect non-visual symptoms of early infection and prevent spread. However, the subtle pathogen-induced physiological alterations that are spectrally detectable are entangled with the dynamics of abiotic stresses. Here, using airborne spectroscopy and thermal scanning of areas covering more than one million trees of different species, infections and water stress levels, we reveal the existence of divergent pathogen- and host-specific spectral pathways that can disentangle biotic-induced symptoms. We demonstrate that uncoupling this biotic–abiotic spectral dynamics diminishes the uncertainty in the Xf detection to below 6% across different hosts. Assessing these deviating pathways against another harmful vascular pathogen that produces analogous symptoms, Verticillium dahliae, the divergent routes remained pathogen- and host-specific, revealing detection accuracies exceeding 92% across pathosystems. These urgently needed hyperspectral methods advance early detection of devastating pathogens to reduce the billions in crop losses worldwide.

Suggested Citation

  • P. J. Zarco-Tejada & T. Poblete & C. Camino & V. Gonzalez-Dugo & R. Calderon & A. Hornero & R. Hernandez-Clemente & M. Román-Écija & M. P. Velasco-Amo & B. B. Landa & P. S. A. Beck & M. Saponari & D. , 2021. "Divergent abiotic spectral pathways unravel pathogen stress signals across species," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26335-3
    DOI: 10.1038/s41467-021-26335-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-26335-3
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-021-26335-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Matthew C. Fisher & Daniel. A. Henk & Cheryl J. Briggs & John S. Brownstein & Lawrence C. Madoff & Sarah L. McCraw & Sarah J. Gurr, 2012. "Emerging fungal threats to animal, plant and ecosystem health," Nature, Nature, vol. 484(7393), pages 186-194, April.
    2. Kevin Schneider & Wopke van der Werf & Martina Cendoya & Monique Mourits & Juan A. Navas-Cortés & Antonio Vicent & Alfons Oude Lansink, 2020. "Impact of Xylella fastidiosa subspecies pauca in European olives," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(17), pages 9250-9259, April.
    3. Karatzoglou, Alexandros & Smola, Alexandros & Hornik, Kurt & Zeileis, Achim, 2004. "kernlab - An S4 Package for Kernel Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i09).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zarco-Tejada, Pablo, 2021. "Advanced monitoring techniques," 2021: Food and Nutrition Security - The Biosecurity, Trade, Health Nexus, 13-14 December 2021 320490, Crawford Fund.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tsukioka, Yasutomo & Yanagi, Junya & Takada, Teruko, 2018. "Investor sentiment extracted from internet stock message boards and IPO puzzles," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 205-217.
    2. Andrea S Martinez-Vernon & James A Covington & Ramesh P Arasaradnam & Siavash Esfahani & Nicola O’Connell & Ioannis Kyrou & Richard S Savage, 2018. "An improved machine learning pipeline for urinary volatiles disease detection: Diagnosing diabetes," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-20, September.
    3. Madhumita Sahoo & Aman Kasot & Anirban Dhar & Amlanjyoti Kar, 2018. "On Predictability of Groundwater Level in Shallow Wells Using Satellite Observations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1225-1244, March.
    4. Grubinger, Thomas & Zeileis, Achim & Pfeiffer, Karl-Peter, 2014. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i01).
    5. Uwe Ligges & Sebastian Krey, 2011. "Feature clustering for instrument classification," Computational Statistics, Springer, vol. 26(2), pages 279-291, June.
    6. Arnout Van Messem & Andreas Christmann, 2010. "A review on consistency and robustness properties of support vector machines for heavy-tailed distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(2), pages 199-220, September.
    7. Ana Patrícia Rocha & Hugo Miguel Pereira Choupina & Maria do Carmo Vilas-Boas & José Maria Fernandes & João Paulo Silva Cunha, 2018. "System for automatic gait analysis based on a single RGB-D camera," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-24, August.
    8. K. Viswanath & P. Sinha & S. Naresh Kumar & Taru Sharma & Shalini Saxena & Shweta Panjwani & H. Pathak & Shalu Mishra Shukla, 2017. "Simulation of leaf blast infection in tropical rice agro-ecology under climate change scenario," Climatic Change, Springer, vol. 142(1), pages 155-167, May.
    9. Gilioli, Gianni & Simonetto, Anna & Colturato, Michele & Bazarra, Noelia & Fernández, José R. & Naso, Maria Grazia & Donato, Boscia & Bosco, Domenico & Dongiovanni, Crescenza & Maiorano, Andrea & Mosb, 2023. "An eco-epidemiological model supporting rational disease management of Xylella fastidiosa. An application to the outbreak in Apulia (Italy)," Ecological Modelling, Elsevier, vol. 476(C).
    10. Valentina del Olmo & Verónica Mixão & Rashmi Fotedar & Ester Saus & Amina Al Malki & Ewa Księżopolska & Juan Carlos Nunez-Rodriguez & Teun Boekhout & Toni Gabaldón, 2023. "Origin of fungal hybrids with pathogenic potential from warm seawater environments," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    11. Huisheng Wu & Maogui Hu & Yaping Zhang & Yuan Han, 2021. "An Empirical Mode Decomposition for Establishing Spatiotemporal Air Quality Trends in Shandong Province, China," Sustainability, MDPI, vol. 13(22), pages 1-10, November.
    12. Shaobo Jin & Sebastian Ankargren, 2019. "Frequentist Model Averaging in Structural Equation Modelling," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 84-104, March.
    13. Tyler C Shimko & Erik C Andersen, 2014. "COPASutils: An R Package for Reading, Processing, and Visualizing Data from COPAS Large-Particle Flow Cytometers," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-5, October.
    14. Zulj, Valentin & Jin, Shaobo, 2024. "Can model averaging improve propensity score based estimation of average treatment effects?," Working Paper Series 2024:1, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    15. Tobias Rentschler & Philipp Gries & Thorsten Behrens & Helge Bruelheide & Peter Kühn & Steffen Seitz & Xuezheng Shi & Stefan Trogisch & Thomas Scholten & Karsten Schmidt, 2019. "Comparison of catchment scale 3D and 2.5D modelling of soil organic carbon stocks in Jiangxi Province, PR China," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-23, August.
    16. Cipollini, Francesca & Oneto, Luca & Coraddu, Andrea & Murphy, Alan John & Anguita, Davide, 2018. "Condition-based maintenance of naval propulsion systems: Data analysis with minimal feedback," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 12-23.
    17. John M Mola & J Morgan Varner & Erik S Jules & Tova Spector, 2014. "Altered Community Flammability in Florida’s Apalachicola Ravines and Implications for the Persistence of the Endangered Conifer Torreya taxifolia," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-8, August.
    18. Maria D. Gonzalez-Lima & Carenne C. Ludeña, 2022. "Using Locality-Sensitive Hashing for SVM Classification of Large Data Sets," Mathematics, MDPI, vol. 10(11), pages 1-21, May.
    19. Martin Schuster & Sreedhar Kilaru & Gero Steinberg, 2024. "Azoles activate type I and type II programmed cell death pathways in crop pathogenic fungi," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    20. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26335-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.