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Rossi JP, Rasplus JY. Climate change and the potential distribution of the glassy-winged sharpshooter (Homalodisca vitripennis), an insect vector of Xylella fastidiosa. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 860:160375. [PMID: 36423847 DOI: 10.1016/j.scitotenv.2022.160375] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 11/17/2022] [Accepted: 11/17/2022] [Indexed: 06/16/2023]
Abstract
Biological invasions represent a major threat for biodiversity and agriculture. Despite efforts to restrict the spread of alien species, preventing their introduction remains the best strategy for an efficient control. In that context preparedness of phytosanitary authorities is very important and estimating the geographical range of alien species becomes a key information. The present study investigates the potential geographical range of the glassy-winged sharpshooter (Homalodisca vitripennis), a very efficient insect vector of Xylella fastidiosa, one of the most dangerous plant-pathogenic bacteria worldwide. We use species distribution modeling (SDM) to analyse the climate factors driving the insect distribution and we evaluate its potential distribution in its native range (USA) and in Europe according to current climate and different scenarios of climate change: 6 General Circulation Models (GCM), 4 shared socioeconomic pathways of gas emission and 4 time periods (2030, 2050, 2070, 2090). The first result is that the climate conditions of the European continent are suitable to the glassy-winged sharpshooter, in particular around the Mediterranean basin where X. fastidiosa is present. Projections according to future climate conditions indicate displacement of climatically suitable areas towards the north in both North America and Europe. Globally, suitable areas will decrease in North America and increase in Europe in the coming decades. SDM outputs vary according to the GCM considered and this variability indicated areas of uncertainty in the species potential range. Both potential distribution and its uncertainty associated to future climate projections are important information for improved preparedness of phytosanitary authorities.
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Affiliation(s)
- Jean-Pierre Rossi
- CBGP (Centre de Biologie pour la Gestion des Populations), INRAE, CIRAD, IRD, Institut Agro, Montpellier, France.
| | - Jean-Yves Rasplus
- CBGP (Centre de Biologie pour la Gestion des Populations), INRAE, CIRAD, IRD, Institut Agro, Montpellier, France.
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Balduque-Gil J, Lacueva-Pérez FJ, Labata-Lezaun G, del-Hoyo-Alonso R, Ilarri S, Sánchez-Hernández E, Martín-Ramos P, Barriuso-Vargas JJ. Big Data and Machine Learning to Improve European Grapevine Moth ( Lobesia botrana) Predictions. PLANTS (BASEL, SWITZERLAND) 2023; 12:633. [PMID: 36771717 PMCID: PMC9921845 DOI: 10.3390/plants12030633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/24/2023] [Accepted: 01/29/2023] [Indexed: 06/18/2023]
Abstract
Machine Learning (ML) techniques can be used to convert Big Data into valuable information for agri-environmental applications, such as predictive pest modeling. Lobesia botrana (Denis & Schiffermüller) 1775 (Lepidoptera: Tortricidae) is one of the main pests of grapevine, causing high productivity losses in some vineyards worldwide. This work focuses on the optimization of the Touzeau model, a classical correlation model between temperature and L. botrana development using data-driven models. Data collected from field observations were combined with 30 GB of registered weather data updated every 30 min to train the ML models and make predictions on this pest's flights, as well as to assess the accuracy of both Touzeau and ML models. The results obtained highlight a much higher F1 score of the ML models in comparison with the Touzeau model. The best-performing model was an artificial neural network of four layers, which considered several variables together and not only the temperature, taking advantage of the ability of ML models to find relationships in nonlinear systems. Despite the room for improvement of artificial intelligence-based models, the process and results presented herein highlight the benefits of ML applied to agricultural pest management strategies.
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Affiliation(s)
- Joaquín Balduque-Gil
- Department of Agricultural Sciences and Natural Environment, AgriFood Institute of Aragon (IA2), University of Zaragoza, Avenida Miguel Servet 177, 50013 Zaragoza, Spain
| | - Francisco J. Lacueva-Pérez
- Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain
| | - Gorka Labata-Lezaun
- Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain
| | - Rafael del-Hoyo-Alonso
- Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain
| | - Sergio Ilarri
- Departamento de Informática e Ingeniería de Sistemas, Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza, María de Luna 1, 50018 Zaragoza, Spain
| | - Eva Sánchez-Hernández
- Department of Agricultural and Forestry Engineering, ETSIIAA, University of Valladolid, Avenida de Madrid 44, 34004 Palencia, Spain
| | - Pablo Martín-Ramos
- Department of Agricultural and Forestry Engineering, ETSIIAA, University of Valladolid, Avenida de Madrid 44, 34004 Palencia, Spain
| | - Juan J. Barriuso-Vargas
- Department of Agricultural Sciences and Natural Environment, AgriFood Institute of Aragon (IA2), University of Zaragoza, Avenida Miguel Servet 177, 50013 Zaragoza, Spain
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Green analytical methodology for grape juice classification using FTIR spectroscopy combined with chemometrics. TALANTA OPEN 2022. [DOI: 10.1016/j.talo.2022.100168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Aguirre-Zapata E, Morales H, Dagatti CV, di Sciascio F, Amicarelli AN. Semi physical growth model of Lobesia botrana under laboratory conditions for Argentina’s Cuyo region. Ecol Modell 2022. [DOI: 10.1016/j.ecolmodel.2021.109803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Rossini L, Bono Rosselló N, Speranza S, Garone E. A general ODE-based model to describe the physiological age structure of ectotherms: Description and application to Drosophila suzukii. Ecol Modell 2021. [DOI: 10.1016/j.ecolmodel.2021.109673] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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