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      Modeling and mapping the current and future distribution of Pseudomonas syringae pv. actinidiae under climate change in China

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          Abstract

          Objective

          Bacterial canker of kiwifruit caused by Pseudomonas syringae pv. actinidiae (Psa) is a major threat to the kiwifruit industry throughout the world and accounts for substantial economic losses in China. The aim of the present study was to test and explore the possibility of using MaxEnt (maximum entropy models) to predict and analyze the future large-scale distribution of Psa in China.

          Method

          Based on the current environmental factors, three future climate scenarios, which were suggested by the fifth IPCC report, and the current distribution sites of Psa, MaxEnt combined with ArcGIS was applied to predict the potential suitable areas and the changing trend of Psa in China. The jackknife test and correlation analysis were used to choose dominant climatic factors. The receiver operating characteristic curve (ROC) drawn by MaxEnt was used to evaluate the accuracy of the simulation.

          Result

          The results showed that under current climatic conditions, the area from latitude 25° to 36°N and from longitude 101° to 122°E is the primary potential suitable area of Psa in China. The highly suitable area (with suitability between 66 and 100) was mainly concentrated in Northeast Sichuan, South Shaanxi, most of Chongqing, West Hubei and Southwest Gansu and occupied 4.94% of land in China. Under different future emission scenarios, both the areas and the centers of the suitable areas all showed differences compared with the current situation. Four climatic variables, i.e., maximum April temperature (19%), mean temperature of the coldest quarter (14%), precipitation in May (11.5%) and minimum temperature in October (10.8%), had the largest impact on the distribution of Psa.

          Conclusion

          The MaxEnt model is potentially useful for forecasting the future adaptive distribution of Psa under climate change, and it provides important guidance for comprehensive management.

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          ORIGINAL ARTICLE: Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar

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            The ability of climate envelope models to predict the effect of climate change on species distributions

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              Overcoming limitations of modelling rare species by using ensembles of small models

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Funding acquisitionRole: MethodologyRole: Project administrationRole: Supervision
                Role: Data curation
                Role: Data curationRole: Investigation
                Role: Writing – review & editing
                Role: Software
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                1 February 2018
                2018
                : 13
                : 2
                : e0192153
                Affiliations
                [1 ] College of Agronomy, Sichuan Agricultural University, Chengdu, Sichuan, China
                [2 ] Sichuan Provincial Rural Economic Information Center, Chengdu, Sichuan, China
                [3 ] The Kiwifruit Institute of Cangxi Country, Cangxi, Sichuan, China
                [4 ] Sichuan Meteorological Observatory, Chengdu, Sichuan, China
                University of the West of England, UNITED KINGDOM
                Author notes

                Competing Interests: We declare that no competing interests exist in employment, consultancy, patents, products in development, marketed products, etc in above funders. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                Author information
                http://orcid.org/0000-0001-9818-8870
                Article
                PONE-D-17-27998
                10.1371/journal.pone.0192153
                5794145
                29389964
                4579e60a-2878-483b-b213-a472ec2d6a4d
                © 2018 Wang et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 26 July 2017
                : 17 January 2018
                Page count
                Figures: 7, Tables: 5, Pages: 21
                Funding
                Funded by: The modern agricultural industry technology system of Sichuan innovation team
                Award ID: 2013-2018
                Award Recipient :
                Funded by: The project of meteorological service for agriculture, rural areas and farmers in Sichuan
                Award ID: 2017-2020
                Award Recipient :
                Our work was supported by the modern agricultural industry technology http://www.scnycxtd.com/ system of Sichuan innovation team and the project of meteorological service for agriculture, rural areas and farmers in Sichuan, and the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Earth Sciences
                Atmospheric Science
                Climatology
                Climate Change
                People and Places
                Geographical Locations
                Asia
                China
                Biology and Life Sciences
                Population Biology
                Population Dynamics
                Geographic Distribution
                Biology and Life Sciences
                Ecology
                Ecological Niches
                Ecology and Environmental Sciences
                Ecology
                Ecological Niches
                Earth Sciences
                Atmospheric Science
                Meteorology
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Forecasting
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Forecasting
                Earth Sciences
                Atmospheric Science
                Atmospheric Chemistry
                Greenhouse Gases
                Physical Sciences
                Chemistry
                Environmental Chemistry
                Atmospheric Chemistry
                Greenhouse Gases
                Ecology and Environmental Sciences
                Environmental Chemistry
                Atmospheric Chemistry
                Greenhouse Gases
                Biology and Life Sciences
                Plant Science
                Plant Pathology
                Custom metadata
                All occurrence data are available from Figshare at: https://dx.doi.org/10.6084/m9.figshare.5734806.

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