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      Mapping the Potential Distribution of Major Tick Species in China

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          Abstract

          Ticks are known as the vectors of various zoonotic diseases such as Lyme borreliosis and tick-borne encephalitis. Though their occurrences are increasingly reported in some parts of China, our understanding of the pattern and determinants of ticks’ potential distribution over the country remain limited. In this study, we took advantage of the recently compiled spatial dataset of distribution and diversity of ticks in China, analyzed the environmental determinants of ten frequently reported tick species and mapped the spatial distribution of these species over the country using the MaxEnt model. We found that presence of urban fabric, cropland, and forest in a place are key determents of tick occurrence, suggesting ticks were likely inhabited close to where people live. Besides, precipitation in the driest month was found to have a relatively high contribution in mapping tick distribution. The model projected that theses ticks could be widely distributed in the Northwest, Central North, Northeast, and South China. Our results added new evidence on the potential distribution of a variety of major tick species in China and pinpointed areas with a high potential risk of tick bites and tick-borne diseases for raising public health awareness and prevention responses.

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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                16 July 2020
                July 2020
                : 17
                : 14
                : 5145
                Affiliations
                [1 ]College of Environment Science and engineering, Huazhong University of Science and Technology, Wuhan 430070, China; yang_xin@ 123456hust.edu.cn (X.Y.); hggz@ 123456hust.edu.cn (Z.G.); wang_luqi@ 123456hust.edu.cn (L.W.); xiaolingjun@ 123456hust.edu.cn (L.X.); hongjuanwu@ 123456mail.hust.edu.cn (H.W.)
                [2 ]School of Automation, Wuhan University of Technology, Wuhan 430070, China; zhoutianli@ 123456whut.edu.cn (T.Z.); jian_zhang@ 123456whut.edu.cn (J.Z.)
                [3 ]UK Centre for Ecology & Hydrology, Wallingford OX10 8BB, UK
                [4 ]Environmental Change Institute, University of Oxford, Oxford OX1 3QY, UK
                Author notes
                [* ]Correspondence: senli@ 123456hust.edu.cn
                Author information
                https://orcid.org/0000-0002-1177-7339
                Article
                ijerph-17-05145
                10.3390/ijerph17145145
                7399889
                32708816
                fd3f2fb1-f327-45e8-a4f4-aa34fc5c478f
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 15 June 2020
                : 10 July 2020
                Categories
                Article

                Public health
                tick,potential distribution,environmental factors,maxent,machine learning
                Public health
                tick, potential distribution, environmental factors, maxent, machine learning

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