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      Climate change influences on the potential geographic distribution of the disease vector tick Ixodes ricinus

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          Abstract

          Background

          Ixodes ricinus is a species of hard tick that transmits several important diseases in Europe and North Africa, including Lyme borreliosis and tick-borne encephalitis. Climate change is affecting the geographic distributions and abundances of arthropod vectors, which in turn influence the geographic distribution and epidemiology of associated vector-borne diseases. To date, few studies have investigated effects of climate change on the spatial distribution of I. ricinus at continental extents. Here, we assessed the potential distribution of I. ricinus under current and future climate conditions to understand how climate change will influence the geographic distribution of this important tick vector in coming decades.

          Method

          We used ecological niche modeling to estimate the geographic distribution of I. ricinus with respect to current climate, and then assessed its future potential distribution under different climate change scenarios. This approach integrates occurrence records of I. ricinus with six relevant environmental variables over a continental extent that includes Europe, North Africa, and the Middle East. Future projections were based on climate data from 17 general circulation models (GCMs) under 2 representative concentration pathway emissions scenarios (RCPs), for the years 2050 and 2070.

          Result

          The present and future potential distributions of I. ricinus showed broad overlap across most of western and central Europe, and in more narrow zones in eastern and northern Europe, and North Africa. Potential expansions were observed in northern and eastern Europe. These results indicate that I. ricinus populations could emerge in areas in which they are currently lacking, posing increased risks to human health in those areas. However, the future of I. ricinus ticks in some important regions such the Mediterranean was unclear owing to high uncertainty in model predictions.

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          Most cited references38

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          The importance of biotic interactions for modelling species distributions under climate change

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            Effects of environmental change on emerging parasitic diseases.

            Ecological disturbances exert an influence on the emergence and proliferation of malaria and zoonotic parasitic diseases, including, Leishmaniasis, cryptosporidiosis, giardiasis, trypanosomiasis, schistosomiasis, filariasis, onchocerciasis, and loiasis. Each environmental change, whether occurring as a natural phenomenon or through human intervention, changes the ecological balance and context within which disease hosts or vectors and parasites breed, develop, and transmit disease. Each species occupies a particular ecological niche and vector species sub-populations are distinct behaviourally and genetically as they adapt to man-made environments. Most zoonotic parasites display three distinct life cycles: sylvatic, zoonotic, and anthroponotic. In adapting to changed environmental conditions, including reduced non-human population and increased human population, some vectors display conversion from a primarily zoophyllic to primarily anthrophyllic orientation. Deforestation and ensuing changes in landuse, human settlement, commercial development, road construction, water control systems (dams, canals, irrigation systems, reservoirs), and climate, singly, and in combination have been accompanied by global increases in morbidity and mortality from emergent parasitic disease. The replacement of forests with crop farming, ranching, and raising small animals can create supportive habitats for parasites and their host vectors. When the land use of deforested areas changes, the pattern of human settlement is altered and habitat fragmentation may provide opportunities for exchange and transmission of parasites to the heretofore uninfected humans. Construction of water control projects can lead to shifts in such vector populations as snails and mosquitoes and their parasites. Construction of roads in previously inaccessible forested areas can lead to erosion, and stagnant ponds by blocking the flow of streams when the water rises during the rainy season. The combined effects of environmentally detrimental changes in local land use and alterations in global climate disrupt the natural ecosystem and can increase the risk of transmission of parasitic diseases to the human population.
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              Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias

              MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one “virtual” derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                5 December 2017
                2017
                : 12
                : 12
                : e0189092
                Affiliations
                [1 ] Biodiversity Institute, University of Kansas, Lawrence, Kansas, United States of America
                [2 ] Zoology Department, Faculty of Science, University of Tripoli, Tripoli, Libya
                [3 ] Entomology Department, Faculty of Science, Ain Shams University, Abbassia, Cairo, Egypt
                University of Toledo College of Medicine and Life Sciences, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-2927-514X
                Article
                PONE-D-17-21201
                10.1371/journal.pone.0189092
                5716528
                29206879
                317a9361-d764-47da-af48-0ab75a75288a
                © 2017 Alkishe 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
                : 2 June 2017
                : 18 November 2017
                Page count
                Figures: 4, Tables: 1, Pages: 14
                Funding
                AAA was supported by the Ministry of Higher Education, Libya. AMS was supported by the Graduate Fulbright Egyptian Mission Program (EFMP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Flowering Plants
                Ricinus
                People and Places
                Geographical Locations
                Europe
                Earth Sciences
                Atmospheric Science
                Climatology
                Climate Change
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Arachnida
                Ixodes
                Biology and Life Sciences
                Ecology
                Ecological Niches
                Ecology and Environmental Sciences
                Ecology
                Ecological Niches
                Medicine and Health Sciences
                Infectious Diseases
                Disease Vectors
                Ticks
                Biology and Life Sciences
                Species Interactions
                Disease Vectors
                Ticks
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Arachnida
                Ixodes
                Ticks
                People and Places
                Geographical Locations
                Africa
                Biology and Life Sciences
                Population Biology
                Population Dynamics
                Geographic Distribution
                Custom metadata
                All relevant data are within the paper and its Supporting Information files. GeoTIFF dataset for different general circulation models are openly available via Figshare repository ( https://doi.org/10.6084/m9.figshare.5067373).

                Uncategorized
                Uncategorized

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