8
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Predicting habitat suitability for Ixodes ricinus and Ixodes persulcatus ticks in Finland

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Ticks are responsible for transmitting several notable pathogens worldwide. Finland lies in a zone where two human-biting tick species co-occur: Ixodes ricinus and Ixodes persulcatus. Tick densities have increased in boreal regions worldwide during past decades, and tick-borne pathogens have been identified as one of the major threats to public health in the face of climate change.

          Methods

          We used species distribution modelling techniques to predict the distributions of I. ricinus and I. persulcatus, using aggregated historical data from 2014 to 2020 and new tick occurrence data from 2021. By aiming to fill the gaps in tick occurrence data, we created a new sampling strategy across Finland. We also screened for tick-borne encephalitis virus (TBEV) and Borrelia from the newly collected ticks. Climate, land use and vegetation data, and population densities of the tick hosts were used in various combinations on four data sets to estimate tick species’ distributions across mainland Finland with a 1-km resolution.

          Results

          In the 2021 survey, 89 new locations were sampled of which 25 new presences and 63 absences were found for I. ricinus and one new presence and 88 absences for I. persulcatus. A total of 502 ticks were collected and analysed; no ticks were positive for TBEV, while 56 (47%) of the 120 pools, including adult, nymph, and larva pools, were positive for Borrelia (minimum infection rate 11.2%, respectively). Our prediction results demonstrate that two combined predictor data sets based on ensemble mean models yielded the highest predictive accuracy for both I. ricinus (AUC = 0.91, 0.94) and I. persulcatus (AUC = 0.93, 0.96). The suitable habitats for I. ricinus were determined by higher relative humidity, air temperature, precipitation sum, and middle-infrared reflectance levels and higher densities of white-tailed deer, European hare, and red fox. For I. persulcatus, locations with greater precipitation and air temperature and higher white-tailed deer, roe deer, and mountain hare densities were associated with higher occurrence probabilities. Suitable habitats for I. ricinus ranged from southern Finland up to Central Ostrobothnia and North Karelia, excluding areas in Ostrobothnia and Pirkanmaa. For I. persulcatus, suitable areas were located along the western coast from Ostrobothnia to southern Lapland, in North Karelia, North Savo, Kainuu, and areas in Pirkanmaa and Päijät-Häme.

          Conclusions

          This is the first study conducted in Finland that estimates potential tick species distributions using environmental and host data. Our results can be utilized in vector control strategies, as supporting material in recommendations issued by public health authorities, and as predictor data for modelling the risk for tick-borne diseases.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13071-022-05410-8.

          Related collections

          Most cited references87

          • Record: found
          • Abstract: not found
          • Article: not found

          Random Forests

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Measuring the accuracy of diagnostic systems.

              J Swets (1988)
              Diagnostic systems of several kinds are used to distinguish between two classes of events, essentially "signals" and "noise". For them, analysis in terms of the "relative operating characteristic" of signal detection theory provides a precise and valid measure of diagnostic accuracy. It is the only measure available that is uninfluenced by decision biases and prior probabilities, and it places the performances of diverse systems on a common, easily interpreted scale. Representative values of this measure are reported here for systems in medical imaging, materials testing, weather forecasting, information retrieval, polygraph lie detection, and aptitude testing. Though the measure itself is sound, the values obtained from tests of diagnostic systems often require qualification because the test data on which they are based are of unsure quality. A common set of problems in testing is faced in all fields. How well these problems are handled, or can be handled in a given field, determines the degree of confidence that can be placed in a measured value of accuracy. Some fields fare much better than others.
                Bookmark

                Author and article information

                Contributors
                ruut.uusitalo@helsinki.fi
                mika.siljander@helsinki.fi
                andreas.linden@luke.fi
                jjtsor@utu.fi
                juha.aalto@fmi.fi
                ghendrickx@avia-gis.com
                eva.r.kallio@jyu.fi
                Andrea.Vajda@fmi.fi
                hilppa.gregow@fmi.fi
                ext.heikki.henttonen@luke.fi
                cmarsboom@avia-gis.com
                essi.m.korhonen@helsinki.fi
                tarja.sironen@helsinki.fi
                petri.pellikka@helsinki.fi
                olli.vapalahti@helsinki.fi
                Journal
                Parasit Vectors
                Parasit Vectors
                Parasites & Vectors
                BioMed Central (London )
                1756-3305
                30 August 2022
                30 August 2022
                2022
                : 15
                : 310
                Affiliations
                [1 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Department of Geosciences and Geography, , University of Helsinki, ; P.O. Box 64, 00014 Helsinki, Finland
                [2 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Department of Virology, , University of Helsinki, ; P.O. Box 21, 00014 Helsinki, Finland
                [3 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Department of Veterinary Biosciences, , University of Helsinki, ; P.O. Box 66, 00014 Helsinki, Finland
                [4 ]GRID grid.22642.30, ISNI 0000 0004 4668 6757, Natural Resources Institute Finland, ; P.O. Box 2, 00791 Helsinki, Finland
                [5 ]GRID grid.1374.1, ISNI 0000 0001 2097 1371, Biodiversity Unit, , University of Turku, ; 20014 Turku, Finland
                [6 ]GRID grid.1374.1, ISNI 0000 0001 2097 1371, Department of Biology, , University of Turku, ; 20014 Turku, Finland
                [7 ]GRID grid.8657.c, ISNI 0000 0001 2253 8678, Weather and Climate Change Impact Research Unit, , Finnish Meteorological Institute, ; P.O. Box 503, 00101 Helsinki, Finland
                [8 ]GRID grid.423833.d, ISNI 0000 0004 6078 8290, Research Department, , AVIA-GIS, ; Zoersel, Belgium
                [9 ]GRID grid.9681.6, ISNI 0000 0001 1013 7965, Department of Biological and Environmental Science and School of Resource Wisdom, , University of Jyväskylä, ; 40014 Jyväskylä, Finland
                [10 ]GRID grid.15485.3d, ISNI 0000 0000 9950 5666, Virology and Immunology, , HUSLAB, Helsinki University Hospital, ; Helsinki, Finland
                [11 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Helsinki Institute of Sustainability Science, , University of Helsinki, ; Helsinki, Finland
                [12 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Institute for Atmospheric and Earth System Research, , University of Helsinki, ; Helsinki, Finland
                Article
                5410
                10.1186/s13071-022-05410-8
                9429443
                36042518
                ebbb6bf9-e1de-489e-bebc-c317bf640e21
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 17 March 2022
                : 15 July 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100002341, Academy of Finland;
                Award ID: 329323
                Award ID: 329323
                Award ID: 329323
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Parasitology
                ixodesricinus,ixodespersulcatus,species distribution modelling,ensemble prediction,tick-borne pathogen,borreliaburgdorferi sensu lato

                Comments

                Comment on this article