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Ecological Niche Modelling of Bank Voles in Western Europe

Measure, Model & Manage Bioresponses (M3-BIORES), Biosystems Department, KU Leuven, Kasteelpark Arenberg 30, Leuven B-3001, Belgium
BIORES, Biosystems Department, KU Leuven, Willem de Croylaan 34, Leuven B-3001, Belgium
National Reference Laboratory for Hantavirus Infections, Laboratory of Clinical Virology, Rega Institute, KU Leuven, Minderbroedersstraat 10, Leuven B-3000, Belgium
Royal Netherlands Meteorological Institute (KNMI), Climate Observations, PO Box 201, De Bilt NL-3730 AE, The Netherlands
Eindhoven University of Technology, Applied Physics, PO Box 513, Eindhoven 5600 MB, The Netherlands
Evolutionary Ecology Group, University of Antwerp, Groenenborgerlaan 171, Antwerpen 2020, Belgium
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2013, 10(2), 499-514;
Received: 9 December 2012 / Revised: 21 January 2013 / Accepted: 21 January 2013 / Published: 28 January 2013
The bank vole (Myodes glareolus) is the natural host of Puumala virus (PUUV) in vast areas of Europe. PUUV is one of the hantaviruses which are transmitted to humans by infected rodents. PUUV causes a general mild form of hemorrhagic fever with renal syndrome (HFRS) called nephropathia epidemica (NE). Vector-borne and zoonotic diseases generally display clear spatial patterns due to different space-dependent factors. Land cover influences disease transmission by controlling both the spatial distribution of vectors or hosts, as well as by facilitating the human contact with them. In this study the use of ecological niche modelling (ENM) for predicting the geographical distribution of bank vole population on the basis of spatial climate information is tested. The Genetic Algorithm for Rule-set Prediction (GARP) is used to model the ecological niche of bank voles in Western Europe. The meteorological data, land cover types and geo-referenced points representing the locations of the bank voles (latitude/longitude) in the study area are used as the primary model input value. The predictive accuracy of the bank vole ecologic niche model was significant (training accuracy of 86%). The output of the GARP models based on the 50% subsets of points used for testing the model showed an accuracy of 75%. Compared with random models, the probability of such high predictivity was low (χ2 tests, p < 10−6). As such, the GARP models were predictive and the used ecologic niche model indeed indicates the ecologic requirements of bank voles. This approach successfully identified the areas of infection risk across the study area. The result suggests that the niche modelling approach can be implemented in a next step towards the development of new tools for monitoring the bank vole’s population. View Full-Text
Keywords: biogeography; bank voles; genetic algorithm; GARP; GIS biogeography; bank voles; genetic algorithm; GARP; GIS
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Amirpour Haredasht, S.; Barrios, M.; Farifteh, J.; Maes, P.; Clement, J.; Verstraeten, W.W.; Tersago, K.; Van Ranst, M.; Coppin, P.; Berckmans, D.; Aerts, J.-M. Ecological Niche Modelling of Bank Voles in Western Europe. Int. J. Environ. Res. Public Health 2013, 10, 499-514.

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