Modelling the Bioclimatic Niche and Distribution of the Steppe Mouse, Mus Spicilegus (Rodentia, Muridae), in Ukraine

Open access


The Steppe mouse, Mus spicilegus, is endemic to Europe and found to be expanding its home range in recent years. In Ukraine there are indications a north- and eastwards expansion and/or reestablishment of M. spicilegus. We suggest that climatic conditions may be the primary factors that foster or limit the range expansion of M. spicilegus in Eastern Europe. Our objective was to complement the knowledge about the distribution of the species with an estimation of the potential distribution of the species in Ukraine using known occurrence sites (in Ukraine and neighbouring areas) and environmental variables in an ecological niche modelling algorithm. After accounting for sampling bias and spatial autocorrelation, we retained 73 occurrence records. The algorithm used in this paper, Maxent (Phillips et al., 2006), is a machine learning algorithm and only needs presence data, besides the environmental layers. Using this approach, we have highlighted the importance and significance of a number of bioclimatic variables, particularly those characterizing wintering conditions, under which higher mean temperatures enhance habitat suitability, whereas increased precipitation leads to an opposite effect. The broadly northwards shift of the home range of the species in Ukraine could generally be due to the increasing (since the 1980s) mean temperature of the winter season. We expect this expansion process will continue together with the changing climate and new records of locations of the species may be used for monitoring such change.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Aars J. Ims R. A. 2002. Intrinsic and climatic determinants of population demography: the winter dynamics of tundra voles. Ecology83 (12) 3449–3456.

  • Aiello-Lammens M. E. Boria R. A. Radosavljevic A. Vilela B. Anderson R. P. 2015. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography38 (5) 541–545.

  • Akaike H. 1974. A new look at the statistical model identification. IEEE Trans. Automatic Control19 716–723.

  • Baldwin R. A. 2009. Use of maximum entropy modeling in wildlife research. Entropy11 (4) 854–866.

  • Beck J. Ballesteros-Mejia L. Nagel P. Kitching I. J. 2013. Online solutions and the “Wallacean shortfall”: What does GBIF contribute to our knowledge of species’ ranges? Diversity and Distributions19 (8) 1043–1050.

  • Berry R. J. 1981. Population dynamics of the house mouse. Symposia of the Zoological Society of London47 395–425.

  • Boonstra R. Rodd F. H. 1983. Regulation of breeding density in Microtus pennsylvanicus. Journal of Animal Ecology52 (3) 757–780.

  • Brauner A. 1928. List of mammals of Askania-Nova. Chapli Steppe Reserve — Askania-Nova. State Publishing House Moscow Leningrad 183–194 [In Russian].

  • Calisher C. H. Mills J. N. Sweeney W. P. Root J. J. Reeder S. A. Jentes E. S. Beaty B. J. 2005. Population dynamics of a diverse rodent assemblage in mixed grass shrub habitat southeastern Colorado 1995–2000. Journal of Wildlife Diseases41 (1) 12–28.

  • Conrad O. Bechtel B. Bock M. Dietrich H. Fischer E. Gerlitz L. Wehberg J. Wichmann V. Böhner J. 2015. System for Automated Geoscientific Analyses (SAGA). Vol. 2.1.4. Geoscientific Model Development8 1991–2007.

  • Coroiu I. Kryštufek B. Vohralík V. 2016. Mus spicilegus. The IUCN Red List of Threatened Species 2016: e.T13984A544549.

  • Elith J. Phillips S. J. Hastie T. Dudík M. Chee Y. E. Yates C. J. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions11 (1) 43–57.

  • Evstafiev I. 2015. Results of a thirty-year study of small mammals of Crimea. Part 1. Introduction composition of fauna home ranges. Proceedings of the Theriological School 12 20–34 [In Russian].

  • Fielding A. H. Bell J. F. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation24 (1) 38–49.

  • Franklin J. 2009. Mapping species distribution: spatial inference and prediction. Cambridge University Press Cambridge 1–340.

  • Guisan A Thuiller W. 2005. Predicting species distribution: offering more than simple habitat models. Ecological Letters8 (9) 993–1009.

  • Hammer Ø. Harper D. A. T. Ryan P. D. 2001. PAST: Paleontological statistics soft ware package for education and data analysis. Palaeontologia Electronica4 (1) 1–9.

  • Hanspach J. Kühn I. Pompe S. Klotz S. 2010. Predictive performance of plant species distribution models depends on species traits. Perspectives in Plant Ecology Evolution and Systematics12 (3) 219–225.

  • Hernandez P. A. Graham C. H. Master L. L. Albert D. L. 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography29 (5) 773–785.

  • Hölzl M. Hoi H. Darolova A. Krištofik J. 2011. Insulation capacity of litter mounds built by Mus spicilegus: physical and thermal characteristics of building material and the role of mound size. Ethology ecology & evolution23 (1) 49–59.

  • Jackson D. M. Trayhurn P. Speakman J. R. 2001. Association between energetics and overwinter survival in short tailed field vole Microtus agrestis. Journal of Animal Ecology70 (4) 633–640.

  • Jobe R. T. Zank B. 2008. Modelling species distributions for the Great Smoky Mountains National Park using Maxent. Unpublished ELECTRONIC FILE:

  • Kondratenko A. V. 1998. The Hillock mouse (Mus spicilegus Petenyi 1882) in the eastern regions of Ukraine. Vestnik Zoologii32 (5–6) 133–136 [In Russian].

  • Kriticos D. J. Webber B. L. Leriche A. Ota N. Macadam I. Bathols J. Scott J. K. 2012. CliMond: global high resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods in Ecology and Evolution3 (1) 53–64.

  • Lichstein J. W. Simons T. R Shriner S. A. Franzreb K. E. 2002. Spatial autocorrelation and autoregressive models in ecology. Ecological Monographs72 (3) 445–463.

  • Lipkovich A. D. 2005. The Hillock mouse (Mus spicilegus Petenyi 1882) in the Rostov Region. Bulletin of the Southern Scientific Center of the Russian Academy of Sciences1 (4) 51–57 [In Russian].

  • Liu C. Berry P. Dawson T. Pearson R. 2005. Selecting thresholds of occurrence in the prediction of species distributions. Ecography28 (3) 385–393.

  • Luis A. D. Douglass R. J. Mills J. N. Bjørnstad O. N. 2010. The effect of seasonality density and climate on the population dynamics of Montana deer mice important reservoir hosts for Sin Nombre hantavirus. Journal of Animal Ecology79 (2) 462–70.

  • Lyalyukhina S. I. Mikhailenko A. G. Kotenkova E. V. 1989. Cadastral reference map of the home range of the Hillock mouse (Mus hortulanus Nordm.) in the USSR. In: House mouse. Institute of Evolutionary Morphology and Animal Ecology Acad. Sci. USSR Moscow 28–51 [In Russian].

  • Merow C. Smith M. J. Silander J. A. Jr. 2013. A practical guide to MaxEnt for modeling species’ distributions: what it does and why inputs and settings matter. Ecography36 (10) 1058–1069.

  • Miller J. 2010. Species Distribution Modeling. Geography Compass4 (6) 490–509.

  • Muntyanu A. I. 1990. Ecological features of an overwintering population of the Hillock Mouse (Mus hortulanus Nordm) in the South-West of the USSR. Biological Journal of the Linnean Society41 (1–3) 73–82.

  • Muscarella R. Galante P. J. Soley-Guardia M. Boria R. A. Kass J. M. Uriarte M. Anderson R. P. 2014. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in Ecology and Evolution5 (11) 1198–1205.

  • Nakazato T. Warren D. L. Moyle L. C. 2010. Ecological and geographic modes of species divergence in wild tomatoes. American Journal of Botany97 (4) 680–693.

  • Nuñez M. A. Medley K. A. 2011. Pine invasions: climate predicts invasion success; something else predicts failure. Diversity and Distributions17 (4) 703–713.

  • Partolin I. V. 2016. The Hillock mouse (Mus spicilegus Petenyi 1882) in novel steppe areas in the northeast of the home range. Protection restoration and study of steppe ecosystems in the XXI century: Materials of the International scientific-practical conference dedicated to the 90th anniversary of the foundation of the Khomutov Steppe Reserve. Publishing House “Knowledge” Donetsk 171 [In Russian].

  • Peterson A. Soberón J. Pearson R. Anderson R. Martínez-Meyer E. Nakamura M. Araújo M. 2011. Ecological Niches and Geographic Distributions. Princeton University Press 1–328.

  • Phillips S. J. Dudík M. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography31 (2) 161–175.

  • Phillips S. J. Anderson R. P. Schapire R. E. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling190 (3–4) 231–259.

  • Polishchuk I. 2012. The Hillock mouse Mus spicilegus (Muridae Rodentia) in Askania-Nova and the Kherson region. Proceedings of the Theriological School11 71–76 [In Russian].

  • Rangel T. F. Diniz-Filho J. A. F. Bini L. M. 2006. Towards an integrated computational tool for spatial analysis in macroecology and biogeography. Global Ecology and Biogeography15 (4) 321–327.

  • Ruzhilenko N. S. 2005. Records of the Hillock mouse Mus spicilegus (Rodentia Muridae) in Kanevskiy Nature Reserve and its surroundings. Vestnik Zoologii39 (3) 76 [In Russian].

  • Rykiel E. J. 1996. Testing ecological models: the meaning of validation. Ecological Modelling90 (3) 229–244.

  • Searcy C. A. Shaffer H. B. 2016. Do ecological niche models accurately identify climatic determinants of species ranges? American Naturalist187 (4) 423–35.

  • Simeonovska-Nikolova D. M. 2000. Strategies in open field behaviour of Mus spicilegus and Mus musculus musculus. Belgian Journal of Zoology130 (1) 115–120.

  • Smirnov N. A. 2009. New information on the occurrence and ecology of the Hillock mouse (Mus spicilegus) on the territory of Bukovina. Problems of studying and protection of wildlife in natural and anthropogenic ecosystems. Proceedings of the International Scientific Conference on the 50th Anniversary of the publication of the regional summaryThe Animal World of Soviet Bukovyna(Chernivtsi November 13) 95–98 [In Russian].

  • Sokolov V. E. Kotenkova E. V. Lyalyukhina S. I. 1990. Biology of House and Mound-building Mice. Nauka Moscow 1–208 [In Russian with English summary].

  • Sokolov V. E.; Kotenkova E. V.; Michailenko A. G. 2008. “Mus spicilegus”. Mammalian Species592 1–6.

  • Swets J. 1988. Measuring the accuracy of diagnostic systems. Science240 (4857) 1285–1293.

  • Szenczi P. Bánszegi O. Dúcs A. Gedeon C. I. Markó G. Németh I. Altbäcker V. 2011. Morphology and function of communal mounds of overwintering moundbuilding mice (Mus spicilegus). Journal of Mammalogy92 (4) 852–860.

  • Tokarsky V. A. Tokarskaya N. V. Fomenko T. C. 2011. The Hillock mouse (Mus spicilegus Rodentia Mammalia) in Kharkiv region. Bulletin of the Karazin Kharkiv National University. Ser.: Biology971 (14) 118–124 [In Russian].

  • Tsvelykh A. N. 2009. Distribution of the Hillock mouse Mus spicilegus (Mammalia) in the Crimean Mountains. Vestnik Zoologii43 (2) 185–188 [In Russian].

  • Vega G. C. Pertierra L. R. Olalla-Tárraga M. A. 2017. MERRAclim a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling. Nature Scientific Data4 170078. doi: 10.1038/sdata.2017.78.

  • Wangen K. Speed J. D. M. Hassel K. 2016. Hyper-oceanic liverwort species of conservation concern: evidence for dispersal limitation and identification of suitable uncolonised regions. Biodiversity and Conservation25 (6) 1053–1071.

  • Warren D. L. Seifert S. N. 2011. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications21 (2) 335–342.

  • Zagorodniuk I. V. Berezovsky V. I. 1994. Mus spicilegus (Mammalia) in the fauna of Podolia and the northern border of the home range of this species in Eastern Europe. Zoological Journal73 (6) 110–119 [In Russian].

Journal information
Impact Factor

Cite Score 2018: 0.41

SCImago Journal Rank (SJR) 2018: 0.324
Source Normalized Impact per Paper (SNIP) 2018: 0.422

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 29 29 29
PDF Downloads 46 46 46