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Mammalia

Editor-in-Chief: Denys, Christiane

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Volume 81, Issue 3 (May 2017)

Issues

The Persian squirrel of Kurdistan Province, western Iran: what determines its geographic distribution?

Maedeh Sadeghi / Mansoureh Malekian
Published Online: 2016-04-23 | DOI: https://doi.org/10.1515/mammalia-2015-0166

Abstract

Here, we used the maximum entropy (MAXENT) method to predict habitat distribution of the Persian squirrel in oak forests of Kurdistan Province, western Iran. We used 70 points with known occurrence of the species and 17 environmental variables (climatic variables represented annual trends in temperature and precipitation, seasonality and extreme or limiting environmental factors) to map the species distribution. The MAXENT model showed high performance. Using a 0.5 logistic probability threshold, the models suggested about 16,783.5 ha of the study area to have high suitability for the Persian squirrel. These areas were thus estimated as “good” habitats. Amongst the environmental variables, land cover had the greatest role in the Persian squirrel’s distribution. Precipitation and temperature were the two major climatic factors that affected the Persian squirrel’s distribution. Gap analysis showed that many parts of the species habitat have remained unprotected what can threaten the survival of the studied species in the region. These findings can be used to develop conservation management plans and boost the network of protected areas in the region.

This article offers supplementary material which is provided at the end of the article.

Keywords: habitat suitability; maximum entropy; oak forest; Persian squirrel; Zagros

References

  • Achard, F., H.D. Eva, H.J. Stibig, P. Mayaux, J. Gallego, T. Richards and J.P. Malingreau. 2002. Determination of deforestation rates of the world’s humid tropical forests. Science 297: 999–1002.Google Scholar

  • Aghtari, H. 2014. Habitat suitability modeling od Persian squirrel using ENFA model in Dena proteced area., M.Sc. Thesis, Payame Noor University, Tehran, Iran.Google Scholar

  • Anderson, R.P. and E. Martí. 2004. Modeling species’ geographic distributions for preliminary conservation assessments: an implementation with the spiny pocket mice (Heteromys) of Ecuador. Biol. Conserv. 116: 167–179.Google Scholar

  • Brook, B.W., N.S. Sodhi and P.K. Ng. 2002. Catastrophic extinctions follow deforestation in Singapore. Tectonophysics 350: 273–282.Google Scholar

  • Brown, J.L. 2014. SDMtoolbox: a python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5: 694–700.Google Scholar

  • Chamani, N. 2014. Habitat suitability modeling of Persian squirrel in Kurdistan province. M.Sc. Thesis, University of Kutdistan, Iran.Google Scholar

  • Elith, J. and J.R. Leathwick. 2009. Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Syst. 40: 677–697.Google Scholar

  • Elith, J., S.J. Phillips, T. Hastie, M. Dudík, Y.E. Chee and C.J. Yates. 2011. A statistical explanation of MaxEnt for ecologists. Divers.Distrib. 17: 43–57.Google Scholar

  • Escalante, T., G. Rodríguez-Tapia, M. Linaje, P. Illoldi-Rangel and R. González-López. 2013. Identification of areas of endemism from species distribution models: threshold selection and Nearctic mammals. TIP 16: 5–17.Google Scholar

  • Fattahi, M. 1996. Investigation on the Zagros Quercus forests and the important deforestation parameters. Iranian Forest and Rangelands Research Institute Press, Tehran, Iran.Google Scholar

  • Fourcade, Y., J.O. Engler, D. Rödder and J. Secondi. 2014. Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS One 9: e97122.Google Scholar

  • Guinotte, J.M. and A.J. Davies. 2014. Predicted deep-sea coral habitat suitability for the US West Coast. PLoS One 9: e93918.Google Scholar

  • Guisan, A. and W. Thuiller. 2005. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8: 993–1009.Google Scholar

  • Guisan, A. and N.E. Zimmermann. 2000. Predictive habitat distribution models in ecology. Ecol. Model. 135: 147–186.Google Scholar

  • Hernandez, P.A., C.H. Graham, L.L. Master and D.L. Albert. 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29: 773–785.Google Scholar

  • Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25: 1965–1978.Google Scholar

  • Joy, M.K. and R.G. Death. 2004. Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural networks. Freshwater Biol. 49: 1036–1052.Google Scholar

  • Khalili, F. 2014. Habitat selection and habitat suitability modeling of Persian squirrel (Sciurus anomalus) in Kohgiluyeh and Boyer-Ahmad protected areas. M.Sc. Thesis, Isfahan university of technology, Isfahan, Iran.Google Scholar

  • Kumar, S. and T.J. Stohlgren. 2009. Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. J. Ecol. Nat. Environ. 1: 094–098.Google Scholar

  • Laurance, W.F. 1999. Reflections on the tropical deforestation crisis. Biol. Conserv. 91: 109–117.Google Scholar

  • Merow, C., M.J. Smith and J.A. Silander. 2013. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36: 1058–1069.Google Scholar

  • Mirzaei, R., M.R. Hemami, A. Esmaili Sari and H.R. Rezaei. 2014. Distribution modelling of Lesser Kestrel (Falco naumanni) in Golestan Province, Iran. Environ. Res. 4: 149–156.Google Scholar

  • Murienne, J., E. Guilbert and P. Grandcolas. 2009. Species’ diversity in the New Caledonian endemic genera Cephalidiosus and Nobarnus (Insecta: Heteroptera: Tingidae), an approach using phylogeny and species’ distribution modelling. Biol. J. Linn. Soc. 97: 177–184.Google Scholar

  • Oldfield, T.E.E., R.J. Smith, S.R. Harrop and N.A. Leader-Williams. 2004. Gap analysis of terrestrial protected areas in England and its implications for conservation policy. Biol. Conserv. 120: 307–313.Google Scholar

  • Pearson, R.G. 2007. Species’ distribution modeling for conservation educators and practitioners, American Museum of Natural History, Retrieved from: http://ncep.amnh.org.

  • Phillips, S. 2005. A brief tutorial on Maxent. AT&T Research, Retrieved from: https://www.cs.princeton.edu/~schapire/maxent/tutorial/tutorial.doc.

  • Phillips, S.J., M. Dudik and R.E. Schapire, 2004. A maximum entropy approach to species distribution modeling. Proceedings of the Twenty-First International Conference on Machine Learning, Alberta, Canada, pp. 655–662.Google Scholar

  • Phillips, S.J., R.P. Andersonb and R.E. Schapired. 2006. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190: 231–259.Google Scholar

  • Rood, E., A.A. Ganie and V. Nijman. 2010. Using presence-only modelling to predict Asian elephant habitat use in a tropical forest landscape: implications for conservation. Divers. Distrib. 16: 975–984.Google Scholar

  • Sadeghi, M. 2014. Habitat change detection of Persian squirrel (Sciurus anomalus) in Kurdistan province. M.Sc. Thesis, Isfahan University of Technology, Isfahan, Iran.Google Scholar

  • Schadt, S., F. Knauer, P. Kaczensky, E. Revilla, T. Wiegand and L. Trepl. 2002. Rule-based assessment of suitable habitat and patch connectivity for the Eurasian lynx. Ecol. Appl. 12: 1469–1483.Google Scholar

  • Scott, J.M., F. Davis, B. Csuti, R. Noss, B. Butterfield, C. Groves, H. Anderson, S. Caicco, F. D’Erchia, T.C. Edwards, J. Ulliman and R.G. Wright. 1993. Gap analysis: a geographic approach to protection of biological diversity. Wildl. Monogr. 123: 3–41.Google Scholar

  • Seoane, J., J. Viñuela, R. Díaz-Delgado and J. Bustamante. 2003. The effects of land use and climate on red kite distribution in the Iberian peninsula. Biol. Conserv. 111: 401–414.Google Scholar

  • Stabach, J.A., N. Laporte and W. Olupot. 2009. Modeling habitat suitability for Grey Crowned-cranes (Balearica regulorum gibbericeps) throughout Uganda. Int. J. Biodivers. Conserv. 1: 177–186.Google Scholar

  • Van Breugel P, Kindt R, Barnekow Lilleso JP, van Breugel M. 2015. Environmental gap analysis to prioritize conservation efforts in eastern Africa. PLoS One 10: e0121444.Google Scholar

  • Wang, X., X. Huang, L. Jiang and G. Qiao. 2010. Predicting potential distribution of chestnut phylloxerid (Hemiptera: Phylloxeridae) based on GARP and Maxent ecological niche models. J. Appl. Entomol. 134: 45–54.Google Scholar

  • Yigit, N., B. Krystufek, M. Sozen, A. Bukhnikashvili and G. Shenbrot. 2012. Sciurus anomalus. In: IUCN 2008. IUCN Red List of Threatened Species. Retrieved from: http://www.iucnredlist.org/details/20000/0.

  • Ziaie, H. 2009. A field guide to the mammals of Iran. Iranian Wildlife Center, Tehran, Iran.Google Scholar

About the article

Received: 2015-10-17

Accepted: 2016-03-18

Published Online: 2016-04-23

Published in Print: 2017-05-01


Citation Information: Mammalia, ISSN (Online) 1864-1547, ISSN (Print) 0025-1461, DOI: https://doi.org/10.1515/mammalia-2015-0166.

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