Skip to main content
    • Aa
    • Aa
  • Get access
    Check if you have access via personal or institutional login
  • Cited by 3
  • Cited by
    This article has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Yen, Shih-Ching Wang, Ying and Ou, Heng-You 2014. Habitat of the Vulnerable Formosan sambar deer Rusa unicolor swinhoii in Taiwan. Oryx, Vol. 48, Issue. 02, p. 232.

    Şekercioğlu, Çağan H. Anderson, Sean Akçay, Erol Bilgin, Raşit Can, Özgün Emre Semiz, Gürkan Tavşanoğlu, Çağatay Yokeş, Mehmet Baki Soyumert, Anıl İpekdal, Kahraman Sağlam, İsmail K. Yücel, Mustafa and Nüzhet Dalfes, H. 2011. Turkey’s globally important biodiversity in crisis. Biological Conservation, Vol. 144, Issue. 12, p. 2752.

    Trisurat, Yongyut and Duengkae, Prateep 2011. Consequences of land use change on bird distribution at Sakaerat Environmental Research Station. Journal of Ecology and Field Biology, Vol. 34, Issue. 2, p. 203.


Combining radio-telemetry and random observations to model the habitat of Near Threatened Caucasian grouse Tetrao mlokosiewiczi

  • Alexander Gavashelishvili (a1) and Zura Javakhishvili (a1)
  • DOI:
  • Published online: 14 October 2010

The distribution of the Near Threatened Caucasian grouse Tetrao mlokosiewiczi, endemic to the Caucasus, was examined to model the species’ nesting habitat, and thus facilitate its conservation and the identification of Key Biodiversity Areas in the Caucasus. The species’ occurrence was defined by field surveys and radio-telemetry. Data were managed and analysed using a geographical information system and various modelling techniques. Grouse locations were divided into training and testing datasets. Habitat variables measured at training locations were used to develop models, and testing locations were used to validate the models. The final best-fit model suggested that Caucasian grouse prefer open habitat, and the most important independent variables accounting for the species' distribution were annual mean temperature, mean temperature of warmest quarter, precipitation seasonality and proximity to deciduous broad-leaf forest. The incorporation of human disturbance and ruggedness into the final model significantly increased its predictive power. This model provides a tool to improve search effectiveness for Caucasian grouse in the Caucasus and for the conservation and management of the species. The model can predict the probable distribution of Caucasian grouse and the corridors between known populations. Threatened and endemic species are often used as species for setting site-based conservation priorities, and this habitat model could help to identify new Key Biodiversity Areas for protection in the Caucasus. The Ministry of Environmental Protection and Natural Resources of Georgia is going to use the results of this study to reshape existing protected areas and identify new ones.

Corresponding author
*Center of Biodiversity Studies, Institute of Ecology, Ilia State University, Chavchavadze Avenue 32, 0179 Tbilisi, Georgia. E-mail
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

F. Adriaensen , J.P. Chardon , G. De Blust , E. Swinnen , S. Villalba , H. Gulinck & E. Matthysen (2003) The application of ‘least-cost’ modelling as a functional landscape model. Landscape and Urban Planning, 64, 233247.

J.D. Clark , J.E. Dunn & K.G. Smith (1993) A multivariate model of female black bear habitat use for a geographic information system. Journal of Wildlife Management, 57, 519526.

J. Elith , C.H. Graham , R.P. Anderson , M. Dudik , S. Ferrier , A. Guisan . (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129151.

T.K. Gottschalk , K. Ekschmitt , S. İsfendiyaroglu , E. Gem & V. Wolters (2007) Assessing the potential distribution of the Caucasian black grouse Tetrao mlokosiewiczi in Turkey through spatial modelling. Journal of Ornithology, 148, 427434.

J. Hanley & B.J. McNeil (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 2936.

P.A. Hernandez , I. Franke , S.K. Herzog , V. Pacheco , L. Paniagua , H.L. Quintana . (2008) Predicting species distributions in poorly-studied landscapes. Biodiversity and Conservation, 17, 13531366.

R.J. Hijmans , S.E. Cameron , J.L. Parra , P.G. Jones & A. Jarvis (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 19651978.

S. Isfendiyaroğlu , G. Welch & M. Ataol (2007) The Caucasian black grouse Tetrao mlokosiewiczi in Turkey: recent survey results and conservation recommendations. Wildlife Biology, 13, 1320.

S. Menard (2002) Applied Logistic Regression Analysis, 2nd edition.Sage Publications, Thousand Oaks, USA.

N. Myers , R.A. Mittermeier , C.G. Mittermeier , G.A.B. Fonseca & J. Kent (2000) Biodiversity hotspots for conservation priorities. Nature, 403, 853858.

S.J. Phillips , R.P. Anderson & R.E. Schapire (2006) Maximum entropy modelling of species' geographic distributions. Ecological Modelling, 190, 231259.

S.J. Phillips & M. Dudik (2008) Modelling of species' distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31, 161175.

R.L. Pressey , C.J. Humphries , C.R. Margules , R.I. Vane-Wright & P.H. Williams (1993) Beyond opportunism—key principles for systematic reserve selection. Trends in Ecology & Evolution, 8, 124128.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

  • ISSN: 0030-6053
  • EISSN: 1365-3008
  • URL: /core/journals/oryx
Please enter your name
Please enter a valid email address
Who would you like to send this to? *


Type Description Title
Supplementary Materials

Gavashelishvili supplementary material
Gavashelishvili supplementary material

 Unknown (36 KB)
36 KB