Land-cover patterns surrounding Caucasian grouse leks in Arasbaran region, East Azerbaijan, Iran
Nader Habibzadeh A B and Omid Rafieyan AA Department of Environmental Science, Tabriz Branch, Islamic Azad University, PO Box 51589-1655, Tabriz, Iran.
B Corresponding author. Email: Habibzadeh@iaut.ac.ir
Wildlife Research 43(3) 267-275 https://doi.org/10.1071/WR15181
Submitted: 22 September 2015 Accepted: 22 March 2016 Published: 3 June 2016
Abstract
Context: To create management strategies with the goal of sustaining a species such as Caucasian grouse (Lyrurus mlokosiewiczi), it is important to identify the habitat requirements of species, not just in terms of a correlation with a given habitat feature, but also the relationship between species presence and vegetation coverage, proximity to other habitat types, and importance at different spatial scales.
Aims: To predict the proportions and spatial configuration of major habitat types that are associated with high probabilities of Caucasian grouse lek occurrence.
Methods: Using minimum mapping-unit scale (i.e. grain) for land cover, we applied spatial analysis at three spatial extents (472-, 702- and 867-m-radius circles) to assess how the importance of different land-cover patterns and patch characteristics surrounding leks of Caucasian grouse changed with scale within the Arasbaran landscape (316.56 km2) in East Azerbaijan, Iran. A set of a priori models has been developed on the basis of landscape metrics linked to hypotheses that could explain the spatial pattern of Caucasian black habitat use at each scale. We used an information-theoretic approach based on Akaike’s information criterion (AIC) within a general additive models framework to model habitat selection, so as to compare the values of landscape metrics calculated for Caucasian grouse lek sites (n = 22) with those calculated for non-lek points (n = 44).
Key results: The probability of lek occurrence at each of the spatial scales increases with a larger amount of open, young forests in the landscape. At each scale, we could indicate the landscape composition and structure required to create an ideal habitat mosaic for Caucasian grouse. Such an ideal habitat mosaic within mountain forests of Arasbaran, for a 702-m-radius area around a potential lek site, would consist of non-square (i.e. more geometrically complex) patches of rangeland cover and deciduous stands with canopy cover of <50%, which encompass over 30% of landscape.
Conclusions: Our results identified differences in black grouse requirements at several scales within the landscape. We believe this will help managers improve the habitat focusing on the area around existing or inactive leks, to adapt the landscape to species requirements, and to encourage targeting new sites.
Implications: These findings demonstrated that not only can we identify important landscape requirements at a range of scales, but by characterising landscape composition and structure across these scales, forest managers can help prioritise combinations of habitats that best serve the conservation of the target species.
Additional keywords: courtship sites (lek), general additive models (GAM), landscape.
References
Addicott, J. F., Aho, J. M., Antolin, M. F., Padilla, D. K., Richardson, J. S., and Soluk, D. A. (1987). Ecological neighborhoods: scaling environmental patterns. Oikos 49, 340–346.| Ecological neighborhoods: scaling environmental patterns.Crossref | GoogleScholarGoogle Scholar |
Alijanpour, A. (1996). Qualitative and quantitative investigation in forest of Arasbaran. M.Sc. Thesis, Faculty of Natural Resources, University of Teheran. [In Persian]
Baskya, S. (2003). Distribution and principal threats to Caucasian black grouse Tetrao mlokosiewiczi in the eastern Karadeniz Mountains in Turkey. Wildlife Biology 9, 377–383.
Burnham, K. P., and Anderson, D. R. (2002). ‘Model Selection and Inference: a Practical Information-Theoretical Approach.’ 2nd edn. (Springer-Verlag: New York.)
Ebrahimi, T. (1995). Plant cartography and phytosociology of Sutanchay experimental forest in Arasbaran. M.Sc. Thesis, University of Tabriz, Iran.
Fielding, A. H., and Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24, 38–49.
| A review of methods for the assessment of prediction errors in conservation presence/absence models.Crossref | GoogleScholarGoogle Scholar |
Forest-Range and Watershed Management Organisation (FRWMO) (2014). ‘Iran Land Cover Map.’ Available at: http://frw.org.ir/00/Fa/StaticPages/Page.aspx?tid=13409 [Accessed 15-April-2014].
Freeman, E. A., and Moisen, G. (2008). PresenceAbsence: an R package for presence absence analysis. Journal of Statistical Software 23, 1–31.
| PresenceAbsence: an R package for presence absence analysis.Crossref | GoogleScholarGoogle Scholar |
Gokhelashvili, R., Reese, K. P., and Gavashelishvili, L. (2003). How much do we know about the Caucasian black grouse Tetrao mlokosiewiczi? Sandgrouse 25, 32–40.
Geary, M., Fielding, A. H., and Marsden, S. J. (2013). Designing mosaic landscapes for black grouse Tetrao tetrix using multi-scaled models. The Ibis 155, 792–803.
| Designing mosaic landscapes for black grouse Tetrao tetrix using multi-scaled models.Crossref | GoogleScholarGoogle Scholar |
Habibzadeh, N., Karami, M., and Tarinejad, A. (2010). Micro-habitat characteristics of breeding display sites (leks) of Caucasian black grouse (Tetrao mlokosiewiczi) in Arasbaran Region, East Azerbaijan, Iran. Russian Journal of Ecology 41, 450–457.
Habibzadeh, N., Karami, M., Alavipanh, S. K., and Riazie, B. (2013). Landscape requirements of Caucasian grouse (Lyrurus mlokosiewiczi) in Arasbarn region, East Azerbijan, Iran. The Wilson Journal of Ornithology 125, 140–149.
Hastie, T., and Tibshirani, R. (1986). Generalized additive models. Statistical Science 1, 297–310.
| Generalized additive models.Crossref | GoogleScholarGoogle Scholar |
Hastie, T., and Tibshirani, R. (1990). ‘Generalized Additive Models.’ (Chapman & Hall/CRC: Boca Raton, FL.)
Huston, M. A. (2002). Critical issues for improving predictions. In ‘Predicting Species Occurrences: Issues of Accuracy and Scale’. (Eds J. M. Scott, P. J. Heglund, and M. L. Morrison.) pp. 7–21. (Island Press: Washington, DC.)
IUCN (2015). The IUCN Red List of Threatened Species. Version 2015-4. Available at http://iucnredlist.org [Accessed 19 December 2015]
Javanshir, K. (1976). ‘Atlas of Woody Plants of Iran.’ (National Society of Natural Resources and Human Environment Conservation: Tehran.) [In Persian]
Kahler, B. M., and Cavalieri, V. S. (2014). Modelling Great Lakes piping plover habitat selection during the breeding period from local to landscape scales. Upper Mississippi River and Great Lakes Region Joint Venture technical report no. 2014-1, Bloomington, MN.
Katherine, M. S., William, S. K., Therese, M. D., and Brian, M. (2008). Stand-level forest structure and avian habitat: scale dependencies in predicting occurrence in a heterogeneous forest. Forest Science 54, 36–46.
Klaus S. Vitovich A. V. 2006
Ludwig, T., Storch, I., and Graf, R. F. (2009). Historic landscape change and habitat loss: the case of black grouse in Lower Saxony, Germany. Landscape Ecology 24, 533–546.
| Historic landscape change and habitat loss: the case of black grouse in Lower Saxony, Germany.Crossref | GoogleScholarGoogle Scholar |
Masoud, M. (2004). The analysis of distribution of a Caucasian black grouse population (Tetrao mlokosiewiczi) in East Azerbaijan Region. The Environment Protection Society of East Azerbaijan, Iran. [In Persian]
Masoud, M., and Fanid, L. E. (2006). A study of Caucasian black grouse (Tetrao mlokosiewiczi) population dispersion confined in Iran. Grouse News: Newsletter of the WPA/BirdLife/IUCN/SSC Grouse Specialist Group 31, 5–8.
McGarigal, K., and Marks, B. J. (1995). FRAGSTATS conceptual background. FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. USDA Forest Service, General Technical Reports PNW GTR-351. Pacific Northwest Research Station, Portland OR.
McGarigal, K., Cushman, S. A., and Ene, E. (2012). ‘FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps.’ (Computer software program produced by the authors at the University of Massachusetts: Amherst, MA.)
Olivier, B., Blaise, P., Christophe, R., Robin, E., Frank, B., Manuela, D., Loic, P., Julien, P., Dorothea, P., Ruben, G. M., Valeria, D. C., Wim, H., Anne, D., Daniel, S., Nicolas, S., and Antoine, G. (2015). ‘Ecospat: Spatial Ecology Miscellaneous Methods. R Package Version 1.1. Department of Ecology and Evolution and Institute of Earth Surface Dynamics, University of Lausanne, Switzerland.
Pearce-Higgins, J. W., Grant, M. C., Robinson, M. C., and Haysom, S. L. (2007). The role of forest maturation in causing the decline of black grouse Tetrao tetrix. The Ibis 149, 143–155.
| The role of forest maturation in causing the decline of black grouse Tetrao tetrix.Crossref | GoogleScholarGoogle Scholar |
QGIS Development Team (2014). ‘QGIS Geographic Information System.’ Open Source Geospatial Foundation Project. Available at http://qgis.osgeo.org (Accessed 03 May 2014)
R Core Team (2014). ‘R: a Language and Environment for Statistical Computing, Version 3.1.1.’ (R Foundation for Statistical Computing: Vienna.) Available at http://www.R-project.org (Accessed 28 September 2014)/
Sagheb-Talebi, K., Amirghasemi, F., and Dargahi, D. (2001). Investigation on the structure of young stands in the mountainous forest of Arasbaran northwest Iran. Schweizerische Zeitschrift fur Forstwesen 152, 383–388.
| Investigation on the structure of young stands in the mountainous forest of Arasbaran northwest Iran.Crossref | GoogleScholarGoogle Scholar |
Schweiger, A. K., Ursula, N. M., and Margit, Z. (2012). Small-scale habitat use of black grouse (Tetrao tetrix L.) and rock ptarmigan (Lagopus muta helvetica Thienemann) in the Austrian Alps. European Journal of Wildlife Research 58, 35–45.
| Small-scale habitat use of black grouse (Tetrao tetrix L.) and rock ptarmigan (Lagopus muta helvetica Thienemann) in the Austrian Alps.Crossref | GoogleScholarGoogle Scholar |
Scott, D. A. (1976). The Caucasian black grouse Lyrurus mlokosiewiczi in Iran. World Pheasant Association Journal 1975–76, 66–68.
Shepherd, F. J. (2006). Landscape-scale habitat use by greater sage grouse (Centrocercus urophaslanus) in southern Idaho. Ph.D. Thesis, University of Idaho, Moscow, ID.
Starling, A. E. (1992). The ecology of black grouse Tetrao Tetrix in North-east England. Ph.D. Thesis, University of Newcastle upon Tyne, UK.
Stauffer, D. F. (2002). Linking populations and habitat: where have we been? Where are we going? In ‘Predicting Species Occurrences: Issues of Accuracy and Scale’. Eds J. M. Scott, P. J. Heglund, and M. L. Morrison) pp. 53–61. (Island Press: Washington, DC.)
Storch, I. (2000). ‘Grouse Status Survey and Conservation Action plan 2000–2004.’ Gland, Switzerland: IUCN and Fordingbridge, UK: World Pheasant Association 121 pp.
Suárez-Seoane, S., and Baudry, J. (2002). Scale dependence of spatial patterns and cartography on the detection of landscape change: relationships with species’ perception. Ecography 25, 499–511.
| Scale dependence of spatial patterns and cartography on the detection of landscape change: relationships with species’ perception.Crossref | GoogleScholarGoogle Scholar |
Sultanov, E., Kərimov, T., Klaus, Z., and Etsold, Y. (2003). ‘Qafqaz Tetrasi. Baku, Azerbaijan, Azerbaijan Ornithological Society. [In Azeri with an English summary: The Caucasian Grouse]
Wiens, J. A. (1981). Scale problems in avian censusing. Studies in Avian Biology 6, 513–521.
Wiens, J. A. (1989). Spatial scaling in ecology. Functional Ecology 3, 385–397.
| Spatial scaling in ecology.Crossref | GoogleScholarGoogle Scholar |
Wood, S. N. (2006). ‘Generalized additive models: an introduction with R.’ (Chapman & Hall/CRC: Boca Raton, FL.)
Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society. Series B. Methodological 73, 3–36.
| Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models.Crossref | GoogleScholarGoogle Scholar |
Yee, T. W., and Mitchell, N. D. (1991). Generalized additive models in plant ecology. Journal of Vegetation Science 2, 587–602.
| Generalized additive models in plant ecology.Crossref | GoogleScholarGoogle Scholar |