Theriologia Ukrainica
(former Proceedings of the Theriological School)

ISSN 2616-7379 (print) ISSN 2617-1120 (online)

2019 Vol. 17 Contents of volume >>>


download pdfTytar, V., Asykulov, T., Hammer, M. Using species distribution modelling to guide survey efforts of the snow leopard (Panthera uncia) in the Central Kyrgyz Ala-Too region. Theriologia Ukrainica. 2019. Vol. 17: 112-118.


 

title

Using species distribution modelling to guide survey efforts of the snow leopard (Panthera uncia) in the Central Kyrgyz Ala-Too region

author(s)

Tytar, V., Asykulov, T., Hammer, M.

affiliation

Schmalhausen Institute of Zoology NAS of Ukraine (Kyiv, Ukraine)
Kyrgyz National University, Faculty of Geography and Ecology (Bishkek, Republic of Kyrgyzstan)
Der Naturschutzbund Deutschland e. V. NABU (Bishkek, Republic of Kyrgyzstan)
Biosphere Expeditions Deutschland (Hoechberg, Germany)

bibliography

Theriologia Ukrainica. 2019. Vol. 17: 112-118.

DOI

http://doi.org/10.15407/pts2019.17.112

   

language

English, with Ukrainian summary, titles of tables, captures to figs

abstract

Listed as Vulnerable (IUCN 2017), the snow leopard is declining across much of its present range. One of major reasons for the snow leopard population decline in the last two decades is a reduction in large prey species that are the cornerstone of the conservation of the snow leopard; in the Central Kyrgyz Ala-Too region such species is primarily Siberian ibex (Capra sibirica). Understanding factors affecting basic requirement of ibex and shaping its distribution is essential for protecting the prey species snow leopards rely on the most. Using a niche modelling approach we explored which environmental features are best associated with ibex occurrence, how well do models predict ibex occurrence, and does the potential distribution of highly suitable ibex habitat correlate with records of snow leopard. A PC analysis was used to capture aspects of ibex ecology and niche. Results of such analysis agree with the herbivore character of the species and bioclimatic habitat requirements of the vegetation it feeds upon, richer in flatter areas, and where plants may benefit from more sunlight. The niche model based on maximum entropy (Maxent) had useful discrimination abilities (AUC = 0.746), enabling to produce a map, where a contour line is drawn around areas of highly predicted probability (> 0.5) of ibex occurrence. In terms of nature conservation planning and setting snow leopard research priorities these areas represent the most interest. With one outlier, most of snow leopard records made in the study area (n = 15) fell within the 10 percentile presence threshold (0.368). Predicted probability of ibex occurrence in places where records were made of snow leopard presence (pugmarks, scrapes etc.) was 0.559, expectedly suggesting areas of high ibex habitat suitability attract the predator.

keywords

Capra sibirica, Panthera uncia, Kyrgyz Ala-Too, species distribution models, Maxent.

   

references

Bellis, L. M., A. M. Pidgeon, V. C. Radeloff, V. StLouis, J. L. Navarro, M. B. Martella. 2008. Modeling habitat suitability for Greater Rheas based on satellite image texture. Ecological Applications, 18 (8): 19561966. https://doi.org/10.1890/07-0243.1
Conrad, O., B. Bechtel, M. Bock, H. Dietrich, E. Fischer, L. Gerlitz, J. Wehberg, V. Wichmann, J. Bohner. 2015. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geoscientific Model Development Discussions, 8 (2): 22712312. https://doi.org/10.5194/gmdd-8-2271-2015
Cruz-Cardenas, G., L., J. L. Lopez-Mata, L. Villasenor, E. Ortiz. 2014. Potential species distribution modeling and the use of principal component analysis as predictor variables. Revista Mexicana de Biodiversidad, 85 (1): 189199. https://doi.org/10.7550/rmb.36723
Elith, J., C. H. Graham, R. P. Anderson, M. Dudik, S. Ferrier, A. Guisan, R. J. Hijmans, F. Huettmann, J. R. Leathwick, A. Lehmann, J. Li, L. G. Lohmann, B. A. Loiselle, G. Manion., C. Moritz, M. Nakamura, Y. Nakazawa, J. M. Overton, A. T. Peterson, S. J. Phillips, K. Richardson, R. Scachetti-Pereira, R. E. Schapire, J. Soberon, S. Williams, M. Wisz, N. E. Zimmermann. 2006. Novel methods improve prediction of species distributions from occurrence data. Ecography, 29 (2): 129151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
Fedosenko, A. K., D. A. Blank. 2001. Capra sibirica. Mammalian Species, No. 675: 113. https://doi.org/10.1644/1545-1410(2001)675<0001:CS>2.0.CO;2
Hijmans, R. J., 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 (15): 19651978. https://doi.org/10.1002/joc.1276
Huang, C., W. L. Yang, H. Collin, G. Zylstra. 2002. Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance. International Journal of Remote Sensing, 23 (8): 17411748. https://doi.org/10.1080/01431160110106113
Hutchinson, G. E. 1957. Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology, No. 22: 415427. https://doi.org/10.1101/SQB.1957.022.01.039
IUCN... 2017. The IUCN Red List of Threatened Species. Version 2017-3. <http://www.iucnredlist.org>. Downloaded on 05 May 2018.
Kauth, R. J., G. S. Thomas. 1976. The Tasselled Cap a graphic description of the spectral-temporal development of agricultural crops as seen by LANDSAT. LARS Symposia. Paper 159.
Lyngdoh, S., S. Shrotriya, S. P. Goyal, H. Clements, M. W. Hayward, B. Habib. 2014. Prey preferences of the snow leopard (Panthera uncia): regional diet specificity holds global significance for conservation. PLoS ONE, 9 (2): e88349. https://doi.org/10.1371/journal.pone.0088349
Marzluff, J. M., K. Ewing. 2001. Restoration of fragmented landscapes for the conservation of birds: a general framework and specific recommendations for urbanizing landscapes. Restoration Ecology, 9 (3): 280292. https://doi.org/10.1046/j.1526-100x.2001.009003280.x
Philipson, P., T. Lindell. 2003. Can coral reefs be monitored from space? AMBIO: A Journal of the Human Environment, 32 (8): 586593. https://doi.org/10.1579/0044-7447-32.8.586
Phillips, S. J., R. P. Anderson, R. E. Schapire. 2006. Maximum entropy modelling of species geographic distributions. Ecological Modeling, 190 (34): 231259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Phillips, S. J., M. Dudik. 2008. Modelling of species distributions with MAXENT: new extensions and a comprehensive evaluation. Ecography, 31 (2): 161175. https://doi.org/10.1111/j.0906-7590.2008.5203.x
Schweiger, A. K., M. Schutz, P. Anderwald, M. E. Schaepman, M. Kneubuhler, R. Haller, A. C. Risch. 2015. Foraging ecology of three sympatric ungulate species Behavioural and resource maps indicate differences between chamois, ibex and red deer. Movement Ecology, 3 (6): 112. https://doi.org/10.1186/s40462-015-0033-x
Swets, J. 1988. Measuring the accuracy of diagnostic systems. Science, 240 (4857): 12851293. https://doi.org/10.1126/science.3287615
Wein, J. 2002. Predicting species occurrences: progress, problems, and prospects. In: Scott M. J. et al. (Eds). Predicting Species Occurrences. Issues of Accuracy and Scale. Island Press, Washington, 739749.


 


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