abstract |
Marmots form a part of the diet of some endangered species such as the snow leopard (Panthera uncia), therefore the knowledge on their distribution and habitat preferences are crucial to the interest of conservation and management of carnivores at high altitudes. Considering this, within a Snow Leopard Project run by Biosphere Expeditions and NABU (Kyrgyzstan), surveys were carried out in summer field seasons of 2014–2019 to assess the distribution of the long-tailed marmots (Marmota caudata) in an area centred around the Karakol Mountain Pass (polygon centroid 74.83°E, 42.37°N) in the Kyrgyz Ala-Too Range. The presence of occupied marmot burrows was recorded using the location (cell) given by a grid, the code of which was displayed in a GPS. Using cells allows examination of data at a wider scale, so information is collected from different cells that are spread from each other, avoiding data autocorrelation. Environmental factors that may affect the spatial distribution of burrow systems were considered: land surface temperature (LST) in winter and summer, summer normalized difference vegetation index (NDVI), a Digital Elevation Model (DEM), and soil type data. The relationship between environmental factors and burrow records was analysed using ecological niche models (Maxent) to predict the distributions of marmot burrows. The models performed well with average test AUC values of 0.939. The contribution orders of the variables in the models were summer NDVI and DEM, winter LST, summer LST, and soil type. The distribution of the suitable areas was largely (up to 38 % permutation importance) affected by summer NDVI. NDVI is an indicator of the feeding conditions of marmots and most of the records were distributed in areas with NDVI in summer ranging from 0.5 to 0.7. According to the prediction maps, suitable marmot habitat (>0.5 predicted probabilities of occurrence) can occupy up to 40 % of study area. These maps are used to direct sampling efforts to areas on the landscape that tend to have greater predicted probabilities of occurrence and accomplish ground validation of snow leopard habitat quality.
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references |
Ahmed, T., M. Shoeb, P. Chandan, A. Khan. 2016. On the status of the Long-tailed Marmot Marmota caudata (Mammalia: Rodentia: Sciuridae) in Kargil, Ladakh (Indian Trans-Himalaya). Journal of Threatened Taxa, 8 (9), 9171-9176. https://doi.org/10.11609/jott.2731.8.9.9171-9176
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): 2271–2312. 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): 189–199. 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): 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
Haque U., M. Hashizume, G. E. Glass, A. M. Dewan, H. J. Overgaard, T. Yamamoto. 2010.The role of climate variability in the spread of malaria in Bangladeshi highlands. PLoS One. 5 (12): e14341. https://doi.org/10.1371/journal.pone.0014341
Hengl T., J. M. de Jesus, R. A. MacMillan, N. H. Batjes, G. B. M. Heuvelink, et al. 2014. SoilGrids1km — Global Soil Information Based on Automated Mapping. PLoS ONE, 9 (8): e105992. https://doi.org/10.1371/journal.pone.0105992
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): 1965–1978. https://doi.org/10.1002/joc.1276
Hutchinson, G. E. 1957. Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology, No. 22: 415–427. https://doi.org/10.1101/SQB.1957.022.01.039
Lu L., Z. P. Ren, Y. J. Yue, X. T. Yu, S. Lu, G. C. Li, H.L. Li, J. C. Wei, J. L. Liu, Y. Mu, [et al.]. 2016. Niche modeling redictions of the potential distribution of Marmota himalayana, the host animal of plague in Yushu County of Qinghai. BMC Public Health, 16: 183. https://doi.org/10.1186/s12889-016-2697-6
Mazzolli, M., M. Hammer. 2013. Sampling and analysis of data for large terrestrial mammals during short-term volunteer expeditions. Biosphere Expeditions, 1–23. https://doi.org/10.13140/RG.2.2.20137.95847
Phillips, S. J., M. Dudik. 2008. Modelling of species distributions with MAXENT: new extensions and a comprehensive evaluation. Ecography, 31 (2): 161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x
Phillips, S. J., R. P. Anderson, R. E. Schapire. 2006. Maximum entropy modelling of species geographic distributions. Ecological Modeling, 190 (3–4): 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Elith, J., C. H. Graham, R. P. Anderson, M. Dudik, S. Ferrier, A. Guisan, et al. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29 (2): 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
Swets, J. 1988. Measuring the accuracy of diagnostic systems. Science, 240 (4857): 1285–1293. https://doi.org/10.1126/science.3287615 |
