Using MaxEnt Model to Predict Suitable Habitat Changes for Key Protected Species in Koshi Basin, Central Himalayas

  • 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    2. University of the Chinese Academy of Sciences, Beijing 100049, China

Received date: 2016-11-05

  Online published: 2017-01-20

Supported by

National Natural Science Foundation of China (41371120); Tibet Key Science and Technology Program (2015XZ01G72); The Australian Government-funded Koshi Basin Programme at the International Centre for Integrated Mountain Development (ICIMOD).


Because of its landscape heterogeneity, Koshi Basin (KB) is home to one of the world’s most abundant, diverse group of species. Habitat change evaluations for key protected species are very important for biodiversity protection in this region. Based on current and future world climate and land cover data, MaxEnt model was used to simulate potential habitat changes for key protected species. The results shows that the overall accuracy of the model is high (AUC > 0.9), suggesting that the MaxEnt-derived distributions are a close approximation of real-world distribution probabilities. The valley around Chentang Town and Dram Town in China, and Lamabagar and the northern part of Landtang National Park in Nepal are the most important regions for the protection of the habitat in KB. The habitat area of Grus nigricollis, Panax pseudoginseng, and Presbytis entellus is expected to decrease in future climate and land cover scenarios. More focus should be placed on protecting forests and wetlands since these are the main habitats for these species.

Cite this article

LIU Linshan, ZHAO Zhilong, ZHANG Yili, WU Xue . Using MaxEnt Model to Predict Suitable Habitat Changes for Key Protected Species in Koshi Basin, Central Himalayas[J]. Journal of Resources and Ecology, 2017 , 8(1) : 77 -87 . DOI: 10.5814/j.issn.1674-764x.2017.01.010


[1] Deng W., Zhang Y. 2014. Resources, Environment and Development of Koshi River Basin. Chengdu: Sichuan Science and Technology Press.
[2] Dong H., Lu G., Zhong X. et al. 2016. Winter diet and food selec?tion of the Black-necked Crane Grus nigricollis in Dashanbao, Yunnan, China. PEERJ, 4(e1968).
[3] Elith J., H. Graham C., P. Anderson R. et al. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2): 129–151.
[4] Hijmans R.J., Cameron S.E., Parra J.L. et al. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15): 1965–1978.
[5] Hu Z., Zhang Y., Yu H. 2015. Simulation of Stipa purpurea distribution pattern on Tibetan Plateau based on MaxEnt model and GIS. Chinese Journal of Applied Ecology, 26(02): 505–511. (in Chinese)
[6] Jiao S., Zeng Q., Sun G. et al. 2016. Improving conservation of cranes by modeling potential wintering distributions in china. Journal of Res-ources and Ecology, 7(1): 44–50.
[7] Khanum R., Mumtaz A.S., Kumar S. 2013. Predicting impacts of climate change on medicinal asclepiads of Pakistan using Maxent modeling. Acta Oecologica-International Journal of Ecology, 49: 23–31.
[8] Kong D., Yang X., Liu Q. et al. 2011. Winter habitat selection by the vulnerable black-necked crane Grus nigricollis in Yunnan, China: Implications for determining effective conservation actions. Oryx, 45(2): 258– 264.
[9] Kumar P. 2012. Assessment of impact of climate change on Rhododend?rons in Sikkim Himalayas using Maxent modelling: limitations and challenges. Biodiversity and Conservation, 21(5SI): 1251–1266.
[10] Namgay R., Wangchuk S. 2016. Habitat preferences and conservation threats to Black-necked Cranes wintering in Bhutan. SPRINGERPLUS, 5(228). doi:10.1186/s40064-016-1923-0
[11] Newbold T., Hudson L.N., Hill S.L.L. et al. 2015. Global effects of land use on local terrestrial biodiversity. Nature, 520(7545): 45.
[12] Paudel B., Zhang Y., Li S. et al. 2016. Review of studies on land use and land cover change in Nepal. Journal of Mountain Science, 13(4): 643–660.
[13] Phillips S.J., Dudik M. 2008. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography, 31(2): 161–175.
[14] Remya K., Ramachandran A., Jayakumar S. 2015. Predicting the current and future suitable habitat distribution of Myristica dactyloides Gaertn. Using MaxEnt model in the Eastern Ghats, India. Ecological Engineering, 82: 184–188.
[15] Sarania B., Devi A., Kumar A. et al. 2016. Predictive distribution modeling and population status of the endangered Macaca munzala in Arunachal Pradesh, India. American Journal of Primatology: doi: 10.1002/ajp.22592
[16] Song H., Zhang Y., Gao H. et al. 2014. Plateau wetlands, an indispensable habitat for the Black-necked Crane (Grus nigricollis)-a review. Wetlands, 34(4): 629–639.
[17] Svenning J.C., Skov F. 2005. The relative roles of environment and history as controls of tree species composition and richness in Europe. Journal of Biogeography, 32(6): 1019–1033.
[18] Wu Q., Wang L., Zhu R. et al. 2016. Nesting habitat suitability analysis of red?crowned crane in Zhalong Nature Reserve based on MAXENT modeling. Acta Ecologica Sinica, 36(12): 1–7. (in Chinese)
[19] Xu Z., Peng H., Peng S. 2015. The development and evaluation of species distribution models. Acta Ecologica Sinica, 35(02): 557–567. (in Chinese)
[20] Xu Z., Zhao C., Feng Z. 2012. Species distribution models to estimate the deforested area of picea crassifolia in arid region recently protected: Qilian Mts. National Natural Reserve (China). Polish Journal of Ecology, 60(3): 515–524.
[21] Yang X., Kushwaha S.P.S., Saran S. et al. 2013. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in the lesser Himalayan foothills. Ecological Engineering, 51: 83–87.
[22] Yu H., Zhang Y., Gao J. et al. 2014. Visualizing patterns of genetic landscapes and species distribution of Taxus wallichiana (Taxaceae), based on GIS and ecological niche models. Journal of Resources and Ecology, 5(03): 193–202.
[23] Yu H., Zhang Y., Liu L. et al. 2015. Combining the least cost path method with population genetic data and species distribution models to identify landscape connectivity during the late Quaternary in Himalayan hemlock. Ecology and Evolution, 5(24): 5781–5791.
[24] Yuan C., Meng G., Fang X. et al. 2012. Age structure and spatial distribution of the rare and endangered plant Alcimandra cathcartii. Acta Ecologica Sinica, (12): 3866–3872. (in Chinese)
[25] Zhang J., Zhang Y., Liu L. et al. 2011. Predicting potential distribution of Tibetan spruce (Picea smithiana) in Qomolangma (Mount Everest) National Nature Preserve using maximum entropy niche-based model. Chinese Geographical Science, 21(4): 417–426.
[26] Zhang Y., Liu L., Wang Z., et al. 2009. HKKH Project Final Report (Internal). Beijing: Institute of Geographic Sciences and Natural Resources Research, CAS
[27] Zhang Y., Gao J., Liu L. et al. 2013. NDVI-based vegetation changes and their responses to climate change from 1982 to 2011: A case study in the Koshi River Basin in the middle Himalayas. Global and Planetary Change, 108(0): 139–148.
[28] Zhang Y., Yao Z., Liu L., et al. 2016. Final report on study on land use and land cover change and erosion in the Koshi River Basin. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, China.
[29] Zhao Z., Wu X., Zhang Y., et al. 2017. Assessment of changes in the value of ecosystem services in the Koshi River Basin, central high Himalayas based on land cover changes and the CA-Markov model, Journal of Resources and Ecology, 8(1): 66-75
[30] Zhu G., Liu G., Bu W. et al. 2013. Ecological niche modeling and its applications in biodiversity conservation. Biodiversity Science, 21(1): 90–98. (in Chinese)