, Volume 21, Issue 4, pp 417-426
Date: 26 Jul 2011

Predicting potential distribution of Tibetan spruce (Picea smithiana) in Qomolangma (Mount Everest) National Nature Preserve using maximum entropy niche-based model

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Abstract

Tibetan spruce (Picea smithiana) is an endemic species of the Himalayas, and it distributes only in a restricted area with very low number. To address the lack of detailed distributional information, we used maximum entropy (Maxent) niche-based model to predict the species’ potential distribution from limited occurrence-only records. The location data of P. smithiana, relative bioclimatic variables, vegetation data, digital elevation model (DEM), and the derived data were analyzed in Maxent. The receiver operating characteristic (ROC) curve was applied to assess the prediction accuracy. The Maxent jackknife test was performed to quantify the training gains from data layers and the response of P. smithiana distribution to four typical environmental variables was analyzed. Results show that the model performs well at the regional scale. There is a potential for continued expansion of P. smithiana population numbers and distribution in China. P. smithiana potentially distributes in the lower reaches of Gyirong Zangbo and Poiqu rivers in Gyirong and Nyalam counties in Qomolangma (Mount Everest) National Nature Preserve (QNNP), China. The species prefers warm temperate climate in mountain area and mainly distributes in needle-leaved evergreen closed to open forest and mixed forest along the river valley at relatively low altitudes of about 2000–3000 m. Model simulations suggest that distribution patterns of rare species with few species numbers can be well predicted by Maxent.

Foundation item: Under the auspices of National Basic Research Program of China (No. 2010CB951704), Institutional Consolidation for Coordinated and Integrated Monitoring of Natural Resources towards Sustainable Development and Environmental Conservation in the Hindu Kush-Karakoram-Himalaya Mountain Complex (No. 76444-000), External Cooperation Program of Chinese Academy of Sciences (No. GJHZ0954)