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Abstract
Microclimate ecology is attracting renewed attention because of its fundamental importance in understanding how organisms respond to climate change. Many hot issues can be investigated in desert ecosystems, including the relationship between species distribution and environmental gradients (e.g., elevation, slope, topographic convergence index, and solar insolation). Species Distribution Models (SDMs) can be used to understand these relationships. We used data acquired from the important desert plant Nitraria tangutorum Bobr. communities and desert topographic factors extracted from LiDAR (Light Detection and Ranging) data of one square kilometer in the inner Mongolia region of China to develop SDMs. We evaluated the performance of SDMs developed with a variety of both the parametric and nonparametric algorithms (Bioclimatic Modelling (BIOCLIM), Domain, Mahalanobi, Generalized Linear Model, Generalized Additive Model, Random Forest (RF), and Support Vector Machine). The area under the receiver operating characteristic curve was used to evaluate these algorithms. The SDMs developed with RF showed the best performance based on the area under curve (0.7733). We also produced the Nitraria tangutorum Bobr. distribution maps with the best SDM and suitable habitat area of the Domain model. Based on the suitability map, we conclude that Nitraria tangutorum Bobr. is more suited to southern part with 0–20 degree slopes at an elevation of approximately 1010 m. This is the first attempt of modelling the effects of topographic heterogeneity on the desert species distribution on a small scale. The presented SDMs can have important applications for predicting species distribution and will be useful for preparing conservation and management strategies for desert ecosystems on a small scale.
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1 Yunnan University, School of Ecology and Environment Science, Kunming, People’s Republic of China (GRID:grid.440773.3) (ISNI:0000 0000 9342 2456); Chinese Academy of Forestry, Research Institute of Forestry Policy and Information, Beijing, People’s Republic of China (GRID:grid.216566.0) (ISNI:0000 0001 2104 9346)
2 Chinese Academy of Forestry, Institute of Forest Resource Information Techniques, Beijing, People’s Republic of China (GRID:grid.216566.0) (ISNI:0000 0001 2104 9346)
3 Tribhuvan University, Institute of Forestry, Kritipur, Kathmandu, Nepal (GRID:grid.80817.36) (ISNI:0000 0001 2114 6728)
4 International Center for Bamboo and Rattan, Key Laboratory of National Forestry and Grassland Administration, Beijing, China (GRID:grid.459618.7) (ISNI:0000 0001 0742 5632)
5 Chinese Academy of Forestry, Research Institute of Forestry Policy and Information, Beijing, People’s Republic of China (GRID:grid.216566.0) (ISNI:0000 0001 2104 9346); The University of British Columbia, Faculty of Forestry, Vancouver, Canada (GRID:grid.17091.3e) (ISNI:0000 0001 2288 9830)