Microclimate ecology is attracting renewed attention because of its fundamental importance in understanding how organisms respond to climate change. A number of 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 the 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 kilometers in the Inner Mongolia region of China to construct the SDMs. We evaluated the performance of the SDMs constructed with both the variants of the parametric and nonpara-metric algorithms (bioclimatic Modelling (BIOCLIM), Domain, Mahalanobi, generalized linear model (GLM), generalized additive model (GAM), random forest (RF), and support vector machine (SVM)). The area under the receiver operating characteristic curve was used to evaluate the algorithms. The SDMs constructed with RF appeared to be the best based on the area under the curve (0.7733). We also generated Nitraria tangu-torum Bobr. distribution maps with the constructed SDMs and the suitable habitat area of the Domain model. Based on the suitability map, we conclude that Nitraria tangutorum Bobr. is more suited to the southern part with a slope of 0-20 degree at an elevation of approximately 1010 m. This is the first attempt of modelling the effects of topographic heterogeneity on desert species distribution on a small scale. The presented SDMs will have important applications for predicting species distribution and will be useful for preparing conservation and management plans for desert ecosystems on a small scale.