# Precipitation Dominates the Distribution of Species Richness on the Kunlun-Pamir Plateau
## 1 Introduction  
The high value species richness means a rich regional biodiversity. The research of species richness is the basis for constructing evolutionary, ecological models and conservation strategies [1, 2]. The global species richness has decreased during the last decades [3], and this trend will continue [4]. Several theoretical hypotheses, such as the productivity hypothesis [5, 6], water-energy dynamic hypothesis [7, 8], ambient energy hypothesis [9, 10], cold tolerance hypothesis [8, 11] and the metabolic theory of ecology [12, 13, 14], were used to explain the distribution patterns of species richness. Among those hypotheses, theproductivity hypothesis and the water-energy dynamic hypothesis were the most widely used. The productivity hypothesis suggests that the higher the productivity, the higher the species richness [5; 6; 15; 16]. The main idea of the water-energy dynamics hypothesis (sometimes referred to as the moisture hypothesis) showed that the large-scale patterns of species richness are determined by a combination of water and energy [7]. The energy in this hypothesis is usually expressed by the potential evapotranspiration, temperature or moisture. The water is generally referred to as liquid water and is commonly expressed as the annual rainfall of a region [7; 17]. Some studies have applied climate data to present energy and moisture variables [18; 19]. Satellite observations provide a rich, long-time series of vegetation productivity products. They can be used to test the productivity hypothesis [20; 21]. The Moderate Resolution Imaging Spectroradiometer (MODIS) data collected by NASA's Terra and Aqua satellites offered various products. This could be utilized for species richness assessments at different scales and include the vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)), Leaf Area Index (LAI) and Fraction of absorbed Photosynthetically Active Radiation (FPAR), as well as Gross Primary Productivity (GPP). The most suitable productivity products vary from region to region. The NDVI and EVI belong to the same MODIS vegetation dataset. The NDVI is the best indicator of vegetation growth status and vegetation cover; however, overfitting exists in densely vegetated areas due to nonlinear transformations. The EVI improves on NDVI saturation. The FPAR and LAI are based on reflectance values of up to seven MODIS spectral bands, while the NDVI and EVI are based on two and three bands, respectively, so the FPAR and LAI provide a closer proxy for vegetation productivity than the NDVI and EVI. GPP products are mainly calculated using FPAR, light and effective radiation (PAR), as well as other surface meteorological data from remote sensing inversion using a light energy utilization model, which is good information for monitoring vegetation growth. Five MODIS products allow a multifaceted and more comprehensive analysis of their relationship with species richness [22]. The Dynamic Habitat Indices (DHIs) based on the productivity hypothesis theory can be calculated from the MODIS products to demonstrate the distribution of species richness [23; 24]. It has been proven that these correlate well with the species richness [22; 25; 26]. The DHIs comprise three indicators: the cumulative annual productivity (CumDHI), minimum productivity (MinDHI) and productivity variation (VarDHI). A good correlation between these indices and the species richness was confirmed on the local and global scales [22; 26; 27; 28; 29; 30; 31; 32]. For example, the biodiversity in China has been detected by the FPAR-derived DHIs [31]. In the United States, the bird species richness correlated with the FPAR-derived DHIs on grasslands, while it denoted a better correlation with the LAI-derived DHIs on woodlands [22]. In Russia, FPAR-derived DHIs combined with climatic variables seemed promising to predict the abundance of large ungulates such as moose [32].