Database of occurrence data This database contains the species richness and 30 environmental factors of orchids in 3499 grid cells of the study area. 1. Species richness The study area was divided into 3 km×3 km grid cells, and the grid cells at the boundaries with more than 1/3 of its area falling out of the study area were excluded, after which 3,499 grid cells retained. Values of energy, water, habitat heterogeneity, and human activity variables for each grid cell was computed as the mean value characterizing the spatial area covered by that grid cell. Then, we also computed orchid species richness for each grid cell. Because it is nearly impossible, within our capacity, to carry out extensive field survey in each grid cell, this was done by two steps. First, inventory of orchid species, including the elevational range of each species, was made for each county within the study area, based on our field-collected data and those available in floras and published literatures. Then, the orchid species richness of each grid cell was computed as the number of orchid species present in the county that the grid cell falls in, and filtered by elevational range of each species. Note that when a grid cell falls at the adjoining area of > 1 counties, it is assumed to fall within the county which contains the largest proportion of its area. 2. Environmental variables To identify environmental variables that can explain the observed spatial distribution of native orchids in the Beipan River Basin, we first downloaded a total of 30 environmental factors categorized into four types: energy, water, habitat heterogeneity, and human activities, covering most of those known to have large impacts on the distribution of terrestrial plant diversity. (1) Energy variables Altogether 13 energy variables were selected, including mean annual temperature (MAT,℃), mean diurnal range (MDR,℃), isothermality (ISO), temperature seasonality (TS,℃), maximal temperature of the warmest month (MTWM,℃), minimal temperature of the coldest month (MTCM,℃), temperature annual range (TAR,℃), mean temperature of the wettest quarter (MTWetQ,℃), mean temperature of the driest quarter (MTDQ,℃), mean temperature of the coldest quarter (MTCQ,℃), mean temperature of the warmest quarter (MTWarmQ,℃), and potential evapo-transpiration (PET,mm). All these twelve variables, except PET (from International Agricultural Database, https://cgiarcsi. community), were downloaded from the Worldclim Database (http://www.worldclim.org) and with the resolution of 30 arc seconds. Additionally, water deficit (WD,mm) was computed as WD=PET-MAP. (2) Water variables We selected 10 water variables, including mean annual precipitation (MAP,mm), precipitation of the wettest month (PWM,mm), precipitation of the driest month (PDM,mm), precipitation seasonality (PS,mm), precipitation of the wettest quarter (PWetQ,mm), precipitation of the driest quarter (PDQ,mm), precipitation of the warmest quarter (PWarmQ,mm), precipitation of the coldest quarter (PCQ,mm), and actual evapo-transpiration (AET,mm). All these nine variables, except AET (Trabucco & Zomer, 2019), were downloaded from the Wordclim Database (http://www. worldclim.org) and with the resolution of 30 arc seconds. Additionally, moisture indes (MI) was computed as MI=(MAP/PET-1)×100. (3) Habitat heterogeneity variables We selected 4 variables that quantify habitat heterogeneity, including elevational range (ER,m), the number of vegetation formations (NVF) (Ran et al., 2012), ranges of MAT and MAP (RMAT (℃) and RMAP (mm), respectively) for each grid. ER was extracted from a 12.5 m digital elevation model (DEM) (https://search. asf.alaska.edu/), and NVF with the resolution of 1km2 from the Center for Resources and Environmental Science and Data, Chinese Academy of Sciences (http://resdc.cn/). (4) Human activities variables We selected 3 virables as proxies of human activity intensity, including human population density (HPD, people/km²) in 2017 with the resolution of 30 arc seconds downloaded from the Global Demographic Dynamics Statistical Analysis Database (https:// landscan.ornl.gov) (Rose et al., 2018), gross domestic product (GDP,10K yuan/km²) in 2015 with the resolution of 1km² from the Center for Resources and Environmental Science and Data, Chinese Academy of Sciences (http://resdc.cn/) (Xu, 2017), and the area of cropland (AOC,m²) in 2015 with the resolution of 1km² from the National Qinghai-Tibet Plateau Scientific Data Center (http://data.tpdc.ac.cn/zh-hans/) .