DOI: 10.5061/dryad.k0p2ngfbx
title: Spatial distribution and its limiting environmental factors of native orchid species diversity in the Beipan River Basin of Guizhou Province, China

description: Understanding the distribution of biodiversity and its determinants,
 particularly that of ecologically sensitive ones, has long been intriguing
 to the science community and will help formulate conservation strategies
 under future climate changes. To this end, we conducted extensive field
 surveys on the distribution of orchid flora in the Beipan River Basin in
 Guizhou Province, which is one of the biodiversity conservation priorities
 in China. The data we acquired, together with those published previously,
 were converted into orchid species richness for each of the 3km × 3km grid
 cells covering the study region. Redundancy analysis (RDA) and
 Geographically Weighted Regression (GWR) were then applied to determine
 which of the 30 environmental factors are potentially critical for the
 spatial distribution of orchid flora we have observed. Despite a moderate
 spatial extent, we found that the Beipan River Basin harbors about 249
 native orchid species belonging to 74 genera, equivalent to 14.5% of
 orchid flora of China. Orchid species richness in this area follows a
 descending gradient from the southeast to the northwest, 70.41% of its
 variation among grid cells can be explained by environmental factors and
 spatial variables, and spatial variables accounted for 63.90% of the
 spatial variation of orchid distribution, indicating that spatial
 variables played a dominant role in the distribution of wild orchidaceae
 species richness. In addition, the main environmental driver is the mean
 temperature of the wettest quarter. Our study provides a good example for
 revealing the main drivers of orchid distribution characteristics, and has
 a certain reference value for the development of orchid conservation
 strategies.
description: 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. <strong>Energy variables</strong>:
 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). 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
 a resolution of 30 arc seconds. Additionally, water deficit (WD) was computed as
 WD=PET－MAP. <strong>Water variables</strong>:
 We selected 10 water
 variables, including mean annual precipitation (MAP), precipitation of the wettest month
 (PWM), precipitation of the driest month (PDM), precipitation seasonality
 (PS), precipitation of the wettest quarter (PWetQ), precipitation of the
 driest quarter (PDQ), precipitation of the warmest quarter (PWarmQ),
 precipitation of the coldest quarter (PCQ), and actual evapo-transpiration
 (AET). All these nine variables, except AET , were downloaded from the
 Wordclim Database (http://www. worldclim.org) and with the resolution of
 30 arc seconds . Additionally, moisture index (MI) was computed
 as
 MI＝(MAP/PET－1)×100. <strong>Habitat heterogeneity
 variables</strong>: We selected 4 variables that quantify habitat
 heterogeneity, including elevational range (ER), the number of vegetation
 formations (NVF), ranges
 of MAT and MAP (RMAT and RMAP, 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/). <strong>Human activities
 variables</strong>: We selected 3 variables as proxies of human
 activity intensity, including human population density (HPD) in 2017 with
 the resolution of 30 arc seconds downloaded from the Global Demographic
 Dynamics Statistical Analysis Database (https://
 landscan.ornl.gov), gross
 domestic product (GDP) in 2015 with the resolution of 1km2 from the Center
 for Resources and Environmental Science and Data, Chinese Academy of
 Sciences (http://resdc.cn/), and the area of cropland (AOC) in 2015 with
 the resolution of 1km2 from the National Qinghai-Tibet Plateau Scientific
 Data Center
 (http://data.tpdc.ac.cn/zh-hans/).