DOI: 10.5061/dryad.xd2547dd3
title: Conservation status of a plant family with high endemism, the Primulaceae, in China

description: Primroses, ca 1000 species from the family Primulaceae, are mostly
 distributed in high mountains and attract people's attention
 with their high ornamental value. However, they are increasingly
 threatened by human activities and climate change in recent
 decades. China harbors more than half of global primrose species, most of
 which are endemic. To strengthen studies on their conservation, we
 established a distribution database for primroses in China,
 including 12 genera and 535 speceies. Based on this database, we explored
 the geographic pattern of
 the richness of primrose species in relation to
 environmental factors and identified conservation priority areas. The
 results showed that the primrose richness was highest in
 the mountain areas from the eastern Himalayas to the
 Hengduan Mountains, with habitat heterogeneity and temperature seasonality
 together shaping that pattern. Furthermore, the
 richnesses of endemic and threatened primroses were strongly
 affected by the long-term climate stability since the Last Glacial
 Maximum. On average, national nature reserves (NNRs) covered 29.6% of
 the distribution areas of primroses. By analyzing less
 protected species, we proposed that nature reserves need to be established
 in several conservation gaps mainly located in Southwest China to protect
 the primrose species in China.
description: <strong>Distribution
 data of primroses in China</strong> Data on the distribution of
 primrose plants in China were obtained from six sources: 1) national,
 provincial and local floras in China, 2) occurrence of endemic species
 derived from the <em>Diversity and Geographical Distribution of
 Endemic Seed Plants in China</em>, 3) scientific articles related to
 field investigations for primrose species, 4) checklists of nature
 reserves, 5) specimen records available from the National Specimen
 Information Infrastructure (NSII, http://nsii.org.cn), 6) records from the
 Global Biodiversity Information Facility (GBIF, https://www.gbif.org/).
 All species names were standardized using the <em>Flora of
 China</em> (Hu and Kelso, 1996) and the <em>Catalogue of
 Life</em> (http://www.catalogueoflife.org/). Among all these sources,
 distribution records from the first four resources are county-level
 occurrence, which usually leads to overestimating the distribution range
 of species. Therefore, we also collected information on the occurrence
 elevations and habitat types of each species. By overlapping the
 county-level records with a digital elevation model (DEM) (with a
 resolution of 30 m from http://reverb.echo.nasa.gov/reverb /redirect/wist)
 and a vegetation map of China (1: 1,000,000) (Editorial Committee of
 Vegetation Map of China, 2007), we selected the grids of suitable habitats
 of the corresponding species in its occurring county as the refined
 distribution areas. Then, these distribution data were uniformly converted
 into grids with a resolution of 25 x 25km. <strong>Environmental
 factors</strong> Contemporary climate data were
 obtained from the WorldClim website (http://www.worldclim.org/) with a
 spatial resolution of 30 arc-second. The climatic variables included mean
 annual temperature (MAT, °C), mean temperature of warmest quarter (MTWQ,
 °C), mean temperature of coldest quarter (MTCQ, °C), mean annual
 precipitation (MAP, mm), temperature seasonality (TSN, unitless) and
 precipitation seasonality (PSN, unitless). In addition, we calculated the
 potential evapotranspiration (PET, mm), actual evapotranspiration (AET,
 mm), moisture index (Im, unitless) and water deficit (WD, defined as the
 difference between PET and AET) using mean monthly temperature and
 precipitation (Thorthwaite and Hare, 1955; Francis and Currie,
 2003). Four
 variables were used to represent habitat heterogeneity: range of altitude
 (RALT, m), number of vegetation types (VEG, unitless), range of MAT (RMAT,
 °C) and MAP (RMAP, mm) within each 25 km grid. RALT is the difference
 between the highest and lowest elevations in each grid, based on the
 abovementioned DEM production. VEG was obtained by overlapping the
 vegetation map of China (1:1,000,000) with the 25 km grid system. RMAT (or
 RMAP) represents climatic heterogeneity, reflected as the difference
 between maximum and minimum MAT (or MAP) within each grid. Long-term anomalies in temperature
 (TANO) and precipitation (PANO) were represented by the absolute values of
 the difference in MAT (and MAP) between the Last Glacial Maximum (LGM) and
 the present. The climate data in LGM were also derived from the WorldClim
 website (Watanabe et al., 2011).