Map of the proportion of threatened endemic species per country in relation with environmental and socioeconomic drivers

This dataset is a shapefile representing the proportion of threatened endemic species (both plants and animals) in 247 countries along with associated environmental and socioeconomic drivers. The geographic coordinate system is World Geodetic System 1984 (EPSG: 4326). Information on a total of 65,125 endemic species including 27,294 globally threatened endemic species (55% threatened plant species, 45% threatened animal species) was extracted from the IUCN Red List. The categories of threatened species used in the analyses included vulnerable (VU), endangered (EN), critically endangered (CR), extinct in the wild (EW) and globally extinct (EX). We calculated the proportion of globally threatened endemic species among the total number of assessed endemic species per country (Chamberlain et al., 2020). Associated environmental socioeconomic regional correlates included: 1) Cropland: The proportion of each country covered by crops (including food, fibre and fodder crops and pasture grasses) was determined based on a FAO global map with a resolution of 5 arc-minutes (von Velthuizen et al., 2007); 2) HANPP: The proportion of net primary production appropriated by humans (HANPP) by harvesting or burning biomass and by converting natural ecosystems to managed lands with lower productivity was derived for the year 2010 from Krausmann et al. (2013); 3) Delta HANPP: We also computed the increase in HANPP over the period 1962-2010 (Krausmann et al., 2013); 4) per area GDP: The per area gross domestic product (GDP, in international $) was obtained by calculating the median value over each country of all 5 arcmin cells of a recently gridded GDP dataset (Kummu et al., 2018); 5) Human Footprint (HFP): The global terrestrial human footprint (HFP) is an index integrating the influence of built environments, population density, electric infrastructure, croplands, pasture lands, roads, railways, and navigable waterways on the environment based on remotely-sensed and bottom-up survey information (Venter et al., 2016). We extracted from a 1 km resolution HFP map the median value over each country in 2009; 6) Delta HFP: We also calculated the increase in median HFP over the period 1993-2009 (Venter et al., 2016); 7) Invasive alien plants: The richness of invasive alien vascular plant species recorded in each country was compiled by Essl et al. (2019); 8) Invasive alien animals: The richness of invasive alien animal species was derived from the Global Register of Introduced and Invasive Species database (http://griis.org/ accessed on 27-6-2018); 9) Delta temperature: Based on decadal climate maps produced by the IPCC over the last century with a 0.5° resolution, we calculated the median of the change in annual mean temperature (in °C) between 1901-1910 and 1981-1990 (Mitchell Jones, 2005); 10) Delta rainfall: The same for annual precipitation (in mm); 11) Velocity temperature: We also calculated the median velocity of climate change based on the formula from Hamann et al. (2015) to evaluate the distance (in °) over which a species must migrate over the surface of the earth to maintain constant temperature conditions; 12) Velocity rainfall: The same for precipitation; 13) Roadless areas: The median area of a roadless fragment (in km²) was calculated from the global map of roadless areas published by Ibisch et al. (2016); 14) Wilderness areas: The proportion of wildlands (categories ‘wild woodlands’ and ‘wild treeless and barren lands’) was calculated from the anthropogenic biome map of Ellis et al. (2010); 15) Protected areas: The proportion of protected areas was estimated from the IUCN’s shapefile of World Database on Protected Areas (https://www.iucn.org/theme/protected-areas/our-work/world-database-protected-areas); 16) Conservation spending: The mean annual conservation spending of each country (in international $) was taken from Waldron et al. (2017) to quantify investment to mitigate biodiversity loss; 17) Completeness of biodiversity information: We used data on the estimated percentage completeness of species records in GBIF, as assessed through comparison with independent estimates of native richness. Inventory effort indices available for vertebrates (Meyer et al., 2015) and vascular plants (Meyer et al., 2016) were merged into a single metric based upon an average weighted by estimated native species richness.

本数据集为一组形状文件（shapefile），涵盖全球247个国家受威胁特有物种（包含植物与动物）的占比，以及与之相关的环境与社会经济驱动因子。其地理坐标系采用世界大地测量系统1984（World Geodetic System 1984，EPSG: 4326）。研究共从IUCN红色名录（IUCN Red List）中提取了共计65125种特有物种的相关信息，其中27294种为全球受威胁特有物种（受威胁植物物种占比55%，受威胁动物物种占比45%）。本分析所使用的受威胁物种等级包括易危（VU）、濒危（EN）、极危（CR）、野外灭绝（EW）以及全球灭绝（EX）。我们计算了各国受评估特有物种总数中，全球受威胁特有物种的占比（Chamberlain等，2020）。与之关联的环境社会经济区域相关因子包括：1. 耕地（Cropland）：各国耕地（涵盖粮食作物、纤维作物、饲料作物与牧草地）的占比，基于分辨率为5弧分的联合国粮食及农业组织（Food and Agriculture Organization of the United Nations, FAO）全球地图确定（von Velthuizen等，2007）；2. 人类占用净初级生产量（Human Appropriation of Net Primary Production, HANPP）：通过收获或焚烧生物质、将自然生态系统转换为生产力更低的管理用地所占用的净初级生产比例，数据取自2010年的Krausmann等，2013研究；3. HANPP变化量（Delta HANPP）：我们同时计算了1962-2010年间HANPP的增幅（Krausmann等，2013）；4. 单位面积国内生产总值（Per Area GDP）：单位面积国内生产总值（GDP，以国际元计）通过计算近期发布的网格化GDP数据集（Kummu等，2018）中每个国家所有5弧分栅格的中位数得到；5. 人类足迹指数（Human Footprint, HFP）：全球陆地人类足迹指数是一个整合了建成环境、人口密度、电力基础设施、耕地、牧草地、道路、铁路与通航水道对环境影响的综合指数，数据基于遥感与自下而上的调查信息（Venter等，2016）。我们从分辨率为1km的HFP地图中提取了2009年各国的中位数数值；6. HFP变化量（Delta HFP）：我们同时计算了1993-2009年间HFP中位数的增幅（Venter等，2016）；7. 外来入侵植物：各国记录的入侵外来维管植物物种丰富度数据由Essl等，2019汇编完成；8. 外来入侵动物：入侵外来动物物种的丰富度数据取自全球引入与入侵物种名录数据库（Global Register of Introduced and Invasive Species, GRIIS，http://griis.org，2018年6月27日访问）；9. 温度变化量（Delta temperature）：基于政府间气候变化专门委员会（Intergovernmental Panel on Climate Change, IPCC）过去一个世纪发布的分辨率为0.5°的十年期气候地图，我们计算了1901-1910年与1981-1990年间年平均气温的变化中位数（单位为°C）（Mitchell Jones, 2005）；10. 降雨量变化量（Delta rainfall）：年降水量（单位为mm）的变化情况同理；11. 温度变化速率（Velocity temperature）：我们同时基于Hamann等，2015提出的公式计算了气候变化的中位数速率，用以评估物种为维持恒定温度条件所需在地球表面迁移的距离（单位为°）；12. 降雨变化速率（Velocity rainfall）：降水量的变化速率同理；13. 无道路区域：无道路斑块的中位数面积（单位为km²）由Ibisch等，2016发布的全球无道路区域地图计算得到；14. 荒野区域：野生林地与野生无林裸地类别的野生土地占比，由Ellis等，2010的人为生物群系地图计算得到；15. 保护区：保护区的占比由IUCN的世界保护区数据库（World Database on Protected Areas）形状文件估算得到（https://www.iucn.org/theme/protected-areas/our-work/world-database-protected-areas）；16. 保护支出：各国的年均保护支出（单位为国际元）取自Waldron等，2017的数据，用以量化缓解生物多样性丧失的投入；17. 生物多样性信息完整性：我们使用了全球生物多样性信息设施（Global Biodiversity Information Facility, GBIF）中物种记录的估计完整百分比数据，该数据通过与本地物种丰富度的独立估算值对比得到。我们将脊椎动物（Meyer等，2015）与维管植物（Meyer等，2016）的调查努力指数，按照本地物种丰富度的加权平均值合并为单一指标。