China Nature Watch
China Nature Watch 2014 is an independent report on the status of China’s biodiversity conservation over the past decade, based on data accumulated by the Center for Nature and Society of Peking University, the Shanshui Conservation Center and the China Bird Watching Society Networks and on data obtained from the public domain. The report included trends of forest cover from 2000 to 2013 based on an interpretation of Global Forest Watch dataset; the distribution pattern, conservation and research on the most concerned endangered species; and the coverage of protected areas with respect to endangered species and forests. Research also revealed that information collected by citizen scientists filled significant gaps in species baseline data. Small protected areas managed by local residents could be an effective alternative to meet the conservation needs of endangered species in populated eastern China. Long-term ecological research and monitoring, and information transparency are urgently needed for biodiversity conservation in China.
We defined all plant and animal species under level-I special state protection, and avian species classified as CR and EN in the IUCN Red List as the most concerned endangered species (MCES). GAP analysis was first applied to national nature reserves (NRs) in China based on the distribution of MCES. Through historical specimen collection data, published research articles on MCES, and bird-watching records, we collected occurrence sites for 96 MCES. We simulated 46 species (with at least 5 known occurrence sites) distributions using a Maxent model and drew scatter diagrams with occurrence sites of 50 other species (with little information). Distribution coverage of each MCES by national NRs was calculated. We defined the geological grid (0.8421º) on which at least 10 MCES would occur as a hotspot for MCES, and the grid on which at least 3 mammals, or 8 birds, or 3 plants of MCES as a hotspot for mammals, birds, and plants of MCES, respectively. The coverage of these hotspots by national NRs was also calculated. We found that there are only 16 MCES with over 10% range covered by national NRs. By 2014, national NRs only covered 8.27% of MCES hotspots, and 10.9%, 1.13%, and 7.26% of mammals, birds and plants MCES hotspots, respectively. The results indicated that there is a significant gap between sufficient coverage of national NRs and MCES distribution and their hotspots in China. The coverage of national NRs for birds, especially long-distance migrants, is exceptionally low, while the greatest gap of NRs occurs in hotspots for MCES in east and south China.
By establishing a set of grading standards, conservation effectiveness was assessed rapidly for 174 of the most concerned endangered species in China, including all the Category I National Protected plant and animal species, and avian species defined as CR and EN by the IUCN Red List. Based on the data from published research articles, satellite remote sensing, field surveys and information collected by citizen scientists, the assessment was obtained by grading population dynamics, information perfections, potential habitat changes and protected area coverage of simulated distributions. Results show that the overall conservation status deteriorated during 2000-2013. The average grades of all four indexes were negative. The conservation status of 26 species improved, while 32 species maintained their status and 116 species worsened. We also investigated the trends of 9,338 published research articles in reference to 746 threatened species during 2000-2013. The numbers of studied species and research articles increased every year, but less than 1/3 of the total threatened species had been studied in any single year. Research concentrated on the star species and the species with economic value, for example, the giant panda (Ailuropoda melanoleuca) contributed to 11.33% of all research articles. On the other hand, scientific research and basic information were lacking in most other threatened species.
Knowing the status and changes in forests is essential for evaluating biodiversity dynamics and making effective conservation action plans. It is also essential knowledge for public awareness and policy. However, before Hansen and his colleagues published the Global Forest Watch (GFW) data online in 2013, there were no datasets of forest distribution and changes publicly available in China. GFW data was produced using Landsat satellite images, grants free access to the general public to study forest changes at 30 m resolutions, and spans a period from 2000 to 2013. We interpreted this dataset and compared results with other datasets, including the National Forest Inventory, ChinaCover and GlobeLand30. Results show that GFW dataset has reliable accuracy, as its forest distribution highly overlaps with GlobeLand30, which has a producer accuracy of 87%, and a user accuracy of 89% within the forest category. With similar forest definitions of tree cover >20%, the total forest area is 1,780,472 km2 in the year 2000, similar to the National Forest Inventory’s forest area of 1,749,092 km2. While other datasets show forest increases between 2,370-433,810 km2 during 2000-2010, GFW identified a net forest loss of 37,551-42,031 km2 in China. 2008 is a turning point of forest loss, as the area of annual forest loss has a rising trend before, and a declining trend afterwards. The southern provinces of Guangxi, Guangdong, Fujian, Jiangxi and Yunnan have the most active forest changes. Forest coverage from nature reserves is insufficient, as by the end of 2013, 407 national level nature reserves, occupying more than 60% of China’s total nature reserve area, covered only 5.03% of total forest area. However, nature reserves have positive outcomes, with reduced forest loss (1.39%), compared to the national average rate (3.46%). This is equivalent to reducing deforestation by 1,856 km2, but there is still an overall net loss of 1,200 km2 inside nature reserves.
Biodiversity Committee, CAS
Botanical Society of China
Institute of Botany, CAS
Institute of Zoology, CAS
Institute of Microbiology, CAS
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