Description:    Gross primary productivity (GPP) is a major component of carbon exchange between the atmosphere and terrestrial ecosystems and a key component of the terrestrial carbon cycle. Because of the large spatial heterogeneity and temporal dynamics of ecosystems, it is a challenge to estimate GPP accurately at global or regional scales. The 8-day MODerate resolution Imaging Spectroradiometer (MODIS) GPP product provides a near real time estimate of global GPP. However, previous studies indicated that MODIS GPP has large uncertainties, partly caused by biases in parameterization and forcing data. In this study, MODIS GPP was validated using GPP derived from the eddy covariance flux measurements at five typical forest sites in East Asia. The validation indicated that MODIS GPP was seriously underestimated in these forest ecosystems of East Asia, especially at northern sites. With observed meteorological data, fraction of photosynthetically active radiation absorbed by the plant canopy (fPAR) calculated using smoothed MODIS leaf area index, and optimized maximum light use efficiency ( ε max ) to force the MOD17 algorithm, the agreement between predicted GPP and tower-based GPP was significantly improved. The errors of MODIS GPP in these forest ecosystems of East Asia were mainly caused by uncertainties in ε max , followed by those in fPAR and meteorological data. The separation of canopy into sunlit and shaded leaves, for which GPP is individually calculated, can improve GPP simulation significantly. Content Type Journal Article Category Special Feature: Original Article Pages 1-10 DOI 10.1007/s10310-012-0369-7 Authors Mingzhu He, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, 901 Mengminwei Building, 22 Hankou Road, Nanjing, 210093 China Yanlian Zhou, School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, 210093 China Weimin Ju, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, 901 Mengminwei Building, 22 Hankou Road, Nanjing, 210093 China Jingming Chen, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, 901 Mengminwei Building, 22 Hankou Road, Nanjing, 210093 China Li Zhang, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China Shaoqiang Wang, Qianyanzhou Ecological Experimental Station, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China Nobuko Saigusa, Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, 305-8506 Japan Ryuichi Hirata, Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, 305-8506 Japan Shohei Murayama, Research Institute for Environmental Management Technology, National Institute of Advanced Industrial Science and Technology (AIST), AIST Tsukuba West, 16-1 Onogawa, Tsukuba, 305-8569 Japan Yibo Liu, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, 901 Mengminwei Building, 22 Hankou Road, Nanjing, 210093 China Journal Journal of Forest Research Online ISSN 1610-7403 Print ISSN 1341-6979

Description:    Soil carbon stocks and sequestration have been given a lot of attention recently in the study of terrestrial ecosystems and global climate change. This review focuses on the progress made on the estimation of the soil carbon stocks of China, and the characterization of carbon dynamics of croplands with regard to climate change, and addresses issues on the mineralization of soil organic carbon in relation to greenhouse gas emissions. By integrating existing research data, China’s total soil organic carbon (SOC) stock is estimated to be 90 Pg and its inorganic carbon (SIC) stock as 60 Pg, with SOC sequestration rates in the range of 20–25 Tg/a for the last two decades. An estimation of the biophysical potential of SOC sequestration has been generally agreed as being 2 Pg over the long term, of which only 1/3 could be attainable using contemporary agricultural technologies in all of China’s croplands. Thus, it is critical to enhance SOC sequestration and mitigate climate change to improve agricultural and land use management in China. There have been many instances where SOC accumulation may not induce an increased amount of decomposition under a warming scenario but instead favor improved cropland productivity and ecosystem functioning. Furthermore, unchanged or even decreased net global warming potential (GWP) from croplands with enhanced SOC has been reported by a number of case studies using life cycle analysis. Future studies on soil carbon stocks and the sequestration potential of China are expected to focus on: (1) Carbon stocks and the sequestration capacity of the earths’ surface systems at scales ranging from the plot to the watershed and (2) multiple interface processes and the synergies between carbon sequestration and ecosystem productivity and ecosystem functioning at scales from the molecular level to agro-ecosystems. Soil carbon science in China faces new challenges and opportunities to undertake integrated research applicable to many areas. Content Type Journal Article Category Review Pages 1-11 DOI 10.1007/s11434-011-4693-7 Authors JuFeng Zheng, Institute for Resource, Ecosystem and Environment of Agriculture, and Research Center of Agriculture and Climate Change, Nanjing Agricultural University, Nanjing, 210095 China Kun Cheng, Institute for Resource, Ecosystem and Environment of Agriculture, and Research Center of Agriculture and Climate Change, Nanjing Agricultural University, Nanjing, 210095 China GenXing Pan, Institute for Resource, Ecosystem and Environment of Agriculture, and Research Center of Agriculture and Climate Change, Nanjing Agricultural University, Nanjing, 210095 China Pete Smith, Institute of Biological and Environmental Sciences, School of Biological Sciences, University of Aberdeen, 23 St Machar Drive, Aberdeen, AB24 3UU UK LianQing Li, Institute for Resource, Ecosystem and Environment of Agriculture, and Research Center of Agriculture and Climate Change, Nanjing Agricultural University, Nanjing, 210095 China XuHui Zhang, Institute for Resource, Ecosystem and Environment of Agriculture, and Research Center of Agriculture and Climate Change, Nanjing Agricultural University, Nanjing, 210095 China JinWei Zheng, Institute for Resource, Ecosystem and Environment of Agriculture, and Research Center of Agriculture and Climate Change, Nanjing Agricultural University, Nanjing, 210095 China XiaoJun Han, Institute for Resource, Ecosystem and Environment of Agriculture, and Research Center of Agriculture and Climate Change, Nanjing Agricultural University, Nanjing, 210095 China YanLing Du, Institute for Resource, Ecosystem and Environment of Agriculture, and Research Center of Agriculture and Climate Change, Nanjing Agricultural University, Nanjing, 210095 China Journal Chinese Science Bulletin Online ISSN 1861-9541 Print ISSN 1001-6538

Description:    Since Westman (1977) and Ehrlich (1982) put forward the concepts of “the service of nature” and “ecosystem service functions”, respectively, methods for conducting value accounting for them, and their practical application have become the subjects of intense study. Based on an overview of available research findings, we discuss three scientific hypotheses. First, the terrestrial ecosystem offers both positive and negative service functions. Second, changes in terrestrial ecosystem service functions lie not only in the number of ecosystem types and the coverage area of each type, but also in their quality. Third, the value of terrestrial ecosystem service functions should be assessed both in terms of the value stocked and the value added. We collected land use data from China during the period 1999–2008, and Normalized Difference Vegetation Index data based on remote sensing images from the Global Inventory Modeling and Mapping Studies for the same period. We then calculated and analyzed spatial and temporal changes in China’s terrestrial ecosystem service values over the 10-year period. Considering temporal change, the total value (stocked) of China’s terrestrial ecosystem service functions decreased from 6.82 trillion Yuan RMB in 1999 to 6.57 trillion Yuan RMB in 2008. During that period, the positive value decreased by 240.17 billion Yuan RMB and the negative value increased by 8.85 billion Yuan RMB. The decrease in total value lies mainly in the humidity control, soil formation, and waste recycling functions. The total value (added) of China’s terrestrial ecosystem service functions increased by 4.31 billion Yuan RMB in 2000, but decreased by 0.13 billion Yuan RMB in 2008 (based on the constant price of China in 1999). The value (added) was a negative figure. From the perspective of spatial change, we can see that the supply of China’s terrestrial ecosystem service functions fell slightly over the past 10 years, mainly in Northeast and Southern China. As a result of human activities on ecosystems, the loss of ecosystem service functions’ value was relatively prominent in Shanxi and Gansu provinces, compared with an increase in value in Shaanxi Province. Terrestrial ecosystem service functions’ value per unit area was relatively high in mid- and East China, showing a prominent spatial change over the 10-year period, but low in Western China. Some conclusions are drawn after an in-depth analysis of the factors causing the spatial and temporal changes in China’s terrestrial ecosystem service functions, in the hope that our suggestions will be helpful for the management of China’s terrestrial ecosystems. Content Type Journal Article Category Article Pages 1-12 DOI 10.1007/s11434-012-4978-5 Authors Yao Shi, State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085 China RuSong Wang, State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085 China JinLou Huang, State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085 China WenRui Yang, Beijing Municipal Institute of City Planning and Design, Beijing, 100045 China Journal Chinese Science Bulletin Online ISSN 1861-9541 Print ISSN 1001-6538

Description:    Four wetland maps for all China have been produced, based on Landsat and CBERS-02B remote sensing data between 1978 and 2008 (1978, 1990, 2000 and 2008). These maps were mainly developed by manual interpretation and validated by substantial field investigation in 2009. Based on these maps, we analyzed the 2008 wetland distribution in China and discussed wetland changes and their drivers over the past 30 years. (i) There were about 324097 km 2 of wetlands in 2008, for which inland marshes or swamps were the most common wetland type (35%), with lakes (26%) second. Most of the wetlands were in Heilongjiang, Inner Mongolia, Qinghai and Tibet, occupying about 55% of the national wetland area. (ii) From 1978 to 2008, China’s wetland area continually and significantly decreased, by about 33% based on changes in the wetland map. This was in sharp contrast to the increase in artificial wetlands, which increased by about 122%. Inland marshes accounted for the main loss of total wetlands from 1978 to 2000. From 2000 through 2008, riverine and lacustrine wetlands constituted the main wetland loss. Fortunately however, the rate of wetland loss decreased from 5523 to 831 km 2 /a. (iii) The change ratio of lost natural wetlands (including inland and coastal wetlands) to non-wetlands has decreased slightly over the past 30 years. From 1978 to 1990, nearly all natural wetlands (98%) lost were transformed into non-wetlands. However, the ratio declined to 86% from 1990 to 2000, and to 77% from 2000 to 2008. (iv) All Chinese provinces were divided into three groups according to patterns of wetland changes, which could relate to the driving forces of such changes. Tibet was completely different from other provinces, as it was one representative example in which there was a net wetland increase, because of global warming and decreased human activity since 1990. Increased economic development caused considerable wetland loss in most eastern provinces, and artificial wetlands increased. Content Type Journal Article Category Article Pages 1-11 DOI 10.1007/s11434-012-5093-3 Authors ZhenGuo Niu, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China HaiYing Zhang, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China XianWei Wang, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China WenBo Yao, Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Institute for Global Change Studies, Tsinghua University, Beijing, 100084 China DeMin Zhou, Resource Environment and Tourism, Capital Normal University, Beijing, 100037 China KuiYi Zhao, Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences, Changchun, 130012 China Hui Zhao, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China NaNa Li, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China HuaBing Huang, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China CongCong Li, Department of Geography and Remote Sensing, Beijing Normal University, Beijing, 100875 China Jun Yang, College of Forestry, Beijing Forestry University, Beijing, 100083 China CaiXia Liu, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China Shuang Liu, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China Lin Wang, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China Zhan Li, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China ZhenZhong Yang, Department of Geography and Remote Sensing, Beijing Normal University, Beijing, 100875 China Fei Qiao, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China YaoMin Zheng, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China YanLei Chen, Department of Environmental Science, Policy and Management, University of California, Berkeley, 94720 USA YongWei Sheng, Department of Geography, University of California, Los Angeles, 90095 USA XiaoHong Gao, Department of Life and Geographic Sciences, Qinghai Normal University, Xining, 810008 China WeiHong Zhu, Department of Geography, Yanbian University, Yanbian, 133002 China WenQing Wang, School of Environment and Ecology, Xiamen University, Xiamen, 361005 China Hong Wang, School of Geographical Information Science, Hohai University, Nanjing, 211100 China YongLing Weng, School of Surveying and Mapping Transportation Engineering, Southeast University, Nanjing, 210096 China DaFang Zhuang, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China JiYuan Liu, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China ZhiCai Luo, School of Surveying and Mapping, Wuhan University, Wuhan, 430079 China Xiao Cheng, Department of Geography and Remote Sensing, Beijing Normal University, Beijing, 100875 China ZiQi Guo, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China Peng Gong, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China Journal Chinese Science Bulletin Online ISSN 1861-9541 Print ISSN 1001-6538

Description:    A method for obtaining a relative deer population density index with low cost and effort is urgently needed in wildlife protection areas that need their own deer management guidelines. We recorded the number of deer sighted during our daily trips on forest roads by car in Ashiu Forest at Kyoto University, Japan, beginning in 2006. We used generalized additive mixed models (GAMMs) to estimate among-year trends in the number of deer sighted. We applied models for the total number of deer (TND), number of adults (NA), and number of fawns (NF) sighted, which included both current-year and 1-year-old fawns. Full models included the terms of year (2007, 2008, 2009, and 2010), weather (fine, cloudy, and rain/snow), and nonlinear effects of season (date) and time (time). The optimal GAMMs for TND, NA, and NF did not include the effect of weather but included those of time, date, and year. The detected among-year trends in deer population may be influenced by differences in snow environments among the years. The modeling of road count data using GAMM quantitatively determined among-year variation in the number of deer sighted. This trend was similar to that of the population density estimated using a block count survey conducted in Ashiu Forest. Content Type Journal Article Category Original Article Pages 1-7 DOI 10.1007/s10310-012-0379-5 Authors Inoue Mizuki, Laboratory of Forest Science, Faculty of Bioresource Sciences, Akita Prefectural University, Akita, 010-0195 Japan Shota Sakaguchi, Laboratory of Forest Biology, Graduate School of Agriculture, Kyoto University, Kyoto, Japan Keitaro Fukushima, Field Science Education and Research Center, Kyoto University, Kyoto, Japan Masaru Sakai, Graduate School of Global Environmental Studies, Kyoto University, Kyoto, Japan Atsushi Takayanagi, Laboratory of Forest Biology, Graduate School of Agriculture, Kyoto University, Kyoto, Japan Daisuke Fujiki, Institute of Natural and Environment Science, University of Hyogo, Tanba, Japan Michimasa Yamasaki, Laboratory of Forest Biology, Graduate School of Agriculture, Kyoto University, Kyoto, Japan Journal Journal of Forest Research Online ISSN 1610-7403 Print ISSN 1341-6979