Representation of critical natural capital in China

Traditional means of assessing representativeness of conservation value in protected areas depend on measures of structural biodiversity. The effectiveness of priority conservation areas at representing critical natural capital (CNC) (i.e., an essential and renewable subset of natural capital) remains largely unknown. We analyzed the representativeness of CNC‐conservation priority areas in national nature reserves (i.e., nature reserves under jurisdiction of the central government with large spatial distribution across the provinces) in China with a new biophysical‐based composite indicator approach. With this approach, we integrated the net primary production of vegetation, topography, soil, and climate variables to map and rank terrestrial ecosystems capacities to generate CNC. National nature reserves accounted for 6.7% of CNC‐conservation priority areas across China. Considerable gaps (35.2%) existed between overall (or potential) CNC representativeness nationally and CNC representation in national reserves, and there was significant spatial heterogeneity of representativeness in CNC‐conservation priority areas at the regional and provincial levels. For example, the best and worst representations were, respectively, 13.0% and 1.6% regionally and 28.9% and 0.0% provincially. Policy in China is transitioning toward the goal of an ecologically sustainable civilization. We identified CNC‐conservation priority areas and conservation gaps and thus contribute to the policy goals of optimization of the national nature reserve network and the demarcation of areas critical to improving the representativeness and conservation of highly functioning areas of natural capital. Moreover, our method for assessing representation of CNC can be easily adapted to other large‐scale networks of conservation areas because few data are needed, and our model is relatively simple.     

Estimation of crop residue in China based on a Monte Carlo analysis

Accurate crop residue resource estimation is important for bioenergy development.This is done by the ratio of residue to grain(R/G),which is usually regarded constant and is widely used for crop residue estimation though uncertainty is inevitable in practice.In this study,a Monte Carlo algorithm was applied to estimate national crop residue by R/G taken from published reports in China.The estimated result was further mapped in pixels by geographic information system.In2009,the amount of crop residue was found to be 802.32 million tons(Mt),with 679.36 and 947.28 Mt as the lower and upper limits for 95%confidence limits.Chinese crop residue was dominated by rice,wheat,and corn,accounting for74.57%(598.29 Mt).From 1949 to 2009,the amount of crop residue increased by four times,accompanied by component change.The spatial distribution of crop residue in China is markedly heterogeneous.Compared to the shortage of crop residue in northwest China,there is an abundant crop residue of about 334 Mt in eastern China,attracting 90%of the country's electricity or heat generation plants.     

Dynamics of a low‐density tiger population in Southeast Asia in the context of improved law enforcement

Recovering small populations of threatened species is an important global conservation strategy. Monitoring the anticipated recovery, however, often relies on uncertain abundance indices rather than on rigorous demographic estimates. To counter the severe threat from poaching of wild tigers (Panthera tigris), the Government of Thailand established an intensive patrolling system in 2005 to protect and recover its largest source population in Huai Kha Khaeng Wildlife Sanctuary. Concurrently, we assessed the dynamics of this tiger population over the next 8 years with rigorous photographic capture‐recapture methods. From 2006 to 2012, we sampled across 624–1026 km² with 137–200 camera traps. Cameras deployed for 21,359 trap days yielded photographic records of 90 distinct individuals. We used closed model Bayesian spatial capture‐recapture methods to estimate tiger abundances annually. Abundance estimates were integrated with likelihood‐based open model analyses to estimate rates of annual and overall rates of survival, recruitment, and changes in abundance. Estimates of demographic parameters fluctuated widely: annual density ranged from 1.25 to 2.01 tigers/100 km², abundance from 35 to 58 tigers, survival from 79.6% to 95.5%, and annual recruitment from 0 to 25 tigers. The number of distinct individuals photographed demonstrates the value of photographic capture–recapture methods for assessments of population dynamics in rare and elusive species that are identifiable from natural markings. Possibly because of poaching pressure, overall tiger densities at Huai Kha Khaeng were 82–90% lower than in ecologically comparable sites in India. However, intensified patrolling after 2006 appeared to reduce poaching and was correlated with marginal improvement in tiger survival and recruitment. Our results suggest that population recovery of low‐density tiger populations may be slower than anticipated by current global strategies aimed at doubling the number of wild tigers in a decade.     

长三角地区人口密度模型及增长模式:1982～2010

基于熵值法对上海、南京和杭州影响力进行综合评价,运用Arc GIS点距离工具测算各空间单元到沪宁杭的距离,然后借鉴重力模型,将长三角地区划分为以上海、南京和杭州为中心的3个城市经济区,并进行区域人口密度模型拟合。相比线性、指数和乘幂模型,对数模型能更好地拟合经济区内各县、县级市或市辖区人口密度与其到各自经济区中心距离的关系。而相比上述基础模型,基于基础模型的二次模型拟合的判定系数更高,拟合效果更好。其中,对数二次模型拟合的判定系数最高,且明显优于基础模型。基于对数二次模型的人口密度变动的空间差异,沪、宁、杭经济区增长模式可以总结为"强向心集聚"和"近域扩散",但不同区域不同时段增长模式存在差异。     

Current Practice and Future Prospects for Social Data in Coastal and Ocean Planning

Coastal and ocean planning comprises a broad field of practice. The goals, political processes, and approaches applied to planning initiatives may vary widely. However, all planning processes ultimately require adequate information on both the biophysical and social attributes of a planning region. In coastal and ocean planning practice, there are well‐established methods to assess biophysical attributes; however, less is understood about the role and assessment of social data. We conducted the first global assessment of the incorporation of social data in coastal and ocean planning. We drew on a comprehensive review of planning initiatives and a survey of coastal and ocean practitioners. There was significantly more incorporation of social data in multiuse versus conservation‐oriented planning. Practitioners engaged a wide range of social data, including governance, economic, and cultural attributes of planning regions and human impacts data. Less attention was given to ecosystem services and social–ecological linkages, both of which could improve coastal and ocean planning practice. Although practitioners recognize the value of social data, little funding is devoted to its collection and incorporation in plans. Increased capacity and sophistication in acquiring critical social and ecological data for planning is necessary to develop plans for more resilient coastal and ocean ecosystems and communities. We suggest that improving social data monitoring, and in particular spatial social data, to complement biophysical data, is necessary for providing holistic information for decision‐support tools and other methods. Moving beyond people as impacts to people as beneficiaries, through ecosystem services assessments, holds much potential to better incorporate the tenets of ecosystem‐based management into coastal and ocean planning by providing targets for linked biodiversity conservation and human welfare outcomes. La Práctica Actual y los Prospectos Futuros para los Datos Sociales en la Planeación Costera y Oceánica     

Expanding Kenya's protected areas under the Convention on Biological Diversity to maximize coverage of plant diversity

Biodiversity is highly valuable and critically threatened by anthropogenic degradation of the natural environment. In response, governments have pledged enhanced protected‐area coverage, which requires scarce biological data to identify conservation priorities. To assist this effort, we mapped conservation priorities in Kenya based on maximizing alpha (species richness) and beta diversity (species turnover) of plant communities while minimizing economic costs. We used plant‐cover percentages from vegetation surveys of over 2000 plots to build separate models for each type of diversity. Opportunity and management costs were based on literature data and interviews with conservation organizations. Species richness was predicted to be highest in a belt from Lake Turkana through Mount Kenya and in a belt parallel to the coast, and species turnover was predicted to be highest in western Kenya and along the coast. Our results suggest the expanding reserve network should focus on the coast and northeastern provinces of Kenya, where new biological surveys would also fill biological data gaps. Meeting the Convention on Biological Diversity target of 17% terrestrial coverage by 2020 would increase representation of Kenya's plant communities by 75%. However, this would require about 50 times more funds than Kenya has received thus far from the Global Environment Facility.     

长江上游支流老河沟鱼类群落结构的时空格局

2012年3月、6月、9月和12月按季度对涪江支流老河沟的14个样点进行了鱼类群落结构调查和分析。共采集鱼类11种,隶属于2目4科;其中,鲤形目鱼类7种,占物种数的63.64%。沿上游源头到下游的纵向梯度方向,老河沟鱼类的种类数和丰度均逐渐增加,具极显著性差异,而鱼类种类数和丰度则无季节差异性。聚类分析(Cluster Analysis)和多维尺度分析(MDS)表明,当Bray-curtis相似度矩阵值为40.77%时,老河沟鱼类群落分成上游、中游和下游3组。利用相似度百分比分析(SIMPER)确定,引起3组差异性的特定鱼类是宽鳍鱲、尖头、似()、齐口裂腹鱼、贝氏高原鳅、红尾荷马条鳅和青石爬鮡。典型相关分析(CCA)表明海拔、底质类型、流速、溶氧、总溶解固体、水温、电导率、河宽、pH值和水深等环境变量是老河沟鱼类群落时空格局差异的影响因子。     

四川省不同类型山洪灾害与主要影响因素的关系

以四川省为例,在明确溪河洪水及其引发的滑坡、泥石流三类山洪灾害分布格局的基础上,构建概念模型,分析降雨、地形、人口资产易损性等引发山洪灾害的基本因子和土壤、河网密度、土地利用等一般因子对不同类型山洪灾害的影响程度,并比较它们的异同。结果表明:(1)四川省山洪灾害主要分布在盆周山区、川西高原和横断山脉一带;(2)如果3项基本因子可以解释山洪灾害空间分布100%的变化,则它们对三类灾害的影响程度分别为:溪河洪水,降雨59%、地形28%、人口资产13%;泥石流,降雨15%、地形73%、人口资产12%;滑坡,降雨48%、地形34%、人口资产18%;(3)总体上,泥石流灾害对地形、岩性、土地利用等下垫面因子的依赖更高,而溪河洪水和滑坡灾害受降雨要素的影响更大。     

基于DMSP/OLS与土地利用的江苏省人口数据空间化研究

准确、高分辨率的人口分布信息是人地关系研究的重要前提。人口数据空间化可实现人口统计数据与空间信息集成,重构人口空间分布特征,为区域可持续发展研究提供数据支持。基于DMSP/OLS夜间灯光数据与土地利用数据,以遥感与地理信息系统理论与方法为基础,采用空间滞后回归模型模拟了江苏省2010年人口空间分布状况,并得到1km×1km的人口密度网格图。通过从县级、乡镇级两种空间尺度对人口数据空间化结果进行检验,结果表明基于DMSP/OLS与土地利用的人口数据空间化能够正确地表达人口空间分布规律,尤其对于人口较为密集地区,具有很高的数据重现精度;但是对于人口密度畸高或畸低的地区,由于人口空间分布异质性较大,数据重现的准确性下降。     

江苏省乡村性空间格局及影响因素研究

基于城乡一体化思想,在分析乡村性内涵基础上,采用人口密度等指标构建乡村性评价体系,以全国第五、六次人口普查等数据为基础,借助SPSS、ArcGIS、Geoda软件,探讨了江苏省县域单元乡村性空间格局及影响因素。结果表明：(1)2000～2010年,江苏全省县域单元乡村性逐渐减弱,并呈现出阶梯状的空间分布态势;(2)各县域乡村性指数并不表现为完全随机状态,而是存在显著的正空间相关性。热点区域分布在沭阳县与泗阳县交界处为核心的圈层结构,冷点区域集中在乡镇工业发达的苏锡常地区;（3）社会环境、交通、资本、工业化和城市化、政策等因素是乡村性变化的主要影响因素;（4）未发现乡村性加强的现象;在数据可获得情况下,以乡镇为单元研究乡村性将具有更强的现实指导意义