The article analyzes the development of the Chinese Ecosystem Research Network, and its mission, mandate, and management mechanisms, with examples of research, demonstration and consultation for policy-setting.
A new form of producing and sharing knowledge has emerged as an international(United States of America,Asia,and Europe) research collaboration,known as the Long-Term Ecological Research(LTER) Network.Although Africa b...A new form of producing and sharing knowledge has emerged as an international(United States of America,Asia,and Europe) research collaboration,known as the Long-Term Ecological Research(LTER) Network.Although Africa boasts rich biodiversity,including endemic species,it lacks the long-term initiatives to underpin sustainable biodiversity managements.At present,climate change may exacerbate hunger and poverty concerns in addition to resulting in ecosystem degradation,land use change,and other threats in Africa.Therefore,ecosystem monitoring was suggested to understanding the effects of climate change and setting strategies to mitigate these changes.This paper aimed to investigate ecosystem monitoring ground sites and address their coverage gaps in Africa to provide a foundation for optimizing the African Ecosystem Research Network(AERN) ground sites.The geographic coordinates and characteristics of ground sites-based ecosystem monitoring were collected from various networks aligned with the LTER implementation in Africa.Additionally,climatic data and biodiversity distribution maps were retrieved from various sources.These data were used to assess the size of existing ground sites and the gaps in description,ecosystems and biomes.The results reveal that there were 1089 sites established by various networks.Among these sites,30.5%,27.5%,and 28.8% had no information of area,year of establishment,current status,respectively.However,68.0% of them had an area equal to or greater than 1 km2.Sites were created progressively over the course of the years,with 68.9% being created from 2000 to 2005.To date,only 41.5% of the sites were operational.The sites were scattered across Africa,but they were concentrated in Eastern and Southern Africa.The unbalanced distribution pattern of the sites left Central and Northern Africa hardly covered,and many unique ecosystems in Central Africa were not included.To sustain these sites,the AERN should be based on operational sites,seeking secure funding by establishing multiple partnerships.展开更多
中国生态系统研究网络(Chinese Ecosystem Research Network,CERN)植物物候观测数据集是CERN生态站植物物候观测数据综合集成的产物,包含21个生态站2003~2015年660余个物种的物候观测记录。因木本植物和草本植物观测的物候期不同,本数...中国生态系统研究网络(Chinese Ecosystem Research Network,CERN)植物物候观测数据集是CERN生态站植物物候观测数据综合集成的产物,包含21个生态站2003~2015年660余个物种的物候观测记录。因木本植物和草本植物观测的物候期不同,本数据集被分为木本子集和草本子集。木本子集主要记录了芽开放期、展叶期、开花始期、开花盛期、果实或者种子成熟期、叶秋季变色期和落叶期等物候信息。草本子集则记录了萌动期、开花期、果实或种子成熟期、种子散布期和黄枯期等物候信息。另外,本数据集还包含生态站代码、年份、样地代码、样地名称、样地类别、植物种名、拉丁名等信息。本数据集可以为环境变化、碳循环、植物对环境变化的响应等方面的研究提供数据支持。展开更多
土壤环境是地球环境的重要组成部分。目前土壤环境问题的关注重点在于土壤污染。我国土壤污染以无机污染为主。中国生态系统研究网络(Chinese Ecosystem Research Network,CERN)自1988年组建以来,在中国主要农田、森林、草原、荒漠、湿...土壤环境是地球环境的重要组成部分。目前土壤环境问题的关注重点在于土壤污染。我国土壤污染以无机污染为主。中国生态系统研究网络(Chinese Ecosystem Research Network,CERN)自1988年组建以来,在中国主要农田、森林、草原、荒漠、湿地生态系统中,按统一的规范,对与土壤环境状况有关的铁、锰、铜、锌、硼、钼、镉、铬、铅、镍、汞、砷、硒元素进行了长期定位监测。通过对CERN典型生态样地表层土壤环境元素监测数据进行加工处理,获得1995~2011年中国陆地生态系统土壤环境元素含量数据集。本数据集中13种土壤环境元素指标测定的相对误差平均为6.55%,重复测定的相对偏差为7.70%。同时附有完整的背景信息,保证了数据在空间和时间上的一致性。本数据集可以为全国和区域土壤环境质量评估、土壤污染风险评价以及环境土壤学研究等工作提供数据基础。展开更多
实物资源是国家生态系统观测研究网络(Chinese National Ecosystem Research Network,CNERN)的宝贵财富,对于开展联网观测、实验、研究具有广泛的用途及重大的价值。研究CNERN实物资源服务的相关问题,探索运行服务阶段CNERN实物资源服...实物资源是国家生态系统观测研究网络(Chinese National Ecosystem Research Network,CNERN)的宝贵财富,对于开展联网观测、实验、研究具有广泛的用途及重大的价值。研究CNERN实物资源服务的相关问题,探索运行服务阶段CNERN实物资源服务的模式,对实现实物资源服务从服务科研创新向全面服务科技、经济和社会发展转变具有重要的理论价值和现实意义。探讨CNERN实物资源服务的内涵、特点、服务内容,并提出基于线上线下融合的实物资源服务模式,为"国家生态系统研究网络科技资源服务系统"的实物资源服务子系统的设计、开发提供理论基础和应用模式,也期望为相关部门开展实物资源服务提供参考。展开更多
为满足中国生态系统研究网络(英文全称Chinese Ecosystem Research Network,简称CERN)对大批量气象规范报表(Excel格式)进行高效快速转换并载入数据库的需求,作者在对当前流行的并行编程方法和技术进行对比研究的基础上,提出了利用Pytho...为满足中国生态系统研究网络(英文全称Chinese Ecosystem Research Network,简称CERN)对大批量气象规范报表(Excel格式)进行高效快速转换并载入数据库的需求,作者在对当前流行的并行编程方法和技术进行对比研究的基础上,提出了利用Python的多进程编程技术实现报表处理的方案,建立了CERN气象规范报表的抽象数据转换模型,并编写了多进程并行处理程序,实现了将大批量气象报表的快速转换载入Oracle数据库的功能。同时,作者对多进程编程的不同参数设置情景下的转换效率进行了测试、比较和分析。实验结果显示,利用Python并行编程技术可以充分利用计算机硬件的潜力和性能,从而大大提高处理效率,且方法简便、事半功倍。作者最后进一步建议通过利用Parallel Python软件包,可更充分利用计算机多核性能,更大程度提高处理效率。更加满足CERN日益增长的数据快速处理需求。同时本文为类似数据处理需求提供了可借鉴的参考方案。展开更多
文摘The article analyzes the development of the Chinese Ecosystem Research Network, and its mission, mandate, and management mechanisms, with examples of research, demonstration and consultation for policy-setting.
基金Under the auspices of National Natural Science Foundation of China(No.31161140355)
文摘A new form of producing and sharing knowledge has emerged as an international(United States of America,Asia,and Europe) research collaboration,known as the Long-Term Ecological Research(LTER) Network.Although Africa boasts rich biodiversity,including endemic species,it lacks the long-term initiatives to underpin sustainable biodiversity managements.At present,climate change may exacerbate hunger and poverty concerns in addition to resulting in ecosystem degradation,land use change,and other threats in Africa.Therefore,ecosystem monitoring was suggested to understanding the effects of climate change and setting strategies to mitigate these changes.This paper aimed to investigate ecosystem monitoring ground sites and address their coverage gaps in Africa to provide a foundation for optimizing the African Ecosystem Research Network(AERN) ground sites.The geographic coordinates and characteristics of ground sites-based ecosystem monitoring were collected from various networks aligned with the LTER implementation in Africa.Additionally,climatic data and biodiversity distribution maps were retrieved from various sources.These data were used to assess the size of existing ground sites and the gaps in description,ecosystems and biomes.The results reveal that there were 1089 sites established by various networks.Among these sites,30.5%,27.5%,and 28.8% had no information of area,year of establishment,current status,respectively.However,68.0% of them had an area equal to or greater than 1 km2.Sites were created progressively over the course of the years,with 68.9% being created from 2000 to 2005.To date,only 41.5% of the sites were operational.The sites were scattered across Africa,but they were concentrated in Eastern and Southern Africa.The unbalanced distribution pattern of the sites left Central and Northern Africa hardly covered,and many unique ecosystems in Central Africa were not included.To sustain these sites,the AERN should be based on operational sites,seeking secure funding by establishing multiple partnerships.
文摘中国生态系统研究网络(Chinese Ecosystem Research Network,CERN)植物物候观测数据集是CERN生态站植物物候观测数据综合集成的产物,包含21个生态站2003~2015年660余个物种的物候观测记录。因木本植物和草本植物观测的物候期不同,本数据集被分为木本子集和草本子集。木本子集主要记录了芽开放期、展叶期、开花始期、开花盛期、果实或者种子成熟期、叶秋季变色期和落叶期等物候信息。草本子集则记录了萌动期、开花期、果实或种子成熟期、种子散布期和黄枯期等物候信息。另外,本数据集还包含生态站代码、年份、样地代码、样地名称、样地类别、植物种名、拉丁名等信息。本数据集可以为环境变化、碳循环、植物对环境变化的响应等方面的研究提供数据支持。
文摘土壤环境是地球环境的重要组成部分。目前土壤环境问题的关注重点在于土壤污染。我国土壤污染以无机污染为主。中国生态系统研究网络(Chinese Ecosystem Research Network,CERN)自1988年组建以来,在中国主要农田、森林、草原、荒漠、湿地生态系统中,按统一的规范,对与土壤环境状况有关的铁、锰、铜、锌、硼、钼、镉、铬、铅、镍、汞、砷、硒元素进行了长期定位监测。通过对CERN典型生态样地表层土壤环境元素监测数据进行加工处理,获得1995~2011年中国陆地生态系统土壤环境元素含量数据集。本数据集中13种土壤环境元素指标测定的相对误差平均为6.55%,重复测定的相对偏差为7.70%。同时附有完整的背景信息,保证了数据在空间和时间上的一致性。本数据集可以为全国和区域土壤环境质量评估、土壤污染风险评价以及环境土壤学研究等工作提供数据基础。
文摘实物资源是国家生态系统观测研究网络(Chinese National Ecosystem Research Network,CNERN)的宝贵财富,对于开展联网观测、实验、研究具有广泛的用途及重大的价值。研究CNERN实物资源服务的相关问题,探索运行服务阶段CNERN实物资源服务的模式,对实现实物资源服务从服务科研创新向全面服务科技、经济和社会发展转变具有重要的理论价值和现实意义。探讨CNERN实物资源服务的内涵、特点、服务内容,并提出基于线上线下融合的实物资源服务模式,为"国家生态系统研究网络科技资源服务系统"的实物资源服务子系统的设计、开发提供理论基础和应用模式,也期望为相关部门开展实物资源服务提供参考。
文摘为满足中国生态系统研究网络(英文全称Chinese Ecosystem Research Network,简称CERN)对大批量气象规范报表(Excel格式)进行高效快速转换并载入数据库的需求,作者在对当前流行的并行编程方法和技术进行对比研究的基础上,提出了利用Python的多进程编程技术实现报表处理的方案,建立了CERN气象规范报表的抽象数据转换模型,并编写了多进程并行处理程序,实现了将大批量气象报表的快速转换载入Oracle数据库的功能。同时,作者对多进程编程的不同参数设置情景下的转换效率进行了测试、比较和分析。实验结果显示,利用Python并行编程技术可以充分利用计算机硬件的潜力和性能,从而大大提高处理效率,且方法简便、事半功倍。作者最后进一步建议通过利用Parallel Python软件包,可更充分利用计算机多核性能,更大程度提高处理效率。更加满足CERN日益增长的数据快速处理需求。同时本文为类似数据处理需求提供了可借鉴的参考方案。
