植物生态学报 ›› 2025, Vol. 49 ›› Issue (典型生态系统数据集): 1-.DOI: 10.17521/cjpe.2025.0001

• • 上一篇    

1998~2010年中国典型生态系统环境要素、物种丰富度和生物量动态数据集

张琳, 袁伟影, 宋创业, 吴冬秀   

  1. 中国科学院植物研究所植被与环境变化重点实验室, 100093
  • 收稿日期:2025-01-02 修回日期:2025-03-17 出版日期:2025-06-20 发布日期:2025-05-07

Dynamic dataset of environmental elements, species richness and biomass of typical ecosystems in China from 1998 to 2010

Zhang Lin, Yuan Weiying, SONG Chuang-Ye, WU Dong-Xiu   

  1. , Institute Of Botany, Chinese Academy Of Sciences 100093,
  • Received:2025-01-02 Revised:2025-03-17 Online:2025-06-20 Published:2025-05-07

摘要: 多尺度联网观测是获取区域生态信息的基础手段,是全面深入认识生态系统动态变化规律,评估生态系统与全球变化及人类活动相互关系的数据源泉。中国生态系统研究网络(Chinese Ecosystem Research Network,CERN)以我国重要生态类型的野外观测试验站为基地,采用统一的规程对我国典型的森林、草地、荒漠、沼泽等生态系统进行长期定位观测和研究,为国家生态环境建设提供科学数据支撑。本数据集涵盖了1998~2010年CERN10个森林站、6个荒漠站、2个草地站、1个沼泽站的84个生物长期监测样地背景、环境要素动态变化、植物群落物种丰富度和生物量信息,数据集经过严格的数据三级审核质控过程,构建了以生物长期监测样地年度观测数据为基本单元的环境要素、物种丰富度和生物量动态数据集,可以为生物资源分布、生物与环境变化和人类活动的关系等提供数据支持。

关键词: 长期监测, 联网研究, 植被类型, 土壤特征, 气候变化

Abstract: Multi-scale networked observation is the basic means to obtain regional ecological information, and it is the source of data for comprehensively and deeply understanding the dynamics of ecosystems and assessing the interrelationships between ecosystems, global changes and human activities. The Chinese Ecosystem Research Network (CERN), based on the field observation stations of important ecosystem types in China, adopts unified protocols to carry out long-term positional observation and research on typical ecosystems such as forests, grasslands, deserts and marshes in China, so as to provide scientific data support for the construction of the national ecological environment. This dataset covers the background, plant community species richness, biomass and habitat information of 84 biological long-term observation plots from 10 forest, 6 desert, 2 grassland and 1 marsh stations of CERN from 1998 to 2010. The dataset has gone through a strict three-tier audit and quality control process, and constructed a biological and habitat dataset with the annual observation data of the biological long-term observation samples as the basic unit. The dataset can provide data support for the distribution of biological resources, the relationship between biological and environmental changes and human activities.

Key words: long-term observation, networking study, vegetation types, soil characteristics, climate change