生物多样性

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全球植被样地调查数据特征

张宇彤1,2, 何志健2,3, 董雪云2,4, 杨成君1,2*, 王洪峰1,2*, 马克平1,2   

  1. 1. 东北林业大学林学院, 哈尔滨 150040; 2. 东北林业大学林学院东北亚生物多样性中心, 哈尔滨 150040; 3. 东北林业大学国家林业和草原局猫科动物研究中心, 哈尔滨 150040; 4. 哈尔滨学院, 哈尔滨 150040
  • 收稿日期:2025-11-25 修回日期:2026-04-27 接受日期:2026-05-22
  • 通讯作者: 王洪峰

Analysis of the characteristics of global vegetation plot data

Yutong Zhang1,2, Zhijian He2,3, Xueyun Dong2,4, Chengjun Yang1,2*, Hongfeng Wang1,2*, Keping Ma1,2   

  1. 1 School of Forestry, Northeast Forestry University, Harbin 150040, China 

    2 Northeast Asia Biodiversity Center, School of Forestry, Northeast Forestry University, Harbin 150040, China 

    3 Feline Research Center of National Forestry and Grassland Administration, School of Forestry, Northeast Forestry University, Harbin 150040, China 

    4 Harbin University, Harbin 150040, China

  • Received:2025-11-25 Revised:2026-04-27 Accepted:2026-05-22
  • Contact: Hongfeng Wang

摘要: 植被样地调查数据在解决重大科学问题中具有重要作用, 如群落构建机制、物种分布以及应对全球环境变化等问题。然而, 数据结构差异、访问受限、重复收录等限制了数据的广泛使用。基于此, 本文通过查阅资料和咨询专家, 收集了全球最重要的十个公共植被样地数据库信息, 分别是GIVD、sPlot、BIEN、VegBank、EVA、FIA、GFBI、BioTIME、ReSurveyEurope和forestREplot。在此基础上, 汇总整理了各库收录的数据集, 并分析了样地调查数据的整体特征, 以提升数据利用效率, 为宏生态学(macroecology)研究提供数据支撑。结果表明, 截至2026年5月, 上述十个样地数据库已整合610.9万个植被样地的调查数据, 但数据仍有明显缺陷。一是空间分布极度不均衡, 样地高度集中于欧洲(407.9万个, 67.1%)和北美洲(135.3万个, 22.1%), 而其余地区的数据严重匮乏。二是数据结构异质性高, 78.5%(437.0万)的样地包含完整维管植物名录, 而21.5%(191.4万)仅记录木本植物, 物种数量特征以盖度为主(63.1%, 338.8万), 记录个体数量或胸径/树高的样地均不足35%。三是关键信息缺失严重, 绝大部分样地(71.1%, 395.2万)的地理信息精度较低(误差 > 25 m), 环境因子记录比例整体较低, 尤其是微地形(5.2%, 22.9万个)以及土壤因子(pH、土壤深度及其他属性均不足10%)。四是数据长时序监测数据匮乏, 仅14.8%(约90.5万个)的样地具有重复调查记录。未来应优先填补数据薄弱区的空白, 推动制定全球统一的数据标准。我们对中国植被数据也进行了总结。中国拥有丰富的植被调查数据, 但被分散在数据中心、植被专著以及数据论文中。对中国而言, 当务之急是建立国家级标准化数据整合平台, 通过数据论文等激励机制促进共享, 并积极引领构建覆盖全亚洲的区域植被数据库。

关键词: 植被样地, 调查数据, 数据库, 生物多样性, 群落生态学

AbstractVegetation plot data play a crucial role in addressing major scientific questions, including theoretical issues such as community assembly mechanisms and species distribution patterns, as well as practical challenges like responses to global environmental change. However, the broad use of these data is constrained by factors such as structural discrepancies, restricted access, and duplicate records. Therefore, this paper identified ten prominent global public vegetation plot databases through literature review and expert consultations, including GIVD, sPlot, BIEN, VegBank, EVA, FIA, GFBI, BioTIME, ReSurveyEurope, and forestREplot. We compiled datasets across these databases and analyzed their characteristics to enhance data usability and support macroecological research.Our results show that, as of May 2026, these 10 vegetation plot databases have integrated data from 6.109 million plots, yet significant limitations remain. First, the spatial distribution is extremely uneven, with plots heavily concentrated in Europe (4.079 million, 67.1%) and North America (1.353 million, 22.1%), while data from other regions are severely scarce. Second, data heterogeneity is high: 78.5% (4.370 million) of the plots contain complete vascular plant checklists, whereas 21.5% (1.914 million) record only woody plants. Species abundance metrics are primarily recorded as cover (63.1%, 3.388 million plots); fewer than 35% of plots record individual counts or diameter at breast height (DBH)/tree height. Third, crucial information is widely lacking: the majority of plots (71.1%, 3.952 million) exhibit low georeferencing precision (error > 25 m), and environmental variables are generally underreported, especially for microtopography (5.2%, 0.229 million plots) and soil factors (soil pH, soil depth, and other soil attributes are all below 10%). Fourth, long-term monitoring data are scarce: only 14.8% (0.905 million plots) of the plots have resurvey records.Future efforts should prioritize filling data gaps in underrepresented regions and promoting the development of globally unified data standards.In addition, China possesses abundant vegetation survey data, but they are scattered across data centers, vegetation monographs, and data papers. For China, an urgent priority is to establish a national standardized data integration platform, promote data sharing through incentives such as data papers, and actively lead the construction of a regional vegetation database covering the entirety of Asia.

Key words: vegetation plot, survey data, database, biodiversity, community ecology