J Plant Ecol ›› 2019, Vol. 12 ›› Issue (6): 1009-1024.doi: 10.1093/jpe/rtz028

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Vegetation classification at the association level under the China Vegetation Classification System: an example of six Stipa steppe formations in China

Changcheng Liu1,2,*, Thomas R. Wentworth2, Xianguo Qiao1,3, Ke Guo1,3,* and Dongjie Hou1,3   

  1. 1State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
    2Department of Plant and Microbial Biology, North Carolina State University, Box 7612, Raleigh, NC 27695-7612, USA
    3University of Chinese Academy of Sciences, Beijing 100049, China
    *Correspondence address. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China. Email: greatwall01@ibcas.ac.cn (C.L.); guoke@ibcas.ac.cn (K.G.)
  • Received:2019-01-15 Revised:2019-03-20 Accepted:2019-05-09 Online:2019-10-30 Published:2019-12-01



The latest China Vegetation Classification System (China-VCS) for natural/semi-natural vegetation has eight hierarchical levels: Association < Association-group < Subformation < Formation < Formation-group < Vegetation-subtype < Vegetation-type < Vegetation-type-group. The classification is based on dominant species and their growth forms and has been completed at the formation level. The principal challenge today in Chinese vegetation classification is to develop the China-VCS at levels below the formation in a way that is consistent with current international standards. We explored the following question: how can existing vegetation plot data help develop the China-VCS and improve its compatibility with other international classification systems?


We compiled 401 plots having plant cover and/or aboveground biomass measurements collected in six Stipa steppe formations and divided them into those with cover data (299 plots) and/or biomass data (283 plots). We applied a combination of hierarchical clustering and ordination to partition the cover and biomass data sets into formations and constituent associations. We then used supervised noise clustering to improve the classification and to identify the core plots representing each association. Diagnostic species were also identified at both association and formation levels. Finally, we compared the classification results based on cover and biomass data sets and combined these results into a comprehensive classification framework for the six formations.

Important Findings

Our results using cover data were comparable with those using biomass data at both formation and association levels. Three Stipa formations were classified into associations based on cover data, two based on biomass data and one based on both biomass and cover data. Twenty-seven associations were defined and proposed within the six formations, using cover or biomass data as consistent classification sections (CCSs). Both dominant species in the dominant stratum and diagnostic species from multiple strata of the core plots were used to characterize vegetation types at both formation and association levels, improving the compatibility of our classification with the International Vegetation Classification. Temperature and precipitation were found to be important climatic factors determining the distribution pattern and species composition of Stipa-dominated vegetation. We propose a framework for plot-based vegetation classification in the China-VCS, using our work with Stipa-dominated steppe vegetation as an example. We applied the concept of CCS to make optimal use of available data representing both plant cover and biomass. This study offers a model for developing the China-VCS to the association level in a way that is consistent with current international standards.

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