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

Abstract

Aims 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? Methods 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.

Figure 4 from: Liu C, Qiao X, Guo K, Zhao L, Pan Q (2022) Vegetation classification of Stipa steppes in China, with reference to the International Vegetation Classification. Vegetation Classification and Survey 3: 121-144. https://doi.org/10.3897/VCS.72875

“…However, lower levels in the China-VCS emphasize dominant species in multiple strata, based on species' relative abundances (or Importance Values). The association, alliance, and vegetation subformation levels of the China-VCS are somewhat similar to the association, alliance, and division levels of EcoVeg, respectively (Liu et al 2019;Faber-Langendoen et al 2020).…”

“…The relative cover of each speces was calculated as: the cover of each species/the sum of cover of all species×100%, and the relative biomass and height were calculated in the same way. Our previous study revealed that the aboveground biomass was highly related with cover in Stipa steppes, and vegetation classification based on cover and aboveground biomass data yielded comparable results (Liu et al 2019). Therefore, in this study, we combined the two data sets of Importance Value to get a whole data set (1337 plots × 1261 species).…”

“…Supervised noise clustering (NC) was used to improve the classifications at the group, alliance, and association level, using results of the previous hierarchical clustering for a priori group memberships (Liu et al 2019). To cast the priori classification into the new NC framework, we first calculated the cluster centroids in the space of the Chord distance and then determined the degree of membership of each plot to each cluster according to two NC parameters, the fuzziness coefficient (m) and the distance to the Noise class (δ) (De Cáceres et al 2010;Wiser and De Cáceres 2013).…”

“…(3) using the Stipa steppe as an example, we discuss how to improve the compatibility of the Chinese vegetation classification with the International Vegetation Classification (IVC) since the China-VCS and EcoVeg approaches have many similarities (Liu et al 2019;Faber-Langendoen et al 2020). Both approaches have eight nested hierarchical classification levels for natural/semi-natural vegetation and use similar principles and criteria for the assignment of vegetation types, including major growth forms (physiognomy), vegetation structure, species composition or floristics, and ecological context (Faber-Langendoen et al 2018Guo et al 2018Guo et al , 2020.…”

The Distribution and Turnover of Bacterial Communities in the Root Zone of Seven Stipa Species Across an Arid and Semi-arid Steppe

“…The genus Stipa refers to a grazing tolerant, drought-resistant perennial bunchgrass species (Gao et al, 2018;Chen et al, 2020) that is an important ecological barrier and basically in the highly specialized stage of the grassland community succession and evolution (Lv et al, 2016;Liu et al, 2019). From eastern to western Inner Mongolia, across various combinations of hydro-thermal characteristic, different moisture ecotypes of Stipa occupy zonal habitats and form meadow steppe, typical steppe, and desert steppe.…”