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Journal of Arid Land  2020, Vol. 12 Issue (2): 303-317    DOI: 10.1007/s40333-020-0121-7
Research article     
Assessing the collapse risk of Stipa bungeana grassland in China based on its distribution changes
QIAO Xianguo1,2, GUO Ke1,2,*(), LI Guoqing3,4, ZHAO Liqing5, LI Frank Yonghong5, GAO Chenguang1
1 State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
2 University of Chinese Academy of Sciences, Beijing 100049, China
3 State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China
4 Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
5 Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
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The criteria used by International Union for Conservation of Nature (IUCN) for its Red List of Ecosystems (RLE) are the global standards for ecosystem-level risk assessment, and they have been increasingly used for biodiversity conservation. The changed distribution area of an ecosystem is one of the key criteria in such assessments. The Stipa bungeana grassland is one of the most widely distributed grasslands in the warm-temperate semi-arid regions of China. However, the total distribution area of this grassland was noted to have shrunk and become fragmented because of its conversion to cropland and grazing-induced degradation. Following the IUCN-RLE standards, here we analyzed changes in the geographical distribution of this degraded grassland, to evaluate its degradation and risk of collapse. Past (1950-1980) distribution areas were extracted from the Vegetation Map of China (1:1,000,000). Present realizable distribution areas were equated to these past areas minus any habitat area losses. We then predicted the grassland's present and future (under the Representative Concentration Pathway 8.5 scenario) potential distribution areas using maximum entropy algorithm (MaxEnt), based on field survey data and nine environmental layers. Our results showed that the S. bungeana grassland was mainly distributed in the Loess Plateau, Hexi Corridor, and low altitudes of the Qilian Mountains and Longshou Mountain. This ecosystem occurred mainly on loess soils, kastanozems, steppe aeolian soils and sierozems. Thermal and edaphic factors were the most important factors limiting the distribution of S. bungeana grassland across China. Since 56.1% of its past distribution area (4.9×104 km2) disappeared in the last 50 a, the present realizable distribution area only amounts to 2.2×104 km2. But only 15.7% of its present potential distribution area (14.0×104 km2) is actually occupied by the S. bungeana grassland. The future potential distribution of S. bungeana grassland was predicted to shift towards northwest, and the total area of this ecosystem will shrink by 12.4% over the next 50 a under the most pessimistic climate change scenario. Accordingly, following the IUCN-RLE criteria, we deemed the S. bungeana grassland ecosystem in China to be endangered (EN). Revegetation projects and the establishment of protected areas are recommended as effective ways to avert this looming crisis. This empirical modeling study provides an example of how IUCN-RLE categories and criteria may be valuably used for ecosystem assessments in China and abroad.

Key wordsclimate change      limiting factors      maximum entropy algorithm      potential distribution      realizable distribution      Red List of Ecosystems      Loess Plateau     
Received: 13 June 2018      Published: 10 March 2020
Corresponding Authors: Ke GUO     E-mail:
About author: *Corresponding author: GUO Ke (E-mail:
Cite this article:

QIAO Xianguo, GUO Ke, LI Guoqing, ZHAO Liqing, LI Frank Yonghong, GAO Chenguang. Assessing the collapse risk of Stipa bungeana grassland in China based on its distribution changes. Journal of Arid Land, 2020, 12(2): 303-317.

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Fig. 1 Framework used to assess the collapse risk of Stipa bungeana grassland based on changes in its spatial distribution. Vegetation Map of China (VMC; 1:1,000,000) was used to extract the pertinent vegetation information for the period 1950-1980. This map was originally generated from field survey data for this period. ENMs is the abbreviation for ecological niche models.
Fig. 2 Locations of the 111 vegetation sites dominated by S. bungeana grassland that were used for predicting its distribution in MaxEnt (a), the current status of S. bungeana grassland in abandoned cropland (b) and artificial Caragana microphylla shrubland where S. bungeana is growing between shrubs (c)
Fig. 3 The past and present realizable distributions (a), limiting factors of the distribution (b), present potential distribution (c) and future potential distribution under Representative Concentration Pathway (RCP) 8.5 (d) of Stipa bungeana grassland in China. ST, soil type; PWM, precipitation of the wettest month; PDM, precipitation of the driest month; PS, precipitation seasonality; MDR, mean diurnal range; ISO, isothermality; TS, temperature seasonality; MTWM, mean temperature of the warmest month; MTCM, mean temperature of the coldest month.
Variable Contribution (%) Present potential suitable habitat category
Core area Medium area Marginal area Unsuitable area
PWM (mm) 22.7 45.0-135.0 41.0-140.0 32.0-159.0 3.0-1244.0
PDM (mm) 0.0 0.0-6.0 0.0-18.0 0.0-18.0 0.0-206.0
PS 0.1 78.3-121.1 37.7-122.5 35.2-141.5 19.1-155.7
MDR (°C) 11.6 10.2-16.1 9.2-16.2 8.7-16.2 1.1-20.1
ISO (%) 2.9 24.2-39.6 23.4-41.8 21.7-45.4 4.4-76.3
TS 2.2 8.0-13.3 6.9-13.5 6.4-14.4 0.0-18.3
MTWM (°C) 9.9 18.0-31.2 17.0-31.8 14.5-32.1 -15.7-40.3
MTCM (°C) 12.8 -22.3- -9.2 -23.5- -7.2 -24.1- -4.3 -48.1- -14.5
ST 37.8 - - - -
Table 1 MaxEnt model-predicted climatic ranges for the present suitable distribution area of Stipa bungeana grassland across China, and the respective contribution of nine environmental variables to these predictions
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