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Plant Diversity, Productivity, and Soil Nutrient Responses to Different Grassland Degradation Levels in Hulunbuir, China

Yuxuan Wu, Ping Wang, Xiaosheng Hu, Ming Li, Yi Ding, Tiantian Peng, Qiuying Zhi, Qiqige Bademu, Wenjie Li, Xiao Guan and Junsheng Li ()
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Yuxuan Wu: Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Ping Wang: Command Center for Comprehensive Survey of Natural Resources, China Geological Survey Bureau, Beijing 100055, China
Xiaosheng Hu: Command Center for Comprehensive Survey of Natural Resources, China Geological Survey Bureau, Beijing 100055, China
Ming Li: Command Center for Comprehensive Survey of Natural Resources, China Geological Survey Bureau, Beijing 100055, China
Yi Ding: Command Center for Comprehensive Survey of Natural Resources, China Geological Survey Bureau, Beijing 100055, China
Tiantian Peng: Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Qiuying Zhi: Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Qiqige Bademu: Command Center for Comprehensive Survey of Natural Resources, China Geological Survey Bureau, Beijing 100055, China
Wenjie Li: Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Xiao Guan: Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Junsheng Li: Command Center for Comprehensive Survey of Natural Resources, China Geological Survey Bureau, Beijing 100055, China

Land, 2024, vol. 13, issue 12, 1-20

Abstract: Grassland degradation could affect the composition, structure, and ecological function of plant communities and threaten the stability of their ecosystems. It is essential to accurately evaluate grassland degradation and elucidate its impacts on the vegetation–soil relationship. In this study, remote sensing data based on vegetation coverage were used to assess the degradation status of Hulunbuir grassland, and five different grassland degradation degrees were classified. Vegetation community composition, diversity, biomass, soil nutrient status, and their relationships in different degraded grasslands were investigated using field survey data. The results showed that grassland degradation significantly affected the species composition of the vegetation community. As degradation intensified, species richness declined, with the proportion of Gramineae and Legume species decreasing and Asteraceae species increasing. Additionally, the proportion of annual species initially increased and then decreased. Degradation also markedly reduced aboveground, belowground, and litter biomass within the communities. Soil moisture, electrical conductivity, organic carbon, total carbon, total potassium, and hydrolyzable nitrogen contents in non-degraded areas were higher than those in severely degraded areas. Conversely, soil total phosphorus content and bulk density gradually increased with degradation. Nitrate nitrogen and ammonium nitrogen levels in severely degraded soils were significantly higher than those in non-degraded soils. Plant diversity in the study area was significantly positively correlated with aboveground biomass and belowground biomass, and it positively correlated with soil nutrient total carbon and available carbon but negatively correlated with soil bulk density. Results of the partial least squares path model showed that grassland degradation had significant negative effects on plant diversity, soil nutrients, and biomass. Soil nutrients were the main factors affecting ecosystem productivity. The direct effect of plant diversity on biomass was not significant, suggesting that soil nutrients may play a more important role than plant diversity in determining biomass during grassland degradation. The results illustrated the relationships among soil nutrients, plant diversity, and biomass in degraded grasslands and emphasized the importance of an integrated approach in the effective management and restoration of degraded grasslands.

Keywords: Hulunbuir grasslands; species diversity; community productivity; soil nutrients; partial least squares path modeling (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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