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Review
. 2024 Apr 25;12(5):854.
doi: 10.3390/microorganisms12050854.

The Impact of Artificial Restoration of Alpine Grasslands in the Qilian Mountains on Vegetation, Soil Bacteria, and Soil Fungal Community Diversity

Xiaomei Yang  1 , Qi Feng  1 , Meng Zhu  1 , Jutao Zhang  1 , Linshan Yang  1 , Ruolin Li  1
Affiliations
Review

The Impact of Artificial Restoration of Alpine Grasslands in the Qilian Mountains on Vegetation, Soil Bacteria, and Soil Fungal Community Diversity

Xiaomei Yang et al. Microorganisms. .

Abstract

To understand how the soil microbial community structure responds to vegetation restoration in alpine mining areas, this study specifically examines the grassland ecosystem in the Qianmalong mining area of the Qilian Mountains after five years of artificial restoration. High-throughput sequencing methods were employed to analyze soil bacteria and fungi microbial characteristics in diverse grassland communities. Combined with modifications in vegetation diversity as well as soil physicochemical properties, the impact of vegetation restoration on soil microbiome diversity in this alpine mining area was investigated. The findings indicated that the dominant plants were Cyperus rotundus, Carex spp., and Elymus nutans. As the extent of the grassland's restoration increased, the number of plant species, importance values, and plant community diversity showed an increasing trend. The plant functional groups were mainly dominated by Cyperaceae, followed by Poaceae. Plant height, density, plant cover, frequency, and aboveground biomass showed an increasing trend, and soil water content (SWC) increased. While soil pH and soil electrical conductivity (EC) exhibited a declining trend, available phosphorus (AP), total phosphorus (TP), total nitrogen (TN), nitrate nitrogen (NO3-N), soil organic carbon (SOC), and soil water content (SWC) showed an increasing trend. The dominant bacterial communities were Actinobacteriota, Proteobacteria, Acidobacteriota, Chloroflexi, Firmicutes, and Gemmatimonadota, while the dominant fungal communities were Ascomycota, Mortierellomycota, Basidiomycota, unclassified_k_Fungi, and Glomeromycota. Significant differences were detected within soil microbial community composition among different degrees of restoration grasslands, with bacteria generally dominating over fungi. SWC, TP, and TN were found to be the main soil physicochemical factors affecting the distribution of soil bacterial communities' structure; however, SOC, TN, and NO3-N were the primary factors influencing the soil distribution of fungal communities. The results of this study indicate that different degrees of vegetation restoration in alpine mining areas can significantly affect soil bacterial and fungal communities, and the degree of restoration has varying effects on the soil bacteria and fungi community structure in alpine mining areas.

Keywords: Qilian Mountains; alpine mining area grassland; artificial restoration; microbial diversity; vegetation diversity.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Location of the study area in the Chinese Qilian Mountains (a,b); LR, low restoration (c); HR, high restoration (d); and NG, natural grassland serving as the CK (e).
Figure 2
Figure 2
Variations in the diversity of grassland vegetation communities across various restoration stages. LR, low restoration; HR, high restoration; NG, natural grassland. Different letters on the back of the values between treatments indicate significant differences at the 0.05 level.
Figure 3
Figure 3
Changes in physicochemical properties of grassland soils in mining zones featuring various levels of restoration. LR, low restoration; HR, high restoration; NG, natural grassland. pH, soil pH; EC, soil electroconductibility; SWC, soil water content; SOC, soil organic carbon; TN, soil total nitrogen; AP, soil available phosphorus; TP, soil total phosphorus; NO3-N, soil nitrate nitrogen. (a) pH and SWC content; (b) EC and SOC content; (c)TN and TP content; (d) NO3-N and AP content, ** indicates extremely significant correlation (p < 0.01), *** indicates extremely significant correlation (p < 0.001), black circle indicates outliers, same as below.
Figure 4
Figure 4
Numbers and proportions of common and unique genera and OTUs of soil bacteria in grasslands in mining zones featuring various levels of restoration.
Figure 5
Figure 5
Relative abundance of soil bacterial communities across both phylum and order levels within grasslands of mining sites with different degrees of restoration (%). (a) Relative abundance of soil bacterial communities of phylum level; (b) Relative abundance of soil bacterial communities of order level.
Figure 6
Figure 6
Differences in the gate-level structure of soil bacterial communities in grasslands of mining zones featuring various levels of restoration. (a) Discriminant analysis of multilevel species differences in soil bacterial Lefse; (b) Differences in soil bacterial species.
Figure 7
Figure 7
Principal component analysis (PCA) and RDA (redundancy analysis) of soil bacteria in grasslands of mining areas with different degrees of restoration. (a) PCA of soil bacteria; (b) RDA of soil bacteria.
Figure 8
Figure 8
Correlation between major bacteria and soil properties at soil gate and phylum levels in grassland soils of mining sites with different degrees of restoration. (a) Correlation between major bacteria and soil properties at soil gate level; (b) Correlation between major bacteria and soil properties at soil phylum level. * indicates extremely significant correlation (p < 0.05), ** indicates extremely significant correlation (p < 0.01), *** indicates extremely significant correlation (p < 0.001).
Figure 9
Figure 9
Co-linearity network analysis of the top 20 soil bacteria species in grasslands with different recovery levels in the Qianmalong mining area. (a) Co-linearity network analysis of the top 20 soil bacteria species in LR; (b) Co-linearity network analysis of the top 20 soil bacteria species in HR; (c) Co-linearity network analysis of the top 20 soil bacteria species in NG. Each species is represented by a node of a different color, where node size is inversely related to its degree. Nodes representing species within the same phylum share identical colors. Positive correlations are denoted by red lines, negative correlations by blue lines, and edges signify significant Spearman correlations with a p-value less than 0.05 (p < 0.05), the same as below.
Figure 10
Figure 10
Numbers and proportions of common and unique genera and OTUs of soil fungal communities in grasslands in mining regions exhibiting varying levels of restoration.
Figure 11
Figure 11
Proportional presence of soil fungal populations at the phylum and order levels in grasslands of mining sites with different degrees of restoration (%). (a) Relative abundance of soil fungal communities of phylum level; (b) Relative abundance of soil fungal communities of order level.
Figure 12
Figure 12
Differences in gate-level makeup of soil fungal populations within grasslands across mining regions at varying restoration levels. (a) Discriminant analysis of multilevel species differences in soil fungal Lefse; (b) Differences in soil fungal species.
Figure 13
Figure 13
Principal component analysis (PCA) and RDA (redundancy analysis) of soil fungi in grasslands across mining regions at varying restoration stages. (a) PCA of soil fungi; (b) RDA of soil fungi.
Figure 14
Figure 14
Correlation between major fungi and soil properties at soil gate and phylum levels in grassland soils of mining sites with different degrees of restoration. (a) Correlation between major fungi and soil properties at soil gate level; (b) Correlation between major fungi and soil properties at soil phylum level. * indicates extremely significant correlation (p < 0.05), ** indicates extremely significant correlation (p < 0.01), *** indicates extremely significant correlation (p < 0.001).
Figure 15
Figure 15
Co-linearity network analysis of the top 20 soil fungi species in grasslands with different recovery levels in the Qianmalong mining area. (a) Co-linearity network analysis of the top 20 soil fungi species in LR; (b) Co-linearity network analysis of the top 20 soil fungi species in HR; (c) Co-linearity network analysis of the top 20 soil fungi species in NG.
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