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Journal of Arid Land  2023, Vol. 15 Issue (3): 359-376    DOI: 10.1007/s40333-023-0006-7
Research article     
Soil microbial community diversity and distribution characteristics under three vegetation types in the Qilian Mountains, China
TONG Shan1,2,3, CAO Guangchao2,3,*(), ZHANG Zhuo1,2,3, ZHANG Jinhu1,2,3, YAN Xin1,2,3
1Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation (Ministry of Education), Qinghai Normal University, Xining 810008, China
2Qinghai Province Key Laboratory of Physical Geography and Environmental Process, College of Geographical Science, Qinghai Normal University, Xining 810008, China
3Academy of Plateau Science and Sustainability, People's Government of Qinghai Province and Beijing Normal University, Xining 810008, China
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Qilian Mountains in Northwest China is a significant ecological security barrier due to its distinctive geographic setting, which has significant biological resource and gene pool. In order to assess the soil quality and ecosystem health in this area, we identified the structural characteristics and functional groups of soil microbial communities. This study focused on Amidongsuo, a typical watershed of the Qilian Mountains, and researched the vertical distribution and dominant populations of soil microorganisms in different habitats, and the relationship between soil microorganisms and environmental factors. Soil microorganisms from three grassland plots, five shrubland plots, and five forest plots in Amidongsuo were studied using high-throughput sequencing. The Venn diagram showed that the types of bacteria were fewer than those of fungi in Amidongsuo. Soil bacteria Acidobacteriota, Proteobacteria, and Methylomirabilota as well as fungi Basidiomycota, Ascomycota, and Mortierellomycota played dominant roles in Amidongsuo, according to the LEfSe (linear discriminant analysis (LDA) effect size) and community structure analyses. According to the ANOSIM (analysis of similarities) result, for both bacteria and fungi, R values of grassland and shrubland were small (R2=0.045 and R2=0.256, respectively), indicating little difference between these two ecosystems. RDA (redundancy analysis) showed a closer relationship between soil nutrients and fungi, and a gradually decreasing correlation between soil nutrients and microorganisms with increasing soil depth. Bacteria were mainly affected by pH, nitrogen (N), and potassium (K), while fungi were mainly affected by K. Overall, fungi had more effect on soil quality than bacteria. Therefore, adjustment of soil K content might improve the soil environment of Amidongsuo in the Qilian Mountains.

Key wordsfungi      bacteria      diversity      soil nutrient      Qilian Mountains     
Received: 30 June 2022      Published: 31 March 2023
Corresponding Authors: * CAO Guangchao (E-mail:
Cite this article:

TONG Shan, CAO Guangchao, ZHANG Zhuo, ZHANG Jinhu, YAN Xin. Soil microbial community diversity and distribution characteristics under three vegetation types in the Qilian Mountains, China. Journal of Arid Land, 2023, 15(3): 359-376.

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Fig. 1 Sample points in the Amidongsuo watershed, Qilian Mountains, China. Grassland plots: CD1, CD2, and CD3; shrubland plots: GC1, GC2, GC3, GC4, and GC5; forest plots: LD1, LD2, LD3, LD4, and LD5.
Code Latitude Longitude Altitude (m) Slope (°) Aspect (°) Vegetation coverage (%)
Grassland CD1 38°05′08″N 100°19′08″E 2928 3.0 53 93
Grassland CD2 38°04′04″N 100°18′14″E 3043 11.0 76 87
Grassland CD3 38°03′46″N 100°16′33″E 3187 14.5 70 76
Forest LD1 38°04′55″N 100°19′04″E 2980 30.5 328 72
Forest LD2 38°04′55″N 100°19′08″E 3018 32.0 330 68
Forest LD3 38°04′51″N 100°18′57″E 2958 35.5 345 57
Forest LD4 38°03′39″N 100°16′33″E 3208 23.5 22 51
Forest LD5 38°04′01″N 100°18′03″E 3078 18.5 29 56
Shrubland GC1 38°05′02″N 100°19′04″E 2929 7.5 47 60
Shrubland GC2 38°04′04″N 100°18′10″E 3041 2.0 46 58
Shrubland GC3 38°03′46″N 100°16′33″E 3187 14.0 62 67
Shrubland GC4 38°03′57″N 100°15′07″E 3064 17.5 67 46
Shrubland GC5 38°03′57″N 100°14′24″E 3530 20.5 51 49
Table 1 Sample information in this study
Ecological systems Soil nutrient Min (g/kg) Max (g/kg) Mean (g/kg) SD
Variance Skewness Kurtosis CV (%)
Grassland TN 4.65 9.69 6.75 2.02 4.09 0.46 -1.63 29.96
TP 1.31 2.53 1.92 0.39 0.16 0.01 -0.83 20.54
TK 20.45 24.37 22.13 1.17 1.38 0.74 0.50 5.31
SOM 111.12 213.70 157.81 31.83 1013.04 0.24 -0.25 20.17
pH 6.60 8.10 7.24 0.55 0.30 0.67 -1.34 7.56
Shrubland TN 1.00 11.99 5.46 3.58 12.80 0.57 -1.10 65.48
TP 1.16 2.38 1.61 0.37 0.13 0.74 -0.39 22.72
TK 17.67 25.29 21.45 2.09 4.35 -0.25 -0.32 9.72
SOM 24.90 205.87 118.54 61.49 3781.13 0.10 -1.44 51.87
pH 6.54 8.26 7.33 0.57 0.33 0.40 -1.60 7.79
Forest TN 1.08 7.28 3.44 1.39 1.93 1.30 3.86 40.37
TP 1.17 1.91 1.49 0.23 0.05 0.52 -1.00 15.40
TK 17.85 21.96 20.15 1.09 1.20 -0.44 0.09 5.43
SOM 24.22 172.12 88.43 34.31 1177.42 0.53 1.87 38.80
pH 6.39 7.96 7.15 0.48 0.23 0.01 -1.11 6.65
Table 2 Soil nutrient characteristics in the study area
Fig. 2 Physical-chemical characteristics of soil in different ecosystems. Different uppercase letters within the same ecosystem indicate significant differences among different soil layers at P<0.05 level; different lowercase letters within the same soil layer indicate significant differences among different ecosystems at P<0.05 level. CD, grassland; GC, shrubland; LD, forest; TK, total potassium; TN, total nitrogen; SOM, soil organic matter; TP, total phosphorus. The abbreviations are the same in the following figures. (a), pH; (b), TK; (c), TN; (d), SOM; (e), TP. Bars are standard errors.
Fig. 3 Dilution curves of soil microbial samples. (a), bacteria; (b), fungi. OTUs, operational taxonomic units
Fig. 4 Venn diagram of OTUs (operational taxonomic units). (a), bacteria; (b), fungi.
Fig. 5 Community characteristics of different ecosystems at the phylum level. (a), bacteria; (b), fungi.
Fig. 6 LEfSe (linear discriminant analysis (LDA) effect size) diagram. (a), bacteria; (b), fungi.
Fig. 7 LDA (linear discriminant analysis) discriminant results. (a), bacteria; (b), fungi.
Ecological systems Soil layer (m) ACE Chao1 Shannon Simpson
Grassland 0-10 848.87±16.42a 860.46±19.15a 8.26±0.03a 0.99±0.00a
10-20 878.71±8.89a 896.87±9.72a 8.24±0.09a 0.99±0.00a
20-30 871.36±13.03a 891.86±14.21a 8.21±0.07a 0.99±0.00a
Shrubland 0-10 865.72±16.99a 874.30±16.69a 8.32±0.03a 0.99±0.00a
10-20 862.82±17.68a 868.80±19.62a 8.24±0.06ab 0.99±0.00a
20-30 865.69±12.19a 877.63±9.56a 8.11±0.03b 0.99±0.00a
Forest 0-10 806.85±51.89a 818.34±53.11a 8.13±0.11a 0.99±0.00a
10-20 827.86±50.60a 851.80±47.64a 8.17±0.09a 0.99±0.00a
20-30 814.32±36.01a 832.69±37.94a 7.90±0.08a 0.99±0.00a
Table 3 Alpha diversity index of bacteria in different ecosystems
Ecological systems Soil layer (cm) ACE Chao1 Shannon Simpson
Grassland 0-10 563.03±35.14a 569.07±32.27a 5.60±0.56a 0.94±0.03a
10-20 532.40±47.18a 542.06±55.10a 5.43±1.04a 0.85±0.11a
20-30 576.54±52.63a 582.42±52.11a 6.22±0.76a 0.94±0.04a
Shrubland 0-10 566.65±39.65a 570.91±38.30a 5.49±0.44a 0.92±0.03a
10-20 554.05±28.73a 559.62±27.26a 5.33±0.40a 0.92±0.02a
20-30 541.54±43.03a 548.26±45.57a 4.96±0.48a 0.86±0.03a
Forest 0-10 395.9±52.24a 390.96±49.98a 3.73±0.55a 0.77±0.09a
10-20 404.26±38.15a 403.06±37.07a 3.98±0.67a 0.78±0.11a
20-30 327.51±18.77a 327.25±20.63a 3.96±0.57a 0.81±0.07a
Table 4 Alpha diversity index of fungi in different ecosystems
Fig. 8 ANOSIM (analysis of similarities) of the soil microbial community. (a1-a4), bacteria; (b1-b4), fungi. In Figure 8, boxes indicate the IQR (interquartile range, 75th to 25th of the data). The median value is shown as a line within the box. Whiskers extend to the most extreme value within 1.5×IQR. Outlier is shown as circle.
Fig. 9 RDA (redundancy analysis) of soil nutrients and microorganisms at the phylum level. (a), (c), and (e) are the RDA of bacteria and soil nutrients; (b), (d), and (f) are the RDA of fungi and soil nutrients; (a) and (b), 0-10 cm soil layer; (c) and (d), 10-20 cm soil layer; (e) and (f), 20-30 cm soil layer.
Ecological systems Bacteria ACE Chao1 Shannon Simpson
Grassland TN -0.73* -0.73* 0.34 0.72*
TP -0.80* -0.79* 0.11 0.46
TK -0.38 -0.31 0.09 0.45
SOM -0.37 -0.41 0.26 0.48
pH 0.82** 0.77* -0.10 -0.53
Shrubland TN 0.01 -0.04 0.31 0.26
TP -0.05 -0.11 0.11 0.20
TK 0.46 0.46 0.08 -0.08
SOM 0.01 -0.07 0.12 0.11
pH 0.53* 0.52* 0.24 0.03
Forests TN -0.08 -0.14 0.22 0.47
TP -0.75** -0.76** -0.54* -0.06
TK 0.10 0.15 -0.19 -0.24
SOM -0.25 -0.28 0.12 0.50
pH -0.10 -0.04 -0.39 -0.44
Table 5 Correlation between bacterial diversity and soil nutrients
Ecological systems Fungi ACE Chao1 Shannon Simpson
Grassland TN -0.60 -0.56 -0.66 -0.42
TP -0.47 -0.44 -0.41 -0.20
TK -0.45 -0.41 -0.34 -0.12
SOM -0.48 -0.48 -0.56 -0.36
pH 0.73* 0.70* 0.60 0.38
Shrubland TN 0.04 0.02 -0.29 -0.25
TP -0.04 -0.04 -0.34 -0.41
TK 0.22 0.23 0.34 0.11
SOM 0.04 0.04 -0.40 -0.42
pH 0.46 0.44 0.72** 0.62*
Forest TN -0.05 -0.05 0.09 0.11
TP -0.00 -0.02 0.02 0.06
TK -0.18 -0.17 -0.29 -0.35
SOM -0.23 -0.24 0.02 0.05
pH 0.09 0.12 0.05 -0.15
Table 6 Correlation between fungal diversity and soil nutrients
Fig. 10 Differences in diversity value across different ecosystems. (a), bacteria; (b), fungi. Different uppercase letters within the same diversity index indicate significant differences among different ecosystems at P<0.05 level. ACE, abundance-based coverage estimator.
Soil nutrient ACE Chao1 Shannon Simpson
TN 0.33* 0.33* 0.11 0.13
TP 0.12 0.14 0.18 0.18
TK 0.34* 0.36* 0.36* 0.20
SOM 0.35* 0.35* 0.15 0.15
pH 0.20 0.21 0.42** 0.21
Table 7 Correlation between soil nutrients and fungal alpha diversity index
Soil nutrient ACE Chao1 Shannon Simpson
TN 0.10 0.06 0.37* 0.52**
TP -0.21 -0.23 0.04 0.36*
TK 0.27 0.28 0.17 0.23
SOM 0.05 0.01 0.28 0.47**
pH 0.17 0.19 -0.05 -0.14
Table 8 Correlation between soil nutrients and bacterial alpha diversity index
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