Soil bacterial characteristics between surface and subsurface soils along a precipitation gradient in the Alxa Desert, China

doi:10.1007/s40333-021-0004-6

Journal of Arid Land

2021, Vol. 13

Issue (3): 257-273 DOI: 10.1007/s40333-021-0004-6

Research article

Soil bacterial characteristics between surface and subsurface soils along a precipitation gradient in the Alxa Desert, China

TENG Zeyu¹^,²^,^*(

), XIAO Shengchun¹, CHEN Xiaohong³, HAN Chao¹^,²

¹Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environmental Resources, Chinese Academy of Sciences, Lanzhou 730000, China
²University of Chinese Academy of Sciences, Beijing 100049, China
³Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

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Abstract

Bacteria in desert soil have unique phylogeny and important ecological functions, and their responses to changes in precipitation need further attention. However, relevant studies have mainly focused on the surface soil, and studies on the responses of bacteria at different soil depths to variations in precipitation are rare. Thus, we used 16S rDNA high-throughput sequencing to investigate the changes in soil bacterial distribution along a mean annual precipitation gradient (50-150 mm) in the Alxa Desert, China, and compared the variation characteristics in the surface soil layer (0-10 cm) and subsurface soil layer (10-20 cm). Results showed that soil bacterial communities significantly changed along the precipitation gradient in both soil layers. However, the subsurface soil layer could support bacterial communities with higher diversity and closer internal relationships but more internal competition than the surface soil layer. Additionally, compared with the surface soil layer, variations in diversity and co-occurrence patterns in the subsurface soil layer were more in line with the changes in the mean annual precipitation, while bacterial community structure was less variable in the subsurface soil layer. Compared with the mean annual precipitation, soil moisture had little influence on the structure and diversity of soil bacterial community but had a high correlation with intercommunity connectivity. Therefore, soil moisture might play a complex role in mediating environmental conditions and soil bacterial community characteristics. Due to the different responses of surface and subsurface soil bacteria to the changes in precipitation, it is necessary to distinguish different soil layers when predicting the trends in desert soil bacterial conditions associated with precipitation, and prediction of subsurface soil bacteria may be more accurate.

Key words： precipitation gradient soil layer soil bacterial community structure diversity co-occurrence pattern

Received: 22 September 2020 Published: 10 March 2021

Corresponding Authors:

About author: * TENG Zeyu (E-mail: Tengzeyu1990@sina.com)

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Cite this article:

TENG Zeyu, XIAO Shengchun, CHEN Xiaohong, HAN Chao. Soil bacterial characteristics between surface and subsurface soils along a precipitation gradient in the Alxa Desert, China. Journal of Arid Land, 2021, 13(3): 257-273.

URL:

http://jal.xjegi.com/10.1007/s40333-021-0004-6 OR http://jal.xjegi.com/Y2021/V13/I3/257

Table 1 Location and environmental characteristics of sampling sites

Fig. 1 Relative abundance of soil bacterial at phylum level in different MAP (mean annual precipitation)-related subregions and soil layers. Td, typical desert (50 mm precipitation); Sd, steppe desert (100 mm precipitation); Ds, desertified steppe (150 mm precipitation); A, 0-10 cm soil layer; B, 10-20 cm soil layer.

Fig. 2 Alpha diversity index in different MAP-related subregions and soil layers. Td, typical desert (50 mm precipitation); Sd, steppe desert (100 mm precipitation); Ds, desertified steppe (150 mm precipitation); A, 0-10 cm soil layer; B, 10-20 cm soil layer; PD, phylogenetic diversity. The lowercase letters indicate significant differences among different treatments at P<0.05 level.

Table 2 Result of analysis of molecular variance (AMOVA) in different MAP-related subregions and soil layers

Fig. 3 Cladograms generated according to LEfSe analysis for the precipitation divisions in layer A (a) and layer B (b). Biomarkers were statistically significant at LDA ≥4. Cladograms indicating the phylogenetic distribution of bacterial lineages associated with the three subregions. Phylum, class, order, family, genus and species levels are listed in order from inside to outside of the cladogram. Red, green and blue circles represent the bacteria enriched in Ds, Sd and Td subregions, respectively, whereas yellow circle represents the taxa showing no significant difference among the three subregions. Labels for biomarkers for the class, order and family levels are abbreviated with a single letter. For the genus and species levels, please see Figures S1 and S2 for details.

Fig. 4 Cladogram generated according to LEfSe analysis for the soil layers and biomarkers showing statistical significance at LDA ≥4. Red and green circles represent the bacteria enriched in the A and B soil layers, respectively.

Index	Td.A	Td.B	Sd.A	Sd.B	Ds.A	Ds.B
SOC (g/kg)	1.11±0.71^a	0.89±0.35^a	1.09±0.53^a	0.84±0.21^a	1.62±1.15^a	1.33±0.86^a
TN (g/kg)	0.16±0.06^b	0.21±0.05^ab	0.26±0.10^ab	0.21±0.10^ab	0.31±0.11^a	0.20±0.10^ab
TC (g/kg)	3.85±0.66^a	4.76±0.53^a	5.87±4.16^a	4.10±1.99^a	4.61±4.02^a	4.07±3.78^a
TP (g/kg)	0.28±0.07^a	0.30±0.11^a	0.25±0.03^a	0.21±0.05^b	0.16±0.04^c	0.15±0.02^c
AN (mg/kg)	15.10±9.82^b	16.28±12.12^b	32.39±9.59^a	33.95±3.69^a	27.54±10.5^ab	25.27±9.11^ab
AP (mg/kg)	1.58±0.20^b	1.42±0.54^b	2.15±0.12^ab	2.25±1.91^ab	3.10±1.03^a	1.58±0.63^b
AK (mg/kg)	99.60±31.33^b	113.43±16.60^b	155.63±40.83^a	112.05±39.01^b	127.27±47.86^ab	135.57±46.21^ab
SMC (%)	1.33±0.78^b	2.47±0.01^a	1.30±0.60^b	2.66±1.00^a	3.07±0.76^a	3.92±1.55^a
SO2- 4 (cmol/kg)	4.77±4.75^b	10.31±5.83^a	1.95±3.31^b	1.92±3.31^b	0.12±0.10^c	0.09±0.06^c
Cl^- (cmol/kg)	17.55±28.02^a	5.76±6.24^a	0.50±0.10^b	0.15±0.10^bc	0.07±0.01^c	0.07±0.01^c
HCO₃^- (cmol/kg)	0.27±0.06^b	0.23±0.04^c	0.36±0.08^ab	0.38±0.08^a	0.38±0.06^a	0.39±0.06^a
Ca²⁺(cmol/kg)	2.97±2.09^b	7.71±4.36^a	1.90±3.07^bc	1.82±3.07^bc	0.32±0.08^c	0.27±0.05^c
Mg²⁺ (cmol/kg)	0.63±0.80^a	0.44±0.29^a	0.18±0.13^b	0.17±0.13^b	0.11±0.08^b	0.12±0.04^b
K⁺ (cmol/kg)	0.13±0.11^a	0.12±0.06^a	0.04±0.04^b	0.04±0.04^b	0.04±0.02^b	0.05±0.01^b
Na⁺ (cmol/kg)	15.78±25.01^a	6.10±6.29^a	0.34±0.21^b	0.32±0.21^b	0.02±0.03^c	0.05±0.02^c
EC (dS/m)	8.04±11.31^a	5.06±3.86^a	1.36±6.14^a	4.34±6.15^a	0.31±0.03^b	0.39±0.05^b
pH	7.44±0.16^b	7.38±0.05^b	7.67±0.16^a	7.80±0.16^a	7.72±0.08^a	7.70±0.12^a
VCS (%)	5.91±2.71^a	3.22±3.63^b	0.22±0.44^c	0.03±0.07^d	0.72±0.66^c	0.33±0.29^c
CS (%)	14.30±5.99^a	8.89±6.82^ab	3.84±1.02^b	5.95±5.24^b	21.07±18.57^a	13.05±11.79^a
MS (%)	21.58±9.17^a	21.28±8.50^a	26.69±8.40^a	25.57±15.04^a	27.67±17.71^a	27.31±16.48^a
FS (%)	43.32±7.39^a	48.24±7.74^a	47.26±10.84^a	50.13±21.77^a	32.89±18.77^a	40.77±12.22^a
VFS (%)	11.13±5.53^ab	12.66±3.23^a	8.78±3.91^ab	7.90±5.14^b	11.43±9.37^ab	11.47±6.58^ab
Silt (%)	3.20±1.61^a	4.82±2.31^a	11.20±11.19^a	9.10±9.37^a	5.33±7.42^a	6.20±7.99^a
Clay (%)	0.57±0.50^a	0.88±0.67^a	2.03±2.27^a	1.32±1.67^a	0.89±1.55^a	0.87±1.51^a
TCn (g/kg)	384.71±6.85^a		377.66 ± 4.24^b		377.23±7.59^b
TNn (g/kg)	18.39±1.92^a		20.45 ± 7.31^a		21.32±6.94^a
TPn (g/kg)	1.23±0.08^ab		1.52 ± 0.78^a		1.15±0.09^b
COV (%)	4.38±1.98^c		18.14 ± 5.67^b		38.95±10.82^a

Table 3 Differences in environmental factors (soil nutrients, moisture, salinity, pH, particle size distribution, N. tangutorum stoichiometric characteristic and vegetation coverage) in different MAP-related subregions and soil layers

Fig. 5 Canonical correlation analysis (CCA) ordination of bacterial community structure at the genus level in relation to major environmental factors in layers A (a) and B (b)

Table 4 Mantel test comparing the correlation between environmental factors and soil bacterial community structure in layer A and layer B

Table 5 Parameters of network topology analysis (genus level) in different MAP-related subregions and soil layers

Fig. S1 Histogram of linear discriminant analysis (LDA) score distribution shows the categories with LDA score greater than the set value (≥ 4) in layer A (0-10 cm) along the precipitation gradient. The length of the histogram indicates the magnitude of the influence of biomarkers.

Fig. S2 Histogram of linear discriminant analysis (LDA) score distribution shows the categories with LDA score greater than the set value (≥ 4) in layer B (10-20 cm) along the precipitation gradient. The length of the histogram indicates the magnitude of the influence of biomarkers.

Fig. S3 Major environmental factors associated with alpha diversity in layers A (a) and B (b). Environmental factors are significantly correlated with at least one alpha diversity index. Asterisks indicate significance of the Spearman's r correlation. *, P<0.05 level; **, P<0.01 level.

Fig. S4 Major environmental factors associated with parameters of network topology analysis. Environmental factors are significantly correlated with at least one parameter. Asterisks indicate significance of the Spearman's r correlation. *, P<0.05 level; **, P<0.01 level.


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