Plant cover of Ammopiptanthus mongolicus and soil factors shape soil microbial community and catabolic functional diversity in the arid desert in Northwest China

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Plant cover of Ammopiptanthus mongolicus and soil factors shape soil microbial community and catabolic functional diversity in the arid desert in Northwest China Yiling Zuoa, Chao Heb, Xueli Hea,*, Xia Lia, Zike Xuea, Xinmei Lia, Shaojie Wanga a b

College of Life Sciences, Hebei University, Baoding 071002, China Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100193, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Soil microbial community Spatial patterns Environmental variables Ammopiptanthus mongolicus Desert ecosystem

Although the spatial patterns of soil microbial community composition are well studied, little is known about the main factors driving microbial distributions in desert ecosystems. Therefore, the objective of this study was to examine the comprehensive effect of spatial scale, plant cover and environmental properties on soil microorganisms under Ammopiptanthus mongolicus canopies at 5 desert locations in Northwest China. Sampling site significantly influenced the soil microbial community structure and metabolic functions under A. mongolicus canopies. Variation of soil microbial communities was mostly attributed to the simultaneous effects of plant cover and soil factors, while purely plant cover explained more variation in catabolic function than did the principle coordinates of neighbour matrices (PCNM) and soil factors. Soil microbial structure and catabolic metabolism were both significantly affected by phosphatase, glomalin and soil organic carbon. Bacterial and actinomycete phospholipid fatty acids (PLFAs) were positively correlated with ammonium nitrogen (N), and the utilization of carbohydrates, carboxylic acids and amino acids was positively correlated with Olsen phosphorus (P). Bacteria (1.43–4.79 nmol∙g−1) were most common in the microbial community, followed by actinomycetes (0.54–1.89 nmol∙g−1) and fungi (0.29–0.48 nmol∙g−1). Carbohydrates (12%–85%) and amino acids (5%–59%) were the main carbon sources for soil microbes. Soil microbial community abundance and catabolic utilization were significantly higher in the rhizosphere of A. mongolicus than in the bulk soils, and principal component analyses (PCAs) significantly separated the rhizospheric soil microbes from those of infertile bulk soils. The results of this study support the conclusion that soil microbial composition and catabolic functional diversity in desert soils are spatially predictable and determined more by specific soil properties and plant cover than by large-scale distance. This research provides a basis for evaluating the management of soil resources and microbial function in desert environments.

1. Introduction Arid desert environments cover a substantial portion of Earth’s terrestrial surface and are characterized by spatial and temporal heterogeneity in rainfall, soil resources, and plant community structure (Niu et al., 2015). In arid desert areas, several indigenous shrubs form rhizospheric “fertility or resource islands”, which are different from the infertile soils in the areas between shrubs (Rango et al., 2006; Kaplan et al., 2013). These rhizospheric below-ground ecosystems represented high productivity and are deeply affected by the activity of soil microbial communities, which act as useful indicators of ecosystem change. Studies of soil microbial variations under affiliated hosts in arid habitats can help elucidate the ecological significance of soil

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microorganisms. Microorganisms are among the most abundant soil organisms and account for over 80% of the total biomass of most soil ecosystems (Fierer and Jackson, 2006). They largely determine the functioning of terrestrial ecosystems and have direct interactions with above- and below- ground parts of plants, thereby creating strong feedbacks between plants and microorganisms. Particularly in desert regions, the interactions between plants and their rhizospheric microbes exert positive effects on water and nutrient absorption and tolerance of host plant to drought stress (Dotaniya and Meena, 2015; Zhang et al., 2017). Studies have contributed to our understanding of the influence of rhizospheric microflora on plant root secretions and soil nutrient cycling, which further affect plant growth (Eisenhauer et al., 2012). While

Corresponding author. E-mail addresses: [email protected] (Y. Zuo), [email protected] (X. He).

https://doi.org/10.1016/j.apsoil.2019.103389 Received 10 March 2019; Received in revised form 11 October 2019; Accepted 15 October 2019 0929-1393/ © 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Yiling Zuo, et al., Applied Soil Ecology, https://doi.org/10.1016/j.apsoil.2019.103389

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abundant and exhibits a heterogeneous distribution. Psammophytic shrubs and grasses (e.g., Haloxylon ammodendron, Caragana korshinskii, Lespedeza davurica and Salix cheilophila) also occur at each site.

studies have highlighted variations in the composition of microbial communities (Pointing and Belnap, 2012; Makhalanyane et al., 2015), information is still lacking with regard tomicrobial catabolic functional diversity in arid ecosystems. Bardgett et al. (2006) described how changes in plant root growth, turnover and carbon supply affected the decomposition rate of organic matter by soil microbes. Furthermore, organic nutrition can provide the organic carbon required for the catabolism of soil microbes and obligate root-associated organisms, such as symbiotic mutualists (He et al., 2010; Zhang et al., 2017). The scale at which environmental variation is considered in association with microbial diversity varies greatly, from tens or thousands of kilometres to metres and even the microscale (Dequiedt et al., 2011; Vos et al., 2013). A meta-analysis of the spatial scale of microbial diversity revealed that different environmental parameters and ecological processes may be associated with observed diversity distributions (BenDavid et al., 2011; Martiny et al., 2011; Andrew et al., 2012; GarciaPichel et al., 2013). Ranjard et al. (2013) found that environmental heterogeneity determined the similarity of soil bacterial communities, and the variation of soil bacterial diversity increased with changes in habitat. Studies have described that the main factors affecting the spatial distribution of arbuscular mycorrhizal fungi (AMF), especially in arid areas, were soil available phosphorus content (Likar et al., 2013) and water availability (Moebius et al., 2013). In addition, numerous studies have demonstrated that the specific abiotic factors have a profound influence on microbial distributions vary spatially at regional or local scalesespecially soil factors (Franklin and Mills, 2003; Drenovsky et al., 2004; Deyn et al., 2011). Although our understanding of the phylogenetic and taxonomic biogeography of soil microbial communities continues to expand (Liu et al., 2018; Xu et al., 2018), there has been limited progress in understanding how the functional capabilities of soil microbial communities change across biomes in arid desert environments. Ammopiptanthus mongolicus (Maxim. ex Kom.) Cheng f. is the only evergreen broad-leaf legume shrub found in the desert area of Northwest China (Li et al., 2015). The long-term evolution of desert ecosystems results in special microbial community compositions and functional traits in the rhizosphere of A. mongolicus, and this species is typically used as a windbreak to protect soil from water loss or wind erosion. The heterogeneity of A. mongolicus along arid desert transects in Northwest China provides the optimal setting for investigations of the spatial variation in soil microbial communities and their responses to environmental change at a large scale. In this study, the objective was to determine the comprehensive effect of plant cover, environmental properties and spatial distance on the composition and function of microbial communities across sampling sites in the arid desert of Northwest China. Such data can contribute to our understanding of the response and ecological distribution of soil microorganisms in desert systems. Considering the ecological efficiency of plants, we expected to identify several edaphic factors that can be used as potential variables for future studies of microbial communities in desert environments.

2.2. Experimental design The experiment followed a completely randomized block design with 5 sample sites × 2 types of soil samples. The 5 sample sites were selected to capture large-scale spatial variation. The 2 types of soil samples included the rhizospheric soils (S) of A. mongolicus and bulk soils (S-K) between the plants without vegetation cover. At each sample site, three sample plots were chosen in July 2016, and five replicate soil cores (3 cm in diameter) of both rhizospheric and bulk soils were randomly collected from a depth of 30 cm in each plot. In total, 150 soil samples were collected (5 sites × 3 plots × 5 replicates × 2 soil types). Before collecting soil samples, the upper layer of soil (approximately 1–5 mm) was removed to clear away litter. Rhizospheric soil cores were then collected at a depth of 0–30 cm within 0–30 cm of the main stem of shrubs in each plot. Additional soil cores were removed from between the plants without vegetation cover as corresponding bulk soil samples. Five subsamples for each plot were mixed into one sample, and a total of 30 soil samples were obtained. The soil samples were placed in individual plastic bags and transported to the laboratory in an insulated container. Before processing, the samples were sieved (2–mm mesh size). One subsample from each replicate was frozen at −80 °C for soil microbial community composition analysis, another was stored at 4 °C for microbial catabolic function and enzyme analyses, and a third subsample was air-dried at room temperature to determine soil properties. Both soil microbial analysis and enzyme determination were completed within half a month. 2.3. Soil analysis

2. Materials and methods

Soil organic carbon (SOC) was estimated by the combustion method, with samples being heated in a muffle furnace (TMF–4–10 T) at 550℃ for 4 h (Heiri et al., 2001). Ammonium nitrogen (N) was determined using a Smartchem 200 (Alliance, France) analyser. Olsen phosphorus (P) was determined by using the chlorostannus-reduced molybdophosphoric blue colour method via extraction with 0.5 M sodium bicarbonate for 30 min (Olsen et al., 1954). Soil pH was determined with a digital pH meter (PHS-3C) in a 1:2.5 soil:water suspension. Soil urease (U) activity was determined using the method of Hoffmann and Teicher (1961) and expressed as μg of NH4+-N released from 1 g of soil over a period of 3 h. Soil acid phosphatase (ACP) and alkaline phosphatase (ALP) were detected following the method of Tarafdar and Marschner (1994), and the unit of phosphatase activity (Eu) was mmol P-nitrophenyl phosphate (pNPP) g−1 soil h−1 that was released by phosphatase. Glomalin in soils was quantified as glomalinrelated soil proteins (GRSPs), which include easily extractable GRSPs (EE-GRSPs, EEGs) and total Bradford-reactive soil proteins (BRSPs) (TBRSPs, TGs). The EEGs and TGs were calculated according to Wright and Upadhyaya (1998).

2.1. Study sites

2.4. Phospholipid fatty acid (PLFA) profiles

Soil samples were collected in desert regions of Northwest China. These areas have a typical semi-arid continental climate, with considerable seasonal and diurnal temperature variations. The average annual temperature is 5–10℃, and the average annual precipitation is 80–350 mm, 150–200 mm, and 45–120 mm in Inner Mongolia, Ningxia, and Gansu, China, respectively. The studied soils comprised entisols and aridisols (Eswaran et al., 2002; Soil Survey Staff, 2014). The five selected sites were Wuhai (WH), Dengkou (DK), and Alxa Left Banner (ALS) in Inner Mongolia; Minqin (MQ) in Gansu; and Shapotou (SPT) in Ningxia (Table S1). The landscape at each location is characterized by desert sand, and the vegetation is dominated by A. mongolicus, which is

Soil microbial community structure was determined by analysing the ester-linked PLFA composition of the soil. Briefly, lipids from approximately weighed (8.0 g) frozen soil subsamples were extracted overnight by the modified Bossio and Scow (1998) method using 23 mL of chloroform:methanol:phosphate buffer (1:2:0.8 v/v/v). The extracts were separated on silica acid columns by sequential elution using organic solvents with increasing polarity, followed by evaporation under N2. The phospholipids were sequentially saponified and methylated, forming fatty acid methyl esters. Individual fatty acid methyl esters were identified and quantified using a gas chromatograph (Agilent 6890 N) equipped with the MIDI 2

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Table 1 Soil microbial community composition in the rhizospheric soils of A. mongolicus and bulk soils and the interaction effect between site and plant cover. Item

Total PLFAs nmol∙g−1

AMF nmol∙g−1

G+ nmol∙g−1

Gnmol∙g−1

Fungi nmol∙g−1

Actinomycetes nmol∙g−1

G+/Gnmol∙g−1

F/B nmol∙g−1

WH S WH S-K DK S DK S-K ALS S ALS S-K MQ S MQ S-K SPT S SPT S-K Site Plant cover Site*Plant cover

6.19cd 0.61f 3.95de 0.61f 9.29ab 0.79f 11.57a 0.52f 8.28bc 1.30ef ** *** **

0.13b 0.00c 0.12b 0.01c 0.31a 0.02c 0.35a 0.00c 0.11b 0.03c *** *** ***

2.22bc 0.40d 1.53c 0.37d 3.14b 0.46d 4.10a 0.31d 3.07b 0.60d ** *** **

2.59bc 0.10d 1.43cd 0.12d 3.78ab 0.15d 4.79a 0.07d 3.72ab 0.25d

0.29bc 0.06d 0.34abc 0.04d 0.48a 0.09d 0.44ab 0.08d 0.31bc 0.20cd

0.86d 4.11ab 1.08d 3.10bc 0.83d 3.44abc 0.86d 4.51a 0.97d 2.32c

***

***

0.96c 0.06f 0.54d 0.06f 1.59b 0.08f 1.89a 0.05f 1.08c 0.22e *** *** ***

0.06de 0.11bc 0.11bc 0.09cd 0.07cde 0.14b 0.05de 0.22a 0.04e 0.23a * *** ***

***

Columns marked with different letters are significantly (P ≤ 0.05) different. * Significant at the 0.05 probability level. ** Significant at the 0.01 probability level. *** Significant at the 0.001 probability level.

functional diversity. SPSS 19.0 software was used for all of the above analyses. Principal component analysis (PCA) was used to visualize the differences in soil microbial community composition and catabolic functional diversity between the rhizospheric and bulk soils, for which CANOCO 4.5 Windows was used. Geographic coordinates were used as the large-scale spatial variable and processed by the principle coordinates of neighbour matrices (PCNM) procedure (Griffith and Peres-Neto, 2006) to obtain all detectable spatial scales in the dataset. Forward selection procedures were used to select subsets of soil environmental factors and PCNM variables. Variation partitioning was performed to estimate the proportion of variation in soil microbial composition and catabolic function explained by host plant cover, the soil environment and spatial distance alone and in combination. The RStudio package vegan (Borcard et al., 2011) was used for analysis and plotting.

software package Sherlock MIS version 4.5 (MIDI Inc., Newark, Delaware, USA) and were analysed for PLFAs. The MIDI software package automatically controlled all gas chromatographic operations, including calibration, subsequent sample sequencing, peak integration, and naming. The calibration standards contained a mixture of straightchain saturated and hydroxy fatty acid methyl esters with a length of 10–20 carbons (MIDI Part No. 1208). 2.5. Microbial catabolic functional diversity Microbial community functional diversity (microbial communitylevel physiological profiles, CLPPs) was determined using Biolog EcoPlatesTM (Zak et al., 1994), which allowed triplicate samples to be analysed on a single 96-well microtitre plate that contained 31 different individual carbon sources divided into 6 types: carbohydrates, carboxylic acids, amino acids, polymers, phenolic acids and amines (Choi and Dobbs, 1999). In brief, 10 g of collected soil was placed in an autoclaved flask, to which 90 mL of 0.85% NaCl was added. After the mouth was sealed, the flask was shaken at 250 rpm for 30 min and then cooled on ice for 2 min. Subsequently, 5 mL of the clear supernatant was transferred to a 100-mL flask filled with 45 mL of sterile distilled water. After 3 dilutions, 1:1000 extract was made and used for an enzyme-linked immunoabsorbent assay (ELISA). A 150-μL aliquot of extraction solution was added into each well of a Biolog EcoPlate that had been prewarmed to 28℃. The plate was incubated at 28℃, and the absorbance at 590 nm was recorded at 24, 48, 72, 96, 120, 144, 168, 192, 216, and 240 h using the ELISA reaction plate reader. The ELISA reaction of the rhizospheric microbial populations was measured as the average well colour development (AWCD) in each microplate. AWCD was calculated using the following formula:

3. Results 3.1. Soil variables and soil enzymes Soil variables in the rhizospheric and bulk soils across all sampling sites were determined (Table S2). The maximum soil pH value of 8.01 was recorded at SPT, and no difference was found between the rhizospheric and bulk soils at any sample sites. All soil parameters except U had the highest value at MQ and the lowest value at DK. At the 5 sites, ALP, U, EEG and P had higher levels in rhizospheric soils than in bulk soils. Furthermore, the availabilities of SOC and N were significantly higher in the rhizospheric soils than in the bulk soils at ALS. The twoway analysis (Table S2) showed that the site-by-plant cover interaction had significant interactiveeffects on soil enzymes and physicochemical properties (except pH).

AWCD= [Σ(C–R)]/31 3.2. Soil microbial community composition

where C is the absorbance of each well hosting one of the 31 carbon sources and R is the corresponding absorbance of the control wells.

The tested microbial composition variables, including total PLFAs, AMF, gram-positive bacteria (G+), gram-negative bacteria (G-), fungi and actinomycetes, exhibited high variation (Table 1). Across all sampling sites, the soil microbial contents were significantly higher in the rhizospheric soils than in the bulk soils, except for G- and fungi. G + and G- played a dominant role in the rhizospheric soil microbial community, in which the microbial groups ranked as follows: bacteria > actinomycetes > fungi > AMF. G + and G- differed significantly among sites, with the lowest frequency at DK (1.53 nmol∙g−1). G + bacteria were more abundant than G- bacteria at WH, ALS, MQ and SPT, while at DK, the opposite pattern was observed.

2.6. Statistical analysis Spatial variations in soil variables, soil microbial community composition and catabolic functional diversity were assessed using one-way analysis of variance (ANOVA), two-way ANOVA and an independentsamples T-test, respectively; comparisons among means were performed using the least significant difference method (P < 0.05). Correlation analysis was used to test the effects of environmental variables on soil microbial community structure and catabolic 3

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Fig. 1. Average well colour development representing microbial metabolism in the rhizospheric soils of A. mongolicus and bulk soils. Table 2 Relative utilization ratio of six groups of carbon sources in the rhizospheric soils of A. mongolicus and bulk soils and the interaction effect between site and plant cover. Item

Carbohydrats

Carboxylic acids

Polymes

Amines

Phenolic acids

Amino acids

WH S WH S-K DK S DK S-K ALS S ALS S-K MQ S MQ S-K SPT S SPT S-K Site Plant cover Site*Plant cover

0.55a 0.48e 0.12g 0.09g 0.79b 0.48e 0.85c 0.68d 0.29f 0.10g *** *** ***

0.33a 0.15f 0.04h 0.03h 0.40b 0.21e 0.44c 0.23d 0.07g 0.03h *** *** ***

0.44b 0.27e 0.10f 0.09f 0.48b 0.44d 0.59b 0.25c 0.10f 0.09f *** *** ***

0.15a 0.02b 0.07f 0.02d 0.15c 0.06b 0.15b 0.04b 0.01de 0.00ef *** *** ***

0.20a 0.02b 0.03g 0.00g 0.19e 0.07c 0.17b 0.02d 0.02f 0.00g *** *** ***

0.46a 0.17f 0.02gh 0.03gh 0.52c 0.24e 0.59b 0.26d 0.05g 0.01h *** *** ***

Columns marked with different letters are significantly (P ≤ 0.05) different. *** Significant at the 0.001 probability level. 4

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Total PLFAs was the highest at MQ (11.57 nmol∙g−1) and the lowest at DK (3.95 nmol∙g−1). However, the fungi to bacteria (F/B) index were the highest at DK. Differences in AMF and actinomycetes were more significant in the rhizospheric soils than in the bulk soils. The two-way analysis (Table 1) of the microbial biomass variables showed that the effects of A. mongolicus cover were more significant than those of site. Plant cover had a significant effect on G-, fungi and G+/G- at all sample sites, based on comparisons between rhizospheric and bulk soils. In most cases, significant interaction effects on total PLFAs, AMF, G+, actinomycetes and F/B were observed.

(PCNM) to soil microbial community composition (Fig. 3A) and catabolic function (Fig. 3B). The complete set of plant cover, soil property and PCNM variables explained 73.8% and 43.8% of the variation in the soil microbial community and catabolic utilization, respectively. The variation in the soil microbial community and catabolic utilization explained by plant cover alone was high, amounting to 18.4% and 21.8%, respectively. However, the plant cover and soil variables simultaneously accounted for 58.5% of the variation in soil microbial composition, which suggested that the effects of plant cover and soil factors were largely dependent on each other. The variable PCNM explained only 4.8% of the variation in catabolic function, which was much less than the variation explained by plant cover and soil factors. Therefore, plant cover and soil variables have a more central impact on the soil microbial community than spatial distance.

3.3. Soil catabolic functional diversity AWCD increased in all tested soil samples and exhibited a sigmoidal pattern with increasing incubation time, and significant differences in the total carbon source utilization of soil microbes were found between the rhizospheric and bulk soils (Fig. 1). Moreover, there were significant differences in the utilization of various carbon sources between the soil types at the five sites (Table 2). The utilization rate of carbohydrates (12%–85%) was the highest, whereas those of amines (1%–15%) and phenolic acids (2%–20%) were the lowest. Significant site-by-plant cover interaction effects on soil microbial catabolic functional diversity were observed (Table 2). In terms of carbohydrate, carboxylic acid, polymer and amino acid carbon metabolism, the sites ranked as MQ > ALS > WH > SPT > DK, while amine and phenolic acid carbon metabolism was the highest at WH and the lowest at SPT.

4. Discussions 4.1. Microbial spatial distribution in a desert ecosystem Studies have reported that the limitation of dispersal by geographic distance and the acceleration of speciation significantly contribute to the distributions of microorganisms (Xiong et al., 2012; Liang et al., 2015). In the present study, the soil microbial community composition and catabolic function under A. mongolicus were significantly different among the 5 sites. Spatial distance (PCNM) explained only 4.8% of the variation in catabolic function, and the effect of spatial distance on soil microbial composition depended primarily on interactions with plant cover (16.8%) and soil factors (7.9%). Our results indicated that environmental selection and habitat heterogeneity are the dominant factors impacting microbial assembly in a desert ecosystem. Collectively, plant cover and soil variables accounted for 58.5% of the variation in soil microbial composition, which indicated that plant cover and soil variables significantly affect the dynamics of soil microorganisms in arid environments. Fierer et al. (2009) proposed that soil parameters are expected to exhibit considerably higher variability in large-scale experiments and have a greater influence on microbial biomass associated with plant productivity. Therefore, the microbial distribution under A. mongolicus was determined more by plant cover and specific soil factors than by geographic distance. These findings are consistent with previous reports of spatial heterogeneity of soil microbial community diversity in selected environments (Lundberg et al., 2012).

3.4. Principal component analysis The PCA results of the soil microbial PLFAs (Fig. 2A) showed that the cumulative contribution of the 2 principal components (PCs) related to soil microbial communities reached 92.4% (PC1 and PC2). The PCA of the fatty acid data clearly separated the rhizospheric and bulk soils based on PC2, revealing a significant difference in soil microbial community composition. The cumulative variance explained by PC1 and PC2 in relation to the metabolic characteristics of the soil microbial carbon sources (192 h) was 50.6% and 21.6%, respectively (Fig. 2B). Along the PC2 axis, rhizospheric soils occurred on the right, and bulk soils occurred on the left, which indicated different patterns of potential catabolic metabolism between the rhizospheric soils and the bulk soils. 3.5. Correlations between environmental factors and microbes

4.2. Variations in microbial communities and catabolic metabolism Pearson’s correlation coefficients demonstrated strong positive correlations between the soil microbial communities and soil factors (Table 3). Bacterial and actinomycete abundances were strongly and positively correlated with the values of ACP, ALP, EEG, TG, SOC and N. No significant effects of soil parameters on fungi were observed, except for significant correlations of fungi with EEG and N. However, there was no correlation between the soil microbial community and temperature, precipitation or altitude, and only altitude was significantly associated with actinomycetes. Correlation analyses between environmental factors and soil catabolic metabolism revealed strong positive correlations between carbohydrate, carboxylic acid and amino acid metabolism and ALP, TG, P and SOC but strong negative correlations between polymer utilization and U, EEG and precipitation (Table 3). Overall, phosphatase, glomalin and organic carbon were the main predictors of the variability in both the soil microbial community and catabolic function. P contributed the most to the high utilization of carbon sources, and N was the driving factor of soil microbial community composition.

Soil microbial communities and carbon source utilization differed and varied considerably along the geographic gradient (i.e., among the 5 desert locations). In this study, based on variability, members of the soil microbial community under A. mongolicus were ranked as bacteria > actinomycetes > fungi > AMF, which was consistent with the results of He et al. (2016), who used a traditional culture method. All microbial groups (except G+) had a maximum abundance at MQ in the west and a minimum at DK in the east, which reflected the poor quality and low nutrient availability at DK. G + bacteria, associated with strong tolerance to nutrient-poor environments (Liao et al., 2013), had a higher abundance at DK than at the other sites. Moreover, the index of F/B, used as an indicator of soil organic matter (De Vries et al., 2012), was also the highest at DK. Carbohydrate, carboxylic acid, polymer and amino acid carbon metabolism was characterized by the same trends at MQ and DK, while amine and phenolic acid carbon metabolism was the highest at WH and the lowest at SPT. All of these results suggest that the soil microbial communities in desert ecosystems adapt to the low-nutrient availability environment by adjusting their reproduction or physiological metabolism (Aliasgharzad et al., 2010). The utilization rates of carbohydrates and amino acids were higher under A. mongolicus than in the bulk soils, whereas those of amines and phenolic acids were lower. However, Xue et al. (2019) reported that

3.6. Variation partitioning analysis Variance partitioning analysis was performed to quantify the contributions of plant cover, soil properties and large spatial distances 5

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Fig. 2. Principal component analysis showing differences in the soil microbial community (A) and carbon resource utilization (B) between the rhizospheric soils of A. mongolicus and bulk soils.

4.3. The effects of plant cover on soil microbial communities and catabolic function

carbohydrates and carboxylic acids contributed most to the metabolic function of the soil microbial community under Hedysarum scoparium in the northwestern desert area of China. This comparison reflects the difference in soil metabolic capacity among host plants. Therefore, we propose that microbes with generalist catabolic metabolism can be used as indicators of the nutritional status of arid environments.

Variation partitioning demonstrated that variation in the soil microbial community (18.4%) and catabolic function (21.8%) was most explained by the effect of plant cover alone. In this study, both the soil microbial community and catabolic functional utilization were significantly higher in the rhizospheric soils than in the bulk soils, which suggests that plant cover plays a pivotal role in the regulation of soil microbial community activities in desert environments (Hinsinger

Table 3 Pearson’s correlation coefficients between environmental factors and soil microbial community and catabolic function. Variable

pH

ACP

ALP

U

EEG

TG

P

SOC

N

Temperature

Precipitation

Altitude

Bacteria Fungi Actinomycetes Carbohydrates Carboxylic acids Polymers Amines Phenolic acids Amino acids

0.21 0.09 0.28 0.06 0.23 0.38 0.13 0.08 0.15

0.75** 0.29 0.80** 0.35 0.56* 0.12 −0.10 −0.37 0.51

0.74** 0.36 0.89** 0.67** 0.78** −0.08 −0.07 −0.19 0.68**

−0.19 −0.00 −0.14 0.08 −0.06 −0.60* −0.08 0.15 −0.15

0.71** 0.58* 0.79** −0.03 0.06 −0.61* −0.30 −0.28 −0.17

0.73** 0.23 0.80** 0.70** 0.88** 0.25 0.01 −0.12 0.73**

0.19 0.35 0.31 0.87** 0.87** 0.38 0.29 0.32 0.73**

0.65** 0.00 0.67** 0.53* 0.74** 0.27 −0.07 −0.23 0.66**

0.64* 0.66** 0.73** 0.24 0.36 −0.24 −0.17 −0.38 0.31

0.16 0.42 0.38 0.18 −0.25 −0.20 0.12 −0.32 −0.16

−0.36 −0.28 −0.37 −0.25 −0.32 −0.66** −0.10 −0.25 −0.24

0.43 0.39 0.58* −0.20 −0.16 −0.44 −0.14 −0.41 −0.07

***Significant at the 0.001 probability level. * Significant at the 0.05 probability level. ** Significant at the 0.01 probability level. 6

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Fig. 3. Variation partitioning analysis was used to assess the relative contributions of plant cover, soil parameters and large-scale distance on the soil microbial community (A) and catabolic function (B). Plant = plant cover; PCNM = large-scale spatial distance; soil = soil factors (including SOC, Olsen P, ammonium N, ACP and EEG). Values below 0 are not shown.

facilitate the growth of some microorganisms, which might partially explain our result in which the nutrient factors EEG and N were positively correlated with fungal abundance. A previous study demonstrated that fungi have exceptional adaptability to nutrient-poor environments, especially in arid regions (Bai et al., 2009). This adaptability may be due to the hyphal structure of fungi, which makes it easy for them to attach to the surface of plant residues and more effectively absorb nutrients. Our results showed that Olsen P contributed the most to the high utilization of carbon sources. Research has revealed that when the carbon supply in the substrate is sufficient, phosphorus, nitrogen and other nutrient elements become the limiting factors for microbial biomass (Ilstedt and Singh, 2005). Under low-phosphorus stress, plants can secrete large amounts of protons, acid phosphatases and organic acids into the rhizospheric soil to release insoluble soil phosphorus (Lambers et al., 2006). Microorganisms can rapidly take up soil phosphorus and concentrate it in the form of polyphosphate within cells, which leads to a reduction in the microbial biomass carbon to phosphorus ratio (Lai et al., 2006). Therefore, to maintain a stable carbon to phosphorus ratio, microbes will utilize significantly larger amounts of quality carbon sources. In arid deserts, the majority of plant root exudates provide important carbon and nitrogen sources for soil microorganisms, which significantly change the soil physical and chemical properties. Changes in these properties affect the abundance and structure of the soil microbial community, the decomposition of soil organic matter and the metabolism of nutrients (Yin et al., 2013).

et al., 2009). According to Yang et al. (2013), high nutrient contents and carbon resource diversity in a plant’s rhizospheric soil are the driving forces of microbial density and activity. In arid deserts, fluctuations in substances and secretions between soil microbes and the roots of canopy plants provide abundant substrate inputs to modify the composition and facilitate the reproduction of microorganisms (Bais et al., 2006; Broeckling et al., 2008). Another reason for the influence of host plants on soil microbial diversity is variation in plant litter quality (Myers et al., 2001). Additionally, A. mongolicus as a leguminous plant, nitrogen fixation occurs in the legume-rhizobium, which affects the physiological processes of soil microorganisms (Chen et al., 2003).

4.4. Important driving factors of soil microbial communities and catabolic function Soil type determines the initial state of the rhizospheric microbial community, which may then be directly affected by complex soil physical and chemical properties (Hu et al., 2014). In the present study, the activities of phosphatase and TG were strongly correlated with not only bacteria and actinomycetes but also the catabolic utilization of carbohydrates, carboxylic acids and amino acids. Studies have indicated that soil enzymes are the main exudates of soil microorganisms and plant roots, and the activity of phosphatase can be used to characterize soil phosphorus cycling. Plants can absorb inorganic P through the enzymatic hydrolysis of organic P (He et al., 2011). The joint effects of soil microorganisms and enzymes can promote the transformation of substances and efficient nutrient and water absorption in desert environments (Xiao et al., 2018). Moreover, Rillig et al. (2003) found that glomalin-related proteinases are secreted by AMF and serve as important sources and components of the soil organic carbon pool in desert environments. The self-contained adhesive force of glomalin is conducive to the enhancement of soil structural stability and fertility, which positively affect the structure of soil microbial communities (Driver et al., 2005). In this study, SOC was strongly positively correlated with soil microbial composition and catabolic function. Numerous studies have identified soil carbon sources as key ecological drivers of microbial community dynamics (Vries et al., 2012), and these relationships are related to decomposing plant litter (Frossard et al., 2013). Moreover, soil organic matter is the dominant carbon source for microbes, even in the presence of sufficient available root-derived carbon substrate (Paterson et al., 2007). Abundant nutrients would be expected to

5. Conclusions In this study, plant cover significantly affected the composition and promoted the catabolic function of the soil microbial community. The soil microbial community and catabolic functional diversity under A. mongolicus were spatially predictable and determined more by specific soil factors associated with host plants than by large-scale distance in the arid desert of Northwest China. Soil phosphatase, glomalin and SOC elicited significant responses from microbes. Ammonia N was the main driving factor of soil microbial community composition, whereas Olsen P contributed the most to the high utilization of carbon sources. Variability in soil microbial communities may facilitate the monitoring of desertification and soil degradation, providing guidance for the restoration of ecological environments. 7

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