Compositional and functional responses of soil microbial communities to long-term nitrogen and phosphorus addition in a calcareous grassland

Journal Pre-proof Compositional and functional responses of soil microbial communities to long-term nitrogen and phosphorus addition in a calcareous grassland Zhirui Wang, Shan Yang, Ruzhen Wang, Zhuwen Xu, Kai Feng, Xue Feng, Tianpeng Li, Heyong Liu, Ruiao Ma, Hui Li, Yong Jiang

PII:

S0031-4056(19)30273-2

DOI:

https://doi.org/10.1016/j.pedobi.2019.150612

Reference:

PEDOBI 150612

To appear in:

Pedobiologia - Journal of Soil Ecology

Received Date:

10 December 2018

Revised Date:

30 November 2019

Accepted Date:

9 December 2019

Please cite this article as: Wang Z, Yang S, Wang R, Xu Z, Feng K, Feng X, Li T, Liu H, Ma R, Li H, Jiang Y, Compositional and functional responses of soil microbial communities to long-term nitrogen and phosphorus addition in a calcareous grassland, Pedobiologia - Journal of Soil Ecology (2019), doi: https://doi.org/10.1016/j.pedobi.2019.150612

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Compositional and functional responses of soil microbial communities to long-term nitrogen and phosphorus addition in a calcareous grassland Zhirui Wanga,b,# , Shan Yanga,c,#, Ruzhen Wanga, Zhuwen Xud, Kai Fengb,e, Xue Fenga,c, Tianpeng Lia,b, Heyong Liua,c, Ruiao Maa,b, Hui Lia,*, Yong Jianga aInstitute

of Applied Ecology, Chinese Academy of Sciences, Shenyang,110016, China, bUniversity of Chinese Academy of Sciences, Beijing, 100049, China, cColledge of Land and Environment, Shenyang Agricultural University, Shenyang, 110866, China, dkey Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot, 010021, China, eCAS Key Laboratory for Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China 

These authors contributed equally to this work. *Corresponding author. Email address: [email protected] (Hui Li) Highlights

N addition decreased the relative abundance of fungi and fungi/bacteria ratio

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Combined N and P addition reduced the proportion of arbuscular mycorrhizal fungi

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N and P addition inhibited the N-acquisition and P-acquisition enzyme, respectively

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Nutrients addition changed microbial functional composition through eutrophication

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N addition changed microbial taxonomic composition through acidification

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Abstract

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Increased nitrogen (N) input into the ecosystem, which is mainly caused by anthropogenic activities, is usually not paralleled by a similar increase in phosphorus (P) input, and thus, can shift the ecosystem from N limitation to P limitation. Although the effects of N enrichment on ecosystem components have been intensively evaluated, the impacts of altered P resource availability and the interactive effects of N and P on the biomass, composition, and function of soil microbial communities are not well understood. Here, based on a 9-year field experiment, we investigated the responses of soil biotic and abiotic properties to N and P additions in a semi-arid calcareous grassland in northern China. We documented a significant increase in the relative abundance of bacteria (copiotrophic group in a broad ecological meaning) and a decrease in the relative abundance of fungi (oligotrophic) and the fungi/bacteria (F/B) ratio under N addition. The proportion of arbuscular mycorrhizal fungi (AMF) decreased under both N addition and P addition. N addition and P addition inhibited N-acquisition enzymes (protease, PRO) and P-acquisition enzymes (alkaline phosphomonoesterases, Alka PME), respectively. N fertilization inhibited most of the soil enzymatic activities by reducing soil pH and microbial biomass. P addition alleviated the negative impacts of N addition on substrate induced respiration (SIR), peroxidase (PER), and Alka PME, likely by reducing the available N content in soils, but strengthened the effects of N on the soil total carbon (TC) content, which might contribute to increased plant productivity. We also found that the overall changes in soil microbial enzyme profiles in response to nutrient addition were mainly caused by

eutrophication (changes in NO3--N and Olsen-P), whereas variations in the broad community structure were driven by soil acidification. Overall, the application of N and P in this natural steppe will cause serious environmental issues and impact ecosystem service and function through changing the compositional and functional profiles of soil microbial communities. Keywords: Grassland, Nitrogen, Phosphorus, Microbial community, PLFA, Soil enzymes 1. Introduction

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With the rapid development of modern industry and agriculture, the input of nitrogen (N) into natural ecosystems has been greatly increased as a result of anthropogenic activities, such as N fertilizer application and fossil fuel combustion (Galloway et al., 2008; Schlesinger, 2009). Increased N input can enhance the availability of N in soils and alleviate N limitation in ecosystems (Aber et al., 1998; Lu et al., 2011). Nevertheless, enriched N resource is not usually paralleled by a similar increase in phosphorus (P) input (Penuelas et al., 2013), and thus, shifts ecosystems from a state of N limitation to that of P limitation (Penuelas et al., 2013; Zheng et al., 2015) due to the constrained stoichiometric ratios of N and P in the biomass of plants and microbes (McGroddy et al., 2004; Sinsabaugh et al., 2008). Amendment of P resources in soil could compensate for the P depletion caused by N addition, decrease the N:P ratio, and improve plant productivity (Perring et al., 2009; Peng et al., 2017). However, the effects of P and combined N and P addition on the biotic and abiotic properties of soil still remain largely unknown. Soil microbes play a key role in soil organic matter decomposition and providing available nutrients to plants (van der Heijden et al., 2008). The influence of N addition on soil microbes had been explored across a variety of ecosystems, including forests, grasslands, agro-ecosystems, and tundra (Campbell et al., 2010; Ramirez et al., 2012; Li et al., 2016; Zhang et al., 2018), but the responses can vary substantially. N addition has been shown to negatively affect microbial biomass in many ecosystems (Treseder, 2008), which might be caused by soil acidification (Yang et al., 2012) and the subsequent activation of soil metal elements, such as aluminum, iron, or manganese (Cai et al., 2017b). The accumulated heavy metals in soil can cause damage of the microbial cell membrane, destruction of the structure of DNA, and may replace essential elements (Bruins et al. 2000; Abdu et al., 2017), all of which are deleterious for microbial growth. In contrast, a few publications documented that N addition might increase microbial biomass by providing more carbon input from plant litter (Johnson et al., 1998; Wang et al., 2014). Microbial biomass might even remain unchanged under conditions of N enrichment (Jing et al., 2016). Phospholipid fatty acid (PLFA)-based studies generally revealed that N addition reduced total microbial

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biomass, the relative abundance of arbuscular mycorrhizal fungi (AMF), and the fungi/bacteria (F/B) ratio (Treseder, 2008; Yang et al., 2017; Zhang et al., 2018). Still, few previous studies proposed that N addition did not alter the abundance of fungi, bacteria, or the F/B ratio (Sun et al., 2015; Zhang et al., 2015). Using next-generation sequencing (NGS) methods, it was observed that the relative abundance of copiotrophic microbial groups (represented by Proteobacteria) increased under N addition, whereas that of oligotrophic classes (represented by Acidobacteria) decreased (Campbell et al., 2010; Ramirez et al., 2012; Li et al., 2016). Though the effect of N enrichment on soil microorganisms has been intensively evaluated in recent years, the impacts of altered P resources and its interactive effects with N on the biomass and composition of soil microbial communities are not completely understood (Elser et al., 2007). It was shown that P fertilization did not exert any significant effect on microbial biomass or the composition of the soil microbial community in agricultural fields (Bunemann et al., 2004; Shi et al., 2013). However, addition of P might increase microbial biomass and change the microbial community structure to that of a more copiotrophic community by decreasing the gram-positive: gram-negative ratio (G+/G-) (Fanin et al., 2015) in tropical P-poor soil, or F/B ratio in temperate Haplic Luvisol soil (Huang et al., 2016). The studies on the response of soil microorganisms to P addition in grassland ecosystem are still lacking, particularly in calcareous soils where P is likely bound by calcium (Raiesi, 2006; Raiesi and Ghollarata, 2006). Chen et al., (2014) observed a shift from a fungal to bacterial dominated microbial community induced by P fertilization in a P-limited grassland. A decreased relative abundance of AMF under P addition is frequently reported across ecosystems (reviewed in Treseder 2004), which may be caused by the declined allocation of carbohydrates from plant sources to AMF. However, a couple of studies observed a contrasting pattern, documenting increased AMF abundance under P amendments (Treseder and Allen, 2002; Liu et al., 2013). These inconsistencies imply that further studies are needed to elucidate the response of soil microbial communities to the variations in P availability, especially in grassland ecosystem under global change scenarios (Peng et al., 2017). Because of the stoichiometry between N and P, it was proposed that P input would unavoidably interact with N input to impact ecosystem components. The counteractive effects of N and P enrichment on a variety of soil properties or processes were observed (Zheng et al., 2015), including soil pH (Mao et al., 2017), dissolved organic P (Wang et al., 2008), microbial respiration (Ren et al., 2016; Poeplau et al., 2016), and soil organic carbon decomposition (Bradford et al., 2008). For instance, Mao et al., (2017) revealed that short-term N addition significantly decreased soil pH, whereas P addition increased soil pH, probably because of the increased biotic uptake of nitrate and phosphate under P input. This implies that exogenous P addition may mitigate N-derived soil acidification and its negative

effects. Conversely, N addition may influence the effect of P. For example, N may ameliorate the effects of P on soil organic carbon decomposition (Bradford et al., 2008). The counteractive effects of N and P are not always observed. P addition may strengthen or have no influence on the effects of N on soil biotic and abiotic properties, and vice versa. For instance, in a semi-arid grassland，it was found that N

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fertilization did not change the observed P effects on soil basal respiration (BR) (Li et al., 2010). A synergistic response of soil N availability to N and P fertilization was also reported in a hardwood forest (Fisk et al., 2014). Soil microorganisms produce extracellular enzymes to excavate biologically available nutrients from soil organic matter for microbial and plant utilization (Zhang et al., 2013; Xiao et al., 2018). Thus, soil enzyme activities are important indicators for assessing microbial nutrient demands and microbial functional responses to environmental changes (Weedon et al., 2011; Wang et al., 2015). The impacts of N and P input on soil enzyme activities are inconsistent in previous studies. A metaanalysis, with a great proportion of data collection from forest ecosystems, reported

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that N addition stimulated C-acquisition -1,4-glucosidase (BG) and P-acquisition acid phosphatase activities (Xiao et al., 2018). The results can be explained by the resource allocation theory (Allison et al., 2011), which suggests that enzyme production is a large microbial investment and predicts that simple, inorganic nutrient addition would suppress the activities of related nutrient-acquisition enzymes and increase the activities of other enzymes. However, another case study documented the negative effects of N addition on C-acquisition and P-acquisition enzyme activities in a semi-arid grassland (Wang et al., 2015). The distinct responses of enzyme activities to N addition indicate that soil enzyme activities are influenced by multiple factors such as soil pH and microbial biomass, not only nutrient availability (Xiao et al., 2018). The suppression in the activity of phosphatases is frequently observed in P addition experiments (Wright and Reddy, 2001; Raiesi and Ghollarata, 2006; Zheng et al., 2015). It is generally believed that phosphatase production and activity are linked to biotic demand for P (Clarholm, 1993), whereas little is known about the impacts of increased P availability on the activities of C-acquisition and N-acquisition enzymes. More data from grassland ecosystems is needed to clarify the driving factors of the response of soil enzyme activities to nutrient addition. The semi-arid temperate grassland in Inner Mongolia constitutes an important part of the Eurasian grassland, with ecological and economic importance in northern China (Xu et al., 2018). It has been predicted that atmospheric nitrogen deposition will increase in the coming decades in this region (Liu et al., 2011). Based on a long-term field experiment with N and P manipulation (started in 2005) in a natural steppe in Duolun, Inner Mongolia, China (Xu et al., 2010; Li et al., 2016), this study documents how soil microbial community composition and enzymatic activities respond to long-term (9 years) N addition, P addition, and a combination of both. We

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hypothesized that (1) N addition would increase the abundance of bacteria, and reduce that of fungi, because bacteria have higher nutrient demands than fungi and would flourish when resources are abundant (Fierer et al., 2007; Mooshammer et al., 2014). We further hypothesized that the abundance of AMF would decrease under N and P addition, since plants do not need AMF to acquire nutrients when available nutrients are enough for plant growth; (2) according to the resource allocation theory, N and P addition would cause negative effects on the activities of N-acquisition and P-acquisition enzymes, respectively. Based on the ecological stoichiometry theory, combined N and P addition would stimulate soil enzymatic activities of C-acquisition; and (3) long-term P addition might partially mitigate the effects of N addition on microbial community composition and function, because the added P nutrients would accelerate the uptake of N by plants and microbes, and thus, alleviate the accumulation of N in soils. 2. Materials and methods 2.1. Field site and experimental design

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The study site is a typical steppe located at Duolun County, Inner Mongolia in northern China (E 116 °17 ′20 ″, N 42 °2 ′ 29 ″, 1324 ma.s.l.). This region has a typical temperate semi-arid climate. Mean annual temperature and precipitation were 2.1 °C and 379.4 mm, respectively. The plant community is dominated by Stipa krylovii, Artemisia frigida, and Agropyron cristattum. Soils in the experimental site is classified as Calcisorthic Aridisol according to the US Soil Taxonomy classification, with 63 % sand, 20 % silt, and 17 % clay (Chen et al., 2009). A field experiment was established in 2005 in this region to evaluate the effects of the increased N deposition and P nutrient inputs on grassland ecosystem components. Seven field blocks containing naturally assembled communities were established. Six 8 m × 8 m plots in each block were randomized of the following treatment: Control (no fertilizer), three N addition levels (5, 10, 15 g N m−2 year−1), P addition (10 g P m−2 year−1), and combined N and P addition (10 g N m−2 year-1+10 g P m−2 year-1). To compare the ecological impacts of N and P addition, four treatments were included in this study, i.e. Control, N addition with 10 g N m−2 year−1, P addition, and integrated N and P addition. The four treatments are referred as Control, N, P, and NP in the following text. There is a 1-m wide buffer zone between two adjacent plots. We used a subset of plots (four replicates were randomly selected) from each treatment for analysis of soil enzymatic activities and community composition. The N and P amendments are applied twice a year in the form of urea and superphosphate, respectively. Half of the fertilizer was applied in early May, and the remaining part was applied in late June in each year. 2.2. Soil sampling and physicochemical characteristics analysis

In August 2013, after 9 year of treatment, soil samples were collected from each plot using an auger (3 cm diameter) at 0-10 cm soil depths. For each plot, soil cores were taken from five randomly selected locations and combined into one sample. The soil sample was sieved by through a 2 mm mesh size to remove the stones and plant residues. Then, these fresh soil samples were placed on ice and transported to the laboratory within two days. Each soil sample was separated into three subsamples parts. One subsample was stored at 4 ℃ for microbial biomass, respiration and enzymatic activities analysis. Another subsample was kept at -20 ℃ until PLFA

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analysis was performed. The last part of subsamples was air dried to constant weight for physicochemical properties determination. The soil pH was determined in a 1: 2.5 soil-to-water ratio using a digital pH meter. Total C and N were determined by a Vario MACRO cube analyzer (Elementar Analysensysteme Vario MACRO cube, Germany). For the determination of total P concentration, the soil was digested using perchloric acid and nitric acid (ratio 3:2). Available P was extracted with 0.5 M NaHCO3 (Olsen et al., 1954) and concentrations of the extracted P were determined by molybdate colorimetry at 880 nm (Murphy and Riley, 1962). Ammonium (NH4+-N) and nitrate (NO3--N) were extracted from 5 g sieved fresh soil with 2 M KCl after shaking for 1 h, and then measured with an autoAnalyser III continuous Flow Analyzer (Bran & Luebbe, Norderstedt, Germany). Soil dissolved organic C (DOC) was extracted by 0.5 M potassium sulfate (K2SO4) and assayed by using a TOC analyzer (High TOC, Elementar).

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2.3. Soil microbial biomass and respiration-related parameters analysis

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Soil microbial biomass C and N were measured using the fumigation-extraction method (Brookes et al., 1985). Soil basal respiration (BR) was determined with Li-COR 8200 Infrared Gas Analyzer (IRGA) (Li-COR Biosciences, Lincoln, NB, USA) as described in Gershenson et al., (2009). After determination of the BR, 2 ml 6 % glucose solution were evenly added to each soil sample to measure the substrate-induced respiration (SIR). We also calculated the C availability index (CAI) by dividing the basal respiration rate with SIR rate (Gershenson et al., 2009).

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2.4. Soil enzyme assays

The activities of β-1,4-glucosidase (BG), N-acetyl--D-glucosaminidase (NAG), acid phosphomonoesterase (Acid PME), and alkaline phosphomonoesterase (Alka PME) were analyzed according to Tabatabai (1994). In brief, 1 g of fresh soil sample was mixed with 4 ml modified universal buffer (pH = 6.0 for BG, 5.5 for NAG, 6.5 Acid PME, and 11 for Alka PME, respectively) and 1 ml substrate (p-nitrophenol-β-D-glucopyranoside for BG p-nitrophenyl-N-acetyl--D-glucosaminide for NAG, and p-nitrophenyl-phosphate for PME, respectively). And then, the mixtures were incubated at 37 °C for 1 h. In the

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case of BG, reactions were stopped by adding 1 ml 0.5 M CaCl2 and 4 ml 0.1 M trihydroxymethyl aminomethane (THAM, pH = 12). In the case of NAG and PME, reactions were stopped by and adding 1 ml 0.5 M CaCl2 and 4 ml 0.5 M NaOH, respectively. Then, the slurries were filtered and measured at 450 nm on a UV-VIS spectrophotometer (UV-1700, Shimadzu). The activities of BG, NAG and PME were expressed in mg p-nitrophenol (PNP) kg-1 h-1. Protease activity (PRO) was determined using casein as substrate. One gram of fresh soil were mixed with 5 ml 2 % casein and 5 ml 0.1 M THAM (pH 8.1), and incubated at 50 °C for 2 h. Then, the reaction was stopped by adding 5 ml 0.92 M trichloroacetic acid (TCA). After centrifugation, 5 ml of supernatant was blended with 7.5 ml of an alkaline reagent (sodium hydroxide-sodium carbonate, copper sulfate and sodium potassium tartrate solution, 100 : 2 : 2 volume ratio) and 5 ml of Folin reagent. Activity of the enzyme was quantified colorimetrically at 700 nm and expressed as mg tyrosine (Tyr) kg-1 h-1 (Ladd, 1978). Cellulase activity (CEL) was measured by the production of glucose, using the carboxymethyl cellulose as the substrate (Tabatabai, 1994). The CEL activity was expressed as mg Tyr kg−1 h−1. The activities of peroxidase (PER) and polyphenol oxidase (PPO) were also assayed by colorimetric-based method. One gram of soil samples were incubated with pyrogallic acid and citrate-phosphate buffer solution at 30 °C for 2 h. An extra of 2 ml of 5 % H2O2 was still required to be added for determining the activity of PER. The PPO and PER was expressed as mg purpurogallin (PPG) g−1 h−1. 2.5. Phospholipid fatty acids (PLFA) analysis

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Soil microbial community composition was estimated by the phospholipid fatty acids analysis (PLFAs) method. The lipids were extracted from 4 g freeze-dried soil with a mixture of chloroform, methanol and citrate buffer (1 : 2 : 0.8, v / v / v). After the extractions, samples were placed into a separation funnel and phospholipids were separated from neutral lipids and glycolipids on solid phase extraction columns filling with silica gel (Supelco Inc., Bellefonte, PA). The collected phospholipids were subjected to a mild alkaline methanolysis and were converted into fatty acid methyl esters. Then, the fatty acid methyl esters were dissolved in hexane (150 μL), and were analyzed by gas chromatograph (Agilent 7890A series) with MIDI Sherlock® Microbial Identification System (Version 4.5; MIDI Inc., Newark, DE, USA). The abundance of individual fatty acid was expressed as nmol lipid g-1 dry soil and calculated based on the concentration of 19:0 internal standard. The generally used fungal PLFA biomarker was 18:2ω6c (Frostegard and Baath, 1996) and the PLFAs of 16:1ω5c, 20:4w6c and 20:5w3c were typically used for summarizing arbuscular mycorrhizal fungi (AMF). The PLFAs of i14:0, a16:0, i15:0, a15:0, i16:0, i17:0, and a17:0 were used as biomarkers for gram-positive(G+)bacteria, and 16:1ω7c, 16:1ω9c,

cy17:0, 17:1ω8c, 18:1ω7c, 18:1ω9c and cy19:0 for gram-negative (G-)bacteria. The biomarkers of G+ bacteria, G- bacteria and 14:0，15:0，16:0，17:0, 18:0n were summed up to represent bacteria. The 10me16:0, 10me17:0, and 10me18:0 were considered as the PLFAs biomarkers for actinomycetes (DeForest et al., 2004). The ratio of fungal to bacterial (F/B ratio) was calculated based on the total fungi and total bacterial PLFAs data. The ratio of Gram-positive to Gram-negative bacteria (G+/G− ratio) was evaluated based on the PLFAs biomass of each group. 2.6. Statistical analyses

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All results were expressed by means (n = 4) and the standard errors (S.E.). One-way analysis of variance (ANOVA), followed by Duncan’s multiple comparisons were performed to identify the differences among Control, N, P and NP treatments. Two-way ANOVAs were used to determine the overall effects of N, P and interactive effects of N and P on soil properties, enzymatic activities and PLFA-based microbial community data. Correlations between the microbial groups, enzyme activities and soil properties were determined using Pearson’s correlation analysis. The statistical significance was accepted at P < 0.05. To illustrate the potentially relationships between soil pH, Mn2+ content and microbial biomass, path analysis was performed by using the standardization of multiple linear regression models. All these statistical analyses were conducted by using IBM SPSS 20.0. Redundancy analysis (RDA) was used to further illustrate the relationships between soil variables and the relative abundance of specific microbial groups, or soil enzymatic activities, as demonstrated by ordination biplots based on species scores and constraining variables scores. RDA was also performed to explore the soil parameters that constrain the overall microbial community composition and the enzymatic activity profiles under different treatments, presenting by ordination biplots based on sites scores and constraining variables scores. Monte Carlo permutation tests (n=499) was used to evaluate the contribution and significance of different soil variables to the variation in the overall microbial community composition and enzymatic activity patterns. Results were statistically significant when P < 0.05. The RDA analysis was conducted by using Canoco 4.5 software. We pre-selected the soil parameters by removing the variables that were significantly correlated with other factors (Pearson’s correlation analysis, P < 0.05). All of the data were log (x+1) transformed before RDA analysis. Structural equation modeling (SEM) was performed to analyze the hypothetical direct and indirect N and P addition effects on the overall microbial community composition and the enzymatic activity patterns. We use principal component analysis (PCA) to create multivariate indexes for microbial community composition and enzymatic activities with multiple variables (Chen et al., 2016), and the first principal components (PC1) of site scores were used in the subsequent SEM analysis. PC1

explained 62.88% and 45.73% of the total variance of the overall microbial community composition and the enzymatic activity changes, respectively (Table S1). The bivariate correlations relationships between variables were examined using linear regression before SEM analysis (Table S2). A conceptual model of hypothetical relationships was constructed (Fig. S1), assuming that N and P addition would directly impact microbial community composition and enzymatic activity, or indirectly through altering soil pH, soil nutrient availability, plant biomass and plant species richness. In the SEM analysis, data were fitted to the model using the maximum likelihood estimation method. This procedure compared the model-implied variance covariance matrix with the observed variance covariance matrix. Adequate model fits

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were indicated by several indices: a non-significant 2 test (P > 0.05), a low RMSEA (root square mean error of approximation), and a low AIC (Akaike information criteria) (Table S3). The final model was improved by removing paths from prior models based on these indices. The SEM analyses were performed using IBM SPSS AMOS 24.0. The PCA analyses were performed using the vegan package in R 3.4.2. To illustrate the response level of soil biotic and abiotic parameters in response to N, P addition alone and combined N and P addition, a meta-analysis-like method was conducted with the “metafor” package in R 3.4.2. adopted. This statistical method can be used not only to combine the results of multiple independent studies but also to measure the difference between treatments and control of multiple independent variables within a single study (Saiya-Cork et al., 2002; Vetter, 2014).We calculated natural log response ratio (ln RR) i.e., ln RR= ln (XE / XC), where XE is the nutrient addition treatment, Xc is the control, and ln is the natural logarithm. Log response ratio > 0 indicates an increase following nutrient additions, while log response ratio < 0 shows a decrease following nutrient additions. The 95% confidence intervals (95% CI) of the response ratio were also calculated. Effect of nutrient additions considered significant if the 95% confidence intervals of the ln RR did not overlap with zero. Only the variables with significant interactive effects of N and P addition were included in this analysis. 3. Results

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3.1. Responses of soil properties to N and P addition N addition significantly (P = 0.001) reduced soil pH, whereas P addition showed no significant impact (P > 0.05) on soil pH in this calcareous grassland (Table 1). N and P addition alone showed no significant impacts on soil TC or total nitrogen (TN) (Duncan’s multiple-range test, P > 0.05), whereas N and P combined (NP) tended to increase TC and TN, showing a synergistic effect of N and P (P < 0.05). Soil NO3--N increased with N addition, and decreased with P addition, exhibiting a counteractive effect of N and P (two-way ANOVAs, P < 0.05) (Table 1, Fig. S2). P addition showed significant positive effects on TP and Olsen-P (two-way ANOVAs, P < 0.001).

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Soil microbial biomass C and N were sharply decreased by N addition (two-way ANOVAs, P < 0.05), approximately 35% and 50% lower than control, respectively. However, P addition alone, or in combination with N, showed no significant effects on soil microbial biomass (Duncan’s multiple-range test, P > 0.05). Soil BR and substrate induced respiration (SIR) were significantly reduced by N and P treatments and NP (Duncan’s multiple-range test, P < 0.05). The negative effect of NP addition on SIR (reduced by 34%) was weaker than that of N addition alone (reduced by 40%), and thus, P amendments tended to alleviate the N impacts on SIR. NP addition showed a weaker negative effect on BR than N and P addition alone. Significant interactive effects of N and P were observed on both soil BR and SIR (Table 1, Fig. S2). N addition significantly enhanced the soil carbon availability index (CAI), which was calculated by dividing BR by SIR (two-way ANOVAs, P < 0.05). There were no effects of P or interactive N and P effects on CAI (Table 1). 3.2. Compositional response of soil microbial community to N and P addition

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N addition significantly reduced the total PLFA concentration (two-way ANOVAs, P < 0.05; Fig. 1a), whereas P addition showed no impacts on total PLFA. Nevertheless, in comparison with addition of N alone, the total PLFA concentration showed a slight increase when combined with P addition (increased by 24%), indicating a significant interactive effect of N and P (P < 0.05; Fig. 1a, Fig. S2). With respect to each specific taxonomic group, the relative abundance of AMF (mol % of total PLFA) declined under both the addition of N and P amendment, with stronger effects being observed under N addition (Fig. 1c). N addition significantly enhanced the relative abundance of bacteria, but reduced that of fungi (Fig. 1e, d). Consequently, the F/B ratio declined under the N treatment (Fig. 1f). In contrast to N addition, P addition tended to increase the relative abundance of fungi as well as the F/B ratio (Fig. 1d, f). Two-way ANOVAs indicated that the relative abundance of actinomycetes, G+, G-, and G+/G- ratio showed no significant response to any nutrient treatment (Fig. 1b and g-i). 3.3. Functional response of soil microbial community to N and P addition

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Overall, N addition exerted a significant negative effect on the activity of BG (two-way ANOVAs, P < 0.01; Fig. 2a). In contrast, P addition significantly enhanced the BG activity (two-way ANOVAs, P < 0.001). CEL showed a weak positive response to N addition (two-way ANOVA, P = 0.091), but no response to P addition (two-way ANOVA, P = 0.352) (Fig. 2b). The activities of lignin-degrading enzymes, in terms of both PER and PPO, were significantly reduced by N addition, P addition, and their combination (Fig. 2c, d). N addition alone reduced the activity of PRO; however, P addition alleviated the negative effect of N, exhibiting a significant interactive effect (Fig. 2e, Fig. S2). The activity of NAG showed a significant positive

response to N addition, but no response to P addition (Fig. 2f). The response patterns of Acid PME and Alka PME to N and P addition were not consistent. The activity of Acid PME was significantly reduced by N and P addition alone, but was not changed by their combination, illustrating a significant interactive effect (Fig. 2g, Fig. S2). The activity of Alka PME was sharply inhibited by all nutrient input treatments, with N addition (reduced about 58%) showing a stronger effect than P treatment (reduced about 42%, Fig. 2h). 3.4. Relationships among the relative abundances of specific microbial groups, soil enzymatic activities, and soil parameters

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Pearson’s correlations revealed that the relative abundance of bacteria has a significant positive correlation with NO3--N, but a negative correlation with soil pH. In contrast, the relative abundance of fungi and F/B ratio showed negative correlations with NO3--N, but positive correlations with soil pH. The relative abundance of AMF was positively correlated with pH and negatively correlated with CAI (Table 2). The RDA analysis based on the relative abundance of each taxonomic group further confirmed that the bacterial group was located at the positive direction of the X-axis, and was closely associated with NO3--N. On the contrary, the fungi, AMF, and F/B ratio, along with soil pH, were all projected at the negative direction of the X-axis (Fig. S4a). The activity of BG was positively correlated with TP and Olsen-P, but was negatively correlated with NO3--N. The activity of CEL was only correlated with soil carbon availability (CAI) (P = 0.032). In general, the activities of the two lignin-degrading enzymes, PER and PPO, showed positive correlations with soil respiration (including BR and SIR), microbial biomass (including MBC and MBN), and soil pH, but had negative correlations with TP and Olsen-P. The activity of PRO had close associations with TC P = 0.024) and TN (P = 0.03). The activity of NAG was mainly correlated with soil NO3--N (P = 0.027), microbial biomass (P = 0.028 for MBC, and P = 0.011 for MBN), and pH (P < 0.001). Both Acid PME and Alka PME were positively correlated with BR and SIR. The Alka PME also showed positive correlations with pH and microbial biomass but exhibited a negative correlation with CAI. The Acid PME was also negatively correlated with NO3--N concentration (Table 2). RDA analysis was fairly consistent with the simple correlation. It was observed that BG had close association with Olsen-P. CEL and NAG were projected at the positive direction of the X-axis along with NO3--N and CAI. The two PMEs and the two lignin-degrading enzymes were distributed in the second and the third quadrants along with soil pH, MBC, and SIR (Fig. S4b). 3.5. Factors driving changes in the overall patterns of soil microbial community structure and soil enzymatic activities

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The RDA biplots with samples being labeled by treatments (Fig. 3a) showed that the microbial community composition was obviously separated by N addition along the X-axis (N factor, P = 0.003). However, the overall changes in microbial community composition were not influenced by P addition (P factor, P = 0.318). Monte Carlo permutation tests revealed that the model accounted for 63.9% of the total variation (P < 0.001). The first axis explained 50.8% of the variations in microbial community composition, and the second axis explained 7.1% of the variation. The N addition was the important factor contributing to the PLFA-pattern of the microbial community, explaining 19.22% of the total variation. Regarding the specific soil parameter, soil pH, NO3--N, SIR, MBC, and CAI showed significant impacts (P < 0.05, Fig. 3c) on soil microbial community composition. The overall changes in the enzymatic activity pattern were clearer in comparison with the PLFA-based measures of community composition (Fig. 3b). It was found that the microbial functional composition was clearly separated by N addition along the X-axis (N factor, P = 0.006). The N-treated samples were loaded on the positive direction of the X-axis, whereas the Control samples were located at the negative direction of the X-axis. The Y-axis differentiated the P-ambient treatments from the P-added ones (P factor, P = 0.004). Specifically, the samples of P and NP treatments distributed at the positive direction of the Y-axis, and the samples of Control and N treatments loaded at the negative direction of the Y-axis. Monte Carlo permutation tests revealed that the model accounted for 61.5% of the total variation (P < 0.001), with the first two canonical axes explaining 27.6% and 13.5%, respectively. Soil NO3--N, Olsen-P, and SIR were the most important soil parameters in relation to variations in the enzymatic activities pattern, accounting for 24.7%, 16.5%, and 13.4% of the total variations, respectively (Fig. 3d). It was proposed that nutrient addition might impact the soil microbial community composition and function through eutrophication (changes in soil available nutrient content, such as NO3--N and Olsen-P), soil acidification (declining pH), or indirectly through changing the aboveground plant community. To disentangle these mechanisms and to mechanistically explain what factors are actually driving the response of microbes to N and P addition, we performed a SEM analysis, hypothesizing that N and P addition influence the overall pattern of microbial community composition and function through changes in soil pH, available nutrients, and their impacts on plants. Most variables examined in final model were correlated with one another, making this data set appropriate for SEM analysis (see Table S2). The final model showed that N addition could influence the overall microbial community composition through reducing soil pH, whereas P addition had no effects on the microbial community composition, consistent with the findings of the RDA analysis (Fig. 3c). N addition explained 58% of the total variance in soil pH. The pathway of soil acidification directly explained 31% of the total variance in microbial

community composition (Fig. 4, Table S4). N and P addition exerted indirect effects on the overall enzymatic activity pattern through enhancing soil NO3--N and Olsen-P resources, respectively. Additionally, we observed that P addition could influence soil enzyme profiles through its negative effects on soil NO3--N. The pathway of eutrophication explained about 72% of the total variance in the overall enzymatic activity changes (Fig. 4b, Table S4). The impacts of plant biomass and species richness on soil microbial PLFA profiles and the enzymatic activities were finally removed from the initial model. 4. Discussion

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4.1. N addition increased the dominance of copiotrophic microbes, and the added N and P reduced the relative abundance of plant-associated fungi

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In line with the findings of most previous studies (Treseder, 2008; Chen et al., 2015; Zhang et al., 2018), N addition reduced the microbial biomass, as estimated by both the fumigation-based method (MBC and MBN) and PLFA-based method (total PLFAs). The decline in microbial biomass may be caused by decreased soil pH under N addition. Soil acidification induced by N addition could enhance the mobilization of aluminum or manganese, which are toxic to microbes (Vitousek et al., 1997). In this study, we did observe that soil pH could indirectly inhibit microbial growth through the negative effect of manganese (Fig. 5). Consistent with our initial hypothesis, N addition stimulated the growth of bacteria, but reduced the relative abundance of fungi and the F/B ratio (Fig. 1e, d and f). In a broad ecological meaning, it has been proposed that bacteria could be classified as copiotrophic members, and fungi, which typically grow slower than bacteria and are usually described as oligotrophic microbes (Six et al., 2006). The present study indicates that enrichment in N resources shifted the microbial community towards the prevalence of copiotrophic members. Similar taxonomic shifts have been observed in several previous studies conducted in grassland ecosystems. For instance, a field experiment conducted in another region in Inner Mongolia documented N-induced declines of fungal biomass and F/B ratio (Wei et al., 2013). Work at the Park Grass, the oldest inorganic N fertilized grassland experiment in the world, has also observed an increase in bacterial biomass, and a decrease in fungal biomass and F/B ratio as a result of N addition (Rousk et al., 2011). The N-amended experiment conducted in a forest ecosystem showed a similar pattern (Frey et al., 2004; Wallenstein et al., 2006), indicating a decline in fungal biomass and F/B ratio under N supply. NGS-based measures of community structure revealed that N enrichment usually enhances the proportion of copiotrophic bacterial groups (e.g., Proteobacteria), whereas it inhibits the growth of oligotrophic taxa (e.g., Acidobacteria) (Leff et al., 2015; Li et al., 2016). In support of our hypothesis, inhibition of AMF growth was observed under N

and P addition, either alone or in combination. The decline in the abundance of AMF under N and P input had been consistently reported across independent field experiments (reviewed in Treseder, 2004). Since AMF is a benefit for plant N and P acquirement (Smith and Read, 2008), one of the widely tested hypotheses is that more carbon (C) should be invested in mycorrhizal symbionts when plant growth is limited by soil nutrients. In detail, with an enhancement of P availability, AMF become more C-limitation because the carbohydrates allocated from plant sources to AMF were reduced (Read, 1991, Smith and Read, 2008). In contrast to the plant investment hypothesis, some other previous publications reported that N or P increased the relative abundance of AMF in tropical ecosystems (Treseder and Allen，2002; Liu et

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al., 2013). The differences in the P effects on AMF might rely on the status of soil P availability in different ecosystems. In the highly weathered tropical areas, P tends to be bound in iron or aluminum sesquioxides, and microbes are predominantly P limited (Liu et al., 2012). Thus, an increase in P availability would alleviate the P-limitation and stimulate the proliferation of AMF (Treseder and Allen, 2002). However, in the temperate grassland with calcareous soil, the soil pH is about 6.5, P is likely bound by calcium in the form of CaHPO4 and Ca(H2PO4)2, resulting in a relative high P bioavailability in comparison with iron- or aluminum-bound P (Holtan et al., 1988). G- bacteria are usually considered to be more copiotrophic than G+, because Gbacteria can use carbon sources and nutrients with high availability (Kramer and Gleixner, 2008). Thus, nutrients enrichment may increase the abundance of G- and decrease the abundance of G+. In a tropical P-poor forest, P input significantly decreased the G+/G- ratio and shifted the microbial community structure to a more copiotrophic microbial community (Fanin et al., 2015). However, in this study, we observed that the relative abundance of G+ and G− bacteria, and the G+/G− ratio showed no response to N and P addition at the rate of 10 g N m-2 year1, implying that these two microbial groups are not sensitive to nutrient enrichment in this calcareous grassland. In fact, the response of G+ , G− bacteria, and the ratio of G+ and G− to N and P addition might not only depend on increased soil nutrient availability. For example, it was reported that N fertilization would directly reduce the abundance of both G- and G+ in a field experiment conducted in typical temperate grasslands because the increased N availability allows plants to outcompete microorganisms in acquiring soil N (Bi et al., 2012). 4.2. N enrichment reduced the activities of most of the measured soil enzymes, and P addition inhibited the activity of phosphomonoesterases The activity of PRO, a soil enzyme involving in proteolysis, was significantly depressed by N addition, consistent with our hypothesis that N addition would depress the activities of N-acquisition enzymes. However, contrary to the initial hypothesis that N addition would stimulate the activities of C- and P- acquisition enzymes, our

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results demonstrated that N amendments reduced the activities of most of the soil enzymes measured in this study, including C-acquisition (BG, PER, PPO), N-acquisition (PRO), and P-acquisition (Acid/Alka PME) enzymes. A similar pattern has been observed in previous studies (Kang and Lee, 2005; Zhang et al., 2013), for example, N addition repressed PRO, phosphodiesterase (Zhang et al., 2013), urease (Tian et al., 2017), and PPO (DeForest et al., 2004) activities. Consequently, the effects of N addition on soil enzymatic activities could not be simply explained by the resource allocation theory (Allison et al., 2011). The sharp decrease in microbial biomass under N addition might be another reason for the declined enzymatic activities, since soil microbial activities were strongly associated with biomass (Rejmánková and Sirová, 2007; Fatemi et al., 2016; Wakelin et al., 2017). The positive correlations (significant or marginal) between microbial biomass and the activities of BG, PER, PPO, PRO, and the two PMEs further supported this speculation (Table 2 and Fig. S1b). Soil acidification induced by N addition may also contribute to the declining activities of several soil enzymes. For example, the activities of PRO and Alka PME were optimal at a pH of 8.0 and 11.0, respectively. The low soil pH under N treatment may further reduce the activities of these Alka enzymes. Such a hypothesis is supported by the positive correlations between the activities of these two enzymes and soil pH (Table 2 and Fig. S1b). In this study, we observed that N fertilization reduced ligninolytic enzymes (PER and PPO). The decrease in lignin-degrading enzymes under N enrichment has been observed in a variety of previous studies (Carreiro et al., 2000; Gallo et al., 2004). The impacts of N addition on ligninolytic enzymes might be indirectly mediated by the variations in the aboveground plant community. A case study demonstrates that N treatment usually improves the plant litter quality by lowering the concentrations of lignin (Hou et al., 2018). The changes in the plant litter chemistry might alter the quality of C input into soils, and subsequently change the activities of the corresponding decomposition enzyme. Consistent with the hypothesis that P addition would inhibit the activity of P-acquisition enzymes, we found that the Acid and Alka PME were significantly suppressed by P addition in this experiment (Fig. 2g and h). Intuitively, microbes do not need to mineralize organic matter to acquire available P resources when P is abundant enough for their growth. Such a hypothesis was evidenced by the negative correlation between the activity of Alka PME and the concentration of Olsen-P (Table 2 and Fig. S1b). Interestingly, the interactive effects of N and P addition on the Acid and Alka PME were not consistent. Addition of N combined with P suppressed the activity of Alka PME but increased that of Acid PME (Fig. 2g), in comparison with P addition alone. This observation may be due to the decreased soil pH under N addition, which meets the optimal pH for Acid PME. 4.3. P interact with N to influence soil biotic and abiotic parameters

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We observed that the effects of N addition on a variety of biotic and abiotic soil properties had been weakened by P addition. For example, even though both N and P showed negative effects on SIR, Alka PME, PER, BR, and total PLFA, the impacts of N were alleviated by combined treatment with P. Thus, the effects of integrated fertilization of N and P were somewhere between that of treatment with N or P alone, as shown by the natural log response ratios in Fig. S2. The antagonism of N and P addition might contribute to the stoichiometric balance between N and P. As a result of the stoichiometric N and P ratios, the added P would stimulate the uptake of N by plants and microbes, and thus, alleviate the accumulation of available resources in soils. This hypothesis was supported by the lower soil NO3--N contents under NP combination treatment in comparison with N addition alone (Table 1). Furthermore, P addition might neutralize the N-effects through changing its influences on the other soil elements, such as calcium. A parallel study conducted in this field experiment found that soil acidification induced by N amendments lead to a decline in exchangeable basic cations (especially Ca2+) (Cai et al., 2017a). The P fertilizer used in this study was added in the form of calcium superphosphate, partially compensating for the loss of exchangeable basic cations after hydrolyzation and enhancing the soil acid buffering capacity. On the other hand, soil acidification under N addition promotes the release and activation of toxic elements, such as aluminum and manganese ions (Cai et al., 2017b; Mao et al., 2017), which would be toxic to the plant and soil microbes. The added P will combine with the aluminum ions under the conditions of pH < 6.5, and ultimately alleviate the aluminum poisoning. Likewise, the impacts of P addition on soil biotic and abiotic properties could also be alleviated by N addition. Though the overall N effects on Olsen-P were not significant, we still observed a slight decrease in Olsen-P under NP treatment in comparison with P addition alone (Table 1), partially attributed to the fact that plants take up more P when extra N is supplied. Additionally, because of the immobilization of phosphorus by iron and aluminum under soil acidification, N addition usually reduces the availability and the effectiveness of P fertilizer, and consequently, offsets the ecological impacts of P amendment (Perring et al., 2009). The negative effects of N and P addition alone on BR, Total PLFA, PRO, and Acid PME faded or disappeared when N and P were added in combination (the natural log response ratios of soil biotic and abiotic parameters mentioned above under NP addition closer to zero as shown in Fig. S2). This indicated the significant interactive effects of N and P addition. However, the ecological impacts of N addition and P addition are not always antagonistic. We observed the synergistic effects of N and P addition on soil TC and the PPO activity, which might be caused by enhanced plant productivity under NP treatment. In this semi-arid grassland, the ecosystem was co-limited by N and P nutrients, because of the biosynthesis of both N-rich proteins, P-rich phospholipids, and nucleic acids (Evans, 1989; Vitousek et al., 2010). Thus, simultaneous addition of

N and P fertilizer greatly increased both the aboveground and belowground plant productivity (Unpublished data). The increased plant productivity subsequently increased the C input into soils, leading to the accumulation of soil TC. 4.4. Nutrient addition influenced the soil microbial functional and compositional profiles through eutrophication and acidification, respectively

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The overall changes in the soil enzymatic activity profile showed significant response to both N and P addition and was clearly separated by N and P treatment in the RDA ordination (Fig. 3b). However, the PLFA-based taxonomic profile was unresponsive to P addition (Fig. 3a). The non-significant effect of P addition on microbial taxonomic composition might partially be caused by the detection methods. The PLFA method evaluates the community composition at a relatively coarse level, and in future work, identification of microbial taxonomic profiles at a finer taxonomic level using NGS techniques would be helpful to generate a more universal conclusion. The lower sensitivity of the microbial taxonomic profile might also contribute to the functional redundancy in microbial populations (Allison and Martiny, 2008). Since specific function would be shared across a variety of taxonomic groups (Burke et al. 2011), microbial species may not directly link with specific functional traits. By using SEM analysis, we found that the compositional and functional response patterns of the soil microbial community to the addition of each nutrient were driven by different mechanisms. N and P addition influenced the overall pattern of soil enzymes through changes in available soil nutrients (NO3--N and Olsen-P), namely the eutrophication process (Fig. 3d and Fig. 4b). In contrast, the overall variation in microbial community structure in response to N addition was driven by decreased soil pH, namely the acidification process (Fig. 3c and Fig. 4a). This finding indicates that the soil microbial functional profiles were more regulated by the available soil nutrient level, whereas the taxonomic profiles were closely linked with soil pH (Pietri and Brookes, 2009; Rousk et al., 2010; Zeng et al., 2019). It is a really interesting finding and would be helpful for explaining the nonsynchronous response of the soil microbial taxonomic composition and functional composition to environmental changes (Frossard et al., 2012; Bowles et al., 2014). The impacts of plant biomass and species richness on soil microbial community composition and enzymatic activities were unexpectedly removed from the hypothetical conceptual model. The decoupling of plant with soil microbes might be due to the sampling time. The influences of the plant biomass on the microbes are mainly mediated by the plant litter and the subsequent nutrients inputs into soils. We collected soil samples in August, and there was no plant litter at that time. During the peak of the growing season, plants might impact soil microbes through nutrient competition or root exudates. 5. Conclusion In this study, we provide deep insights into the changes in soil microbial

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community composition and enzymatic activities in response to N and P addition in a semi-arid typical grassland in northern China. We concluded that N addition increased the relative abundance of bacteria which was usually nominated as the copiotrophic microbes but decreased that of fungi (typically classified as oligotrophic microbes) and F/B ratio. N fertilization inhibits most of the soil enzymatic activity, especially protease, probably through reducing soil pH and microbial biomass. P addition suppresses the growth of AMF and the activity of P-acquisition enzyme (Alka PME). Overall, though the application of N and P fertilizer would increase plant productivity in this pastoral region, they might bring serious environmental problems through widespread eutrophication of local land, and impact ecosystem service and function by changing soil biological properties. In this study, we found that the compositional and functional responses of the soil microbial community to nutrient addition were driven by different mechanisms. N and P addition influenced the overall pattern of soil enzymes through eutrophication (changes in NO3--N and Olsen-P), whereas the broad scale microbial community structure was more sensitive to soil acidification (changes in pH). Consequently, we observed that the microbial PLFA profiles are responsive to only N addition, whereas the enzymatic activity profiles are responsive to both N and P addition. The present study improves our understanding on the response of belowground organisms to nutrient input under global change scenarios induced by human activities.

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Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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This work was financially supported by the Major State Research Development Program of China (2016YFC0500601), and the National Science Foundation of China (31770525 and 31870441). We thank all members of the Duolun Restoration Ecology Research Station, Inner Mongolia, for providing the experimental sites.

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Fig. 1. Total PLFA concentration (a), relative abundance of different soil microbial groups (b)-(e), (g), (h), and ratios of F/B (f) and G+/G- (i) in response to the N, P and NP addition treatments. Values are means of four replicates (±S.E.). Effects of nitrogen addition (N), phosphorus addition

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(P) and interaction between nitrogen and phosphorus (N×P) on these variables were estimated by two-way ANOVAs. Asterisks indicate significant effects, *P < 0.05; **P < 0.01;***P < 0.001. Only the significant treatment effects are labeled on the figure. Different lowercase letters above the bars indicate significant differences among treatments as estimated by one-way ANOVA followed by Duncan’s multiple-range test. The raw data of treatments of Control and N had been previously reported in a closely related research (Yang et al. 2017).

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Fig.2. Microbial extracellular enzymes activities of BG (a), CEL (b), PER(c), PPO(d), PRO(e), NAG(f), Acid PME(g), Alka PME(h)in response to the N, P and NP addition treatments. Values are means of four replicates (±S.E.).Effects of nitrogen addition (N) and phosphorus addition (P) and interaction between nitrogen and phosphorus (N×P) on these variables were estimated by two-way ANOVAs. Asterisks indicate significance of the main treatment effects,*P < 0.05; **P < 0.01;***P < 0.001. Only the significant treatment effects were labeled on the figure. Different lowercase letters above the bars indicate significant differences among treatments as estimated by one-way ANOVA followed by Duncan’s multiple-range test. Alka PME: alkaline PME. The raw data of treatments of Control and N had been previously reported in a closely related research (Yang et al. 2017).

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Fig.3. Ordination biplots based on the redundancy analysis (RDA) of microbial community composition (a) and soil enzymatic activities profiles (b). The ordination diagrams present sites scores and constraining variables scores. Soil properties variables are indicated by red arrows. N and P effects are represented by red triangles. N represents nitrogen addition, and P represents phosphorus addition. NO3: soil NO3--N. The contribution and significance of different variables to the variation in the overall microbial community composition (c) and soil enzymatic activity profiles (d) was tested by Monte Carlo permutations. * P < 0:05, **P < 0.01, ***P < 0.001. The significant P values are displayed in boldface.

Fig.4. Structural equation model (SEM) of the effects of N or P fertilizer addition on the overall microbial community composition and the enzymatic activity patterns. The final models resulted in good fit to the data, with model 2 = 9.600, df = 13, P = 0.726, RMSEA = 0.000，AIC = 39.600.

Square boxes indicate variables included in the model. Percentages close to the variables indicate the variance explained by the model (R2).The symbols “↑”and “↓” in square boxes indicate positive or negative PC1 value of the variable, respectively. The arrows indicate statistically significant paths at P < 0.05.Solid and dashed arrows indicate positive and negative relationship, respectively. The width of the arrows is proportional to the strength of the relationship. Numbers adjacent to the arrows represent standardized path coefficients.

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Fig.5. Path diagrams illustrating the effects of soil pH and soil Mn2+ content on microbial biomass C. The value beside each arrow represented the path coefficient. Solid arrows denoted positive correlation, and dashed one was negative correlation. The data of soil Mn2+ content were provided by Cai et al. (2017b).

Table 1 Changes in soil chemical and microbial characteristics in response to N addition, P addition and their combination (NP) (means±standard errors, n=4). To measure the overall effects of nitrogen (N), phosphorus (P) and their interaction (N×P) on soil variables, two-way ANOVAs are performed and the P and F values are shown in the right panel of the table. The significant P < 0.05 differences are displayed in boldface. Different lowercase letters indicate significant differences among treatments as estimated by one-way ANOVA and the following Duncan’s multiple-range test. N Variables

Control

N

P

5.97±0.11 6.41±0.08a

5.98±0.10b

6.34±0.09a

TC (g kg-1)

18.71±0.14b

18.18±0.29b

18.56±1.16b

b

F

P

F

P

F

P

27.3 9

0.00 1

0.29

0.60 1

0.17

0.69 1

21.42±1.1 1a

2.40

0.15 6

4.26

0.06 9

5.15

0.04 9

2.45

0.15 2

3.74

0.08 5

5.01

0.05 2

0.98

0.34 8

283. 74

<0. 001

0.21

0.65 8

2.10

0.18 1

1.99

0.19 2

0.01

0.91 3

44.1 6

<0. 001

24.4 0

0.00 1

11.5 1

0.00 8

0.97

0.35 1

0.03

0.86 1

0.44

0.52 6

0.23

0.64 2

215. 20

<0. 001

0.05

0.83 0

47.9 9

<0. 001

0.51

0.49 4

0.00

0.96 9

21.2 9

0.00 1

0.94

0.35 7

2.70

0.13 5

3.54

0.09 3

18.0 2

0.00 2

30.8 3

<0. 001

22.9 6

0.00 1

3.24

0.10 6

9.20

0.01 4

7.82

0.02 1

0.00

0.98 7

0.58

0.46 5

2.20±0.09

TP (mg

kg-1)

1.94±0.02b

1.89±0.04b

1.91±0.12b

a

316.39±10.1

269.25±6.75

850.39±38.3

833.05±49

6b

b

8a

.10a

240.85±4.99a

248.02±3.96a

247.85±6.83a

-p

TN (g

253.99±9.

DOC (mg kg-1)

78a

(mg

kg-1)

(mg

kg-1)

Olsen-P (mg

kg-1)

NO3

7.42±0.15bc

15.09±1.12a

re

8.72±0.76

--N

6.24±0.10c

b

15.66±1.6

NH4

14.19±1.64a 14.87±2.75b 213.64±25.0

MBN (mg kg-1) soil

h-1)

30.93±3.93a 0.54±0.004a

ur

BR (μg CO2-Cg-1

9a

15.25±0.64a

151.57±4.

12.37±0.35b

1a

78a

140.97±8.42

221.58±14.1

148.07±21

b

8a

.00b

14.17±1.30b

23.93±2.12a

0.44±0.02bc

15.97±1.4

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soil

CAI

2b 0.46±0.01

0.41±0.003c

SIR (μg CO2-Cg-1 h-1)

3a

158.33±19.4

na

MBC (mg

kg-1)

16.26±1.52a

lP

+-N

N×P

ro of

pH

kg-1)

P

NP

b 4.34±0.21

6.57±0.14a

3.92±0.14b

4.94±0.63b

0.08±0.002b

0.11±0.01a

0.09±0.02ab

b 0.11±0.00 5ab

Control: no fertilizer; N: 10 g N m-1 year-1; P: 10 g P m-1 year-1; NP: 10 g m-1 year-1of N + 10 g m-1 year-1 of P. The raw data of pH, DOC, NO3--N, NH4+-N, MBC, MBN and soil respiration in treatments of Control and N had been previously reported in a closely related research (Yang et al., 2017).

Table 2 Pearson’s correlations between the relative abundances of dominant microbial groups, soil enzymatic activities, and soil parameters across all samples.

Total PLFA Actinomyc etes AM Fungi Fungi Bacteria F/B ratio GG+ G+/G- ratio

pH

TC

TN

TP

NO3--N

0.620 * 0.009

0.008

0.058

-0.007

-0.396

OlsenP -0.075

0.273

0.312

0.185

0.029

0.126

0.752 ** 0.447

-0.265

-0.104

-0.580*

-0.136

0.086

-0.28 0 0.100

0.510*

-0.613*

0.466

-0.79 0** 0.525 * 0.210

0.256

0.201

0.010

0.556*

0.085

0.021

0.042

0.454

0.401

0.268

0.264

-0.039

-0.633* * -0.053

-0.45 4 0.362

0.017

0.124

0.105

0.208

0.073

-0.04 4 0.109

-0.131

-0.069

-0.220

0.205

0.353

0.367

-0.82 2** 0.224

0.389

0.355

0.785* * 0.056

-0.644* * 0.552*

0.758* * 0.036

0.458

0.476

0.064

-0.568*

0.004

0.625 ** 0.049

-0.139

-0.14 4 0.544 * 0.157

-0.347

-0.484

-0.384

0.367

-0.483

0.288

-0.235

0.444

-0.263

-0.02 8 -0.46 9

-0.550 * -0.360

-0.146

-0.534 * -0.345

-0.127

MBC

MBN

BR

SIR

CAI

0.498 * -0.46 2 0.609 * 0.383

0.489

0.417

0.254

-0.15 2 0.716 ** 0.213

-0.25 2 0.405

-0.62 8** 0.448

-0.67 7** 0.293

-0.13 7 -0.08 3 -0.02 7

0.123

-0.11 2 -0.27 1 -0.06 4 0.303

-0.23 2 0.748 ** 0.117

-0.37 3 0.286

-0.17 3 0.244

-0.20 6 0.208

0.01 8 0.16 7 -0.57 8* -0.12 7 0.28 0 -0.14 4 0.02 3 0.06 6 -0.03 2

0.195

0.180

0.110

-0.54 8* 0.145

-0.61 7* 0.462

0.506 * 0.360

0.780 ** 0.424

-0.46 1 -0.14 2 0.598 * 0.767 ** 0.450

-0.41 7 0.209

-0.25 1 0.560 * 0.570 *

0.088

-0.38 7 0.748 ** 0.549 *

Alka PME PRO Cellulase

-0.03 3 0.359

0.562 * 0.113 -0.025

PER 0.614 *

-0.477

na

PPO

lP

Acid PME

re

NAG

-p

Enzymes BG

-0.362

-0.46 0 0.165 0.179

ro of

Microbial groups

0.641 **

0.755 ** 0.469

-0.45 1 0.612 * 0.855 ** 0.426

-0.37 8 0.39 3 -0.40 9 -0.54 1* -0.24 9 0.53 6* -0.47 7 -0.34 2

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The values are correlation coefficients. *P < 0.05; **P < 0.01. Soil variables that showed no significant correlations with any microbial parameters were not included in this table, including DOC and NH4+-N.