Crop-litter type determines the structure and function of litter-decomposing microbial communities under acid rain conditions

Science of the Total Environment 713 (2020) 136600

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Crop-litter type determines the structure and function of litterdecomposing microbial communities under acid rain conditions Hui Wei a,b,c, Rui Ma a, Jiaen Zhang a,b,c,⁎, Muhammad Saleem d, Ziqiang Liu a, Xiaoran Shan a, Jiayue Yang a, Huimin Xiang a,b,c a

Department of Ecology, College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China Guangdong Provincial Key Laboratory of Eco-circular Agriculture, Guangzhou 510642, China Key Laboratory of Agro-Environment in the Tropics, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China d Department of Biological Sciences, Alabama State University, Montgomery, United States b c

H I G H L I G H T S • Microbial properties during litter decomposition under acid treatments were analyzed. • Litter type greatly determined litter decomposing microbial community properties. • Litter-inhibiting microbial communities were highly resistant to acid rains.

G R A P H I C A L

A B S T R A C T

microbial community composition and functions during its decomposition, while acid rain treatment had neglectable effects during the experimental period.

Crop leaf litters

Zea mays L. Acid rain

Oryza sativa L.

Glycine max (Linn.) Merr. Various resource availability indicated by litter C, N, and P contents and stoichiometric ratios

a r t i c l e

i n f o

Article history: Received 3 October 2019 Received in revised form 6 January 2020 Accepted 7 January 2020 Available online 09 January 2020 Editor: Fang Wang Keywords: Acid deposition Litter decay Phospholipid fatty acids Extracellular enzyme activities Soil microbial communities Subtropics

a b s t r a c t Acid rain has been one of the major environmental problems in industrial countries. While it may affect the litter decomposition, a highly important microbial-driven biogeochemical process, knowledge about the impact of acid rain on litter-decomposing microbial communities and their functions remains unclear. Therefore, this experiment was conducted to investigate how acid rain treatments would alter microbial communities and their functions during litter decomposition of three major commodity crops (maize, rice, and soybean) for six months from June to December 2018. We used litterbag method to determine litter decomposition,while the phospholipid fatty acid (PLFA) and fluorometric methods were used to reveal changes in the litter-adhering microbial community parameters and activities of enzymes involved in the litter decomposition and nutrient release (including carbon [C], nitrogen [N], and phosphorus [P]), respectively. Our results showed that microbial community composition and functions were significantly different among litter types, but not significantly altered by acid rain treatments during the experimental period. The enzyme activities significantly correlated with each other, thus suggesting that microbial requirements for C, N, and P were coupled together during litter decomposition. Moreover, the enzyme activities, at large, did not correlate to microbial community composition, thus indicating the asymmetric relationship between microbial community structure and functions. Our results imply that crop litter type and substrate availability determined the microbial community composition and functions, while litter-inhabiting microbial communities demonstrated substantial resilience under acid rain pressure throughout the experimental period. These results also predict that litter (crop residues) decomposition

⁎ Corresponding author at: Department of Ecology, College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China. E-mail address: [email protected] (J. Zhang).

https://doi.org/10.1016/j.scitotenv.2020.136600 0048-9697/© 2020 Elsevier B.V. All rights reserved.

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H. Wei et al. / Science of the Total Environment 713 (2020) 136600

may not be altered by acid rains in the subtropical agroecosystem, due to relatively high resilience of litterdecomposing microbial communities. © 2020 Elsevier B.V. All rights reserved.

1. Introduction The atmospheric acid deposition, which results from emissions of acidic compounds into the atmosphere, consists of dry and wet precipitation with pH b 5.6. Depending on its intensity, acid deposition can result in different ecological consequences across spatiotemporal scales (Burns et al., 2016; Likens and Bormann, 1974; Likens and Butler, 2018). Traditionally, there are three acid rain (AR) centers that are located in northern America, Europe, and eastern Asia (Duan et al., 2016; Rodhe et al., 2002). In Northern America and Europe, several control measures are adapted to reduce AR occurrence and its effects on ecosystems and human health (Johnson et al., 2018; Likens and Butler, 2018). In the Asian AR center, however, the emissions of SO2 and NOx keep steadily increasing, mainly due to rapidly increasing industrialization and urbanization. Consequently, this phenomenon causes acid deposition onto a considerable portion of land (e.g., in China, Japan, Korea, and India) (Duan et al., 2016). Although environmental conditions are improving in China in recent years due to some control measures, the country has been experiencing AR pollution since the 1980s (Yu et al., 2017). Therefore, there is a pressing need to determine the impacts of changes in AR properties (e.g., level of acidity) on ecosystem processes, such as litter decomposition. The AR may influence litter decomposition, which is an important biogeochemical process that determines many ecosystem functions and services, such as returning nutrients to the soil and stabilizing the soil organic matter (Cotrufo et al., 2015; Olson, 1963; Sayer and Tanner, 2010). It is widely accepted that environmental conditions, litter quality, and decomposer communities predominantly regulate the decomposition of litter (Coûteaux et al., 1995; Zhang et al., 2008; Zhang et al., 2019). However, the role of decomposer communities has received relatively less attention, since they are considered to work at small scales and thus, have a less independent effect on the litter decomposition (Bradford et al., 2016; Wall et al., 2008). Nevertheless, a recent modelling study predicts that microbial communities may regulate the turnover of litter at relatively large regional scales (Bradford et al., 2017), thus highlighting the importance of microbial communities in litter decomposition. Therefore, it becomes essential to study the impact of AR on litter-decomposing microbial communities. The AR effects on the litter decomposition could derive from the fact that acid rain may alter microbial communities that are involved in the litter decomposition process (Lv et al., 2014; Tang et al., 2019; Wolters, 1991). Previous studies have reported that acid rain may have positive, negative, or no effect on soil microbial communities, depending on specific contexts, for instance, level of acidity, composition of acid rain, decomposition period, composition and/or acid-resistance of microbial communities (Liu et al., 2017; Oulehle et al., 2018; Pennanen et al., 1998). However, most of these studies have focused on microbial communities of soil adjacent to litter bags or even bulk soil but not the litter-associated (or adhering) soil (Hobbie et al., 2012; Tang et al., 2019). These kinds of sampling strategy may limit our ability to understand changes in the litter decomposition process and subsequently the role of microbial communities in it under acid rain influence, although changes in the soil microbial communities are often related to the litter decomposition (Liu et al., 2017; Wang et al., 2010). In particular, the properties of litter-inhabiting microbial communities do not necessarily change with that of soil microbial communities at a comparable magnitude under environmental changes (Saiya-Cork et al., 2002; Smith et al., 2015).

The microbial communities are highly sensitive to changes in soil pH, and thus could adapt to changing environmental conditions by changing their abundance, community composition and diversity (Fierer and Jackson, 2006; Lauber et al., 2009; Saleem, 2015). In this regard, the phospholipid fatty acid (PLFA) profiles of microbial communities are used to predict microbial community composition (Hobbie et al., 2012; Qiao et al., 2019; Zelles, 1999), and the content of PLFAs can indicate microbial biomass (Bååth and Anderson, 2003; Wei et al., 2019). Acid rain may increase or decrease the total microbial PLFAs and alter the microbial community composition and abundance (Liu et al., 2017; Lv et al., 2014; Pennanen et al., 1998). In addition to PLFAs profiles, the extracellular enzyme activities are used to predict microbial functions since they process organic matter and release resources such as carbon (C), nitrogen (N), and phosphorus (P). The simulated acid rain of different chemical composition and intensities consistently reduced the soil microbial enzyme activities such as cellulase and urease that are involved in C- and N- release, but it stimulated the acid and alkaline phosphatase activities in two subtropical forest soils (Lv et al., 2014). Moreover, some studies reported that microbial communities could respond differently to AR treatments (Liu et al., 2017; Wang et al., 2014). These opposing findings could be due to the negative and positive effects of AR on microbial communities. For instance, in one scenario, the AR may accelerate soil acidification and suppress microbial communities, but, in another scenario, it may stimulate microbial communities by nutrient input (e.g., N and sulfur [S]). Therefore, it is anticipated that the changes in the size, composition, and activity of soil microbial communities may subsequently influence the decomposition of litter under acid rain conditions (Bradford et al., 2017; Wang et al., 2010; Zhang et al., 2019). However, due to limited studies, it is difficult to interpret the effects of acid rain on microbial communities during the litter decomposition process. The maize, rice, and soybean are the major commodity crops that produce millions of tons of litter after each crop cycle while extensively mining nutrients from agricultural soils. Therefore, the amount and type of litter produced in the agroecosystems and its relevance for the soil ecosystem processes bear much broader implications in soil health, fertility, and productivity (Karlen et al., 1994; Lenka et al., 2019; Wilhelm et al., 2004). That is why the use of crop residues has received much attention to sustain soil health (Bijay-Singh et al., 2008). While, the leaf litter of different crops may have different microbial communities due to varying leaf chemistry, differential microbial nutrient demands and life-history traits (copiotrophic/oligotrophic) (Chen et al., 2012; Fanin et al., 2019; Fierer et al., 2007). Nevertheless, how the microbial communities involved in the decomposition of crop (leaves) residues respond to various AR conditions, remains poorly understood. We conducted this experiment to study how ARs of varying acidity affect litter-decomposing microbial communities and enzyme activities. We hypothesized that mild AR treatment would stimulate the microbial communities, primarily, due to nutrient input. However, severe AR (low pH) treatment may suppress microbial communities, specifically bacteria that are higher sensitive to environmental changes compared with fungi (Rousk et al., 2010), during litter decomposition. Consequently, there might be a decrease in the ratio of bacteria to fungi under the AR than control treatments. Corresponding to AR effects on microbial communities, we also anticipated changes in the enzymatic activities of litterdecomposing microbial communities.

H. Wei et al. / Science of the Total Environment 713 (2020) 136600

2. Materials and methods 2.1. Experimental materials and site descriptions The study was conducted in the ecological farm (23°08′N, 113°15′E) of South China Agricultural University in Guangzhou city, China. This region has a subtropical monsoon climate with obvious cool-dry and warm-wet seasons. In the study site, the average air temperature was around 22 °C and average precipitation in a recent decade reached up to 2000 mm, of which the most (~80%) occurred in the warm-wet season ranging from April to September in each year. The soil has been used for agricultural practices for years and is classified as Ultisol according to American Soil Taxonomy (Soil Survey Staff, 2014). The contents of total C and N were 1.04 ± 0.43 and 0.16 ± 0.043%, respectively, and the soil pH was 6.98 ± 0.13. Two graminaceous crops such as maize (Zea mays L.) and rice (Oryza sativa L.), and a leguminous crop soybean (Glycine max L. Merr.), which are widely grown in this region, were used in this study. In the next half year of 2017, the leaf samples were collected after harvesting these crops, and then air-dried at the room temperature. The dry leaves were stored for experimental use. The leaves were cut into pieces (~4 cm) before the decomposition experimentation. The initial contents of the litter C, N and P were analyzed. The results showed that the three leaf litter types had substantially different chemistry and quality, as indicated by the differences in their C, N, and P contents and the stoichiometric ratios (Table S1). 2.2. Experimental design and treatments Our experimental setup included two main factors such as species and acid rain treatment while each factor further included three levels in this study, thus following a two-factorial experimental design. The three crop species, as mentioned above, were regarded as three levels of species with different litter quality. Two acid rain treatments, i.e., acid rain with pH being 5.0 (AR1) or 3.0 (AR2), were set up and a non-acid addition treatment was also established as the control, constituting three levels of acid rain treatments in this study. The AR solutions were prepared by mixing different amounts of concentrated sulfuric (analytical reagent, 95–98%; Guangzhou Chemical Reagent Factory, Guangzhou, China) and nitric acids (analytical reagent, 65–68%; Guangzhou Chemical Reagent Factory, Guangzhou, China). The AR1 treatment contained the acidity that approximated to the annual rainfall pH (4.94) from 2001 to 2016 in Guangdong Province (Statistics Bureau of Guangdong Province, 2017), while the AR2 treatment simulating a severe acid rain condition, representing a relatively low pH and a high acidity (Cao et al., 2009). At the end of the experiment, the AR treatments decreased the soil pH by 0.1 and 0.3 units in the AR1 and AR2 than control treatments, respectively (Fig. S1). In the study site, we built a rainshed (with the size of 5 m × 18 m) using a transparent plastic sheet as a rooftop to avoid confounding effects of natural precipitation during the experimental period. Under the rainshed, twelve 1.2 m × 2.0 m plots were established by inserting polyvinyl chloride (PVC) panels around the plots with 20 cm belowground and 20 cm above the surface. Additionally, every two plots had a buffering area of 1.5 m distance between each other. The acid rain treatments were randomly assigned to the plots, with four replicates for each. Within the experimental period, each plot received 25 L of simulated acid rains or controlled water once every five days corresponding to the assigned treatment. The spraying treatments continued for six months and resulted in a 375 mm amount of acid rain in each plot. This number corresponded to the 750 mm of acid rain in a year, a figure that approximated to annual acid rain amount in the study site. The acid rain records showed that the annual acid rain amount was 721.6 mm on average in the Guangdong Province from 2001 to 2016 (Statistics Bureau of Guangdong Province, 2017).

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The simulated acid rain contained a ratio of sulfate to nitrate being 4 that approximated to the value in natural precipitation in this region, that were 202 and 52 μeq L−1, respectively (Huang et al., 2009). Combining with the annual rainfall amount, it could be estimated that the deposition load of sulfate and nitrate were 171.1 and 88.1 mmol m−2 yr−1, respectively, in the study site. The control plots received the same amount of tap water (pH = 7.8) to avoid the effects of water addition on litter-decomposing microbial communities. For each species, 48 samples were prepared by weighing 20 g of leaf litter into nylon sample bags with a size of 20 × 20 cm and net mesh of 1 mm, thus making a total of 144 sample bags (3 species × 3 AR levels × 4 replicates × 4 samplings). Some previous studies suggested that a mesh size of 1 mm permits to a variety of soil decomposers including small earthworms (Hobbie et al., 2012), however, the soil macrofauna was excluded (Coleman et al., 2004). Then, 4 bags of sample for each species (total 12 bags for the three species) were randomly buried at a 5 cm depth in each plot. Among these, we took one bag at the end of the 1st, 2nd, 4th and 6th months after the AR treatments to study microbial community composition and functions. We set the experimental duration up to 6 months due to a reduced amount of litter left for analyses after this period as a function of the decomposition process. At each sampling time, we collected all the samples to first weigh the fresh mass of the remaining litter. Then, each litter sample was completely mixed and divided into two parts; one part was dried to determine the remaining litter to calculate the decomposed litter mass, while the other one was used to investigate the litter-adhering microbial community composition and functions. The soil microbial respiration rate was determined once every 7–15 days throughout the experimental period, using a Li-cor 8100 automated soil CO2 flux system with the 103 chamber (LI-COR, Inc., Lincoln, Nebraska, USA), and the soil samples at 0–10 cm depths were collected for pH and mineralized N analysis at the end of the experiment. During the experimental period, the litter of maize, rice, and soybean decayed up to 58.7 ± 2.7, 69.8 ± 1.6, and 79.5 ± 1.8%, respectively (Fig. S2). However, the AR treatments did not significantly affect the litter decomposition, although the AR treatments increased the soil microbial respiration rate, which could be associated with changes in soil nitrogen availability (Fig. S1). 2.3. Soil analyses and investigations of litter-decomposing microbial communities The soil pH was determined by mixing samples with deionized water at a ratio of 1:2.5. The pH of liquid supernate was measured using a Pro10 Handheld pH meter (YSI Inc., Yellow Springs, Ohio, USA). The soil ammonium and nitrate contents were determined by using the indophenol blue colorimetry and dual-wavelength ultraviolet spectrophotometry after extracted by the KCl solution (Norman et al., 1985). The microbial community composition was studied using the phospholipid fatty acid (PLFA) method, following Frostegård and Bååth (1996), with a few modifications. Despite some limitations (Frostegård et al., 2011), the PLFA method is widely used to efficiently reveal variations in the size and composition of microbial communities (Chen et al., 2019a; Hobbie et al., 2012; Qiao et al., 2019), while comparative studies show that variations in PLFAs can efficiently reveal changes in the soil microbial community composition and are comparable with DNA-based techniques (Grayston et al., 2004; Widmer et al., 2001). Briefly, the frozen-dried and ground samples were extracted twice using an extractant that contained chloroform, methanol and phosphate buffer with a ratio of 1:2:0.8. The extracted phospholipids in the organic phase were then separated by a 3 mL silica column (ANPEL Laboratory Technologies, Inc., Shanghai, China). The collected fatty acids were identified using gas chromatography (Agilent 7890A, Agilent Technologies, Inc., Wilmington, DE, USA) equipped with a Sherlock Microbial Identification System (Version 6.2, MIDI, Inc.,

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Table 1 Results of the linear mixed model with repeated measures testing the significance of fixed effects for species and acid rain (AR) treatment on microbial community properties. The abbreviations of PLFAs, G+, G-, Acti, Bac, and Fun stand for phospholipid fatty acids, gram positive bacteria, gram negative bacteria, actinomycetes, bacteria and fungi, respectively. BG stands for β-1,4-glucosidase, NAG stands for β-1,4-N-acetyl-glucosaminidase, and AP stands for acid phosphatase. Statistical F values with significance indicated by stars are presented in cells. In the cells, * indicates the significance level at p b .05, ** indicates the significance level at p b .01, and *** indicates the significance level at p b .001. AR

Species × AR

Microbial community composition and structure Total PLFAs 28.501*** G+ 167.325*** G145.079*** Acti 27.282*** Bac 12.074*** Fun 0.730 G+/G264.218*** Fun/Bac 1.461

Species

0.108 0.348 0.816 1.131 0.081 0.429 0.226 0.385

0.875 0.093 0.800 0.388 1.099 0.927 0.112 0.949

Enzyme activities BG NAG AP

0.225 0.014 2.892

1.265 1.253 2.552*

19.792*** 6.441** 5.614**

Newark, DE, USA). The contents of individual PLFAs were determined based on the added internal standard 19:0 and the total amount of individual PLFAs (nmol g−1). The total PLFAs were calculated to indicate the size of microbial communities due to a well-established correlation between total PLFAs and microbial biomass (Bååth and Anderson, 2003; Wei et al., 2019). All fatty acids (N0.5%) were categorized to present the relative abundance of microbial functional groups (%mol), i.e., grampositive (G+) bacteria by iso- and anteiso- fatty acids, gram-negative (G-) bacteria by cyclopropane and mono-unsaturated fatty acids, actinomycetes by carboxylic acids with methyl functioning on the tenth C, and fungi by 18:2w6c and 18:1w9c (Hobbie et al., 2012; Kourtev et al., 2002; Wei et al., 2017). Moreover, the ratios of G+/Gand fungal/bacterial PLFAs were also calculated to reveal changes in microbial community structure under various experimental treatments. The activities of three hydrolytic enzymes such as β-1,4-glucosidase (BG, EC 3.2.1.21), β-1,4-N-acetyl-glucosaminidase (NAG, EC 3.2.1.1.14) and acid phosphatase (AP, EC 3.1.3.2) that regulate C-, N- or P- release, were determined using methylumbelliferone (MUB)-labeled substrates following the methods of Saiya-Cork et al. (2002). We conducted preliminary experiments to determine the necessary parameters such as saturated substrate concentrations, incubation time, and

concentration range of standard curve (German et al., 2011). In brief, 0.5 g of subsamples were homogenized and extracted in 100 mL of 50 mM acetate buffer (pH = 5.0) for 2 min. Then, the sample suspensions were pipetted into deep-well plates for incubation with acetate buffer and MUB substrates, using an 8-channel pipettor (Eppendorf Xplorer, Eppendorf AG, Hamburg, Germany). This makes the total volume of the reaction system to be 600 μL and the final substrate concentration 400 μM that was the saturating concentration for our assays. After incubation in the dark under 37 °C for 3 h, 250 μL of the supernatants were pipetted into black 96-well plates to determine the fluorescence readings using a microplate reader (Synergy H1, BioTek Instruments, Inc., Vermont, USA) with 365 nm excitation and 450 nm emission filters. Then the potential activity of BG, NAG and AP enzymes (μmol h−1 g−1) was calculated based on the fluorescence readings after corrected by controls (German et al., 2011). 2.4. Statistics Repeated measures analysis of variances (ANOVAs) was employed to test the treatment effects on soil microbial respiration rate and litter decomposition. Linear mixed models with repeated measures were conducted to test the significance of the main and interactive effects of crop species and acid rain treatment on the tested microbial community properties. Moreover, principal component analysis (PCA) was used to detect differences in microbial community composition indicated by the PLFA profiles. Due to the insignificant effects of AR (Table 1), data were pooled together to mainly compare the main effects of litter type on the microbial community properties, using linear mixed models with the Least Significance Difference method. The correlation analysis combining with a simple bootstrap sampling method was conducted to calculate the Pearson r coefficient and the 95% Confidence Interval (CI), as well as the significance level, between each two of the three tested enzyme activities. All the statistical analyses were performed in the IBM SPSS statistics 22 (IBM Corp., New York, USA) and the figures were drawn in the SigmaPlot 10.0 (Systat Software Inc., California, USA). 3. Results 3.1. Effects of litter types on litter-decomposing microbial communities under AR conditions The PCA showed that microbial community composition was mainly regulated by crop species and decomposition duration, but not by the

Fig. 1. Principal component analysis on microbial community composition identified by the phospholipid fatty acid method. The first two principal components (PCs) are used to profile the microbial community composition and the plots are labeled by species (a), experimental duration (b) or acid rain (AR) treatment (c). In the panels, M1, M2, M4 and M6 stand for the experimental duration of 1, 2, 4 and 6 months, while AR1 and AR2 stand for AR treatments of pH 5.0 and 3.0, respectively.

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AR treatments in this study (Fig. 1). Within the experimental period, the litter-decomposing microbial community of the leguminous species soybean differed from that of the non-leguminous maize and rice species along the PC axis 1 that explained 42.5% of the total variance in the microbial community composition, with the microbial community composition of the latter two being similar (Fig. 1a). During decomposition, the microbial community composition consistently demonstrated a directional shift along the PC axis 2 regardless of crop species and AR treatments (Fig. 1b). The AR treatments, however, did not seem to significantly affect the composition of litter-decomposing

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microbial communities of three crops during the experimental period (Fig. 1c). Consistently, the linear mixed models also indicated that the microbial community composition was greatly shaped by crop species identity than by AR treatment (Table 1). The main effect of crop species identity was statistically significant on the total PLFAs and abundance of different microbial functional properties including G+, G-, actinomycetal, and total bacterial PLFAs contents(p b .001); whereas there was no change in the fungal PLFAs across various treatments (p = .484, Table 1). The total amount of microbial PLFAs was

Fig. 2. Total amount of phospholipid fatty acids (PLFAs; a) and relative abundance of bacterial (b), Gram-negative bacterial (G-; c), Gram-positive bacterial (G+; d), actinomycetal (e) and fungal (f) PLFAs. Data are presented as the mean and standard error. In each panel, statistical p values of species effects in each sampling occurrence are calculated in linear mixed models, with different lowercase letters indicating significant differences at p b .05 among species at each sampling occurrence.

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Fig. 3. Ratios of Gram-positive to Gram-negative bacterial PLFAs (G+/G-) and fungal to bacterial PLFAs within the investigation period. Data are presented as the mean and standard error. In each panel, statistical p values of species effects in each sampling occurrence are calculated in linear mixed models, with different lowercase letters indicating significant differences at p b .05 among species at each sampling occurrence.

significantly higher in soybean than maize or rice litters (p b .05, Table 1 and Fig. 2a). After assigning the microbial PLFAs to different functional groups, we observed that relative abundance of total bacterial PLFAs was significantly higher in soybean than maize or rice litters (Fig. 2b), mainly due to higher abundance of G-bacterial PLFAs in soybean than maize or rice litters (on average 36.4% vs. 24.6%; Fig. 2c). Contrarily, the relative abundance of G+ and actinomycetal PLFAs was significantly higher in maize and rice than in soybean litters during the experimental period (p b .05, Table 1 and Fig. 4d and e) and there was no significant difference among crop species for fungal PLFAs (p N .05, Table 1 and Fig. 2f). Within the experimental period, there appeared to be an increasing trend for the total amount of PLFAs and relative abundance of G+, G-, actinomycetal and total bacterial PLFAs whereas fungal PLFAs tended to decrease with the decomposition process, regardless of species (Fig. 2). This was associated with an increase and decrease in the G+/G- and fungal/bacterial PLFAs ratios, respectively (Fig. 3). Moreover, the soybean litter had a significantly lower G+/G- bacterial ratio than maize and rice litters during the decomposition (p b .05, Table 1 and Fig. 3a) but there was no significant difference in the

fungal/bacterial PLFAs ratio among crop litter species except in the early stage of decomposition (p N .05, Table 1 and Fig. 3b). The AR treatments neither significantly changed the microbial community composition nor showed interactive effects with the crop litter species during decomposition (p N .05, Table 1). 3.2. Effects of litter types on litter-decomposing enzyme activities under AR conditions The activity of C-decomposing enzyme BG was context-dependent and was influenced by crop litter species (p b .001) though AR treatments did not significantly affect it (p = .799, Table 1). The BG activity was significantly lower in rice litter than that in the other two species (p b .05, Table 1 and Fig. 4a). Similarly, the activity of Ndecomposing enzyme NAG was also significantly different among species, with its highest activity in maize litter followed by soybean and rice litters (p b .05, Table 1 and Fig. 4b), while AR did not change this pattern (p = .292, Table 1). The AP activity, however, was significantly higher in the soybean than other crops litters (p b .05,

Fig. 4. Potential enzyme activities involving in litter carbon, nitrogen and phosphorus release within the investigation period. BG stands for β-1,4-glucosidase, NAG stands for β-1,4-Nacetyl-glucosaminidase, and AP stands for acid phosphatase. Data are presented as the mean and standard error. In each panel, statistical p values of species effects in each sampling occurrence are calculated in linear mixed models, with different lowercase letters indicating significant differences at p b .05 among species at each sampling occurrence.

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Fig. 5. Biplots of potential enzyme activities. Data were logarithmically transformed with the natural base. BG stands for β-1,4-glucosidase, NAG stands for β-1,4-N-acetyl-glucosaminidase, and AP stands for acid phosphatase. AR1 and AR2 stand for acid rain treatments of pH 5.0 and 3.0, respectively.

Table 1 and Fig. 4c), while AR treatments did not significantly affect AP activity during the experimental period (p = .059, Table 1). The linear mixed models revealed that there were no significant interactive effects of crop species and AR treatments on BG and NAG activities (p N .05, Table 1). However, the interactive effect of crop litter species and AR treatments on AP activity was significant (p = .043, Table 1). The correlation analysis combined with a simple bootstrap sampling method showed that the three microbial enzyme activities were significantly correlated with each other (p b .05, Fig. 5). The Pearson correlation coefficient r was 0.769 (p b .001, 95%CI = 0.699–0.830) between BG and NAG, 0.399 (p b .001, 95%CI = 0.245–0.527) between BG and AP, and 0.541 (p b .001, 95%CI = 0.404–0.650) between NAG and AP, respectively. The AR treatments did not significantly change linear relationships among the three enzyme activities, as indicated by the comparable slope index among control and AR treatments (p N .05, Fig. 5a–c). Moreover, regardless of species and AR treatments, the activity of BG was relatively higher than that of NAG (41.0 ± 2.0 μmol h−1 g−1 vs. 29.3 ± 1.3 μmol h−1 g−1) and AP activity (16.7 ± 0.9 μmol h−1 g−1) was the lowest relative to BG and NAG activities (p b .05 for all, Fig. 4). Resultantly, the ratios of BG/NAG and NAG/AP were mostly N1, implying that microbial communities were mostly C and N or N limited but were rarely P limited within the experimental period (especially for maize and soybean litters; Fig. 6). For rice litter, however, microbial communities tended to be P or C and P limited in the late stage of decomposition (Fig. 6). Moreover, in spite of the significant correlations between each two of the texted enzymes, the correlation analyses showed that only BG activity significantly correlated with the microbial community composition,

Fig. 6. Microbial resource limitations as indicated by ratios of β-1,4-glucosidase (BG), β1,4-N-acetyl-glucosaminidase (NAG) and acid phosphatase (AP).

i.e., positively with the total (p = .021) and G- (p = .009) PLFAs but negatively with G+ (p = .003), G+/G- ratio (p = .004) and actinomycetal (p = .003) PLFAs. 4. Discussion The soil microbial communities, especially bacteria, are influenced by acidification across spatiotemporal scales. The abundance and diversity of soil bacterial communities are linked to alterations in soil pH in several studies (Fierer and Jackson, 2006; Rousk et al., 2010). Considering the adverse consequences of AR on microbial-driven ecosystem processes, we investigated its impact on litterdecomposing soil microbial communities of three important crops. In this study, the two AR treatments (i.e. pH 5.0 and pH 3.0) did not significantly alter the litter-adhering microbial community composition, enzyme activities, and litter decomposition than those of control treatments during the experimental periods (Table 1 and Fig. S2). The minor effects of AR treatments on soil microbial communities were certainly surprising to us, though we initially hypothesized that AR of relatively high pH (mild acidic) may stimulate bacteria communities but that of low pH may favor fungi communities due to their greater acidity tolerance than bacteria. Previous studies showed that individual PLFAs could respond differently to changes in soil acidity (Bååth and Anderson, 2003; Bååth et al., 1995), indicating different levels of microbial tolerance to the acidification. The environmental stresses caused by AR treatments should be detrimental to microbial taxa with low acidity tolerance while these should favor those with high acidity tolerance. Overall, these negative and positive effects may, therefore, alter microbial community composition. Whether microbial groups respond to acid rain treatments, should nevertheless, depend on the tolerance threshold of microbial groups under acidic conditions. The microbial communities that live in the acidic soils evolve resistance to acidification, as we have observed that high load of N deposition, which is one of the major causes of rising soil acidity (Lu et al., 2015), did not change the PLFA-derived soil microbial community composition in an acidic forest soil (Wei et al., 2017). Moreover, the sulfur (S) than N enriched AR could exert relatively weaker effects on the soil microbial communities (Liu et al., 2017; Lv et al., 2014). Therefore, we suggest that a relatively high tolerance of microbial communities to acidification and the relatively low effect of S-enriched AR might have collectively contributed to the neutral effects of AR on microbial communities (Table 1 and Fig. 1c) and then on the litter decomposition (Fig. S2). Meanwhile, we may not rule out the significance of the nature of the litter-mixed soil environment in determining the microbial resilience to AR treatments. The litter-mixed soils usually demonstrate higher

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resource and habitat heterogeneities that are essential to maintaining and stabilizing microbial communities under anthropogenic perturbations (Dilly et al., 2004; Meng et al., 2019; Purahong et al., 2016). Nevertheless, because previous studies have shown that litterdecomposing microbial communities could respond to environmental changes differently from soil microbial communities (Saiya-Cork et al., 2002; Smith et al., 2015), there is a need for further research to discern the impact of AR on litter-adhering microbial communities. Apart from the AR effects, the identity of crop litter significantly influenced the size, composition, and activity of microbial communities (Table 1, Fig. 1a and 2–4). This could result from differences in the substrate availability, since substrate availability could greatly regulate the proportional abundance of different microbial functional groups (Bååth et al., 1995; Chen et al., 2019b; Rinnan and Bååth, 2009). The higher microbial PLFA content and lower G+/G- bacterial PLFA ratio indicate that the leaf litter of soybean might have relatively higher resource availability, since low G+/G- ratios may imply high C availability for soil bacterial communities (Fanin et al., 2019). This prediction is supported by relatively higher C, N, and P contents of the soybean leaf litter than that of the other two litter types (Table S1). Moreover, although bacteria and fungi have different substrate preferences (Griffiths et al., 1999; Rinnan and Bååth, 2009), the effects of substrate availability on microbial communities could become smaller at the late stage of decomposition, due to litter quality turning comparable among crop litter types after decomposition. Therefore, this change resulted in fungal/bacterial PLFAs ratio similar among crop litter species at the late stage of decomposition (Fig. 3b). The enzyme activity is often a product of microbial cellular metabolism and can be influenced by the substrate availability in the soil environment (Burns et al., 2013; Sinsabaugh et al., 2009; Sinsabaugh et al., 2008). Our results showed that microbial enzyme activities involving in C-, N- and P- releases were significantly different among crop litter species (Table 1 and Fig. 4), thus suggesting that different resource requirements of the litter-decomposing microbial communities depended on crop litter types. We also observed significant positive correlations between the enzyme activities (Fig. 5), indicating that the C, N and P needs for microbial communities are coupled and proportional (Smith et al., 2015). Furthermore, the enzyme activities and their coupled relationships were also not altered by the acid rain treatments (Fig. 5), thus highlighting the resilience of litterdecomposing microbial communities and their functions to the acidity. Regarding the link between enzyme activity and microbial community structure, only the BG activity significantly correlated with the microbial community composition and structure, thus implying that microbial community functions are not necessarily linked with microbial community composition (Marschner et al., 2003; Saleem et al., 2019; Waldrop et al., 2000). Overall, our results predicted that the microbial community functions could not show similar fluctuations in association with changes in microbial community composition under anthropogenic pressure. The asymmetric relationship between microbial community structure and functions, could be due to resistance, resilience, dormancy and/or functional redundancy of microbial species (Allison and Martiny, 2008; Fierer, 2017; Saleem, 2015). 5. Conclusions In the present study, we observed that microbial community composition and functions were significantly different among litter crop species, while these were determined by the litter quality, regardless of AR treatments during the experimental period. These results imply that litter quality may regulate the litter-decomposing microbial communities at a greater magnitude, as compared to the AR effects, while AR could not alter the litter decomposition process even at high acidity (pH 3.0), irrespective of crop litter types. Consequently, the associated release of elements also depended on the litter quality,

and it was not influenced by the AR. Moreover, the microbial enzyme activities, responsible for C, N, and P releases, correlated with each other. These findings predict a simultaneous uptake of C, N, and P by microbial communities during litter decomposition, irrespective of litter crop identity. However, the microbial community structure did not correlate with enzyme activities, thus suggesting asymmetric interactions between microbial community composition and functions. Overall, asymmetric responses of microbial communities to environmental changes suggest their resilience and adaptability in the agroecosystem. These findings potentially contribute to existing knowledge on litter-decomposing communities and their functions under AR scenarios. Declaration of competing interest The authors declare there is no conflict of interest. Acknowledgements This work was supported by the National Natural Science Foundation of China (U1701236) and Science and Technology Planning Project of Guangdong Province of China (2019B030301007). We are grateful to Dr. Qian Zhao, Dr. Yanxia Nie and Ms. Yongxia Jia from South China Botanical Garden, Chinese Academy of Sciences for their helps on the microbial community analyses. The handling Editor and two anonymous referees are appreciated to evaluate our work and provide many helpful suggestions for improving the quality of our manuscript. Appendix A. Supplementary table and figures Supplementary materials to this article can be found online at https://doi.org/10.1016/j.scitotenv.2020.136600. References Allison, S.D., Martiny, J.B.H., 2008. Resistance, resilience, and redundancy in microbial communities. Proc. Natl. Acad. Sci. U. S. A. 105, 11512–11519. Bååth, E., Anderson, T.H., 2003. Comparison of soil fungal/bacterial ratios in a pH gradient using physiological and PLFA-based techniques. Soil Biol. Biochem. 35, 955–963. Bååth, E., Frostegård, Å., Pennanen, T., Fritze, H., 1995. Microbial community structure and pH response in relation to soil organic matter quality in wood-ash fertilized, clear-cut or burned coniferous forest soils. Soil Biol. Biochem. 27, 229–240. Bijay-Singh, S.Y.H., Johnson-Beebout, S.E., Yadvinder-Singh, B.R.J., 2008. Crop residue management for lowland rice-based cropping systems in Asia. Adv. Agron. 98, 117–199. Bradford, M.A., Berg, B., Maynard, D.S., Wieder, W.R., Wood, S.A., Cornwell, W., 2016. Understanding the dominant controls on litter decomposition. J. Ecol. 104, 229–238. Bradford, M.A., Veen, G.F.C., Bonis, A., Bradford, E.M., Classen, A.T., Cornelissen, J.H.C., et al., 2017. A test of the hierarchical model of litter decomposition. Nat. Ecol. Evol. 1, 1836–1845. Burns, R.G., DeForest, J.L., Marxsen, J., Sinsabaugh, R.L., Stromberger, M.E., Wallenstein, M.D., et al., 2013. Soil enzymes in a changing environment: current knowledge and future directions. Soil Biol. Biochem. 58, 216–234. Burns, D.A., Aherne, J., Gay, D.A., Lehmann, C.M.B., 2016. Acid rain and its environmental effects: recent scientific advances. Atmos. Environ. 146, 1–4. Cao, Y., Wang, S., Zhang, G., Luo, J., Lu, S., 2009. Chemical characteristics of wet precipitation at an urban site of Guangzhou, South China. Atmos. Res. 94, 462–469. Chen, D., Zhou, L., Wu, J., Hsu, J., Lin, Y., Fu, S., 2012. Tree girdling affects the soil microbial community by modifying resource availability in two subtropical plantations. Appl. Soil Ecol. 53, 108–115. Chen, D., Saleem, M., Cheng, J., Mi, J., Chu, P., Tuvshintogtokh, I., et al., 2019a. Effects of aridity on soil microbial communities and functions across soil depths on the Mongolian Plateau. Funct. Ecol. 33, 1561–1571. Chen, D., Xing, W., Lan, Z., Saleem, M., Wu, Y., Hu, S., et al., 2019b. Direct and indirect effects of nitrogen enrichment on soil organisms and carbon and nitrogen mineralization in a semi-arid grassland. Funct. Ecol. 33, 175–187. Coleman, D.C., Crossley, D.A., Hendrix, P.F., 2004. Fundamentals of Soil Ecology. Second edition. Elsevier Academic Press, San Diego, California, USA. Cotrufo, M.F., Soong, J.L., Horton, A.J., Campbell, E.E., Haddix Michelle, L., Wall, D.H., et al., 2015. Formation of soil organic matter via biochemical and physical pathways of litter mass loss. Nat. Geosci. 8, 776–779. Coûteaux, M.M., Bottner, P., Berg, B., 1995. Litter decomposition, climate and liter quality. Trends Ecol. Evol. 10, 63–66. Dilly, O., Bloem, J., Vos, A., Munch, J.C., 2004. Bacterial diversity in agricultural soils during litter decomposition. Appl. Environ. Microbiol. 70, 468–474.

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