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Single-Species Artificial Grasslands Decrease Soil Multifunctionality in a Temperate Steppe on the Qinghai–Tibet Plateau

Single-Species Artificial Grasslands Decrease Soil Multifunctionality in a Temperate Steppe on... agronomy Article Single-Species Artificial Grasslands Decrease Soil Multifunctionality in a Temperate Steppe on the Qinghai–Tibet Plateau 1 2 , 3 1 1 1 1 1 1 Kelu Chen , Huakun Zhou , Bingbing Lu , Yang Wu , Jie Wang , Ziwen Zhao , Yuanze Li , Mei Wang , 1 1 1 , 4 1 , 3 , 4 , Yue Zhang , Wenjing Chen , Guobin Liu and Sha Xue * State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China; klchen@nwafu.edu.cn (K.C.); lubingbing@nwafu.edu.cn (B.L.); wuyang0521@nwsuaf.edu.cn (Y.W.); jwang1454@nwafu.edu.cn (J.W.); zhaozw18@nwsuaf.edu.cn (Z.Z.); lyzasz@nwafu.edu.cn (Y.L.); wangmei182@163.com (M.W.); 17854262662@nwafu.edu.cn (Y.Z.); chenwj886@163.com (W.C.); gbliu@ms.iswc.ac.cn (G.L.) Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Regions, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China; hkzhou@nwipb.cas.cn State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China * Correspondence: xuesha100@163.com; Tel.:+86-13679211517 Abstract: Artificial grasslands have been regarded as an effective method to improve grass production and quality, especially on the Qinghai–Tibet Plateau. Soil ecosystem multifunctionality (EMF) plays an important role in sustainable regional development. However, few studies have investigated the Citation: Chen, K.; Zhou, H.; Lu, B.; impacts of artificial grasslands on soil EMF. Here, we constructed single-species artificial grasslands in Wu, Y.; Wang, J.; Zhao, Z.; Li, Y.; a natural temperate steppe and investigated soil microbial communities, abiotic factors (soil moisture Wang, M.; Zhang, Y.; Chen, W.; et al. and pH), and functions related to biogeochemical cycles to explore (1) how the transformation Single-Species Artificial Grasslands from temperate steppe to artificial grasslands affected soil EMF and (2) the roles of species and Decrease Soil Multifunctionality in a phylogenetic microbial diversities, microbial community composition, and abiotic factors in driving Temperate Steppe on the differences in soil EMF. Our results showed that artificial grasslands decreased soil EMF regardless Qinghai–Tibet Plateau. Agronomy of planting species; that the bacterial and fungal community composition contributed more to soil 2021, 11, 2092. https://doi.org/ EMF prediction than species and phylogenetic diversities; and that microbial phylogenetic diversities 10.3390/agronomy11112092 were negatively associated with soil EMF. Soil pH played an important role in the effects of artificial Academic Editor: Alwyn Williams grasslands on soil EMF—artificial grasslands increased soil pH, which was negatively associated with soil EMF. Overall, the benefits of establishing artificial grasslands, for example, higher grass Received: 17 September 2021 production and quality, might be at the expense of soil EMF. Further studies should explore mixed- Accepted: 15 October 2021 species artificial grasslands. Published: 20 October 2021 Keywords: artificial grasslands; soil microbial diversity; ecosystem multifunctionality; Publisher’s Note: MDPI stays neutral microbial sequencing with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction Grasslands are a major vegetation type on the Qinghai–Tibet Plateau; they provide many vital ecological functions, such as carbon storage, water and soil conservation, and Copyright: © 2021 by the authors. animal habitat [1,2]. However, continuous overgrazing has degraded grasslands in this Licensee MDPI, Basel, Switzerland. region. In response, artificial grasslands have been constructed because of their higher This article is an open access article production and quality compared with natural grasslands [3]. In addition to their effects distributed under the terms and on vegetation, artificial grasslands also impact multiple soil and ecosystem functions, with conditions of the Creative Commons their impact varying depending on the species planted [4–6]. For example, Avena sativa Attribution (CC BY) license (https:// improves soil nematode biodiversity and fungal community diversity [4,5]. Additionally, creativecommons.org/licenses/by/ Elymus nutans and Elymus sibircus decreased microarthropod diversity, whereas Medicago 4.0/). Agronomy 2021, 11, 2092. https://doi.org/10.3390/agronomy11112092 https://www.mdpi.com/journal/agronomy Agronomy 2021, 11, 2092 2 of 12 sativa increased microarthropod diversity [6]. Therefore, investigating multiple ecological functions together, rather than focusing on individual functions, could help us compre- hensively predict the ecological efficiency of artificial grasslands and determine optimal planting species [7]. Soil ecosystem multifunctionality (EMF) is the capacity to sustain multiple soil func- tions simultaneously [7,8]. Measuring EMF takes into account the trade-offs among mul- tiple functions. The EMF of many types of soil ecosystems has been determined [9–13]. There is a consensus that soil microbial diversity plays an extremely important role in driving soil EMF [14]. Many studies found that soil microbial diversity was threatened with climate change and land-use change [15,16]. To solve the problem and effectively uti- lize soil microorganisms, the continuously developing prebiotic and probiotic approaches might become an effective tool to sustain soil biodiversity and functions [17,18]. Although many studies have found positive impacts of soil microbial diversity on soil EMF [12], the relative importance of different aspects of diversity, for example phylogenetic or species diversity, for determining EMF remains to be elucidated. Previous studies that focused on species diversity reported that biodiversity was important in maintaining EMF [10,12,14]. In addition, phylogenetic diversity may also contribute to EMF [19–21]. Even though the effects of soil microbial phylogenetic diversity on soil EMF have been reported [14], there is a knowledge gap concerning the role of phylogenetic diversity in artificial grasslands transformed from natural grassland. In addition, the influence of -diversity (commu- nity composition) on EMF has attracted increasing attentions in recent years [22]. It is commonly accepted that community composition could play a more important role than species diversity in predicting EMF [23]. For example, community composition indicates whether specific species exist and their potential function, but species and phylogenetic diversities do not [7]. Given the skewed distribution of species abundance, some common species, not diversity, have been found to have determined EMF [24]. Although some studies have explored the role of soil microbial composition in sustaining soil EMF [9,23], its relative importance compared with species diversity or phylogenetic diversity is still not clear. To fill these knowledge gaps, we explored the effects of different types of artificial grasslands on soil EMF in a temperate steppe on the Qinghai–Tibet Plateau. We hypothe- sized that (1) artificial grasslands can decrease the EMF of the temperate steppe; this effect would vary with different plant species and would be mediated by microbial organisms and abiotic factors (soil moisture and pH); (2) community composition would be a stronger predictor of soil EMF than species diversity and phylogenetic diversity; and (3) microbial diversity would be positively associated with soil EMF. The results could help us select the optimum planting species and improve management practices to sustain high EMF. 2. Materials and Methods 2.1. Study Area This study was conducted at the Qinghai Forage Thoroughbred Breeding Factory (35.25 N, 100.65 E, 3280 m a.s.l.), Tongde County, Qinghai Province, China. This location has a plateau continental climate. The mean annual temperature was 0.2 C and the mean annual precipitation was 429.8 mm. The vegetation was a typical alpine meadow dominated by Achnatherum splendens, Stipa capillata, Orinus thoroldii, and Saussurea japonica. The soil was a dark chestnut soil [25]. 2.2. Experiment Design We constructed artificial single-species grasslands of Elymus nutans, Poa crymophila, Puccinellia tenuiflora, Elymus sinosubmuticus, Elymus breviaristatus, Elymus sibircus, and Roegneria pauciflora on a natural temperate steppe in 2013. These grasses were regarded as adaptive species in the Qinghai–Tibet Plateau [25]. The adjacent natural temperate steppe was regarded as the control group (CK). We established three plots for each artificial grassland and five plots for CK, which were regarded as three replications. Each plot was Agronomy 2021, 11, 2092 3 of 12 3 m  4 m. A total of 24 plots were randomly distributed and separated by a 1.5-m-wide buffer strip to avoid mutual interference. These plots were excluded from grazing by fencing and were not fertilized. First, 18 g of seeds of the planting species were sowed into each plot for artificial grasslands. Weeds in artificial grasslands were removed during the growing season. In the middle of September every year, the aboveground biomass of the artificial grasslands was harvested, except for 5 cm of stubble. 2.3. Soil Sampling and Analysis After surface litter was removed, soil samples (5 cm diameter, 0–10 cm) randomly distributed in each plot were collected in 2019. Three soil samples from one plot were mixed to form one composite sample, sealed in a polyethylene bag, stored on ice, and transported to the laboratory. Further treatment followed [26]. The soil organic carbon (SOC) content was determined using the H SO –K Cr O 2 4 2 2 7 oxidation method [27], and the total soil nitrogen (TN) concentration was estimated using the Kjeldahl method [28]. After digestion with H SO and HClO , the soil total phosphorus 2 4 4 (TP) concentration was measured using the colorimetric method [29]. The chloroform fumigation-extraction method was used to determine microbial biomass carbon (MBC) and nitrogen (MBN) concentrations [26]. The soil samples were cultured at 25 C for 7 days with 60% water content. The samples were fumigated with chloroform for 24 h. Then the fumigated and unfumigated samples were extracted with 0.5M K SO solution. 2 4 Quantitative filter paper was filtered and stored in 20 C refrigerator for measuring MBC and MBN. MBC was measured using the Liqui TOCII analyzer (Elementar, Germany) and MBN by the Kjeldahl method [28]. The activities of cellobiohydrolase (CBH), -1,4-glucosidase (BG), -1,4-xylosidase (XYL), leucine aminopeptidase (LAP), N-acetyl- -D-glucosaminidase (NAG), L-alanine aminopeptidase (ALA), and phosphatase (PHOS), which are related to carbon, nitrogen, and phosphorous cycling, were measured using a fluorometric microplate enzyme assay as follows. First, 3 g of fresh soil was weighed and 125 mL Tris buffer was added (HCl was used to adjust the pH value of Tris buffer to the pH of the soil sample) to prepare the soil suspension. Then, an eight-channel pipette was used to siphon 150 L suspension into 96 microporous plates, and 50 L corresponding fluorescence substrate was added into each sample well to prepare sample micropores. At the same time, the control microhole was set. One-hundred-and-fifty microliters of suspension and 50 L Tris buffer were added to blank micropores; in addition, 150 L Tris buffer and 50 L 4-methylumbelliferone or 7-amino-4-methylcoumarin standard micropores were added. Negative control was added with 150 L Tris buffer and 50 L fluorescence substrate. The micropores were cultured with ALP for half an hour; NAG, LAP and ALT for 2 hours, and BG, CBH, and BX for 4 hours. All samples were cultured under dark condition at 25 C. After culture, 10 L sodium hydroxide (NaOH) solution with a concentration of 0.5 mol/L was added to each well to stop the reaction, and the fluorescence of the samples was at 360 nm excitation and 460 nm emission using a microplate reader (Biotek Synergy 2, Winooski, VT, USA) [30]. Soil pH was determined by pH meter (Metrohm 702, Herisau, Switzerland) in a suspension in which soil-to-water ratio was 1:5 (v/w). Soil samples were dried at 105 C overnight, and then the weight before drying minus the weight after drying divided by the weight before drying were regarded as soil moisture. [31]. 2.4. Microbial DNA Extraction, Sequencing, and Data Processing Total microbial DNA was extracted from 0.25 g frozen soil samples using a Power Soil DNA Isolation Kit (MO BIO Laboratories). Microbial communities were profiled by targeting a region of the internal transcribed spacer (ITS1) for fungi and the 16S rRNA gene for bacteria. Corresponding polymerase chain reaction (PCR) assays were performed using the ITS1F/ITS2-2043R and 338F/806R primer pairs. Further detailed information can be found in [26]. High-throughput sequencing analysis was performed using the Illumina Hiseq 2500 platform (2  250 paired ends). Agronomy 2021, 11, 2092 4 of 12 To obtain high-quality clean tags, the acquired sequences were filtered using QIIME for quality control as previously described [32]. A barcode sequence was used to sort the samples using the UCHIME algorithm, and then all chimeric sequences were removed [33]. The sequences were then clustered using UPARSE software and assigned to operational taxonomic units (OTUs) using a similarity cutoff of 97%. The bacterial and fungal repre- sentative sequences were assigned to taxonomic lineages within the SILVA database and UNITE + INSDC (UNITE database and the International Nucleotide Sequence Database Collaboration) using the RDP Classifier, BLAST, and QIIME software [26]. The functional profiles of soil bacteria and fungi were separately analyzed using FAPROTAX [34] and FUNGuild [35]. The results of the metagenomic workflow can be found in Table S1. 2.5. Assessing Soil Multifunctionality SOC, TN, TP, MBC, MBN, ALA, PHOS, BG, CBH, LAP, XYL, NAG, and the predicted microbial functional genes related abundances were selected in this study. All of the above single ecosystem functions were Z-Score standardized as follows: EFstd = (EF – E mean)/SD, with EFstd, EF, EFmean, and SD representing the standardized value, the raw value, mean of raw values, and standard deviation of each single function, respectively. The EMF was calculated as the average value of all these Z-Score standardized single functions [12]. 2.6. Data Analysis Microbial alpha diversity was calculated using the “vegan” package in R v.3.5.3. Phylogenetic diversity (PD) was estimated based on Faith’s approach using the “picante v.1.8.2” package [36] in R. Microbial community composition was evaluated by principal coordinate analysis (PCOA) and permutational multivariate analysis of variance based on the Bray-Curtis distance at the OTU level [31]. For single soil functions, EMF, microbial diversities, and soil abiotic factors, if the data satisfied normality (Shapiro–Wilk test) and variance homogeneity (Bartlett test), one-way analysis of variance with Tukey’s post hoc test at a significance level of 0.05 was performed to reveal the effects of the seven types of artificial grasslands. Otherwise, the Kruskal–Wallis test was adopted. Pearson’s correlation analysis was performed to test the relationship between microbial diversity (species richness, phylogenetic diversity, and community composition) and both soil EMF and single functions. Random forest modeling (RFM) was conducted to determine the drivers of soil EMF. All of the above analyses were performed in IBM SPSS Statistics 22.0 and R v.3.5.3 using the “picante v.1.8.2” [36], “multifunc v.0.8.0” [37], “multcomp v.1.4-13” [38], “vegan v.2.5-7” [39], “randomForest v.4.6-14” [40], and “ggplot2 v.3.5.3” [41] packages. 3. Results 3.1. Impacts of Artificial Grasslands on Soil EMF, Single Functions, Abiotic Factors and Microbial Diversity of the Temperate Steppe All types of artificial grasslands significantly decreased the soil EMF of the temperate steppe (Figure 1). There were no differences in soil EMF among the artificial grasslands with different planting species (Figure 1). Moreover, MBC, MBN, SOC, TN, ALA, BG, LAP, XYL, and NAG, as well as the carbon cycle, compound transformation, nitrogen cycle, and symbiosis or parasitic genes relative abundances in CK were significantly higher than those in all types of artificial grasslands. Additionally, PHOS and CBH, as well as the other, bacterial pathogen, and S cycle genes relative abundances in CK were higher than those in most types of artificial grasslands. There were no differences in the ectomycorrhizal and mycorrhizal genes relative abundances among all treatments. All the above single functions of the temperate steppe were decreased by artificial grasslands. Other functions were improved by artificial grasslands. For example, TP and the chemoheterotrophy genes relative abundances in CK were significantly lower than those in most types of artificial grasslands. The fungi pathogen genes’ relative abundances in CK were lower than that in E. nutans, E. breviaristatus, and P. crymophila grasslands (Figure 2). Agronomy 2021, 11, x FOR PEER REVIEW 5 of 12 most types of artificial grasslands. There were no differences in the ectomycorrhizal and mycorrhizal genes relative abundances among all treatments. All the above single func- tions of the temperate steppe were decreased by artificial grasslands. Other functions were improved by artificial grasslands. For example, TP and the chemoheterotrophy genes rela- tive abundances in CK were significantly lower than those in most types of artificial grass- Agronomy 2021, 11, 2092 5 of 12 lands. The fungi pathogen genes' relative abundances in CK were lower than that in E. nu- tans, E. breviaristatus, and P. crymophila grasslands (Figure 2). Figure 1. Soil EMF at natural and artificial grasslands: Effects of artificial grasslands. CK: Natural temperate steppe. E Figure 1. Soil EMF at natural and artificial grasslands: Effects of artificial grasslands. CK: Natural temperate steppe. E breviaristatus: Elymus breviaristatus. E nutants: Elymus nutans. E sibircus: Elymus sibircus. E sinosubmuticus: Elymus sino- breviaristatus: Elymus breviaristatus. E nutants: Elymus nutans. E sibircus: Elymus sibircus. E sinosubmuticus: Elymus submuticus. P crymophila: Poa crymophila. P tenuiflora: Puccinellia tenuiflora. R pauciflora: Roegneria pauciflora. *: p < 0.05. sinosubmuticus. P crymophila: Poa crymophila. P tenuiflora: Puccinellia tenuiflora. R pauciflora: Roegneria pauciflora. *: p < 0.05. The pH at all types of artificial grasslands were higher than CK (Figure 2). Soil moistures of the majority of artificial grasslands were not significantly different with CK (Table S2). The species diversities of both bacteria and fungi were not affected by artificial grass- lands (Figure S1). Most types of artificial grassland increased the soil bacterial phylogenetic diversity of the temperate steppe (Figure S1). The phylogenetic diversity of soil fungi was not significantly different among the artificial grasslands with different planting species. Artificial grasslands improved the community composition of fungi but not bacteria com- pared with temperate steppe, regardless of planting species (Figure 3). 3.2. Relationships between Soil Microbial Diversity and Ecosystem Functionality Bacterial species diversity and phylogenetic diversity were negatively correlated with soil EMF. The bacterial and fungal community compositions were positively correlated with soil EMF (Figure 4). Bacterial species diversity was negatively correlated with TN, MBC, PHOS, and BG, as well as the nitrogen cycle, other, and symbiosis or parasitic genes relative abundances, but positively correlated with TP and the chemoheterotrophy genes relative abundances. Bacterial phylogenetic diversity was negatively correlated with SOC, TN, MBN, MBC, BG, LAP, ALA, NAG, and XYL, as well as the carbon cycle, compound transformation, nitrogen cycle, other, pathogen 1, and symbiosis or parasitic genes relative abundances. Fungal species diversity was only positively correlated with the ectomycorrhizal genes relative abundances, and fungal phylogenetic diversity was positively correlated with SOC, TN, and NAG, but negatively correlated with the arbuscular mycorrhizal genes’ relative abundances. The community compositions of both bacteria and fungi were positively correlated with SOC, TN, MBN, MBC, PHOS, BG, LAP, ALA, NAG, XYL, and CBH, as well as the mycorrhizal, carbon cycle, compound transformation, nitrogen cycle, other, pathogen 1, S cycle, and symbiosis or parasitic genes’ relative abundances, but negatively correlated with TP and the pathogen and chemoheterotrophy genes’ relative abundances (Figure 5). 3.3. Possible Drivers of Soil EMF The random forest model explained 91.7% of the soil EMF variance. The contributions of different drivers to the model from high to low were bacterial community composition, soil pH, fungal community composition, and bacterial PD. Fungal PD, soil moisture, and bacterial and fungal species diversity did not play a role in the prediction of soil EMF (Figure 6). Agronomy 2021, 11, 2092 6 of 12 Agronomy 2021, 11, x FOR PEER REVIEW 6 of 12 Figure 2. Soil single functions and soil pH at natural and artificial grassland. CK: Natural temperate Figure 2. Soil single functions and soil pH at natural and artificial grassland. CK: Natural temperate steppe. E breviaristatus: Elymus breviaristatus. E nutants: Elymus nutans. E sibircus: Elymus sibircus. steppe. E breviaristatus: Elymus breviaristatus. E nutants: Elymus nutans. E sibircus: Elymus sibircus. E sinosubmuticus: Elymus sinosubmuticus. P crymophila: Poa crymophila. P tenuiflora: Puccinellia ten- E sinosubmuticus: Elymus sinosubmuticus. P crymophila: Poa crymophila. P tenuiflora: Puccinellia uiflora. R pauciflora: Roegneria pauciflora. The different lowercase letters above the bars denoted sig- tenuiflora. R pauciflora: Roegneria pauciflora. The different lowercase letters above the bars denoted nificant differences (p < 0.05). significant differences (p < 0.05). The pH at all types of artificial grasslands were higher than CK (Figure 2). Soil mois- tures of the majority of artificial grasslands were not significantly different with CK (Table S2). The species diversities of both bacteria and fungi were not affected by artificial grass- lands (Figure S1). Most types of artificial grassland increased the soil bacterial phyloge- netic diversity of the temperate steppe (Figure S1). The phylogenetic diversity of soil fungi was not significantly different among the artificial grasslands with different planting spe- cies. Artificial grasslands improved the community composition of fungi but not bacteria compared with temperate steppe, regardless of planting species (Figure 3). Agronomy 2021, 11, 2092 7 of 12 Agronomy 2021, 11, x FOR PEER REVIEW 7 of 12 PERMANOVA PERMANOVA p=0.001 p=0.001 Kruskal-Wallis Test D Kruskal-Walls Test P=0.09 p=0.09 Artificial plot Natural plot Kruskal-Wallis Test Kruskal-Walls Test P<0.001 p<0.001 Agronomy 2021, 11, x FOR PEER REVIEW 8 of 12 relative abundances. The community compositions of both bacteria and fungi were posi- Artificial plot Natural plot tively correlated with SOC, TN, MBN, MBC, PHOS, BG, LAP, ALA, NAG, XYL, and CBH, Figure 3. Principal coordinates analysis and within stage dissimilarity of bacteria (A, C, D) and fungal (B, E, F) communi- Figure 3. Principal coordinates analysis and within stage dissimilarity of bacteria (A, C, D) and fungal (B, E, F) communities as well as the mycorrhizal, carbon cycle, compound transformation, nitrogen cycle, other, patho- ties at artificial and natural grassland. E breviaristatus: Elymus breviaristatus. E nutants: Elymus nutans. E sibircus: Elymus at artificial and natural grassland. E breviaristatus: Elymus breviaristatus. E nutants: Elymus nutans. E sibircus: Elymus sibircus. gen 1, S cycle, and symbiosis or parasitic genes' relative abundances, but negatively corre- sibircus. E sinosubmuticus: Elymus sinosubmuticus. P crymophila: Poa crymophila. P tenuiflora: Puccinellia tenuiflora. R pau- E sinosubmuticus: Elymus sinosubmuticus. P crymophila: Poa crymophila. P tenuiflora: Puccinellia tenuiflora. R pauciflora: lated with TP and the pathogen and chemoheterotrophy genes' relative abundances (Figure ciflora: Roegneria pauciflora. *: p < 0.05, **: p < 0.01, *** p < 0.001. Roegneria pauciflora. *: p < 0.05, **: p < 0.01, *** p < 0.001. 5). 3.2. Relationships between Soil Microbial Diversity and Ecosystem Functionality Bacterial species diversity and phylogenetic diversity were negatively correlated with soil EMF. The bacterial and fungal community compositions were positively corre- lated with soil EMF (Figure 4). Bacterial species diversity was negatively correlated with TN, MBC, PHOS, and BG, as well as the nitrogen cycle, other, and symbiosis or parasitic genes relative abundances, but positively correlated with TP and the chemoheterotrophy genes <0.001 relative abundances. Bacterial phylogenetic diversity was negatively correlated with SOC, <0.001 <0.001 0.047 TN, MBN, MBC, BG, LAP, ALA, NAG, and XYL, as well as the carbon cycle, compound transformation, nitrogen cycle, other, pathogen 1, and symbiosis or parasitic genes relative abun- β diversi ty dances. Fungal species diversity was only positively correlated with the ectomycorrhizal genes relative abundances, and fungal phylogenetic diversity was positively correlated with SOC, TN, and NAG, but negatively correlated with the arbuscular mycorrhizal genes' Figure 4. The relationships between soil EMF and Shannon indexes, phylogenetic diversities, and communities composi- Figure 4. The relationships between soil EMF and Shannon indexes, phylogenetic diversities, and communities compositions tions (β diversity) of bacteria and fungi. ( diversity) of bacteria and fungi. Phylogenetic diversity of fungi Phylogenetic diversity of bacteria Shannon’s diversity of fungi Shannon’s diversity of bacteria β diversity of fungi β div ersity of bacteria Figure 5. The relationships between single soil functions and Shannon indexes, phylogenetic diversities, and communities compositions of bacteria and fungi. *: p < 0.05, **: p < 0.01, *** p < 0.001. 3.3. Possible Drivers of Soil EMF The random forest model explained 91.7% of the soil EMF variance. The contribu- tions of different drivers to the model from high to low were bacterial community com- position, soil pH, fungal community composition, and bacterial PD. Fungal PD, soil mois- ture, and bacterial and fungal species diversity did not play a role in the prediction of soil EMF (Figure 6). Agronomy 2021, 11, x FOR PEER REVIEW 8 of 12 relative abundances. The community compositions of both bacteria and fungi were posi- tively correlated with SOC, TN, MBN, MBC, PHOS, BG, LAP, ALA, NAG, XYL, and CBH, as well as the mycorrhizal, carbon cycle, compound transformation, nitrogen cycle, other, patho- gen 1, S cycle, and symbiosis or parasitic genes' relative abundances, but negatively corre- lated with TP and the pathogen and chemoheterotrophy genes' relative abundances (Figure 5). <0.001 0.047 <0.001 <0.001 β diversi ty Agronomy 2021, 11, 2092 8 of 12 Figure 4. The relationships between soil EMF and Shannon indexes, phylogenetic diversities, and communities composi- tions (β diversity) of bacteria and fungi. Phylogenetic diversity of fungi Phylogenetic diversity of bacteria Shannon’s diversity of fungi Shannon’s diversity of bacteria β diversity of fungi β div ersity of bacteria Agronomy 2021, 11, x FOR PEER REVIEW 9 of 12 Figure 5. Figure The r 5. The elationships relationships be between tween sing single le so soil il functions and Shannon indexes, functions and Shannon indexes, phylogenetic phylogenetic diver diversities, sitieand s, and communities communities compositions of bacteria and fungi. *: p < 0.05, **: p < 0.01, *** p < 0.001. compositions of bacteria and fungi. *: p < 0.05, **: p < 0.01, *** p < 0.001. 3.3. Possible Drivers of Soil EMF The random forest model explained 91.7% of the soil EMF variance. The contribu- tions of different drivers to the model from high to low were bacterial community com- position, soil pH, fungal community composition, and bacterial PD. Fungal PD, soil mois- ture, and bacterial and fungal species diversity did not play a role in the prediction of soil EMF (Figure 6). R²=91.7 p<0.001 Figure 6. The contribution of bacterial and fungi Shannon indexes, phylogenetic diversities (PD), Figure 6. The contribution of bacterial and fungi Shannon indexes, phylogenetic diversi- and communities compositions ( diversity) to the prediction to soil EMF. MSE is the mean square ties (PD), and communities compositions (β diversity) to the prediction to soil EMF. error. The different lowercase letters above the bars denoted significant differences (p < 0.05). MSE is the mean square error. The different lowercase letters above the bars denoted significant differences (p < 0.05). 4. Discussion Although artificial grasslands are regarded as a feasible method to improve grass production and quality [42], our results showed that they decreased soil EMF, which is important for sustainable development [43], compared with natural temperate steppes. This partly verifies our first hypothesis and could partly be explained by the different aboveground plant community compositions of artificial and natural grasslands and their effects on soil EMF [23,42]. In addition, the Qinghai–Tibet Plateau has experienced acidi- fication since the 1980s [44]. During this process, soil microorganisms could adapt to acid- ification. However, artificial grasslands that are harvested could have lower soil nutrient inputs and increase soil pH [25,45], which might inhibit the soil microorganisms being adaptive to acidified condition and thus possibly decrease soil functions. Thus, as shown in our results, soil pH plays a role in determining soil EMF, and all types of artificial grass- lands significantly increased soil pH and decreased EMF. In addition to the aboveground plant community composition, the belowground community composition can significantly affect soil EMF [23]. However, for artificial grasslands, especially in the Qinghai–Tibet Plateau, such a relationship has not yet been established [23]. The community composition of both fungi and bacteria contributed more than diversity to soil EMF and was strongly associated with most single functions, verify- ing our second hypothesis. This suggests that the microbial community composition is a major driver of soil EMF and supports the notion that community composition might be more important than community diversity [46]. More specifically, the transformation from natural temperate steppe to single-species artificial grasslands altered plant commu- nity composition [42]. Plants mediated a shift in the soil microbial community, increasing Agronomy 2021, 11, 2092 9 of 12 4. Discussion Although artificial grasslands are regarded as a feasible method to improve grass production and quality [42], our results showed that they decreased soil EMF, which is important for sustainable development [43], compared with natural temperate steppes. This partly verifies our first hypothesis and could partly be explained by the different aboveground plant community compositions of artificial and natural grasslands and their effects on soil EMF [23,42]. In addition, the Qinghai–Tibet Plateau has experienced acidification since the 1980s [44]. During this process, soil microorganisms could adapt to acidification. However, artificial grasslands that are harvested could have lower soil nutrient inputs and increase soil pH [25,45], which might inhibit the soil microorganisms being adaptive to acidified condition and thus possibly decrease soil functions. Thus, as shown in our results, soil pH plays a role in determining soil EMF, and all types of artificial grasslands significantly increased soil pH and decreased EMF. In addition to the aboveground plant community composition, the belowground community composition can significantly affect soil EMF [23]. However, for artificial grasslands, especially in the Qinghai–Tibet Plateau, such a relationship has not yet been established [23]. The community composition of both fungi and bacteria contributed more than diversity to soil EMF and was strongly associated with most single functions, verify- ing our second hypothesis. This suggests that the microbial community composition is a major driver of soil EMF and supports the notion that community composition might be more important than community diversity [46]. More specifically, the transformation from natural temperate steppe to single-species artificial grasslands altered plant community composition [42]. Plants mediated a shift in the soil microbial community, increasing the relative abundances of one or few particular species and decreasing the relative abundances of others [47]. Specific soil microbial species, even rare taxa, can determine soil EMF [12,48]. In addition, our results showed that soil bacterial PD was a better predictor of EMF than species diversity, which was in accordance with previous conclusions obtained in above- ground ecosystems [49,50]. Therefore, phylogenetic diversity should be included in future soil multifunctionality predictions in artificial grasslands on the Qinghai–Tibet Plateau. Although most evidence supports the positive effect of soil microbial diversity on soil EMF [12], we found that soil bacterial species diversity and PD were negatively associated with soil EMF, rejecting our third hypothesis. Similar results were also found in some previous studies [31,51]. Wang et al. found bacterial richness was negatively related to soil EMF at degraded alpine meadow [31]. Becker et el. also reported negative biodiversity– ecosystem functioning relationships caused by antagonistic interactions [51]. We propose two hypotheses that could explain the negative relationship. (1) It is possible that specific bacterial or fungal species rather than the total diversity contributed to the soil EMF in our study, similar to other recent studies in which all found that some specific microbial taxa play a major role in driving soil EMF [12,31,48]. (2) There might be functional redundancy in the soil microbial community, which could be inferred based on the lower PD in the natural temperate steppe than that in artificial grasslands, and the fact that it is widespread in terrestrial ecosystems [52]. 5. Conclusions We concluded that single-species artificial grasslands decreased the soil EMF of the temperate steppe, regardless of the planting species. Soil microbial community composition contributed more than diversity to the prediction of soil EMF. Phylogenetic diversity better explained the variation in soil EMF variance. Artificial grasslands increased soil pH, which might inhibit the soil microorganisms being adaptive to acidified condition, and thus possibly decreased soil EMF. These conclusions suggest that the benefits of establishing artificial grasslands, such as higher grass production and quality, might be at the expense of soil EMF. Further studies should explore mixed-species artificial grasslands. Agronomy 2021, 11, 2092 10 of 12 Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/agronomy11112092/s1, Table S1: Soil moisture at artificial and natural grasslands. Table S2: Soil moisture at artificial and natural grasslands. Figure S1: Effects of artificial grasslands on shannon index of bacteria and fungi and phylogenetic diversities of bacteria and fungi. Author Contributions: W.C., H.Z., G.L. and S.X. designed the experiment; W.C., Y.W., Z.Z., Y.L., Y.Z., B.L. performed the experiment; K.C., M.W., J.W. and W.C. analyzed the data and wrote the manuscript. All authors made important contributions to the manuscript and approved publication. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported financially by the Qinghai innovation platform construction project by the Chinese Academy of Sciences (2021-ZJ-Y01) and Joint Research Project of Three-River- Resource National Park funded by Chinese Academy of Sciences and Qinghai Provincial People’s Government (LHZX-2020-08), and Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Regions, Northwest Institute of Plateau Biology (2020-KF-04). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request. Acknowledgments: Authors appreciated the Qinghai innovation platform construction project by the Chinese Academy of Sciences (2021-ZJ-Y01) and Joint Research Project of Three-River-Resource National Park funded by Chinese Academy of Sciences and Qinghai Provincial People’s Government (LHZX-2020-08), and Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Regions, Northwest Institute of Plateau Biology (2020-KF-04). Conflicts of Interest: The authors declare that they have no conflict of interest. References 1. Dong, S.; Sherman, R. Enhancing the resilience of coupled human and natural systems of alpine rangelands on the qinghai-tibetan plateau. Rangel. J. 2015, 37, i–iii. [CrossRef] 2. Wei, D.; Liu, Y.; Wang, Y.; Wang, Y. 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Single-Species Artificial Grasslands Decrease Soil Multifunctionality in a Temperate Steppe on the Qinghai–Tibet Plateau

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agronomy Article Single-Species Artificial Grasslands Decrease Soil Multifunctionality in a Temperate Steppe on the Qinghai–Tibet Plateau 1 2 , 3 1 1 1 1 1 1 Kelu Chen , Huakun Zhou , Bingbing Lu , Yang Wu , Jie Wang , Ziwen Zhao , Yuanze Li , Mei Wang , 1 1 1 , 4 1 , 3 , 4 , Yue Zhang , Wenjing Chen , Guobin Liu and Sha Xue * State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China; klchen@nwafu.edu.cn (K.C.); lubingbing@nwafu.edu.cn (B.L.); wuyang0521@nwsuaf.edu.cn (Y.W.); jwang1454@nwafu.edu.cn (J.W.); zhaozw18@nwsuaf.edu.cn (Z.Z.); lyzasz@nwafu.edu.cn (Y.L.); wangmei182@163.com (M.W.); 17854262662@nwafu.edu.cn (Y.Z.); chenwj886@163.com (W.C.); gbliu@ms.iswc.ac.cn (G.L.) Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Regions, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China; hkzhou@nwipb.cas.cn State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China * Correspondence: xuesha100@163.com; Tel.:+86-13679211517 Abstract: Artificial grasslands have been regarded as an effective method to improve grass production and quality, especially on the Qinghai–Tibet Plateau. Soil ecosystem multifunctionality (EMF) plays an important role in sustainable regional development. However, few studies have investigated the Citation: Chen, K.; Zhou, H.; Lu, B.; impacts of artificial grasslands on soil EMF. Here, we constructed single-species artificial grasslands in Wu, Y.; Wang, J.; Zhao, Z.; Li, Y.; a natural temperate steppe and investigated soil microbial communities, abiotic factors (soil moisture Wang, M.; Zhang, Y.; Chen, W.; et al. and pH), and functions related to biogeochemical cycles to explore (1) how the transformation Single-Species Artificial Grasslands from temperate steppe to artificial grasslands affected soil EMF and (2) the roles of species and Decrease Soil Multifunctionality in a phylogenetic microbial diversities, microbial community composition, and abiotic factors in driving Temperate Steppe on the differences in soil EMF. Our results showed that artificial grasslands decreased soil EMF regardless Qinghai–Tibet Plateau. Agronomy of planting species; that the bacterial and fungal community composition contributed more to soil 2021, 11, 2092. https://doi.org/ EMF prediction than species and phylogenetic diversities; and that microbial phylogenetic diversities 10.3390/agronomy11112092 were negatively associated with soil EMF. Soil pH played an important role in the effects of artificial Academic Editor: Alwyn Williams grasslands on soil EMF—artificial grasslands increased soil pH, which was negatively associated with soil EMF. Overall, the benefits of establishing artificial grasslands, for example, higher grass Received: 17 September 2021 production and quality, might be at the expense of soil EMF. Further studies should explore mixed- Accepted: 15 October 2021 species artificial grasslands. Published: 20 October 2021 Keywords: artificial grasslands; soil microbial diversity; ecosystem multifunctionality; Publisher’s Note: MDPI stays neutral microbial sequencing with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction Grasslands are a major vegetation type on the Qinghai–Tibet Plateau; they provide many vital ecological functions, such as carbon storage, water and soil conservation, and Copyright: © 2021 by the authors. animal habitat [1,2]. However, continuous overgrazing has degraded grasslands in this Licensee MDPI, Basel, Switzerland. region. In response, artificial grasslands have been constructed because of their higher This article is an open access article production and quality compared with natural grasslands [3]. In addition to their effects distributed under the terms and on vegetation, artificial grasslands also impact multiple soil and ecosystem functions, with conditions of the Creative Commons their impact varying depending on the species planted [4–6]. For example, Avena sativa Attribution (CC BY) license (https:// improves soil nematode biodiversity and fungal community diversity [4,5]. Additionally, creativecommons.org/licenses/by/ Elymus nutans and Elymus sibircus decreased microarthropod diversity, whereas Medicago 4.0/). Agronomy 2021, 11, 2092. https://doi.org/10.3390/agronomy11112092 https://www.mdpi.com/journal/agronomy Agronomy 2021, 11, 2092 2 of 12 sativa increased microarthropod diversity [6]. Therefore, investigating multiple ecological functions together, rather than focusing on individual functions, could help us compre- hensively predict the ecological efficiency of artificial grasslands and determine optimal planting species [7]. Soil ecosystem multifunctionality (EMF) is the capacity to sustain multiple soil func- tions simultaneously [7,8]. Measuring EMF takes into account the trade-offs among mul- tiple functions. The EMF of many types of soil ecosystems has been determined [9–13]. There is a consensus that soil microbial diversity plays an extremely important role in driving soil EMF [14]. Many studies found that soil microbial diversity was threatened with climate change and land-use change [15,16]. To solve the problem and effectively uti- lize soil microorganisms, the continuously developing prebiotic and probiotic approaches might become an effective tool to sustain soil biodiversity and functions [17,18]. Although many studies have found positive impacts of soil microbial diversity on soil EMF [12], the relative importance of different aspects of diversity, for example phylogenetic or species diversity, for determining EMF remains to be elucidated. Previous studies that focused on species diversity reported that biodiversity was important in maintaining EMF [10,12,14]. In addition, phylogenetic diversity may also contribute to EMF [19–21]. Even though the effects of soil microbial phylogenetic diversity on soil EMF have been reported [14], there is a knowledge gap concerning the role of phylogenetic diversity in artificial grasslands transformed from natural grassland. In addition, the influence of -diversity (commu- nity composition) on EMF has attracted increasing attentions in recent years [22]. It is commonly accepted that community composition could play a more important role than species diversity in predicting EMF [23]. For example, community composition indicates whether specific species exist and their potential function, but species and phylogenetic diversities do not [7]. Given the skewed distribution of species abundance, some common species, not diversity, have been found to have determined EMF [24]. Although some studies have explored the role of soil microbial composition in sustaining soil EMF [9,23], its relative importance compared with species diversity or phylogenetic diversity is still not clear. To fill these knowledge gaps, we explored the effects of different types of artificial grasslands on soil EMF in a temperate steppe on the Qinghai–Tibet Plateau. We hypothe- sized that (1) artificial grasslands can decrease the EMF of the temperate steppe; this effect would vary with different plant species and would be mediated by microbial organisms and abiotic factors (soil moisture and pH); (2) community composition would be a stronger predictor of soil EMF than species diversity and phylogenetic diversity; and (3) microbial diversity would be positively associated with soil EMF. The results could help us select the optimum planting species and improve management practices to sustain high EMF. 2. Materials and Methods 2.1. Study Area This study was conducted at the Qinghai Forage Thoroughbred Breeding Factory (35.25 N, 100.65 E, 3280 m a.s.l.), Tongde County, Qinghai Province, China. This location has a plateau continental climate. The mean annual temperature was 0.2 C and the mean annual precipitation was 429.8 mm. The vegetation was a typical alpine meadow dominated by Achnatherum splendens, Stipa capillata, Orinus thoroldii, and Saussurea japonica. The soil was a dark chestnut soil [25]. 2.2. Experiment Design We constructed artificial single-species grasslands of Elymus nutans, Poa crymophila, Puccinellia tenuiflora, Elymus sinosubmuticus, Elymus breviaristatus, Elymus sibircus, and Roegneria pauciflora on a natural temperate steppe in 2013. These grasses were regarded as adaptive species in the Qinghai–Tibet Plateau [25]. The adjacent natural temperate steppe was regarded as the control group (CK). We established three plots for each artificial grassland and five plots for CK, which were regarded as three replications. Each plot was Agronomy 2021, 11, 2092 3 of 12 3 m  4 m. A total of 24 plots were randomly distributed and separated by a 1.5-m-wide buffer strip to avoid mutual interference. These plots were excluded from grazing by fencing and were not fertilized. First, 18 g of seeds of the planting species were sowed into each plot for artificial grasslands. Weeds in artificial grasslands were removed during the growing season. In the middle of September every year, the aboveground biomass of the artificial grasslands was harvested, except for 5 cm of stubble. 2.3. Soil Sampling and Analysis After surface litter was removed, soil samples (5 cm diameter, 0–10 cm) randomly distributed in each plot were collected in 2019. Three soil samples from one plot were mixed to form one composite sample, sealed in a polyethylene bag, stored on ice, and transported to the laboratory. Further treatment followed [26]. The soil organic carbon (SOC) content was determined using the H SO –K Cr O 2 4 2 2 7 oxidation method [27], and the total soil nitrogen (TN) concentration was estimated using the Kjeldahl method [28]. After digestion with H SO and HClO , the soil total phosphorus 2 4 4 (TP) concentration was measured using the colorimetric method [29]. The chloroform fumigation-extraction method was used to determine microbial biomass carbon (MBC) and nitrogen (MBN) concentrations [26]. The soil samples were cultured at 25 C for 7 days with 60% water content. The samples were fumigated with chloroform for 24 h. Then the fumigated and unfumigated samples were extracted with 0.5M K SO solution. 2 4 Quantitative filter paper was filtered and stored in 20 C refrigerator for measuring MBC and MBN. MBC was measured using the Liqui TOCII analyzer (Elementar, Germany) and MBN by the Kjeldahl method [28]. The activities of cellobiohydrolase (CBH), -1,4-glucosidase (BG), -1,4-xylosidase (XYL), leucine aminopeptidase (LAP), N-acetyl- -D-glucosaminidase (NAG), L-alanine aminopeptidase (ALA), and phosphatase (PHOS), which are related to carbon, nitrogen, and phosphorous cycling, were measured using a fluorometric microplate enzyme assay as follows. First, 3 g of fresh soil was weighed and 125 mL Tris buffer was added (HCl was used to adjust the pH value of Tris buffer to the pH of the soil sample) to prepare the soil suspension. Then, an eight-channel pipette was used to siphon 150 L suspension into 96 microporous plates, and 50 L corresponding fluorescence substrate was added into each sample well to prepare sample micropores. At the same time, the control microhole was set. One-hundred-and-fifty microliters of suspension and 50 L Tris buffer were added to blank micropores; in addition, 150 L Tris buffer and 50 L 4-methylumbelliferone or 7-amino-4-methylcoumarin standard micropores were added. Negative control was added with 150 L Tris buffer and 50 L fluorescence substrate. The micropores were cultured with ALP for half an hour; NAG, LAP and ALT for 2 hours, and BG, CBH, and BX for 4 hours. All samples were cultured under dark condition at 25 C. After culture, 10 L sodium hydroxide (NaOH) solution with a concentration of 0.5 mol/L was added to each well to stop the reaction, and the fluorescence of the samples was at 360 nm excitation and 460 nm emission using a microplate reader (Biotek Synergy 2, Winooski, VT, USA) [30]. Soil pH was determined by pH meter (Metrohm 702, Herisau, Switzerland) in a suspension in which soil-to-water ratio was 1:5 (v/w). Soil samples were dried at 105 C overnight, and then the weight before drying minus the weight after drying divided by the weight before drying were regarded as soil moisture. [31]. 2.4. Microbial DNA Extraction, Sequencing, and Data Processing Total microbial DNA was extracted from 0.25 g frozen soil samples using a Power Soil DNA Isolation Kit (MO BIO Laboratories). Microbial communities were profiled by targeting a region of the internal transcribed spacer (ITS1) for fungi and the 16S rRNA gene for bacteria. Corresponding polymerase chain reaction (PCR) assays were performed using the ITS1F/ITS2-2043R and 338F/806R primer pairs. Further detailed information can be found in [26]. High-throughput sequencing analysis was performed using the Illumina Hiseq 2500 platform (2  250 paired ends). Agronomy 2021, 11, 2092 4 of 12 To obtain high-quality clean tags, the acquired sequences were filtered using QIIME for quality control as previously described [32]. A barcode sequence was used to sort the samples using the UCHIME algorithm, and then all chimeric sequences were removed [33]. The sequences were then clustered using UPARSE software and assigned to operational taxonomic units (OTUs) using a similarity cutoff of 97%. The bacterial and fungal repre- sentative sequences were assigned to taxonomic lineages within the SILVA database and UNITE + INSDC (UNITE database and the International Nucleotide Sequence Database Collaboration) using the RDP Classifier, BLAST, and QIIME software [26]. The functional profiles of soil bacteria and fungi were separately analyzed using FAPROTAX [34] and FUNGuild [35]. The results of the metagenomic workflow can be found in Table S1. 2.5. Assessing Soil Multifunctionality SOC, TN, TP, MBC, MBN, ALA, PHOS, BG, CBH, LAP, XYL, NAG, and the predicted microbial functional genes related abundances were selected in this study. All of the above single ecosystem functions were Z-Score standardized as follows: EFstd = (EF – E mean)/SD, with EFstd, EF, EFmean, and SD representing the standardized value, the raw value, mean of raw values, and standard deviation of each single function, respectively. The EMF was calculated as the average value of all these Z-Score standardized single functions [12]. 2.6. Data Analysis Microbial alpha diversity was calculated using the “vegan” package in R v.3.5.3. Phylogenetic diversity (PD) was estimated based on Faith’s approach using the “picante v.1.8.2” package [36] in R. Microbial community composition was evaluated by principal coordinate analysis (PCOA) and permutational multivariate analysis of variance based on the Bray-Curtis distance at the OTU level [31]. For single soil functions, EMF, microbial diversities, and soil abiotic factors, if the data satisfied normality (Shapiro–Wilk test) and variance homogeneity (Bartlett test), one-way analysis of variance with Tukey’s post hoc test at a significance level of 0.05 was performed to reveal the effects of the seven types of artificial grasslands. Otherwise, the Kruskal–Wallis test was adopted. Pearson’s correlation analysis was performed to test the relationship between microbial diversity (species richness, phylogenetic diversity, and community composition) and both soil EMF and single functions. Random forest modeling (RFM) was conducted to determine the drivers of soil EMF. All of the above analyses were performed in IBM SPSS Statistics 22.0 and R v.3.5.3 using the “picante v.1.8.2” [36], “multifunc v.0.8.0” [37], “multcomp v.1.4-13” [38], “vegan v.2.5-7” [39], “randomForest v.4.6-14” [40], and “ggplot2 v.3.5.3” [41] packages. 3. Results 3.1. Impacts of Artificial Grasslands on Soil EMF, Single Functions, Abiotic Factors and Microbial Diversity of the Temperate Steppe All types of artificial grasslands significantly decreased the soil EMF of the temperate steppe (Figure 1). There were no differences in soil EMF among the artificial grasslands with different planting species (Figure 1). Moreover, MBC, MBN, SOC, TN, ALA, BG, LAP, XYL, and NAG, as well as the carbon cycle, compound transformation, nitrogen cycle, and symbiosis or parasitic genes relative abundances in CK were significantly higher than those in all types of artificial grasslands. Additionally, PHOS and CBH, as well as the other, bacterial pathogen, and S cycle genes relative abundances in CK were higher than those in most types of artificial grasslands. There were no differences in the ectomycorrhizal and mycorrhizal genes relative abundances among all treatments. All the above single functions of the temperate steppe were decreased by artificial grasslands. Other functions were improved by artificial grasslands. For example, TP and the chemoheterotrophy genes relative abundances in CK were significantly lower than those in most types of artificial grasslands. The fungi pathogen genes’ relative abundances in CK were lower than that in E. nutans, E. breviaristatus, and P. crymophila grasslands (Figure 2). Agronomy 2021, 11, x FOR PEER REVIEW 5 of 12 most types of artificial grasslands. There were no differences in the ectomycorrhizal and mycorrhizal genes relative abundances among all treatments. All the above single func- tions of the temperate steppe were decreased by artificial grasslands. Other functions were improved by artificial grasslands. For example, TP and the chemoheterotrophy genes rela- tive abundances in CK were significantly lower than those in most types of artificial grass- Agronomy 2021, 11, 2092 5 of 12 lands. The fungi pathogen genes' relative abundances in CK were lower than that in E. nu- tans, E. breviaristatus, and P. crymophila grasslands (Figure 2). Figure 1. Soil EMF at natural and artificial grasslands: Effects of artificial grasslands. CK: Natural temperate steppe. E Figure 1. Soil EMF at natural and artificial grasslands: Effects of artificial grasslands. CK: Natural temperate steppe. E breviaristatus: Elymus breviaristatus. E nutants: Elymus nutans. E sibircus: Elymus sibircus. E sinosubmuticus: Elymus sino- breviaristatus: Elymus breviaristatus. E nutants: Elymus nutans. E sibircus: Elymus sibircus. E sinosubmuticus: Elymus submuticus. P crymophila: Poa crymophila. P tenuiflora: Puccinellia tenuiflora. R pauciflora: Roegneria pauciflora. *: p < 0.05. sinosubmuticus. P crymophila: Poa crymophila. P tenuiflora: Puccinellia tenuiflora. R pauciflora: Roegneria pauciflora. *: p < 0.05. The pH at all types of artificial grasslands were higher than CK (Figure 2). Soil moistures of the majority of artificial grasslands were not significantly different with CK (Table S2). The species diversities of both bacteria and fungi were not affected by artificial grass- lands (Figure S1). Most types of artificial grassland increased the soil bacterial phylogenetic diversity of the temperate steppe (Figure S1). The phylogenetic diversity of soil fungi was not significantly different among the artificial grasslands with different planting species. Artificial grasslands improved the community composition of fungi but not bacteria com- pared with temperate steppe, regardless of planting species (Figure 3). 3.2. Relationships between Soil Microbial Diversity and Ecosystem Functionality Bacterial species diversity and phylogenetic diversity were negatively correlated with soil EMF. The bacterial and fungal community compositions were positively correlated with soil EMF (Figure 4). Bacterial species diversity was negatively correlated with TN, MBC, PHOS, and BG, as well as the nitrogen cycle, other, and symbiosis or parasitic genes relative abundances, but positively correlated with TP and the chemoheterotrophy genes relative abundances. Bacterial phylogenetic diversity was negatively correlated with SOC, TN, MBN, MBC, BG, LAP, ALA, NAG, and XYL, as well as the carbon cycle, compound transformation, nitrogen cycle, other, pathogen 1, and symbiosis or parasitic genes relative abundances. Fungal species diversity was only positively correlated with the ectomycorrhizal genes relative abundances, and fungal phylogenetic diversity was positively correlated with SOC, TN, and NAG, but negatively correlated with the arbuscular mycorrhizal genes’ relative abundances. The community compositions of both bacteria and fungi were positively correlated with SOC, TN, MBN, MBC, PHOS, BG, LAP, ALA, NAG, XYL, and CBH, as well as the mycorrhizal, carbon cycle, compound transformation, nitrogen cycle, other, pathogen 1, S cycle, and symbiosis or parasitic genes’ relative abundances, but negatively correlated with TP and the pathogen and chemoheterotrophy genes’ relative abundances (Figure 5). 3.3. Possible Drivers of Soil EMF The random forest model explained 91.7% of the soil EMF variance. The contributions of different drivers to the model from high to low were bacterial community composition, soil pH, fungal community composition, and bacterial PD. Fungal PD, soil moisture, and bacterial and fungal species diversity did not play a role in the prediction of soil EMF (Figure 6). Agronomy 2021, 11, 2092 6 of 12 Agronomy 2021, 11, x FOR PEER REVIEW 6 of 12 Figure 2. Soil single functions and soil pH at natural and artificial grassland. CK: Natural temperate Figure 2. Soil single functions and soil pH at natural and artificial grassland. CK: Natural temperate steppe. E breviaristatus: Elymus breviaristatus. E nutants: Elymus nutans. E sibircus: Elymus sibircus. steppe. E breviaristatus: Elymus breviaristatus. E nutants: Elymus nutans. E sibircus: Elymus sibircus. E sinosubmuticus: Elymus sinosubmuticus. P crymophila: Poa crymophila. P tenuiflora: Puccinellia ten- E sinosubmuticus: Elymus sinosubmuticus. P crymophila: Poa crymophila. P tenuiflora: Puccinellia uiflora. R pauciflora: Roegneria pauciflora. The different lowercase letters above the bars denoted sig- tenuiflora. R pauciflora: Roegneria pauciflora. The different lowercase letters above the bars denoted nificant differences (p < 0.05). significant differences (p < 0.05). The pH at all types of artificial grasslands were higher than CK (Figure 2). Soil mois- tures of the majority of artificial grasslands were not significantly different with CK (Table S2). The species diversities of both bacteria and fungi were not affected by artificial grass- lands (Figure S1). Most types of artificial grassland increased the soil bacterial phyloge- netic diversity of the temperate steppe (Figure S1). The phylogenetic diversity of soil fungi was not significantly different among the artificial grasslands with different planting spe- cies. Artificial grasslands improved the community composition of fungi but not bacteria compared with temperate steppe, regardless of planting species (Figure 3). Agronomy 2021, 11, 2092 7 of 12 Agronomy 2021, 11, x FOR PEER REVIEW 7 of 12 PERMANOVA PERMANOVA p=0.001 p=0.001 Kruskal-Wallis Test D Kruskal-Walls Test P=0.09 p=0.09 Artificial plot Natural plot Kruskal-Wallis Test Kruskal-Walls Test P<0.001 p<0.001 Agronomy 2021, 11, x FOR PEER REVIEW 8 of 12 relative abundances. The community compositions of both bacteria and fungi were posi- Artificial plot Natural plot tively correlated with SOC, TN, MBN, MBC, PHOS, BG, LAP, ALA, NAG, XYL, and CBH, Figure 3. Principal coordinates analysis and within stage dissimilarity of bacteria (A, C, D) and fungal (B, E, F) communi- Figure 3. Principal coordinates analysis and within stage dissimilarity of bacteria (A, C, D) and fungal (B, E, F) communities as well as the mycorrhizal, carbon cycle, compound transformation, nitrogen cycle, other, patho- ties at artificial and natural grassland. E breviaristatus: Elymus breviaristatus. E nutants: Elymus nutans. E sibircus: Elymus at artificial and natural grassland. E breviaristatus: Elymus breviaristatus. E nutants: Elymus nutans. E sibircus: Elymus sibircus. gen 1, S cycle, and symbiosis or parasitic genes' relative abundances, but negatively corre- sibircus. E sinosubmuticus: Elymus sinosubmuticus. P crymophila: Poa crymophila. P tenuiflora: Puccinellia tenuiflora. R pau- E sinosubmuticus: Elymus sinosubmuticus. P crymophila: Poa crymophila. P tenuiflora: Puccinellia tenuiflora. R pauciflora: lated with TP and the pathogen and chemoheterotrophy genes' relative abundances (Figure ciflora: Roegneria pauciflora. *: p < 0.05, **: p < 0.01, *** p < 0.001. Roegneria pauciflora. *: p < 0.05, **: p < 0.01, *** p < 0.001. 5). 3.2. Relationships between Soil Microbial Diversity and Ecosystem Functionality Bacterial species diversity and phylogenetic diversity were negatively correlated with soil EMF. The bacterial and fungal community compositions were positively corre- lated with soil EMF (Figure 4). Bacterial species diversity was negatively correlated with TN, MBC, PHOS, and BG, as well as the nitrogen cycle, other, and symbiosis or parasitic genes relative abundances, but positively correlated with TP and the chemoheterotrophy genes <0.001 relative abundances. Bacterial phylogenetic diversity was negatively correlated with SOC, <0.001 <0.001 0.047 TN, MBN, MBC, BG, LAP, ALA, NAG, and XYL, as well as the carbon cycle, compound transformation, nitrogen cycle, other, pathogen 1, and symbiosis or parasitic genes relative abun- β diversi ty dances. Fungal species diversity was only positively correlated with the ectomycorrhizal genes relative abundances, and fungal phylogenetic diversity was positively correlated with SOC, TN, and NAG, but negatively correlated with the arbuscular mycorrhizal genes' Figure 4. The relationships between soil EMF and Shannon indexes, phylogenetic diversities, and communities composi- Figure 4. The relationships between soil EMF and Shannon indexes, phylogenetic diversities, and communities compositions tions (β diversity) of bacteria and fungi. ( diversity) of bacteria and fungi. Phylogenetic diversity of fungi Phylogenetic diversity of bacteria Shannon’s diversity of fungi Shannon’s diversity of bacteria β diversity of fungi β div ersity of bacteria Figure 5. The relationships between single soil functions and Shannon indexes, phylogenetic diversities, and communities compositions of bacteria and fungi. *: p < 0.05, **: p < 0.01, *** p < 0.001. 3.3. Possible Drivers of Soil EMF The random forest model explained 91.7% of the soil EMF variance. The contribu- tions of different drivers to the model from high to low were bacterial community com- position, soil pH, fungal community composition, and bacterial PD. Fungal PD, soil mois- ture, and bacterial and fungal species diversity did not play a role in the prediction of soil EMF (Figure 6). Agronomy 2021, 11, x FOR PEER REVIEW 8 of 12 relative abundances. The community compositions of both bacteria and fungi were posi- tively correlated with SOC, TN, MBN, MBC, PHOS, BG, LAP, ALA, NAG, XYL, and CBH, as well as the mycorrhizal, carbon cycle, compound transformation, nitrogen cycle, other, patho- gen 1, S cycle, and symbiosis or parasitic genes' relative abundances, but negatively corre- lated with TP and the pathogen and chemoheterotrophy genes' relative abundances (Figure 5). <0.001 0.047 <0.001 <0.001 β diversi ty Agronomy 2021, 11, 2092 8 of 12 Figure 4. The relationships between soil EMF and Shannon indexes, phylogenetic diversities, and communities composi- tions (β diversity) of bacteria and fungi. Phylogenetic diversity of fungi Phylogenetic diversity of bacteria Shannon’s diversity of fungi Shannon’s diversity of bacteria β diversity of fungi β div ersity of bacteria Agronomy 2021, 11, x FOR PEER REVIEW 9 of 12 Figure 5. Figure The r 5. The elationships relationships be between tween sing single le so soil il functions and Shannon indexes, functions and Shannon indexes, phylogenetic phylogenetic diver diversities, sitieand s, and communities communities compositions of bacteria and fungi. *: p < 0.05, **: p < 0.01, *** p < 0.001. compositions of bacteria and fungi. *: p < 0.05, **: p < 0.01, *** p < 0.001. 3.3. Possible Drivers of Soil EMF The random forest model explained 91.7% of the soil EMF variance. The contribu- tions of different drivers to the model from high to low were bacterial community com- position, soil pH, fungal community composition, and bacterial PD. Fungal PD, soil mois- ture, and bacterial and fungal species diversity did not play a role in the prediction of soil EMF (Figure 6). R²=91.7 p<0.001 Figure 6. The contribution of bacterial and fungi Shannon indexes, phylogenetic diversities (PD), Figure 6. The contribution of bacterial and fungi Shannon indexes, phylogenetic diversi- and communities compositions ( diversity) to the prediction to soil EMF. MSE is the mean square ties (PD), and communities compositions (β diversity) to the prediction to soil EMF. error. The different lowercase letters above the bars denoted significant differences (p < 0.05). MSE is the mean square error. The different lowercase letters above the bars denoted significant differences (p < 0.05). 4. Discussion Although artificial grasslands are regarded as a feasible method to improve grass production and quality [42], our results showed that they decreased soil EMF, which is important for sustainable development [43], compared with natural temperate steppes. This partly verifies our first hypothesis and could partly be explained by the different aboveground plant community compositions of artificial and natural grasslands and their effects on soil EMF [23,42]. In addition, the Qinghai–Tibet Plateau has experienced acidi- fication since the 1980s [44]. During this process, soil microorganisms could adapt to acid- ification. However, artificial grasslands that are harvested could have lower soil nutrient inputs and increase soil pH [25,45], which might inhibit the soil microorganisms being adaptive to acidified condition and thus possibly decrease soil functions. Thus, as shown in our results, soil pH plays a role in determining soil EMF, and all types of artificial grass- lands significantly increased soil pH and decreased EMF. In addition to the aboveground plant community composition, the belowground community composition can significantly affect soil EMF [23]. However, for artificial grasslands, especially in the Qinghai–Tibet Plateau, such a relationship has not yet been established [23]. The community composition of both fungi and bacteria contributed more than diversity to soil EMF and was strongly associated with most single functions, verify- ing our second hypothesis. This suggests that the microbial community composition is a major driver of soil EMF and supports the notion that community composition might be more important than community diversity [46]. More specifically, the transformation from natural temperate steppe to single-species artificial grasslands altered plant commu- nity composition [42]. Plants mediated a shift in the soil microbial community, increasing Agronomy 2021, 11, 2092 9 of 12 4. Discussion Although artificial grasslands are regarded as a feasible method to improve grass production and quality [42], our results showed that they decreased soil EMF, which is important for sustainable development [43], compared with natural temperate steppes. This partly verifies our first hypothesis and could partly be explained by the different aboveground plant community compositions of artificial and natural grasslands and their effects on soil EMF [23,42]. In addition, the Qinghai–Tibet Plateau has experienced acidification since the 1980s [44]. During this process, soil microorganisms could adapt to acidification. However, artificial grasslands that are harvested could have lower soil nutrient inputs and increase soil pH [25,45], which might inhibit the soil microorganisms being adaptive to acidified condition and thus possibly decrease soil functions. Thus, as shown in our results, soil pH plays a role in determining soil EMF, and all types of artificial grasslands significantly increased soil pH and decreased EMF. In addition to the aboveground plant community composition, the belowground community composition can significantly affect soil EMF [23]. However, for artificial grasslands, especially in the Qinghai–Tibet Plateau, such a relationship has not yet been established [23]. The community composition of both fungi and bacteria contributed more than diversity to soil EMF and was strongly associated with most single functions, verify- ing our second hypothesis. This suggests that the microbial community composition is a major driver of soil EMF and supports the notion that community composition might be more important than community diversity [46]. More specifically, the transformation from natural temperate steppe to single-species artificial grasslands altered plant community composition [42]. Plants mediated a shift in the soil microbial community, increasing the relative abundances of one or few particular species and decreasing the relative abundances of others [47]. Specific soil microbial species, even rare taxa, can determine soil EMF [12,48]. In addition, our results showed that soil bacterial PD was a better predictor of EMF than species diversity, which was in accordance with previous conclusions obtained in above- ground ecosystems [49,50]. Therefore, phylogenetic diversity should be included in future soil multifunctionality predictions in artificial grasslands on the Qinghai–Tibet Plateau. Although most evidence supports the positive effect of soil microbial diversity on soil EMF [12], we found that soil bacterial species diversity and PD were negatively associated with soil EMF, rejecting our third hypothesis. Similar results were also found in some previous studies [31,51]. Wang et al. found bacterial richness was negatively related to soil EMF at degraded alpine meadow [31]. Becker et el. also reported negative biodiversity– ecosystem functioning relationships caused by antagonistic interactions [51]. We propose two hypotheses that could explain the negative relationship. (1) It is possible that specific bacterial or fungal species rather than the total diversity contributed to the soil EMF in our study, similar to other recent studies in which all found that some specific microbial taxa play a major role in driving soil EMF [12,31,48]. (2) There might be functional redundancy in the soil microbial community, which could be inferred based on the lower PD in the natural temperate steppe than that in artificial grasslands, and the fact that it is widespread in terrestrial ecosystems [52]. 5. Conclusions We concluded that single-species artificial grasslands decreased the soil EMF of the temperate steppe, regardless of the planting species. Soil microbial community composition contributed more than diversity to the prediction of soil EMF. Phylogenetic diversity better explained the variation in soil EMF variance. Artificial grasslands increased soil pH, which might inhibit the soil microorganisms being adaptive to acidified condition, and thus possibly decreased soil EMF. These conclusions suggest that the benefits of establishing artificial grasslands, such as higher grass production and quality, might be at the expense of soil EMF. Further studies should explore mixed-species artificial grasslands. Agronomy 2021, 11, 2092 10 of 12 Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/agronomy11112092/s1, Table S1: Soil moisture at artificial and natural grasslands. Table S2: Soil moisture at artificial and natural grasslands. Figure S1: Effects of artificial grasslands on shannon index of bacteria and fungi and phylogenetic diversities of bacteria and fungi. Author Contributions: W.C., H.Z., G.L. and S.X. designed the experiment; W.C., Y.W., Z.Z., Y.L., Y.Z., B.L. performed the experiment; K.C., M.W., J.W. and W.C. analyzed the data and wrote the manuscript. All authors made important contributions to the manuscript and approved publication. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported financially by the Qinghai innovation platform construction project by the Chinese Academy of Sciences (2021-ZJ-Y01) and Joint Research Project of Three-River- Resource National Park funded by Chinese Academy of Sciences and Qinghai Provincial People’s Government (LHZX-2020-08), and Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Regions, Northwest Institute of Plateau Biology (2020-KF-04). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request. Acknowledgments: Authors appreciated the Qinghai innovation platform construction project by the Chinese Academy of Sciences (2021-ZJ-Y01) and Joint Research Project of Three-River-Resource National Park funded by Chinese Academy of Sciences and Qinghai Provincial People’s Government (LHZX-2020-08), and Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Regions, Northwest Institute of Plateau Biology (2020-KF-04). Conflicts of Interest: The authors declare that they have no conflict of interest. References 1. Dong, S.; Sherman, R. Enhancing the resilience of coupled human and natural systems of alpine rangelands on the qinghai-tibetan plateau. Rangel. J. 2015, 37, i–iii. [CrossRef] 2. Wei, D.; Liu, Y.; Wang, Y.; Wang, Y. 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Journal

AgronomyMultidisciplinary Digital Publishing Institute

Published: Oct 20, 2021

Keywords: artificial grasslands; soil microbial diversity; ecosystem multifunctionality; microbial sequencing

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