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Microbial taxonomic and functional attributes consistently predict soil CO2 emissions across contrasting croplands
Journal Pre-proofs Microbial taxonomic and functional attributes consistently predict soil CO2 emissions across contrasting croplands Yu-Rong Liu, Manuel Delgado-Baquerizo, Ziming Yang, Jiao Feng, Jun Zhu, Qiaoyun Huang PII: DOI: Reference:
S0048-9697(19)34877-6 https://doi.org/10.1016/j.scitotenv.2019.134885 STOTEN 134885
To appear in:
Science of the Total Environment
Received Date: Revised Date: Accepted Date:
17 July 2019 12 September 2019 6 October 2019
Please cite this article as: Y-R. Liu, M. Delgado-Baquerizo, Z. Yang, J. Feng, J. Zhu, Q. Huang, Microbial taxonomic and functional attributes consistently predict soil CO2 emissions across contrasting croplands, Science of the Total Environment (2019), doi: https://doi.org/10.1016/j.scitotenv.2019.134885
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Microbial taxonomic and functional attributes consistently predict soil CO 2 emissions across contrasting croplands
Yu-Rong Liu,1,
2*
Manuel Delgado-Baquerizo,3 Ziming Yang,4 Jiao Feng,1,
2
Jun Zhu2,
Qiaoyun Huang1, 2
1
State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
2 3
College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China Departamento de Biología, Geología, Física y Química Inorgánica, Escuela Superior de Ciencias Experimentales y Tecnología, Universidad Rey Juan Carlos, c/ Tulipán s/n, 28933 Móstoles, Spain
4
Department of Chemistry, Oakland University, Rochester, MI 48309, USA
* Corresponding author: Dr. Yu-Rong Liu. Affiliations: College of Resources and Environment, Huazhong Agricultural University Post address: No. 1, Shizishan Street, Hongshan District, Wuhan, 430070, China. E-mail: [email protected]; Phone: (+86) 27-87671033
1
Abstract Despite distinct roles of soil microbes in regulating carbon (C) respiration in diverse environments, it remains unclear whether microbial taxonomic and functional attributes can consistently predict soil C emissions across contrasting ecosystems. Here, we conducted a large-scale sampling event across two contrasting croplands (rice and wheat-corn crop rotation) to identify specific soil microbial phylotypes and functional genes associated with soil respiration rates. The results of structural equation modeling indicated that bacterial community composition had a strong link with C respiration rates in the two contrasting cropland types; however, this link was weaker for fungal communities. More importantly, we found
that
the
relative
abundances
of
bacterial
Solirubrobacterales_480-2,
Myxococcales_mle1-27 and fungal Westerdykella had consistently negative correlation with respiration rates across paddy and upland soils. We also identified taxa that are significantly correlated to C respiration in the paddy (e.g. Methylocaldum) and upland soils (e.g. Kribbella), respectively. Further, we found multiple associations between functional genes involved in microbial C metabolism and soil respiration rates. Our findings provide novel insights into understanding microbial predictors of soil CO2 emissions in diverse croplands, which have important implications for improving C emission predictions in terrestrial ecosystems. Keywords: bacteria, carbon cycling, prediction, microbial community, metagenome, functional gene
2
1. Introduction Soil stores more than three times the carbon (C) of plants and atmospheric pools (Schmidt et al., 2011). Approximately 60 Pg C are released annually from the terrestrial surface to the atmosphere through soil microbial respiration (Kirschbaum Miko, 2004; Nazaries et al., 2015), which is considered as a major driver for C emissions and climate change regulation (Dorrepaal et al., 2009; Singh et al., 2010). Many abiotic and biotic factors, including geographic and climatic conditions, soil properties and plant attributes are known to regulate soil C emissions worldwide (Campbell et al., 2004; García-Palacios et al., 2012; Knowles et al., 2015; Weihermüller et al., 2018). These factors have been used to parameterize models of predicting changes in global C cycling; however, a large portion of variations in the distribution of C emissions remain unexplained, which hampers our ability to predict C cycling under global change scenarios (Wieder et al., 2017; Liu et al., 2018a; Monteux et al., 2018). One of the most promising areas in searching for new predictors of soil C cycling includes microbial ecology (Allison et al., 2010; Delgado-Baquerizo et al., 2017; Delgado-Baquerizo et al., 2018a; Trivedi et al., 2018). Most predictive models for C cycling use microbial communities as a “black-box” (Schimel and Schaeffer, 2012, Sun, et al., 2016), though this has been recently advanced by the inclusion of microbial diversity into the model (Allison, 2012; Liu et al., 2018a). Theoretical and empirical evidence suggests that functional and taxonomic attributes of soil microbial communities could be used to predict soil C fluxes (Allison et al., 2010; Wieder et al., 2013; Whitaker et al., 2014; Zhang et al., 2017b; Li et al., 2018). For example, laboratory and local studies demonstrated that shifts of soil microbial community composition influence 3
soil C respiration and fixation (Liu et al., 2018a; Monteux et al., 2018; Müller et al., 2018). We thus expect that information about soil microbial communities can improve the prediction of soil C emissions at a large spatial scale. Moreover, recent advances in soil metagenomics can infer the functional potential of microbial communities (Leff et al., 2018), which may identify important indicators for soil C respiration potential. Despite that previous efforts have been successful in linking microbial extracellular enzyme activity to the biomass regulating soil C respiration (Allison et al., 2008; Kaiser et al., 2010), how changes in functional genes affect soil C emissions in natural ecosystems remain much unexplored. In particular, little is known about associations between microbial attributes and soil C emission across contrasting ecosystems. Paddy and upland fields are two typical cropland types in agriculture, often producing rice and wheat/corn respectively, and their distributions continue to increase in order to feed a continuously growing human population (Merino et al., 2012). These two contrasting agricultural ecosystems are critical not only for the maintenance of human welfare but also for global C cycling. These agricultural land types are responsible for producing a majority of food provisions globally (Tilman et al., 2002), but cropping can cause a 30–40% loss of soil organic C through microbial respiration compared to natural or semi-natural vegetation (Don et al., 2010; Poeplau and Don, 2015). Further, different land use types in agriculture often result in wide gradients of soil properties, nutrient availability and microbial communities, which are important factors influencing soil respiration (Campbell et al., 2004; Knowles et al., 2015; Weihermüller et al., 2018). Therefore, understanding soil C emissions in the contrasting croplands is critical for the prediction and management of soil C stock. Moreover, 4
identifying the associations between microbial communities and C respiration across the contrasting croplands is of paramount importance if we were able to predict how shifts of cropland types will affect climate regulation worldwide. In addition, there are great differences in microbial community composition in between paddy and upland soils (Hernández et al., 2017), which may result in their distinct abilities to use C for respiration. However, it remains unclear whether microbial taxonomic and functional attributes can consistently predict soil C emissions across contrasting ecosystems. Herein, we aimed to identify the potential of microbial community composition in predicting soil respiration in two contrasting croplands (paddy and upland). We hypothesized that (i) shifts of microbial community composition affect C emissions in both paddy and upland soils; (ii) there are specific microbial phylotypes and functional attributes that are consistently associated with respiration rates across the two contrasting cropland types. To test our hypotheses, we collected 141 soil samples from 47 paddy (rice) and upland (wheat-corn crop rotation) fields of major grain-producing areas across southwest China. The selected paddy and upland soils were derived from the same parent materials under similar climate conditions, providing a unique opportunity to test for the importance of microbial communities and functional genes on the regulation of C cycling in crop ecosystems. We conducted amplicon sequencing of 16S rRNA and ITS genes to characterize soil bacterial and fungal communities and shotgun metagenomics to assess the relative abundance of functional genes in our soil samples. Identifying the consistent links between particular soil microbial phylotypes/functional genes and respiration rates across contrasting soil environments could open a door to improve predictions of C flux from diverse croplands. 5
2. Materials and Methods 2.1 Study area and sampling Soil samples were collected from 24 sites of major grain-producing areas across Hunan and Guizhou provinces in southwest China (Figure S1). Two typical types of agricultural land use, rice paddy fields and surrounding uplands (wheat-corn crop rotation), were chosen to compare the land-use effects on soil respiration rates. The sampling areas are influenced by subtropical humid climate, with 1200-1400 mm of annual average rainfall and 17 °C of annual mean temperature. The soil in the region belongs to Haplic Alisol that was mainly derived from carbonate rocks. Sampling was conducted before crop harvesting at the beginning of August 2016, when the paddy fields were saturated (> 100% water content) and the upland fields were unsaturated (9.5% to 25.4% water content). Three soil replicates (0-15 cm depth) from each site were collected to account for small scale variations in soil properties, resulting in a total of 141 soil samples. Each collected soil was sealed in a plastic bag, and immediately shipped back to the laboratory in an iced cooler. Collected moist soils were homogenized and sieved through an ethanol-cleaned 2.0 mm sieve (Delgado-Baquerizo et
al., 2016), and then sieved soil was divided into two subsamples. One subsample was
stored at -20 °C for microbial analysis, while the other was kept at 4 °C for soil property analyses. 2.2 Analyses of soil respiration rates and chemical characteristics For each measurement of potential aerobic respiration, soils were pre-incubated at 25 °C for a week to minimize the initial disturbance (Chen et al., 2018). Briefly, 10 g of homogenized soil (fresh weight) was incubated in a 120 ml glass vessel at 25 °C for 20 h (Ge et al., 2016), 6
basing on a pre-experiment that showed an unsaturated status of CO2 concentration by that time. We adjusted soil moisture content to 80% of maximum water holding capacity before incubation (Whitaker et al., 2014). All the containers were sealed with caps equipped with rubber septa for gas sampling. At the end of this period, headspace gases were sampled using a 10 ml glass gas-tight syringe, and the CO2 concentrations were subsequently measured using an Agilent-7890a gas chromatograph equipped with a flame ionization detector (Agilent Technologies, Wilmington, DE, USA). We then calculated soil respiration rates from the net accumulation of CO2 over time. The same exact standardized procedure allows the direct comparison of CO2 emissions in all soils, though the physical disturbance had effects on the soil respiration. This method has been widely used in many studies in order to explore some underlying associations and mechanisms (Whitaker et al., 2014; Ge et al., 2016; Monteux et al., 2018), despite of the limitation in linking natural C emission to soil abiotic and biotic attributes. Soil pH was determined with a fresh soil to water ratio of 1:2.5 using a Delta pH-meter, and soil organic carbon (SOC) was measured using the K2CrO7 oxidation titration method (Walkley and Black, 1934). Total carbon (TC) and total nitrogen (TN) in soils were determined on a LECO Macro-CN analyzer (LECO, St. Joseph, MI, USA). The carbon in microbial biomass (MBC) was determined using the fumigation-extraction method (Vance et al., 1987; Liu et al., 2018b). Concentrations of dissolved organic carbon in the control and fumigated soils were both determined using a TOC analyzer (TOC-L Analyzer, Shimadzu, Japan) after an extraction using 0.5 M K2SO4. The major soil properties are shown in Table S1. 2.3 Analyses of soil bacterial and fungal communities 7
Microbial DNA was extracted from 0.30 g of soil using a MoBio PowerSoil DNA Isolation kit (QIAGEN Inc., USA) following the manufacturer’s instructions. To characterize bacterial and fungal community composition, the V4 region of the bacterial 16S rRNA and fungal ITS genes were amplified using the primer pairs of 338F/806R (Liu et al., 2016) and ITS1F /2043R (Zhao et al., 2016), respectively. PCR amplification was performed in 50 μl mixtures consisting of 25 μl PremixTaq™ (Takara Biotechnology, Dalian, China), 1 μl of each primer (10 μM), 3 μl of template DNA, and 20 μl of sterilized ddH2O. The barcoded PCR products were purified using the Wizard® SV Gel and PCR Clean-Up System (Promega, San Luis Obispo, USA). The purified amplicons were equimolarly mixed, and 2 × 250 bp paired-end sequencing was carried out on an Illumina MiSeq sequencer (Illumina Inc., San Diego, USA). Raw reads generated from the MiSeq paired-end sequencing were merged together using the fast length adjustment of short reads (FLASH). A chimera filtering approach using UPARSE was employed as the operational taxonomic unit (OTU or phylotype) picking strategy at 97% sequence similarity. Representative sequences from individual OTUs generated in UPARSE were processed using the Quantitative Insights into Microbial Ecology (QIIME) pipeline. Even sequence numbers of bacteria (30,212 reads) and fungi (40,000 reads) per sample were randomly selected to compensate for variability in sequencing depth before the downstream analysis. Shifts in the microbial community composition were visualized by the non-metric multidimensional scaling (NMDS) ordinations based on the Bray-Curtis dissimilarity matrix. Taxonomy assignments of bacterial and fungal phylotypes were performed in reference to the SILVA gold and UNITE databases, respectively (Quast et al., 2013). The raw sequence data generated in this study were deposited into the NCBI short reads archive (SRA) database 8
under BioProject number PRJNA522337. 2.4 Analyses of functional genes from soil metagenomes To link variations in gene abundance to soil respiration rates, we selected 10 representative soil samples (7 paddy soils and 3 upland soils) for the metagenomic analysis. Because of higher bacterial and fungal diversity and CO2 emissions in the paddy soil than those in the upland soils, we selected higher number of paddy soils than upland soils for the analysis. Shotgun sequencing was performed using an Illumina PE150 (Illumina Inc.) at Majorbio, Inc., Shanghai, China. Raw reads (150 bp in length) were trimmed to remove low quality reads. Paired reads of shotgun metagenomic sequences were merged with FLASH using default parameters (Magoč and Salzberg, 2011); merged reads were also mapped against the protein sequence of the KEGG database using MBLASTX (E-value cutoff 1e-6) (Zhang et al., 2017b) and the relative abundance of each KO gene was calculated. Additional details on methodology are provided in the Supporting Information. 2.5 Statistical analyses We first used structural equation modeling (SEM) to evaluate the effects of bacterial and fungal community composition (axes of NMDS ordinations) and on soil respiration after accounting for soil properties. The SEM allowed us to separate the influence of the microbial communities on respiration from multiple soil variables (Grace, 2006). We established an a priori model according to our current knowledge of both abiotic and biotic impacts on soil respiration (Figure S2). The data matrix was fitted to the model using the maximum-likelihood estimation method. Since there is no single universally accepted test of overall goodness of fit for SEM, we used the Chi-square test (χ2; the model has a good fit 9
when 0 ≤ χ2/d.o.f ≤ 2 and 0.05 < P ≤ 1.00) and the root mean square error of approximation (RMSEA; the model has a good fit when RMSEA 0 ≤ RMSEA ≤ 0.05 and 0.10 < P ≤ 1.00 (Schermelleh-Engel et al., 2003). Finally, we also calculated the standardized total effects of the included variables on the soil respiration rates to aid with the final interpretation of the SEMs. The SEM analyses were performed using AMOS 21.0 software (SPSS Inc., Chicago, IL, USA). We also used Spearman’s correlations to evaluate the correlations between soil respiration rates and all microbial genera in the two contrasting soils; thus, we identified consistent patterns of the association (either positive or negative) between them. We then identified bacterial and fungal predictors (genera) of respiration rates using classification random forest analysis (Delgado-Baquerizo et al., 2016; Trivedi et al., 2016). The major aim of the analyses was to test what genera are significant predictors of the respiration rates in the two soils. Unlike traditional classification and regression tree (CART) analyses, the fit of each tree is assessed using randomly selected cases, which are withheld during its construction (out-of-bag or OOB cases) (Delgado-Baquerizo et al., 2016). To estimate the importance of these phylotypes, we used percentage increases in the MSE (mean squared error) of the relative abundance of phylotypes: higher MSE% values imply more important phylotypes. This accuracy importance measure was computed for each tree and averaged over the forest (5,000 trees). These analyses were conducted using the rfPermute package (Archer, 2016) of the R statistical software 3.5.0 (http://cran.r-project.org/). Additionally, we used the random forest model to identify the statistically significant functional genes (from KEGG KO analyses by metagenomics) that predict soil respiration rates. After that, we used Spearman’s 10
correlations to evaluate the correlations between the relative abundances of the predictive genes and soil respiration rates in the two contrasting soils. Finally, we used linear regressions to evaluate relationships between the relative abundances of the selected predictive genera and genes and the soil respiration rates.
3. Results 3.1 Effects of microbial community composition on soil respiration rates Our structural equation modeling (SEM) demonstrated that the microbial community composition was strongly associated with soil respiration rates, even when considering other key soil properties in the models. Specifically, bacterial community composition had a significant (P < 0.05) association with soil respiration rate in both paddy and upland soils (Figure 1). In contrast, our SEM suggested that the fungal community structure had a lower capacity to predict changes in respiration rates in the contrasting soils (0.05 < P < 0.10). Importantly, the models further indicated that the effects of soil properties, such as pH, total (TN) and soil organic carbon (SOC), on respiration rates were mainly indirectly driven by changes in the composition of bacterial and fungal communities. Similar results were observed when we analyzed the standardized total effects (sum of directs and indirect effects) of microbial communities on soil respiration rates (Figure S3). 3.2 Microbial taxonomic attributes as predictors of soil respiration rate We identified multiple phylotypes that had consistent (59 genera are positive and 139 ones are negative) association patterns in the relative abundance with soil respiration rates across the contrasting soils (Table S2). Further, our random forest modeling suggested that the 11
multiple bacterial predictors of respiration included the phylotypes within phyla/classes Actinobacteria,
Bacteroidetes,
Alphaproteobacteria,
Betaproteobacteria,
Gammaproteobacteria, and Firmicutes; fungal genera predictors of respiration rates were intensively distributed among Ascomycota and Basidiomycota (Figures S4 and S5; Table S3). Some of these predictive genera had significant correlations (Spearman’s correlations) with soil respiration rates (Table S4). Importantly, we found some genera had consistent correlations with respiration rates in both contrasting soils. For example, soil respiration rates were negatively associated with the relative abundance of bacterial unclassified Solirubrobacterales_480-2 and Myxococcales_mle1-27 across the two soils (Figure 2, P < 0.05). Similarly, there were consistently negative relationships between the relative abundance of the selected fungal genera (e.g. Westerdykella) and soil respiration rates in the contrasting soils (Figure 3, P < 0.05). We also found specific taxa that were strongly correlated with respiration rates in single soil types. For example, soil C respiration had linear correlations with the relative abundance of bacterial Methylocaldum in paddy soils and Kribbella in upland soils, respectively (Figures S6, P < 0.05). The representative sequences of these selected sensitive taxa predicting soil respiration rates are provided in Table S5. 3.3 Microbial functional attributes as predictors of soil respiration rates Random forest analysis identified 31 out of total 1008 functional genes as significant (P < 0.05) predictors of soil respiration (Figure 4a; Table S6 and S7). For example, the respiration rates were positively correlated with the elevated relative abundances of genes encoding indolepyruvate ferredoxin oxidoreductase (R = 0.64, P = 0.046), while negatively correlated with the elevated relative abundance of the major gene predictors relating to 12
UDP-N-acetylmuramate dehydrogenase (R = -0.83, P = 0.003), pyruvate dehydrogenase E1 component (R = -0.79, P = 0.007) and malate synthase (R = -0.69, P = 0.026) (Figure 4b). Most of those genes are involved in carbohydrate metabolism and CO2 fixation; however, we also observed significant correlations between soil respiration rates and the genes involved in Fe-S cluster assembly protein, ribosomal protein and others (Table S7).
4. Discussion Our study identified important microbial taxonomic and functional attributes that can potentially predict the changes in soil respiration rates across contrasting soil environments (i.e., paddy and upland soils). Our SEM demonstrated a strong association of bacterial community composition with the respiration rates in both contrasting soils (Figure 1), suggesting that changes in the community composition of soil bacteria could result in predictable variations in soil respiration. As such, the different levels of soil respiration rates in the two soils (Figure S7) could be partially explained by the shifts in the community composition of bacteria under the contrasting land uses (Figure S8). The saturated paddy soil could facilitate growth and metabolism of anaerobic microbes, while the unsaturated upland soil was dominated by aerobic microbes. The distinct types of microbes in the two soils have different abilities to regulate soil CO2 emission. These results agree with recent studies emphasizing an important role of microbial community composition in soil respiration (Whitaker et al., 2014; Liu et al., 2018b; Monteux et al., 2018).
Importantly, our SEM
showed that soil properties such as pH and SOC indirectly influenced soil respiration rates via shifting of the soil microbial community, rather than in a direct manner (Ramirez et al., 2010; Whitaker et al., 2014). Our findings further demonstrate the importance of microbial 13
communities as predictive parameters in soil C models (Fierer and Jackson, 2006; Allison et al., 2010; Aanderud et al., 2013; Whitaker et al., 2014). Even so, our results suggest that fungal communities have a much lower capacity than bacteria to predict changes in soil respiration (0.05 < P < 0.1). This is consistent with the common expectation that ecosystem processes in agricultural ecosystems are mainly governed by bacterial, rather than by fungal communities (Van Der Heijden et al., 2007), suggesting that future studies aiming to identify and culture key soil taxa controlling C emissions should initially focus on bacterial communities. These findings can be used to improve our prediction of global C feedback to a changing environment and might also have implications for the future management of soil C stock in widespread and economically important croplands. We subsequently identified significant bacterial and fungal taxa (genera) capable of predicting the variations in respiration rates in a consistent manner for the two contrasting soils. Identifying microbial taxa that consistently predict soil respiration rates across contrasting croplands is critical if we want to use this knowledge to model C emissions in different
environments.
Our
results
suggest
Solirubrobacterales_480-2
and
Solirubrobacterales family as important predictors of soil microbial respiration across the contrasting land use types (Figures 2 and 3), and these sensitive taxa associated with soil C respiration could potentially be used to improve the predictive power of C cycling models. The negative correlation between the relative abundance of bacterium unclassified Solirubrobacterales_480-2 and soil C emission suggest that the Solirubrobacterales family is a potential category of oligotrophic bacteria, as a similar pattern had been found in deeper soil layers showing an increasing dominance of Solirubrobacterale but a decline in carbon 14
and nutrient levels (Taş et al., 2018). Therefore, it is likely the taxa utilize little C for respiration and thus can be used as an important indicator for soil C emission. Our results also suggest the unclassified Myxococcales cluster1-27 as an important predictor of soil respiration, though the mechanism of the association between this phylotype and C emission remains unexplored. On the other hand, we found some fungal genera were able to predict large-scale changes in soil respiration rates, despite of a relatively weak capacity of overall fungal community composition to predict changes in soil respiration rates (Figure 1). For example, changes in the relative abundance of Westerdykella were negatively correlated to soil C emissions, highlighting the role of fungal phylotypes in soil C cycling. Fungal Westerdykella were reported to have potential to synthesize carbohydrates (Xu et al., 2017), thus this taxa may fixed C and resulted in a decrease of CO2 emission from the soils. Future studies using pure cultures may provide direct evidence on their role in C cycling. Together, these findings support one of our hypotheses that there are phylotypes that can be used to consistently predict soil C respiration across contrasting habitats. In addition, our study also identified specific microbial genera associated with soil respiration in a single soil type, which could be useful in predicting soil C fluxes within specific ecosystems. Most of these taxa have been reported to be involved in microbial C metabolism. For example, the best predictor (the predictor scoring the importance in Figure S4) of C respiration in the paddy soil was bacterium Methylocaldum, a well-known methanotroph capable of utilizing methane as a source of carbon and energy (Bodrossy et al., 1997). This could explain the positive correlation between the relative abundance of this bacterial taxon and soil respiration (Figure S6a). Future work on the microbial communities 15
responsible for CH4 production and consumption will strengthen our understanding of C cycling in the paddy ecosystem. In upland soils, we also identified specific genera predictors of respiration rates (Figure S6b). For example, Kribbella within the Actinobacteria phylum can utilize sugars as C sources for respiration (Ozdemir-Kocak et al., 2017), which has been selected as one of most important predictors for soil C respiration in this soil type. These taxa predictors can be helpful in explaining variations in soil C emissions in different agricultural ecosystems. Our results suggest that the type of land use should to be considered when identifying taxa to predict C emissions in local climatic models. Furthermore, our findings indicate multiple associations between soil respiration rates and functional genes involved in microbial C metabolism, providing novel insights into the potential microbial mechanisms regulating CO2 emissions in terrestrial environments. Our random forest analysis revealed a new list of functional genes of soil microbiomes, which are important soil C emission predictors across different environmental gradients. These genes associated with microbial C metabolism could have important implications for understanding soil C turnover in croplands, and they may be involved in both microbial respiration and C fixation in soil. Soil microbial respiration is usually associated with the microbial C cycling that is driven by extracellular enzymes (Fenner et al., 2005). Therefore, changes in microbial community composition capable of predicting soil respiration may be driven by parallel changes in the abundance of functional genes (Trivedi et al., 2016). For example, indolepyruvate ferredoxin oxidoreductase is an essential enzyme in C metabolism that produces CO2, which could explain the positive correlation between the relative abundance of this gene and soil respiration rates (Figure 4b). In contrast, some of the selected genes were 16
negatively correlated with soil respiration. UDP-N-acetylmuramate dehydrogenase is an enzyme responsible for amino sugar and nucleotide sugar metabolism (Dumont Marc et al., 2013) and fixes CO2 to carbohydrate. In addition, pyruvate dehydrogenase and malate synthase are critical in carbohydrate synthesis (Hartig et al., 1992; Fleige et al., 2007). Consequently, these biosynthesis processes utilized CO2, rather than respired soil C, could help predict decreases of the soil C respiration potential. Although there are current knowledge gap between microbial genes and soil respiration due to highly diverse and poorly characterized soil microbial communities (Fierer, 2017; Delgado-Baquerizo et al., 2018b), our study paves the initial step in linking microbial functional genes to soil C emissions across environmental gradients. Future study on how these genes related to soil C transformations are expected to further improve our prediction of terrestrial C cycling.
5. Conclusions Taken together, our study identified novel microbial taxonomic and functional attributes capable of consistently predicting soil respiration across contrasting environments; a result which is fundamental if we want to use this knowledge to predict C fluxes across the different croplands. Particularly, bacterial Solirubrobacterales_ 480-2, Myxococcales_mle1-27 and fungal Westerdykella are effective in consistently predicting soil respiration rates in two contrasting soils. We also identified taxa that could be used to predict soil respiration in paddy (e.g. Methylocaldum) and upland soil (e.g. Kribbella), respectively, which provide new insights into the prediction of soil CO2 release in specific habitats. Further, we suggest multiple functional genes as significant predictors of soil respiration, advancing our understanding of soil C emissions from croplands. These findings provide potential microbial 17
tools to monitor changes in soil respiration and C balance in agricultural ecosystems by targeting highly dynamic soil microbial communities as indicators. The results from this work may also help us better understand how omics- technologies can be used to improve the prediction of C emission via inclusions of soil microbial taxonomic and functional attributes.
Acknowledgements This research was supported by the National Natural Science Foundation of China (41877120, 41830756) and the Fundamental Research Funds for the Central Universities (Program No. 2662019PY010). M.D-B. is supported by the Marie Sklodowska-Curie Actions of the Horizon 2020 Framework Program H2020-MSCA-IF-2016 under REA grant agreement n° 702057. Z.Y. acknowledges support from the U.S. National Science Foundation under Grant CBET-1841301.
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Figure Legends Figure 1. Structural equation modeling (SEM) showing the effects of soil properties and microbial community on the respiration rates in paddy (a) and upland (b) soils. Black lines and arrows indicate significant effects, while no figure is showed when the effect is not significant. Numbers adjacent to arrows are path directions and coefficients, and width of the arrows is proportional to the strength of path coefficients. R2 denotes the proportion of variance explained. TC and TN stand for total carbon and total nitrogen, respectively and SOC is soil organic carbon. Bacterial and fungal community compositions are represented using the first two axis of non-metric multidimensional scaling ordination (NMDS) derived from Bray-Curtis similarities. Significance levels are as follows: *P < 0.05, **P < 0.01, aP < 0.10. Figure 2. Linear regressions between the relative abundances of selected bacterial genera and soil respiration rates across contrasting croplands. Figure 3. Linear regressions between the relative abundances of fungal Westerdykella and soil respiration rates across contrasting croplands. Figure 4. Random forest analysis identifying the significant (P < 0.05) predictive genes of soil respiration rates (a). Linear relationships between soil respiration rates and the identified predictive genes (b). These functional genes were annotated according to Kyoto Encyclopedia of Genes and Genomes (KEGG) using metagenomic data derived from a subset of our soil samples. Additional information on the KEGG genes is available in Table S6.
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Highlights Ø Multiple taxa are consistently related to CO2 emission across contrasting soils. Ø Multiple functional genes are associated with soil respiration. Ø Bacterial community had stronger link with soil respiration than that of fungi. Ø Our findings have implications for improving our ability to predict soil C balance.
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S0048-9697(19)34877-6 https://doi.org/10.1016/j.scitotenv.2019.134885 STOTEN 134885
To appear in:
Science of the Total Environment
Received Date: Revised Date: Accepted Date:
17 July 2019 12 September 2019 6 October 2019
Please cite this article as: Y-R. Liu, M. Delgado-Baquerizo, Z. Yang, J. Feng, J. Zhu, Q. Huang, Microbial taxonomic and functional attributes consistently predict soil CO2 emissions across contrasting croplands, Science of the Total Environment (2019), doi: https://doi.org/10.1016/j.scitotenv.2019.134885
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Microbial taxonomic and functional attributes consistently predict soil CO 2 emissions across contrasting croplands
Yu-Rong Liu,1,
2*
Manuel Delgado-Baquerizo,3 Ziming Yang,4 Jiao Feng,1,
2
Jun Zhu2,
Qiaoyun Huang1, 2
1
State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
2 3
College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China Departamento de Biología, Geología, Física y Química Inorgánica, Escuela Superior de Ciencias Experimentales y Tecnología, Universidad Rey Juan Carlos, c/ Tulipán s/n, 28933 Móstoles, Spain
4
Department of Chemistry, Oakland University, Rochester, MI 48309, USA
* Corresponding author: Dr. Yu-Rong Liu. Affiliations: College of Resources and Environment, Huazhong Agricultural University Post address: No. 1, Shizishan Street, Hongshan District, Wuhan, 430070, China. E-mail: [email protected]; Phone: (+86) 27-87671033
1
Abstract Despite distinct roles of soil microbes in regulating carbon (C) respiration in diverse environments, it remains unclear whether microbial taxonomic and functional attributes can consistently predict soil C emissions across contrasting ecosystems. Here, we conducted a large-scale sampling event across two contrasting croplands (rice and wheat-corn crop rotation) to identify specific soil microbial phylotypes and functional genes associated with soil respiration rates. The results of structural equation modeling indicated that bacterial community composition had a strong link with C respiration rates in the two contrasting cropland types; however, this link was weaker for fungal communities. More importantly, we found
that
the
relative
abundances
of
bacterial
Solirubrobacterales_480-2,
Myxococcales_mle1-27 and fungal Westerdykella had consistently negative correlation with respiration rates across paddy and upland soils. We also identified taxa that are significantly correlated to C respiration in the paddy (e.g. Methylocaldum) and upland soils (e.g. Kribbella), respectively. Further, we found multiple associations between functional genes involved in microbial C metabolism and soil respiration rates. Our findings provide novel insights into understanding microbial predictors of soil CO2 emissions in diverse croplands, which have important implications for improving C emission predictions in terrestrial ecosystems. Keywords: bacteria, carbon cycling, prediction, microbial community, metagenome, functional gene
2
1. Introduction Soil stores more than three times the carbon (C) of plants and atmospheric pools (Schmidt et al., 2011). Approximately 60 Pg C are released annually from the terrestrial surface to the atmosphere through soil microbial respiration (Kirschbaum Miko, 2004; Nazaries et al., 2015), which is considered as a major driver for C emissions and climate change regulation (Dorrepaal et al., 2009; Singh et al., 2010). Many abiotic and biotic factors, including geographic and climatic conditions, soil properties and plant attributes are known to regulate soil C emissions worldwide (Campbell et al., 2004; García-Palacios et al., 2012; Knowles et al., 2015; Weihermüller et al., 2018). These factors have been used to parameterize models of predicting changes in global C cycling; however, a large portion of variations in the distribution of C emissions remain unexplained, which hampers our ability to predict C cycling under global change scenarios (Wieder et al., 2017; Liu et al., 2018a; Monteux et al., 2018). One of the most promising areas in searching for new predictors of soil C cycling includes microbial ecology (Allison et al., 2010; Delgado-Baquerizo et al., 2017; Delgado-Baquerizo et al., 2018a; Trivedi et al., 2018). Most predictive models for C cycling use microbial communities as a “black-box” (Schimel and Schaeffer, 2012, Sun, et al., 2016), though this has been recently advanced by the inclusion of microbial diversity into the model (Allison, 2012; Liu et al., 2018a). Theoretical and empirical evidence suggests that functional and taxonomic attributes of soil microbial communities could be used to predict soil C fluxes (Allison et al., 2010; Wieder et al., 2013; Whitaker et al., 2014; Zhang et al., 2017b; Li et al., 2018). For example, laboratory and local studies demonstrated that shifts of soil microbial community composition influence 3
soil C respiration and fixation (Liu et al., 2018a; Monteux et al., 2018; Müller et al., 2018). We thus expect that information about soil microbial communities can improve the prediction of soil C emissions at a large spatial scale. Moreover, recent advances in soil metagenomics can infer the functional potential of microbial communities (Leff et al., 2018), which may identify important indicators for soil C respiration potential. Despite that previous efforts have been successful in linking microbial extracellular enzyme activity to the biomass regulating soil C respiration (Allison et al., 2008; Kaiser et al., 2010), how changes in functional genes affect soil C emissions in natural ecosystems remain much unexplored. In particular, little is known about associations between microbial attributes and soil C emission across contrasting ecosystems. Paddy and upland fields are two typical cropland types in agriculture, often producing rice and wheat/corn respectively, and their distributions continue to increase in order to feed a continuously growing human population (Merino et al., 2012). These two contrasting agricultural ecosystems are critical not only for the maintenance of human welfare but also for global C cycling. These agricultural land types are responsible for producing a majority of food provisions globally (Tilman et al., 2002), but cropping can cause a 30–40% loss of soil organic C through microbial respiration compared to natural or semi-natural vegetation (Don et al., 2010; Poeplau and Don, 2015). Further, different land use types in agriculture often result in wide gradients of soil properties, nutrient availability and microbial communities, which are important factors influencing soil respiration (Campbell et al., 2004; Knowles et al., 2015; Weihermüller et al., 2018). Therefore, understanding soil C emissions in the contrasting croplands is critical for the prediction and management of soil C stock. Moreover, 4
identifying the associations between microbial communities and C respiration across the contrasting croplands is of paramount importance if we were able to predict how shifts of cropland types will affect climate regulation worldwide. In addition, there are great differences in microbial community composition in between paddy and upland soils (Hernández et al., 2017), which may result in their distinct abilities to use C for respiration. However, it remains unclear whether microbial taxonomic and functional attributes can consistently predict soil C emissions across contrasting ecosystems. Herein, we aimed to identify the potential of microbial community composition in predicting soil respiration in two contrasting croplands (paddy and upland). We hypothesized that (i) shifts of microbial community composition affect C emissions in both paddy and upland soils; (ii) there are specific microbial phylotypes and functional attributes that are consistently associated with respiration rates across the two contrasting cropland types. To test our hypotheses, we collected 141 soil samples from 47 paddy (rice) and upland (wheat-corn crop rotation) fields of major grain-producing areas across southwest China. The selected paddy and upland soils were derived from the same parent materials under similar climate conditions, providing a unique opportunity to test for the importance of microbial communities and functional genes on the regulation of C cycling in crop ecosystems. We conducted amplicon sequencing of 16S rRNA and ITS genes to characterize soil bacterial and fungal communities and shotgun metagenomics to assess the relative abundance of functional genes in our soil samples. Identifying the consistent links between particular soil microbial phylotypes/functional genes and respiration rates across contrasting soil environments could open a door to improve predictions of C flux from diverse croplands. 5
2. Materials and Methods 2.1 Study area and sampling Soil samples were collected from 24 sites of major grain-producing areas across Hunan and Guizhou provinces in southwest China (Figure S1). Two typical types of agricultural land use, rice paddy fields and surrounding uplands (wheat-corn crop rotation), were chosen to compare the land-use effects on soil respiration rates. The sampling areas are influenced by subtropical humid climate, with 1200-1400 mm of annual average rainfall and 17 °C of annual mean temperature. The soil in the region belongs to Haplic Alisol that was mainly derived from carbonate rocks. Sampling was conducted before crop harvesting at the beginning of August 2016, when the paddy fields were saturated (> 100% water content) and the upland fields were unsaturated (9.5% to 25.4% water content). Three soil replicates (0-15 cm depth) from each site were collected to account for small scale variations in soil properties, resulting in a total of 141 soil samples. Each collected soil was sealed in a plastic bag, and immediately shipped back to the laboratory in an iced cooler. Collected moist soils were homogenized and sieved through an ethanol-cleaned 2.0 mm sieve (Delgado-Baquerizo et
al., 2016), and then sieved soil was divided into two subsamples. One subsample was
stored at -20 °C for microbial analysis, while the other was kept at 4 °C for soil property analyses. 2.2 Analyses of soil respiration rates and chemical characteristics For each measurement of potential aerobic respiration, soils were pre-incubated at 25 °C for a week to minimize the initial disturbance (Chen et al., 2018). Briefly, 10 g of homogenized soil (fresh weight) was incubated in a 120 ml glass vessel at 25 °C for 20 h (Ge et al., 2016), 6
basing on a pre-experiment that showed an unsaturated status of CO2 concentration by that time. We adjusted soil moisture content to 80% of maximum water holding capacity before incubation (Whitaker et al., 2014). All the containers were sealed with caps equipped with rubber septa for gas sampling. At the end of this period, headspace gases were sampled using a 10 ml glass gas-tight syringe, and the CO2 concentrations were subsequently measured using an Agilent-7890a gas chromatograph equipped with a flame ionization detector (Agilent Technologies, Wilmington, DE, USA). We then calculated soil respiration rates from the net accumulation of CO2 over time. The same exact standardized procedure allows the direct comparison of CO2 emissions in all soils, though the physical disturbance had effects on the soil respiration. This method has been widely used in many studies in order to explore some underlying associations and mechanisms (Whitaker et al., 2014; Ge et al., 2016; Monteux et al., 2018), despite of the limitation in linking natural C emission to soil abiotic and biotic attributes. Soil pH was determined with a fresh soil to water ratio of 1:2.5 using a Delta pH-meter, and soil organic carbon (SOC) was measured using the K2CrO7 oxidation titration method (Walkley and Black, 1934). Total carbon (TC) and total nitrogen (TN) in soils were determined on a LECO Macro-CN analyzer (LECO, St. Joseph, MI, USA). The carbon in microbial biomass (MBC) was determined using the fumigation-extraction method (Vance et al., 1987; Liu et al., 2018b). Concentrations of dissolved organic carbon in the control and fumigated soils were both determined using a TOC analyzer (TOC-L Analyzer, Shimadzu, Japan) after an extraction using 0.5 M K2SO4. The major soil properties are shown in Table S1. 2.3 Analyses of soil bacterial and fungal communities 7
Microbial DNA was extracted from 0.30 g of soil using a MoBio PowerSoil DNA Isolation kit (QIAGEN Inc., USA) following the manufacturer’s instructions. To characterize bacterial and fungal community composition, the V4 region of the bacterial 16S rRNA and fungal ITS genes were amplified using the primer pairs of 338F/806R (Liu et al., 2016) and ITS1F /2043R (Zhao et al., 2016), respectively. PCR amplification was performed in 50 μl mixtures consisting of 25 μl PremixTaq™ (Takara Biotechnology, Dalian, China), 1 μl of each primer (10 μM), 3 μl of template DNA, and 20 μl of sterilized ddH2O. The barcoded PCR products were purified using the Wizard® SV Gel and PCR Clean-Up System (Promega, San Luis Obispo, USA). The purified amplicons were equimolarly mixed, and 2 × 250 bp paired-end sequencing was carried out on an Illumina MiSeq sequencer (Illumina Inc., San Diego, USA). Raw reads generated from the MiSeq paired-end sequencing were merged together using the fast length adjustment of short reads (FLASH). A chimera filtering approach using UPARSE was employed as the operational taxonomic unit (OTU or phylotype) picking strategy at 97% sequence similarity. Representative sequences from individual OTUs generated in UPARSE were processed using the Quantitative Insights into Microbial Ecology (QIIME) pipeline. Even sequence numbers of bacteria (30,212 reads) and fungi (40,000 reads) per sample were randomly selected to compensate for variability in sequencing depth before the downstream analysis. Shifts in the microbial community composition were visualized by the non-metric multidimensional scaling (NMDS) ordinations based on the Bray-Curtis dissimilarity matrix. Taxonomy assignments of bacterial and fungal phylotypes were performed in reference to the SILVA gold and UNITE databases, respectively (Quast et al., 2013). The raw sequence data generated in this study were deposited into the NCBI short reads archive (SRA) database 8
under BioProject number PRJNA522337. 2.4 Analyses of functional genes from soil metagenomes To link variations in gene abundance to soil respiration rates, we selected 10 representative soil samples (7 paddy soils and 3 upland soils) for the metagenomic analysis. Because of higher bacterial and fungal diversity and CO2 emissions in the paddy soil than those in the upland soils, we selected higher number of paddy soils than upland soils for the analysis. Shotgun sequencing was performed using an Illumina PE150 (Illumina Inc.) at Majorbio, Inc., Shanghai, China. Raw reads (150 bp in length) were trimmed to remove low quality reads. Paired reads of shotgun metagenomic sequences were merged with FLASH using default parameters (Magoč and Salzberg, 2011); merged reads were also mapped against the protein sequence of the KEGG database using MBLASTX (E-value cutoff 1e-6) (Zhang et al., 2017b) and the relative abundance of each KO gene was calculated. Additional details on methodology are provided in the Supporting Information. 2.5 Statistical analyses We first used structural equation modeling (SEM) to evaluate the effects of bacterial and fungal community composition (axes of NMDS ordinations) and on soil respiration after accounting for soil properties. The SEM allowed us to separate the influence of the microbial communities on respiration from multiple soil variables (Grace, 2006). We established an a priori model according to our current knowledge of both abiotic and biotic impacts on soil respiration (Figure S2). The data matrix was fitted to the model using the maximum-likelihood estimation method. Since there is no single universally accepted test of overall goodness of fit for SEM, we used the Chi-square test (χ2; the model has a good fit 9
when 0 ≤ χ2/d.o.f ≤ 2 and 0.05 < P ≤ 1.00) and the root mean square error of approximation (RMSEA; the model has a good fit when RMSEA 0 ≤ RMSEA ≤ 0.05 and 0.10 < P ≤ 1.00 (Schermelleh-Engel et al., 2003). Finally, we also calculated the standardized total effects of the included variables on the soil respiration rates to aid with the final interpretation of the SEMs. The SEM analyses were performed using AMOS 21.0 software (SPSS Inc., Chicago, IL, USA). We also used Spearman’s correlations to evaluate the correlations between soil respiration rates and all microbial genera in the two contrasting soils; thus, we identified consistent patterns of the association (either positive or negative) between them. We then identified bacterial and fungal predictors (genera) of respiration rates using classification random forest analysis (Delgado-Baquerizo et al., 2016; Trivedi et al., 2016). The major aim of the analyses was to test what genera are significant predictors of the respiration rates in the two soils. Unlike traditional classification and regression tree (CART) analyses, the fit of each tree is assessed using randomly selected cases, which are withheld during its construction (out-of-bag or OOB cases) (Delgado-Baquerizo et al., 2016). To estimate the importance of these phylotypes, we used percentage increases in the MSE (mean squared error) of the relative abundance of phylotypes: higher MSE% values imply more important phylotypes. This accuracy importance measure was computed for each tree and averaged over the forest (5,000 trees). These analyses were conducted using the rfPermute package (Archer, 2016) of the R statistical software 3.5.0 (http://cran.r-project.org/). Additionally, we used the random forest model to identify the statistically significant functional genes (from KEGG KO analyses by metagenomics) that predict soil respiration rates. After that, we used Spearman’s 10
correlations to evaluate the correlations between the relative abundances of the predictive genes and soil respiration rates in the two contrasting soils. Finally, we used linear regressions to evaluate relationships between the relative abundances of the selected predictive genera and genes and the soil respiration rates.
3. Results 3.1 Effects of microbial community composition on soil respiration rates Our structural equation modeling (SEM) demonstrated that the microbial community composition was strongly associated with soil respiration rates, even when considering other key soil properties in the models. Specifically, bacterial community composition had a significant (P < 0.05) association with soil respiration rate in both paddy and upland soils (Figure 1). In contrast, our SEM suggested that the fungal community structure had a lower capacity to predict changes in respiration rates in the contrasting soils (0.05 < P < 0.10). Importantly, the models further indicated that the effects of soil properties, such as pH, total (TN) and soil organic carbon (SOC), on respiration rates were mainly indirectly driven by changes in the composition of bacterial and fungal communities. Similar results were observed when we analyzed the standardized total effects (sum of directs and indirect effects) of microbial communities on soil respiration rates (Figure S3). 3.2 Microbial taxonomic attributes as predictors of soil respiration rate We identified multiple phylotypes that had consistent (59 genera are positive and 139 ones are negative) association patterns in the relative abundance with soil respiration rates across the contrasting soils (Table S2). Further, our random forest modeling suggested that the 11
multiple bacterial predictors of respiration included the phylotypes within phyla/classes Actinobacteria,
Bacteroidetes,
Alphaproteobacteria,
Betaproteobacteria,
Gammaproteobacteria, and Firmicutes; fungal genera predictors of respiration rates were intensively distributed among Ascomycota and Basidiomycota (Figures S4 and S5; Table S3). Some of these predictive genera had significant correlations (Spearman’s correlations) with soil respiration rates (Table S4). Importantly, we found some genera had consistent correlations with respiration rates in both contrasting soils. For example, soil respiration rates were negatively associated with the relative abundance of bacterial unclassified Solirubrobacterales_480-2 and Myxococcales_mle1-27 across the two soils (Figure 2, P < 0.05). Similarly, there were consistently negative relationships between the relative abundance of the selected fungal genera (e.g. Westerdykella) and soil respiration rates in the contrasting soils (Figure 3, P < 0.05). We also found specific taxa that were strongly correlated with respiration rates in single soil types. For example, soil C respiration had linear correlations with the relative abundance of bacterial Methylocaldum in paddy soils and Kribbella in upland soils, respectively (Figures S6, P < 0.05). The representative sequences of these selected sensitive taxa predicting soil respiration rates are provided in Table S5. 3.3 Microbial functional attributes as predictors of soil respiration rates Random forest analysis identified 31 out of total 1008 functional genes as significant (P < 0.05) predictors of soil respiration (Figure 4a; Table S6 and S7). For example, the respiration rates were positively correlated with the elevated relative abundances of genes encoding indolepyruvate ferredoxin oxidoreductase (R = 0.64, P = 0.046), while negatively correlated with the elevated relative abundance of the major gene predictors relating to 12
UDP-N-acetylmuramate dehydrogenase (R = -0.83, P = 0.003), pyruvate dehydrogenase E1 component (R = -0.79, P = 0.007) and malate synthase (R = -0.69, P = 0.026) (Figure 4b). Most of those genes are involved in carbohydrate metabolism and CO2 fixation; however, we also observed significant correlations between soil respiration rates and the genes involved in Fe-S cluster assembly protein, ribosomal protein and others (Table S7).
4. Discussion Our study identified important microbial taxonomic and functional attributes that can potentially predict the changes in soil respiration rates across contrasting soil environments (i.e., paddy and upland soils). Our SEM demonstrated a strong association of bacterial community composition with the respiration rates in both contrasting soils (Figure 1), suggesting that changes in the community composition of soil bacteria could result in predictable variations in soil respiration. As such, the different levels of soil respiration rates in the two soils (Figure S7) could be partially explained by the shifts in the community composition of bacteria under the contrasting land uses (Figure S8). The saturated paddy soil could facilitate growth and metabolism of anaerobic microbes, while the unsaturated upland soil was dominated by aerobic microbes. The distinct types of microbes in the two soils have different abilities to regulate soil CO2 emission. These results agree with recent studies emphasizing an important role of microbial community composition in soil respiration (Whitaker et al., 2014; Liu et al., 2018b; Monteux et al., 2018).
Importantly, our SEM
showed that soil properties such as pH and SOC indirectly influenced soil respiration rates via shifting of the soil microbial community, rather than in a direct manner (Ramirez et al., 2010; Whitaker et al., 2014). Our findings further demonstrate the importance of microbial 13
communities as predictive parameters in soil C models (Fierer and Jackson, 2006; Allison et al., 2010; Aanderud et al., 2013; Whitaker et al., 2014). Even so, our results suggest that fungal communities have a much lower capacity than bacteria to predict changes in soil respiration (0.05 < P < 0.1). This is consistent with the common expectation that ecosystem processes in agricultural ecosystems are mainly governed by bacterial, rather than by fungal communities (Van Der Heijden et al., 2007), suggesting that future studies aiming to identify and culture key soil taxa controlling C emissions should initially focus on bacterial communities. These findings can be used to improve our prediction of global C feedback to a changing environment and might also have implications for the future management of soil C stock in widespread and economically important croplands. We subsequently identified significant bacterial and fungal taxa (genera) capable of predicting the variations in respiration rates in a consistent manner for the two contrasting soils. Identifying microbial taxa that consistently predict soil respiration rates across contrasting croplands is critical if we want to use this knowledge to model C emissions in different
environments.
Our
results
suggest
Solirubrobacterales_480-2
and
Solirubrobacterales family as important predictors of soil microbial respiration across the contrasting land use types (Figures 2 and 3), and these sensitive taxa associated with soil C respiration could potentially be used to improve the predictive power of C cycling models. The negative correlation between the relative abundance of bacterium unclassified Solirubrobacterales_480-2 and soil C emission suggest that the Solirubrobacterales family is a potential category of oligotrophic bacteria, as a similar pattern had been found in deeper soil layers showing an increasing dominance of Solirubrobacterale but a decline in carbon 14
and nutrient levels (Taş et al., 2018). Therefore, it is likely the taxa utilize little C for respiration and thus can be used as an important indicator for soil C emission. Our results also suggest the unclassified Myxococcales cluster1-27 as an important predictor of soil respiration, though the mechanism of the association between this phylotype and C emission remains unexplored. On the other hand, we found some fungal genera were able to predict large-scale changes in soil respiration rates, despite of a relatively weak capacity of overall fungal community composition to predict changes in soil respiration rates (Figure 1). For example, changes in the relative abundance of Westerdykella were negatively correlated to soil C emissions, highlighting the role of fungal phylotypes in soil C cycling. Fungal Westerdykella were reported to have potential to synthesize carbohydrates (Xu et al., 2017), thus this taxa may fixed C and resulted in a decrease of CO2 emission from the soils. Future studies using pure cultures may provide direct evidence on their role in C cycling. Together, these findings support one of our hypotheses that there are phylotypes that can be used to consistently predict soil C respiration across contrasting habitats. In addition, our study also identified specific microbial genera associated with soil respiration in a single soil type, which could be useful in predicting soil C fluxes within specific ecosystems. Most of these taxa have been reported to be involved in microbial C metabolism. For example, the best predictor (the predictor scoring the importance in Figure S4) of C respiration in the paddy soil was bacterium Methylocaldum, a well-known methanotroph capable of utilizing methane as a source of carbon and energy (Bodrossy et al., 1997). This could explain the positive correlation between the relative abundance of this bacterial taxon and soil respiration (Figure S6a). Future work on the microbial communities 15
responsible for CH4 production and consumption will strengthen our understanding of C cycling in the paddy ecosystem. In upland soils, we also identified specific genera predictors of respiration rates (Figure S6b). For example, Kribbella within the Actinobacteria phylum can utilize sugars as C sources for respiration (Ozdemir-Kocak et al., 2017), which has been selected as one of most important predictors for soil C respiration in this soil type. These taxa predictors can be helpful in explaining variations in soil C emissions in different agricultural ecosystems. Our results suggest that the type of land use should to be considered when identifying taxa to predict C emissions in local climatic models. Furthermore, our findings indicate multiple associations between soil respiration rates and functional genes involved in microbial C metabolism, providing novel insights into the potential microbial mechanisms regulating CO2 emissions in terrestrial environments. Our random forest analysis revealed a new list of functional genes of soil microbiomes, which are important soil C emission predictors across different environmental gradients. These genes associated with microbial C metabolism could have important implications for understanding soil C turnover in croplands, and they may be involved in both microbial respiration and C fixation in soil. Soil microbial respiration is usually associated with the microbial C cycling that is driven by extracellular enzymes (Fenner et al., 2005). Therefore, changes in microbial community composition capable of predicting soil respiration may be driven by parallel changes in the abundance of functional genes (Trivedi et al., 2016). For example, indolepyruvate ferredoxin oxidoreductase is an essential enzyme in C metabolism that produces CO2, which could explain the positive correlation between the relative abundance of this gene and soil respiration rates (Figure 4b). In contrast, some of the selected genes were 16
negatively correlated with soil respiration. UDP-N-acetylmuramate dehydrogenase is an enzyme responsible for amino sugar and nucleotide sugar metabolism (Dumont Marc et al., 2013) and fixes CO2 to carbohydrate. In addition, pyruvate dehydrogenase and malate synthase are critical in carbohydrate synthesis (Hartig et al., 1992; Fleige et al., 2007). Consequently, these biosynthesis processes utilized CO2, rather than respired soil C, could help predict decreases of the soil C respiration potential. Although there are current knowledge gap between microbial genes and soil respiration due to highly diverse and poorly characterized soil microbial communities (Fierer, 2017; Delgado-Baquerizo et al., 2018b), our study paves the initial step in linking microbial functional genes to soil C emissions across environmental gradients. Future study on how these genes related to soil C transformations are expected to further improve our prediction of terrestrial C cycling.
5. Conclusions Taken together, our study identified novel microbial taxonomic and functional attributes capable of consistently predicting soil respiration across contrasting environments; a result which is fundamental if we want to use this knowledge to predict C fluxes across the different croplands. Particularly, bacterial Solirubrobacterales_ 480-2, Myxococcales_mle1-27 and fungal Westerdykella are effective in consistently predicting soil respiration rates in two contrasting soils. We also identified taxa that could be used to predict soil respiration in paddy (e.g. Methylocaldum) and upland soil (e.g. Kribbella), respectively, which provide new insights into the prediction of soil CO2 release in specific habitats. Further, we suggest multiple functional genes as significant predictors of soil respiration, advancing our understanding of soil C emissions from croplands. These findings provide potential microbial 17
tools to monitor changes in soil respiration and C balance in agricultural ecosystems by targeting highly dynamic soil microbial communities as indicators. The results from this work may also help us better understand how omics- technologies can be used to improve the prediction of C emission via inclusions of soil microbial taxonomic and functional attributes.
Acknowledgements This research was supported by the National Natural Science Foundation of China (41877120, 41830756) and the Fundamental Research Funds for the Central Universities (Program No. 2662019PY010). M.D-B. is supported by the Marie Sklodowska-Curie Actions of the Horizon 2020 Framework Program H2020-MSCA-IF-2016 under REA grant agreement n° 702057. Z.Y. acknowledges support from the U.S. National Science Foundation under Grant CBET-1841301.
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Figure Legends Figure 1. Structural equation modeling (SEM) showing the effects of soil properties and microbial community on the respiration rates in paddy (a) and upland (b) soils. Black lines and arrows indicate significant effects, while no figure is showed when the effect is not significant. Numbers adjacent to arrows are path directions and coefficients, and width of the arrows is proportional to the strength of path coefficients. R2 denotes the proportion of variance explained. TC and TN stand for total carbon and total nitrogen, respectively and SOC is soil organic carbon. Bacterial and fungal community compositions are represented using the first two axis of non-metric multidimensional scaling ordination (NMDS) derived from Bray-Curtis similarities. Significance levels are as follows: *P < 0.05, **P < 0.01, aP < 0.10. Figure 2. Linear regressions between the relative abundances of selected bacterial genera and soil respiration rates across contrasting croplands. Figure 3. Linear regressions between the relative abundances of fungal Westerdykella and soil respiration rates across contrasting croplands. Figure 4. Random forest analysis identifying the significant (P < 0.05) predictive genes of soil respiration rates (a). Linear relationships between soil respiration rates and the identified predictive genes (b). These functional genes were annotated according to Kyoto Encyclopedia of Genes and Genomes (KEGG) using metagenomic data derived from a subset of our soil samples. Additional information on the KEGG genes is available in Table S6.
25
Highlights Ø Multiple taxa are consistently related to CO2 emission across contrasting soils. Ø Multiple functional genes are associated with soil respiration. Ø Bacterial community had stronger link with soil respiration than that of fungi. Ø Our findings have implications for improving our ability to predict soil C balance.
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