Impactsoftherhizosphereeffectandplantspecieson ...ir.yic.ac.cn/bitstream/133337/24862/1/Impacts of the... · 2020. 7. 8. · 2 FEMSMicrobiologyEcology,2019,Vol.95,No.9 INTRODUCTION

FEMS Microbiology Ecology, 95, 2019, fiz120

doi: 10.1093/femsec/fiz120Advance Access Publication Date: 25 July 2019Research Article

RESEARCH ARTICLE

Impacts of the rhizosphere effect and plant species onorganic carbon mineralization rates and pathways,and bacterial community composition in a tidalmarsh.Yuxiu Liu1, Min Luo1,2,*,†, Rongzhong Ye3, Jiafang Huang1, Leilei Xiao4,Qikai Hu1,2, Aijv Zhu1 and Chuan Tong1

1Key Laboratory of Humid Subtropical Eco-Geographical Process, Ministry of Education, Fujian NormalUniversity, Fuzhou 350007, China, 2School of Environment and Resource, Fuzhou University, Fuzhou 350116,China, 3Pee Dee Research & Education Centers, Clemson University, Florence, SC 29506, USA and 4KeyLaboratory of Coastal Biology and Biological Resources Utilization, Yantai Institute of Coastal Zone Research,Chinese Academy of Sciences, Yantai 264003, China∗Corresponding author: Xueyuan Road #2, New District, Fuzhou 350116, Fujian Province, China. Tel: +86059122866078; E-mail: [email protected]

One sentence summary: The rhizosphere effect of the tidal marsh plants not only promotes total soil organic carbon decomposition rates, but alsoshifts the terminal metabolic pathways and reshapes the bacterial community structures.

Editor: Gary King†Min Luo, http://orcid.org/0000-0003-3284-629X

ABSTRACT

Despite the growing recognition regarding the carbon cycle in the rhizosphere of upland ecosystems, little is knownregarding the rhizosphere effect on soil organic carbon (SOC) mineralization in tidal marsh soils. In the current study, in siturhizobox experiments (including rhizosphere and inner and outer bulk soil) were conducted in an estuarine tidal marsh.Our results showed that a higher abundance of total bacteria, Geobacter, dsrA and mcrA and lower α-diversity were observedin the rhizosphere relative to the bulk soil. Rhizosphere effects shifted the partition of terminal metabolic pathways fromsulfate reduction in the bulk soil to the co-dominance of microbial Fe(III) and sulfate reduction in the rhizosphere. Althoughthe rhizosphere effect promoted the rates of three terminal metabolic pathways, it showed greater preference towardsmicrobial Fe(III) reduction in the tidal marsh soils. Plant species had little impact on the partitioning of terminal metabolicpathways, but did affect the potential of total SOC mineralization together with the abundance and diversity of totalbacteria. Both the rhizosphere effect and plant species influenced the bacterial community composition in the tidal marshsoils; however, plant species had a less pronounced impact on the bacterial community compared with that of therhizosphere effect.

Keywords: rhizosphere effect; SOC mineralization; Fe(III) reduction; sulfate reduction; methanogenesis; bacterialcommunity composition; tidal marsh plants

Received: 11 January 2019; Accepted: 18 July 2019

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2 FEMS Microbiology Ecology, 2019, Vol. 95, No. 9

INTRODUCTION

Tidal marshes occur at the intersection of terrestrial and aquaticecosystems and play an important role in the Earth’s carboncycle (Megonigal and Neubauer 2009). Globally, tidal marshesshow high primary productivity (930−7600 g C m−2 yr−1; Mitschand Gosselink 2015) and act as significant ‘blue carbon’ sinks(Kirwan and Mudd 2012). Mineralization of organic carbon intidal marsh soils regulates both the amount of soil organic car-bon (SOC) that remains stored and the amount of mineralizedcarbon that is released to the atmosphere as greenhouse gases(e.g., CO2 or CH4; Sutton-Grier et al. 2011). The rhizosphere is ahotspot for SOC mineralization in tidal marsh soils and also acritical ecological niche for complex and dynamic interactionsbetween plants, microbes and soils (Hiltner 1904; Weiss et al.2003; Berg and Smalla 2009; Tobias and Neubauer 2009; Mueller,Jensen and Megonigal 2016). Tidal marsh plants can transportsubstantial amounts of photosynthetically fixed C into the soilby releasing root exudates, which can serve as substrates formicrobial terminal metabolism (Strom et al. 2003; Wolf et al.2007). In addition, the survival and growth of marsh plant rootsin anaerobic marsh soils are dependent on an adequate supplyof O2 via the aerenchyma, a process known as root oxygen loss(ROL; Connell, Colmer and Walker 1999; Armstrong et al. 2000;Koop-Jakobsen et al. 2018). ROL can change the redox conditionsurrounding the roots and also reoxidize toxic substances, e.g.,dissolved iron and sulfide in the root zone (Koretsky et al. 2003;Hyun et al. 2009).

Tidal marsh plants have been reported to stimulate fasterSOC mineralization potential in the rhizosphere compared withthat of bulk soil (Hines, Knollmeye and Tugel 1989; Hines et al.1999; King and Garey 1999; Neubauer et al. 2005; Koretsky et al.2008a, 2008b). SOC mineralization in tidal marsh ecosystemsis accomplished via the reduction of several terminal electronacceptors (TEAs) along a cascade of decreasing free energy yields(Froelich et al. 1979). The order of energy yield is nitrate reduc-tion, manganese reduction, ferric iron (Fe(III)) reduction, sulfatereduction and then methanogenesis (Sivan, Shusta and Valen-tine 2016). However, these terminal metabolic pathways are notexclusive and can coexist in tidal marsh soils because the tra-ditional order is based on assuming an even TEA distribution(Sutton-Grier et al. 2011). In fact, microbial nitrate and man-ganese reduction are always negligible in tidal marsh ecosys-tems because of low TEA contents (Kostka, Roychoudhury andCappellen 2002b; Neubauer et al. 2005; Luo et al. 2017, 2018).Microbial sulfate reduction has been reported as the majority(32–95%) of SOC mineralization in brackish and salt marsh soils(Hines, Knollmeye and Tugel 1989; Kostka, Roychoudhury andCappellen 2002b; Neubauer et al. 2005; Luo et al. 2017, 2018).In freshwater tidal marsh soils, where SO4

2– concentrationsare low, microbial Fe(III) reduction or methanogenesis becomethe dominant pathways of anaerobic SOC mineralization (e.g.,Roden and Wetzel 1996; Neubauer et al. 2005). Moreover, thedominant terminal metabolic pathways change between vege-tated and unvegetated habitats (Kostka, Roychoudhury and Cap-pellen 2002b; Gribsholt, Kostka and Kristensen 2003) under dif-ferent inundation frequencies (Luo et al. 2016) or among differ-ent soil types (Sutton-Grier et al. 2011). From the rhizosphere tobulk soil, where rhizosphere effects can reallocate C substratesand O2, the partition of terminal metabolic pathways remainsenigmatic.

Rhizosphere bacteria are important drivers of C cycling intidal marsh soils (Yarwood 2018). Previous studies have sug-gested that the rhizosphere effect can significantly impact thebacterial community structure in the root zones of wetlandplants (Ravit, Ehrenfeld and Haggblom 2003; Lu et al. 2004, 2006;Ravit et al. 2007; Rietl et al. 2016; Li et al. 2019). In the rhizo-sphere of Limoniu mvulgare, a higher relative abundance of Chlo-rofexi, Plantomycetes and δ-proteobacteria and a lower rela-tive abundance of γ -proteobacteria were observed comparedwith bulk soil (Wang, Yang and Salles 2016). The rhizobiomesof the seagrasses Zostera marina, Zostera noltii and Cymodoceanodosa were dominated by members of the classes γ -, δ- andε-proteobacteria and Bacteroidia, while communities from thebulk soils were predominantly composed of members from theα-, γ - and δ-proteobacteria classes (Cucio et al. 2016). Basedon the 16S rRNA technique, Li et al. (2019) found that the rel-ative abundance of Oxobacter, Lachnospiraceae, Coprococcus, α-Proteobacteria, Rhodospirillales, Rhodospirillaceae and Magne-tospirillum were much higher in rhizosphere soils comparedwith bulk soil. Breidenbach, Pump and Dumont (2016) sug-gested that numerous phyla were affected by the presence ofrice roots, but the strongest effects were observed for Gem-matimonadetes, Proteobacteria and Verrucomicrobia. Further-more, this study also suggested that, with respect to func-tional groups of microorganisms, potential iron reducers (e.g.,Geobacter, Anaeromyxobacter) and fermenters (e.g., Clostridiaceae,Opitutaceae) were notably enriched in the rhizosphere environ-ment compared with bulk soil. However, a few studies havealso shown no significant difference between the rhizosphereand bulk soil (Ahn, Gillevet and Sikaroodi 2007). Nie et al. (2009)explained that the inundation or anaerobic condition in tidalmarshes may mask the rhizosphere effect on bacterial commu-nity compositions. Plant species can also influence the rhizo-sphere microbial community composition in tidal marsh soils(Hines et al. 1999; Nielsen et al. 2001; Batten et al. 2006; Berg andSmalla 2009; Prasse, Baldwin and Yarwood 2015). Tidal marshplants differ from each other in terms of morphology (size andrelative abundance of rhizomes and roots), total biomass, O2

transport capability and root exudates (Howes and Teal 1994;Armstrong et al. 2000). Several cases in tidal marsh soils havedemonstrated that the abundance of bacteria, fungi, actino-mycetes, and denitrifiers, as well as microbial community com-position, differs in rhizosphere soils of different plant speciesin wetland soils (Ravit, Ehrenfeld and Haggblom 2003; Berg andSmalla 2009).

According to the aforementioned analysis, soil bacteria oftidal marshes are sensitive to both the rhizosphere effect andplant species. Despite many existing studies in tidal marshes,most previous studies have considered that even though thereis an impact of rhizosphere effect or plant species on bac-terial community composition, it is reliant on the change inrelative abundance between aerobic and anaerobic bacteria oracidophilic and non-acidophilic bacteria. Few have examinedwhether the change in bacterial community composition is sig-nificantly influenced by the shift in terminal metabolic path-ways. To address this issue, we established a series of rhizoboxesto investigate the rates and pathways of the total SOC mineral-ization between the rhizosphere and bulk soil of two tidal marshplants (Spartina alterniflora Loisel. and Phragmites australis (Cav.)Trin. ex. Steud) in the Min River Estuary, East China Sea.

The specific goal of this study was to (i) clarify the ratesand pathways of SOC mineralization when transitioning from

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the rhizosphere to bulk soil; (ii) identify the abundance of iron-reducing bacteria (Geobacter), sulfate-reducing bacteria (dsrAgene) and methanogen (mcrA gene), and overall bacterial abun-dance and community composition (16S rRNA gene) in the rhi-zosphere and bulk soil; and (iii) explore the relationship betweenthe rhizosphere effect or plant species and bacterial communitycomposition across the rhizoboxes.

MATERIAL AND METHODS

Soil preparation

The soil utilized in this study was collected from the 0−20 cmlayer of mudflat near the study site located on the barrier islandof the Shanyu Tidal Marsh in the Min River Estuary, East ChinaSea (26◦1′58′′N, 119◦37′50′′E). The tidal marsh is brackish (meansalinity: 0.25± 0.04%) and flooded twice a day, with the inunda-tion frequency ranging from 26 to 31% (Luo et al. 2017). The par-ticle size composition of the soil was characterized as sandy clayloam (clay 21.9 ± 1.9%, silt 22.9 ± 2.8% and sand 55.2 ± 4.7%). TheSOC content was 1.48 ± 0.33% and the in situ salinity was 0.27 ±0.03% (the test method is detailed below).

Experimental setup

A specially designed acrylic plexiglass rhizobox (Fig. S1) wasused during the experiment. The rhizobox was the mesocosmused for investigating the root-soil interactions (Li, Fan and Shen2008; Neumann, George and Plassard 2009; Bengtson, Barker andGrayston 2012; Hafner et al. 2014; Li et al. 2016; Ma et al. 2018). Therhizobox could be separated as one root compartment (i.e. rhi-zosphere) and two side soil compartments (i.e. bulk soil). Meshpartitions between the compartments prevented the roots fromentering the soil compartments and synchronously ensured theexchange between the rhizosphere and bulk soil (Luo et al. 2018;Wang et al. 2018). However, some fine root hairs and hyphae maystill have penetrated into the adjacent bulk soil (Ma et al. 2018)and thus the rhizosphere effect may have somehow extended itsimpact into the special bulk soil zone (Wang et al. 2018). Accord-ing to previous studies, the range of this special bulk zone waswithin 5 mm (Luo et al. 2018). In this study, we operationallydefined the bulk soil zone 5 mm outward from the mesh parti-tion as the ‘inner bulk soil’ and the remainder as the ‘outer bulksoil’.

Sextuplicate rhizoboxes for S. alterniflora and P. australis wereestablished in the tidal flat on 12 March 2016. The cultivation soilwas fully pooled, mixed, homogenized and subsequently passedthrough a 2 mm sieve. The S. alterniflora and P. australis seedlingswere first rinsed in the tidal water to remove the attached soiland then carefully washed again using sterile deionized water.Afterwards, six seedlings of each plant were evenly planted inthe root chambers.

After 130 days of cultivation, all the marsh plants in therhizoboxes grew vigorously with roots full in the root cham-bers. The rhizoboxes were then taken out of the ground andwrapped with a plastic cloth. The rhizoboxes were stored in aportable refrigerator and taken back to the laboratory within 1 h.After transport to the laboratory, the rhizoboxes were placed intoan anaerobic glove box (AtmosBag, Sigma-Aldrich, USA). Soil inthe same zones was pooled and homogenized and then passedthrough a 2 mm mesh nylon screen for further chemical, incuba-tion and extracellular enzyme analyses. The soil for the molec-ular genetic analyses was snap frozen in liquid nitrogen andstored at −80 ◦C. The porewater was extracted by centrifugation

(4000 × g for 20 min) for the SO42– and dissolved organic carbon

(DOC) analyses. The porewater samples were filtered through0.45 μm syringe filters with hydrophilic GH Polypro membranes(Acrodisc, Pall Corporation, USA) and drained into 10 ml glassvials. The filters used for the DOC analyses were prewashed withdiluted hydrochloric acid (HCl).

Soil and plant analysis

The SOC concentrations were determined using a dry combus-tion method on an Elementar analyzer (Vario MAX CN, Elemen-tar, Germany; detection limit = 0.3%) following grinding andacidification of the samples using 10% HCl. The DOC concen-trations were analyzed with an automatic total organic carbonanalyzer (TOC-V CPH, Shimadzu, Japan; detection limit = 0.002mg C L−1). The porewater SO4

2− concentrations were determinedusing ion chromatography (ICS-2000, Dionex, USA; detectionlimit = 0.09 mg L−1). The solid-phase Fe(II) and Fe(III) contentswere extracted using 0.5 M HCl-extractable Fe(II) and citrate-bicarbonate-dithionite (0.35 M glacial acetic acid/0.2 M sodiumcitrate, buffered with 50 g·L−1 sodium dithionite), respectively(Kostka and Luther III 1994). The total reduced sulfur (TRS) sam-ples were extracted by acid chromium chloride solution with theaddition of zinc grains (the procedure developed by Burton et al.2008). The extractions for the Fe and S species were performed bythe 1, 10-phenanthroline colorimetric method (detection limit= 1 μM, SD = 1%; APHA 2005) and the methylene blue method(detection limit = 1 μM; SD = 1%; Cline 1969), respectively, usingspectrophotometry (UV-2450, Shimadzu, Japan). The dissolvedCH4 concentrations were analyzed using a headspace techniquebased on the vapor liquid equilibrium with a Shimadzu GC2010gas chromatograph (Japan) with a flame ionization detector (FID)(detection limit = 10 ppm; Tong et al. 2012). For the root biomass,all remaining soil on the roots and rhizome was washed away.The washed roots were then oven-dried at 70 ◦C to a constantweight and the dry weight was recorded as the root biomass.

Anaerobic incubation

The measurements for rates of SOC mineralization, Fe(III)reduction, sulfate reduction and methanogenesis followed themethod reported in previous studies (see Luo et al. 2017).

Briefly, approximately 5 g of soil sub-samples were preparedand loaded into 7 mL glass incubation bottles for the sulfateand Fe(III) reductions. For the microbial Fe(III) reduction, sodiummolybdate at a final concentration of 20 mM was added to thebottles to inhibit microbial sulfate reduction because Fe(III) canalso be chemically reduced by the newly yielded HS− (a prod-uct of the sulfate reduction; Hyun, Smith and Kostka 2007). Themicrobial Fe(III) and sulfate reduction rates were determined bythe generation of solid-phase Fe(II) (Fe(III) reduction) and TRS(sulfate reduction), respectively, at 0, 24, 48, 72 and 96 h. Theincubation times were linearly regressed to the rates of micro-bial Fe(III) reduction (r2 = 0.82, p < 0.05, n = 30) and microbial sul-fate reduction (r2 = 0.81, p < 0.05, n = 30). The assays for Fe(II) andTRS were performed as described above. We acknowledge thatthe estimates of sulfate reduction rates in this study should betreated with some caution given that the method we chose maydiscount sulfide reoxidation and underestimate the actual rates.

For the SOC mineralization and methanogenesis rates,approximately 20 g of soil sub-samples were prepared andloaded into 150 mL glass incubation bottles. Deoxygenated andsterilized tide water was added to the bottles to create a slurrywith a 2:1 ratio of water to soil by volume. The CH4 (g) and CO2 (g)

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values were determined (for total SOC mineralization) at 0, 24,48, 72 and 96 h. Prior to sampling, each bottle was shaken ona rotary shaker at 240 rpm for 1 h to drive all the CO2 and CH4

that escaped the slurry into the headspaces. Subsequently, 4 mLsub-samples of the gas were collected for the CO2 and CH4 con-centration analyses. After each gas sample was collected, theincubation bottles were backfilled with 4 mL of N2 to re-establishnormal atmospheric pressure. The CO2 and CH4 concentrationswere measured using a gas chromatograph fitted with a thermalconductivity detector to measure CO2 and a flame-ionizationdetector to measure CH4 (GC2014, Shimadzu, Japan). The CO2

and CH4 concentration measurements were corrected for stan-dard pressure and temperature using the ideal gas law in whichthe concentrations were multiplied by the headspace volume(Tong et al. 2012). The incubation times were linearly regressedto the rates of total SOC mineralization (r2 = 0.96, p < 0.05, n =30) and methanogenesis (r2 = 0.92, p < 0.05, n = 30).

Terminal metabolic pathway rates were unified in C units(μmol C·g−1·d−1) using Fe:C = 4:1, S:C = 1:2 and C–CH4:C = 1:2theoretical stoichiometry (Kostka et al. 2002a; Neubauer et al.2005). The relative contributions of Fe(III) reduction, sulfatereduction and methanogenesis were calculated from the ratiosof the individual anaerobic terminal metabolic pathway ratesversus the rates for the total SOC mineralization.

Extracellular enzymatic activity

The extracellular enzymatic activity (including β-glucosidase(βG) and cellobiohydrolase (CBH)) was measured usingfluorometric and colorimetric microplate assays with 4-methylumbelliferone (MUB)-linked substrate as described inSaiya-Cork, Sinsabaugh and Zak (2002). Briefly, approximately3 g of soil sub-samples were homogenized in 125 mL 50 mMacetate buffer (pH = 5.0) using a magnetic stirrer. Next, 200μL aliquots of the sample suspension were dispensed into a96-well black Optiplate microplate (PerkinElmer, MA, USA) bypipette. The microplate was incubated in the dark for 4 h at20◦C, then a 10 mL aliquot of 1 M NaOH was added to eachwell of the microplate to stop the reaction. The sample fluo-rescence was measured using a microplate fluorometer with365 nm excitation and 450 nm emission filters on a Synergy H4microplate reader (BioTek, Hybrid Technology, USA).

Molecular genetic analyses

The soil DNA extractions followed the procedures from the Pow-erSoil DNA Isolation Kit (MoBio Laboratories, CA, USA) using0.8% agarose gels to check the purity and quality of the genomicDNA.

The microbial abundances were quantified by real-timequantitative PCR (qPCR) using SYBR R© Premix Ex Taq II (TliRNaseH Plus), ROX plus (Takara, Japan) and a Stepone plus ther-mocycler (Applied Biosystems, CA, USA). The bacteria (16S rRNAgene) were targeted with the primers Eub338 and Eub517 (Mor-rissey and Franklin 2015). The ME1 and mcrA-rev primers wereused to target the methyl coenzyme-M reductase encoding themcrA functional gene (Steinberg and Regan 2009). The dsrA 290Fand dsrA 660R primers were employed to quantify the abun-dance of the dsrA gene (δ-proteobacteria clade; Pereyra et al.2010), and the GM3 and 825R primers were used to quantify theGeobacter abundance (putative iron-reducing bacteria; Holmeset al. 2002). The reactions (20 μL) were performed with 6 ng ofDNA template and 0.2 μM concentrations of each primer. Thethermal cycling conditions included incubation at 94◦C for 5

min, followed by 30 cycles of denaturing at 94◦C for 30 s, anneal-ing at 55◦C for 30 s, extending at 72◦C for 30 s and extending onceagain at 72◦C for 10 min. The data were analyzed using Bio-RadCFX Manager (Version 2.1).

The V3-4 hypervariable region of the bacterial 16S rRNAgene was amplified with the primers 338F (ACTCCTACGGGAG-GCAGCAG) and 806R (GGACTACHVGGGTWTCTAAT) (Munyakaet al. 2015). For each soil sample, 10-digit barcode sequenceswere added to the 5′ end of the forward and reverse primers (pro-vided by Allwegene Company, Beijing, China, http://tech.allwegene.com). The PCR reactions were conducted using a Mastercy-cler Gradient (Eppendorf, Germany) with 25 μL reaction volumescontaining 12.5 μL 2 × Taq PCR MasterMix, 3 μL BSA (2 ng μL−1),2 Primer (5 μM), 2 μL template DNA and 5.5 μL distilled deion-ized water (ddH2O). The cycling parameters were 95◦C for 5 min,followed by 32 cycles of 95◦C for 45 s, 55◦C for 50 s and 72◦C for 45s with a final extension at 72◦C for 10 min. Three PCR productsper sample were pooled to mitigate reaction-level PCR biases.The PCR products were purified using a QIAquick Gel ExtractionKit (Qiagen, Hilden, Germany), quantified using real-time PCRand sequenced at the Allwegene Company. The deep sequencingwas performed on a Miseq platform at the Allwegene Company.After the run, image analysis, base calling and error estimationswere performed using Illumina Analysis Pipeline version 2.6.

Statistical analysis

All environmental, enzymatic activity and terminal metabolicpathways and total SOC mineralization rates were provided asthe mean ± standard deviation (SD). The datasets were testedbefore meeting the assumptions of homogeneity and normal-ity. If these assumptions were not met, the raw data was log10

transformed before further statistical analyses were performed.Most notably, all microbial abundances (bacteria, mcrA, dsrAand Geobacter) required a log10 transformation prior to statisti-cal analysis.

Two-way analysis of variance (ANOVA) analyses were utilizedto compare the indices (including soil geochemistry, enzymaticactivities, total SOC mineralization rates, terminal metabolicpathways rates and microbial abundances) across the rhi-zoboxes. In some cases, ANOVA was followed by the Tukey posthoc test to resolve significant differences. Independent t-tests(two-tails) were performed to examine whether the root biomassdifferences between two plants were significant. The relation-ships between the aforementioned indices were assessed usingPearson correlations. For all the analyses, a significance level ofα = 0.05 was used, and the difference was considered to be sta-tistically significant when p < 0.05. The statistical analyses wereperformed using SPSS 22.0 software.

The raw data for the bacterial communities were firstscreened and sequences were removed from consideration ifthey were shorter than 200 bp, had a low-quality score (≤20), contained ambiguous bases, or did not exactly matchthe primer sequences and barcode tags. Qualified reads wereseparated using the sample-specific barcode sequences andtrimmed using Illumina Analysis Pipeline version 2.6 and thenthe datasets were analyzed using QIIME 1.8 (Caporaso et al.2010). The sequences were clustered into operational taxonomicunits (OTUs) at a similarity level of 97% to generate rarefac-tion curves and to calculate the richness and diversity indices.OTUs accounting for less than 0.005% of the sequences wereremoved prior to the downstream analyses (Bokulich et al. 2013).The Ribosomal Database Project (RDP) Classifier tool was used to

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classify the sequences into different taxonomic groups. Taxon-omy was assigned to the OTUs with reference to a subset of theSILVA 128 database (http://www.arb-silva.de). A total of 987 012high-quality bacterial sequences (samples: 18; min: 19 278; max:103 533; median: 49 848) were obtained after trimming via Illu-mina high-throughput sequencing. All of the statistics concern-ing bacterial communities were based on the rarefied OTUs table(total sequences of each sample were normalized to 19 278).

The alpha diversities (including the Shannon indices andChao 1 index) were calculated to represent the OTU diversity foreach sample using the ‘vegan’ package R × 64 (3.2.2). The betadiversity (weighted Bray-Curtis distances; Beals 1984) was visu-alized using clustering analyses and nonmetric multidimen-sional scaling analyses (NMDS) with the ‘hcluster’ and ‘ape’package R × 64 (3.2.2). The two-way non-parametric multivari-ate analysis of variance (PerMANOVA) was used to test the dif-ferences in the soil bacterial communities across the rhizoboxesand between two plants using the ‘vegan’ package R × 64 (3.2.2)(Wang et al. 2018). Venn diagrams were created using 3 Venn dia-grams (Microsoft, Redmond, WA, USA) by tracing the results cal-culated using the venneuler function from the ‘venneuler’ pack-age R × 64 (3.2.2) (Wilkinson and Urbanek 2010).

To compare the membership and structure of communitiesin different samples, heatmaps were generated with the top 25OTUs using the ‘heatmap’ package R × 64 (3.2.2) (Jami et al. 2013).The evolutionary distances between the bacterial communitieswere represented as an unweighted pair group method witharithmetic mean (UPGMA) clustering tree describing the dissim-ilarity between multiple samples using MEGA 7.0 (www.megasoftware.net). The generated Newick-formatted tree file was plot-ted using iTOL (itol.embl.de).

Canonical correlation analysis (CCA) and variation partition-ing analysis (VPA) were performed to determine the extent towhich the terminal metabolic pathways could explain the bac-terial community composition using Canoco 5.0 (www.canoco5.com). For selecting the variables, those containing redundantinformation were reduced to a minimum number and only thevariables that had significant impacts were maintained. The sig-nificance of the CCA model was tested using ANOVA. Accordingto the CCA results, VPA was performed to determine the contri-bution of each individual variable or groups of variables to thetotal variations in the soil bacterial community compositions(Zhou et al. 2008).

All sequences from this study are available in the NationalCenter for Biotechnology Information (NCBI) GenBank (www.ncbi.nlm.nih.gov) under the accession number SRP095149.

RESULTS

Plant and soil characteristics

The root biomasses (g cm–3) were comparable in the root com-partments for S. alterniflora (11.59 ± 0.34) and P. australis (10.98 ±1.60; independent t-test, p > 0.05). The rhizosphere effect did nothave a significant influence on the SOC contents (%) and pore-water SO4

2– concentrations (mM; both two-way ANOVA, Table 1).The DOC concentrations (mM, Table 1) significantly decreasedfrom the rhizosphere (mean: 4.3) to the outer bulk soil (mean:1.1; two-way ANOVA, Tukey; Table 1). The extractable Fe(III) con-centrations (μmol Fe g–1, Table 1) also gradually decreased fromthe rhizosphere (mean: 146.9) to the outer bulk soil (mean: 83.4;two-way ANOVA, Tukey). By contrast, the extractable Fe(II) con-centrations (μmol Fe g–1, Table 1) were significantly higher inthe outer bulk soil (mean: 52.9), followed by the inner bulk

soil (mean: 40.4) and then the rhizosphere (mean: 24.5; two-way ANOVA, Tukey). The total reduced sulfur (TRS) concentra-tions (μmol S g–1, Table 1) increased by ∼4.0 times from the rhi-zosphere to the outer bulk soil (two-way ANOVA, Tukey). Thedissolved CH4 concentrations (mM, Table 1) were significantlyhigher in the rhizosphere (mean: 27.4) and inner bulk soil (mean:28.5) compared with the outer bulk soil (mean: 13.5, two-wayANOVA, Tukey). Plant species did not exert a significant influ-ence on the aforementioned soil and porewater geochemistry,except for the DOC concentrations (two-way ANOVA, Table 1).The DOC concentrations were higher in the S. alterniflora rhi-zoboxes (mean: 3.1) compared with those in the P. australis boxes(mean: 2.3, two-way ANOVA).

SOC mineralization and anaerobic terminal metabolicpathways

The accumulation of CO2, CH4, Fe(II) and TRS provided evidencethat total SOC mineralization and methanogenesis, as well asmicrobial Fe(III) and sulfate reduction, were occurring in boththe rhizosphere and bulk soil (Fig. 1a to d). The methanogene-sis rates (nmol C g–1 d–1) were several orders of magnitude lowercompared with those of the SOC mineralization and microbialFe(III) and sulfate reductions (μmol C g–1 d–1) across the rhi-zoboxes. The total SOC mineralization rates (μmol C g–1 d–1;Fig. 1a) significantly decreased from the rhizosphere (mean:33.73), followed by the inner bulk soil (mean: 23.37) and thenthe outer bulk soil (mean: 18.81; two-way ANOVA, Tukey). Themicrobial Fe(III) reduction (Fig. 1d) pattern was similar to thetotal SOC mineralization with the following order: rhizosphere(mean: 13.66) > inner bulk soil (mean: 6.27) > outer bulk soil(mean: 2.30; two-way ANOVA, Tukey). The methanogenesis andsulfate reduction rates (Fig. 1b and c) in the rhizosphere (mean:methanogenesis = 160.35; sulfate reduction = 16.62) and innerbulk soil (mean: methanogenesis = 119.09; sulfate reduction= 15.59) were significantly higher when compared with thosethat of the outer bulk soil (mean: methanogenesis = 92.50; sul-fate reduction = 11.25; two-way ANOVA, Tukey). The impactof the rhizosphere effect on rates of microbial Fe(III) reduction(an increase of 5–7 times) was much higher when comparedwith that on sulfate reduction (an increase of 1.4–1.5 times)or methanogenesis (an increase of 1.5–2.0 times). Plant speciesexert their influence on total SOC mineralization rates (two-way ANOVA; Fig. 1a), which were higher in the S. alterniflora rhi-zoboxes compared with those in the P. australis rhizoboxes (two-way ANOVA). Plant species had no effect on methanogenesisand Fe(III) and sulfate reduction rates (Fig. 1b and d).

The Fe(III) reduction contributions across the rhizoboxes fol-lowed this order: rhizosphere (mean: 41%) > inner bulk soil(mean: 24%) > outer bulk soil (mean: 11%). The sulfate reduc-tion contribution was significantly higher in the inner (mean:68%) and outer bulk soil (mean: 60%) when compared with rhi-zosphere (mean: 50%). The methanogenesis contribution wascomparable (<1%) across the rhizoboxes. Plant species had noeffect on the partition of terminal metabolic pathways.

Microbial abundance and enzymatic activity

The rhizosphere effect had a significant influence on the abun-dance of total bacteria (16S rRNA gene), iron-reducing bacteria(Geobacter), sulfate-reducing bacteria (dsrA) and methanogens(mcrA) as well as β-Glucosidase (βG) activities (two-way ANOVA,p < 0.05; Table 2). The β-Glucosidase (βG; nmol h–1 g–1; Table 2)activities followed the order: rhizosphere (mean: 1.92) > inner

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Table 1. Mean ± standard deviation (SD) of the environmental parameters (n = 6) across the three soil zones (rhizosphere and inner and outerbulk soil) of the tidal marsh plants (S. alterniflora and P. australis) in the Min River Estuary, East China Sea.

Soil zones across the rhizoboxes Factor F p

Rhizosphere Inner bulk soil Outer bulk soil

SOC (%) R 0.600 0.555S. alterniflora 1.56 ± 0.08 1.54 ± 0.05 1.48 ± 0.08 P 0.444 0.510P. australis 1.50 ± 0.16 1.52 ± 0.09 1.49 ± 0.10 I 0.320 0.729DOC (mM) R 87.289 <0.001S. alterniflora 5.0 ± 0.7 2.9 ± 0.5 1.5 ± 0.3 P 15.202 0.001P. australis 3.6 ± 0.9 2.7 ± 0.6 0.7 ± 0.2 I 3.134 0.058SO4

2– (mM) R 1.683 0.203S. alterniflora 9.6 ± 1.0 8.9 ± 1.3 8.2 ± 1.0 P 0.276 0.603P. australis 8.9 ± 1.2 8.7 ± 1.2 8.6 ± 0.7 I 0.853 0.436Fe(III) (μmol Fe g–1) R 46.884 <0.001S. alterniflora 154 ± 24 108 ± 9 80 ± 20 P 0.013 0.910P. australis 140 ± 14 114 ± 12 87 ± 11 I 1.518 0.236Fe(II) (μmol Fe g–1) R 70.646 <0.001S. alterniflora 26.2 ± 4.0 42.7 ± 4.3 52.2 ± 8.4 P 1.214 0.279P. australis 22.8 ± 7.3 38.1 ± 3.0 53.6 ± 6.2 I 0.868 0.430TRS (μmol S g–1) R 159.457 <0.001S. alterniflora 12.9 ± 2.2 31.2 ± 3.5 39.3 ± 3.9 P 0.202 0.657P. australis 11.4 ± 1.9 29.6 ± 5.0 44.2 ± 6.3 I 2.492 0.100Dissolved CH4 (mM) R 6.373 0.005S. alterniflora 22.3 ± 10.4 28.5 ± 14.8 18.3 ± 5.7 P 0.001 0.972P. australis 32.4 ± 12.8 28.5 ± 15.5 8.6 ± 5.1 I 2.227 0.125

Factor: ’R’ denotes rhizosphere effect, ’P’ denotes plant species and ’I’ denotes interaction among soil zones and plant species.

Bold letters denote a significant difference of p < 0.05.

Table 2 Mean ± standard deviation (SD) of the environmental parameters (n = 6) across the three soil zones (rhizosphere and inner and outerbulk soil) of the tidal marsh plants (S. alterniflora and P. australis) in the Min River Estuary, East China Sea.

Soil zones across the rhizoboxes Factor F p

Rhizosphere Inner bulk soil Outer bulk soil

Abundance (log10 copies g–1)Bacteria (16S rRNA gene) R 17.617 <0.001S. alterniflora 8.85 ± 0.05 8.79 ± 0.05 8.65 ± 0.10 P 0.019 0.039P. australis 8.78 ± 0.04 8.74 ± 0.11 8.57 ± 0.06 I 2.046 0.147Iron-reducing bacteria (Geobacter) R 364.865 <0.001S. alterniflora 6.69 ± 0.05 6.31 ± 0.05 5.76 ± 0.10 P 0.040 0.843P. australis 6.68 ± 0.05 6.33 ± 0.05 5.78 ± 0.15 I 0.116 0.891Sulfate-reducing bacteria (dsrA) R 32.457 <0.001S. alterniflora 8.30 ± 0.06 8.27 ± 0.07 8.06 ± 0.08 P 0.471 0.498P. australis 8.30 ± 0.02 8.24 ± 0.02 8.05 ± 0.15 I 0.134 0.875Methanogen (mcrA) R 239.407 <0.001S. alterniflora 5.14 ± 0.04 4.91 ± 0.11 4.44 ± 0.08 P 2.156 0.152P. australis 5.04 ± 0.03 4.91 ± 0.10 4.43 ± 0.05 I 1.793 0.184Enzymatic activity (nmol h–1 g–1)β-glucosidase (βG) R 7.711 0.002S. alterniflora 1.90 ± 0.38 1.52 ± 0.28 1.33 ± 0.38 P 0.0001 0.987P. australis 1.94 ± 0.39 1.56 ± 0.52 1.25 ± 0.37 I 0.076 0.9271,4-β-cellobiosidase (CBH) R 1.471 0.246S. alterniflora 0.34 ± 0.12 0.31 ± 0.06 0.22 ± 0.05 P 0.364 0.551P. australis 0.27 ± 0.10 0.28 ± 0.08 0.26 ± 0.13 I 0.954 0.397Alpha diversity (bacteria)Shannon index R 22.842 <0.001S. alterniflora 6.01 ± 0.07 6.59 ± 0.06 6.61 ± 0.04 P 25.025 <0.001P. australis 6.24 ± 0.09 6.76 ± 0.19 6.55 ± 0.10 I 17.121 <0.001Chao-1 index R 62.312 <0.001S. alterniflora 2596 ± 143 3629 ± 108 3433 ± 143 P 31.295 <0.001P. australis 3049 ± 92 3961 ± 179 3354 ± 170 I 31.167 <0.001

Factor: ’R’ denotes rhizosphere effect, ’P’ denotes plant species and ’I’ denotes interaction among the soil zones and plant species.Bold letters denote a significant difference of p < 0.05.

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Figure 1. Rates of (A) SOC mineralization, (B) methanogenesis and (C) sulfate reduction and (D) microbial Fe(III) in the rhizosphere, inner bulk soil, and outer bulk soilacross the rhizoboxes of S. alterniflora and P. australis in the Min River Estuary, East China Sea. The box-and-whisker plots show the median and interquartile range(IQR); the whiskers show the 1.5 × IQR of the lower and upper quartiles. The relative contributions of methanogenesis and microbial Fe(III) and sulfate reduction areprovided at the top of the box-and-whisker plots. Two-way ANOVA analysis of the rhizosphere effect and that of plant species on SOC mineralization rates of each

terminal metabolic pathway are provided. S. alterniflora: darker bar, P. australis: lighter bar. Rhizosphere: orange, inner bulk soil: green and outer bulk soil: blue.

bulk soil (mean: 1.55) > outer bulk soil (mean: 1.29; Tukey). The1,4-β-cellobiosidase (CBH; nmol h–1 g–1; Table 2) showed statisti-cally similar activities across the rhizoboxes (two-way ANOVA).Plant species had no significant effect on the abundance oftotal bacteria, Geobacter, sulfate-reducing bacteria (dsrA) andmethanogens (mcrA) as well as enzymatic activities; however,total bacteria (16S rRNA gene) was significantly higher in the rhi-zosphere of S. alterniflora than that of P. australis (Table 2).

Bacteria diversity and community composition

Both the Shannon (range: 6.01–6.76) and Chao-1 (range: 2596–3961) indices showed that the bulk soil (particularly the innerbulk soil) had greater OTU diversity than that of the rhizosphere(two-way ANOVA, Tukey; Table 2). The Chao-1 and Shannonindices from the S. alterniflora rhizoboxes were relatively lowcompared with those of the P. australis boxes (two-way ANOVA;Table 2).

A total of 953 OTUs (20%) synchronously occurred in the rhi-zosphere, inner and outer bulk soil (Fig. 2a). A total of 818 (17.1%),1054 (22.1%) and 1021 (21.4%) OTUs preferred the rhizosphere,inner bulk soil and outer bulk soil, respectively. The bacterialcommunities could be clearly separated across three differentsoil zones and between two plant species based on the NMDS ofthe OTUs (Fig. 2b). Combined with the results from PerMANOVA,

they suggested that both rhizosphere effect and plant specieshave a significant influence on bacterial community composi-tions (Table 3). The cluster analysis results (assessed as UniFracdistances; Lozupone and Knight 2005; Fig. 2c) also confirmedthat the rhizosphere and inner bulk soil samples independentlyclustered with the dissimilarity indices tested. Individual plantspecies have a less pronounced effect than the rhizosphereeffect in structuring bacterial communities in the tidal marshes(Table 3).

The dominant phylum level of bacteria (≥2% of sequencesanalyzed in different clone libraries) in the rhizosphere andbulk soil included Proteobacteria, Chloroflexi, Acidobacteria,Nitrospirae and Latescibacteria (listed according to taxonomy;Fig. 3a). In terms of the relative abundance (% of OTUs), thefive most abundant phyla recovered accounted for 72.9–83.9%of these sequences. All the soil zones were dominated by Pro-teobacteria, contributing to 35.2 ± 5.6% of the total OTUs.However, the relative abundances of the Proteobacteria werecomparable across the rhizoboxes, while significantly changedbetween plant species (Fig. 3a). By contrast, the relative abun-dances of Chloroflexi, Acidobacteria, Nitrospirae and Latescibac-teria differed across the rhizoboxes, which were comparablebetween plant species (Fig. 3a).

The dominant class level of bacteria (≥3% of sequences ana-lyzed in different clone libraries) in the rhizosphere and bulk

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Figure 2. (A) Bacterial community structure and Venn diagram comparing the microbial structure similarities across the root zones (rhizosphere and inner and outer

bulk soil) of the S. alterniflora and P. australis rhizoboxes in the Min River Estuary, East China Sea. The Venn diagram shows the shared phylotypes. (B) Nonmetricmultidimensional scaling analysis and (C) cluster analysis were based on the Paired group (UPGMA) algorithm and the Bray-Curtis similarity index. The colors denotethe three zones in the rhizoboxes, including the rhizosphere (orange), inner bulk soil (green) and outer bulk soil (blue). Circles: S. alterniflora; triangles: P. australis.

Table 3 Summary of the permutation tests used to investigate the soil microbial community composition based on the OTUs across the threesoil zones (rhizosphere and inner and outer bulk soil) of the tidal marsh plants (S. alterniflora and P. australis) in the Min River Estuary, EastChina Sea. Dissimilarity tests were performed by two-way PerMANOVA based on (Euclidean, Bray–Curtis and Horn) distance.

Dissimilarity Rhizosphere effect Plant species Interaction

df F p df F p df F p

Euclidean 2 37.133 <0.001 1 16.332 <0.001 2 9.9006 <0.001Bray-Curtis 2 38.126 <0.001 1 13.856 <0.001 2 7.2717 <0.001Horn 2 144.81 <0.001 1 36.293 <0.001 2 12.545 <0.001

Bold letters denote a significant difference of p < 0.05.

soil included δ-Proteobacteria, Anaerolineae, β-Proteobacteria,Nitrospira, γ -Proteobacteria and α-Proteobacteria (listed accord-ing to taxonomy, Fig. 3b). The relative abundance of thesesix classes accounted for 39.3–68.4% of the total OTUs. More-over, δ-proteobacteria had the highest abundance (11.8–16.3%)at the class-level (Fig. 3b). The relative abundances of the δ-proteobacteria were comparable across the rhizoboxes, whilethe other five classes showed significant differences across therhizoboxes (Fig. 3b). Only δ-proteobacteria and β-proteobacteriasignificantly changed between plant species (Fig. 3b).

The genus-level species with relative abundances less than1� were filtered out. From the filtered results, the top 25 dom-inant genera were constructed into a heatmap to depict therelative abundance of the OTUs that best represent the dis-similarities between the bacterial communities in the rhizo-sphere versus inner or outer bulk soil (Fig. 4). They belonged toeight phyla: Proteobacteria, Chloroflexi, Acidobacteria, Nitrospi-rae, Actinobacteria, Cyanobacteria, Spirochaetes and Ignavibac-teriae. There were 17 dominant genera derived from the phy-lum Proteobacteria and 11 dominant genera belonged to the δ-Proteobacteria class. Several of the genera have been reported byprevious researchers to have iron- or sulfate-reducing and den-itrification abilities (the reference is provided in Table S1).

Based on the CCA and VPA, only Fe(III) reduction (contri-bution = 24.7%, F = 4.7, p < 0.01; Fig. 5b) and sulfate reduc-tion (contribution = 8.8%, F = 2.0, p < 0.05; Fig. 5b) significantlycontributed to the bacterial community composition (Fig. 5a).Methanogenesis did not make a significant contribution to bac-terial community composition.

DISCUSSION

Rhizosphere effect on SOC mineralization rates andpathways in the tidal marshes

Previous studies have reported that the rhizosphere effect pro-moted the potential of total SOC mineralization in tidal marshes(Kostka et al. 2002a; Kostka, Roychoudhury and Cappellen 2002b;Gribsholt, Kostka and Kristensen 2003; Wolf et al. 2007; Mueller,Jensen and Megonigal 2016; Bernal, Megonigal and Mozdzer2017). In line with this, the total SOC mineralization rates in therhizosphere increased by 77% to 82% in the rhizosphere of P. aus-tralis and S. alterniflora relative to outer bulk soil in the Min RiverEstuary (Fig. 1a). The rhizosphere effect efficiency (i.e., promo-tion of total SOC mineralization rates from bulk soil to the rhizo-sphere) in this study was comparable with that of mineral tidalmarshes (Kostka et al. 2002a; Kostka, Roychoudhury and Cap-pellen 2002b; Gribsholt, Kostka and Kristensen 2003), but wasmuch lower than (167% to 182%) that of organic tidal marshes(Mueller et al. 2015; Wolf et al. 2007). The SOC contents in thoseorganic tidal marshes (> 40%) far outstripped the current studyand other mineral tidal marshes (< 2%). The distinctive SOC con-tents may explain, at least partly, the rhizosphere effect site dif-ferences among the different sites.

Roots support SOC mineralization by supplying labile organicC to soils (Hines et al. 1999; Neubauer, Emerson and Megonigal2008; Yarwood 2018), explaining the positive correlation betweenthe total SOC mineralization rates and DOC concentrations inthis study (Pearson, r = 0.856, p < 0.001). Because the samplingperiod was during the vigorous growth period of the tidal marshplants, DOC would be enriched in organic acid and glucose asreported in previous studies (Hines et al. 1999; Derrien, Marol and

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Figure 3. Taxonomic composition at the bacterial phylum level (A; at least one phylum relative abundance > 2%) and at the bacterial class level (B; at least one classrelative abundance > 3%) across the root zones (rhizosphere and inner and outer bulk soil) of the S. alterniflora and P. australis rhizoboxes in the Min River Estuary, East

China Sea. Two-way ANOVA analysis of the rhizosphere effect and plant species on each phylum or class are provided. Significant differences across the root zones orplant species are indicated by ∗ when 0.01 < p < 0.05; ∗∗ when 0.001 < p < 0.01; and ∗∗∗ when p < 0.001 (two-way ANOVA).

Balesdent 2004; Tobias and Neubauer 2009). This could explainwhy DOC concentrations were also in relation to β-Glucosidaseactivities (Pearson, r = 0.870, p < 0.001), an extracellular enzymetargeted at labile C (Kogel-Knabner 2002). Furthermore, DOCconcentrations were also in accordance with the total bacte-rial abundance (16S rRNA gene; Pearson, r = 0.547, p < 0.01).These combined results highlighted a possible mechanism forthe rhizosphere effect on SOC mineralization: the living rootsof the tidal marsh plants release labile C into the surroundingsoils, leading to an increase in the total bacterial growth and β-Glucosidase enzymatic activity further facilitating the potentialof total SOC mineralization in the rhizosphere.

Similar to the total SOC mineralization rates, the micro-bial Fe(III) and sulfate reduction rates and methanogenesis alsoincreased from the bulk soil to rhizosphere (Fig. 1b to d). Therhizosphere effect had a more dominant influence on Fe(III)reduction (an increase of 5–7 times) compared with that of sul-fate reduction (an increase of 1.4–1.5 times) or methanogenesis(an increase of 1.5–2.0 times). In accordance with the terminalmetabolic rates, the Geobacter abundance also showed a greaterincrease (∼8.5 times) compared with that of dsrA (1.7 times) andmcrA (4.5 times) towards the roots (Table 2). All three terminalmetabolic rates were deeply influenced by DOC concentrations

(Pearson, r > 0.709, p < 0.001). However, regarding Fe(III) reduc-tion, the rates were also associated with changes in Fe(III) con-tents across the rhizoboxes (Fig. S2). The Fe(III) could be real-located by the ROL of the tidal marsh plants (Mueller, Jensenand Megonigal 2016), since root-derived O2 could oxidize Fe2+

in the rhizosphere soils, precipitating a reddish Fe plaque onthe root surface (Khan et al. 2016). This Fe plaque was enrichedin bioavailable Fe(III) that was easily accessed by iron-reducingbacteria (Armstrong et al. 2000). In our previous study, we alsofound a logarithmically decreasing pattern against the distancefrom the roots in the tidal marsh soils because of ROL (Luoet al. 2018). Accordingly, the enhancement of microbial Fe(III)reduction rates appeared much stronger compared with thoseof sulfate reduction and methanogenesis when approaching theroots. The premise for this preference for Fe(III) reduction is thattidal marsh soils, particularly mineral tidal marshes, are anaer-obic (owing to inundation) and enriched in Fe(III) minerals. Inother ecosystems (e.g., upland soils), if the soil redox conditionis relatively aerobic, the plants would not develop such strongROL capabilities as those of marsh plants (Khan et al. 2016). Thispreference for Fe(III) reduction would thus be rather limited inupland soils but may be applicable to some Fe(III)-rich paddysoils.

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Figure 4. Heatmap showing the relative abundance of the bacterial OTUs of the top 25 genera across the root zones (rhizosphere and inner and outer bulk soil) of theS. alterniflora and P. australis rhizoboxes in the Min River Estuary, East China Sea. The colors correspond to the relative sequence abundance of the OTUs as indicatedby the color legend.

Figure 5. Canonical correspondence analyses (CCA) and variation partition analysis (VPA) based on the 16S rRNA gene amplicon sequences across the root zones(rhizosphere and inner and outer bulk soil) of the S. alterniflora and P. australis rhizoboxes in the Min River Estuary, East China Sea. S. alterniflora: circle; P. australis:triangle. Rhizosphere: orange; inner bulk soil: green; and outer bulk soil: blue.

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To the best of our knowledge, few studies have quantifiedthe partition of terminal metabolic pathways between the rhi-zosphere and bulk soil in tidal marshes. Microbial sulfate reduc-tion has been widely recognized as the most common terminalmetabolic pathway in coastal and marine ecosystems becauseof high porewater SO4

2– concentrations (Hines, Knollmeye andTugel 1989; Kostka et al. 2002a; Hyun et al. 2007, 2009). The pres-ence of plants can somehow increase the FeR% competitive-ness (from 28% to 96%) in coastal wetlands, including mangroves(Kristensen et al. 2000; Kristensen and Alongi 2006), salt marshes(Gribsholt and Kristensen 2002; Kostka et al. 2002a; Luo et al.2017) and freshwater marshes (Neubauer et al. 2005). For the firsttime, we have observed that the rhizosphere effect shifts thedominant terminal metabolic pathways from the predominantlysulfate reduction in bulk soil to the co-domination of Fe(III) andsulfate reduction in the rhizosphere. This result highlights thatwhen studying C cycling in tidal marshes, it is necessary to con-sider the heterogeneity of SOC mineralization rates and path-ways in millimeter-scale zones.

The rhizosphere effect also enhanced the methanogenesisrates from the bulk soil to the rhizosphere, with methanogenesiscontinually accounting for <1% of the total SOC mineralizationacross the rhizoboxes (Fig. 1b). There are two potential explana-tions for the low methanogenesis contribution: (i) methanogen-esis suppression from the perspective of energy yield by bothFe(III) and sulfate reduction (Froelich et al. 1979) and (ii) newlyyielded CH4 immediately aerobically and/or anaerobically beingoxidized by both microbial Fe(III) and sulfate reduction (Kuselet al. 2008; Beal, House and Orphan 2009; Wang et al. 2018).

Bacterial community composition coinciding withterminal metabolic pathways

As expected, the rhizosphere effect increased the abundance oftotal bacteria, Geobacter, dsrA and mcrA in the rhizosphere rela-tive to bulk soil, as higher DOC concentrations approached theroots (Table 2). However, it was surprising that the bacterial α-diversity showed a lower value in the rhizosphere comparedwith that in the bulk soil (Table 2). A similar result has also beenreported in coastal mangrove sediments (Jiang et al. 2013). Li et al.(2019) suggested that the analysis technique may be responsi-ble for the low diversity in the rhizosphere, because 16S rRNAgene sequence analysis would detect both active and inactivebacteria. That research further suggested that the rhizosphereeffect does support high bacterial richness, when only consider-ing active bacteria.

Proteobacteria was the largest phylum and the δ-Proteobacteria the largest class in the present study (Fig. 3aand b). Surprisingly, rhizosphere effects did not exert signifi-cant influence on both of these (Fig. 3a and b). When furtherexamining bacteria at a genera level, we found that there was alarge proportion (36–52%) of iron- and sulfate-reducing bacteriawithin the δ-Proteobacteria class (Fig. 4). Several importantiron-reducing bacteria were identified, including Geobacter,Anaeromyxobacter and Ignavibacterium (Fig. 4 and Table S1).Geobacter is among the most abundant iron-reducing bacteriaon Earth (Lovley, Holmes and Nevin 2004). Anaeromyxobacter arewell-known iron-reducing bacteria in many paddy soil systemsand are capable of acetate oxidation (Hori et al. 2010; Ahn et al.2014; Yuan et al. 2016). Ignavibacteria has been isolated from iron-reducing enrichment cultures of hot-spring sediments fromYellowstone National Park (Fortney et al. 2018). There was alsoa clade of typical sulfate-reducing bacteria, e.g., Desulfoprunum,

Desulfatiglans, Desulfobacca and Desulfobulbus. Sulfate-reducingbacteria have previously been found in mangrove (Yang, Tsaiand Chang 2018) and estuarine marsh sediments (Jiang et al.2009; Zeleke et al. 2013).

Based on the heatmap, the relative abundance of iron-reducing bacteria decreased from the rhizosphere to the bulksoil, while the sulfate-reducing bacteria showed a higher relativeabundance in the bulk soil (particularly the inner bulk soil) com-pared with that of the rhizosphere (Fig. 4). Such transitions pro-vided microbiological evidence for the shift in Fe(III) and sulfatereduction across the rhizoboxes. To further explore this point,we applied CCA and VPA analysis to confirm the relationshipbetween the bacterial community compositions and partition inthe terminal metabolic pathways. Results suggested that micro-bial Fe(III) and sulfate reduction could both explain a portion ofthe changes observed in the bacterial community compositionsacross the rhizoboxes (Fig. 5a). Microbial Fe(III) reduction (24.7%)had a greater influence on the bacterial community composi-tions than that of sulfate reduction (8.8%; Fig. 5b). These resultsprove the hypothesis that a shift in partition in the terminalmetabolic pathways could partly explain bacterial communitycompositions in the tidal marsh soils.

Abundance and composition of bacterial communitiesbetween the S. alterniflora and P. australis rhizoboxes

Plant species can influence rhizosphere microbial communi-ties in natural wetlands (Ravit et al. 2007; Prasse, Baldwin andYarwood 2015). There have been a wide variety of publishedresults assessing different microbial communities in S. alterni-flora and P. australis in tidal marsh ecosystems (Ravit, Ehren-feld and Haggblom 2003; Angeloni et al. 2006; Ravit et al. 2007;Nie et al. 2009; Prasse, Baldwin and Yarwood 2015; Yang et al.2016). Most studies have suggested that plant species differ intheir microbial abundance and biomass (Burke, Hamerlynck andHahn 2002; Lu et al. 2006). In accordance with these studies, wealso found that S. alterniflora soils have a higher abundance oftotal bacteria (16S rRNA gene) compared with that of the P. aus-tralis soils in our study (Table 2). The well-developed root sys-tem and greater root biomass of S. alterniflora relative to P. aus-tralis are used to provide the most reasonable explanation forthe different abundances of total bacteria in undisturbed nat-ural tidal marshes (Ravit, Ehrenfeld and Haggblom 2003; Ravitet al. 2007; Yang et al. 2016). However, probably because of thelimited space in the root compartments, root biomass was com-parable between the two plant species. Most of the environmen-tal parameters did not show a significant difference between thetwo plant species, except DOC concentrations (Table 1). A pre-vious study has suggested that the root exudation rates of S.alterniflora can be >12–104 μg C g–1 root h–1, higher than P. aus-tralis (9 μg C g–1 root h–1) during growth periods (Zhai et al. 2013).Thus, even with similar root biomass, the roots of S. alternifloracan release more labile C substrates in the rhizosphere thanthose of P. australis. Difference in root exudates can influence rhi-zosphere bacterial abundance in tidal wetlands (Ravit et al. 2007;Prasse, Baldwin and Yarwood 2015). Thus, it was not surprisingthat the abundance of total bacteria was significantly higher inthe rhizosphere of S. alterniflora than that of P. australis (Table 2).Furthermore, higher abundance of total bacteria could explainthe higher total SOC mineralization rates in the S. alterniflora rhi-zoboxes compared with those of P. australis (Fig. 1a).

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It was beyond our expectation that bacteria in the P. australisrhizoboxes would be more diverse than those of the S. alterni-flora (Table 2). However, a lower bacterial α diversity was alsofound in S. alterniflora stands than that in P. australis stands inother estuarine marsh sediments (Zeleke et al. 2013). A reason-able explanation is that the S. alterniflora may only favor thedominance of certain bacterial species. As root exudates dif-fer among plant species, the bacterial diversity was also influ-enced by the types of organic compounds present in the rhizo-sphere (Wang, Chen and Li 2007). If this was the case, it couldalso explain the difference in the bacterial community compo-sitions between two plant species (Fig. 2b and Table 3). Previousstudies have reported that a greater abundance of Acidobacteria,δ-Proteobacteria and Bacteroidetes, which all have special het-erotrophic metabolic capabilities, are found in the rhizospheresoils of S. alterniflora compared with those of P. australis (Tanget al. 2011; Rietl et al. 2016; Nie et al. 2019). Similarly, Proteobacte-ria at a phylum level, and δ- and β-Proteobacteria at the bacterialclass level were identified, and had relatively lower abundancesin the rhizosphere of P. australis compared with that of S. alterni-flora (Fig. 3). By contrast, they showed a relatively higher abun-dance in the bulk soil sample from P. australis compared withthat of S. alterniflora. Both δ- and β-Proteobacteria contain abun-dant anaerobic degraders and facultative anaerobes; this couldexplain why the plant species did not have a significant impacton the pathways of organic carbon mineralization.

CONCLUSIONS

In this study, we investigated the rhizosphere effect and theinfluence of plant species on the total SOC mineralization andeach terminal metabolic pathway using in situ rhizoboxes inan estuarine tidal marsh. The results suggested that the rhi-zosphere effect significantly increased the SOC mineralization,Fe(III) reduction and sulfate reduction rates and also methano-genesis from the bulk soil to the rhizosphere. However, becauseof the ability of reallocation of Fe(III), we found that the rhi-zosphere effect showed a greater preference towards microbialFe(III) reduction relative to the other two terminal metabolicpathways. For the first time, we have shown that the rhizosphereeffect shifts the dominant terminal metabolic pathways fromsulfate reduction in bulk soil to the co-dominance of microbialFe(III) and sulfate reduction in the rhizosphere. The relative con-tribution of methanogenesis remained negligible (<1%) in boththe rhizosphere and bulk soil. More importantly, the shift in theterminal metabolic pathways was associated with the bacterialcommunity composition across the rhizoboxes. Plant speciesdid not affect the partitioning of terminal metabolic pathways,but did influence the total SOC mineralization rates as well asthe total bacterial abundance. Although the rhizosphere effectand that of plant species both influence the bacterial commu-nity, our study shows that plant species had a less pronouncedimpact on the bacterial community when compared with thatof the rhizosphere effect.

SUPPLEMENTARY DATA

Supplementary data are available at FEMSEC online.

Acknowledgments

This work was financially supported by the National ScienceFoundation of China (Grant No. 41501252), the Science Foun-dation of the Fujian Province (Grant No. 2016J05097), Key Lab-oratory of Wet Subtropical Ecology and Geography process ofthe Ministry of Education (Grant No. 2017KFJJ02), Key Labora-tory of Coastal Environmental Processes and Ecological Remedi-ation, YICCAS (Grant No. 2018KFJJ10) and the Open Test Fund forvaluable instruments and equipment from Fuzhou University(2018T013). We thank the Elsevier Web shop for its assistancewith language editing during the preparation of this manuscript.

Authors’ contributions: ML designed the experiment. YL andML co-wrote the manuscript with substantial contributions fromJH, RY, LX and CT. All authors conducted the research and ana-lyzed the data. All authors edited the manuscript.

Conflict of interest. None declared.

REFERENCES

APHA. Standard methods for the examination of water and wastew-ater. Baltimore, MD: American Public Health AssociationPress, 2005.

Ahn JH, Choi MY, Kim BY et al. Effects of water-saving irrigationon emissions of greenhouse gases and prokaryotic commu-nities in rice paddy soil. Microb Ecol 2014;68:271–283.

Ahn C, Gillevet PM, Sikaroodi M. Molecular characterization ofmicrobial communities in treatment microcosm wetlandsas influenced by macrophytes and phosphorus loading. EcolIndic 2007;7:852–863.

Angeloni NL, Jankowski KJ, Tuchman NC et al. Effects of an inva-sive cattail species (Typha × glauca) on sediment nitrogen andmicrobial community composition in a freshwater wetland.FEMS Microbiol Lett 2006;263:86–92.

Armstrong W, Cousins D, Armstrong J et al. Oxygen distribu-tion in wetland plant roots and permeability barriers to gas-exchange with the rhizosphere: a microelectrode and mod-elling study with Phragmites australis. Ann Bot 2000;86:687–703.

Batten KM, Scow KM, Davies KF et al. Two invasive plants altersoil microbial community composition in serpentine grass-lands. Biol Invasions 2006;8:217–230.

Beal EJ, House CH, Orphan VJ. Manganese- and iron-dependentmarine methane oxidation. Science 2009;325:184–187.

Beals EW. Bray-Curtis Ordination: An effective strategy for anal-ysis of multivariate ecological data. Adv Ecol Res 1984;14:1–55.

Bengtson P, Barker J, Grayston SJ. Evidence of a strong couplingbetween root exudation, C and N availability, and stimulatedSOM decomposition caused by rhizosphere priming effects.Ecol Evol 2012;2:1843–1852.

Berg G, Smalla K. Plant species and soil type cooperatively shapethe structure and function of microbial communities in therhizosphere. FEMS Microbiol Ecol 2009;68:1–13.

Bernal B, Megonigal JP, Mozdzer TJ. An invasive wetlandgrass primes deep soil carbon pools. Global Chang Biol2017;23:2104–2116.

Bokulich NA, Subramanian S, Faith JJ et al. Quality-filteringvastly improves diversity estimates from Illumina ampliconsequencing. Nat Methods 2013;10:57.

Breidenbach B, Pump J, Dumont MG. Microbial communitystructure in the rhizosphere of rice plants. Front Microbiol2016;6:1537.

Dow


ic.oup.com/fem




https://academic.oup.com/femsec/article-lookup/doi/10.1093/femsec/fiz120#supplementary-data

Liu et al. 13

Burke DJ, Hamerlynck EP, Hahn D. Interactions among plantspecies and microorganisms in salt marsh sediments. ApplEnviron Microb 2002;68:1157–1164.

Burton ED, Sullivan LA, Bush RT et al. A simple and inexpensivechromium-reducible sulfur method for acid-sulfate soils.Appl Geochem 2008;23:2759–2766.

Caporaso JG, Kuczynski J, Stombaugh J et al. QIIME allows anal-ysis of high-throughput community sequencing data. NatMethods 2010;7:335.

Cline JD. Spectrophotometric determination of hydrogen sulfidein natural waters. Limnol Oceanogr 1969;14:454–458.

Connell EL, Colmer TD, Walker DI. Radial oxygen loss from intactroots of Halophila ovalis as a function of distance behind theroot tip and shoot illumination. Aquat Bot 1999;63:3–4.

Cucio C, Engelen AH, Costa R et al. Rhizosphere microbiomes ofEuropean seagrasses are selected by the plant, but are notspecies specific. Front Microbiol 2016;7:440.

Derrien D, Marol C, Balesdent J. The dynamics of neutral sug-ars in the rhizosphere of wheat. An approach by 13C pulse-labelling and GC/C/IRMS. Plant Soil 2004;267:243–253.

Fortney NW, He S, Kulkarni A et al. Stable isotope probing formicrobial iron reduction in Chocolate Pots Hot Spring, Yel-lowstone National Park. Appl Environ Microb 2018;84:02894–02817.

Froelich PN, Klinkhammer GP, Bender ML et al. Early oxida-tion of organic matter in pelagic sediments of the easternequatorial Atlantic: suboxic diagenesis. Geochim CosmochimAc 1979;43:1075–1090.

Gribsholt B, Kristensen E. Effects of bioturbation and plant rootson salt marsh biogeochemistry: a mesocosm study. Mar EcolProg Ser 2002;241:71–87.

Gribsholt B, Kostka JE, Kristensen E. Impact of fiddler crabs andplant roots on sediment biogeochemistry in a Georgia salt-marsh. Mar Ecol Prog Ser 2003;259:237–251.

Hafner S, Wiesenberg GL, Stolnikova E et al. Spatial distributionand turnover of root-derived carbon in alfalfa rhizospheredepending on top-and subsoil properties and mycorrhiza-tion. Plant Soil 2014;380:101–115.

Hiltner L. Uber neuere Erfahrungen und Probleme aufdem Gebiete der Bodenbakteriologie unter besondererBerucksichtigung der Grundungung und Brache. Arb DLG1904;98:59–78.

Hines ME, Evans RS, Sharak-Genthner BR et al. Molecular phylo-genetic and biogeochemical studies of sulfate-reducing bac-teria in the rhizosphere of spartina alterniflora. Appl EnvironMicrob 1999;65:2209.

Hines ME, Knollmeye SL, Tugel J. Sulfate reduction and othersedimentary biogeochemistry in a northern New Englandsaltmarsh. Limnol Oceanogr 1989;34:578–590.

Holmes DE, Finneran KT, O’Neil RA et al. Enrichment of mem-bers of the family Geobacteraceae associated with stimulationof dissimilatory metal reduction in uranium-contaminatedaquifer sediments. Appl Environ Microb 2002;68:2300–2306.

Hori T, Muller A, Igarashi Y et al. Identification of iron-reducingmicroorganisms in anoxic rice paddy soil by 13C-acetateprobing. Isme J 2010;4:267–278.

Howes BL, Teal JM. Oxygen loss from Spartina alterniflora andits relationship to salt marsh oxygen balance. Oecologia1994;97:431–438.

Hyun JH, Mok JS, Cho HY et al. Rapid organic matter mineraliza-tion coupled to iron cycling in intertidal mud flats of the HanRiver estuary, Yellow Sea. Biogeochemistry 2009;92:231–245.

Hyun JH, Smith AC, Kostka JE. Relative contributions of sulfate-and iron(III) reduction to organic matter mineralization and

process controls in contrasting habitats of the Georgia salt-marsh. Appl Geochem 2007;22:2637–2651.

Jami E, Israel A, Kotser A et al. Exploring the bovine rumen bacte-rial community from birth to adulthood. Isme J 2013;7:1069–1079.

Jiang XT, Peng X, Deng GH et al. Illumina sequencing of 16S rRNAtag revealed spatial variations of bacterial communities in amangrove wetland. Microb Ecol 2013;66:96–110.

Jiang L, Zheng Y, Peng X et al. Vertical distribution and diversityof sulfate-reducing prokaryotes in the Pearl River estuarinesediments, Southern China. Fems Microbiol Ecol 2009;70:93–106.

Khan N, Seshadri B, Bolan N et al. Root iron plaque on wetlandplants as a dynamic pool of nutrients and contaminants. AdvAgron 2016;138:1–96.

King GM, Garey MA. Ferric iron reduction by bacteria associatedwith the roots of freshwater and marine macrophytes. ApplEnviron Microb 1999;65:4393–4398.

Kirwan ML, Mudd SM. Response of salt-marsh carbon accumu-lation to climate change. Nature 2012;489:550–553.

Kogel-Knabner I. The macromolecular organic composition ofplant and microbial residues as inputs to soil organic mat-ter. Soil Biol Biochem 2002;34:139–162.

Koop-Jakobsen K, Mueller P, Meier RJ et al. Plant-sediment inter-actions in salt marshes–an optode imaging study of O2,pH, and CO2 gradients in the rhizosphere. Front Plant Sci2018;9:541.

Koretsky CM, Cuellar A, Haveman M et al. Influence of Spartinaand Juncus on saltmarsh sediments. II. Trace element geo-chemistry. Chem Geol 2008b;255:100–113.

Koretsky CM, Haveman M, Cuellar A et al. Influence of Spartinaand Juncus on saltmarsh sediments. I. Porewater geochem-istry. Chem Geol 2008a;255:87–99.

Koretsky CM, Moore CM, Lowe KL et al. Seasonal oscillation ofmicrobial iron and sulfate reduction in saltmarsh sediments(Sapelo Island, GA, USA). Biogeochemistry 2003;64:179–203.

Kostka JE, Gribsholt B, Petrie E et al. The rates and pathways ofcarbon oxidation in bioturbated saltmarsh sediments. LimnolOceanogr 2002a;47:230–240.

Kostka JE, Luther GW, III. Partitioning and speciation of solidphase iron in saltmarsh sediments. Geochim Cosmochim Ac1994;58:1701–1710.

Kostka JE, Roychoudhury A, Cappellen PV. Rates and con-trols of anaerobic microbial respiration across spatial andtemporal gradients in saltmarsh sediments. Biogeochemistry2002b;60:49–76.

Kristensen E, Alongi DM. Control by fiddler crabs (Uca vocans)and plant roots (Avicennia marina) on carbon, iron, and sul-fur biogeochemistry in mangrove sediment. Limnol Oceanogr2006;51:1557–1571.

Kristensen E, FØ Andersen, Holmboe N et al. Carbon and nitrogenmineralization in sediments of the Bangrong mangrove area,Phuket, Thailand. Aquat Microb Ecol 2000;22:199–213.

Kusel K, Blothe M, Schulz D et al. Microbial reduction of ironand porewater biogeochemistry in acidic peatlands. Biogeo-sciences 2008;5:2165–2196.

Li YL, Fan XR, Shen QR. The relationship between rhizospherenitrification and nitrogen-use efficiency in rice plants. PlantCell Environ 2008;31:73–85.

Li H, Su J-Q, Yang X-R et al. Distinct rhizosphere effect on activeand total bacterial communities in paddy soils. Sci Total Env-iron 2019;649:422–430.

Dow


ic.oup.com/fem





Li J, Yu J, Liu J et al. The effects of sulfur amendments on thegeochemistry of sulfur, phosphorus and iron in the man-grove plant (Kandelia obovata (S. L.)) rhizosphere. Mar PollutBull 2016;114:733.

Lovley DR, Holmes DE, Nevin KP. Dissimilatory Fe(III) and Mn(IV)reduction. Adv Microb Physiol 2004;49:219.

Lozupone C, Knight R. UniFrac: a new phylogenetic methodfor comparing microbial communities. Appl Environ Microb2005;71:8228.

Lu Y, Murase J, Watanabe A et al. Linking microbial communitydynamics to rhizosphere carbon flow in a wetland rice soil.Fems Microbiol Ecol 2004;48:179–186.

Lu Y, Rosencrantz D, Liesack W et al. Structure and activityof bacterial community inhabiting rice roots and the rhizo-sphere. Environ Microbiol 2006;8:1351–1360.

Luo M, Huang J, Tong C et al. Iron dynamics in a subtropical estu-arine tidal marsh: effect of season and vegetation. Mar EcolProg Ser 2017;577:1–15.

Luo M, Liu Y, Huang J et al. Rhizosphere processes inducechanges in dissimilatory iron reduction in a tidal marsh soil:a rhizobox study. Plant Soil 2018;433:83–100.

Luo M, Zeng CS, Tong C et al. Iron reduction along an inunda-tion gradient in a tidal sedge (Cyperus malaccensis ) marsh:the rates, pathways, and contributions to anaerobic organicmatter mineralization. Estuar Coast 2016;39:1–15.

Ma X, Zarebanadkouki M, Kuzyakov Y et al. Spatial patterns ofenzyme activities in the rhizosphere: Effects of root hairs androot radius. Soil Biol Biochem 2018;118:69–78.

Megonigal P, Neubauer SC. Biogeochemistry of tidal freshwaterwetlands. Coastal Wetlands: An Integrated Ecosystem Approach.Elsevier, 2009.

Mitsch WJ, Gosselink JG. Wetlands, 5th ed. John Wiley & Sons,2015.

Morrissey EM, Franklin RB. Evolutionary history influences thesalinity preference of bacterial taxa in wetland soils. FrontMicrobiol 2015;6:1013.

Mueller P, Jensen K, Megonigal JP. Plants mediate soil organicmatter decomposition in response to sea level rise. GlobalChang Biol 2016;22:404–414.

Mueller CW, Rethemeyer J, Kao-Kniffin J et al. Large amounts oflabile organic carbon in permafrost soils of northern Alaska.Global Chang Biol 2015;21:2804–2817.

Munyaka PM, Khafipour A, Wang H et al. Mo1774 Prenatalantibiotic treatment increases offspring’s susceptibility toexperimental colitis: a role of the gut microbiota. PLoS One2015;10:e0142536.

Neubauer S, Emerson D, Megonigal J. Microbial oxidation andreduction of iron in the root zone and influences on metalmobility. Biophysico-chemical processes of heavy metals and met-alloids in soil environments. John Wiley and Sons, 2008, 339–371.

Neubauer SC, Givler K, Valentine SK et al. Seasonal patterns andplant-mediated controls of subsurface wetland biogeochem-istry. Ecology 2005;86:3334–3344.

Neumann G, George TS, Plassard C. Strategies and methods forstudying the rhizosphere—the plant science toolbox. PlantSoil 2009;321:431–456.

Nie M, Zhang X-D, Wang J-Q et al. Rhizosphere effects on soilbacterial abundance and diversity in the Yellow River Deltaicecosystem as influenced by petroleum contamination andsoil salinization. Soil Biol Biochem 2009;41:2535–2542.

Nielsen LB, Finster K, Welsh DT et al. Sulphate reduction andnitrogen fixation rates associated with roots, rhizomes and

sediments from Zostera noltii and Spartina maritime meadows.Environ Microbiol 2001;3:63–71.

Pereyra L, Hiibel S, Riquelme M et al. Detection and quantificationof functional genes of cellulose-degrading, fermentative, andsulfate-reducing bacteria and methanogenic archaea. ApplEnviron Microb 2010;76:2192–2202.

Prasse CE, Baldwin AH, Yarwood SA. Site history and edaphicfeatures override the influence of plant species on microbialcommunities in restored tidal freshwater wetlands. Appl Env-iron Microbiol 2015;81:3482–3491.

Ravit B, Ehrenfeld JG, Haggblom MM. A comparison of sedimentmicrobial communities associated with Phragmites australisand spartina alterniflora in two brackish wetlands of New Jer-sey. Estuar Coast 2003;26:465–474.

Ravit B, Ehrenfeld JG, Haggblom MM et al. The effects of drainageand nitrogen enrichment on Phragmites australis, Spartinaalterniflora, and their root-associated microbial communities.Wetlands 2007;27: 915.

Rietl AJ, Overlander ME, Nyman AJ et al. Microbial communitycomposition and extracellular enzyme activities associatedwith Juncus roemerianus and Spartina alterniflora vegetatedsediments in Louisiana saltmarshes. Microb Ecol 2016;71:290–303.

Roden EE, Wetzel RG. Organic carbon oxidation and suppressionof methane production by microbial Fe(III) oxide reduction invegetated and unvegetated freshwater wetland sediments.Limnol Oceanogr 1996;41:1733–1748.

Saiya-Cork KR, Sinsabaugh RL, Zak DR. The effects of long termnitrogen deposition on extracellular enzyme activity in anAcer saccharum forest soil. Soil Biol Biochem 2002;34:1309–1315.

Sivan O, Shusta SS, Valentine DL. Methanogens rapidly transi-tion from methane production to iron reduction. Geobiology2016;14:190–203.

Steinberg LM, Regan JM. mcrA-targeted real-time quantitativePCR method to examine methanogen communities. ApplEnviron Microb 2009;75:4435.

Strom L, Ekberg A, Mastepanov M et al. The effect of vascularplants on carbon turnover and methane emissions from atundra wetland. Global Chang Biol 2003;9:1185–1192.

Sutton-Grier AE, Keller JK, Koch R et al. Electron donors andacceptors influence anaerobic soil organic matter mineral-ization in tidal marshes. Soil Biol Biochem 2011;43:1576–1583.

Tang Y-S, Wang L, Jia J-W et al. Response of soil microbialcommunity in Jiuduansha wetland to different successionalstages and its implications for soil microbial respiration andcarbon turnover. Soil Biol Biochem 2011;43:638–646.

Tobias C, Neubauer SC. Chapter 16 - Salt MarshBiogeochemistry—An Overview, In: Perillo G.M.E. WolanskiE. Cahoon D.R. Hopkinson C.S. (Eds.), Coastal Wetlands 2009,539–596.

Tong C, Wang WQ, Huang JF et al. Invasive alien plants increaseCH4 emissions from a subtropical tidal estuarine wetland.Biogeochemistry 2012;111:677–693.

Wang X., Chen R., Jing Z et al. Root derived carbon transportextends the rhizosphere of rice compared to wheat. Soil BiolBiochem 2018;122:211–219.

Wang M, Chen J-K, Li B. Characterization of bacterial communitystructure and diversity in rhizosphere soils of three plantsin rapidly changing salt marshes using 16S rDNA. Pedosphere2007;17:545–556.

Wang M, Yang P, Salles JF. Distribution of root-associated bac-terial communities along a salt-marsh primary succession.Front Plant Sci 2016;6:1188.

Dow


ic.oup.com/fem




Liu et al. 15

Wang W, Zeng C, Sardans J et al. Soil methane production, anaer-obic and aerobic oxidation in porewater of wetland soils ofthe Minjiang River Estuarine, China. Wetlands 2018;38:627–640.

Weiss JV, Emerson D, Backer SM et al. Enumeration of Fe(II)-oxidizing and Fe(III)-reducing bacteria in the root zone ofwetland plants: implications for a rhizosphere iron cycle. Bio-geochemistry 2003;64:77–96.

Wilkinson L, Urbanek S. venneuler: Venn and Euler Diagrams. Rpackage version 1.0–4, 2010.

Wolf AA, Drake BG, Erickson JE et al. An oxygenmediated positivefeedback between elevated carbon dioxide and soil organicmatter decomposition in a simulated anaerobic wetland.Global Chang Biol 2007;13:2036–2044.

Yang C-W, Tsai L-L, Chang B-V. Anaerobic degradation of sul-famethoxazole in mangrove sediments. Sci Total Environ2018;643:1446–1455.

Yang W, Yan Y, Jiang F et al. Response of the soil microbial com-munity composition and biomass to a short-term Spartina

alterniflora invasion in a coastal wetland of eastern China.Plant Soil 2016;408:443.

Yarwood SA. The role of wetland microorganisms in plant-litterdecomposition and soil organic matter formation: a criticalreview, Fems Microbiol Ecol 2018;94:175.

Yuan H-Y, Ding L-J, Wang N et al. Geographic distance and amor-phous iron affect the abundance and distribution of Geobac-teraceae in paddy soils in China. J Soil Sediment 2016;16:2657–2665.

Zeleke J, Sheng Q, Wang JG et al. Effects of spartina alterniflorainvasion on the communities of methanogens and sulfate-reducing bacteria in estuarine marsh sediments. Front Micro-biol 2013;4:243.

Zhai X, Piwpuan N, Arias CA et al. Can root exudates from emer-gent wetland plants fuel denitrification in subsurface flowconstructed wetland systems? Ecol Eng 2013;61:555–563.

Zhou J, Kang S, Schadt CW et al. Spatial scaling of functional genediversity across various microbial taxa. P Natl Acad Sci USA2008;105:7768–7773.

Dow


ic.oup.com/fem




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