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Applied Soil Ecology 96 (2015) 88–98

Contents lists available at ScienceDirect

Applied Soil Ecology

jo u r n al homep ag e: www.elsev ier .com/ locate /apsoi l

hort-term response of nitrifier communities and potentialitrification activity to elevated CO2 and temperature interaction

n a Chinese paddy field

uan Liua,b, Huimin Zhoua, Jianqing Wanga, Xiaoyu Liua, Kun Chenga, Lianqing Lia,inwei Zhenga, Xuhui Zhanga, Jufeng Zhenga, Genxing Pana,∗

Institute of Resource, Ecosystem and Environment of Agriculture, Nanjing Agricultural University, 1 Weigang, Nanjing 210095, ChinaDepartment of Bioengineering, College of Life Sciences, Huaibei Normal University, 235000 Huaibei, Anhui, China

r t i c l e i n f o

rticle history:eceived 26 January 2015eceived in revised form 9 June 2015ccepted 10 June 2015

eywords:mmonia oxidizerlevated CO2

armingitrification activityice paddy field

a b s t r a c t

While microbial nitrogen transformations in terrestrial ecosystems are known to be affected by globalclimate change, changes in abundance and composition of mediating microorganisms in agriculturalsoils under climate change have not yet been well characterized. Here, using well-established molecu-lar fingerprinting techniques and biochemical assays, we tried to analyze changes in nitrifier abundance,composition and activity in the rhizosphere under simultaneously elevated atmospheric CO2 and temper-ature conditions in a Chinese paddy field. Abundance, rather than community composition of ammoniaoxidizing archaea and bacteria (AOA and AOB) responded to the climate change treatments, and the effectwas greater on the heading and ripening stages (36–52%) than on the tillering stage (8–26%). Elevatedatmospheric CO2 significantly increased AOA and AOB abundance at the heading and ripening stages.Treatment of WA (warming of canopy air) alone did not affect the abundance or community structure ofAOA or AOB in the rice rhizosphere at any growth stage. The simultaneous application of CO2 enrichmentand warming affected ammonia oxidizer communities differently than independent application of CO2

enrichment or warming, with warming negating the stimulating effect of CO2 enrichment. Phylogenicanalysis indicated that all AOA clones fell within the soil and sediment lineage while all AOB cloneswere classified as Nitrosospira. Although no changes to soil NH4

+ or NO3− contents were found, potential

nitrification rate generally increased under the treatments with elevated CO2 at all rice growth stages.This could imply a complexity of the joint effect by elevated CO2 on soil properties, plant N uptake and

microbial growth. These results suggest that impacts of climate change on N transformation in the ricepaddy occur through interactions between effects of climate change on ammonia oxidizer communitiesand soil properties. Further studies would be required on multiple effects by simulated climate changeson soil properties, N transformations and microbial communities, for a sound understanding of potentialchanges in N cycling and rice productivity under global climate change.

. Introduction

Climate model projections suggested that atmospheric carbon

ioxide (CO2) concentration was likely to double and global averageemperature would probably rise by another 1.1–6.4 ◦C during the1st century (IPCC, 2013). Global climate change had been shown

Abbreviations: AOA, ammonia oxidizing archaea; AOB, ammonia oxidizingacteria; SMBC, soil microbial biomass carbon; T-RFLP, terminal-restriction frag-ent length polymorphism; qPCR, quantitative PCR; PNR, potential nitrification

ate.∗ Corresponding author.

E-mail address: pangenxing@aliyun.com (G. Pan).

ttp://dx.doi.org/10.1016/j.apsoil.2015.06.006929-1393/© 2015 Elsevier B.V. All rights reserved.

© 2015 Elsevier B.V. All rights reserved.

to profoundly affect terrestrial ecosystem properties and functions(Rosenzweig et al., 2007). Elevated atmospheric CO2 enhancedplant photosynthesis and root production, increasing labile car-bon input into soil (Cardon et al., 2001; Austin et al., 2009), whilewarming had been associated with increased soil microbial respi-ration (Bradford et al., 2008) and net plant productivity (Trumbore,1997). Previous studies had demonstrated that belowground pro-cesses, mainly regulated by soil microorganisms, were central tothe effects of climate change on ecological systems (Hu et al., 1999;Morgan, 2002). Although microbial communities regulate nutri-

ent biogeochemistry, the responses of these organisms to globalclimate change are still unclear.

As a critical process in soil N transformation and cycling in ter-restrial ecosystem, nitrification played a key role in plant uptake

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Y. Liu et al. / Applied S

f inorganic N, emission of nitrous oxide, and nitrate leaching tourface and groundwater (Zhalnina et al., 2012). Ammonia oxidiza-ion to nitrite was already known as the rate-limiting step in soilitrification, which was performed by microbial communities ofmmonia oxidizing bacteria (AOB) and archaea (AOA) (Leiningert al., 2006; Nicol and Schleper, 2006; Jackson et al., 2008). As

main N transforming process in aerobic condition, nitrificationould be potentially affected by climate change (Barnard et al.,005; Brown et al., 2012), probably due to indirect changes in soil2 concentration and in biological activity caused with elevatedO2 and warming (Rustad et al., 2001). Shifts in community struc-ure and abundance of ammonia oxidizers had been reported inesponse to increased temperature, CO2 enrichment and N fertil-zation (Horz et al., 2004; Nicol et al., 2008; Tourna et al., 2008; Dit al., 2009). However, the responses of AOB and AOA to elevatedO2 were highly variable in the literature. Elevated CO2 reducedOB abundance (Horz et al., 2004) but did not change AOB abun-ance or activity in grass ecosystems (Schortemeyer et al., 1996;arnard et al., 2004b). Rapid responses to short-term experimen-al warming of ammonia oxidizer activity and community structureinking to inorganic N availability had been reported in a laboratorytudy using grassland soil (Avrahami and Conrad, 2003), and in aeld study under forest (Szukics et al., 2010). The warming effect onomposition and activity of soil ammonia oxidizers was attributedargely to the changes in soil properties such as soil moisture andH as well as ammonium contents (Horz et al., 2004). While mosttudies described above were conducted under a single climatehange factor either of elevated atmospheric CO2 concentration orf warming (Horz et al., 2004; Castro et al., 2010; Gray et al., 2011),here had been few works on the multi-factorial effects of climatehange, particularly in agricultural soils.

It could be critical for predicting future changes in rice produc-ion and ecosystem functioning to understand microbial responseso elevated CO2 and warming in rice paddies (Das et al., 2011;ee et al., 2015). In flooded paddy fields, the rice rhizosphere pro-ided an aerobic microhabitat for soil microorganisms, which coulde affected potentially by climate change via the interaction ofice plant physiology and soil properties (Whipps, 2001). Effectsad been frequently reported of CO2 enrichment on rice ecosys-em function including rice grain yield and quality, plant nutrientsptake and greenhouse gas emission (Hasegawa et al., 2013; Usuit al., 2014; Tokida et al., 2011). Correspondingly, activity and com-unity composition of ammonia oxidizer in rice rhizosphere could

e affected by elevated CO2 through root exudates and rhizode-osition as well as soil organic carbon (Bhattacharyya et al., 2013;kubo et al., 2014). The changes to nitrifying activity of agricul-

ural soils under elevated atmospheric CO2 and warming conditionsad been shown to depend on soil pH, inorganic N availability,nd ammonia oxidizer community structure and abundance, buthese changes were difficult to correlate with those in nitrifyingctivity (Yao et al., 2011; Long et al., 2012). Whereas, there hadeen insufficient knowledge on changes of ammonia oxidizers andheir nitrification activity in rice paddy fields under climate changeonditions, particularly under simultaneous CO2 enrichment andarming.

We hypothesized that simulated climate changes could alterhe abundance, community composition and metabolic potentialf ammonia oxidizer in rice rhizosphere, but the effects couldiffer between a single factor and multifactorial treatment andelated to rice growth. Therefore, the objective of this study, using

field experiment with simulation of climate change conditions,as to address how multiple factorial climate change treatment

ffected soil ammonia oxidizer communities and their activities.e aimed to provide sound information for understanding the

otential impacts of future climate change in N transforming pro-esses and rice production.

logy 96 (2015) 88–98 89

2. Materials and methods

2.1. Site description and experimental setup

The field experiment with simulated climate change was estab-lished in 2010 in Kangbo village (31◦30′ N, 120◦33′ E), Jiangsu,China. The area was controlled under a subtropical monsoonclimate with an annual mean temperature of 16 ◦C and mean pre-cipitation of 1100–1200 mm. The soil is a Gleyic Stagnic Anthrosolformed on clayey lacustrine deposit and cultivated for hundreds ofyears with rice-wheat rotation. The basic properties of the studiedtopsoil were: soil pH (H2O) 7.0, soil organic carbon 1.6%, total nitro-gen 1.9 g kg−1, and bulk density 12 g cm−3. As described in the workby Liu et al. (2014), the treatments included elevated atmosphericCO2 concentration up to 500 ppm (CE), warming of crop canopyair by 2 ◦C over ambient (WA), and simultaneously elevated atmo-spheric CO2 concentration and canopy air (CW), and a control ofambient condition (CK)). For CO2 enrichment, pure CO2 was sup-plied with a liquid CO2 pool tank and injected into the plot using theperforated pipes surrounding the ring, CO2 release was automati-cally manipulated for direction and speed and air CO2 concentrationwas controlled with CO2 analyzers over the ring. Canopy air warm-ing was performed with infrared heaters, hanging over the plot. Gaspipes and heaters were all inserted in control rings, without work-ing. All the simulated climate change conditions were consistentlytreated across the crop growing season. The treatments were con-ducted in triplicates and the rings arranged in a split row design.All the rings were buffered by adjacent open fields to avoid anytreatment cross-over.

Rice production was managed with local conventional practices,including a soil water regime of flooding during seedling to tiller-ing stages, intermittent irrigation during heading and drainage forripening. Urea and ammonium bicarbonate were applied as basalfertilizers at a rate of 150 kg N ha−2 (123 kg N ha−2 as urea and27 kg N ha−2 as ammonium bicarbonate) on June 21. Chlorpyrifoswas applied as a pesticide at a rate of 800–1000 g ha−2 at the head-ing stage. The management practices were consistent across all thetreatments.

2.2. Soil sampling

Rhizosphere soils were sampled at the tillering, heading andripening stages of rice growing season in 2012. The rhizosphere of 5individual rice plants were randomly collected in depth of 0–15 cmfrom each plot, following the procedure described by Butler et al.(2003). The rhizosphere soil (about 1 cm thick soils) was carefullyremoved from the roots and homogenized to form a compositesample. Soil samples were sieved through 2-mm-mesh sieves andimmediately sealed in plastic ice bags before shipping to labora-tory within one day. Fresh samples were stored at 4 ◦C prior to soilphysico-chemical analyses within 1 week, and a portion was storedat −20 ◦C prior to DNA extraction within 1 week. The sampling wascompleted in a same day across the treatments, the sampling depthwas the same throughout the rice growing stages.

2.3. Soil physical-chemical analysis

Soil pH was measured using a glass electrode (1/2.5 soil/waterratio, v/v) with a Mettler-Toledo pH meter. For analysis of soilexchangeable NH4

+ and NO3−, 10 g of soil were extracted with

25 ml 0.25 M K2SO4 and vigorously shaken for 30 min (Robertsonet al., 1999). Extracts were then filtered, and NH4

+ and NO3− con-

centrations were determined colorimetrically in an automated flowinjection analyzer (Skalar Analytical B.V., The Netherlands). Soilmicrobial biomass carbon (SMBC) was determined by fumigationand extraction method (Wu et al., 1990). Fumigated (with ethanol

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ree chloroform for 24 h at 25 ◦C) and unfumigated samples werextracted with 0.5 mM K2SO4 for 30 min on a shaker. K2SO4 extractsere analyzed for extractable C by an automated TOC Analyzer

TOC-500, Japan) and a KEC of 0.45 was used to convert the mea-ured C to SMBC values.

.4. Measurement of potential nitrification rate

The potential nitrification rate (PNR) of soil was analyzed withn incubation method described by Schmidt and Belser (1994).riefly, 20 g moist soil were added to a 250 mL cotton-stoppedask with 100 ml of 0.5 mM phosphate buffer (pH 7.4), 0.5 mMNH4)2SO4 and 10 mM KClO3 to stop further oxidation of NO2

−.hen, the flask was incubated for 24 h on a shaker at approximately75 rpm at 25 ◦C. At specified intervals (4, 8, 12, 16, 20 and 24 h),O2

− was extracted by 5 ml of 3 M KCl and determined with aolorimeter at 540 nm with N-(1-naphthyl) ethylenediamine dihy-rochloride. Linearity of NO2

− concentration versus time for a givenoil was checked (R2 > 0.93). The net increase in the NO2

− concen-ration during 12 h was used to calculate the potential nitrifyingctivity.

.5. DNA extraction and real-time PCR assay

Total DNA was extracted from 0.35 g of fresh soil with a MoBioowerSoilTM DNA isolation kit (MoBio, CA, USA) following the man-facturer’s instructions. DNA quality was assessed on an agarose gelhile DNA quantity was determined using a Nanodrop spectropho-

ometer (Thermoscientific, DA, USA).The abundances of amoA (AOB and AOA) in all soil samples

ere determined in triplicate using a 7500 real-time PCR sys-em (Applied Biosystems, Germany). The DNA concentration ofll soil samples was adjusted to 15 ng �l−1. The amoA genesf AOA and AOB were amplified using primers Arch-amoAFSTAATGGTCTGGCTTA GACG)/Arch-amoAR (GCGGCCATCCATCTG-ATGT) and amoA1F (GGGGTTTCT ACTGGTGGT)/amoA2R (CCC-TCKGSAAAGCCTTCTTC), respectively (Francis et al., 2005; Shent al., 2008). The thermal cycling conditions for the amoA (AOB andOA) gene were as follows: an initial denaturation step at 95 ◦C

or 2 min; 40 cycles of 95 ◦C for 15 s, 55 ◦C for 30 s, 72 ◦C for 45 s,0 ◦C for 20 s; final extension at 72 ◦C for 1 min, followed by theelting curve analysis. Each reaction was performed in a 25 �l vol-

me containing 15 ng of DNA, 1 �l of 10 �M of each primer and2.5 �l of SYBR premix EX Taq TM (Takara Shuzo, Shinga, Japan).he amplified PCR products of AOA and AOB genes were ligatednto pEASY-T3 cloning vector and cloned into Escherichia coli DH5�.lones containing correct inserts were chosen as the standardsor qPCR. Standard curves were generated using triplicate 10-foldilutions of plasmid DNA. PCR efficiencies were obtained between8.5% and 106.6%, with R2 values > 0.98.

.6. T-RFLP analysis of AOB and AOA communities

Terminal-restriction fragment length polymorphism (T-RFLP)as used for analyzing AOB and AOA community structure. Briefly,OB and AOA amoA genes were amplified by PCR using therimer pairs amoA1F/amoA2R and Arch-amoAF/Arch-amoAR, asentioned above with the 5′ end of the amoA1F and Arch-amoAF

rimers labeled with 6-carboxyfluorescein (6-FAM). All PCRs wereerformed in duplicate and pooled for subsequent restriction and-RFLP analysis. PCR products were separated by 1.5% agaroseel, and purified using the PCR solution purification kit (Takara,

alian, China). Purified PCR products were used in a restrictionigest AfaI (Takara, Dalian, China) for archaeal amoA gene and HhaITakara, Dalian, China) for bacterial amoA gene reported by manu-acturer’s instructions. Digests were incubated at 37 ◦C for 5 h and

logy 96 (2015) 88–98

subsequently inactivated by heat denaturation at 65 ◦C for 20 min.Fragment analysis was achieved by capillary electrophoresis (ABI3100 Genetic Analyzer; Applied Biosystems, CA), using a GeneScanROX-labeled GS500 internal size standard. T-RFLP patterns wereproduced using the GeneMapper software (Applied Biosystems),and peaks between 50 and 550 bp were selected to avoid T-RFscaused by primer-dimers. The relative abundance of a true T-RFwithin a given T-RFLP pattern was generated as a ratio of the respec-tive peak height. The peaks with heights < 2% of the total peak heightwere not included for further analyses. The Shannon diversity index(H′) was used to calculate the diversity of AOA and AOB based onthe following equations:

H′ = −∑

pi ln(pi) (1)

where pi is the relative abundance of each T-RF compared to all theT-RFs in a sample.

2.7. Clone library, sequencing and phylogenetic analysis

In order to identify the main T-RFs appeared in the profile, AOAand AOB clone libraries were constructed using the same primersets as T-RFLP but without 6-FAM labeled. Based on the obtainedT-RFLP results, soil samples at the ripening stage with the highestrepresentative T-RF diversity were chosen to establish clone library.The purified PCR products were ligated with pEASY-T3 vector andthen transformed into competent cells of E. coli JM109 (Takara,Japan). 103 AOA clones and 106 AOB clones were sequenced suc-cessfully with ABI PRISM 3730 sequencer (Applied Biosystems). Allsequences were checked for chimera by using Bellerophon, andthen were grouped into operational taxonomic units (OTUs) usingthe furthest-neighbor clustering algorithm of the DOTUR softwarewith a 97% threshold (Schloss and Handelsman, 2005). The vir-tual digests with AfaI and HhaI were carried out on the sequencesretrieved from the clone libraries to allow the assignment of phy-logenetic identity to individual peaks. As discrepancies between insilico and observed T-RF sizes might occur (Schütte et al., 2008),we further referred to the silico T-RF value. The closest relativesof each sequence were checked using a BLAST search within Gen-Bank. The representative sequences recorded in this study havebeen deposited in the GenBank database under accession numberKP182965–KP182980.

2.8. Statistical analysis

Statistical analysis was performed using SPSS 20.0. For each vari-able measured in the rhizosphere, the data were analyzed by one-way ANOVA with Tukey’s HSD test to compare the means of differ-ent climate change treatments at each growth stage. Repeated mea-sures ANOVA were used to determine the effect of climate changefactors and plant growth stage on soil properties, PNR and ammoniaoxidizer abundance (log10-transformed AOB and AOA amoA geneabundances). Spearsman’s correlation analyses were performed toassess the relationships among soil properties, PNR and ammoniaoxidizer abundances. Redundancy analysis (RDA) with the MonteCarlo permutation’s test (499 permutations) was used to evaluatethe relationship among AOB and AOA community structures andsoil properties using the Canoco 4.5 software. The probability levelp < 0.05 was considered to be statistically significant.

3. Results

3.1. Soil properties and potential nitrification rate

As shown in Table 1, Soil pH ranged from 7.2 to 8.2 acrossthe treatments and growing stages. Compared to the control

Y. Liu et al. / Applied Soil Ecology 96 (2015) 88–98 91

Table 1Soil properties of the studied soils under different climate change treatments.

Stage Treatment pH NH4+ (mg kg−1) NO3

− (mg kg−1) SMBC (g kg−1)

Tillering CK 8.08 ± 0.09a 8.90 ± 1.41a 10.58 ± 2.56a 0.40 ± 0.04bCE 7.99 ± 0.22a 10.16 ± 1.50a 17.79 ± 1.30a 0.79 ± 0.12aCW 8.11 ± 0.05a 9.37 ± 2.05a 9.08 ± 4.51a 0.70 ± 0.10aWA 8.19 ± 0.04a 7.68 ± 1.82a 14.94 ± 5.46a 0.37 ± 0.14b

Heading CK 7.91 ± 0.11a 5.50 ± 0.32a 13.84 ± 7.61a 0.24 ± 0.08bCE 7.75 ± 0.03a 5.33 ± 0.25a 9.76 ± 2.23a 0.49 ± 0.15aCW 7.33 ± 0.08b 5.04 ± 0.62a 6.45 ± 2.28a 0.46 ± 0.04aWA 7.84 ± 0.08a 6.07 ± 1.03a 10.54 ± 4.79a 0.34 ± 0.10ab

Ripening CK 7.93 ± 0.14a 2.76 ± 0.83a 14.99 ± 3.35a 0.45 ± 0.18aCE 7.65 ± 0.22a 3.14 ± 0.58a 17.10 ± 1.09a 0.76 ± 0.20aCW 7.16 ± 0.17b 2.72 ± 0.63a 17.67 ± 1.10a 0.77 ± 0.17aWA 7.90 ± 0.04a 3.23 ± 0.86a 17.35 ± 1.91a 0.64 ± 0.12a

CK: ambient CO2 and ambient temperature; CE: atmospheric CO2 enrichment; CW: atmospheric CO2 enrichment and warming of canopy air; WA: warming of canopyair. SMBC: soil microbial biomass carbon. Data were presented as means of three replicates ± standard error; different letters within the same column indicate significantdifferences among treatments within a single growth stage (p < 0.05).

Table 2Repeated measures ANOVA for the effects of climate change factor, growth stage and their interaction on soil properties, PNR and ammonia oxidizer abundance in therhizosphere soils.

Factor pH NH4+ NO3

− SMBC PNR AOA abundance AOB abundance

CO2 0.157 0.476 0.441 0.012 0.012 0.048 0.018Warming 0.921 0.920 0.601 0.315 0.158 0.928 0.565CO2 × warming 0.005 0.993 0.364 0.009 0.024 0.942 0.072Time (T) <0.001 <0.001 0.040 0.012 <0.001 0.003 <0.001CO2 × T 0.394 0.332 0.058 0.792 0.008 0.026 0.003Warming × T 0.052 0.383 0.414 0.146 0.203 0.017 0.232

S

tWpr(oNaaihr

FCs

CO2 × warming × T <0.001 0.758 0.158

MBC: soil microbial biomass carbon; PNR: potential nitrification rate.

reatment, the soil pH did not significantly change under CE andA treatments, however a significant decrease of 0.58 and 0.77

H units was found under the CW treatment at the heading andipening stages, respectively. The variation of soil NH4

+ content2.72–10.16 mg kg−1) with rice growth stage was opposite to thatf NO3

− contents (6.45–17.79 mg kg−1) across all treatments.either elevated CO2, warming nor their combination significantlyffected soil inorganic N (NH4

+ and NO3−) in the rhizosphere at

single growth stage. Compared to CK, SMBC was significantly

ncreased at the tillering and heading stages, but the WA treatmentad no significant effect on SMBC regardless of the growth stage. Aepeated measures ANOVA showed that the interactive effects of

ig. 1. Potential nitrifying rate (PNR) in the rhizosphere of the studied soils. CK: ambient CO2 enrichment and warming of canopy air; WA: warming of canopy air. Different letteingle growth stage (p < 0.05).

0.753 0.032 0.291 0.158

CO2 enrichment and warming were significant on pH and SMBC,but not on NH4

+ or NO3− contents, whereas CO2 enrichment

alone only significantly affected SMBC (Table 2). Although soilpH, SMBC, NH4

+ and NO3− contents significantly differed among

growth stages, the effects of CO2 enrichment, warming and theirinteraction did not depend on the growth stage. The potentialnitrification rate ranged from 0.68 (WA, ripening) to 3.75 (CE,tillering) mg NO2

−–N kg−1 soil h−1 across all treatments andgrowth stages, with the highest values at the tillering stage then

a sharp declined through later stages (Fig. 1). The PNR was alsosignificantly influenced by CO2 enrichment, growing stage andtheir interaction (p < 0.05). Potential nitrification rate increased

O2 and ambient temperature; CE: atmospheric CO2 enrichment; CW: atmosphericrs above the columns indicate significant differences among treatments within a

92 Y. Liu et al. / Applied Soil Ecology 96 (2015) 88–98

Table 3Ammonia oxidizing archaea (AOA) and bacteria (AOB) gene abundance and Shannon index (H′) of the studied soils under different climate change treatments.

Stage Treatment Abundance Shannon index (H′)

AOA (×107) AOB (×107) AOA AOB

Tillering CK 1.21 ± 0.39a 0.48 ± 0.18a 0.74 ± 0.03b 1.19 ± 0.04cCE 1.24 ± 0.56a 0.49 ± 0.20a 0.85 ± 0.01a 1.29 ± 0.05bCW 0.95 ± 0.09a 0.53 ± 0.12a 0.83 ± 0.05a 1.32 ± 0.06bWA 0.66 ± 0.07a 0.43 ± 0.20a 0.78 ± 0.06ab 1.44 ± 0.03a

Heading CK 2.07 ± 0.15b 0.82 ± 0.13b 0.98 ± 0.02a 1.24 ± 0.02bCE 5.72 ± 0.45a 2.02 ± 0.15a 1.00 ± 0.03a 1.37 ± 0.05aCW 2.19 ± 0.75b 1.25 ± 0.55ab 0.99 ± 0.04a 1.38 ± 0.03aWA 3.00 ± 0.89b 1.38 ± 0.60ab 0.96 ± 0.03a 1.43 ± 0.05a

Ripening CK 1.66 ± 0.81b 0.50 ± 0.05b 0.83 ± 0.01c 1.28 ± 0.11aCE 3.80 ± 0.56a 1.24 ± 0.15a 1.00 ± 0.04a 1.33 ± 0.05aCW 1.86 ± 0.79b 0.96 ± 0.11a 0.99 ± 0.04a 1.30 ± 0.13aWA 2.12 ± 1.23b 0.60 ± 0.24b 0.91 ± 0.03b 1.37 ± 0.03a

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As shown in Table 4, soil pH was negatively related to bothAOA and AOB abundance in the rhizosphere. NH4

+ concentrationwas positively correlated only to AOB abundance. PNR was signifi-cantly positively correlated with both soil pH and SMBC across all

Table 4Correlation coefficients of soil properties, potential nitrification rate (PNR) and abun-dance of ammonia oxidizing archaea (AOA) and bacteria (AOB).

pH NH4+ NO3

− SMBC PNR

AOA −0.730** −0.402 −0.083 0.110 −0.358

K: ambient CO2 and ambient temperature; CE: atmospheric CO2 enrichment; CWata were presented as means of three replicates ± standard error; different letters wrowth stage (p < 0.05).

ignificantly under both CE and CW treatments at all growthtages, but at the heading stage only under WA treatment.

.2. Abundance of AOA and AOB

The archaeal amoA gene abundance under all treatmentsanged from 6.58 × 106 (WA, tillering) to 5.72 × 107 (CE, heading)opies g−1 dry soil weight, and was greater than the bacterial amoAene abundance, which ranged from 4.28 × 106 (WA, tillering) to.02 × 107 (CE, heading) copies g−1 dry soil weight, across the threerowth stages (Table 3). Elevated CO2 alone, rice growth stage andheir interaction significantly affected the abundances of both AOAnd AOB (Table 2). The variation in AOA with the rice growth stagesollowed a similar trend to AOB abundance. Both AOA and AOBenes exhibited the highest abundance at the heading stage, andere slightly reduced at the ripening stage. In this study, no sig-ificant changes in gene abundance of AOA and AOB with climatehange treatments were observed at rice initial growth stage. How-ver, both AOA and AOB abundance were significantly increasednder CE treatment at the heading and ripening stages. When abun-ances under the CE treatment were compared to under the CKreatment, archaeal amoA gene counts was increased by 176% and29% and bacterial amoA gene counts by 148% and 149% at theeading and ripening stages, respectively. Whereas, WA and CWreatments had no significant effects on AOA and AOB abundance.

.3. Community structure and diversity of AOA and AOB

The AOA and AOB community structures were analyzed using-RFLP fingerprints. T-RFLP fingerprinting of archaeal amoA genesevealed that the 59 and 298 bp TRFs were dominant in all treat-ents, with their relative abundances ranging from 89.2 to 94.3%

Fig. 2A). The relative abundance of the 59 bp TRF was decreased,hile that of the 298 bp TRF increased under CE, CW and WA treat-ents. The relative abundances of 89, 196 and 283 bp TRFs were

elatively low in all treatments. Six main TRFs of bacterial amoAenes were detected from all soil samples, and significant differ-nces in relative abundance of 64 and 116 bp TRFs were observedmong all treatments. The relative abundance of the 64 bp TRFas lowest under CK treatment and highest under CW treatment

Fig. 2B), whereas the relative abundance of the 116 bp TRF showedhe opposite response to CW treatment. The effect of climate

hange on the relative abundance of AOB TRFs was insignificantor 62 bp, 97 bp, 128 bp and 133 bp TRFs.

Principal component analysis (PCA) of the T-RFLP profiles ofOA and AOB at the three growth stages yielded summaries of

spheric CO2 enrichment and warming of canopy air; WA: warming of canopy air. the same column indicate significant differences among treatments within a single

data, as 72.5% for AOA and 64.2% for AOB of the total variabilitywas explained respectively by PC1 and PC2 (Fig. 3). PCA analysisshowed no clear differences in AOA and AOB communities amongclimate change treatments at a single growth stage. In addition,both AOA and AOB community profiles were similar both betweenthe treatments and across the growing stages. Data of the commu-nity diversity is presented in Table 3. Compared to CK, the diversityof AOA significantly increased under CE, CW and WA treatmentsat tillering and ripening stages but no change at heading stage.Unlike AOA, the increase in AOB diversity under all climate changetreatments was seen at the tillering and heading stages.

3.4. Phylogenetic analysis of AOA and AOB

A total of 103 archaeal and 106 bacterial amoA gene clonesequences were achieved based on the clone library. Cloning andsequencing of AOA amoA genes showed that all sequences wereaffiliated with soil and sediment lineage (Fig. 4). Soil cluster 1 and 3were detected across all treatments with cluster 1 dominating. Theclones of 59 and 196 bp TRFs were assigned to soil cluster 1, whilethose of the 298 bp TRF were assigned to soil cluster 3. Phyloge-netic analysis revealed that all AOB sequences were affiliated withNitrosospira cluster 3a, cluster 3b and cluster 4 (Fig. 5). The 62 bp,64 bp, 97 bp, and 133 bp TRFs were detected in AOB sequences. Theclones of 62 bp and 64 bp were assigned to cluster 3a and 3b, whilethose of 133 bp and 97 bpTRF were assigned to cluster 3b and 4,respectively. In addition, the AOB community was dominated bycluster 3b (62%).

3.5. Correlation analysis

AOB −0.683* −0.565* −0.247 −0.013 −0.576*PNR 0.521* 0.864** 0.001 0.544* –

SMBC: soil microbial biomass carbon; PNR: potential nitrification rate.Significance at p < 0.05 and p < 0.01, respectively

Y. Liu et al. / Applied Soil Ecology 96 (2015) 88–98 93

F r diffa anopym

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ig. 2. Relative abundance of TRFs for AOA (A) and AOB (B) in the paddy soil undetmospheric CO2 enrichment; CW: atmospheric CO2 enrichment and warming of ceans.

ice growth stages and negatively correlated to AOB abundance.DA was conducted to determine the correlation of soil propertiesith AOA and AOB community structure Fig. S1). Soil pH, NH4

+ andMBC were significantly correlated with AOB structure throughouthe rice growing stages. Significant correlations were also observedetween the AOA community structure and SMBC and soil pHhroughout the rice growing stages.

Supplementary Fig. S1 related to this article can be found, in thenline version, at http://dx.doi.org/10.1016/j.apsoil.2015.06.006

. Discussion

.1. Responses of AOA and AOB communities to elevated CO2 andarming

It is generally accepted that carbon assimilation could benhanced under CO2 enrichment (Ainsworth and Long, 2005). Rootiomass and tiller number of wild rice were reported to signifi-antly increase under elevated CO2 (Inubushi et al., 2003; Kim et al.,

003). Accordingly, increased carbon input in the rhizosphere soilhough rice root exudates and rhizodeposition was observed underlevated CO2 (Bhattacharyya et al., 2013; Okubo et al., 2014). Asicrobial communities in soil can be generally mediated by carbon

erent climate change treatments. CK: ambient CO2 and ambient temperature; CE: air; WA: warming of canopy air. The error bars indicate the standard error of the

substrate availability, an increase in below-ground carbon inputcould bring changes in soil microbial biomass under climate changeconditions (Rakshit et al., 2012). Our study showed an increase inSMBC in paddy soils under elevated CO2, possibly attributed toincreased flux of rhizodeposition, root exudates, secretions, etc.in the rhizosphere. Such increase was also observed in laboratoryincubation with elevated CO2 and temperature treatment in ricesoil (Das et al., 2011). Autotrophic nitrifiers, potentially transfor-ming C–CO2 into organic C using the energy liberated by ammoniaoxidation, could be stimulated by increases in available C substrates(Marsh et al., 2005; Hatzenpichler, 2012). As shown in Table 1, ele-vated CO2 significantly increased the AOA and AOB abundance atthe heading and ripening stages, whereas much higher SMBC wasfound under both CE and CW treatments across all growth stages.However, gene abundance of AOB was seen increased in forestecosystems under elevated CO2 but no change in AOA was observed(Lesaulnier et al., 2008; Long et al., 2012). Soil AOA abundancewas more closely correlated with soil levels of small molecularorganic matter than with levels of inorganic N in a forest ecosystem

(Stopnisek et al., 2010). On the other hand, potential increases insoil C/N ratio, could bring about an increase in N immobilization anda reduction in pools of available N (Cardon et al., 2001; Austin et al.,2009). At the SoyFACE site of Champaign, USA, elevated CO2 had no

94 Y. Liu et al. / Applied Soil Ecology 96 (2015) 88–98

F d AOBH 2 and

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ig. 3. Principal component analysis (PCA) analysis of T-RFLP patterns of AOA (A) aneading stage (gray); Ripening stage (black). The symbols are as follows: ambient COO2 enrichment and warming canopy air (CW), circles; warming canopy air (WA), d

ignificant effect on AOA or AOB abundance in the rhizosphere ofaize or soybean (Nelson et al., 2010).In our study, the WA treatment (warming of canopy air only)

id not affect the abundance or community structure of AOA andOB at any growth stages. This could be due to not significanthanges in carbon input as WA treatment had no significant effectn rice biomass production, and even reduced grain yield (dataot shown). In this study, with artificial infrared light heating overhe rice paddy ring, the canopy air temperature was continuouslyept 2 ◦C (1.6–1.8 ◦C on average across the growing period) higherver the ambient temperature, though air temperature did varyiurnally. The 2 ◦C warming of rice canopy air had caused a small

ncrease in soil temperature (<1 ◦C), which was in the range ofaily/seasonal fluctuations (Liu et al., 2014). In grass chambersith a similar warming treatment of 3 ◦C over ambient, Malchair

t al. (2010a) demonstrated no warming influence on AOB richnessr community composition after 14–28 months treatment. How-

ver, warming could modify microbial abundance and communityomposition through changes in soil temperature and moistureegimes (Barnard et al., 2005). Under experimental warming inon-irrigated soils, there could be joint changes in soil moisture

(B) gene fragments in the rhizosphere from the study soils. Tillering stage (white);temperature (CK), squares; atmosphere CO2 enrichment (CE), triangles; atmospherends. The error bars indicate the standard error of the means.

content and substrate availability, which could confound the directtemperature effects on ammonia oxidizers compositions (Allisonand Treseder, 2008).

Warming mitigated the increased abundance of AOA and AOBobserved under CE treatment, resulting in similar gene abundanceunder CW compared to under CK treatment across all growthstages. This points to a confounding interaction between CO2enrichment and warming on the ammonia oxidizer communities.In other words, the general increase in abundance of both AOA andAOB observed under CE treatment was potentially offset by theabiotic effect of air warming, though the direct impact of warm-ing on soil temperature could be much smaller (Malchair et al.,2010a). Likewise in wheat crop soil, the abiotic impact of short termdrought has also been demonstrated to neutralize the increase inabundances of AOA and AOB observed under elevated CO2, whichalone stimulated plant production and soil carbohydrate pool (Wallet al., 2006). This is in contrast to the finding by Luo et al. (2008) who

demonstrated a two-way positive interaction between CO2 enrich-ment and warming effects on net primary production (NPP) andheterotrophic respiration (Rh) of ecosystems, though the interac-tion was inconsistent across American climate zones. These results

Y. Liu et al. / Applied Soil Ecology 96 (2015) 88–98 95

Fig. 4. Phylogenetic tree of representative archaeal amoA sequences retrieved from the paddy soil in the ripening stage and reference sequences from GenBank. The sequencesfrom this study are shown in bold and include the information: accession number followed the T-RF length digested by Afa I determined from in silico analysis then thenumber of clones. Bootstrap values of >50% are indicated at branch points. The scale bar indicates the expected number of changes per sequence position.

Fig. 5. Phylogenetic tree of representative bacterial amoA sequences retrieved from the paddy soil in the ripening stage and reference sequences from GenBank. The sequencesfrom this study are shown in bold and include the information: accession number followed by the T-RF length digested by Hha I determined from in silico analysis then thenumber of clones. Bootstrap values of >50% are indicated at branch points. The scale bar indicates the expected number of changes per sequence position.

9 oil Eco

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6 Y. Liu et al. / Applied S

uggest that multiple climate change factors synergistically affectoil microorganisms, and that multifactor experiments in diversecosystem are necessary to predict acute microbial responses inerrestrial ecosystems to future climate change.

Soil AOA and AOB genotypes had distinct physiological charac-eristics and ecological niches (Prosser and Nicol, 2012), affectinghe overall performance and responses of the subgroups to elevatedO2 and warming. Community composition of ammonia oxidizersad been shown significantly affected by soil environmental prop-rties such as ammonia availability (Verhamme et al., 2011), soil pHNicol et al., 2008), and soil temperature and moisture (Horz et al.,004). For example, AOA and AOB community structures could beffected rather by soil niche properties (e.g., soil pH and DOC/DON)han by elevated CO2 in a temperate forest ecosystem (Long et al.,012). In contrast, altered AOB community structure after exposedo elevated CO2 in a grassland was attributed to increased root exu-ation through enhanced photosynthesis under elevated CO2 (Horzt al., 2004). In this study, simulated climate change treatmentsxerted generally insignificant or slight effects on AOA and AOBommunity structure, though redundancy analysis showed com-unity structure of AOA and AOB were primarily affected by abiotic

oil properties. Phylogenetic analyses of AOB genes identified thatitrosospira species were predominant among all the treatments,ith Nitrosospira cluster 3b dominated in all treatments (Fig. 5).ther studies have also identified Nitrosospira as the most com-on species in paddy soils (Chen et al., 2008; Wang et al., 2009). AllOA sequences identified were affiliated with the soil and sediment

ineage, and none was affiliated with the marine lineage, suggest-ng that AOA in the tested soils have a similar origin with soil andediment sequences. So were those identified in a grassland (Shent al., 2011). Despite of possible coexistence of soil/sediment andarine lineages, the soil/sediment lineage tends to dominate AOA

n agricultural soils (He et al., 2007; Shen et al., 2008). Recently,he predominance of Nitrosospira or Nitrosospira-like species in theitrifier community genotypes from forest soil was linked to lowoil pH and high C/N ratios under elevated CO2 (Long et al., 2012).oil pH has been often considered a key factor affecting the com-unity structure of soil microbes, as different functional groups of

oil microbes could have selective adaptation to soil chemical reac-ion (Rousk et al., 2009) and substrate availability (ammonium anditrate) in soil (Kemmitt et al., 2006). Whereas, both rhizosphereH and N substrates here were only slightly changed with climatehange treatments in a single growth stage (Table 1).

.2. Responses of potential nitrification rate to elevated CO2 andarming

Generally, a neutral soil pH and high NH4+ availability could

acilitate microbial nitrification in aerobic soils (Prosser and Nicol,012). In this study, PNR increased significantly under CE andW (both with enriched CO2) at all three rice stages (Fig. 1) buto change in the NO3

− availability was observed. This may bettributed to the dynamic change in denitrification processes underlevated CO2. Denitrification is generally favored by high avail-bility of NO3

− as substrate and of labile carbon as energy sourcen anaerobic soil. An increase in SMBC was observed under ele-ated CO2 treatments, which provides more energy for denitrifier.hus, more NO3

− in paddy soils is reduced during denitrifica-ion process. The response of the nitrifying activity to climatehange is insufficient to explain the magnitude of soil NO3

− contenthange, therefore, the response of the denitrifying activity to cli-ate change has also to be taken into account in the interpretation

f our findings. Such an increase of PNR was greater at tillering andeading stages (by 37–46%) when flooded than at ripening stage (by7–28%) when non-flooded, suggesting that the nitrifying activityas affected by water logging and the resultant redox potential

logy 96 (2015) 88–98

(though data of redox are not provided in this study). ElevatedCO2 can stimulate the delivery of oxygen to the rice rhizosphereby increasing rice root biomass and tiller number (Inubushi et al.,2003; Kim et al., 2003; Hatzenpichler, 2012). Oxygen availabilitycan stimulate the nitrifying activity in the rice rhizosphere underflooding at tillering and heading stages, when the bulk soil wereanaerobic. In rice paddy fields, plant growth and associated soilenvironmental conditions could play a key role in the dynamics ofammonia oxidizers along rice growing stages (Fischer et al., 2013;Ke et al., 2013).

An increase in PNR of rice rhizosphere soil was found underenriched CO2 in this study. This was contrast to a minor effect ofelevated CO2 on potential nitrification in EU-forest sites where soilNH4

+ and NO3− contents were relatively low (Barnard et al., 2005).

Enhanced nitrification under elevated CO2 had been observedmostly in grassland soils, where active microbial groups or nitri-fiers could be stimulated with the increased labile C inputs (Zaket al., 1993; Carnol et al., 2002; Brown et al., 2012). In contrastto elevated CO2, warming had an insignificant effect on soil PNR.This can be explained by the minimal changes in soil temperaturedespite the 2 ◦C increase in canopy air temperature in this study.There had been a number of similar findings of either insignifi-cant (Barnard et al., 2004b; Niboyet et al., 2011) or slight changes(Malchair et al., 2010b; Larsen et al., 2011) in nitrification activitywith experimental warming.

Despite of a greater gene abundance of archaeal amoA than thebacterial counterpart, soil AOB rather than AOA functionally drovemicrobial ammonia oxidation in agricultural soils (Jia and Conrad,2009). In agreement, the AOA gene abundance was much higherthan that of AOB across all the treatments and growing stages ofrice rhizosphere, shown in Table 3 of this study. Under elevatedCO2, in particular, significant increases in nitrification activity werecoincident with significant increases in gene abundance both ofAOA and AOB, but significantly correlated only to AOB abundance.Being generally consistent with the findings by Horz et al. (2004) ina multifactorial climate change experiment, CE and CW treatmentshere exerted similar effects on PNR and AOB abundance as AOBabundance and PNR was well correlated. These findings suggesta more potent influence on PNR by AOB abundance than by AOAabundance. Nevertheless, this study indicated that altered nitrifierabundance changes as an important mechanism underpinned cli-mate change impacts in rice paddy N transformation, though thesechanges could be inconsistent among the communities and ecosys-tems. The effect of elevated CO2 alone on soil microbial activitywould be insufficient to explain the magnitude of the nitrifica-tion activity response to the ongoing climate change. Furthermore,alterations of soil environmental conditions under climate changeconditions should necessarily be considered (Arnone and Bohlen,1998; Barnard et al., 2004a). In addition, changes in ammonia oxi-dizer communities were assessed by analysis of the DNA-basedamoA gene, which did not allow a direct link to changes in nitri-fying activity in this study. Of course, this study was conducted inan open paddy field with treatment ring area of 50 m2 each, and inrelatively shot term (2 years after establishment of simulated cli-mate change conditions). With prolonging the field experiments,new and more in depth studies need to conduct, taking into accountof soil dynamics, particularly of redox, and nitrifier dynamics withamoA gene transcript abundance and composition.

5. Conclusion

Our data demonstrated distinct changes in the abundance, com-position and related activity of ammonia oxidizers in responseto elevated atmospheric CO2 and warming in a rice paddy field.Elevated simultaneous CO2 and warming positively influenced

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itrification activity in the rhizosphere throughout all the studiedice growth stages. Nitrification activity was controlled by interac-ions of soil properties and ammonia oxidizer communities, whichre potentially modified both by elevated CO2 and warming. Theolecular fingerprinting data demonstrated that AOA and AOB

esponded similarly to elevated CO2 and warming when thesereatments were applied independently with no apparent shiftsn the community structure of ammonia oxidizers. The increasedbundance of AOA and AOB observed under elevated CO2 treat-ent was mitigated by warming when paddies were exposed to

imultaneous CO2 and warming treatments. The interactive effectsf elevated CO2 and warming on ammonia oxidizers suggest theesponses of these microorganisms to a single climate change factoray be not sufficient to understand global climate change factors

ffect on N transforming microbial communities and thus on Nycling in global ecosystems. Future studies will be required focus-ng the mutual interaction of changes in soil dynamics, nitrifierommunities and nitrification activities under the simulated cli-ate change conditions in the rice paddy.

cknowledgements

The present research was funded by “State Special Fund forgro-scientific Research in the Public Interest” (Impact of cli-ate change on agricultural production of China, grant number:

00903003), and by the Priority Academic Program Developmentf Jiangsu Higher Education Institutions (PAPD). The authors arerateful for the anonymous referees for their very constructiveomments and suggestions for improving the scientific level andhe English fluency.

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