RESEARCH ARTICLE

Characterizing changes in soil bacterial community structure in response to short-term warming Jinbo Xiong1,2, Huaibo Sun1, Fei Peng3, Huayong Zhang1, Xian Xue3, Sean M. Gibbons4,5, Jack A. Gilbert4,6 & Haiyan Chu1 1

State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China; 2School of Marine Sciences, Ningbo University, Ningbo, China; 3Key Laboratory of Desert and Desertification, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, China; 4Argonne National Laboratory Biosciences Division, Argonne, IL, USA; 5Graduate Program in Biophysical Sciences, University of Chicago, Chicago, IL, USA; and 6Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA

Correspondence: Haiyan Chu, State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China. Tel.: 86 25 86881356; fax: 86 25 86881000; e-mail: [email protected] Received 5 November 2013; revised 20 December 2013; accepted 17 January 2014. DOI: 10.1111/1574-6941.12289

MICROBIOLOGY ECOLOGY

Editor: John Priscu Keywords Tibetan plateau; soil warming; bacterial community.

Abstract High altitude alpine meadows are experiencing considerably greater than average increases in soil surface temperature, potentially as a result of ongoing climate change. The effects of warming on plant productivity and soil edaphic variables have been established previously, but the influence of warming on soil microbial community structure has not been well characterized. Here, the impact of 15 months of soil warming (both +1 and +2 °C) on bacterial community structure was examined in a field experiment on a Tibetan plateau alpine meadow using bar-coded pyrosequencing. Warming significantly changed (P < 0.05) the structure of the soil bacterial community, but the alpha diversity was not dramatically affected. Changes in the abundance of the Actinobacteria and Alphaproteobacteria were found to contribute the most to differences between ambient (AT) and artificially warmed conditions. A variance partitioning analysis (VPA) showed that warming directly explained 7.15% variation in bacterial community structure, while warming-induced changes in soil edaphic and plant phenotypic properties indirectly accounted for 28.3% and 20.6% of the community variance, respectively. Interestingly, certain taxa showed an inconsistent response to the two warming treatments, for example Deltaproteobacteria showed a decreased relative abundance at +1 °C, but a return to AT control relative abundance at +2 °C. This suggests complex microbial dynamics that could result from conditional dependencies between bacterial taxa.

Introduction Climate warming has proceeded at an accelerating pace, and the global surface temperature has increased by about 0.5 °C since 1975 (Hansen et al., 1999), and recent warming has been significantly faster than anticipated (IPCC, 2007). Thus, both natural and managed ecosystems are currently facing an uncertain future. Warming can increase the quantity of soil dissolved organic carbon (Luo et al., 2001; Zhang et al., 2005) and stimulates plant photosynthesis (Niu et al., 2008). On the other hand, warming has also been shown to increase soil CO2 efflux and mineralization rates (Bergner et al., 2004; Zhou et al., FEMS Microbiol Ecol && (2014) 1–13

2012), which consequently lead to soil C loss (Conant et al., 2008). These complex responses have lead to considerable uncertainties in models describing the soil C balance under warming (Luo et al., 2001; Conant et al., 2008). Elucidating these differential and interdependent response relationships is crucial as microorganisms drive soil biogeochemical processes (Walther et al., 2002; He et al., 2012; Xiong et al., 2012a) and as such are key regulators of the magnitude and direction of terrestrial ecosystem feedback to future climate change (Singh et al., 2010). It is known that microbial communities regulate the temperature dependency of soil C turnover rates ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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(Bardgett et al., 2008). However, recent studies have observed divergent results regarding microbial temperature sensitivity in soil. For example, there are reports of dramatic shifts in microbial communities in response to warming after only a few years (Zhang et al., 2005; Flury & Gessner, 2011; Yergeau et al., 2011) and after 8 years (Zhou et al., 2012), but there is also evidence showing no significant community changes due to warming over similar timescales (Allison et al., 2010). Interestingly, there have been similarly mixed results regarding functional adaptions of microbial communities to temperature changes, with some cases showing thermal adaption (Luo et al., 2001; Rousk et al., 2012); and others not (Bergner et al., 2004; Zhou et al., 2012). Rinnan et al. (2007) suggested that it could take more than a decade to detect the initial effects of warming on soil microbial community structure, referring to the time required for gradual changes in plant biomass to drive subsequent changes in belowground community composition. In addition, no systematic studies have yet been performed on the effect of warming magnitude on microbial communities. For example, using an elevation gradient to mimic elevated warming levels, Yuan et al. (2014) found that elevated temperature did not significantly alter soil bacterial community structure. In contrast, incubation experiments showed linear response to warming, that is, increasing temperature resulted in higher relative bacterial growth (B
arcenas-Moreno et al., 2009), and compositional and functional shifts in microbial communities (Zogg et al., 1997). For these reasons, there is no consensus on what timescales these changes will occur (Clemmensen et al., 2006) and sustain (Rinnan et al., 2009), and whether microbial communities respond consistently to warming magnitude, although cumulative effects of long-term warming appeared to be more frequently observed in vulnerable alpine ecosystems (Elmendorf et al., 2012). The Tibetan plateau, the largest (2 9 106 km2) and highest (average c. 4500 m above sea level) plateau on Earth, is disproportionately threatened by climate change, due to a significantly greater rate of regional temperature increase when compared to the global average (Liu & Chen, 2000), making this area more ecologically sensitive to climate change. It is thought that climate warming in these plateau meadows may have significant biogeochemical consequences because of the huge amount of organic carbon stored in the soils (Wang et al., 2002), which, as recent studies show, can become labile due to temperature-accelerated microbial decomposition (Bronson et al., 2008; Briones et al., 2010; Na et al., 2011). There is extensive evidence that warming has critical effects on the soil nutrient supplies, greenhouse gases emission and plant phenology in this area (Liu & Chen, 2000; Klein et al., 2007; Na et al., 2011; Shi et al., 2012; Yuan et al., ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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2014). In contrast, the focus on the response of microbial communities to warming only started recently, resulting in inconsistent evidence for the microbial responses to warming (Shi et al., 2012; Zheng et al., 2012). Much of this uncertainty may be due to the divergent responses of different soil microbial taxa to warming (e.g. bacterial taxa show inconsistent changes in abundance in response to increasing temperature), as conditional dependences between organisms alter response trajectories under multiple warming scenarios. Conditional dependencies refer to interactions between taxa (i.e. indirect or direct), whose magnitudes and directions are conditionally dependent upon additional factors (e.g. other organisms, edaphic variables, temperature, etc.; Armbrecht et al., 2004; HilleRisLambers et al., 2012). Here, we hypothesize that climate warming will alter the microbial community structure by modifying the belowground microenvironment (Fierer et al., 2007; Goldfarb et al., 2011). We also hypothesize that different microbial taxa will show inconsistent responses to increasing soil temperature due to conditional dependencies between taxonomic groups (e.g. competition between a given species pair may rapidly increase/decrease above a particular environmental threshold). To test these hypotheses, we experimentally manipulated soil temperature by actively warming plots to two elevated levels (+1 and +2 °C) using infrared heaters in a free-air temperature enhancement (FATE) system for an alpine meadow on the Tibetan plateau. Microbial community structure was analyzed by 16S rRNA gene amplicon pyrosequencing and correlated to changes in plant phenotype and soil edaphic parameters (1) to evaluate how soil bacterial communities respond to warming, (2) to determine which factors are driving this response and further (3) to test for inconsistent responses of a given taxa to warming.

Materials and methods Experimental design and soil sampling

The field experiment was carried out in an alpine meadow ecosystem starting on July 1, 2010. The site was located in the Yangtze River source region, near the Beilu River research station (92°560 0300 E, 34°490 2200 N, 4635 m above sea level) on the Tibetan plateau, where the mean annual temperature (MAT) is 3.8 °C with the minimum temperature in January (27.9 °C) and maximum temperature in July (19.2 °C). Mean annual precipitation is 290.9 mm with over 95% falling during warm growing season (from May to October). The experiment was based on a randomized complete block design (n = 5, 10–50 m apart) with each block (three 2 m 9 2 m plots, spaced at least 4 m from each FEMS Microbiol Ecol && (2014) 1–13

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Bacterial community response to warming

other to avoid cross-contamination) containing three treatments: ambient (AT), elevated temperature +1 °C (ET1) and +2 °C (ET2). A single infrared heater (165 9 15 cm; Kalglo Electronics, Bethlehem, PA) was suspended 1.5 m above the ground in each warmed plot; heater radiation output in ET1 was 130 and 150 W m2 in ET2. Reflector surfaces of the heaters were adjusted so as to generate evenly distributed radiant input to soil surface (Kimball, 2005). The control plot had a dummy heater to mimic the shading effects. All the sensors were connected to a CR1000 (Campbell Scientific Inc) data logger. The outputs of heater radiation were automatically regulated to maintain elevated 1 and 2 °C, respectively. On September 30, 2011, 15 months (< 2 growing seasons) following the start of the experiment, soil columns were collected (5 cm in diameter and 15 cm in depth). In each of the 15 plots (three treatments 9 five biological replicates), samples were collected from four representative points and composited as a single replicate sample. The soil samples were kept in wet ice and shipped within 48 h to the laboratory. Visible plant roots and residues were removed, and then the soil was sieved through 2mm mesh. Subsamples were temporarily archived at 4 °C for subsequently soil geochemical characterization and at 80 °C for gDNA extraction. Soil biogeochemical analyses

Soil pH values were measured using a pH probe (Starter 2100; Ohaus) with 1 : 2.5 (w/v) of soil to deionized water, and moisture was measured gravimetrically (ovendry, 65 °C for 48 h). Soil total organic carbon (TOC) and nitrogen (TN) contents were determined by combustion (Muti N/C 3100, Jena, Germany). Labile carbon (LC), ammonium, and nitrate were extracted by adding 50 mL of 1 M KCl to 10 g fresh soil, shaking for 1 h, fil tering through filter paper. NHþ 4 and NO3 were determined by an automated procedure (Skalar SANplus segmented flow analyzer); LC was detected with a CN Analyzer (Vario Max CN; Elementar, Germany). At the center of each plot, a Model HMP45C probe was connected to a CR1000 data logger (Campbell Scientific, Inc) to automatically monitor soil temperature every 10 min at a depth of 10 cm, and at 5 cm aboveground. These data were used to calculate daily temperature averages. We also measured plant height, coverage and aboveground biomass variables as described previously (Dai et al., 2009). DNA extraction and purification

Soil DNA was extracted using a FastDNA Spin Kit (Bio 101; Carlsbad, CA) from 0.5 g of wet soil according to FEMS Microbiol Ecol && (2014) 1–13

the manufacturer’s protocol. The crude DNA was purified using a 1% (w/v) low melting point agarose gel in TAE buffer to obtain genomic DNA. The DNA bands were excised and then extracted using an Agarose Gel DNA purification kit (TaKaRa). Purified genomic DNA extracts were quantified with a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE) and stored at 20 °C until use. Bacterial 16S rRNA gene amplification and 454 sequencing

An aliquot (50 ng) of purified DNA from each sample was used as template for PCR amplification. The V4–V5 hypervariable region of bacterial 16S rRNA gene (Biddle et al., 2008) was amplified using the primer set: F515: GTGCCAGCMGCCGCGG with the Roche 454 ‘A’ pyrosequencing adapter and a unique 7-bp barcode sequence (Supporting Information, Table S1), and R907: CCGTCAATTCMTTTRAGTTT with the Roche 454 ‘B’ sequencing adapter at the 50 -end of each primer. Each sample was amplified in triplicate (50 lL reactions) with a specific barcode primer under the following conditions: 30 cycles of denaturation at 94 °C for 30 s, annealing at 55 °C for 30 s, and extension at 72 °C for 30 s; with a final extension at 72 °C for 10 min. PCR products were pooled together and purified using a PCR fragment purification kit (TaKaRa). The purified PCR products were quantified using the Quant-ItTM PicoGreen kit (Invitrogen, Carlsbad, CA). An equimolar amount of PCR products (assuming the same size of amplicons had similar molar mass) for each sample was combined in a single tube and to be run on a Roche FLX 454 pyrosequencing machine (Roche Diagnostics Corporation, Branford, CT), producing reads from the forward direction (F515 with barcode). Processing of pyrosequencing data

Sequencing reads were quality-filtered and chimerachecked using the Quantitative Insights Into Microbial Ecology (QIIME) workflow with minor modification (Caporaso et al., 2010a; Xiong et al., 2012b). In brief, reads outside the range of 200–400 bp (after F515 trimming) were discarded, homopolymer runs of length exceeding six were removed by PyroNoise (Quince et al., 2009), and sequences with the same barcode were sorted into the same sample (Caporaso et al., 2010a). Bacterial phylotypes were identified using UCLUST (Edgar, 2010) and assigned to operational taxonomic units (OTUs, 97% similarity). Representative sequences from each phylotype were aligned using PYNAST (Caporaso et al., 2010b), and the most abundant sequence in each OTU cluster was ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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selected as the representative sequence. Taxonomic identity of each phylotype was determined using the Greengenes database (DeSantis et al., 2006). The sequence data generated in this study were deposited in the DDBJ (http://www.ddbj.nig.ac.jp/) Sequence Read Archive under the project number DRA000983. To correct for varying sampling efforts, we used a randomly selected subset of 3300 sequences per sample (corresponding to the smallest sequencing effort for any of the samples), to calculate distances between samples.

subset of variables was used to construct abiotic property matrix for the VPA, that is, based on the set of significant variables in each variable category (soil geochemical parameters, and plant variables), variation in bacterial community structure (dissimilarities between samples) was partitioned into the pure effects of those factors and into their interactions (Legendre & Legendre, 1998) using unbiased estimators of the fractions (Peres-Neto et al., 2006). Changes were considered significant at a P ≤ 0.05 baseline.

Statistical analysis

Results

Our experimental design was paired, consisting of FATE plot with adjacent AT plot to diminish high heterogeneity of the sampling sites by only comparing adjacent plots, which maximize the statistical power to detect subtle difference between treatments (Yergeau et al., 2011). Therefore, paired t-test was employed to compare each FATE plot with the directly adjacent AT plot as a global test of the difference between FATE and AT plots, and a oneway ANOVA was used to test for significant differences across the treatments in SPSS 13.0. The effects of elevated temperature (ET) on abundance of a given phylotype were analyzed by computing the response ratio (RR). In pffiffiffiffiffi brief, the 95% confidence interval = rri  1.96 9 Vi , where rri ¼ ln ðxi =yi Þ ði ¼ 1    nÞ, x is the mean of OTUs reads number at AT samples, y is the mean of OTUs reads number at ET1 or ET2 samples; the variance s2 s2 (vi) is, vi ¼ mxxix2 þ myyiy2 ði ¼ 1    nÞ, where s is the SD i i i i of OTU i in AT samples and in ET1 or ET2, m is the number of OTU i in AT samples and in ET1 or ET2 (Luo et al., 2006). SIMPER (Similarity Percentage) was applied to assess which taxa are primarily responsible for the observed differences between groups of samples using PAST (Clarke, 1993; Hammer et al., 2001). The following analyses were implemented in R v.2.11.0 (using the vegan package; R Development Core Team, 2011): Nonmetric multidimensional scaling (NMDS) was used to evaluate the overall differences of microbial community structure, based on pairwise Jaccard distances (Legendre & Legendre, 1998). The soil and plant variables were standardized to have means equal to zero, and standard deviations equal to 1. An OTU abundance matrix was used to calculate the Jaccard index, which gives an estimate of bacterial community dissimilarity between samples, while similarity matrices of environmental variables were calculated using Euclidean distance (Legendre & Legendre, 1998). To reduce multicollinearity, Mantel test and the BioEnv procedure were used to identify the combination of environmental factors (except for elevated temperature) that contributed the strongest correlation with microbial community variation (Clarke & Ainsworth, 1993). The same ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

Effect of elevated temperature on soil and plant properties

The warming treatments showed significant (P < 0.05) MAT increases of 1.12 and 2.42 °C (for the ET1 and ET2 plots, respectively) at a soil depth of 10 cm. Ground surface temperatures (5 cm aboveground) also increased, on average, 1.92 and 3.15 °C from July 1, 2010 to July 1, 2011, respectively, relative to the AT control. No signifi cant differences for TOC, NHþ 4 , TN, NO3 and C/N ratios were found between treatments (Table 1). However, soil moisture significantly decreased, and pH significantly increased in both warming treatments (Table 1). Plant height and biomass were significantly greater under ET2 relative to AT, and ET1 (P < 0.05, one-way ANOVA), but changes were not detected for ET1 vs. AT (Table 1). Table 1. Summary of the soil and plant variables (includes plant height, coverage and aboveground dry weight biomass per square meter), and mean annual temperature (MAT) at a depth of 10 cm Parameter TOC (%) TN (%) C/N LC (g kg1) NHþ 4 -N (mg kg1) NO 3 -N (mg kg1) Moisture pH Height (cm) Coverage (%) Biomass (g m2) MAT (°C)

AT 8.75 0.70 12.3 2.18 8.21

ET1     

4.59a 0.34a 0.68a 1.00a 1.58a

1.69  0.78a 16.02 7.91 2.93 73.0 179.7 1.12

     

7.20a 0.05c 1.10b 28.7a 48.4b 0.31c

7.99 0.73 11.0 1.89 8.40

ET2     

3.51a 0.34a 0.59a 0.91ab 1.16a

1.50  0.28a 14.47 8.13 3.66 77.0 205.0 2.24

     

7.2b 0.09b 0.30b 12.7a 7.8b 0.31b

7.81 0.66 11.8 1.76 8.51

    

2.86a 0.24a 0.76a 0.67b 0.84a

1.72  0.66a 14.46 8.27 5.72 79.9 272.8 3.54

     

6.1b 0.07a 0.94a 14.2a 31.0a 0.33a

AT, ambient; ET1, elevated temperature 1 °C; ET2, elevated temperature 2 °C; TOC, total organic carbon; TN, total nitrogen; LC, labile carbon. The values in bracket are standard deviation (N = 5). Different small letters in a given parameter indicate significant difference (P < 0.05) between treatments based on one-way ANOVA, followed with Tukey test.

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Bacterial community structure and diversity response to elevated temperature

Across all soil samples, we obtained in total of 72 383 sequences after quality control, with 3397–7688 sequences per sample (mean = 4825), of which 83.7% could be taxonomically classified. The five replicates in each treatment showed extremely similar structure of the microbial community (Fig. 1), which suggests that the application of the 454-pyrosequencing to amplicon analysis is quite reproducible, despite prior results showing the opposite (Zhou et al., 2011). A total of 6356 OTUs were detected in at least two of the three treatments (AT, ET1 or ET2), and a core microbiome of 797 OTUs was detected across AT, ET1 and ET2, while a considerable portion were unique to each treatment (Table S2). Overall, the bacterial community structure was markedly different between AT, ET1 and ET2 (Figs 1 and S1) and was significantly different between AT and ET2 (P = 0.043), but not between AT and ET1 (P = 0.069), ET1 and ET2 (P = 0.110). However, the overall community diversity [measured by species richness, Shannon diversity, Phylogenetic diversity (calculated according to Faith, 1992)] and evenness did not change significantly between treatments (Table S2). The abundant community (> 10 reads per OTU) remained unchanged, while the composition of the rare community (< 10 reads per OTU) changed significantly between treatments (Fig. S2a and b). However, this is not surprising when we consider that these rarer taxa were probably not sufficiently covered by the existing sequencing effort, leading to stochastic occurrence in the dataset. In addition, there was no clearly block effects on bacterial communities (Fig. S2c).

Changes in the abundance of Alphaproteobacteria and Actinobacteria were primarily responsible for the community structural differences between treatments

The top 20 OTUs that contributed the most difference to bacterial community structure between treatments accounted for 8.0% and 7.9% dissimilarities between AT and ET1, AT and ET2, respectively (Table 2). These OTUs were mainly derived from Alphaproteobacteria and Actinobacteria, and 13 of them (65%) were shared across all treatments (Table 2), suggesting that those taxa may be good indicators for the response of the Tibetan plateau meadows to warming. Changes in LC concentration were significantly correlated with the relative abundances of Actinobacteria, Alphaproteobacteria and other dominant phyla (Fig. S3). Additionally, the average ratio of the abundances of Alphaproteobacteria-to-Acidobacteria increased from 3.2 (AT) to 3.6 and 4.3 for ET1 and ET2, respectively (significant for AT vs. ET2, P = 0.037). The abundances of phyla and subphyla-level taxonomic groups divergently changed in response to elevated temperature

Several phyla showed inconsistent responses to warming, for example, compared with AT their abundances deceased at ET1, but then increased, or returned to a relative abundance similar to AT, in the ET2 treatment (Fig. 2). In addition, specific populations showed changes in abundance due to elevated temperature at Class or lower taxonomic levels, even though some of the parent Phyla did not show a significant response. For example, the relative abundance of the phylum Bacteroidetes was not significantly altered by elevated temperature, but nearly all OTUs in the Family Sphingobacteria had a significantly lower abundance in respond to elevated temperatures (Fig. 3). Additionally, while Acidobacteria did significantly respond to the temperature increase, different subphyla classifications responded divergently; for example the Order GP4 increased, while GP6 and GP16 demonstrated lower abundances in response to warming (Fig. 3). These results show that different taxa within a phylum can respond divergently to warming (Fig. S3). Linking bacterial community structure to soil and plant properties

Fig. 1. NMDS of the bacterial communities based on Jaccard distances and symbols coded by treatments.

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Bacterial community abundance was significantly correlated with soil edaphic properties (P = 0.001), but not with plant phenotypic variables (P = 0.480) (Table 3). However, individual taxonomic groups showed different correlative relationships; for example the Actinobacteria, ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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Table 2. The top 20 OTUs that primarily contribute to differences in bacterial community structure between ambient (AT) and elevated temperature (ET1 or ET2) Contribution (%) OTU ID

Phylotype

AT vs. ET1

21854 Acidobacteria; Gp4 15501 Actinobacteria 1444 Actinobacteria 19978 Actinobacteria; Actinomycetales; Micrococcaceae; Arthrobacter 8651 Actinobacteria; Actinomycetales; Nocardioidaceae; Marmoricola 4745 Actinobacteria; Actinomycetales; Nocardioidaceae; Nocardioides 16342 Actinobacteria; Actinomycetales; Nocardioidaceae; Nocardioides 13372 Actinobacteria; Actinomycetales; Pseudonocardiaceae; Pseudonocardia 1330 Actinobacteria; Solirubrobacterales; Solirubrobacter 23304 Archaea; Crenarchaeota; Thermoprotei 5234 Bacteria; unclassified 12489 Bacteria; unclassified 21921 Bacteroidetes; Flavobacteria; Flavobacteriaceae; Flavobacterium 23 Firmicutes; Clostridia; Clostridiales; Veillonellaceae; Pelosinus 12928 Alphaproteobacteria 1131 Alphaproteobacteria; Burkholderiales; Comamonadaceae 18355 Alphaproteobacteria; Burkholderiales; Oxalobacteraceae 21491 Alphaproteobacteria; Rhizobiales 1743 Alphaproteobacteria; Rhizobiales 14348 Alphaproteobacteria; Rhizobiales; Hyphomicrobiaceae 7950 Alphaproteobacteria; Rhizobiales; Hyphomicrobiaceae 18691 Alphaproteobacteria; Rhizobiales; Methylobacteriaceae; Microvirga 2979 Alphaproteobacteria; Rhizobiales; Rhizobiaceae; Rhizobium 19221 Betaproteobacteria; Burkholderiales; Comamonadaceae 21864 Deltaproteobacteria; Desulfuromonadales; Geobacteraceae; Geobacter 2494 Gammaproteobacteria; Pseudomonadales; Pseudomonadaceae; Pseudomonas 8005 Gammaproteobacteria; Xanthomonadales; Pseudoxanthomonas Total contribution (%)

Alphaproteobacteria, Betaproteobacteria, Deltaproteobacteria, Clostridia and BRC1 were significantly (P < 0.05) correlated with the selected soil variables (the combination of soil TOC, TN, LC, pH and moisture), while Acidobacteria_Gp10, Flavobacteria and WS3 were significantly (P < 0.05) correlated with plant variables (plant coverage, mean height and aboveground biomass; Table 3). VPA demonstrated that the combination of selected soil (including LC, moisture TC and TN) and plant (coverage, height and aboveground biomass) properties, and warming, showed a significant (P = 0.043) correlation with the bacterial community structure (pairwise Jaccard distances between samples). These variables explained 58.8% of the observed variation, leaving 41.2% of the variation unexplained. Warming was able to independently explain 7.15% (P = 0.004) of the variation observed (3rd of the eight variables tested). Combined soil variables explained 28.3% (P = 0.003), and plant properties alone explained 20.6% (P = 0.082) of the variation (Fig. 4). The interaction between warming, soil, and plant variables was only 2.7% (Fig. 4). ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

0.623 0.262 1.182 0.274 0.420 0.343 0.277 0.388 0.361 0.346 0.582 0.270 0.296 0.293 0.297 0.498 0.284

0.356 0.293 0.381

8.0

AT vs. ET2 0.261 0.547 0.290 1.429

0.364 0.509 0.320 0.536 0.293 0.342 0.382 0.295 0.273 0.263 0.314 0.301 0.320 0.349 0.263 0.276 7.9

Discussion The short-term (15 months) warming treatment used in this experiment significantly increased subsurface (10 cm depth) soil MAT, leading to soil drying and C loss, with consistent decreases in TOC and LC at warming sites (Table 1). Aboveground plant biomass increased significantly in the warming treatments, which is consistent with previous observations in a tall-grass prairie (Luo et al., 2001), and in an adjacent alpine meadow after 2 years of ecosystem warming (Na et al., 2011). The changes in soil pH, moisture, substrate quality and plant properties (Table 1) are known to directly influence soil microorganisms (Xiong et al., 2010; Lundin et al., 2012). Consistent with some previous studies, after 8 months (Flury & Gessner, 2011), or a few years (< 3 years) of warming effects (Luo et al., 2001; Yergeau et al., 2011), our results showed that short-term warming had significant effects on the structure of soil bacterial communities. The taxa that contributed to the variations between AT and elevated temperatures (ET1 or ET2) were mainly affiliated with Actinobacteria and Alphaproteobacteria. The

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Bacterial community response to warming

(a)

(b)

Fig. 2. Averages of scaled abundances (per 3300 sequences) for the dominant phyla or class (a) and rare phyla (b) at AT, ET1 and ET2 samples. A one-way ANOVA was used to test for significant differences between the treatments. Data presented as means  standard errors (N = 5).

abundances of these phyla are known to be positively correlated with soil available C pools (Nemergut et al., 2010), and the supply of fresh C could accelerate the microbial decomposition of soil C (Fontaine et al., 2004). Thus, warming-induced changes in bacterial community may in turn modify the physiochemical environment, altering terrestrial ecosystem function and climate feedbacks (Dorrepaal et al., 2009; Singh et al., 2010). Consistently, we detected that the soil CO2 efflux rates were significantly increased under warming treatments (F. Peng, X. Xue, Q. You, X. Zhou, T. Wang, unpublished data), which may lead to significant C loss over the long run (Blankinship et al., 2011). There is ample evidence that warming strongly intensifies plant production and influences turnover of the soil nutrient pool, along with associated microenvironmental changes (Na et al., 2011; Zhou et al., 2012). These changes may lead to shifts in the structure and taxonomic FEMS Microbiol Ecol && (2014) 1–13

interdependences (co-associations) of soil microbial communities. The effect of elevated temperature may trigger both direct and indirect impacts on microbial communities. For example, genes involved in LC degradation have been shown to be significantly more abundant in warmed plots, which may lead to C loss, despite no change in TOC (Zhou et al., 2012), suggesting that warming may directly modify microbial community structure and function. However, the daily changes in soil temperature experienced in the alpine meadows of the Tibetan Plateau are actually much higher (> 5 °C) than the treatments applied in our study. This suggests that the direct effects of warming on soil microbial communities may be minor relative to potential long-term indirect effects, such as changes in soil microclimate and plant productivity (Na et al., 2011; Zhou et al., 2012). Consistent with this hypothesis, we found that warming only explained 7.15% of the total variation in bacterial community structure, ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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Fig. 3. OTUs that exhibited significant changes in abundance at an ET2 relative to AT controls. Significance was determined using RR methods (Luo et al., 2006) at a 95% confidence interval.

which was much less than soil variables at 28.3%, and plant properties at 20.6%. Therefore, it is not surprising to detect a low interaction effect (2.7%) among warming, soil and plant variables on the bacterial community structure. The unexplained variation (41.2%) may be the result of other unmeasured factors, such as root exudates. ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

Similarly, a low interaction (2.8%) between soils variables, plant and elevated CO2 has also been reported in a manipulated eCO2 experiment (He et al., 2012). Plant properties have been considered key factor in controlling the diversity and structure of microbial communities (Xiong et al., 2010). Here, 20.6% variation of the FEMS Microbiol Ecol && (2014) 1–13

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Table 3. Partial Mantel analysis of the relationship between the abundance of bacterial classes and soil or plant variables. Abiotic variables were normalized before analyses Plant,† partial soil

Soil,* partial plant Phylum All detected Acidobacteria Actinobacteria Bacteroidetes BRC1 Firmicutes Proteobacteria

WS3

Class

r

P‡

r

P

Acidobacteria_Gp10 Actinobacteria Flavobacteria BRC1_genera_incertae_sedis Clostridia Alphaproteobacteria Betaproteobacteria Deltaproteobacteria WS3_genera_incertae_sedis

0.426 0.110 0.386 0.311 0.340 0.339 0.315 0.373 0.200 0.139

0.001 0.213 0.007 0.010 0.021 0.017 0.014 0.003 0.049 0.822

0.026 0.325 0.034 0.433 0.113 0.293 0.117 0.136 0.222 0.328

0.480 0.042 0.473 0.028 0.279 0.982 0.232 0.763 0.934 0.033

Bold values indicate significant (P < 0.05). *Selected soil variables included soil total organic carbon, total nitrogen, labile carbon (LC) and moisture. † Plant variables included plant height, coverage and aboveground biomass (dry weight) per square meter. ‡ Only significantly (P < 0.05) changed phylotypes are shown.

Fig. 4. VPA of the effects of warming, soil and plant variables on the soil bacterial community structure.

bacterial community was constrained by plant variables, consistent with the notion that plants help mediate microbial responses to climate change (Singh et al., 2010; He et al., 2012). Warming stimulated plant allocation to soils might mitigate C loss by increased respiration (Thomson et al., 2010; Pritchard, 2011), resulting in a moderate decreased C pool here. However, it should be noted that soil variables may be affected by plant phenotypic properties, and the observed correlations with soil edaphic variables could be proxies for changes in the plant properties. Overall, the above analysis suggested that warming had a moderate direct impact on the microbial community structure and that soil properties were more important, perhaps via indirect warming effects on edaphic and plant community variables in this alpine meadow ecosystem.

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Warming had inconsistent effects on the abundances of different taxa. For example, the abundance of Actinobacteria, and the Alphaproteobacteria-to-Acidobacteria ratio significantly increased, but Chloroflexi and Firmicutes abundance decreased in response to warming. Previous studies have shown increases in the relative abundances of Actinobacteria and Alphaproteobacteria in warming soils (Yergeau et al., 2011). A possible reason for this is the increased availability of organic carbon in warmed soils, which may favor fast-growing (copiotrophic) microorganisms (e.g. Proteobacteria) that may outcompete oligotrophs (e.g. Acidobacteria) via species sorting (Fierer et al., 2007). Therefore, an increase in Alphaproteobacteria-to-Acidobacteria ratio could be an indicator of changing soil nutrient availability (Thomson et al., 2010). We observed an increase in the ratio of Alphaproteobacteriato-Acidobacteria under warming, as well as concomitant significant shifts in soil edaphic properties, which lend support for this theory. However, causality cannot be inferred from this experiment, that is, we cannot distinguish whether bacteria drive shifts in the physicochemical properties of the system, or whether environmental conditions shape bacterial community composition. That being said, it is likely that this interaction is bidirectional and that the relationship is highly complex. Many of the taxa in our study demonstrated inconsistent relationships to increased temperature. This may be due to the complex interaction between plants and soil bacteria (Singh et al., 2010). Specifically, at ET2, the significantly higher plant biomass may intensify competition for belowground labile nutrients, promoting oligotrophic growth (Fierer et al., 2007), such as Acidobacteria, whose abundance was significantly correlated with plant variables (Table 3). Similarly, an adjacent alpine meadow

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acted as a net source, although plant biomass increased after 2 years of warming (Na et al., 2011). In addition, there are considerable differences in nutrient responsiveness between bacterial ecotypes that cannot yet be predicted or explained (Johnson et al., 2006). Concordantly, no linear response pattern was detected for a given phylum to consistent warming. It should be noted that taxa which contributed to the differences between AT and ET1, and AT and ET2 were shared (13 in 20 OTUs) at finer level (Table 2). Future work in this area, across more comprehensive temperature gradients, will be necessary to identify the exact conditional dependences that influence these inconsistent responses in soil systems.

Conclusions A short-term (15 month) warming treatment led to rapid shifts in the structure of soil bacterial communities. Notably, the variation of bacterial community is primarily contributed by warming-induced changes in soil and plant variables, while warming directly explained 7.15% variation. We also found that the abundances of Acidobacteria and Bacteroidetes significantly decreased, while Actinobacteria rose in abundance, in response to warming. These taxa may be sensitive indicators for climate warming in this ecosystem. The significant changes in the bacterial community structure were correlated with soil CO2 efflux rates, which should result in overall C losses. We also identified inconsistent responses of certain taxa to warming, which we hypothesize results from conditional dependences between taxa in this system. Overall, soil ecosystem function and microbial community structure in Tibetan plateau region are strongly affected by climate warming. Future experimental work should focus on determining the mechanisms and magnitudes of these impacts.

Acknowledgements This work was supported by the National Natural Science Foundation of China (41071167 and 41101228), the ‘Hundred Talents Program’ of the Chinese Academy of Sciences to X. Xue and H. Chu, and partly by the US Dept. of Energy under Contract DE-AC02-06CH11357. Funding for S.M. Gibbons was provided by an EPA STAR Fellowship. The authors declare no conflict of interests.

References Allison SD, McGuire KL & Treseder KK (2010) Resistance of microbial and soil properties to warming treatment seven years after boreal fire. Soil Biol Biochem 42: 1872–1878.

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Armbrecht I, Perfecto I & Vandermeer J (2004) Enigmatic biodiversity correlations: ant diversity responds to diverse resources. Science 304: 284–286. B
arcenas-Moreno G, G
omez-Brand
on M, Rousk J & B a ath E (2009) Adaptation of soil microbial communities to temperature: comparison of fungi and bacteria in a laboratory experiment. Glob Chang Biol 15: 2950–2957. Bardgett RD, Freeman C & Ostle NJ (2008) Microbial contributions to climate change through carbon cycle feedbacks. ISME J 2: 805–814. Bergner B, Johnstone J & Treseder KK (2004) Experimental warming and burn severity alter soil CO2 flux and soil functional groups in a recently burned boreal forest. Glob Change Biol 10: 1996–2004. Biddle JF, Fitz-Gibbon S, Schuster SC, Brenchley JE & House CH (2008) Metagenomic signatures of the Peru Margin subseafloor biosphere show a genetically distinct environment. P Natl Acad Sci USA 105: 10583–10588. Blankinship JC, Niklaus PA & Hungate BA (2011) A meta-analysis of responses of soil biota to global change. Oecologia 165: 553–565. Briones MI, Garnett MH & Ineson P (2010) Soil biology and warming play a key role in the release of ‘old C’ from organic soils. Soil Biol Biochem 42: 960–967. Bronson DR, Gower ST, Tanner M, Linder S & Van Herk I (2008) Responses of soil surface CO2 flux in a boreal forest to ecosystem warming. Glob Change Biol 14: 856–867. Caporaso JG, Kuczynski J, Stombaugh J et al. (2010a) QIIME allows integration and analysis of high-throughput community sequencing data. Nat Methods 7: 335–336. Caporaso JG, Bittinger K, Bushman FD et al. (2010b) PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26: 266–267. Clarke KR (1993) Non-parametric multivariate analysis of changes in community structure. Aust J Ecol 18: 17–143. Clarke KR & Ainsworth M (1993) A method of linking multivariate community structure to environmental variables. Mar Ecol Prog Ser 92: 205–219. Clemmensen KE, Michelsen A, Jonasson S & Shaver GR (2006) Increased ectomycorrhizal fungal abundance after long-term fertilization and warming of two arctic tundra ecosystems. New Phytol 171: 391–404. Conant RT, Steinweg JM, Haddix ML, Paul EA, Plante AF & Six J (2008) Experimental warming shows that decomposition temperature sensitivity increases with soil carbon recalcitrance. Ecology 89: 2384–2391. Dai X, Jia X, Zhang W et al. (2009) Plant height–crown radius and canopy coverage–density relationships determine above-ground biomass–density relationship in stressful environments. Biol Lett 5: 571–573. DeSantis TZ, Hugenholtz P, Larsen N et al. (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72: 5069–5072.

FEMS Microbiol Ecol && (2014) 1–13

Bacterial community response to warming

Dorrepaal E, Toet S, van Logtestijn RP et al. (2009) Carbon respiration from subsurface peat accelerated by climate warming in the subarctic. Nature 460: 616–619. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26: 2460–2461. Elmendorf SC, Henry GH, Hollister RD et al. (2012) Global assessment of experimental climate warming on tundra vegetation: heterogeneity over space and time. Ecol Lett 15: 164–175. Faith DP (1992) Conservation evaluation and phylogenetic diversity. Biol Conserv 61: 1–10. Fierer N, Bradford MA & Jackson RB (2007) Toward an ecological classification of soil bacteria. Ecology 88: 1354– 1364. Flury S & Gessner MO (2011) Experimentally simulated global warming and nitrogen enrichment effects on microbial litter decomposers in a marsh. Appl Environ Microbiol 77: 803– 809. Fontaine S, Bardoux G, Abbadie L & Mariotti A (2004) Carbon input to soil may decrease soil carbon content. Ecol Lett 7: 314–320. Goldfarb KC, Karaoz U, Hanson CA et al. (2011) Differential growth responses of soil bacterial taxa to carbon substrates of varying chemical recalcitrance. Front Microbiol 2: 1–10. Hammer Ø, Harper DAT & Ryan PD (2001) PAST: paleontological statistics software package for education and data analysis. Palaeontol Electronica 4: 9. Hansen J, Ruedy R, Glascoe J & Sato M (1999) GISS analysis of surface temperature change. J Geophys Res 104: 30997– 31022. He Z, Piceno Y, Deng Y et al. (2012) The phylogenetic composition and structure of soil microbial communities shifts in response to elevated carbon dioxide. ISME J 6: 259–272. HilleRisLambers J, Adler PB, Harpole WS et al. (2012) Rethinking community assembly through the lens of coexistence theory. Annu Rev Ecol Evol Syst 43: 227–248. IPCC (2007) Climate Change 2007: The Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK. Johnson ZI, Zinser ER, Coe A et al. (2006) Niche partitioning among Prochlorococcus ecotypes along ocean-scale environmental gradients. Science 311: 1737–1740. Kimball BA (2005) Theory and performance of an infrared heater for ecosystem warming. Glob Change Biol 11: 2041– 2056. Klein JA, Harte J & Zhao X (2007) Expremental warming, not grazing, decrease rangeland quality on the Tibetan Plateau. Ecol Appl 17: 541–557. Legendre P & Legendre L (1998) Numerical Ecology. Elsevier, New York, NY. Liu X & Chen B (2000) Climatic warming in the Tibetan Plateau during recent decades. Int J Climatol 20: 1729–1742. € € Andersson AF & Lundin D, Severin I, Logue JB, Ostman O, Lindstr€ om ES (2012) Which sequencing depth is sufficient

FEMS Microbiol Ecol && (2014) 1–13

11

to describe patterns in bacterial a- and b-diversity? Environ Microbiol Rep 4: 367–372. Luo Y, Wan S, Hui D & Wallace LL (2001) Acclimatization of soil respiration to warming in a tall grass prairie. Nature 413: 622–624. Luo Y, Hui D & Zhang D (2006) Elevated CO2 stimulates net accumulations of carbon and nitrogen in land ecosystems: a meta-analysis. Ecology 87: 53–63. Na L, Wang GX, Yang Y, Gao YH & Liu GS (2011) Plant production, carbon and nitrogen source pools, are strongly intensified by experimental warming in alpine ecosystems in the Qinghai-Tibet plateau. Soil Biol Biochem 43: 942– 953. Nemergut DR, Cleveland CC, Wieder WR, Washenberger CL & Townsend AR (2010) Plot-scale manipulations of organic matter inputs to soils correlate with shifts in microbial community composition in a lowland tropical rain forest. Soil Biol Biochem 42: 2153–2160. Niu S, Li ZL, Xia J, Han Y, Wu M & Wan S (2008) Climatic warming changes plant photosynthesis and its temperature dependence in a temperate steppe of northern China. Environ Exp Bot 63: 91–101. Peres-Neto PR, Legendre P, Dray S & Borcard D (2006) Variation partitioning of species data matrices: estimation and comparison of fractions. Ecology 87: 2614–2625. Pritchard SG (2011) Soil organisms and global climate change. Plant Pathol 60: 82–99. Quince C, Lanz
en A, Curtis TP et al. (2009) Accurate determination of microbial diversity from 454 pyrosequencing data. Nat Methods 6: 639–641. R Development Core Team (2011) R: A Language and Environment for Statistical Computing. R 21 Foundation for Statistical Computing, Vienna, Austria. Rinnan R, Michelsen A, B a ath E & Jonasson S (2007) Fifteen years of climate change manipulations alter soil microbial communities in a subarctic heath ecosystem. Glob Change Biol 13: 28–39. Rinnan R, Stark S & Tolvanen A (2009) Responses of vegetation and soil microbial communities to warming and simulated herbivory in a subarctic heath. J Ecol 97: 788–800. Rousk J, Frey SD & B a ath E (2012) Temperature adaptation of bacterial communities in experimentally warmed forest soils. Glob Change Biol 18: 3252–3258. Shi F, Chen H, Chen H et al. (2012) The combined effects of warming and drying suppress CO2 and N2O emission rates in an alpine meadow of the eastern Tibetan Plateau. Ecol Res 27: 725–733. Singh BK, Bardgett RD, Smith P & Reay DS (2010) Microorganisms and climate change: terrestrial feedbacks and mitigation options. Nature 8: 779–790. Thomson B, Ostle N, McNamara N et al. (2010) Vegetation affects the relative abundances of dominant soil bacterial taxa and soil respiration rates in an upland grassland soil. Microb Ecol 59: 335–343. Walther G, Post E, Convey P et al. (2002) Ecological responses to recent climate change. Nat Rev 416: 389–395.

ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

12

Wang G, Qian J, Cheng G & Lai Y (2002) Soil organic carbon pool of grassland soils on the Qinghai-Tibetan Plateau and its global implication. Sci Total Environ 291: 207–217. Xiong J, Wu L, Tu S et al. (2010) Microbial communities and functional genes associated with soil arsenic contamination and rhizosphere of the arsenic hyper-accumulating plant Pteris vittata L. Appl Environ Microbiol 76: 7277–7284. Xiong J, He Z, Van Nostrand JD et al. (2012a) Assessing the microbial community and functional genes in a vertical soil profile with long-term arsenic contamination. PLoS ONE 7: e50507. Xiong J, Liu Y, Lin X et al. (2012b) Geographic distance and pH drive bacterial distribution in alkaline lake sediments across Tibetan Plateau. Environ Microbiol 14: 2457–2466. Yergeau E, Bokhorst S, Kang S et al. (2011) Shifts in soil microorganisms in response to warming are consistent across a range of Antarctic environments. ISME J 6: 692–702. Yuan Y, Si G, Wang J et al. (2014) Bacterial community in alpine grasslands along an altitudinal gradient on the Tibetan Plateau. FEMS Microbiol Ecol, 87: 121–132. Zhang W, Parker KM, Luo Y, Wan S, Wallace LL & Hu S (2005) Soil microbial responses to experimental warming and clipping in a tallgrass prairie. Glob Change Biol 11: 266–277. Zheng Y, Yang W, Sun X et al. (2012) Methanotrophic community structure and activity under warming and grazing of alpine meadow on the Tibetan Plateau. Appl Microbiol Biotechnol 93: 2193–2203. Zhou J, Wu L, Deng Y et al. (2011) Reproducibility and quantitation of amplicon sequencing-based detection. ISME J 5: 1303–1313.

ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

J. Xiong et al.

Zhou J, Xue K, Xie J et al. (2012) Microbial mediation of carbon-cycle feedbacks to climate warming. Nat Clim Change 2: 106–110. Zogg GP, Zak DR, Ringelberg DB et al. (1997) Compositional and functional shifts in microbial communities due to soil warming. Soil Sci Soc Am J 61: 475–481.

Supporting Information Additional Supporting Information may be found in the online version of this article: Table S1. List of soil samples and corresponding barcodes used in this study. Table S2. The numbers and percentages of OTUs overlap, uniqueness, diversity, evenness, and dominance indices ( 1 standard deviation, N = 5) for ambient (AT) and elevated temperature (ET1 and ET2) among 3300 sequences. Fig. S1. Cluster analysis using the algorithm of paired group by Jaccard similarity. Fig. S2. Nonmetric multidimensional scaling (NMDS) of the bacterial communities with the OTUs abundance values. Fig. S3. OTUs having significantly different abundances at an elevated temperature of 1 °C (ET1) determined using the response ratio methods at a 95% confidence interval.

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