ARTICLE

Variations in bacterial communities during foliar litterdecomposition in the winter and growing seasons in analpine forest of the eastern Tibetan PlateauYeyi Zhao, Fuzhong Wu, Wanqin Yang, Bo Tan, and Wei He

Abstract: Bacterial communities are the primary engineers during litter decomposition and related materialcycling, and they can be strongly controlled by seasonal changes in temperature and other environmental factors.However, limited information is available on changes in the bacterial community from winter to the growingseason as litter decomposition proceeds in cold climates. Here, we investigated the abundance and structure ofbacterial communities using real-time quantitative PCR and denaturing gradient gel electrophoresis (DGGE)during a 2-year field study of the decomposition of litter of 4 species in the winter and growing seasons of an alpineforest of the eastern Tibetan Plateau. The abundance of the bacterial 16S rRNA gene was relatively high duringdecomposition of cypress and birch litter in the first winter, but for the other litters 16S rRNA abundance duringboth winters was significantly lower than during the following growing season. A large number of bands wereobserved on the DGGE gels, and their intensities and number from the winter samples were lower than those fromthe growing season during the 2-year decomposition experiment. Eighty-nine sequences from the bands ofbacteria that had been cut from the DGGE gels were affiliated with 10 distinct classes of bacteria and an unknowngroup. A redundancy analysis indicated that the moisture, mass loss, and elemental content (e.g., C, N, and P) of thelitter significantly affected the bacterial communities. Collectively, the results suggest that uneven seasonalchanges in climate regulate bacterial communities and other decomposers, thus affecting their contribution tolitter decomposition processes in the alpine forest.

Key words: alpine forest, bacterial community, DGGE, foliar litter decomposition, q-PCR.

Résumé : Les communautés bactériennes sont les agents principaux de la décomposition de la litière et durecyclage de matières connexes. Les variations de température saisonnières et autres facteurs environnementauxont un impact important sur ces communautés. Cependant, il existe peu d’information sur les variations subiespar la communauté bactérienne entre l’hiver et la saison de croissance, au moment où la décomposition de litières’active dans les climats froids. Dans la présente étude, nous avons examiné l’abondance et la structure decommunautés bactériennes au moyen de la PCR quantitative en temps réel et de l’électrophorèse sur gel engradient dénaturant (DGGE) dans le cadre d’une étude sur le terrain de 2 ans se penchant sur la décomposition de4 espèces de litière en hiver et lors des saisons de croissance, dans une forêt alpine a l’est du plateau tibétain. Ona noté une abondance relativement élevée de gènes d’ARNr 16S bactérien lors de la décomposition de cyprès et debouleau lors du premier hiver, mais l’abondance dans d’autres litières était significativement moindre par rapporta la saison de croissance. On a observé un grand nombre de bandes sur les gels de DGGE; or, les intensités et lenombre de bandes des échantillons hivernaux étaient inférieurs a ceux issus de la saison de croissance, tout aulong de l’expérience de décomposition de 2 ans. Quatre-vingt-neuf séquences de bandes découpées de gels deDGGE préparés avec des échantillons bactériens étaient affiliées a 10 classes de bactéries distinctes ainsi qu’a ungroupe non identifié. Une analyse de la redondance a indiqué que l’humidité, la perte de masse et le contenuélémentaire (p. ex. le C, N et P) de la litière avaient une incidence significative sur les communautés bactériennes.Dans l’ensemble, les résultats indiquent que les variations climatiques saisonnières inégales régissent les com-munautés bactériennes, affectant ainsi les processus de décomposition de la litière dans la forêt alpine. [Traduitpar la Rédaction]

Mots-clés : forêt alpine, communauté bactérienne, DGGE, décomposition de la litière foliaire, q-PCR.

Received 17 July 2015. Revision received 3 October 2015. Accepted 5 October 2015.

Y. Zhao, F. Wu, W. Yang, B. Tan, and W. He. Long-term Research Station of Alpine Forest Ecosystems, Institute of Ecology andForest, Sichuan Agricultural University, Chengdu 611130, People’s Republic of China. Collaborative Innovation Center of EcologicalSecurity in the Upper Reaches of Yangtze River, Chengdu 611130, People’s Republic of China.Corresponding author: Wanqin Yang (e-mail: scyangwq@163.com).

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Can. J. Microbiol. 62: 1–14 (2016) dx.doi.org/10.1139/cjm-2015-0448 Published at www.nrcresearchpress.com/cjm on 22 October 2015.

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IntroductionBacterial communities are the primary engineers dur-

ing litter decomposition and are important in essentialprocesses of biogeochemical cycles in terrestrial ecosys-tems (Jordan 1982; Aerts 1997; Didham 1998; Berg andMcClaugherty 2008). However, among microbial decom-poser communities, bacteria are the most sensitive tochanges in biotic and abiotic environments (Taylor et al.2002; Young and Crawford 2004; Moscatelli et al. 2005;Monson et al. 2006). Minor changes in the environmentoften have strong effects on the structure and function ofbacterial communities and on the decomposition of lit-ter. Freezing temperatures in winter and warm temper-atures in the growing season are the primary seasonalcharacteristics in cold climates; however, limited re-search has focused on seasonal changes in the bacterialcommunity with respect to litter decomposition.

The harsh environments in winter have long been con-sidered to force microorganisms into dormancy or causemicroorganism death (Uchida et al. 2005); thus, manystudies have focused on bacterial communities in thegrowing season (Dilly et al. 2004; Korkama-Rajala et al.2008; Thoms and Gleixner 2013). However, a growingnumber of cold-resistant bacterial groups were observedin winter beginning in the 1970s (Campbell et al. 2005;Zinger et al. 2009; Wang et al. 2010; Wilhelm et al. 2011).Therefore, it has been suggested that these active bacte-rial populations might play an essential role in litterdecomposition during winter.

Increasing evidence has shown that the loss of littermass primarily occurs in the snow-covered season in coldbiomes (Hobbie 1996; Aerts 1997, 2006; Konstantin 2010;Wu et al. 2013), which indicates that cold-adapted, cold-resistant, and cryophilic microorganisms play importantroles in litter decomposition in cold biomes. Recently,Zhu et al. (2013) conducted a 2-year litter decompositionexperiment along a subalpine forest gradient and dem-onstrated that the control of biological factors duringlitter decomposition in the growing season is signifi-cantly different from that in the nongrowing season.

Cold biomes at high altitudes and latitudes are oftencharacterized by obvious seasonal snow cover and freeze–thaw cycles. Baptist et al. (2010) found that microorgan-isms and seasonal freeze–thaw cycles played significantroles in litter decomposition. Moreover, the biologicalactivity of cold-adapted microorganisms has been de-tected in frozen soils (Clein and Schimel 1995), suggest-ing that litter decomposition in winter is primarilyregulated by these microbes. During the growing season,favorable temperatures and humidity levels and ade-quate nutrient supply provide conditions for the prolif-eration and survival of most decomposers and lead to adiverse community structure that functions in the de-composition of litter. Compared with other microorgan-isms (such as fungi, actinomycetes, and protozoan),bacteria often display a much stronger tolerance for cold

temperatures and represent a major component of themicrobial communities during litter decomposition inalpine regions (Neufeld et al. 2004; Mackelprang et al.2011). Briefly, bacterial communities in foliar litter mightregulate litter mass loss at different critical periods inthe growing and nongrowing seasons in cold biomes.However, limited research has focused on the relation-ships between litter mass loss and bacterial communitiesat different periods in cold biomes.

The alpine forests located along the upper reaches ofthe Yangtze River and eastern Tibetan Plateau play im-portant roles in storing fresh water, conserving soil andwater, nurturing biodiversity, regulating regional cli-mate, sequestering carbon dioxide, and providing cli-mate change indicators (Liu 2002; Yang et al. 2005, 2006,2007). In previous studies, microbial diversity has beenobserved in completely frozen soils in winter throughthe use of denaturing gradient gel electrophoresis (DGGE)and polymerase chain reaction (PCR) (Liu et al. 2010; Wanget al. 2010, 2012), and the mass loss of foliar litter inwinter was 40%–65% of the total loss for the year (Wuet al. 2013). Moreover, although significant changes havebeen observed in the diversity of bacteria in relation toclimate dynamics and freeze–thaw patterns in soil (Wanget al. 2010; Wang 2012) and although bacterial activitymay directly contribute to the decomposition of litter,these relationships have received little attention. On thebasis of previous studies, it is hypothesized that theabundance, dominant groups, and structure of bacterialcommunities in foliar litter change as litter decomposi-tion proceeds, and that these changes control the litterdecomposition at different critical periods in the grow-ing and nongrowing seasons in alpine forest ecosystems.

To test these hypotheses, a field litterbag experimentwas conducted in a Mingjiang fir (Abies faxoniana) primaryforest. DGGE and real-time quantitative polymerase chainreaction (q-PCR) were used to evaluate the changes inbacterial abundance and community structure that oc-cur during the decomposition of foliar litter of 4 differ-ent species throughout the winter and growing seasonsover 2 years. The results of this study may clarify themechanisms underlying bacterial decomposition of fo-liar litter and provide further insight into the process offoliar litter decomposition in alpine forests.

Materials and methods

Site descriptionThe study was conducted in the Bipenggou Nature Re-

serve located in Lixian County in southwestern China(102°53=–102°57=E, 31°14=–31°19=N; 2458–4619 m above sealevel), which is in the transitional area between the SichuanBasin and Tibetan Plateau (Zhu et al. 2013). The meanannual temperature of the study site is 2–4 °C, the max-imum temperature is 23 °C, and the minimum tempera-ture is –18.0 °C. The annual precipitation is approximately850 mm. The soil is classified as Cambic Umbrisol (IUSS

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Working Group WRB 2007) and has a pH of 6.2 ± 0.3(Gong et al. 2007). Snow cover remains for approximately6 months in the winter season. The sample sites werelocated in Daxuetang (102°53=E, 31°15=N; 3582 m abovesea level), which has an annual precipitation of approxi-mately 801 mm and mean annual temperature of 2.9 °C.The canopy vegetation is dominated by fir (Abies faxoniana),birch (Betula albo-sinensis), larch (Larix mastersiana), andcypress (Sabina saltuaria). The shade density is approxi-mately 0.7, and the average tree age is 130 years. The dom-inant understory plants are barberry (Berberis sargentiana),sedge (Carex spp.), fern (Cystopteris montana), sheep fescue(Festuca ovina), and azalea (Rhododendron delavayi) (Tanet al. 2014).

Foliar litter decomposition experimentIn September 2010, the fresh senescent foliar litter

from larch, fir, cypress, and birch was collected from theforest floor at the sample sites. The fresh foliar litter wasair-dried for more than 2 weeks at room temperature,the air-dried litter was weighed to determine the mois-ture content, and the initial dry mass of the litter sam-ples was determined by oven-drying (65 °C, 48 h).Samples of air-dried foliar litter were placed inside nylonmesh bags (20 cm × 20 cm; 0.055 mm mesh size nylonmesh of bags’ bottom side; 1.0 mm mesh size nylon meshof bags’ top side; 10.00 g per bag) (Keane 2008; Xia et al.2011), and a total of 240 litterbags (3 sample plots × 4 spe-cies × 4 sample dates × 5 replicates) were prepared. All ofthe litterbags with foliar litter were placed on the forestfloor under closed canopy in the sampled primary firforests on 26 October 2010, and intervals of at least 2 cmwere placed between each litterbag to avoid mutual dis-turbance upon collection (He et al. 2013; Wu et al. 2013).

On the basis of previous studies, 3 sample plots (25 m ×25 m) with similar environmental characteristics wererandomly selected within the sampled primary fir for-

ests, and the plots were separated by at least 100 m. Todetermine the characteristics of bacterial abundanceand community structure in winter and the growing sea-son during foliar litter decomposition, the litterbagswere collected on the following dates over the 2-yearexperiment on the basis of previous studies: 3 March 2011(1st winter stage, W1), 19 August 2011 (1st growing season,G1), 7 March 2012 (2nd winter, W2), and 25 August 2012(2nd growing season, G2). Five litterbags were randomlycollected from each of the sample sites on each sampledate and then immediately transported to the labora-tory in cold storage. Additionally, Thermochron iBut-ton DS1923-F5 loggers (Maxim Integrated, San Jose,California, USA) were set up on 26 October 2010 to recordthe litterbag and air temperature every 2 h (Fig. 1). Thepositive accumulated temperature and negative accumu-lated temperature were calculated, and 0 °C was consid-ered the normal threshold (Coakley et al. 1982) (Table 1).

Chemical analysesForeign materials such as roots and soil debris were

carefully removed from the litterbags. The samples weredivided into 2 parts, with one part used for the chemicalanalyses and mass loss measurements and the other partused for bacterial community analyses. The loss of foliarlitter mass was calculated as follows: M (in grams) = M0 – Mt,where M0 is the dry litter mass (in g) when the bag wasplaced at the sample site and Mt is the mass (in g) of thedry litter from the litterbag after it was removed fromthe site (Zhu et al. 2012). The litterbag foliar litter wasoven-dried at 65 °C for 48 h to a constant mass and thenground to pass through a 1 mm sieve to determine thetotal C, total N, and total P as described by Lu (1999).

DNA extraction and PCR and DGGE analysesDNA was extracted from approximately 0.3 g (dry

mass) of litter using a DNA out kit (Tiandz, Beijing,

Fig. 1. Dynamics of soil and air temperature during foliar litter decomposition from 26 October 2010 to 25 October 2012.

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China). The extracted DNA was purified through agaroseelectrophoresis using an E.Z.N.A. Gel Extraction kit(Omega Bio-Tek, Norcross, Georgia, USA). The DNA pu-rity was verified using electrophoresis on a 1% agarosegel. For our community analysis, DNA was pooled inequimolar amounts from the 5 replicates of each sample,species, and sampling date combination to reduce thenumber of samples and the variation across replicates(Stone et al. 2015).

The primer pair 341f (5=-CCTACGGGAGGCAGCCAG-3=)and 534r (5=-ATTACCGCGGCTGCTGG-3=) was selected onthe basis of the conserved sequence of the V3 region ofthe 16S rRNA gene sequence to amplify the bacterial genefragments. The purified genomic DNA was used as a tem-plate, and the 341f primer was modified with a GC-clamp:CGCCCGCCGCGCCCCGCGCCCGGCCCGCCGCCCCCGCCCC(Muyzer et al. 1993). To obtain the amplified target DNA,PCR was performed using the C1000™ Thermal Cycler(Bio-Rad, Hercules, California, USA) in 25 �L reactionsthat contained 12.5 �L of Premix Ex Taq (TaKaRa, Dalian,China), 0.4 mg·mL−1 bovine serum albumin, 200 nmol·L−1

each primer, and 1.0 �L of purified DNA (1–10 ng) as atemplate. The following conditions were used for thePCR: initial denaturation at 94 °C for 5 min; 35 cycles ofdenaturation at 94 °C for 50 s, annealing at 55 °C for 60 s,chain extension at 72 °C for 50 s; and a final extensionstep at 72 °C for 15 min. The GC-clamp product amplifi-cation was performed using a C.B.S. Denaturing GradientGel Electrophoresis System (C.B.S. Scientific Company,Inc.) according to the manufacturer’s instructions. Theelectrophoresis was performed using 10% acrylamidegels with a denaturing gradient of 35%–65%. The PCRproducts (25 �L) were used for each DGGE analysis.Acrylamide gels were run in 1× TAE buffer (40 mmol·L−1

Tris–HCl, 40 mmol·L−1 CH3COOH, and 1 mmol·L−1 EDTA;pH = 7.2) at 60 °C for 16 h at 100 V. The gels were stainedwith silver nitrate and photographed with a GS-800™Calibrated Imaging Densitometer (Bio-Rad), and the pho-tographs were then digitized with Quantity One version4.0.1 software (Bio-Rad), and the dominant DGGE bandswere excised.

Phylogenetic analysis of bacterial genesEighty-nine PCR products from the bands cut from the

DGGE gels were gel-purified and ligated into the pMD19-Tvector (TaKaRa). The ligation products were cloned intoEscherichia coli DH5� competent cells according to themanufacturer’s instructions. After the recombinant cloneswere reamplified using the vector-specific primer pairM13-47 and RV-M (TaKaRa), the identity of the positiveclones was validated through sequencing and the Na-tional Center for Biotechnology Information (NCBI) BasicLocal Alignment Search Tool (BLAST) program for eachsample date. The 16S rRNA gene sequences identifiedfrom the samples were compared with related 16S rRNAgene sequences in GenBank using the NCBI BLAST pro-gram (Altschul et al. 1990). On the basis of the nucleotideT

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sequences, the phylogenetic analysis was performedusing MEGA (molecular evolutionary genetics analysis)version 4.0 (Tamura et al. 2007), and neighbor-joiningphylogenetic trees were constructed from dissimilardistance and pairwise comparisons with the Kimura2-parameter correction with 1000 replicates to producethe bootstrap values.

Quantification of bacteria using q-PCRThe PCR-amplified products were gel-purified and

cloned as described above. The ligation products werecloned into E. coli DH5� competent cells according to themanufacturer’s instructions. After the recombinant cloneswere reamplified using the vector-specific primer pairM13-47 and RV-M (TaKaRa), the identity of the positiveclones was validated through sequencing and the NCBIBLAST program. The verified positive clones were se-lected for plasmid DNA extraction using an E.Z.N.A. Plas-mid Extraction kit (Omega Bio-Tek), and these plasmidswere used as the bacterial 16S rRNA gene standards. Aultraviolet-visible (UV-VIS) BioPhotometer (Eppendorf,Hamburg, Germany) was used to determine the concen-tration of plasmid DNA, and the number of bacterial16S rRNA gene copies was directly calculated from theconcentration of the extracted plasmid DNA (Okanoet al. 2004). Tenfold serial dilutions of a known numberof copies of the plasmid DNA that contained the se-quenced bacterial 16S rRNA gene fragments were sub-jected to q-PCR in triplicate to generate an externalstandard curve.

The bacterial 16S rRNA gene from the purified foliarlitter sample DNA was amplified by PCR using the primer

pair Eub 338 (5=-ACTCCTACGGGAGGCAGCAG-3=) andEub 518 (5=-ATTACCGCGGCTGCTGG-3=) (Lane 1991). Theq-PCR was performed in 20 �L reactions that contained12.5 �L of SsoFast™ Eva Green Supermix (Bio-Rad),0.4 mg·mL−1 bovine serum albumin, 200 nmol·L−1 eachprimer, and 1.0 �L of purified foliar litter sample DNA(1–10 ng) as a template with the CFX96™ Real-Time sys-tem (Bio-Rad). Triplicate replicates were analyzed foreach sample, and the optimized reaction conditionswere as follows: predenaturation at 98 °C for 30 s;35 cycles of denaturation at 98 °C for 10 s, annealing at54 °C for 30 s, and extension at 72 °C for 30 s. The plateswere read at 72 °C after each cycle, and the productspecificity was confirmed using a melting curve anal-ysis (65–95 °C, 0.5 °C per read with a hold time of 5 s).

Statistical analysesThe digital images of the DGGE gels were obtained and

analyzed using the Quantity One version 4.0.1 software(Bio-Rad). Densitometric data obtained from the DGGEimages were used to perform a principal componentanalysis (PCA) and multivariate redundancy analysis(RDA) with CANOCO version 4.5 for Windows (Wagenin-gen UR, Netherlands). Correlations between the bacterialabundance and foliar litter mass loss, litter elementalcomposition, and environmental factors were analyzedusing Pearson’s correlation coefficients in SPSS statisti-cal software (version 20.0, IBM, USA).

Nucleotide sequence accession numbersThe 16S rRNA gene sequences obtained in this study

were deposited in GenBank under the following acces-

Fig. 2. Changes in the number of copies per gram of dry litter of the bacterial 16S rRNA gene with foliar litter decomposition. Errorbars indicate the standard error of the mean of triplicate quantitative polymerase chain reaction reactions, and different lettersdenote significant differences between different stages. W1, 1st winter; G1, 1st growing season; W2, 2nd winter; G2, 2nd growingseason.

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sion numbers: KJ616619, KJ616622–KJ616624, KJ616640–KJ616652, KR707618–KR707684.

Results

Bacterial abundanceOver the 2-year decomposition period, the number of

bacterial 16S rRNA gene copies was 1.34 × 106 – 1.26 × 109,7.45 × 106 – 7.55 × 108, 2.99 × 107 – 6.49 × 109, and 1.92 ×108 – 1.47 × 1010 (g dry litter)–1 for larch, fir, cypress, andbirch, respectively (Fig. 2). The abundance of bacterial16S rRNA gene copies in the cypress and birch litter inwinter was the highest during W1, whereas for larch andfir litter it was highest during G1, with these values sub-sequently decreasing to a minimum during W2 for allspecies except birch, which presented its lowest abun-dance during G2. The number of bacterial 16S rRNA genecopies was significantly correlated with the total P, C/N,

C/P, and N/P in all seasons, and temperature influencedthe abundance of bacteria throughout the experiment(Table 2). In summary, the abundance of bacteria waslower in the 2nd year than the 1st year.

Diversity and dynamics of the bacterial communitySignificant differences were observed in the structure

of the bacterial community in the 4 different types offoliar litter during the decomposition process in winterand the growing seasons. The number and intensity ofthe bands were lower in winter than the growing season,and certain bands disappeared completely (Fig. 3); how-ever, only cypress and birch litter produced a greaternumber of bands in winter than in the growing stage inthe 2nd year (Table 3). In a comparison between winterand the growing seasons, the Shannon–Wiener indiceswere lower in winter and inversely related to variationsin the Simpson indices generally, but for cypress andbirch in the 2nd years, both indices were inversed

Table 2. Correlation analyses among the abundance of bac-teria, C, N, P, accumulated and average temperature.

W1 G1 W2 G2Wholeresearch

Log ratio ofbacteria

0.801** 0.892** 0.794** 0.930** 0.627**

Moisture –0.439* –0.698** –0.414 0.013 –0.361**Mass loss –0.103 0.150 0.799** –0.174 –0.479**TC –0.610** 0.383 –0.618* 0.413* 0.081TN 0.740** –0.398 0.870** –0.484* 0.230*TP –0.636** 0.722** –0.594* 0.566** –0.240*C/N –0.693** 0.477* –0.754** 0.545** –0.182C/P 0.726** –0.643** 0.665** –0.551** 0.409**N/P 0.754** –0.472* 0.825** –0.507* 0.309**PAT — — — — –0.340**NAT — — — — –0.353**Du-AT — — — — –0.339**Air — — — — –0.331**

Note: *, correlation is significant at the 0.01 level (2-tailed). **, correla-tion is significant at the 0.05 level (2-tailed). TC, total organic carbon;TN, total nitrogen; TP, total phosphorus; PAT, positive accumulatedtemperature; NAT, negative accumulated temperature; Du-AT, dura-tion average temperature; W1, 1st winter; G1, 1st growing season;W2, 2nd winter; G2, 2nd growing season.

Fig. 3. Denaturing gradient gel electrophoresis profiles of bacterial communities at different stages of foliar litter decomposition.Lanes: M, marker; W1, 1st winter; G1, 1st growing season; W2, 2nd winter; G2, 2nd growing season.

Table 3. Bacterial diversity index of foliar litter decomposition.

Species

Samplecollectionperiod

No. ofbands

Shannon–Wienerindex

Simpsonindex

Larch W1 11 2.387 0.093G1 39 3.584 0.029W2 11 2.348 0.099G2 18 2.865 0.058

Fir W1 13 2.456 0.093G1 41 3.684 0.026W2 23 3.046 0.051G2 32 3.413 0.034

Cypress W1 17 2.571 0.093G1 34 3.490 0.031W2 29 3.253 0.043G2 22 3.060 0.048

Birch W1 17 2.786 0.064G1 29 3.318 0.037W2 41 3.645 0.028G2 29 3.312 0.038

Note: W1, 1st winter; G1, 1st growing season; W2, 2nd winter;G2, 2nd growing season.

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(Table 3). To provide a more effective analysis of the bac-terial community composition at 2 different stages dur-ing foliar litter decomposition and of the DGGE bandintensity, a PCA was performed to determine the degreeof similarity among samples in the winter and growingseasons. The first 2 PCA factors accounted for 69.8% of thevariability (Fig. 4), and variations were observed in thebacterial community between the winter and growingseasons, although the communities were similar duringthe foliar litter decomposition process. Additionally, thefoliar litter species acting as substrates affected the bacte-rial community.

Phylogenetic analysisUsing the sequences from the primary bands, all of the

bacterial sequences were grouped into 10 distinct classes(Actinobacteria, Alphaproteobacteria, Bacilli, Betaproteobacteria,Deltaproteobacteria, Epsilonproteobacteria, Flavobacteria,Gammaproteobacteria, and Sphingobacteria) and an unclas-sified group on the basis of neighbor-joining phylo-genetic trees (Fig. 5). The composition of these bacterialgroups varied notably between the winter and growingseasons (Fig. 6). Gammaproteobacteria dominated in win-ter largely in the larch and cypress litters, whereasAlphaproteobacteria and Sphingobacteria dominated in the1st and 2nd growing seasons, respectively, in fir litter andwere the dominant groups in the birch litter decomposi-tion process, although their abundance was variable inthe different stages. The bacterial community structurein winter was simpler than in the growing season.

Redundancy analysisThe multivariate RDA showed that environmental fac-

tors and foliar litter elemental contents regulated thedynamics of the bacterial community structure (Fig. 7),and the primary factor varied with the different stages ofthe litter decomposition process. In the 1st winter, mois-ture correlated with Alphaproteobacteria; mass loss corre-lated with Betaproteobacteria, Gammaproteobacteria, andSphingobacteria; and C/N ratio affected Flavobacteria. In the2nd winter, moisture was the primary influencing factoron Betaproteobacteria, Flavobacteria, and Sphingobacteria,and mass loss and total C content correlated withAlphaproteobacteria and Gammaproteobacteria, respec-tively. In the 1st growing season, Actinobacteria, Bacilli,Deltaproteobacteria, and Epsilonproteobacteria were affectedby the total P content and C/P ratio; Alphaproteobacteria,Gammaproteobacteria, and Flavobacteria correlated withmoisture; and Sphingobacteria were affected by the total Ccontent. Moreover, the Bacilli, Epsilonproteobacteria, andFlavobacteria were significantly affected by the total Ncontent; moisture correlated with the Gammaproteobacteriaand Sphingobacteria; C/P and the total C correlated withActinobacteria and Alphaproteobacteria, respectively.

DiscussionTwo combined analysis methods were used here to

explore the bacterial abundance and community compo-sition of 4 litter species throughout a 2-year decomposi-tion process in an alpine forest. Relatively abundantgroups of bacteria were detected during foliar litter

Fig. 4. Principal component analysis (PCA) using denaturing gradient gel electrophoresis band intensities. Points representindividual samples with different times, and the diameter of each point is proportional to the measured Shannon–Wienerindex value. L, Larch; F, Fir; C, Cypress; B, Birch; W1, 1st winter; G1, 1st growing season; W2, 2nd winter; G2, 2nd growingseason.

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Fig. 5. Neighbor-joining phylogenetic tree based on bacterial 16S rRNA gene sequences derived from the denaturing gradient gel electrophoresis fingerprint profiles.The numbers at branch points indicate bootstrap percentages of the Kimura 2-parameter. P

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decomposition in winter, and bacterial abundance andcommunity composition were influenced by variationsin the moisture and elemental contents in the litter (C, N,and P) in both the winter and growing seasons. Theseresults support our hypothesis that the abundance, dom-inant groups, and structure of bacterial communities infoliar litter change as litter decomposition proceeds andthat these changes control the litter decomposition atdifferent critical periods in the growing and nongrowingseasons in alpine forest ecosystems.

Bacteria survive in subzero temperatures and remainactive at extreme cold temperatures (Carpenter et al.2000; Junge et al. 2002). The bacterial community is animportant part of the overall decomposer communityand actively participates in the process of foliar litterdecomposition. With continued foliar litter decomposi-tion, seasonal variations in the number of bacterial16S rRNA gene copies reflected the dynamics of bacterialabundance, and a statistical analysis of the q-PCR resultsindicated that variations in bacterial abundance wererelated to variability in environmental factors and ele-mental litter contents. The environment in winter, in-cluding freeze–thaw cycles, is particularly damaging tomicroorganisms (Walker et al. 2006) because of the for-mation of ice and reduction in soil solute concentra-tions, which result in protein denaturation, cell dehydration,and low metabolic rates (Nedwell 1999; Rodrigues andTiedje 2008). In general, low temperatures reduce micro-bial diversity and even kill certain bacteria species thathave poor cold tolerance. Therefore, bacterial abun-dance should increase from winter to the growing seasonin cold regions (Deslippe et al. 2012), and higher abun-dances of bacteria should be observed during the grow-

ing season. Although the abundance of bacteria in larchand fir litter increased from winter to the growing sea-son throughout the experiment, abundance was ob-served to decrease in the cypress and birch litters fromthe 1st winter to the growing season, which may be ex-plained by several possible underlying mechanisms.First, the lack of a snowpack during winter exposed thelitter to extreme environmental conditions (e.g., largediurnal temperature changes); therefore, the region didnot experience a relatively stable environment requiredto maintain bacterial activity in winter (Drotz et al. 2010;Bokhorst et al. 2010). Second, the foliar litter substrate onthe forest floor, which lacked the protection of the snow-pack, experienced increased mechanical disruptionfrom the freeze events and freeze–thaw cycles in thewinter, which resulted in an improved substrate envi-ronment for bacteria because of the greater availabilityof microbial substrates in the subsequent growing sea-son (Berg 2000; Groffman et al. 2001; Herrmann andWitter 2002; Hentschel et al. 2008). Third, after the 1styear of decomposition, labile constituents were decom-posed, which reduced the availability of nutrients re-quired to sustain bacterial activities in the subsequentyear (Dijkstra et al. 2011; Cotrufo et al. 2013). Fourth, theavailable nutrients decreased with the litter mass lossand might have been leached into the soil by rainfall (Wuet al. 2013); in addition, this loss may have been greaterwith the broad-leaved litter than the needle litter, whichcould explain the reduced abundance of bacteria in birchlitter in the 2nd year of this study (Neff and Asner 2001;Park and Matzner 2003; Müller et al. 2009).

The diversity indices and bacterial group variationswere direct indicators of bacterial community responses to

Fig. 6. Relative abundance of the bacterial groups as foliar litter decomposition proceeds. W1, 1st winter; G1, 1st growingseason; W2, 2nd winter; G2, 2nd growing season.

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Fig. 7. Redundancy analysis (RDA) ordination diagrams for the first 2 dimensions of the relationship with the bacterial community structure at the class level.W1, 1st winter; G1, 1st growing season; W2, 2nd winter; G2, 2nd growing season. TP, total phosphorus; TC, total carbon; TN, total nitrogen.

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the decomposition process of foliar litter. The Shannon–Wiener and Simpson indices were notably lower andhigher, respectively, in winter compared with the grow-ing season, and the values were consistent with the re-sults for bacterial abundance. Although a large numberof bacteria might have been killed and the Shannon–Wiener diversity index of the bacterial community wasnotably reduced in winter, certain bacterial speciesmight increase their predominance in the communitybecause of high tolerance for harsh environmental con-ditions (Walker et al. 2006; Wilson and Walker 2010). Inaddition, broad-leaved litter under freeze–thaw cyclescan provide additional substrates for bacterial groups inwinter, and improved environmental conditions, includ-ing sufficient moisture and mechanical disruptions ofthe litter caused by repeated freeze–thaw cycles, provideabundant substrates in the growing season and a goodenvironment for bacterial communities (Lipson and Schmidt2004); however, the low exchange of nutrients and wateravailability with longer periods of winter may present agreater stress to bacteria (Morozova and Wagner 2007).Changes in the relative abundance of the bacterial groupsduring foliar litter decomposition (Fig. 6) highlight the im-portance of understanding the relationships betweenchanges in bacterial communities and environmental fac-tors. The elemental contents, litter mass loss, and temper-ature during foliar litter decomposition might change thebacterial community structure. On the basis of a comparisonof the results with the NCBI, the bacterial groups includedActinobacteria, Alphaproteobacteria, Bacilli, Betaproteobacteria,Deltaproteobacteria, Epsilonproteobacteria, Flavobacteria,Gammaproteobacteria, and Sphingobacteria. The bands fromthe winter DGGE gels were consistent with Pseudomonas(Gammaproteobacteria) in foliar litter, which was detectedfrom alpine and glacial environments in other studies,indicating that these species can better tolerate theharsh environmental conditions during winter (Shivajiet al. 2004; Jiang et al. 2006; Zinger et al. 2009; Swan et al.2010). However, Gammaproteobacteria numbers reducedin the broad-leaf birch litter in this present study. More-over, the bacterial communities in the subsequent grow-ing season and winter were abundant, which suggeststhat the surviving and cold-active bacteria, which weremasked by the more abundant species, adjusted theirmetabolic profiles (Nedwell 1999; Schimel et al. 2007) inthe subsequent growing season and winter. The multi-variate RDA was used to illustrate the relationships be-tween the bacterial community structure and environmentalfactors during foliar litter decomposition (Fig. 7). Theelemental contents, mass loss, and moisture affected thebacterial community structure, although various pri-mary factors affected the dominant groups of bacteria. Inthe 1st winter, the mass loss and elemental contents ofthe litter were the primary factors limiting Gammaproteo-bacteria, which was the primary bacterial group. In the2nd year, the moisture and elemental contents were the

primary factors that affected the dominant groups. Inthe growing season, however, the dominant groups ofbacteria were correlated with moisture (Fig. 7).

In summary, the relatively rich abundance of bacteriadetected in winter was notable and highlighted changesin the bacterial community structure between the win-ter and growing seasons during foliar litter decompo-sition. Changes in the loss of mass, moisture, andelemental contents in foliar litter are significantly re-lated to the abundance and community composition ofbacteria. Foliar litter substrates and environmental fac-tors affected the bacterial community structure, whichmight have significance for understanding the process offoliar litter decomposition in the cold biomes. In thefuture, we should pay more attention to seasonal fluctu-ations of microorganisms and their related influencingfactors as litter decomposition proceeds in the alpineforest. In addition to that, more advanced methodsshould be used to study the microorganisms to clearlyunderstand their abundance, distribution, and functionsin litter decomposition.

AcknowledgementsWe gratefully acknowledge the reviewer and editor for

their constructive comments and suggestions. This re-search was conducted in compliance with the laws of thePeople’s Republic of China, and the study was financiallysupported by the National Natural Science Foundation ofChina (31170423 and 31270498), National Key Technolo-gies R&D in China (2011BAC09B05), Sichuan Youth Sci-tech Foundation (2012JQ0008 and 2012JQ0059), and ChinaPostdoctoral Science Foundation (2012T50782). We arevery grateful to Qiqian Wu for the nice suggestions onthe manuscript, and to Chunzi Wang for the help inlaboratory analysis work.

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