deterministic processes dominate soil microbial …of microbial communities: ‘‘everything is...

Submitted 18 September 2018Accepted 8 March 2019Published 7 May 2019

Corresponding authorBaofeng Chai bfchaisxueducn

Academic editorXavier Le Roux

Additional Information andDeclarations can be found onpage 14

DOI 107717peerj6746

Copyright2019 Zhao et al

Distributed underCreative Commons CC-BY 40

OPEN ACCESS

Deterministic processes dominate soilmicrobial community assembly insubalpine coniferous forests on the LoessPlateauPengyu Zhao1 Jiabing Bao1 Xue Wang1 Yi Liu2 Cui Li2 and Baofeng Chai1

1 Institute of Loess Plateau Shanxi University Shanxi Key laboratory of Ecological Restoration of Loess PlateauTaiyuan China

2College of Resources and Environment Shanxi University of Finance and Economics Taiyuan China

ABSTRACTMicrobial community assembly is influenced by a continuum (actually the trade-off)between deterministic and stochastic processes An understanding of this ecologicalcontinuum is of great significance for drawing inferences about the effects of com-munity assembly processes on microbial community structure and function Here weinvestigated the driving forces of soil microbial community assembly in three differentenvironmental contexts located on subalpine coniferous forests of the Loess Plateauin Shanxi China The variation in null deviations and phylogenetic analysis showedthat a continuum existed between deterministic and stochastic processes in shaping themicrobial community structure but deterministic processes prevailed By integratingthe results of redundancy analysis (RDA) multiple regression tree (MRT) analysis andcorrelation analysis we found that soil organic carbon (SOC) was themain driver of thecommunity structure and diversity patterns In addition we also found that SOC had agreat influence on the community assembly processes In conclusion our results showthat deterministic processes always dominated assembly processes in shaping bacterialcommunity structure along the three habitat contexts

Subjects Ecology MicrobiologyKeywords Ecological process Community assembly Phylogenetic structure Soil microbialcommunity

INTRODUCTIONUnderstanding the fundamental ecological mechanisms that drive the assembly processesof microbial communities is a major challenge in community ecology (Shen et al 2013)particularly microbial ecology The assembly processes of the microbial community in alocal community are generally influenced by two types of ecological processes includingdeterministic and stochastic processes First deterministic factors such as organism traitsinterspecies relationships (eg competition predation mutualisms and trade-offs) andenvironmental factors (eg pH temperature salt and moisture) govern the communitystructure (Chase amp Myers 2011 Dumbrell et al 2010 Ofiţeru et al 2010) Ecologists havetraditionally appreciated that the environmental context determines the assembly processesof microbial communities lsquolsquoEverything is everywhere but the environment selectsrsquorsquo (Baas

How to cite this article Zhao P Bao J Wang X Liu Y Li C Chai B 2019 Deterministic processes dominate soil microbial communityassembly in subalpine coniferous forests on the Loess Plateau PeerJ 7e6746 httpdoiorg107717peerj6746

Becking 1934) For example environmental factors such as pH (Tripathi et al 2018)temperature (Anderson amp Laurel 2013) or nitrogen levels (Xiong et al 2016) may bemajor determinants of microbial community structure

For the other type of community assembly processes (ie stochastic processes) it isassumed that community structures are independent of organism traits and are governedby birth death colonization extinction drift and speciation (Hubbell amp Borda-de-Aacutegua2004) and it is hypothesized that species are all ecologically equivalent (Woodcock et al2007) Previous studies have confirmed that both deterministic and stochastic processesact concurrently to regulate the assembly of ecological communities (Diniandreote et al2016 Diniandreote et al 2015 Zhou amp Ning 2017) but the relative importance may varyin different environmental contexts (Tian et al 2017) This may be because the variationin ecological selection strength and the rates of dispersal on different habitat contexts caninfluence the relative importance of deterministic and stochastic processes across temporaland spatial scales in addition to within entire ecosystems (Chisholm amp Pacala 2011 Jurburget al 2017) Therefore investigation into community driving forces in different habitatscan enrich the understanding on the community assembly process

In this study soil was sampled from 23 soil plots in subalpine coniferous forests locatedon the Loess Plateau in Shanxi province China The 16S ribosomal RNA genes of bacteriawere analyzed using high-throughput sequencing To investigate the driving forces of soilmicrobial community assembly we sampled three sites having different environmentalcharacteristics Sampling was performed along three different altitudinal gradients Thisstudy can largely enrich the understanding on microbiology of subalpine mountainsOur aims were as follows (i) to quantify the relative roles of deterministic and stochasticprocesses in bacterial community dynamics for three different habitat contexts and moreprecisely (ii) to evaluate the effects of environmental factors on microbial communityassembly

MATERIAL AND METHODSSite and samplingA total of 23 soil plots were sampled (Table S1 Fig S1) in August 2016 and August 2017The sites were selected because their vegetation was subalpine mountain coniferous forestsand they were located between 1900 m and 3055 m above mean sea level (amsl) Thestudy area has a warm temperate continental monsoon climate and mostly cinnamon soil

This study focused on response patterns along environmental gradients ratherthan exploring differences among treatment groups Thus we sampled along threealtitudinal gradients without replicates Previous studies have shown that for continuousenvironmental drivers gradient designs further allow for better extrapolationcharacterization of (nonlinear) response functions and consequently quantitative outputsbetter suited for ecological models than replicated designs (Cottingham Lennon amp Brown2005)

To avoid the interference of vegetation factors we sampled plots in the singlevegetation type (ie Larix principis-rupprechtii forests) These sites located on subalpine

Zhao et al (2019) PeerJ DOI 107717peerj6746 219

ecological environments possess pronounced climatic gradients and climosequenceswithin short distances with a high level of environmental heterogeneity (Siles amp Margesin2017) Therefore the sites with different altitudinal gradients corresponded to differentenvironmental contexts and different environment characteristics

Eight plots were sampled from the Wutai Mountain site (WT) which ranges from 1900m and 3055 m amsl Ten plots were sampled from the Pangquangou Natural Reserve site(PQG) ranging from 1950 m to 2650 m amsl The last five plots were sampled from theLuya Mountain site (LY) which ranges from 2000 m and 2400 m amsl The details ofeach sample plots were added in the Supplemental Files (Table S1) At each sampling sitea 1 m times 1 m sampling plot was established in situ along the elevation gradient Five soilcores at a depth of 15 cm were taken at each sampling plot and then combined to forma single independent soil sample Then the soil samples were sealed in plastic bags andrefrigerated immediately transported to the laboratory and sieved using a 2 mm meshThe soil samples were then stored at minus80 C until further analysis

The soil samples were subsampled for molecular analysis and the DNA from of 1 g of soilwas extracted using an EZNA Soil DNA Kit (Omega Bio-tek Inc Norcross GA USA)The quality and quantity of the DNA extracts were measured using an Infinite 200 PROplate reader (TECAN Maumlnnedorf Switzerland) The DNA purity was assessed based onthe A260A280 absorbance ratios and only DNA extracts with absorbance ratios of 18sim20were used for further analyses Three DNA samples were extracted from each soil samplewhich were then combined and sequenced at Shanghai Personal Biotechnology Co Ltdon an Illumina MiSeq sequencing platform based on the bacterial v3ndashv4 hypervariableregion using bacterial 16S universal primers (341F 5prime-ACTCCTACGAGGAGCA- 3prime and805R 5prime-TTACCGCGGCTGCTGGCAC- 3prime) (Tripathi et al 2018)

Bioinformatics analysisThe sequencing data were analyzed using the QIIME pipeline (v180 httpqiimeorg)(Caporaso et al 2010) The filtered sequence alignments were denoised by DeNoiser(Reeder amp Knight 2010) and then screened for chimeras using UCHIME (Edgar et al2011) The Archaea and unknown sequences were removed The sequences were clusteredinto operational taxonomic units (OTUs) at a 97 similarity level using the averageneighbor method and taxonomy was blast to SILVA database by k-mer searching usingMOTHUR (Pruesse et al 2007) TheOTU table was rarefied to 4020 sequences per sampleTen independent maximum-likelihood phylogenetic trees based on JukesndashCantor distancewere then constructed using FastTree2 (Price Dehal amp Arkin 2009) after the removalof gaps and hypervariable regions using a Lane mask supplied by QIIME to supportphylogenetic diversity calculations

Environmental variablesIn the laboratory soil total carbon (TC) total nitrogen (TN) and total sulfur (TS) weremeasured using an elemental analyzer (Vario EL MACRO cube Elementar HanauGermany) nitrate nitrogen (NO3

minus_N) ammonium nitrogen (NH4+_N) and nitritenitrogen (NO2

minus_N) were measured by an Automated Discrete Analysis Instrument


(CleverChem 380 DeChem-Tech Hamburg Germany) After shaking the soil watersuspension (125 massvolume) for 30 mins the soil pH was measured using a pH meter(Hl 3221 Italy) The soil organic carbon in each soil sample was measured using thepotassium dichromate volumetric method (Nelson amp Sommers 1982)

Null model analysisA null model was constructed to account for changes in β-diversity while controlling forstochastic variation and associated changes in α-diversity (ie local species richness 999iterations) (Chase et al 2011) We considered the null deviation as the relative differencebetween the observed β-diversity and the null-model β-diversity (Tucker et al 2016) Assuch null deviation values may represent communities that are more similar than expectedby chance (a negative null deviation value) less similar than expected by chance (a positivenull deviation value) or close to the chance expectation (values near zero) (Tucker et al2016)

Phylogenetic analysisOur study used phylogenetic turnover between communities to infer ecological processes(Stegen et al 2015) To quantify phylogenetic turnover between communities we usedthe between community mean-nearest-taxon-distance (βMNTD) metric βMNTD wascalculated in R (R Core Team 2018) lsquocomdistntrsquo (abundanceweighted = TRUE packagelsquolsquopicantersquorsquo) Then we evaluated β-Nearest Taxon Index (βNTI) which expresses thedifference between observed βMNTD and the mean of the null distribution in units ofstandard deviations (Stegen et al 2013)

In addition to distinguish more details in the assembly processes we used the RaupndashCrick metric (Chase et al 2011) extended to incorporate speciesrsquo relative abundancesreferred to as RCbray The R script of RCbray can be found at httpsgithubcomstegenStegen_etal_ISME_2013

In a given community we estimated the relative influence of variable selection orhomogeneous selection as the fraction of their comparisons with βNTI gt +2 or βNTIlt minus2 respectively We regard the fraction of the between community comparisons with|βNTI|lt 2 and RCbray gt +095 as dispersal limitation while |βNTI|lt 2 and RCbray lt

minus095 is considered homogenizing dispersal (Diniandreote et al 2015 Stegen et al 2013Stegen et al 2015)

Network analysisThe co-occurrence network was constructed based on the Spearman correlation matrixoffered in the lsquopsychrsquo package in R In this network the nodes represent OTUs andthe edges that connect these nodes represent correlations between OTUs Only thoseconnections with correlation coefficients gt06 and P lt 005 were used in the networkThus positive correlations indicate co-occurring OTUs based on abundances whereasnegative correlations indicate that the OTUs are mutually exclusive (Barberaacuten et al 2012)P-values were false discovery rate (FDR) adjusted to control for the analysis (FDRlt 005)The network analysis was completed using the lsquoigraphrsquo package in R


Statistical analysisAll statistical analyses were performed in the R environment using the lsquoveganrsquo lsquoggplot2rsquolsquoggpubrrsquo and lsquocorrplotrsquo packages A Venn diagram was used to visualize the shared OTUsamong the sites A correlation matrix graph was used to demonstrate the correlationbetween soil physicochemical factors and was constructed using the lsquocorrplotrsquo packagesin R Multivariate regression tree analysis (MRT) was used to explain the relationshipbetween bacterial α-diversity estimates and environmental variables in a visualized treeand diversity indices were normalized to the same mean before performing MRT analysis(Ge et al 2008) Based on the longest gradient lengths from the results of detrendedcorrespondence analysis (DCA) we selected redundancy analysis (RDA) to quantify theeffects of environmental variables on microbial community composition (Mo et al 2018)Forward selection of PCNM variables based on permutation tests was chosen to identifytwo of the 23 extracted PCNM variables that significantly (P lt 005) explained the spatialstructure The PCNM eigenfunctions which represent the lsquospectral decomposition of thespatial relationship across sampling locationsrsquo can be considered as the spatial variables inthe ordination-based analysis The contributions of environmental filtering and the spacevariable (PCNM) to the variation in bacterial community composition were calculatedby using variance partitioning analysis (VPA) (CANOCO for Windows Version 50) Themantel test was performed in the R environment using the lsquoveganrsquo packages

RESULTSPhysicochemical properties of the soils from the different sitesThe soil physicochemical properties varied across the different sampling sites (Fig 1)Briefly the contents of ammonium nitrogen and nitrite nitrogen were the highest at LYsites (3691 and 016 mg kgminus1 respectively) and were lowest at WT sites (1741 and 004mg kgminus1 respectively) The contents of nitrate nitrogen (645 mg kgminus1) SOC (7029 mggminus1) TC (64) and TN (051) were the highest at WT sites and were the lowest at LYsites

TN was significantly positively correlated with TC and SOC (P lt 005) and significantlynegatively correlated with pH value (P lt 005 Fig 2) TC and pH showed a significantnegative correlation (P lt 005) SOC was significantly positively correlated with nitratenitrogen (P lt 005) and significantly negatively correlated with nitrite nitrogen (P lt 005)This indicated that the sites sampled had different environment characteristics

Dynamics of bacterial community composition and diversityA total of 4258 OTUs were identified from 1062241 high-quality sequences recoveredfrom 23 soil samples Goodrsquos coverage ranged from 9519 to 9975 indicating thatthe identified sequences were representative of most of the bacterial sequences in thecollected soil samples Rarefaction curve analyses which generally yielded asymptoticcurves indicated that the numbers of sampling plots were enough Detailed informationof the sequencing results is provided in Table S2

The soil microbial community composition varied across the different sampling sites(Fig 3) There were 15 bacterial phyla with relative abundances of more than 001


ns

ns

ns

NSns

ns

nsns

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ns

NSns

TN TS

pH SOC TC

ammoniumnitrogen nitratenitrogen nitritenitrogen

LY PQG WT LY PQG WT

LY PQG WT LY PQG WT LY PQG WT


000

005

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a b c

d e f

g h

Figure 1 Bar plots indicating the soil physicochemical factors at different sites (A) Ammonium nitro-gen (B) nitrate nitrogen (C) nitrite nitrogen (D) pH (E) SOC (F) TC (G) TN (H) TS

Full-size DOI 107717peerj6746fig-1

(Fig 3A) As shown in the Venn diagram 869 bacterial shared OTUs were observedin all sampling sites There were 46 bacterial phyla identified (Fig 3B) The abundanceof Proteobacteria at all sites was the highest (mean relative abundance = 3059) andfollowed by Acidobacteria (1963) Actinobacteria (1651) and Chloroflexi (1322)Briefly the mean relative abundance of Proteobacteria was the most at PQG (3439)and that of Actinobacteria was the highest at LY (2629) The mean relative abundancesof Acidobacteria (2868) and Chloroflexi (1609) were the highest at WT There were31 bacterial families with relative abundances of more than 001 (Fig 3C) Based onthe clustering graph the sampling plots of each of the sites roughly clustered together(Fig 3D) The community α-diversity indices varied at the different sites (Fig 4) Brieflythe phylogenetic diversity (pd) and the number of observed species (sobs) were the highest


Figure 2 Correlation matrix graph indicating the correlation between soil physicochemical factorsOnly the environmental factors with significantly difference represented in the figure


at WT sites (P lt 005) There was no significant difference in the ACE index Chao indexShannon index and Simpson index at the different sites (P gt 005) This indicated that thesites sampled had different soil microbial community structure

Effects of environmental factors on microbiome dynamicsBased on the results of the DCA (axis length = 102) we used RDA to identify theabiotic environmental drivers that influenced bacterial community composition (Fig 5permutation test P lt 001) The results demonstrated that Proteobacteria Bacteroidetesand Cyanobacteria were mainly driven by pH while SOC TC and TN were the mainabiotic drivers of Parcubacteria and Planctomycetes

In the MRT analysis (Fig 6) we observed that the diversity indices (normalized)were mainly split by SOC explaining 3675 in the first spilt The correlation analysisshowed similar results SOC was significantly correlated with bacterial communities at thephylum level (eg Proteobacteria Bacteroidetes and Chloroflexi) Given its contribution toexplaining community distribution patterns SOC was further used as a descriptor for theenvironmental gradients


Figure 3 Microbial community composition and structure Relative abundance of the dominant bac-terial phyla (A) and family levels (C) across the sites Venn diagram (B) showing the shared OTUs in allplots In the heat map (D) the horizontal coordinate represents the sample name and the vertical coordi-nate represents the species name A color gradient is used to represent the proportion of species The valueon each site represent average values of sampling plots


The variation partitioning analysis showed that environmental variables (203)explained more variation of microbial community structure than spatial variables (19)This suggested that both deterministic and stochastic processes were involved in theassembly of microbial communities and that deterministic processes were dominant Theunexplained variable was 786 (Fig 7)

Nonrandom co-occurrence patterns of the microbial communityNetwork analysis was applied to explore the interspecific relationship patterns in themicrobial communities (Fig 8) Compared with the LY- and WT- network the PQG-network exhibited more edges (87) more vertices (40) more modularity (0691) higheraverage degree (435) and average clustering coefficients (0858) but less the numbers ofmodules (6) (Table S3) Strong positive correlations were observed at all sites while negativecorrelations were rare The size of the nodes corresponds to betweenness centralizationvalues


ns

ns

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Shannon Simpson Sobs

ACE Chao Pd



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2700

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a b c

d e f

Figure 4 Bacterial community diversity at the different sites (A) ACE (B) Chao (C) Pd (D) Shannon(E) Simpson (F) Sobs


The bacterial community assembly processesAccording to the nullmodel analysis our results demonstrated that the null deviation valuesvaried at different sites (ranging from 029 to 057 Fig 9A) The bacterial communitiesat WT deviated significantly from the null expected value (relative null deviation = 045)and were greater than that at LY site and PQG site (relative null deviation= 032 and 034respectively) (P lt 005)

Most importantly we observed that the microbial community was more greatly shapedby variable selection (βNTI gt +2) (Fig 9B) From LY to WT we observed a gradualincrease in the relative role of deterministic processes compared to stochastic processes(Fig 9C) Based on the regression analysis of the environmental variables with assemblyprocess parameters we found that SOC had a great influence on community assembly


TN

TC

pH

SOC

minus04

minus02

00

02

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minus10 minus05 00 05RDA1 2362

RD

A2

518

AcidobacteriaActinobacteriaBacteroidetesChloroflexiCyanobacteriaFirmicutesGemmatimonadetesLatescibacteriaNitrospiraeParcubacteriaPlanctomycetesProteobacteriaSaccharibacteriaunclassified_k__norankVerrucomicrobia

Figure 5 RDA of the bacterial communities and the response of these communities to significant soilphysicochemical properties


Figure 6 MRT of bacterial α-diversity data associated with key environmental factors (A) correlationanalysis (B) based on spearman correlation of microbial community composition and soil physico-chemical factors



Environmentalfactors

Spatialvariable

0203 0019

Residuals = 0786

Values lt0 not shown

Figure 7 Variation partitioning analysis showing the percentages of variance in bacterial communitiesexplained by environment factors and spatial variable (PCNM)


processes (Fig 9D) The mantel test between βNTI and SOC matrices indicated the similarconclusion (P lt 005 R= 0509)

DISCUSSIONCompared to LY themicrobial community atWTwasmore greatly driven by deterministicprocesses The driving effects of the deterministic processes gradually increased from LYto WT Given this we inferred that a continuum existed between deterministic andstochastic processes in the assembly of microbial communities in the study area This isconsistent with previous studies (Chase et al 2011 Tucker et al 2016 Jurburg et al 2017Tian et al 2017) which pointed out the relative importance of the two processes variedin the different environmental contexts For example in terms of plants aggregationin temperate forests reflect stronger environmental correlations suggesting a key rolefor species-sorting processes (deterministic processes) (Myers et al 2013) In terms ofmicroorganisms previous studies have noted that bacterial community assembly is largelygoverned by stochastic processes in early successional soils with the relative roles of


Figure 8 Network of co-occurring OTUs A and D represent the network of the microbial communityfor LY B and E for PQG C and F for WT Furthermore A B and C represent the network analysis coloredby phylum where D E and F represent the network analysis colored by modular class A red line indicatesa positive interaction between two individual nodes while a blue line indicates a negative interaction Thesize of the nodes corresponds to betweenness centralization values


deterministic processes increasing progressively in later successional soils (Diniandreote etal 2015 Ferrenberg et al 2013 Hanson et al 2012)

Previous research has confirmed this continuum could be dependent on varyingenvironmental conditions and the characteristics of organisms (Zhou et al 2013)Environmental factors such as salinity (Lozupone amp Knight 2007) pH (Fierer amp Jackson2006 Griffiths et al 2011) CN ratio (Bates et al 2011) soil C (Drenovsky et al 2004)nitrogen levels (Xiong et al 2014) and the structure of the plant community (Lundberget al 2012) may be major determinants of microbial community structure Our resultsdemonstrated that pH SOC TC and TN were the main abiotic drivers of microbialcommunity compositions More importantly based on the integrated results of the MRTanalysis RDA and correlation analysis we identified SOC as a general descriptor thatencompassed the environmental gradients by which the communities responded to

Our results demonstrated that SOC differed significantly at different sites and wassignificantly correlated with nitrate nitrogen nitrite nitrogen and TN (P lt 005) Thisindicated that SOC was closely related to soil fertility and possessed the highest weightingLitters from the trees will impact SOC which in turn will impact the community assemblystructure and this is perhaps the reason explaining why variable selection increases fromLY to WT sites The relationships between SOC and bacterial community assembly havealso been reported across a broad range of microbial ecosystems (Bastida et al 2013) Mostimportantly we also observed that SOCwas closely associatedwith the community assemblyprocess Similar results reported that the relative roles of stochastic and deterministicprocesses can vary with the successional age of soils and can primarily be attributed to thecovariance of soil pH with age (Tripathi et al 2018) The unexplained variation in VPA


ns

00

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04

06

LY PQG WT

devi

atio

n

a

ns

minus2

0

2

4

LY PQG WT

βNT

I

b

R2 = 067 p = 000045

035

040

045

050

055

0 20 40 60 80SOC

Dev

iatio

n

C

R 2= 025 p = 0025

2

3

0 20 40 60 80SOC

βNT

I

d

Figure 9 Microbial community assembly processes The β-diversity null model analysis showing thenull deviation of the bacterial communities at different sites (A) A null deviation close to zero suggeststhat stochastic processes are more important in structuring the community whereas larger positive ornegative null deviations suggest that deterministic processes play more important roles Bar plot indicatesthat βNTI values varied among sites but were all greater than+2 (B) Regression analysis of the environ-mental variables based on the results of the assembly processes parameters (C D) We used the analysis ofvariance (ANOVA) to evaluate differences in the different indices ns not significantly 001lt P le 005 0001lt P le 001 P le 0001


(786) could be due to stochastic influences (eg drift or speciation Caruso et al 2011)unmeasured soil physicochemical properties (eg metal ion concentration Gombeer et al2015) or interactions between species (eg competition Caruso et al 2011) In fact inother studies of microbial communities using VPA the unexplained portions may alsoaccount for more than 50 (Liao et al 2016 Mo et al 2018)

In deterministic processes not only environmental filtering but also interspeciesinteractions have a great influence on community assembly Ecologists recentlyaccepted that competition and environmental processes act simultaneously (Zhang et


al 2018) In the network analysis the higher modularity indicates that the networkbecame denser suggesting that the microbial communities are highly complex(Olesen et al 2007) Interestingly the modularity was the highest at PQG (0691) Thismay be related to the greater sampling scales and elevation gradients and thus greaterenvironmental heterogeneity at PQG The average path distance represents the shortestpath between two nodes (Wang et al 2016) which demonstrated irregular variation at WT(Zheng et al 2017) Strong positive correlations were observed among sites while negativecorrelations were rare (Figs 8Andash8C) This implied that microbes might cooperate in orderto adapt to similar niches In the network positive links could be attributed to nicheoverlap and cross-feeding while negative relationships could be attributed to competitionand amensalism (Faust amp Raes 2012) From an ecological perspective the peripherals mayrepresent specialists whereas module hubs and connectors may be more generalists andnetwork hubs may be super-generalists (Figs 8Dndash8F) (Deng et al 2012) It is interestingto observe that the module hubs and connectors differed at the different sites

CONCLUSIONWe quantified the importance of the deterministic and stochastic processes driving thebacterial community assembly on different sites in subalpine coniferous forests and showedthat deterministic processes prevailed Moreover SOC was closely related to microbialcommunity structure and greatly influenced the processes of community assembly

ACKNOWLEDGEMENTSWe are grateful to all the scientists who contributed to the collection of data used in thisstudy

ADDITIONAL INFORMATION AND DECLARATIONS

FundingThis work was supported by the National Natural Science Foundation of China (No31772450 and 31600308) and the Project of Service to Industrial Innovation of HigherEducation Shanxi province Discipline Group of Ecological Remediation of Soil PollutionThe funders had no role in study design data collection and analysis decision to publishor preparation of the manuscript

Grant DisclosuresThe following grant information was disclosed by the authorsNational Natural Science Foundation of China 31772450 31600308Project of Service to Industrial Innovation ofHigher Education Shanxi province DisciplineGroup of Ecological Remediation of Soil Pollution

Competing InterestsThe authors declare there are no competing interests


Author Contributionsbull Pengyu Zhao conceived and designed the experiments performed the experimentsanalyzed the data contributed reagentsmaterialsanalysis tools prepared figures andortables authored or reviewed drafts of the paper approved the final draftbull Jiabing Bao Xue Wang and Yi Liu performed the experiments approved the final draftbull Cui Li performed the experiments analyzed the data approved the final draftbull Baofeng Chai conceived and designed the experiments authored or reviewed drafts ofthe paper approved the final draft

Data AvailabilityThe following information was supplied regarding data availability

The raw data are available in the Supplementary Files The bacterial sequences have beendeposited in the SRA database SRP135838

Supplemental InformationSupplemental information for this article can be found online at httpdxdoiorg107717peerj6746supplemental-information

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Ge Y He JZ Zhu YG Zhang JB Xu Z Zhang LM Zheng YM 2008 Differences in soilbacterial diversity driven by contemporary disturbances or historical contingenciesISME Journal 2254ndash264 DOI 101038ismej20082


Gombeer S Ramond JB Eckardt FD Seely M Cowan DA 2015 The influence ofsurface soil physicochemistry on the edaphic bacterial communities in con-trasting terrain types of the Central Namib Desert Geobiology 13494ndash505DOI 101111gbi12144

Griffiths RI Thomson BC James P Bell T Bailey MWhiteley AS 2011 The bac-terial biogeography of British soils Environmental Microbiology 131642ndash1654DOI 101111j1462-2920201102480x

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Jurburg SD Nunes I Stegen JC Roux XL Priemeacute A Salles JF 2017 Autogenic succes-sion and deterministic recovery following disturbance in soil bacterial communitiesScientific Reports 745691 DOI 101038srep45691

Liao J Cao X Zhao LWang J Gao Z CaiwangM Huang Y 2016 The importanceof neutral and niche processes for bacterial community assembly differs be-tween habitat generalists and specialists FEMS Microbiology Ecology 92fiw174DOI 101093femsecfiw174

Lozupone CA Knight R 2007 Global patterns in bacterial diversity Proceedings ofthe National Academy of Sciences of the United States of America 10411436ndash11440DOI 101073pnas0611525104

Lundberg DS Lebeis SL Paredes SH Yourstone S Gehring J Malfatti S Tremblay JEngelbrektson A Kunin V Rio TGD 2012 Defining the core Arabidopsis thalianaroot microbiome Nature 48886ndash90 DOI 101038nature11237

Mo Y ZhangW Yang J Lin Y Yu Z Lin S 2018 Biogeographic patterns of abundantand rare bacterioplankton in three subtropical bays resulting from selective andneutral processes ISME Journal 122198ndash2210 DOI 101038s41396-018-0153-6

Myers JA Chase JM Jimeacutenez I Joslashrgensen PM Araujomurakami A Paniaguazam-brana N Seidel R 2013 Beta-diversity in temperate and tropical forests reflectsdissimilar mechanisms of community assembly Ecology Letters 16151ndash157DOI 101111ele12021

Nelson DW Sommers LE 1982 Dry combustion method using medium temperatureresistance furnace In Page AL et al edsMethods of soil analysis Part 2 chemicaland microbial properties Soil Science Society of America and American Society ofAgronomy Book Series no 9 Second edition Madison Soil Science Society ofAmerica and American Society of Agronomy 539ndash579

Ofiţeru ID LunnM Curtis TPWells GF Criddle CS Francis CA SloanWT 2010Combined niche and neutral effects in a microbial wastewater treatment communityProceedings of the National Academy of Sciences of the United States of America10715345ndash15350 DOI 101073pnas1000604107


Olesen JM Bascompte J Dupont YL Jordano P 2007 The modularity of pollinationnetworks Proceedings of the National Academy of Sciences of the United States ofAmerica 10419891ndash19896 DOI 101073pnas0706375104

Price MN Dehal PS Arkin AP 2009 FastTree computing large minimum evolutiontrees with profiles instead of a distance matrixMolecular Biology amp Evolution261641ndash1650 DOI 101093molbevmsp077

Pruesse E Quast C Knittel K Fuchs BM LudwigW Peplies J Gloumlckner FO 2007SILVA a comprehensive online resource for quality checked and aligned ribosomalRNA sequence data compatible with ARB Nucleic Acids Research 357188ndash7196DOI 101093nargkm864

R Core Team 2018 R a language and environment for statistical computing Version351 Vienna R Foundation for Statistical Computing Available at httpswwwR-projectorg

Reeder J Knight R 2010 Rapidly denoising pyrosequencing amplicon reads by exploit-ing rank-abundance distributions Nature Methods 7668ndash669

Shen C Xiong J Zhang H Feng Y Lin X Li X LiangW Chu H 2013 Soil pH drives thespatial distribution of bacterial communities along elevation on Changbai MountainSoil Biology amp Biochemistry 57204ndash211 DOI 101016jsoilbio201207013

Siles JA Margesin R 2017 Seasonal soil microbial responses are limited to changes infunctionality at two Alpine forest sites differing in altitude and vegetation ScientificReports 72204 DOI 101038s41598-017-02363-2

Stegen JC Lin X Fredrickson JK Chen X Kennedy DWMurray CJ RockholdMLKonopka A 2013 Quantifying community assembly processes and identifyingfeatures that impose them ISME Journal 72069ndash2079 DOI 101038ismej201393

Stegen JC Lin X Fredrickson JK Konopka AE 2015 Estimating and mapping ecolog-ical processes influencing microbial community assembly Frontiers in Microbiology6370ndash395

Tian J Qiao YWu B Chen H LiW Jiang N Zhang X Liu X 2017 Ecological suc-cession pattern of fungal community in soil along a retreating glacier Frontiers inMicrobiology 81028ndash1041 DOI 103389fmicb201701028

Tripathi BM Stegen JC KimM Dong K Adams JM Lee YK 2018 Soil pH mediatesthe balance between stochastic and deterministic assembly of bacteria ISME Journal121072ndash1083

Tucker CM Shoemaker LG Davies KF Nemergut DR Melbourne BA 2016 Differenti-ating between niche and neutral assembly in metacommunities using null models ofβ-diversity Oikos 125778ndash789 DOI 101111oik02803

Wang Y Zhang R Zheng Q Deng Y Van Nostrand JD Zhou J Jiao N 2016 Bacte-rioplankton community resilience to ocean acidification evidence from microbialnetwork analysis Ices Journal of Marine Science 73865ndash875DOI 101093icesjmsfsv187

Woodcock S Van DG Christopher J Bell T LunnM Curtis TP Head IM SloanWT 2007 Neutral assembly of bacterial communities FEMS Microbiology Ecology62171ndash180 DOI 101111j1574-6941200700379x


Xiong Q Pan K Zhang LWang Y LiW He X Luo H 2016Warming and nitrogendeposition are interactive in shaping surface soil microbial communities near thealpine timberline zone on the eastern QinghaiacircTibet Plateau southwestern ChinaApplied Soil Ecology 10172ndash83 DOI 101016japsoil201601011

Xiong J Sun H Peng F Zhang H Xue X Gibbons SM Gilbert JA Chu H 2014 Char-acterizing changes in soil bacterial community structure in response to short-termwarming FEMS Microbiology Ecology 89281ndash292 DOI 1011111574-694112289

Zhang Q GobernaM Liu Y Cui M Yang H Sun Q InsamH Zhou J 2018 Compe-tition and habitat filtering jointly explain phylogenetic structure of soil bacterialcommunities across elevational gradients Environmental Microbiology 202386ndash2396

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Zhou J LiuW Deng Y Jiang YH Xue K He Z Van Nostrand JDWu L Yang YWangA 2013 Stochastic assembly leads to alternative communities with distinct functionsin a bioreactor microbial communityMbio 449ndash52

Zhou J Ning D 2017 Stochastic community assembly does it matter in microbialecologyMicrobiology amp Molecular Biology Reviews 81e00002ndash00017


Becking 1934) For example environmental factors such as pH (Tripathi et al 2018)temperature (Anderson amp Laurel 2013) or nitrogen levels (Xiong et al 2016) may bemajor determinants of microbial community structure

For the other type of community assembly processes (ie stochastic processes) it isassumed that community structures are independent of organism traits and are governedby birth death colonization extinction drift and speciation (Hubbell amp Borda-de-Aacutegua2004) and it is hypothesized that species are all ecologically equivalent (Woodcock et al2007) Previous studies have confirmed that both deterministic and stochastic processesact concurrently to regulate the assembly of ecological communities (Diniandreote et al2016 Diniandreote et al 2015 Zhou amp Ning 2017) but the relative importance may varyin different environmental contexts (Tian et al 2017) This may be because the variationin ecological selection strength and the rates of dispersal on different habitat contexts caninfluence the relative importance of deterministic and stochastic processes across temporaland spatial scales in addition to within entire ecosystems (Chisholm amp Pacala 2011 Jurburget al 2017) Therefore investigation into community driving forces in different habitatscan enrich the understanding on the community assembly process

In this study soil was sampled from 23 soil plots in subalpine coniferous forests locatedon the Loess Plateau in Shanxi province China The 16S ribosomal RNA genes of bacteriawere analyzed using high-throughput sequencing To investigate the driving forces of soilmicrobial community assembly we sampled three sites having different environmentalcharacteristics Sampling was performed along three different altitudinal gradients Thisstudy can largely enrich the understanding on microbiology of subalpine mountainsOur aims were as follows (i) to quantify the relative roles of deterministic and stochasticprocesses in bacterial community dynamics for three different habitat contexts and moreprecisely (ii) to evaluate the effects of environmental factors on microbial communityassembly

MATERIAL AND METHODSSite and samplingA total of 23 soil plots were sampled (Table S1 Fig S1) in August 2016 and August 2017The sites were selected because their vegetation was subalpine mountain coniferous forestsand they were located between 1900 m and 3055 m above mean sea level (amsl) Thestudy area has a warm temperate continental monsoon climate and mostly cinnamon soil

This study focused on response patterns along environmental gradients ratherthan exploring differences among treatment groups Thus we sampled along threealtitudinal gradients without replicates Previous studies have shown that for continuousenvironmental drivers gradient designs further allow for better extrapolationcharacterization of (nonlinear) response functions and consequently quantitative outputsbetter suited for ecological models than replicated designs (Cottingham Lennon amp Brown2005)

To avoid the interference of vegetation factors we sampled plots in the singlevegetation type (ie Larix principis-rupprechtii forests) These sites located on subalpine
























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deterministic processes dominate soil microbial …of microbial communities: ‘‘everything is...

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