ORIGINAL ARTICLE

Phylogenetic beta diversity in bacterial assemblagesacross ecosystems: deterministic versus stochasticprocesses

Jianjun Wang1,2, Ji Shen1, Yucheng Wu3, Chen Tu4, Janne Soininen5, James C Stegen6,Jizheng He2, Xingqi Liu1, Lu Zhang1 and Enlou Zhang1

1State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences, Nanjing, China; 2State Key Laboratory of Urban and Regional Ecology,Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China; 3State KeyLaboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences,Nanjing, China; 4Key Laboratory of Coastal Zone Environmental Processes, Yantai Institute of Coastal ZoneResearch, Chinese Academy of Sciences, Yantai, China; 5Department of Geosciences and Geography,University of Helsinki, Helsinki, Finland and 6Fundamental and Computational Sciences Directorate,Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

Increasing evidence has emerged for non-random spatial distributions of microbes, but knowledgeof the processes that cause variation in microbial assemblage among ecosystems is lacking. Forinstance, some studies showed that deterministic processes such as habitat specialization areimportant, while other studies hold that bacterial communities are assembled by stochastic forces.Here we examine the relative influence of deterministic and stochastic processes for bacterialcommunities from subsurface environments, stream biofilm, lake water, lake sediment and soilusing pyrosequencing of the 16S ribosomal RNA gene. We show that there is a general pattern inphylogenetic signal in species ecological niches across recent evolutionary time for all studiedhabitats, enabling us to infer the influences of community assembly processes from patterns ofphylogenetic turnover in community composition. The phylogenetic dissimilarities among-habitattypes were significantly higher than within them, and the communities were clustered according totheir original habitat types. For communities within-habitat types, the highest phylogenetic turnoverrate through space was observed in subsurface environments, followed by stream biofilm onmountainsides, whereas the sediment assemblages across regional scales showed the lowestturnover rate. Quantifying phylogenetic turnover as the deviation from a null expectation suggestedthat measured environmental variables imposed strong selection on bacterial communities fornearly all sample groups. For three sample groups, spatial distance reflected unmeasuredenvironmental variables that impose selection, as opposed to spatial isolation. Such characteriza-tion of spatial and environmental variables proved essential for proper interpretation of partialMantel results based on observed beta diversity metrics. In summary, our results clearly indicate adominant role of deterministic processes on bacterial assemblages and highlight that bacteria showstrong habitat associations that have likely emerged through evolutionary adaptation.The ISME Journal advance online publication, 28 February 2013; doi:10.1038/ismej.2013.30Subject Category: Microbial population and community ecologyKeywords: bacteria; community composition; distance–decay relationship; evolutionary nicheconservatism; neutral theory; phylogenetic beta diversity

Introduction

A major theme of ecological research is characteriz-ing the processes underlying spatial variation inbiotic communities (that is, beta diversity) across

Earth’s ecosystems (Hubbell, 2001; Martiny et al.,2006; Anderson et al., 2011). It has been recognizedthat habitat specialization resulting from the adap-tive evolution by means of natural selection(Darwin, 1859) has a pivotal role in determiningcommunity composition (for example, Graham andFine, 2008; Cavender-Bares et al., 2009). This is aclassic deterministic process driven by contempor-ary environmental heterogeneity. However, variationin communities is also influenced by stochasticprocesses such as dispersal limitation, mass effectsand random demographics (Hubbell, 2001; Leibold

Correspondence: J Wang, State Key Laboratory of Lake Scienceand Environment, Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences, 73, East Beijing Road, Nanjing,Jiangsu 210008, China.E-mail: JJWang@niglas.ac.cnReceived 18 September 2012; revised 23 January 2013; accepted28 January 2013

The ISME Journal (2013), 1–12& 2013 International Society for Microbial Ecology All rights reserved 1751-7362/13

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et al., 2004; Cottenie, 2005; Martiny et al., 2006;Vellend, 2010; Chase and Myers, 2011).

Although stochastic and deterministic processeshave long been studied in plant and animal systems,extensive study of these processes in microbialcommunities has emerged only in the past decade(Besemer et al., 2012; Hanson et al., 2012 andreferences therein). These studies provide strongevidence of biogeographical patterns for microbes,the distance–decay relationship being one example(for example, Martiny et al., 2006). However, stilllittle is known about the processes that underlienon-random distributions of microbes.

Meta-analysis have shown that bacterial commu-nities differ substantially among-habitat types (forexample, Lozupone and Knight, 2007; Delmontet al., 2011), with salinity being a key factorstructuring microbial communities (Lozupone andKnight, 2007). These patterns suggest a dominantrole of habitat specialization for microbial commu-nity assembly. However, the inconsistency of publicmicrobial gene sequences or experimental methodsand inaccessibility of consistent environmentalinformation prevent more detailed profiles on therelative influence of deterministic and stochasticprocesses. Further, processes governing between-habitat variation in community composition maydiffer from those responsible for variation withinhabitats. Until now, few studies on microbialcommunities have characterized biogeographicalpatterns and underlying processes across- andwithin-habitat types.

Even with consistent microbial and environmentaldata sets, there are important factors to be consideredwhen charactering underlying processes. For exam-ple, contemporary environmental variables measuredin the field are typically spatially autocorrelated andsome ecologically important variables may be leftunmeasured, both factors complicate the inferencesrelated to the relative influences of stochastic anddeterministic processes. However, methods frommacro-organism ecology may provide good solutionsto these challenges. In particular, coupling the spatialvariation of phylogenetic community structure (Webbet al., 2002) with null models can help characterizethe relative influences of deterministic and stochasticprocesses (Graham and Fine, 2008; Chase and Myers,2011; Stegen et al., 2012).

Inferring underlying ecological processes usingphylogenetic information requires phylogenetic signalin habitat association (Losos, 2008; Cavender-Bareset al., 2009). Following detection of phylogeneticsignal, within a phylogenetic framework (Graham andFine, 2008; Chase and Myers, 2011; Fine and Kembel,2011; Stegen et al., 2012), we propose a three-stepprocedure to characterize the relative influences ofdeterministic and stochastic processes.

First, we test for an influence of deterministicprocesses by comparing observed phylogeneticturnover between assemblages to a stochasticexpectation that controls for observed turnover in

taxonomic composition. Significant deviations fromthe stochastic expectation indicate that determinis-tic processes such as environmental filteringstrongly influence community composition.

Second, we use a new approach to reveal influencesof unmeasured environmental variables by relatingphylogenetic null model deviations to environmentaland spatial distances. A significant relationship withspatial distances, after controlling for environmentaldistances, suggests that unmeasured environmentalvariables impose deterministic processes that over-whelm any influences of stochastic processes. This isbecause stochastic processes should not cause phylo-genetic null model deviations (Hardy, 2008).

Third, we evaluate the relative influences ofdeterministic and stochastic processes by consider-ing results from the previous step and additionalanalyses that relate observed phylogenetic turnoverto spatial and environmental distances: a strongerinfluence of stochastic versus deterministic pro-cesses can be inferred if null model deviations arenot related to spatial distance and if observedphylogenetic turnover increases with spatial dis-tance to a greater degree than environmentaldistance. We infer that deterministic processes havemore influence than stochastic processes if observedphylogenetic turnover correlates more strongly withenvironmental than with spatial distances (Stegenand Hurlbert, 2011). We also infer a greater influ-ence of deterministic processes if null modeldeviations increase with spatial distance, whichsuggests that any influence of spatial isolation isoverwhelmed by environmental selection imposedby unmeasured environmental variables.

Here, we analyzed bacterial assemblages fromaquatic and terrestrial subsurface environments, aswell as lake water, stream biofilm, lake sedimentand soil using pyrosequencing of the 16S ribosomalRNA gene. It should be noted that although previousmicrobial work has examined most of Earth’s majorecosystems, aquatic and terrestrial subsurfaceenvironments have received little attention. Com-pared with previous meta-analyses that incorporateamong-habitat comparisons (for example, Lozuponeand Knight, 2007), we examine a broad rangeof habitats using more consistent methods tocharacterize community and environmental data.

In addition to evaluating phylogenetic signal andinferring ecological processes across habitats, ourstudy aims to (1) examine whether bacteria showclear habitat associations, that is, whether communitycomposition differs among sampled habitat types; and(2) compare among-habitat types the rate at whichcommunity composition turns over through space.

Materials and methods

Data collectionWe collected bacterial samples from aquatic orterrestrial subsurface sediment, lake water, stream

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biofilm, lake surface sediment and soil (Table 1).Sequences generated from pyrosequencing ofbacterial 16S ribosomal RNA gene amplicons wereprocessed using QIIME pipeline (v1.2) (Caporasoet al., 2010). Details related to sample collection,DNA sequencing and analyses are described in thesupplementary text and Wang et al. (2012a). Opera-tional taxonomic units (OTUs) were defined using a97% sequence similarity cutoff. We accounted fordifferences in sampling effort among samples byrandomly subsampling 1000 sequences per samplefor 1000 times.

Statistical analysesTo evaluate phylogenetic signal across a range ofphylogenetic depths, we used Mantel correlogramswith 999 randomizations for significance tests(Oden and Sokal, 1986; Diniz-Filho et al., 2010)with the function ‘mantel.correlog’ in the R packageVegan v2.0-2 (http://vegan.r-forge.r-project.org). Wepartitioned phylogenetic distances into classes (thatis, evolutionary time steps; here 0.02 units) andwithin each distance class we found the correlationcoefficient relating between-OTU phylogenetic dis-tances to environmental-optimum distances (Diniz-Filho et al., 2010). This method has the advantage ofcharacterizing shifts in phylogenetic signal acrossphylogenetic distance classes (Diniz-Filho et al.,2010). An environmental-optimum for each OTUwas found for each environmental variable as inStegen et al. (2012). Between-OTU environmental-optimum differences were calculated as Euclideandistances using optima for all the environmentalvariables.

To quantify phylogenetic turnover in communitycomposition between a given pair of samples (that is‘phylogenetic beta diversity’ or ‘phylobetadiver-sity’), we used unweighted Unifrac (Lozupone andKnight, 2005) and the mean nearest taxon distance

separating OTUs in two communities (betaMNTD)(Fine and Kembel, 2011; Stegen et al., 2012).BetaMNTD is the mean phylogenetic distance tothe closest relative in a paired community for alltaxa (Fine and Kembel, 2011) and is sensitive to thechanges of lineages close to the phylogenetic tips.

We performed non-metric multidimensionalscaling based on unweighted Unifrac andbetaMNTD to depict community composition intwo dimensions. To test the hypothesis that habitattypes structure the distribution of bacteria, permuta-tional multivariate analysis of variance was used(Anderson, 2001).

For each habitat, a standardized effect size(ses.betaMNTD) was computed as the number ofstandard deviations that observed betaMNTDdeparted from the mean of null distribution (999null iterations) based on random shuffling of OTUlabels across the tips of the phylogeny (Hardy, 2008;Fine and Kembel, 2011; Stegen et al., 2012). Thisrandomization holds constant observed speciesrichness, species occupancy and species turnover.It therefore provides an expected level of betaMNTDgiven observed species richness, occupancy andturnover. The absolute magnitude of ses.betaMNTDreflects the influence of deterministic processes; thelarger the magnitude, the greater the influence ofdeterministic, niche-based processes.

To examine variation in phylobetadiversity withinthe nine sample groups (those with samplenumberX10, Table 1), we used a distance-basedapproach (Martiny et al., 2006; Tuomisto andRuokolainen, 2006) akin to distance–decay analysiswhere phylogenetic dissimilarity is related to spatialand environmental distance among sampled com-munities. Environmental distance was measured asEuclidean distance using all environmental vari-ables standardized to have a mean of zero and astandard deviation of one. Phylobetadiversity wasregressed against spatial or environmental distances

Table 1 Brief description of the sample groups with different habitat types

Sample group Location Sample numbera Sampling date Habitat types Description

KL1 31.4411N, 120.8141E 12 2010-01 Surface sediments (0–1 cm) Kuilai LakeKL7 31.4411N, 120.8141E 12 2010-07 Surface sediments (0–1 cm) Kuilai LakeThS 31.1921N, 120.1061E 27 2010-06 Surface sediments (0–1 cm) Taihu LakeSC 261–341N, 981–1031E 25 2009-10 Surface sediments (0–1 cm) Lakes in Sichuang RegionsKSS 35.7371N, 92.8811E 2 2010-09 Surface sediments (0–1 cm) Kusai Lakeb

KS 35.7371N, 92.8811E 28 2010-09 Subsurface sedimentsc Kusai LakeLG 27.7131N, 100.7721E 20 2008-10 Subsurface sediments Lugu LakeNJ 32.0611N, 118.7891E 5 2010-11 Subsurface soilc A park in Nanjing CityZJ 29.9441N, 121.0151E 7 2010-06 Surface soils (0–4 cm) Fields in Zhejiang ProvinceHX 35.7821N, 92.9741E 10 2010-09 Surface soils (0–4 cm) Qinghai–Tibetan plateauSTR 26.8431N, 99.9641E 24 2009-11 Stream biofilm Laojun MountainsThW 31.1921N, 120.1061E 27 2010-06 Lake water Taihu Lake

aNon-metric multidimensional scaling was applied for all sample groups. Mantel correlograms (that is, the test of phylogenetic signal) andvariations in phylobetadiversity (Unifrac, mean nearest taxon distance separating OTUs in two communities (MNTD) or standardized effect sizeof MNTD) were only examined for sample groups withX10 samples.bKusai Lake is a saline lake with a salinity of B17%. Owing to the paleo-environmental changes in the Kusai Lake regions, the lake salinity variedin the past 2000 years (Liu et al., 2009). The other lakes or streams (Wang et al., 2011) are freshwater.cSubsurface sediments: 1 cm below lake floor; subsurface soils: 10 cm below land surface.

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using a Gaussian generalized linear model. Signifi-cance was determined using Mantel tests (Spear-man’s correlation) with 9999 permutations(Legendre and Legendre, 1998). We used an analysisof covariance with permutation to test the hypoth-esis that the regression slopes do not differ amongthe sample groups. Further, partial Mantel tests wereused to assess the relationship between phyloge-netic turnover and spatial or measured environ-mental distance after accounting for measuredenvironmental distance or spatial distance, and thesignificance was assessed using 9999 permutations.These analyses were performed in the R environ-ment with Picante v1.4 (http://picante.r-forge.r-project.org) and Vegan v2.0-2

Finally, we inferred the underlying processesfollowing the three steps listed in the Introductionsection. For instance, the variation in the magnitudeof ses.betaMNTD should be driven primarily byvariation in the influence of deterministic processesas deviations from the betaMNTD null modelexpectation are primarily due to niche-basedprocesses (Hardy, 2008). Significant increasesin ses.betaMNTD with increasing environmentaldistances therefore implies that the influence ofniche-based processes grows with increasingly largeshifts in measured environmental conditions (Fine

and Kembel, 2011; Stegen et al., 2012). Importantly,significant increases in ses.betaMNTD with increas-ing spatial distances implies that the influence ofniche-based processes grows with increasingly largeshifts in spatially structured, but unmeasuredenvironmental conditions.

Results

Mantel correlograms consistently showed signifi-cant positive correlations across short phylogeneticdistances for all nine sample groups (Po0.05,Figures 1a–i). The phylogenetic distance acrosswhich there was significant phylogenetic signalvaried from 10% to 30% of the maximum phyloge-netic distance within each phylogeny. For nearly allsample groups (except for KL1 in Figure 1a),there were significant negative correlations at inter-mediate phylogenetic distances (Po0.05) and non-significant relationships across longer distances(Figures 1b–i).

Non-metric multidimensional scaling using bothunweighted Unifrac and betaMNTD showed thatsamples were phylogenetically segregated byhabitat type (Figures 2a and c). For each samplegroup, the Unifrac metric showed higher values than

Figure 1 (a-i) Pearson correlation resulting from Mantel correlogram between the pairwise matrix of OTU niche distances and phylogeneticdistances (with Jukes–Cantor model) for each sample group with 9999 permutations. Significant correlations (Po0.05, solid circles) indicatephylogenetic signal in species ecological niches, and were consistently found across short phylogenetic distances for all sample groups.

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betaMNTD and the range of unweighted Unifrac wassmaller than that of betaMNTD (Figures 2b and d).Community differences among-habitat types werealso observed across short spatial distances, that is,there were large differences in community composi-tion between surface sediments and lake water inTaihu Lake (Supplementary Figure S1A) or betweensoils and lake sediments in Kusai Lake regions(Supplementary Figure S1B). Analyses of permuta-tional multivariate analysis of variance showedthat habitat type explained 50.0% and 21.2% ofthe variation in community composition, usingbetaMNTD and Unifrac metrics, respectively(Po0.001, 9999 permutations).

A plot of pairwise phylobetadiversity versusspatial distance showed that there was a significantdistance–decay relationship for most of the samplegroups: six out of nine using Unifrac (Figure 3,Supplementary Table S1) or five out of nine usingbetaMNTD (Supplementary Figure S2, Supple-mentary Table S2). For both metrics, the slope ofthis relationship varied significantly among mostgroups (Po0.01), with the following decreasingorder in spatial turnover rates: KS4LG4STR4ThS4ThW4SC.

Except for sample groups KL1, KL7 and ZJ, meanvalues of ses.betaMNTD were significantly differentfrom the expected value of zero (Po0.001, t-test;Supplementary Figure S3). After controllingfor spatial distance, environmental distance was

significantly correlated with ses.betaMNTD withinseven sample groups (Table 2). Spatial distance wassignificantly (Po0.05) correlated with ses.beta-MNTD for five sample groups (SupplementaryFigure S4). However, after controlling for environ-mental distance, spatial distance was significantlycorrelated with ses.betaMNTD only in ThS, KS andSTR (partial Mantel test, Po0.05; Table 2).

There were six sample groups in which ses.be-taMNTD was not significantly related to spatialdistance after controlling for environmental distance(Table 2). In five out of these six groups unweightedUnifrac and betaMNTD were more strongly relatedto environmental distance (after controlling forspatial distance) than to spatial distance (aftercontrolling for environmental distance; Supple-mentary Table S1, S2). The exception was groupHX, for which all beta diversity metrics showed norelationship to spatial or environmental distances.

Discussion

Here we studied a broad range of ecosystems toassess patterns of microbial community compositionand the processes that underlie these patterns. To doso, we have characterized patterns of spatial turn-over in the phylogenetic composition of microbialcommunities, and have inferred processes bycomparing phylogenetic turnover to null model

Figure 2 Non-metric multidimensional scaling plots (a, c), or boxplots (b, d) of community dissimilarities within-habitat groups usingunweighted Unifrac and betaMNTD, respectively. The samples are colored by habitat groups. The habitat types include lake surfacesediments (KL, ThS and SC), lake subsurface sediments/soils (KS LG and NJ), surface soils (ZJ and HX), stream biofilm (STR) and lakewater (ThW). Two surface sediments from Kuisai Lake (KSS) are not included in KS group. KL1 and KL7 indicate the surface sedimentssampled from Kuilei Lake in January and July, respectively. More details on the abbreviation of groups, see Table 1 and thesupplementary text. Components of the box are: top of the box, upper hinge; midline of box, median; bottom of box, lower hinge; bars, 1.5times length of box (1.5 times the horizontal spread); dots, values that are 4 or o1.5 times the horizontal spread of the distribution, plusthe upper or lower hinge.

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Figure 3 The relationships between unweighted Unifrac and spatial distance for different sample groups. (a–d) Lake surface sediments;(e, f) lake subsurface sediments; (g) soils; (h) stream biofilm and (i) lake water. The regression slopes of the linear relationships based onGaussian generalized model are shown with solid (statistically significant, ranked Mantel test, 9999 permutations, Po0.05) or dashed(statistically nonsignificant, P40.05) lines. The significant slope (unweighted Unifrac per 103 km) is shown in each sample group panel.Detailed Mantel statistics are shown in Supplementary Table S1.

Table 2 Mantel and partial Mantel tests for the correlation between ses.betaMNTD and the explanatory distances (elevational,geographic and environmental distance) using Spearman’s rho for different habitat types and spatial scales

Ses.betaMNTD Local Regional

Effects of Controlling for Surfacesediments

Lakewater

Subsurfacesediments

Streambiofilm

Surfacesediments

KL1 KL7 ThS ThW LG KS STR SC

Geographic 0.151 0.202 0.353*** 0.244** 0.177* 0.394*** 0.376** 0.145Environmental 0.338** 0.404* 0.406*** 0.386*** 0.407*** 0.413** 0.410*** 0.387**Elevational 0.734*** 0.157Geographic Environmental 0.078 0.140 0.173* 0.074 � 0.067 0.317*** 0.240* 0.029

elevational � 0.413* 0.125**Elevational Environmental 0.668* 0.059

geographic 0.745* 0.139**Environmental Geographic 0.315* 0.380* 0.273* 0.316** 0.378*** 0.341** 0.296* 0.364*

elevational � 0.007* 0.363**

The significances are tested based on 10 000 permutations. ***Po0.001; **Po0.01; *Po0.05. For the HX group, both the geographic andenvironmental distances were not significantly related to bacterial phylogenetic turnover (P40.05). For ZJ and NJ groups, the Mantel analyses arenot conducted because of their lower sample numbers (7 and 5, respectively).

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expectations. This pattern-to-process linkagerequires phylogenetic signal in microbial habitatassociations. Compared with previous microbialstudies, we used a more statistically robust methodto test phylogenetic signal. We used this updatedmethod to provide the broadest evaluation of phylo-genetic signal in microbes to date, and find signifi-cant phylogenetic signal across all evaluated habitats.Further, we used a novel statistical approach to testfor deterministic processes governed by unmeasuredenvironmental variables. The ability to detect deter-ministic influences by unmeasured environmentalvariables proved critical for understanding therelative balance between deterministic and stochasticprocesses.

Phylogenetic signal varies with phylogenetic distanceInferring ecological processes using phylogeneticinformation requires phylogenetic signal (Losos,2008) in ecological niches (Cavender-Bares et al.,2009). Recent studies on freshwater Actinobacteria(Newton et al., 2007), marine bacterioplankton(Andersson et al., 2010) and subsurface bacteria(Stegen et al., 2012) have indicated there is apositive relationship between phylogenetic dis-tances and ecological differences among closerelatives. Our Mantel correlogram analyses supportthis finding: significant phylogenetic signal wasconsistently detected across all studied habitattypes, but only across short phylogenetic distances.This is also supported by regressing habitat differ-ences against phylogenetic distances between pairsof OTUs (the same procedures in Stegen et al.,2012), which also showed a clear positive relation-ship across the short phylogenetic distances for allsample groups (Supplementary Figure S5). Morequantitatively, significant phylogenetic signal wasfound at up to 10–30% of the maximum observedphylogenetic distance. This is consistent withStegen et al. (2012) who found up to 13–15% ofthe maximum phylogenetic distance for terrestrialsubsurface bacteria. This general pattern in phylo-genetic signal strongly indicates that closely relatedbacterial taxa are ecologically coherent and thatinterspecies gene exchange, such as horizontal genetransfer (Popa and Dagan, 2011), does not eliminatesuch ecological coherence at the scale of bacterialmetacommunities (see also in Philippot et al., 2010;Wiedenbeck and Cohan, 2011; Stegen et al., 2012).

Unexpectedly, across intermediate distances therewere significant negative correlations between phy-logenetic and ecological distances. This may suggestconvergent evolution, that is, that distinctly relatedlineages acquire the similar ecological niches, acrossintermediate phylogenetic distances. We are una-ware of other work showing convergent evolutionacross free-living bacteria, but the same genes areoften lost in obligate intracellular bacteria fromdifferent phyla, suggesting evolutionary conver-gence (Merhej et al., 2009). More generally,

evolutionary convergence may have a role incommon functions for complex symbiont commu-nities across phylogenetically divergent hosts (Fanet al., 2012). However, the causes and consequencesof convergent evolution in free-living microbialcommunities are unclear, but warrant further study.

Taken together, our results combined with previousstudies, indicate a general pattern in the phylogeneticstructure of bacterial ecological niches: conservedniches/traits across short phylogenetic distances,convergent niches/traits across intermediate distancesand random niches/traits across large distances. Asfunctional and phylogenetic beta diversity for soilmicrobes were closely correlated (Fierer et al., 2012),and there is a phylogenetic signal for 93% functionaltraits in micro-organisms (Martiny et al., 2012), itwould be interesting to use other molecular markers(functional genes, for instance) to test phylogeneticsignal at finer phylogenetic scales within a hierarchyof environmental factors (see Martiny et al., 2009).Nevertheless, this observation has two importantimplications. First, strong phylogenetic signal acrossshort phylogenetic distances indicates that ecologicalprocesses can be inferred by studying spatial ortemporal patterns in the phylogenetic structure ofcommunities. Second, it suggests that ecologicalinferences are most robust when made using metricsof nearest neighbor distances (for example, beta-MNTD). These metrics focus on relatively shortphylogenetic distance such that phylogenetic struc-ture carries ecologically relevant information.

Turnover rate in community composition varies amonghabitatsOur results showed much greater turnover incommunity composition between habitats thanwithin habitats. This suggests that bacteria arespecialized on particular habitats and is consistentwith former meta-analyses on bacteria (for example,Lozupone and Knight, 2007; Delmont et al., 2011;Nemergut et al., 2011; Zinger et al., 2011).

Within habitats, unweighted Unifrac andbetaMNTD both showed significant distance–decaypatterns across six out of the nine habitat types(67%) studied here. This is consistent with Hansonet al. (2012), who found that microbial communitiesshowed significant spatial patterns in 68% of 54reviewed data sets. In addition, there was substan-tial across-habitat variation in the rate of spatialdistance–decay. As expected, the turnover rate inphylogenetic community composition was highestfor shallow terrestrial subsurface environments (LGand KS). This high rate of turnover in phylogeneticcommunity composition is consistent with a pre-vious observation of high turnover rate in taxonomiccomposition in a terrestrial subsurface environment(Wang et al., 2008). Such high turnover rates may beexplained by strong dispersal limitation and steepenvironmental gradients in subsurface environ-ments (Wang et al., 2008).

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In amphibian, bird, mammal or plant assem-blages, beta diversity is typically higher in mountai-nous regions than in regions with less topo-graphic relief, presumably due to species specializ-ing on particular elevations (for example, McKnightet al., 2007). Our results are consistent with thisobservation: turnover rate was significantly higherfor the biofilm bacterial communities in mountain-side streams than for other habitat types (exceptsubsurface environments). This high elevationalturnover rate of bacteria is also consistent with theresults for diatoms and macroinvertebrates in thesame streams (Wang et al., 2012b). On the otherhand, high turnover across elevations for bacteria issomewhat different from a previous result obtainedusing denaturing gradient gel electrophoresis alongthe same elevational gradient, which showed nosignificant elevational distance–decay relationship(Wang et al., 2012b). This difference may haveresulted from different resolution of the two meth-ods: a method with lower resolution may fail todetect a significant distance–decay relationshipbecause of undetected endemism (Morlon et al.,2008; Hanson et al., 2012).

Our samples covered a wide range of horizontalspatial extents, which potentially affects theobserved turnover rate. Below the spatial extent of10 km, we did not find significant distance–decay inKL1, KL7 or HX sample groups. At a spatial extentofo100 km, as within habitats of Taihu Lake (ThSand ThW) for instance, we found significantdifferences in turnover rates across-habitat types:bacterial communities in surface sediments showeda significantly higher turnover rate than theircorresponding free-living communities (1.4 and 1.0unweighted Unifrac per 103 km, respectively). Whenlarger spatial extents were considered (4 100 km),the lakes from mountain regions (SC) for instance,the sediment bacterial communities showed asignificantly lower turnover rate than other habitats,especially within habitats (that is, ThS) (Figure 3).

Previous work has also found the distance–decayrelationship for microbes to be scale dependent,where significant relationships occurred only acrosslocal or relatively short spatial extents (for example,King et al., 2010; Martiny et al., 2011). However, forlarger spatial extents similar to those we consideredhere, former reports indicate that the bacterialdistance–decay relationships range from significantin lake surface sediments across the Tibetan Plateau(Xiong et al., 2012) to nonsignificant in NorthAmerica soils (Fierer and Jackson, 2006). Theseresults collectively suggest that the rate of distance–decay in bacteria shows strong context-dependency,potentially driven by among-habitat variation in thedegree of environmental spatial autocorrelation andin the degree of dispersal limitation.

Summarily, the pairwise phylobetadiversity sig-nificantly increased with spatial distance for most ofthe sample groups and clearly showed that amongthe studied habitats there were significant

differences in the rates at which community com-position changes through space. In general, thehorizontal turnover rates of bacterial communitiesfrom lakes or soils, or from local or regional scales,were significantly lower than the rates from subsur-face environments or mountain regions. In addition,the community turnover rate in subsurface environ-ments was highest.

Deterministic processes govern communitycomposition across habitatsBy leveraging phylogenetic information and follow-ing the three-step procedure proposed here, weinferred the relative influences of deterministic andstochastic processes across a broad range of habitattypes. The first step is to examine distributions ofses.betaMNTD; a distribution mean that deviatessignificantly from zero suggests a strong influence ofdeterministic processes (Fine and Kembel, 2011;Stegen et al., 2012). In 9 of the 11 sample groups,ses.betaMNTD distributions deviated significantlyfrom zero (Supplementary Figure S3). Furthermore,seven groups showed distributions greater than zero(Supplementary Figure S3), suggesting that acrosscommunities there are shifts in environmentalconditions that deterministically cause changes incommunity composition. Two groups from TaihuLake (ThS and ThW) had mean ses.betaMNTDvalues that were less than zero (SupplementaryFigure S3), suggesting that for both groups there wasrelative consistency in the environmental condi-tions that deterministically governed communitycomposition. Although many features of theobserved abiotic environment in Taihu Lake variedacross sampled communities, a high level ofeutrophication was maintained across communities(Duan et al., 2009). It may therefore be that higheutrophication imposed strong environmental filter-ing on microbial communities in Taihu Lake. Moregenerally, our observation of ses.betaMNTD valuesranging from negative to null to positive highlightsthe fact that the influence of deterministic ecologicalprocesses varies across systems; deterministic pro-cesses can minimize spatial variation in, have littleinfluence over, or drive large shifts in communitycomposition. A major challenge for future work is tomechanistically understand variation in the influ-ence of deterministic processes.

The second step in the process-inference proce-dure focuses on revealing which process is theprimary cause of significant, partial Mantel coeffi-cients relating turnover in community compositionto spatial distance. A common interpretation is thatsuch a relationship is caused by stochastic pro-cesses. Although intuitive, this interpretation maybe premature, especially if turnover in communitycomposition is quantified using a observed or ‘raw’metric; a raw metric such as betaMNTD simplymeasures the difference in composition betweentwo communities.

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Consider a scenario in which the turnover incommunity composition is quantified usingbetaMNTD and in which there is an unmeasuredenvironmental variable that changes across sampledmicrobial communities. If this unmeasured variablegoverns community composition, it can cause asignificant partial Mantel coefficient relatingbetaMNTD to spatial distances. The standard (andincorrect) inference would be that communitycomposition is governed by stochastic processes.

To make a more robust inference we use ses.betaMNTD as the turnover metric. In this case, thepartial Mantel coefficient related to spatial distanceshould reflect deterministic processes governed byunmeasured environmental variables. The reason istwofold: (i) the influence of measured environmen-tal variables has already been accounted for becausewe are dealing with partial Mantel coefficients; and(ii) the magnitude of phylogenetic null modeldepartures (that is, ses.betaMNTD) should only beinfluenced by deterministic processes; stochasticprocesses should have no influence (Hardy, 2008).

When using ses.betaMNTD as the turnover metric,a significant, partial Mantel coefficient related tospatial distances should indicate that stochasticprocesses are overwhelmed by deterministic pro-cesses governed by unmeasured environmentalvariables. Similarly, if the partial Mantel coefficientis nonsignificant, it would indicate that unmeasuredenvironmental variables have little influence overcommunity composition.

In ThS, KS and STR groups, spatial distanceswere significantly related to ses.betaMNTD aftercontrolling for measured environmental distances(Table 2). We therefore infer that in these habitatsthere are unmeasured, spatially structured environ-mental variables that influence community compo-sition by imposing deterministic processes. We alsoinfer that deterministic processes imposed byunmeasured variables overwhelm any influencesof stochastic processes. In contrast, for the other sixgroups, unmeasured environmental variables havelittle influence. For these six groups, a significantinfluence of stochastic processes can be indicated bya significant relationship between spatial distanceand Unifrac or betaMNTD (after controlling formeasured environmental variables).

The third step in our process-inference procedureevaluates the relative balance between deterministicand stochastic processes. To begin, we infer a greaterinfluence of deterministic processes for the threegroups characterized by significant influences ofmeasured and unmeasured environmental variables(ThS, KS and STR). This inference assumes that ifstochastic processes were more influential thandeterministic processes, spatial distances alone wouldnot be related to ses.betaMNTD; stochastic processesacting alone should cause there to be no relationshipbetween ses.betaMNTD and spatial distance.

For the six sample groups in which ses.betaMNTDwas not related to spatial distances (by partial

Mantel), we use the relative magnitudes of partialMantel coefficients from the analyses of Unifrac andbetaMNTD. The reason we use these observed or‘raw’ beta diversity metrics instead of ses.betaMNTDis because they can increase with an increasedinfluence of stochastic processes. That is, increasedstochasticity should increase taxonomic turnoverand increased taxonomic turnover should, by itself,cause increases in phylogenetic turnover. For five ofthe six sample groups, the partial Mantel coefficientwas larger for environmental distance than forspatial distance (Supplementary Tables S1 and S2).We take this as evidence that deterministic pro-cesses are more influential than stochastic processesin these five groups. The exception was the HXgroup, which was characterized by nonsignificantpartial Mantel tests, which does not provide anyclear ecological inferences.

It is worth noting that some inferences drawn hereare opposite to those which we would have madeusing ‘traditional’ approaches without null models.When a higher partial Mantel coefficient is observedfor spatial distance than for environmental distance,the standard inference is that stochastic processeshave a stronger influence than deterministic pro-cesses. Using this approach (for example, for KS andSTR), partial Mantel coefficients based onbetaMNTD would suggest a stronger influence ofstochastic processes (Supplementary Table S2).

Considering that ses.betaMNTD is significantlyrelated to spatial distance in KS and STR, however,implies that the larger coefficient on spatial distanceis actually driven by unmeasured environmentalvariables that deterministically govern communitycomposition. This reverses the inference from thedominance of stochastic processes to the dominanceof deterministic processes. We suggest that theapproach used here provides more informed infer-ences than the standard Mantel framework. It wouldbe informative to apply the approach to thepreviously studied microbial and macro-organismsystems. Substantial changes in our new under-standing of these systems may result. We stress,however, that both approaches are important andthe relative utility of each will depend on thecontext and questions of interest.

Concluding remarks

Although our results suggest that bacterial commu-nities are governed primarily by deterministicprocesses, stochastic processes are also important.For instance, many pairwise comparisons producednonsignificant ses.betaMNTD values and the ses.be-taMNTD distributions for the Kuilei Lake were notsignificantly different from zero (SupplementaryFigure S3). In addition, bacterial communities insurface sediments near river mouths (islands orwaterway channels) were more phylogeneticallysimilar to the lake water than the other locations

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in Taihu Lake, with an increasing trend in dissim-ilarities from the river mouth to the lake center(Supplementary Figure S1). This pattern suggests astronger influence of dispersal and mass effects nearthe river mouth, potentially due to physical mixing.These observations emphasize the need to under-stand what governs the relative balance betweenstochastic and deterministic processes and whatconditions would lead to stochastic processes over-whelming deterministic processes.

By integrating former theoretical reviews (Leiboldet al., 2004; Martiny et al., 2006), Lindstrom andLangenheder (2012) depicted the relationshipbetween stochastic and deterministic processesalong gradients of dispersal rate and selectivestrength imposed by local habitat conditions(Figure 4). Our results across a broad range ofecosystems suggest that the dominance of stochasticprocesses might occur only when the selectivestrength of local habitat conditions is below aconceptual threshold (Figure 4). Microbial commu-nities are above this threshold when they areconsidered across-habitat types or across spacewithin highly environmentally heterogeneous

habitats. This is likely driven by habitat specializa-tion, reflected here by significant phylogeneticsignal in environmental associations across shortphylogenetic distances. In systems with less envir-onmental variation or with regional species poolscharacterized by environmental generalists, stochas-tic processes may overwhelm deterministic pro-cesses, as suggested in previous work (for example,Ofiteru et al., 2010).

Acknowledgements

We are grateful to Yong Zhang, Rong Wang, Bo Yao,Zhitong Yu, Yong Wang, Kun Yang and many colleaguesfor field sampling. Great thanks to Professor JenniferMartiny for insightful comments on the manuscript,to Dr Christopher van der Gast for valuable discussionsand to two anonymous reviewers for helpful comments.J Shen thanks to 973 Program (2012CB956100). J Wangappreciates key project of NSFC (41030211), NIGLAS(NIGLAS2012135004), NSFC (40903031, 41273088),Jiangsu NSF (BK2010605), CPSF (2011M500397) andCAS oversea visiting scholarship (2011-115). Y Wuwas supported by the Youth Innovation PromotionAssociation, CAS. L Zhang was supported by NSFC(41271468). J Soininen acknowledges the State KeyLaboratory of Lake Science and Environment (China).J Stegen is supported by a Linus Pauling DistinguishedPostdoctoral Fellowship at PNNL.

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