Global Biogeographic Analysis of Methanogenic Archaea Identifies Community-Shaping

13 Pages·2017·3.16 MB·English

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ORIGINALRESEARCH
published:18July2017
doi:10.3389/fmicb.2017.01339
Global Biogeographic Analysis of
Methanogenic Archaea Identifies
Community-Shaping Environmental
Factors of Natural Environments
XiWen1,2*,SizhongYang1,3,FabianHorn1,MatthiasWinkel1,DirkWagner1and
SusanneLiebner1*
1Section5.3Geomicrobiology,GFZGermanResearchCentreforGeosciences,Potsdam,Germany,2CollegeofElectrical
Engineering,NorthwestUniversityforNationalities,Lanzhou,China,3StateKeyLaboratoryofFrozenSoilEngineering,
NorthwestInstituteofEco-EnvironmentandResources,ChineseAcademyofSciences,Lanzhou,China
Methanogenic archaea are important for the global greenhouse gas budget since
they produce methane under anoxic conditions in numerous natural environments
such as oceans, estuaries, soils, and lakes. Whether and how environmental change
will propagate into methanogenic assemblages of natural environments remains
largely unknown owing to a poor understanding of global distribution patterns and
environmental drivers of this specific group of microorganisms. In this study, we
performed a meta-analysis targeting the biogeographic patterns and environmental
Editedby: controls of methanogenic communities using 94 public mcrA gene datasets. We
HongchenJiang,
show a global pattern of methanogenic archaea that is more associated with habitat
MiamiUniversity,UnitedStates
filtering than with geographical dispersal. We identify salinity as the control on
Reviewedby:
JosephYavitt, methanogenic community composition at global scale whereas pH and temperature
CornellUniversity,UnitedStates
are the major controls in non-saline soils and lakes. The importance of salinity for
WeiXie,
TongjiUniversity,China structuringmethanogeniccommunitycompositionisalsoreflectedinthebiogeography
*Correspondence: of methanogenic lineages and the physiological properties of methanogenic isolates.
XiWen Linking methanogenic alpha-diversity with reported values of methane emission
[email protected]
identifies estuaries as the most diverse methanogenic habitats with, however, minor
SusanneLiebner
[email protected] contributiontotheglobalmethanebudget.Withsalinity,temperatureandpHourstudy
identifiesenvironmentaldriversofmethanogeniccommunitycompositionfacingdrastic
Specialtysection:
changesinmanynaturalenvironmentsatthemoment.However,consequencesofthis
Thisarticlewassubmittedto
TerrestrialMicrobiology, for the production of methane remain elusive owing to a lack of studies that combine
asectionofthejournal
methaneproductionratewithcommunityanalysis.
FrontiersinMicrobiology
Received:14May2017 Keywords:methanogenicarchaea,mcrA,biogeography,environmentaldrivers,salinity,pH,temperature
Accepted:03July2017
Published:18July2017
Citation: INTRODUCTION
Wen X,Yang S,Horn F,Winkel M,
Wagner DandLiebner S(2017) Methane (CH ) is a major greenhouse gas. Its emission from natural environments such as
4
GlobalBiogeographicAnalysis wetlands,oceans,andsedimentsaccountsforover70%ofatmosphericmethaneglobally(IPCC,
ofMethanogenicArchaeaIdentifies
2007). An assessment of published data revealed different methane emission rates for wetlands,
Community-ShapingEnvironmental
lakes, rivers, estuaries, and oceans (in decreasing order, Supplementary Figure S1). Natural
FactorsofNaturalEnvironments.
wetlandsaloneaccountfor62%ofthebiogenicCH production(Kirschkeetal.,2013;Nazaries
Front.Microbiol.8:1339. 4
doi:10.3389/fmicb.2017.01339 et al., 2013) and wetland emissions dominate the inter-annual variability of methane sources
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Wenetal. MethanogenicBiogeographyinNaturalEnvironments
(Bousquet et al., 2006). In contrast, the vast area of marine global exploration of the correlations between microbial
ecosystems only contribute about 8% to the natural sources of community composition and environmental conditions
CH (Nazaries et al., 2013). Within the global oceanic methane (Lozupone and Knight, 2007). At present, only a few studies
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emission,lessthan10%iscontributedbyestuaries(Bangeetal., address dispersal and habitat filtering of methanogenic
1994). communities (Auguet et al., 2010; Barreto et al., 2014).
Methanogenesis, the biological formation of methane, is We hypothesize that methanogenic assemblages are mainly
performed by methanogenic archaea which produce methane influenced by habitat filtering and that it is driven by global
primarily from H /CO , methyl groups or acetate at anoxic environmental controls. Considering that methane emission
2 2
conditions (Thauer et al., 2008). This reaction is catalyzed rates differ largely between natural ecosystems, the explicit
by the methyl-coenzyme M reductase (MCR). The mcrA gene integration of the composition, diversity and biogeography
encoding a subunit of this enzyme is a commonly used gene of methanogenic assemblages in these ecosystems may be
marker in molecular surveys (Conrad, 2007; Bridgham et al., fundamental to determine the response of methane production
2013). The advantage of the mcrA gene marker is to capture to current and future climate change. This meta-study is
boththephylogeneticandfunctionalsignaturesofmethanogens, performed to fill the gaps associated with methanogenic
offering a high sequencing depth for this particular function biogeography, diversity and its environmental controls by
(Luton et al., 2002; Borrel et al., 2013; Yang et al., 2014). using publicly available mcrA sequence data and literature
A large number of mcrA sequences were retrieved from a complemented through physiological data of methanogenic
variety of natural environments. The public mcrA data set isolates.
allowsforextractinggeneralecologicalpatternsandinvestigating
the shaping environmental gradients at global and regional
scales. Additionally, a database summarizing the physiological MATERIALS AND METHODS
properties of 152 methanogenic isolates is available1 (Jabłon´ski
et al., 2015). Recently, genome binning revealed unusual mcrA Data Collection
sequences in the new class of Methanofastidiosa (Nobu et al., We retrieved mcrA sequences available in GenBank (January
2016) and the new phyla of Bathyarchaeota (Evans et al., 2015) 2016)2. For each hit, the original paper was checked and the
and Verstraetearchaeota (Vanwonterghem et al., 2016). These accordingmcrAsequenceswereparsedbyacustomPerlscript.
new findings expanded our knowledge about the diversity of As we focused on natural environments, methanogenic mcrA
potential methanogens but did not obscure the applicability of sequences were obtained from natural habitats and classified as
the mcrA gene as a molecular marker for the large majority of soil, lake-, estuary-, marine and hydrothermal sediments, and
methanogeniccommunities. mud volcanos. Five libraries from next generation sequencing
To date, methanogenic communities have been detected (NGS)wereincludedinadditiontosequencesofclonelibraries.
in wetlands, sediments, permafrost areas, rice paddies, Sequencesweredownloadedwithouttakingintoaccountrelative
digesters, geothermal springs and hydrothermal vents (Conrad, abundance in the original dataset. Because sequences of clone
2007; Thauer et al., 2008; Wagner and Liebner, 2009). The libraries mainly covers the abundant phylotypes while NGS
methanogenic community structure was found to be associated can capture much deeper diversity, we made a compromise
withenvironmentalpH,temperature,salinity,groundwaterlevel in order to use the NGS data but mitigated a potential error
andvegetationdynamicsatdifferentspatialandtemporalscales due to different resolution of sequencing methods. Therefore,
(Megonigaletal.,2005;Milferstedtetal.,2010;Frank-Fahleetal., we only chose the representative sequences of abundant
2014;McCalleyetal.,2014;Cuietal.,2015;Liebneretal.,2015). OTUs with a relative abundance higher than 1%. We further
Forexample,acetoclasticmethanogenesisisgenerallyhampered rejected those NGS sequences which failed the translation
bylowpHasitreducestheacetatedissociation(Megonigaletal., check from nucleotide to protein sequences or with a low
2005; Kotsyurbenko et al., 2007). The vegetation can supply quality(sequences<250bps).Finally,weconstructedadataset
labile, high-quality organic carbon to fuel methanogens in the containing 4466 unique mcrA sequences from 94 globally
formofrootexudatesordetritussothatplantexudatesgenerally distributedsites(Figure1andSupplementaryTablesS1,S2).In
favor acetoclastic methanogens primarily in fens (Bridgham addition, we did not subtract the mcrA sequences of potential
etal.,2013).Sulfatefromseawaterinhibitsmethaneproduction archaeal methanotrophs from the dataset, which was inevitably
in tidal wetlands, and salinity has consequently been used as a detected in the genomic survey (Conrad, 2007). This part is
general predictor for methane emissions (Holm et al., 2016). beyondthefocusofthisstudy.
A study on Tibetan lake sediments showed that increasing The geographical coordinates and environmental settings
salinity inhibits hydrogenotrophic methanogens but enhances including pH, salinity, elevation, mean annual air temperature
acetoclastic methanogenesis (Liu et al., 2016). These studies (MAAT) and mean annual precipitation (MAP) were extracted
indicatedenvironmentaldriversformethanogeniccommunities, for each research site considered in this study from the
buthavefocusedonsinglehabitatsorlimitedspatialscales. correspondingpublicationsgiventhatthedataareavailable(see
Understanding the adaptation of methanogens to different SupplementaryTableS1).Toaccountformissingenvironmental
environmental changes, however, requires a systematic and parametersacrossmultiplestudies,wequalitativelydefinedsome
1http://metanogen.biotech.uni.wroc.pl/ 2http://www.ncbi.nlm.nih.gov/
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FIGURE1|Locationofthesitesusedforthisstudy.Thesitesweregroupedintosixdifferentcategoriesaccordingtotheirhabitatsandwerelabeledwithdifferent
colors.AsummaryofthesixhabitatsisgiveninSupplementaryTablesS1,S2.
environmentalvariablesaccordingtothesitedescriptionsinthe blasted both the nucleotide and the protein sequence in NCBI
relevant literature, and then converted these category data into and determined the final taxonomic identity by taking into
semi-metric numeric values, for example, we defined marine accountthequerycoverage(>95%),identity(>84%),ande-value
sediments,hydrothermalsediments,volcanicmudandsodalake (<1E-5). For protein sequences, the cutoff at the genus level
sediment samples as “saline,” soil and freshwater lake sediment referredtothethresholdof83.5%(Hungeretal.,2011).
samples as “non-saline,” and mangrove and estuary samples as
“mixed”samples. Ecological and Statistical Analysis
ThestatisticalanalysiswasdonebyvariousRpackages.Principal
Raw Sequence Processing
coordinates analysis (PCoA) ordinations were generated based
The sequence processing was implemented with the Mothur on Jaccard distance matrices constructed using the vegan
softwareplatform(Schlossetal.,2009).Sequencesfromdifferent packagev2.2.0(Oksanenetal.,2015).PermutationalMANOVA
libraries were pooled prior to processing. Sequences with a (multivariate analysis of variance) was conducted to assess
length less than 350 bp or more than 8 ambiguous bases were the source of variation in the Jaccard matrix (McArdle and
discarded. Subsequently, these sequences were aligned against Anderson, 2001) in vegan with 104 permutations. The Jaccard
a pre-aligned subset of mcrA sequences, which were retrieved distance measures are based on the presence/absence of the
fromtheFunGenedatabaseathttp://fungene.cme.msu.edu/(Fish species, which is more suitable for our dataset as most
etal.,2013).ChimericsequenceswereidentifiedwiththeMothur studies only provided the representative sequences while the
softwareusingtheuchimemethod(Edgaretal.,2011)withthe information about abundances is missing. The taxonomic
datasetitselfasreference.Then,thevalidmcrAgenenucleotide incidence frequencies across habitats were visualized through
sequenceswereusedtocomputeuncorrectedpairwisedistances bubbleplotswiththeggplot2package(v1.0.0)(Wickham,2009).
between aligned DNA sequences and further assigned into Hierarchical clustering analysis of the non-saline soil and lake
operational taxonomic units (OTU) at a cutoff of 84% which sedimentcommunitieswasperformedbytheRfunction‘hclust’
corresponds to 97% for 16S rRNA gene (Yang et al., 2014). (R Core Team, 2014). The obtained community clusters were
The abundance of each mcrA OTU was only accounted as described according to the pH and temperature regime of the
presenceorabsence.Weincreasedtheaccuracyofthetaxonomic original samples because using PCoA beforehand we identified
classification of the OTUs by considering both nucleotide and bothparameterspHandtemperaturetoinfluencemethanogenic
amino acid sequences. At DNA level, the taxonomic identity communitycompositioninnon-salinesoilsandlakesediments.
was assigned by the Mothur platform according to a reference Theassociationofeachmethanogeniclineagewitheachofthese
database(Yangetal.,2014).Atproteinlevel,thealignedprotein clusterswasdeterminedusingcorrelation-basedindicatorspecies
sequences were used to construct a tree in ARB, and then the analysis (Dufrene and Legendre, 1997). Indicator species are
taxonomicassignmentwasbasedonthecorrespondingdatabase. defined here as those that are both abundant in a specific type
If the assignment of an OTU was inconsistent, we manually of habitat (specificity) and predominantly found in this type
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of habitat (fidelity). In this study, the indicator taxa, similar Inmarinesediments,MethanoculleusandMethanosaetaarethe
to the indicator species concept, for non-saline soils and lake mostcommonlineages,followedbyMethanolinea.
sediments were picked according to an indicator value (IndVal Even though many taxa were detected in different
value)bytheRpackagelabdsv(Roberts,2016)iftheprobabilityof environments, some still show environmental preferences.
obtaininganindicatorvalueashighasobservedoverthespecified Methanoregula, the taxon frequently occurring in non-marine
iterations is less than 0.05. The Chao2 indices were calculated and transitional environments (soils, lake sediments and
for each sample using the vegan package. The Wilcoxon rank estuaries), is absent from marine habitats (marine sediments,
sum test of Chao2 indices between habitats was performed by hydrothermalsedimentsandmudvolcanos).Methanobacterium
theRfunction‘wilcox.test’(RCoreTeam,2014).Toconsolidate and Methanocella, which prevail in the non-marine and
theimpactofhabitatfilteringonmethanogenicarchaeaisolates, transitionalenvironments,arerarelyfoundinmarinehabitats.In
the physiological and biochemical characteristics of described contrast,Methanococcoides,asapredominantlineageinmarine
methanogenic cultures were retrieved from the3 ‘Methanogenic sediments,hydrothermalsedimentsandmudvolcanos,isbarely
archaea database’ (Jabłon´ski et al., 2015). Among them, the observedinsoilsandlakesediments.Moreover,Methanogenium
isolates with information of optimum NaCl requirement were and Methanolacinia are only observed in estuary and marine
filtered, categorized and plotted according to their isolation sedimentswhereasMethanospirillumandMethanosphaerulaare
source. onlyfoundinterrestrialenvironments.Inaddition,somespecific
To examine the influence of dispersal limitation on taxaareexclusivelyfoundinhydrothermalsediments,including
methanogenic community structure, a linear regression Methanocaldococcus, Methanothermococcus, Methanopyrus,
analysiswasperformedbasedonageographicaldistancematrix Methanotorris,andMethanococcus.Althoughsomelineagessuch
and community Jaccard distance matrix by the R function ‘lm’ as Methanosaeta are present in most of the environments, no
(RCoreTeam,2014).WeperformedMantelandpartialMantel lineagecanberegardedasomnipresent.
teststoevaluatetheeffectsofdispersallimitationaccordingtothe Thehighestrichnessoflineagesoccurredinestuarysediments
twomatricesagainusingtheveganpackageinR(Oksanenetal., which also harbor more even incidence frequencies of various
2015). Further, multivariate spatial analysis (spatial PCA) was lineages.Incontrast,mudvolcanoandhydrothermalecosystems
appliedto16Europeansoilandlakesedimentsamplesbasedon display relatively low methanogenic diversity. Soils and lake
Moran’sI indextoexplorethespatialstructureofmethanogens sediments similar to estuaries are characterized by diverse
by the function “multispati” in the R ade4 package (Dray and methanogenicassemblages.
Dufour, 2007). In addition, the Ward’s Minimum variance
clustering which was based on the Jaccard distance matrix was Alpha-Diversity of Methanogenic
implementedonthese16samplesusingtheRfunction“hclust” Communities in Natural Environments
(R Core Team, 2014) and we further projected the clustering
The richness of methanogenic archaea according to the Chao2
results on to a geographical map. The European shapefile for
index varied largely between ecosystem types (Figure 3). For
mapping at state level is available at the GSHHG Database
the purpose of directly comparing the alpha diversities and for
(v2.3.6)4.ThemapwasgeneratedbyusingQGISv2.18.25.
obtaining a reasonable trade-off among samples, subsampling
to 15 sequences for each site was performed. The Chao2
index shows that estuary sediments encompass the highest
RESULTS
species richness of methanogens along the six ecosystem types
(Supplementary Table S3), which underlines the results of the
Biogeography of Methanogenic Archaea
bubbleplot(Figure2).Soilsandlakesediments,showinglower
in Natural Environments richness than estuary samples, have significantly higher Chao2
The mcrA gene sequences from 94 globally distributed natural indicesthanmarinesedimentsandhydrothermalsedimentsand
environments were retrieved. The location and ecosystem with no significant differences between marine sediments and
type of each of these 94 sites is depicted in Figure 1. The hydrothermalsediments(SupplementaryTableS3).
incidence (presence/absence) frequencies of methanogenic
lineagesweremergedaccordingtoecosystemtypeandillustrated Global Controls on Methanogenic
in Figure 2. Briefly, Methanoregula is the most frequently Communities in Natural Environments
observed taxon in soils, together with Methanobacterium,
The 94 globally distributed methanogenic communities were
Methanosaeta, Methanocella, Methanomassiliicoccus, and
clustered into an ordination plot by applying PCoA based on
Methanosarcina. In estuary sediments, sequences from
Jaccarddistancematrix.AccordingtothePCoAanalysis,thefirst
Methanosaeta, Methanobacterium, Methanoregula, and
andsecondaxestogetherexplain16.3%ofthetotalvariance.The
Methanoculleus were commonly detected. Moreover in lake
variationsamongthesamplescantherebybelargelyexplainedby
sediments, Methanoregula and Methanosaeta mainly occurred.
salinity (Figure 4). Since initial data on the salt concentrations
were unavailable in some cases, we qualitatively assigned these
3http://metanogen.biotech.uni.wroc.pl/ samplesassaline,mixed(intermediate)andnon-salinesamples
4https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html asdescribedabove.Thesalineandnon-salinesampleseffectively
5http://qgis.osgeo.org separate along the first axis. The mixed samples overall group
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FIGURE2|Bubbleplotshowingtheincidencefrequenciesofmethanogeniclineagesindifferentnaturalenvironments.Therankorderalongtheverticalaxis
correspondstothedecreasingtotalincidencefrequenciesofthelineages.Thetaxonomyisshownforthegenuslevel.Ifanassignmenttothegenuslevelwasnot
possiblethenexthigherassignabletaxonomiclevelwasused.Thenumberofsamples(n)isgivenforeachhabitat.
in-betweenthesalineandnon-salinesamples.Thepermutational communitiesfromthesetwohabitatswhichaccountfor33study
MANOVAbasedontheJaccarddistancematrixalsosuggeststhat sites in total. Community based cluster analysis for these two
salinityistheprimaryabioticfactorcontrollingthedistribution types of habitats revealed four clusters based on the Jaccard
of global methanogenic communities (R2 = 0.099, P < 0.001) distance matrix (Supplementary Figure S3). Permutational
(Table1). MANOVA suggest that both pH (R2 = 0.099, P < 0.001) and
Additionally,wecheckedforapotentialrelationbetweenthe temperature(R2=0.069,P<0.001)influencethemethanogenic
isolationsourceofmethanogenicpureculturesandtheoptimum β-diversity in non-saline soils and lake sediments (Table 2).
concentrationofNaClforgrowth.Theoptimalconcentrationof Accordingly,weassignedthefourclusterstothepHandMAAT
NaClofthemethanogenicpureculturesdemonstratedadecline of the initial sampling site and obtained largely consistent
from marine to estuaries, lake sediments and soil ecosystems subgroups to the community clustering (Figures 5C,D). The
(SupplementaryFigureS2).Afewoutlierisolatesoriginatefrom combination of environmental characteristics and these four
sodalakesedimentsorhypersalinesoils. communityclustersenablesustodefinethesefoursubgroupsas
neutral and cold group 1, acidic and cold group 2, acidic and
moderate group 3, and neutral and warm group 4 (Figure 5).
Environmental Controls and
Further PCoA ordination based on the Jaccard dissimilarity
Methanogenic Indicator Taxa in
matrix suggests that along the PC1 axis, most of the samples
Non-saline Soils and Lake Sediments from group 2 and group 3 are from acidic soils and lake
On a global scale, methanogenic communities from non-saline sedimentswhilegroup1andgroup4weremainlyfromneutral
soils and lake sediments cluster closely (Figure 4) so that we environments (Figure 5A). Moreover, samples from moderate
further analyzed the environmental controls of methanogenic sites(group3)separatedfromthoseofwarmandcoldsitesalong
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TABLE1|PermutationalMANOVAanalysisonJaccarddistancematrixofallthe
samplesfromthesixhabitatstotesttheassociationofcommunityvariancewith
differentenvironmentalvariables.
Notsubsampled Subsampledto15
sequences
Environmental R2 P R2 P
variables
Salinity 0.09890 0.0001∗∗∗ 0.09998 0.0001∗∗∗
Elevation 0.01894 0.0003∗∗∗ 0.02026 0.0158∗
Latitude 0.02145 0.0002∗∗∗ 0.03136 0.0001∗∗∗
ThestatisticalsignificancewithP-values<0.001andP<0.05werehighlighted
by‘∗∗∗’and‘∗’,respectively.
TABLE2|PermutationalMANOVAbasedonaJaccarddistancematrixof
non-salinesoilandlakesedimentsamplestotesttheassociationofcommunity
variancewithdifferentenvironmentalvariables.
Notsubsampled Subsampledto15
sequences
FIGURE3|BoxplotofChao2indicesofthedifferentecosystemtypes.The
plotisbasedonsubsampleddatasetscontaining15sequencesforeachsite Environmental R2 P R2 P
tomakethecomparisononalphadiversitymeasuresmorerobust.The variable
numberofsamplesineachhabitatisgivenas‘n’underneaththehabitatlabel.
pH 0.0992 0.0001∗∗∗ 0.09334 0.0001∗∗∗
The‘ns’donatesnostatisticalsignificanceinWilcoxontest.Thestatistical
resultofalphadiversityatOTUlevelisgiveninSupplementaryTableS3. MAAT 0.0699 0.0001∗∗∗ 0.06342 0.0006∗∗∗
MAP 0.0447 0.0098∗∗ 0.03375 0.2595
Elevation 0.0577 0.0009∗∗∗ 0.04788 0.0211∗
ThestatisticalsignificancewithP-values<0.001,P<0.01,andP<0.05were
highlightedby‘∗∗∗’,‘∗∗’,and‘∗’,respectively.MAAT,meanannualairtemperature;
MAP,meanannualprecipitation.
We examined the occurrence of methanogenic lineages
in each subgroup based on incidence frequencies (Figure 6).
Methanoregula prevails in all types of non-saline habitats. In
addition to Methanoregula, the neutral and cold subgroup
(group 1) displays a high abundance of Methanosaeta,
Methanobacterium, and Methanosarcina. The acidic and cold
group2isrepresentedbyMethanobacterium,Methanocella,and
MethanosarcinawhileMethanosaetaisabsenthere.Methanocella
and Methanosaeta are common in the acidic and moderate
group 3. In the neutral and warm group 4, Methanolinea and
Methanosaeta are important members. This group is the only
groupwhereMethanoculleuswasidentified.
For all four groups, the taxa having a high incidence
FIGURE4|Principalcoordinatesanalysis(PCoA)ordinationbasedonthe are Methanoregula, Methanobacterium, Methanosarcina,
JaccarddistancematrixofmethanogenicmcrAgenesequencelibraries Methanosaeta, Methanomassiliicoccus, and Methanocella. The
comparing94samples.ThePCoAiscoloredbysalinity:Theredsymbols specialisttaxa,whicharesignificantlymorerepresentedinmost
indicatenon-salineenvironments,theblueonesindicatesalineenvironments,
of the sites within a given group, were detected according to
andthegreenonesindicateintermediateenvironments.Differentsymbols
depictdifferentenvironments.Thepercentageofthevariationexplainedby indicator species analysis as described before. In total, six out
theplottedprincipalcoordinatesisindicatedontheaxes. of the 31 taxa showed a significant indicator value (P < 0.05)
(labeled with asterisk in Figure 6). Group 1 (neutral and
cold) showed the largest number of specialist with lineages
PC2,whereasthesamplesfromthewarmenvironments(group4) of Methanosaeta, Methanolobus, and Methanomethylovorans.
separated from the other samples along PC3 (Figure 5B). Methanobacterium served as a specialist taxon in the cold and
TherebythefirstthreeaxesofthePCoAordinationexplain38.8% acidic group 2 while Methanolinea was identified as a specialist
ofthetotalvariation. ingroup4(neutralandwarm)butwashardlyobservedinother
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FIGURE5|PrincipalcoordinatesanalysisplotbasedonaJaccarddistancematrixfor33non-salinesoilsandlakesediments.Subplot(A)showsPC1andPC2and
symbolsarecoloredbypH,andsubplot(B)showsPC2andPC3andsymbolsarecoloredbytemperature.Thefirstthreecomponentsexplain17.1,11.7,and10%
ofthevariance.TheboxplotsshowthepH(C)andMAAT(meanannualairtemperature)(D)offouridentifiedsub-groups.TheboxcolorinfigureCcorrespondsto
thepHcategoryin(A).ThecolorsoftheboxplotsshowstatisticalsignificancebasedonapairwiseWilcoxontest(P<0.05),wherethesampleswiththesamecolor
donotsignificantlydifferfromeachother.Similarly,colorin(D)followsthetemperaturegroupingin(B).Thesub-groupsrefertothehierarchicalclusteranalysisof
communitysimilarities.
groups.Inaddition,Methanoregulaislargelyrepresentedinthe effects. Plotting the geographical distance against Jaccard
acidicandmoderategroup3. community similarity shows no clear linear trend but patterns
whichmainlyresultfromtheglobaldistributionofthesampling
Dispersal Limitation points(seeSupplementaryFigureS4).
A linear regression analysis (R2 = 0.05, P < 0.001) indicated In order to further analyze the influence of dispersal, we
a weak correlation between geographical distance and limited our analysis to Europe, which was sampled densest
methanogenic community structure on the global dataset. and most even. Mantel tests and partial Mantel tests on
Atthesametime,aManteltestshowedthattheenvironmental this subset reproduced the trend that the community data
variables have a higher correlation to the community structure is higher correlated to the environmental variables than to
thanthegeographicaldistances(seeTable3).Thistrendisalso geographical distances (see Table 3). The partial Mantel test
confirmedbyapartialManteltestcontrollingforautocorrelation controlling for environmental variables could not detect any
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FIGURE6|Incidencefrequenciesofmethanogeniclineageswithinthefourdefinedgroupsofmethanogeniccommunitiesinsoilsandlakesediments.Thevertical
axisisarrangedinalphabeticorder.Thebarlengthcorrespondstotheaverageincidencefrequenciesforeachlineagewithinthecorrespondinggroup.Theerror
barsrepresentthestandarddeviationofagiventaxonoverdifferentsamplesinthatgroup.AsterisksshowthespecialisttaxawithP-value<0.05.Thetaxonomyis
shownforthegenuslevel.Ifanassignmenttothegenuslevelwasnotpossiblethenexthigherassignabletaxonomiclevelwasused.Thedescriptionforthefour
groupsisgiveninFigure5.
TABLE3|MantelandpartialManteltestanalysesforthedeterminationoftheinfluenceofenvironmentalvariablesandgeographicaldistanceontothemicrobial
distributionfortheglobaldatasetandasubsampleof16Europeansamples.
Test Matrices Globalsamples Europeansamples
r P r P
Mantel Geodistvs.Jacdist 0.2153 <0.001 0.3884 0.0032
Envdistvs.Jacdist 0.3838 <0.001 0.4421 0.0019
PartialMantel Geodistvs.Jacdist(Envdistconditioned) 0.1436 <0.001 0.0598 0.2685
Envdistvs.Jacdist(Geodistconditioned) 0.3525 <0.001 0.2364 0.0380
Geodist,Geographicaldistance;Envdist,Environmentaldistance;Jacdist,Jaccarddistance.
statisticallysignificantcorrelationbetweenmicrobialcommunity reproduce the separation of the spatial PCA along the Baltic
and geographical distance. A spatial PCA analysis on these 16 Sea. Accordingly, some sites which are geographically very
European sites implies a spatial structure of the methanogenic closetoeachotherexhibitdissimilarmethanogeniccommunity
community (23.7% of the total variance was explained by this structures and assemble with different clusters. On the other
structure)whichcorrespondstoapositivespatialautocorrelation hand, some geographically very distant sites show very similar
of the sites as indicated by the Moran’s I index (Moran’s communitycompositionsandclustertogether(Figure7B).The
I = 0.4018). Only the first eigenvalue was stable and regional dispersal of the groups in Europe does not seem to
corresponded to a separation of the samples between Central correspondtoortobelimitedbyageographicalstructure.
Europe and the Baltic States (Supplementary Figure S5). The
small data set, however, complicates a robust assignment of
this observed spatial structure to geographical, environmental DISCUSSION
variablesortobothofthem.Wethereforecarriedoutacluster
analysis on the methanogenic communities as described above Identifyingandapplyingconceptsofbiogeographyonmicrobial
and revealed three groups (Figure 7A) which we projected communitiesisofmajorinterestinmicrobialecology.Microbial
on a geographical map (Figure 7B). The clustering did not biogeography is believed to be governed by the evolutionary
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FIGURE7|HierarchicalclusteranalysisofJaccarddistancematrixamong16Europeansoilandlakesedimentsamples.TheWardclusteringmethodwasusedfor
theanalysis.Thesitesweredividedintothreegroupscoloredbydifferentrectanglesinclusteringdendrogram(A)andwereprojectedonaEuropeanmap(B).
and ecological interplay of four major processes: habitat expected by chance (Weiher and Keddy, 2001; Cornwell et al.,
filtering, dispersal, drift and mutation (Hanson et al., 2012). 2006;Ulrichetal.,2010).Ourresultshowthatlargedifferences
Even though the influence of drift and mutation is beyond among methanogenic community composition occur between
the interpretation power of this study, we show that global marineandwetlandandlakeecosystems,whileestuariescluster
patternsofmethanogeniccommunitiesinnaturalenvironments inbetween.Biogeographypatternsbasedonasetofgenesurveys
exist. This study demonstrates a global biogeographic pattern onenvironmentalsampleswerealsoreportedforgeneralbacteria
of methanogenic communities that is more associated with (Nemergutetal.,2011),themethaneseepmicrobiome(Ruffetal.,
habitatfilteringthanwithgeographicaldispersal.Methanogenic 2015), ammonia-oxidizing archaea (Cao et al., 2013), marine
communities from soda lake sediments, for example, cluster pelagicandbenthicbacteria(Zingeretal.,2011),andnitrogen-
closely with geographically distant marine samples (Figure 4) cycling microorganisms (Church et al., 2008). Habitat filtering
andverysimilarmethanogeniccommunitiesoccurinEuropean was specifically reported for uncultured archaea (Auguet et al.,
soilandlakesedimentsdespitelocatedinlargedistancebetween 2010),entirebacterialcommunitiesindiverseenvironments(von
eachother(Figure7).Overall,ourattemptstodisclosepotential Meringetal.,2007;Chaffronetal.,2010)orinSouthAmerican
dispersal limitation revealed a weak influence of geographical peatlandsatregionalscale(Olooetal.,2016),aswellasspecific
locationonmethanogeniccommunitystructurewhichcontrasts bacterial groups such as methane-oxidizing (Knief, 2015) and
a clear influence of environmental conditions. A conclusive nitrogen-fixingbacteria(Nelsonetal.,2016).
exclusionofspatialeffectsontothemicrobialcommunitiesisnot Our results indicate that at the global scale, salinity
possiblewiththeavailabledatapoints.Samplingpointsfocuson substantially regulates methanogenic community composition
certainhabitatsand/orareaswhiletheoverallnumberofsamples anddetermineslargedifferencesbetweenmarineandterrestrial
is low. If there is an effect, we assume spatial effects to occur methanogenic assemblages. Also methanogens from soda lake
on regional or local scale only. Local dispersal limitations were sedimentsclusterwiththosefrommarinesediments(Figure4)
reported for hyperthermophilic archaea caused by geographical highlighting the global influence of salinity. This result is in
barriers (Whitaker et al., 2003), ammonia-oxidizing bacteria accordance with other studies based on the 16S rRNA gene
at local salt marshes, but not regional and continental scales disclosing that salinity is a primary factor shaping global
(Martiny et al., 2011), microorganisms in deep-sea sediments patterns of overall bacterial and overall archaeal communities
togetherwithenvironmentalsettings(Schaueretal.,2009),and (Lozupone and Knight, 2007; Auguet et al., 2010; Caporaso
for bacteria of a large set of heterogeneous snow environments et al., 2011; Cao et al., 2013). A low influence of geographical
mostlycausedbytheavailabilityofallochthonouscarbon(Lutz separation but a strong impact of salinity on general microbial
et al., 2016). A minor influence of dispersal limitation on communities was also observed in previous studies (Logares
methanogensinnaturalenvironmentsmeansthatmethanogens et al., 2013; Yang et al., 2016). Accordingly, salinity largely
can randomly distribute over space, are successfully selected by determines which lineages can survive. In various habitats
thelocalenvironmentiftheirphysiologicalrequirementsaremet methane production activity was negatively correlated with
and can establish stable communities (Martiny et al., 2006; von salinity (Bartlett et al., 1987; Potter et al., 2009; Poffenbarger
Meringetal.,2007). et al., 2011). The inhibition of methane production through
The concept of habitat filtering implies that species with salinity is thereby suggested to coincide with a reduced
similarecologicalrequirementsshouldco-occurmoreoftenthan methanogenicpopulationsize(Pattnaiketal.,2000).Theeffectof
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salinity on hydrogenotrophic, acetotrophic and methylotrophic neutral environments, which is consistent with other studies
methanogenesis thereby depends on the level of salinity and (Rosenberg et al., 2014) and underlines the robustness of our
is different for the different pathways of methanogenesis (Liu approach. Generally, the geochemical conditions surrounding
et al., 2016). Currently, there is no clear mechanism to explain methanogenic communities will lead to niche differentiation.
the impact of salinity on community structure but several Since the niche sorting tends to leave the adaptive specialists
hypotheses may serve as possible explanation. Physiologically, (Langenheder and Székely, 2011), the progressive long-term
salinity influences the external and internal osmolarity of cells. environmental selection generated a variety of niches that were
Thenon-salinemethanogeniccellshavedevelopedphysiological filled by an array of endemic habitat specialists, which may
adaptions to counter internal turgor pressure, while the salt- be less represented or absent in other different environmental
adaptedcellshavelostsuchfeature(Zinder,1993).Inaddition, conditions.Thecommunityisalsoshapedbybioticfactors,such
increasing salinity can induce methanogens to synthesize or asecologicalinteractions,dynamics,competitionandsymbiosis.
take up an increased proportion of compatible solutes at a Despitethosebiologicalfactors,vonMeringetal.(2007)found
significant energetic and thus metabolic cost (McGenity, 2010). that habitat preferences are often remarkably stable over time
The trait of salt tolerance is even manifested in the optimum and the distinctive taxonomic composition of environmental
concentrationofNaClforgrowthofmethanogenicpurecultures communities, in turn, may be an important indicator of their
since we found that the isolates from marine sediments and ecologyandfunction.
hydrothermal sediments have significantly higher optimum Consistenttothehabitatpreferenceofmethanogenicarchaea,
NaCl concentration than those from soils (Supplementary it appears that closely related methanogenic strains were
FigureS2). often isolated from comparable environments. For example,
In the non-saline terrestrial ecosystems, specifically in Methanoregulaceae seems to be quite diverse in natural
soils and lake sediments, the methanogenic community environments (Yang et al., 2017) and neutral strains may resist
composition is controlled by the combination of temperature culturingsofar.ThecurrentlydescribedstrainsofMethanoregula
and pH. Accordingly, methanogens of these environments arebothobtainedfromslightlyacidophilicenvironments,while
could be classified in four groups (Figure 6). Unlike marine thetworepresentativesofMethanolineaarefromrelativelywarm
ecosystems, the non-saline terrestrial ecosystems show a large habitats such as digester sludge and rice field soil, respectively
natural variability both of pH and temperature. Temperature (Rosenberg et al., 2014). Although Methanobacterium strains
can affect not only the methanogenic pathway but also wereisolatedfromvariousenvironments,approximatelyhalfof
methanogenic populations themselves (Conrad, 2007; Rooney- the existing isolates of this genus exhibit pH optima slightly
Vargaetal.,2007).Methaneproductioncanbegreatlyenhanced less than 7. This means that the indicator lineages, which were
if temperatures rise as a consequence of the temperature- identified based on the environmental sequences, could reflect
sensitivestepsduringfermentationandacetogenesis(Megonigal the differentiations of physiology and sources of the existing
et al., 2005; Kotsyurbenko et al., 2007). In addition, low pH methanogenic cultivars. For example, the habitat salinity, as a
can substantially limit the availability of acetate by preventing generalpropertyofhabitat,canprogressivelyexposeorganisms
acetate from dissociating and thus negatively affect acetoclastic to strong environmental selection and filter the assembly of a
methanogenesis (Fukuzaki et al., 1990; Bridgham et al., 2013). newsetofspecieswhicharebestsuitedfortheambientsalinity
ThiscouldbeapossiblereasonthatMethanosaetawasabsentin (Logaresetal.,2013).
the group2, while Methanosarcina can switch between different Biodiversity conservation and management is a primary
sourcesandwasnotsubstantiallyinfluenced.Moreover,pHcan challenge of our current society. Here we show that
regulatetheefficiencyofmethaneproductionandmethanogenic methanogenic archaea of natural environments are most
pathways from ombrotrophic to minerotrophic peatlands, diverse in estuary sediments. Estuaries are transition zones
throughdirectinhibitionofbothmethanogenesispathwaysand between marine and terrestrial ecosystems. This allows for
indirectly through its effects on fermentation (Ye et al., 2012). two major processes that can contribute to species richness.
Therefore, both temperature and pH can directly or indirectly Firstly, microbes from the sea and land mix at the estuaries
regulate metabolic steps associated with methanogenesis and and eventually encompass an overall high diversity (McLusky
the upstream fermentation, which provides substrate for and Elliott, 2004). For example the high diversity of estuary
methanogens. bacteria, archaea, fungi, and even specific bacteria performing
Methanoregula is ubiquitous and very abundant in all unique functions have been observed (Cunliffe et al., 2008;
four groups of terrestrial habitats (Figure 6) but virtually Mosier and Francis, 2008; Crump et al., 2012). Another aspect
absent from the marine system and may thus prove to be is the high nutrient level due to the terrestrial, tidal inputs
a proxy for freshwater influence in the marine realm. Its for estuary organisms to feed on (McLusky and Elliott, 2004;
global relevance was recently reported elsewhere (Yang et al., Statham, 2012). In this context, the estuary environments are
2017). Despite its ubiquitous distribution in soils and lake of importance in recovering generic novelty for methanogens.
sediments, Methanoregula occurs as an indicator lineage in So far, the effects of species diversity on ecosystem processes
acidic habitats with moderate temperatures. Moreover, (1) have attracted substantial research efforts. The link between
Methanolinea appears to have particularly adapted to the biodiversity and ecosystem function is still under debate and
warm, neutral terrestrial environments, (2) Methanobacterium remainselusiveformicrobialcommunities(Loreauetal.,2001;
to the cold, acidic environments, and (3) Methanosaeta to pH Tilman et al., 2014). Even though soils and lake sediments
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