Potential sources of microbial colonizers in an initial soil ecosystem after retreat of an alpine glacier

17 Pages·2016·2.43 MB·English

by  Thomas Rime

TheISMEJournal(2016)10,1625–1641
©2016InternationalSocietyforMicrobialEcology Allrightsreserved 1751-7362/16
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ORIGINAL ARTICLE
Potential sources of microbial colonizers in an initial
soil ecosystem after retreat of an alpine glacier
Thomas Rime, Martin Hartmann and Beat Frey
Rhizosphere Processes Group, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
Rapid disintegration of alpine glaciers has led to the formation of new terrain consisting of mineral
debris colonized by microorganisms. Despite the importance of microbial pioneers in triggering the
formation of terrestrial ecosystems, their sources (endogenous versus exogenous) and identities
remainelusive. We used 454-pyrosequencing to characterize thebacterial and fungalcommunities in
endogenous glacier habitats (ice, sub-, supraglacial sediments and glacier stream leaving the glacier
forefront) andinatmospheric deposition (snow, rain andaeolian dust). We compared these microbial
communities with those occurring in recently deglaciated barren soils before and after snow melt
(snow-coveredsoilandbarrensoil).Atmosphericbacteriaandfungiweredominatedbyplant-epiphytic
organismsanddifferedfromendogenousglacierhabitatsandsoilsindicatingthatatmosphericinputof
microorganismsisnotamajorsourceofmicrobialpioneersinnewlyformedsoils.Wefound,however,
that bacterial communities in newly exposed soils resembled those of endogenous habitats, which
suggests that bacterial pioneers originating from sub- and supraglacial sediments contributed to the
colonization of newly exposed soils. Conversely, fungal communities differed between habitats
suggesting a lower dispersal capability than bacteria. Yeasts putatively adapted to cold habitats
characteristicofsnowandsupraglacialsedimentsweresimilar,despitethefactthatthesehabitatswere
notspatiallyconnected.Thesefindingssuggestthatenvironmentalfilteringselectsparticularfungiin
coldhabitats.AtmosphericdepositionprovidedimportantsourcesofdissolvedorganicC,nitrateand
ammonium. Overall, microbial colonizers triggering soil development in alpine environments mainly
originate from endogenous glacier habitats, whereas atmospheric deposition contributes to the
establishmentofmicrobialcommunitiesbyprovidingsourcesofCandN.
TheISME Journal (2016) 10,1625–1641; doi:10.1038/ismej.2015.238; published online15January 2016
Introduction weathering rock minerals and incorporating organic
matter (Frey et al., 2010; Smittenberg et al., 2012).
Knowledge about the dispersal of microorganisms
Despitetheimportanceofpioneermicroorganisms
and how they colonize newly exposed habitats is
in triggering soil formation, their origin and identity
essential for understanding the biogeographical
are largely unknown (Bradley et al., 2014). Mineral
patterns of microbial diversity (Martiny et al., 2006;
debris released after glacier retreat is exposed
Tedersoo et al., 2012). Most alpine glaciers are
tocontinuousatmosphericinput (exogenoussource)
currently retreating leading to formation of new
ofmicrobes(Womacketal.,2010)throughprecipita-
terrestrial ecosystems.Glacier ecosystems have been tions (Zhang et al., 2010; Šantl-Temkiv et al., 2012)
used extensively to investigate dynamics of soil
and dry deposition, such as wind-blown dust and
formation in relation to shifts in microbial commu-
plant debris (Chuvochina et al., 2011; Bowers et al.,
nities(forexample,Bernasconietal.,2011;Zumsteg
2012; DeLeon-Rodriguez et al., 2013). The atmo-
et al., 2012; Bajerski and Wagner, 2013; Rime et al.,
sphere in particular is often wrongly considered
2015).Theyalsorepresentidealecosystemstostudy
sterile because of harsh ultraviolet (UV) radiation
natural establishment of microbial communities in
and extreme C and nutrient limitations (Womack
terrestrial environments (Edwards et al., 2014).
et al., 2010). However, recent studies have reported
When glacier ice melts, entrapped rocks, sediments
that the atmosphere harbours complex and viable
and fine debris are released and microbial pioneers
microbial communities (Vaïtilingom et al., 2010;
present on these debris initiate soil formation by
Šantl-Temkiv et al., 2012; Stres et al., 2013).
For example, Amato et al. (2005) isolated active
bacteria and fungi from cloud droplets. Similarly,
Correspondence: B Frey, Rhizosphere Processes Group, Swiss Šantl-Temkiv et al. (2013) reported that viable
FederalResearchInstituteWSL,Zürcherstrasse111,Birmensdorf methane-oxidizing bacteria were found in rain and
CH-8903,Switzerland.
Favetetal.(2013)retrievedlivingalgae(forexample,
E-mail:[email protected]
Chlorella) and fungi (for example, Aspergillus) in
Received 20 June 2015; revised 9 October 2015; accepted
8November2015;publishedonline15January2016 wind-blown desert dust. These studies thus suggest
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thatmicroorganismsmaybetransportedthroughthe contents and are mainly characterized by lichens,
atmosphere and may represent a non-negligible algae and mosses (Bernasconi et al., 2011).
reservoir of microbes able to colonize recently
deglaciated mineral debris. In contrast to these
exogenous sources, microorganisms already present Experimental procedures
In our sampling approach, snow (referred hereafter
at the surface of the glacier, entrapped in ice,
to as Snow), as well as dry and wet atmospheric
transported by glacial water runoff and thriving
summer deposition (Deposition) were considered
under basal ice (endogenous sources) can also
exogenous sources of microbial colonizers, whereas
potentially colonize deglaciated debris and partici-
basal glacier ice (Ice), glacier stream water (Glacier
pate in soil formation (Edwards et al., 2014).
stream), sub- (Subglacial sediment) and supraglacial
Hamilton et al. (2013) reported that viable micro-
(Supraglacial sediment) sediments were considered
organisms thrive under ice. In addition, several
endogenous sources of microbial colonizers.
studies showed that cryoconite holes and supragla-
Microbialcommunitieslivinginsnowmayoriginate
cialdebrisarehot-spotsofmicrobialdiversityontop from more distant soils, aerosolized and deposited
of glaciers (Anesio et al., 2009, 2010; Edwards et al., with snow (Cameron et al., 2015). We therefore
2013b;Franzettietal.,2013)andmaythusconstitute considered this habitat as an exogenous source
part of the microbial pioneer communities in of microbial pioneers. Owing to the fact that
recently deglaciated barren soils. the Damma glacier ice is covered by sediments,
Although these exogenous and endogenous wecouldnotidentifycryoconiteholes.Wetherefore
sources have been investigated separately, their sampled mineral debris and referred to them as
relative contribution to microbial colonization of supraglacial sediments. Snow was sampled on
mineral debris recently exposed after glacier retreat 23 June 2014. Barren soils, considered as a poten-
has not been assessed yet. To address this gap, tiallycolonizablehabitat,weresampledbeforesnow
we simultaneously characterized the microbial melt (Snow-covered soil; collected on 23 June 2014)
communities in exogenous and endogenous sources and 3 weeks after snow melt (Barren soil; collected
andcomparedthemwiththemicrobialcommunities on 10 July 2014) to determine whether microbial
found in barren soils formed after the retreat of the pioneers present in snow were also found in barren
Damma glacier (Switzerland). In particular, we used soils after snow melt. We sampled all other habitats
barcoded 454-pyrosequencing of archaeal, bacterial 3 weeks (collected on 10 July 2014) after snow melt,
andfungalribosomalmarkers.Ourstudyspecifically as recommended by Musilova et al. (2015). Detailed
addresses the following questions: (1) What is the informationaboutthesamplingprocedureandsample
origin (exogenous versus endogenous sources) of locations is given in Supplementary Table S1 and
the microbes colonizing newly formed terrain after Supplementary Figure S1.
glacier retreat? (2) Which microbial taxa are Briefly,approximately2 litresnowsurfaces(0–2cm
characteristic of the exogenous and endogenous depth) were sampled in triplicate during the melting
sources? (3) Which environmental factors shape the phase, as described by Lazzaro et al. (2015), with
microbial taxa characteristic of these habitats? EtOH-cleaned laboratory spoons in autoclaved poly-
ethylene (PE) bags. After sampling of the snow
patches, we removed snow aseptically to collect
Materials and methods around 100g of barren soil (0–2cm depth). After
snow melt, around 100g of barren soil (0–2cm
Site description depth) were sampled at the same sites, which were
Samples were collected in the Damma glacier marked with sticks during snow sampling. We
catchment in the Central Alps of Switzerland. installed three sets of sampler pair to collect atmo-
This site was selected because it has been studied sphericdepositionatthesamelocationsasthesnow
extensively in various scientific disciplines, includ- andsoilsamples.Eachsampler consisted ofasterile
ing hydrology, botany, soil carbon dynamics and (acidandEtOHrinsed)1litrePEbottlecoupledwith
microbiology (Bernasconi et al., 2011; Menon et al., a sterile 12-cm diameter PE funnel fixed on a stake
2014), and thus detailed information about the 1m above the ground. The samplers were left open
ecosystem dynamics in this glacier forefield was for 2 weeks (from 30 June 2014 to 9 July 2014) to
available. The climate in the catchment area is collect passively atmospheric dust at ambient tem-
characterized by annual precipitation of 2200mm peratureandweresampleddirectlyafterarainevent
and high seasonal temperature fluctuations, ranging (on9July2014)inordertorecoverbothatmospheric
from −8°C to 4°C, with an annual average tempera- dry and wet inputs while limiting aftergrowth.
ture of 2°C (Bernasconi et al., 2011). The growing Around 1 litre of rain per bottle resulting in
season lasts from June to September. Before the approximately 2 litre of rain per sampler pair were
glacier terminus, the initial soils sampled in our collected in July after a Saharan dust event,
study are located 20m away from the glacier tongue and in August (left open from 31 July 2014 to
(deglaciated for approximately 2 years) and consist 9August2014;collectedon9August2014),whenno
of barren sandy rocks with low carbon and nitrogen Saharan dust event was detected at the Jungfraujoch
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researchstation(CollaudCoenetal.,2003).Owingto DNA extraction, PCR reactions and 454-
the absence of significant differences in α- and β- pyrosequencing
diversitiesandphysico-chemicalfactorsbetweenthe Total DNA from soils and sediments (approximately
two sampling times (data not shown), we averaged 1g) was extracted with the UltraClean Soil DNA
thesesamplesandtreatedthemaspoolstorepresent extraction kit(MoBio),whereas the 0.22μm pore-size
atmospheric deposition throughout the growing filters were extracted with the RapidWater DNA
season. We collected three replicates of 2 litre of Isolation Kit (MoBio) according to the manufacturer
stream water at the mouth of the glacier terminus in protocols. DNA was quantified with PicoGreen
individual sterile 1 litre PYREX bottles previously (Invitrogen,Carlsbad,CA,USA)andstoredat−20°C.
rinsed three times with Milli-Q water before being AstheicesamplesyieldedsmallamountsofDNA,
autoclaved. We accessed basal glacier ice by pene- fourindividuallyextractedsubsampleswerepooled,
trating into a cave naturally formed by glacier precipitated and resuspended in 50μl elution
melting. Snow blocked access to the cave until the solution according to the manufacturer instructions
end of June (beginning of snow melt), ensuring us to form three independent replicates. Amplicons
that the ice had not been exposed to atmospheric were generated using methods similar to those
contaminants. Twelve subsamples of approximately applied by Hartmann et al. (2014) and Rime et al.
2litre of ice were collected aseptically with an ice (2015). Briefly, DNA was pre-treated with 1μg
pick in autoclaved PE bags, following methods used bovineserumalbuminml−1at95°Cfor5minbefore
in previous studies (Felip and Sattler, 1995; PCR reactions to bind PCR inhibitors. The regions
Hamilton et al., 2013). In the same cave, 100g of V1–V3 and V3–V5 of the bacterial and archaeal
subglacialsedimentsdepositedduringicemeltwere small-subunit ribosomal RNA genes, respectively,
sampled aseptically in triplicate. Approximately as well as the ITS-2 region of the fungal ribosomal
200g of supraglacial sediments were collected operonwereamplifiedbyperformingthreePCRswith
in triplicate on the glacier ice above the cave. 10ng template DNA and the HotStar Taq amplifica-
All samples were kept cold during transportation, tion kit (Qiagen, Hilden, Germany) in a final volume
stored and/or melted at 4°C overnight. Melted of 25μl. All PCRs were conducted on a standardized
samples of snow and ice, glacier stream water and amount of DNA to account for different microbial
atmospheric deposition were each divided into two cellnumbersinthehabitatssampled.PCRconditions
portionsandfilteredthedayaftersampling.Thefirst are given in the Supplementary Information.
portionwasfilteredthrough0.22μmpore-sizewater The purified PCR products with different barcoded
filters (MoBio, Carlsbad, CA, USA) for DNA extrac- primers were pooled in equimolar concentrations
tion, whereas the other portion was filtered through and shipped to the sequencing facility (Genome
0.44μm pore-size water filters (MoBio) for chemical Quebec Innovation Center, Montreal, Canada)
analyses. Filters (for DNA extraction) and filtrates where they were unidirectionally sequenced with
(for chemical analyses) were kept frozen at −20°C. the454-pyrosequencingGS-FLXTitaniumtechnology
Soil and sediment samples were homogenized (Roche 454 Life Sciences, Branford, CT, USA) from
through a 2mm sieve and divided into two sub- the primers 27f, 349f and ITS4.
samples. The first subsamples were weighed and
frozen at −20°C in DNA extraction buffer (MoBio),
whereas the second subsamples were oven-dried Sequence analyses
overnightat105°CandextractedwithMilli-Qwater Bioinformaticsqualitychecksofthepyrotaggedreads
(1:5m/v ratio) by using an overhead shaker were performed in MOTHUR v.1.32 (Schloss et al.,
overnight. Soil and sediment slurries were first 2009)accordingtotheestablishedprocedurerecently
filtered through glass fibre filters (Whatman GF/F described (Hartmann et al., 2014; Rime et al., 2015).
glassmicrofiberfilter,GEHealthcare,LittleChalfont, A detailed description of the sequence curation
UK) precombusted at 450°C. These subsamples were procedure is given as Supplementary_Materials and
then filtered through 0.44μm pore-size filters to Methods.doc. Curated sequences as well as opera-
obtaina particlesizesimilar to thatofthedeposition, tionaltaxonomicunit(OTU)tablesareavailableinthe
snow,iceandglacierstreamfiltrates.Allfiltrateswere SupplementaryInformation.Lessthan6%ofarchaeal
collected in acid-cleaned 100ml PE bottles and pH sequencescouldbeclassifiedatthephylumlevel(see
was determined with a pH-metre (FEP20-FiveEasy Discussionsectionfordetails).Therefore,ourin-depth
Plus, Mettler Toledo GmbH, Switzerland). Nitrate analyses only focused on the bacterial and fungal
(NO−), phosphate (PO3−), sulphate (SO2−) and chlor- sequences.
3 4 4
ide (Cl-) concentrations were measured by ion
chromatography with a IC:DX-120 chromatograph
(DionexCorp.,Sunnyvale,CA,USA),andammonium Quantitative real-time PCR reactions
(NH+) concentrations was measured photometrically Relative abundances of bacterial 16S and fungal ITS
4
with a FIAS 300 (PerkinElmer, Waltham, MA, USA) rRNA gene copies were determined by quantitative
and dissolved organic carbon (DOC) and nitrogen real-time PCR on an ABI7500 Fast Real-Time PCR
(DON) concentrations were measured with a TOC-V system (Applied Biosystems, Foster City, CA, USA)
analyser (Shimadzu, Tokyo, Japan). as described in Rime et al. (2015). Quantitative
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real-time PCR reactions were performed using 2ng determined by performing a one-way ANOVA fol-
DNA in a total volume of 25μl containing 0.5μM of lowedbyTukeyHSDtests.Allgraphsweregenerated
eachprimer,0.2mgml−1bovineserumalbumin and in R with the graphics (R development Core Team,
12.5μl of QuantiTect SYBR Green PCR master mix 2012) or ggplot2 (Wickham, 2009) packages.
(Qiagen). Cycling conditions were similar to those We additionally conducted an indicator species
usedforthepyrosequencingapproach.Standardcurves analysis to identify taxon–habitat association
(correlations⩾0.997)wereobtainedusing10-foldserial patterns. This procedure identifies OTUs as indica-
dilutions (10−1 to 10−9 copies) of plasmids generated tor species independently from their abundance
from cloned targets (Frey et al., 2011). in the total data set. However, for this analysis,
single- and doubleton OTUs were removed as they
hold little indicator information. This analysis
Statistical analyses was done with the multipatt function (number
We estimated the observed richness (S ), Shannon of permutations=99999) implemented in the
obs
diversity (H) and Gini coefficient (G), a measure of indicspecies R package (De Cáceres et al., 2010). To
inequality where values close to 0 correspond to accountformultipletesting,P-valueswerecorrected
even communities (Dorfman, 1979; Edwards et al., by calculating false discovery rates (q-values) with
2013b), based on evenly rarefied OTU matrices the q-value function implemented in the BiocLite R
(Bacteria: 3764 sequences; Fungi: 675 sequences). package(DabneyandStorney,2014).IndicatorOTUs
We assessed differences in α-diversity indices with qo0.05 were considered significant. Indicator
between exogenous and endogenous sources as taxawererepresentedinbipartitenetworksbyusing
well as among different soil habitats by performing the edge-weighted spring embedded algorithm
aone-way analysis ofvariance (ANOVA) in R v.2.15 layout implemented in Cytoscape v.3.0.2 (Shannon
(RdevelopmentCoreTeam,2012).Tukey'sHonestly etal.,2003)wherepointbiserialcorrelationvalues,a
Significant Difference (Tukey HSD) post hoc tests measureofspecies–habitatassociation,wereusedto
were conducted to examine pairwise differences weight the edges between nodes constituting the
among habitats with the HSD.test function imple- habitatsandindicatorOTUs(Hartmannetal.,2015).
mented in the agricolae package in R (De We further mapped these indicator OTUs on taxo-
Mendiburu, 2012). As the library sizes differed nomic networks generated in Cytoscape v.3.0.2 to
among samples, we also investigated rarefaction investigate potential taxa–habitat associations
curves. Rarefaction curves for bacterial and fungal (Hartmann et al., 2015). Indicator OTUs classified
observed richness were generated by pooling all atthegenuslevelweredisplayedinataxonomictree
sequencesforeachhabitatwiththerarefaction.single generatedin iTOL (Letunic and Bork, 2011) together
function as implemented in MOTHUR using 1000- with the positive point biserial correlation values
fold resampling without replacement. Bray–Curtis associated with each habitat. To find patterns of
dissimilarities were calculated based on square-root co-occurrence among OTUs characteristic of the
transformedrelativeabundancesofOTUs.Differences habitats studied, we analysed correlations among
in community structures (β-diversity) were assessed the relative abundances of all indicator OTUs
by conducting a permutational ANOVA (PERMA- (co-correlations) by calculating Spearman rank
NOVA, number of permutations=99999) with the correlation values (Spearman, 1904) in R with the
function adonis and an analysis of similarity (ANO- functioncorr.testimplementedinthepackagepsych.
SIM, number of permutations=99999) implemented Multiple testing was accounted for by correcting
in the vegan R package (Oksanen et al., 2012) and P-values with a false discovery rates of 5%
displayed with principal coordinate analysis ordina- (q-valueo0.05). Bacterial and fungal indicator
tions. As significant differences detected by PERMA- OTUs that were significantly co-correlated were
NOVA may arise because of within-habitat variation displayedinnetworkswiththeedge-weightedspring
(Hartmann et al., 2012), we performed an analysis of embedded algorithm layout implemented in Cytos-
homogeneity of group dispersion (Dispersion) cape where Spearman correlation values were used
(Legendre andAnderson,1999) by using thefunction to weight the edges between the nodes representing
betadisper implemented in the vegan package the OTUs (Hartmann et al., 2015).
(Oksanen et al., 2012). In our multivariate analyses, Differences in relative abundances of ribosomal
rare OTUs, that is, OTUs represented by one (single- gene copies and in environmental factors between
tons) or two (doubletons) sequences, were not habitats were assessed by conducting a one-way
removed, unless mentioned otherwise, because they ANOVAfollowedbyTukeyHSDtests.Toinvestigate
may represent real organisms and they do not the effect of environmental factors on microbial
substantially influence changes in β-diversity (Gobet communities, we conducted a DistLM analysis
et al., 2010). Changes in α- and β-diversities of the (Legendre and Anderson, 1999) using a step-wise
most abundant (relative abundance 41% of the total selection procedure with the adjusted-R2 criterion
sequence number) phyla or classes among the selection in PRIMER6+ (Clarke and Gorley, 2006).
Proteobacteria were assessed using analyses similar Selected factors were used to build distance-based
tothatappliedtothetotalcommunities.Differencesin redundancy ordinations (db-RDA). Similarly to the
relative abundances of the most abundant taxa were indicator species analysis, we calculated Spearman
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rank correlation values between environmental communities were dominated by Proteobacteria
factorsusedindb-RDAsandtherelativeabundances (56%)followedbyBacteroidetes(13%),Cyanobacteria
of all OTUs with the corr.test function implemented (8%),Actinobacteria(7%),Acidobacteria(4%),Chlor-
in the psych R package (Revelle, 2014). This oflexi(3%),Gemmatimonadetes(2%),Planctomycetes
approach enabled us to identify significant associa- (1%)andTM7(2%),whereaso1%wereunclassified.
tions between particular OTUs and environmental Combined, the remaining 35 phyla represented 3.5%
factors. We accounted for multiple testing ofthetotalbacterialsequences.AmongProteobacteria,
by correcting P-values using a false discovery rate Betaproteobacteriawasthemostabundantclass(32%)
of 5% (qo0.05). OTUs classified at the genus level followed by Alphaproteobacteria (12%), Gammapro-
were displayed in a taxonomic tree generated with teobacteria (9%) and Deltaproteobacteria (2%).
iTOL where the positive Spearman rank correlation The fungal communities consisted of Ascomycota
values between OTUs and the selected environmen- (64%), Basidiomycota (23%) and unclassified fungi
tal factors were mapped. (12%),whereasChytridiomycota,Glomeromycotaand
Zygomycota represented o1% of the total fungal
sequences.
Results
Environmental factors and relative abundances of
ribosomal gene copies Alpha-diversity
All environmental factors except for NH+ and PO3− For bacteria and fungi, as well as their most
concentrations varied between habitats4(Table 14). abundant taxa, α-diversity indices differed between
DON, NO− and SO2− concentrations were highest in endo-andexogenoussourcesofmicrobialcolonizers
3 4
deposition and DOC was highest in barren soil, and andamongsoilhabitats(Table2).Observedrichness
pH was highest in deposition and glacier stream. (S ) and Shannon diversity (H) of bacteria were
obs
Relative abundances of bacterial 16S and fungal ITS highest in barren soil and lowest in deposition and
gene copy numbers significantly differed between snow (Figure 1). The bacterial Gini coefficient (G)
habitats (Table 2) and were highest in barren soil, was lowest in barren soil and highest in deposition
snow-covered soil and supraglacial sediments and and snow indicating that the bacterial communities
were lowest in ice (Figure 1). in deposition and snow were dominated by a few
abundant OTUs. Fungal S , H and G differed
obs
between subglacial sediments, barren soils and
Community compositions snow-covered soils. Among the most abundant
We obtained 174096 (7254±1957 per sample) bacterial and fungal phyla or classes of Proteobac-
bacterial 16S and 157292 (5826±2501 per teria,S andHweregenerallyhighestinbarrensoil
V1-V2 obs
sample) fungal ITS high-quality sequences resulting and snow-covered soil, whereas G was lowest in
in 3942 (624±346 per sample) bacterial and 1611 barren soil and highest in deposition and snow
(157±66 per sample) fungal OTUs. The bacterial (Supplementary Figure S2). Rarefaction curves
Table1 Statisticaldifferences(F-values)andmeans(±s.e.,n=3)ofenvironmentalfactorscharacterizingthedifferenthabitats
investigated
Habitats pH DOC DON NH+ NO- PO3- SO2- Cl-
[H2O] 4 3 4 4
(μgl-1)
F A=30.4*** F =6.8*** F =10.0*** F =2.5NS F =15.4*** F =1.0NS F =15.5*** F =18.1***
1,8 1,8 1,8 1,8 1,8 1,8 1,8 1,8
DepositionB 6.5±0.1aC 3.4±0.2bcd 2.9±0.8a 0.9±0.5 1.4±0.2a 0.4±0.3 1.0±0.2a 0.8±0.5b
Snow 6.2±0.0ab 0.7±0.1cd 0.1±0.0b 0.1±0.0 0.1±0.1b 0.1±0.0 0.1±0.0c 0.1±0.0b
Glacierstream 6.5±0.0a 0.0±0.0d 0.2±0.0b 0.0±0.0 0.5±0.0b 0.1±0.0 0.4±0.0bc 0.1±0.0b
Ice 5.8±0.1bc 1.0±0.6cd 0.1±0.0b 0.0±0.0 0.1±0.0b 0.1±0.0 0.0±0.0c 0.1±0.0b
Subglacial 5.4±0.1c 12.5±4.8ab 0.8±0.1b 0.4±0.1 0.2±0.1b 0.1±0.0 0.3±0.0bc 1.9±0.1a
sediments
Supraglacial 6.5±0.1a 10.6±0.1abc 0.5±0.0b 0.4±0.1 0.1±0.0b 0.1±0.0 0.3±0.1bc 0.2±0.0b
sediments
Barrensoil 5.4±0.1c 17.1±3.2a 1.0±0.3b 0.8±0.3 0.1±0.0b 0.1±0.0 0.5±0.0bc 0.3±0.0b
Snow-covered 5.5±0.0c 9.5±0.0abcd 1.4±0.2b 0.7±0.1 0.6±0.2b 0.2±0.0 0.2±0.0bc 1.9±0.2a
soil
Abbreviations:Cl-,chloride;DOC,dissolvedorganiccarbon;DON,dissolvedorganicnitrogen;NH+,ammonium;NO-,nitrate;pH ,pH
4 3 [H2O]
measuredinwater;PO3-,phosphate,SO2-,sulphate;.
4 4
Significancelevels:***Po0.001;**Po0.01;*Po0.05;NS,nonsignificant.
ASignificanceoftestsbasedonF-ratioswheretheindicesarethedegreesoffreedomanderrorterms.
BDepositionstandsforaeolianandwetatmosphericdepositioncollectedinsummer.
CSmalllettersindicatedifferencesbetweenindividualmeansassessedbyTukeyHSDtests.
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Table2 Statisticaldifferencesinα-diversity,β-diversityandrelativeabundancesofbacteria,fungiandtheirmostabundanttaxabetween
thedifferenthabitatsinvestigated
Taxa α-Diversity β-Diversity Rel.abund.a
(F )
1,8
S H G PERMANOVA ANOSIM Dispersion
obs
(F b) (F ) (F ) (pseudo-F) (Rc) (F )
1,8 1,8 1,8 1,8
Bacteria 42.0*** 53.0*** 28.0*** 8.3*** 0.97*** 1.6NS 102.5***
Acidobacteria 16.9*** 23.0*** 9.0*** 6.0*** 0.92*** 0.3NS 10.3***
Actinobacteria 27.7*** 23.3*** 15.4*** 5.8*** 0.83*** 2.0NS 22.3***
Bacteroidetes 33.6*** 45.7*** 22.3*** 7.0*** 0.94*** 1.8NS 12.0***
Chloroflexi 14.9*** 48.8*** 29.3*** 3.7*** 0.72*** 3.7* 6.3**
Cyanobacteria 43.8*** 22.3*** 17.6*** 6.9*** 0.89*** 3.8* 133.0***
Gemmatimonadetes 22.7*** 104.8*** 18.6*** 5.5*** 0.78*** 1.8NS 11.0***
Planctomycetes 19.1*** 110.2*** 15.5*** 3.6*** 0.94*** 7.0** 6.3**
Alphaproteobacteria 53.8*** 55.9*** 28.6*** 10.1*** 0.95*** 3.6* 12.3***
Betaproteobacteria 15.4*** 43.0*** 11.5*** 8.3*** 0.90*** 1.0NS 12.3***
Deltaproteobacteria 20.9*** 77.8*** 18.9*** 3.0*** 0.80*** 3.2* 13.9***
Gammaproteobacteria 15.5*** 43.0*** 10.1*** 7.3*** 0.89*** 2.1NS 32.4***
TM7 43.9*** 44.2*** 29.0*** 3.5*** 0.66*** 11.1*** 5.0**
Fungi 9.9*** 11.3*** 6.3** 6.7*** 0.97*** 11.7*** 120.5***
Ascomycota 12.4*** 10.4*** 5.6** 5.0*** 0.91*** 6.5*** 309.5***
Basidiomycota 8.2*** 6.6** 5.1** 7.3*** 0.81*** 13.6*** 35.0***
Abbreviations:ANOSIM,analysisofsimilarities;Dispersion,multivariatehomogeneityofgroupdispersion;G,Ginicoefficientofinequality;
H,Shannondiversity;PERMANOVA,permutationalanalysisofvariance;Rel.abund.,relativeabundance;S ,observedrichness.
obs
Significancelevels:***Po0.001;**Po0.01;*Po0.05;NS,nonsignificant.
aDifferencesinrelativeabundancesofeachdomainwerebasedonquantitativereal-timePCRdata,whereasthedifferencesinrelativeabundances
ofthetaxawerebasedon454-pyrosequencingdata.
bSignificanceoftestswerebasedonF-ratioswheretheindicesarethedegreesoffreedomanderrorterms.
cStatisticRvalueswereusedtoassesscompositionalsimilarities.
showed that the α-diversity differed among habitats of habitats (Supplementary Table S2) and were
(SupplementaryFigureS3).Thebacterialα-diversity represented in bipartite networks (Figures 2c and d).
was highest in snow-covered and barren soils and Thestructureofthebipartitenetworksresembledthe
lowest insnow anddeposition. Similarly, the fungal principal coordinate analysis ordinations, showing
α-diversity in deposition was lowest. In contrast strong differences in indicator taxa characteristic
to bacteria, fungal α-diversity was highest in supra- of exogenous sources (deposition in particular)
glacial sediments and similar in snow-covered soils endogenous sources and soil habitats. Four hundred
and snow. bacterial indicator OTUs were shared between the
different habitats, whereas48 fungalindicatorOTUs
were shared between habitats. Among the bacterial
Beta-diversity indicator, OTUs shared among habitats, 94 OTUs
Principal coordinate analysis ordinations and (14% of the total number of indicator OTUs) were
PERMANOVA revealed that the structures of the shared between newly exposed soils (barren and
total bacterial and fungal communities as well as snow-covered) and subglacial sediments, 71 OTUs
their most abundant taxa also changed among the (11%)betweennewlyexposedsoilsandsupraglacial
different habitats investigated (Figures 2a and b, sediments, 55 OTUs (9%) between newly exposed
Table2andSupplementaryFigureS4).Theseresults soils andglacierstream,and20 OTUs (3%) between
were supported by the ANOSIM statistics. However, newly exposed soils and ice. The evaluation of
the analysis of within-habitat community dispersion co-correlation patterns of indicator OTUs revealed
showed that structural differences in Chloroflexi, that bacterial indicator OTUs characteristic of exo-
Cyanobacteria, Planctomycetes, Alphaproteobacteria, genoussources(depositionorsnow)werenegatively
Deltaproteobacteria, TM7 as well as the total fungi, correlated with indicator OTUs of other habitats
AscomycotaandBasidiomycotawereatleastpartially (soils or endogenous sources), whereas fungal indi-
caused by different within-habitat heterogeneities. cator OTUs associated with snow and supraglacial
Further, the relative abundances of all taxa investi- sediments were positively co-correlated (Figure 3,
gated significantly varied between habitats. For Supplementary Table S3).
example, the relative abundance of Acidobacteria Among the indicator OTUs, we identified 139
and Deltaproteobacteria was highest in barren soil, bacterial and 47 fungal indicator OTUs that were
whereasthatofCyanobacteriaandBasidiomycotawas classified atleastdownto thegenus level (Figure4),
highest in snow (Supplementary Figure S5). representing 42% and 47% of the total bacterial and
We identified 670 bacterial and 176 fungal fungal sequences, respectively. Most of the OTUs
indicator OTUs, which were significantly (qo0.05) affiliated to genera belonging to Bacteroidetes were
associatedwithaparticularhabitatoracombination positively associated with glacier stream while no
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Figure 1 Changes in bacterial and fungal α-diversity indices among the habitats investigated. Horizontal lines represent the median,
whereastheboxesrepresenttheinterquartilerangeofthefirstandthirdquartiles.Theverticallines(whiskers)representthemaximaland
minimalvalues.Pointswithineachboxplotrepresentthemeans(n=3).Lettersindicatedifferencesbetweenindividualmeansassessed
withTukeyHSDposthoctests.
otherparticularphylum–habitatassociationcouldbe representative OTUs affiliated to Geobacter (Proteo-
observed (Supplementary Figure S6). For example, bacteria) were only associated with barren soil and
representatives of genera positively associated snow-covered soil. OTUs belonging to Candidatus
with deposition were distributed among Actino- Koribacter (Acidobacteria) and Desulfosporosinus
bacteria(RhodococcusandMycetocola),Bacteroidetes (Firmicutes) were associated with barren soil. Simi-
(Sediminibacterium), and Alpha- (Methylibium and larly for the fungi, representatives belonging to
Sphingomonas), Beta- (Variovorax) and Gammapro- Leucosporidium and Rhodotorula (both Basidiomy-
teobacteria(Pseudomonas).Similarly,indicatorOTUs cota) were specifically prominent in snow and
associated to barren soil were affiliated to various supraglacial sediments, whereas OTUs affiliated to
phyla,suchasAcidobacteria(CandidatusKoribacter), Aureobasidium (Ascomycota) and Mucor (Zygomy-
Firmicutes (Desulfosporosinus), Alphaproteobacteria cota) were associated with deposition.
(Telmatospirillum), Gammaproteobacteria (Rhodo- The environmental factors that explained most of
ferax) and Verrucomicrobia (Opitutus). However, at the variability in bacterial and fungal β-diversities
thegenuslevel,somepatternsemerged.Forexample, were DOC, DON, Cl-, pH and SO2- concentrations
4
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10 OTUs
0.4
13 OTUs
62 OTUs 64 OTUs
0.2
%)
acteriaO 2 (13 0.0
BC
P 6 OTUs
-0.2 5 OTUs Exogenous sources
400 OTUs Deposition
Snow
55 OTUs
-0.6 -0.4 -0.2 0.0 0.2 0.4 53 OTUs Endogenous sources
PCO 1 (27%) Glacier stream
Ice
Subglacial sed.
Supraglacial sed.
22 OTUs 23 OTUs
1 OTU Newly exposed soils
0.4
Barren
%) Snow-covered
ungi2 (16 0.2 5 OTUs 17 OTUs
FO
C 0.0 48 OTUs
P
13 OTUs
-0.2
35 OTUs
-0.6 -0.4 -0.2 0.0 0.2 0.4
12 OTUs
PCO 1 (23%)
Figure 2 Differences in total bacterial (a) and fungal (b) community structures shown by principal coordinate analysis ordinations
(PCOs).Thevarianceexplainedbyeachaxisisgiveninparentheses.Coloursandsymbolsaredescribedinthefigure.Bacterial(c)and
fungal(d)bipartiteassociationnetworksshowingsignificant(qo0.05)positiveassociationsbetweenindicatorOTUsandspecifichabitats,
represented as nodes. Edges, that is, the connections between the habitat and an OTU, were weighted according to the point biserial
correlationvalues.Nodesizesrepresentthesquare-rootoftherelativeabundancesofeachOTUs.Coloursandsymbolsusedtoillustrate
association between habitats and OTUs are identical to those in the PCOs. In addition, white hexagons represent OTUs that are
significantlyassociatedwithmorethanonehabitat(sharedOTUs).ThenumberofindicatorOTUsismentionedforeachhabitat.
(Figures5aandb,Table3)andaccountedfor42%and catchment (endogenous sources: ice, glacier stream,
51%oftheoverallvariabilityobservedinthebacterial sub- and supraglacial sediments; exogenous sources:
and fungal communities, respectively. We identified aeolian dust, rain and snow). We used 454-
137 bacterial and 37 fungal OTUs that were signifi- pyrosequencing to identify microorganisms that may
cantly correlated (qo0.05) with the selected environ- colonize mineral debris present at the surface of
mental factors(SupplementaryTableS4).Thirty-nine recentlydeglaciatedsoils.Wefoundthatα-diversityin
ofthebacterialand12ofthefungalOTUssignificantly the investigated habitats was in accordance to values
associated with these environmental factors were reported by others in glacial ecosystems (in high-
classified at the genus level (Figure 5c). OTUs elevation streams: Wilhelm et al., 2013; in glacier
affiliated to Acidobacteria, Cyanobacteria, OP10 and debris: Franzetti et al., 2013; in barren soils: Brown
Planctomycetes were positively correlated with DOC and Jumpponen,2014; Rimeetal., 2015).Despitethe
and Cl-, whereas OTUs affiliated to Actinobacteria, occurrence of harsh conditions in the investigated
Bacteroidetes(exceptforanOTUrelatedtothegenus habitats,suchashighUVradiation,dailyandseasonal
Segetibacter) and Ascomycota (except for OTUs temperature fluctuations, as well as paucity of
related to Cladiophialophora and Trichoderma) were nutrients and C, our study supports the existing
positivelycorrelatedwithSO42−,pHandDON.Among body of evidence that alpine environments have a
Alphaproteobacteria, OTUs affiliated to the genera high genetic pool of microorganisms. Despite all
Hyphomicrobium and Rhodoplanes were positively
precautions applied during sequence processing and
correlatedwithDOCandCl-,whereastheotherOTUs
statistical analyses, conclusions concerning microbial
were correlated with SO42−, pH and DON. α-diversity and, to a lesser extent, β-diversity should
be considered carefully given the difficulty to prop-
Discussion erly explore microbial diversity (Hughes et al., 2001).
Taxon–habitat association patterns revealed distinct
This study investigated the microbial communities dispersal patterns between bacterial and fungal
present in various habitats of the Damma glacier communities (Figures 2 and 3). Communities from
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Bacteria Fungi
Exogenous Endogenous Newly exposed
sources sources soils
Deposition Glacier stream Barren
Snow Ice Snow-covered
Subglacial sed.
Supraglacial sed.
Figure3 Networks of significantly (qo0.05) co-correlated bacterial andfungal indicator OTUs. The nodesrepresentOTUs whilethe
edgesconnectingnodesarepositiveSpearmanrankcorrelationvalues.Nodesizesrepresentthesquare-rootoftherelativeabundanceof
each OTU and node shapes illustrate the habitat type with which each OTU is associated. White hexagons represent OTUs that are
associatedwithmorethanonehabitat(sharedOTUs).
endogenous sources shared many bacterial indicator revealed interesting patterns. Sixty percent (400)
OTUs with soils but only a few with snow and ofthebacterialindicatorOTUsweresharedbetween
atmospheric deposition. Conversely, fungal commu- atleasttwodifferenthabitats,whereasonly27%(48)
nities were generally distinct, with a few indicator of the fungal indicator OTUs characterized more
OTUssharedamonghabitats.Interestingly,thefungal than one habitat, suggesting different dispersal
community in snow resembled that of supraglacial patterns between bacterial and fungal communities.
sediments although these habitats were not in direct Most of the bacterial OTUs classified at the genus
contact during sampling (Supplementary Table S1). levelthatwerecharacteristicofthebarrensoilswere
Advected sediment-associated fungi may have alsopresentinendogenoushabitatsorsnow-covered
been deposited on the snow surface, which would soils. This finding is in agreement with previous
explain these similarities. Our findings suggest that studies that reported similar bacterial communities
atmospheric deposition of microorganisms is not the indifferentbutspatiallyconnectedhabitats(Hodson
dominant sources of microbial colonizers of recently et al., 2008; Řeháková et al., 2010; Edwards et al.,
deglaciated mineral debris for the period sampled. 2013a). At a larger biogeographical scale, bacteria
However, inputs of C, N (as DON, nitrate and affiliated to the genus Polaromonas have been
ammonium) and other nutrients (for example, recorded throughout the cryosphere worldwide
sulphateandphosphate)fromatmosphericdeposition (Priscu and Christner, 2004; Darcy et al., 2011;
represent important inputs to nutrient and C pools, Larose et al., 2013; Hell et al., 2013). In contrast,
which are known to be small in barren soils 73%(154)ofthefungalindicator OTUsinourstudy
(Bernasconi et al., 2011; Smittenberg et al., 2012). were only associated with one habitat, indicating a
Therefore, these inputs may facilitate the establish- lower dispersal efficiency than bacteria. Similar to
mentofpioneermicrobialcommunitiesinthemineral our results, Naff et al. (2013) observed that fungal
debris (Frey et al., 2010, 2013; Brunner et al., 2011). zoospores affiliated to Chytrids differed between
The simultaneous investigation of bacterial and geographically close snowpacks. Further, Edwards
fungal communities present in atmospheric deposi- et al. (2013b) reported that fungal community
tion, endogenous glacial habitats and barren soils structures differed between moraine debris, tundra
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Figure 4 Taxonomic tree representing the bacterial and fungal indicator OTUs classified at the genus level. The phyla within each
domainaremappedonthebranchesofthetaxonomictreeasnumbers.Circlesmappedintheouterrimofthetreerepresentthepositive
pointbiserialcorrelationvaluesbetweeneachOTUandthehabitatsinvestigated.
soils and cryoconite holes in three different Arctic andfungalcommunitiesmaybeduetodifferencesin
glaciers. Their study also identified unknown fungi the size and physiological capabilities of bacteria
in cryoconite holes that were not retrieved from and fungi as discussed by Schmidt et al. (2014).
surrounding moraine debris or tundra soils, indicat- Dispersal is thought to be a key factor driving
ing that cryoconite holes may represent specific biogeographical patterns of bacterial communities
reservoirsof fungalbiodiversity.Wesuggestthatthe (Lindström and Langenheder, 2012). Overall, our
difference in dispersal patterns between bacterial study indicates that differences in dispersal ability
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