Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield

Vertical distribution of the soil microbiota along asuccessional gradient in a glacier forefield

THOMAS RIME,* MARTIN HARTMANN,*† IVANO BRUNNER,* FRANCO WIDMER,†JOSEF ZEYER‡ and BEAT FREY*

*Forest Soils and Biogeochemistry, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland, †Molecular Ecology,

Institute for Sustainability Sciences, Agroscope, 8046 Z€urich, Switzerland, ‡Institute of Biogeochemistry and Pollutant

Dynamics, Federal Institute of Technology (ETH Z€urich), 8092 Z€urich, Switzerland

Abstract

Spatial patterns of microbial communities have been extensively surveyed in well-

developed soils, but few studies investigated the vertical distribution of micro-organ-

isms in newly developed soils after glacier retreat. We used 454-pyrosequencing to

assess whether bacterial and fungal community structures differed between stages of

soil development (SSD) characterized by an increasing vegetation cover from barren

(vegetation cover: 0%/age: 10 years), sparsely vegetated (13%/60 years), transient (60%/

80 years) to vegetated (95%/110 years) and depths (surface, 5 and 20 cm) along the

Damma glacier forefield (Switzerland). The SSD significantly influenced the bacterial

and fungal communities. Based on indicator species analyses, metabolically versatile

bacteria (e.g. Geobacter) and psychrophilic yeasts (e.g. Mrakia) characterized the barren

soils. Vegetated soils with higher C, N and root biomass consisted of bacteria able to

degrade complex organic compounds (e.g. Candidatus Solibacter), lignocellulolytic

Ascomycota (e.g. Geoglossum) and ectomycorrhizal Basidiomycota (e.g. Laccaria). Soildepth only influenced bacterial and fungal communities in barren and sparsely vege-

tated soils. These changes were partly due to more silt and higher soil moisture in the

surface. In both soil ages, the surface was characterized by OTUs affiliated to Phormi-dium and Sphingobacteriales. In lower depths, however, bacterial and fungal communi-

ties differed between SSD. Lower depths of sparsely vegetated soils consisted of

OTUs affiliated to Acidobacteria and Geoglossum, whereas depths of barren soils were

characterized by OTUs related to Gemmatimonadetes. Overall, plant establishment

drives the soil microbiota along the successional gradient but does not influence the

vertical distribution of microbiota in recently deglaciated soils.

Keywords: 454-pyrosequencing, DNA metabarcoding, glacier retreat, indicator species analysis,

soil formation, vertical distribution

Received 18 May 2014; revision received 16 December 2014; accepted 17 December 2014

Introduction

Spatial structuring of soil microbial communities and

the corresponding occurrence of microbially driven pro-

cesses have received increasing attention (Ettema &

Wardle 2002). Most microbial processes in soil vary

with depth (Watanabe et al. 2010; Marhan et al. 2011;�Stursov�a et al. 2012) and are directly linked to changes

in microbial biomass, activity and diversity (Ekelund

et al. 2001; Hansel et al. 2008). For example, �Stursov�a

et al. (2012) showed that shifts in microbial community

structures with depth in forest soils affected decomposi-

tion processes, which might in turn influence soil car-

bon dynamics. Changes in microbial processes mainly

result from differences in nutrient availability, organic

C, moisture regime and O2 concentration (Hartmann

et al. 2010a, b; Will et al. 2010; Eilers et al. 2012).

Despite the importance of spatial organization in soil

microbial communities, there is limited knowledgeCorrespondence: Beat Frey, Fax: 0041 44 739 22 15;

E-mail: [email protected]

© 2014 John Wiley & Sons Ltd

Molecular Ecology (2015) 24, 1091–1108 doi: 10.1111/mec.13051

about the vertical distribution of micro-organisms in

developing soils (<150 years), in particular after retreat

of Alpine glaciers. The few studies that have addressed

the vertical distribution of microbial communities in

developing soils took place in either polar (Sch€utte et al.

2009) or Antarctic glacier forefields (Bajerski & Wagner

2013) and were of limited resolution due to the use of

genetic profiling. Soils formed after the retreat of Alpine

glaciers have been extensively used to study the physi-

cal, chemical and biological dynamics of initial soil

development (Bernasconi et al. 2011; Schmidt et al. 2012;

Guelland et al. 2013). The continuum of stages of soil

development (SSD) formed after glacier retreat repre-

sents an ideal framework to study trajectories of micro-

bial succession (Schmidt et al. 2014). Shifts in microbial

communities along soil chronosequences have often

been reported as a consequence of increasing C and

nutrient availability, plant colonization and associated

changes in pH (Ohtonen et al. 1999; Blaalid et al. 2012;

Knelman et al. 2012). Nemergut et al. (2007) reported an

increase in a-diversity and a change in b-diversity (Beta-

proteobacteria to Alphaproteobacteria) in top soil (0–5 cm)

during soil development. Similarly, Knelman et al.

(2012) reported an enrichment in Alphaproteobacteria and

Acidobacteria in top soil due to lower pH associated with

plant colonization after glacier retreat. Zumsteg et al.

(2012) and Jumpponen (2003) found a shift from Asc-

omycota to Basidiomycota, particularly due to an increase

in ectomycorrhizas, with the establishment of plants

during soil development. Further, Brown & Jumpponen

(2014) showed that bacterial succession was more influ-

enced by plant establishment than fungal communities

during soil formation. This study further posited that

bacterial communities in barren soils were driven by

stochastic colonization and converged during soil devel-

opment, indicating that establishment of vegetation

might drive more deterministic processes in bacterial

communities. These investigations have, however, only

focused on microbial communities living in the top soil

(0–5 cm) and neglected the distribution of micro-organ-

isms at different depths of a developing soil. The inves-

tigation of the vertical distribution of microbial

communities during soil development might give addi-

tional insight about establishment of microbial commu-

nities in terrestrial ecosystems. Soils in initial

ecosystems feature unique environmental conditions

that might influence microbial community structures

along the depth profile. The surface of barren soils at

higher altitude is exposed to high UV radiation, large

moisture and temperature fluctuations and higher

atmospheric deposition, such as wind-blown plant deb-

ris and arthropods (Schmidt et al. 2008; Zumsteg et al.

2011; Jumpponen et al. 2012; Mladenov et al. 2012),

while lower strata of newly deglaciated soils are pro-

tected from solar radiation, show more constant micro-

climatic conditions and receive less atmospheric input

of organic matter. The surface of vegetated soils

receives organic matter as plant litter, whereas lower

strata are directly influenced by root exudates of colo-

nizing plants and weathered bedrock material (Knel-

man et al. 2012).

Our study used high-throughput barcoded 454-py-

rosequencing (Margulies et al. 2005) of amplicons from

the bacterial small-subunit ribosomal RNA (16S rRNA)

gene and the fungal ribosomal internal transcribed

spacer (ITS) to determine both bacterial and fungal lin-

eages associated with specific soil habitats (different

stages of soil development (SSD): 10, 60, 80 and

110 years, and depths: surface, 5 and 20 cm) in a Swiss

glacier forefield. We specifically addressed the follow-

ing questions. (i) Do bacterial and fungal communities

differ with SSD and depth in a developing ecosystem?

(ii) Which bacterial and fungal taxa are characteristic of

the different SSD and depth? and (iii) Are shifts in bac-

terial and fungal community structures correlated with

changes in environmental conditions?

Materials and methods

Site description

The Damma glacier forefield is located in the Central

Alps of Switzerland (N46°380, E8°280) between 1900 and

2100 m.a.s.l. The glacier front has been retreating at an

approximate rate of 10 m per year (Zumsteg et al.

2012). The climate is typical for a high alpine environ-

ment with a short growing season between June and

September. The annual mean precipitation reaches

2300 mm, and the annual mean temperature has been

estimated around 2.2 °C with high daily and seasonal

fluctuations (�4 °C to 8 °C across the seasons). The

bedrock material is mainly composed of coarse-grained

metamorphic granite along the forefield (Bernasconi

2008). The soil texture ranges from coarse sand in the

youngest sites to more loamy sand in vegetated soils.

Four sites were sampled based on the locations of the

interdisciplinary BigLink project (Bernasconi et al. 2011).

These sites have been deglaciated for 10, 60, 80 and

110 years resulting in vegetation cover of 0%, 13%, 60%

and 95% of the surface, respectively. The 10-year-old

soils are located between the glacier terminus and the

1992 moraine and consist of barren sandy rock mainly

colonized by mosses and lichens (Fig. 1a). The 60- and

80-year-old soils are located between the 1992 and 1928

moraines, 250 and 500 m away from the glacier termi-

nus, respectively, and their vegetations mainly consist

of grass (e.g. Agrostis gigantea and Festuca rubra) and

shrubs (Salix spp.). The 110-year-old soils are located


1092 T. RIME ET AL.

after the 1928 moraine, that is 720 m away from the gla-

cier terminus, and are characterized by shrubs (e.g. Rho-

dodendron ferrugineum and Salix spp.) and grass (e.g.

Festuca rubra and Agrostis gigantea).

Sampling procedure

Soils were sampled in three parallel transects, constitut-

ing the replication units, at the defined SSD. One of the

replicates was taken at the BigLink reference site, and

the other two replicates were taken 30 m on both sides

of the reference site (Fig. 1a). For the 10-year-old soils,

however, the distance between the centre replicate and

each side replicate was reduced to 20 m due to the

smaller investigation area (Fig. 1a). Each replicate con-

sisted of four pooled subsamples within a 1-m2 area.

Due to the absence of defined soil horizons, we sam-

pled three soil depths, that is the 0- to 1-cm (surface,

referred to 0 cm in tables and figures), 4- to 6-cm (mid-

dle, referred to 5 cm) and 18- to 20-cm (deepest,

referred to 20 cm) soil layers, by digging a soil profile

with an ethanol-cleaned spade at each subsampling site.

It was not possible to dig beyond this depth due to the

presence of granitic rocks. To avoid mixing of the dif-

ferent depths, the deepest soil layer was sampled first,

followed by the middle and the top layers. Approxi-

mately 100 g soil was collected in individual plastic

bags and kept cold during transportation to the labora-

tory. Same amounts of subsamples were sieved (2 mm)

and pooled after the fine roots were removed. Overall,

our sampling approach resulted in 36 independent sam-

ples (three transects 9 four SSD 9 three depths). Por-

tions of the soil samples were stored at �20 °C(biological analyses) or 4 °C (physico-chemical analyses)

until further processing.

Soil characteristics

Soil texture was determined by the hydrometer tech-

nique according to Gee & Bauder (1986). The water-

holding capacity (WHC) was determined according to

Schimel & Gulledge (1999). Soil samples were dried

overnight at 105 °C to measure their gravimetric water

content (SM). Around 2 g of well-homogenized soil

was milled with a Teflon ball mill, and around 40 mg

of soil was subsequently weighed in tin caps for mea-

surement of the total carbon (TC) and nitrogen (TN)

contents with a CHN analyser (Shimadzu, Tokyo,

Japan). Dried soil was extracted with milliQ water

(1:10 m/v) in 250-mL PE bottles placed in an overhead

shaker overnight at room temperature and filtered

through folded paper filters (0790 1/2 Whatman paper

filter, Whatman Inc., CA, USA). The pH was measured

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100% OthersUnclassifiedGammaproteobacteriaDeltaproteobacteriaBetaproteobacteriaAlphaproteobacteriaPlanctomycetesGemmatimonadetesCyanobacteriaChloroflexiBacteroidetesActinobacteriaAcidobacteria

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

OthersUnclassifiedZygomycotaGlomeromycotaBasidiomycotaAscomycota

Rel

ativ

e ab

unda

nce

of b

acte

rial t

axa

(%)

Rel

ativ

e ab

unda

nce

of fu

ngal

taxa

(%)

B SV T V All SSD

(b)

(a)

Fig. 1 Sample locations (a) and mean relative abundances

(b) of the most abundant bacterial (phyla or Proteobacteria

classes > 1.5% of the total sequence number) and fungal taxa

(phyla > 1% of the total sequence number). Each stage of

soil development (SSD) is specified by a letter and a colour

(B: barren, grey, 10 years; SV: sparsely vegetated, cyan,

60 years; T: transient, orange, 80 years; V: vegetated, green,

110 years). The colours are used in all figures to illustrate

the SSD. The symbols represent the replicate units, which

are located at the BigLink reference sites (square) or 30 m

apart on each side of this reference (triangle or circle). The

distance between replicates was reduced in the youngest

soils due to the smaller area of this site. Taxa that repre-

sented less than 1.5% of the bacterial or 1% of the fungal

sequence numbers are mentioned as ‘Others’. Distinction of

relative abundances of each taxa between depths of each

SSD, pairwise comparisons and overall statistical tests are

reported in Fig. S3 (Supporting information) and Table 2,

respectively.


DEPTH AFFECTS MICROBIOME IN DEGLACIATED SOILS 1093

in each sample using a glass electrode linked to a pH

meter (FEP20-FiveEasy Plus, Mettler-Toledo GmbH,

Switzerland). The concentrations of dissolved organic

carbon (DOC) and nitrogen (DON) were measured with

a TOC-V analyzer (Shimadzu, Tokyo, Japan). The anion

(SO2�4 , PO3�

4 , NO�3 and NO�

2 ) content was measured by

ion chromatography with an IC:DX-120 chromatograph

(Dionex Corp., CA, USA). The concentrations of ammo-

nium (NHþ4 ) were determined photometrically with a

FIAS 300 (Perkin-Elmer, MA, USA). The cation (Al3+,

Ca2+, Fe2+, K+, Mg2+, Mn2+, Na+ and Zn2+) content was

measured by inductive plasma atomic spectrometry

with an Optima 3000 (Perkin-Elmer). The cation

exchange capacity (CEC) was calculated as the sum of

the measured cations (mmolc/kg). The base saturation

was defined as the sum of Ca2+, Mg2+, K+ and Na+ con-

centrations divided by the CEC (%). Roots and moss rhi-

zoids were collected during sieving and dried overnight

at 60 °C to measure the root biomass in depths of each

SSD.

Bacterial activity and fungal biomass

The bacterial activity and fungal biomass were mea-

sured at Lund University with the 3H-leucine incorpo-

ration method (B�a�ath 1994) as modified by B�a�ath et al.

(2001) and by extracting ergosterol (B�a�ath 2001). As

samples were kept frozen during transportation, they

were thawed at room temperature 24 h before mea-

surement start. Both assays were conducted at room

temperature. Compared to the original protocols, the

amount of soil was increased to c. 5 g fresh soil for3H-leucine incorporation and 2 g for ergosterol extrac-

tion instead of 1 g and the incubation time after the

addition of 3H-leucine was extended to 3 h instead of

2 h.

DNA extraction, PCRs and pyrosequencing of taggedamplicons

Total DNA for metabarcoding analyses was extracted

from c. 1 g soil with the UltraClean Soil DNA extraction

kit (MoBio, Carlsbad, CA, USA) according to manufac-

turer instructions. The obtained DNA was quantified by

PicoGreen (Invitrogen, Carlsbad, CA, USA) and stored

at �20 °C. DNA was adjusted to 10 ng DNA/lL in

H2O and pretreated with 1 lg BSA/mL (BSA concentra-

tion in sample: 10 mg/mL) at 95 °C for 5 min to bind

PCR-inhibiting substances. Region V1–V3 of the bacte-

rial 16S rRNA gene (16SV1–V3) and the fungal ITS-2

region were amplified using the barcoded primer pairs

27f/519r (Amann et al. 1995) and ITS3/ITS4 (White

et al. 1990), respectively. The primer and multiplex-tag

(MID) sequences are provided in Table S1 (Supporting

information). PCR amplification was performed with

20 ng soil DNA and the HotStar Taq amplification kit

(Qiagen, Hilden, Germany) in a final volume of 50 lLper samples (16SV1–V3: 15 min at 95 °C/30 cycles: 40 s

at 94 °C, 40 s at 60 °C, 1 min at 72 °C/10 min at 72 °C;ITS-2: 15 min at 95 °C/40 cycles: 40 s at 94 °C, 40 s at

58 °C, 1 min at 72 °C/10 min at 72 °C). For each sam-

ple, targets were amplified in triplicate reactions and

subsequently pooled prior to purification and quantifi-

cation. Amplicons with different barcoded primers were

finally pooled in equimolar concentrations and sent for

pyrosequencing to the Genome Quebec Innovation Cen-

ter, Montreal, Canada. The PCR products were unidi-

rectionally sequenced from primers 27f and ITS4 with

the GS-FLX Titanium technology (Roche 454 Life Sci-

ences, Brandford, CT, USA).

Sequence analyses

Quality control of bacterial and fungal reads was per-

formed according to Hartmann et al. (2014). Reads con-

taining more than two mismatches to the target

specific primers or more than one mismatch to the

barcode were removed. In parallel, reads with flow-

grams smaller than 360 were discarded, while reads

with more than 720 flows were trimmed to 720 flows

(Quince et al. 2011). The PyroNoise algorithm imple-

mented in MOTHUR v.1.28 (Schloss et al. 2009) was

then applied to remove erroneous reads due to homo-

polymeric signal misinterpretation (Quince et al. 2009).

Primer and barcode sequences were trimmed off. Sub-

sequently, the V1–V2 region of the bacterial 16S ribo-

somal sequence and the fungal ITS-2 region were

extracted with V-Xtractor (Hartmann et al. 2010b) and

Fungal ITS Extractor (Nilsson et al. 2010), respectively.

In this step, we verified the basic authenticity of the

sequences and extracted a phylogenetically comparable

segment of the targeted region (e.g. region V1–V2 of

the bacterial 16S rRNA). This accounts for the limited

read length provided by the GS-FLX Titanium technol-

ogy, which prevents the sequencing of the whole tar-

geted region (e.g. region V1–V3 of the bacterial 16S

rRNA). Based on hidden Markov models, conservative

sequences framing the targeted segments were kept to

reduce the formation of spurious OTUs during cluster-

ing. The number of single base errors due to PCR

amplification was reduced using the SeqNoise algo-

rithm (Quince et al. 2011) implemented in MOTHUR.

Most chimeric sequences were identified and removed

using UCHIME (Edgar et al. 2011) in MOTHUR. The

processed sequences were subsequently clustered in

operational taxonomic units (OTU) defined at 97%

similarity using CROP (Hao et al. 2011). Taxonomic

assignments were obtained using MOTHUR by


1094 T. RIME ET AL.

querying the bacterial and fungal reads against the

GREENGENES (DeSantis et al. 2006) and UNITE (Aba-

renkov et al. 2010) reference databases, respectively,

using a na€ıve Bayesian classifier (Wang et al. 2007)

with a minimum bootstrap support of 60%. The con-

sensus taxonomy was determined with MOTHUR as

the taxonomic path represented at least 80% of the

sequences within an OTU.

Quantitative real-time PCR

Relative abundances of bacterial 16S rRNA gene and

fungal ITS rRNA copies were determined by quantita-

tive real-time PCR (qPCR) on an ABI7500 Fast Real-

Time PCR system (Applied Biosystems, Foster City,

CA, USA) with the same primers and cycling conditions

as used for the pyrosequencing approach. Additionally,

we quantified the relative abundances of cyanobacterial

16S rRNA gene copies according to Frey et al. (2013).

qPCR were performed using 2.5 ng DNA in a total vol-

ume of 25 lL containing 0.5 lM of each primer, 0.2 mg/

mL BSA and 12.5 lL of QuantiTect SYBR Green PCR

master mix (Qiagen, Hirlen, Germany). Three standard

curves per target region (correlations ≥0.997) were

obtained using tenfold serial dilutions (10�1 to 10�9

copies) of plasmids generated from cloned targets (Frey

et al. 2011).

Statistical analyses

Indices of a-diversity, that is the local diversity (Whit-

taker 1960), observed richness (Sobs) and Shannon

diversity (H0), were estimated for total bacteria and

fungi as well as their most abundant taxa (phyla and

classes from the Proteobacteria >1.5% for the bacteria

and phyla >1% for the fungi) based on OTU abundance

matrices rarefied to the lowest sequence numbers (4640

for bacteria and 5038 for fungi). Changes in abiotic and

biological variables, a-diversity indices, Good’s cover-

age (Good 1953) and the relative abundances of the

most abundant taxa were assessed by conducting a

two-way analysis of variance (ANOVA, SSD and depth as

categorical factors, where the factor ‘depth’ was nested

in ‘SSD’) in R v.2.15.0 (R Development Core Team

2012). Differences were considered significant at

P < 0.05 unless mentioned otherwise. Normality of the

residual distribution and variance homoscedasticity

were examined to ensure validity of the test. If these

conditions were not fulfilled, the data were log-trans-

formed. For significant omnibus tests, Tukey’s honestly

significant difference post hoc tests were conducted in

R with the HSD.test function implemented in the agrico-

lae package (De Mendiburu 2012) to identify differences

between SSD and within depths of each individual

SSD. The influence of environmental variables on bacte-

rial activity, fungal biomass, abundances of ribosomal

markers and a-diversity indices was assessed by calcu-

lating Pearson’s correlation values and assessing their

significance in R using the cor.test function imple-

mented in the stats package (R Development Core

Team 2012).

Rare OTUs, that is single- and doubleton OTUs, were

kept for multivariate analyses, unless mentioned other-

wise, because they only marginally influence these

analyses (Gobet et al. 2010). Standardization of the OTU

abundance matrices was carried out by dividing the

number of sequences of each OTU by the total number

of sequences in each sample. Bray–Curtis distance

matrices were generated based on the standardized and

square-root-transformed data of the sequence abun-

dances. The overall variability in bacterial and fungal

community structures was examined with principal

coordinate analyses (PCO). Significant differences in

community structure among SSD and depths, that is

changes in b-diversity, were assessed using the PERMA-

NOVA routine (Anderson 2001) implemented in the soft-

ware Primer6+ (Clarke & Gorley 2006) with 9999

permutations. Pairwise tests among individual groups

were adjusted using the Holm method (Holm 1979).

The DistLM procedure (McArdle & Anderson 2001) was

performed in Primer6+ to identify environmental vari-

ables that are best predicting differences in microbial

community structures using the adjusted R2 selection

criterion with the ‘forward’ procedure. The variables

selected were subsequently used to build a constrained

ordination plot using a distance-based redundancy

analysis (db-RDA) (Legendre & Anderson 1999). The

goodness of fit of the constraining models was exam-

ined by calculating the Procrustes sum of squares (m12)

(Peres-Neto & Jackson 2001) between the PCO and db-

RDA ordination scores with the procrustes function as

implemented in the vegan package in R. The results of

these analyses were investigated in parallel to infer dif-

ferences between microbial community structures with

SSD, depth and changes in environmental variables.

Indicator species analysis was performed using the mul-

tipatt function implemented in the indicspecies package

in R with 99 999 permutations and allowing combina-

tions between habitats (De C�aceres et al. 2010) to iden-

tify OTUs, leading to the changes in multivariate

patterns. For this analysis, single- and doubleton OTUs

were removed because they contain little indicator

information. Multiple testing corrections of P-values

were performed in R using the fdrtool function imple-

mented in the fdrtool package (Strimmer 2008) with a

false discovery rate of 10% (q < 0.1). All graphs were

generated with R using the vegan (Oksanen et al. 2012)

and ggplot2 packages (Wickham 2009).



Results

Community compositions

We obtained 244 867 bacterial 16S rRNA (6686 � 550

per sample) and 255 249 fungal ITS-2 curated sequences

(7057 � 576 per sample). Clustering of the sequences

resulted in a total of 6819 bacterial (1051 � 30 per sam-

ple) and 2390 fungal OTUs (246 � 10 per sample). The

classification success of bacterial and fungal sequences

decreased at lower taxonomic level (Fig. S1, Supporting

information). The bacterial communities were character-

ized by nine dominant phyla (>1.5% of the total number

of sequences) and 27 less abundant phyla (referred as

‘Others’ in Fig. 1b and Fig. S1a, Supporting informa-

tion) representing 85% and 11% of the sequence num-

ber, respectively, while 4% of the sequences was

unclassified at the phylum level. Among the Proteobacte-

ria, Betaproteobacteria were the most abundant class (18%

of the sequence number) followed by Alphaproteobacteria

(16%), Gammaproteobacteria (8%) and Deltaproteobacteria

(5%). The fungal communities consisted of four domi-

nant phyla (>1% of the total number of sequences) and

two less abundant phyla (referred as ‘Others’ in Fig. 1b

and Fig. S1b, Supporting information) constituting 94%

and 1% of the sequence number, respectively, whereas

5% of the sequences were not classified at the phylum

level. A detailed description of the data set as well as

the taxonomic path and the abundances of each OTU

(Damma_Depth_OTU_data.zip) are accessible from the

Dryad Digital Repository (DOI: 10.5061/dryad.gp302).

Effect of stage of soil development

Bacterial and fungal observed richness (Sobs) and Shan-

non diversity (H0) were significantly influenced by SSD

(Table 1). Observed richness (Sobs) of Acidobacteria, Chlo-

roflexi, Cyanobacteria, Alphaproteobacteria, Deltaproteobacte-

ria and of all fungal phyla changed with SSD. Shannon

diversity (H0) of most phyla or classes except for Acido-

bacteria, Bacteroidetes, Cyanobacteria, Gemmatimonadetes,

Planctomycetes and Basidiomycota significantly varied

with SSD (Fig. S2, Supporting information). Stage of soil

development (SSD) also influenced the b-diversity of

bacteria and fungi (Fig. 2a,b, Table 2 and Table S2, Sup-

porting information). Relative abundances of the most

abundant bacterial and fungal taxa except for Bacteroide-

tes, Deltaproteobacteria and Ascomycota significantly

varied between SSDs (Table 2, Fig. S3, Supporting infor-

mation). Relative abundances of Acidobacteria, Actinobac-

teria, Alphaproteobacteria, Basidiomycota and Zygomycota

increased, while those of Cyanobacteria and Gemmatimo-

nadetes decreased with SSD. The main abiotic factors

explaining the changes in total bacterial and fungal

community structures were pH and clay (Fig. 2c,d,

Tables 3 and Table S3, Supporting information). DON

and PO3�4 also contributed to the shift in bacterial com-

munity structure with SSD, while water-holding capac-

ity (WHC) and base saturation (BS) contributed to the

changes in fungal community structure. The model

used to build the db-RDA constraining the variability of

the fungal community was, however, less reliable

(m12 = 0.52) than that used to constrain the variability

of the bacterial community (m12 = 0.02). The selected

abiotic factors explained 30% and 29% of the variability

in the bacterial and fungal community structure, respec-

tively. These abiotic factors as well as bacterial and fun-

gal rRNA gene copy numbers, 3H-leucine incorporation

rates and ergosterol significantly varied with soil age

(Table 4). Clay, BS, WHC, DON, PO3�4 , total bacterial

16S, cyanobacterial 16S and ITS rRNA gene copy num-

bers, 3H-leucine incorporation rates and ergosterol

increased, while pH decreased with SSD (Fig. S4,

Table S4, Supporting information). 3H-leucine incorpo-

ration rates, ergosterol and most of the a-diversity indi-

ces negatively correlated with pH, while they positively

correlated with WHC, DOC and root biomass (Table S5,

Supporting information).

Indicator species analysis revealed 518 bacterial and

263 fungal OTUs significantly (q < 0.1) associated with

a specific SSD or a combination of SSD (Table S6, Sup-

porting information). Eighty-eight bacterial (Fig. 3) and

83 fungal OTUs (Fig. 4) were classified at the genus

level. Bacterial and fungal genera significantly associ-

ated with the barren (10 years) and sparsely vegetated

(60 years) soils were similar but differed from the indi-

cator taxa characteristic of the vegetated (80 and

110 years) soils.

Effect of soil depth and its interaction with stage ofsoil development

Soil depth significantly influenced bacterial and fungal

Sobs and H0 (Table 1). Observed richness (Sobs) of all

taxa except for Bacteroidetes, Deltaproteobacteria and

Zygomycota and H0 of Acidobacteria, Bacteroidetes, Chloro-flexi, Planctomycetes, Alphaproteobacteria, Gammaproteobac-

teria and Ascomycota were significantly influenced by

soil depth (Table 1, Fig. S2, Supporting information).

Soil depth also significantly changed the b-diversity of

bacteria, fungi and their most abundant taxa in the bar-

ren (10 years) and sparsely vegetated (60 years) soils,

but not in the vegetated soils (80 and 110 years)

(Fig. 2a,b, Tables 2 and Table S7, Supporting informa-

tion). Except for Acidobacteria, Betaproteobacteria and Del-

taproteobacteria, the relative abundances of all bacterial

taxa changed with soil depth, while only the relative

abundance of Glomeromycota varied with depth among


1096 T. RIME ET AL.

http://dx.doi.org/10.5061/dryad.gp302

the fungal phyla (Table 2). The relative abundance of

Glomeromycota was highest in the lower depths of the

sparsely vegetated soils, while the relative abundances

of Alphaproteobacteria and Zygomycota were highest in

the surface of the 110-year-old soils (Fig. S3, Supporting

information). The main abiotic factors influencing the

changes in total bacterial and fungal community struc-

tures in the 10- and 60-year-old soils were silt and soil

moisture (SM) (Fig. 2c,d, Tables 3 and Table S3, Sup-

porting information). These abiotic factors as well as

bacterial 16S rRNA gene and fungal ITS rRNA gene

copy numbers, 3H-leucine incorporation rates and

ergosterol were significantly influenced by soil depth

(Table 4). 3H-leucine incorporation rates, ergosterol and

root biomass were highest in the 110-year-old soil

surface, while silt was highest in the 60-year-old soil

surface (Fig. S4, Table S4, Supporting information).

Indicator species analysis revealed 30 bacterial and 20

fungal OTUs significantly (q < 0.1) associated with

depths of the 10- and 60-year-old soils (Table S8, Sup-

porting information). Only three bacterial and seven

fungal OTUs were classified at the genus level; there-

fore, bacterial and fungal OTUs assigned at different

taxonomic levels are also reported in Fig. 5. The

Table 1 Effect of stage of soil development (SSD), depth and their interactions on observed richness (Sobs) and Shannon diversity

(H0) of total bacteria, fungi and their most abundant taxa (phyla or classes > 1.5% for bacteria and phyla > 1% for fungi) as well as

the direction of change in observed richness and Shannon diversity.

Variables Taxa SSD (F3,6†) Direction of change‡ Depth (F2,16) Direction of change SSD 9 Depth (F2,16)

Sobs Bacteria 4.0* ↑ 6.2** — 1.4n.s.

Acidobacteria 6.7** ↑ 12.3*** ↑ 1.7n.s.

Actinobacteria 0.6n.s. — 4.3* ↑ 0.5n.s.

Bacteroidetes 1.7n.s. — 1.4n.s. — 1.5n.s.

Chloroflexi 4.0* — 13.6*** ↑ 0.6n.s.

Cyanobacteria 14.0** ↓ 43.1*** ↓ 9.0***

Gemmatimonadetes 1.1n.s. — 5.2* ↑ 0.2n.s.

Planctomycetes 5.7* ↑ 37.9*** ↑ 5.4**

Alphaproteobacteria 4.4* ↑ 12.2*** ↓ 0.8n.s.

Betaproteobacteria 1.9n.s. — 4.6* — 0.6n.s.

Deltaproteobacteria 3.9* — 2.9n.s. — 1.2n.s.

Gammaproteobacteria 2.2n.s. — 7.1** ↑ 1.8n.s.

Fungi 4.0* ↑ 4.5* — 0.4n.s.

Ascomycota 7.0** ↑ 5.0* — 1.4n.s.

Basidiomycota 4.9** ↑ 3.5* — 0.4n.s.

Glomeromycota 14.3*** ↑ 5.2** — 0.4n.s.

Zygomycota 7.6*** ↑ 0.2n.s. — 0.8n.s.

H0 Bacteria 4.5* ↑ 10.1*** — 3.8**

Acidobacteria 0.5n.s. — 19.1*** ↑ 2.6*

Actinobacteria 6.6** ↑ 0.0n.s. — 0.3n.s.

Bacteroidetes 1.8n.s. — 4.0* — 0.6n.s.

Chloroflexi 5.6** ↑ 3.4* — 0.9n.s.

Cyanobacteria 1.4n.s. — 1.6n.s. — 0.2n.s.

Gemmatimonadetes 0.6n.s. — 1.9n.s. — 1.3n.s.

Planctomycetes 4.1n.s. — 16.2*** — 4.7**

Alphaproteobacteria 3.0* ↑ 6.4** — 2.1n.s.

Betaproteobacteria 3.9* — 2.4n.s. — 1.0n.s.

Deltaproteobacteria 10.2*** ↑ 0.8n.s. — 0.5n.s.

Gammaproteobacteria 13.1*** — 6.8** — 1.9n.s.

Fungi 5.4** ↑ 7.9** ↓ 1.1n.s.

Ascomycota 4.5* ↑ 7.7** ↓ 0.7n.s.

Basidiomycota 2.8n.s. — 0.7n.s. — 0.6n.s.

Glomeromycota 32.5*** ↑ 2.6n.s. — 0.7n.s.

Zygomycota 4.4* ↑ 0.1n.s. — 0.7n.s.

Significance levels: n.s., not significant; *P < 0.05, **P < 0.01, ***P < 0.001. Values in bold highlight significant effects of SSD, depth

or their interaction.†F-ratio tests (F) were conducted as main tests to assess the effects of SSD, depth and interactions on the variables selected. The indi-

ces are the degrees of freedom and error terms for each factor. Means, standard errors and post hoc test significances are reported in

Fig. S2 (Supporting information).‡Increase (↑), decrease (↓) or no clear pattern (—) in the selected variables with SSD or depth (surface to lowest depth).



surfaces of the 10- and 60-year-old soils were character-

ized by similar bacterial and fungal OTUs while the

lower depths of the 10- and 60-year-old soils by differ-

ent indicator OTUs. For example, OTUs affiliated to

Phormidium (Cyanobacteria), Rhizobiales (Alphaproteobacte-

ria) and Asterophora (Basidiomycota) were associated with

surface layers of both soil ages. OTUs related to Phycisp-

haerales (Planctomycetes) and Betaproteobacteria were char-

acteristic of the lower depths of the 10-year-old soil,

while OTUs affiliated to Arthrobacter crystallopoietes (Ac-

tinobacteria), Acetobacteraceae (Alphaproteobacteria) and Cu-

doniella acicularis (Ascomycota) were associated with the

lower depths of the 60-year-old soil.

Discussion

Here, we report on the first high-resolution assessment

of both bacterial and fungal communities present in dif-

ferent depths of a developing soil formed after retreat

of an Alpine glacier. Bacterial and fungal diversity

changed with soil development, whereas bacterial and

fungal community structures only differed with depth

in the barren and sparsely vegetated soils (10 and

60 years), but not in the vegetated soils. These findings

could be partly attributed to different texture and mois-

ture in the soil surface. In addition, we performed an

indicator species analysis, similar to Hartmann et al.

(2012), identifying bacterial and fungal OTUs character-

istic of the different habitats to inspect potential

changes in community composition and related

ecological features with soil depth within each SSD. We

are aware nevertheless that our interpretations may suf-

fer from caveats. First, our analyses of microbial com-

munities are based on DNA. Therefore, our survey

potentially includes inactive or dormant micro-organ-

isms, which have little influence on ecosystem function-

ing at the time of sampling. Second, the identification of

indicator species might not entirely shed light on eco-

system functioning related to microbial community

composition due to our limited knowledge about the

majority of micro-organisms as well as their ecological

functions. Nevertheless, this tool is useful to highlight

OTUs which lead to changes in overall multivariate pat-

terns and thus respond to ecological changes occurring

either with soil development or along the soil profiles

of different SSD.

During soil development after glacier retreat, C, N

and other nutrients accumulate (Sch€utte et al. 2009;

Bernasconi et al. 2011; Bajerski & Wagner 2013), mainly

due to plant colonization (Knelman et al. 2012), leading

to higher microbial biomass (Welc et al. 2012) and activ-

ity (G€oransson et al. 2011). Our study adds to the body

of evidence that such changes occur in the Damma gla-

cier forefield. Shifts in microbial diversity related to

increases in organic matter and nutrients during soil

development have already been reported in Alpine,

Arctic and Antarctic glacier forefields by genetic profil-

ing (Sigler & Zeyer 2002; Sch€utte et al. 2009; Zumsteg

et al. 2012; Bajerski & Wagner 2013). However, our 454-

pyrosequencing approach provided a much higher

PCO1 (23%)

PC

O2

(14%

)–0

.3–0

.10.

1–0

.8–0

.20.

2

PC

O2

(11%

)–0

.4–0

.10.

2

(a)

010 60 80 110

5 20

(b)

–0.4 –0.2 0.20.0 0.4

–1.0 –0.5 0.50.0 1.0

PCO1 (25%)–0.4 –0.2 0.20.0 0.4

Depth (cm)

db-RDA1(34% fitted, 19% total variation)

–1.0 –0.5 0.50.0 1.0db-RDA1

(35% fitted, 21% total variation)

db-R

DA

2(2

0% fi

tted,

11%

tota

l var

iatio

n)

–0.6

0.0

0.4

db-R

DA

2(1

3% fi

tted,

8%

tota

l var

iatio

n)

SMClay

WHCSilt

BS

pHpH

Silt

SMDON

ClayPO4

3–

(c)

Bacteria Fungi

(d)

SSD (years) Fig. 2 Changes in bacterial and fungal

community structures between stages of

soil development (SSD) and depth. PCO

representing the overall variability in

bacterial (a) and fungal communities (b).

db-RDAs with selected environmental

variables that explained most of the vari-

ability in bacterial (c) and fungal commu-

nity structures (d). According to the

Procrustes analysis, the model used to

build the db-RDA constraining the bacte-

rial community was accurate (m12 = 0.02,

P < 0.001), while the model explaining

the variability in fungal community was

less reliable (m12 = 0.52, P < 0.001). Silt:

silt content; Clay: clay content; BS: base

saturation; SM: gravimetric soil moisture;

WHC: water-holding capacity; DON:

concentration of dissolved organic nitro-

gen; PO3�4 : phosphate concentration.


1098 T. RIME ET AL.

coverage of the bacterial and fungal diversities present

in the different SSD leading to a robust assessment of

a-diversity and high-resolution description of the com-

munity composition. For example, ectomycorrhizal

fungi, such as Cortinarius (Favre 1960), Laccaria (Jump-

ponen 2003) and Hygrocybe (Seitzman et al. 2011), were

characteristic of the vegetated (80 and 110 years) soils.

These fungi might contribute to plant growth by pro-

viding plant-available nutrient sources. As plants grow,

they provide in turn environmental conditions benefi-

cial to bacterial taxa involved in the degradation of

complex organic compounds (e.g. Arthrobacter, Candida-

tus Solibacter and Sporosarcina) (Haichar et al. 2008; Uroz

et al. 2014) as well as fungal plant pathogens (e.g. Asco-

chyta and Cadophora) and lignocellulolytic saprophytes

(e.g. Coniochaeta and Geoglossum) that are able to

degrade more recalcitrant organic material (Lopez et al.

2007). In contrast, the barren (10 years) and sparsely

vegetated (60 years) soils were characterized by spore-

forming and metabolically versatile bacterial genera

Table 2 Effect of stage of soil development (SSD), depth and their interactions on b-diversity and relative abundances of total bacte-

ria, fungi and their most abundant taxa (phyla or classes > 1.5% for bacteria and phyla > 1% for fungi) as well as the direction of

changes in relative abundances with SSD and depth.

Variables Taxa SSD (F3,6†) Direction of change‡ Depth (F2,16) Direction of change SSD 9 Depth (F2,16)

b-diversity Bacteria 4.5*** n.a. 3.1*** n.a. 1.3*

Acidobacteria 7.3*** n.a. 5.3*** n.a. 1.4n.s.

Actinobacteria 6.0*** n.a. 3.0*** n.a. 1.0n.s.

Bacteroidetes 4.7*** n.a. 2.9*** n.a. 1.2n.s.

Chloroflexi 4.1*** n.a. 2.7*** n.a. 1.3*

Cyanobacteria 3.1*** n.a. 3.0*** n.a. 1.0n.s.

Gemmatimonadetes 5.1*** n.a. 3.8*** n.a. 1.2n.s.

Planctomycetes 2.2*** n.a. 2.3*** n.a. 1.0n.s.

Alphaproteobacteria 5.6*** n.a. 4.5*** n.a. 1.5*

Betaproteobacteria 6.2*** n.a. 3.3*** n.a. 1.1n.s.

Deltaproteobacteria 3.4*** n.a. 3.5*** n.a. 1.2n.s.

Gammaproteobacteria 4.6*** n.a. 2.5*** n.a. 1.3n.s.

Fungi 5.5*** n.a. 2.0* n.a. 0.1n.s.

Ascomycota 6.1*** n.a. 2.0* n.a. 0.9n.s.

Basidiomycota 5.0*** n.a. 1.8* n.a. 0.8n.s.

Glomeromycota 7.0*** n.a. 1.5n.s. n.a. 1.1n.s.

Zygomycota 5.4*** n.a. 5.4*** n.a. 1.2n.s.

Abundance Acidobacteria 15.2*** ↑ 1.8n.s. — 1.1n.s.

Actinobacteria 6.0**. ↓ 8.7** ↑ 1.0n.s.

Bacteroidetes 2.6n.s. — 13.1*** ↑ 2.6*

Chloroflexi 6.0** ↑ 15.1*** ↑ 1.4n.s.

Cyanobacteria 11.7*** ↓ 31.7*** ↓ 6.1***

Gemmatimonadetes 17.0*** ↓ 10.3*** ↑ 2.4n.s.

Planctomycetes 3.1*. — 6.4** ↑ 1.4n.s.

Alphaproteobacteria 28.9*** ↑ 17.6*** ↓ 1.4n.s.

Betaproteobacteria 7.5** ↓ 2.4n.s. — 1.6n.s.

Deltaproteobacteria 2.9n.s. — 1.1n.s. — 1.8n.s.

Gammaproteobacteria 5.5** — 5.3** ↑ 3.7**

Ascomycota 2.4n.s. — 0.3n.s. — 0.4n.s.

Basidiomycota 3.2*. ↑ 0.0n.s. — 0.1n.s.

Glomeromycota§ 8.3*** ↑ 7.7** ↑ 1.4n.s.

Zygomycota§ 22.0*** ↑ 1.5n.s. — 6.5***

b-diversity: differences in community structures assessed by PERMANOVA. Abundance: relative abundance calculated by dividing the

number of sequences of an individual phylum by the total number of sequences in each replicate.

Significance levels: n.s., not significant; *P < 0.05; **P < 0.01; ***P < 0.001. Values in bold highlight significant effects of SSD, depth or

their interaction.†F-ratio tests (F) were conducted as main tests to assess the effects of SSD, depth and interactions on the variables selected. The indi-

ces are the degrees of freedom and error terms for each factor. Means, standard errors and pairwise comparisons between SSD and

depth are reported in the Supporting information (Fig. S3, Tables S2 and S7).‡Increase (↑), decrease (↓) or no clear pattern (—) in the selected variables with SSD or depth (surface to lowest depth). It was not

possible to assign a change of direction for the b-diversity analyses (n.a., not applicable).§Data were log-transformed.



known to grow anaerobically, such as Clostridium,

Desulfosporosinus, Sporotalea, Anaeromyxobacter and

Geobacter (Hartmann et al. 2014). The presence of such

micro-organisms indicates that oxygen depletion occurs

in these soils due to water table fluctuations during

snow and glacier ice melting (Magnusson & Kobierska

2014). We also found psychrophilic yeasts, such as

Cryptococcus, Leucosporidium and Mrakia (Buzzini et al.

2012), characterizing these SSD. These micro-organisms

can cope with low temperatures, which are common

near the glacier terminus; psychrophilic yeasts might

therefore be favoured in these habitats.

Our study investigated in particular the changes in

microbial communities with depth during early SSDs

(<110 years). As for studies conducted in grassland and

forest soils (e.g. �Snajdr et al. 2008; Will et al. 2010),

bacterial activity and fungal biomass decreased with

depth at all SSD and with C depletion. Bacterial a-diversity did not consistently decrease with soil depth

and was not correlated with reduction in soil C. On the

other hand, fungal a-diversity was highest in the soil

surface of the vegetated soils and was positively corre-

lated with increasing DOC and root biomass. The

absence of trend in bacterial a-diversity with soil depth

might result from high variability in microclimatic con-

ditions occurring in Alpine soils (Lazzaro et al. 2012)

and diverse resources of organic matter (Schurig et al.

2013), creating a high diversity of ecological niches. In

contrast, our study suggests that changes in fungal a-diversity with depth of developing soils (<110 years)

might be mainly influenced by plant establishment and

subsequent C accumulation.

In forest and grassland soils, shifts in community struc-

tures were usually related to decrease in C with depth

(Will et al. 2010; Baldrian et al. 2011). On the contrary,

here, bacterial and fungal b-diversities did not change in

vegetated soils with depth although soil C decreased.

Micro-organisms present in the Damma glacier forefield

must thrive under C limitation due to seasonal fluctua-

tions (Lazzaro et al. 2012). Thus, variation in soil C with

depth does not constitute a determining factor structuring

the bacterial and fungal communities in relatively young

soils (<110 years) formed after glacier retreat.

Surprisingly, we observed shifts in bacterial and fun-

gal b-diversities between depths in the barren (10 years)

and sparsely vegetated (60 years) soils. These changes

could be partly attributed to changes in soil moisture

and texture, particularly silt, with depth. Zumsteg et al.

(2013) already reported that bacterial and fungal com-

munities present in barren soils were drastically

affected by fluctuations in soil moisture. Extreme drying

events occur in barren soils due to high temperature

and solar radiations (Zumsteg et al. 2011). Fluctuations

in soil moisture might affect carbon and nutrient avail-

ability, which influences in turn the microbial commu-

nity structure and activity (Brockett et al. 2012; Zumsteg

et al. 2013). In addition, we observed a decrease in silt

with depth of the barren soils, which could also influ-

ence microbial communities by modifying soil moisture

and nutrient availability (Lauber et al. 2008). Although

we could not clearly identify the main environmental

driver structuring the microbial communities with

depth of the barren soils, our study suggests that

changes in soil moisture and texture affect bacterial and

fungal community structures in barren and sparsely

vegetated soils.

Our indicator species analysis allowed us to identify

characteristic OTUs associated with a particular depth

and thus leading to changes in observed multivariate

patterns. For example, an OTU affiliated to the

Table 3 Variance of the bacterial and fungal community struc-

tures constrained by environmental variables obtained in dis-

tance-based redundancy analyses using the DistLM procedure.

Variables Variance (%)†

Pseudo-F values‡

Sequential test§ Marginal test§

Bacteria

pH 12.4 4.4*** 4.4***

Silt 9.9 3.8*** 4.1***

SM 5.8 2.3*** 2.0*

DON 4.5 1.9** 3.9***

Clay 3.8 1.6* 1.8*

PO3�4 3.4 1.5* 3.1***

Fungi

Silt 11.9 4.2*** 4.2***

pH 8.2 3.1*** 4.0***

SM 6.6 2.6*** 2.1*

Clay 5.2 2.4** 1.6*

WHC 3.9 1.6* 1.5n.s.

BS 3.7 1.6* 3.7***

SM: gravimetric soil moisture; WHC: water-holding capacity;

BS: base saturation; DON: concentration of dissolved organic

nitrogen; PO3�4 : phosphate concentration.

Significance levels: not significant; *P < 0.05, **P < 0.01,

***P < 0.001.†The values of variance reported here are those obtained from

the sequential tests. Values of variance obtained from the mar-

ginal test are reported in the Supporting information

(Table S3).‡Permutational tests based on pseudo-F values were conducted

as main tests to assess the effect of each environmental variable

on the community structures. Here, we show the variables that

significantly affected the community structures.§The sequential test reports variance constrained by an envi-

ronmental variable after the previous variables used in the

model have been fitted (selection procedure: forward; selection

criterion: adjusted R2). The marginal test inspects the variance

constrained by each individual variable independently of the

others.


1100 T. RIME ET AL.

Gemmatimonadetes was associated with the lower (5 and

20 cm) depths of the barren soils. In addition, this phy-

lum was particularly abundant in these depths (up to

8%, Fig. S3, Supporting information). Representatives of

this phylum were also found in glacier forefields

(Zumsteg et al. 2012; Bajerski & Wagner 2013), alpine

and arid environments (DeBruyn & Nixon 2011) as well

as agricultural soils (Chaudhry et al. 2012). Zeng et al.

(2014) recently isolated a photoheterotrophic representa-

tive of this phylum from a freshwater lake, suggesting

that this phylum has broad physiological potentials.

Their widespread distribution and their various metab-

olism types suggest that they can colonize different

environments under various climatic conditions. How-

ever, their ecological functions are hardly known

because they have been rarely cultivated (Zhang 2003;

DeBruyn et al. 2013; Zeng et al. 2014). Our indicator spe-

cies analysis also revealed a mucilaginous Cyanobacte-

rium affiliated to the genus Phormidium in the soil

surface. Phormidium isolates retain water in soil and

produce pigments and sheath protecting them against

high UV exposure (Janatkov�a et al. 2013). High UV radi-

ation also puts high selective pressure on microbial

communities in unvegetated environments, such as gla-

cier forefields (Frey et al. 2013), rocks (Sigler et al. 2003)

and desert soil crusts (Garcia-Pichel et al. 2003), which

leads to the selection of UV-resistant micro-organisms,

such as Cyanobacteria (Freeman et al. 2009). In our study,

cyanobacterial a-diversity and abundance were highest

in the surface of the barren soils and decreased with

soil depth. The analysis of both our environmental vari-

ables and bacterial indicator OTUs therefore suggests

that fluctuations in water regime and high UV radiation

at the soil surface influence the microbial community

Table 4 Effect of stage of soil development (SSD), depth and their interactions on the abiotic and biological variables as well as the

direction of change in abiotic and biological variables with SSD and depth.

Variables SSD (F3,6†) Direction of change‡ Depth (F2,16) Direction of change SSD 9 Depth (F2,16)

Sand 12.6*** ↑ 11.9*** ↑ 1.9n.s.

Silt 13.3*** ↑ 11.2*** ↓ 1.9n.s.

Clay§ 5.3** ↑ 8.5** ↓ 4.1**

pH 31.5*** ↓ 12.9*** ↑ 1.3n.s.

CEC 1.0n.s. — 14.3** ↓ 1.5n.s.

BS 6.7** ↑ 1.1n.s. — 1.0n.s.

SM 0.7n.s. — 7.2** — 1.3n.s.

WHC 4.2* ↑ 23.7*** ↓ 1.6n.s.

TC§ 12.0* ↑ 14.1*** ↓ 1.9n.s.

DOC 8.5*** ↑ 12.5*** ↓ 3.0*

TN 12.7** ↑ 12.7*** ↓ 7.9**

DON 10.7*** ↑ 13.0*** ↓ 5.1*

PO3�4 24.4*** ↑ 30.2*** ↓ 13.8***

SO2�4 2.7n.s. — 8.3*** ↓ 1.1n.s.

NO�3 6.1* ↑ 0.8n.s. — 1.5n.s.

NHþ4 10.9***. ↑ 6.4* ↓ 0.8n.s.

DNA§ 23.0*** ↑ 30.9*** ↓ 2.5n.s.

Total 16S§ 5.5* ↑ 24.6*** ↓ 2.5n.s.

Cya 16S§ 3.6* ↓ 53.4*** ↓ 3.8**

ITS2§ 7.3** ↑ 27.5*** ↓ 1.9n.s.

Leucine§ 15.2*** ↑ 19.6*** ↓ 2.0n.s.

Ergosterol§ 14.5*** ↑ 20.0*** ↓ 1.3n.s.

Root 9.9** ↑ 8.4** ↓ 4.0**

CEC: cation exchange capacity; BS: base saturation; SM: gravimetric soil moisture; WHC: water-holding capacity; TC: total carbon;

DOC: concentration of dissolved organic carbon; TN: total nitrogen; DON: concentration of dissolved organic nitrogen; PO3�4 : phos-

phate concentration; SO2�4 : sulfate concentration; NO�

3 : nitrate concentration; NHþ4 : ammonium concentration; DNA: Total DNA

concentration; Total 16S: numbers of total 16S rRNA gene copies; Cya 16S: numbers of cyanobacterial 16S rRNA gene copies; ITS2:

numbers of ITS gene copies; Leucine: 3H-leucine incorporation rates; Ergosterol: concentration of ergosterol; Root: root biomass.

Significance levels: n.s., not significant; *P < 0.05, **P < 0.01, ***P < 0.001. Values in bold highlight significant effects of SSD, depth or

their interaction.†F-ratio tests (F) were conducted as main tests to assess the effects of SSD, depth and interactions on the abiotic and biotic variables.

The indices are the degrees of freedom and error terms for each factor. Means, standard errors and post hoc test significances are

reported in the Supporting information (Fig. S4, Table S4).‡Increase (↑), decrease (↓) or no clear pattern (—) in the selected variables with SSD or depth (surface to lowest depth).§Data were log-transformed.



structures within the profile of barren and sparsely veg-

etated soils.

Plant colonization is a determining factor shaping

microbial community structures in glacier forefields

(Jumpponen 2003; Blaalid et al. 2012; Knelman et al.

2012; Schulz et al. 2013; Brown & Jumpponen 2014).

Our study further indicates that plant establishment

influences the vertical distribution of bacterial and fun-

gal communities in the sparsely vegetated soils. As soil

progressively becomes vegetated, arbuscular mycorrhi-

zas, such as OTUs affiliated to Archaeosporaceae and

Glomeraceae, are particularly present in lower depths

where roots are most abundant. Following the establish-

ment of plants and higher root biomass in lower

Otu3103Otu4470

Otu0124Otu0270Otu0653Otu1059Otu1205Otu1514Otu1521Otu1689Otu2293Otu2501Otu2664Otu2836Otu3113Otu3148Otu3344Otu3820Otu3858Otu3921Otu4303Otu4540Otu4623Otu5100Otu5261Otu5394Otu6048

Otu4089Otu5700Otu6427



Otu0150Otu3938

Otu3561

Otu0837

Otu2026Otu2214Otu2489Otu6389

Otu1646Otu5757

Azospirillum Otu2491Bosea Otu4965

Magnetospirillum Otu2638Rhizobium Otu0246

Telmatospirillum Otu6807Rhodoplanes Otu5388Burkholderia Otu2475

Collimonas Otu6471Cupriavidus Otu3675

Massilia Otu4175Methylotenera Otu5825Polaromonas Otu6803

Otu2957Otu3288

Anaeromyxobacter Otu4277Geobacter Otu2403

Rhodanobacter Otu4340Aquicella Otu6019

Luteolibacter Otu0615

Otu5637

Otu5711Otu6085

Acidobacteria

Actinobacteria

Bacteroidetes

Firmicutes

Gemmatimonadetes

Planctomycetes

Alphaproteobacteria

Betaproteobacteria

DeltaproteobacteriaGammaproteobacteriaVerrocomicrobia

Candidatus KoribacterCandidatus Koribacter

Otu0002Candidatus SolibacterCandidatus SolibacterCandidatus SolibacterCandidatus SolibacterCandidatus SolibacterCandidatus SolibacterCandidatus SolibacterCandidatus SolibacterCandidatus SolibacterCandidatus SolibacterCandidatus SolibacterCandidatus SolibacterCandidatus SolibacterCandidatus SolibacterCandidatus Solibacter

Candidatus SolibacterCandidatus Solibacter

Candidatus Solibacter


Candidatus SolibacterCandidatus SolibacterCandidatus SolibacterCandidatus Solibacter


Candidatus SolibacterCandidatus Solibacter

Otu3051ArthrobacterArthrobacterArthrobacterArthrobacter

Catenulispora Otu2231Phycicoccus Otu5386

CytophagaOtu3193Otu1131

Propionicimonas

CytophagaCytophagaCytophaga

Flavobacterium Otu2258Hymenobacter Otu2186

Bacillus Otu0250BacillusBacillusBacillus

Otu0082ClostridiumClostridiumClostridium

DesulfosporosinusDesulfosporosinus

Otu0159Paenibacillus Otu0326Sporosarcina Otu6557

Sporotalea Otu0080Sporotalea

Gemmatimonas Otu1052GemmatimonasGemmatimonasGemmatimonasGemmatimonas

Gemmata Otu0060Planctomyces

Relative abundance(%)

SSD (years)10 60 80 110

0.0 0.4 0.8

Indicator values<0.250.50–0.75

0.25–0.50>0.75

Otu0885PlanctomycesPlanctomyces

Chthoniobacter Otu2155ChthoniobacterChthoniobacter

Otu3741MC18MC18

Otu4053OpitutusOpitutusOpitutus

Fig. 3 Bacterial genera identified as

indicator taxa significantly (q < 0.1) asso-

ciated with a stage of soil development

(SSD) or a combination of SSD. The bars

represent the cumulative relative abun-

dance of each indicator OTU in all the

samples. The size of each circle defines

the association strength (indicator value)

of a particular OTU with the different

SSD, such as 0–0.25: not characteristic;

0.25–0.5: weakly characteristic; 0.5–0.75:characteristic; 0.75–1.0: strongly charac-

teristic. The taxonomic path, indicator

values, P-values, q-values of all bacterial

indicator OTUs are reported in Table S6

(Supporting information).


1102 T. RIME ET AL.

depths, fungal saprophytes, such as Geoglossum nigritum

(Ascomycota) and Mortierella alpina (Zygomycota), as well

as chemoorganoheterotrophic bacterial OTUs, such as

Arthrobacter crystallopoietes (Actinobacteria) and OTUs

affiliated to the Acidobacteria, are able to colonize these

habitats, likely due to their capability to degrade plant

recalcitrant organic compounds (�Stursov�a et al. 2012).

Our results therefore suggest that plant establishment

already influences the bacterial and fungal communities

in lower depths of early stages of soil development

(60 years), probably through rhizodeposition, while the

surface remains influenced by UV radiation and tem-

perature fluctuations due to the absence of a thick vege-

tation cover.


AscochytaBeauvariaBisporella

Bryoglossum

CatenuliferaChalara

CheilymeniaCladophialophora

ConiochaetaCosmospora

CudoniellaExophiala

FontanosporaGeoglossum

Gleotinia

GyoerffyellaHaptocillium

HypodermaKernia

Lachnum

LecanoraMycocentrospora

OidiodendronPhaeosphaeria

PhialocephalaPhialophora

SchizoblastosporionSphaerostilbella

SpirosphaeraTetracladium

TolypocladiumTrichoderma

VenturiaZalerion

Asterophora

BovistaClavaria

ClavicornaCortinarius

Cryptococcus

Entoloma

FibulrhizoctoniaGalerina

Hygrocybe

LaccariaLeucosporidium

MrakiaMycena

OmphalinaZymoxenogloea

ClydaeaKappamycesAcaulospora

ClaroideoglomusMortierella

Mucor

Acarospora Otu0660Otu0716

Otu1470Otu1972Otu0908Otu1519Otu0731Otu0130Otu1626Otu0161Otu0059Otu2293Otu0966Otu1944

Otu1385Otu1182Otu2042Otu2219Otu1743Otu1865Otu1783Otu0585Otu0085Otu0083Otu1887Otu0011Otu1176Otu1136Otu0111Otu1622Otu1399Otu1333Otu0292Otu1688Otu0075Otu1965Otu2170Otu0306Otu0105Otu2165Otu2226Otu1233Otu1567Otu2150Otu0026Otu0086Otu1169Otu1552Otu0539Otu0191Otu0348Otu1837Otu0211Otu0616Otu0160Otu0238Otu1798Otu0200Otu0844Otu0099Otu0104Otu0228Otu0234Otu1439Otu1802Otu0055Otu0020Otu1973Otu0067Otu0609Otu0090Otu0840Otu0212Otu0113Otu0768Otu1130Otu1463Otu1804Otu0123

Holwaya

Gibberella

Otu0016

Ascomycota

Relative abundance(%)

0 2 4 6<0.25 0.25–0.500.50–0.75 >0.75

Indicator values

SSD (years)10 60 80 110

Basidiomycota

Chytridiomycota

Glomeromycota

Zygomycota

CadophoraCadophora

Cladophialophora

Geoglossum

Gleotinia

Lachnum

Phaeosphaeria

Tetracladium

Trichoderma

Asterophora

Clavaria

CryptococcusCryptococcus

Entoloma

Galerina

Hygrocybe

Leucosporidium

Mycena

Zymoxenogloea

Acaulospora

MortierellaMortierellaMortierellaMortierella

Fig. 4 Fungal genera identified as indica-

tor taxa significantly (q < 0.1) associated

with a stage of soil development (SSD)

or a combination of SSD. The bars repre-

sent the cumulative relative abundance

of each indicator OTU in all the samples.

The size of each circle defines the associ-

ation strength (indicator value) of a par-

ticular OTU with the different SSD, such

as 0–0.25: not characteristic; 0.25–0.5:weakly characteristic; 0.5–0.75: character-istic; 0.75–1: strongly characteristic. The

taxonomic path, indicator values, P-val-

ues, q-values of all fungal indicator OTUs

are reported in Table S6 (Supporting

information).




1104 T. RIME ET AL.

In conclusion, our study corroborates findings from

numerous studies that have shown microbial succession

during soil development in glacier forefields. However,

our approach combining high-throughput sequencing

and the identification of indicator species revealed that

bacterial and fungal community structures differed

along the profiles of barren and sparsely vegetated soils

due to differences in soil texture, fluctuations in water

regime and high UV exposure at the soil surface. As soil

becomes vegetated, bacterial and fungal communities in

lower depths shift towards plant-related micro-organ-

isms. In the later stages of soil ecosystems, the bacterial

and fungal communities did not differ between depths.

Soil C, usually thought as a determining factor structur-

ing microbial communities with depth, had a minor

influence on bacterial and fungal diversities but was cor-

related to the increasing microbial biomass and activity

in surface during soil development. With the disintegra-

tion of ice biomes worldwide, the comprehension of soil

formation processes in both time and space is of extreme

importance. In that respect, our study unravelled only

part of the vertical stratification of micro-organisms dur-

ing soil development and should therefore encourage

further investigations.

Acknowledgements

This study was funded by the Swiss National Science Foun-

dation (SNSF) under the Grant number: 31003A-138321. The

authors gratefully thank Dr. E. B�a�ath, who hosted TR during

his visit at Lund University to measure 3H-leucine incorpora-

tion rates and ergosterol. We also thank the Central Labora-

tory, Roger K€ochli, Noureddine Hajjar and Beat Stierli (Swiss

Federal Research Institute WSL) for the soil analyses and

help in the laboratory. We also wish to acknowledge the

Genetic Diversity Centre (GDC) of the ETH Zurich and the

contribution of scientists at the McGill University and

G�enome Qu�ebec Innovation Center, Montr�eal, Canada, for

the 454-pyrosequencing.

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Data accessibility

DNA sequences: raw sequences uploaded as publicly

accessible SRA (ENA: PRJEB6728); quality-checked

sequences (DNA_seqs_MEC2014.zip) accessible from

the Dryad Digital Repository: doi:10.5061/dryad.gp302.

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ma_Depth_OTU_data.zip) accessible from the Dryad

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Supporting information

Additional supporting information may be found in the online ver-

sion of this article.

Fig. S1 Overview of the bacterial (a) and fungal (b) community

compositions.

Fig. S2 Effect of stage of soil development (SSD) and depth on

observed richness (Sobs) and Shannon diversity (H0) indices

for bacteria (A and B), fungi (C and D) and their most abun-

dant taxa (phyla or classes > 1.5% for bacteria and phyla > 1%

for fungi).


the relative abundance of the most abundant bacterial (A) and

fungal (B) taxa (phyla or classes >1.5% for bacteria and phyla

>1% for fungi).


bacterial activity, measured as 3H-leucine incorporation rates,

and fungal biomass, measured as ergosterol.

Table S1 Sequences of primer sets and multiplex-tag (MID)

barcodes used to amplify the partial bacterial small-subunit

ribosomal RNA genes (region V1-V3 of 16S) and the fungal

internal transcribed spacers genes (region ITS-2).

Table S2 Differences in bacterial and fungal community struc-

tures between stages of soil development (SSD) assessed by

pairwise t-tests as post-hoc tests.

Table S3 Variance explained by each environmental variable

on the bacterial and fungal community structures.

Table S4 Abiotic and biological variables of different stages of

soil development (SSD) and depth (mean � SE; n = 3).

Table S5 Correlations between microbial activity, biomass,

relative abundances of rRNA genes and a-diversity indices

and edaphic variables.

Table S6 Taxonomic path and indicator values of bacterial (A)

and fungal (B) indicator OTUs associated with a specific stage

of soil development (SSD) or a combination of SSD.

Table S7 Effect of soil depth on community structures of the

total bacteria and fungi as well as their most abundant taxa

(phyla or classes >1.5% for bacteria and phyla >1% for fungi)

determined by PERMANOVA.

Table S8 Taxonomic path and indicator values of bacterial (A)

and fungal (B) indicator OTUs associated with a specific depth

or a combination of depths within the 10 and 60 year-old soils.


1108 T. RIME ET AL.




Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield

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