Dominant Tree Species and Soil Type Affect the Fungal CommunityStructure in a Boreal Peatland Forest

Hui Sun,a,b,c Eeva Terhonen,b Andriy Kovalchuk,b Hanna Tuovila,d Hongxin Chen,b Abbot O. Oghenekaro,b Jussi Heinonsalo,c

Annegret Kohler,e Risto Kasanen,b,f Harri Vasander,b Fred O. Asiegbub

Collaborative Innovation Center of Sustainable Forestry in Southern China, College of Forestry, Nanjing Forestry University, Nanjing, Chinaa; Department of ForestSciences, University of Helsinki, Helsinki, Finlandb; Department of Food and Environmental Sciences, University of Helsinki, Helsinki, Finlandc; Department of Biological andEnvironmental Science, University of Jyväskylä, Jyväskylä, Finlandd; UMR 1136 INRA/Université de Lorraine, Interactions Arbres/Microorganismes, INRA, Institut National dela Recherche Agronomique, Centre INRA de Nancy, Champenoux, Francee; Natural Resources Institute Finland (LUKE), Vantaa, Finlandf

Boreal peatlands play a crucial role in global carbon cycling, acting as an important carbon reservoir. However, little informa-tion is available on how peatland microbial communities are influenced by natural variability or human-induced disturbances.In this study, we have investigated the fungal diversity and community structure of both the organic soil layer and buried woodin boreal forest soils using high-throughput sequencing of the internal transcribed spacer (ITS) region. We have also comparedthe fungal communities during the primary colonization of wood with those of the surrounding soils. A permutational multivar-iate analysis of variance (PERMANOVA) confirmed that the community composition significantly differed between soil types(P < 0.001) and tree species (P < 0.001). The distance-based linear models analysis showed that environmental variables weresignificantly correlated with community structure (P < 0.04). The availability of soil nutrients (Ca [P � 0.002], Fe [P � 0.003],and P [P � 0.003]) within the site was an important factor in the fungal community composition. The species richness in woodwas significantly lower than in the corresponding soil (P < 0.004). The results of the molecular identification were supplementedby fruiting body surveys. Seven of the genera of Agaricomycotina identified in our surveys were among the top 20 genera ob-served in pyrosequencing data. Our study is the first, to our knowledge, fungal high-throughput next-generation sequencingstudy performed on peatlands; it further provides a baseline for the investigation of the dynamics of the fungal community in theboreal peatlands.

Boreal and subarctic peatlands play a crucial role as carbon (C)reservoirs, because they contain approximately 15% to 30% of

the world’s soil C in the form of peat, though they cover less than3% of Earth’s land surface (1). Peatlands can act as a sink foratmospheric C for millennia, owing to the imbalance betweentheir net primary production and decomposition rates (2). How-ever, direct and indirect environmental changes caused by humanactivities may have serious impacts on the function of peatlandecosystems. It has been hypothesized that global warming andpeatland drainage will increase the rate of decomposition in borealpeatlands, potentially causing a long-term net flux of carbon di-oxide (CO2) into the atmosphere (3–5). The decomposition rateof organic material in peatland may also be influenced by micro-bial community composition, which in turn might be affected byenvironmental changes. Changes in plant communities inducedby peatland drainage and other factors may have effects on the soilmicrobial communities. Therefore, it is important to improve ourunderstanding of the effects of environmental factors (e.g., waterlevel and nutrient availability) on the structure of microbial com-munities and improve our predictions about the effect of anthro-pogenic disturbances on the function of peatland ecosystems.

Fungi are key players in nutrient cycling in boreal ecosystems(6, 7). They degrade soil organic matter (SOM) due to their abilityto produce and secrete a broad range of appropriate hydrolyticand oxidative enzymes (8). White-rot and brown-rot fungi of thephylum Basidiomycota play a key role in the decay of recalcitrantmaterials of plant origin, in particular the polymers of plant cellwalls. White-rot fungi are the only organisms capable of substan-tial lignin decay (9).

About six hundred fungal species have been reported from

different types of peatlands worldwide, with the majority of thembelonging to the two dominant groups, Ascomycota and Basidi-omycota (10). Most of the available data on peatland fungal diver-sity are based upon the results of either cultivation of fungal iso-lates or direct identification by microscopy (10). The impact ofnatural factors on the structure of fungal communities in peat-lands has received little attention compared to other terrestrialecosystems. A successional trend from mycorrhizal fungi towardsaprotrophs after water level drawdown has been suggested by oneof the previous studies (11). However, most of the available resultswere obtained using low-resolution methods. Fungal communi-ties of forest ecosystems have been studied in more detail. Theeffects of different environmental factors on the structure of forestfungal communities have been assessed, and it has been demon-strated that dominant tree species have the strongest effect on thecomposition of fungal communities in forest litter (12).

Received 30 November 2015 Accepted 15 February 2016

Accepted manuscript posted online 19 February 2016

Citation Sun H, Terhonen E, Kovalchuk A, Tuovila H, Chen H, Oghenekaro AO,Heinonsalo J, Kohler A, Kasanen R, Vasander H, Asiegbu FO. 2016. Dominant treespecies and soil type affect the fungal community structure in a boreal peatlandforest. Appl Environ Microbiol 82:2632–2643. doi:10.1128/AEM.03858-15.

Editor: J. E. Kostka, Georgia Institute of Technology

Address correspondence to Hui Sun, Hui.Sun@helsinki.fi.

H. Sun, E. Terhonen, and A. Kovalchuk contributed equally to this article.

Supplemental material for this article may be found at http://dx.doi.org/10.1128/AEM.03858-15.

Copyright © 2016, American Society for Microbiology. All Rights Reserved.

crossmark

2632 aem.asm.org May 2016 Volume 82 Number 9Applied and Environmental Microbiology

on Novem

ber 3, 2020 by guesthttp://aem

.asm.org/

Dow

nloaded from

In this paper, we present the results of our analysis of fungalcommunity structure across different soil types (pristine anddrained peatland and mineral soil) with different dominant treespecies (Scots pine and Norway spruce) in a boreal forest. Thecomparison between pristine and drained plots should provide aninsight into the potential effects of lowering the water table level.With this approach, we were aiming to address the following ques-tions. (i) How diverse are fungal communities in different types ofpeat soils? (ii) Do the fungal community structure and functioncorrelate with site-related factors, such as dominant tree species,nutrient availability, and water table level? (iii) What is the impactof environmental variables on fungal community structure in theorganic soil layer versus in decaying wood?

The data presented here contribute to a better understandingof the importance of the fungal community to ecosystem pro-cesses and function in relation to the environmental variables inboreal peatlands. The study also provides important informationfor estimating and modeling the response of boreal peatlands to achanging climate (13).

MATERIALS AND METHODSStudy plots. The study plots are located at Lakkasuo (61°48=N, 24°19=E,ca. 150 m above sea level), a boreal peatland complex in central Finland.The characteristics of the 10 selected study plots are shown in Table 1.Four pristine peatland (PP) plots, four drained peatland (DP) plots, andtwo plots lacking peat layer (named here mineral soil [MS] plots) wereincluded. All the peatland soils had clear horizons of organic material(humus or organic layer), which were the sampled horizons. The plotsPine-DP1, Pine-DP2, and Spruce-DP4 were drained in 1965, and theditches were again cleaned in 1988. The plot Spruce-DP3 was drained in1928, and the ditches were cleaned in 1990. The dominant forest treespecies in each plot was either Scots pine (Pinus sylvestris L.) or Norwayspruce [Picea abies (L.) H. Karst], each of which naturally regenerated inall plots. A detailed description of Lakkasuo and the study plots can befound elsewhere (14, 15).

Soil sampling. Soil samples were taken from the organic layer at thebeginning of August 2010. Within each plot, three replicate soil cores(3-cm diameter and 5-cm depth) were taken from the organic layer of thepeat or mineral soil after removal of the litter layer. The distance betweenreplicate core samples was 10 m. In total, 6 replications for mineral soil(MS) (2 plots), 12 replications for pristine undrained peatland (PP) (4plots), and 12 replications for drained peatland (DP) (4 plots) were sam-pled. To reduce complexity in the further analysis, the samples were sub-divided based on tree species, vegetation, and soil type, resulting in 10separate plots that were geographically close to each other. The sampleswere stored on ice, delivered to the laboratory, and stored at �20°C untilfurther processing.

Sampling of primary wood-colonizing fungi. Four of the 10 plotsdescribed in Table 1 were used for this experiment: two pristine peatlandplots (Pine-PP2 and Spruce-PP4) and two mineral soil plots (Pine-MSand Spruce-MS). Norway spruce wood cubes (1 by 1 by 1 cm) were usedin the experiment. The wood cubes were weighed; then, they were auto-claved for 30 min at 120°C and dried for 3 days at 65°C in the oven. Threepreweighed wood cubes were placed into a mesh bag (mesh size, 1 mm).The mesh bags with wood cubes were buried 2 to 3 cm beneath the surfaceof the litter layer in the four selected plots at the beginning of June 2010.For each plot, 6 replicate mesh bags containing wood cubes were preparedand placed in pairs at 3 locations. After a 2-month incubation, one meshbag from each location, a total of three per plot, was transported on ice tothe lab, where the wood cubes were cleaned to remove all nonhyphalsurface materials and particles. The cleaned wood cubes were stored at�80°C until further processing. After 4 months of incubation, the processwas repeated. The fungal community of the soil sampled from nearby sites

within the plot where the mesh bags were buried served as a referencepopulation (reference soil samples were collected in August 2010).

Wood decay in peatland and mineral soil. Wood decay measure-ments were performed in the same way as wood colonization experi-ments, except that they were carried out at all 10 study plots (Table 1).Sterilized preweighed Norway spruce wood cubes were placed into meshbags and buried 2 to 3 cm beneath the surface of the litter layer. After 4months of incubation, the mesh bags were collected. The wood cubes werecleaned to remove all additional superficial materials and weighed afterbeing dried for 3 days at 65°C in the oven. The total wood weight loss wascalculated.

DNA extraction, amplification of internal transcribed spacer re-gion, and pyrosequencing. The wood cube was ground in liquid nitrogenwith a grinder (Kinematica, Switzerland). DNA was isolated from 0.5 g ofthe homogenized sample using the PowerSoil DNA isolation kit (MoBioLaboratories, Carlsbad, CA, USA), and the internal transcribed spacer(ITS) region was amplified using the fungus-specific primers ITS1-F(containing 454 pyrosequencing A-adapter and a 6-bp barcode) and ITS4(containing 454 pyrosequencing B-adapter) (16, 17). The possible ampli-fication of contaminants was evaluated with a negative PCR control, inwhich the template DNA was replaced with sterile water. These remainedfree of PCR amplicons. PCR amplicons were sequenced using the ITS1-Fprimer at the Institute of Biotechnology (University of Helsinki, Finland)using the 454 GS-FLX titanium protocol (Life Sciences/Roche Diagnos-tics, CT, USA), which yielded read lengths of �400 bp.

Pyrosequencing data processing. The sequence data were analyzedusing the mothur pipeline v. 1.31.2 (18), following a modified standardoperation procedure (19). In summary, the raw reads were subjected toquality control, and each sequence was screened for a match to the se-quencing primer (ITS1-F) and valid DNA tag. Sequences were removed ifthey contained (i) ambiguous (N) bases; (ii) homopolymers longer thaneight nucleotides; or (iii) an average Phred quality score lower than 25.Due to the lack of reference templates for sequence alignment, each se-quence that passed the quality filtering was truncated to a 250-bp lengthafter the primer (ITS1-F) and tag were removed. To remove sequencesthat were likely due to pyrosequencing errors, the remaining sequenceswere preclustered within a distance of 1 bp using a pseudosingle linkagealgorithm implemented in mothur. All potentially chimeric sequenceswere identified using the mothur-embedded uchime de novo algorithm(20) and removed. Unique sequences were pairwise aligned with theNeedleman method (21), and the aligned distance matrices were clusteredinto operational taxonomic units (OTUs) using the average neighbor-joining algorithm at 97% similarity. All global singletons (OTUs contain-ing only one sequence across all samples) were omitted due to their un-certain origin (22). Observed and estimated species richness Chao 1 (23),a diversity index (inverse Simpson’s complement), and Good’s coverage(complement of the ratio between local singleton OTUs and the totalsequence count) were estimated.

To correct for differences in survey effort and ensure comparable es-timators across samples, the number of sequences of the smallest size(1,000 reads/sample) among all samples was randomly subsampled (rar-efaction) and used for calculation of estimators and for fungal structurecomparison between communities.

To determine the taxonomic affinities, the whole sequence data set wasphylogenetically classified using a mothur-formatted copy of the fullUNITE � INSD data sets version 6 (24) with an 80% confidence thresholdin mothur (18). Nonfungal sequences were removed using the removelineage command in mothur.

Field inventory of fungal fruiting bodies. Three circular plots of 50m2 (radius, 3.99 m), 10 m apart, were placed in the plots so that the centerof the plot was located approximately at the site where the sample forpyrosequencing was collected. All fruiting bodies within the plots weredocumented. The majority of fruiting bodies were photographed and col-lected for further identification. The survey was carried out twice, on 20August 2010 and on 20 September 2010. The survey was designed to

Structure of Fungal Communities in Boreal Forest

May 2016 Volume 82 Number 9 aem.asm.org 2633Applied and Environmental Microbiology

on Novem

ber 3, 2020 by guesthttp://aem

.asm.org/

Dow

nloaded from

TA

BLE

1D

escriptionof

the

study

plots

Plot a

SoilclassP

eat/min

eralsoiltypeD

omin

ant

treespecies

Oth

ertree

speciesO

ther

plant

speciesT

reevol

(m3

·ha

�1)

Avg

water

tabledepth

b(cm)

SoilpH

cC

/Nb

Valu

e(m

g·cm

�3)

bof:

Th

ickness

ofpeatlayer

b(m

)N

PK

Ca

Fe

Pin

e-PP

1P

ristine

peatsoil

Moderately

decomposed

sedge-Sphagnumpeat

Scotspin

eB

irchC

arexlasiocarpa,C

.rostrata,M

enyanthestrifoliata,

Betula

nana,Vaccinium

oxycoccos,Sphagnumfallax

1710

4.6436

1.000.04

0.060.31

0.39A

bout

2

Pin

e-PP

2P

ristine

peatsoil

Weakly

decomposed

Eriophorum

-Sphagnumpeat

Scotspin

eE

riophorumvaginatum

,A

ndromeda

polifolia,Betula

nana,Sphagnumspp.

3118

4.1141

0.900.04

0.040.12

0.12�

2

Spruce-P

P3

Pristin

epeat

soilM

oderatelydecom

posedw

oody-sedgepeat

Norw

ayspru

ceB

irch,alder

Scotspin

eM

esotrophic

mire

herbs

(Calam

agrostispurpurea,

Potentilla

palustris,Caltha

palustris),Salixspp.

19915

4.0326

1.900.12

0.060.39

1.38�

1

Spruce-P

P4

Pristin

epeat

soilM

oderatelydecom

posedw

oody-Sphagnumpeat

Norw

ayspru

ceB

irchSphagnum

spp.,forestm

osses,V

acciniumm

yrtillus,V

.vitis-idaea,Carex

globularis

20430

3.8733

1.800.09

0.070.29

0.24�

1.5

Pin

e-DP

1D

rained

peatsoil

Moderately

decomposed

sedge-Sphagnumpeat

Scotspin

eSphagnum

spp.,forestm

osses,V

acciniumm

yrtillus,V

.vitis-idaea

19145

3.8932

1.780.07

0.030.12

0.14�

2.6

Pin

e-DP

2D

rained

peatsoil

Weakly

decomposed

Eriophorum

-Sphagnumpeat

Scotspin

eForest

mosses

(Pleurozium

schreberi),Vaccinium

myrtillus,V

.vitis-idaea,C

allunavulgaris

10035

3.8721

2.340.08

0.030.28

0.35�

1.7

Spruce-D

P3

Drain

edpeat

soilM

oderatelyw

elldecomposed

woody-sedge

peatN

orway

spruce

Alder,Scots

pine

Ferns,O

xalisacetosella,R

ubusidaeus

54840

4.2719

2.590.13

0.020.35

1.10�

1.3

Spruce-D

P4

Drain

edpeat

soilM

oderatelydecom

posedw

oody-Sphagnumpeat

Norw

ayspru

ceB

irch,Scots

pine

Forestm

osses(P

leuroziumschreberi,D

icranumspp.),

Sphagnumspp.,V

acciniumm

yrtillus

30550

4.2034

1.580.06

0.040.19

0.23�

1.8

Pin

e-MS

Min

eralsoil

Nu

trient-poor

Scotspin

eForest

mosses

(Pleurozium

schreberi,Dicranum

spp.),V

acciniumvitis-idaea,

V.m

yrtillus

430217

4.26N

Dd

0.08

Spruce-M

SM

ineral

soilN

utrien

t-richN

orway

spruce

Birch

Forestm

osses(P

leuroziumschreberi,D

icranumspp.),

Oxalis

acetosella,Vaccinium

myrtillus

53065

4.35N

Dd

0.12

aP

P,pristin

epeat;D

P,drain

edpeat;M

S,min

eralsoil.b

Data

were

obtained

fromLain

eet

al.(2004)(14).

cSoilpHvalu

ew

ascalcu

latedby

the

mean

ofthree

replicatesan

dm

easured

bydissolvin

g5

gofsoilin

to25

mlofdistilled

water.

dN

D,n

otdeterm

ined.

Sun et al.

2634 aem.asm.org May 2016 Volume 82 Number 9Applied and Environmental Microbiology

on Novem

ber 3, 2020 by guesthttp://aem

.asm.org/

Dow

nloaded from

complement the pyrosequencing results rather than to provide an exhaus-tive overview of the fungi present in the study area.

Statistical analysis. After the assumptions of data normality and con-stant variance were checked, the permutational multivariate analysis ofvariance (PERMANOVA) package (25) in PRIMER 6 (26) was used to testthe difference in species richness, diversity, and community structure be-tween samples. The soil types were used as the first factor, and the treespecies were used as the second factor. The data from mineral soils wereexcluded, because there was no replication within treatment combina-tions. The principal coordinate analysis (PCoA) in PERMANOVA wasused to visualize the community structure based on the OTU abundancefrom each plot. The OTU abundances were transformed by taking thesquare root prior to the PCoA. The environmental variables were plottedin PCoA with a Spearman correlation, and the distance-based linear mod-els (DistLM) were performed to calculate the correlations between envi-ronmental variables and community structure. The community structurein wood and in the corresponding soil based on the OTU abundance wasvisualized by PCoA in PERMANOVA.

The Jaccard index (presence/absence) (27) was used to calculate thedendrogram describing the dissimilarity between communities based onthe number of shared and unique OTUs in mothur. To determinewhether clustering within the tree was statistically significant, a parsimonytest of similarity (28) was performed.

Nucleotide sequence accession number. The raw sequence data areavailable in the European Nucleotide Archive at the European Bioinfor-

matics Institute under accession number PRJEB6211 (http://www.ebi.ac.uk/ena/data/view/PRJEB6211).

RESULTSImpact of soil type and dominant tree species on fungal diver-sity and community structure. All of the sequence reads that passedthe quality control were assigned to the domain Fungi, and 94% ofthem were classified below the domain level. They were clustered in967 OTUs (1,742 OTUs with singletons included), which were fur-ther classified into four fungal phyla, i.e., Ascomycota, Basidiomy-cota, former Zygomycota, and Chytridiomycota. The Ascomycotagroup was the most species rich, whereas Basidiomycota dominatedthe sequence counts (Fig. 1; see Fig. S1 and S2 in the supplementalmaterial). More than 35% of the identified OTUs (342 OTUs) wereassigned down to the genus or species level. Of these, 86 OTUs (whichaccounted for 3% of the total sequence reads) belonged to the Asco-mycota phylum, and 234 OTUs (which accounted for 56% of thereads) belonged to the Basidiomycota phylum.

The OTUs identified in our sequencing effort were assigned to135 fungal genera. The most abundant genera were the ectomy-corrhizal (ECM) basidiomycetes Cortinarius, Russula, Piloderma,and Tomentella (Table 2; Fig. 2). Among the soil saprotrophs, themost abundant were the genera Mortierella, Mycena, and Syzy-

BA

DC

050

100150200250300350400450

OTUs

Sequence(x100)

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

(%)

OTUs

Sequence

0

50

100

150

200

250

300

OTUs

Sequence(x100)

0,0

10,0

20,0

30,0

40,0

50,0

60,0

(%)

OTUs

Sequence

FIG 1 Bar charts showing identification to the phylum level of operational taxonomic units (OTUs) as number of OTUs and as sequence reads from all 10 plots(total of 967 OTUs and 53,975 reads) (A) and from woods (total of 512 OTUs and 55,704 reads) (C), and the relative proportion of OTUs and sequence readsfrom all 10 plots (B) and from woods (D).

Structure of Fungal Communities in Boreal Forest

May 2016 Volume 82 Number 9 aem.asm.org 2635Applied and Environmental Microbiology

on Novem

ber 3, 2020 by guesthttp://aem

.asm.org/

Dow

nloaded from

gospora (Table 2; Fig. 2). Of the top 20 most abundant genera insoil, ECM fungi comprised 60% of the genera, whereas sapro-trophs made up only 30%. The genus Meliniomyces (Ascomycota),which is able to form both ericoid-type mycorrhiza (ErM) andectomycorrhiza, was also found among the most abundant genera(Table 2; Fig. 2).

In pine-dominated plots, we identified 652 OTUs. Only 11% ofthem were shared by the three soil types (MS, DP, and PP),whereas the fraction of unique OTUs in each soil type ranged from13% to 29% (Fig. 3A). In the spruce-dominated plots, 696 OTUswere detected; 17% of them were common to all three soil types,whereas the percentage of OTUs unique to a given soil type rangedfrom 8.5% to 29% (Fig. 3B).

The sampled soils showed great variation in fungal diversityand species richness (see Table S1 in the supplemental material).The PERMANOVA analysis demonstrated that neither soil typenor tree species had a significant effect on fungal diversity (inverseSimpson’s complement) or species richness (Chao 1). However,the interaction effect between soil and tree species significantlyinfluenced the fungal species richness (P � 0.02).

PCoA separated the communities between spruce- and pine-dominated plots and between pristine and drained peat plots (Fig.4). The PERMANOVA confirmed that the community composi-tion significantly differed between soil types (P � 0.001) and treespecies (P � 0.001). The interaction between soil type and treespecies also had significant effects on community structure (P �0.001). The DistLM analysis showed that the environmental vari-ables were significantly correlated with community structure (Fig.4) (r2 � 0.5425, P � 0.04). The availabilities of soil nutrients (Ca[r2 � 0.2314, P � 0.002], Fe [r2 � 0.3375, P � 0.003], and P [r2 �0.2092, P � 0.003]) within the site were important factors in thefungal community composition. The water depth in each plot alsohad a significant effect on the community composition (r2 �

0.2114, P � 0.003), but it explained only 8.8% of the total varia-tion. The effect of other tested factors on community structurewas not statistically significant.

Identification of fungal species by fruiting body surveys. Thefruiting body surveys were performed to complement the resultsof molecular analysis. In total, we identified members of 32 fungalgenera based on collected sporocarps (see Table S2 in the supple-mental material). The top 20 genera are shown in Figure 2. Most ofthe identified genera belonged to Basidiomycota, with the excep-tion of three genera of Ascomycota: Cudonia (Helotiales), Hypomy-ces (Hypocreales) and Otidea (Pezizales). The order Agaricales (Ba-sidiomycota) was the most diverse and the most abundant group inour surveys, as indicated by the number of identified genera andby total number of collected sporocarps. Cortinarius appeared tobe the most common genus by sporocarp number, which is con-sistent with the results of molecular analysis. The number of sap-rotrophs among identified genera was slightly higher than thenumber of mycorrhiza formers, but the mycorrhiza formers werebetter represented in the numbers of collected sporocarps.

Influence of soil type and dominant tree species on fungaldiversity and community structure during primary wood colo-nization. We documented 512 OTUs across all the wood samples.The fungal diversity index and richness (Chao 1 estimator) inwood cubes differed between the 2-month and 4-month incuba-tion periods (see Table S3 in the supplemental material). The spe-cies richness in wood was significantly lower than that of the cor-responding soil (P � 0.004) (see Tables S1 and S3).

PCoA distinguished the fungal community structures of pine-and spruce-dominated plots and of mineral and peat soil (Fig. 5).PERMANOVA confirmed a significant difference between treespecies (P � 0.01) and between soil types (P � 0.02). Neither thesoil type nor the dominant tree species had a significant effect on

TABLE 2 Top 20 fungal genera in peatland soil during wood colonization and in decayed logs based on relative abundance of reads

Plot no.

Humus soil Wood colonization Decayed logsa

Genus Functionb Genus Functionb Genus Functionb

1 Cortinarius ECM Meliniomyces ErM Fomitopsis SAP/BR2 Russula ECM Mycena SAP/WR Heterobasidion SAP/WR/PP3 Piloderma ECM Penicillium SAP Phellinus SAP/WR4 Tomentella ECM Xenasmatella SAP/WR Antrodia SAP/BR5 Mortierella SAP Botryobasidium SAP Resinicium SAP/WR6 Alnicola ECM Athelia SAP/PP Exidia SAP7 Lactarius ECM Chalara SAP/PP Hyphodontia SAP8 Thelephora ECM Cryptococcus SAP/PP Spadicoides SAP9 Tylospora ECM Galerina SAP/WR Mycena SAP/WR10 Mycena SAP/WR Acephala Root endophyte Hyphoderma SAP/WR

11 Syzygospora SAP/MP Mortierella SAP Leptodontidium SAP/endophyte12 Amphinema ECM Xenopolyscytalum SAP/endophyte Junghuhnia SAP/WR13 Meliniomyces ErM Haplographium SAP/PP Russula ECM14 Suillus ECM Lactarius ECM Phialophora Endophyte15 Amanita ECM/SAP Trichoderma SAP/MP Mariannaea SAP16 Inocybe ECM Coniochaeta SAP/PP Lophodermium Endophyte17 Archaeorhizomyces Root endophyte Dactylellina SAP/predator Tubulicrinis SAP18 Rhodocollybia SAP/WR Hyphodontia SAP Phialocephala Endophyte19 Trechispora SAP/WR Hypocrea SAP Sistotrema SAP/ECM20 Sugiyamaella SAP Rhodotorula SAP Ascocoryne SAP/endophytea Data from Ovaskainen et al. (2013) (29).b ECM, ectomycorrhizae; ErM, ericroid-mycorrhizae; BR, brown rot; SAP, saprotroph; WR, white rot; PP, plant pathogen; MP, mycoparasite.

Sun et al.

2636 aem.asm.org May 2016 Volume 82 Number 9Applied and Environmental Microbiology

on Novem

ber 3, 2020 by guesthttp://aem

.asm.org/

Dow

nloaded from

wood decay rate under experimental conditions (see Fig. S3 in thesupplemental material).

Pairwise analysis showed that, in mineral soil, the relativeabundance of Ascomycota and Basidiomycota in wood significantlydiffered from that in the corresponding soil (P � 0.02 and P �0.03, respectively). The Ascomycota were more abundant in wood,whereas Basidiomycota were more abundant in the soil.

Between the two incubation periods, a shift in relative abun-dance of dominant taxa was observed. The relative abundance ofAscomycota increased, whereas the Basidiomycota abundance de-creased (see Fig. S4 in the supplemental material). The analysis ofvariance (ANOVA) of sequences showed a significant shift incommunity composition in wood cubes between incubation in-

tervals within each plot (P � 0.001). Within each plot, the twoincubation periods formed separate fungal clusters (see Fig. S5).Among plots, spruce and pine also formed distinct fungal com-munity in both pristine and mineral soils.

The OTUs in the wood chips were classified into phyla Asco-mycota, Basidiomycota, and former Zygomycota (Fig. 1). Morethan 45% of the total OTUs belonged to the Ascomycota phylum(233 OTUs), accounting for half of the total reads (55,704). Ba-sidiomycota (162 OTUs) represented 32% of the total OTUs, cov-ering 32% of the total reads. Former Zygomycota and unknownphyla made up 4% and 19% of the OTUs, respectively, and ac-counted for 1% and 17% of the sequence count, respectively. Ofthe OTUs, 29%, representing 32% of the total sequences, were

A

B

0

10

20

30

40

50

60

70

80

Rela�ve

abun

dance(%

)

Sugiyamaella

Trechispora

Rhodocollybia

Archaeorhizomyces

Inocybe

Amanita

Suillus

Meliniomyces

Amphinema

Syzygospora

Mycena

Tylospora

Thelephora

Lactarius

Alnicola

Mortierella

Tomentella

Piloderma

Russula

Cor�narius

0

10

20

30

40

50

60

70

80

90

100

Rela�ve

abun

dance(%

)

Entoloma

Cystoderma

Clitocybe

Cantharellus

Naucoria

Hygrocybe

Galerina

Collybia

Auriscalpium

Ampulloclitocybe

Russula

Rhodocollybia

Paxillus

Gymnopus

Cudonia

Amanita

Marasmiellus

Mycena

Lactarius

Cor�narius

FIG 2 The relative abundance of the top 20 genera of fungal ITS sequences classified (A) and fungi identified in fruiting bodies surveys (B) from the mineral andpeat soil forest plots. Sequence proportions were calculated based on the sequences classified with an 80% confidence threshold. A description of each plot ispresented in Table 1.

Structure of Fungal Communities in Boreal Forest

May 2016 Volume 82 Number 9 aem.asm.org 2637Applied and Environmental Microbiology

on Novem

ber 3, 2020 by guesthttp://aem

.asm.org/

Dow

nloaded from

further assigned to the genus or species level. Of these, most be-longed to Basidiomycota (15%) and Ascomycota (11%).

At the genus level, the sequences were classified into 87 genera.The most abundant genus in wood cube was the ascomycete Me-liniomyces (5.6% of reads). It was followed by a diverse range ofsoil saprotrophs and one ECM genus, Lactarius (Fig. 6). The twoincubation periods in each plot shared a considerable fraction ofthe genera (�43%) (see Fig. S6 in the supplemental material).

DISCUSSION

The boreal soil and peatland investigated in this study possessed adiverse fungal community with high variation across the sampled

plots. We identified more fungal species (if OTUs are regarded asproxy for species) than all previous studies combined (10). Similarto some previously published works (30, 31), we found no corre-lation between the fungal diversity (inverse Simpson’s comple-ment) and the dominant tree species. One reason that could atleast partly explain the absence of such a correlation is the highvariability of the fungal diversity across the studied plots.

The fungal community structure was significantly influencedby the dominant tree species and the soil type, as confirmed byPCoA and by PERMANOVA (Fig. 4). This observation agreeswith the results of previous studies, demonstrating the profound

118

189

8549

114 2671

Pine_DP Pine_MS

Pine_PP

Venn diagram at distance 0.03

(11%)

(7.5%)(18%) (13%)

(17.5%) (4%)

(29%)

203

113

5973

89 38121

Spruce_DP Spruce_MS

Spruce_PP

Venn diagram at distance 0.03

(17%)

(10.5%)(29%) (8.5%)

(13%) (5.5%)

(16%)

A B

FIG 3 Venn diagrams showing unique and shared OTUs in mineral soil (MS), pristine peat (PP), and drained peat (DP) in pine-dominated (A) and spruce-dominated (B) plots.

FIG 4 Principal coordinate (PCO) analysis based on the OTU abundance showing the fungal community structure with environmental variables in pristine anddrained peat soils.

Sun et al.

2638 aem.asm.org May 2016 Volume 82 Number 9Applied and Environmental Microbiology

on Novem

ber 3, 2020 by guesthttp://aem

.asm.org/

Dow

nloaded from

FIG 5 Principal coordinate (PCO) analysis based on the OTU abundance showing the fungal community structure in wood after 2 and 4 months of incubationin pristine peat and mineral soil (A) and the fungal community structure between wood cubes and the corresponding soils (B).

Structure of Fungal Communities in Boreal Forest

May 2016 Volume 82 Number 9 aem.asm.org 2639Applied and Environmental Microbiology

on Novem

ber 3, 2020 by guesthttp://aem

.asm.org/

Dow

nloaded from

influence of the dominant tree species on the structure of thefungal community in soil and litter (11, 32). Trees affect the com-position of soil microbial communities in different ways, for ex-ample, by influencing the chemical composition of litter and rootexudates but also by creating a spatially heterogeneous environ-ment and by influencing understory vegetation and fauna (11).Additionally, many fungal root symbionts are tree specific (33),and dominant tree species have a particularly strong effect inECM-dominated fungal communities.

Soil microbial communities may be influenced by soil chemis-try and nutrient availability, such as the soil pH, C-to-N ratio, andnitrogen and phosphorus content, and by the precipitationamount and the physical soil properties (31, 34–37). In our study,we have identified the availability of Ca, Fe and P as the factorsdriving the fungal community structure. However, there are alsoreports showing no significant effect of soil chemistry on fungalcommunities (11), suggesting that the role of these factors mightvary between sites. It is possible that chemical elements present atgrowth-limiting concentrations have the strongest effect on themicrobial soil communities.

The level of the water table also had a statistically significanteffect on the fungal community structure in our experiment, in-dicating that drainage might influence fungal communities inpeatlands, though it explained only 8.8% of total variation. The

percentage of observed variation agrees with the findings of pre-vious work performed on the same field plots, which showed thatwater level has only a minor impact on microbial communitystructure (12). However, the water table level might have an effecton functional activities of soil microbial communities, which mer-its further investigation.

Among the top 20 fungal genera most abundant in soil, themycorrhizal fungi were represented by 15 genera, whereas only 5genera belonged to soil saprotrophic fungi (Table 2). Mycorrhizalfungi were also the most prominent group based on the number ofsequence reads. Specialized wood-degrading fungi were scarcelyrepresented in our samples, e.g., the entire order Polyporalesaccounted for less than 0.4% of reads, confirming earlier observa-tions that this ecological group is relatively uncommon in peat-lands (10). Previous studies have demonstrated that soil sapro-trophic fungal taxa are more abundant in the litter horizon of theforest floor, whereas mycorrhizal fungi increase in abundancewith soil depth (31, 38). The increased abundance of ECM fungi inlate summer or autumn has been reported previously (39–41).Our samples were collected at the beginning of autumn and onlyfrom the organic soil layer. Thus, the sampling time and strategymay have biased our results in favor of the mycorrhiza-formingspecies.

Results of molecular identification were supplemented by

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

Rela�ve

abun

dance(%

)Rhodotorula

Hypocrea

Hyphodontia

Dactylellina

Coniochaeta

Trichoderma

Lactarius

Haplographium

Xenopolyscytalum

Mor�erella

Acephala

Galerina

Cryptococcus

Chalara

Athelia

Botryobasidium

Xenasmatella

Penicillium

Mycena

Meliniomyces

FIG 6 Relative abundance of fungal genera (top 20) in wood cubes after 2 and 4 months of incubation in each plot. Proportions were calculated based on thesequences classified with an 80% confidence threshold. The number after the plot indicates 2 or 4 months after incubation. MS, mineral soil; PP, pristine peat.

Sun et al.

2640 aem.asm.org May 2016 Volume 82 Number 9Applied and Environmental Microbiology

on Novem

ber 3, 2020 by guesthttp://aem

.asm.org/

Dow

nloaded from

sporocarp surveys. Seven of the genera of Agaricomycotina identi-fied in our surveys were among the top 20 genera according topyrosequencing data. Cortinarius was the most abundant genusbased both on number of sequence reads and on sporocarp sur-veys. It is one of the largest genera among agaricoids, and thelargest genus of ECM fungi (42). The high numbers of species ofAgaricales and Boletales identified in the surveys correlate withtheir abilities to produce easily detectable macroscopic fruitingbodies (mushrooms). At the same time, basidiomycetes with re-supinate fruiting bodies and ascomycetes were clearly underrep-resented in the fruiting body surveys, as those species are oftendifficult to spot in the field. This was probably the main reasonwhy some of the genera from the top 20 list (e.g., Piloderma, To-mentella, Tylospora, and Amphinema) were not collected duringthe sporocarp surveys. Additionally, the absence of microscopicfungi, such as Mortierella and Meliniomyces, in the fruiting bodysurveys is not unexpected. At the same time, 9 genera identified inthe surveys were not detected by molecular methods. This is animportant result, showing that even relatively common taxa (e.g.,Marasmiellus) can escape detection by molecular methods. Possi-ble explanations include the great spatial heterogeneity of soil fun-gal communities and certain biases during sampling. In our case,we collected samples from organic soil layer after removing thelitter and so probably excluded some of litter-degrading species.For example, this could explain the absence of the genus Maras-miellus from fungal taxa identified by next-generation sequenc-ing, since members of this genus are specialized decomposers ofspruce needles, and they are probably present in the very top litterlayer only. Though the results of our fruiting body inventory aresuggestive, they reflect the outcomes of most of previous studies offungal diversity in forests of the Northern Hemisphere (43–45).

The fungal communities colonizing the wood showed pro-nounced differences compared to those of the surrounding soils.The composition of the soil fungal community with respect tospecies diversity is more limited even than that of the communityin the wood, but it has greater number of unique species. Thecommunity structure of wood-inhabiting fungi differed betweenthe two incubation time points, probably as a result of the earlysteps of natural succession of wood colonizers that result fromchanges in nutrient availability and competition between species.The fungal community structure also differed between plots dom-inated either by pine or spruce. Species of Ascomycota prevailedover Basidiomycota in the analyzed wood samples. This was mostlikely because the 4-month incubation period used in our studyrepresents the very early stage of decay, when ascomycetes prevailover basidiomycetes (46–48). Our results agree with the previousobservations that ascomycetes (Geomyces, Penicillium, and Oidio-dendron) and zygomycetes (Mucor) were among the fungal taxamost frequently recovered from spruce wood blocks buried be-neath the peat surface for 8 to 12 months (49).

The 20 most abundant genera from soil and wood sampleshave only 4 genera in common (Table 2), confirming earlier ob-servations that the structure of the wood-inhabiting fungal com-munity does not necessarily reflect the fungal community in thesurrounding soil (48).

From the functional point of view, wood-colonizing fungalcommunities were significantly enriched in saprotrophic species,whereas the frequency of ECM fungi was lower than that in soilsamples. This might be explained by the reduced ability of ECMfungi to degrade plant cell wall polymers due to a decrease in the

number of glycosyl hydrolases and other carbohydrate-acting en-zymes (50, 51) and due to species succession in decomposingwood (52).

We have shown that ECM fungi are the dominant componentof the fungal community in the organic soil layer along the borealpeatland gradient. Their role in providing their plant hosts withnitrogen and phosphorus, the limiting nutrients in many borealecosystems, is generally accepted (53). On the global scale, ECMfungi contribute to the long-term carbon sequestration in a borealzone (7). Remarkably, some species of ECM fungi may be able toefficiently decompose plant litter while mobilizing nitrogen fromsoil organic matter (54). Saprotrophic species were less abundantthan ECM fungi in the analyzed samples. It is possible that thelower abundance of saprotrophs in the organic soil layer could bedue to limited nutrient availability, lower oxygen content, andcompetition with other soil microorganisms. The low abun-dance of saprotrophic fungi in the organic soil layer observedin our experiment agrees well with the results of previous stud-ies, which have demonstrated that saprotrophic communitiesare largely confined to litter layers and that saprotrophic androot-associated microbial soil communities are vertically sep-arated (30, 38, 55, 56).

Specialized fungal species known as white and brown rotters(52) play the primary role in wood decomposition. These speciesare commonly found in dead logs and other types of decayingwood material (29), but their abundance in soil is low. Our resultsalso show that wood-degrading fungi constitute a minor fractionof fungal community in the organic soil layer of peatland soils, buttheir abundance may increase under favorable conditions. Themost probable factors limiting the spread of wood-degradingfungi in soils are their specialization for certain microenviron-ments and low competitiveness compared to other soil microor-ganisms. For example, ectomycorrhizal fungi obtain carbohy-drates directly from their symbiotic host tree and therefore have acompetitive advantage over decomposers that need to obtain all oftheir energy from the decomposition of low-energy carbon com-pounds. It has been shown that white rot fungi are poor peatdecomposers (57), which might be an additional factor limitingtheir spread in peatlands. At the same time, different species ofascomycetes may efficiently contribute to the process of Sphag-num peat decomposition (58).

The presented results show the strong effect of the dominanttree species and availability of certain nutrients (Ca, P, and Fe) onthe composition of fungal communities in boreal peatland forests.Our study provides a baseline for the investigation of the dynam-ics of the fungal community in the boreal peatland in Finland.Establishing the link between the fungal community structure andits functional properties constitutes an important challenge forthe future research of microbial communities in boreal forests.

ACKNOWLEDGMENTS

We thank The Academy of Finland for research funding. Eeva Terhonenthanks the Biological Interaction Graduate School (BIOINT) and FinnishCultural Foundation (Pehr August and Saga Stenius Foundation grant00110952) for a doctoral student scholarship. Hui Sun thanks the PriorityAcademic Program Development (PAPD) of Jiangsu Higher EducationInstitutions for research funding and for the Jiangsu Specially AppointedProfessorship Program (2015-2018). We also thank Pauli Rainio for as-sistance in the field work and Dmitry Schigel and Hsiao-Che Kuo for theirhelp during the field trip for fruiting body surveys.

Structure of Fungal Communities in Boreal Forest

May 2016 Volume 82 Number 9 aem.asm.org 2641Applied and Environmental Microbiology

on Novem

ber 3, 2020 by guesthttp://aem

.asm.org/

Dow

nloaded from

FUNDING INFORMATIONThis work, including the efforts of Fred O. Asiegbu, was funded bySuomen Akatemia (Academy of Finland). This work, including the effortsof Eeva Terhonen, was funded by Biological Interactions Doctoral Pro-gramme (BIOINT). This work, including the efforts of Eeva Terhonen,was funded by Suomen Kulttuurirahasto (Finnish Cultural Foundation)(00110952). This work, including the efforts of Hui Sun, was funded byPriority Academic Program Development (PAPD). This work, includingthe efforts of Hui Sun, was funded by Jiangsu Specially Appointed Profes-sorship Program (165010015).

REFERENCES1. Limpens J, Berendse F, Blodau C, Canadell JG, Freeman C, Holden J,

Roulet N, Rydin H, Schaepman-Strub G. 2008. Peatlands and the carboncycle: from local processes to global implications a synthesis. Biogeosci-ences 5:1475–1491. http://dx.doi.org/10.5194/bg-5-1475-2008.

2. Vasander H, Kettunen A. 2006. Carbon in boreal peatlands, 165–194. InWieder RK, Vitt D (ed), Ecological studies: boreal peatland ecosystems.Springer, Berlin, Germany.

3. Gitay H, Brown S, Easterling W, Jallow B, Antle J, Apps M, Beamish R,Chapin T, Cramer W, Frangi J. 2001. Ecosystems and their goods andservices, p 235–342. In McCarthy JJ, Canziani OF, Leary NA, Dokken DJ,White KS (ed), Climate change 2001, impacts, adaptation, and vulnera-bility, contribution of working group ii to the third assessment report ofthe intergovernmental panel on climate change. Cambridge UniversityPress, Cambidge, United Kingdom.

4. Gorham E. 1991. Northern peatlands: role in the carbon cycle and prob-able responses to climatic warming. Ecol Appl 1:182–195. http://dx.doi.org/10.2307/1941811.

5. Trettin CC, Laiho R, Minkkinen K, Laine J. 2006. Influence of climatechange factors on carbon dynamics in northern forested peatlands. Can JSoil Sci 86:269 –280. http://dx.doi.org/10.4141/S05-089.

6. Lindahl BO, Taylor AFS, Finlay RD. 2002. Defining nutritional con-straints on carbon cycling in boreal forests: towards a less phytocen-tric perspective. Plant Soil 242:123–135. http://dx.doi.org/10.1023/A:1019650226585.

7. Clemmensen KE, Bahr A, Ovaskainen O, Dahlberg A, Ekblad A, Wal-lander H, Stenlid J, Finlay RD, Wardle DA, Lindahl BD. 2013. Rootsand associated fungi drive long-term carbon sequestration in boreal for-est. Science 339:1615–1618. http://dx.doi.org/10.1126/science.1231923.

8. Burns RG, DeForest JL, Marxsen J, Sinsabaugh RL, Stromberger ME,Wallenstein MD, Weintraub MN, Zoppini A. 2013. Soil enzymes in achanging environment: current knowledge and future directions. Soil BiolBiochem 58:216 –234. http://dx.doi.org/10.1016/j.soilbio.2012.11.009.

9. Floudas D, Binder M, Riley R, Barry K, Blanchette RA, Henrissat B,Martinez AT, Otillar R, Spatafora JW, Yadav JS, Aerts A, Benoit I, BoydA, Carlson A, Copeland A, Coutinho PM, de Vries RP, Ferreira P,Findley K, Foster B, Gaskell J, Glotzer D, Gorecki P, Heitman J, HesseC, Hori C, Igarashi K, Jurgens JA, Kallen N, Kersten P, Kohler A, KuesU, Kumar TKA, Kuo A, LaButti K, Larrondo LF, Lindquist E, Ling A,Lombard V, Lucas S, Lundell T, Martin R, McLaughlin DJ, Morgen-stern I, Morin E, Murat C, Nagy LG, Nolan M, Ohm RA, Patyshaku-liyeva A, et al. 2012. The Paleozoic origin of enzymatic lignin decompo-sition reconstructed from 31 fungal genomes. Science 336:1715–1719.http://dx.doi.org/10.1126/science.1221748.

10. Thormann MN, Rice AV. 2007. Fungi from peatlands. Fungal Divers24:241–299.

11. Peltoniemi K, Strakova P, Fritze H, Iraizoz PA, Pennanen T, Laiho R.2012. How water-level drawdown modifies litter-decomposing fungal andactinobacterial communities in boreal peatlands. Soil Biol Biochem 51:20 –34. http://dx.doi.org/10.1016/j.soilbio.2012.04.013.

12. Urbanova M, Snajdr J, Baldrian P. 2015. Composition of fungal andbacterial communities in forest litter and soil is largely determined bydominant trees. Soil Biol Biochem 84:53– 64. http://dx.doi.org/10.1016/j.soilbio.2015.02.011.

13. Makiranta P, Laiho R, Fritze H, Hytonen J, Laine J, Minkkinen K. 2009.Indirect regulation of heterotrophic peat soil respiration by water level viamicrobial community structure and temperature sensitivity. Soil BiolBiochem 41:695–703. http://dx.doi.org/10.1016/j.soilbio.2009.01.004.

14. Laine J, Komulainen V, Laiho R, Minkkinen K, Rasinmäki A, SallantausT, Sarkkola S, Silvan N, Tolonen K, Tuittila ES, Vasander H, Päivänen

J. 2004. Lakkasuo: a guide to mire ecosystem. Department of Forest Ecol-ogy, University of Helsinki, Helsinki, Finland.

15. Andersen R, Poulin M, Borcard D, Laiho R, Laine J, Vasander H,Tuittila ET. 2011. Environmental control and spatial structures in peat-land vegetation. J Veg Sci 22:878 – 890. http://dx.doi.org/10.1111/j.1654-1103.2011.01295.x.

16. Terhonen E, Sun H, Buee M, Kasanen R, Paulin L, Asiegbu FO. 2013.Effects of the use of biocontrol agent (Phlebiopsis gigantea) on fungal com-munities on the surface of Picea abies stumps. Forest Ecol Manag 310:428 –433. http://dx.doi.org/10.1016/j.foreco.2013.08.044.

17. Gardes M, Bruns TD. 1993. ITS primers with enhanced specificity forbasidiomycetes: application to the identification of mycorrhizae andrusts. Mol Ecol 2:113–118. http://dx.doi.org/10.1111/j.1365-294X.1993.tb00005.x.

18. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB,Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B,Thallinger GG, Van Horn DJ, Weber CF. 2009. Introducing Mothur:open-source, platform-independent, community-supported software fordescribing and comparing microbial communities. Appl Environ Micro-biol 75:7537–7541. http://dx.doi.org/10.1128/AEM.01541-09.

19. Schloss PD, Gevers D, Westcott SL. 2011. Reducing the effects of PCRamplification and sequencing artifacts on 16S rRNA-based studies. PLoSOne 6:e27310. http://dx.doi.org/10.1371/journal.pone.0027310.

20. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. 2011. UCHIMEimproves sensitivity and speed of chimera detection. Bioinformatics 27:2194 –2200. http://dx.doi.org/10.1093/bioinformatics/btr381.

21. Needleman SB, Wunsch CD. 1970. A general method applicable to thesearch for similarities in the amino acid sequence of two proteins. J MolBiol 48:443– 453. http://dx.doi.org/10.1016/0022-2836(70)90057-4.

22. Tedersoo L, Nilsson RH, Abarenkov K, Jairus T, Sadam A, Saar I,Bahram M, Bechem E, Chuyong G, Koljalg U. 2010. 454 Pyrosequencingand Sanger sequencing of tropical mycorrhizal fungi provide similar re-sults but reveal substantial methodological biases. New Phytol 188:291–301. http://dx.doi.org/10.1111/j.1469-8137.2010.03373.x.

23. Chao A. 1984. Nonparametric estimation of the number of classes in apopulation. Scand J Stat 11:265–270.

24. Kõljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AF, BahramM, Bates ST, Bruns TD, Bengtsson-Palme J, Callaghan TM, Douglas B,Drenkhan T, Eberhardt U, Duenas M, Grebenc T, Griffith GW, Hart-mann M, Kirk PM, Kohout P, Larsson E, Lindahl BD, Lucking R,Martin MP, Matheny PB, Nguyen NH, Niskanen T, Oja J, Peay KG,Peintner U, Peterson M, Poldmaa K, Saag L, Saar I, Schussler A, ScottJA, Senes C, Smith ME, Suija A, Taylor DL, Telleria MT, Weiss M,Larsson KH. 2013. Towards a unified paradigm for sequence-based iden-tification of fungi. Mol Ecol 22:5271–5277. http://dx.doi.org/10.1111/mec.12481.

25. Anderson MJ. 2001. A new method for nonparametric multivariateanalysis of variance. Austral Ecol 26:32– 46. http://dx.doi.org/10.1111/j.1442-9993.2001.01070.pp.x, http://dx.doi.org/10.1046/j.1442-9993.2001.01070.x.

26. Clarke KR, Gorley RN. 2006. PRIMER v6: user manual/tutorial. PRIM-ER-E, Plymouth, United Kingdom.

27. Jaccard P. 1912. The distribution of the flora in the alpine zone. NewPhytol 11:37–50. http://dx.doi.org/10.1111/j.1469-8137.1912.tb05611.x.

28. Maddison W, Slatkin M. 1990. Parsimony reconstructions of ancestralstates do not depend on the relative distances between linearly orderedcharacter states. Syst Zool 39:175–178. http://dx.doi.org/10.2307/2992455.

29. Ovaskainen O, Schigel D, Ali-Kovero H, Auvinen P, Paulin L, NordenB, Norden J. 2013. Combining high-throughput sequencing with fruitbody surveys reveals contrasting life-history strategies in fungi. ISME J7:1696 –1709. http://dx.doi.org/10.1038/ismej.2013.61.

30. Baldrian P, Kolarik M, Stursova M, Kopecky J, Valaskova V, VetrovskyT, Zifcakova L, Snajdr J, Ridl J, Vlcek C, Voriskova J. 2012. Active andtotal microbial communities in forest soil are largely different and highlystratified during decomposition. ISME J 6:248 –258. http://dx.doi.org/10.1038/ismej.2011.95.

31. O’Brien HE, Parrent JL, Jackson JA, Moncalvo JM, Vilgalys R. 2005.Fungal community analysis by large-scale sequencing of environmentalsamples. Appl Environ Microbiol 71:5544 –5550. http://dx.doi.org/10.1128/AEM.71.9.5544-5550.2005.

32. Kubartová A, Ranger J, Berthelin J, Beguiristain T. 2009. Diversity anddecomposing ability of saprophytic fungi from temperate forest litter.Microb Ecol 58:98 –107. http://dx.doi.org/10.1007/s00248-008-9458-8.

Sun et al.

2642 aem.asm.org May 2016 Volume 82 Number 9Applied and Environmental Microbiology

on Novem

ber 3, 2020 by guesthttp://aem

.asm.org/

Dow

nloaded from

33. Tedersoo L, Bahram M, Toots M, Diedhiou AG, Henkel TW, Kjøller R,Morris MH, Nara K, Nounhra E, Peay KG, Pölme S, Ryberg M, SmithME, Koljalg U. 2012. Towards global patterns in the diversity and com-munity structure of ectomycorrhizal fungi. Mol Ecol 21:4160 – 4170. http://dx.doi.org/10.1111/j.1365-294X.2012.05602.x.

34. Christ S, Wubet T, Theuerl S, Herold N, Buscot F. 2011. Fungalcommunities in bulk soil and stone compartments of different forest andsoil types as revealed by a barcoding ITS rDNA and a functional laccaseencoding gene marker. Soil Biol Biochem 43:1292–1299. http://dx.doi.org/10.1016/j.soilbio.2011.02.022.

35. Gomez-Hernandez M, Williams-Linera G. 2011. Diversity of macromy-cetes determined by tree species, vegetation structure, and microenviron-ment in tropical cloud forests in Veracruz, Mexico. Botany 89:203–216.http://dx.doi.org/10.1139/B11-007.

36. Högberg MN, Hogberg P, Myrold DD. 2007. Is microbial communitycomposition in boreal forest soils determined by pH, C-to-N ratio, thetrees, or all three? Oecologia 150:590 – 601.

37. Janssens IA, Dieleman W, Luyssaert S, Subke JA, Reichstein M, Ceule-mans R, Ciais P, Dolman AJ, Grace J, Matteucci G, Papale D, Piao SL,Schulze ED, Tang J, Law BE. 2010. Reduction of forest soil respiration inresponse to nitrogen deposition. Nat Geosci 3:315–322. http://dx.doi.org/10.1038/ngeo844.

38. Lindahl BD, Ihrmark K, Boberg J, Trumbore SE, Hogberg P, Stenlid J,Finlay RD. 2007. Spatial separation of litter decomposition and mycor-rhizal nitrogen uptake in a boreal forest. New Phytol 173:611– 620. http://dx.doi.org/10.1111/j.1469-8137.2006.01936.x.

39. Davey ML, Heegaard E, Halvorsen R, Ohlson M, Kauserud H. 2012.Seasonal trends in the biomass and structure of bryophyte-associated fun-gal communities explored by 454 pyrosequencing. New Phytol 195:844 –856. http://dx.doi.org/10.1111/j.1469-8137.2012.04215.x.

40. Högberg MN, Briones MJI, Keel SG, Metcalfe DB, Campbell C, Mid-wood AJ, Thornton B, Hurry V, Linder S, Nasholm T, Hogberg P. 2010.Quantification of effects of season and nitrogen supply on tree below-ground carbon transfer to ectomycorrhizal fungi and other soil organismsin a boreal pine forest. New Phytol 187:485– 493. http://dx.doi.org/10.1111/j.1469-8137.2010.03274.x.

41. Wallander H, Nilsson LO, Hagerberg D, Baath E. 2001. Estimation ofthe biomass and seasonal growth of external mycelium of ectomycorrhizalfungi in the field. New Phytol 151:753–760. http://dx.doi.org/10.1046/j.0028-646x.2001.00199.x.

42. Teasdale SE, Beulke AK, Guy PL, Orlovich DA. 2013. Environmentalbarcoding of the ectomycorrhizal fungal genus Cortinarius. Fungal Divers58:299 –310. http://dx.doi.org/10.1007/s13225-012-0218-1.

43. Peter M, Ayer F, Egli S, Honegger R. 2001. Above- and below-groundcommunity structure of ectomycorrhizal fungi in three Norway spruce(Picea abies) stands in Switzerland. Can J Bot 79:1134 –1151. http://dx.doi.org/10.1139/cjb-79-10-1134.

44. DeBellis T, Kernaghan G, Bradley R, Widden P. 2006. Relationshipsbetween stand composition and ectomycorrhizal community structure inboreal mixed-wood forests. Microb Ecol 52:114 –126. http://dx.doi.org/10.1007/s00248-006-9038-8.

45. O’Hanlon R, Harrington TJ. 2012. Macrofungal diversity and ecology infour Irish forest types. Fungal Ecol 5:499 –508. http://dx.doi.org/10.1016/j.funeco.2011.12.008.

46. Frankland JC. 1998. Fungal succession: unravelling the unpredictable.Mycol Res 102:1–15. http://dx.doi.org/10.1017/S0953756297005364.

47. Osono T. 2007. Ecology of ligninolytic fungi associated with leaf litterdecomposition. Ecol Res 22:955–974. http://dx.doi.org/10.1007/s11284-007-0390-z.

48. Voríšková J, Baldrian P. 2013. Fungal community on decomposing leaflitter undergoes rapid successional changes. ISME J 7:477– 486. http://dx.doi.org/10.1038/ismej.2012.116.

49. Rice AV, Tsuneda A, Currah RS. 2006. In vitro decomposition of Sphag-num by some microfungi resembles white rot of wood. FEMS MicrobiolEcol 56:372–382. http://dx.doi.org/10.1111/j.1574-6941.2006.00071.x.

50. Martin F, Aerts A, Ahren D, Brun A, Danchin EGJ, Duchaussoy F,Gibon J, Kohler A, Lindquist E, Pereda V, Salamov A, Shapiro HJ,Wuyts J, Blaudez D, Buee M, Brokstein P, Canback B, Cohen D, CourtyPE, Coutinho PM, Delaruelle C, Detter JC, Deveau A, DiFazio S,Duplessis S, Fraissinet-Tachet L, Lucic E, Frey-Klett P, Fourrey C,Feussner I, Gay G, Grimwood J, Hoegger PJ, Jain P, Kilaru S, Labbe J,Lin YC, Legue V, Le Tacon F, Marmeisse R, Melayah D, Montanini B,Muratet M, Nehls U, Niculita-Hirzel H, Oudot-Le Secq MP, Peter M,Quesneville H, Rajashekar B, Reich M, et al. 2008. The genome ofLaccaria bicolor provides insights into mycorrhizal symbiosis. Nature 452:88 –92. http://dx.doi.org/10.1038/nature06556.

51. Martin F, Kohler A, Murat C, Balestrini R, Coutinho PM, Jaillon O,Montanini B, Morin E, Noel B, Percudani R, Porcel B, Rubini A,Amicucci A, Amselem J, Anthouard V, Arcioni S, Artiguenave F, AuryJM, Ballario P, Bolchi A, Brenna A, Brun A, Buee M, Cantarel B,Chevalier G, Couloux A, Da Silva C, Denoeud F, Duplessis S, GhignoneS, Hilselberger B, Iotti M, Marcais B, Mello A, Miranda M, Pacioni G,Quesneville H, Riccioni C, Ruotolo R, Splivallo R, Stocchi V, TisserantE, Viscomi AR, Zambonelli A, Zampieri E, Henrissat B, Lebrun MH,Paolocci F, Bonfante P, Ottonello S, et al. 2010. Perigord black trufflegenome uncovers evolutionary origins and mechanisms of symbiosis. Na-ture 464:1033–1038. http://dx.doi.org/10.1038/nature08867.

52. Rajala T, Peltoniemi M, Hantula J, Makipaa R, Pennanen T. 2011. RNAreveals a succession of active fungi during the decay of Norway spruce logs.Fungal Ecol 4:437– 448. http://dx.doi.org/10.1016/j.funeco.2011.05.005.

53. Smith SE, Read DJ. 2008. Mycorrhizal symbiosis, 3rd ed. Academic Press,New York, NY.

54. Rineau F, Shah F, Smits MM, Persson P, Johansson T, Carleer R,Troein C, Tunlid A. 2013. Carbon availability triggers the decompositionof plant litter and assimilation of nitrogen by an ectomycorrhizal fungus.ISME J 7:2010 –2022. http://dx.doi.org/10.1038/ismej.2013.91.

55. McGuire KL, Allison SD, Fierer N, Treseder KK. 2013. Ectomycorrhizal-dominated boreal and tropical forests have distinct fungal communities,but analogous spatial patterns across soil horizons. PLoS One 8:e68278.http://dx.doi.org/10.1371/journal.pone.0068278.

56. Clemmensen KE, Finlay RD, Dahlberg A, Stenlid J, Wardle DA, Lin-dahl BD. 2015. Carbon sequestration is related to mycorrhizal fungalcommunity shifts during long-term succession in boreal forests. New Phy-tol 205:1525–1536. http://dx.doi.org/10.1111/nph.13208.

57. Thormann MN. 2008. In vitro decomposition of Sphagnum-derived ac-rotelm and mesotelm peat by indigenous and alien basidiomycetousfungi. Mires and Peat 8: article 03:1–12.

58. Thormann MN. 2006. The role of fungi in boreal peatlands, p 101–123. InWider RK, Vitt DH (ed), Ecological studies: boreal peatland ecosystems.Springer, Berlin, Germany.

Structure of Fungal Communities in Boreal Forest

May 2016 Volume 82 Number 9 aem.asm.org 2643Applied and Environmental Microbiology

on Novem

ber 3, 2020 by guesthttp://aem

.asm.org/

Dow

nloaded from