ORIGINAL PAPER

Global sampling of plant roots expands the describedmolecular diversity of arbuscular mycorrhizal fungi

Maarja Öpik & Martin Zobel & Juan J. Cantero &

John Davison & José M. Facelli & Inga Hiiesalu &

Teele Jairus & Jesse M. Kalwij & Kadri Koorem &

Miguel E. Leal & Jaan Liira & Madis Metsis &

Valentina Neshataeva & Jaanus Paal & Cherdchai Phosri &Sergei Põlme & Ülle Reier & Ülle Saks & Heidy Schimann &

Odile Thiéry & Martti Vasar & Mari Moora

Received: 12 November 2012 /Accepted: 28 January 2013 /Published online: 20 February 2013# Springer-Verlag Berlin Heidelberg 2013

Abstract We aimed to enhance understanding of the mo-lecular diversity of arbuscular mycorrhizal fungi (AMF) bybuilding a new global dataset targeting previously unstudiedgeographical areas. In total, we sampled 96 plant speciesfrom 25 sites that encompassed all continents except

Antarctica. AMF in plant roots were detected by sequencingthe nuclear SSU rRNA gene fragment using either cloningfollowed by Sanger sequencing or 454-sequencing. A totalof 204 AMF phylogroups (virtual taxa, VT) were recorded,increasing the described number of Glomeromycota VT

Electronic supplementary material The online version of this article(doi:10.1007/s00572-013-0482-2) contains supplementary material,which is available to authorized users.

M. Öpik (*) :M. Zobel : J. Davison : I. Hiiesalu : T. Jairus :J. M. Kalwij :K. Koorem : J. Liira : J. Paal : S. Põlme :Ü. Reier :Ü. Saks :O. Thiéry :M. Vasar :M. MooraDepartment of Botany, University of Tartu, 40 Lai Street,51005 Tartu, Estoniae-mail: maarja.opik@ut.ee

J. J. CanteroDepartamento de Biología Agrícola, Facultad de Agronomía yVeterinaria, Universidad Nacional de Rio Cuarto,Ruta Nac. 36, Km. 601,Código Postal X5804BYA Río Cuarto, Córdoba, Argentina

J. M. FacelliSchool of Earth and Environmental Sciences,University of Adelaide, Adelaide, South Australia 5005, Australia

I. HiiesaluDepartment of Biology, University of Regina, Regina, SK,Canada S4S 0A2

M. E. LealAlbertine Rift Program, Wildlife Conservation Society,2300 Southern Boulevard,New York, NY, USA

M. E. LealCentral Africa Program, Missouri Botanical Garden,BP 7847, Libreville, Gabon

M. MetsisCentre for Biology of Integrated Systems,Tallinn University of Technology, Akadeemia tee 15A,Tallinn 12618, Estonia

V. NeshataevaKomarov Botanical Institute, Russian Academy of Sciences,2 Professor Popov Street,197376 Saint Petersburg, Russia

C. PhosriMicrobiology Programme, Faculty of Science and Technology,Pibulsongkram Rajabhat University, Phitsanulok 65000, Thailand

C. PhosriResearch and Development Institute, Pibulsongkram RajabhatUniversity, Phitsanulok 65000, Thailand

H. SchimannINRA–Joint Research Unit Ecology of Guiana Forests (Ecofog),campus agronomique, BP 709, 97387 Kourou cedex,French Guiana, France

Mycorrhiza (2013) 23:411–430DOI 10.1007/s00572-013-0482-2

from 308 to 341 globally. Novel VT were detected from 21sites; three novel but nevertheless widespread VT (Glomusspp. MO-G52, MO-G53, MO-G57) were recorded from sixcontinents. The largest increases in regional VT numberwere recorded in previously little-studied Oceania and inthe boreal and polar climatic zones — this study providingthe first molecular data from the latter. Ordination revealeddifferences in AM fungal communities between differentcontinents and climatic zones, suggesting that both biogeo-graphic history and environmental conditions underlie theglobal variation of those communities. Our results show thata considerable proportion of Glomeromycota diversity hasbeen recorded in many regions, though further large increasesin richness can be expected in remaining unstudied areas.

Keywords Diversity . Glomeromycota . Fungalmacroecology . 454-sequencing . Biogeography . Database

Introduction

Arbuscular mycorrhizal fungi (AMF; phylumGlomeromycota;Schüßler et al. 2001) are among the world’s most common soilmicroorganisms and associate with more than 80 % of vascularplant species (Smith and Read 2008). They are obligate plant-root symbionts that gain all of their carbon from a host plant,whilst delivering a range of benefits to the plant, such asimproved nutrient acquisition and resistance to pathogens andabiotic stress (Smith and Read 2008). The global carbon fluxfrom plants to AMF is estimated to be 5 billion tons per year(Bago et al. 2000), while the global biomass of AMF in roots isestimated to be 1.4 Pg dry weight (Treseder and Cross 2006). Ithas also been suggested that AMF play a key role in determin-ing the distribution and abundance of plant species (Fitter 2005;Rosendahl 2008; van der Heijden et al. 2008). In order tounderstand the role of AMF in shaping the structure andcomposition of plant communities, information about theirdiversity and distribution is needed. However, the cryptic life-style of AMF means that knowledge about global patterns ofspecies distribution and abundance generally remains scarce,although new information is gradually accumulating (Fitter2005; Öpik et al. 2006, 2010; Treseder and Cross 2006;Chaudhary et al. 2008; Kivlin et al. 2011; Moora et al. 2011;Turrini and Giovannetti 2012; Yang et al. 2012).

There are currently 241 described morphospecies ofGlomeromycota (A. Schüßler’s Glomeromycota phylogeny,http://schuessler.userweb.mwn.de/amphylo/; 2 January2013), and the taxonomy is constantly being developed:new species are regularly being described and higher leveltaxa reorganized (e.g., Schüßler and Walker 2010; Oehl et al.2011b; Goto et al. 2012; Krüger et al. 2012), as overviewed byStürmer (2012) and Young (2012). Morphological character-istics of the largely soil-borne spores have remained the main

species-distinguishing traits of Glomeromycota (Schüßler andWalker 2010; Oehl et al. 2011c). However, in order todescribe root-colonising AMF communities, morphology-independent approaches are required, because the intraradicalstructures of Glomeromycota — hyphae, arbuscules,vesicles — only permit taxon identification to the fam-ily level at best (Merryweather and Fitter 1998; Sanders2004). Glomeromycota can be detected in planta using DNA-based methods, with the majority of current approaches tar-geting nuclear rRNA genes (Helgason et al. 1998;Wubet et al.2006; Lee et al. 2008; Krüger et al. 2009). This procedureprovides an assessment of the AMF taxa which are present inroots and presumably form active mycorrhizae, as opposed tospores in soil that may be dormant (Sanders 2004).Furthermore, analysis of soil spores and root-colonisingAMF may record different species (Clapp et al. 1995), prob-ably reflecting variation in the sporulation dynamics ofGlomeromycota species (Pringle and Bever 2002).

The ITS (internal transcribed spacer) region has beenproposed as a general fungal barcoding marker (Schoch etal. 2012). However, it may not provide optimal speciesdistinction for certain groups including basal fungal lineagessuch as Glomeromycota (Schoch et al. 2012). Another com-monly used marker region, the LSU rRNA gene, also pro-vides equivocal species distinction among Glomeromycota(Stockinger et al. 2010). Most data concerning the naturaldiversity of Glomeromycota have been obtained using theSSU rRNA gene (>170 papers using SSU, >70 papers usingITS, >70 papers using LSU; based on ISI Web of Science,status 3 January 2013). The SSU rRNA gene providesintraspecific variation that is comparable to that of theLSU gene in Glomeromycota (Thiéry et al. 2013) and gen-erally provides sufficient phylogenetic signal to allow de-limitation of sequence groupings (phylogroups) thatcorrespond roughly to the species level (Lee et al. 2008)or slightly above (see below). Where SSU rRNA genesequence groupings have been delineated based on phylo-genetic relationships, using evolutionary models, rather thanfixed-level sequence similarity, they have been useful inexplaining the distribution of organisms in relation to abioticand biotic factors (Powell et al. 2011), indicating that thegroupings are biologically meaningful. In light of this andthe large and geographically diverse set of existing SSUsequence data, this marker region was chosen for use in thisstudy.

The use of sequence groupings as a proxy for approxi-mately species level taxa allows natural occurrence patternsof organisms to be described at the DNA sequence leveleven when species identification is not possible, as is cur-rently the case with most DNA-based microorganism surveydata. The virtual taxon (VT) approach to classifying rRNAsequences, as implemented in the MaarjAM database (Öpiket al. 2009, 2010) and applied elsewhere (Liu et al. 2011;

412 Mycorrhiza (2013) 23:411–430

Merckx et al. 2012; Yang et al. 2012), generates a consis-tently named system of SSU rRNA gene sequence phy-logroups that can be used as proxy for species and/orhigher level organism identification in ecological research.Thus, sequence identification is constant between databaseversions and when used in different dataset queries, withdifferences arising only from inclusion of new VT and splitsor merges of existing VT (all such changes are recorded inVT history logs in MaarjAM). The VT system and othersequence grouping approaches do not replace classical tax-onomic identification and description. Nevertheless, the VTsystem provides a means to identify organisms while apply-ing the same principles universally across a dataset; allowseasy re-identification by other researchers or after collectingadditional data (provided that the sequences are made pub-licly available); and provides comparability among studiesvia the consistent nomenclature.

Öpik et al. (2010) provided a global overview of studiesof AMF in plant roots based on the SSU rRNA gene, andcompiled a database summarizing publicly availableGlomeromycota DNA sequence data, comprising 282Glomeromycota VT (although this number has increased since2010). These VT included 71 of the 222 Glomeromycotamorphospecies then described, as well as many unidentifiedVT that may represent as yet undescribed species ofGlomeromycota. An alternative, ITS-based VT taxonomyyielded a comparable number of 305 ITS GlomeromycotaVT (Yang et al. 2012). For fungi in general, 37 % of ITSsequence clusters contained purely environmental DNAsequences, whilst 50 % of all ITS clusters containedspecimen-based but no environmental sequences (Hibbett etal. 2011). These numbers indicate the vast yet undescribedorganismal diversity hidden among “environmental” sequencedata (Hibbett and Glotzer 2011).

While the number of Glomeromycota VT recorded glob-ally in plant roots exceeds the number of known morpho-species, there are several reasons to suppose that actualmolecular diversity within this group of fungi is still under-estimated. The analysis of VT distribution across continentsand climatic zones indicates a degree of specificity in VToccurrence (Öpik et al. 2010; Kivlin et al. 2011; Yang et al.2012). Most information concerning the distribution ofAMF in natural ecosystems comes either from Europe orNorth America: such data constitute ca. 70 % of the 2274records from natural or semi-natural habitats based on thecentral fragment of the SSU rRNA gene in the MaarjAMdatabase (http://maarjam.botany.ut.ee/, cf. Öpik et al. 2010,status 20 September 2012: Europe, 1,342 records; NorthAmerica, 305 records). However, even within these conti-nents many regions and ecosystems remain unstudied.Rarefaction curves generated for the less studied continentsof Asia, South America, Africa and Oceania showed nosigns of taxon saturation (Öpik et al. 2010). Therefore,

further AMF molecular diversity can be expected from themany geographic areas and major ecosystems that remainlittle studied to date.

A comprehensive reference sequence database is essen-tial for sequence identification when recording species oc-currence or analysing diversity patterns in moleculardatasets. For common similarity-based identity assignmentmethods, the accuracy of sequence assignment is dependenton the comprehensiveness of the reference database (Bik etal. 2012). Therefore, further reference sequence data fromformerly untargeted areas are much needed in order toimprove reference databases and to allow the detection ofmore accurate global AMF diversity patterns.

We aimed to enhance knowledge of the global moleculardiversity of AMF by collecting a dataset from different geo-graphical regions using a consistent methodology. While ourfocus here is on improving AMF reference sequence collec-tions by providing full-amplicon-length sequences, we alsoinvestigate large scale patterns of AMF community composi-tion and anticipate that these data will provide a solid basis forfurther such studies of natural AMF distribution patterns. Inparticular, we expected that targeting little studied areas ofNorth America and Europe, as well as much less studiedcontinents (Asia, South America, Africa, Oceania), wouldcontribute towards a more complete list of molecular taxa(VT) of Glomeromycota and of their occurrence globally indifferent continents and climatic zones.

Materials and methods

Study sites

Samples were collected from six continents — Africa, Asia,Oceania, Europe, North America, South America — andfive climatic zones — polar, boreal, temperate, subtropicaland tropical (Table 1, Fig. 1; continental and climatic zonedefinitions follow those in Öpik et al. 2010). When choosingstudy sites, we targeted the typical regional vegetation. Wealso aimed to sample the ecosystems present locally thatwere least disturbed by human activities. The biomes sampledincluded tropical, subtropical, temperate and boreal forests,subtropical and temperate grasslands (including savannas),tropical and subtropical deserts and shrublands (includingfynbos), and polar tundras (Table 1).

Sampling

At each study site, two plots, representing the same type ofvegetation in similar habitat conditions, were sampled. Theplots were approximately 30×30 m, and the distance be-tween plots ranged from several hundred meters up to 5 km,depending on the local conditions. In each plot, four

Mycorrhiza (2013) 23:411–430 413

Tab

le1

Listof

stud

ysitesfrom

where

rootswerecollected

forAMFdetectionin

planta

Cou

ntry

Clim

atic

zone

Ecosystem

Biome

Habitat

Site

andplot

Coo

rdinates

Altitude

(ma.s.l.)

Sam

pling

Date

Africa

CapeVerde

Tropical

Shrub

land

Desertsandxeric

shrublands

Sem

idesert

Sal

116

°48′19

″N22

°55′16

″W54

27.10.20

09

Sal

216

°48′48

″N22

°54′57

″W13

627

.10.20

09

CapeVerde

Tropical

Shrub

land

Tropicalshrubland

Aridmon

tane

shrubland

Santiago

115

°10′49

″N23

°41′27

″W84

629

.10.20

09

Santiago

215

°10′41

″N23

°41′12

″W88

629

.10.20

09

Gabon

Tropical

Forest

Tropicalmoist

broadleafforest

Tropicalrainforest

Tchim

bele

10°37′02″N

10°23′57″E

300

8.05

.200

9

Tchim

bele

20°37′02″N

10°24′49″E

400

9.05

.200

9

Sou

thAfrica

Sub

trop

ical

Shrub

land

Sub

trop

ical

shrubland

Fyn

bos

Jonk

ershoek1

33°59′17

″S18

°57′20

″E31

92.02

.201

0

Jonk

ershoek2

33°59′35

″S18

°58′27

″E37

92.02

.201

0

Sou

thAfrica

Sub

trop

ical

Grassland

Sub

trop

ical

grasslands

andsavann

asSavanna

Nylstroom

124

°40′40

″S28

°39′39

″E1,09

628

.01.20

10

Nylstroom

224

°45′05

″S28

°34 ′34

″E1,15

328

.01.20

10

Asia

China

Tem

perate

Forest

Tem

perate

broadleaf

andmixed

forest

Tem

perate

mixed

forest

Chang

baimou

ntains

42°22′49

″N12

8°05′35″E

754

1.10

.200

9

China

Sub

trop

ical

Forest

Sub

trop

ical

moist

broadleafforest

Broadleaf

forest

GutianshanNatural

Reserve

29°24′N

118°11′E

1,01

91.10

.200

8

China

Sub

trop

ical

Forest

Sub

trop

ical

moist

broadleafforest

Broadleaf

forest

Hon

gYuan1

29°27′N

118°08′E

435

1.10

.200

8

Hon

gYuan2

29°28′N

118°08′E

466

1.10

.200

8

Georgia

Tem

perate

Forest

Tem

perate

broadleaf

andmixed

forest

Beech

forest

Borjomi-Kharagauli1

41°56′00

″N43

°25′24

″E1,10

013

.05.20

10

Borjomi-Kharagauli2

41°56′27

″N43

°25′09

″E1,40

013

.05.20

10

Israel

Sub

trop

ical

Shrub

land

Desertsandxeric

shrublands

Desert

Negev

Highlands

130

°35′36

″N34

°42′41

″E82

325

.03.20

09

Negev

Highlands

230

°35′34

″N34

°42′44

″E82

325

.03.20

09

Russia

Boreal

Forest

Borealforest

Boreallarchforest

Kam

chatka,Kozyrevsk

156

°03′36

″N15

9°54′44″E

5020

.08.20

10

Kam

chatka,Kozyrevsk

256

°03′30

″N15

9°54′15″E

5020

.08.20

10

Russia

Boreal

Forest

Borealforest

Hem

iborealbirchforest

Kam

chatka,Petropavlov

sk1

53°02′33

″N15

8°40′11 ″E

8013

.08.20

10

Kam

chatka,Petropavlov

sk2

53°02′25

″N15

8°40′19″E

8013

.08.20

10

Russia

Boreal

Forest

Borealforest

Taiga

pine

forest

WestSiberia,Barsova

Gora1

61°15′13

″N73

°09′59

″E60

28.08.20

10

WestSiberia,Barsova

Gora2

61°15′37

″N73

°08′53

″E50

28.08.20

10

Thailand

Tropical

Forest

Tropicaldrybroadleaf

forest

Seasonaltrop

ical

dipterocarpforest

Phitsanulok

116

°51′34

″N10

0°27′55″E

190

1.12

.200

9

Phitsanulok

216

°51′34

″N10

0°27′55″E

190

1.12

.200

9

414 Mycorrhiza (2013) 23:411–430

Tab

le1

(con

tinued)

Cou

ntry

Clim

atic

zone

Ecosystem

Biome

Habitat

Site

andplot

Coo

rdinates

Altitude

(ma.s.l.)

Sam

pling

Date

Europ

e

Eston

iaTem

perate

Grassland

Tem

perate

seminatural

grassland

Woo

dedmeado

wLaelatu

158

°25′04

″N23

°34′11″E

922

.08.20

08

Laelatu

258

°35′92

″N23

°34′05

″E9

22.08.20

08

Finland

Polar

Shrub

land

Tun

dra

Woo

dedtund

raKevo

69°47′54

″N27

°05′11″E

213

16.08.20

08

Skallo

vaara

69°48′49

″N27

°10′25

″E32

016

.08.20

08

Norway

Polar

Shrub

land

Tun

dra

Arctic

tund

raSvalbard,

Lon

gyearbyen1

78°13′15

″N15

°36′60

″E61

24.06.20

09

Svalbard,

Lon

gyearbyen2

78°12′02

″N15

°48′55

″E20

24.06.20

09

North

America

Canada

Tem

perate

Grassland

Tem

perate

natural

grassland

Mixed-grass

prairie

White

Butte

150

°46′N

104°37′W

582

5.11.200

8

White

Butte

250

°46′N

104°37′W

582

5.11.200

8

Ocean

ia

Australia

Sub

trop

ical

Forest

Sub

trop

ical

dry

broadleafforest

Dry

eucalyptus

forest

Mou

ntLofty

Ranges1

35°07′03

″S13

8°39′53″E

257

1.03

.200

9

Mou

ntLofty

Ranges2

35°07′03

″S13

8°39′53″E

257

1.03

.200

9

Australia

Sub

trop

ical

Shrub

land

Desertsandxeric

shrublands

Desert

AliceSprings

123

°46′01

″S13

3°52′32″E

556

3.12

.201

0

AliceSprings

223

°46′00

″S13

3°52′36″E

553

3.12

.201

0

Australia

Sub

trop

ical

Forest

Sub

trop

ical

dry

broadleafforest

Eucalyp

tusforest

Syd

ney1

33°40′31

″S15

1°12′51″E

184

5.12

.201

0

Syd

ney2

33°40′31

″S15

1°12′52″E

184

5.12

.201

0

Australia

Sub

trop

ical

Grassland

Sub

trop

ical

grasslands

andsavann

asSavanna

Woo

hlpo

oerState

Forest1

37°20′00

″S14

2°09′00″E

227

1.12

.201

0

Woo

hlpo

oerState

Forest2

37°20′00

″S14

2°09′00″E

227

2.12

.201

0

SouthAmerica

Argentin

aSub

trop

ical

Grassland

Mon

tane

grassland

Mon

tane

pampa

grassland

Cordo

bamou

ntainrang

e1

32°46′08

″S64

°56′26

″W1,63

210

.12.20

07

Cordo

bamou

ntainrang

e2

32°45′22

″S64

°55′28

″W1,64

710

.12.20

07

FrenchGuy

ana

Tropical

Forest

Tropicalmoist

broadleafforest

Tropicalrainforest

Kaw

14°33′24″N

52°12′31″W

304

7.11.200

7

Kaw

24°33′15″N

52°12′36″W

301

7.11.200

7

FrenchGuy

ana

Tropical

Forest

Tropicalmoist

broadleafforest

Tropicalrainforest

Paracou

15°15′26 ″N

52°55′47″W

4910

.11.20

07

Paracou

25°15′30″N

52°55′47″W

3010

.11.20

07

Ineach

site,twoplotswith

similarvegetatio

nandenvironm

entalcharacteristicsweresampled,ind

icated

with

numbers1and2afterthesitename.Clim

aticzone,ecosystem

,biomeandhabitatd

ata

follo

wtheclassificatio

nsdescribedby

Öpiket

al.(201

0)andused

intheMaarjAM

database

ofGlomerom

ycotasequ

ence

records(http

://maarjam

.botany.ut.ee/)

Mycorrhiza (2013) 23:411–430 415

arbuscular mycorrhizal plant species that were abundant inthe plot were selected (fewer plant species were sampled atseveral sites; Table 2). Ten randomly chosen individuals ofeach plant species were excavated, and soil and other mate-rial adhering to the roots was carefully removed by hand asquickly as possible, without making the roots wet. A rootsample suitable for DNA extraction (length >20 cm) waswrapped in tissue paper and placed in a plastic bag contain-ing silica gel. Altogether, 25 study sites and 96 plant specieswere sampled (Tables 1 and 2).

Molecular methods

DNA was extracted from 30 to 100 mg of dried roots fromeach plant individual (sample) with PowerSoil-htp™ 96Well Soil DNA Isolation Kit (MO BIO Laboratories, Inc.,Carlsbad, CA, USA) with the following modifications. First,roots were milled to powder in 2-ml tubes with one to two 3-mm tungsten carbide beads with Mixer Mill MM400(Retsch GmbH, Haan, Germany) per tube, instead of millingin the Bead Plate as suggested by the manufacturer. 750 μlof Bead Solution was added to the tubes, mixed, and theslurry transferred to the Bead Plate. Second, to increase theDNAyield, Bead Plates were shaken at a higher temperature

than in the default protocol (60 °C, as suggested by themanufacturer as a variation in order to increase the yield)for 10 min at 150 rpm in a shaking incubator. Third, in orderto increase DNA yield but maintain DNA concentration,final elution was performed twice with 75 μl of Solution C6.

Glomeromycota were identified in plant roots using oneof two approaches: cloning followed by Sanger sequencingor 454-sequencing. A first set of samples was targeted withcloning and Sanger sequencing in order to obtain high-quality, full-amplicon-length sequences suitable to serve asreference sequences for future environmental sequenceidentification. With a second set of samples, we used thenow commonly applied 454-sequencing technique in orderto capture maximum AMF diversity from the study sites,since 454-sequencing has previously been shown to consid-erably increase the richness recorded at a site (Öpik et al.2009). Thus, all Sanger sequences and 454-reads of fullamplicon length could be used in phylogenetic analysis toidentify novel VT. All sequence data could then be assignedto existing or novel VT using a BLAST-based assignmentmethod with the aim of describing diversity (see bioinfor-matics and phylogenetic analysis description below).Table 2 indicates which approach was applied to particularsamples.

Fig. 1 Map showing sampling sites used in this study and earlierstudies recorded in the MaarjAM database of Glomeromycota sequencerecords (http://maarjam.botany.ut.ee/; Öpik et al. 2010; as of

September 2012). Symbols representing MaarjAM data are graduatedin size to represent the numbers of records coming from individuallocations

416 Mycorrhiza (2013) 23:411–430

Table 2 List of plant taxa sampled at each study site (Table 1), showing the sequencing method applied (cloning followed by Sanger sequencing or454-sequencing of bar-coded amplicons) and the number of samples taken (plant individuals)

Country Site and plot Plant family(or higher rank)

Plant genus Plant species Sequencingmethod

No. ofsamples

Africa

Cape Verde Sal 1 Poaceae Aristida funiculata Sanger 2

Cucurbitaceae Citrullus colocynthis Sanger 1

Boraginaceae Heliotropium ramosissimum Sanger 1

Cape Verde Sal 2 Poaceae Aristida funiculata Sanger 2

Cucurbitaceae Citrullus colocynthis Sanger 1

Fabaceae Lotus brunneri Sanger 1

Asteraceae Pulicaria diffusa Sanger 2

Cape Verde Santiago 1 Campanulaceae Campanula jacobaea Sanger 2

Fabaceae Lotus jacobaeus Sanger 2

Crassulaceae Umbilicus schmidtii Sanger 2

Cape Verde Santiago 2 Campanulaceae Campanula jacobaea Sanger 2

Asteraceae Conyza pannosa Sanger 2

Fabaceae Lotus jacobaeus Sanger 2

Gabon Tchimbele 1 Begoniaceae Begonia susaniae 454 2

Annonaceae Polyalthia suaveolens 454 2

Arecaceae Podococcus barteri 454 2

Burseraceae Santiria trimera 454 2

Gabon Tchimbele 2 Begoniaceae Begonia susaniae 454 2

Fabaceae Hymenostegia ngounyensis 454 2

Arecaceae Podococcus barteri 454 2

Dryopteridaceae Triplophyllum vogelii 454 2

South Africa Jonkershoek 1 Fabaceae sp. Sanger 2

Montiniaceae Montinia caryophyllacea Sanger 2

Polygalaceae Muraltia sp. Sanger 2

Poaceae Cymbopogon nardus Sanger 2

South Africa Jonkershoek 2 Asteraceae Gerbera crocea Sanger 2

Rosaceae Cliffortia cuneata Sanger 2

Montiniaceae Montinia caryophyllacea Sanger 2

Poaceae Cymbopogon nardus Sanger 2

South Africa Nylstroom 1 Fabaceae Acacia erioloba Sanger 1

Fabaceae Dichrostachys cinerea Sanger 2

Poaceae Digitaria eriantha Sanger 2

Poaceae Panicum maximum Sanger 2

South Africa Nylstroom 2 Commelinaceae Commelina benghalensis Sanger 2

Fabaceae Dichrostachys cinerea Sanger 2

Poaceae Digitaria eriantha Sanger 2

Poaceae Panicum maximum Sanger 2

Asia

China Changbai mountains Rosaceae Rubus sp. Sanger 2

Poaceae Calamagrostis angustifolia Sanger 2

China Gutianshan Natural Reserve Arecaceae Trachycarpus fortunei 454 2

China Hong Yuan 1 Arecaceae Trachycarpus fortunei 454 2

China Hong Yuan 2 Arecaceae Trachycarpus fortunei 454 2

Georgia Borjomi-Kharagauli 1 Asparagaceae Polygonatum glaberrimum Sanger 2

Araliaceae Hedera colchica Sanger 2

Boraginaceae Trachystemon orientalis Sanger 1

Georgia Borjomi-Kharagauli 2 Araliaceae Hedera colchica Sanger 2

Melanthiaceae Paris incompleta Sanger 2

Araceae Arum italicum subsp. albispathum Sanger 2

Israel Negev Highlands 1 Cistaceae Helianthemum sp. Sanger 1

Mycorrhiza (2013) 23:411–430 417

Table 2 (continued)

Country Site and plot Plant family(or higher rank)

Plant genus Plant species Sequencingmethod

No. ofsamples

Liliales sp. Sanger 2

Plantaginaceae Plantago afra Sanger 1

Israel Negev Highlands 2 Lamiaceae Ballota undulata Sanger 2

Geraniaceae Erodium crassifolium Sanger 1

Liliales sp. Sanger 2

Plantaginaceae Plantago afra Sanger 2

Russia Kamchatka, Kozyrevsk 1 Poaceae Calamagrostis purpurea Sanger 2

Caprifoliaceae Linnaea borealis Sanger 2

Asteraceae Solidago spiraeifolia Sanger 2

Russia Kamchatka, Kozyrevsk 2 Poaceae Calamagrostis purpurea Sanger 2

Ranunculaceae Thalictrum minus Sanger 1

Russia Kamchatka, Petropavlovsk 1 Asteraceae Cacalia kamtschatica Sanger 1

Poaceae Calamagrostis purpurea Sanger 2

Ranunculaceae Thalictrum minus Sanger 2

Melanthiaceae Veratrum oxysepalum Sanger 2

Russia Kamchatka, Petropavlovsk 2 Poaceae Calamagrostis purpurea Sanger 2

Liliaceae Lilium debile Sanger 2

Ranunculaceae Thalictrum minus Sanger 2

Russia West Siberia, Barsova Gora Poaceae Festuca ovina Sanger 1

Rosaceae Rubus saxatilis Sanger 1

Asteraceae Solidago virgaurea Sanger 1

Russia West Siberia, Barsova Gora 2 Caprifoliaceae Linnaea borealis Sanger 1

Asparagaceae Maianthemum bifolium Sanger 2

Asteraceae Solidago virgaurea Sanger 1

Thailand Phitsanulok 1 Acanthaceae sp. Sanger 2

Araceae Amorphophallus sp. Sanger 2

Asteraceae Elephantopus scaber Sanger 1

Poaceae Pennisetum polystachyon Sanger 1

Thailand Phitsanulok 2 Acanthaceae sp. Sanger 2

Poaceae Vietnamosasa pusilla Sanger 2

Araceae Amorphophallus sp. Sanger 2

Schizaeaceae Lygodium flexuosum Sanger 2

Europe

Estonia Laelatu 1 Asteraceae Centaurea jacea Sanger 2

Plantaginaceae Plantago lanceolata Sanger 2

Lamiaceae Prunella vulgaris Sanger 2

Poaceae Sesleria caerulea Sanger 2

Estonia Laelatu 2 Asteraceae Centaurea jacea Sanger 2

Ranunculaceae Hepatica nobilis Sanger 2

Poaceae Sesleria caerulea Sanger 2

Finland Kevo Poaceae Deschampsia flexuosa Sanger 2

Caprifoliaceae Linnaea borealis Sanger 1

Asteraceae Solidago virgaurea Sanger 2

Primulaceae Trientalis europaea Sanger 2

Finland Skallovaara Cornaceae Cornus suecica Sanger 1

Poaceae Deschampsia flexuosa Sanger 1

Asteraceae Solidago virgaurea Sanger 2

Primulaceae Trientalis europaea Sanger 2

Norway Svalbard, Longyearbyen 1 Ranunculaceae Ranunculus sulphureus Sanger 1

Saxifragaceae Saxifraga oppositifolia Sanger 1

Norway Svalbard, Longyearbyen 2 Ranunculaceae Ranunculus sulphureus Sanger 2

North America

418 Mycorrhiza (2013) 23:411–430

Glomeromycota sequences were amplified from rootDNA extracts using the primers NS31 and AML2, which

target a ca. 560-bp central fragment of the SSU rRNA genein Glomeromycota (Simon et al. 1992; Lee et al. 2008), the

Table 2 (continued)

Country Site and plot Plant family(or higher rank)

Plant genus Plant species Sequencingmethod

No. ofsamples

Canada White Butte 1 Asteraceae Artemisia frigida 454 2

Asteraceae Heterotheca villosa 454 2

Asteraceae Solidago missouriensis 454 2

Canada White Butte 2 Asteraceae Artemisia ludoviciana 454 2

Asteraceae Solidago missouriensis 454 2

Oceania

Australia Mount Lofty Ranges 1 Asparagaceae Lomandra multiflora subsp. dura 454 2

Fabaceae Platylobium obtusangulum 454 2

Australia Mount Lofty Ranges 2 Asparagaceae Lomandra multiflora subsp. dura 454 2

Fabaceae Pultenaea hispidula 454 2

Australia Alice Springs 1 Poaceae Aristida contorta Sanger 2

Poaceae Triraphis mollis Sanger 2

Australia Alice Springs 2 Poaceae Aristida contorta Sanger 2

Poaceae Cenchrus ciliaris Sanger 3

Poaceae Eragrostis barrelieri Sanger 4

Poaceae Triraphis mollis Sanger 2

Australia Sydney 1 Fabaceae Pultenaea daphnoides Sanger 1

Poaceae Stipa pubescens Sanger 2

Australia Sydney 2 Apiaceae Actinotus minor Sanger 1

Magnoliidae sp. Sanger 2

Australia Woohlpooer State Forest 1 Goodeniaceae Goodenia geniculata Sanger 2

Asteraceae Leptorhynchos squamatus Sanger 2

Poaceae Themeda triandra Sanger 2

Australia Woohlpooer State Forest 2 Asteraceae Leptorhynchos squamatus Sanger 2

Xanthorrhoeaceae Tricoryne elatior Sanger 2

Poaceae Themeda triandra Sanger 2

South America

Argentina Cordoba mountain range 1 Rubiaceae Spermacoce eryngioides 454 2

Verbenaceae Glandularia dissecta 454 2

Plantaginaceae Plantago argentina 454 2

Poaceae Sorghastrum pellitum 454 2

Argentina Cordoba mountain range 2 Rubiaceae Spermacoce eryngioides 454 2

Apiaceae Oreomyrrhis andicola 454 2

Plantaginaceae Plantago argentina 454 2

Poaceae Sorghastrum pellitum 454 2

French Guyana Kaw 1 Costaceae Chamaecostus congestiflorus 454 2

Urticaceae Cecropia obtusa 454 2

French Guyana Kaw 2 Arecaceae Bactris rhaphidacantha 454 2

Urticaceae Cecropia obtusa 454 2

French Guyana Paracou 1 Fabaceae Dicorynia guianensis 454 1

Fabaceae Eperua grandiflora 454 2

French Guyana Paracou 2 Fabaceae Eperua falcata 454 2

Fabaceae Eperua grandiflora 454 2

Total: 257

Two individuals of each plant species were analysed from the total pool of sampled individuals per plot, with a few exceptions; in many cases onlyone individual provided Glomeromycota sequences. No Glomeromycota sequences were detected in the NS31-AML2 PCR products from roots ofthe following plant species: Crotalaria microphylla (Sal, Cape Verde), Cardamine bulbifera (both plots in Georgia), Linnaea borealis (BarsovaGora plot 1, Russia)

Mycorrhiza (2013) 23:411–430 419

most widely used marker in AMF surveys to date (Öpik etal. 2010; Kivlin et al. 2011). For cloning, PCR productswere generated under the following conditions: 12.5 μl ofQiagen HotStarTaq Master Mix (Qiagen Gmbh, Germany),10 pmol of each of the primers and 1 μl of template DNA, ina total volume of 25 μl. The reactions were run on aThermal cycler 2720 (Applied Biosystems) under the fol-lowing conditions: 95 °C for 15 min; five cycles of 94 °C for30 s, 52 °C for 30 s, 72 °C for 1 min; 30 cycles of 94 °C for30 s, 58 °C for 30 s, 72 °C for 1 min; followed by 72 °C for10 min. Several PCR products from the same sample werecombined if necessary to produce a sufficient quantity ofDNA for cloning. PCR products were purified using theMSB® Spin PCRapace Kit (STRATEC Molecular GmbH,Berlin, Germany), ligated into the pTZ57R/T vector(Fermentas UAB, Vilnius, Lithuania) and cloned in chemi-cally competent Escherichia coli DH5α. At least 16 putativepositive colonies were selected and grown. The presence ofthe correct insert was checked by PCR with universal pri-mers M13F and M13R. Sixteen positive products of colonyPCR per sample were sequenced at Macrogen Inc. (Seoul,South Korea) with primer M13R. Cloning procedures wereperformed by Icosagen Cell Factory OÜ (Tartu, Estonia).

In the case of 454-sequencing, PCR primers were linked to454-sequencing adaptors A and B, respectively. In order toidentify sequences originating from different samples, we useda set of 8-bp bar-codes designed following Parameswaran et al.(2007). The bar-code sequences were inserted between theadaptor A and NS31 primer sequences and between the adap-tor B and AML2 primer sequences. We employed a two-stepPCR approach: in the first PCR reaction PCR primers werelinked to bar-codes and partial 454-sequencing adaptors A andB; the second reaction was performed with the full 454-adaptors A and B serving as PCR primers, thus completingthe addition of the full 454-adaptor+bar-code+PCR primerconstruct to the amplicon. Thus, the composite forward primerin the first PCR reaction was: 5′-GTCTCCGACTCAG(NNNNNNNN) TTGGAGGGCAAGTCTGGTGCC-3′; andthe reverse primer: 5′-TTGGCAGTCTCAG (NNNNNNNN)GAACCCAAACACTTTGGTTTCC-3′, where the A and Badaptors are underlined, the bar-code is indicated by N-s inparentheses, and the specific primers NS31 and AML2 areshown in italics. Ten times diluted product of the first PCRreaction was used in the second PCR with primers A (5′-CCATCTCATCCCTGCGTGTCTCCGACTCAG-3′) and B(5′-CCTATCCCCTGTGTGCCTTGGCAGTCTCAG-3′).The reaction mix contained 5 μl of Qiagen HotStarTaq MasterMix (Qiagen), 0.2μMeach of the primers and 1 μl of templateDNA, in a total volume of 10 μl. Negative controls without atemplate were included. The reactions were run on a Thermalcycler 2720 (Applied Biosystems) under the following con-ditions: 95 °C for 15 min; five cycles of 42 °C for 30 s, 72 °Cfor 90 s, 92 °C for 45 s; 35 cycles (first PCR) or 20 cycles

(second PCR) of 65 °C for 30 s, 72 °C for 90 s, 92 °C for 45 s;followed by 65 °C for 30 s and 72 °C for 10min. PCR productswere separated by electrophoresis through a 1.5 % agarose gelin 0.5×TBE, and the PCR products were purified from the gelusing theQiagenQIAquick Gel Extraction kit (Qiagen Gmbh).A total of 2 μg of the resulting DNA mix was sequenced on aGenome Sequencer FLX System, using Titanium Seriesreagents (Roche Applied Science) at GATC Biotech(Konstanz, Germany).

Bioinformatics and phylogenetic analysis

The cloned Sanger sequences (2,693 sequences) werestripped of primer and vector sequences, and 74 sequenceswith length <500 or >550 nucleotides were removed. Fortypotential chimeras were detected and removed from the datausing UCHIME (Edgar et al. 2011) in reference databasemode (MaarjAM) and the default settings. Remaining sequen-ces were submitted to BLAST searches against the MaarjAMdatabase (status as of 20 September 2012; Öpik et al. 2010)and the International Nucleotide Sequence Database (INSD)in order to identify putative Glomeromycota for phylogeneticanalysis.

The 454-reads that carried correct bar-code and forwardprimer sequences and were ≥170 bp long (excluding the bar-code and primer sequence) (52,235 reads) were stripped ofany remaining 454-adaptor nucleotides, and bar-code andforward primer sequences. 544 potential chimeras wereidentified and removed from this set of data using theprocedure described above. Remaining reads were submittedto BLAST searches against the MaarjAM and the INSD data-bases in order to identify putative Glomeromycota. We iden-tified 725 reads that spanned the majority of the NS31-AML2amplicon (>500 bp) and were putative Glomeromycota.

In order to identify novel VT, the obtained Sangersequences and the 725 full-amplicon-length 454-reads wereautomatically aligned together with the non-redundant se-quence set of the MaarjAM database (status as of 20September 2012; Öpik et al. 2010) using the MAFFT ver-sion 6 (Katoh and Toh 2008) multiple sequence alignmentonline service using automatic settings, adjusting for se-quence number. Neighbor-joining (NJ) analysis (F84 modelwith gamma substitution rates) was implemented inTOPALi version 2.5 (Milne et al. 2009). VT were delimitedbased on the NJ tree at ≥ 97 % sequence similarity followingthe procedure used to update the MaarjAM database de-scribed by Öpik et al. (2010). One novel VT defined onthe basis of a single 454 read was discarded, because there isa strong possibility that such singleton taxa are artefactualwhen identified by pyrosequencing (Tedersoo et al. 2010).Related sequences obtained from published datasets (thosenot yet in MaarjAM), 308 VT type sequences from theMaarjAM database, and representative sequences of all VT

420 Mycorrhiza (2013) 23:411–430

detected in this study were subjected to sequence alignmentand Bayesian Inference analysis using BEAST (version 1.6.1;Drummond and Rambaut 2007) in order to depict the phylo-genetic placement of the VT. The GTR + G + I nucleotidesubstitution model was selected on the basis of the BayesianInformation Criterion (jModeltest; Posada 2008). A burn-incorresponding to approximately 30 % of samples was dis-carded and trees were drawn every 9,000 generations fromthree independent runs of 45 million generations. The resultsare summarized on a maximum clade credibility tree.

In order to capture AMF diversity in both datasets, allSanger sequences and clean 454 reads were assigned to VTfollowing the methodology of Davison et al. (2012). Inshort, we conducted BLAST searches (soft masking withthe DUST filter) against the MaarjAM database plus thenewly identified VT from this study with the followingcriteria required for a match: (1) sequence similarity≥97 %; (2) the alignment length should not differ from thelength of the shorter of the query (Sanger sequence or 454read) and subject (reference database sequence) sequencesby more than ten nucleotides; (3) a BLAST e-value <1×20−50. Singleton VT in the 454 dataset that were not en-countered in the cloning dataset were discarded. We inves-tigated those 454 reads that BLAST did not match againstthe MaarjAM database amended with newly detected VT byconducting a further BLAST search against the INSD data-base using the same parameters. A BLAST-based approachof VT identification has the benefit of recording stable VTidentifiers and of representing a strict quality filter, sincesequences that diverge significantly from reference sequen-ces, e.g., non-target organisms or chimeric sequences, areunlikely to be recorded (Bik et al. 2012). This work wascarried out in part in the High Performance ComputingCenter of the University of Tartu.

At least one sequence of each VT per plant species perstudy site was submitted to EMBL under accession numbersHE798692-HE799285 and HF566437-HF567439.

Statistical analyses

VT records from this study were filtered in the same way asis done for submission to the MaarjAM database, i.e., twosequences (or one where only one sequence was recorded;the longest suitable 454 reads were selected) of each VT perplant species per site were retained. These data were com-bined with VT record data from MaarjAM, effectively pro-ducing a version of the MaarjAM database updated withrecords from this study. We used rarefaction analysis toassess the richness estimates in different continents andclimatic zones in this updated MaarjAM record set. Curveswere produced using the rarefy() function from R packagevegan (Oksanen et al. 2011) after pooling records intocontinent or climatic zone categories.

Variation in AM fungal community composition wasvisualized using non-metric multi-dimensional scaling(NMDS; implemented using R package vegan). In order toavoid methodological bias, the datasets derived usingSanger sequencing and 454-sequencing were analysed sep-arately. Ordination was performed using both presence–ab-sence and quantitative data. In the latter case, counts ofreads in each sample were transformed to proportions ofreads corresponding to different VT. Because the results ofthe two analyses largely coincided, we present only theresults of the quantitative analysis. Stress values for theordinations were in the range 0.19–0.21. PerManova (imple-mented in R package vegan) was used to make statisticalcomparisons between AM fungal communities in differentcontinents (Africa, Asia, Europe, North America, SouthAmerica, Oceania), and in different climatic zones (polar,boreal, temperate, subtropical, tropical).

Results

General patterns

A total of 257 root samples from 96 plant species at 25 siteson six continents were analysed using cloning followed bySanger sequencing or using 454 sequencing (Tables 1 and 2,Fig. 1). Sanger sequencing of 186 samples from 17 sitesyielded a total of 2,353 good quality Glomeromycotasequences in 143 phylogroups (virtual taxa, VT). 454-sequencing of 71 samples from eight sites yielded a totalof 22,391 good quality Glomeromycota sequences in 173VT (Fig. S1; see Materials and methods section for qualitycriteria and filtering of sequences). A total of 204 VTwere recorded: 18 Acaulosporaceae,1 one Ambisporaceae,six Archaeosporaceae, six Claroideoglomeraceae, sixDiversisporaceae, seven Gigasporaceae, 156 Glomeraceae(formerly Glomus group A) and four Paraglomeraceae VT(Table 3, Fig. S1). No Geosiphonaceae or Pacisporaceaewere recorded. Only 37 VT (17 %) included describedmorphospecies. Sanger sequences with no matches to theMaarjAM database matched Ascomycota, Basidiomycota orplant sequences in the INSD. Among the 454-reads with nomatches to the MaarjAM database, 70 % also had no matchin the INSD, 15 % matched a plant sequence, 6 % aGlomeromycota sequence (almost all “uncultured Glomus”from environmental samples), 3 % a Metazoa sequence and2 % a non-glomeromycotan fungal sequence.

We mapped the recent genus and family level taxonomicrearrangements onto the phylogeny of all GlomeromycotaVT, including VT containing sequenced morphospecies and

1 The family-level taxonomy of Glomeromycota follows Schüßler andWalker (2010), unless otherwise stated.

Mycorrhiza (2013) 23:411–430 421

those known based on DNA sequences alone (Fig. S1). Ofthe recently erected families, ClaroideoglomeraceaeC.Walker & A.Schüßler (Schüßler and Walker 2010) =Entrophosporaceae Oehl & Sieverd., emend. Oehl,Sieverd., Palenz. & G.A.Silva (Oehl et al. 2011b) is wellsupported, but no SSU rRNA gene sequences are availablefor Sacculosporaceae Oehl, Sieverd., G.A.Silva, B.T.Goto,I .C.Sanchez & Palenz. (Oehl et al . 2011b) andIntraornatosporaceae B.T.Goto & Oehl (Goto et al. 2012),meaning that we could not compare their phylogeneticplacement. Of the newly erected genera, ClaroideoglomusC.Walker & A. Schüßler (Schüßler and Walker 2010) is wellsupported, but new genera in Diversisporales C.Walker &A.Schüßler (Schüßler and Walker 2010) are not clearlydistinguishable from many related environmental VT ordue to limited sequence variation in Diversispora and

Gigasporaceae. In the case of former Glomus group A(Schüßler et al. 2001), there are two alternative familyemendations with four alternative genera in each: (1)Glomeraceae Piroz. & Dalpé, emend. C.Walker &A.Schüßler (Schüßler and Walker 2010), including generaFunneliformis C.Walker & A.Schüßler, Glomus Tul. &C.Tul., emend. C.Walker & A.Schüßler, RhizophagusP.A.Dang and Sclerocystis Berk. & Broome, and (2)Glomeraceae Piroz. & Dalpé, emend. Oehl, G.A.Silva &Sieverd. (Oehl et al. 2011a), including genera FunneliformisC.Walker & A.Schüßler, emend. Oehl, G.A.Silva &Sieverd., Glomus Tul. & C.Tul., emend. Oehl, G.A.Silva& Sieverd., Septoglomus Sieverd., G.A.Silva & Oehl andSimiglomus Sieverd., G.A.Silva & Oehl. These eight generacannot be unequivocally delimited when sequences frommorphospecies are analysed together with environmental

Table 3 Numbers of Glomeromycota SSU rRNA gene phylogroups(virtual taxa, VT sensu Öpik et al. 2009, 2010) by family and asrecorded from different continents and climatic zones: richness

recorded in this dataset is compared with information from recentupdates of the MaarjAM database of Glomeromycota sequence records(http://maarjam.botany.ut.ee/)

MaarjAM 2010April

MaarjAM 2011September

This dataset Novel VT Newrecords

Total VTcurrent

Increase (%)

Fungal familya

Acaulosporaceae 46 47 18 3 50 6

Ambisporaceae 2 3 1 0 3 0

Archaeosporaceae 8 9 6 1 10 11

Claroideoglomeraceae 10 12 6 1 13 8

Diversisporaceae 11 11 6 2 13 18

Geosiphonaceae 1 1 0 0 1 0

Gigasporaceae 10 11 7 0 11 0

Glomeraceae 186 203 156 25 228 12

Pacisporaceae 2 2 0 0 2 0

Paraglomeraceae 6 9 4 1 10 11

Continent

Africa 50 62 129 26 64 152 145

Asia 72 79 145 25 63 167 111

Europe 155 190 37 5 7 202 6

North America 101 115 67 15 14 144 25

Oceania 14 26 95 23 61 110 323

South America 77 78 115 25 56 157 101

Climatic zone

Boreal 3 3 25 2 20 25 733

Polar 0 0 28 5 23 28 NA

Subtropical 103 141 170 28 51 220 56

Temperate 194 209 86 17 4 230 10

Tropical 120 125 142 29 49 203 62

Total 282 308 204 33 NA 341 10

The numbers of VT detected in this study are shown (“this dataset”), along with counts of VT that were recorded for the first time in this study(“novel VT”) and records of existing VT from new continents and climatic zones (“new records”). The total numbers of currently registered VT areshown in the “Total VT current” column. The “Total” line at the bottom of table shows total VT counts irrespective of family, climate or continentcategorisationa The family-level taxonomy of Glomeromycota follows Schüßler and Walker (2010)

422 Mycorrhiza (2013) 23:411–430

sequences (Fig. S1). Also, several genus- or family-level cladesrelated to the orders Paraglomerales and Archaeosporales can-not be clearly placed into currently recognised familiesand they most probably represent new clades withinGlomeromycota (Fig. S1, subtree 14).

Novel phylogroups

Among the recorded VT, 33 (16 %) were novel, i.e.,recorded for the first time and having no closely relatedsequences among published datasets in the INSD. Five andfour novel VT were recorded only in the cloned Sanger-sequenced and 454-sequencing datasets, respectively. NovelVT constituted 16 % of sequences (20 % of VT) in thecloned-Sanger sequenced dataset and 26 % of reads (16 %of VT) in the 454-sequencing dataset. Novel VT weredetected from 13 of the 17 sites targeted with cloning andSanger-sequencing and from all eight sites targeted with454-sequencing (Table 4). Study sites targeted withcloning-Sanger sequencing revealed 0–6 novel VT; sitestargeted with 454-sequencing revealed 12–22 novel VT(Table 4). Across both datasets, three novel VT (Glomusspp. MO-G52, MO-G53, MO-G572) were detected from allsix continents studied, and ten, six, four, three and seven novelVT recorded from five, four, three, two and one continent,respectively. Novel VTwere recorded in all detected familiesexcept Ambisporaceae and Gigasporaceae (Table 3).

Continental and climatic patterns

The continental and climatic distributions of recorded VTare presented in Table 3. The overall number of known VTincreased by 11 %: from 308 to 341 VT. Increases in thenumbers of VT recorded from particular continents rangedfrom 6 % in Europe to 323 % in Oceania (where there wasan increase from 26 to 110 VT) and in particular climaticzones from 10 % in the temperate zone to 733 % in theboreal zone (where there was an increase from three to 25VT). No data were previously available for the polar zone;this zone is now represented by 28 VT (Table 3).Rarefaction curves calculated at the level of continents andclimatic zones suggest that a considerable proportion ofGlomeromycota diversity has already been registered inmost regions, whilst further large increases can be expectedin less studied areas, in particular in Oceania and in theboreal and polar climatic zones (Fig. 2).

NMDS ordination revealed similar patterns of variationin AM fungal communities in the cloned Sanger-sequencedand 454-sequencing datasets (Fig. 3). In both data setsfungal communities differed significantly among continents

(PerManova; Sanger: pseudo-F=4.61, R2=0.07, P<0.001;454: pseudo-F=4.05, R2=0.20, P<0.001) and climaticzones (PerManova; Sanger: pseudo-F=5.01, R2=0.10,P<0.001; 454: pseudo-F=4.52, R2=0.12, P<0.001).

Discussion

Knowledge about the distribution of Glomeromycota de-rived using DNA sequence-based (molecular) approacheshas been largely confined to Europe and North America(Öpik et al. 2010; Kivlin et al. 2011; Yang et al. 2012).Data obtained by traditional morphological identification ofspores is considerably more substantial (Robinson-Boyer etal. 2009) and of wider geographical origin, but little hasbeen done to generalize such information. A recent over-view of Glomeromycota in the world’s protected areas,incorporating both morphospecies (i.e., spore identification)and molecular species, still revealed large areas— mostly inAfrica and Asia — from where no information is available(Turrini and Giovannetti 2012). Here, we present data that aimto generate a better global understanding of Glomeromycotaoccurrence and richness, and to contribute towards a referencedataset for DNA sequence-based Glomeromycota identifica-tion in molecular surveys. Our study sites spanning six con-tinents and five climatic zones yielded a total of 204phylogenetically defined molecular operational taxonomicunits (OTUs), here called virtual taxa (VT). VT nomenclaturedevelops around type sequences, is based on uniform princi-ples, and is consistent in time, thus allowing comparabilityamong datasets and DNA sequence-based organism detectionwith consistent phylogroup naming (Öpik et al. 2009, 2010;Liu et al. 2011; Moora et al. 2011; Davison et al. 2012;Merckx et al. 2012). Of the 204 VT detected in this study,16 % were recorded for the first time from anywhere in theworld. However, the total increase in the number ofGlomeromycota VT — 11 %, from 308 to 341 — wasmoderate despite the extensive sampling. This result suggeststhat the recorded number of Glomeromycota VT might beapproaching the actual number of VT. On the other hand,anticipated large increases in the number of recorded VTwereobserved in some of the formerly less studied regions, con-firming that data on the distribution of Glomeromycota arestill limited.

There are several likely reasons for the observed moder-ate increase in the total number of Glomeromycota molec-ular taxa despite the extensive sampling of formerlyuntargeted areas, which we initially expected to yield morenovel VT. First, the proportion of VT recorded for the firsttime (i.e., novel) is clearly smaller in this dataset (20 and16 % in the cloned-Sanger sequenced and 454-sequencedsubsets of data) than a decade ago when the majority ofrecorded AMF OTUs were new to science (e.g., Helgason et

2 New VT acquire numerical codes of MaarjAM VT nomenclature afterdata from a publication have been uploaded to the MaarjAM database.

Mycorrhiza (2013) 23:411–430 423

Tab

le4

Glomerom

ycotaSSU

rRNA

gene

phylog

roup

s(virtual

taxa,VTsensuÖpiket

al.20

09,20

10)recorded

forthefirsttim

ein

thisstud

y

DataSou

rce

MO-

Ar1

MO-

A8

MO-

A10

MO-

A9

MO-

D1

MO-

D2

MO-

G37

MO-

G38

MO-

G39

MO-

G40

MO-

G41

MO-

G42

MO-

G43

MO-

G44

MO-

G45

MO-

G46

Sang

er-sequenced

clon

es

Africa

Santiago

24

16

Africa

Sal

565

Africa

Jonk

ershoek

662

Africa

Nylstroom

15

Asia

Chang

baimou

ntain

1Asia

Borjomi-Kharagauli

1Asia

Negev

Highlands

Asia

Kam

chatka,

Petropavlov

sk1

10

Asia

Phitsanulok

114

12

5Europ

eKevo

1Oceania

AliceSprings

2Oceania

Syd

ney

11

2Oceania

Woo

hlpo

oer

State

Forest

130

No.

ofstud

ysitesperVT

11

12

13

31

32

20

12

12

454-sequ

ences

Africa

Tchim

bele

15

911,13

52

22

1Asia

Gutianshan

Natural

Reserve

8118

817

243

22

Asia

Hon

gYuan

171

716

530

74

2North

America

White

Butte

221

646

7024

481

93

Oceania

Adelaide

13

212

9692

1Sou

thAmerica

Cordo

bamou

ntain

rang

e1

1018

02

51

Sou

thAmerica

Kaw

14

1020

162

175

1522

11

2Sou

thAmerica

Paracou

4354

41

No.

ofstud

ysitesperVT

01

44

00

78

86

28

13

36

Total

no.of

stud

ysitesperVT

12

56

13

109

118

48

25

48

DataSou

rce

MO-

G47

MO-

G48

MO-

G49

MO-

G50

MO-

G51

MO-

G52

MO-

G53

MO-

G54

MO-

G55

MO-

G56

MO-

G57

MO-

G59

MO-

G60

MO-

G61

MO-

G62

MO-

GB1

MO-

P1

No.

ofVT

persite

Sang

er-sequenced

clon

es

Africa

45

Africa

2

Africa

12

12

6

Africa

61

15

424 Mycorrhiza (2013) 23:411–430

Tab

le4

(con

tinued)

DataSou

rce

MO-

G47

MO-

G48

MO-

G49

MO-

G50

MO-

G51

MO-

G52

MO-

G53

MO-

G54

MO-

G55

MO-

G56

MO-

G57

MO-

G59

MO-

G60

MO-

G61

MO-

G62

MO-

GB1

MO-

P1

No.

ofVT

persite

Asia

1

Asia

1

Asia

11

13

Asia

2

Asia

16

Europ

e1

58

15

Oceania

1468

44

Oceania

111

56

Oceania

73

11

21

11

24

04

21

01

10

1

454-sequ

ences

Africa

15

66

76

58

25

14

20

Asia

133

929

54

85

110

1219

Asia

114

915

78

71

16

North

America

393

7469

310

771

284

3135

133

20

Oceania

26

12

365

16

116

Sou

thAmerica

11

9910

472

12

Sou

thAmerica

22

71

54117

33

255

59

22

Sou

thAmerica

183

19

76

26

13

115

25

77

48

85

26

85

74

01

0

36

98

59

109

210

106

85

11

1

Num

bersof

sequ

encesperVTdetected

from

each

stud

ysite(Table1)

areshow

n.New

VTacqu

irethenu

mericalMaarjAM

VTno

menclatureafterdatafrom

apu

blicationhave

been

uploaded

tothe

MaarjAM

database

VTarecodedas

follo

ws:MO-ArArcha

eospora,

MO-A

Acaulospo

ra,MO-D

Diversispora,

MO-G

Glomus,MO-G

BClaroideoglom

us,MO-P

Parag

lomus

Mycorrhiza (2013) 23:411–430 425

al. 1998; Öpik et al. 2003). This is most likely a reflection ofthe state of the art, i.e., commonly occurring AMF have now

probably been detected and described for many regions.Second, the procedure that we used in order to define novel

0 500 1000 1500 2000

050

100

150

200

250

MaarjAM − extended records

Mea

n V

T R

ichn

ess

borealpolarsubtropicaltemperatetropical

a

0 500 1000 1500 2000

050

100

150

200

MaarjAM − extended records

AfricaAsiaEuropeNorth AmericaOceaniaSouth America

bFig. 2 Rarefaction analysis ofMaarjAM database recordscombined with representativerecords from this study. Twosequences per VT per plantspecies per site was added fromthis study in order to make datacomparable with those alreadyin the MaarjAM database.Rarefaction curves arepresented for separate climaticzones (a) and continents (b)

−1.0 −0.5 0.0 0.5 1.0

−1.

5−

1.0

−0.

50.

00.

51.

0

NMDS 1

NM

DS

2

borealpolarsubtropical

temperatetropical

a

−1.0 −0.5 0.0 0.5 1.0

−1.

5−

1.0

−0.

50.

00.

51.

0

NMDS 1

NM

DS

2

AfricaAsia

EuropeOceania

b

−1.0 −0.5 0.0 0.5 1.0 1.5 2.0

−1.

5−

1.0

−0.

50.

00.

51.

0

NMDS 1

NM

DS

2

subtropicaltemperatetropical

c

−1.0 −0.5 0.0 0.5 1.0 1.5 2.0

−1.

5−

1.0

−0.

50.

00.

51.

0

NMDS 1

NM

DS

2

AfricaAsiaN. AmericaOceaniaS. America

d

Fig. 3 Nonmetric Multidimensional Scaling plots showing relation-ships between fungal communities. Data recorded using different se-quencing approaches are presented separately: a and b cloningfollowed by Sanger sequencing; c and d 454-sequencing. Samples

are categorised according to climatic zone (a and b); or continent (band d). Solid lines show dispersion ellipses (1 standard deviation)around groups of samples

426 Mycorrhiza (2013) 23:411–430

VT was careful and conservative — that is, novel VT weredefined either on the basis of cloned and Sanger sequenceddata, or on the basis of the 454-reads that were full-amplicon-length. This means that only a small subset of454-reads were used for novel VT delimitation, whilst thefull set of 454-sequencing data was used only for diversityestimation. It is possible that the “unused” part of the 454data set might still harbour additional novel VT. It is impor-tant to stress that mostly the same novel VT were detectedwith the two sequencing approaches: 24 of the 33 novel VToccurred in both datasets, and only four novel VT weredefined solely on the basis of 454-reads. Third, the markerregion used in this study can distinguish Glomeromycotawith a taxonomic resolution corresponding to the specieslevel or slightly higher (Fig. S1). Species in some genera,including Ambispora, Diversispora, Gigaspora andScutellospora, cannot be resolved based on the central frag-ment of the SSU rRNA gene (de Souza et al. 2004; Walkeret al. 2007; Gamper et al. 2009; Fig. S1). Thus, SSU rRNAgene sequence VT diversity at least partially underestimatesthe actual species diversity of Glomeromycota.

The global number of Glomeromycota species has beenrecently estimated to be similar to or higher than indicated inthe present study: 305 ITS-VT with a 90 % sequence sim-ilarity cut-off level (Yang et al. 2012); 563 and 669 OTUsbased on recorded SSU rRNA and LSU rRNA gene sequen-ces with a 97 % sequence similarity cut-off level, or 789 and1321 OTUs based on the Chao1 index of total richness(Kivlin et al. 2011). Considering the SSU data, the highOTU count in comparison with the figures we present heremight have resulted from inclusion of unpublished datasetsby Kivlin et al. (2011) or from differences in OTU delimi-tation principles. However, both Kivlin et al. and this studydelimit OTUs by sequence similarity and shared phyloge-netic placement. It should also be noted that OTU delimita-tion at a level slightly above that of the species when usingSSU (Lee et al. 2008) might be expected to generate relativelylow OTU counts and total richness estimates. However,none of SSU, ITS or LSU has been able to distinguishclosely related species unequivocally, due to intraspecificsequence variation of Glomeromycota exceeding interspecificvariation in some instances (Fig. S1 in Stockinger et al. 2010).Thus, the total number of Glomeromycota species (asopposed to VTs or OTUs) might still be higher than any of thepublished estimates.

In a previous analysis, Öpik et al. (2010) found restricteddistribution ranges of AM fungi reflecting continents andclimatic zones. They concluded that AM fungal distributionpatterns reflect the effect of regional biogeographic historyas well as the different ecology of AM fungal taxa, thoughthe limited available data might also have influenced theobserved pattern. In the current paper, we analysed twodiscrete global datasets where community data were

gathered using the same sampling design, but differentsequencing approaches. Again, we found distinct continentaland geographic patterns, though in both datasets (but espe-cially the 454-sequencing data) the continent and climatecategorisations were somewhat confounded (Table 2;Fig. 3), meaning that the effects of the two variables couldnot be completely separated. There is accumulating evidencethat not every fungus is everywhere (Peay et al. 2010).Continental differences in AM fungal community composi-tion provides further support to the notion of Morton et al.(1995) that local AMF diversity has a strong historical com-ponent, related to the dispersal of Glomeromycota speciesover geological time. Distinction between AMF communitiesin different climatic zones either across or within continentsindicates that AMF community composition in different envi-ronments is to some extent driven by the varying ecologicalrequirements of AMF (Fitter 2005; Öpik et al. 2006, 2010;Chaudhary et al. 2008; Dumbrell et al. 2010). However, moredetailed community-level data is needed to specifically disen-tangle the effects of various single ecological factors.

Our data brought about a massive increase in the numberof Glomeromycota VT recorded in areas that were formerlyentirely or largely unstudied — boreal and polar climaticzones and Oceania. Remarkably, this increase largely con-sisted of new records of already described VT, rather thannovel VT as might be expected based on the previouslyreported selectivity of AMF for geographical areas andclimatic zones (Öpik et al. 2010; Kivlin et al. 2011; Yanget al. 2012). For example, 137 previously known VT (44 %)were recorded from at least one “new” continent and 115VT (37 %) were recorded from at least one “new” climaticzone. This result does however support our previous con-clusion that there is a set of ubiquitous VT with a globaldistribution (Öpik et al. 2010). In addition to new records ofexisting VT, all continents other than Europe and NorthAmerica yielded over 20 VT that are new to science, andexhibited at least a 2-fold increase in the total number ofrecorded VT. However, we included relatively few sitesfrom Europe and North America in this study comparedwith the other continents. This sampling bias, allied to themore comprehensive pre-existing knowledge, probably con-tributed to the smaller increases in recorded VT in theseareas.

In common with the continental patterns, the relativelywell-studied temperate climatic zone exhibited only a mod-est increase in recorded VT and a low number of novel VT,whilst approximately 30 novel VT were detected from thesubtropical and tropical zones, with an increase in recordedVT of ca 60 %. There had previously only been three VTrecorded from the boreal climatic zone (Öpik et al. 2010)and none from the polar zone. AM symbiosis is howeverquite common in the polar zone (Newsham et al. 2009), andT-RFLP fingerprinting data from the Kilpisjärvi site in polar

Mycorrhiza (2013) 23:411–430 427

Finland (Pietikäinen et al. 2007) suggested that local AMFspecies richness in the polar zone might be comparable tothat in temperate natural ecosystems. Thus, it was fullyexpected that the number of VT recorded in the boreal zoneincreased considerably — by an order of magnitude — andthat a number of VT were recorded in the polar zone.Overall, our rarefaction analysis suggests that furtherincreases in the number of recorded VT or species ofGlomeromycota can be expected in the future from allcontinents and climatic zones, but may be greatest inOceania and the boreal and polar climatic zones, whichremain less studied.

The proportion of recorded VT that could be assigned tomorphospecies in this dataset was low – only 17 % (37 VTrepresenting 63 morphospecies; Gigasporaceae VT com-monly contain many morphospecies due to low variabilityin the central part of the SSU rRNA gene in this family —see Fig. S1). This proportion is even lower than the ca. 25–30 % of OTUs per study belonging to morphospeciesreported in recent surveys applying cloning-Sanger se-quencing (van der Heijden et al. 2008), but this is probablydue to the increased detection of rarer OTUs by 454 se-quencing (Öpik et al. 2009; Lentendu et al. 2011; Table 4 ofthis study). The large discrepancy between Glomeromycotadiversity detected and delimited morphologically on onehand and molecularly on the other could partly be attributedto the fact that only a proportion of Glomeromycota mor-phospecies have been sequenced: 76 species for the SSUregion and 91 species for the combined ITS–LSU region(Krüger et al. 2012), out of the total of 241 morphospeciesof Glomeromycota currently known (http://schuessler.userweb.mwn.de/amphylo/; as of 2 January 2013). Althoughfurther species are steadily being sequenced, the speed of newspecies description and new molecular species detection ismuch faster than that of morphospecies sequencing (Hibbett etal. 2009; Hibbett and Glotzer 2011; Pringle et al. 2011).Clearly, further taxonomic work is needed in many groupsof Glomeromycota, employing both traditional morphologicalcharacters and DNA sequence data in species delimitation.Furthermore, systematic sequencing of the morphospeciesavailable in culture collections and herbaria has the potentialto greatly increase the proportion of molecular species withmorphospecies identity (Brock et al. 2009; Nagy et al. 2011).

The far higher number of DNA-based OTUs or VT (Öpiket al. 2010; Kivlin et al. 2011; Yang et al. 2012; this study)than currently known morphospecies of Glomeromycotasuggests that many species remain to be described in thisphylum. There have been major rearrangements ofGlomeromycota taxonomy during recent years at the levelsof genera, families and higher taxa (Schüßler and Walker2010; Oehl et al. 2011c), of which several are not distin-guishable when the many related environmental VT areincluded in the phylogenetic analysis. Furthermore, our data

suggest that not only new species or genera, but also higherlevel groups within Glomeromycota remain to be “discov-ered” in the traditional manner, possibly brought into cultureand formally taxonomically described. In the absence ofmorphological specimens, but where DNA sequence evi-dence is available, formal descriptions of solely DNAsequence-based molecular taxa represents an alternative tomorphospecies description (Hibbett et al. 2011). Clearly,DNA-based taxa represent a substantial fraction of organis-mal diversity that should not be ignored in taxonomic sur-veys and ecological studies. Therefore, we believe that thecurrent study makes a valuable contribution towards im-proving basic information about the global distribution ofDNA sequence-based AMF taxa and will allow more de-tailed analysis of AMF distribution and diversity patterns.

Acknowledgments Elise Vanatoa assisted with laboratory proce-dures. We thank the numerous colleagues and institutions who offeredlocal support and contributed to field work: Christina Birnbaum, HelgeBruelheide, Jeff Cole, Karen J. Esler, Evelina Facelli, LarsGötzenberger, Andres Koppel, Eliane Louisanna, Eric Marcon, JohnMorgan, Jodi N. Price, Marina Semchenko, Sawarng Sitawan, EliseVanatoa, Wang Wei, CENAREST (Gabon) for granting us permissionto carry out research in Gabon and the ANPN for allowing us to carryout fieldwork in the national park of Monts de Cristal, GutianshanNatural Reserve (China), Western Cape Nature Conservation Board(South Africa), and Wildlife Conservation, Development andExtension Station, Phitsanulok (Thailand). Some of the voucher speci-mens of AM host plants are preserved in the herbarium of Botanicaland Mycological Museum (TU), Natural History Museum ofUniversity of Tartu. We are grateful to Herbier de Guyane (CAY) andin particular Dr. Jean-Jacques de Granville for kindly helping with plantidentification. This research was funded by grants from the EstonianScience Foundation (9050, 9157, 7738), targeted financing(SF0180098s08), the European Regional Development Fund (Center ofExcellence FIBIR), the Parrot project (14541QM) and the Hubert CurienPartnership between France and Estonia.

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