Research
High diversity of arbuscular mycorrhizal fungi in a boreal
herb-rich coniferous forest
Blackwell Publishing Ltd
Maarja Öpik1,2, Mari Moora2, Martin Zobel2, Ülle Saks2, Ron Wheatley1, Frank Wright3 and Tim Daniell1
1Scottish
Crop Research Institute, Invergowrie, Dundee DD2 5DA, UK; 2Department of Botany, Institute of Ecology and Earth Sciences,
University of Tartu, 40 Lai St., 51005 Tartu, Estonia; 3Biomathematics and Statistics Scotland, SCRI, Invergowrie, Dundee DD2 5DA, UK
Summary
Author for correspondence:
Maarja Öpik
Tel: +372 7376224
Fax: +372 7376222
Email [email protected]
Received: 13 March 2008
Accepted: 17 April 2008
• Here, the diversity of arbuscular mycorrhizal (AM) fungi was determined in a
boreal herb-rich coniferous forest in relation to environmental variables.
• Root samples of five plant species (Fragaria vesca, Galeobdolon luteum, Hepatica
nobilis, Oxalis acetosella and Trifolium pratense) were analysed from stands differing
in age and forest management intensity.
• Thirty-four Glomeromycota taxa (small-subunit ribosomal RNA gene (SSU rDNA)
sequence groups) were detected from 90 root samples (911 clones), including eight
new taxa. Sequence groups related to Glomus intraradices were most common
(MO-G3 and MO-G13). Samples of H. nobilis were colonized by more AM fungal
taxa (3.68 ± 0.31) than those of O. acetosella (2.69 ± 0.34), but did not differ significantly in this respect from those of F. vesca (3.15 ± 0.38). Effects of forest management,
host plant species (except above) or season on the number or composition of
fungal taxa in root samples were not detected, and neither were they explained by
environmental variables (vegetation, soil and light conditions).
• This is the most taxon-rich habitat described to date in terms of root-colonizing
Glomeromycota. The data demonstrate the importance of temperate coniferous
forests as habitats for AM fungi and plants. Lack of obvious fungal community patterns
suggests more complex effects of biotic and abiotic factors, and possibly no adverse
effect of common forest management practices on AM fungal diversity.
Key words: arbuscular mycorrhiza (AM), boreal forest, community structure,
diversity, forest management intensity, small subunit ribosomal RNA gene (SSU
rDNA).
New Phytologist (2008) 179: 867–876
© The Authors (2008). Journal compilation © New Phytologist (2008)
doi: 10.1111/j.1469-8137.2008.02515.x
Introduction
Arbuscular mycorrhizal (AM) fungi (Ph. Glomeromycota) are
ubiquitous plant root symbionts that can be considered as
‘keystone mutualists’ in terrestrial ecosystems, forming a link
between biotic and abiotic ecosystem components via carbon
and nutrient fluxes that pass between plants and fungi in the
soil (O’Neill et al., 1991). AM fungal diversity affects plant
community diversity and productivity (van der Heijden et al.,
1998). There can be large differences in functional complementarity between coexisting plants and AM fungi (Helgason
et al., 2002; Moora et al., 2004a,b). Therefore it is essential to
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understand the fine-scale structure and dynamics of AM
fungal communities in natural and managed ecosystems.
However, possible approaches to link the taxon diversity of
Glomeromycota communities with functional significance
are still under debate because of the practical difficulties of
working with such obligate symbiotic organisms (van der
Heijden & Scheublin, 2007).
The diversity and composition of intraradical AM fungal
communities vary among habitat types around the globe
(Öpik et al., 2006a). When comparing studies using the small
subunit ribosomal RNA gene (SSU rDNA) region to identify
AM fungi, the highest taxon richness in a single site has been
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868 Research
reported from tropical rain forest in Panama – 30 taxa in
the roots of three host species (Husband et al., 2002a,b; 29
according to the taxon synonymy used by Öpik et al., 2006a).
Taxon-rich fungal communities (over 20 taxa) are also known
from temperate grassland and forest locations (Vandenkoornhuyse et al., 2002; Saito et al., 2004; Helgason et al., 2007).
Tropical forest may exhibit significantly higher mean fungal
richness than other ecosystems (expressed as number of fungal
taxa per plant species: 18; Öpik et al., 2006a). By contrast,
human-impacted habitats such as arable fields (Helgason
et al., 1998; Daniell et al., 2001) or sites polluted with heavy
metals (Whitfield et al., 2004) may exhibit low AM fungal
taxon diversity. However, recent evidence of higher richness in
such habitats (Hijri et al., 2006; Vallino et al., 2006) suggests
that the relationship with management is complex.
AM fungal communities in deciduous forests of the temperate
zone have been described from the UK by Helgason and
colleagues (Helgason et al., 1998, 1999, 2002, 2007). They
have recorded > 20 AM fungal taxa from roots of six host
plant species in a single forest location. Nine taxa, most of
them unique, were detected in 25 pooled root samples from
a warm-temperate broadleaved forest in Japan (Yamato &
Iwase, 2005). However, the presence of AM fungi in coniferous forests has been largely ignored. Only two AM fungal
taxa were detected in the roots of Taxus baccata in a Norway
spruce (Picea abies) forest in Germany (Wubet et al., 2003)
and 10 taxa in the roots of two Pulsatilla species in a Scots
pine (Pinus sylvestris) forest in Estonia (Öpik et al., 2003).
To our knowledge, no further information (including that
from spore surveys) is available relating to AM fungal richness
in boreal forest ecosystems. The land area covered by boreal
forest constitutes one-third of the world’s forest and provides
important ecological functions as well as 20–50% of the
world’s pulp, newsprint, sawn wood, paper and paper board
(Reich et al., 2001). There is a considerable wealth of information regarding the diversity of ectomycorrhizal fungi –
the symbionts of the dominant overstorey plants in temperate/
boreal coniferous forests (Horton & Bruns, 2001; Johnson
et al., 2005; Tedersoo et al., 2006). However, in herb-rich
temperate/boreal coniferous forests, where there are plentiful
AM plant species, Glomeromycota should not be overlooked
as an ecosystem component. In order to obtain a good overview
of biodiversity in coniferous forests, more information about
AM fungi is required.
Different management practices may have significant impacts
on the diversity and composition of boreal forest plant communities (Reich et al., 2001; Ramovs & Roberts, 2003). The
impact of logging can directly influence vegetation via the
disturbance of soil or forest floor, altered habitat structure,
removal of nutrients, or altered microclimate (Roberts &
Gilliam, 1995; Bergeron & Harvey, 1997). Clearcut logging
may have a significant impact on ectomycorrhizal fungal communities (Jones et al., 2003). There is, however, no information
about the impact of boreal forest management on AM fungi.
New Phytologist (2008) 179: 867–876
In the present paper, we aimed to investigate the taxon composition and community structure of AM fungi in a herb-rich
boreal coniferous forest. In particular, we asked: (1) what is the
taxon richness and composition of the AM fungal communities
of the studied coniferous forest, and (2) what is the impact of
local environmental conditions and forest management (clearfelling and cultivating clear-cut areas versus old growth) on the
taxon richness and composition of AM fungal communities?
Materials and Methods
Study area
The study area is located at Koeru, central Estonia (58°58′N;
26°03′E). The landscape of the region is flat, consisting of a
mosaic of cultivated areas and forest. The climate is transitional
between maritime and continental. The mean annual precipitation
is 700–750 mm. The mean annual air temperature in the region
is 4.3–6.5°C, ranging between –7°C in January and 17.4°C in
July (Jaagus, 1999). The study area is a 130-ha patch of Hepatica
nobilis Mill. site type spruce forest (typification after Lõhmus,
2004). The soil is a calcaric cambisol (typification after Food
and Agriculture Organization of the United Nations (FAO)).
Soil conditions vary little across the study area (Zobel et al., 2007).
The predominant tree species is Norway spruce (Picea abies
(L.) H. Karst.) with Corylus avellana L. prevailing in the shrub
layer. Altogether 70 herbaceous vascular plant species have been
recorded in the field layer: Oxalis acetosella L., Fragaria vesca
L. and H. nobilis were the most abundant plant species;
Dicranum scoparium Hedw. and Cirriphyllum piliferum (Hedw.)
Grout were the most common bryophytes (Moora et al., 2007).
The area has been maintained as forest since at least 1828
(from map evidence). The forest has been managed with
clear-cutting in patches of approx. 1–2 ha. However, areas of
the forest can still be classified as old growth, with different
age classes present, and the oldest spruce trees being 130–140 yr
old. In these areas selective felling has been practised.
Root sampling
We sampled forest ecosystems of different age and management
intensity. Mature spruce forests with a heterogeneous canopy
represented old-growth stands, where the intensity of forest
management has been low and the ecosystems are close to
their natural state. Early successional stages were represented
by young dense stands in areas that were clear-cut 20–25 yr
ago and then replanted with Norway spruce. Young stands
have been thinned repeatedly since planting.
Both old and young forest stand types were replicated three
times on similar soil conditions. From each of the six stands,
plant roots were sampled from a 10 × 10 m plot divided into
1 × 1 m subplots. 1 × 1 m subplots were further divided into
six equal parts for six sampling times: the beginning of June,
end of July, and beginning of October 2003 and 2004.
www.newphytologist.org © The Authors (2008). Journal compilation © New Phytologist (2008)
Research
Thus, a 1 × 1 m subplot could be sampled six times consecutively without disturbing the soil and breaking the fungal
mycelium. The following five vascular plant species were
sampled for this study: O. acetosella (Oxalidaceae), H. nobilis
(Ranunculaceae) and F. vesca (Rosaceae), which were the most
frequent species in the field layer and present in abundance in
all succession stages; Galeobdolon luteum Huds. (Lamiaceae,
syn. Lamium galeobdolon), which was patchily distributed and
present only in two stands of old forest; and Trifolium pratense
L. (Fabaceae), which was present only in young stands. Entire
plants (several individuals if roots were very small) of each
species were excavated from the 1/6-m2 subplot if present and
placed in plastic bags. In the laboratory, roots were cleaned,
dried with silica gel and stored dry until analysis. Please note that
only two samples of each plant species per plot per sampling
time and the first three sampling times were used in this study
(Table 1).
Vegetation analysis and environmental conditions
In all 1 × 1 m subplots within six 10 × 10 m plots (600
altogether) we recorded the per cent coverage of all vascular
plant species in the field layer, but not the shrub layer, and the
total cover of all bryophytes before root sampling. In each
subplot, local environmental conditions were characterized as
follows. Topsoil samples (1–10 cm) were taken from the centre
of each subplot for the determination of pH and the content
of mineral nitrogen (N) ( NO3− -N, NO+4 -N and total N),
phosphorus (P) and dissolved organic material (DOC). Soil
pH was measured in 0.01 M CaCl2 (10 g of soil in a 50-ml
solution). DOC and mineral N were extracted from 10 g of
soil with 1 M KCl (soil:extractant ratio 1 : 4) and filtered
through Whatman No. 1 filter paper (Wheatley et al., 1989).
Available soil P was extracted using the sodium bicarbonate
(Olsen) method (Olsen et al., 1954). N, P and DOC concentrations were determined colourimetrically on a segmented
flow autoanalyser (Skalar Analytical, Breda, the Netherlands).
Light availability was estimated using photographs taken at
the height of 30 cm at the centre of each subplot with a Nikon
CoolPix 950 digital camera equipped with a hemispherical
lens. All photographs were taken at times when the sun was
blocked by clouds to ensure homogeneous illumination of the
overstorey canopy and correct contrast between canopy and sky.
We calculated an indirect site factor (ISF) and a direct site factor
(DSF) by using WinSCANOPY software (Regent Instruments
Inc., Québec, Canada) assuming the standard overcast sky model
(Anderson, 1966). ISF and DSF are defined as the proportion
of diffuse and direct radiation received below the tree canopy
as a fraction of that received above the canopy (Rich, 1990).
Molecular analyses
AM fungi were identified on the basis of sequence variation
within an SSU rDNA region in two individuals from each of
Table 1 Sampling scheme showing the number of plants successfully
analysed for molecular diversity of arbuscular mycorrhizal (AM) fungi
in roots
Plant species
Plot
Sampling time
Young stands
T
1
2
3
T total
R
1
2
3
R total
S
1
2
3
S total
Young stands total
Old stands
Z
1
2
3
Z total
W
1
2
3
W total
Y
1
2
3
Y total
Old stands total
Plant samples total
FV
GL
HN
OA
TP
Total
0
0
1
1
0
0
0
NP
NP
NP
NP
NP
NP
0
2
1
3
2
2
2
6
0
2
0
2
11
NA
NA
NA
2
0
2
4
5
2
2
2
6
2
2
2
6
1
2
2
5
17
2
4
4
10
4
6
6
16
4
5
6
14
40
2
2
2
6
2
1
2
5
0
2
2
4
15
20
0
2
1
3
NP
NP
NP
2
2
2
6
1
2
1
4
2
0
2
4
14
31
2
2
2
6
2
1
2
5
0
2
2
4
15
26
NP
NP
NP
0
1
2
3
6
6
0
2
2
4
0
1
2
3
7
NP
NP
NP
NP
NP
NP
NP
NP
NP
7
6
8
7
21
5
4
5
14
2
5
8
15
50
90
Two plants were subjected to analysis per sampling time, species and
plot, except for one sample each of HN from plot W at sampling times
1 and 3. Elsewhere values < 2 indicate no success in PCR or cloning.
FV, Fragaria vesca; GL, Galeobdolon luteum; HN, Hepatica nobilis,
OA; Oxalis acetosella; TP, Trifolium pratense. NP, plant species not
present in this plot; NA, not analysed. Plots (10 × 10 m) are
designated T, R, P in young stands and Z, W, Y in old stands.
five plant species, six stands and three sampling times
(Table 1); each plant individual was sampled once. A representative subsample (approx. 20 cm) of a root system of a
plant was pulverized with 1.1-mm tungsten carbide beads
(BioSpec Products, Inc., Bartlesville, OK, USA) with Mixer
Mill 301 (Retsch GmbH, Haan, Germany). DNA was then
extracted using the Nucleospin® 96 Plant kit (Macherey-Nagel,
Düren, Germany) eluting in a final volume of 100 µl. Following
optimization for template quantity, 5 µl of the DNA extraction
was used in a 25-µl volume PCR reaction containing Expand
High Fidelity Buffer (Roche Applied Science, Mannheim,
Germany) with 15 mM MgCl2, 100 nM of each of the
dNTPs, 200 nM of each of the primers NS31 and AM1
(Simon et al., 1992; Helgason et al., 1998), 20 mg ml–1 bovine
© The Authors (2008). Journal compilation © New Phytologist (2008) www.newphytologist.org
New Phytologist (2008) 179: 867–876
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serum albumin (BSA) and 0.7 units of Expand High Fidelity
enzyme mix (Roche Applied Science). Thermocycling conditions
were as follows: 94°C for 2 min; 10 cycles of 94°C for 15 s,
58°C for 30 s and 72°C for 45 s; 20 cycles of 94°C for 15 s,
58°C for 30 s and 72°C for 45 s + 5 s per cycle; 72°C for
7 min using a DNAEngine PTC Dyad thermocycler (MJ
Research, Reno, NV, USA). Positive PCR products were
purified using the MinElute PCR Purification kit (Qiagen,
Crawley, UK), cloned and sequenced following the method of
Griffiths et al. (2006). Purified PCR products were inserted
into the pGEM-T Easy vector (Promega, Madison, WI, USA)
and transformed into Escherichia coli DH10B electrocompetent
cells prepared in the laboratory following the method of Tung
& Chow (1995). From each sample, 16–32 colonies were grown
in 1 ml of 2 × Luria-Bertani (LB) broth with 0.15 mg ml–1
ampicillin in deep-well microtitre plates. Plasmids were purified
with a Multiscreen Plasmid Minipreparation Kit (Millipore,
Bedford, MA, USA) following the manufacturer’s instructions.
Sequencing was performed in a volume of 10 µl with a 1 : 8
dilution using the BigDye® Terminator v3.1 Cycle Sequencing
kit (Applied Biosystems, Warrington, UK) with vector primers
directed against the SP6 or T7 promoter regions. Sequencing
reactions were purified using 96- or 384-well Geneclean plates
(Genetix, Queensway, New Milton, UK) following the manufacturer’s instructions and run on an ABI Prism 3700 DNA
Analyzer (Applied Biosystems).
Phylogenetic sequence analyses
Raw sequences from 911 clones were aligned using the freeware
poa (Lee et al., 2002). First, all root-derived sequences were
submitted to neighbour-joining analysis (F84 model with
gamma substitution rates) implemented in TOPALi version 1
(Milne et al., 2004). From the apparent sequence groups
second strands of representative clones were sequenced; the
double-stranded sequences were submitted to a blast search.
Retrieved sequences of closely related ‘known’ fungi and
environmental samples, sequences of major clades of Glomeromycota, and double-stranded sequences obtained in this
study were then aligned automatically using the MAFFT
multiple sequence alignment web service in JalView
version 2.3 (Clamp et al., 2004). Representative sequences
of detected sequence groups were submitted to the European
Molecular Biology Laboratory (EMBL) Nucleotide Sequence
Database (accession numbers AM849253–AM849327).
Phylogenetic analysis of rRNA alignment can be significantly
improved by taking into account RNA secondary structure.
Analysis methods that model stem region as doublets (i.e. 16
possible states) taking base pairing into account are better than
simple four-state nucleotide models (Telford et al., 2005). We
therefore annotated each position in our alignment as belonging
to a loop or to a stem based on structure coordinates for the
Geosiphon pyriformis X86686 structure obtained from the
European ribosomal RNA database (http://bioinformatics.psb.
New Phytologist (2008) 179: 867–876
ugent.be/webtools/rRNA/). These coordinates were processed
into MrBayes nexus format using the Ystem python script
(Telford et al., 2005) which identified position type (stem or
loop) and the coordinates of pairs of nucleotides in the stem.
Loop regions were then modelled according to a standard
4 × 4 nucleotide substitution model. Model selection was based
on the six nucleotide substitution matrix models available in
the MrBayes software (Ronquist & Huelsenbeck, 2003) with
or without rate heterogeneity modelled by the gamma distribution. Rather than the conventional approach of comparing
these 12 models based on a single phylogenetic tree, comparisons
were based on a PhyML maximum likelihood tree (Guindon
& Gascuel, 2003) estimated for each model. Based on all model
selection criteria, the general time-reversible model with gamma
distribution of remaining sites (GTR + G) was chosen. Stem
regions were modelled according to the doublet model available in MrBayes which includes a rate heterogeneity term.
A preliminary Bayesian inference (BI) analysis using MrBayes
software revealed that the Markov Chain Monte Carlo (MCMC)
steady state was reached after less than 50 000 generations. A
conservative burn-in of 250 000 generations was chosen and
a full analysis of 750 000 generations was carried out with
sampling every 1000 generations, resulting in 1000 trees from
two independent runs. The potential scale reduction factor
(PRSF) values of all 35 parameters were less than 1.11 (31 had
values < 1.03) suggesting good convergence (i.e. less than a
PRSF threshold of 1.2 as suggested by Gelman et al., 1995)
of the two runs. Another convergence diagnostic, the standard
deviation of split frequencies between simultaneous runs, was
close to zero (< 0.03), confirming convergence. The tree was
rooted with Geosiphon pyriformis, Paraglomus brasilianum and
Paraglomus occultum.
Statistical data analyses
Effects of forest management intensity (stand type), host
plant species identity and sampling time on the number of
AM fungal taxa in root samples were estimated using linear
mixed models with the residual maximum likelihood method
(REML). Forest management (two levels), plant species (three
levels), and sampling time (three levels) were included as fixed
factors and site (three levels) as a random factor nested in
forest management type. Raw counts were analysed. A square
root transformation improved the residual plot characteristics
only slightly and did not affect significance patterns.
The variation in AM fungal community composition was
analysed by principal coordinates analysis (PCoA, or metric
multidimensional scaling). PCoA is a multivariate technique
that allows placing of nonmetric or semimetric distances (sample
similarities) into Euclidean space, so that a linear ANOVA
model can be applied to the obtained PCoA axis values (see
Legendre & Anderson, 1999). Sample similarity matrixes
were calculated using ‘ecological’ (1 – |xi – xj|/range unless
xi = xj = 0) and Jaccard (if xi = xj = 1, then 1; if xi = xj = 0, then
www.newphytologist.org © The Authors (2008). Journal compilation © New Phytologist (2008)
1
1
1
1
2
2
2
3
4
4
4
4
5
5
5
5
5
7
8
10
12 12
13
15
24 15
27
Fungal taxa are ordered by frequency of occurrence across all samples. FV, Fragaria vesca; GL, Galeobdolon luteum; HN, Hepatica nobilis, OA; Oxalis acetosella; TP, Trifolium pratense. Fungal taxon codes
are as in Fig. 1, excluding the MO prefix.
274 151 108 54 54 45
Total
0
1
0
0
1
0
0
1
0
1
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
2
0
0
2
0
0
0
0
0
0
0
3
0
3
0
2
0
1
3
0
0
0
0
0
0
3
0
0
3
0
1
0
1
2
0
1
0
0
1
0
2
0
0
2
0
0
0
0
0
0
0
0
0
0
0
3
1
0
4
0
4
0
0
4
0
2
0
0
2
0
2
0
1
3
2
1
1
0
4
0
11
3
1
15
0
0
6
2
8
1 4
2 8
0 0
0 0
3 12
0
0
0
0
0
0
4
0
0
4
911
28
8 0
26 20
13 9
5 3
52 32
28
20
25
15
88
32
31
59
25
147
Young stands
FV
HN
OA
TP
Total
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
2
0
0
2
0
0
0
0
0
0
2
0
0
2
0
0
0
0
0
0
0
1
0
1
0
0
1
3
4
1
0
0
0
1
0
0
2
0
2
0
1
3
0
4
2
0
0
1
3
2
0
1
2
5
0
0
0
5
5
1
1
1
0
3
1
0
3
2
6
20 2
15 8
4 8
15 6
54 24
0
0
5
0
5
16
0
5
4
25
0 7
0 0
1 0
20 4
21 11
0
0
0
0
0
5
0
0
0
5
0
0
9
0
9
0
0
0
0
0
0
0
3
5
8
1
0
0
0
1
77
180
124
60
441
0
18
1
4
23
0 1
0 16
0 2
0 2
0 21
0 6
8 4
36 9
12 3
56 22
21
14
9
19
63
57
19
30
21
127
Old stands
FV
GL
HN
OA
Total
142
75
131
122
470
G3 G13 G7 G4 G5 G20 G33 G18 G2 G21 G27 G17 A5 G30 G15 G26 G28 A6 A7 GC1 GC2 G16 A3 G24 G25 G31 G22 G10 G19 G32 A4 G11 G23 G29 Total
Plant species
Table 2 Number of clones of arbuscular mycorrhizal (AM) fungal taxa detected in studied plant species in old and young forest stand types
Research
0; if xi ≠ xj, then 0) similarity measures for relative abundance
and presence/absence data, respectively. Relative abundances
of fungal taxa were obtained by dividing the number of clones
belonging to the given taxon by the number of clones
sequenced in the sample. The experimental factors (forest
management type, site, host plant species, and sampling time)
were visualized on the plots of PCoA axis scores using different
colours. As no clear patterns were observed, no further statistical
analyses were applied.
The relations of the environmental variables (ISF, DSF, soil
NO3− -N and NO+4 -N, P, pH and DOC), cover of bryophytes
and cover and richness of vascular plants in a 1 × 1 m subplot
with the number of AM fungal taxa or the fungal community
composition in root samples were assessed by plotting the
values for the number of AM fungal taxa or scores of the first
five PCoA axes against the above variables, again visualizing
the experimental factors using colours. Again, no clear patterns
were observed and no further statistical analyses were applied.
All above statistical analyses were implemented in GenStat
version 10 using only samples from the three plant species
(F. vesca, H. nobilis and O. acetosella) that occur in all study
sites; site-specific G. luteum and T. pratense were not included
in these analyses.
The effect of sampling effort on fungal taxon accumulation
was assessed by calculating the number of detected fungal taxa
(Sobs) as a function of the number of samples using EstimateS
version 7.5.1 (Colwell, 2005) based on presence/absence of
fungal taxa in individual samples of the five plant species.
Results
AM fungal taxa
We recorded 34 AM fungal SSU rDNA taxa in the roots of
five host plant species (90 plant samples; 911 clones
sequenced) in the Koeru boreal forest. These taxa comprised:
five Acaulospora, two Glomus group C, and 23 Glomus s. str.
sequence groups (Fig. 1, Table 2). Eleven sequence groups
clustered with a known species or isolate, with two of these
not having been registered from environmental samples
(roots) previously; 15 groups have been previously detected
from plant roots, but were not represented in sequence
databases by any known species or isolate; eight groups
represented previously unknown taxa. Altogether 23 sequence
groups detected in this study have been previously recorded
from environmental samples.
Variation in AM fungal taxon richness
In total we identified 26 AM fungal taxa from the roots of
H. nobilis (31 individuals), 20 taxa from F. vesca (20), 21 taxa
from O. acetosella (26), 11 taxa from T. pratense (7) and 11
taxa from G. luteum (6). Sampling effort curves (Fig. 2) indicate
a trend for the actual number of fungal taxa associated with
© The Authors (2008). Journal compilation © New Phytologist (2008) www.newphytologist.org
New Phytologist (2008) 179: 867–876
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Fig. 1 Glomeromycota sequences detected in this study and database sequences of known Glomeromycota and of those from environmental
samples of the small-subunit ribosomal RNA gene (SSU rDNA) fragment between the NS31 and AM1 primers. A Bayesian analysis with a general
time-reversible model with gamma distribution of remaining sites (GTR + G) is shown.
New Phytologist (2008) 179: 867–876
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Research
Table 3 Percentage variation explained by the first five axes of
principal coordinate analyses of arbuscular mycorrhizal fungal
community composition in root samples of Fragaria vesca, Hepatica
nobilis and Oxalis acetosella using relative abundance or presence/
absence data
Relative abundance
Presence/absence
Fig. 2 Expected arbuscular mycorrhizal (AM) fungal taxon
accumulation curves (Mao Tau) of the studied plant species. One may
observe that a larger number of root samples could have increased
the number of fungal taxa detected in the host species Galeobdolon
luteum and Trifolium pratense. Plant species codes: FV, Fragaria
vesca; GL, Galeobdolon luteum; HN, Hepatica nobilis, OA; Oxalis
acetosella; TP, Trifolium pratense.
H. nobilis to be higher than that of other host species. One can
expect that more samples of T. pratense and G. luteum would
have revealed more fungal taxa associated with these plant
species, as the curves follow those of F. vesca and O. acetosella,
showing higher total numbers of AM fungal taxa.
A mean of 3.17 (± 0.24; SE of the estimate) AM fungal taxa
colonized a root sample for the three abundant plant species
(F. vesca, H. nobilis and O. acetosella). The effect of plant
species identity on the number of fungal taxa per plant individual was marginally nonsignificant (P = 0.056). The
estimated mean number of fungal taxa per sample of H. nobilis
(3.68 ± 0.31; mean ± SE of the estimate) was higher than that
per sample of O. acetosella (2.69 ± 0.34), but did not differ
significantly from that per sample of F. vesca (3.15 ± 0.38).
Forest management intensity (stand type), sampling time, and
their interaction had no significant effect on the AM fungal
taxon richness in samples. We could not detect any relations
between the number of AM fungal taxa and explanatory environmental variables (data not shown). The two stand-specific plant
species, G. luteum and T. pratense, were not included in this
model; a mean of 3.5 (± 1.38, SD) and 3.71 (± 1.60) AM fungal
taxa were observed per sample for these species, respectively.
Variation in AM fungal community composition
Principal coordinate analysis (PCoA) of fungal community
composition based on presence/absence and relative abundance
data for AM fungi in root samples did not yield obvious
1
2
3
4
5
14.05
16.02
7.03
7.34
6.49
6.52
5.68
5.14
4.39
4.89
groupings of samples; neither were there patterns relating to
forest management type, site, plant species or sampling time.
The percentage variation described by the first five PCoA
axes was 39.91 and 37.64% for presence/absence and relative
abundance data, respectively (Table 3). We could not detect
any relations between the fungal community composition
and explanatory environmental variables. Example plots of
first PCoA axis against plant cover, plant species richness,
soil P and N content, soil pH and light availablity (ISF) are
presented in Supplementary Material Fig. S1.
Five of the 34 detected AM fungal taxa occurred in the
roots of all studied plant species and in all plots: Glomus sp.
MO-G3 (in 60% of samples/30% of clones, related to the
Glomus intraradices group), Glomus sp. MO-G13 (60/17%,
related to Glomus vesiculiferum), Glomus sp. MO-G4 (30/6%),
Glomus sp. MO-G7 (30/12%, related to Glomus hoi), and
Glomus sp. MO-G20 (20/5%). G4 and G7 were half as
frequent (in terms of the proportion of samples colonized) in
spring as in summer and autumn (data not shown). Eight
fungal taxa were detected from one host species only, but were
all represented by only one or two clones or samples. Seven
and eight taxa were detected from young and old stands only,
respectively. Most frequent among these, occurring in > 5%
of root samples, were Glomus sp. MO-G27 (young stand),
Glomus sp. MO-G5 (old stand), and Glomus sp. MO-GC1
(old stand).
Discussion
Communities of AM fungi colonizing the roots of five understorey plant species in an Estonian herb-rich boreal coniferous
forest were found to be remarkably rich. We recorded 34 AM
fungal taxa in total, comparable to the fungal richness described
in tropical rain forests in Panama (Husband et al., 2002a,b)
and higher than that in temperate grassland and broad-leaved
forest locations (Vandenkoornhuyse et al., 2002; Saito et al.,
2004; Helgason et al., 2007). To our knowledge the only
other boreal or temperate forest systems where AM fungal
community dynamics has been studied are a Scots pine forest
in Estonia, a Norway spruce forest in Germany, a warmtemperate broadleaved forest in Japan, and a broadleaved
forest in the UK (Helgason et al., 2002, 2007; Öpik et al.,
2003; Wubet et al., 2003; Yamato & Iwase, 2005).
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New Phytologist (2008) 179: 867–876
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874 Research
The number of samples in our study is higher than (90 vs
20–54), and the number of sequenced clones comparable to
or lower than (911 vs 558–2001), those in the studies cited
above. Forty-one taxa were reported from roots of three host
species from tropical forest and pasture locations in Costa
Rica, although forest-specific richness cannot be deduced from
these data (Aldrich-Wolfe, 2007). The number of root samples
processed can significantly affect the number of root-colonizing
AM fungi detected (Öpik et al., 2006a). Furthermore, because
of the patchy distribution of colonization units, small subsamples
of a root system can contain different AM fungi (Öpik et al.,
2006b). Here the individual samples were large (20 cm length)
but contained on average three AM fungal taxa in an ecosystem
supporting at least 34 taxa. This indicates a trade-off between
the number of samples and the size of a root sample needed
to reasonably describe the diversity of root colonizers in an
ecosystem, an issue that needs to be addressed in future field
surveys. In conclusion, care is required when comparing
AM fungal richness data acquired with different methodologies,
including differences in sample number, screened/sequenced
clone number, spatiotemporal sampling design, and number
of sampled plant species.
In this study we identified eight previously undescribed
AM fungal taxa and 23 taxa that are known from molecular
analysis of plant roots only. This high proportion of ‘knownas-sequence-only’ taxa reflects the accumulation of molecular
diversity data of AM fungi as a result of the increasing number
of studies of Glomeromycota in natural ecosystems. More
matches with known fungal species would be expected if or
when more intensive sequencing, of relevant genome regions,
of identified isolates maintained in culture collections occurs.
The taxon richness of AM fungi per root sample was higher
for H. nobilis than for F. vesca and O. acetosella. However,
there were no differences among forest management types or
seasons (sampling times). It has been shown that co-occurring
plant species can be colonized by AM fungal communities of
different composition (Helgason et al., 2002; Vandenkoornhuyse
et al., 2002), but differences in the number of fungal taxa
associated with host plant species have not been previously
demonstrated. We can hypothesize that these trends are linked
to the host preferences of AM fungi, different symbiont ranges
of AM plant hosts, or different sizes of fungal colonization
units in roots resulting in variable fungal taxon densities.
Variable symbiont ranges of plants could be related to plant
functional types, for example life forms, plant growth rates,
rooting traits and types of clonal growth. However, why
fungi or plants should ‘prefer’ one host to another requires
further research.
There is evidence that disturbance can decrease AM fungal
taxon richness (Helgason et al., 1998; Whitfield et al., 2004).
Therefore, we expected to observe smaller numbers of AM
fungal taxa in the more intensively managed young forest stands.
However, these ecosystems have preserved a high richness and
the common management practices have not had an adverse
New Phytologist (2008) 179: 867–876
impact on the AM fungal biodiversity. In contrast to the findings
of Helgason et al. (1998) and Whitfield et al. (2004), habitats
with moderate management intensities may still display rather
high numbers of AM fungal taxa (Hijri et al., 2006; Vallino
et al., 2006). In the studied ecosystem the soil disturbance
associated with clearcut logging and planting of tree saplings
is less intense than that associated with recurrent ploughing,
which in combination with no soil disturbance during subsequent years may aid maintenance of fungal diversity through
management activity. Thus we propose that the severity and
recurrence of disturbance events influence the magnitude of
the reaction of AM fungal communities following disturbance.
Furthermore, it is worth noting the lack of clear management- or
environment-related patterns of AM fungal communities in soil
despite the obvious differences among these forest ecosystems
apparent to the naked eye. The (lack of) variability in diversity of
soil micro-organisms in relation to common disturbances and
natural environmental gradients warrants further investigation.
The two most common fungi in our study, Glomus sp.
MO-3 and MO-13, are related to the G. intraradices group,
which has been detected from world-wide locations of both
stable and disturbed ecosystems (Öpik et al., 2006a) and many
host species (Helgason et al., 2007). Glomus intraradices is
sometimes considered to be an aggressive species. It can depress
plant growth even if it provides all the P acquired by the plant
(Smith et al., 2003). Glomus intraradices isolates from the same
or geographically distant locations can affect plant growth differentially (Hart & Reader, 2002; Koch et al., 2006). Such variation
may be explained if this group contains several functionally
different taxa as proposed by van der Heijden et al. (2004).
Thus, it is reasonable to hypothesize that the G. intraradices
species group, as identified using the NS31/AM1 primer
pair, contains several cryptic taxa with differences in various
ecological properties such as disturbance tolerance, mycelial
growth, root colonization rate and sporulation traits. Even if
it is considered as a group of multiple taxa, this is the most
common group in both the studied boreo-nemoral forest and
many ecosystems world-wide, and deserves further attention.
The third most common fungus in the studied ecosystem
was G. hoi (here Glomus sp. MO-G7), detected in one-third
of samples and all plots and host species. A host specificity of
G. hoi towards Acer pseudoplatanus has been demonstrated in
one ecosystem (Helgason et al., 2002), but otherwise the taxon
appears to be widespread with no specialization in regard to
habitat type or host species (Öpik et al., 2006a; Helgason
et al., 2007).
An old stand-specific taxon, Glomus sp. MO-G5, has been
previously reported from forests and grasslands, but not from
disturbed habitats (Öpik et al., 2006a), and from 11 host
species (as Glo2; Helgason et al., 2007). MO-G5 was dominant
in experimental plants inoculated with boreal Scots pine forest
soil (Öpik et al., 2003), and in natural grassland habitats
(Öpik et al., 2006a). Here, the taxon was detected in c. 10%
of samples.
www.newphytologist.org © The Authors (2008). Journal compilation © New Phytologist (2008)
Research
As regards the methodology, it is obvious that the SSU
rDNA gene region used in this study does not separate all
Glomeromycota groups very well because of limited nucleotide
variation among some clades, including the G. intraradices and
G. caledonium clades (Fig. 1). Investigations of mitochondrial
large subunit (LSU) sequences, including that of G. intraradices,
suggest that better marker regions with more interspecific
variability might be available (Raab et al., 2005). However, these
have not been rigorously tested on a range of related taxa, on
isolates of the same taxa and on environmental samples. Apart
from these imperfections the SSU region provides comparability
of data for ecological studies as a result of the accumulation
of database entries and publications based on this gene region
(Öpik et al., 2006a).
In conclusion, the observed unexpectedly high richness of
Glomeromycota in a temperate coniferous forest indicates the
need to obtain comparable descriptive soil fungal community
data from a more diverse range of ecosystems. This almost
unique richness of Glomeromycota could be speculatively
attributed to the relative stability of the ecosystem and/or a
high diversity of host species. Comparative data from a range
of ecosystems along disturbance and plant richness gradients
and from a range of plant hosts would help to test these
hypotheses. The described taxon richness patterns and apparent
lack of taxon composition patterns deserve further evaluation
in order to establish the role of host plant identity, plant species
richness, light availability, and soil conditions as determinants
of Glomeromycota taxon distribution at a small scale. Furthermore, it is essential to evaluate the observed diversity patterns
in functional terms.
Acknowledgements
MÖ received short-term scholarship from the European
Molecular Biology Organisation (EMBO) and Kristjan Jaak
scholarship from the Archimedes Foundation (Estonia) for
visits to SCRI, UK. The study was supported by Estonian
Science Foundation grants 6533, 7366 and SF0180098s08,
EU FP6 integrated project ALARM (GOCECT-2003506675) and EU Marie Curie Fellowship grant MEIF-CT2005-024657 (MÖ). TJD acknowledges the support of the
Scottish Government Rural and Environment Research and
Analysis Directorate (RERAD). We are grateful to Lauri
Laanisto and Eve Eensalu (UoT) for their help with taking
fish-eye photographs and calculating the light parameters of
the study sites, and to James McNicol (BIOSS) for help with
statistical analyses.
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Supplementary Material
The following supplementary material is available for this
article online:
Fig. S1 (a) Principal coordinate analysis of arbuscular mycorrhizal (AM) fungal community composition based on (a)
relative abundance data and (b) presence/absence data, scores
of axis 1, vs selected environmental variables.
This material is available as part of the online article from:
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10.1111/j.1469-8137.2008.02515.x
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Please note: Blackwell Publishing are not responsible for the
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