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Ectomycorrhizal-Dominated Boreal and Tropical Forests
Have Distinct Fungal Communities, but Analogous
Spatial Patterns across Soil Horizons
Krista L. McGuire
1
*, Steven D. Allison
2,3
, Noah Fierer
4,5
, Kathleen K. Treseder
2
1 Department of Biology, Barnard College, Columbia University, New York, New York, United States of America, 2 Department of Ecology & Evolutionary Biology,
University of California Irvine, Irvine, California, United States of America, 3 Department of Earth System Science, University of California Irvine, Irvine, California, United
States of America, 4 Department of Ecology & Evolutionary Biology, University of Colorado, Boulder, Colorado, United States of America, 5 Cooperative Institute for
Research in Environmental Sciences, University of Colorado, Boulder, Colorado, United States of America
Abstract
Fungi regulate key nutrient cycling processes in many forest ecosystems, but their diversity and distribution within and
across ecosystems are poorly understood. Here, we examine the spatial distribution of fungi across a boreal and tropical
ecosystem, focusing on ectomycorrhizal fungi. We analyzed fungal community composition across litter (organic horizons)
and underlying soil horizons (0–20 cm) using 454 pyrosequencing and clone library sequencing. In both forests, we found
significant clustering of fungal communities by site and soil horizons with analogous patterns detected by both sequencing
technologies. Free-living saprotrophic fungi dominated the recently-shed leaf litter and ectomycorrhizal fungi dominated
the underlying soil horizons. This vertical pattern of fungal segregation has also been found in temperate and European
boreal forests, suggesting that these results apply broadly to ectomycorrhizal-dominated systems, including tropical rain
forests. Since ectomycorrhizal and free-living saprotrophic fungi have different influences on soil carbon and nitrogen
dynamics, information on the spatial distribution of these functional groups will improve our understanding of forest
nutrient cycling.
Citation: McGuire KL, Allison SD, Fierer N, Treseder KK (2013) Ectomycorrhizal-Dominated Boreal and Tropical Forests Have Distinct Fungal Communities, but
Analogous Spatial Patterns across Soil Horizons. PLoS ONE 8(7): e68278. doi:10.1371/journal.pone.0068278
Editor: Jean Thioulouse, CNRS - Universite
´
Lyon 1, France
Received November 1, 2012; Accepted May 31, 2013; Published July 9, 2013
Copyright: ß 2013 McGuire et al. This is an open-access article distri buted under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Funding for this work came from the National Science Foundation grant to KKT, a NOAA Climate and Global Change Postdoctoral Fellowship to SDA,
and Doctoral Dissertation Improvement Grant to KLM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of
the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: kmcguire@barnard.columbia.edu
Introduction
Ectomycorrhizal (ECM) and saprotrophic fungi are major
contributors to nutrient cycling in forest ecosystems [1]. These
functional groups are globally distributed and coexist in many
forest ecosystems. Approximately 6000 tree species worldwide
depend on ECM fungi for nutrient acquisition [2], and the
distribution of ECM trees spans the globe ranging from northern
boreal regions to tropical rain forests. Strikingly, a disproportion-
ate number of the dominant trees in temperate, boreal and certain
tropical forests form ECM associations [3–6], suggesting that
ECM fungi are likely responsible for a significant quantity of C, N,
and P cycling worldwide. In boreal forests, ECM fungi contribute
up to 86% of total plant N [7]. Saprotrophic fungi are also critical
to nutrient cycling, and are the major decomposers of complex,
organic molecules such as lignin. Thus, understanding how ECM
and saprotrophic fungi are distributed within and across ecosys-
tems is critical for making inferences about nutrient cycling and
related ecosystem functions in forest communities.
It is well established that mycorrhizal fungi interact with other
soil organisms such as bacteria and invertebrates, but interactions
among mycorrhizal and decomposer fungi have been more
challenging to evaluate [1,8]. There is evidence from boreal and
temperate forests that ECM and saprotrophic fungal taxa
vertically segregate in soils [9–11], suggesting physiological
specialization of fungi on organic substrates in various levels of
decay [10]. However, there have been few studies of fungal spatial
dynamics in tropical ECM forests, so it is unclear if the patterns
detected in boreal and temperate forests are similar to those found
in the tropics.
While the majority of trees in temperate and boreal forests form
ECM associations, most species of trees in lowland tropical rain
forests form arbuscular mycorrhizal (AM) associations. When
tropical trees do form ECM symbioses, they are more likely to
become locally dominant [6] or in some cases regionally dominant
(e.g., the Dipterocarpaceae in Southeast Asia). At this point, we do
not know if generalizations can be made about ECM forests at a
global scale or if tropical ECM forests contain unique fungal
communities that function differently from ECM fungi at higher
latitudes. From the data that have been collected, it seems that
tropical forests have lower ECM diversity than temperate and
boreal ecosystems [12,13], although there is clearly a gap in our
knowledge and a paucity of belowground studies in tropical ECM
forests. Since tropical forests harbor 40% of all terrestrial biomass
and are responsible for 32% of terrestrial net primary production
[14,15], understanding the dynamics of fungal distribution and
function in tropical forests is important for making inferences
about global nutrient cycles.
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In this study, we used sequence-based approaches to assess the
distribution of fungal taxa in a tropical forest located in central
Guyana and a boreal forest in Delta Junction, Alaska. The tropical
forest site contained two types of rain forest: an ectomycorrhizal
monodominant forest and a non-ectomycorrhizal mixed forest
[16,17]. Our objectives were to: 1) examine the level of taxonomic
similarity in fungal community composition across the two ECM
forests in different biomes, 2) compare fungal community
composition across organic and mineral soil horizons within each
ecosystem, and 3) determine if patterns of functional group
separation across soil horizons were analogous in the boreal and
tropical forest. Since tropical ECM forest dynamics have been
shown to be significantly different than non-EM forests within the
same biome [6,18], we predicted that the boreal forest and tropical
ECM forest would exhibit more similar fungal community
patterns than the ECM and non-EM tropical forest.
Materials and Methods
Study Site and Sample Collection
Samples used for this study were collected from sites in Alaska,
USA (63u559N, 145u449W) and Guyana (5u49 N, 59u58 W)
between 2007 and 2009. In Alaska, the site consisted of boreal
spruce forest that has not burned in over 80 years [19].The forest
canopy was dominated by Picea mariana (Mill.) Britton, Sterns &
Poggenb. (Pinaceae), which forms ECM associations and com-
prises the vast majority of the canopy trees [19,20]. Likewise, the
ECM forest site in Guyana consisted of mature forest dominated
by the ECM tree Dicymbe corymbosa Spruce ex. Benth (Caesalpi-
niaceae), in which D. corymbosa comprises up to 90% of canopy
trees [16,21]. Dicymbe corymbosa was also the only ECM host in the
plots used for this study, thereby making it an ideal comparative
site to the Picea-dominated boreal forest. As a non-ECM
comparison, three plots from mixed forest in Guyana were also
analyzed, which do not contain dominant ECM species [17].
Since the majority of boreal forest trees are ECM, we did not have
a non-ECM forest comparison for the boreal biome. Permits for
the field research in Guyana were granted by the Guyana
Environmental Protection Agency and the Ministry of Amerindian
Affairs. The sites were not located on private or protected land and
did not involve endangered or protected species.
In each ecosystem, samples were separately collected from the
litter and upper soil horizons (0–20 cm) from previously estab-
lished plots at both sites. Plots in both sites were at least 100 m
apart. In the boreal forest, a total of three plots were sampled, with
each plot having dimensions of 10610 m. One composite soil
sample was derived from five soil cores taken from each plot.
These plots were also used as control sites in a previous study [22].
In the tropical forest, ten composite soil samples were taken from
three previously established forest plots (306100m) in the Dicymbe-
dominated forest [21]. At the same points of soil core sampling, we
also collected litter samples from the forest floor. Plot sizes across
sites were different, as these study sites were established
independently without the original intention of comparative
analyses. However, since samples were collected in a similar
manner and DNA was extracted with the same protocol, the
extracts were sequenced together for comparative analyses of
vertical fungal separation, rather than comparisons of fungal
species richness.
To evaluate litter fungi in a more controlled way so that only the
dominant tree litter was used, we set out freshly fallen leaf litter in
mesh bags on the forest floor at both sites. In the boreal forest, 4 g
air-dried Picea mariana leaf litter was placed in litter bags composed
of 2 mm mesh (window screen) lined with 0.5 mm mesh (bridal
veil) to prevent loss of needle fragments. Leaf litter in the mesh
bags from Guyana was composed of 10 g air-dried, freshly-fallen
D. corymbosa leaves. After one year of incubation on the surface of
the forest floor, decomposed litter from six bags in the boreal forest
and ten bags in the tropical forest was transported to the
laboratory, where it was frozen at –80uC until analysis. All samples
remained frozen during transport, which was less than 10 h for
both sites.
Molecular Analyses
To examine fungal community composition across ecosystems,
we first analyzed environmental soil and litter samples from the six
plots at each site using 454 pyrosequencing. To homogenize the
soil samples, each composite sample was passed through a 2 mm
sieve that had been sterilized with ethanol and 15 min of uv
radiation. All homogenizations were accomplished in a sterile,
benchtop PCR hood (AirClean Systems, Inc, Raleigh, NC). Litter
was hand homogenized with sterile gloves. Since the litter was
highly decomposed, mechanical grinding was not necessary. From
each composite soil and litter sample for each plot, total DNA was
extracted from three 0.25g subsamples to obtain a representative
sample [23] using a Powersoil DNA extraction kit (MoBio,
Carlsbad, CA) according to the manufacturer’s instructions. These
three DNA extracts were pooled to create one representative soil
DNA extract. General fungal primers (SSU817f and SSU1196r)
targeting a portion of the 18S rRNA gene were modified for 454
sequencing [24]. PCR amplifications were done as described
previously [24–26] with 30 mM of each primer (0.25
ml), 22.5 ml
Platinum PCR SuperMix (Invitrogen, Carlsbad, CA), and 3
mlof
DNA template. Three PCR reactions per sample were pooled for
analysis. PCR products were sequenced at the Environmental
Genomics Core Facility at the University of South Carolina
(Columbia, SC) on a Roche 454 Gene Sequencer with Titanium
chemistry. Sequenced amplicons were quality checked, aligned,
and grouped into operational taxonomic units (OTUs) at a 97%
sequence similarity cutoff with the Quantitative Insights Into
Microbial Ecology (QIIME) pipeline [27]. The centroid sequence
from each OTU cluster was chosen and used to create a
phylogenetic tree with the FastTree algorithm [28]. Taxonomic
information for each OTU was determined using the BLAST
algorithm [29] against identified sequences in both Genbank and
the SILVA database [30]. Ultimately, we used an open-reference
for OTU picking and sequences ,400 bp were removed. The
(phred) quality score cutoff was 25 and sequences containing
ambiguous characters and those having an unreadable barcode
were also removed. Non-fungal sequences were manually removed
following taxonomic assignment. The average sequence length was
,450 bp. Fungal sequences have been deposited in the Sequence
Read Archive of Genbank (Accession # SRP009079.1).
To gain more detailed taxonomic information about soil and
litter fungi, we used clone library sequencing on the same soil
samples analyzed in the pyrosequencing runs and on litter from
incubated leaf litter bags to standardize for litter species and
decomposition time. The same DNA extracts used in pyrose-
quencing were analyzed for the composite soil samples and DNA
from litter bags was extracted with a PowerSoil DNA kit (Mo Bio
Laboratories, Inc, CA) as described above. Three DNA extrac-
tions of each sample were again pooled for each site. Fungal DNA
was selectively amplified from soil and litter DNA extractions
using the ITS1-F forward primer [31] and the TW13 reverse
primer [32]. These primers target ,600 bp of the ITS region and
,700bp of the 59 portion of the 28S region. The reason for
choosing these primers is that amplification of the 28S region
allowed for alignment of amplicons and phylogenetic community
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analysis, whereas the hypervariable ITS region allowed for higher
taxonomic resolution at the subgeneric level [31,33]. PCR
reactions were carried out in 30
mL volumes with 200 mM Tris-
HCl PCR buffer, 1.23 mM MgSO
4
, 0.2 mM each dNTP, 0.5 mg
mL
21
BSA, 0.1 mM each primer, and 0.01 U mL
21
Platinum Taq
DNA Polymerase (Invitrogen, Carlsbad, CA), and 0.13
mL
template DNA uL
21
reaction volume. PCR reactions were done
in an iCycler thermocycler (BioRad) with the following program:
5 min initial denaturation at 95uC, followed by 35 cycles of 30 sec
at 95uC, 45 sec of annealing at 50uC, 6 min of elongation at 72uC,
and a final elongation for 10 min at 72uC. While these PCR
conditions are frequently used in the literature, we acknowledge
that the high cycle number may have skewed the mixed template
amplifications in favor of more abundant groups [34,35].
PCR products were gel-purified by running each sample on a
1.5% agarose gel; target bands were cut from the gel and cleaned
up with a Qiaquick gel extraction kit (Qiagen, Valencia, CA).
Clone libraries were constructed with the gel-purified PCR
products using the Topo TA Cloning Kit for Sequencing with
PCR 4-TOPO vector (Invitrogen) following the manufacturer’s
instructions. This vector allowed for blue/white colony screening,
such that only chemically competent E. coli cells that were white in
color were selected for sequencing. We picked 96 colonies from
each clone library, 384 sequences total for a total of 4 clone
libraries; one for each organic soil fraction in each ecosystem to
identify the dominant fungal taxa. Clones were bi-directionally
sequenced at the Laboratory for Genomics and Bioinformatics at
the University of Georgia (Athens, GA).
Raw DNA sequences were edited using using CodonCode
Aligner version 2.0 (CodonCode Corporation, Dedham, MA) and
Bioedit. Contiguous sequences were constructed for forward and
reverse DNA sequences using Geneious version 3.7.0 (Biomatters
Ltd., Auckland, New Zealand). Contiguous sequences have been
deposited to Genbank (Accession #: JN889716 - JN890544).
Alignments were made in ClustalW [36] using only the 28S
portion of the DNA, as the ITS portions are too variable for
alignment. Distance matrices were generated using the default
parameters of Phylip DNADIST [37]. 28S sequences having $
99% sequence similarity, as determined by DOTUR [38], were
assigned the same Operational Taxonomic Unit (OTU). OTUs
were assigned to taxa using the BLASTn algorithm against known
sequences in GenBank [29] and the UNITE database [39]. For all
BLAST searches the full consensus sequences were used, rather
than just the 28S portion, for better identification resolution. A
taxonomic name was assigned to an OTU only if the name
occurred within the top ten best BLAST matches, query coverage
was .95%, and the e-value was 0.0. If the top ten matches were
all ‘uncultured’ or ‘unidentified’, then ‘unknown’ was assigned to
the OTU. Chimeras were identified by separately BLAST
searching the 28S and ITS regions; if the top five hits in GenBank
did not match for both regions of DNA, the sequence was
considered chimeric and discarded. Functional group assignments
(saprobe, EM, pathogen, etc.) were given to OTUs with assigned
identities only if the taxonomic affiliation could reliably be placed
in a group where the majority of species are known to have that
particular function. For a few groups (notably Amanitaceae,
Entolomataceae, and Clavulinaceae), the ECM function was
assigned since it is the dominant function of that family or if the
OTUs aligned to genera known to be ECM, even though there are
some cases of fungal taxa in those families that can be saprotrophic
[40,41]. Ambiguous genera or families in which there was not a
predominant function were listed as unknown functional groups.
Statistical Analyses
To determine differences in fungal community composition
across soil and litter horizons in the forest plots, fungal sequences
were rarified to 1000 sequences [42] and proportional counts of
sequences per OTU group were then square-root transformed to
minimize the influence of rare taxa. OTU abundance data were
then analyzed by generating distance matrices with the Bray-
Curtis coefficient followed by Analysis of Similarity [ANOSIM;
43] using Primer-version 6 software (Primer-E, Plymouth, UK).
Nonmetric multidimensional scaling plots and dendrograms were
used to visualize similarity in fungal community composition
across sites and horizons. In the ECM forests at both sites, the
relative proportions of the most abundant ECM fungal families
were analyzed across sites and horizons (litter versus soil) using a
multivariate general linear model. For the clone library data, a
two-way ANOVA was used to assess differences in fungal
taxonomic richness between soil horizons and recently-shed litter
within and across ecosystems.
Results
We obtained 31,942 sequences from pyrosequencing with an
average of 1330 sequences per sample and approximately 450 bp
in length. Prior to downstream analyses, all non-fungal and
unclassifiable sequences were removed, which represented ap-
proximately 7% of the sequences. Thus, a total of 29,837
sequences were used for downstream analyses. Of the sequences
that could be identified as fungi from the 18S pyrosequencing
data, an average of 327 unique operational taxonomic units
(OTUs) were observed for each sample. Across all samples, 28% of
sequences were Ascomycota, 55% were Basidiomycota, 9% were
Chytridiomycota, 1% were Glomeromycota, 1% were basal fungal
lineages, and 5% could not be assigned to a phylum. The inability
to assign a phylum to these sequences may in part be due to the
presence of deeply diverging fungal lineages that have not yet been
characterized in Genbank [44].
Ordination of pyrosequencing data showed that fungal com-
munities were distinct across tropical and boreal ecosystems and
across horizons within site (Fig. 1A). These patterns were
confirmed by ANOSIM for both site (P = 0.02) and horizon (litter
versus soil) within site (Alaska P,0.01; Guyana P,0.001). When
fungal communities in the tropical forests were analyzed separately
from boreal samples, fungal taxa in the ECM forest were distinct
from the non-EM forest across horizons in each forest type
(P,0.001; Fig. 1B).
When pyrosequencing-derived fungal OTUs from soil and litter
samples were compared across tropical and boreal sites, the
proportional abundances of fungal phyla were significantly
different in litter samples (P,0.01 for all comparisons; Fig. 2).
However, the proportional abundance of fungal phyla in soil
samples were not significantly different, with the exception of the
Glomeromycota (F (1,11) = 7.4, P = 0.02), which was more
abundant in the tropical soils. The Ascomycota and Basidiomy-
cota comprised 80–90% of fungal OTUs in both horizons at both
sites. Thus, to determine if these phyla were differentially driving
the observed biogeographical patterns of fungi, we separately
analyzed OTUs assigned to each phylum. Cluster analysis showed
that fungi in both phyla displayed similar patterns across sites and
horizons (Fig. 3).
While pyrosequencing data provide limited taxonomic resolu-
tion as the sequences cannot be reliably identified beyond the
family level, some fungal families are exclusively or mostly ECM
and could be compared across ECM forests. In both boreal and
tropical ECM sites, there were six predominantly ECM fungal
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families that were among the 10 most abundant ECM families in
both litter and soil horizons collected from the plots (in terms of
sequence abundance), so these taxa were used to compare ECM
communities across the boreal and tropical ECM forests (the non-
EM tropical forest was excluded, as very few ECM taxa were
detected). The relative abundance of these six predominantly
ECM families was not significantly different across boreal and
tropical forests with the exception of the Clavulinaceae, which had
higher abundance in the tropical ECM forest (F (1,8) = 237.0;
P,0.001; Fig. 4). Of these six ECM families, there were
significantly more ECM taxa detected in soil compared to litter
for all families except for the Clavulinaceae, which had a high
relative abundance in the boreal litter.
Clone library sequencing generated a total of 119 unique OTUs
out of 329 analyzed sequences; 56 of these OTUs were detected
from the boreal forest and 63 were detected from the tropical rain
forest. From the original 384 sequences, a total of 55 sequences
were discarded based on poor sequencing quality and chimera
formation (5 chimeras detected). In both ecosystems there were
more Basidiomycota than Ascomycota in the O
a
(top mineral)
horizons, but approximately equal representation of Ascomycota
and Basidiomycota in the recently shed leaf litter. We found a total
of 44 Ascomycota (22 in the boreal forest, 22 in the tropical forest),
73 Basidiomycota (34 in the boreal forest, 39 in the tropical forest)
and 2 unknown taxa, both detected in the tropical forest.
Based on alignments unknown clone library sequences in
GenBank using the BLASTn algorithm [29], the dominant fungal
taxa were found to be distinct in each organic soil horizon. In the
boreal forest, the fungal community from the upper soil horizons
(0–20 cm) was dominated by the Agaricales, and particularly, the
Figure 1. Non-metric multidimensional scaling plot for pyrosequencing OTUs in litter and soil horizons based on Bray-Curtis
distance across sites (A) and across forest types (EM vs. non-EM) in the tropical ecosystem (B).
doi:10.1371/journal.pone.0068278.g001
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ECM genus Cortinarius. The next most abundant identified order
in the boreal soil was the Helotiales, from sequences closely
matched to pathogenic fungi. This order was also the second most
abundant order of fungi in the boreal litter bag samples. The most
abundant order on the boreal litter was the Microbotryomycetes
(incertae sedis), specifically Zymoxenogloea sp., of which little
ecological information is known. The Agaricales were the most
common order of fungi found in both the soil and litter bag
samples in the tropical rain forest.
Since ecological function could only be assigned to exclusively
ECM families in the pyrosequencing data, we used the informa-
tion from the clone library sequence identifications to determine
how the dominant ECM and non-EM fungi were distributed. We
were able to assign an ecological function (EM fungus, saprotroph,
pathogen, etc.) to 96 of the 119 unique OTUs from clone library
sequencing, as inferred from the GenBank sequence alignments.
We found that saprotrophs occurred in recently-shed leaf litter and
ECM fungi in underlying soil horizons (0–20 cm) in both the
boreal and tropical ecosystem (Fig. 5).
Discussion
While numerous sporocarp surveys have been done in tropical
forests [e.g., 45,46,47], our study provides some of the first
molecular evidence that confirms biogeographical separation of
fungal communities across a tropical and boreal forest, despite the
occurrence of dominant trees that form ectomycorrhizae in both
ecosystems. As has been found in other tropical ECM forests, the
major ECM fungal lineages reflect those already known to
dominate temperate and boreal ecosystems [48,49]. Additionally,
fungal communities were unique across soil and litter horizons
within the same ecosystem, possibly due to fungal specialization on
substrates in differing levels of decay [50]. Clone library
sequencing and pyrosequencing showed analogous results in both
ecosystems indicating that these patterns are robust to sequencing
technology and gene region targeted, which has been a major
concern among microbial ecologists [51]. While the clone library
sequencing gave more reliable taxonomic information for the
environmental DNA sequences (i.e., longer sequence reads), the
OTUs generated from pyrosequencing aligned to similar taxa,
probably as a result of incomplete coverage of fungal reference
sequences in Genbank. However, because pyrosequencing allows
for greater sequencing depth (for this study samples were rarified
to 1000 sequences each), we can more reliably say that we have
fully characterized the fungal community of a sample. Thus, the
tandem use of these technologies provides strong support for our
results in terms of fungal community characterization and
taxonomic placement of environmental sequences. Another result
supported by both pyrosequencing and clone library sequencing
was that within the tropical and boreal ECM forests, ECM fungi
were not prevalent in litter horizons from the forest floor, but
rather occupied lower organic and mineral soil horizons.
Findings that ECM fungi were more abundant in deeper soil
depths have also been observed in temperate [9] and Swedish
boreal forests [11], indicating that vertical segregation of ECM
Figure 2. Proportional abundances of fungal taxa from pyrosequencing data assigned to each phylum across sites and horizons.
Asterisks denote significance between boreal and tropical forests at P,0.05.
doi:10.1371/journal.pone.0068278.g002
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and saprotrophic fungi in soils may be a widespread phenomenon
in ECM-dominated forest ecosystems. The reasons for spatial
segregation of these fungal groups are likely due to the distribution
of C and nutrients in litter versus soil. Since ECM fungi have
decomposer abilities [52,53], but are not C-limited like other
saprotrophs due to their access to plant photosynthate, ECM fungi
may reside below the freshly-fallen litter layer in deeper horizons
to target substrates richer in other nutrients [54,55]. An
alternative, but not mutually exclusive explanation for the
predominance of ECM fungi in the soil horizons may be due to
antagonistic relationships between ECM and saprotrophic fungi
[56–58]. Since these fungal groups compete for some of the same
resources, they may vertically segregate to avoid competitive
exclusion [10].
Within the tropical ecosystem, pyrosequencing showed that soil
fungal communities were distinct between the ECM and the
diverse, non-ECM forests, indicating that at a local scale, the
presence of an ECM tree can dramatically alter the general fungal
community. The magnitude of differentiation in soil fungal
communities across these tropical forests was almost as dramatic
as the differentiation observed across biomes, and previous
research in this site has shown that soil physicochemical properties
are not responsible for determining these patterns [18]. Fungi
detected in forest floor litter were also clustered by forest type in
Figure 3. Dendrograms derived from cluster analyses are shown for pyrosequencing OTUs identified as Ascomycota (A) and
Basidiomycota (B). Similar clustering patterns by site (Guyana versus Alaska) and horizon was observed for fungal communities in both phyla.
doi:10.1371/journal.pone.0068278.g003
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the tropical system, although the magnitude of difference was
much less. This result may be due to the fact that the same species
of non-ECM trees are present in both tropical forests [16,21], so
the chemical composition of the leaf litter is somewhat similar
[18]. However, there is an overwhelming abundance of litter in the
ECM forest from the ECM, monodominant tree ( Dicymbe
corymbosa), which may explain the differences in the litter fungal
communities across these forests. In another study, a reciprocal
litter decomposition experiment has shown that leaf litter of
Dicymbe and non-ECM trees decomposes slower in the ECM forest
relative to the non-ECM forest [18], indicating that these
differences in fungal communities may result in altered nutrient
cycling.
In the boreal biome, the results of this study suggest that related
fungal taxa may dominate the organic layers in boreal forest soils
across different systems. For example, the genus Cortinarius was the
most abundant in our boreal soil samples, and this genus also
dominates Swedish boreal forest soil [11]. In an earlier study,
Allison et al. [59] also found that the ECM genus Cortinarius was
the dominant taxon from our Alaskan study site. Future work
focusing on the function of Cortinarius in decomposition would be
valuable, as it is a globally distributed genus and known to occupy
litter at late stages of decomposition [60]. However, other than
protease ability [61], its complete enzymatic capabilities are still
unknown. We also found Cortinarius taxa in the tropical samples,
Figure 4. Proportional abundances of sequences derived from 454 pyrosequencing (calculated on a per-sample basis using total
sequences as the denominator) of predominantly ectomycorrhizal fungal families found to be abundant in the boreal and tropical
forests. Different letters indicate significance levels at P,0.05.
doi:10.1371/journal.pone.0068278.g004
Figure 5. Distribution of clone library OTUs aligning with saprotrophic (black bars) and ectomycorrhizal fungi (white bars) across
organic litter and underlying soil horizons (0–20 cm) in the boreal and tropical forest ecosystems. OTUs for this analysis were derived
from clone library sequencing.
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although our sequence analysis indicated that they are different
genotypes than the boreal taxa.
Some of the ECM genera we detected from the Agaricales in
the tropical samples are known to associate with the dominant
ECM tree, Dicymbe corymbosa, as they have been described by
mycologists working in that region [62,63]. However, some of the
sequences we generated are likely undescribed taxa. This is
probably true for the numerous Clavulina species we observed from
the Cantharellales in the tropical soil, which was the second most
abundant order. Clavulina diversity is known to be high in this
region [64], which reflects what we detected in our environmental
pyrosequencing data.
The finding that fungal communities are distinct in litter
horizons also has implications for environmental sampling of
fungal communities. For a comprehensive understanding of
microbial community composition, sampling should incorporate
both the organic and underlying soil horizons. In addition,
environmental changes that affect one soil layer more than
another may have disproportionate consequences for the two
fungal groups. For instance, forest fires primarily burn the upper
soil horizons (depending on severity), so direct effects of fire may
be stronger on saprotrophic fungi than on ECM fungi. Making
inferences about fungal communities from only mineral samples
may, therefore, underestimate diversity and provide an incomplete
picture of community composition.
Acknowledgments
We thank Jennifer Martiny, Ivan Edwards, and Terry Henkel for valuable
intellectual contributions to this work. We are also grateful to the
Patamona Amerindian tribe in Guyana for assisting with field work,
Margaret and Malcolm Chana-Sue and Raquel Thomas for logistical
support, and the Guyana EPA for granting permits.
Author Contributions
Conceived and designed the experiments: KLM SDA KKT. Performed
the experiments: KLM SDA KKT. Analyzed the data: KLM SDA NF
KKT. Contributed reagents/materials/analysis tools: KLM SDA NF
KKT. Wrote the paper: KLM SDA NF KKT.
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