FEMS Microbiology Ecology, 92, 2016, fiw061
doi: 10.1093/femsec/fiw061
Advance Access Publication Date: 16 March 2016
Research Article
RESEARCH ARTICLE
Temporal distance decay of similarity of
ectomycorrhizal fungal community composition
in a subtropical evergreen forest in Japan
Shunsuke Matsuoka1,∗ , Eri Kawaguchi2 and Takashi Osono1
1
Center for Ecological Research, Kyoto University, Hirano 2-509-3, Otsu 520-2113, Shiga, Japan and
Department of Ophthalmology and Visual Sciences, Graduate School of Medicine, Kyoto University,
Kyoto 606-8507, Japan
2
∗
Corresponding author: Center for Ecological Research, Kyoto University, Hirano 2-509-3, Otsu 520-2113, Shiga, Japan. Tel: +81-77-549-8240;
Fax: +81-77-549-8201; E-mail: [email protected]
One sentence summary: Successive sampling of ectomycorrhizal fungi over a 2-year period demonstrated that the community composition of
ectomycorrhizal fungi changed with time independently of season or year.
Editor: Ian Anderson
ABSTRACT
Community compositions of ectomycorrhizal (ECM) fungi are known to show spatial distance decay of similarity, which
arises from both deterministic niche-based processes and stochastic spatial-based processes (e.g. dispersal limitation).
Recent studies have highlighted the importance of incorporating the spatial-based processes in the study of community
ecology of ECM fungi. However, few studies have investigated the temporal distance decay of similarity of ECM fungal
communities. More specifically, the role of stochastic temporal-based processes, which could drive the temporal distance
decay of similarity independently of niche-based processes, in the temporal variation of the communities remains unclear.
Here we investigated ECM fungi associated with roots of Castanopsis sieboldii at 3-month intervals over a 2-year period. We
found that dissimilarity of the ECM fungal community composition was significantly correlated with temporal distance but
not with environmental distance among sampling dates. Both climatic and temporal variables significantly explained the
temporal variation of the community composition. These results suggest that temporal variations of ECM fungi can be
affected not only by niche-based processes but also by temporal-based processes. Our findings imply that priority effects
may play important roles in the temporal turnover of ECM fungal community at the site.
Keywords: fungal community; ectomycorrhiza; niche-based process; temporal process; species richness; temporal variation
INTRODUCTION
Ectomycorrhizal (ECM) fungi live in mutualistic association with
fine roots of tree species belonging to such families as Fagaceae,
Pinaceae, Betulaceae, Salicaceae, Polygonaceae and Dipterocarpaceae, which can be found in a wide range of forest ecosystems throughout the world (Brundrett 2009). Because host trees
depend largely on ECM fungi for their acquisition of soil nutrients, ECM fungi are key elements in forest ecosystems con-
trolling the cycling of carbon and nutrients and the dynamics
of forest communities (Smith and Read 2008). Previous studies
have demonstrated that the structure of ECM fungal communities varies with space (e.g. Tedersoo et al. 2003; Bahram et al. 2012;
Põlme et al. 2013) and time (e.g. Buée, Vairelles and Garbaye 2005;
Izzo, Agbowo and Bruns 2005; Smith, Douhan and Rizzo 2007).
Among such community structural features, spatial variations
of species richness and community composition of ECM fungi
Received: 2 December 2015; Accepted: 11 March 2016
C FEMS 2016. All rights reserved. For permissions, please e-mail: [email protected]
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 5
have received particular attention and have been related to spatial variations of such deterministic niche-based factors as the
host taxa (Ishida, Nara and Hogetsu 2007; Tedersoo et al. 2013),
soil type and nutrient availability (Toljander et al. 2006; Peay et al.
2010), and soil depth (Landeweert et al. 2003; Genney, Anderson
and Alexander 2006).
The spatial distance decay of similarity, diminishing similarity of community compositions with increasing geographic
distance, arises not only from deterministic niche-based processes, or changes in environmental conditions with increasing geographic distance, but also from stochastic spatial-based
processes. The spatial-based processes are caused by intrinsic
processes of individual organisms such as dispersal limitation
(Hubbell 2001; Cottenie 2005; Peay, Garbelotto and Bruns 2010;
Peay et al. 2012). Recent studies have investigated ECM fungal
community, focusing on the spatial structure, and separated the
relative effects of niche-based processes from those of spatialbased processes on the spatial variation of the community. The
results of these studies have revealed that these two processes
can drive the community dynamics at the same time (Tedersoo
et al. 2011; Bahram et al. 2013; Põlme et al. 2013) and that spatialbased processes could sometimes explain the spatial variation
of ECM fungal communities better than niche-based processes
(Tedersoo et al. 2011; Bahram et al. 2013). The importance of incorporating spatial-based processes in the study of community
ecology of ECM fungi is a focus of current research, and neglecting the effects of spatial-based processes would result in
overlooking crucial patterns in the community (Cottenie 2005;
Bahram et al. 2013).
In contrast to spatial variations, few studies have focused
on the temporal structure of ECM fungal community composition and separated the effects of deterministic niche-based processes (i.e. temporal changes in environmental conditions) and
stochastic temporal-based processes on the temporal variation
of ECM fungal communities. Previous studies have shown the
relationships between temporal changes in ECM fungal communities and niche-based processes. For example, Bruns (1995)
postulated that ECM fungal communities respond rapidly to seasonal fluctuations in temperature and moisture. In fact, seasonal
shifts of ECM fungal communities have been reported (Buée,
Vairelles and Garbaye 2005; Koide et al. 2007; Courty et al. 2008;
note that Koide et al. found a seasonal signal only with extraradical hyphae but not with root tips). Buée, Vairelles and Garbaye
(2005) suggested that the seasonal shift of ECM fungal community was related to the season, temperature and soil moisture.
These studies imply that the temporal structure of ECM fungal community composition is expected to show a cyclic pattern when examined across multiple years because of the effect of cyclic changes in seasonal variables (e.g. temperature).
In contrast, Smith, Douhan and Rizzo (2007) demonstrated that
few ECM fungal species showed seasonal patterns of occurrence
despite the strong seasonal changes in environmental conditions such as temperature, and that the community gradually
changed over their sampling period.
The findings of Smith, Douhan and Rizzo (2007) implied a
possible role of temporal-based processes, which could lead to
temporal distance decay of similarity of the ECM fungal community. The biological processes responsible for the temporalbased process are not fully understood but may include effects
of the sequences of species arrival on community structures,
which are commonly referred to as ‘priority effects’ (Alford and
Wilbur 1985; Chase 2003; Dickie et al. 2012; Fukami 2015). That
is, once an ECM fungus colonizes a root tip, the fungus persists for a given period and prevents colonization of later arrivals
through preemption of shared resources (Kennedy 2010; Fukami
2015). Therefore, if community assembly is driven by temporalbased processes, community compositions could show temporal distance decay of similarity that reflect community assembly history (i.e. sequences and timing in which species join a
community; Chase 2003; Dickie et al. 2012; Fukami 2015), independent of seasonal fluctuations of environmental conditions.
However, previous studies have addressed temporal gradients of
ECM fungal communities only within a single year, and it therefore remains unclear whether the temporal structure shows
the cyclic pattern across multiple years or whether the temporal structure shows the temporal distance decay of similarity.
Studies conducted across multiple years could provide a critical
step toward distinguishing the stochastic temporal-based processes (which is seemingly akin to the spatial variation; Bahram
et al. 2013; Bahram, Peay and Tedersoo 2015) from deterministic
niche-based processes.
The objective of our study was to investigate the temporal
structure and the contribution of temporal-based processes to
the temporal variations of ECM fungal community composition.
We conducted successive sampling of ECM fungi at 3-month intervals over a 2-year period in a subtropical forest. We also measured environmental and climatic variables and temporal and
spatial distances between samples. We thereby tested following
two hypotheses: (1) ECM fungal community composition shows
temporal distance decay of similarity and (2) the temporal variation of the ECM fungal community is associated with not only
niche-based processes but also temporal-based processes.
MATERIALS AND METHODS
Study site
The study was conducted in an evergreen broad-leaved subtropical forest in the University Forest, University of the Ryukyus,
in the northern part of Okinawa Island, southwestern Japan
(26◦ 9 N, 128◦ 5 E, ca. 275 m a.s.l.). The 30-year mean annual temperature is 21.8◦ C, and the 30-year mean annual precipitation
at the University Forest is 2680 mm (the climatic data were obtained from the office of Yona Experimental Forest of the University of the Ryukyus, located 3 km west of the study site; Xu
et al. 1998). The topography is hilly and dissected. The bedrock is
composed of sandstone and slate, and red yellow soil (Kanno et
al. 2008) has developed. The dominant tree species are Castanopsis sieboldii (Fagaceae) and Schima wallichii ssp. liukiuensis (Nak.)
Bloemb. (Theaceae) (Enoki 2003). Forest age is approximately 60
years old and logging and other forest practices have not been
carried out for at least 50 years.
Sampling design
We established two 200 m2 (20 m × 10 m) study plots, which
were 10 m apart from each other. Each plot was divided into
two 10 m × 10 m subplots, and each of the subplots was further subdivided into 25 squares (2 m × 2 m), making a total of
100 squares for the two plots (Fig. S1, Supporting Information).
Castanopsis sieboldii was the only one of 25 tree species encountered in the plots that was known to host ECM fungi. Castanopsis
sieboldii accounted for 55% of the total basal area of trees with
diameter of breast height (DBH) > 5 cm in the plots. Sampling
of the forest floor was conducted eight times at 3-month intervals in July and October 2009, January, April, July and October
2010, and January and April 2011. On each sampling occasion,
Matsuoka et al.
five squares were randomly chosen from each of the four subplots (i.e. 20 squares in total). After removing surface litter layer,
blocks of fermentation-humus (FH) layer (10 cm × 10 cm, 5 cm
in depth) including tree roots were collected from the center of
the 20 squares. When we came to choose the same square as
the previous sampling occasion, we collected a soil block near
the center of the square. Thus, a total of 160 blocks (20 replicates × eight times) were used for the study. The blocks were
kept in plastic bags and frozen at −20◦ C during the transport to
the laboratory.
In the laboratory, FH materials in the blocks were gradually
melted at room temperature and gently loosened. Then, 5 g
(wet) of samples were used for the measurement of pH as described below. Fine roots of trees were extracted from the remaining samples and washed with tap water using a 2-mm
mesh sieve to remove soil particles and debris. In each block, 20
individual root segments (approximately 5 cm in length) were
randomly selected, and one root tip (1–2 mm in length) was
randomly collected from each root segment under a binocular
(20×) microscope. The collected root tips included ECM as well
as other mycorrhizal or non-mycorrhizal fungi. The resultant 20
root tips were pooled for each block and washed serially in 70%
ethanol (w/v) and 0.005% aerosol OT (di-2-ethylhexyl sodium
sulfosuccinate) solution (w/v) and rinsed with sterile distilled
water. The root tips were then transferred to tubes containing cetyltrimethylammonium bromide (CTAB) lysis buffer and
stored at −20◦ C until DNA extraction. After the collection of root
tips, root segments from which ECM root tips were detached
were pooled for each square, transferred into paper bags, ovendried at 40◦ C and used for chemical analyses as described below.
On each sampling date, we collected fruit bodies of ECM
fungi in the plots to obtain reference sequences for molecular identification of taxa. Fresh tissue samples were aseptically transferred to 1.5-mL tubes containing CTAB buffer on
the day of collection and stored at room temperature until
DNA extraction.
DNA extraction, PCR amplification and pyrosequencing
Whole DNA was extracted from root tips in 160 samples using the modified CTAB method described by Gardes and Bruns
(1993). For direct 454 sequencing of the fungal internal transcribed spacer 1 (ITS1), we used a semi-nested PCR protocol.
The ITS region has been proposed as the formal fungal barcode
(Schoch et al. 2012). First, the entire ITS region and 5 end region
of LSU were amplified using the fungus-specific primers ITS1F
(Gardes and Bruns 1993) and LR3 (Vilgalys and Hester 1990). PCR
was performed in a 20 μl volume with the buffer system of KOD
FX NEO (TOYOBO, Osaka, Japan), which contained 1.6 μl of template DNA, 0.3 μl of KOD FX NEO, 9 μl of 2× buffer, 4 μl of dNTP,
0.5 μl each of the two primers (10 μM) and 4.1 μl of distilled water. The PCR conditions were as follows: an initial step of 5 min
at 94◦ C; followed by 20 cycles of 30 s at 95◦ C, 30 s at 58◦ C for annealing and 90 s at 72◦ C; and a final extension of 10 min at 72◦ C.
The PCR products were purified using ExoSAP-IT (GE Healthcare,
Little Chalfont, Buckinghamshire, UK) and diluted by adding
225 μl of sterilized water. The second PCR was then conducted
targeting the ITS1 region using the ITS1F fused with the 454
Adaptor A (5 -CCA TCT CAT CCC TGC GTG TCT CCG ACT CAG3 ) and the eight-base-pair DNA tag for post-sequencing sample identification (Hamady et al. 2008) and the reverse universal primer ITS2 (White et al. 1990) fused with the 454 Adaptor B (5 -CCT ATC CCC TGT GTG CCT TGG CAG TCT CAG-3 ).
PCR was performed in a 20 μl volume with the buffer system
3
of Blend Taq Plus (TOYOBO), which contained 1.0 μl of template DNA, 0.2 μl of Blend Taq Plus, 2 μl of 10× buffer, 2 μl
of dNTP, 0.8 μl each of the two primers (5 μM) and 13.2 μl of
distilled water. The PCR conditions were as follows: an initial
step of 5 min at 94◦ C; followed by 20 cycles of 30 s at 95◦ C, 30
s at 60◦ C and 90 s at 72◦ C; and a final extension of 10 min at
72◦ C. PCR products were checked by agarose gel electrophoresis, purified with ExoSAP-IT and quantified with Nanodrop. Amplicons were equimolarly pooled into four libraries and purified using a QIAquick PCR Purification Kit (Qiagen, Hilden, Germany). The pooled products were sequenced in the four 1/16 regions of a sequencing reaction of a GS-FLX sequencer (Roche 454
Titanium) at the Graduate School of Science, Kyoto University,
Japan.
For fruit bodies, whole DNA was extracted by the CTAB
method. Whole ITS and the 5 end of the LSU region were amplified with the same procedure as that used for the first PCR of
root tip samples, except that the number of cycles was changed
to 30 cycles. Amplicons were purified using a QIA quick PCR Purification kit (Qiagen) and sequenced with a BigDye Terminator
v3.1 Cycle Sequencing Kit using an ABI 3130xl Sequencer (Applied Biosystems, CA, USA). The DNA sequences of fungal ITS
and LSU regions are available in INSD (accession no. AB973713–
AB973818).
Bioinformatics
In the pyrosequencing, 165 466 reads were obtained. The full
dataset of the runs was deposited in the Sequence Read Archive
of DNA Data Bank of Japan (accession: DRA002424). The pyrosequencing reads were trimmed with a minimum quality value of
27 at the 3 tails (Kunin et al. 2010), and the trimmed reads were
sorted into individual samples using the sample-specific tags.
The 5 - and 3 -primer sequences and tag sequences were then
removed from the sorted reads. Of the sorted reads, those that
had sequence length shorter than 150 bp were excluded, and
those longer than 380 bp were shortened to 380 bp by removing bases from the 3 end. The remaining 127 952 reads were assembled using Assams assembler v0.1.2013.08.10 (Tanabe 2013).
This assembler is a highly parallelized and extended version
of the Minimus assembly pipeline (Sommer et al. 2007). First,
reads in each sample were assembled with 99.5% similarity, and
we removed singletons, potentially chimeric sequences and pyrosequencing errors. Potentially chimeric sequences were eliminated using UCHIME v4.2.40 (Edgar et al. 2011) with a relatively
rigorous chimera check option (the value for minimum score to
report a chimera was 0.8), and pyrosequencing errors were eliminated using an algorithm in CD-HIT-OTU (Li et al. 2012) with a
value to report errors of 0.8 (default value of Assams). After these
filtering procedures, we had obtained 72 014 reads. Reads from
15 samples that had less than 50 filtered reads were not used
in the following analyses because such low read numbers could
lead to the underestimation of ITS richness. Thus, the remaining 71 829 reads from 145 samples were used for further analyses. The number of sequencing reads per sample ranged from
59 to 1053 (495±191, mean±S.D.). All sequences were assembled
across the samples using Assams at a threshold similarity of
97%, which is widely used for fungal ITS region (Osono 2014), and
the resulting consensus sequences represented molecular operational taxonomic units (OTUs). Consensus sequences of the
OTUs are listed in Table S1 (Supporting Information).
To systematically annotate the taxonomy of the OTUs, we
used Claident v0.1.2013.08.10 (Tanabe and Toju 2013), built upon
automated BLAST-search by means of BLAST+ (Camacho et al.
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 5
Table 1. Environmental and climatic factors and their mean values.
n
Mean ± SD
(a) Environmental variables with spatial and temporal duplication
pH
pH of FH materials
C (%)
Carbon concentration of ECM roots
N (%)
Nitrogen concentration of ECM roots
160
160
160
3.78 ± 0.22
42.1 ± 3.21
1.29 ± 0.15
(b) Environmental variables with spatial duplication only
WC (%)
Soil water content
CO (%)
Canopy openness
Basal area of C. sieboldii
BA (m2 )
DIST (m)
Distance to nearest C. sieboldii
100
100
100
100
44.5 ± 4.43
7.68 ± 1.39
1.04 ± 0.73
2.14 ± 1.15
(c) Climatic variables with temporal duplication only
Mean daily temperature for 2 weeks before each sampling date
t2w (◦ C)
Cumulative precipitation for 2 weeks before each sampling date
p2w (mm)
8
8
20.2 ± 5.34
142 ± 122
Abbr.
Variable
2009) and the NCBI taxonomy-based sequence identification engine. Using the reference database took from INSDC for taxonomic assignment, sequences homologous to the ITS sequence
of each query were fetched, and then taxonomic assignment
was performed based on the lowest common ancestor algorithm
(Huson et al. 2007). The results of Claident and the number of
reads for the OTUs are given in Table S1 (Supporting Information). To screen for ECM fungi, we referred to reviews by Tedersoo, May and Smith (2010) and Tedersoo and Smith (2013) to
assign OTUs to the genera and/or families that were predominantly ECM fungi and OTUs that had high-sequence similarity
(>99%) with fruit bodies collected from the study site (described
above) and classified as ECM fungi. The resultant ECM fungal
OTUs (ECM OTUs) were used for further analyses (see Table S1,
Supporting Information).
Environmental and climatic data
To investigate possible relationships between niche-based factors (i.e. environment and climatic factors) and ECM community (OTU richness and OTU composition), we measured and
calculated variables, which were categorized into three groups
(Table 1). The first group included three environmental variables measured for each block on each sampling occasion (Table
1a). The pH of FH materials was measured in a 1 N potassium
chloride solution with Docu-pH meter+ (Sartorius, Goettingen,
Germany). Total carbon (C) and nitrogen (N) concentrations of
root segments from which ECM root tips had been detached were
determined with combustion method by automatic gas chromatography (JM 1000 CN, J-Science Co. Ltd, Kyoto, Japan).
The second group included four environmental variables
measured for each square only once (Table 1b). Soil water content was measured with a Moisture Meter HH2 (Delta-T Devices Ltd, Cambridge, UK) by putting 6-cm-length sensors into
soil from the surface of the forest floor in January 2011. To
measure the light regime on the forest floor, hemispherical
fish-eye photographs were taken 50 cm above ground at the
center of each square in April 2012. The photographs were
taken under an overcast sky with a Canon EOS60D digital camera equipped with a Sigma 4.5-mm F2.8 EXDC circular lens
(Sigma, Tokyo, Japan), which was kept horizontal with a leveling device. The photographs were analyzed with CanopOn 2
software (http://takenaka-akio.org/etc/canopon2/) to calculate
canopy openness of each square as the percentage relative to
the total area of the sky. To evaluate the effect of host trees on
ECM community, we calculated two parameters: basal area of
C. sieboldii (denoted as BA) and distance to nearest C. sieboldii
(denoted as DIST). All C. sieboldii trees (height >1 m) within the
plots were measured for the DBH, and the square from which the
stem occurred was recorded. We also established 68 additional
squares (2 m × 2 m) directly outside the plots (Fig. S1, Supporting
Information), and the DBHs of C. sieboldii trees within these additional squares were recorded. For each of the 100 squares within
the plots, we calculated BA as the sum of the basal area of C.
sieboldii present in the square and in the eight squares surrounding that particular square. We calculated DIST as the distance
from the center of a given square to the center of the nearest
square that harbored C. sieboldii. We considered that temporal
variations of these variables were negligible in our study plots,
because the plots are in a mature evergreen forest and no serious
disturbance occurred during our sampling period.
The third group included two climatic variables representing each sampling date (Table 1c). We calculated the mean daily
temperature during the 2 weeks before each sampling date (t2w )
and the cumulative amount of precipitation during 2 weeks
before each sampling date (p2w ). Data of air temperature and
rainfall during the study period were obtained from the Automatic Metrological Data Acquisition System (Japan Meteorological Agency) Oku station, located 7 km north of the study site.
Mean air temperature and cumulative amount of precipitation
during 1 month from 2009 to 2011 in the study site are described
in Fig. S2 (Supporting Information). We considered that spatial
variation of these climatic variables was negligible in our study
plots, because the plots were closely located and each plot is
not as large as containing variations in air temperature and precipitation (Fig. S1, Supporting Information). Temporal variations
in environmental and climatic variables with temporal duplication (i.e. pH, C and N concentration, t2w, and p2w) are shown in
Table S3 (Supporting Information).
Data analyses
The presence or absence of the ECM OTUs was recorded for
each sample, regardless of the number of 454 reads, because
there are known issues with quantitative use of read numbers
from 454 sequencing (Amend, Seifert and Bruns 2010). The presence/absence data was used for all statistical analyses as the
binary data. All analyses were performed using the R v. 3.0.1 (R
Core Team 2013). Differences in the sequencing depth of individual samples affect the number of OTUs retrieved, often leading
Matsuoka et al.
to underestimation of OTU richness in those samples that had
low sequence reads. Hence, some studies examined rarefaction
curves depicting OTU numbers with respect to sequence reads
and calculated OTU richness at a given sequence read to standardize the effect of sequencing depth. In this study, however,
we used the raw OTU richness for further analyses, rather than
such rarefied OTU richness. This was because (i) samples with
fewer than 50 sequence reads were removed as described above,
(ii) the rarefaction curves for the majority of still-low-read samples reached asymptotes (Fig. S3, Supporting Information) and
(iii) the raw ECM OTU number was not significantly correlated
with the read number for 145 samples (Pearson’s correlation coefficient r = 0.117, P = 0.16).
We analyzed the variation of ECM OTU richness (i.e. the
number of ECM OTU per sample) of total ECM fungi and four major families (Russulaceae, Thelephoraceae, Boletaceae and Cortinariaceae) during the sampling period using a generalized linear model (GLM). Error structure and link function of the GLM
were Poisson and log, respectively. Additional GLMs were used
to examine the relationship between the OTU richness of total
ECM and four major families and environmental and climatic
variables, using Poisson and log function. The exception was
the GLM for total ECM fungi, in which the error structure was
negative binomial, instead of Poisson, because the frequency
distribution of OTU richness of total ECM fungi was strongly
overdispersed from Poisson distribution. No significant multicollinearity was present in the environmental or climatic variables (variation inflation factors, VIF < 10). P values of each
model were calculated with the likelihood ratio test by χ 2 approximation and were adjusted by the Benjamini–Hochberg procedure (Benjamini and Hochberg 1995) to correct multiple testing. To show the strength of the relationships between OTU
richness and environmental or climatic variables, we calculated
pseudo R2 according to Nagelkerke (1991).
The effect of the seasons (as a categorical variable) on temporal variation of the ECM community composition was tested
to whether the community composition showed a cyclic pattern
or not by permutational MANOVA (PERMANOVA, ‘adonis’ command in the vegan package). To determine whether the dissimilarity of ECM fungal community composition was related to temporal distance and/or environmental distance among sampling
dates, Mantel tests were performed, respectively (‘mantel’ command in the vegan package, respectively). Then, to characterize
the scale of temporal clustering of ECM fungal community composition, Mantel correlogram analysis was performed (‘mantel.correlog’ command in the vegan package). A Mantel correlogram draws a graph in which Mantel correlation value rM is
plotted as a function of the temporal distance classes among the
sampling dates. A positive (and significant) rM indicates that for
the given distance class, the multivariate dissimilarity among
samples is lower than expected by chance (i.e. the mean withinclass dissimilarity is lower than the mean among-class dissimilarity) (Borcard and Legendre 2012). Presence/absence data of
ECM OTUs for each sampling date were merged (ECM OTUs in
Table S2, Supporting Information) and converted into a dissimilarity matrix using Simpson’s index (Simpson 1943). This index focus on compositional differences among sampling units
(i.e. species turnover) more than differences in species richness
and is less affected by adding and losing species compared to
other major indices such as Jaccard and Sorensen. The dissimilarity of community was differentiated into two components:
turnover (species replacement) and nestedness (species loss)
(Baselga 2010). We preliminarily conducted this differentiation
of the total dissimilarities (β SOR ) of ECM fungal communities into
5
turnover (β SIM ) and nestedness (β NES ) according to the procedures described in Baselga (2010). The equation was 0.814 (β SOR )
= 0.773 (β SIM ) + 0.041 (β NES ), indicating that the turnover was
responsible for most of the dissimilarity. Sampling dates and
mean values of environmental and climatic variables (pH, C and
N concentration, t2w and p2w ) for each sampling date were converted into temporal and environmental dissimilarity matrices
using Euclidean dissimilarity index, respectively. All environmental and climatic variables were standardized before calculating the dissimilarity. We preliminary tested the relationships
between the dissimilarity matrix of the community composition
and the distance matrixes of individual environmental/climatic
variables and found no significant correlations (Mantel test, P >
0.1). Therefore, we showed only the result of the pooled environmental distance matrix.
We used variation partitioning based on distance-based redundancy analysis (db-RDA, ‘capscale’ command in the vegan
package) to quantify the contribution of the environmental, climatic, spatial and temporal variables to the community structure of ECM OTUs. The relative weight of each fraction (pure and
shared fractions and unknown fractions) was estimated following the methodology described by Peres-Neto et al. (2006). Presence/absence data of ECM OTUs for each sample (ECM OTUs
in Table S1, Supporting Information) were converted into a dissimilarity matrix using Simpson’s index. We also conducted differentiation of total dissimilarities into turnover and nestedness components. The equation used was 0.989 (β SOR ) = 0.983
(β SIM ) + 0.006 (β NES ), indicating that the turnover was responsible for most of the dissimilarity. We then constructed four
models (environmental, climatic, spatial and temporal). First,
we constructed environmental and climatic models by applying
the forward selection procedure (999 permutations with an alpha criterion = 0.05) of Blanchet, Legendre and Borcard (2008) to
the environmental and climatic variables, respectively (Table 1).
Then, we constructed models using spatial and temporal variables extracted based on principal components of neighbor matrices (PCNM, Borcard et al. 2004). The PCNM analysis produced a
set of orthogonal variables derived from the geographical coordinates of the sampling locations (position of sampling squares)
and temporal coordinates of sampling dates. We used the PCNM
vectors that best accounted for autocorrelation and then conducted forward selection (999 permutations with an alpha criterion = 0.05) to select spatial and temporal variables that significantly influenced community dissimilarities. Based on these
four models, we performed variation partitioning by calculating
adjusted R2 values for each fraction (Peres-Neto et al. 2006).
RESULTS
Taxonomic assignment
In total, the filtered 71 829 pyrosequencing reads from 145 soil
samples were grouped into 372 OTUs with 97% sequence similarity (Table S1, Supporting Information). Among them, 123 OTUs
(25 345 read) belonged to ECM fungal taxa, with 121 OTUs being Basidiomycota and 2 OTUs being Ascomycota. At the family level, 123 OTUs belonged to 14 families, and common families were Russulaceae (40 OTUs, 32.5% of the total number of
ECM fungal OTUs), Thelephoraceae (28 OTUs, 22.8%), Boletaceae
(20 OTUs, 16.3%) and Cortinariaceae (19 OTUs, 15.5%). The most
common OTUs were OTU 77 (Tuber sp., Tuberceae, 18 out of the
145 samples), OTU 107 (Boletaceae sp., 16 samples) and OTU 38
(Tomentella stuposa, Thelephoraceae, 14 samples). Fifty-six OTUs
(45.5%) were found in only a single sample.
6
0.5
0.3 (0.33)
0.1 (0.28)
0.2 (0.16)
0.1 (0.1)
0.2 (0.13)
OTU richness of ECM fungi
2.5
0.8
0.5
0.6
0.5
0.1
0.4
0.2 (0.21)
0.2 (0.24)
0.2 (0.27)
0.2 (0.14)
0.2 (0.14)
±
±
±
±
±
±
2.8
0.7
0.6
0.7
0.4
0.4
0.4
0.2 (0.36)
0.3 (0.31)
0.2 (0.19)
0.1 (0.09)
0.1 (0.05)
2.1
0.6
0.3
0.3
0.7
0.2
±
±
±
±
±
±
0.4
0.2 (0.29)
0.1 (0.15)
0.1 (0.19)
0.2 (0.23)
0.1 (0.14)
2.5
1.0
0.7
0.3
0.3
0.3
±
±
±
±
±
±
0.6
0.3 (0.39)
0.3 (0.16)
0.2 (0.11)
0.2 (0.07)
0.1 (0.27)
3.4
1.1
1.1
0.6
0.4
0.2
±
±
±
±
±
±
19
47
15
28
18
26
Oct.
0.4
0.3 (0.26)
0.2 (0.25)
0.2 (0.25)
0.2 (0.13)
0.1 (0.11)
3.7
0.9
0.9
0.9
0.6
0.4
±
±
±
±
±
±
20
46
n
Total number of
ECM fungal OTUs
OTU richness
Total ECM
Russulaceae
Thelephoraceae
Boletaceae
Cortinariaceae
Other families
Jul.
Numbers in parentheses are mean values of the proportion of the number of OTUs in each ECM family at each sampling date.
Other families included Amanitaceae, Cantharellaceae, Clavulinaceae, Entolomataceae, Hygrophoraceae, Elaphomycetaceae, Hymenochaetaceae, Gomphaceae, Tuberaceae and Tricholomataceae.
0.4
0.2 (0.25)
0.2 (0.28)
0.2 (0.27)
0.1 (0.04)
0.1 (0.16)
0.7
0.3 (0.23)
0.2 (0.13)
0.1 (0.41)
0.2 (0.21)
0.1 (0.02)
17
27
20
34
±
±
±
±
±
±
Oct.
Jul.
Apr.
Jan.
2010
2009
Sampling date
Table 2. Total number of ectomycorrhizal (ECM) fungal OTU and mean values of ECM fungal OTU richness at each sampling date. Values are mean ± SE.
2.6
0.7
0.8
0.6
0.2
0.4
±
±
±
±
±
±
19
32
Jan.
2011
2.6
0.9
0.6
0.4
0.3
0.4
±
±
±
±
±
±
17
25
Apr.
FEMS Microbiology Ecology, 2016, Vol. 92, No. 5
The total number of ECM fungal OTU at each sampling date
ranged from 25 to 47 (Table 2). Mean values of ECM fungal OTU
richness at each sampling date ranged from 2.1 to 3.7, and that
of Russulaceae, Thelephoraceae, Boletaceae and Cortinariaceae
ranged from 0.6 to 1.1, 0.3 to 1.1, 0.3 to 0.9 and 0.2 to 0.7, respectively (Table 2). Mean values of ECM fungal OTU richness, and
those of Russulaceae, Boletaceae, Thelephoraceae and Cortinariaceae, were not significantly different among sampling dates
(GLM, P > 0.05).
Of the 45 GLM models [five groups (total ECM fungi, Russulaceae, Thelephoraceae, Boletaceae and Cortinariaceae) × nine
environmental factors], only one model was statistically significant. The OTU richness of Cortinariaceae decreased significantly
with increasing pH (GLM, Deviation = 7.42, P = 0.045, pseudo R2 =
0.08). The other 44 models, including eight environmental variables (C and N concentration of ECM roots, soil water content,
canopy openness, basal area of C. sieboldii, distance to nearest
C. sieboldii, mean daily temperature for 2 weeks before each
sampling date and cumulative precipitation for 2 weeks
before each sampling date), were not statistically significant (GLM, Deviation = 0.00-6.32, pseudo R2 = 0.00–0.06,
P > 0.05).
Temporal structure of the community composition
The seasons did not significantly affect the temporal variation of the ECM community composition (PERMANOVA, F =
1.132, P = 0.328). The Mantel test revealed that the dissimilarity of ECM fungal community among sampling dates was
significantly and positively correlated with temporal distance
(r = 0.350, P = 0.038, Fig. 1a) but not significantly correlated
with environmental distance (r = 0.055, P = 0.417, Fig. 1b).
These results indicated the ECM fungal community changed
with time independently of season or year. To characterize the
scale of temporal clustering of ECM fungal community, Mantel correlogram analysis was performed. In the Mantel correlograms (Fig. 2), only the temporal distance class of 0–110
days exhibited significant temporal autocorrelation, and no significant autocorrelations were found for the longer temporal
distance classes.
Contribution of environmental, climatic, spatial
and temporal variables to the community composition
Forward selection was performed for four full models, namely
environmental model with seven factors, climatic model with
two factors, spatial model with 29 PCNM vectors with positive eigenvalues and temporal model with four PCNM vectors
with positive eigenvalues. Canopy openness and DIST (Table 1)
were selected as environmental factors; t2w and p2w (Table 1)
were selected as climatic factors; four spatial PCNM vectors (vectors 1, 2, 6 and 8) were selected as spatial variables and three
temporal PCNM vectors (vectors 1, 2 and 3) were selected as temporal variables. The percentages explained by the environmental, climatic, spatial and temporal fractions were 1.7%, 1.7%, 4.3%
and 3.6%, respectively (Fig. 3). The shared fraction between environmental and spatial variables was 0.1%, and that between
climatic and temporal variables was 0.1% (Fig. 3). In total, 11.5%
of the community variation was explained and the remaining
88.5% was unexplained. The results of RDA are visualized in
Fig. S4 (Supporting Information).
Matsuoka et al.
7
Figure 1. Relationships between community dissimilarity and temporal distance (a) and environmental distance (b). The dissimilarity of community composition
among sampling dates (n = 8) was used. (a Pearson r = 0.350, P = 0.038; b Pearson r = 0.055, P = 0.417, Mantel tests).
DISCUSSION
Temporal structure of the community composition
Figure 2. The Mantel correlogram showing the extent of temporal autocorrelation of the ECM fungal community composition. The dissimilarity of community
composition among sampling dates (n = 8) was used. Filled and open boxes indicate the significant and non-significant correlation at 5% level, respectively.
Figure 3. Venn diagram showing pure and shared effects of environment, spatial,
temporal and climatic factors on ECM fungal community as derived from variation partitioning analysis. The dissimilarity of community composition among
FH blocks (n = 145) was used. Numbers indicate the proportions of explained
variation.
To our knowledge, this is the first study that demonstrated the
temporal distance decay of similarity of ECM fungal community
and quantified the effects of temporal variables on the temporal variation of ECM fungal community composition. The results
showed that the community dissimilarity was significantly correlated with temporal distance (Fig. 1a) but not with environmental distance (Fig. 1b). We also found that the pure temporal
fractions explained the temporal variation of ECM fungal community composition (Fig. 3). These results support our hypotheses that ECM fungal community composition shows temporal
distance decay of similarity and that not only niche-based processes but also temporal-based processes could affect the temporal variation of ECM fungal community. Our finding suggests
the importance of taking into account temporal structure and
stochastic temporal-based processes as well as deterministic
niche-based processes in the study of temporal changes in ECM
fungal community.
The temporal pattern of ECM fungal community composition reflected the patterns of occurrence of major ECM fungal
OTUs. That is, most of the major ECM fungal OTUs occurred for
several months with a single peak during the 2-year study period. For example, three major Thelephoraceae OTUs, OTU 38
(Tomentella stuposa), OTU 48 (Thelephora sp.) and OTU 40 (Thelephoraceae sp.) occurred for 6 to 9 months with a peak in July
2009, April 2010 and January 2011, respectively (Table S2, Supporting Information). In contrast, only a few OTUs showed seasonal patterns of occurrence. For example, OTU 288 (Rossbeevera
vittatispora) increased in July for 2 years (Table S2, Supporting Information). Such patterns of occurrence resulted in the Mantel
correlogram showing that only the temporal distance class of
0–110 days, which corresponds to the period between two and
three consecutive sampling events, exhibited a significant temporal autocorrelation of ECM fungal community composition
(Fig. 2). These results suggest that once an ECM fungus colonized
a root tip, the fungus could persist for several months regardless of variations in environmental and/or climatic conditions.
Although our study is observational and thus the underlying
biological processes cannot be thoroughly determined, the temporal change in ECM fungal community composition can have
derived in part from priority effects. There are some evidences
8
FEMS Microbiology Ecology, 2016, Vol. 92, No. 5
that priority effects could play a large role in community dynamics of ECM fungi. For example, Kennedy and colleagues observed
that the timing of colonization had a significant effect on the
outcome of competition between ECM fungal species (i.e. the
presence of one ECM species had a significant negative influence on the ability of later arrival species to colonize host roots)
in both laboratory and field studies (Kennedy and Bruns 2005;
Kennedy et al. 2007; Kennedy, Peay and Bruns 2009). This negative influence on later arrival species may cause the temporal
distance decay of similarity by preventing rapid response of ECM
fungal community to the seasonal fluctuations of environmental condition. On the other hand, niche-based processes may
also contribute to the temporal structure of ECM fungal community composition because the pure temporal fraction in variation
partitioning (Fig. 3) could also indicate the potential effects of
unmeasured environmental variables that are temporally structured. For instance, the temporal distance decay of similarity
may also reflect the successional change of the ECM fungal community, which affected by successional changes in environmental conditions in our study site, even though our sampling was
carried out in a relatively mature forest stand. Because these
processes are not mutually exclusive, further investigation is
necessary to fully understand the causality of the temporal distance decay of similarity.
Temporal variation of the community composition
The result that the climatic (niche-based) factors had small explanatory power compared to the temporal variables (Fig. 3)
could have resulted partly from the relative lack of a seasonal
pattern in the ECM fungal community in our subtropical study
site, as well as from potential influences of unmeasured nichebased variables. The weak seasonal pattern could also be attributable to the relatively little seasonality in temperature and
precipitation in the study region. Buée, Vairelles and Garbaye
(2005) and Courty et al. (2008) found that the ECM fungal community showed seasonal shifts in temperate forests, where seasonal changes in temperature are more evident than in subtropical forests. Whether a weak seasonal pattern of the ECM fungal
community is common to other subtropical and tropical forests
is unknown, as few data are currently available regarding the
temporal variation of ECM fungi. To clarify the effects of seasonal variations in environmental and climatic conditions on
ECM fungal community composition, comparative studies between climatic regions with annual and seasonal samplings are
needed.
In variation partitioning, there was large unexplained variation of ECM fungal community composition (Fig. 3 and Fig.
S4, Supporting Information). The unexplained variation may
be related to the following three factors which were untested:
(1) niche-based factors such as temporal and spatial change in
fine root turnover (Koide et al. 2007; Vořı́šková et al. 2014), soil
chemistry (e.g. the availability of N and cations; Toljander et al.
2006; Aponte et al. 2010) and litter input (Vořı́šková et al. 2014).
For example, Matsuoka et al. (unpublished) observed temporal
changes of ECM fungal community composition with fine root
turnover in the same study site with this study. Also, Xu et
al. (1998) showed that litter input increased in spring and autumn. (2) Short-term temporal-based factors that require more
frequent sampling than the current study (3-month interval).
(3) Random extinction and colonization of individual ECM fungi
(Hubbell 2001). In addition to these untested factors, the relatively low species richness in each FH block (Table 2) and using
only presence/absence data could have decrease the probability
of detecting the temporal and spatial pattern of the ECM fungal community composition. Therefore, we cannot clearly determine the relative importance of niche-based processes and
temporal-based processes on the temporal and spatial variation in ECM fungal community composition. Nevertheless, our
results stress the importance of incorporating temporal-based
processes in the study of temporal variation of ECM fungal community. Further studies with explicit consideration of temporalbased processes, as well as niche-based processes, will help to
clarify the importance and generality of the temporal-based processes in determining the ECM fungal community.
Spatial variation of the community composition
In this study, spatial variables explained a similar proportion
of the spatial and temporal variation of the community composition to temporal variables (Fig. 3). The lower explanatory
power of niche-based factors for the spatial variation of ECM
fungal community than spatial variables (Fig. 3) could partly result from the lack of spatial variation in environment, such as
host species, in our study plot. It could also be attributed to in
part from that potential effects of unmeasured niche-based variables on the ECM fungal community (see above). Despite the limitation of our spatial sampling scale, the result that both nichebased processes and spatial-based processes simultaneously explained the spatial variation of the community composition
(Fig. 3) is consistent with recent findings that these two processes drive the community dynamics at the same time (Tedersoo et al. 2011; Bahram et al. 2013). This result stresses the importance of incorporating spatial-based processes in the study
of community ecology of ECM fungi (Bahram et al. 2013).
Temporal variation of ECM fungal OTU richness
Our observation that ECM fungal OTU richness was not significantly different among sampling dates appears contradictory
to previous studies reporting significant temporal variations of
ECM fungal species richness in temperate forests (Buée, Vairelles
and Garbaye 2005; Courty et al. 2008; Longo, Urcelay and Nouhra
2011). The temporal variations of ECM fungal richness in temperate forests have often been attributed to seasonal patterns of
tree root elongation (e.g. Izzo, Agbowo and Bruns 2005; Koide et
al. 2007; Courty et al. 2008). For example, Courty et al. (2008) studied temporal variation of ECM fungal community in a temperate
forest by monthly sampling and found increases of ECM fungal
richness in spring and autumn, when tree root elongation rate
was high (e.g. Teskey and Hinckley 1981). In our subtropical site,
the abundance of fine roots did not change significantly over a
1-year period, and the production of fine roots was continuous
throughout the year (Matsuoka et al. unpublished). Lack of significant variation of the abundance of fine roots and/or continuous production of fine roots has also been reported in aseasonal
tropical forests (e.g. Ostertag 2001; Valverde-Barrantes, Raich
and Russell 2007). The continuous supply of fine roots in our
study site may have led to the lack of significant temporal variation of ECM fungal OTU richness.
Relationship between niche-based factors
and ECM fungal OTU richness
The OTU richness of Cortinariaceae decreased with increasing pH. The lower taxonomic richness of Cortinariaceae under
higher pH conditions has been reported also in temperate and
boreal forests (Douglas, Parker and Cullings 2005; Horton et al.
Matsuoka et al.
2013, but see Lilleskov et al. 2002). The causal relationship between pH and taxonomic richness of Cortinariaceae remains
unclear, but the effect of pH on Cortinariaceae might be indirect. Soil pH often correlates with the availability of soil nutrients such as N (e.g. Lilleskov et al. 2002; Toljander et al. 2006).
Lilleskov et al. (2002) studied the effect of N deposition on ECM
fungal community and suggested that changes of N availability, rather than pH, caused the change in taxonomic richness of
Cortinariaceae.
CONCLUSION
In this study, we demonstrated the temporal distance decay
of similarity of ECM fungal community by successive sampling
across 2 years and separated the effects of niche-based processes and temporal-based processes on the temporal variation of ECM fungal community composition. The results supported our hypothesis that temporal-based processes could lead
to temporal distance decay of similarity of ECM fungal community. Although the underlying biological processes were not
identified, priority effects may have played a significant role in
driving the temporal turnover of ECM fungal community at the
site. Our finding suggests the importance of explicit consideration of not only niche-based processes but also temporal-based
processes in the study of temporal changes in ECM fungal community. However, relative contribution of these two processes
could not be deduced by our observation and can vary depending on the range of temporal or seasonal variations in environmental and climatic conditions. Therefore, further comparative
studies between climatic regions with explicit consideration of
temporal-based processes, as well as niche-based processes, will
help to clarify the relative importance of niche-based processes
and temporal-based processes for temporal variations in ECM
fungal community.
SUPPLEMENTARY DATA
Supplementary data are available at FEMSEC online.
ACKNOWLEDGEMENTS
We thank Dr Atsushi Takashima and the staffs of Yona Experimental Forest, University of the Ryukyus for their assistance in
fieldwork; Dr Akira S. Mori for useful discussions; Dr Kiyokazu
Agata and Dr Shigenobu Yazawa for help in pyrosequencing; Dr
Osamu Nishimura and Dr Akifumi S. Tanabe for their help in
bioinformatics; Dr Ryo Kitagawa for help in statistical analyses;
and Dr Hirotoshi Sato, Dr Shinichi Tatsumi and Dr Koichi Ito for
helpful suggestions.
FUNDING
This study received partial financial support from Grants-in-Aid
for Japan Society for the Promotion of Science Fellows to S.M.,
the Ministry of Education, Culture, Sports, Science, and Technology of Japan (No. 19780114, No. 15K07480), The Sumitomo Foundation, Nissan Global Foundation, and Nippon Life Inst. Foundation to T.O., and the Global COE Program A06 and Grants for Excellent Graduate Schools, Ministry of Education, Culture, Sports,
Science, and Technology, Japan ( 12-01) to Kyoto University.
Conflict of interest. None declared.
9
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