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Relationships between soil fungal and woody plant assemblages
differ between ridge and valley habitats in a subtropical
mountain forest
Cheng Gao1, Nan-Nan Shi1,2, Liang Chen1,2, Niu-Niu Ji1,2, Bin-Wei Wu1,2, Yong-Long Wang1,2, Ying Xu1,2,
Yong Zheng1, Xiang-Cheng Mi3, Ke-Ping Ma3 and Liang-Dong Guo1,2
1
State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China; 2College of Life Sciences, University of Chinese Academy of Sciences,
Beijing 100049, China; 3State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
Summary
Author for correspondence:
Liang-Dong Guo
Tel: +86 10 6480 7510
Email: [email protected]
Received: 29 May 2016
Accepted: 26 September 2016
New Phytologist (2017) 213: 1874–1885
doi: 10.1111/nph.14287
Key words: 454 pyrosequencing,
composition, habitat type, ITS2, plant–
fungus relationship, richness, subtropical
forest.
Elucidating interactions of above-ground and below-ground communities in different habi-
tat types is essential for understanding biodiversity maintenance and ecosystem functioning.
Using 454 pyrosequencing of ITS2 sequences we examined the relationship between subtropical mountain forest soil fungal communities, abiotic conditions, and plant communities
using correlation and partial models.
Ridge and valley habitats with differing fungal communities were delineated. Total, saprotrophic and pathogenic fungal richness were significantly correlated with plant species richness and/or soil nutrients and moisture in the ridge habitat, but with habitat convexity or
basal area of Castanopsis eyrei in the valley habitat. Ectomycorrhizal (EM) fungal richness was
significantly correlated with basal area of C. eyrei and total EM plants in the ridge and valley
habitats, respectively. Total, saprotrophic, pathogenic and EM fungal compositions were significantly correlated with plant species composition and geographic distance in the ridge habitat, but with various combinations of plant species composition, plant species richness, soil
C : N ratio and pH or no variables in the valley habitat.
Our findings suggest that mechanisms influencing soil fungal diversity and community composition differ between ridge and valley habitats, and relationships between fungal and
woody plant assemblages depend on habitat types in the subtropical forest ecosystem.
Introduction
Interactions between above-ground and below-ground communities strongly influence, and are strongly influenced by, biodiversity and ecosystem functions (Wardle et al., 2004; Van Dam &
Heil, 2011). As key components of below-ground communities,
fungi interact with plants in diverse ways according to their
lifestyle ranges (e.g. mycorrhizal, pathogenic, endophytic, or
saprotrophic) (Peay et al., 2008). Plants influence fungal communities through host specificity and generating diverse organic substrates and microhabitats (Hooper et al., 2000; Wardle, 2006;
Dickie, 2007). In contrast, according to plant–soil feedback theory soil fungi may influence plant communities through increasing soil nutrient availability and/or mediating plant coexistence
(e.g. van der Putten et al., 2013; Bever et al., 2015; Bennett &
Cahill, 2016). For example, mycorrhizal fungi affect plant communities through formation of underground common mycorrhizal networks that redistribute nutrients among plants (e.g. van
der Heijden et al., 1998; Booth, 2004). In addition, the Janzen–
Connell hypothesis proposes that herbivores and pathogens affect
plant communities through a community compensatory trend
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that results in rare plant species exhibiting a higher per-capita survival rate than common plant species (Janzen, 1970; Connell,
1971; Connell et al., 1984; Bagchi et al., 2014).
Numerous previous studies have found that diversities of
plants and animals generally either monotonically decrease or
have a hump-shaped pattern with increasing altitude (e.g. Rahbek, 2005; Sundqvist et al., 2013). However, recent studies have
found that the diversity of microbes may show various responses
to increases in altitude, including no significant change, increases,
reductions, or hump-shaped patterns (e.g. Fierer et al., 2011;
Bahram et al., 2012; Pellissier et al., 2014; Matsuoka et al.,
2016). These variations in plant and microbial altitudinal diversity patterns may arise because plant diversity is mainly determined by temperature and dispersal limitation (e.g. Vetaas &
Grytnes, 2002; Sundqvist et al., 2013), while microbial diversity
is influenced not only by plant traits such as diversity, composition, identity, productivity and leaf phenology (e.g. Peay et al.,
2011; Martınez-Garcıa et al., 2015; Urbanova et al., 2015), but
also by abiotic factors such as soil nutrient status, pH, altitude
and slope (e.g. Dumbrell et al., 2010; Bahram et al., 2012;
Tedersoo et al., 2014; Matsuoka et al., 2016). Furthermore,
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relationships between fungal and plant communities may be
influenced by traits of the fungi, such as trophic type, dispersal
ability, diversity, identity, abundance, and phylogeny (Peay et al.,
€
2010, 2013; Tedersoo et al., 2014, 2015; Zobel & Opik,
2014;
Garcıa de Leon et al., 2016). For example, both Peay et al. (2013)
and Tedersoo et al. (2014, 2015) found that diversities of plants
were more strongly correlated with those of fungal biotrophs,
such as mycorrhizal and pathogenic fungi, than with those of
saprotrophic fungi in examined ecosystems. Taken together, the
lack of resemblance of plant and microbial diversities along altitudinal gradients suggests that the relationships between plant
and microbial assemblages vary among habitat types.
In forested ecosystems, various habitat types have frequently
been demonstrated to be defined by topographic features such as
altitude, convexity, slope, and aspect (e.g. Harms et al., 2001;
Condit et al., 2002; Legendre et al., 2009). Of these habitat types,
ridge and valley (with different convexity and altitude characteristics) are the most common habitats determining distinct plant
communities in subtropical and tropical montane forest ecosystems. Fungi may have stronger dispersal ability in valley than in
ridge habitats, because there is more opportunity for dispersal of
propagules of fungi inhabiting leaves, litter and surface soil via
wind, gravity and forest floor surface runoff into concave valley
habitats than into convex ridge habitats. Ridge and valley habitats
also differ in soil and microclimatic variables, which affect fungal
communities (e.g. Pellissier et al., 2014; Prober et al., 2015; Matsuoka et al., 2016) and thus may shift relationships between fungal and plant assemblages. However, associated variations in
relationships between fungal and plant assemblages in different
habitats in natural forest ecosystems have not been elucidated.
Subtropical mountain forests are widely distributed in south
and east China, and have high plant species diversity and wide
variations in topographic and soil conditions, which presumably
provide niches capable of accommodating diverse fungi (Legendre et al., 2009; Gao et al., 2013, 2015). Woody plants, as the
most dominant vegetation type in subtropical forest ecosystems,
make major contributions to global carbon (C) cycling and the
gross primary production of terrestrial ecosystems (Yu et al.,
2014). Five types of habitats hosting differing woody plant communities have been defined in terms of specific convexity and altitude criteria in a Chinese subtropical mountain forest (Legendre
et al., 2009). In addition, these habitat types differ in convexity,
soil, and microclimatic variables that influence fungal dispersal
and community assembly, and thus may shift relationships
between fungal and woody plant assemblages, as mentioned earlier. Therefore, in this study we hypothesized that: (H1) this
forest could be divided into topographic habitats supporting distinct soil fungal communities; (H2) the main factors affecting
total fungal and trophic group diversities and community compositions in the different topographic habitats would differ; and
(H3) the relationships between fungal and woody plant assemblages would vary among topographic habitat types in the forest.
To test these hypotheses, we examined soil fungal communities in quadrats representing the five woody plant habitat types in
the subtropical mountain forest using 454 pyrosequencing. We
delineated fungal habitat types with similarities in topographic
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conditions and fungal community composition, then analyzed
the relationships among fungal, plant and abiotic variables in the
delineated fungal habitats in the forest.
Materials and Methods
Study site and sampling
The study was conducted in a 24 ha permanent plot in a subtropical broad-leaved forest in the Gutianshan National Nature
Reserve (29°080 1800 –29°170 2900 N, 118°020 1400 –118°110 1200 E, c.
81 km2, 250–900 m above sea level (asl)), with an annual mean
temperature of 15.4°C and annual mean precipitation of
1964 mm (Legendre et al., 2009). The 24 ha plot contains secondary forest c. 160–180 yr old that was heavily disturbed by
agriculture and charcoal production c. 80 yr ago. Currently, most
of the forest is in middle and late successional stages. In the 24 ha
plot, 140 676 individual woody plants (with diameter at breast
height ≥ 1 cm) belonging to 159 species and 49 families were previously identified (Legendre et al., 2009). Of the 159 plant
species, Castanopsis eyrei, Schima superba and Pinus massoniana
are abundant (accounting for, respectively, 32.8%, 19.2% and
12.8% of total plant basal area) and the other 156 species are rare
(each accounting for < 2%). The 24 ha plot was divided into 600
quadrats (20 m 9 20 m), in which various descriptors of the plant
community (species richness, composition, and basal area) and
topography (convexity, altitude, slope, eastness, and northness)
have been previously measured (Legendre et al., 2009).
The convexity of each quadrat was calculated (for modeling
relationships between topography and both plant and soil variables) by subtracting the mean altitude of the eight surrounding
quadrats from its altitude (Legendre et al., 2009). A negative convexity value indicates that the focal quadrat lies in a hollow or
local depression in the landscape, while a positive convexity value
indicates a hillock. Depressions and hillocks are probably zones
with relatively high influxes and effluxes, respectively, of moisture, nutrients, and organic material. Third-degree polynomial
equations were calculated for altitude, convexity and slope, thus
allowing their application in the modeling of nonlinear relationships between the topographic predictors and the plant and soil
variables (Legendre et al., 2009). Soil properties (total C, total
nitrogen (N), total phosphorus (P), C : N ratio, C : P ratio,
+
NO
3 –N, NH4 –N, available N, available P, available calcium
(Ca), available ferrum (Fe), bulk density, moisture, and pH) have
previously been assessed by Zhang et al. (2011). Briefly, 893 soil
samples were collected from the 24 ha plot using regular and random sampling techniques, then (following analysis) the ordinary
kriging method was used to obtain the soil variables for every
quadrat (as detailed in Supporting Information Table S1).
Detrended component analysis (DCA) of the 14 soil variables
using the DECORANA command in the VEGAN package (Oksanen
et al., 2007) showed that the largest ‘axis lengths’ of the first four
axes was 0.741 (< 4), suggesting that linear gradients largely governed the relationships among soil variables. Thus, to reduce
these 14 measured soil variables to a convenient number of predictors, principal component (PC) analysis was applied, using
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the RDA command in the VEGAN package (Oksanen et al., 2007).
Each of the first four soil PCs included total C, total N, total P,
+
C : N ratio, C : P ratio, NO
3 –N, NH4 –N, available N, available
P, available Ca, available Fe, bulk density, moisture, and pH
(Table S1). The first four soil PCs (largely reflecting variables in
parentheses) explained 81.1% of the total variation of soil variables: PC1 (nutrients and moisture), 45.9%; PC2 (C : N ratio),
18.8%; PC3 (pH), 12.5%; and PC4 (available N), 7.5%
(Table S1). The DCA of the basal areas of the 159 plant species
showed that the largest ‘axis lengths’ of the first four axes was
3.343 (< 4), suggesting that linear gradients largely governed the
relationships among them. Thus, PC analysis was also applied to
reduce the basal areas of the 159 plant species into a convenient
number of predictors, and the first three PCs explained 89.3% of
the total variation of plant basal area: PC1 (basal area of C. eyrei),
57.9%; PC2 (basal area of P. massoniana), 21.0%; and PC3
(basal area of S. superba), 10.4% (Table S2). Spatial eigenvectors
based on geographical coordinates were extracted from principal
coordinates of neighbor matrices (PCNMs), and PCNM eigenfunctions with positive eigenvalues were used as explanatory variables to analyze the spatial variation using the PCNM command in
the PCNM package (Dray et al., 2006). In total, 13, seven and
26 of the calculated PCNM vectors were positive in the ridge
habitat, valley habitat and whole forest, respectively (Table S3),
and were thus used in further analysis.
Legendre et al. (2009) have delineated five habitat types for
woody plant communities in the 600 quadrats of the 24 ha plot
based on convexity and altitude, designated habitats I (237
quadrats; convexity < 0.585, altitude < 642.9 m), II
(269 quadrats; convexity > 0.585, altitude < 642.9 m), III (42
quadrats; convexity < 3.3, 682.8 m > altitude > 642.9 m), IV
(eight quadrats; convexity < 3.3, altitude > 682.8 m), and V (44
quadrats; convexity > 3.3, altitude > 642.9 m). Habitats I and II
are separated from habitats III, IV and V by the altitude breakpoint of 642.9 m; habitat I in the valleys is separated from habitat
II on the mid-altitude ridges by a breakpoint in the convexity
variable at 0.585; habitats III and IV occupy the less convex
high-altitude quadrats, but habitat III is at lower altitudes than
habitat IV (breakpoint, 682.8 m); habitat V contains the most
convex quadrats (convexity > 3.3). Habitat I was significantly
correlated with plant species Camellia fraterna and Neolitsea
aurata, habitat IV with Quercus serrata, Lyonia ovalifolia and
Rhododendron mariesii, and habitat V with Albizia kalkor,
Lindera reflexa, Platycarya strobilacea and Sorbus folgneri. There
are no significant indicative plant species in habitats II and III.
A total of 62 quadrats representing the five habitat types (27
quadrats in habitat I, 20 quadrats in habitat II, six quadrats in
habitat III, three quadrats in habitat IV, and six quadrats in habitat V), covering various ranges of the plant diversity and soil characteristics, in the 24 ha plot were selected, spaced > 28 m apart
(Fig. S1). In October 2011, 16 soil cores (2 cm in diameter,
10 cm deep) were collected from evenly spaced spots within each
quadrat, pooled, passed through a 2 mm sieve and stored at
80°C until DNA extraction. Information on the plant, soil and
topographical variables of the 62 quadrats is summarized in
Table S4.
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Molecular analysis
Total DNA was extracted from 1.0 g of each frozen soil sample
using a FastDNA Spin Kit for Soil (MP Biomedicals, Santa Ana,
CA, USA). The fungal internal transcribed spacer (ITS) region
was amplified following Gao et al. (2015) using a forward 454
primer containing a DNA capture bead anneal adaptor and an
ITS1OF primer (Taylor & McCormick, 2008), and a reverse
454 primer combining a 454 sequencing adaptor, a 9-base tag
(Table S5) and an ITS4 primer (White et al., 1990). Amplicon
libraries generated from each sample using three dilutions (910,
950 and 9100 template DNA solution) with three PCR replicates per dilution, were purified using a PCR Product Gel Purification Kit (Axygen, Union City, CA, USA). The yields of
purified PCR products were measured using a TBS 380 Fluorescence Spectrophotometer (Promega), and 50 ng of DNA from
each of the 62 samples was pooled and adjusted to 10 ng ll1.
The pooled product was subjected to pyrosequencing (1/2 plate)
using a FLX Titanium Roche Genome Sequencer (454 Life
Sciences, Branford, CT, USA). The raw sequence data have been
submitted to the Sequence Read Archive of the GenBank
database under accession no. SRA064714.
Bioinformatics analysis
Noise signals generated during the sequencing process were
detected and removed using the SHHH.FLOW command in
MOTHUR 1.31.2 (Schloss et al., 2009). Subsequently, sequences
with no valid primer sequence or DNA tag, containing ambiguous bases, homopolymers > 8 bases, < 250 bp long, or with an
average quality score < 25 were removed using the TRIM.SEQS
command in MOTHUR. The ITS2 region of each remaining
sequence was extracted using the ITSx software package
(Bengtsson-Palme et al., 2013), and potential chimeras were subsequently detected and discarded using the CHIMERA.UCHIME command in MOTHUR by comparison with entries in the unified
system for the DNA based fungal species linked to the classification (UNITE) database (K~oljalg et al., 2013). The remaining
nonchimeric ITS2 sequences were dereplicated, sorted and clustered into operational taxonomic units (OTUs) at a 97% similarity level using the UPARSE pipeline (Edgar, 2013). A
representative (the most abundant) sequence of each OTU was
selected and searched against the international nucleotide
sequence databases collaboration (INSDC), UNITE, and global
fungal ITS2 databases (Tedersoo et al., 2014) using a basic local
alignment search tool (BLAST) (Altschul et al., 1990). Fungal
OTUs and trophic groups (i.e. saprotrophic, pathogenic, animal
parasitic, mycoparasitic, ectomycorrhizal (EM), and arbuscular
mycorrhizal (AM) fungi) were identified following the criteria of
Tedersoo et al. (2014) (detail in Table S6). To eliminate the
effects of different read numbers among the plots on the fungal
community analysis, the number of sequences per quadrat was
normalized to the smallest sample size using the SUB.SAMPLE command in MOTHUR. The representative fungal OTU sequences
have been submitted to the European Nucleotide Archive (ENA)
under accession nos. LN909523–LN912774.
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Statistical analysis
A multivariate regression tree (MRT) was computed to delineate
habitat types that were similar in topographic conditions and
fungal OTU composition (Hellinger-transformed read number
dataset), using the MVPART command in the mvpart package
(De’Ath, 2002). As two fungal habitat types were delineated by
MRT, t-tests were applied to explore the differences in richness
of total fungi and trophic groups between these two habitats.
Both linear and quadratic models were constructed to depict the
relationships between plant species richness and total, saprotrophic, pathogenic, EM and AM fungal richness in each habitat
type and the whole forest (i.e. all 62 quadrats). Akaike information criterion (AIC) values were then employed to judge whether
the quadratic model was better (had a 10 unit smaller AIC value)
than the linear model (Burnham & Anderson, 2002).
To depict the community compositions that were independent
of the variation of richness, pairwise modified Raup–Crick dissimilarity matrices were calculated for total fungi, trophic groups
and plants (Chase et al., 2011). Permutational analysis of variance
(PERMANOVA) was applied to explore differences in community compositions of total fungi and trophic groups between fungal habitats, using the ADONIS command in the VEGAN package
(Oksanen et al., 2007). Pairwise Euclidean dissimilarity matrices
were calculated for the basal areas of total plants, EM plants and
plant PC1–PC3, convexity, altitude, aspect, slope, soil PC1–
PC4, and geographic distance. To understand correlations
between plant, soil, topographic and geographic distances and
total, saprotrophic, pathogenic, EM and AM fungal compositions, distance matrices of these variables were subjected to multiple regression of distance matrices (MRM) in the ecodist package
(Goslee & Urban, 2007), with forward-selection until Padj < 0.05
for all variables. If more than one variable was retained in the
final model, hierarchical partitioning was applied to explore the
independent contribution of every variable.
The fungal and plant dissimilarity matrices were subjected to
principal coordinate (PCo) analysis using the CMDSCALE command in the VEGAN package (Oksanen et al., 2007), to generate
vectors to be used in the following variation partitioning. The
variations of total, saprotrophic, pathogenic, EM and AM fungal
richness and compositions (PCo vectors) were partitioned among
the plant (richness of total, EM and AM plants, basal areas of
total plants, EM plants and plant PC1–PC3, and PCo vectors),
soil (PC1–PC4), topographic (convexity, altitude, slope, eastness,
and northness), and spatial (positive PCNM vectors) variables
using the VARPART command in the VEGAN package (Oksanen
et al., 2007). The variables within each category were forwardselected using the FORWARD.SEL command in the packfor package
(Dray et al., 2009) then subjected to variation partitioning
(Legendre & Legendre, 2012). Spatial distribution of the significant PCNM vectors was graphically depicted in Fig. S2. To
understand correlations among these plant, soil, topographic and
spatial vectors and total, saprotrophic, pathogenic, EM and AM
fungal richness, these variables were subjected to multiple regression modeling and forward-selection until Padj < 0.05 for all variables. If more than one variable was retained in the final model,
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hierarchical partitioning was applied to explore the independent
contribution of every variable.
Total, saprotrophic, pathogenic, EM and AM fungal community compositions were ordinated using nonmetric multidimensional scaling (NMDS) using the METAMDS command in the
VEGAN package (Oksanen et al., 2007), and significant plant, soil,
topographic and spatial variables were fitted as vectors onto the
NMDS graphs using the envfit function based on 999 permutations in the VEGAN package (Oksanen et al., 2007). Procrustes
analysis was applied to explore correlations between NMDS
structures of plant species composition and total, saprotrophic,
pathogenic, EM and AM fungal compositions using the
PROCRUSTES command in the VEGAN package (Oksanen et al.,
2007).
In addition, as a correlation between pathogenic fungal richness and plant species richness was detected, relationships
between pathogenic fungal richness and basal areas of common
plants (PC1–PC3, accounting for 89.3% of total basal area;
largely reflecting basal areas of C. eyrei, S. superba, and
P. massoniana) and rare plants (PC4–PC11, accounting for 8.4%
of total basal area) were explored by linear mixed-effects models,
including random effects of PC axes using the LME command in
the LME4 package (De Boeck et al., 2011). Rarefaction curves of
observed fungal OTUs were calculated in each fungal habitat
type and the whole forest (i.e. all 62 quadrats), using the SPECACCUM command in the VEGAN package (Oksanen et al., 2007). All
analyses were carried out in R 3.1.1 (R Development Core Team,
2011), and statistical significance measures were corrected using
the Bonferroni method (Padj).
Results
General characterization of 454 pyrosequence data
After removing sequences that did not meet the quality criteria,
the remaining nonchimeric ITS2 sequences (365 003 in total)
were clustered into 4304 nonsingleton OTUs at a 97% similarity
level. Of these 4304 OTUs, 3301 (294 530 reads) were identified
as fungal. As the fungal read numbers obtained from the 62
quadrats ranged from 3109 to 6743, the read numbers were normalized to 3109, resulting in a normalized dataset containing
3252 fungal OTUs (192 758 reads) (Fig. S3; Table S6). The represented fungi included 2081 Ascomycota, 961 Basidiomycota,
81 Cryptomycota, 58 Zygomycota, 43 Glomeromycota, nine
Chytridiomycota, and 19 unknown fungi (Fig. S4a). Of the 3252
fungal OTUs, 2415 were assigned to six fungal trophic groups:
saprotrophic fungi (1478), EM fungi (476), pathogenic fungi
(293), animal parasitic fungi (66), mycoparasitic fungi (59), and
AM fungi (43) (Fig. S4b).
Delineation of habitats for fungal communities
Multivariate regression tree analysis classified the 62 quadrats
into valley (n = 20) or ridge (n = 42) habitat types, with differing
fungal communities, separated by a convexity threshold of
3.783 (Fig. 1). Of the 3252 fungal OTUs, 2813 (130 578
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Fig. 1 Results of multiple regression tree analysis of the fungal community
composition with topographic variables, dividing the 62 quadrats into
ridge (42) and valley (20) habitats according to convexity.
reads) were recovered from the ridge habitat and 2575 (62 180
reads) from the valley habitat (Fig. S3). The richness values of
total, saprotrophic, pathogenic, mycoparasitic and animal parasitic fungi were significantly higher in valley than in ridge habitats, but EM and AM fungal richness did not differ significantly
between these habitat types (Table 1). PERMANOVA revealed
significant differences in compositions of total (R2 = 0.311,
Padj < 0.001), saprotrophic (R2 = 0.297, Padj < 0.001), EM
(R2 = 0.125, Padj = 0.008), and animal parasitic (R2 = 0.127,
Padj = 0.007) fungi between the valley and ridge habitats, but not
in compositions of pathogenic (R2 = 0.068, Padj = 0.252), mycoparasitic (R2 = 0.076, Padj = 1) and AM (R2 = 0.071,
Padj = 0.491) fungi between these habitat types.
Fungal richness in ridge habitat, valley habitat and whole
forest
Total and saprotrophic fungal richness were significantly linearly
related to plant species richness in the ridge habitat and whole
forest (total 62 quadrats), but not in the valley habitat (Fig. S5a,
b). Pathogenic fungal richness was significantly related to plant
species richness (linearly in the ridge habitat, quadratically in the
whole forest), but not in the valley habitat (Fig. S5c). EM and
AM fungal richness were not significantly related to host plant
species richness in the ridge and valley habitats, or the whole
forest (Fig. S5d,e).
The stepwise multiple regressions showed that total and saprotrophic fungal richness were significantly predicted by plant
species richness and soil PC1 (nutrients and moisture) in the
ridge habitat, by convexity or plant PC1 (C. eyrei basal area) in
the valley habitat, and by soil PC2 (C : N ratio), plant PCo1 and
either soil PC1 (nutrients and moisture) or plant PC1 (C. eyrei
basal area) in the whole forest (Table 2). EM fungal richness was
significantly predicted by plant PC1 (C. eyrei basal area) in the
ridge habitat and whole forest, and by EM plant basal area in the
valley habitat (Table 2). Pathogenic fungal richness was significantly predicted by plant species richness in the ridge habitat, by
convexity2 in the valley habitat, and by soil PC2 (C : N ratio),
total basal area and plant PCo1 in the whole forest (Table 2).
AM fungal richness was not significantly predicted by any tested
variable in the ridge and valley habitats or the whole forest
(Table 2). The variation partitioning showed that various combinations of soil, spatial, plant and topographic vectors explained
0–54%, 0–72% and 22–72% of the variations in richness of
total, saprotrophic, pathogenic, EM and AM fungi in the ridge
habitat, valley habitat, and whole forest, respectively (Fig. S6).
Linear mixed-effects models showed that pathogenic fungal
richness was significantly correlated with the basal area of common plants negatively (Fig. 2a) and that of rare plants positively
(Fig. 2b) in the ridge habitat, but had no significant correlation
with the basal area of either common or rare plants in the valley
habitat (Fig. 2a,b).
Fungal community composition in ridge habitat, valley
habitat and whole forest
Nonmetric multidimensional scaling and envfit analyses showed
that total, saprotrophic, EM and pathogenic fungal compositions
were significantly related to soil PC1 (nutrients and moisture),
plant PCo1 and various combinations of soil PC2 (C : N ratio),
plant PCo2, basal areas of total plants, plant PC1 (C. eyrei) and
PC2 (P. massoniana), altitude, plant species richness, and
PCNM5 in the ridge habitat (Fig. 3a,d,g,j); and to convexity, soil
PC1–PC2 (nutrients and moisture), basal areas of total plants,
plant PC1 (C. eyrei) and PC2 (P. massoniana), plant PCo1 and
various combinations of soil PC3 (pH), plant PCo2, altitude,
plant species richness, and PCNM1 and 12 in the whole forest
(Fig. 3c,f,i,l). In the valley habitat, total and saprotrophic fungal
compositions were significantly related to plant species richness,
plant PCo1, total basal area, plant PC1 (C. eyrei basal area) and/
or PCNM2 (Fig. 3b,e), but EM fungal composition was only
Table 1 Richness (mean SE) of total fungi and trophic groups in the ridge and valley habitats, and results of t-tests of differences between them
Fungal richness
Ridge (n = 42)
Valley (n = 20)
t
Padj
Total fungi
Saprotrophic fungi
Ectomycorrhizal (EM) fungi
Pathogenic fungi
Animal parasitic fungi
Mycoparasitic fungi
Arbuscular mycorrhizal (AM) fungi
443.45 10.40
227.05 5.65
55.91 1.72
34.19 1.55
9.14 0.64
8.74 0.49
2.69 0.30
544.45 11.11
276.35 6.93
55.25 4.25
52.60 2.33
13.05 1.00
12.8 0.66
3.6 0.37
6.636
5.517
0.143
6.584
3.277
4.932
1.896
< 0.001
< 0.001
1
< 0.001
0.017
< 0.001
0.452
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Table 2 Stepwise multiple regressions of total, saprotrophic, ectomycorrhizal (EM), pathogenic and arbuscular mycorrhizal (AM) fungal richness against
indicated plant, soil and topographic variables in the ridge habitat, valley habitat and whole forest
Habitat
Fungal richness
Variable
Ridge
Total fungi
Plant species richness
Soil principal component 1 (PC1)
(nutrients and moisture)
Plant species richness
Soil PC1 (nutrients and moisture)
Plant PC1 (C. eyrei basal area)
Plant species richness
No variable retained in the final model
Convexity
Plant PC1 (C. eyrei basal area)
EM plant basal area
Convexity2
No variable retained in the final model
Soil PC1 (nutrients and moisture)
Soil PC2 (C : N ratio)
Plant PCo1
Soil PC2 (C : N ratio)
Plant PC1 (C. eyrei basal area)
Plant PCo1
Plant PC1 (C. eyrei basal area)
Soil PC2 (C : N ratio)
Total basal area
Plant PCo1
No variable retained in the final model
Saprotrophic fungi
Valley
Whole
forest
EM fungi
Pathogenic fungi
AM fungi
Total fungi
Saprotrophic fungi
EM fungi
Pathogenic fungi
AM fungi
Total fungi
Saprotrophic fungi
EM fungi
Pathogenic fungi
AM fungi
Slope
SD
t
6.740
41.514
1.692
11.446
3.982
3.627
0.004
0.012
3.107
23.260
21.573
1.370
0.990
6.695
4.731
0.264
3.138
3.474
4.560
5.194
0.048
0.019
0.001
< 0.001
14.340
106.410
33.785
0.151
3.217
16.981
7.623
0.046
4.457
6.266
4.432
3.309
< 0.001
< 0.001
0.005
0.050
41.354
38.865
111.767
17.893
47.479
84.700
26.966
5.994
7.245
27.993
11.946
8.946
28.525
4.843
11.555
11.314
5.111
1.404
1.772
3.312
3.462
4.344
3.918
3.694
4.109
7.486
5.276
4.268
4.089
8.452
0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
Padj
Independent
contribution (%)
R2adj
Padj
29.6
26.6
0.54
< 0.001
23.2
26.1
0.466
< 0.001
0.326
0.388
0.001
< 0.001
0.525
0.668
0.495
0.344
< 0.001
< 0.001
0.005
0.05
0.616
< 0.001
0.628
< 0.001
0.306
0.700
< 0.001
< 0.001
24.3
12.2
27.0
11.5
16.0
37.0
12.5
18.0
40.9
N, nitrogen; C, carbon; C. eyrei, Castanopsis eyrei.
related to plant species richness (Fig. 3h), and pathogenic fungal
composition to none of the tested variables (Fig. 3k). AM fungal
composition was not significantly related to any of the tested variables in either the ridge and valley habitats or the whole forest
(Fig. 3m–o).
Procrustes analysis demonstrated that total and saprotrophic
fungal compositions were significantly related to plant species
composition in the ridge habitat, valley habitat and whole forest
(Table S7). EM and pathogenic fungal compositions were significantly related to plant species composition in the ridge habitat
and whole forest, but not in the valley habitat (Table S7). AM
fungal composition was not significantly related to plant species
composition in the ridge habitat, valley habitat, or the whole
forest (Table S7).
The final MRM models showed that total, saprotrophic,
pathogenic and EM fungal compositions were significantly
predicted by plant species composition and geographic distance
in the ridge habitat, and by soil PC1 (nutrients and moisture),
plant species composition, and either total basal area or geographic distance in the whole forest (Table 3). The final MRM
models for communities in the valley habitat included plant
species composition and plant species richness for total fungi,
these two variables plus soil PC2 (C : N ratio) for saprotrophic
fungi, soil PC3 (pH) for EM fungi or no variables for pathogenic
fungi (Table 3). AM fungal composition was not significantly
predicted by any of the tested variables in the ridge and valley
habitats and the whole forest (Table 3). The variation partitioning showed that various combinations of soil, plant, spatial and
topographic variables explained 0–42%, 0–70% and 5–36% of
the variations in compositions of total, saprotrophic, pathogenic,
EM and AM fungal communities in the ridge habitat, valley
habitat and whole forest, respectively (Fig. S7).
Fig. 2 Relationships between pathogenic
fungal richness and basal area of common
plants (principal component (PC)1–PC3,
accounting for 89.3% of total basal area) (a)
and rare plants (PC4–PC11, accounting for
8.4% of total basal area) (b) in the ridge
(blue lines) and valley (red lines) habitats, as
explored by linear mixed-effects models,
including random effects of plant PC axes.
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(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)
(m)
(n)
(o)
Fig. 3 Nonmetric multidimensional scaling (NMDS) of total (a–c), saprotrophic (d–f), ectomycorrhizal (g–i), pathogenic (j–l) and arbuscular mycorrhizal (m–
o) fungal community compositions in the ridge habitat (a, d, g, j, m), valley habitat (b, e, h, k, n) and whole forest (c, f, i, l, o). Significant plant, soil,
topographic and spatial variables were fitted as vectors onto the NMDS graphs using the envfit function. Soil principal component 1 (PC1), nutrients and
moisture; soil PC2, carbon : nitrogen ratio; soil PC3, pH; plant PC1, Castanopsis eyrei basal area; plant PC2, Pinus massoniana basal area.
In addition, to assess the effect of the imbalance in sample
numbers between the ridge (42 quadrats) and valley (20
quadrats) habitats on the results, we randomly selected 20
quadrats from the ridge habitat, and found that the main results
were similar to those obtained from the 42-quadrat dataset
(Fig. S8–S13; Table S8–S10).
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Discussion
The convexity-based ridge and valley fungal habitats delineated in
this study have clear partial similarities with the five plant habitats
previously delineated by topographic convexity and altitude in the
subtropical forest (Legendre et al., 2009). These results suggest that
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Table 3 Multiple regressions on distance matrices of total, saprotrophic, pathogenic, ectomycorrhizal (EM) and arbuscular mycorrhizal (AM) fungal
community compositions as predicted by plant, soil, topographical and geographic variables in the ridge habitat, valley habitat and whole forest
Independent
contribution (%)
Habitat
type
Fungal composition
Independent variable
Slope
Padj
Ridge
Total fungi
Geographic distance
Plant species composition
Geographic distance
Plant species composition
Geographic distance
Plant species composition
Geographic distance
Plant species composition
No variable retained in the final model
Plant species composition
Plant species richness
Soil PC2 (C : N ratio)
Plant species composition
Plant species richness
No variable retained in the final model
Soil PC3 (pH)
No variable retained in the final model
Soil PC1 (nutrients and moisture)
Total basal area
Plant species composition
Soil PC1 (nutrients and moisture)
Total basal area
Plant species composition
Soil PC1 (nutrients and moisture)
Plant species composition
Geographic distance
Soil PC1 (nutrients and moisture)
Plant species composition
No variable retained in the final model
< 0.001
0.273
< 0.001
0.286
< 0.001
0.324
< 0.001
0.205
0.003
< 0.001
< 0.001
< 0.001
0.05
< 0.001
0.002
< 0.001
3.5
8.9
3.5
9.9
2.2
9.7
3.5
5.3
0.289
0.013
0.049
0.225
0.008
0.002
0.003
0.003
0.004
0.002
11.3
9.1
8.7
14
9.8
0.137
< 0.001
0.098
0.118
0.202
0.08
0.108
0.201
0.117
0.191
< 0.001
0.074
0.141
0.003
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
0.004
< 0.001
< 0.001
< 0.001
< 0.001
Saprotrophic fungi
Pathogenic fungi
EM fungi
Valley
AM fungi
Total fungi
Saprotrophic fungi
Whole
forest
Pathogenic fungi
EM fungi
AM fungi
Total fungi
Saprotrophic fungi
Pathogenic fungi
EM fungi
AM fungi
7.6
5.4
7.3
6.9
5.7
8.4
9
6.4
2.5
3.7
3.5
Model R2
Model Padj
0.124
< 0.001
0.134
< 0.001
0.119
0.088
< 0.001
< 0.001
0.204
< 0.001
0.324
< 0.001
0.119
< 0.001
0.203
< 0.001
0.209
< 0.001
0.153
< 0.001
0.096
< 0.001
N, nitrogen; C, carbon.
distributions of both the fungal and plant communities are strongly
influenced by convexity, but the fungal communities are less sensitive than the plant communities to the variations in altitude of the
quadrats (450–710 m asl) in the studied forest. However, recent
studies have detected significant changes in soil fungal communities
across substantially larger altitudinal gradients in natural ecosystems
in Argentina (400–3000 m asl; Geml et al., 2014), Switzerland
(400–3200 m asl; Pellissier et al., 2014) and China (530–2200 m
asl; Shen et al., 2014). This is not surprising as large altitudinal gradients are tightly correlated with dramatic changes in climate, soil
and vegetation types, resulting in shifts in soil fungal communities
(Procter et al., 2014; Tedersoo et al., 2014).
In the whole forest, total, saprotrophic, pathogenic and EM fungal richness were mainly correlated with various combinations of
edaphic and plant-related variables, as reported in previous studies
(e.g. Kerekes et al., 2013; Tedersoo et al., 2014, 2015; Prober et al.,
2015). However, we found that the factors related to total, saprotrophic, pathogenic and EM fungal richness differed between ridge
and valley habitats. For example, total, saprotrophic and
pathogenic fungal richness were correlated to plant species richness
and/or soil nutrients and moisture in the ridge habitat, but to convexity and/or C. eyrei basal area in the valley habitat (Table 2). At
the study site, soil nutrients, moisture and plant species richness are
lower in ridge than in valley habitats (by 14–43%, 10% and 15%,
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respectively), but C. eyrei basal area is 37% lower in valley than in
ridge habitats (Table S4). Therefore, resource limitations may be
primarily responsible for the differences in these fungal diversities
between the habitats, as proposed by Tilman (1982). In addition,
the finding that total and pathogenic fungal richness were correlated with convexity in the valley habitat, but not in the ridge habitat, is consistent with findings that total and pathogenic fungal
richness were substantially higher in valley than in ridge habitats,
by 23% and 54%, respectively (Table 1). This is indicative of community coalescence (Rillig et al., 2015) potentially caused by more
dispersal of propagules of fungi inhabiting leaves, litter and surface
soil via wind, gravity and forest floor surface runoff into concave
quadrats in valley habitat than in convex quadrats in ridge habitat
in the mountain forest ecosystem. In addition, fungi may have
higher dispersal ability as a result of higher abundance and cultivability in wet, nutrient-rich valley habitat than in dry, nutrientpoor ridge habitat (Table S4). It is not possible to determine from
data collected in this study whether variation in the dispersal of
fungal propagules between habitats was causal or consequential,
but it would be valuable to include estimates of fungal abundance
or fungal dispersal ability (e.g. cultivability) in models as explanatory variables in future studies.
By contrast, EM fungal richness was correlated with the basal
area of the dominant EM plant C. eyrei and total EM plants in
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1882 Research
the ridge and valley habitats, respectively. However, C. eyrei basal
area accounted for 57% of total EM plant basal area in the ridge
habitat, suggesting that the observed effect of C. eyrei may reflect
its dominance (and hence total effect of EM plants) in this habitat. These results indicate that supplies of organic substrates from
host plants are major determinants of EM fungal abundance and
diversity, in accordance with previous findings (e.g. Taniguchi
et al., 2007; Tedersoo et al., 2014, 2015). Besides, although EM
fungal richness was not significantly different between the ridge
and valley habitats, EM fungal composition was significantly different between these habitats. This suggests that different EM
fungal species (at the same richness levels) are inhabiting the different habitats. The difference in EM fungal composition might
be that C. eyrei is more common in the ridge than in the valley
habitats and thus might associate with a different suite of EM
fungi, as EM fungi might be plant species-specific (e.g. Molina
et al., 1992; Tedersoo et al., 2008, 2010).
In addition, we found positive correlations between plant species
richness and total, saprotrophic and pathogenic fungal richness in
the ridge habitat, but not in the valley habitat. This may be because
plant species richness is a key limited factor in shaping these fungal
diversities in the ridge habitat, but not in the valley habitat, as mentioned earlier. By contrast, we found no indications that host plant
species richness was correlated with AM and EM fungal richness,
as reported in some previous studies (e.g. Kernaghan & Harper,
2001; Gao et al., 2013, 2015; Prober et al., 2015). However, several previous studies have demonstrated significant correlations
between EM fungal richness and host plant species richness in
ecosystems (e.g. Kernaghan et al., 2003; Tedersoo et al., 2014).
The correlation between plant species richness and mycorrhizal
fungal richness used to be attributed to host preference (Hart et al.,
2003; Dickie, 2007), and evidence corroborating this hypothesis
has been obtained in some studies (e.g. Molina et al., 1992;
Tedersoo et al., 2008, 2010), but not in investigations of several
tropical forests (Smith et al., 2011; Tedersoo et al., 2011). Besides,
AM fungi generally show no or very weak host preference com€
pared with EM fungi (e.g. Opik
& Moora, 2012; Davison et al.,
2015), which might explain the lack of correlation between plant
€
species richness and AM fungal richness (Opik
et al., 2008, 2010;
Prober et al., 2015). In addition, relationships between fungi and
plants might also be influenced by other organisms in the soil and
rhizosphere, such as bacteria, archaea and protists, as well as other
abiotic factors, such as microhabitat and soil properties apart from
the measured soil variables.
In the whole forest, total, saprotrophic, pathogenic and EM fungal community compositions were significantly related to various
combinations of edaphic, geographic and plant-related variables, as
reported in previous studies (e.g. Pellissier et al., 2014; Tedersoo
et al., 2014; Prober et al., 2015; Matsuoka et al., 2016). Furthermore, we found that different combinations of abiotic variables were
correlated with these fungal compositions between ridge and valley
habitats. For example, geographic distance was correlated with these
fungal compositions in the ridge habitat, but not in the valley habitat
(Table 3), suggesting that dispersal limitation is a stronger factor in
the ridge habitat than in the valley habitat. This may be because
fungi have stronger dispersal ability in the concave valley habitat
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than in the convex ridge habitat, as a result of higher dispersal of fungal propagules, higher fungal abundance or higher cultivability in
wet, nutrient-rich valley habitat than in dry, nutrient-poor ridge
habitat (Table S4). Furthermore, in the valley habitat, saprotrophic
and EM fungal compositions were correlated with soil C : N ratio
and pH, respectively, but not in the ridge habitat. In the study site,
soil C : N ratio is 7.9% lower in valley than in ridge habitats, but the
soil pH range is 76% higher in valley (3.73–5.00) than in ridge
(4.20–4.93) habitats (Table S4), suggesting that resource limitation
may be responsible for the differences in saprotrophic fungal
composition, but the pH niche axis length may be responsible for
the differences in EM fungal composition in the habitats.
In addition to abiotic variables, we found that different combinations of plant variables were correlated with total, saprotrophic,
EM and pathogenic fungal compositions in the ridge and valley
habitats. In addition to plant species composition, total and
saprotrophic fungal compositions were correlated with plant
species richness in the valley habitat, but not in the ridge habitat.
This may be a result of the 18% higher plant species richness in
valley than in ridge habitats (Table S4), generating more diverse
organic substrates that affect fungal community assembly.
Pathogenic and EM fungal compositions were correlated with
plant species composition in the ridge habitat, but not in the valley habitat, possibly because, although these biotrophs may have
stronger dispersal ability in valley than in ridge habitats (as mentioned earlier), they have poor ability to colonize hosts in valley
habitat as a result of the ‘priority effect’ (Kennedy et al., 2009).
However, AM fungal richness and composition were not correlated with any of the many biotic and abiotic variables investigated in this study, suggesting that factors influencing the AM
fungi in this subtropical forest have strongly stochastic elements.
It should be noted that both the number of samples (62
quadrats in total) and sequencing depth (the observed fungal
OTU richness did not reach the asymptote) were limited in this
study, and thus the robustness of the relationships between plants
and fungi should be tested with more samples covering larger
areas, or completely different sites, with much deeper fungal
sequencing to saturation. Furthermore, although we delineated
fungal OTUs into trophic groups using public databases (Tedersoo et al., 2014), the correspondence between phylogenetic relations and trophic status is controversial (Peay et al., 2008). Thus,
confirmation of the trophic status of the detected taxa by collecting fungi from diseased plant tissue, litter and mycorrhizal root
samples would be valuable in future studies.
In conclusion, this study first delineated ridge and valley fungal
habitats based on convexity, in partial accordance with five plant
habitats delineated by convexity and altitude criteria in the focal
subtropical forest. Total, saprotrophic, EM and pathogenic fungal richness and compositions were significantly correlated with
different combinations of plant, soil, geographic and topographic
variables in the ridge and valley habitats. Our findings suggest
that the mechanisms responsible for maintaining the diversity
and community composition of soil fungi differ between ridge
and valley habitats, and the relationships between fungal and
woody plant assemblages depend on habitat types in the examined subtropical forest ecosystem.
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Acknowledgements
We are grateful to staff in the Gutianshan Research Station of
Forest Biodiversity and Climate Change for sample collection.
This work was supported by the National Natural Science Foundation of China (grant nos. 31210103910, 30930005 and
31470545). We thank Prof. Helge Bruelheide from Martin
Luther University, Dr Tesfaye Wubet and Prof. Francßois Buscot
from UFZ–Helmholtz Centre for Environmental Research, and
Dr Lei Chen from the Institute of Botany, Chinese Academy of
Sciences, for their valuable suggestions on data analysis and organization of this paper.
Author contributions
C.G. and L-D.G. designed the experiments, analyzed the data
and wrote the manuscript. C.G., N-N.S., L.C., N-N.J., B-W.W.,
Y-L.W., Y.X. and Y.Z. performed sampling and molecular work.
X-C.M. and K-P.M. provided soil, topography and plant data.
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Supporting Information
Additional Supporting Information may be found online in the
Supporting Information tab for this article:
Research 1885
arbuscular mycorrhizal fungal richness in the 20 selected quadrats
in ridge habitat.
Fig. S1 Distribution of the 62 quadrats used in this study in the
24 ha plot in a Chinese subtropical forest.
Fig. S12 Nonmetric multidimensional scaling (NMDS) of total,
saprotrophic, ectomycorrhizal, pathogenic and arbuscular mycorrhizal fungal compositions in the 20 selected quadrats in ridge
habitat.
Fig. S2 Spatial distribution of principal coordinates analysis of
neighbor matrix (PCNM). PCNM5 and PCNM9 in the ridge
habitat, PCNM1, PCNM2, PCNM6 and PCNM7 in the valley
habitat, and PCNM1, PCNM4, PCNM7 and PCNM8 in the
whole forest.
Fig. S13 Results of variation partitioning analyses showing pure
and shared effects of plant, topographic, soil and spatial variables
on total, saprotrophic, pathogenic, ectomycorrhizal and arbuscular mycorrhizal fungal community compositions in the 20
selected quadrats in ridge habitat.
Fig. S3 Rarefaction of observed fungal operational taxonomic units
(OTUs) in the ridge habitat, valley habitat and whole forest.
Table S1 Principal component analysis (PCA) of soil variables
Fig. S4 Numbers of fungal operational taxonomic units (OTUs)
belonging to indicated phyla and indicated trophic groups.
Fig. S5 Relationships between plant species richness and richness
of total, saprotrophic, pathogenic, ectomycorrhizal (EM) and
arbuscular mycorrhizal (AM) fungi in the ridge habitat, valley
habitat and whole forest.
Fig. S6 Results of variation partitioning analyses showing pure
and shared effects of plant, topographic, soil and spatial variables
on total, saprotrophic, pathogenic, ectomycorrhizal and arbuscular mycorrhizal fungal richness in the ridge habitat, valley habitat
and whole forest.
Fig. S7 Results of variation partitioning analyses showing pure
and shared effects of plant, topographical, soil and spatial variables on total, saprotrophic, pathogenic, ectomycorrhizal and
arbuscular mycorrhizal fungal community compositions in the
ridge habitat, valley habitat, and whole forest.
Fig. S8 Rarefaction of observed fungal operational taxonomic
units (OTUs) in the 20 selected quadrats in ridge habitat and valley habitat.
Fig. S9 Relationships between plant species richness and richness
of total, saprotrophic, pathogenic, ectomycorrhizal (EM) and
arbuscular mycorrhizal (AM) fungi in the 20 selected quadrats in
ridge habitat.
Fig. S10 Relationships between pathogenic fungal richness and
basal area of common plants (PC1–PC3, accounting for 89.3%
of total basal area) and rare plants (PC4–PC11, accounting for
8.4% of total basal area, in the 20 selected quadrats in ridge habitat and valley habitat, as explored by linear mixed-effects models
including random effects of plant PC axes.
Fig. S11 Results of variation partitioning analyses showing pure
and shared effects of plant, topographic, soil and spatial variables
on total, saprotrophic, pathogenic, ectomycorrhizal and
Ó 2016 The Authors
New Phytologist Ó 2016 New Phytologist Trust
Table S2 Principal component analysis (PCA) of basal area of
plant species
Table S3 Principal coordinates analysis of neighbor matrix
(PCNM) eigenfunctions with positive eigenvalues in the ridge
habitat, valley habitat and whole forest
Table S4 T test of plant, soil and topographical variables (mean
SE) between ridge and valley habitats
Table S5 The DNA tag of the 62 quadrats in this study
Table S6 Molecular identification of fungi in this study
Table S7 Relationships between total fungal and trophic group
compositions and plant species composition in the ridge habitat,
valley habitat and whole forest, as determined by Procrustes test
Table S8 Richness (mean SE) of total fungi and trophic groups
in the 20 selected quadrats in ridge habitat and valley habitat,
and results of t-tests of differences between them
Table S9 Stepwise multiple regressions of total, pathogenic,
saprotrophic, ectomycorrhizal (EM) and arbuscular mycorrhizal (AM) fungal richness against indicated plant, soil,
topographic and spatial variables in the 20 selected quadrats
in ridge habitat
Table S10 Multiple regressions on distance matrices of total,
saprotrophic, pathogenic, ectomycorrhizal (EM) and arbuscular
mycorrhizal (AM) fungal compositions as predicted by plant,
soil, topographical and geographic variables in the 20 selected
quadrats in ridge habitat
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directed to the New Phytologist Central Office.
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