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Research 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 1874 New Phytologist (2017) 213: 1874–1885 www.newphytologist.com 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, Ó 2016 The Authors New Phytologist Ó 2016 New Phytologist Trust New Phytologist 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 Ó 2016 The Authors New Phytologist Ó 2016 New Phytologist Trust Research 1875 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 New Phytologist (2017) 213: 1874–1885 www.newphytologist.com New Phytologist 1876 Research 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. New Phytologist (2017) 213: 1874–1885 www.newphytologist.com 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. Ó 2016 The Authors New Phytologist Ó 2016 New Phytologist Trust New Phytologist 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, Ó 2016 The Authors New Phytologist Ó 2016 New Phytologist Trust Research 1877 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 New Phytologist (2017) 213: 1874–1885 www.newphytologist.com New Phytologist 1878 Research 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 New Phytologist (2017) 213: 1874–1885 www.newphytologist.com Ó 2016 The Authors New Phytologist Ó 2016 New Phytologist Trust New Phytologist Research 1879 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. Ó 2016 The Authors New Phytologist Ó 2016 New Phytologist Trust New Phytologist (2017) 213: 1874–1885 www.newphytologist.com New Phytologist 1880 Research (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). New Phytologist (2017) 213: 1874–1885 www.newphytologist.com 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 Ó 2016 The Authors New Phytologist Ó 2016 New Phytologist Trust New Phytologist Research 1881 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%, Ó 2016 The Authors New Phytologist Ó 2016 New Phytologist Trust 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 New Phytologist (2017) 213: 1874–1885 www.newphytologist.com 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 New Phytologist (2017) 213: 1874–1885 www.newphytologist.com New Phytologist 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. Ó 2016 The Authors New Phytologist Ó 2016 New Phytologist Trust New Phytologist 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. References Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. 1990. 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Plant and Soil 347: 211–220. € Zobel M, Opik M. 2014. Plant and arbuscular mycorrhizal fungal (AMF) communities – which drives which? Journal of Vegetation Science 25: 1133– 1140. Ó 2016 The Authors New Phytologist Ó 2016 New Phytologist Trust New Phytologist 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 Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. 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