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Environmental Microbiology (2014)

doi:10.1111/1462-2920.12559

Network analysis reveals that bacteria and fungi form modules that correlate independently with soil parameters

Alexandre B. de Menezes,1* Miranda T. Prendergast-Miller,2 Alan E. Richardson,1 Peter Toscas,3 Mark Farrell,2 Lynne M. Macdonald,2 Geoff Baker,1 Tim Wark4 and Peter H. Thrall1 1 Black Mountain, CSIRO Agriculture Flagship, Canberra, ACT 2601, Australia. 2 Waite Campus, CSIRO Agriculture Flagship, Glen Osmond, SA, Australia. 3 CSIRO Digital Productivity and Services Flagship, Clayton South, VIC, Australia. 4 CSIRO Digital Productivity and Services Flagship, QCAT, Pullenvale, QLD, Australia. Summary Network and multivariate statistical analyses were performed to determine interactions between bacterial and fungal community terminal restriction length polymorphisms as well as soil properties in paired woodland and pasture sites. Canonical correspondence analysis (CCA) revealed that shifts in woodland community composition correlated with soil dissolved organic carbon, while changes in pasture community composition correlated with moisture, nitrogen and phosphorus. Weighted correlation network analysis detected two distinct microbial modules per land use. Bacterial and fungal ribotypes did not group separately, rather all modules comprised of both bacterial and fungal ribotypes. Woodland modules had a similar fungal : bacterial ribotype ratio, while in the pasture, one module was fungal dominated. There was no correspondence between pasture and woodland modules in their ribotype composition. The modules had different relationships to soil variables, and these contrasts were not detected without the use of network analysis. This study demonstrated that fungi and bacteria, components of the soil microbial communities usually treated as

Received 4 April, 2014; revised 2 July, 2014; accepted 2 July, 2014. *For correspondence. E-mail [email protected]; Tel. +61 (2) 62465041; Fax +61 (2) 62464950.

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd

separate functional groups as in a CCA approach, were co-correlated and formed distinct associations in these adjacent habitats. Understanding these distinct modular associations may shed more light on their niche space in the soil environment, and allow a more realistic description of soil microbial ecology and function. Introduction A major focal point of soil microbial ecology is to understand the environmental drivers of microbial community structure and diversity, and how microbial activity can in turn influence soil physical and chemical parameters, in particular soil nutrient pools and their provision of ecological services (Prosser et al., 2007; Bissett et al., 2013). However, studies tend to relate individual microbial groups, e.g. bacteria, fungi or archaea, to their soil environment, but as these groups interact in the environment (Singh et al., 2008), such approaches limit our understanding of the complexity of microbial ecology (Fuhrman, 2009). To overcome this limitation, researchers have recently started to investigate microbial co-occurrence patterns with the use of correlation network analysis. Co-occurrence patterns have the potential to reveal important information about shared niche spaces and interactions between poorly known microbial taxa (Barberán et al., 2012). Exploration of data through network analyses emphasizes co-occurrences, and shows that microbial groups should not be compartmentalized, but ideally, should be studied using a whole soil systematic approach (Bissett et al., 2013). Such information is particularly needed in soils where the use of new molecular technologies has revealed a high degree of microbial diversity previously unknown, such as the soil Verrucomicrobia, a phylum that can constitute c. 50% of soil bacterial 16S rRNA sequences but of which the ecological role is still poorly understood (Janssen, 2006; Bergmann et al., 2011; Naether et al., 2012; Fierer et al., 2013). The few studies that have applied correlation network analysis to soil microbial communities have demonstrated that these show non-random co-occurrence patterns and are modular in nature, i.e. the community or

2 A. B. de Menezes et al. a subset of it is structured into microbial modules which consists of groups of organisms that form distinct associations across a sample set (Duran-Pinedo et al., 2011; Zhou et al., 2011; Barberán et al., 2012; Sul et al., 2013). In many biological systems, modularity is a useful feature allowing the integration of highly dimensional data, reducing the number of variables analysed and allowing more robust statistical inferences (Langfelder and Horvath, 2008; Duran-Pinedo et al., 2011). Exploring the relationships between microbial modules and environmental variables could significantly improve our understanding of the interactions between microbial communities and abiotic factors by highlighting important details of the microbial community not apparent when microbial communities are treated as arbitrarily compartmentalized groups. Our objective was to determine the relationships between soil microbial communities and soil properties in two co-occurring land uses, native remnant woodlands and managed pastures, which are prominent in the landscape of SE Australia. We used terminal restriction length polymorphism (T-RFLP) (Liu et al., 1997) to characterize soil microbial community structure. T-RFLP is an accurate, reproducible, cost-effective and rapid method to assess variability of microbial community across a large number of samples (McCafferty et al., 2012), which has continued applicability in a variety of ecosystems such as aquatic sediments (Tischer et al., 2013), marine environments (Kim et al., 2014) and soils (Ke et al., 2013; Zumsteg et al., 2013). We first explored microbial community composition using permutational multivariate analysis of variance (PERMANOVA) and canonical correspondence analysis (CCA) to identify land use differences and broad relationships between soil variables and bacterial or fungal communities. We then used weighted correlation network analysis (WCNA) to determine if modules could be detected in the soil microbial community, and whether bacterial and fungal ribotypes would be located in separate modules or together in the same modules, i.e. evidence of interkingdom microbial co-occurrence patterns. We also compared soil variables correlating with microbial modules to determine whether different modules showed distinct relationships to soil variables that were not observed when analysing the community as a whole. Results Broad relationships between microbial communities, land use and soil variables PERMANOVA analysis revealed that differences in bacterial and fungal community composition between woodland and pastures were significant when analysing all sites together, as well as when focusing on each individual site (Table 1A). PERMANOVA further revealed that

overall bacterial and fungal community composition both differed significantly between sites (Table 1A). β-Diversity estimates determined by multivariate dispersion indices (MVDISP and PERMDISP) showed that fungal communities were significantly more heterogeneous in the woodlands compared with pastures, whereas the heterogeneity of bacterial communities was similar in both land uses (Table 1B). Bogo bacterial community showed the greatest heterogeneity and Glenrock the least, whereas fungal community heterogeneity was similar in all three sites (Table 1B). Soil parameters varied across the six sampling sites, and pasture sites were generally more nutrient rich in terms of nitrogen (N) and phosphorus (P) pools compared with woodland sites, which were characterized by higher dissolved organic carbon (DOC) concentrations (see Table 2). CCA analysis (Fig. 1) shows that shifts in bacterial and fungal community composition were positively correlated with high DOC in woodland soils, in contrast to pasture soils, where changes in microbial community composition were positively correlated with high moisture, inorganic and organic P (Fig. 1). In addition to DOC, shifts in fungal community composition in woodland soils were also correlated with increasing clay and total C, but nutrient pools, especially high NO3−-N, were mostly correlated with changes in fungal community composition in pasture soils. In contrast, shifts in bacterial community composition in woodland soils correlated with NH4+-N and FAA-N, but site differences were evident. Differences in biogeochemistry between sites were important in structuring bacterial communities, where for example, pasture communities were influenced by high moisture content, particulate organic carbon (POC), pH, NO3−-N and inorganic P (Talmo pasture), high FAA-N, microbial biomass C (MBC) and clay, but low C and NH4+-N (Bogo pasture), and high C, NO3−-N and NH4+-N (Glenrock pasture). Low MBC, high C and NH4+-N correlated with shifts in bacterial community composition at Glenrock and Bogo woodland, whereas changes in woodland community composition at Talmo were correlated with higher MBC and clay, but lower NH4+-N. Changes in pasture fungal community composition at Talmo were correlated with high soil moisture, and higher N and P concentrations at Glenrock and Bogo. Fungal and bacterial co-occurrence patterns and relationships to soil variables Detection of microbial modules. In the natural environment, bacterial and fungal communities interact with each other as well as with soil variables; therefore, WCNA was performed on combined bacterial and fungal groups to identify microbial modules operating in woodland or pasture sites. Land use was not combined because of the

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology

Network analysis of microbial community structure

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Table 1. A, PERMANOVA values for differences in community composition between land uses and sites (main test); pairwise comparisons show PERMANOVA values for differences in microbial community composition between woodland and pasture within each site and also between sites within each land use. Pseudo-F values are shown for the main test; for pairwise tests, the t-statistics are shown. B, Global multivariate dispersion for each land use and each site (MVDISP) and significance of differences in homogeneity of dispersion between groups (PERMDISP). Higher dispersion indicates higher β-diversity. A Bacteria Test

Factor

Main test

Land-use Site Site versus Land-use Site Site Site Site Site Site Land-use Land-use Land-use

Pairwise – Woodland

Pairwise – Pasture

Pairwise TO Pairwise GK Pairwise BO

Pairwise comparison

Pseudo-F

t statistic

30.199 28.527 33.518 TO versus GK TO versus BO BO versus GK TO versus GK TO versus BO BO versus GK Pasture versus Woodland Pasture versus Woodland Pasture versus Woodland

5.05 4.57 3.28 4.79 7.17 8.16 5.172 4.602 6.840

Fungi P-value

Pseudo-F

0.001 0.001 0.001

32.947 11.369 9.763

0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001

t statistic

P-value 0.001 0.001 0.001

2.67 2.35 2.26 4.90 4.12 3.50 4.498 4.669 3.374

0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001

B Bacteria Groups

MVDISP Dispersion

woodland pasture woodlands versus pasture TO GK BO TO versus GK TO versus BO BO versus GK

0.999 1.001

Fungi PERMDISP P-value

MVDISP Dispersion

PERMDISP P-value

1.323 0.677 0.515

1.047 0.709 1.237

0.001 1.028 0.955 1.016

0.001 0.001 0.001

0.416 0.926 0.334

TO, Talmo; GK, Glenrock; BO, Bogo.

clear difference in microbial community composition in pasture and woodland sites (see Fig. 1). Network analysis resulted in the detection of two modules in the woodland sites and two modules in the pasture sites (Fig. 2), with each module consisting of bacterial and fungal ribotypes (Table 3A). There was little overlap in ribotype composition between the modules occurring in the pasture and woodland sites; module C had higher contributions of fungal ribotypes than modules A, B and D (Table 3A). Analysis of network properties in Cytoscape (Shannon et al., 2003) revealed that the pasture network had higher densities [proportion of actual connections between nodes to all possible connections (Schaub et al., 2007)], clustering coefficients [density measure of local connections, i.e. how connected nodes are to their immediate neighbours (Dong and Horvath, 2007)] and average number of neighbours (average number of nodes to which a node is connected) than the woodland network, whereas module B in the woodlands in

particular had a very low density compared with all the others (Table 3B). Bacterial and fungal associations within the networks. Figures 2 and 3 and Table 3A show that the relative number of fungal to bacterial ribotypes in each module varied, and whereas in the woodlands, both modules detected had a similar ratio of fungal to bacterial ribotypes (0.9), in the pasture the ratio was of 1.3 for module C and 0.6 for module D. Interestingly, within the non-modular ribotype groups, the ratio of fungal ribotypes was greater than bacterial ribotypes, being particularly high in the woodland sites (3.7) compared with the pasture sites (2.8). In order to determine the relative importance of bacteria and fungi to each module, the 20 ribotypes with highest module membership were counted and identified (Table 4). Module membership is a measure of the importance of a ribotype to the module: the higher the module membership of a ribotype, the more

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology

28.38 ± 5.82bc 6.04 ± 1.25f 11.68 ± 2.11b 7.00 ± 1.29b 79.57 ± 13.42 13.02 ± 0.90a 2.17 ± 0.39c 2.96 ± 0.75 3.44 ± 5.22 0.75 ± 0.54c 15.88 ± 2.68c 284.53 ± 45.63e 236.21 ± 32.91d 48.32 ± 17.97d 333.94 ± 80.54cd 35.37 ± 10.25b 5.46 ± 0.12d 33.60 ± 2.58d 325.48 ± 43.37b 0.05 ± 0.06a

25.53 ± 8.09b 2.87 ± 1.00a 9.37 ± 2.56b 6.49 ± 1.55ab 85.12 ± 27.47 25.15 ± 3.94d 1.06 ± 0.43a 2.33 ± 1.05 2.03 ± 1.67 0.18 ± 0.29a 11.19 ± 2.99b 128.34 ± 42.38a 104.36 ± 35.29a 23.98 ± 10.42b 367.21 ± 101.18d 23.18 ± 9.65a 5.43 ± 0.21d 15.97 ± 3.38b 346.04 ± 52.56b 0.03 ± 0.04a

Total C (mg g−1) POC (mg g−1) HOC (mg g−1) ROC (mg g−1) DOC (mg g−1) C:N Total N (mg g−1) FAA-N (mg kg−1) NH4+-N (mg kg−1) NO3−-N (mg kg−1) DON (mg kg−1) Total P (mg kg−1) Organic P (mg kg−1) Inorganic P (mg kg−1) MBC (mg g−1) MBN (mg g−1) pH Moisture (%) Clay (mg g−1) Fungal : bacterial ratio

26.06 ± 7.13bc 3.48 ± 1.21b 10.06 ± 3.05b 5.93 ± 1.49a 112.43 ± 34.52 27.11 ± 3.26e 0.97 ± 0.27a 2.68 ± 1.11 5.36 ± 11.58 0.20 ± 0.26ab 9.13 ± 3.51a 115.13 ± 30.44a 98.47 ± 23.64a 16.66 ± 8.86a 156.79 ± 76.44a 20.70 ± 8.04a 4.96 ± 0.14a 12.52 ± 3.17a 252.28 ± 58.57a 0.05 ± 0.09b

Glenrock Woodland 25.10 ± 5.54b 3.55 ± 0.80bc 10.67 ± 1.71b 6.51 ± 1.11b 91.47 ± 27.38 15.10 ± 1.57b 1.67 ± 0.32b 3.08 ± 0.98 5.91 ± 4.84 1.20 ± 1.12d 10.81 ± 3.31b 230.38 ± 37.97d 166.47 ± 26.59c 63.91 ± 19.04e 195.28 ± 45.59a 21.25 ± 9.35a 5.22 ± 0.13c 16.42 ± 4.41b 247.50 ± 35.47a 0.01 ± 0.00a

Glenrock Pasture

Where indicated, superscript letters show significant site and land use interaction (P < 0.05).

Talmo Pasture

Talmo Woodland

Soil property 30.31 ± 14.72b 4.74 ± 2.59e 11.95 ± 3.69b 7.91 ± 2.20c 113.68 ± 45.89 19.68 ± 2.67c 1.58 ± 0.82b 3.37 ± 1.14 3.99 ± 3.13 0.37 ± 0.51b 11.89 ± 4.35b 192.13 ± 77.87c 158.77 ± 70.23c 33.36 ± 13.21c 280.20 ± 159.2b 34.20 ± 19.78b 5.40 ± 0.33d 15.00 ± 4.02b 396.53 ± 55.30c 0.01 ± 0.01a

Bogo Woodlands

Table 2. Soil variables (means ± standard deviation) from paired native woodland and managed pasture sites (n = 3).

19.45 ± 3.91a 4.24 ± 0.86d 8.01 ± 1.61a 5.48 ± 0.95a 99.57 ± 28.42 12.82 ± 0.70a 1.52 ± 0.32b 3.67 ± 1.78 4.47 ± 2.13 0.65 ± 0.60c 14.60 ± 5.41c 167.86 ± 34.02b 137.69 ± 29.59b 30.17 ± 7.51c 320.88 ± 76.26bc 34.07 ± 8.50b 5.11 ± 0.14a 18.65 ± 3.20c 336.85 ± 60.38b 0.03 ± 0.02a

Bogo Pasture

ns < 0.001 ns 0.05 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 ns < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 ns

Site

0.01 < 0.001 ns 0.022 0.001 < 0.001 < 0.001 < 0.001 0.007 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 ns < 0.001 ns < 0.001 < 0.001 ns

Land use P value

< 0.001 < 0.001 < 0.001 < 0.001 ns < 0.001 < 0.001 ns ns 0.001 0.042 < 0.001 < 0.001 < 0.001 0.024 0.001 < 0.001 < 0.001 0.003 < 0.001

Interaction P value

4 A. B. de Menezes et al.

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Network analysis of microbial community structure

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Fig. 1. CCA biplot of soil variables that influence bacterial and fungal T-RFLP communities in pasture and woodland soils at three sites, Glenrock, Talmo and Bogo. (See further details in Table S1 and S2, Appendix S1.)

connected it will be with other ribotypes in the module (Langfelder and Horvath, 2008). The number of fungal ribotypes in the 20 with highest module membership was 9 in module A, 7 in module C, but only 1 and 0 in modules B and D. Module associations with soil variables. Figure 3 shows that the overall strength of the correlations between modules and soil variables was lower in the woodland than in pasture sites. In addition, the modules detected in the pasture and woodland networks showed different relationships to the measured traits. In the woodland network,

module A only correlated positively with pH, MBC and moisture; all other correlations were insignificant (P > 0.05). The larger module B was positively correlated (P < 0.05) with FAA-N, NH4+-N, POC and DOC, whereas significant negative correlations were detected with pH, moisture, MBC and clay. The non-modular ribotypes correlated positively with organic P, C, POC, pH, soil moisture, NH4+-N, dissolved organic nitrogen (DON), FAA-N, microbial biomass N (MBN) and MBC. Within the pasture network, module C correlated positively and significantly with NH4+-N, NO3−-N and inorganic P; significant negative correlations were found between

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology

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A. B. de Menezes et al.

Fig. 2. Network plots of the woodland (A) and pasture (B) bacterial and fungal communities. Circles and rhombuses represent bacterial and fungal ribotypes respectively; node size is proportional to ribotype relative abundance. In the woodland network, blue and purple nodes represent modules A and B respectively; in the pasture network, green and dark blue nodes represent modules C and D respectively. Edges (red lines) represent associations between nodes (ribotypes); edge width is proportional to edge weight, which is a measure of the strength of the connection between two nodes. Node module membership in weighted correlation networks are characteristically continuous, with some nodes in intermediary positions between modules, which can be seen in the woodland network, where blue and purple nodes overlap at the boundary between modules A and B.

In general, correlations were stronger within the pasture compared with the woodland network; for example, whereas in the woodland network, the correlations (r values) ranged from +0.19 to +0.31 (module A), −0.51 to +0.32 (module B) and +0.20 to +0.41 (nonmodular ribotypes), in the pastures, correlations ranged from −0.74 to +0.46 (module C), −0.64 to +0.38 (module D) and −0.38 to +0.83 (non-modular ribotypes).

module C and pH, soil moisture, POC, clay, organic P, MBN and MBC. The module D had significant positive correlations with FAA-N, clay, DOC, MBN and MBC, whereas significant negative correlations were found with soil pH, moisture, NO3−-N, C, inorganic and organic P. The non-modular group in the pasture sites correlated positively with soil moisture, POC, organic P, pH, clay, C, MBN and MBC; negative correlations were observed with NH4+-N, NO3−-N and DOC.

Table 3. Module properties of woodland and pasture networks. A, number of ribotypes per module and number of shared ribotypes between woodland and pasture modules; B, network parameters for whole network and individual modules detected in the woodland and pasture networks (edge weight threshold > 0.01). A

Woodland network

Pasture network

Number of shared ribotypes in woodland versus pasture modules

Module

Total number of ribotypes

Number of Fungal ribotypes

Number of Bacterial ribotypes

Module A Module B Non-modular Module C Module D Non-modular

19 200 143 81 140 144

9 95 113 46 52 106

10 105 30 35 88 38

Shared ribotypes A versus C 3

A versus D 7

B versus C 39

B versus D 69

B Woodland network

Pasture network

Network parameter

Whole network

Module A

Module B

Whole network

Module C

Module D

Clustering coefficient Network density Average number of neighbours

0.485 0.072 10.127

0.456 0.267 4.000

0.485 0.086 10.810

0.730 0.180 38.676

0.697 0.192 15.333

0.803 0.310 41.600

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Network analysis of microbial community structure

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Fig. 3. Heatmap showing strength of correlations between modules and soil variables in woodland and pasture networks. Fungi/bacteria ratio was calculated based on the number of fungal versus bacterial ribotypes in each module. Stronger colours (red is positive, green is negative) in the heatmap represent stronger correlations; the correlation (r) is indicated in the plot for significant correlations (P < 0.05).

Discussion Soil microbial community composition and relationships to soil variables Using CCA and PERMANOVA, we showed that land use does affect bacterial and fungal community composition and broad relationships with soil variables that defined pasture (moist and nutrient rich) or woodland (C rich) soil were outlined. Therefore, fungal and bacterial communities in woodland soils were correlated mainly with DOC, clay and total C content. By contrast, in the relatively nutrient-rich pasture soils which receive regular inputs of P-fertilizer and have high potential for biological N-fixation through subterranean clover (Peoples and Baldock, 2001), N and P pools and moisture content structured microbial communities. Although this was a snapshot of the soil properties at these sites, studies conducted in the same region (south and south-eastern Australia) and in soils under similar land uses (i.e. remnant eucalyptus woodlands and managed pastures) as well as in cropping systems have found similar associations in Australian soils before (Wakelin et al., 2008; 2009; Osborne et al., 2011). Based on CCA, interpretation of microbial composition is limited to differences which seem to be mainly due to local variation in biogeochemistry between sites (Fig. 1). We therefore used WCNA to improve our understating of the relationships between microbial communities and soil variables. WCNA revealed that there are modules, shared between all sites in each land use, with distinct associations with soil variables. Importantly, the microbial community was not constituted of separate

bacteria- or fungi-only modules; rather bacterial and fungal ribotypes correlated together in the same modules, but their ratio varied, particularly in the pastures where module C had twice the contribution of fungal ribotypes than module D. WCNA also showed that the different modules had contrasting relationships to the soil variables measured. Within the woodland correlation network, the larger woodland B module, dominated by bacterial ribotypes, correlated with NH4+-N and DOC, which agreed with CCA analysis which showed that these two variables were correlating with bacterial community composition variability in the woodlands. CCA suggested that woodland communities had little relationship with soil variables other than C; however, the smaller module A detected by WCNA was positively correlated to moisture and pH, indicating that soil variables that are important to a subset of the microbial community may not appear important when analysing the overall community. These data indicate a possible partition of most of the woodland soil community into two functional groups, i.e. the bacterial-dominated Table 4. Number of fungal and bacterial ribotypes in the 20 ribotypes with highest module membership for its respective module. Modules in Woodland network

Module A* Module B

Fungi

Bacteria

9 1

10 19

Modules in Pasture network

Module C Module D

*Module A had a total of 19 ribotypes.

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology

Fungi

Bacteria

7 0

13 20

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A. B. de Menezes et al.

module B, potentially involved in the metabolism of DOC, and the fungal-dominated non-modular ribotypes, most of which are likely associated with C and POC degradation, as well as organic P metabolism. In the pasture sites, the microbial modules detected (C and D) were distinct in their composition, with double the contribution of fungi to module C compared with module D (Fig. 3). The fungaldominated module C correlated positively with both inorganic N and inorganic P, but negatively with moisture. This suggests that bacterial and fungal member species of module C may be involved in N transformations in soil. The importance of fungi in nutrient cycling in semi-arid and temperate grassland soils has been demonstrated (Laughlin and Stevens, 2002; Crenshaw et al., 2008), but the presence of a fungal-dominated module points to potential bacterial–fungal interactions that need investigating. The bacterial-dominated module D correlated positively with clay, FAA-N and DOC, while being negatively correlated to organic and inorganic P, pH, C and soil moisture. Again, a subset of the microbial community was detected using WCNA, compared with the broad relationships described by CCA, and this suggests a possible specialization of microbes in module D for uptake of dissolved C and N. In contrast, the non-modular ribotypes were positively correlated with POC as was the case in the woodland soil community. Most of the ribotypes in the non-modular group were fungal in both pasture and woodlands, which agrees with the expected role of fungi in soils as degraders of cellulosic biomass from plant litter (Fontaine et al., 2011). Other factors determining microbial community structure A striking difference between woodland and pasture soil microbial communities was the lower strength of association between modules and soil variables in woodland sites, as well as the lower densities and clustering coefficients in the woodland compared with pasture networks, which indicate looser connectivities between ribotypes in the woodlands. Furthermore, CCA showed lower correlations between microbial community and soil variables in woodland sites. The lower correlations between modules and soil variables in the woodland sites could be caused by other, unquantified, factors that could be more important in determining the microbial community in the woodlands, such as plant composition (Kennedy et al., 2006; de Vries et al., 2012). The woodland sites were characterized by their variability in tree species, tree density, ground litter cover and presence of grassy patches. Soil bacteria and fungi are known to form associations with the roots of different plant species, for example N-fixing rhizobia which colonize roots of legume species such as acacias (Leary et al., 2006). Trees will thus undoubtedly have effects on microbial community composition as well

as soil chemistry (Mitchell et al., 2010; Prober and Wiehl, 2011). The heterogeneity in plant cover may therefore have reduced the overall apparent contribution of soil physicochemical properties in shaping microbial community composition. For example, Bennett and colleagues (2009) showed that soil fungal community composition was affected by proximity to trees. In contrast, Osborne and colleagues (2011) found that closeness to trees had no statistically significant effect on the microbial community in a similar woodland habitat to this study. The woodlands had lower overall moisture content compared with the pasture sites, and relatively lower moisture may have decreased the importance of competition in community assembly processes, as observed by Li and colleagues (2013) in biological soil crust microbial communities, where improved microhabitat conditions, i.e. wetter and more nutrient rich, shifted biotic interactions from facilitation to competition. Decreased prevalence of competition in biotic interactions in the drier woodland soils could therefore lead to a shift in the relative importance of stochastic and trait-based community assembly processes, resulting in lower correlations between microbial ribotypes. An additional explanation for the lower woodland correlations is dormancy: soils harbour a significant number of dormant microbial cells (Felske and Akkermans, 1998), and the relatively drier woodland soils may harbour a greater proportion of dormant cells and spores, which will be mostly inert and potentially decrease the apparent importance of soil variables in determining the community. It is possible, however, that differences in spatial variability between woodlands and pastures could also explain the lower correlations in the woodlands, as the same sampling grid was used for both land uses. MVDISP indicated that the level of heterogeneity of the bacterial community was similar between land uses; however, the heterogeneity of fungal communities was twice in the woodlands compared with the pastures. The greater heterogeneity of the fungal community in the woodlands may have therefore contributed to the lower levels of correlation between modules and soil variables in woodlands as seen in Fig. 3. In addition to the modules detected, a large number of ribotypes were not placed in any module. Fungal ribotypes were more abundant in the non-modular groups than in the modules, especially from woodland soil samples, suggesting that the fungal community is more variable and less structured than bacterial communities at the spatial scales sampled here. Studies investigating soil fungal diversity across landscapes have found that fungal communities are less even than bacterial communities, that plant communities have an important role in structuring soil fungal communities (Peay et al., 2013) and that the dominant species are more spatially restricted (Baldrian et al., 2012). Moreover, due to dispersal limita-

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology

Network analysis of microbial community structure tions and host specificity of mycorrhizal species (Gilbert and Webb, 2007; Galante et al., 2011; Liu et al., 2012; Peay et al., 2012), soil fungi can have a patchy distribution, with community similarity between samples decreasing significantly in the scale of a few metres (Peay et al., 2008). Indeed, Bennett and colleagues (2009) found that resource heterogeneity in Australian semi-arid Allocasuarina luehmannii woodlands led to greater fungal diversity at the landscape level, while only moderate correlations were found between shifts in the fungal community composition and soil resources. Greater patchiness in community composition can explain the higher number of fungal ribotypes that were not members of any module as seen in this study. Conclusions We demonstrated that the microbial modules detected were not fungal or bacterial specific, but importantly that these two microbial groups shared the same modules, and that the dominance of bacterial or fungal ribotypes within a module also varied. Furthermore, rather than the whole fungal or bacterial community being affected by the same variables in each land use, there were modules comprising bacteria and fungi with contrasting relationships to soil properties. While we acknowledge that soil microbial community also include archaea and microeukaryotes which were not investigated here, this study highlights that treating fungi and bacteria as separate groups with distinct ecological roles is an oversimplification that prevents investigation of intermicrobial relationships; this is essential if we are to progress our understanding of real soil microbial community structure and function. We conclude that the more detailed picture of the interactions between microbial groups (as broadly outlined by CCA) provided by WCNA is a useful strategy for framing future studies investigating the ecological roles of soil biota.

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native woodland (mixed Eucalyptus and Acacia spp.). The pasture sites were well established (> 40 years old), and receive typical district rates of phosphorus fertilizer (∼ 10 kg P ha−1 year−1). The woodland at Talmo is adjacent to native woodland in the Burrinjuck Nature Reserve, NSW, and had little or no ground vegetation cover. The native woodland sites at Glenrock and Bogo are patches of remnant woodland located within the farm property, with patchy grass ground cover. The pasture areas were c. 56 ha (Talmo), 36 ha (Glenrock) and 20 ha (Bogo). The three woodlands were dominated by Eucalyptus spp.; however, Talmo had the highest tree density with an infrequent population of Acacia dealbata and Acacia implexa, and small isolated patches of native Australian grasses. The woodlands at both Glenrock and Bogo were relatively open with scattered individuals of A. dealbata present in the understory. At both of these sites, while not extensive, patches of native Australian grasses were somewhat more common than at Talmo (some exotic grasses were present in the Bogo woodland site). The woodland at Talmo abutted the edge of the Burrinjuck Nature Reserve, and the sampling plot was located in a block of woodland/forest c. 9 km2 in area; the plot at Glenrock was situated in a triangular block of woodland c. 60 ha in size, whereas at Bogo the woodland within which the plot was situated is c. 2 km long and varied from 500 to 2 km in width. The woodlands were all situated at slightly higher elevations than their paired pastures and nutrient run-off from pastures should therefore be minimized. The sites are within 15 km of each other; therefore, local climatic conditions are quite comparable. The approximate distance between the woodland and pasture plots at each site was 160 m (Talmo), 200 m (Glenrock) and 440 m (Bogo). Adjacent sites were selected to minimize differences in parent soil type between land use. At each woodland or pasture site, a 100 × 100 m sampling plot was marked out at 25 m intervals, plus three subplots marked out at 12.5 m intervals. Sites were sampled to measure soil microbial composition and soil chemistry in May 2012. Two soil cores (0–10 cm depth, 2 cm diameter) were taken at the intersections of the grids within the plot and composited. Forty soil samples were therefore collected at each plot, giving a total of 240 individual samples from the three paired plots.

Analysis of soil biogeochemistry Experimental procedures Study sites and sampling design The study was established to determine the soil factors related to microbial community composition in replicated paired land uses in New South Wales (NSW), Australia. The land uses selected were pockets of remnant native woodland adjacent to areas of improved pasture within farm properties. Three paired study sites were selected in the BookhamYass districts of NSW (see Fig. S1). The farms [Talmo (34.936976°S, 148.625293°E), Glenrock (34.858413°S, 148.56724°E) and Bogo (34.813746°S, 148.704558°E)] are used for extensive sheep grazing. Improved pasture with Trifolium subterraneum (subterranean clover) and a mix of annual and perennial grasses, including Phalaris aquatica (phalaris) at each site was selected adjacent to remnant

Any visible leaf litter and root material was removed from each sample. Soil was then sieved (< 5 mm) and homogenized. A subsample (2 g) was frozen in liquid nitrogen and stored at −80°C for molecular analyses. Soil DOC, total dissolved nitrogen (TDN), ammonium (NH4+-N), nitrate (NO3−-N) and free amino acids (FAA-N) were determined after extracting field moist soils with cold 0.5 M K2SO4 at 1:5 (w/v) ratio (Rousk and Jones, 2010). MBC and MBN were determined using the chloroform fumigation extraction method [correction factor was 0.45 (Wu et al., 1990) and 0.54 (Brookes et al., 1985) for C and N respectively] (Vance et al., 1987), and fumigated soil samples were extracted with 0.5 M K2SO4 (1:5 w/v ratio). Extracts were filtered (Whatman #42) and frozen (−20°C) before quantitative analysis. Ammonium (Mulvaney, 1996) and NO3−-N (Miranda et al., 2001) concentrations were determined colorimetrically on a microplate reader

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(SynergyMX, BioTek; Winooski, VT, USA), and FAA-N using the fluorimetric o-phthalaldehyde-β-mercaptoethanol method (Jones et al., 2002). Concentrations of DOC and TDN were measured using a Thermalox TOC/TN analyser (Analytical Sciences, Cambridge, UK). DON was calculated by subtracting NH4+-N and NO3−-N concentrations from TDN. Total, inorganic and organic P were determined by the ignition-extraction procedure (Olsen and Sommers, 1982). Inorganic P was determined colorimetrically on a 0.5 M sulphuric acid extract of an oven-dried soil sample (105°C overnight; un-ignited sample) using malachite green (Irving and McLaughlin, 1990). A second soil sample was treated in a muffle furnace at 450°C for 4 h (ignited sample) and again extracted with 0.5 M sulphuric acid for determination of total P with malachite green. Organic P was estimated by difference between total P (ignited samples) and inorganic P (unignited samples). Soil pH was measured on a 1:10 (w/v) ratio in distilled water using a pH meter (Denver Instruments). Soil moisture was determined after drying at 105°C. All soil data are expressed on a dry weight basis. Air-dried soil subsamples were finely ground in a Retsch MM400 grinding mill (28 Hz, 180 s) before being analysed for total C and N (LECO CN analyser) and diffuse reflectance mid-infrared (MIR) spectra (Nicolet 6700 FTIR spectrometer equipped with a KBr beam splitter and a DTGS detector, Thermo Fisher Scientific, MA, USA) acquired over 8000– 4000 cm−1 (resolution of 8 cm−1) as described in Baldock and colleagues (2013). The MIR spectra were used to estimate the size of the soil organic C fractions [particulate organic carbon (POC), humus organic carbon (HOC) and resistant organic carbon (ROC)] according to the Australian national prediction algorithms developed by Baldock and colleagues (2013), and clay content was estimated using the prediction algorithms of Janik and Skjemstad (1995). The MIR predicted data were accepted where the error statistics were below the threshold limit set by the respective methods [Hotellings Ratio in the case of C fractions (Baldock et al., 2013); and an F-value in the case of clay content (Janik and Skjemstad, 1995)] indicating that the spectral characteristics within the sample set fell within the scope of the calibration dataset.

DNA extraction, polymerase chain reaction (PCR) and T-RFLP analysis DNA was extracted from 0.25 g of soil using MoBio PowerSoil® DNA isolation kit (Carlsbad, CA, USA) according to the manufacturer’s instructions except that the samples were shaken in a Qiagen TissueLizer (Venlo, the Netherlands) shaker for 2 min at full speed after the introduction of buffer C1. DNA concentrations were normalized across all samples. Duplicate PCRs (25 μl) were carried out per sample and pooled. Bacterial 16S rRNA gene was amplified using primers 27f (Lane, 1991) and 519r (Lane et al., 1985) and the fungal ITS region using primers ITS1f (Gardes and Bruns, 1993) and ITS4r (White et al., 1990). In both cases, the forward primers were labelled with 6-carboxyfluorescein at the 5’end. For T-RFLP analysis, the resulting amplicon mixtures were digested with AluI (New England Biolabs). Restriction fragment profiles were obtained from GENEMAPPER® (Applied Biosystems, Mulgrave, Australia) and filtered using a custom R script (R Development Core Team, 2014) to

remove spurious baseline peaks (peaks were removed if they were smaller than the minimum height of 20 fluorescence units, and smaller than two times the standard deviation calculated over all peaks). Further details of the PCR amplification and T-RFLP data processing methods are described in the Appendix S1.

Quantitative PCR (qPCR) Fungal : bacterial ratios were calculated based on the method of Fierer and colleagues (2005), using the primers Eub338 (Lane, 1991) and Eub518 (Muyzer et al., 1993) for bacteria and 5.8S and ITS1F for fungi. Details of qPCR cycling conditions are described in Appendix S1.

Data analysis Means in soil variables between the six sites were compared using a two-way analysis of variance (GenStat v15, Hemel Hempstead, UK). Skewed data (C, N, NH4+-N, NO3−-N, FAA-N, MBN, POC, DOC) were square-root transformed before analysis. In order to conduct statistical analysis of microbial community data, the bacterial and fungal T-RFLP community data were first square-root transformed, and a Bray–Curtis similarity matrix calculated. Differences in community composition (we use the term ‘community composition’ to refer to the ribotype composition across samples as determined by T-RFLP) between land uses and sites were determined by PERMANOVA, while differences in bacterial and fungal β-diversity between land uses and sites were determined using multivariate dispersion index analysis (MVDISP) and a test of homogeneity of dispersion (PERMDISP). PERMANOVA, MVDISP and PERMDISP were conducted in the PRIMER-E package for ecological statistical analysis (Clarke and Gorley, 2006). PERMANOVA was performed using a non-nested, fixed factors design, with type III partial sums of squares and 999 permutations under a reduced model; there were two factors: site, with three levels (Talmo, Glenrock and Bogo), and land use, with two levels (woodland and pasture). We tested significance of differences between pasture and woodlands, between sites (Talmo versus Glenrock versus Bogo), and the interaction between land use and sites. MVDISP was conducted using land use and site as the factor values; PERMDISP was conducted using 9999 permutations and either three groups (site test) or two groups (land use test). The relationship between microbial community composition and soil variables was analysed by CCA using MVSP (v3; Kovach Computing Services, Anglesey, Wales). CCA allows bacterial or fungal T-RFLP ribotypes to be related to soil variables (Ter Braak, 1987). Soil characteristics are represented by vectors, with magnitude and angle of the vector indicating its correlation with an ordination axis. Soil variables were log-transformed (except pH) and standardized, while bacterial and fungal ribotypes were square-root transformed, before performing CCA (Ter Braak, 1987). Soil variables with high variance inflation factors (VIF > 10) were removed from the analysis (see Tables S1 and S2, Appendix S1). High VIF indicate multicollinearity, and so total N, C:N ratio, HOC, ROC and total P were not included in the CCA.

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology

Network analysis of microbial community structure Network correlation analysis of bacterial and fungal communities Network construction. WCNA (Langfelder and Horvath, 2008) was applied to the T-RFLP dataset and used to determine the existence of co-varying modules containing both bacterial and fungal species. All network analyses were performed in R (R Development Core Team, 2014). We analysed the presence of microbial modules in the woodlands and pastures separately and quantified the association of these modules to the soil properties measured. The soft-threshold (sft = 5) was determined by raising power until an approximate scale-free topology was reached (scale-free topology fit index > 0.9) (Langfelder and Horvath, 2008). In WCNAgenerated networks, modules are groups of nodes [genes, operational taxonomic units (OTUs) or ribotypes] of strong topological overlap, which is interpreted as nodes that co-correlate strongly (Yip and Horvath, 2007). The modules are detected using average linkage hierarchical clustering coupled with topological overlap matrix-based dissimilarity, and correspond therefore to branches in a node (OTUs, genes or ribotypes) hierarchical clustering tree. The WCNA procedure also allows the calculation of module and trait (e.g. soil variable) associations; first, the first principal component of each module (the module eigengene E) is calculated. The eigengene (E) summarizes the ribotype relative abundance profiles within a module. For each module, the eigengenes are then correlated with the external traits to provide a measure of trait and module relationship. In this study, network construction was carried out using the combined bacterial and fungal T-RFLP profile data in order to detect modules containing both bacterial and fungal ribotypes. In order to simplify the dataset and remove poorly represented ribotypes, those ribotypes not occurring in at least 20 samples (number chosen based on the size of each individual plot, 40 samples) were removed from the analysis. The soft-threshold used was the same for both woodland and pasture network construction (sft = 5). Ribotypes that were not part of any module were grouped into the miscellaneous ‘non-modular ribotypes’ group. For the module versus soil variable association analysis, the same variables were selected as used for CCA; the environmental variable data were log transformed and normalized. Network properties were analysed in Cytoscape (Shannon et al., 2003).

Acknowledgements We are particularly grateful to Tony Armour (Glenrock), Chris Shannon (Talmo) and Malcolm Peake (Bogo) for their enthusiastic support of this research, and their willingness to allow us access to their properties; Andrew Bissett (CSIRO Agriculture Flagship) and Bronwyn Harch for comments and suggestions in the running of the project; and Shamsul Hoque (CSIRO Agriculture Flagship), Bruce Hawke (CSIRO Agriculture Flagship) and Thomas Carter (CSIRO Agriculture Flagship) who provided excellent technical support in the lab and field.

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Supporting information Additional Supporting Information may be found in the online version of this article at the publisher’s web-site: Fig. S1. Map showing sampling locations. Table S1. CCA: Bacteria. Table S2. CCA: Fungi. Appendix S1. Supplementary Methods.

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology