Soil fungi influence the distribution of microbial functional groups that mediate forest greenhouse gas emissions

Soil Biology & Biochemistry 53 (2012) 112e119

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Soil fungi influence the distribution of microbial functional groups that mediate forest greenhouse gas emissions David J. Burke a, b, *, Kurt A. Smemo a, c, Juan C. López-Gutiérrez a, d, Jared L. DeForest e a

The Holden Arboretum, Kirtland, OH 44094, USA The Department of Biology, Case Western Reserve University, Cleveland, OH 44106, USA c Department of Biological Sciences, Kent State University, Kent, OH 44242, USA d Ecosystem Science and Management Program, University of Northern British Columbia, Prince George, British Columbia V2N 4Z9, Canada e Department of Environmental and Plant Biology, Ohio University, Athens, OH 45701, USA b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 December 2011 Received in revised form 2 May 2012 Accepted 13 May 2012 Available online 2 June 2012

The distribution of microbial functional groups in soil may be governed by the interaction between the soil environment and the presence of other microbial competitors or facilitators. In forest soils, one of the most important groups of organisms are fungi, which are vital to many ecosystem processes such as nutrient cycling and decomposition, and can form direct connections to primary producers. Nevertheless, the overall effect of soil fungi on the structure and distribution of the other soil microbial functional groups has not been thoroughly investigated. We hypothesized that by altering the soil environment, fungi create favorable conditions for Archaea, methane oxidizing bacteria (MOB) and denitrifying bacteria (DNB), thereby potentially influencing the ability of forest soils to produce or consume greenhouse gases. To test these hypotheses, we studied the distribution of microbial functional groups and fungi in forest soil using molecular methods and related that distribution to soil environment and extracellular enzyme activity as a measure of microbial activity and metabolic effort. Non-metric multidimensional scaling of terminal restriction fragment length (TRFLP) profiles found that DNB and MOB largely separated within ordination space, suggesting little overlap of these bacteria in soil cores. In addition, DNB were significantly positively correlated with fungal biomass and with chitinase activity while MOB were negatively correlated with both. Most archaeal TRFs were also negatively correlated with fungal biomass, suggesting that forest Archaea and MOB have similar relationships to fungal biomass. Soil chemistry including soil carbon (C), nitrogen (N) and bicarbonate extractable phosphorus (P) were not significantly correlated with DNB, MOB or Archaea. Our results suggest that soil fungi might influence the spatial distribution of important prokaryotic groups in forests, including some groups that mediate the production and consumption of important greenhouse gases. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Archaea Denitrifying bacteria (DNB) Methane oxidizing bacteria (MOB) Fungi Nutrients TRFLP Hardwood forest Extracellular enzymes

1. Introduction Soil microbes play an important role in ecosystem function and may act as filters or valves that regulate the intra-system cycling of soil nutrients (Pastor et al., 1984). Important functions carried out by soil microbes include maintenance of soil fertility (i.e. nutrient cycling and organic matter decomposition), and regulating the emission and consumption of important greenhouse gases such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) (Bardgett et al., 2008; Forster et al., 2007). Changes in microbial diversity or community structure could have dramatic impacts on * Corresponding author. The Holden Arboretum, 9500 Sperry Road, Kirtland, OH 44094, USA. Tel.: þ1 440 602 3858; fax: þ1 440 602 8005. E-mail addresses: [email protected], [email protected] (D.J. Burke). 0038-0717/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.soilbio.2012.05.008

ecosystem processes (Prosser, 2002), and the ability of microbes to maintain soil fertility or regulate nutrient cycling may be largely dependent on the composition of soil microbial communities (Robertson et al., 2000; Singh et al., 2010). While past work addressing the spatial structure and distribution of soil microbial communities has focused on assembly rules for microbial groups (e.g. Green et al., 2004; Horner-Devine et al., 2004) or environmental controls such as redox (e.g. Pett-Ridge and Firestone, 2005), resource availability (e.g. Zak et al., 2003), and pH (e.g. Rousk et al., 2010), less attention has been given to the spatial relationship between different microbial groups as a factor that may affect their distribution and persistence. For example, in temperate hardwood forests, soil fungi are a key component of the soil food web acting as both decomposers (saprotrophs) and plant mutualists (mycorrhizas) (Rayner and Boddy,

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1988; Leake et al., 2002; Parniske, 2008; Smith and Read, 2008). In previous work, we found that fungal communities associated with tree roots vary seasonally in a mature hardwood forest, and fungal distribution is influenced by fine scale spatial variation in soil nutrient content as well as the presence of both of trees and herbaceous plants (Burke et al., 2009). Bacterial communities and species can also be affected by spatial changes in soil nutrient content (Lynch, 1988; Poly et al., 2001), fertilization of forest soil (Burke et al., 2006a), and leaf litter from different plant species (Burke and Chan, 2010). The spatial structure of fungal biomass can also potentially create a patchy soil environment for other microbial groups with respect to redox, pH, and nutrient/substrate availability (Reid, 1984; Rygiewicz et al., 1984; Linderman, 1988; Grayston et al., 1997; Smith and Read, 2008). Such heterogeneity in the soil environment could be important for the distribution and function of microbial functional groups in forest soil. However, the overall influence of fungal biomass on environmental conditions and the distribution of other microbial groups are still poorly understood and additional research into spatial patterns of forest microbes and their interrelationships is needed. In this study, we examined the distribution of microbial functional groups and fungal biomass in forest soil using molecular methods and related that distribution to soil environment and extracellular enzyme activity as a measure of microbial activity and metabolic effort. We chose to specifically examine how the distribution of fungal hyphae, that could alter soil environmental conditions in ways important for other microbial groups, affected the distribution of soil Archaea, anaerobic denitrifying bacteria (DNB), and aerobic CH4-oxidizing bacteria (MOB). We chose these groups because factors controlling their distribution in mature hardwood forests are poorly understood (Bates et al., 2011); yet, their distribution and community structure could potentially affect whether forest soils act as a source or a sink for CO2, CH4, and N2O (Forster et al., 2007; Bardgett et al., 2008). Specifically, we hypothesized that 1) because many described soil Archaea are from groups responsible for ammonia oxidation or nitrification (Leininger et al., 2006) and CH4 production (methanogenesis) under anoxic conditions (Pesaro and Widmer, 2002), archaeal community structure and the relative abundance of archaeal taxa will be positively associated with fungal biomass due to reduced soil redox conditions and enhanced substrate availability and; 2) the community structure and relative abundance of MOB and archaeal taxa will be positively associated with fungal biomass in forests because MOB may be substrate-limited and therefore prevalent near zones of active CH4 production; and 3) because DNB distribution is often explained by C availability and NO 3 pools (Wallenstein et al., 2006), we hypothesized a negative relationship between fungal biomass and the community structure and relative abundance of DNB taxa. Specifically, we expected a negative relationship because fungi and DNB may compete directly for soil C and nutrients. 2. Materials and methods 2.1. Site description and sampling We established our study site in a 80-ha section of old growth beech-maple forest within Stebbins Gulch, a mature, 360 ha, northern hardwood forest located at The Holden Arboretum in northeastern Ohio, USA (41360 N and 81160 W) and a part of the Holden Natural Areas National Natural Landmark (http://www. nature.nps.gov/nnl/site.cfm?Site¼HOLD-OH). Total precipitation averages around 116-cm per year, with an average of 287-cm of snowfall per season. The soils at the site are classified as a Mahoning silt loam (Aeric Epiaqualfs). These soils are characterized by

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gently sloping ground (2e6% slope), somewhat poorly drained soils that form on till plains. Soil organic matter is concentrated in the top 5-cm and a perched water table is common, especially in early spring. To capture natural variation in microbial distribution, soil cores were collected at 5-m intervals along three randomly selected 100-m transects (Supplemental Fig. 1) in September of 2006 to a depth of 5-cm using a 10-cm diameter metal soil corer, transported on ice to the laboratory, and subsequently sieved (2 mm) to separate soil from root tissue. We did not collect recent leaf litter but restricted our sampling to the Oe, Oa and A horizons. Of the 60 total samples collected as part of a larger study (Burke et al., 2009), 22 of those samples were chosen for a detailed analysis of microbial community functional groups and fungal biomass (reported here). A portion of field fresh soil was used for analysis of pH and soil moisture, and the remaining soil frozen and stored at 70 C for subsequent microbial and chemical analysis. At each sample point along the 100-m transects, we estimated percent herbaceous plant cover within a 0.25 m radius and identified and measured diameter at breast height (dbh) of all overstory trees (>2 cm dbh) within a 5 m radius. 2.2. Soil chemical analyses Field fresh soil was used to measure soil pH (1:1H2O) and gravimetric water content and is expressed here as [(g water g FW soil1)  100%]. Soil for C, N and P was oven-dried and pulverized in a Precellys homogenizer (Bertin Technologies, Montigny-leBretonneux, France). Soil C and N was measured on an ECS 4010 CHNSO elemental analyzer (Costech Analytical, Valencia, CA). Dried soil was used to determine labile soil inorganic phosphorus (Pi; readily available) and organic phosphorus (Po; easily mineralizable) using colorimetric techniques. Labile soil P was extracted by adding 0.5 M NaHCO3 (pH 8.5) and shaking at 100 rpm on an orbital shaker (Lab-Line, Melrose Park, IL) for 30 min (Olsen et al., 1954). Pi was determined using the modified ascorbic acid method (Kuo, 1996) directly on the NaHCO3 extracts, while Po was determined by the increase in Pi after NaHCO3 extracts were digested with 1.8 N H2SO4 and (NH4)2S2O (EPA, 1971). 2.3. Enzyme assays and PLFA Potential soil extracellular enzyme (EE) activity was measured using soil slurries and high throughput 96-well microplate protocols (Saiya-Cork et al., 2002; DeForest et al., 2004). Methylumbelliferone (MUF)-linked model substrates were used to fluorometrically estimate the activity of enzymes associated with the breakdown of cellulose (b-1,4-Glucosidase (BG)), chitin (b-1,4N-Acetylglucosaminidase (NAG)), starches (a-1,4-Glucosidase (AG)), and phosphate monoesters (acid phosphatase (AP)). Fluorescence was measured at 20  C with a Synergy HT microplate reader (BioTek, Winooski, VT, USA). Microbial biomass and abundance of broad taxonomic microbial groups (such as bacteria, fungi, actinomycetes and arbuscular mycorrhizae) was estimated using phospholipid fatty acid (PLFA) analysis (Tunlid et al., 1989; Vestal and White, 1989; Zelles, 1999; Olsson and Wilhelmsson, 2000). Analytical recovery was determined by adding phospholipid 19:0 standard (Avanti Polar Lipids, Inc., Alabaster, AL, USA). Total lipids were extracted from freezedried soil, and polar and nonpolar lipids were separated and quantified using an HP GC-FID (HP6890 series, Agilent Technologies, Inc. Santa Clara, CA, USA) as described elsewhere (DeForest et al., 2004; DeForest and Scott, 2010). Biomarkers were identified using the Sherlock System (v. 6.1, MIDI, Inc., Newark, DE, USA). We also assessed fungal biomass in each soil core by extraction and quantification of ergosterol via high-pressure liquid

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chromatography using a PerkinElmer Series 200 HPLC (PerkinElmer Inc., Waltham, MA) following general procedures in Gong et al. (2001). In brief, 500-mg of field wet soil was shaken for 1 h at room temperature, centrifuged for 10 min at 11,000 rpm and vacuum centrifuged in the dark to completely evaporate the methanol and concentrate the ergosterol. Dried ergosterol was suspended in 75-mL of fresh methanol and used for HPLC analysis and fungal dry weight determined as per Montgomery et al. (2000). 2.4. DNA extraction and amplification DNA was extracted from soil using a bead beating protocol (Burke and Chan, 2010). Briefly, 500-mg fresh soil was placed in a 1.5-mL bead beating tube containing 500 mg of sterile glass beads (300-mg of 400 mM glass beads [VWR, West Chester, PA, USA], 200mg 1 mm glass beads [Chemglass, Vineland, NJ, USA]) and 750 mL of 2% CTAB (cetyltrimethyl-ammonium bromide). Samples were then beaten for 40 s in a Precellys homogenizer at 6500 rpm and approximately 500-mL of the supernatant was removed and DNA purified by phenol/chloroform extraction and precipitation with 20% polyethylene glycol 8000 in 2.5 M NaCl (Burke et al., 2005, 2006b). DNA was suspended in 100 mL TE (Tris EDTA) buffer and 25 mL of the DNA was further purified using a Wizard SV Gel and PCR Clean Up System (Promega, Madison, WI, USA) according to the manufacturer’s instructions. Following gel extraction, DNA was suspended in PCR grade water and stored at 20  C. 2.5. Molecular analysis of microbial functional groups We targeted the 16S rRNA gene to analyze soil Archaea using labeled primers ARCH915-forward (6-carboxyfluorescein [6FAM]) (Amann, 1995) and UNI-b-reverse (4, 7, 20 , 40 , 50 , 70 -hexachloro-6carboxyfluorescein [HEX]) (Brandt et al., 2001) using PCR conditions as described by Pesaro and Widmer (2002). PCR product was digested with restriction enzymes MspI and HaeIII (Promega, Madison, WI, USA). To amplify the community of DNB, we targeted the functional gene nitrous oxide reductase (nosZ) using the primers nosZFb-forward (6FAM) and nosZRb-reverse (HEX) (Rösch and Bothe, 2005), following conditions as described in Rösch et al. (2002) and digested the product with restriction enzymes MboI and TaqI (Promega, Madison, WI, USA). Finally, we described the MOB community by targeting the functional gene particulate methane monooxygenase (pmoA) with labeled A189-forward (6FAM) and mb661-reverse primers (Costello and Lidstrom, 1999). We initially followed PCR conditions as described in Costello and Lidstrom (1999); however, these conditions resulted in the production of 2 PCR bands, one approximately 585 base pairs (bp) and another 510 bp in size. Subsequent cloning and sequencing revealed that the larger band consisted of non-specific amplification product whereas the smaller band showed high affinity to the pmoA gene. Consequently, we adjusted the PCR conditions to eliminate the larger band and amplify only the smaller specific band. We used a touchdown PCR protocol consisting of an initial denaturation step of 4 min at 95  C followed by 33 cycles where the denaturation step was 95  C for 1 min, the annealing step was held for 1 min, and an extension step at 72  C for 1 min. Annealing temperatures decreased by 1  C, lowering from 62  C to 55  C during the protocol as follows: 62  C for 2 cycles, 61  C for 2 cycles, 60  C for 3 cycles, 59  C for 3 cycles, 58  C for 4 cycles, 57  C for 4 cycles, 56  C for 4 cycles, and 55  C for 11 cycles. A final 5-min extension at 72  C completed the protocol. Purified pmoA PCR product was digested with restriction enzymes MspI and HaeIII (Promega, Madison, WI, USA). PCR for all bacterial groups was carried out in 50 mL reaction volumes using 1-mL of gel purified DNA, 0.2 mm of primers, 2.0 mM

MgCl, 0.2 mM dNTP, 0.25 mg Bovine Serum Albumin, and 1.0 unit Taq DNA polymerase (Promega, Madison, Wisconsin, USA) on an PTC 100 Thermal Cycler (MJ Research, Boston, USA). Terminal restriction fragment length polymorphisms (TRFLPs) were completed through the Life Sciences Core Laboratories Center (Cornell University) using an Applied BioSystems 3730xl DNA Analyzer and the GS600 LIZ size standard. Profiles were analyzed using Peak ScannerÔ Software (version 1.0, Applied Biosystems 2006). For our analyses, only peaks that accounted for greater than 50 fluorescence units (scale of 5000) and greater than 1% of the relative peak area were included (i.e., major TRFs; Burke et al., 2008). We have found that peaks with less than 1% of total profile area are generally not repeatable between replicate samples (Burke et al., 2008; Burke and Chan, 2010) and although excluding these peaks may provide a more conservative estimate of microbial diversity and community structure, it reduces the chances that non-specific TRFs will be included in our analysis (Dunbar et al., 2001). PCR was also conducted for all bacterial groups using unlabeled primers for cloning and sequencing of the targeted groups. Cloning and sequencing was intended to confirm the specificity of the respective amplifications and provide a snapshot of the groups most commonly found in our forest soils. PCR conditions were as noted above and PCR product was gel extracted prior to cloning using the Wizard SV Gel and PCR Clean Up System (Promega, Madison, WI, USA). Gel extracted PCR product was cloned using a pGEM-T Easy vector system (Promega, Madison, WI), following the manufacturer’s instructions. Randomly selected colonies were incubated overnight at 37  C in LB medium, and plasmids were harvested using a Wizard Plus SV Miniprep DNA purification system (Promega, Madison, WI). Sequence assignments were determined for Archaea using the Naive Bayesian rRNA Classifier from the Ribosomal Database Project (Wang et al., 2007) with the confidence threshold set at 90%. Sequence assignments were determined for DNB (nosZ) and MOB (pmoA) by comparing the generated sequences to EMBL/GenBank/DDBJ database entries using the NCBI Blast tool through the European Bioinformatics Institute (http://www.ebi.ac.uk/). 2.6. Statistical analyses Detected TRFs were used as operational taxonomic units (OTU) and are considered proxy measures of microbial taxa, even though each TRF may represent many microbial species (Burke et al., 2006b, 2008; Feinstein et al., 2009; Burke and Chan, 2010). The relative peak area of each TRF for detected Archaea and DNB and MOB was used for non-metric multidimensional scaling (NMS) analysis of community structure using PC-ORD 4 (MjM Software, OR). All peak area data was arcsine-square root transformed prior to analysis. The Sørenson distance with a random starting configuration was used for these analyses. A maximum of 400 iterations were used for 50 runs, with data for the Monte Carlo test randomized. Archaea, DNB and MOB TRFs were analyzed together in the same ordination. We used Archaea community profiles generated with the HEX-labeled reverse primer using restriction enzyme HaeIII; profiles generated with MboI and the HEX-labeled reverse primer were used to represent the DNB; and profiles generated with HaeIII and the 6FAM-labeled forward primer to represent the MOB in NMS analysis. These enzymes and labeled fragments were chosen for analysis because they provided the largest number of distinct TRFs for the respective communities. NMS analysis of microbial community structure was performed on these three groups of soil microbes only. Soil physiochemical conditions, enzyme activity, fungal and bacterial biomass (PLFA) and plant data were considered to be environmental features and

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NMS was used to determine whether any of these environmental features was correlated with community structure determined by TRFLP. Consequently, fungal community composition was not examined in this study or included in the ordination, but fungal biomass was considered an environmental feature that could influence archaeal, DNB and MOB community structure and taxa distribution. Significance of correlations was determined using the critical values for correlation coefficients (Zar, 1998). For all samples (n ¼ 21, excluding 1 sample outlyer), P was <0.05 for an r of >0.423 (two-tailed test). 2.7. Accession numbers

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Table 2 Phospholipid fatty acid content of microbial groups present in soil from Stebbins Gulch. Means and standard errors in parenthesis. Taxonomic group

nmol PLFA g1 soil

Total Bacteriaa Generalb Fungic Arbuscular mycorrhizad Actinomycete and Protozoae Unknownf

324.9 182.9 100.0 19.8 10.6 11.0 0.5

(95.7) (65.0) (23.8) (2.3) (3.3) (3.1) (0.3)

a Includes biomarkers i15:0, i16:0, 16:1u9c, 10Me16:0, i17:0, a17:0, cy17:0, c17:0, 18:1u7c, 18:1u7t, 18:1u5c, cy19:0a and c19:0. b Includes biomarkers i14:0, c14:0, 16:1u7c, c16:0 and c18:0. c Includes biomarkers 18:2u6 and 18:1u9c. d Includes biomarker 16:1u5c. e Includes biomarkers 10Me18:0, 18:3u3 and 20:4u6. f Includes biomarker 20H-16:0.

All sequences recovered in this study were deposited into the EMBL/GenBank/DDBJ database through the European Bioinformatics Institute. Sequence accession numbers for archaeal clones are HE603013 e HE603078; DNB sequence accession numbers are HE602974 e HE603012; and MOB sequence accession numbers are HE602960 e HE602973.

of the PLFA biomass was classified as “general” and is associated with both bacteria and fungi (Table 2).

3. Results

3.3. Molecular analysis of archaeal soil communities

3.1. Soil chemistry and enzyme activity

Using HaeIII and the reverse labeled primer, we detected a total of 18 archaeal TRFs in our samples. The NMS ordination produced a 2-dimensional solution with a final stress of 9.94% and a cumulative coefficient of determination of 0.910. We did not find clear differences in the archaeal communities among our samples (Fig. 1). However, archaeal TRFs appeared significantly negatively correlated with NAG activity (r ¼ 0.509), BG activity (r ¼ 0.435), fungal PLFA biomarkers (18:1u9c) in soil (r ¼ 0.469), and fungal biomass as measured using ergosterol (r ¼ 0.440). We found no significant relationships between Archaea and the AMF PLFA biomarker (16:1u5c). Archaeal TRFs clustered with MOB TRFs in the ordination, although some archaeal TRFs did not appear associated with either MOB or DNB TRFs. Out of 75 isolated plasmids, we successfully sequenced 66 archaeal clones from our soil samples. Archaeal sequences recovered in this study matched sequences previously described from the phyla Crenarchaeota and Euryarchaeota (Supplemental Table 1). 13 sequences were positively identified as Euryarchaeota (% ID > 90%) and 2 other clones were putatively affiliated with Euryarchaeota (%ID > 85%) (Supplemental Table 1). Crenarchaeota dominated our library, and 31 sequences were positively identified as Crenarchaeota (%ID > 90%) and 10 other clones were putatively affiliated with Crenarchaeota (%ID > 85%). An additional 10 sequences were most closely affiliated with the Crenarchaeota but had low identity (%ID < 85%) (Supplemental Table 1). Most of our sequences could be positively assigned to a known class of archaea, with the Crenarchaeota class Thermoprotei and the Euryarchaeota class Thermoplasmata being the only class assignments our sequences could be placed. Below the class level, sequence assignment to known groups was poor.

Soil gravimetric water content ranged from 25 to 45% with a mean of 34.6% (Table 1). Soils were strongly acidic, with pH ranging between 3.4 and 5.3 and a mean of 3.9  0.1. Soil N ranged from 2.5 to 11.2 mg N g soil1 soil while soil C ranged from 32.1 to 209.1 mg C g soil1. Bicarbonate available inorganic P ranged from 5.5 to 75.9 mg P kg1 soil with a mean of 33.6  3.6 while bicarbonate available organic P ranged from 19.5 to 128.1 mg P kg1 soil with a mean of 57.7  7.7. AP activity varied greatly in our samples, ranging between 7 and 1917 nmol h1 g soil1 with a mean of 671  114 nmol h1 g soil1. BG and NAG activity was less than that of AP with mean rates of 322  63 and 157  51 nmol h1 g soil1, respectively. We generally detected very low levels of AG activity (Table 1). 3.2. PFLA analysis of soil microbial biomass Bacterial specific PLFA biomass ranged from 30 to 1510 nmol PLFA g1 soil with a mean of 183  65 nmol PLFA g1 soil, and on average dominated microbial biomass and accounted for 56% of the total PLFA biomass (Table 2). Fungal biomass ranged between 7 and 50 nmol PLFA g1 soil, representing on average 6% of total PLFA microbial biomass. Biomass of AMF ranged from approximately 1e75 nmol PLFA g1 soil, and represented a relatively small portion of total biomass on average (w3.2%; Table 2). A significant portion

Table 1 Soil chemical analysis and enzyme activity of forest soil samples from Stebbins Gulch. Means  standard error of the mean are shown.

3.4. Molecular analysis of DNB and MOB communities

Soil parameter Water content pH N (mg g1) C (mg g1) C:N Labile Pi (mg kg1) Labile Po (mg kg1) Acid phosphatase (nmol h1 g1) a-1,4Glucosidase(nmol h1 g1) b-1,4Glucosidase(nmol h1 g1) b-1,4-N-Acetylglucosaminidase (nmol h1 g1)

34.6 3.9 4.9 78.3 15.5 33.6 57.7 671.3 13.1 322.2 156.7

(1.4) (0.1) (0.4) (8.3) (0.4) (3.6) (7.7) (114.1) (4.7) (62.8) (50.9)

We observed 42 and 19 TRFs of DNB and MOB, respectively, among soil samples using restriction enzymes MboI and HaeIII. Although we were able to amplify DNA from both groups, we found evidence for spatial separation between these bacterial groups as only 27% of samples contained both groups of bacteria. Forty-one percent of cores contained only MOB while 32% of the cores contained only DNB. NMS ordination confirmed this spatial separation (Fig. 1). The analysis revealed clear separation between samples containing DNB and MOB although a small number of samples

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Axis 2 (55.4%)

0.5

Allium tricoccum

Beech 30-60 cm dbh

Fungal Biomass (Ergosterol) NAGase

β-Glucosidase

Fungal Biomass (F18:1w9c)

-0.5

-1.5 -1.5

-0.5

0.5

1.5

Axis 1 (35.6%) Fig. 1. Non-metric multidimensional scaling ordination based on the distribution of archaeal, DNB and MOB TRFs (considered OTUs based on proportional peak area). DNB TRFs are represented by Xs, crosses represent MOB TRFs, and filled circles represent archaeal TRFs. Soil samples are represented by open triangles and ellipses show clustering of DNB (solid ellipse) and MOB (dashed ellipse). Joint plots of significant (r > 0.423) environmental variables are shown. The joint plot vector lengths indicate the strength and direction of the strongest correlations. The proportion of variance explained by each axis is shown.

contained both. We found that DNB TRFs and samples containing only DNB were significantly positively correlated NAG activity (r ¼ 0.509), BG activity (r ¼ 0.435), fungal PLFA biomarkers (18:1u9c) in soil (r ¼ 0.469), and fungal biomass as measured using ergosterol (r ¼ 0.440), while MOB were negatively correlated with these same soil parameters. We found no significant relationships between these bacterial functional groups and the AMF PLFA biomarker (16:1u5c) or with soil C, N or bicarbonate available P. We saw some suggestion that the distribution of patches of Allium tricoccum (wild leek) was positively correlated with MOB distribution (r ¼ 0.423). We successfully sequenced 39 nosZ clones representing the DNB from our soil samples out of 48 isolated plasmids. nosZ sequences recovered in this study primarily matched sequences of uncultured forest denitrifying bacteria with some sequences matching uncultured denitrifying bacteria found in rice paddy soils (Supplemental Table 2). Successfully isolated pmoA clones showed high identity to methanotrophic bacteria isolated from aquatic and soil environments. Two clones showed high identity to species in the genus Methylocystis (Supplemental Table 3).

4. Discussion We hypothesized that fungal biomass is an important environmental feature that affects the overall distribution of microbial functional groups in forest soil and we found evidence to support this concept and the idea that fungal biomass may help to structure microbial niches in soil and facilitate niche separation of different groups. Fungal biomass was negatively correlated with the distribution of Archaea and MOB in our forest soil, but positively correlated with the distribution of DNB. This suggests that Archaea and MOB taxa are less likely to be found in areas with high levels of

fungal biomass, whereas DNB taxa are more likely to be found in areas with high fungal biomass. This could indicate that fungi either facilitate or share similar environmental preferences and may interact more with DNB than with either MOB or Archaea in forest soils.

4.1. Archaea-fungal biomass relationships We expected that soil fungi might foster conditions favorable to soil Archaea, and therefore positive correlations might be found between fungal biomass and archaeal distribution. However, fungal biomass appeared negatively correlated with most archaeal TRFs, supporting our alternative hypothesis and suggesting that most forest Archaea are found in soil with low levels of fungal biomass. Soil fungi can alter physicochemical conditions in soil, including soil oxygen (O2) status through fungal respiration, altering availability of nutrients such as N and P, and altering microsite pH (Reid, 1984; Rygiewicz et al., 1984; Linderman, 1988; Grayston et al., 1997; Smith and Read, 2008). Fungal biomass might also serve as an important C source as hyphae senesce and die. We expected that fungal activity, and potentially greater soil N content in response to fungal enzyme activity, would be favorable to Archaea. However, we found that only the community structure and distribution of DNB taxa were positively affected by fungal biomass. It is unclear whether this pattern is driven by niche separation or substrate competition. Our study focused only on the distribution of Archaea in the top 5-cm of the soil profile, and we found Archaea in every sample analyzed and had strong PCR amplification. Archaea are now commonly found in acidic forest soils (Pesaro and Widmer, 2002; Bomberg et al., 2010; Bates et al., 2011) with the Crenarcheota often dominating clone libraries as was the case in our study. We

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also found Euryarcheota in our samples, and these clones belonged entirely to the order Thermoplasmatales, which have also been previously detected in forest soils (Pesaro and Widmer, 2002). The distribution of Crenarchaeota in anaerobic tropical peatlands appeared correlated with deeper soil layers (>20-cm) with low levels of enzyme activity whereas bacteria were found in more shallow layers with higher levels of enzyme activity (Jackson et al., 2009). This is in agreement with our study since we found negative correlations between archaeal TRF distribution and soil enzyme activity. Because enzyme activity is often linked to rates of organic matter decomposition and can be considered a proxy measure of nutrient cycling, our results suggest that most Archaea in forest soils are associated with areas of low organic matter and nutrient cycling. This could mean that Archaea either have little effect on organic matter turnover and nutrient cycling in forests, or that they are more tolerant of stressful conditions where resource availability is low. Our results are somewhat in contrast to a large study by Bates et al. (2011) that examined archaeal distribution across 146 sites from North and South America and Antarctica. Only C:N ratio was consistently correlated with archaeal abundance in soil (Bates et al., 2011). We may not have seen a positive response to C:N because our study was limited to one old growth forest site, and the combined relative effects of fungal biomass on Archaea may have been greater than soil chemistry alone. Although the exact functional role Archaea may play in forest soils is uncertain, previous studies have found that Crenarchaeota may be an important if not the major contributor to nitrification (Wuchter et al., 2006; Bates et al., 2011) and that some possess the amoA gene for nitrification (Treusch et al., 2005). Some Archaea are also capable of denitrification through the ANAMOX pathway (Dalsgaard et al., 2003), but whether these groups exist in forest soil will require additional study. Overall, our understanding of chrenarchaeal function is limited by the fact that none have yet been isolated in pure culture (Poplawski et al., 2007). Euryarchaea are a ubiquitous phylum of the Archaea that are known to produce and oxidize CH4, fix atmospheric N, reduce NO 3, and contribute to ANAMOX, but they have not been widely detected in aerobic forest soils (Poplawski et al., 2007). In two separate studies, Bomberg et al. (2003, 2010) found that the soil around plant roots colonized by mycorrhizal fungi contained diverse groups of Archaea, including methanogens within the order Methanosarcinales (Euryarcheota). Despite our cloning efforts and identification of Euryarcheota, we did not detect any known groups of methanogens. This does not mean that methanogens were not present, as many are yet undescribed, and our cloning effort was not exhaustive and may have missed some taxa. We also examined soil samples that included both mycorrhizospshere (i.e. soil immediately around mycorrhizal roots) and bulk soil. Although Bomberg et al. (2003) were able to amplify Archaea from the mycorrhizosphere, fungal external mycelium did not contain Archaea. This suggests that archaeal distribution is highly microsite specific, and our sampling method might have hindered our ability to isolate methanogens that intimately associate with root surfaces colonized by mycorrhizal fungi. Additional work is needed to understand how microsite variability affects groups of Archaea in forest soil and the resource requirements of these different groups. 4.2. MOB-fungal biomass relationships Although many studies have examined the taxonomic diversity and functional activity of MOB in soils including forest soils (Henckel et al., 2000; Horz et al., 2005; Singh and Tate, 2007), few studies have attempted to identify environmental features associated with the distribution of MOB. Soil fungi can potentially alter the environment for bacteria, including MOB, by changing chemical

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conditions including soil pH, N and P availability, and the amount and type of organic carbon found within soil (Reid, 1984; Rygiewicz et al., 1984; Linderman, 1988; Grayston et al., 1997; Smith and Read, 2008). In this study we attempted to discern the soil conditions associated with MOB and we found that MOB were negatively correlated with fungal biomass and enzyme activity, suggesting that MOB are more likely found in areas with low C and nutrient cycling rates. Like Archaea, MOB appear to prefer sites with low levels of nutrient and organic matter decomposition and cycling, and may inhabit more mineral areas of the soil environment. Thus, the MOB are found in sites that are preferred by potential CH4 producers (i.e. Euryarchaea) suggesting that spatial organization may be the result of substrate source. Interestingly, we saw some positive correlation between the distribution of MOB and % cover of the herbaceous plant A. tricoccum (wild leek). In wetlands, MOB are influenced by the presence of plant roots that alter soil O2 concentration, pH, and C and N availability (Dunfield et al., 1993; King, 1996; Calhoun and King, 1997; Van der Nat and Middelburg, 1998; van Bodegom et al., 2001; Bodelier and Laanbroek, 2004). Although the exact cause of the association between MOB and wild leek is uncertain, plants can have a strong affect on bacteria in many soil types and systems (Burke et al., 2002; Zak et al., 2003). Although it has been thought that MOB use CH4 as their sole C and energy source, increasing evidence suggests that MOB may have greater metabolic flexibility with respect to electron donors and acceptors (Ward et al., 2004) and that different groups of MOB occupy different niches in soil that may be governed by CH4, O2 and N concentrations (Shrestha et al., 2008). Our results suggest that soil fungal biomass and the activity of fungi could also affect the distribution of this bacterial functional group in forest soils. Previous studies have found a strong correlation between MOB and pH in forest and agricultural soil (Knief et al., 2003). However, we did not see any relationships between soil chemistry and MOB. The pH in our forest was generally more acidic, ranging narrowly between 3.4 and 5.3, than in the study by Knief et al. (2003) where pH ranged from 4.3 to as high as 8.0. Effects of pH on MOB in our study may therefore not be as great due to the narrow pH range. 4.3. DNB-fungal biomass relationships Environmental factors such as pH, temperature, O2 and C availability may be the primary control on the distribution of DNB in soils (Wallenstein et al., 2006). Consequently, we expected fungal biomass in surface soils to have a strong negative effect on communities of DNB because of their potential to compete for soil C and N. Contrary to our expectation, DNB were positively correlated with both fungal biomass and C and nutrient cycling as indicated by enzyme activity. DNB were associated with BG and NAG activity, suggesting again that DNB are correlated with sites high in organic matter and nutrient cycling and turnover. Interestingly, DNB communities were not correlated with soil C or N status in our study, suggesting that overall N availability may not be important for structuring the distribution of DNB communities. This observation is in contrast to other studies that reported positive correlations between DNB communities and C:N ratio or a lack of correlations with soil NO 3 concentrations or N supply (Mergel et al., 2001; Rich and Myrold, 2004; Haase et al., 2008). Although NO 3 concentration did not govern DNB distribution, Mergel et al. (2001) did find that DNB distribution was always highest in the top 5-cm of a forest soil profile. Because fungal biomass is often highest in the most shallow soil layers (Bååth and Söderström, 1982), it may be that in terms of CN requirements fungi create soil conditions more favorable to DNB. It is possible therefore that organic matter quality, not overall amounts of C and N, are important for structuring these communities and that fungi

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positively influence organic matter quality. However, plant roots can also control DNB distribution and activity through the excretion of substantial amounts of carbon into forest soil (Philippot et al., 2007) and more than 90% of fine root biomass at our study site is found within the top 5-cm of soil (Burke et al., 2009). Nonetheless, we found no correlation between DNB communities and herbaceous plant or tree distribution, indicating that fungal biomass may be more important in structuring the distribution of the DNB communities than plants at our study site. 5. Conclusions We found that soil fungi are correlated with the distribution of important bacterial functional groups in forests, including some groups that mediate the production and consumption of important greenhouse gases. DNB were positively correlated with fungal biomass and enzyme activity, suggesting that both DNB and fungi are associated with each other and areas with high levels of organic C and nutrient cycling. On the other hand, MOB and Archaea were negatively correlated with fungal biomass, suggesting that these groups are more likely found in areas with low organic C, nutrient availability and organic matter turnover. Our results indicate that niche separation may exist between these microbial groups in forest soils, and that soil fungi may be associated with some functional groups in forest soil, possibly due to shared preferences for sites with elevated levels of C and nutrient cycling. Acknowledgements This work was supported by funding from The Holden Arboretum Trust and the Corning Institute for Education and Research. Appendix A. Supplementary material Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.soilbio.2012.05.008. References Amann, R.I., 1995. Fluorescently labeled, ribosomal-rna-targeted oligonucleotide probes in the study of microbial ecology. Molecular Ecology 4, 543e553. Bååth, E., Söderström, B., 1982. Seasonal and spatial variation in fungal biomass in a forest soil. Soil Biology & Biochemistry 14, 353e358. Bardgett, R.D., Freeman, C., Ostle, N.J., 2008. Microbial contributions to climate change through carbon cycle feedbacks. ISME Journal 2, 805e814. Bates, S.T., Berg-Lyons, D., Caporaso, J.G., Walters, W.A., Knight, R., Fierer, N., 2011. Examining the global distribution of dominant archaeal populations in soil. ISME Journal 5, 908e917. Bodelier, P.L.E., Laanbroek, H.J., 2004. Nitrogen as a regulatory factor of methane oxidation in soils and sediments. FEMS Microbiology Ecology 47, 265e277. Bomberg, M., Jurgens, G., Saano, A., Sen, R., Timonen, S., 2003. Nested PCR detection of Archaea in defined compartments of pine mycorrhizospheres developed in boreal forest humus microcosms. FEMS Microbiology Ecology 43, 163e171. Bomberg, M., Montonen, L., Timonen, S., 2010. Anaerobic Eury- and Crenarchaeota inhabit ectomycorrhizas of boreal forest Scots pine. European Journal of Soil Biology 46, 356e364. Brandt, K.K., Vester, F., Jensen, A.N., Ingvorsen, K., 2001. Sulfate reduction dynamics and enumeration of sulfate-reducing bacteria in hypersaline sediments of the Great Salt Lake (Utah, USA). Microbial Ecology 41, 1e11. Burke, D.J., Chan, C.R., 2010. Effects of the invasive plant garlic mustard (Alliaria petiolata) on bacterial communities in a northern hardwood forest soil. Canadian Journal of Microbiology 56, 81e86. Burke, D.J., Dunham, S.M., Kretzer, A.M., 2008. Molecular analysis of bacterial communities associated with the roots of Douglas fir (Pseudotsuga menziesii) colonized by different ectomycorrhizal fungi. FEMS Microbiology Ecology 65, 299e309. Burke, D.J., Hamerlynck, E.P., Hahn, D., 2002. Interactions among plant species and microorganisms in salt marsh sediments. Applied and Environmental Microbiology 68, 1157e1164. Burke, D.J., Kretzer, A.M., Rygiewicz, P.T., Topa, M.A., 2006a. Soil bacterial diversity in a loblolly pine plantation: influence of ectomycorrhizas and fertilization. FEMS Microbiology Ecology 57, 409e419.

Burke, D.J., Lopez-Gutierrez, J.C., Smemo, K.A., Chan, C.R., 2009. Vegetation and soil environment influence the spatial distribution of root-associated fungi in a mature beech-maple forest. Applied and Environmental Microbiology 75, 7639e7648. Burke, D.J., Martin, K.J., Rygiewicz, P.T., Topa, M.A., 2005. Ectomycorrhizal fungi identification in single and pooled root samples: terminal restriction fragment length polymorphism (TRFLP) and morphotyping compared. Soil Biology & Biochemistry 37, 1683e1694. Burke, D.J., Martin, K.J., Rygiewicz, P.T., Topa, M.A., 2006b. Relative abundance of ectomycorrhizas in a managed loblolly pine (Pinus taeda) genetics plantation as determined through terminal restriction fragment length polymorphism profiles. Canadian Journal of Botany-Revue Canadienne De Botanique 84, 924e932. Calhoun, A., King, G.M., 1997. Regulation of root-associated methanotrophy by oxygen availability in the rhizosphere of two aquatic macrophytes. Applied and Environmental Microbiology 63, 3051e3058. Costello, A.M., Lidstrom, M.E., 1999. Molecular characterization of functional and phylogenetic genes from natural populations of methanotrophs in lake sediments. Applied and Environmental Microbiology 65, 5066e5074. Dalsgaard, T., Canfield, D.E., Petersen, J., Thamdrup, B., Acuna-Gonzalez, J., 2003. N2 production by the anammox reaction in the anoxic water column of Golfo Dulce, Costa Rica. Nature 422, 606e608. DeForest, J.L., Scott, L.G., 2010. Available organic soil phosphorus has an important influence on microbial community composition. Soil Science Society of America Journal 74, 2059e2066. DeForest, J.L., Zak, D.R., Pregitzer, K.S., Burton, A.J., 2004. Atmospheric nitrate deposition, microbial community composition, and enzyme activity in northern hardwood forests. Soil Science Society of America Journal 68, 132e138. Dunbar, J., Ticknor, L.O., Kuske, C.R., 2001. Phylogenetic specificity and reproducibility and new method for analysis of terminal restriction fragment profiles of 16S rRNA genes from bacterial communities. Applied and Environmental Microbiology 67, 190e197. Dunfield, P., Knowles, R., Dumont, R., Moore, T.R., 1993. Methane production and consumption in temperate and sub-arctic peat soils e response to temperature and pH. Soil Biology & Biochemistry 25, 321e326. EPA, 1971. Methods of Chemical Analysis of Water and Wastes. EPA, Cincinnati, OH. Feinstein, L.M., Sul, W.J., Blackwood, C.B., 2009. Assessment of bias Associated with incomplete extraction of microbial DNA from soil. Applied and Environmental Microbiology 75, 5428e5433. Forster, P., Artaxo, V.R.P., Berntsen, T., Betts, R., Fahey, D.W., Haywood, J., Lean, J., Lowe, D.C., Myhre, G., Nganga, J., Prinn, R., Raga, G., Schulz, M., Van Dorland, R., 2007. Changes in atmospheric constituents and in radiative forcing. In: Solomon, D.Q., Manning, S.M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Gong, P., Guan, X., Witter, E., 2001. A rapid method to extract ergosterol from soil by physical disruption. Applied Soil Ecology 17, 285e289. Grayston, S.J., Vaughan, D., Jones, D., 1997. Rhizosphere carbon flow in trees, in comparison with annual plants: the importance of root exudation and its impact on microbial activity and nutrient availability. Applied Soil Ecology 5, 29e56. Green, J.L., Holmes, A.J., Westoby, M., Oliver, I., Briscoe, D., Dangerfield, M., Gillings, M., Beattie, A.J., 2004. Spatial scaling of microbial eukaryote diversity. Nature 432, 747e750. Haase, S., Philippot, L., Neumann, G., Marhan, S., Kandeler, E., 2008. Local response of bacterial densities and enzyme activities to elevated atmospheric CO2 and different N supply in the rhizosphere of Phaseolus vulgaris L. Soil Biology & Biochemistry 40, 1225e1234. Henckel, T., Jackel, U., Schnell, S., Conrad, R., 2000. Molecular analyses of novel methanotrophic communities in forest soil that oxidize atmospheric methane. Applied and Environmental Microbiology 66, 1801e1808. Horner-Devine, M.C., Lage, M., Hughes, J.B., Bohannan, B.J.M., 2004. A taxa-area relationship for bacteria. Nature 432, 750e753. Horz, H.P., Rich, V., Avrahami, S., Bohannan, B.J., 2005. Methane-oxidizing bacteria in a California upland grassland soil: diversity and response to simulated global change. Applied and Environmental Microbiology 71, 2642e2652. Jackson, C.R., Liew, K.C., Yule, C.M., 2009. Structural and functional changes with depth in microbial communities in a tropical Malaysian Peat Swamp forest. Microbial Ecology 57, 402e412. King, G.M., 1996. In situ analyses of methane oxidation associated with the roots and rhizomes of a bur reed, Sparganium eurycarpum, in a Maine wetland. Applied and Environmental Microbiology 62, 4548e4555. Knief, C., Lipski, A., Dunfield, P.F., 2003. Diversity and activity of methanotrophic bacteria in different upland soils. Applied and Environmental Microbiology 69, 6703e6714. Kuo, S., 1996. Phosphorus. In: Sparks, D.L. (Ed.), Methods of Soil Analysis, Part 3, Chemical Methods. SSSA and ASA, Madisoni, WI, pp. 869e919. Leake, J.R., Donnelly, D.P., Boddy, L., 2002. Interactions between ecto-mycorrhizal and saprotrophic fungi. In: Heijden, M.G.A., Sanders, I.R. (Eds.), Mycorrhizal Ecology. Springer-Verlag, Berlin, pp. 345e371. Leininger, S., Urich, T., Schloter, M., Schwark, L., Qi, J., Nicol, G.W., Prosser, J.I., Schuster, S.C., Schleper, C., 2006. Archaea predominate among ammoniaoxidizing prokaryotes in soils. Nature 442, 806e809.

D.J. Burke et al. / Soil Biology & Biochemistry 53 (2012) 112e119 Linderman, R.G., 1988. Mycorrhizal interactions with the rhizosphere microflora e the mycorrhizosphere effect. Phytopathology 78, 366e371. Lynch, J.M., 1988. The terrestrial environment. In: Lynch, J.M., Hobbie, J.E. (Eds.), Micro-organisms in Action: Concepts and Applications in Microbial Ecology. Blackwell Scientific Publications, Inc., Palo Alto, California, USA, pp. 103e131. Mergel, A., Schmitz, O., Mallmann, T., Bothe, H., 2001. Relative abundance of denitrifying and dinitrogen-fixing bacteria in layers of a forest soil. FEMS Microbiology Ecology 36, 33e42. Montgomery, H.J., Monreal, C.M., Young, J.C., Seifert, K.A., 2000. Determination of soil fungal biomass from soil ergosterol analyses. Soil Biology & Biochemistry 32, 1207e1217. Olsen, S.R., Cole, C.V., Watanabe, F.S., Dean, L.A., 1954. Estimation of Available Phosphorus in Soils by Extraction with Sodium Bicarbonate. U.S. Government Print Office, Washington D.C. Olsson, P.A., Wilhelmsson, P., 2000. The growth of external AM fungal mycelium in sand dunes and in experimental systems. Plant and Soil 226, 161e169. Parniske, M., 2008. Arbuscular mycorrhiza: the mother of plant root endosymbioses. Nature Reviews Microbiology 6, 763e775. Pastor, J., Aber, J.D., McClaugherty, C.A., Melillo, J.M., 1984. Above-ground production and N and P cycling along a nitrogen mineralization gradient on Blackhawk Island, Wisconsin. Ecology 65, 256e268. Pesaro, M., Widmer, F., 2002. Identification of novel Crenarchaeota and Euryarchaeota clusters associated with different depth layers of a forest soil. FEMS Microbiology Ecology 42, 89e98. Pett-Ridge, J., Firestone, M.K., 2005. Redox fluctuation structures microbial communities in a wet tropical soil. Applied and Environmental Microbiology 71, 6998e7007. Philippot, L., Hallin, S., Schloter, M., 2007. Ecology of denitrifying prokaryotes in agricultural soil. Advances in Agronomy 96, 249e305. Poly, F., Ranjard, L., Nazaret, S., Gourbiere, F., Monrozier, L.J., 2001. Comparison of nifH gene pools in soils and soil microenvironments with contrasting properties. Applied and Environmental Microbiology 67, 2255e2262. Poplawski, A.B., Martensson, L., Wartiainen, I., Rasmussen, U., 2007. Archaeal diversity and community structure in a Swedish barley field: specificity of the EK510R/ (EURY498) 16S rDNA primer. Journal of Microbiological Methods 69, 161e173. Prosser, J.I., 2002. Molecular and functional diversity in soil micro-organisms. Plant and Soil 244, 9e17. Rayner, A.D.M., Boddy, L., 1988. Fungal Decomposition of Wood, its Biology and Ecology. John Wiley & Sons Inc., Chichester, UK. Reid, C.P.P., 1984. Mycorrhizae: a rootesoil interface in plant nutrition. In: Todd, R.L., Giddens, J.E. (Eds.), ASA (American Society of Agronomy) Special Publication, No. 47. MicrobialePlant Interactions; Proceedings of a Symposium, Fort Collins, Colo., USA, Aug. 5e10, 1979. Soil Science Society of America, American Society of Agronomy, Crop Science Society of America, Madison, Wis., USA, pp. 29e50. Rich, J.J., Myrold, D.D., 2004. Community composition and activities of denitrifying bacteria from adjacent agricultural soil, riparian soil, and creek sediment in Oregon, USA. Soil Biology & Biochemistry 36, 1431e1441. Robertson, G.P., Paul, E.A., Harwood, R.R., 2000. Greenhouse gases in intensive agriculture: contributions of individual gases to the radiative forcing of the atmosphere. Science 289, 1922e1925. Rösch, C., Bothe, H., 2005. Improved assessment of denitrifying, N-2-fixing, and total-community bacteria by terminal restriction fragment length polymorphism analysis using multiple restriction enzymes. Applied and Environmental Microbiology 71, 2026e2035. Rösch, C., Mergel, A., Bothe, H., 2002. Biodiversity of denitrifying and dinitrogenfixing bacteria in an acid forest soil. Applied and Environmental Microbiology 68, 3818e3829. Rousk, J., Baath, E., Brookes, P.C., Lauber, C.L., Lozupone, C., Caporaso, J.G., Knight, R., Fierer, N., 2010. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME Journal 4, 1340e1351.

119

Rygiewicz, P.T., Bledsoe, C.S., Zasoski, R.J., 1984. Effects of ectomycorrhizae and solution ph on N-15 nitrate uptake by coniferous seedlings. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere 14, 893e899. Saiya-Cork, K.R., Sinsabaugh, R.L., Zak, D.R., 2002. The effects of long term nitrogen deposition on extracellular enzyme activity in an Acer saccharum forest soil. Soil Biology & Biochemistry 34, 1309e1315. Shrestha, M., Abraham, W.R., Shrestha, P.M., Noll, M., Conrad, R., 2008. Activity and composition of methanotrophic bacterial communities in planted rice soil studied by flux measurements, analyses of pmoA gene and stable isotope probing of phospholipid fatty acids. Environmental Microbiology 10, 400e412. Singh, B.K., Bardgett, R.D., Smith, P., Reay, D.S., 2010. Microorganisms and climate change: terrestrial feedbacks and mitigation options. Nature Reviews Microbiology 8, 779e790. Singh, B.K., Tate, K., 2007. Biochemical and molecular characterization of methanotrophs in soil from a pristine New Zealand beech forest. FEMS Microbiology Letters 275, 89e97. Smith, S.E., Read, D.J., 2008. Mycorrhizal Symbiosis. Academic Press Inc., London, UK. Treusch, A.H., Leininger, S., Kletzin, A., Schuster, S.C., Klenk, H.P., Schleper, C., 2005. Novel genes for nitrite reductase and Amo-related proteins indicate a role of uncultivated mesophilic crenarchaeota in nitrogen cycling. Environmental Microbiology 7, 1985e1995. Tunlid, A., Ringelberg, D., Phelps, T.J., Low, C., White, D.C., 1989. Measurement of phospholipid fatty-acids at picomolar concentrations in biofilms and deep subsurface sediments using gas-chromatography and chemical ionization mass-spectrometry. Journal of Microbiological Methods 10, 139e153. van Bodegom, P., Stams, F., Mollema, L., Boeke, S., Leffelaar, P., 2001. Methane oxidation and the competition for oxygen in the rice rhizosphere. Applied and Environmental Microbiology 67, 3586e3597. Van der Nat, F., Middelburg, J.J., 1998. Effects of two common macrophytes on methane dynamics in freshwater sediments. Biogeochemistry 43, 79e104. Vestal, J.R., White, D.C., 1989. Lipid analysis in microbial ecology e quantitative approaches to the study of microbial communities. Bioscience 39, 535e541. Wallenstein, M.D., Myrold, D.D., Firestone, M., Voytek, M., 2006. Environmental controls on denitrifying communities and denitrification rates: insights from molecular methods. Ecological Applications 16, 2143e2152. Wang, Q., Garrity, G.M., Tiedje, J.M., Cole, J.R., 2007. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology 73, 5261e5267. Ward, N., Larsen, O., Sakwa, J., Bruseth, L., Khouri, H., Durkin, A.S., Dimitrov, G., Jiang, L.X., Scanlan, D., Kang, K.H., Lewis, M., Nelson, K.E., Methe, B., Wu, M., Heidelberg, J.F., Paulsen, I.T., Fouts, D., Ravel, J., Tettelin, H., Ren, Q.H., Read, T., DeBoy, R.T., Seshadri, R., Salzberg, S.L., Jensen, H.B., Birkeland, N.K., Nelson, W.C., Dodson, R.J., Grindhaug, S.H., Holt, I., Eidhammer, I., Jonasen, I., Vanaken, S., Utterback, T., Feldblyum, T.V., Fraser, C.M., Lillehaug, J.R., Eisen, J.A., 2004. Genomic insights into methanotrophy: the complete genome sequence of Methylococcus capsulatus (Bath). PLOS Biology 2, 1616e1628. Wuchter, C., Abbas, B., Coolen, M.J., Herfort, L., van Bleijswijk, J., Timmers, P., Strous, M., Teira, E., Herndl, G.J., Middelburg, J.J., Schouten, S., Sinninghe Damste, J.S., 2006. Archaeal nitrification in the ocean. Proceedings of the National Academy of Sciences of the United States of America 103, 12317e12322. Zak, D.R., Holmes, W.E., White, D.C., Peacock, A.D., Tilman, D., 2003. Plant diversity, soil microbial communities, and ecosystem function: are there any links? Ecology 84, 2042e2050. Zar, J.H., 1998. Biostatistical Analysis. Prentice Hall, Upper Saddle River, NJ, 929 pp. Zelles, L., 1999. Identification of single cultured micro-organisms based on their whole-community fatty acid profiles, using an extended extraction procedure. Chemosphere 39, 665e682.