• fen soil;
  • temperature;
  • soil water content;
  • microcosms;
  • T-RFLP;
  • greenhouse gases


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

In this study, microcosms were used to investigate the influence of temperature (4 and 28 °C) and water content (45% and 90% WHC) on microbial communities and activities in carbon-rich fen soil. Bacterial, archaeal and denitrifier community composition was assessed during incubation of microcosms for 12 weeks using terminal restriction fragment length polymorphism (T-RFLP) profiling of 16S rRNA and nitrous oxide reductase (nosZ) genes. In addition, microbial and denitrifier abundance, potential denitrification activity and production of greenhouse gases were measured. No detectable changes were observed in prokaryote or denitrifier abundance. In general, cumulatively after 12 weeks more carbon was respired at the higher temperature (3.7 mg CO2 g−1 soil), irrespective of the water content, whereas nitrous oxide production was greater under wet conditions (98–336 μg N2O g−1 soil). After an initial lag phase, methane emissions (963 μg CH4 g−1 soil) were observed only under warm and wet conditions. T-RFLP analyses of bacterial 16S rRNA and nosZ genes revealed small or undetectable community changes in response to temperature and water content, suggesting that bacterial and denitrifying microbial communities are stable and do not respond significantly to seasonal changes in soil conditions. In contrast, archaeal microbial community structure was more dynamic and was strongly influenced by temperature.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Temperature and soil water content are two of the most important environmental factors influencing soil organic matter decomposition and production of greenhouse gases in terrestrial environments (Kirshboum, 1995, 2006). Temperature positively correlates with microbial activity, but microorganisms maintain activity at temperatures close to 0 °C, even below snowpack, and contribute to global budgets of greenhouse gas emissions (Sommerfeld et al., 1993). Changes in temperature can influence bacterial and archaeal community structure. For example, the soil bacterial community structure, measured using denaturing gradient gel electrophoresis (DGGE) analysis of 16S rRNA genes, was altered following geothermal heating in Yellowstone National Park soil (Norris et al., 2002), terminal restriction fragment length polymorphism (T-RFLP) analysis demonstrated temperature effects on the structure and function of the methanogenic archaeal community in a rice field soil (Chin et al., 1999) and temperature influenced soil ammonia-oxidizer community structure during long-term incubation of soil microcosms (Avrahami & Conrad, 2003; Tourna et al., 2008).

Soil water content is important in regulating oxygen diffusion, with maximum aerobic microbial activity occurring at moisture levels between 50% and 70% of water-holding capacity (WHC) (Linn & Doran, 1984; Franzluebbers, 1999). A high moisture content (matric potential >−0.01 MPa) decreases rates of organic matter decomposition, due to low oxygen supply, while low soil moisture decreases microbial activity by reducing diffusion of soluble substrates, microbial mobility and intracellular water potential (Csonka, 1989; Killham et al., 1993; Stark & Firestone, 1995; Zhou et al., 2002; Schjønning et al., 2003). Soil water content may also determine microbial community structure and Zhou et al. (2002) suggested that free water connecting soil particles may influence diversity patterns, by controlling nutrient availability and cell movement.

Despite their importance, the influence of temperature and soil water content on microbial community structure and associated ecosystem functions is poorly understood. Soil water content is particularly important in fens, which are peatlands predominantly fed by groundwater rich in minerals. Temperature and water table levels influence pathways of greenhouse gas emissions and rates of organic matter decomposition (Happell & Chanton, 1993; Conrad, 1996). Both factors also directly control carbon and nitrogen mineralization rates in fen soils (Augustin et al., 1996; Keller et al., 2004). Seasonal changes in these two important environmental parameters may, therefore, influence function–structure relationships of microbial communities responsible for greenhouse gas emissions.

We have investigated previously the influence of temperature and soil water content on microbial communities and activities in Ljubljana Marsh, a carbon-rich fen soil that was drained 200 years ago and has since been used mainly for agricultural production (Hacin et al., 2001; Kraigher et al., 2006). Fluctuations in the water table usually occur between 12 and 55 cm below the soil surface, but during prolonged dry summer conditions, the water table may drop below 100 cm. Flooding conditions that bring the water table to the soil surface occur sporadically during the early winter and late spring (Hacin et al., 2001). Our field studies indicated that nitrogen content and nitrogen mineralization in the predominant soil type (Histosol) were 10–40% greater at low than high water table levels (Hacin et al., 2001). Seasonal variations in soil moisture and temperature were also reflected in the microbial biomass as determined by substrate-induced respiration (SIR) and in dehydrogenase activity, both of which were lowest after prolonged dry summer, when soil moisture was very low (42% WHC). A slight increase in biomass and dehydrogenase activity was detected under wet and cold conditions (March and May). However, the structure of the bacterial community in the field remained stable despite changing environmental parameters (Kraigher et al., 2006).

This field study, therefore, indicates that seasonal changes in environmental factors influence microbial activity and biomass, but not community structure. The aim of this study was to use well-controlled, open soil microcosm systems to investigate the individual and combined effects of temperature and soil water content on the activity, abundance and structure of ‘total’ bacterial and archaeal communities and on denitrifiers in this soil. Archaea and bacteria were characterized because we were interested in prokaryotic responses to water content and temperature and because they are responsible for the two specific ecosystem functions (denitrification and methane production) that were measured in our experiments.

Microcosms were incubated at two temperatures (4 and 28 °C) and two soil moisture conditions (45% and 90% WHC). Warm and dry conditions (28 °C, 45% WHC) are common in the summer period (June–August), whereas cold and wet conditions (4 °C, 90% WHC) are re-encountered during the winter period (December–February). In contrast, warm and wet conditions (28 °C, 90% WHC) may occur during summer flooding and cold and dry conditions (4 °C, 45% WHC) may occur in winters with long periods without precipitation. The lower moisture content used is typical of prolonged dry summer conditions, and may be encountered more frequently in the future due to global warming. The higher moisture content, typical of winter conditions, reflects conditions recommended for restoration of drained and degraded fens (Rosenthal, 2003).

The aim of this study was to use well-controlled, open soil microcosm systems to investigate the individual and combined effects of temperature and soil water content on the activity, abundance and structure of ‘total’ bacterial and archaeal communities and on denitrifiers in this soil. We hypothesized that mineralization of organic matter, assessed as CO2 emission, would decrease in rewetted fen soils due to anoxia, while CH4 and N2O emissions would increase, especially at the higher incubation temperature. In contrast, extremely dry soil conditions would show low gas fluxes under both cold and warm conditions. In addition, we predicted that, although no seasonal changes in the bacterial community structure were observed in the field (Kraigher et al., 2006), other communities, such as the archaeal community and the denitrifier community, might still be responsive to environmental fluctuations in this soil. It has been shown previously that the archaeal community is highly responsive to temperature and water content (Chin et al., 1999; Jerman et al., 2007) while denitrifiers, which represent up to 5% of general bacterial community (Henry et al., 2006), are positively affected by temperature, anaerobiosis and availability of nitrate. In addition, we predicted that uneven distribution of guilds might increase the resolution of microbial community profiling approach when functional guilds are analyzed independently, because when the general bacterial community structure is analyzed, minute changes of functional guilds become diluted out. We, therefore, used 16S rRNA genes to characterize bacterial and archaeal communities and the nosZ gene, encoding nitrous oxide reductase, to characterize the denitrifier community. While open soil microcosms have been exploited to study the influence of temperature or water table on greenhouse gas emissions and mineralization processes (Chimer & Cooper, 2003), we believe that this is the first investigation of the influence of both factors on the function and structure of microbial communities and their activities in drained fen grassland soils.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Site description and soil characteristics

Soils were obtained from Ljubljana Marsh grassland, located south of Ljubljana, Slovenia (45°58′N, 14°28′E), and the soil characteristics are: Corg content=150±15 mg gdry soil−1, Norg content=13±1 mg gdry soil−1, C/N ratio=11.8±0.3, WHC=1.7±0.2, pH 6.6, and texture: 0.1% sand, 60.3% clay and 39.6% silt (Kraigher et al., 2006). The mean annual precipitation rate is 1400 mm and the mean air annual temperature is 10 °C. Seasonal variation in soil moisture content was estimated on the basis of gravimetric measurements in soil sampled during the winter period (December–January, 2005/6; n=10) and during the summer period (June–August, 2005 and 2006; n=12). The average % WHC for the winter period was 58 (SD 6.4) for the upper 0–20-cm soil horizon and 76 (SD 9) for the 20–40-cm soil horizon. For the summer period, the average % WHC for the 0–20-cm soil horizon was 43 (SD 11.7) and for the 20–40-cm soil horizon % WHC was 55 (SD 16.4). For the microcosm study, soil cores (15 cm long × 7 cm diameter; Eijkelkamp, Giesbeek, NL) were taken systematically from the 0–30 cm soil horizon, classified as histosol (Hacin et al., 2001; Kraigher et al., 2006), using a 1 m × 1 m grid on a 100 m2 area to form one homogenized composite soil sample. Soil was sieved (mesh size 5 mm) to form one homogenized composite soil sample. The majority of soil macro aggregates passed the sieve unrestricted. Homogenized soil was stored in loosely closed plastic bags at 4 °C for 5 days until the establishment of microcosms and determination of soil properties. Soil WHC was measured by packing soil to a bulk density of 0.36±0.01 g dry soil cm−3 into soil sample rings (standard diameter 53 mm, volume 100 cm3; Eijkelkamp, Giesbeek, the Netherlands) fitted with a cloth at the bottom and immersing the rings in water for 24 h. Soil moisture content at saturation (100% WHC) was measured after allowing the rings to drain freely on a funnel until they reached a constant weight (7 h). Soil water content was determined after drying at 60 °C for 24 h and is presented on a gravimetric basis. This information was used to maintain the moisture content of soil throughout the experiments.

Establishment of microcosms

A microcosm experiment was designed with four different combinations of soil moisture (45% and 90% WHC) and temperature (4 and 28 °C), sacrificing three replicate microcosms at each sampling time at 3-week intervals (Fig. 1). Glass jars (750 mL, n=120) were filled with 400 g of 45% WHC soil and preincubated at 28 °C for 3 weeks (to). Microcosms were then randomly divided into four treatments (A, B, C, D), and water content was adjusted to 45% or 90% WHC and incubated at two different temperatures for 12 weeks, as presented in Fig. 1. In order to maintain the original % WHC, gravimetric water loss through evaporation was replenished every 2 days using sterile-deionized water. Microcosms were open to the air and were closed only for 1.5 h, during measurement of gas fluxes.


Figure 1.  Schematic representation of the microcosm experiment setup. The microcosm experiment was designed with combinations of soil moisture (45% and 90% WHC) and temperature (4 and 28°C). Glass jars (750 mL, n=120) were filled with 400 g of 45% WHC soil and preincubated at 28°C for 3 weeks (to) and then exposed to four incubation conditions (A, B, C, D) for 12 weeks. At 3-week intervals, three replicate microcosms were sacrificed for each condition. Microcosms were open to the air during incubation and were closed only for 1.5 h, during measurement of gas fluxes.

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To characterize the availability of electron acceptors for denitrification, soil NO2 and NO3 were measured at each microcosm sampling date, following standard 2 M KCl extraction, using a continuous flow analyzer (FlowSys, Alliance Instruments, Salzburg, Austria). Total soil organic carbon (TOC) content was determined initially (t0) and after 12 weeks (t12) using the method published by Yeomans & Bremner (1989). Microcosm headspace gas samples were removed using a syringe inserted through a septum in the lid of the jar at times 0, 30, 60 and 90 min after the closure of microcosms incubated at the appropriate temperature. CO2, CH4 and N2O emission rates were measured weekly using a Network GC System 6890 N (Agilent Technologies) gas chromatograph equipped with a GS-Carbon PLOT capillary column (30 m × 0.53 mm ID × 3-μm-film) (Agilent Technologies), methanizer, flame ionization and electron capture detectors. Gas emissions were corrected for temperature effects and water-dissolved gases according to Tiedje (1994).

Three microcosms were sacrificed per experimental treatment at each sampling time (every 3 weeks) and used for measurements of soil moisture, NOx, acridin orange direct counts (AODC), culturable cell concentration of denitrifiers [most probable number (MPN) method] and potential denitrification activity (PDA). Archaeal and bacterial communities were characterized using T-RFLP analysis of 16S rRNA genes and denitrifiers by analysis of nosZ genes at 0 and 12 weeks.

Measurement of abundance of culturable denitrifying bacteria and total cells

MPN enumeration of denitrifying bacteria was performed at each sampling date for each sacrificed microcosm. Cell suspensions were made by horizontally shaking 10 g soil in 90 mL MgSO4 (1 g L−1) in 500-mL flasks for 20 min at 150 r.p.m. Ten-fold serial dilutions were prepared to 10−8 in a D30 medium (Chèneby et al., 2004) and five replicates of 1-mL samples from each dilution were inoculated into 9-mL portions of the same medium in Hungate tubes. The atmosphere was exchanged for N2, supplemented with 10% (v/v) acetylene and tubes were incubated at 28 °C in the dark for 4 weeks, after which they were analyzed for N2O according to Tiedje (1994). Estimates of culturable denitrifier cell concentration were determined according to Tiedje (1994), using an MPN calculator ( Total cell densities in cell suspensions were determined by direct counts following staining with acridine orange (Bloem, 1995), using a Nikon Eclipse TE 300 fluorescence microscope equipped with a DXM 1200 digital camera.

Microbial biomass and activity

Microbial biomass was evaluated as SIR, using a shaken soil-slurry method (Anderson & Domsch, 1978) with the modifications suggested by West & Sparling (1986) and Sparling (1995). The water content of 30-g soil samples was normalized to 3 g H2O g−1 dry soil and supplemented with glucose (10 mg g−1 soil) in 250-mL incubation flasks at 28 °C. Empty flasks and flasks containing soil without glucose amendment served as controls. The headspace was sampled and the amount of CO2 released was measured at 0, 45 and 90 min of incubation.

Denitrification potential rates (PDA) were determined by the modified method of Tiedje (1994) without addition of chloramphenicol, as preliminary analyses showed a linear increase in N2O through 5 h of incubation. Measurement of PDA using soil supplemented with a nitrate and carbon source enables determination of the number/concentration of specific enzymes capable of performing denitrification as a process. Five replicate 10-g soil samples from sacrificed microcosms were amended with 40 mL of anoxic 5 mM glucose and nitrate at pH 6.8 (28 °C) in 150-mL flasks capped with butyl rubber stoppers, evacuated and flushed three times (3 × 3 min) with N2 in the presence of 10% (v/v) acetylene. N2O production was monitored by sampling the headspace every 15 min, following establishment of anoxic conditions, during incubation for 90 min with shaking at 250 r.p.m. at 28 °C in the dark.

The gas phase composition was determined as described above.

Bacterial, archaeal and denitrifier community structure

For each independent soil microcosm, soil DNA was extracted from duplicate samples using the Ultra Clean Soil DNA Isolation Kit (MO BIO Laboratories Inc., CA) according to the manufacturer's instructions. Duplicate extracts were combined to obtain a more representative DNA sample. Extracts from independent triplicate microcosms (1, 2, 3) of four experimental treatments sampled at 12 weeks, and one at 0 weeks, were used to compare communities. DNA was quantified using the Quant-iT dsDNA Assay Kit (Molecular Probes Inc., Eugene, OR; Invitrogen, Karlsruhe, Germany). Bacterial community structure was determined by amplification of 16S rRNA genes from extracted DNA using Bacteria-specific FAM-labelled forward primer 27f (5′-AGA GTT TGA TCC TGG CTC AG-3′) and 907r reverse primer (5′-CCG TCA ATT CCT TTR AGT TT-3′) (Egert et al., 2004). Archaeal communities were characterized by amplification of 16S rRNA genes using Archaea-specific primers: FAM-labelled forward primer Ar109f (5′-ACK GCT CAG TAA CAC GT-3′) and Ar915r reverse primer (5′-GTG CTC CCC CGC CAA TTC CT-3′) (Egert et al., 2004). Amplification was performed in volumes of 50 μL containing 5 μL of 10 × buffer, 4 μL of 25 mM MgCl2, 10 μL of 10 mM dNTP, 1 μL of 10 μM of each primer 27f/907r or Ar109f/Ar915r, 1 μL of 10 mg mL−1 BSA, 2.5 U of aTaq DNA Polymerase (Promega, Madison, WI), 2 μL template DNA and 34.5 μL of nuclease-free water. The thermal cycler was programmed as: denaturation at 94 °C for 1 min, annealing at 53 °C for 1 min and extension at 72 °C for 1 min for 30 cycles, followed by a final extension at 72 °C for 10 min. NosZ-specific PCR amplification from pure cultures and DNA extracted from soil was performed using the forward primer nosZ-F-1181 (5′-CGY TGT TCM TCG ACA GCC AG-3′) and the reverse primer nosZ-R-1880 (5′-CAT GTG CAG NGC RTG GCA GA-3′) (Rösch et al., 2002; Rich et al., 2003). The specificity of these primers and PCR conditions are as described by Rösch et al. (2002) and Rich et al. (2003), with the exception that the forward primer nosZ-F-1181 was labelled at the 5′ end with 6-Fam (6-carboxyfluorescein).

PCR products were resolved on a 1% agarose gel and detected by ethidium bromide staining. PCR products were purified using a PCR Purification Kit (Qiagen, Hilden, Germany) and amplicons were quantified using the Quant-iT dsDNA Assay Kit (Molecular Probes Inc.; Invitrogen). Purified nosZ and bacterial and archaeal 16S rRNA gene-specific PCR products were digested using New England Biolabs restriction endonucleases HaeIII and HhaI (MBI Fermentas Inc., Burlington, ON, Canada). Digestion of 180 ng of PCR products was carried out in a 30 μL volume using 3 μL of R+red buffer, 0.5 μL of HaeIII and HhaI (10 U μL−1) in separate reactions. The volume was adjusted to 30 μL with nuclease-free water and incubated at 37 °C overnight. Digestions were inactivated for 15 min at 85 °C and purified by ethanol precipitation.

Separation, detection and basic GeneScan analysis of fluorescently labelled T-RFs were performed on an automated ABI PRISM 310 DNA sequence analyzer (Applied Biosystems Inc., Foster City, CA). Purified digested products were mixed with 0.5 μL internal-lane DNA standard (Genescan 500 ROX, Applied Biosystems Inc.) and 10 μL deionized formamide. Before analysis, DNA samples were denatured for 2 min at 95 °C and than immediately placed on ice. The T-RFLP raw data were automatically converted to a digitized form using bionumerics software (Applied Maths NV, Belgium). For each treatment three replicates were analyzed.

Data analysis

Abundances and PDA were compared using Student's t-test and a significance level of 0.05 was used to assess differences between treatments. T-RFLP profiles were analyzed using bionumerics software (Applied Maths NV). This software allows for lane definition, background subtraction, marker-assisted normalization, compensation for intensity differences between the lanes and assignment of the different bands within each lane. A matrix of similarities for the densitometric curves of the band patterns was calculated based on Pearson product–moment correlation coefficient and dendrograms were created using UPMGA linkage. Relevant and nonrelevant clusters were further delineated by cluster cut-off methods and variance in densitometric curves was assessed by a cophenetic correlations approach (Applied Maths NV).

Weekly measurements of greenhouse gas emissions as end-products of microbial metabolism served as a proxy for overall microbial activity that is itself a function of resource availability (Balser et al., 2006). A number of proposed models from the recent literature were evaluated but did not fit the data well (Townsend et al., 1995; Paul et al., 1999; Holtan-Hartwig et al., 2002; Waldrop & Firestone, 2004; Sleutel et al., 2005; Curiel Yuste et al., 2007). The only two models that provided acceptable fit (r2>0.8) to the data were simple linear kinetics and the sigmoid kinetic model. Nonlinear emissions' data were described best by a sigmoid function:

  • image(1)

where C is the concentration of emitted gas (CO2, CH4 or N2O), A is the available substrate concentration, k is a rate constant, t is time and t1/2 is the time at which the concentration of emitted gas reached 50% of the available substrate concentration. Available substrate concentration (A) and the rate constant (k) were determined using an iterative best-fit technique, allowing both A and k to vary, using the origin® 6.1 statistical program (Origin Corporation). The emission rate (K) was derived from the above equation:

  • image(2)

The fraction of initial organic carbon or nitrogen was calculated as the ratio between available substrate pool and total organic carbon or nitrogen. The amounts of total organic carbon and nitrogen used for calculation were determined by Kraigher et al. (2006).


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Abundance and activity

Total cell concentrations, determined by acridine orange direct enumeration, ranged from 8.61 (SD 0.24) to 8.81 (SD 0.31) log cells g−1 drained peat soil (Table 1). There was no statistical difference between the four treatments at the end of the 12-week microcosm experiment (t-test, P>0.05). In addition, MPN counts were performed to determine whether the conditions investigated influenced the abundance of denitrifiers, rather than activity or community structure. The abundance of denitrifiers, estimated by MPN, ranged from 6.96 (SD 0.24) to 7.32 SD 0.31) log cells g−1 soil in different treatments and was also not statistically different (P>0.05) (Table 1). In contrast, PDA, which is a measure of denitrification enzymes present in the examined soil sample, was determined at the end of the experiment (12 weeks). PDA was the highest in microcosms incubated under wet–warm conditions (treatment B), intermediate at low-temperature incubations irrespective of water content (treatments C and D) and the lowest under warm–dry conditions (treatment A) (Table 1). Moisture content also affected the PDA values for warm treatments (A, B) but had no detectable effect on cold treatments (C, D). Microbial biomass was determined using SIR to assess changes in the abundance of general microbial biomass to changed conditions. Microbial biomass as determined by SIR was not significantly different between treatments A, C and D (Table 1). Microbial biomass in microcosms incubated at a high temperature and a high moisture content (treatment B) was lower than in other treatments.

Table 1.   AODC, SIR, nitrate concentration (NOx-N), denitrifier MPN and PDA in microcosms sampled after incubation for 12 days
 Microcosm conditions
A – dry– warmB – wet– warmC – dry– coldD – wet– cold
  • Values represent means and SD (in parentheses) for each treatment determined in samples taken from triplicate microcosms after incubation for 12 weeks.

  • *

    Values that are not significantly different. For details of experimental design, see Fig. 1.

AODC (log × g−1)8.81 (0.31)*8.74 (0.43)*8.61 (0.24)*8.69 (0.12)*
SIR (mL CO2 h−1 g−1)2.56 (0.05)1.39 (0.44)4.14 (0.19)3.21 (0.17)
NOx -N (mg N g−1)0.2 (0.01)0.01 (0.01)0.1 (0.01)*0.1 (0.01)*
MPN (log × g−1)6.96 (0.24)*7.09 (0.33)*7.19 (0.23)*7.32 (0.31)*
PDA (μg N h−1 g−1)70.5 (2.87)98.0 (4.32)85.7 (2.42)*85.0 (4.11)*

Microbial community structure

NosZ, bacterial and archaeal 16S rRNA gene fragments were successfully amplified from initial (ABCD-t0) and 12-week incubated soils (A-t12, B-t12, C-t12 and D-t12). The concentration of amplified DNA ranged between 11 and 40 ng μL−1. Analysis of the nosZ profiles indicated two distinct denitrifying communities at to and t12 (Fig. 2a). In addition, incubation led to the development of two clusters delineating nosZ T-RFLP profiles from microcosms with low water content (45% WHC, treatments A and C) and high water content (90% WHC, treatments B and D). The separation of the two clusters was not entirely consistent (e.g. see D-t12-1) and was not supported by the cluster cut-off method. Temperature had no effect on the clustering of nosZ T-RFLP profiles. Similar clustering patterns were observed for bacterial T-RFLP profiles (Fig. 2c). Initial and 12-week bacterial community structures were different, as indicated by cluster cut-off values and cophenetic correlations between them. The separation of high-water (B, D) and low-water regimes (A, C) at the end of the incubation was small, and was not statistically significant.


Figure 2.  Cluster analysis of the T-RFLP profiles for nitrous oxide reductase (nosZ) genes (a), archaeal (b) and bacterial (c) 16S rRNA genes amplified from soil. A, B, C and D represent microcosm incubation conditions indicated in Fig. 1; t0 and t12 indicate analysis of samples taken after incubation for 0 and 12 weeks, respectively, and 1, 2 and 3 indicate replicates of T-RFLP profiles. Replicates for which too low a fluorescence signal was obtained were omitted from cluster analyses. Gray lines delineate relevant clusters separated by the cluster cut-off method and cophenetic correlations are shown as numbers at each branch. Horizontal bars represent the reliability and internal consistency of the branch.

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In contrast to both denitrifier and bacterial communities, archaeal community structure exhibited a very different pattern in different treatments after incubation for 12 weeks (Fig. 2b). At a low temperature (treatments C and D), there was no major effect of incubation on archaeal community structure but a significant change in archaeal community structure was observed at high temperatures (treatments A and B). In both treatments, the archaeal community structure was different from the initial archaeal structure and from communities in C and D treatments. The effect was more pronounced when the water content was low (treatment A).

Microbial activities

Weekly estimates of emissions of the greenhouse gases CO2, CH4 and N2O were used as a measure of overall microbial activity, presented as cumulative gas concentrations (Fig. 3). The fitted parameters to the experimental data are given in Table 2. In general, sigmoid models gave a better fit when rates of gas production were high, while linear models gave the best fit when the rates were low. Cumulative-respired CO2 was the highest at a high temperature irrespective of water content over the 12-week incubation period (Fig. 3a; Table 2). This indicates that substrate for carbon mineralization was available in the drained fen grassland soil. Cumulative CO2 evolution (Table 2, CO2 model, parameter A in treatment A) was 17% lower than in treatment B but kinetic parameters (k) for these two treatments were very similar (Table 2). In contrast, cumulative CO2 emissions at low temperatures were very low, irrespective of the water content in the soil microcosms (Fig. 3a). Significant amounts of CH4 were detected only under wet–warm conditions (treatment B) after a long lag phase of 55 days (Fig. 3b). Emission rates of CH4 from microcosms in treatments A, C and D were c. 70 times lower than in treatment B (Table 2).


Figure 3.  Cumulative emissions of carbon dioxide (a), methane (b) and nitrous oxide (c) during incubation of microcosms for 12 weeks. A, B, C and D represent the four sets of incubation conditions indicated in Fig. 1. Data from triplicates and best-fitting model curves are shown.

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Table 2.   Parameter values for sigmoid and linear models of microbial CO2, CH4 and N2O production: calculated available substrate pool (A), rate constant (k), emission rate (K), lag time of the microbial process and the percentage of initial carbon and nitrogen content respired
 Microcosm conditions
A – dry–warmB – wet–warmC – dry–coldD – wet–cold
  1. –, not applicable.

  2. Values represent calculated means and SD (in brackets) for each treatment in three microcosms. For details of experimental design, see Fig. 1.

CO2 modelSigmoidSigmoidLinearLinear
A (μg C-CO2 g−1)3943 (111)4740 (110)
k (day−1)0.061 (0.005)0.055 (0.002)
K (μg C-CO2 g−1 day−1)60.1 (5.2)65.2 (2.8)−1.41 (0.41)−1.78 (0.99)
Lag (day)014
Fraction of initial Corg (%)2.6 (0.27)3.16 (0.32)
CH4 modelLinearSigmoidSigmoidLinear
A (μg C-CH4 g−1)1968 (121)14.75 (2.42)
k (day−1)0.104 (0.014)0.186 (0.08)
K (μg C-CH4 g−1 day−1)0.007 (0.003)51.2 (7.59)0.69 (0.32)0.006 (0.003)
Lag (day)5575
Fraction of initial Corg (%)1.3 (0.15)0.01 (0.002)
N2O modelLinearSigmoidLinearSigmoid
A (μg N-N2O g−1)96.5 (1.8)414.8 (24.2)
k (day−1)1.14 (0.33)0.053 (0.007)
K (μg N-N2O g−1 day−1)0.08 (0.01)27.5 (7.9)0.127 (0.017)5.5 (1)
Lag (day)010
Fraction of initial Norg (%)0.74 (0.06)3.19 (0.31)

In contrast to CO2 emissions, which were temperature-dependent, N2O emissions showed greatest variation with moisture content (Fig. 3c), with little N2O production under dry conditions. Under wet–warm conditions, the highest initial rates of N2O emissions (27.5 μg N-N2O g−1 day−1) were observed during the first 3 weeks of incubation (treatment B, Table 2). After c. 1 week of incubation, N2O emissions stopped, coinciding with nitrate depletion in this treatment (data not shown). Under wet–cold conditions (treatment D), the concentration of N2O increased throughout incubation at a rate of 5.5 μg N-N2O g−1 day−1 (Table 2) and the nitrogen substrate pool converted to N2O after incubation for 12 weeks was approximately four times larger than under wet–warm conditions (Table 2).


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The rates of microbially mediated soil processes depend on the abundance and community structure of relevant functional groups of microorganisms and on the environmental conditions. Assessment of the relative importance of these different factors is made difficult by the high spatial variability and heterogeneity in soil characteristics, plant and microbial communities and by high variability in environmental conditions. Microcosms provide experimental systems in which environmental conditions can be controlled and manipulated and have proved very useful tools for elucidating the basic principles that shape microbial communities in natural habitats (Ellis, 2004; Jessup et al., 2004; Srivastava et al., 2004; Balser et al., 2006; Prosser et al., 2007). In a previous field study, we have observed seasonal effects on microbial abundance and activity, but not community structure, in a drained fen grassland soil. In this study, we used open soil microcosm systems containing soil from this grassland site, to investigate the influence of major changes in temperature and soil moisture on microbial activity, abundance and community structure under controlled conditions. This was achieved by incubation at combinations of two temperatures (4 and 28 °C), representative of winter and summer, respectively, and two soil water contents (45% and 90% WHC), representing summer and winter extremes, respectively.

Microbial biomass and abundance of denitrifiers

The overall abundances of microbial communities, estimated by direct counts and denitrifier MPN counts, suggest that the size of microbial communities in these grassland soils is stable and does not respond significantly to changes in temperature and soil moisture. There was a small decrease in microbial biomass under wet–warm conditions, as estimated by SIR. This is not surprising because less energy can be gained from anaerobic fermentative and respiratory processes, and thus less biomass is generated. However, overall, SIR was higher in soil exposed to cold than to warm conditions. These results are consistent with our field study where biomass estimated by SIR was also higher in cold spring than in warm and dry summer (Kraigher et al., 2006). While the results of direct counts and MPN suggest stability, this is difficult to judge from these measurements, because community composition may change significantly without measurable changes in abundance. In addition, the methods applied lack precision and alternative approaches, such as quantitative PCR, might be better in detecting subtle changes in abundance of the targeted communities. Nevertheless, SIR and microbial activities showed some significant differences and were responsive to the environmental changes and will be discussed below.

Influence of temperature and soil water content on greenhouse gas production

We hypothesized that mineralization of organic matter, assessed as CO2 emission, would decrease in reflooded fen soils due to anoxia, while CH4 and N2O emissions would increase, especially at the higher incubation temperature. In contrast, an extremely dry soil condition would show low gas fluxes under both cold and warm conditions. The results indicate that soil temperature is the key factor determining carbon availability in the drained fen grassland soil and, as predicted, CO2 production was significantly higher at 28 °C than at 4 °C, regardless of the water content. Under dry–warm conditions, production was constant for 80 days and slowed down during the last 10 days of incubation, while at a high soil moisture content, CO2 production continued for 12 weeks. It should be noted that the long incubation periods (up to 180–360 days), as used by others (e.g. Petterson & Bååth, 2003) and also in our experiments, did not significantly alter carbon availability. The rate constant (k) was slightly higher under dry–warm conditions and no lag in CO2 production was observed, in contrast to wet–warm conditions, which exhibited a lag of 14 days and a lower rate constant. This is consistent with the observation of lower microbial biomass and slower microbial metabolism in anoxic soils (Fenchel & Finlay, 1995). Nevertheless, the emission rates and % overall carbon respired were not significantly influenced by moisture content.

Temperature influenced both CO2 production and N2O production. However, N2O was only detected in microcosms with high moisture and the persistence of N2O emissions at low temperature and high water content (treatment D) is intriguing. Biological processes respond positively to temperature upshift and several studies have confirmed that this is also true for the denitrification process. In experiments addressing both N2O production and reduction to N2, the product ratio (N2O/N2) has been found to increase with decreasing temperature (Bailey & Beauchamp, 1973; Keeney et al., 1979; Avalakki et al., 1995). One possible explanation might be that N2O reduction has higher activation energy than N2O production. However, Holtan-Hartwig et al. (2002) reported that this temperature effect is not due to higher activation energy for N2O reduction as compared with N2O production, at least when process rates between 5 and 20 °C were monitored. The rates measured at 0 °C, however, were much lower than those predicted by the Arrhenius function based on data in the temperature range 5–20 °C and the effect was found to be more severe for N2O reduction. Further studies are required to determine the activation energies of N2O reduction and production in Ljubljana marsh soils because it is known that temperature responses of the estimated net N2O emission potentials are different in different soils and might also be microbial community dependent (Holtan-Hartwig et al., 2002).

An increase in the N2O emissions was detected previously in soil microcosms exposed to a stepwise decrease in temperature from 15 to 0 °C, with the peak at 0 °C (Koponen et al., 2004). Recently, Gödde & Conrad (1999) and Öquist et al. (2007) noted that a substantial fraction of N2O produced in cold soils is derived from nitrification. It is also possible that the higher solubility and diffusion through wet soils at a low temperature (Davidson et al., 1997; Lüdemann et al., 2000) may have relieved oxygen limitation of nitrification in the top layer of treatment D microcosms, allowing continued oxidation of ammonia to nitrate. This is consistent with our observation that nitrate was not depleted from the soil even after 12 weeks of incubation. Therefore, coupling between nitrification and denitrification may take place due to favorable conditions for both nitrification and denitrification in neighboring microhabitats (Wrage et al., 2001) at a low temperature and a high water content in drained fen grassland soils.

Temperature was also important for CH4 production, which was detected only at a high temperature and a high moisture content and after a relatively long lag period. This suggests that methanogenic archaea were at low abundance, which may also explain our ability to detect significant changes in the archaeal community structure. Alternatively, other electron acceptors may have been present that inhibit methanogenesis and our recent results suggest that high iron concentrations may inhibit methane production in these drained fen grassland soils (Jerman et al., 2007). The absence of significant CH4 production at a low water content could be explained by aerobic conditions that inhibit methane production, while low methane production under wet–cold conditions is consistent with the temperature dependence of methanogenesis (Daulat & Clymo, 1998; Fey & Conrad, 2003).

Influence of temperature and soil water content on the structure of bacterial, archaeal and denitrifying communities

We predicted that, although no seasonal changes in the bacterial community structure were observed in the field (Kraigher et al., 2006), prolonged exposure (12 weeks) to constant conditions might lead to detectable shifts in community structure, especially if specific functional genes were targeted. We, therefore, used 16S rRNA genes to characterize bacterial and archaeal communities and nosZ genes to characterize the denitrifier community. T-RFLP profiling of 16S rRNA genes indicated that there was no effect of temperature on bacterial community structure and only a small difference in communities in microcosms at high and low soil moisture contents. These findings, therefore, support the conclusion of Kraigher et al. (2006) that seasonal differences do not lead to significant changes in bacterial community structure. There are conflicting reports in the literature about the effect of soil temperature and moisture on microbial communities. For example, Smit et al. (2001) and Liesack et al. (2000) reported substantial effects, whereas Watanabe et al. (2007) found that archaeal methanogenic community structure was largely unaffected by strong abiotic seasonal changes. Peat soils are carbon-rich environments and Zhou et al. (2002) found that a high soil carbon content is capable of maintaining the initial community structure despite differences in aqueous connectivity. Similar observations have been made by Agnelli et al. (2004). It is thus likely that, in high carbon soils such as those investigated here, the effects of moisture and temperature in combination or separately are not sufficiently strong to shift the bacterial community structure significantly.

The absence of seasonal changes in the community structure of high-level taxonomic groups may result from high diversity and redundancy, and we predicted that changes may be more apparent in microbial communities involved in specific processes whose rates exhibit significant seasonal changes. In particular, we used nosZ genes to target the denitrifying community (Sharma, 2003; Stres et al., 2004; Rösch & Bothe, 2005), but denitrifier communities also showed very little change in structure after incubation for 12 weeks. There was some evidence of an effect of soil water content, as T-RFLP profiles of nosZ genes formed two separate clusters, which correlated with soil water content. The combined results of bacterial 16S rRNA and nosZ genes therefore indicate that temperature and water content had no significant effect on bacterial or denitrifier communities. However, we do observe that incubation alone, regardless of environmental conditions, influenced both bacterial and denitrifier communities, indicating a ‘microcosm’ effect, possibly due to release of organic matter when homogenizing and repacking the soil into the microcosms as also observed in Ranjard et al. (2006). It is interesting that the incubation effect was reflected only in the bacterial and denitrifier community but not in the archaeal community.

The most significant change in microbial community structure due to environmental conditions was observed in T-RFLP profiles of archaeal communities, suggesting that these organisms are more responsive to the environmental parameters tested, in particular to temperature. Archaeal community structure changed significantly at the high but not at the low temperature, while water content had additional effects on archaeal community structure. While Watanabe et al. (2007) report a very stable community of methanogenic archaea throughout the year in paddy soils, temperature downshift induced a change in the composition of archaeal community in anoxic soil microcosms (Chin et al., 1999). In addition, Tourna et al. (2008) report an effect of temperature on crenarchaeal community structure as determined by analysis of 16S rRNA genes and of crenarchaeal ammonia monooxygenase (amoA) genes.

Overall, our results support the idea that, in the absence of plant cover, changes in water content and temperature play a minor role in shaping bacterial and denitrifier community structures but significantly influence their activity. These results suggest that environmental parameters such as temperature and water content act at the level of substrate availability and control the expression of specific enzymes involved in the microbial processes studied, while the abundances and community structure of total bacterial and denitrifying communities remain largely unaffected. In contrast, the archaeal community structure is much more responsive to environmental changes in drained fen grassland soil.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

We dedicate this work to Prof. Dr Ivan Mahne, the previous head of our laboratory, who always stressed the importance of controlled laboratory experiments, when addressing questions in microbial ecology. Prof. Dr Ivan Mahne died in November, 2006. We would like to thank the three anonymous reviewers and Prof. J.I. Prosser for their constructive comments that improved the manuscript. This work was supported by the Slovenian Ministry of Higher Education ARRS grants J4-6149-0481-04, P4-0116, and the Centre of Excellence for Environmental Technologies grant 3211-05000183.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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