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Genetic and Environmental Controls on Nitrous Oxide Accumulation in Lakes

  • Jatta Saarenheimo ,

    Affiliation Department of Biological and Environmental Science, University of Jyväskylä, 40014, Jyväskylä, Finland

  • Antti J. Rissanen,

    Affiliation Department of Biological and Environmental Science, University of Jyväskylä, 40014, Jyväskylä, Finland

  • Lauri Arvola,

    Affiliation Lammi Biological Station, University of Helsinki, 16900, Lammi, Finland

  • Hannu Nykänen,

    Affiliation Department of Biological and Environmental Science, University of Jyväskylä, 40014, Jyväskylä, Finland

  • Moritz F. Lehmann,

    Affiliation Department for Environmental Science, University of Basel, CH-4058, Basel, Switzerland

  • Marja Tiirola

    Affiliation Department of Biological and Environmental Science, University of Jyväskylä, 40014, Jyväskylä, Finland

Genetic and Environmental Controls on Nitrous Oxide Accumulation in Lakes

  • Jatta Saarenheimo, 
  • Antti J. Rissanen, 
  • Lauri Arvola, 
  • Hannu Nykänen, 
  • Moritz F. Lehmann, 
  • Marja Tiirola


We studied potential links between environmental factors, nitrous oxide (N2O) accumulation, and genetic indicators of nitrite and N2O reducing bacteria in 12 boreal lakes. Denitrifying bacteria were investigated by quantifying genes encoding nitrite and N2O reductases (nirS/nirK and nosZ, respectively, including the two phylogenetically distinct clades nosZI and nosZII) in lake sediments. Summertime N2O accumulation and hypolimnetic nitrate concentrations were positively correlated both at the inter-lake scale and within a depth transect of an individual lake (Lake Vanajavesi). The variability in the individual nirS, nirK, nosZI, and nosZII gene abundances was high (up to tenfold) among the lakes, which allowed us to study the expected links between the ecosystem’s nir-vs-nos gene inventories and N2O accumulation. Inter-lake variation in N2O accumulation was indeed connected to the relative abundance of nitrite versus N2O reductase genes, i.e. the (nirS+nirK)/nosZI gene ratio. In addition, the ratios of (nirS+nirK)/nosZI at the inter-lake scale and (nirS+nirK)/nosZI+II within Lake Vanajavesi correlated positively with nitrate availability. The results suggest that ambient nitrate concentration can be an important modulator of the N2O accumulation in lake ecosystems, either directly by increasing the overall rate of denitrification or indirectly by controlling the balance of nitrite versus N2O reductase carrying organisms.


Nitrous oxide (N2O) is an important greenhouse gas and the single most important ozone destroying chemical [1]. N2O in the biosphere is produced as an intermediate molecule in denitrification or nitrifier-denitrification, or as a by-product during nitrification or dissimilatory nitrate reduction to ammonium (DNRA) [2, 3]. The denitrification pathway includes four enzymatically catalyzed reductive steps: nitrate reduction (nar), nitrite reduction (nir), nitric oxide reduction (nor), and nitrous oxide reduction (nos) [4]. Reduction of nitrite, where the first gaseous form of fixed nitrogen (N) (i.e. NO) is produced, is catalyzed by two analogous genes: nirK and nirS genes encoding a copper nitrite reductase and a cytochrome cd1-nitrite reductase, respectively [4]. These two genes prevail in different organisms and their differential distributions in nature seem to be modulated by the redoxconditions, with nirS being preferentially expressed under low dissolved oxygen conditions [5, 6]. Recent studies have also revealed that nosZ genes encoding N2O reductase actually belong to two phylogenetically distinct clades [7, 8], here referred to as nosZI and nosZII, which need to be analyzed by separate PCR primer sets. As with nir genes, the relative importance of nos genes seems to systematically differ between habitats and with environmental conditions [8], yet the exact controls that modulate their relative abundance in nature are uncertain. Some denitrifiers are lacking the nosZ gene completely and perform the truncated denitrification pathway, where N2O is produced as an end-product [9]. In fact, genome sequencing showed that one third of the cultivated denitrifying bacteria lack the nosZ gene [10].

Since denitrifier community structure is likely to have an effect on net N2O production and emission [11, 12], denitrifier communities have been studied through the analysis of sequence variation and/or the abundance of nirS, nirK, and nosZ genes in many ecosystems [13, 14, 15, 16, 17]. High availability of nitrate and nitrite has been shown to be conducive to N2O accumulation [18, 19], fostering the increase the N2O/(N2O+N2) ratio in the gaseous denitrification products [20, 21]. Such correlations may simply indicate nitrate-induced enhancement of denitrification rates (and thus N2O accumulation), but they may also be the result of microbial community adaptation. Philippot et al. [22], for example, demonstrated that the relative abundance of the nosZ gene was a strong predictor of the N2O/(N2O+N2) production ratio.

In soils, microbially produced N2O is likely lost to the atmosphere by turbulent diffusive escape. In contrast, in aquatic environments, the diffusivity of gases is much slower (Kz values on the order of 10−5 to 10−6 cm2 s−1, [23]), reducing diffusive loss rates and improving the N2O availability for nosZ carrying bacteria. More complete denitrification and lower N2O/N2 gas emission ratios should, therefore, be expected for the aquatic versus soil environments. Still, lake ecosystems have shown to be important sites of N2O emissions [19, 24], and, as in soils, N2O production and accumulation in lakes appears to be dependent on the ambient nitrate and oxygen concentrations [25, 26, 24, 27]. Although the importance of lacustrine N2O production is well recognized [19, 26], and albeit the fact that benthic denitrifier community structure has been studied in some lakes [28, 29], it is not known whether variations in the accumulation of N2O are mostly directly dependent on the environmental conditions, or whether they rather are indirectly constrained by the denitrifying community structure. With some recent exceptions [7, 8] the role of the nosZII clade remained mostly unconsidered in this context.

Here, we evaluated genetic and environmental factors that likely modulate N2O production and accumulation in lake ecosystems, especially focusing on the benthic abundance of nirS, nirK, nosZI, and nosZII genes during the summertime N2O accumulation period. Anticipating close links between nitrate concentrations and the N2O accumulation, we hypothesized 1) that high hypolimnetic nitrate concentrations would decrease the relative abundance of the nosZ genes (i.e., increase the nir/nos ratio) within lacustrine sediments, and 2) that higher nir/nos ratios would lead to enhanced N2O accumulation. The linkage between benthic denitrification gene frequency and N2O accumulation was assessed in an inter-lake study of 12 boreal lakes in southern Finland, pooling the lakes into two groups based on their hypolimnetic nitrate concentrations (high-NO3-lakes and low-NO3-lakes). In addition, denitrification gene abundance and N2O accumulation was investigated along a littoral-to-pelagic transect in a large stratified lake (Vanajavesi) with relatively high hypolimnetic nitrate levels (24.0−44.9 μmol l−1).


Comparison of denitrification genes in high- versus low-nitrate lakes

Considerable inter-lake variation was observed with regards to the nitrate (0.4−79.1 μmol l−1), ammonium (0.6−61.4 μmol l−1), and oxygen (1.9−333.4 μmol l−1) concentrations (S1 Table). The lakes were classified into two groups based on their nitrate concentration, which generally reflected land use in the catchment area: high-NO3-lakes comprised lakes mostly with extensive agricultural activity in their catchment area and one urban lake (Jyväsjärvi), while low-NO3-lakes included lakes mostly with little agricultural land in their catchment area. Other environmental parameters did not differ significantly between the two groups (Table 1).

Table 1. Environmental parameters (mean and SE) for high-NO3-lakes (n = 6) and low-NO3-lakes (n = 6), and results of a t-test or Mann-Whitney U-test* comparing the oxygen, nitrate, ammonium and phosphate concentrations, temperature, catchment field area (ha), averaged N2Oexcess concentrations, and maximum observed N2excess concentrations between the two lake groups.

Throughout the studied lakes, the abundances of nirS, nirK, nosZI, and nosZII relative to 16S rRNA genes varied between 0.6−12.9% (Table 2), and the gene copy numbers ranged between 4.8 and 580 per ng of DNA (S2 Table). The ratio of nirS/nirK ranged between 0.5−2.0 (average 1.0), and the ratio of nosZI/nosZII varied between 0.5−5.7 (average 1.9). Neither environmental factors (oxygen, temperature, nitrate concentration) nor N2O accumulation showed any significant correlation with the gene abundance, gene copy numbers, or with nirS/nirK or nosZI/nosZII gene ratios (Pearson correlations, p values >0.05). The relative proportion of the previously unaccounted nosZII gene was of a similar magnitude as that of nosZI, but showed a markedly higher inter-lake variability (Table 2). Although not statistically significant, nosZI and nosZII seemed slightly more abundant in the low-NO3 group of lakes, while nirS and nirK seemed less abundant, (Fig. 1A). The (nirS+nirK)/nosZI ratio was higher in high-NO3-lakes than in low-NO3-lakes (Fig. 1B). In addition, the (nirS+nirK)/nosZI gene ratio correlated positively with the estimated net N2O production, as well as with nitrate and phosphate concentrations (Table 3.). As for (nirS+nirK)/nosZII and (nirS+nirK)/(nosZI+nosZII), we also observed a tendency for higher ratios in the high-NO3-lakes compared to low-NO3 lakes (Fig. 1B). However, correlation between nitrate and (nirS+nirK)/(nosZI+nosZII) was only weakly significant (p = 0.06) (Table 3).

Fig 1. Abundance of nirS, nirK, nosZI, and nosZII genes relative to the amount of 16S rRNA genes (A), and ratios of nir and nos genes in sediments of lakes with high and low nitrate concentrations (high-NO3-lakes and low-NO3-lakes) (B).

* = significantly different between the two lake groups (Mann-Whitney U-test, p = 0.006).

Table 2. Copy numbers (mean ±SE) of nirS, nirK, nosZI, and nosZII gene amplicons as percentages of 16S rRNA gene copy numbers (nd, no data).

Table 3. Correlations of functional gene ratios and accumulated N2O and N2 gas concentrations with environmental parameters in the inter-lake dataset.

Correlation coefficients with 0.01 < p < 0.05 and p < 0.01 are written in normal text and bold, respectively.

N2O and N2 accumulation in high- versus low-nitrate lakes

During the summer sampling (late July), most of the study lakes were oversaturated with respect to N2O (i.e. the depth-integrated mean N2Oexcess was >0). N2Oexcess in the water column varied between 0.9−37.1 nmol l−1 (11−337% oversaturation). The highest N2Oexcess concentrations were observed either in near-bottom waters of the lakes or, in the case of stratified lakes (five lakes were stratified with regards to oxygen and displayed an anoxic hypolimnion), at the oxic-anoxic interface within the water column (S1 Fig.). Maximum N2excess concentrations measured using membrane inlet mass spectrometry (MIMS) were generally slightly higher than the equilibrium concentration at given temperatures (<2% oversaturation). N2excess was significantly higher in high-NO3 lakes than in low-NO3 lakes (Table 1) and correlated with nitrate concentrations (Table 3). Moreover, the depth-integrated N2Oexcess concentrations (0−20.3 μmol m−3) and net N2O production rates (0−11.2 μmol N m−2 d−1) estimated from the N2O concentration profiles were significantly higher in high-NO3-lakes than in low-NO3-lakes (Table 1), and both correlated with NO3 concentration (Table 3). Maximum N2excess concentrations were found to correlate with the depth-integrated N2Oexcess concentration (Table 3).

Denitrification genes and N2O accumulation in Lake Vanajavesi

In Lake Vanajavesi, hypolimnetic temperature and oxygen concentrations were tightly correlated, indicating the effect of thermal water column stratification on the vertical distribution of dissolved oxygen (correlation r = -0.98 and p = 0.000). Sampling sites 1−3 (water depths 2−6 m) were fully aerated, sites 4−6 (water depths 8−12 m) displayed lower oxygen concentrations, and the two deepest sampling sites (water depths 14 and 16 m) were anoxic at the bottom of the hypolimnion (S3 Table). Nitrate concentrations (24.0−44.9 μmol l−1) were consistently high at all sampling sites, whereas ammonium (1.1−57.4 μmol l−1) and phosphate (0.03−0.7 μmol l−1) concentrations displayed strong variability between strongly oxygen-depleted and oxygen-replete conditions (S3 Table).

The relative abundances of nirS, nirK, nosZI, and nosZII genes in Lake Vanajavesi varied between 0.6 and 6.2% of the total 16S rRNA genes (Table 2), with nosZI or nosZII being the least abundant of the denitrifying genes at all sites. In contrast to observation at the inter-lake scale (where nitrate concentrations were generally lower and more variable), we observed a strong positive correlation between nitrate concentrations and the (nirS+nirK)/nosZI+II ratio (r = 0.98 and p = 0.001) (Fig. 2). The correlation with either nosZI or nosZII only was not significant (p > 0.05).

Fig 2. Relationship between hypolimnetic nitrate concentration and the sedimentary (0–2cm) (nirS+nirK)/nosZI+II gene ratio (r = 0.98 and p = 0.001), and depth-integrated N2Oexcess (r = 0.89 and p = 0.02) in Lake Vanajavesi.

At all sampling sites, essentially the entire water column was oversaturated with respect to equilibrium N2O concentrations (S2 Fig.). The N2O profiles of Sites 1, 2, and 3 indicated a homogenized water column, with an equal degree of oversaturation throughout. At the deeper Sites 4, 5, and 6, a markedly higher N2O oversaturation was observed at the bottom of the lake. The degree of N2O oversaturation was even higher at the oxic-anoxic interface in the water column of Sites 7 and 8 (S2 Fig.). Depth-integrated N2Oexcess varied between 5.7−36.0 nmol l−1 (62−337% oversaturation) and correlated positively with the nitrate concentration in Lake Vanajavesi (r = 0.89 and p = 0.02) (Fig. 2). A negative correlation was observed with respect to the oxygen concentration (r = -0.90 and p = 0.002) and temperature (r = -0.95 and p < 0.001).


To our knowledge, this is the first study combining N2O measurements and molecular analyses of denitrification genes in lake ecosystems. This is also the first time that the abundance of nirS and nirK genes together with both clades of nosZ genes were investigated in freshwater sediments. The total nir/nos ratio was above 1:1 in nearly all study lakes, indicating that the microbial community had a higher potential to produce N2O than to reduce it. This implies that the accumulation of N2O is linked to genetic factors.

All the studied denitrification genes (nir and nos variants) were present in the lake sediments, although their abundance largely varied among the lakes and along the Vanajavesi transect. The qPCR results also revealed that nosZII genes are as frequent as the canonical nosZI genes in the freshwater sediments, which emphasizes the need to further study the ecology of nosZII encoding organisms in future studies. The relatively high abundance of individual nirS, nirK, nosZI, and nosZII genes highlights the important biogeochemical role of denitrification in boreal lake sediments. For comparison, the abundance of individual denitrification genes nirS, nirK, and nosZ have previously been found to range between 0.5 and 6.8% of the 16S rRNA gene abundance in various soil and sediment samples [14, 21, 30, 31]. Bioavailability of copper (Cu) and iron (Fe) can control the expression and activity of nitrite and nitrous oxide reductases. While nirK and nosZ are copper-containing reductases, nirS is an iron containing cd1-type reductase. Possible Cu limitation may lead to nirS dominance and, thus, to increased N2O accumulation. Unfortunately, data on Fe and Cu concentrations were not available, and we cannot fully exclude Cu versus Fe limitation as a controlling factor in N2O accumulation in the study lakes. Yet, the equal abundance of nirS and nirK genes does not suggest any adaptation of the microbial community to Cu limitation.

Data on the nirS/nirK gene ratios in lakes are rare. The only study we know of in this context is by Martins et al. [31], who reported that nirS genes were more abundant than nirK genes in sediments of freshwater lakes on the Azores. In contrast, the average nirS/nirK gene ratio observed in this study was 1:1. Different from the subtropical lakes studied by Martins et al. [31], boreal lakes experience seasonal variations in redox and other physico-chemical conditions, which may increase the diversity of ecological niches and prevent certain microbial ecotypes from dominating an ecosystem. Since the distribution of nirS and nirK genes is phylogenetically scattered [10], the ratio of these two evolutionarily separate, but functionally equivalent, nitrite reductase gene types does not necessarily reflect the dominance of one taxonomical group over another as a function of environmental conditions. Instead, the relatively strong variability in the nirS and nirK gene ratio between the existing studies highlights the need to quantify both genes when studying the factors affecting N2O accumulation. Although the nir/nos ratio at the DNA level does not necessarily correspond to the respective ratios at the level of mRNA transcripts or enzyme molecules on short-term time scales, it may indicate longer-term genetic adaptation, which was the focus of this study.

When comparing lakes at different spatial scales and between various geographical regions, denitrification rates have shown a clear positive correlation with nitrate availability [32]. This correlation was further corroborated by the observed co-variation of NO3 and N2excess in the lakes studied here. Our study also showed the linkage between NO3 concentration and N2O accumulation, which is in agreement with previous work in boreal lakes [18]. Based on previously published N2 production rates for five of the lakes in this study [29, 32] (unpublished results), the N2O production rates reported here correspond to 0.2−1.7% of the total gaseous N production (N2O/(N2+N2O) ratio). These values fall within the range of previously reported estimates (0.1–4.1%) for freshwater systems [33]. Besides total denitrification rates, it is the balance between nitrite reduction and N2O reduction which controls the build-up of N2O. This balance has been shown to be sensitive to changes in redox conditions [34]; however, the role of longer-term nitrate availability in modulating this balance is uncertain. NO3 is generally the preferred electron acceptor for the denitrifying community when compared to N2O (except for some nosZII carrying organisms, see the discussion below). Hence, when the competition for nitrate is tighter, reduction of N2O becomes a more feasible trait for the heterotrophic micro-organisms [35].

At the inter-lake scale, nir/nosZI ratios correlated with the nitrate concentrations and N2Oexcess. These correlations suggest that the denitrifying communities were adapted to varying nitrate levels within the lake and that they control the ratio of N2O production versus reduction. Moreover, both at the inter-lake scale and within the Lake Vanajavesi transect the combined nir/nos ratio (i.e. [nirS+nirK]/nosZI+II) correlated with ambient nitrate. In contrast, the nir/nosZI ratio did not display any statistically significant correlation with (the less variant) nitrate concentration in Lake Vanajavesi. This apparent difference with regards to the role of nosZI and nosZII may be related to the known genetics of nosZII carrying organisms. The N2O reductase nosZI has only been found for Alpha-, Beta-, and Gammaproteobacteria and some archaea, whereas nosZII reductases may be common in a wider range of bacterial and archaeal phyla [7, 8]. While most of the typical nosZI-harboring microbes have the complete set of denitrification genes, less than half of the known nosZII-carrying microorganisms possess genes of the “upstream” denitrification steps, and nosZII-type reductase was thus named as “non-denitrifier nitrous oxide reductase” [7]. As a consequence, many of the nosZII-carrying microbes are incapable of using nitrate (or nitrite) as an electron acceptor, and are, therefore, less affected by ambient nitrate availability. The variable prevalence of denitrifying versus non-denitrifying nosZII subsets may explain the above-described differences in the correlation analyses between the inter-lake and intra-lake studies (genetic relationships versus NO3 levels).

Although it has been shown that denitrification is the major N2O source in lake ecosystems [19, 27], it is likely that nitrifiers (i.e. ammonium oxidation and nitrifier-denitrification) also contribute to N2O production in these environments. In the lake transect, where sampling sites where characterized by different hypolimnetic oxygen regimes, N2O accumulation patterns were clearly linked to oxygen concentration. Concentration of N2O peaked near the oxic-anoxic interface, which was located either in the sediment surface or in the water column. This could be due to O2 availability just above the oxic-anoxic interface, which would increase N2O production via nitrification [36, 37]. On the other hand, the presence of O2 even at low levels likely inhibits N2O reduction compared to other reduction steps in denitrification [37]. Therefore, truncated denitrification would also lead to observed accumulation patterns of N2O, with concentration maxima in the vicinity of the redox transition zones. The lack of N2O accumulation in the anoxic water layers of the lakes further supports the notion that stable anoxic conditions are conducive to full denitrification to N2, while microaerophilic conditions would rather support truncated denitrification and/or slowed nitrous oxide reduction. In addition, dissimilatory nitrate reduction to ammonium (DNRA), in which N2O can also be formed as a by-product [38], competes with denitrification for nitrate. The most important factor controlling competition between these two processes appears to be the C:N ratio [39, 40], where high ratios favors DNRA over denitrification. In addition, the supply of nitrate relative to nitrite and microbial generation time are identified as key environmental factors in controlling whether nitrate is reduced to nitrogen gas in denitrification, or retained in the ecosystem as ammonium in DNRA [41]. In our study lakes, the C:N ratio of sediment organic material varied between 9 and 27 (on average 17.7), and thus DNRA may have had some role on NO3 reduction. However, the actual contribution of N2O production by organisms carrying out DNRA in lake ecosystems is currently unresolved.

This study provided putative evidence for the control of both denitrifier gene composition and N2O accumulation by nitrate concentration. This suggests that N2O emissions from denitrification would be modulated by nitrate-induced changes in the denitrifier communities. In turn, the study indicates that recent increases in the land-based and atmospheric anthropogenic nitrogen loadings from agriculture and energy production may have caused shifts in the lacustrine denitrifier communities as well as stimulated N2O emissions from lake ecosystems.

Experimental Procedures

Study sites and the sampling procedure

The study lakes are located within the same region in southern Finland (61°01−61°52 N and 25°02−24°09 E), except Lake Jyväsjärvi which is located 150 km north of the other lakes (62°13 N and 25°44 E) (S1 Table). The lakes are located on state land with open access, thus no permits were required for collection of samples. Further, the locations are not protected in any way and the study did not involve endangered or protected species. All the study lakes were sampled in July 2011. The lakes were chosen to cover a wide variety of lake characteristics: size (surface area 25−12000 ha), maximum depth (2−85 m), and nutrient concentrations (S1 Table). All the study lakes are ice-covered from November until the beginning of May. We divided the selected lakes into two groups based on their hypolimnetic nitrate concentrations. High-NO3-lakes (n = 6) comprised lakes with NO3 concentrations between 15.7−79.4 μmol l−1 and low-NO3-lakes (n = 6) included lakes with NO3 concentrations between 0.6−1.5 μmol l−1 (S1 Table).

Depths of the sampling sites were recorded with an echo-sounder (S1 Table) and the water samples were taken with a Limnos tube sampler (height 30 cm, volume 2.1 l). Water samples for gas analyses were collected at ca. 0.5 m, 1 m, 3 m, and 5 m above the lake bottom (if the lake was deep enough) and below/under the surface (0.5 m water depth). Three replicates (30 ml) were taken from each depth for N2O concentration measurements in 60 ml polypropylene syringes, which were closed with three-way stopcocks after removing any gas bubbles, and transported to the laboratory on ice. Nitrogen gas (N2) samples for membrane inlet mass spectrometry (MIMS) measurements were taken in 12 ml borosilicate glass tubes (six replicates) with screw-capped butyl rubber septa (Labco Ltd.). We allowed water overflow for at least three volumes to avoid atmospheric contamination, and samples with air bubbles were discarded. Microbial processes in borosilicate glass tubes were stopped by adding 100 μl ZnCl through the septum with a needle under water. Water for nutrient analyses were collected in 1-L bottles from the near-bottom waters of the lakes and all samples were transported to the laboratory on ice. Sediment core samples for analyses of the denitrifier communities were collected in all of the lakes using a mini gravity corer with plexiglass tubes (ø = 3.5 cm).

Water column profiles of temperature and oxygen concentrations were measured in situ using a portable field meter (YSI model 58, Yellow Springs Instruments). Dissolved inorganic phosphorus [42], nitrate [43], and ammonium [44] were determined with a flow injection analyzer using standard methods (QuikChem 8000) from filtered (0.2 mm filter; Millipore) water samples.

Quantification of nirS, nirK, and nosZ genes

Sediment samples were collected from the surface layer (0–2 cm) of the sediment cores and freeze-dried for further use (Alpha 1–4 LD plus, Christ). DNA extraction was performed from 0.03 g of dry sediment using the bead-beating and phenol-chloroform extraction protocol of Griffiths et al. [45]. Two extractions were made from each site. The DNA concentrations were measured with a Qubit 2.0 Fluorometer (Invitrogen) and the DNA concentration of each sample was adjusted to yield a concentration of 10 ng μl−1.

For qPCR quantification of the nirK, nirS, nosZI, and nosZII genes, partial 16S rRNA was used as a reference gene, and commonly used primers were selected from previous studies (S4 Table). Amplification of qPCR and fluorescent data collection was carried out with a Bio-Rad CFX96 thermal cycler (Bio-Rad Laboratorios) in a reaction mixture of 0.5 μM of each primer for the selected target gene (except for nosZII 1 μM of each primer), 10 μl 2XiQ SYBR Green supermix (BioRad), 1 μl of DNA (10 ng), and PCR-grade water (Fermentas) to yield a total volume of 20 μl. Three replicate qPCR amplifications were performed for each sample.

The PCR procedure for 16S rRNA included an initial denaturation step at 95°C for 15 min and 40 cycles of amplification (95°C for 20 s, 53°C for 35 s and 72°C for 70 s). Finally, an increase of 0.5°C s−1 from 65 to 95°C was performed to obtain the melting curve analysis of PCR products. The thermal cycling conditions for other genes were the same as the one just described, except that the annealing temperature was 55°C for nirS, 60°C for nirK and nosZI, and 54°C for nosZII. Standard curves were constructed from PCR amplicons extracted from agarose gel with a BioRad Gel Extraction Kit (BioRad). Amplicons were re-amplified and the resulting products were purified with Agencourt AMPure XP (Beckman Coulter). A dilution series of 107–102 gene copies were used as standards in each qPCR run. Gene abundances were calculated as relative abundances from the abundance of the reference gene (16S rRNA). Replicate results were averaged (n = 6) and standard errors were calculated. Inhibition was tested from the dilution series (1, 1–10 and 1–100) and no inhibition was detected.

N2O gas concentrations

N2O samples were analyzed according to Maljanen et al. [46] with a gas chromatograph (Agilent 6890N, Agilent Technologies) equipped with an auto sampler (Gilson) and an electron capture detector (ECD). The N2O samples were processed according to Bellido et al. [47], and two replicates from each depth were measured. N2O equilibrium concentrations were calculated based on Henry’s law (modified from IPCC Fourth Assessment Report: Climate Change 2007 and [48]). Concentration of N2O accumulated due to microbial reactions (N2Oexcess) was calculated from the difference between observed N2O concentration and the calculated equilibrium concentration. The overall amount of accumulated N2O per square meter was estimated from integration of the N2Oexcess concentration profiles, and the depth-integrated N2Oexcess per m3 was obtained by division through the water depth at the sampling site. All study lakes undergo complete spring mixing after ice-off (with equilibrium concentrations throughout the water column). Assuming cumulative N2O production in the hypolimnion, with low atmospheric exchange after the mixing period, net N2O production rates can be estimated according to Mengis et al. [25] (with slight modifications) by dividing the amount of accumulated N2O per square meter by the number of days since ice-off (i.e. the onset of water column stratification in early May) to the sampling date (end of July). These estimates need to be considered conservative, as at least some turbulent diffusive loss to the atmosphere is indicated by the partial N2O pressure gradient between surface water and the atmosphere (see S1 Fig.).

Natural N2 gas concentrations

N2/Ar gas concentration ratios were determined using membrane inlet mass spectrometry (MIMS) as described in Kana et al. [49]. Equilibrium concentrations were calculated according to Weiss [50]. N2excess was then calculated from N2/Ar ratio in the sample divided by the N2/Ar ratio at equilibrium for a given temperature.

Statistical analyses

Data analyses were conducted using PASW 18.0 (PASW Statistics 18, Release Version 18.0.0, SPSS 2009). The normality assumption was tested with the Shapiro-Wilks test. In our dataset, the effect of nitrate concentration on process parameters and denitrifier communities was specifically addressed by comparing high-NO3-lake and low-NO3-lake data either using independent samples t-test (normally distributed variables) or Mann-Whitney U-test (non-normally distributed variables). In addition, correlation analysis (Pearson or Spearmann correlation) was performed to study potential relationships among environmental parameters (NO3 concentration, oxygen concentration, ammonium concentration, phosphorus concentration, depth, gene abundances, and N2O concentrations.

Supporting Information

S1 Fig. Vertical profiles of measured N2O concentrations, calculated N2O equilibrium concentrations, oxygen concentrations, and temperatures in different lakes.

The grey line indicates the respective oxic-anoxic interface.


S2 Fig. Vertical profiles of measured N2O concentrations, calculated N2O equilibrium concentrations, oxygen concentrations, and temperatures at the different sampling sites along the depth transect of Lake Vanajavesi.

The grey line indicates the oxic-anoxic interface.


S1 Table. Study site information, hypolimnetic nutrient concentrations, and oxygen status of the study lakes.


S2 Table. Gene copy numbers of nirS, nirK, nosZI, and nosZII gene amplicons per ng of DNA (nd, no data).


S3 Table. Water temperature and pH, as well as nutrient and oxygen concentrations at various sampling sites in Lake Vanajavesi.


S4 Table. Gene-specific primer pairs used in the qPCR assays.



We want to thank Saara-Maria Haapala for help in the field and Simo Jokinen for assistance in measuring N2O samples. We thank Lammi Biological Station for helping and providing all facilities. We are grateful to M. Rollog for his laboratory assistance during MIMS analyses. Finally, we thank Jari Syväranta for valuable comments on an earlier version of the manuscript, and Sara Hallin for providing positive control samples and assistance with the nosZII qPCR analyses.

Author Contributions

Conceived and designed the experiments: JS LA MT. Performed the experiments: JS HN MFL. Analyzed the data: JS AJR. Contributed reagents/materials/analysis tools: HN LA MFL MT. Wrote the paper: JS AJR LA HN MFL MT.


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