Microbial mechanisms responsible for diurnal dynamics of N 2 O fluxes in a nutrient-poor subarctic permafrost environment

Microbial mechanisms responsible for diurnal dynamics of N2O fluxes in a nutrient-poor subarctic permafrost environment | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Microbial mechanisms responsible for diurnal dynamics of N 2 O fluxes in a nutrient-poor subarctic permafrost environment View ORCID Profile Dhiraj Paul , Wasi Hashmi , Nathalie Ylenia Triches , Matej Znaminko , Henri M.P. Siljanen , Ivan Mammarella , Mathias Göckede , Christina Biasi , Maija E Marushchak doi: https://doi.org/10.1101/2025.11.04.686510 Dhiraj Paul 1 Department of Environmental and Biological Sciences, University of Eastern Finland , Kuopio Campus, P.O. Box 1627, FI-70211 Kuopio, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dhiraj Paul For correspondence: dhiraj.paul{at}uef.fi Wasi Hashmi 1 Department of Environmental and Biological Sciences, University of Eastern Finland , Kuopio Campus, P.O. Box 1627, FI-70211 Kuopio, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nathalie Ylenia Triches 2 Max Planck Institute for Biogeochemistry , Hans-Knöll-Str. 10, 07745 Jena, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Matej Znaminko 1 Department of Environmental and Biological Sciences, University of Eastern Finland , Kuopio Campus, P.O. Box 1627, FI-70211 Kuopio, Finland 4 Institute of Ecology, University of Innsbruck , Innsbruck, Austria Find this author on Google Scholar Find this author on PubMed Search for this author on this site Henri M.P. Siljanen 1 Department of Environmental and Biological Sciences, University of Eastern Finland , Kuopio Campus, P.O. Box 1627, FI-70211 Kuopio, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ivan Mammarella 3 Institute for Atmospheric and Earth System Research (INAR), University of Helsinki , Helsinki, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mathias Göckede 2 Max Planck Institute for Biogeochemistry , Hans-Knöll-Str. 10, 07745 Jena, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christina Biasi 1 Department of Environmental and Biological Sciences, University of Eastern Finland , Kuopio Campus, P.O. Box 1627, FI-70211 Kuopio, Finland 4 Institute of Ecology, University of Innsbruck , Innsbruck, Austria Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maija E Marushchak 1 Department of Environmental and Biological Sciences, University of Eastern Finland , Kuopio Campus, P.O. Box 1627, FI-70211 Kuopio, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Arctic soils affected by permafrost are generally low in nutrients but highly sensitive to climate warming, which may enhance reactive N availability and subsequently N 2 O emissions. A significant yet often overlooked phenomenon in the Arctic is the diurnal variation of N 2 O fluxes. This variation is a well-documented occurrence in other ecosystems, however, the underlying microbial mechanisms that drive it remain entirely unknow. Here, we studied the microbial factors behind the daily variations in N 2 O exchange in nutrient-poor subarctic permafrost peatland, where higher atmospheric N 2 O consumption occurs at night than during the day. By quantitative PCR, we assessed the abundance and expression of denitrifier genes related to N 2 O production ( nirK and nir S) and consumption ( nos Z clade I and II). Although all four genes were present, only gene nir K and nos Z clade I were expressed. Remarkably, we found that the diurnal pattern of nir K/ nos Z gene expression ratio closely aligns with the diurnal pattern of N 2 O flux data, with dominance of N 2 O production rather than N 2 O reduction activity driving the day-to-night variation in N 2 O flux. Our analysis of 1,635 metagenome-assembled genomes (MAGs) sites indicates that partial denitrifiers followed by complete denitrifiers are key players in driving diurnal N 2 O variations within this acidic Arctic ecosystem, rather than nitrification processes. To the best of our knowledge, this is the first study to uncover the microbial mechanisms underlying the diurnal dynamics of N 2 O flux. Our findings not only advance our understanding of nitrogen cycling in Arctic ecosystems but also underscore the critical need for similar research in diverse ecological contexts to fully grab the implications of climate change on greenhouse gas emissions. Introduction Nitrous oxide (N₂O) is a significant greenhouse gas (GHG), ranking third in the contribution to radiative forcing (behind carbon dioxide and methane), and the leading contributor to ozone depletion in the twenty-first century ( Ravishankara et al., 2009 ). Its global warming potential is 298 times greater than that of carbon dioxide (CO₂) over a 100 year period. Soils are the dominant natural source of N₂O, accounting for roughly two thirds of global emissions, with about one third originating from managed soils strongly influenced by fertilizer inputs (Tian et al., 2024). Notably, about one third of the global N 2 O emissions come from natural terrestrial sources, mainly driven by microbial processes mediating mineral N transformations in the soil (Tian et al. 2024). Among unmanaged soils, tropical soils are the ones with highest emissions due to their rapid nitrogen turnover rates in the warm climate, followed by temperate forest soils ( Tian et al., 2020 ) The emissions from permafrost soils were previously considered negligible. However, recent research has challenged this view by unveiling a common occurrence of N 2 O sources among northern permafrost soils ( Voigt et al., 2020 ). Most of the existing N 2 O measurements stem from short campaigns in the peak summer, and have been usually collected by manual, opaque chambers in the daytime with a lower-than-daily frequency. Thus, the possibility of diurnal variability and any effect of light for N 2 O emissions from permafrost has largely been ignored. This is a serious shortcoming since diurnal variability of N 2 O flux is a widespread phenomenon, as shown by a recent meta-analysis of autochamber data from natural and managed soils. Around 80% of the studies included in the review showed significant diurnal variability, with prevalence of higher emissions in the day than in the night. However, there are currently no studies that directly investigate the microbial mechanisms behind this phenomenon, despite the fact that microbes are key players in both the formation and consumption of N 2 O in natural environments. Therefore, in our study, we hypothesize that N 2 O uptake occurs at night and that microbial factors, in addition to abiotic factors, play a crucial role in this process. Currently, the only known pathway for N 2 O uptake or consumption is through microbial-mediated reduction of N 2 O to N 2 . This process is facilitated by the N 2 O reductase enzyme, which is encoded by the nos Z gene ( Sanford et al. 2012 ; Jones et al. 2013 ). The nos Z gene is phylogenetically divided into two distinct clades: Clade I and Clade II ( Sanford et al. 2012 ; Jones et al. 2013 ). Organisms within nos Z Clade II are generally non-denitrifying, diverse, and often possess the unique capability to consume atmospheric N 2 O directly, even in the absence of other denitrification genes, and they are known for their potential to increase the N 2 O sink/uptake capacity of soils and other environments ( Graf et al. 2014 ; Jones et al. 2014 , Domeignoz-Horta et al. 2015 , Yin et al. 2020 ). In contrast, nos Z Clade I organisms typically harbor complete denitrification pathways, including nir S or nir K and other genes, which allow them to utilize NO 3 or NO 2 as substrates and convert them efficiently to N 2 O and N 2 ( Graf et al. 2014 ). The nir K and nir S are the key rate limiting genes responsible for NO to N 2 O conversion mean N 2 O production ( Graf et al. 2014 ; Jones et al. 2014 ). In addition to complete denitrifiers, nir K and nir S are also found in incomplete denitrifiers or partial denitrifiers ( Pold et al., 2025 ), where the nosZ gene may be lacking. Therefore, the relationship between the nos Z gene (which may be coming from non-denitrifiers or complete denitrifiers) and the nir K + nir S genes (which may be coming from complete or partial denitrifiers) is important for N 2 O emissions. Based on this understanding, we hypothesize that we would observe higher expression levels of nos Z genes, particularly non-denitrifying nos Z Clade II genes, during the night than in the day, explaining the higher N 2 O uptake in the night. This would imply that the trends of N 2 O flux and nos Z gene expression are inversely related. Additionally, we can formulate an alternative hypothesis, suggesting that differences in N 2 O production instead of N 2 O uptake would explain the observed diurnal pattern, namely that higher N 2 O production in daytime than nighttime would explain why emissions are higher in the day. This phenomenon is influenced by the microbial mechanisms involved in denitrification, which can be either complete (having both nir and nos genes) or incomplete (having only nir genes). In this scenario, the ratio of ( nir S + nir K) to nos Z would be notably high during the daytime, while at night, this ratio would be low. Our study site is Stordalen Mire, an important permafrost ecosystem located in the subalpine tundra of northern Sweden. Over the past three years, comprehensive N 2 O flux measurements at Stordalen Mire covering different parts of the snow-free period have been carried out by an optimized manual chamber method ( Triches et al. 2025 ). These measurements have unveiled distinct variations in N 2 O fluxes during the mid-summer months in dark and light conditions, particularly during the peak growing season in wet habitats (fen and bog) ( Triches et al. 2025 ; unpublished). While the extensive dataset confirmed the role of photosynthetically active radiation (PAR) as a key driver of the diurnal pattern ( Triches et al. 2025 ), the actual underlying mechanisms and the role of microbes remained unresolved. Therefore, in the present study we take a novel approach by measuring field fluxes using transparent chambers in alignment with the natural environmental conditions and combine these measurements with microbial sampling. We hypothesize that while the observed diurnal variability of a N 2 O flux likely arises from a complex interplay of biotic and abiotic factors, microbial activities are the main driver behind this phenomenon. Based on the prior understanding on the crucial role of denitrifiers for N 2 O production and consumption processes in permafrost peatlands ( Gil et al. 2021 , Palmer et al. 2012 ), we expected their major contribution to the observed fluxes also here. To test our hypothesis and delve deeper into the fundamental mechanisms driving fluctuations in N 2 O flux, we designed our study to reveal novel insights into the phenomenon of day-night variations in N 2 O flux. To the best of our knowledge, this is the first published study looking into microbial mechanisms for the unique day-night variations of N 2 O flux. This pioneering research not only significantly enhances our comprehension of diurnal N 2 O dynamics in these unique environments but also makes vital contributions to the field. It underscores the urgent need to reevaluate the significance of these nutrient-limited ecosystems in the global nitrogen cycle and their essential role in climate feedback. Materials and methods Study site and sample collection: Our study centers on the Stordalen Mire, a permafrost peatland located just 10 kilometers east of the Abisko Natural Sciences Research Station, lying between the E10 Kiruna-Narvik highway and the Lake Torneträsk (68°35’N, 19°04’E). Spanning approximately 25 hectares, it exemplifies eastern continental peatland, perched upon permafrost, with its central area covering around 15 hectares ( Sonesson et al., 1980 ). Permafrost thawing has been occurring rapidly in this region, therefore isolated permafrost is currently only present in the dry uplifted areas on the peatland (Palsa) ( Sjögersten et al., 2023 ). The Stordalen mire ecosystem has three distinct habitats with variable permafrost and moisture conditions, representing a gradient of thaw intensity: 1. The Palsa, ombrotrophic peat surfaces uplifted by a permafrost core. The vegetation in this area is primarily dominated by lichens (Cladonia spp.), along with shrubs such as Empetrum hermaphroditum, Betula nana, Vaccinium uliginosum, Vaccinium vitis-idaea, and Rubus chamaemorus. Additionally, some mosses like Dicranum elongatum and Sphagnum fuscum can also be found here. In the current study, we divided the palsa into two categories: Palsa moss-dominated areas, which are covered by moss, and Palsa lichen-dominated areas, which are covered by lichen. 2. The Bog is characterized by a series of hollows within the palsa, which are filled with stagnant, ombrotrophic water. This environment supports a rich diversity of graminoid species, including Sphagnum balticum, Sphagnum lindbergii, and Sphagnum riparium, as well as vascular plant sedges such as Carex rotundata and Carex rostrata. 3. In contrast, the Fen is situated in the lower regions of the mire, where free-flowing water promotes the growth of large aquatic graminoid species, including Sphagnum balticum, Sphagnum lindbergii, and Sphagnum riparium, along with vascular plants like cotton grass (Eriophorum vaginatum and Eriophorum angustifolium). For our study, we selected a subset of 12 chamber flux plots in two transects on a dry to wet thawing gradient from palsa to bog to fen, with three replicates for each of the four microhabitat types: Palsa lichen (PL), Palsa moss (PM), Bog (B), and Fen (F). These flux plots represented a subset from the 24 plots studied by Triches et al. (2025) and were selected for this microbial process study based on their spatial variability. During a field campaign at the Stordalen mire in August 17 to 24, 2024, we collected surface peat samples for microbiological and geochemical analysis, in parallel to in situ N 2 O flux measurements conducted in comparable conditions in the day and the following night ( Figure 1a-f ). We chose these dates during the late growing season because of potential nutrient availability in the soils, and the reoccurrence of dark nights. Download figure Open in new tab Figure 1: Sampling Location and Diurnal N 2 O Emissions in Arctic Peatland: (a) A photo depicting four different niches selected for N 2 O flux measurement and sampling, where “PL,” “PM,” “F,” and “B” represent Palsa Lichen, Palsa Moss, Fen, and Bog, respectively. (b) A photo illustrating sampling conducted during both day and night. (c) Box plots displaying daytime and nighttime N 2 O fluxes from the various niches. Box plots show lower and upper quartiles, median (thick black line), smallest and largest values without outliers (thin black line) and outliers (circles); n = 6 independent samples. Estimation of (d) DOC (dissolved organic carbon); (e) DON (dissolved organic nitrogen); (f) NH 4 (ammounium) and (g) NO 3 (nitrate) from 2 cm depth from different niches. Gold and gray colors indicate daytime and nighttime sampling, respectively. Two-way ANOVA was performed to check the statistical different among the micro-niche, and day-night. Two and three asterisks denote statistical significance levels of p < 0.01 and p < 0.001, respectively. To investigate the microbial and geochemical variability at two different depths, we collected soil samples from both 2 cm and 15 cm depths. For microbiological sampling, peat samples were collected using a sterile forceps in triplicate from the vicinity of each flux plot, pooled into a single sample from each depth into a sterile falcon tube (pre-treated with DEPC (Diethyl pyrocarbonate)-water), rapidly flash-frozen in liquid nitrogen in the field, and transported on dry ice to our laboratory. For nutrient analysis, samples were collected in sterile falcon tube, subsequently stored and transported in cooler gel pack. The samples were stored at -80 °C to preserve their integrity for further processing. N 2 O flux measurements N 2 O flux measurements for Palsa Lichen (PL), Palsa Moss (PM), Bog (B), and Fen (F) were performed combining the manual chamber method with high-precision portable gas analyzers (PGAs), previously documented in detail by Triches et al. (2025) . Preinstalled round chamber base collars (PVC) with a diameter of 25 cm have enabled detailed flux measurements consistently over the last three years ( Triches et al. 2025 ). Our manual chamber fluxes were recorded during both daytime (11:00–18:00) and nighttime (22:00–03:00), chosen to catch maximum amplitude in the photosynthetically active radiation (PAR). To measure N 2 O flux, we paired a transparent acryl-glass chamber (height 25 cm, diameter 25 cm) with a state-of-the-art PGA (Aeris MIRA Ultra N 2 O/CO 2 ) with a precision of less than 2 ppb for N 2 O detection. For the first night measurements (12 flux data) another portable laser Li-7820 (Licor) was used due to unavailability of the default PGA. Duplicate measurements were subsequently conducted at each plot for a robust flux estimate. We measured gas concentrations in the chamber headspace at 1-second intervals over a 5-minute enclosure period. Additionally, parameters such as temperature, soil moisture, photosynthetically active radiation (PAR), and Specific Ultraviolet Absorbance (SUVA) were simultaneously recorded during the field flux measurements and used as auxiliary data for the flux analyses. For N 2 O flux calculation we followed our previously published work ( Triches et al. 2025 ). Briefly, we calculated fluxes by utilizing all data points from the 5-min chamber closure. To account for the initial time delay—during which the concentration from the chamber had yet to reach the sensors of the online laser analyzers—we strategically excluded the first 8 seconds of the measurement period as an equilibration period. We derived flux estimates using both linear models (LM) and nonlinear models (HM) through the goFlux package in R (v 0.2.0., Rheault 2025). This open-source tool is specifically designed to work with high-throughput, laser-based GHG data, incorporating both linear and nonlinear models. Additionally, factors such as temperature and relative humidity are considered in the flux calculations, ensuring that our data is both robust and highly reliable. Geochemical analysis Soil characteristics were determined from the vicinity of each flux plot during both day and night measurement (n = 3) within one week from the sampling. The samples were homogenized by hand and large roots and living vascular plants were removed. Soil pH and electrical conductivity (EC) were measured using a slurry with 1:3 (v/v) soil-to-water ratio. Dissolved form of anions including nitrite (NO 2 ‒ ), nitrate (NO 3 ‒ ), sulfate (SO 4 ²‒ ), phosphate (PO 4 ³ ‒ ), and chloride (Cl ‒ ), along with dissolved organic carbon (DOC) and dissolved nitrogen (DN) content were extracted using a soil-to-water ratio of 3:10 (v/v) and filtered through filter paper (Whatman, no. 42). Anion concentrations were analyzed using ion chromatography (Dionex ICS-2100, Thermo Scientific), with a standard dilution curve ranging from 0.25 to 10 mg L ‒1 . For the analysis of DOC and DN, the water extracts were filtered using 0.45 µm syringe filters and analyzed on a Shimadzu TOC-V CPH (Shimadzu Scientific, Japan), utilizing standard dilution curves for DOC and DN at specified concentrations. The DON content was derived by subtracting water soluble mineral N pools from DN. For all analyses from the water, extraction blanks (n = 3) were subtracted from the sample concentrations. For C and N content and C:N ratio, oven-dried samples were ground by a ball mill and analyzed on an EA-IRMS (Elemental Analyzer – Isotope Ratio Mass Spectrometry). Spectral absorbance was measured with a laboratory benchtop spectrophotometer (UV-1800, Shimadzu, Kyoto, Japan) between 200 and 800 nm with a 10 mm pathlength quartz cell (acquisition step: 1 nm, scan speed: slow). Wavelength-specific UV-absorbance at 254 nm (SUVA254) was calculated as the absorbance divided by the DOC concentration and used as a proxy for DOC aromaticity ( Weishaar et al., 2003 ). DNA and RNA extraction Total community DNA and RNA were extracted from all samples using the RNeasy PowerSoil RNA Extraction Kit (Qiagen, Germany), followed by the Soil DNA Elution Kit (Qiagen, Germany), according to the manufacturer’s protocol with some modification. In brief, to separate out the phenol and aqueous phase properly we used 1-1.5 g soil instead of 2 g. This modified protocol was based on our test extractions and resulted in a better DNA and RNA yield from the sample material used in this study. 1.2 g fresh soil of the total of 48 samples (2 times of the day * 4 microhabitat * 2 depths * 3 replicates), each weighing approximately was used for DNA and RNA extraction. The quality of the total RNA and DNA was assessed using a Nanodrop spectrophotometer. The Qubit HS RNA and HS DNA assay kits were utilized to quantify the extracted RNA and DNA, respectively. For quantitative PCR (qPCR), a portion of the RNA underwent DNase treatment followed by CDNA synthesis. To ensure that all the DNA was degraded from the eluted RNA, we checked the Qubit readings for DNA after the DNase treatment and also performed 16S rRNA gene PCR prior to CDNA synthesis to confirm the absence of DNA amplification. After confirming that there was no amplification, we proceeded to convert the RNA into first-strand CDNA. Both CDNA and DNA samples are stored at -20 °C and RNA stored at -80 °C qPCR We used quantitative real-time PCR (qPCR) to accurately quantify key functional genes involved in denitrification (nirS and nirK) and N 2 O consumption (nosZ clade I and nosZ clade II), along with essential bacterial and archaeal 16S rRNA genes. For specific PCR conditions, please refer to Supplementary Table S2. To determine the total copy number and expression of these genes, we performed qPCR using DNA and CDNA as templates, respectively. Each qPCR reaction was conducted with a total volume of 10 µL, including three biological replicates and two technical replicates, using the GOTAG SYBR Green qPCR kit. Each reaction contained 10 ng of template DNA and 0.5–1.0 µM of each primer to ensure accuracy and consistency. To test for potential PCR inhibition, we amplified a known amount of the pGEM-T plasmid (Promega) with plasmid-specific T7 and SP6 primers added to the extracted DNA, as well as a negative control. There was no inhibition of the PCR reaction with the amount of template DNA used. We also conducted melting curve analysis to validate the specificity of the qPCR products and followed this with agarose gel electrophoresis to confirm the amplicon size, further reinforcing the reliability of our findings. Metagenome sequencing and assembly Extracted community DNA was used for DNA fragmentation and library construction using the NEBNext Ultra II DNA Library kit (New England Biolabs, USA). The quality and quantity of the fragment library were assessed using the Qubit dsDNA HS assay kit (Thermo Fisher Scientific, USA) and the Agilent 2200 TapeStation (Agilent, USA), respectively. A high-quality library was normalized, pooled, and subsequently sequenced using 2×150 bp chemistry on the NovaSeq X Plus Series (150-bp paired-end) at Novogen, Germany. A 5% PhiX spike-in was included during library loading in the sequencing cartridge. To cover all the niche and depth samples, a total of 8 samples from four microhabitats, at two different depths (2 cm and 15 cm), were used for sequencing. The quality of the raw paired-end reads was evaluated using FastQC (Andrews, 2010). Good quality sequences (Q > 30) were selected using Trimmomatic ( Bolger et al., 2014 ) for further analysis. After trimming, an around 1600 million high-quality reads were obtained from our present study, which were then used for metagenome assembly and downstream analysis. The MetaWRAP pipeline ( Uritskiy et al., 2018 ) was utilized for generating metagenome-assembled genomes (MAGs) from the metagenome sequences. In this pipeline, metagenome assembly was conducted using Megahit, followed by binning of contigs for MAG generation using MaxBin2 and metaBAT2, with subsequent bin refinements. The quality of the bins was checked using CheckM. After refining the bins and removing redundancy with the DAS tool, a total of 595 MAGs were formed and were selected for taxonomic and functional annotation. Taxonomic assignments of MAGs were performed using GTDB-Tk (v2.1.15). Functional annotations were carried out using Prokka, KEGG, SwissProt, and in-house HMMER profiles. To enhance our understanding of the microbial potential influencing the nitrogen (N 2 ) cycle in this ecosystem, we undertook a comprehensive MAGs analysis that integrates our own study MAGs sequencing data with an existing database of MAGs, generated from samples taken at the Stordalen Mire study sites between 2010 and 2017 as part of the EMERGE project ( Li et al., 2025 ). More information about the constructed database is available in McGivern et al. (2024), with a brief description provided below. The database contains a total of 13,290 MAGs (Viviana Freire-Zapata, 2025) with at least 70% completeness and less than 10% contamination, as determined using CheckM ( Parks et al., 2015 ). For this study, we were only interested in the 1405 MAGs reconstructed from metagenomic data collected from depths of 0 to 15 cm at the Palsa Mire, Bog, and Fen to match with the current data set. As a result, around 1629 MAGs, combining data from our study and previous studies, were utilized for further functional analysis. To identify functional genes associated with nitrogen fixation, nitrification and denitrification—specifically nif H, Amo A, Tamo A, nir K, nir S, nos Z, nor B, nap A, and nar G— we utilized targeted gene hammer profiles to all 1629 MAGs. This enabled us to search for sequences of each functional gene with a maximum E-value cutoff of E <0.00001 using our in-house database tailored for each gene. For further confirmation of gene function and to determine the closest cultured relatives, each sequence was then queried against the UniProt Trembl and Swiss-Prot databases using the tblastx method through the Diamond sequence search accelerator. Statistical analysis All experimental observations that include in-situ N 2 O flux results, geochemical analysis and qPCR data were recorded with three biological replica (n=3) and data were expressed as mean ± SD. The pairwise t-test was used to compare the day and night flux variation at 5% significant level (p<0.05). The means were compared using analysis of two-way ANOVA at 5% significant level (p<0.05). Association between the variables were performed using spearman correlation. R packages such as ggplot2, Reshape2, vegan, ggpubr, corrplot, etc in Rstudio version 4.2.2 were used for data visualizations. Results Diurnal variability in insitu N 2 O fluxes During flux measurements at daytime, the PAR (Photosynthetically Active Radiation) values averaged around 561.24±183.70 μmol m −2 s −1 and nighttime around 0.1±0.23 μmol m −2 s −1 (Supplementary Table 1). Our findings demonstrate that, in the majority of instances, daytime N 2 O flux levels were higher than those recorded at night ( Figure 1e ), where positive mean flux value indicted emission and negative mean flux value indicating consumption. Fen plots demonstrated a significant difference (p<0.05) in N 2 O fluxes between day and night. During the day, it exhibited a positive mean flux (1.09± 0.43 μg N 2 O-N m −2 h −1 ), while at night, there was a negative mean flux (-0.36± 0.66 μg N 2 O-N m −2 h −1 ). In the Palsa moss plots, daytime measurements revealed a positive flux with a mean of 0.33±0.02 μg N 2 O-N m −2 h −1 , whereas nighttime assessments indicated a negative flux averaging -1.76± 1.6 μg N 2 O-N m −2 h −1 with significance (p<0.05) differences. In contrast, the Palsa lichen plots, consistently exhibited negative fluxes during both daylight (-0.15±0.04 μg N 2 O-N m −2 h −1 ) and nighttime hours (- 0.72±1.23 μg N 2 O-N m −2 h −1 ). Whereas, in comparison, the Bog samples, displayed a lower positive flux than the Fen site, averaging 0.17±0.08 μg N 2 O-N m −2 h −1 during the day; this variation was not significant. However, the nighttime negative flux for the Bog samples mirrored the results from the Fen plot, with a mean of -0.67± 0.08 μg N 2 O-N m −2 h −1 ( Figure 1g , Supplementary Table 1). During day-night sampling, no significant difference was observed in case of soil temperature (Palsa lichen day: value I need to add) and moisture (Palsa lichen day: 28.59±9.1%, night: 29.5±11.8%; Palsa moss day: 33.53±0.05%, night 33.49±0.03%; Bog day: 57.4±3.7%, night:56.3±6.02%; Fen day:84.2±11%, night:83±13.1%) (Supplementary table 1). Geochemical characterization At both depths of all studied surface types, we observed a low pH of around 4 and a low electrical conductivity (EC) ranging from 10 to 20 (µS cm-1) (Supplementary table 2). Importantly, we found no diurnal variation in the total carbon (TC) and total nitrogen (TN) content at either depth (Figure S2). While it was observed that dissolved organic carbon (DOC) levels were higher during the daytime than night at 2 cm depth ( Figure 1f ). We observed similar results regarding NO 3 content, with elevated concentrations found in the Fen habitat at a depth of 2 cm. While there was some variation between day and night conditions, it was not significant. The concentration of dissolved NH 4 varied across day and night conditions in most cases; however, this variation was not significant except for the Palsa lichen ( Figure 1h ). At a depth of 15 cm, we noted significant differences among the microhabitats in terms of DOC and TON content, with higher levels observed in the Fen (Supplementary Figure 1). Diurnal pattern of gene abundance and expression The copy numbers of all targeted genes were estimated, indicating all the targeted genes were present at DNA level (see Figure S3-S4). It was observed that the functional genes were generally more abundant at a depth of 15 cm compared to 2 cm (Figure S4). Notably, the abundance of the nir K gene was higher than that of the nir S and nos Z clades I and II in both depths. In the 15 cm samples, nir K was particularly abundant in the Bog and Fen samples (Figure S4b). For the nir S gene, higher abundance was noted in the Fen sample at both depths (Figure S4c,d). Regarding nos Z clade I, higher abundance was found in both the Fen and Palsa moss samples, where their amounts were nearly equal (Figure S4e,f). For nos Z clade II, at a depth of 2 cm, higher abundance was observed in the Palsa moss, lichen, and Bog samples, while at 15 cm, Palsa moss and Bog samples exhibited greater abundance (Figure S4g,h). However, we could not explain the fluctuations in N 2 O flux by the copy numbers of the involved genes. Nonetheless, small but non-significant variations in gene copy numbers were detected under different day-night conditions, likely influenced by factors such as microbial growth rates, cell lysis, and DNA degradation due to DNase activity. To test the first hypothesis that higher N 2 O uptake at night is related to higher consumption of atmospheric N 2 O, we quantified the expression of the non-denitrifying nos Z clade II gene mediating N 2 O reduction to N 2 at the RNA level. Surprisingly, we did not detect any expression of the nos Z clade II gene at any of the land cover types or sampling depths despite its presence in environmental DNA (eDNA) and employment of a set of undiluted and diluted templates (stock, 10 times and 100 times dilutions) for the qPCR. Based on this RNA-based qPCR, we can conclude that the first hypothesis—which suggests that uptake mechanisms significantly influence the day-night variation of N 2 O flux and that the non-denitrifying nos Z clade II gene plays a major role—is not supported. To test the second hypothesis—that high N 2 O emissions occur during the daytime compared to nighttime—and to examine the denitrification process (which might be complete and partial) involved in this, we focused on the expression patterns of the nos Z clade I and nir genes. We conducted qPCR on RNA samples for the nos Z clade I, nir S, and nir K genes ( Figure 2 ). Interestingly, we observed positive gene expression for nos Z clade I at the RNA level. We observed that the nos Z clade I gene expression was higher in 15 cm depth specifically in case of Palsa lichen ( Figure 2b ). Whereas we noted that in case 2 cm depth samples higher expression was noted in case Bog with a significant day-night variation (p <0.05) ( Figure 2a ). Our finding indicated that nos Z clade I gene expression showed opposite pattern with the N 2 O flux except for Palsa moss at 2 cm depth, although in 15 cm depth the nos Z clade I gene expression was higher compared to 2 cm depth ( Figure 2a,b ). Download figure Open in new tab Figure 2: Gene expression estimation of N 2 O production and consumption genes using quantitively PCR at transcript level from four microhabitats (Palsa lichen, Palsa moss, Bog and Fen) with two different depths. Gene expression of nirK at a) 2 cm and b) 15 cm depth. Gene expression of nosZ clade I at c) 2 cm and d) 15 cm depth. Ratio of (nirK + nirS)/nosZ genes expression at e) 2 cm and f) 15 cm depth. Gold and gray colors indicate daytime and nighttime sampling, respectively. Day and night fold change of nirK (g) and nosZ (h) gene at RNA level at 2 cm and 15 cm depth. i) Correlation analyses were conducted to examine the relationships among N 2 O flux, N-cycling genes, and geochemical properties (where * indicates p < 0.01 and ** indicates p < 0.05). The quantification of nirK, and nosZ clades I at RNA levels, were utilized for the correlation analysis, with values obtained from qPCR analysis. NB: not found nosZ clade II and nirS gene qPCR Cq value. Interestingly, in the case of the nir S gene, we did not detect any Cq values at the RNA level, despite its presence at the DNA level. We tested various dilution templates (including stock, 10-fold, and 100-fold dilutions) but were unable to confirm any positive expression of the nir S gene, even though it was detectable at the genetic level (see supplementary figure 4). This situation may have a similar explanation to our findings regarding the nos Z clade II gene. We observed the expression of the nir K gene at the RNA level. Notably, the expression pattern of nir K aligned with most of the flux measurements, except for the Palsa lichen samples at a depth of 2 cm ( Figure 2c ). Additionally, there was a significant variation in nir K gene expression between day and night (p < 0.05) in both the Fen and Palsa moss at this depth. In contrast, at a depth of 15 cm, nir K gene expression was higher; however, it did not correspond to the N 2 O flux pattern ( Figure 2d ). Based on our second hypothesis, we postulated that the ratio of the genes nir K + nir S to nos Z would play a crucial role in our findings. We observed that this ratio intriguingly aligned with the flux patterns at a depth of 2 cm ( Figure 2e,f ). We observed a significant variation (p < 0.05) in the day-night expression ratio of the nir K to nos Z genes in both the Fen and Palsa moss samples (see Figure 2e ). This finding suggests that fluctuations in N 2 O flux are related to emission processes and that microbial denitrification plays a crucial role in this variation. Notably, the gene ratio was high at a depth of 15 cm; however, in most cases, it did not correspond with the diurnal variation in N 2 O flux. Additionally, fold change analysis of nir K and nos Z clade I gene expression revealed that the nir K gene expression was higher during the day compared to night at both depths ( Figure 2g ). In contrast, the nos Z clade I gene expression at a depth of 2 cm exhibited higher levels at night in most instances ( Figure 2h ). Correlation analysis among the geochemical parameters confirmed that photosynthetically active radiation (PAR) positively correlates with N 2 O flux ( Figure 2i ). This suggests that during the daytime, when PAR levels are high, N 2 O flux is also elevated. Conversely, at night, when PAR levels drop, N 2 O flux decreases as well. Additionally, PAR exhibits a positive association with dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and ammonium (NH 4 ) ( Figure 2i ). This indicates that during the day, when PAR is high, there is an increase in DOC, DON, and NH 4 concentrations. Furthermore, N 2 O flux shows a positive association with the nir K/ nos Z ratio and a negative correlation with the carbon-to-nitrogen (C/N) ratio ( Figure 2i ). This implies that during the daytime, when PAR is elevated, the nir K/ nos Z ratio also increases. It suggests that high PAR levels during the day positively influence the nir K/ nos Z ratio, leading to increased N 2 O emissions, while the opposite occurs at night. Genome based analysis Analysis of the Metagenome Assembled Genomes (MAGs) revealed a remarkable abundance of nitrogen fixation genes ( nif H) across many of the MAGs studied. Out of 1,629 MAGs, 1,446 contained nif H genes. Additionally, significant denitrification genes—including nir S, nir k, nos Z, and nor B—were identified in 23, 312, 85, and 333 MAGs, respectively ( Figure 3a ). Notably, no single MAG contained the nitrification genes Amo A and Tamo A ( Figure 3a ). The metagenomic analysis further emphasized this trend, revealing a very limited presence of reads associated with Ammonia-Oxidizing Bacteria (AOB), and no single Ammonia-Oxidizing archaeal genes ( Figure 3b ). These findings strongly suggest that biological nitrogen fixation and denitrification processes are dominant features of this acidic nutrient-poor permafrost ecosystem, highlighting its unique biogeochemical dynamics. Download figure Open in new tab Figure 3: Genome Reconstruction and Features of N 2 O Production and Reduction-Related Genes: a) Abundance of N 2 cycling genes across selected metagenome-assembled genomes (MAGs). b) Relative abundance of N 2 cycling functional genes in the studied metagenomic samples. c) Distribution of the denitrification process across the MAGs. In the denitrification process, we categorized each genome into three groups: complete denitrifiers (those that possess the nir, nor, and nos Z genes), partial denitrifiers (those lacking either nir or nos Z), and non-denitrifying organisms (those lacking both nir and nor but having nos Z) ( Pold et al., 2025 ) ( Figure 3d ). We identified 32 genomes, representing 5.5% of the total, that carry the nir, nor, and nos Z genes, which are responsible for the complete reduction of NO 2 - to N 2 . Notably, there was significant variation among different phyla (Supplementary table 4). The complete denitrification trait was found in seven distinct bacterial phyla (see Fig. 1c and Supplementary Data 3), with the most prevalent being Gammaproteobacteria (34% of complete denitrifiers) and Alphaproteobacteria (34%) and Acidobacteriota, with Myxococcota at 18% (see Fig. 1d ). In contrast, partial denitrifiers were found in the majority (55%) of the genomes, primarily within the Acidobacteriota, Gammaproteobacteria, and Bacteroidota (Supplementary table 5). The ratio of the sum of nir K and nir S to nos Z indicates a greater genomic potential for N 2 O emission ( Figure 3 ). Thus, our genome-based analysis suggests that while complete denitrifiers are present, there is a greater abundance of partial denitrifiers. Discussion Our study shows that N 2 O emissions of a palsa mire are lower at night than during the day, corroborating earlier observations by Triches et al. (2025) , who compared fluxes measured with light and dark chambers. Our findings indicate, for the first time, that microbial activity plays a crucial role in influencing this distinct day-night pattern of N 2 O emissions, highlighting a previously underexplored mechanism in northern permafrost peatlands. Beside the acidic character of the peat of the samples, nutrient analysis showed higher dissolved organic carbon (DOC), dissolved ammonium, and dissolved nitrate levels in the Fen and Palsa moss samples during the daytime, closely matching with the observed N 2 O flux patterns. Microbial analysis indicates a similar pattern: the expression of nos Z and nir K genes is high during the day for most of the samples. This suggested that the increased availability of nutrients in daylight boosted microbial activity associated with denitrification, resulting in high N 2 O emissions. Additionally, our findings demonstrate a positive correlation between photosynthetically active radiation (PAR) and N 2 O flux and a moderate positive association with nos Z clade I and nir K gene expression patterns. This points toward a coupling between photosynthesis-driven substrate inputs and microbial processes. Our research indicates that both nos Z clades I and II, which are associated with N 2 O consumption, were present in the total gene pool of acidic permafrost peat samples examined in this study. Interestingly, nos Z clade II, which is predominantly found in non-denitrifying organisms and diverse in nature ( Jones et al., 2013 ), was detected through DNA analysis in the present study, but it remained unexpressed. In contrast, nos Z clade I, which is less diverse and primarily linked to denitrifying organisms ( Jones et al., 2013 ), demonstrated significant RNA expression. As expected from a nutrient-poor system, nitrate (NO 3 — ) was available in limited amounts, resulting in low N 2 O emission in our study sites compared to other ecosystems. Previous studies have shown that nos Z clade II has a low affinity for N 2 O, whereas nos Z clade I possess a higher affinity for N 2 O and is actively expressed in nitrogen-limited scenarios ( Yoon et al. 2016 ). Under conditions of limited nitrate, microbes are compelled to utilize all available mineral nitrogen forms (NO 2 , NO 3 ) as well as gases, which are intermediates in denitrification (NO, N 2 O), as electron acceptors. In addition, a recent study indicated that nos Z clade I-denitrifiers were relatively less sensitive to low soil pH (Yunpeng et al., 2024), whereas in the case of nos Z clade II, general increase abundance is noted in neutral or higher pH ( Jones et al. 2014 , Samad et al. 2016 ). This fits well with acidic peat soils. Thus, nos Z clade I microbes emerge as crucial influence factors, as most belong to denitrifying organisms and harbor associated genes like nir S and nir K and are less sensitive to acidic pH (Yunpeng et al., 2024). This highlights the essential role of nos Z clade I in sustaining microbial activity in a highly acidic nutrient-poor subarctic permafrost peatland environment, such as the Stordalen mire. Our data show the presence of both nir S and nir K genes at the genetic level; however, it is noteworthy that only the nir K gene was actively expressed. Previous studies indicated that the abundance and diversity of genes encoding nir K and nir S varies among the habitats and also in response to environmental changes (Philippot et at., 2009; Abell et al., 2010). It was noted that the nir S gene is highly sensitive to environmental challenges, including low pH. In contrast, the nir K gene can cope with various environmental stresses and tolerate low pH levels ( Herold et al., 2018 ; Ligi et al., 2014 ; Pold et al., 2025 ). In our study, we observed that although both genes are present, the acidic soil pH led to support the expression of the nir K gene, which has the ability to tolerate environmental stress. Although denitrifying genes, nos Z clade I and nir K were expressed at detectable level and showed trend with the day-night N 2 O flux patterns in most of the cases, the gene expression patterns in case of Palsa lichen surface were noticeably inconsistent with the flux data. However, upon analyzing the nir K/ nos Z expression ratio, our findings revealed that the gene expression ratios of surface samples (2 cm depth) closely aligned with the flux patterns, in contrast to the gene expression at 15 cm depth. This also emphasizes that depth is also important in understanding microbial gene expression related to N 2 O exchange and its impact on GHG emission from soil to the atmosphere. It might be possible that, despite higher gene expression at 15 cm depth, the upward diffusion of N₂O enables microbial consumption to occur closer to the surface. Furthermore, the surface layer might be important for determining the magnitude of atmospheric N 2 O sink, because this process is limited by N 2 O diffusion to the peat profile, particularly in water-logged sites, such as fen. Consequently, the actual exchange of N₂O with the atmosphere to be more closely linked to gene expression ratios at the 2 cm depth. Interestingly, the presence and expression of the nos Z clade I and nir gene suggested a complete denitrification process is important in this ecosystem. However, high ratio of nir K to nos Z gene expression in most instances—raises a crucial question: if only complete denitrification process were indeed taking place, we would expect these gene expression levels to be roughly equal. The current discrepancy suggests that the end product of one enzyme is not being effectively utilized by the downstream enzyme of the same organisms. Subsequently, more than 2000 genome-resolved analyses reveal a significant abundance of both partial denitrifiers and complete denitrifiers, pointing to the fact that nir gene expression stems from both groups. This dual contribution likely explains the high nir K/ nos Z gene ratio we have observed in this ecosystem. In addition, recent study also indicated that acidic soils favor N 2 O-producing over N 2 O-consuming microorganisms, thereby enhancing N 2 O emissions over consumption ( Cuhel et al., 2010 ; Friedl et al., 2021 ; Yunpeng et al., 2024), which is also in line with our findings. However, this also documents that nitrification is one of the main driver of high N 2 O emissions from arctic peat soils ( Kozlowski et al., 2016 ; Siljanen et al., 2019 ), as it regulates the supply of NO 2 − and NO 3 − for N 2 O production through denitrification, as shown for bare peat during dry years ( Gil J et al., 2017 ). In contrust our analysis, we did not detect nitrification-related genes in the genome-based evaluation ( Figure 2 , supplementary table 3), even after analyzing deeper soil samples that included 13,000 genomes from the study sites. However, metagenome analysis revealed the presence of AOB (ammonium-oxidizing bacteria) genes, albeit in very low abundance compared to denitrification genes. These findings are consistent with other research indicating that soil acidification inhibits both ammonium-oxidizing archaea (AOA) and AOB, ultimately reducing nitrification (Yunpeng et al., 2024). Our study samples are acidic in nature, indicating that nitrification may not provide enough NO 3 − for denitrification and is also not the factor responsible for N 2 O emission in the acidic peat ecosystem. The nutrient supply here, including NO 3 − , might be coming from other sources that include rainfall, surface water, and groundwater (Clymo et al., 1982; Malmer et al., 1994 ), but rarely from nitrification. Consequently, under these conditions, only genes that exhibit a high affinity for substrates and are capable of thriving in acidic environments are expressed at the RNA level. In summary, our analysis of microbial gene expression and its correlation with diurnal N₂O flux demonstrates that in this nutrient-deficient, acidic peatland, microbially mediated complete and partial denitrification play a crucial role in driving gross N 2 O emissions. This mechanism explained the observed day–night flux patterns, as evidenced by our analysis of microbial gene expression and its correlation with diurnal N 2 O flux and PAR. Since the variation was also found previously between light-transparent and dark chambers ( Triches et al., 2025 ), and persists between day and night, the observed dynamics point toward a light- or plant-mediated influence on microbial activities. One possible explanation could be light-induced N₂O production, as recently documented in marine ecosystems ( Leon-Palmero et al., 2025 ), but given the very low concentrations of NO₃⁻ in our study sites, this mechanism is unlikely to be a major contributor. Instead, the results strongly suggest that plants and associated microbial processes are key drivers. At the same time, the contribution of abiotic processes, which are well-established regulators of both N₂O production and consumption, remains to be resolved in this nutrient-poor Arctic environment. Based on our results, here we propose a conceptual microbial mechanistic model that elucidates the interactions of biotic and abiotic factors driving the diurnal N 2 O flux phenomenon in a subarctic, nutrient-poor, acidic environment (Figure 4). This insight emphasizes the importance of understanding microbial contributions to N 2 O dynamics in fragile ecosystems. Conclusion To conclude, our results first time indicate that arctic permafrost peatland, which is nutrient-poor and acidic in nature, dissolved nutrient availability and specific microbial mechanisms significantly impact the day-night variation of gross N 2 O emission in this ecosystem during summer. We found that instead of N 2 O uptake mechanisms during nighttime, N 2 O emission mechanisms are key factors for this variation. The microbial process, specifically partial denitrification and complete denitrification is the key player in gross N 2 O emission in this acidic environment. Interestingly, while essential genes for N 2 O consumption ( nos Z clades I and II) and emission ( nir K and nir S) are present, it is the less diverse nos Z clade I that is actively expressed. Additionally, the expression of the nir K for N 2 O emission gene over nir S highlights the impact of the acidic pH. Ultimately, this research reveals the unique microbial contributions to diurnal N 2 O flux variations in acidic and nutrient-poor peatlands, shedding light on a crucial aspect of N 2 O dynamics that has previously been overlooked. Further, in Arctic permafrost ecosystems, the influence of light on N₂O dynamics, and even the N 2 O budget might be more important than at lower latitudes, because of the long polar days that amplify the potential impact of light–plant–microbe interactions on emission patterns. Thus, the dark chamber method used in most of previous studies could significantly underestimate the emissions. Conflicts of interest The authors declare no conflicts of interest for this manuscript. Funding The project was financially supported by the Academy of Finland projects Thaw-N, N-Perm, PERNO (Christinas Austrian funding) Supplementary Figure 1: Estimation of (a) DOC (dissolved organic carbon); (b) DON (dissolved organic nitrogen); (c) NH 4 (ammonium) and (d) NO 3 (nitrate) from 15 cm depth from four microhabitat. Gold and gray colors indicate daytime and nighttime sampling, respectively. Two-way ANOVA was performed to check the statistical different among the micro-niche, and day-night. Two and three asterisks denote statistical significance levels of p < 0.01 and p < 0.001, respectively. Supplementary Figure 2: Estimation of CN using EA-IRMS: estimation of C% at a) 2 cm and b) 15 cm depth. Estimation of N% at c) 2 cm and d) 15 cm depth; estimation of delta C at e) 2 cm and f) 15 cm depth and estimation of delta N at g) 2 cm and h) 15 cm depth. Gold and gray colors indicate daytime and nighttime sampling, respectively. Two-way ANOVA was performed to check the statistical different among the micro-niche, and day-night. Two and three asterisks denote statistical significance levels of p < 0.01 and p < 0.001, respectively. Supplementary Figure 3: Relative abundance of 16S rRNA gene from bacterial and archaeal community at DNA and RNA level using qPCR. Relative abundance of bacterial 16S rRNA gene from four microhabitat from a) 2 cm and b) 15 cm depth at DNA level. Relative abundance of archaeal 16S rRNA gene from four microhabitat from c) 2 cm and d) 15 cm depth at DNA level. Relative abundance of bacterial 16S rRNA gene from four microhabitat from e) 2 cm and f) 15 cm depth at RNA level. Relative abundance of archaeal 16S rRNA gene from four microhabitat from g) 2 cm and h) 15 cm depth at RNA level. Gold and gray colors indicate daytime and nighttime sampling, respectively. Two and three asterisks denote statistical significance levels of p < 0.01 and p < 0.001, respectively. Supplementary Figure 4: Relative abundance of N 2 O production and consumption genes at DNA level from four microhabitats (Palsa lichen, Palsa moss, Bog and Fen) with two different depths. Relative abundance of nirk gene at a) 2 cm and b) 15 cm depth. Relative abundance of nirS gene at c) 2 cm and d) 15 cm depth. Relative abundance of nosZ clade I gene at e) 2 cm and f) 15 cm depth. Relative abundance of nosZ clade II gene at g) 2 cm and h) 15 cm depth. Gold and gray colors indicate daytime and nighttime sampling, respectively. Two and three asterisks denote statistical significance levels of p < 0.01 and p < 0.001, respectively. Supplementary Table 1: Estimation of N 2 O flux and other axillary parameter during flux measurement Supplementary Table 2: Physiochemical analysis of day-night sampling of the studied subarctic permafrost Acknowledgements DP acknowledge Abisko station for logistic support during sampling. References 1. ↵ Ravishankara , A. R. , Daniel , J. S. , & Portmann , R. W . ( 2009 ). Nitrous oxide (N2O): the dominant ozone-depleting substance emitted in the 21st century . science , 326 ( 5949 ), 123 – 125 . OpenUrl Abstract / FREE Full Text 2. ↵ Tian , H. , Xu , R. , Canadell , J. G. , Thompson , R. L. , Winiwarter , W. , Suntharalingam , P. , … & Yao , Y. ( 2020 ). A comprehensive quantification of global nitrous oxide sources and sinks . Nature , 586 ( 7828 ), 248 – 256 . OpenUrl CrossRef PubMed 3. Tian , Y. , Tao , L. , Wang , J. , Lu , H. , Zhou , L. , Campbell , D. E. , & Ren , H . ( 2020 ). Canopy and understory nitrogen additions did not significantly change the community structure of soil fauna under a mature subtropical forest . Science of the Total Environment , 718 , 137438 . OpenUrl PubMed 4. ↵ Voigt , C. , Marushchak , M. E. , Abbott , B. W. , Biasi , C. , Elberling , B. , Siciliano , S. D. , … & Martikainen , P. J. ( 2020 ). Nitrous oxide emissions from permafrost-affected soils . Nature Reviews Earth & Environment , 1 ( 8 ), 420 – 434 . OpenUrl 5. ↵ Sanford , R. A. , Wagner , D. D. , Wu , Q. , Chee-Sanford , J. C. , Thomas , S. H. , Cruz-García , C. , … & Löffler , F. E. ( 2012 ). Unexpected nondenitrifier nitrous oxide reductase gene diversity and abundance in soils . Proceedings of the National Academy of Sciences , 109 ( 48 ), 19709 – 19714 . OpenUrl Abstract / FREE Full Text 6. ↵ Jones , C. M. , Graf , D. R. , Bru , D. , Philippot , L. , & Hallin , S . ( 2013 ). The unaccounted yet abundant nitrous oxide-reducing microbial community: a potential nitrous oxide sink . The ISME journal , 7 ( 2 ), 417 – 426 . OpenUrl PubMed 7. ↵ Domeignoz-Horta , L. A. , Spor , A. , Bru , D. , Breuil , M. C. , Bizouard , F. , Léonard , J. , & Philippot , L . ( 2015 ). The diversity of the N2O reducers matters for the N2O: N2 denitrification end-product ratio across an annual and a perennial cropping system . Frontiers in Microbiology , 6 , 971 . OpenUrl PubMed 8. ↵ Yin , C. , Fan , X. , Yan , G. , Chen , H. , Ye , M. , Ni , L. , … & Liang , Y. ( 2020 ). Gross N2O production process, not consumption, determines the temperature sensitivity of net N2O emission in arable soil subject to different long-term fertilization practices . Frontiers in Microbiology , 11 , 745 . OpenUrl PubMed 9. ↵ Graf , D. R. , Jones , C. M. , & Hallin , S . ( 2014 ). Intergenomic comparisons highlight modularity of the denitrification pathway and underpin the importance of community structure for N2O emissions . PloS one , 9 ( 12 ), e114118 . OpenUrl CrossRef PubMed 10. ↵ Pold , G. , Saghaï , A. , Jones , C. M. , & Hallin , S . ( 2025 ). Distribution of alternative routes for completing denitrification in microbial genomes and metagenomes of global biomes . bioRxiv , 2025 – 01 . 11. ↵ Triches , N. Y. , Engel , J. , Bolek , A. , Vesala , T. , Marushchak , M. E. , Virkkala , A. M. , … & Göckede , M. ( 2025 ). Advancing N 2 O flux chamber measurement techniques in nutrient-poor ecosystems . Atmospheric Measurement Techniques Discussions , 2025 , 1 – 28 . OpenUrl 12. ↵ Gil , J. A. , Marushchak , M. E. , Rütting , T. , Baggs , E. M. , Pérez , T. , Novakovskiy , A. , … & Biasi , C. ( 2021 ). Sources of nitrous oxide and fate of mineral nitrogen in sub-Arctic permafrost peat soils . Biogeosciences Discussions , 2021 , 1 – 25 . OpenUrl 13. ↵ Palmer , Katharina , Christina Biasi , and Marcus A. Horn . " Contrasting denitrifier communities relate to contrasting N2O emission patterns from acidic peat soils in arctic tundra ." The ISME journal 6 , no. 5 ( 2012 ): 1058 – 1077 . OpenUrl PubMed 14. ↵ Sonesson , M. , Jonsson , S. , Rosswall , T. , & Rydén , B. E . ( 1980 ). The swedish ibp/pt tundra biome project objectives-planning-site . Ecological Bulletins , 7 – 25 . 15. ↵ Sjögersten , S. , Ledger , M. , Siewert , M. , De La Barreda-Bautista , B. , Sowter , A. , Gee , D. , … & Boyd , D. S. ( 2023 ). Optical and radar Earth observation data for upscaling methane emissions linked to permafrost degradation in sub-Arctic peatlands in northern Sweden . Biogeosciences , 20 ( 20 ), 4221 – 4239 . OpenUrl 16. Rheault , K. , Riis Christiansen , J. , & Steenberg Larsen , K. ( 2025 , April). goFlux: A user-friendly tool for calculating GHG fluxes regardless of experience . In EGU General Assembly Conference Abstracts (pp. EGU25 – 3658 ). 17. ↵ Weishaar , J. L. , Aiken , G. R. , Bergamaschi , B. A. , Fram , M. S. , Fujii , R. , & Mopper , K . ( 2003 ). Evaluation of specific ultraviolet absorbance as an indicator of the chemical composition and reactivity of dissolved organic carbon . Environmental science & technology , 37 ( 20 ), 4702 – 4708 . OpenUrl PubMed 18. ↵ Bolger , A. M. , Lohse , M. , & Usadel , B . ( 2014 ). Trimmomatic: a flexible trimmer for Illumina sequence data . Bioinformatics , 30 ( 15 ), 2114 – 2120 . OpenUrl CrossRef PubMed Web of Science 19. ↵ Uritskiy , G. V. , DiRuggiero , J. , & Taylor , J . ( 2018 ). MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis . Microbiome , 6 ( 1 ), 158 . OpenUrl CrossRef PubMed 20. ↵ Li , Z. , Riley , W. J. , Marschmann , G. L. , Karaoz , U. , Shirley , I. A. , Wu , Q. , … & Brodie , E. L. ( 2025 ). A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry . Nature Communications , 16 ( 1 ), 2186 . OpenUrl PubMed 21. Freire Zapata , V. E. ( 2025 ). Soil Microbial Adaptations to Climate Disturbance: Integrated Multi-Omics Insights from Permafrost and Arid Ecosystems . 22. ↵ Parks , D. H. , Imelfort , M. , Skennerton , C. T. , Hugenholtz , P. , & Tyson , G. W . ( 2015 ). CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes . Genome research , 25 ( 7 ), 1043 – 1055 . OpenUrl Abstract / FREE Full Text 23. ↵ Yoon , S. , Nissen , S. , Park , D. , Sanford , R. A. , & Löffler , F. E . ( 2016 ). Nitrous oxide reduction kinetics distinguish bacteria harboring clade I NosZ from those harboring clade II NosZ . Applied and Environmental Microbiology , 82 ( 13 ), 3793 – 3800 . OpenUrl Abstract / FREE Full Text 24. ↵ Jones , C. M. , Spor , A. , Brennan , F. P. , Breuil , M. C. , Bru , D. , Lemanceau , P. , … & Philippot , L. ( 2014 ). Recently identified microbial guild mediates soil N2O sink capacity . Nature Climate Change , 4 ( 9 ), 801 – 805 . OpenUrl 25. ↵ Samad , M. S. , Biswas , A. , Bakken , L. R. , Clough , T. J. , de Klein , C. A. , Richards , K. G. , … & Morales , S. E. ( 2016 ). Phylogenetic and functional potential links pH and N2O emissions in pasture soils . Scientific Reports , 6 ( 1 ), 35990 . OpenUrl PubMed 26. Philippot , Laurent , Janet Andert , Christopher M. Jones , David Bru , and Sara Hallin . " Importance of denitrifiers lacking the genes encoding the nitrous oxide reductase for N2O emissions from soil ." Global change biology 17 , no. 3 ( 2011 ): 1497 – 1504 . OpenUrl 27. ↵ Herold , M. B. , Giles , M. E. , Alexander , C. J. , Baggs , E. M. , & Daniell , T. J . ( 2018 ). Variable response of nirK and nirS containing denitrifier communities to long-term pH manipulation and cultivation . FEMS microbiology letters , 365 ( 7 ), fny035 . OpenUrl 28. ↵ Ligi , T. , Truu , M. , Truu , J. , Nõlvak , H. , Kaasik , A. , Mitsch , W. J. , & Mander , Ü . ( 2014 ). Effects of soil chemical characteristics and water regime on denitrification genes (nirS, nirK, and nosZ) abundances in a created riverine wetland complex . Ecological Engineering , 72 , 47 – 55 . OpenUrl CrossRef Web of Science 29. ↵ Cuhel , J. , Šimek , M. , Laughlin , R. J. , Bru , D. , Chèneby , D. , Watson , C. J. , & Philippot , L . ( 2010 ). Insights into the effect of soil pH on N2O and N2 emissions and denitrifier community size and activity . Applied and environmental microbiology , 76 ( 6 ), 1870 – 1878 . OpenUrl Abstract / FREE Full Text 30. ↵ Friedl , J. , Rowlings , D. , Scheer , C. , De Rosa , D. , & Grace , P. ( 2021 ). Soil pH drives magnitude and N2: N2O partitioning of denitrification losses from an acidic sugarcane soil . In Soil Science Australia and the New Zealand Society of Soil Science Joint Conference: Soils, Investing in our future . 31. Qiu , Y. , Zhang , Y. , Zhang , K. , Xu , X. , Zhao , Y. , Bai , T. , … & Hu , S. ( 2024 ). Intermediate soil acidification induces highest nitrous oxide emissions . Nature Communications , 15 ( 1 ), 2695 . OpenUrl PubMed 32. ↵ Kozlowski J , Stieglmeier M , Schleper C , Klotz MG , Stein LY . Pathways and key intermediates required for obligate aerobic ammonia-dependent chemolitho-trophy in bacteria and Thaumarchaeota . ISME J . 2016 ; 10 : 1836 – 45 . doi: 10.1038/ismej.2016.2 . OpenUrl CrossRef PubMed 33. ↵ Gil J , Pérez T , Boering K , Martikainen PJ , Biasi C . Mechanisms responsible for high N2O emissions from subarctic permafrost peatlands studied via stable isotope techniques . Global Biogeochem Cy . 2017 ; 31 : 172 – 89 . doi: 10.1002/2015GB005370 . OpenUrl CrossRef 34. ↵ Siljanen HMP , Alves RJE , Ronkainen JG , Lamprecht RE , Bhattarai HR , Bagnoud A , et al. Archaeal nitrification is a key driver of high nitrous oxide emissions from arctic peatlands . Soil Biol Biochem . 2019 ; 137 : 107539 . doi: 10.1016/j.soilbio.2019.107539 . OpenUrl CrossRef 35. Clymo RS , Hayward PM. The ecology of sphagnum. In: Bryophyte Ecology . Springer Netherlands ; 1982 , p. 229 – 89 . 36. ↵ Malmer N , Svensson BM , Wallén B . Interactions between sphagnum mosses and field layer vascular plants in the development of peat-forming systems . Folia Geobot Phytotaxonomica . 1994 ; 29 : 483 – 96 . OpenUrl 37. ↵ Leon-Palmero , E. , Morales-Baquero , R. , Thamdrup , B. , Löscher , C. , & Reche , I . ( 2025 ). Sunlight drives the abiotic formation of nitrous oxide in fresh and marine waters . Science , 387 ( 6739 ), 1198 – 1203 . OpenUrl PubMed View the discussion thread. Back to top Previous Next Posted November 04, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Microbial mechanisms responsible for diurnal dynamics of N2O fluxes in a nutrient-poor subarctic permafrost environment Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Microbial mechanisms responsible for diurnal dynamics of N 2 O fluxes in a nutrient-poor subarctic permafrost environment Dhiraj Paul , Wasi Hashmi , Nathalie Ylenia Triches , Matej Znaminko , Henri M.P. Siljanen , Ivan Mammarella , Mathias Göckede , Christina Biasi , Maija E Marushchak bioRxiv 2025.11.04.686510; doi: https://doi.org/10.1101/2025.11.04.686510 Share This Article: Copy Citation Tools Microbial mechanisms responsible for diurnal dynamics of N 2 O fluxes in a nutrient-poor subarctic permafrost environment Dhiraj Paul , Wasi Hashmi , Nathalie Ylenia Triches , Matej Znaminko , Henri M.P. Siljanen , Ivan Mammarella , Mathias Göckede , Christina Biasi , Maija E Marushchak bioRxiv 2025.11.04.686510; doi: https://doi.org/10.1101/2025.11.04.686510 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Microbiology Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17697) Bioengineering (13894) Bioinformatics (41951) Biophysics (21455) Cancer Biology (18592) Cell Biology (25507) Clinical Trials (138) Developmental Biology (13380) Ecology (19903) Epidemiology (2067) Evolutionary Biology (24321) Genetics (15610) Genomics (22509) Immunology (17737) Microbiology (40398) Molecular Biology (17182) Neuroscience (88618) Paleontology (667) Pathology (2833) Pharmacology and Toxicology (4825) Physiology (7641) Plant Biology (15158) Scientific Communication and Education (2046) Synthetic Biology (4296) Systems Biology (9825) Zoology (2271)