Active DNRA and denitrification in oxic hypereutrophic waters

harbor anaerobic nitrogen (N) cycling processes due to low-oxygen


Introduction
Nitrogen (N) and phosphorus (P) are the main limiting nutrients for aquatic life ( Canfield et al., 2010 ).With the start of Abbreviations: C org , Organic carbon; DIN, Dissolved inorganic nitrogen; DIP, Dissolved inorganic phosphorus; DNRA, Dissimilatory nitrate reduction to ammonium; DON, Dissolved organic nitrogen; GeTMM, Gene length corrected trimmed mean of M-values; POC, Particulate organic carbon; PON, Particulate organic nitrogen; RT-qPCR, Quantitative reverse transcription PCR; TDN, Total dissolved nitrogen; TN, Total nitrogen.
E-mail addresses: elias.broman@su.se(E.Broman), stefano.bonaglia@gu.se(S.Bonaglia).synthetic fertilizer production more than a hundred years ago, coastal environments have received increasing inputs of dissolved N and P, which have led to cultural eutrophication and subsequent phytoplankton blooms ( Howarth and Marino 2006 ).Mineralization of such planktonic material accelerates oxygen (O 2 ) consumption, which potentially leads to anoxia, especially in scarcely ventilated and stratified aquatic systems ( Breitburg et al., 2018 ;Carpenter et al., 1998 ).Under these conditions P is quickly recycled through internal feedback, and diazotrophs such as bloom-forming cyanobacteria capable of N 2 fixation become the main source of biological N ( Montoya et al., 2004 ).The arising conditions, in which the system presents nuisance algal blooms, low visibility ( < 1.5 m) and extremely high ( > 50 μg L −1 ) chlorophyll a concentrations, are called hypereutrophic ( Paerl et al., 2011 ).https://doi.org/10.1016/j.watres.2021.1169540043-1354/© 2021 The Author(s).Published by Elsevier Ltd.This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ) Under oxic conditions, nitrifying bacteria oxidize the fixed ammonium (NH 4 + ) to nitrate (NO 3 − ), which is further reduced to N 2 under anoxic conditions through denitrification and anaerobic ammonium oxidation (anammox) ( Canfield et al., 2005 ).In addition to these two NO 3 − -depending pathways, NO 3 − may be reduced by another metabolic process, the dissimilatory nitrate reduction to ammonium (DNRA), which recycles fixed N in the system.Laboratory reactor studies have demonstrated that this process is in competition for free NO 3 − with denitrification ( van den Berg et al. 2015 ), and its activity may increase when organic carbon is in surplus over NO 3 − (i.e. a high C:NO 3 − ratio) ( Kraft et al., 2014 ).Recent literature has also shown that this process is ubiquitous in multiple aquatic environments due to the presence of oxicanoxic interfaces, where NO 3 − is supplied via the oxic interface, and together with the availability of electron donors (organic matter, sulfide, etc.) facilitate DNRA activity (e.g.Caffrey et al., 2019, Hellemann et al., 2020, Klawonn et al., 2015, Stief et al., 2018 ).
In eutrophic lakes and coastal waters, the strictly anaerobic NO 3 − reduction pathways are mostly constrained to the top mm sediment layers ( Canfield et al., 2005 ).Because of the vast size of benthic ecosystems, the oxic-anoxic interface in sediments constitutes the largest N-loss environment on Earth ( Seitzinger et al., 2006 ).Water column anoxia can also host intense anaerobic Ncycling processes when NO 3 − is available and NO 3 − reduction pathways move vertically from the sediment to the water column (e.g.Bonaglia et al., 2016 ).In addition to anoxic sediment and waters, DNRA and denitrification have also been described in a number of other anoxic microenvironments found on zooplankton carcasses (e.g.Stief et al., 2018 ) and phytoplankton aggregates (e.g.Klawonn et al., 2015 ).Bulk water incubation experiments aimed at quantifying rates of NO 3 − reduction have therefore generally focused on anoxic sulfidic waters (e.g.Brettar and Rheinheimer 1991 ), suboxic waters of oxygen minimum zones (e.g.Dalsgaard et al., 2003 ), and on Baltic Sea waters at the oxic-anoxic interface ( Bonaglia et al., 2016 ).Except for a recent study reporting on coupled nitrification-denitrification associated with suspended sediment particles in riverine water ( Xia et al., 2017 ), to our knowledge, studies quantifying NO 3 − reduction in oxic waters are still missing.
In order to bridge this gap, we carried out a combination of geochemical and molecular assessments of an array of aerobic and anaerobic N-cycling processes in a hypereutrophic model system, the Curonian Lagoon (Baltic Sea), which constitutes the largest lagoon in Europe.Due to external loading and internal nutrient cycling, this lagoon presents massive phytoplankton blooms throughout the summer and chlorophyll a concentrations well above 50 μg L −1 ( Zilius et al., 2014 ).The water column might therefore contain anoxic microenvironments, making DNRA and denitrification possible even though the water column is oxygenated.We hypothesized that: (1) the lagoon oxic bottom waters have the potential for hosting active NO 3 − reduction processes; (2) in the highly organic and eutrophic waters, DNRA rates are higher than denitrification; and (3) sediment NO 3 − reduction is quantitatively higher than in the water column.We tested these hypotheses by applying a combination of 15 N incubation experiments, metagenomic analysis, and quantitative reverse transcription PCR (RT-qPCR) of both pelagic and benthic environments.

Field sampling
Water and sediment samples were collected on the 26th of August 2019 in the south-central half of the Curonian Lagoon (close to the resort town Nida, 55 °17.2388N, 21 °01.2898E; Fig. 1 ).This part of the lagoon has a 3.5 m water depth, a water re- newal time of 190 days (annual mean), and the water is situated above fluffy, oxygen-depleted, and organic-rich sediment ( Umgiesser et al., 2016 ;Zilius et al., 2014 ).During sampling, in situ temperature, oxygen and salinity profiles were measured in the water column using an YSI 460 multiprobe (Xylem; Fig. S1).In addition, vertical profiles of photosynthetically active radiation (PAR) were measured with a LI-192 underwater quantum sensor (LI-COR).Water samples were collected in triplicate, from the surface (~0.5 m depth) and bottom (~3 m depth) layers, using a 2 L Ruttner water sampler and transferred to: 1) sterilized 1 L amber borosilicate bottles for molecular and flow-cytometer analysis; 2) opaque 2 L HPDE bottles for chemical analyses of environmental variables; and 3) plastic 10 L tanks for measurements of nutrient transformation rates.Additional samples (40 mL) from both the surface and bottom layers were preserved in 50 mL centrifuge tubes with acetic Lugol's solution for microscopic phytoplankton counting.Finally, 200 L of bottom water was collected for preincubation and incubation procedures in the laboratory.
During sampling 16 large (i.d. 8 cm, 30 cm length) and 7 small (i.d.4.6 cm, 25 cm length) intact sediment cores were collected using a hand-corer, within 50-150 m of the sampling station.The large cores were used for benthic net flux and NO 3 − reduction pro-  ( Montoya et al., 1996 ).cesses measurements, while the small cores were used for sediment characterization and nucleic acids extraction.All water and sediment samples were transported back to the laboratory within 1 hour on ice (except for the 10 and 25 L plastic tanks), and immediately analysed.Water and sediment cores collected in the field were used in various incubation experiments to quantify different N-cycling pathways (see Table 1 for an overview of these experiments).

Water column N 2 fixation experiment
Pelagic N 2 fixation was determined using the 15 N 2 technique according to Montoya et al. (1996) .The samples were filled without air bubbles into 500 mL transparent HDPE bottles, and through a gas-tight septum each sample received 0.5 mL 15 N 2 (98% 15 N 2 , Sigma-Aldrich).As the isotopic equilibration takes up to several hours ( Mohr et al., 2010 ) we incubated the samples for 24 h ( Mulholland et al., 2012 ;Wannicke et al., 2018 ).Surface water samples ( n = 3) were incubated in outdoor tanks at ambient irradiance (14 h light and 10 h dark), while bottom water samples ( n = 3) were wrapped in aluminum foil as in situ irradiance was below 1% of surface PAR at these depths.Three controls (without 15 N 2 tracer) each for the surface and bottom water were incubated in parallel.After incubation the suspended material was collected on pre-combusted (8 h at 450 °C) Advantec GF75 glass fiber filters (0.3 μm pore size) for particulate organic (PO) 15 N analyses.N 2 fixation rates (μmol L −1 h −1 ) were calculated following Montoya et al. (1996) as where at% 15 N is the atom percentage of 15 N in the PON pool in 15 N-amended samples ( at% 15 N PON ) and in control samples ( at% 15 N control ), and in the dissolved N 2 pool ( at% 15 N N 2 ), PON is the amount of particulate organic nitrogen and t is the incubation time.All samples were stored frozen until analysis.Volumetric rates of N 2 fixation were calculated by converting areal rates taking into account the depth of the water column and the thickness of each layer ( Montoya et al., 1996 ).

Water column NH 4 + assimilation experiment
Ammonium assimilation rates in the water column were conducted based on a method previously described by Bartl et al. (2018) .Briefly, 27 × 250 mL polycarbonate bottles were filled with collected field water to the top, avoiding bubbles, sealed and assigned, in triplicates, to the following treatments: (a) 9 × bottom water in dark; (b) 9 × surface water in light; and (c) 9 × surface water in dark.A volume of 200 μL from a 312.5 μM 15 NH 4 Cl stock solution (99% 15 N atom, Sigma-Aldrich) was injected to a final concentration of 0.3 μM 15 NH 4 + through the butyl septum.This was followed by short incubations with three time points T 0 , T 1 (1.5 h), and T 2 (3 h).At each time point, bottles ( n = 3) were sacrificed and 60-70 mL of water were filtered on a pre-combusted 25 mm Advantec GF75 glass fiber filters for PO 15 N analyses.Filters were frozen at -20 °C until analysis.Additional aliquots of filtered water (15 mL) were frozen at -20 °C for nutrient analyses (see below).
Ammonium consumption rates were calculated following Glibert et al. (1982) , Klawonn et al. (2019) .During incubations, the 15 NH 4 + concentrations commonly decrease exponentially with time due to the concurrent assimilation of 15 NH 4 + and dilution through 14 NH 4 + regeneration.To account for this exponential decrease, we used a non-linear curve fitting where C t and C 0 are the 15 NH 4 + concentrations at time t and time zero, respectively.Note that we did not measure the 15 NH 4 + concentrations directly but calculated them as the difference between the initially added amount of 15 NH 4 + and the amount of 15 N in the PON at each time point.Since total NH 4 + concentrations were not significantly different between time points ( t -test, p range = 0.07-1.00),NH 4 + consumption and remineralisation rates were similar, and calculated as k × C , with C as the mean NH 4 + concentrations during incubations ( Glibert et al., 1982 ).Since we used the amount of 15 N in the PON to calculate NH 4 + consumption rates, those can be considered as NH 4 + assimilation rates.

Water column nutrient transformation and NO 3 − reduction experiment
Eight large plexiglass cylinders (i.d. 8 cm, 30 cm length) filled with ~1.3 L of bottom water each were submerged into an incubation tank containing approximately 20 L of aerated collected field water and maintained at constant temperature (21.3 ± 0.2 °C).Two stirrer bars, driven by an external magnet at 40 rpm, were inserted in each cylinder approximately at 10 cm height distance to each other to avoid water stagnation during incubation.
A first incubation for oxygen consumption and nutrient transformation processes (NH 4 + , NO 3 − , NO 2 − ) was conducted by sealing four of the cylinders with rubber stoppers, while avoiding bubbles.The incubation was conducted in the dark and lasted six hours, and before and at the end a 20 mL of aliquot was subsampled, filtered (GF-75 filters) and frozen at -20 °C for nutrient analysis.Dissolved O 2 was monitored with a pre-calibrated oxygen microsensor (OX-50 microsensor, Unisense A/S).A second incubation, using the other set of four cylinders, was performed to target NO 3 − reduction processes by means of the 15 NO 3 − tracer addition technique.The water inside each cylinder was spiked with 15 N-NO 3 − from a stock solution (20 mM Na 15 NO 3 , 98% 15 N atom, Sigma Aldrich) to a final concentration of 15 μM 15 N. To calculate the exact isotopic enrichment, water samples for NO 3 − analysis were collected prior and after the isotope addition.At the start of the timeseries incubation, the cylinders were sealed with gas-tight plastic lids, avoiding bubbles.At intervals of approximately 4 h, and over an incubation time of 8 h, the cores were sampled through the perforated lid using a glass syringe equipped with a Viton tubing.The water removed was replaced with new water from the tank and the dilution factor was considered in the calculations.From each core a 40 mL subsample was transferred to a 12 mL exetainer (Labco Ltd) containing 200 μL of 7 M ZnCl 2 for 15 NH 4 + and 15 N 2 measurements, or filtered on a pre-combusted GF-75 filter that was frozen (-20 °C) for later PO 15 N analysis.Denitrification and DNRA rates were calculated according to Bonaglia et al. (2016) : (3) where 30 N 2r is the production rate of labelled N 2 ; 15 NH 4 + r is the production rate of labelled NH 4 + ; and F NO3 − is the fraction of 15 N in the NO 3 − pool.

Benthic nutrient flux and NO 3 − reduction rate measurements
A total of 16 large sediment cores were submerged into an incubation tank containing 200 L of aerated collected field water kept at a constant temperature (21.3 ± 0.2 °C).Cores were left uncapped in the tank to allow for full water mixing between the water inside the cores and tank bottom water.A stirrer bar, driven by an external magnet at 40 rpm, was inserted to the water phase of each core approximately 15 cm above the sediment surface to avoid water stagnation during incubation.After preincubation overnight, a gas-tight lid was placed on each core and dark incubations started.Four cores were immediately uncapped and 40 mL water aliquots were collected from each, transferred into 12 mL Exetainers (Labco Ltd), and fixed with 200 μL of 7 M ZnCl 2 for further gas measurements.A second 40 mL water aliquot was filtered (GF-75 filters) into a plastic test tube and frozen immediately at -20 °C for dissolved N analyses (see 2.6 section for details).At 1 h time intervals, four cores were uncapped and subsampled for respective analysis.The cores were then returned back to tank for subsequent incubation.
The second incubation for NO 3 − reduction followed the rationale of the revised isotope pairing technique (r-IPT, Risgaard-Petersen et al., 2003 ).The overlying water inside each core was spiked with 15 N-NO 3 − from a stock solution (20 mM Na 15 NO 3 , 98% 15 N atom, Sigma Aldrich) to final concentrations of 15 μM 15 N ( n = 8 cores) and 30 μM 15 N ( n = 8 cores).To calculate the exact isotopic enrichment, water samples for NO 3 − analysis were collected prior and after the isotope addition.The two different 15 NO 3 − concentrations were used to validate the method assumptions and to test for anammox activity ( Risgaard-Petersen et al., 2003 ).Since the resulting rates were not significantly different, assumptions were met, anammox activity disregarded and denitrification rates pooled.The cores were left uncapped for 45 min to allow enough time to establish a stable NO 3 − concentration in the surface layer of the sediment.At the start of the time-course incubation, all cores were capped with rubber stoppers and every 1.5 h, four cores (two per treatment) were sacrificed and sediments were gently mixed with the overlying water down to the depth of 5-10 cm.Thereafter, 20 mL aliquots of the slurry were transferred into 12 mL exetainers (Labco Ltd) and fixed with 200 μL of 7 M ZnCl 2 for 29 N 2 and 30 N 2 analysis.An additional 40 mL subsample was collected and treated with 1.3 M KCl, vigorously shaken for 30 min, centrifuged supernatant (30 0 0 rpm for 10 min) was filtered, and frozen at -20 °C for analyses of the exchangeable NH 4 + pool and the 15 NH 4 + fraction.All benthic rates were calculated following established protocols ( Bonaglia et al., 2014 ).

Water analysis
Water samples were filtered (GF-75 filters) within 1.5 h of collection into 10 mL PE tubes for dissolved inorganic nutrient (NH 4 + , NO 2 − , NO x − , DIP) and 25 mL glass vials for organic nitrogen (DON) analysis, and frozen immediately (-20 °C).All dissolved inorganic nutrient concentrations from field sampling and experiments were determined with a continuous flow analyser (San ++ , Skalar) using standard colorimetric methods ( Grasshoff 1983 ).NO 3 − was calculated as the difference between NO x − and NO 2 − .Total dissolved nitrogen (TDN) was analysed by high temperature combustion at 680 °C, followed by the catalytic oxidation/NDIR method using a Shimadzu TOC 50 0 0 analyser with a TN module.DON was calculated as the difference between TDN and DIN (NH 4 + + NO 2 − + NO 3 − ).400 mL water samples for chlorophyll a were filtered through Whatman GF/F filters (pore size 0.7 μm) and extracted with 90% acetone (24 h at 4 °C) and measured by spectrophotometry ( Jeffrey and Humphrey 1975 ;Parsons et al., 1984 ).
Dissolved O 2 was quantified from the O 2 :Ar ratio measured by membrane inlet mass spectrometer (MIMS) at Ferrara University (Bay Instruments, Kana et al., 1994 ) and corrected for Ar concentration and solubility based on temperature and salinity ( Colt 2012 ).The 15 N-atom% in the dissolved N 2 pool in samples from the N 2 fixation experiment was also estimated using MIMS.Isotopic samples for 29 N 2 and 30 N 2 production were analysed by gas chromatography-isotopic ratio mass spectrometry (GC-IRMS) at the University of Southern Denmark. 15 NH 4 + from the isotope pairing technique experiments were analysed by the same GC-IRMS after conversion of NH 4 + to N 2 ( De Brabandere et al. 2015 ) by the addition of alkaline hypobromite ( Warembourg 1993 ).Filters for PO 15 N analyses were analysed with a continuous-flow isotope ratio mass spectrometer (IRMS; Delta S, Thermo-Finnigan) at the Leibniz Institute for Baltic Sea Research Warnemünde (IOW).

Sediment characteristics
One of the small sediment cores were used to measure sediment oxygen penetration depth, and three small cores were sliced (top 0-2 cm sediment layer) and used for bulk density (dry weight per unit volume), organic carbon (C org ), total N (TN), and its isotopic composition ( δ 13 C, δ 15 N).Before slicing, three O 2 microprofiles with a 100 μm resolution were measured in one random selected core using a pre-calibrated Clark-type oxygen microelectrode (OX-50, Unisense A/S) mounted on a motorized micromanipulator (MM33, Unisense).Measurements were performed in the dark at in situ temperature (21 °C).An overlying water column of 2-3 cm was left in the sediment core and aerated by a flow of atmospheric air to ensure sufficient water stirring during measurements.The oxygen penetration depth was defined as the depth at the bottom of the oxygen profile where [O 2 ] was < 0.3 μM, which is the detection limit of the microelectrode.
From each core the sliced sediment was homogenised (5 mL) and dried at 60 °C for 48 h.In a subsample of dry sediment (2.9-4.5 mg), C org and TN content and their isotopic composition ( δ 13 C, δ 15 N) were measured with a mass spectrometer (Delta V, Thermo Scientific) coupled to an element analyser (FlashEA 1112, Thermo Electron Corporation) at the Center for Physical Sciences and Technology, Vilnius, Lithuania.Before measurements the samples were acidified with 1 M HCl in order to remove carbonates.

Nucleic acids extraction, cDNA synthesis, and sequencing
Water samples collected from the surface ( n = 3) and bottom water ( n = 3) were filtered (20 0-30 0 mL) by gentle vacuum (0.3 bar) onto sequential 10 μm polycarbonate membrane (GE Healthcare) and 0.22 μm MCE membrane filters (Frisenette ApS).Filters were transferred into sterile 2 mL cryotubes, immediately frozen and stored at -80 °C until DNA and RNA extraction.The filters were combined for nucleic acid extraction to ensure the representation of the whole microbial community.Three small intact sediment cores were sliced (0-0.5 and 0.5-1.0cm sediment surface layers, n = 3), homogenized, and subsampled with a sterile spatula in order to sample the sediment microbial community.Nucleic acids were then extracted from 0.65-0.85g of sediment sample.
DNA was extracted and purified using the QIAamp Fast DNA Stool Mini Kit (QIAGEN) following the manufacturer's instructions.For the improvement of bacterial cell rupture, lysis temperature was increased to 90 °C and for increasing the final DNA concentration in the eluate, elution volume of 50 μL was used.Quantity and quality of the DNA was measured on a NanoDrop One spectrophotometer (ThermoFisher Scientific).Metagenome sequencing was prepared with the library kit SMARTer ThruPLEX (Takara Bio) and sequenced at SciLife laboratory in Stockholm, Sweden.All samples were sequenced together on one lane using the Illumina No-vaSeq S-Prime platform with a 2 × 150 bp setup.
RNA extraction was conducted with an initial incubation using lysozyme (20 mg mL −1 ) and mutanolysin (250 U mL −1 ) for 90 min at 37 °C.After incubation, 1 mL of Trizol was added and the samples were subjected to four cycles of bead beating with glass beads (for 2 min) and resting on ice (for 3 min) followed by incubation at room temperature (for 5 min).Thereafter, samples were cleaned following the protocol by Samuiloviene et al. (2019) .Additional, RNA cleaning was performed using the RNeasy Mini Kit (QIAGEN) according to protocol instructions.Any leftover DNA in the extracted RNA was removed using the TURBO DNase kit (Invitrogen) according to the manufacturer's instructions.Contamination with residual DNA was tested by PCR amplification of the V3 region of the 16S rRNA gene, using primer pair Probio_Uni (5'-CCTACGGGRSGCAGCAG-3') and Probio_Rev (5'-ATTACCGCGGCTGCT-3') by ( Milani et al., 2013 ).cDNA was generated with random primers using a SuperScriptIII Reverse Transcriptase (Invitrogen) with 5 min at 25 °C, 50 min at 55 °C, and 15 min at 70 °C, and the quantity and quality were measured on a NanoDrop One spectrophotometer.The cDNA samples were kept at -20 °C until transcript quantification by RT-qPCR.

RT-qPCR
Four functional genes involved in N-cycling ( nirS, nrfA, amoA , and nifH ) were targeted for quantification of its transcript abundance using the synthesized cDNA from the extracted RNA.qPCR standards were prepared using DNA from reference bacterial strains and specific primers (Table S1).Each amplification for the qPCR standards was performed under the following conditions: 5 min initial denaturation at 94 °C; 35 cycles at, 94 °C for 30 s, specific annealing temperature of the primer set for 30 s (Table S1), 72 °C for 45 s, 72 °C for 10 min, using 11 μL of Platinum Green Hot Start 2X Master Mix (Invitrogen), 0.3 μM of each primer, 1.25 μg μL −1 of BSA and 2 μL of template in total volume of 22 μL.Amplification products were purified using PureLink PCR Purification Kit (Invitrogen), quantified using Qubit 3.0 (Invitrogen) and sequenced (BaseClear B.V) to confirm their identity.Ten-fold serial dilutions ranging from 10 3 to 10 7 of copy number of the standard were used in RT-qPCR reactions in triplicate to generate an external quantification standard.
RT-qPCR reactions were performed on the StepOnePlus Real Time PCR system (ABI 7900 HT Sequence Detection System, PE Biosystems) using optical grade 96-well plates.The reaction mixture (20 μL) contained 10 μL of SYBR Green master mix, 0.2 μM of respective forward and reverse primers (Table S1), 2 mM of MgCl 2 , 1.25 μg μL −1 of BSA and 2 μL of diluted cDNA sample (diluted 1:100 to 0.1 ng μL −1 cDNA).The thermocycling conditions were as follows: 50 °C for 2 min; initial denaturation at 94 °C for 10 min; 40 cycles at 94 °C (30 s), primer annealing temperature (see Table S1 for each primer temperature; 1 min), 72 °C (2 min).Specificity for RT-qPCR reactions was tested with a melting curve analysis (60 °C-94 °C, with 0.3 °C ramp increment) in order to identify unspecific PCR products such as primer dimers or fragments with unexpected fragment lengths.Each sample was analysed in triplicate and the average Ct-value was used to calculate transcript copy numbers per sample (mL −1 for water or mg −1 for wet sediments).Triplicate no-template controls were included in each RT-qPCR assay.
The paired without unpaired (PwU) reads from trimmomatic were used with MEGAHIT 1.1.2to construct a co-assembly ( Li et al., 2016a ) that consisted of 9,148,477 contigs with an average length of 705 bp (range 200-311,543 bp).This was followed by gene prediction and gene annotation using the PROKKA 1.12 software suit ( Seemann 2014 ).PROKKA uses Prodigal 2.6.3 for gene prediction ( Hyatt et al., 2010 ) and BLAST 2.6.0 + ( Altschul et al., 1990 ) for annotation against the UniProtKB/Swiss-Prot database (database downloaded: 31 January 2019).The quality trimmed reads were mapped on the assembly using bowtie2 on default settings.htseqcount from the HTSeq python package 0.9.1 ( Anders et al., 2015 ) were used to estimate mapped sequence counts, and data was normalized within and between samples as Gene length corrected Trimmed Mean of M-values (GeTMM) ( Smid et al., 2018 ).The final metagenomic data consisted of 152,288 unique UniProtKB/Swiss-Prot identifiers and 43,533 unique genes.The UniProtKB identifiers were used on UniProt's website (function: Retrieve/ID mapping) to retrieve reference taxonomy, gene names, and protein names.The nitrogen metabolism pathway as shown on the KEGG website (2020-07-28) was used to determine prokaryotic genes involved in nitrogen cycling for further analysis.
The pipeline for taxonomic identification followed the Kraken2 + Bracken2 protocol (with the same options) as described in ( Broman et al., 2020 ).In brief, all quality trimmed metagenome sequences were taxonomically classified using Kraken2 2.0.8 ( Wood et al., 2019 ) against the NCBI RefSeq genome database (database downloaded: 1 January 2020), and relative abundances estimated on genus level using Bracken 2.5 ( Lu et al., 2017 ).The final data were analysed in the software Explicet 2.10.5 ( Robertson et al., 2013 ) and normalized as relative abundances (%).See Data S1 for a list of sample names, number of sequences yielded before and after quality trimming, read lengths, quality scores, and number of reads classified with Kraken2.The sequence data has been uploaded to the NCBI BioProject: PRJNA645809.

Statistics
Benthic fluxes/rates were calculated from the least square linear regression of the solute concentration against time.The slope of the linear regression multiplied by the incubation cylinder's water column height gave the net solute increase (positive flux) or decrease (negative flux) per time and area.Measured volumetric rates (μmol L −1 d −1 ) from the water were converted to areal scales (mmol m −2 d −1 ) taking into account the lagoon's water column depth and thickness of each layer: surface -1 m and bottom -2.5 m.Shannon's H alpha diversity was based on the RefSeq lowest classified taxonomic level (i.e.genera), and was analysed in Explicet by first sub-sampling counts to the lowest sample size (6,003,552 counts) followed by bootstrap × 100 (with the bootstrap mean being reported).One-Way ANOVA tests (Shapiro-Wilk and Levene's tests were used to confirm assumptions for ANOVA were met) were then used to test for differences in Shannon's H alpha diversity between water and sediment layers.Bray-Curtis beta diversity based on the RefSeq lowest classified taxonomic level (i.e.genera) was analysed in the software past 4.0 ( Hammer et al., 2001 ) and statistically tested with PERMANOVA (9999 permutations) between water and sediment layers.Significance level was set at P < 0.05, and all data are shown as mean ± standard error.

Characteristics of water column and sediment
During sampling, the surface and bottom water were oxygenated (181 and 81% saturation, respectively: Table 2 & Fig. S1).The water salinity was 0.3 and the pH ~9 (Fig. S1).The water temperature was ~25 °C in the surface and ~22 °C in the bottom.The depth of the euphotic zone (Z eu = 1% of surface PAR) was 1.4 m (Fig. S1).
Based on data measured in the field, lagoon conditions were clearly hypereutrophic ( Fig. 1 ) and the dissolved nutrient concentrations, phytoplankton biomass, and bacteria number were distributed evenly throughout the water column ( Table 2 ).This was also indicated by chlorophyll a concentrations of 72 and 77 μg L −1 in the surface and bottom water, respectively.DIN concentrations (i.e.NH 4 + and NO x each < 1 μM) were low compared to DON (40-50 μM).N limitation was indicated by the very low calculated DIN:DIP ratio ( < 6).The N 2 -fixing cyanobacteria constituted 25% of the total phytoplankton biomass (both in surface and bottom water), with species belonging to the genus Dolichospermum spp.(formerly planktonic Anabaena ( Li et al., 2016b )) having the highest abundance (Data S2).The most abundant nondiazotrophic cyanobacteria included species belonging to the genus Planktothrix (42% of total phytoplankton biomass; Data S2).The top 0-2 cm sediment layers were depleted in 13 C and enriched in 15 N ( δ 13 C = −31.2‰ and δ 15 N = 5.8 ‰ ), and had a C org content of ~13%.In the sediments surface oxygen penetrated 1.5 mm ( Table 2 ).assimilation rates in the surface water are based on light and dark incubation values combined together based on a 14 h light and 10 h dark period.For more info see Table S2.

Nitrogen transformation rates
The incubation experiments showed that total lagoon O 2 consumption was ~4 times higher in the bottom water compared to the sediment surface ( Table 3 ; for raw rates see Table S2).DIN was produced in both the bottom water and the sediment, with substantially higher rates in the bottom water.NH 4 + production was 9.7-fold higher than NO x -in the bottom water (~34 compared to 3.5 mmol m −2 d −1 ), while NH 4 + assimilation rates were similar in the bottom and surface water (~18 mmol m −2 d −1 ).Raw N2 fixation rate was four times higher in the surface water (2.4 μMN 2 fixation was higher in the surface water than in the bottom water (~2.4 compared to ~1.5 mmol m −2 d −1 ; Table 3 ).Isotope labeling experiments with 15 NO 3 − indicated that both DNRA (NO 3 − → NH 4 + ) and denitrification (NO 3 − → N 2 ) occurred in the oxygenated bottom water (255 μM O 2 ) and in the sediment surface (Table S2; Table 3 ; see Fig. S2 for oxygen concentrations measured during the incubations).DNRA rates were higher than denitrification rates in the bottom water (~0.19 compared to ~0.04 mmol m −2 d −1 ; Table 3 ), while in the sediment denitrification rates were higher than DNRA rates (~0.95 compared to 0.04 mmol m −2 d −1 ; Table 3 ).The sum of the total NO 3 -reduction rates from DNRA and denitrification [(DNRA)/(DNRA + Denitrification) = (0.19 + 0.04)/((0.19+ 0.04) + (0.04 + 0.95)) = 18.9%] results in total DNRA accounting for approximately 19% of the fixed N undergoing NO 3 -reduction.From the DNRA and denitrification experiments our results also showed that the 15 N amount on filters (i.e., PO 15 N) did not increase with time.Anammox activity was excluded since the 14 N-N 2 production in cores incubated at 15 μM 15 NO 3 − did not significantly differ from that in cores incubated at 30 μM 15 NO 3 − (Student's t -test, t = −0.483,P = 0.640).
Based on the Bray Curtis dissimilarity index, the sediment had a different microbial community composition when compared to the water column (Bray Curtis, PERMANOVA, df = 11, pseudo-F = 319.1,P = 0.0019).Likewise, the sediment also had a higher alpha diversity (Shannon's H, 8.4 ± 0.01, data compared between all sediment ( n = 6) and water samples ( n = 6), One-Way ANOVA, df = 11, F = 258.5,P < 0.001).However, there was no difference in beta and alpha diversity between the two sliced sediment sections (0-0.5 cm and 0.5-1 cm).Phyla with the highest relative abundance in the sediment included Actinobacteria, Firmicutes, and Proteobacteria (classes Alpha, Beta, and Gamma; Fig. 2 ).Top dominant genera in the sediment included e.g.actinobacterial Streptomyces , gammaproteobacterial Pseudomonas , betaproteobacterial Burkholderia and Cupriavidus , and firmicutes Thermobaculum (Fig. S3).See Data S3 for a full list of classified taxa in the dataset.

N-cycling genes and transcripts
There were slightly more classified functional genes in the sediment (21,346 unique gene names including data from all samples) compared to the surface and bottom water samples (20,426 genes).See Data S4 for a full list of detected functional genes in the metagenomic dataset.The water column (surface and bottom, n = 6) contained more normalized counts for N-cycling genes compared to the sediment (238 GeTMM compared to 94 GeTMM in the sediment (both depth layers, n = 6) for the analysed genes; Fig. 4 A).Considering the data was normalized for gene length and sequence depth (as GeTMM), this indicates that the func-Fig.3. The top dominant genera in the water column according to the metagenomic data (NCBI RefSeq database).The heatmap shows the lowest level of taxonomic classification (genera).The dataset was delimited to taxonomic groups > 0.5% relative abundance (average of all samples).Fig. 4. A) Distribution and taxonomy of selected N-cycling genes in the metagenomic data (based on UniProtKB/Swiss-Prot database).The y-axis shows different nitrogen metabolic processes in each sampled water or sediment layer, with: nitrification (genes amoAB ); assimilatory nitrate reduction (ANRA, genes narB, nosAB, nirA ); denitrification (genes narGHI, napAB, nirKS, norBC, nosZ ); dissimilatory nitrate reduction (DNRA, genes nirBD, nrfAH ); N 2 fixation (genes nifDKH, vnfK ).The x-axis shows normalized sequence counts (GeTMM-values).The coloured stacked bars show the relative proportion (%) of the taxonomy, phyla, or proteobacteria classes attributed to the various metabolic processes.The error bars show the standard error ( n = 3).B) DNRA and denitrification genes and their associated reference species in the UniProtKB database.The heatmap shows GeTMM-values (labels on dark cells have been coloured white).The dataset was delimited to database hits > 1 GeTMM (average of all samples).tional genes in the water column contained proportionally more N-cycling genes than the sediment.Denitrification genes ( narGHI, napAB, nirKS, norBC, nosZ ) were more prominent in the water column (surface + bottom, n = 6) when compared to the sediment (both sliced depths, n = 6) (104 ± 4 compared to 38 ± 1 GeTMM, df = 11, F = 196.9,P < 0.01; Fig. 4 A).Similarly, DNRA genes ( nirBD and nrfAH ) also had more mapped reads in the water compared to the sediment (57 ± 2 compared 27 ± 1 GeTMM, df = 11, F = 148.3,P < 0.01; Fig. 4 A).However, in contrast to denitrification that had no difference in GeTMM-values between the surface and bottom

Table 4
The table shows the RT-qPCR transcript copy numbers per 1 mL water or 1 mg sediment sample.The numbers in parenthesis behind the labels denote the biological replicate number (i.e.sediment core), and dashes in the table denote below detection limit.The studied genes code for denitrification ( nirS ), DNRA ( nrfA ), nitrification ( amoA ), and N 2 fixation ( nifH ).

Sample
Transcript water, DNRA had higher GeTMM-values in the bottom water (df = 5, F = 7.7, P < 0.05; Fig. 4 A).Both of these processes were attributed to Proteobacteria classes Alpha, Beta, Delta, and Gamma.
In addition, Firmicutes and the archaeal phyla Euryarchaeota were attributed to denitrification genes and Bacteroidetes to DNRA.Some examples of reference species in the UniProtKB database with the highest GeTMM-values attributed to DNRA genes included e.g.Parabacteroides distasonis , Desulfovibrio vulgaris and Wolinella succinogenes ( Fig. 4 B).Furthermore, the DNRA genes for these three species had higher GeTMM-values in surface and bottom water compared to the sediment (One-Way ANOVA with Tukey tests, df = 11, F = 55-802, P < 0.05).Species associated with denitrification genes included e.g.Escherichia coli, Pseudomonas aeruginosa, Achromobacter cycloclastes , and Brucella melitensis ( Fig. 4 B).These denitrification genes attributed to species also had higher GeTMMvalues in the water when compared to the sediment (One-Way ANOVA with Tukey tests, df = 11, F = 20-135, P < 0.05).Assimilatory NO 3 -reduction genes ( narB, nosAB, nirA ) were attributed to Cyanobacteria, Firmicutes, and Euryarchaeota with no statistical difference between the water and sediment layers (18 ± 1 GeTMM for the whole dataset; Fig. 4 A).N 2 fixation genes ( nifDKH and vnfK ) were more prominent in the water compared to the sediment (50 ± 2 compared to 13 ± 1 GeTMM, df = 11, F = 238.8,P < 0.01; Fig. 4 A).However, there was no difference between the layers for N 2 fixation.In the water column, N 2 fixation genes were attributed to Chloroflexi, Cyanobacteria, Firmicutes, and Proteobacteria classes Alpha, Beta, and Gamma.This was different compared to the sediment, in which N 2 fixation genes were attributed to Euryarchaeota, Cyanobacteria, Firmicutes, and Gammaproteobacteria ( Fig. 4 A).
The RT-qPCR results showed that transcripts for nirS, nrfA , and nifH genes were present in all water and sediment layers, with the exception of the amoA transcript used in nitrification that was missing in the deeper anoxic 0.5-1 cm sediment layer ( Table 4 ).Interestingly, the anaerobic process DNRA (gene nrfA , 3314 ± 518 transcripts per mL water) had significantly higher transcript numbers than nitrification (gene amoA , 1101 ± 65 transcripts; One-Way ANOVA, df = 5, F = 18.4,P < 0.05, n = 3) in the surface water.There was no significant difference between nrfA and amoA when compared to the denitrification gene nirS in the surface water.Finally, the bottom water was indicated to have the highest number of DNRA nrfA transcripts (32,250-62,602, n = 2) as well as nirS transcripts (1329-2716, n = 2; Table 4 ).

Water column nitrate reduction processes
We found that the oxic bottom water of the hypereutrophic model system hosted NO 3 − reduction processes, with both active DNRA and denitrification.Similar findings have been reported from other pelagic coastal systems, but only after turning the water anoxic ( Zeng et al., 2019 ).Consequently, apparent denitrification rates in the previous study (6-107 nmol L −1 h −1 ) were 1-3 orders of magnitude higher than our measured rates (Table S2).To our knowledge, this is the first report of genuinely active water column NO 3 − reduction in shallow estuarine systems.By combining results from the isotope tracer experiments and flux measurements in the benthic and pelagic compartments, we were able to reconstruct the whole lagoon N-cycle ( Fig. 5 ).Our data show that NO 3 − reduction in the water column was dominated by DNRA (83%), while in the sediment denitrification dominated (96%).Interestingly, wholelagoon DNRA was estimated to retain 19% of the fixed N 2 that goes through the NO 3 − pool, intended as DNRA / (DNRA + denitrification).This highly organic and eutrophic estuarine system, with DNRA dominance over denitrification in the water column, differs from the deep anoxic basins of the open Baltic Sea, where denitrification is more prominent than DNRA ( Bonaglia et al., 2016 ;Dalsgaard et al., 2013 ;Hietanen et al., 2012 ).The fact that water column DNRA was higher than denitrification might be explained by the very high C/NO 3 − ratio in the pelagic environment, which renders DNRA thermodynamically more favourable ( Kraft et al., 2014 ;Tiedje et al., 1983 ).
All water column processes were measured at full O 2 saturation (255 μM O 2 ; Fig. S2).During stagnation events, when O 2 respiration exceeds its supply, bottom water can rapidly undergo hypoxia in the Curonian Lagoon (Fig. S4; Zilius et al. (2014) ).Under hypoxia, the potential of the water column to significantly contribute to N-recycling and N-loss via DNRA and denitrification, respectively, will increase dramatically ( Klawonn et al., 2015 ).Considering that the lagoon was N-limited and phytoplankton biomass was high, we cannot exclude that a portion of the added 15 NO 3 − was assimilated by the phytoplankton and re-mineralized to NH 4 + ( Klawonn et al., 2015 ).However, we have three lines of evidence against this possibility.First, we could not measure any 15 NO 3 − being incorporated into PO 15 N. We acknowledge the fact that our 15 NO 3 − experiment was carried out in dark only, and thus we cannot exclude that minimal NO 3 − incorporation into PO 15 N might have happened in light conditions, but since the bottom waters of the lagoon are dark, we are confident that 15 NO 3 − was carried out dissimilatory.Second, we detected the highest amounts of nrfA RNA transcripts in the bottom water compared to surface water and sediment.These transcripts translate for a nitrite reductase enzyme used specifically in DNRA ( Mohan et al., 2004 ).RNA transcripts have previously been observed for this gene in oxic waters in the hypereutrophic Lake Taihu, China ( Krausfeldt et al., 2017 ).However, here we also conducted incubation experiments with 15 NO 3 − additions in both water and sediment to confirm that this was an active process.The NO 3 − reduction experiments indicated that DNRA activity was higher than denitrification in the bottom water, a pattern that was further confirmed by the higher number of nrfA transcripts compared to nirS ones.Third, the major players in these waters ( Planktothrix, Anabaena and Dolichospermum ) have never been shown, to our knowledge, to be able to carry out dark NO 3 − assimilation.Thus, interpreting our chemistry and molecular results together, we suggest that DNRA was an active process in the oxygenated lagoon waters.Our results thus imply that DNRA in the oxic waters can facilitate a complete NH 4 + cycling: NH 4 + uptake → mineralization/NH 4 + release → nitrification to NO 3 − (not directly measured in our study but supported The measured rates of N-cycling processes were used to calculate the whole water column and sediment fluxes.The bacterial phyla and proteobacteria classes associated with genes for specific processes were based on the most abundant groups in the metagenomic data.The values shown represent average rates in mmol m −2 d −1 and were calculated based on a 2.5 m aphotic bottom water column (except for N 2 fixation and NH 4 + uptake that were measured both in surface and bottom water, and were therefore based on the whole 3.5 m water column).Pelagic nitrification rates were not directly measured in this study, and were estimated from production rates of NO x − from the flux experiments.Arrow thickness indicates the processes that had the highest measured rates, and the orange coloured arrows denote the novel water column nitrate reduction pathways, denitrification and DNRA.(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)by the RT-qPCR amoA transcript data) → reduction to NH 4 + via DNRA.

Pelagic niches for anaerobic process
The transcription of marker genes encoding for NO 3 − reduction and its derivatives typically occurs under low oxygen conditions ( Härtig and Zumft 1999 ).In our study, this might have been associated with anoxic microniches on, e.g.zooplankton carcasses ( Stief et al., 2018 ) and, most likely, on phytoplankton aggregates ( Klawonn et al., 2015 ).Since the lagoon's water column was dominated by filamentous cyanobacteria, it is very likely that anoxic microniches were present inside cyanobacterial aggregates ( Klawonn et al., 2015 ).We cannot exclude that denitrification and/or DNRA associated with suspended sediment particles ( Xia et al., 2017 ) may have contributed to the measured rates.However, we consider the latter process negligible since the lagoon is an extremely low-energy ecosystem, which is characterized by stagnant conditions especially during summer months, with a water residence time of more than 4 months at the sampling site ( M ėžin ė et al., 2019 ).
Bacteria capable of DNRA are diverse and include, e.g.Bacteroidetes, Firmicutes and Proteobacteria ( Mohan et al., 2004 ), which were also detected and attributed to DNRA in our metagenome data for both the water and sediment.Notably, Bacteroidetes were uniquely associated with DNRA genes compared to the other N-cycling pathways.Most of the DNRA genes were attributed to species Parabacteroides distasonis and Wolinella succinogenes in the UniProtKB database.Parabacteroides distasonis has been detected in e.g. the human gut ( Ravcheev and Thiele 2014 ), and in an anaerobic industrial wastewater reactor amended with NO 3 -to promote DNRA and denitrification ( Xie et al., 2015 ).Wolinella succinogenes is a known N-cycling bacterium capable of DNRA and reduction of N 2 O to N 2 ( Hollocher 1996 ;Luckmann et al., 2014 ).Moreover, members of Bacteroidetes are often found closely associated with filaments of cyanobacteria ( Allgaier and Grossart 2006 ;Eigemann et al., 2019 ), likely benefiting from cyanobacterial exudates ( Adam et al., 2016 ).Most of the denitrification genes were attributed to Achromobacter cycloclastes and Pseudomonas aeruginosa .Both Pseudomonas aeruginosa and Achromobacter sp. have been described capable of aerobic denitrification ( Kathiravan and Krishnani 2014 ).In addition, the human pathogens Brucella melitensis and E. coli that have previously been detected in sediment and water ( Zhou et al., 2019 ) were also found to be associated with the detected denitrification genes.
Our results suggest that low oxygen or anoxic microniches associated with cyanobacteria and other algae might have been essential for DNRA and denitrification to function.Furthermore, our measured chlorophyll a values are in the same range as those reported for other hypereutrophic waters in, e.g.Asia ( Paerl et al., 2011 ;Xing et al., 2005 ) and North America ( Bigham et al., 2009 ;Norris and Laws 2017 ).Thus, our findings of active NO 3 − reduction in oxic waters are relevant for a large number of aquatic systems worldwide.

Recycling dominates over N 2 fixation
The nifH transcripts in our study indicated active diazotrophic communities in the water column and sediments.The highest N 2 fixation activity was measured in the surface water layer, where also the cyanobacterial biomass was higher, likely explained by enhanced growth due to photosynthesis ( Castenholz 2015 ).Yet, we could detect relatively high N 2 fixation rates in the dark bottom water.More work is required to understand how long these cyanobacteria can fix N 2 in dark conditions under high energetic cost.Due to gas vesicle and mixing events, heterocystous cyanobacteria can migrate upward to the surface layer ( Walsby et al., 1995 ), where they can gain energy during photosynthesis.
Measured δ 15 N (2.21 ‰ ) from living material and NH 4 + uptake suggest that the pelagic communities of bacteria plus phytoplankton use a combined source of N for their growth, since the particulate organic nitrogen from N 2 alone would have a lower signal (ca.−2 ‰ ) ( Voss et al., 1997 ).Our results show that N 2 fixation rates only partly (13%) meet nutritional needs for the microbial community ( Fig. 5 ).However, up to 50% of this fixed N could be released as NH 4 + ( Adam et al., 2016 ).It seems likely that recycling of organic matter to NH 4 + supplies the major amount of required N, which has previously been reported for other systems (see e.g.Présing et al., 2001 ), and recently also for the coastal Baltic Sea ( Klawonn et al., 2019 ).Measured high NH 4 + uptake rates and low standing concentration ( < 1 μM) further indicate high turnover rates of NH 4 + in the water column.We suggest that under N limitation, the lagoon efficiently fixes and recycles N via coupled N 2 fixation, organic matter oxidation, and assimilative processes.Thus, this recycling allows the conspicuous presence of non-fixing cyanobacteria (52% of total phytoplankton biomass) and other phytoplankton (23% biomass, based on microscopy counts; Data S2), which typically thrive in periods of higher inorganic N availability ( Paerl and Otten 2013 ).
The study site is representative of 55% of the lagoon's surface water ( Zilius et al., 2014 ), which allows for upscaling processes in a wider context including N inputs, removal, and recycling.The physical conditions are generally stable in the summer that lead to stratified waters, but strong wind events are observed in autumn and winter that cause resuspension ( M ėžin ė et al., 2019 ) making our extrapolation representative only for the summer period.In this area, pelagic N 2 fixation delivers 45% of the total N input (N 2 fixation, DIN release, and TN riverine input) during summer with intensive cyanobacterial blooms (July-September), assuming that the main tributary delivers 10,075 t of N ( Zilius et al., 2018 ).Considering the denitrification rates, only 11% of N input (1133 t) is removed from the lagoon ecosystem.This is supported by a calculated low denitrification efficiency (22%) sensu Eyre and Ferguson (2009) .Thus, the largest part of N is recycled in the system, including via DNRA in the water column.

Conclusions
We have shown that hypereutrophic oxygenated waters contain active DNRA and to a lesser extent denitrification, which were likely conducted in anoxic microniches related to the large growth of filamentous algae.Microbial genera associated with detected DNRA genes were predominated by Parabacteroides and Wolinella , while denitrification genes were associated with Pseudomonas, Achromobacter, and Brucella .With DNRA being active in the processing of nitrate reduction to the more bioavailable and less mobile ammonium, this process might sustain the growth of the pelagic community even further.Our data support the presence of a complete ammonium (re)cycling within the oxic waters of the lagoon, with clear implications for the enhancement of eutrophication.Since the conditions found in the studied lagoon (e.g.chlorophyll a concentrations) are similar to those found in other hypereutrophic systems ( Bigham et al., 2009 ;Norris and Laws 2017 ;Paerl et al., 2011 ;Xing et al., 2005 ), we argue that our findings of active DNRA in oxic waters are relevant for a large number of aquatic systems worldwide.Enhanced water column DNRA might lower the capacity for these ecosystems to provide services such as water filtration, carbon sequestration, and fish supplies.

Fig. 1 .
Fig. 1.Satellite image showing the Curonian Lagoon.The image was taken by the operational land imager (OLI) onboard Landsat-8 satellite on September 18, 2014.The samples were collected at a station (red circle) in the south-central part of the lagoon, just outside the resort town Nida.LT = Lithuania, RUS = Russia, with the black line denoting the border between the two countries.(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2 .
Fig. 2. Stacked bars showing Phyla with Proteobacteria divided into classes in the studied water and sediment layers (based on metagenomic data classified against the NCBI RefSeq database).Taxonomic groups less than 0.5% relative abundance (average of all samples) are grouped as "Other".

Fig
Fig. 5.The measured rates of N-cycling processes were used to calculate the whole water column and sediment fluxes.The bacterial phyla and proteobacteria classes associated with genes for specific processes were based on the most abundant groups in the metagenomic data.The values shown represent average rates in mmol m −2 d −1 and were calculated based on a 2.5 m aphotic bottom water column (except for N 2 fixation and NH 4 + uptake that were measured both in surface and bottom water, and were therefore based on the whole 3.5 m water column).Pelagic nitrification rates were not directly measured in this study, and were estimated from production rates of NO x − from the flux experiments.Arrow thickness indicates the processes that had the highest measured rates, and the orange coloured arrows denote the novel water column nitrate reduction pathways, denitrification and DNRA.(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Table 1
Overview of the experimental activities carried out for quantifying different N-cycling pathways in the water column and sediment.Isotope additions denote target 15 N atom% ( 15 N 2 ) or concentrations ( 15 NH 4 + and 15 NO 3 -) in the incubation vessel.Substrate enrichments for the N 2 fixation experiments were estimated based on rationale provide in

Table 2
Measured in situ environmental variables in the surface (~0.5 m) and bottom water (~3 m), as well as the top 0-2 cm sediment surface.The values show the mean ± standard error based on three replicates (except biomass and POC (particulate organic carbon) -two samples).

Table 3
Measured nitrogen cycling processes from two set of incubations: 1) water column and 2) intact sediment cores with bottom water and sediment (i.e.sediment fluxes were measured).The water data has been upscaled from L −1 d −1 units to m −2 d −1 based on a 1 m surface water column and a 2.5 m deep bottom water column (see TableS2for measured μmol L −1 d −1 values).The outline of the experiments that resulted in these rates, and number of replicates, are available in Table1.