Regulation of nitrous oxide production in low oxygen waters off the coast of Peru

. Oxygen deficient zones (ODZs) are major sites of net natural nitrous oxide (N 2 O) production and 15 emissions. In order to understand changes in the magnitude of N 2 O production in response to global change, knowledge on the individual contributions of the major microbial pathways (nitrification and denitrification) to N 2 O production and their regulation is needed. In the ODZ in the coastal area off Peru, the sensitivity of N 2 O production to oxygen and organic matter was investigated using 15 N-tracer experiments in combination with qPCR and microarray analysis of total and active functional genes targeting archaeal amoA and nirS as marker genes for 20 nitrification and denitrification, respectively. Denitrification was responsible for the highest N 2 O production with a mean of 8.7 nmol L -1 d -1 but up to 118 ± 27.8 nmol L -1 d -1 just below the oxic-anoxic interface. Highest N 2 O production from ammonium oxidation (AO) of 0.16 ± 0.003 nmol L -1 d -1 occurred in the upper oxycline at O 2 concentrations of 10 - 30 µmol L -1 which coincided with highest archaeal amoA transcripts/genes. Hybrid N 2 O formation (i.e. N 2 O with one N atom from NH 4 + and the other from other substrates such as NO 2 – ) was the 25 dominant species, comprising 70 – 85 % of total produced N 2 O from NH 4 + , regardless of the ammonium oxidation rate or O 2 concentrations. Oxygen responses of N 2 O production varied with substrate, but production and yields were generally highest below 10 µmol L -1 O 2 . Particulate organic matter additions increased N 2 O production by denitrification up to 5-fold suggesting increased N 2 O production during times of high particulate organic matter export. High N 2 O yields of 2.1% from AO were measured, but the overall contribution by AO to N 2 O production 30 was still an order of magnitude lower than that of denitrification. Hence, these findings show that denitrification is the most important N 2 O production process in low oxygen conditions fueled by organic carbon supply, which implies a positive feedback of the total oceanic N 2 O sources in response to increasing oceanic deoxygenation. Quantification Kit (life technologies) was used for cDNA quantification. abundances of total and active nirS and archaeal amoA communities determined by quantitative PCR (qPCR) with assays based on SYBR Green staining according methods al. 2013, Peng et al. 2013). Primers nirS1F and nirS3R (Braker et al. 1998) were used to amplify a 260-bp 260 conserved region within the nirS gene. The nirS primers are not specific for epsilon-proteobacteria (Murdock et al. 2017), but in previous metagenomes from the ETSP epsilon-proteobacteria where below 3-4 % of the reads or not found, in very sulfidic, coastal stations Ganesh Schunck al. Kavelage al. Primers Arch-amoAF and Arch- amoAR et al. 2005) were used to quantify archaeal amoA abundance. A standard curve containing 6 serial dilutions of a plasmid with either an archaeal amoA fragment or a nirS fragment was used on respective assay plates. Assays were performed in a StratageneMx3000P qPCR cycler (Agilent Technologies) in triplicates of 20- 25ng DNA or cDNA, along with a no primer control and a no template control. Cycle thresholds (Ct values) were determined automatically and used to calculate the number of nirS or archaeal amoA copies in each reaction, which was then normalized to copies per milliliter of seawater (assuming 100% recovery). The detection limit was around 15 copies mL -1 based on the Ct values of the no template control. not change the results from Bray-Curtis Analysis. These data indicate that the extent to


Introduction
Nitrous oxide (N2O) is a potent greenhouse gas (IPCC 2013) and precursor for nitric oxide (NO) radicals, which can catalyze the destruction of ozone in the stratosphere (Crutzen 1970, Johnston 1971, and is now the single most important ozone-depleting emission (Ravishankara et al. 2009). The ocean is a significant N2O source, accounting for up to one third of all-natural emissions (IPCC 2013) and this source may increase substantially as a result of eutrophication, warming, and ocean acidification (see e.g. Capone and Hutchins 2013, Breider et al. 40 2019). Major sites of oceanic N2O emissions are regions with steep oxygen (O2) gradients (oxycline), which are usually associated with coastal upwelling regions with high primary production at the surface. There, high microbial respiratory activity during organic matter decomposition leads to the formation of anoxic waters also called oxygen deficient zones (ODZs), in which O2 may decline to functionally anoxic conditions (O2 <10 nmol kg -1 , Tiano et al. 2014). The most intense ODZs are found in the eastern tropical North Pacific (ETNP), the eastern 45 tropical South Pacific (ETSP) and the northwestern Indian Ocean (Arabian Sea). The anoxic waters are surrounded by large volumes of hypoxic waters (below 20 µmol L -1 O2) which are strong net N2O sources (Codispoti 2010;Babbin et al. 2015). Latest estimates of global, marine N2O fluxes ) agree well with the 3.8 Tg N y -1 (1. 8 -9.4 Tg N y -1 ) reported by the IPCC (2013), but have large variability in the resolution on the regional scale, particularly along coasts where N2O cycling is more dynamic. The expansion of 50 ODZs is predicted in global change scenarios and has already been documented in recent decades (Stramma et al. 2008, Schmidtko et al. 2017. This might lead to further intensification of marine N2O emissions, which will constitute a positive feedback on global warming (Battaglia and Joos, 2018). However, decreasing N2O emissions have also been predicted based on reduced nitrification rates due to reduced primary and export production (Martinez-Rey et al. 2015, Landolfi et al. 2017) and ocean acidification (Beman et al. 2011, Breider et al. 2019.

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The parametrization of N2O production and consumption in global ocean models is crucial for realistic future predictions, and therefore better understanding of their controlling mechanisms is needed. N2O can be produced by both nitrification and denitrification. Nitrification is a two-step process, comprising the oxidation of ammonia (NH3) to nitrite (NO2 -) (ammonia oxidation, AO) and NO2to nitrate (NO3 -) (NO2oxidation). The relative contributions to AO by autotrophic ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) have been inferred, based on the abundance of the archaeal and bacterial amoA genes, which encode subunit A of the key enzyme ammonia monooxygenase (e.g. (Francis et al. 2005, Mincer et al. 2007, Santoro et al. 2010, Wuchter et al. 2006). These studies consistently revealed the dominance of archaeal over bacterial ammonia oxidizers, particularly in marine settings (Francis et al. 2005, Wuchter et al. 2006, Newell et al. 2011. In oxic conditions, AO by AOB and AOA forms N2O as a by-product (Anderson 1964;Vajrala et al. 2013;Stein 2019) and AOA contribute significantly to N2O production in the ocean Löscher et al. 2012). While hydroxylamine (NH2OH) was long thought to be the only obligate intermediate in AO, NO has recently been identified as an obligate intermediate for AOB (Caranto and Lancaster 2017) and presumably AOA (Carini et al. 2018). Both intermediates are present in and around ODZs and correlated with nitrification activity (Lutterbeck et al. 2018, Korth et al. 2019. Specific details about the precursor of NO to form N2O in AOA remains 70 controversial. Stiegelmeier et al. (2014) concluded that NO is derived from NO2reduction to form N2O, while Carini et al. (2018) hypothesized that NO is derived from NH2OH oxidation, which can then form N2O. A hybrid N2O production mechanism in AOA has been suggested, where NO from NO2reacts with NH2OH from NH4 + , which is thought to be abiotic, i.e., non-enzymatic (Koslovski et al. 2016). Abiotic N2O production, also known as chemodenitrification, from intermediates like NH2OH, NO or NO2can occur under acidic conditions (Frame et boundaries (Kock et al. 2016). N2O accumulation during denitrification is mostly linked to O2 inhibiting the N2O reductase, but other factors such as sulfide accumulation , pH (Blum et al. 2018), high NO3or NO2concentrations , or copper limitation (Granger and Ward 2003) may also be relevant. Recent studies contrast the view of nitrification vs. denitrification as the main N2O source in ODZs (Nicholls et al. 2007, Babbin et al. 2015, Ji et al. 2015a, Yang et al. 2017. They show the importance of denitrification in N2O production 100 in the ETNP from model outputs (Babbin et al. 2015) and in the ETSP from tracer incubation experiments , Ji et al. 2015a, based on natural abundance isotopes in N2O (Casciotti et al. 2018) or from water mass analysis of apparent N2O production (N2O) and O2 utilization (AOU) (Carrasco et al. 2017). 45,46 N2O production from the addition of 15 N-labeled NH4 + , NO2and/or NO3revealed nitrification as a source of N2O within the oxic-anoxic interface, but overall denitrification dominated N2O production with higher rates at the 105 interface and in anoxic waters (Ji et al. 2015a(Ji et al. , 2018a. Denitrification is driven by organic matter exported from the photic zone and fuels blooms of denitrifiers leading to high N2 production , Jayakumar et al. 2009, Babbin et al. 2014. Denitrification to N2 is enhanced by organic matter additions and the degree of stimulation varies with quality and quantity of organic matter (Babbin et al. 2014). Because N2O is an intermediate in denitrification, we hypothesize that its production should also be stimulated by organic matter, possibly leading 110 to episodic and variable N2O fluxes.
N2O concentration profiles around ODZs appear to be at steady state (Babbin et al. 2015), but are much more variable in regions of intense coastal upwelling where high N2O emissions can occur (Arévalo-Martínez et al. 2015). The contributions of and controls on the two N2O production pathways under different conditions of O2 and organic matter supply, are not well understood and may contribute to this variability. Hence, the goal of this 115 study is to understand the factors regulating N2O production around ODZs in order to better constrain how future changes in O2 concentration and carbon export will impact production, distribution and emissions of oceanic N2O.
Our goal was to determine the impact of O2 and particulate organic matter on N2O production rates using 15 N tracer experiments in combination with qPCR and functional gene microarray analysis of the marker genes, nirS for denitrification and amoA for AO by archaea, to assess how the abundance and structure of the community impacts 120 N2O production rates from the different pathways. 15 N-labelled NH4 + and NO2was used to trace the production of single-( 45 N2O) and double-labelled ( 46 N2O) N2O to investigate the importance of hybrid N2O production during AO along an O2 gradient.

Sampling sites, sample collection and incubation experiments
Seawater was collected from 9 stations in the upwelling area off the coast of Peru in June 2017 onboard R/V Meteor ( Figure 1). Water samples were collected from 10 L Niskin bottles on a rosette with a conductivitytemperature-depth profiler (CTD, seabird electronics 9plus system). In-situ O2 concentrations (detection limit 2 µmol L -1 O2), temperature, pressure and salinity were recorded during each CTD cast. NO2and NO3 -130 concentrations were measured on board by standard spectrophotometric methods (Hydes et al. 2010) using a QuAAtro autoanalyzer (SEAL Analytical GmbH, Germany). NH4 + concentrations were determined fluorometrically using ortho-phthaldialdehyde according to Holmes et al. (1999). For N2O, bubble-free triplicate samples were immediately sealed with butyl stoppers and aluminum crimps and fixed with 50 μL of saturated mercuric chloride (HgCl2). A 10 mL He headspace was created and after an equilibration period of at least 2 hours 135 the headspace sample was measured with a gas chromatograph equipped with an electron capture detector (GC/ECD) according to Kock et al. (2016). The detection limit for N2O concentration is 2nM  0.7nM. At all experimental depths nucleic acid samples were collected by filtering up to 5 L of seawater onto 0.2 µm pore size Sterivex-GP capsule filters (Millipore, Inc., Bedford, MA, USA). Immediately after collection filters were flash frozen in liquid nitrogen and kept at -80°C until extraction.

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Three different experiments were carried out at coastal stations, continental slope and offshore stations.
Experiments 1 and 2 aimed to investigate the influence of O2 concentration along a natural and artificial O2 gradient and experiment 3 targeted the impact of large particles (>50 µm) on N2O production. Serum bottles were filled from the Niskin bottles with Tygon tubing after overflowing three times to minimize O2 contamination. Bottles were sealed bubble free with grey butyl rubber septa (National Scientific) and crimped with aluminum seals 145 immediately after filling. The grey butyl rubber septa were boiled in MilliQ for 30min to degas and kept in a He atmosphere until usage. A 3 mL helium (He) headspace was created and samples from anoxic (O2 < below detection) water depths were He purged for 15min. He purging removed dissolved oxygen contamination which is likely introduced during sampling and the headspace prevents possible oxygen leakage from the rubber seals (DeBrabandere et al. 2012). Natural abundance 2000 ppb N2O carrier gas (1000 µL in He) was injected to trap the 150 produced labeled N2O and to ensure a sufficient mass for isotope analysis. For all experiments, 15 N-NO2 -, 15 N-NO3 -, and 15 N-NH4 + tracer ( 15 N/( 14 N+ 15 N) = 99 atom-%) were injected into five bottles each from the same depth to a final concentration of 0.5 μmol L -1 , except for the NO3incubations where 2 µmol L -1 final concentration were anticipated to obtain 10 % label of the NO3pool. The fraction labeled of the substrate pools was 0.76 -0.99 for NH4 + , 0.11 -0.99 for NO2 -, 0.055 -0.11 for NO3 -. In the 15 N-NO3treatment, 14 N-NO2was added to trap the label in the product pool for NO3reduction rates and in the 15 N-NH4 + treatment, 14 N-NO2was added to a final concentration of 0.5 µmol L -1 to trap the label in the product pool for AO rates.
For the O2 manipulation experiments, all serum bottles were He purged and after the addition of different amounts of air saturated site water a final headspace volume of 3 mL was achieved. Site water from the incubation depth was shaken and exposed to air to reach full O2 saturation. Then 0, 0.2, 0.5, 2 and 5mL O2 saturated seawater 160 was added into serum bottles and to reach final measured O2 concentration of 0 ± 0.18 µM, 0.4 ± 0.24 μM, 1.6 ± 0.12 μM, 5.2 ± 0.96 μM and 11.7 ± 1.09 μM in seawater. For the 15 N-NO3incubations two more O2 treatments with 21.5 ± 2.8 and 30.2 ± 3.35 µM O2 were carried out to extend the range of a previous study in which N2O production from 15 NO3did not decrease in the presence of up to 7 µM O2 .The O2 concentration was monitored with an O2 sensor spot in one serum bottle per treatment using an O2 probe and meter (FireSting,

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PyroScience, Aachen, Germany; Figure S1). The sensor spots are highly sensitive in the nanomolar range and prepared according to Larsen et al. (2016).
For the organic matter additions, concentrated particles > 50 µm from 3 different depths were collected with a Challenger stand-alone pump system (SAPS in situ pumps, Liu et al. 2005), autoclaved and He purged.
200µL of POC solution were added to each serum bottle before 15 N-NO3or 14 N-NO2tracer injection. The final 170 particle concentrations and C/N ratios varied between 0.18 -1.37 µM C and 8.1 -15.4, respectively ( Table 2).
The concentration and C/N ratio of PON and POC of the stock solutions were analyzed by mass spectrometry using GV Isoprime mass spectrometer.
A set of five bottles was incubated per time course. One bottle was sacrificed at t0, two bottles at t1 and two at t2 to determine a single rate. Total incubation times were adjusted to prevent bottle effects, which become 175 significant after 20 h based on respiration rate measurements (Tiano et al. 2014). Hence, experiments lasted from 12 hours (at the shelf stations) to 24 hours (at the slope stations). Incubation was terminated by adding 0.1 mL saturated mercuric chloride (HgCl2). All samples were stored at room temperature in the dark and shipped back to the lab.

Isotope measurement and rate determination
The total N2O in each incubation bottle was extracted with a purge-trap system according to Ji et al. (2015). Briefly, serum bottles were flushed with He for 35 min (38 ml min -1 ), N2O was trapped by liquid nitrogen, H2O removed with an ethanol trap, a Nafion® trap and a Mg(ClO4)2 trap and CO2 removed with an Ascarite CO2-Adsorbance column and afterwards mass 44, 45, 46 and isotope ratios 45/44, 46/44 were detected with a GC-

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IRMS system (Delta V Plus, Thermo). Every two to three samples, a 20 mL glass vial with a known amount of N2O gas was measured to calibrate for the N2O concentration (linear correlation between N2O peak size and concentration, r 2 = 0.99  (Sigman et al. 2001, Weigand et al. 2016. Measured and expected values were compared based on a binominal distribution of 15 N and 14 N within the N2O pool (Frame et al. 2017).
After N2O analysis, samples incubated with 15 NH4 + and 15 NO3were analyzed for 15 NO2to determine rates of NH4 + oxidation and NO3reduction, respectively. The individual sample size, adjusted to contain 20 nmol azide in acetic acid (McIlvin and Altabet, 2005) and the nitrogen isotope ratio was measured on a Delta V Plus (Thermo).
For each serum bottle, total N2O concentration (moles) and 45 N2O/ 44 N2O and 46 N2O/ 44 N2O ratios were converted to moles of 44 N2O, 45 N2O and 46 N2O. N2O production rates were calculated from the slope of the increase 200 in mass 44, 45 and 46 over time ( Figure S2). To quantify the pathways for N2O production, rates were calculated based on the equations for N2 production for denitrification and anammox (Thamdrup and Dalsgaard, 2002). In incubations with 15 NH4 + and unlabeled NO2 -, it is assumed that AO produces 46 N2O from two labeled NH4 + (equation 1) and some 45 N2O-labeled N2O based on binomial distribution (equation 2). If more single labelled N2O is produced than expected (equation 2 and 3), a hybrid formation of one nitrogen atom from NH4 + and one from 205 NO2 -(equation 4) is assumed to be taking place as found in archaeal ammonia oxidizers (Kozlowski et al. 2016).
In incubations with 15 NO2 -, we assume that 46 N2O comes from nitrifier-denitrification or denitrification, which cannot be distinguished (equation 1). Hence, any production of 45 N2O not attributed to denitrification stems from hybrid N2O formation by archaeal nitrifiers (equation 4). In incubations with 15 NO3 -, denitrification produces 46 N2O and was the only process considered and hence was calculated based on equation (1 where fN is the fraction of 15 N in the substrate pool (NH4 + , NO2or NO3 -), which is assumed to be constant over the incubation time. Hence, changing fN due to any other concurrent N-consumption or production process during the incubation is neglected. Nevertheless, the assumption of constant fN has implications that may affect the results. There is a potential for overestimating hybrid N2O production in 15 NO2incubations by 5% in samples 220 with high NO3reduction rates. But in incubations from anoxic depths with high NO3reduction rates, no hybrid N2O production was found at all. For example, accounting for a decrease in fN of the NO3pool by active NO2oxidation, the process with highest rates (Sun et al. 2017), had an effect of only ± 0.2% on the final rate estimate.  Lam et al. 2009) is assumed to produce 0.02 nM 15 NH4 + during the 24 h incubations and all of it is oxidized (maximum N2O production from AO 0.16 nM d -1 , this study) its contribution to 46 N2O production is likely minor and within the standard error of the high N2O production rates from NO3. Hence an overestimation of the N2O production rates is unlikely. The same applies in incubations with 15 N-NO2when DNRA produces 15 NH4 + , additional 46 N2O can be produced with a hybrid 230 mechanism by AO. In 15 NO2incubations with high starting fN (>0.7) the production of 14 NO2by NO3reduction (which decreases fN) leads to an underestimation by up to 9%, whereas in incubations with a low fN (<0.3) the effect is less (up to 3% underestimation of N2O production rates). In 15 NH4 + incubations (fN >0.9), maximum DNRA rate would lead to an underestimation of 3.5%. Slope of 46 N2O and slope of 45 N2O represent the 46 N2O and 45 N2O production rates, which were tested for significance based on a linear regression (n=5, student t-test, R 2 > 0.80, p<0.05). Linear regressions that were not significantly different from zero were reported as 0. The error for each N2O production rate was calculated as the standard error of the slope. Detection limits were 0.002 nmol L -1 d -1 for N2O production from AO and 0.1 nmol L -1 d -1 for N2O production from denitrification based on the average measured standard error for rates . The curve-fitting tool of Sigma Plot was used for the O2 sensitivity experiments. A one-way ANOVA was performed on the N2O production rates to determine if rates 240 were significantly different between POM treatments.
The rates (R) of NH4 + oxidation to NO2and NO3reduction to NO2were calculated based on the slope of the linear regression of 15 NO2enrichment over time (n = 5) (equation 6).
where fN is the fraction of 15 N in the substrate pool (NH4 + or NO3 -).

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Yield (%) of N2O production during NH4 + oxidation was defined as the ratio of the production rates (equation 7).
Yields of N2O production during denitrification were calculated based on the fact that N2O is not a side product during NO3reduction to NO2but rather the next intermediate during denitrification (equation 8).
All rates, yields and errors are reported in Table S3.

Molecular Analysis -qPCR, Microarrays
DNA and RNA were extracted using the DNA/RNA ALLPrep Mini Kit (Qiagen) followed by immediate cDNA Synthesis from purified and DNA-cleaned RNA using a SuperScript III First Strand Synthesis System 255 (Invitrogen). The PicoGreen dsDNA Quantification Kit (Invitrogen) was used for DNA quantification and Quant-iT OliGreen ssDNA Quantification Kit (life technologies) was used for cDNA quantification.
The abundances of total and active nirS and archaeal amoA communities were determined by quantitative PCR (qPCR) with assays based on SYBR Green staining according to methods described previously . Primers nirS1F and nirS3R (Braker et al. 1998) were used to amplify a 260-bp 260 conserved region within the nirS gene. The nirS primers are not specific for epsilon-proteobacteria (Murdock et al. 2017), but in previous metagenomes from the ETSP epsilon-proteobacteria where below 3-4 % of the reads or not found, except in very sulfidic, coastal stations (Stewart et al. 2011, Wright et al. 2012, Ganesh et al. 2012, Schunck et al. 2013, Kavelage et al. 2015. Primers Arch-amoAF and Arch-amoAR (Francis et al. 2005) were used to quantify archaeal amoA abundance. A standard curve containing 6 serial dilutions of a plasmid with either 265 an archaeal amoA fragment or a nirS fragment was used on respective assay plates. Assays were performed in a StratageneMx3000P qPCR cycler (Agilent Technologies) in triplicates of 20-25ng DNA or cDNA, along with a no primer control and a no template control. Cycle thresholds (Ct values) were determined automatically and used to calculate the number of nirS or archaeal amoA copies in each reaction, which was then normalized to copies per milliliter of seawater (assuming 100% recovery). The detection limit was around 15 copies mL -1 based on the Ct Microarray experiments were carried out to describe the community composition of the total and active nirS and archaeal amoA groups using the DNA and cDNA qPCR products. Pooled qPCR triplicates were purified and cleaned using the QIAquick PCR Purification Kit (Qiagen). Microarray targets were prepared according to Ward and Bouskill (2011). Briefly, dUaa was incorporated into DNA and cDNA targets during linear amplification 275 with random octomers and a Klenow polymerse using the BioPrime kit (Invitrogen) and then labeled with Cy3, purified and quantified. Each probe is a 90-mer oligonucleotide consisting of a 70-mer archetype sequence combined with a 20-mer reference oligo as a control region bound to the glass slide. Each archetype probe represents a group of related sequences with 87 ± 3% sequence identity of the 70-mer sequence. Microarray targets were hybridized in duplicates on a microarray slide, washed and scanned using a laser scanner 4200 (Agilent

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Technologies) and analyzed with GenePix Pro 6.0. The resulting fluorescence ratio (FR) of each archaeal amoA or nirS probe was divided by the FR of the maximum archaeal amoA or nirS FR on the same microarray to calculate the normalized FR (nFR). nFR represents the relative abundance of each archetype and was used for further analyses.

Data analysis
Spearman Rank correlation was performed from all N2O production rates, AO and NO3reduction rates, environmental variables, nirS and archael amoA gene and transcript abundance as well as the 20 most abundant archetypes of total and active nirS and amoA using R. Only significant values (p<0.05) are shown. Archetype 295 abundance (nFR) data were square-root transformed and beta-diversity was calculated with the Bray-Curtis coefficient. Alpha diversity of active and total nirS and amoA communities was estimated by calculating the Shannon diversity index using PRIMER6. Bray-Curtis dissimilarities were used to perform a Mantel test to determine significant differences between active and total communities of nirS and amoA using R (Version 3.0.2, package "vegan" (Oksanen et al., 2019). Canonical Correspondence Analysis (CCA) (Legendre & Legendre 2012) 300 was used to visualize differences in community composition dependent upon environmental conditions using the software PAST (Hammer et al. 2001). Before CCA analysis, a forward selection (Borcard et al. 1992) of the parameters that described the environmental and biological variables likely to explain the most significant part of the changes in the archetypes was performed.
The make.lefse command in MOTHUR was used to create a linear discriminant analysis (LDA) effect 305 size (LEfSe) (Segata et al. 2011) input file from the MOTHUR shared file. This was followed by a LEfSe (http://huttenhower.sph.harvard.edu/lefse/) to test for discriminatory archetypes between O2 levels. With a normalized relative abundance matrix, LEfSe uses the Kruskal-Wallis rank sum test to detect features with significantly different abundances between assigned archetypes in the different O2 levels and performs an LDA to estimate the effect size of each feature. A significant alpha of 0.05 and an effect size threshold of 2 were used for 3. Results

Hydrographic conditions
The upwelling system off Peru is a hot spot for N2O emissions (Arévalo-Martínez et al. 2015) with most intense upwelling in austral winter but maximum chlorophyll during December to March (Chavez and Messié, 2009;Messié and Chavez, 2015 Figure S3). Generally,
amoA gene and transcript number decreased in the ODZ to 1000 -6500 gene copies mL -1 and 20 -250 transcript copies mL -1 . The profiles of nirS gene and transcript abundance were similar to each other (Figure 3(II) d, e) with highest abundance in the ODZ up to 1 x 10 6 copies mL -1 and 2.9 x 10 5 copies mL -1 , respectively. Denitrifier nirS genes and transcripts peaked in the anoxic layer and were significantly correlated with N2O production from NO2 -355 but not from NO3 -. Archaeal amoA gene and transcript abundances were significantly correlated with AO and, N2O production from AO ( Figure S5). N2O concentrations did not correlate with any of the measured variables ( Figure S5).
However, this response was only significant in sample S11 (Figure 4d, e). There was no significant response to O2 concentration of N2O production from NO3 -. O2 did not inhibit N2O production from NO3up to 23 µmol L -1 ( Figure 4f).

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The proportion of hybrid N2O produced during AO, i.e., the formation of N2O from one 15 NH4 + and one N compound (excluding NH4 + ) such as NO2 -, NH2OH or NO, was consistently between 70 -85 % across different O2 concentrations for manipulated and natural O2 concentrations (Figure 5a, c). Hybrid formation during N2O production from NO2varied between 0 and 95% along the natural O2 gradient (Figure 5b). In manipulated O2 treatments hybrid formation from NO2did not change across different O2 treatments but with respect to the 375 original depth, 0% in sample S11 which originated from 145 m of station 892 or 78% in sample S19 from 120m of station 894 ( Figure 5d).
Highest N2O yields during AO (over 1%) occurred between 1.4 and 2 µmol O2 L -1 , and decreased at both higher and lower O2 concentrations ( Figure 6a). However, only the increase in yield from nmol O2 to 1.4 -2 µmol L -1 O2 was significant (t-test, p<0.05) and the following decrease in yield was not (t-test, p>0.05). In the 380 manipulated O2 treatment of sample S19 ( Figure 6c) the same significant pattern was observed, whereas in S11 highest yield was found at 12 µmol L -1 O2. N2O yield during NO3reduction to NO2decreased to zero at 8.4 µmol L -1 O2 along the natural O2 gradient (Figure 6b) while no significant response occurred in the manipulated O2 treatments (Figure 6d). There, NO3reduction was decreasing with increasing O2 but N2O production was steady with increasing O2 leading to high yields between 38.8 ± 9 % -91.2 ± 47 % at 23 µmol L -1 O2.

Effect of large particulate organic matter on N2O production
The autoclaving of the concentrated POM solution liberated NH4 + from the particles, reducing the N/C ratio of the particles compared to non-autoclaved particles ( Table 2). The highest NH4 + accumulation is found in samples with the largest difference in N/C ratios between autoclaved and non-autoclaved particles ( Table 2, 904-zone), the quality (N/C ratio) or the quantity of the organic matter on the magnitude of the increase. Only samples S20 and S17 were not stimulated by particle addition and N2O production from denitrification did not significantly differ from the control (Figure 7b).

Diversity and community composition of total and active nirS and amoA assemblages and its correlation with environmental parameters
nFR values from functional gene microarrays were used to describe the nitrifier and denitrifier community composition of AOA and nirS assemblages, respectively. nFR was averaged from duplicate microarrays, which replicated well (R 2 = 0.89 -0.99). Alpha-diversities of nirS and archaeal amoA were not statistically different for total and active communities (students t-test, p > 0.05), but were overall lower for RNA (3.2 ± 0.3) than DNA (3.8 ± 0.4) ( Table S1). Principle Coordinate Analysis of Bray-Curtis similarity for each probe group on the microarray indicated that the community structure of archaeal amoA genes was significantly different from that of archaeal 405 amoA transcripts whereas community structure of nirS genes and transcripts did not differ significantly ( Figure   S4). To identify which archetypes were important in explaining differences in community structure of key nitrification and denitrification genes, we identified archetypes that accounted for more than 1% of the total fluorescence for their probe set and that were significantly different with respect to ambient O2 using a lefse analysis (Table S2). Furthermore, we used CCA to test whether the community composition, or even single 410 archetypes, could explain the N2O production rates.
The nFR distribution showed greater variability in the active (cDNA) AOA community than in the total community (DNA) among depths, stations and O2 concentrations (Figure 8a, b). Archetypes over 1% made up between 76% (DNA) -83% (cDNA) of the amoA assemblage and only 61% (DNA) -68% (cDNA) of the nirS assemblage. The 4 most abundant AOA archetypes AOA55, AOA3, AOA21 and AOA32 made up 20% -65% of 415 the total and active community (Figure 8a, b). DNA of archetypes AOA55 and AOA79, both related to uncultured AOA in soils, significantly correlated with in situ NH4 + concentrations ( Figure S5). DNA and cDNA from AOA3 and AOA83 were significantly enriched in oxic waters and AOA7, closely related with crenarchaeote SCGC AAA288-M23 isolated from station ALOHA near Hawaii (Swan et al. 2011), was significantly enriched in anoxic and hypoxic waters for DNA and cDNA respectively (Table S2). All other archetypes did not vary with O2 levels.

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DNA of AOA 3, closely related to Nitrosopelagicus brevis (CN25), identified as the only archetype to be significantly correlated with N2O production and yield from AO ( Figure S5).
The total and active denitrifier communities were dominated by Nir7, derived from an uncultured clone from the ODZ in the ETSP (Lam et al. 2009), and Nir7 was significantly more enriched in the active community ( Figure 8c, d). DNA from ODZ depths of the eddy, S15 (907, 130 m) and S17 (912, 90 m), diverged most obviously 425 from the rest and from each other (Figure 8c, d). Interestingly, these two samples were not divergent among the active nirS community (Figure 8c, d; Figure S4). DNA of Nir35, belonging to the Flavobacteriaceae derived from coastal waters of the Arabian Sea (Goréguès et al., 2004), was most abundant (12.3 %) at the eddy edge (S15) as opposed to the eddy center (S17) where nir167, representing Anammox sequences from Peru, was most abundant (12.0 %). Interestingly, Nir4 and Nir14, among the top 5 abundant archetypes, were significantly enriched in oxic 430 water masses (Table S2). nFR signal of nir166, belonging to Scalindua, and Nir23 were among the top 5 abundant archetypes and significantly enriched in anoxic depths.
CCA is a direct gradient analysis, where the gradient in environmental variables is known a priori and the archetypes are considered to be a response to this gradient. Composition from total and active AOA community sample with lowest NO3concentration (8 µmol L -1 ), highest temperature and salinity of the data set and the DNA is positively related with O2 and driven by AOA55, AOA32 and AOA79. RNA of S17 (912, 90 m) clusters with AOA70. AOA55 was abundant and its distribution is driven by O2 and NH4 + ( Figure S5).
CCA clustered the denitrifier community DNA into one main group with a few exceptions (Figure S6 c).
Two surface samples (S16, S18) clustered separate and were positively correlated with Nir4 and Nir14 and O2.

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Two anoxic samples from the eddy core (S17) and eddy edge (S15) clustered separate with S17 being driven by 3 nirS archetypes -Nir54, Nir10 and Nir167 and S15 by Nir23, Nir35 and Nir133 (Figure S6 c). Total and active nirS community composition did not differ as a function of O2. Although, composition of active and total nirS communities were not significantly different, the active community clustered slightly differently. For nirS RNA, surface and oxycline samples (S16 and S10) grouped together and were correlated positively with O2, temperature 445 and salinity, whereas the anoxic eddy samples did not differ from the rest ( Figure S6d). N2O production from NO2significantly correlated with nirS gene and transcript abundance but both reductive N2O production pathways were not linked with a single dominant nirS archetype ( Figure S5 production rates correlations with physical and chemical parameters were not consistent. On one hand, oxidative N2O production from NH4 + positively correlated with temperature, salinity, oxygen and negatively with depth and 460 PO4 3concentration. On the other hand, reductive N2O production from NO2positively correlated with NH4 + and NO2concentrations, but negatively with NO3concentrations ( Figure S5), suggesting when NO3is abundant, denitrifiers are less likely to use NO2for N2O production during denitrification. Both oxidative (AO) and reductive (NO2and NO3reduction) N cycling processes produced N2O with differential effects of O2 on them. Measured N2O production rates were always highest from NO3 -, followed by NO2and NH4 + , which is consistent with 465 previous studies that showed denitrification as a dominant N2O source in Peruvian coastal waters harboring an ODZ (Ji et al. 2015a, Casciotti et al. 2018. A low contribution of AO to N2O production in low O2 waters is in line with a previous study in this area estimating N2O production based on isotopomer measurements combined with a 3-D Reaction-Advection-Diffusion Box model (Bourbonnais et al. 2017). The low percentage that AO contributed to total N2O production was between 0.5 -6%, with one exception in the shallowest sample S5 with 470 30 µmol L -1 O2 where AO contributed 86% to total N2O production. We found strong positive effects of decreasing O2 concentration and increasing particulate matter concentrations on N2O production in the upper oxycline. and 50 nmol L -1 d -1 (Farìas et al. 2009), were obtained using N2O isotope and isotopomer approaches, which provide time and process integrated signals. Hence, the deviation of maximum rates can be explained by 1) the different approaches and 2) the sampling of the core of the eddy. N2 production measurements (from anammox and denitrification) were not performed in this study, but should be in future studies to account for potential 485 artefacts by co-occurring NO3reduction processes. Here, it cannot be determined whether the eddy only stimulated N2O production but not N2 production from denitrification (i.e. increasing the N2O/N2 yield) or if the eddy also increased complete denitrification to N2 by 10 times compared to stations outside of the eddy. Considering that at some depths only incomplete denitrification (also known as "stop-and go" denitrification) to N2O is at work, it would not be surprising that N2O production can reach the same order of magnitude as N2 production from 490 complete denitrification. Aged eddies also show lower N2O concentration maxima at the upper oxycline (Arévalo-Martínez et al. 2016), which was not the case in this study where a young eddy was just about to detach from the coast. In fact, the eddy stations show the highest N2O peak in the upper oxycline within this data set. Eddies and their age imprint mesoscale patchiness and heterogeneity in biogeochemical cycling. It appears that young eddies close to the coast with high N2O concentrations and high N2O production rates have a great potential for high N2O 495 emissions compared to aged eddies or waters surrounding eddies.

Effect of O2 on reductive and oxidative N2O production
The relationship between O2 concentrations and N2O production by nitrification and denitrification is very complex in ODZs. While poorly constrained, the reported O2 threshold level (1.7 µmol L -1 O2) for reductive 500 N2O production is lower ) than the reported O2 threshold level (8 µmol L -1 ) for N2O consumption in the ETSP (Cornejo and Farías 2012). Nevertheless, the suboxic zone between 1 -8 µmol L -1 O2 carries high N2O concentrations indicating higher N2O production than consumption. In this study, we focused on this suboxic water masses above the ODZ and determined bulk kinetics of O2 sensitivity in batch experiments, which reflect the metabolism of the microbial community. The effect of O2 on N2O production differed between 505 natural O2 concentrations with varying communities vs. manipulated O2 concentrations within a community. While N2O production from NO2and NO3decreased exponentially along the natural O2 gradient, it did not always decrease for the manipulated O2 treatments. Unchanged N2O production with higher O2 levels in NO3treatments showed that at least a portion of the community can respond very differently to a sudden increase in O2 than predicted from natural O2 gradients with communities acclimated to a certain O2 concentration. In the ETNP, this 510 pattern has been observed before (Ji et al. 2018a) but the mechanism behind it is unknown. Different responses of N2O production rates to O2 between in situ assemblages and incubated samples were not unexpected because different rates at different depths were likely not only due to O2 differences but also other factors such as different organic matter fluxes and different amounts and types of N2O producers at different depths. In addition, sampling with Niskin bottles and purging can induce stress responses (Stewart et al. 2012) and shift the richness and structure like H2S during purging is another potential artefact. However, it is unlikely as measurable H2S concentrations have mostly been found at very shallow coastal stations (< 100 m deep) (Callbeck et al. 2018), not the environment of this study. On the contrary, high abundances (up to 12%) of sulfur oxidizing gamma proteobacteria, like SUP05 can be found in eddy-transported offshore waters where they actively contributed to autotrophic denitrification 520 (Calbeck et al. 2018). In this study, we cannot differentiate between autotrophic or organotrophic denitrification, but a contribution of autotrophic denitrification in the eddy center is likely. Off the Chilean coast, active N2O production by denitrification was found at up to 50 µmol L -1 O2 (Farías et al. 2009). These results reinforce prior studies showing that distinct steps of multistep metabolic pathways, such as denitrification, can differ in O2 sensitivity , Bristow et al. 2016a, 2016b. In various bacterial strains and natural 525 communities, the NO3reductase enzyme (Nar) which catalyzes the first step in denitrification, is reportedly the most O2 tolerant, followed by the more O2 sensitive steps of NO2reduction (Nir) and N2O reduction (Körner und Zumft 1989, McKenney et al. 1994, Kalvelage et al. 2011. The fact that N2O production is insensitive to manipulated O2 in the NO3treatments and not in the NO2treatments is evidence that it is not due to inhibition of the reduction of N2O to N2 at higher O2 because then both treatments would look similar. It further indicates that 530 high N2O production from NO3in high oxygen treatments is unlikely an effect of anoxic microniches. While anoxic microniches in batch incubations can never be fully ruled out, there is no reason why they should systematically change N2O production in NO3from NO2incubations at the same oxygen treatment. We suggest a stimulation of incomplete denitrification, which leads to the accumulation of N2O in the serum bottles rather than a stimulation of overall denitrification rates to N2. While NO3reduction was inhibited by higher O2 concentrations, N2O production was not, leading to very high yields of N2O production per NO2produced. We hypothesize that there is a direct channeling of reduced NO3to N2O without exchange of an internal NO2pool with the surrounding NO2 -. Long turnover times for NO2have been inferred from  18 O of NO2 -, which was fully equilibrated with water in the offshore waters (Bourbonnais et al. 2015) and more dynamic in the coastal waters (Hu et al. 2016) supporting our hypothesis. If NO2does not exchange, our rate estimates for NO3reduction based 540 on produced 15 N-NO2are underestimated resulting in high yields. A low NO2exchange rate has been shown before (Ji et al. 2018b). Based on the assumption that all labelled N2O from 15 NO3has gone through the NO2pool, we include the NO2pool into calculating fN. In 15 NO3incubations the enrichment of the substrate pool was low (fN = 0.05 -0.1) and including NO2resulted in an underestimation of no more than 5 % depending on the in situ NO2concentration, and thus does not explain the high rates.

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One N2O producing process not considered in this study is fungal denitrification, but it deserves mentioning because in soils and coastal sediments it contributes substantially to N2O production (Wankel et al. 2017, Shoun et al. 2012. With 15 N-labelling experiments it is not possible to distinguish between bacterial and fungal denitrification. In ODZs, marine fungal communities show a wide diversity (Jebaraj et al. 2012) and a high adaptive capability is suggested (Richards et al. 2012). Most fungal denitrifiers lack the capability to reduce N2O 550 to N2, hence all NO3reduction results in N2O production (Richards et al. 2012). In a culture study, the fungus, Fusarium oxysporum, needed O2 exposure before it started to denitrify (Zhou et al. 2001). To what extent marine fungi play a role in denitrification in open ocean ODZs and their O2 sensitivity remains to be investigated.
N2O production from NH4 + did not decrease exponentially with increasing O2 as shown previously for the ETSP (Qin et al. 2017, Ji et al. 2018a). N2O production rather increased with increasing in situ oxygen and had an optimum between 1.4 -6 µmol O2 L -1 in manipulated O2 treatments. A similar optimum curve was observed in cultures of the marine AOA Nitrosopumilus maritimus, where N2O production reached maxima at O2 concentrations between 2 -10 µmol L -1 (Hink et al. 2017a). Furthermore, N2O production by N.
viennensis and N. maritimus was not affected by O2 but instead by the rate of AO (Stieglmeier et al. 2014, Hink et al. 2017a). To find out if this is the case in our study, we plotted AO rate against N2O production from NH4 + for 560 natural and manipulated O2 samples ( Figure S7). The resulting significant linear fit (R 2 = 0.75, p<0.0001) implies that the rate of AO was the main driver for the intensity of N2O production from NH4 + and oxygen had a secondary effect.
Discrepancies in estimates of the O2 sensitivity of N2O production by nitrification and denitrification are likely due to a combination of taxonomic variation as well as differences in sensitivity among the various enzymes 565 of each pathway.

N2O yields and hybrid N2O formation from NH4 +
N2O yields of AO were 0.15 -2.07 % (N2O-N mol/ NO2 --N mol = 1.5 x 10 -3 -20.7 x 10 -3 ) which are at the higher end of most marine AOA culture or field studies (Hink et al 2017b, Qin et al. 2017, 570 Stieglmeier et al. 2014. Only in 2015 off the coast of Peru a higher maximum yield of 3.14% was reported (Ji et al. 2018a). While high N2O yields are usually found in low O2 waters (<6 µmol L -1 ), in this study AO had also high yields at higher oxygen concentrations, 0.9 % at 30 µmol L -1 O2 compared to previous studies (0.06% at > 50 µmol L -1 Ji et al. 2018a).. In near coastal regions, higher N2O yield at higher O2 concentrations expands the overall water volume where N2O production by AO contributes to high N2O concentration, which is more likely to be 575 emitted to the atmosphere.
Insights into the production mechanism of N2O is gained from hybrid-N2O formation based on differentiating between production of single ( 45 N2O) and double ( 46 N2O) -labelled N2O. If the production of 45 N2O is higher than what is expected based on the binomial distribution, then an additional source of 14 N can be assumed.
In 15 NH4 + incubations, as potential 14 N substrates (besides NH4 + ), NO2 -, NH2OH and HNO are most likely. Even 580 though, in situ NH4 + is below detection in almost all water depths (fN > 0.9), there remains the potential for 15 NH4 + pool dilution by remineralization and DNRA during the incubation. Studies have shown fast turnover for NH4 + , despite low NH4 + concentrations (e.g.. Klawonn et al. 2019). Even if hybrid N2O production rates are overestimated, it remains the major N2O production mechanisms from AO in this study. In future 15 N -labelling studies, co-occurrence of NH4 + production by DNRA or degradation should be measured along with N2O 585 production to account for pool dilution. Whether hybrid N2O formation is purely abiotic, a mix of biotic and abiotic or biotic reactions, is debatable (Stieglmeier et al. 2014, Kozlowski et al. 2016, Carini et al. 2018, Lancaster et al. 2018, Stein 2019. Hybrid N2O production from NO2was variable with depth and oxygen, which can be explained by the different proportions of nitrifier versus denitrifier NO2reduction to N2O. For example, in the interface sample S19 (892, 144 m, 3.69 µmol L -1 NO2 -) N2O production from NO2 -(0.72  0.19 nmol L -1 d -1 ) was 20 times 590 higher than from NH4 + (0.033  0.0004 nmol L -1 d -1 ) and no hybrid N2O formation from NO2was found ( Figure   5d). There, the major N2O production mechanism seems to be by denitrification rather than nitrification, and even if there was a hybrid production we were not able to detect it within the given error ranges. Hybrid N2O production from NH4 + was independent of the rate at which N2O production took place and independent of the O2 concentration and varied little (70 -86% of total N2O production) during AO. Therefore, a purely abiotic reaction outside and without the vicinity of the cell can be excluded because concentrations of potential substrates for abiotic N2O production like Fe(II), Mn, NO, NH2OH vary with depth and O2 concentration (Zhu-Barker et al. 2015, Kondo and Moffet 2015, Lutterbeck et al. 2018, Korth et al. 2019. Additionally, at four depths the potential for abiotic N2O production in 15 NO2addition experiments showed variations with depth and no significant impact of HgCl2 fixation ( Figure S9). Hence, any 14 N which is integrated into N2O to produce a hybrid/single labelled N2O has to be passively or actively taken up by the cell first ( Figure 9). There, it reacts with an intermediate product ( 15 NO or 15 NH2OH) of AO inside the cell. With this set of experiments, it is not possible to disentangle if hybrid production is based on an enzymatic reaction or an abiotic reaction inside the cell. Caranto et al. (2017) showed that the main substrate of NH2OH oxidation is NO, making NO an obligate intermediate of AO in AOB and suggested the existence of an unknown enzyme that catalyzes NO oxidation to NO2 -(further details also in 605 Stein 2019). If NO is an obligate intermediate of AO in AOA (Lancaster et al. 2018), a constant rate of spontaneous abiotic or enzymatic N2O production is very likely, which always depends on the amount of NO produced in the first place. This could explain why we consistently find ~80% hybrid formation at high as well as at low AO rates.
Further studies are needed to investigate the full mechanisms.

Effect of particulate organic matter on N2O production
A positive stimulation of N2O production from denitrification by particulate organic matter was found, indicating carbon limitation of denitrification in the ETSP. The experimental POM amendments simulated a low POC export flux and represented a flux that happens over 2 -15 days, assuming an export flux of 3.8 mmol m -2 d -1 and that 8% of the total POC pool is >50 µm (Boyd et al. 1999, Martin et al. 1987, Haskell et al. 2015. We are 615 aware that the POM collected by in situ pumps is a mix of suspended and sinking particles and hence the flux should be considered a rough estimate. However, the particle size (>50 μm) used in the experiments is indictive of sinking particles. The stimulation of N2 production from denitrification by particulate organic matter has been shown in ODZs before (Ward et al. 2008, Chang et al. 2014, with quantity and quality of organic matter influencing the degree of stimulation (Babbin et al. 2014). In this study, amendments of POM at different 620 degradation stages resulted in variable magnitudes of N2O production from NO2and NO3with no significant correlations between magnitude of the rates and amount, origin or quality of POM added. The processing of the particles has reduced the original N/C ratios of POM from the mixed layer more than of the POM from the ODZ, resulting in similar N/C ratios of particles from different depths. This could be one possible explanation for a lack of correlation of N2O production with origin of the POM. Furthermore, N2 production was not quantified and 625 hence it is not possible to evaluate potential relationships between overall N loss and POM additions or whether the partitioning between N2O and N2 varied among treatments and depths. N2O/N2 production ratio can vary from 0 -100% , Bonaglia et al. 2016. A temporary accumulation of N2O before further reduction to N2 in the incubations can be ruled out as N2O accumulated linearly over time. The only station, where POM additions did not stimulate N2O production was in the center of the young eddy (912-S17). There, the highest rates 630 of N2O production from NO3 -(118 nmol L -1 d -1 ) were found, indicating that denitrification was not carbon limited. This is consistent with previous studies on anti-cyclonic eddies, which have shown high N loss in the core of a young eddy that weakened with aging of the eddy (Stramma et al. 2013, Bourbonnais et al. 2015. A direct link between the freshly produced POM fueling N loss on one hand, and decreased N loss with aging due to POM export out of the eddy on the other hand, was proposed (Bourbonnais et al. 2015, Löscher et al. 635 2016. In this study, the young eddy is a hot spot for N2O production. Besides carbon availability as electron donor for denitrification, copper limitation and high NO3availability may play a role. Copper limitation has been argued to lead to N2O accumulation by inhibiting the copper-dependent N2O reductase (Granger andWard 2003, Bonaglia et al. 2016), but it was not a limiting factor for denitrification in the three major ODZs previously (Ward et al. 2008). Water sampling from Niskin bottles in our study was not trace metal clean and could be contaminated with Copper from the sampling system, making a limitation of trace metals in our incubations unlikely. However, OM fueled N2O production may have become limited by the availability of copper during the incubation.
High NO3availability increases N2O production from denitrification in salt marshes (Ji et al. 2015b) and in soils (Weier et al. 1993), systems which are generally not carbon limited. Also, at the oxic -anoxic interface of Chesapeake Bay, the ratio of NO2to NO3concentration was identified as a driver for high N2O production from NO3 - (Ji et al. 2018b). This study also found higher N2O production rates from NO3than NO2 -, which linearly correlated with the ratio of NO2 -/NO3concentrations ( Figure S8). Intracellularly produced NO2does not seem to exchange with the surrounding pool, but ambient NO3is directly converted to N2O, a process identified as "NO2shunting" in N2 production studies , Chang et al. 2014. POM as electron donor is an 650 important regulator for reductive N2O production.

Effect of abundance of total and active community composition on N2O production rates
The abundances of both amoA and nirS genes found in the ETSP are similar to those reported in earlier studies in the ETSP , Ji et al. 2015a. The amoA gene abundances were 655 similar to those reported for the coastal ETSP by Lam et al. (2009), but nirS abundances reported here were higher than the nirS abundances in that study, probably due to the use of different PCR primers. The community composition of AOA did not significantly differ along the O2 gradient as shown previously ), but a significant correlation between archaeal amoA transcript abundance and N2O production was shown in this study.
The combination of qPCR and microarray analysis offered a great advantage to relate the total abundances to the 660 production rates and additionally link particular community components to biogeochemical activities. To determine whether a particular archetype drives the correlation of N2O production by AO, a Bray-Curtis dissimilarity matrix revealed archetype AOA3 related to Nitrosopelagicus brevis (CN25) to be significantly correlated with the N2O production by AO. This clade is abundant in the surface ocean and typically found in high abundances in the lower euphotic zone (Santoro et al. , 2015. With the demonstration of high abundances of 665 AOA3 coincident with high nitrification rates and high N2O production rates, we suggest that Nitrosopelagicus brevis related AOA likely play an important role in N2O production in near surface waters in the Eastern Tropical South Pacific. The lack of significant correlation between community composition or single members of the community and reductive N2O production is consistent with the fact that nirS is not the enzyme directly synthesizing N2O and 670 nirS communities are sources as well as sinks for N2O. Taxonomic analysis of the nirS gene and transcripts suggested that there is high taxonomic diversity among the denitrifiers, which is likely linked to a high variability of the total denitrification gene assembly (including nos, nor, nir). In particular the abundance and diversity of nitric oxide reductase (nor), the enzyme directly synthesizing N2O, would be of interest, but it is present in nitrifiers and denitrifiers (Casciotti and Ward 2005) and one goal of this study was to differentiate among N2O produced by 675 nitrifiers and denitrifers. However, nirS gene and transcript abundance correlated with N2O production from NO2making it a possible indicator for one part of reductive N2O production. It is also worth noting that anammox related nirS genes and transcripts (nirS 166, 167) contribute up to 12% of the total copy numbers putting a wrinkle on nirS abundance as marker gene for denitrifiers only. The subtraction of the anammox related nirS genes from total copy numbers did not change the results from Bray-Curtis Analysis. These data indicate that the extent to which gene or transcript abundance patterns or community composition of marker genes of processes can be used as proxies for process rate measurements is variable, likely due to complex factors, including the relative dominance of different community members, the modular nature of denitrification, differences in the level of metabolic regulation (transcriptional, translational, and enzymatic), and the range of environmental conditions being observed.

Summary and conclusion
In this study we used a combined approach of 15 N tracer techniques and molecular techniques in order to investigate the factors that control N2O production within the upper oxycline of the ODZ in the ETSP. Our results suggest that denitrification is a major N2O source along the oxic -anoxic interface of the upper oxycline. Highest

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N2O production rates from NO2and NO3were found at or below the oxic-anoxic interface, whereas highest N2O production from AO was slightly shallower in the oxycline. Overall, in situ O2 threshold below 8 µmol L -1 favored NO3and NO2reduction to N2O and high N2O yields from AO up to 2.2%. A different pattern was observed for the community response to increasing oxygen, with highest N2O production from NH4 + and NO2between 1.4 -6 µmol L -1 O2 and high N2O production from NO3even at O2 concentrations up to 22 µmol L -1 . This study highlights 695 the diversity of N2O production regulation and the need to conduct further experiments where single community members can be better constrained. Our experiments provide the first insights into N2O regulation by particulate organic matter in the ETSP with particles greatly enhancing N2O production (up to 5fold). Furthermore, the significant positive correlation between Nitrosopelagicus brevis (CN25) and N2O produced from AO could indicate its importance in N2O production and points out the great value of combining biogeochemical rate 700 measurements with molecular analysis to investigate multifaceted N2O cycling. This study shows that short term oxygen increase can lead to high N2O production even from denitrification and extends the existing O2 thresholds for high reductive N2O production up to 22 µmol L -1 O2. Together with high N2O yields from AO up to O2 levels of 30 µmol L -1 , an expansion of low oxygenated waters around ODZs predicted for the future can significantly increase marine N2O production.

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Regardless of which processes are responsible for N2O production in the ODZ, high N2O production at the oxic-anoxic interface of the upper oxycline sustains high N2O concentration peaks with a potential for intense N2O emission to the atmosphere during upwelling events. An average total N2O production rate of 3.1 nmol N2O L -1 d -1 in a 50 m thick suboxic layer with 0 -20 µmol L -1 O2 leads to an annual N2O efflux of 0.5 Tg N y -1 in the into the short-term response of N2O production to oxygen and particles. With the further parametrization of POM export as a driver for N2O production from denitrification, models may be able to better predict N2O emissions in highly productive coastal upwelling regions and to evaluate how fluxes might change with changing stratification and deoxygenation.  (10) Tables   Table 1: Overview of characteristics of samples. bd -below detection limit of Winkler method and seabird sensor 1150 (2 µmol L -1 ), x -analysis includes qPCR and microarray with qPCR products, x* -only qPCR, no microarray     is N2O production along natural O2 gradient from all stations. Figure 4 (b, c) are additionally zoomed in to oxygen concentrations below 5µmol L -1 .Lower panel (d-f) is N2O production in manipulated O2 experiments with water from oxic -anoxic interface from slope station 892 (S11, 0 µmol L -1 O2, 145m) and shelf station 894 (S19, 0 µmol 1175 L -1 , 120 m). Note different scale for N2O production rates from NH4 + . Vertical error bars represent ± Standard error (n = 5 per time course). Horizontal error bars represent ± Standard error of measured O2 over the time of incubations (n = 6).