The suspended small-particle layer in the oxygen-poor Black Sea: a proxy for delineating the effective N₂-yielding section

The shallower oxygen-poor water masses of the ocean confine a majority of the microbial communities that can produce up to 90 % of oceanic N₂. This effective N₂-yielding section encloses a suspended small-particle layer, inferred from particle backscattering (b_bp) measurements. It is thus hypothesized that this layer (hereafter, the b_bp-layer) is linked to microbial communities involved in N₂ yielding such as nitrate-reducing SAR11 as well as sulfur-oxidizing, anammox, and denitrifying bacteria – a hypothesis yet to be evaluated. Here, data collected by three BGC-Argo floats deployed in the Black Sea are used to investigate the origin of this b_bp-layer. To this end, we evaluate how the key drivers of N₂-yielding bacteria dynamics impact the vertical distribution of b_bp and the thickness of the b_bp-layer. In conjunction with published data on N₂ excess, our results suggest that the b_bp-layer is at least partially composed of the bacteria driving N₂ yielding for three main reasons: (1) strong correlations are recorded between b_bp and nitrate; (2) the top location of the b_bp-layer is driven by the ventilation of oxygen-rich subsurface waters, while its thickness is modulated by the amount of nitrate available to produce N₂; and (3) the maxima of both b_bp and N₂ excess coincide at the same isopycnals where bacteria involved in N₂ yielding coexist. We thus advance that b_bp and O₂ can be exploited as a combined proxy to delineate the N₂-yielding section of the Black Sea. This proxy can potentially contribute to refining delineation of the effective N₂-yielding section of oxygen-deficient zones via data from the growing BGC-Argo float network.

1 Introduction

Oxygen-poor water masses (O₂ < 3 µM) host the microbial communities that produce between 20 % and 40 % of oceanic N₂ mainly via heterotrophic denitrification and anaerobic oxidation of ammonium (Gruber and Sarmiento, 1997; DeVries et al., 2013; Ward, 2013). The shallower oxygen-poor water masses (∼ 50–200 m) are the most effective N₂-producing section because this is where the microbial communities that condition the process mainly develop and generate up to 90 % of the N₂ (Ward et al., 2009; Dalsgaard et al., 2012; Babbin et al., 2014). These microbial communities include nitrate-reducing SAR11 and anammox, denitrifying, and sulfur-oxidizing bacteria (e.g., Canfield et al., 2010; Ulloa et al., 2012; Ward, 2013; Tsementzi et al., 2016; Callbeck et al., 2018). It is thus important to unravel the biogeochemical parameters that trigger the accumulation of such bacteria in the ocean's oxygen-poor water masses. This information is crucial for understanding and quantifying how bacterial biomass and related N₂-yielding bacteria can respond to the ongoing expansion of oceanic regions with low oxygen (Keeling and Garcia, 2002; Stramma et al., 2008; Helm et al., 2011; Schmidtko et al., 2017). Ultimately, greater accuracy in this domain can contribute to improving mechanistic predictions on how such expansion will affect the oceans' role in driving the Earth's climate by sequestering atmospheric carbon dioxide (e.g., Oschlies et al., 2018).

In oxygen-poor water masses, the biogeochemical factors that can affect the abundance of denitrifying and anammox bacteria are the levels of O₂, organic matter (OM), nitrate (NO${}_{\mathrm{3}}^{-}$), ammonium (NH${}_{\mathrm{4}}^{+}$), and hydrogen sulfide (H₂S) (Murray et al., 1995; Ward et al., 2008; Dalsgaard et al., 2014; Bristow et al., 2016). Therefore, to elucidate what triggers the confinement of such bacteria, we need to investigate how the above biogeochemical factors drive their vertical distribution, with high temporal and vertical resolution. To this end, we should develop multidisciplinary approaches that allow us to permanently monitor the full range of biogeochemical variables of interest in oxygen-poor water masses.

Optical proxies of tiny particles can be applied as an alternative approach to assess the vertical distribution of N₂-yielding microbial communities in oxygen-poor water masses (Naqvi et al., 1993). For instance, nitrate-reducing SAR11 and anammox, denitrifying, and sulfur-oxidizing bacteria are found as free-living bacteria (0.2–2 µm) and can be associated with small suspended (> 2–30 µm) and large sinking (> 30 µm) particles (Fuchsman et al., 2011, 2012a, 2017; Ganesh et al., 2014, 2015). Therefore, particle backscattering (b_bp), a proxy for particles in the ∼ 0.2–20 µm size range (Stramski et al., 1999, 2004; Organelli et al., 2018), can serve to detect the presence of these free-living bacteria and those associated with small suspended particles.

Time series of b_bp acquired by biogeochemical Argo (BGC-Argo) floats highlight the presence of a permanent layer of suspended small particles in shallower oxygen-poor water masses (b_bp-layer) (Whitmire et al., 2009; Wojtasiewicz et al., 2020). It has been hypothesized that this b_bp-layer is linked to N₂-yielding microbial communities such as nitrate-reducing SAR11 and denitrifying, anammox, and sulfur-oxidizing bacteria. However, this hypothesis has not yet been clearly demonstrated. To address this, the first step is to evaluate (1) potential correlations between the biogeochemical factors that control the presence of the b_bp-layer and such arrays of bacteria (O₂, NO${}_{\mathrm{3}}^{-}$, OM, H₂S; Murray et al., 1995; Ward et al., 2008; Fuchsman et al., 2011; Ulloa et al., 2012; Dalsgaard et al., 2014; Bristow et al., 2016) and (2) the possible relationship between the b_bp-layer and N₂ produced by microbial communities.

This first step is thus essential for identifying the origin of the b_bp-layer and, ultimately, determining whether BGC-Argo observations of b_bp can be implemented to delineate the oxygen-poor water masses where such bacteria are confined. The Black Sea appears a suitable area for probing into the origin of the b_bp-layer in low-oxygen waters in this way. It is indeed a semi-enclosed basin with permanently low O₂ levels where N₂ production and related nitrate-reducing SAR11 and denitrifying and anammox bacteria are mainly confined within a well-defined oxygen-poor zone (Kuypers et al., 2003; Konovalov et al., 2005; Kirkpatrick et al., 2012). In addition, a permanent b_bp-layer is a typical characteristic of this region, which is linked to such microbial communities and inorganic particles (Stanev et al., 2017, 2018; see details in Sect. 2).

The goal of our study is therefore to investigate the origin of the b_bp-layer in the oxygen-poor waters of the Black Sea using data collected by BGC-Argo floats. More specifically, we aim to evaluate, within the oxygen-poor zone, how (1) two of the main factors (O₂ and NO${}_{\mathrm{3}}^{-}$) that drive the dynamics of denitrifying and anammox bacteria impact the location and thickness of the b_bp-layer, (2) NO${}_{\mathrm{3}}^{-}$ controls the vertical distribution of b_bp within this layer, (3) temperature drives the formation of the b_bp-layer and consumption rates of NO${}_{\mathrm{3}}^{-}$, and (4) particle content inferred from b_bp and N₂ produced by microbial communities can be at least qualitatively correlated. Ultimately, our findings allow us to infer that b_bp can potentially be used to detect the presence of the microbial communities that drive N₂ production in oxygen-poor water masses – including nitrate-reducing SAR11 and sulfur-oxidizing, denitrifying, and anammox bacteria.

2 Background nature of the small particles contributing to the b_bp-layer and their links with N₂ yielding

The oxygen-poor water masses of the Black Sea are characterized by a permanent layer of suspended small particles constituted of organic and inorganic particles (Murray et al., 1995; Kuypers et al., 2003; Konovalov et al., 2005; Kirkpatrick et al., 2012). In the oxygen-poor (O₂ < 3 µM) section with detectable NO${}_{\mathrm{3}}^{-}$ and undetectable H₂S levels, organic particles are mainly linked to microbial communities involved in the production of N₂, and these include nitrate-reducing SAR11 and anammox, denitrifying, and sulfur-oxidizing bacteria (Kuypers et al., 2003; Lam et al., 2007; Yakushev et al., 2007; Fuchsman et al., 2011; Kirkpatrick et al., 2012). The first group listed, SAR11, provides NO${}_{\mathrm{2}}^{-}$ for N₂ yielding and makes the largest contribution (20 %–60 %) to N₂-yielding bacteria biomass (Fuchsman et al., 2011, 2017; Tsementzi et al., 2016). Meanwhile, the second and third groups of bacteria make a smaller contribution to microbial biomass (∼10 %; e.g., Fuchsman et al., 2011, 2017) but dominate N₂ yielding via anammox (${\mathrm{NO}}_{\mathrm{2}}^{-}+{\mathrm{NH}}_{\mathrm{4}}^{+}\to {\mathrm{N}}_{\mathrm{2}}+\mathrm{2}{\mathrm{H}}_{\mathrm{2}}\mathrm{O}$) and heterotrophic denitrification (${\mathrm{NO}}_{\mathrm{3}}^{-}\to {\mathrm{NO}}_{\mathrm{2}}^{-}\to {\mathrm{N}}_{\mathrm{2}}\mathrm{O}\to {\mathrm{N}}_{\mathrm{2}}$) (Murray et al., 2005; Kirkpatrick et al., 2012; DeVries et al., 2013; Ward, 2013). The last group can potentially produce N₂ via autotrophic denitrification (e.g., $\mathrm{3}{\mathrm{H}}_{\mathrm{2}}\mathrm{S}+\mathrm{4}{\mathrm{NO}}_{\mathrm{3}}^{-}+\mathrm{6}{\mathrm{OH}}^{-}\to \mathrm{3}{\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}+\mathrm{2}{\mathrm{N}}_{\mathrm{2}}+\mathrm{6}{\mathrm{H}}_{\mathrm{2}}\mathrm{O}$; Sorokin, 2002; Konovalov et al., 2003; Yakushev et al., 2007). Finally, Epsilonproteobacteria are the major chemoautotrophic bacteria that form organic particles in the sulfidic zone (e.g., oxygen-poor section with detectable sulfide levels (> 0.3 µM) but undetectable NO${}_{\mathrm{3}}^{-}$; Coban-Yildiz et al., 2006; Yilmaz et al., 2006; Grote et al., 2008; Canfield and Thamdrup, 2009; Glaubitz et al., 2010; Ediger et al., 2019). However, they can also be involved in the production of N₂ and linked formation of organic particles in the oxygen-poor section with detectable levels of sulfide and NO${}_{\mathrm{3}}^{-}$ (see Fig. 1, e.g., Epsilonproteobacteria Sulfurimonas acting as an autotrophic denitrifier; Glaubitz et al., 2010; Fuchsman et al., 2012b; Kirkpatrick et al., 2018).

The inorganic component is mainly due to sinking particles of manganese oxides (Mn, III, IV) that are formed due to the oxidation of dissolved Mn (II, III) pumped from the sulfidic zone (e.g., $\mathrm{2}{\mathrm{Mn}}^{\mathrm{2}+}\left(\mathrm{l}\right)+{\mathrm{O}}_{\mathrm{2}}+\mathrm{2}{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\to \mathrm{2}{\mathrm{MnO}}_{\mathrm{2}}\left(\mathrm{s}\right)+\mathrm{4}{\mathrm{H}}^{+}$; Konovalov et al., 2003; Clement et al., 2009; Dellwig et al., 2010). Ultimately, sinking particles of manganese oxides are dissolved back to Mn (II, III), mainly via chemosynthetic bacteria that drive sulfur reduction (e.g., ${\mathrm{HS}}^{-}+{\mathrm{MnO}}_{\mathrm{2}}\left(\mathrm{s}\right)+\mathrm{3}{\mathrm{H}}^{+}\to {\mathrm{S}}^{\mathrm{0}}+{\mathrm{Mn}}^{\mathrm{2}+}\left(\mathrm{l}\right)+\mathrm{2}{\mathrm{H}}_{\mathrm{2}}\mathrm{O}$; Jørgensen et al., 1991; Konovalov et al., 2003; Johnson, 2006; Yakushev et al., 2007; Fuschman et al., 2011; Stanev et al., 2018). Overall, these arrays of bacteria mediate the reactions described above by using electron acceptors according to the theoretical “electron tower” (e.g., ${\mathrm{O}}_{\mathrm{2}}\to {\mathrm{NO}}_{\mathrm{3}}^{-}\to \mathrm{Mn}\left(\mathrm{IV}\right)\to \mathrm{Fe}\left(\mathrm{III}\right)\to {\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$; Stumm and Morgan, 1970; Murray et al., 1995; Canfield and Thamdrup, 2009). Therefore, the vertical distributions of NO${}_{\mathrm{3}}^{-}$, N₂ excess, and content of small particles are driven by the reactions that occur in the chemical zones of oxygen-poor water masses (e.g., nitrogenous and manganous zones, which correspond to the sections where NO${}_{\mathrm{3}}^{-}$ and Mn(IV), respectively, are predominantly used as electron acceptors; Murray et al., 1995; Konovalov et al., 2003; Yakushev et al., 2007; Canfield and Thamdrup, 2009; see also Sect. 4.2 and 4.3).

3 Methods

3.1 Bio-optical and physicochemical data measured by BGC-Argo floats

We used data collected by three BGC-Argo floats that profiled at a temporal resolution of 5–10 d in the first 1000 m depth of the Black Sea from December 2013 to July 2019 (Fig. 1). These floats – allocated World Meteorological Organization (WMO) numbers 6900807, 6901866, and 7900591 – collected 239, 301, and 518 vertical profiles, respectively. BGC-Argo float 6901866 was equipped with four sensors: (1) a SBE-41 CP conductivity–T–depth sensor (Sea-Bird Scientific), (2) an Aanderaa 4330 optode (serial number 1411, O₂ range 0–1000 µM, with an accuracy of 1.5 %), (3) a WETLabs ECO Triplet Puck, and (4) a Satlantic Submersible Ultraviolet Nitrate Analyzer (SUNA). These sensors measured upward profiles of (1) temperature (T), conductivity, and depth, (2) dissolved oxygen (O₂), (3) chlorophyll fluorescence, total optical backscattering (particles + pure seawater) at 700 nm, and fluorescence by colored dissolved organic matter, and (4) nitrate (NO${}_{\mathrm{3}}^{-}$; detection limit of ∼0.5 µM with T ∕ salinity correction processing) and bisulfide (HS⁻, detection limit of ∼0.5 µM; Stanev et al., 2018). Floats 6900807 and 7900591 were equipped with only the first three sensors.

Raw data of fluorescence and total backscattering were converted into chlorophyll concentration (chl) and particle backscattering (b_bp) following standard protocols, respectively (Schmechtig et al., 2014, 2015). Spike signals in vertical profiles of chl and b_bp and due to particle aggregates were removed by using a median filter with a window size of three data points (Briggs et al., 2011). NO${}_{\mathrm{3}}^{-}$, HS⁻, and O₂ data were processed following BGC-Argo protocols (Bittig and Körtzinger, 2015; Johnson et al., 2018; Thierry et al., 2018). Sampling regions covered by the three floats encompassed most of the Black Sea area (Fig. 1 and Appendix A). However, we only used data collected during periods without a clear injection of small particles derived from the productive layer and Bosporus plume (e.g., advection of water masses, Stanev et al., 2017). This restriction allowed us to focus on the in situ 1D processes driving local formation of the b_bp-layer, with minimal interference from any possible external sources of small particles.

We only describe the time series of data collected by float 6901866 because this was the only float carrying a NO${}_{\mathrm{3}}^{-}$/HS⁻ sensor. Data acquired by floats 6900807 and 7900591 are described in Appendix A and nevertheless used as complementary data to those of float 6901866 to corroborate (1) qualitative correlations between O₂ levels and the location of the b_bp-layer and (2) consistency in the location of the b_bp maximum within the b_bp-layer.

3.2 Defining the oxygen-poor zone, mixed-layer depth, and productive layer

We used O₂ and NO${}_{\mathrm{3}}^{-}$ to, respectively, define the top and bottom isopycnals of the oxygen-poor zone where denitrifying and anammox bacteria are expected to be found. To set the top isopycnal, we applied an O₂ threshold of ∼ 3 µM because denitrifying and anammox bacteria seem to tolerate O₂ concentrations beneath this threshold (Jensen et al., 2008; Dalsgaard et al., 2014; Babbin et al., 2014). The bottom isopycnal was defined as the deepest isopycnal at which NO${}_{\mathrm{3}}^{-}$ was detected by the SUNA sensor (0.23±0.32 µM). NO${}_{\mathrm{3}}^{-}$ was used to set this isopycnal because heterotrophic denitrification and subsequent reactions cannot occur without NO${}_{\mathrm{3}}^{-}$ (Lam et al., 2009; Bristow et al., 2017). HS⁻ was not used to delimit the bottom of this zone because the maximum concentration of HS⁻ that denitrifying and anammox bacteria tolerate is not well established (Murray et al., 1995; Kirkpatrick et al., 2012; see also Sect. 4.1).

Mixed-layer depth (MLD) was computed as the depth at which density differed from 0.03 kg m⁻³ with respect to the density recorded at 1 m depth (de Boyer Montégut et al., 2004). We used chl to define the productive layer where living phytoplankton were present and producing particulate organic carbon. The base of this layer was set as the depth at which chl decreased below 0.25 mg m⁻³. This depth was used only as a reference to highlight the periods when surface-derived small particles were clearly injected into the oxygen-poor zone.

3.3 Complementary cruise data on N₂ excess and NO${}_{\mathrm{3}}^{-}$

Published data on N₂ : Ar ratios and NO${}_{\mathrm{3}}^{-}$ collected in the southwest of the Black Sea in March 2005 (Fuchsman et al., 2008, 2019) were exploited to complement discussion of our results. N₂ produced by anaerobic microbial communities (N₂ excess, µM) was estimated from N₂ : Ar ratios and argon concentrations at atmospheric saturation (Hamme and Emerson, 2004). N₂ excess data were used to (1) describe the oxygen-poor zone where N₂ is expected to be predominantly produced and (2) highlight qualitative correlations between N₂ excess, the location of the b_bp-layer, and vertical distribution of small particles within the b_bp-layer.

Figure 1 (a) Sampling locations of float 6901866 between May 2015 and July 2019. Colored circles indicate the date (color bar) for a given profile. The white star in (a) marks the sampling site of the cruise (March 2005). The white x in (a) highlights the float location on 6 April 2016. Float profiles of (b) log (O₂), (c) NO${}_{\mathrm{3}}^{-}$, (d) log (b_bp), and (e) HS⁻ collected on 24 November 2018.

4 Results and discussion

4.1 Description of the oxygen-poor zone

The top and bottom of the oxygen-poor zone are located around isopycnals (mean ± standard deviation) 15.79±0.23 and 16.30±0.09 kg m⁻³, respectively. The two isopycnals therefore delimit the oxygen-poor water masses where nitrate-reducing SAR11 and denitrifying, anammox, and sulfur-oxidizing bacteria are expected to be found (zone hereafter called the OP_DA, Fig. 2; Kuypers et al., 2003; Lam et al., 2007; Yakushev et al., 2007; Fuschman et al., 2011; Kirkpatrick et al., 2012). The top location of the OP_DA shows large spatial–temporal variability ranging between 80 and 180 m (or σ_θ between 15.5 and 15.9 kg m⁻³, Fig. 2). Similarly, the OP_DA thickness varies between 30 and 80 m, which corresponds to a σ_θ separation of ∼ 0.50 kg m⁻³. The bottom of the OP_DA is slightly sulfidic (HS${}^{-}=\mathrm{11.4}\pm \mathrm{3.53}$ µM, n=86) and deeper than suggested (e.g., σ_θ=16.20 kg m⁻³ and H₂S≤10 nM; Murray et al., 1995). However, our results coincide with the slightly sulfidic conditions of the deepest isopycnal at which anammox bacteria can still be recorded (σ_θ=16.30 kg m⁻³ and H₂S ≥ 10 µM; Kirkpatrick et al., 2012).

Figure 2 Time series of (a) salinity (S), (b) O₂, (c) NO${}_{\mathrm{3}}^{-}$, (d) log(b_bp), and (e) HS⁻. The blue lines in (a) and (b) indicate the mixed-layer depth. The red lines in (a), (b), and (d) show the base of the productive region. Isopycnals 15.79 and 16.30 kg m⁻³ describe the top and bottom of the oxygen-poor zone (OP_DA), respectively. SU, A, W, and SP stand for summer, fall, winter, and spring, respectively. The colored horizontal line in (b) indicates the sampling site for a given date (Fig. 1). The horizontal white lines in (d) are the profiles used to (1) delimit the OP_DA and (2) compute correlations between b_bp, NO${}_{\mathrm{3}}^{-}$, and T within the OP_DA.

4.2 NO${}_{\mathrm{3}}^{-}$, O₂, and MnO₂ as key drivers of the thickness and location of the suspended small-particle layer

The permanent b_bp-layer is always confined within the two isopycnals that delimit the OP_DA (Fig. 2). It follows that the thickness and top location of this layer demonstrate the same spatial and temporal variability as the one described for the OP_DA (Fig. 2 and Appendix A). This correlation indicates that variations in the thickness and top location of the b_bp-layer are partially driven, respectively, by (1) the amount of NO${}_{\mathrm{3}}^{-}$ available to produce N₂ inside the OP_DA via the set of bacteria communities involved and (2) downward ventilation of oxygen-rich subsurface waters (Fig. 2 and Appendix A).

NO${}_{\mathrm{3}}^{-}$ and O₂ are two of the key factors that modulate the presence of (1) denitrifying and anammox bacteria working in conjunction with nitrate-reducing SAR11 (Fuschman et al., 2011; Ulloa et al., 2012; Tsementezi et al., 2016; Bristow et al., 2017), and probably with chemoautotrophic ammonia-oxidizing bacteria (in this case, only with anammox, e.g., γAOB; Ward and Kilpatrick, 1991; Lam et al., 2007), and (2) sulfur-oxidizing bacteria (e.g., SUP05 and potentially Epsilonproteobacteria Sulfurimonas; Canfield et al., 2010; Glaubitz et al., 2010; Fuschman et al., 2011, 2012b; Ulloa et al., 2012; Kirkpatrick et al., 2018). Therefore, the results described above highlight that at least a fraction of the b_bp-layer should be due to this array of bacteria. This notion is supported by three main observations. Firstly, the top location of the b_bp-layer is driven by the intrusion of subsurface water masses ($S\le \mathrm{20.36}\pm \mathrm{0.18}$ psu) with O₂ concentrations above the levels tolerated by denitrifying and anammox bacteria (O₂≥3 µM, Jensen et al., 2008; Babbin et al., 2014; Fig. 2). As a result, in regions where O₂ is ventilated to deeper water masses, the top location of the b_bp-layer is also deeper. The contrary is observed when O₂ ventilation is shallower (Fig. 2 and Appendix A). Secondly, nitrate-reducing SAR11 and denitrifying, anammox, and sulfur-oxidizing bacteria reside between isopycnals 15.60 and 16.30 kg m⁻³ (Fuchsman et al., 2011, 2012a; Kirkpatrick et al., 2012), while the b_bp-layer is formed between isopycnals ∼ 15.79 and 16.30 kg m⁻³. We can thus infer coexistence of such bacteria between the coincident isopycnals where the b_bp-layer is generated. Thirdly, NO${}_{\mathrm{3}}^{-}$ declines from around isopycnal 15.79 kg m⁻³ to isopycnal 16.30 kg m⁻³ due to the expected N₂ production via the microbial communities involved (Figs. 2–3 and Kirkpatrick et al., 2012).

The ventilation of subsurface O₂ is also key in driving the depth at which MnO₂ is formed (O₂ ≤ 3–5 µM; Clement et al., 2009) and can thus contribute to setting the characteristics of the b_bp-layer via its subsequent accumulation and dissolution (Konovalov et al., 2003; Clement et al., 2009; Dellwig et al., 2010). Thus, in regions where subsurface O₂ (e.g., O₂ ≥ 3–5 µM and $S\le \mathrm{20.36}\pm \mathrm{0.18}$ psu) is ventilated to deeper water masses, both the formation of MnO₂ and top location of the b_bp-layer can be expected to be deeper and vice versa (Fig. 2). Finally, the dissolution of MnO₂ should also influence the thickness of the b_bp-layer because it occurs just beneath the maxima of the optical particles inside this layer (Konovalov et al., 2006; see the explanation in Sect. 4.3).

Overall, the qualitative evidence presented above points out that particles of MnO₂ as well as nitrate-reducing SAR11 and denitrifying, anammox, and sulfur-oxidizing bacteria appear to define the characteristics of the b_bp-layer (Johnson, 2006; Konovalov et al., 2003; Fuchsman et al., 2011, 2012b; Stanev et al., 2018). This observation leads us to argue, in the next section, that the b_bp-layer is partially composed of the main group of microbial communities involved in N₂ yielding as well as of MnO₂.

4.3 Role of the removal rate of NO${}_{\mathrm{3}}^{-}$, MnO_2, and temperature in the vertical distribution of small particles

We propose that the removal rate of NO${}_{\mathrm{3}}^{-}$ is a key driver of the vertical distribution of small particles and N₂ excess within the OP_DA. This is because the vertical profiles of small particles and of N₂ excess are qualitatively similar, and both profiles are clearly related to the rate at which NO${}_{\mathrm{3}}^{-}$ is removed from the OP_DA (Figs. 3–4). For instance, maxima of N₂ excess and b_bp coincide around isopycnal 16.11±0.11 kg m⁻³ (Fig. 3; Konovalov et al., 2005; Fuchsman et al., 2008, 2019). At this isopycnal, the mean concentration of NO${}_{\mathrm{3}}^{-}$ is 1.19±0.53 µM. We thus propose that this NO${}_{\mathrm{3}}^{-}$ threshold value splits the OP_DA into two sub-zones with distinctive biogeochemical conditions (e.g., nitrogenous and manganous zones; Canfield and Thamdrup, 2009). Ultimately, these two different sets of conditions drive the rates at which NO${}_{\mathrm{3}}^{-}$ and small particles are removed and formed within the OP_DA, respectively (Fig. 3 and explanation below).

The first sub-zone is thus located between the top of the OP_DA (σ_θ=15.79 kg m⁻³) and around isopycnal 16.11 kg m⁻³. Here, removal rates of NO${}_{\mathrm{3}}^{-}$ ($-\mathrm{0.16}\pm \mathrm{0.10}$ µM m⁻¹, Fig. 4) are likely to be boosted by (1) high content of organic matter (dissolved organic carbon = 122±9 µM, Margolin et al., 2016) and NO${}_{\mathrm{3}}^{-}$ ($\ge \mathrm{1.19}\pm \mathrm{0.53}$ µM) and (2) O₂ levels staying between a range that maintains the yielding of N₂ (0.24±0.04 µM ≥ O${}_{\mathrm{2}}\le \mathrm{2.8}\pm \mathrm{0.14}$ µM, n=100, the means of the minima and maxima of O₂, respectively, in the first sub-zone) and promotes the formation of MnO₂ (e.g., maximum of Mn(II) oxidation is at O₂ levels ∼0.2 µM; Clement et al., 2009). Consequently, the formation of biogenic and inorganic small particles (and related N₂ excess) increases from the top of the OP_DA to around isopycnal 16.11 kg m⁻³ (Fig. 3). This hypothesis is (1) in part confirmed by significant and negative power-law correlations between the suspended small-particle content and NO${}_{\mathrm{3}}^{-}$ in this sub-zone (Fig. 3) and is (2) in agreement with the progressive accumulation of MnO₂ from around isopycnal 15.8 kg m⁻³ to isopycnal 16.10 kg m⁻³ (e.g., Konovalov et al., 2006).

Figure 3 (a) Cruise profiles of NO${}_{\mathrm{3}}^{-}$ and N₂ excess, collected in March 2005 (Fuchsman et al., 2019). (b) Float profiles of NO${}_{\mathrm{3}}^{-}$, b_bp, and log (O₂) measured on 6 April 2016. Profiles in (a) and (b) were conducted in the northwest of the basin (see Fig. 1). The top and bottom of the OP_DA are described in (a) and (b) as horizontal blue and red lines, respectively. The b_bp maximum is the horizontal black line in (b). The first and second sub-zones of the OP_DA are, respectively, highlighted in (b) as blue and red squares. NO${}_{\mathrm{3}}^{-}$ vs. b_bp in (c) are the first and (d) second sub-zones of the float profile in (b). The number of data points visualized in (c) is lower than in (b) for the first sub-zone because b_bp and NO${}_{\mathrm{3}}^{-}$ are not always recorded at the same depths. (e) Frequency distributions of correlation coefficients (R, blue bars) and root mean square error (RMSE, white bars) for NO${}_{\mathrm{3}}^{-}$ vs. b_bp in the first sub-zone. (f) Same as (e) but for the second sub-zone. (g) Frequency distributions of the isopycnals at which b_bp maxima are found within the OP_DA. Dotted, dashed, and solid black lines in (g) are data collected by floats 7900591, 6901866, and 6900807, respectively. Gray bars include all data.

The second sub-zone is located between isopycnal 16.11 kg m⁻³ and the bottom of the OP_DA (σ_θ=16.30 kg m⁻³, Fig. 3). Here, NO${}_{\mathrm{3}}^{-}$ is low ($\le \mathrm{1.19}\pm \mathrm{0.53}$ µM) and O₂ is relatively constant (0.23±0.02 µM, n=2284; mean of O₂ calculated in the second sub-zone for all profiles) or lower than the minimum of O₂ recorded by this sensor (0.22±0.02 µM, n=89). These constant (or lower) levels of O₂ roughly correspond to those at which anammox and heterotrophic denitrification are inhibited by ∼50 % (0.21 and 0.81 µM, respectively; Dalsgaard et al., 2014). In addition, low levels of NO${}_{\mathrm{3}}^{-}$ necessarily promote the microbial use of Mn(IV) as an electron acceptor, ultimately dissolving the particles of MnO₂ into Mn(II) (e.g., manganous zone; Konovalov et al., 2006; Yakushev et al., 2007; Canfield and Thamdrup, 2009). As a result, this sub-zone exhibits a decline in removal rates of NO${}_{\mathrm{3}}^{-}$ ($-\mathrm{0.04}\pm \mathrm{0.01}$ µM m⁻¹, Fig. 4) along with inhibited formation of biogenic small particles and dissolution of MnO₂. Ultimately, both the content of small particles and related N₂ excess decrease from around isopycnal 16.11 kg m⁻³ to the bottom of the OP_DA (Fig. 3). These results are in agreement with (1) significant and positive exponential correlations computed between the small-particle content inferred from b_bp and NO${}_{\mathrm{3}}^{-}$ within this sub-zone (Fig. 3) and (2) the overlap of nitrogenous and manganous zones in this sub-zone because the content of MnO₂ particles and dissolved Mn(II) concurrently declines and increases just beneath isopycnal 16.11 kg m⁻³, respectively (e.g., Murray et al., 1995; Konovalov et al., 2003, 2005, 2006; Yakushev et al., 2007; Canfield and Thamdrup, 2009).

Strong-positive linear correlations are also recorded between b_bp and T in the first sub-zone of the OP_DA (Fig. 4). This is likely to indicate that the formation of small particles is sensitive to very tiny increments in T (0.003±0.001 ^∘C m⁻¹, n=133). We thus infer a tendency for the decline rates of NO${}_{\mathrm{3}}^{-}$ and related production of N₂ to increase with T. This hypothesis is at least partially supported by the significant correlation between NO${}_{\mathrm{3}}^{-}$ decline rates and T increase rates in this sub-zone (Fig. 4). Within the second sub-zone, T continues increasing while b_bp decreases, likely due to inhibition of the formation of small particles for the reasons described above (Fig. 4). These observations suggest that the production of small particles is likely to have first- and second-order covariations with NO${}_{\mathrm{3}}^{-}$ and T, respectively – a likelihood backed up by a lack of correlation between NO${}_{\mathrm{3}}^{-}$ decline rates and T increase rates in this sub-zone (Fig. 4). Finally, more information is needed to investigate the physical and/or biogeochemical processes driving the correlation between the increase rates of T and declines rates of NO${}_{\mathrm{3}}^{-}$ in the first sub-zone. This is however beyond the scope of our study.

Figure 4 Float profiles of (a) NO${}_{\mathrm{3}}^{-}$ and b_bp and (b) T and log (O₂) collected on 10 September 2017. Horizontal blue and red lines in (a) and (b) are the top and bottom of the OP_DA. The b_bp maximum is indicated in (a) and (b) as horizontal black lines. The first and second sub-zones of the OP_DA are, respectively, highlighted in (a) and (b) as blue and red squares. (c) b_bp vs. T for the first sub-zone of the profile in (b). (d) Frequency distributions of correlation coefficients (R, blue bars) and root mean square errors (RMSE, white bars) for b_bp vs. T in the first sub-zone, including data collected by the three floats. Decrease rates of NO${}_{\mathrm{3}}^{-}$ vs. increase rates of T in (e) the first sub-zone and (f) the second sub-zone.

To summarize, BGC-Argo float data combined with a proxy of N₂ production suggest that in regions without the Bosporus plume influence, the b_bp-layer systematically tracks and delineates the effective N₂-yielding section independently of (1) the biogeochemical mechanisms driving N₂ yielding and (2) the contribution that MnO₂ and other microorganisms can be expected to make to the formation of the b_bp-layer (e.g., Lam et al., 2007; Fuchsman et al., 2011, 2012a; Kirkpatrick et al., 2018). It is thus finally inferred that this b_bp-layer is at least partially composed of the predominant anaerobic microbial communities involved in the production of N₂, such as nitrate-reducing SAR11 and anammox, denitrifying, and sulfur-oxidizing bacteria. These results also suggest that N₂ production rates can be highly variable in the Black Sea because the characteristics of the b_bp-layer show large spatial–temporal variations driven by changes in NO${}_{\mathrm{3}}^{-}$ and O₂ (Figs. 2 and 4). Finally, we propose that b_bp and O₂ can be exploited as a combined proxy for defining the N₂-producing section of the oxygen-poor Black Sea. We consider that this combined proxy can delineate the top and base of this section by applying an O₂ threshold of 3.0 µM and the bottom isopycnal of the b_bp-layer, respectively. This section should thus be linked to free-living bacteria (0.2–2 µm) and those associated with small suspended particles (> 2–20 µm) as well as to small inorganic particles (0.2–20 µm).

4.4 New perspectives for studying N₂ yielding in oxygen-deficient zones

The conclusions and inferences of this study, especially those related to the origin and drivers of the b_bp-layer, primarily apply to the Black Sea. However, these findings may also have a wider application. In particular, the shallower water masses of oxygen-deficient zones (ODZs) are similarly characterized by the formation of a layer of suspended small particles that can be optically detected by b_bp and the attenuation coefficients of particles (Spinrad et al., 1989; Naqvi et al., 1993; Whitmire et al., 2009). This layer is mainly linked to N₂-yielding microbial communities because (1) its location coincides with the maxima of N₂ excess, microbial metabolic activity, and nitrite (NO${}_{\mathrm{2}}^{-}$, the intermediate product of denitrification and anammox that is mainly accumulated in the N₂-yielding section: Spinrad et al., 1989; Naqvi et al., 1991, 1993; Devon et al., 2006; Chang et al., 2010, 2012; Ulloa et al., 2012; Wojtasiewicz et al., 2020), and (2) MnO₂ is not accumulated as in the Black Sea (Martin and Knauer, 1984; Johnson et al., 1996; Lewis and Luther, 2000). Therefore, our findings suggest that highly resolved vertical profiles of b_bp and O₂ can potentially be used as a combined proxy to define the effective N₂-production section of ODZs. Such a definition can be key to better-constrained global estimates of N₂ loss rates because it can allow us to (1) accurately predict the oxygen-poor water volume where around 90 % of N₂ is produced in the ODZ core (Babbin et al., 2014) and (2) evaluate how the location and thickness of the N₂-yielding section vary due to changes in the biogeochemical factors that modulate anammox and heterotrophy denitrification.

Global estimates of N₂ production differ by 2–3-fold between studies (e.g., 50–150 Tg N yr⁻¹, Codispoti et al., 2001; Bianchi et al., 2012, 2018; DeVries et al., 2012; Wang et al., 2019). These discrepancies are caused in part by inaccurate estimations of the oxygen-poor volume of the N₂-production section. Other sources of uncertainties arise from the methods applied to estimate the amount of particulate organic matter (POM) that fuels N₂ production. For instance, POM fluxes and their subsequent attenuation rates are not well resolved because they are computed, respectively, from satellite-based primary-production algorithms and generic power-law functions (Bianchi et al., 2012, 2018; DeVries et al., 2012). POM-flux estimates based on these algorithms visibly exclude (1) POM supplied by zooplankton migration (Kiko et al., 2017; Tutasi and Escribano, 2020), (2) substantial events of POM export decoupled from primary production (Karl et al., 2012), and (3) the role of small particles derived from the physical and biological fragmentation of larger ones (Karl et al., 1988; Briggs et al., 2020), which are more efficiently remineralized by bacteria in ODZs (Cavan et al., 2017). In addition, these estimates do not take into consideration the inhibition effect that O₂ intrusions may have on N₂-yielding rates (Whitmire et al., 2009; Ulloa et al., 2012; Dalsgaard et al., 2014; Peters et al., 2016; Margolskee et al., 2019).

Overall, mechanistic predictions of N₂ production misrepresent the strong dynamics of the biogeochemical and physical processes that regulate them. Consequently, it is still debated whether the oceanic nitrogen cycle is in balance or not (Codispoti, 2007; Gruber and Galloway, 2008; DeVries et al., 2012; Jayakumar et al., 2017; Bianchi et al., 2018; Wang et al., 2019). The subsiding uncertainty points to a compelling need for alternative methods that allow accurate refinement of oceanic estimations of N₂ production.

Our study supports the proposition that robotic observations of b_bp and O₂ can be used to better delineate the N₂-yielding section at the appropriate spatial (e.g., vertical and regional) and temporal (e.g., event, seasonal, interannual) resolutions. In addition, POM fluxes and N₂ can be simultaneously quantified using the same float technology (BGC-Argo, Bishop and Wood, 2009; Dall'Olmo and Mork, 2014; Reed et al., 2018; Boyd et al., 2019; Estapa et al., 2019; Rasse and Dall'Olmo, 2019). These robotic measurements can contribute to refining global estimates of N₂ production by better constraining both the oxygen-poor section where N₂ is produced and POM fluxes that fuel its loss. Ultimately, O₂ intrusions into the N₂-yielding section can potentially be quantified by BGC-Argo floats to assess their regulatory effect on N₂ production.

5 Conclusions

Our results along with those from previous studies suggest that the b_bp-layer of the oxygen-poor Black Sea is at least partially composed of nitrate-reducing SAR11 and anammox, denitrifying, and sulfur-oxidizing bacteria. The location and thickness of this layer show strong spatial–temporal variability, mainly driven by the ventilation of oxygen-rich subsurface waters and nitrate available to generate N₂, respectively. Such variations in the characteristics of the b_bp-layer highlight that N₂-production rates can be highly variable in the Black Sea. We therefore propose that high-resolution measurements of O₂ and b_bp can potentially be exploited as a combined proxy to delineate the effective N₂-yielding section of ODZs. This proposition is in part supported by evidence that the b_bp-layer and a majority of N₂-yielding microbial communities are both confined in the shallower oxygen-poor water masses of ODZs. We however recommend investigation into the key biogeochemical drivers of the b_bp-layer for each ODZ. This information will be critical for validating the applicability of the b_bp-layer in assessing spatial–temporal changes in N₂ production.

Finally, it is evident that BGC-Argo float observations can acquire essential proxies of N₂ production and associated drivers at appropriate spatial and temporal resolutions. The development of observation–modeling synergies therefore has the potential to deliver an unprecedented view of N₂-yielding drivers if robotic observations become an integrated part of model validation. Ultimately, this approach could prove essential for reducing present uncertainties in the oceanic N₂ budget.

Appendix A: Sampling locations and time series of floats 7900591 and 6900807

Figure A1 Sampling locations of floats (a) 7900591 and (b) 6900807 between December 2013 and July 2019. Colored squares and hexagons indicate the date (color bar) for a given profile of floats 6900807 and 7900591, respectively.

Figure A2 Time series of (a) S, (b) O₂, (c) log (chl), and (d) log (b_bp) for float 7900591. The blue line in (c) indicates the mixed layer depth. The red lines in (c) and (d) show the base of the productive region. Isopycnals 15.79 and 16.30 kg m⁻³ describe the top and bottom of the oxygen-poor zone (OP_DA), respectively. SU, A, W, and SP stand for summer, autumn, winter, and spring, respectively. The colored horizontal line at the bottom indicates the sampling site for a given date (Fig. A1). The horizontal white lines in (d) are the profiles used to (1) delimit the OP_DA and (2) find the isopycnals at which b_bp is maximum in the OP_DA. chl is set to zero in the OP_DA due to fluorescence contamination (Stanev et al., 2017).

Figure A3 Same as Fig. A2 but for float 6900807.