We thank this reviewer for their positive feedback on our manuscript. The typo in the abstract has been corrected

We are grateful for the detailed comments on the biological portion of our analysis.

Nearby hydrothermal vents are depicted in Fig. 5C (shown with green stars) using the InterRidge Vents Database (Beaulieu et al., 2013), with additional vent coordinates from (Baker et al., 2019). This has been clarified and highlighted for readers in the discussion.

Details regarding the metatranscriptomic processing and amplicon sequencing analysis have now been included in the methods:

“A translated metatranscriptome was used as the protein database (Cohen et al., 2021). Briefly, the metatranscriptomic data was generated by extracting RNA from 3–51-µm size fraction filters, purifying RNA, removing ribosomal RNA, converting RNA to cDNA and amplifying, and fragmenting to 200 bp. Libraries were sequenced on the Illumina HiSeq platform, and raw data is available through National Center for Biotechnology (NCBI) under Bioproject PRJNA555787. Bioinformatic processing consisted of adaptor trimming, de novo assembly, open reading frame (protein) prediction, and read mapping (Cohen et al. 2021). Taxonomic and functional annotations were performed using the custom-built database PhyloDB, which includes marine prokaryotic and eukaryotic references (https://github.com/allenlab/PhyloDB), and additional iron oxidation, reduction, storage and acquisition annotations were assigned using FeGenie (Garber et al., 2020).”

“Taxonomic composition was further assessed using 18S and 16S ribosomal RNA (rRNA) amplicon sequencing from the 3–51-µm filter size fraction (Cohen et al. 2021). The V3–V5 and V9 regions were targeted of 16S and 18S rRNA fragments, respectively, and sequenced using the Roche 454 platform. The full cDNA prep and bioinformatic processing details are described in Bertrand et al. (2015). The 16S rRNA OTUs were taxonomically annotated using the SILVA rRNA database (release 111) (Quast et al., 2013), and 18S rRNA OTUs using the Protist Ribosomal Reference v.4.11.1 database (Guillou et al., 2013). Principal coordinate analysis (PCoA) of OTU data was performed using Bray-Curtis dissimilarity on center-log-ratio transformed values and implemented with the R package phyloseq (McMurdie and Holmes, 2013).”

 The maps in Fig. 1 have been modified to more clearly present stations and ocean basin context.

The low dMn concentrations measured in the deep South Pacific are not unique to the region, and dMn similarly follows this distribution in the North Atlantic and North Pacific Oceans. In these regions, surface concentrations are elevated and deep concentrations decrease to a minimum of ~0.1-0.2 nM (Bruland et al., 1994; Van Hulten et al., 2017).  High surface concentrations are maintained by the photodegradation of particulate Mn oxides in the presence of organics (Sunda et al., 1983), but decrease deeper in the water column as Mn oxides form, which is partly driven by Mn-oxidizing bacteria. It is likely that the Mn-oxidizing bacteria keep dMn inventories low in the deep ocean, and in regions such as Oxygen Minimum Zones where oxidation is inhibited, both dissolved Mn and cobalt (Co) (which adsorbs onto Mn oxides) accumulate (Johnson et al., 1996; Saito et al., 2017).

We agree that additional samples from the distal hydrothermal plume would strengthen the analysis. However, as described in the beginning of Section 3.3 (see below), real-time tracking of the plume was not performed on the ship, and it was not known that the core of the distal plume had moved since the original observation two decades before. This is therefore a descriptive analysis that can be used to generate future hypotheses, and additional studies are required to confirm metabolic differences between distal plume vs. background waters. 

“Since hydrothermal vents were not the focus of this expedition, real-time instrumentation for tracking hydrothermal signatures was not onboard the ship. Instead, seawater samples and biomass was collected at the same location where hydrothermal activity had been observed previously, at St. 12 (Lupton et al. 2004), and analyzed back in the laboratory. We therefore were unaware that the largest metal signatures were at St. 13, or that present-day plume maxima at St. 12 were at 1,200 and 2,200 m, and so biomass was not collected at these depths. However, biomass was obtained in the vicinity of hydrothermal influence, at St. 12 (15°S, 173.1°W) at 1,900m, where dissolved and particulate metals were higher than background concentrations. Other deep (≥ 200 m) samples collected across the transect served as background, non-hydrothermally-influenced vent sites (n=20).”

The “remnant phototrophic metabolism” paragraph has been moved to its own section entitled “Potential vertical transport of surface phytoplankton”. We do not believe that a significant fraction of the protein biomass detected on the 1,900 m filter is derived from contamination. The 1, 900 m (“distal hydrothermal plume”) pump collected 0.5 µg/L of protein in 178 L, while surface pumps from <100 m collected on average 7.5 µg/L of protein in 354 L (Table S1). For all of the biomass collected on the 1,900 m filter to be surface contamination, approximately 23 L of surface water would be required to pass through. We believe this is unlikely as the pumps are not programmed to turn on while being deployed or recovered back on the ship’s deck, and there was no evidence of a pump misfire. The seawater would furthermore need to passively filter, without pumping, through three membranes (51, 3 and 0.2 µm). Other McLane pumps deployed at this station appeared to collect biomass at the correct depths, with surface and deep samples distinct based on transcriptome content (Cohen et al. 2021). Finally, the metaproteomic analysis carried out in this study shows similarities in the community metabolism recovered from 1,900 m and other hydrothermal vent environments, suggesting that the protein signatures are reflective of deep water. Future analyses will benefit from replication at distal plume depths. 

“Contamination cannot be completely ruled out, but is unlikely to be solely responsible for the 1, 900 m protein biomass signal, as approximately 23 L of surface seawater would be required to passively move through the 1,900 m filter during vertical transport to produce the protein levels collected (Table S1). Future experiments will benefit from replicated biomass collection with large volumes filtered, and examinations into the degradation state of proteins at depth.”

We could not find information regarding Prochlorococcus degradation rates in the deep ocean. However, there is growing support for fresh dissolved organic material (DOM) exported to the deep ocean that is utilized by heterotrophic bacteria similarly as surface DOM (Bergauer et al., 2018; Kirchman, 2018). We have included statements about these points in the manuscript.

Baker, E. T., Walker, S. L., Massoth, G. J. and Resing, J. A.: The NE Lau Basin: Widespread and Abundant Hydrothermal Venting in the Back-Arc Region Behind a Superfast Subduction Zone, Front. Mar. Sci., 6, 382, doi:10.3389/fmars.2019.00382, 2019.

Beaulieu, S. E., Baker, E. T., German, C. R. and Maffei, A.: An authoritative global database for active submarine hydrothermal vent fields, Geochemistry, Geophys. Geosystems, 14(11), 4892–4905, doi:10.1002/2013GC004998, 2013.

Bergauer, K., Fernandez-Guerra, A., Garcia, J. A. L., Sprenger, R. R., Stepanauskas, R., Pachiadaki, M. G., Jensen, O. N. and Herndl, G. J.: Organic matter processing by microbial communities throughout the Atlantic water column as revealed by metaproteomics, Proc. Natl. Acad. Sci. U. S. A., 115(3), E400–E408, doi:10.1073/pnas.1708779115, 2018.

Bruland, K. W., Orians, K. J. and Cowen, J. P.: Reactive trace metals in the stratified central North Pacific, Geochim. Cosmochim. Acta, 58(15), 3171–3182, doi:10.1016/0016-7037(94)90044-2, 1994.

Cohen, N. R., McIlvin, M. R., Moran, D. M., Held, N. A., Saunders, J. K., Hawco, N. J., Brosnahan, M., DiTullio, G. R., Lamborg, C., McCrow, J. P., Dupont, C. L., Allen, A. E. and Saito, M. A.: Dinoflagellates alter their carbon and nutrient metabolic strategies across environmental gradients in the central Pacific Ocean, Nat. Microbiol., doi:10.1038/s41564-020-00814-7, 2021

Garber, A. I., Nealson, K. H., Okamoto, A., McAllister, S. M., Chan, C. S., Barco, R. A. and Merino, N.: FeGenie: A Comprehensive Tool for the Identification of Iron Genes and Iron Gene Neighborhoods in Genome and Metagenome Assemblies, Front. Microbiol., 11, doi:10.3389/fmicb.2020.00037, 2020.

Johnson, K. S., Coale, K. H., Berelson, W. M. and Gordon, R. M.: On the formation of the manganese maximum in the oxygen minimum, Geochim. Cosmochim. Acta, 60(8), 1291–1299, doi:10.1016/0016-7037(96)00005-1, 1996.

Kirchman, D. L.: Microbial proteins for organic material degradation in the deep ocean, Proc. Natl. Acad. Sci. U. S. A., 115(3), 445–447, doi:10.1073/pnas.1720765115, 2018.

Lupton, J. E., Pyle, D. G., Jenkins, W. J., Greene, R. and Evans, L.: Evidence for an extensive hydrothermal plume in the Tonga-Fiji region of the South Pacific, Geochemistry, Geophys. Geosystems, 5(1), doi:10.1029/2003GC000607, 2004.

McMurdie, P. J. and Holmes, S.: Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data, PLoS One, 8(4), doi:10.1371/journal.pone.0061217, 2013.

Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J. and Glöckner, F. O.: The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools, Nucleic Acids Res., 41(D1), doi:10.1093/nar/gks1219, 2013.

Saito, M. A., Noble, A. E., Hawco, N., Twining, B. S., Ohnemus, D. C., John, S. G., Lam, P., Conway, T. M., Johnson, R., Moran, D. and McIlvin, M.: The acceleration of dissolved cobalt’s ecological stoichiometry due to biological uptake, remineralization, and scavenging in the Atlantic Ocean, Biogeosciences, 14(20), 4637–4662, doi:10.5194/bg-14-4637-2017, 2017.

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The careful review by Reviewer #3 is much appreciated, and we are grateful for their effort in strengthening the geochemical aspects of this manuscript. We have specific responses to comments below. Importantly, we clarify that our dissolved metals “partial recovery” method using indium is technically a matrix correction, as indium is not introduced to the column prior to sampling binding, and instead is added into the elution acid. For the particulate metal analysis, indium is used as a proper internal standard, and is added to samples before the digestion. This has been clarified in the text. Specific comments are addressed below.

Line 125: Filters were not rinsed with DI to remove salts before freezing since the elements of interest do not require salt correction. It is possible low levels of dissolved metals on dried seawater were also included in the particulate analysis. This has been specified in the methods.

Line 134: Regarding the pH during sample loading, we followed the manufacturer’s (Elemental Scientific) default guidelines in which acidified samples are mixed with concentrated and heavily buffered ammonium acetate (4M) of pH 6 at a 4:1 sample:buffer ratio, enabling sample loading onto the column at pH ~6 (Rapp et al., 2017; Wuttig et al., 2019). This is consistent with reports of complete column recovery from Nobias PA-1 resin for Fe, Mn, Ni, Cu, and Zn with a sample pH between 5.5-7 (Lagerström et al., 2013; Sohrin et al., 2008). Unfortunately, the pH of the waste was not checked, but we will do so going forward.

We tested the pH of a 4:1 acidified seawater sample:buffer mixture to simulate the seaFAST buffering step, which produced a pH of 5.5. Metal binding to WAKO resin at this pH shows similar performance to pH 6 for all metals apart from Mn, where binding (and count rates) increase with higher pH (Rapp et al. 2017).

Rapp et al. (2017) report a recovery of approximately 83.1 ± 2.6% for Fe with Nobias resin at a pH of 6.1, close to our assumed matrix correction of ~83% using In. Other metals with close corrections to ours include Cd (85.5 ± 3.1%) and Zn (95.0 ± 3.4). However, that study shows other metals with recoveries slightly larger (Cu: 102.7 ± 3.4%) or much lower (Ni: 38.5 ± 1.4%; Mn: 24.1 ± 0.2%), and if applied to our analysis, would suggest that our final concentrations are either artificially elevated or severely underestimated, respectively. We do not believe this was the case for Ni or Mn, as the Geotraces GSC sample concentrations match consensus values (Table 1). It is possible our Cu concentrations are elevated compared to reference material and previously published studies partly due to underestimation of column recovery. We have included a note about this in the text.

Line 135: We did not test the difference in recovery between DI water and 1% nitric rinse solutions. A 1% nitric acid rinse solution was included in an early seaFAST protocol compiled by Kohler (2017), and was logical as we similarly flush between ICP-MS injections using a weak nitric acid rinse solution.

Line 147: We used a quartz cyclonic spray chamber (Thermo Scientific). This has been included in the text.

Line 155: We tested the reviewer’s proposed recovery method using Geotraces reference material by subtracting out blanks from the measured GSC sample concentration, dividing by concentration factor, and dividing by GSC consensus values. We obtain the following averaged estimates (n = 3):

Fe: 89 +/- 6%

Mn: 82 +/- 5%

Ni: 81 +/- 8%

Cu: 108 +/- 11%

Zn: 76 +/- 19%

Cd: 87 +/- 8%

These are close to the average In matrix correction of 83% in our study, for all metals except Cu. It is possible that labile Cu concentrations increase over time in storage, and the consensus values are not reflective of this. We have included a note about this in the text to more clearly present this possibility to readers.

“It is possible that our higher dCu concentrations are a result of long-term acidified storage (8 years), during which time strongly binding refractory organic complexes could degrade and increase labile Cu (Little et al., 2018). Along these lines, Posacka et al. (2017) determined labile Cu concentrations in non-UV-oxidized seawater samples increase with storage time, with long term sample storage at low pH (>4 years) demonstrating similar concentrations to those UV-oxidized and measured within 2 months. Dissolved Cu has previously been reported as 3.1 nM in the deep southwest Pacific using Nobias-chelate PA1 resin (Takano et al., 2017), whereas the maximum raw dCu concentration we obtained in the southwest Pacific was approximately 4.2 nM. It is furthermore possible that the matrix corrections based on In are not reflective of Cu, which has been demonstrated to show both high (103%) and low (~50%) recoveries from Nobias resin at pH of 6.1 (Quéroué et al., 2014; Rapp et al., 2017). In addition, ArNa+ interferences on 63Cu cannot be completely ruled out given the abundance of Na+ in seawater (Diemer et al., 2002), although a cooled spray chamber was used to minimize such polyatomic interferences. The exact mechanism behind these elevated Cu concentrations is unclear at present, and an intercalibration exercise within the trace metal community using long-term stored seawater would be useful to further understand these offsets.”

 We have included a supplemental figure (attached) showing our St. 7, 8, and 9 compared to GP16 St. 36, which is the closest station to ours from the East Pacific Zonal Transect (EPZT) expedition (~ 1,000 km to the east). For example, here is how the deep (5 km) concentrations (nmol/kg) compare between GP16 St. 36 (left) and Metzyme St. 8 (right):

Fe: 0.44,          0.65

Mn: 0.06,         0.13

Ni: 7.62,           8.06

Zn: 7.22,          7.72

Cd: 0.79,          0.85

Due to the potential storage-related issues with Cu, we are not directly comparing with GP16 data. The Fe and Cd concentrations are from John et al. (2017) and (2018) (https://www.bco-dmo.org/dataset/643809), and Zn, Ni, and Mn from the Bruland data set (https://www.bco-dmo.org/dataset-deployment/643427).

Line 175: It is possible ArNa⁺ interfered with ⁶³Cu detection, which is known to occur with a seawater matrix containing high Na⁺ (Diemer et al. 2002), although the column chemistry should remove most of the Na. This has been included as an additional mechanism possibly explaining our high Cu concentrations compared to consensus values.

Line 180: It is difficult to say with certainty which factors are responsible for the Cu offset between our data and previously published values. We believe the long storage time of over 8 years could have played a role in liberating labile Cu (Little et al. 2018, Pasacka et al. 2017). We have found the accurate quantification of Cu can be difficult as consensus values may not reflect long storage times, and the community may benefit from additional methodological comparisons. There has been recent discussion within the trace metal community regarding the accuracy of Cu methods applied to long term archives similar to the trends we have observed here. 

Line 200: Regarding our claim that a fraction of Co is lost even after UV exposure, this is an unpublished finding that occurred during GEOTRACES intercomparison efforts. We have not published it since it involved other groups’ data, but it is worth working with other authors on revisiting this since increasingly groups with ICP-MS datasets are submitting total dissolved and labile methods that are unverified and often not UV-irradiated making their interpretation and intercomparison difficult. Since these matters don’t concern this study, we have deleted the sentence.

Line 210: We later determined that soaking acid-rinsed filter blanks in oligotrophic seawater for ≥1 week were effective at removing the background contamination, but this was years after the initial digestions were performed and the original batch of filters were not available. It was unlikely to be MQ contamination as this same MQ was used as seaFAST blanks, which were generally low in metal concentration. Indium was used to check recovery and was added with nitric acid prior to the digestion, serving as both an internal standard and matrix correction. Reference material was not used.

Due to high filter blank contamination, samples corresponding to pFe and pMn minimums largely within OMZs were selected from each of the three ICP-MS runs to serve as “blanks”, and primarily to compare to the large pFe and Mn signals at St. 12. As a result these blanks are likely overestimated, making the resulting data conservative, but because particulate material in the upper water column is much higher than at depth, this is a relatively small conservative bias. Estimated “blank” concentrations were calculated by converting from cps to pM and scaling up based on In recovery. They are as follows:

St. 1, 3, 5, 6:    Fe blank = 130 pM (St. 1 400 m). Mn blank = 5 pM (St. 5 800 m).

St. 10, 11, 12, 2, 7.        Fe blank = 117 pM (St. 2. 450m). Mn blank = 3 pM (St. 2 225 m).

St. 4, 8, 9.         Fe blank = 240 pM (St. 4 150m). Mn blank = 12 pM (St. 4 400m).

The text has been corrected to indicate the exact depths in which each blank is derived and estimated concentrations.

Our Milli-Q system is an Element system intended for low metal use. It has also been modified to not include the metal nut on the dispenser system, and instead uses a foot operated teflon tube dispenser.

Note: while calculating particulate metal blanks, we realized our dissolved metal blanks were not scaled to take into account the In matrix correction (they assumed no matrix effects). The corrected blank and LOD values are included in Table 2 (attached).

Line 299: There is a slight enrichment in dMn at St. 2 at approximately 200 m, where dCo reaches the highest concentrations and coincides with an oxygen minimum (Fig. 2&3). Dissolved Co distributions are more driven by biological influences, whereas the dMn distribution is strongly driven by Mn oxide photoreduction in surface waters and Mn oxidation at depth. More intense dMn signals have been observed elsewhere in OMZs due to inhibited Mn oxidation (e.g., Johnson et al., (1996), Lewis and Luther (2000).

Line 457: The exponential fit would be largely driven by one point, and we do not have enough data points to confidently suggest an exponential relationship, however this figure has been included for reference as a supplemental figure (attached).

“Fig. S6B: The relationship between dFe measured at St. 13 and ³He from Lupton et al. (2004) shown in Fig. 5C alongside an exponential curve fit (in purple). Although the exponential fit is strongly supported by a high R2 value, it is largely driven by one data point.”

Table 3: Depths that ³He was measured by Lupton et al., (2004) were matched to the depths of dFe values obtained in this study. No upper water column ³He values were available for 400-800 m, and they were therefore linearly extrapolated. In addition, He concentrations were not available in Lupton et al. (2004) and instead were estimated using nearby concentrations, at similar depths, from Jenkins et al., (2019). The ratios were obtained from the regression of dFe and ³He individual sample points, not integrated. This has been clarified in the Table 3 caption.

Line 625: The InterRide Vents database includes polymetallic massive sulfides identified at vent sites in this region. At Eastern Lau Spreading Center vents, high metal concentrations suggest metal-sulfide formation during vent fluid mixing with background seawater, preventing sulfide oxidation by microbes (Hsu-Kim et al., 2008). If this similarly occurs in the NE Lau Basin, it could reduce sulfide availability to microbes. This has been included in the text.

“Metals and sulfide derived from vent fluid likely formed inorganic metal-sulfide clusters, reducing metal toxicity in the microbial community (Edgcomb et al., 2004) and limiting the bioavailability of sulfide to sulfide-oxidizing organisms, as observed in the Eastern Lau Spreading Center (Hsu-Kim et al., 2008; Sheik et al., 2015). Supporting this possibility, polymetallic massive sulfides have been observed in the NE Lau Basin (Beaulieu et al., 2013; Hawkins, 1986).”

Line 628: This was an important reference and has been included in the bacterial-mediated Mn oxidation discussion section.

“Lastly, heterotrophic Mn-oxidizing bacteria are a major conduit for Mn oxide precipitation in non-buoyant plumes (Cowen et al., 1990). Multicopper oxidase enzymes responsible for heterotrophic Mn oxidation in cultured hydrothermal bacteria (Dick et al., 2006), however, were not detected in the plume-influenced sample. Lithotrophic Mn oxidation is also theorized to occur in Mn(II)-rich vent fluids (Templeton et al., 2005) and has recently been described for the first time in Nitrospirae bacteria of tap water, which show high 16S rRNA sequence similarity to Nitrospirae from the Lo’ihi Seamount seafloor lava (Yu and Leadbetter, 2020). In this analysis, Nitrosopirae proteins were not enriched in distal plume-influenced seawater (>3 µm fraction), although proteins of this phyla were detected elsewhere in the transect. Genes expressed in Nitrospirae and hypothesized to play a role in lithotrophic Mn(II) oxidation, including outer membrane c-type cytochromes and porin-cytochrome c complexes (Yu and Leadbetter, 2020), were similarly not enriched. It is possible that environmental Mn oxidation proteins differ from those characterized in our reference databases, and are therefore missed during bioinformatic annotations.”

All minor errors pointed out have been corrected.

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