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. 2022 Nov 11;9:688. doi: 10.1038/s41597-022-01809-1

Global Ocean Particulate Organic Phosphorus, Carbon, Oxygen for Respiration, and Nitrogen (GO-POPCORN)

Tatsuro Tanioka 1, Alyse A Larkin 1, Allison R Moreno 2,3, Melissa L Brock 2, Adam J Fagan 1, Catherine A Garcia 1,4, Nathan S Garcia 1, Skylar D Gerace 1, Jenna A Lee 1,5, Michael W Lomas 6, Adam C Martiny 1,2,
PMCID: PMC9652364  PMID: 36369310

Abstract

Concentrations and elemental stoichiometry of suspended particulate organic carbon, nitrogen, phosphorus, and oxygen demand for respiration (C:N:P:−O2) play a vital role in characterizing and quantifying marine elemental cycles. Here, we present Version 2 of the Global Ocean Particulate Organic Phosphorus, Carbon, Oxygen for Respiration, and Nitrogen (GO-POPCORN) dataset. Version 1 is a previously published dataset of particulate organic matter from 70 different studies between 1971 and 2010, while Version 2 is comprised of data collected from recent cruises between 2011 and 2020. The combined GO-POPCORN dataset contains 2673 paired surface POC/N/P measurements from 70°S to 73°N across all major ocean basins at high spatial resolution. Version 2 also includes 965 measurements of oxygen demand for organic carbon respiration. This new dataset can help validate and calibrate the next generation of global ocean biogeochemical models with flexible elemental stoichiometry. We expect that incorporating variable C:N:P:-O2 into models will help improve our estimates of key ocean biogeochemical fluxes such as carbon export, nitrogen fixation, and organic matter remineralization.

Subject terms: Marine chemistry, Element cycles


Measurement(s) particulate matter • particulate carbon oxygen demand • particulate phosphorus
Technology Type(s) elemental analyzer • potassium dichromate • ash-hydrolysis
Factor Type(s) location • period
Sample Characteristic - Environment ocean
Sample Characteristic - Location global

Background & Summary

The elemental ratio between carbon (C), nitrogen (N), phosphorus (P), and oxygen (O2) demand for respiration is a fundamental quantity that couples nutrient uptake by primary producers, organic carbon export, and remineralization13. Most ocean biogeochemical models from the pre-CMIP6 era have exclusively used the fixed canonical Redfield C:N:P and respiration quotient -O2:C of 106:16:1 and 1, respectively, to link nutrient uptake and convert to and from organic carbon. However, it is now widely accepted in the oceanographic community that C:N:P:-O2 in the surface ocean are variable through space and time. Previous global compilation studies4,5 have shown that C:P and N:P are systematically higher than the Redfield ratios of 106:1 and 16:1 in the nutrient-deplete subtropical gyres, lower in the nutrient-rich subpolar and polar regions, and approximately equal to the Redfield values in the tropical and upwelling regions. The respiration quotient of particulate organic matter (POM) in terms of -O2:C and -O2:P has also been shown to be spatially variable through direct observations and inverse modeling68. In light of these recent observations, our understanding of the oceanic ecosystem elemental stoichiometry has evolved rapidly over the last ten years.

Here we present Version 2 (“v2”) of the Global Ocean Particulate Organic Phosphorus, Carbon, Oxygen for Respiration, and Nitrogen (GO-POPCORN) dataset (Fig. 1). We refer to Version 1 (“v1”) as a previously published data compilation9, in which POC/N/P was collated from 70 cruises and time-series between 1971 and 2010. Version 1 has served multiple purposes, such as calibration and validation of ocean biogeochemical models, including those used in the latest coupled model intercomparison project (CMIP6)1012, and identifying drivers of global-scale spatiotemporal variability in C:N:P13,14. However, several limitations of GO-POPCORN v1 were identified. First, there was a significant bias towards regions of frequent oceanographic research, leading to samples being concentrated in the North Atlantic, Eastern North Pacific Ocean, Mediterranean Ocean, and near the Palmer Station in the Southern Ocean (Fig. 1). Second, aggregated data samples were collected using different techniques, such as differing blank measurements and detection limits. Third, a large proportion of measurements came from time-series studies at a fixed geographical location: Hawaiian Ocean Time-series (HOT), Bermuda Atlantic Time-series Study (BATS), and CARIACO Ocean Time-series program.

Fig. 1.

Fig. 1

Distribution of paired POC/N/P measurements in the surface ocean. Samples from GO-POPCORN v1 (n = 580) and v2 (n = 2093) are shown in blue and red, respectively.

GO-POPCORN v2 is a new compendium of global POC/N/P collected between 2011 and 2020 as part of Bio-GO-SHIP (the Biological initiative for the Global Ocean Ship-based Hydrographic Investigations Program)15,16 and the Arctic Integrated Ecosystem Research Program (IERP)17. The v2 dataset contains 2581 paired measurements (of which 2093 measurements are from the surface ocean) of POC/N/P and 965 measurements of particulate chemical oxygen demand (PCOD), which is the oxygen needed for full respiration of organic carbon7. The new version has a comprehensive geographic range, and the samples were collected across all major oceanic regions from 70°S to 73°N (Fig. 2) across 2188 stations using a consistent methodology and quality control (Table 1).

Fig. 2.

Fig. 2

Geographical distribution of paired POC/N/P measurements in the surface ocean. The number of paired POC/N/P measurements binned by (a) every 20° of latitude, (b) every 30° of longitude, and (c) by oceanographic basins for GO-POPCORN v1 (blue) and v2 (red). [Abbreviations: ATL = Atlantic Ocean, PAC = Pacific Ocean, IND = Indian Ocean, SO = Southern Ocean, ARC = Arctic Ocean].

Table 1.

Summary of data in GO-POPCORN Version 2, including the number of stations and particulate organic matter (POM) samples and the mean elemental ratios.

Cruise (Program) Year #Stations Latitude Longitude POC PON POP PCOD C:P N:P C:N −O2:C Ref.
min max min max (# Samples) (Geometric mean)
AE1319 (NSF) 2013 15 32 55 −69 −40 123 111 111 0 145 12 11.6 NA 25,31,45
AMT-28 (PML AMT, SOCCOM, NSF) 2018 709 −48 50 −53 −6 741 741 775 771 155 23 6.7 1.2 8,24,34
BVAL46 (BATS, NSF) 2011 18 20 39 −66 −64 0 0 197 0 NA NA NA NA 26,31,45
C13.5 (GO-SHIP) 2020 112 −41 35 −74 17 112 112 112 0 155 22 7.1 NA 20
I07N (GO-SHIP) 2018 719 −30 18 40 69 732 733 727 0 121 19 6.4 NA 21
I09N (GO-SHIP) 2016 238 −31 18 85 110 235 235 236 0 134 19 7.1 NA 22,30,31,34
NH1418 (NSF) 2014 88 −3 19 −158 −150 159 159 180 0 142 23 6.1 NA 27,31,33
P18 (GO-SHIP) 2016–2017 193 −70 29 −116 −100 194 194 194 194 130 21 6.2 1.1 7,23,32
OS1701 (Arctic IERP) 2017 30 67 72 −169 −154 106 106 105 0 96 13 7.4 NA This study
OS1901 (Arctic IERP) 2019 38 63 73 −171 −154 137 137 137 0 150 21 7.2 NA This study
SKQ201709S (Arctic IERP) 2017 14 63 69 −173 −165 72 72 72 0 142 18 8.0 NA This study
SKQ201813S (Arctic IERP) 2018 14 63 69 −172 −164 53 53 53 0 113 17 6.7 NA This study
Summary 2011–2020 2188 −70 73 −173 110 2664 2653 2899 965 137 21 6.7 1.1

We operationally define the sampling station as a distinct pair of longitude and longitude. Similar descriptions for GO-POPCORN Version 1 are listed in Table 1 of Martiny et al.9. [Abbreviations: POC = Particulate Organic Carbon, PON = Particulate Organic Nitrogen, POP = Particulate Organic Phosphorus, PCOD = Particulate Chemical Oxygen Demand, BATS = Bermuda Atlantic Time-series Study, GO-SHIP = Global Ocean Ship-based Hydrographic Investigations Program, NSF = National Science Foundation, PML AMT = Plymouth Marine Laboratory Atlantic Meridional Transect, SOCCOM = Southern Ocean Carbon and Climate Observations and Modeling project, IERP = Integrated Ecosystem Research Program].

Median C:N:P for paired surface POM samples from GO-POPCORN v1 and v2 are 140:19:1 and 136:21:1, respectively (Fig. 3). The data spread is noticeably smaller in v2 compared to v1. Specifically, the interquartile range (IQR) in v2 is reduced by a factor of 2–3 compared to that of v1 (IQR of C:P, N:P, C:N in versions 1 and 2 are [103, 13, 2] and [43, 6, 1], respectively). About 90% of observed C:P and N:P from v2 are above the Redfield ratios of 106 and 16, respectively (Fig. 3a,b). This contrasts with v1, where only 75% of samples collected have C:P and N:P above the Redfield ratios. In both versions, the observed mode for C:N is around the Redfield C:N of 6.7, but values are more tightly clustered around 5–8 in v2 (Fig. 3c). The median -O2:C from v2 is 1.14, with an IQR of 0.17 (Fig. 3d). Thus, surface organic matter is generally more reduced than pure carbohydrate, with a respiration quotient of 1 (i.e., Redfield -O2:C)18,19. In summary, both the quantity and the quality of the data have significantly improved in v2 over v1.

Fig. 3.

Fig. 3

Summary of observed C:N:P:−O2 in the surface ocean. The histogram of (a) C:P, (b) N:P, (c) C:N, and (d) −O2:C from GO-POPCORN v1 (blue) and v2 (red). Black dashed lines are Redfield C:N:P and −O2:C of 106:16:1 and 1.0, respectively, for comparison. Please note a difference in the total number of observations for each elemental ratio and that −O2:C was not measured in v1.

Methods

GO-POPCORN v1 is an exhaustive compilation of POM collected by 70 independent studies and cruises from 1971 to 2010. Refer to the original description paper9 for more details on how the v1 dataset was compiled.

GO-POPCORN v2 comprises samples from 12 recent cruises between 2011 and 2020 (Table 1). These sampling efforts have been supported by GO-SHIP (C13.520, I07N21, I09N22, and P1823), SOCCOM and Plymouth Marine Laboratory Atlantic Meridional Transect (AMT-2824), National Science Foundation Dimensions of Biodiversity (AE131925, BVAL4626, NH141827), and North Pacific Research Board Arctic Integrated Ecosystem Research Program (OS170128, OS190128, SKQ201709S29, SKQ201813S29).

The POM samples were collected and analyzed using the consistent sampling method described previously3033. Briefly, 3–8 L seawater was collected from the flow-through underway system or CTD. Samples from underway systems were filtered using 30 µm nylon mesh to remove large particles from the sample. Samples were then collected on GF/F filters (Whatman, nominal pore size 0.7 µm) that were precombusted at 500 °C for 5 h to remove any traces of inorganic carbon as well as organic contaminants. Whenever possible, POC, PON, and POP were sampled in triplicate, and PCOD was sampled in sextuplicate. Triplicate sampling occurred hourly in cruises AMT-28 and I07N; every 4 hours for C13.5, I09N, and P18; and once a day for AE1319, BVAL46, NH1418, OS1701, OS1901, SKQ201709S, and SKQ201813S. Differences in the sample collection are based on differences in the hypotheses being tested. For example, hourly sampling in AMT-28 and I07N is aimed toward capturing the diurnal changes in elemental stoichiometry34.

POC and PON samples were measured using a CN Flash 1112 EA or 240-XA/440-XA elemental analyzer and were calibrated using a known quantity of atropine (C17H23NO3). Inorganic carbonates were removed using concentrated hydrochloric acid fumes before analysis by storing filters in a desiccator for 24 hours. The mean detection limits for POC and PON, defined as ~3x standard deviation of the low standards, are ~2.4 μg and ~3.0 μg, respectively. POP was analyzed using the modified ash-hydrolysis method described previously with spectrophotometric detection at 885 nm35,36. The detection limit for POP is ~0.3 μg. It is important to note that measured particulate N and P are not devoid of inorganic N (e.g., aerosol-derived particulate nitrogen species) and P (e.g., polyphosphate granules), respectively. Furthermore, POM analyzed using this protocol includes contributions of dead materials in addition to live plankton cells, including a wide diversity of heterotrophs.

PCOD was quantified using the new, modified assay7 based on the determination of residual potassium dichromate following organic matter oxidation with silver sulfate as the catalyst under the strongly acidic condition at 150 °C for 2h3739. As dichromate does not oxidize ammonium, the assay aims explicitly to quantify the oxygen demand from organic carbon (but not organic nitrogen). To remove the interference of chloride ions from the precipitation of silver chloride, mercuric sulfate was added40. Dichromate was quantified by absorbance at 600 nm using HACH-certified phthalate-based COD standards. We could not directly quantify the detection limit for PCOD as the PCOD chemistry method is highly sensitive (see Technical Validation).

Data Records

Data of GO-POPCORN are publicly available in CSV format uploaded to Dryad for Version 1 (10.5061/dryad.d702p)41 and Version 2 (10.5061/dryad.05qfttf5h)42. GO-POPCORN datasets are distributed under a CC0 1.0 Universal Public Domain Dedication license.

Technical Validation

In GO-POPCORN v1, most studies used similar techniques and sample volumes, but there are many slight deviations in the technical approach, including the measurement sensitivity, detection limits, the number of replicates, and the overall cleanliness (i.e., contamination) of procedures9. It is also worth noting that the POP measurements were grossly undersampled compared to POC and PON measurements in GO-POPCORN v1.

In GO-POPCORN v2, the POM samples were collected and quantified using consistent protocols. Before POM sampling, all the carboys used were rinsed at least twice with the pre-filtered underway seawater. The filtered volume of seawater was consistent between all POM (POC/N and POP) samples at each station and varied on a per-station basis to ensure that the amount of collected material was minimally impacted by the difference in filtration time. Initial rinsing and the large sampling volume were aimed at reducing the effect of a time delay caused by the underway system. The methods used for quantifying POC/N43 and POP36 are based on previously described and validated standard techniques.

POM described in this dataset are “small size-class” samples, where a 30 µm nylon mesh pre-filter was attached to the underway outlet to remove large plankton and particulates. In the Southern Ocean Section of the P18 cruise, we have separately collected “large-class” of POM >30 µm and showed that the larger particles constitute, on average, 17% of the total POC and PON concentrations and 31% of the total POP concentration32. The same study showed that a large size fraction of POM in P18 had statistically lower C:P, C:N, and N:P compared to a small size fraction of POM. However, the general effect of particle size on the C:N:P stoichiometry of POM is not yet clear.

For the technical validation of the novel PCOD assay, we tested for (1) interference using standard additions of a HACH-certified phthalate-based COD standard, (2) a linear correspondence between input amounts and absorbance, (3) the degree of variance with respect to POC measurement technique, and (4) biases for different substrates. In summary, we found that (1) the sample interference is limited, (2) there is indeed a linear relationship between filtered sample volume and PCOD, (3) variance for PCOD is higher compared to POC; hence it is vital to prepare and oxidize the high volume of POC to minimize relative error and ensure accurate determination of -O2:C, and (4) a high correspondence between theoretical and observed values for different substrates. A full detailed description of PCOD assay validation is described elsewhere7.

Usage Notes

This dataset is the most comprehensive global compilation of surface POM and PCOD. By combining this dataset with datasets of temperature, nutrients, and plankton community composition, regional and global drivers of C:N:P:-O2 can be identified. The dataset is also useful for evaluating outputs from ocean biogeochemical models with flexible C:N:P:-O2 stoichiometry, with important implications for future ocean carbon, nitrogen, and oxygen dynamics.

Acknowledgements

We want to acknowledge the captains and crew of the R/V Atlantic Explorer, R/V New Horizon, R/V Ronald H. Brown, R/V Roger Revelle, R/V Sikuliaq, R/V Ocean Star and the R.R.S. James Clark Ross, as well as all the members of Bio-GO-SHIP and IERP. We also thank Andy Rees from Plymouth Marine Laboratory. This work was supported by National Science Foundation (GRFP to ARM, OCE-1046297, 1559002, 1848576, and 1948842 to ACM, OCE-1045966 and 1258836 to MWL), NASA (NESSF16R to CAG, 80NSSC21K1654 to ACM), NOAA (101813-Z7554214 to ACM and NOAA Cooperative Institutes, Award #NA19NES4320002, at the Cooperative Institute for Satellite Earth System Studies), National Institutes of Health (T32AI141346 to MLB), UCI Graduate Division (Chancellor’s Club Fellowship to ARM), Simons Foundation (Postdoctoral Fellowship in Marine Microbial Ecology #724483 to TT) and North Pacific Research Board (Arctic IERP Project A92 & A96 to MWL). The Atlantic Meridional Transect is funded by the UK Natural Environment Research Council through its National Capability Long-term Single Centre Science Programme, Climate Linked Atlantic Sector Science (grant number NE/R015953/1). This study contributes to the international IMBeR project and is contribution number 383 of the AMT programme.

Author contributions

A.C.M. and M.W.L. conceived the study and supervised the investigation. T.T., A.A.L., A.R.M., C.A.G., M.W.L. and A.C.M. developed the methodology and collected metadata. A.A.L., A.R.M., C.A.G., N.S.G., J.A.L., A.J.F., M.L.B., S.D.G. and M.W.L. processed and/or analyzed samples. T.T. wrote a draft and made figures with substantial input from A.A.L., A.R.M., M.W.L. and A.C.M.

Code availability

Code and data used to reproduce all the figures and tables are available in the GitHub repository https://github.com/tanio003/GOPOPCORN_Data_Codes and archived here (10.5281/zenodo.6967484)44.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Redfield, A. C., Ketchum, B. H. & Richards, F. A. The influence of organisms on the composition of Seawater. in The composition of seawater: Comparative and descriptive oceanography. The sea: ideas and observations on progress in the study of the seas (ed. Hill, M. N.) vol. 2 26–77 (Interscience Publishers, 1963).
  • 2.Moreno AR, Martiny AC. Ecological Stoichiometry of Ocean Plankton. Ann Rev Mar Sci. 2018;10:43–69. doi: 10.1146/annurev-marine-121916-063126. [DOI] [PubMed] [Google Scholar]
  • 3.Deutsch C, Weber T. Nutrient Ratios as a Tracer and Driver of Ocean Biogeochemistry. Ann Rev Mar Sci. 2012;4:113–141. doi: 10.1146/annurev-marine-120709-142821. [DOI] [PubMed] [Google Scholar]
  • 4.Martiny AC, et al. Strong latitudinal patterns in the elemental ratios of marine plankton and organic matter. Nat Geosci. 2013;6:279–283. doi: 10.1038/ngeo1757. [DOI] [Google Scholar]
  • 5.Martiny AC, Vrugt JA, Primeau FW, Lomas MW. Regional variation in the particulate organic carbon to nitrogen ratio in the surface ocean. Global Biogeochem Cycles. 2013;27:723–731. doi: 10.1002/gbc.20061. [DOI] [Google Scholar]
  • 6.DeVries T, Deutsch C. Large-scale variations in the stoichiometry of marine organic matter respiration. Nat Geosci. 2014;7:890–894. doi: 10.1038/ngeo2300. [DOI] [Google Scholar]
  • 7.Moreno AR, et al. Latitudinal gradient in the respiration quotient and the implications for ocean oxygen availability. Proceedings of the National Academy of Sciences. 2020;117:22866–22872. doi: 10.1073/pnas.2004986117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Moreno AR, et al. Regulation of the Respiration Quotient Across Ocean Basins. AGU Advances. 2022;3:e2022AV000679. doi: 10.1029/2022AV000679. [DOI] [Google Scholar]
  • 9.Martiny AC, Vrugt JA, Lomas MW. Concentrations and ratios of particulate organic carbon, nitrogen, and phosphorus in the global ocean. Sci Data. 2014;1:140048. doi: 10.1038/sdata.2014.48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Teng Y-C, Primeau FW, Moore JK, Lomas MW, Martiny AC. Global-scale variations of the ratios of carbon to phosphorus in exported marine organic matter. Nat Geosci. 2014;7:895–898. doi: 10.1038/ngeo2303. [DOI] [Google Scholar]
  • 11.Wang W-L, Moore JK, Martiny AC, Primeau FW. Convergent estimates of marine nitrogen fixation. Nature. 2019;566:205–211. doi: 10.1038/s41586-019-0911-2. [DOI] [PubMed] [Google Scholar]
  • 12.Séférian R, et al. Tracking Improvement in Simulated Marine Biogeochemistry Between CMIP5 and CMIP6. Curr Clim Change Rep. 2020;6:95–119. doi: 10.1007/s40641-020-00160-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Galbraith ED, Martiny AC. A simple nutrient-dependence mechanism for predicting the stoichiometry of marine ecosystems. Proceedings of the National Academy of Sciences. 2015;112:8199–8204. doi: 10.1073/pnas.1423917112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Sharoni S, Halevy I. Nutrient ratios in marine particulate organic matter are predicted by the population structure of well-adapted phytoplankton. Sci Adv. 2020;6:eaaw9371. doi: 10.1126/sciadv.aaw9371. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Larkin AA, et al. High spatial resolution global ocean metagenomes from Bio-GO-SHIP repeat hydrography transects. Sci Data. 2021;8:107. doi: 10.1038/s41597-021-00889-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Clayton, S. et al. Bio-GO-SHIP: The Time Is Right to Establish Global Repeat Sections of Ocean Biology. Front Mar Sci8 (2022).
  • 17.Baker MR, et al. Integrated ecosystem research in the Pacific Arctic – understanding ecosystem processes, timing, and change. Deep Sea Research Part II: Topical Studies in Oceanography. 2020;177:104850. doi: 10.1016/j.dsr2.2020.104850. [DOI] [Google Scholar]
  • 18.Anderson LA. On the hydrogen and oxygen content of marine phytoplankton. Deep-Sea Research Part I. 1995;42:1675–1680. doi: 10.1016/0967-0637(95)00072-E. [DOI] [Google Scholar]
  • 19.Paulmier A, Kriest I, Oschlies A. Stoichiometries of remineralisation and denitrification in global biogeochemical ocean models. Biogeosciences. 2009;6:923–935. doi: 10.5194/bg-6-923-2009. [DOI] [Google Scholar]
  • 20.Martiny A, Garcia N, Tanioka T, Fagan A. 2022. POM concentrations for carbon, nitrogen, and phosphorus from GO-SHIP Line C13.5/A13.5 in 2020. Biological and Chemical Oceanography Data Management Office (BCO-DMO) [DOI]
  • 21.Martiny A, Garcia C, Moreno AR, Tanioka T. 2022. POM concentrations for carbon, nitrogen, and phosphorus from GO-SHIP Line I07N RB1803 in the Western Indian Ocean from April to June 2018 (Ocean Stoichiometry Project) Biological and Chemical Oceanography Data Management Office (BCO-DMO) [DOI]
  • 22.Martiny A, Lomas MW. 2021. Particulate organic matter (PON, POC, POP) concentrations collected on R/V Roger Revelle cruise RR1604 along the hydrographic line IO9 in the Eastern Indian Ocean from March to April 2016. Biological and Chemical Oceanography Data Management Office (BCO-DMO) [DOI]
  • 23.Martiny A, Garcia C, Lee J, Moreno A, Larkin AA. 2020. POM concentrations for carbon, nitrogen, phosphorus, and chemical oxygen from GO-SHIP Line P18 Legs 1 and 2 in 2016 and 2017.  Biological and Chemical Oceanography Data Management Office (BCO-DMO) [DOI]
  • 24.Larkin A, Lee JA, Martiny A. POC, PON, and POP from surface underway water samples collected during AMT28/JR18001. British Oceanographic Data Centre, National Oceanography Centre, NERC, UK. 2020 doi: 10.5285/d76d90bb-5d7a-5415-e053-6c86abc0d182. [DOI] [Google Scholar]
  • 25.Lomas MW, Martiny A. 2020. Depth profile data from R/V Atlantic Explorer AE1319 in the NW Atlantic from Aug-Sept. 2013. Biological and Chemical Oceanography Data Management Office (BCO-DMO) [DOI]
  • 26.Lomas MW, Martiny A. 2020. Depth profile data from Bermuda Atlantic Time-Series Validation cruise 46 (BVAL46) in the Sargasso Sea from Sept-Oct. 2011. Biological and Chemical Oceanography Data Management Office (BCO-DMO) [DOI]
  • 27.Lomas MW, Martiny A. 2020. Depth profile data from R/V New Horizons NH1418 in the tropical Pacific from Sept-Oct. 2014. Biological and Chemical Oceanography Data Management Office (BCO-DMO) [DOI]
  • 28.Stabeno P. 2021. EcoFOCI hourly-averaged Ice Draft Data and statistics at Icy Cape, Alaska station C2; 10/2017 to 08/2018 (3 parts/phases), and 08/2018 to 08/2019 (2 parts/phases), Arctic Integrated Ecosystem Research Program, 2017–2019. Axiom Data Science. [DOI]
  • 29.Danielson S. 2021. Conductivity, temperature, and depth data from CTDs deployed on mooring N1 for the ASGARD Project, Bering Sea, 2017–2019. Axiom Data Science. [DOI]
  • 30.Garcia CA, et al. Nutrient supply controls particulate elemental concentrations and ratios in the low latitude eastern Indian Ocean. Nat Commun. 2018;9:4868. doi: 10.1038/s41467-018-06892-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Garcia CA, et al. Linking regional shifts in microbial genome adaptation with surface ocean biogeochemistry. Philosophical Transactions of the Royal Society B: Biological Sciences. 2020;375:20190254. doi: 10.1098/rstb.2019.0254. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Lee, J. A., Garcia, C. A., Larkin, A. A., Carter, B. R. & Martiny, A. C. Linking a Latitudinal Gradient in Ocean Hydrography and Elemental Stoichiometry in the Eastern Pacific Ocean. Global Biogeochem Cycles35, (2021).
  • 33.Lomas MW, et al. Varying influence of phytoplankton biodiversity and stoichiometric plasticity on bulk particulate stoichiometry across ocean basins. Commun Earth Environ. 2021;2:143. doi: 10.1038/s43247-021-00212-9. [DOI] [Google Scholar]
  • 34.Garcia NS, et al. The Diel Cycle of Surface Ocean Elemental Stoichiometry has Implications for Ocean Productivity. Global Biogeochem Cycles. 2022;36:e2021GB007092. doi: 10.1029/2021GB007092. [DOI] [Google Scholar]
  • 35.Solórzano L, Sharp JH. Determination of total dissolved phosphorus and particulate phosphorus in natural waters. Limnology and Oceanography. 1980;25:754–758. doi: 10.4319/lo.1980.25.4.0754. [DOI] [Google Scholar]
  • 36.Lomas MW, et al. Sargasso Sea phosphorus biogeochemistry: An important role for dissolved organic phosphorus (DOP) Biogeosciences. 2010;7:695–710. doi: 10.5194/bg-7-695-2010. [DOI] [Google Scholar]
  • 37.Vyrides I, Stuckey DC. A modified method for the determination of chemical oxygen demand (COD) for samples with high salinity and low organics. Bioresour Technol. 2009;100:979–982. doi: 10.1016/j.biortech.2008.06.038. [DOI] [PubMed] [Google Scholar]
  • 38.Moore WA, Kroner RC, Ruchhoft CC. Dichromate Reflux Method for Determination of Oxygen Consumed. Anal Chem. 1949;21:953–957. doi: 10.1021/ac60032a020. [DOI] [Google Scholar]
  • 39.Baumann FJ. Dichromate Reflux Chemical Oxygen Demand. Proposed Method for Chloride Correction in Highly Saline Wastes. Anal Chem. 1974;46:1336–1338. doi: 10.1021/ac60345a039. [DOI] [Google Scholar]
  • 40.Dobbs RA, Williams RT. Elimination of Chloride Interference in the Chemical Oxygen Demand Test. Anal Chem. 1963;35:1064–1067. doi: 10.1021/ac60201a043. [DOI] [Google Scholar]
  • 41.Martiny AC, Vrugt JA, Lomas MW. 2015. Data from: Concentrations and ratios of particulate organic carbon, nitrogen, and phosphorus in the global ocean. Dryad. [DOI] [PMC free article] [PubMed]
  • 42.Tanioka T, 2022. Global Ocean Particulate Organic Phosphorus, Carbon, Oxygen for Respiration, and Nitrogen (GO-POPCORN) data from Bio-GO-SHIP cruises. Dryad. [DOI] [PMC free article] [PubMed]
  • 43.Ducklow, H. & Dickson, A. Shipboard sampling procedures. JGOFS 1–210 (1994).
  • 44.Tanioka T. 2022. tanio003/GOPOPCORN_Data_Codes: Initial Submission. Zenodo. [DOI]
  • 45.Baer SE, Lomas MW, Terpis KX, Mouginot C, Martiny AC. Stoichiometry of Prochlorococcus, Synechococcus, and small eukaryotic populations in the western North Atlantic Ocean. Environ Microbiol. 2017;19:1–23. doi: 10.1111/1462-2920.13672. [DOI] [PubMed] [Google Scholar]

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This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Martiny A, Garcia N, Tanioka T, Fagan A. 2022. POM concentrations for carbon, nitrogen, and phosphorus from GO-SHIP Line C13.5/A13.5 in 2020. Biological and Chemical Oceanography Data Management Office (BCO-DMO) [DOI]
  2. Martiny A, Garcia C, Moreno AR, Tanioka T. 2022. POM concentrations for carbon, nitrogen, and phosphorus from GO-SHIP Line I07N RB1803 in the Western Indian Ocean from April to June 2018 (Ocean Stoichiometry Project) Biological and Chemical Oceanography Data Management Office (BCO-DMO) [DOI]
  3. Martiny A, Lomas MW. 2021. Particulate organic matter (PON, POC, POP) concentrations collected on R/V Roger Revelle cruise RR1604 along the hydrographic line IO9 in the Eastern Indian Ocean from March to April 2016. Biological and Chemical Oceanography Data Management Office (BCO-DMO) [DOI]
  4. Martiny A, Garcia C, Lee J, Moreno A, Larkin AA. 2020. POM concentrations for carbon, nitrogen, phosphorus, and chemical oxygen from GO-SHIP Line P18 Legs 1 and 2 in 2016 and 2017.  Biological and Chemical Oceanography Data Management Office (BCO-DMO) [DOI]
  5. Lomas MW, Martiny A. 2020. Depth profile data from R/V Atlantic Explorer AE1319 in the NW Atlantic from Aug-Sept. 2013. Biological and Chemical Oceanography Data Management Office (BCO-DMO) [DOI]
  6. Lomas MW, Martiny A. 2020. Depth profile data from Bermuda Atlantic Time-Series Validation cruise 46 (BVAL46) in the Sargasso Sea from Sept-Oct. 2011. Biological and Chemical Oceanography Data Management Office (BCO-DMO) [DOI]
  7. Lomas MW, Martiny A. 2020. Depth profile data from R/V New Horizons NH1418 in the tropical Pacific from Sept-Oct. 2014. Biological and Chemical Oceanography Data Management Office (BCO-DMO) [DOI]
  8. Stabeno P. 2021. EcoFOCI hourly-averaged Ice Draft Data and statistics at Icy Cape, Alaska station C2; 10/2017 to 08/2018 (3 parts/phases), and 08/2018 to 08/2019 (2 parts/phases), Arctic Integrated Ecosystem Research Program, 2017–2019. Axiom Data Science. [DOI]
  9. Danielson S. 2021. Conductivity, temperature, and depth data from CTDs deployed on mooring N1 for the ASGARD Project, Bering Sea, 2017–2019. Axiom Data Science. [DOI]
  10. Martiny AC, Vrugt JA, Lomas MW. 2015. Data from: Concentrations and ratios of particulate organic carbon, nitrogen, and phosphorus in the global ocean. Dryad. [DOI] [PMC free article] [PubMed]
  11. Tanioka T, 2022. Global Ocean Particulate Organic Phosphorus, Carbon, Oxygen for Respiration, and Nitrogen (GO-POPCORN) data from Bio-GO-SHIP cruises. Dryad. [DOI] [PMC free article] [PubMed]
  12. Tanioka T. 2022. tanio003/GOPOPCORN_Data_Codes: Initial Submission. Zenodo. [DOI]

Data Availability Statement

Code and data used to reproduce all the figures and tables are available in the GitHub repository https://github.com/tanio003/GOPOPCORN_Data_Codes and archived here (10.5281/zenodo.6967484)44.


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