Contributors | Affiliation | Role |
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Bundy, Randelle M. | University of Washington (UW) | Principal Investigator |
Hoffman, Colleen L. | University of Washington (UW) | Co-Principal Investigator |
Rauch, Shannon | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
Sampling and cruise transect
Samples were collected as part of the 2017-2018 U.K. GEOTRACES GA13 section cruise along the Mid-Atlantic Ridge. Water samples from 11 venting and near venting locations were collected using a Seabird 911 conductivity, temperature, and depth (CTD) titanium rosette using conducting Kevlar wire with an oxidation-reduction potential (ORP) sensor to detect plumes. Teflon coated OTE (Ocean Test Equipment) bottles were pressurized to approximately 7 psi with 0.2 micrometers (μm) filtered air using an oil-free compressor. A Sartobran 300 (Sartorius) filter capsule (0.2 μm) was used to collect filtered seawater samples into clean 250 milliliter (mL) LDPE sample bottles. Bottles and caps were rinsed 3 times with the filtered sample before being filled. Samples were stored frozen at -20 degrees Celsius (°C) for organic iron-binding ligand characterization by voltammetry and mass spectrometry.
Iron-binding ligand concentration and binding strengths measured with competitive ligand exchange-adsorptive cathodic stripping voltammetry
Iron-binding ligand concentrations and binding strengths were determined by competitive ligand exchange-adsorptive cathodic stripping voltammetry (CLE-ACSV) with a BASi controlled growth mercury electrode (CGME) with an Ag/AgCl- reference electrode and platinum auxiliary electrode (Bioanalytical Systems Incorporated). Using previously established methods (Buck et al., 2015, 2018; Bundy et al., 2018; Abualhaija and van den Berg, 2014; Hawkes et al., 2013 (Planet. Sci. Lett.)). 40 frozen filtrate (<0.2 µm) samples with dissolved iron concentrations between 0.41-11.67 nanomolar (nM) (Table S1-S2 of Hoffman et al., 2023) were thawed in a 4°C fridge prior to analysis. A 15-point titration curve was analyzed for each sample. Briefly, within each titration, every point sequentially received 10 mL of sample, 7.5 micromolar (µM) of borate-ammonium buffer, 10 µM salicylaldoxime (SA) as the added ligand, and a dissolved Fe addition. Data was collected using the Epsilon Eclipse Electrochemical Analyzer (v.213) with a deposition time of 120 seconds and analyzed using ElectroChemical Data Software (v2001-2014) and ProMCC (v2008-2018) to determine peak areas and iron-binding ligand parameters, respectively.
Reverse Titration-CLE-ACSV
Reverse titration-CLE-ACSV (RT-CLE-ACSV) (Hawkes et al., 2013 (Anal. Chim. Acta)) was completed on 10 samples from Broken Spur, and TAG hydrothermal vent fields with dissolved Fe concentrations between 19.01-90.25 nM (Table S1-S2 of Hoffman et al., 2023). Briefly, a 10-point titration curve was analyzed for each sample with each titration point consisting of 10 mL of sample buffered with 7.5 µM boric acid and the competitive ligand 1-nitroso-2-napthol (NN). All samples were analyzed on a BASi Controlled Growth Mercury Electrode (CGME) with the Epsilon Eclipse Electrochemical Analyzer (v.213) and deposition time of 120 seconds. For each sample, the competitive ligand NN was added in concentrations of 0.5, 1, 2, 3, 4, 6, 9, 15, 20, and 40 µM. Samples were equilibrated overnight and purged with N₂ (99.99% UHP) for 5 minutes before analysis. At the end of each titration, three dissolved iron additions (3-15 nM) were added to the final titration point to get the total concentration of iron in equilibrium with the natural ligands. Data was analyzed using ElectroChemical Data Software (v2001-2014) to acquire peak areas and a package in R using the model parameters of βFeNN3 = 5.12 x 10¹⁶, Xmin = 0.8, Xmax = 0.9, and c1high = 0.75 to determine the iron-binding ligand parameters (Hawkes et al., 2013 (Anal. Chim. Acta)). These parameters were chosen based on the recommendations for undersaturated samples and titrations curves where ipmax was not reached (Hawkes et al., 2013 (Anal. Chim. Acta)). All other parameters within the model we kept at the default values.
Forward titrations were analyzed using ElectroChemical Data Software (v2001-2014) and ProMCC (v2008-2018) to determine peak areas and Fe-binding ligand parameters, respectively.
Reverse titration data was analyzed using ElectroChemical Data Software (v2001-2014) to acquire peak areas and a package in R using the model parameters of βFeNN3 = 5.12 x 10¹⁶, Xmin = 0.8, Xmax = 0.9, and c1high = 0.75 to determine the Fe-binding ligand parameters (Hawkes et al., 2013 (Anal. Chim. Acta)).
Additional data for siderophore analyses have been deposited on Massive under the DOI 10.25345/C5V97ZW7N.
Microbial 16S rRNA data have been deposited on GenBank under the accession number BioProject #PRJNA865382.
- Imported original file "FRidge_GA13_CSV_data_final_bcodmo.xlsx" into the BCO-DMO system.
- Flagged "NaN" as a missing data identifier. Missing data are empty/blank in the final CSV file.
- Renamed fields to comply with BCO-DMO naming conventions.
- Saved the final file as "923065_v1_ga13_diss_org_fe-binding_ligands.csv".
File |
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923065_v1_ga13_diss_org_fe-binding_ligands.csv (Comma Separated Values (.csv), 4.15 KB) MD5:da194bf71e6b69636cd9adff12eac119 Primary data file for dataset ID 923065, version 1 |
Parameter | Description | Units |
Cruise | cruise number for the expedition | unitless |
Latitude_deg_N | Latitude in degrees north | decimal degrees |
Longitude_deg_E | Longitude in degrees east (negative values = West) | decimal degrees |
Station | Station number | unitless |
Geotraces_ID | Geotraces number for the corresponding sample | unitless |
Depth_m | sample depth | meters (m) |
dFe_nM | dissolved iron concentration | nanomoles per liter (nmol L-1) |
L1_nM | dissolved organic iron-binding ligand concentration of L1 ligands | nanomoles per liter (nmol L-1) |
L1_err_nM | 95% confidence interval of dissolved organic iron-binding ligand L1 concentration | nanomoles per liter (nmol L-1) |
L2_nM | dissolved organic iron-binding ligand concentration of L2 ligands | nanomoles per liter (nmol L-1) |
L2_err_nM | 95% confidence interval of dissolved organic iron-binding ligand L2 concentration | nanomoles per liter (nmol L-1) |
L3_nM | dissolved organic iron-binding ligand concentration of L3 ligands | nanomoles per liter (nmol L-1) |
L3_err_nM | 95% confidence interval of dissolved organic iron-binding ligand L3 concentration | nanomoles per liter (nmol L-1) |
LT_nM | dissolved organic iron-binding ligand concentration of total ligands | nanomoles per liter (nmol L-1) |
LT_err_nM | 95% confidence interval of dissolved organic iron-binding ligand total concentration | nanomoles per liter (nmol L-1) |
logK1 | dissolved organic iron-binding ligand strength of L1 ligands | unitless |
logK1_err | 95% confidence interval of dissolved organic iron-binding ligand L1 strength | unitless |
logK2 | dissolved organic iron-binding ligand strength of L2 ligands | unitless |
logK2_err | 95% confidence interval of dissolved organic iron-binding ligand L2 strength | unitless |
logK3 | dissolved organic iron-binding ligand strength of L3 ligands | unitless |
log_K3_err | 95% confidence interval of dissolved organic iron-binding ligand L3 strength | unitless |
Dataset-specific Instrument Name | BASi Controlled Growth Mercury Electrode |
Generic Instrument Name | BASi Controlled Growth Mercury Electrode |
Dataset-specific Description | All samples were analyzed on a BASi Controlled Growth Mercury Electrode (CGME) with the Epsilon Eclipse Electrochemical Analyzer (v.213). |
Generic Instrument Description | Bioanalytical Systems (BASi) Mercury drop electrodes are generated by the BASi Controlled Growth Mercury Electrode (CGME) in three modes:
DME (Dropping Mercury Electrode) - mercury is allowed to flow freely from the reservoir down the capillary and so the growth of the mercury drop and its lifetime is controlled by gravity. (The optional 100 um capillary is recommended for this mode.)
SMDE (Static Mercury Drop Electrode) - the drop size is determined by the length of time for which the fast-response capillary valve is opened, and the drop is dislodged by a drop knocker. The dispense/knock timing is microprocessor-controlled and is typically coordinated with the potential pulse or square-wave waveform. This mode can also used to generate the Hanging Mercury Drop Electrode required for stripping experiments.
CGME (Controlled Growth Mercury Electrode) - the mercury drop is grown by a series of pulses that open the capillary valve. The number of pulses, their duration, and their frequency can be varied by PC control, providing great flexibility in both the drop size and its rate of growth. This CGME mode can be used for both polarographic and stripping experiments.
http://www.basinc.com/products/ec/cgme.php |
Dataset-specific Instrument Name | Epsilon Eclipse Electrochemical Analyzer |
Generic Instrument Name | BASi EC-epsilon 2 Autoanalyzer |
Dataset-specific Description | All samples were analyzed on a BASi Controlled Growth Mercury Electrode (CGME) with the Epsilon Eclipse Electrochemical Analyzer (v.213). |
Generic Instrument Description | The Bioanalytical Systems EC epsilon is a family of potentiostat/galvanostats for electrochemistry. The most basic epsilon instrument can be used for standard techniques, as well as chronopotentiometry for materials characterization (e.g., characterization of transition metal complexes by cyclic voltammetry and controlled potential electrolysis, or of biosensors by cyclic voltammetry and constant potential amperometry). Pulse, square wave, and stripping techniques can be added by a software upgrade, and a second channel can be added by a hardware upgrade.
http://www.basinc.com/products/ec/epsilon/ |
Dataset-specific Instrument Name | Seabird 911 CTD |
Generic Instrument Name | CTD Sea-Bird 911 |
Generic Instrument Description | The Sea-Bird SBE 911 is a type of CTD instrument package. The SBE 911 includes the SBE 9 Underwater Unit and the SBE 11 Deck Unit (for real-time readout using conductive wire) for deployment from a vessel. The combination of the SBE 9 and SBE 11 is called a SBE 911. The SBE 9 uses Sea-Bird's standard modular temperature and conductivity sensors (SBE 3 and SBE 4). The SBE 9 CTD can be configured with auxiliary sensors to measure other parameters including dissolved oxygen, pH, turbidity, fluorescence, light (PAR), light transmission, etc.). More information from Sea-Bird Electronics. |
Website | |
Platform | RRS James Cook |
Start Date | 2017-12-20 |
End Date | 2018-02-01 |
Description | See more about this cruise at: https://www.bodc.ac.uk/resources/inventories/cruise_inventory/report/16349/ |
NSF Award Abstract:
This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).
Iron is one of the most abundant elements in the Earth’s crust, but it is extremely diluted in the ocean. Iron-poor surface waters limit the growth of microscopic marine life, called phytoplankton, and their ability to remove carbon from the atmosphere and surface ocean. However, over the last few decades, our understanding of how iron enters the ocean has evolved. Recent data has shown that deep-sea hot springs, also known as hydrothermal vents, impact global iron budgets and are important for surface iron supply. Hydrothermal vents are found globally along volcanic spreading centers where new seafloor is created through tectonic activity. The new porous seafloor allows seawater to circulate through the hot, chemically reactive rocks to create hydrothermal fluids. These fluids are less dense (hotter, 300-400°C) than deep ocean waters (2°C), so the water exiting the vents rises while mixing with ambient seawater, eventually forming hydrothermal plumes. These nutrient-rich plumes can extend for 10-1000s of kilometers into the ocean interior. To account for the long-range transport of hydrothermal iron into the ocean interior, models have shown that stabilizing agents (i.e. organic ligands) are needed to prevent iron from precipitating and settling to the seafloor. However, we still do not know the sources and identities of these organic ligands, as well as how common they are in various hydrothermal systems across the global ocean. Investigating these mechanism(s) for hydrothermal iron stabilization across different vent systems will provide insight into both local and long-range iron utilization by deep-sea marine microorganisms and phytoplankton in the surface ocean.
In this project, the sources, concentration, and identities of iron-binding organic ligands in hydrothermal plumes from four different volcanic spreading centers will be examined to understand their impact on iron stabilization and transport into the ocean interior. The major aim of this research is to test whether (1) the concentrations of strong organic ligands tightly control the distal transport of hydrothermally derived dissolved iron in neutrally buoyant plumes across a variety of hydrothermal vent systems and (2) investigate if microbes from hydrothermal systems are responsible for production of these strong organic ligands (i.e. siderophores). This work will use a combination of existing samples and samples of opportunity that will be collected during an upcoming field expedition, each from distinct spreading centers. These findings would significantly enhance our understanding of hydrothermal iron transport and aid in future modeling efforts on the fate of hydrothermal iron in the global iron cycle. This project will support the training of two early career scientists, an undergraduate intern, and STEM workshop kits for middle school programs about deep-sea environments, which will be developed in collaboration and made freely available through the NOAA Pacific Marine Environmental Education and Outreach webpage.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Funding Source | Award |
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NSF Division of Ocean Sciences (NSF OCE) |