Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Processing Description
• Related Publications
• Related Datasets
• Parameters
• Instruments
• Deployments
• Project Information
• Funding

Water column sampling:
The full water column of 21 stations near the West Antarctic Peninsula was sampled between 11 September 2016 and 10 October 2016 aboard the R/V IB Nathaniel B. Palmer. Depth profile samples were collected using 12-liter (L) Niskin bottles (OceanTestEquipment, Inc) mounted on a trace metal clean rosette sampling system (SBE32, Seabird; TMC CTD).

From the trace metal clean rosette, samples for humic-like substances were filtered through pre-cleaned 0.2-micrometer (µm) polyethersulfone membrane filters (Acropak 200, Pall Corporation). Samples were collected in acid-cleaned, narrow-mouth 500-milliliter (mL) fluorinated high-density polyethylene bottles (FLPE; Nalgene) and stored at -20 degrees Celsius (ºC) until shore-based humic-like substance and ligand analyses at Oregon State University.

Sample analyses – humic-like substances:
Electroactive, Fe-binding humic-like substances (HS) were measured using adsorptive cathodic stripping voltammetry based on the method first developed by Laglera et al. (2007) and updated by Sukekava et al. (2018). All samples were analyzed using a polarographic 797 VA Computrace (Metrohm) stand equipped with a hanging mercury drop electrode, Ag/AgCl reference electrode, platinum rod auxiliary electrode, and an acid-cleaned Teflon analytical cell. Briefly, frozen samples were set out to thaw overnight at room temperature in the dark. The following day, 10 mL sub-samples were aliquoted into each of 2 paired acid-cleaned, pre-conditioned 50 mL polypropylene vials (MetalFree, Labcon), followed by the addition of 50 microliters (μL) of 1.5 molar (M) boric acid buffer to maintain a solution pH of 8.2. Iron standard was added to one of the paired vials (vial B, final concentration 60 nanomolar (nM)) to saturate all the humic-like substance binding groups in the solution, while vial A was left under ambient Fe conditions. Vial A represented the amount of Fe bound to HS in-situ (Fe-HS) and vial B represented the total amount of Fe-binding HS in the sample (HS_T). Both aliquots were left to equilibrate for a minimum of 14 hours. After the equilibration period, vial A was added to the Teflon voltametric cell with an addition of 500 μL of 0.4M KBrO₃- catalyst. The sample was then purged for 300 seconds with high-purity N₂ gas, and then electrochemical analysis was performed with a 60-120 second deposition period with stirring at -0.1 volts (V) followed by linear sweep voltammetry (-0.1 to -1V, scan rate 50 millivolts per second (mV s-1)). Vial B was analyzed immediately following vial A under the same conditions, then three successive standard additions of Fe-saturated Suwannee River Fulvic Acid (SRFA) standard were added. Default standard additions were 0.1, 0.2, and 0.3 milligrams (mg) SRFA per liter, and up to 0.6 mg SRFA per liter for samples with high HS concentrations. A 70 second N₂ purge was included after each addition to ensure oxygen removal. All measurements were measured in triplicate, and the Teflon analytical cell was rinsed thoroughly with Milli-Q water between samples to minimize carryover. Due to high HS concentrations, 13 samples required dilutions with Milli-Q water prior to analysis. Dilutions were 1:10 (n = 2), 1:5 (n = 10), and 5:1 (n = 1) volume sample : volume Milli-Q.

Peak heights were extracted from scans using ECDSOFT software (Omanović, 2025; Omanović and Branica, 1998), then the peak heights from vial A and vial B converted into Fe-HS and HS_T concentrations, respectively in micrograms (μg) SRFA eq per liter using the slope of curve generated by the three standard additions. Units were then converted to nM Fe eq using the measured binding capacity of SRFA in seawater 14.6 ± 0.4 nmol Fe per mg SRFA. Median variability between replicate scans was 0.02 nM Fe eq (average 0.03) for Fe-HS and 0.02 nM Fe eq (average 0.06) for HS_T. The limit of detection (LOD) was calculated as three times the median standard deviation of replicate scans and was estimated to be 0.06 nM Fe eq. Only one sample had a concentration below the LOD (Stn 16–33m, Fe-HS = 0.03 nM Fe eq).

Data were flagged using the SeaDataNet quality flag scheme recommended by GEOTRACES (https://www.geotraces.org/geotraces-quality-flag-policy/) and described below. Notes specific to the application of these flags to this dataset are noted in brackets […].

0: No Quality Control: No quality control procedures have been applied to the data value. This is the initial status for all data values entering the working archive. [Not used].

1: Good Value: Good quality data value that is not part of any identified malfunction and has been verified as consistent with real phenomena during the quality control process. [Not used].

2: Probably Good Value: Data value that is probably consistent with real phenomena, but this is unconfirmed or data value forming part of a malfunction that is considered too small to affect the overall quality of the data object of which it is a part. [Used when no replicates or reference samples were available to further verify the quality of the data; used for most data here].

3: Probably Bad Value: Data value recognized as unusual during quality control that forms part of a feature that is probably inconsistent with real phenomena. [Used for Fe-HS data when Fe-HS > Fe concentrations by more than 0.02 nM, the median std dev of triplicate scans].

4: Bad Value: An obviously erroneous data value. [used when variability in triplicate scans exceeded measured value or for suspected bottle misfire].

5: Changed Value: Data value adjusted during quality control. Best practice strongly recommends that the value before the change be preserved in the data or its accompanying metadata. [Not used].

6: Value Below Detection Limit: The level of the measured phenomenon was less than the limit of detection (LOD) for the method employed to measure it. [used when Fe-HS or HS_T were less than LOD of 0.06 nM Fe eq].

7: Value in Excess: The level of the measured phenomenon was too large to be quantified by the technique employed to measure it. The accompanying value is the measurement limit for the technique. [Not used].

8: Interpolated Value: This value has been derived by interpolation from other values in the data object. [Not used].

9: Missing Value: The data value is missing. Any accompanying value will be a magic number representing absent data [When sample was not analyzed the notation ‘nda’ for no data available was used].

A: Value Phenomenon Uncertain: There is uncertainty in the description of the measured phenomenon associated with the value such as chemical species or biological entity. [Not used]

- Imported original file "BCO_DMO_Moore_Kisiday_WestAnt_HS-FeL.xlsx" into the BCO-DMO system.
- Converted DATE (%m-%d-%y) and GMT (%H:%M) (interpreted as UTC) into a combined datetime field ISO_DateTime_UTC formatted as %Y-%m-%dT%H:%MZ (UTC).
- Removed the original DATE and GMT columns.
- Saved final file as "988663_v1_westant_hs-fel.csv".

This project focuses on an important group of photosynthetic algae in the Southern Ocean (SO), diatoms, and the roles associated bacterial communities play in modulating their growth. Diatom growth fuels the SO food web and balances atmospheric carbon dioxide by sequestering the carbon used for growth to the deep ocean on long time scales as cells sink below the surface. The diatom growth is limited by the available iron in the seawater, most of which is not freely available to the diatoms but instead is tightly bound to other compounds. The nature of these compounds and how phytoplankton acquire iron from them is critical to understanding productivity in this region and globally. The investigators will conduct experiments to characterize the relationship between diatoms, their associated bacteria, and iron in open ocean and inshore waters. Experiments will involve supplying nutrients at varying nutrient ratios to natural phytoplankton assemblages to determine how diatoms and their associated bacteria respond to different conditions. This will provide valuable data that can be used by climate and food web modelers and it will help us better understand the relationship between iron, a key nutrient in the ocean, and the organisms at the base of the food web that use iron for photosynthetic growth and carbon uptake. The project will also further the NSF goals of training new generations of scientists and of making scientific discoveries available to the general public. The project supports early career senior investigators and the training of graduate and undergraduate students as well as outreach activities with middle school Girl Scouts in Rhode Island, inner city middle and high school age girls in Virginia, and middle school girls in Florida.

The project combines trace metal biogeochemistry, phytoplankton cultivation, and molecular biology to address questions regarding the production of iron-binding compounds and the role of diatom-bacterial interactions in this iron-limited region. Iron is an essential micronutrient for marine phytoplankton. Phytoplankton growth in the SO is limited by a lack of sufficient iron, with important consequences for carbon cycling and climate in this high latitude regime. Some of the major outstanding questions in iron biogeochemistry relate to the organic compounds that bind >99.9% of dissolved iron in surface oceans. The investigators' prior research in this region suggests that production of strong iron-binding compounds in the SO is linked to diatom blooms in waters with high nitrate to iron ratios. The sources of these compounds are unknown but the investigators hypothesize that they may be from bacteria, which are known to produce such compounds for their own use. The project will test three hypotheses concerning the production of these iron-binding compounds, limitations on the biological availability of iron even if present in high concentrations, and the roles of diatom-associated bacteria in these processes. Results from this project will provide fundamental information about the biogeochemical trigger, and biological sources and function, of natural strong iron-binding compound production in the SO, where iron plays a critical role in phytoplankton productivity, carbon cycling, and climate regulation.