Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Supplemental Files
• Related Publications
• Parameters
• Instruments
• Deployments
• Project Information
• Funding

Methodology:
The following methods are provided from a manuscript currently in preparation (Burns et al. in prep.).

Sampling and analytical procedures:

Sample Collection:

Depth profile samples were collected in austral spring 2016 aboard the R/V/I/B Nathaniel B. Palmer using a SeaBird GEOTRACES style SBE32 rosette system deployed on a conducting Kevlar line (Cutter and Bruland 2012) with OceanTestEquipment, Inc. X-Niskin samplers modified for trace element sampling. Samples were filtered by gravity through 0.2 µm Pall AcroPakTM membrane filter capsules and collected in 125 mL low density polyethylene (LDPE) bottles (Nalgene; Fisher Scientific) that had been cleaned first in a soap (3% Citranox, Fisher) bath, rinsed three times with ultrahigh purity water (Milli-Q, >18.2 MΩ cm), soaked in 25% nitric acid (HNO3, Fisher, Trace Metal Grade) for at least one month, rinsed three times with ultrahigh purity water and stored filled with ~0.024 M hydrochloric acid (HCl, Fisher, Trace Metal Grade). Bottles were rinsed three times with sample seawater prior to filling. Samples were acidified to pH 1.8 (0.024 M HCl, Fisher, Optima) and stored double bagged in buckets at room temperature until analyzed at the University of South Florida.

Dissolved Trace Metals:
Extraction and pre-concentration of the dissolved samples was performed using the seaFAST-pico system (Elemental Scientific) offline (Lagerström et al. 2013; Bown et al. 2017; Rapp et al. 2017). The commercially available Nobias-chelate PA1 resin (Sohrin et al. 2008; Sohrin and Bruland 2011; Biller and Bruland 2012) in the seaFAST preconcentration column concurrently extracts the trace metals of interest in this study: Mn, Fe, Co, Ni, Cu, Zn, Cd, and Pb. To sufficiently extract dissolved Co and Cu, ultraviolet (UV) oxidation of the dissolved samples was conducted prior to seaFAST extraction (Achterberg et al. 2001; Milne et al. 2010; Biller and Bruland 2012). To accomplish this, dissolved samples were poured into acid-cleaned Teflon™ 30-mL vials (Savillex) with Teflon™ caps custom-fitted with transparent quartz window, and UV oxidized for 90 minutes at ~20 mW cm-2 in a UVO-Cleaner® (Jelight Model No. 342) after a 30-minute system warm-up.

During the seaFAST extraction process, UV-oxidized samples were buffered to a target pH range of 6.0 to 6.5 (Lagerström et al. 2013). To conserve the buffer reagent, the seaFAST buffer flow rate was adjusted in the submethod from 400-650 to 400-350 sec-μL/min. To make the ammonium acetate (NH4Ac) buffer, a solution of 5.3 M glacial acetic acid (HAc, Fisher, Trace Metal Grade) and 2.6 M ammonium hydroxide (NH4OH, Fisher, Optima) in Milli-Q was adjusted to pH 7.4 ± 0.2 with small additions of either HAc or NH4OH.

For preconcentration of the extracted trace metals, the seaFAST software method was programmed to take up one 10-mL loop of sample seawater and elute the extracted trace metals with 400 μL of elution acid. The elution acid was 0.74 M triple-distilled nitric acid (HNO3) containing 10 ppb indium (In) and rhodium (Rh) internal standards. The HNO3 was triple-distilled using a Savillex DST-1000 Acid Purification System prior to use. The eluent was eluted into acid-cleaned, 2.0 mL PVDF vials (Elemental Scientific) with TeflonTM caps (Elemental Scientific). A 0.30 M HNO3 (Fisher, Trace Metal Grade) rinse for the seaFAST autosampler probe was used between each sample.

Quality control (QC) checks were included in seaFAST runs. GEOTRACES 2008 GS and SAFe 2004 D2 reference samples were measured to assess accuracy. Additionally, QC seawater samples were run approximately every 15 samples to monitor instrument precision over time. The first QC was offshore seawater from the Antarctic Circumpolar Current (ACC), acidified to pH 1.8 (0.024 M) with Optima (Fisher) HCl. The second QC was from offshore Eastern Pacific Zone seawater, acidified to pH 1.8 (0.024 M) with Optima (Fisher) HCl.

Two sets of standard curves were made for these analyses: one set in ACC QC seawater (acidified to 0.024 M with Optima HCl) and a second set in the elution acid (0.74 M triple-distilled HNO3 containing 10 ppb In and Rh). For the mixed metal standard curves, stock solutions were made in 1.49 M Optima (Fisher) HNO3 using 1,000 ppm standards (ULTRA Scientific) of Mn, Fe, Co, Ni, Cu, Zn, Cd, and Pb. Each curve was a minimum of six points and made to cover the concentration ranges of the dissolved trace metals in incubation samples.

The eluents from the seaFAST were analyzed on a Thermo Scientific magnetic sector Element XR High Resolution Inductively Coupled Plasma Mass Spectrophotometer (HR-ICP-MS). In between each sample, the autosampler probe was rinsed twice in 0.74 M TraceMetalGrade (Fisher) HNO3, to avoid sample carryover. To account for any interference of MoO+ on Cd counts, a three-point Mo calibration curve was made in elution acid and the slope of the Mo counts plotted against Cd counts was used to adjust Cd counts.

Trace metals were quantified by standard addition from the seawater standard curves. The average counts for each trace metal were normalized to the In internal standard counts per sample, to account for daily drift in ICP-MS measurements. Dissolved trace metal concentrations in each seawater sample were calculated from the seawater calibration curve slope. The In-normalized average trace metal counts were divided by the seawater standard curve slope to yield the trace metal concentrations in each eluent.

Air blanks were measured with a minimum of three replicates per seaFAST and Element XR run. For the air blanks, the seaFAST method was run as usual, but taking up air instead of acidified seawater. For the dissolved trace metal concentrations presented here, the average air blank concentrations per seaFAST run were subtracted from the dissolved sample concentrations to account for the procedural blank.

Sample analyses for dissolved trace metals were performed by Shannon Burns (USF); ORCID ID: https://orcid.org/0000-0002-1569-3060.

Quality Flags: The standard Ocean Data View qualifying flags were used (reference all flags at https://www.bodc.ac.uk/data/codes_and_formats/odv_format/). Additional notes specific to the application of these flags to this project are noted in brackets […].

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. [See Table 1 for reference sample data.]
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. [Not used.]
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 when data appeared anomalous.]
4: Bad Value: An obviously erroneous data value. [Not used.]
5: Changed Value: Data value adjusted during quality control. [Not used.]
6: Value Below Detection Limit: The level of the measured phenomenon was too small to be quantified by the technique employed to measure it. The accompanying value is the detection limit for the technique or zero if that value is unknown. [Not used. See Table 1 for detection limits.]
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. [Not 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.]

Data were processed using ESI SC version 2.9.0.380 software.

BCO-DMO Data Manager Processing Notes:
* Extracted data submitted in xlsx format as csv file.
* added a conventional header with dataset name, PI name, version date
* modified parameter names to conform with BCO-DMO naming conventions
** Multiple "FLAG" columns renamed to reflect the trace metal concentration they describe e.g. FLAG renamed to Mn_D_CONC_FLAG columns. 
* blank values in this dataset are displayed as "nd" for "no data."  nd is the default missing data identifier in the BCO-DMO system.
* Date format converted to ISO 8601 date format yyyy-mm-dd
* Added ISO_DateTime_UTC column in format ISO 8601 yyyy-mm-ddTHH:MMZ from columns "Date" and "GMT"
* Latitude and longitude rounded to 4 decimal places

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.