Table of Contents

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

Samples from the submarine groundwater wells and overlying water column were collected quarterly from November 2022 through March 2024 aboard U.S. Geological Survey (USGS) small boats. This sampling effort was led by collaborator Chris Smith at USGS. Water column samples were collected by lowering the sampling line, acid-cleaned ½” OD Bev-A-Line tubing attached to ¼” synthetic line that was weighed down by a 30 lb kettlebell to target depths. Surface samples were collected at ~1 m depth and bottom samples were collected from ~1 m above the sea floor. A separate sampling line of acid-cleaned 3/8” OD Bev-A-line connected to a custom-made PVC adapter was used for sampling the submarine groundwater wells, which had been installed by USGS. Different Teflon diaphragm pumps were used for the water column and wells samples to avoid cross contamination. Samples for macronutrient analysis were filtered through 0.2 µm Pall Acropak Super membrane filter capsules, collected in acid-cleaned and triple-rinsed 15-mL Falcon tubes and stored in ziplock bags and frozen (-20 ºC) until analyzed following recommended practices (Becker et al., 2020).

Samples for riverine and estuarine end members were collected by collaborators from the University of South Florida as described in Conway and Hunt (submitted). These macronutrient samples were filtered using an acid-cleaned and triple-rinsed polypropylene syringe coupled with a PVDF 0.2 µm filter, collected in acid-cleaned and triple-rinsed 15-mL Falcon tubes and stored in ziplock bags and frozen (-20 ºC) until analyzed following recommended practices (Becker et al., 2020).

Macronutrients samples were analyzed for phosphate, nitrate+nitrite, silicate, and nitrite on a QuAAtro39 AutoAnalyzer (SEAL Analytical) according to standard colorimetric methods (Strickland and Parsons, 1972). All reagents were prepared in dedicated labware with high purity Milli-Q (>18 MΩ cm) water. Working standards were prepared fresh daily in an artificial seawater (ASW; 35 g/L sodium chloride, 0.5 g/L sodium bicarbonate) matrix using calibrated volumetric pipettes.

Nine-point standard curves were made in artificial seawater analyzed at the beginning of each run following a series of reagent blanks. Quality control checks were conducted every twelfth sample using a combination of ASW blanks and standards. The highest standard from the calibration curve was analyzed approximately every twenty samples to check/correct for drift during the runs. Subsamples of reference material for nutrients in seawater (Kanso) were measured in each run (see accompanying Table 1 in supplemental file).

Detection limits for each parameter were determined from three times the standard deviation of replicate lowest standards. Limits of detection were 0.01 µM for phosphate, 0.04 µM for nitrate+nitrite, 0.04 µM for silicate, and 0.01 µM for nitrite. Values below detection are reported as 0 µM with accompanying QC Flag 6. Limits of quantification (LOQ) for each parameter were determined as ten times the standard deviation of replicate analyses of the lowest standards. These are provided in the supplemental file, Table 2, as an alternative estimate of the analytical limitations for these parameters.

Sample analyses for macronutrients were performed by senior researcher Dr. Salvatore Caprara in the College of Earth, Ocean, and Atmospheric Sciences at Oregon State University.

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. [See supplemental file, Table 1, for certified reference material values obtained in this study].

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 replicate measurements were available to check the quality of the data].

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. [Not used].

4: Bad Value: An obviously erroneous data value. [Not used].

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. The accompanying value is the detection limit for the technique or zero if that value is unknown. [Values below detection are reported as 0 and should be interpreted using the associated quality flag = 6. Detection limits for each parameter are provided in the methods, and calculated limits of detection and limits of quantification are provided in the accompanying Table 2 in the supplemental file].

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 [Used when no data is available for this parameter].

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].

- Loaded CSV file "BCO-DMO_Buck_STING-Land_Nutrients_260206.csv" with headers from row 1, skipping row 2 (units row); missing values defined as "", "nd", "na", and "nda"
- Combined DATE_LOCAL (MM/DD/YY format) and TIME_LOCAL (HH:MM format) using America/New_York timezone to create DATETIME_LOCAL_ET as a datetime column formatted as ISO 8601 (YYYY-MM-DDTHH:MM); rows with missing time values produce null
- Created DATETIME_UTC by converting the same DATE_LOCAL and TIME_LOCAL inputs from America/New_York to UTC, formatted as ISO 8601
- Reordered columns placing STING_ID, CRUISE_ID, DATETIME_UTC, DATETIME_LOCAL_ET, DATE_LOCAL, TIME_LOCAL, and TIME_ZONE first, followed by location, sample metadata, and nutrient measurement columns
- Converted DATE_LOCAL from MM/DD/YY to ISO 8601 date format (YYYY-MM-DD), preserving America/New_York timezone context
- Renamed column TYPE to SITE_TYPE and SAMPLE to SAMPLE_DESC
- Renamed table from "res1" to "997784_v1_sting_land_nutrients"
- Output written to "997784_v1_sting_land_nutrients.csv"

NSF Award Abstract:
This project will investigate how groundwater discharge delivers important nutrients to the coastal ecosystems of the West Florida Shelf. Preliminary studies indicate that groundwater may supply both dissolved organic nitrogen (DON) and iron in this region. In coastal ecosystems like the West Florida Shelf that have very low nitrate and ammonium concentrations, DON is the main form of nitrogen available to organisms. Nitrogen cycling is strongly affected by iron availability because iron is essential for both photosynthesis and for nitrogen fixation. This study will investigate the sources and composition of DON and iron, and their influence on the coastal ecosystem. The team will sample offshore groundwater wells, river and estuarine waters, and conduct two expeditions across the West Florida Shelf in winter and summer. Investigators will participate in K-12 and outreach activities to increase awareness of the project and related science. The project will fund the work of six graduate and eight undergraduate students across five institutions, furthering NSF’s goals of education and training.

Motivated by preliminary observations of unexplained, tightly-correlated DON and dissolved iron concentrations across the West Florida Shelf (WFS), the proposed work will quantify the flux and isotopic signatures of submarine groundwater discharge (SGD)-derived DON and iron to the WFS, and evaluate the bioavailability of this temporally-variable source using four seasonal near-shore campaigns sampling offshore groundwater wells, estuarine, and riverine endmembers and two cross-shelf cruises. The work will evaluate whether SGD stimulates nitrogen fixation on the WFS, and the potential for the stimulated nitrogen fixation to further modify the chemistry of DON and dissolved iron in the region. The cross-shelf cruises will investigate hypothesized periods of maximum SGD and Trichodesmium abundance (June), and reduced river discharge and SGD (February), thus comparing two distinct biogeochemical regimes. The concentrations and isotopic compositions of DON and dissolved iron, molecular composition of DON, and the concentration and composition of iron-binding ligands will be characterized. Nitrogen fixation rates and Trichodesmium spp. abundance and expression of iron stress genes will be measured. Fluxes of DON and iron from SGD and rivers will be quantified with radium isotope mass balances. The impacts of SGD on nitrogen fixation and DON/ligand production will be constrained with incubations of natural phytoplankton communities with submarine groundwater amendments. Two hypotheses will be tested: 1) SGD is the dominant source of bioavailable DON and dissolved iron on the WFS, and 2) SGD-alleviation of iron stress changes the dominant Trichodesmium species on the WFS, increases nitrogen fixation rates and modifies DON and iron composition. Overall, the work will establish connections between marine nitrogen and iron cycling and evaluate the potential for coastal inputs to modify water along the WFS before export to the Atlantic Ocean. This study will thus provide a framework to consider these boundary fluxes in oligotrophic coastal systems and the relative importance of rivers and SGD as sources of nitrogen and iron in other analogous locations, such as coastal systems in Australia, India, and Africa, where nitrogen fixation and SGD have also been documented.

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.