Table of Contents

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

Riverine and estuarine samples were collected on land. Submarine groundwater samples collected on various shore boats by Co-PI Christopher Smith. Riverine, estuarine and submarine groundwater well samples were collected between November 2022 and March 2024 using a trace metal clean pump and filtered through a 0.2 µm Pall Acropak Super membrane filter capsule, collected into 60 mL HDPE bottles and frozen at -20 C until analysis on land. 

TDP concentrations were measured using a Shimadzu UV-1800 and a Shimadzu UV-1900i. All reagents were prepared in dedicated labware with high purity Milli-Q (>18.2 MΩ cm) water. Samples were calibrated using a 6 point calibration curve with concentrations that bracketed sample concentrations. Each run included multiple Milli-Q water and reagent blanks, as well as three internal standards including adenosine triphosphate, a polyphosphate compound, glyphosate, a polyphosphonate compound, and 0.2 µm filtered oligotrophic surface water from the Gulf to check the consistency of the TDP concentration analysis over time.

TDN concentrations were measured using a Thermo 42i NOx analyzer and a Teledyne T200 NOx analyzer and were calibrated with standards that bracketed the concentration range of samples. Additionally, as an internal check on the completeness of the persulfate oxidation method, DON standards were included as “unknowns” in each set of persulfate oxidation, in this case USGS 40 (L-glutamic acid) and IAEA600 (caffeine). 

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 Table 1 for blank and 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. [Used when all replicate measurements were too high to be consistent with real phenomena].

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 0.00 µM in the data file. Detection limits for each parameter are listed in the “methods and sampling” section of this form].

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 collected the notation ‘na’ for ‘not applicable’ was used; when sample collected but there is no result for this parameter, 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.]

- Opened "BCO-DMO_STING_LandSampling_TDN_TDP_Conc_v2.xlsx" in Excel and moved 'NDA' values from the 'TDP_FLAG' and 'TDN_FLAG' fields to the 'TDP' and 'TDN' fields, respectively
- Replaced all 'NDA' values from the 'TDP_FLAG' and 'TDN_FLAG' with '9' to represent missing data, as described in the Data Processing section
- Saved file as "BCO-DMO_STING_LandSampling_TDN_TDP_Conc_v2_dm_adjust_keys.xlsx" and uploaded the file again to the submission
- Imported "BCO-DMO_STING_LandSampling_TDN_TDP_Conc_v2_dm_adjust_keys.xlsx" into the BCO-DMO system
- Removed the second "STING_ID" parameter, since it's values were the same as the first "STING_ID" parameter
- Renamed CRUISE_ID to CRUISE_TYPE to better describe the contents of the parameter
- Exported file as "989225_v1_sting_land_tdn_tdp.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.