Table of Contents

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

Cellular trace elements collected on cruise RR1604 (GO-SHIP transect IO9N) in the Eastern Indian Ocean from March to April 2016.

Samples were collected from 5 L Teflon-coated Niskin-X bottles hung on non-metal line, and clean techniques were followed throughout (Bruland et al., 1979). Niskin-X bottles were transferred to a clean lab immediately after retrieval. At most stations a single sample was collected from 20 m. Four-depth profiles extending to 200 m were collected at 6 stations along the transect.

A small aliquot of unfiltered seawater was reserved from the surface-most Niskin-X bottle to collect phytoplankton cells for synchrotron X-ray fluorescence (SXRF) analysis. SXRF samples were collected following protocols described in Twining et al. (2015). Cellular metals were analyzed with the 2-ID-E microprobe beamline at the Advanced Photon Source, Argonne National Laboratory. Incident beam energy was 10 keV to enable the excitation of Kα fluorescence for elements ranging in atomic number from Si (14) to Zn (30). Approximately 15 cells were analyzed at each station. Flagellated autotrophs with diameters ranging from 1.65 - 6.6 µm were analyzed using 2D raster scans with 0.5 μm pixel step sizes and detector dwell times from 10-20 sec/pixel.

Element quantification was performed by averaging the spectra from pixels representing the cells of interest. Spectra were also extracted from a background area close to each cell. The spectra were then fit with MAPS, a custom fitting software package (Vogt, 2003).  Concentrations were calculated based on conversion factors obtained by running the thin-film standards NBS 1832, NBS 1833, and custom Si, P, and Fe standards made by Micromatter XRF. Cell volume was calculated based on measurements taken from bright field images of the cells and using the equations of Hillebrand et al. (1999). Cellular C was then calculated from the volumes using the equations described in Menden-Deuer and Lessard (2000).

SXRF data quality was assessed and outliers identified in two ways. First, measurements were excluded if the relative standard deviation of the element peak fit by the model was greater than 20%, indicating poor precision of the model fit.  Second, outliers were identified by assessment of cellular log-transformed metal:biomass ratios using an ANCOVA model that included log volume, station, and cell type as effects (JMP, SAS). Ratios were removed if the Jackknife distances of the studentized residuals of this model were greater than 3. Roughly 1% of data were removed from the dataset through this process. Outliers appear as blanks with "3" flags.

Data quality: 
Data are flagged with the following:
1 = Good data, passed QC;
3 = Questionable or suspect data, used when a data point was oceanographically inconsistent;
4 = Bad;
6 = Below detection limit.

BCO-DMO Processing: 
- modified parameter names (removed units, replaced hyphens and spaces with underscores, added "_flag" to flag columns, replaced blank cells with "nd");
- 2022-07-12 - removed unnecessary columns (named "SXRF_map_filename" and "SXRF_spectrum_filename") and added supplemental file of light images.

Description from NSF award abstract:
The Indian Ocean accounts for nearly a fifth of global ocean photosynthesis and is likely a key component in global ocean nutrient and carbon cycles. However, the Indian Ocean may be the least studied major marine body on the planet. Our limited understanding suggests extensive variations in physical and chemical environmental conditions, but how this variation influences biodiversity, nutrient stress, and more broadly regional differences in the functioning of phytoplankton is unknown. To help address these gaps, the investigators will conduct a study by joining an already-funded major research cruise to this region. It will cover a northern region with some of the highest temperatures recorded in open ocean waters, an area around 10°S of predicted (but not tested in situ) iron stress, and a southern subtropical gyre with unique nitrogen to phosphorous(or N:P) ratios. The focus of this project is to quantify and synthesize the interconnectedness of environmental conditions, phytoplankton diversity and genome content, and nutrient biogeochemistry, with the goal of understanding how these may lead to unique biogeochemical regions in Indian Ocean. The research will have broader impacts on many levels. First, it will increase public awareness of the role of phytoplankton on ocean functioning, climate, and people's lives through a new partnership with the Aquarium of the Pacific (AOP), which is the fourth most-attended aquarium in the nation. Secondly, the project will train a postdoctoral scholar as well as a graduate and undergraduate students. Third, the research will dramatically increase our basic knowledge ocean biogeochemistry and in many cases will be the first measurements of their kind made in the Indian Ocean.

This project will address two major questions: How do environmental conditions, phytoplankton diversity, phytoplankton physiology, and biogeochemistry vary across the central Indian Ocean? Are there distinct biogeochemical regimes in the central IO? The researchers hypothesize that environmental conditions, including the relative availability of nitrogen (N) and iron (Fe), lead to three distinct phytoplankton communities and biogeochemical regimes. They will employ a series of advanced analytical tools including high sensitivity measurements of dissolved and particulate nutrients (nitrogen, phosphorus, and iron), genomics, bioassays to test for nutrient stress, and cell-sorting of specific taxa followed by measures of nutrient content and uptake. A focus of this project is to quantify and synthesize the interconnectedness of environmental conditions, phytoplankton diversity and genome content, and nutrient biogeochemistry, and how these lead to unique biogeochemical regions in Indian Ocean. This extensive set of observations can ultimately be linked to ocean models and satellite data to provide a comprehensive view of regional differences in chemistry, biodiversity and phytoplankton biogeochemical functioning in the Indian Ocean.