Table of Contents

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

At each of the 66 stations, we sampled surface water (~5 meters (m)) using a (SIO) STS 36-place yellow rosette sampler. We prepared samples for single-cell trait analyses in clean conditions by resting 3 to 5 drops (~10 microliters (µL)) of seawater on SF200 Ni grids (Cat: SF200-NI, Electron Microscopy Sciences) using a micropipette. The grids were air-dried for approximately 50 seconds between each drop of seawater. We removed the excess water using a tapered rolled corner of fresh Kimwipe, to avoid excess accumulation of salt particles on the grid. The process was repeated 3 to 5 times for each grid. In total, 6 to 10 grids were prepared for the surface water samples at each station. The samples were analyzed onshore in the Analytical Research Core's (ARC) electron microscope facility at Colorado State University, Fort Collins. We measured the dissolved nutrients, including ammonium, nitrite, nitrate, phosphate, and silicate onboard using a Seal Analytical continuous-flow AutoAnalyzer 3. Temperature, salinity, and fluorescence (chlorophyll) data were obtained from the sensors mounted on the CTD rosette sampler.

We exported the spectral output generated by the Oxford EDAX system as an '.emsa' file type and processed the spectra using the freely available NIST-based DTSA II  (Desktop Spectral Analyzer) software program (Jupiter release available at: http://www.cstl.nist.gov/div837/837.02/epq/dtsa2/). After stripping the background radiation from each spectrum, we determined the total counts within the pre-selected ranges using the DTSA software. The background-stripped counts in each characteristic peak were generated for both the sample and the film, and the film signal was subsequently subtracted from the sample signal. The elemental content in counts per second was further converted to cellular elemental content in femtomoles using the C, N, and P calibration factors. From these values, we calculated the individual traits (i.e., C:N, N:P, and C:P) for each microbe. We analyzed between 24 and 88 individual cells per station. The calibration factors for C, N, and P were 13.64, 10.52, and 49.81 counts per second per femtomole, respectively. We used the probability density function to characterize the trait distributions using the "ggridges" package in RStudio (2025.05.1). Additionally, we computed the functional richness, functional evenness, and functional divergence of the trait distributions using the TPD package in RStudio. We selected an alpha value of 0.95 for each trait to make the analyses less sensitive to outliers. We also used ImageJ software to measure the cell area of microbes. It is free software available at https://imagej.net/ij/download.html. In ImageJ, we processed the SEM images of microbes, where the software automatically outlined the cells and calculated their corresponding area in square micrometers (µm²).

- Imported the sample log from the original file "I08S_EDS_Sample_Log_DS_4302026.csv" as table "sample_log" in the BCO-DMO data processing system.
- Imported the primary cell-level data from the original file "IO8S_cell_info_with_environmental_DS_4302026_bco.csv" as table "cell_info" in the BCO-DMO data processing system.
- Applied find/replace on "Sample Id" in cell_info and "Sample" in sample_log to remove space-underscore patterns (" " → "") and normalize Sample IDs in both tables.
- Stripped "_Box 1" or "_Box 2" suffixes from "Sample" column in sample_log to normalize Sample IDs in both tables.
- Renamed columns in sample_log: Sample → Sample_Id, Date_UTC (mm/dd/yyyy hh:mm) → DateTime_UTC, Time_UTC (hh:mm) → Time_UTC, Lat → Latitude_sample_log, Long → Longitude.
- Renamed "Sample Id" → "Sample_Id" in cell_info to align join keys.
- Performed half-outer join of sample_log into cell_info on compound key {Sample_Id}{Niskin_num} (source) / {Sample_Id}{Niskin number}, bringing in Cruise_ID, DateTime_UTC, EXPOCODE, Grid_Locations, Grid_location_box_num, Latitude_sample_log, Longitude, Niskin_num, Sample_descrip, Time_UTC, WOCE_LINE from the sample_log.
- Converted DateTime_UTC (format "%m/%d/%Y %H:%M", UTC) to ISO 8601 datetime column "ISO_DateTime_UTC" (format "%Y-%m-%dT%H:%MZ").
- Deleted the original DateTime_UTC and Time_UTC columns.
- Multiplied Latitude_sample_log by "-1" to correct sign convention for Southern Hemisphere.
- Multiplied Latitude (from cell_info) by "-1" to correct sign convention for Southern Hemisphere; renamed to "Latitude_cell_info".
- Renamed columns to comply with BCO-DMO naming conventions.
- For rows where Sample_Id "S7_2.8 m", replaced Grid_Locations value "A1, A3, A5" with "Box 1: J4, J6, J8; Box 2: A1, A3, A5".
- For rows where Sample_Id "S7_2.8 m", replaced Grid_location_box_num value "2" with "Box 1: 1; Box 2: 2".
- Renamed table "cell_info" to "1002701_v1_io8s_cell_info".
- Filled all EXPOCODE values with "325020240221".
- Reordered columns to final order.
- Saved the final file as "1002701_v1_io8s_cell_info.csv".

- Imported original file "IO8S_stats.csv" into the BCO-DMO data processing system.
- Renamed columns to comply with BCO-DMO naming conventions.
- Saved the final file as "1002701_v1_supplemental_io8s_stats.csv".

NSF Award Abstract:
The elemental ratios of carbon, nitrogen, and phosphorus (C:N:P) have been considered fixed proportions in marine environments but recent work has demonstrated changes across latitudes. Such variation can have large implications for marine biogeochemistry and atmospheric CO_2 levels. As such, future variations in ocean community C:N:P could be a key feedback to global change. The elemental composition of particulate organic matter (POM) represents the aggregate value of diverse microorganisms as well as non-living particles. However, we currently do not understand how individual cells and particles contribute to observed variation in the C:N:P of POM. This project is determining the biomass C:N:P of individual microbial cells grown under a range of conditions and sampled from diverse ocean biomes. Because different individuals are likely to have different fates (e.g., loss to sinking, lysis, predation), understanding how the trait distribution of microbial biomass C:N:P relates to cell size and trophic mode and how environmental conditions affect each trait’s distribution offers new perspectives and insight into marine C, N, and P cycles. This project supports two PhD students and multiple undergraduate students. The PhD students are integrated into existing networks on microbiome science at each institution with opportunities to collaborate across diverse disciplines. Undergraduate students at both institutions are being recruited through existing training programs that target underrepresented groups. In addition, PI Hall is part of a collaborative of Ecologists and Poets at CSU, that look at ways to translate ecological relationships to non-traditional media to make it more accessible and impactful to the general public. This group is exploring the nature of individuality within the marine microbiome by creating trait distributions of written text and removing different modes of individuals (e.g., words) to better understand and communicate how individuals from the smallest organisms on the planet (marine microorganisms) can have large effects on the surrounding ecosystem (i.e., the planet). Results from this project are being incorporated into future projects of the working group at Colorado State University including public presentations, art installations, and published materials.

The aim of this project is to quantify the relationship between environmental conditions and marine microbial C:N:P by assessing the individuality in microbial elemental stoichiometry. To achieve this the project uses energy dispersive spectroscopy (EDS) to assess the stoichiometric trait distribution of populations and communities under different resource and temperature treatments and oceanographic field work across a broad latitudinal gradient. The researchers hypothesize that the relationship between the stoichiometry of particulate organic matter (POM) and environmental conditions are masked by distinct responses of individual constituents of the marine microbiome. They hypothesize that these distinct responses result in a multi-modal distribution of particulate carbon (C), nitrogen (N), and phosphorus (P) of the marine microbiome. The investigators are characterizing the distribution of three stoichiometric traits (biomass C:N, C:P, and N:P) for 50 marine isolates (both autotrophs and heterotrophs) under different resource and temperature environments. They are characterizing the same trait distribution for marine communities sampled from different regions of the ocean for the same resource and temperature environments as the population experiments. They are participating on an ocean going cruise to characterize stoichiometric trait distribution of the marine microbiome across natural gradients of resources and temperature. Understanding how constituent members of microbial communities alter their biomass in response to environmental change is providing a missing link between the variation in the ocean’s environment and particulate C:N:P ratios for diverse marine environments.

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.