Table of Contents

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

Additional funding description:
This dataset was supported by NSF OCE-2401646, OCE-2401645, OCE-2401644, University of Washington Royalty Research Fund, NIH NIEHS grant R21ES034337-01, NSF IOS-2041497, NIH fellowship F31 ES032733-01A1

Two samples (one bacterial, one phytoplankton) were collected for downstream flow cytometry analyses at each time point. The bacterial fraction was isolated for flow cytometry by collecting 1 mL of whole water from a glass bottle rinsed three times with incoming seawater. The 1 mL aliquot was transferred to a cryovial and immediately fixed with 20 µL glutaraldehyde (25% EM-grade, MilliporeSigma) at 4 °C for 25 minutes and flash-frozen in liquid nitrogen in the field prior to long-term storage in the lab at −80 °C. For flow cytometry analysis of the phytoplankton fraction, whole water (3.5 mL) was subsampled from the same triple rinsed glass bottle and then fixed with 100 uL formaline: hexamine (18%:10% v/w) at 4 °C for 25 minutes. The sample was immediately frozen in liquid nitrogen in the field prior to long-term storage in the lab at −80 °C.

Methodology is from the results paper Nunn et al. (2024, doi:10.1038/s41597-024-04013-5).

Each sample was analyzed on a Guava EasyCyte HT (Luminex) flow cytometer using the native software package Incyte. For bacteria, 190 µL of each sample was stained with 10 µL of 200X SYBR Green I (Invitrogen, Catalog number S7563) for 30 min in the dark at room temperature and distinguished based on plots of forward scatter and green fluorescence (512 nm). Biological duplicates were analyzed and averages and standard deviations were calculated. For phytoplankton, three major phytoplankton groups (cyanobacteria, picoeukaryotes (2–5 microns), and nanoeukaryotes (5–10 microns)) were distinguished by size based on plots of forward scatter vs. red fluorescence (692 nm). Predefined gates for each group were set up based on forward scatter and red fluorescense determined using variable size beads to calculate the forward scatter. The cyanobacteria is further distinguished by gates that target cells that have lower forward scatter with lower chlorophyll and higher orange fluorescence (phycoerythrin) relative to the picoeukaryotes. Biological triplicates were analyzed, measurements were averaged and standard deviations were calculated.   

Data processing is from the results paper Nunn et al. (2024, doi:10.1038/s41597-024-04013-5).

* Sheet 1 of submitted file "Nunn_OrcasIsland_Data_FlowCytometry.xlsx" was exported as csv and imported into the BCO-DMO data system for this dataset.  Table will appear as Data File: 984014_v1_flow-cytometry.csv (along with other download format options).

Missing Data Identifiers:
* In the BCO-DMO data system missing data identifiers are displayed according to the format of data you access. For example, in csv files it will be blank (null) values. In Matlab .mat files it will be NaN values. When viewing data online at BCO-DMO, the missing value will be shown as blank (null) values.

* Column names adjusted to conform to BCO-DMO naming conventions designed to support broad re-use by a variety of research tools and scripting languages. [Only numbers, letters, and underscores.  Can not start with a number]

* Local date and time columns (Pacific Time) were kept, but dates were converted to ISO 8601 format.

* Local Date_PT column was converted to ISO 8601 format. (no time zone change).
* Additional column ISO_DateTime_UTC added from Date and Time (local US/Pacific timezone) columns.

NSF Award Abstract:
Floating, single-celled algae, or phytoplankton, form the base of marine food webs. When phytoplankton have sufficient nutrients to grow quickly and generate dense populations, known as blooms, they influence productivity of the entire food web, including rich coastal fisheries. The present research explores how the environment (nutrients) as well as physical and chemical interactions between individual cells in a phytoplankton community and their associated bacteria act to control the timing of bloom events in a dynamic coastal ecosystem. The work reveals key biomolecules within the base of the food web that can inform food web functioning (including fisheries) and be used in global computational models that forecast the impacts of phytoplankton activities on global carbon cycling. A unique set of samples and data collected in 2021 and 2022 that captured phytoplankton and bacterial communities before, during, and after phytoplankton blooms, is analyzed using genomic methods and the results are used to interrogate these communities for biomolecules associated with blooms stages. The team mentors undergraduates, graduate students, and postdoctoral researchers in the fields of biochemical oceanography, genome sciences, and time-series multivariate statistics. University of Washington organized hackathons develop publicly accessible portals for the simplified interrogation and visualization of 'omics data by high schoolers and undergraduates and are implemented in investigator-led undergraduate teaching modules and the University of Rhode Island Ocean Classroom. The research team also returns to Orcas Island, WA, where the field sampling takes place, to host a series of annual Science Weekends to foster scientific engagement with the local community.

Phytoplankton blooms, from initiation to decline, play vital roles in biogeochemical cycling by fueling primary production, influencing nutrient availability, impacting carbon sequestration in aquatic ecosystems, and supporting secondary production. In addition to environmental conditions, the physical and chemical interactions between individual phytoplankton can significantly modulate blooms, influencing the growth, maintenance, and senescence of phytoplankton. Recent work in steady-state open ocean ecosystems has shown that important chemicals are transferred amongst plankton on time-dependent metabolic schedules that are related to diel cycles. It is unknown how these metabolic schedules operate in dynamic coastal environments that experience perturbations, such as phytoplankton blooms. Here, the investigators are examining metabolic scheduling using long-term, diel sample sets to reveal how chemical and biological signals associated with the initiation, maintenance, and cessation of phytoplankton blooms are modulated on both short (hrs) and long (days-weeks) time scales. Findings are advancing the ability to predict and manage phytoplankton dynamics, providing crucial insights into ecological stability and future oceanographic sampling strategies. Additionally, outcomes of this study are providing a new foundational understanding of the succession of microbial communities and their chemical interactions across a range of timescales. In the long term, this research has the potential to identify predictors of the timing of phytoplankton blooms, optimize fisheries management, and guide future research on carbon sequestration.