Table of Contents

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

Experimental setup:

The experiments were designed to test the combined effects of four temperatures, and eight light intensities on growth and photophysiology of the diatom T. pseudonana CCMP1335 in a multifactorial design. Four temperatures were tested: 15°C, 18°C, 22°C, and 26°C. Within each temperature, eight light levels were tested: 30, 40, 70,90,105,125,140 and 265 µmol photons m-2 s-1.  All lights were set at a 12 h day: 12 h dark cycle. For logistical reasons, experiments were partially conducted in series.

Experiments were conducted in Multicultivator MC-1000 OD units (Photon Systems Instruments, Drasov, Czech Republic). Each unit consists of eight 85 ml test-tubes immersed in a thermostated water bath, each independently illuminated by an array of cool white LEDs set at specific intensity and timing. A 0.2µm filtered ambient air was bubbled through sterile artificial seawater, and the humidified air was supplied to each tube.  Each experiment was split into two phases: An acclimation phase spanning 3 days, was used to acclimate cultures to their new environment. Pre-acclimated, exponentially-growing cultures were then inoculated into fresh media and incubated through a 4-day experimental phase during which assessments of growth, photophysiology, and nutrient cycling were carried out daily. All sampling started 6 hours into the daily light cycle to minimize effects of diurnal cycles.

Experiments were conducted with artificial seawater (ASW) prepared using previously described methods (Kester et. al 1967), and enriched with 50mL per liter of UV sterilized natural seawater and nitrate (NO3), phosphate (PO4), silicic acid (Si[OH]4), at levels ensuring that the cultures would remain nutrient-replete over the course of the experiment. Trace metals and vitamins were added as in f/2 (Guillard 1975). The pH of the growth media was measured spectrophometrically using the m-cresol purple method (Dickson 1993), and adjusted using 0.1N HCl or 0.1M NaOH.

pH measurements:

Three ml samples were taken at the start and end of the experiment to assess pH. The pH was measured with a spectrophotometer (Genesys 10SVIS) using the indicator dye m-cresol purple (Sigma Aldrich) at 25°C. The absorbance was measured at 730 nm, 578 nm, and 434 nm before and after dye addition (Clayton & Byrne 1993, Fangue et al. 2010). A TRIS buffer solution in synthetic seawater with known pH, supplied by A. Dickson (Scripps Institution of Oceanography, USA) was used to calibrate the dye.

Dissolved Inorganic Carbon (DIC) measurements:

DIC was measured in freshly prepared media, and at the end of the experiment phase. 25 ml of the sample was siphoned into clean glass serum vials, fixed with HgCL2 (0.035 % final conc. v/v), and sealed with butyl rubber septa. Samples were stored at 4°C until analyzed. Prior experiments had confirmed that no gas exchange, and/or change in DIC occurred during sample storage for up to 30 days using this method. Total dissolved inorganic carbon (TCO2) samples were analyzed using an automated infrared inorganic carbon analyzer (AIRICA).  The AIRICA-23 (MARIANDA, Kiel, Germany), is a high precision instrument used to measure total dissolved inorganic carbon in seawater. The system uses a high precision syringe and a mass flow controller to deliver a known volume of sample into a stripper where it is then acidified, converting the inorganic carbon species into CO2 and delivered under constant flow to nondispersive infrared detector. The CO2 is then carried using an inert reference gas (N2) into a LICOR-7000 that measures pCO2 using the difference in infrared absorbance between a sample and reference cell. The pCO2 is recorded over time and integrated by the AIRICA software. This integrated value is proportional to the amount of dissolved inorganic carbon evolved from the sample and converted to carbon units using a conversion factor (CT Factor). The CT Factor is determined by calibration of the system against a certified reference material of known value (Dickson et al. 2007. Guide to Best Practices for Ocean CO2 Measurements). The value is converted to gravimetric units (umol/kg) using the volume, temperature, and salinity of the sample. In order to check for analytical stability of the system throughout a run, a certified reference material is used in between every 5 samples. Replicate DIC measurements were averaged.

Macronutrient concentrations:

Media was filtered through 0.2 µm filters into clean (plastic) bottles and stored at -20°C until analyses for nutrients.  Phosphate (PO4), Nitrate (NO3) + Nitrite (NO2), and Silicic Acid (Si(OH)4) were measured by Flow injection analysis (FIA) using a QuikChem 8500 Series 2 AutoAnalyzer (Lachat Instruments, Zellweger Analytics, Inc.).

BCO-DMO Processing Notes:
- data submitted in Excel file "BCODMO_Series 1B - 1_pH_DIC_Nuts_4Aug2020.xlsx" sheets "pH_DIC" and "Nutrients" extracted to csv
- the three sheets were joined into a single table
- added conventional header with dataset name, PI name, version date
- renamed columns to conform with BCO-DMO naming conventions (removed spaces and special characters

The overarching goal of this project is to develop a framework for understanding the response of phytoplankton to multiple environmental stresses. Marine phytoplankton, which are tiny algae, produce as much oxygen as terrestrial plants and provide food, directly or indirectly, to all marine animals. Their productivity is thus important both for global elemental cycles of oxygen and carbon, as well as for the productivity of the ocean. Globally the productivity of marine phytoplankton appears to be changing, but while we have some understanding of the response of phytoplankton to shifts in one environmental parameter at a time, like temperature, there is very little knowledge of their response to simultaneous changes in several parameters. Increased atmospheric carbon dioxide concentrations result in both ocean acidification and increased surface water temperatures. The latter in turn leads to greater ocean stratification and associated changes in light exposure and nutrient availability for the plankton. Recently it has become apparent that the response of phytoplankton to simultaneous changes in these growth parameters is not additive. For example, the effect of ocean acidification may be severe at one temperature-light combination and negligible at another. The researchers of this project will carry out experiments that will provide a theoretical understanding of the relevant interactions so that the impact of climate change on marine phytoplankton can be predicted in an informed way. This project will engage high schools students through training of a teacher and the development of a teaching unit. Undergraduate and graduate students will work directly on the research. A cartoon journalist will create a cartoon story on the research results to translate the findings to a broader general public audience.

Each phytoplankton species has the capability to acclimatize to changes in temperature, light, pCO2, and nutrient availability - at least within a finite range. However, the response of phytoplankton to multiple simultaneous stressors is frequently complex, because the effects on physiological responses are interactive. To date, no datasets exist for even a single species that could fully test the assumptions and implications of existing models of phytoplankton acclimation to multiple environmental stressors. The investigators will combine modeling analysis with laboratory experiments to investigate the combined influences of changes in pCO2, temperature, light, and nitrate availability on phytoplankton growth using cultures of open ocean and coastal diatom strains (Thalassiosira pseudonana) and an open ocean cyanobacteria species (Synechococcus sp.). The planned experiments represent ideal case studies of the complex and interactive effects of environmental conditions on organisms, and results will provide the basis for predictive modeling of the response of phytoplankton taxa to multiple environmental stresses.