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Cassondra DeFoor

University of Nevada, Reno

Research Mentor: Lisa Eisner and Jens Nielsen

Project: Factors that Influence Phytoplankton Biomass and Productivity in the Chukchi Sea in 2017

During the 10-week summer JISAO internship I worked with Dr. Jens Nielsen and Dr. Lisa Eisner on our Project entitled, “Factors that Influence Phytoplankton Biomass and Productivity in the Chukchi Sea in 2017”. Phytoplankton are an important area of study because they are the base of the marine food web. These microscopic marine algae are autotrophs that depend on light, nutrients, and temperature to assimilate carbon and produce oxygen. Changes in temperature and salinity create stratification that limits vertical mixing of deeper water to surface water, often resulting in nutrient depleted surface waters and nutrient rich deeper waters. In these waters, it is possible for phytoplankton blooms to occur. We characterized phytoplankton blooms as having a peak in chlorophyll-a concentration of at least 1 microgram per liter above the median for the cast. We then split the phytoplankton blooms into two categories: surface blooms which occurred in the upper 15 meters of the water column and subsurface blooms that happened below 15 meters. We were particularly interested in subsurface blooms because this is more commonly an effect of stratified waters. We assessed the prevalence of phytoplankton blooms in spring (June) and late summer (August-September) in the Northern Bering and Chukchi Sea in 2017. We then identified the main drivers of surface and subsurface blooms. Finally, we used theoretical models using temperature, irradiance, and nutrient availability to predict realized growth.

We determined that in spring 7% of casts were surface blooms, 26% were subsurface blooms, and 67% were non-blooms. In fall, 8% were surface blooms, 41% were subsurface blooms, and 51% were non-blooms. Thus, subsurface blooms were more prevalent in fall than spring 2017 while surface blooms were equally prevalent. We created graphs of chlorophyll, temperature, salinity, and density along the water column and then identified that many phytoplankton blooms which were detected by peaks in chlorophyll correlated with changes in temperature, salinity and density. This suggests that when there is a change in water mass there tends to be a change in phytoplankton biomass. We also examined how phytoplankton biomass changed with various nutrients such as nitrate, silicate, ammonium, nitrite, and phosphate. We focused on nitrate because it is considered a limiting nutrient in these locations and the other nutrients generally followed the same patterns. One micromole per kilogram nitrate is considered a threshold level for phytoplankton growth to occur. We looked at the nitrate profile along the water column and determined that many subsurface and surface blooms were limited in nitrate in the upper water columns and then increased to above the threshold level nitrate concentration in lower waters. The upper and lower water column were identified as the mixed layer depth where the change in density was greater than 0.1 kilogram per meter cubed from the value at 5 meters. The average light percentage at the chlorophyll-a max for surface and subsurface blooms in both fall and spring were analyzed. The values were all under 12% suggesting that phytoplankton blooms were positioned deep in the water column where light levels were low.

For the models, we looked at a cast that did not have a phytoplankton bloom and was not nitrate limited and a cast with a phytoplankton bloom that was nitrate limited. Data along the water column were used in temperature, irradiance, and nitrate availability equations from literature to predict phytoplankton growth. We then compared the modeled phytoplankton growth to the measured realized growth from the data. We found that for the non-bloom cast the measured growth best followed the model that was temperature and light dependent. Since it was not limited in nitrate there was no change in the predicted growth between the model that was temperature and light dependent versus the model that was temperature, light, and nitrate dependent suggesting that this cast was best described as light dependent. For the subsurface bloom, the predicted growth for the temperature and light model and the temperature, light and nitrate model differed greatly. The measured growth for this cast followed the model that used all three criteria as dependency supporting that the growth for this cast was nutrient dependent.

We concluded that there were more subsurface blooms in fall than spring. Also, most casts appeared to be primarily limited by nutrient availability in surface water. For these casts, phytoplankton growth depends on nutrient availability and will experience tradeoffs of light and temperature to acquire nutrients. For non-nutrient limited casts, phytoplankton growth was best described as light dependent. Better understanding the relationship between phytoplankton growth and nutrient dynamics in the Northern Bering and Chukchi Seas will improve our understanding of how future climate warming might impact these ecosystems.

Cassondra's research poster