Gulf of Alaska | Lower Trophic Levels
Seasonal and interannual drivers of planktonic food web dynamic in the northwestern Gulf of Alaska shelf Presenter: Ludivine Conte , luconte@ucsc.edu, UCSC Jerome Fiechter , fiechter@ucsc.edu Suzanne Strom , stroms@wwu.edu Seth Danielson , sldanielson@alaska.edu, University of Alaska Fairbanks, College of Fisheries and Ocean Sciences Ana Aguilar-Islas , amaguilarislas@alaska.edu Russell Hopcroft , rrhopcroft@alaska.edu, University of Alaska Fairbanks, CFOS The northwestern Gulf of Alaska (NGA) was recently designated as a Long-Term Ecological Research site to investigate the impact of climate change on ecosystem dynamic. However, it is challenging to understand the long-term effects of climate change when organisms are naturally exposed to strong environmental variability on seasonal, interannual and decadal time scales. Here, we explore the main biotic and abiotic drivers of seasonal and interannual variability of the coastal NGA planktonic food web using monthly averaged model output from a 25 year-long (1995-2020) physical- biogeochemical regional ocean simulation. After evaluating simulated nutrients, Chl-a and zooplankton biomasses against observations, shelf-wide relationships between simulated biological (nanophytoplankton, diatoms, micro and meso-zooplankton) and environmental (salinity, temperature, nutrients, winds, freshwater inputs, mixed layer depth (MLD) and light intensity) variables were identified using pairwise linear correlations. Results show that yearly variation in cumulated phytoplankton production is dominated by diatom variability, and simulated diatom concentrations are primarily driven by nitrate availability prior to the spring bloom. Nutrient concentrations during winter are in turn related to basin-scale dynamics and negatively correlated to the Pacific Decadal Oscillation. Years with high diatom concentration in the model correspond to positive anomalies of mesozooplankton and euphausiids via bottom-up effect. In contrast, simulated microzooplankton variability is primarily controlled by variations in nanophytoplankton concentrations. While none of the environmental variables tested were strongly correlated with annual variation in cumulative nanophytoplankton concentrations, our analysis suggests that different drivers control nanophytoplankton variability throughout the year. In spring, nanophytoplankton is primarily driven by changes in MLD whereas summer variability is negatively correlated to ammonium concentration and diatom biomass, likely due to competition for recycled production after nitrate has been depleted. In summary, our study uses a long-term historical simulation to provide a comprehensive representation of trophic linkages of the NGA planktonic food web and lay the foundation for a better understanding of energy transfers across the pelagic food web in response to low and high productivity years, extreme event disturbances, and secular climate change.
Alaska Marine Science Symposium 2023 141
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