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Funded Research

Predicting, Validating, and Understanding Zooplankton Distributions from Space in an Eddy Rich Ocean

Coles, Victoria: University of Maryland Center for Environmental Science (Project Lead)

Project Funding: 2017 - 2020

NRA: 2016 NASA: Interdisciplinary Research in Earth Science   

Funded by NASA, Other US Funding: NASA

Remote sensing observations of marine zooplankton have great potential for enhancing global climate models and ecosystem-based management of ocean resources. As the crucial link between primary production and upper trophic levels (such as fish) zooplankton grazing and biomass influence both carbon export and fisheries production. The importance of mesoscale features such as fronts and eddies on zooplankton dynamics are well established, but high frequency large-scale sampling of these features is currently only possible using satellites. Next generation sensors will observe the ocean at higher spatial resolutions, including proxies for surface currents (e.g. Surface Water Ocean Topography), as well as highly spectrally-resolved measurements for detecting phytoplankton functional types and rate processes (e.g. Pre-Aerosol, Cloud, ocean Ecosystem). To translate these fine-scale measurements of ocean physics and phytoplankton into fisheries relevant quantities, we must understand their effects on zooplankton. Our goal is to use space-based high-resolution observations to predict zooplankton and grazing in relation to mesoscale eddy features. A cornerstone of our strategy will be linking an existing coupled ocean ecosystem model with explicit microzooplankton to a Lagrangian individual-based model of two forms of mesozooplankton (each with five stages and stage-specific size, behavior, growth, and grazing properties). We will use this model for developing both Eulerian and Lagrangian algorithms that relate surface ocean fields in the model results (such as: currents, temperature, size-specific phytoplankton carbon biomass and production rates) to microzooplankton and mesozooplankton abundance and grazing. The coupled models will serve as a known system for understanding error propagation. The algorithms will then be applied to satellite-derived fields (e.g. primary productivity, size-specific phytoplankton carbon biomass, net change in phytoplankton abundance, temperature, thermocline or nutricline depth) to predict micro and mesozooplankton abundance. Because zooplankton algorithm development is a challenging problem, we propose to evaluate three complementary algorithm approaches in a shared quantitative framework: empirical algorithms based on machine learning and statistical inference, mechanistic algorithms based on equations describing growth and grazing of phytoplankton, and theoretical algorithms based on the flow of energy from one trophic level to the next using allometric scaling. We will then validate the space-based estimates of zooplankton fields in two test regions, the Gulf of Mexico and the California Current, selected based on the availability of extensive existing zooplankton databases. We will quantitatively evaluate the skill of the satellite-derived zooplankton fields in relation to observations of micro- and meso-zooplankton, and eddy variability. The realized zooplankton distributions will be used to answer the question; How do fronts and eddies in the ocean influence trophic transfer of energy up the food web into zooplankton? Larval fish often aggregate in fronts and eddies, indicating that trophic levels above zooplankton respond to the eddying environment. We hypothesize that grazing and trophic transfer by zooplankton will be greater in an eddying ocean when compared to large spatial and temporal averages of these parameters. This proposed effort advances NASA research objectives through improving space-based prediction of ocean ecosystems that can be applied to ecosystem based management of the oceans at scales relevant to emerging technologies and planned NASA missions.

More details may be found in the following project profile(s):