Energy transfer in marine ecosystems based on phytoplankton size structure from satellite remote sensing

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Although the connection from unicellular phytoplankton to predatory fish in the marine food web is well known, our current understanding on how the community composition of phytoplankton (e.g. their prominent size structure) may affect energy fluxes and transfer efficiency to higher trophic levels is still limited. This has implications for fisheries productivity, nutrient recycling and incorporation of carbon into seabed sediments. This project will aim at enhancing our understanding of low tropic level processes in marine food webs by (a) applying the state-of-the-art ocean colour algorithms, and further developing remote-sensing-based models to derive phytoplankton size structure from remote-sensing data;(b) combining satellite-derived information on phytoplankton size structure to the in situ observations on phytoplankton and zooplankton taxa, and planktivorous fish; and (c) developing a tri-trophic food web model to evaluate the implications of these size-structures and environmental parameters for energy transfer to higher trophic levels.The student will work on the interface between computer-based modelling, satellite remote sensing (at the University of Reading with S. Roy), and laboratory and field observations (at CEFAS with E. Capuzzo and J. Van Der Kooij), in close collaboration with ICES (S. Jennings). In particular, the student will (a) take part in the activities within annual multidisciplinary fish surveys (carried out by Cefas), to estimate biomass of fish communities (e.g. sprat, sardine, mackerel, herring), as well as their distribution in relation to their environment; (b) generate satellite-derived variables on phytoplankton size and community structure, using the state-of-the-art satellite algorithms (e.g. [1]), and validate the satellite-based estimates with the survey data that covered the western English Channel and eastern Celtic Sea; (c) use the abundance and distribution data on pelagic fish obtained from acoustic data (collected during pelagic trawl operations), the environmental data (subsurface temperature, salinity, and chlorophyll), and the abundance, biomass and size of the main planktonic functional groups, phytoplankton pigments by HPLC, microzooplankton and mesozooplankton, to improve the ecological context to the fish observations (e.g. [2]);(d) build a tri-trophic food-web model connecting satellite-derived phytoplankton size structure and survey-based biological data to estimate trophic energy fluxes and transfer efficiencies, and uncertainties consumer biomass (e.g. [3]).Key references:(1) Roy et al. (2013) The global distribution of phytoplankton size spectrum and size classes from their light-absorption spectra derived from satellite data. Remote Sensing of Environment, 139, 185-197;(2) Capuzzo et al. (2015) Decrease in water clarity of the southern and central North Sea during the 20th century. Global change biology 21(6), 2206-2214.(3) Jennings and Collingridge (2015) Predicting consumer biomass, size-structure, production, catch potential, responses to fishing and associated uncertainties in the world's marine ecosystems. PloS one 10 (7), e0133794.