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ASSESSING BIODIVERSITY OF PHYTOPLANKTON COMMUNITIES FROM OPTICAL REMOTE SENSING

Explore the impact of phytoplankton diversity on marine biogeochemical processes with ocean-color based discrimination methods. Discover key insights into primary production modeling and global distribution of phytoplankton groups.

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ASSESSING BIODIVERSITY OF PHYTOPLANKTON COMMUNITIES FROM OPTICAL REMOTE SENSING

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  1. ASSESSING BIODIVERSITY OF PHYTOPLANKTON COMMUNITIES FROM OPTICAL REMOTE SENSING Julia Uitz, Dariusz Stramski, and Rick A. Reynolds Scripps Institution of Oceanography University of California San Diego NASA Biodiversity Team Meeting – May 2010 – Washington DC

  2. WHY STUDYING PHYTOPLANKTON DIVERSITY? • Phytoplankton diversity influences many important biogeochemical processes • Photosynthetic efficiency • Fate of carbon fixed via photosynthesis • Marine biological pump of carbon • Key questions to be addressed • Understanding of marine biogeochemical cycles and modeling capabilities • Distribution and variability on scales relevant to environment and climate changes

  3. Project objectives and strategy

  4. Ocean-color based discrimination of different phytoplankton groups • Satellite measurements of ocean color • Surface Chla concentration • Quasi-global spatial scale • Daily to decade • New generation of algorithms for discriminating different phytoplankton groups from ocean color • Dominance (Alvain et al. 2005) • Surface Chla (Devred et al. 2006; Hirata et al. 2008) • Vertical profile of Chla (Uitz et al. 2006)

  5. Ocean color-based primary production model P(t,z) = Chla(z,t) a*(z,t) PAR(z,t) Φc(z,t) Absorbed light energy Conversion to C • P: Primary production (g C m-3 d-1) • PAR: Irradiance available for photosynthesis (mol quanta m-2 s-1) • Chla: Concentration of chlorophyll a (mg m-3) • a*: Chla-specific absorption coefficient of phytoplankton [m2 (mg Chla)-1] • Φc: Quantum yield of carbon fixation [mol C (mol quanta)-1]

  6. Primary production at the phytoplantkon group level Ppg(t,z) = Chlapg(z,t) apg*(z,t) PAR(z,t) Φc,pg(z,t)

  7. (mg m-3) 10-year time series of SeaWiFS surface Chl (1997-2007) 1. Computation of Chla vertical profiles from surface Chla(Uitz et al. 2006) Chlamicro Chlanano Chlapico 2. Bio-optical model of Morel (1991) + photophysiological properties of Uitz et al. (2008) φmicro φnano φpico 3. Computation of group-specific primary production rates (Uitz et al. GBC in press) Pmicro Pnano Ppico Methodology Ppg(t,z) = Chlapg(z,t) apg*(z,t) PAR(z,t) Φc,pg(z,t)

  8. GLOBAL ANNUAL PRIMARY PRODUCTION

  9. CLIMATOLOGY OF MICROPHYTOPLANKTON PRODUCTION • Boreal winter/Austral summer • (Dec-Jan-Feb) • Boreal summer/Austral winter • (Jun-Jul-Aug) • Temp/subpolar latitudes in summer: high contribution (e.g. Atl Nord >50%) • Near-coastal upwelling systems: 70% (1 g C m-2 d-1) • South Pacific Subtropical Gyre: Minimum contribution (0.02 g C m-2 d-1)

  10. CLIMATOLOGY OF PICOPHYTOPLANKTON PRODUCTION • Boreal winter/Austral summer • (Dec-Jan-Feb) • Boreal summer/Austral winter • (Jun-Jul-Aug) • Maximum contribution in oligotrophic subtropical gyres (40-45%) • Contribution reduced to ~15% at high latitudes

  11. CLIMATOLOGY OF NANOPHYTOPLANKTON PRODUCTION • Boreal winter/Austral summer • (Dec-Jan-Feb) • Boreal summer/Austral winter • (Jun-Jul-Aug) • Substantial contribution on global scale: 0.07-1 g C m-2 d-1 (30-60%) • Can be found in extremely diverse environmental conditions (subtropical gyres vs. winter subantarctic waters  Biodiversity? (see Liu et al. PNAS 2009)

  12. CONCLUSIONS AND PERSPECTIVES • First climatology of phytoplankton group-specific primary production on global scale over seasonal to interannual scales • Significant contribution to our ability to understand and quantify marine carbon cycle with implications for carbon export • Key elements required to calibrate/validate new biogeochemical models (e.g. Le Quéré et al. 2005) • Benchmark for monitoring responses of marine pelagic ecosystems to climate change

  13. CONCLUSIONS AND PERSPECTIVES • Chla-based approaches • Describe general trends across various trophic regimes • But do not necessarily account for specific local conditions • New complementary approaches need to be developed • Explore the potential of hyperspectral optical measurement for discriminating different phytoplankton groups • Hyperspectral optical measurements have matured into powerful technologies in the field of remote sensing • Yet remain largely unexplored for open ocean applications

  14. HYPERSPECTRAL OPTICAL APPROACH • “Pilot” study • Small set of stations from Eastern Atlantic open ocean • HPLC pigments • Optical data • Encouraging results • Best classification with hyperspectral derivative spectra Evaluation of performance (Torecilla et al. in prep.)

  15. HYPERSPECTRAL OPTICAL APPROACH • 2nd cruise in the Atlantic Ocean almost completed!

  16. THANK YOU FOR YOUR ATTENTION

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