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 2

3. P ROJECT OBJECTIVES AND STRATEGY Phytoplankton diversity in the world’s open oceans from optical remote sensing 1. Exploit current Chla- based approach 2. Explore the potential of hyperspectral approach 3

4. O CEAN - 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) 4

5. Conversion to CAbsorbed light energy O CEAN COLOR - BASED PRIMARY PRODUCTION MODEL P(t,z) = Chla(z,t) a*(z,t) PAR(z,t) Φ c (z,t) ▫ 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 [m 2 (mg Chla) -1 ] ▫ Φ c : Quantum yield of carbon fixation [mol C (mol quanta) -1 ] 5

6. P RIMARY PRODUCTION AT THE PHYTOPLANTKON GROUP LEVEL Group-specific profiles of Chla (Uitz et al. JGR 2006) Group-specific photophysiology (Uitz et al. L&O 2008) Group-specific primary production (Uitz et al. GBC in press) P pg (t,z) = Chla pg (z,t) a pg *(z,t) PAR(z,t) Φ c,pg (z,t) 6

7. P micro P nano P pico 3. Computation of group-specific primary production rates (Uitz et al. GBC in press) 2. Bio-optical model of Morel (1991) + photophysiological properties of Uitz et al. (2008) φ micro φ nano φ pico Chla micro Chla nano 1. Computation of Chla vertical profiles from surface Chla (Uitz et al. 2006) Chla pico M ETHODOLOGY P pg (t,z) = Chla pg (z,t) a pg *(z,t) PAR(z,t) Φ c,pg (z,t) (mg m -3 ) 10-year time series of SeaWiFS surface Chl (1997-2007) 7

8. GLOBAL ANNUAL PRIMARY PRODUCTION Total primary production Pmicro + Pnano + P pico = 46 Gt C y-1 Consistent with previous estimates (37-56 Gt C y -1 ; e.g. Antoine et al. 1996; Behrenfeld & Falkowski 1997; Westberry et al. 2008) Group-specific primary production Pmicro (diatoms) 15 Gt C y-1 (32% of total) P nano (prymnesiophytes) 20 Gt C y -1 (44%) P pico (cyanobacteria) 11 Gt C y -1 (24%) Carbon export towards deep oceans 10-16 Gt C y -1 (20-40%; review by Tréguer et al. 2003) 8

9. CLIMATOLOGY OF MICROPHYTOPLANKTON PRODUCTION 9 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 10 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 11 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 12

13. 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 13 CONCLUSIONS AND PERSPECTIVES

14. HYPERSPECTRAL OPTICAL APPROACH -Input dataset- Pigment composition Cluster analysis -Reference classification- -Input dataset- Optical measurements (ocean reflectance and absorption spectra) Derivative analysis Cluster analysis 14 Evaluation of performance (Torecilla et al. in prep.) “Pilot” study Small set of stations from Eastern Atlantic open ocean ▫ HPLC pigments ▫ Optical data Encouraging results ▫ Best classification with hyperspectral derivative spectra

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

16. THANK YOU FOR YOUR ATTENTION 16