1. Assimilation Numbers?? Phytoplankton Absorption: a Strong Predictor of Primary Productivity in the Surface Ocean Or: Throw Away that Lab Fluorometer John Marra, LDEO Chuck Trees, CHORS Jay O’Reilly, NOAA

2. The Leaf Analogy: Photosynthesis Measurement Phytoplankton are ‘tiny leaves’ (in solution)

3. Pigments and Phytoplankton Ecology 1. Environmental factors drive phytoplankton community structure [Margalef, 1978] 2. Community structure can be defined by pigment composition, i.e., absorption properties [Mackey et al. (1996), Vidussi et al. (2001)] 3. Therefore, absorption properties are a response to environmental conditions [Claustre et al., (2005)], and may indicate physiological rates

4. Measuring phytoplankton absorption: No perfect method Collect phytos on a filter Scan filter in a spec Apply corrections; MeOH wash and rescan Advantage: in vivo Disadvantage: other colored stuff on filter gets washed off Extract pigments HPLC a ph ( ) =  a i *( )C i Advantage: only pigments Disadvantages: –Solvents variations –Unknown a*( ) Filter Pad TechniquePigment Reconstruction

5. FPT >> Pigment Reconstruction Overestimate by FPT is caused by other colored compounds (Nelson et al., 1993; Bricaud et al., 2004) 1:1

6. Pigment Spectra

7. Data Sources: JGOFS Process Studies NABE (1989) EqPac (1991-1992) Arabian Sea Expedition (1995) Antarctic Ecosystems, Southern Ocean Process Study (AESOPS) (1997-1998) http://www.usjgofs.whoi.edu

8. Productivity and Fluorometer Chlorophyll-a ( <1 mg m -3 ), near surface

9. Productivity and HPLC Chl-a, near surface r 2 = 0.78

10. Productivity and a ph (pig), near surface r 2 = 0.82

11. Productivity and Chl-a extracts for HPLC (all data)

12. Absorption and PP, Chl-a > 1 Pigment reconstructions fpt

13. Conclusions Productivity in the ocean varies with phytoplankton absorption, not always with the quantity of chlorophyll-a How pigments are arranged (‘packaged’) in cells is important in many ocean regimes, more important than the quantity of Chl-a Phytoplankton absorption integrates variability in nutrients, temperature, and irradiance

14. CAVEATS No temperate or central gyre data (however temperate bloom species similar to Antarctic) Based on incubation methodology (Agrees* or Disagrees § with daytime in situ  CO 2 No method for phycobiliproteins (cyanobacteria?) Haven’t yet extended analysis to depth (we expect that PP/aph to decline linearly with depth) Largest effect where Chl-a > 1 (includes areas responsible for most export, trophic transfer) § Marra et al., 1995 *Chipman et al., 1993

15. RAMIFICATIONS P/a ph may be a simpler approach to estimating P from ocean color (maybe, Lee et al. 2002?), or from shipboard “Assimilation No.” may actually be relatively invariant throughout the ocean’s surface layer if defined as P/a ph ‘C/Chl’ may not apply everywhere Grinding up the ‘tiny leaves’ and extracting chemicals isn’t the way to go

16. Thanks!

17. Absorbed Irradiance and Quantum Yield

18. P B opt and Temperature? Temperature (ºC) P B opt [(mgC)(mgChl) -1 h -1 )] (thanks to J. Cullen’s presentation at the Bangor Productivity Conference, March 2002)

19.  Our results mean that productivity in the ocean varies with phytoplankton absorption, not always with the quantity of chlorophyll-a;  Our results mean that we can throw out many of the so- called ‘standard’ models for calculating productivity from space;  Our results mean that we have been mislead by measuring chlorophyll-a and other pigments chemically, when how they behave inside the cell is the most important factor in determining photosynthetic rates;  Our results mean that productivity from space-borne sensors will be much easier and straightforward than we have realized;  Our results mean that estimating productivity at sea (within  10%!) will be much easier, and afford a way to avoid costly, time-consuming, incubations; and  Our results mean that we’ll need to redesign drastically most of the models of phytoplankton growth that have been produced over the years.