Assimilation Numbers??

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

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

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. 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 aph() = ai*()Ci Advantage: only pigments Disadvantages: Solvents variations Unknown a*() Measuring phytoplankton absorption:No perfect method Filter Pad Technique Pigment Reconstruction

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

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 r2 = 0.78

10. Productivity and aph(pig), near surface r2 = 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 DCO2 • 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) *Chipman et al., 1993 §Marra et al., 1995

15. RAMIFICATIONS • P/aph 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/aph • ‘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. PBopt and Temperature? (thanks to J. Cullen’s presentation at the Bangor Productivity Conference, March 2002) PBopt [(mgC)(mgChl)-1 h-1)] Temperature (ºC)

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.