# Presentation: 933163

## 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

## The Leaf Analogy: Photosynthesis Measurement

- Phytoplankton are ‘tiny leaves’

- (in solution)

## 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

## 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
- aph()  = ai*()Ci
- _Advantage_: only  pigments
- _Disadvantages_:
- Solvents variations
- Unknown a*()

- _Filter Pad Technique_

- _Pigment Reconstruction_

## FPT >> Pigment Reconstruction

- Overestimate by FPT is caused by other colored compounds (*Nelson et al., 1993; Bricaud et al., 2004*)

**1:1**

## Pigment Spectra

## 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

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

## Productivity and HPLC Chl-a, near surface

- r2 = 0.78

## Productivity and aph(pig), near surface

- r2 = 0.82

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

## Absorption and PP, Chl-a > 1

***Pigment reconstructions***

***fpt***

## 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

## 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 CO2
- 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*

## 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

## Thanks!

## Absorbed Irradiance and Quantum Yield

## PBopt 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)

- 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.