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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. The Leaf Analogy: Photosynthesis Measurement. (in solution).

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assimilation numbers

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

the leaf analogy photosynthesis measurement
The Leaf Analogy: Photosynthesis Measurement

(in solution)

Phytoplankton are ‘tiny leaves’

pigments and phytoplankton ecology
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


aph() = ai*()Ci

Advantage: only pigments


Solvents variations

Unknown a*()

Measuring phytoplankton absorption:No perfect method

Filter Pad Technique

Pigment Reconstruction

fpt pigment reconstruction
FPT >> Pigment Reconstruction


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

data sources jgofs process studies
Data Sources: JGOFS Process Studies
  • NABE (1989)
  • EqPac (1991-1992)
  • Arabian Sea Expedition (1995)
  • Antarctic Ecosystems, Southern Ocean Process Study (AESOPS) (1997-1998)
absorption and pp chl a 1
Absorption and PP, Chl-a > 1

Pigment reconstructions


  • 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
  • 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

  • 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

PBopt and Temperature?

(thanks to J. Cullen’s presentation at the Bangor Productivity Conference, March 2002)

PBopt [(mgC)(mgChl)-1 h-1)]

Temperature (ºC)

 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.