MODELING PHYTOPLANKTON COMMUNITY STRUCTURE: PIGMENTS AND SCATTERING PROPERTIES Stephanie Dutkiewicz 1 Anna Hickman 2 , Oliver Jahn 1 , Watson Gregg 3 , Mick Follows 1 1. Massachusetts Institute of Technology 2. University of Essex 3. NASA Goddard Space Flight Center stephanie dutkiewicz http://ocean.mit.edu/~stephd
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MODELING PHYTOPLANKTON COMMUNITY STRUCTURE: PIGMENTS AND SCATTERING PROPERTIES
MODELING PHYTOPLANKTON COMMUNITY STRUCTURE: PIGMENTS AND SCATTERING PROPERTIES Stephanie Dutkiewicz 1 Anna Hickman 2 , Oliver Jahn 1 , Watson Gregg 3 , Mick Follows 1 1. Massachusetts Institute of Technology 2. University of Essex 3. NASA Goddard Space Flight Center. stephanie dutkiewicz - PowerPoint PPT Presentation
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MODELING PHYTOPLANKTON COMMUNITY STRUCTURE:
PIGMENTS AND SCATTERING PROPERTIES
Stephanie Dutkiewicz1
Anna Hickman2, Oliver Jahn1, Watson Gregg3, Mick Follows1
1. Massachusetts Institute of Technology2. University of Essex
3. NASA Goddard Space Flight Center
stephanie dutkiewiczhttp://ocean.mit.edu/~stephd
http://darwinproject.mit.edu
modeling the marine ecosystem
nutrients
light
many (100+)phytoplankton
zooplankton
detritus
some sinks out to depthsDarwin Project Model(Follows et al., Science 2007)
PO4NO3
FeSi
randomly assignedgrowth rates
grazing rates
http://darwinproject.mit.edu
modeling the marine ecosystem
nutrients
light
phytoplankton
zooplankton
detritus
some sinks out to depths
PO4NO3
FeSi grazing rates
randomly assignedgrowth rates
Darwin Project Model(Follows et al., Science 2007)
environment 1
http://darwinproject.mit.edu
modeling the marine ecosystem
Darwin Project Model(Follows et al., Science 2007)
nutrients
light
phytoplanktonzooplankton
detritus
some sinks out to depths
PO4NO3
FeSi
grazing rates
environment 2
randomly assignedgrowth rates
http://darwinproject.mit.edu
log10(biomass)
Initial Biomass of 100 phytoplankton types
http://darwinproject.mit.edu
log10(biomass)
Annual Biomass after 10 years simulation
stephanie dutkiewiczhttp://ocean.mit.edu/~stephd
EMERGENT COMMUNITY
By putting in appropriate trait trade-offs, environment selects the appropriatecommunity structure:
- K versus r strategies (Dutkiewicz et al, GBC, 2009)
-nitrogen fixing (Monteiro et al, GBC, 2010,2011)
-nitrate assimilation ability (Bragg et al, PlosOne 2010)-size/grazing pressure (Ward et al. in prep)-pigments/absorption (Hickman et al, MEPS, 2010) Phytoplankton Functional Types
stephanie dutkiewiczhttp://ocean.mit.edu/~stephd
EMERGENT COMMUNITY
Phytoplankton Functional Types
By putting in appropriate trait trade-offs, environment selects the appropriatecommunity structure:
- K versus r strategies (Dutkiewicz et al, GBC, 2009)
-nitrogen fixing (Monteiro et al, GBC, 2010,2011)
-nitrate assimilation ability (Bragg et al, PlosOne 2010)-size/grazing pressure (Ward et al. in prep)-pigments/absorption (Hickman et al, MEPS, 2010)
stephanie dutkiewiczhttp://ocean.mit.edu/~stephd
(Data courtesy: M. Zubkov, J. Heywood)
(Hickman et al, MEPS, 2010)
AMT15
Vertical distribution of phytoplankton types
OBSERVATIONS
ONE DIMENSIONAL MODEL
stephanie dutkiewiczhttp://ocean.mit.edu/~stephd
Pigments as trait• Different pigment allow absorption of light at different wavebands
Remote sensing beginning to resolve aspects ofphytoplankton community and functionality:
e.g. PHYSAT (Alvain et al), PHYTODAS (Bracher et al), Aiken et al, Sathyendranath et al, Balch et al, Hirata et al, Uitz et al, Giotti+Bricaud, Mouw+Yoder, Kostadinov et al, etc
Models also resolving community structure:
By resolving optical properties of model ocean can werelate more to the remotely sensed products?
stephanie dutkiewiczhttp://ocean.mit.edu/~stephd
SUMMARY
We are currently working to include radiative transfer code (spectral) and explicit absorption and backscattering.
- will provide a closer link with satellite (and other optical) studies- additional remote sensed products could be used
to validate model- potential for data assimilation- model may then help untangle the mechanisms
leading to variability and trend observed in satellite products