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Modelling Marine Ecosystems
Mick Follows
Dept of Earth, Atmospheric and Planetary Sciences,
Massachusetts Institute of Technology
http://ocean.mit.edu/~mick/Downloads.html
What is the marine ecosystem?
• Food web
• Focus on
phytoplankton
Bacteria, archaea
Why model marine ecosystems?
• To understand and quantify global carbon cycle
and relationship to climate
– e.g. how much carbon is sequestered in the ocean
due to the formation of sinking organic particles?
• To understand and describe fundamental
ecological controls on marine ecosystem and its
evolution
– e.g. why do particular species or functional types of
phytoplankton occupy particular ocean regions?
Where is phytoplankton biomass in
the oceans?
• Satellite based observations of
Chlorophyll-a, annual cycle 2005 (MODIS)
Where is phytoplankton biomass in
the oceans?
• Upwelling regions of tropics and subpolar oceans bring
nutrients to surface, sustaining high productivity
Seasonal cycle of “plant pigment” at
Georges Bank – in situ observations
(Riley, 1946)
Phytoplankton
abundance
month
Modelling Marine Phytoplankton
Riley (1946)
dP
= P  μ−K r −gZ 
dt
Modelling Marine Phytoplankton
Riley (1946)
dP
= P  μ−K r −gZ 
dt
P = phytoplankton
biomass (mol C m-3)
Modelling Marine Phytoplankton
Riley (1946)
dP
= P  μ−K r −gZ 
dt
Growth
rate (s-1)
P = phytoplankton
biomass (mol C m-3)
Modelling Marine Phytoplankton
Riley (1946)
dP
= P  μ−K r −gZ 
dt
Growth
rate (s-1)
P = phytoplankton
biomass (mol C m-3)
Respiration
rate (s-1)
Modelling Marine Phytoplankton
Riley (1946)
dP
= P  μ−K r −gZ 
dt
Growth
rate (s-1)
P = phytoplankton
biomass (mol C m-3)
Respiration
rate (s-1)
Grazing
g = grazing rate
Z = abundance of
grazers
Riley’s (1946) model of seasonal cycle at
Georges Bank
observed
Phytoplankton
abundance,
P
modelled
dP
= P  μ−K r −gZ 
dt
month
Growth rate, μ , depends on
environmental conditions
• Information from laboratory cultures
Growth rate (day-1)
Chisholm lab, McCarthy (1981)
Temperature
light intensity
nutrient
Global marine ecosytem model
• Embed a model like Riley’s in a description
of ocean circulation, temperature, nutrient
and light distributions
• Capture broads regional and seasonal
dynamics
• Understand nutrient and light controls
Captures regional and seasonal
patterns of biomass and productivity
modelled 0-50m biomass (uM N)
Stephanie Dutkiewicz
Model captures observed regional
and seasonal patterns
Remote Chl-a observations
modelled biomass
Reveals environmental regulation of
primary production
Iron
limited
Light limited
Nitrogen limited
Current Question: What regulates
phytoplankton “community structure”?
coccolithophores
(CaCO3 plates)
Prochlorococcus,
Synechococcus
4 μm
20 μm
diatoms
(Silicate frustule)
0.5 μm
Small, buoyant,
locally recycled.
Inefficient export of
organic carbon
Large, blooming, aggregating. Efficient export of organic carbon
Observations of phytoplankton
community structure
• Pigment observations (Aiken et al, 2000)
Diatoms, coccolithophores
Prochlorococcus
Global phytoplankton community
structure (January)
• Interpretations of remote ocean color
observations (Alvain et al, 2006)
January: dominant
functional types from
SeaWifS (Alvain et al, 2005)
red
Green
yellow
blue
- diatoms
- Prochlorococcus
- Synechococcus-like
- includes coccolithophores
What determines phytoplankton
community structure?
physical and
chemical
environment
genetics and
physiology
competition
predation
selection
ecosystem structure
and function
How can we reflect this in
mathematical model?
• Seed model with many potentially viable
phytoplankton types (c.f. Riley’s model with only
one)
• Competition for resources, ability to avoid
predation, etc… determine “fitness” of each
phytoplankton type
• Model “self-organizes” selecting for
phytoplankton with “fittest” physiological
characteristics
Follows,Dutkiewicz, Grant and Chisholm (Science, March 30th 2007)
How can we reflect this in a model?
DP j
= P j  μ j −k Rj −g j Z 
Dt
• Represent a large variety of phytoplankton types
(index j)
• Provide characteristics (μ, KR, g) for each type by
random draw from ranges determined in lab
• Explicit “natural selection” in model brings to the
fore the “fittest” types for model environment
Follows, Dutkiewicz, Grant and Chisholm (Science, March 30th 2007)
Modeled ecosystem structure
Characterize dominant types in terms of real-world functional types
Prochlorococcus analogs
Synechococcus & small eukaryotes
.
Diatoms
Other large eukaryotes
January: dominant
functional types from
SeaWifS (Alvain et al, 2005)
red
green
yellow
blue
MODEL: January dominant
functional types
red
green
blue
brown
- diatoms
- Prochlorococcus
- other “small”
- other “large”
- diatoms
- Prochlorococcus
- Synechococcus-like
- haptophytes
What now?
• Use model ecosystem as platform to
explore and test ecological theories
• Explore relationships between ecosystem,
nutrient cycles and climate change
Summary and Outlook
• Mathematical and numerical models of marine
ecosystems are developed to help understand
marine ecology and biogeochemical cycles
• Recent models embrace complexity of
ecosystem and help us understand relationship
of “community structure” and environment
• New, genetically based observations of marine
ecosystems provide new opportunities and
challenges