Phytoplankton Stoichiometry

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Phytoplankton Stoichiometry

• Christopher A. Klausmeier
• Kellogg Biological Station, Michigan State
  University

Elena Litchman Kellogg Biological Station,
Michigan State University
Tanguy Daufresne INRA Centre de Toulouse
Simon A. Levin Princeton University
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Phytoplankton Stoichiometry

• Christopher A. Klausmeier
• Kellogg Biological Station, Michigan State
  University

Elena Litchman Kellogg Biological Station,
Michigan State University
Tanguy Daufresne INRA Centre de Toulouse
Simon A. Levin Princeton University
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Alfred C. Redfield
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Phytoplankton C106N16P1
(Redfield 1934, 1958)
Alfred C. Redfield
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Phytoplankton C124N16P1S1.3K1.7Mg0.56Ca0.5Fe0.00
75Zn0.0008Cu0.00038Cd0.00021Co0.00019 (Quigg et
al. 2003, Nature 425 291294 Ho et al. 2003, J.
Phycol. 11451159)
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Phytoplankton C124N16P1S1.3K1.7Mg0.56Ca0.5Fe0.00
75Zn0.0008Cu0.00038Cd0.00021Co0.00019 (Quigg et
al. 2003, Nature 425 291294 Ho et al. 2003, J.
Phycol. 11451159)
Coastal Ocean C2333N21.4P1S28,000K10,000Mg53,500
Ca10,400Fe0. 894Zn0.31Cu0.157Cd?Co0.0017
Open Ocean C?N14.5P1S?K?Mg?Ca?Fe0.
079Zn3.12Cu1.01Cd0.346Co11.8
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Phytoplankton C124N16P1S1.3K1.7Mg0.56Ca0.5Fe0.00
75Zn0.0008Cu0.00038Cd0.00021Co0.00019 (Quigg et
al. 2003, Nature 425 291294 Ho et al. 2003, J.
Phycol. 11451159)
Coastal Ocean C2333N21.4P1S28,000K10,000Mg53,500
Ca10,400Fe0. 894Zn0.31Cu0.157Cd?Co0.0017
Supply/Demand C18.8N1.34P1S21,500K5880Mg95,500Ca
20,800Fe119Zn388Cu413Cd?Co8.95
Open Ocean C?N14.5P1S?K?Mg?Ca?Fe0.
079Zn3.12Cu1.01Cd0.346Co11.8
Supply/Demand C?N0.913P1S?K?Mg?Ca?Fe10.5Zn3900Cu
2660Cd1650Co62,100
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Phytoplankton C124N16P1S1.3K1.7Mg0.56Ca0.5Fe0.00
75Zn0.0008Cu0.00038Cd0.00021Co0.00019 (Quigg et
al. 2003, Nature 425 291294 Ho et al. 2003, J.
Phycol. 11451159)
Coastal Ocean C2333N21.4P1S28,000K10,000Mg53,500
Ca10,400Fe0. 894Zn0.31Cu0.157Cd?Co0.0017
Supply/Demand C18.8N1.34P1S21,500K5880Mg95,500Ca
20,800Fe119Zn388Cu413Cd?Co8.95
Open Ocean C?N14.5P1S?K?Mg?Ca?Fe0.
079Zn3.12Cu1.01Cd0.346Co11.8
Supply/Demand C?N0.913P1S?K?Mg?Ca?Fe10.5Zn3900Cu
2660Cd1650Co62,100
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Overall Stoichiometry Structure Stores
µ8
µ, growth rate
Qmin
Q, cell quota
(Droop 1968, Caperon 1968)
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Overall Stoichiometry Structure Stores
µ8
µ, growth rate
structure
stores
Qmin
Q, cell quota
(Droop 1968, Caperon 1968)
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Q1) What determines overall stoichiometry
(structure stores)?
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Nutrient Storage Model
QP
P
B
QN
N
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Dynamics NPin10 (N-limitation)
biomass
P N
AP
cell NP
time (days)
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Two Kinds of Chemostat Experiments

• Goldman experiments fix NP supply and vary
  dilution (growth) rate
• Rhee experiments fix dilution (growth) rate and
  vary NP supply

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Goldman Experiments Vary Growth Rate
N-limitation
P-limitation
(Goldman et al. 1979, reprinted from Sterner
Elser 2002)
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Goldman experimentsFix NPin, vary dilution
(growth rate)
Cell NP (QNQP)
optimal NP Qmin,N/Qmin,P
µ (day-1)
(Klausmeier et al. 2004 LO)
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Rhee Experiments Vary NP Supply
(Rhee 1974, reprinted from Sterner Elser 2002)
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Rhee experimentsFix dilution (growth rate),
vary NPin
µ0.59 day-1 (Rhee)
QNQP
Cell NP (QNQP)
NPin
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Does it work in the field?
Lake Pond Mesocosms
Lakes
NP Ratio of Plants or Seston, mass
NP Supply Ratio, mass
Spencer Hall Indiana University
(Hall et al. Ecology 861894-1904)
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Does it even work in cultures?!
Algal Cultures
NP Ratio of Plants or Seston, mass
NP Supply Ratio, mass
Spencer Hall Indiana University
(Hall et al. Ecology 861894-1904)
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Does it even work in cultures?!
Algal Cultures










Rhee 1978

NP Ratio of Plants or Seston, mass



NP Supply Ratio, mass
Spencer Hall Indiana University
(Hall et al. Ecology 861894-1904)
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Rhee experimentsFix dilution (growth rate),
vary NPin
µ0.59 day-1 (Rhee)
µ0.9 day-1
Cell NP (QNQP)
µ1.05 day-1
NPin
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Other solutions (Hall et al.)

• Grazers
• Interspecific competition
• Quota-dependent nutrient uptake

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Quota-dependent uptake

• Negative feedback of internal store of nutrient
  on uptake of same

vmax
Q
Rhee Gotham 1981, Morel 1987, Andersen 1997
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A Dynamic Model of Uptake Regulation
AP proportion of uptake dedicated to P
uptake (after Aksnes Egge 1991) AN1-AP Vmax,P
? AP Vmax,N? AN
Klausmeier, Litchman Levin in review
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Optimal Strategy Leads to Colimitation
AP
NPin
Klausmeier, Litchman Levin in review
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A Dynamic Model of Uptake Regulation
Klausmeier, Litchman Levin in review
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Dynamics (c0.001 day-1)
biomass
P N
AP
cell NP
time (days)
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Dynamics (c0.05 day-1)
biomass
P N
AP
cell NP
time (days)
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Dynamics (c1.0 day-1)
biomass
P N
AP
cell NP
time (days)
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Dynamics (c50 day-1)
biomass
P N
AP
cell NP
time (days)
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Dynamics (c0.05 day-1)
biomass
P N
AP
cell NP
time (days)
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Does phytoplankton NP match NP supply?
time
Cell NP (QNQP)
NPin
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Q2) What determines structural stoichiometry
(optimal NP)?
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Optimal NP Ratios
Redfield
(Klausmeier et al., 2004, Nature 429 171-174)
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Allocation model

• Cell is made up of different types of machinery
  and factory walls
• Uptake robots, Ru per carbon
• Assembly robots, Ra per carbon
• Each component has its own NP stoichiometry (Nx,
  Px)
• Uptake machinery should be N-rich
  (proteins/chloroplasts)
• Assembly machinery should be N- and P-rich
  (ribosomes)
• Trade-off between uptake and assembly machinery

(Growth Rate Hypothesis)
Jim Elser Arizona State University
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Allocation Strategy Determines Model Parameters
(Klausmeier et al., 2004, Nature 429 171-174)
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How to find an optimal optimal NP

• During exponential growth, maximize µmax
• At competitive equilibrium, minimize R for the
  limiting resource

(Klausmeier et al., 2004, Nature 429 171-174)
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Exponential growth
Light-limitation
I
µmax
N-limitation
P-limitation
N
P
Ra
Ra
(Klausmeier et al., 2004, Nature 429 171-174)
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Optimal NP Ratios
µmax
Redfield
I
N
P
(Klausmeier et al., 2004, Nature 429 171-174)
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Phytoplankton N16P1 Ocean N15P1 Interesting
Alfred C. Redfield
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Q3) Why do phytoplankton and ocean NPs (almost)
match?
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Redfields ideas

• Coincidence
• Plankton adapt to match ocean NP
• Ocean adapts to match phytoplankton NP

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Redfields ideas

• Coincidence
• Plankton adapt to match ocean NP
• Ocean adapts to match phytoplankton NP

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Nature 400 525-531
Mixed layer
N, P
Bfix, Bnon
Deep layer
Nin, Pin
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N/Pnon16
N
nonfixers win
Nin/Pin
N/Pnon


coex
(Pin,Nin)
time
P
(Schade et al. 2005, Oikos 109 40-51)
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N/Pnon16
N
nonfixers win
Nin/Pin
N/Pnon


(Pin,Nin)

coex
time
P
(Schade et al. 2005, Oikos 109 40-51)
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N/Pnon16
N
nonfixers win
Nin/Pin
N/Pnon

(Pin,Nin)



coex
time
P
(Schade et al. 2005, Oikos 109 40-51)
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N/Pnon16
N
nonfixers win
Nin/Pin

(Pin,Nin)
N/Pnon




coex
time
P
(Schade et al. 2005, Oikos 109 40-51)
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N/Pnon16
N
nonfixers win

Nin/Pin
(Pin,Nin)


N/Pnon




coex
time
P
(Schade et al. 2005, Oikos 109 40-51)
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denitrification
N/Pnon16
N
nonfixers win
Nin/Pin

(Pin,Nin)
N/Pnon






coex
time
P
(Schade et al. 2005, Oikos 109 40-51)
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Two extensions

• NP of N-fixers barely matters NP of non-fixers
  does

(Lenton Klausmeier in review)
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N/Pnon48
nonfixers win

N
(Pin,Nin)
Nin/Pin


N/Pnon




coex
time
P
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Two extensions

• NP of N-fixers barely matters NP of non-fixers
  does
• Mechanism still works if N-fixers are excluded
  from large portions of the ocean (Fe- or
  light-limitation)

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One Wrinkle
(Tyrell 1999)
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One Wrinkle
(Tyrell 1999)
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biomass
PS NS
Ns/PS ND/PD
time (years)
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biomass
PS NS
Ns/PS ND/PD
time (years)
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Multigroup, Multinutrient Model of Oceanic
Phytoplankton Litchman et al.
Zooplankton
Grazers
Phytoplankton
Diatoms
Coccos
Dinos
Greens
Mixed Layer Depth
Resources
P
Si
Fe
Light
NO3
NH4

• 0-D model
• Explicit light gradient
• Forced by mixed layer depth and irradiance

• Variable internal stores
• Liebigs Law of the Minimum

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Modern Ocean VerificationJGOFS Sites
Ocean Weather Station Papa Shallow MLD, much
less seasonality, abundant Prasinophytes
NABE Deep winter MLD, strong seasonal
successional pattern, spring diatom bloom
www.orbimage.com/ news/img_week/image07.html
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Modern Ocean Verification

• Qualitative and quantitative agreement with the
  data
• presence or absence of certain functional groups
• seasonal succession and magnitude of blooms
• nutrient concentrations and drawdown patterns

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Modern Ocean Verification
North Atlantic Site (NABE)
zoo
NO3-
Chl(mg L-1) Zoo(umol C L-1)
phyto
uM
Si
Day of year
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Modern Ocean Verification
N. Pacific (Sta. Papa)
zoo
Chl(mg L-1) Zoo(umol C L-1)
Si
uM
phyto
NO3-
Day of year
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Global Change Scenarios

• Changes in Mixed Layer Depth Dynamics
• Changes in N and P in Deep Water
• Increase in NP (2xN or 1/2xP)
• Changes in Fe concentration
• (2xFe or 1/2xFe)

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Change in Mixed Layer Depth
Global Coupled Atmosphere-Ocean-Ice Model
(Miller and Russell 1997) 150-year simulations,
CO2 increases at 0.5 a year SST increases
N. Atlantic
N. Pacific
future
Depth (m)
future
modern

• SST 1.5?C (winter), 0.85?C (summer)
• Shoaling of MLD ca. month later
• Stratification lasts longer
• Shallower MLD in summer

• SST 1.0?C (winter), 0.6?C (summer)
• Shoaling of MLD earlier
• Stratification lasts longer
• Shallower MLD in summer

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Global Change ScenariosNorth Atlantic (NABE)
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Global Change ScenariosNorth Pacific (Sta. Papa)
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• Klausmeier et al. (2004) make matters even
  more confusing
• (Leonardos Geider 2004, LO 49 2105-2114)

Supported by NSF Biocomplexity Grants to Simon
Levin and Paul Falkowski Andew Mellon Foundation
Grant to Simon Levin James S McDonnell Foundation
Grant to C. Klausmeier NSF Grants to C.
Klausmeier E. Litchman
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Dynamics (c0 day-1)
biomass
P N
AP
cell NP
time (days)
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Dynamics (c0.1 day-1)
biomass
P N
AP
cell NP
time (days)
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Dynamics (c10 day-1)
biomass
P N
AP
cell NP
time (days)
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Dynamics (c1000 day-1)
biomass
P N
AP
cell NP
time (days)
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Competition in a pulsed environment

2 wins
-
-
log cinv
log c2
1 wins
1 wins

2 wins
log cres
log c1