TRAIT TRADE OFFS AND CELL SIZE FOR OCEAN ECOSYSTEM MODELING Stephanie Dutkiewicz and Mick Follows...

TRAIT TRADE OFFS AND CELL SIZE FOR OCEAN ECOSYSTEM

MODELING

Stephanie Dutkiewicz and Mick FollowsMassachusetts Institute of Technology

Darwin Project People:Oliver JahnJason BraggFanny MonteiroAnna HickmanBen Ward

Penny Chisholm Andrew BartonChris KempesSophie ClaytonChris Hill

“Everything is everywhere, but, the environment selects” Lourens Baas-Becking

genetics

physics,nutrient

communitystructure

TRAIT TRADE OFFS AND CELL SIZE FOR OCEAN ECOSYSTEM MODELING

OUTLINE OF TALK:• Trait-based ecology framework• Example from our ecosystem model: Trade-offs are key!• Size as “master” trait – a brief review• Models with explicit size spectrum – a brief review• Preliminary results from MIT self-organizing ecosystem model• Where next …

(from Litchman+Klausmeier, 2008)

TRAIT-BASED APPROACH TO ECOLOGY

HOW DO THESE TRAITS TRADE OFF AGAINST EACH OTHER?

• Competitive ability for different resources - diatoms (Fe versus light) - diazotrophs (N versus Fe)

• Grazer resistance and nutrient acquisition

• Maximum growth rate and nutrient acquisition: - K versus r strategy (gleaners/opportunists)

(from Litchman and Klausmeier)

HOW DO THESE TRAITS TRADE OFF AGAINST EACH OTHER?

• Maximum growth rate and nutrient acquisition: - K versus r strategy (gleaners/opportunists)

K strategy (gleaner): optimize for low nutrient requirements

r strategy (opportunist): optimize for fast growth rate

Test this is a numerical simulation

(see: MacArthur+Wilson, 1967 Kilham+Kilham, 1980)

TRAIT TRADE OFFS AND CELL SIZE FOR OCEAN ECOSYSTEM MODELING

OUTLINE OF TALK:• Trait-based ecology framework• Example from our ecosystem model: Trade-offs are key!• Size as “master” trait – a brief review• Models with explicit size spectrum – a brief review• Preliminary results from MIT self-organizing ecosystem model• Where next …

P1

P

● initialize with many potentially viable organism types and interactions● parameters (rates) are chosen

randomly within a reasonable range● allow the system to self-organize … Pi

PjPPPn

NZ1

D

Z2

N

D

Z2Z1

competitionpredationselection

physical and chemical

environment

genetics andphysiology

SELF ORGANIZING ECOSYSTEM MODEL(Follows et al, 2007)

choices and trade-offs on growth parameters

•biogeochemical cycling of N, P, Si, Fe

•78 phytoplankton•2 zooplankton classes

opportunists(r-strategy)

gleaners(K-strategy)

SELF ORGANIZING ECOSYSTEM MODEL(Follows et al, 2007)

high max growth rate

low nutrient half saturation

(Dutkiewicz et al, GBC – submitted http://ocean.mit.edu/~stephd)

biomass of opportunists/total biomass

gleaner(low nutrient requirementsmatter)

opportunists(fast growth matters)

10th yearannual 0-50m mean

RESULTS FROM NUMERICAL SIMULATION: IMPORTANCEOR BIOGEOGRAPHY

(from Dutkiewicz et al, GBC – submitted http://ocean.mit.edu/~stephd)

ECCO2 MODEL WITH ECOSYSTEM: DOMINANT FUNCTIONAL TYPEred/yellow=opportunists, green/blue=gleaners; opacity=total biomass

Oliver Jahn

ECCO2 MODEL WITH ECOSYSTEM: DOMINANT FUNCTIONAL TYPEred/yellow=opportunists, green/blue=gleaners; opacity=total biomass

(from Litchman+Klausmeier, 2008)

Trade-offs are the key!

Trade-offs are the key!

(from Litchman+Klausmeier, 2008)

How to model these in a consistent manner?

“Size is the most structuring dimension of ecological systems” (Maury et al, 2007)

• consistent regulation of trade-offs (hopefully)

• closer interface with spectral resolution of remotely-sensed data - e.g. particle back-scattering

BENEFITS OF USING CELL SIZE AS A “MASTER” TRAIT:

TRAIT TRADE OFFS AND CELL SIZE FOR OCEAN ECOSYSTEM MODELING

OUTLINE OF TALK:• Trait-based ecology framework• Example from our ecosystem model: Trade-offs are key!• Size as “master” trait – a brief review• Models with explicit size spectrum – a brief review• Preliminary results from MIT self-organizing ecosystem model• Where next …

CELL SIZE INFLUENCES:• Metabolic rates and Maximum growth rates• Nutrient acquisition• Chl content and Light absorption• Sinking speeds• Maximum and minimum cell quota• and ….

many of the above are related tocell size by, where S can be V,C,r:

baSX

CELL SIZE INFLUENCES:• Metabolic rates and Maximum growth rates

Bigger phytoplankton grow slower

(from Tang 1995)

growth rate versus cell sizebaVmax

25.0b

• b=-0.25 appears to work over very large range of scales (Platt and Silvert, 1981; West et al 2002)• but b has been found between -0.15 and -0.3 but various studies (Chisholm 1992)

Chris’s work

Kempes et al(in prep)

data from Chrisholm et al (1992)

theoreticalcurve (m-1/4)

CELL SIZE INFLUENCES:• Nutrient acquisition

Bigger phytoplankton require more nutrients

(from Litchman et al, 2007)

half saturation for nitrate versus cell volume

bn aV 33.0b

rate at which molecular diffusionsupplies nutrients to the surfaceof the cell(Aksnes+Egge, 1991; Munk+Riley, 1952 )

(from Chisholm, 1992)

CELL SIZE INFLUENCES:• Chl content and Light absorption

(from Ciotti et al, 2002)

intercellular Chl a versuscell diameter

Bigger phytoplankton absorb light less efficiently

absorption spectra normalized by Chl-a and phaeopigments

(from Finkel et al, 2004)

“packaging effect”

CELL SIZE INFLUENCES:• Sinking speeds

Bigger phytoplankton sink quicker

(from Smayda,1970)

bp arw

17.1b Stokes Law suggest b=2

SO WHY ARE THERE ANY BIG CELLS:• Grazing Pressure - e.g. Thingstad et al 2005

• Susceptibility to Viruses - e.g. Raven et al 2006

• Respiration/Loses - e.g. Laws 1975

• Photo-inhibition

– e.g. Raven et al 2006

• “Luxury quota”• Taxonomically related advantage

SO WHY ARE THERE ANY BIG CELLS:• “Luxury quota”

Scaling of size dependent parameters: X=aSb

gro

wth

ra

te

size

ANALYTICAL MODEL OFVERDY ET AL, MEPS, 2009

SO WHY ARE THERE ANY BIG CELLS:• Taxonomically related advantage

SIZE RELATIONSHIP NOT SO GROWTH CLEAR:(e.g. Chisholm 1992, Raven et al, 2006)

especially for picoplankton e.g. (<1um) Prochloroccus 1 d-1

(4um) Thalassiosira spp. 3 d-1

(from Chisholm 1992)

SO WHY ARE THERE ANY BIG CELLS:• Taxonomically related advantage

(from Irwin et al, 2006)

25.0max

aV

SO WHY ARE THERE ANY BIG CELLS:• Taxonomically related advantage

(from Irwin et al, 2006)

Irwin et al, 2006b=-0.25

Baird, 2008b=-0.15

baVmax

TRAIT TRADE OFFS AND CELL SIZE FOR OCEAN ECOSYSTEM MODELING

OUTLINE OF TALK:• Trait-based ecology framework• Example from our ecosystem model: Trade-offs are key!• Size as “master” trait – a brief review• Models with explicit size spectrum – a brief review• Preliminary results from MIT self-organizing ecosystem model• Where next …

NUMERICAL MODELING WITH SIZE AS TRAIT:

some examples-Baird and Sutherland (2007) -Maury et al. (2007)-Stock et al (2007)-Mei, Finkel and Irwin (in prep)

Baird+Sutherland, J. Plankton Res (2007)

(from Baird+Sutherland, 2007)

Schematic of size-resolved biology model

<1um

78mm

Phytoplankton size determines: carbon content/growth/sinking/half saturation/swimming/predation

Maury et al, Prog. Ocean, 2007

Size-dependent physiology and metabolism, using the Dynamic Energy Budget theory (Kooijman, 2001)

Based on Droop’s Growth Model,3 classes of plankton

run in global 3-D MITgcm setup

Phytoplankton size determines: cell quota/growth/uptake/half saturation/mortality

currently adding size-dependent grazing

TRAIT TRADE OFFS AND CELL SIZE FOR OCEAN ECOSYSTEM MODELING

OUTLINE OF TALK:• Trait-based ecology framework• Example from our ecosystem model: Trade-offs are key!• Size as “master” trait – a brief review• Models with explicit size spectrum – a brief review• Preliminary results from MIT self-organizing ecosystem model• Where next …

SELF ORGANIZING ECOSYSTEM MODEL(Follows et al, 2007)

modified Dutkiewicz et al 2009, Monteiro et al, Hickman et al

opportunists gleaners

10’s to 1000’s phytoplankton “types”:choices and trade-offs on growth parametersT, I, nutrients

decision tree on initialized phytoplankton

SELF ORGANIZING ECOSYSTEM MODEL

SIZE SPECTRUM VERSION

10’s to 1000’s phytoplankton “types”:choices and trade-offs• size: growth parameters, nutrient half-saturation, sinking rates grazing

• T, I, types of nutrients

decision tree on initialized phytoplankton

Si No-Si

NH4, NO2, NO3

Diatomanalogues

Non-diatom eukaryoteanalogues

NH4, NO2, NO3

NH4, NO2

NH4

Pico-Eukaryote analogues

HL Prochl.analogues

LL Prochl.analogues

Synechococcusanalogues

SIZE SPECTRUMbigger smaller

SELF ORGANIZING ECOSYSTEM MODELSIZE SPECTRUM VERSION

cell diameter (um)

P – ProchloroccusS – SynochcoccusA – diazotrophC – coccolithophersF - dinoflagellatesD – diatoms

17.16.5 rwp

33.01.0 Vp

25.0max

aV“a” has taxanomicdifferences(following Irwin et al, 2006)

(Smayda, 1970)

(Irwin et al, 2006)

SELF ORGANIZING ECOSYSTEM MODELSIZE SPECTRUM VERSION

gra

zin

g r

ate

SIZE DEPENDENT GRAZING(following Baird+Sutherland 2007)

529.0max 00271.0 zrg

min predator-prey ratio: 3.0max predator-prey ratio: 22.6(parameters from Hansen et al 1994,1997)

SELF ORGANIZING ECOSYSTEM MODELSIZE SPECTRUM VERSION

1-D SIMULATION(S. Atlantic subtropical gyre)

green: <1miconcyan: 1-2 micronsblue: 2-3 microns

nitratephytoplanktonbiomass

de

pth

(m)

(100 plankton types, no temp, light or grazing differences in this version)

SELF ORGANIZING ECOSYSTEM MODELSIZE SPECTRUM VERSION

1-D SIMULATION(S. Atlantic subtropical gyre)

green: <1miconcyan: 1-2 micronsblue: 2-3 microns

(100 plankton types, no temp, light or grazing differences in this version)

SELF ORGANIZING ECOSYSTEM MODELSIZE SPECTRUM VERSION

1-D SIMULATION(S. Atlantic subtropical gyre)

green: <1miconcyan: 1-2 micronsblue: 2-3 microns

nitratephytoplanktonbiomass

de

pth

(m)

(100 plankton types, no temp, light or grazing differences in this version)

SELF ORGANIZING ECOSYSTEM MODELSIZE SPECTRUM VERSION

3-D SIMULATION:PRELIMINARY RESULTS

(78 plankton types, no temp, light in this version)

total biomass (uM)

biomassweightedcell diameter (um)

nitrate (uM)

cell diameter (um)

gro

wth

ra

te (

1/d

)

WHERE WE ARE GOING:

• continuous size spectrum determining many of the rates/parameters• quota based • pigment specific light absorption (with Anna Hickman, see poster)• explicit radiative transfer model (with Watson Gregg)• run in the eddy-permitting ECCO2 framework

ECCO2 with 78-phytoplankton self-organizing model

Oliver Jahn

ECCO2 with 78-phytoplankton self-organizing model

Oliver Jahn

WHERE WE ARE GOING:

• continuous size spectrum determining many of the rates/parameters• quota based • pigment specific light absorption (see poster)• explicit radiative transfer model• run in the eddy-permitting ECCO2 framework

SELF ORGANIZING ECOSYSTEM MODEL

modified Hickman et al

10’s to 1000’s phytoplankton “types”:choices and trade-offs on growth parametersT, I, nutrients

decision tree on initialized phytoplankton

Large Small

Si No-Si

NH4, NO2, NO3

Diatomanalogues

Non-diatom eukaryoteanalogues

NH4, NO2, NO3

NH4, NO2

NH4

Pico-Eukaryote analogues

HL Prochl.analogues

LL Prochl.analogues

Synechococcusanalogues

* = m . a*from absorption spectra

ADDITIONAL OF PIGMENT SPECIFIC ABSORPTION SPECTRA

see poster