TRAIT TRADE OFFS AND CELL SIZE FOR OCEAN ECOSYSTEM MODELING Stephanie Dutkiewicz and Mick Follows Massachusetts Institute of Technology Darwin Project People: Oliver Jahn Jason Bragg Fanny Monteiro Anna Hickman Ben Ward Penny Chisholm Andrew Barton Chris Kempes Sophie Clayton Chris Hill

“Everything is everywhere, but, the environment selects” Lourens Baas-Becking genetics physics, nutrient community structure

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 …

P1P1 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 … PiPi PjPj P P PnPn N Z1Z1 D Z2Z2 N D Z2Z2 Z1Z1 competition predation selection physical and chemical environment genetics and physiology 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

biomass of opportunists/total biomass gleaner (low nutrient requirements matter) opportunists (fast growth matters) 10 th year annual 0-50m mean RESULTS FROM NUMERICAL SIMULATION: IMPORTANCE OR BIOGEOGRAPHY (from Dutkiewicz et al, GBC – submitted

ECCO2 MODEL WITH ECOSYSTEM: DOMINANT FUNCTIONAL TYPE red/yellow=opportunists, green/blue=gleaners; opacity=total biomass Oliver Jahn

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

(from Litchman+Klausmeier, 2008) 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 to cell size by, where S can be V,C,r:

CELL SIZE INFLUENCES: Metabolic rates and Maximum growth rates Bigger phytoplankton grow slower (from Tang 1995) growth rate versus cell size 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 and -0.3 but various studies (Chisholm 1992)

Chris’s work Kempes et al (in prep) data from Chrisholm et al (1992) theoretical curve (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 rate at which molecular diffusion supplies nutrients to the surface of 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 versus cell 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) 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=aS b growth rate size ANALYTICAL MODEL OF VERDY 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)

SO WHY ARE THERE ANY BIG CELLS: Taxonomically related advantage (from Irwin et al, 2006) Irwin et al, 2006 b=-0.25 Baird, 2008 b=-0.15

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 opportunistsgleaners 10’s to 1000’s phytoplankton “types”: choices and trade-offs on growth parameters T, 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 SiNo-Si NH4, NO 2, NO 3 Diatom analogues Non-diatom eukaryote analogues NH4, NO 2, NO 3 NH4, NO 2 NH4 Pico- Eukaryote analogues HL Prochl. analogues LL Prochl. analogues Synechococcus analogues SIZE SPECTRUM bigger smaller

SELF ORGANIZING ECOSYSTEM MODEL SIZE SPECTRUM VERSION cell diameter (um) P – Prochloroccus S – Synochcoccus A – diazotroph C – coccolithophers F - dinoflagellates D – diatoms “a” has taxanomic differences (following Irwin et al, 2006) (Smayda, 1970) (Irwin et al, 2006)

SELF ORGANIZING ECOSYSTEM MODEL SIZE SPECTRUM VERSION grazing rate SIZE DEPENDENT GRAZING (following Baird+Sutherland 2007) min predator-prey ratio: 3.0 max predator-prey ratio: 22.6 (parameters from Hansen et al 1994,1997)

SELF ORGANIZING ECOSYSTEM MODEL SIZE SPECTRUM VERSION 1-D SIMULATION (S. Atlantic subtropical gyre) green: <1micon cyan: 1-2 microns blue: 2-3 microns nitrate phytoplankton biomass depth(m) (100 plankton types, no temp, light or grazing differences in this version)

SELF ORGANIZING ECOSYSTEM MODEL SIZE SPECTRUM VERSION 1-D SIMULATION (S. Atlantic subtropical gyre) green: <1micon cyan: 1-2 microns blue: 2-3 microns (100 plankton types, no temp, light or grazing differences in this version)

SELF ORGANIZING ECOSYSTEM MODEL SIZE SPECTRUM VERSION 1-D SIMULATION (S. Atlantic subtropical gyre) green: <1micon cyan: 1-2 microns blue: 2-3 microns nitrate phytoplankton biomass depth(m) (100 plankton types, no temp, light or grazing differences in this version)

SELF ORGANIZING ECOSYSTEM MODEL SIZE SPECTRUM VERSION 3-D SIMULATION: PRELIMINARY RESULTS (78 plankton types, no temp, light in this version) total biomass (uM) biomass weighted cell diameter (um) nitrate (uM) cell diameter (um) growth rate (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 parameters T, I, nutrients decision tree on initialized phytoplankton LargeSmall SiNo-Si NH4, NO 2, NO 3 Diatom analogues Non-diatom eukaryote analogues NH4, NO 2, NO 3 NH4, NO 2 NH4 Pico- Eukaryote analogues HL Prochl. analogues LL Prochl. analogues Synechococcus analogues  * =  m. a  * from absorption spectra ADDITIONAL OF PIGMENT SPECIFIC ABSORPTION SPECTRA see poster