1. A biodiversity-inspired approach to marine ecosystem modelling
Jorn Bruggeman
Bas Kooijman
Theoretical biology
Vrije Universiteit Amsterdam

2. Context: biological carbon pump
Biota-controlled transport of CO2 between atmosphere and deep
CO2 (g)
surface
CO2 (aq)
ecosystem
POC
thermocline
Focus: mass fluxes (carbon!) rather than individual species

3. It used to be so simple… nitrogen phytoplankton zooplankton detritus
NO3-
NH4+
DON
zooplankton
detritus
labile
stable

4. 1. Omnipotent population
phototrophy
detritivory
biomass
predation …
Standardization: one model for all species
Dynamic Energy Budget theory (Kooijman 2000)
Species differ in allocation to metabolic strategies
Allocation parameters: traits

5. 2. Continuity in traits: distributions
Phototrophs and heterotrophs: a section through diversity
bact 1
heterotrophy
bact 2
bact 3 ? ? ?
mix 1
mix 2
mix 3 ?
phyt 1
mix 4 ?
phyt 2 ?
phyt 2
phyt 3
phototrophy

6. 3. Succession & persistence of species
The environment evolves
External forcing (light, mixing)
Ecosystem dynamics (e.g. depletion of nutrients)
Changing environment drives succession
Niche presence = time- and space-dependent
Trait value combinations define species & niche
Trait distribution will change in space and time
Assumption: all species can invade; actual invasion depends on niche presence
Implementation: continuous immigration of trace amounts of all species
Similar to assumptions of minimum biomass (Burchard et al. 2006) , constant variance of trait distribution (Wirtz & Eckhardt 1996)

7. In practice: mixotroph
Trait 1: investment in light harvesting +
light harvesting
nutrient
nutrient +
structural biomass +
organic matter
Trait 2: investment in organic matter harvesting
organic matter
organic matter harvesting +

8. How to deal with trait distributions?
Discretize
E.g. 2 traits  15 x 15 grid = 225 state variables (‘species’)
Flexible: any distribution shape (multimodality) possible
High computational cost
Simplify via assumptions on distribution shape
Characterize trait distribution by moments: mean, variance, etc.
Express higher moments in terms of first moments (moment closure)
Evolve first moments
E.g. 2 traits  2 x (mean, variance) = 4 state variables

9. Moment-based mixotroph
variance of allocation to autotrophy
mean allocation to autotrophy
nitrogen
biomass
detritus
mean allocation to heterotrophy
variance of allocation to heterotrophy

10. Setup General Ocean Turbulence Model (GOTM)
1D water column
Depth- and time-dependent turbulent diffusivity
Configured for k-ε turbulence model
Scenario: Bermuda Atlantic Time series Study (BATS)
Surface forcing from ERA-40 dataset
Initial state: observed depth profiles temperature/salinity
Parameter fitting
Fitted internal wave parameterization to temperature profiles
Fitting biological parameters to observed depth profiles of chlorophyll and DIN simultaneously

11. Results
DIN
chlorophyll

12. Autotrophy and heterotrophy

13. Conclusions Simple mixotroph + biodiversity model shows
Good description of BATS chlorophyll and DIN
Depth-dependent species composition: subsurface chlorophyll maximum
Time-dependent species composition: autotrophic species (e.g. diatoms) replaced by mixotrophic/heterotrophic species (e.g. dinoflagellates)
“Non-mass state variables”, but in this case:
Representatives of biodiversity  mechanistic derivation, not ad-hoc
Direct (measurable) implications for mass- and energy balances

14. Outlook Selection of traits, e.g. Biodiversity-based ecosystem models
Metabolic strategies
Individual size
Biodiversity-based ecosystem models
Rich dynamics through succession rather than physiological detail
Use of biodiversity indicators (variance of traits)
Effect of biodiversity on ecosystem functioning
Effect of external factors (eutrophication, toxicants) on diversity