Transcript Bruggeman, J. 2006/04/06 A biodiversity

A biodiversity-inspired approach to
marine ecosystem modelling
Jorn Bruggeman
Bas Kooijman
Theoretical biology
Vrije Universiteit Amsterdam
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
It used to be so simple…
NO3-
NH4+
nitrogen
phytoplankton
DON
labile
zooplankton
detritus
stable
1. Omnipotent population
biomass

Standardization: one model for all species
–


Dynamic Energy Budget theory (Kooijman 2000)
Species differ in allocation to metabolic strategies
Allocation parameters: traits
2. Continuity in traits: distributions
Phototrophs and heterotrophs: a section through diversity
bact 1
heterotrophy
bact 3
?
bact 2
?
?
mix 1
mix 2
mix 3
mix 4
?
phyt 1
?
phyt 2
?
phyt 3
phototrophy
phyt 2
3. Succession & persistence of species

The environment evolves
–
–

Changing environment drives succession
–
–
–

External forcing (light, mixing)
Ecosystem dynamics (e.g. depletion of nutrients)
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)
In practice: mixotroph
Trait 1: investment in light harvesting
+
light harvesting
nutrient
+
nutrient
structural biomass
+
organic
matter
organic matter harvesting
+
Trait 2: investment in organic matter harvesting
organic
matter
How to deal with trait distributions?
1.
Discretize
–
–
–
2.
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.
2.
Express higher moments in terms of first moments (moment closure)
3.
Evolve first moments
E.g. 2 traits  2 x (mean, variance) = 4 state variables
1.
Moment-based mixotroph
variance of allocation to autotrophy
mean allocation to autotrophy
nitrogen
biomass
mean allocation to heterotrophy
variance of allocation to heterotrophy
detritus
Setup

General Ocean Turbulence Model (GOTM)
–
–
–

Scenario: Bermuda Atlantic Time series Study (BATS)
–
–

1D water column
Depth- and time-dependent turbulent diffusivity
Configured for k-ε turbulence model
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
Results
DIN
chlorophyll
Autotrophy and heterotrophy
autotrophy
heterotrophy
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
Outlook

Selection of traits, e.g.
–
–

Biodiversity-based ecosystem models
–

Metabolic strategies
Individual size
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