Transcript A biodiversity-inspired approach to marine ecosystem modeling

A biodiversity-inspired approach to
marine ecosystem modelling
Jorn Bruggeman
Bas Kooijman
Theoretical biology
Vrije Universiteit Amsterdam
It used to be so simple…
NO3-
NH4+
nitrogen
Le Quére et al. (2005):
10 plankton types
assimilation
phytoplankton
DON
death
labile
zooplankton
death
detritus
stable
Step 1
The “omnipotent” population
biomass
N2 fixation
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Standardization: one model for all species
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Dynamic Energy Budget theory (Kooijman 2000)
Species differ in allocation to metabolic strategies
Allocation parameters: traits
Step 2
Continuity in traits
Phototrophs and heterotrophs: a section through diversity
bact 1
heterotrophy
bact 3
?
bact 2
?
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mix 1
mix 2
mix 3
mix 4
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phyt 1
?
phyt 2
?
phyt 3
phototrophy
phyt 2
Step 3
“Everything is everywhere; the environment selects”
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Every possible species present at all times
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The environment changes because of
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implementation: continuous immigration of trace amounts of all species
similar to: minimum biomass (Burchard et al. 2006), constant variance of
trait distribution (Wirtz & Eckhardt 1996)
external forcing, e.g. periodicity of light, mixing
ecosystem dynamics, e.g. depletion of nutrients
Changing environment drives succession
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niche presence = time- and space-dependent
trait value combinations define species & niche
trait distribution will change in space and time
In practice: mixotroph
nutrient
Trait 1: investment in light harvesting
+
maintenance
light harvesting
nutrient
+
structural biomass
organic matter
+
organic matter harvesting
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Trait 2: investment in organic matter harvesting
death
organic matter
Setting
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General Ocean Turbulence Model (GOTM)
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Scenario: Bermuda Atlantic Time-series Study (BATS)
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1D water column
depth- and time-dependent turbulent diffusivity
k-ε turbulence model
surface forcing from ERA-40 dataset
initial state: observed depth profiles temperature/salinity
Parameter fitting
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fitted internal wave parameterization to temperature profiles
fitting biological parameters to observed depth profiles of chlorophyll and
DIN simultaneously
Result: evolving trait distribution
Results: nutrient, biomass, detritus
Results: autotrophy & heterotrophy
Simpler trait distributions
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Before: “brute-force”
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2 traits  20 x 20 grid = 400 state variables (‘species’)
flexible: any distribution shape (multimodality) possible
high computational cost
Now: 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
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Moment-based mixotroph
variance of allocation to autotrophy
mean allocation to autotrophy
nitrogen
biomass
mean allocation to heterotrophy
variance of allocation to heterotrophy
detritus
Approximation visualized
Results: data assimilation
DIN
chlorophyll
Conclusions
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Simple mixotroph + biodiversity model shows
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Time-dependent species composition: autotrophic species (e.g. diatoms)
replaced by mixotrophic/heterotrophic species (e.g. dinoflagellates)
Depth-dependent species composition: subsurface chlorophyll maximum
Good description of BATS chlorophyll and DIN
Modeled biodiversity adds flexibility “in a good way”:
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Moments represent biodiversity  mechanistic derivation, not ad-hoc
Direct (measurable) implications for mass- and energy balances
Outlook
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Selection of traits, e.g.
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Biodiversity-based ecosystem models
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Metabolic strategies
Individual size
Rich dynamics through succession rather than physiological detail
Use of biodiversity indicators (variance of traits)
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Effect of biodiversity on ecosystem functioning
Effect of external factors (eutrophication, toxicants) on diversity