a biodiversity-inspired approach to marine ecosystem modelling
DESCRIPTION
A biodiversity-inspired approach to marine ecosystem modelling. Jorn Bruggeman Dept. of Theoretical Biology Vrije Universiteit Amsterdam. Intro: it used to be so simple…. nitrogen. phytoplankton. Le Quére et al. (2005): 10 plankton types. NO 3 -. NH 4 +. assimilation. DON. - PowerPoint PPT PresentationTRANSCRIPT
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A biodiversity-inspired approach to marine ecosystem modelling
Jorn Bruggeman
Dept. of Theoretical Biology
Vrije Universiteit Amsterdam
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phytoplankton
zooplankton
Intro: it used to be so simple…
nitrogen
NO3-
detritus
NH4+
DON
labile
stable
assimilation
death
predation
de
ath
mineralization
Le Quére et al. (2005):10 plankton types
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Layout
Theory: modeling biodiversity Test case 1: the phytoplankton community Intermezzo: a simple approximation Test case 2: mixotrophy, phytoplankton and bacteria Conclusion and outlook
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Modeling biodiversity: step 1The “omnipotent” population
N2 fixation
predation
phototrophy heterotrophy
Standardization: one model to describe any species– Dynamic Energy Budget theory (Kooijman 2000)
Species differ in allocation to metabolic strategies Allocation parameters: traits
calcification
biomass
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Modeling biodiversity: step 2Continuity in traits
Phototrophs and heterotrophs: a section through diversity
phototrophy
heterotrophy
phyt 2
phyt 1
phyt 3
bact 1
bact 3 bact 2?
? ?
mix 2
mix 4
?
?
mix 3
mix 1
?
phyt 2
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Modeling biodiversity: step 3“Everything is everywhere; the environment selects”
Every possible species present at all times– implementation: continuous immigration of trace amounts of all species– similar to: constant variance of trait distribution (Wirtz & Eckhardt 1996)
The environment changes– external forcing: periodicity of light, mixing, …– ecosystem dynamics: 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
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Test case 1: phytoplankton diversity
structural biomass
light harvesting
nutrient harvesting
+
+ +
+
nutrient
Trait 1: investment in light harvesting
maintenance
Trait 2: investment in nutrient harvesting
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Physical setting
General Ocean Turbulence Model (GOTM)– 1D water column– depth- and time-dependent turbulent diffusivity, k-ε turbulence model
Scenario: Bermuda Atlantic Time-series Study (BATS)– surface forcing from ERA-40 dataset– initial state: observed depth profiles temperature/salinity
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Result: trait distribution characteristics
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Intermezzo: simpler trait distributions
1. Before: “brute-force”– 2 traits 25 x 25 grid = 625 ‘species’ state variables– flexible: any distribution shape possible, e.g. multimodality– high computational cost
2. Now: simplify via assumptions on distribution shape1. characterize trait distribution by moments: mean, (co)variance, …2. express higher moments in terms of first moments = moment closure3. evolve first momentsE.g. 2 traits 2 x (mean, variance) + covariance = 5 state variables
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New state variables
nitrogen
mean light harvesting investment
variance of light harvesting investment
mean nutrient harvesting investment
variance of nutrient harvesting investment
biomass covariance of investments
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Quality of approximation
biomass 1.2 ± 1.9
mean light harvesting 5.1 ± 4.0mean nutrient harvesting 8.3 ± 6.7
variance light harvesting 11.3 ± 7.7variance nutrient harvesting 12.7 ± 9.2covariance light & nutrient harv. 7.1 ± 5.9
variable deviation (%)
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Test case 2: mixotrophy
structural biomass
light harvesting
organic matter harvesting
+
+
+
+nutrient
nutrientTrait 1: investment in light harvesting
Trait 2: investment in organic matter harvesting
organic matter
maintenance
death
organic matter
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Result: mass variables
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Result: autotrophy & heterotrophy
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Result: generalists vs. specialists
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Conclusion
Phytoplankton + diversity– Light-driven succession in space (shade flora)– Nutrient-driven succession in time (Margalef’s Mandala)
Moment-based approximation– Multiple traits, potentially correlated– Formulated as tracers that advect and diffuse normally– Deviations of 1%, 6%, 12% for biomass, mean, variance, respectively
Mixotroph + biodiversity– Spring bloom of autotrophs, and autumn bloom of mixotrophs– Mixotrophy near surface, pure autotrophy and heterotrophy in deep
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Discussion: variance dynamics matter!
Variance determines trait flexibility Example: simulated phytoplankton size at NABE site
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Where does diversity come from?
Without external source of variance– variance → 0– mean → constant– despite spatial & temporal heterogeneity
Quick fixes– lateral input (assumes heterogenity in horizontal plane)– input from below (assumes high biodiversity in the deep)– constant variance
Long-term generic solution needed!
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Outlook
Short-term– Upcoming: paper on phytoplankton diversity in 1D (L&O)– Study (co)variance of bivariate trait distributions in 0D– Write up mixotrophy in 1D
Long-term– Traits for stoichiometry– Physiologically-structured population models (intraspecific and
interspecific variation in size)