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
A biodiversity-inspired approach to marine ecosystem modelling
Jorn Bruggeman
Dept. of Theoretical Biology
Vrije Universiteit Amsterdam
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
Layout
Theory: modeling biodiversity Test case 1: the phytoplankton community Intermezzo: a simple approximation Test case 2: mixotrophy, phytoplankton and bacteria Conclusion and outlook
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
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
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
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
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
Result: trait distribution characteristics
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
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
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 (%)
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
Result: mass variables
Result: autotrophy & heterotrophy
Result: generalists vs. specialists
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
Discussion: variance dynamics matter!
Variance determines trait flexibility Example: simulated phytoplankton size at NABE site
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!
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)