A biodiversityinspired approach to marine ecosystem modelling Jorn
A biodiversity-inspired approach to marine ecosystem modelling Jorn Bruggeman Bas Kooijman Theoretical biology Vrije Universiteit Amsterdam
Context: biological carbon pump l Biota-controlled transport of CO 2 between atmosphere and deep CO 2 (g) surface CO 2 (aq) ecosystem POC thermocline l Focus: mass fluxes (carbon!) rather than individual species
It used to be so simple… NO 3 - NH 4+ nitrogen phytoplankton DON labile zooplankton detritus stable
1. Omnipotent population hy ot ph op otr de trit ivo ry biomass pre da l l n … Standardization: one model for all species – l tio 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 l The environment evolves – – l Changing environment drives succession – – – l 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 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 l General Ocean Turbulence Model (GOTM) – – – l Scenario: Bermuda Atlantic Time series Study (BATS) – – l 1 D 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 l Simple mixotroph + biodiversity model shows – – – l 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 l Selection of traits, e. g. – – l Biodiversity-based ecosystem models – l 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
- Slides: 14