1. A biodiversity-inspired approach to marine ecosystem modelling Jorn Bruggeman Bas Kooijman Theoretical biology Vrije Universiteit Amsterdam

2. phytoplankton zooplankton It used to be so simple… nitrogen NO 3 - detritus NH 4 + DON labile stable assimilation death predation death mineralization Le Quére et al. (2005): 10 plankton types

3. Step 1 The “omnipotent” population N 2 fixation predation phototrophy heterotrophy Standardization: one model for all species – Dynamic Energy Budget theory (Kooijman 2000) Species differ in allocation to metabolic strategies Allocation parameters: traits calcification biomass

4. Step 2 Continuity 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

5. 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: minimum biomass (Burchard et al. 2006), constant variance of trait distribution (Wirtz & Eckhardt 1996) The environment changes because of – external forcing, e.g. periodicity of light, mixing – ecosystem dynamics, e.g. 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

6. In practice: mixotroph structural biomass light harvesting organic matter harvesting + + + + nutrient Trait 1: investment in light harvesting Trait 2: investment in organic matter harvesting organic matter maintenance death organic matter

7. 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 Parameter fitting – fitted internal wave parameterization to temperature profiles – fitting biological parameters to observed depth profiles of chlorophyll and DIN simultaneously

8. Result: evolving trait distribution

9. Results: nutrient, biomass, detritus

10. Results: autotrophy & heterotrophy

11. Simpler trait distributions 1. Before: “brute-force” – 2 traits  20 x 20 grid = 400 state variables (‘species’) – flexible: any distribution shape (multimodality) possible – high computational cost 2. Now: simplify via assumptions on distribution shape 1. 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

12. Moment-based mixotroph nitrogendetritus mean allocation to autotrophy variance of allocation to autotrophy mean allocation to heterotrophy variance of allocation to heterotrophy biomass

13. Approximation visualized

14. Results: data assimilation DIN chlorophyll

15. Conclusions Simple mixotroph + biodiversity model shows – 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”: – Moments represent biodiversity  mechanistic derivation, not ad-hoc – Direct (measurable) implications for mass- and energy balances

16. Outlook Selection of traits, e.g. – Metabolic strategies – Individual size Biodiversity-based ecosystem models – 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