A biodiversity-inspired approach to marine ecosystem modelling

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

2. It used to be so simple… nitrogen phytoplankton Le Quére et al. (2005): 10 plankton types NO3- NH4+ assimilation DON mineralization death predation zooplankton detritus labile death stable

3. Step 1The “omnipotent” population • Standardization: one model for all species • Dynamic Energy Budget theory (Kooijman 2000) • Species differ in allocation to metabolic strategies • Allocation parameters: traits phototrophy heterotrophy biomass predation calcification N2 fixation

4. Step 2Continuity in traits Phototrophs and heterotrophs: a section through diversity bact 1 heterotrophy bact 2 bact 3 ? ? ? mix 1 mix 2 mix 3 ? phyt 1 mix 4 ? phyt 2 ? phyt 2 phyt 3 phototrophy

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. Trait 1: investment in light harvesting Trait 2: investment in organic matter harvesting In practice: mixotroph nutrient maintenance + light harvesting nutrient + structural biomass + organic matter harvesting organic matter 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 • Before: “brute-force” • 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. • express higher moments in terms of first moments (moment closure) • evolve first moments E.g. 2 traits  2 x (mean, variance) = 4 state variables

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

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