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. Context: biological carbon pump • Biota-controlled transport of CO2 between atmosphere and deep CO2 (g) surface CO2 (aq) POC ecosystem thermocline • Focus: mass fluxes (carbon!) rather than individual species

3. It used to be so simple… nitrogen phytoplankton NO3- NH4+ DON zooplankton detritus labile stable

4. 1. 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 detritivory biomass predation …

5. 2. Continuity in traits: distributions 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

6. 3. Succession & persistence of species • The environment evolves • External forcing (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 • 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)

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

8. How to deal with trait distributions? • Discretize • 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. • Express higher moments in terms of first moments (moment closure) • Evolve first moments E.g. 2 traits  2 x (mean, variance) = 4 state variables

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

10. Setup • General Ocean Turbulence Model (GOTM) • 1D water column • Depth- and time-dependent turbulent diffusivity • Configured for 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

11. Results DIN chlorophyll

12. Autotrophy and heterotrophy autotrophy heterotrophy

13. Conclusions • Simple mixotroph + biodiversity model shows • 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

14. 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