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

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

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

1. Omnipotent population phototrophy detritivory biomass predation … Standardization: one model for all species 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 2 bact 3 ? ? ? mix 1 mix 2 mix 3 ? phyt 1 mix 4 ? phyt 2 ? phyt 2 phyt 3 phototrophy

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)

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

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

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

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

Results DIN chlorophyll

Autotrophy and heterotrophy

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

Outlook Selection of traits, e.g. Biodiversity-based ecosystem models 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