A biodiversity-inspired approach to marine ecosystem modelling

Jorn BruggemanBas Kooijman

Theoretical biologyVrije Universiteit Amsterdam

phytoplankton

zooplankton

It used to be so simple…

nitrogen

NO3-

detritus

NH4+

DON

labile

stable

assimilation

death

predation

death

mineralization

Le Quére et al. (2005):10 plankton types

Step 1The “omnipotent” population

N2 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

Step 2Continuity 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

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

In practice: mixotroph

structural biomass

light harvesting

organic matter harvesting

+

+

+

+nutrient

nutrientTrait 1: investment in light harvesting

Trait 2: investment in organic matter harvesting

organic matter

maintenance

death

organic matter

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

Result: evolving trait distribution

Results: nutrient, biomass, detritus

Results: autotrophy & heterotrophy

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 shape1. characterize trait distribution by moments: mean, variance, etc.2. express higher moments in terms of first moments (moment closure)3. evolve first momentsE.g. 2 traits 2 x (mean, variance) = 4 state variables

Moment-based mixotroph

nitrogen detritus

mean allocation to autotrophy

variance of allocation to autotrophy

mean allocation to heterotrophy

variance of allocation to heterotrophy

biomass

Approximation visualized

Results: data assimilation

DIN

chlorophyll

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

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