Accounting for biodiversity in marine ecosystem models

Jorn BruggemanS.A.L.M. Kooijman

Dept. of Theoretical Biology

Vrije Universiteit Amsterdam

Interspecific differences quantified by traits

How to capture biodiversity in models? Species-specific models are incomparable Approach: one omnipotent species Parameter values determine the species Species-determining parameters: traits

Ecosystem diversity

Phototrophs and heterotrophs: a section through diversity

phototrophy

heterotrophy

phyto 2

phyto 1

phyto 3

bact 1

bact 3 bact 2?

? ?

mix 2

mix 4

?

?

mix 3

mix 1

?

phyto 2

Infinite diversity continuity in traits

Species = investment strategy

Why not ‘just’ do everything well? Good qualities must be paid for

– costs for directly associated machinery

(photosynthesis, phagocytosis)– costs for containment if qualities conflict

(nitrogen fixation requires anoxic environment) Budget is limited make choices! Usefulness of qualities depends on environment

– No photosynthesis in dark environments Species define niche by choosing qualities to invest in (‘strategy’)

Cost-aware phytoplankton population

structural biomassnutrient

++

++

structural biomass

light harvesting

nutrient harvesting

+

++

+

nutrient

κL

κN

Functional group: phytoplankton

Discretized trait distribution– 15 x 15 trait values = 225 ‘species’

Start with homogeneous distribution, low densities

Realistic setting

Bermuda Atlantic Time-series Study (BATS)– 10 years of monthly depth profiles for physical/biological variables

Turbulent water column model (1D)– General Ocean Turbulence Model (GOTM)– upper 250 meter– k-ε model for turbulence parameterization– realistic forcing with meteorological data (ERA-40)

Biota: chlorophyll

Modeled light harvesting equipment chlorophyll

BATS measured chlorophyllaveraged over 10 years

Succession: average trait values in time

Modeled nutrient harvesting equipment surface-to-volume 1/cell length

Modeled light harvesting equipment cell-specific chlorophyll

Trends

Cell-specific chlorophyll increases with depth– High-chlorophyll species do better in low-light deep– Thus: succession (‘shade flora’), not photo-acclimation (Geider)

Seasonal succession: large small species– Small species fare better in oligotrophic environment– Bloom start with high nutrient level, large species– Small species gain upper hand as bloom proceeds (Margalef)

Conclusions and perspectives

Trait-based approach demonstrates diversity in space and time– increase in chlorophyll content with increasing depth– decrease in cell size between start of bloom and winter

Description of BATS– Qualitatively ‘reasonable’ with current (5 parameter!) model– Space for improvement; parameter fitting with base no-trait model

Aim: collapse trait distribution– Loss of state variables fitting becomes possible

Future: traits for ecosystems– heterotrophy– predation/defense– body size