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Accounting for biodiversity in marine ecosystem models Jorn Bruggeman S.A.L.M. Kooijman Dept. of Theoretical Biology Vrije Universiteit Amsterdam.

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Presentation on theme: "Accounting for biodiversity in marine ecosystem models Jorn Bruggeman S.A.L.M. Kooijman Dept. of Theoretical Biology Vrije Universiteit Amsterdam."— Presentation transcript:

1 Accounting for biodiversity in marine ecosystem models Jorn Bruggeman S.A.L.M. Kooijman Dept. of Theoretical Biology Vrije Universiteit Amsterdam

2 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

3 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

4 Infinite diversity  continuity in traits

5 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’)

6 Cost-aware phytoplankton population structural biomass nutrient structural biomass light harvesting nutrient harvesting nutrient κLκL κNκN

7 Functional group: phytoplankton Discretized trait distribution – 15 x 15 trait values = 225 ‘species’ Start with homogeneous distribution, low densities

8 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)

9 Biota: chlorophyll Modeled light harvesting equipment  chlorophyll BATS measured chlorophyll averaged over 10 years

10 Succession: average trait values in time Modeled nutrient harvesting equipment  surface-to-volume  1/cell length Modeled light harvesting equipment  cell-specific chlorophyll

11 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)

12 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


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