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Accounting for biodiversity in marine ecosystem models

Accounting for biodiversity in marine ecosystem models. Jorn Bruggeman S.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

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Accounting for biodiversity in marine ecosystem models

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  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 bact 1 heterotrophy bact 2 bact 3 ? ? ? mix 1 mix 2 mix 3 ? phyto 1 mix 4 ? phyto 2 ? phyto 2 phyto 3 phototrophy

  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. light harvesting + + structural biomass nutrient + + nutrient harvesting Cost-aware phytoplankton population + + structural biomass nutrient + + κL κN

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

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

  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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