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A biodiversity-inspired approach to marine ecosystem modelling

A biodiversity-inspired approach to marine ecosystem modelling. Jorn Bruggeman Dept. of Theoretical Biology Vrije Universiteit Amsterdam. Intro: it used to be so simple…. nitrogen. phytoplankton. Le Quére et al. (2005): 10 plankton types. NO 3 -. NH 4 +. assimilation. DON.

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A biodiversity-inspired approach to marine ecosystem modelling

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  1. A biodiversity-inspired approach to marine ecosystem modelling Jorn Bruggeman Dept. of Theoretical Biology Vrije Universiteit Amsterdam

  2. Intro: it used to be so simple… nitrogen phytoplankton Le Quére et al. (2005): 10 plankton types NO3- NH4+ assimilation DON mineralization death predation zooplankton detritus labile death stable

  3. Layout • Theory: modeling biodiversity • Test case 1: the phytoplankton community • Intermezzo: a simple approximation • Test case 2: mixotrophy, phytoplankton and bacteria • Conclusion and outlook

  4. Modeling biodiversity: step 1The “omnipotent” population • Standardization: one model to describe any species • Dynamic Energy Budget theory (Kooijman 2000) • Species differ in allocation to metabolic strategies • Allocation parameters: traits phototrophy heterotrophy biomass predation calcification N2 fixation

  5. Modeling biodiversity: step 2Continuity in traits 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

  6. Modeling biodiversity: 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: constant variance of trait distribution (Wirtz & Eckhardt 1996) • The environment changes • external forcing: periodicity of light, mixing, … • ecosystem dynamics: 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

  7. Trait 1: investment in light harvesting Trait 2: investment in nutrient harvesting Test case 1: phytoplankton diversity light harvesting + + structural biomass maintenance nutrient + + nutrient harvesting

  8. Physical 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

  9. Result: trait distribution characteristics

  10. Intermezzo: simpler trait distributions • Before: “brute-force” • 2 traits  25 x 25 grid = 625 ‘species’ state variables • flexible: any distribution shape possible, e.g. multimodality • high computational cost • Now: simplify via assumptions on distribution shape • characterize trait distribution by moments: mean, (co)variance, … • express higher moments in terms of first moments = moment closure • evolve first moments E.g. 2 traits  2 x (mean, variance) + covariance = 5 state variables

  11. New state variables variance of light harvesting investment mean light harvesting investment nitrogen biomass covariance of investments mean nutrient harvesting investment variance of nutrient harvesting investment

  12. Quality of approximation variable deviation (%) biomass 1.2 ± 1.9 mean light harvesting 5.1 ± 4.0 mean nutrient harvesting 8.3 ± 6.7 variance light harvesting 11.3 ± 7.7 variance nutrient harvesting 12.7 ± 9.2 covariance light & nutrient harv. 7.1 ± 5.9

  13. Trait 1: investment in light harvesting Trait 2: investment in organic matter harvesting Test case 2: mixotrophy nutrient maintenance + light harvesting nutrient + structural biomass + organic matter harvesting organic matter death + organic matter

  14. Result: mass variables

  15. Result: autotrophy & heterotrophy

  16. Result: generalists vs. specialists

  17. Conclusion • Phytoplankton + diversity • Light-driven succession in space (shade flora) • Nutrient-driven succession in time (Margalef’s Mandala) • Moment-based approximation • Multiple traits, potentially correlated • Formulated as tracers that advect and diffuse normally • Deviations of 1%, 6%, 12% for biomass, mean, variance, respectively • Mixotroph + biodiversity • Spring bloom of autotrophs, and autumn bloom of mixotrophs • Mixotrophy near surface, pure autotrophy and heterotrophy in deep

  18. Discussion: variance dynamics matter! • Variance determines trait flexibility • Example: simulated phytoplankton size at NABE site

  19. Where does diversity come from? • Without external source of variance • variance → 0 • mean → constant • despite spatial & temporal heterogeneity • Quick fixes • lateral input (assumes heterogenity in horizontal plane) • input from below (assumes high biodiversity in the deep) • constant variance • Long-term generic solution needed!

  20. Outlook • Short-term • Upcoming: paper on phytoplankton diversity in 1D (L&O) • Study (co)variance of bivariate trait distributions in 0D • Write up mixotrophy in 1D • Long-term • Traits for stoichiometry • Physiologically-structured population models (intraspecific and interspecific variation in size)

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