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A trait-based approach for downscaling complexity in plankton ecosystem models

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  • Merico, Agostino
  • Bruggeman, Jorn
  • Wirtz, Kai

Abstract

Although predator–prey cycles can be easily predicted with mathematical models it is only since recently that oscillations observed in a chemostat predator–prey (rotifer–algal) experiment offer an interesting workbench for testing model soundness. These new observations have highlighted the limitations of the conventional modelling approach in correctly reproducing some unexpected characteristics of the cycles. Simulations are improved when changes in algal community structure, resulting from natural selection operating on an assemblage of algal clones differing in competitive ability and defence against rotifer predation, is considered in multi-prey models. This approach, however, leads to extra complexity in terms of state variables and parameters. We show here that multi-prey models with one predator can be effectively approximated with a simpler (only a few differential equations) model derived in the context of adaptive dynamics and obtained with a moment-based approximation. The moment-based approximation has been already discussed in the literature but mostly in a theoretical context, therefore we focus on the strength of this approach in downscaling model complexity by relating it to the chemostat predator–prey experiment. Being based on mechanistic concepts, our modelling framework can be applied to any community of competing species for which a trade-off between competitive ability and resistance to predators can be appropriately defined. We suggest that this approach can be of great benefit for reducing complexity in biogeochemical modelling studies at the basin or global ocean scale.

Suggested Citation

  • Merico, Agostino & Bruggeman, Jorn & Wirtz, Kai, 2009. "A trait-based approach for downscaling complexity in plankton ecosystem models," Ecological Modelling, Elsevier, vol. 220(21), pages 3001-3010.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:21:p:3001-3010
    DOI: 10.1016/j.ecolmodel.2009.05.005
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    2. Christopher A. Klausmeier & Elena Litchman & Tanguy Daufresne & Simon A. Levin, 2004. "Optimal nitrogen-to-phosphorus stoichiometry of phytoplankton," Nature, Nature, vol. 429(6988), pages 171-174, May.
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    1. Gunnar Brandt & Agostino Merico & Björn Vollan & Achim Schlüter, 2012. "Human Adaptive Behavior in Common Pool Resource Systems," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-9, December.
    2. Castellani, Marco & Rosland, Rune & Urtizberea, Agurtzane & Fiksen, Øyvind, 2013. "A mass-balanced pelagic ecosystem model with size-structured behaviourally adaptive zooplankton and fish," Ecological Modelling, Elsevier, vol. 251(C), pages 54-63.
    3. Acheampong, E. & Nielsen, M.H. & Mitra, A. & St. John, M.A., 2012. "Towards an adaptive model for simulating growth of marine mesozooplankton: A macromolecular perspective," Ecological Modelling, Elsevier, vol. 225(C), pages 1-18.
    4. Klauschies, Toni & Coutinho, Renato Mendes & Gaedke, Ursula, 2018. "A beta distribution-based moment closure enhances the reliability of trait-based aggregate models for natural populations and communities," Ecological Modelling, Elsevier, vol. 381(C), pages 46-77.
    5. Wan, Nian-Feng & Jiang, Jie-Xian & Li, Bo, 2014. "Modeling ecological two-sidedness for complex ecosystems," Ecological Modelling, Elsevier, vol. 287(C), pages 36-43.

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