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Coupling agent-based with equation-based models to study spatially explicit megapopulation dynamics

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  • Marilleau, Nicolas
  • Lang, Christophe
  • Giraudoux, Patrick

Abstract

The incorporation of the spatial heterogeneity of real landscapes into population dynamics remains extremely difficult. We propose combining equation-based modelling (EBM) and agent-based modelling (ABM) to overcome the difficulties classically encountered. ABM facilitates the description of entities that act according to specific rules evolving on various scales. However, a large number of entities may lead to computational difficulties (e.g., for populations of small mammals, such as voles, that can exceed millions of individuals). Here, EBM handles age-structured population growth, and ABM represents the spreading of voles on large scales. Simulations applied to the spreading of a montane water vole population demonstrated that our model is quite efficient in representing the pattern observed and might help to highlight some key parameters during population expansion. This method paves the way for further developments, including the introduction of density-dependent parameters (predation, diseases, etc.) capable of triggering population declines in an explicitly spatial context.

Suggested Citation

  • Marilleau, Nicolas & Lang, Christophe & Giraudoux, Patrick, 2018. "Coupling agent-based with equation-based models to study spatially explicit megapopulation dynamics," Ecological Modelling, Elsevier, vol. 384(C), pages 34-42.
    • Handle: RePEc:eee:ecomod:v:384:y:2018:i:c:p:34-42
    DOI: 10.1016/j.ecolmodel.2018.06.011
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    References listed on IDEAS

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    1. Ardi Tampuu & Tambet Matiisen & Dorian Kodelja & Ilya Kuzovkin & Kristjan Korjus & Juhan Aru & Jaan Aru & Raul Vicente, 2017. "Multiagent cooperation and competition with deep reinforcement learning," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-15, April.
    2. Thierry, Hugo & Sheeren, David & Marilleau, Nicolas & Corson, Nathalie & Amalric, Marion & Monteil, Claude, 2015. "From the Lotka–Volterra model to a spatialised population-driven individual-based model," Ecological Modelling, Elsevier, vol. 306(C), pages 287-293.
    3. Vincenot, Christian Ernest & Giannino, Francesco & Rietkerk, Max & Moriya, Kazuyuki & Mazzoleni, Stefano, 2011. "Theoretical considerations on the combined use of System Dynamics and individual-based modeling in ecology," Ecological Modelling, Elsevier, vol. 222(1), pages 210-218.
    4. Rolf A. Ims & Harry P. Andreassen, 2000. "Spatial synchronization of vole population dynamics by predatory birds," Nature, Nature, vol. 408(6809), pages 194-196, November.
    5. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
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    Cited by:

    1. Zaatour, Wajdi & Marilleau, Nicolas & Giraudoux, Patrick & Martiny, Nadège & Amara, Abdesslem Ben Haj & Miled, Slimane Ben, 2021. "An agent-based model of a cutaneous leishmaniasis reservoir host, Meriones shawi," Ecological Modelling, Elsevier, vol. 443(C).
    2. Donadello, Carlotta & Nguyen, Thi Nhu Thao & Razafison, Ulrich, 2021. "On the mathematical modeling of vole populations spatial dynamics via transport equations on a graph," Applied Mathematics and Computation, Elsevier, vol. 396(C).

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