Understanding Ecosystem Complexity via Application of a Process-Based State Space rather than a Potential Surface

Cédric Gaucherel (1) , F. Pommereau (2) , Christelle Hély (3)

1 UMR AMAP - Botanique et Modélisation de l'Architecture des Plantes et des Végétations
2 IBISC - Informatique, BioInformatique, Systèmes Complexes
3 UMR ISEM - Institut des Sciences de l'Evolution de Montpellier

Ecosystems are complex objects, simultaneously combining biotic, abiotic, and human components and processes. Ecologists still struggle to understand ecosystems, and one main method for achieving an understanding consists in computing potential surfaces based on physical dynamical systems. We argue in this conceptual paper that the foundations of this analogy between physical and ecological systems are inappropriate and aim to propose a new method that better reflects the properties of ecosystems, especially complex, historical nonergodic systems, to which physical concepts are not well suited. As an alternative proposition, we have developed rigorous possibilistic, process-based models inspired by the discrete-event systems found in computer science and produced a panel of outputs and tools to analyze the system dynamics under examination. e state space computed by these kinds of discrete ecosystem models provides a relevant concept for a holistic understanding of the dynamics of an ecosystem and its abovementioned properties. Taking as a specific example an ecosystem simplified to its process interaction network, we show here how to proceed and why a state space is more appropriate than a corresponding potential surface.

Domains: Biodiversity and Ecology; Environmental Sciences

Cédric Gaucherel, F. Pommereau, Christelle Hély. Understanding Ecosystem Complexity via Application of a Process-Based State Space rather than a Potential Surface. Complexity, 2020, 2020, pp.1-14. ⟨10.1155/2020/7163920⟩. ⟨hal-02974933⟩