Understanding Ecosystem Complexity via Application of a Process-Based State Space rather than a Potential Surface
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/7163920 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832567830128623616 |
|---|---|
| author | C. Gaucherel F. Pommereau C. Hély |
| author_facet | C. Gaucherel F. Pommereau C. Hély |
| author_sort | C. Gaucherel |
| collection | DOAJ |
| description | 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. The 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. |
| format | Article |
| id | doaj-art-ca8597eacdb04e07a8bbf34a941578c1 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ca8597eacdb04e07a8bbf34a941578c12025-02-03T01:00:28ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/71639207163920Understanding Ecosystem Complexity via Application of a Process-Based State Space rather than a Potential SurfaceC. Gaucherel0F. Pommereau1C. Hély2AMAP-INRAE, CIRAD, CNRS, IRD, Université de Montpellier, Montpellier, FranceIBISC, Université d’Evry, Evry, FranceInstitut des Sciences de l’Évolution de Montpellier (ISEM), EPHE, PSL University, Université de Montpellier, CNRS, IRD, Montpellier, FranceEcosystems 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. The 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.http://dx.doi.org/10.1155/2020/7163920 |
| spellingShingle | C. Gaucherel F. Pommereau C. Hély Understanding Ecosystem Complexity via Application of a Process-Based State Space rather than a Potential Surface Complexity |
| title | Understanding Ecosystem Complexity via Application of a Process-Based State Space rather than a Potential Surface |
| title_full | Understanding Ecosystem Complexity via Application of a Process-Based State Space rather than a Potential Surface |
| title_fullStr | Understanding Ecosystem Complexity via Application of a Process-Based State Space rather than a Potential Surface |
| title_full_unstemmed | Understanding Ecosystem Complexity via Application of a Process-Based State Space rather than a Potential Surface |
| title_short | Understanding Ecosystem Complexity via Application of a Process-Based State Space rather than a Potential Surface |
| title_sort | understanding ecosystem complexity via application of a process based state space rather than a potential surface |
| url | http://dx.doi.org/10.1155/2020/7163920 |
| work_keys_str_mv | AT cgaucherel understandingecosystemcomplexityviaapplicationofaprocessbasedstatespaceratherthanapotentialsurface AT fpommereau understandingecosystemcomplexityviaapplicationofaprocessbasedstatespaceratherthanapotentialsurface AT chely understandingecosystemcomplexityviaapplicationofaprocessbasedstatespaceratherthanapotentialsurface |