Computational Model Library

Evolution of Ecological Communities: Testing Constraint Closure (1.0.0)

Ecosystems are among the most complex structures studied. They comprise elements that seem both stable and contingent. The stability of these systems depends on interactions among their evolutionary history, including the accidents of organisms moving through the landscape and microhabitats of the earth, and the biotic and abiotic conditions in which they occur. When ecosystems are stable, how is that achieved? Here we look at ecosystem stability through a computer simulation model that suggests that it may depend on what constrains the system and how those constraints are structured. Specifically, if the constraints found in an ecological community form a closed loop, that allows particular kinds of feedback may give structure to the ecosystem processes for a period of time. In this simulation model, we look at how evolutionary forces act in such a way these closed constraint loops may form. This may explain some kinds of ecosystem stability. This work will also be valuable to ecological theorists in understanding general ideas of stability in such systems.

Release Notes

Purpose

This model has been designed to test how specially defined ecological communities maintain stability even in the presence of stochastic turnover. Using Vellend’s (Vellend 2016) proposed set of high-level processes for conceptually framing ecological communities, i.e., selection, species drift, speciation, and dispersal, we hypothesize that these provide constraint closure in the way proposed by Moreno and Mossio (Moreno and Mossio 2015) that contribute to biological autonomy. Moreover we hypothesize specifically that these will allow stable ecological signatures to arise and be maintained through constraint closure. In this way we seek to exemplify the possibility of creating some measure of generalizability in ecosystem community structure.

Associated Publications

Preprint doi: https://doi.org/10.1101/2020.01.28.924001

This release is out-of-date. The latest version is 1.0.1

Evolution of Ecological Communities: Testing Constraint Closure 1.0.0

Ecosystems are among the most complex structures studied. They comprise elements that seem both stable and contingent. The stability of these systems depends on interactions among their evolutionary history, including the accidents of organisms moving through the landscape and microhabitats of the earth, and the biotic and abiotic conditions in which they occur. When ecosystems are stable, how is that achieved? Here we look at ecosystem stability through a computer simulation model that suggests that it may depend on what constrains the system and how those constraints are structured. Specifically, if the constraints found in an ecological community form a closed loop, that allows particular kinds of feedback may give structure to the ecosystem processes for a period of time. In this simulation model, we look at how evolutionary forces act in such a way these closed constraint loops may form. This may explain some kinds of ecosystem stability. This work will also be valuable to ecological theorists in understanding general ideas of stability in such systems.

Release Notes

Purpose

This model has been designed to test how specially defined ecological communities maintain stability even in the presence of stochastic turnover. Using Vellend’s (Vellend 2016) proposed set of high-level processes for conceptually framing ecological communities, i.e., selection, species drift, speciation, and dispersal, we hypothesize that these provide constraint closure in the way proposed by Moreno and Mossio (Moreno and Mossio 2015) that contribute to biological autonomy. Moreover we hypothesize specifically that these will allow stable ecological signatures to arise and be maintained through constraint closure. In this way we seek to exemplify the possibility of creating some measure of generalizability in ecosystem community structure.

Version Submitter First published Last modified Status
1.0.1 Steve Peck Fri Apr 16 22:17:46 2021 Wed Apr 28 08:47:47 2021 Published Peer Reviewed DOI: 10.25937/5g4z-ky07
1.0.0 Steve Peck Sun Dec 6 19:37:54 2020 Thu Dec 5 06:37:18 2024 Published Peer Reviewed DOI: 10.25937/xqfk-hf84

Discussion

This website uses cookies and Google Analytics to help us track user engagement and improve our site. If you'd like to know more information about what data we collect and why, please see our data privacy policy. If you continue to use this site, you consent to our use of cookies.
Accept