Complexes

Highlights • The useful of quantitative indicators of ecological complexity is evaluated. • Chaos should not be confused with complexity. • Light and temperature cause different ranges of complexity in the gradient. • Homoeostasis variation is related to the seasonal changes and transitions. • Autopoiesis reveals groups with higher and lower degree of autonomy. Abstract Measuring complexity is fast becoming a key instrument to compare different ecosystems at various scales in ecology. To date there has been little agreement on how to properly describe complexity in terms of ecology. In this regard, this manuscript assesses the significance of using a set of proposed measures based on information theory. These measures are as follows: emergence, self-organization, complexity, homeostasis and autopoiesis. A combination of quantitative and qualitative approaches was used in the data analysis with the aim to apply these proposed measures. This study system...

In this chapter review measures of emergence, self-organization, complexity, homeostasis, and autopoiesis based on information theory. These measures are derived from proposed axioms and tested in two case studies: random Boolean networks and an Arctic lake ecosystem. Emergence is defined as the information produced by a system or process. Self-organization is defined as the opposite of emergence, while complexity is defined as the balance between emergence and self-organization. Homeostasis reflects the stability of a system. Autopoiesis is defined as the ratio between the complexity of a system and the complexity of its environment. The proposed measures can be applied at multiple scales, which can be studied with multi-scale profiles. Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis Nelson Fernandez, Carlos Maldonado, Carlos Gershenson http://arxiv.org/abs/1304.1842