We present a set of Matlab/Octave functions to compute measures of emergence, self-organization, and complexity applied to discrete and continuous data. These measures are based on Shannon’s information and differential entropy. Examples from different datasets and probability distributions are provided to show how to use our proposed code.
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.
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 systematically reviews the data previously …