Measuring Complexity in an Aquatic Ecosystem

TL;DR

This paper applies information-theoretic measures to analyze the complexity of an Arctic lake's physiochemical components, revealing insights into ecological dynamics and seasonal variations.

Contribution

It introduces a novel application of formal information measures to quantify ecological complexity in an aquatic ecosystem.

Findings

Higher emergence in variables with homogeneous distributions

Seasonal homeostasis variations observed

Autopoiesis indicates biological independence from environment

Abstract

We apply formal measures of emergence, self-organization, homeostasis, autopoiesis and complexity to an aquatic ecosystem; in particular to the physiochemical component of an Arctic lake. These measures are based on information theory. Variables with an homogeneous distribution have higher values of emergence, while variables with a more heterogeneous distribution have a higher self-organization. Variables with a high complexity reflect a balance between change (emergence) and regularity/order (self-organization). In addition, homeostasis values coincide with the variation of the winter and summer seasons. Autopoiesis values show a higher degree of independence of biological components over their environment. Our approach shows how the ecological dynamics can be described in terms of information.