Measuring Complexity in an Aquatic Ecosystem

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 232)


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.


Complex Systems Information Theory Complexity Self-organization Emergence Homeostasis Autopoiesis 


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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Laboratorio de Hidroinformática, Facultad de Ciencias BásicasUnivesidad de PamplonaPamplonaColombia
  2. 2.Centro de Micro-electrónica y Sistemas DistribuidosUniversidad de los AndesMéridaVenezuela
  3. 3.Departamento de Ciencias de la Computación, Instituto de Investigaciones en Matemáticas Aplicadas y en SistemasUniversidad Nacional Autónoma de MéxicoMéxico CityMéxico
  4. 4.Centro de Ciencias de la ComplejidadUniversidad Nacional Autónoma de MéxicoMéxicoMéxico

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