%0 Book Section %B Advances in Computational Biology %D 2014 %T Measuring Complexity in an Aquatic Ecosystem %A Fernández, Nelson %A Gershenson, Carlos %E Castillo, Luis F. %E Cristancho, Marco %E Isaza, Gustavo %E Pinzón, Andrés %E Corchado Rodríguez, Juan Manuel %X 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. %B Advances in Computational Biology %S Advances in Intelligent Systems and Computing %I Springer %V 232 %P 83-89 %G eng %U http://arxiv.org/abs/1305.5413 %R 10.1007/978-3-319-01568-2_12