Modeling and specification of the aquatic ecological emergence using genetic programming

A major endeavor of ecology is to understand the emergence of complexity. This task requires the integration of knowledge and theories, moving from physical to social sciences. We use genetic programming to develop mathematical relationships between ecological emergence and variables such as self-organization, homeostasis, autopoiesis and complexity. These variables were initially formalized on the basis of information theory. The emergence models found were applied and tested with a case study involving an arctic lake and a tropical lake. In these lakes, the variables of limiting nutrients, biomass and physico-chemical components were taken into account for the automated generation of the model equations. The results show that the model follows in the dynamics of the aquatic ecological components selected accurately. In this context, ecological emergence can be calculated and studied.

[1]  Carlos Gershenson,et al.  Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis , 2013, ArXiv.

[2]  Mary L. Cadenasso,et al.  Dimensions of ecosystem complexity: Heterogeneity, connectivity, and history , 2006 .

[3]  Di Paolo,et al.  Homeostatic adaptation to inversion of the visual field and other sensorimotor disruptions , 2000 .

[4]  Carlos M. Fonseca,et al.  'Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[5]  Mikhail Prokopenko,et al.  An information-theoretic primer on complexity, self-organization, and emergence , 2009 .

[6]  Carlos Gershenson,et al.  When Can We Call a System Self-Organizing? , 2003, ECAL.

[7]  Giovanni Zurlini,et al.  Order and disorder in ecological time-series: Introducing normalized spectral entropy , 2013 .

[8]  Christopher G. Langton,et al.  Computation at the edge of chaos: Phase transitions and emergent computation , 1990 .

[9]  J. Aguilar,et al.  Fuzzy Classifier System and Genetic Programming on system identification problems , 2001 .

[10]  Lael Parrott,et al.  Measuring ecological complexity , 2010 .

[11]  Friedrich Recknagel,et al.  Applications of machine learning to ecological modelling , 2001 .

[12]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[13]  Nelson Fernández,et al.  Sistemas Dinámicos como Redes Computacionales de Agentes para la evaluación de sus Propiedades Emergentes , 2012 .

[14]  J. Aguilar Genetic Programming-Based Approach for System Identification , 2022 .

[15]  C. Bernard Leçons sur les propriétés physiologiques et les altérations pathologiques des liquides de l'organisme / par Claude Bernard. , 1859 .

[16]  Stuart A. Kauffman,et al.  ORIGINS OF ORDER , 2019, Origins of Order.

[17]  Carlos Gershenson,et al.  Complexity and information: Measuring emergence, self-organization, and homeostasis at multiple scales , 2012, Complex..

[18]  X. R. Wang,et al.  Relating Fisher information to order parameters. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Carlos Gershenson,et al.  Measuring Complexity in an Aquatic Ecosystem , 2014 .

[20]  Peter Nordin,et al.  Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications , 1998 .

[21]  L. Hayek,et al.  On richness and evenness within and between communities , 2005, Paleobiology.

[22]  Lael Parrott,et al.  Measures of structural complexity in digital images for monitoring the ecological signature of an old-growth forest ecosystem , 2008 .

[23]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[24]  Sanford Weisberg,et al.  An R Companion to Applied Regression , 2010 .