University of Windsor Scholarship at UWindsor Electronic Theses and Dissertations Theses, Dissertations, and Major Papers 2014 Individual-Based Modeling and Nonlinear Analysis for Complex Systems with Application to Theoretical Ecology Abbas Ghadri Golestani University of Windsor Follow this and additional works at: https://scholar.uwindsor.ca/etd Recommended Citation Ghadri Golestani, Abbas, "Individual-Based Modeling and Nonlinear Analysis for Complex Systems with Application to Theoretical Ecology" (2014). Electronic Theses and Dissertations. 5253. https://scholar.uwindsor.ca/etd/5253 This online database contains the full-text of PhD dissertations and Masters’ theses of University of Windsor students from 1954 forward. These documents are made available for personal study and research purposes only, in accordance with the Canadian Copyright Act and the Creative Commons license—CC BY-NC-ND (Attribution, Non-Commercial, No Derivative Works). 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Individual-Based Modeling and Nonlinear Analysis for Complex Systems with Application to Theoretical Ecology By Abbas Ghadri Golestani A Dissertation submitted to the Faculty of Graduate Studies through the Department of Computer Science in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy at the University of Windsor Windsor, Ontario, Canada 2014 © 2014 Abbas Ghadri Golestani Individual-Based Modeling and Nonlinear Analysis for Complex Systems with Application to Theoretical Ecology by Abbas Ghadri Golestani APPROVED BY: ___________________________________________ Dr. J. Liu, External Examiner Hong Kong Baptist University ______________________________________________ Dr. D. Heath Great Lakes Institute for Environmental Research ______________________________________________ Dr. B. Boufama School of Computer Science ______________________________________________ Dr. Z. Kobti School of Computer Science ______________________________________________ Dr. R. Gras, Advisor School of Computer Science December 5, 2014 Declaration of Co-Authorship / Previous Publication I. Co-Authorship Declaration I hereby declare that this dissertation incorporates material that is result of joint research with Dr. Robin Gras, my supervisor. This dissertation also incorporates the outcome of a joint research undertaken in collaboration with Dr. M. Cristescu and Dr. A.P Hendy under the supervision of professor Robin Gras. The collaboration is covered in Chapter 4 of the dissertation. In all cases, the key ideas, primary contributions, experimental designs, data analysis and interpretation, were performed by the author, and the contribution of co-author was primarily through the provision of required background biological information . In Chapter 3, Ms. Khater also contributed in explaining the materials. I am aware of the University of Windsor Senate Policy on Authorship and I certify that I have properly acknowledged the contribution of other researchers to my dissertation, and have obtained written permission from each of the co-author(s) to include the above material(s) in my dissertation. I certify that, with the above qualification, this dissertation, and the research to which it refers, is the product of my own work. II. Declaration of Previous Publication This dissertation includes 13 original papers and patents that have been previously published/submitted for publication in peer reviewed journals, as follows: iii Dissertation Publication title/full citation Publication Chapter status* Chapter 2 R. Gras, A. Golestani, M. Hosseini, M. Khater, Y.M. Farahani, M. Published Mashayekhi, M. Sina, A. Sajadi, E. Salehi and R. Scott, EcoSim: an individual-based platform for studying evolution, European Conference on Artificial Life, pp 284-286, 2011. Chapter 2 M. Mashayekhi, A. Golestani, Y.M. Farahani, R. Gras, An enhanced Published artificial ecosystem: Investigating emergence of ecological niches, International Conference on the Simulation and Synthesis of Living Systems (ALIFE 14), pp 693-700, 2014. Chapter 4 A. Golestani, R. Gras, and M. Cristescu, Speciation with gene flow in a Published heterogeneous virtual world: can physical obstacles accelerate speciation?, Proceedings of the Royal Society B: Biological Sciences , vol. 279 no. 1740, pp 3055-3064, 2012. Chapter 4 A. Golestani, R. Gras, A New Species Abundance Distribution Model Based Published on Model Combination, International Journal of Biostatistics (IJB) , 9(1): 1– 16, 2013. Chapter 4 A. Golestani, R. Gras, Using Machine Learning Techniques for Identifying Published Important Characteristics to Predict Changes in Species Richness in EcoSim, an Individual-Based Ecosystem Simulation", International Conference on Machine Learning and Data Analysis (ICMLDA'12), vol 1, pp 465-470, San Francisco, 2012. Chapter 4 R. Gras, A. Golestani, M. Cristescu, and A.P. Hendry, Speciation without Submitted pre-defined fitness functions, PLOS ONE, 2014. Chapter 5 A. Golestani, R. Gras, Regularity Analysis of an individual-based Ecosystem Published Simulation, journal of Chaos: An Interdisciplinary journal of Nonlinear Science, CHAOS 20, 043120. pp 1-13, 2010. Chapter 5 A. Golestani, R. Gras, Identifying Origin of Self-Similarity in EcoSim, an Published Individual-Based Ecosystem Simulation, Using Wavelet-based Multifractal Analysis, International Conference on Modeling, Simulation and Control (ICMSC'12), vol 2, pp 1275-1282, San Francisco, 2012. Chapter 5 Y.M. Farahani, A. Golestani, R. Gras, Complexity and Chaos Analysis of a Published Predator-Prey Ecosystem Simulation, COGNITIVE, ISBN: 978-1-61208- 001-7, pp: 52-59, 2010. Chapter 5 A. Golestani, R. Gras, "Multifractal Phenomena in EcoSim, a Large Scale Published Individual-Based Ecosystem Simulation", ICAI (International Conference on iv Artificial Intelligence), Las Vegas, USA, pp 991-999, 2011. Chapter 6 A. Golestani, R.Gras, Can We Predict the Unpredictable?, Scientific Reports Published 4, 6834; DOI:10.1038/srep068342014. Chapter 6 A. Golestani, R. Gras, System and Process for Predictive Chaos Analysis , In press Filed under US Patent Application Serial Number 61/882863. Chapter 6 A. Golestani, R. Gras, Method And Apparatus For Prediction Of Epileptic In press Seizures, Filed under US Patent Application Serial Number 62/042535. I certify that I have obtained a written permission from the copyright owner(s) to include the above published material(s) in my dissertation. I certify that the above material describes work completed during my registration as graduate student at the University of Windsor. I declare that, to the best of my knowledge, my dissertation does not infringe upon anyone’s copyright nor violate any proprietary rights and that any ideas, techniques, quotations, or any other material from the work of other people included in my dissertation, published or otherwise, are fully acknowledged in accordance with the standard referencing practices. Furthermore, to the extent that I have included copyrighted material that surpasses the bounds of fair dealing within the meaning of the Canada Copyright Act, I certify that I have obtained a written permission from the copyright owner(s) to include such material(s) in my dissertation. I declare that this is a true copy of my dissertation, including any final revisions, as approved by my dissertation committee and the Graduate Studies office, and that this dissertation has not been submitted for a higher degree to any other University or Institution. v ABSTRACT One approach to understanding the behaviour of complex systems is individual-based modeling, which provides a bottom-up approach allowing for the consideration of the traits and behaviour of individual organisms. Ecosystem models aim to characterize the major dynamics of ecosystems, in order to synthesize the understanding of such systems and to allow predictions of their behaviour. Moreover, ecosystem simulations have the potential to help scientists address theoretical questions as well as helping with ecological resource management. Because in reality biologists do not have much data regarding variations in ecosystems over long periods of time, using the results of ecological computer simulation for making reasonable predictions can help biologists to better understand the long-term behaviour of ecosystems. Different versions of ecosystem simulations have been developed to investigate several questions in ecology such as how speciation proceeds in the absence of experimenter-defined functions. I have investigated some of these questions relying on complex interactions between the many individuals involved in the system, as well as long-term evolutionary patterns and processes such as speciation and macroevolution. Most scientists now believe that natural phenomena have to be looking as a chaotic system. In the past few years, chaos analysis techniques have gained increasing attention over a variety of applications. I have analyzed results of complex models to see whether chaotic behaviour can emerge, since any attempt to model a realistic system needs to have the capacity to generate patterns as complex