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Journal of Environmental Informatics

Online ISSN 1684-8799 / Print ISSN 1726-2135

 

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   Volume 6   Number 2   December  2005 = complimentary

doi:10.3808/jei.200500056 About DOIs

JEI 6(2) 2005, Pages 58-71  

© 2005 ISEIS. All rights reserved.

Complexity and Organized Behaviour within Environmental Bounds (COBWEB): An Agent-Based Approach to Simulating Ecological Adaptation

B. Bass1* and E. Chan2

  1. Adaptation & Impacts Research Group, Environment Canada at the University of Toronto, Institute for Environmental Studies, 33 Willcocks Street, Toronto, ON M5S 3E8, Canada
  2. Department of Medicine, Faculty of Health Sciences, McMaster University, 1280 Main Street West Hamilton, ON L8S 4L8, Canada

*Corresponding author. Email: brad.bass@ec.gc.ca

 

Abstract

An agent-based simulation model, Complex Organization and Behaviour within Environmental Bounds (COBWEB), has been developed to simulate how a group of agents adapts to environmental variability and change. The environment is an abstraction with properties that are common to many complex systems. The environment is two-dimensional and is characterized by a cellular automaton with multiple resources, variable rates of growth, famines and the presence of immovable objects. The agents are genetic algorithms with modifiable behavioural traits and resource preferences. The agents' strategies are defined by movement, consumption, asexual and sexual reproduction, communication and sharing or stealing resources from other agents. The strategies are determined genetically and through new information acquired from the environment or from other agents. COBWEB has been used to examine the linkages between population growth, resources, the availability of energy and surprise suggesting a role for this software in studying ecological processes, specifically the adaptation of different species to environmental change. The current research results illustrate how COBWEB agents can adapt to variability and change in response to changing resource availability, available energy and crossing threshold values. The second set of experiments indicates the importance of genetic diversity and the ability of an invader to extract more energy from the landscape than the indigenous species. The results illustrate that COBWEB is a useful tool for this sort of research and is capable of generating new insights.


Keywords: Complexity, ecological simulation, environmental change, genetic algorithms, invasive species

 

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Cite this paper as: B. Bass and E. Chan, 2005. Complexity and Organized Behaviour within Environmental Bounds (COBWEB): An Agent-Based Approach to Simulating Ecological Adaptation. Journal of Environmental Informatics, 6(2), 58-71. http://dx.doi.org/10.3808/jei.200500056


References: 18

  1. Bass, B., Byers, R.T. and Lister, N.M. (1998). Integrating research on ecohydrology and land use change with land use management. Hydrol. Process., 12, 2217-2233.
  2. Bass, B., Hill, J. and Suh, N. (2002). Simulating adaptation to environmental change: Complexity and Organized Behaviour Within Environmental Bounds (COBWEB). J. CASYS, 13, 21-33.
  3. Byers, R.E. and Hansell, R.I.C. (1996). Implications of semi-stable attractors for ecological modelling. Ecol. Model., 89, 59-65.
  4. Board on Agriculture and Natural Resources (BANR) , Board on Life Sciences (BLS) (2002). Predicting Invasions of Nonindigenous Plants and Plant Pests, Washington.
  5. Groves, R.H. and Burton, J.J. (1986). Ecology of Biological Invasions, Australian Academy of Science, Cambridge University Press, Canberra.
  6. Hansell, R.I.C. and Bass, B. (1998). Holling¡¯s figure-eight model: A technical reevaluation in relation to climate change and biodiversity. J. Environ. Manage., 49, 157-168.
  7. Hansell, R.I.C., Craine, I.T. and Byers, R.E. (1997). Predicting change in non-linear systems. J. Environ. Manage., 46, 175-190.
  8. Hofstadter, D.R. (1985). Metamagical Themas: Questing for the Essence of Mind and Pattern, Basic Books, NY.
  9. Holling, C.S. (1986). The resilience of terrestrial ecosystems: local surprise and global change, in Clark, W.C. and Munn, R.E. (Eds.), Sustainable Development of the Biosphere, IIASA, Cambridge University Press, pp. 292-317.
  10. Miller, T.E., Kneitel, J.M. and Burns, J.H. (2002). Effect of community structure on invasion success and rate. Ecol., 83(4), 898-905.
  11. M¨¹ller, F. and Fath, B. (1998). The physical basis of ecological goal functions-fundamentals, problems and questions, in F. Muller (Ed.), Eco-targets, Goal Functions and Orientors, Springer-Verlag, Berlin-Heidelberg, pp. 15-18.
  12. National Research Council (NRC), Board on Agriculture and Natural Resources (BANR), Board on Life Sciences (BLS), (2002). Predicting Invasions of Noningigenous Plants and Plant Pests: Washington.
  13. Olson, R.L. and Sequeira, R.A. (1995). Emergent computation and the modeling and management of ecological systems. Comput. Electron. Agric., 12, 183-209.
  14. Patz, J.A., Epstein, P.R., Burke, T.A. and Balbus, J.M. (1996). Global climate change and emerging infectious diseases. J. Am. Med. Assoc., 275(3), 217-223.
  15. Scheffer, M. (1990). Multiplicity of stable states in freshwater systems. Hydrobiol., 200/201, 475-486.
  16. Sakai, A.K., Allendorf, F.W., Holt, J.S., Molofsky, J., With, K.A., Baughman, S., Cabin, R.J., Cohen, J.E., Ellstrand, N.C., McCauley, D.E., O'Neil, P., Parker, I.M., Thompson, J.N. and Weller, S.G. (2001). The population biology of invasive species. Annu. Rev. Ecol. Syst., 32, 305-322.
  17. Severinghaus, J.P. and Brook, E.J. (1999). Abrupt climate change at the end of the last glacial period inferred from trapped air in polar ice. Sci., 286(5441), 930-934.
  18. Shigesada, N. and Kawasaki, K. (1997). Biological Invasions: Theory and Practice, Oxford University Press, NY.
  19.  



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