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Part of the book series: Emergence, Complexity and Computation ((ECC,volume 6))

Abstract

Conventional approaches to modelling ecological dynamics often do not include evolutionary changes in the genetic makeup of component species and, conversely, conventional approaches to modelling evolutionary changes in the genetic makeup of a population often do not include ecological dynamics. But recently there has been considerable interest in understanding the interaction of evolutionary and ecological dynamics as coupled processes. However, in the context of complex multi-species ecosytems, especially where ecological and evolutionary timescales are similar, it is difficult to identify general organising principles that help us understand the structure and behaviour of complex ecosystems. Here we introduce a simple abstraction of coevolutionary interactions in a multi-species ecosystem.We model non-trophic ecological interactions based on a continuous but low-dimensional trait/niche space, where the location of each species in trait space affects the overlap of its resource utilisation with that of other species. The local depletion of available resources creates, in effect, a deformable fitness landscape that governs how the evolution of one species affects the selective pressures on other species. This enables us to study the coevolution of ecological interactions in an intuitive and easily visualisable manner.We observe that this model can exhibit either of the two behaviouralmodes discussed in the literature; namely, evolutionary stasis or Red Queen dynamics, i.e., continued evolutionary change.We find that which of these modes is observed depends on the lag or latency between the movement of a species in trait space and its effect on available resources. Specifically, if ecological change is nearly instantaneous compared to evolutionary change, stasis results; but conversely, if evolutionary timescales are closer to ecological timescales, such that resource depletion is not instantaneous on evolutionary timescales, then Red Queen dynamics result. We also observe that in the stasis mode, the overall utilisation of resources by the ecosystem is relatively efficient, with diverse species utilising different niches, whereas in the Red Queen mode the organisation of the ecosystem is such that species tend to clump together competing for overlapping resources. These models thereby suggest some basic conditions that influence the organisation of inter-species interactions and the balance of individual and collective adaptation in ecosystems, and likewise they also suggest factors that might be useful in engineering artificial coevolution.

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References

  1. Adami, C.: Learning and complexity in genetic auto-adaptive systems. Physica D 80, 154–170 (1995)

    Article  MATH  Google Scholar 

  2. Carrol, L.: The Complete Works. CRW Publishing Limited, London (2005)

    Google Scholar 

  3. Cliff, D., Miller, G.F.: Tracking the red queen: Measurements of adaptive progress in co-evolutionary simulations. In: Morán, F., Moreno, A., Merelo, J.J., Chacón, P. (eds.) Third European Conference on Artificial Life, pp. 200–218. Springer, Berlin (1995)

    Chapter  Google Scholar 

  4. Cliff, D., Miller, G.F.: Co-evolution of pursuit and evasion II: Simulation methods and results. In: Maes, P., Mataric, M.J., Meyer, J.A., Pollack, J., Wilson, S.W. (eds.) From Animals to Animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pp. 506–515. The MIT Press, Cambridge (1996)

    Google Scholar 

  5. Dawkins, R., Krebs, J.R.: Arms races between and within species. Proc. R. Soc. Lond. B 205, 489–511 (1979)

    Article  Google Scholar 

  6. Dieckmann, U., Doebeli, M.: On the origin of species by sympatric speciation. Nature 400, 354–357 (1999)

    Article  Google Scholar 

  7. Ebner, M.: A three-dimensional environment for self-reproducing programs. In: Kelemen, J., Sosík, P. (eds.) ECAL 2001. LNCS (LNAI), vol. 2159, pp. 306–315. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  8. Ebner, M.: Coevolution and the red queen effect shape virtual plants. Genetic Programming and Evolvable Machines 7(1), 103–123 (2006)

    Article  Google Scholar 

  9. Ebner, M., Watson, R.A., Alexander, J.: Co-evolutionary dynamics on a deformable landscape. In: Zalzala, A., Fonseca, C., Kim, J.H., Smith, A., Yao, X. (eds.) Proceedings of the 2000 Congress on Evolutionary Computation, vol. 2, pp. 1284–1291. IEEE Press, San Diego (2000)

    Google Scholar 

  10. Ebner, M., Watson, R.A., Alexander, J.: Coevolutionary dynamics of interacting species. In: Di Chio, C., et al. (eds.) EvoApplicatons 2010, Part I. LNCS, vol. 6024, pp. 1–10. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  11. Faro, J., Velasco, S.: An approximation for prey-predator models with time delay. Physica D 110, 313–322 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  12. Floreano, D., Nolfi, S.: God save the red queen! Competition in co-evolutionary robotics. In: Koza, J.R., Deb, K., Dorigo, M., Fogel, D.B., Garzon, M., Iba, H., Riolo, R.L. (eds.) Genetic Programming 1997: Proceedings of the Second International Conference on Genetic Programming, July 13-16, pp. 398–406. Morgan Kaufmann Publishers, San Francisco (1997)

    Google Scholar 

  13. Floreano, D., Nolfi, S., Mondada, F.: Competitive co-evolutionary robotics: From theory to practice. In: Pfeifer, R., Blumberg, B., Meyer, J.A., Wilson, S.W. (eds.) From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior, pp. 515–524. The MIT Press, Cambridge (1998)

    Google Scholar 

  14. Gillespie, J.H.: Molecular evolution over the mutational landscape. Evolution 38, 1116–1129 (1984)

    Article  Google Scholar 

  15. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company, Reading (1989)

    MATH  Google Scholar 

  16. Higgs, P.G., Derrida, B.: Genetic distance and species formation in evolving populations. Journal of Molecular Evolution 35, 454–465 (1992)

    Article  Google Scholar 

  17. Hillis, W.D.: Co-evolving parasites improve simulated evolution as an optimization procedure. In: Langton, C.G., Taylor, C., Farmer, J.D., Rasmussen, S. (eds.) Artificial Life II, SFI Studies in the Sciences of Complexity, pp. 313–324. Addison-Wesley (1991)

    Google Scholar 

  18. Hutchinson, G.E.: The niche: an abstractly inhabited hypervolume. In: Hutchinson, G.E. (ed.) The Ecological Theater and the Evolutionary Play, Yale University Press, New Haven (1965)

    Google Scholar 

  19. Juille, H., Pollack, J.B.: Coevolving the ideal trainer: Application to the discovery of cellular automata rules. In: Koza, J.R. (ed.) Proceedings of the Third Annual Conference on Genetic Programming, University of Wisconsin, Madison, WI, pp. 519–527. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  20. Kauffman, S.A.: The Origins of Order. Self-Organization and Selection in Evolution. Oxford University Press, Oxford (1993)

    Google Scholar 

  21. Luke, S., Wiegand, R.P.: Guaranteeing coevolutionary objective measures. In: De Jong, K.A., Poli, R., Rowe, J.E. (eds.) Foundations of Genetic Algorithms VII, pp. 237–251. Morgan Kaufman, San Francisco (2002)

    Google Scholar 

  22. MacArthur, R.H.: The theory of the niche. In: Lewontin, R.C. (ed.) Population Biology and Evolution, Syracuse University Press, Syracuse (1968)

    Google Scholar 

  23. MacArthur, R.H., Levins, R.: Competition, habitat selection and character displacement in a patchy environment. Proc. Nat. Acad. Sci. USA 51, 1207–1210 (1964)

    Article  Google Scholar 

  24. May, R.M., MacArthur, R.H.: Niche overlap as a function of environmental variability. Proc. Nat. Acad. Sci. USA 69, 1109–1113 (1972)

    Article  Google Scholar 

  25. Metz, J.A.J., Geritz, S.A.H., Meszena, G., Jacobs, F.J.A., van Heerwaarden, J.S.: Adaptive dynamics: a geometrical study of the consequences of nearly faithful reproduction. In: Strien, S.J.V., Lunel, S.M.V. (eds.) Stochastic and Spatial Structures of Dynamical Systems, North Holland, Amsterdam, The Netherlands, pp. 183–231 (1996)

    Google Scholar 

  26. Miller, G.F., Cliff, D.: Protean behavior in dynamic games: Arguments for the co-evolution of pursuit-evasion tactics. In: Cliff, D., Husbands, P., Meyer, J., Wilson, S.W. (eds.) From Animals to Animats III: Proceedings of the Third International Conference on Simulation of Adaptive Behavior, pp. 411–420. The MIT Press, Cambridge (1994)

    Google Scholar 

  27. Pavlicev, M., Cheverud, J.M., Wagner, G.P.: Evolution of adaptive phenotypic variation patterns by direct selection for evolvability. Proceedings of the Royal Society B: Biological Sciences 278, 1903–1912 (2011)

    Article  Google Scholar 

  28. Pelletier, F., Garant, D., Hendry, A.P.: Eco-evolutionary dynamics. Phil. Trans. R. Soc. B 364, 1483–1489 (2009)

    Article  Google Scholar 

  29. Post, D.M., Palkovacs, E.P.: Eco-evolutionary feedbacks in community and ecosystem ecology: interactions between the ecological theatre and the evolutionary play. Phil. Trans. R. Soc. B 364, 1629–1640 (2009)

    Article  Google Scholar 

  30. Potter, M.A., Couldrey, C.: A cooperative coevolutionary approach to function optimization. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 374–383. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  31. Ray, T.S.: Is it alive or is it GA? In: Belew, R.K., Booker, L.B. (eds.) Proceedings of the Fourth International Conference on Genetic Algorithms, University of California, San Diego, pp. 527–534. Morgan Kaufmann Publishers, San Mateo (1991)

    Google Scholar 

  32. Ray, T.S.: Evolution, complexity, entropy and artificial reality. Physica D 75, 239–263 (1994)

    Article  MATH  Google Scholar 

  33. Reynolds, C.W.: Competition, coevolution and the game of tag. In: Brooks, R.A., Maes, P. (eds.) Artificial Life IV, July 6-8, pp. 59–69. The MIT Press, Cambridge (1994)

    Google Scholar 

  34. Ridley, M.: The Red Queen: Sex and the Evolution of Human Nature. Penguin Books, New York (1994)

    Google Scholar 

  35. Riolo, R.L., Cohen, M.D., Axelrod, R.: Evolution of cooperation without reciprocity. Nature 414, 441–443 (2001)

    Article  MATH  Google Scholar 

  36. Rosenzweig, M.L., Brown, J.S., Vincent, T.L.: Red queens and ESS: The coevolution of evolutionary rates. Evolutionary Ecology 1, 59–94 (1987)

    Article  Google Scholar 

  37. Roughgarden, J.: Coevolution in ecological systems: results from “loop analysis” for purely density-dependent evolution. In: Christiansen, F.B., Fenchel, T.M. (eds.) Measuring Selection in Natural Populations, pp. 499–517. Springer, Berlin (1977)

    Chapter  Google Scholar 

  38. Maynard Smith, J.: A comment on the red queen. Amer. Natur. 110, 325–330 (1976)

    Article  Google Scholar 

  39. Stenseth, N.C., Maynard Smith, J.: Coevolution in ecosystems: Red queen evolution or stasis? Evolution 38(4), 870–880 (1984)

    Article  Google Scholar 

  40. Tregenza, T., Butlin, R.K.: Speciation without isolation. Nature 400, 311–312 (1999)

    Article  Google Scholar 

  41. Van Valen, L.: A new evolutionary law. Evolutionary Theory 1, 1–30 (1973)

    Google Scholar 

  42. Valentine, J.W.: Conceptual models of ecosystem evolution. In: Schopf, T.J.M. (ed.) Models in Paleobiology, pp. 192–215. Freeman Cooper, San Francisco (1972)

    Google Scholar 

  43. Vygotsky, L.S.: Mind and Society: The Development of Higher Psychological Processes. Harvard University Press, Cambridge (1978)

    Google Scholar 

  44. Watson, R.A., Pollack, J.B.: Coevolutionary dynamics in a minimal substrate. In: Spector, L. (ed.) Proceedings of the Genetic and Evolutionary Computation Conference, pp. 702–709 (2001)

    Google Scholar 

  45. Wiegand, R.P.: An Analysis of Cooperative Coevolutionary Algorithms. George Mason University Fairfax, VA (2004)

    Google Scholar 

  46. Wright, S.: The roles of mutation, inbreeding, crossbreeding and selection in evolution. In: Jones, D.F. (ed.) Proceedings of the Sixth International Congress on Genetics, Ithaca, NY, pp. 356–366 (1932)

    Google Scholar 

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Watson, R.A., Ebner, M. (2014). Eco-Evolutionary Dynamics on Deformable Fitness Landscapes. In: Richter, H., Engelbrecht, A. (eds) Recent Advances in the Theory and Application of Fitness Landscapes. Emergence, Complexity and Computation, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41888-4_12

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  • DOI: https://doi.org/10.1007/978-3-642-41888-4_12

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