Effects of Random and Selective Mass Extinction on Community Composition in Communities of Digital Organisms

Chapter

Abstract

Digital evolution is a computer-based instantiation of Darwinian evolution in which short self-replicating computer programs compete, mutate, and evolve. It is an excellent platform for addressing topics in long-term evolution and paleobiology, such as mass extinction and recovery, with experimental evolutionary approaches. We evolved model communities with ecological interdependence among community members, which were subjected to 2 principal types of mass extinction: a pulse extinction which killed randomly, and a selective press extinction involving an alteration of the abiotic environment to which the communities had to adapt. These treatments were applied at 2 different strengths, along with unperturbed control experiments. We examined effects on the ecotype composition of the model communities. Empirical abundance distributions (EADs) which ignore ecotype identity were altered only temporarily by mass extinction and showed little enduring change, though evenness was affected by changes in richness. A metric sensitive to ecotype identity showed that the different treatments resulted in strongly differing degrees of community turnover relative to both the immediate pre-extinction state and to each other, and these could differ based on temporal perspective. Conclusions about community dynamics in digital evolution experiments depend on what aspects of the system are investigated, and how they are measured.

Keywords

Mass Extinction Computational Function Strong Press Abundance Distribution Median MRS10 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Gabriel Yedid and Lise Heier thank Trond Reitan for advice and assistance with statistical analyses, and Barbara Fischer, Øistein Holen, Antonieta Labra, Lee Hsiang Liow, Anna Nilsson, and Thomas Svennungsen (all from CEES, University of Oslo) for helpful comments on the talk from which this chapter is derived. Gabriel Yedid was supported by Research Council of Norway #179569/V40.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Centre for Ecological and Evolutionary Synthesis, Department of BiologyUniversity of OsloOsloNorway

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