Date of Award

Winter 2014

Publication Type

Master Thesis

Degree Name

M.Sc.

Department

Computer Science

Keywords

Biological sciences, Applied sciences, Divergent eating, Food chains, Individual basedmodel, Sympatric speciation, Sympatric speciation prediction

Supervisor

Gras, Robin

Rights

info:eu-repo/semantics/openAccess

Abstract

The study of sympatric speciation in evolutionary biology is facing the obstacle of unifying empirical studies with existing theoretical investigations. Disruptive selection due to preferential food resource usage is considered as the main hypothesis to explain the sympatric speciation occurrence in empirical studies. We extend an individual based evolving predator-prey ecosystem platform called "EcoSim" [Gras et al. 2009a] to model a dual resource system. We investigated whether and in which conditions the selective pressures acting on foraging behaviors drove sympatric speciation. We observed clear results showing some behavioral modifications occurring as a consequence of preferential resource usage. We also observed many cases where the sympatric speciation criteria described in the literature were fulfilled. Using several machine learning techniques, we extracted explicit rules that can predict with a very high accuracy the occurrence of sympatric speciation based on ecological factor observations. Moreover, we confirmed that the existence of a second food resource is determinant for the emergence of sympatric phenomenon. We also proved that our method is able to discover very generic rules which may later be used to structure empirical studies.

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