October 28, 2015

Speaker: Dr. Robin Gras, Canada Research Chair, School of Computer Science,Cross-appointed by the Biology Department, Cross-appointed by the Great Lakes Institute for Environmental Research

Title: How species emerge? An evolving ecosystem simulation approach

Abstract: We have conceived EcoSim, a simulation platform designed to investigate several broad ecological questions, as well as long-term evolutionary patterns and processes such as speciation and macroevolution. EcoSim uses a modified version of the Fuzzy Cognitive Map (FCM) model adapted for behavioral modeling. The FCM is used as the behavioral model of the agents and is coded in their genome and therefore subject to evolution. This approach offers compactness with a very low computational requirement while having the capacity to represent complex high level notions. Therefore, each agent possesses its unique FCM and the system can still manage several hundreds of thousands of agents simultaneously in the world with reasonable computational requirements. In a typical run, more than one billion of agents can be born and several thousands of species can emerge or become extinct. This tool generates a huge amount of data representing all the events, the mental state and action of every agent saved for every time step of every run. This thorough tracking system allows for a deep statistical analysis of the whole system using several dedicated tools to extract, measure and correlate parameters that could be useful to understand the underlying and emerging properties of such a complex system.

This simulation is now the framework for the study of numerous specific ecological questions in collaboration such as the species abundance distribution, patterns and rates of speciation, the evolution of sexual and asexual populations, the interaction and diffusion of an invasive species into an existing ecosystem, etc. One such project concerns speciation mechanisms. The forces promoting and constraining speciation are often studied in theoretical models because the process is hard to observe, replicate, and manipulate in real organisms. In EcoSim, simulations including natural selection led to strong and distinct phenotypic/genotypic clusters between which hybridization was low. By contrast, simulations without natural selection (i.e., behavioural model turned off) but with spatial isolation (i.e., limited dispersal) produced weaker and overlapping clusters. Simulations without natural selection or spatial isolation (i.e., behaviour model turned off and high dispersal) did not generate clusters. These results confirm the leading role of natural selection in speciation by showing its importance even in the absence of pre-defined fitness functions.