title: Different Evolutionary Paths to Complexity for Small and Large Populations of Digital Organisms.
abstract: A major aim of evolutionary biology is to explain the respective roles of adaptive versus non-adaptive changes in the evolution of complexity. While selection is certainly responsible for the spread and maintenance of complex phenotypes, this does not automatically imply that strong selection enhances the chance for the emergence of novel traits, that is, the origination of complexity. Population size is one parameter that alters the relative importance of adaptive and non-adaptive processes: as population size decreases, selection weakens and genetic drift grows in importance. Because of this relationship, many theories invoke a role for population size in the evolution of complexity. Such theories are difficult to test empirically because of the time required for the evolution of complexity in biological populations. Here, we used digital experimental evolution to test whether large or small asexual populations tend to evolve greater complexity. We find that both small and large-but not intermediate-sized-populations are favored to evolve larger genomes, which provides the opportunity for subsequent increases in phenotypic complexity. However, small and large populations followed different evolutionary paths towards these novel traits. Small populations evolved larger genomes by fixing slightly deleterious insertions, while large populations fixed rare beneficial insertions that increased genome size. These results demonstrate that genetic drift can lead to the evolution of complexity in small populations and that purifying selection is not powerful enough to prevent the evolution of complexity in large populations.
authors.affiliation: Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, United States of America.|Ecology, Evolutionary Biology, and Behavior Program, East Lansing, Michigan, United States of America.|BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, United States of America.
authors.forename: Thomas
authors.identifier: 0000-0003-3869-6720
authors.initials: T
authors.lastname: LaBar
authors.affiliation: Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, United States of America.|Ecology, Evolutionary Biology, and Behavior Program, East Lansing, Michigan, United States of America.|BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, United States of America.|Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, United States of America.
authors.forename: Christoph
authors.identifier: 0000-0002-2915-9504
authors.initials: C
authors.lastname: Adami
mesh_terms: D003198:Computer Simulation; D019143:Evolution, Molecular; D040961:Genetic Drift; D005828:Genetics, Population; D016678:Genome / Q000235:genetics; D008957:Models, Genetic