title: Heterogeneous adaptive trajectories of small populations on complex fitness landscapes.
abstract: BACKGROUND
  Small populations are thought to be adaptively handicapped, not only because they suffer more from deleterious mutations but also because they have limited access to new beneficial mutations, particularly those conferring large benefits.
  
  
  RESULTS
  Here, we test this widely held conjecture using both simulations and experiments with small and large bacterial populations evolving in either a simple or a complex nutrient environment. Consistent with expectations, we find that small populations are adaptively constrained in the simple environment; however, in the complex environment small populations not only follow more heterogeneous adaptive trajectories, but can also attain higher fitness than the large populations. Large populations are constrained to near deterministic fixation of rare large-benefit mutations. While such determinism speeds adaptation on the smooth adaptive landscape represented by the simple environment, it can limit the ability of large populations from effectively exploring the underlying topography of rugged adaptive landscapes characterized by complex environments.
  
  
  CONCLUSIONS
  Our results show that adaptive constraints often faced by small populations can be circumvented during evolution on rugged adaptive landscapes.
authors.affiliation: Department of Genetics, Wageningen University, Wageningen, The Netherlands. daniel.rozen@manchester.ac.uk
authors.forename: Daniel E
authors.initials: DE
authors.lastname: Rozen
authors.forename: Michelle G J L
authors.initials: MG
authors.lastname: Habets
authors.forename: Andreas
authors.initials: A
authors.lastname: Handel
authors.forename: J Arjan G M
authors.initials: JA
authors.lastname: de Visser
mesh_terms: D000220:Adaptation, Biological; D003198:Computer Simulation; D004777:Environment; D004926:Escherichia coli / Q000235:genetics*; D019143:Evolution, Molecular; D008957:Models, Genetic; D009154:Mutation / Q000235:genetics; D012641:Selection, Genetic