Application of network methods for understanding evolutionary dynamics in discrete habitats

Mol Ecol. 2017 Jun;26(11):2850-2863. doi: 10.1111/mec.14059. Epub 2017 Mar 23.

Abstract

In populations occupying discrete habitat patches, gene flow between habitat patches may form an intricate population structure. In such structures, the evolutionary dynamics resulting from interaction of gene-flow patterns with other evolutionary forces may be exceedingly complex. Several models describing gene flow between discrete habitat patches have been presented in the population-genetics literature; however, these models have usually addressed relatively simple settings of habitable patches and have stopped short of providing general methodologies for addressing nontrivial gene-flow patterns. In the last decades, network theory - a branch of discrete mathematics concerned with complex interactions between discrete elements - has been applied to address several problems in population genetics by modelling gene flow between habitat patches using networks. Here, we present the idea and concepts of modelling complex gene flows in discrete habitats using networks. Our goal is to raise awareness to existing network theory applications in molecular ecology studies, as well as to outline the current and potential contribution of network methods to the understanding of evolutionary dynamics in discrete habitats. We review the main branches of network theory that have been, or that we believe potentially could be, applied to population genetics and molecular ecology research. We address applications to theoretical modelling and to empirical population-genetic studies, and we highlight future directions for extending the integration of network science with molecular ecology.

Keywords: gene flow; network science; network theory; patch network; population genetics.

Publication types

  • Review

MeSH terms

  • Biological Evolution*
  • Ecology
  • Ecosystem*
  • Gene Flow*
  • Genetics, Population*
  • Models, Genetic
  • Population Dynamics