Spatiotemporal landscape genetics: Investigating ecology and evolution through space and time.

Academic Article

Abstract

  • Genetic time-series data from historical samples greatly facilitate inference of past population dynamics and species evolution. Yet, although climate and landscape change are often touted as post-hoc explanations of biological change, our understanding of past climate and landscape change influences on evolutionary processes is severely hindered by the limited application of methods that directly relate environmental change to species dynamics through time. Increased integration of spatiotemporal environmental and genetic data will revolutionize the interpretation of environmental influences on past population processes and the quantification of recent anthropogenic impacts on species, and vastly improve prediction of species responses under future climate change scenarios, yielding widespread revelations across evolutionary biology, landscape ecology and conservation genetics. This review encourages greater use of spatiotemporal landscape genetic analyses that explicitly link landscape, climate and genetic data through time by providing an overview of analytical approaches for integrating historical genetic and environmental data in five key research areas: population genetic structure, demography, phylogeography, metapopulation connectivity and adaptation. We also include a tabular summary of key methodological information, suggest approaches for mitigating the particular difficulties in applying these techniques to ancient DNA and palaeoclimate data, and highlight areas for future methodological development.
  • Authors

    Status

    Publication Date

  • January 2020
  • Has Subject Area

    Published In

  • Molecular Ecology  Journal
  • Keywords

  • ancient DNA
  • climate change
  • ecological genetics
  • genome-environment association
  • genotype-environment correlation
  • spatiotemporal population dynamics
  • Digital Object Identifier (doi)

    Pubmed Id

  • 31758601
  • Start Page

  • 218
  • End Page

  • 246
  • Volume

  • 29
  • Issue

  • 2