Details

Autor(en) / Beteiligte

• Chan, Lauren M.
• Brown, Jason L.
• Yoder, Anne D.

Titel

Integrating statistical genetic and geospatial methods brings new power to phylogeography

Ist Teil von

• Molecular phylogenetics and evolution, 2011-05, Vol.59 (2), p.523-537

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2011

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• [Display omitted] ► Genetic and geospatial data complement one another in phylogeography. ► Practical examples to illustrate how these data can be integrated. ► Variety of tools and approaches can be applied to many biological systems. The field of phylogeography continues to grow in terms of power and accessibility. Initially uniting population genetics and phylogenetics, it now spans disciplines as diverse as geology, statistics, climatology, ecology, physiology, and bioinformatics to name a few. One major and recent integration driving the field forward is between “statistical phylogeography” and Geographic Information Systems (GIS) ( Knowles, 2009). Merging genetic and geospatial data, and their associated methodological toolkits, is helping to bring explicit hypothesis testing to the field of phylogeography. Hypotheses derived from one approach can be reciprocally tested with data derived from the other field and the synthesis of these data can help place demographic events in an historical and spatial context, guide genetic sampling, and point to areas for further investigation. Here, we present three practical examples of empirical analysis that integrate statistical genetic and GIS tools to construct and test phylogeographic hypotheses. Insights into the evolutionary mechanisms underlying recent divergences can benefit from simultaneously considering diverse types of information to iteratively test and reformulate hypotheses. Our goal is to provide the reader with an introduction to the variety of available tools and their potential application to typical questions in phylogeography with the hope that integrative methods will be more broadly and commonly applied to other biological systems and data sets.