Das Zoologische Forschungsmuseum Alexander Koenig

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Population genetics revisited

AutorInnen: 
Habel, J. C., Zachos, F. E., Dapporto, L., Rödder, D., Radespiel, U., Tellier, A., Schmitt, T.
Erscheinungsjahr: 
2015
Vollständiger Titel: 
Population genetics revisited – towards a multidisciplinary research field.
Autor/-innen des ZFMK: 
Org. Einordnung: 
Publiziert in: 
Biological Journal of the Linnean Society
Publikationstyp: 
Zeitschriftenaufsatz
DOI Name: 
10.1111/bij.12481
Keywords: 
census population size; dispersal; effective population size; environmental stress; landscape permeability; migration; population differentiation; variability
Bibliographische Angaben: 
Habel, J. C., Zachos, F. E., Dapporto, L., Rödder, D., Radespiel, U., Tellier, A., Schmitt, T. (2015): Population genetics revisited – towards a multidisciplinary research field. - Biological Journal of the Linnean Society, 2015, 115: 1–12; doi: 10.1111/bij.12481
Abstract: 

Population genetic research has transformed from the study of genetic structures into a much wider and highly multidisciplinary research field. In this contribution, we outline the limitations of classic population genetic data and highlight the potential of combining molecular data with additional environmental, ecological and biological data sets in multidisciplinary approaches. The combination of data sets from various fields allows a more comprehensive understanding of extrinsic and intrinsic evolutionary processes affecting populations such as the distinction between genetic drift and natural selection. The integration of population size estimates and demographic dynamics as well as individual behaviour (embracing aspects like dispersal behaviour) allows for testing of adaptations to local environments and understanding time-lags frequently observed in molecular data. Modelling approaches are integrated at increasing rates into population genetic research to identify potential barriers and corridors. In our review, we therefore highlight multiple synergistic effects when combining population genetic data with morphological, ecological, behavioural data and modelling. We outline strengths and limitations and indicate future possibilities and challenges.