Conservation Genetics

, Volume 13, Issue 5, pp 1421–1426 | Cite as

Relative sensitivity of neutral versus adaptive genetic data for assessing population differentiation

  • Erin L. LandguthEmail author
  • Niko Balkenhol
Short Communication


Population differentiation is often quantified using putatively neutral genetic markers. While adaptive (i.e., selection-driven) genetic markers are becoming increasingly popular, they are mostly used for research on evolutionary processes, such as local adaptation or speciation. Here, we use simulations to evaluate the potential of adaptive genetic data for estimating population differentiation under a range of gene flow, population size, and selection scenarios. Our results suggest that reduced migration can lead to more pronounced genetic differentiation in adaptive versus neutral genetic differentiation, provided that a difference in local selection pressures among spatial locations exists (i.e., spatial selection gradients). These results encourage the use of adaptive genetic data for quantifying genetic differentiation, even in studies focusing on contemporary or recent processes, such as habitat loss and fragmentation. Furthermore, our results illustrate that not testing for selection in putatively neutral markers may lead to incorrect inferences about the processes underlying population differentiation.


Dest Computer simulations Landscape genetics CDPOP Migration rates Two-locus selection Barriers Gene flow Spatial selection gradients 



We would like to thank Norman Johnson, Gernot Segelbacher, and two anonymous reviewers for their comments.


  1. Angeloni F, Wagemaker N, Vergeer P, Ouborg J (2011) Genomic toolboxes for conservation biologists. Evol Appl. doi: 10.1111/j.1752-4571.2011.00217.x Google Scholar
  2. Bonin A, Nicole F, Pompanon F, Miaud C, Taberlet P (2007) Population adaptive index: a new method to help measure intraspecific genetic diversity and prioritize populations for conservation. Conserv Biol 21:697–708PubMedCrossRefGoogle Scholar
  3. Eimes JA, Bollmer JL, Whittingham LA et al (2011) Rapid loss of MHC class II variation in a bottlenecked population is explained by drift and loss of copy number variation. J Evol Biol 24:1847–1856PubMedCrossRefGoogle Scholar
  4. Frankham R (2006) Genetics and landscape connectivity. In: Crooks KR, Sanjayan M (eds) Connectivity conservation. Cambridge University Press, New York, pp 72–96CrossRefGoogle Scholar
  5. Gavrilets S (2004) Fitness landscapes and the origin of species. Princeton University Press, Princeton, NJGoogle Scholar
  6. Hoffmann AA, Willi Y (2008) Detecting genetic responses to environmental change. Nat Rev Genet 9(421):432Google Scholar
  7. Holderegger R, Wagner HH (2008) Landscape genetics. BioSciences 58:199–207CrossRefGoogle Scholar
  8. Jost L (2008) GST and its relatives do not measure differentiation. Mol Ecol 17:4015–4026PubMedCrossRefGoogle Scholar
  9. Landguth EL, Cushman SA (2010) CDPOP: a spatially-explicit cost distance population genetics program. Mol Ecol Resour 10:156–161PubMedCrossRefGoogle Scholar
  10. Landguth EL, Cushman SA, Murphy M, Luikart G (2010) Relationships between migration rates and landscape resistance assessed using individual-based simulations. Mol Ecol Resour 10:854–862PubMedCrossRefGoogle Scholar
  11. Landguth EL, Cushman SA, Johnson NA (2012) Simulating natural selection in landscape genetics. Mol Ecol Resour 12:363–368Google Scholar
  12. Luikart G, England PR, Tallman D, Jordan S, Taberlet P (2003) The power and promise of populations genomics: from genotyping to genome typing. Nat Rev Genet 4:1811–1832CrossRefGoogle Scholar
  13. Manel S, Joost S, Epperson BK et al (2010) Perspectives on the use of landscape genetics to detect genetic adaptive variation in the field. Mol Ecol 19:3760–3772PubMedCrossRefGoogle Scholar
  14. Michel AP, Sim S, Powell THQ, Taylor MS, Nosil P, Feder JL (2010) Widespread genomic divergence during sympatric speciation. Proc Natl Acad Sci 107:9724–9729PubMedCrossRefGoogle Scholar
  15. Palsbøll PJ, Bérubé M, Allendorf FW (2007) Identification of management units using population genetic data. Trends Ecol Evol 22:11–16PubMedCrossRefGoogle Scholar
  16. Schwartz MK, Luikart G, McKelvey KS, Cushman SA (2010) Landscape genomics: a brief perspective. In: Cushman SA, Huettmann F (eds) Spatial complexity, informatics, and wildlife conservation. Springer, Heidelberg, pp 165–174. doi:  10.1007/978-4-431-87771-4_9
  17. Storfer A, Murphy MA, Spear SF, Holderegger R, Waits LP (2010) Landscape genetics: where are we now? Mol Ecol 19:3496–3514PubMedCrossRefGoogle Scholar
  18. Sutton J, Nakagawa S, Robertson BC, Jamieson IG (2011) Disentangling the roles of natural selection and genetic drift in shaping variation at MHC immunity genes. Mol Ecol 20:4408–4420PubMedCrossRefGoogle Scholar
  19. Vitalis R, Dawson K, Boursot P (2001) Interpretation of variation across marker loci as evidence of selection. Genetics 158:1811–1823PubMedGoogle Scholar
  20. Wright S (1932) The roles of mutation, inbreeding, crossbreeding and selection in evolution. Proc VI Int Congress Genet 1:356–366Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Division of Biological SciencesUniversity of MontanaMissoulaUSA
  2. 2.Department of Forest Zoology & Forest ConservationUniversity of GoettingenGoettingenGermany

Personalised recommendations