Chinese Science Bulletin

, Volume 56, Issue 24, pp 2531–2540

When central populations exhibit more genetic diversity than peripheral populations: A simulation study

Open Access
Article Special Topic Conservation Biology of Endangered Wildlife

Abstract

The central-peripheral population hypothesis (CPH) predicts that peripheral populations have reduced genetic variability. Therefore, it is often assumed that they deserve higher conservation priority over central populations. We examined this hypothesis using computer simulations with the objective of determining the range of species properties (parameters) under which a species is likely to exhibit the CPH pattern. The interaction between migration, genetic drift, and time of population establishment was examined; in particular, various parameters of migration, such as mode of dispersal, migration rate, and maximum migration distance, were investigated. The CPH pattern was observed only within a narrow parameter window of various species properties. Active dispersers with low migration rate and moderate maximum migration distance were more likely to have higher genetic diversity in the central populations than in the peripheral populations. Newly established populations were also more likely to exhibit the CPH pattern. Although migration rate appeared to be the most important determining factor, sensitivity analysis suggested that the interaction between parameters is probably more important than any single parameter. Our findings have important implications for conservation programs.

Keywords

central-peripheral population hypothesis migration genetic drift population establishment time active disperser passive disperser conservation 

Supplementary material

11434_2011_4605_MOESM1_ESM.pdf (350 kb)
Supplementary material, approximately 349 KB.

References

  1. 1.
    Mayr E. Animal Species and Evolution. Cambridge: Harvard University Press, 1963Google Scholar
  2. 2.
    Lesica P, Allendorf F W. When are peripheral populations valuable for conservation? Conserv Biol, 1995, 9: 753–760CrossRefGoogle Scholar
  3. 3.
    Lesica P, AIlendorf F W. Are small populations of plants worth preserving? Conserv Biol, 1992, 6: 135–139CrossRefGoogle Scholar
  4. 4.
    Shumaker K M, Babbel G R. Patterns of allozyme similarity in ecologically central and marginal populations of Hordeumjubatum in Utah. Evolution, 1980, 34: 110–116CrossRefGoogle Scholar
  5. 5.
    Gapare W J, Aitken S N, Ritland C E. Genetic diversity of core and peripheral Sitka spruce (Picea sitchensis (Bong.) Carr) populations: Implications for conservation of widespread species. Biol Conserv, 2005, 123: 113–123CrossRefGoogle Scholar
  6. 6.
    Crozier R H. Genetic diversity and the agony of choice. Biol Conserv, 1992, 61: 11–15CrossRefGoogle Scholar
  7. 7.
    Levin D A. Local speciation in plants: The rule not the exception. Syst Bot, 1993, 18: 197–208CrossRefGoogle Scholar
  8. 8.
    Durka W. Genetic diversity in peripheral and subcentral populations of Corrigiola litoralis L. (Illecebraceae). Heredity, 1999, 83: 476–484CrossRefGoogle Scholar
  9. 9.
    Shea K L, Furnier G R. Genetic variation and population structure in central and isolated populations of balsam fir, Abies balsamea (Pinaceae). Am J Bot, 2002, 89: 783–791CrossRefGoogle Scholar
  10. 10.
    Cassel A, Tammaru T. Allozyme variability in central, peripheral and isolated populations of the scarce heath (Coenonympha hero: Lepidoptera, Nymphalidae): Implications for conservation. Conserv Genet, 2003, 4: 83–93CrossRefGoogle Scholar
  11. 11.
    Lönn M, Prentice H C. Gene diversity and demographic turnover in central and peripheral populations of the perennial herb Gypsophila fastigiata. Oikos, 2002, 99: 489–498CrossRefGoogle Scholar
  12. 12.
    Zhan A, Li C, Fu J. Big mountains but small barriers: Population genetic structure of the Chinese wood frog (Rana chensinensis) in the Tsinling and Daba Mountain region of northern China. BMC Genetics, 2009, 10: 17CrossRefGoogle Scholar
  13. 13.
    Guries R P, Ledig F T. Genetic diversity and population structure in pitch pine (Pinus rigida Mill). Evolution, 1982, 36: 387–402CrossRefGoogle Scholar
  14. 14.
    Betancourt J L, Schuster W S, Mitton J B, et al. Fossil and genetic history of a pinyon pine (Pings edults) isolate. Ecology, 1991, 72: 1685–1697CrossRefGoogle Scholar
  15. 15.
    Garner T W J, Pearman P B, Angelone S. Genetic diversity across a vertebrate species’ range: A test of the central-peripheral hypothesis. Mol Ecol, 2004, 13: 1047–1053CrossRefGoogle Scholar
  16. 16.
    Garcia-Ramos G, Kirkpatrick M. Genetic models of adaptation and gene flow in peripheral populations. Evolution, 1997, 51: 21–28CrossRefGoogle Scholar
  17. 17.
    Griffin S R, Barrett S C H. Post-glacial history of Trillium grandiflorum (Melanthiaceae) in Eastern North America: Inferences from phylogeography. Am J Bot, 2004, 91: 465–473CrossRefGoogle Scholar
  18. 18.
    Amato M, Brooks R J, Fu J. A phylogeographic analysis of populations of the wood turtle (Glyptemys insculpta) throughout its range. Mol Ecol, 2008, 17: 570–581Google Scholar
  19. 19.
    Lammi A, Siikamäki P, Mustajärvi K. Genetic diversity, population size, and fitness in central and peripheral populations of a rare plant Lychnis viscaria. Conserv Biol, 1999, 13: 1069–1078CrossRefGoogle Scholar
  20. 20.
    Miller S D, MacInnes H E, Fewster R M. Detecting invisible migrants: An application of genetic methods to estimate migration rates. In: Thomson D L, Cooch E G, Conroy M J, eds. Modeling Demographic Processes in Marked Populations, Volume 3. New York: Springer, 2009. 417–437CrossRefGoogle Scholar
  21. 21.
    Saltelli A, Chan K, Scott E M. Sensitivity Analysis. New York: Wiley, 2000Google Scholar
  22. 22.
    Dancik G M, Jones D E, Dorman K S. Parameter estimation and sensitivity analysis in an agent-based model of Leishmania major infection. J Theor Biol, 2010, 262: 398–412CrossRefGoogle Scholar
  23. 23.
    Monod H, Naud C, Makowski D. Uncertainty and sensitivity analysis for crop models. In: Wallach D, Makowski D, Jones J W, eds. Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization and Applications. Amsterdam: Elsevier, 2006. 55–100Google Scholar
  24. 24.
    Lamboni M, Makowski D, Monod H. Multivariate global sensitivity analysis for dynamic crop models. Field Crop Res, 2009, 113: 312–320CrossRefGoogle Scholar
  25. 25.
    Lamboni M, Makowski D, Monod H. Multisensi: Multivariate Sensitivity Analysis. R package version 1.0–3. http://CRAN.R-project.org/package=multisensi, 2010.
  26. 26.
    R Development Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing, 2011Google Scholar
  27. 27.
    Lande R, Barrowclough G F. Effective population size, genetic variation, and their use in population management. In: Soulé M E, ed. Viable Populations for Conservation. Cambridge: Cambridge University Press, 1987. 87–123CrossRefGoogle Scholar
  28. 28.
    Theodorou K, Couvet D. Genetic load in subdivided populations: Interactions between the migration rate, the size and the number of subpopulations. Heredity, 2006, 96: 69–78Google Scholar
  29. 29.
    Webb T, Bartlein P J. Global changes during the last 3 million years: Climatic controls and biotic responses. Ann Rev Ecol Syst, 1992, 23: 141–173CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Chengdu Institute of BiologyChinese Academy of SciencesChengduChina
  2. 2.Department of ZoologyUniversity of British ColumbiaVancouverCanada
  3. 3.Department of Integrative BiologyUniversity of GuelphGuelphCanada

Personalised recommendations