Conservation Genetics

, Volume 11, Issue 2, pp 421–433 | Cite as

The use of approximate Bayesian computation in conservation genetics and its application in a case study on yellow-eyed penguins

Research Article

Abstract

The inference of demographic parameters from genetic data has become an integral part of conservation studies. A group of Bayesian methods developed originally in population genetics, known as approximate Bayesian computation (ABC), has been shown to be particularly useful for the estimation of such parameters. These methods do not need to evaluate likelihood functions analytically and can therefore be used even while assuming complex models. In this paper we describe the ABC approach and identify specific parts of its algorithm that are being the subject of intensive studies in order to further expand its usability. Furthermore, we discuss applications of this Bayesian algorithm in conservation studies, providing insights on the potentialities of these tools. Finally, we present a case study in which we use a simple Isolation-Migration model to estimate a number of demographic parameters of two populations of yellow-eyed penguins (Megadyptes antipodes) in New Zealand. The resulting estimates confirm our current understanding of M. antipodes dynamic, demographic history and provide new insights into the expansion this species has undergone during the last centuries.

Keywords

Approximate Bayesian computation Historical demography Likelihood-free Isolation-Migration model Megadyptes antipodes Population genetics 

Abbreviations

ABC

Approximate Bayesian computation

MCMC

Markov chain Monte Carlo

Supplementary material

10592_2009_32_MOESM1_ESM.doc (46 kb)
(DOC 46 kb)

References

  1. Allendorf FW, Luikart G (2007) Conservation and the genetics of populations. Mammalia 2007:189–197Google Scholar
  2. Allendorf FW, Leary RF, Soule ME (1986) Conservation biology: the science of scarcity and diversity. Sinauer Associates, SunderlandGoogle Scholar
  3. Amos W, Balmford A (2001) When does conservation genetics matter? Heredity 87:257–265PubMedGoogle Scholar
  4. Anderson CNK, Ramakrishnan U, Chan YL, Hadly EA (2005) Serial SimCoal: a population genetics model for data from multiple populations and points in time. Oxford University Press, Oxford, pp 1733–1734Google Scholar
  5. Aspi J, Roininen E, Kiiskilä J, Ruokonen M, Kojola I, Bljudnik L, Danilov P, Heikkinen S, Pulliainen E (2009) Genetic structure of the northwestern Russian wolf populations and gene flow between Russia and Finland. Conserv Genet 10:815–826Google Scholar
  6. Avise JC (1996) The scope of conservation genetics. In: Avise JC, Hamrick JL (eds) Conservation genetics: case histories from nature. Chapman & Hall, New York, pp 1–9Google Scholar
  7. Barnosky AD, Hadly EA, Maurer BA, Christie MI (2001) Temperate terrestrial vertebrate faunas in north and south America: interplay of ecology, evolution, and geography with biodiversity. Conserv Biol 15:658Google Scholar
  8. Beaumont M (2008) Joint determination of topology, divergence time, and immigration in population trees. In: Matsumura S, Forster P, Renfrew C (eds) Simulations, genetics, and human prehistory. McDonald Institute for Archaeological Research, Cambridge, pp 135–154Google Scholar
  9. Beaumont MA, Zhang W, Balding DJ (2002) Approximate Bayesian computation in population genetics. Genetics 162:2025–2035PubMedGoogle Scholar
  10. Beaumont M, Cornuet JM, Marin JM, Robert CP (2009) Adaptivity for ABC algorithms: the ABC-PMC scheme. Biometrika. doi:10.1093/biomet/asp052 Google Scholar
  11. Berry O, Tocher MD, Sarre SD (2004) Can assignment tests measure dispersal? Mol Ecol 13:551–561PubMedGoogle Scholar
  12. Birdlife International (2008) Species factsheet: megadyptes antipodes. In: IUCN(ed) 2007 IUCN red list of threatened species. http://www.iucnredlist.org
  13. Blum M (2009) Approximate Bayesian computation: a non-parametric perspective. Arxiv preprint arXiv:0904.0635Google Scholar
  14. Blum MGB, Francois O (2009) Non-linear regression models for approximate Bayesian computation. Stat Comput. doi:10.1007/s11222-009-9116-0 Google Scholar
  15. Boessenkool S, King TM, Seddon PJ, Waters JM (2008) Isolation and characterization of microsatellite loci from the yellow-eyed penguin (Megadyptes antipodes). Mol Ecol Resour 8:1043–1045Google Scholar
  16. Boessenkool S, Austin JJ, Worthy TH, Scofield P, Cooper A, Seddon PJ, Waters JM (2009a) Relict or colonizer? Extinction and range expansion of penguins in southern New Zealand. Proc Biol Sci 276:815PubMedGoogle Scholar
  17. Boessenkool S, Star B, Waters JM, Seddon PJ (2009b) Multilocus assignment analyses reveal multiple units and rare migration events in the recently expanded yellow-eyed penguin (Megadyptes antipodes). Mol Ecol 18:2390–2400PubMedGoogle Scholar
  18. Boessenkool S, Star B, Seddon PJ, Waters JM (this issue) Temporal genetic samples indicate small effective population size of the endangered yellow-eyed penguin. Conserv Genet. doi:10.1007/s10592-009-9988-8
  19. Bortot P, Coles SG, Sisson SA (2007) Inference for stereological extremes. J Am Stat Assoc 102:84–92Google Scholar
  20. Carnaval AC, Hickerson MJ, Haddad CFB, Rodrigues MT, Moritz C (2009) Stability predicts genetic diversity in the Brazilian Atlantic Forest hotspot. Science 323:785PubMedGoogle Scholar
  21. Chan YL, Anderson CNK, Hadly EA (2006) Bayesian estimation of the timing and severity of a population bottleneck from ancient DNA. PLoS Genet 2:e59PubMedGoogle Scholar
  22. Cornuet JM, Santos F, Beaumont MA, Robert CP, Marin JM, Balding DJ, Guillemaud T, Estoup A (2008) Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation. Bioinformatics 24:2713PubMedGoogle Scholar
  23. De Mita S, Ronfort J, McKhann HI, Poncet C, El Malki R, Bataillon T (2007) Investigation of the demographic and selective forces shaping the nucleotide diversity of genes involved in nod factor signaling in Medicago truncatula. Genetics 177:2123PubMedGoogle Scholar
  24. Del Moral P, Doucet A, Jasra A (2006) Sequential Monte Carlo samplers. J R Stat Soc B 68:411–436Google Scholar
  25. Del Moral P, Doucet A, Jasra A (2008) An adaptive sequential Monte Carlo method for approximate Bayesian computation. Working paper, Department of Statistics, University of British ColumbiaGoogle Scholar
  26. DeSalle R, Amato G (2004) The expansion of conservation genetics. Nat Rev Genet 5:702–712PubMedGoogle Scholar
  27. Estoup A, Clegg SM (2003) Bayesian inferences on the recent island colonization history by the bird Zosterops lateralis lateralis. Mol Ecol 12:657–674PubMedGoogle Scholar
  28. Estoup A, Wilson IJ, Sullivan C, Cornuet JM, Moritz C (2001) Inferring population history from microsatellite and enzyme data in serially introduced cane toads, Bufo marinus. Genetics 159:1671–1687PubMedGoogle Scholar
  29. Estoup A, Beaumont M, Sennedot F, Moritz C, Cornuet JM (2004) Genetic analysis of complex demographic scenarios: spatially expanding populations of the cane toad, Bufo marinus. Evolution 58:2021–2036PubMedGoogle Scholar
  30. Evans BJ, McGuire JA, Brown RM, Andayani N, Supriatna J (2008) A coalescent framework for comparing alternative models of population structure with genetic data: evolution of Celebes toads. Biol Lett 4:430PubMedGoogle Scholar
  31. Excoffier L, Estoup A, Cornuet JM (2005) Bayesian analysis of an admixture model with mutations and arbitrarily linked markers. Genetics 169:1727–1738PubMedGoogle Scholar
  32. Fabre V, Condemi S, Degioanni A (2009) Genetic evidence of geographical groups among Neanderthals. PLoS One 4(4):e5151Google Scholar
  33. Fagundes NJR, Ray N, Beaumont M, Neuenschwander S, Salzano FM, Bonatto SL, Excoffier L (2007) Statistical evaluation of alternative models of human evolution. Proc Natl Acad Sci USA 104:17614PubMedGoogle Scholar
  34. François O, Blum MGB, Jakobsson M, Rosenberg NA (2008) Demographic history of European populations of Arabidopsis thaliana. PLoS Genet 4(5):e1000075Google Scholar
  35. Frankham R (1995) Effective population size/adult population size ratios in wildlife—a review. Genet Res 66:95–107Google Scholar
  36. Frankham R (2005) Genetics and extinction. Biol Conserv 126:131–140Google Scholar
  37. Frankham R, Ballou JD, Briscoe DA (2002) Introduction to conservation genetics. Cambridge University Press, CambridgeGoogle Scholar
  38. Grelaud A, Robert CP, Marin JM (2009) ABC methods for model choice in Gibbs random fields. C R Math. doi:10.1016/j.crma.2008.12.009 Google Scholar
  39. Haddrill PR, Thornton KR, Charlesworth B, Andolfatto P (2005) Multilocus patterns of nucleotide variability and the demographic and selection history of Drosophila melanogaster populations. Cold Spring Harbor Laboratory Press, New York, pp 790–799Google Scholar
  40. Hamilton G, Currat M, Ray N, Heckel G, Beaumont M, Excoffier L (2005) Bayesian estimation of recent migration rates after a spatial expansion. Genetics 170:409–417PubMedGoogle Scholar
  41. Hey J, Nielsen R (2004) Multilocus methods for estimating population sizes, migration rates and divergence time, with applications to the divergence of Drosophila pseudoobscura and D. persimilis. Genetics 167:747–760PubMedGoogle Scholar
  42. Hickerson MJ, Meyer CP (2008) Testing comparative phylogeographic models of marine vicariance and dispersal using a hierarchical Bayesian approach. BMC Evol Biol 8:322PubMedGoogle Scholar
  43. Hickerson MJ, Dolman G, Moritz C (2005) Comparative phylogeographic summary statistics for testing simultaneous vicariance. Mol Ecol 15:209–223Google Scholar
  44. Hickerson MJ, Stahl EA, Lessios HA (2006) Test for simultaneous divergence using approximate bayesian computation. Evolution 60:2435–2453PubMedGoogle Scholar
  45. Hickerson MJ, Stahl E, Takebayashi N (2007) msBayes: pipeline for testing comparative phylogeographic histories using hierarchical approximate Bayesian computation. BMC Bioinformatics 8:268PubMedGoogle Scholar
  46. Hudson RR (1983) Properties of a neutral allele model with intragenic recombination. Theor Popul Biol 23:183–201PubMedGoogle Scholar
  47. Hudson RR (1990) Gene genealogies and the coalescent process. Oxf Surv Evol Biol 7:1–44Google Scholar
  48. Hudson RR (2002) Generating samples under a Wright-Fisher neutral model of genetic variation. Bioinformatics 18:337–338PubMedGoogle Scholar
  49. Ihaka R, Gentleman R (1996) R: a language for data analysis and graphics. J Comput Graph Stat 5:299–314Google Scholar
  50. Jabot F, Chave J (2009) Inferring the parameters of the neutral theory of biodiversity using phylogenetic information and implications for tropical forests. Ecol Lett 12:239–248PubMedGoogle Scholar
  51. Jobin MJ, Mountain JL (2008) REJECTOR: software for population history inference from genetic data via a rejection algorithm. Bioinformatics 24:2936PubMedGoogle Scholar
  52. Johnson JA, Tingay RE, Culver M, Hailer F, Clarke ML, Mindell DP (2009) Long-term survival despite low genetic diversity in the critically endangered Madagascar fish-eagle. Mol Ecol 18:54–63PubMedGoogle Scholar
  53. Joshi S (2007) Estimating selection coefficient using the ancestral selection graph. In: Department of Biological Science. The Florida State University, TallahasseeGoogle Scholar
  54. Joyce P, Marjoram P (2008) Approximately sufficient statistics and Bayesian computation. Stat Appl Genet Mol Biol 7:26Google Scholar
  55. Kayser M, Lao O, Saar K, Brauer S, Wang X, Nürnberg P, Trent RJ, Stoneking M (2008) Genome-wide analysis indicates more Asian than Melanesian ancestry of Polynesians. Am J Hum Genet 82:194–198PubMedGoogle Scholar
  56. Kimura M (1969) The number of heterozygous nucleotide sites maintained in a finite population due to steady flux of mutations. Genetics 61:893–903PubMedGoogle Scholar
  57. Kimura M, Ohta T (1978) Stepwise mutation model and distribution of allelic frequencies in a finite population. Proc Natl Acad Sci USA 75:2868–2872PubMedGoogle Scholar
  58. Kingman JF (1982) The coalescent. Stoch Process Appl 13:235–248Google Scholar
  59. Koerich LB, Wang X, Clark AG, Carvalho AB (2008) Low conservation of gene content in the Drosophila Y chromosome. Nature 456:949–951PubMedGoogle Scholar
  60. Lambert DM, Ritchie PA, Millar CD, Holland B, Drummond AJ, Baroni C (2004) Rates of evolution in ancient DNA from Adélie penguins. Science 295:2270–2273Google Scholar
  61. Lande R (1988) Genetics and demography in biological conservation. Science 241:1455PubMedGoogle Scholar
  62. Laval G, Excoffier L (2004) SIMCOAL 2.0: a program to simulate genomic diversity over large recombining regions in a subdivided population with a complex history. Oxford University Press, Oxford, pp 2485–2487Google Scholar
  63. Leaché AD, Crews SC, Hickerson MJ (2007) Two waves of diversification in mammals and reptiles of Baja California revealed by hierarchical Bayesian analysis. Biol Lett 3:646PubMedGoogle Scholar
  64. Legrand D, Tenaillon M, Matyot P, Gerlach J (2009) Species-wide genetic variation and demographic history of Drosophila sechellia, a species lacking population structure. Genetics. doi:10.1534/genetics.108.092080 PubMedGoogle Scholar
  65. Legras J, Merdinoglu D, Cornuet JM, Karst F (2007) Bread, beer and wine: Saccharomyces cerevisiae diversity reflects human history. Mol Ecol 16:2091–2102PubMedGoogle Scholar
  66. Loader CR (1996) Local likelihood density estimation. Ann Stat 24:1602–1618Google Scholar
  67. Lopes JS, Beaumont M (2009) ABC: a useful Bayesian tool for the analysis of population data. Infect Genet Evol. doi:10.1016/j.meegid.2009.10.010 PubMedGoogle Scholar
  68. Lopes JS, Balding D, Beaumont MA (2009) PopABC: a program to infer historical demographic parameters. Bioinformatics. doi:10.1093/bioinformatics/btp487 PubMedGoogle Scholar
  69. Marchant S, Higgin PJ (1990) Handbook of Australian. New Zealand and Antarctic birds. Oxford University Press, Melbourne, AustraliaGoogle Scholar
  70. Marjoram P, Tavaré S (2006) Modern computational approaches for analysing molecular genetic variation data. Nat Rev Genet 7:759–770PubMedGoogle Scholar
  71. Marjoram P, Molitor J, Plagnol V, Tavaré S (2003) Markov chain Monte Carlo without likelihoods. Proc Natl Acad Sci USA 100:15324–15328PubMedGoogle Scholar
  72. McKinlay B (2001) Hoiho (Megadyptes antipodes) recovery plan 2000–2025. Department of Conservation, WellingtonGoogle Scholar
  73. Miller N, Estoup A, Toepfer S, Bourguet D, Lapchin L, Derridj S, Kim KS, Reynaud P, Furlan L, Guillemaud T (2005) Multiple transatlantic introductions of the western corn rootworm. Science 310:992PubMedGoogle Scholar
  74. Neigel JE (2002) Is F ST obsolete? Conserv Genet 3:167–173Google Scholar
  75. Neuenschwander S, Largiader CR, Ray N, Currat M, Vonlanthen P, Excoffier L (2008) Colonization history of the Swiss Rhine basin by the bullhead (Cottus gobio): inference under a Bayesian spatially explicit framework. Mol Ecol 17:757–772PubMedGoogle Scholar
  76. Nielsen R, Wakeley J (2001) Distinguishing migration from isolation: a Markov chain Monte Carlo approach. Genetics 158:885–896PubMedGoogle Scholar
  77. Nordborg M (2001) Coalescent theory. In: Balding DJ, Bishop M, Cannings C (eds) Handbook of statistical genetics. Wiley, Chichester, pp 602–635Google Scholar
  78. Padon S (2008) Computational methods for complex problems in extreme value theory. In: Dipartimento di Scienze Statistiche. Universita degli Studi di Padova, PadovaGoogle Scholar
  79. Paetkau D, Slade R, Burden M, Estoup A (2004) Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation-based exploration of accuracy and power. Mol Ecol 13:55–65PubMedGoogle Scholar
  80. Palero F, Lopes JS, Abelló P, Macpherson E, Pascual M, Beaumont MA (2009) Rapid radiation in spiny lobsters (Palinurus spp.) as revealed by classic and ABC methods using mtDNA and microsatellite data. BMC Evol Biol 9:263PubMedGoogle Scholar
  81. Patin E, Laval G, Barreiro LB, Salas A, Semino O, Santachiara-Benerecetti S, Kidd KK, Kidd JR, Van der Veen L, Hombert JM (2009) Inferring the demographic history of African farmers and Pygmy hunter—gatherers using a multilocus resequencing data set. PLoS Genet 5(4):e1000448Google Scholar
  82. Peters GW, Fan Y, Sisson SA (2008) On sequential Monte Carlo, partial rejection control and approximate Bayesian computation. Arxiv preprint arXiv:0808.3466v1Google Scholar
  83. Pritchard JK, Seielstad MT, Perez-Lezaun A, Feldman MW (1999) Population growth of human Y chromosomes: a study of Y chromosome microsatellites. Mol Biol Evol 16:1791–1798PubMedGoogle Scholar
  84. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedGoogle Scholar
  85. Ratmann O, Jørgensen O, Hinkley T, Stumpf M, Richardson S, Wiuf C (2007) Using Likelihood-free inference to compare evolutionary dynamics of the protein networks of H. pylori and P. falciparum. PLoS Comput Biol 3:e230PubMedGoogle Scholar
  86. Riddle BR, Dawson MN, Hadly EA, Hafner DJ, Hickerson MJ, Mantooth SJ, Yoder AD (2008) The role of molecular genetics in sculpting the future of integrative biogeography. Progr Phys Geogr 32:173Google Scholar
  87. Rosenblum EB, Hickerson MJ, Moritz C (2007) A multilocus perspective on colonization accompanied by selection and gene flow. Evolution 61:2971–2985PubMedGoogle Scholar
  88. Schwartz MK, Luikart G, Waples RS (2007) Genetic monitoring as a promising tool for conservation and management. Trends Ecol Evol 22:25–33PubMedGoogle Scholar
  89. Shriner D, Liu Y, Nickle DC, Mullins JI (2006) Evolution of intrahost HIV-1 genetic diversity during chronic infection. Evolution 60:1165–1176PubMedGoogle Scholar
  90. Sisson SA, Fan Y, Tanaka MM (2007) Sequential Monte Carlo without likelihoods. Proc Natl Acad Sci USA 104:1760PubMedGoogle Scholar
  91. Slabbert R, Bester AE, D’Amato ME (2009) Analyses of genetic diversity and parentage within a South African hatchery of the Abalone Haliotis midae Linnaeus using microsatellite markers. J Shellfish Res 28:369–375Google Scholar
  92. Sousa VM, Fritz M, Beaumont MA, Chikhi L (2009) Approximate Bayesian computation (ABC) without summary statistics: the case of admixture. Genetics. doi:10.1534/genetics.108.098129 PubMedGoogle Scholar
  93. Storz JF, Beaumont MA (2002) Testing for genetic evidence of population expansion and contraction: an empirical analysis of microsatellite DNA variation using a hierarchical Bayesian model. Evolution 56:154–166PubMedGoogle Scholar
  94. Tajima F (1983) Evolutionary relationship of DNA sequences in finite populations. Genetics 105:437–460PubMedGoogle Scholar
  95. Tallmon DA, Koyuk A, Luikart G, Beaumont MA (2008) ONeSAMP: a program to estimate effective population size using approximate Bayesian computation. Mol Ecol Resour 8:299–301Google Scholar
  96. Tanaka MM, Francis AR, Luciani F, Sisson SA (2006) Using approximate bayesian computation to estimate tuberculosis transmission parameters from genotype data. Genetics 173:1511–1520PubMedGoogle Scholar
  97. Tavaré S, Balding DJ, Griffiths RC, Donnelly P (1997) Inferring coalescence times from DNA sequence data. Genetics 145:505–518PubMedGoogle Scholar
  98. Thornton K, Andolfatto P (2006) Approximate Bayesian inference reveals evidence for a recent, severe bottleneck in a Netherlands population of Drosophila melanogaster. Genetics 172:1607–1619PubMedGoogle Scholar
  99. Toni T, Stumpf MPH (2009) Parameter inference and model selection in signaling pathway models. In: Topics in computational biology, Methods in molecular biology series. Humana Press, TotowaGoogle Scholar
  100. Toni T, Welch D, Strelkowa N, Ipsen A, Stumpf MPH (2009) Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. J R Soc Interface 6:187–202PubMedGoogle Scholar
  101. Topp CM, Winker K (2008) Genetic patterns of differentiation among five landbird species from the Queen Charlotte Islands, British Columbia. Auk 125:461–472Google Scholar
  102. Verdu P, Austerlitz F, Estoup A, Vitalis R, Georges M, Théry S, Froment A, Le Bomin S, Gessain A, Hombert JM (2009) Origins and genetic diversity of Pygmy hunter-gatherers from western Central Africa. Curr Biol 19:312–318PubMedGoogle Scholar
  103. Voje KL, Hemp C, Flagstad O, Saetre GP, Stenseth N (2009) Climatic change as an engine for speciation in flightless Orthoptera species inhabiting African mountains. Mol Ecol 18:93–108PubMedGoogle Scholar
  104. Waits LP, Talbot SL, Ward RH, Shields GF (1998) Mitochondrial DNA phylogeography of the North American brown bear and implications for conservation. Conserv Biol 408–417Google Scholar
  105. Walsh PS, Metzger DA, Higuchi R (1991) Chelex® 100 as a medium for simple extraction of DNA for PCR-based typing from forensic material. Biotechniques 10:506–513PubMedGoogle Scholar
  106. Weiss G, von Haeseler A (1998) Inference of population history using a likelihood approach. Genetics 149:1539–1546PubMedGoogle Scholar
  107. Whitlock MC, McCauley DE (1999) Indirect measures of gene flow and migration: FST 1/(4Nm+1). Heredity 82:117–125PubMedGoogle Scholar
  108. Wilson IJ, Balding DJ (1998) Genealogical inference from microsatellite data. Genetics 150:499–510PubMedGoogle Scholar
  109. Wilson GA, Rannala B (2003) Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163:1177–1191PubMedGoogle Scholar
  110. Witzenberger KA, Hochkirch A (2008) Genetic consequences of animal translocations: a case study using the field cricket, Gryllus campestris L. Biol Conserv 141:3059–3068Google Scholar
  111. Wright S (1950) Genetical structure of populations. Nature 166:247–249PubMedGoogle Scholar
  112. Wright SI, Bi IV, Schroeder SG, Yamasaki M, Doebley JF, McMullen MD, Gaut BS (2005) The effects of artificial selection on the maize genome. American Association for the Advancement of Science, Washington, DC, pp 1310–1314Google Scholar
  113. Zhang DX, Hewitt GM (2003) Nuclear DNA analyses in genetic studies of populations: practice, problems and prospects. Mol Ecol 12:563–584PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.School of Biological SciencesUniversity of ReadingReadingUK
  2. 2.Department of ZoologyUniversity of OtagoDunedinNew Zealand
  3. 3.National Centre for Biosystematics, Natural History MuseumUniversity of OsloOsloNorway

Personalised recommendations