Journal of Mathematical Biology

, Volume 74, Issue 1–2, pp 447–467 | Cite as

Algorithmic improvements to species delimitation and phylogeny estimation under the multispecies coalescent

Article

Abstract

The focus of this article is a Bayesian method for inferring both species delimitations and species trees under the multispecies coalescent model using molecular sequences from multiple loci. The species delimitation requires no a priori assignment of individuals to species, and no guide tree. The method is implemented in a package called STACEY for BEAST2, and is a extension of the author’s DISSECT package. Here we demonstrate considerable efficiency improvements by using three new operators for sampling from the posterior using the Markov chain Monte Carlo algorithm, and by using a model for the population size parameters along the branches of the species tree which allows these parameters to be integrated out. The correctness of the moves is demonstrated by tests of the implementation. The practice of using a pipeline approach to species delimitation under the multispecies coalescent, has been shown to have major problems on simulated data (Olave et al. in Syst Biol 63:263–271. doi:10.1093/sysbio/syt106, 2014). The same simulated data set is used to demonstrate the accuracy and improved convergence of the present method. We also compare performance with *BEAST for a fixed delimitation analysis on a large data set, and again show improved convergence.

Keywords

Species delimitation Multispecies coalescent Bayesian analysis Markov chain Monte Carlo 

Mathematics Subject Classification

92B10 62P10 

Supplementary material

285_2016_1034_MOESM1_ESM.pdf (300 kb)
Supplementary material 1 (pdf 299 KB)

References

  1. Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu CH, Xie D, Suchard MA, Rambaut A, Drummond AJ (2014) BEAST 2: A software platform for Bayesian evolutionary analysis. PLoS Comput Biol 10(4):e1003,537. doi:10.1371/journal.pcbi.1003537 CrossRefGoogle Scholar
  2. Degnan JH, Rosenberg NA (2009) Gene tree discordance, phylogenetic inference and the multispecies coalescent. Trends Ecol Evol 24:332–340CrossRefGoogle Scholar
  3. Edwards SV (2009) Is a new and general theory of molecular systematics emerging? Evolution 63:1–19CrossRefGoogle Scholar
  4. Felsenstein J (2003) Inferring phylogenies. Sinauer Associates, Sunderland. doi:10.1016/S0022-0000(02)00003-X Google Scholar
  5. Flot JF (2015) Species delimitation’s coming of age. Syst Biol 64(6):897–899CrossRefGoogle Scholar
  6. Giarla T, Esselstyn J (2015) The challenges of resolving a rapid, recent radiation: empirical and simulated phylogenomics of Philippine shrews. Syst Biol 64(5):727–740. doi:10.1093/sysbio/syv029 CrossRefGoogle Scholar
  7. Heled J, Drummond A (2010) Bayesian inference of species trees from multilocus data. Mol Biol Evol 27:570–580CrossRefGoogle Scholar
  8. Hey J, Nielsen R (2007) Integration within the felsenstein equation for improved markov chain Monte Carlo methods in population genetics. Proc Natl Acad Sci 104:2785–2790CrossRefGoogle Scholar
  9. Höhna S, Defoin-Platel M, Drummond AJ (2008) Clock-constrained tree proposal operators in Bayesian phylogenetic inference. In: 8th IEEE international conference on bioinformatics and bioengineering, Athens, Greece, pp 1–7, 8–10 Oct 2008Google Scholar
  10. Huang H, He Q, Kubatko LS, Knowles LL (2010) Sources of error for species-tree estimation: impact of mutational and coalescent effects on accuracy and implications for choosing among different methods. Syst Biol 59:573–583CrossRefGoogle Scholar
  11. Huelsenbeck JP, Andolfatto P (2007) Inference of population structure under a Dirichlet process model. Genetics 175:1787–1802CrossRefGoogle Scholar
  12. Jones G, Aydin Z, Oxelman B (2014) DISSECT: an assignment-free Bayesian discovery method for species delimitation under the multispecies coalescent. Bioinformatics. doi:10.1093/bioinformatics/btu770 Google Scholar
  13. Liu L, Pearl DK, Brumfield RT, Edwards SV (2008) Estimating species trees using multiple allele DNA sequence data. Evolution 62(8):2080–2091CrossRefGoogle Scholar
  14. Olave M, Solà E, Knowles LL (2014) Upstream analyses create problems with DNA-based species delimitation. Syst Biol 63:263–271. doi:10.1093/sysbio/syt106 CrossRefGoogle Scholar
  15. Plummer M, Best N, Cowles K, Vines K (2006) CODA: convergence diagnosis and output analysis for MCMC. R News 6(1), 7–11. http://CRAN.R-project.org/doc/Rnews/
  16. Pritchard JK, Stephens M, Donnelly PJ (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959Google Scholar
  17. Rannala B (2015) The art and science of species delimitation. Curr Zool 61:846–853CrossRefGoogle Scholar
  18. Rannala B, Yang Z (2003) Bayes estimation of species divergence times and ancestral population sizes using DNA sequences from multiple loci. Genetics 164:1645–1656Google Scholar
  19. Rannala B, Yang Z (2013) Improved reversible jump algorithms for Bayesian species delimitation. Genetics 194:245–253CrossRefGoogle Scholar
  20. Solís-Lemus C, Knowles LL, Ane C (2015) Bayesian species delimitation combining multiple genes and traits in a unified framework. Evolution 69:492–507CrossRefGoogle Scholar
  21. Yang Z (2002) Likelihood and Bayes estimation of ancestral population sizes in hominoids using data from multiple loci. Genetics 162:1811–1823Google Scholar
  22. Yang Z, Rannala B (2010) Bayesian species delimitation using multilocus sequence data. Proc Natl Acad Sci USA 107:9264–9269CrossRefGoogle Scholar
  23. Yang Z, Rannala B (2014) Unguided species delimitation using DNA sequence data from multiple loci. Mol Biol Evol 31(12):3125–3135. doi:10.1093/molbev/msu279 CrossRefGoogle Scholar
  24. Zhang C, Rannala B, Yang Z (2014) Bayesian species delimitation can be robust to guide-tree inference errors. Syst Biol 63:993–1004. doi:10.1093/sysbio/syu052 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Biological and Environmental SciencesUniversity of GothenburgGöteborgSweden

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