Abstract
Original language | English (US) |
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Pages (from-to) | 743-759 |
Number of pages | 17 |
Journal | Evolution |
Volume | 68 |
Issue number | 3 |
DOIs | |
State | Published - Oct 13 2013 |
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In: Evolution, Vol. 68, No. 3, 13.10.2013, p. 743-759.
Research output: Contribution to journal › Research Article › peer-review
TY - JOUR
T1 - Ancestral character estimation under the threshold model from quantitative genetics
AU - Revell, L.J.
N1 - Cited By :29 Export Date: 17 April 2018 CODEN: EVOLA Correspondence Address: Revell, L.J.; Department of Biology, University of Massachusetts Boston, Boston, MA 02125, United States; email: [email protected] References: Akaike, H., A new look at the statistical model identification (1974) IEEE Trans. Autom. Control, 19, pp. 716-723; Beaulieu, J.M., O'Meara, B.C., Donoghue, M.J., Identifying hidden rate changes in the evolution of a binary morphological character: the evolution of plant habit in the campanulid angiosperms (2013) Syst. 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PY - 2013/10/13
Y1 - 2013/10/13
N2 - Evolutionary biology is a study of life's history on Earth. In researching this history, biologists are often interested in attempting to reconstruct phenotypes for the long extinct ancestors of living species. Various methods have been developed to do this on a phylogeny from the data for extant taxa. In the present article, I introduce a new approach for ancestral character estimation for discretely valued traits. This approach is based on the threshold model from evolutionary quantitative genetics. Under the threshold model, the value exhibited by an individual or species for a discrete character is determined by an underlying, unobserved continuous trait called "liability." In this new method for ancestral state reconstruction, I use Bayesian Markov chain Monte Carlo (MCMC) to sample the liabilities of ancestral and tip species, and the relative positions of two or more thresholds, from their joint posterior probability distribution. Using data simulated under the model, I find that the method has very good performance in ancestral character estimation. Use of the threshold model for ancestral state reconstruction relies on a priori specification of the order of the discrete character states along the liability axis. I test the use of a Bayesian MCMC information theoretic criterion based approach to choose among different hypothesized orderings for the discrete character. Finally, I apply the method to the evolution of feeding mode in centrarchid fishes. © 2013 The Society for the Study of Evolution.
AB - Evolutionary biology is a study of life's history on Earth. In researching this history, biologists are often interested in attempting to reconstruct phenotypes for the long extinct ancestors of living species. Various methods have been developed to do this on a phylogeny from the data for extant taxa. In the present article, I introduce a new approach for ancestral character estimation for discretely valued traits. This approach is based on the threshold model from evolutionary quantitative genetics. Under the threshold model, the value exhibited by an individual or species for a discrete character is determined by an underlying, unobserved continuous trait called "liability." In this new method for ancestral state reconstruction, I use Bayesian Markov chain Monte Carlo (MCMC) to sample the liabilities of ancestral and tip species, and the relative positions of two or more thresholds, from their joint posterior probability distribution. Using data simulated under the model, I find that the method has very good performance in ancestral character estimation. Use of the threshold model for ancestral state reconstruction relies on a priori specification of the order of the discrete character states along the liability axis. I test the use of a Bayesian MCMC information theoretic criterion based approach to choose among different hypothesized orderings for the discrete character. Finally, I apply the method to the evolution of feeding mode in centrarchid fishes. © 2013 The Society for the Study of Evolution.
U2 - 10.1111/evo.12300
DO - 10.1111/evo.12300
M3 - Research Article
SN - 0014-3820
VL - 68
SP - 743
EP - 759
JO - Evolution
JF - Evolution
IS - 3
ER -