Inferring ancestral states without assuming neutrality or gradualism using a stable model of continuous character evolution

Michael G. Elliot*, Arne Ø Mooers

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

35 Citaten (Scopus)


Background: The value of a continuous character evolving on a phylogenetic tree is commonly modelled as the location of a particle moving under one-dimensional Brownian motion with constant rate. The Brownian motion model is best suited to characters evolving under neutral drift or tracking an optimum that drifts neutrally. We present a generalization of the Brownian motion model which relaxes assumptions of neutrality and gradualism by considering increments to evolving characters to be drawn from a heavy-tailed stable distribution (of which the normal distribution is a specialized form).

Results: We describe Markov chain Monte Carlo methods for fitting the model to biological data paying special attention to ancestral state reconstruction, and study the performance of the model in comparison with a selection of existing comparative methods, using both simulated data and a database of body mass in 1,679 mammalian species. We discuss hypothesis testing and model selection. The stable model outperforms Brownian and Ornstein-Uhlenbeck approaches under simulations in which traits evolve with occasional large "jumps" in their value, but does not perform markedly worse for traits evolving under a truly Brownian process.

Conclusions: The stable model is well suited to a stochastic process with a volatile rate of change in which biological characters undergo a mixture of neutral drift and occasional evolutionary events of large magnitude.

Originele taal-2English
Aantal pagina's15
TijdschriftBMC Evolutionary Biology
StatusPublished - 28-nov-2014
Extern gepubliceerdJa

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