Biologists frequently attempt to infer the character states at ancestral nodes of a phylogeny from the distribution of traits observed in contemporary organisms. Because phylogenies are normally inferences from data, it is desirable to account for the uncertainty in estimates of the tree and its branch lengths when making inferences about ancestral states or other comparative parameters. Here we present a general Bayesian approach for testing comparative hypotheses across statistically justified samples of phylogenies, focusing on the specific issue of reconstructing ancestral states. The method uses Markov chain Monte Carlo techniques for sampling phylogenetic trees and for investigating the parameters of a statistical model of trait evolution. We describe how to combine information about the uncertainty of the phylogeny with uncertainty in the estimate of the ancestral state. Our approach does not constrain the sample of trees only to those that contain the ancestral node or nodes of interest, and we show how to reconstruct ancestral states of uncertain nodes using a most-recent-common-ancestor approach. We illustrate the methods with data on ribonuclease evolution in the Artiodactyla. Software implementing the methods (BayesMultiState) is available from the authors.

We describe a Bayesian method for investigating correlated evolution of discrete binary traits on phylogenetic trees. The method fits a continuous-time Markov model to a pair of traits, seeking the best fitting models that describe their joint evolution on a phylogeny. We employ the methodology of reversible-jump (RJ) Markov chain Monte Carlo to search among the large number of possible models, some of which conform to independent evolution of the two traits, others to correlated evolution. The RJ Markov chain visits these models in proportion to their posterior probabilities, thereby directly estimating the support for the hypothesis of correlated evolution. In addition, the RJ Markov chain simultaneously estimates the posterior distributions of the rate parameters of the model of trait evolution. These posterior distributions can be used to test among alternative evolutionary scenarios to explain the observed data. All results are integrated over a sample of phylogenetic trees to account for phylogenetic uncertainty. We implement the method in a program called RJ Discrete and illustrate it by analyzing the question of whether mating system and advertisement of estrus by females have coevolved in the Old World monkeys and great apes.

Greek speakers say "omicronupsilonrho", Germans "schwanz" and the French "queue" to describe what English speakers call a 'tail', but all of these languages use a related form of 'two' to describe the number after one. Among more than 100 Indo-European languages and dialects, the words for some meanings (such as 'tail') evolve rapidly, being expressed across languages by dozens of unrelated words, while others evolve much more slowly--such as the number 'two', for which all Indo-European language speakers use the same related word-form. No general linguistic mechanism has been advanced to explain this striking variation in rates of lexical replacement among meanings. Here we use four large and divergent language corpora (English, Spanish, Russian and Greek) and a comparative database of 200 fundamental vocabulary meanings in 87 Indo-European languages to show that the frequency with which these words are used in modern language predicts their rate of replacement over thousands of years of Indo-European language evolution. Across all 200 meanings, frequently used words evolve at slower rates and infrequently used words evolve more rapidly. This relationship holds separately and identically across parts of speech for each of the four language corpora, and accounts for approximately 50% of the variation in historical rates of lexical replacement. We propose that the frequency with which specific words are used in everyday language exerts a general and law-like influence on their rates of evolution. Our findings are consistent with social models of word change that emphasize the role of selection, and suggest that owing to the ways that humans use language, some words will evolve slowly and others rapidly across all languages.

The radiation of the mammals provides a 165-million-year test case for evolutionary theories of how species occupy and then fill ecological niches. It is widely assumed that species often diverge rapidly early in their evolution, and that this is followed by a longer, drawn-out period of slower evolutionary fine-tuning as natural selection fits organisms into an increasingly occupied niche space. But recent studies have hinted that the process may not be so simple. Here we apply statistical methods that automatically detect temporal shifts in the rate of evolution through time to a comprehensive mammalian phylogeny and data set of body sizes of 3,185 extant species. Unexpectedly, the majority of mammal species, including two of the most speciose orders (Rodentia and Chiroptera), have no history of substantial and sustained increases in the rates of evolution. Instead, a subset of the mammals has experienced an explosive increase (between 10- and 52-fold) in the rate of evolution along the single branch leading to the common ancestor of their monophyletic group (for example Chiroptera), followed by a quick return to lower or background levels. The remaining species are a taxonomically diverse assemblage showing a significant, sustained increase or decrease in their rates of evolution. These results necessarily decouple morphological diversification from speciation and suggest that the processes that give rise to the morphological diversity of a class of animals are far more free to vary than previously considered. Niches do not seem to fill up, and diversity seems to arise whenever, wherever and at whatever rate it is advantageous.

Phylogenetic comparative methods are increasingly used to give new insights into the dynamics of trait evolution in deep time. For continuous traits the core of these methods is a suite of models that attempt to capture evolutionary patterns by extending the Brownian constant variance model. However, the properties of these models are often poorly understood, which can lead to the misinterpretation of results. Here we focus on one of these models – the Ornstein Uhlenbeck (OU) model. We show that the OU model is frequently incorrectly favoured over simpler models when using Likelihood ratio tests, and that many studies fitting this model use datasets that are small and prone to this problem. We also show that very small amounts of error in datasets can have profound effects on the inferences derived from OU models. Our results suggest that simulating fitted models and comparing with empirical results is critical when fitting OU and other extensions of the Brownian model. We conclude by making recommendations for best practice in fitting OU models in phylogenetic comparative analyses, and for interpreting the parameters of the OU model.