A cautionary note on the use of Ornstein Uhlenbeck models in macroevolutionary studies

2015/12/01 by Natalie Cooper, Gavin H. Thomas, Chris Venditti +3 · 352 citations
Biochemistry, Genetics and Molecular Biology · Earth and Planetary Sciences · Environmental Science · Mathematics · #Biology #Computer science #Econometrics #Evolution and Paleontology Studies #Evolutionary biology #Genetic diversity and population structure #Genetics #Mathematics #Phylogenetic comparative methods #Phylogenetic tree #Statistical physics #Statistics #Trait #Variance (accounting) #Wildlife Ecology and Conservation

paper · pdf · doi:10.1111/bij.12701

published in Biological Journal of the Linnean Society 118(1), 64-77 (Oxford University Press)

openalex publication_date 2015/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

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.

Citations

• TESTING FOR PHYLOGENETIC SIGNAL IN COMPARATIVE DATA: BEHAVIORAL TRAITS ARE MORE LABILE

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