School of Biological and Chemical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, UK.
Department of Genetics, Evolution and Environment, University College London, Gower Street, London WC1E 6BT, UK.
Syst Biol. 2019 Nov 1;68(6):967-986. doi: 10.1093/sysbio/syz015.
Discrete morphological data have been widely used to study species evolution, but the use of quantitative (or continuous) morphological characters is less common. Here, we implement a Bayesian method to estimate species divergence times using quantitative characters. Quantitative character evolution is modeled using Brownian diffusion with character correlation and character variation within populations. Through simulations, we demonstrate that ignoring the population variation (or population "noise") and the correlation among characters leads to biased estimates of divergence times and rate, especially if the correlation and population noise are high. We apply our new method to the analysis of quantitative characters (cranium landmarks) and molecular data from carnivoran mammals. Our results show that time estimates are affected by whether the correlations and population noise are accounted for or ignored in the analysis. The estimates are also affected by the type of data analyzed, with analyses of morphological characters only, molecular data only, or a combination of both; showing noticeable differences among the time estimates. Rate variation of morphological characters among the carnivoran species appears to be very high, with Bayesian model selection indicating that the independent-rates model fits the morphological data better than the autocorrelated-rates model. We suggest that using morphological continuous characters, together with molecular data, can bring a new perspective to the study of species evolution. Our new model is implemented in the MCMCtree computer program for Bayesian inference of divergence times.
离散形态数据已被广泛用于研究物种进化，但定量（或连续）形态特征的应用较少。在这里，我们使用贝叶斯方法来估计使用定量特征的物种分歧时间。使用布朗扩散模型来模拟定量特征的进化，该模型考虑了特征相关性和种群内的特征变异。通过模拟，我们证明忽略种群变异（或种群“噪声”）和特征之间的相关性会导致分歧时间和速率的估计出现偏差，尤其是在相关性和种群噪声较高的情况下。我们将我们的新方法应用于分析食肉目哺乳动物的定量特征（颅骨地标）和分子数据。我们的结果表明，时间估计受到分析中是否考虑相关性和种群噪声的影响。估计还受到分析中所使用的数据类型的影响，仅分析形态特征、仅分析分子数据或两者结合分析，这会导致时间估计之间存在明显差异。食肉目物种之间形态特征的速率变化似乎非常高，贝叶斯模型选择表明独立速率模型比自相关速率模型更适合形态数据。我们建议使用形态连续特征与分子数据相结合，可以为物种进化研究带来新的视角。我们的新模型已在 MCMCtree 计算机程序中实现，用于进行贝叶斯分歧时间推断。
