Bayesian Estimation of Species Divergence Times Using Correlated Quantitative Characters
Bayesian Estimation of Species Divergence Times Using Correlated Quantitative Characters
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DOI：
10.1093/sysbio/syz015
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发表时间：
2019-11-01
影响因子：
6.5
通讯作者：
Dos Reis, Mario
中科院分区：
文献类型：
--
作者：
Alvarez-Carretero, Sandra;Goswami, Anjali;Dos Reis, Mario
离散形态学数据已被广泛用于研究物种进化，但使用的数量（或连续）形态特征是不常见的。在这里，我们实现了贝叶斯方法来估计物种的分歧时间，使用数量性状。数量性状的进化模型使用布朗扩散与字符的相关性和字符的变化在人口。通过模拟，我们证明，忽略人口的变化（或人口的“噪音”）和字符之间的相关性，导致偏差的估计的分歧时间和速度，特别是如果相关性和人口噪声高。我们将我们的新方法应用于食肉类哺乳动物的数量特征（颅骨标志）和分子数据的分析。我们的研究结果表明，时间估计的影响，是否相关性和人口噪声占或忽略的分析。估计值还受到分析数据类型的影响，仅分析形态特征，仅分析分子数据或两者的组合;显示时间估计值之间存在明显差异。食肉动物物种之间的形态特征的变化率似乎是非常高的，贝叶斯模型的选择表明，独立率模型拟合的形态数据比自相关率模型。我们认为，利用形态学连续特征，结合分子生物学数据，可以为物种进化研究带来新的视角。我们的新模型在MCMCCtree计算机程序中实现，用于发散时间的贝叶斯推断。
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.