Beyond Brownian Motion and the Ornstein-Uhlenbeck Process: Stochastic Diffusion Models for the Evolution of Quantitative Characters
Beyond Brownian Motion and the Ornstein-Uhlenbeck Process: Stochastic Diffusion Models for the Evolution of Quantitative Characters
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DOI：
10.1086/706339
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发表时间：
2020-02-01
影响因子：
2.9
通讯作者：
Moreau, Cheyenne M.
中科院分区：
文献类型：
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作者：
Blomberg, Simone P.;Rathnayake, Suren, I;Moreau, Cheyenne M.
高斯过程，如布朗运动和奥恩斯坦-乌伦贝克过程，一直是数量性状进化的流行模型，并广泛用于系统发育比较方法。然而，它们的缺点限制了它们的效用。在这里，我们描述了新的非高斯随机微分方程（扩散）模型的数量性状进化。我们提出了推导新的扩散模型的一般方法，并开发了新的软件来拟合非高斯进化模型到特征数据。随机过程理论为理解当前和未来系统发育比较方法的特性提供了一个数学框架。注意性状进化和多样化模型的数学细节有助于避免在使用随机过程建模宏观进化时的一些缺陷。
Gaussian processes, such as Brownian motion and the Ornstein-Uhlenbeck process, have been popular models for the evolution of quantitative traits and are widely used in phylogenetic comparative methods. However, they have drawbacks that limit their utility. Here we describe new, non-Gaussian stochastic differential equation (diffusion) models of quantitative trait evolution. We present general methods for deriving new diffusion models and develop new software for fitting non-Gaussian evolutionary models to trait data. The theory of stochastic processes provides a mathematical framework for understanding the properties of current and future phylogenetic comparative methods. Attention to the mathematical details of models of trait evolution and diversification may help avoid some pitfalls when using stochastic processes to model macroevolution.