非线性驱动种群动态：一种非参数凝聚方法
Non-Linear Drivers of Population Dynamics: a Nonparametric Coalescent Approach
AI总结：
本文提出一种非参数凝聚方法，通过高斯过程先验整合协变量，以更准确地建模环境和流行病学因素对种群动态的影响。
摘要：
有效种群大小（Ne(t)）是人口遗传学和流行病学中一个基本参数，用于量化遗传多样性并揭示人口历史。基于凝聚的方法通过从分子序列数据重建的系统发育树，能够推断出随时间变化的Ne(t)轨迹。理解生态和环境因素对种群动态的驱动作用需要将Ne(t)与外部协变量联系起来。现有方法通常假定协变量与Ne(t)之间存在对数线性关系，这可能无法捕捉复杂的生物学过程，并且当真实关系非线性时会引入偏差。我们提出了一种灵活的贝叶斯框架，通过高斯过程（GP）先验将协变量整合到具有分段常数Ne(t)的凝聚模型中。GP作为一种函数的分布，能够自然地容纳非线性协变量效应，而无需限制性的参数假设。这种形式提高了协变量-Ne(t)关系的估计，减轻了非线性关联下的偏差，并产生了在协变量空间中变化的可解释不确定性量化。为了在全局协变量驱动模式与局部时间动态之间取得平衡，我们将GP先验与高斯马尔可夫随机场相结合，以强制Ne(t)轨迹的平滑性。通过模拟研究和三个实证应用——巴西黄热病病毒动态（2016-2018年）、晚第四纪麝牛人口动态，以及喀麦隆HIV-1 CRF02-AG进化——我们证明了我们的方法在适当的情况下确认了线性关系，并揭示了其他方法可能遗漏或误表征的非线性协变量效应。该框架通过更准确和生物上更现实地建模环境和流行病学因素如何随时间塑造种群大小，推动了流行病学推断。
英文摘要：
Effective population size (Ne(t)) is a fundamental parameter in population genetics and phylodynamics that quantifies genetic diversity and reveals demographic history. Coalescent-based methods enable the inference of Ne(t) trajectories through time from phylogenies reconstructed from molecular sequence data. Understanding the ecological and environmental drivers of population dynamics requires linking Ne(t) to external covariates. Existing approaches typically impose log-linear relationships between covariates and Ne(t), which may fail to capture complex biological processes and can introduce bias when the true relationship is nonlinear. We present a flexible Bayesian framework that integrates covariates into coalescent models with piecewise-constant Ne(t) through a Gaussian process (GP) prior. The GP, a distribution over functions, naturally accommodates nonlinear covariate effects without restrictive parametric assumptions. This formulation improves estimation of covariate-Ne(t) relationships, mitigates bias under nonlinear associations, and yields interpretable uncertainty quantification that varies across the covariate space. To balance global covariate-driven patterns with local temporal dynamics, we couple the GP prior with a Gaussian Markov random field that enforces smoothness in Ne(t) trajectories. Through simulation studies and three empirical applications - yellow fever virus dynamics in Brazil (2016-2018), late-Quaternary musk ox demography, and HIV-1 CRF02-AG evolution in Cameroon - we demonstrate that our method both confirms linear relationships where appropriate and reveals nonlinear covariate effects that would otherwise be missed or mischaracterized. This framework advances phylodynamic inference by enabling more accurate and biologically realistic modeling of how environmental and epidemiological factors shape population size through time.
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