Birth-death processes have given biologists a model-based framework to answer questions about changes in the birth and death rates of lineages in a phylogenetic tree. Therefore birth-death models are central to macroevolutionary as well as phylodynamic analyses. Early approaches to studying temporal variation in birth and death rates using birth-death models faced difficulties due to the restrictive choices of birth and death rate curves through time. Sufficiently flexible time-varying birth-death models are still lacking. We use a piecewise-constant birth-death model, combined with both Gaussian Markov random field (GMRF) and horseshoe Markov random field (HSMRF) prior distributions, to approximate arbitrary changes in birth rate through time. We implement these models in the widely used statistical phylogenetic software platform RevBayes, allowing us to jointly estimate birth-death process parameters, phylogeny, and nuisance parameters in a Bayesian framework. We test both GMRF-based and HSMRF-based models on a variety of simulated diversification scenarios, and then apply them to both a macroevolutionary and an epidemiological dataset. We find that both models are capable of inferring variable birth rates and correctly rejecting variable models in favor of effectively constant models. In general the HSMRF-based model has higher precision than its GMRF counterpart, with little to no loss of accuracy. Applied to a macroevolutionary dataset of the Australian gecko family Pygopodidae (where birth rates are interpretable as speciation rates), the GMRF-based model detects a slow decrease whereas the HSMRF-based model detects a rapid speciation-rate decrease in the last 12 million years. Applied to an infectious disease phylodynamic dataset of sequences from HIV subtype A in Russia and Ukraine (where birth rates are interpretable as the rate of accumulation of new infections), our models detect a strongly elevated rate of infection in the 1990s.

中文翻译：

---

局部自适应贝叶斯出生死亡模型成功检测出缓慢和快速的速率变化

出生死亡过程为生物学家提供了一个基于模型的框架，以回答有关系统发育树谱系的出生和死亡率变化的问题。因此，出生死亡模型是宏观进化和系统动力学分析的核心。由于出生和死亡率曲线随时间的限制选择，使用出生-死亡模型研究出生和死亡率的时间变化的早期方法面临困难。仍然缺乏足够灵活的随时间变化的生死模型。我们使用分段恒定的出生-死亡模型，结合高斯马尔可夫随机场（GMRF）和马蹄马尔可夫随机场（HSMRF）的先验分布，以近似随时间变化的出生率的任意变化。我们在广泛使用的统计系统发育软件平台RevBayes中实现这些模型，使我们能够在贝叶斯框架中共同估算出生-死亡过程参数，系统发育和有害参数。我们在各种模拟的多元化情景下测试了基于GMRF和基于HSMRF的模型，然后将它们应用于宏观进化和流行病学数据集。我们发现，这两个模型都能够推断出可变的出生率，并能够正确拒绝可变的模型，而有利于有效的恒定模型。通常，基于HSMRF的模型比GMRF对应的模型具有更高的精度，几乎没有损失精度。将基于GMRF的模型检测到澳大利亚壁虎科Pygopodidae的宏观进化数据集（其中出生率可解释为物种形成率），在最近1200万年中，基于GMRF的模型检测到物种形成率快速下降。 。