Variation in a sample of molecular sequence data informs about the past evolutionary history of the sample's population. Traditionally, Bayesian modeling coupled with the standard coalescent, is used to infer the sample's bifurcating genealogy and demographic and evolutionary parameters such as effective population size, and mutation rates. However, there are many situations where binary coalescent models do not accurately reflect the true underlying ancestral processes. Here, we propose a Bayesian nonparametric method for inferring effective population size trajectories from a multifurcating genealogy under the $\Lambda-$coalescent. In particular, we jointly estimate the effective population size and model parameters for the Beta-coalescent model, a special type of $\Lambda-$coalescent. Finally, we test our methods on simulations and apply them to study various viral dynamics as well as Japanese sardine population size changes over time. The code and vignettes can be found in the phylodyn package.

翻译：分子序列数据样本中的变异反映了该样本所属种群过去的演化历史。传统上，我们采用贝叶斯建模结合标准溯祖理论，来推断样本的分叉谱系以及有效种群大小、突变率等人口统计学与演化参数。然而，在许多情况下，二元合并的溯祖模型并不能准确反映真实的祖先过程。本文提出一种贝叶斯非参数方法，用于在$\Lambda-$溯祖框架下，从多分叉谱系推断有效种群大小的动态轨迹。具体而言，我们联合估计了有效种群大小以及Beta-合并模型（$\Lambda-$溯祖的一种特殊类型）的参数。最后，我们通过模拟测试了所提方法，并将其应用于研究多种病毒动态以及日本沙丁鱼种群大小随时间的变化。相关代码与示例可在phylodyn软件包中获取。