CAREER: Quantitative assessment of models for phylogenetic data

了解物种之间的进化关系和生命树上物种形成事件的时间对生物学，医学和生物化学的许多领域至关重要。准确地模拟生物进化是一个越来越复杂的过程，涉及到模拟生物体的分子和化石特征如何随着地质时间的推移而变化。该项目将提供有关如何最好地建模数据，以获得深时间进化动力学的准确图片的见解。数据密集型和实验密集型工作在生物科学中越来越普遍。为了让未来的劳动力为数据密集型的未来做好准备，本科生培训必须进行改革，以包括更多的定量和计算学习。本项目将提高不同学生群体的计算和定量技能，并分析结果，以确定如何将定量思维最有效地融入早期本科学习。推断过时系统发育树的方法的最新进展，如生物出生-死亡过程（FBD），将现存和灭绝的数据作为同一多样化过程的一部分。FBD通常被实现为分层贝叶斯模型，涉及分子和/或形态特征进化的模型，描述进化速率如何在树中分布的模型，以及在焦点分类群中如何进行多样化的模型。这些方法提供了许多优于旧方法的优点，例如能够将仅从形态学数据已知的标本放置在树上。尽管这些模型在数学上很优雅，但它们也很复杂，这使得研究人员很难将其应用于数据集并评估其性能。这个项目将开发方法来评估一个复杂的系统发育模型是否能够充分捕捉到经验数据的变幻莫测。该项目将制作软件，使研究人员能够利用经验数据真实的评价和了解模型的性能。该项目由系统学和生物多样性科学计划和刺激竞争性研究的既定计划（EPSCoR）共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Understanding the evolutionary relationships among species and the timing of speciation events on the tree of life is critical to many fields of biology, medicine, and biochemistry. Accurately modeling biological evolution is an increasingly complex process, involving modeling how molecular and fossil features of organisms change over geological time. This project will provide insights about how to best model data to obtain an accurate picture of deep-time evolutionary dynamics. Data- and mathematically-intensive work is increasingly common in the biological sciences. To prepare a future workforce for a data-intensive future, undergraduate training must be reformed to include more quantitative and computational learning. This project will increase computational and quantitative skills in a diverse student body and analyze outcomes to determine how quantitative thinking can be most effectively integrated into early undergraduate learning.Recent advances in methods for inferring dated phylogenetic trees, such as the fossilized birth-death process (FBD), model the extant and extinct data together as part of the same process of diversification. The FBD is typically implemented as a hierarchical Bayesian model involving a model of molecular and/or morphological character evolution, a model describing how rates of evolution are distributed across the tree, and a model of how diversification has proceeded in the focal taxa. These methods offer many advantages over older methods, such as being able to place specimens known from only morphological data on the tree. Despite their mathematical elegance, these models are also complex, which can make it difficult for researchers to apply them to their datasets and evaluate their performance. This project will develop methods to assess if a complex phylogenetic model is adequately capturing the vagaries of empirical data. The project will produce software to enable researchers to evaluate and understand the performance of models in real time with empirical data. This project is jointly funded by the Systematics and Biodiversity Science Program and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.