CAREER: Quantitative assessment of models for phylogenetic data

职业：系统发育数据模型的定量评估

• 批准号: 2045842
• 负责人: April Wright
• 金额: $ 115.09万
• 依托单位: Southeastern Louisiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045842&HistoricalAwards=false
• 关键词: CAREER; Quantitative; assessment; models; phylogenetic

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.

了解物种之间的进化关系和生命之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都至关重要。准确地模拟生物进化是一个越来越复杂的过程，包括模拟生物的分子和化石特征如何随着地质时间而变化。该项目将提供关于如何最好地建模数据以获得深度时间进化动力学的准确图像的见解。数据和数学密集型工作在生物科学领域越来越普遍。为了让未来的劳动力为数据密集型的未来做好准备，本科培训必须进行改革，以包括更多的定量和计算学习。该项目将提高不同学生群体的计算和定量技能，并分析结果，以确定如何最有效地将定量思维融入本科早期学习。最近在推断年代久远的系统发育树的方法方面取得的进展，如化石出生-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一起建模。FBD通常作为一个层次贝叶斯模型来实现，包括分子和/或形态特征进化模型、描述进化速率如何在整个树中分布的模型以及焦点分类群中如何进行多样化的模型。这些方法比旧的方法有许多优点，例如能够将仅从形态学数据中知道的标本放置在树上。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它们应用于他们的数据集并评估它们的性能。该项目将开发方法，以评估是否一个复杂的系统发育模型是充分捕捉经验数据的变幻莫测。该项目将开发软件，使研究人员能够利用经验数据实时评估和理解模型的性能。本项目由系统学和生物多样性科学计划和促进竞争性研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Lessons learned from organizing and teaching virtual phylogenetics workshops

从组织和教授虚拟系统发育学研讨会中汲取的经验教训

• DOI: 10.18061/bssb.v1i2.8425
• 发表时间: 2022
• 期刊: Bulletin of the Society of Systematic Biologists
• 作者: Barido-Sottani, Joëlle;Justison, Joshua A.;Borges, Rui;Brown, Jeremy M.;Dismukes, Wade;Do Rosario Petrucci, Bruno;Guimarães Fabreti, Luiza;Höhna, Sebastian;Landis, Michael J.;Lewis, Paul O.
• 通讯作者: Lewis, Paul O.

Revticulate: An R framework for interaction with RevBayes

Revticulate：与 RevBayes 交互的 R 框架

• DOI: 10.1111/2041-210x.13852
• 发表时间: 2022
• 期刊: Methods in Ecology and Evolution
• 影响因子: 6.6
• 作者: Charpentier, Caleb P.;Wright, April M.
• 通讯作者: Wright, April M.

Handling Logical Character Dependency in Phylogenetic Inference: Extensive Performance Testing of Assumptions and Solutions Using Simulated and Empirical Data

处理系统发育推断中的逻辑字符依赖性：使用模拟和经验数据对假设和解决方案进行广泛的性能测试

• DOI: 10.1093/sysbio/syad006
• 发表时间: 2023
• 期刊: Systematic Biology
• 影响因子: 6.5
• 作者: Simões, Tiago R;Vernygora, Oksana V;de Medeiros, Bruno A;Wright, April M
• 通讯作者: Wright, April M

Integrating Fossil Observations Into Phylogenetics Using the Fossilized Birth–Death Model

使用化石出生死亡模型将化石观察整合到系统发育学中

• DOI: 10.1146/annurev-ecolsys-102220-030855
• 发表时间: 2022
• 期刊: and Systematics
• 作者: Wright, April M.;Bapst, David W.;Barido-Sottani, Joëlle;Warnock, Rachel C.M.
• 通讯作者: Warnock, Rachel C.M.