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探索贝叶斯出生-死亡天际线模型从仅存在现生物种的系统发育中检测大规模灭绝事件的能力。

Exploring the power of Bayesian birth-death skyline models to detect mass extinction events from phylogenies with only extant taxa.

机构信息

Real Jardín Botánico (RJB), CSIC, Plaza de Murillo 2, 28014, Madrid, Spain.

Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule Zürich, 4058, Basel, Switzerland.

出版信息

Evolution. 2019 Jun;73(6):1133-1150. doi: 10.1111/evo.13753. Epub 2019 May 9.

DOI:10.1111/evo.13753
PMID:31017656
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6767073/
Abstract

Mass extinction events (MEEs), defined as significant losses of species diversity in significantly short time periods, have attracted the attention of biologists because of their link to major environmental change. MEEs have traditionally been studied through the fossil record, but the development of birth-death models has made it possible to detect their signature based on extant-taxa phylogenies. Most birth-death models consider MEEs as instantaneous events where a high proportion of species are simultaneously removed from the tree ("single pulse" approach), in contrast to the paleontological record, where MEEs have a time duration. Here, we explore the power of a Bayesian Birth-Death Skyline (BDSKY) model to detect the signature of MEEs through changes in extinction rates under a "time-slice" approach. In this approach, MEEs are time intervals where the extinction rate is greater than the speciation rate. Results showed BDSKY can detect and locate MEEs but that precision and accuracy depend on the phylogeny's size and MEE intensity. Comparisons of BDSKY with the single-pulse Bayesian model, CoMET, showed a similar frequency of Type II error and neither model exhibited Type I error. However, while CoMET performed better in detecting and locating MEEs for smaller phylogenies, BDSKY showed higher accuracy in estimating extinction and speciation rates.

摘要

大规模灭绝事件(MEEs)被定义为物种多样性在极短时间内的显著损失,由于它们与重大环境变化有关,因此引起了生物学家的关注。MEEs 传统上是通过化石记录进行研究的,但出生-死亡模型的发展使得根据现存分类群的系统发育来检测其特征成为可能。大多数出生-死亡模型将 MEEs 视为同时从树上移除大量物种的瞬时事件(“单脉冲”方法),与古生物学记录相反,MEEs 具有时间持续时间。在这里,我们通过“时间切片”方法下灭绝率的变化来探索贝叶斯出生-死亡天空线(BDSKY)模型检测 MEEs 特征的能力。在这种方法中,MEEs 是灭绝率大于物种形成率的时间间隔。结果表明,BDSKY 可以检测和定位 MEEs,但精度和准确性取决于系统发育的大小和 MEE 的强度。BDSKY 与单脉冲贝叶斯模型 CoMET 的比较表明,两种模型的第二类错误频率相似,且都没有出现第一类错误。然而,虽然 CoMET 在较小的系统发育中检测和定位 MEEs 的效果更好,但 BDSKY 在估计灭绝和物种形成率方面的准确性更高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d85/6767073/2d4e81aa63a6/EVO-73-1133-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d85/6767073/abe37541475e/EVO-73-1133-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d85/6767073/c7aa65d4e7a9/EVO-73-1133-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d85/6767073/3953926edb7f/EVO-73-1133-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d85/6767073/1b5d671d5da1/EVO-73-1133-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d85/6767073/3bfa08236d1b/EVO-73-1133-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d85/6767073/2d4e81aa63a6/EVO-73-1133-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d85/6767073/abe37541475e/EVO-73-1133-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d85/6767073/c7aa65d4e7a9/EVO-73-1133-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d85/6767073/3953926edb7f/EVO-73-1133-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d85/6767073/1b5d671d5da1/EVO-73-1133-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d85/6767073/3bfa08236d1b/EVO-73-1133-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d85/6767073/2d4e81aa63a6/EVO-73-1133-g006.jpg

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