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8 results for “Birth-death model”
Data from: Skyline fossilized birth-death model is robust to violations of sampling assumptions in total-evidence dating
<p>Several total-evidence dating studies under the fossilized birth-death (FBD) model have produced very old age estimates, which are not supported by the fossil record. This phenomenon has been termed "deep root attraction (DRA)". For two specific datasets, involving divergence time estimation for the early radiations of ants, bees and wasps (Hymenoptera) and of placental mammals (Eutheria), it has been shown that the DRA effect can be greatly reduced by accommodating the fact that extant species in these trees have been sampled to maximize diversity, so called diversified sampling. Unfortunately, current methods to accommodate diversified sampling only consider the extreme case where it is possible to identify a cut-off time such that all splits occurring before this time are represented in the sampled tree but none of the younger splits. In reality, the sampling bias is rarely this extreme, and may be difficult to model properly. Similar modeling challenges apply to the sampling of the fossil record. This raises the question of whether it is possible to find dating methods that are more robust to sampling biases. Here, we show that the skyline FBD (SFBD) process, where the diversification and fossil-sampling rates can vary over time in a piecewise fashion, provides age estimates that are more robust to inadequacies in the modeling of the sampling process and less sensitive to DRA effects. In the SFBD model we consider, rates in different time intervals are either considered to be independent and identically distributed, or assumed to be autocorrelated following an Ornstein-Uhlenbeck (OU) process. Through simulations and reanalyses of the Hymenoptera and Eutheria data, we show that both variants of the SFBD model unify age estimates under random and diversified sampling assumptions. The SFBD model can resolve DRA by absorbing the deviations from the sampling assumptions into the inferred dynamics of the diversification process over time. Although this means that the inferred diversification dynamics must be interpreted with caution, taking sampling biases into account, we conclude that the SFBD model represents the most robust approach available currently for addressing DRA in total-evidence dating.</p>
Online appendix and simulated data sets for assesment of Birth-Death Exposed-Infectious (BDEI) phylodynamic model estimators
<p>The birth-death exposed-infectious (BDEI) phylodynamic model describes the transmission of pathogens featuring an incubation period (when there is a delay between the moment of infection and becoming infectious, as for Ebola and SARS-CoV-2), and permits its estimation along with other parameters, from time-scaled phylogenetic trees.</p> <p>We implemented a highly parallelizable estimator for the BDEI model in a maximum likelihood framework (<a href="https://github.com/evolbioinfo/bdei">PyBDEI</a>) using a combination of numerical analysis methods for efficient equation resolution. This dataset contains the assessment of PyBDEI in comparison with a Bayesian implementation in <a href="http://www.beast2.org/">BEAST2</a> (mtbd package) and a deep learning estimator <a href="https://github.com/evolbioinfo/phylodeep">PhyloDeep</a>: the parameter values estimated by the 3 tools.<br><br>The PyBDEI and the theoretical findings behind it are described in A Zhukova, F Hecht, Y Maday, and O Gascuel. Fast and Accurate Maximum-Likelihood Estimation of Multi-Type Birth-Death Epidemiological Models from Phylogenetic Trees Syst Biol 2023. This dataset contains the online Appendix (Fig S1-S3 and Table S1).</p>
Data from: Skyline fossilized birth-death model is robust to violations of sampling assumptions in total-evidence dating
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Online appendix and simulated data sets for assesment of Birth-Death Exposed-Infectious (BDEI) phylodynamic model estimators
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A Multi-Type Birth-Death model for Bayesian inference of lineage-specific birth and death rates
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Data from: Exploring the power of Bayesian birth-death skyline models to detect mass extinction events from phylogenies with only extant taxa
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 phylogenies 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.
Data from: Exploring the power of Bayesian birth-death skyline models to detect mass extinction events from phylogenies with only extant taxa
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Supplement to Inferring the evolutionary history of the Sino-Himalayan biodiversity hotspot using a Bayesian birth-death skyline model
<p>This repository contains the supplementary files for:</p> <p>Allen BJ, Vaughan TG, du Plessis L, Schouten TLA, Yuan Z, Willett SD, Stadler T. 2024. Inferring the evolutionary history of the Sino-Himalayan biodiversity hotspot using a Bayesian birth-death skyline model. Geological Society of London Special Publications, 549.</p> <p><strong>Description of files</strong></p> <p>This repository contains the cleaned tree file, raw log files, simulated trees, XML files for running the analyses in BEAST2, and R code to process the datasets.</p> <p>Liu_et_al_SinoHimalayan.nex - the phylogeny inferred by Liu et al. (2021), trimmed to only include the 8864 tips associated with genetic data</p> <p>Skyline_logs.zip - skyline logs produced by BEAST2 analyses (see below for naming convention)</p> <p>Regression_results.zip - results of the linear modelling between global palaeotemperature and diversification estimates</p> <p>Sim_trees.trees - the phylogenies simulated by ReMASTER</p> <p>Adequacy_logs.zip - skyline logs produced from the analyses using the simulated phylogenies</p> <p> </p> <p><strong>Description of BEAST2 XMLs</strong></p> <p>The XML files contain the BEAST2 configurations for:</p> <p>Liu_et_al_bd.xml, Liu_et_al_bd_high.xml, Liu_et_al_bd_mid.xml, Liu_et_al_bd_low.xml - skyline analyses using equal length time bins, with beta sampling prior, high fixed sampling, mid fixed sampling, and low fixed sampling respectively</p> <p>Liu_et_al_bd_geol.xml, Liu_et_al_bd_geol_high.xml, Liu_et_al_bd_geol_mid.xml, Liu_et_al_bd_geol_low.xml - skyline analyses using geological time bins, with beta sampling prior, high fixed sampling, mid fixed sampling, and low fixed sampling respectively</p> <p>Remaster_simulation.xml - simulating new phylogenies based on the inferred skylines using ReMASTER</p> <p>Sim_trees.xml - skyline analyses conducted on the simulated phylogenies</p> <p> </p> <p><strong>Description of R code</strong></p> <p>The R code is subdivided into the following files:</p> <p>BDSKY_skylines.R - code for processing and plotting skyline data from the BEAST2 log files</p> <p>BDSKY_adjacent_bins.R - code for the analyses examining the increase or decrease in evolutionary rates between adjacent skyline bins</p> <p>Plot_palaeotemp_comparison.R - code for plotting the diversification estimates against global palaeotemperature, as taken from Scotese et al. (2021)</p> <p>Palaeotemperature_regressions.R - code for linear modelling between global palaeotemperature and diversification estimates</p> <p>Remaster_processing.R - code for processing and plotting inferred skylines from the simulated datasets</p>
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