Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts

Birth-death processes have given biologists a model-based framework to answer questions about changes in the birth and death rates of lineages in a phylogenetic tree. Therefore birth-death models are central to macroevolutionary as well as phylodynamic analyses. Early approaches to studying temporal variation in birth and death rates using birth-death models faced difficulties due to the restrictive choices of birth and death rate curves through time. Sufficiently flexible time-varying birth-death models are still lacking. We use a piecewise-constant birth-death model, combined with both Gaussian Markov random field (GMRF) and horseshoe Markov random field (HSMRF) prior distributions, to approximate arbitrary changes in birth rate through time. We implement these models in the widely used statistical phylogenetic software platform RevBayes, allowing us to jointly estimate birth-death process parameters, phylogeny, and nuisance parameters in a Bayesian framework. We test both GMRF-based and HSMRF-based models on a variety of simulated diversification scenarios, and then apply them to both a macroevolutionary and an epidemiological dataset. We find that both models are capable of inferring variable birth rates and correctly rejecting variable models in favor of effectively constant models. In general the HSMRF-based model has higher precision than its GMRF counterpart, with little to no loss of accuracy. Applied to a macroevolutionary dataset of the Australian gecko family Pygopodidae (where birth rates are interpretable as speciation rates), the GMRF-based model detects a slow decrease whereas the HSMRF-based model detects a rapid speciation-rate decrease in the last 12 million years. Applied to an infectious disease phylodynamic dataset of sequences from HIV subtype A in Russia and Ukraine (where birth rates are interpretable as the rate of accumulation of new infections), our models detect a strongly elevated rate of infection in the 1990s. Author summary Both the growth of groups of species and the spread of infectious diseases through populations can be modeled as birth-death processes. Birth events correspond either to speciation or infection, and death events to extinction or becoming noninfectious. The rates of birth and death may vary over time, and by examining this variation researchers can pinpoint important events in the history of life on Earth or in the course of an outbreak. Time-calibrated phylogenies track the relationships between a set of species (or infections) and the times of all speciation (or infection) events, and can thus be used to infer birth and death rates. We develop two phylogenetic birth-death models with the goal of discerning signal of rate variation from noise due to the stochastic nature of birth-death models. Using a variety of simulated datasets, we show that one of these models can accurately infer slow and rapid rate shifts without sacrificing precision. Using real data, we demonstrate that our new methodology can be used for simultaneous inference of phylogeny and rates through time

License GPL-3

Title Fast simulation of reconstructed phylogenetic trees under time-dependent birth-death processe

Data from: A Bayesian approach for detecting the impact of mass-extinction events on molecular phylogenies when rates of lineage diversification may vary

The paleontological record chronicles numerous episodes of mass extinction that severely culled the Tree of Life. Biologists have long sought to assess the extent to which these events may have impacted particular groups. We present a novel method for detecting the impact of mass-extinction events on molecular phylogenies, even in the presence of tree-wide diversification-rate variation and in the absence of additional information from the fossil record. Our approach is based on an episodic stochastic-branching process model in which rates of speciation and extinction are constant between events. We model three types of events: (i) instantaneous tree-wide shifts in speciation rate; (ii) instantaneous tree-wide shifts in extinction rate and (iii) instantaneous tree-wide mass-extinction events. Each type of event is modelled as an independent compound Poisson process (CPP), where the waiting times between events are exponentially distributed with event-specific rate parameters. The magnitude of each event is drawn from an event-specific prior distribution. Parameters of the model are then estimated in a Bayesian statistical framework using a reversible-jump Markov chain Monte Carlo algorithm. This Bayesian approach enables us to distinguish between tree-wide diversification-rate variation and mass-extinction events by specifying a biologically informed prior on the magnitude of mass-extinction events and empirical hyperpriors on the diversification-rate parameters. We demonstrate via simulation that this method has substantial power to detect the number of mass-extinction events and provides unbiased estimates of the timing of mass-extinction events, while exhibiting an appropriate (i.e. <5%) false-discovery rate, even when background diversification rates vary. Finally, we provide an empirical demonstration of this approach, which reveals that conifers experienced a major episode of mass extinction ≈23 Ma. This new approach – the CPP on Mass-Extinction Times (CoMET) model – provides an effective tool for detecting the impact of mass-extinction events on molecular phylogenies, even when the history of those groups includes temporal variation in diversification rates and when the fossil history of those groups is poorly known

Approximate Bayesian Computation of diversification rates from molecular phylogenies:Introducing a new efficient summary statistic, the nLTT

Molecular phylogenies form a potential source of information on rates of diversification, and the mechanisms that underlie diversification patterns. Diversification models have become increasingly complex over the past decade, and we have reached a point where the computation of the analytical likelihood of the model given a phylogeny is either unavailable or intractable. For such models, a likelihood-free approach such as Approximate Bayesian Computation (ABC) offers a solution. ABC is a Bayesian framework that uses one or more summary statistics instead of the likelihood function. Crucial to the performance of an ABC algorithm is the choice of summary statistics. Here, we analyse the applicability of three traditional and often-used summary statistics (Gamma statistic, Phylogenetic Diversity and tree size) within an ABC framework and propose a new summary statistic: the normalized Lineages-Through-Time (nLTT) statistic. We find that the traditional summary statistics perform poorly and should not be used as a substitute of the likelihood. By contrast, we find that the nLTT statistic performs on par with the likelihood. We suggest to include the nLTT statistic in future ABC applications within phylogenetics. We argue that the use of ABC in diversification rate analysis is a promising new approach, but that care should be taken which summary statistics are chosen

CoMET data archive

This data archive contains the scripts used to generate and analyze all the data in the accompanying manuscript (May et al., MEE), as well as the simulated data that were analyzed for that work. The archive is broken into five subdirectores, each corresponding to one set of simulations/analyses performed in the manuscript. The descriptions of each subdirectory are provided in the README.txt file

Data from: Critically evaluating the theory and performance of Bayesian analyis of macroevolutionary mixtures

Bayesian analysis of macroevolutionary mixtures (BAMM) has recently taken the study of lineage diversification by storm. BAMM estimates the diversification-rate parameters (speciation and extinction) for every branch of a study phylogeny and infers the number and location of diversification-rate shifts across branches of a tree. Our evaluation of BAMM reveals two major theoretical errors: (i) the likelihood function (which estimates the model parameters from the data) is incorrect, and (ii) the compound Poisson process prior model (which describes the prior distribution of diversification-rate shifts across branches) is incoherent. Using simulation, we demonstrate that these theoretical issues cause statistical pathologies; posterior estimates of the number of diversification-rate shifts are strongly influenced by the assumed prior, and estimates of diversification-rate parameters are unreliable. Moreover, the inability to correctly compute the likelihood or to correctly specify the prior for rate-variable trees precludes the use of Bayesian approaches for testing hypotheses regarding the number and location of diversification-rate shifts using BAMM

supplementary_data

supplementary_dat

https://github.com/brianrmoore/BAMM_critique

This repo hosts modified BAMM code that we used in our project exploring theoretical issues and statistical problems with BAMM

RevGadgets: An R package for visualizing Bayesian phylogenetic analyses from RevBayes

10.1111/2041-210X.13750METHODS IN ECOLOGY AND EVOLUTION132314-32