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Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.

Species diversification—the balance between speciation and extinction—is fundamental to our understanding of how species richness varies in space and time and throughout the Tree of Life. Phylogenetic approaches provide insights into species diversification by enabling support for alternative diversification scenarios to be compared and speciation and extinction rates to be estimated. Here, we review the current toolkit available for conducting such analyses. We first highlight how modeling efforts over the past decade have fostered a notable transition from overly simplistic evolutionary scenarios to a more nuanced understanding of how and why diversification rates vary through time and across lineages. Using the latitudinal diversity gradient as a case study, we then illustrate the impact that modeling choices can have on the results obtained. Finally, we review recent progress in two areas that are still lagging behind: phylogenetic insights into microbial diversification and the speciation process. [ABSTRACT FROM AUTHOR]

Phylogenetic inferences using combined datasets of both extant and extinct species have grown increasingly popular, in part thanks to the development of the fossilized birth–death (FBD) process. The FBD process provides a powerful model for the evolution of past and present lineages and can be used for both inference and simulation. Simulations in particular are very helpful for new users to gain better understanding of the model and its different components. In this work, we present FossilSimShiny, a visual application for simulating phylogenies, fossil samples, and fossil taxonomies under the FBD process. The app integrates a wide range of simulation models and presents the simulation results in clear, customizable figures. As a teaching tool, FossilSimShiny allows lecturers to create illustration plots and students to directly experiment with the model. For research applications, the app can help researchers save time and effort by testing and calibrating simulation setups before running them on a large scale. [ABSTRACT FROM AUTHOR]

For much of terrestrial biodiversity, the evolutionary pathways of adaptation from marine ancestors are poorly understood and have usually been viewed as a binary trait. True crabs, the decapod crustacean infraorder Brachyura, comprise over 7600 species representing a striking diversity of morphology and ecology, including repeated adaptation to non-marine habitats. Here, we reconstruct the evolutionary history of Brachyura using new and published sequences of 10 genes for 344 tips spanning 88 of 109 brachyuran families. Using 36 newly vetted fossil calibrations, we infer that brachyurans most likely diverged in the Triassic, with family-level splits in the late Cretaceous and early Paleogene. By contrast, the root age is underestimated with automated sampling of 328 fossil occurrences explicitly incorporated into the tree prior, suggesting such models are a poor fit under heterogeneous fossil preservation. We apply recently defined trait-by-environment associations to classify a gradient of transitions from marine to terrestrial lifestyles. We estimate that crabs left the marine environment at least 7 and up to 17 times convergently, and returned to the sea from non-marine environments at least twice. Although the most highly terrestrial- and many freshwater-adapted crabs are concentrated in Thoracotremata, Bayesian threshold models of ancestral state reconstruction fail to identify shifts to higher terrestrial grades due to the degree of underlying change required. Lineages throughout our tree inhabit intertidal and marginal marine environments, corroborating the inference that the early stages of terrestrial adaptation have a lower threshold to evolve. Our framework and extensive new fossil and natural history datasets will enable future comparisons of non-marine adaptation at the morphological and molecular level. Crabs provide an important window into the early processes of adaptation to novel environments, and different degrees of evolutionary constraint that might help predict these pathways. [Brachyura; convergent evolution; crustaceans; divergence times; fossil calibration; molecular phylogeny; terrestrialization; threshold model.] [ABSTRACT FROM AUTHOR]

Relaxed clock Bayesian evolutionary inference (BEI) enables the co‐estimation of phylogenetic trees and evolutionary parameters associated with models of character and lineage evolution. Fast advances in new model developments over the past decade have boosted BEI as a major macroevolutionary analytical framework using morphological and/or molecular data across vastly different study systems. However, there is limited availability of bioinformatic tools to pre‐ and postprocess data from BEI, such as identifying morphological data partitions, or statistically testing and creating publication quality plots of evolutionary hypotheses.Here, we introduce EvoPhylo, an r package to perform automated morphological character partitioning and analyse macroevolutionary parameter from relaxed clock (time‐calibrated) BEI outputted by the programs Mr.Bayes and BEAST2. These include rates of evolution and mode of selection for each character partition, diversification rate parameters, and handling fossil‐only posterior trees.We present the theoretical background behind EvoPhylo's functions and analytical tools for evolutionary hypothesis testing, its potential uses, and interpretation of its results with a series of vignettes and links to a step‐by‐step tutorial using examples from two empirical datasets.EvoPhylo will facilitate the use of Bayesian relaxed clocks as a tool for macroevolutionary inference across a wide range of users and fields of research, especially those that make usage of morphological datasets, from paleontological to total evidence dating analyses. [ABSTRACT FROM AUTHOR]

Bayesian total-evidence approaches under the fossilized birth-death model enable biologists to combine fossil and extant data while accounting for uncertainty in the ages of fossil specimens, in an integrative phylogenetic analysis. Fossil age uncertainty is a key feature of the fossil record as many empirical data sets may contain a mix of precisely dated and poorly dated fossil specimens or deposits. In this study, we explore whether reliable age estimates for fossil specimens can be obtained from Bayesian total-evidence phylogenetic analyses under the fossilized birth-death model. Through simulations based on the example of the Baltic amber deposit, we show that estimates of fossil ages obtained through such an analysis are accurate, particularly when the proportion of poorly dated specimens remains low and the majority of fossil specimens have precise dates. We confirm our results using an empirical data set of living and fossil penguins by artificially increasing the age uncertainty around some fossil specimens and showing that the resulting age estimates overlap with the recorded age ranges. Our results are applicable to many empirical data sets where classical methods of establishing fossil ages have failed, such as the Baltic amber and the Gobi Desert deposits. [Bayesian phylogenetic inference; fossil age estimates; fossilized birth-death; Lagerstätte; total-evidence.] [ABSTRACT FROM AUTHOR]

Phylodynamic models generally aim at jointly inferring phylogenetic relationships, model parameters, and more recently, the number of lineages through time, based on molecular sequence data. In the fields of epidemiology and macroevolution, these models can be used to estimate, respectively, the past number of infected individuals (prevalence) or the past number of species (paleodiversity) through time. Recent years have seen the development of "total-evidence" analyses, which combine molecular and morphological data from extant and past sampled individuals in a unified Bayesian inference framework. Even sampled individuals characterized only by their sampling time, that is, lacking morphological and molecular data, which we call occurrences , provide invaluable information to estimate the past number of lineages. Here, we present new methodological developments around the fossilized birth–death process enabling us to (i) incorporate occurrence data in the likelihood function; (ii) consider piecewise-constant birth, death, and sampling rates; and (iii) estimate the past number of lineages, with or without knowledge of the underlying tree. We implement our method in the RevBayes software environment, enabling its use along with a large set of models of molecular and morphological evolution, and validate the inference workflow using simulations under a wide range of conditions. We finally illustrate our new implementation using two empirical data sets stemming from the fields of epidemiology and macroevolution. In epidemiology, we infer the prevalence of the coronavirus disease 2019 outbreak on the Diamond Princess ship, by taking into account jointly the case count record (occurrences) along with viral sequences for a fraction of infected individuals. In macroevolution, we infer the diversity trajectory of cetaceans using molecular and morphological data from extant taxa, morphological data from fossils, as well as numerous fossil occurrences. The joint modeling of occurrences and trees holds the promise to further bridge the gap between traditional epidemiology and pathogen genomics, as well as paleontology and molecular phylogenetics. [Birth–death model; epidemiology; fossils; macroevolution; occurrences; phylogenetics; skyline.] [ABSTRACT FROM AUTHOR]

Phylogenetic estimation is, and has always been, a complex endeavor. Estimating a phylogenetic tree involves evaluating many possible solutions and possible evolutionary histories that could explain a set of observed data, typically by using a model of evolution. Values for all model parameters need to be evaluated as well. Modern statistical methods involve not just the estimation of a tree, but also solutions to more complex models involving fossil record information and other data sources. Markov chain Monte Carlo (MCMC) is a leading method for approximating the posterior distribution of parameters in a mathematical model. It is deployed in all Bayesian phylogenetic tree estimation software. While many researchers use MCMC in phylogenetic analyses, interpreting results and diagnosing problems with MCMC remain vexing issues to many biologists. In this manuscript, we will offer an overview of how MCMC is used in Bayesian phylogenetic inference, with a particular emphasis on complex hierarchical models, such as the fossilized birth-death (FBD) model. We will discuss strategies to diagnose common MCMC problems and troubleshoot difficult analyses, in particular convergence issues. We will show how the study design, the choice of models and priors, but also technical features of the inference tools themselves can all be adjusted to obtain the best results. Finally, we will also discuss the unique challenges created by the incorporation of fossil information in phylogenetic inference, and present tips to address them., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Barido-Sottani J et al.)