Search for: All records

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. 

Over the past decade, a new set of methods for estimating dated trees has emerged. Originally referred to as the fossilized birth–death (FBD) process, this single model has expanded to a family of models that allows researchers to coestimate evolutionary parameters (e.g., diversification, sampling) and patterns alongside divergence times for a variety of applications from paleobiology to real-time epidemiology. We provide an overview of this family of models. We explore the ways in which these models correspond to methods in quantitative paleobiology, as the FBD process provides a framework through which neontological and paleontological approaches to phylogenetics and macroevolution can be unified. We also provide an overview of challenges associated with applying FBD models, particularly with an eye toward the fossil record. We conclude this review by discussing several exciting avenues for the inclusion of fossil data in phylogenetic analyses. 

Abstract 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 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 The fossilized birth–death (FBD) process provides an ideal model for inferring phylogenies from both extant and fossil taxa. Using this approach, fossils are directly integrated into the tree, leading to a statistically coherent prior on divergence times. Since fossils are typically not associated with molecular sequences, additional information is required to place fossils in the tree. We use simulations to evaluate two different approaches to handling fossil placement in FBD analyses: using topological constraints, where the user specifies monophyletic clades based on established taxonomy, or using total‐evidence analyses, which use a morphological data matrix in addition to the molecular alignment. We also explore how rate variation in fossil recovery or diversification rates impacts these approaches. We find that the extant topology is well recovered under all methods of fossil placement. Divergence times are similarly well recovered across all methods, with the exception of constraints which contain errors. We see similar patterns in datasets which include rate variation, however, relative errors in extant divergence times increase when more variation is included in the dataset, for all approaches using topological constraints, and particularly for constraints with errors. Finally, we show that trees recovered under the FBD model are more accurate than those estimated using non‐time calibrated inference. Overall, we show that both fossil placement approaches are reliable even when including uncertainty. Our results underscore the importance of core taxonomic research, including morphological data collection and species descriptions, irrespective of the approach to handling phylogenetic uncertainty using the FBD process. 

Abstract Simulations are playing an increasingly important role in paleobiology. When designing a simulation study, many decisions have to be made and common challenges will be encountered along the way. Here, we outline seven rules for executing a good simulation study. We cover topics including the choice of study question, the empirical data used as a basis for the study, statistical and methodological concerns, how to validate the study, and how to ensure it can be reproduced and extended by others. We hope that these rules and the accompanying examples will guide paleobiologists when using simulation tools to address fundamental questions about the evolution of life. 

No abstract available.