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Barido-Sottani J, Schwery O, Warnock RCM, Zhang C, Wright AM. Practical guidelines for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC). OPEN RESEARCH EUROPE 2024; 3:204. [PMID: 38481771 PMCID: PMC10933576 DOI: 10.12688/openreseurope.16679.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/30/2024] [Indexed: 06/06/2024]
Abstract
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.
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Affiliation(s)
- Joëlle Barido-Sottani
- Institut de Biologie de l’ENS (IBENS), École normale supérieure, CNRS, INSERM, Université PSL, Paris, Île-de-France, 75005, France
| | - Orlando Schwery
- Department of Biological Sciences, Southeastern Louisiana University, Hammond, Louisiana, 70402, USA
- Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, 24061, USA
- Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana, 70803, USA
| | - Rachel C. M. Warnock
- GeoZentrum Nordbayern, Department of Geography and Geosciences, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Bavaria, 91054, Germany
| | - Chi Zhang
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing, 100044, China
| | - April Marie Wright
- Department of Biological Sciences, Southeastern Louisiana University, Hammond, Louisiana, 70402, USA
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Matzig DN, Marwick B, Riede F, Warnock RCM. A macroevolutionary analysis of European Late Upper Palaeolithic stone tool shape using a Bayesian phylodynamic framework. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240321. [PMID: 39144489 PMCID: PMC11321859 DOI: 10.1098/rsos.240321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 06/04/2024] [Accepted: 07/19/2024] [Indexed: 08/16/2024]
Abstract
Phylogenetic models are commonly used in palaeobiology to study the patterns and processes of organismal evolution. In the human sciences, phylogenetic methods have been deployed for reconstructing ancestor-descendant relationships using linguistic and material culture data. Within evolutionary archaeology specifically, phylogenetic analyses based on maximum parsimony and discrete traits dominate, which sets limitations for the downstream role cultural phylogenies, once derived, can play in more elaborate analytical pipelines. Recent methodological advances in Bayesian phylogenetics, however, now allow us to infer evolutionary dynamics using continuous characters. Capitalizing on these developments, we here present an exploratory analysis of cultural macroevolution of projectile point shape evolution in the European Final Palaeolithic and earliest Mesolithic (approx. 15 000-11 000 BP) using a Bayesian phylodynamic approach and the fossilized birth-death process model. This model-based approach leaps far beyond the application of parsimony, in that it not only produces a tree, but also divergence times, and diversification rates while incorporating uncertainties. This allows us to compare rates to the pronounced climatic changes that occurred during our time frame. While common in cultural evolutionary analyses of language, the extension of Bayesian phylodynamic models to archaeology arguably represents a major methodological breakthrough.
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Affiliation(s)
- David N. Matzig
- Department of Archaeology and Heritage Studies, Aarhus University, Højbjerg, Denmark
| | - Ben Marwick
- Department of Anthropology, University of Washington, Seattle, WA, USA
| | - Felix Riede
- Department of Archaeology and Heritage Studies, Aarhus University, Højbjerg, Denmark
| | - Rachel C. M. Warnock
- GeoZentrum Nordbayern, Friedrich-Alexander-University Erlangen, Erlangen, Germany
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Mendes FK, Landis MJ. PhyloJunction: a computational framework for simulating, developing, and teaching evolutionary models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.15.571907. [PMID: 38168278 PMCID: PMC10760140 DOI: 10.1101/2023.12.15.571907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
We introduce PhyloJunction, a computational framework designed to facilitate the prototyping, testing, and characterization of evolutionary models. PhyloJunction is distributed as an open-source Python library that can be used to implement a variety of models, through its flexible graphical modeling architecture and dedicated model specification language. Model design and use are exposed to users via command-line and graphical interfaces, which integrate the steps of simulating, summarizing, and visualizing data. This paper describes the features of PhyloJunction - which include, but are not limited to, a general implementation of a popular family of phylogenetic diversification models - and, moving forward, how it may be expanded to not only include new models, but to also become a platform for conducting and teaching statistical learning.
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Affiliation(s)
- Fábio K. Mendes
- Department of Biology, Washington University in St. Louis, St. Louis, MO
| | - Michael J. Landis
- Department of Biology, Washington University in St. Louis, St. Louis, MO
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