Affiliation:
1. Museum of Comparative Zoology & Department of Organismic and Evolutionary Biology Harvard University Cambridge Massachusetts USA
2. Institute for Quantitative Social Science Harvard University Cambridge Massachusetts USA
3. Institut de Biologie de l'ENS (IBENS), École Normale Supérieure, CNRS, INSERM Université PSL Paris France
Abstract
Abstract
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.
Funder
Harvard University
Natural Sciences and Engineering Research Council of Canada
Subject
Ecological Modeling,Ecology, Evolution, Behavior and Systematics
Cited by
4 articles.
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