Uncertainty in Bayesian phylogenetic analysis using morphological data: A brief overview
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Abstract:
Phylogenetic analysis reconstructs the evolutionary history of different groups of organisms based on morphological and/or molecular data. Fossils, as the only remaining evidence for most extinct species, offer a direct glimpse into the evolutionary events that have shaped the diversification of life on Earth. Consequently, phylogenetic analysis based on discrete morphological data is essential for understanding the early origins and evolution of various organisms. Over the past decade, phylogenetic analysis has primarily relied on the maximum parsimony method. Re cently, model-based Bayesian inference has gained prominence in paleobiology. The Bayesian method provides robust estimates of phylogeny along with clear measures of phylogenetic uncertainty, making the results both accurate and easily interpretable. In this study, we discuss the likely factors influencing uncertainty in the phylogenetic analysis, such as data set composition, substitution models, and Markov chain parameters, to improve confidence in phyloge netic inference. In a simulation study, we investigated the effects of different versions of the Markov model and the stability of the Markov chain on the phylogenetic tree, using a pubulished bryozoa data set. Results indicate that these factors influence both topology and posterior probabilities, underscoring the need to carefully consider them in phylo genetic analysis.
