What is the best method for estimating ancestral states from discrete characters?

作者：Joseph N. Keating · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2023 · DOI：10.1101/2023.08.31.555762 · 被引用次数：7 · 研究领域：Evolution and Paleontology Studies、Genetic diversity and population structure、Genomics and Phylogenetic Studies

Abstract Ancestral state estimation is a formal phylogenetic method for inferring the nature of ancestors and performing tests of character evolution. As such, it is among the most important tools available to evolutionary biologists. However, there are a profusion of methods available, the accuracy of which remains unclear. Here I use a simulation approach to test between parsimony and likelihood methods for estimating ancestral states from discrete binary characters. I simulate 500 characters using 15 different Markov generating models, a range of tree sizes (8-256 tips) and three topologies representing end members of tree symmetry and branch length heterogeneity. Simulated tip states were subjected to ancestral state estimation under the Equal Rates (ER) and All-Rates-Different (ARD) models, as well as under parsimony assuming accelerated transformations (ACCTRAN). The results demonstrate that both parsimony and likelihood approaches obtain high accuracy applied to trees with more tips. Parsimony performs poorly when trees contain long branches, whereas the ER model performs well across simulations and is reasonably robust to model violation. The ER model frequently outperforms the ARD model, even when data are simulated using unequal rates. Furthermore, the ER model exhibits less transition rate error when compared to ER models. These results suggest that ARD models may be overparameterized when character data is limited. Surprisingly, the difference in likelihood-based ...