He, M., Hautphenne, S., & Chan, Y. (2024). Approximate Bayesian computation for Markovian binary trees in phylogenetics. arXiv preprint arXiv:2309.00194v2.
This paper aims to develop a new method for inferring diversification rates (speciation, extinction, and transition rates) from phylogenetic trees using Approximate Bayesian Computation (ABC) with Markovian Binary Trees (MBTs). The authors focus on scenarios where species can exist in one of two possible states (phases) and investigate both reducible (unidirectional transitions) and irreducible (bidirectional transitions) MBT models.
The researchers employ an ABC-PMC (Population Monte Carlo) algorithm to infer MBT parameters from phylogenetic trees. They develop and utilize a suite of summary statistics to compare observed and simulated trees, including average branch length, tree height, normalized lineage-through-time (nLTT) curve, Colless balance index, and novel phase-specific balance and transition statistics. The accuracy of their method is evaluated through simulation studies, comparing their results to those obtained using maximum likelihood estimation (MLE) with the BiSSE model. Finally, they apply their method to a real-world dataset of squamata (reptiles) to infer diversification rates associated with oviparity and viviparity.
The study demonstrates the potential of ABC and MBTs as a powerful and versatile approach for inferring diversification rates in phylogenetics. The authors argue that their method offers a more flexible and potentially more accurate alternative to traditional likelihood-based methods, particularly for complex evolutionary scenarios.
This research contributes to the field of phylogenetics by introducing a novel and promising method for inferring diversification rates. The use of ABC with MBTs allows for greater flexibility in modeling evolutionary processes and may lead to more accurate estimations of diversification parameters.
The study primarily focuses on MBT models with two phases and limited transition possibilities. Future research could explore the applicability of this method to MBT models with more phases and unrestricted transitions. Additionally, investigating the impact of incomplete lineage sorting and other confounding factors on the accuracy of the method would be beneficial.
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