Morphological Phylogenetics Evaluated Using Novel Evolutionary Simulations.

Area of Science:

• Evolutionary biology
• Phylogenetics
• Computational biology

Background:

• Reliable phylogenies are crucial for evolutionary inferences.
• Debate exists between maximum parsimony and Bayesian methods for morphological data analysis.
• Ambiguity in morphological evolution modes and lack of appropriate models hinder progress.

Purpose of the Study:

• To investigate phylogenetic inference methods using novel simulation models.
• To compare Bayesian and parsimony approaches under different evolutionary scenarios.
• To assess the impact of morphological evolution modes on phylogenetic accuracy.

Main Methods:

• Developed two novel simulation models: stochastic evolution and selection-based evolution.
• Generated character data and lineage splitting simultaneously, with trees as emergent properties.
• Compared Bayesian (Mki) and parsimony (equal/implied weighting) consensus methods using RF, SPR, and TBR metrics.

Main Results:

• Bayesian methods yielded fewer incorrect nodes and quartets than parsimony methods.
• Morphological coherence was a more significant factor for phylogenetic accuracy than homoplasy.
• Selection-based models showed lower homoplasy, lower coherence, and higher tree inaccuracy.

Conclusions:

• Bayesian phylogenetic inference generally provides more accurate trees than parsimony.
• Selection as a driver of morphological evolution can confound phylogenetic inference.
• Future research should focus on identifying modes of morphological evolution for improved probabilistic models.