Phylogenetic trees establish a historical context for the study of organismal form and function. Most phylogenetic trees are estimated using a model of evolution. For molecular data, modeling evolution is often based on biochemical observations about changes between character states. For example, there are four nucleotides, and we can make assumptions about the probability of transitions between them. By contrast, for morphological characters, we may not know a priori how many character states there are per character, as both extant sampling and the fossil record may be highly incomplete, which leads to an observer bias. For a given character, the state space may be larger than what has been observed in the sample of taxa collected by the researcher. In this case, how many evolutionary rates are needed to even describe transitions between morphological character states may not be clear, potentially leading to model misspecification. To explore the impact of this model misspecification, we simulated character data with varying numbers of character states per character. We then used the data to estimate phylogenetic trees using models of evolution with the correct number of character states and an incorrect number of character states. The results of this study indicate that this observer bias may lead to phylogenetic error, particularly in the branch lengths of trees. If the state space is wrongly assumed to be too large, then we underestimate the branch lengths, and the opposite occurs when the state space is wrongly assumed to be too small.

Funding provided by: National Science FoundationROR ID: https://ror.org/021nxhr62Award Number: DEB 2045842 Funding provided by: National Science FoundationROR ID: https://ror.org/021nxhr62Award Number: DBI 2113425 Funding provided by: Deutsche ForschungsgemeinschaftROR ID: https://ror.org/018mejw64Award Number: HO 6201/1-1 Funding provided by: European Research CouncilROR ID: https://ror.org/0472cxd90Award Number: GA 101043187

The datasets are simulated under Mk model. In this study, we examine the effectiveness of partitioning by state during a Bayesian morphological phylogenetic analysis. So, the datasets that are simulated are analysed under partitioning by state and unpartitioned models.