Partition models are widely used in phylogenomic analyses to account for heterogeneous evolutionary processes across different regions or loci of a sequence alignment. How alignment regions are grouped into partitions (the partitioning scheme) affects both the degree of over- or under-parameterization and the accuracy of downstream phylogenetic inferences. PartitionFinder is a widely adopted framework for selecting an optimal partitioning scheme. Using Akaike information criterion (AIC) and Bayesian information criterion (BIC), PartitionFinder merges partitions with similar evolutionary processes to avoid model overfitting. However, AIC and BIC are based on conditional likelihoods that treat partition assignments as fixed, whereas the recently introduced marginal AIC (mAIC) averages site likelihoods over the models of all partitions, providing a more appropriate criterion for inferring global parameters such as tree topology and branch lengths (Susko et al. 2026). Here, we implement mAIC for partition models in IQ-TREE 3 and integrate it into the PartitionFinder algorithms. Using a range of simulated and empirical DNA and protein datasets, we show that PartitionFinder-mAIC yields partitioning schemes with fewer partitions than those selected by AIC or BIC, and additionally improves phylogenetic inference at most key branches of the green plant evolution. The new PartitionFinder-mAIC is available in IQ-TREE version 3.1.4 and a tutorial for using it can be found here: https://iqtree.github.io/doc/Advanced-Tutorial#choosing-the-right-partitioning-scheme.
Ren, H., Wong, T. K. F., Jiang, C., Susko, E., Lanfear, R., Minh, B. Q.
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