PartitionFinder-mAIC: Phylogenetic Partitioning using Marginal Akaike Information Criterion
- 1Australian National University
Abstract
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 with the command-line option -merit mAIC.
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BibTeX
@misc{ren2026partitionfindermaic,
title = {{PartitionFinder-mAIC: Phylogenetic Partitioning using Marginal Akaike Information Criterion}},
author = {H. Ren and T. K. F. Wong and C. Jiang and E. Susko and R. Lanfear and B. Q. Minh},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.04.749328},
url = {https://www.biorxiv.org/content/10.64898/2026.09.04.749328v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - PartitionFinder-mAIC: Phylogenetic Partitioning using Marginal Akaike Information Criterion
AU - H. Ren
AU - T. K. F. Wong
AU - C. Jiang
AU - E. Susko
AU - R. Lanfear
AU - B. Q. Minh
PY - 2026
DA - 2026/09/04
PB - bioRxiv
DO - 10.64898/2026.09.04.749328
UR - https://www.biorxiv.org/content/10.64898/2026.09.04.749328v1
AB - 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 with the command-line option -merit mAIC.
ER -