DOI: 10.5061/dryad.1hh515h
title: Data from: Homoplasy-based partitioning outperforms alternatives in Bayesian analysis of discrete morphological data

description: Bayesian analysis of morphological data is becoming increasingly popular
 mainly (but not only) because it allows for time-calibrated phylogenetic
 inference using relaxed morphological clocks and tip dating whenever
 fossils are available. As with molecular data, recent studies have shown
 that modeling among character rate variaton (ACRV) in morphological
 matrices greatly improves phylogenetic inference. In a likelihood
 framework this may be accomplished, for instance, by employing a hidden
 Markov model (HMM) to assign characters to rate categories drawn from a
 (discretized) Γ distribution and/or by partitioning datasets according to
 rate heterogeneity and estimating per-partition branch lengths,
 conditioned on a single topology. While the first approach is available in
 many phylogenetic analysis software, there is still no clear consensus on
 how to partition data, except perhaps in the simplest cases (e.g. “by
 codon” partitioning of coding sequences). Additionally, there is a
 trade-off between improvement in likelihood scores and the number of free
 parameters in the analysis, which rises quickly with the number of
 partitions. This trade-off may be dealt with by employing statistics that
 penalize overfitting of complex models, such as Akaike or Bayesian
 information criteria (AIC and BIC), or the more recently introduced
 stepping-stone (SS) method for marginal likelihood approximation. We
 applied the latter to three distinct matrices of discrete morphological
 data and demonstrated that sorting characters by homoplasy scores
 (obtained from implied weighting parsimony analysis) outperformed other
 partitioning strategies (anatomically-based and PartitionFinder2). The
 method was in fact so efficient in segregating characters by rates of
 evolution that no within-partition ACRV modeling was necessary, while
 among partition rate variation (APRV) was adequately accommodated by rate
 multipliers. We conclude that partitioning by homoplasy is a powerful and
 easy-to-implement strategy to address ACRV in complex datasets. We provide
 some guidelines focusing on morphological matrices, although this approach
 may be also applicable to molecular datasets.
description: data_nexusData in Nexus.Supplementary_Material_Table_S1Table S1. Clarke's et al. matrix (Unordered), results.Supplementary_Material_Table_S2Table S2. O’Connor and Zhou’s dataset modified by Lee et al. matrix, results.Supplementary_Material_Table_S3Table S3. Scolebythidae matrix, results.Supplementary_Material_Table_S4Table S4. Clarke's et al. matrix (Ordered), results.Supplementary_Material_Table_S5Table S5. Comparisons between variable and informative coding.Supplementary_Material_Table_S6Table S6. Partition Finder partitions for Clarke's et al. matrix, O’Connor and Zhou’s dataset modified by Lee et al. and Scolebythidae matrix.Supplementary_Table_S7_to_S32Supplementary Tables S7 to S32. Partitions schemes.Supplementary_Figures_S1_to_S3Supplementary Figures S1 to S3.Supplementary_File_SM1Supplementary file SM1: Systematic of Scolebythidae’ matrix.Supplementary_File_SM2Supplementary file SM2: Ordered versus unordered characters.Supplementary_File_SM3Supplementary file SM3: Tree topologies and clade support.Supplementary_File_SM4Supplementary file SM4: Joint posterior distributions.