Reconstructing phylogeny from morphological data remains mired in investigator biases, including subjective inclusion and discretization of phenotypic variation. Geometric morphometrics and multivariate statistical analyses provide an alternative array of tools for studying variation in morphological traits. However, direct analysis of landmark data is often unreliable for phylogeny reconstruction. Morphological variation is typically highly correlated among nearby landmarks and may evolve saltationally between adaptive peaks instead of gradually, thereby violating the assumptions of typical continuous models. To address these concerns, we developed an approach to more objectively discretize morphometric data and applied it to 3D surface scans of mandibles and postcranial elements of Macropodiformes (kangaroos, bettongs, and rat-kangaroos). The scanned elements were partitioned into sets of locally co-varying landmarks, which approximate functional units. These subregions were discretized into "atomized" characters using novel approaches to combine the objectivity of continuous shape variation for delineating discrete states with the model flexibility offered for multistate and binary characters. This allows us to 1) potentially reduce the influence of non-independence among neighboring landmarks, 2) accommodate multimodal variation from saltational evolution, 3) accommodate missing data, such as from fragmentary fossils, and 4) promote tree-search efficiency. We built discrete morphological character matrices using three alternative approaches: commonly used clustering algorithms (UPGMA, k-means, k-medoids, and Gaussian mixture modeling), a minimum evolution branch length criterion, and a tree sampling procedure. Our phylogenetic analyses with these novel matrices generally succeeded in recovering genera and several deep-level macropodiform clades, but failed to accurately reconstruct intergeneric relationships within the rapid diversification of the macropodine subfamily; those relationships were also not recovered with continuous morphological data or traditionally discretized characters and are the most poorly resolved with DNA data. On balance, our atomized characters, which derive from only mandibular and three postcranial elements, show promise for improving objectivity, accuracy, and clocklikeness in morphological phylogenetics and provide pathways for accommodating correlated homoplasy and for more accurately estimating rates of morphological evolution, and thereby better integrating phenotypic and genomic data for phylogenetic inference.
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