Year: 2026
Type: preprint
Abstract: Summary Reconstructing ancestral morphologies on a phylogenetic tree is a central task in evolutionary morphometrics. Established reconstruction methods, including multivariate Brownian-motion approaches, rely on linear assumptions and do not directly model the correlations between landmarks within a shape, which can oversimplify the reconstructed morphology. The DICAROS method—Diffeomorphic Independent Contrasts for Ancestral Reconstruction of Shapes (Severinsen et al., 2026)— instead fuses sibling shapes along branches with large-deformation diffeomorphic (LDDMM) landmark dynamics that model these correlations, so that ancestors remain on the shape manifold. DICAROS was shown to outperform ordinary least-squares, Brownian-motion, and penalized-likelihood reconstruction, particularly on non-symmetric trees. dicaros repackages that pipeline as a documented, pip-installable tool that runs on arbitrary landmark datasets from a single command. It handles 2D and 3D landmarks, Newick and NEXUS trees, a choice of Euclidean or Fréchet species means, optional anchor-based alignment, and tips backed by a single specimen, and it returns the reconstructed shapes for all nodes together with the tree relabelled at its internal nodes. We demonstrate dicaros on two new datasets—a 2D leaf dataset (217 species) and a 3D guenon skull dataset (22 species). Availability and Implementation dicaros is a pip-installable Python package, freely available without registration under the MIT license at https://github.com/MichaelSev/DICAROS , with the two example datasets, documentation and reproduction scripts. Contact [email protected] Supplementary information Supplementary data (CPU and GPU runtime benchmarks) are available at Bioinformatics online.
Language: English
Subfield: Geometry and Topology
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funder: Villum Fonden
Award: G7291770527
OpenAlex ID: W7203976459