Assessing the application of landmark-free morphometrics to macroevolutionary analyses

Abstract The study of phenotypic evolution has been transformed in recent decades by methods allowing precise quantification of anatomical shape, in particular 3D geometric morphometrics. While this effectiveness of geometric morphometrics has been demonstrated by thousands of studies, it generally requires manual or semi-automated landmarking, which is time-consuming, susceptible to operator bias, and limits comparisons across morphologically disparate taxa. Emerging automated methods, particularly landmark-free techniques, offer potential solutions, but these approaches have thus far been primarily applied to closely related forms. In this study, we explore the utility of automated, landmark-free approaches for macroevolutionary analyses. We compare an application of Large Deformation Diffeomorphic Metric Mapping (LDDMM) known as Deterministic Atlas Analysis (DAA) with a high-density geometric morphometric approach, using a dataset of 322 mammals spanning 180 families. Initially, challenges arose from using mixed modalities (computed tomography (CT) and surface scans), which we addressed by standardising the data by using Poisson surface reconstruction that creates watertight, closed surfaces for all specimens. After standardisation, we observed a significant improvement in the correspondence between patterns of shape variation measured using manual landmarking and DAA, although differences emerged, especially for Primates and Cetacea. We further evaluated the downstream effects of these differences on macroevolutionary analyses, finding that both methods produced comparable but varying estimates of phylogenetic signal, morphological disparity and evolutionary rates. Our findings highlight the potential of landmark-free approaches like DAA for large scale studies across disparate taxa, owing to their enhanced efficiency. However, they also reveal several challenges that should be addressed before these methods can be widely adopted. In this context, we outline these issues, propose solutions based on existing literature, and identify potential avenues for further research. We argue that by incorporating these improvements, the application of landmark-free analyses could be expanded, thereby enhancing the scope of morphometric studies and enabling the analysis of larger and more diverse datasets.