Biology subjects

Pedersen, M. A.

Publications and source records attributed to Pedersen, M. A..

2 recordsLinked to original sources

bioRxiv2025

Stochastic Phylogenetic Models of Shape

Phylogenetic modeling of morphological shape in two or three dimensions is one of the most challenging statistical problems in evolutionary biology. As shape data are inherently correlated and non-linear, most naive methods for phylogenetic analysis of morphological shape fail to capture the biological realities of evolving shapes. In this study we propose a novel framework for evolutionary analysis of morphological shape which facilitates stochastic character mapping on landmark shapes. Our framework is based on recent advances in mathematical shape analysis and models the evolution of shape as a diffusion process that accounts for the evolutionary correlation between nearby landmarks. The diffusion process we consider is parametrized in terms of meaningful parameters describing the evolutionary rate and the degree of spatial autocorrelation among landmarks. The framework we propose assumes that the phylogenetic tree is fixed and uses a Metropolis-Hastings Markov Chain Monte Carlo sampling scheme for inferring ancestral shapes and parameters of the model. We evaluate the new inference algorithm using simulations and show that the method leads to improved estimates of the shape at the root and well-calibrated credible sets of shapes at internal nodes. In addition, we also compare the diffusion parameter describing the degree of spatial autocorrelation to an existing metric of integration and find that they quantify integration in a shape in a similar way. To illustrate the method, we also apply it to a previously published data set of butterfly wing images.

evolutionary biology↗

bioRxiv2025

Diffeomorphic Independent Contrasts for Ancestral Reconstruction of Shapes

AO_SCPLOWBSTRACTC_SCPLOWAncestral reconstruction is a fundamental challenge in evolutionary biology, requiring methods that can capture complex morphological changes while accounting for phylogenetic relationships. Current approaches are based on linear assumptions that often oversimplify the spatial relationships between anatomical features and fail to account for landmark correlations within shapes. Here, we introduce a novel method that combines the ability of Large Deformation Diffeomorphic Metric Mapping (LDDMM) to model smooth, invertible transformations between shapes while preserving the relationships between landmarks with Felsensteins Independent Contrasts (IC) to iteratively reconstruct ancestral shapes along the branches of a phylogenetic tree. We call this method Diffeomorphic Independent Contrasts for Ancestral Reconstruction of Shapes (DICAROS). We validate DICAROS against two existing methods: (1) Linear predictors using Ordinary Least Squares and (2) Ancestral character estimation using maximum likelihood under Brownian Motion and apply DICAROS to a dataset of swallowtail butterfly species (Family Papilonidae, Order Lepiodetra) to reconstruct the ancestral shape and visualize evolutionary trajectories in a phylomorphospace from the contrasts. We conclude that DICAROS outperforms the existing methods in terms of accuracy and provides a more accurate reconstruction of the ancestral shape for non-symmetric phylogenetic trees. With DICAROS we show a transition between un-tailed and tailed papilinodae species while also illustrating how images of modern species would look under the DICAROS ancestral reconstruction

evolutionary biology↗