Data from: A method for mapping morphological convergence on three-dimensional digital models: the case of the mammalian saber-tooth

Morphological convergence can be assessed through a variety of statistical methods. None of the methods proposed to date enable the visualization of convergence. All are based on the assumption that the phenotypes either converge, or do not. However, between species, morphologically similar regions of a larger structure may behave differently. Previous approaches do not identify these regions within the larger structures or quantify the degree to which they may contribute to overall convergence. Here we introduce a new method to chart patterns of convergence on three-dimensional models, deployed with the R function conv.map. The convergence between pairs of models is mapped onto them to visualize and quantify the morphological convergence. We applied conv.map to a well-known case study, the saber-tooth morphotype which has evolved independently among distinct mammalian clades, from placentals to metatherians. Although previous authors have concluded that saber-tooths kill using a stabbing 'bite' to the neck, others have presented different interpretations for specific taxa, including the iconic Smilodon and Thylacosmilus. Our objective was to identify any shared morphological features among the saber-tooths that may underpin similar killing behaviours. From a sample of 49 placental and metatherian carnivores, we found stronger convergence among saber-tooths than for any other taxa. We found that the morphological convergence is most apparent in the rostral and posterior parts of the cranium. The extent of this convergence implies similarity in function among these phylogenetically distant species. In our view this function is most likely the killing of relatively large prey by a stabbing bite.1. Title of Dataset: Data from: A method for mapping morphological convergence on three-dimensional digital models: the case of the mammalian saber-tooth 2. Author Information A. First author Contact Information Name: Marina Melchionna Institution: DiSTAR - University of Naples Federico II Address: Via Cinthia, 21 - 80126, Monte Sant'Angelo, Naples Email: marina.melchionna@unina.it B. Corresponding author Contact Information Name: Pasquale Raia Institution: DiSTAR - University of Naples Federico II Address: Via Cinthia, 21 - 80126, Monte Sant'Angelo, Naples Email: pasquale.raia@unina.it DATA & FILE OVERVIEW - File List: Appendix S1.docx: a general overview of all supplementary materials and methods included in the manuscript. tree.txt: phylogenetic tree in Newick format. Table S1.csv: list of used specimens with ID code, sex and museum location. F = female, M = male, U = unknown. Table S2.csv: anatomical definition of sampled landmarks on each specimen. Fig. S1.tif: landmark configuration (blue) and semilandmark patch (yellow) used in the analysis. Fig. S2.tif: the process of retrodeformation on Barbourofelis fricki. A-B, the specimen before and after the retrodeformation process. C, the original specimen showing the modified areas during the retrodeformation in terms of expansion (blue) and contraction (red). Fig. S3.tif: the process of retrodeformation on Homotherium serum. A-B, the specimen before and after the retrodeformation process. C, the original specimen showing the modified areas during the retrodeformation in terms of expansion (blue) and contraction (red). Fig. S4.tif: illustration of the workflow followed to perform conv.map. A. We first performed a Principal Component Analysis on the sample. B. We used the search.conv function ('RRphylo' R package) to identify convergent species. C. We performed a Relative Warp Analyses on the shape data, setting the alpha parameter to 1. The RW scores obtained by RWA were fed to conv.map to select the axes best-embodying convergence, by using the ede function ('inflection' R package). After the RW axes were selected, their scores were used to compute pairwise angle distances between species. D. conv.map plots highlighting the areas responsible for convergence (in blue). data.RDA: data used in the manuscript, any relevant information can be found in the script file. script.R: script to perform the analyses with the R Cran software