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Serial disparity in the carnivoran backbone unveil a complex adaptive role in metameric evolution


Figueirido Castillo, Francisco Borja et al. (2020), Serial disparity in the carnivoran backbone unveil a complex adaptive role in metameric evolution, Dryad, Dataset,


Multi-element systems such as the vertebral column of vertebrates represent a major challenge to phenotypic quantification and macroevolutionary analyses. The vertebral column is a metameric structure, composed of serially repeated subunits, and much of what is known so far has been inferred from sparse anatomical samples, providing little insight into local-scale (i.e. vertebra-to-vertebra) variation and its macroevolutionary importance. This limits understanding of how evolutionary constraints and functional adaptation interact during the evolution of multi-element phenotypes. Here, we quantify morphological disparity across all subunits (vertebrae) of the pre-sacral column in the mammalian order Carnivora. We address how vertebral morphology varies among elements, and the extent to which these patterns have been structured by constraints and/or evolutionary adaptation to locomotory capabilities, using 3D geometric morphometrics and multivariate analyses for high-dimensional phenotypes. We find that lumbars and posterior thoracics exhibit high individual disparity but low serial differentiation. These vertebrae are pervasively recruited into locomotory functions, exhibiting high-dimensional ecomorphological signals and patterns of evolution indicative of relaxed constraints. Cervical and anterior thoracic vertebrae have low individual disparity and greater serial differentiation. Individual vertebrae in these regions unexpectedly also show signals of locomotory adaptation that were not generally recognized by previous studies. These are characterized by low-dimensional ecomorphological signals and overall constrained patterns of evolution. Our findings support the hypothesis that the lumbar region is a key innovation that increases ecological versatility of mammalian locomotion. Nevertheless, locomotory adaptation is more widely distributed along the mammalian axial skeleton. This has been masked by local-scale variation and low phenotypic variability in comparison with other skeletal structures such as the skull or limbs. Our analyses demonstrate that the strength of ecomorphological signal does not have a predictable influence on macroevolutionary outcomes even within the same structure, and undermine the traditional view that highly constrained skeletal units are strongly limited in their potential to adapt to new ecological avenues. Our findings emphasize the importance of quantifying local-scale variation in functionally versatile, multi-element phenotypes such as the vertebral column, or indeed, the vertebrate skeleton as a whole.


All presacral vertebraewere scanned in 3D with either micro-computed tomography (CT) scanning or a NextEngine® surface scanner. CT scans were segmented in Avizo. This resulted in 3D models of 1097 vertebrae. The 3D models were imported into Meshlab (Cignoni et al. 2008) to reduce the size of the models. In addition, we used the software Netfabb to edit the 3D models and to separate each vertebrae of a given vertebral column.

To capture the morphology of the vertebrae, we digitized 34 homologous landmarks on the cervical vertebrae (C03-C07), 32 homologous landmarks on the thoracic vertebrae (T01-T14), and 36 homologous landmarks on the lumbar vertebrae (L1-L7) (see Fig. 2; Table S2). The landmarks were digitized with the software Landmark from IDAV (Wiley et al. 2005) and the x,y,z coordinates of each landmark were exported as a Text file.

Raw landmark data were imported into the R package geomorph version 3.1.0 (Adams et al. 2019). Each region of the vertebral column (cervical, thoracic, and lumbar) was separated into different datasets, and therefore, all subsequent analyses were performed independently.


Ministerio de Ciencia, Innovación y Universidades, Award: CGL-2015-68300P

Ministerio de Ciencia, Innovación y Universidades, Award: CGL-2017-92166-EXP

European Union’s Horizon 2020 research and innovation program

H2020 European Research Council, Award: TEMPO-677774

European Union’s Horizon 2020 research and innovation program