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Data from: Origins of mammalian vertebral function revealed through digital bending experiments

Cite this dataset

Jones, Katrina; Angielczyk, Ken; Pierce, Stephanie (2024). Data from: Origins of mammalian vertebral function revealed through digital bending experiments [Dataset]. Dryad.


Unravelling the functional steps that underlie major transitions in the fossil record is a significant challenge for biologists due to the difficulties of interpreting functional capabilities of extinct organisms. New computational modelling approaches provide exciting avenues for testing function in the fossil record. Here we conduct digital bending experiments to reconstruct vertebral function in non-mammalian synapsids, the extinct forerunners of mammals, to provide insights into the functional underpinnings of the synapsid-mammal transition. We estimate range of motion and stiffness of intervertebral joints in eight non-mammalian synapsid species alongside a comparative sample of extant tetrapods, including salamanders, reptiles, and mammals. We show that several key aspects of mammalian vertebral function evolved outside crown Mammalia. Compared to early diverging non-mammalian synapsids, cynodonts stabilized the posterior trunk against lateroflexion, while evolving axial twisting in the anterior trunk. This was later accompanied by posterior sagittal bending in crown mammals, and perhaps even therians specifically. Our data also supports the prior hypothesis that functional diversification of the mammalian trunk occurred via co-option of existing morphological regions in response to changing selective demands. Thus, multiple functional and evolutionary steps underlie the origin of remarkable complexity of the mammalian backbone.


The enclosed datasets include the data and code required to generate range of motion data from digital models of vertebrae using AutoBend (see Jones, K. E., R. J. Brocklehurst, and S. E. Pierce. "AutoBend: an automated approach for estimating intervertebral joint function from bone-only digital models." Integrative Organismal Biology 3.1 (2021): obab026.)

Detailed descriptions of the files are as follows:

synapsid data.csv -

CSV file containing the range of motion data generated in AutoBend. Data was generated for each specimen, each joint, and each iteration of AutoBend. Parameters that were varied were js (Joint spacing, mm), it (intersection threshold, %), and strain (unitless). Data that was generated were Tor (ROM in axial rotation, L/R mean), Latero (ROM in lateroflexion, L/R mean), Dorso (ROM in dorsoflexion), and Ventro (ROM in ventroflexion). ROM data are in degrees of motion. The 'type' columns indicate which constraint prevented motion for each itteration of AutoBend (e.g., 'intersect' is bony intersection). DORSO, VENTRO, and LATERO are the stiffness metrics which were calculated as described in the methods.

synapsid morph data.csv

CSV file containing the morphological data taken from the vertebrae, used for the stiffness calculation. Centrum and arch measurements are presented for each joint, where CL (centrum length) and ArchH (arch height) are the mean of the anterior and posterior vertebra. Measurements are in mm.

Wrapper_vert bending_synapsids.PY

Wrapper python code for operating AutoBend through MayaPy. This code applies AutoBend to each specimen and specifies the parameters used, as well as generating the results file. Use this file to specify the locations of your models and where results should be saved.


The code used to run Mayapy from the command line. This allows AutoBend to run automatically without directly opening Maya, and is much faster as the graphics interface is not needed. This code directs Mayapy to the python wrapper code above which contains details of how AutoBend should be run.


A series of R functions used to process the data generated by AutoBend in R. These include functions which read and reformat the data, as well as plotting functions. 


Range of motion of vertebral joints of extant amniotes and non-mammalian synapsids based on digital simulation using AutoBend software.


National Science Foundation, Award: EAR-1524523, EAR

National Science Foundation, Award: EAR-1524938, EAR

Royal Society, Award: URF\R1\201547