Tail Base Deflection but not Tail Curvature Varies with Speed in Lizards: Results from an Automated Tracking Analysis Pipeline

Abstract

Tail movement is an important component of vertebrate locomotion and likely contributes to dynamic stability during steady-state locomotion. Previous results suggest that the tail plays a significant role in lizard locomotion, but little data are available on tail motion during locomotion and how it differs with morphological, ecological, and phylogenetic parameters. We collected high-speed vertical climbing and horizontal locomotion video data from 43 lizard species from four taxonomic groups (Agamidae, Gekkota, Scincidae, and Varanidae) across four habitats. We introduce a new semi-automated and generalizable analysis pipeline for tail and spine motion analysis including markerless pose-estimation, semi-automated kinematic recognition, and muti-species data analysis. We found that step length relative to snout-vent length (SVL) increased with tail length relative to SVL. Examining spine cycles agnostic to limb stride phase, we found that ranges of inter-tail bending compared with inter-spine bending increased with relative tail length, while ranges of tail deflection relative to spine deflection increased with relative speed. Considering stepwise strides, we found the angular velocity and acceleration of the tail center of mass increased with relative speed. These results will provide general insights into the biomechanics of tails in sprawling locomotion enabling biomimetic applications in robotics, and a better understanding of vertebrate form and function. We look forward to adding more species, behaviors, and locomotor speeds to our analysis pipeline through collaboration with other research groups.

Multilevel dynamic adjustments of geckos ( Hemidactylus frenatus ) climbing vertically: head-up versus head-down

“…The kinematic analysis was conducted using a custom Python script (DOKA; electronic supplementary material, data S1), which extracts desired kinematic parameters automatically from DLC output files. Stance and stride phases were automatically detected from the velocity of the feet relative to that of the body, after smoothing each with a Butterworth low pass filter and a Savitzky-Golay smoothing filter (for a detailed description, see [28,29]). On a whole-body level, we extracted direction of climbing (up or down), body deflection from vertical (for filtering data), defined as the average deviation of the body axis (shoulder, hip) from the vertical axis of the race track, speed, duty factor, stride frequency and stride length.…”