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The study of animal locomotion can be logistically challenging, especially in the case of large or unhandleable animals in uncontrolled environments. Recent technological advances have permitted the use of Global Positioning System and inertial sensors in locomotion studies, but these methods require manual access to each study subject. Here we demonstrate the utility of a low cost unmanned aerial vehicle (UAV) in measuring two-dimensional running kinematics from free-roaming giraffes (Giraffa camelopardalis giraffa) in the Free State Province, South Africa. We collected 120 Hz video of running giraffes, and calibrated each video frame using metatarsal length as a constant object of scale. We tested a number of methods to measure metatarsal length. The method with the least variation used close range photography and a trigonometric equation to spatially calibrate the still image, and derive metatarsal length. In the absence of this option, a spatially calibrated surface model of the study terrain was used to estimate topographical dimensions in video footage of interest. Data for the terrain models were collected using the same equipment, during the same study period. We subsequently validated the accuracy of the UAV method by comparing similar speed measurements of a running human subject, with a gold standard method. We recommend that future users maximise the camera focal distance, and keep the subject in the central field of view.
The studied giraffes used a grounded rotary gallop with a speed range of 3.4 to 6.9 ms-1 (never cantering, trotting or pacing), and lower duty factors when compared with other cursorial quadrupeds. As this pattern might result in adverse increases in peak vertical limb forces with speed, it was notable to find that contralateral limbs became more in-phase with speed. Considering the latter pattern and the modest maximal speed of giraffes, we speculate that tissue safety factors are maintained within tolerable bounds this way. Furthermore, the angular kinematics of the neck were frequently isolated from the pitching of the body during running; this may be a result of the large mass of the head and neck. Further field experiments and biomechanical models are needed to robustly test these speculations.
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Regression statistics describing how gait parameters vary with speed
Coefficients are in the form y = au+b. Significant p-values (green shading) were interpreted in the context of the Benjamini-Hochberg critical values, which are adjusted to account for multiple statistical comparisons.