Locomotion is one of the major energetic costs faced by animals and various strategies have evolved to reduce its cost. Birds use interspersed periods of flapping and gliding to reduce the mechanical requirements of level flight while undergoing cyclical changes in flight altitude, known as undulating flight. Here we equipped free-ranging marine vertebrates with accelerometers and demonstrate that gait patterns resembling undulating flight occur in four marine vertebrate species comprising sharks and pinnipeds. Both sharks and pinnipeds display intermittent gliding interspersed with powered locomotion. We suggest, that the convergent use of similar gait patterns by distinct groups of animals points to universal physical and physiological principles that operate beyond taxonomic limits and shape common solutions to increase energetic efficiency. Energetically expensive large-scale migrations performed by many vertebrates provide common selection pressure for efficient locomotion, with potential for the convergence of locomotory strategies by a wide variety of species.

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The development of multisensor animal‐attached tags, recording data at high frequencies, has enormous potential in allowing us to define animal behaviour. The high volumes of data, are pushing us towards machine‐learning as a powerful option for distilling out behaviours. However, with increasing parallel lines of data, systems become more likely to become processor limited and thereby take appreciable amounts of time to resolve behaviours. We suggest a Boolean approach whereby critical changes in recorded parameters are used as sequential templates with defined flexibility (in both time and degree) to determine individual behavioural elements within a behavioural sequence that, together, makes up a single, defined behaviour. We tested this approach, and compared it to a suite of other behavioural identification methods, on a number of behaviours from tag‐equipped animals; sheep grazing, penguins walking, cheetah stalking prey and condors thermalling. Overall behaviour recognition using our new approach was better than most other methods due to; (1) its ability to deal with behavioural variation and (2) the speed with which the task was completed because extraneous data are avoided in the process. We suggest that this approach is a promising way forward in an increasingly data‐rich environment and that workers sharing algorithms can provide a powerful library for the benefit of all involved in such work.