Abstract
Techniques for non-invasive sampling of ecophysiological data in wild animals have been developed in response to challenges associated with studying captive animals or using invasive methods. Of these, drones, also known as Unoccupied Aerial Vehicles (UAVs), and their associated sensors, have emerged as a promising tool in the ecophysiology toolkit. In this review, we synthesise research in a scoping review on the use of drones for studying wildlife ecophysiology using the PRISMA-SCr checklist and identify where efforts have been focused and where knowledge gaps remain. We use these results to explore current best practices and challenges and provide recommendations for future use. In 136 studies published since 2010, drones aided studies on wild animal body condition and morphometrics, kinematics and biomechanics, bioenergetics, and wildlife health (e.g. microbiomes, endocrinology, and disease) in both aquatic and terrestrial environments. Focal taxa are biased towards marine mammals, particularly cetaceans. While conducted globally, research is primarily led by institutions based in North America, Oceania, and Europe. The use of drones to obtain body condition and morphometric data through standard colour sensors and single camera photogrammetry predominates. Techniques such as video tracking and thermal imaging have also allowed insights into other aspects of wildlife ecophysiology, particularly when combined with external sampling techniques such as biologgers. While most studies have used commercially available multirotor platforms and standard colour sensors, the modification of drones to collect samples, and integration with external sampling techniques, have allowed multidisciplinary studies to integrate a suite of remote sensing methods more fully. We outline how technological advances for drones will play a key role in the delivery of both novel and improved wildlife ecophysiological data. We recommend that researchers prepare for the influx of drone-assisted advancements in wildlife ecophysiology through multidisciplinary and cross-institutional collaborations. We describe best practices to diversify across species and environments and use current data sources and technologies for more comprehensive results.