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Abstract Studying the detailed biomechanics of flying animals requires accurate three‐dimensional coordinates for key anatomical landmarks. Traditionally, this relies on manually digitizing animal videos, a labor‐intensive task that scales poorly with increasing framerates and numbers of cameras. Here, we present a workflow that combines deep learning–powered automatic digitization with filtering and correction of mislabeled points using quality metrics from deep learning and 3D reconstruction. We tested our workflow using a particularly challenging scenario: bat flight. First, we documented four bats flying steadily in a 2 m³wind tunnel test section. Wing kinematic parameters resulting from manually digitizing bats with markers applied to anatomical landmarks were not significantly different from those resulting from applying our workflow to the same bats without markers for five out of six parameters. Second, we compared coordinates from manual digitization against those yielded via our workflow for bats flying freely in a 344 m³enclosure. Average distance between coordinates from our workflow and those from manual digitization was less than a millimeter larger than the average human‐to‐human coordinate distance. The improved efficiency of our workflow has the potential to increase the scalability of studies on animal flight biomechanics. 

Abstract Bat wing membranes are composed of specialized skin that is covered with small sensory hairs which are likely mechanosensory and have been suggested to help bats sense airflow during flight. These sensory hairs have to date been studied in only a few of the more than 1,400 bat species around the world. Little is known about the diversity of the sensory hair network across the bat phylogeny. In this study, we use high‐resolution photomicrographs of preserved bat wings from 17 species in 12 families to characterize the distribution of sensory hairs along the wing and among species. We identify general patterns of sensory hair distribution across species, including the apparent relationships of sensory hairs to intramembranous wing muscles, the network of connective tissues in the wing membrane, and the bones of the forelimb. We also describe distinctive clustering of these sensory structures in some species. We also quantified sensory hair density in several regions of interest in the propatagium, plagiopatagium, and dactylopagatia, finding that sensory hair density was higher proximally than distally. This examination of the anatomical organization of the sensory hair network in a comparative context provides a framework for existing research on sensory hair function and highlights avenues for further research.