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Abstract With a great variety of shapes and sizes, compound eye morphologies give insight into visual ecology, development, and evolution, and inspire novel engineering. In contrast to our own camera-type eyes, compound eyes reveal their resolution, sensitivity, and field of view externally, provided they have spherical curvature and orthogonal ommatidia. Non-spherical compound eyes with skewed ommatidia require measuring internal structures, such as with MicroCT (µCT). Thus far, there is no efficient tool to characterize compound eye optics, from either 2D or 3D data, automatically. Here we present two open-source programs: (1) the ommatidia detecting algorithm (ODA), which measures ommatidia count and diameter in 2D images, and (2) a µCT pipeline (ODA-3D), which calculates anatomical acuity, sensitivity, and field of view across the eye by applying the ODA to 3D data. We validate these algorithms on images, images of replicas, and µCT eye scans from ants, fruit flies, moths, and a bee. 

Abstract Characterising the frequency and timing of biological processes such as locomotion, eclosion or foraging, is often needed to get a complete picture of a species' ecology. Automated trackers are an invaluable tool for high‐throughput collection of activity data and have become more accurate and efficient with advances in computer vision and deep learning. However, tracking activity of small and fast flying animals remains a hurdle, especially in a field setting with variable light conditions. Commercial activity monitors can be expensive, closed source and generally limited to laboratory settings.Here, we present a portable locomotion activity monitor (pLAM), a mobile activity detector to quantify small animal activity. Our setup uses inexpensive components, builds upon open‐source motion tracking software, and is easy to assemble and use in the field. It runs off‐grid, supports low‐light tracking with infrared lights and can implement arbitrary light cycle colours and brightnesses with programmable LEDs. We provide a user‐friendly guide to assembling pLAM hardware, accessing its pre‐configured software and guidelines for using it in other systems.We benchmarked pLAM for insects under various laboratory and field conditions, then compared results to a commercial activity detector. They offer broadly similar activity measures, but our setup captures flight and bouts of motion that are often missed by beam breaking activity detection.pLAM can automate laboratory and field monitoring of activity and timing in a wide range of biological processes, including circadian rhythm, eclosion and diapause timing, pollination and flower foraging, or pest feeding activity. This low cost and easy setup allows high‐throughput animal behaviour studies for basic and applied ecology and evolution research.