Capturing vocal communication in a free-living corvid: high-resolution data from low-impact miniaturized tags.

Understanding vocal communication is essential to unraveling avian social behavior and cognition; however, audio recording remains particularly challenging in field studies involving wild populations. In this study, we deployed a lightweight, multi-sensor biologging device (MiniDTAG) designed for medium- to large-sized birds. The device integrates a microphone, accelerometer, magnetometer, and pressure sensors into a 12.5 g package, enabling high-fidelity acoustic and behavioral data collection. We deployed 52 MiniDTAGs over three breeding seasons in free-ranging cooperatively breeding carrion crows (Corvus corone) in northern Spain. The auto-releasing attachment method allowed the birds to free themselves from the tag after 18.5 days, on average. We recovered 87% tags, collecting over 83 h of data per device on average. Using a machine learning model (Voxaboxen), we detected over 127,000 vocalizations and assigned them to focal tagged individuals, adult conspecifics, crow chicks, and parasitic great spotted cuckoo nestlings (Clamator glandarius) with high precision and recall. We also explored the potential of accelerometer data to identify specific behaviors within a cooperative context, namely anti-predator mobbing. To evaluate logger impact, we analyzed 825 h of video from 22 crow groups and found minimal effects on brood feeding rates and reproductive success. Our results highlight the MiniDTAG's potential to advance the study of animal communication by capturing vocalizations across the whole range of amplitudes. This approach opens new avenues for exploring the mechanisms of cooperation and information exchange in complex social systems and lays the groundwork for future comparative studies in corvid communication.