Determining Activity Patterns and Home Range of Wild Bats Using a Proximity Biologging System Based on the Internet of Things (IoT)

ABSTRACT Understanding spatial patterns in small, elusive species is critical for behavioral ecology and conservation, yet traditional tracking methods often face logistical constraints. We evaluated the utility of an Internet of Things (IoT)‐based proximity biologging system as an automated tool for determining the activity patterns and utilization distributions (UDs) of small, free‐ranging animals. We monitored female Bechstein's bats ( Myotis bechsteinii ) using a grid of 65 logging stations over two breeding seasons. Individual UDs were estimated using proximity data via autocorrelated kernel density estimation. We analyzed kinship‐driven space‐sharing and site fidelity through spatial overlap metrics. To validate system accuracy, we conducted a human‐mediated field simulation for comparing proximity against GPS‐derived UDs. Female bats exhibited individualized home ranges; however, mother‐daughter pairs showed significantly higher overlap than non‐related pairs. Repeatedly tagged individuals showed site fidelity. These results converge with previous patterns reported using VHF data. Field simulations demonstrated 88% (95% AKDE—home range) and 78% (50% AKDE—core area) of spatial congruence between proximity‐based and GPS‐derived UDs, confirming high locational accuracy within the detection grid. While the grid‐based approach limits UD estimations to the monitored area, the IoT proximity system provides a reliable and automated alternative for studying fine‐scale activity patterns. Our findings highlight the potential for this technology to identify activity hotspots and spatial dynamics in conservation‐priority habitats of species with relatively small home ranges and high site fidelity, such as the Bechstein's bat.