Monitoring breeding seabird colonies and marked individuals using a remote-controlled camera system

Abstract Colonial-breeding seabirds are among the most threatened avian groups—their breeding colonies are exposed to predation, disease outbreak, coastal pollution, and other anthropogenic disturbances. Real-time identification of threats and tracking of demographic trends are top priorities for researchers and conservationists. Rapid advances in remote-controlled camera technology provide efficient and non-invasive methods to monitor seabird colonies and identify individuals marked with colored bands. By employing these tools, researchers can gather extensive datasets on population dynamics, breeding success, and behavioral patterns without disturbing birds in their natural environments. The project described here focuses on a breeding colony of two tern species, Sterna hirundo (Common Tern) and Sternula albifrons (Little Tern). Since 2021, two remote-controlled cameras have been used for monitoring this colony, comprising ∼1,300 pairs, while traditional monitoring methods, including individual marking, have been conducted since 2010. Using these cameras enabled real-time threat identification, reading of coded bands, and the counting of nests, chicks, and adults. We assessed potential biases in band identification under varying lighting conditions and between different age groups. Our findings demonstrate that remote-controlled cameras significantly enhance the identification rates of marked terns and reveal variations in detection success influenced by band color and bird age. For example, black, yellow, and white bands, but not green and red, were successfully identified at night. Furthermore, the use of cameras improved our ability to assess the size of the breeding population and to identify unusual mortality events. The methodology described here could improve monitoring capabilities and efficiency without increasing direct human activity in sensitive breeding areas. Future research directions include the application of artificial intelligence methods for automated monitoring and data analysis, which may further enhance the effectiveness of conservation efforts for colonial seabirds.