Hentati Sundberg, Jonas
- Department of Aquatic Resources (SLU Aqua), Swedish University of Agricultural Sciences
Research article2025Peer reviewed
Hentati-Sundberg, Jonas; Berglund, Per-Arvid; Olin, Agnes B.; Hejdstrom, Aron; Osterblom, Henrik; Carlsen, Astrid A.; Queiros, Quentin; Olsson, Olof
In 2008, we built an artificial nesting construction for Common Murres Uria aalge, the Karls & ouml; Auk Lab, on an island in the Baltic Sea (Hentati-Sundberg et al., 2012). The aim was to create an environment in which the birds could be readily monitored and accessed, and technological equipment easily installed. In this current paper, we report on murre recruitment to the Auk Lab over the first decade, assess the performance of the birds living on the lab compared to natural cliff ledges, and revisit the original research questions. We conclude that the tremendous developments in sensor technology (video surveillance, automated scales, thermal cameras, weather sensors) and artificial intelligence was not anticipated 10 years ago. Several major scientific insights, including the effects of eagle disturbances and heat stress on the murres, have come as surprises and have been driven mainly by the technology's potential to deliver data with a resolution unattainable using traditional field studies. The dramatic increase in data volumes has partly been paired by automated analysis methods, but some aspects of the new technology, notably individual identification, have been more difficult than anticipated. The investment costs for information technology infrastructure, data storage, and processing capacity have also been substantial. We finish the paper by sketching out new research questions that will guide the next decade at the Auk Lab and repeating an invitation for research collaborations beyond our planned research focus.
automation; artificial intelligence; big data; Common Guillemot; information technology; long-term studies; seabirds
Marine Ornithology
2025, volume: 53, number: 1, pages: 21-33
Publisher: PACIFIC SEABIRD GROUP
Ecology
Zoology
Computer Science
https://res.slu.se/id/publ/140478