Combining Individual-Based Radio-Tracking With Whole-Genome Sequencing Data Reveals Candidate for Genetic Basis of Partial Migration in a Songbird.

Partial migration is a phenomenon where migratory and resident individuals of the same species co-exist within a population, and has been linked to both intrinsic (e.g., genetic) as well as environmental factors.

Bats surf storm fronts during spring migration.

Long-distance migration, common in passerine birds, is rare and poorly studied in bats. Piloting a 1.2-gram IoT (Internet of Things) tag with onboard processing, we tracked the daily location, temperature, and activity of female common noctules () during spring migration across central Europe up to 1116 kilometers.

Foxtrot migration and dynamic over-wintering range of an Arctic raptor.

Advances in tracking technologies have revealed the diverse migration patterns of birds, which are critical for range mapping and population estimation. Population trends are usually estimated in breeding ranges where birds remain stationary, but for species that breed in remote areas like the Arctic, these trends are often assessed in over-wintering ranges. Assessing population trends during the wintering season is challenging due to the extensive movements of birds in these ranges, which requires a deep understanding of the movement dynamics.

Tracking individual animals can reveal the mechanisms of species loss.

As biodiversity loss continues, targeted conservation interventions are increasingly necessary. Stemming species loss requires mechanistic understanding of the processes governing population dynamics. However, this information is unavailable for most animals because it requires data that are difficult to collect using traditional methods.

Time synchronisation for millisecond-precision on bio-loggers.

Time-synchronised data streams from bio-loggers are becoming increasingly important for analysing and interpreting intricate animal behaviour including split-second decision making, group dynamics, and collective responses to environmental conditions. With the increased use of AI-based approaches for behaviour classification, time synchronisation between recording systems is becoming an essential challenge. Current solutions in bio-logging rely on manually removing time errors during post processing, which is complex and typically does not achieve sub-second timing accuracies.