Seabirds racing home over the ocean can get jet-lagged

Autonomous navigation systems may need to account for internal clock drift in flying machines.

Manx shearwaters, seabirds capable of flying hundreds of kilometers daily over the North Atlantic, exhibit symptoms of jet lag, according to a new study published in Science. Researchers analyzed over 20 years of GPS tracking data from more than a thousand birds, revealing that their internal clocks become misaligned with local time during long flights. Because these birds navigate using the sun, this temporal displacement causes them to veer off course. The study found that a shearwater flying east might veer clockwise, while one flying west would veer anticlockwise, by up to 15 degrees. These navigational errors, while not crippling due to the birds' ability to self-correct using other navigation methods, do add distance to their journeys. The findings suggest that jet lag is not exclusively a human ailment and may affect other animals with internal clocks that travel across time zones.

This research demonstrates that the biological phenomenon of jet lag is not limited to humans and can impact animal navigation, particularly for species that undertake long-distance directional flights. The Manx shearwater's reliance on the sun for navigation, coupled with its internal clock's inability to perfectly synchronize across time zones, leads to measurable directional errors. This suggests a potential vulnerability for any biological or artificial navigation system that depends on accurate solar positioning relative to time.

The implications extend to the design of bio-inspired autonomous navigation systems, which could benefit from incorporating mechanisms to correct for temporal discrepancies or utilizing redundant navigation strategies. Future research could explore how animals that consistently experience jet lag have evolved coping mechanisms, potentially offering insights for mitigating its negative effects in other species, including humans, or improving the resilience of robotic navigators in complex environments.