Route optimisation and solving Zermelo's navigation problem during long distance migration in cross flows.

Area of Science:

• Animal behavior and migration
• Oceanography and marine biology
• Mathematical modeling in ecology

Background:

• The Zermelo navigation problem, concerning optimal paths in cross-flows, was established over 80 years ago.
• Limited research has explored how migrating animals, like sea turtles, navigate complex ocean currents during long-distance journeys.
• Ocean currents significantly displace marine animals, impacting their migration routes and energy expenditure.

Purpose of the Study:

• To investigate whether migrating marine turtles follow the theoretically optimal Zermelo navigation path.
• To analyze the migration strategies of sea turtles using GPS tracking and ocean current data.
• To understand the role of course corrections in the final stages of marine turtle migration.

Main Methods:

• Utilized long-term GPS tracking data from open-ocean marine turtle movements covering thousands of kilometers.
• Integrated high-resolution, basin-wide physical ocean models to accurately estimate ocean currents.
• Developed a robust mathematical framework to compare turtle trajectories with optimal Zermelo routes.

Main Results:

• Marine turtles successfully reach their target destinations during long-distance migrations.
• Turtle migration routes were found to deviate from the mathematically optimal Zermelo path.
• Sea turtles primarily employ course corrections in coastal (neritic) waters during the final migration phase.

Conclusions:

• Migrating marine turtles do not adhere to the Zermelo optimal path, suggesting alternative navigation strategies.
• The reliance on late-stage course corrections highlights a distinct approach to goal attainment in marine animal migration.
• This study offers a novel perspective for analyzing wildlife tracking data and understanding animal navigation complexity.