A new experimental design to uncorrelate visual cues in honeybees' altitude control

International audienceMovement of an observer through a static environment generates a retinal pattern of optic flow specifying the self-motion direction. In the honeybee Apis mellifera, ability to navigate along tunnels solely on basis of optic flow has been largely investigated [1-3]. But what component of this optic flow is actually relevant in the regulation of honeybees’ altitude? Two specific optical invariants should be considered: optical scrolling speed and base lines’ splay angle rate of change. We are designing a new paradigm, in which these two optical invariants could be uncorrelated, individually suppressed or distorted. Our flight tunnel (220-cm long, 71-cm high and 25-cm wide) is textured with parallel red stripes on the four surfaces oriented perpendicularly to the direction of flight. This pattern provides to a flying insect an optical scrolling speed. The floor pattern can be replaced by a uniform white one to remove the ventral optical scrolling speed. The right wall consists of an insect netting lined with the same pattern allowing trajectories’ record at a 100Hz acquisition frequency with a Dalsa Génie HM640 camera . The splay angle formed by base lines can be changed when a bee travels along the tunnel by two motorized carbon sticks. Sticks’ speed is controlled by a brushless motor, coupled to a two-way metric worm with two rotating carriages driving sticks orientation. When a honeybee flies through the flight tunnel, mobile sticks could converge or diverge, providing bees the illusion they will be going up or down, respectively. By filming their 2D trajectory in the vertical plane, we will be able to assess whether they use or not each optical invariant to control their altitude. We hypothesize the splay angle rate of change is an optical invariant used by A. mellifera to control its altitude, as pilots do in a similar situation. [1] G. Portelli, J.R Serres and F.Ruffier (2017) Altitude control in honeybees: joint vision-based learning and guidance, Scientific Reports (7) 9231[2] J.R Serres and F.Ruffier (2017) Optic flow-based collision-free strategies: From insects to robots, Arthropod structure & development (46) 5, 703-717[3] M.V Srinivasan (2011) Visual control of navigation in insects and its relevance for robotics, Current Opinion in Neurobiology (24) 4, 535-54