Blinkered teaching: tandem running by visually impaired ants

During tandem runs, one ant worker recruits another to an important resource. Here, we begin to investigate how dependent are tandem leaders and followers on visual cues by painting over their compound eyes to impair their vision. There are two ways in which Temnothorax albipennis might use vision during tandem running. First, the follower might track the movements of the leader by keeping it in sight. Our results suggest that the ants do not use vision in this way. For example, in all four classes of tandem run (those with either leader or follower, both, or neither of their participants with visual impairments) progress was most smooth at about 3 mm/s. This suggests that communication between leaders and followers during tandem runs is not based on vision and is purely tactile and pheromonal. Second, the leader and the follower might be using vision to navigate and our results support this possibility but also suggest that these ants have other methods of navigation. Ants with visual impairments were more likely to follow than to lead, but could occupy either role, even though they had many fully sighted nestmates. This might help to explain why the ants did not focus grooming on their most visually impaired nestmates. Wild-type tandem runs, with both participants fully sighted and presumably taking time to learn landmarks, were overall significantly slower, smoother, and a little less tortuous, than the other treatments. All four classes of tandem run significantly increased mean instantaneous speeds and mean absolute changes in instantaneous acceleration over their journeys. Moreover, tandems with sighted followers increased their speed with time more than the other treatments. In general, our findings suggest that eyesight is used for navigation during tandem running but that these ants also probably use other orientation systems during such recruitment and to learn how to get to new nest sites. Our results suggest that the ants' methods of teaching and learning are very robust and flexible.