Insects possess small brains but exhibit sophisticated behaviour, specifically their ability to learn to navigate within complex environments. To understand how they learn to navigate in a cluttered environment, we focused on learning and visual scanning behaviour in the Australian nocturnal bull ant, Myrmecia midas, which are exceptional visual navigators. We tested how individual ants learn to detour via a gap and how they cope with substantial spatial changes over trips. Homing M. midas ants encountered a barrier on their foraging route and had to find a 50-cm gap between symmetrical large black screens, at 1m distance towards the nest direction from the centre of the releasing platform in both familiar (on-route) and semi-familiar (off-route) environments. Foragers were tested for up to 3 learning trips with the changed conditions in both environments. Results showed that on the familiar route, individual foragers learned the gap quickly compared to when they were tested in the semi-familiar environment. When the route was less familiar, and the panorama was changed, foragers were less successful at finding the gap and performed more scans on their way home. Scene familiarity thus played a significant role in visual scanning behaviour. In both on-route and off-route environments, panoramic changes significantly affected learning, initial orientation and scanning behaviour. Nevertheless, over a few trips, success at gap finding increased, visual scans were reduced, the paths became straighter, and individuals took less time to reach the goal. 

Data were collected from the field site at Macquarie University’s North Ryde campus in Sydney, Australia (33°46`11`` S, 151°06`40`` E), from November 2018 to March 2019. The goniometer data were analysed with circular statistics (Batschelet 1981) using the circular statistics software Oriana Version 4 (KOVACH Computing Service, UK). To examine the foragers’ initial orientation, Rayleigh’s tests were conducted, testing if data met the conditions of a uniform distribution (P>0.05) or if the distribution of headings was nonrandomly distributed. V-tests were conducted, if the data were non-uniform, to determine whether the distribution of initial heading directions was significantly clustered in the nest direction. We also examined whether the nest direction fell within the 95% confidence intervals of the mean vectors of heading distributions. We used a digitizing software, Graph-Click (www. arizona-soſtware.ch/graph-click), for digitizing the paths of individual foragers. A custom-written MATLAB (MATLAB 2019b) program was used to plot the paths of the foragers and measure the path straightness of individual foragers in all conditions. For the Control Conditions, the straight-line distance for individual foragers was 7 m. In the Gap Learning Conditions, we divided the path into two sections: Section-A, from release point to the gap, and Section-B, from the gap to the nest. For path straightness in both experiments, we conducted repeated-measures ANOVAs to compare across the 3rd Control run in the Control Condition and the 3 trips of the Gap Learning Condition. In Experiment-2, ANOVAs were conducted separately for path straightness in Section-A and Section-B. Each learning trip in the Gap Learning Condition was compared to Control run 3, using a Bonferroni correction. We did not observe any notable differences between the control runs so that we chose only the 3^rd Control trip to compare to the other conditions. In Section-A, foragers’ duration was timed when individual ants started their journey from the releasing point until they reached the gap between the black screens (or where the screens would be) in both the Control Condition and the Learning Condition. In Section-B, foragers’ duration was calculated from the point when they either crossed the gap (or where the gap would be) or were released at the middle of the gap until they reached the nest location. Statistical tests using ANOVA were conducted using the same statistical procedure as that used for path straightness.  

All of the variables are documented with their units and clearly classified. Files are classified according to their nature. The data can be used following the procedure mentioned in the methods section.