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Virtual reality (VR) offers an appealing experimental framework for studying visual performances of insects under highly controlled conditions. In the case of the honeybee Apis mellifera, this possibility may fill the gap between behavioural analyses in free-flight and cellular analyses in the laboratory. Using automated, computer-controlled systems, it is possible to generate virtual stimuli or even entire environments that can be modified to test hypotheses on bee visual behaviour. The bee itself can remain tethered in place, making it possible to record neural activity while the bees is performing behavioural tasks. Recent studies have examined visual navigation and attentional processes in VR on flying or walking tethered bees, but experimental paradigms for examining visual learning and memory are only just emerging. Behavioural performances of bees under current experimental conditions are often lower in VR than in natural environments, but further improvements on current experimental protocols seem possible. Here we discuss current developments and conclude that it is essential to tailor the specifications of the VR simulation to the visual processing of honeybees to improve the success of this research endeavour.

The movement pattern of a searching animal affects how it interacts with the environment, thus playing an important role in many environmental processes. Social hymenopteran foragers make good models for studying search patterns because their only role is to search for food and bring it back to the nest or hive. Search patterns in ants and bees, the most studied social hymenopterans, consist of loops around the origin of search that expand in size as the search goes on. Different factors influence search structure, including the type and distribution of the resource that is targeted, and the kind and amount of information available for navigation. In the distributions of the lengths of search segments, bees exhibit a Levy walk pattern, which follows a power law in which occasional long segments are interspersed within many short segments, and yet longer segments are interspersed within the long segments. Such a scale-free and heavy-tailed distribution has been shown to be optimal in searching for sparse targets. Ants on the other hand show a composite Brownian search distribution, composed of two exponential distributions at different scales. We argue that such composite Brownian searches serve to approximate a Levy walk. Both Levy walks and composite Brownian searches are found in a range of other animals, including hunter–gatherer humans. These patterns may be ancient, as they have been found in fossils dating to at least 50 million years.

In a synthetic approach to studying navigational abilities in desert ants, we review recent work comparing ants living in different visual ecologies. Those living in a visually rich habitat strewn with tussocks, bushes, and trees are compared to those living in visually barren salt pans, as exemplified by the Central Australian Melophorus bagoti and the North African Cataglyphis fortis, respectively. In bare habitats the navigator must rely primarily on path integration, keeping track of the distance and direction in which it has travelled, while in visually rich habitats the navigator can rely more on guidance by the visual panorama. Consistent with these expectations, C. fortis performs better than M. bagoti on various measures of precision at path integration. In contrast, M. bagoti learned a visually based associative task better than C. fortis, the latter generally failing at the task. Both these ants, however, exhibit a similar pattern of systematic search as a 'back up' strategy when other navigational strategies fail. A newly investigated salt-pan species of Melophorus (as yet unnamed) resembles C. fortis more, and its congener M. bagoti less, in its path integration. The synthetic approach would benefit from comparing more species chosen to address evolutionary questions. This article is part of a Special Issue entitled: CO3 2013.

Australian desert ants, Melophorus bagoti, return home after foraging by means of path integration and visual navigation. If these mechanisms do not deliver them exactly at the inconspicuous nest entrance, they engage in a systematic search. Here we describe the structure of this search pattern in detail. Trained ants ran home from a feeder in the natural visual setting where they navigated very accurately, and were then captured for tests on a distant test field after they had almost reached their nest. The search pattern consisted of loops and was centred on the position where the nest was most likely to be located. At first, it covered a rather small area, but then gradually extended outwards to cover a larger area. The search density was also adapted to the preceding outbound foraging distance, with longer distances leading to flatter, wider search distributions. Since the visual surround at the time of capture was similar for ants with all outbound distances, we suggest this is an adaptation to the cumulative error of the homing vector. The frequency distribution of segment lengths in the paths of searching ants does not show characteristics of a Levy walk strategy. Instead, it is well described by a double exponential model, lending support to a theoretically optimal strategy that consists of a mixture of two random walks, as in the composite Brownian walk strategy.