Neurobiology, Faculty of Biology, Universität Bielefeld, Germany.
PLoS Comput Biol. 2020 Oct 13;16(10):e1008272. doi: 10.1371/journal.pcbi.1008272. eCollection 2020 Oct.
Returning home is a crucial task accomplished daily by many animals, including humans. Because of their tiny brains, insects, like bees or ants, are good study models for efficient navigation strategies. Bees and ants are known to rely mainly on learned visual information about the nest surroundings to pinpoint their barely visible nest-entrance. During the return, when the actual sight of the insect matches the learned information, the insect is easily guided home. Occasionally, modifications to the visual environment may take place while the insect is on a foraging trip. Here, we addressed the ecologically relevant question of how bumblebees' homing is affected by such a situation. In an artificial setting, we habituated bees to be guided to their nest by two constellations of visual cues. After habituation, these cues were displaced during foraging trips into a conflict situation. We recorded bumblebees' return flights in such circumstances and investigated where they search for their nest entrance following the degree of displacement between the two visually relevant cues. Bumblebees mostly searched at the fictive nest location as indicated by either cue constellation, but never at a compromise location between them. We compared these experimental results to the predictions of different types of homing models. We found that models guiding an agent by a single holistic view of the nest surroundings could not account for the bumblebees' search behaviour in cue-conflict situations. Instead, homing models relying on multiple views were sufficient. We could further show that homing models required fewer views and got more robust to height changes if optic flow-based spatial information was encoded and learned, rather than just brightness information.
回家是许多动物(包括人类)每天都要完成的一项关键任务。由于昆虫的大脑很小,因此蜜蜂或蚂蚁等昆虫是研究高效导航策略的良好模型。众所周知,蜜蜂和蚂蚁主要依赖于关于巢周围环境的习得的视觉信息来精确定位它们几乎看不见的巢入口。在返回过程中,当昆虫实际看到的与所学信息匹配时,昆虫很容易被引导回家。当昆虫在觅食过程中,视觉环境可能会发生变化。在这里,我们解决了一个与生态学相关的问题,即这种情况如何影响大黄蜂的归巢。在一个人工环境中,我们使蜜蜂习惯于通过两个视觉线索星座来引导它们回到巢中。在习惯化之后,在觅食过程中,这些线索会在冲突情况下被转移。我们在这种情况下记录了大黄蜂的归巢飞行,并研究了它们在两个与视觉相关的线索之间的位移程度之后,它们在哪里寻找巢入口。大黄蜂主要在虚拟巢位置搜索,这由两个线索星座中的任意一个指示,但从未在它们之间的妥协位置搜索。我们将这些实验结果与不同类型的归巢模型的预测进行了比较。我们发现,引导代理通过对巢周围环境的单一整体视图的模型无法解释大黄蜂在线索冲突情况下的搜索行为。相反,依赖于多个视图的归巢模型就足够了。我们还可以进一步表明,如果基于光流的空间信息被编码和学习,而不仅仅是亮度信息,则归巢模型需要的视图更少,并且对高度变化更鲁棒。