Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia.
PLoS One. 2012;7(11):e50451. doi: 10.1371/journal.pone.0050451. Epub 2012 Nov 29.
Home is a special location for many animals, offering shelter from the elements, protection from predation, and a common place for gathering of the same species. Not surprisingly, many species have evolved efficient, robust homing strategies, which are used as part of each and every foraging journey. A basic strategy used by most animals is to take the shortest possible route home by accruing the net distances and directions travelled during foraging, a strategy well known as path integration. This strategy is part of the navigation toolbox of ants occupying different landscapes. However, when there is a visual discrepancy between test and training conditions, the distance travelled by animals relying on the path integrator varies dramatically between species: from 90% of the home vector to an absolute distance of only 50 cm. We here ask what the theoretically optimal balance between PI-driven and landmark-driven navigation should be. In combination with well-established results from optimal search theory, we show analytically that this fractional use of the home vector is an optimal homing strategy under a variety of circumstances. Assuming there is a familiar route that an ant recognizes, theoretically optimal search should always begin at some fraction of the home vector, depending on the region of familiarity. These results are shown to be largely independent of the search algorithm used. Ant species from different habitats appear to have optimized their navigation strategy based on the availability and nature of navigational information content in their environment.
家对于许多动物来说是一个特别的地方,提供了躲避自然元素、免受捕食和同种聚集的场所。毫不奇怪,许多物种已经进化出高效、强大的归巢策略,这些策略被用作每次觅食旅程的一部分。大多数动物使用的一个基本策略是通过积累觅食过程中所走过的净距离和方向,找到最短的回家路线,这种策略被称为路径整合。这种策略是在不同景观中占据的蚂蚁导航工具箱的一部分。然而,当测试和训练条件存在视觉差异时,依赖路径整合器的动物所走过的距离在物种之间差异很大:从归巢向量的 90%到绝对距离只有 50 厘米。我们在这里询问 PI 驱动和地标驱动导航之间的理论最佳平衡应该是什么。结合最优搜索理论的成熟结果,我们从理论上分析表明,在各种情况下,这种对归巢向量的分数使用是一种最优的归巢策略。假设存在一条蚂蚁可以识别的熟悉路线,那么根据熟悉区域,理论上最优搜索应该始终从归巢向量的某个分数开始。这些结果在很大程度上独立于所使用的搜索算法。来自不同栖息地的蚂蚁物种似乎已经根据其环境中导航信息内容的可用性和性质优化了它们的导航策略。