Spontaneous formation of multiple routes in individual desert ants (Cataglyphis velox)

Mangan, Michael and Webb, Barbara (2012) Spontaneous formation of multiple routes in individual desert ants (Cataglyphis velox). Behavioral Ecology, 23 (5). pp. 944-954. ISSN 1045-2249

Full content URL: http://dx.doi.org/10.1093/beheco/ars051

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Desert ants make use of various navigational techniques, including path integration and visual route following, to forage
efficiently in their extremely hostile environment. Species-specific differences in navigation have been demonstrated, although
it remains unknown if these divergences are caused by environmental adaptation. In this work, we report on the navigational
strategies of the European ant Cataglyphis velox, which inhabits a visually cluttered environment similar to the Australian honey
ant Melophorus bagoti, although it is more closely related to other North African Cataglyphis species. We show that C. velox learn
visually guided routes, and these are individual to each forager. Routes can be recalled in the absence of global path integration
information or when placed in conflict with this information. Individual C. velox foragers are also shown to learn multiple routes
through their habitat. These routes are learned rapidly, stored in long-term memory, and recalled for guidance as appropriate.
Desert ants have previously been shown to learn multiple routes in an experimental manipulation, but this is the first report
of such behavior emerging spontaneously. Learning multiple paths through the habitat over successive journeys provides
a mechanism by which ants could memorize a series of interlaced courses, and thus perform complex navigation, without
necessarily having a map of the environment. Key words: Cataglyphis velox, desert ant, foraging, learning, navigation, route, visual
navigation. [Behav Ecol]

Subjects:G Mathematical and Computer Sciences > G730 Neural Computing
D Veterinary Sciences, Agriculture and related subjects > D300 Animal Science
Divisions:College of Science > School of Computer Science
ID Code:23577
Deposited On:03 Aug 2016 12:07

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