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Journal Articles Current Biology - CB Year : 2020

Reinforcement Learning Enables Resource-Partitioning in Foraging Bats


Every evening, from late spring to midsummer , tens of thousands of hungry lactating female Lesser long-nosed bats (Leptonycteris yerbabuenae) emerge from their roost and navigate over the Sonoran Desert seeking for nectar and pollen [1,2]. The bats roost in a huge maternal colony which is far from the foraging grounds, but allows their pups to thermoregulate [3] while the mothers are foraging. Thus, the mothers have to fly tens of kilometers to the foraging sites-fields with thousands of Saguaro cacti [4,5]. Once at the field, they must compete with many other bats over the same flowering cacti. Several solutions have been suggested for this classical foraging task of exploiting a resource composed of many renewable food-sources whose locations are fixed. Some animals randomly visit the food sources [6], some actively defend a restricted foraging territory [7–11], or use simple forms of learning such as ‘win-stay lose-switch’ strategy [12]. Many species have been suggested to follow a trapline, that is, to re-visit the food sources in a repeating ordered manner [13–22]. We thus hypothesized that lesser long-nosed bats would visit cacti in a sequenced manner. Using miniature GPS devices, aerial imaging and video recordings, we tracked the full movement of the bats and all of their visits to their natural food-sources. Based on real data and evolutionary simulations, we argue that the bats use a reinforcement learning strategy, that requires minimal memory, to create small non-overlapping cacti-cores and exploit nectar efficiently, without social communication.
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hal-02989489 , version 1 (07-11-2020)



Aya Goldshtein, Michal Handel, Ofri Eitan, Afrine Bonstein, Talia Shaler, et al.. Reinforcement Learning Enables Resource-Partitioning in Foraging Bats. Current Biology - CB, 2020, 30 (20), pp.4096-4102.e6. ⟨10.1016/j.cub.2020.07.079⟩. ⟨hal-02989489⟩
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