Reinforcement Learning Enables Resource Partitioning in Foraging Bats

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2020
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Handel, Michal
Eitan, Ofri
Bonstein, Afrine
Shaler, Talia
Collet, Simon
Greif, Stefan
Medellín, Rodrigo A.
Emek, Yuval
Yovel, Yossi
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Current biology : CB. Elsevier. 2020, 30(20), pp. 4096-4102.e6. ISSN 0960-9822. eISSN 1879-0445. Available under: doi: 10.1016/j.cub.2020.07.079
Zusammenfassung

Every evening, from late spring to mid-summer, 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 that 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], and 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 revisit 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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Fachgebiet (DDC)
570 Biowissenschaften, Biologie
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nectar feeding bats, reinforcement learning, resource partitioning, trapline, behavioral ecology, movement ecology, territories
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ISO 690GOLDSHTEIN, Aya, Michal HANDEL, Ofri EITAN, Afrine BONSTEIN, Talia SHALER, Simon COLLET, Stefan GREIF, Rodrigo A. MEDELLÍN, Yuval EMEK, Yossi YOVEL, 2020. Reinforcement Learning Enables Resource Partitioning in Foraging Bats. In: Current biology : CB. Elsevier. 2020, 30(20), pp. 4096-4102.e6. ISSN 0960-9822. eISSN 1879-0445. Available under: doi: 10.1016/j.cub.2020.07.079
BibTex
@article{Goldshtein2020Reinf-54868,
  year={2020},
  doi={10.1016/j.cub.2020.07.079},
  title={Reinforcement Learning Enables Resource Partitioning in Foraging Bats},
  number={20},
  volume={30},
  issn={0960-9822},
  journal={Current biology : CB},
  pages={4096--4102.e6},
  author={Goldshtein, Aya and Handel, Michal and Eitan, Ofri and Bonstein, Afrine and Shaler, Talia and Collet, Simon and Greif, Stefan and Medellín, Rodrigo A. and Emek, Yuval and Yovel, Yossi}
}
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