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Reinforcement Learning Enables Resource Partitioning in Foraging Bats.

Aya Goldshtein¹, Michal Handel¹, Ofri Eitan¹

¹School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.

Current Biology : CB

|August 22, 2020

Summary

Lesser long-nosed bats use a reinforcement learning strategy to efficiently forage for nectar. This minimal-memory approach allows them to exploit Saguaro cactus resources without social communication.

Keywords:

behavioral ecology movement ecology nectar feeding bats reinforcement learning resource partitioning territories trapline

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Area of Science:

Ecology
Animal Behavior
Bio-inspired Computing

Background:

Lactating female lesser long-nosed bats (Leptonycteris yerbabuenae) undertake long nightly flights to forage for nectar and pollen.
These bats must navigate and efficiently exploit fixed Saguaro cactus food sources, often facing competition.

Purpose of the Study:

To investigate the foraging strategy of lesser long-nosed bats visiting Saguaro cacti.
To determine if bats employ a traplining or other systematic foraging method.

Main Methods:

Tracking bat movements using miniature GPS devices.
Utilizing aerial imaging and video recordings to document foraging visits.
Employing evolutionary simulations to model bat foraging behavior.

Main Results:

Lesser long-nosed bats exhibit a non-random, potentially sequenced foraging pattern.
Data supports a reinforcement learning strategy for efficient nectar exploitation.
The bats create small, non-overlapping foraging areas (cacti-cores) with minimal memory.

Conclusions:

Lesser long-nosed bats utilize an efficient reinforcement learning strategy for foraging.
This strategy allows for optimal exploitation of renewable Saguaro cactus resources.
Foraging behavior appears to be individually optimized, without reliance on social communication.