干旱区生态环境知识资源中心(Arid): Reinforcement Learning Enables Resource Partitioning in Foraging Bats

Arid

DOI	10.1016/j.cub.2020.07.079
	Reinforcement Learning Enables Resource Partitioning in Foraging Bats
	Goldshtein, Aya; Handel, Michal; Eitan, Ofri; Bonstein, Afrine; Shaler, Talia; Collet, Simon; Greif, Stefan; Medellin, Rodrigo A.; Emek, Yuval; Korman, Amos; Yovel, Yossi
通讯作者	Yovel, Y
来源期刊	CURRENT BIOLOGY
ISSN	0960-9822
EISSN	1879-0445
出版年	2020
卷号	30 期号:20 页码:4096-+
英文摘要	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.
类型	Article
语种	英语
开放获取类型	Green Published, hybrid, Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000579853000039
WOS关键词	NECTAR-FEEDING BAT ; BEHAVIOR ; BEES ; POLLINATORS ; MONKEYS ; MEMORY ; TIME
WOS类目	Biochemistry & Molecular Biology ; Biology ; Cell Biology
WOS研究方向	Biochemistry & Molecular Biology ; Life Sciences & Biomedicine - Other Topics ; Cell Biology
来源机构	Universidad Nacional Autónoma de México
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/326901
作者单位	[Goldshtein, Aya; Handel, Michal; Eitan, Ofri; Bonstein, Afrine; Shaler, Talia; Yovel, Yossi] Tel Aviv Univ, Fac Life Sci, Sch Zool, IL-6997801 Tel Aviv, Israel; [Collet, Simon; Korman, Amos] Res Inst Fdn Comp Sci IRIF, CNRS, F-75013 Paris, France; [Collet, Simon; Korman, Amos] Univ Paris, F-75013 Paris, France; [Greif, Stefan; Yovel, Yossi] Tel Aviv Univ, Sagol Sch Neurosci, IL-6997801 Tel Aviv, Israel; [Medellin, Rodrigo A.] Univ Nacl Autonoma Mexico, Inst Ecol, Dept Ecol Biodiversidad, Ciudad De Mexico 04510, Mexico; [Emek, Yuval] Technion Israel Inst Technol, Fac Ind Engn & Management, IL-3200003 Haifa, Israel
推荐引用方式 GB/T 7714	Goldshtein, Aya,Handel, Michal,Eitan, Ofri,et al. Reinforcement Learning Enables Resource Partitioning in Foraging Bats[J]. Universidad Nacional Autónoma de México,2020,30(20):4096-+.
APA	Goldshtein, Aya.,Handel, Michal.,Eitan, Ofri.,Bonstein, Afrine.,Shaler, Talia.,...&Yovel, Yossi.(2020).Reinforcement Learning Enables Resource Partitioning in Foraging Bats.CURRENT BIOLOGY,30(20),4096-+.
MLA	Goldshtein, Aya,et al."Reinforcement Learning Enables Resource Partitioning in Foraging Bats".CURRENT BIOLOGY 30.20(2020):4096-+.