文章基本信息

标题：Automated measurement of long-term bower behaviors in Lake Malawi cichlids using depth sensing and action recognition
本地全文：下载
作者：Zachary V. Johnson ; Manu Tej Sharma Arrojwala ; Vineeth Aljapur 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2020
卷号：10
期号：1
页码：1-15
DOI：10.1038/s41598-020-77549-2
出版社：Springer Nature
摘要：In the wild, behaviors are often expressed over long time periods in complex and dynamic environments, and many behaviors include direct interaction with the environment itself. However, measuring behavior in naturalistic settings is difficult, and this has limited progress in understanding the mechanisms underlying many naturally evolved behaviors that are critical for survival and reproduction. Here we describe an automated system for measuring long-term bower construction behaviors in Lake Malawi cichlid fishes, in which males use their mouths to sculpt sand into large species-specific structures for courtship and mating. We integrate two orthogonal methods, depth sensing and action recognition, to simultaneously track the developing bower structure and the thousands of individual sand manipulation behaviors performed throughout construction. By registering these two data streams, we show that behaviors can be topographically mapped onto a dynamic 3D sand surface through time. The system runs reliably in multiple species, across many aquariums simultaneously, and for up to weeks at a time. Using this system, we show strong differences in construction behavior and bower form that reflect species differences in nature, and we gain new insights into spatial, temporal, social dimensions of bower construction, feeding, and quivering behaviors. Taken together, our work highlights how low-cost tools can automatically quantify behavior in naturalistic and social environments over long timescales in the lab.
其他摘要：Abstract In the wild, behaviors are often expressed over long time periods in complex and dynamic environments, and many behaviors include direct interaction with the environment itself. However, measuring behavior in naturalistic settings is difficult, and this has limited progress in understanding the mechanisms underlying many naturally evolved behaviors that are critical for survival and reproduction. Here we describe an automated system for measuring long-term bower construction behaviors in Lake Malawi cichlid fishes, in which males use their mouths to sculpt sand into large species-specific structures for courtship and mating. We integrate two orthogonal methods, depth sensing and action recognition, to simultaneously track the developing bower structure and the thousands of individual sand manipulation behaviors performed throughout construction. By registering these two data streams, we show that behaviors can be topographically mapped onto a dynamic 3D sand surface through time. The system runs reliably in multiple species, across many aquariums simultaneously, and for up to weeks at a time. Using this system, we show strong differences in construction behavior and bower form that reflect species differences in nature, and we gain new insights into spatial, temporal, social dimensions of bower construction, feeding, and quivering behaviors. Taken together, our work highlights how low-cost tools can automatically quantify behavior in naturalistic and social environments over long timescales in the lab.