Automated measurement of long-term bower behaviors in Lake Malawi cichlids using depth sensing and action recognition

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...

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Autores principales:	Zachary V. Johnson, Manu Tej Sharma Arrojwala, Vineeth Aljapur, Tyrone Lee, Tucker J. Lancaster, Mark C. Lowder, Karen Gu, Joseph I. Stockert, Rachel L. Lecesne, Jean M. Moorman, Jeffrey T. Streelman, Patrick T. McGrath
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/48ac8af7396144dab905b7bd5ebb1129
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spelling	oai:doaj.org-article:48ac8af7396144dab905b7bd5ebb11292021-12-02T16:09:10ZAutomated measurement of long-term bower behaviors in Lake Malawi cichlids using depth sensing and action recognition10.1038/s41598-020-77549-22045-2322https://doaj.org/article/48ac8af7396144dab905b7bd5ebb11292020-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77549-2https://doaj.org/toc/2045-2322Abstract 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.Zachary V. JohnsonManu Tej Sharma ArrojwalaVineeth AljapurTyrone LeeTucker J. LancasterMark C. LowderKaren GuJoseph I. StockertRachel L. LecesneJean M. MoormanJeffrey T. StreelmanPatrick T. McGrathNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-15 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zachary V. Johnson Manu Tej Sharma Arrojwala Vineeth Aljapur Tyrone Lee Tucker J. Lancaster Mark C. Lowder Karen Gu Joseph I. Stockert Rachel L. Lecesne Jean M. Moorman Jeffrey T. Streelman Patrick T. McGrath Automated measurement of long-term bower behaviors in Lake Malawi cichlids using depth sensing and action recognition
description	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.
format	article
author	Zachary V. Johnson Manu Tej Sharma Arrojwala Vineeth Aljapur Tyrone Lee Tucker J. Lancaster Mark C. Lowder Karen Gu Joseph I. Stockert Rachel L. Lecesne Jean M. Moorman Jeffrey T. Streelman Patrick T. McGrath
author_facet	Zachary V. Johnson Manu Tej Sharma Arrojwala Vineeth Aljapur Tyrone Lee Tucker J. Lancaster Mark C. Lowder Karen Gu Joseph I. Stockert Rachel L. Lecesne Jean M. Moorman Jeffrey T. Streelman Patrick T. McGrath
author_sort	Zachary V. Johnson
title	Automated measurement of long-term bower behaviors in Lake Malawi cichlids using depth sensing and action recognition
title_short	Automated measurement of long-term bower behaviors in Lake Malawi cichlids using depth sensing and action recognition
title_full	Automated measurement of long-term bower behaviors in Lake Malawi cichlids using depth sensing and action recognition
title_fullStr	Automated measurement of long-term bower behaviors in Lake Malawi cichlids using depth sensing and action recognition
title_full_unstemmed	Automated measurement of long-term bower behaviors in Lake Malawi cichlids using depth sensing and action recognition
title_sort	automated measurement of long-term bower behaviors in lake malawi cichlids using depth sensing and action recognition
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/48ac8af7396144dab905b7bd5ebb1129
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Automated measurement of long-term bower behaviors in Lake Malawi cichlids using depth sensing and action recognition

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