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dc.contributor.author
Kembro, Jackelyn Melissa
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dc.contributor.other
Enderling, Heiko
dc.contributor.other
Kirschner, Denise
dc.date.available
2023-04-04T10:38:58Z
dc.date.issued
2021
dc.identifier.citation
Accelerometers as a tool to characterize reproductive behavior within social groups in long term experiments: The case of the Japanese Quail; SMB 2020 Annual Meeting; Estados Unidos; 2020; 1-2
dc.identifier.uri
http://hdl.handle.net/11336/192575
dc.description.abstract
Accelerometers are devices that convert movement into three signals belonging to each component of the acceleration vector at a high acquisition rate, up to 25 data per second. When they are fixed to an animal, each action performed by the individual leads to a particular shape in these signals that, when depicted in a computer, can be isolated and classified. Hence, accelerometer recordings can be combined with machine learning techniques in order to automatically classify signals into behavioral categories. This is particularly useful in the context of long-term social behavior studies in large or natural environments were recording from visual observation is difficult and time consuming. Herein, we placed accelerometers on the back of adult male quails (Coturnix japonica) to register their activity when they are released into a home box containing two female quails during a 1-hour period. At the same time, the experiment was video-recorded to obtain a time series of the different behaviors performed by the male and their corresponding duration by direct inspection. The accelerometric signals and behavioral time series obtained were used to train a neuronal network. Our neuronal network was able to classify reproductive behavior of males at high temporal resolution. In particular, we showed, first, that the duration of some reproductive events can be much shorter than those reported previously and transitions between different behaviors are very fast (of the order of ~100ms). Second, reproductive behavior appears to begins earlier and finish later than it is possible to observe visually using video recordings. Our results show that combining accelerometer recordings with neural network processing is a powerful method to automatically register reproductive behaviors within social groups with high accuracy. This is of particular importance given that it has the potential to replace registering from visual observation of social behavior. Moreover, the long, high resolution reproductive time series obtained by this approach can be useful for studding long-term reproductive behavioral rhythms in poultry.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Society for Mathematical Biology
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ACELEROMETER
dc.subject
BEHAVIOR
dc.subject
QUAIL
dc.subject
POULTRY
dc.subject.classification
Biología
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dc.subject.classification
Ciencias Biológicas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Accelerometers as a tool to characterize reproductive behavior within social groups in long term experiments: The case of the Japanese Quail
dc.type
info:eu-repo/semantics/publishedVersion
dc.type
info:eu-repo/semantics/conferenceObject
dc.type
info:ar-repo/semantics/documento de conferencia
dc.date.updated
2022-11-09T16:59:16Z
dc.journal.pagination
1-2
dc.journal.pais
Estados Unidos
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dc.description.fil
Fil: Kembro, Jackelyn Melissa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; Argentina
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://2020.smb.org/Jackelyn-M-Kembro/
dc.conicet.rol
Autor
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Autor
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Autor
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Autor
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Autor
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dc.coverage
Internacional
dc.type.subtype
Reunión
dc.description.nombreEvento
SMB 2020 Annual Meeting
dc.date.evento
2020-08-17
dc.description.paisEvento
Estados Unidos
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dc.type.publicacion
Journal
dc.description.institucionOrganizadora
Society for Mathematical Biology
dc.source.revista
SMB 2020 Annual Meeting
dc.date.eventoHasta
2020-07-20
dc.type
Reunión
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