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Time is of the essence: an application of a relational event model for animal social networks

Understanding how animal social relationships are created, maintained and severed has ecological and evolutionary significance. Animal social relationships are inferred from observations of interactions between animals; the pattern of interaction over time indicates the existence (or absence) of a s...

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Autores principales: Patison, K. P., Quintane, E., Swain, D. L., Robins, G., Pattison, P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405283/
https://www.ncbi.nlm.nih.gov/pubmed/25926713
http://dx.doi.org/10.1007/s00265-015-1883-3
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author Patison, K. P.
Quintane, E.
Swain, D. L.
Robins, G.
Pattison, P.
author_facet Patison, K. P.
Quintane, E.
Swain, D. L.
Robins, G.
Pattison, P.
author_sort Patison, K. P.
collection PubMed
description Understanding how animal social relationships are created, maintained and severed has ecological and evolutionary significance. Animal social relationships are inferred from observations of interactions between animals; the pattern of interaction over time indicates the existence (or absence) of a social relationship. Autonomous behavioural recording technologies are increasingly being used to collect continuous interaction data on animal associations. However, continuous data sequences are typically aggregated to represent a relationship as part of one (or several) pictures of the network of relations among animals, in a way that parallels human social networks. This transformation entails loss of information about interaction timing and sequence, which are particularly important to understand the formation of relationships or their disruption. Here, we describe a new statistical model, termed the relational event model, that enables the analysis of fine-grained animal association data as a continuous time sequence without requiring aggregation of the data. We apply the model to a unique data set of interaction between familiar and unfamiliar steers during a series of 36 experiments to investigate the process of social disruption and relationship formation. We show how the model provides key insights into animal behaviour in terms of relationship building, the integration process of unfamiliar animals and group building dynamics. The relational event model is well suited to data structures that are common to animal behavioural studies and can therefore be applied to a range of social interaction data to understand animal social dynamics. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00265-015-1883-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-44052832015-04-27 Time is of the essence: an application of a relational event model for animal social networks Patison, K. P. Quintane, E. Swain, D. L. Robins, G. Pattison, P. Behav Ecol Sociobiol Methods Understanding how animal social relationships are created, maintained and severed has ecological and evolutionary significance. Animal social relationships are inferred from observations of interactions between animals; the pattern of interaction over time indicates the existence (or absence) of a social relationship. Autonomous behavioural recording technologies are increasingly being used to collect continuous interaction data on animal associations. However, continuous data sequences are typically aggregated to represent a relationship as part of one (or several) pictures of the network of relations among animals, in a way that parallels human social networks. This transformation entails loss of information about interaction timing and sequence, which are particularly important to understand the formation of relationships or their disruption. Here, we describe a new statistical model, termed the relational event model, that enables the analysis of fine-grained animal association data as a continuous time sequence without requiring aggregation of the data. We apply the model to a unique data set of interaction between familiar and unfamiliar steers during a series of 36 experiments to investigate the process of social disruption and relationship formation. We show how the model provides key insights into animal behaviour in terms of relationship building, the integration process of unfamiliar animals and group building dynamics. The relational event model is well suited to data structures that are common to animal behavioural studies and can therefore be applied to a range of social interaction data to understand animal social dynamics. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00265-015-1883-3) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2015-03-24 2015 /pmc/articles/PMC4405283/ /pubmed/25926713 http://dx.doi.org/10.1007/s00265-015-1883-3 Text en © The Author(s) 2015 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
spellingShingle Methods
Patison, K. P.
Quintane, E.
Swain, D. L.
Robins, G.
Pattison, P.
Time is of the essence: an application of a relational event model for animal social networks
title Time is of the essence: an application of a relational event model for animal social networks
title_full Time is of the essence: an application of a relational event model for animal social networks
title_fullStr Time is of the essence: an application of a relational event model for animal social networks
title_full_unstemmed Time is of the essence: an application of a relational event model for animal social networks
title_short Time is of the essence: an application of a relational event model for animal social networks
title_sort time is of the essence: an application of a relational event model for animal social networks
topic Methods
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405283/
https://www.ncbi.nlm.nih.gov/pubmed/25926713
http://dx.doi.org/10.1007/s00265-015-1883-3
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