A framework for the identification of long-term social avoidance in longitudinal datasets

Kasha Strickland; Alexis L Levengood; Vivienne Foroughirad; Janet Mann; Ewa Krzyszczyk; Celine H Frere

doi:10.1098/rsos.170641

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A framework for the identification of long-term social avoidance in longitudinal datasets

Journal article

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Peer reviewed

A framework for the identification of long-term social avoidance in longitudinal datasets

Kasha Strickland, Alexis L Levengood, Vivienne Foroughirad, Janet Mann, Ewa Krzyszczyk and Celine H Frere

Royal Society Open Science, Vol.4, 170641

2017

DOI: https://doi.org/10.1098/rsos.170641

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Abstract

avoidance

social behaviour

longitudinal data

spatial distribution

social preference

Animal sociality is of significant interest to evolutionary and behavioural ecologists, with efforts focused on the patterns, causes and fitness outcomes of social preference. However, individual social patterns are the consequence of both attraction to (preference for) and avoidance of conspecifics. Despite this, social avoidance has received far less attention than social preference. Here, we detail the necessary steps to generate a spatially explicit, iterative null model which can be used to identify non-random social avoidance in longitudinal studies of social animals. We specifically identify and detail parameters which will influence the validity of the model. To test the usability of this model, we applied it to two longitudinal studies of social animals (Eastern water dragons (Intellegama lesueurii) and bottlenose dolphins (Tursiops aduncus)) to identify the presence of social avoidances. Using this model allowed us to identify the presence of social avoidances in both species. We hope that the framework presented here inspires interest in addressing this critical gap in our understanding of animal sociality, in turn allowing for a more holistic understanding of social interactions, relationships and structure.

Details

Title: A framework for the identification of long-term social avoidance in longitudinal datasets
Authors: Kasha Strickland (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and Engineering
Alexis L Levengood (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and Engineering
Vivienne Foroughirad (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and Engineering
Janet Mann (Author) - Georgetown University, United States
Ewa Krzyszczyk (Author) - Georgetown University, United States
Celine H Frere (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and Engineering
Publication details: Royal Society Open Science, Vol.4, 170641; 15
Publisher: Royal Society Publishing
Date published: 2017
DOI: 10.1098/rsos.170641
ISSN: 2054-5703
Copyright note: Copyright © 2017 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
Organisation Unit: School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; GeneCology Research Centre - Legacy; School of Science, Technology and Engineering
Language: English
Record Identifier: 99450355202621
Output Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Zoology

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