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Analysing animal social network dynamics: the potential of stochastic actor‐oriented models

1. Animals are embedded in dynamically changing networks of relationships with conspecifics. These dynamic networks are fundamental aspects of their environment, creating selection on behaviours and other traits. However, most social network‐based approaches in ecology are constrained to considering...

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Autores principales: Fisher, David N., Ilany, Amiyaal, Silk, Matthew J., Tregenza, Tom
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6849756/
https://www.ncbi.nlm.nih.gov/pubmed/28004848
http://dx.doi.org/10.1111/1365-2656.12630
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author Fisher, David N.
Ilany, Amiyaal
Silk, Matthew J.
Tregenza, Tom
author_facet Fisher, David N.
Ilany, Amiyaal
Silk, Matthew J.
Tregenza, Tom
author_sort Fisher, David N.
collection PubMed
description 1. Animals are embedded in dynamically changing networks of relationships with conspecifics. These dynamic networks are fundamental aspects of their environment, creating selection on behaviours and other traits. However, most social network‐based approaches in ecology are constrained to considering networks as static, despite several calls for such analyses to become more dynamic. 2. There are a number of statistical analyses developed in the social sciences that are increasingly being applied to animal networks, of which stochastic actor‐oriented models (SAOMs) are a principal example. SAOMs are a class of individual‐based models designed to model transitions in networks between discrete time points, as influenced by network structure and covariates. It is not clear, however, how useful such techniques are to ecologists, and whether they are suited to animal social networks. 3. We review the recent applications of SAOMs to animal networks, outlining findings and assessing the strengths and weaknesses of SAOMs when applied to animal rather than human networks. We go on to highlight the types of ecological and evolutionary processes that SAOMs can be used to study. 4. SAOMs can include effects and covariates for individuals, dyads and populations, which can be constant or variable. This allows for the examination of a wide range of questions of interest to ecologists. However, high‐resolution data are required, meaning SAOMs will not be useable in all study systems. It remains unclear how robust SAOMs are to missing data and uncertainty around social relationships. 5. Ultimately, we encourage the careful application of SAOMs in appropriate systems, with dynamic network analyses likely to prove highly informative. Researchers can then extend the basic method to tackle a range of existing questions in ecology and explore novel lines of questioning.
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spelling pubmed-68497562019-11-15 Analysing animal social network dynamics: the potential of stochastic actor‐oriented models Fisher, David N. Ilany, Amiyaal Silk, Matthew J. Tregenza, Tom J Anim Ecol Behavioural Ecology 1. Animals are embedded in dynamically changing networks of relationships with conspecifics. These dynamic networks are fundamental aspects of their environment, creating selection on behaviours and other traits. However, most social network‐based approaches in ecology are constrained to considering networks as static, despite several calls for such analyses to become more dynamic. 2. There are a number of statistical analyses developed in the social sciences that are increasingly being applied to animal networks, of which stochastic actor‐oriented models (SAOMs) are a principal example. SAOMs are a class of individual‐based models designed to model transitions in networks between discrete time points, as influenced by network structure and covariates. It is not clear, however, how useful such techniques are to ecologists, and whether they are suited to animal social networks. 3. We review the recent applications of SAOMs to animal networks, outlining findings and assessing the strengths and weaknesses of SAOMs when applied to animal rather than human networks. We go on to highlight the types of ecological and evolutionary processes that SAOMs can be used to study. 4. SAOMs can include effects and covariates for individuals, dyads and populations, which can be constant or variable. This allows for the examination of a wide range of questions of interest to ecologists. However, high‐resolution data are required, meaning SAOMs will not be useable in all study systems. It remains unclear how robust SAOMs are to missing data and uncertainty around social relationships. 5. Ultimately, we encourage the careful application of SAOMs in appropriate systems, with dynamic network analyses likely to prove highly informative. Researchers can then extend the basic method to tackle a range of existing questions in ecology and explore novel lines of questioning. John Wiley and Sons Inc. 2017-02-01 2017-03 /pmc/articles/PMC6849756/ /pubmed/28004848 http://dx.doi.org/10.1111/1365-2656.12630 Text en © 2016 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Behavioural Ecology
Fisher, David N.
Ilany, Amiyaal
Silk, Matthew J.
Tregenza, Tom
Analysing animal social network dynamics: the potential of stochastic actor‐oriented models
title Analysing animal social network dynamics: the potential of stochastic actor‐oriented models
title_full Analysing animal social network dynamics: the potential of stochastic actor‐oriented models
title_fullStr Analysing animal social network dynamics: the potential of stochastic actor‐oriented models
title_full_unstemmed Analysing animal social network dynamics: the potential of stochastic actor‐oriented models
title_short Analysing animal social network dynamics: the potential of stochastic actor‐oriented models
title_sort analysing animal social network dynamics: the potential of stochastic actor‐oriented models
topic Behavioural Ecology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6849756/
https://www.ncbi.nlm.nih.gov/pubmed/28004848
http://dx.doi.org/10.1111/1365-2656.12630
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