A multiplex centrality metric for complex social networks: sex, social status, and family structure predict multiplex centrality in rhesus macaques

Members of a society interact using a variety of social behaviors, giving rise to a multi-faceted and complex social life. For the study of animal behavior, quantifying this complexity is critical for understanding the impact of social life on animals’ health and fitness. Multilayer network approach...

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Main Authors: Brianne Beisner, Niklas Braun, Márton Pósfai, Jessica Vandeleest, Raissa D’Souza, Brenda McCowan
Format: Article
Language:English
Published: PeerJ Inc. 2020-03-01
Series:PeerJ
Subjects:
Online Access:https://peerj.com/articles/8712.pdf
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spelling doaj-3714951e9ed7443292cfaa78d4ba747b2020-11-25T02:38:14ZengPeerJ Inc.PeerJ2167-83592020-03-018e871210.7717/peerj.8712A multiplex centrality metric for complex social networks: sex, social status, and family structure predict multiplex centrality in rhesus macaquesBrianne Beisner0Niklas Braun1Márton Pósfai2Jessica Vandeleest3Raissa D’Souza4Brenda McCowan5Department of Population Health & Reproduction, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States of AmericaDepartment of Mechanical and Aerospace Engineering, University of California, Davis, Davis, CA, United States of AmericaDepartment of Computer Sciences, University of California, Davis, Davis, CA, United States of AmericaNeuroscience and Behavior Unit, California National Primate Research Center, Davis, CA, United States of AmericaDepartment of Mechanical and Aerospace Engineering, University of California, Davis, Davis, CA, United States of AmericaDepartment of Population Health & Reproduction, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States of AmericaMembers of a society interact using a variety of social behaviors, giving rise to a multi-faceted and complex social life. For the study of animal behavior, quantifying this complexity is critical for understanding the impact of social life on animals’ health and fitness. Multilayer network approaches, where each interaction type represents a different layer of the social network, have the potential to better capture this complexity than single layer approaches. Calculating individuals’ centrality within a multilayer social network can reveal keystone individuals and more fully characterize social roles. However, existing measures of multilayer centrality do not account for differences in the dynamics and functionality across interaction layers. Here we validate a new method for quantifying multiplex centrality called consensus ranking by applying this method to multiple social groups of a well-studied nonhuman primate, the rhesus macaque. Consensus ranking can suitably handle the complexities of animal social life, such as networks with different properties (sparse vs. dense) and biological meanings (competitive vs. affiliative interactions). We examined whether individuals’ attributes or socio-demographic factors (sex, age, dominance rank and certainty, matriline size, rearing history) were associated with multiplex centrality. Social networks were constructed for five interaction layers (i.e., aggression, status signaling, conflict policing, grooming and huddling) for seven social groups. Consensus ranks were calculated across these five layers and analyzed with respect to individual attributes and socio-demographic factors. Generalized linear mixed models showed that consensus ranking detected known social patterns in rhesus macaques, showing that multiplex centrality was greater in high-ranking males with high certainty of rank and females from the largest families. In addition, consensus ranks also showed that females from very small families and mother-reared (compared to nursery-reared) individuals were more central, showing that consideration of multiple social domains revealed individuals whose social centrality and importance might otherwise have been missed.https://peerj.com/articles/8712.pdfMultilayer networksAnimal behaviorComplex societiesNetwork analysisSocial behaviorNonhuman primates
collection DOAJ
language English
format Article
sources DOAJ
author Brianne Beisner
Niklas Braun
Márton Pósfai
Jessica Vandeleest
Raissa D’Souza
Brenda McCowan
spellingShingle Brianne Beisner
Niklas Braun
Márton Pósfai
Jessica Vandeleest
Raissa D’Souza
Brenda McCowan
A multiplex centrality metric for complex social networks: sex, social status, and family structure predict multiplex centrality in rhesus macaques
PeerJ
Multilayer networks
Animal behavior
Complex societies
Network analysis
Social behavior
Nonhuman primates
author_facet Brianne Beisner
Niklas Braun
Márton Pósfai
Jessica Vandeleest
Raissa D’Souza
Brenda McCowan
author_sort Brianne Beisner
title A multiplex centrality metric for complex social networks: sex, social status, and family structure predict multiplex centrality in rhesus macaques
title_short A multiplex centrality metric for complex social networks: sex, social status, and family structure predict multiplex centrality in rhesus macaques
title_full A multiplex centrality metric for complex social networks: sex, social status, and family structure predict multiplex centrality in rhesus macaques
title_fullStr A multiplex centrality metric for complex social networks: sex, social status, and family structure predict multiplex centrality in rhesus macaques
title_full_unstemmed A multiplex centrality metric for complex social networks: sex, social status, and family structure predict multiplex centrality in rhesus macaques
title_sort multiplex centrality metric for complex social networks: sex, social status, and family structure predict multiplex centrality in rhesus macaques
publisher PeerJ Inc.
series PeerJ
issn 2167-8359
publishDate 2020-03-01
description Members of a society interact using a variety of social behaviors, giving rise to a multi-faceted and complex social life. For the study of animal behavior, quantifying this complexity is critical for understanding the impact of social life on animals’ health and fitness. Multilayer network approaches, where each interaction type represents a different layer of the social network, have the potential to better capture this complexity than single layer approaches. Calculating individuals’ centrality within a multilayer social network can reveal keystone individuals and more fully characterize social roles. However, existing measures of multilayer centrality do not account for differences in the dynamics and functionality across interaction layers. Here we validate a new method for quantifying multiplex centrality called consensus ranking by applying this method to multiple social groups of a well-studied nonhuman primate, the rhesus macaque. Consensus ranking can suitably handle the complexities of animal social life, such as networks with different properties (sparse vs. dense) and biological meanings (competitive vs. affiliative interactions). We examined whether individuals’ attributes or socio-demographic factors (sex, age, dominance rank and certainty, matriline size, rearing history) were associated with multiplex centrality. Social networks were constructed for five interaction layers (i.e., aggression, status signaling, conflict policing, grooming and huddling) for seven social groups. Consensus ranks were calculated across these five layers and analyzed with respect to individual attributes and socio-demographic factors. Generalized linear mixed models showed that consensus ranking detected known social patterns in rhesus macaques, showing that multiplex centrality was greater in high-ranking males with high certainty of rank and females from the largest families. In addition, consensus ranks also showed that females from very small families and mother-reared (compared to nursery-reared) individuals were more central, showing that consideration of multiple social domains revealed individuals whose social centrality and importance might otherwise have been missed.
topic Multilayer networks
Animal behavior
Complex societies
Network analysis
Social behavior
Nonhuman primates
url https://peerj.com/articles/8712.pdf
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