Abstract
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.
Highlights
We present a new approach for quantifying multilayer network centrality, called ‘consensus ranking’ (Pósfai et al, 2019), and illustrate its utility for uncovering novel information about individuals’ social roles and social structure using rhesus macaques (Macaca mulatta)
Behavioral network data were collected from seven captive social groups of rhesus macaques (Macaca mulatta) housed in half-acre (0.2 ha) outdoor enclosures at the California National Primate Research Center in Davis, CA
High-ranking males with high certainty of their dominance were more central than other males, females from the largest families were more central than females from medium-sized families, and mother-reared individuals were more central than nursery-reared individuals
Summary
Social relationships arise from the patterning of these different types of interactions, and social structure arises from the patterning of these relationships within a society (Hinde, 1976; Whitehead & Dufault, 1999a). Traditional analytical approaches cannot adequately represent or quantify such multilayer complexity. Given the importance of social relationships for the fitness, health and well-being of social animals (e.g., Akinyi et al, 2013; Silk et al, 2009; Silk et al, 2010; Cameron, Setsaas & Linklater, 2009; Stanton & Mann, 2012), accurate quantification and representation of this type of social complexity is critical to advance our understanding of the selective forces acting on individuals, the impact individuals may exert on their conspecifics, and the overall social structure of animal societies. We present a new approach for quantifying multilayer network centrality, called ‘consensus ranking’ (Pósfai et al, 2019), and illustrate its utility for uncovering novel information about individuals’ social roles and social structure using rhesus macaques (Macaca mulatta)
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