Social inheritance can explain the structure of animal social networks.

Amiyaal Ilany,Erol Akçay

doi:10.1038/ncomms12084

Abstract

The social network structure of animal populations has major implications for survival, reproductive success, sexual selection and pathogen transmission of individuals. But as of yet, no general theory of social network structure exists that can explain the diversity of social networks observed in nature, and serve as a null model for detecting species and population-specific factors. Here we propose a simple and generally applicable model of social network structure. We consider the emergence of network structure as a result of social inheritance, in which newborns are likely to bond with maternal contacts, and via forming bonds randomly. We compare model output with data from several species, showing that it can generate networks with properties such as those observed in real social systems. Our model demonstrates that important observed properties of social networks, including heritability of network position or assortative associations, can be understood as consequences of social inheritance.

Highlights

The social network structure of animal populations has major implications for survival, reproductive success, sexual selection and pathogen transmission of individuals
After positively interacting with the parents’ social contacts, young individuals are likely to form social bonds with these conspecifics, as was found in African elephants, Loxodonta africana[36]. We demonstrate that this simple social inheritance process can result in networks that match both the degree and local clustering distributions of real-world animal social networks, as well as their modularity
The values we find suggest that social inheritance is stronger in hyena and hyrax than in dolphins and sleepy lizards (Table 1)

Summary

Introduction

The social network structure of animal populations has major implications for survival, reproductive success, sexual selection and pathogen transmission of individuals. Most applications of social network analysis to non-human animals have been at a descriptive level, using various computational methods to quantify features of social structure and individuals’ position in it These methods, combined with increasingly detailed data ‘reality mining’[23] about social interactions in nature, provided valuable insights about the complex effects of social interaction on individual behaviours and fitness outcomes. Seyfarth[24] used a generative model of grooming networks based on individual preferences for giving and receiving grooming, and showed that a few simple rules can account for complex social structure. Real-world human and animal networks exhibit significantly more clustering than random or preferential attachment models predict[13,27]

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Conclusion