Disease implications of animal social network structure: A synthesis across social systems

Abstract

The disease costs of sociality have largely been understood through the link between group size and transmission. However, infectious disease spread is driven primarily by the social organization of interactions in a group and not its size. We used statistical models to review the social network organization of 47 species, including mammals, birds, reptiles, fish and insects by categorizing each species into one of three social systems, relatively solitary, gregarious and socially hierarchical. Additionally, using computational experiments of infection spread, we determined the disease costs of each social system. We find that relatively solitary species have large variation in number of social partners, that socially hierarchical species are the least clustered in their interactions, and that social networks of gregarious species tend to be the most fragmented. However, these structural differences are primarily driven by weak connections, which suggest that different social systems have evolved unique strategies to organize weak ties. Our synthetic disease experiments reveal that social network organization can mitigate the disease costs of group living for socially hierarchical species when the pathogen is highly transmissible. In contrast, highly transmissible pathogens cause frequent and prolonged epidemic outbreaks in gregarious species. We evaluate the implications of network organization across social systems despite methodological challenges, and our findings offer new perspective on the debate about the disease costs of group living. Additionally, our study demonstrates the potential of meta-analytic methods in social network analysis to test ecological and evolutionary hypotheses on cooperation, group living, communication and resilience to extrinsic pressures.

Social structure contains epidemics and regulates individual roles in disease transmission in a group‐living mammal

“…Our disease simulations showed that the spatial structure of empirically derived badger contact networks reduced the probability of large epidemics. This provides evidence for the importance of a "social bottleneck" (Nunn, Craft, et al, 2015;Sah et al, 2018;VanderWaal et al, 2014;White et al, 2017) in disease transmission functioning at a population level. We also found considerable variation between individuals in the size of epidemics they generated in networks with realistic spatial and social group structure that varied according to both their local (direct connections) and global position (role in broader network connectivity) F I G U R E 5 (a) The proportion of variation in (a) the number of secondary infections and (b) epidemic size explained by the choice of the initially infected individual in the three network types for each transmission probability used in the study.…”

“…However, recent advances in bio-logging technology to collect high-resolution social contact data, and methods of network analysis, have enabled the quantification of social interactions among wild animals (Blyton, Banks, Peakall, Lindenmayer, & Gordon, 2014;Hamede, Bashford, McCallum, & Jones, 2009;Hirsch, Reynolds, Gehrt, & Craft, 2016;Pinter-Wollman et al, 2013;White et al, 2017). This has facilitated modeling work that has provided many important insights into how social systems and network structure influences the transmission of directly transmitted infections in nonhuman animals (e.g., Sah et al, 2018). However, an important gap remains in understanding the role of stable social group structure at a population level (cf.…”

“…However, an important gap remains in understanding the role of stable social group structure at a population level (cf. work within groups; Nunn, Craft, illespie, Schaller, & Kappeler, 2015;Sah et al, 2018;VanderWaal et al, 2014;White et al, 2017), and in particular how it interacts with the role of individuals in the spread of infection.…”

A multi-species repository of social networks

“…For the first time, there is thus an opportunity to carry out comparative network studies across multiple species to identify general patterns and generate broad principles [19]. However, such studies are currently challenging due to differences in data collection methods, a lack of standardized formats for published data, and the absence of a centralized data repository [20]. While such repositories exist for human interaction data [2,3,16,1], there is a gap for social network datasets across multiple taxonomic groups.…”

And the winner of the inaugural Sidnie Manton Award is…

“…Weinstein's (2018) Review of a computer vision for animal ecology (Weinstein 2018). Sah's (2018) Review on disease implications of animal social network structure (Sah, Mann, & Bansal, 2018).…”