One contribution of 14 to a theme issue 'The sociality-health -fitness nexus in animal societies'. Increased risk of infectious disease is assumed to be a major cost of group living, yet empirical evidence for this effect is mixed. We studied whether larger social groups are more subdivided structurally. If so, the social subdivisions that form in larger groups may act as barriers to the spread of infection, weakening the association between group size and infectious disease. To investigate this 'social bottleneck' hypothesis, we examined the association between group size and four network structure metrics in 43 vertebrate and invertebrate species. We focused on metrics involving modularity, clustering, distance and centralization. In a meta-analysis of intraspecific variation in social networks, modularity showed positive associations with network size, with a weaker but still positive effect in cross-species analyses. Network distance also showed a positive association with group size when using intraspecific variation. We then used a theoretical model to explore the effects of subgrouping relative to other effects that influence disease spread in socially structured populations. Outbreaks reached higher prevalence when groups were larger, but subgrouping reduced prevalence. Subgrouping also acted as a 'brake' on disease spread between groups. We suggest research directions to understand the conditions under which larger groups become more subdivided, and to devise new metrics that account for subgrouping when investigating the links between sociality and infectious disease risk.

Culturally transmitted traits are observed in a wide array of animal species, yet we understand little about the costs of the behavioural patterns that underlie culture, such as innovation and social learning. We propose that infectious diseases are a significant cost associated with cultural transmission. We investigated two hypotheses that may explain such a connection: that social learning and exploratory behaviours (specifically, innovation and extractive foraging) either compensate for existing infection or increase exposure to infectious agents. We used Bayesian comparative methods, controlling for sampling effort, body mass, group size, geographical range size, terrestriality, latitude and phylogenetic uncertainty. Across 127 primate species, we found a positive association between pathogen richness and rates of innovation, extractive foraging and social learning. This relationship was driven by two independent phenomena: socially contagious diseases were positively associated with rates of social learning, and environmentally transmitted diseases were positively associated with rates of exploration. Because higher pathogen burdens can contribute to morbidity and mortality, we propose that parasitism is a significant cost associated with the behavioural patterns that underpin culture, and that increased pathogen exposure is likely to have played an important role in the evolution of culture in both non-human primates and humans.

The transmission of infectious disease through a population is often modeled assuming that interactions occur randomly in groups, with all individuals potentially interacting with all other individuals at an equal rate. However, it is well known that pairs of individuals vary in their degree of contact. Here, we propose a measure to account for such heterogeneity: effective network size (ENS), which refers to the size of a maximally complete network (i.e., unstructured, where all individuals interact with all others equally) that corresponds to the outbreak characteristics of a given heterogeneous, structured network. We simulated susceptible-infected (SI) and susceptible-infected-recovered (SIR) models on maximally complete networks to produce idealized outbreak duration distributions for a disease on a network of a given size. We also simulated the transmission of these same diseases on random structured networks and then used the resulting outbreak duration distributions to predict the ENS for the group or population. We provide the methods to reproduce these analyses in a public R package, “enss.” Outbreak durations of simulations on randomly structured networks were more variable than those on complete networks, but tended to have similar mean durations of disease spread. We then applied our novel metric to empirical primate networks taken from the literature and compared the information represented by our ENSs to that by other established social network metrics. In AICc model comparison frameworks, group size and mean distance proved to be the metrics most consistently associated with ENS for SI simulations, while group size, centralization, and modularity were most consistently associated with ENS for SIR simulations. In all cases, ENS was shown to be associated with at least two other independent metrics, supporting its use as a novel metric. Overall, our study provides a proof of concept for simulation-based approaches toward constructing metrics of ENS, while also revealing the conditions under which this approach is most promising.

Social substructure can influence pathogen transmission. Modularity measures the degree of social contact within versus between “communities” in a network, with increasing modularity expected to reduce transmission opportunities. We investigated how social substructure scales with network size and disease transmission. Using small-scale primate social networks, we applied seven community detection algorithms to calculate modularity and subgroup cohesion, defined as individuals’ interactions within subgroups proportional to the network. We found larger networks were more modular with higher subgroup cohesion, but the association’s strength varied by community detection algorithm and substructure measure. These findings highlight the importance of choosing an appropriate community detection algorithm for the question of interest, and if not possible, using multiple algorithms. Disease transmission simulations revealed higher modularity and subgroup cohesion resulted in fewer infections, confirming that social substructure has epidemiological consequences. Increased subdivision in larger networks could reflect constrained time budgets or evolved defences against disease risk.