Person:

Nunn, Charles

Social learning has been documented in a wide diversity of animals. In free-living animals, however, it has been difficult to discern whether animals learn socially by observing other group members or asocially by acquiring a new behaviour independently. We addressed this challenge by developing network-based diffusion analysis (NBDA), which analyzes the spread of traits through animal groups and takes into account that social network structure directs social learning opportunities. NBDA fits agent-based models of social and asocial learning to the observed data using maximum-likelihood estimation. The underlying learning mechanism can then be identified using model selection based on the Akaike information criterion. We tested our method with artificially created learning data that are based on a real-world co-feeding network of macaques. NBDA is better able to discriminate between social and asocial learning in comparison to diffusion curve analysis, the main method that was previously applied in this context. NBDA thus offers a new, more reliable statistical test of learning mechanisms. In addition, it can be used to address a wide range of questions related to social learning, such as identifying behavioural strategies used by animals when deciding whom to copy.

Living in a large social group is thought to increase disease risk in wild animal populations, but comparative studies have provided mixed support for this prediction. Here, we take a social network perspective to investigate whether patterns of social contact within groups influence parasite risk. Specifically, increased modularity (i.e. sub-grouping) in larger groups could offset the increased disease risk associated with living in a large group. We simulated the spread of a contagious pathogen in random social networks to generate theoretically grounded predictions concerning the relationship between social network connectivity and the success of socially transmitted pathogens. Simulations yielded the prediction that community modularity (Q) negatively impacts parasite success. No clear predictions emerged for a second network metric we considered, the eigenvector centralization index (C), as the relationship between this measure and parasite success depended on the transmission probability of parasites. We then tested the prediction that Q reduces parasite success in a phylogenetic comparative analysis of social network modularity and parasite richness across 19 primate species. Using a Bayesian implementation of phylogenetic generalized least squares and controlling for sampling effort, we found that primates living in larger groups exhibited higher Q, and as predicted by our simulations, higher Q was associated with lower richness of socially transmitted parasites. This suggests that increased modularity mediates the elevated risk of parasitism associated with living in larger groups, which could contribute to the inconsistent findings of empirical studies on the association between group size and parasite risk. Our results indicate that social networks may play a role in mediating pressure from socially transmitted parasites, particularly in large groups where opportunities for transmitting communicable diseases are abundant. We propose that parasite pressure in gregarious primates may have favored the evolution of behaviors that increase social network modularity, especially in large social groups.