  Social network structures play an important role in the lives of animals by
affecting individual fitness, and the spread of disease and information.
Nevertheless, we still lack a good understanding of how these structures emerge
from the behaviour of individuals. Generative network models based on empirical
knowledge about animal social systems provide a powerful approach that can help
close this gap. In this study: 1) we develop a general model for the emergence
of social structures based on a key generative process of real animal social
networks, namely social preferences for traits (such as the age, sex, etc. of
social partners); 2) we use this model to investigate how different trait
preferences affect social network structure and function. We find that the
preferences used in a population can have far-reaching consequences for the
population, via effects on the transmission of disease and information and the
robustness of the social network against fragmentation when individuals
disappear. The study thus shows that social preferences can have consequences
that go far beyond direct benefits individuals gain from social partner
selection. It also shows that these consequences depend both on the preference
types, and on the types of traits they are used with. We discuss the
implications of the results for social evolution.
