Social relationships among animals emerge from interactions in multiple ecological and social situations. However, we seldom ask how each situation contributes to the global structure of a population, and whether different situations contribute different information about social relationships and the position of individuals within the social fabric. Griffon vultures (Gyps fulvus) interact socially in multiple situations, including communal roosting, joint flights, and co-feeding. These social interactions can influence population-level outcomes, such as disease transmission and information sharing that determine survival and response to changes. We examined the unique contribution of each social and ecological situation to the social structure of the population and individuals' positions within the overall social network using high-resolution GPS tracking. We found that the number of individuals each vulture interacted with (degree) was best predicted by diurnal interactions-both during flights and on the ground (such as when feeding). However, the strength of social bonds, that is, the number of interactions an individual had (strength), was best predicted by interactions on the ground-both during the day (e.g., while feeding) and at night (e.g., while roosting) but not by interactions while flying. Thus, social situations differ in their impact on the relationships that individuals form. By incorporating the ecological situations in which social interactions occur we gain a more complete view of how social relationships are formed and which situations are important for different types of interactions.

Studying the spatial-social interface requires tools that distinguish between social and spatial drivers of interactions. Testing hypotheses about the factors determining animal interactions often involves comparing observed interactions with reference or null models. One approach to accounting for spatial drivers of social interactions in reference models is randomizing animal movement paths to decouple spatial and social phenotypes while maintaining environmental effects on movements. Here, we update a reference model that detects social attraction above the effect of spatial constraints. We explore the use of our wrap-around method and compare its performance to the previous approach using agent-based simulations. The wrap-around method provides reference models that are more similar to the original tracking data, while still distinguishing between social and spatial drivers. Furthermore, the wrap-around approach results in fewer false-positives than its predecessor, especially when animals do not return to one place each night but change movement foci, either locally or directionally. Finally, we show that interactions among GPS-tracked griffon vultures (Gyps fulvus) emerge from social attraction rather than from spatial constraints on their movements. We conclude by highlighting the biological situations in which the updated method might be most suitable for testing hypotheses about the underlying causes of social interactions. This article is part of the theme issue The spatial-social interface: a theoretical and empirical integration.