Functional explanations advocated for mixed-species groups (mainly foraging and anti-predator advantages) are based on top-down approaches that implicitly assume the species as fixed and static social structures and shift the analysis of fitness benefits and costs from the level of individuals to the one of the groups/species. According to Farine, this operation of over-simplification leads to cursory inferences. On the contrary, a bottom-up approach, usually used to study intraspecific sociality, could provide an insight and a better understanding of the emergent and dynamic properties at each level of the social system. In this perspective, the author proposes Social Network Analysis (SNA) as a tool to measure inter-individual interaction within mixed species flocks. His aim is to explore the different roles within the flocks and the individual variations of sociality, as well as the fitness consequences that such variations leads to both individual and community structure. SNA main strength is the possibility to graphically represent and quantify, at any scale between dyads and groups, specific aspects of social relationships, such as number, intensity, frequency of direct and indirect interactions and individual roles, centrality (i.e. “keystone” individuals). As an istance, Loussou & Newman (2004) found a “keystone” individual in a bottlenose dolphin community in Scotland that acted as a bridge between two subgroups, therefore playing a critical role in keeping the connection between them. However, SNA current applications raise some issues concerning spatial and temporal constraints (Wey et al. 2008). On one hand, pattern of associations vary on a case by case basis depending on habitat topology and distribution of resources, while on the other hand, it’s important to distinguish genuine social network structures from relationships explained solely by shared space use. Furthermore, traditional graph models are limited in addressing temporal questions regarding changes that may occur over a given time period. Future work must also go beyond simple description, beginning to link network structure to biological and evolutionary consequences, in order to answer the “why” questions in sociobiological and ethological studies (Hock & Fefferman 2011).
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