Data from: Identifying timescales of change in vulture social networks

Animal social interaction patterns change over time, but the continuous
nature of social interactions makes selecting a timescale for temporal
analysis challenging. We applied both a heuristic approach and a
multilayer reducibility analysis approach to study timescales of change in
social networks of free-ranging griffon vultures. We analyzed social
networks in two behavioral situations: in-flight interactions, such as
during foraging movements, which we expected to fluctuate seasonally but
to exhibit a relatively constant pattern of change over the course of a
season; and diurnal ground interactions, such as interactions while
feeding, which we expected to show a pulsed temporal pattern that followed
the pattern of carcass availability on the landscape. The heuristic method
confirmed the suitability of a 3-10 day aggregation window for studying
temporal change in vulture social networks. It also highlighted how
different timescales of aggregation offer different insights about
longer-term patterns of change. Multilayer reducibility analysis confirmed
that substantial change was happening at every aggregation timescale we
tested, with no redundancy in network layers; that is, social interactions
in this population were not oversampled. However, it revealed more
similarity between non-adjacent layers in the flight networks as compared
to the feeding networks, further supporting the influence of carcasses as
drivers of social network structure. Multilayer reducibility analysis over
a multi-season timescale did not reveal seasonal similarities in network
structure extensive enough to override the substantial demographic and tag
coverage turnover between seasons. While multilayer analysis of temporal
dynamics in social networks may prove useful for the study of change in
links among a fixed subset of individuals, we highlight its limitations as
a tool for studying long-term social network structural change, especially
in free-living animal populations.