  Recent technological advances and long-term data studies provide interaction
data that can be modelled through dynamic networks, i.e a sequence of different
snapshots of an evolving ecological network. Most often time is the parameter
along which these networks evolve but any other one-dimensional gradient
(temperature, altitude, depth, humidity, . . . ) could be considered.Here we
propose a statistical tool to analyse the underlying structure of these
networks and follow its evolution dynamics (either in time or any other
one-dimensional factor). It consists in extracting the main features of these
networks and summarise them into a high-level view.We analyse a dynamic animal
contact network and a seasonal food web and in both cases we show that our
approach allows for the identification of a backbone organisation as well as
interesting temporal variations at the individual level.Our method, implemented
into the R package dynsbm, can handle the largest ecological datasets and is a
versatile and promising tool for ecologists that study dynamic interactions.
