Revealing the hidden structure of dynamic ecological networks. 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.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Marina Knight, Kathryn Leeming, Guy Nason, Matthew Nunes: Generalized Network Autoregressive Processes and the GNAR Package (2020) not zbMATH
- Wikle, Nathan B.; Hanks, Ephraim M.; Hughes, David P.: A dynamic individual-based model for high-resolution ant interactions (2019)
- Matias, Catherine; Miele, Vincent: Statistical clustering of temporal networks through a dynamic stochastic block model (2017)