Revealing the hidden structure of dynamic ecological networks

Abstract : 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.
Type de document :
Pré-publication, Document de travail
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Contributeur : Catherine Matias <>
Soumis le : jeudi 5 janvier 2017 - 15:59:35
Dernière modification le : mardi 16 janvier 2018 - 16:14:51
Document(s) archivé(s) le : jeudi 6 avril 2017 - 12:16:15


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  • HAL Id : hal-01426652, version 1
  • ARXIV : 1701.01355



Vincent Miele, Catherine Matias. Revealing the hidden structure of dynamic ecological networks. 2017. 〈hal-01426652〉



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