Network 3D: Visualizing and Modelling Food Webs and other Complex Networks


Food webs, networks of who eats whom in an ecosystem, are central to our understanding of ecosystems. They are also a much-studied and important class of networks in burgeoning field of network science. Network3D is a software package for highly interactive and flexible three-dimensional visualizations of ecological networks and other complex networks, and for network analysis and modelling. The visualizations generated by Network3D facilitate data exploration and communication of concepts fundamental to the study of food webs, other ecological networks such as pollination networks and more broadly other complex networks such as protein interaction networks, citation networks and organizational networks (Eric Berlow: How complexity leads to simplicity). Network3D can also be used to analyse the structure of complex networks, test a number of stochastic models of network structure and test the robustness of networks to the targeted removal of species (nodes).

Network3D Features:

  • 3-dimensional visualization of complex networks; pan, zoom and rotate the visualization.
  • Force-directed, constrained force directed or property-based network layouts.
  • Manipulate network element (node and link) colors and sizes; maps node and link properties onto element colors and sizes.
  • Visualize paths through the network – highlight directed or undirected paths, loops through a node or connections between nodes.
  • Compute network topological properties – connectance; fraction of species that are top, intermediate, herbivore, basal, cannibals, omnivores; similarity statistics; standard deviation of generality, vulnerability and connectivity; distribution entropy, characteristic path length; mean clustering coefficient; nestedness (NODF).
  • Compare stochastic network models with empirical data.  Implements the random model (Erdős & Rényi 1959), cascade model (Cohen et al. 1990), niche model (Williams & Martinez 2000), nested hierarchy model (Cattin et al. 2004) and relaxed niche model (Williams & Martinez 2008).
  • Perform extinction experiments such as in (Dunne & Williams 2009); perform MaxEnt degree distribution analysis (Williams 2010; Williams 2011); aggregate topologically identical (Cohen et al. 1990) or similar (Martinez 1991) species.

Related publications

  • Cattin M.-F., Bersier L.-F., Banasek-Richter C., Baltensperger R. & Gabriel J.-P. (2004). Phylogenetic constraints and adaptation explain food-web structure. Nature, 427, 835-839.
  • Cohen J.E., Briand F. & Newman C.M. (1990). Community food webs:  data and theory. Springer, Berlin.
  • Dunne J.A. & Williams R.J. (2009). Cascading extinctions and community collapse in model food webs. Phil. Trans. R. Soc. Lond. B, 364, 1711-1723.
  • Erdős P. & Rényi A. (1959). On random graphs I. Publicationes Mathematicae Debrecen, 6, 290-297.
  • Martinez N.D. (1991). Artifacts or Attributes - Effects of Resolution on the Little-Rock Lake Food Web. Ecol. Monogr., 61, 367-392.
  • Williams R.J. (2010). Simple MaxEnt models explain food web degree distributions. Theoretical Ecology, 3, 45-52.
  • Williams R.J. (2011). Biology, Methodology or Chance? The Degree Distributions of Bipartite Ecological Networks. PLoS ONE, 6, e17645.
  • Williams R.J. & Martinez N.D. (2000). Simple rules yield complex food webs. Nature, 404, 180-183.
  • Williams R.J. & Martinez N.D. (2008). Success and its limits among structural models of complex food webs. J. Anim. Ecol., 77, 512–519.


Food web from Little Rock Lake


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