Abstract
We analyze the local structure of model and empirical food webs through the statistics of three-node subgraphs. We study analytically and numerically the number of appearances of each subgraph for a simple model of food web topology, the so-called generalized cascade model, and compare them with 17 empirical community food webs from a variety of environments, including aquatic, estuarine, and terrestrial ecosystems. We obtain analytical expressions for the probability of appearances of each subgraph in the model, and also for randomizations of the model that preserve species' numbers of prey and number of predators; their difference allows us to quantify which subgraphs are over- or under-represented in both the model and the empirical food webs. We find agreement between the model predictions and the empirical results. These results indicate that simple models such as the generalized cascade can provide a good description not only of the global topology of food webs, as recently shown, but also of its local structure.
Original language | English (US) |
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Pages (from-to) | 260-268 |
Number of pages | 9 |
Journal | Journal of Theoretical Biology |
Volume | 246 |
Issue number | 2 |
DOIs | |
State | Published - May 21 2007 |
Funding
We thank R. Guimerà and M. Sales-Pardo for stimulating discussions and helpful suggestions. JC thanks the Spanish CICYT (FIS2006-12296-C02-01) and the Direcció General de Recerca (2005 SGR 000 87) for support. DBS acknowledges the NU ChBE Murphy Fellowship and NSF-IGERT “Dynamics of Complex Systems in Science and Engineering” (DGE-9987577). LANA acknowledges a Searle Leadership Fund Award, National Institute of General Medical Sciences/National Institutes of Health K25 Career Award, the J.S. McDonnell Foundation, and the W.M. Keck Foundation.
Keywords
- Complex networks
- Food webs
- Motifs
- Network structure
ASJC Scopus subject areas
- General Agricultural and Biological Sciences
- Applied Mathematics
- General Biochemistry, Genetics and Molecular Biology
- General Immunology and Microbiology
- Statistics and Probability
- Modeling and Simulation