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A simple model of bipartite cooperation for ecological and organizational networks

Nature volume 457, pages 463466 (22 January 2009) | Download Citation

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Abstract

In theoretical ecology, simple stochastic models that satisfy two basic conditions about the distribution of niche values and feeding ranges have proved successful in reproducing the overall structural properties of real food webs, using species richness and connectance as the only input parameters1,2,3,4. Recently, more detailed models have incorporated higher levels of constraint in order to reproduce the actual links observed in real food webs5,6. Here, building on previous stochastic models of consumer–resource interactions between species1,2,3, we propose a highly parsimonious model that can reproduce the overall bipartite structure of cooperative partner–partner interactions, as exemplified by plant–animal mutualistic networks7. Our stochastic model of bipartite cooperation uses simple specialization and interaction rules, and only requires three empirical input parameters. We test the bipartite cooperation model on ten large pollination data sets that have been compiled in the literature, and find that it successfully replicates the degree distribution, nestedness and modularity of the empirical networks. These properties are regarded as key to understanding cooperation in mutualistic networks8,9,10. We also apply our model to an extensive data set of two classes of company engaged in joint production in the garment industry. Using the same metrics, we find that the network of manufacturer–contractor interactions exhibits similar structural patterns to plant–animal pollination networks. This surprising correspondence between ecological and organizational networks suggests that the simple rules of cooperation that generate bipartite networks may be generic, and could prove relevant in many different domains, ranging from biological systems to human society11,12,13,14.

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Acknowledgements

We thank J. Dunne, R. Guimerà, J. Kertész, M. Sales-Pardo, D. Stouffer and R. Williams for comments and suggestions. F.R.-T. acknowledges funding from the European Commission under the FP6 NEST Pathfinder Initiative ‘Tackling Complexity in Science’ (MMCOMNET project, contract no. 012999). S.S. held a Doctoral Research Studentship funded by MMCOMNET and CONACYT, and currently is supported by a Postdoctoral Fellowship at the Oxford University Corporate Reputation Centre in conjunction with the CABDyN Complexity Centre.

Author Contributions B.U. provided the NYGI data; F.R.-T. designed the research; S.S., F.R.-T. and B.U. analysed the data; S.S. ran the simulations; S.S. and F.R.-T. wrote the paper.

Author information

Affiliations

  1. Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK

    • Serguei Saavedra
  2. CABDyN Complexity Centre,

    • Serguei Saavedra
    •  & Felix Reed-Tsochas
  3. Corporate Reputation Centre,

    • Serguei Saavedra
  4. James Martin Institute, Saïd Business School, University of Oxford, Oxford OX1 1HP, UK

    • Felix Reed-Tsochas
  5. Kellogg School of Management and Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois 60208, USA

    • Brian Uzzi
  6. Haas School of Business, University of California, Berkeley, California 94720, USA

    • Brian Uzzi

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Corresponding author

Correspondence to Felix Reed-Tsochas.

Supplementary information

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    Supplementary Information

    This file contains Supplementary Text 1 (Comparison of mutualistic models), Supplementary Tables S1-S5, Supplementary Text 2 (Pollination-network datasets), Supplementary Text 3 (The New York garment industry network), Supplementary Figures S1-S3 and Supplementary References.

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DOI

https://doi.org/10.1038/nature07532

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