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Theoretical Ecology

, Volume 8, Issue 1, pp 15–21 | Cite as

Inferring topology from dynamics in spatial networks

  • Luis J. Gilarranz
  • Alan Hastings
  • Jordi Bascompte
Original Paper

Abstract

We examine the dynamics of oscillating populations in habitats described as networks of connected patches where the connections are not regular. This system would be typically analysed focusing either on the population dynamics, or measuring dispersal directly or indirectly. We focus on the question of the degree to which the dynamical patterns, as reflected in synchrony, reveal the underlying dispersal pathways. This would represent a bridge between two major spatial approaches: topological and dynamical. We show how local populations can be synchronized even if there is no direct dispersal route between them, while the stepping-stone populations are not synchronized. This leads to the surprising result that the topological structure of the underlying network is not reflected simply in patterns of synchrony across space in population dynamics. This shows that, with our current tools, the complex relationship between the underlying dispersal patterns and population dynamics prevent us from determining network structure through the observation of population dynamics.

Keywords

Metapopulation dynamics Modularity Synchronization Functional connectivity 

Notes

Acknowledgments

This work was funded by the European Research Council through an Advanced Grant (to JB), the Spanish Ministry of Education trough a FPU PhD Fellowship (to LJG), and US National Science Foundation Grant EF-0742674 (to AH).

Supplementary material

12080_2014_231_MOESM1_ESM.pdf (5.7 mb)
(PDF 5.73 KB)

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Luis J. Gilarranz
    • 1
  • Alan Hastings
    • 2
  • Jordi Bascompte
    • 1
  1. 1.Integrative Ecology GroupEstación Biológica de Doñana, CSICSevilleSpain
  2. 2.Department of Environmental Science and PolicyUniversity of California, DavisDavisUSA

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