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Local interactions predict large-scale pattern in empirically derived cellular automata

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  • J. Timothy Wootton

    (The University of Chicago)

Abstract

An important unanswered question in ecology is whether processes such as species interactions that occur at a local scale can generate large-scale patterns seen in nature1,2. Because of the complexity of natural ecosystems, developing an adequate theoretical framework to scale up local processes has been challenging. Models of complex systems can produce a wide array of outcomes; therefore, model parameter values must be constrained by empirical information to usefully narrow the range of predicted behaviour. Under some conditions, spatially explicit models of locally interacting objects (for example, cells, sand grains, car drivers, or organisms), variously termed cellular automata3,4 or interacting particle models5, can self-organize to develop complex spatial and temporal patterning at larger scales in the absence of any externally imposed pattern1,6,7,8. When these models are based on transition probabilities of moving between ecological states at a local level, relatively complex versions of these models can be linked readily to empirical information on ecosystem dynamics. Here, I show that an empirically derived cellular automaton model of a rocky intertidal mussel bed based on local interactions correctly predicts large-scale spatial patterns observed in nature.

Suggested Citation

  • J. Timothy Wootton, 2001. "Local interactions predict large-scale pattern in empirically derived cellular automata," Nature, Nature, vol. 413(6858), pages 841-844, October.
  • Handle: RePEc:nat:nature:v:413:y:2001:i:6858:d:10.1038_35101595
    DOI: 10.1038/35101595
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    Cited by:

    1. J Timothy Wootton & James D Forester, 2013. "Complex Population Dynamics in Mussels Arising from Density-Linked Stochasticity," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-12, September.
    2. Craig, Peter D., 2010. "Imposed and inherent scales in cellular automata models of habitat," Ecological Modelling, Elsevier, vol. 221(20), pages 2425-2434.
    3. Perry, George L.W. & Enright, Neal J., 2007. "Contrasting outcomes of spatially implicit and spatially explicit models of vegetation dynamics in a forest-shrubland mosaic," Ecological Modelling, Elsevier, vol. 207(2), pages 327-338.
    4. Li, Hong & Arias, Mijail & Blauw, Anouk & Los, Hans & Mynett, Arthur E. & Peters, Steef, 2010. "Enhancing generic ecological model for short-term prediction of Southern North Sea algal dynamics with remote sensing images," Ecological Modelling, Elsevier, vol. 221(20), pages 2435-2446.
    5. Convertino, M., 2011. "Neutral metacommunity clustering and SAR: River basin vs. 2-D landscape biodiversity patterns," Ecological Modelling, Elsevier, vol. 222(11), pages 1863-1879.
    6. Huan Cao & Tian Li & Shuxia Li & Tijun Fan, 2017. "An integrated emergency response model for toxic gas release accidents based on cellular automata," Annals of Operations Research, Springer, vol. 255(1), pages 617-638, August.
    7. Clancy, Damian & Tanner, Jason E. & McWilliam, Stephen & Spencer, Matthew, 2010. "Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo," Ecological Modelling, Elsevier, vol. 221(10), pages 1337-1347.

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