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Imposed and inherent scales in cellular automata models of habitat

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  • Craig, Peter D.

Abstract

Both observational and modelling studies of the natural environment are characterised by their ‘grain’ and ‘extent’, the smallest and largest scales represented in time and space. These are imposed scales that should be chosen to ensure that the natural scales of the system are captured in the study. A simple cellular automata model of habitat represents only the presence or absence of vegetation, with global and local interactions described by four empirical parameters. Such a model can be formulated as a nonlinear Markov equation for the habitat probability. The equation produces inherent space and time scales that may be considered as transition scales or the scales for recovery from disturbance. However, if the resolution of the model is changed, the empirical parameters must be changed to preserve the properties of the system. Further, changes in the spatial resolution lead to different interpretations of the spatial structure. In particular, as the resolution is reduced, the apparent dominance of one habitat type over the other increases. The model provides an ability to compare both field and model investigations conducted at different resolutions in time and space.

Suggested Citation

  • Craig, Peter D., 2010. "Imposed and inherent scales in cellular automata models of habitat," Ecological Modelling, Elsevier, vol. 221(20), pages 2425-2434.
    • Handle: RePEc:eee:ecomod:v:221:y:2010:i:20:p:2425-2434
    DOI: 10.1016/j.ecolmodel.2010.07.011
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    References listed on IDEAS

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    1. Colasanti, R.L. & Hunt, R. & Watrud, L., 2007. "A simple cellular automaton model for high-level vegetation dynamics," Ecological Modelling, Elsevier, vol. 203(3), pages 363-374.
    2. Pawlowski, Christopher W. & McCord, Christopher, 2009. "A Markov model for assessing ecological stability properties," Ecological Modelling, Elsevier, vol. 220(2), pages 86-95.
    3. J. Timothy Wootton, 2001. "Local interactions predict large-scale pattern in empirically derived cellular automata," Nature, Nature, vol. 413(6858), pages 841-844, October.
    4. Mathey, Anne-Hélène & Krcmar, Emina & Dragicevic, Suzana & Vertinsky, Ilan, 2008. "An object-oriented cellular automata model for forest planning problems," Ecological Modelling, Elsevier, vol. 212(3), pages 359-371.
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    Cited by:

    1. Hui, Cang, 2011. "Forecasting population trend from the scaling pattern of occupancy," Ecological Modelling, Elsevier, vol. 222(3), pages 442-446.

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