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Power laws reveal phase transitions in landscape controls of fire regimes

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  • Donald McKenzie

    (Pacific Wildland Fire Sciences Lab, US Forest Service, 400 N 34th St #201, Seattle, Washington 98103, USA.)

  • Maureen C. Kennedy

    (School of Environmental and Forest Sciences, University of Washington, Box 352100, Seattle, Washington 98195, USA.)

Abstract

Understanding the environmental controls on historical wildfires, and how they changed across spatial scales, is difficult because there are no surviving explicit records of either weather or vegetation (fuels). Here we show how power laws associated with fire-event time series arise in limited domains of parameters that represent critical transitions in the controls on landscape fire. Comparison to a self-organized criticality model shows that the latter mimics historical fire only in a limited domain of criticality, and is not an adequate mechanism to explain landscape fire dynamics, which are shaped by both endogenous and exogenous controls. Our results identify a continuous phase transition in landscape controls, marked by power laws, and provide an ecological analogue to critical behaviour in physical and chemical systems. This explicitly cross-scale analysis provides a paradigm for identifying critical thresholds in landscape dynamics that may be crossed in a rapidly changing climate.

Suggested Citation

  • Donald McKenzie & Maureen C. Kennedy, 2012. "Power laws reveal phase transitions in landscape controls of fire regimes," Nature Communications, Nature, vol. 3(1), pages 1-6, January.
    • Handle: RePEc:nat:natcom:v:3:y:2012:i:1:d:10.1038_ncomms1731
    DOI: 10.1038/ncomms1731
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    Cited by:

    1. Morales, Juan Manuel & Mermoz, Mónica & Gowda, Juan Haridas & Kitzberger, Thomas, 2015. "A stochastic fire spread model for north Patagonia based on fire occurrence maps," Ecological Modelling, Elsevier, vol. 300(C), pages 73-80.
    2. Pană, Gabriel Tiberiu & Nicolin-Żaczek, Alexandru, 2023. "Motifs in earthquake networks: Romania, Italy, United States of America, and Japan," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    3. Furniss, Tucker J. & Hessburg, Paul F. & Povak, Nicholas A. & Salter, R. Brion & Wigmosta, Mark S., 2022. "Predicting future patterns, processes, and their interactions: Benchmark calibration and validation procedures for forest landscape models," Ecological Modelling, Elsevier, vol. 473(C).
    4. Mark D Penney & Yigit Yargic & Lee Smolin & Edward W Thommes & Madhur Anand & Chris T Bauch, 2021. "“Hot-spotting” to improve vaccine allocation by harnessing digital contact tracing technology: An application of percolation theory," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-15, September.

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