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Self-organized multistability in the forest fire model.

Diego Rybski1, Van Butsic2, Jan W Kantelhardt3

  • 1Potsdam Institute for Climate Impact Research-PIK, Member of Leibniz Association, P.O. Box 601203, 14412 Potsdam, Germany Department of Environmental Science Policy and Management, University of California Berkeley, 130 Mulford Hall #3114, Berkeley, California 94720, USA; and Complexity Science Hub Vienna, Josefstädterstrasse 39, A-1090 Vienna, Austria.

Physical Review. E
|August 20, 2021
PubMed
Summary
This summary is machine-generated.

This forest fire model study reveals self-organized multistability and quasiperiodic behavior, persisting even at low tree growth probabilities. The system exhibits periodic attractors and 1/f² scaling, transitioning towards white noise over time.

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Area of Science:

  • Statistical physics
  • Complex systems modeling
  • Dynamical systems theory

Background:

  • Forest fire models are critical systems.
  • Understanding criticality is key in statistical physics.

Purpose of the Study:

  • Investigate the behavior of a forest fire model near criticality.
  • Analyze the impact of tree growth probability on system dynamics.
  • Characterize emergent phenomena like multistability and noise scaling.

Main Methods:

  • Simulations of a forest fire model.
  • Analysis of system dynamics at varying tree growth probabilities (p).
  • Examination of power-spectrum scaling.

Main Results:

  • Identified periodic attractors with oscillating tree density for high p.
  • Observed persistent self-organized multistability with increasing states for lower p.
  • Characterized quasiperiodic behavior (frequency ≈p) approaching chaos.
  • Detected 1/f² scaling (Brownian noise) at low frequencies, transitioning to white noise.

Conclusions:

  • The forest fire model exhibits complex dynamics near criticality.
  • Self-organized multistability and quasiperiodicity are robust features.
  • The model demonstrates a transition from Brownian to white noise characteristics.