Abstract

AbstractIn low-severity fire regimes of the American West and elsewhere, landscape memory of fire events is registered in fire-scarred trees, with temporal record lengths often exceeding 200 years^1-5^. 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). 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. We used stochastic simulations iteratively with Monte Carlo inference to replicate the spatio-temporal structure of historical fire-scar records in forested watersheds of varying topographic complexity. We find that the balance between endogenous and exogenous controls on fire spread shifts with topographic complexity, where in the most complex landscapes the endogenous controls dominate and the pattern exhibits criticality. Comparison to an self-organized criticality (SOC) model^6,7^ 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 behavior in physical and chemical systems^8-11^. This explicitly cross-scale analysis provides a paradigm for identifying critical thresholds in landscape dynamics that may be crossed in a rapidly changing climate.

Highlights

  • Marked by power laws, and provide an ecological analogue to critical behavior in physical and chemical systems[8,9,10,11]

  • We provide an alternative interpretation of spatial patterns of fire to that of selforganized criticality (SOC) and other theories that do not adequately incorporate heterogenous landscape controls on fire

  • When we vary pspread between 0.35 and 1.0 and remove the size constraint, we find two domains in which simulated s distance2 (SD) variograms follow power laws (Figure 3), one of which is centered on the percolation threshold, and represents a transition between fires that are mostly small and cannot propagate across a landscape and larger ones that can

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Summary

Introduction

Marked by power laws, and provide an ecological analogue to critical behavior in physical and chemical systems[8,9,10,11]. When we vary pspread between 0.35 and 1.0 and remove the size constraint, we find two domains in which simulated SD variograms follow power laws (Figure 3), one of which is centered on the percolation threshold, and represents a transition between fires that are mostly small and cannot propagate across a landscape and larger ones that can.

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