Wildfires, complexity, and highly optimized tolerance

doi:10.1073/pnas.0508985102

Wildfires, complexity, and highly optimized tolerance

^*Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720; Departments of ^†Geography and ^§Physics, University of California, Santa Barbara, CA 93106; ^‡Department of Earth Sciences, California Polytechnic State University, San Luis Obispo, CA 93407; and ^∥Department of Control and Dynamical Systems, California Institute of Technology, Pasadena, CA 91125

Communicated by James S. Langer, University of California, Santa Barbara, CA, October 19, 2005 (received for review July 26, 2004)

Abstract

Recent, large fires in the western United States have rekindled debates about fire management and the role of natural fire regimes in the resilience of terrestrial ecosystems. This real-world experience parallels debates involving abstract models of forest fires, a central metaphor in complex systems theory. Both real and modeled fire-prone landscapes exhibit roughly power law statistics in fire size versus frequency. Here, we examine historical fire catalogs and a detailed fire simulation model; both are in agreement with a highly optimized tolerance model. Highly optimized tolerance suggests robustness tradeoffs underlie resilience in different fire-prone ecosystems. Understanding these mechanisms may provide new insights into the structure of ecological systems and be key in evaluating fire management strategies and sensitivities to climate change.

resilience

Footnotes

↵ ¶ To whom correspondence should be addressed. E-mail: carlson{at}physics.ucsb.edu.
Author contributions: M.A.M., J.M.C., and J.D. designed research; M.A.M., M.E.M., L.A.S., J.M.C., and J.D. performed research; M.A.M., M.E.M., L.A.S., J.M.C., and J.D. contributed new reagents/analytic tools; M.A.M., J.M.C., and J.D. analyzed data; and M.A.M., J.M.C., and J.D. wrote the paper.
Conflict of interest statement: No conflicts declared.
Abbreviations: HOT, highly optimized tolerance; LPNF, Los Padres National Forest; PLR, probability–loss–resource.