Landscape Ecology

, Volume 25, Issue 10, pp 1561–1573 | Cite as

Using a stochastic model and cross-scale analysis to evaluate controls on historical low-severity fire regimes

Research Article


Fire-scarred trees provide a deep temporal record of historical fire activity, but identifying the mechanisms therein that controlled landscape fire patterns is not straightforward. We use a spatially correlated metric for fire co-occurrence between pairs of trees (the Sørensen distance variogram), with output from a neutral model for fire history, to infer the relative strength of top-down vs. bottom-up controls on historical fire regimes. An inverse modeling procedure finds combinations of neutral-model parameters that produce Sørensen distance variograms with statistical properties similar to those observed from two landscapes in eastern Washington, USA, with contrasting topography. We find the most parsimonious model structure that is able to replicate the observed patterns and the parameters of this model provide surrogates for the predominance of top-down vs. bottom-up controls. Simulations with relatively low spread probability produce irregular fire perimeters and variograms similar to those from the topographically complex landscape. With higher spread probabilities fires exhibit regular perimeters and variograms similar to those from the simpler landscape. We demonstrate that cross-scale properties of the fire-scar record, even without historical fuels and weather data, document how complex topography creates strong bottom-up controls on fire spread. This control is weaker in simpler topography, and may be compromised in a future climate with more severe weather events.


Fire history Sørensen distance Neutral model Top-down vs. bottom-up controls Scaling 

Supplementary material

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Supplementary material 1 (DOC 179 kb)
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Supplementary material 2 (DOC 15707 kb)
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Supplementary material 3 (DOC 225 kb)
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Supplementary material 4 (DOC 43 kb)


  1. Cushman SA, McKenzie D, Peterson DL, Littell JS, McKelvey KS (2007) Research agenda for integrated landscape modeling. USDA Forest Service general technical report RMRS-GTR-194. Rocky Mountain Research Station, Fort Collins, COGoogle Scholar
  2. Diggle PJ (1983) Statistical analysis of spatial point patterns. Academic Press, New YorkGoogle Scholar
  3. Drossel B, Schwabl F (1992) Self-organized critical forest-fire model. Phys Rev Lett 69:1629–1632CrossRefPubMedGoogle Scholar
  4. Everett RL, Schellhaas R, Keenum D, Spurbeck D, Ohlson P (2000) Fire history in the ponderosa pine/Douglas-fir forests on the east slope of the Washington Cascades. For Ecol Manage 129:207–225CrossRefGoogle Scholar
  5. Falk DA (2004) Scaling rules for fire regimes. Ph.D. dissertation, University of Arizona, Tucson, AZGoogle Scholar
  6. Falk DA, Miller C, McKenzie D, Black AE (2007) Cross-scale analysis of fire regimes. Ecosystems 10:809–823CrossRefGoogle Scholar
  7. Finney MA (2001) Design of regular landscape fuel treatment patterns for modifying fire growth and behavior. For Sci 47:219–227Google Scholar
  8. Finney MA (2004) FARSITE: fire area simulator-model development and evaluation. USDA forest service research paper RMRS-RP-4. Rocky Mountain Research Station, Fort Collins, COGoogle Scholar
  9. Gardner RH, Urban DL (2007) Neutral models for testing landscape hypotheses. Landscape Ecol 22:15–29CrossRefGoogle Scholar
  10. Gedalof Z, Peterson DL, Mantua N (2005) Atmospheric, climatic, and ecological controls on extreme wildfire years in the northwestern United States. Ecol Appl 15:154–174CrossRefGoogle Scholar
  11. Hessl AE, McKenzie D, Schellhaas R (2004) Drought and Pacific Decadal Oscillation linked to fire occurrence in the inland Pacific northwest. Ecol Appl 14:425–442CrossRefGoogle Scholar
  12. Heyerdahl EK, Brubaker LB, Agee JK (2001) Spatial controls of historical fire regimes: a multiscale example from the interior West, USA. Ecology 82:660–678CrossRefGoogle Scholar
  13. Keane RE, Finney MA (2003) The simulation of landscape fire, climate, and ecosystem dynamics. In: Veblen TT, Baker WL, Montenegro G, Swetnam TW (eds) Fire and climatic change in temperate ecosystems of the western Americas. Springer-Verlag, New York, pp 32–68CrossRefGoogle Scholar
  14. Kellogg L-KB, McKenzie D, Peterson DL, Hessl AE (2008) Spatial models for inferring topographic controls on historical low-severity fire in the eastern Cascade Range of Washington, USA. Landscape Ecol 23:227–240CrossRefGoogle Scholar
  15. Kilgore BH, Taylor D (1979) Fire history of a sequoia-mixed conifer forest. Ecology 60(1):129–142CrossRefGoogle Scholar
  16. Kimura M (1983) The neutral theory of molecular evolution. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  17. Legendre P, Legendre L (1998) Numerical ecology, 2nd edn. Elsevier Science B.V., AmsterdamGoogle Scholar
  18. Lertzman K, Fall J, Dorner B (1998) Three kinds of heterogeneity in fire regimes: at the crossroads of fire history and landscape ecology. Northwest Sci 72:4–23Google Scholar
  19. Littell JS, McKenzie D, Peterson DL, Westerling AL (2009) Climate and wildfire area burned in western U.S. ecoprovinces, 1916–2003. Ecol Appl 19:1003–1021CrossRefPubMedGoogle Scholar
  20. McKenzie D, Kennedy MC (2010) Scaling laws and complexity in fire regimes. In: McKenzie D, Miller C, Falk DA (eds) The landscape ecology of fire, Chap. 2. Dordrecht, The Netherlands, Springer (in press)Google Scholar
  21. McKenzie D, Peterson DL, Alvarado E (1996) Extrapolation problems in modeling fire effects at large spatial scales: a review. Int J Wildland Fire 6:65–76CrossRefGoogle Scholar
  22. McKenzie D, Hessl AE, Kellogg L-KB (2006) Using neutral models to identify constraints on low-severity fire regimes. Landscape Ecol 21:139–152CrossRefGoogle Scholar
  23. Millar CI, Stephenson NL, Stephens SL (2007) Climate change and forests of the future: managing in the face of uncertainty. Ecol Appl 17:2145–2151CrossRefPubMedGoogle Scholar
  24. Millington JDA, Perry GLW, Malamud BD (2006) Models, data, and mechanisms: quantifying wildfire regimes. In: Cello G, Malamud BD (eds) Fractal analysis for natural hazards. Special Publication 261. London, Geological Society, pp 155–167Google Scholar
  25. Parisien M-A, Miller C, Ager AA, Finney MA (2010) Use of artificial landscapes to isolate controls on burn probability. Landscape Ecol 25:79–93CrossRefGoogle Scholar
  26. Parsons RA, Heyerdahl EK, Keane RE, Dorner B, Fall J (2007) Assessing accuracy of point fire intervals across landscapes with simulation modeling. Can J For Res 37:1605–1614CrossRefGoogle Scholar
  27. Peterson DL, Johnson MC (2007) Science-based strategic planning for hazardous fuel treatment. Fire Manage Today 67:13–18Google Scholar
  28. Press WH, Teukolsky SA, Vetterling WT, Flannery BP (2003) Numerical recipes in C++. Cambridge University Press, CambridgeGoogle Scholar
  29. R Development Core Team (2009) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0.
  30. Swetnam TW, Allen CD, Betancourt JL (1999) Applied historical ecology: using the past to manage for the future. Ecol Appl 9:1189–1206CrossRefGoogle Scholar
  31. Turley MC, Ford ED (2009) Definition and calculation of uncertainty in ecological process models. Ecol Modell 220(17):1968–1983CrossRefGoogle Scholar
  32. Weisberg PJ, Dongwook K, Py C, Bauer JM (2008) Modeling fire and landform influences on the distribution of old-growth pinyon-juniper woodland. Landscape Ecol 23:931–943Google Scholar
  33. Westerling AL, Bryant BP (2008) Climate change and wildfire in California. Clim Change 87(Suppl 1):S231–S249CrossRefGoogle Scholar
  34. With KA, King AW (1997) The use and misuse of neutral landscape models in ecology. Oikos 79(2):219–229CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.School of Forest Resources, College of the EnvironmentUniversity of WashingtonSeattleUSA
  2. 2.Pacific Wildland Fire Sciences LabUS Forest ServiceSeattleUSA

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