Landscape Ecology

, Volume 33, Issue 7, pp 1159–1176 | Cite as

The changing landscape of wildfire: burn pattern trends and implications for California’s yellow pine and mixed conifer forests

  • Zachary L. SteelEmail author
  • Michael J. Koontz
  • Hugh D. Safford
Research Article



Wildfire spatial patterns drive ecological processes including vegetation succession and wildlife community dynamics. Such patterns may be changing due to fire suppression policies and climate change, making characterization of trends in post-fire mosaics important for understanding and managing fire-prone ecosystems.


For wildfires in California’s yellow pine and mixed-conifer forests, spatial pattern trends of two components of the post-fire severity matrix were assessed for 1984–2015: (1) unchanged or very low-severity and (2) high-severity, which represent remnant forest and stand-replacing fire, respectively. Trends were evaluated for metrics of total and proportional burned area, shape complexity, aggregation, and core area. Additionally, comparisons were made between management units where fire suppression is commonly practiced and those with a history of managing wildfire for ecological/resource benefits.


Unchanged or very low-severity area per fire decreased proportionally through time, and became increasingly fragmented. High-severity area and core area increased on average across most of California, with the high-severity component also becoming simpler in shape in the Sierra Nevada. Compared to suppression units, managed wildfire units lack an increase in high-severity area, have less aggregated post-fire mosaics, and more high-severity spatial complexity.


Documented changes in severity patterns have cascading ecological effects including increased vegetation type conversion risk, habitat availability shifts, and remnant forest fragmentation. These changes likely benefit early-seral-associated species at the expense of mature closed-canopy forest-associated species. Managed wildfire appears to moderate some effects of fire suppression, and may help buy time for ecosystems and managers to respond to a changing climate.


Fire ecology Burn severity Landscape heterogeneity Disturbance Patch dynamics Climate change 



We thank two anonymous reviewers, along with Mark Schwartz and Malcolm North for providing critiques of early versions of this article, and Jay Miller for assistance in accessing burn severity data. Funding was provided by the United States Forest Service Pacific Southwest Region, and the University of California, Davis.

Supplementary material

10980_2018_665_MOESM1_ESM.csv (1 kb)
File 1 Parameter estimates for core area sensitivity analysis. Distance thresholds range from 50-400 m. The mean estimate, standard deviation, and 90% credible interval is presented for the intercepts, and the effect of year. The probability that effect of year is positive (i.e. the proportion of the parameter posterior distribution above zero) is listed as Prob_Positive. Supplementary material 1 (CSV 0 kb)
10980_2018_665_MOESM2_ESM.pdf (162 kb)
File 2 Correlation matrices and bivariate scatterplots for the landscape metrics modeled in the state-wide and managed wildfire analyses. Severity area (SevArea), shape complexity (EdgeArea), and severity core area (CoreArea) are on the log scale. Severity proportion (SevProp), the aggregation metric percent-like adjacency (PLA), and management area (Management) are on the proportion scale. Elevation, topographic roughness (Roughness), and mean 100 h fuel moisture (FuelMoisture) are in meters, meter standard deviations, and degrees Celsius, respectively. Supplementary material 2 (PDF 162 kb)
10980_2018_665_MOESM3_ESM.csv (11 kb)
File 3 Parameter estimates for each state-wide model (unique outcome metric and severity level combination). The mean estimate, standard deviation, and 90% credible interval is presented for the intercepts of California (\(\alpha\)) and each bioregion (\(\alpha + \alpha_{{br_{j} }}\)), and the effect of year on California (\(\beta_{yr}\)) and each bioregion (\(\beta_{yr} + \beta_{{br_{j} }}\)). The probability that effect of year is positive (i.e. the proportion of the parameter posterior distribution above zero) is listed as Prob_Positive. Supplementary material 3 (CSV 11 kb)
10980_2018_665_MOESM4_ESM.csv (10 kb)
File 4 Parameter estimates for each managed wildfire model (unique outcome metric and severity level combination). The mean estimate, standard deviation, and 90% credible interval is presented for each model parameter and the following derived parameters of interest: intercepts of fires burning fully in suppression units (Int_Supp = \(\alpha + \beta_{pmw} *PMW_{min}\)) and managed wildfire units (Int_MW = \(\alpha + \beta_{pmw} *PMW_{max}\)), the effect of year on fires in suppression units (Year_Supp = \(\beta_{yr} + \beta_{yr:pmw} *PMW_{min}\)) and managed wildfire units (Year_MW = \(\beta_{yr} + \beta_{yr:pmw} *PMW_{max}\)), where \(PMW_{min}\) and \(PMW_{max}\) represent proportional area within managed wildfire units of 0 and 1 on the z-scale, respectively. The estimated difference (e.g. \(\beta_{pmw} *PMW_{min} - \beta_{pmw} *PMW_{max}\)) between suppression and managed wildfire intercepts and effects of year are also presented. The probability that an effect or difference is positive (i.e. the proportion of the parameter posterior distribution above zero) is listed as Prob_Positive. Supplementary material 4 (CSV 9 kb)


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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Environmental Science and PolicyUniversity of CaliforniaDavisUSA
  2. 2.Graduate Group in EcologyUniversity of CaliforniaDavisUSA
  3. 3.Department of Plant SciencesUniversity of CaliforniaDavisUSA
  4. 4.United States Department of Agriculture, Forest ServicePacific Southwest RegionVallejoUSA

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