International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

Predicting spatial patterns of fire on a southern California landscape

Alexandra D. Syphard A E , Volker C. Radeloff A , Nicholas S. Keuler B , Robert S. Taylor C , Todd J. Hawbaker A , Susan I. Stewart D and Murray K. Clayton B
+ Author Affiliations
- Author Affiliations

A Department of Forest and Wildlife Ecology, University of Wisconsin, Madison, WI 53706, USA.

B Department of Statistics, University of Wisconsin, Madison, WI 53706, USA.

C National Park Service, Santa Monica Mountains National Recreation Area, Thousand Oaks, CA 91360, USA.

D USDA Forest Service, Northern Research Station, Evanston, IL 60201, USA.

E Corresponding author. Email: asyphard@yahoo.com

International Journal of Wildland Fire 17(5) 602-613 https://doi.org/10.1071/WF07087
Submitted: 29 July 2007  Accepted: 19 November 2007   Published: 3 October 2008

Abstract

Humans influence the frequency and spatial pattern of fire and contribute to altered fire regimes, but fuel loading is often the only factor considered when planning management activities to reduce fire hazard. Understanding both the human and biophysical landscape characteristics that explain how fire patterns vary should help to identify where fire is most likely to threaten values at risk. We used human and biophysical explanatory variables to model and map the spatial patterns of both fire ignitions and fire frequency in the Santa Monica Mountains, a human-dominated southern California landscape. Most fires in the study area are caused by humans, and our results showed that fire ignition patterns were strongly influenced by human variables. In particular, ignitions were most likely to occur close to roads, trails, and housing development but were also related to vegetation type. In contrast, biophysical variables related to climate and terrain (January temperature, transformed aspect, elevation, and slope) explained most of the variation in fire frequency. Although most ignitions occur close to human infrastructure, fires were more likely to spread when located farther from urban development. How far fires spread was ultimately related to biophysical variables, and the largest fires in southern California occurred as a function of wind speed, topography, and vegetation type. Overlaying predictive maps of fire ignitions and fire frequency may be useful for identifying high-risk areas that can be targeted for fire management actions.

Additional keywords: fire frequency, fire ignitions, generalised linear model, predictive mapping, wildland–urban interface.


Acknowledgements

We are grateful to the USDA Forest Service Northern Research Station and the Pacific Northwest Research Station for their support. We also thank the editor, the associate editor, and our anonymous reviewers for their insightful comments and recommendations that greatly improved the manuscript. Thanks also to Janet Franklin for her statistical advice.


References


Agresti A (1996) ‘An Introduction to Categorical Data Analysis.’ 1st edn. (Wiley: New York)

Allen CD, Savage M, Falk DA, Suckling KF, Swetnam TW, Schulke T, Stacey PB, Morgan P, Hoffman M , Klingel JT (2002) Ecological restoration of south-western ponderosa pine ecosystems: a broad perspective. Ecological Applications  12, 1418–1433.
CrossRef |

Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. USDA Forest Service, Intermountain Research Station, General Technical Report INT-167. (Ogden, UT)

Andrews PL, Bevins CD, Seli RC (2005) BehavePlus Fire Modeling System, version 3.0: user’s guide. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-106WWW Revised. (Ogden, UT)

Badia-Perpinya A , Pallares-Barbera M (2006) Spatial distribution of ignitions in Mediterranean periurban and rural areas: the case of Catalonia. International Journal of Wildland Fire  15, 187–196.
CrossRef |

Bautista D, Arana E, Marti-Bonmati L , Paredes R (1999) Validation of logistic regression models in small samples. Journal of Clinical Epidemiology  52, 237–241.
CrossRef | CAS | PubMed |

Belsey D, Kuh E, Welsch RE (Eds) (1980) ‘Regression Diagnostics: Identifying Influential Observations and Sources of Collinearity.’ (Wiley Sons: New York)

Bradstock RA, Gill AM, Kenny BJ , Scott J (1998) Bushfire risk at the urban interface estimated from historical weather records: consequences for the use of prescribed fire in the Sydney region of south-eastern Australia. Journal of Environmental Management  52, 259–271.
CrossRef |

Brillinger DR, Preisler HK, Benoit JW (2003) Risk assessment: a forest fire example. In ‘Science and Statistics: a Festschrift for Terry Speed’. (Ed. DR Goldstein) pp. 177–196. (Institute of Mathematical Statistics: Beachwood, OH)

Burgan RE, Rothermel RC (1984) BEHAVE: fire prediction and fuel modelling system – FUEL subsystem. USDA Forest Service, Intermountain Research Station, General Technical Report INT-167. (Ogden, UT)

Calkin DE, Gebert KM, Jones JG , Neilson RP (2005) Forest Service large fire area burned and suppression expenditure trends, 1970–2002. Journal of Forestry  103, 179–183.


Cardille JA, Ventura SJ , Turner MG (2001) Environmental and social factors influencing wildfires in the Upper Midwest, Unites States. Ecological Applications  11, 111–127.
CrossRef |

Chou YH (1992) Spatial autocorrelation and weighting functions in the distribution of wildland fires. International Journal of Wildland Fire  2, 169–176.
CrossRef |

Chou YH, Minnich RA , Chase RA (1993) Mapping probability of fire occurrence in San Jacinto Mountains, California, USA. Environmental Management  17, 129–140.
CrossRef |

Countryman CM (1972) The fire environment concept. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, General Technical Report PSW-7. (Berkeley, CA)

DellaSala DA, Williams JE, Williams CD , Franklin JF (2004) Beyond smoke and mirrors: a synthesis of fire policy and science. Conservation Biology  18, 976–986.
CrossRef |

Diaz-Avalos C, Peterson DL, Alvarado E, Ferguson SA , Besag JE (2001) Space–time modelling of lightning-caused ignitions in the Blue Mountains, Oregon. Canadian Journal of Forest Research  31, 1579–1593.
CrossRef |

Dickson BG, Prather JW, Xu Y, Hampton HM, Aumack EN , Sisk TD (2006) Mapping the probability of large fire occurrence in northern Arizona, USA. Landscape Ecology  21, 747–761.
CrossRef |

Fielding AH , Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation  24, 38–49.
CrossRef |

Forestry Canada Fire Danger Group (1992) Development and structure of the Canadian Forest Fire Behaviour Prediction System. Forestry Canada, Science and Sustainable Development Directorate, Report ST-X-3. (Ottawa, ON)

Franklin J (1998) Predicting the distribution of shrub species in Southern California from climate and terrain-derived variables. Journal of Vegetation Science  9, 733–748.
CrossRef |

Franklin J, McCullough P, Gray C (2000) Terrain variables used for predictive mapping of vegetation communities in Southern California. In ‘Terrain Analysis: Principles and Applications’. (Eds J Wilson, J Gallant) pp. 331–353. (Wiley: New York)

Fried JS, Winter G , Gilless JK (1999) Assessing the benefits of reducing fire risk in the wildland–urban interface: a contingent valuation approach. International Journal of Wildland Fire  9, 9–20.
CrossRef |

Haidinger TL , Keeley JE (1993) Role of high fire frequency in destruction of mixed chaparral. Madrono  40, 141–147.


Haight RG, Cleland DT, Hammer RB, Radeloff VC , Rupp TS (2004) Assessing fire risk in the wildland–urban interface. Journal of Forestry  104, 41–48.


Halsey RW (2005) ‘Fire, Chaparral, and Survival in Southern California.’ (Sunbelt Publications: San Diego, CA)

Hernandez PA, Graham CH, Master LL , Albert DL (2006) The effect of sample size and species characteristics on performance of different species distribution models. Ecography  29, 773–785.
CrossRef |

Jacobsen AL, Pratt RB, Ewers FW , Davis SD (2007) Cavitation resistance among twenty-six chaparral species of southern California. Ecological Monographs  77, 99–115.
CrossRef |

Keeley JE (1982) Distribution of lightning and man-caused wildfires in California. In ‘Proceedings of the International Symposium on the Dynamics and Management of Mediterranean-type Ecosystems’, 22–26 June 1981, San Diego, CA. (Eds CE Conrad, WC Oechel) USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, General Technical Report PSW 58, pp. 431–437. (Berkeley, CA).

Keeley JE (2000) Chaparral. In ‘North American Terrestrial Vegetation’. (Eds MG Barbour, WD Billings) pp. 202–253. (Cambridge University Press: Cambridge, MA)

Keeley JE (2005) Fire history of the San Francisco East Bay region and implications for landscape patterns. International Journal of Wildland Fire  14, 285–296.
CrossRef |

Keeley JE, Fotheringham CJ (2003) Impact of past, present, and future fire regimes on North American Mediterranean shrublands. In ‘Fire and Climatic Change in Temperate Ecosystems of the Western Americas’. (Eds TT Veblen, WL Baker, G Montenegro, TW Swetnam) pp. 218–262. (Springer-Verlag: New York)

Keeley JE, Fotheringham CJ , Morais M (1999) Reexamining fire suppression impacts on brushland fire regimes. Science  284, 1829–1832.
CrossRef | CAS | PubMed |

Lachenbruch PA (1967) An almost unbiased method for the probability of misclassification in discriminant analysis. Biometrics  23, 639–645.
CrossRef | CAS | PubMed |

Larjavaara M, Pennanen J , Tuomi TJ (2005) Lightning that ignites forest fires in Finland. Agricultural and Forest Meteorology  132, 171–180.
CrossRef |

Littell RC, Milliken GA, Stroup WW, Wolfinger RD (1996) ‘SAS System for Mixed Models.’ (SAS Institute Inc.: Cary, NC)

Mercer DE , Prestemon JP (2005) Comparing production function models for wildfire risk analysis in the wildland–urban interface. Forest Policy and Economics  7, 782–795.
CrossRef |

Miller C , Urban DL (2000) Modeling the effects of fire management alternatives on mixed-conifer forests in the Sierra Nevada. Ecological Applications  10, 85–94.


Moritz MA (2003) Spatiotemporal analysis of controls on shrubland fire regimes: age dependency and fire hazard. Ecology  84, 351–361.
CrossRef |

National Park Service (2002) Final general management plan and environmental impact statement, Vol. 1 of 2. USDI, National Park Service. (Thousand Oaks, CA)

National Park Service (2005) Final environmental impact statement for a fire management plan, Santa Monica Mountains National Recreation Area. USDI, National Park Service. (Thousand Oaks, CA)

Odion DC, Frost EJ, Strittholt JR, Jiang H, DellaSalla DA , Moritz MA (2004) Patterns of fire severity and forest conditions in the western Klamath Mountains, north-western California. Conservation Biology  18, 927–936.
CrossRef |

Pew KL , Larsen CPS (2001) GIS analysis of spatial and temporal patterns of human-caused wildfires in the temperate rainforest of Vancouver Island, Canada. Forest Ecology and Management  140, 1–18.
CrossRef |

Potts JP , Elith J (2006) Comparing species abundance models. Ecological Modelling  199, 153–163.
CrossRef |

Preisler HK, Brillinger DR, Burgan RE , Benoit JW (2004) Probability-based models for estimation of wildfire risk. International Journal of Wildland Fire  13, 133–142.
CrossRef |

Prestemon JP, Pye JM, Butry DT, Holmes TP , Mercer DE (2002) Understanding broadscale wildfire risks in a human-dominated landscape. Forest Science  48, 685–693.


PROC GLIMMIX (2005) ‘SAS/STAT Software, version 9.1.3 of the SAS System for Unix.’ (SAS Institute Inc.: Cary, NC)

Pyne SJ (2001) ‘Fire in America.’ (Princeton University Press: Princeton, NJ)

Pyne SJ, Andrews PL, Laven RD (1996) ‘Introduction to Wildland Fire.’ (Wiley: New York)

R Development Core Team (2005) R: a language and environment for statistical computing. (R Foundation for Statistical Computing: Vienna, Austria) Available at http://www.R-project.org [Verified 11 August 2008]

Radeloff VC, Hammer RB, Stewart SI, Fried JS, Holcomb SS , McKeefry JF (2005) The wildland–urban interface in the United States. Ecological Applications  15, 799–805.
CrossRef |

Radtke KWH, Arndt AM, Wakimoto RH (1982). Fire history of the Santa Monica Mountains. (Eds CE Conrad, WC Oechel) USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, General Technical Report PSW-58, pp. 438–443. (Berkeley, CA)

Reed WJ, Larsen CPS, Johnson EA , MacDonald GM (1998) Estimation of temporal variations in historical fire frequency from time-since-fire map data. Forest Science  44, 465–475.


Rollins MG, Morgan P , Swetnam T (2002) Landscape-scale controls over 20th century fire occurrence in two large Rocky Mountain (USA) wilderness areas. Landscape Ecology  17, 539–557.
CrossRef |

Rundel PW , King JA (2001) Ecosystem processes and dynamics in the urban/wildland interface of Southern California. Journal of Mediterranean Ecology  2, 209–219.


Scheller RM, Mladenoff DM, Crow TR , Sickley TA (2005) Simulating the effects of fire reintroduction versus continued fire absence on forest composition and landscape structure in the Boundary Waters Canoe Area, northern Minnesota, USA. Ecosystems  8, 396–411.
CrossRef |

Steele BM, Reddy SK , Keane RE (2006) A methodology for assessing the departure of current plant communities from historical conditions over large landscapes. Ecological Modelling  199, 53–63.
CrossRef |

Stephens SL (2005) Forest fire causes and extent on United States Forest Service lands. International Journal of Wildland Fire  14, 213–222.
CrossRef |

Syphard AD, Radeloff VC, Keeley JE, Hawbaker TJ, Clayton MK, Stewart SI , Hammer RB (2007a) Human influence on California fire regimes. Ecological Applications  17, 1388–1402.
CrossRef | PubMed |

Syphard AD, Clarke KC , Franklin J (2007b) Simulating fire frequency and urban growth in southern California coastal shrublands. Landscape Ecology  22, 431–445.
CrossRef |

Tanskanen H, Venäläinen A, Puttonen P , Granström A (2005) Impact of stand structure on surface fire ignition potential in Picea abies and Pinus sylvestris forests in southern Finland. Canadian Journal of Forest Research  35, 410–420.
CrossRef |

US Census (2000) ‘Census 2000 TIGER/Line Files [machine-readable data files].’ (US Census Bureau: Washington, DC)

USDA USDI (2001) Urban–wildand interface communities within vicinity of Federal lands that are at high risk from wildfire. Federal Register  66, 751–777.


Venables WN, Ripley BD (1999) ‘Modern Applied Statistics with S-Plus.’ 3rd edn. (Springer: New York)

Wells ML, O’Leary JF, Franklin J, Michaelson J , McKinsey DE (2004) Variations in a regional fire regime related to vegetation type in San Diego County, California (USA). Landscape Ecology  19, 139–152.
CrossRef |

Whelan RJ (1995) ‘The Ecology of Fire.’ (Cambridge University Press: Cambridge, UK)

Wotton BM , Martell DL (2005) A lightning fire occurrence model for Ontario. Canadian Journal of Forest Research  35, 1389–1401.
CrossRef |

Yang J, He HS, Shifley SR , Gustafson EJ (2007) Spatial patterns of modern period human-caused fire occurrence in the Missouri Ozark Highlands. Forest Science  53, 1–15.

CAS |

Zedler PH (1995) Plant life history and dynamic specialization in the chaparral/coastal sage scrub flora in southern California. In ‘Ecology and Biogeography of Mediterranean Ecosystems in Chile, California, and Australia’. (Eds MTK Arroyo, PA Zedler, MD Fox) pp. 89–115. (Springer-Verlag: New York)

Zedler PH, Clayton RG , McMaster GS (1983) Vegetation change in response to extreme events: the effect of a short interval between fires in California chaparral and coastal scrub. Ecology  64, 809–818.
CrossRef |



Export Citation Cited By (101)