Spatially Heterogeneous Estimates of Fire Frequency in Ponderosa Pine Forests of Washington, Usa

Fire Ecology Volume 6, Issue 3, 2010 Kernan and Hessl: Spatially Heterogeneous Fire Frequency doi: 10.4996/fireecology.0603117 Page 117 PRACTICES AND APPLICATIONS IN FIRE ECOLOGY SPATIALLY HETEROGENEOUS ESTIMATES OF FIRE FREQUENCY IN PONDEROSA PINE FORESTS OF WASHINGTON, USA James T. Kernan1* and Amy E. Hessl2 1Department of Geography, State University of New York at Geneseo, 1 College Circle, Geneseo, New York 14454, USA 2Department of Geology and Geography, West Virginia University, G49 Brooks Hall, Morgantown, West Virginia 26506, USA *Corresponding author: Tel.: 001-585-245-5463; e-mail: [email protected] ABSTRACT Many fire history studies have evaluated the temporal nature of fire regimes using fire interval statistics calculated from fire scars. More recently, researchers have begun to evaluate the spatial properties of past fires as well. In this paper, we describe a technique for investigating spatio-temporal variability using a geographic information system (GIS). We used a dataset of fire-scarred trees collected from four sites in eastern Washington, USA, ponderosa pine (Pinus ponderosa C. Lawson) forests. The patterns of past fires recorded by individual trees (points) were converted to two-dimensional representations of fire with inverse distance weighting (IDW) in a GIS. A map overlay approach was then used to extract a fine-grained, spatially explicit reconstruction of fire frequency at the four sites. The resulting classified maps can supplement traditional fire interval statistics and fire atlas data to provide detailed, spatially heterogeneous estimates of fire frequency. Such information can reveal ecological relationships between fire and the landscape, and provide managers with an improved spatial perspective on fire frequency that can inform risk evaluations, fuels reduction efforts, and the allocation of fire-fighting resources. Keywords: fire frequency, GIS, inverse distance weighting, Pinus ponderosa, ponderosa pine, spatial, Washington state Citation: Kernan, J.T., and A.E. Hessl. 2010. Spatially heterogeneous estimates of fire frequency in ponderosa pine forests of Washington, USA. Fire Ecology 6(3): 117-135. doi: 10.4996/ fireecology.0603117 INTRODUCTION gal 2009); influences resource availability, nu- trient cycling, water yield, mass wasting, and Fire is a key ecological process as it inter- erosion (Agee 1993); affects air quality (Samp- acts with other processes (Agee 1993, Dale et son et al. 2000); and may exert climate feed- al. 2001); controls landscape patterns and spe- backs (Houghton and Hackler 2000, Wester- cies diversity (Swetnam and Betancourt 1997, ling et al. 2006). Given the significant role of Norman and Taylor 2003, Haire and McGari- fire in natural systems, there is continued dis- Fire Ecology Volume 6, Issue 3, 2010 Kernan and Hessl: Spatially Heterogeneous Fire Frequency doi: 10.4996/fireecology.0603117 Page 118 cussion among managers, scientists, and the sediment deposits may be spatially ambiguous, public regarding fuel management, prescribed and scars and sediments may be lost through fire, and the suppression of wildfires (Hunter natural processes (Kilgore and Taylor 1979, 1993, Agee 1997, Allen et al. 2002, Nash Fall 1998, Baker and Ehle 2001, Van Horne 2003). Reconstructing fire regimes can guide and Fulé 2006, Parsons et al. 2007). As a re- fuel reduction and controlled burns to reduce sult, data can be incomplete and may incorpo- fire risk, inform decisions for controlling wild- rate false negatives (unrecorded fires or de- fires, and provide targets for ecological resto- stroyed scars) in both spatial and temporal in- ration (Allen et al. 1995, Fulé et al. 1997, quiries, potentially underestimating fire size Swetnam and Betancourt 1997, Morgan et al. and frequency (Baker and Ehle 2001, Collins 2001, Parsons et al. 2007, Brown et al. 2008). and Stephens 2007). Regardless of these un- Furthermore, reconstructions can characterize certainties, investigations of historical fire re- the range of natural variability in fire frequen- gimes that use fire scar data often discuss fire cy and extent to help scientists distinguish be- occurrence (Swetnam and Betancourt 1997, tween climatic and anthropogenic influences Hessl et al. 2004) or fire frequency (McBride on fire (Swetnam and Westerling 2003), define 1983, Grissino-Mayer 1999, Everett et al. the factors that control fire (Heyerdahl et al. 2000, Heyerdahl et al. 2001) as summary mea- 2001, Hessl et al. 2004), evaluate the relation- sures of fire regimes. Fire frequency, often ex- ship between forest structure and fire (Beatty pressed as a fire interval, provides a site-scale and Taylor 2001), and predict process-driven estimate of the prevalence of fire, and allows vegetation responses to a changing climate generalized comparisons between sites. How- (Brown 2006). Natural variability has been ever, such measures do not provide the spatial- discussed in the context of its applications and ly explicit data needed for comprehensive eco- limitations in management, most notably in a logical analyses and informed management series of papers in Ecological Applications in (Heyerdahl and Card 2000, Baker and Kipfm- 1999 (volume 9 number 4). Many authors ueller 2001). concluded that, at a minimum, an understand- ing of natural variability can guide broad man- Fire Interval Statistics agement objectives in many dry forests in the western United States (Cissel et al. 1999, Lan- Fire interval statistics have been used for dres et al. 1999, Moore et al. 1999, Swetnam decades to describe surface fire regimes record- et al. 1999). More recent literature has contin- ed by fire scarred trees (Agee 1993, Baker and ued to discuss the importance of reconstruct- Ehle 2001). Mean fire interval (MFI) is com- ing patterns of natural variability to inform monly used to estimate fire frequency (Agee management decisions (Baker and Kipfmuel- 1993, Heyerdahl 1997, Baker and Ehle 2001). ler 2001, Morgan et al. 2001, Allen et al. 2002, A mean point fire interval (MPFI) is calculated Hessl et al. 2007, Lombardo et al. 2009). from a single tree, indicating fire frequency at Many fire history investigations have em- that point (Agee 1993). The MPFI is suscepti- phasized the temporal aspects of historical fire ble to false negatives (Kilgore and Taylor 1979, regimes (Allen et al. 1995, Baker and Kipfm- Fall 1999, Baker and Ehle 2001), as fires may ueller 2001, Parsons et al. 2007). This may be not be recorded or scars may be lost by subse- due in part to the inherent spatial uncertainty quent fires, so this measurement may overesti- of available data sources, such as fire-scarred mate fire interval. Sampling several trees in trees and charcoal sediment, which may chal- close proximity, averaging the MPFIs, and lenge spatial reconstructions. Not all fires are treating them as a point (Agee 1993) may help recorded by fire scars; the area represented by address such false negatives. However, a tree Fire Ecology Volume 6, Issue 3, 2010 Kernan and Hessl: Spatially Heterogeneous Fire Frequency doi: 10.4996/fireecology.0603117 Page 119 or a cluster of trees only records fire at a single tistical approaches (Everett et al. 2000, Kel- location on the landscape, and may not repre- logg et al. 2008, Haire and McGarigal 2009 sent broader areas. MPFI has been described among others) to provide a spatially explicit as a minimum measure of fire interval (esti- context that can inform management decisions mates longer fire intervals), and may provide a (Heyerdahl and Card 2000, Morgan et al. conservative measure of fire frequency (Baker 2001, Baker and Kipfmueller, 2001). Morgan and Ehle 2001). et al. (2001) reviewed wildland fire mapping Composite mean fire intervals (CMFI) are and discussed four methods: rule-based maps calculated by incorporating fire scars from derived from fire history data, modeled maps, multiple trees in a master list (Agee 1993). atlases (for contemporary fire regimes), and The CMFI can offset the occurrence of some the interpretation of fire scar data to infer rela- false negatives in that most events in a low-se- tive fire extents. The authors presented a broad verity surface fire regime should be recorded overview of the role of mapping in manage- by at least one tree, potentially identifying new ment and science and affirmed the usefulness intervals that cannot be accounted for using of mapping fire regime parameters. MPFI. The CMFI can also represent broader In this paper, we review a very specific as- areas, but is scale sensitive (McKenzie 2000, pect of wildland fire mapping, the reconstruc- McKenzie et al. 2006). The larger the study tion or estimation of fire perimeters in the con- area, the more fire events are incorporated and text of fire history research. The review was the lower the CMFI; thus, CMFI may repre- not intended to be exhaustive, but to sample a sent a maximum estimation (estimates shorter range of approaches and identify representative fire intervals) of fire frequency (Baker and studies. Our discussion then focuses on the Ehle 2001). However, CMFI homogenizes the methods of mapping perimeters in order to fire interval within the study area and does not demonstrate the usefulness of a geographic in- allow finer grain analyses. Compositing is also formation system (GIS) approach that inte- performed by analyzing subsets of data based grates spatial and temporal data. We identified on how many trees recorded a given fire event. four methods for mapping fire perimeters that Researchers have analyzed widespread fires were frequently described (Table 1). First, that scarred ≥25 % of trees (Grissino-Mayer et there have been numerous studies in regions al. 2004, Gonzalez et al. 2005), and fires that with high-severity fire regimes in which the au- scarred ≥50 % of trees to isolate large fire thors reconstructed fire boundaries using stand events (Barton et al. 2001). Hessl et al. (2004) age mapping, often in conjunction with fire composited fire years that scarred ≥10 % of scar data (Heinselman 1973, Hemstrom and trees to eliminate small spot fires, and fire Franklin 1982, Romme 1982, Agee et al.

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