Landscape-scale controls over 20th century fire occurrence in two largeRocky Mountain (USA) wilderness areas
Matthew G. Rollins1,*, Penelope Morgan2 and Thomas Swetnam3
1USDA Forest Service, Fire Sciences Laboratory, 5775 Hwy 10 West, Missoula, Montana 59807, USA;2Department of Forest Resources, College of Natural Resources University of Idaho, Moscow,Idaho 83844-1133, USA; 3Laboratory of Tree-Ring Research, The University of Arizona, Tucson,Arizona 85721, USA; *Author for correspondence (e-mail: [email protected])
Received 6 March 2001; accepted in revised form 30 April 2002
Key words: Fire atlases, Fire ecology, Fire history, Fire regimes, Pattern-process interactions, Rocky Mountains
Abstract
Topography, vegetation, and climate act together to determine the spatial patterns of fires at landscape scales.Knowledge of landscape-fire-climate relations at these broad scales (1,000s ha to 100,000s ha) is limited and islargely based on inferences and extrapolations from fire histories reconstructed from finer scales. In this study,we used long time series of fire perimeter data (fire atlases) and data for topography, vegetation, and climate toevaluate relationships between large 20thcentury fires and landscape characteristics in two contrasting areas: the486,673-ha Gila/Aldo Leopold Wilderness Complex (GALWC) in New Mexico, USA, and the 785,090-ha Sel-way-Bitterroot Wilderness Complex (SBWC) in Idaho and Montana, USA. There were important similarities anddifferences in gradients of topography, vegetation, and climate for areas with different fire frequencies, bothwithin and between study areas. These unique and general relationships, when compared between study areas,highlight important characteristics of fire regimes in the Northern and Southern Rocky Mountains of the WesternUnited States. Results suggest that amount and horizontal continuity of herbaceous fuels limit the frequency andspread of surface fires in the GALWC, while the moisture status of large fuels and crown fuels limits the fre-quency of moderate-to-high severity fires in the SBWC. These empirically described spatial and temporal rela-tionships between fire, landscape attributes, and climate increase understanding of interactions among broad-scaleecosystem processes. Results also provide a historical baseline for fire management planning over broad spatialand temporal scales in each wilderness complex.
Introduction
Fire frequency, along with fire severity and size, af-fects the composition, structure, and successional tra-jectories of Rocky Mountain forests in the WesternUnited States (Habeck and Mutch 1973; Arno 1980;Romme and Knight 1981; Romme 1982; Johnson1992; Agee 1993; Turner et al. 1997). At fine spatialand temporal scales (individual trees and foreststands, seconds to hours), the physics of fire spreadare well known (Albini 1976; Rothermel 1983). Theclimatic and anthropogenic constraints on fire occur-rence have also been documented in many studiesconducted at much broader scales (regions, centuries,
and millennia; Barrett and Arno (1982) and Gruell(1985), Baisan and Swetnam (1990), Swetnam(1993), Barrett et al. (1997), Swetnam and Betancourt(1998)). Few studies, however, have empirically de-termined the factors that determine the variability offire patterns at intermediate spatial and temporalscales: 1,000s to 100,000s of ha, and over the decadesof the 20thcentury (but see Chou et al. (1990) andBarton (1994), McKelvey and Busse (1996), Kushlaand Ripple (1997), Heyerdahl et al. (2001)). Under-standing temporal and spatial constraints on fire fre-quency at these intermediate scales is important forboth fire and vegetation management and for increas-
ing the understanding of how landscape patterns in-fluence fire processes and vice versa.
Centennial to seasonal climate variability entrainsfire regimes (Swetnam and Betancourt 1998; Veblenet al. 1999; Grissino-Mayer and Swetnam 2000).Drought directly affects fire occurrence by determin-ing fuel moisture and by constraining productivity(Schroeder and Buck 1970; Agee 1993). Anthropo-genic ignitions locally affected fire frequencies in theUnited States prior to European settlement (Gruell1985; Boyd 1999); and fire suppression and land-usepatterns have dramatically affected twentieth-centuryfire frequencies (Minnich 1983; Covington et al.1994; Swetnam and Baisan 1996; Rollins et al. 2001).While the temporal patterning of fire is important forevaluating the effects of changing fire regimes, thispaper focuses on factors that influence the spatial pat-terning of fire frequency across landscapes.
Quantitative understanding of the interactions be-tween landscape patterns and ecosystem processes isa main focus of landscape ecology and is necessaryfor managing landscapes with an ecosystem perspec-tive (Turner et al. 1995; Christensen et al. 1996). Thescarcity of research into the influence of landscapepatterns on fire processes limits understanding of theeffects of changing fire regimes and how landscapeswill respond to these changes. Lack of knowledge offire-landscape-climate interactions also hinders effortsto assess long-term consequences of ecologicallybased fire management strategies that involve bothwildland fire use and wildfire suppression (Kaufmannet al. 1994; Jensen and Bourgeron 1993). The increas-ing demand for information on the frequency of firein wilderness and other areas is a direct consequenceof fire management strategies focused on 1) assessingbroad-scale fire hazard, 2) restoring fire to its naturalrole in ecosystems, and 3) strategic fire suppressionplanning. Knowledge of the factors that determinelandscape-scale spatial and temporal fire patterning iscritical for 1) predicting fire effects under changingclimate, 2) mapping departures from pre-20th centuryfire regimes, 3) planning the restoration of fire as anecosystem process, and 4) mitigating potentially haz-ardous fire conditions at broad scales.
Several direct and indirect environmental gradientsact together to constrain fire patterns in time andspace. Topography influences fire occurrence, behav-ior, and effects at landscape to regional scales (Agee1993). Temperature and water availability changewith elevation, contributing to shifts in vegetation anddifferent seasonal fuel moistures (Whittaker and Nier-
ing 1965; Stephenson 1990). Mountain ranges affectlarge air masses via orographic lifting, which in-creases precipitation, changes the timing and severityof storms, and contributes to the potential for ignitionof fires by lightning (Schroeder and Buck 1970; Fu-quay et al. 1979). Slope angles affect fire spread byincreasing the efficiency of radiant energy transferfrom flaming fronts to upslope fuels (Albini 1976;Rothermel 1983; Agee 1993). Aspect affects fuelmoisture status by determining insolation (Frank andLee 1966; Waring and Running 1998). Steep, south-west-facing aspects tend to have the highest irradi-ance and driest conditions; however, lack of sufficientfuels on these slopes because of low productivity maylimit fire ignition and spread. Northeast aspects aremore productive than other aspects in general, and infuel-limited ecosystems may have the fuel continuitynecessary for fire spread (Agee 1993).
Fires are most frequent at elevations where neitherfuel continuity nor moisture is limiting (Martin’s1982). According to this theoretical model of the spa-tial distribution of fire frequency, middle elevationssupport frequent fires because sites are sufficientlyproductive (fuels are continuous) and dry (ignitionpotential is high and flaming fronts spread readily) toallow fire spread. In general, lower and upper eleva-tion sites have lower productivity and moist condi-tions, respectively. This reduces ignition probabilityand the likelihood of fire spread (Martin’s 1982; Bar-ton 1994). Here we use Martin’s (1982) theoreticalmodel of fire frequency to formulate hypotheses. Wealso extend it to other landscape gradients (e.g. slopeand aspect) that control horizontal fuel continuity andmoisture status.
To evaluate relationships between fire regime,landscape characteristics, and climate we analyzed20th century time series of fire perimeter data (fire at-lases) along topographic, biophysical, and climaticgradients in the 486,673-ha Gila Aldo Leopold Wil-derness Complex (GALWC) in New Mexico, USAand the 785,090-ha Selway-Bitterroot WildernessComplex (SBWC) in Idaho and Montana, USA. Ourobjectives were to determine how probabilities forareas to burn multiple times are related to topogra-phy, vegetation, and climate using spatially and tem-porally continuous data for fire occurrence, topogra-phy, potential vegetation type, and climate. In aseparate paper we focus on assessing the amount andrate of burning over time in our study areas (Rollinset al. 2001). Comparative analyses both within andbetween these dramatically different wilderness com-
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plexes provide opportunities to both generalize ourfindings and allow identification of specific, uniquerelationships between landscape factors and the prob-ability of an area to burn multiple times, therebystrengthening inferences about how landscape pat-terns influence fire processes. It is important to notethat we use the term fire frequency to describe thenumber of times a specific area (in this case, a pixelor grid cell) burned during the period of record forthe fire atlas from each wilderness complex.
We expect that landscape-scale patterns of fire fre-quency will be related to aspect, slope, elevation, andpotential vegetation type. Steeper slopes and slopesthat receive higher insolation will tend to have rela-tively high fire frequencies. Functional processes be-hind these relationships will be related to both hori-zontal fuel continuity and local fuel moisture status.We hypothesize that years with the most extensivefires will be dry, preceded by moist years similar toresults from Swetnam and Betancourt (1990) and Ve-blen et al. (1999), Grissino-Mayer and Swetnam(2000).
Study areas
Gila/Aldo Leopold Wilderness Complex
The 486,673-ha Gila/Aldo Leopold Wilderness Com-plex (Figure 1) encompasses the headwaters of theGila River, the Mogollon Mountains, and the BlackRange 70 km north of Silver City, New Mexico,USA, and is mostly contained within the Gila Na-tional Forest and the Gila Cliff Dwellings NationalMonument. Elevations range from 1,300 m near themain stem of the Gila River to 3,300 m on top of theMogollon Mountains. The Aldo Leopold Wildernessportion is rugged, with elevations ranging from 1,500m near the Mimbres River to 2,900 m on McKnightMountain in the Black Range. Parent material of theGALWC results from volcanic events in the late Cre-taceous (USGS 1965). The Gila Conglomerate, a ter-tiary sedimentary formation, is exposed in tall, pinna-cle-like rock formations along the Middle and EastForks of the Gila River.
Average annual precipitation in the GALWC var-ies from 250 mm in the valleys to 760 mm in themountain ranges (Beschta 1976). Precipitation overthe year is bi-modal, with a wet period from Decem-ber to March, and monsoonal storms from the Gulfof Mexico occurring between July and September.
Mean daily temperatures vary from below freezing inthe winter to extremely hot in the mid-summer (30°C). Spring conditions are usually dry with thunder-storm activity increasing in early July. Thunderstormsoccur nearly daily during summer months, resultingfrom rapid lifting of moist air masses from the Gulfof Mexico. The GALWC has perhaps the highestlevel of lightning-caused fire occurrence in the nation,with up to five lightning-caused fires per 100 ha peryear (Barrows 1978). Fire season in the GALWC be-gins as early as April and extends through September.The GALWC is dominated by low-severity (low for-est mortality) surface fire regimes with mixed sever-ity regimes found at upper elevations (Swetnam andDieterich 1985; Abolt 1996). During dry years of the20th century, fire behavior has been extreme withstand-replacing fire common across all elevations.
At upper elevations, forests of the GALWC com-prise mixed stands of Douglas-fir (Pseudotsuga men-ziesii), southwestern white pine (P. strobiformis), En-glemann spruce (P. engelmannii), subalpine fir (Abieslasiocarpa), white fir (A. concolor), and aspen (Pop-ulus tremuloides). At middle elevations, ponderosapine (P. ponderosa) stands cover extensive mesasabove the west and middle forks of the Gila River.Piñon-oak-juniper woodlands (P. edulus Englemannii,Juniperus deppeana, J. monosperma, and Quercusspp.) gain dominance as elevation decreases. Piñon-oak-juniper woodlands and ponderosa pine forestsmake up a large proportion of the GALWC (21% and23% respectively). Broad valleys at the lowest eleva-tions support desert scrub and grasslands (Ceanothus,Artemisia, and Yucca spp.)
Selway-Bitterroot Wilderness Complex
The Selway-Bitterroot Wilderness Complex (SBWC)in Idaho and Montana, USA (Figure 1) is a785,090-ha wilderness area, second in size (in theconterminous United States) only to the adjacentFrank Church River-of-No-Return Wilderness inIdaho. The area is characterized by extremely ruggedterrain with broad topographic variation. Portions ofthe wilderness are found on the Bitterroot, Clearwa-ter, Lolo, and Nez Perce National Forests. The east-ern portion of the wilderness consists of the BitterrootMountains. These large (3,000 + m), granitic moun-tains follow the eastern boundary of the wildernessfor 70 km and comprise most of the upper subalpineand alpine forest ecosystems of the SBWC. East/west-oriented glacial valleys dissect the mountains to
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valley bottoms as low as 1,000 m. The central/south-ern portion of the wilderness comprises the majorityof the Selway River basin. This area consists of rug-ged terrain with complex high ridges dissected bysteep canyons as low as 500 m. The northwesternportion of the wilderness falls within the LochsaRiver drainage with high mountains (the ClearwaterMountains) and forested valleys with over 1,500 mof relief. Mountainous geology of the SBWC is char-acterized by granitic rock shaped by Pleistocene gla-ciation. Lower elevations are dominated by substrateformed in a series of volcanic events during the Me-sozoic and Cenozoic periods (Greenwood and Morri-
son 1973). The canyons and valleys of the centralSelway and Clearwater River drainages were dis-sected prior to the eruption of the Columbia Riverbasalt (Habeck 1972).
Average precipitation in the SBWC is higher thanthe GALWC. Along the main stems of the Lochsa andSelway Rivers close to 1000 mm of precipitation fallsannually, with values as high as 1800 mm in the cen-tral and Bitterroot mountain ranges; over 50% of thisprecipitation falls as snow (Finklin 1983). January isusually the wettest month with average monthly pre-cipitation ranging from 75 mm to 250 mm (Finklin1983). Late summer (July and August) is the driest
Figure 1. Vegetation for the 486,673 ha Gila/Aldo Leopold and the 785,090 ha Selway-Bitterroot Wilderness Complexes.
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time of year with average monthly precipitation be-tween 20 and 30 mm. Summertime precipitation var-ies widely; values have ranged from 7 mm in July1969 to 160 mm in July 1975 (Finklin 1983). The fireseason in the SBWC begins in June and may extendthrough September. Fire regimes are mixed, withpatchy stand-replacement fire dominant in upper el-evation forests (70% of the SBWC) and lower sever-ity, surface fire dominant at lower elevations. Stand-replacement fires occur across all elevations duringextreme years (Brown et al. 1994; Habeck 1972).Though less frequent than in the GALWC, large thun-derstorms occur throughout the spring and summer,with a peak in activity during the early summer. Drythunderstorms are common in the late summer andearly fall. Monthly mean temperatures range from−10 °C in January to 20 °C in July and August.
At elevations around 2,500 m, forests in the cen-tral, southern and eastern portions of the SBWC aredominated by subalpine forests containing assem-blages of Englemann spruce, subalpine fir, whitebarkpine (P. albicaulis), and lodgepole pine (P. contorta).Stands of lodgepole pine result from widespread firesand are extensive, with relatively homogenous standstructure and ages (Arno et al. 1993). Seventy percentof the SBWC is comprised of subalpine forests withhighest subalpine elevations characterized by mixedwhitebark pine/alpine larch (Larix lyallii) forests (Ha-beck 1972) and middle elevations characterized bymixed ponderosa pine, Douglas-fir, and western larch(L. occidentalis) forests. As elevation decreases theseassemblages change to more mesic Douglas-fir/grandfir (A. grandis) forests. Diverse, Pacific maritime for-ests distinguish the lowest elevations, along the mainstem of the Lochsa and Selway Rivers, with assem-blages of western redcedar (Thuja plicata), westernhemlock (Tsuga heterophylla), western white pine (P.monticola), and Douglas-fir ranging from 500 m to1,500 m.
Methods
Spatial data
Fire atlasesTwentieth-century fire perimeters were obtained fromarchival fire data at the Gila National Forest and digi-tized for the GALWC. For the SBWC, perimeterswere obtained in digital form from the Bitterroot,Clearwater, and Nez Perce National Forests. Fire ar-
chives consisted of old fire reports or operational fireperimeter maps (Rollins 2000; Rollins et al. 2001).Each annual set of fire perimeters was converted intoa grid representing burned and unburned areas. Theseannual grids were added together to create continu-ous surfaces of fire frequency for each study area(Figure 2). Again, in this paper we use the term firefrequency to describe the number of times a specificpixel burned during the period of record for each fireatlas. To include fires that burned over the wildernessboundary we ‘clipped’ the final fire atlases to a 5-kmbuffer around the wilderness boundary for each studyarea. This boundary defined the extent of the wilder-ness complexes for the purpose of our analyses.
TopographyDigital elevation data were obtained from the USDAForest Service, Rocky Mountain Research StationFire Sciences Laboratory in Missoula, Montana. Dig-ital elevation data were developed as two compiledsets of USGS 7.5-minute digital elevation models(DEMs, 30 m cells) for the GALWC and SBWC byKeane et al. (1998, 2000). For both areas, a combi-nation of Level 1 and Level 2 DEMs was used to ob-tain complete coverage of the study area. Level 1DEMs sometimes show a horizontal banding pattern,and this was the case in some of the DEMs used. Anequal weight, directional filter (seven cells high andone cell wide) was applied to the level 1 DEMs tosmooth areas where horizontal banding was most ap-parent. The DEMs were then tiled together, and edgesbetween Level 1 and Level 2 DEMs were filtered tosmooth transitions between adjoining datasets (Keaneet al. (1998, 2000)). Each DEM was ‘clipped’ to theextent of the fire atlas. Slope and aspect surfaces werederived from the final DEMs using the Arc/Grid com-mands ‘slope’ and ‘aspect’ (ESRI 1998). We derivedan insolation index based on sun angles (elevationand azimuth) calculated for 1900 GMT (12 noon) forthe 15th of each month of the fire season for each area(April–September for the GALWC and May–Septem-ber for the SBWC). Using ENVI (ENvironment forVisualizing Images) image processing software andthe DEMs from each area, the data from each monthwere averaged to yield a single surface for each studyarea integrating slope, aspect and geographic locationand representing relative amounts of insolation acrossthe landscape.
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Potential vegetationWe used potential vegetation type (PVT) to describebiophysical settings in each wilderness area (Keaneet al. (1998, 1999, 2000)). Potential vegetation typesrepresent aggregated habitat types and were namedfor the late successional community presumed to ex-ist for a specific site in the absence of disturbance(Daubenmire 1968a; Pfister and Arno 1980; Cooperet al. 1991). We used PVTs, as opposed to finer-grained habitat types because the relatively coarseresolution of PVT maps more closely matched theminimum mapping unit of the digital fire atlases. Po-
tential vegetation types have been used to describedistributions of plant habitats across broad landscapesfor Rocky Mountain forests (Daubenmire 1968b;Pfister and Arno 1980; Keane et al. 1999). Thoughoriginally based on single-pathway succession(Daubenmire 1968a), PVTs remain valid for multiple-pathway succession systems (Cooper et al. 1991).Vegetation communities rarely reach late successionalstages because of recurring disturbances and non-lin-ear successional trajectories (Noble and Slayter 1980;Cooper et al. 1991), but indicator species are usuallypresent even in disturbed areas.
Figure 2. Reburned areas in each wilderness area based on archives of 20th century fire perimeters. Data extend from 1909 to 1993 in theGALWC and from 1880 to 1996 in the SBWC.
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Classifications of PVTs were developed from mod-els incorporating geographic location, field data, to-pography, local productivity, and soil characteristics.With these data, area ecologists and local scientistsformulated series of rules to assign PVT to differentpermutations of location and topographic classifica-tions for each wilderness complex (Keane et al.(1998, 2000)). These rules were then implemented asa spatial model within a GIS. Thematic accuracies ofthese layers were determined by comparison withfield data by Keane et al. (1998, 2000). Overall accu-racies of PVT maps were 65% in the SBWC (Khat0.57) and 89% in the GALWC (Khat 0.45; Keane etal. (1998, 2000)). We excluded areas of development,riparian forests, and water bodies from our analyses.It is important to note that although PVTs were namedfor the late successional tree species presumed to oc-cupy a given site, and are meant to represent biophys-ical conditions, the PVT classifications used for eachwilderness area also represented the distribution ofdominant existing forest types in each wildernesscomplex.
Climate dataEstimates of summer Palmer Drought Severity Index(PDSI) were available for the 20th century from theNational Oceanic and Atmospheric AdministrationNational Climatic Data Center in Boulder, Colorado(http://www.ncdc.noaa.gov/ol/climate/climatedata.ht-ml). PDSI is a measure of prolonged (monthly andyearly) periods of water status and accounts for pre-cipitation, evapotranspiration, and soil moisture con-ditions (Palmer 1965). Typical values range from neg-ative 6 (drought) to positive 6 (moist conditions).Data were summarized within state by climate divi-sions. For each wilderness complex we used Aprilthrough October PDSI to represent drought statusduring the months when fires were most likely to oc-cur.
Data analysis procedures
Using digital fire atlases we document unburned areaand areas burned once, twice, and three or more timesfor each wilderness complex, by PVT. Percentageswere calculated as the proportion of the total area ofeach PVT in each fire frequency class. In the SBWC,subalpine forests were divided into lower subalpinedry and moist and upper subalpine dry and moist toexamine differences between fire frequencies on dryversus moist subalpine sites in the SBWC and to fa-
cilitate comparison with the mixed conifer andspruce/fir PVTs in the GALWC.
We used a graphical/statistical approach to assessrelationships between fire frequency and topography,vegetation, and climate. Maps of 20th century fire fre-quency were cross-tabulated with data for elevation,slope, aspect, insolation index, and PVT to derivedistributions of fire frequency over landscape varia-bles. Distributions of landscape variables (i.e. topog-raphy and vegetation) within each fire frequency class(unburned, burned once, burned twice, and burnedthree or more times) were compared with distribu-tions of the landscape variables over the entire studyarea to test for random conditions. We derived pro-portions of area burned in each fire frequency classfor each landscape variable to enable direct compari-son on identical scales, despite uneven distribution ofarea burned in different fire frequency classes. De-spite conversion to proportions, our use of the termfire frequency remains the same, except we now referto the proportion of pixels in a given landscape var-iable that burned a specific number of times. Cumu-lative distributions of proportions were compared us-ing two sample Kolmogorov-Smirnov tests (KS-tests)for differences in empirically derived, continuous dis-tributions (Ho: distributions are not different, P >0.10). For discrete distributions of PVT and aspect ineach wilderness complex we used log-likelihood testsfor goodness-of-fit (G-tests) to assess differences be-tween observed and expected proportional areas indifferent fire frequency classes (Ho: distributions arenot different, P > 0.10).
We used superposed epoch analysis (SEA, Loughand Fritts (1987) and Swetnam (1993), Grissino-Mayer and Swetnam (2000)) to determine whetherperiods of drought or moist conditions were relatedto years with large areas burned. Superposed epochanalysis has been used to evaluate the influence ofclimate conditions prior to and during years with ex-tensive fires at landscape to regional scales (Swetnam1993; Swetnam and Betancourt 1998; Veblen et al.1999). In this study, we used SEA to analyze climateprior to and during the 8 years with the largest areasburned in each wilderness complex. We excluded1992 and 1993 from SEA in the GALWC as largefires during these years were managed as prescribednatural fires and were allowed to reach large sizes dueto relatively wet conditions (Garcia 1997). We con-ducted separate SEA on ponderosa pine and Douglas-fir (GALWC), mixed conifer and spruce/fir (GAL-WC), Douglas-fir (SBWC), and subalpine (SBWC)
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forest PVTs to assess drought relationships withinsimilar biophysical settings between study areas.
Results
Archival mapped fire perimeters extended from 1909to 1993 in the GALWC and from 1880 to 1996 in theSBWC (Rollins 2000; Rollins et al. 2001). Mappeddata indicated that 139,716 ha burned in 220 fires inthe GALWC and 472,921 ha burned in 524 fires inthe SBWC. In the GALWC, 7,021 ha burned three ormore times; 5,727 ha (82%) of this were in ponde-rosa pine and Douglas-fir PVTs, which make up 44%of the study area (Table 1). In the SBWC, 7,379 haburned three or more times, with the majority of re-burned areas in Douglas-fir and lower subalpine PVTs(Table 1). More area burned multiple times in lowerthan in upper subalpine forest PVTs. While slightlyhigher proportions of moist subalpine forests burnedonce and twice, overall, xeric subalpine PVTs ac-
counted for the majority of area in subalpine forestsburned more than once in the 20th century (Table 2).
Ponderosa pine and Douglas-fir PVTs had thehighest proportions burned in each study area. Morearea burned in these PVTs than expected if burn pat-terns were random (Figure 3). The piñon/oak/juniperPVT in the GALWC and the lower subalpine PVT inthe SBWC burned less often than expected if burnedareas were randomly distributed across the study ar-eas. Large areas that burned multiple times in ponde-rosa pine and Douglas-fir PVTs in the GALWC andwestern redcedar and Douglas-fir PVTs in the SBWCcontributed most to the overall significance of log-likelihood tests.
Areas burned multiple times in the GALWC werefound at higher elevations than expected based on thedistribution of elevation for the entire area (Figure 4).The majority of areas burned three or more times inthe GALWC were found between 2,300 m and 2,600m. Mean elevation in the GALWC was 2,200 m. Two-sample KS-tests for differences in distributionsshowed significant differences between the distribu-
Table 1. Area burned (ha) and percent of potential vegetation type (PVT) burned by fire frequency class for the entire study area and PVTswithin each wilderness complex. The majority of each wilderness complex remained unburned in the 20th century. Reported areas excludedeveloped lands, riparian forests, and water.
Gila/Aldo Leopold Wilderness Complex
Unburned Once Twice Three or more times Total
Ha % Ha % Ha % Ha % Ha %
Entire study Area 383,286 79 72,580 15 23,038 5 7,020 1 485,924 100
tion of elevation for all fire frequency classes (exclud-ing unburned) and the distribution of elevation forboth wilderness areas (P < 0.10, Table 3). Areasburned multiple times in the SBWC were found be-tween 1,000 m and 1,700 m (Figure 4). Mean eleva-tion in the SBWC was 1,758 m. KS-tests indicatedno significant differences between distributions ofslope for areas with different fire frequencies in ei-ther study area (Table 3). Log-likelihood analyses in-dicated that northeastern aspects in the GALWC andwestern and southwestern aspects in the SBWC havehigher fire frequencies than expected under randomconditions (P < 0.10, Figure 5).
In the GALWC, cumulative distributions of inso-lation index for areas that burned three or more timeswere significantly different from the distribution ofinsolation across the entire area (P < 0.05, Table 3).In general, areas burned three or more times hadlower insolation values (Figure 6). There were no sig-nificant differences between insolation index for otherfire frequencies in the GALWC, and we found no dif-ference between distributions of insolation for differ-ent fire frequency classes in the SBWC.
Annual area burned was compared to drought sta-tus using superposed epoch analysis. In the GALWCcurrent or lagged year drought status were not relatedto years with large areas burned (Figure 7). Whenpartitioned by PVT, SEA indicated that years in whichlarge areas burned in the upper elevation mixed coni-fer and spruce-fir PVTs of the GALWC were signifi-cantly drier than average (P < 0.01). In the SBWC,years with extensive fires were significantly dry (P <0.01). This pattern remained the same when SEA wasperformed separately for Douglas-fir and Subalpineforests (Figure 7).
Discussion
Spatial distributions of fire frequency during the 20th
century are related to gradients of landscape variablesand climate in each wilderness complex. Relation-ships between landscape characteristics and areasburned multiple times showed key differences andsimilarities between the two wilderness complexes.These unique and general patterns highlight importantaspects of fire regimes for the forests of the Northernand Southern Rocky Mountains.
Two main limitations for using fire atlases to studythe landscape controls over fire frequency over broadareas and for long periods of time are:
1. database quality and2. anthropogenic effects on the size and frequency of
firesIt was impossible to completely assess the spatial ac-curacy of these historical spatial data. Fire mappingmethods change over time in archived sets of fire pe-rimeters, resulting in an uneven temporal distributionof precision and accuracy. For the most part, fires thathad important financial, ecological, or social signifi-cance tend to be mapped and archived (McKelveyand Busse 1996). This may bias the data toward large,high severity events. The degree of this bias is unclearand probably varies through time. However, themapped fires likely represent a large proportion of thearea actually burned (Strauss et al. 1989). Compari-son with the National Interagency Fire ManagementIntegrated Database (USDA Forest Service. 1993)suggested that less than 1% of fires less than 50 hawere accounted for in the mapped perimeter data. Onthe other hand over 95% of fires over 1,000 ha wereincluded.
Fire atlases used in these analyses lacked informa-tion describing fire severity patterns or unburned ar-
Table 2. Area burned (ha) and percent of PVT burned in each fire frequency class for subalpine PVTs in the SBWC. Dryer subalpine forestsburned more frequently.
eas within fire perimeters. Fire atlases with broadtemporal extents typically do not contain this infor-mation. Care must be taken to incorporate existingknowledge of general fire regimes in the study area(e.g. from dendroecological analysis) when makinginferences based on fire atlases that contain no infor-mation on severity. Fire atlases from the GALWC andthe SBWC also contained no information about dif-ferent levels of fire suppression imposed on the spa-tial patterns of mapped fires. By conducting this re-search in wilderness areas, the effects ofanthropogenic fire exclusion were reduced relative toareas with extensive road networks or land-use pat-terns. Fires in these areas, while suppressed, were not
subject to the same degree of mechanical manipula-tion as fires in adjacent ‘managed’ landscapes. Thatis, there were no extensive areas of fireline construc-tion using mechanical equipment. For the most part,the effect of fire suppression was more of a matter offire exclusion. That is, fires that may have reachedlarge sizes were extinguished at an early stage by ag-gressive initial attack by aerially delivered firefight-ers and/or aerial retardant operations. However, firesuppression effects may have obscured some land-scape-fire frequency relationships to the degree thatour interpretations of landscape fire relationshipswere tenuous.
Figure 3. Areas burned two or more times for different potential vegetation types. A plus (+) indicates a significant positive difference anda minus (−) a significant negative difference (P < 0.10) between the spatial distribution of area burned multiple times in different PVTs andthe distribution of PVT over the entire wilderness complex.
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Our results were quite similar to findings of otherstudies that used fire history information to investi-gate landscape-fire relationships (Barton 1994; McK-elvey and Busse 1996; Kushla and Ripple 1997). Fur-ther, 20th century relationships between topography,vegetation, and climate in the GALWC and SBWC
are similar to results from previous research in otherareas using pre-20th century fire history databases(Romme and Knight 1981; Martin’s 1982; Engelmark1987; Brown et al. 1994; Barton 1994; Heyerdahl etal. 2001). This supports the assertion that patterns re-vealed by analyses of 20th century fire patterns were
Figure 4. Cumulative distributions of elevation for areas burned multiple times and the distribution of elevation over each wilderness com-plex. Distributions are plotted as proportions to equalize scales. Shifts in the distributions of elevation for areas burned multiple times indi-cate higher fire frequencies in high elevations in the GALWC and low elevations in the SBWC. The differences in distributions between thetwo study areas are related to differences in gradients of fuel moisture status and fuel continuity. Two-sided Kolmogorov-Smirnov testsindicated that distributions of elevation for areas burned multiple times were significantly different from elevation over the entire area (P <0.10).
Table 3. Probabilities for the two-sided Kolmogorov-Smirnov tests for differences in distributions of different fire frequencies and landscapevariables over the entire study area. Results are reported for the entire period of record. Significant differences (P < 0.10) are shown in bold.
GALWC SBWC
Elevation Slope Insol. Index Elevation Slope Insol. Index
Unburned 0.43 1.00 1.00 0.00 0.97 1.00
Once 0.00 1.00 0.99 0.00 0.97 1.00
Twice 0.00 0.99 0.77 0.00 0.30 1.00
Three or more Times 0.00 0.13 0.04 0.00 0.22 0.48
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also the major controls over that governed the spatialpatterns of burning prior to anthropogenic manipula-tion of wildland fire patterns.
Despite limitations, empirical analyses of land-scape-fire relationships using digital fire atlases pro-vide useful information for evaluating the factors thatcontrol fire regimes at landscape to regional scales.The fire atlases used here have both broad temporaland spatial extents which makes them invaluable forevaluating the causal agents of fire patterns at land-scape scales. Fire atlases represent a useful source offire history information and allow spatial analyses offire location, fire size distribution, and analyses of re-cent fire frequency. These analyses are often lackingfrom fire history research based on fire-scarred trees(Morgan et al. 2001; Rollins et al. 2001).
Landscape variables
The piñon/juniper PVT in the GALWC and the lowersubalpine forest PVT in the SBWC re-burned signifi-cantly less often than expected relative to the distri-bution of these types over the entire study area (Table1, Figure 3). This could suggest that these areas hadbeen more affected by fire exclusion than other veg-etation types. However, the temporal extent of our fireatlases may have been too short to represent the longfire return intervals (i.e. low fire frequency) previ-ously described for these forests (Swetnam and Diet-erich 1985; Abolt 1996; Barrett and Arno 1991;Brown et al. 1994). Throughout the 20th century, firefrequencies in the GALWC and SBWC were lowcompared to reconstructions of pre-20th century fireregimes (Swetnam and Dieterich 1985; Barrett andArno 1991; Brown et al. 1994; Abolt 1996). This islikely due to reduction of fine fuels because of exten-sive grazing in the GALWC (Swetnam and Dieterich1985; Savage and Swetnam 1990) and aggressivesuppression of fires in both the GALWC and SBWC(Gruell et al. 1982; Brown et al. 1994; Covington etal. 1994; Swetnam and Baisan 1996). Elevationranges corresponding with areas burned multipletimes were different in each wilderness complex (Fig-ure 4); however, the ponderosa pine/Douglas-fir PVTsfound at these different elevations are quite similarwith regard to dominant overstory tree species. Un-derstories are similar with a larger shrub componentin the Douglas-fir PVT in the SBWC. Previous re-search based on pre-20th century fire history data sug-gests that Douglas-fir and ponderosa PVTs in eachwilderness complex are the most similar with regard
to fire frequency (Swetnam and Dieterich 1985; Bar-rett and Arno 1991; Brown et al. 1994; Abolt 1996).
Douglas-fir and ponderosa pine PVTs are found atlow to moderate elevations in each wilderness com-plex and are characterized by conditions that are dryrelative to higher elevations. Prior to European settle-ment in the Western United States, ponderosa pineand Douglas-fir PVTs were characterized by openstand structures maintained by frequent low-to-mod-erate severity surface fires that created patchy standage and size distributions (Weaver 1951; Cooper1961; Arno (1976, 1980); Gruell et al. 1982; Swet-nam and Dieterich 1985). Fine fuel loads, fuel struc-ture and continuity, and local topography dominatefire behavior in these stands. During the latter part ofthe 20th century, stand-replacing fires have occurredin some areas where closed-canopy conditions devel-oped during the fire suppression era (Gruell et al.1982; Agee 1993; Covington and Moore 1994). In1975 both wilderness areas implemented prescribednatural fire management programs (currently referredto as wildland fire use) as part of efforts to restore fireas a keystone disturbance process (Garcia et al. 1978;Frost 1982). These efforts have increased the amountof fire in each wilderness complex, but some PVTshave been affected more than others (Brown et al.1994; Rollins et al. 2001). In the GALWC, 68% ofPNF-era area burned was in ponderosa pine and Dou-glas-fir PVTs. In the SBWC, 71% of the area burnedafter 1975 was in ponderosa pine and Douglas-fir for-ests.
Although areas that burned multiple times ap-peared, graphically, to occur on steeper slopes in bothwilderness complexes, there was no statistically sig-nificant relationship evident. Slope is an importantdeterminant of fire behavior and subsequent fire ef-fects (Agee 1993; Rothermel 1983; Albini 1976). En-gelmark (1987) found higher fire frequencies onsteeper slopes in northern Sweden. In addition tomore efficient heat transfer to upslope fuels via con-vection (Agee 1993), higher fire frequencies onsteeper slopes may result from a ‘chimney’ effectbased on landform (Swanson 1981) or from lower soilmoistures on steeper slopes (Brown 1972). Appar-ently, these effects are important at local scales (e.g.first-order watersheds) and may not be as evident atthe landscape scales analyzed here for the GALWCand SBWC. The coarse resolution of the fire atlasesmay account for the lack of significant differences infire frequencies relative to slope, as many of the moresubtle relationships between topography and fire
550
spread are not captured by relatively coarse spatialfire perimeter data used in this study.
Relationships between aspect and fire frequencywere nearly opposite between the GALWC andSBWC (Figure 5). This ‘mirror image’ in the relation-ship between aspect and fire frequency highlights im-portant differences in fire regime characteristics ineach area. Fires in the GALWC burn, in general, withlow-to-moderate severities (i.e. low tree mortality andfire intensity). In this semi-arid landscape, fires areoften constrained by the amount and horizontal con-
tinuity of fuel. Northern and northeastern aspects aremore productive, leading to higher herbaceous fuelcontinuity, and thus, a higher probability of re-burn.Fires in the Southwestern United States become muchmore infrequent and intense as forests transition tomore mesic and dense mixed conifer and spruce/firforests (Baisan and Swetnam 1990; Grissino-Mayeret al. 1995; Swetnam and Baisan 1996; Abolt 1996),but these forests represent only 9% of the GALWC.
In contrast, fire regimes in the SBWC are charac-terized by infrequent moderate-to-high severity fires
Figure 5. Aspect for different fire frequencies and aspect over the entire area in each wilderness complex. Chi-square tests indicated signif-icant differences in distributions, with inverse aspect-fire frequency relationships between the GALWC and SBWC. + indicates a significantpositive difference and − a significant negative difference (P < 0.05) between the distribution of area burned multiple times in different aspectclasses and the distribution of aspect over the entire wilderness complex.
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that kill a large proportion of trees (Barrett and Arno1991; Brown et al. 1994). These fire regimes occur insubalpine forests (70% of the SBWC) and moistgrand fir and western redcedar PVTs. Widespreadfires in subalpine forests are constrained by the avail-ability and moisture status of large, woody fuels.Southern and southwestern aspects receive more di-rect insolation, causing desiccation and preheating offuels (Agee 1993). High insolation and xeric condi-tions on south-facing slopes may slow vegetation re-growth following severe burns.
Climate-fire frequency
In the GALWC, areas that burned three or more timesexperienced significantly lower insolation than thosethat burned less often or not at all (Figure 6). Thissupports assertions that northern-facing slopes in theGALWC re-burn more frequently because of higherproductivity leading to more biomass to fuel fires. Inthe GALWC, areas with the highest insolation (i.e.steep south-facing slopes) are likely to have low finefuel continuity because the extremely dry conditionslimit biomass production. No significant differenceswere found in the distribution of insolation for areaswith different fire frequencies in the SBWC (Table 3).Some 72% of the area burned in the SBWC burnedin very large (>20,000 ha) fires prior to 1935. Thesefires were strongly related to summer drought andburned primarily in dense, low-elevation forests(Moore 1996; Larsen and Delaven 1922). Large, deadfuels accounted for most areas that re-burned in theearly 20th century in low elevation, mesic forest types(Larsen and Delaven 1922). In these situations, localtopography plays a smaller role than wind and ante-cedent moisture conditions in constraining fire pat-terns.
Years with extensive fire in the GALWC andSBWC corresponded with regional April–Octoberdrought. In the GALWC, large high elevation fires inthe 1940s and 1950s corresponded with the most in-tense, sustained drought since 1580 (Swetnam andBetancourt 1998). Years with extensive fire yearstended to be much drier in the SBWA than theGALWC (Figure 7). This supports the assertion thatfuel continuity, rather than fuel moisture, is a mainconstraint on fire frequency in the GALWC. Wide-spread fire years corresponded with significantly dryyears in the mixed conifer and spruce/fir PVTs in theGALWC and all PVTs in the SBWC (Figure 7). Thissuggests that fire regimes in upper elevation forests
of the GALWC are similar to fire regimes of theSBWC. That is, fires in upper elevation forests of theGALWC are probably limited by fuel moisture as op-posed to fuel continuity as in the PVTs dominant atlower elevations of the GALWC.
Regional climate and annual area burned arestrongly related in the Southwestern United States.The strength of this regional-scale forcing of fire re-gimes is related to El Niño Southern Oscillation(ENSO) precipitation events (Swetnam and Betan-court (1990, 1998)). Our results were not entirelyconsistent with the findings of Swetnam and Betan-court (1998). Using long time series of fire and cli-mate data from a network of tree-ring fire historyreconstructions in the Southwestern United States,Swetnam and Betancourt (1998) show that wide-spread fires in the Southwestern United States tendedto occur during a dry year preceded by one to threewet years and that fire regimes in ponderosa pine for-ests of the Southern Rocky Mountains are constrainedby fine fuel amount and continuity as much as by fuelmoisture.
Barrett et al. (1997) found no relationship betweenregional drought and multi-year ‘fire episodes’ (i.e.5-year intervals with high fire occurrence), attributinghigh fire frequencies instead to inter-regional weathervariations (e.g. intense, local storms), mass lightning
Figure 6. Insolation index for different fire frequency classes andover the entire GALWC. Insolation index was calculated from sunangles determined using ENVI image processing software andrange from 0 (low insolation) to 255 (high insolation). Lower val-ues of insolation had proportionately higher fire frequencies thanareas with high insolation.
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ignitions, and strong local winds created from wide-spread fire events. However, lack of annual precisionin Barrett’s reconstructed fire episodes and use of cli-mate reconstructions from Oregon rather than themore regionally appropriate reconstructions for theNorthern Rockies (e.g. Cook et al. (1999)) may havecontributed to the overall lack of significant corre-spondence between large fire years and drought indi-ces.
Drought years in the GALWC occurred on normalto wet years in the SBWC and vice versa. Precipita-
tion variability over the Western United States hasbeen shown to pivot near 40° N on time scales fromdecades to centuries (Dettinger et al. 1998). Whenconditions are wet in northern states, conditions tendto be dry in southern states and vice versa (Dettingeret al. 1998; McCabe and Dettinger 1999). This pivotin precipitation is especially pronounced during ElNiño Southern Oscillation (ENSO) events, which en-train fire regimes throughout the Southwestern UnitedStates (Swetnam and Betancourt (1990, 1998); Det-tinger et al. 1998; McCabe and Dettinger 1999). This
Figure 7. Results from superposed epoch analysis (SEA) in each wilderness complex and A) SEA for ponderosa-pine/Douglas-fir and mixedconifer/spruce/fir PVTs in the GALWC and B) SEA for Douglas-fir (includes seral ponderosa pine) and subalpine PVTs in the SBWC. Barsindicate mean April-October PDSI values for the eight largest fire years (year 0) and during four lagged years. The solid, dashed, and dottedlines indicate 99.9, 95, and 90 percent confidence intervals, respectively, based on Monte Carlo simulation. Large fire years correspondedwith significantly dry years in the SBWC and in the mixed-conifer PVT in the GALWC.
553
relationship is graphically evident in PDSI and areaburned from each area. For example, dry years withlarge areas burned in the GALWC often correspondwith normal or wet periods with less area burned inthe SBWC (e.g. 1909, 1913, 1951) and vice versa(e.g. 1919, 1979, and 1988). Improved predictabilityof ENSO events, along with cross-dated, dendroeco-logical analyses of synoptic fire-drought relation-ships, may provide a powerful predictive tool for de-termining the timing and effects of predicted climatechange on fire regimes. Because ENSO events can beidentified as they develop, such a tool will also beuseful in strategic planning for fire suppression dur-ing upcoming dry years and management of lightningand human ignited fires in other years.
Conclusions
In summary, our interpretations of the observed pat-terns are as follows. The amounts and horizontal con-tinuity of surface fuel appear to be the most importantfactors leading to high fire frequencies in theGALWC. This leads to high fire frequencies on rela-tively productive sites. In the SBWC, the moisturecontent of large fuels and tree crowns appears to bemost important in influencing the spatial distributionof fire frequency in the area, leading to higher firefrequencies on dryer sites. Past research has evaluatedvariability in fire behavior at fine spatial and tempo-ral scales (Kushla and Ripple 1997; Finney 1998;Perry 1998) and how fire frequency varies across re-gions and centuries (Arno 1980; Rothermel 1983;Swetnam and Baisan 1996). By evaluating the spatialand temporal variability of fire frequency at interme-diate spatial and temporal scales our results provide abridge between fine-scale and broad-scale under-standing of factors that determine fire regimes of theforests of the Rocky Mountains.
This research demonstrates the utility of using his-torical fire perimeter data to examine fire-landscape-climate interactions over space and time. The com-parative nature of our research strengthens inferencesabout causal relationships between landscape at-tributes and fire patterns, providing additional valid-ity to the extrapolation of results from this researchto other areas. Our results provide baseline empiricalinformation for implementing existing mechanisticmodels of landscape change in research focused onhow landscape patterns and processes change underdifferent climate and disturbance regimes.
Acknowledgements
This paper was written and prepared by US Govern-ment employees on official time, and therefore is inthe public domain and not subject to copyright. Theuse of trade or firm names in this paper is for readerinformation and does not imply endorsement by theUS Department of Agriculture of any product or ser-vice. This research was partially supported by theAldo Leopold Wilderness Research Institute, Mis-soula, Montana (#INT-94980-RJVA), the RockyMountain Research Station, Fort Collins, Colorado(RMRS-99145-RJVA), the National Science Founda-tion (SBR-9619411), and the Joint Fire Sciences Pro-gram. This material is partially based upon work sup-ported by the National Science Foundation underGrant No. SBR-9619411. Any opinions, findings, andconclusions or recommendations expressed in thismaterial are those of the author(s) and do not neces-sarily reflect the views of the National Science Foun-dation. We thank the personnel of the Bitterroot,Clearwater, Gila, and Nez Perce National Forests forproviding facilities, data, metadata, and scanning sup-plies. We thank James Riser, Robert Keane, DonLong, and Jim Menakis for providing technical assis-tance and GIS data.
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