Relationships between fire severity and post-fire landscape pattern following a large mixed-severity fire in the Valle Vidal, New Mexico, USA

Forest Ecology and Management 261 (2011) 1392–1400

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Relationships between fire severity and post-fire landscape pattern following a large mixed-severity fire in the Valle Vidal, New Mexico, USA James J. Hayes a,∗ , Scott M. Robeson b a b

Department of Geography, California State University, Northridge, Sierra Hall 150, 18111 Nordhoff St., Northridge, CA 91330, United States Department of Geography, Indiana University, Bloomington, Student Building 120, 701 E. Kirkwood Ave., Bloomington, IN 47405, United States

a r t i c l e

i n f o

Article history: Received 28 September 2010 Received in revised form 17 January 2011 Accepted 18 January 2011 Available online 18 February 2011 Keywords: Mixed-severity fire Ponderosa pine Fire ecology Spatial heterogeneity Landscape metrics Landscape pattern Moving-window metrics

a b s t r a c t The predominant fire regime associated with ponderosa pine (Pinus ponderosa) forests in the southwestern US has shifted from the historic norm of frequent, low-severity fires to less frequent mixed-severity and crown fires. This change in the severity of fire has altered ponderosa pine forests from the open stands typical of pre-settlement times to even-aged, high-density stands at increased risk of crown fire. As a result, restoration plans and post-fire management practices must consider the spatial and temporal variability of fire severity in both mixed-severity and crown fire events because fire-severity patterns strongly influence post-fire ecological conditions. This study examines the landscape pattern of fire severity in the Ponil Complex Fire and applies a moving-window approach to post-fire landscape pattern measurement. The moving-window approach allows examination of the quantitative and spatial variability of landscape pattern, producing a more nuanced description of forest pattern when compared to whole-landscape or patch-based metrics. The fire resulted in a complex mosaic of fire patches and foreststructure changes. In high-severity fire patches, mean and median values of many post-fire landscape metrics were markedly different from those in low and moderate-severity patches. Landscape pattern in high-severity patches also had the greatest variability of metric values, suggesting that high-severity fire patches require a spatially mediated management response to fire. Categorical fire-severity maps and traditional landscape-pattern assessment would not be able to identify these spatially variable post-fire conditions. © 2011 Elsevier B.V. All rights reserved.

1. Introduction In ponderosa pine (Pinus ponderosa) forests, changes in land-use and management practices following Euro-American settlement have altered stand age-structure and density, composition, and ecological processes (Covington and Moore, 1994). Grazing, intensive timber extraction, and fire suppression, in particular, have changed ponderosa pine forests from the open stands typical of presettlement times to even-aged, high-density stands at increased risk of crown fire and altered ecological processes outside the historic range of variability (Friederici, 2003). Southwestern ponderosa pine (PIPO) systems have historically been characterized by a low-severity, high-frequency fire regime that promoted unevenaged, low-density, park-like stands within a matrix of grasses (Covington and Moore, 1994; Swetnam and Baisan, 1996; Fule et al., 1997; Mast et al., 1999). The life-history characteristics of PIPO were shaped by and thus well suited to the historical low-severity fire regime (Moore et al., 1999). The stand-replacing crown fires

∗ Corresponding author. Tel.: +1 818 677 3519; fax: +1 818 677 2723. E-mail address: [email protected] (J.J. Hayes). 0378-1127/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2011.01.023

that have replaced high-frequency, low-severity fires have altered forest structure, function, and species composition in formerly lowdensity PIPO landscapes (Fule et al., 1997; Moore et al., 1999; Covington, 2003; Savage and Mast, 2005). The presence of mixedconifer forests and shrubland communities, which historically have a longer fire-free period (Barbour and Billings, 1999; Allen et al., 2002), provide clear evidence of the alterations in the landscape. Ultimately, changes in species composition and disturbance regime alter the entire ecosystem, change landscape patterns in nutrient and energy cycling, and affect plant and wildlife dynamics. Restoring PIPO forests to pre-settlement conditions has been documented as a priority in order to move forest ecosystem processes toward their historic range of variability and away from current threshold conditions that seem to place PIPO forests at their limit of ecological resilience (Allen et al., 2002; Bailey and Covington, 2002; Friederici, 2003; Savage and Mast, 2005). Although many PIPO forests are in need of restoration, restoration techniques must be chosen using information on how landscape characteristics vary across the restoration site. Post-fire recovery depends on the spatial heterogeneity, or landscape pattern, of a number of ecological variables following the fire (Turner, 1989; Bailey and Covington, 2002; Fule et al., 2002, 2003; Lee et

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Fig. 1. Study area and elevation, including the perimeter of the Ponil Fire on the Carson National Forest. Dark shaded area (“Study Area”) represents overlap between the fire perimeter (“Ponil Fire”) and National Forest boundary.

al., 2009). Environmental heterogeneity in space and time demand a flexible and adaptive approach to PIPO restoration (Allen et al., 2002). Ponderosa pine restoration practices must take environmental heterogeneity into account at the landscape scale as well as at the stand and patch scale. Land managers also must recognize that heterogeneity has both spatial and temporal components. This study examines the heterogeneity of landscape pattern and its spatial variation following a large PIPO wildfire. Traditional approaches for landscape-pattern assessment yield summary values for each individual patch, class type, or the whole landscape. Patch-based metrics tend to generalize landscape-scale spatial patterns and assume within-patch homogeneity, ignoring variability of the metrics within patches and across assemblages of patches. The landscape-pattern information needed to understand the spatial and temporal variability of post-fire landscape characteristics can be derived from a spatially and quantitatively continuous data model. In this study, a moving-window approach is used to integrate the two approaches (patch-based and raster data) for landscape analysis by calculating a raster dataset of landscape metric values. The moving-window approach has been used to identify areas within landscapes that have metric values well below or above averages for the landscape as a whole—such locations could go unnoticed (“averaged out”) using the typical summaryvalue approach (Riitters et al., 2000, 2002; Wade et al., 2003; Lung and Schaab, 2006; Hayes and Robeson, 2009). Using this approach for the Valle Vidal following the Ponil Complex Fire (hereafter Ponil Fire), we found that there was little or no change in pattern when averaging over the entire landscape (Hayes and Robeson, 2009). Maps and frequency distributions of the metrics, however, revealed areas of the landscape that experienced large changes in pattern. We suspected that spatial variability of changes in landscape pattern was associated with fire severity, but that hypothesis was not explored in Hayes and Robeson (2009). The present study extends the moving-window method to examine pattern variability within specific levels of fire severity. Though land managers may focus restoration and recovery efforts on areas of highest fire severity, the moving-window approach may help to distinguish between areas of the landscape that are most amenable to restoration and those more in need of environmental mitigation (soil and water protection). In this paper we focus on the effects of fire severity on forest-cover change and the spatial variation of metric values within and across classes of different fire-severity levels.

2. Materials and methods 2.1. Study site The study area is located on the Valle Vidal Unit of the Carson National Forest in northwestern Colfax County, near Cimarron, New Mexico (36◦ 30 N and 104◦ 55 W; Fig. 1). The Ponil Fire began as three separate fires ignited by lightning strikes, first reported on 2 June, 2002. By 6 June, they had merged to form the Ponil Complex (USFS, 2003). Years of fire suppression, high fuel load, dry conditions, and the remote location of the fires are likely reasons for the rapid coalescence, spread, and size of the fire (New Mexico Energy, Minerals, and Natural Resources Dept., 2004, pers. comm.; United States Forest Service, 2004, pers. comm.). The fire burned over 37,000 ha of public and private lands; however, we focus on the 9600 ha in Carson National Forest that is within the fire perimeter. The Ponil Fire burned in a mixed-severity pattern on the Valle Vidal, creating a complex mosaic of fire scars and post-fire conditions consistent with accounts of mixed-severity fire regimes (Agee, 2005; Lentile et al., 2005; Keyser et al., 2008; Thompson and Spies, 2010). Although several large crown fires burned in the forest, about 40% of the fire was low severity, 23% was moderate severity, and 24% was high-severity. The remaining 13% of the study area was unburned (Table 1, see Section 3). The landscape is dominated by PIPO forest of varying densities. Patches of grassland, shrubland, and mixed-conifer forest also are found within the study area. Elevation ranged from 2400 m to Table 1 Fire severity classification and description of fire severity classes. Fire classification

Description

Unburned

No evidence of vegetation killed by fire. No charring on tree stems. Ground fire. Herbaceous plants and some shrubs killed. Some charring of tree stems to about 20% of tree height. Less than 25% of trees killed. Ground fire and burning lower tree limbs. Herbaceous plants and some shrubs killed. Stem charring to about 70% of tree height. Between 25% and 75% of trees killed. Ground and canopy fire. All shrubs and herbaceous plants killed. More than 75% of stem height charred. More than 75% of trees killed.

Low severity

Moderate severity

High severity

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Table 2 Pre- and post-fire land-cover classification and description of each class.a Land cover classification

Description

Water Herbaceous: Mixed scrub:

Small seasonal lakes Trees absent, grass, herbs and few small shrubs Few or no trees >3 cm dbh or >2 m tall, may have mix of Qg, Js, Jc, Pp, Cm and some herbs and grass. Open canopy with few trees though may contain some trees mixed in with majority shrubs. Low-density forest: PIPO forest with trees >3 cm dbh at density between 150 and 450 stems/ha Intermediate-density forest: PIPO forest with trees >3 cm dbh at density between 450 and 750 stems/ha High-density forest: PIPO forest or mixed coniferous forest with trees >3 cm dbh at density >700 stems/ha. Mixed coniferous forest composed of Pp, Pm, Ac. Burned/unvegetated (post-fire) No vegetation, previously forested. a Qg, Quercus gambelii; Js, Juniperus scopulorum; Jc, Juniperus communis; Pp, Pinus ponderosa; Cm, Cercocarpus montanus; Pm, Pseudotsuga menziesii; Ac, Abies concolor.

2800 m, sloping generally toward the northeast. Monthly average temperatures from 1971 to 2000 range from 0.3 ◦ C (January) to 19.8 ◦ C (July). Annual mean precipitation from 1971 to 2000 was 45.5 cm, peaking in July and August with 6.8 and 8.8 cm respectively (NCDC, 2004). 2.2. Data To examine the relationships between landscape pattern and fire severity, Landsat Enhanced Thematic Mapper Plus (ETM+) data were classified to create categorical maps of pre-fire vegetation cover, post-fire vegetation cover and fire severity. Classifications of the study area were checked for accuracy using a combination of ground-reference data and aerial photography. Ground-reference data were collected in June 2003 (one year post-fire) on forest composition, structure, and fire severity to aid in the interpretation and accuracy assessment of vegetation and fire severity classes derived from Landsat ETM+ data. Data collection plots were chosen in a stratified random sampling framework based on four vegetation types delineated from visual interpretation of United States Geological Survey digital orthophoto quarter-quadrangles (USGS DOQQ) (1-m resolution images, viewed at approximately 1:5000 scale). Following field work the vegetation types were refined into forest structure categories to be classified and a fifth class, “mixed scrub”, was added (Table 2). A total of 63 points were located and used to construct fire severity and forest structure classes, and aid the visual interpretation of aerial photos for accuracy assessment. Each ground reference point was the center of a 10 m radius data-collection plot. A 10 m radius plot was chosen because it was more accurately constructed than a square plot and captured the plot-level variability in forest conditions. As we targeted homogeneous areas for our plot locations, larger plots would have been somewhat redundant. We also noted that the data collected for ground referencing captures different levels of information than the data collection of the ETM+ sensor. Although we could have attempted to create 30 m square sampling plots to match the resolution of the ETM+ sensor, it would be incorrect to conclude that the information derived from such plots has a 1:1 correspondence to an ETM+ pixel. In each plot all trees were counted and identified to species. Trees >3 cm dbh were measured and dbh was recorded. It was noted whether each tree was living or dead. If dead trees had most or all of their branches charred the death was attributed to fire. Dead trees that could not be associated with fire were not used in estimating fire severity. Mean canopy

Table 3 Differenced normalized burn ratio (dNBR) classification and class boundaries. Class

Class boundary

Unburned Low severity Moderate severity High severity

<50 dNBR ≥50 and ≤220 dNBR ≥221 and ≤400 dNBR >400 dNBR

height was calculated using the height of each tree (>3 cm) estimated using a meter tape and clinometer. Char height on tree stems was measured with a meter tape if less than 2 m, otherwise it was estimated using a clinometer and meter tape. These observations of char height and percent mortality were used to assign each plot to a qualitative fire-severity class (see Table 1). Two cloud-free Landsat ETM+ images (14 October 1999, and 6 October 2002; WRS path/row 33/35) were used to categorize and map forest cover and fire severity. Landsat ETM+ reflective bands used for this analysis have a nominal resolution of 30 m × 30 m covering a ground area of 900 m2 . Both images were received from the United States Geological Survey radiometrically and geometrically corrected to National Land Archive Production System processing standards. All subsequent image processing was carried out with ERDAS Imagine 8.7 (Leica Geosystems, 2005) following standard practices for image normalization (NASA, 2009), image to image registration (RMSE of 15 m), and accuracy assessment. The normalized burn ratio (NBR) (Key and Benson, 2006), a standardized ratio of ETM+ bands 4 and 7, was calculated for each image. NBR ranges from −1 to +1 where lower values are associated with areas of little vegetation and low moisture. Higher values indicate the presence of vegetation and moister conditions. Temporal differencing of NBR (dNBR) subtracts pre-fire from post-fire NBR images: dNBR = 1000 × (NBRpre-fire − NBRpost-fire ) Areas unaffected by fire will have near-zero values while positive values indicate stressed or reduced vegetation in the post-fire image (Key and Benson, 2006; Miller and Yool, 2002). Cocke et al. (2005) found dNBR to be sensitive to the differential effects of fire on canopy, understory, and soils. Relative measures of fire severity have been shown to produce accurate results under some conditions (e.g., Miller and Thode, 2007), dNBR, however, has been shown to have overall accuracy as good or better than relative measures though results may vary with regional conditions (Soverel et al., 2010). It is recognized that year 0 imagery presents a different perspective on fire severity, compared to one or two years following a fire, due to a potential lag in tree mortality. As a result, fire-severity categories were calibrated to observations of fire severity in the field one year following the fire to mitigate the problem of potential lag in tree mortality (e.g., Weber et al., 2009). 2.3. Image classification and accuracy The 1999 and 2002 images were classified into 6 and 7 land cover classes, respectively, to represent vegetation structure and forest density (Table 2; Fig. 2). The additional class for 2002 was a class for former vegetation completely removed by fire. Unsupervised classification of vegetation structure and density for both images was performed using the iterative self-organizing data analysis (ISODATA) algorithm (Leica Geosystems, 2005). To map burn severity (Fig. 3), dNBR values were classified into four fire-severity categories. Class boundaries for fire severity (Table 3) were determined by comparing fire severity observed at ground-reference plots with dNBR values from the ETM+ data. This method required modifying Key and Benson’s (2006) class bound-

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Fig. 2. Maps of categorical forest vegetation cover (before and after fire) from classified Landsat ETM+ data.

aries to better suit the Ponil Fire site and fire characteristics, as indicated by observational data in the field (Lentile et al., 2006). To assess classification accuracy, 210 points were plotted on aerial photography in a stratified random fashion (Congalton, 1991; Jensen, 2005). Each point was assigned a vegetation and fire severity class based on visual interpretation of the photos and observations made in the field. These class assignments were then compared with the ETM+ classifications. Overall accuracy of the pre-fire classification was 80.6% with a Khat statistic of 0.72 while overall classification accuracy of the post-fire image improved to 85.2% with a Khat statistic of 0.81. Overall accuracy for the fire-

Fig. 3. Burn severity map illustrating spatial distribution of low, moderate, and highseverity fire scars.

severity classification was approximately 80.0% and the Khat was 0.72. 2.4. Spatial analysis Fire severity is expected to influence the spatial configuration and arrangement of forest patches, contributing to and influencing ecological processes during post-fire recovery and succession. As a result, we analyzed measures of landscape spatial pattern and composition using image data on fire severity and post-fire forest patches. We used three steps in our analysis of fire severity and its effects on the post-fire landscape (1) landscape-pattern analysis on fire severity patches; (2) image-change detection in ERDAS Imagine to determine the effects of the fire on forest composition; and (3) analysis of post-fire forest-patch metrics using the moving-window tool in Fragstats 3.3 (McGarigal et al., 2002). To describe and assess the areal distribution and landscape composition of fire severity patches we used the percentage of the landscape covered by each class, the number of individual patches, mean patch size (MPS), and the area-weighted mean patch size (AM). The spatial arrangement of fire severity patches was assessed using patch density (PD), patch richness (PR), mean shape index (MSI), and the interspersion and juxtaposition index (IJI) (Fragstats, 2010). Spatial pattern of post-fire forest patches was assessed using MPS, PD, PR, and MSI calculated with the moving-window tool in Fragstats 3.3. These metrics are intended to measure the key spatial changes in areal distribution, configuration, and arrangement. Both MPS and AM are reported because patch-size summaries are often skewed by high frequency of small patches. AM helps account for this bias by weighting larger patches more heavily in the computation of the summary statistic (Fragstats, 2010). While size and density are essential patch characteristics in this analysis, shape and spatial arrangement can have important influences on post-fire processes. MSI is a measure of patch-shape complexity calculated as the perimeter of a patch divided by the minimum possible perimeter for a patch of the same size (perimeters are calculated using the number of pixels along the outside edge of the patch). As a ratio, MSI is dimensionless and equals 1 for a maximally compact patch shape

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Table 4 Distribution and size of patches with varying fire severity. Fire severity

% of Landscape

No. of patches

MPS (ha)

AM (ha)

Patches/100 ha

MSI

IJI

Low Moderate High

40 23 24

161 373 147

23.7 6.0 15.4

562.7 77.9 469.7

1.7 3.9 1.5

1.73 1.68 1.47

77.1 77.8 44.1

and increases as shape becomes more irregular (Fragstats, 2010). IJI is used in this study to describe differences in spatial arrangements among the different fire-severity classes. IJI is calculated as the observed interspersion of different patch types divided by the maximum interspersion for a given number of patches and converted to a percent; IJI ranges from 0 to 100% where higher values indicate greater intermixing (adjacencies) of different patch types and values approaching 0 indicate uneven distribution of adjacencies among the patch types. A moving-window approach was used to assess the spatial variability of post-fire forest pattern. MPS, PD, PR, and MSI were analyzed for pre- and post-fire forest patches using the movingwindow method implemented in FRAGSTATS 3.3 (Fig. 4) (McGarigal et al., 2002; Hayes and Robeson, 2009). A moving window of 15 × 15 Landsat ETM+ pixels (450 m2 ) was used to calculate each metric for the area within the moving window, with the result assigned to the center pixel (focus) stored in a new image file. The window moves pixel by pixel across and down the classified image until each pixel has served as the focus, with the exception of edge pixels. Edge pixels were any foci that caused the moving window to extend beyond the study-area boundary. Moving-window metrics of post-fire forest vegetation patches were compared across fire-severity classes to assess potential relationships between fire severity and landscape pattern. The moving-window landscape metrics were summarized using a zonal analysis based on fire-severity classes. The distributions of the metric values within each fire-severity class were graphically compared using histograms of metric value frequencies. We also examined differences in measures of central tendency and dispersion within each severity class. Significance testing using ANOVA and post-hoc difference of means tests for class differences was not performed because the large number of data values produces “significant” results for even the smallest differences observed, even after considering the effects of spatial autocorrelation. A moving window size of 15 × 15 pixels was chosen since a window of this size is larger than 95% of the patches in the 1999 image. Using a window that is larger than 95% of all patches in the landscape allows us to identify areas of heterogeneity while it also preserves areas of homogeneity that are associated with the largest, landscape-dominating patches. This approach allows the visualization and analysis of a spatially derived statistical distribution of metric values across the landscape rather than summary statistics for the entire landscape. Dependence of the results on the window size remains a methodological issue within the moving-window analysis; however, moving window statistics can be calculated at any location in the landscape and, therefore, measure landscape pattern more continuously than patch-based metrics. 3. Results 3.1. Landscape pattern of fire scars The Ponil Fire created a mosaic of 681 patches, 54% of which were moderate-severity fire; approximately 23% of the patches were low severity and 22% were high severity. Each of the fireseverity classes had distinctive characteristics as measured by the landscape metrics (Table 4). Moderate-severity fire was the patchi-

est of all with the smallest mean patch size, the greatest patch density, and the highest level of interspersion and juxtaposition. Low severity and high severity had similar patch densities, but high-severity fire had a smaller mean patch size and was much less intermixed with other severity classes. The fire created a new mosaic of forest structure on the landscape as well with lowseverity fire associated with the most diverse mix of forest structure including 50% low-density forest, and 11 and 22% intermediate and high-density forest respectively. In contrast, high-severity fire was approximately 90% bare/unvegetated soil (crown fire) after the fire (Table 5). Forty percent of the study area was classified as low severity, 23% as moderate severity, and 24% as high severity while 13% (primarily open meadows and standing water) was unburned. Overall, lowseverity fire produced a relatively small number of patches that were, on average, larger than the other fire classes (161 patches with a MPS of 23.7 ha). Moderate-severity fire produced a large number of small patches (373 patches with a MPS of 6.0 ha). Highseverity fire (147 patches with a MPS of 15.4 ha) produced a similar number of patches to low-severity fire, but its MPS size was 2/3 that of low-severity fire. Although the low-severity fire class covered the greatest area and produced the largest patches, the high MSI (complex shape pattern) (Table 4) and high IJI, revealed that low-severity areas burned in a complex pattern intermixed with high and moderate-severity patches. In contrast, high-severity fire had a simple, compact shape pattern with low MSI and IJI (Fig. 2). Moderate-severity fire has a complex shape with an MSI value similar to that for low severity. This complex shape, and the high IJI value, corroborates the observation that patches of moderateseverity fire tend to intermix with the other classes on the map or sometimes “surround” the areas of high-severity fire. The high PD for moderate severity is due to the large number of small patches making up this class, but illustrates a danger of relying on a summary value of PD calculated for the for the entire landscape, i.e., high PD does not necessarily mean that the class is heavily clustered or aggregated. Conversely, the low values of PD, MSI, and IJI for high severity all suggest that this class is made up of compact, isolated patches, which is not the case for all of the study area (Fig. 2). The fire-severity patches also differed in the composition and spatial structure of the post-fire vegetation classes (see Table 5). As expected, though, high-severity fire often resulted in crown fire with near total canopy removal. Ninety percent of the area within the high-severity fire class was in the burned/unvegetated category in the post-fire vegetation classification. In general little low-density forest was observed in areas affected by high-severity fire, but was mostly found in low and moderate-severity fire areas. High-severity fire was less likely to occur in low-density areas in the first place (Hayes and Robeson, 2009) and when it did it was not sustained over large areas. All fire-severity classes included some area affected by crown fire (areas classified as bare/unvegetated), indicating that there were instances of canopy-removing fire even in areas classified as low or moderate severity. Likewise, high-severity fire also left behind low and intermediate-density forest in some locations within larger high-severity burn scars. The nature of the fire-severity categories, which are based on ranges of dNBR values that produce a typical damage response (% mortality for instance), causes the classification map to include variability in fire outcomes.

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Fig. 4. Moving-window metric maps of MPS, PD, PR, and MSI, illustrating the spatial variability of these metrics across the burn mosaic.

3.2. Fire severity and post-fire landscape pattern (moving window results) While whole-landscape summaries are useful, we are particularly interested in spatial variability of the post-fire response across the fire-severity categories. Moving-window-based metrics indicate clear differences in the post-fire landscape pattern that are associated with fire-severity levels (Table 6). High-severity fire areas tended to result in a simpler landscape as measured by the moving-window metrics. High-severity areas had the largest MPS, and lowest values for PD, PR, and MSI. Low-severity fire was associated with a more complex post-fire landscape with many small patches (low MPS) of different vegetation classes (high PD). Areas of low-severity fire had the highest values for PD, PR, and MSI, but the lowest MPS. Moving-window metrics for moderate-severity fire were intermediate of high and low-severity, though closer to that of low severity.

3.3. Variability of metric values The frequency distributions of post-fire landscape metrics varied widely across fire-severity classes. Metric values for low and moderate severity fire-affected areas tended to exhibit similarities in the shape of their frequency distributions and had less dispersion (Fig. 5 and Table 6). With the exception of MPS (which had a fairly uniform distribution), metrics in high-severity fire areas tended to have a skewed frequency distribution with values clustered toward lower values. As a result, high-severity fire consistently had the highest coefficient of variation (standard deviation divided by the mean). Frequency distributions of metric values for low and moderate-severity fire areas were similar, although there were fewer moderate-severity pixels overall. Frequency distribution patterns for high-severity fire areas consistently differed from low and moderate fire areas. In practical terms, this translates into greater spatial variability of post-fire spatial pattern in areas of

Table 5 Distribution of post-fire vegetation (% of area for each fire-severity class). Fire severity

Scrub

Low density forest

Intermediate density

High density

Low Moderate High

10% 6% 0.2%

50% 45% 9%

11% 6% 0.6%

22% 3% 0.2%

Bare/unvegetated 7% 41% 90%

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Table 6 Post-fire forest pattern metrics (mean) within each fire-severity class. Standard deviation and coefficient of variation in parentheses.

Low Moderate High

MPS

PD

PR

MSI

2.9 (1.5, 0.51) 3.7 (2.8, 0.75) 7.5 (6.0, 0.80)

40.0 (14.2, 0.35) 35.1 (15.3, 0.43) 22.0 (14.1, 0.64)

4.2 (1.0, 0.23) 3.8 (1.1, 0.28) 2.8 (1.2, 0.42)

1.30 (0.10, 0.07) 1.29 (0.12, 0.09) (0.16, 0.13)

high-severity fire (i.e., its effects on landscape pattern were less consistent).

fire regimes in PIPO systems, suggesting that these fire regimes may undergo changes spatially and temporally (Shinneman and Baker, 1997; Brown et al., 1999; Fule et al., 2003). The legacy effects of these fires remain poorly understood, though they can influence future disturbance events (Peterson, 2002). In any case, more severe fires tend to reinforce and perpetuate high-severity burns (Savage and Mast, 2005; Wimberly and Kennedy, 2008). In southwest PIPO forests, low, and moderate-severity fire are likely to reduce short- and long-term fire risk due to reduction of fuels and maintenance of low tree density (Fule et al., 2003; Keyser et al., 2008). In low-severity areas, however, crown fire remains a possibility because the canopy is usually left intact (Agee and Skinner, 2005). Our results support these relationships between low- and moderate-severity fire and post-fire conditions. We observed that low-severity fire had the most low-density forest as well as the greatest percentage of remaining high-density forest. Additionally, we observed a high degree of similarity in post-fire landscape pattern between low and moderate severity fire. Although we did not measure coarse woody debris, observations of standing dead trees for the fire-severity classification also support lower fuel loads in these areas compared to high-severity areas. However, coarse woody debris in moderate-severity burns is likely to be more abundant after these fires and adjacency to highseverity fire patches is likely to increase the risk of future fires. But this risk also may vary spatially and depend on how past disturbances have affected landscape pattern. Coupling moving-window measures of landscape pattern with predictive models of fire severity based on topography (Holden et al., 2009) and fuel models (Scott and Burgan, 2005) could be an effective way to further explore these relationships. While high-severity fire scars are often the focus of mitigation and restoration efforts, there are areas within high-severity scars that are characterized by low MPS, and high measures of PD, PR,

4. Discussion Mixed-severity fires create complex patchiness in landscapes, particularly considering that fires act on landscapes with previous disturbance and management treatment histories (Agee, 2005; Fule et al., 2002; Lentile et al., 2005; Keyser et al., 2008). The patchiness observed in the Ponil Fire has implications for PIPO restoration and natural regeneration and succession. Mixedseverity fire regimes often produce complex landscape mosaics due to the variation in regeneration and recruitment conditions they create. High, moderate, and low-severity fire each have differing effects on regeneration due to differences in the post-fire canopy density, amount of surface fuel consumed or left in place, the amount of mineral soil exposed, and the presence of, and distance to, a seed source (Lentile et al., 2005; Savage and Mast, 2005; Keyser et al., 2008). This is reflected in how post-fire patch size, shape, density, diversity, and adjacency vary with fire severity. Post-fire heterogeneity in these landscape metrics, therefore, should influence restoration plans. Much of the research on mixed-severity fires and their spatial variability has been done in the Pacific Northwest where fire often produces a mosaic of high and low-severity burn scars (Agee, 1993; Keane et al., 2008; Thompson and Spies, 2010). Mixed-severity fires in the southwest and their effects on PIPO ecology are not as well understood. Fire regimes in southwestern PIPO landscapes may be viewed as transitioning from historical low-severity fire to standreplacing fire regimes. Mixed-severity fire has been identified as a possible stage in a dynamic process of changing fire regimes over time in PIPO forests (Fule et al., 2003). Some studies have shown evidence and discussed implications of mixed and high-severity

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High

Fig. 5. Histogram matrix of frequency distributions of moving-window metric values across the fire severity classes. Columns represent low, moderate, and high-severity fire metrics from left to right.

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and MSI. These heterogeneous portions of high-severity burn scars may be more resilient, resulting in lower likelihoods of future highseverity burns (Keane et al., 2008). Regeneration densities are also more likely to be moderate, making these areas less susceptible to establishment of invasive exotics and non-forest community development. This also may be the case in areas with heterogeneous burn patterns of intermixed small patches of different fire-severity levels, which our results suggest tend to be associated with higher levels of post-fire patch density and structural diversity. In contrast, areas of high-severity fire that are associated with high MPS – but low PD, PR, and MSI values – are more likely to need mitigation of erosion and restoration of vegetation composition and structure over the long term. The typical crown-fire scar is characterized by low complexity with large patches of bare/unvegetated ground that are susceptible to erosion and invasion by exotic pioneer species (Crawford et al., 2001). In the case of the Ponil Fire, the high-severity areas were most likely treated with seeding of grasses to stabilize soils and reduce run-off (USFS, 2003). Longerterm effects of seeding and other common mitigation practices on recovery and succession for PIPO and the native herbaceous layer are uncertain (e.g., Robichaud et al., 2003; Kruse et al., 2004; Wagenbrenner et al., 2006). High-severity fire areas, nevertheless, contained the most variable measures of post-fire landscape pattern, in spite of the apparent homogeneity based on land-cover classification. Median post-fire metrics for high-severity fire were vastly different from low and moderate-severity fire for all metrics considered. Low and moderate-severity fire differed in MPS and PD, although there was little or no difference in PR or MSI. Dispersion of metric values, as measured by the coefficient of variation, varied widely for all metrics across fire-severity classes, with moderate severity fire being intermediate in dispersion, but more similar to low severity than high. The variability of metrics within high-severity areas was consistently dissimilar from that of low and moderate-severity areas.

5. Conclusions By examining post-fire conditions using a classification based on a range of fire impacts, we found large differences in post-fire vegetation classes between fire-severity levels. Using a moving-window approach, we also found large spatial variability of post-fire vegetation pattern within each of the fire-severity levels. Although post-fire management efforts should usually be directed toward areas of high-severity fire first, there is considerable variation within those areas and it may be worthwhile to discriminate within areas of high-severity fire rather than treating them as homogenous. Moving-window metrics of landscape composition and arrangement could help guide such discrimination. High-severity, canopy-replacing fires often require immediate environmental management and remediation to protect watershed resources, such as water and soil quality. The results of this study suggest that variability within high-severity fire areas may create regions of smaller patches and a diversity of conditions. High-severity fire zones must continue to be a focus of forest management; however, variability of landscape pattern in high-severity fire scars suggests that a spatially mediated management response can help maintain PIPO dominance and mitigate the most severe effects of future fires. Low and moderate-severity burns tend to receive less attention; nevertheless, there is relevant variability in these areas that can be identified using the moving-window approach. This should help to identify areas that require different management prescriptions and where natural regeneration might proceed in different ways. This spatially differentiated approach to management prescription within mixed-severity fires could help to distinguish between

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heterogeneous areas that simply need monitoring of post-fire recovery, and areas where mitigation, remediation, and restoration practices might be of greatest value. The results presented here suggest that visual analysis of fireseverity maps may not be adequate to plan post-fire management or to provide sufficient information about regeneration patterns for different parts of a forest. Variability within fire scars may alter the chances of success for management practices or the course of unmanaged regeneration. The US Forest Service plan for postfire mitigation of damage to soil and water quality relied heavily on broadcast seeding of grasses, as well as in-stream and stream bank erosion and sediment control. Fire-response plans are inherently general due to the lack of detailed spatial information on forest structure in the short-term and a chronic lack of funding for ecosystem restoration. Although silvicultural activity will be site and stand specific, it is likely to occur only in forests with reasonable commercial timber value—which the Carson does not have, as it is managed primarily for wildlife and recreation. Even so, the limited resources that are available could be used more strategically and managers need sound data and evidence to justify the use of these resources. However, the development of a comprehensive and spatially explicit relationship between landscape pattern and regeneration that holds at both the landscape and patch scale remains elusive. Additional empirical data on forest recovery and regeneration is needed to gain a better understanding of how regeneration is related to post-fire landscape pattern and pattern variability. Acknowledgements We thank Phil Keating for supporting field work and for providing Landsat and GIS data; Shanon Donnelly and Norma Froelich for assistance in the field; and the staff of the Carson National Forest, Questa Ranger District for supporting our project. We also thank David Deis for assistance in preparing maps and figures and two anonymous reviewers for suggestions that improved the manuscript. References Agee, J.K., 1993. Fire Ecology of Pacific Northwest Forests. Island Press. Agee, J.K., 2005. The complex nature of mixed severity fire regimes. In: Taylor, L., Zelnik, J., Cadwallader, S., Hughes, B. (Eds.), Mixed Severity Fire Regimes: Ecology and Management Symposium Proceedings. Spokane, Washington, November, 2004. Agee, J.K., Skinner, C.N., 2005. Basic principles of forest fuel reduction treatments. Forest Ecology and Management 211, 83–96. Allen, C.D., Savage, M., Falk, D.A., Suckling, K.F., Swetnam, T.W., Schulke, T., Stacey, P.B., Morgan, P., Hoffman, M., Klingel, J.T., 2002. Ecological restoration of southwestern Ponderosa pine ecosystems: a broad perspective. Ecological Applications 12, 1418–1433. Bailey, J.D., Covington, W.W., 2002. Evaluating Ponderosa pine regeneration rates following ecological restoration treatments in northern Arizona, USA. Forest Ecology and Management 155, 271–278. Barbour, M.G., Billings, W.D., 1999. North American Terrestrial Vegetation. University Press, Cambridge. Brown, P.M., Kaufmann, M.R., Shepperd, W.D., 1999. Long-term landscape patterns of past fire events in a Montane Ponderosa pine forest of Central Colorado. Landscape Ecology 14, 513–532. Cocke, A.E., Fule, P.Z., Crouse, J.E., 2005. Comparison of burn severity assessments using differenced normalized burn ratio and ground data. Wildland Fire 14, 189–198. Congalton, R.G., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37, 35–46. Covington, W.W., 2003. The evolutionary and historical context. In: Friederici, P. (Ed.), Ecological Restoration of Southwestern Ponderosa Pine Forests. Island Press. Covington, W.W., Moore, M.M., 1994. Southwestern Ponderosa pine forest structure: changes since Euro-American settlement. Journal of Forestry 92, 39–47. Crawford, J.A., Wahren, C.H.A., Kyle, S., Moir, W.H., 2001. Responses of exotic plant species to fires in Pinus Ponderosa forests in northern Arizona. Journal of Vegetation Science 12, 261–268. Fragstats, 2010. Fragstats Documentation. Available from http://www.umass.edu/ (accessed landeco/research/fragstats/documents/fragstats documents.html June 2010).

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