Effect of vegetation on the impact of a severe blowdown in the southern Rocky Mountains, USA

Forest Ecology and Management 168 (2002) 63–75

Effect of vegetation on the impact of a severe blowdown in the southern Rocky Mountains, USA William L. Bakera,*, Patrick H. Flahertya, Jeremiah D. Lindemanna, Thomas T. Veblenb, Karen S. Eisenhartb, Dominik W. Kulakowskib a

Department of Geography and Recreation, University of Wyoming, Laramie, WY 82071, USA b Department of Geography, University of Colorado, Boulder, CO 80301, USA Received 12 March 2001; accepted 14 August 2001

Abstract In October 1997, a storm with winds estimated at 200–250 km/h blew down a large percentage of trees in over 10,000 ha of subalpine forest in northern Colorado, USA. In a case study, we analyzed the effect of pre-blowdown tree density, cover-type, and stand structural stage on the percentage of trees blown down. Low tree density led to somewhat lower levels of blowdown than did higher density. Effects of cover-type and habitat structural stage on the pattern of damage from the blowdown varied spatially. At lower elevations, farther from the source of the winds coming over the Continental Divide, aspen forests were less susceptible to blowdown than expected, whereas spruce–fir forests were more susceptible than expected. At higher elevations, closer to the source of the winds, habitat structural stages representing earlier stages of stand development were much less susceptible to blowdown than expected, whereas more advanced structural stages were generally more susceptible to blowdown than expected. Overall, the effects of density, composition, and structural stage on the pattern of damage were modest, but evident. That there is a detectable effect of vegetation composition and structure across this large blowdown implies that, even during extreme wind events, vegetation can influence the extent and pattern of damage, more strongly so in some places than in others. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Subalpine forest; Colorado; GIS; Tree density; Composition; Forest structure

1. Introduction Rates and patterns of ecological processes, such as succession, may be contingent on historical legacies, such as remnant physical structures and surviving organisms (e.g. Fastie, 1995; Berlow, 1997; Turner et al., 1998). Ecologists have often viewed natural disturbance as a process relatively independent of the

*

Corresponding author. Tel.: þ1-307-766-2925; fax: þ1-307-766-3294. E-mail address: [email protected] (W.L. Baker).

affected vegetation, but the influence of vegetation structure on disturbance is receiving more attention (e.g. Malanson and Butler, 1984; Veblen et al., 1994; Minnich and Chou, 1997). For example, the mosaic structure produced by past fires may limit subsequent fires until a threshold of fire intensity is exceeded (Turner and Romme, 1994). Similarly, past fires and snow avalanches may shape the pattern of response to even severe insect outbreaks in the Rocky Mountains (Veblen et al., 1994). To what extent may vegetation similarly shape the response to extreme winds? This study investigates how the historical legacy of predisturbance forest structure shaped the response to a

0378-1127/02/$ – see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 1 1 2 7 ( 0 1 ) 0 0 7 3 0 - 7

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severe wind event, called the ‘‘Routt-Divide blowdown’’. This storm of 25 October 1997, from an unusual low-level jet stream with winds of 200– 250 km/h (Wesley et al., 1998), blew down a large percentage of trees over about 10,000 ha of upper

montane and subalpine forest (Lindemann and Baker, 2001). The winds blew from the east across the Continental Divide and down onto the west slope of the Park Range northeast of Steamboat Springs, Colorado (Fig. 1).

Fig. 1. Map of the study area, which is located at 1068W408N.

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Severe winds affect forests throughout the world, including the Rocky Mountains. Wind disturbance, if frequent, can be an important ecological process for re-setting plant succession (White, 1979), and may effectively prevent some forests from reaching an equilibrium in species composition (Whitmore, 1974; Pontailler et al., 1997). In other forests or stages of stand development, wind storms may accelerate succession towards dominance by late-successional species (Veblen et al., 1989). In some forests, maintenance of species richness is believed to depend on repeated disturbance by wind storms (Ball, 1980). The role of wind storms in patterning vegetation may become more important if the frequency and intensity of such disturbance events increase, as predicted by some climate models (Wendland, 1977; Emanuel, 1987; Gray, 1990). Ecological legacies of severe wind storms include abiotic patterns and biological residuals of the predisturbance forest which may have important influences on subsequent patterns of ecosystem recovery (Everham and Brokaw, 1996). These ecological legacies result from the interaction of pre-existing physical conditions (e.g. topography) and forest conditions with wind storms. Storms act in a heterogeneous way in time and space according to storm characteristics (wind speed, orientation and turbulence) and the nature of the obstacle (topography and the morphology of individual trees and their populations) (Pontailler et al., 1997). Although wind may act uniformly, local gusts and turbulence also can produce otherwise inexplicable patterns of tree damage and survival (Foster, 1988a). Wind damage typically increases linearly or has a unimodal relationship with tree height (Smith and Weintknecht, 1915; Foster, 1988b; Everham and Brokaw, 1996). Rooting depth also influences resistance to wind (Everham and Brokaw, 1996). Variation in wood density and other mechanical properties may confer varying tolerance to wind (King and Loucks, 1978). Denser forests are generally more resistant to blowdown (Foster, 1988b; Everham and Brokaw, 1996). When deciduous trees drop their leaves, wind drag is reduced, and these species may be less susceptible to wind damage than are evergreen conifers (Moore, 1988; Everham and Brokaw, 1996). Spatial variation in wind, site, and biotic factors, and interactions among them, may lead to a complex geographic pattern of disturbance (Foster and Boose, 1992).

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While blowdowns of the magnitude of the one we studied are rare, they are not without precedent in the relatively short period of historical observation in the central and southern Rocky Mountains. For example, in 1987, about 6000 ha of forest were blown down in the Teton Wilderness area in Wyoming (Fujita, 1989). In Colorado, coincident release dates of suppressed trees in stands separated by 12–15 km suggest that regional wind storms are responsible for small, but synchronous blowdowns over large areas of forest (Veblen et al., 1991). In a nearby area of about 300 ha of subalpine forest, 71% of the tree mortality was attributed to wind action during 12 years of observation (Alexander and Buell, 1955). Although the ecological consequences of blowdowns of the magnitude of the Routt-Divide blowdown have not previously been investigated in the southern Rocky Mountains, the stand-level consequences of smallerscale blowdowns have been examined (Veblen et al., 1989). In Colorado subalpine forests, smaller-scale blowdown can accelerate succession by releasing previously suppressed late-successional species (Veblen et al., 1989), and, at intermediate severity, may increase understory species diversity (Savage et al., 1992). Studies conducted in a wide range of forest types around the world (reviewed by Everham and Brokaw, 1996) suggest that previous disturbance by wind or other agents may influence forest responses to severe wind storms by (1) altering canopy conditions that change wind turbulence, (2) selectively removing more susceptible trees, and (3) changing species composition to more wind-resistant forms. In this study, we investigate how pre-blowdown forest structure, including tree density, species composition, and structural stage, influenced the amount and severity of blowdown. We developed three hypotheses about the blowdown, based on previous findings: (1) forests with lower tree density will have a higher percentage of trees blown down than expected based on chance, as denser forests in the past have been shown to be better able to resist wind damage; (2) spruce–fir forests will have greater area blown down than expected, and aspen forests will have less area blown down than expected, as spruce and fir trees are morphologically more susceptible and aspen trees less susceptible to blowdown; and (3) stand structures characterized by larger tree diameters and

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greater crown cover will experience a higher amount of blowdown per unit area than expected. This case study of the Routt-Divide blowdown was conducted at a broad spatial scale (ca. 62,000 ha) on the basis of aerial-photographic interpretation, and is complemented by intensive field sampling of blowdown patterns in relation to stand structure and composition at a stand-scale (Veblen et al., 2001).

2. Methods 2.1. The map of blowdown patches We obtained color aerial-photographs, at about 1:12,000-scale, taken by the US Forest Service a few days after the blowdown using standard procedures for aerial-photography quality control. Patch boundaries were traced on mylar overlays while viewing these photos with a zoom stereoscope. A patch is defined by the minimum polygon sufficient to enclose areas of five or more proximal blown down trees. Isolated blown down trees were not mapped. Some patches were tentatively subdivided if variation in the percentage of blown down trees was evident. A sampling plot, as described below, was then used to determine whether they truly differed in either pre-blowdown tree density or the percentage of blown down trees. Patch boundaries were checked separately by two of the authors, and minor errors were fixed. The final map contains 756 patches, ranging in size from 0.07 to 310.30 ha in area (Lindemann and Baker, 2001). A small transparent sampling plot was placed over the aerial-photo in a representative area in each patch to estimate the percentage of trees blown down. The plot was about 3 mm  22 mm (approximately 1.7 ha on the ground), but a smaller plot about 3 mm  9 mm (approximately 0.7 ha on the ground) was used for smaller patches. Where a patch was too small to contain a plot, the patch boundary itself was used in counting the percentage of trees blown down. Due to varying distortion from the camera, variation in the elevation and orientation of the airplane, and topography (Wolf, 1983), the scale of the aerial-photo at each plot was calculated and used to estimate plot area on the ground. The numbers of fallen trees and trees left standing were tallied and the percentage of trees blown down

relative to the number of pre-blowdown trees was calculated for each of the 756 patches. The counts are primarily of larger trees in the canopy of the forest. Smaller trees are not clearly visible in 1:12,000-scale photos, and some trees may be blown down and buried beneath others. This problem may be more important in denser stands. Pre-blowdown density from aerial-photos is, thus, only a relative and somewhat imprecise measure of actual tree density. Preblowdown tree density (trees/ha) for the patch was calculated based on the number of trees within the estimated area of the plot on the ground. The aerial-photo estimates of percent down and patch boundaries were ground-truthed in two ways. First, in the field, we sampled 30 randomly located sites within the blowdown area, and compared the estimate of percent down from field sites with the aerial-photo estimates from the same patch. In the field at each site, the starting point for a 100–150 m transect was randomly located. Then, 10 m  10 m plots (5–15 per transect) were systematically placed along the transect until approximately 100 trees (>5 cm dbh) were sampled. Regression analysis indicates that the relationship between our aerialphoto and field estimates of percent down is linear, lacks trends in the residuals, and has a reasonably good fit, with an R2adj of 68.3%. The mean difference between the two estimates is 9%. One of the 30 sites was omitted from this analysis, as it is a statistical outlier, probably because it is found in an area with considerable local variation in percent down, and our aerial-photo plot may not match the exact location of the field site. Second, we walked through some of the accessible parts of the blowdown area, and compared our mapped boundaries with the boundaries of blowdown patches visible in the field. Mapped boundaries were generally accurate, as they are quite distinct on the aerial-photos. However, blowdown in a few aspen stands was difficult to detect in the aerialphotos, because the leaves had already fallen and the white trunks were difficult to see against the backdrop of light snow. These boundaries were verified in the field. To build a map base in a geographical information system (GIS), patch boundaries and aerial-photos were scanned, and an orthorectification was performed to remove distortion and create a planimetric map (Baltsavias, 1996; ERDAS Inc., 1997). To do

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so, we used a digital elevation model (DEM) and control points from 7.50 US Geological Survey quadrangles and digital orthophoto quads. Patch boundaries were then digitized using ArcView (ESRI Inc., 1998). Percent down and pre-blowdown tree density were assigned as patch attributes. 2.2. Potential predictors and analysis Data for potential vegetation predictors of percent down came from aerial-photo analysis and a US Forest Service database, the Region 2 Resource Information System (R2RIS). Pre-blowdown tree density came from our aerial-photo analysis, as described above. Data on cover-type and habitat structural stage came from the R2RIS database, which contains attribute information about polygons, derived from aerialphoto interpretation and field inventory (USDA Forest Service, 2001). Forest cover-types represent the three major categories of forest found in the study area: aspen (Populus tremuloides), spruce–fir (Picea engelmannii–Abies lasiocarpa), and lodgepole pine (Pinus contorta). Habitat structural stages (HSS) are five classes representing increasing tree size and canopy cover, reflecting stand development (Table 1). Only stages 1–4 are found in our analysis area. R2RIS represents the best available data, but these data were collected over the last few decades, so some changes may have occurred, and the accuracy of the data is uncertain. We found a few mis-classified stands in our field work. To analyze the effect of cover-type and HSS on the blowdown, a rectangular analysis area (about 14 km  44 km) was drawn and digitized to enclose the approximate extent of blowdown patches (Fig. 1). The blowdown affected about 10,140 ha of forest in 756 patches in the approximately 62,130 ha analysis area. As a first step, the fraction of the area of Table 1 Habitat structural stages (USDA Forest Service, 2001) Structural stage

Tree diameters (cm)

Crown cover (%)

1 2 3 4 5

Any <4 4–23 >23 >23

0–10 11–100 11–100 11–100 71–100

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blowdown in each cover-type (and separately in each HSS) was compared to the fraction of the area that was not blown down in that cover-type (or HSS) within the analysis area. These fractions or proportions (e.g. fraction of total blowdown area that is aspen forest) should be approximately equal, inside the blowdown area and outside it, if there is no effect of cover-type or HSS on blowdown response. Since this is an analysis of proportions, a change in the size of the analysis area or the absolute amount of each vegetation type would likely have little effect on the results. The areas of cover-types and HSSs that we report do not necessarily sum to the expected analysis area or blowdown area because forest makes up only part of these areas. Also, the R2RIS cover-type and HSS data are missing in a few areas. We used all available data in the analysis. Chi-square analysis is often used to determine the significance of observed differences in proportions (Agresti, 1996), but there are two concerns with using w2 analysis here. First, we obtained a census, not a sample of the blowdown and analysis area, and a direct comparison of proportions shows the population differences between the areas blown down and not blown down. In this sense, a statistical inference about a larger population is inappropriate. However, we cannot be sure that the analysis area contains the population of areas affected by strong winds, as some areas with no physical evidence of blowdown may have received and simply resisted the strong winds of this storm, and there is also some measurement error. Second, classical w2 analysis assumes that the observations are independent. However, in a separate study spatial auto-correlation in predictor variables (e.g. elevation) was evident and modest up to distances of about 0.5 km (Lindeman and Baker, in press). Spatial auto-correlation from non-independent observations typically leads to null hypotheses that are too often rejected, as observations do not each represent a full degree of freedom (Legendre, 1993). One solution is to obtain observations that are independent by spacing them so that auto-correlation is unimportant (Legendre, 1993). We obtained a sample of independent observations using a systematic grid of points with a randomly chosen starting location (x, y), with points spaced 0.5 km apart, within the blowdown and outside the blowdown. Because there is more area outside the blowdown than inside, there were more

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points outside also, so we randomly subsampled the outside points to obtain an equal number (401 points) inside and outside the blowdown. This further reduces spatial auto-correlation by increasing sample spacing. These point samples were then used to estimate frequencies for the w2 analysis. The variables of interest here are the two vegetation variables (covertype, habitat structural stage), but there are several potentially confounding variables. Preliminary analysis (Lindeman and Baker, in press) of non-vegetation influences on the blowdown revealed that the blowdown was primarily influenced by five other topographically-related variables that also influence cover-type and habitat structural stage (Table 2). Our hypothesis is that the two vegetation variables have an influence on the blowdown, while controlling for the

potentially confounding effects of the five topographically-related variables. To test that the topography variables are confounding, an analysis of two-dimensional contingency tables (e.g. cover-type versus distance from Continental Divide) was first completed, and the null hypothesis of independence was tested using Fisher’s Exact Test (Agresti, 1996). Where this null hypothesis is rejected, we used Cochran–Mantel–Haenzel contingency table analysis, which controls for confounding variables (Agresti, 1996), to test that the vegetation variables have an effect on the blowdown. Contingency table analysis was completed using SAS 8.1 (SAS, 2000), except that standardized residuals were calculated using SPSS 10.0 (SPSS, 2000).

3. Results Table 2 Categorical variables analyzed using contingency table analysisa Abbreviation

Variable

Variable of interest BLOW Blowdown Potential influencing variables COVER Cover-type

HSS

Habitat structural stage

Potential confounding variables CONT Distance from Continental Divide EXPOS ELEV

Exposure at 158 angle Elevation

ASPECT

Aspect

SLOPE

Slope

a

Categories 0 ¼ not blown down 1 ¼ blown down 1 2 3 1 2 3

¼ ¼ ¼ ¼ ¼ ¼

aspen forest spruce–fir forest lodgepole pine forest HSS 1 and 2 HSS 3 HSS 4

1 2 3 0 1 1 2 3 1 2 3 4 1 2 3

¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼

0–4 km 5–8 km 9–12 km not exposed exposed <2875 m 2875–3083 m >3083 m 315–458 (N) 45–1358 (E) 135–2258 (S) 225–3158 (W) 98 10–168 178

Exposure at a 158 angle was determined using the EXPOS model (Boose et al., 1994). The 158 angle is the angle at which the wind dips below horizontal when passing over the terrain.

The relationship between estimated percent down and pre-blowdown tree density in the 756 patches is not linear, as the regression has an R2adj of only 2.8%, and there is substantial scatter (Fig. 2). The graph suggested there might be less blowdown below about 53 trees/ha (Fig. 2). A two-sample t-test (Minitab 12.1; Minitab Inc., 1998) led to rejection of the null hypothesis that mean percent down is equal for patches with pre-blowdown density < 53 trees/ha (mean ¼ 31:6% down) versus 53 trees/ha or greater (mean ¼ 44:6% down) (t ¼ 8:13, n ¼ 500, P ¼ 0:000). The 95% confidence interval for the estimated increase in mean percent down with 53 trees/ha or greater pre-blowdown tree density is an added 9.9– 16.2%. There might be less blowdown above about 140 trees/ha (Fig. 2), but sample size is insufficient for an adequate test. These findings do not support the first hypothesis, as low tree density appears to decrease susceptibility to blowdown. A greater proportion of blowdown occurred in the spruce–fir cover-type than expected, given the percentage of spruce–fir in the analysis area outside the blowdown patches (Fig. 3a). In lodgepole pine, there was a lower percentage blown down relative to the percentage of lodgepole pine in the analysis area outside the blowdown patches. Less aspen also was blown down than expected given the percentage of aspen within the analysis area outside the blowdown patches. The second hypothesis, that spruce–fir forests

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69

Fig. 2. Percent down vs. pre-blowdown tree density. Each dot represents one of the 756 plots.

would be more susceptible and aspen forests less susceptible is, thus, supported by this analysis of proportions, but we did not anticipate the lower susceptibility of lodgepole pine. Habitat structural stage had only slight effect on the pattern of blowdown (Fig. 3b). Stands dominated by the largest trees (HSS 4) had the greatest land

area affected within the blowdown area (Fig. 3b). However, there is no evidence of higher susceptibility of HSS 4 stands, as a little less than expected was actually blown down (Fig. 3b). Very little of the study area contains forests in HSS 1 and 2, but there appears to be less blown down than expected. There is perhaps a slightly enhanced susceptibility in HSS 3. Thus, the

Fig. 3. Comparison of the percentage of area in the blowdown patches to the percentage of area in the analysis area, outside the blowdown patches for (a) three cover-types, and (b) four habitat structural stages. The bars for inside the blowdown patches sum to 100%, as do the bars for outside the blowdown patches. The number above each bar is the land area, in hectares.

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Table 3 Contingency table analysis of the influence of topographic variables on cover-type and habitat structural stagea Null hypotheses COVER is independent of CONT COVER is independent of EXPOS15 COVER is independent of ELEV COVER is independent of ASPECT COVER is independent of SLOPE HSS is independent of CONT HSS is independent of EXPOS15 HSS is independent of ELEV HSS is independent of ASPECT HSS is independent of SLOPE

n 687 687 687 687 687 802 802 802 802 802

P

Contingency coefficient 

0.000 0.644 0.000 0.001 0.001 0.000 0.001 0.000 0.000 0.000

0.410 0.394 0.205 0.166 0.313 0.127 0.252 0.220 0.172

a

Significance level is from the Fisher’s Exact Test. Starred P-values indicate that the chosen significance level of 0.10, corrected for multiple tests using a Bonferroni correction, was exceeded, and the null hypothesis of independence is rejected. The contingency coefficient measures the magnitude of the association (0–1 scale) where it is significant. Variable abbreviations are in Table 2.

third hypothesis is not fully supported, as the most advanced structural stage (HSS 4) was not more susceptible, and the earlier stage (HSS 3) was not less vulnerable, although the earliest stages (HSS 1 and 2) may be less susceptible. The null hypotheses that cover-type and habitat structural stage are independent of the topography variables are rejected, with the exception that one null hypothesis, that cover-type is independent of exposure, is not rejected (Table 3). Thus, these five variables are potentially confounding of a relationship between cover-type or habitat structural stage and the blowdown. However, the Cochran–Mantel–Haenzel analysis shows that even though the topography variables are confounding, the null hypothesis that cover-type and habitat structural stage are independent of the blowdown are rejected in all cases where there is potential confounding (Table 4). Two-dimensional contingency tables reveal where the influence of cover-type and habitat structural stage is expressed relative to the classes of the potential confounding variables, and the contingency coefficient expresses the magnitude of the relationship (Table 4). The blowdown was influenced by covertype where the setting is: (1) 5–12 km from the Continental Divide (63% of the 687 sample points), (2) exposed to the wind or not (100% of the 687 sample points), (3) below 2875 m in elevation (35% of the 687 sample points), (4) on west-facing aspects (26% of the 687 sample points), and (5) on both shallow (32% of the 687 sample points) and on steep

slopes (35% of the 687 sample points). Thus, covertype primarily affects the blowdown at low elevations away from the direct impacts of the wind coming over the Continental Divide and on west-facing aspects, probably where the winds were weaker. In contrast, the blowdown was influenced by habitat structural stage where the setting is: (1) 0–4 km from the Continental Divide (42% of the 802 sample points), (2) exposed to the wind (82% of the 802 sample points), (3) at elevations of 2875 m and above (67% of the 802 sample points), (4) on all aspects except southfacing (74% of the 802 sample points), and (5) on all slopes (100% of the 802 sample points). Thus, habitat structural stage influences the blowdown at high elevations, close to the source of the winds coming over the Continental Divide, and where directly exposed to those winds, mostly without regard to slope or aspect. However, the relationships are all only modest, with contingency coefficients ranging from 0.229 to 0.437 on a 0–1 scale (Table 4). Standardized residuals from the two-dimensional contingency tables (Agresti, 1996) reveal how each cover-type and habitat structural stage contributes to the effect on the blowdown, where there is an effect of these variables (Table 5). Where there is an effect of cover-type on the blowdown, high negative residuals indicate that the consistent primary contributor to the effect is that less aspen forest than expected was blown down, although residuals for spruce–fir forest are consistently positive, but of lesser magnitude, indicating that more spruce–fir than expected was

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Table 4 Analysis of contingency tablesa Null hypotheses

n

w2

d.f.

P

BLOW is independent of COVER controlling for CONT BLOW is independent of COVER for 0–4 km BLOW is independent of COVER for 5–8 km BLOW is independent of COVER for 9–12 km

687 252 332 103

13.82

1

0.001 0.881 0.000 0.000

BLOW and COVER for EXPOS BLOW is independent of COVER for not exposed BLOW is independent of COVER for exposed BLOW is independent of COVER controlling for ELEV BLOW is independent of COVER for <2875 m BLOW is independent of COVER for 2875–3083 m BLOW is independent of COVER for >3083 m BLOW is independent of COVER controlling for ASPECT BLOW is independent of COVER for N aspects BLOW is independent of COVER for E aspects BLOW is independent of COVER for S aspects BLOW is independent of COVER for W aspects BLOW is independent of COVER controlling for SLOPE BLOW is independent of COVER for 98 BLOW is independent of COVER for 10–168 BLOW is independent of COVER for 178 BLOW is independent of HSS controlling for CONT BLOW is independent of HSS for 0–4 km BLOW is independent of HSS for 5–8 km BLOW is independent of HSS for 9–12 km BLOW is independent of HSS controlling for EXPOS BLOW is independent of HSS for not exposed BLOW is independent of HSS for exposed BLOW is independent of HSS controlling for ELEV BLOW is independent of HSS for <2875 m BLOW is independent of HSS for 2875–3083 m BLOW is independent of HSS for >3083 m BLOW is independent of HSS controlling for ASPECT BLOW is independent of HSS for N aspects BLOW is independent of HSS for E aspects BLOW is independent of HSS for S aspects BLOW is independent of HSS for W aspects BLOW is independent of HSS controlling for SLOPE BLOW is independent of HSS for 98 BLOW is independent of HSS for 10–168 BLOW is independent of HSS for 178

118 569 687 239 253 195 687 117 197 190 183 687 218 230 239 802 335 357 110 802 141 661 802 267 269 266 802 150 213 212 227 802 272 260 270

7.55 30.68 7.11

2 2 1

7.53

1

7.58

1

19.98

1

43.90

1

31.67

1

32.82

32.16

1

1

0.023 0.000 0.008 0.000 0.255 0.052 0.006 0.372 0.079 0.015 0.000 0.006 0.004 0.146 0.000 0.000 0.000 0.546 0.537 0.000 0.068 0.000 0.000 0.104 0.000 0.000 0.000 0.000 0.000 0.058 0.000 0.000 0.000 0.000 0.000

Contingency coefficient

0.284 0.333

0.288

0.301

0.261 0.313

0.323

0.283 0.398 0.437 0.297 0.281 0.280 0.310 0.276

a The Cochran–Mantel–Haenzel w2 statistic and its significance are reported for three-dimensional tables with a controlling variable. For two-dimensional tables, significance (P) is from Fisher’s Exact Test. Starred P-values indicate that the chosen significance level of 0.10, with Bonferroni correction for multiple tests, was exceeded, and the null hypothesis of independence is rejected. The contingency coefficient measures the magnitude of the association (0–1 scale) where it is significant. Variable abbreviations are in Table 2. Note that COVER and EXPOS were found to be independent (Table 3), so the Cochran–Mantel–Haenzel analysis is not completed, and the two-dimensional results are presented directly.

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Table 5 Standardized residuals from two-dimensional contingency tables where the contingency table analysis showed a significant effect of covertype or habitat structural stage on the blowdown (see Table 4)a Cover-type

Aspen

Spruce–fir

Lodgepole pine

Distance from Continental Divide: 5–8 km Distance from Continental Divide: 9–12 km Elevation: <2875 m Aspect: west-facing Slope: 98 Slope: 178

2.60 2.32 3.02 2.67 2.46 2.65

0.89 1.49 1.58 1.11 1.41 1.26

0.69 0.50 0.37 0.35 0.93 1.01

Habitat structural stage

HSS 1 and 2

HSS 3

HSS 4

Distance from Continental Divide: 0–4 km Exposure: exposed Elevation: 2875–3083 m Elevation: >3083 m Aspect: north-facing Aspect: east-facing Aspect: west-facing Slope: 98 Slope: 10–168 Slope: 178

4.06 5.16 2.51 4.29 3.14 2.73 2.73 2.93 3.06 2.84

2.07 2.42 1.66 2.51 2.68 1.43 1.14 1.22 0.85 2.20

2.11 1.52 0.30 1.81 0.47 0.01 1.14 1.29 0.99 0.42

a

Standardized residuals that have an absolute value >2 indicate lack of fit in that cell of the table (Agresti, 1996), and these are highlighted in bold. Standardized residuals are shown here only for the blowdown part of the table, but the non-blowdown part of the table has corresponding residuals that are very similar, but opposite in sign. Negative residuals indicate less was blown down than expected, while positive residuals indicate more was blown down than expected.

blown down. There is no consistent effect from lodgepole pine. Where there is an effect of habitat structural stage on the blowdown, high negative residuals indicate that the consistent primary contributor to the effect is that less of habitat structural stages 0, 1, and 2 was blown down than expected, while a second contributor is that more of habitat structural stage 3 was blown down than expected, particularly in the settings closest to the source of the winds coming over the Continental Divide. There is a generally consistent, but weaker effect of habitat structural stage 4 in which more than expected was blown down. This effect, too, is strongest in the highest elevation, exposed settings close to the source of the winds coming over the Continental Divide.

4. Discussion The hypothesis that blowdown severity will be higher in forests with lower tree density is not supported. Past studies have shown that higher tree

density can lead to less blowdown damage (Alexander and Buell, 1955; Foster, 1988a; Everham and Brokaw, 1996), perhaps by physically absorbing or resisting wind damage to some extent. However, low-density forests may have resisted wind damage in this study in part because the wind is able to move through the stands, dissipating energy in the process. Also, when a blowdown occurs in low-density forests, there is less chance for falling trees to knock down adjacent trees. Low-density forests may also generally allow more wind to penetrate, increasing pre-conditioning stress that confers resistance to more severe winds (Everham and Brokaw, 1996). Our previous research at the stand-scale showed percent damage was unaffected by stand density in 30 stands overall, but damage increased with stand density in 12 stands that sustained <75% damage (Veblen et al., 2001), partly reflecting our results at the landscape scale. Cover-type response to the blowdown varies as expected, based on previous research in this and other areas. The strongest cover-type effect, much lower than expected aspen blowdown, is consistent with

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previous research. Aspen may escape wind damage when wind events occur after leaf fall, as in this case, because wind drag is reduced. Also, aspen trunks are more flexible than those of conifers, and thus, able to withstand intense wind velocities without uprooting (Moore, 1988). This result, at the landscape scale, is consistent with our previous findings at the stand-scale (Veblen et al., 2001), where these factors and the clonal, expansive root system of aspen were suggested to confer resistance to wind damage. While spruce–fir is the dominant forest type in the analysis area, spruce–fir forests were more susceptible to blowdown than expected based on chance alone (Fig. 3a, Table 5). Greater ages and denser tree canopies in spruce–fir may partly explain their susceptibility. Trees with dense crowns are more subject to blowdown (Smith and Weintknecht, 1915; Foster, 1988b). Other factors contributing to spruce–fir susceptibility to blowdown, relative to lodgepole pine, may be shallow rooting (Alexander, 1987) and root or bole decay by several wood-rotting fungi (Hinds and Hawksworth, 1966). Shallow roots make trees less wind firm, especially in wet soils. Rot weakens tree boles, contributing to stem or root breakage. Lodgepole pine forests were a little less susceptible to wind damage than expected at the landscape scale overall, but this relationship is not consistent across the potential confounding variables, suggesting lodgepole pine probably is not strongly resistant or susceptible to wind. This is consistent with stand-scale results (Veblen et al., 2001). Larger trees typical of late-successional forests have been found in other areas to be more likely to be damaged or blown over, and early-successional stands with small trees may better resist blowdown (Baker, 1915; Peterson and Rebertus, 1997). The general increase in canopy density with age, and increasing wind speed at greater tree heights, might theoretically lead to greater blowdown susceptibility in latesuccessional forests (Curtis, 1943; Foster, 1988b). The analysis of proportions does not indicate structural stage to be very important (Fig. 3b), which is consistent with the absence of a strong influence of stand age on damage pattern found in our research at the stand-scale (Veblen et al., 2001). However, the contingency table analysis does show that where habitat structural stage affected the blowdown, HSS 1 and 2 are blown down much less than expected and

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HSS 3 and, to some extent, HSS 4 are blown down more than expected (Table 5). The greater damage found for HSS 3 and 4 here is consistent with the greater susceptibility of taller trees, typical of later structural stages, found in our stand-level study (Veblen et al., 2001). Moreover, this structural stage effect occurs consistently within the part of the blowdown where winds were probably strongest, closest to the Continental Divide at higher elevations in exposed settings (Table 5). Elevated susceptibility of the intermediate structural stage, HSS 3, may reflect the known susceptibility of intermediate-size trees that are tall enough to be blown down, but not yet sufficiently pre-conditioned to tolerate severe wind stress (Everham and Brokaw, 1996). Low-intensity fires may be strongly limited and shaped by existing fuels and forest structures (e.g. stand boundaries), while high-intensity fires may burn with little or no limitation by forest structure (Turner and Romme, 1994). We cannot determine whether there is a similar threshold for wind events, since we lack lower-intensity events for comparison. However, there is some evidence of vegetation resistance even to the high-intensity winds of the Routt-Divide event. The Routt-Divide blowdown is an extreme wind event, so it is perhaps not surprising that the pre-blowdown vegetation pattern had, on average, only a modest influence on the response to the event. Low tree density appears to have offered some resistance to the blowdown, but even in these forests, a modest to large percentage of trees were blown down. The Cochran–Mantel–Hantzael analysis (Table 4) shows that both cover-type and habitat structural stage influenced the blowdown in spite of confounding effects of topography. These vegetation properties, thus, generally influenced the outcome of the blowdown, although the effects are modest (Fig. 3, Table 4). Previous research has suggested that vegetation may influence the pattern of damage in topographically complex landscapes (Foster and Boose, 1992). In this study, the influence of cover-type and habitat structural stage is strongest in some parts of the blowdown and is not evident in other parts of the blowdown (Table 4). The influence of cover-type was strongest away from the Continental Divide at lower elevations, where the wind was likely weaker, but also where there is more cover-type diversity. Cover-type

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had no apparent influence at higher elevations closer to the source of the winds, where the effect of habitat structural stage is most apparent (Table 4). Habitat structural stage had an effect over a larger part of the blowdown, but had no effect in many of the places where cover-type played a role (Table 4). Past natural disturbances, including fires and beetle outbreaks, shaped this mosaic of cover-types and habitat structural stages, which is also shaped by the topography (Table 3). Historical influences and the indirect effect of topography on vegetation influenced the pattern of damage from the blowdown, but only rather modestly overall, with these factors most important in particular places.

Acknowledgements This material is based upon work supported by the National Science Foundation under Grant no. SBR9808070. For field assistance, we thank Mauro Gonzalez, Jason Sibold, Rosemary Sherriff, and Jason Eiler. For assistance with orthorectification, we thank Annette Green. We thank Dennis Knight and Richard Marston for comments that improved the manuscript.

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