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2001 Modeling plant diversity and post-fire regeneration in a 31-year-old burn-Vermilion Pass, Canadian Rockies

Chernoff, Gregory William

Chernoff, G. W. (2001). Modeling plant diversity and post-fire regeneration in a 31-year-old burn-Vermilion Pass, Canadian Rockies (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/22043 http://hdl.handle.net/1880/41211 master thesis

University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. Downloaded from PRISM: https://prism.ucalgary.ca UNIVERSITY OF CALGARY

Modeling Plant Diversity and Post-Fire Regeneration in a 31 -Year-Old Bum -

Vermilion Pass, Canadian Rockies

Gregory William Chemoff

A THESIS SUBMITIED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

DEPARTMENT OF GEOGRAPHY

CALGARY, ALBERTA AUGUST 2001

8Gregory Wliarn Chemoff 2001 National Library BibliotMque nationale 1+1 OfCanada du Canada uisitions and Acquisitions el 7.Bi tographic Services services bibliographiques 395 W.llircgkn Street 395, rue Wellington Ottawa ON KIA ON4 OltawaON K1AON4 CPMdQ -

The author has granted a non- L'auteur a accorde une licence non exclusive licence allowing the exclusive pennettant a la National Lidnary of Canada to Bibiioth&quenationale du Canada de reproduce, loan, distribute or sell reproduke, pr&er, distribuer ou copies of this thesis in microform, vendre des copies de cette these sous paper or electronic formats. la fonne de microfiche/film, de reproduction sur papier ou sur format electronique.

The author retains ownership of the L'auteur conserve la propriW du copyright in this thesis. Neither the droit d'auteur qui proege cette these. thesis nor substantial extracts fiom it Ni la these ni des extraits substantiels may be printed or otherwise de celle-ci ne doivent Stre imprimes reproduced without the author's ou autrement reproduits sans son permission. autorisation. In response to a recognized need for improved understanding of the regenerative response of vegetation to fire, this thesis describes the composition and patterns of distribution of plant communities in a 2,430ha subalpine area in the Vermilion Pass, Canadian Rockies, which burned in July, 1968.

Phnt community composition and distribution is modeled through a multi-stage process that incorporates field and ancillary data through an integration of rnulivariate statistics (cluster analysis), GIs and geostatistics (DEM-derivation of site characteristics, spatial interpolation), and remote sensing (maximum likelihood classification) methods. The result is a map of the current

(1 999) distribution of plant communities in the Vermilion bum, which is used to compare plant communities in terms of composition, location in Vermilion Pass, and site preference.

This thesis is intended as the second stage in a longitudinal study, which will record the vegetative regeneration of the Vermilion bum through a complete fire cycle.

iii Numerous people and organizations were of tremendous help throughout the course of this research. I would like to express my humblest and most sincere thanks to:

P Pa& Canada - especially Rob Walker and Dave Gilbride at the KootenayNoholLake Louise Field Office in Radium, for support in the form of a warm and dry place to stay

during field season, digital data and other resources, and a grant that enabled the hiring of

a field assistant.

P The Province of Alberta and the Faculty of Graduate Studies. who contributed research

grants to this thesis research.

P The entire Department of Geography at the University of Calgary, but especially the

following people: Dr. Mryka Hall-Beyer (thesis supemisor), Dr. Damn Sjogren, Dr. Wayne

Strong, Dr. Nigel Waters, Dr. Clarence Woudsma, Dr. Stuart Hanis, Ms. Medina Deuling,

and Mr. Robin Poitras.

P Richard Norman, field assistant extraordinaire, for smiling in the face of rain, cold, and

adversity, bringing the cookies, eating 'hungn for supper every night, continuing on his

own after Ifell off a log and sprained my ankle, knitting nice toques, and working four

months before receiving a pay cheque.

P Mollie Ferris, for her support, patience, kindness, and love.

P All the rest of my family and friends who have offered encouragement, kinship, community

and support. Dedicaed to the whitebark pine, Ltre wolverine, and ihe western Indian paintbrush, and to the modher of us all. TABLE OF CONTENTS

Approval Page ...... ii Abstract ...... iii Acknowledgements ...... iv Dedication ...... v Table of Contents ...... vi ... List of Tables ...... VIII List of Figures ...... ix List of Equations ...... xii ... Epigraph ...... xln CHAPTER 1 - INTRODUCTION ...... 1 CHAPTER 2 - BACKGROUND ...... 4 CHAPTER 3 - LITERATURE REVIEW ...... 10 3.1 - Subalpine Forest Fire Ecology ...... 10 3.2 - Modeling Composition and Distribution of Vegetation ...... 12 3.3 - Past Vegetation Studies in and Around Vermilion Pass ...... 17 3.4 - Literature Review Summary ...... 26 CHAPTER 4 - METHODS ...... 27 4.1 - Field Data Collection, Input and Verif'ition ...... 27 4.2 - Cluster Analysis ...... 30 4.3 - Incorporation of Spatial Data ...... 33 4.4 - Identification of Diagnostic Species ...... 39 4.5 - Modeling Distributions of Diagnostic Species ...... 44 vi 4.6 .Modeling Distribution of Plant Communities ...... 52 CHAPTER 5 .RESULTS ...... 57 5.1 .Field Data Set ...... 57 5.2 .Cluster Analysis ...... 58 5.3 .Incorporation of Spatial Data ...... 59 5.4 .Identification of Diagnostic Species ...... 61 5.5 .Modeling Distributions of Diagnostic Species ...... 71 5.6 .Modeling Distribution of Plant Communities ...... 83

CHAPTER 6 = DISCUSSION ...... 85 6.1 .The Plant Communities of the Vermilion Bum ...... 85 6.2 .Critique of the Model ...... 117

6.3 = Suggestions for Further Research ...... 122 CHAPTER 7 .CONCLUSION ...... 127 REFERENCES ...... 130

APPENDIX 1 .Data and Software Used in this Thesis ...... 136

APPENDIX 2 = Model Flow Chart ...... 137 APPENDIX 3 .Cartographic Model ...... 138 APPENDIX 4 .Instrument Used for Recording of Field Data ...... 141 APPENDIX 5 .Complete list of Species Identified in the Vermilion Bum ...... 142 APPENDIX 6 .Summary of Plant Spedes Composition by Cluster ...... 145 APPENDIX 7 .DEM-Derived Ancillary Data Layers ...... 156 APPENDIX 8 .Summary Statistics for Tree Spedes ...... 163

vii LIST OF TABLES

Table 3.1 .Lower Subalpine Ecmites of Vermilion Pass ...... 18 Table 3.2 .Upper Subalpine Ecosites of Vermilion Pass ...... 19

Table 5.1 .Summary of Main Features of Field Data Set ...... 51 Table 5.2 .Plant Communities of the Vermilion Bum ...... 59 Table 5.3 .Correlation Matrix for Potential Diagnostic Species and Ancillary Variables ...... 62

Table 5.4 .Parameters for Spatial Dependence Models ...... 76 Table 5.5 .Error Matrix For Maximum Likelihood Classification ...... 83

viii UST OF FIGURES

Figure 2.1 .Map of Vermilion Pass and the Vermilion Bum ...... 5 Figure 2.2 .Mean Monthly Temperature and Precipitation. Vermilion Pass ...... 8 Figure 3.1 .1972 Distribution of New Pine Seedlings ...... 23

Figure 3.2 .1972 Distribution of New Spruce and Fir Seedlings ...... 24 Figure 3.3 .1972 Distribution of Dominant Shrub Species ...... 24 Figure 3.4 .1972 Distribution of Dominant Herb Species ...... 25 Figure 4.1 .Diagram of a Sample Plot ...... 28 Fgure 4.2 .Conversion of Raw Aspect Values to Solar and Cross-Valley Aspect ...... 34 Figure 4.3 .3x3 Convolution Matrix for Deriving Curvature ...... 37 Figure 4.4 .Hypothetical Experimental Semivariogram and Spatial Dependence Model ...... 50 Figure 4.5 .Hypothetical Maximum Likelihood Classification ...... 53 Figure 5.1 .Dendrogram of Cluster Analysis Results ...... 58

Figure 5.2 = Map of Locations of Sample Plots ...... 60 Figure 5.3 .OEM of Vermilion Pass ...... 61

Figure 5.4 = Equip robability Contour Diagram for Lodgepole Pine and Rusty Menziesia ...... 63

Figure 5.5 = Coincident Histogram for Lodgepole Pine ...... 64 Figure 5.6 .Coincident Histogram for Rusty Menziesia ...... 65 Figure 5.7 .Equiprobability Contour Diagram for Canadian Bunchberry and Grouseberry ...... 66 Figure 5.8 .Coincident Histogram for Canadian Bunchbeny ...... 67 Figure 5.9 .Coincident Histogram for Grousebeny ...... 68

Figure 5.1 0 = Results of KM-WallisNon-Parametric Test for Sample Independence ...... 70

Figure 5.1 1 = Generalized (Regression) Distribution of Lodgepole Pine ...... 72

ix Figure 5.1 2 .Generalized (Regression) Distnstnbutionof Rusty Menziesia ...... 73 Figure 5.1 3 .Generalized (Regression) Distribution of Grouseberry ...... 75

Figure 5.1 4 .Kriging Interpolated Surface of Regression Residuals for Lodgepole Pine ...... 77 Figure 5.1 5 .Kriging Interpolated Surface of Regression Residuals for Rusty Menziesia ...... 78

Figure 5.16 = Kriging Interpolated Surface of Regression Residuals for Grousebeny ...... 79

Figure 5.17 = Modeled Distribution of Lodgepole Pine ...... 80 Figure 5.1 8 .Modeled Distribution of Rusty Menziesia ...... 81 Figure 5.1 9 .Modeled Distribution of Grouseberry ...... 82 Figure 5.20 .Modeled Distribution of Plant Communities ...... 84 Figure 6.1 .Modeled Distribution of Mount Whyrnper Open PinelBuffaloberry (Cluster 1) ...... 87 Figure 6.2 .Modeled Distribution of Subalpine Meadows and Avalanche Tracks (ClusteR) ...... 89 Figure 6.3 .Modeled Distribution of Stonn Mountain Grousebeny (Cluster 3) ...... 92

Figure 6.4 .Modeled Distribution of South Side Open PinelMenziesia (Cluster 4) ...... 94 Figure 6.5 .Modeled Distribution of Open PinelMenziesialGrousebeny-Bunchberry(Cluster 5) .96 Figure 6.6 .Modeled Distribution of Ribbon of Menziesia (Cluster 6) ...... 99 Figure 6.7 .Modeled Distribution of Dog Hair Pine (Cluster 7) ...... 101 Figure 6.8 .Modeled Distribution of Bottomlands Dense Pine (Cluster 8) ...... 103 Figure 6.9 .Modeled Distribution of Midslope Closed PineIMenziesia (Cluster 9) ...... 106 Figure 6.1 0 .Modeled Dist. of Closed PineMaalobviGmuseberry-Twinflower (CI . 10) ...... 108 Figure 6.1 1 .1999 Distribution of Lodgepole Pine by Plant Community ...... 109 Figure 6.12 .1999 Distribution of Engelmann Spruce and Subalpine Fir by Plant Community ... 11 1

Figure 6.13 g 1999 Distribution of Dominant Shrub Species by Plant Community ...... 113 Figure 6.14 .1999 Distribution of Dominant Herb Species by Plant Commun* ...... 115

X Figure 9.1 .DEMderived Slope. Vermilion Pass ...... 156

Figure 9.2 .DEMderived Solar Aspect. Vermilion Pass ...... 157

Figure 9.3 - DEMderived Cross-Valley Aspect. Vermilion Pass ...... 158 Figure 9.4 - DEM-derived Solar Slope-Aspect Index. Vermilion Pass ...... 159 Figure 9.5 .DEMdenved Cross-Valley Slope Aspect Index. Vermilion Pass ...... 160 Figure 9.6 - DEMderived DownSlope Curvature. Vermilion Pass ...... 161 Figure 9.7 - DEMderived CrossSlope Curvature. Vermilion Pass ...... 162 LIST OF EQUATIONS

Equation (1) .Squared Euclidean Measure of Distance ...... 32

Equation (2) .Calculation of Slope-Aspect Index ...... 35 Equation (3) .Calculation of Slope Vector ...... 37

Equation (4) .Calculation of X-Component of Slope ...... 38 Equation (5) .Calculation of Y-Component of Slope ...... 38 Equation (6) .Calculation of Azimuth ...... 38

Equation (7) .Calculation of Curvature ...... 38 Equation (8) .Pearson's Correlation Coefficient ...... 40 Equation (9) .Kruskal-Wallis H-Statistic ...... 44

Equation (1 0) .General Simple Linear Regression Equation ...... 46 Equation (1 1) .General Multiple Linear Regression Equation ...... 47

Equation (12) .Experimental Semivariogram ...... 49 Equation (13) .Ordinary Kriging Estimator ...... 51

Equation (14) .Kappa Index of Agreement Statistic ...... 55

Equation (15) .Regression Equation for Lodgepole Pine ...... , . 71 Equation (16) .Regression Equation for Rusty Menziesia ...... 71 Equation (17) .Regression Equation for Grouseberry ...... 71

xii EPIGRAPH

xiii CHAPTER 1 - INTRODUCTION Forest fires, naturally occumng phenomena in the Canadian Rockies, play a significant role in shaping the ecological landscape. In fact, there exist few vegetated areas within the ecoregions of the Rockies that have not, at one time or another, been affected by fire [Gadd, 1995;

Hallworth 8 Chinnappa, 19973. It is a natural regenerative process that is essential to the continued health and vitality of the natural environment. The fire cycle, at every stage in its gradual evolution, alters the landscape so as to render it more habitable for same species of flora and fauna, and less habitable for others. Fire facilitates the weaving of the intricate mosaic of forest communities in the landscape, improves soil quality, and provides increased resistance to disease and pestilence

[Kenhaw, MacKinnon & Pojar, 19981. It is an important aspect of the constant, dynamic change that is required for the preservation of ecological health and biodiversity.

Only relatively recently have popular attitudes towards fire and its role in the maintenance of ecological balance begun to change. It is, perhaps, due to this fact that the regeneration of natural plant communities from fire is a process that is not well documented or understood, and that further exploration into the nature of this process is required [Bailey, 1996; Schimmel &

Granstrom, 1996; Achuff et al., 1984; Harris, 19761. The research documented in this report has been directed towards the goal of advancing the understanding of this process. Specifically, the research described here relates to the vegetative regeneration that has taken place within a 31- Introducion 2 year-old (July 1968) bum located in Vermilion Pass in the Eastern Main Ranges of the Central

Canadian Rocky Mountains.

The goal of this research is to, as accurately and at as high spatial resolution as possible, model the vegetative composition and spatial distribution of plant communities within the Vermilion

Bum. This goal is achieved by meeting the following objectives:

1. Using field collected (sample plot) vegetation data and multivariate statistical techniques, to group sample plots of similar composition into plant communities.

2. Through the incorporation of ancillary data, to identify a set of 'diagnostic speciesn, which serve to distinguish each plant community from the others by satisfying the following three criteria: (a) species must occur in all or nearly all sample plots; (b) species must demonstrate statistically significant correlation to one or more ancillary (DEMderived) variable; and (c) species' intracommunity similarity and intercommunity distinction must be sufficient to clearly separate communities or groups of communities from one another;

3. Through the relating of discrete, field-collected (point) data to continuous, ancillary data values at discrete sample plot locations, and through the combined use of regression and geostatistical spatial interpolation techniques, to develop continuous spatial representations of diagnostic species for the bum area;

4. To classify the bum area using the modeled diagnostic species' distributions as inputs and

Maximum Likelihood Classification methods; and thus, to generate a map of the spatial distribution of plant communities within the Vermilion Bum; and finally,

5. To describe these plant communities with respect to their dominant species, location in the

Vermilion bum, and general preferred site conditions. lntroducion 3 Plant communities identified through this process will be compared to those identified in similar areas through the Kootenay National Park Ecological Land Classification [Achuff et al.,

19841, so that the relevance of these results in comparison to other similarly situated, similarly aged bum areas may be established.

These plant communities and their spatial distribution will also be compared to the findings of an earlier study by Hams [1976], part of which examined the regeneration of vegetation within the Vermilion Bum during the years immediately following the fire. The goal of this comparison is to add a temporal aspect to the study, and thus improve understanding of the dynamics of bum regeneration over time. This will provide future researchers with two 'time slices" of the regeneration to which they can add, the anticipated end result being documentation of the full fire cycle at regular intervals, and improved understanding of the natural regeneration process.

It is important to state at the outset that this research is descriptive rather than predictive in nature. That is, the goal is to accurately describe the present conditions and situation of vegetation within the bum, and not to predict how regeneration may be expected to proceed from this point fomard. It is anticipated that this research will be of interest to conservationists and Parks Canada staff, ecologists, wildlife biologists, foresters, and biogeographers. 4 CHAPTER 2 - BACKGROUND The Vermilion Pass bum straddles the continental divide and the border between Alberta and British Columbia. It is accessible by road - Highway 93 makes use of Vermilion Pass to connect Castle Junction and the Trans-Canada Highway to the Kootenay River Valley, Radium Hot

Springs and southeastern British Columbia. The Pass is a glacially eroded saddle with an elevation of 1,639m, and is located approximately at 51 "1 5'N Latitude and 116OO'W Longitude. Figure 2.1 shows the relative location and spatial extent of the Vermilion bum.

Physiographically, Vermilion Pass is located in the eastern section of the Main Ranges

(Eastern Main Ranges) of the Canadian Rocky Mountains. The Main Ranges, a narrow band of structurally and lithologically distinct mountains, stretch along the continental divide from the British

Columbia-Montana border at Lake Koocanusa, north to the southern tip of Williston Lake on British

Columbia Highway 97. Compared to neighbouring physiographic regions, the Eastern Main

Ranges are generally characterized by nearly flat-lying, older (as old as 800 million years) sedimentary rock, and high peaks. Their height and situation on the continental divide result in cold temperatures and high levels of precipitation - consequently, higher peaks are commonly glaciated. Glacial erosion, combined with the flat or near-flat layering of bedrock, contributes to an irregular drainage pattern with valleys radiating in all directions from mountain peaks [Gadd,1995].

Vermilion Pass sits between the Bow and Ball Ranges, both of which are members of the Eastern

Main Ranges, and which contain the highest peaks in Kootenay National Park, which include

Deltaform Mountain (3,424m), Mount Ball (3312m), Mount Allen (3,301 m), Mount Tuzo (3,249m),

Mount Fay (3,238m). and Neptuak Mountain of the Wenkchemna Peaks (3236m) [Acuff et al.,

19841. In this area, the Eastern Main Ranges are bordered to the west by the Western Main Background 5

ION PASS 9 CANADIAN ROCaS

I LEGEND I Named Mountain Peaks

Locaticm of LighbmlingSbike (Ignition) Vegetated Areas @ekw tree line)

Streams, Creeks, and Rivers Glaciers

Major Roads and Highways Lakes

Alberta - Brifish Cdumbia Border Figure 2.1: Map showrirg relatiire localion of VemiIiun Pass and ignition pointcspatia/ extent of bum. Background 6 Ranges, including Hawk Ridge, and the Vermilion and Mitchell Ranges. The dividing Res between the Eastern and Western Main Ranges are Haffner and Tokkum Creeks, just down the Vermilion

River Valley from the southwestern edge of the bum (see Figure 2.1). The Western Main Ranges differ significantly from the Eastern Main Ranges in their morphology - the Vermibn and Mitchell Ranges are characterized by long and narrow serrated ridges, castellate peaks, and welldefined cirques and hanging glacial valleys [Achuff et al., 19841. To the east, the Eastern Main Ranges are bordered by the Front Ranges of the Rocky Mountains, the boundary of which is demarcated by the Simpson Pass thrust fault. Compared to the Easten Main Ranges, the Front Ranges are not as high, much drier, and characterized by thrust faults, southwestwarddipping sedimentary layers, and very regular trellis drainage networks comprised of transverse and longitudinal valleys [Gadd,

19951.

Lower Cambrian Gog Quartzite, and middle Cambrian limestone and dolomite deposits of the Cathedral Formation and shale of the Stephen Fonation compose the bedrock geology of the valley bottoms and lower hillslopes. Mountain slopes are primarily composed of middle Cambrian limestone of the Eldon and Pika Formations, shale of the Arctomys Formation, and Waterfowl

Fonation dolomite. Crowning only the highest peaks of the area are early Cambrian shales of the

Sullivan Formation [Cook, 19731. The characteristic near-horizontal orientation of sedimentary layers combines with the alternation of bedrock between cliff-forming, erosion-resistant limestone and laminar, more erosive shales to create a 'stair-step" topography of relatively gradually sloping areas, separated by abrupt cliffs [Gadd, 19951.

Generally, the lower valleys, cirques, ods, and valley bottoms in the Eastern Main Ranges comprise the lower subalpine emregion and are abundantly vegetated. The upper valley walls, Background 7 crags, and cliffs tend to be more barren and make up the upper subalpine ecoregion [Achuff et al.,

19841. The vegetation surrounding the bum is of highly variable composition and maturity. The lower subalpine is made up of dense stands dominated by Engelmann spruce (Pba engelmannii

Parry ex Engelm.) and subalpine fir (Abies lasiocarpa var. lasimrpa (Hook.) Nutt.) in more mature areas, and by lodgepde pine (Pinus cdntorta Dougl. ex Loud.) in areas that were burned more recently. Higher up the valley walls in the upper subalpine, the forest cover begins to thin and the vegetation changes to open forests of limber pine (Pinus flexilk James), interspersed with hearty shrubs and sparse meadows containing more resilient, mat-forrning ground cover species.

Krummholz communities are also present, comprised of dwarf tree and shrub species from the lower subalpine. In many instances, especially along the Vermilion River valley, the Vermilion fire appears to have burned right up to tree line (see Figure 2.1).

Situated atop the continental divide, Vermilion Pass is a transition zone between the dryer, continental climate to the east and the moister, maritime climate to the west [Hams, 19761. The interplay between these two climates frequently results in dramatic microdimatic differences over relatively short distances [Achuff et al., 19841.

Figure 2.2 shows the average monthly temperature and precipitation over a period of five years, taken from eight weather stations located within the Vermilion bum - the eight weather stations were placed at various locations, aspects and elevations (1,524m, 1,646m, 1,676m (four stations), and 1,981 m (two stations)), five stations within the bum and three immediately adjacent to it.

Generally, the climate in Vermilion Pass is quite cdd, with a shoct growing season - only Background 8 three months -June, July and August, recorded average temperatures above the suggested growing season threshold temperature of 5oC [Peddle and Duguay, 19951, under which most plants remain dormant This is due largely to the influence of elevation -the bum itself ranges in elevation from just over 1,500m along the Vermilion River, to just under 2,400m near Aster Lakes on the shoulder of Storm Mountain. The area has an average annual precipitation of 809mm - relatively moist when compared to other lower subalpine areas1.

Mean Monthfy Temperature and Precipitation Vermilion Pass, Canadian Rockies

Figure 2.2: Mean monthly temperature and pm@ilatiim (source: 1970-75 data, from Hams [1976J1

The Vermilion Pass fire was ignited on July 9m, 1968 by a dry lightning strike, in the

Statistic calculated by a 3-year averaging of data taken from Marble Canyon Meteorological Branch weather station - mean for seven lower subalpine stations in and around Kootenay National Park was 747mrn [Achuff et al., 19841; Hams study [I9761 data shows average annual precipitation equal to 434.2mm from seven weather stations within the bum. Background 9 Tokkum Creek Valley high on the shoulder of Mount Whymper (see Figure 2.1). The fire bumed fiercely for five days, consuming 2,430 ha of mature forest by the time it was controlled on July

13% Spot fires continued into early August [Hams, 19761. The Vermilion fire was the largest recorded between the years of 1961 and 1985 in the lower subalpine ecoregions of Kwtenay,

Yoho, and Banff National Parks [Johnson 8 Miyanishi, 19911. One can infer from the large area of the bum that the fuel moisture in the summer of 1968 was likely "so low that differences in vegetation type and age, topographic position, aspect, and elevation made little difference"

[Johnson 8 Mianishi, 1991, p.851.

The Vermilion bum is situated entirely within Kootenay and Banff National Parks, on lands that are managed by the Lake Louise, Kootenay and Yoho Field Unit of Parks Canada. An important part of the mission statement of Parks Canada is a commitment to the preservation of ecological integrity. It behooves Parks staff, therefore, to ensure that TKootenay Nationall park's ecosystems and their component native species and natural processes are free to function and evolve" [Parks Canada, 2000, p.131. This mandated "laissez-fairenapproach to land management has allowed the vegetation of the Vermilion bum to regenerate naturally from the 1968 fire, free from the human intervention that is common in bumed areas that fall under different land management strategies. 10 CHAPTER 3 - LITERATURE REVIEW Examination of the dynamics and complexities associated with the regeneration of natural plant communities (and, more broadly, ecosystems) from the effects of fire is a burgeoning field of academic study. This chapter provides a brief ove~ewof current research regarding fire ecology and the fire cycle within subalpine pinelsprucelfir forests. Other attempts to model the distribution of plant communities (in various circumstances and environments) using fieldcollected andlor ancillary data are also discussed. Finally, this chapter will provide the reader with a summary of past research on the vegetative composition of the Vermilion bum and surrounding areas.

3.1 - SUBALPINE FOREST FIRE ECOLOGY The dynamics of the subalpine forest structure and ecological landscape are largely governed by the cycle of fire and regeneration [Kenhaw et a!., 1998). Due to the serotinous (resin- sealed) nature of its cones, lodgepole pine commonly requires the heat of fire for its seeds to be released [Gadd, 19951. Other species such as larch [Carlson et al., 19901 and broadleaf deciduous tree species [Schier & Campbell, 19781 also rely on fire for continued survival (in both cases, fires of low to moderate intensity are reported to be most beneficial). Conversely, the seeds of tree species such as spruce and fir are killed by fire. These species must recolonize an area by the dispersal, usually wind-borne, of seeds from mature forest located either at the edge of the bum or in some area within the bum that suwived the fire [Greene & Johnson, 1995; Greene 8 Johnson,

19961. The combination of various different species' rates and means of reestablishment within a burned area, along with the site characteristics which favour the establishment of one type of plant community over another, create a complex, diverse, and ever changing mosaic of plant communities within the subalpine bum. Literature Review 11 Some of the conventional ideas of fire succession and fire ecology in the subalpine forest landscape have recently been re-examined. The "traditional linear" view of fire succession states that lodgepole pine is the first species of tree to appear in a recently burned area. The growing pine trees create shade and leaf litter necessary for the establishment of subalpine fir and Engelmann spruce seedlings. These seedlings grow in the understory of lodgepole pine until they eventually outcompete the pine for essential resources and thus come to make up the 'climax vegetation"

[Cormack, 1953; Horton, 19561.

Johnson and Miyanishi [1991] have challenged this traditional view of succession, citing lack of consideration of such important factors as species and age-specific growth and mortality rates as the source of the confusion. An alternative hypothesis has been proposed which incorporates current knowledge regarding the fire regime and species-specific population dynamics. The result is a new paradigm in the understanding of fire succession: lodgepole pine, subalpine fir, and Engelmann spruce all commence regeneration immediately following a fire. The most significant factors impacting reestablishment of all species are availability, location, and transport of seeds. Due to higher juvenile growth rates than Engelmann spruce and subalpine fir, lodgepole pine will form the early regenerating forest canopy. Eventually, growth rate declines in lodgepole pine and remains constant in Engelmann spruce and subalpine fir, and older forests become increasingly dominated by the latter. All species continue to recruit, albeit with high mortality, at a diminished rate even under the closed canopy of a mature forest [Johnson &

Miyanishi, 19911.

A signifiint body of research exists that has attempted to empirically characterize the fire regime within the subalpine ecoregion of the Rocky Mountains. Lightning caused fires are Literature Rewiew 12 characterized by high intensity, high consumption levels of organic matter from the forest floor, and fire cycles of 90-1 30 years [Johnson 8 Lawn, 1991 ; Johnson & Miyanishi, 1991 ;Masters, 19901.

Fire cyde is defined as '...the time required to bum an area equivalent to the study area at least once. It is not necessary that every location in the study area bum once, some locations may bum more than once, some not at all during the fire cyclen[Johnson & Miyanishi, 1991, p.821. Though it exhibits spatial variabilii - other studies involving similar vegetation communities in the United States have calculated fire cycles anywhere from 'decadal" [Fall, 19981 to upwards of 300 years

[Renkin & Despain, 19921 - the local fire cyde appears to have remained relatively constant since the mid-1700's. Before this time (the beginning of the Tile ice age"), the regional climate was warmer and drier, resulting in shorter fire cycles [Johnson & Larsen, 1991). Forest stand age and composition were found not to be correlated to fire ignition, and it was deduced that climate, specifically lightning-laden weather patterns caused by tropospheric blocking, is the main factor governing the fire cycle [Bessie & Johnson, 1995; Johnson 8 Miyanishi, 19911.

3.2 - MODELING COMPOSlTlON AND DISTRIBUTION OF VEGETATION Modeling is the process of empirically defining and describing the relationship between one phenomenon (in this case, plant community composition and distribution) and one or more associated phenomena. Two general types of models are discussed - non-spatial (composition) models, which describe the component species and other site characteristics of a community, and spatial (distribution) models, which generalize the patterns of relative location and distribution of communles, commonly employing a GIs (Geographic Information System). Non-spatial models commonly generalize the relationship between associated phenomena at discrete (sample point) locations; spatial models commonly involve the inference of phenomenon distribution based on a Litemture Review 13 combination of discrete (sample point) observations and continuous (ancillary) data on associated

phenomena. Although few have dealt directly with the effects of fire, there are many documented

studies that model vegetation patterns on a landscape using different combinations of field-

collected and ancillary data.

Without the aid of GIs, Strong and Leggat [I 9811 mapped the ecological communlies

(ecoregions) of Alberta, in which ecological communities are distinguished from each other by a

broader range of features than plant communities, incorporating climate and soil characteristics

into their definition. They mapped out the ecoregions of Alberta using rigorous field sampling and a

manual overlay of existing forest cover maps, Landsat and conventional aerial photography, soil

reports, and climatic data.

La Roi et al. [I 9881 experimented with various methods of classifying understory plant communities, and found generally that the furthest neighbour and minimum variance (a.k.a.

Ward's) methods of cluster analysis (as well as Braun-Blanquet tabular comparison and two-way indicator species analysis (lWINSPAN)) 'produced potentially useful results" [La Roi et al., 1988, p.87. As a means of establishing the separability of understory plant communities, an ordination of sample plots was undertaken using a modified version of principal components analysis, detrended correspondence analysis (DECORANA). DECORANA, first developed by M.O. Hill of

Comell University in 1979, and modified by Oksanen and Minchin [I997 to correct program instabilities under reordering of input data, is similar to principal components analysis (PCA) in that it reduces data into a number of 'supervariablesn (principal components) that describe the maximum variability for the data set. DECORANA differs from PCA in that it eliminates the arching tendency, or trend, of vegetative data that is ordinated in this fashion w.L Strong, personal Litemture Review 14 communication, November 20001.

There have been many applications of GIs and geostatistics to modeling the associations between topographic or environmental factors and plant community distributions. In a restored tidal marsh in Connecticut, Banett and Niering (19931 modeled the success of indigenous species in reclaiming a formerly impounded area, using hydrology as a determining factor. Bolliger and

Sherrer (19931, as part of a Swiss national environmental protection policy, made combined use of aerial photography and digitally derived topographic data to model the distribution of vegetation in a fen on the Lake of Zurich.

In the Passeier Valley in the central Alps of northern Italy, Tappeiner et al. [I9981 used a combination of natural (soil humidity, hydrology, altitude, and slope derived from a high-resolution

(5xSm pixels) DEM) and anthropogenic (empirical measures of present and historical land use, including mowing, forestry, grazing, fertilization, and irrigation) factors to model the distribution of plant communities. The authors used discriminant analysis (specifically, the SORT classification and ordination software) to group a randomly generated set of sample locations into 21 distinguishable vegetation classes based on the above variables and on fieldcollected vegetation data. The discriminant analysis resulted in calculation of a discriminant function that was used to classify the entire study area. Using a pre-existing vegetation map of the study area for pixel-to- pixel comparison, the overall classification accuracy was calculated to be 78%.

As part of the Blue River Landscape Study [Cissel et al., 19991 in the Cascade Mountains of central western Oregon, Kushla and Ripple [I9961 modeled past fire seventy in a Douglas fir forest that most recently burned in 1991, using topographic predictor variables. It was determined Literature Revbw 15 that the model was more accurate when the study area was divided into four broad landscape classes and a separate fire severity prediction equation computed for each area, than was the case when the study area was regarded as a homogenous landscape.

Lister et al. [2000] modeled the distribution of a spruce-fir vegetation community type over a large (approximately 1.7 million ha) area in northern New England (New Hampshire and

Vermont) based on ancillary data from DEM, satellite imagery, and climate data sources. Ancillary data included raster coverages over the study area for elevation, slope, aspect (computed from a

1:24,000 scale DEM), satellite (Landsat) imagery, distance to and density of roads, and precipitation. The research sought to determine an accurate technique to model continuous surfaces representing the distribution of the two species using discrete point (sample plot) vegetation data and its relationship to the ancillary variables listed above. It was determined that the most accurate method was a combination of two modeling approaches. First, linear regression was performed using the vegetation data as the dependent variable and the significantly correlated ancillary data values, spotted for each point in the vegetation data set, as independent variables

(spotted values are ancillary (exhaustive) data values, extracted from a continuous (raster) coverage only at locations for which discrete data values (sample plots) exist). The regression residuals were recorded for each plot, and used to create a new discrete (point) data set -that is, a new value was assigned to each vegetation sample plot location, equal to the portion of the percent cover not described by the linear regression equation. Next, a surface or continuous coverage was generated from this residual data set by kriging, a rigorous geostatistical spatial interpolation process. Finally, a percent cwer distribution of Re spruce-fir vegetation community type was modeled by adding the continuous surface generated by the spatial expression of the Literature Review 16 linear regression equation to the surface generated by kriging interpolation of the residual spatial data set.

Various attempts have been made to model vegetation patterns in the Rocky Mountains using topographic and climatic variables as determining factors. Hiemstra et al. [2000] postulated that winddistributed snow accumulations were the main variable determining plant community distribution in the alpine-subalpine ecotone of the Medicine Bow Mountains in southcentral

Wyoming. Hence they modeled wind and the accumulation of snow for a broad alpine (3,000+ m) ridge, using a program called SnowTrandD [Liston and Sturrn, 19981 and a DEM, an empirically developed tree elevation model (TEM), and meteorological driving variables (wind speed, wind direction, temperature, relative humidity, and precipitation) as inputs. The model computes a wind speed and direction value for each pixel in three dimensions. Model accuracy was tested by comparing model-derived values to field-recorded values at regular intervals along a trans- The model accurately located areas of wind erosion and deposition of snow, but generally tended to overestimate depth of both erosion and deposition.

Peddle and Duguay [I 9951 calculated a series of topoclimatic index variables to incorporate into the satellite image analysis of an alpine ridge in the Front Ranges of the Colorado

Rocky Mountains. The goal of this research was to improve the prediction and explanation of ecological community distribution in a landscape by including variables representing various climatic factors. Five topoclimatic indices were calculated for the study area:

1. An orogenic precipitation index calculated as a function of altitude and distance from the continental divide;

2. A slopeaspect index with aspect measured as angular separation of true aspect from due west; Literature Review 17 3. A snow probability index calculated as the proportion of past satellite images in which snow was present in a given pixel;

4. An insolation index calculated as a summation of all possible sources of incident solar radiation for each pixel; and finally,

5. A cumulative measure for each pixel of growing degree days, where the daily number of growing degree days is equal to the number of degrees (OC) by which the average daily temperature exceeds the minimum temperature required for plants to grow (defined as 5oC).

Peddle and Duguay found that the best results were obtained by inclusion of all five indices in the modeling process. Individually, the growing degree days and orogenic precipitation indices were found to demonstrate the highest ecological community classification accuracies.

3.3 - PAST VEGETATION STUDIES IN AND AROUND VERMILION PASS This thesis relies (and to some extent, builds) upon two past studies that classified and mapped the vegetation of Kwtenay National Park and the Vermilion bum.

The Alberta Institute of Pedobgy, on behalf of Parks Canada, carried out and published an

Ecological Land Classification (ELC) of Kootenay National Park in the early 1980's [Achuff et al.,

19841. In comparison to the mapping of plant communities, ELC involves a more complex and exhaustive consideration of factors (soil properties and lithology, climate, hydrology, and wildlife habitat suitability) in the designation and mapping of ecological communities (ecosites); therefore, the definlion and distribution of mapping unlfor the 1984 Kootenay National Park ELC will differ from those identified in this thesis. However, the ecosites mapped for the area in and around

Vermilion Pass are valuable in terms of a general description of ecological communities of the bum, and also for comparison between plant communities as they are mapped in this study and

Literature Review 20 those mapped by Achuff et al.. The vegetation types described in the Kootenay National Park ELC win be matched as closely as possible to the plant communities identified in this thesis. It is important to note, however, that in the ever evolving dynamics of the fire cyde, the plant communities recorded in 1984 are likely to be different to some extent from those recorded in

1999. Tables 3.1 and 3.2 summarize plant diversity analysis and provide a brief description of the respective locations and qualities of the ecosites identified in the Vermilion bum through the

Kootenay National Park ELC.

Vegetation for the entire area of Kootenay National Park was mapped on a 1:50,000 scale.

The method was first to outline areas of homogenous vegetative composition on air photos. A sample plot was then established in each homogenous area, and the composition of each was recorded in the field using square sample plots of various dimensions - sample plots were 20X20m in forested vegetation, 15x15rn in shrubby areas, and 1OX1 Om in herbaceous and dwarf shrub locations. Smaller plots (5XSm or 1x1m) were used in intricately patterned areas to ensure homogeneity [Achuff et al., 19841. Along with vegetation data, the following physical environmental factors were also recorded for each sample plot: elevation, slope, aspect, topographic position, relief shape. landform, soil subgroup and drainage class, and moisture regime. Vegetation was first organized into five general physiognomic (structural) classes (Closed Forest (C), Open Forest (0),

Shrub (S), Low Shrub-Herb (L), and Herb-Dwarf Shrub (H)), and then classified using a combination of manual tabular comparison and ordination procedures. This resulted in the grouping of sample plots possessing similar vegetative characteristics into units called vegetation types (VTs). A total of 56 different VTs were identified for Kootenay National Park - 28 closed forest, 9 open forest, 7 shrub, 2 low shrub herb, and 10 herb-dwarf shrub VTs. Separate VTs were Literature Review 21 not designated for burned areas; rather, an ecosite modifier (in this case, the letter %")as suffixed to an area that had recently burned?

Addressing a perceived need for greater understanding of the effects of fire, Or Stuart A.

Hams of Harris Environmental Consultants, Limited (Calgary Alberta), in 1971, undertook a rigorous research project, the goal of which was to quantify all aspects of the environmental conditions resulting from the 1968 fire in Vermilion Pass. Not limited to a vegetation analysis, the

Harris Study [I9761 looked at the bum and the early stages of regeneration in terms of changes in climate, soils and soil microflora, vegetation, and fauna (small mammals and birds). Since this thesis focuses exclusively on regenerative changes in vegetation, only those sections of the Hams study that deal directly with vegetation will be discussed in detail. These findings will be used as a basis of comparison between the state of regeneration immediately following the fire and that which is extant today, and are envisioned as the first two rime slicesn in a longitudinal study that traces the vegetative changes of the Vermilion bum throughout the entire fire cycle. In order to improve comparability of study results, the research undertaken in this thesis has purposely employed a similar sampling resolution to that employed by Hams [I9761 and Willard 8 Harris

[I 9721 in the vegetation study of 1972.

Initially, a study was undertaken in 1971 to determine the general vegetative changes over the course of the fire cycle. Four plots in and around Vermilion Pass, each representative of a different regenerative stage (3,55,120, and 235 years since fire), were selected, and herbaceous and shrub layer vegetation in these four plots was recorded and compared. Hams deduced that 3

2 other emsite modifiers areas that were frequentfy snow avalanched (A), failed slopes (F), and liicareas (X). So, for example, an area determined to belong to the Sawback ecosectian, ecosite 3 that had recently been burned and that experienced frequent snow avalanches would be designated S83AB". Litenture ReVrew 22 years following fire, the dominant understory Jpades were heartleaf arnica (Amoordifolia

Hook), fireweed (Epilobium angustiolium ssp. angustiolium L.), and rusty menziesia (Menziesia femginea Sm.). These were replaced at 55 years with whortlebeny (Vaainium myMus var. oreophilum (Rydb.) Dom) as the dominant shrub to still-common rusty rnenziesia, and diminishing heartleaf arnica and fireweed. After 120 years, Hams credited the increased shade provided by denser canopy coverage for the resurgence of rusty menziesia as the dominant shrub species over whortlebeny, and for the diversification of the herb layer (high incidences of strawberryleaf raspberry (Rubus pedatus Sm.), Canadian bunchberry (Comus canadensis L.), bog Labrador tea

(Ledum groen/andicum Oeder), and twinflower (Linnaea borealis L.)). The 120-year old stand had the densest recorded understory (95% ground cover) of the four plots. In the oldest (235-year) stand of forest, rusty menziesia continued to dominate with considerable amounts of Canadian bunchbeny, strawberryleaf raspberry, and whortlebeny in an otherwise abundantly mossy understory. Hams cautioned that these four plots demonstrate only very generally the vegetative composition of the forest at different stages of regeneration, and are not necessarily representative of the entire range of vegetation associations to be found in regenerating bums. Nor, suggested

Hams, do the four plots sampled in the preliminary vegetation study provide a comprehensive sampling of communities of their respective ages.

Willard and Hams [I9721 mapped the vegetation within the Vermilion bum. A sampling grid was laid out, consisting of a 300X300m array, oriented parallel to the bottom of the Vermilion River

Valley, with survey pins located at the grid intersection points. These survey pins were used as the centre for radius sample plots (5m radius for trees, 2m radius for shrub and herb layers). Recorded vegetation data was neither classified nor ordinated, but rather, recorded values of certain species Literature Rewiew 23 and their relative locations were then used to manually create generalized (isoline) vegetation maps showing the distribution of lodgepole pine, combined Engelmann spruce and subalpine fir, and dominant herb and shrub species. Figures 91-34 reproduce the Willard and Hams 119721 vegetation maps.

Figure 3.1 shows the reestablishment of lodgepole pine seedlings in the Vermilion bum as of 1972. The distribution was noted to demonstrate a high degree of spatial variability, ranging from

Figure 3.1: 1972 map showing post-ire distn'bution and abundance of hni'le pine seedlings in the Vermilbn Pass bum. Regular grid of dots represent sample pbt locafbns. Source: Ham's, 1976.

0 new seedlings in many areas (some higher elevations, avalanche chutes, the valley bottom, around Altrude Lakes) to a maximum of 70 seedlings on one plot near the northeastern end of the bum. Hams postulated that the favourable site conditions for the establishment of pine seedlings were lower elevations and higher amounts of early season (i.e. snow melt) moisture; however, the relationship among these variables was not quantified in the Hams study.

Figure 3.2 shows the combined distribution of Engelmann spruce and subalpine fir figure 3.2: 1972 map showing post-fire dMb&n and abundance of Engemann spruce and subalpine fir seedlings in the Vetmilion Pass bum. Sourn: Ham's, 1976.

seedlings within the bum. Harris noted that their reestablishment at this early stage in the

regenerative process was spatially confined to areas in close proximity to a seed source (i.e. the

edge of the bum or an isolated island of trees that escaped the fire), and also remarked on the comparative abundance of subalpine fir relative to Engelmann spruce. As of 1976, these two

_IPt). SHRUBS - WRAUDN BURN

r SPIREA COTCNTILLA L LORNKCM ar IK)SA-IYENZILSIA 3 SHE)~EID(A-smatlr 0 NO rnauu ~m. Figure 3.3: 1972 map showing post-fife dMbution and abundbnce of dominant shmb species h the Vermilbn Pass bum. Source= Ha&, 1976. Litemture Review 25 species were beginning to re-establish themselves with increased vigour.

Figures 3.3 and 3.4 show the areas of dominance of shrub and herb species, respectively, in 1972.

Dominance was assigned for a given sample plot to that species accounting for at least 50% of the total number of plants in that stratum. Although Hams acknowledged the large areas of rusty menziesiadominated shrubs and apparent competition between fireweed and heartleaf arnica for dominance in the herbaceous layer, he did not propose explanatory hypotheses for these patterns.

Figure 3.4: 1972 map showrirg post-fire distribution and abundance of dominant herb species in the Vermiliun Pass bum. Source: Ham', 1976.

Vermilion Pass, especially the higher slopes of Mount Whyrnper, experience frequent snow avalanches that, even to the untrained eye, noticeably affect some vegetation patterns within and beyond the Vermilion bum. Both the Hams study [I 9761 and the Kootenay National Park €LC

[Achuff et al., 19841 paid special attention to the vegetative pattems and communities assodated with avalanche disturbance. As part of the Harris study, Winterbottom [I9741 mapped the vegetation in the avalanche chutes of the Vermilion bum and compared them to those in similar, unburned, adjacent areas. It was concluded that the avalanche chutes had widened and Litemture ReVre w 26 lengthened as a resutt of the fire. Achuff et al. [I9841 identified three distinct avalanche complexes in Kootenay National Pak The avalanche complexes vary according to ecoregional situation and vegetation composition, and are mostly comprised of low-lying herb and shrub (with the exception of one aspen-dominated closed forest) vegetation types. Achuff et al. did not examine the effects of fire on avalanche tracks. Although the response of vegetation communities to the avalanche disturbance (and moreover, to combined disturbances of fire and avalanche) is an intriguing field of study, time constraints prohibit the consideration of such modeling problems within the scope of this thesis.

3.4 - LITERATURE REVIEW SUMMARY This thesis relies heavily upon past research in fire ecology, modeling of distribution and composition of plant and ecological communities, and past vegetation studies in Vermilion Pass and surrounding areas. This chapter has provided a brief discussion of the existing body of literature, with particular focus on those methods that have been employed in this thesis - a more detailed discussion of these methods is provided in the following chapter. Modeling techniques used in this thesis were selected according to time and resource constraints, optimization of anticipated results, and ease of calculation. The other techniques for modelling plant communities that are desaibed in this chapter are equally effective and valid, and are included in order to illustrate the myriad of possible approaches that exist beyond the scope of this thesis. 27 CHAPTER 4 - METHODS This chapter provides an outline of the field and analytical research methods employed in this thesis. For purposes of clarity and convenience, it has been divided into sections, each of which describes a separate stage of research. A list of the spatial data resources (complete with sources and accessibility) and analytical software used in this study is included in Appendix 1.

Appendix 2 is a flow chalt that summarizes the procedure outlined in this chapter. Appendix 3 is a cartographic model that illustrates the modeling process, as well as the creation and incorporation of data layers in the analysis.

4.1 - RELD DATA COLLECTION, INPUT AND VERIFICATION The vegetative composition and other field data used in this thesis were collected between late June and early August of 1999. Prior to starting work in the field, it was necessary to devise a field sampling strategy. In order to maximize comparability between this and the earlier [Harris,

19761 study of the bum, a similar sampling strategy was used. Two 1:20,000 scale British

Columbia Ministry of Environment Terrain Resource Inventory (TRIM) Maps (82N.020, covering the southern part, and 82N.030, covering the northern part of Vermilion Pass) were comounted for field use. A sampling grid consisting of 300X300m cells, identical to the grid used in the Hanis study, was superimposed on the maps. A letter was assigned to each row (running along the Vermilion

River Valley, assigned the letten 'A" through 'IT, inclusive), and a numbar to each column

(running across the valley, assigned the numbers 1 through 36 inclusive) in the sampling grid. A corresponding alphanumeric identifier was thus assigned to each cell within the grid.

In the field, one sample plot was chosen at random from within each cell of the sample grid that was estimated to be more than 50% burned. Figure 4.1 is a configuration diagram of the Metihods 28 sample plot. 10XlOm square sample plots were delineated (with flagging tape that was subsequently removed) for analysis of A-sbatum (trees higher than 3m) and Bstratum (shrubs and young trees less than 3m high) plants. Within each sample plot, four lXlm subplots were delineated for the purpose of C-stratum (herbaceous layer - comprised of herbs, and shrubs and trees less than 1m in height) vegetation analysis. As a rule, C-stratum data was recorded for four subplots in each plot. In plots of low diversity or barren plots, three subplots were occasionally sampled. In plots of high diversity it was occasionally necessary to sample a fifth subplot - in such cases, this fisubplot was located in the centre of the sample plot. Methods 29 Plots were precisely located using a hand-held, 12channel Garrnin global positioning system (GPS42) (for the first two weeks of the field season, pkts were located by triangulation with a Brunton pocket tmnsit compass). In 1999, with the intentional displacement of GPS locations

(known as Selective Availability (SA)) still in place, the accuracy of GPS readings was approximately +I- 100m. General site characteristics were recorded for each plot, including slope, aspect, elevation (estimated from TRIM maps), and site morphology. The date of sampling was recorded, as well as any remarkable features regarding the site (e.g. evidence of animal activity, or proximity to features such as streams, avalanche chutes, or the edge of the bum). Field identification of all plant species was based on Kershaw et al. [I 9981 and Gadd [1995].

A-stratum trees were identified and counted for each sample plot. An estimate was made of total percent coverage (crown cover) and average crown diameter for each identified species. A representative tree or number of trees for each species was selected for detailed measurements, and each of these trees was assigned a unique identification code. The number of trees for which detailed data was recorded was equal to 10% of the total number of trees of a given species in a given plot, not to exceed 8 trees for each species. For each representative tree, height (in metres) was calculated by measuring the angle between the top of the tree and an observation point at known distance from, and level with, the base of the tree. Diameter at breast height (DBH,in millimetres) was also determined. Finally, a core was extracted from each tree, and was labelled and stored for determining absolute age at a later date.

For the B-stratum, species were identified and counted, and percentage cover for each species was estimated. Trees that were less than 3m in height were also recorded here, and age was determined (by whorlcounting) for coniferous 6-layer trees. Where multiple &layer trees of a Methods 30 given species were present, the average, minimum and maximum age were recorded. Only percent coverage data was collected in the Csttratum for herb, bryophyte, and lichen species, as well as for juvenile shrubs and trees (average age was determined by whorloounting for juvenile

(i.e., less than 1m in height) coniferous trees). A copy of the instrument used to record field data is included in this report as Appendix 4.

The taxonomy of plant species identified in the field was verified using the Integrated

Taxonomic Information System (ITfS) [ITIS'? 20011. Lichen species were verified using

Hawksworth at al. [I9801 and Hale and Culberson [1970]. Data were excluded from all sample plots that were located in mature forest around or within the bum, so that only data collected from those plots located within regenerating forest area remained. All fieldcollected data was entered into a spreadsheet, including A-stratum tree ages, determined by counting the rings of extracted tree cores. The data for species that occurred in both the 6 and Cstrata were combined; Values such as age that were averaged over the total number of trees in a given stratum were combined through an average-weighting process (for example, if there were 4 6-stratum and 6 C-stratum lodgepole pines with average ages of 10 years and 4 years, respectively, the weighted average age would be [0.4 X 10]+[0.6 X 41 = 6.4 years). Each C-stratum species' percentage was averaged over the number of subplots sampled, and entered as a single variable for each sample plot-

4.2 - CLUSTER ANALYSIS Hypothesis: field collected vegetation data is divisible into meaningful groups representing distinct plant communities.

Test: Perfom Wads(minirnum variance) method of hierarchical cluster analysis on the field data.

Cluster analysis is a multivariate statistical procedure that arranges individual 'cases" into Methods 31 meaningful groups, or clusters. The term 'case" refers to the collection of all observed variable values for a single object in the data set. In this thesis, individual cases represent distinct sample plots within the bum. Generally, this partitioning or subd'nriding of the data set is obtained through the definition of some unifying characteristics that all members of an individual cluster share, and that also distinguish them from all of the other clusters. Cluster analysis is a general term used to describe the numerous techniques of subdividing data into groups. The specific type of cluster analysis used in this thesis is Ward's method of hierarchical cluster analysis, employing the squared Euclidean measure of distance. A brief explanation of this clustering method follows, adapted from Davis [I 9861 unless otherwise specified.

Hierarchical cluster analysis first groups or combines those cases that demonstrate the highest degree of mutual similarity. The next most mutually similar groups are then combined, and so on in an iterative process until two large groups of cases, demonstrating the highest degree of mutual dissimilanty, remain. The results of hierarchical cluster analysis are frequently represented in an inverted tree-shaped linkage diagram called a dendrograrn, which shows the levels of similarity at which each respedve group of cases is related to every other group (for an example of a dendrogram, see Figure 5.1 in the next chapter).

The first step in performing a hierarchical cluster analysis is to establish a measure of similarity between each case and every other case in the dataset, with respect to all variables being considered. The measure of similarity used in this instance is squared Euclidean distance, which calculates the distance dh or dissimilarity, between two cases, iand j, in the multidimensional space defined by a set of m variables, by the following equation: Methods 32

where rn is the number of variables,

Xk represents the kth of m variables' value, recorded in case i, and

&R represents the WI variable's value, recorded in case j.

When a low value is returned, the two cases are close together in rndimensional space

(hence, similar with respect to the m variables considered in the analysis); when a high value is returned, the two cases are located at a greater distance from one another, and are therefore less similar. The squared Euclidean distance statistic is calculated for every case pair in the data set, and the result, given n total cases, is an mn similarity matrix. Once the similanty matrix has been computed, cases are grouped into clusters according to highest mutual similanty.

In the next step of the hierarchical cluster analysis procedure, the similarities between clusters and groups of clusters are quantified by iteratively applying a cluster combination process until all clusters are joined, and by recording a measure of similarity that associates one cluster (or group of clusters) with the next joining cluster. Ward's minimum variance clustering method (also referred to as Orloci's clustering method in ecological literature [Lane,2001]), one of many clustering methods available, was used in this thesis. This method combines groups based on a criterion of maximum efficiency. Groups are joined only if the resulting increase in cumulative variance - that is, the greatest Euclidean distance, in mdimensional space, between the two most disparate cases in the newly combined group - is less than the cumulative variance that would Methods 33 result from the combination of any other pair of groups. This method, though camputationally complex, is the one most commonly used in ecological research [Lane, 20011.

SPSS was used to perfom a hierarchical cluster analysis (Ward's method, squared

Euclidean measure of distance) of all sample plots within the bum, using only species percentage variables as inputs. This process subdivided the data into sample plots of similar vegetative composition - in other words, plant communities. Results of the cluster analysis are reported in the next chapter, and a discussion of these results is provided in chapter 6.

4.3 - INCORPOFIATION OF SPATIAL DATA All of the modeling and spatial analysis in this thesis was done using the IDRlSl raster- based GIs program. However, many different GIs and image analysis programs were used in the compilation and creation of the spatial database (for a complete listing of GIs and other software used in this thesis, consult Appendix 1). All data described in this section, regardless of the software that was used to produce or abr it, was imported into IDRlSl for actual analysis and modeling.

A point coverage was generated in Arclnfo from the GPS-recorded UTM coordinates of the sample plots. One sample plot that was obviously and severely mislocated (its recorded UTM coordinates placing it wet outside of its designated sample grid cell) was omilted before spatial analysis was performed.

The boundary of the bum, kcation of avalanche chutes, and mature (i.e. unburned in

1968) forested areas within the bum were precisely delineated from orthorectified IRS and Landsat

TM satellite imagery in PCI. Landsat Imagery was already rectified when it was received, but the Methods 34 IRS imagery had to be orthorectified by matching features visible in the IRS image to the planimetric representation of the same features found in the BC TRlM digital data (see Appendices

1 and 3). Polygon coverages were subsequently constructed in Arclnfo -these polygon coverages were made into mask layers in IDRISI, used to confine the analysis of spatial data in certain layers to the burned area, or to specific areas within it. The 'general vegetation mask" covered the entire bum, excluding unburned areas. The "A-stratum vegetation masK also excluded those areas identified as avalanche chutes, where no tall trees are present.

Digital TRlM planimetric data (1 :20,000 scale) was used in Arclnfo to locate roads, streams and lakes, and glaciers, and to generate a DEM of Vermilion Pass (a grid of 25m pixels with a vertical step of 1m, interpolated from 20m elevation contours). This DEM was then used in

Arclnfo to generate raster coverages for slope and aspect

ASPECT CONVERSION

daplct cro#-vrlky- t mapwtvdw

I 1 Figurn 4.2 - Conversion of raw aspect values to solar and cross-valley aspecl van'abes. Methods 35 The raw aspect values generated in Ardnfo range from 0-3600: it was therefore necessary to convert this circular variable, which places geographically adjacent aspects of 00 and 3590 at opposite ends, into a more meaningful measure. Transformation followed Peddle and Duguay

[I 9953 - raw aspect values were converted to a symmetrical scale variable, ranging from 0-1800, with the axis of symmetry oriented in the direction of the maximum anticipated variability, or of the aspect-relatedfeature that was to be modeled. Figure 4.2 illustrates the conversion of raw values that were used in the calculation of the two aspect variables used in this thesis. The first, called

"solar asp&, was designed to capture the effects of insolation on vegetation patterns, and was therefore minored about a north-south axis. North and south aspects retain their raw aspect values

(00 and 1800, respectively), but due east and due west aspects both assumed a value of 900. The second aspect variable was designed to identQ and explore the apparent abtupt change in vegetation patterns, especially among 6-stratum and C-stratum species, from one side of the

Vermilion River Valley to the other. This transition was most evident in the valley bottom and on lower valley slopes. The second aspect variable, called "cross-valley aspecr, was thus calculated to confirm and identify the variability in vegetation between one side of the valley and the other.

The axis of symmetry in this case was oriented orthogonal to the Vermilion River Valley (east- southeast to west-northwest).

The converted aspect variables were combined with the DEMderived slope to calculate two separate slope-aspect indikes, SAI, after Peddle and Duguay [I 9951. Cross-valley (SAM and solar (SAlsr) slopeaspect indices were computed using the following formula:

SAI = sin(s ,) asp Methods 36 where sr is the slope, measured in radians, and asp is the aspect variable, derived through the application of either one of the two conversions described above.

Site morphology is a surrogate measure of soil moisture (drier areas on ridges and promontories, moister areas in gullies and depressions), which is thought to signif'kantly effect the distribution of vegetation [Bailey, 19961. To incorporate a measure of site morphdogy, the DEM and the convolution module of Terra Fima [Eyton, 19921 were used to derive crossslope and down-slope curvature variables. These curvature variables give a discrete measure of rate of change of elevation, and may be used to quantitatively infer the morphology of a landscape. The description of these cuwature calculation methods is adapted from Eyton [1991].

Curvature values are derived from a DEM using a 3x3 convolution algorithm. 5x5 convolutions can also be calculated in Terra Firrna, but a 3x3 convolution matrix was chosen for its ability to capture more localized site morphology than the 5x5 matrix. The process, illustrated in

Figure 4.3, is as follows:

Assume a matrix of 3x3 cells on a raster grid, each with a & value representing elevation.

The computation of both the cross-slope and down-slope curvature values (in the example above, for the middle cell with the elevation value &) is essentially the same. Implicit in this calculation are the measures of slope and azimuth (aspect). The magnitude and orientation of the slope vector

(line ab in figure 4.3) must be determined before the cross-slope and downslope curvature values can be computed, since the slope vector, and specr'illy the azimuth, identifies the down-slope direction for the grid cell in question. Slope is calculated by applying Pythagoras' theorem to its two Methods 37 Cartesian 0( and Y) components, sx and s, The magnitude of the slope vector, s,is compud as follows:

where sx and sy are the two Cartesian (X - horizontal, presumably east-west, and Y - vertical. presumably north-south) slope components of the slope vector.

3x3 CONVOLUTION MATRIX

I] Figure 4.3 - Diagm of lhe 3x3 conwluliwr maink used to detemme curvature values kx the mtnlpiael (la. The d&edmw mptz3sents fhe =in&, abng wfiM !he endpoints (a and b) of the slope vector an, located.

Cartesian components of slope are calculated as discrete partial derivatives -that is, by Medhuds 38 averaging the changes in elevation between the central cell of the matrix over a known, uniform distance (D) between its centre and the centre of the adjacent cell in either Cartesian direction.

Equations 4 and 5 show the calculation of sx and s+

Azimuth (asp) is cakulated as a function of slope - generally, it is obtained by calculating the angle of separation between the slope vector and its horizontal (X) Cartesian component. It is computed as follows:

The slope vector (represented in Figure 4.3 by the points a and b on the dashed arrow) has a definite length equal to 24or the distance that connects the centres of three adjacent horizontally or vertically oriented cells. The measure of directional cumture (dc, either cross-slope cuwature (xsc) or down-sbpe cuwature (dsc)) is obtained by finding the discrete (finite) second derivative of the elevation values (with respect to distance) along the slope vector.

where & and are linearly interpolated elevation values at either end of the slope vector.

The calculation of dsc and xsc are identical, except in the case of xsc the vector through the centre of the target cell is orthogonal to the slope vector ab.

A total of eight ancillary topographical variables (layers) were generated for analytical use: - elevation, from the DEM; Methods 39 - slope, derived in Arclnfo; - two aspect variables (cross-valley and solar aspects, converted fmm raw aspect data derived in Arclnfo); - two slope-aspect indices, (calculated in IDRlSl using the two aspect conversions); and - the two (cross-slope and down-slope) curvature variables (calculated using Terra Firma). The resolution (pixel size) of each layer is 25m. Values at sample plot locations were spotted for each ancillary variable, and were incorporated into the data set for analysis.

4.4 - IDENTIFICATION OF DIAGNOSTIC SPECIES Hy-sis: There exist diagnostic plant species, whose presence and comparative abundana, h a given sample plot indicate mernbetship in a specific plant amnnunw.

Test: ldentfmtion of diagnostic species based on three selectrbn criteria: ubiquity, high correlation to topographic variables, and improved separability of plant communities.

Prior to deciding on species composlon (percent cover) variables as the best type of diagnostic variables, some preliminary exploratory statistics were performed on ancillaly topographical variables. The goal of this exploration was to determine whether these ancillary variables could be used directly to model the distribution of plant communities. It was determined by the same methods that were used to assess suitability of diagnostic species (see below) that ancillary variables were not sufficiently diagnostic of the uniqueness of plant communities to be used directly in the modeling process. This dedsion-making process is documented in the model flow chart, found in Appendix 2.

The next step in the modeling process was therefore to select the most suitable from a list of possible 'diagnostic spedesn. Diagnostic species are ubiquitous or nearly ubiquitous plant Methods 40 species within the bum, but their abundance vanes greatly between (and comparatively little within) different plant communities. The abundance of each diagnostic species in comparison to all other diagnostic species at a given location is the basis for modeling the distribution of plant communities of the Vermilion bum.

The following criteria were used to determine the most suitable diagnostic species: 1. Spiesmust be ubiquitous or neady ubiquitous within the bum - A list of the ten species that were identified in the greatest number of sample plots was compiled.

2. Species must demonstrate statistically significant correlation to one or more ancillary topographical variable - -use diagnostic species' continuous distributions were to be modeled from the discrete point data set based on raster topographical values (see section 4.5, below, for a more detailed discussion of the modeling process), i?was necessary to ensure that there is at least one ancillary variable, X, correlated with each potential diagnostic species, Y. To this end, a linear correlation analysis was performed for all combinations of Xand Y using the ten potential diagnostic species and all eight ancillary variables as inputs. This involved the calculation of Pearson's correlation coefficient, r, computed as follows (Lane, 20011:

where rm is Pearson's coefficient of correlation between variables Xand Y; and

N is the total number of observations (cases) on X and K Methods 41 Assuming m total variables input into the amelation analysis, the result is an mm similarity matrix, symmetric about the main diagonal (r~=m), and with all diagonal entries in the matrix equal to 1. Only those species with a statistically significant correlation to one or more ancillary variables was considered as a potential diagnostic species.

3. Species must impow separability of plant communities in the -1. The ultimate goal of this thesis is to use the distribution of individual plant species to model the distribution of plant communities. Therefore, a most desired quality of a diagnostic species is the ability of the distribution of its values to statistically demonstrate the separation of one or many clusters - in other words, species were selected according to the degree to which their inclusion in the model would improve cluster separability. The diagnostic species that were selected demonstrate percent cover values that are sufficiently similar within a cluster to give it coherence, and sufficiently different between one cluster and the next to render them distinguishable and unique. Cluster separability was assessed first graphically using probability ellipses (equiprobability contours) and coincident histograms, and was subsequently verified using the Kruskal-Wallis non-parametric test for sample independence.

Equiprobability contour (or probability ellipse) diagrams are drawn on two-axis (two variable) scatterplots, and are used to provide a visual estimate of cluster separability. Examples of equiprobability contour diagrams can be found in figures 5.4 and 5.7 of the following chapter.

First, cluster means of each variable are plotted (given m clusters, a total of m points).

Three ellipses, or equal probability contours, are then drawn around each cluster mean with the horizontal dimensions (breadth) of the ellipses equal to twice, four times, and six times the standard deviation in variable X from the cluster mean. The vertical dimensions of the ellipses are Methods 42 determined in a similar fashion, using multiples of ?he standard deviation from the mean in variable

Y. Points located within the area defined by each successively smaller ellipse around a cluster mean are successively more likely to be associated with that cluster. Two points located along the path of the same equiprobabilii contour are equally likely to belong to the same cluster. A point that is located at the intersection between equiprobabilii contours of the same magnitude (e.g. both contours bounded by a distance of 1 standard deviation from the means of two different clusters) is equally likely to be associated with either cluster.

The equiprobability contour diagram is interpreted by assessing the degree of overlap between the probability ellipses of one cluster and those of another. If even the largest ellipses of two adjacent clusters do not overlap one another, then there is a high degree of separability between those two clusters using the variables X and Y. Conversely, if there is a significant degree of overlap between two clusters, then those clusters are not separable wllesand 8 Kiefer, 19941.

The advantages of using equiprobability contour diagrams are that they are intuitive and easy to interpret, and they allow for simultaneous assessment, based on two variables, of cohesion within individual clusters and differentiation between them. The main disadvantages are that they provide only a qualitative means of separability assessment, and that they assume a normal distribution of data within clusters, in that they use standard deviation from the mean as a probability measure%This latter shortcoming is addressed by another graphical separability assessment tool, the coincident histogram plot.

3 Equiprobability contours tend to underestimate the probability of cluster membership for platykurtic distributions, and overestimate the same for leptokurtic diiiutions. The distribution of species data within clusters is discussed briefly in the next chapter, section 5.4. Methods 43 Unlike equiprobability contour diagrams, coincident histograms only allow for the assessment of cluster separability based on one variable at a time. They are useful, however, in that they not only show the range of values in a given cluster, but they show the actual pattern of the distribution of those values as well [Lillesand 8 Kiefer, 1994). As the name implies, a coincident histogram is simply a collection of the histograms of each cluster on a given variable, plotted on the same axes. For examples of coincident histograms, see figures 5.5,5.6,5.8 and 5.9 in the next chapter.

Finally, the ability of potential diagnostic species to distinguish plant communities was verified quantitatively using the Kruskal-Wallis non-parametric test for sample independence. The

Kruskal-Wallis test is similar to the one-way analysis of variance test (ANOVA), except unlike

ANOVA, Kruskal-Wallis does not assume a normal distribution (hence non-parametric), and can be calculated on ordinal-level data. Instead of comparing the means of samples to the population mean, mean ranks for each sample are the objects of comparison. The test determines the level of distinction, or independence, of samples or subsets of a data set (in this case, clusters) with respect to a given variable. First, all observations on the variable for the entire data set are grouped together and rank-ordered from highest to lowest value; That is, given N observations, the highest value for the data set is given a rank value of N, the next highest a rank of N-1 , and so on.

Equal values are all assigned the same rank, averaged over the number of tied variables. The

Kruskal-Wallis Hstatistic is then calculated, which compares the mean ranks of each sample

(cluster) to the mean rank for the dataset as a whole. The Kruskal-Wallis Hstatistic is calculated as follows [Levin & Fox, 19941: Methods 44

where N is the total number of observations; k is the number of samples (clusters) being tested for independence; n is the number of observations in a given sample (cluster); and

R is the rank-order value assigned to a given observation.

Once calculated, the Kruskal-Wallis Hstatistic is compared to the ~2 (chi-squared) value at

'1 degrees of freedom and a user-specified confidence level. A low value of Hwill be close to the

~2 value and will support the null hypothesis that samples' (clusters') respective distributions are similar to those of the entire dataset for the variable being tested. A high value of Hcompared to the ~2 value means that the null hypothesis can be rejected, and that samples display statistically significant independence in their distributions. When performing the Kruskal-Wallis test in SPSS, the comparison between the Kruskal-Wallis H static and the ~2 value is automatic, and the test returns a measure, based on this comparison, called asymptotic significance. This is a probability- based significance value @value), based on the asymptotic distribution of the Kwskal-Wallis H statistic. Generally, an asymptotic significance value of 0.05 or lower is considered to be statistically signif'kant [SPSS Manual, 19981.

4.5 - MODELING MSTRl6UTlONS OF MAGNOSTlC SPECIES Hypofhe8is1: The genetalized distribution of diagnostic species h the Vermilion bum can be descn'bed using DEM-derived ancillary topographkal values.

Testt: Linear regWon analysis of diagnostic species' pemtcover values against significantly Methods 45 conelated ancillary variable values at sample plot locations.

Hypothesis2: The vabbjlity h diagnrasb speces'percent cover values that is not explained by linear regression (i.8. regression residuals) is spatially dependent, and can therefore be modeled using geostatistkal lnte~polationtechniques.

Tea:Calculation of tbe experimental semivariogtams for regression residuals, and mdeling of spatial dependence.

Once diagnostic species had been identified, continuous coverages (distribution maps) were generated for these species over the entire bum area. The goal was to identw the most accurate method of spatially modeling a continuous coverage fmdiscrete point data and continuous ancillary (DEMderived) data layers. The solution was adopted from Lister et al. [2000], who found that optimal results were obtained through a combination of two modeling procedures

(for a more detailed description of this research and its findings, see section 3.2 in the previous chapter). First, linear regression analysis was performed of a point vegetation data set against ancillary data coverages, in order to determine the relationship between plant species abundance

(percent cover) and site characteristics. This created a generalized spatial model of the species' distribution. Next, a continuous spatial model of the regression residuals was interpolated from the residual values at every point in the original data set using the kriging geostatistical process. The two spatial models were then additively combined to generate the model of vegetative distribution.

A common practice in the modeling of any continuous variable from known values at sample points is a random 'splitting" of the data set into a %antrol" ("assessmenf') subset and a hodelingwsubset. The control data subset is used to compare the modeled distribution of a phenomenon, generated using the modeling data subset, to known values at control sample point Methods 46 locations, thereby quantitatively determining the accuracy of the modeled distribution. In this thesis,

no %ontrolWsample plots were excluded from the data set, for Noreasons. First, the density of

sample plots was such that splitting the data and removing a control set of data points would result

in failure to capture the intricacies of spatial vegetative change within the bum. Second, splitting

the data set would also compromise the stated objective of maintenance of the same sampling

density as that used in the Hams study [1976].

Diagnostic species' percent cover values (over the entire data set) were regressed against

those ancillary variables to which each one is most significantly correlated, as determined by the

process described in section 4.4, above. Both single variable and multivariate linear regression

models were used in this thesis, and are described below.

Single variable (or simple) linear regression generalizes the influence that the independent variable Xhas on the dependent variable K The generalized relationship between the two variables is the "best fii line to the data, and is expressed by the regression equation (or regression line):

where Y is the dependent variable;

Xis the independent variable; ais the constant (Y-intercept); fl is the regression cmffiiient (slope) for X; and

€is a random error term (unless the data are perfectly correlated (rpl or I),there will be a random error). Methd 47 For any value of X that does not fall exactly on the regression line, there will be a residual, which is defined as the portion at location Xo along the Xaxis, of any Y value, YO, which is not described by the regression equation. Alternatively, it can be expressed as the difference between the actual value YOand the Y value predicted at Xo by the regression line. The best fit" for the regression line is the line that minimizes the sum of squared residuals. Regression line Tiis assessed by determining the R value, which is equal to the sum of squared predicted values of Y at every value of X, divided by the sum of squared actual Yi values for every X. in the data set

[Steel et al., 19973. The FP value will always be higher when the dependent and independent variables are highly correlated.

The procedure for multiple linear regression is identical to simple linear regression, except in multiple linear regression, the predictive capability of the best fit" regression line is theoretically improved through the inclusion of more than one independent variable. The general form of the multiple linear regression equation, using n independent variables, is:

where Y is the dependent variable;

XI is the independent variable; ais the constant ( Y-intercept); j37 is the regression coefficient (slope) for XI;

#32 is the regression coefficient for XZ; n is the number of independent variables; and

&is a random error term. Methods 48 In this model, the dependent variables are the diagnostic species' percent cwer variables.

The independent variables, which were used to predict the changing values of diagnostic species' percentages, are the DEMderived ancillary data that displayed the highest correlation to each respective diagnostic species. A regression line and resulting @ value was calculated for each diagnostic species' percent cover (dependent variable) that generalizes its relation to some ancillary (independent) topographical variable(s). These regression equations were then used to create generalized models (coverages) of diagnostic species' distributions in IDRISI. The residuals from each regression were recorded and used to create new point data coverages in IDRISI, from which continuous residual surfaces were interpolated using kriging.

Spatial interpolation estimates a value for a continuous (i.e. rasterized) attribute at all unknown locations using only known attribute data at discrete (sample point) locations. Kriging is a generic term that refers to a host of related geostatistical spatial interpolation procedures [Mias &

Mitasova, 19991. All kriging interpolation procedures are least-squares regression algorithms with the common goal of minimizing variance of interpolation error, under the constraint of an expected interpolation error of 0. This is accomplished by first modeling the spatial variance between values at all sample point locations, and then assigning a value to an unknown location that minimizes the error between Re predicted values according to the spatial variance model and actual values at sample locations. This approach is referred to as the best linear unbiased estimator [Eastman,

19991.

Spatial variance is assessed through the creation of an experimental semivariogram, and through the subsequent fitting of a generalized spatial variance model to this experimental semivariogram. The experimental semivariogram ylh) is an average measure of the differences in Mefhods 49 known values of a variable between all pairs of sample points separated by a given distance (or lag), h. The equation

calculates the experimental semivariogram gh), where N(h) is the number of data pairs in the point data set that are separated by a lag of h; z(u,) is the known value of the attribute z at location u,; z(u,+h) is the known value of the attribute tat location ua+h.

The semivariogram value is calculated for a user-specified number and distance of lags, providing an idea of the spatial dissimilarity or variance of known data values. Semivariograrns can be omnidirectional or direction-dependent. Directiondependent semivariograms are used when there is a known anisotropy of spatial variance of data. Once the experimental semivariogram is created, it is generalized to create a spatial variance (or spatial dependence) model using GStat, a geostatistical analysis program that has been integrated into IDRISI. Figure 4.4 is a generic illustration of some key features of both the experimental semivariogram and the spatial dependence model.

As might be expected, experimental semivariograms usually return a higher y(h) value for higher h values, and a lower dh) values for data pairs that are closer together. This inverse relationship between distance and correlation of attribute values, calculated and quantified in the experimental semivariogram, is assumed in other popular interpolation procedures, such as the inverse distance-weighted (IDW) algorithm. The parameters that control the shape of spatial variance model are the nugget, range, sill, and type of model. The nugget is the inlial spatially Methods 50 ------

SPATIAL DEPENDENCE OF VARIABILITY IN AlTRlBUTE z

I' 11 Figure 4.4 - Semivanbgrarn graph, showing the hypofhehlspalial dependence of vam;abiIily ri7 attribute z The grey dkamoncb represent actual &lated dh) values, and and wconnected by a dotted line, fhe experimental sernivarbgram. The dark line repmlsthe spatial variance mode/. dependent variance in attribute z, or the intercept of the Mh) axis. The range is the total distance from the origin over which the variance in attribute z is spatially dependent. The sill is the spatially independent level of dh), or the ylh) limit of the spatial dependence model. The type of model defines the shape of the spatial variance curve through the range. In IDRISI, a choice of 11 different model types is available - the model types may be used individually or combined

(Eastman, 19991. Model fit is optimized by minimizing the sum of squared residuals. Once generated, the spatial dependence model is used to assign distance-related weights to sample Methods 51 points in the kriging interpdation process to generate a continuous, smooth surface that preserves the known attribute values at sample plot locations. [Bumugh &.McDonnell, 19911.

The specific type of kriging used in this thesis is ordinary kriging. Ordinary kriging allows for the regional variations in mean attribute values to be considered by limiting the domain of stationarity of the mean to a userspecified local neighbourhood centred on the location for which the attribute value is being estimated [Goovaerts, 1997). The general formula for assigning attribute values to all unknown locations using ordinary kriging, constrained by unbiasedness and minimum variance, is as follows [Goovaerts, 19971:

where T(u) is the estimation of the attribute value z at any location (u); n(u) is the number of sample points within the user-specified local neighbourhood;

L(u) is the distance-dependent weight (based on the modeled spatial dependence) assigned to a known attribute value z at the location u$ qua) is this known attribute value zat the location ud and m(u) is the local mean of all known attribute values in the neighbouhood.

The interpolated residual species percentage coverages were combined with the regression coverages in IDRlSl using an additive overlay. The coverages that resulted were the modeled distributions of diagnostic species in the Vermilion bum. Methods 52 4.6 MODELING DISTRIBUTION OF PLANT COMMUNrnES

Hymsis:M'imum IikeIihood c/ass&ation, using modeled diagnostic spedes' distributions as

input channels, is a viable method by whkh to madel the distn'bution of plant cwnmuniles h the

Vermilion bum.

resf: Error matrix analysis and caku/ation of fhe kappa index of agreement.

The final step in the analylical process was to use the modeled distributions of diagnostic

species to infer the distributions of plant communities with which these species' percent cover

values are associated, and thus, to map the plant communities of the Vermilion bum. Essentially,

the goal of this last stage was to quantitatively assess each raster cell (pixel) within the bum

according to the relative abundance of the identified diagnostic species in the appropriate modeled

layer; then, based on the results of this assessment, to place each pixel in the cluster (plant

community) to which it was most likely to belong. The maximum likelihood classification algorithm was used to classify the vegetation of the Vermilion bum. The procedure for maximum likelihood classification is as follows:

Maximum likelihood classification divides an image into a user-specified number of classes according to the location of each pixel in an n-dimensional space, which is defined by the n variables or input channels (in this case, the variables represent the diagnostic species' percent cover distributions) that have been determined to best differentiate among classes. This separation is established through an analysis of the variance in n dimensions within the pixel values of a given class, and the covariance of pixel values between classes. The mean value and standard deviation for each of the n variables in each class are calculated using training sites, collections of pixels representing areas within the image that are known to belong to a particular class. In this case, the Methods 53 training sites used in the classifikation are the sample plot locations, at which percent cover values for each diagnostic species are known.

Once the separability of classes using the values for n variables at training site locations has been satisfactorily verified, each pixel in the image is placed in the class to which, based its observed values over n variables and on the established variancelcovariance of training sites, it is most likely to belong.

MAXI MUM LlKELlHOOD CLASS1FlCATlON

Figure 4.5 - Diagram of a hypothefkal mardmum likelihood clhlrbnof three points, 1,2,and 3, into thm CI~SSBS, a, b, and c, bas8d on data from two input vakble X and Y. €llipses of the same shade represent eguipmhibililiiy mtours calculated from the variants values of miring site pixels on each class.

The logic underlying the maximum likelihood classifion process can be visualized using a simple, two-variable (Xand Y) example, illustrated in Figure 4.5. Normal distribution of training Methods 54 site values around the mean is assumed in the maximum likelihood classification algorithm.

Equiprobability contours (also lcnown as probability density functions) have been plotted representing the variancelcovariance within and between training sites representing three classes, a, 6, and c. Each of the pixels, points 1,2 and 3 in the diagram, are classified based on their Xand

Y values and their resulting location with respect to the probability density functions of the three classes. Point 1 will certainly be assigned to class c, since it is within the 1-standard deviation equiprobability contour of this class, and located far away in Y space from any other class' probability density function. Point 2 will be placed in class b, since it is located inside of the "two- sigman equiprobability contour for class 6, but only the 'three-sigmawequiprobabilii contour for class a. Ambiguity and potential emoccur in the classification of pixels with locations in X-Y space such as point 3 in the diagram. Point 3 is located at the intersection of the @three-sigman equiprobabilii contoun for both classes a and b, and is thus exactly equally likely to belong to either class [Lillesand 8 Kiefer, 19941.

Classification accuracy is assessed in two ways: by the creation of an error (confusion) matrix, and by the calculation of the kappa index of agreement (KHATstatistic). The error matrix compares the classified image to known class values at sample plot (training site) locations by listing known class values (from sample plots) in matrix columns, and class values resulting from the classifikation procedure in matrix rows. Correctly classified sample plots will be listed along the main diagonal of the error matrix, and the overall classiliition emis obtained by calculating the proportion of incorrectly classified sample plots to total sample plots, obtained by subtracting the sum of the main diagonal entries from the total number of sample plots. Two distinct types or error are commonly calculated: errors of commission (user's em) and errors of omission (producer's Methods 55 error). Errors of commission occur when sample plots are included in a class to which they are known not to belong, and are calculated by dividing the sum of non-diagonal row entries by the row total. Errors of omission occur when the classification algorithm fails to place a sample plot in its known class, and is calculated by dividing the sum of nondiagonal column entries by the column total [Lillesand & Kiefer, 19943. For an example of an error matrix, see table 5.4 in the next chapter.

The kappa index of agreement (KHAT) statistic is an empirical measure of the proportion of classification accuracy that has resulted from chance. Possible KHATvalues range from 0 to 1 - a high value, of 0.87, for example, means that the classification being tested is 87% better than a completely random assignment of class values to pixels. Conversely, a very low KHATvalue means that the classification being tested has produced results that are no better (or only marginally better) than those that would result from a random assignment of class values. The

KHAT is computed as follows [Lillesand & Kiefer, 19941:

r N*C~xi- (xi+*x+i) i=I i-1 KHAT = *

where N is the total number of entries in the error matrix; r is the number of rows in the error matrix; a is the entry in row iand column i(that is, along the main diagonal of the matrix); xi, is the sum of all entries in row i;and x4 is the sum of all entries in column i.

The plant community distribution maps developed through this modeling process were compared to the 1972 distributions of dominant plant species found in Harris [1976], in order to Methods 56 assess changes in plant community distribution over the past three decades. 57 CHAPTER 5 - RESULTS This chapter summarizes the results of the research conducted in this thesis. Results have been divided to correspond with the sections laid out in chapter 4. A detailed discussion of these results follows in chapter 6.

5.1 - FIELD DATA SET Field sampling of data was recorded for a total of 227 sample plots in Vermilion Pass. After removing cases from sample plots that were on or beyond the bum's edge, data from 218 sample plots remained. Table 5.1 summarizes the occurrence of the ten (most common) potential diagnostic plant species selected for analysis.

Table 5.1 - General summary staf&fisfor the vegetaiim of the Venni/km bum Results 58 Eighty-four plant species were identified within the bum: 5 A-stratum species. 17 6-stratum species, and 62 C-stratum species. A complete and verified list of species' vernacular and scientific names is included in this repolt as Appendix 5.

5.2 - CLUSTER ANALYSIS: Hierarchical cluster analysis using Ward's clustering method and the squared Euclidean measure of distance resulted in the grouping of sample plots into ten clusters, based solely on vegetative composition. Figure 5.1 is a reproduction of the dendrogram created by the cluster analysis in SPSS.

DENDROGRAM Sample Plots by Plant Community Results of Hiirarchiil Cluster Analysis'

@ure5.1 - Uendrogram depicting mu& of cluster analysis of sample pbfs based on vegetative compmitbn. Row beneath cluster numbem shows rnembersh4 (number of sampk pbls) in each cluster.

The measure of similarity value has been rescaled. It does not represent actual distance Results 59 values calculated using the squared Eucliian distance statistic (equation (1)); rather, it provides

an idea of the relative similarities and proximities of various plant communities to one another.

Table 5.2 lists the cluster number and the name given to each of these plant communities.

PLANT COMMUNmES OF THE VERMILION BURN d CLUSTER MEMBERSHIP 1 28 Mount Whyrnper Open Pine~Buffaloberry I 2 43 Subalpine Meadows and Avalanche Tracks I 3 3 Storm Mountain Gmusebeny I

4 27 South Side Open PineNendesia I 5 29 Open PinelMenziesialGrousebeny-Bunchbeny r 6.12 .Ribbon of Menziesia 7 11 Dog Hair Pine I 8 28 Bottomlands Dense Pine 9 12 Midslope Closed PineNenziesia L 10 25 Closed Pine/BMaloberrv/Grouseberrv-fwinf lower Dominant species in strata am sqmted by a kmmd slash, andcodmtinant specias am separated bya hyphen, as in the wgetatbn types khtiWbyActtuffet al. [IWJ. Tabk, 52 - Plant communities of the Vermilion bum.

A detailed description and discussion of these ten plant communities is provided in the

next chapter; a chart detailing plant composition and average percent coverage for each cluster is

provided in Appendix 6.

5.3 - INCORPORATION OF SPATIAL DATA

Figure 5.2 shows the distribution of sample plots within the Vermilion bum. It also shows the sample plots that were rejected in the process of "cleaningn the data - nine sample plots that were removed due to location within mature forest, and one sample plot that was found to be inaccurately located. The mislocated sample plot was used in all stages of analysis except those stages that were spatially dependent - see chapter 4. -Results ------60 > SAMPLE PLOT LOCATIONS - VERMILION PASS

y figure 52 - Locations of sample pbb h ihs Vetmilion bum. Boundary d bum k tbl~headby dashed line.

Figure 5.3 is the DEM for Vermilion Pass, with important features identified. This OEM, derived from 1:20,000 scale planimetric data, was used to derive all other ancillary variables. Maps of the other seven ancillary data layers (see section 4.3) are included in Appendix 7. Results 61 DIGITAL ELEVATION MODEL (DEM) - VERMILION PASS LEGEND

ELEVATION (m) 1,350 - 1,513 1,513- 1,675 1,675 - 1,838 m1,8~-2,ooo Zoo0 - 2,163 2,163 - 2,325 2,325 - 2,488 I2,488 - 2,650 m2.650 - 2,813 2,8 I3- 2,975 =2,975 - 3,138 0 3,138 - 3,300

P Figure 5.3 - Generalized OEM of VemillEon Pass, created usmg TRIM digital data.

5.4 - IDEEmFlCATlON OF DIAGNOSTIC SPECIES Table 5.1 shows the ten most common plant species within the Vermilion bum; these were the species initially selected as potential diagnostic species. Table 5.3 is a summary of the similarity matrix, consisting of Pearson's correlation coefficients for every possible combination between each potential diagnostic species' percentage variable and each ancillary topographical data variable. Statistically significant conelations appear in bold typeface, and the four species that were retained as potential diagnostic species (those associated with the highest ancillary variable Results 62 correlations - A-stratum lodgepole pine, rusty mendesia, grouseberry, and Canadian bunchbeny) are highlighted in grey.

Pemm's Codaion Coefficient* - Potential Diagjnortk Spades vr. Ancillary Variables ] I Ancilialy Variables I

Table 5.3 - Pa&/ comlatiun maink forptenW dhgmdic species and ancilhqty data variables (from left to mht: elevafbn, &upIsolar and crass-valleyaspects, sku and cross-valley sbpe-aspect indkxsI down-slope curvature, and class-skpe cutvaturn).

The next step in the process of selecting diagnostic species was to assess the extent to which potential species could be used to identify and dinerentiate plant communities. To this end, the following equiprobability contour graphs and coincident histograms were created.

Figures 5.4 through 5.6 demonstrate the cluster separabilii based on percentage values for A-stratum lodgepole pine and rusty menziesia. Figure 5.4 demonstrates a relatively clear differentiation of clusters - the separation resulting from these two variables is remarkably better than that which resulted from any of the ancillary data variables (see section 4.4). There is no mutual overlapping of the '1 sigmau(standard deviation) equiprobability contours. The overlapping of contours is most noticeable and potentially problematic nearer the origin, specifically in clusters

1 through 5. The fact that these clusten are less mutually differentiable and more similar based on Results 63 dominant plant species is not surprising - the dendrogram of the cluster analysis shows that they are closely related with respect to vegetative composition, as are clusters 7 through 10, inclusive.

I I Fgure 5.4 - Equipmbabilify oonfour dhgram of A-stratum bcii~@e pine and Nsty mmiesia by cluster.

There are no equiprobability contours for cluster 3 in figure 5.4, since there are no observed occurrences of either lodgepole pine or rusty menziesia in any cluster 3 sample plots. If the modeling of distributions of plant communities was to proceed using only lodgepole pine and rusty menziesia as diagnostic species, then it is likely that any location in which there is neither of these species were present would be associated with the plant community represented by cluster

3. These assodations may be inaccurate, since cluster 3 sample plots appear to be highly localized and uncommon in the bum, restricted to a small region near Aster Lakes (see figure 5.2).

In many cases, some sections of the equiprobability contours cover areas to the left of the vertical

(lodgepole pine) axis, or beneath the horizontal (rusty menziesia) axis, and above 100% on both Results 64 axes. Since these values are impossible for species percentage variables, this demonstrates the shortcomings associated with representing cluster separability in this fashion. It is, nonetheless, a useful means by which to acquire a general idea of the bivariate distribution and ordination of plant communities.

In order to obtain an idea of the actual distribution of species percentage variables, both within and between individual clusters, coincident histograms of the four potential diagnostic species that satisfied the first selection criterion (see above, and -.on 4.4) were plotted. This method of separability assessment allows for the comparison of actual variable distributions for each cluster, one variable at a time.

I I Fgum 5.5 - CoinciMnt histogram of A-siraturn ba@poIe pine by cluster

Figure 5.5 compares the distributions of A-stratum lodgepole pine for all clusters. From this Results 65 diagram, it is observed that clusters 1,7, and 8 are readily differentiable from all other clusters using lodgepole pine as a diagnostic species. Confusion would likely arise between dusters 9 and

10 based exclusively on lodgepole pine percent cover values, with similar measures of central tendency but different distributions. There is a group of four clusters with high frequencies of low lodgepole pine values (clusters 2,4,5, and 6); no lodgepole pine were observed in cluster 3 sample plots.

I I Figure 5.6 - Cornc-t histugram of tusty menziesia by cluster

Figure 5.6 assesses the separability of clusters based exclusively on rusty menziesia percent cover values. From the comparative representation of cluster distributions in the coincident histogram, clusters 4,6, and 9 are cleariy differentiable. Two other distinguishable but similarly distributed groups of clusters ate noted - clusters 5 and 8 forming the first, and clusters 1,2,7, and Resuits 66 10 forming the second. Again, there were no rusty menziesia recorded in cluster 3 sample plots. It

is clear that including rusty menziesia as a diagnostic species would help to resolve some of the

intercluster confusion that is evident in the lodgepole pine coincident histogram. Specifically, comparing the rusty menziesia percent cover values considerably reduces confusion between clusters 4,5, and 6 and between clusters 9 and 10. The most indistinguishable cluster separations that remain are between clusters 2 and 5, and between cluster 3 and any cluster with frequent low percent cover values for either lodgepole pine or rusty menziesia.

I I figure 5.7 - Equipr~babilifycontour dbgram forgmusebeny and Canadian bumhbeny - on& ellipses of dimensions equal to me stmhrd deviath h either directrbn hum the mean are imluded.

Figure 5.7 is the equiprobability contour plot for the other two potential diagnostic species: grouseberry and Canadian bunchbeny. Due to extensive overlapping of contour ellipses, only the ellipses representing one standard deviation fmm the mean of each cluster are included. Clearly, Results 67 neither grouseberry nor Canadian bunchberry are as effective cluster separators as lodgepole pine or rusty menziesia. The diagram shows that cluster 3 is clearly distinguishable from all other clusters by the high grousebeny percent cover values recorded in its sample plots. Two other groups of clusters are barely discernible in the diagram: the first consisting of clusten 1 and 2

(again, close to heorigin), and the second consisting of clusten 4 through 10, inclusive (with low mean percentage values for both species). The minimal separation between cluster groups (and cluster 3) that is evident in this diagram is mostly along the horizontal (grouseberry) axis.

1 I Fgure 5.8 - Cdmmth&nogam of Canadian bumhbeny by cluster.

Figure 5.8 is the coincident histogram for Canadian bunchbeny. It confirms that there is little in the relative distributions of Canadian bunchbeny values that distinguishes any one cluster

(or group of clusten) from any other. There are two barely dkcemable groups of clusters, Results 68 differentiated from one another by the location of the cluster mode (most frequent observation).

Clusters 1,2,3, and 5 all have modes between 0 and 2.5% (in the first bin of the histogram), and clusters 4,6,7,8,9, and 10 all have modes between 2.5 and 12.5% (in the second and third bins of the histogram). Cluster 5 is the only one with a sample plot - an extreme outlier value - in which a Canadian bunchbeny percentage value greater than 22.5% was recorded. This outlier explains the comparatively bngn(vettically) shape of the probability ellipse for cluster 5 in figure 5.7. Bassd on its incapacity to improve intercluster separation or intracluster cohesion, Canadian bunchbeny was rejected as a potential diagnostic species in the model.

I 1 Fgum 5.9 - Co~~thktugm of gmusebeny by cluster.

Figure 5.9 demonstrates importance of grouseberry percentage values to the separation of cluster 3 from all other variables. Cluster 3 is the only cluster in wh'kh gmusebeny percentage Results 69 values over 47.5% were recorded; furthermore, no values of less than 46% were recorded for cluster 3. Clusters 1 and 2 are modal closest to 0% again with respect to grouseberry, and clusters

5 through 10 inclusive all have modes belween 2.5 and 12.5%. The separabilii of cluster 4 may be marginally improved by inclusion of the grousebeny percentage variable in the model.

Some general trends were evident in the distributions of cluster data on all four potential diagnostic variables that were considered. Almost all clusters' distributions demonstrated a moderate degree of positive skewness. Distributions that were primarily in the lower bins of the histogram (is. the to the left of the coincident histograms above, nearest the vertical (frequency) axis) tended to be more highly positively skewed than those distributions located in the higher bins.

Also, most cluster distributions displayed moderate to high degrees of negative kurtosis (i.e. platykurtic). These features made the coincident histogram a more effective visual tool for separability assessment, and were also the impetus for the use of a non-parametric test to quantify sample (cluster) independence.

In order to verify the visual separability assessment and quantify the cluster differentiation capabilities, the Kruskal-Wallis non-parametric test for sample independence was performed in

SPSS on all of the four potential diagnostic species' percent cover variables. Figure 5.1 0 summarizes the results of this test.

The bars in figure 5.10 represent the mean rank order for each species by clusteer; high mean rank-orders represent high percent awer values for a given species and cluster, and low mean rank-orders represent low species percent cover values compared to other clusters. A large difference between mean rank-orders for a given variable between species represents a greater degree of intercluster separability, whereas two or more clusters that share the same or similar Results 70

I Knrdcdl-Walk Ted hdb- Rrrk by Cl- by -&s I

I Figure 5.10 - Summary of resub hwn KiuskaI-Walk m-paramebic test ibrsample indepemhce. Asymptotic signrTlcance (p) for comparison of each varkbk's If-stamto chi squared value (9 degmof hedam) b reported next to species' names in legend. mean rank-orders for a given species are indistinguishable based on that species' percent cover variable. Although the p (asymptotic significance) values are well below the statistical significance threshold value (stated in SPSS) of 0.05, the Canadian bunchbeny percent cover variable resulted in the highest pvalue.

Due mainly to the visual (qualitative) det8mination that the inclusion of the Canadian bunchbeny species percent cover variable does little to improve separability of clusters, it was rejected as a potential diagnostic variable. The three diagnostic species that were selected to model plant community distribution in the Vermilion bum are A-stratum lodgepole pine, rusty menziesia, and grouseberry. Resufis 71 5.5 - MODELING MSTRlBUnONS OF DIAGNOSTIC SPECIES Correlation analysis, reported in table 5.3, identified those ancillary variables that are most highly correlated to each of the three diagnostic species. Linear regression was performed in SPSS of diagnostic species' percent cover values against these ancillary data values at sample plot locations. The results of regression analysis are summarized in the following three regression equations:

Percent cover of A-stratum lodgepole pine (LP.):

(R*=0.233), where elev is DEMderived elevation (in metres); and sd is the DEMderived slope (in degrees);

Percent cover of rusty menziesia (MZ):

(R2=0.230), where SAl, is the solar slope-aspect index; and

Percent cover of grouseberry (GRB):

(RW.1 32), where SAI, is the cross-valley slope-aspect index.

The R2 values reported by each of the three regression analyses are consistent with the correlation coefficient values reported in table 5.3. These values demonstrate that linear regression does not describe enough of the variabilii of species' percent cover values for them b be used Results 72 exclusively in the modeling of diagnostic species' distribution. Rather, the regression models are

useful in that they provide a general idea d the influence of terrain-related features on vegetative

distribution.

mmkdglpd, Phng-rOdnrt rl-md-

m 0-10% 10-20% 20-30% 30-40% 40-50% =m-60% 60 - 70% mi70-80% 1'160-90% I1greater than 90%

I Figure 5.1 1 -GeneraIued dr'stribm mode/ fot A-stratum kdgepole pine, cahlated by applying the LP. regression equation (equation 15) to the continuous siope and elewfkm Pornraps.

Figures 5.1 1 through 5.1 3 show the regression-modeled distributions over the entire study area of lodgepole pine, rusty menziesia, and grouseberry, respectively. In the regressed IodgepoIe Results 73 pine model, figure 5.1 1, tree percent cover increases with a decrease in either slope or elevation - that is, the model suggests that lodgepole pine prefer lower, flatter areas to steeper, higher ones.

GENERALIZED (REGRESSION) DISTRIBUTION OF RUSTY MENZlESlA

Figure 5.12 4eneraIhed d~~ br rusty mentiesia, calcuhted by apptying fhe MZ regmsbn equation (qua- 16) to the continuous solar shyemirrdex (MI#)mverage.

The regression equation applied to the ancillary data has resulted in significant areas of the coverage - very steep areas of very high elevation - returning a negative percentage value for lodgepole pine. Though this is not a possible percent cover value in reality, the negative values

represent the extreme unlikelihood associated with finding lodgepole pine in either of these Figure 5.1 2 shows the generalized distri'bution of rusty rnenziesia as determined by linear regression against the solar slope-aspect index (SAI). The general trend identified in this case is that rusty menziesia percentages tend to decrease with a corresponding increase in either slope or aspect. That is, the regressed surface suggests that rusty menziesia tend generally to prefer flatter, north-facing slopes to steeper, south-facing ones. There is no predicted effect of elevation on the distribution of rusty menziesia (other than the indirect association between slope and elevation - that is, glacially eroded valleys tend to be steeper at higher elevations). Again, a significant proportion of the regressed rusty menziesia coverage has resulted in the calculation of an impossible negative percent cover value, this time in very steep or directly south-facing areas.

Figure 5.13 illustrates the generalized distribution of grouseberry as determined by linear regression against the cross-valley slope-aspect index (SAIm).The relationship, like that of the other two diagnostic species to their respective predictor variables, is an inverse one. Generally, an increase in SAlmcan be expected to correspond with a decrease in the percentage of ground covered by grousebeny. This means that grousebeny is more likely to be located on the southeast side of the Vermilion River valley, in less steep areas. The three sample plots in which the highest percentage values for grouseberry were recorded are all located on the southeast side of the valley, in a less steeply sloping area. In the case of the generalized grouseberry model, the negative values were recorded on steeper (and coincidentally, higher) locations on the northwest side of the valley. Results 75 GENERALIZED (REGRESSION) DISTRIBUTION OF GROUSEBERRY

Y Frgure 5.13 -Generalized disbibuth model fbrgmuse6eny, cakulated by appwng the GRB regressbn equahn (equation 17) 16 the continuous ems-valley sbp-aspct index (SAL) coverage.

Table 5.4 summarizes the characteristics of the spatial dependence models that were developed in the IDRlSl GStat interface, and which best ft the experimental semivariogram data for each species. An omnidirectional (i.e. direction-independent) spatial variance model was created for each of the three diagnostic species' experimental semivariograms. Models that

included a nugget value greater than zero were frequently found to over-generalize the variability of data -that is, to underestimate local maxima and overestimate local minima in the interpolation process.

- - -- I SPATIAL DEPENDENCE MODEL PARAMETERS I

In spatial vanmrmMs that mk'm tvvo types of &, the adual sill valw is 6qual lo hdf of the sill value that mould occur if the rnadel wmto be used i-ntiy.

I I Table 5.4 - Parameters of best Mbirg spatid dependence models fbr lfiree dhgmiic species -generated using the GStat mterfam in lDRISI (manual model fit method).

Once the spatial dependence of the diagnostic species' residual values was modeled, the variograms were used as inputs to the ordinary kriging interpolation procedure. The following parameters were used in the ordinary kriging procedure to generate all three surfaces: raw input values (i.e. no data transformation), the minimum acceptable sample plots were set at 10, the maximum allowable sample plots was set at 100, and a sample value search radius (local neighbouhood) of 2,000m was specified. Figures 5.14 through 5.16 show the continuous surfaces interpolated from sample plot residual value data for all three diagnostic species. Obviously, kriging did not assign values to every grid cell over the entire area. This is an artefact of the specified

2,000m sample point search radius and minimum of ten sample plots required for interpolation of unknown values. No value was interpolated for any location within 2,000m of which any less than ten sample plots were located. In the case of the kriged residual surfaces, negative values are meaningful, with negative values (darker areas on figures 5.14-5.16) representing overestimation R~SURS n by the regression function at a given kcation, and positive values (lighter areas on figures 5.14-

5.16) representing value underestimation by the regression function.

e Figurn 5.14 - The continuous surface htetpolated by dhaty wing of ltre hwnregmion msiduak at sample plot locatb~for A-stratum l@epok pine.

Overestimation of lodgepole pine (figure 5.14) appears to have occurred frequently along the valley floor and in areas around the Continental Divide, whereas values midway up the valley

sides around the Continental Divide appear to have been frequently underestimated. Results 78 KRlGlNG INTERPOLATION OF RESIDUALS - RUSTY MENZlESlA I

r Fgure 5.15 - 7he cunhuous sudkwe mterpolated by ordhary kr@r;ng of Ihe known regmsion residuak at sample pbt locab'wrsfornrsly~.

Overestimation of rusty menziesia values (figure 5.15) is most common on the northwest side of the valley, and underestimation occurs primarily in sample plots that were grouped together in cluster 6. Wth the exception of cluster 6 plots, rusty menziesia values on the Alberta side of the

Continental divide seem generally to be overestimated by the regression equation. Results 79 KRlGlNG INTERPOLATION OF RESIDUALS - GROUSEBERRY

From figure 5.16 it is evident that the most severe underestimation by the regression equation of grousebeny values has occurred in the cluster 3 plots on Storm Mountain. There are no other immediately apparent spatial patterns in the distribution of either positive or negative residuals.

Figures 5.17 through 5.19 illustrate the modefed distributions of the three diagnostic species that were subsequently used to model plant community distribution in the Vermilion bum. Results 80 These models were obtained by additive overlay of the regression surface and the kriged residual surface for each of the three species. The three modeled distributions were then masked so that only the bum area was included. The mask that was applied to the modeled distribution of rusty menziesia and grouseberry was the area of the entire bum, minus any unburned areas within the outer bum boundary. The mask applied to the modeled distribution of lodgepole pine excluded the

MODELED DISTRIBUTION OF A-STRATUM LODGEPOLE PINE

11 Figure 5.17 - Modeled distn'ufbn of A-stratum wlepine A the Vermilion bum, genented by addib'vely &bhhg the regressed LP, surface [figure 5.11) and the herface interpolated from LPa regnessmn msjduak at sample plot bcahs (figure 5.14). Dark areas mpresent hbh mmfrabions of bo?gqwlepine. The dashed line repmts fhe bum penineter. Results 81 same area as the mask for the other two species, but also masked avalanche chutes, as frequent avalanches in these areas deter the growth of lodgepole pine and other tree species.

Figure 5.17 shows that the highest modeled concentrations of lodgepole pine are located on the lower slopes of Mount Whymper on the northwest side of the Vermilion River valley, at the mouth of the Stanley Glacier hanging valley, and near Vista Lakes at the northeast end of

MODELED DISTRIBUTION OF RUSTY MENZlESlA J

Y Fgure 5.18 - Modeled disthbubh of rusty menzfesr;a in fite Venniiion bum, generated by aWitivety combining the regressed MZ surface (mure 5.12) and fhe surface interpolated from MZ regmsbn miduak at sample plot locahns (figure 5.15). Dark areas represent high concenb;tlions of rusty medsia. me dashed line represents the bum penineter. Results 82 the bum. The highest modeled concentrations of rusly mentiesia (figure 5.18) are found in a linear pattern along the middle southeastern (left) slopes of the Vermilion River valley, and at the bum's edge on the eastem shoulder of Mount Whyrnper. There are large, continuous areas, specifically on the northwest side of the valley and along Altrude creek from Altrude Lakes to the northeast edge of the bum, in which rusty menziesia is absent.

MODELED DISTRIBUTION OF GROUSEBERRY I

Fgum 5.19 -Modeled dwbhof gmusebeny in fhe Vennilbn bum, generated by aWitive/y combining the reg& GRB surface (figure 5.13) and dhe surface interpolaied frwn GRB fegre&n residuals at sample pbt Iocations (Qure 5.16). Dark areas repmmt hr"gh cmcenbatfons of grouseberry. The dashed line represents the bum perimeter. Results 83 From figure 5.19, the location of highest modeled concentration of grousebeny is obvious.

Aside from the comparative abundance of grousebeny on the shoulder of Storm Mountain, it is more or less evenly distributed at low percent cover values throughout large areas in the rest of the bum. The exceptions, areas in which grouseberry is generally not present, include the eastem end of the bum, most of the slopes of Mount Whymper, and the area around Alhude Lakes.

5.6 MODELING DISTRIBUTION OF PLANT COMMUNITIES

The cluster separability measures attained in this analysis are repocted in section 5.4, above. figure 5.20 is the final objjveof this thesis, a map of the distribution of plant communities within the Vermilion Pass bum. The map was generated using the maximum likelihood classification module in IDRISI, with modeled diagnostic species' percent cover distributions as input "channels." Equal prior probabilities were assigned to each cluster, and a forced classification of the entire image (i.e. 0% unclassified) was imposed. A detailed discussion of the distribution, composition, and site characteristics of the 10 plant communities of the Vermilion bum follows in section 6.1.

ERROR MATRIX ANALYSIS

CLUSTEU 1 2 3 4 5 6 7 8 8 10 WTOTAL 117 300 100 00 4 25 32% 2 83700 200 30 2 52 m 3 0 030 000 00 0 3 OW 4 o 0025 200 00 o n 7% S 2 3022300 10 1 32 28% 6 0 000 0120 00 0 12 OK 7 0 000 0011 to 0 12 m 8 0 000 000210 1 22 PIL 0 0 000 000 212 0 14 14% 10 1000 100 0016 10 11%

CA- ~AL28 a 3 n 29 12 11 20 12 24 217 bmrdGnlrdoK5Q)L11KmC%zl%mbmb2s%OwJ5W, 18.43%- '~-~~~ "CA-dl6Iwrrl)of *** Tddsgirtmb,am. 45 16% (1327%-P5gKJ SX inmJ; annl hdPdrgmwl (KUtZgfiC Table 5.5 - E'r mahk anafysis of maximum likelihood cIa&kation of plant communities. KHAT value report81 footnote at bottom of tabk. ResuIYs 84 Table 5.5 h the emmatrix, gsnera$d in IDRISI, for the clasMcation procedure described above. A detailed discussion of this emmatrix and possible sources of emand inaccuracy is included in seclion 6.2 of the next chapter-

PLANT COMMUNITIES OF THE VERMILION BURN

CLUSTER 1 CLUSTER2 CLUSTER 3 =CLUSTER4 CLUSTERS CLUSTER6 I=f CLUSTER7 maurn8 CLUSTER 9 rn CLUSTER 10 This chapter is subdivided into three The first section describes in detail the plant communities of the Vermilion bum and their modeled distributions. A comparison between the present vegetation in the bum and the findings of the Hams study [I 9761 is also included in this section. The second section is a critical look at the underpinnings of the modeling approach used in this thesis, which attempts to explain some of the potential advantages and shortcomings associated with this approach. Finally, the third section suggests some potential areas for further research.

6.1 - THE PLANT COMMUNITIES OF THE VERMILION BURN It must be emphasized that the research described in this thesis models the distribution of plant (vegetative) communities, and not ecological communities. These distributions have been modeled exclusively based on terrain, location, and vegetation data, and not on the ecological qualities or amibutes that a certain location or community may possess. It may well be possible, however, to infer the ecological qualities (such as soil nutrient and moisture regime or wildlife habitat suitability) of a given area from the assemblage of plants that are present. Such inferences ate the domain of those who will use the information presented here for its intended purpose, and are beyond the scope of this thesis.

This section will describe the principle characteristics of each of the ten plant communities of the

Vermilion bum. Up to this point, these plant communities have been referred to in this thesis only by the number assigned to them as a result of the cluster analysis classifiition procedure. In this section, each cluster will be given a name that generally describes its vegetative composition, preferred topographic site conditions, and where appropriate, its specific location in the Vermilion Discussion 86 bum. Any similarities between the awnposition of plant communities and the vegetation types

(VTs) identified by Achuff et al. [I9841 will be noted. And finally, the modelled distribution of the three most common tree spedes, as well as current patterns of shrub- and herb-layer dominance in the bum, will be compared with the vegetative characteristics of the bum three decades ago, as described by Harris [19761.

In most cases, only those species that are present in more than 60% of the plots in a given cluster are included in the description - excepting those clusters which demonstrate a high degree of heterogeneity. The frequency of occurrence (or constancy) and percent cover of all three diagnostic species are noted for every cluster, regardless of dominance. A complete list of plant species found in each cluster, separated into ten percent constancy intervals (from most common to least common in the plots of that cluster), can be found in Appendix 6. Species percent ground cover, unless otherwise noted, is averaged over only the plots in which the species was present, not over the total number of plots in a given cluster - this provides a better idea of the abundance of a particular species in the areas where it is present. A complete list of the tree species found in each plant community, along with tree age, height, and diameter at breast height (DBH) data, is included in Appendix 8.

Where a dominant species is present for the A-stratum of a given plant community, it is invariably lodgepole pine. However, the percent cover of lodgepole pine varies greatly among plant communities (as has already bwn demonstrated), as does the understory composition.

Figures 6.1 through 6.10 below show the modeled distribution in the bum of each respective plant community. Discussion 87 -I CLUSTER 1 - NIT. WHYMPER OPEN PINEIBUFFALOBERRY

I' Fgure 6.1 - Dbtdwtkm of the Mount Wnympefopen ~~bbeny(cluster 1) phtmmuniiy in the VemiIrion Pass bum.

CLUSTER 1 - MOUNT WHYMPER OPEN PINE/BUFFALOBERRV: So named because of its preponderance on the lower to high middle elevations of the bum, especially on the slopes of Discussion 88 Mount Whyrnper. This plant community k an open forest type - defined by Achuff et el. [I9841 as

any area in which the distance between cmsof trees is between two and five times the average

crown diameter, usually between 5% and 20% cover - with an average of 18.6% cover in the A- stratum by lodgepole pine (average age 20 years). The dominant &stratum species is russet

buffaloberry (Shepherdh canadensis (L.) Nutt.) (average 11 .O% cover), with a frequent but low

subdominance of juvenile lodgepole pine (average age 9 years). The most common C-stratum

species, in descending order of abundance, are downy ryegrass (Leymus innovatus (Beal) Pilger)

(quite abundant with an average cover of 7.5%), twinflower (4.5%), and less abundantly showy

aster (Aster conspkuus Lindl.), wild strawberry (Fragana Mginima Duchesne), Rocky Mountain

goldenrod (Solidago mulhiadiata At), fireweed, prickly rose (Rosa acicularis MI.), and alpine

groundsel (Senecio paucitows Pursh). Grouseberry (3.8% cover) and rusty menziesia (5.0% cover) are found less frequently, and where they occur, less abundantly than in other areas. This is

not surprising, considering that most accurrences of this plant community are in areas with

unfavourable aspects for these two species. The composition of this plant community closely matches that of VT C19 identified by Achuff et al. [1984], and noted in the Kootenay National Park

€LC to be common in Vermilion Pass.

Although not commonly occupying current avalanche tracks in the bum, this plant community is frequently located immediately adjacent to or beneath areas which experience frequent snow avalanches. General site characteristics include moderately steep slope (average

250), a broad range of elevations (1,554m to 2,068m, average 1,76Om), and south-facing aspects on the nocthwest side of the Vermilion River valley, except most notably areas adjacent to avalanche tracks in and around Stanley valley. Discussion 89 /CLUSTER 2 - SUBALPINE MEADOWS AND AVALANCHE TRACKS

I! Fi6.2 - DMutkm of the subabine meadows and awMetracks (cluster 2) phtmmunity h the Vermilh Pass bum.

CLUSTER 2 - SUBALPINE MEADOWS AND AVALANCHE TRACKS: This is likely a combination of two or more different types of plant communities found within the bum that have Discussion 90 been grouped together based on the common feature of sparseness of vegetation. Unless otherwise not* all species' average percent covers are less than 5%. This is the most

heterogeneous of all plant communities, with no species occurring in more than 80% of all sample plots in this cluster, and only seven species occurring in more than half of the plots. The most frequently occurring species in this plant community can be subdivided into two main types. The first group is comprised of resilient, low-lying species that would commonly be found in a frequently avalanched area. Species of this type found here include willow species (Salk L.) and sedge

(Carex L.) species, alpine groundsel, common ymw(Achilles mil/efolium war. a/pr'coIa (Rydb.)

Garrett), shrubby cinquefoil (Dasiphora floribunda (Pursh) Kaltesz, comb. nov. ined.), dicranoweisia moss (Dicranoweisia crispula (Hedw.) Lindb. ex Milde), Rocky Mountain goldenrod,

mountain deathcamas (Zigadenus elegans Pursh), and scarlet Indian paintbrush (Castilleja miniata

Dougl. ex Hook.). This vegetative composition is consistent with the first of three distinct avalanche complexes identified by Achuff et al. [19&4l, the lower subalpine avalanche complex, comprised of

VTs S2, S16, H5, and C22. Another component of the avahnche complex is 'unvegetated areasn, which were also likely to have been classified as belonging to this plant community; that is, areas for which 0% cover had been modeled for all three diagnostic species were likely classified as belonging to this plant community - see coincident histograms, figures 5.5,5.6, and 5.9.

The second discernible group within this plant community consists of plant species that are

likely to make up meadows in more stable high lower to upper subalpine areas, and are similar to the VT identified by Achuff et al. [I9841 as 01 0. These species include A-stratum lodgepole pine

(infrequently and with low (5.7%) percent cover, average age 19 years), B-stratum russet

buffaloberry, Engehnann spruce (average age 13 yean), Canadian gooseberry (Ribs Dkussbn 91 oxyacanthoides L.), lodgepole pine (average age 13 years), limber pine (Pinus &xi/& James)

(average age 12 years), and common juniper (Junipems communis L.), and Gstratum wild strawberry, firewed, twinf lower, downy lyegrass, prickly rose (Rosa ackuaris Lindl.), kinnikinnick

(Arctostaphyllos uva-ursi (L.) Spreng.), yellow columbine (AquiIegia flavescens S. Wats.), fewflower meadowrue ( Thalidmm ssp~siflorumTurcz. ex RCh. & C.A. Mey.), and grouseberry (infrequently and at low (3.5%) percent values). Rusty menziesia is present in less than one fM of the sample plots in this community, with sparse cover (5.1 %) where it is found.

Aside from avalanche tracks, the modeled distribution of this plant community is concentrated mainly at higher elevations in the bum (highest recorded elevation: 2,246m; lowest

1,586m; average: 1,930m). It generally occurs on steep slopes (average 310, maximum 510). The two notable exceptions to this general tendency are a small portion of the large, sparsely vegetated alluvial fan at the confluence of the stream flowing from Aster Lakes and the Vermilion River (this area is quite possibly erroneously classified - see description of cluster 5 for more discussion of the alluvial fan), and some smaller areas near Ahde and Vista Lakes. It is difficult to generalize regarding the preferred aspect of this plant community, as its aspect values are spread almost across the entire range, and the distribution seems to be aspect-independent.

CLUSTER 3 - STORM MOUNTAIN GROUSEBERRY: This plant community derives its name from two immediately noticeable and unique features its highly localized distribution in the bum, exclusively on a high-elevation, south- and southwest-facing slope near Aster Lakes in the cique on the west side of Storm Mountain; and the unique extent to which grouseberry is dominant over all other vegetation. This plant community displays a significant amount of homogeneity - 50% of all species identified occurred in all three sample plots. II CLUSTER 3. STORM MOUNTAIN GROUSEBERRY

figure 6.3 - Distn'b- of the SIorm Mountan g-ny (cluster 3) plant community m the Vemilbn Pass bum.

There were four juvenile or dwarf adult tree species identified. Limber pine was found in all three plots, with an average pemnt aver of 4.7% and an average age of 14 years. In the

Kootenay National Park ELC. Achuff et al. (1 9841 suggest that whitebark pine (Pinus albhulis Discussion 93 Engkn.), and not limber pine is associated with this plant community. It is probable that whitebark pine was mistakenly identifiled as limber pine at this location in the bum, due to the preponderance of limber pine in other high-elevation locations in the bum, the d'fl~uItyin discerning between the two pine species, and the absence of whitebark pine from all other parts of Vermilion Pass.

Subalpine larch (Larix lya/\ii Pad.) was found in two plots, with an average percent cover of 6.0% and an average age of 14 years. Engelmann spruce (5.5% awer and an average age of 11 years) and subalpine fir (2.0% cover and an average age of 12 years) were each found in only one plot.

The Cstratum is unquestionably dominated by gmusebeny, with an average percent cover of 53% over the three plots, by far the highest concentration of grouseberry of all the sample plots in the bum. Other Gstratum species found in all three plots include fireweed, heartleaf arnica

(Arnica cordifoIia Hook.), pink mountainheath (PhyIIodm empehifomis (Sm.) D. Don), ribbed pixie lichen (Cladmia cariosa (Ach.) Spreng., Rocky Mountain goldenrod, juniper polytrichum moss

(Po/yfrichumjuniperinurn Hedw.), and alpine groundsel. Greater pixie stick lichen (Cladonia muta(L.) Hoffm.) and common yarrow were found in two of the three sample plots. Neither of the other two diagnostic species (A-stratum lodgepole pine and rusty mendesia) was present in this plant community. This assemblage of plants very closely matches the CIS VT identified (and mapped in the same location) by Achuff et al. [1984].

The preferred site for this plant community is high (average 2.208m) elevation, moderately sloping (199, and south- (though not directly south) facing. It would seem that there are other preferred site characteristics of this plant community that are not identified by this research, since there are other areas within the bum that meet the above physical description, but nonetheless are Dkussrbn 94 not occupied by this highly localized plant community. Achuff et al. [I9841 note the preference for well drained morainal or colluvial locations.

II CLUSTER 4 - SOUTH SIDE OPEN PlNEIMENZlESlA

I' Faun 6.4 - Di&b& of Ihe south side open piMnem&i& (clusfer4) plant mmuf?ityh the Vefmillian Pass bum. CLUSTER 4 - SOUTH SIDE OPEN PINE/MENaESIA: This plant community is found mostly on the southeast side of the Vermilion River valley, in areas close to and on either side of the Continental Divide. This is a fairly homogeneous plant community - tree species are welldispersed lodgepole pine (average 10.7% cover, average age 22 years) and juvenile Engelmann spruce

(average 5.7% coverage, average age 14 years). Just less than hatf of the sample plots in this plant community had low percent covers of A-stratum Engelmann spruce, averaging 3.6% cover

(average age 19 years). The only &stratum species in this plant community is rusty menziesia, found in all sample plots with an average perce~tcover of 28.3%. Dominant in the C-stratum is grousebeny, also found in every plot, with an average percent cover of 12.9%. Fireweed is also omnipresent in this plant community, but at an average percent cover of only 3.3%. Other less constant species in the herbaceous layer include Cadian bunchbeny, sulphur pixie cup lichen

(Cladonia sulphurina (Michx.) Fr.). knights plume moss (Plilium cn'sa-cassfrensis (Hedw.) De Not.), heartleaf arnica, twinflower, juniper polytnchum moss, willow species, and greater pixie stick lichen, all with average percent covers under 5%. This plant community is not directly comparable to any of the VTs identified by Achuff et al. [I9841; however, it bears meresemblance to VTs C10 and

C14.

The south side open pine/menziesia plant community is viable over a wide range of slopes

(from 50 to 30,average 180) and elevations (from 1,590m to 2,066m,average 1,767m). It is generally restricted, however, to north facing areas (sdar asped average 520. ranging from 10to

1080) on the southeast side of the valley (cross-valley aspect average 35O, ranging from 00to an anomalous 1580). Discussion 96 CLUSTER 5 - OPEN PlNElMENUESWGROUSEBERRY-BUNCHBERRY

I' Fgure 6.5 - DWibutb of the open pine/rn~gmuse6eny-bund,beny(cIuster 5) plant wmmunily in the VedIriOn Pass bum.

CLUSTER 5 - OPEN PINWEWESWOROUSEBERRY4UNCHBERRY: This plant community is similar in many ways to the south side open pinelmenziesia plant community (above) in terms of its composition. It is also similar (though slightly more homogeneous) to the subalpine meadows and avalanche tracks plant community, in terns of its sparseness of vegetation -there are only 11 species of plant with constancies of greater than 60%. This plant community is dispersed widely throughout the bum, and is commonly located towards the termini of avalanche tracks, on alluvial fans and along stream and river valley bottoms, and generally in rocky, sparsely vegetated areas.

A-stratum lodgepole pine (average 11.6% cover, average age 20 years) and B and C stratum Engelmann spruce (average 9.7% cover, average age 14 years) are the most common tree species. Rusty menziesia is the dominant 0-stratum species with an average of 12.3% cover; willow species are the only other shrubs that are frequently present. The herbaceous layer is co- dominated by grousebevy (10.9% cover) and Canadian bunchberry (10.1 % mer). Fireweed is present in all sample plots in this plant community, but in low abundance (average 4.3% cover).

Other common ground layer species are juniper polytnchum moss, twinflower (average 5.2% cover where present), heartleaf arnica, and sulphur pixie cup lichen. There are no ideal matches to this site in the VTs of Adruff et al. [1984]; the one that comes the closest is 011, but this VT has no record of either Engelmann spruce or rusty menziesia

From the ancillary data values for sample plots, it is diiiult to make any generalizations about preferred site conditions for this plant community. Slope values range from 70 to 300, but on the average are of moderate steepness (190). Elevation values range almost as widely as for the bum itself - from a low plot of 1565m to a high plot of 2,160m (with the average squarely in the middle at 1,789m). The range of both aspect measures also approaches the maximum possible - solar aspect values range from 140 to 1660, (average of 729, whereas cross-valley aspect values range from 20 to l7OP (with a mean of 430) displaying a possible preference of sites located on the DI'scussioon 98 southeast side of the valley. Interestingly, both solar and cross-valley slope-aspect index averages were low for this site (solar range 3-64, average 23.6, cross-valley range 1-77, average 14.5). This suggests a preference of gradually sloping areas on north- or northwestfacing slopes, or either a combination of: (a) extremely flat and south- or southeast-facing slopes; or (b) steeper and north- or northwest-facing slopes. This plant community covers most of the large alluvial fan at the mouth of the stream that drains the outflow from Aster Lakes and the adjacent valley (which contains a rock glacier) into the Vermilion Rier.

CLUSTER 6 - RIBBON OF MENDESIA: This plant community is an anomaly, both in comparison to the vegetation found in the rest of the bum, and to the VTs identified in the Kootenay National

Park ELC. For an empirical measure of how different this plant community is from all the others in the bum, see the cluster analysis dendmgrarn, figure 5.1 in &.on 5.2. It consists of an area that runs in a nearly straight, nearly continuous line along one of the more gently-sloping shale benches between limestone bedrock layers (for an explanation of local topography, see chapter 2), and a small, isolated area near the Upper Vermilion Rier on the lower eastem slopes of Mount Whyrnper. Some other interesting features were noted in the field along this bench - specifiilly, several seepage areas and emerging springs. Also remarkable were a few very narrow strips of mature forest (often less than 25m wide), oriented parallel to the Vermilion River, and surrounded on all sides by forest regenerating from the 1968 bum. It seems dome very unique and highly localized phenomena, possibly related to ground moisture conditions, would have been necessary in this particular area for a fire that burned w completely and with such intensity to have skipped over such a small patch of mature forest. Discussion 99 TI ti CLUSTER 6 - RIBBON OF MENZlESlA

t' Fgum 6.6 - DW6ufim of lfre ribbon of mena'esia (cluster 6)plant community in the Vemilh Pass bum.

The vegetation in this community is an open (average 14.7% cover, average age 21 years) pine forest, with few juvenile Engelmann spruce (average 4.3% cover, average age 14 years). As its name implies, its B-stratum is overwhelmingly dominated by rusty menziesia, which is present in Discussion 100 all sample plots, boasting an average percent cover of 70.7%. Willow species are also common in the shrub layer, but are much less abundant, with an average percent cover of 4.0%. The C- stratum is dominated by grousebeny (average 12.6%), followed by fireweed (average 4.9%), which are both found in every plot in this plant mmun~ty.Other common ground-layer species include greater pixie stick lichen, juniper polybichum moss, and pohlia moss (Pohlia nutans (Hedw.)

Lindb.).

Aside from the site characteristics that have already been mentioned, the ribbon of mendesia is confined to the 250m between 1,659m and 1,898m (average 1,788m), and is found on moderately sloping (average 219 benches. With the exception of the two sample plots on the lower eastern slopes of Mount Whymper, it is found exclusively in north-facing slopes on the southeast side of the valley.

CLUSTER 7 - DOG HAlR PINE: Another highly localized phenomenon, this plant community is found in few areas in the bum, most commonly in valley bottoms (of the Vermilion River, and of

Stanley and Altrude Creeks). A large contiguous area covered by the dog hair pine plant community occurs on the alluvial fan created by the creek that drains the large cirque on the south side of Mount Whymper. Two isolated patches occur on the middle-southeastern slopes of Mount

Whym per.

'Dog hair is the vernacular term commonly used in reference to very dense stands of thin, younger pine trees that are often found in areas regenerating from fire. During this short stage, a low (less than I%), density-independent mortality rate is reported [Johnson & Miyanishi, 1991 until trees are large enough to begin showing the effects of competition with one another for space Discussion 101 11 r CLUSTER 7 - DOG HAIR PINE

I' I] Fgum 6.7 - Dbfributitm of fhe dog hair phe (duster I) phtcwnrnuniljtin the VemiIkw Pass bum. and resources.

As the name implies, this plant community is characterized by extremely dense stands of Discussion 102 closed canopy lodgepole pine forest (A-stratum trees present in all plots with average 87.8% cover, average age 21 years; juvenile trees and seedlings also frequent with average 10.2% cover and average age 17 years). The lodgepole pine in this plant community are unusually small in diameter, perhaps due to the high density of stems (see Appendix 8). The shrub layer is most commonly dominated by russet buffaloberry (average 14.3% cover), with willow species less constant and abundant. The codominant Gstratum species in the plant community are grwsebeny (average

7.3% cover) and twinflower (average 5.1 % corer), both occuning in all sample plots. Other less abundant Gstratum species include fireweed (omnipresent in this plant community), Canadian bunchberry, downy ryegrass, showy aster, heartleaf arnica, prickly rose, and knights plume moss.

Rusty menziesia is present in just over half of the sample plots in this plant community (average

8.9% cover over those plots in which it occurs). The dog hair pine plant community is a close match to the C6 VT identified by Achuff et al. [1984], although the Kwtenay National Park ELC only maps its distribution at higher elevations along the Vermilion River valley.

The site characteristics of this plant community are diffiarlt to define. Slopes upon which it is located range from 3 to 280, with an average of a moderate 180 steepness. It is generally present at middle elevations of the bum, ranging roughly 300m (1,570m - 1,849m) around a mean elevation of 1,656m. It is difficult to assign a prefened aspect to the dog hair pine plant community, with both solar and cross-valley aspects spread over nearly the widest possible range (OP1680, average 1030, and 150-1700, average 1100). Curiously, both slope-aspect indices have low ranges and averages (solar: 0-75, average 36, and cross-valley: 2-78, average 37), which given the values for slope and aspect, must mean a that dog hair pine is found on either gentlygloping south to south-east facing slopes, or on steeper north to northwest-facing slopes. Discussion 103 CLUSTER 8 -BOTTOMLANDS DENSE PINE

II Frgure 6.8 - Disfribufimof the bottom- deinse pine (cluster 8) plant mtnunity in the Vemribn Pass bum.

CLUSTER 8 - BOlTOMUNDS DENSE PINE: Distributed principally in large, contiguous patches at lower elevations and in the bottom of the Stanley CreaWOlacier valley, this plant community is comprised of dense, closed canopy stands of l0dgepol8 pine. Lodgepole pine is the only species Discussion 104 present in all sample plots, with an average of 64.5% cover (average age 20 years) in the A- stratum and 5.6% (average age 15 yeas) where present in the understory. There is an abundant and diverse understory, particularly in the shrub layer. Dominance in the 6-stratum is shared by three species: rusty menziesia (the most abundant where present, at an average of 17.0% cover), russet buffalobeny (average 8.8% cover), and B and C-stratum Engelmann spruce (averaging

9.0% cover (average age 15years) where present). Willow species are also common in the 6- stratum, though in lower abundance. It is unusual in the bum to see the same locations shared by russet buffalobeny (most frequently dominant on the slopes of Mount Whyrnper and other south- to southeast-facing locations) and rusty menziesia (rarely found on the right side of the Vermilion

River valley - see figure 5.18 in the previous chapter). The Gstratum is dominated by grouseberry (average 10.2% cover), with twinflower roughly half as abundant (average 5.4% cover). Other common Gstratum species include Canadian bunchberry, heartleaf arnica, fireweed, and juniper polytrichum moss. The vegetation of the bottomlands dense pine plant community is most similar to that found in areas covered by dog hair pine. Its composition most closely resembles that of the closed forest VT C18, which was sampled near Marble Canyon (at the confluence of Tokkum

Creek and the Vermilion River) for the Kootenay National Park ELC. Some discrepancy exists between C18 and the vegetative composition recorded here, most notably in the ground (C) layer.

This plant community generally occurs at lower ehtions (average 1,6961~1,but ranging from 1,537m to 1,942m), with the notable exception of certain areas in the bottom of the Stanley valley. Large portions of the bum along the Vermilion River are occupied by this type of vegetation, which is the inspiration for its name. Slopes are generally slightly more gradual than in other plant communities, with an average of 160 (though they range from 30 up to 420 in one extreme case). Discussion 105 Aspect values are once again wide-ranging (solar average 1060, range 350-1 740, cross-valley average along the valley axis at 920, but ranging almost as widely as possible, from 20-1 779, but slope-aspect indices are again both low (solar average 31, ranging from 5-100, cross-valley average more concentrated about the mean of 25, ranging from 0-75). This suggests a preponderance of this plant community in similar areas to those described for the dog hair pine plant community.

CLUSTER 9 - MIDSLOPE CLOSED PINEIYEWESIA: This plant community and the next are similarly composed, but they resemble each other less than the dog hair pine and bottomlands dense pine plant communities (for a graphical representation, see figure 5.1 in section 5.2).

Midslope closed pineimenziesia, as the name implies, is a closed canopy forest, found mainly in large, contiguous patches, at middle elevations and most commonly on the southeast side of the

Vermilion River valley, the two exceptions a large patch on the eastern slope of Mount Whymper, and the valley bottom at the Continental Divide.

The vegetation in this plant community is quite homogeneous. Lodgepole pine is present in all strata, most abundantly in the A-stratum (present in all plots, with an average of 51 .I% cover and an average age of 21 years; lower strata average 2.4% cover where present, average age 16 years). The only common shrub is rusty menziesia (omnipresent, with an average 44.2% cover).

The C-stratum is again dominated by grouseberry (present in all plots, average 10.9% cover) with juniper polytrichum moss (also present on all plots), Canadian bunchberry, greater pixie stick lichen, sulphur pixie cup lichen, fireweed, twinflower, and heartleaf arnica common though less abundant. The VT from the Kootenay National Park ELC that best matches this assemblage of vegetation is C20, which is mapped by Achuff et al. 09841 over substantial portions of Vermilion Discussbn 106 CLUSTER 9 - MIDSLOPE CLOSED PINUMENZIESIA

I' Fyum 6.8 - D&&i'b& of the rn- cbsed~~(cIuster 9) plant mmunily in Lhe Vmilion Pass bum.

Pass.

The site charactefistics of this plant community, particularly slope and elevation, are Discussbn 107 refreshingly well defined. Sample plot slopes ranged from 10to 119, with an average slope of 140, quite gradual in comparison to the average slopes for other plant communities. Sample plots were typically located on middle elevations in the bum, ranging less than 200m (from 1,673m-1,847m) and averaging 1,735m. The aspect values are wide-ranging, suggesting a lack of aspect preference for this plant community.

CLUSTER 10 - CLOSED PINEliBUFFALOBERRYN3ROUSEBERRY-TWINFLOWER: This last plant community of the Vermilion bum is the most widespread, with large contiguous areas along the valley bottom from one end to the other. The northeast end of the bum is classified almost exclusively as belonging to this plant community.

Like the previous plant community, this is a closed pine forest (though not as heavily covered, with A-strahnn lodgepole pine present in all plots, averaging 39.0% cover (average age

21 years), and not frequently found in the understory). The shrub layer is diverse, with russet buffaloberry most common (average 9.4% cover), along with rusty menziesia (less common, but abundant, averaging 13.2% cover, where found), willow species, and juvenile Engelmarm spruce

(only 3.3% abundant where present, averaging 16 years in age). In the C-stratum, twinflower is found in every plot, with en average of 4.4% cover; grousebeny, less constant, is abundant where found (average 8.8% ground cover). Other Gslratum species include Canadian bunchbeny, downy ryegrass, and prickly rose. Again, VT C20 most closely resembles this plant community.

The average slope (150, ranging from 10 to a single exheme value of 340) is among the flattest of all plant communities in the bum, and the average elevation is lowest (1,678m, ranging from 1,549m to a single extreme value, found in the same sample plot as the extreme slop value, Discussion I08 of 1,903m). As in the case of the last plant community, little can be generalized from the wide- ranging aspect values and slope-aspect indices.

11 CLUSTER 10 - CLOSED PINElBUFFALOBERRY/GROUSEBEERRYNFLOWER /

P F~ure- 6.10 - Distrbubion of the closedpine/buffa&beny/gmusebeny-Mfkwer (cluster 10) plant community h the Vemilrion h bum. Discussion 109 In order to facilitate direct comparison of the current state of vegetative regeneration in the bum to that which was recorded in the Hanis study [1976], A series of four maps (figures 6.1 1 through 6.14, inclusive) has been created in a fashion similar to the four vegetation maps from the

Hanis report [I 9761, reproduced in figures 3.1-3.4 in section 3.3 of this report. In the 1972 distribution maps, tree seedling abundance was measured in stems per 78.5m2 (the area covered by the Sm radius plot used in the hems study - see sedion 3.3) - here it is measured in average

GENERALIZED DISTRIBUTION OF LODGEPOLE PlNE - VERMILION BURN AMRAGE LODGEPOLE PlNE DISTRIBUTION BY PLANT COMMUNIM

- areas represent highest concen~~sof bo@pok pme. Discussion 110 percent cover for each respective cluster.

Figure 6.1 1 is a generalized distribution map for lodgepole pine in all strata. In the Harris report (1 9761, the pine distribution map (figure 3.1) showed highest concentrations of new pine seedlings at the northeast end of the bum, with pockets of dense seedling establishment on the lower eastern slope of Mount Whymper, and in the Vermilion valley just below and west of Stanley headwal. Another denser pocket of pine seedlings was found on the main slopes of Mount

Whymper, close to the main drainage of the bowl into the Vermilion River. There were few areas within the bum in 1972 that were devoid of pine -the largest contiguous area was along Altrude

Creek between Ahde and Vista lakes.

There are some elements of similarity between the past and present distributions of pine. as evident in examination of figure 6.1 1. The densest concentrations of seedlings in 1972 have become, in most instances (e.g. the main and eastern slopes of Mount Whyrnper, mid-valley locations at the northeast end of the bum, and a small patch just west of Stanley valley), the densest stands of maturing pine in the present-day forest. Furthermore, some new (or previously unidentified) dense patches of pine can be found, specifically in the bottom of the Stanley valley, and at the southwest end of the bum near Marble Canyon. Larger areas of the main slopes of

Mount Whyrnper are densely covered, and generally, pine is more ubiquitous now than it was thilty years ago. The lowest concentrations of lodgepole pine are still along the southeast side of the valley, and at high elevations on both sides of the Vermilion River valley. Especially banen of lodgepole pine are the avalanche tracks and high meadows on the slopes of Mount Whympec, and facing the glacier and headwa! on the east side of the Stanley valley. Discussion Ill In 1972, the distribution of Engelmann spruce and subalpine fir within the bum (figure 3.2) was minimal, and limited only to those areas closest to mature trees (seed sources), either at the bum's edge or in areas within the bum that had managed to sunrive the fire. The densest recruitment of seedlings was occumng in the pine-free area around Altrude Lakes, with other dense areas on the southwestern end of the bum on the southeast side of the valley.

I GENERALIZED DlSTRlBUTlON OF ENGELMANN SPRUCE AND SUBALPINE FIR - VERMILION BURN

-INED AVERAGE ENGELMANN SPRUCE AND SUBALPINE FIR MSTRlBUTlON BY PUNT

From figure 6.12, it is apparent that Engelmann spruce and subalpine fir have begun to Discussion 112 establish themselves more widely within the bum, though the areas of past highest density appear to be presenred. New densely populated areas have appeared, including most notably areas around Storm Mountain high on the valley sides, and near the southern intersection of the bum boundary and the highway. With the exception of avalanche tracks and high-elevation meadows in the bum, there are currently no forested locations in which Engelmann spruce or subalpine fir are more abundant than lodgepole pine. The areas in which Engelmann spruce and subalpine fir are absent frequently coincide with the areas of maximum lodgepole pine density (dog hair pine stands on the slopes of Mount Whyrnper and at the northeast end of the bum). This is possibly indicative of competitive exclusion of spruce and fir h these areas by lodgepole pine.

In 1972, the vast majority of the Vermilion bum was dominated in the shrub (0) layer by rusty menziesia (see figure 3.3). Harris [I9761 delineated an 'avalanche complex" on the main slopes of Mount Whymper, and a patchy area towards the northeastern end of the bum that exhibited a heterogeneous assemblage of small shrub communities with various different dominant species (white spirea, prickly rose, shrubby cinquefoil, a small area dominated by russet buffaloberry), and areas of co-dominance between these species. Rusty menziesia was repotted to be dominant over the rest of the bum, except those areas devoid of shrubs, at high elevations and a narrow patch at middle elevation on the southeast side of the Vermilion valley.

Figure 6.13 shows that the B-stratum dominance in the Vermilion bum has changed considerably over the last three decades, with menziesia losing ground to other species, most notably russet buffalobeny. Large areas of the bum are still dominated by rusty menziesia, particularly on the southeast side of the Vermilion River valley. The avalanche tracks and subalpine meadows are still easily distinguishable from other areas in the bum, with russet buffalobeny and Discussion 113

GENERALIZED DISTRIBUTION OF DOMINANT 0-STRATUM (SHRUB) SPECIES - VERMILION BURN

P - -- A Fiquure- 6.13 - Generalized 7999 d&tnbubion of dominant Bsfratum (shrub) species m the Vennilnbn bum. In the legend, twu &inant species are separated by a hyphen ( - ). willow species co dominant, but also commonly home to shorter, resilient, spreading shrubs such as shrubby cinquefoil, prickly rose, common juniper, and rock willow (Salk vestita Pursh). Seedling and juvenile Engelmann spruce and subalpine fir are also common in these areas. For further discussion on the establishment of Engelmann spruce and subalpine fir in avalanche tracks, see the discussion of potential further research in section 6.3.

The high, grouseberry-dominated plant community (cluster 3) on the shoulder of Storm Discussion 114 Mountain has little in the way of tall shrub species, though there are juvenile subalpine larch and

limber pine growing amidst the grouseberry in the 9- and Gstrata.

Aside from these two exceptions, the shrub layer of the entire bum is dominated or co-

dominated by two species: russet buffaloberry and rusty mendesia. Russet buffaloberry dominates

on most of the main stable slopes of Mount Whyrnper, and in some bottomlands at the

northeastem end of the bum (the only area in which they were mapped as being dominant in

1972). Rusty menziesia is dominant in large areas, already discussed above. Co-dominance of

russet buffaloberry and rusty menziesia is present over large, continuous areas in the valley

bottom, and on the lower slopes on either side. This may represent an ecotone, or a zone of transition between one broadly defined type of vegetative composition and another.

There is no legend on the map of 1972 Gstratum dominance in the Harris report [I 9761

(figure 3.4). However, it is possible to decipher the species distribution for most areas, and to speculate based on current C-stratum species as to the interpretation of other codes on the map.

In the 1976 report, Hams states that 'a major battle for dominance [of the herbaceous layer] was detected between Epilobium angust#o/jum and Am& cordifolia" [pp.91-921. This "battle" is evident in the dominance of these two species over a considerable poltion of the total bum area in 1972.

Harris also notes a large area occupied by "GRASSES" on the slopes of Mount Whymper, in areas referred to in the B-stratum 1972 distribution map as being occupied by the 'avalanche complex".

As in the shrub distribution map, Hams recorded an absence of vegetation at high elevations on the southeast side of the valley. Also similar to the shrub map was the increased diversity of dominant herbaceous species at the northeast end of the bum, with small areas dominated by

'ERIG." (taken to refer to Erigeron spp. L., comnwnly known as fleabane), "H." (taken to refer to ------either Hedy~a~msulphumscens Rydb., commonly called white sweetvetch, or Hiemcium spp. L, commonly called hawkweed), and a few other patches dominated by "GRASSES".

GENERALIZED DISTRIBUTION OF DOMINANT C-STRATUM (HERB) SPECIES - VERMILION BURN

Figure 6.14 - Generalized 1999 distribution of dominant Gsttatum (herb) species in the Vermilion bum. In the legend, two codurninant species are separated by a hyphen ( - ), and a dominant and sub-dominant species are separated by a '@waterinan's@ ( > ).

Althwgh both fireweed and heartleaf arnica are still present and in fact quite common throughout the bum, they are no longer as dominant in the herbaceous layer as they were in 1972.

The presently dominant Gstratum species in the bum is unquestionably grouseberry, which is Discussh 116 actually not a herb, but a low shrub. Grousebeny dominates exclusively in the Storm Mountain grousebeny plant community (cluster 3), and is codominant or dominant over the entire southeast side of the Vermilion River Valley, the valley bottom, the lower eastern and southeastern slopes of

Mount Whyrnper, and except a few isolated patches (most notably around Altrude and Vista

Lakes), the entire northeast end of the bum. This pattem of dominance is similar to that reported in

Hams [I 9761 as being characteristic of a 55-year old fire succession stand.

Two separate patterns of gouseberry co-dominance are identified in the C-stratum: the first, in which grouseberry dominates with Canadian bunchberry, is found in large patches at middle elevations on the left side of the valley, and in large patches along the eastern slopes of

Mount Whymper and along the Vermilion River at the Continental Divide. Grousebeny co- dominates with twinflower and Canadian bunchbeny in large, contiguous areas along the valley bottom and up to middle elevations on both sides of the Vermilion River valley, as well as near the ignition point of the 1968 bum in the Tokkurn Creek valley, and over large pollions of the northeast end of the bum. Grousebeny is dominant over the significantly subdominant fireweed over large contiguous areas on the southeast slopes of the Vermilion River valley, especially on either side of the continental divide. This is a significant part of the area that was dominated by fireweed in 1972.

Those areas in which grousebeny is not dominant are covered by two distinct dominance patterm: the first is the diverse assemblage of avalanche track and subalpine meadow plant species, and has no clearly dominant C-stratum species (see the description of the subalpine meadows and avalanche tracks (cluster 2) plant community). The second pattem coven many of the areas that the Hams report mapped as being composed dominantly of 'grassesD in 1972, and is common at middle to high elevations on the slopes of Mount Whyrnper and the Stanley valley, Discussion 117 and on the steep south-facing slopes to the north of Albude Lakes, and in other isolated patches.

In these regions the co-dominant herbs are downy ryegrass and twinflower, common understory species in the open pinerbuffalobeny plant community (cluster 1 - see description above).

It can be concluded from discussion of figures 6.1 1 - 6.14, and comparison of these to the distribution maps found in Hanis [I 9761, that three decades of regeneration have resulted in considerable change to the vegetative composition of the Vermilion bum. In some areas, new species have established themselves and are now dominant - in others, the relative abundance of species to one another has changed, so that formerly dominant species are now less abundant.

Species have been recruited to re-establish in the large, previously unvegetated areas on the upper slopes of the Vermilion valley. Generally, the vegetation in the bum has become more abundant, as might well be expected. The patterns of distribution and dominance have become far more complex as various recolonizing species spread over the landscape to occupy their preferred sites, and to compete for increasingly limited space and resources.

6.2 - CRITIQUE OF THE MODEL The purpose of this section is to scrutinize the underpinnings of the modeling process used in this thesis, and to identify and discuss the consequences associated with potential sources of error or discrepancy in the model, and the robustness of model components. Discussion begins with the results of the maximum likelihood classifiion procedure, and works back through the process of modeling diagnostic species' distributions, the generation of the OEM and associated topographic variables, hierarchical cluster analysis, and the collection of field data

The error matrix for the maximum likelihood classification of the Vermilion bum into plant Discussion 118 communities is presented in table 5.4. This matrix calculates the accuracy of classification only at those points of known class value - in this case, at sample plot locations. The maximum likelihood classification algorithm is based on the assumption of normal distribution of sample plot (training site) data for each class. As has already been mentioned, the percent cover values at sample plot locations for diagnostic species by cluster are not normally distributed - they typically exhibit a mimimal degree of skewness (usually positive), and medegree of platykurtosis. This specific type of non-normality is expected to have little effect on the outcome of the classification procedure, as it is likely that percent cover values will be concentrated more closely around the cluster mean than the probability density function assumed by the maximum likelihood classif'mtion algorithm.

The accuracy of the classifition at other locations in the bum is dependent to a significant degree on the accuracy with which the distributions of the three diagnostic species have been modeled.

The accuracy of these modeled distributions is difficult to assess quantitatively, for two main reasons. First, the modeling method that was used in this thesis preserves the percentage values for diagnostic species at sample plot locations. Secondly, no sample plots were excluded from the data set for the purpose of checking the accuracy of the modeled distributions at control points (see section 4.5). However, the two separate phases of the process of modeling diagnostic species' distributions (regression and interpolation) can be examined qualitatively, and a general assessment of model accuracy can thus be obtained.

As has already been mentioned (see section 5.5), the W values that resulted from linear Discussion 119 regression of diagnostic species' percent cover values against the most highly correlated ancillary variables were low. Less than 25% of the variability in each diagnostic species' percent cover was explained by the best M regression line. If regression was to be used exclusively to model distributions, these R values would not be acceptable. The purpose of regression, however, was not to explain all of the variability in species' percentage values, but rather, was to quantify and model the general relationships that existed bebetwe the distribution of these species and some topographic site characteristic or characteristics. Despite low R values in each case, the regression analysis did identify sane significant and ecologically defensible trends in the data set - namely, that lodgepole pine tend generally to grow better at lower elevations and on more gradual slopes, that rusty menziesia generally grows best on gradual, north-facing slopes, and that grouseberry generally grows best on gradual, northwest-facing slopes. Better-fitting regression lines, and deeper insight into the preferred site condiiions for diagnostic species, may have been obtained from the inclusion of other ancillary variables, such as an orogenic precipitation index

[Peddle & Duguay, 19951, or a wind-related variable [Peddle & Duguay, 1995; Hiemstra, Liston, &

Reiners, 2000j. Some ancillary variables that were expected to demonstrate high correlation to species distribution, specffkally the curvature variables, proved to be of little use in predicting species' percent cover values at sample plot locations. Possible explanations for the inability of curvature variables to increase model accuracies include locational errors (see below), problems in the way that curvature was calculated, and the inappropriate use of curvature values as a surrogate for soil moisture variabilii in the landscape.

The regression residual surfaces interpolated using ordinary Kriging (see section 5.5) appear to have local maxima or minima at sample plot locations (see figures 5.14 - 5.16) -this is a Discussion 120 commonly observed trait of kriged surfaces, but is less pronounced when using this method than it is when using other interpolation procedures (Mi& Miiva, 1999). In the model described here, the potentially ecologically spurious result is a tendency of plant community distributions to be centred on sample plot locations. This observed characteristic of the interpolated residual surfaces may be related to the choice of spatial dependence model in each case. Theoretically, kriging that uses a spatial dependence model with a short range and high sill (i.e. in which variability increases dramatically over a short initial increase in distance, quickly attaining a high degree of distance-independent spatial variability - see figure 4.4) will be more likely to interpolate a surface in which each sample plot location is a local maximum or minimum.

The overall error associated with the maximum likelihood classification, reported in the emmatrix (table 5.4), is 18.43%, meaning that 40 of 21 7 plots were not classified in the same cluster into which they were placed by the cluster analysis procedure. The kappa index of agreement is 0.788, which means that the maximum likelihood classifier produced results that were

79% better than those that would have been generated by a random assignment of plant communities to pixels within the bum. There exists an apparent pattern to the erroneously classified sample plots -they appear generally to have been placed in plant communities to which they were closely related in either the separability analysis (figures 5.4 - 5.9) or the cluster analysis dendrograrn (figure 5.1). This pattem calls into question the clarity of distinction between different clusters generated in through cluster analysis. Davis [I9861 points out a tendency, in cluster analysis algorithms, for variables with larger absolute values (e.g. magnitude of 100) to be weighted more heavily in the cluster allocation pmcess than variables with smaller absolute values

(e.g. magnitude of 0.01). This means that species that are more common and abundant within the Discussion 121 bum (like tree species) will have more influence on cluster association than those that are more rare and sparse (like uncommon 3- or C-stratum species) - essentially, that clusters' memberships are established based more on dominant species than less dominant ones. In any case, it seems probable, from analysis of the error matrix and based on the properties of the cluster analysis procedure described above, that those sample plots repocted to have been erroneously classified were likely outliers in the cluster to which they were assigned in the cluster analysis, possibly ambivalent between membership in two or more different clusters.

There is a potential for error in the methods that were used to assess percent cover of plant species in the field. A-Stratum species' percent cover values were determined by estimating the average crown area of a representative tree in the sample plot, and then multiplying this average crown area by the total number of trees. 6 and GStratum percent cover value was simply estimated based on the percent of the sample plot (or subplot) that was covered by a given species. It is difficult to assess the margin of enor associated with such an approach, but it can generally stated that these were, to some degree, subjective estimates of percent cover values - their inaccuracy is dependent largely on the diligence and consistency of the estimator. It is not likely that these inaccuracies would lead to the placement of sample plots in the wrong cluster, as the cluster analysis procedure assessed similarity between cases in 84 dimensions, and clusters may have been differentiable based on the presence or absence of any number of species. In any case, the lines that are drawn to delineate the boundary between one naturally mmngplant community and the next are most often blurry, and ambivalence between membership in two or more clusters is inevitable for those sample plots that are located in transitional areas.

One last source of error that needs to be addressed is the potential for inaccurate location Discussion 122 of plots. Only one sample plot was excluded from analysis due to obvious and severe mislocation

(see figure 5.2). However, sample plots were located using a handheld GPS receiver, and data was collected in the summer of 1999, prior to the removal of selective availability (SA), the intentional misrepresentation of location. imposed on GPS signals by the US rnilitaly in the interest of national security. SA decreased the locational accuracy of the GPS being used to locate plots by up to 100m - that is, the recorded location of a sample plot could diier from its actual location on the ground by as much as lOOm (4 pixels on the GIs image) in any direction. A concerted effort was made to obtain the most accurate GPS reading possible - locations were checked against features on the 1:20,000 scale TRIM map used in the field. For ancilbry data variables, which were spotted using the digitized sample plot locations, this could result in erroneous values being recorded at mislocated sample plots, and hence, poor or spurious ancillary variable correlations to sample plot data. This is particularly true of ancillary data that vary greatly over short distances, such as the down-slope and cross-slope curvature variables calculated using Terra Firma (see figures 9.6 and 9.7 in Appendix 7).

6.3 - SUGGESTIONS FOR FURTHER RESEARCH The following are a number of suggestions for future research, based on observations in the field, acknowledged opportunities for improvement of the modeling process employed in this thesis, and a general interest in the improved description and documentation of the role of fire in shaping natural landscapes.

First and foremost, it is recommended that a study of the vegetative regeneration of the

Vermilion bum, similar in scope and objectives to this study and to the vegetation portion of the

1976 Hams study, be undertaken no longer than thirty years after the date of this thesis. The goal Discussion 123 of continually revisiting the same bum at regular intervals is to provide a record of regeneration through the complete course of the fire cycle in Vermilion Pass. In an area whose average fire return interval is 130 years [Johnson & Mianishi, 1991], vegetation studies conducted at thirty year intervals would produce only four (on average) 'tine slices" of the constantly changing and dynamics of fire cycle vegetation.

The effects of fire are very specific to the landscape in which the fire occurs, the conditions and qualities of the fire itself, and to the influence local and regional climate [Johnson 8 Miianishi,

19911. Comparing the vegetation and regeneration patterns in other burned areas in the subalpine ecoregion may provide a means by which to quantify this specificity, and to establish the regional- level site factors governing regeneration. It may also be beneficial to undertake a comparative study that models the regeneration of plant communities in a similarly situated burned area that has been subject to more rigorous land management and forest engineering practices.

As far as the Vermilion bum is concerned, there are some q~estionsthat the impositions ernplaced by time and resources have left unanswered. Most obvious is the observation that this model seems to have grouped avalanche tracks and high subalpine meadows, two very distinct environments, in the same plant community. As part of the Hank (19761 report, K.M. Winterbottom

[I 9741 described the effects of fire on avalanche tracks, and mapped out the vegetation in the avalanche tracks on the main slopes of Mount Whymper. The model described in this thesis would be considerably improved if a more detailed analysis of the vegetation in the avalanche tracks of the bum, and of the differentiation between avalanche tracks and meadows, were to be undertaken. Due to the highly heterogeneous nature of vegetation in these areas, a higher field sampling density would be required for such a modeling exercise. Discussion 124 Harris [I 9761 and Winterbottom [I 974) reported that the fire had the effect of lengthening and in some cases, widening the existing avalanche tracks within the Vermilion bum. However, casual observations in the field, specifically of the lateral extent of the tracks before the fire (as determined by the presence of snags - standing dead trees), compared to the present width of tracks, suggest that the avalanche tracks are narrowing. These areas are being encroached upon by seedling and juvenile tree, mostly Engelmann spruce and subalpine fir (limber pine at higher elevations), some of which had obviously stood up to frequent avalanches, judging by their shape and scars on the uphill side of trunks The question arising from this observation which begs to be answered is: does fire create condiiions whiih lead to the longer-term stabilization of avalanche tracks?

This thesis did not provide the opportunity to consider a number of factors that most likely play a significant role in the regeneration of burned landscapes. Potential improvements on this modeling approach include: - Incorporation of othur ancillary variables: This thesis has examined the effects of certain terrain-related ancillary variables on the regenerative distribution of vegetation in a burned area.

Ancillary variables to consider were selected based on availability, greatest anticipated influence on distribution of vegetation, and time and resource constraints. Other potential ancillary data to consider are effects of wind and snow redistribution [Hiemstm, Liston, & Reinen, 20001, the calculation of an orogenic precipitation index [Peddle & Duguay, 19951, and the inclusion of soil and lithology data. Wth improved location accuracy, the cuwature indices calculated in this thesis might provide more useful results, and are thus worthy of reconsideration. - Informdon wingcomposition d surrounding matun torwt: Time was not available Discussion 125 during the field season or in the course of analysis to include in the modeling process information regarding surrounding mature forest types and their composition. Most species that regenerate within a bum do so by the transport of seeds, usually by wind from a source (mature plant) into the clearing created by fire [Greene & Johnson, 19961. It would therefore seem likely that the makeup of the forest adjacent to a recently burned area would play a considerable role in the distribution and regeneration of plants within that area. This data could be included through field sampling, satellite image analysis, or alternatively, through inference from time-since fire maps of the bum and surrounding area, if such maps exist (As of 1991, there were no time-since fire maps for

Kootenay National Park [Johnson & Miyanishi, 19911). - The impact of snags on regemration: This may at first seem an unlikely factor to influence the regeneration of vegetation within a bum, but its inclusion here stems from numerous field obsenrations of tree seedlings of various species growing in the shelter of snags (standing dead trees from the pre-fire mature forest). Snags have the potential to provide shade and shelter from the elements, as well as to act as 'snow fences" and create drifts, allowing for increased soil moisture during the spring thaw. Snags also provide important habitat for many species of wildlife, especially birds, which play an important role in the dispersal of seeds for many plant species

[Gadd, 19953. The number of snags on a given sample plot could easily be recorded and added to the spatial data base as a potential factor influencing regeneration.

If Parks Canada is to fulfill its mandate of prese~ngecological integrity, and if the natural effects of fire are to be considered in the evolution of more ecologically sensitive and holistic forest land management strategies, then a concerted effolt must be made on the part of scientists of all kinds - ecologists, biologists, forest engineers, and biogeographers - to provide land manages Discussrbn 126 with the took and the knowledge required to better understand the effects of fire on the landscape. and the importance of fire to the maintenance of ecological balance. 127 CHAPTER 7 - CONCLUSION The purpose of this thesis has been to generate a descriptive model of the vegetative response over the past three decades to a fire that burned 2,430ha of mature spruce and fir forest in Vermilion Pass in July, 1968. The Vermilion bum is unique due to its size, one of the largest fires in the subalpine ecoregion of the southern Canadian Rocky Mountains in the past half century. It is also unique for its location in two of Canada's National Parks, affording it the opportunity to regenerate naturally, and with minimal human intenrention.

The modeled distribution and composition of plant communities was produced by combining an assortment of methods from various fields - ecology, biogeography and landscape modeling, Geostatistics and GIs, and remote sensing.

Vegetation and site characteristic data from 218 sample plots were collected during the summer of 1999 from the Vermilion bum, and subsequently entered into a spreadsheet.

Hierarchical cluster analysis was then used to group vegetation data from similarly composed sample plots into ten distinct clusters or plant communities.

A point coverage was created in a GIs from GPSobtained locations of sample plots. A digital elevation model (DEM) of the study area was obtained, and a series of seven terrain-related ancillary layers were derived from the DEM - measures of slope, solar and cross-valley aspect, solar and cross-valley slope-aspect indices, and cross-slope and down-slope curvature values were all generated. The values for each of these variables were than spotted a sample plot locations in the bum, and added to the thematic data set.

Next, three diagnostic species, lodgepole pine, rusty menziesia, and grouseberry, were Conclusr'on 128 selected, to be used to model the distribution of plant communities in the bum. Diagnostic species were selected based on the following criteria, which were evaluated sequentially: (a) species had to be ubiquitous or near ubiquitous within the bum; (b) species' had to demonstrate statistically signif'mnt correlation to at least one of the DEMderived ancillary variables; and (c) species percent cover values within and between individual pMcommunities (i.e. variance and covariance) had to be such that they enabled or improved the separability of at least one plant community or group of plant communities from all others.

Once selected, continuous distributions were modeled for each of the three diagnostic species percent cover within the bum, through a three step process. First, a general distribution of the species was modeled from the regression of species' percent cover values against the most highly correlated ancillary variables at sample plot locations. Next, a continuous surface was interpolated from the regression residuals at sample plot locations using a rigorous geostatistical process called ordinary kriging. Finally, the generalized distribution (regression) layer and the interpolated residual layer were additiiely combined to produce a modeled distribution map for each diagnostic species.

The three modeled percent cover distribution maps were then used as input channels for a maximum likelihood classifition procedure, which assigned a plant community identifier value

(between 1 and 10) to each cell in the raster grid, representing 25mX25m on the ground, according to the percent cover values for each of the three diagnostic species in that pixel.

The modeled distribution of the 10 plant communities was used to assign an appropriate name to each plant community, based on its vegetative composition, site preference, and speclic Conclusrbn 129 location in the bum. Separate generalized distribution maps were created for lodgepole pine, combined Engelmann spruce and subalpine fir, and dominant species in the 8-stratum and C- stratum. These generalized distribution maps were used to compare current vegetative regeneration in the Vermilion bum to the plant communily distribution that was present immediately after the 1968 fire, as identified and mapped by Hams [I 9761.

This thesis is directed towards the goal of improving the understanding of the spatial dynamics of vegetative regeneration in a natural ecosystem from the effects of fire. It is anticipated that this will be the second in a series of studies of the vegetation of the Vermilion bum, aimed at acquiring a full record of the fire cycle in this particular location. References 130 REFERENCES

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Bailey, Robert G., 1996; Ecosystem Geography; Springer-Verlag Inc., New Yo& New York, USA.

Banett, Neb E., & Niering, William A., 1993; mdal Mash Restontion: Trends in Vegetation Change Using a Geographical Information System (GIs); in Restomtion Ecology, March 1993, pp. 18-28; Society for Ecological Restoration.

Bessie, W.C., & Johnson. E.A., 1995; The Relative lmpotfance of Fuek and Weather on fire Behaviour h Subalpine Forestq in Ecology, vol.76 no.3, pp.747-762; Ecological Society of America.

Bolliger, P., & Schemer, H., 1993; Vegetation Mapping with Aerial Photos and GIS; in Anthas, February 1993, pp.22-25.

Burrough, Peter A., 8 McDonnell, Rachael A., 1991 ; Pnnc@lesof Geographicallnfomation System; Oxford University Press, Inc., New York, New York, USA.

Carlson, Clinton E., Amo, Stephen F., & Menakis, James, 1990; Hybrid larch ofthe Carlton Ridge Research Natural Area in western Montana; in Natural Areas Journal, vol. 10 no. 3; pp.134- 139.

Cissel, John H., Swanson, Frederick J., & Weisberg, Peter J., 1999; Landscape Management Using HistoricalFke Regimes: Blue River, Oregon; in Ecdogia/Applicafions, vo1.9 no.4, pp.1217-1231.

Connack, R.G.H., 1953; A Survey of Conifemus Forest Succession h ihe Eastern Rockies; in Forest Chmnides, vol.29, pp.218-232.

Davis. John C., 1986; Statistics and Defa Analysis in Geology; John W~ley8 Sons; New Yo* 1986. References 131 Eastman, J. Ronald, 1999; IDRlS/ 32 - Guide to GIs and lmage Processing, vols. 1&2; Clark Labs, Clark University, Worcester, Massachusetts, USA.

Eyton, J. Ronald, 1991 ; Rate-of-Change Maps; in Carfognphy and Geographic lnfomation Systems, vol. 18 n0.2, p.87.

Fall, Patricia L., 1997; FKe history and composition of the subalpine forest of western Colorado dunig the UoIoc8ne; in Journal of Biogeography, vol.24 no.3, pp.309-325; Blackwell Science Ltd., Oxford, UK.

Gadd, Ben, 1995; Handbook of the Canadian Rockies, Carax Press, Jasper, Alberta, Canada.

Gwvaerts, Pierre, 1997; Geostatistks for Natural Resuumes Evaluatioq Oxford University Press, Inc., New York, New York, USA.

Greene, D.F., 8 Johnson, E. A., 1995; Long-Distance Wnd Dispersal of Tres Seeds; in Canadian Joumal of Botany, vol.73, pp. 1036-1 045.

Greene, D.F., 8 Johnson, E.A., 1996; Wnd Dispersal of Seeds from a Forest into a Clearing, in Ecology, vol.77 no.2, pp.595-609; Ecological Society of America.

Hale, Jr., M.E., d Culberson, W.L., 1970; A Fourth Checklist for the Lidrens of the Continental United Sfafesand Canada; in BryoIogy, vol.73, pp.499-543.

Hallworth, Beryl, & Chinnappa, Chendanda Chengappa, 1997; Plants of Kananaskjs Country in the Rocky Mountains of Albeda; University of Calgary Press, Alberta, Canada. Hams, Stuart A,, 1976; The Vennillion Pass Fire - The First Seven Years; Harris Environmental Research Ltd., Calgary, Alberta, Canada.

Hawksworth, D.L., James, P.W., 8 Coppins, B.J., 1980; Checklist of Brilish Lkhen-forming, Lchenicokus and Allied Fungk in Lkhenology, vol. 12 no. 1; p. 11 5.

Horton, K.W., 1956; 7718 8~0Iogyof Lodgepole Pine h Alberta and its Role in Succession; Technical Note no.45; Canadian Department of Northern Affairs and Natural Resources, Forestry Branch. Refemnces 132 Johnson, Edward A., & Larsen, C.P.S. 1991 ; Climatkally Induced Change in Fire Frequency in the Southern Canadian Rucbs; in Edlogy, vo1.72 no.1, pp.194-201; Ecological Society of America.

Johnson, Edward A., & Miyanishi, Kiyoko, 1991; Fre and Population Dynamks of Lodgepole Pine and Engelmann Spruce Forests in the Southern Canadian Rockieq in Cbniferous Forest EcoIogy hman International Perspective, pp.77-91; SPB Academic Publishing, The Hague, The Netherlands.

Johnson, E.A., & Van Wagner, C.E., 1984; The Theory and Use of TWOFire History Models; in the Canadian Joumal of Forest Research, vol. 15, pp.214-220. Johnston, R. J., 1980; Multtvariate Statistical Analysis in Geography - A Primer on the General Linear Modet Longman Scientific and TechnicaVJohn Wiley and Sons, Inc., New York, New York, USA.

Kenhaw, Linda. MacKinnon, Andrew, 8 Pojar, Jim, 1998; Plants of the Rocky Mountains Lone Pine Publishing, Edmonton, Alberta, Canada.

Kushla, J.D., & Ripple, W.J., 1997; Tha Role of Temin in a Fire Mwk of a Tempente Conferous Forest in Forest Ecology and Management, vol.95 no.2, pp.97-107.

La Roi, George H., Strong, Wayne L. & Pluth, Donald J., 1988; Understory Plant Community Classifications as Predictors d Forest Site Quality for Lodgepole Pine and White Spruce h West-Central Alberta; in the Canadian Journal of Forest Research, vo1.18, pp.875-887.

Levin, Jack, & Fox, James Alan, 1994; Elementary Statistics h Social Research (6m Ed.); HarperCollins College Publishing, New York, New York, USA.

Lillesand, Thomas M., & Kiefer, Ralph W., 1994; Remote Sensing and lmage interpretation (3d Ed.); John Wiley & Sons, Inc., New York, New York, USA.

Liston, G.E., 8 Sturm, M., 1998; A Snow-Transport Model for Complex Temiq in Journal of Glaciology, vol.44 no. 148, pp.498-5 16.

Masters, A.M.; 1990, Tempotal and Spatial Change h Forest Fire History of Kootenay National Park, Canadian Rockies; in Canadian Journal of Botany, voI.68, pp.1763-1767. References 133 Mitas, 1,& Miasova, H., 1999; Spatial Interpotation; in Geogmphkal Infomation Systems. vol.1 (2nd Ed., Longley, Paul A., Goodchild, Michael F., Maguire, David J., 6. Rhind, David W., Editors) ;chapter 34, pp.481-492; John Wiley & Sons, Inc., New Yo&, New York, USA.

Oksanen, J, 8 Minchin, P.R., 1997; Instabilityof Oldnation Resuls Under Changes in Input Data Order: Explanations and Remedies; in Joumal of Vegetation Science, vol.8, pp.447-454.

Parks Canada, 2000; Kootenay National Park of Canada Management PIanl Minister of Public Works and Government Services Canada.

Peddle, Derek R., & Duguay, Claude R., 1995; Incorporating Topographic and Climatic GIs Data into Satellite Image Analysis of an Abine Tundra EcosystempFmnt Range, Colorado Rocky Mountains in Gemrto international, vol. 10 no.4, pp.43-60; Geocarto International Centre, Hong Kong.

Renkin, R.A., & Despain, D.G., 1992; Fuel MoisturepFoest Tjpe, and Lightning-CausedEre in Yellowstone National Park; in Canadian Joumal of Forest Research, v01.22, pp.37-45.

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Schimmel, Johnny, & Granstrom, Anders, 1996; Fire Sevenfy and Vegetation Response k the Boreal Swedish Forest in Ecology, vol.77 no.5, pp.1436-1450; Ecological Society of America.

SPSS version 9.0.0 User's Manual, 1998; SPSS Inc., December 1998.

Steel, Robert G.D., Torrie, James H., 8 Dickey, David A., 1997; Pn'nciples and Procedures of Statistics - A BiometricaiApproach (3d Ed.); WCBIMcGraw-Hill, New York, New York USA. Strong, W.L., & Leggat, K.R., 1981; Ecoregrbns of Alberta, ENR Technical Report no.T/4; Province of Alberta, Department of Energy and Natural Resources, Office of Resource Information Se~ces,Edmonton, Alberta, Canada. References 134 Tappeiner, U., Tasser, E., & Tappeiner, G., 1998; Mudelling Vegetation Patterns Using Natumland Anthmpogenic lntluence Factors: Preliminary Experienoe with a GIs Based Model AppliM to an Alpine Area; in Ecological Modekng, ~01.113, pp.225-237; Elsevier Science.

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INTERNET REFERENCES

Hiemstra, Christopher A., Liston. Glen E.. & Aeiners, William A., 2000; Wind Redistribution of Snow at Treeline in ihe Medicine Bow Mountains of Wjmming; from proceedings of the 4th International Conference on Integrating GIs and Environmental Modeling (GISJEM4): Problems, Prospects and Research Needs; Banff, Alberta, Canada, September 2 - 8,2000. URL: http.Jl~~~.colorado.eddre~8a~cire~anff/up~~525~ ITIS'ca, 2001 ; - Integrated Taxonomic Information System (Canadian version) - Taxon based biological information system; Government of Canada, Department of Agriculture and Agri- References 135 food Canada URL: http'llsis.agr.gc.calpIsTrti~aget?pPPrfx=aafc

Lane, David M.. 2001 ;Hyperstat Online Statistics Text Book; David M. lane 1993-2001. URL: httplldavidmlane.com/hyperstat/

Lister, Andrew, Riemann, Rachel, & Hoppus, Michael, 2000; Use of Regression and Geostatistiml Techniques to Predict Tree Species Distribufibns at Regional Scales; from proceedings of the 4th International Conference on Integrating GIs and Environmental Modeling (GISlEM4): Prcblems, Prospects and Research Needs; Banff, Alberta, Canada, September 2 - 8,2000. URL: h~l/~~~..colorado.edulre~8a1~h/cire&anff/upl~d218/ Appendix I 136 APPENDIX 1 - DATA AND SOFIWARE USED IN THIS THESIS

DIGITAL DATA

PROGRAM VERSION 0 APPLICATION Arclnfo GIs 8.1 ESRI,Inc.,1992-2000 vector-based GIs analysis ArcView GIs 3.2a ESRI, lnc., 1992-2000 vector-based GIs analysis GStat - GNUIEdzer Pebesma, 1999 geostatistical analysis' IDRIS132 GIs 132.1 1 Clark Labs, 1987-2000 raster-based GIs analysis PC1 7.0 PC1 Geomatics Inc., 1999 satellite image manipulation and analysis SPSS 9.0.0 SPSS Inc., 1989-1999 statisticsl analysis Terra Firma - J.R.Eyton,1991 generation of curvature indices

* The GStat sofhvare is htegrated into IDRISI32 GIs. Appendix 2 137 APPEND(X 2 - MODEL FLOW CHART

1.Data I I CIuS1er Analysis [cleaning of Field L 1 I Derivation of * Anallary Sunrival Stands. and Topgraphical Variables

Variables

used directly to model

Selection of A-stratum Lodgepole Pine. Rusty Menziesia. and Grouseberry as Diagnostic Species

+ I Regression Regression Analysis Against -0 Residual Point Analysis Against -# Residual Point Analysis Against + Residual Point Ancillary Variables

------...... ------_ I led Oistribution of ( 1 Modeled Dishibution of 1 odel led M'mutim of 1 1

1 MODELED OlSTRlBUTlON Of PLANT COMMUNITIES 1 I OF THE VERMIUON BURN I Appendix 3 138 APPENDIX 3 - CARTOGRAPHIC MODEL DERIVATION OF DEM AND ANCILLARY TOPOGRAPHICAL VARIABLES

BC TRlM 82N.030

+ I slopegrid as~ectgtid ARClDRlS gridaxii+ command in command in command in command in Arclnfo Arclnfo fDRlSl Arclnfo v f 9 [wcupclgld] p-w-m)-- f ARClDRlS ARClDRlS Down-Slope Calculation ' Cross-Slope Calculation command in command in in ACONVO.ME in ACONVO.EXE IDRlSl IDRlSl module of *Terra Firma module of Terra Finna

conversion to BILIDRIS BILfDRIS radians using Image command in Calculator in IDfllSl . IDRlSl IDRlSl

aspect. st > 130 ? NO aspecterst> 180 ?

+-llmage Calculator in lmage Calculator in Image Calculator in el calculation

------Image Claculator In Idrisi: -4 sin(abperad.mJ masp~xv.nt1

(MD) - denotes procedures camed out by Medina Deuling. Appendix 3 139 DELINEATION OF BURN FROM SATELLITE IMAGERY AND CREATION OF SAMPLE PLOT LAYERS

IRS Imagery GPS Locations and 8C TRIM Planimetric B2NID1 h 82W ID'S of Sample Plots Data for Study Area

studyareainPCI(~o) in Arclnfo C -- sunphplotsvecturpoint Deiiiation of Bum. Landsat TM5 Imagey in Avalanche Tracks, and of Study Area * h Mature Stands within ARClDRlS the Bum, in PC1 command in IDRISI , Ilmpart tines into Anlnfo

Create Potygon Topology Usbtg Build and Clean POINTRAS Commands in Archfo command in IDRlSl Classinof Potygom &Mature Forest. 1 =Regenerating Bum, 2=Avalanche Tracks Wthin Bum fie: vegmask-rst afeature dehifile: sarnpleplob.rst ,I

ARCIDRIS 1command in 1 I Iremove sample pbt1 ample plot

ASSIGN command in of Feld Data Set

w [RECUSS command in IDRISI: I RECLASS command in IDRISI: 0 ta (x-lH, -1, (x+l) to 10=0 Appendix 3 140 MODELING DISTRIBUTIONS OF DIAGNOSTIC SPECIES AND PLANT COMMUNITIES

Isd-aJr.mt sri_nr.nt f * Image Calculator in IDRISI: Image Calculator in IDRISI: Image Calculator in IDRISI: 144.4 - O.OG(basemap.rst) - 0.504(sbpe.rst) 26.7 - 0.368(sai-slr.rst) 11.3 - 0.1 13(sai-xv-rst) I I I

C Save Regression Save Regression Save Regression Residuafs Residuals Residuals

ASSIGN ASSIGN ASSIGN Command Command Command in IDRlSl+ in IDRlSl in IDRlSl 1 residwlwllP.rst1 re~idw~rb.rst( + + + Calculation of Calculation of Calculation of Experimental Experimental Experimentat Semivariograrn in Semivarbgram in Semivariogram in

(I Generation of Generation of rrgnr-~p-nt( Spatial Dependence Spatial Dependence Spatial Dependence Madel in Model in

Ordinary Kriging Ordinary Kriging Ordinary Kriging Interpolation in Interpolationin v Interpalation in GstatnDFllSl I-*** 1 GstaUlDRlSl

[krigrarld-lllod- v OVERLAY (addiin) OVERLAY (addition) Command in IDRlSl Command in IDRlSl Command in lDRlSl > ~tmemask.rstI I-mI ma

OVERLAY (mulikation) OVERLAY (rnuliilicatian) OVERLAY (mulitplication) 4-- Command in IDRlSl Command m IDRlSl Command in IDRlSl *

mrrbn~dal-mmt ma~moc~_glb.rstl I I I 1 MAKESIG Command in 1 IDRISI - Assigns Diagnostic Species' 1-1 b-1 Percent Cover Values to siz

Appendix 5 142 APPENDIX 5 - COMPLETE UST OF PLANT SPECIES, VERMIUON BURN The following tables list the vernacular and scientific names of all species found within the

Vermilion bum. Species taxa have been verified using the Government of Canada's Integrated

Taxonomic Information System [ITISa, 200q for all plants, and Hale & Culbenon [I 9701 and

Hawksworth et al. [I9801 for all lichen species. Species' scientific names are alphabetized (in the right column), and are listed only in the first stratum in which they occur - for example, subalpine larch, a species of tree, is listed in the second table with 6-stratum (shrub) species, since there are no subalpine larch within the bum that are taller than 3m.

A-STRATUM (TREE) SPECIES VERNACULAR NAME SCIENTIFIC NAME AND AUTHORITY subalpine fir Abies lasiocarpa var. lasimtpa (Hwk.) Nutt. Engelmann spruce Picea engelmmii Parry ex Engelm. lodgepole pine Pinus contorta Dougl. ex Loud. limber pine Pinus flexilis James quaking aspen Populus tremuloides Mihx.

B-STRATUM------(SHRUB1------SPECIES------

VERNACULAR- - - ~- NAME 1 SClENnFlC. - NAME-- AND- ---- AUTHORITY------I Douglas maple ~cerglabnm war. douglasii (Hook.) Dippel mountain alder Atnus viridis ssp. cispa (Aii.) Turrill Saskatwn servicebeny I Amelanchier alnifolia (Nutt.) Nutt. ex M. Roemer I dwarf birch Betula nana L. shrubbv cinauefoil Dasiohom flonbunda (Pursh) Kartesz. comb. nov. ined. common juniper I Juniperus communis L. 1 subalpine larch Larix lyallii Pad. bearbeny honeysuckle Lonicefa involucrata Banks ex Spreng. rusty menziesia Men2iesia femginea Sm. white rhododendron Rhododendron albiflomm Hook. Canadian gooseberry I Ribs oxyacandhoides L. I common red raspberry Rubus idbeus L. undetermined-. willow s~ecies Salk L. rock willow I Salk vestita Pursh red elderberry I Sambucus racemasa var. racemosa L. I russet buffaloberry Shepherdia canadensis (L.) Nutt. Greene's mountain ash Sorbus seo~ulinaGreene Appendix 5 143 GSTRATUM (HERB) SPECIES VERNACULAR NAME SCIENTIFIC NAME AND AUTHORITY common yarrow Achillea mil(efo/ium w.aborh (Rydb.) Garrett nodding onion Allium cemuum Roth pearly everlasting Anaphalis margatitacea (L.) Benth. Drummond's anemone Anemone drummondii S. Wats. smaltflowered anemone Anemone pamora Mihx. raceme pussytoes Antennaria racemow Hook. yellow columbine Aquik~hflavescens S. Wats. Kinnikinnick Arctostaphyb uva-ursi (L.) Spreng. heartleaf amica Arnica cordifolia Hook. showy aster Aster conspicuus Lindl. Barbilophozia Barbilophozh lywpodioides (Wallr.) Loeske Haller's campylium moss Campylium halleri (Hedw.) Lindb. undetermined sedge species Carex L. scarlet Indian paintbrush CastdIeja miniata Dougl. ex Hook. , western Indian paintbrush Castille~identalkTon. ribbed pixie lichen Cladonia &ma (Ach.) Spreng. pixie cup lichen Cladonia chlorophaea (FI6rke ex Sommerf.) Spreng. greater pixie stick lichen Cladania comuta (L.) Hofhn. orange-faoted pixie lichen Ckdonia mocyna (Ach.) Nyl. sulphur pixie cup lichen Cladonia sulphurina (Michx.) Fr. Canadian bunchbeny Comus canadensis L. dicranoweisia moss Dkranoweisia cnspula (Hedw.) Lindb. ex Milde Fireweed Epibbium angustifolium ssp. angustifolium L. western horsetail EquEsetum arvense L. aspen fleabane Engeron speciosus (Lindl.) DC. red fescue Festuca mbra L. wild strawberry Fngana virginiana Duchesne annual gentian Gentianelk amarella (L.) Boerner white sweetvetch Haiysamm sulphumscens Rydb. houndstongue hawkweed Hieracium cynugIossoides AN.-Touv. slender hawkweed Hiencium gracile Hook. bog Labrador tea Ledum gtvenlandkum Oeder downy ryegrass Leymus innovatus (Beal) Pilger Twinflower Linnaea borealis 1. smallflowered woodrush Luzula pandlora ssp. paMlora (Ehrh.) Desv. stiff club moss Lycopodium annotinum L. Groundcedar Lympodium cwtplanaturn L. starburst lichen PamreIEopsis ambigua (Wutfen) N y I. fringed grass of Pamassus Pamask fimbriata var. fimMata Koenig bracted lousewort Pedkularis bracteosa Benth. rr------

I VERNACULARNAME 1 SClENTlFlC NAME AND AUTHORITY-- - 1 freckle-pelt lichen Peltiqeta aphdhusa dog pelt lichen Peitigeta canina fuzzytongue penstemon Penstemon enanthems Pursh pink mountainheath PhylEodoc8 empetriform& (Sm.) D. Don Kentuckv bluearass Poa oratensis L I Pohria nutans (Hedw.) Lindb. I I juniper polytrichum moss I Po~humjunipetinum Hedw. I sockeye psora lichen Psora dixipiens knights plume moss Ptilium crbta-castrensk (Hedw.) De Not. undetermined winterareen s~ecies PvroIa L. - - - pricklyTe Rasa ackulad Lindl. arctic blackberry Rubus amusL. spotted saxifrage Saxifiaga bronchialis L. lanceleaf stonecrop Sedum lanc6olatum Tom.

alpine aroundsel- - Senecio paucfioms Pursh I Rocky Mountain goldenrod &/idagdmu1tiradiafa Kt. white spirea Spiraea betulifol. Pallas western featherbells Stenanthiurn occiidentale Gray fewflower meadowrue Tha/kt~msparsiflorum Turn. ex Fisch. 8 C.A. Mey. Grousebem Vacdnium man'urn bib. ex Coville I Valeriana sitchensis Bong. I mountain deathcamas 1 Zgadenus elegans Push Appendk 6 145 APPENMX 6 - PLANT SPECIES COMPOSITION BY CLUSTER

The chart on the following ten pages breaks down the vegetative composition of sample plots by cluster. The headings along the top row of each page refer to the cluster being described, and the headings along the left margin of each page show the constancy range to whidr each species belongs. Constancy is a measure of frequency of occurrence within a given cluster. For example, if a given species occurs in 7 of the 10 sample plots in a given cluster, then the measure of constancy is 70%. Species in each constancy range are listed (vernacular and scientific names) by stratum, and in descending order of dominance within each stratum. Species percentages, to the right of each respective species, have been averaged over only those sample plots in which the species was present -this approach provides the reader with a better idea of the relative abundance of a given species in those areas in which it was found.

The three species that were identified as index species for modelling cluster distributions

(see chapter 5, section 5.4) appear in bold face. downy ryegrass [Leymus rirmlus] 7.5% twinflower [tinnaea hmalis] 4.5% $ st~~~vyaster[~er~us] 23% wild strawbeny [Fagaria vimiana ] 20%

&GStrata lodgepde pine [Pinus contorts ] 3.6% russet buffaloberry [Sh@m@ anadlensis] 9.W Rocky Mountain gddenrod [W&p muIb'ta&ta 1 1.7% wilb species [Salkspp .I 5.8% $ fireweed [Epilobiumangusfifdium ] 1.6% wild strawbeny [Fmgnir virgniana ] 3.39, K fireweed [Epilobium anguMdium ] 249,

priddy rc~S8[Rosa ackuhtis] 1.5% BtGStrata Engelrnann spruce [PE'aba mg3Imanniij 3.1 9, alpine gmunw [smciops~~~l 0.9% alpine gmuml~el[~emio pauciflonrs] 1 h% .a

willow species [Salkspp. j 6.4% twinflower [~mmhrealis] 6.8'3 WStrata Engelmann spnree [P~Mengelmmnii] 29% downy ryegrass [Leynrus inmhrs] 27% Saskatoon servicebeny [Amelandier alnifdh ] 23% common yam[Achilka m~lkbliurn] 1.3'3 " gmurony [~ac~ikturnsapmiurn ] 3.8% common yam[Adrr~llea millefdium ] 0.6% ribbed pixie lichen (Oiacarba] 1.2% ASbltum hbepoh pino [ffnus CO~W~] 5.m heartleaf arnica [Arnica ~~dia1 0.8% Canadian goosebeny [Riboxyac%fnfh~] 4.m I WStrata lodsepole pine [Pinus contoda] 4.5% shrubby anpuefoil [Dasiphora fibnbunda] 4.B dicrammoss [Dimno- crispula ] 22?4 prickly rose [Rosa aciculansI 21% Rocky Mountain goldenrod [Sdidap multrndiita] 2m muumain deathcamas (Zigdmus elegensl 1.1% common juniper [&nipems mmunis1 2696 common iuniper [Junims mmunisl 3.9% Canadian bunchberry [Comus umdmis] 29% B+GSirata limber pine (Pinus WIk] 27% fewflower meadowrue [Thalidnrm sparsib~m] 22% kinnikinnick [Ardm&@y& w~] 5.m $ mountain dealhcamas [Zjgad8nus ebgms] 0.996 gmmdmy [Vacddrn 8cOmw 3.5% Si scarlet Indian pahtbrush [CaMlq& minhta] 20% sedsespecies~~~spp.1 1.a yellow columbine [Aquiwa %wescms] 0.991 fewflower meadowrue [Thalicfrum spaWk?rurn] 0.6% k6 147 CLUSTER 1 2 &Stratum Engelmann spruce [Pbaengdm~ns'] 14.3% rack wibw [Shbx w&a ] 120% fusty mmrkrb [Umdrrb hmghw ] 5.01 dwarf birch [8sW nana ] 9.4% bC-Sffata fimber pine [Pinus kx&] 3.0% B+C-Strata quaking aspen [Popukrs tremuloaes ] 3.7% shrubby cinquefol [Dasiphom bribunda] 2.3% Saskatoon sawiceberry [Amdsnchiera/nilbfia1 3.4% white sweetvetch [Hedjwamm su@hurescsns] 25% &C-Strata subalpine fir [Abies Uocurpa] 1.2% aspen fleabane [Erigem spedosus] 1.I% heaftleaf arnica [Arnica codrfoh ] 2.3% ' b- kinnikinnkk [Anlwlsphyk?~m-ursi] 0.9% ribbed pixie lichen [Cladonit ani7sa I 1.8% slender hawkweed [Hiemuum gn&] 0.9% whii swemtwtch [tfetfysa~rnsulphum~~dns] 1.8% dicranoweisia moss [Oicranoweisia crispula ] 0.8% 88pem fleabane [Erigeron speciosus] 1.8% pixie cup lichen [Cladonit chlomphaea ] 0.3% showy aster [Aster wnspicuus I 1.4% juniper polytrkhum moss [Polyrrichum juniperinum] 12% Drummond'sanemone [Anemone dmmmondii] 0.7% western In&an paintbrush [Castilkja oceidenralis] 0.7% &Stratum subalpine fir [Abies ili,siocarpa1 6.0% A-Stratum Engelmann spruce [Pima engelmannii] 2.6% rock wilbw (Sahvesfira ] 5.8% Douglas mapb [Awr gl8brum 1 B+C-Strata subalpine fir [Abies Wmrpa1 3.8% NW mend#b [Ymdm& knuOInu1 common red raspberry [Rubus idaeus ] 3.7% spotted saxifrage [Saxifraga bmnch-] dwad birch [BeWa nana 1 3.3% dog peJlt lichen [Pe/figefa mha] bearberry honeysuckk [Lonicera involucrafa 1 2.3% pohlii moss [Pohfia nulans] Canadian gooseberry [Ribes oxyacanbhoides] 2.3% Canadian bunchberry [Comus canadensis 1 B+C-Strata quaking aspen [Populus mmuloides] 21% knights plume moss [PbEum crista-etlsrrensis] knights plume moss [Pb'Eum cristsau~sis] 6.5% fringed gmsa of Pamasws [Pamas& fmbriala ] $- pohlia moss [Pohlia nufm] 2.8% sockaye psora lichen [Psm dsdpiens 1 - sockeye psara lichen [Pmdedpiens] 1.6% slender hawkweed [Hiendum gncrle ] yellow columbine [A~uiktgiafhvescens] i2% annual gentian [Ganfianelh8mamIla 1 scarlet lndian paintbrush [CaNleia mmiata 1 1.2% nodding onion [AIKum cemuum ] dog pelt lichen [Peltigem canina ] 1.0% Halter's campyliim moss [Campylium hdkri] sulphur pixie cup lichen [Cladonia su$hurina] 0.6% sulphur pixie cup lichen (Chdonb sulphurina 1 juniper polylrichum moss [Po/yt&um juniperinurn 1 0.6% nodding onion [Alfium cemuum ] 0.5% red fescue [Feslvca ~bm] 0.4% starburst lichen [PameGopsismbigua I 02%1 A-Stratum limber pine [Pinus IlexiIis] 12.0%~k~tratumlimber pine [Pinus Aexflis] Douglas maple [Acergkb~m1 5.0% kSlratum quaking aspen [Populus tremulo~iles] white rhododendron [Rhododendron8lbM0~m 1 4.0% mountain alder [Alnus wiridis] mountain alder [Alnw viridis 1 3.0% bog Labrador tea [Ledum gmenlandiarm ] funytongue penstemon [Pentsfemon erianlhenrs] 5.0% common red raspberry [Rubus idaeus] white spirea [Spiraea behrlilbfia 1 4.3% white rhododendron [Rhododendron alb&Nm 1 raceme pussytoes [Antennaria racemoss 1 3.0% bearbany honeysuckle [Lonicera hvolucrata ] fringed grass of Pamasus [Pamess& limbnitfa] 1.% B+C-Slmta subalpine larch [Lsrix lyslfi] stiff club moss [Lycopodium annorinurn] 1.3% pearly everlesting [An8ph8&smargsritacea ] bracted lousewon [Pedidaris bmcteose 1 1.3% houndslongue hawkweed [Hiemcium cynoglossoides] Hallefs campylium moss [Campflium hallen] 0.5% Kentucky bluegrass [Papmtensis 1 western feathefbelk [Srenanlhivm midentale] 0.5% smaRRowered woodrush [Lumlapa~Mora I g sedge species [Carsx spp. 1 0.5% fmckls-pelt Gchen [PeMgera aphfhosa] +- Drummondk anemone [Anemone d~mmondii] 0.4% raceme pussyfaat [Antennaria mwmosa ] smalHlawerad anemone [Anemone prvihbm] 0.4% SHkr vrlsrian [VhEeriam sjtchensis] western Indian paintbrush [Casfileja occidentalis] 0.4% western horsetail [Equrjehrm rmnse] &GSbata hber pine [husRedis] 4.7% m~(m-1 20.3% orowsbsny[Vmm=P'-l 53.0% Oloorsbay[V'Wm~n] 12.9% fireweed [@~lbbiurnmg&Mum 1 7.8% frewed [mbbiumengusWium] 3.Wo o heartWamica[AmiaQKdMi] 4.4% 8 pirk mournhe* [Pnfl- empemmrjl 4.1 % ribbed pixie Schen [Cbdonb canbse] 23% Rocky Mountah goldenrod [SoI&go mulfiradbta ] 1396 juniper po~ummoss urn junnperinum] 1.5% alpine goundsel [Seepa&nrs] 0.6% B+GStrata Engebnann spruce [PCaea engelmannr?] 5.7% Canadan bunchberry [Comus canadensis] 4.3% $ suwur pixie cup Eehen [Cbdoni suIphuri~] 1.WO

A~~Pw~~l10.7% Wights plume moss [Pliumakta-cesirensis] 5.0% hearlleaf arnica (Amrice cud&& ] 4.1 % twinfbwr. . [tiieabomlis] 3.1% I I ! hperpotyWtum moss [~@tridwrnjuniperinurn] 13% 1 B+C-Srata sumire larch [LsriXlpfIii] 6.0% wibwspedes[=spp.] 4.6% g greater pb6e sSek Schen [C&&nk mmuta] 1 -9% greater pide slick Cchen [Cbdonia cwmuta I 1 .We mmnyarrow [Millea miIMurn] 0.9%

A-Stralum Engeharm spruoe [Rba engelmannii] 3.6% white rhododendron [Rh&&ndmn albifforurnj 7.8% russet buffdobefly [Swpherdiacanadens~s 1 5.Wa $ CaMdian gposeberty [Ribes oqmmnhkA8] 4.496 Z &C-S&& hrbr phe [Rnus kdD] 27% bog Labrador tea [Ledurn gmn/ardicum 1 27% &C-Sbata bdgepole pire [Pinus cwnlorta] 2.0% pohk moss [PohIia nu$ns] 1.2% EtC-Strata Engehm spw[FWa engelmannil 5.5% ribbed pixie lichen (Cktdonm CB~S~] 1.9% B+C-Sltatasub~inefir [Mes~~] 2.0% sbn dub moss [Lyar;lpodiurnannobnum] 1.WC Cm&i bunchberry [Comus aura-] 0.896 dog pelt Cdren (Fb&xm canina I 1.S fredde-pelt~[~aphlYKlsal 0.8% 0.896 scarlet Indian painlbrush [Casbiw miniata] 0.8% dogpeum[~mcenrm] 0.5% Appendix 6 149 CUIsTEn 3 1 red elderbsny[Sembucus mcemosa ] 3.6% 64-Strata subapira tir [Abies leshafpa 1 3.0% fredde-pel Man[Psl@era ~phfhosa1 32% western horsePll [IEquIjblum mnse] 1.O% *me gfoundsel [Sen- paudb~s1 03% rT houndstongue hawkweed [Hieradurn cynogbsso&sl 0.7% Kl

A-Slrafum sub*ine fir [Mas&sioa?fpa 1 1.7% Gmmne's mountsir ash [Sorbus scopuina ] 33% bearberry honeysuckb [Lonken mwlucreb, ] 2.7% common red mspbeny [Rubus Uaeus] 1.6% B+C-Strata quahg aspem [Populus temubkfes] 1.O% &C-S- ~~batrirek~h [Laklyaiyal] 0.7% priday rose [Rosa aciarhris] 3.8% downy ryegass [Leymus innovatus] 22% showy aster [Asmr conspicvus 1 13% B socbaye pmra Men[Pan decpkns] 1.6% - dhowekia moss [Dicmnowisie&pula ] 15% wmtergeen species [@mhspp.] 1.3% groundcedar [Ly~opodiumoomplensbm 1 12% wld strawberry [Fn~gariaMghiana ] 1 .O% 51 smaWbwerad woodrush [LwLparvilbn j 0.9% Aady Mountsin gohnrod [SoMago mulbiadiale] 0.8% SIll annual gentian [Genhnesb amam& I 05% 0

rockwbw[SeTamsrnI 7.5% mountain alder [husriMis] 6.5% spolrsd sadrage [Sax#iaga bronchialisl 4.0% pearly everlesting [hapha& maguritacea I 14% scarlet Indian paintbrush [CasPleja miniata 1 1.E% common yarrow [AchiLa miAblblium 1 1.6% Kentucky blragrsu [Poa pramnsis] 1.5% aspen Rsfhns [Erigeran Jpebbsus] 1 3% starburst khen [Pannebpjisambgua 1 1 2% frirged grass of Pamassus [Pama& fimbriara I 1.O% sedge species [Carex spp. I 0.9% pixie cup Men[Cledonia chlbmphaea 1 0.8% $ fewfbwer meadowrue [Thekturn spadurum] 0.8% - orange-foosd pinis 1Sthsn [Chdonia ecnmjma] 0 .8% Halbr's campylium moss [Campflum halkeril 0.5% sbndur hawheed [Hhraciumgrauk] 0.4% bmcted busswort [Micub& bmcdeosa] 0.3% pbwcolumbine [Aquikgia Lwscens] 03% Appendix 6 150 CLUSTER 5 6 10.9% NItyllKmbh[mwh] 70.7% 4.3% gmumbmy [V(~cckrlunrscup#riun] 12.6% (ireweed (Epl70bium angusi&lium] 4.9% sulphur pixie cup Cchen [Cladonh sulphunna] 1.3%

-Y~[~fsmglirsol 123% wbw~[Wuspp.] 4.0% &C-Strata Engehann spruce (fkaen@mannii] 9.7% mrpixie slid< Sehen [Cladon* cum& ] 5.2% $- juniper polytriehum moss [Po~urnjuniperinum] 1.5%

AIStmtmkdgspokpine[~~~canlorEs] 11.6% A-Stntum kdgspok pine [ffnus cmtortr] 14.7% s wbwspedes[Salixspp.] 5.0% WSMa Engeknann spruce [Maengelmannil 4.3% juniper polytrichum moss [Cb~umjuniperinurn] 32% $v

CaMdian bunchberry (Cumus canadensis] 10.1 % pohia moss [PohIb nutans] 0.6% $ twinfbwer [Unmea borealis] 52% 9 heartleaf arnica [Arnica 00rdMI'a ] 4.5% I sulphur pb6e cup Cehen [CladbnP wlphurii] 0.9% russet buffdoberry [Shephwdia mnadensis] 5.3% bitsplume moss [Rlium aista-c~rnsis] 27% &C-Strata subalpii lir [NSlasium~pa] 4.7% heaRleaf arnica [Arnicacordifolb ] 2.5% $- knighis plume moss [Ffilum ais$-~~M] 28% Canadii bunchberry [Cornus camdensis] 2.2% * &bed pixie Sehen [CladonM ~MsB] 1 -4%

pohk mass [Pohh nutans j 1 2% WStrata limber pire [tinus hdlis] 1 .% alpine goundsel [SeneaiopaWnrs] 0.9% fredde-pelt Sdren [PeH@em aphltiosa ] 4.1% $ greater pixie slidc lidm [Cladonia mmu$] 0.9% dog pelt lichen [AMgen mnina] 0.8% Z ribbed pixie Cdren [Ckdonb caniasa] 0.8%

A-Stratum Engehann spruce [PEbea engdmannii] 4.3% bog Labrador tea [Ledurn gmlandhm] 7.6% bog Labrador tea [Ledurngruenlendiarm] I.% EtC-Strata subdpii fir [Abas lasbarpa] 3.Wo &C-SbaQ linber pine [&us IRedIiS] 3.5% IkC-Sbata bdgepde pire [tinus contar$ ] 28% $ Cmdian goorberry [Ribes o-rW&s] 3.1 % twirfbwer [tinnaea 60-1 1.7% s; B+C-Strata bdgepde phe [Pinus~rte] 2.4% downy ryegrass [Leymus innovatus] 2.7% fredde-pel richen [&I'm ephfba] 1.9% I - I I iwhb rhododendron [~hoddanironabifioNm] 123% l~hibrhododendron [Rhodocfendfun abilk~m1 113% mdcwbw [Sehrmsfita 1 3.6% radrw&~[~~~&] 52% bearberry honeysudds [Lonicenr inw/ucm$] 2.6% russet buRebberry [Shephsrdia canadensis] 23% dog pelMen [Pelfigem canine] 20% sockeye psom Men[Psom dscrpiens] 12%

mountain alder [Alnus riridis ] 5.0% red elderberry [Ssmbucusracemosa 1 2.0% &C-Strata q* aspen [Popufus (remubides 1 2.6% bearberry honeysudda [Lonicem hvoluaata 1 15% Greene's mountah ash [Sorbus soopulina I 1.6% &C-Slrate quailing aspen [Populus fremuloides] 1.5% Kentucky bluegrass [Poapramnsis ] 7.4% barbbphoda [Bed~~Bphozfi?&opadmides ] 53% barbbphozia [Barbdophoaie ~4dioides] 3.8% sliff club moss [Ljwpodium annotinurn] 1 2% showy aster [hercon~picuus 1 2.0% pew everlasbirg[Anaph- mgaritaoea 1 0a% wild sttawberry [Fmgaria tiginiana] 13% sWburst khm [PdmTapsisambigua ] 0.8% p- rose [Rosa a&le&] 1.7% smslbwsred woodrush [LmlapaNilkra ] 0.6% scarlet hdii paitlbrush [CssIbje miniam] 1.5% whlorgrssn spscies [Pymb spp.1 0.6% t western honetal[EquiJebrm amse1 1 .O% R* Mountain galdenrod (Soa;dagomu/lfradiata 1 0.5% - annual gentian [Gsntiunelle amamPle j 1.O% houndstongue hewheed [Hmracium cynogbssoidess] 0.4% tiiranowebii moss [Dimnowish cnjpuh ] 1.0% orange-barn pids Cehsn [Cledonia scrnocyna ! 0.4% Wigroundsel [Senecio puci&ms 1 0.4%

A-Sbatum suba@ii fir [Abies hsiocarpo 1 2.0% A-SWum Engehann spruce [Picea engelmnnii] dwarf birch [Bebrle nana] 218% kSlrahtm suba@ime fir (A6ieslasiocarpa I M-Strata subapme larch [Larix@P] 7,7% SasJmon serviceberry [Amelancf?&r ahW] Saskbon senriceberry [Amelendrier adnilblia 1 2.8% Gmene's mountah ash [Sorbus soopubna j red eUerbeny (Sambucus mcemosa I 22% B+C-Strata subalpine larch [Lsrkfyaaii] common juniper [Junbe~Sammunis] 1B% common red raspberry [Rubus idaeus] common red mpberry [Rubus idaeus I 0.6% downy ryegag [Leymus innovalus j fringed grass of Pmasuls [Psmassie fimbriam 1 5.1 % Kentucky bluegas8 {Pea pra$nsb] yebw columbine [Aquikgie lwmns1 32% pixie cup men[Cledonie drbrophaea 1 pirk mountainheath [Pnflkdbmempeailbnnis 1 2.4% diaanoweisiemoss [DicranowesiaWula 1 lewfbwer meMownre [ThaliclNm spe~rum] 2.0% groundcedar [Lppodium wmplanabrmI aspen fleabane [Efigeron spaaiosusl 1.7% scarbt Indian paharush [CasbJleja miniam 1 spoltled saxfmge [-#a brondrialis 1 1.5% 5 srnanDwered anemone [hemnopsnifknr ] 1 A% sedge species [Camx spp. I 1.3% houndstongua hrmlweed [Hiemairm eynogbssoi&s] 1.O% smalbvrered anemone [Anemone prNinDm] 0.6% malfbwered woodrush (Luzufe pund&n] 0.6% groundcedar [Lywpodiumwmphnabrm 1 0.6% arcric b-rry [Rubus amWs ] 03% slender hawkmeed [Hmracrirmgm'b] 05% pacup lichen [Cledoniacjlb10pkda) 0.5% starburst Men[Pambpsb ambm I 0.5% wintergreen species [Pphspp. ] 0.4% common yarrow [Achdba mdklbfum 1 0.4% Appendix 6 15: I CUlSTOl

~~v=-rcoprkml 7.3% MhIlowr [Linnaea hMi] 5.1% Candim bunchberry [Comus cmdmskJ 3.6% Weed[Eplbbium angustiMurn1 329L

russet buffabbemy [ShepherdEiPcam-] 14.3% nrsset buff&beny [S~@~rdiecanadensis] B+C-Sbah bdgepde pire [Knusmbrta] 10.2% gmmbawy[V~cc~~] downy ryegrass [Le~mmmfusj 5.0% twinfbwer [Linneea hreelis] showy aster [Aster aMspiaus] 4.8% Canadian bunchberry [Cornus canawl heartW ami= [Amhoodiblia] fireweed [EpI'Iobiumangusfibhum] Wrow spedes [Salixspp.] 3.1% B+C-Sbata Engelmann spruce [Fbaengelmannil heartleaf arnica [Arnica curdMia] 3.1% &C-Sbata lodgepole phe [Pinus aontorta] pw [a- 8&&&] 22%

bights plume moss [Rilium akta-] 4.5% rusty n#mkub [Mmzbh &nugma] 17m wbw spedes [Salixspp. 1 3.4% juniper po~ummoss [Po~umjuniperinurn] 1.3%

1 l-Ynrmrr*~u*law-~ U%(showy aster [Merawrsp#rus] 1.6% wid strawberry [Fmpr& urghiima 1 1.7% subhut pix& cup tichen [Cladon& sulphunira ] 1.1% 2 1sulphur pixie cup tichen [Cladonia sulphurina 1 0.5% I

ribbed pixie lichen [Chdonia mrksa] 0.9% downy fpgras [Leymus innovadvs] 3.7% junper polytrichum moss [Powurnjunipdnum] 0.6% hn@& plume moss [Riburn cris$aM] 2.0%

BtC-Shata Engehann sprum [hen~elmnnii] 3.3% bog Labrador tea [Ledurn gmnlandiam] 75% 1 white spirea [spiraea btmri] 1.- &GStratasubalpirefir[AbEes&~rpa] 3.3% hedde-peRCehen[~m8phM] 1.6% wld strawberry [Fm~ahIlirgrniena ] 2.4% alpinegoundsel[Senedopa&m] 0.9% priddy rose [Rosa a&laris] 1296 ,- ~~spedes~~~.]0.8% pixie stick Ccherr [Clammuta] t .l% dog pel! Echen [&l/&n canha] 0.8% Rody Mwntein goldenrod [SoTrdago muIb'mdLta] 1.O% starburstc$len [pemriambfgue] 0.4% atpan Ib&ma [Bgerun spedosus] 0.6% dieranwebis moss [Drbrsmweisie aiTpuh j 1.6% sodmye psora men[Psos dernns] 05% phfamoss [PohP nudans] 0.9% dog pelt Kchen [&lagera canine 1 0.5% starburst Men(Pamkpsis ambigua ] 0.4% alpine groundsel [Sendpauci&~s] 0.3%

bog Labrador tea [Ledum gmnhndicum1 65% A-Stratum Engehann spruce [Picea engellnannii] 3.3% bearberry holreysuc#s [Lonicen rirwkrcmbt 1 2.0% B*C-Sbate qu- aspen [Populus mmulorires] &C-Strata linbet pine [PTnus Le] 13% Greeneesmountair ash [Sorbus scopufia ] lewfbwer meadowrue [Thiirim sparsBorum] 13% Canadian gooseberry [RBes aryamnfho~iles] pohfie moss [Pohb nutans] 1.5% common jun@er [hnperus cummunb J western horsehi [Equiselum afwense 1 0.5% bearberry honeysudde [Lonam invplumfa I &C-Strata linber pine [tinus r$elrrlis] pbrie cup khen [Cladonia chkrophaea 1 frsc#b-pt lichen [k/tigeraapMhosa1 slJQenReabane [mmnspeciosus] tC - kwlbwer me&hwrue [TnBIklrum spafsikrum) common yarrow (AchifIse m~lbIbBum] &bed pinis Wwn [Cladonie CBMSB] &ye psora khetn [Pscrra dmpiens I sbnder hawlawbed [Hieradm gracile] wintergreen species [pLrole spp. 1

5.0% kStrahrm suba@ime tir [Abies bsiocarpa I Douglas mepb [Aasrgbbrum] 3.0% rock W&W [ah~dh ] Canadii gooseberry [Rhsoayamndho~es ] 25% Douglas mspb [AcerglebfUmI &C-Sm subwme fir [Abies &siocdrpe 1 2.0% whb rhododendron [Rhododendronalbilkrum 1 greater pastick Cchen [Chdonia wmu$] 1 .8% dwarf birch [Belule nana 1 Rocky Mountail ~0ldenfod[SoIMago mulflmdiebt 1 1.6% shrubby cinquefol [Das@bonIRorCbunda 1 common yarrow [AchJbamillelbfium] 1.O% whls spiea [Sphea behrMbk I stlf club moss [Lycopodim annotinurn] 08% whb sweehreteh [Hafysanrrn su@hum~~~ns] annual gentian [Gentieneh amarek~] 0.5% rawme puss@as [Antbnnafia mcemra 1 pirie cup men [Ciadona chbrophaea] 03% wesdsrn horsetail [Equiselum awns3 ] scarlet Indian pamarush [Castlleja minieta] 0.3% mountah deathcamas [Zigadenus ekgans] red fescue [Feshrm tuba ] barbbphorie[&thJophorie &opodioMss ] Kentucky bluegasj [Paa pralnJir] stiff club moss [Lppodium annofinurn 1 western featherbeb [Stenanfhium ocdden?ab] Appendix 6 154

NItY-r-f-1 142% W-@-Ww"i-rtaprrkanl 10.9% Chn&i bunchberry[Comuscanadens&] 5.5% juniperpohlbiehummopi[PRl)bidumiun@enirum] 21% g gererp~erski~en[~&doniammu~a] 1-6% subhur pixie cup Schen [Cladonia sulphun'ne] 1 .0%

hearkif arnica [Arnica curdm] 24% ~m-i@w~Ikmrgharl *wspecies[-spp.l $-z z B+GSbata Engebnann spruoe [F3bea engelmannii] ~ p~rose[Rosaadntlarisl &C- Engelmann spruce [Plioea engdmnnii] 1.9% heartleaf arnica [Arnica wrdWia] hniits phme moss [Rliurnc&a~nsis] 27% showy asbr [Aster uwgpkuus] $ pohk moss [Pohiia nudsm] 1.7% ki@splume moss [Pffliurn ais$-c~slrem's] 3; wild sbawbeny [Fig& uginn'ana ]

russet buffakbeny [Shepherdia cena&nsis] 7.1% 1bog Labrador $a [Ledurn gmlandEarrn] 7.1% Saskawn semiwberry [hekndrmr elnibk] 1.5% Rocky Mountain goldenrod [W&w muIb'rediata ] 1 .% dog pelt ktm [M~eracanine] 1.4% ~gra~ndsel[Senecrb~Udb~S] 0.696 juniper polybiehum moss [Fb&fddtumjmiperinum] 0.696 Appendix 6 CLUSTER I a to orangs-footed phie Men[Cbdonia ecmocyna] 1B% k-m Engehmn spruce (Picsa engelmnnfi] 5.0% frec&peR lichen [&?@era ephfhosa ] 1.1% Canadian goowberry [Rhso*yacanUtoides) 7.0% I I ldog pea Men[Pelfigera mnim 1 0.6% Idiiowebiamoss [Diaamweisia #ispu&] 1 3% pixh cup Wen [Cladonia drbmphaee 1 0.6% IswRower meadowrue [Thakfrum spatsdbrum] 1 2% I IIsU?club moss [Lppodium annofhum] 0.4% I grealer pbda stick hen[Cfadonia cornuh] 0.4%

I I ]bog Labrador ma [Ledumgraenlendkum] 2.6% l~oug~esmapb [~cerg~~m] 6.4% 3tC-Strata bnber pine [Pinus Ikm] 1.0% &C-SInm qualdng sspsn [Populus bgmubkfes] bwny ryegrsrs [Leymus innomfus] 2.4% shrubby cirquefoil[Dasphora lknbunda ] ,arbitophozia [Berbibphotie lpopodbidbs) 1.6% common jmipet [Juniperus communis] mbumEhen [Parmehpsu amqua ] 0.6% whb sprea [Spiraea befu/iiP ] v# strawberry [Fagaria virgniana 1 0.6% hinnMnidt[hctosaaphfis vva-umi] &ye psora Schen [Psom deMmns1 aspen Mabane [En'geron spdosus 1 pohL moss [Pohba nudens] rbbed pirie Wlen [Cledonia cariosa] stll club moss [Lycapddiumannotinum wintergreen specas [&role spp.1 starbum lichen [P8mw,liopsisamb~u8I pixie cup lichen [Cledonia Chlorophe~98j

I I 1 ired elderberry [Sambucus mcemosa ] 1.8% ired elderbarry [Sambucus mcemosa 1 37.0% whim rhododendron [Rhododendron albiffomm] 1 .O% dwarf birch [8ehrie nana] 6.0% Rocky Mountain g~ldenrad[Solidago muliriadiehl] 1 .I % mountain alder [Ahus wMisl 5.0% showy aster [AsLsr wnspiwus] 0.5% bearberry honeysuclQ [Lonicera hwlucmta j 3 6% slender haw laneed [Hmmaum gaak ] 05% Greene's mountain ash [Sorbus s~puline1 2.0% dimowehis moss [Dimno weis& cr@u& 1 0.3% whle rhodadendron[Rhododendron albmorum 1 2.0% western horsetal [Equissfum a~wnse] 0.1 % M-Sbate hrbbr pine [Pinus LxiIL] 1.O% priddy rose [ROJBaaiarleris 1 0.1% Kentucky bluegrass [Poa prabnsis] 7.3% western horsetail [Equ&lum amnS8 ] 5.0% fringed grass of Pamassus [Pamassia fimbriata 1 25% scarlet lndii paintbrush [Caslillej~miniera 1 2.4% groundcedar [Lmpodium wmphnafum 1 1 9% o~fooMdpirib Men[Cladonia eanocyna ] 1.3% pearly ever-g [kraphslis margaritscea] 1.3% freddb-pet Men[Ps/f&m~ aphthosa] 1.3% mountain deathmas [Zgadenus ekgans] 0.6% nodding onion [Miurn cemuum ] 0.5% Habfs wmpyCum moss [Campyfium Iralbri] 0.5% haundslongue hawheed [Hiurndurn eymgbssoides 1 0.4% I Drummond's anemone [Anemone d~fnmOndfJ 0.3% white sweatvelth [Hdys??mmsu@hum~~~ns] 0.3% common yarrow [Adriaba rnikfolium ] 0.1% Appendix 7 156 APPENDIX 7 - DEM9ERIVED ANCILLARY VARIA0LES

The following seven figures display the ancillary data layers derived from the DEM of

Vermilion Pass (see figure 5.3). The caption beneath each figure provides a brief description of the process involved in generating the terrain-related variable.

SLOPE - VERMILION PASS

LL Fgum 9. I - Skpe VCIIWfor VemiIim Pass, generated in Mnfo. Appendix 7 157 I SOLAR ASPECT - VERMILION PASS I

Figure 9.2 - Solar aspect - raw aspect vaIues deMh Arclnfb, lhen anwerted (see chapter 4, section 4.3). CROSS-VALLEY ASPECT - VERMILION PASS I i ~V~ ; ASPECT (*) ; ' 0-11.25 f 1125-2250 2250 -33.75 j 33.75 - 45.00 . 4.00 -5625 f 56.25 -67.50 f 67.50 -78.75 f 7a75 -w.w I W.W-101.25 f 101.25-11250 11250 -123.75 1 123.75-l3S.W t 135.00 - 146.25 j 146.25 -151.50 i 1Is7.S - 168.75 f a 168.75-llYI 1

I Figure 9.3 - CrowkvaIley aspect - converted from raw aspect values as above. I SOLAR SLOPE-ASPECT INDEX - VERMILION PASS SOLAR SAI 0 -7.50 7.a - t5.60 15.00 - 22.50 m2250 - 30.00 m30.00 - 37.50 37.50 - 45.00 45.00 - 5250 5250 - 80.00 60.00 -67.50 67.50 - 75.00 75.00 - 82.50 8250 - 90.00 5 90.00 - 9750 F=q 97.50 - 105.00 Iilo!j.Oo - 112.50 I=i 1t 250 - 158.04

U 1 Figurn 9.4 - Sdar slopaspect Index (SAIJ - Cacculateci k 1DRISI using sbpe sobr-cunlwrted aspect variables (br ewlanatb see chapter 4, sedh 4.3). I CROSS-VALLEY SLOPE-ASPECT INDEX - VERMILION PASS 1 CROSS-VALLEY SAI 0 -7.50 7.50 - 15.00 15.00-2250 2250-30.00 36.00 - 37-50 37.50 - 45.00 45-00 - 5250 5250 - 60.00 60.00 - 67.50 67.50 - 75.00 75-00 - 82.50 18250 - 90.00 90.00 - 97.50 97.50 - 105.00 105.00 - 112.50 L-i L-i 11 250 - 169.48

X figure 9.5 - C#ss-valley s@eaqmcf rjrdex (W) - akulated h IDRISI usrig sbpe ctvss-valleyanverted aspect van'ables (farercplanalkm see chapter 4, sectrbn 4.3). I DOWN-SLOPE CURVATURE - VERMILION PASS DOWNSLOPE CURVATURE ,; j I less thm -65.63 -65.63 - -5625 i 56.25-4.M [ 46.88-37.50 1 -3750 - -28.1 3 1 -28.13 --18.75 1 -18 -75 - -9 38 -9.38 - 0 0 - 9.38 i 9.38 - 18.75 ] 18.75-28.13 i 28.13 - 37-50 i 37.50-46.88 i 46.88 - 58.25 1 5625 - 65.63 greak than 65.63

[I Fgure 9.6 - bwn-slope cumture -determined using Tern Firma by cakulating the discfete second derivative of elemth of each cell along fhe aainuih (see chapter 4, section 4.3). I CROSS-SLOPE CURVATURE - VERMILION PASS 1 CROSS-SLOPE 11 CURVATURE i . kss than -65.63 -85.63 - -5625 -58 25 - 46.88 46.88 - -37.50 -37.50 - -28.1 3 -21.13 - -18.75 -1 8.75 - -9.38 -9.38 - 0 0 - 9.38 9.38 - 18.75 18.75 - 28.13 28.13 - 37.9 37.50 - 46.88 46.88 - 56.25 5625 - 65.63 gmhr than 65.6

'I figure 9.7 - Gloss-sbpe curvaiure - detemined Tern Fma by mblating ttre discrete second derivative of elevat&n of each cell orthogonal b the azhulh (see chapter 4, section 4.3). Appendix 8 163 APPENDlX 8 -TREE SPECIES STA- BY CLUSTER

.- . .- - - 1 subalpine fir 14% 3.8% 16

limber pine 40% 2.7% 12 - -

21 % 12% 12

4 limber Pine 41% 2.7% 15 - -

Pemnt arveris alltlmgmlonlyowlhasepk6 in which Iree Jpedes wus bud. To &$in awqp pemnt mrowrihe en* cbsbr, mu/@& consfamy bypemnt ~r wW. Pine~mdusder3maybewh~~pine(~~~uk~.),es~rirAcnulCetal.[1984j Appendix 8 164

su babire fir 420/6 2.3% 15 - o