UNIVERSITY OF CALGARY
Modeling Plant Diversity and Post-Fire Regeneration in a 31-Year-Old Bum -
Vermilion Pass, Canadian Rackies
Gregory William Chemoff
A THESIS SUBMIlTED TO THE FACULTY OF GRADUATE SNDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE
DEPARTMENT OF GEOGRAPHY
CALGARY, ALBERTA AUGUST 2001
Q Gregory Wliarn Chemoff 2ûû1 Natioiial Library Bibliottieque nationaie 1+1 ,Canada du Canada uisitions and Acquisitions et 7.Bi iographic Services seivices bibliographiques 395 W.llirigkn Street 395, rue Weiîingtm Ottawa ON K1A ON4 OltawaON K1AûN4 CpMdQ -
The author has grantecl a non- L'auteur a accordé une licence non exclusive licence dowing the exclusive mettant a la National Lihaq of Canada to Bibliothèque nationale du Canada de reproduce, 10- diskibute or seIl reproduire, prêter, distribuer ou copies of this thesis in microform, vendre des copies de cette thèse sous paper or electronic formats. la forme de microfiche/nlm, de reproduction sur papier ou sur format électronique.
The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor substantial extracts fiom it Ni la thèse ni des extraits substantiels may be printed or otherwise de celle-ci ne doivent être imprimés 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 Vemiiion Pass, Canadian Rockies, which bumed in Juiy, 1968.
Phnt community composition and distribution is modeled through a muiti-stage process that incorporates field and anciilary data through an integration of muhivariate statistics (ckister analysis), GIS and geostatistics (DEM-derivation of site characteristics, spatial interpolation), and remote sensing (maximum likelihood classification) methods. The resuit is a map of the curent
(1 999) distribution of plant communiaes in the Vemiiion bum, which is used to compare plant communities in ternis of composition, location in Vemilion Pass, and site preference.
This thesis is intended as the second stage in a longitudinal stuây, which will record the vegetative regeneration of the Vemilion burn through a complete Cre cycle.
iii Numerous people and organizations were of tremendous help throughout the course of this research. I would like ta 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 fomi of a wam 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 supenrisor), Dr. Darren Sjogren, Dr. Wayne
Strong, Dr. Nigel Waters, Dr. Clarence Woudsma, Dr. Stuart Harris, 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 "mungnfor supper every night, continuing on his
own after I fell off a log and sprsined my ankle, knitüng nice toques, and working four
months before receiving a pay cheque.
P Mollie Fems, for her support, patience, kindness, and love.
P All the rest of my family and friends who have offered encouragement, kinship, community
and support. Dedrcated to the whitebark phe, Ltie woivenne, and Be western lndian painBmsh, and to the mother of us al/. TABLE OF CONTENTS
Approval Page ...... ii Abstract ...... iii Acknowledgements ...... iv Dedication ...... v Table of Contents ...... vi ... List of Tables ...... wii List of Figures ...... ix List of Equations ...... xii Epigraph ...... xinS.. CHAPTER 1 - INTRODUCTION ...... 1 CHAPTER 2 - BACKGROUND ...... 4 CHAPTER 3 - LITERATURE REVIEW ...... 10 3.1 - Subalpine Forest Fne Ecology ...... 10 3.2 - Modeling Composition and Distribution of Vegetation ...... 12 3.3 - Past Vegetation Studies in and Around Vermilion Pas ...... 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 Communiaes ...... 52
CHAPTER 5 O RESULTS ...... 57
5.1 O Field Data Set ...... 57
5.2 O Cluster Anaiysis ...... 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 Cornmunilies ...... 83
CHAPTER 6 O DISCUSSION ...... 85 6.1 .nie Plant Communities of the Vermilion Bum ...... 85 6.2 .Critique of the Model ...... 117
6.3 O Suggestions for Further Research ...... 122 CHAPTER 7 .CONCLUSION ...... 127 REFERENCES ...... 130
APPENDIX 1 O Data and Software Used in this Thesis ...... 136 APPENDIX 2 .Model Flow Chart ...... 137 APPENDIX 3 .Cartographie Model ...... 138 APPENDIX 4 .Instrument Used for Recording of Field Data ...... 141
APPENDIX 5 O Complete List of Species Identifiied in the Vermilion Bum ...... 142 APPENOIX 6 .Summary of Plant Species Composition by Cluster ...... 145 APPENDIX 7 .DEM-Derived Ancillary Data Layers ...... 156
APPENDIX 8 .Summary Sbtistics for Tree Species ...... 163
vii LIST OF TABLES
Table 3.1 O 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 O Correiation Mat& for Potential Diagnostic Species and Ancillary Variables ...... 62
Table 5.4 O Parameters for Spatial Dependence Models ...... 76
Table 5.5 O Error Matrix For Maximum Likelihood Classification ...... 83
viii UST OF FIGURES
Figure 2.1 .Map of Vermilion Pass and the Vermilion Burn ...... 5 Figure 2.2 .Mean Monthly Temperature and Pracipitation. Verniilion 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 Shnib 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 CroûîValey Aspect ...... 34 Figure 4.3 .3x3 Convolution Math for Deriving Cunrature ...... 37 Figure 4.4 .Hypothetical Experimental Semivariogram and Spatial Dependence Model ...... 50 Figure 4.5 .Hypothetical Maximum Likelihood Classifion ...... 53 Figure 5.1 .Dendrogram of Cluster Analysis Results ...... 58
Figure 5.2 O Map of Locations of Sample Plots ...... 60
Figure 5.3 O DEM of Vermilion Pass ...... 61
Figure 5.4 O Equiprobability Contour Diagram for Lodgepole Pine and Rusty Menziesia ...... 63
Figure 5.5 O Coincident Histogram for Lodgepole Pine ...... 64 Figure 5.6 .Coincident Histogram for Rusty Menziesia ...... 65
Figure 5.7 O Equiprobability Contour Diagram for Canadian Bunchberry and Grousebeny ...... 66
Figure 5.8 O Coincident Histogram for Canadian Bunchbeny ...... 67 Figure 5.9 .Coincident Histogram for Giousebeny ...... 68
Figure 5.1 0 O Results of KM-WallisNon-Peramehfc Test for Sample Independence ...... 70
Figure 5.1 1 = Generalized (Regression) Distribution of Lodgepole Pine ...... 72
ix Figure 5.1 2 .Generalized (Regression) Distribution of Rusty Menziesia ...... 73 Figure 5.1 3 .Generalized (Regression) Distribution of Grousebeny ...... 75
Figure 5.1 4 .Kriging lnterpolated Surface of Regression Residuals for Lodgepole Pine ...... 77
Figure 5.1 5 O Kriging Interpolated Surface of Regression Residuals for Rusty Menziesia ...... 78
Figure 5.16 O Kriging Interpolated Surface of Regression Residuals for Grousebeny ...... 79
Figure 5.17 = Modeled Distribution of Loâgepole Pine ...... 80 Figure 5.1 8 .Modeled Distribution of Rusty Menziesia ...... 81
Figure 5.1 9 O Modeled Distribution of Grouseberry ...... 82 Figure 5.20 .Modeled Distribution of Plant Communities ...... 84
Figure 6.1 O Modeled Distribution of Mount Whyrnper Open Pine/Buffaloberry (Cluster 1) ...... 87
Figure 6.2 O Modeled Distribution of Subalpine Meadows and Avalanche Tracks (Clustee) ...... 89
Figure 6.3 O Modeled Distribution of Stonn Mountain Grousebeny (Cluster 3) ...... 92
Figure 6.4 O 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 O Modeled Distribution of Ribbon of Menziesia (Cluster 6) ...... 99 Figure 6.7 .Modeled Distribution of Dog Hair Pine (Cluster 7) ...... 101
Figure 6.8 O 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 Pine/8uffaalob~îGmuseberry-Twinfiower (CI . 10) ...... 108 Figure 6.1 1 .1999 Distribution of Lodgepole Pine by Plant Cornmunity ...... 109
Figure 6.12 O 1999 Distribution of Engelmann Spruce and Subalpine Fir by Plant Community ... 11 1 Figure 6.13 .1999 Distribution of Dominant Shrub Species by Plant Community ...... 113
Figure 6.14 O 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. Verniilion Pas ...... 158 Figure 9.4 - DEM-derived Solar SlopeAspect Index. Vermilion Pas ...... 159 Figure 9.5 .DEMderived Cross-Valley Slope Aspect Index. Vermilion Pass ...... 160 Figure 9.6 - DEMderived DomiSIope Curvature. Vermilion Pass ...... 161 Figure 9.7 - DEMderived Cross-Slope Curvature. Vermilion Pas ...... 162 LIST OF EQUATîONS
Equation (1) .Squarad Euclidean Measure of Distance ...... 32
Equation (2) .Calculation of Slope-Aspect Index ...... 35
Equation (3) O Cafculation of Slope Vector ...... 37
Equation (4) O Cakulation of X-Component of Slope ...... 38 Equation (5) .Cakulatîon of Y-Component of Slope ...... 38 Equation (6) .Calculation of Arimuth ...... 38
Equation (7) .Cahlation of Curvature ...... 38 Equation (8) .Pearson's Correlation Coefficient ...... 40
Equation (9) O Kniskal-Wallis H-Statistic ...... 44
Equation (1 0) O 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) œ Kappa Index of Agreement Statistic ...... 55
Equation (15) O Regression Equation for Lodgepole Pine ...... 71 Equation (16) .Regression Equation for Rusty Menziesia ...... 71 Equation (17) .Regrassion Equation for Grousebeny ...... 71
xii xiii CHAPTER 1 - INTRODUCTION Forest fires, naturally occumng phenomena in the Canadian Rodaes, play a significant role in shaping the edogical 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 continuad health and vitality of the natural environment. The fire cycle, at every stage in its gradua1 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 inthte mosaic of forest communities in the landscape, improves soi1 quality, and provides increased resistance to disease and pestilence
[Kershaw, MacKinnon & Pojar, 19981. lt is an important aspect of the constant, dynamic change that is required for the preservation of ecological hsalth and biodivenity.
Only relatively recently have popular attitudes towards fire and its role in the maintenance of ecological balance begun to change. It S. pehaps, due to this fact that the regeneration of natural plant communities from fire is a process that is not well doaimented or understood, and that further exploration into the nature of this process is required [Bailey, 1996; Schimmel8
Granstrom, 1996; Achuff et al., 1984; Harris, 19761. The research documented in this report has been directeci towards the goal of advancing the understanding of this process. Specificaliy, 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 Verniilion 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 species", which serve to distinguish each plant community from the others by satisfying the following three criteria: (a) species must occur in al1 or nearly al1 sample plots; (b) species must demonstrate statistically signifiant correlation to one or more ancillary (DEMdeiived) variable; and (c) species' intracommunity similarity and intermmrnunity distinction must be sufficient to clearly separate communities or groups of communities from one another;
3. Through the relating of disaete, field-collected (point) data to continuous, ancillary data values at discrete sample plot locations, and through the combined use of regression and geostatistiical spatial interpolation techniques, to develop continuous spatial representations of diagnostic species for the bum area;
4. To classify the burn area using the mdeled diagnostic species' distributions as inputs and
Maximum Likelihood Classification methods; and thus, to generate a map of the spatial distribution of plant communities within aie 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 thmugh this process will be compared to those identified in simihr areas through the Kootenay National Park Ecological Land Classification [Achuff et al.,
19841, so that the relevance of these results in comparison to other simihriy situated, similarly aged burn 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 examaied the regeneration of vegetation within the Vermilion Bum during the years immediately following the fre. The goal of this comparison is to add a temporal aspect to the study, and thus irnprove understanding of the dynamics of bum regeneration over time. This will provide future researchers with two 'tirne slices" of the regeneration to which they can add, the anticipated end result being documentation of the full fire cycle at regular intewals, and impcoved 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, foresten, and biogeographen. 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 Vemilion Pass to connect Castle Junction and the Trans-Canada Highway to the Kootenay River Valley, Radium Hot
Springs and southeastem 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 1l6OO'W Longitude. Figure 2.1 shows the relative location and spatial extent of the Vemilion bum.
Physiographically, Vermilion Pass is located in the eastem 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 akng the continental divide from the British
Columbia-Montana border at Lake Koocanusa, north to the southem tip of Williston Lake on British
Columbia Highway 97. Compared to neighbouring physiographic regions, the Eastern Main
Ranges are generaliy characterized by nearly flat-lying, older (as old as 800 million years) sedimentary rock, and high peaks. lheir 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 al1 directions from mountain peaks [Gadd,1995].
Vermilion Pass sits between the Bow and Ball Ranges, both of which are memben of the Eastem
Main Ranges, and which contain the highest peaks in Kootenay National Park, which include
Deltaforni Mountain (3,424m), Mount Ball(W2m), Mount Allen (3,301 m), Mount Tuzo (3,249m),
Mount Fay (3,238m). and Neptuak Mountain of the Wenkchemna Peak (3236m) [Acuff et al.,
19841. In this area, the Eastem Main Ranges are bordered to the west by the Western Main LEGEND
A Narned Mountain Peaks Amburned Juiy 913.1968
f Locatiori of Lighlning Sbike (Ignition) Vegetated Areas @ekw tiee lin@
Stream, Creeks, and Rivas Glaciers
Major Roads and Hïghways Lakes
Alberta - Briüsh Cdumbia Border Figure 2.1: Map showhg relative localion of Vermi/hn 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 Man Ranges differ signifcantly from the Eastern Main Ranges in their morphology - the Vermibn and Mitchell Ranges are cheracterirecl by long and nanow serrated ridges, castellate peaks, and welldefined cirques and hanging glacial valleys [Achuff et al., 19841. To the east, the Eastem Main Ranges are bordered by the Front Ranges of the Rocky Mountains, the boundary of whkh is demarcated by the Simpson Pass thnist fault Compared to the Easten Main Ranges, the Front Ranges are not as high, much drier, and characterized by thrust faults, southwestwarddipping sedirnentary 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 Cathedra1 Formation and shale of the Stephen Fonation compose the bedrock geology of the valley bottoms and lower hillslopes. Mountah slopes are primarily composed of middle Carnbrian 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 eaily Carnbrian shales of the
Sullivan Formation [Cook, 19731. The characteristic near-horizontal orientation of sedirnentary layers combines with the altemation of bedrack between cliff-fonning, erosion-resistant limestone and laminar, more erosive shales to mate a 'stak-step" topography of relatively gradually sloping areas, separated by abrupt cliffs [Gadd, 19951.
Generally, the lower valleys, cirques, cols, and valley bottoms in the Eastem Main Ranges comprise the lower subalpine scoregion and are abundantly vegetated. The upper valley walls, Background 7 crags, and cliffs tend to be more barnand make up the upper subalpine ecoregion [Achuff et al.,
19841. The vegetation surrounding the bum is of highly variable composition and matunty. The lower subalpine is made up of dense stands dominated by Engelmann spruce (Pba engelmannii
Pany ex Engelm.) and subalpine fir (Abies lasiocarpa var. lasimrpa (Hook.) Nutt.) in more mature areas, and by lodgepde pine ($Mus mntorta Dougl. ex Loud.) in areas that were bumed 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 tlexilis James), interspersed with heaity shrubs and sparse meadows containing more resilient, mat-formng ground merspecies.
Krummholz cornmunities are also present, comprised of dwarf tree and shnib species from the lower subalpine. In many instances, especially along the Vermilion River valley, the Vermilion fire appears to have bumed right up to tree Iine (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 aie west [Harris, 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 Vennilion bum - the eight weather stations were placed at various locations, aspects and alevations (1,524m, 1,646m, 1,676m (four stations), and 1,981m (two stations)), five stations within the burn 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, recordeci average temperatures above the wggested growing season threshold temperature of SOC[Peddle and Duguay, 19951, under which most plants remain donnant This is due largely to the influence of elevation -the bum itseif 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 Mountani. The area has an average annual precipitation of 809mm - relatively moist when compared to ooier lower subalpine areas'.
Mean Monîhfy Tempeiature and Precipitation Vernilion Pass, Canadian Rocl Figure 2.2: Mean month& temperature and pm@ilatiim (source: 197û-75 data, hmHams [1976J1 The Vermilion Pass fire was ignited on July 9m, 1968 by a dry lightning strike, in the l Statistic calculated by a 3-year averaging of data taken ftom Marble Canyon Meteomlogical Branch weather station - mean for seven lower subalpine stations in and munâ Kootenay National Park was 747mrn [Achuff et al., 19841; Hams study [1976] data shows average annual precipitation equal to 434.2mm hmseven weather stations wiîhin 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 contrdled on July 13% Spot fires continued into early August [Harris, 19761. The Vermilion fire was the largest recorded between the years of 1961 and 1985 in the lower subalpine ecoregions of Kootenay, 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, topographie 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 cornmitment to the preservation of ecological integrity. It behooves Parks staff, therefore, to ensure that TKootenay National] 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 burn to regenerate naturally from the 1968 fire, free from the human intervention that is cornmon in bumed areas that fall under different land management strategies. 10 CHAPTER 3 - LIERATURE REVlEW Examination of the dynarnics and cbmplexities associatecl 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 oveMew of cuvent 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 disaissed. Finally, this chapter will provide the reader with a summary of past research on the vegetative composition of the Vermilion bum and sunounding areas. 3.1 - SUBALPINE FOREST FlRE ECOLOGY The dynamics of the subalpine forest structure and ecological landscape are largely govemed by the cycle of fire and regeneration [Kenhaw et al., 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 suwival (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 bumeâ 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 rnosaic of plant communities within the subalpine bum. Literature Review Il Some of the carventional ideas of fire succession and fire ecology in the subalpine forest landscape have recently been re-examined. The "taditional lineaf view of fire succession States that lodgepole pine is the first species of tree to appear in a recentiy bumed area. The growing pine trees create shade and leaf litter necessary for the establishment of subalpine fir and Engelmann spruce seedlings. These seedlings gnmr in aie undentory of lodgepole pine until they eventually outcornpete the pine for essential resources and thus corne 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 pmposed which incorporates cunent 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 Cr, and Engelmann spruce al1 commence regeneration immediately following a fire. The most signifiant factors impacting reestablishment of al1 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 eariy regenerating forest canopy. Eventually, growth rate dedines 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 remit, albeit with high mortality, at a diminished rate even under the closed canopy of a mature forest [Johnson & Miyanishi, 19911. A signifkant body of research exists that has attempted to empirically charactenze the fire regime within the subalpine ecoregion of the Rocky Mountains. Lightning caused fires are Litemture Rewiew 12 characterized by high intensity, high consumption levels of organic matter from the forest flcor, and fire cycles of 90-1 30 years [Johnson 8 Larsen, 1991 ; Johnson & Miyanishi, 1991 ;Masten, 19901. Fire cyde is defined as '...the time required to burn 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 al1 dunng the fire cyclen[Johnson 8 Miyanishi, 1991, p.821. Though it exhibiis 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 8 Despain, 19921 - the local fire cyde appears to have remained relatively constant since the mid-1700's. Before this time (the beginning of the Yile ice age"), the regional climate was warmer and drier, resulting in shorter fire cycles [Johnson & Larsen, 19911. 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 goveming the fire cycle [Bessie & Johnson, 1995; Johnson 8 Miyanishi, 19911. 3.2 - MODELING COMPOSlTlON AND DISTRIBUTION OF VEGETATION Modeling is the process of empirically definng 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, whkh generalize the patterns of relative location and distribution of communles, commonly employing a GIS (Geographic Infornation System). Non-spatial models commonly generalize the relationship betwaen associated phenomena at discrete (sample point) locations; spatial models commonly involve the inference of phenomenon distribution based on a combination of discrete (sample point) observations and continuous (ancillary) data on associatecl phenomena. Although few have dealt directiy with the effects of Cm, there are many documented studies that model vegetation patterns on a landscape using tiierent combinations of field- collected and ancillaiy data. Wthout the aid of GIS, Strong and Leggat [1981] mapped the ecological communlies (ecoregions) of Alberta, in which ecological communities are disthguished from each other by a broader range of features than plant communities, incorpomting climate and soi1 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, soi1 reports, and climatic data. La Roi et al. [198û] 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 BraunBlanquet tabular comparison and two-way indicator species analysis (ïWINSPAN)) '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 modifmd version of principal mponents analysis, detrended correspondence analysis (DECORANA). DECORANA, first developed by M.O. Hill of Comell University in 1979, and modified by Oksanen and Minchin [1997] to correct program instabilities under reordering of input data, is similar to principal mponents analysis (PCA) in that it reduces data into a number of 'supervariablesn (principal components) that desaibe the maximum variability for the data set. DECORANA diffen 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 rechiming a forrneriy impounded area, using hydrology as a detenninng factor. Bolliger and Sherrer (19931, as part of a Swiss national environmentai protection policy, made combined use of aeriai 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. [1998] used a mbination of natural (soi1 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, forestty, 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 field-collected 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 aawracy 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 [1996] modeled past fire seventy in a Douglas fir forest that most recently bumed in 1991, using topographic predictor variables. It was detennined 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. [2ûûû] modeled the distribution of a spruce-fir vegetation community type over a large (approximately 1.7 million ha) area in northem New England (New Hampshire and Vermont) based on ancillary data from DEM, satellite imagery, and climate data sources. Ancilhry data included raster cwerages 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 detennine an accurate technique to model continuous surfaces representing the distribution of the two species using diswete point (sample plot) vegetation data and its relationship to the ancillary variables listed above. It was detemined that the most accurate method was a combination of two modeling approaches. First, linear tegression was performed using the vegetation data as the dependent variable and the significantly conelated ancillary data values, spotted for each point in the vegetation data set, as independent variables (spotted values are ancillaiy (exhaustive) data values, extracted fmm a conthuous (raster) coverage only at locations for which discrete data values (sample plots) exist). The regression residuals were recorded for each plot, and used to mate 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 ngorous geostatistical spatial interpolation pracess. Finally, a percent merdistribution of the spruce-fir vegetation community type was modeled by adding the continuous surface generated by th8 spatial expression of the Literature Review 16 linear regression equatbn to the surface generated by kriging interpolation of the residual spatial data set. Various attempts have been made to mode1 vegetation patterns in the Rocky Mountains using topographie and clirnatic variables as determining factors. Hiemstra et al. [2ûûû] postulated that winddistributed snow accumulations were the main variable determining plant community distribution in the alpinesubalpine 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 Sturm, 19981 and a DEM, an empirically developed tree elevation model (TEM), and meteorological drivïng variables (wind speed, wind direction, temperature, relative humidity, and precipitation) as inputs. The model aimputes a wind speed and direction value for each pixel in three dimensions. Model accuracy was tested by comparing model-derived values to field-recwded values at regular intervals along a transeet. The model accurately located areas of wind erosion and deposition of snow, but generally tended to overestmate depth of both erosion and deposition. Peddle and Duguay [1995] calculated a series of topoclimatic index variables to incorporate into the satellite image analysis of an alpine ndge 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 cakulated for the study area: 1. An orogenic precipitation index calculated as a function of altihide 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 calcuhted 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 al1 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 al1 five indices in the modeling process. Ind~dually,the growing degree days and orogenic precipitation indices were found to demonstrate the highest ecological community classification accuracies. 3.3 - PAST VEGEtAnON STUDIES IN AND AROUND VERMILION PASS This aiesis 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 lnstitute of Pedobgy, on behalf of Park Canada, camed 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 propeities and lithology, climate, hydrology, and wildlife habitat suitability) in the designation and mapping of ecological communities (ecosites); therefore, the definlion and distribution of mapping un* for the 1984 Kootenay National Park ELC will differ from those identifid in this thesis. However, the ecosites mapped for aie area in and around Vermilion Pass are valuable in ternis of a general description of edogical communities of the bum, and also for comparison between plant communities as they are mapped in mis study and Literature Revie w 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 evohring dynamics of the fire cyde, the plant communities recorded in 1964 are likely to be different to some extent from those recordd in 1999. Tables 3.1 and 3.2 summarize plant divenity 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, l5Xl5m in shrubby areas, and lOX1Om in herbaceous and dwarf shrub locations. Smaller plots (5XSm or 1x1m) were used in intricately pattemed areas to ensure homogeneity [Achuff et al., 19û41. Along with vegetation data, the following physical environmental factors were also recorded for each sample plot: elevation, slope, aspect, topographie position, relief shape. landform, soi1 subgroup and drainage class, and moisture regime. Vegetation was first organized into five general physiognomic (structural) classes (Closed Forest (C), Open Forest (O), 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 (Vis). A total of 56 different VTs were identifid 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 bumed areas; Mer, an ecosite modifier (in this case, the letter %")as suffixed to an area that had recently bumed? Addressing a perceived need for greater understanding of the effects of fire, Dr Stuart A. Hams of Harris Environmental Consultants, Limited (Calgary Alberta), in 1971, undertook a rigorous research project, the goal of whkh was to qumtify al1 aspects of the environmental conditions resulting from the 1968 fire in Vermilion Pass. Not limited to a vegetation analysis, the Harris Study [1976] lmked at the bum and the early stages of regeneration in tenns of changes in climate, soils and soi1 microflora, vegetation, and fauna (small marnmals and birds). Since this thesis focuses exclusively on regenerative changes in vegetation, only those sections of the Harris study that deal directly with vegetation will be discussed in detail. These findings will be used as a bais of cornparison 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 thmughout the entire fire cycle. In order to improve comparability of study results, the research undertaken in this thesis has purposely employed a similar sarnpling resolution to that employed by Hams [1976] and Willard 8 Harris [1972] 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 rnodiers areas mat were frequentiy snow avalancheci (A), faim slopes (F), and liicareas (X). So, for example, an area detemined to belong to the Sawback ecosectian, ecosite 3 that had recently been burned and that experienced freqoent snow avalanches would be designated S83AB". Litenturi ReVrew 22 yean following fire, the dominant understoiy Jpacies were heartleaf amica (Amka cordifolia Hwk), fireweed (Epilobium angustifolium ssp. angustifolium L.), and rusty menziesia (Menziesia femginea Sm.). These were replaced at 55 years with whortlebeny (Vaccinium myrtillus var. oreophilum (Rydb.) Dom) as the dominant shmb to still-cornmon wsty menziesia, and diminishing heartleaf arnica and fireweed. After 120 yean, Harris credited the increased shade provided by denser canopy coverage for the resurgence of wsty menziesia as the dominant shrub species over whortlebeny, and for the diversification of the herb layer (high incidences of strawbnyleaf raspbeny (Rubus pedatus Sm.), Canadian bunchbeny (Comus canadensis L.), bog Labrador tea (Ledum groen/andicum Oder), and tmnflower (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, msty menziesia continued to dominate with considerable amounts of Canadian bunchbeny, strawbenyleaf raspberry, and whortlebeny in an othewise abundantly mossy understory. Hams cautioned that these four plots dernonstrate 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 burns. Nor, suggested Harris, do the four plots sampled in the preliminary vegetation study provide a comprehensive sampling of communities of their respective ages. Wilhrd and Hams [1972] mapped the vegetation within the Vermilion bum. A sampling grid was laid out, consisting of a 300X300m anay, oriented parallel to the bottom of the Vermilion River Valley, with survey pins lacated 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 mate generalized (isoline) vegetation maps showing the distribution of lodgepole pine, cornbined Engelmann spnice and subalpine fir, and dominant herb and shrub species. Figures 91-34 reproduce the Willard and Harris 119721 vegetation maps. Figure 3.1 shows the reestablishment of ladgepde pine seedlings in the Vermilion bum as of 1972. The distribution was noted to demonstrate a high degree of spatial variability, ranging from o. 3 Km. Figure 3.1: 1972 map showing post-ire disîn'bution and abundance of hni'le pine seedlings in aie VermiIbn Pass bum. ReguIar gM of dots repmsent sample pbt locaîbns. Source: Ham's, 1976. O 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 northeastem end of the bum. Harris postulated that the favourable site conditions for the establishment of pine seedlings were lower elevations and higher amounts of earîy season (i.e. snow men) moisture; however, the relationship among these variables was not quantified in the Harris study. Figure 3.2 shows the mbined distribution of Engelmann spruce and subalpine fir figure 3.2: 1972 map showing pst-fire dkinbuthn and abundance of Engelmann spruce and subalpine fr seedlings in dhe Vetmiliin Pas bum. Soum: Ham's, 1976. seeâlings within the bum. Harris noted that their reestablishment at this eaily 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 Figure 3.3: 1972 map showing post-fia dr'stnbuîion and abundbnce of dominant shmb species in the VermiIbn Pass bum. Source= Ha&, 1976. Litemture Review 25 species were beginning to re-establish themsehres wiai increased vigour. Figures 3.3 and 3.4 show the areas of dominance of shmb 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 strahim. Although Harris acknowledged the large areas of rusîy menziesiadominated shnibs and apparent cornpetition between fireweed and heartleaf arnica for dominance in the herbaceous layer, he did not propose exphnatory hypotheses for these patterns. Figure 3.4: 1972 map showhg post-fire dbtn'buîhn and abundance of dominant herb species in the Vermiliun Pas bum. Source: Ha*, 1976. Vermilion Pass, especially the higher slopes of Mount Whymper, experience frequent snow avalanches that. even to the untrained eye, noticeably affect sorne vegetation patterns within and beyond the Vermilion bum. 60th the Hams study [l976]and the Kootenay National Park ELC [Achuff et al., 19841 paid special attention to the vegetative pattems and communities associated with avalanche disturbance. As part of the Harris study, Winteibottom [1974] mapped the vegetation in the avalanche chutes of the Vermilion bum and compared them to those in similar, unbumed. adjacent areas. It was concluded that the avalandie chutes had widened and Litmature ReVre w 26 lengniened as a resutt of the fire. Achuff et al. [1984] identified three distinct avalanche complexes in Kootenay National Pak The avalanche complexes Vary according to ecoregional situation and vegetation composition, and are mostly mprised of low-lying herb and shrub (with the exception of one aspen-dominated closeci forest) vegetaüon types. Achuff et al. did not examine the effects of fire on avalanche tracks. Although the response of vegetation aimmunities to the avalanche disturbance (and moreover, to combineci disturbances of fire and avalanche) is an Mguing field of study, time mstraints prohibit the consideration of such modeling problems within the scope of this thesis. 3.4 - LERATURE REVlEW 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 surounding areas. This chapter has provided a brief discussion of the existing body of literature, with particulaifoais 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 cakulation. 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 - METHOOS This chapter provides an outline of the field and analytical research methods employed in this thesis. For purposes of clarity and canvenience, it has been divided into sections, each of which describes a separate stage of research. A list of the spatial data resources (mplete with sources and accessibil'ty) and analytiil software used in this study is included in Appendix 1. Appendix 2 is a flow chait that summarizes the procedure outlined in this chapter. Appendix 3 is a cartographie mode1 that illustrates the modeling proces, as well as the creation and incorporation of data layers in the analysis. 4.1 - RELD DATA COLLECTION, INPVT AND VERlFlCATlON The vegetative composition and other field data used in this thesis were collected between late June and eariy August of 1999. Prior to staiong work in the field, it was necessary to devise a field sampling strategy. In order to maximize comparability between this and the eariier [Harris, 19761 study of the hm, a simihr sarnpling strategy was used. Two 1:20,000 sale British Columbia Ministry of Environment Terrain Resource lnventory (TRIM) Maps (82N.020, covering the southem part, and 82N.030, covering the northem 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 (ninning along the Vermilion River Valley, assigned the letten 'A" through 'W, inclusive), and a numbar to each cdumn (running across the valley, assigned the numbers 1 through 36 inclusive) in the sampling grid. A conesponding alphanumeric identifier was thus assigned to each cell within the grid. In the field, one sample plot was chosen at random fmm wwin each cell of the sample grid that was estimated to be more than 50% bumed. Figure 4.1 is a configuration diagram of the Meo7oàs 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 Bstmtum (shrubs and young trees lesthan 3m high) plants. Within each sample plot, four lXlm subplots were delineated for the purpose of C-stratum (herbaceous layer - comprisecl of heh, and shrubs and trees les 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 banen plots, three subplots were occasionally sampled. In plots of high diversity it was occasionally necessary to sample a fisubplot - in such cases, this fisubplot was Iocated in the centre of the sample plot. Methods 29 Plots were precisely Iocated using a hand-held, 12channel Gamin global positioning system (GPS42) (for the first two weeks of the field season, plots were Iocated by triangulation with a Bninton pocket tmnsit compass). In 1999, with the intentional displacement of GPS locations (known as Selactive Avaiiability (SA)) sMI in place, the accuracy of GPS readings was approximately +/- 100m. General site characteristics were recorded for each plot, including slope, aspect, elevation (estimated hmTRlM 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). FieM identification of al1 plant species was based on Kershaw et al. [1998] 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 detailad measurements, and each of these trees was assigned a unique identification code. The number of trees for which detailed data was rscorded 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. Foi 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 detemined. Finally, a core was extracted from each tree, and was labelled and stored for deterrnining 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 detemined (by whorlcounting) for miferous 6-layer trees. Where multiple 6-layer trees of a MeWs 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 Taxonomie Information System (ITiS) [ITIS'? 20011. Lichen species were verified using Hawksworth et al. [1980] 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 hm those plots located within regenerating forest area remaineci. All field-collected data was entered into a spreadsheet, including A-stratum tree ages, determineci by counting the rings of extracted tree cores. The data for species that occurred in bdh 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-strahim 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 sarnpled, and entered as a single variable for each sample plot- 4.2 - CLUSTER ANALYSIS Hypothesis: field collected vegetation data is divisible into meaningful groups repfesenting distinct plant mmmunities. Test: Perfom Wads(minimum variance) method of hierarchical cluster analysis on the field data. Cluster analysis is a mulavariate statistical procedure that arranges individual 'cases" into Medhods 31 meaningful groups, or clusten. The term 'case" refers to the collection of al1 obsewed variable values for a single object in the data set. In this thesis, individuai cases represent distinct sample plots within the bum. Generaliy, this partkning or subdividing of the data set is obtained through the definition of some unifying characteristics that al1 mernbers of an ind~dualcluster share, and that also distinguish them from al1 of the other clusten. Cluster anaîysis is a general term used to describe the numerous techniques of subdividing data into gmups. Tha specifii type of cluster analysis used in this thesis is Ward's mettiod of hierarchical cluster analysis, employing the squared Euclidean measure of distance. A brief explanation of this clustering method follows, adapted from Davis [1986] unless otherwise specified. Hieratchical cluster analysis first groups or combines those cases that demonstrate the highest degree of mutual similarity. The next moût mutually sknilar groups are then cambined, and so on in an iterative process until Iwo large groups of cases, demonstrating the highest degree of mutual dissimilanty, remain. The resuits of hierarchical cluster analysis are frequently represented in an inverted tree-shaped linkage diagram called a dendrograrn, which shows the levels of similanty at which each respective group of cases is related to every other group (for an example of a dendrogram, seFigure 5.1 in the next chapter). The fint step in perfoming a hiearchical cluster analysis is to establish a measure of similarity between each case and every other case in the dataset, with respect to al1 variables being considered. The measure of similarity used in this instance is squared Euclidean distance, which cakulates the distance dh or dissimilarity, between two cases, iand j, in the rnultidimensional space defined by a set of m variables, by the following equation: where rn is the number of variables, & represents the kth of m variables' value, recorded in case i, and &R represents the Wi variable's value, racorâed in case j. When a low value is retumed, the two cases are close together in indimensional space (hence, similar with respect to the m variables considered in the analysis); when a high value is retumed, the two cases are located at a greater distance from one anooier, and are therefore less similar. The squared Euclidean distance statistic is calculated for every case pair in the data set, and the resut, given n total cases, is an mn similarity matrix. Once the similanty matrix has been computed, cases are grouped into ciusteers 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 al1 clusters are joined, and by recording a measure of slnilarity that associates one cluster (or group of clusters) with the next joining cluster. Ward's minimum variance clustering method (also refened to as Orioci's clustering method in ecological Merature [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 resulong increase in cumulative variance - that is, the greatest Euclidean distance, in mdimensional space, between the two most disparate cases in the newfy 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 commonîy used in ecological research [Lane, 20011. SPSS was used to perfom a hierarchical cluster analysis (Ward's meaiod, squared Euclidean measure of distance) of al1 sample plots within the bum, using only species percentage variables as inputs. This process subd~dedthe data into sarnple 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 IDRISI 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, consuit Appendix 1). All data described in this section, regardless of the software that was used to produce or abr it, was impoited into IDRISI 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 welf outside of its designated sarnple grid cell) was omilted before spatial analysis was perfomied. The boundary of the burn, location of avalanche chutes, and mature (i.e. unbumed in 1968) forested areas within the bum were precisely delineated from oithorectified IRS and Landsat TM satellite imagery in PCI. Landsat Imagery was already rectifieci when it was received, but the Medhods 34 IRS imagery had to be orthorectified by matching feahires 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 bumed area, or to specific areas within it. The 'general vegetation mask" covered the entire bum, excluding unbumed areas. The "A-stratum vegetation masK also excluded those amas identified as avalanche chutes, wtiere no tall trees are present. Digital TRlM planimetric data (1 :20,ûûû scale) was used in Arclnfo to locate roads, strearns 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 Figum 4.2 - Conversion of raw aspect values to soIar and cross-Wey aspecl vanables. Meütods 35 The raw aspect values generated in Ardnfo range from 0-3600: t was therefore necessary to convert this circular variable, whiiplaces geographically adjacent aspects of 00 and 3590 at opposite ends, into a more meaningful measure. Transformation followed Peddle and Duguay [1995] - raw aspect values were converted to a symmetrical scaie variable, ranging from 0-1800, with the axis of symmetry oriented in the direction of the maximum anticipated variabili, or of the aspect-rebted featute that was to be modeled. Figure 4.2 illustrates the conversion of raw values that were used in the cakuiation of the two aspect variables used in this thesis. The first, called "sohr aspea". was designed to capture the effects of insolation on vegetatian patterns, and was therefore minored about a north-south axis. North and south aspects tetain 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 identify and explore the apparent abtupt change in vegetation patterns, especially among 6-stratum and C-stratum species, fmm one sida of the Vermilion River Valley to the other. This transition was most avident in the valley bottom and on lower valley slopes. The second aspect variable, called "cross-valley aspecr, was thus cakulated to confirm and identify the variability in vegetation between one side of the valley and the other. The ais 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 DEMdefived slope to calculate two separate slope-aspect inckes, SA/, after Peddle and Duguay [1995]. Cross-valley (SAM and solat (SAIS) slope-aspect 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 variabie, derived through the application of either one of the two convenions described above. Sie morphology is a surrogate measure of sol moisture (drier areas on ridges and promontones, moister areas in gullies and depressions), whkh is thought to signifkantly effect the distribution of vegetation [Bailey, 19961. To incorporate a measure of site morphofogy, the DEM and the convolution module of Terra Fima [Eyton, 19921 were used to derive crossalope and dom-dope 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 rnethods is adapted from Eyton [1991]. Cuwature values are derived from a MMusing a 3x3 convolution algorithm. 5x5 convolutions can also be calculated in Terra Firma, but a 3x3 convolution matnx 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 boîh the cross-slope and down-dope curvature values (in the example above, for the middle cell with the elevation value &) is essentially the same. lmplicit in this calculation are the rneasures 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 down-slope curvature values can be computed, since the dope vector, and specr'iliy the azimuth, identifies the down-slope direction for the grid cell in question. Slope is calculated by applying Pythagoras' theorem to L two Methods 37 Cartesian (X and Y) mponents, sx and sy.The magnitude of the slope vector, ~v,is compufed as follows: where sx and sy are the two Cartesian (X - horizontal, presumably east-west, and Y - vertical, presumably noith-south) slope components of the slope vector. fl 3x3 CONVOLUTION MATRIX l l j 1 r Y i 1 v ,' Figure 4.3 - Diagram of Ihe 3x3 conwlutbn rnaînk used to detemrine curvature values for the cenlralpiKel&). The dashed amw repmsents the minuth, abng wfiM îhe endpoinîs (a and 6) of Ihe slope vector are located. Cartesian components of slope are cakulated 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 caiculation of sx and SC Azimuth (asp) is cakulated as a function of dope - generally, it is obtained by calcuhting the angle of separation between the slope vector and its horizontal (X) Cartesian component. % is cornputed as follows: The slope vector (represented in Figure 4.3 by the points a and b on the dashed anow) 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 cuivature (dc, either crossîlope cuwature (xsc) or down-sbpe cuwature (da)) is obtained by finding the discret0 (finite) second derivative of the eievation values (with respect to distance) along the dope vector. where Zp and are linearly interpolated elevation values at either end of the dope vector. The calculation of daand xsc are identical, except in the case of xsc the vector through the centre of the target ceIl is orthogonal to the dope vector ab. A total of eight ancillary topographical variables (layers) were generated for analytical use: - elevation, from the DEM; Methods 39 - slope, denved in Arclnfo; - two aspect variables (cross-valley and solar aspects, canverted hmraw aspect data denved in Arclnfo); - two slope-aspect indices, (calculatecl in IDRISI using the two aspect conversions); and - the two (cross-slope and downîlope) 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 indkate mernbetship in a specfii plant commun@t Test: ldentfmtion of diagnostic species based on dhrm selectr.On criteria: ubiquity, high correlation to topogmphic variables, and irnproved 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 perfonned on ancillaiy 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 detemined by the same methods that were used to assess suitability of diagnostic species (see below) that ancilkry variables were not suffiiiently diagnostic of the uniqueness of plant communities to be used directiy in the modeling proaess. This decisionniaking process is dacumented in the model flow chart, found in Appendix 2. The next step in the modeling proces was therefore to select the most suitable from a list of possible 'diagnostic speciesn. Diagnostic species are ubiquitous or nearly ubiquitous plant species within the bum. but their abundance varies greatly between (and mparativeiy liWe within) different plant communiaes. The abundance of each diagnostic species in comparison to al1 other diagnostic species at a given location is the bais for rnodeling the distribution of plant cornmunities of the Vermilion bum. The following criteria were used to detemine the most suitable diagnostic species: 1. Spiesmust be ubiqu#our or neady ubiquitous minthe burn - A list of the ten species that were identified in the greatest number of sample plots was compiled. 2. Specier must ckmonsaate staüstically signifiant correlation to one or more ancilbry topographical variable - Bacause 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 conelation analysis was perfomed for al1 combinations of Xand Y using the ten potential diagnostic species and al1 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 conelation between variables Xand Y; and N is the total number of observations (cases) on X and Y. Methods 41 Assuming m totai variables input into the amelation anaiysis, the result is an mm similarity matrix, syrnmetrk 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 potenüal diagnostic species. 3. Species must impow seprability of piant communitks in the el.The uitimate goal of this thesis is to use the distribution of individual plant species to mode1 the distribution of plant communities. Therefore, a most desired quality of a diagnostic species is the ability of the distribution of its values to staüstically demonstrate the separation of one or many clusters - in other words, species were selected according to the degree to which their inclusion in the mode1 would improve cluster separability. The diagnostic species that were selected demonstrate percent cover values that are suffiiientiy similar within a cluster to give it coherence, and suffiiently different between one cluster and the next to render them distinguishable and unique. Cluster separabiiity was assessed first graphically using probability ellipses (equiprobability contours) and coincident histograrns, and was subsequently verifid using the Kruskal-Wallis non-parametnc test for ample independence. Equiprobability contour (or probability ellipse) diagrams are drawn on two-mis (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 arwnd each cluster mean with the horizontal dimensions (breadth) of the ellipses equal to twice, four times, and six times the standarâ deviation in variable X from the cluster mean. The vertical dimensions of the ellipses are Mebhods 42 detennined in a similar fashion, using muîtiples of the standard deviation from the mean in variable Y. Points located within the area defined by each successively smaller ellipse amund a cluster mean are successively more likely to be associated with that cluster. Two points located along the path of the same equiprobabiiii contour are equally likely to belong to the same cluster. A point that is located at the intersection between equiprobabiiii contours of the same magnitude (e.g. both contours bounded by a distance of 1 standard deviation from the rneans of two different clusters) is equally likely to be açsaciated with either cluster. The equiprobability contour diagram is interpreted by assessing the degree of overiap between the probability ellipses of one cluster and those of another. If even the largest ellipses of two adjacent clusters do not oveilap one another, then there is a high degree of separablity between those two clusters using the variables X and Y. Conversely, if there is a significant degree of overiap 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 clusten, in that they use standard deviation from the mean as a probability measurea. This latter shortwming is addressed by another graphical separability assessment tool. the coincident histogram plot. 3 Equiprobabili contours tend to underestimate the probability of cluster membership for platykurtic distributions, and overestimate the same for leptokurtî diiiutions. The distribution of species data within clusters is discuçsed briefiy in the next ctiapter, section 5.4. Methods 43 Unlike eguiprobability contour diagrams, coincident histograms only allow for the assessrnent of cluster separability based on one variable ai 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 histograrns, 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 KruskaI-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. lnstead of cornparhg the means of samples to the population mean, mean ranks for each sample are the obiects of cornparison. The test detemines 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, al1 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 al1 assigned the same rank, averaged over the number of tied variables. The Kruskal-Wallis Hstatistic is then cakulated, 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: where N is the total nurnber of observations; k is the number of samples (clusters) being testeci for independence; n is the number of observations in a given sample (cluster); and R is the rank-order value assigneci to a given obsewation. Once calailated, the Kniskal-Wallis Hstatistic is compared to the ~2 (chi-squared) value at '1 degrees of freedom and a user-specifieâ confidence level. A low value of Hwill be close to the ~2 value and will support the nuIl hypothesis that samples' (clusters') respective distributions are simibr to those of the entire dataset for the variable being tested. A high value of Hcompared to the ~2 value means that the nuIl hypothesis can be rejected, and that sarnples display statistically signifiant independence in their distributions. When performing the Kniskal-Wallis test in SPSS, the cornparison between the Kruskal-Wallis H static and the ~2 value is automatic, and the test retums a measure, based on this cornparison, called asymptotic significance. This is a probabilii based significance value @value), based on the asymptotic distribution of the Kwskal-Wallis H statistic. Generally, an asyrnptotic significance value of 0.05 or lower is considered to be statistically signif'kant [SPSS Manual, Igg8j. 4.5 - MODELING MSTRl6UTlONS OF MAGNOSTlC SPEClES Hypofhe8is1: The gene&lUed distribution of diagnostk species h Itie Vermilion bum can be descn'bed ushg DEM-derived ancillary topographrW1 values. Testt: Linear regWon analysis of diagnostic species' pemtcover values against significantly Methods 45 conelatd ancillary variable values at mpleplot locations. Hypothesis2: The vaiability h diagnosb spcies' percent cover values that is not explained by linear regression fie. regression residuals) is spatial4 dependent, and can therefore &e modeled using geostatistkal lnte~polationtechniques. Tests Calculation of aie expen'mental 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 rnost accurate mealod 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 mbination of two modeling procedures (for a more detailed description of this research and L findings, see section 3.2 in the previous chapter). First, linear regression analysis was perfonned 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 h useci 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 quantitatkely 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 rernoving 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 Harris study [1976]. Diagnostic species' percent cover values (over the entire data set) were regressed against those ancillary variables to which each one is most significantiy correlated, as determined by the process described in section 4.4, above. Both single variable and muitiiariate linear regression models were used in this thesis, and are described below. Single variable (or simple) Iinear regression generalizes the influence that the independent variable Xhas on the dependent variable Y. The generalized relationship between the two variables is the %est filine to the data, and is expressed by the regression equation (or regression line): where Y is the dependent variable; X is the independent variable; ais the constant (Y-intercept); fl is the regression cmffiiient (slope) for X; and gis a random error terni (unless the data are perfectly conelated (rpl or -1). 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, whkh 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 Y0 and the Y value predicted at Xo by the regression line. The %est fit" for the regression line is the line that minimizes the sum of squared residuals. Regression line Tiis assessed by detennining 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 Menthe dependent and independent variables are highly correlated. The procedure for multiple Iinear regression is identical to simple Iinear regression, except in multiple linear regression, the predictive capability of the %est firegression 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 (dope) for XI; #32 is the regression coefficient for XZ; n is the number of independent variables; and E~Sa random enor tem. Methuds 48 In this model, the dependent variaôies are the diagnostic species' percent mervariables. 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 resuiting @ value was calculated for each diagnostic species' percent cover (dependent variable) that generalizes its relation to some ancilbry (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 al1 unknown locations using only known attribute data at disûete (sample point) locations. Kriging is a generic terni that refers to a host of related geostatistical spatial interpolation procedures [Mitas & Mitasova, 19991. All kriging interpolation procedures are least-squares tegression algorithms with the common goal of minimizing variance of interpolation error, under the constraint of an expected interpolation error of O. This is accomplished by first modeling the spatial variance between values at al1 sample point locations, and then assigning a value to an unknown location that minimizes the error between the predicted values according to the spatial variance model and actual values at sample locations. This approach is referred to as the best Iinear unbiased estimator [Eastman, 19991. Spatial variance is assesseci through the creation of an experimental semivariogram, and through the subseguent ringof a generalized spatial variance mode1 to this experimental semivariogram. The experimental semivariogram y(h) is an average measure of the differences in Mefhods 49 known values of a vaMble between al1 pairs of sarnple points separatd by a given distance (or lag), h. The equation calculates the experirnental semivariogram gh), where N(h) is the nurnber of data pairs in the point data set that are separated by a lag of h; au,) 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 cakulated for a userîpecified number and distance of lags, providing an idea of the spatial dissimilarity or variance of known data values. Sernivariograrns can be omnidirectional or directiondependent. 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) mode1 using GStat, a geostatistical analysis program that has been integrated into IDRISI. Figure 4.4 is a generk illustration of some key features of both the experimental semivariogram and the spatial dependence model. As might be expected, experimental semivariograms usually retum a higher y(h) value for higher h values, and a lower dh) values for data pairs that are doser together. This inverse relationship between distance and correlation of amibute values, calculated and quantifid in the experimental semivariogram, is assumed in other popubr interpolation procedures, such as the inverse distance-weighted (IDW) algorithm. The parameters that control the shape of spatial variance model are the nugget, range, sll, and type of model. The nugget is the inlial spatially Methods JO5 SPATIAL DEPENDENCE OF VARlABlUTY IN ArrWBUTE z CI =24 nup.! Figure 4.4 - Semivanbgram graph, showing the hypofhebCal spatial dependence of varnabiIily in attribute z The grey dkamoncb represent actual cakulated dh) values, ami and wconnecteô by a doffed Iine, fhe experimental sernivarbgram. The dark Iine repmlsthe spaîial variance maiel. dependent vaMnce in attribut0 z, or the intercept of the Mh) ais. The range is the total distance from the origin over which the variance in attribue z is spatially dependent. The sill is the spatially independent level of yIh), or the ylh) Iimit of the spatial dependence model. The type of mode1 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 mmbined [Eastman, 19991. Model fit is optimized by rninimizing 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 intemation process to generate a continuous, smooth surface that preserves the knom attribute values at sample plot locations. [Bunwgh &.McDonnell, 19911. The specifii type of kriging used in this thesis is ordinary kriging. Ordinary kriging allows for the regional variations in mean attributs values to be considered by limiting the domain of stationarity of the mean to a user-specified local neighbourhood centred on the location for which the attribute value is being estimated [Goovaerts, 1997). The general formula for assigning attribute values to al1 unknown locations using ordinary krïging, constranied 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 userîpecified local neighbourhood; L(u) is the distancedependent weight (based on the modeled spatial dependence) assigned to a known attribute value z at the location u,; aua) is this known attribute value zat the location ua; and m(u) is the local mean of al1 known attribute values in the neighbouhood. The interpolated residual species percentage coverages were mbinedwith the regression coverages in IDRISI using an additive overiay. The coverages that resulted were the modeled distributions of diagnostic species in the Vermilion bum. Methods 52 4.6 MOûELlNG ûlSTRIBUT1ON OF PLANT COMMUNiïiES Hypothesis: M'imum IikeIihood c/ass&ation, using maleled diagnostic pies' distributions as input channels, is a viable methal by whkh to madel the dktn'bution of plant cwnmunioes h the Vemilion bum. resf: Emor matnx analysis and caku/ation of îhe kappa index of agreement. The final step in the analylical pmswas to use the modeled distributions of diagnostic species to infer the distributions of plant cornmunioes with which these species' percent cover values are associated, and thus, to map the plant communities of aie Vemilion bum. Essentialiy, 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 oie results of this assesment, 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 Vemilion bum. The procedure for maximum likelihood classifiition is as follows: Maximum likelihood classifnaion divides an image into a user-specifed number of classes according to the location of each pixel in an Aimensional 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 anaiysis 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 calculateci 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 sampie plot locations, at which percent cover values for each diagnostic species are known. Once aie separabiliîy of classes using the values for n variables at training site locations has been satisfactoriiy verified, each pixel in the image is placed in the class to which, based its obsewed values over n variables and on the established variancelcovariance of training sites, it is most likeiy to belong. MAXIMUM LlKELl HOOD CLASSIFICATION Figure 4,.5 - Diagram of a hypothe~lmhum likelihood clhiionof three points, 1,2,and 3, into thtw cIasses, a, b, and c, baseà on data hmîwo input YiUCdbk X and Y. €llipses of the same shade repmsmt eguiprobabililiiy contours cakulated from ttie variancs values of training site pixels on each class. The logic underiying the maximum likelihood classifion process cm be visualized using a simple, two-variable (Xand Y) example, illustrated in Figure 4.5. Normal distribution of training Medhods 54 site values around the mean is aswmed in the maximum îikelihood classification algorithm. Equiprobability contours (also known as probability density functions) have been plotted representing the variance/covariance 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 pmbability density functions of the three classes. Point 1 will ceitainly be assigned to class c, since it is within the 1-standard deviation equiprobability contour of this class, and located far away in X- Y space from any other class' probabili density function. Point 2 will be placed in class b, since it is located inside of the Wo- sigman equiprobability contour for class 6, but only the 'three-sigmawequiprobability contour for class a. Ambiguity and potential emoccur in the classifikation of pixels with locations in X-Y space such as point 3 in the diagram. Point 3 is located at the intersection of the 'Mree-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 acwracy 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 enor math compares the classified image to known clas values at sample plot (training site) locations by listing known class values (from sample plots) in mata cdumns, and class values resulting from the classifikation procedure in matrix rows. Correctly classified sample plots will be listed along the main diagonal of the enor matrix, and the overail classiliition emis obtained by calculating the proportion of incorrectiy classified sampie plots b total mple 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 calculateci: emKs of commission (user's em) and enors of omission (producer's Methods 55 enor). Emrs of commission occur when sample plots are included in a class to which they are known not to belong, and are cakuiated by d~dingthe sum of non-diagonal row entries by the row total. Enors of omission occur Men the classification algoriîhm fails to place a sample plot in its known class, and is calculated by diingthe sum of nondiagonal column entries by the column total [Lillesand & Kiefer, 19943. For an exarnple of an enor matix, see table 5.4 in the next chapter. The kappa index of agreement (KHAT) statistic is an empirical measure of the proportion of classification aariracy that has resulted from chance. Possible KHATvalues range from O to 1 - a high value. of 0.87, for example, rneans that the classifiin being tested is 87% better than a completely iandom assignment of class values to pixels. Conversely, a very low KHATvalue means that the classifiion being tested has produced results that are no better (or oniy marginally better) than those that would result from a random assignment of class values. The KHAT is computed as follows [Lillesand 8 Kiefer, 19941: where N is the total number of entries in the enor matrix; ris the number of rows in the enor matrix; xi is the entry in row iand column i (that is, along the main diagonal of the matrix); xi, is the sum of al1 entries in row i;and x4 is the sum of al1 entries in column i. The plant community distribution maps developed through Ihis madeling process were compared to the 1972 distdbutions of dominant plant species found in Harris [1976], in order to Methods 56 assess changes in piant cornmunity distribution over the past three decades. This chapter surnmarizes the resuits of the research conducted in this thesis. Results have been d~dedto 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 recordeci 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 mple plots remained. Table 5.1 summarizes the occurrence of the ten (most common) potential diagnostic plant species selected for analysis. Table 5.1 - General summaty staî&bGs for the vegetaîim of the Venni/ùm bum Results 58 Eighty-four plant species were identified minthe bum: 5 A-straturn species, 17 6-sbatum species, and 62 C-stratum species. A cornplate and verifid list of species' vemacular and scientific names is included in this revit 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 clusteen, based solely on vegetative composition. Figure 5.1 is a reproduction of the dendrogram created by the cluster analysis in SPSS. DENDROGRAM Sample Piots by Piant Ccnnmunity Resuits of Hierarchiil Cluster Analysis' The measure of similarity value has been rescaled. It does not represent achial distance Results 59 values cahlated using the squared Eucliian distance statistic (equation (1)); rather, it provides an idea of the relative similarities and pmximities 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 COMMUNlTiES- OF- THE VERMILION BURN d CLUSER MEMBEFlSHlP 1 28 Mount Whymper Open Pine~Buffaloberry I 2 43 Subalpine Meadow and Avalanche Tracks I 3 3 Storm Mountain Grousebenva 1 4 1 27 19~1thSide Open PinelMenziesia 1 6 12 Ribbon of Menziesia , 7 11 Dog Hair Pine I 8 28 Bottomlands Dense Pine 9 12 Midslope Closed PineNenzksia L 1O 25 Closed Pine/BMaloberrv/Grouseberrv-fwinf lower Dominant sWes in cMllen?nt sirata am separsteif by a ûmmd slash, and OOQbminant spci?~am separated l bya hyphen, as in the vegetaii;on types idmüibdby Metal. [IWJ. 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 eadi cluster is provided in Appendix 6. 5.3 - INCORPORATION OF SPATIAL DATA Figure 5.2 shows the distribution of sarnple plots within the Vernilion 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 locatd. The mislocated sample plot was used in al1 stages of analysis except those stages that were spatialty dependen?- see chapter 4. Results 60r SAMPLE PLOT LOCATIONS - VERMILION PASS Fgure 52 - Lacations of sample pbis R the Vetmilhn bum. ûoundary of bum k delheated by dashed line. Figure 5.3 is the DEM for Vermilion Pass, wio, important features identified. This DEM, derived from 1:20,000 scale planimetric data, was used to derive al1 other ancillary variables. Maps of aie other seven ancillary data layers (see section 4.3) are included in Appendix 7. DIGITAL ELEVATION MODEL (DEM) - VERMILION PASS LEGEND ELEVATiON (m) Figure 5.3 - GeneraIUed DEM of VemiIlEon Pas, created usmg TRIM digital data. Table 5.1 shows the ten most cornmon plant species within the Vermilion bum; these were the species initially selected as potential diagnostic species. Table 5.3 is a surnmary of the similarity matrix, consisting of Pearson's carrelation coefficients for every possible combination between each potential diagnostic species' percentage variable and eadi ancillary topographical data variable. Statisticaliy significant correlations appear in bold typeface, and the four species that were retained as potential diagnostic species (those associated w*M the highest ancillary variable Results 62 correlations - A-stratum lodgepole pire, rusty menziesia, grousebeny, and Canadian bunchbeny) are highlighted in grey. Pemm's Codaion Coefficient* - PoMaIDiagjnosüc Sprdrr vr AncUwy Variables ] The next step in the pmcess of selecting diagnostic species was to asses the extent to which potential species could be used to identify and diierentiate plant communities. To ths end, the following equiprobabilii 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 rwty menziesia. Figure 5.4 demonstrates a relatively clear differentiaüon of clusters - th8 separation resulüng from aiese two variables is remaikably better than that whkh resulted from any of the ancillary data variables (see section 4.4). There is no mutual overbpping of the '1 sigmau(standard deviation) equiprobaôility contours. The overbpping of contours is most noticeable and potentialty problematic nearer the ongin, specifiilly in clusten 1 through 5. The fact that these clusten are less mutually dierentiable and more similar based on Results 63 dominant plant species is not surprising - the dendmgram of the cluster analysis shows that they are closely related with respect to vegetative composition, as are clusten 7 through 10, inclusive. Fgure 5.4 - Equri,robabilify oonfour dMgram of A-sûahrm Wi~@ephe and Nsty menziesia 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 ru* 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 associations 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 lefl 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, mis demonstrates the shortwmings associated with representing cluster separabiiii in this fashion. H is, noneoieless, a useful means by which to acquire a general idea of th8 biwariate 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 histograrns of the four potential diagnostic species that satisfied the first seledion criterion (see above, and -.on 4.4) were plotted. This method of separability assessrnent allows for the cornparison of actual variable distributions for each cluster, one variable at a time. Figure 5.5 compares the distributions of A-stratum lodgepole pine for al1 clusten. From this Results 65 diagram, it is observed that clusters 1,7, and 8 are readily diierentiabie from al1 other clusters using lodgepoie pine as a diagnostic spscies. Confusion would likely arise between dustem 9 and 1O based exclusively on lodgepole pine percent cover values, with similar measures of central tendency but different distributions. There is a group of four clusten with high frequencies of low lodgepole pine values (clusters 5 4,5, and 6); no kdgepole pine were observed h cluster 3 sample plots. Figure 5.6 - Cornci'dent hisîugram of tusiy menzWa by cluster Figure 5.6 assesses the separability of clusters based exclusively on nisty menziesia percent cover values. Frorn the comparative representation of cluster distributions in the coincident histogram, clusters 4,6, and 9 are cleariy differentiable. Two oaier distinguishable but similarly distributed groups of clusters are noted - clusters 5 and 8 foming the first, and clusters 1,2,7, and Resuits 66 IO forrning the second. Again, there were no rusty menziesia mrded in cluster 3 sample plots. It is clear that including rusty menziesia as a diagnostic species would help to resohre some of the intercluster confusion that is evident in the lodgepole pine coincident histogram. Specifically, comparing the NS!~menziesia percent mervalues cwiderabîy 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 behveen ckister 3 and any cluster with frequent low percent cover values for either lodgepde pine or rusty menDesia. figure 5.7 - Equipr~babiliîycontour diagram for gmusebeny and Canadian bumhbeny - on& ellipses of dimensions qua1 to me standard deviath h eiiher dimeth hmthe mean are imluded. Figure 5.7 is the equiprobabiliicontour plot for the other two potential diagnostic species: grousebeny and Canadian bunchbeny. Due to extensive overhpping of contour ellipses, only the ellipses representing one standard deviation fmm the mean of each duster are included. Cleady, Resuîts 67 neither grouseberry nor Canadian bunchbany are as effective cluster separators as lodgepoie pine or rusty menriesia. The diagram shows that cluster 3 is cleady distinguishable from al1 other clusten 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 me ongin), and the second consisting of clusten 4 through 10, inclusive (with low mean percentage values for both species). The minimal separation between cluster gmups (and cluster 3) that is evident in this diagm is mostly along the horizontal (grouseberry) ais. Figure 5.8 is the coincident histograrn for Canadian bunchbeny. lt confimis that there is little in the relative distributions of Canadian bunchbeny values that dbtinguishes any one cluster (or group of clusten) from any other. There are two barely discemaMe groups of clusters, Results 68 differentiated from one another by the location of the cluster mode (most frequent obseivation). Ciusters 1,2,3, and 5 al1 have modes between O and 2.5% (in the first bin of the histogram), and clusters 4,6,7,8,9, and 10 al1 have modes between 2.5 and 12.5% (in the second and ttiird bins of the histogram). Cluster 5 is the only one with a sarnple 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. Figure 5.9 demonstrates importance of grousebeny percentage values to the separation of cluster 3 from ail other variables. Cluster 3 h the only cluster in wh'kh gmusebeny percentage Resuits 69 values over 47.5% were recordai; furthemore, no values of less îhan 46% were recoràeâ for cluster 3. Clusters 1 and 2 are moâal closest to 0% again with respect to grouseberry, and clusters 5 through 10 inclusive al1 have modes belween 2.5 and 12.5%. The separabiiii 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 al1 four potential diagnostic variables that were considerad. Alma al1 clusters' distributions demonstrated a moderate degree of positive skewness. Distributions that were primarily in the lower bins of the histogram (i.8. the to the left of the coincident histograms above, nearest the vertical (frequency) mis) tended to be more highly posioiely skewed than those distributions Iocated in the higher bins. Also, rnost cluster distributions displayed moderate to high degrees of negative kurtosis (i.e. platykuitic). These features made the coincident histogram a more effective visual tool for separabiiii assessment, and were also the impetus for the use of a non-parametric test to quantify sample (cluster) independence. In order to vecify the visual separabiiii assessment and quantify the cluster differentiation capabilities, the Kruskal-Wallis non-paramet* test for sampk independence was performed in SPSS on al1 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 mervalues for a given species and cluster, and low mean rank-orders represent low species percent cover values compared to oîher clusters. A large difference between mean rankorders for a given variable between species represents a greater degree of intercluster separability, whereas two or more clusten that share the rame or simihr zoo- 150 - Figure 5. IO - Summary of mulis hmKiuskaI- WaIk m-paramehic test for samgle independenCs. Asymptoüc signrTlcance (pl for cornpanSon of each varxabk's If-staüsîks to chi squared value (9 degrees of fteedom) is reporteci next to species' names in legend. mean rank-ordenfor a given species are indistinguishable based on that species' percent cover vanable. Allhough the p (asymptotic signifnance) 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 rnainly to the visual (qualitative) det8mination that the inclusion of the Canadian bunchbeny species percent cover variable does little to improve separability of clusten, it was rejected as a potential diagnostic variable. The thrw diagnostic species that were selected to mode1 plant community distribution in the Vermilion bum are A-stratum lodgepole pine, rusty menziesia, and grousebeny. Resufis 71 5.5 - MODEUNG MSTRlBUnONS OF DIAGNOSTIC SPECIES Correlation anaiysis. reported in table 5.3, identifid those ancillary variables that are most highly correlated to each of the three diagnostic species. Linear regression was perfomed in SPSS of diagnostic species' percent cover values against these ancillary data values at sample plot locations. The results of regression anaîysis are summarized in the following three regression equations: Percent cover of A-stratum lodgepole pine (P.): (R*=0.233), where elev is DEMderived elevation (in metres); and sd is the DEMderived dope (in degrees); Percent cover of rusty menziesia (MZ): (R2=û.230), where SA/, is the solar dope-aspect index; and Percent cover of grousebeny (GRB): (RW.1 32). whe re SA/, 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 reportad 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 regresion models are useful in that they provide a general idea d the influence of terrain-related features on vegetative distribution. Figures 5.1 1 through 5.1 3 show the regression-modeled distributions over the entire study area of lodgepole pine, wsty menziesia, and grouseberry, respectiveiy. In the regres~edlodgepole Resu& 73 pine model, figure 5.1 1, tree percent cover inmases with a decrease in either slope or elevation - that is, the model suggests that lodgepole pine prefer lower, flatter amas to steeper, higher ones. GENERALIZED (REGRESSION) DISTRIBUTION OF RUSTY MENZlESlA Figure 5.12 -Genera/izeûdistribution nnnj8I fPI nrsty nmziesh, dcuhted by apptying fhe MZ regmsbn equation (qua- 16) to the continuous solar slope-aspect Wx(SA/#) mverage. The regression equation applied to the ancillary data has resulted in signifiint areas of the coverage - very steep areas of very high elevatbn - retuming 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 assaciated w'fi finding lodgepole pine in eiaier of these Figure 5.1 2 shows the generalized distriobutionof rusty rnenziesia as detemined by linear regression against the solar slope-aspect index (SA/). The general trend identified in this case is that nisty menziesia percentages tend to decrease with a cwresponding increase in either dope or aspect. That is, the regressed suiface suggests tha rusty menziesia tend generally to prefer flatter, north-facing dopes to steeper, south-facing ones. There is no predicted effect of elevation on the distribution of rusty menaesia (other than the indirect association between slope and elevation - that is, glacially eroded valleys tend to be steeper at higher elevations). Again, a signifiant proportion of the regressed rusty menziesia coverage has resulted in the caluilation 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 detemined by linear regression against the cross-valley dope-aspect index (SAIm).The relationship, like mat of the other two diagnostic species to their respective predidor variables, is an inverse one. Generally, an increase in SAlmcan be expeded to correspond with a decrease in the percentage of ground covered by grousebeny. This means that grousebeny is more likely to be lacated on the southeast side of the Vermilion River valley, in less steep areas. The thrett sample plots in which the highest percentage values for grouseberry were recorded are al1 located on the southeast side of the valey, in a less steeply sloping area. In the case of the generalized grouseberry model, the negative values were recordeci on steeper (and coincidentally, higher) locations on oie nollhwest side of the valley. GENERALIZED (REGRESSION) DISTRIBUTION OF GROUSEBERRY wwesbnequatiim (equation 17) 16 ote continuous cross-valley slope-aspect index (SAL) csverage. Table 5.4 summarizes the characteristics of the spatial dependence models that were developed in the IDRISI GStat interface, and which best fR the experimental semivariogram data for each species. An omnidiredianal (i.e. direction-independent) spatial variance mode1 was created for each of the three diagnostic species' experimental samivariograms. Models that included a nugget value greater îhan zero were frequently found to over-generalize the variability of data - that is, to underestimate local maxima and overestimate local minima in the interpolation - - - -- I SPATIAL DEPENDENCE MOOEL PARAMETERS I I I Table 5.4 - Parsmeters of best Mbiig spath/dependence modefs for lfime diagmsüc species - genetated usr'ng the GStat mterfam in lDRISI (manual mode1 fit mettrod). Once the spatial dependence of the diagnostic species' residual values was modeled, the variogram were used as inputs to the ordhary kriging interpolation procedure. The followhg parameters were used in the ordinary kriging procedure to generate al1 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 al1 three diagnostic species. Obviously, kriging did not assign values to every grid cell over the entire area. This is an artefact of the specifiad 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,Oûûm of which any lesthan 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~SMS n by the regression function at a given Iocation, and positive values (Iighter areas on figures 5.14- 5.1 6) representing value underestimation by the regmion function. KRlGlNG INTERPOLATION OF RESIDUALS - LODGEWLE PlNE Overestimation of lodgepole pine (figure 5.14) appears to have occuned frequently along the valley flwr and in areas around aie Continental Dide, whereas values midway up the valley sides around the Continental DMde appear to have been frequentiy underestimated. KRlGlNG INTERPOLATION OF RESIDUALS - RUSPI MENZlESlA 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 duster 6 plots, rusty menziesia values on the Alberta side of the Continental divide seem generally to be overestimated by the regression equation. 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 poslive or negative residuals. Figures 5.17 through 5.19 illustrate the modefed distributions of the three diagnostic species that were subsequentiy used to mode1 plant community distribution in the Vermilion bum. Results 80 These models were obtained by addiove oveilay of the regression surface and the kriged residual surface for each of the three species. The three modeleci distributions were then masked so that only the burn area was included. The mask that was applied to the modeled distribution of ~sty menziesia and grouseberry was the area of the entire bum. minus any unbumed areas within the outer burn boundary. The mask applied to the rnodeled distribution of lodgepole pine excluded the MODELED DISTRIBUTION OF A-STRATUM LODGEPOLE PlNE 11 figure 5.17 - Modeled dkût"~of A-siratum -le pine I?I oie Vermilion bum, genented by addiüvely 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 gmwth of kdgepde pine and other tree species. Figure 5.17 shows that the highest modeled concentrations of lodgepole pine are located on the lower dopes of Mount Whyrnper on the northwest side of the Vernilion River valley, at the mouth of the Stanley Glacier hanging valley, and near Vista Lakes a? the northeast end of MODELED DISTRIBUTION OF RUSTY MENZIESIA figure 5.18 - Modeled disîribuôüm of ~stymedeska in ihe VermiIbn bum, generated by aWitiveiy cornbining the regmsseà MZ surface (ligure 5.12) and üksurface interpolated from MZ regnkû?n mriduab at sahple plat hhns (figure 5.15). Dark areas reprisent h@h concenbaliions of msty rnenzkk. The dashed Iine repmnts the bum penmete~: Results 82 the bum. The highest modeled concentrations of rusly mentiesia (figure 5.18) are found in a Iinear pattern along the middle southeastem (Mt)dopes 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 Fgum 5.19 - Modeled dwbhof gmusebeny in îhe VerniIbn bum, gmerated by aWitive/y combining the reg& GR6 surface (figure 5.13) and oie surface interpoIaied frwn GR6 re(lre&n msiduaIs at sam~leobt 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 Stotm Mountain, it is more or les 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 dopes of Mount Whymper, and the area arwnd Alhude Lakes. 5.6 MODELING MSTRIBUT1ON OF PLANT COMMUNmES 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 likelihaad classifiion 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 imposeci. A âetaileâ discussion of the distribution, composition, and sbcharacteristics of the 10 plant communities of the Vermilion bum follows in section 6.1. Table 5.5 - E'r matnX ana&& of maximum /&e/iihood cIasçification of plant communities. KHAT value mported in footnote at bottom of tabk Table 5.5 is the emmatrix, gsnecated in IDRISI, for the classbtion describecl above. A detaiîed dmionof this enor mat& and possible sources of enor and inamracy is induded in sedion 6.2 of the next chapter. PLANT COMMUNITIES OF THE VERMILION BURN 1 This chapter is subd~dedinto three The first section desaibes in detail the plant communities of the Vermilion bum and their modeled distributions. A comparim between the present vegetation in the bum and the findings of the Harris study [1W6]is also included in this section. The second section is a critical look at the underpinnings of the modeling approach used in this thesis, whnh attempts to explain Mme of the potential advantages and shoitcomings associated with this approach. Finally, the third section suggests soma potential areas for fumer research. 6.1 - THE PLANT COMMUNIVES 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 rnodeled 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 sol nutrient and moisture regime or wildlife habitat suitability) of a given area from the assemblage of plants that are ptesent. Such inferences are the domain of those who will use the information presented here for its intended purpose, and are beyond the çcope 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 refened to in this thesis only by the number assigneci to them as a resutt 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 simihrities between the composition of plant communities and the vegetation types (VTs) identified by Achuff et al. [1984] will be noted. And finally, the modelled distribution of the three most common tree species, as well as curent patterns of shnib- and herb-layer dominance in the bum, will be compared with the vegetative charaderistics of the bum three decades ago, as described by Hams [1976]. In most cases, only those species that are present in more than 60% of the plots in a given duster are included in the description - excepting those clusters which demonstrate a high degree of heterogeneity. The frequency of occurrence (oc constancy) and percent cover of al1 three diagnostic species are noted for every cluster, regardless of dominance. A axnplete list of plant species found in each cluster, separateci 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 Meridea of the abundance of a parücular species in the areas wliere it is present. A complet8 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 Astratum of a given plant community, it is invariably lodgepole pine. However, the percent cover of lodgepole pine varies greatly among plant communiües (as has already bwn demonstrated), as daes the understory composition. Figures 6.1 through 6.10 below show the modeled distribution in the bum of each respective plant community. Discussion -, 87 CLUSTER 1 - MT. WHYMPER OPEN PINEIBUFFALOBERRY CLUSER 1 - MOUNT WHYMPER OPEN PINWUFFALOBERRY: 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 is an open forest type - defined by Achuff et al. [1984] as any area in which the distance between cmsof trees is between two and five times aie average crown diameter, usually between 5% and 20% cover - wiai an average of 18.6% cover in the A- stratum by lodgepole pine (average age 20 years). The dominant B-stratum species is russet buffalobeny (Shephedb 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 domy 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 strawbeny (Fragana viginima Duchesne), Rocky Mountain goldenrd (Solidago mulhiadiata AL), fireweed. prickly rose (Rosa acicuans MI.), and alpine groundsel (Senecio paucitows Pursh). Grouseberry (3.8% wver) and msty menziesia (5.0% cover) are found lem frequently, and where they occur. less abundantly than in other areas. This is not surprising, considering that most acairrences of this plant community are in areas with unfavourable aspects for aiese two species. The composition of this plant community closely matches that of VT Cl9 identified by Achuff et al. [19W], and noted in the Kootenay National Park ELC to be common in Vermilion Pass. Although not commonly occupying cunent avalanche tracks in the bum, this plant cornmunity 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 swth-facing aspects on the nodhwest side of the Vermilion River valley, except most notably areas adjacent to avalanche tracks in and around Stanley valley. Discussion 89 /CLUSTER2 - SUBALPINE MEADOWS AND AVALANCHE TRACKS Li Fi6.2 - DMuaion of the subarpine meadows and awland,e ûadcs (cluster 2) phtmmunity m the Vennilh Pass butn. CLUSTER 2 - SUBALPINE MEADOWS AND AVALANCHE TRACKS: This is likely a combination of two or more different types of plant mmunities found within the bum that have Discussion 90 been grouped together based on the -mon featwe of speneness of vegetation. Unless othemise noteci, al1 species' average percent covers are leas than 5%. This is the most heterogeneous of al1 plant communities, with no species accuning in more than 80% of al1 sample plots in this cluster, and only seven species occumng in more than half of the plots. The most frequently occumng species in this plant cornmunity cm be subdivided into two main types. The first group is comprised of resilient, low-lying species that would cornrnonly 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(Achillea mil/efolium var. a/prCoIa (Rydb.) Garrett), shrubby cinquefoil (Dasiphora floribunda (Pursh) Kaitesz, cornb. nov. ined.), dicranoweisia moss (Dicranoweisia crispula (Hedw.) Lindb. ex Y Ide), Rocky Mountain goldenrod, mountain deathcamas (Zigadenus elegans Pursh), and scariet lndian paintbrush (Castilleja miniata Dougl. ex Hook.). This vegetative composition is consistent wia, 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 areasnl 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 al1 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 discemible group within this plant community consists of plant species that are likely to make up meadows in more stable high lower to upper wbalpine areas, and are similar to the VT identified by Achuff et al. [19û4] as 01 0. These species include A-stratum lodgepde pine (infrequently and with low (5.7%) percent cover, average age 19 years), B-stratum russet buffaloberry, Engehnann spruce (average age 13 yean), Canadian goosebeny (Rjbes Dkussbn 91 oxyacanthoides L.), lodgepole pine (average age 13 years), limber pine (Phus &xi113 James) (average age 12 years), and cornmon juniper (Junipems cornmunis L.), and Gstratum wild strawbeny ,firewwd, twinf lower, domy iyegrass, prickly rose (Rosa acricularis Lindl.), kinnikinnick (Arctostaphyllosuva-ursi (L.) Spreng.), yellow columbine (AquiIegia flavescens S. Wats.), fewflower meadowrue (Thalic?mmspatsiflo~rn Tum. ex RCh. & C.A. Mey.), and grouseberry (infrequently and at low (3.5%) percent values). Rwty menziesia is present in less than one fM of the sample plots in this community, with sparse mver (5.1 %) where it is found. Aside from avalanche tracks, the modeled distribution of this plant community is concentrateci mainly at higher elevations in the bum (highest recorded elevetion: 2,246m; lowest 1,586m; average: 1,930m). It generaliy 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 Rier (this area is quite possibly emneously classifieci - 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 prefened aspect of this plant community, as its aspect values are spread almost across the entire range. and the distribution seems to b aspect-independent. CLUSTER 3 - STORM MOUNTAIN GROUSEBERRY: This plant community derives its name from two immediately noticeable and unique featurer its highly localized distribution in the bum, exclusiveiy on a high-elevation, south- and southwest-facing dope near Aster Lakes in the cique on the west side of Storm Mountain; and the unique extent to which grousebeny is dominant over al1 other vegetation. This plant community displays a signifiant arnount of homogeneity - 50% of sll species identifid oarined in al1 three sample plots. CLUSTER 3 = STORM MOUNTAIN GROUSEBERRY Fgure 6.3 - DnStnbutb of ihe SIorm Mountan gtwse6eny (cluster 3) plant community m îhe VerniIbn Pass bum. There were four juvenile or dwarf aduk tree species identifid. Limber pine was found in al1 three plots, with an average pemnt merof 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 associateci with this plant cornmunity. It is probable that whitebark pine was mistakenly idenafi as limber pine at this location in the bum, due to the preponderance of Iimber pine in other high-devation locations in the bum, the d'fl~cultyin disceming between the two pine species, and the abdence of whitebark pine from al1 other parts of Vernilion Pass. Subalpine larch (La* iya/\ii Pari.) was found in two plots, wiai an average percent cover of 6.0% and an average age of 14 years. Engelmann spruce (5.5% merand 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 grousebeny of al1 the sarnple plots in the bum. Other Gstratum species found in al1 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/yfnchum junipnnum Hedw.), and alpine groundsel. Greater pixie stick lichen (Cladonia muta(L.) Hoffm.) and common yanow were found in two of the three sarnple plots. Neither of the other two diagnostic species (A-stratum lodgepole pine and rusty menziesia) was present in this plant community . This assemblage of plants very closely matches the Cl 5 VT identified (and mapped in the sarne location) by Achuff et al. [1984]. The prefened site for this plant community is high (average 2.208m) elevation, moderately slopng (199, and south- (though not diredly souai) facing. It would seem that there are other preferred site characteristics of this plant community that are not identif ied by this research. since there are other areas within the bum that meet the above physical description, but nonetheless are Dhssrion 94 not occupied by this highly locaiized plant community. Achuff et al. [1984] note the preference for well drained morainaf or colluvia1 locations. II CLUSTER 4 = SOUTH SlDE OPEN PlNElMENZlESlA Il Frgum 6.4 - 0~~ of the souîi~sùie open pI'vitdmeneésh (cluster 4) plant cmmunity h the VemiIb Pass bum. CLUSER 4 - SOüTH SlDE OPEN PINE/MENaESIA: This plant community is found mostly on the southeast side of the Vernilion River valley, in areas close to and on either side of the Continental DM&. This is a fairly homogeneous plant community - tree species are welldispersed lodgepole pine (average 10.7% cover, average age 22 years) and juvenile Engelmann spnice (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 Bstratum species in this plant community is rusty menziesia, found in al1 sample plots with an average percefit cover 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%. Oîher less constant species in the herbaceous layer include Cadian bunchbeny, sulphur pixie cup lichen (Cladonia sulphurina (Michx.) Fr.). knights plume moss (Palium cn'sa-cassfrensis (Hedw.) De Not.), heartleaf arnica, twinflower, juniper poîytnchum mas, willow species, and greater pixie stick lichen, al1 with average percent covers under 5%. This plant community is not directly comparable to any of the VTs identifid by Achuff et al. [IW];however, it bears meresemblance to VTs Cl0 and C14. The south side open pine/menziesia plant community is viable over a wide range of slopes (from 50 to 3Raverage 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 00 to an anomalous 1580). Discussion 96 CLUSTER 5 - OPEN PlNElMENUESWGROUSEBERRY=BUNCHBERRY Fgure 6.5 - DrSln'butb of the open pine/rn~gmUSB68ny-bund,beny(duster 5) plant wmmuniîy in the CLUSTER 5 - OPEN PINWEWESWOROUSEBERRY4UNCHBERRY: This plant mmunity is similar in many ways to the south side open pinelmenziesia plant community (above) in terms of Dkussion 97 its composition. It is alao similar (though slightly more hmogeneous) to the subalpine meadows and avalanche tracks plant commundy, in tems of its sparseness of vegetation - there are only 11 species of plant with constancies of greater than 60%. This plant cornmunity is dispersed widely throughout the bum, and is mmonly Iocated towards the tenini of avalanche tracks, on alluvial fans and along Stream and river valley bottoms, and generally in rod 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 rnenziesia is the dominant 0-stmturn species with an average of 12.3% covei; willow species are the only other shrubs that are frequently present. The herbaceous layer is CO- dorninated by grousebeny (10.9% cover) and Canadian bunchbeny (10.1 % mer). Fireweed is present in al1 sarnpie plots in mis plant community, but in low abundance (average 4.3% cover). Other common gmund layer species are juniper poiytnchum 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 Adiuff et al. [19&4]; the one that comes the closest is 011, but this VT has no record of either Engelmann spruce or ~stymenziesia From the ancillary data values for sample plots, it is âiiiltto make any generalizations about preferred site conditions for this plant cornmunity. Slope values range from 70 to 300, but on the average are of moderate steepness (190). Elevation values range amost 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 rniddle 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-valiey slope-aspect index averages were low for this site (sohr range 3-64, average 23.6, cms-valley range 1-77, average 14.5). This suggests a preference of gradually sloping areas on nom- or northwestfacing slopes, or either a combination of: (a) extremely flat and south- or southeast-facing slopes; or (b) steeper and north- or nodhwest-facing slopes. This plant community covers most of the large alluvial fan at the mouth of the stream that drains the outflow from Aster Mesand the adjacent valley (which contains a rock glacier) into the Vemlilion Rier. CLUSTER 6 - RlBBON OF MENDESIA: This plant community is an anomaly, both in cornparison 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 al1 the others in the bum, see the cluster anaiysis dendmgrarn, figure 5.1 in section 5.2. It consists of an area that runs in a nearly straight, ne* cantinuous Ene dong one of the more gently-sloping shale benches between limestone bedrock layers (for an explanation of local topography, see chapter 2), and a small, isclated 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 namw strips of mature forest (often less than 25m wide), oriented parallel to the Vermilion River, and sunounded on al1 sides by forest regenerating from the 1968 bum. It seems dome very unique and highly lacalized phenomena, possibly related to gmnd moisture conditions, would have been necessaiy in this particular area for a fire that bumed w completeîy and with such intensity to have skipped over such a small patch of mature forest. Discussion 99 CLUSTER 6 - RlBBON OF MENZlESlA t' Fgum 6.6 - DM6ulion of lfre ribbon of menaésia (cluster 6)plant community in the Vemilh Pass bum. The vegetation in this mmunity is an open (average 14.7% cover, average age 21 years) pine forest, with few juvenife Engelmann spruce (average 4.3% mer, average age 14 years). As its name implies, its B-stratum is overwhelmingly dominabci by msty menziesia, which is presant in Discussion 100 al1 sarnple plots, boasting an average percent cowtr of 70.7%. Wilkw species are also common in the shrub layer, but are much less abundant, wiih 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 community. ûther common ground-layer species include greater pixie stick lichen, juniper polybichum moss, and pohlia moss (Pohüa nutans (Hedw.) Lindb.). Aside from the site charaderistics 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. WAh the exception of the iwo sample plots on the lower eastem dopes of Mount Whymper, it is found exdusively in nom-facing slopes on the southeast side of the valley. CLUSTER 7 - DOG HAlR PINE: Another highly hlizedphenomenon, 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 bige cirque on the south side of Mount Whymper. Two isolated patches muron the middle-southeastem slopes of Mount Whym per. 'Dog haif is the vemacular term mnmonly used in reference to very dense stands of aiin, younger pine trees that are often found in areas regeneratng from fire. During this short stage. a low (less than 1%), density-independent mortalii rate is reported [Johnson & Miyanishi, 1991k until trees are large enough to begn showing the effects of competition with one another for space Discussion 101 Il ? CLUSTER 7 - DOG HAlR PlNE Fgum 6.7 - Dbûibutitm of Che &g hair phe (ciuster 7') phtmmuniijt in the VerniIbn 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-sbatum trees present in ail plots with average 87.8% cover, average age 21 years; jwenile trees and seedlings also frequent wilh 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 buffalobeny(average 14.3% cover), with willow species less constant and abundant. The CO-dominantGstratum species in the plant community are grwsebeny (average 7.3% cover) and twinflower (average 5.1 % mer), both ocairring in al1 sample plots. ûther les abundant Gstratum species include fireweed (omnipresent in this plant community), Canadian bunchberry, downy ryegrass, showy aster, heaitleaf arnica, prickly rose, and knights plume moss. Rusty menziesia is present in just over haif of the sarnple plots in this plant mmmunity (average 8.9% cover over those plots in which it occuis). The dog hair pine plant mmmunity is a close match to the C6 VT identified by Achuff et al. [1984], although the Kwtenay National Park ELC onîy maps its distribution at higher elevations along the Vermilion River valley. The site characteristics of this plant community are diffiailt to define. Slopes upon which it is located range from 3 to 280, wiü~an average of a moderate 180 steepness. It is generally present at middle elevations of the bum, ranging roughly 3ûûm (1,570m - 1,84Qm) 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 neariy the widest possible range (OP1 680, average 1030, and 150-1700, average 110o). Curiousfy, both slope-aspect indices have low ranges and averages (da?:0-75, average 36, and cross-valley: 2-78, average 37), which given the values for dope and aspect, must mean a that dog hair pine is found on esmergently-sloping south to south-east facing slopes, or on steeper north to northwest-facing slopes. Discussion 103 CLUSTER 8 -BOTTOMLANDS DENSE PlNE Frgum 6.8 - Disîribuîim of the boîtmands dense ph(clusîer 8) plant cammuniîy Ni îhe VerniIbn Pass bum. CLUSTER 8 - BOTTOMLANDS ENSE FINE: Distributed principally in large, contiguous patches at lower elevations and in the bottom of the Stanley CreeWGlacier valley, this plant community is comprised of dense, closed canopy stands of bdgepol8 pine. Lodgepole pine is the only species Discussion 104 present Yi al1 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 shnib layer. Dominance in the 6-stratum is shared by three species: rusty menziesia (the most abundant where present, at an average of 17.0% mer), 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 mmonin 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 nisty menziesia (rarely found on the right side of the Vermilion River valley - see figure 5.18 in the previws chapter). The Gstratum is dominated by grouseberry (average 10.2% cover), with îwinflower mughly half as abundant (average 5.4% cover). Other common Gstratum species include Canadian bunchbeny, heartleaf arnica, fireweed, and juniper polytrichum moss. The vegetation of the bottomlands dense pine plant community is rnost similar to that found in areas covered by dog hair pine. Its composition most closely resembles that of the closed forest VT Cl8, which was sarnpled near Marble Canyon (at the confluence of Tokkum Creek and the Vermilion River) for the Kootenay National Park ELC. Some discrepancy exists between Cl8 and the vegetative composition recordad hm, most notably in the ground (C) layer. This plant community generally ocairs at lower ehtions (average 1,6961~1,but ranging from 1,537m to 1,942m), wiîh the notabie 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 gradua1 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 vaiues are once again wide-ranging (soiar average 1060, range 350-1 740, cnwivalley average along the valley axis at 920, but ranging aimost 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 describeci for the dog hair pine plant community. CLUSTER 9 - MlDSLOPE 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 communiaes (for a grsphical representation, see figure 5.1 in section 5.2). Midslope closed pindmenziesia, 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 eastem dope of Mount Whymper, and the valley bottom at the Continental DMde. The vegetation in this plant community is quite homogeneous. Lodgepole pine is present in al1 strata, most abundantly in the A-strahim (present in alplots, with an average of 51.1% cover and an average age of 21 years; bwer strata average 2.4% cmrwhere present, average age 16 years). The only common shrub is ~stymenziesia (omnipresent, with an average 44.2% cover). The C-stratum is again dominated by grouseberry (present in al1 plots, average 10.9% cover) with juniper polytrichum moss (also present on al1 plots), Canadian bunchbeny, greater pixie stick lichen, sulphur pixie cup lichen, Zreweed, twinflower, and heaitleaf amica 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 The site charactefistics of this plant community, particularly skpe and elevation, are Discussbn 107 refreshingly wel defined. Sample plot sbps ranged hwn 10to 119, mai an average siope of 140, quite gradua1 in comparison to the average slopes for 0thplant communiaes. Sarnple plots were typically located on middle elevatbns in the burn, ranging less aian 2Wm (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, mai 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 th8 previous plant community, this is a cbsed pine forest (though not as heavily covered, with A-strahnn lodgepole pine present in al1 plots, averaging 39.0% cover (average age 21 years), and not frequently found in the understory). The shrub layer is diverse, with russet buffalobeny most common (average 9.4% cover), along with nsty menziesia (les common, but abundant, averaging 13.2% cover, where found), willow species, and juvenile Engelmann spnice (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 dope (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 108 of 1$(Mm). As in the case of the last plant community, little can be generalized from the wide- ranging aspect values and slopeaspect indices. CLUSTER 10 - CLOSED PINEIBUFFALOBERRYlGROUSEBERRY-TWINFLOWER 1' Fgure 6.10 - DWbuéOn of the closedpine/buîYàkbeny/gm~sebeny-hnnfkwer(cluster IO) plant community in the Discussion 109 In order to facilitate direct cornparin of the current state of vegetative regeneration in the bum to that which was recocded in the Hams study [1976], A series of four maps (figures 6.1 1 through 6.14, inclusive) has been created in a fashion simihr to the four vegetaüon maps from the Harris report [1976], 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 Harris study - see sedion 3.3) - here it is measured in average 1 GENERALIZED DISTRIBUTION OF LODGEPOLE PlNE - VERMILION BURN AMRAGE LOOGEPOLE PlNE DISTRiBlMON BY PLANT COMMUNIM Discussion 110 percent cuver for each respective cluster. Figure 6.1 1 is a generalized distribution rnap for lodgepde pine in al1 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 burn, 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 danser pocket of pine seedlings was found on the main slopes of Mount Whyrnper, close to the main drainage of the bowl into the Vermilion River. There were few areas within the burn in 1972 that were devoid of pine -the largest contiguous area was along Altrude Creek between Ahde and Vista Mes. 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 skpes of Mount Whyrnper, mid-valtey locations at the northeast end of the burn. and a small patch just west of Stanley valley), the densest stands of maturing pine in the present-day forest. Furthemore, menew (or previously unidentified) dense patches of pine can be found, speciflcally in the bottom of the Stanley valley, and at the southwest end of the burn near Marble Canyon. Larger areas of the main slopes of Mount Whyrnper are densely covered, and generally, pine is more ubiguitous now than it was thiity years ago. The lowest concentrations of lodgepole pine are still along the sautheast 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 tir mthin 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 ares within the bum that had managed to suMve the Ire. The densest recniibnent of seedlings was ocairring in the pine-free area around Ahde Lakes, with other dense areas on the southwestern end of the bum on the southeast side of the vaffey. GENERALIZED DlSTRlBUTlON OF ENGELMANN SPRUCE AND SUBALPINE FIR - VERMILION BURN -INED AVERAGE ENGELMANN SPRUCE AND SUW1NE FR MSTRlBUTlON BY PUNT WMUNlTV Darkest areas mpresent h@mî mmûabtons of Eirgeknann spme and subalpine fit. From figure 6.12, it is apparent that Engelmann spnne and subalpine fir have begun to Discussion 112 establish themsehres more widely within the burn, though the areas of past highest density appear to be presenred. New densely populated amas have appeared, includng most notably areas around Storm Mountain high on the valley sides, and near the southem intersection of the burn boundary and the highway. Withe exception of avalanche tracks and high-eievation meadows in the bum. there are cunently no forested locations in which Engelmann spmce or subalpine fir are more abundant than lodgepole pine. The areas in which Engelmann spruce and subalpine fir are absent frequently coincide wiîh the areas of maximum lodgepde pine density (dog hair pine stands on the slopes of Mount Whyrnper and at the northeast end of the burn). This is possibly indicative of competitive exclusion of spnice and fir in these areas by lodgepde pine. In 1972, the vast majority of the Vermilion burn was dominated in the shnib (0) layer by msty menziesia (see figure 3.3). Hams il9761delineated an 'avalanche cornplex" on the main slopes of Mount Whymper, and a patchy area towards the northeastern end of the burn that exhibited a heterogeneous assemblage of small shnib cornmunities with various different dominant species (white spirea, priclûy rose, shnibby cinquefoil, a small area dominated by russet buffaloberry), and areas of co-dominance between these species. Rusty menziesia was repocted to be dominant over the rest of the bum, except those areas devoid of shrubs, at high elevations and a nanow patch at middle elevation on the southeast side of the Vemilion valley. Figure 6.13 shows that the B-stratum dominance in the Vemilion burn has changed considerably over the last three decades, with menziesia losing gmund to other species, most notably russet buffalobeny. Large areas of the burn 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 - -- - Fgure 6.13 - GeneraIrZed 7999 d&tnbubion of dominant 5sfraîum (shrub) species m the Vennilion bum. In îhe kgenû îwu Ci04(0minantspecies are aparated by a hyphen (0). willow species CO dominant, but also commonly home to shoiter, resilient, spreading shrubs such as shrubby cinquefoil, prickly rose, common juniper, and rock willow (Salu vestita Pursh). Seedling and juvenile Engelmann spruce and subalpine fir are also common in these areas. For fumer discussion on the establishment of Engelmann spruce and subalpine fir in avalanche tracks, see the discussion of potential fumer research in section 6.3. The high, grousebeny-dominated plant community (cluster 3) on the shoulder of Storm Discussion 114 Mountain has Iittle in the way of tall shrub species, though there are juvenile subalpine larch and limber pine growing amidst the grousebeny in the B and Gsbata Aside from these two exceptions, the shrub layer of the entire bum is dominated or co- dominated by two species: russet buffaloberry and rusty menziesia. Russet bufFalobeny dominates on most of the main stable slopes of Mount Whyrnper, and in some bottomlands at the northeastem end of the burn (the only area in which they were mapped as being dominant in 1972). Rusty menziesia is dominant in large areas, already disaisseci 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 rnap of 1972 Gstratum dominance in the Harris report [1976] (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 baffle for dominance [of the herbaceous layer] was detected between Epilobium angustiioium and Amka cordifolia" [pp.91-921. This "battle" is evident in the dominance of these two species over a considerable portion of the total bum area in 1972. Hams also notes a large area occupied by "GRASSES" on the slopes of Mount Whymper, in areas referred to in the B-stratum 1972 distribution rnap as kingaccupied by the 'avalanche complef. As in the shnib distribution map, Harris recorded an absence of vegetation at high elevations on the southeast side of the valley. Also similar to the shrub map was the irtcreased diversity of dominant herbaceous species at the noitheast end of the bum, with small areas dominated by 'ERIG." (taken to der to Erigeron spp. L., comnwnly known as fleabane), "H." (taken to refer to Di&cussion 115 either Hedy~a~msulphumscens Rydb., canmoniy called white sweetvetch, or Hiemcium spp. L, commonly calied hawkweed), and a few other patches dominated by "GRASSES". GENERALIZED DISTRIBUTION OF DOMINANT C-STRATUM (HERB) SPECIES - VERMILION BURN Figure 6.14 - GeneraIUed 1999 disinbution of dominant Gsttatum (herb) species in the Vermilion bum. In the @end, two coduminant species are separated by a hyphm ( - ), and a dominant and sub-dominant species are separated by a '@waterinanws@n( > ). Althwgh both fireweed and heartleaf arnica are still present and in fact quite mmon throughout the bum, they are no longer as dominant in the herbaceous layer as they were in 1972. The presentfy dominant Gstratum species in the bum is unquestionably grousebeny, which is actually not a herb, but a low shrub. Grousebeny dominates exclusively in the Storm Mountain grousebeny plant community (cluster 3), and is cIdominant or dominant over the entire southeast side of the Vermilion River Valley, the valley bottom, the lower eastem and southeastem slopes of Mount Whyrnper, and except a few isolated patches (most notably around Ahde and Vista Lakes), the entire noitheast end of the bum. This pattem of dominance is smilar to that repotted in Hams il9761 as being characteristic of a 55-year old tire succession stand. Two separate patterns of gousebeny co-dominance are identified in the C-stratum: the first, in which grousebeny 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 eastem slopes of Mount Whymper and along the Vernilion River at the Continental D~de.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 poilions of the northeast end of the bum. Grousebeny is dominant over the signifiintly subdominant fireweed over large contiguous areas on the southeast slopes of the Vertnilion River valley, especially on either side of the continental divide. This is a signifiint 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 pattern: the first is th8 diverse assemblage of avalanche track and subalpine meadow plant species, and has no cleariy 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 domnantly 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 nom of Albude Lakes, and in other isolateci patches. In these regions the CO-dominantheibs are domy ryegiass and hniinfiower, camion 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 amiparison of these to the distribution maps found in Hanis [1976], 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 thernsehres and are now dominant - in others, the relative abundance of species to one another has changed, so that formerly dominant species are now les abundant. Species have been recniited 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 ocaipy their preferred sites, and to compte for increasingly limited space and resources. 6.2 - CRITIQUE OF THE MODEL The purpose of this section is to scnitinize the underpinnings of the modeling process used in this thesis, and to identify and disais the consequences associated with potential sources of error or discrepancy in the model, and the mbustness of mode1 components. Discussion begins with the results of the maximum likelihood classifiition procedure, and woiks back through the procas of modeling diagnostic species' distributions, the generation of the DEM and associated topographie variables, hierarchical cluster analysis, and the collection of field data The error matrix for the maximum likefihood classification of the Vermilion burn into plant Discussion 118 communities is presented in table 5.4. This math 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-nonnality is expectd to have liîtle effed on the outcorne of the classification procdure, as it is likely that percent cover values will be concentrateci more closely around the cluster mean than the probability density function assumed by the maximum likelihood classification algorithm. The accuracy of the classification at otfter locations in the bum is dependent to a signifiant degree on the accuracy with which the distributions of the aime diagnostic species have been modeled. The accuracy of these modeled distributions is dlicult to assess quantitatively, for two main reasons. First, the mdeling 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 assessrnent 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 aganist 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 regressian Ine. If regression was to be used exclusively to mode1 distributions, these R values would not be acceptable. The purpose of regression, however, was not to explain al1 of the variabilii in species' percentage values, but rather, was to quantify and mode1 the general relationships that existed bebetwn the distribution of these species and soma topographie site characteristic or characteristicS. Despite low R values in each case, the regression analysis did identify mesignificant and eoologically defensible trends in the data set - namely, that lodgepole pine tend generally to gmw better at lower elevations and on more gradual slopes, that rusty menziesia generally grows best on gradual, nom-facing slopes, and that grousebeny generally grows best on gradual, noithwest-facing slopes. Better-fiiing regression lines, and deeper insight into the prefened site condiiions for diagnostic species, may have ken obtained from the inclusion of other ancillary vanables, 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, speafkally the curvature vanables, proved to be of little use in predicting species' percent cover values at sample plot locations. Possible explanations for the inabiliof curvature variables to increase mode1 accuracies incfude locational emrs (see below), problems in the way that curvature was cakulated, and the inappropriate use of curvature values as a surrogate for soi1 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 mode1 described here, the potentially ecologically spuriow result is a tendency of plant community distributions to be cantred on sample plot locations. This observed characterisüc 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 mode1 with a short range and high siIl (i.e. in which variability increases drarnatically over a short initial increase in distance, quickly attaining a high degree of distanceindependent 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 enor associated with the maximum likelihood classification, reported in the emx math (table 5.4), is 18.43%, meaning that 40 of 21 7 plots were not classified in the same cluster into whidi they were placed by the cluster analysis procedure. The kappa index of agreement is 0.788, which means that the maximum likelihood classifier produceci results that were 79% better than those that wouM have been generated by a random assignment of plant communities to pixels wiaiin the bum. There exists an apparent pattern to the enoneously 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 difierent clusters generated in through cluster analysis. Davis [1986] points out a tendency, in cluster analysis algorithms, for variables with larger abdute 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' membenhips are established based more on dominant species than less dominant ones. In any case, it seems probable, from analysis of the enor matrix and based on the properties of the cluster analysis procedure described above, that those sarnple plots repo 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 detemined by estimating the average crown area of a representative tree in the sarnple pkt, and then mukiplying this average crown area by the total nmber 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 diffiiult to assess the margin of enor associateci with such an approach, but it can generaliy stated that these were, to some degree, subjective estimates of percent mver 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 dierentiable based on the presence or absence of any number of species. In any case, the lines that are dravm to delineate the boundary between one naturaliy occumng plant community and the next are most obn 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 addreSSBC) is the potential for inaccurate location Discussion 122 of plots. Oniy one sample plot was excluded hmanalysis due to obvious and severe mislocation (see figure 5.2). However, sample plots were located using a hand-held GPS receiver, and data was collecteci in the summer of 1999, prior to the removal of selective availability (SA), the intentional misrepresentation of location. imposed on GPS signais by the US rnilitary in the interest of national securily. SA decreasad the locational accuracy of the GPS king used to locate plots by up to 1ûûm - 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 sale TRlM map used in the field. For ancilhry data variables, which wece spotted using the digitized sample plot locations, this could result in enoneous values being recorded at mislocated sample plots, and hence, poor or spurious ancillary variable correlations to ample plot data. This is parücularly true of ancillary data that Vary greatly over short distances, such as the dom-dope and cross-slope curvature vaRables calculated using Terra Firma (see figures 9.6 and 9.7 in Appendix 7). 6.3 - SUGGESTIONS FOR FURTHECI RESEARCH The following are a number of suggestions for future research, based on observations in the field, acknowledged oppoflunities for improvement of the modeling process employed in this thesis, and a general interest in the improved description and documentation of the role of Cre in shaping natural landscapes. First and foremost, it is recmmmended that a study of the vegetative regmeration of the Vermilion bum, similar in scope and objectives to this study and to the vegetation portion of the 1976 Hanis study, be undettaken no longer than thirty yean after the date of aiis thesis. The goal Discussion 123 of continually revisiting the same bum at regular intervais is to praiide a record of regeneration Mrough the wmplete course of the fire cycle in Vermilion Pass. In an area whose average fire retum interval is 130 years [Johnson & Mianishi, 1991], vegetation studies conducted at aiirty year intervals would produce only four (on average) 'tme slkes" of the mstantly changing and dynamics of fire cycle vegetation. The effects of fire are very specific to the landscape in which the fire accus, 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 bumed areas in the subalpine ecoregion may provide a means by which to quantify this specbity, and to establish the regional- level site factors governing regeneration. It may also be benefiiial to undertake a comparative study that models the regeneration of plant communities in a similady situated bumed area that has been subjed to more rigorous land management and forest engineering practkes. As far as the Vernilion bum is concemed, there are some q~estionsthat the impositions emplaced by time and resources have left unanswered. Most obvious is the observation that this model seems to have gmuped avalanche tracks and high subalpine meadows, two very distinct environments, in the same plant community. As part of the Harris (19761 report, K.M. Winterbottom [1974] described the effeds of fire on avalanche tracks, and mappeâ out the vegetation in the avalanche tracks on the main slopes of Mount Whymper. The model descrbed in this thesis would be considerably improved if a more detailed analysis of the vegetation in aie avalanche tracks of the bum, and of the diierentiation 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 execcise. Discussion 124 Harris [1976] and Winterbottom [1974] reporkd that the fire had the effect of lengthening and in some cases, widening the existing avalanche tracks moiin the Vermilion bum. However, casual obsedons in the field, specificalîy of the lateral extent of the tracks before the fire (as detennined by the presence of snags - standing dead trees), compared to the present wïdth of tracks, suggest that the avalanche tracks are nanowing. These areas are being encroached upon by seedling and juvenile trw, mostly Engelmann spruce and subalpine fir (limber pine at higher elevations), soma of which had obviously stood up to frequent avalanches, judging by their shape and scars on the uphill side of tninks The question arising from this observation which begs to be answered is: does fire create condiions whiih lead to the longer-tem stabilization of avalanche tracùs? This thesis did not provide the opportunity to consider a number of factors that most likely play a significant role in the regeneration of bumed landscapes. Potential improvements on this modeling approach include: - lncorpofation of othur ancilhry variables: This thesis has exarnined the effects of certain terrain-related ancillary variables on the regenerative distribution of vegetation in a bumed area. Ancillary vanables to consider were selected based on availability, greatest anticipated influence on distribution of vegetation, and time and resource constrainta Other potential ancillary data to consider are effects of wind and snow redistribution [Hiemstm, Liston, & Reinen, 20001, the cakulation of an orogenic precipitation index [Peddle & Duguay, 19951, and the inclusion of soi1 and lithology data. Wth impmved location aaxiracy, the cuwature indices calculated in this thesis might provide more useful results, and are thus worthy of reconsideration. - Info~onngvding composition d sunounding mature toiwt: Time was not available Discussion 125 during the field season or in the course of analysis to include in the modeling proces information regarding sunounding mature forest types and thei composition. Most species that regenerate within a burn do so by the transport of se&, 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 bumed area would play a considerable role in the distribution and regeneration of plants within that area. This data coukl be included through field sampling, satellite image analysis, or altemativeiy, through inference from time-since fire maps of the bum and sunounding area, if such maps exist (As of 1991, there were no time-since fire maps for Kootenay National Park [Johnson & Miyanishi, l99l]). - 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 efements, as well as to act as 'snow fences" and create drifts, allowing for increased soi1 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 sarnple plot could easily be recorded and added to the spatial data base as a potential factor influencing regeneration. If Pa& Canada is to fulfill its mandate of piesenring ecological integnty, and if the natural effects of fire are to be msidered in the evolution of more ecdogically sensitive and holistic forest land management strategies, then a concerted effoit must be made on the part of scientists of al1 kinds - ecologists, biologists, forest engineers, and biogeographen - to provide land managers Discussbn 126 with the toob and the knowiedge 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 modal of the vegetative response over the past three decades to a fire that bumed 2,430ha of mature spruce and fir forest in Vermilion Pass in July, 1988. The Vermilion bum is unique due to its size, one of the largest fires in the subalpine ecoregion of the southem Canadian Rocky Mountains in the past half century. It is also unique for its location in two of Canada's National Paris, affording it the opportunity to regenerate naturally, and with minimal human intewention. The modeled distribution and composition of plant communities was produced by combining an assortment of methods from various fields - ecdogy, biogeography and hndscape modeling, Geostatistics and GIS, and remote sensing. Vegetation and site charaderistic data from 218 sample plots were collected duhg th8 summer of 1999 from th8 Vermilion bum, and subsequently entered into a spreadsheet. Hierardical cluster analysis was then used to group vegetation data from simlarly 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 mode1 (DEM) of the study area was obtained, and a series of seven terrain-related ancHlary layers were derived from the DEM - measures of dope, solar and cross-valley aspect, solar and cross-valley slope-aspect indices, and cross-slope and dom-dope curvature values were al1 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, lodgepde pine, rusty menziesia, and grousebeny, were Conclusr"0n 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 evaiuated sequentiaily: (a) species had to be ubiquitous or near ubiquitous within aie bum; (b) species' had to demonstrate statisticaliy signif'mnt correlation to at least one of the DEMderived ancillary variables; and (c) species percent cover values within and between individual pMcommunities (Le. variance and covariance) had to be such that they enabled or improved the separabili of at least one plant community or group of plant communities from al1 others. Once selected, continuous distributions were modeled for each of the three diagnostic species percent cover within the bum, through a three slep process. First, a general distribution of the species was modeleci from the regression of species' percent cover values against the mat highly correlated ancillary variables at sample plot locations. Next, a continuous surface was interpolated from the regression residuals ab 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 classifkation 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 Conclusion 129 location in the bum. Separate generalized distribution maps were created for lodgepde pine, combined Engelmann spruce and subalpine fir, and dominant species in the 8-stratum and C- stratum. These generalized distribution rnaps were used to compare curent vegetative regeneration in the Vermilion bum to the plant community distribution that was present irnmediately after the 1968 fire, as identaied and mapped by Hams [1976]. This thesis is directed towards the goal of improving the understanding of the spatial dynarnics of vegetative regeneration in a natural ecosystem from the effects of fie. It is anticipated that this will be the second in a series of Miesof the vegetation of the Vermilion bum, aimed at acpuinng a full record of the fire cycle in this paiacular location. References 130 REFERENCES Achuff , P.L., Holland, W.D., Coen, G.M., & Van Tighem, K. 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Johnson, Edward A., & Miyanishi, Kiyoko, 1991; Fre and Population Dynamks of Lodgepole Pine and Engelmann SpmForests in the Southern Canadian Rockieq in Cbniferous Forest EcoIogy hman International Perspecfive, pp.TI-91; SPB Academic Publishing, The Hague, The Netherlands. Johnson, E.A.. & Van Wagner, C.E., 1984; The Theory and Use of TWORe History Models; in the Canadian Joumal of Forest Uesearch, vol. 15, pp.214-220. Johnston, R. J., 1980; Multhriate StatistrCaI Analysis in Geogiaphy - A Primer on the General Linear Moûet 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 RoIe of Temin in a Fire MosaS 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 Communily Classifications as Predktors d Forest Site Quality for Lodgepde Pine and White Spnne h West-Central Alberta; in the Canadian Journal of Forest Research, vo1.18, pp.875-887. Levin, Jack, & Fox, James Alan, 1994; Elementaiy Statistks h Social Reseatch (6m Ed.); HarpeCollins College Publishing, New York, New York, USA. Lillesand, Thomas M., & Kiefer, Ralph W., 1994; Remote Sensing and /mage lnterpmtation (3d Ed.); John Wiley & Sons, Inc., New York, New York, USA. Liston, GE.,8 Sturm, M., 1998; A Snow-Transport Mode1for Complex Temin; in Journal of Glaciology, vol.44 no. 148, pp.498-5 16. Masters, A.M.; 1990, Tempoml and Spatial Change h Forest Fire Histoiy of Kootenay National Park, Canadian Rakies; in Canadian Journal of Bofany, voI.68, pp.1763-1767. Referenaes 133 Mitas, 1,& Miasova, H., 1999; Spatial Interpolation; in Geographkal lnfonnation Systerns, vol.1 (2nd Ed., Longley, Paul A., Goaddrild, Miihael F., Maguire, David J., h Rhind, David W., Editors) ;chapter 34, pp.481-492; John Wiley & Sons, Inc., New York, New York, USA. Oksanen, J, 8 Minchin, PR, 1997; Instability of Ordination Resuls Under Changes k Input Data Order: Ekplanations and Remedies; in Joumal of Vegetation Science, vol.8, pp.447-454. Parks Canada, 2000; Kootenay National Park of Canada Management Plan; Minister of Public Works and Government SeMces Canada. Peddle, Derek R., & Duguay, Claude R., 1995; Incorporating Topographie and CIHnatic GIS Data into Satellite Image Analysis of an A@ne Tundra Ecosystern9Fmnt Range, Colorado Rocw Mountains in Gemrto International, vol.10 no.4, pp.43-60; Geocarto International Centre, Hong Kong. Renkin, RA., & Des pain, D.G., 1992; Fuel Moisture9Focest Tjpe, and dightning-CausedFire in Yellowstone National Park; in Canadian Joumal of Forest Research, vol.22, pp.37-45. Schier, George A., 8 Campbell, Robert B., 1978; Aspen Suc& Flegeneration Folbwing Buming and Clearcutting on Two Sites in the Rocky Mountains; in Forest Sence, vol.24 240.2, pp.303-308. Schimmel, Johnny , & Granstrom, Anders, 1996; Fife Sevenfy and Vegetation Response in the Boreal Swedish Forest in Ecology, vol.ïï no.5, pp.1436-1450; Eodogical Society of America. SPSS version 9.0.0 User's Manual, 1998; SPSS Inc., December 1998. Steel, Robert GD., Torrie, James H., 8 Diikey, David A., 1997; Pn'nciples and Procedures of Statistics - A BiometriCa/ Appmch (3d Ed.); WCBIMcGraw-Hill, New York, New York, USA. Strong, W.L., & Leggat, KR, 1981; Ecomgbns of Alberta, ENR Technical Report no.T/4; Province of Alberta, Department of Energy and Natural Resources, ûffice of Resource Information SeMces, Edmonton, Alberta, Canada. References 134 Tappeiner, U., Tasser, E., & Tappeiner, O., 1998; Malelling Vegetation Patterns Using Natudand Anthmpogenic lntluence Factors: PreIiminary Experienoe with a GIS Ba& Mode1 Appliéd to an Alpine Area; in EcologicaI Modekng, ~01.113, pp.225-237; Elsevier Science. Wilhrd, T, 8 Harris, S.A., 1972; Report on the Vegetation of Me VenniIion Pass Bum; (unpublishd report) for Pa& Canada, Western Regional Office, Calgary. Winterbottom, K.M., 1974; The Effects of Slope Angle, Aspect, and Fire on Snow Avalanching in the Field, Lake Louiw, and Marbe Canyon Region of the Canadian Rocky Mountains; MSc. Thesis, Department of Geography, University of Calgary, Alberta, Canada. MAPS Cook, DG.; 1973; Geology of the Msw, Ranges of the Rmky Mountains from VermiIlion Pass to Blaebeny River and Bow Lake, British Columbia-Alberla;GGeogical Suwey of Canada; Ottawa, Ontario. British Cokrrnbia Ministry of Enwonment Temin Resouia, Inventoty Maps (TRIM) Nos. 82N.020 & 82N.030 (1:2O,ûOû scale) (digital and hard copies); McElhanney Consulüng SeMces Ltd., Vancouver, British Columbia, Canada. Topoglilphic Map No. 82 N/1- Mount Goodsir (150,000 scale) ;1996; Canada Centre for Mapping, Natural Resources Canada; Her Majesty the Queen in Right of Canada. INTERNET REFERENCES Hiemstra, Christopher A., Liston. Glen E.. & Aeiners, William A., 2000; Wind Redistributlion of Snow at Treeline in Ihe Medicine Bow Mountains of Wjmming; from prwdings of the 4th International Conference on Integrating GIS and Environmental Modeling (GISEM4): Problems, Prospects and Research Needs; Banff, Alberta, Canada, September 2 - 8,2000. URL: http.Jl~~~.colorado.eddre~8a~cire~anff/up~~525~ ITIS? 2001 ; - lntegrated Taxonomie Information System (Canadian version) - Taxon based biological information system; Govemment of Canada, Department of Agriculture and Agn- References 135 food Canada URL: http.llsis.agr.gc.calpIsTrtiScalfaxaget?pPPifx=aafc Lane, David M.. 2001 ;HyperStat Online Statistics Text Book; David M. lane 1993-2W1. URL: httplldavidmlane.comBlyperstat/ Lister, Andrew, Riemann, Rachel, & Hoppus, Michael, 2000; Use of Regression and Geostatistiml Techniques to Predict Tree Species Disttibufibns at Regional ScaIes; from praceedings of the 4th International Conference on lntegrating 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~d21û/ Appendix 1 136 APPENûlX 1 - DATA AND SOFIWARE USED IN THIS THESIS MGITAL DATA SOFWARE PROGRAM VERSON O APPUCATION 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 PCI 7.0 PCI Geomatics Inc., 1999 satellite image manipulation and analyçis SPSS 9.0.0 SPSS lm., 1989-1999 statistica l analysis Terra Firma - J.R.Eyton,1991 generation of curvature indices * The GStat sofhvare is htegrated inîo IDRISI32 GIS. Appendh 2 137 APPEND(X 2 - MODEL FLOW CHART Acquisibjoli of Feu Interpolation of DEM Cleanmg of Field Derivation of Oata Anallary Sunnval Stands. and + Topgraphical Avabnche Tracks Feld Data Set Variables - 1 I t Incorporation of Cluster and Gght Ancillary niuo vegatation Ancillary Data into Fidd Data Set Variables used direcîly lo mode1 Selection of A-stratum Lodgepole Pine. Rusty Menziesia. and Grouseberry as Diagnostic Species Regression Regression Analysis Agaïnsl Fiesidual Point IHJAnc~mVariabtes Regression Regession Regreççion 1 Maximum Likelihood Classification 1 MûûECEO DISTRIBUTiûN ûf PLANT COMYUNmES 1 I OF THE VERMiUON BURN 1 Appendix 3 138 APPENMX 3 - CARTOGRAPHIC MODEL DERIVATION OF DEM AND ANCILLARY TOPOGRAPHICAL VARIABLES BC TRlM 82N.030 + + slopegrid a~p-gtid ARCIDRIS gndaxii command in command in command in command in Arclnfo Arclnfo IDRISI Arclnfo v f 9 [wcupclgld] p-w-nt)-- f ARCIDRIS ARCIDRIS command in command in in ACONVO.ME in ACONVO.EXE IDRISI IDRISI module of Terra Firma module of Terra Finna 1 conversion to BILfDRIS 1 radians using mage 1 r-7command in command in IDRISI +llmage Calculator in lmage Calculator in Image Cakulator in IDRISI: d c-4 l 1 31 O - aspect.rst 1 1 ------Image Claculator In Idrisi: -4 sin(sbpeiad.rst) nt1 (MD) - denotes procedures camed out by Medina Deuling. Appendix 3 139 DELlNEATlON OF BURN FROM SATELLiTE IMAGERY AND CRMION OF SAMPLE PLOT GPS Locations and 8C TRlM Planimetric Data for Study Area Merge and Clip to -snrdyareainPcI(~0) Deiiiation of Bum, Landsat TM5 lmagery Avalanche Tracks, and * of Shidy Area Mature Stands wiihii command in [impart ines nt. ~nfo] Creaîe Potygon Topology Usng Buiid and Clean Commands in Arclrifo Classinof Poiygors &Mature Forests 1=Regmerathg Bum, + 2=Avaîanche Tracics Woiin Bum EXTRACT cornmand in IDRISI image me: vegmask.rst bumpoiy vector poIygon rfeabire definiifile: hampleploLPrst, average in Arclnfo L ASSIGN command in ResuL of Cluster Analysk iDRISI command in of Feld Data Set IDRISI 1 Xbumpofy.rst [RECUSS co&and in IDRISI: 1 1 REC& mmand in IDRISI: ( 1 RECLASS cornmanci in IDRISI: Appendix 3 140 MODELING DISTRIBUTIONS OF DlAGNOSTiC SPECIES AND PLANT COMMUNlTlES Residuais ASSIGN 1 Command 1 Command in IDRIS1 in IDRISI ( 1 Generation of 1 Spatial Dependence 1 Spatial Dependence ( 1 Spatial Dependence / Command in IDRISI Comrnand in IDRISI Cammand in IDRISI OVERLAY (muSikation) 1 Command in IDRISI 1 MAKESIG Command in 1 A IDRISI - Assigns Percent Cover Values to Appendix 5 142 APPENMX 5 - COMPLETE UST OF PLANT SPECIES, VERMIUON BURN The following tables list the vemacular and scientifïi narnes of alspecies found within the Vermilion bum. Species taxa have ben verifid using the Govemment of Canada's lntegrated Taxonomie Information System [ITISa, 200q for al1 plants, and Hale & Culbenon [1970] and Hawksworth et al. [1980] for al1 lichen species. SpeQest scientifc 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 (shnib) species, since there are no subalpine larch within the burn that are taller than 3m. A-STRATUM (TREE) SPECIES VERNACUUR NAME SClENTlFlC NAME AND AUTHORrrV subalpine fir Abies lasiocarpa var. lasibcatpa (Hwk.) Nutt. Engelmann spruce PECea engelmmii Parry ex Engelm. lodgepole pine Pinus contorta Dougl. ex Laud. limber pine Pinus flexilis James auakina amen Podus tremuloides Michx. B-STRATUM SHRUBI SPECIES . ------Douglas maple ~cerglabnm var. douglasii (Hook.) Dippel mountain alder Atnus vinds ssp. cnSpa (Ai.) Turrill 1 Saskatoon servicebeny 1 Amelanchier alnifolia (Nutt.) Nutt. ex M. Roemer 1 dwarf birch Betula nana L. shrubbv cinauefoil Dasiohom flonbunda (Pursh) Kartesz. comb. nov. ined. 1 common juniper 1 Junipems cornmunis L. 1 subalpine larch Larix lyallii Parl. bearbeny honeysuckle Lonicera involucrata Banks ex Spreng. Menriesia femrainea Sm. rustvn menziesia - white rhododendron 1 Rhaludendmn albiflomm Hook. 1 1 Canadian gooseberry 1 Ribes oxyacandhoides L. 1 common rdraspbeny Rubus &eus L. undetemineci willow s~ecies Salk L. rock willow Salk vestita Pursh red elderberry Sambucus racemm var. racernosa L. russet buffalobeny Shepherdia canadensis (L.) Nutt. Greene's mountain ash Sorbus seo~ulinaGreene Appendix 5 143 GSTRATUM (HERB) SPECIES VERNACULAR NAME SClENnflC NAME AND AUTHORITY common yarrow Achillea mi&fo/ium W. a@noih(Rydb.) Garrett nodding onion Allium cemuum Roth peariy everlasting Anaphalis margatitacea (L.) Benth. Drummond's anemone Anemone drummondii S. Wats. smaltflowered anemone Anemone panMora Mihx. raceme pussytoes Antemaria racernosa Hooù. yellow calumbine Aquik~kflavescens S. Wats. Kinnikinnick Arctostaphyb uva-ursi (L.) Spreng. heartleaf amica Arnica cordifolïa Hook. showy aster Aster conspicuus Lindl. Barbilophozia Barbilophozk lywpodioides (Wallr.) Loeske Haller's campylium moss Campylium halleri (Heûw.) Lindb. undetermined sedge species Carex L. scarlet lndian paintbrush CastdIeja miniata Dougl. ex Hook. western lndian paintbrush Castilleja occidentalis Torr. ribbed pixie lichen Cladonia &osa (Ach.) Spreng. pixie cup khen Cladonia chlorophaea (Florke ex Sommerf.) Spreng. mater ~ixiestick lichen Cladonia cornuta (L.1 Hofhn. -- 1 orange-footed pielichen 1 Ckdonia eanocyna (Ach.) N$1 1 sulphur pixie cup lichen Cladonia subhurina (Michx.) Fr. Canadian bunchbeny Comus canadensis L. dicranoweisia moss Dkmoweisia crispula (Hedw.) Lindb. ex Milde Fireweed Epilobium angustifolium ssp. angustifolium L. Gentianella amaiella (L.) Ekmmer 1 white sweetvetch Hedysamm sulphurescens Rydb. houndstongue hawkweed Hieracium cynogIossoides AN.-Touv. slender hawkweed Hiencium gracile Hook. bog Labrador tea Ledum gtvenlandkum Oeder domy ryegrass Leymus innovatus (Beal) Pilger Twinflower Linnaea boreaIis 1. smallflowered woodrush 1 Luzula ~andlorasa. oanliflora (Ehrh.) Desv. 1 stiff club moss I LvcoDOdium annotinum L. 1 starburst khen Pannelbpsis ambigua (Wuif en) Ny 1. fringed gras of Pamassus Pamassia fimbnata var. fimbrrata Koenig bracted lousewort Pedkularis bracteosa Benth. rr------ 1 VERNACULARNAME 1 SClENTlFlC NAME AND AUTHORïTY-- - 1 freckle-pelt lichen Pelüqeta apha1usa dog peit lichen Peitigeta mina fuzzytongue penstemon Penstemm enanthems Pursh pink mountainheath PhylEodoc8 empetriformrS (Sm.) D. Don Kentuckv bluearass Pm oratensis L 1 Pohlia nutans (Hedw.) Lindb. I 1 juniper polytrichum moss 1 Po~humjunipennum Hm. 1 sockeye psora lichen Psora dixipiens knights plume moss Ptilium crbta-castrensk (Hedw.) De Nat. undetemined winterareen s~ecies PvroIa L. - - - pricklyTe Rasa ackulad Lindl. arctic blackbeny Rubus arclrcus L. spotted saxifrage SaXifiaga bronchialis L. lanceleaf stonecrop Sedum lanc6olatum Torr. alpine aroundsel- - Senecio paucifloms Pursh I Rocky Mountain goldenrad &/idag~mulfkadiafa Ait. white spirea Spiraea betulifol. Pallas western featherbells Stenanthium occiidentale Gray fewflower meadowrue Tha/rict~msparsifbrum Tum. ex Fisch. 8 C.A. Mey. Grousebem Vacdnium scman'urn Ceib. ex Coville 1 Valeriana sitchensis Bong. 1 mountain deathcamas 1 Zïgadenus elegans Punh Appendk 6 145 APPENMX 6 - PLANT SPECIES COMPOSITION BY CLUSTER The chart on the following ten pages breaks dom 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 whkh each species belongs. Constancy is a rneasure of frequency of occurrence within a given cluster. For example, if a given species murs 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 (vemacular 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 whii it was found. The three species that were identified as index species for rnodelling cluster distributions (see chapter 5, section 5.4) appear in bdd face. kGSîfata lodgepde pine [Pinus contorîa ] 3.B ?o&y Mountain gddenrod [W&p muIb'mûhta 1 1.7% ireweed [Epilobiumangusüfdium ] 1.m -- tiiddy rose [Rasa aci#rh&] 1.5% kç~hataEngelmann spw [PEOcba m~lmanniij 3.1% dpim grwndsel [Senedo paucifbms] 0.9% lpine gmumlsel [Smcio pauaflonrs] 1.a wiiow speW [Salk spp. j 6.4% WStrata Engelmann spniœ [P~Mengelmmnii] 29% iaskatoon servicebeny [Amelandier alnifdh ] 23% ~rouseberty[V~cciniurn #aywlum] 3.8% ammon yam[Adrr~liea millefdium ] 0.6% ibbed pixie lichen (Cladonia carima] 1.29é teartleaf arnica [Arnica ardiildia 1 0.8% kmon iunipei [Junipenrs ammunis1 kc-snata limber pine (Pinus WIk] innikinnick [Ardm&@y& wd] [v~~~~ml cadet lnc&n pahtbrush [Casfilmmidta] edsespecies~~~spp.1 dlow cdurnbine [Aquiwa %we~cms] mflower meadowrue [Thalicfrum spamYbrurn 1 I 1 I 2 IA-Smtum Engelmann rpfuœ [Pbaengdm~në] 14.361rodl wabw [ShCx WC& 1 120% msty mrnrkrb [Umdrrb mhu ] 5.01 dwarf birch [&WU nana ] BIC-Sirata Sm ber pine [Pinus kxih1 3.0% B+C-Strata quaking aspen [Popukrs tremuloaes ] shrubby cinquefol [Dasiphon bribunda] 2.3% Saskatoon sewkeberry [Amdandriera/nilbfia1 white sweetvatch [Hedjwamm su@hurescsns] 25% &C-Strata aibaipine Sr [Abies ~0~4ip8] aspen fleabane [En'peron speaosus] 1.?% headeaf arnica [Arnica corcii'iL ] -$ kinnikülnick [Anlwlsphyios milmi] 0.9% ribbed puie Gchen [Cladonla ani7sa 1 dender hawkweed [HienQum gn&] 0.9% whii swe)eWtdi [Hmfysamrn sulphumscens] dicranoweisia moss [Oicranoweisia crispula ] 0.8% aspan fleabm [E~ronspeüosusl pirie cup lichen [Cladonla chlorophaea 1 0.3% showy aster [Aster wnspKuus 1 iuniper polytrichum moss [Polyrrichum juniperinum 1 OrummondL anemone [Anemone dmmmondii] western In&an painfinish [Ca&lkja oceidenralis] 0.7% A-Stranim subalpine iir [Abies ili,siocarpa 1 6.0% A-Stratum Engelmann spniœ [Pima engelmmnii] 2.6% rock wilbw (Sanvesfira ] 5.8% Dougias mapie [Amr glsbnrm 1 ü+C-Strata subaipine fi? [Abies Wmrpa1 3.8% NW m@nd#b[UHiokrli knuO-1 common red raspberry [Rubus idaeus ] 3.7% spotted saxifrage [S-ga bmnch~] dwad birch [Berda nana 1 3.3% dog peAt lichen [PeIiigem mna1 bearbeny honeysuckk [Lonicera involucrafa 1 2.3% pohiii mos[Pohfia nulans] Canadii goosebeny [Ribes oxyacanbhoides] 2.3% Canadian bunchbeny [Comus canadensis 1 ü+C-Strata quaking aspen [Populus lremuloides] 21 % kniihts plume moss [PbEum crista-etisrfensis] knights plume moss [Polium cristsau~sis] 6.5% fringed gmsa of Pamasws [ParnaSsia fmbnala 1 $- pohGa moss [Pohlh nufans] 2.8% soekeye paora lichen [Psm dscipens 1 - sockeye psara lichen [Pmdecipiens] 1.6% slender hawkwsed [Hieracium gracile ] yellow columbine [Awiktgia fhvescens] i9% annual genthn [Genüanelh 8mamIIa 1 searlet lndian pdntbnish [Casbiieia mmiata] 1.2% nodding onion [AIKum œmuum ] dog pelt lichen [Pelügera canin8 ] 1,O% Helleh campyïim moss [Campyfium haikri] sulphur ph& cup iiihen [Cladonia su$hunna] 0.6% sulphur pixie arp lichen (Chd~niasulphurina 1 juniper polylrichum mas [Poiytriclium junipennum 1 0.6% nodding onion [Alfium œmuum ] 0.5% red fescue [Feslvca ~bra] 0.4% starbuist lichen [PameGopsis ambigua 1 0.2% A-Stratum Iimber pine [Pinus IlexiIis] 12.0% kSlratum Gmber pine [Pinus ABXfliS] Douglas maple [Acergkb~m1 5.0% kSlratum quaking aspen [Populus tremuloiles] white rhododendron [Rhododendron8lbM0~m 1 4.0% mountain aider [Alnus wiridis] mountain aiâer [Ahw viridis 1 3.0% bog Labrador tee [Ledum gmenlandiarm ] funytongue panstemon [Penlslemon enanlhenrs] 5.0% common rdraspberry [Rubus idaeus] white spire8 [Spiraea behrlilbfia 1 4.3% white rhododendron[Rhododendmn albiiï~~m] raceme pwsytoes [Anlennaria racernosa] 3.0% bearbsny honeysuckle [Loniœm hvolucrata ] fringed gras of Parnasus [Pamessd lunbnhfa] 1.9% B+C-Slmta subaipine lanh [Lunx lyslai] süff club moss [Lycopodium annorinum] 1.3% peady everlesüng [An8ph8&smargsritacea ] bracted lo~wort[Pedidaris bmcteose 1 1.3% houndsiongue hawkweed [Hiemcium cynoglossoides] Haller's campylium moss [Campflium hallen] 0.5% Kentucky bluegnss [Poe pmtensis 1 western feathefûelk [Stsnanthium midentale] 0.5% smainioweied woodnid, [LumlapaMïJ4ra ] gr sedge species [Carex @p. 1 0.5% fmcüs-pell Gchen [PeMgera aphütosa] r Dnimmondk anemone [Anemono d~mmondii] 0.4% raceme pussyfaat [Antennana mwmosa ] smalHlawerad anemone [Anemone paivihbm] 0.4% Smvrlsrim [VhEeriam &chensial western lndian psinlbrush [Casfileje occidentalis] 0.4% western hometail [EqurJehrm rmnse] 3 &GSbata hber piie [fius Redis] orowsbsnyw-mrcapuiun1 fireweed [m~lbbiummg&Mum 1 O heartWamica[AmiaQKdMi] phi kGStmîa Engebnann spruœ [%ea enge/manni] 5.7% hadhbunchbeiry [Cornus canadensis] 4.3% uphur pïxïe cup Eehen [Cbdonk suIphuri~] 1.O% r-straîum Engehiann spruœ [Rba engelmanniil 3.6% rhâe rhododendron [Rh&&ndmn albifforurnj 7.8% ~sse!buffdobeny [Shepherdka canadens~s1 5.2% hadiagposeberty [Ribes oqmmnhE'des] 4.4% kclsbasa hiber piie [Piirus krii&] 27% og Labrador tea [Ledum gmnkvdicum 1 27% hC-Sbata bdgepoie pire [n'nus annlorta] 20% ohk moss [Pohlia nu$ns] 1.% EtC-Sbata hgeinam çpruœ [Risaengelmanniil 5.5% bbed pixie Cehen [Cktdonm c~ni;Osa] 1.9% B+C-Sltatasub~iiefir [Mesk3&cmpr] 2.0% î?f dub moss [Lyar;ipodium annobnum] 1.3% Canaai bundrberry [Cornus auia-] 0.896 ks m I- aina1 1 3% fredde-pelt~[~aphlYKisa] 0.8% 0.896 scariet lndipaiilbrush [Casbüw miniata] 0.8% dogpeUlicheri[~rncenrm] 0.5% 7 red eîûeibsrry[Sembucus mcemosa 1 3.69 &C-Sirata subapiia tir [AWSle&fpa 1 3.09 fredde-pelMen [Psi@era 8phIIios~] 329 western horsePll [îEquaSlum mnse] 1.O9 *me gfoundsei [Sem& paudb~s1 O39 houndstongue hawlweed [Hieraaum cynogbsso&sl 0.79 A-Sbriàim subaPine fir [Mies &sioa?fpa1 Greene's mountsir ash [Sorbussoopuha] bearbbrry honeysuckb [Lonhm mwlucreb, ] mmmon red mspberry [Rubus &eus] ü+C-Strata quahg a-n [Populus aemubkfes] M-S- SU~M~WLaich [LakIyaiyallU] prC#v rose [Rosa aclarhris] downy ryegass [Leymus innovatus] chowy aster [hm?conspÉvus 1 &ye pwaMen [PandecQmns] jicranowehin moss [Oicnnowisie &pu4 ] amîergeen spccibs [plnale spp. j pundcbdar [Lypopodïum oomplensbm 1 ald strarrberry [Fragarh Mghiana ] smaWbwerad woodrush [Lm& parvilbn j Sady Mountsn goüenrod [SoMago muMadiehi] mnual gentian [Gentiane& amam& 1 Wwbw[SeTa~~~~ nountaii alder [husriiidis] ipollsd sadiage [Sax#iaga bronchialisl iearly eveilesting [haphal& maguritaœa1 icailet lndm pahtbrush [CasPleja miniata j ammon ymw[AdriLa miiblblium 1 (enhicky blragrsu [Poa pmmnsis] tspen Rsabane [E.msp&sus] ;taiburst Men [Pannebpjisambgua 1 mged grass of Parnassus [Parna& fimbriata j iedge species [Carex spp. 1 ficup Men[Chdonia chlbmphaea 1 ewfbwer msadowrue [Thekturn spadurum] ,range-fooisd pSQs 1Sthsn [Chdonia (~m~~yna] Wor's campyhm moss [Campflum hal&riJ hndur hawiweed [Hhmciumgmuk] ir8eted busswort [Wicu4nSbmcdeosa] abw coiumbne [Aquikgia Lwsœnsj pamdmy [V(~cckrlunr~riun] 12.6'1 lireweed (@Wium angusi&lium] 4.99 suiphur pixie aip Cchen [Cladonh sulphunna] 1.35: ~~[~fsmglirsol123% [S&spp.I 4.6% kç-Sûata Engehann spruœ (fkaengielmannii] 9.7% -pater- pixie slid< Sehen [Cladon* carnuta] 5.2% uniper polytriehum moss [PoiyM~umjunipennum] 1.5% LISimtunkdgspokpine[~~~canlorEs] 11.6% rbwspeOes[sfilixspp.] 5.0% iniper polytrichum moss [Pb~umjunipennum] 32% badian bunchbeny (Cumuscanadensis] 10.1% hfbwer [Linnaea borealis 1 52% eartleaf arnica [Arnim 0ordMh ] 4.5% ulphur phk~aip Cehen [Cladbnh ~Iphurii] 0.9% isset buifaiobeny [Shepheda mnadensrS] 5.3% mi@& plume moss [RIiumcrista~sfmnsi~] 27% kc-StraQ subaipii lir [Abres iasium~pa1 4.7% ieaRleaf mica [Amita cordifolh ] 2.5% nights plume moss [Fülum CY~~~-CBM]28% hadiibunchberry [Cornus cenadensis] 2.2% bbed pixie Sehen [CladonM m&a] 1 -4% WMgooseberry [Riborryamnihoides] 5.0% kCISbata hber pb[Pinus hxilis] 1 .% redde-pen~[~&Pm~] 4.1% bg peit ïchen [AMgen mina] 0.8% bbed piraie Cdren [C&don& caniasa ] 0.8% -Straaim Engelmann spruce [PEOea engelmanni] 4.3% og Labrador tea [Ledum gmlandECUrn] 7.6% Labrador tea [Ledumgruenlendiarm] 7.3% K-Sûatasubdpi fir [Abas lasbarpa] 3.Wo +C-Sirata hiber pine [kIRedIis] 3.5% i+C-Sbatabdgepde pha [tinus contada ] 28% aMdm goosebeny [Ribes o~ndhoides] 3.1 % niifbwer [tinnaea 60-1 1.7% +C-Sba$ bdgepde piie [n'nusoontorte] 2.4% myryegrass [Leymus innovaius] 2.1% edde-pel Men[&I'm ephihose] 1.9% CU s whb rhododendron [Rhododbndron abifio~m] 1239 mdcwbw [Sehriiasa$] 3.69 bearbeny honeysudds [Loniceni invaiucm$] 2.69 dog peR Men[Pbifigem mina] 209 socheye psom Men[Bon dscrpbns] 12% E Mi dub moss [Lrcopodiumannoünum ] f -1% mountah alder [Alnus riridis] &C-Shata quaihg aspan [Popukrs temubides 1 eaiberry honeysudda [Lonicem involuaata 1 15% Greene's mounteii mh [Surbus soopulina 1 kC-Slratequahg aspen [Populus ûemuloides] 1.5% Kentucky bluegisu [Poapramnsis ] arbbphoda[Be~~Bphoaà ~padm~es] 53% barbbphozia [BarbdophoaérLaopodioides] in club moss [Ljwpodium annotinum1 1 2% showy aster [AsBr conqoicuus] eafty everiasbiig [Anaph- mgaritaœa 1 OB% wild sttawbeny [Fmgaria mghiana] twbunt Men[Pdr~~~?Tapsis ambgua ] 0.8% prÉ#v rose [Rosa aciarleris] mslbwsred woodiush [LmlapaNilkra ] 0.6% scarlet hdën paiilbnish [Cssüibje miniafa] 'irlorgrssn spscéspymh spp.1 0.6% western honetaï [EquiJebrm amnse 1 t* Mountain galdenrod (Sondago mu/üradiaîa ] 0.5% C-I annuai gentimn [Gsnüunelh amamila] oundstongue hewheed [Hmraciumcynogbssoides ] 0.4% tiiranowebii mou[DicmnowrSYi crispufa] range-fiooû~Ipii8s Ëhsn [Chdonia scrnocyna ! 0.4% pigoundsel [Senecio pucilkms] 0.4% A-Sbahim subapii fir [Abriss fasiocarpo 1 -Sbahim Engeînann sprues [Picsa engelmnniï] dwarf biich [Bebrle nana] -Slrahtm uiba@ine iir (A6ies lasiocarpa 1 M-Strata subapme &ch [Larix@P] twîmon seniieaberry [AmelandikwahW ] Sasiabon senricebsrry [Amelandrier ainilblia 1 me ne's mounîain ash [Soribus soopubna ] red eiderbeny (Sambucus niamosa] 4-Straai û~balpiielarch [LBrfxl)gBii] common juniper [Junbd~Sammunis] mmon rdiaspberry [Rubus ideeus] common red mpberry [Rubus idaeus 1 Dwny rysgag [Leymus innova fus^ friiged grass of Pmasuis [Psmassie fimbriam 1 enîwky bçiegass{Poa pra$nslp] yebw colimbime [Aquiiagie lwsœns1 cup men(Cladonie drbrophaea 1 piik mountainheafh [Pnflkdbœempeailbnnis 1 Eianoweisémoss [Dicranowesia~ula] lewfbwer meMownre [Thalicinrm spersillbrum] foundcedar [Lppodium wmplanabrm] aspan ûeabane [Efigeronspaaiosusl wbt lndiipair@rush [CasbJleja miniam 1 spoltied saxfmge [-#a brondrialis] 5 srnaübwered anemone [kismne psMfknr ] sedge spcies [Camx m. 1 houndstongua himkweed [Hianûum cynogbssoid8s] smalbvrered anmono [kremone prNinDm] maübwered woodrush (Luzuh puMnon] gioundwdar [Lyoopodium wmphnabrm] arcüc b-rry [Rubusafcëcus ] slender harrlareed [Hœraciüm gm'b 1 pixi~~cup lichen [Chdonia cjibmphaea] starburst Mien [Pambpsbambm ] winîeqreen species [Wbspp. ] common yanow [Achdba mdklbfurn] -- CUI TOI 7 ireweed [EpiIobiurn angu~um] 24% kC-Sbata Engelmann spniœ [Fbaengelmanni] 9.0% - -- hights piume moss [Rilium cnkh-1 4.5% ;howy aster [Asfierctmspkuus] 1.6% uphur ph& cup fichen [Cladonîa sulphuniia ] 1.1% lbbed pixie lichen [Chdona c~ksa] 0.9% bwny ryegtass [Leymus innovadvs] 3.7% unpet polytrichum moss [Pomumjunijwdnum] 0.6% ngMç piume moss jmiurn cris$c~strensis] 2.0% iog Labrador tea [Ledum gmnlandiam ] 76% kGstratasubalpiiefr[AbEeslasiiocarpa] 3.3% vld sbawbeny [Fm~ahIrgniena] 2.4% lriddy rose [Rosa aciwkris] 12% Frpirae stickçcheri [C&-muta] t .l% bciy Mwnten goldenrod [Soirdam muIb'mdkita] 1.O% diioweisis mos[Dr;aSmweisrs aapuh j 1.6% pohk mou[Poh& nuarns] 0.9% dog peR Hen[Pelagelil canina 1 0.5% daibursî Men(Pambpsis ambigua ] 0.4% alpine groundsei [Sen& pauci&~s] 0.3% mg Labrador ma [Ledum gmnlandimm 1 65% ~arbenyholreysuc#s [Lonicen mwkrcnbt 1 2.0% M-Sbatequ- aspen [Populus aemuloiires] kC-Smlinber pine [finus Le] 19% Greene's mountaii ash [Sorbus smpuha ] ewRower meadownie [Tharim spanilborum] 1.9% Canadian goosebeny [RBes aryamnfho~ües] mhiie rnoss [Pohb nutans] 1.5% iommon juniper [&?@?rusammunis] mestem horsetaï [Equblum afwense 1 0.5% bearbsrry honeysudde [Lonam Mvplumfa] &C-Stiata linber pine [tinus &dis] )Oré cup khen [Chdonia chkrophaea 1 hsc#b-pe& ïchen [R?îtigeraapMhosa1 mnReabane [G@eronspeciosus] bwîbwer me&hwrue [~I~msparsilibrum) mmmon yarrow (AchiiIse m~lbbfum] abed p& Wwn [Chdonie cariosa ] &mye psora khetn [Pscrra Wiens1 Jsnder hawlawbed [Hieracium graule] wintergresn specms spp. 1 hrubby chquefail [DaJphoni IlOrbunda ] 5 .O% Iougb mepb [Aasrgbbrum] :anadien goosebsrry [Rboayamndho&s ] kC-Smsuba@ime fi [Mies &siocdrpe 1 ireater pastick Cchen [Chdonia wmu$] lochy Mount* ~0ldenfod[Soiidago mulümdiebt 1 ammon yarrow [Achiibamilblbfium] tlf club moss [Lycopodum annotinum] vinual gentan [Gentieneh amarelie] iirie cup men[Ciadonè chbrophaea] cariet indan pahtbrush [Castlleja minw cwm 9 10 ~~bmmVw~~~51.1% AStntum-pkia[mm] 39s Uw-[-fwl 442% hiviiffower [Linmeta 60-1 4.49 F-@-Ww"i-rtaprrkanl 10.9% bunchberiy[Camuscanadensff] 5.5% iniper polytiichum moss [~urniunipenirumj 21% pater pixïe sîick iichen [Cladonia carnufa] 1 .6% ubhur pixie cap Schen [Cladonia sulphun~] 1 .O% kC-Sûata bdgepde phe [Pïnusawrtorta ] 24% [ViinCum -un] 8.89 Mwer[Linnaea bomIis] 4.8% CaMdian bunchberry [Cornus canadernsis] 5.991 domiy ryegass [Lemsinnovafus] 3.1 % earkaf arnica [Arnica curdm] i@ts plume moss [RTurn cn'sta-Q1sbBnsis] 27% ohia moss (Pohiia nudsm] 1.7% bog Miador $a [Ledum gmlandEarrn] 7.1% Sadabon semiwberry [Amelandrmrelnilb&] 1.5% Rociiy Mount* gddenrod [W&w muIb'rediata ] 1 .% dog pelt ïctm [M~eraœnine] 1.4% ~giaundsel[&fW&~Udb~S] 0.696 juniper polybÉhum moss [CiolVbfchumjmiperinum] 0.696 CL a tO irangs-fooîed pkb khen [Cbdonia ecmocyna 1 i8% &Sliahrm Engehmn spruce (Pics8 engelmnnfi] 5.09: rec&peR Men[RsMgem ephfhosa ] 1.1% :anadi gooseberry [Raes o*yacanütoides] 7.0% log peR Men[Pelügera mnim 1 0.6% icranoweisia moss [Diaamweisia #ispu&] 1 39: iixh wp Wen [Cladonia driomphaea1 0.6% Wüwer meadowrue [ïhakfrum spafsihrum] 12% ;tn ciub moss [Lppodium annobnum ] 0.4% reaîer pgoa sück Men [Ciadonia cornu$] 0.44: iog Labrador ma [Ledum groenlendEcum] 2.6% lougies mapb [AarrgW~m] 6.4% W-Strate bnber pine [Pinus Ikm] t .O% kC-Simla quahg sspsn [Populus bgmubkfes] 2.1% lowny ryegrsrs [Leymus innovafus] 2.4% hnibby cirquefoii[Dasphora lknbunda 1 f .8% iarbitophozà [Berbibphotie ipopodbidbs) 1.6% ornmon junipet [Juniperus communis] 1.O% Wbunt Ehen [Parmelhpsuambigu8 ] 0.6% thta sprea [Spimea beîuïiiia] 6.0% r# wawtterry [Fragaria virgniana 1 0.6% nnlannÉk[Ilreb,saaphfis vva-umi] 5.2% &y8 psora Schen [Psom deapmns] 1.8% spen fbabane [Hgeron speaiosus 1 15% ohhm mou[Pohba nudens] 12% bbed p9iie Wien [Chdonia cariosa] 12% tll club moss [Lycapodiumannotinum 0.8% ~intergresnspeciss [Fymh spp.1 0.6% tarbum lichen [P8mw,IiopSaamb~u81 0.5% ixie cup lichen [Chdonia ~hlorophaeaj 0.4% ihii ttiododindton[Rhododendron aibiffo~m] wari biich [&3hrie nana ] tochy Mountaii g~idenrod[Solidago muliiiadbhi 1 iountah aider [Ahus whllis] howy aster [AsLsr wnspicuusI sarbeny honeysuclQ [Lonicera mwlucmta j bnder hawlaneed [Hmmaum gaula] ireene's mountain ash [Sorbus swpuüna 1 icfanoweisismoss [Di#anoweis& cr@u& 1 hle rhodadendron [Rhododendron albmorum 1 restem horsetal [Equissfum a~wnse] +C-Smhrùer pme [Pinus LxiIL] riddy rose [Rom aciculeris 1 eniuchy bluegiau [Poe pfa$nsis] estern horsetel[Equ&Mum amnse ] nged gras of Piunassus [Pamassia fimbriaia 1 werlet Indii painlbrush [Caslillej~mgriefa 1 roundcedar [Lmpodium wmphnafum] range=fooMdpmb Men[Chdonia eanocyna 1 oarly everiasaig [kiaphslis margaritsœa ] sddb-pet Men[PsIb;gers aphIhosa] ~ountahdeathcamas [Zgadenus ekgans] adding onion [AIPium œmuum ] abfs wmpyCum moss [Campyfium Iralbri] aundslongue hawkeed [Hiumcium cynoObssoides 1 rummond's anornane [AnemOne d~mmondil] #hitesweamlth [H&ys??mmsu@humscens ] mmon ymow [Adriha mikWum] 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 proces involved in generating the terrainielatecl variable. SLOPE - VERMILION PASS Fgum 9.1 - Skye VCIIWfor VemiIim Pas, gmerated in Mnfo. Appendk 7 157 SOLAR ASPECT - VERMILION PASS Figure 9.2 - Solar aspect - mw aspect vaIues derived ri, Arclnfb, lhen conwerted (see chapter 4, secb;on 4.3). CROSSVALLEY ASPECT - VERMILION PASS SOLAR SLOPE-ASPECT INDEX - VERMILION PASS CROSS-VALLEY SLOPE-ASPECT INDEX - VERMILION PASS figure 9.5 - Closs-vaIleyskpe-aspect àidex (Ml)- akulated iin IDRISI usrig sbpe ctvss-valleyanverted aspec van'ables (farercplanalnon see chapter 4, sectrQn 4.3). 1 DOWN-SLOPE CURVATURE - VERMILION PASS Fgure 9.6 - hm-dopecumture - detemined usmg Tema Firma by cakulating the discfete second derivative ( elemîh of each ce// abng îhe aainuîh (see chapfer 4, sedon 4.3). CROSS-SLOPE CURVATURE - VERMILION PASS 1 CRûSS-SLûPE / CURVATURE figure 9.7 - Closs-slope curvaîure - atennined ushg Tema Fma by tablating îhe dkrete second derivative I eleva&n of each ce// oraiogonal b the azWnulh (see chapter 4, section 4.3). AVERAGE I