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A quantitative evaluation of porcupine-habitat relationships in the Kalum Valley, B. C. Lawson, Andrea L 1991

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A QUANTITATIVE EVALUATION OF PORCUPINE-HABITAT RELATIONSHIPS IN THE KALUM VALLEY, B.C. by ANDREA L. LAWSON B.Sc., The University of Western Ontario, 1987 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES Department of Botany  We accept this thesis as conforming  THE UNIVERSITY OF BRITISH COLUMBIA December 1991 © Andrea L. Lawson, 1991  In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission.  Department of The University of British Columbia Vancouver, Canada  Date  DE-6 (2/88)  ii ABSTRACT  The implications that the interactions between animals and the communities in which they exist have to wildlife management are just being realized. The assessment of species habitat -  interactions necessitates the consideration of many variables and the adaptation of multivariate statistics to ecology has made possible the multidimensional consideration of habitats. The purpose of this thesis is to evaluate the usefulness of a multivariate approach to an applied management problem: porcupinehabitat interactions in the Kalum Valley B.C. In recent years an alarming amount of damage caused by the winter feeding of porcupines has been occurring on the north coast of B.C. There is concern that silvicultural practices, such as thinning of crop trees, are predisposing stands to attack. The three specific objectives addressed in this thesis are: 1) To test the hypothesis that thinned stands incur more damage than unthinned stands 2) To investigate the process by which porcupines are selecting habitats and individual trees 3) To determine the variable or combination of variables that best predicts damage.  Four 100 ha. blocks of forest were selected in the Kalum Valley. Two stands had been spaced and two were unmanaged. Within these stands 25 sampling plots were randomly selected. Three sets of  iii variables were recorded in each plot: recent and past porcupine damage to trees, % cover of all species of vegetation, % cover of vegetation strata.  The results indicate that thinned stands do not incur more damage than unthinned stands. In fact, unthinned stands incur more new damage than do spaced areas, indicating that porcupines demonstrate choice at the level of large blocks of forest perhaps on the basis of stand properties such as density or basal area of trees. Hemlock was almost exclusively attacked with damage peaking in the 20.1-20.5 diameter class. Within stands, damage is not related to density, basal area of trees or any site features at the level of the plot except the number of stumps. Damage is related to the cover of a few herbs. This result is probably related to the greater amounts of light reaching the forest floor in damaged areas resulting from the dead tops of the hemlock trees.  Thus,^damage is predictable from the individual tree characteristics of species and diameter class of trees. Trees in unthinned areas appear to be more likely to incur damage.  ^  iv  TABLE OF CONTENTS PAGE ABSTRACT^  ii  LIST OF TABLES^  vi  LIST OF FIGURES^  vii  AKNOWLEDGEMENTS^  ix  CHAPTER 1^INTRODUCTION^  1  1.1^Herbivore-Habitat Interactions; the porcupine problem in B.C.^  1  1.2^General Porcupine Ecology^  5  1.3^Habitat and Feeding Preferences^10 1.4^Relevant Studies in the Pacific Northwest^14 1.5^Objectives^  16  CHAPTER 2 METHODS^  17  2.1^Study Site^  17  2.2^Stand Structure and Damage Assessment ^25 2.3^Habitat Description^  29  2.3.i Vegetation Description^ 29 2.3.ii Site Description ^ 30 2.5^Data Analysis^  35  2.5.i^Stand Structure^ 38 2.5.ii Damage Data^ 39 2.5.iii Site and Vegetation Data^40 2.5.iv Relating Damage to Site and Vegetation Data^ 43  ^  V TABLE OF CONTENTS continued^ CHAPTER 3 RESULTS^  PAGE 45  3.1^Stand Structure^  45  3.2^Incidence of Damage^  47  3.3^Damage Index^  57  3.4^Vegetation and Site Variables^  63  3.5^Relating Damage to Site and Vegetation ^69 CHAPTER 4^DISCUSSION^  78  4.1^Comparability of Stands^  78  4.2^Effects of Thinning^  78  4.3^Process of Selection by Porcupines ^79 4.3.i Evidence for a hierarchical process of selection^ 80 4.3.ii Evidence for selective feeding ^83 4.4^Recommendations^  85  4.5 A Comment on the Use of Multivariate Methods in Wildlife Management.^  86  LITERATURE CITED^  88  vi  LIST OF TABLES PAGE Table 2.1.  Description of the four study areas. ^22  Table 2.2.  Index used for percent cover of plant species 30  Table 2.3.  Variables measured in the site description. ^31  Table 3.1.  Summary of the number and species of trees sampled and those incurring damage.^45  Table 3.2.  Estimates of density and basal area for all tree species and for hemlocks only. ^46  Table 3.3.  The incidence of damage to western hemlock. ^55  Table 3.4.  The results of regressions of damage on density and basal area of all tree species and of hemlocks only. ^ 62  Table 3.5. The treatment means of the site, species and vegetation stata variables, and the results of the MANOVA testing for differences between grids.^  65  Table 3.6. The results of the canonical correlation analysis of the site and vegetation matrices.^  67  Table 3.7. The correlations (loadings) between the site and vegetation variables and their first two canonical discriminant axes. 68 Table 3.8.  The results of a canonical redundancy analysis for the first two canonical variables of the CCA of site and vegetation variables. 70  Table 3.9.  The results of regressions of damage on sets of habitat variables.^  72  Table 3.10. The canonical loadings of all variables on the discriminant axis from the canonical discriminant analysis with respect to the 'high' or 'low' damage categories. ^76  vi i  LIST OF FIGURES PAGE Figure 1.1^Photograph of a porcupine. ^  6  Figure 1.2^Photograph of porcupine damage. ^8 Figure 1.3^Photograph of a 'spike top'. ^9 Figure 2.1^Map of the north coast of B.C. showing the Kalum Valley.^  18  Figure 2.2^Photograph of the general appearence of forests in the Kalum Valley. ^19 Figure 2.3^The biogeoclimatic zones of northern B.C.^  20  FIgure 2.4^Map showing the location of the four study grids.^  21  Figure 2.5a Photograph of the appearance of a spaced forest area.^  23  Figure 2.5b Photograph of the appearance of an unspaced forest area.^  24  Figure 2.6^Photograph showing the use of the relescope.^  26  Figure 2.7^Photograph showing damage assessment of a hemlock tree.^  28  Figure 2.8^Photograph showing a sampling plot.^36 Figure 2.9^Photograph showing a soil pit. ^36 Figure 3.1a Graph of the mean and standard error of the density (stems/ha.) of all trees in each of the four grids. 48 Figure 3.1b Graph of the mean and standard error of the density (stems/ha.) of hemlocks in each of the grids. ^  48  Figure 3.2a Graph of the mean and standard error of the basal area (m 2 /ha.) of all trees in each of the grids. 49  ^  viii  LIST OF FIGURES continued Figure 3.2b Graph of the mean and standard error of the basal area (m 2 /ha.) of hemlocks in each of the grids.^  49  Figure 3.3^Graph of the mean and standard error of the diameter at breast height of hemlocks in each of the grids. ^  50  Figure 3.4^Graph of the mean and standard error of the number of wounds per hemlock in each of the grids.^  51  Figure 3.5^Bar graph of the number of old and new wounds per hemlock with respect to tree diameter class.^  53  Figure 3.6^Graph of a quadratic regression of the number of new wounds per hemlock with respect to tree diameter class. ^54 Figure 3.7a Bar graph of the number of old wounds per hemlock in each third of the tree with respect to tree diameter class. 56 Figure 3.7b Bar graph of the number of new wounds per hemlock in each third of the tree with respect to tree diameter class. 56 Figure 3.8^Graph of the variables 'number of wounds per hemlock' and 'circumference girdled per hemlock' showing the first PC-axis. ^58 Figure 3.9a Graph of the mean and standard error of the damage summary index, calculated on the basis of old wounds, in each grid. 60 Figure 3.9b Graph of the mean and standard error of the damage summary index, calculated on the basis of new wounds, in each grid. 60 Figure 3.10^Graph of the variables 'number of wounds per hemlock' and 'circumference girdled per hemlock' showing the division for the categorical damage variable.^73 Figure 3.11^Box plots of the categorical damage variable against the discriminant function. ^  74  LX  ACKNOWLEDGEMENTS I would like to thank John Krebs without whose effort and expertise my own research would not have gotten off the ground. I would also like to thank Martin Geertsema for his assisstance with the soil descriptions. Funding and logisitical support for this research was provided by Mammal Pest Management, The B.C. Ministry of Forests, G. Lawson, J. Millar—Kane and Dr. G. Bradfield. I would like to thank Dr. Alan Slemon and Dr. Roger Green of The University of Western Ontario for their suggestions regarding the data analysis. Thanks also to Dr. G. Bradfield and Dr. D. Schluter for their helpful comments on the manuscript. A special thanks to my parents, George and Grace Lawson.  1  CHAPTER 1 INTRODUCTION  1.1 HERBIVORE-HABITAT INTERACTIONS: THE PORCUPINE PROBLEM IN B.C.  A special issue of Bioscience published in December 1988 contained a series of articles that reflected the emerging realization that the interactions between animals and their environment have long-term implications for ecosystem dynamics. The significant ecological roles that animals may play in communities go beyond the immediate requirements for food and habitat. In many cases they are responsible for biogeochemical, successional and landscape alterations that may persist for centuries (Naiman 1988). Changes occurring at the community level have implications on different spatial scales which reverberate throughout all trophic levels, sometimes causing unpredictable alterations to the dynamics of the system (Naiman 1988; Naiman et al. 1988; Pastor et al. 1987; Pastor et al. 1988). The substantial and long-lasting consequences of these interactions to wildlife management are just now being realized.  There are a number of herbivorous mammals in North America whose winter habits of feeding on the foliage and vascular tissues of coniferous trees are of concern to the management of forest crops. In the province of British Columbia an alarming amount of caused by the winter feeding of porcupines has been observed in  2 recent years. Damage has been reported over a wide range of biogeoclimatic subzones and environmental conditions although outbreaks seem to be occurring mainly in second growth western hemlock (Tsuga heterophylla Raf.(Sarg.)) and Douglas fir (Psuedotsuga menziesii (Mirbel) Franco) (Dodge and Cannutt 1969; Hooven 1971; Sullivan et al. 1986; Sullivan and Cheng 1989). The Prince George and Prince Rupert regions sustain the majority of the damage in the province.  It is possible that the recent expansion of porcupine populations in the northern half of the province is the result of logging and silvicultural practices in these areas. Such practices provide a mosaic of forest types, increasing the suitable habitat for porcupines who seem to prefer early successional stages and second-growth stands over mature, oldgrowth forest (Golley 1956; Van Duesen and Myers 1962). Population outbreaks reported in California (Lawrence 1957; Yocom 1971; c.f. Sullivan and Cheng 1989) have occurred in stands associated with recent logging, similar to the situation now occurring in B.C. and the Alaska Panhandle (Sullivan et al. 1986; Sullivan and Cheng 1989). Porcupines appear to prefer feeding on vigorous stems which are usually the dominant and codominant trees in natural and managed stands (Sullivan et al. 1986; Sullivan and Cheng 1989). This damage may have severe impacts on the forest industry, leading to a state where some areas do not have enough merchantable timber, or lengthening the  3  rotation time to reach merchantable volume. This would impact on the annual allowable cut (Sullivan and Cheng 1989). There is also evidence that silvicultural activities such as thinning and fertilization may increase the susceptibility of trees to attack (Sullivan and Cheng 1989).  Most fundamental to wildlife management is an understanding of the interactions between an animal and its environment (Sanderson et al. 1979). The demonstrated complexity and multidimensional nature of these interactions necessitates the consideration of many variables when assessing species-habitat relationships and highlights the inappropriateness of one dimensional approaches (i.e. measurements of response to on resource such as food, protective cover etc.). The development of multidimensional treatments of habitats was concurrent with the adaptation of multivariate statistical methods to ecology. This has made possible the application of multidimensional thinking to field studies of species environment relationships (Carey 1981), as discussed by Green (1971, 1974). Many ecological problems involve numerous variables and numerous individuals or samples. Community data are multivariate because each sample site is described by the abundances of a number of species and because numerous environmental factors affect communities. Multivariate analysis of community data cannot replace experimental manipulation, but neither can experimentation replace multivariate analysis (Pimentel 1979). Resear-  4 chers in community ecology and wildlife management are increasingly using multivariate statistical procedures to identify meaningful habitat variables.  It was the purpose of this thesis to evaluate the utility of a multivariate approach in the consideration of porcupine-habitat relationships in the north coast region of British Columbia. Practically, it was hoped that the identification of variables important in determining the susceptibility of particular forest stands to porcupine attack would allow for recommendations to be made regarding damage reduction measures. Such information would also, perhaps, lend some insight into the general relationships between herbivores, their food resources, and other aspects of their habitats.  This chapter includes a brief introduction to the ecology of the porcupine followed by a review of the relevant research to date regarding the habitat selection and feeding preferences of these animals. This information was necessary in the selection of pertinent factors to be included in this study. Finally the specific objectives of this thesis are stated.  Chapter two, Methods, describes the study site, and variables measured and the methods of data analysis. Chapter three, Results, presents the results of the analyses, and chapter four follows with a Discussion of these results.  5 1.2 GENERAL PORCUPINE ECOLOGY  The porcupine (Erithizon dorsatum Allen) (Figure 1.1) is a solitary animal, occurring in a number of forest types across Canada, southeast Alaska and the northern and western United States (Bannan 1974). Porcupine densities are generally low, but numbers in an area tend to fluctuate greatly depending upon the season and the availability of food. There are no reliable estimates of porcupine densities for the north coast of British Columbia. Nevertheless, this animal, which lives only 5 to 10 years and produces only one offspring per year, has demonstrated a surprisingly ability to attain high densities (Sullivan and Cheng 1989).  Porcupines are strictly herbivorous, feeding on grasses and herbs duing the summer months and moving up to 10 km in the autumn to search for food and cover or denning sites (Sullivan et al. 1986; Sullivan and Cheng 1989). These seasonal migrations could be due to the requirement for den sites, shelter or food. The switch in habitat is accompanied by a switch in diet. During the winter porcupines feed on the vascular tissues, the phloem and cambium, of coniferous trees and hardwoods. Winter home ranges are reported to be between 0.1 and 12.1 ha and probably vary according to the availability and quality of food, protective cover etc. In northern coastal climates, most winter feeding occurs near den sites and often on the same trees in  6  Figure^1.1.^The^porcupine,^Erithizon^dorsatum.  7 successive years (Sullivan et al. 1986; Sullivan and Cheng 1989).  Porcupine feeding during the winter months causes broad prominent, horizontal and diagonal incisor marks on exposed sapwood (Figures 1.2a and b), usually concentrated in the upper parts of the tree. Complete girdling of the tree results in death of the stem above the point of injury, leaving the tree with a characteristic 'spike top' (Figure 1.3). Partial girdling can greatly affect the vigour of the tree for up to ten years as has been shown in the case of ponderosa pine (Storm and Halverson, 1965). The physiological stress of lost vascular tissues and increased vulnerability to attack from insects and diseases probably accounts for the reduced vigour of semi-girdled trees.  Porcupine damage is significant in many forested areas of North America. Damage is common in the coniferous-hardwood forests of the northeast and north-central United States (Curtis 1941, 1944; Rudolph 1949; Cook and Hamilton 1957; Krefting et al. 1962) and in the dry western forests (Taylor 1935; Curtis and Wilson 1953; Lawrence 1957; Van Deusen and Meyers 1962; Storm and Halverson 1967; Hooven 1971). Damage has also been reported in the western hemlock - Sitka spruce (Picea sitchensis (Bong.) forests of southeast Alaska (Meehan 1974; Ruth and Harris 1979). In Canada it has been reported that porcupine damage has occurred to limber pine (Pinus flexilis James) and Douglas-fir  8  Figure 1.2. The results of porcupine feeding on hemlock.  9  Figure 1.3. The results of porcupine feeding on hemlock; ce  +° P ' •  10 in southern Alberta (Gill and Cordes 1972; Harder 1979), and to white spruce (Picea glauca (Moench) Voss), balsam fir (Abies balsamea (L.) Mill.) and other species in the Maritimes (Reeks 1942; Radvanyi 1953; Speer and Dilworth 1978), and juvenile lodgepole pine (Pines contorta Dougl.) in central British Columbia (Sullivan and Sullivan 1982a).  Surveys have indicated that porcupine damage in the northern half of B.C. is presently increasing due to expanding populations of these animals (Sullivan and Cheng 1989). The availability of suitable winter habitats is probably most crucial to the survival of porcupines. Bark is not very nutritious and the quality of this food source will depend upon the age, species and condition of the tree being utilized. Porcupines have been found dead from starvation during the winter, yet with a gut full of bark. Thus, there may only be one point in the year, a 'critical period' (Lack, 1954), when porcupine populations are limited by a shortage of food.  1.3 HABITAT SELECTION AND FEEDING PREFERENCES  The selectivity of porcupine feeding behaviour is an important consideration before recommendations can be put forward for damage reduction methods. Harder (1980) points out that the selection of individual trees by porcupines could occur by either of two processes: a) porcupines may seek out an  11 appropriate community and then, having found one, search for an appropriate patch within this and, finally, decide upon an attractive tree; b) alternatively, they may simply search for a particular species of tree.  There exist published accounts which characterize these animals as selective feeders (process b). Harder (1980) comments, however, that it is difficult to assess the accuracy of many reports for three reasons: data were not provided regarding the availability of tree species; preference rankings were subjective (Gabrielson 1928; Taylor 1935; Curtis and Kozicky 1944; Shapiro 1949; c.f. Harder 1980); the data presented were not in support of conclusions (Krefting et al. 1962; Gill and Cordes 1972; c.f. Sullivan 1986a). Porcupines do, however, demonstrate preferences on the basis of tree species in some areas (Curtis 1941; Rudolph 1948; Brander 1973). One study by Tennison and Oring (1985), which addressed the interaction of porcupine damage and a number of inter- and intra-community variables, found that used versus unused areas differed with respect to tree species composition. In a study by Roze (1984) individuals had narrower diets than the population as a whole, each animal specializing, at least temporarily, on one or two species.  Some studies have indicated that habitat selection proceeds hierarchically (process a).^Landscapes are characterized by  12 spatial gradients of habitat variables such as elevation, slope, soil moisture and nutrient status. The prevalence of porcupines in a particular forest area may be determined by the physical features of the area as much as, or more, than the tree types available (Taylor 1935; Van Duesen and Meyers 1962). Speer and Dilworth (1978) measured porcupine use and a number of habitat variables in mixed coniferous/hardwood forests in New Brunswick and concluded that moist areas were preferred over more mesic habitats. In Harder's (1980) study in Alberta there were marked community preferences for low density stands in leeward communities.  Whether or not porcupines are proceeding in a hierarchical way, it is important to consider how individual trees within a stand are selected. In Speer and Dilworth's (1978) study, every species of tree was attacked which comprised more than 5% of the stand, with the exception of red maple (Acer rubrum); it was attacked at even lower densities. Eastern hemlock (Tsuga  canadensis), present in small numbers, was not utilized although other studies have found it to be the preferred food where it was common. Thus, porcupine winter food may vary at the scale of the stand such that feeding may occur on any species comprising a substantial part of the stand. Roze (1984), working in New York state, also found that the primary food of porcupines was the most abundant food. Obviously, this would increase foraging efficiency since less time would be spent  13 searching for specific food types (MacArthur and Pianka 1966; c.f. Roze 1984).  Harder (1980) concluded that porcupines demonstrated a lack of preference between the coniferous species comprising the communities he studied. He argued that in the preferred stands trees were larger, taller, and generally younger, and suggested that if porcupines select on the basis of size and/or vigour, the collective population response would be manifest in intercommunity preferences similar to those observed. The size of trees fed on by porcupines ranges from 3 cm dbh to almost 90  cm dbh but the bulk of feeding occurs on pole-sized trees from 7.6 to 38.1 cm dbh. A predilection may exist in some cases for a particular size class of tree regardless of geographical location or species of tree. Perhaps, as proposed by Curtis and Wilson (1953), porcupines are most adept at climbing or maintaining feeding positions in this size of tree. Several studies have concluded that relatively large, fast growing trees are preferred (Rudolph 1949; Curtis and Wilson 1953; Krefting et al. 1962; Spencer 1964; c.f. Harder 1979). Vigorous trees with large open crowns produce a large annual increment of phloem and provide more foliage than crowded trees (Bannan 1955; Grillos and Smith 1959) and provide more large branches allowing greater access to food. Porcupines may be identifying vigorous trees by their obvious physical features (Harder 1979).  14 The implications to the silvicultural industry of such selective feeding are that practices such as thinning may predispose the trees to attack due to their increased vigour and accessibility. Porcupine preference for crop trees in thinned stands has been reported by Van Deusen and Meyers (1962), and Dodge and Canutt (1969) in the Pacific northwestern U.S., by Eglitis and Hennon (1987), in Alaska, and by damage surveys in the Kalum Valley of B.C. by Sullivan and Cheng (1989).  1.4 RELEVANT STUDIES IN THE PACIFIC NORTHWEST  Selectivity in feeding on the basis of size and vigour has been reported in the Khuzeymateen Inlet on the north coast of British Columbia (Sullivan et al. 1986). Studies have concluded that in second growth stands, feeding damage, measured as the average number of wounds per tree based on crown class, was the highest for dominant and codominant trees which were usually larger than those in intermediate or suppressed crown classes. This pattern was also recorded for the percentage of trees completely girdled. Damage peaked (83.3%) among second growth hemlock in the 27.5-32.4 cm dbh class. Sitka spruce sustained less damage than hemlock, while amabilis fir and western redcedar were not attacked (Sullivan et al. 1986).  These conclusions are supported by studies of porcupine feeding damage in Alaska (Eglitis and Hennon 1986, 1987) and elsewhere  15 (Curtis 1941; Rudolph 1949; Curtis and Wilson 1953; Krefting et al. 1962; Van Deusen and Meyers 1962). Comparable damage patterns have been observed in studies of red squirrel feeding on juvenile lodgepole pine (Sullivan and Sullivan 1982a and b; Sullivan and Vyse 1987). Spencer (1964), Storm and Halverson (1967), and Harder (1979) have also reported a preference by porcupines to feed on vigorous stems for various species of conifers in the interior of western North America (Sullivan and Cheng 1989).  In the Kalum Valley of B.C. (Prince Rupert District) the annual rate of attack has increased from 0.6% in 1986 to 1.8% in 1987 for western hemlock, which is the major species in managed stands, whereas damage to Sitka spruce has remained constant. Lodgepole pine is also attacked were it grows (Sullivan and Cheng 1989).  Severe damage has been incurred by both western hemlock and Sitka spruce in managed stands on Mitkof Island in Alaska and the degree of attack may depend on their relative abundances in a given managed stand (Eglitis and Hennon 1986, 1987). Sullivan and Cheng (1989) comment that this severe example may represent the future situation for the Kalum Valley.  16  1.5 OBJECTIVES  The overall objective of this thesis was to evaluate the relationships existing between porcupine damage due to the winter barking of trees and relevant vegetational and environmental variables in managed and unmanaged stands. Three specific questions were addressed:  1.)  Do thinned areas sustain more damage than unthinned areas?  2.)  Are porcupines choosing on the basis of a community type (a suite of properties such as manifested in the vegetation, soil nutrient status, etc.), or on the basis of the species, size or vigour of individual trees?  3.)^Which variables or combination of variables best pre dict the amount of damage that an area will incur?  Answers to these questiions are necessary before considering the appropriateness and efficacy of damage reduction measures. It is hoped that the study would also contribute to the understanding of relationships between herbivores and the communities in which they exist.  17 CHAPTER 2 METHODS  2.1 STUDY SITE  The Kalum Valley District (Figure 2.1) was selected as an especially appropriate area within the Prince Rupert District in which to carry out the study. This is an extensive area of relatively young (12-15 years) managed stands. Silvicultural work has been put into approximately 9,086 ha and another 48,560 ha have been reforested at a total investment of 68.6 million dollars. These stands are now entering the susceptible stage (15-35 years) for intensive porcupine attack. Damage to western hemlock has increased three fold over the last two years. An additional 32,000 ha is proposed for stand-tending over the next ten years (Sullivan and Cheng 1989). Figure 2.2 illustrates the general appearance of the forests in the area of the study sites. These forests are within the Coastal Western Hemlock Zone, Northern Drier Maritime Subzone (CWHf) (Figure 2.3).  Four blocks of forest in the Kalum Valley, 100 ha each, were selected on the basis of walk-throughs and forest cover maps. They were located on the north side of the Kitsumkalum River, north of Terrace, B.C. (Skeena Cellulose TFL #1) as shown in Figure 2.4. These blocks were similar in species composition, topography, elevation, height class of trees and years since logging (Table 2.1). All four areas had regenerated naturally  ^ ^  u^ 33,5:.^• ,^•^' "  '^01770 -^k  -  rea  v 2459'  Cs  yy^.  10  ‘-'2653  I  COW, .  d  01101 SO  Hogan 5080, immie Mt  Rimer^Birnie  '  4550  TERRACE  Firllayso  59.  ,47  rgetern —  Gee ,,  Trentlm  1.00.5 -  Pt 00-  fSIMPS [A  g1altielt  M et I ugwell rriote Is^ r.^ Lucy  4  c •rr-a ct i a  e.  raS^  4  ,,  50 55  `51i.phens  V 4f  T  NORTH  •  ,  Ae  ,Prescott  500  •  S  Larrerer, ;,,,  • )^Island^  11250 -•  watisrnif^ 2450 '^,'' •4 • .1.•i'4^te • RP-let-ANON\Sa  ^'PON lLl?50 ^1  50  orsey  icking  ORCNlR  40,50) •  0a  2:150  Ir  31'^50'^40'^30'^20'^10'  4  6*  ,.,!  ^  .)...,  ; ^5623:--^2350  L  -- — --,0 _,  .P• , ,A ..  5850 0  6250.  01.40  Ø*1.  /^  50'^40'  N' - -.  ^i^  ,÷^\ ....^  4:,..  .- 7 ^\^1^L•P--,. 6t,'^Ll DVIsiii.  .^i___  ^\^'^*-ls,^' .  0150  6350 Nllimal  (  30  ...  Kama •  000  , .  45p^P^s 0  1  1  55517.  (-.7",  (\\,_a544  `. ""^  "—", 11.93^"or  rake^Pi^650=7  —  e  --- ^,. .)^ . \\.\ \ s.:50  ..,,,-„f--__:_ ^  38.50  P. ^ t m° 01,3"'n.P.C • ',a^  130 °  ,..  4650  '5550  ,:3650 Ken,n,ady  7i  •-•  ,  5750,- :F " .  Rachel lap^IsQ^ Lelu I  Miro  ,--'^  -^-  A  Kinakan9iclleY  .,__---.■-...,-,--"--  72^  1,- )^7 --r"^  5650  t7335°I''^385'f  Islan  0,004 Archibald^  'c  450,1,  PRINCE RUPERT  :^tak  601` ^6450 --:—^' •  we Lek.  340 reeM:e vat  o Diby  Tree Nob ,P.  L  .  8  50  20'  10'  ^  129°  M  SO'^40'^30'^20'  Figure 2.1. Location of the Kalum Valley in north coastal B.C. (yellow)  10'  1—$ co  Z . 2 .^T 414 e-^&Jr:671_^ -  r) pear-a,  ANCE  %Port Edward  .  41  cm —A  .  .';otte Sandipt  AI Figure 2.3. The biogeoclimatic zones of the north coast of B.C. Green represents the coastal western hemlock zone.  21  Figure 2.4. The location of the four grids on the north side of the Kitsumkalum River.  22 after deforestation and two of the areas had been thinned (spaced several years previous to this study) in an effort to reduce the effects of competition between crop trees. Figure 2.5a presents an example of the typical appearance of an area which has been spaced compared to Figure 2.5b, an unspaced area.  Table 2.1. Description of the four study areas.  Grid  Location  Species Class  Logged  Spaced  A  128 44'E 34'N  Hemlock/Balsam  1960  1984  B  128 45'E 38'N  Hemlock/Balsam  1956/58  1980/84  C  128 43'E 36'N  Hemlock/Balsam  1958  not  D  128 50'E 50'N  Hemlock/Balsam  1957/60  not  Using hip string and a compass, one 100 ha grid was laid out and flagged in each forest block, the exact shape of the grid being dependent on the topography. Stations were flagged every 100m, the intersection of grid lines being possible plot locations. Twenty-five sampling plots were then randomly selected on each grid. Twenty-one intersections on grid C had to be excluded from the random draw because they were discovered to be unspaced even though they were classified as spaced on the forest cover maps. Two plots were excluded on D and 5 on B because they were in swamps. Two more on B were excluded as they fell in gravel  22  Figure 2.5 a. The general appearance of a thinned forest area.  24  Figure 2.5 b. ^The general appearance of an unspaced forest area.  25 pits. Plots that landed directly on a skid road or on an old landing area (ground severely compacted) were offset by 15m at right angles to the road.  2.2 STAND STRUCTURE AND DAMAGE ASSESSMENTS  All damage data were collected in May, June and July of 1989. Data was recorded on rainproof Cruise Tally Sheets (Province of B.C., MOF). Trees greater than 10 cm dbh were selected using a relescope (Bitterlich, 1985) (Figure 2.6). The number of bands  used in the relescope viewfinder (determining basal area for trees in to be included) was held constant in each area in order to achieve an average number of trees/plot of between 10 and 12. Thus, the size of the sample plots for assessing damage and tree specific variables varied dependent on tree density, but was consistently larger than 50m 2 , the area of the vegetation and site description plots (see below). Only live trees were included.  Each tree was tagged and the following variables recorded: species; dbh (diameter at 1.3 m above point of germination) to nearest 0.1 cm; crown class, ranked from 1 to 3 (1=dominant; 2=codominant; 3=intermediate/suppressed); and tree height. The height of trees up to 12 m was measured using a height pole. Trees taller than 12 m were measured using a clinometer and measuring tape. In this case the height was calculated  25  IFT  a  4■111■11  Figure 2.6. The use of a relescope to select sample trees.  27  according to the formula Height = (B+T)(cos(tan -I B)SD))+correction where B is the angle of the tree bottom, as seen through the clinometer, T is the angle of the tree top, SD is the distance from the clinometer to the tree at breast height in m, and Correction is the height person at bottom of tree, since clinometer measurements were taken to top of the head.  My original intention was to calculate the growth form of each tree as the ratio between its height and dbh. However, so many of the damaged trees had dead tops, reflecting the extent of girdling, that this was not possible.  Basal area/ha. and number of stems/ha were calculated at the grid level and at the level of the plots. These variables were considered to be important indicators of stand structure and possible factors in porcupine choice. They are properties which may be manifest at the level of the whole stand or a smaller community level, such as the plot.  Porcupine damage data were obtained by climbing nearly all the trees in the plots (Figure 2.7) unless they could be assessed from the ground. For each tree I recorded the number of new (1988-89 winter) and old (prior to 1988-89) damage wounds and their position on the stem, in thirds (lower, middle, upper).  2'6  Figure 2.7. Climbing a hemlock to assess damage.  29 I also recorded the circumference of stem girdled (classes: 125, 24-49, 50-75, 76-99, and 100%) of the most severe wound in each third of the tree. The total number of wounds and the total wound circumference was computed as the sum of the values for each third of the tree.  2.3 HABITAT DESCRIPTION  2.3.i Vegetation  All vegetation and site description data were taken in a circle of 3.99 m radius (50m 2 ) positioned at the center of the damage plots (Figure 2.8). Data were recorded on standard, rainproof, ecological classification reconnaissance forms (Province of B.C., MOF).  The percent ground cover of all species of herbs and shrubs occurring within the plots was recorded and coded as shown in Table 2.2.  Comparison charts were used in the field and trial plots were done to establish consistency.  Distribution codes were also recorded for each species but were not included in the analysis.  30 Table 2.2. Percent cover codes for plant species data. After Walmsley et al. (1980)  1 - present outside of plot 2 - 0-1% 3 - 1-5% 4 - 5-25% 5 - 25-50% 6 - 50-75% 7 - 75-100% 8 - 100%  Plants were identified using Hitchcock and Cronquist (1973), Coupe et al. (1982), Lyons (1952).  Strata coverage, the percent cover of trees, shrubs, herbs, mosses and lichens was also recorded as per Klinka et al. (1981). These variables were treated as a separate data matrix for many of the analyses.  2.3.ii Site Description  The variables measured for the complete site description are presented in Table 2.3.^The methods of description follow  31 those given in 'Describing Ecosystems in the Field' (Walmsley et al. 1980), unless otherwise stated. The gradients described for the variables were tailored to the particular biogeoclimatic subzone in which the study was carried out.  A soil pit, usually about 2 feet deep, was dug at each plot (Figure 2.9) and a complete soil description was recorded. The soil descriptions used to designate the Humus Form Class were also used in the evaluation of the variables Ecological Nutrient Regime, Moisture Regime and Drainage.  Table 2.3.^Description of variables recorded for the site decription (after Walmsley et al., 1980).  SLOPE - in degrees, measured using clinometer.  ASPECT - the direction perpendicular to maximum slope, measured using compass.  SITE POSITION - Upper, Mid or Lower, receiving Slope  SURFACE SHAPE - Categories apply within Site Position and refer to the surface profile of the site, concave, convex or flat.  ^  32 Table 2.3 continued  MICROTOPOGRAPHY - categories refer to the variability of the surface of a site:  1. Smooth^- no mounds 2. Microrounded^- mounds less than 0.3m high 3. Slightly Mounded - mounds 0.3m to lm high, over 7m apart 4. Moderately Mounded - mounds 0.3m to lm high, 3m to 7m apart 5. Strongly Mounded ^- mounds 0.3m to lm high, im to 3m apart 6. Severely Mounded - mounds 0.3m to im high, 0.3 to 1m apart 7. Extremely Mounded - mounds more than lm high and 3m apart 8. Ultra Mounded ^- mounds more than im high and less than 3m apart.  ECOLOGICAL MOISTURE REGIME (Hygrotope) - Categories were relative to the macroclimatic conditions of the biogeoclimatic subzone; regime signifies the actual amount of moisture available for plant growth, and integrates many interrelated environmental and biotic variables (Walmsley et al. 1980). The field assessment was completed by  33 evaluating a combination of soil properties, physical site factors and indicator plants (Klinka et al. 1989).  0. Very Xeric 1.  Xeric  2.  Subxeric  3.  Submesic  4.  Mesic  5.  Subhygric  6.  Hygric  7. Subhydric  ECOLOGICAL NUTRIENT REGIME (Trophotope) - Scale was appropriate to climatic conditions of the biogeoclimatic zone; regime signifies on a relative scale the nutrient supply available for plant growth (Walmsley et al. 1980). Integrates many environmental and biotic parameters which, in combination, determine the avialable nutrients. Trophotope was evaluated by a qualitative examination of soils and indicator species (Klinka et al. 1989).  1.  Oligotrophic (Very Poor)  2.  Submesotrophic (Poor)  3.  Mesotrophic (Medium)  4.  Permesotrophic (Rich)  5. Eutrophic (Very Rich)  34  Table 2.3 continued  SOIL DRAINAGE - Seven classes assessed from topography, position, vegetation and soil characteristics as per Walmsley et al. (1980).  1.  Very Rapidly Drained  2.  Rapidly Drained  3.  Well Drained  4.  Moderately Well Drained  5.  Imperfectly Drained  6.  Poorly Drained  7. Very Poorly Drained  HUMUS FORM CLASS - Determined from the soil profiles as per Klinka et al. (1981). Humus fell into 2 orders: Mors and Moders, and was thus coded according to the 14 groups of humus taxa within these orders. The procedure is too elaborate to describe here. For details see Klinka et al. 1981. Humus Form Class was considered to be a continuous variable since it was coded in such a way that it provides for segregation along the soil moisture gradient and, more loosely, the soil nutrient gradient. The Mor order encompasses the least biologically active humus forms of the two orders, with fungi being dominant in the upper profile.  35 Moders, however, have soil fauna dominant in the upper soil horizons and provide generally more available nutrients than Mors (Klinka et al. 1981). Soil analysis was performed, or confirmed, by Martin Geertsema, a pedologist with the MOF, Smithers, B.C.  SURFACE SUBSTRATE - The percent cover of the following variables was recorded: Humus, Dead Wood, Bedrock, Rocks (includes rocks >7.5 cm in diameter, which may be covered by an organic layer <2 cm deep, moss or lichen), Exposed Mineral Soil and Standing Water.  2.4 DATA ANALYSIS  Multivariate analysis examines numerous variables simultaneously, summarizing the data and revealing its structure. One of the main purposes of this research was to explore multivariate methods as they apply to a wildlife management problem. A number of exploratory procedures were examined and several were chosen as particularily useful.  There was an overlap between the Objectives and in the data required to address them and, thus, in the methods of analysis. First, though, it was necessary to summarize and to simplify  36  -  - e, T  41W1017:,%  :4010 -•  Figure 2.8. A typical plot.  Figure 2.9. An example or a soil pit__  37 some of the data and to compare the areas on the basis of damage, site and vegetation variables. Analyses were performed using BMDP, SAS and the Systat package software.  The general approach used in analysing the data was to do parametric tests because of the greater power they afford. This assumes that the variables have a normal distribution which may not be the case considering the nature of the variables involved. However, sample sizes were such (80-100) that most tests would be robust. Nonparametric tests were used to confirm analyses wherever possible and results were unaltered.  In biological situations, as means increase so often do variances. Significance tests for differences between variables are extremely powerful, and may pick up even minor differences (Pimentel 1979). Log transformations often tended to render data more normally distributed and to equalize variances but results were generally unchanged. Also, I was not interested in the prediction of, for example, log(damage). Hence, I present results based on the untransformed variables. I did not detect any appreciable non-linear relationships between variables, and so I did not correct for it by transforming data.  2.4.i Stand Structure  Nested analysis of variance procedures were used to test for  38 differences between treatments (thinned and unthinned) and grids within treatments in the density (stems/ha.) and the basal area (m 2 /ha.) of trees. These estimates were calculated (as per Bitterlich 1985) for trees of all species in the plots, and then for hemlock only as it was the only species utilized by porcupines. The ANOVA model predicts the dependent variable for each grid by a sample mean. The difference between the actual and the predicted response is the residual error. It is the sum of squares of residual errors that is minimized by the procedure in the fitting of parameters. Because grids are nested within treatments the total sum of squares (SS) can be partitioned into a treatment SS, a between grids within treatment SS , or nested factor, and a within grid SS. No interaction can be obtained because grids are not completely crossed with treatment, but are nested under treatment. One-tailed tests were used here since the a priori expectations were for lower values in the treatment areas.  The estimates of stems/ha. and basal area (m 2 /ha.) were derived for each individual plot for use in regression against the damage index. For this purpose, plots with no hemlock trees were excluded from the analyses. In these plots damage was, obviously, undefined.  Differences between treatments, between grids within treatments and between all four grids in the size structure of the trees  39 (mean dbh) were assessed using nested analysis of variance. Mean dbh was also calculated at the plot level for use in regression against damage.  2.4.ii Damage Data  It was necessary to summarize the damage to trees in order to compute a measure of damage on a 'per plot' basis. Two aspects of damage had been measured, the number of wounds per hemlock and the circumference of the most severe wound in each third of the tree. For each plot I computed the 'number of wounds per hemlock' (WPH) as the total number of wounds on hemlocks divided by the number of hemlocks present. I also calculated for each plot the 'circumference girdled per hemlock per plot' (CPH) as the sum of the circumferences of wounds on all hemlocks present divided by the number of hemlocks.  The variables WPH and CPH were highly correlated and so a summary measure of damage per plot was derived that retained as much information as possible but that was free from redundancy. Principal components analysis (PCA) was performed on the correlation matrix of the untransformed damage variables WPH and CPH. In a PCA the original variables are transformed to variables that have zero intercorrelations. The transformation rotates the original axes but maintains the original relationship among data points (Pimentel 1979). The new PC-axes are  40 linear combinations of the original variables with coefficients equal to the eigenvectors of the correlation matrix. The first principal component was used as the new damage variable, referred to as 'PC1D total', 'PC1D old' and 'PC1D new', depending on whether it was calculated on the basis of total, old or new wounds.  This summary variable, PC1D, was used in an ANOVA to test for differences between grids in the amount of damage incurred. It was the intention to test the degree to which damage could be predicted from tree specific variables (species, dbh, crown class and growth form) using analysis of variance and regression procedures. However, because of the resulting stand structure, and the fact that so many of the damaged trees had dead tops, it was not possible to evaluate crown class.  2.4.iii Site and Vegetation Data  Of the site and vegetation variables measured, I selected those that occurred in at least 25% of the plots as these were considered to be most useful to this applied management problem. Rarer plants or site characteristics, though they may sometimes correlate highly with damage in the few plots in which they occurred, would not be useful for the future practical prediction of threatened areas because of their rarity.  41 A multivariate analysis of variance (MANOVA) would have been the preferred method for detecting the effect of treatment on the total set of site and vegetation variables, but this was not possible due to a lack of degrees of freedom (so many variables and only two treatments). Therefore, univariate ANOVAs were performed for each of the variables separately. The probability level (0) for significance tests was set to 0.05 divided by the number of univariate ANOVAs performed (Bonferroni standard procedure). There were a resulting 11 variables in both the site and vegetation submatrices after those variables occurring with a frequency of less than 25% were dropped. Thus, P=0.004.  A nested MANOVA was used to test for differences between grids within treatments for sets of site, species and vegetation strata variables separately. The methodology of MANOVA is the same as that of ANOVA, however, since multiple measurements have been made in each plot, the method is multivariate.  Finally, I attempted to explore the relationship between vegetation data the site data using canonical correlation analysis (CCA). The strategy of CCA is to search simultaneously for linear combinations of each set of variables (the canonical vectors) such that the correlation between the sets is maximized (Cooley and Lohnes 1971; Gittens 1985). The variables with the greatest contribution to the canonical vectors were determined by their standardized correlations with the canonical variates.  42 A redundancy analysis was included in the CCA. Although the squared correlation coefficient is a measure of the overlap between canonical variates it may not be a very good indicator of the importance of the linear combination of variables, since these may not account for much of the variation in the original data sets (Gittens 1985). The redundancy associated with a canonical variate is a useful index of the predictive or explanatory power of each canonical variate in relation to the variation in the opposite set of variables. Redundancy is the proportion of total variance in one domain (set of variables) that is predictable from a linear composite of the other domain, given the availability of the second domain (Gittens 1985). This quantity is arrived at if the variance extracted by the kth canonical variate is multiplied by the squared canonical correlation coefficient which expresses the proportion of the variance of one of a pair of canonical variates shared by the other.  2.4.iv Relating Damage to Site and Vegetation Data  This part of the data analysis most directly addresses the second and third objectives stated in the Introduction: Objective 2) By what process are porcupines selecting habitats and trees and 3) What variable or combination of variables best predicts the amount of damage an area will incur. Similar methods of analysis were employed to address these objectives  43 because of the obvious degree of overlap.  I initially used CCA in an attempt to relate the damage variables WPH and CPH to site or vegetation variables, because this approach was considered useful for detecting associations. However, this was abandoned because the canonical vector of damage variables proved not to be an index of damage, but an uninterpretable ratio of WPH and CPH. Hence, regression using the damage index PC1D was then used as the alternative.  For the purposes of regression, PC1D was considered to be the dependant variable. Stepwise regression was considered but not employed because it was found that the set of variables included by the stepwise selection procedure was highly sensitive to the order in which they were added. Variables added last to the model were not themselves related to damage, but were simply selected relative to the other variables already included. Multiple regression was used instead. Regressions were performed within thinned and control grids as treatment had an effect on the damage index and on the stand structure.  Another method explored because of the possible applications to wildlife management was Canonical Discriminant Analysis (CDA). Given a classification variable and several quantitative variables, CDA derives canonical variables (linear combinations of the original quantitative variables) that summarize variation  44 between classes. On the basis of a graph of WPH against CPH the plots were coded as low or high damage areas. CDA was used to test if these two categories of total damage could be discriminated on the basis of the complete subset of site, species and vegetation strata variables within the control grids.  45 CHAPTER 3 RESULTS  3.1 STAND STRUCTURE  A summary of the number, species of trees sampled, and trees incurring damage, is presented in Table 3.1. Of 884 trees sampled, almost all were western hemlock or amabilis fir. The calculated mean and standard errors for the estimates of stems/ha. and mean basal area/ha. for each grid are shown in Table 3.2.  Table 3.1. Summary of the damage data. Total number of trees examined, and numbers showing damage (in parentheses), in the four grids. There were 25 plots per grid. Plots are variable in size, and No./Plot does not indicate tree density.  SPECIES  Total Grid No.Trees  Hemlock  Fir  Spruce Redcedar  Average No./Plot  A  259  75 (26)  180  (2)  4  0  10.2  B  195  76 (35)  116 (3)  2  1  7.7  C  224  125  (45)  99 (3)  0  0  9.0  D  213  100  (40)  113 (1)  0  0  8.5  508  6  1  9.0  Total 891  376 (146)  (9)  46 Table 3.2. The estimated mean number of stems/ha., and basal area (m 2 ) /ha. with standard errors, for each grid. Means are given for trees of all the species counted and for hemlocks only.  STEMS/HA.^  All Trees  BASAL AREA  Hemlock Only  All Trees  Hemlock Only  Grid  Mean  S.E.  Mean  S.E.  Mean  S.E.  Mean  S.E.  A  429.5  33.4  153.2  30.1  15.9  1.2  4.8  0.8  B  384.3  43.3  179.5  36.5  23.5  2.2  8.7  1.6  C  1100.1 110.2  677.1  92.4  27.4  2.6  15.4  2.6  D  1946.1 271.2  1019.7  182.4  43.1  4.5  20.5  4.5  The estimated total density of trees, and density of hemlocks alone, was lower on the experimental (thinned) grids (A and B) than on the control grids (C and D) (Table 3.2, Figure 3.1). This effect of treatment was statistically significant for hemlock density (nested ANOVA, F=15.76, df=1,2, P=0.029), and nearly so for the density of all trees (F=6.94, df=1,2, P=0.060). Thus, thinning did appear to reduce tree density. The trend was the same for basal area of trees (Table 3.2,  47 Figure 3.2) although it was significant for only hemlocks alone (F=12.36, df=1,2, P=0.036), but not for all trees (F=3.19, df=1,2, P=0.11).  The variable dbh (tree breast height diameter) was considered to be an important character of stand structure. Only hemlocks were considered for this analysis as they were almost exclusively attacked by porcupines (Table 3.1). A nested ANOVA comparing treatments in the mean of this variable revealed no significant difference (F=5.86, df=1,2, P=0.136) although the trend is towards larger trees in thinned grids (Figure 3.3). There was a highly significant difference between grids within treatments (F=10.24, df=2,372, P<0.001). Tukey pairwise comparisons indicated that all grids differed significantly from one another in the mean dbh of hemlocks with the exception of grid C from D. Tests repeated on log transformed data gave identical results.  3.2 INCIDENCE OF DAMAGE  Damage was confined almost exclusively to western hemlock (Table 3.1). Thirty-two percent of the hemlocks from the first grid were damaged, 42% from grid B, 33% from grid C and 37% from grid D. Damage to amabilis fir was incidental and western red cedar and Sitka spruce were not attacked at all. This comparison shows that incidence of damage was not noticibly greater in  4+1  Cd  0  a 2000  Cl) a)  1000 ED  • 0 1500 Cd  N  0  Z  1000  a) c(i)  8 E  500  I  0  A B C D Grid  Figure 3.1. The mean estimated density (number of stems/ha.) and standard error for all tree species (a) and for hemlocks only (b) in each of the grids (n=25/grid).  49  50 773 cNi  E 40 a(D's tea  30 JD  scc•  2  20  78 0  F-  10 25  C\j  E  co -6 03  0 E (D  20 1 10 5  0  B C  D  Grid Figure 3.2. The mean estimated basal area (m 2 /ha.) and standard error of all tree species (a) and of hemlocks only (b) in each of the grids (n.25).  50  27  E  0 2  5 24 0 0 E  0 .y  6 21  4  L.  O  E ces  18  I  ai a)  15  A^B^C  ^  D  Grid Figure 3.3. The mean and standard error of the diameter at breast height of hemlocks in each _of the grids. All grids are significantly (P<0.001) different from one another with the exception of C and D (Tukey pairwise comparisons). - •  51  0  A^B^C  ^  D  Grid Figure 3.4. The mean and standard errors of the number of wounds per hemlock in each of the grids.  52 thinned grids (A and B) than in unthinned grids.  The incidence of western hemlock attack by porcupines during the 1988-89 winter and prior to this period is shown in Table 3.3. The total incidence of damage was 39%, of which 15% were trees with new wounds.  The number of wounds per hemlock was considered as a measure of porcupine feeding intensity. The number of wounds per hemlock was also not greater in thinned grids (Figure 3.4; nested ANOVA, F=0.004, df=1,2, P=0.480). However, there was substantial variation in the mean number of wounds per hemlock between grids within each treatment (F=3.65, df=2,83, P=0.03).  The average number of wounds per hemlock with respect to tree diameter class during and prior to the winter of 1988-1989 is illustrated in Figure 3.5. The number of old wounds accumulates steadily as the trees grow larger until the largest (presumably oldest) diameter classes, in which severely damaged trees have probably died. A quadratic regression on the mean number of new wounds against ordered tree diameter class indicated a significant trend (tree diameter class P=0.02, tree diameter class 2 P=0.01) with damage peaking in the 25.1 cm to 30.0 cm tree diameter class as shown in Figure 3.6.  53  7  F  6 5 4 3  V  2  1 s10 10.1- 15.1- 20.1- 25.1- 30.1- 35.1- >40 15.0 20.0 25.0 30.0 35.0 40.0  Hemlock diameter (cm)  Figure 3.5. The mean number of wounds per hemlock with respect to tree diameter class. The solid bars are new (1988/89 winter) wounds and the hatched bars are old (prior to 1988/89 winter) damage.  54  2.0  1^I^I  0  1.5 N  co D c 1.0 c (i> 0  0.5  a)  2  0.0  1Q1- 15.1- 20.1- 26.1- 30.1- sal- >40 150 200 260 300 350 4Q0  Hemlock diameter (cm) Figure 3.6. Quadratic regression of the mean number of new (1988/89 winter) wounds per hemlock with respect to tree diameter class. R 2 =0.76; for DBH, P=0.016, for size 2 ' P=0.012.  ^  55 Table 3.3. Number of western hemlock trees with porcupine damage inflicted during the 1988-89 winter season ('New Damage'), and during previous seasons ('Prior Damage').  Prior Damage  None^Number Damaged Total  None^230^98^328  New Damage Number^22^26^48 Damaged  Total^252^124^376  The average number of wounds per hemlock, new and prior to the 1988-89 winter, in each third of the trees with respect to tree diameter class is shown in Figure 3.7a and b respectively. In general, the pattern of new and old wounds with respect to the position in the tree appears similar. The results of three pairwise t-tests indicated significantly more wounds in the  56  1.0 0.8 0.6 0.4 0  E _c  Q u)  0 C  0.2 0.0 4 3 2  1 10 10.1- 15.1- 20.1- 25.1- 30.1- 35.1- >40 15.0 20.0 25.0 30.0 35.0 40.0  Hemlock diameter (cm)  Figure 3.7. The mean number of a) new (1988/89 winter) wounds per hemlock and b) old (prior to 1988/89 winter) wounds per hemlock with respect to tree diameter class. ^Position on tree:lertop third, lir middle third, C,74: lower third.  57 middle third of the stems than in the upper or lower thirds (P<0.01 in each case, DF=375). Fewer wounds were recorded in the upper stems in cases where the trees were stripped almost completely because individual wounds could not be distinguished.  It was not possible to evaluate the effect of crown class owing to the even-aged structure of these second-growth stands. Nearly all of the trees (296) fell into the codominant crown class; only 14 were classified dominant. Although 65 trees were classified as intermediate/suppressed, they were represented by the smallest size classes only, (<=20.0 cm dbh).  3.3 DAMAGE INDEX  A plot of the two variables 'wounds per hemlock' (WPH) and 'circumference girdled per hemlock' (CPH) (Figure 3.8) indicates that the error in these measurements increases as the variables increase.  The variables WPH and CPH were highly correlated (86%), meaning that trees with the most wounds also had the most severe wounds. Principal components analysis was employed to create a summary variable of these two measures, free of redundancy yet retaining as much information as possible. The first PC-axis of the damage variables accounted for 96% of the variation in the data (Figure 3.8). The variable contributions to this axis were  58  Mean circumf. wounds per hemlock Figure 3.8. The mean number of wounds per hemlock versus the mean circumference of wounds per hemlock calculated on a plot basis.^The line shows the first PC-axis from a principle components analysis of these two variables. The broken line is the deviation of the observation from the PC-axis.  59 relatively equal (component loadings, WPH= 0.63 and CPH=0.51). This PC1-axis was considered as a new variable (as in methods) and will be referred to hereafter as 'PC1D total', since it was calculated on the basis of old and new wounds. The damage summary indices calculated on the basis of new wounds and old wounds separately were also calculated and are referred to in the text as 'PC1D new' and 'PC1D old' respectively.  There was no significant treatment effect for the total damage summary index, 'PC1D total', (F=0.05, df=1,2, P=0.901) nor were there any differences between grids nested within treatments (F=1.61, df=3,82, P=0.192) for this variable. There was no difference between treatments in "PC1D old', the damage summary index calculated on the basis of old wounds (F=0.005, df=1,2, P=0.952; Figure 3.9a), as expected, since 'old' damage was inflicted before the treatments were imposed, though there was a significant difference between grids nested within treatments (F=3.34, df=2,83, P=0.040). However, there was significantly more new damage in unthinned areas than in thinned areas (F=25.84, df=1,2, P=0.037; Figure 3.9 b) while there was no significant difference between grids within treatments (F=0.186, df=2,83, P=0.831). With regards to Objective 1, the hypothesis that thinned areas incur more damage than do unthinned areas can be rejected. In fact, the opposite would seem to be true. With regard to Objective 2, it is possible that porcupines are choosing at the scale of large blocks of forest perhaps on the  60  a) old wounds 5  1 0 1.8  0.0  A  B  C  D  Grid Figure 3.9. The mean and standard error for the damage summary index in each of the four grids, a) calculated on the basis of old wounds ('PC1D old') and b) calculated on the basis of new wounds ('PC1D new').  61 basis of properties such as stand density or the basal area of trees. It is not likely that they are responding to the diameter classes of trees. Although porcupines demonstrate a predilection for a particular size of trees they are not attacking more trees in the thinned areas were the trend, though not significant, is towards the preferred size classes of trees.  Regression of damage, 'PC1D total', 'PC1D new' and PC1D old', calculated at the level of the plot on the stand structure variables of density and basal area were performed for treatment grids and control grids separately. The results indicated no significant relationships using either the total number of trees or only hemlocks (Table 3.4). Porcupines are not, therefore, choosing at the level of the plot on the basis of these properties.  62 Table 3.4. Results of regressions of the damage index, calculated on the basis of new, old and total wounds, and the density (stems/ha.) and basal area (m 2 /ha.)of all tree species and of hemlocks only. (For control grids, total damage, N=46, df=1,44; old damage and new damage, N=47, df=1,47. For treatment grids, new damage, N=40, df=1,38).  CONTROL GRIDS  F-Ratio  P  TREATMENT GRIDS  F-Ratio  P  NEW DAMAGE Total Density  0.296  0.589  0.408  0.527  Hemlock Density  0.691  0.410  2.181  0.148  Total Basal Area  0.360  0.551  0.014  0.905  Hemlock Basal Area  1.768  0.190  0.194  0.662  Total Density  1.767  0.190  Hemlock Density  1.350  0.251  Total Basal Area  0.821  0.370  Hemlock Basal Area  0.004  0.953  Total Density  2.358  0.132  Hemlock Density  1.045  0.312  Total Basal Area  0.936  0.339  Hemlock Basal Area  0.768  0.386  OLD DAMAGE  TOTAL DAMAGE  63 3.4 VEGETATION AND SITE VARIABLES  The plant species present in at least 25% of the plots, and therefore retained for analysis (see Methods) were: Athyrium filix-femina, Clintonia unifoliata, Cornus canadensis, Dryopteris assimilis, Epilobium angustifolium, Oplopanax horridus, Rubus pedatus, Rubus spectabilis, Streptopus streptopoides, Tiarella unifoliata, and Vaccinium alaskense.  The site variables occurring in at least 25% of the plots were: Slope, Aspect, Surface Shape, Moisture Regime, Nutrient Regime, Number of Stumps, % Cover Humus, % Cover Dead Wood, Microtopography, Drainage, and Humus Form.  Analysis of variance on the vegetation, site and vegetation strata variables indicated that the only variables significantly different between treatments were % Cover Humus (F=36.31, df=1,2, P=0.003) and % Cover Dead Wood (F=21.15, df=1,2, P=0.004). These results are probably reflect the amount of slash (downed wood) lying on the ground in the thinned areas. The mean % Cover of Humus in thinned areas was 55.76 and in unthinned areas was 71.35, since the humus in unthinned areas was not covered in slash. The mean % Cover Dead Wood was, of course, higher in thinned areas, 42.00 as compared to unthinned areas, 26.76.  A nested MANOVA was used to test for the effect of grids within  64 treatments for each of the site, vegetation species and vegetation strata matrices. Multivariate test statistics of Wilks' Lambda, Pillai Trace and Hotelling Lawley Trace revealed no significant effect for the site variables (Table 3.5). However, there was a highly significant difference between grids within treatments for the species variables as well as for the vegetation strata variables (Table 3.5). There was a significant degree of difference in the % cover of the herbs Cornus canadensis, Epilobium angustifolium and the shrub Vaccinium alaskense between grids. This was also the case for the % cover of trees, shrubs and mosses.  Canonical correlation analysis was used to relate the site variables and the vegetation variables. The overall CCA was significant as indicated by the multivariate tests of Wilks' Lambda (F=1.67, df=121,518, P<0.001), Hotelling-Lawley Trace (F=1.74, df=121,648, P<0.001) and Pillai trace (F=1.53, df=121, 814, P<0.001). The first two canonical correlations were significant and accounted for 55% of the variation on the data (Table 3.6). The adjusted canonical correlation coefficients of the first two canonical correlations were 0.649 and 0.544 respectively (Table 3.6). These values would indicate that the goodness of fit of one set of canonical variates to the other is reasonably good, but not strong.  The site variable contributing most strongly to the determination of the first canonical variable (Table 3.7) was Microtopography (0.64). The species Tiarella unifoliata and Vaccinium  65 Table 3.5. The differences between the means of variables within the spaced and unspaced grids and the results of nested MANOVAs testing for differences between grids within treatments for the habitat variables. Significance, P<0.05, is indicated by the astrix (*), and P<0.001 by the double astrix (**).  MEANS  Spaced^Unspaced ^Univariate F  0.71  0.46  5.63  59.75  44.35  Surface Shape^0.36  0.35  2.36  Mosture Regime^0.13  0.07  0.14  Nutrient Regime^0.31  0.32  1.34  Number of Stumps^0.05  0.74  1.16  4.48  2.75  0.23  Dead Wood^ 4.30  0.50  0.37  Microtopography^0.07  1.09  2.94  0.35  0.49  1.31  Humus Form^0.41  0.57  0.28  Slope^ Aspect^  Humus^  Drainage^  Multivariate Test Statistic  F-Statistic  4.34 *  DF  Wilks' Lambda  1.36  22, 144  Pillai Trace  1.35  22, 146  Hotelling-Lawley Trace  1.36  22, 142  ^  66  Table 3.5 continued Athyrium felix-femina  0.98  0.36  0.91  Clintonia unifoliata  0.34  0.80  4.00  Cornus canadensis  0.22  1.30  3.74^*  Dryopteris assimilis  0.75  0.33  1.86  Epilobium angustifolium  0.72  0.36  3.80^*  Oplopanax horridus  0.44  0.01  0.40  Rubus pedatus  0.27  0.71  1.88  Rubus spectabilis  0.56  0.14  0.62  Streptopus streptopoides 0.60  0.28  2.11  Tiarella unifoliata  0.06  0.67  2.46  Vaccinium alaskense  1.15  1.43  8.11^**  Multivariate Test Statistic Wilks' Lambda  F-Statistic 3.17  Pillai Trace ^22, 3.09 Hotelling-Lawley Trace  3.24  DF 22,^144^* * ^146^* * 22,^142^**  % Cover Trees  24.31  12.59  7.04 *  % Cover Shrubs  14.20  5.41  3.14 *  % Cover Herbs  1.90  7.24  1.17  13.30  10.43  % Cover Mosses  Multivariate Test Statistic  F-Statistic  3.94 *  DF  Wilks' Lambda  3.60  8, 158 **  Pillai trace  3.53  8, 160 **  Hotelling-Lawley Trace  3.67  8, 156 **  67 alaskense were similarly the major contributors to the first canonical axis of the species variables, having correlations of 0.44 and -0.56 respectively. Moisture Regime, Nutrient Regime, Drainage and Humus Form Class were most highly correlated with their second canonical variable. Athyrium felix-femina (0.72), Dryopteris assimilis (0.73) and Rubus spectabilis (0.67) were most highly correlated with their second canonical vector.  The squared canonical correlation coefficients are also presented in Table 3.6. but although this statistic is a measure of the overlap between canonical variates it may not be a very good  Table 3.6 Results of the CCA using site and vegetation variables.  Adjusted^Squared^Eigenvalue Proportion Pr>F Canonical^Canonical Correlation Correlation  1  0.649  0.550  1.221  0.361  0.000  2  0.544  0.431  0.759  0.224  0.023  3  0.461  0.339  0.513  0.151  0.253  4  0.423  0.269  0.367  0.109  0.663  5  0.248  0.179  0.218  0.065  0.927  68  Table 3.7 Canonical correlations of the site variables on the first 2 canonical axes.  1  2  0.376  -0.192  -0.209  0.385  Surface Shape  0.309  -0.082  Moisture Regime  0.235  0.536  Nutrient Regime  0.310  0.780  -0.046  0.094  0.108  -0.014  % Cover Dead Wood  -0.092  0.030  Microtopography  -0.639  0.422  Drainage Class  -0.220  0.585  Humus Form Class  0.178  0.718  Athyrium felix-femina  0.213  0.725  Clintonia unifoliata  0.293  0.152  Cornus canadensis  -0.105  0.438  Dryopteris assimilis  -0.052  0.735  Epilobium angustifolium  -0.340  0.338  Oplopanax horridus  -0.049  0.252  Rubus pedatus  -0.138  0.295  Rubus spectabilis  0.246  0.671  Streptopus streptopoides  0.195  0.546  Tiarella unifoliata  0.440  0.319  Vaccinium alaskense  -0.562  0.268  Canonical Loadings  Slope Aspect  Stumps % Cover Humus  69 indicator of the importance of the linear combination of variables comprising the variate to the original data set. Therefore a canonical redundancy analysis has been included (Table 3.8).  The total redundancy value for the site variables and their first two canonical variables is 0.13, indicating that the actual overlap between the two batteries of variables on the relevant canonical axis, as seen from the perspective of the species variables added to an already available set of site variables, is not great. The redundancy value is identical for the species variables. The total redundancy value for the site variables and the species variables and their opposite canonical variables is 0.06. Thus it could be concluded that neither set of variables is an excellent predictor of the other set, though there is some relationship between the two, not surprisingly.  3.5 RELATING DAMAGE TO SITE AND VEGETATION VARIABLES  CCA was considered an appropriate procedure to address Objectives 2 and 3, by relating the damage matrix of variables WPH and CPH to the matrices of habitat variables. It was not carried far, however, because it was immediately evident that the first canonical vector of the damage matrix, which included the two damage variables WPH and CPH, was not in fact a measure of damage. The canonical coefficients standardized by sample standard deviations on the first canonical vector were 1.81 for WPH and 0.97 for CPH, an unusual negative relationship. This  70 TABLE 3.8 The results of the canonical redundancy analysis for the site and species variables. Variances are standardized.  ^ Redundancy of the Redundancy of the Site ^ Species Variables Variables and the ^ and the Canonical Canonical Variables of Variable of  Their Own The Opposite Their own The Opposite R2  ^  Set^Set^Set^Set  Variate 1^0.55^0.05^0.03^0.04^0.02  2^0.43^0.08^0.03^0.09^0.04  Total  ^  0.13^0.06  ^  0.13^0.06  procedure was abandoned for the purpose of relating damage to vegetation and site variables and regression was used instead.  The results of the multiple regression of the damage indices, 'PC1D total', 'PC1D new' and 'PC1D old' on the subsets of habitat variables are presented in Table 3.9. Regression was not performed on the habitat variables of the treatment grids using 'PC1D old' or "PC1D total' as some of the old damage may have been incurred before the thinning took place. Only the regression of 'PC1D  71 total' was significant (R 2 =0.49). The plant species with the most significant partial correlation were Athyrium felix-femina (P=0.093) and Rubus pedatus (P=0.077). The regression coefficients for these two species were both positive, indicating that they increase in % cover as damage increases. This regression was also performed using analysis of covariance and the results were unaltered.  Although it is impossible to determine cause and effect from these data, it might be expected that areas of high damage would be associated with higher % covers of certain herbs since highly damaged trees often had spike tops allowing greater amounts of light to penetrate to the forest floor.  One further analysis used to explore the relationship between damage and habitat variables was canonical discriminant analysis. Recall Figure 3.8, which plotted WPH against CPH. On the basis of the dispersion of these variables, plots were coded as having incurred 'low' or 'high' damage, a value of 3 being chosen as the arbitrary cutoff point for the variables WPH and CPH (Figure 3.10).  This categorical variable for damage, calculated on the basis of both new and old damage, was used in a CDA for relating damage to the habitat variables in a 2-group canonical discriminant analysis. This analysis was performed only for the control grids and used the matrix which included all of the habitat variables. The multivariate statistics of Wilks' Lambda, Pillai Trace and Hotelling-  72 Lawley Trace, all of which tested the hypothesis that the group means are equal, were significant (df=26,19, P=0.019).  Table 3.9. The F-ratios and P values from the results of multiple regressions of the damage index and subsets of the habitat variables.  CONTROL GRIDS^TREATMENT GRIDS  F-Ratio^P^F-Ratio NEW DAMAGE VEGETATION  0.39  0.95  0.58  0.82  STRATA  1.37  0.27  0.60  0.67  SITE  0.85  0.60  0.98  0.50  OLD DAMAGE VEGETATION  1.18  0.34  STRATA  0.36  0.84  SITE  1.02  0.45  VEGETATION  2.94  0.01  STRATA  0.47  0.76  SITE  1.52  0.17  TOTAL DAMAGE  Figure 3.11 shows box-plots for the low and high damage categories against the discriminant function. The data for the 'high' category is obviously skewed as indicated by the asymmetry of the interquartiles, indicating very little overlap in the discrimin-  73  10 0 0 0  OMB  high  E  a)  Q. 6  0  D C  4  0^0 0  0  0  C  ai a)  0  2  0^ 0  1  ^  2 3 4 5 6  Mean circumf, wounds per hemlock Figure 3.10. The mean number of wounds per hemlock versus the mean circumference of wounds per hemlock calculated on a plot basis. The shaded area designates plots that were classified as having 'low' damage and those outside of the shaded area were designated as having 'high' damage.  74  4  0 0 4  ^0 --  E°  CCS C  E -2 17_  ROM  0 SO  C)  -4 -6  low^high Damage category  Figure 3.11. Box plots of the damage class variable, 'PC1D total' against the discriminant function. From the CDA of all habitat variables (P<0.001).  75 ation.  The variables significant in the discrimination between areas of low and high damage were Stumps, % Cover Herbs, % Cover Athyrium felix-femina, Rubus pedatus, Streptopus streptopoides and Tiarella unifoliata (Table 3.10). As indicated by the canonical loadings (Table 3.10), the species variables Athyrium filixfemina (0.202) and Rubus pedatus (0.257) made the greatest contribution to the discrimination. The mean of Athyrium filixfemina in areas of low damage was 0.50 (standard error=0.19) and in areas of high damage was 1.83 (standard error=0.49). Rubus pedatus had a mean % cover in areas of low damage of 0.70 (standard error=0.18) and in areas of high damage of 2.33 (standard error=0.48). The site variable Stumps contributed heavily to the discrimination (0.205) with a mean in areas of low damage of 1.35 (standard error=0.30) and in areas of high damage of 3.50 (standard error=0.79), perhaps indicative of more denning opportunities.  76 Table 3.10 The results of the CDA using the categorical damage variable:^F-values,^P^values^and canonical^loadings^for the habitat variables on the discriminant axis.  F -value  P  Canonical Loading  (df=1,44)  Slope  0.013  0.909  -0.009  Aspect  0.563  0.457  -0.060  Surface Shape  1.117  0.296  0.085  Moisture Regime  0.080  0.779  -0.023  Nutrient Regime  0.929  0.340  0.077  Number of Stumps  6.526  0.014  0.205  % Humus  2.899  0.096  -0.137  % Dead Wood  3.998  0.052  0.161  Microtopography  1.679  0.202  -0.104  Drainage  0.255  0.616  -0.041  Humus Form Class  1.740  0.194  0.106  Athyrium filix-femina  6.295  0.016  0.202  Clintonia unifoliata  0.713  0.403  0.068  Cornus canadensis  1.527  0.223  -0.099  Dryopteris assimilis  1.588  0.214  0.101  Epilobium angustifolium  2.966  0.092  0.138  Oplopanax horridus  0.004  0.952  0.005  10.253  0.003  0.257  Rubus pedatus  77  Table 3.10 continued Rubus spectabilis  0.314  0.578  0.045  Streptopus streptopoides  4.828  0.033  0.177  Tiarella unifoliata  6.060  0.018  0.189  Vaccinium alaskense  0.022  0.883  0.012  % Cover Trees  1.175  0.284  -0.087  % Cover Shrubs  1.164  0.286  0.087  % Cover Herbs  4.131  0.048  0.163  % Cover Moss  1.741  0.194  0.106  78 CHAPTER 4 DISCUSSION  4.1 COMPARABILITY OF STANDS  The four study areas (grids) in which this research was carried out had been chosen, using forest cover maps and walk-throughs, to be comparable in general site features. This was confirmed by statistical analysis. However, there was a significant difference between grids within treatments in both the species variables and the vegetation strata variables, as discussed in the Results. It was expected that there would be variation between grids in the habitat data that could be used as a basis for comparison with porcupine usage.  4.2 THE EFFECT OF THINNING ON DAMAGE  Regarding Objective 1, thinned stands did not sustain more damage than did unmamaged stands. On the contrary, unthinned areas sustained significantly more new damage than did thinned stands. As thinned stands differed from unthinned stands in the density and basal area of the preferred food species, western hemlock, it appears that porcupines are exercising choice on the basis of these properties. This was somewhat surprising as thinned areas had more trees in the preferred diameter class, though the trend was not significant. ^The result is also contrary to earlier studies.^Harder (1980) found that  79 porcupines preferred low density stands.  It is quite possible that the porcupines were not responding to hemlock density at all, but to the amount of slash left on the ground after thinning. This slash was dense and often piled high and may have hampered movements by the porcupines. Porcupines are not agile animals (personal observation). Of 15 adult skeletons examined, Roze (1984) found that 9 showed evidence of healed fractures, probably incurred from falling from trees. In thinned areas, slash is sometimes 0.6 m deep and of less than optimum diameter for porcupines to walk on. However, for some of the winter (December to March) the slash in the area of the study would be well covered in snow.  On the basis of this research the hypothesis that thinned areas sustain more damage than unthinned areas can be rejected. However, it is possible that the vigour of trees in thinned areas may in the future surpass that of unthinned stands, making these stands more attractive to porcupines and predisposing them to attack.  4.3 PROCESS OF SELECTION BY PORCUPINES  The process by which porcupines are selecting trees as food, whether porcupines actually select food and habitat or merely rely upon chance, is central to understanding the observed  80 patterns of use (Objective 2), especially when considering recommendations for damage reduction measures.  4.3.i Evidence for a Hierarchical Process of Selection  Porcupines seem to be choosing at the level of large blocks of forest, as thinned stands are incurring less damage than unthinned areas. Other studies have documented the response of wildlife to stand structure (Sullivan and Vyse 1987; Hodorff et al. 1988).  Studies that have also presented evidence for choice by porcupines on the basis of large forest units include Harder's (1980) study, mentioned above. Harder suggested that the occurrence of intercommunity preferences for low density stands resulted from the greater abundance of large, vigorous trees in low density stands, In an earlier study Harder (1979) found that stands sustained a degree of damage that depended on their species composition. Tenneson and Oring (1985) also found that areas used heavily for winter feeding differed in the tree species composition from less used areas. Stand density was uniform in their study and its effects on damage could not be tested. The stands in my own study were similar in species composition and so the effect of this variable on stand-level damage could not be tested. The results of this research, however, indicate little evidence  81 for porcupines choosing areas of feeding in successively smaller units. One level lower, that of the plots, could be considered as a smaller community type or association of habitat variables. Regression analysis revealed no significant relationship between the amount of damage in a plot (calculated on the basis of total, new or old wounds) and the structural properties of tree density and total tree basal area.  It is possible that porcupines were choosing in a hierarchical way within a stand at a smaller unit size characterized by some combination of site or vegetation factors. Regression analysis indicated that the herbs Athyrium filix-femina and Rubus pedatus were increasingly abundant as the damage index variable calculated on the basis of total wounds increased. Canonical discriminant analysis confirmed these results. It is difficult to disentangle cause and effect here, but the reason for a greater cover of these species is probably greater levels of light reaching the forest floor in highly damaged areas. Many heavily damaged trees had dead tops, creating openings in the canopy through which a greater amount of light could pass. However, although these two species typically occur in semiopen, coniferous forests, they are both considered to be shade tolerant (Klinka et al. 1989) and are probably not characteristic gap species. The fact that the site variables were correlated, though not strongly, with the vegetation variables and yet damage was significantly related to one set (vegetation)  82 but not the other (site), would support the conclusion that these two plants were responding to some damage-related condition.  The only significant relationship between the amount of damage at the plot level and the site variables, as revealed by the CDA, involved the number of stumps. Porcupines typically use stumps as denning sites and insular cover is important to these animals in the winter (Roze 1987). Porcupines demonstrate a high level of den fidelity which probably results in repeated damage to nearby trees (Roze 1987).  In Harder's (1980) study, communities (on the scale of approximately 30 ha in size) within major blocks of forest differed from one another in repeated use of individual trees, with repeated use being highest on leeward slopes. This pattern is suggestive of selective behaviour at a forest unit within the level of the whole stand.  At a smaller community level (30 m radius plots), Speer and Dilworth (1978) found that porcupines used winter areas that were moister and closer to standing water though they felt that porcupines were not in fact using the water for drinking as it was frozen for most of the winter. However, in the present study, attempts to relate damage to community level factors of sites and vegetation through regression analyses indicated no  83  trend strong enough to be of use in prediction.  4.3.ii Evidence for Selective Feeding on the Basis of Individual Tree Characteristics  The results of the damage assessments indicated that western hemlock was the only species attacked, though the sites were comprised almost exclusively of this species and amabilis fir. Western hemlock was also the preferred  species in previous  studies of porcupine barking damage on the north coast (Sullivan et al. 1986; Sullivan and Cheng, 1989).  Porcupines have been  known to feed on balsam fir where it was present but it would seem not to be a preferred food in any area where studies have been carried out. Speer and Dilworth (1978) and Radvyani (1952), working in central New Brunswick, found that porcupines preferred eastern larch (Larix laricina) and red spruce (Picea rubens) over balsam fir. Curtis (1944) and Dodge (1982) have reported that feeding in their study areas was more common on spruce than on balsam.  The total incidence of damage to western hemlock was high at 41% and the fact that 10% of trees had been damaged in the winter previous to the study is alarming. With hemlocks representing 43% of the trees sampled and an annual rate of attack of 10%, within which 15% had never been damaged before, it is not likely that these areas will ever be worth harvesting. This will be  84 true even if only a small fraction of the attacked trees are severely reduced in vigour or possess spike tops. Such a rate of attack is certainly precedented. An earlier study in the Kalum Valley reported that the annual rate of attack had increased from 0.6% in 1986 to 1.8% in 1987 for western hemlock (Sullivan and Cheng 1989).  The intensity of attack in the 1988-89 winter, as indicated by the mean number of new wounds, peaked significantly in the 25.1 to 30.0 cm tree diameter class. The fact that the total incidence of damage (new and prior) was greatest in the size class above this (30.1 to 35.0 cm dbh) is reasonable as the trees were probably attacked when they were in the preferred size class of 25.1 to 30.0 but have put on growth since the time of attack. In a study by Sullivan et al. (1986) in the Khutzeymateen Inlet, damage peaked at 83.3% among second-growth hemlock in the 27.5-32.4 cm dbh class.  Such preferences for particular size classes of trees would seem to be typical of porcupines. Tenneson (1983) found that the amount of damage to trees resulting from the winter bark removal by porcupines increased with increasing dbh classes, preferred trees being over 15 cm dbh. Harder (1979) found a predilection for a particular size class irrespective of geographic location or tree species. This is in agreement with the suggestion by Curtis and Wilson (1953) that porcupines may be most adept at  85 climbing trees with a dbh between 15 and 25 cm. It is likely easier for porcupines to maintain a feeding position in trees of a particular diameter, and this could explain their preference.  It is also possible that trees of the preferred diameter classes provide superior quality of food. Quality of a food item is a complex property that involves not only its energy and nutrient content but also the digestion and assimilation efficiency of the consumer (Longhurst et al. 1968, Harder 1979). Porcupines may prefer to feed on trees producing a large annual increment of phloem yet without a tough bark.  Thus, in this study there is evidence that porcupines are choosing feeding areas through a hierarchical process, evaluating large stands of forest but then within these stands are simply choosing on the basis of individual tree species and diameters.  4.4 RECOMMENDATIONS  The third objective of this study, the determination of variables most useful in predicting damage, pertained most directly to the ability to put forward recommendations for forest management. However, the only variables highly related to  86 damage are tree species and dbh. This information could be used to space, or replant, in favour of less preferred species or to leave patches of sacrifice trees of the preferred species, western hemlock. It does not seem possible to evaluate areas in terms of susceptibility to attack on the basis of site or vegetation characteristics.  At the present rates of damage, however, it is expected that the damage situation will become so severe as to have broader implications for the whole ecosystem. If this study were considered to represent a kind of baseline data there would be a future opportunity to determine the ecosystem effects of this herbivore by a continued monitoring of the variables measured in this study.  4.5 A COMMENT ON MULTIVARIATE METHODS IN WILDLIFE MANAGEMENT  There has been some concern that multivariate statistical analyses have the potential to fabricate relationships not inherent in the data. At one extreme Green (1971) has excused the use of some mutivariate methods in violation of assumptions if the results make some ecological sense. However, Rexstad et al. (1988) questioned the use of the multivariate techniques in studies of wildlife habitat stating that "Sophisticated multivariate techniques cannot be expected to replace careful, a priori thinking and design;". Although this statement is  87 reasonable, their own efforts to discredit certain mutivariate methods, including PCA and CCA, by using a set of meaningless data, were unsupported by their analyses (Taylor 1990). This issue would be worth pursuing.  Williams (1983) differentiates between exploratory and confirmatory analyses. The methods used in this thesis were intended as an exploration of the data with the aim to better understand the relationship between a destructive herbivore and its habitat. Surely animals respond to many variables in their environment simultaneously. Though one must be thoughtful when using multivariate techniques, and remain aware of the limitations, they assist in a realistic consideration of habitats.  88 LITERATURE CITED Austin, M.P. 1968. An ordination study of a chalk grassland community. J. Ecol. 56: 739-757. Bannan, A.W.F. 1974. The Mammals of Canada. University of Toronto Press, Toronto, Ontario. Begon, M., J.L. Harper and C.R. Townsend. 1986. 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