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Analytical study of plant/environment interactions in thimbleberry and devil's club Mason, Rosemary 1990

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ANALYTICAL STUDY OF PLANT/ENVIRONMENT INTERACTIONS IN THIMBLEBERRY AND DEVIL'S CLUB By ROSEMARY MASON B.Sc. (Hons.), University of Victoria, 1965 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR A DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES Botany Department We accept this thesis as conforming to the required standard  THE UNIVERSITY OF BRITISH COLUMBIA September 1990 ® Rosemary Mason, 1990  In  presenting  degree freely  at  the  available  copying  of  or of  thesis  in  partial  fulfilment  University  of  British  Columbia,  for  this  department publication  this  reference  thesis by  this  for  his  and  scholarly  or  thesis  study.  her  for  /2o~rZ(  of  financial  /^y  The University of British C o l u m b i a Vancouver, Canada  Date  DE-6  (2/88)  Se f>k  W?€  r  Si I,  I  I further  purposes  /??Q  gain  the  shall  requirements  agree  that  agree  may  representatives.  permission.  Department  of  be  It not  that  the  Library  by  understood be  an  advanced  shall  permission for  granted  is  for  allowed  the  make  extensive  head  that  without  it  of  copying my  my or  written  ii  Abstract The morphology, phenology and stem demography of devil's club and thimbleberry were examined to elucidate their niche utilization strategies. The study was conducted in the Kitimat River valley in west central B.C. during the 1986 and 1987 growing seasons. Thimbleberry was sampled in a grded alder site and a nondrded alder site, whereas devil's club was sampled in an old growth forest. The variation in the plant characters, as summarized by principal components axes, was apportioned within and among clones, between sites, years, and species. Except for the thimbleberry vegetative phenology, within-sites differences accounted for most variation and variation between-sites often exceeded that between years. Moreover, between-spedes differences accounted for less variation than within-species differences for morphology and phenology.  The variation in plant characters was also examined in relation to canopy cover, soils and adjacent vegetation using multivariate methods. The rate of vegetative development for devil's club in 1986 increased as canopy cover decreased; other environmental measures were uncorrelated with devil's club. Both vegetative and reproductive rates of development increased with disturbance due to girdling and increasing moisture for the combined grded and ungrded thimbleberry data set. Similarly, morphdodcal size was greater fa the combined thimbleberry data set with increasing moisture and dsturbance. Environmental correlations were reflected differently within-sites, however, with rates of development, plant size and the number of flowers decreasing with increasing moisture at the nongirded thimbleberry she. The relationship between plant characters was also assessed. Phenology and morphology were correlated for both devil's dub and thimbleberry; stem development began earlier and was more rapid with increasing stem size. Demography and phenology were unrelated. Both spedes displayed different niche utilization strategies; thimbleberry being more flexible than devil's club. In contrast to devil's dub, thimbleberry is morphologically and  iii  phenoioglcaily responsive to disturbance ana is mizomatous ratner man stoioniTerous. stems and lateral branches also had several phonological and developmental possibilities. This flexibility imparted an advantage to thimbleberry in the fluctuating conditions of its earlier successional niche. The differing correlation structure between and within thimbleberry sites suggests that several scales of observation are necessary to clarify plant-environment relationships. Moreover, as envronmental characters interact differently with plants from site to site, management must be site specific. Alder girdling may be a judicious management technique at drier sites, where thimbleberry is not as prolific under an open canopy.  iv  Table of Contents Abstract List of Tables List of Figures Acknowledgement  ii vii x xii  Chapter I  INTRODUCTION 1.1 OBJECTIVES 1.2 PHENOLOGY, MORPHOLOGY AND DEMOGRAPHY 1.3 VARIATION IN PHENOLOGY, MORPHOLOGY AND DEMOGRAPHY 1.4 FACTORS CORRELATED WITH VARIATION 1.5 THIMBLEBERRY 1.6 DEVIL'S CLUB  ..  Chapter II  STUDY AREAS 2.1 DESCRIPTION OF STUDY AREA 2.1.1 GEOGRAPHIC LOCATION 2.1.2 EDAPHIC ENVIRONMENT 2.1.3 DISTURBANCE HISTORY 2.2 CLIMATE  Chapter III  SAMPLING METHOD & DATA COLLECTION 3.1 DESIGN 3.2 CANOPY COVER 3 3 SOILS 3^ PLANT NEIGHBOURS '"!]]!!!!!!!!!!!*.".'.'.!'.'.!!!!!!!!!.'!!!!!!!!!!! 3.5 MORPHOLOGY 3.6 PHENOLOGY 3.7 DEMOGRAPHY  Chapter IV  ANALYTICAL TECHNIQUES 4.1 TECHNIQUES 4.1.1 PRINCIPAL COMONENT ANALYSIS (PCA) 4.1.2 ANOVA 4.1.3 CANONICAL CORRELATION (CANCORR) 4.1.4 WEIGHTED MULDTIMENSIONAL SCALING (WMDS)  Chapter V  DATA ANALYSIS 5.1 GRAPHICAL ANALYSES 5.2 ENVIRONMENT  1 2 2 4 5 11 12 14 14 14 14 16 17 19 19 21 21 22 23 25 26 27 27 27 28 29 30 33 33 33  V  5.3 5.4 5.5 5.6 5.7  MORPHOLOGY PHENOLOGY DEMOGRAPHY CANCORR AND WMDS COMPUTATION  35 37 38 41 42  Chapter VI RESULTS 6.1 ENVIRONMENTAL RESULTS 6.1.1 CANOPY COVER 6.1.2 SOILS VARIABLES 6.1.3 PLANT NEIGHBOURS-PRESENCE/ABSENCE 6.1.4 PLANT NEIGHBOURS-FREQUENCY 6.2 MORPHOLOGY 6.2.1 UNIVARIATE ANALYSES AND GRAPHS 6.2.2 PRINCIPAL COMPONENT ANALYSES 6.2.3 PARTITIONINGOFVARIATION 6.2.4 MORPHOLOGICAL-ENVIRONMENTAL RELATIONSHIPS 6.3 PHENOLOGY 6.3.1 UNIVARIATE ANALYSES AND GRAPHS 6.3.2 PRINCIPAL COMPONENT ANALYSES 6.3.3 PARTITIONINGOFVARIATION 6.3.4 PHENOLOGICAL-ENVIRONMENTAL RELATIONSHIPS 6.4 DEMOGRAPHY 6.4.1 UNIVARIATE ANALYSES AND GRAPHS 6.4.2 PRINCIPAL COMPONENT ANALYSES 6.4.3 PARTITIONINGOFVARIATION 6.4.4 DEMOGRAPHIC-ENVIRONMENTAL RELATIONSHIPS 6.5 MORPHOLOGY-PHENOLOGY-DEMOGRAPHYINTERACTIONS  43 43 43 44 51 60 68 68 71 77 79 81 81 89 92 95 99 99 106 111 112 115  Chapter VII DISCUSSION 7.1 MORPHOLOGY 7.2 PHENOLOGY 7.3 DEMOGRAPHY 7.4 NICHE UTILIZATION AND LIFEHISTORY STRATEGY 7.5 MANAGEMENT 7.6 FURTHER RESEARCH  119 119 124 127 130 134 135  LITERATURE CITED  136  APPENDIX 1 - MANN-WHITNEY U TESTS FOR THE DIFFERENCES IN MORPHOLOGY BETWEEN THIMBLEBERRY SITES IN 1986 AND 1987, BETWEEN YEARS (1986 AND 1987), AND WITHIN PLANTS BETWEEN STEMS AND BRANCHES  151  vi  APPENDIX 2- MANN-WHITNEY U TESTS FOR DIFFERENCES IN GENERATIVE AND VEGETATIVE PHENOLOGIES BETWEEN THIMBLEBERRY SITES IN 1986 AND 1987, BETWEEN YEARS (1986 AND 1987) AND WITHIN PLANTS BETWEEN STEMS AND BRANCHES APPENDIX 3 - CANCORR RELATIONSHIPS BETWEEN MORPHOLOGICAL PHENOLOGICAL AND DEMOGRAPHIC CHARACTERS FOR TB, KIT, WED AND DC AND SOILS, FREQUENCY AND PRESENCE/ABSENCE OF PLANT NEIGHBOUR MATRICES APPENDIX 4 - WMDS RELATIONSHIPS BETWEEN MORPHOLOGICAL. PHENOLOGICAL AND DEMOGRAPHIC CHARACTERS FOR TB, KIT, WED AND DC AND SOILS, FREQUENCY AND PRESENCE/ABSENCE OF PLANT NEIGHBOUR MATRICES  vii List of Tables  1.  Phonological, morpholoojcal and demographic sampling in 1966 and 1987  24  2.  Phenological codes  25  3.  Analytical methods for environmental data  34  4.  Analytical methods for morphological data  36  5.  Analytical methods for phenological data  39  6.  Analytical methods for demographic data  40  7.  TB soils - PCA axes relationships  45  8.  KIT soils - PCA axes rdationsh  47  9.  WED soils - PCA axes relationships  48  10. 11. 12.  DC soils - PCA axes relationships TB presence/absence of plant neighbours - PCA axes relationships KIT presence/absence of plant neighbours - PCA axes relationships  49 52 55  13.  WED presence/absence of plant neighbours - PCA axes relationships  56  14.  DC presence/absence of plant neighbours - PCA axes relationships  56  15.  TB frequency of plant neighbours - PCA axes relationships  60  16.  KIT frequency of plant neighbours - PCA axes relationships  63  17.  WED frequency of plant neighbour - PCA axes relationships  65  18.  DC frequency of plant neighbour - PCA axes relationships  66  19.  Eigenvalues of the PCAs used in ANOVA only  71  20.  Morphology - PCA axes relationships for TB data in 1967  72  21.  Morphology-PCA axes relationships for KIT data in 1987  73  22.  Morphdogy-PCA axes relationships for WED data in 1987  75  viii  23. 24.  Morphology-PCA axes relationships for DC data in 1987 Morphological variation apportioned between years, species, sites and within and among quadrats by ANOVA  25.  Canopy cover-morphological PCA relationships  26.  Summary of relationships between TB, KIT, WED and DC morphological variables and envronmental characters  76 78 79 79  27.  Phenology - PCA relationships for TB data  89  28.  Phenology - PCA relationships for KIT data  90  29.  Phenology - PCA relationships for WED data  90  30.  Phenology - PCA relationships for DC data  91  31.  Percentage of phenological variation apportioned between years, species, sites and within and among quadrats for devil's club and thimbteberry  32.  Canopy cover - phenology relationships  33.  Summary of relationships between TB, KIT, WED and DC phenological variables and environmental characters  93 95 96  34.  Regression analysis of thimbleberry stem longevity and stem density  105  35.  Regression analysis of devil's club stem age and density  105  36.  Eigenvalues of 1986 and 1987 PCA for the number of stems surviving to each age class 106 Demography - PCA axes fa TB data for the number of stems dying during each age class 107 Demography - PCA axes for KIT data for the number of stems dying during each age class 108 Demographic - PCA axes for WED data for the number of stems dying during each age class 109 Demography - PCA axes for DC data for the number of stems in each age class 110 Demographic variation apportioned between years, sites and within and between quadrats by ANOVA 111 Canopy cover - demographic relationships 112  37. 36. 39. 40. 41. 42.  ix  43.  Canopy cover - age distribution relationships  44.  Summary of relationships between TB, KIT, WED and DC demographic variables and environmental characters Summary of relationships between TB, KIT, WED and DC morphological, phendoacal and demographic variables  45.  113 114 115  X  list of Figure?  1.  Map oftiiestudy area showing the location of each site in relation to the river system and each other  2.  Weather in the study areas from January to August 31,1986 and 1987  3.  Map of quadrats and transects in each study site  4.  WMDS and CANCORR analyses  5.  Percentage canopy cover over 63 quadrats at KIT, WED and DC in July, 1987  15 18 20 42 43  6.  Graph of axes I and II of the soils PCA fa TB showing individual quadrats ....  46  7.  Graph of axes I and II of the soils PCA fa DC showing individual quadrats ..  50  8.  Graph of axes I and II of the presence/absence of plant neighbours PCA fa TB showing individual quadrats Graph of axes I and II of the presence/absence of plant neighbours PCA fa DC showing individual quacrats Graph of axes I and II of the frequency of plant neighbours PCA fa TB showing individual quacrats Graph of axes I and II of the frequency of plant neighbours PCA fa DC showing individual quacrats Number of thimbleberry flowers per quadrat at WED and KIT on June 22, 1987  9. 10. 11. 12. 13.  Number of devil's club racemes per quadrat in 1986 and 1987  14.  Total number of stems per quadrat fa devil's club on June 30,1986 and 1987  15.  Vegetative phonological codes at KIT and WED in 1986 and 1987  16.  Vegetative phonological codes fa devil's club in 1967 and 1987  17.  Reproductive phenoJogical codes at KIT and WED in 1986 and 1987  54 59 62 67 68 70 70 81 63 85  xi  18.  Reproductive phendogjcal codes fa devil's dub in 1986 and 1987  19.  Survivorship of thimbleberry stems produced in 1986 and 1987 at the WED and KIT sites 100 Percentage of stems dying within distinct time periods at KIT and WED sites in 1986 and 1987 101 Cumulative number of devil's dub stems older than the time indicated on the x-axis 103 Percentage of devil's club stems in distinct age classes on June 30,1987 .... 104  20. 21. 22.  87  xii  Acknowledgement I would like to acknowledge then continuing support and friendship of my supervisor, Gary Bradfield, during the formulation, analysis and writing of the thesis. My committee, Jack Maze, Roy Turkington and Jim Pojar, were also especially helpful at different times throughout the process. Jack Maze was encouraging, challenging and warm throughout. Roy Turkington was especially constructive with editing and writing suggestions in the final manuscript. Jim Pojar, deserves special mention, for his understanding, patience and positive input throughout 'the whole process. r  Lacey Samuels offered encouragement and friendship and with Mishtu Banerjee and Len Dyck provided stimulating discussion and exchange of new and interesting ideas. Finally I am most grateful to and appreciative of Rick Caswell, for his excellent field assistance, warmth, understanding and caring during this entire thesis.  I. INTRODUCTION Rubus parvfflorus Nutt. (thimbleberry) and Oplopanax horridus (Smith) Miq. (devil's club) are two clonal iteroparous shrubs which occur in different successional stages in B.C. forests. Thimbleberry is found in disturbed areas and early to mid-successional sites whereas devil's club is found in climax forests. The dfferences observed between species, such as thimbleberry and devil's club, are often ascribed to drfferent environmental conditions found in early and late successional seres along a successional gradient (Lee et al. 1986). Variation observed within and among populations and years within a species is linked to other factors. Variation between two genetically similar populations at drfferent sites implies that there are environmental differences between sites which maintain this variation (Van Cauteren and Lefebvre 1986; Huenneke 1987; Matlock 1987; Maddox et al. 1989; Moot et al. 1989; Cartsson and Callaghan 1990). Variation within populations has been linked to genetic and environmental influences (Primack 1980, 1985; Huenneke 1987; Menges 1987; Schlichting 1989). Genetic variability can account for differences between clones of a single population living in a common environment (Huenneke 1987). Variation observed within individuals, which is manifested as plasticity, has been seen as evidence of a heterogeneous environment (Primack 1985; Kephart 1987; Schlichting 1989). Temporal variation, which may also be manifested as plasticity, has also been observed and is often explained by climatic heterogeneity (Kjellson 1985; Rathke 1988).  Observations of variability at these differing levels suggest a study quantifying the variation within and among populations and individuals. Investigating factors which maintain that variability may clarify genetic and environmental relationships (Koehl 1989). In this thesis I examine rdationships within and among individuals and populations of thimbleberry and devil's club to gain insight Into the major factors shaping the development of these two species in the Krtimat valley. As ecologically drfferent species, it was of interest to determine whether they responded drfferentJy to drfferent environmental conditions. My study was also motivated by a need within the B.C. Forest Service for information about life histories of these two species which are important understory components. By comparing problem with  2  nonproblem species, the study win elucidate me method ot niche utilization employed Dy eacn. I also had the personal goal to learn more about multivariate statistical techniques and plant ecology. 1.1  OBJECTIVES  The objectives of this study are: 1) to document the morphology, phenology and demography of thimbleberry and devil's club in the Kitimat River valley in west central B.C. during the 1986 -1987 growing seasons. 2) to apportion variation in morphology, phenology and demography of devil's club and thimbleberry within and among clones, between sites, species and years and 3) to ascertain to what extent canopy cover, soils and adjacent vegetation are correlated with phenology, demography and morphology at these dfferent levels of organization. 1.2  PHENOLOGY. MORPHOLOGY AND DEMOGRAPHY  Phenology, morphology and demography were examined for both species in 1986 and 1987 at each level in the above hierarchy. Phenology is the study of periodic phenomena in an organism's life in response to environmental factors (Funk and Wagnalls 1978). As plants are sedentary organisms changes in timing of responses such as flowering and fruiting may be an effective strategy in afluctuatingenvironment and may be critical in a harsh environment (Gill and Mahall 1986). More information on phenology can be found in Rathcke and Lacey (1985). Morphology is the study of the form and shape of plants and animals (Funk and Wagnalls 1978) and has also been related to function and strategy in differing environments (Barnes 1986; Lee et al. 1986; Hancock and Pritts 1987). Morphological relationships have been described with techniques such as plant growth analysis (Hunt 1982; Hardwick 1984; Hun 1984; Jolliffe and Courtney 1984), morphometries (Pimentel 1979) and allometry (White 1981; Joirffe and Courtney 1984; Welter 1987).  3  Rant demographic studies consist of actuarial measures. When correlated with biotic and abiotic factors these measures may explain community (Harper 1980; Crawley 1986; Moot et al. 1989) or population (Hutchings 1976,1986; Harper and Bell 1979; Blom 1988) dynamics. Ecological demographers document the growth of genets or genetically distinct individuals (Harper and Bell 1979) by enumerating genets and their constituent parts, ramets or modules (Hutchings 1976). Modules which nave been selected for study include leaves (Clark 1980; Lovett Doust 1981; Lovett Doust and Eaton 1982; White 1985; Gamier and Roy 1988), buds (Maillette 1982), rhizomes (Bell 1976), shoots (White 1980), branches (McGraw and Antonovics 1983) and flowers (Lovett Doust and Eaton 1982). Male and female plants within a population have also been counted (AKiende and Harper 1989). Commonly, stems are considered the most meaningful subunit for demographic analysis of clonal individuals (Sarukhan and Harper 1973; Hartnett and Bazzaz 1985). Reviews of demographic theory are provided by Harper and White (1974), White (1979), Harper (1980), Blom (1988) and Balogh and Grigal (1988). A comparative study of plant demography and plant growth analysis is described by Hunt (1978), Bazzaz and Harper (1979), Hunt and Bazzaz (1980) and JoWfe and Courtney (1984). Differences within and among species, populations and individuals have often been described for phenology, morphology and demography. Recent studies attempt to link dfffering phenological, morphological and demographic patterns observed in various environments using life-history strategies though overall this approach has not proven conclusive (Steams 1977; Grime 1979; Southwood 1988).  4 1.3  VARIATION IN MORPHOLOGY. PHENOLOGY AND DEMOGRAPHY Variability or variation within- and among-groups has seldom been quantified. Scagel  and Maze (1984) found that within individual variance exceeded the amount of between individual variance among two Stipa species and an intermediate when they apportioned morphological variation with ANOVA. Differences between the three groups accounted for the most variation (Scagel and Maze 1984). Other studies have described the morphological variation of individuals and clones in different populations (Aarssen and Turkington 1985; Barnes 1986; Herndon 1989; Maddox et al. 1989). Substantial variation was found for clonal structure both within and between populations (Maddox et al. 1989). Morphological measures of individual traits, such as leaf length or awn length, revealed variation within populations to be large (Aarssen and Turkington 1985; Barnes 1986; Herndon 1987). These studies support earlier research which also described large amounts of morphological variation among (Burdon 1980) and within (Fowler et al. 1983) populations. Reinartz and Popp (1987) reported that morphological variation may also be substantial within and between individuals. Clonal variation was large for shoot height, above and below ground biomass, and the pattern of root connections of northern prickly ash both within and among individuals (Reinartz and Popp 1987). The extent of temporal and spatial variation has been quantified for demography. Demographic variation between years has been reported to be greater than variation occurring between populations in a single year (Weiss 1981; Mack and Pyke 1983; Waite 1984). Angevine (1983) found spatial demographic variation to be greater among populations within a species than between related species. At a finer scale of observation, greater demographic variation was found within a population than between populations at different sites (Huenneke 1987). Spatial demographic variation has also been observed within and between individuals, with genetic (Burdon et al. 1983; Ennos 1985) and environmental (Werner 1985) influences invoked to account for the variation. Several studies have quantified the relative amounts of variation stemming from genetic and environmental effects (Sarukhan et al.1984; Blom and Lotz  5  1985; Cook 1985; Billington et al. 1990). In all cases cited environment accounted for most of the variation (Oka 1976; Sarukhan et al. 1984; Blom and Lotz 1985; Cook 1985). In a recent study, Billington et al. (1990) reported that environment accounted for up to 100 percent of the demographic variation in adjacent grass populations. 1.4  FACTORS CORRELATED WITH VARIATION  Factors which maintain variation have been measured at many different scales of observation. Phenological studies have compared similar communities, such as Asian rainforests (Newton 1988) or temperate bogs (Wieder et al. 1984). in widely divergent areas to seek common patterns. At this large scale of observation ii is dfficult to assess factors which control phenological cffferences, due to the interactive nature of biotic and abiotic influences, though climatic differences are often cited (Primack 1985). Within-community studies comparing phenological development of dtfering strata have often been descriptive. Examples include phenological comparisons between the shrub layer and the canopy layer (Ralhan et al. 1985), between deciduous and evergreen trees (Gill and Mahall 1986) or the effects of canopy cover on understory plants (Niison 1986). Studies examining constancy or changes in phenological pattern between years have also been done (Helenurm and Barrett 1987). When several or all species in the community were observed, a sequential pattern of flowering or a divergence of flowering times has been reported (Pojar 1974; Heinrich 1976). Competition for pollinators seems to be the most widely accepted explanation for this asynchrony. Other studies nave stated that nonrandom distributions of phenological data indicate that competition has occurred (Rathke 1984, 1988; Wieder et al. 1984; Kjeilson 1985). Flowering phenology has also been viewed as a secondary trait related to fruit and seed production. According to this idea fruiting phenology is selected for times when seed dispersers are prevalent or seed predators are rare and flowering phenology is subject to these selection pressures (Stiles 1980; Primack 1985). Other explanations of flowering phenology have been purely physiological focussing on the availability of resources within the environment (Heinrich 1976; Kawano et al. 1982; Primack 1985).  6  Bioflc and abiotic influences nave been linked 10 demograpnic venation wrtnin communities or guilds though interpretations applied to these influences are often conflicting (Cook 1979; Bakker et al. 1980; Huenneke 1983; Beatty 1984; Verkaar and Shenkeveid 1984; Sakai and Sluak 1985; Dunn 1986; Huenneke and Sharitz 1986; Van der Toorn and Pons 1988; Balogh and Grigal 1988) Demographic comparisons between species focus on early versus late serai species in an overall successional development (Bierzychudek 1982; Whitney 1986). Morphological comparisons between species are also made at drfferent points along a successional gradient. Though morphological traits are correlated with environmental and ontogenetic differences between species (Antos and Zobd 1984; Barnes 1986; Lee et al. 1986), reproductive effort is most often quantified. Harper et al. (1970) argue that reproductive effort is expected to be maximized by annual species found early in succession, when competition is minimal, whereas perennial species, which predominate in later successional seres, minimize reproductive effort to devote higher proportions of energy to vegetative structures which may confer a competitive advantage (Harper et al. 1970). Since this idea was first espoused ft has been elaborated by several others (GadgJI and Sdbrig 1972; Abrahamson and Gadgjl 1973). Although several studies have measured reproductive effort, few have accounted for below ground biomass. It is also unclear whether patterns in reproductive allocation are phenotypic or genotypic Hi origin (Hancock and Prftts 1987).  Demographic (Law et al. 1977; Whitney 1986; Forte 1989) and morphological (Gamier and Roy 1988) patterns were also compared within genera though in many cases no overriding trends were discernible and environmental factors accounting for the variation observed were not identifiable. Environmental causes have been inferred for maintaining morphological, phonological and demographic variation among populations of a single species. Environmental influences were arroborated when drfferent phonological races found in drffering environments were examined in between population studies (Van Cauteren and Lefebvre 1986). Other studies have suggested environmental variables which maintain morphological variation among  7  populations though the relationships were not measured (Aarssen and Turkington 1985; Maddox et al. 1989). Environmental influences have also been suggested by adjacent dissimilar populations found at opposite ends of a gradent, such as moisture, that morphologically integrade where they overlap on the gradient (Barnes 1986). Finally, comparative studies between drfferent populations have provided dues to the environmental correlates of demographic variation. In some cases, no differences in mortality were observed between sites (Gamier and Roy 1988), whereas in other cases sdls properties (Sakai and Sluak 1985; Matlock 1987; Moot et al. 1989) and moisture (Sakai and Sluak 1985; Moot et al. 1989; Carisson and Callaghan 1990) were found to affect longevity. Phendogjcal variation among indviduals has been documented to examine the genetic component of phonological raits (Primack 1980; Primack 1985; Somers and Grant 1981; Kephart 1987). Successful selection of early and late developing subspedes in agriculture is evidence of the strong genetic component to phendogy (Rathcke and Lacey 1985). Mcrphdogjcal studes at the level of the indviduaJ or done have frequently dealt with three ideas. First, morphdogical variation has been explained as an adaptation to dffering environmental factors such as light or sdl moisture (Prtefka et al. 1980; Gawler et al. 1987; Menges 1987). Secondy. the effects of physiological integration on the development of large long-lived donal indviduals has been examined (Hartnett and Bazazz 1983; Slade and Hutchings 1987a; Hutchings 1987b; Schmid and Bazazz 1987; Lau and Young 1988). Physidogical integration implies that ramets in favorable areas may translocate resources to other ramets of the same done in stressed areas and increase the longevity or growth of the redpient ramets. This physidogical integration may buffer ramets within indement areas and facilitate rapid growth of these ramets if condtions improve. Several studes have documented this translocation of resources and examined donal architecture through a spatially heterogeneous area (Hartnett and Bazazz 1983; Slade and Hutchings 1987a; Slade and Hutchings 1987b; Lau and Young 1988). These studes found that rhizomes between stem ramets tended to be fewer and longer through areas of nutrient stress (Slade and Hutchings 1987a; Slade and Hutchings 1987b) and water stress (Hartnett and Bazazz 1983). Clumps of  8  stem ramets may therefore be widely separated or densely packed within a site. Architectural differences may affect competitiveness and/a cooperation between individuals (Hartnett and Bazazz 1985; Schmid and Bazazz 1987). Thirdly relative resource allocations between sexual and vegetative reproductive have been documented (Rtelka et al. 1980; Eriksson 1985; Jurik 1985; Loehie 1987; Cid-Benevento 1987). Sexual and vegetative reproduction may maintain and spread a clonal individual under differing conditions. Abrahamson (1980) considered vegetative reproduction advantageous in early successional sites as it may more rapidly fill a site than sexual reproduction. In later successional sites, where density and competition are greater, sexual reproduction was thought to be a more effective method of clone maintenance (Abrahamson 1980). This idea has since been expanded to include other condtions than competitive intensity and density. Loehie (1987) predicts that 1)  sexual reproduction will increase under condtjons which the plant perceives as favorable to germination, 2) where nutrients are available sexual reproduction will be less costly and will increase, 3) vegetative reproduction will increase relative to sexual reproduction when the opportunities diminish for sexual progeny, 4) as the value of vegetative reproduction diminishes sexual reproduction will increase and 5) when both vegetative and sexual reproduction value decrease concurrently, the ratio of sexual to vegetative reproduction will increase. Other researchers have examined the relationship between environment and reproductive allocation though patterns observed are not always consistent and predictable (Piteika et al. 1980; Eriksson 1985; Jurik 1985; Cid-Benevento 1987). Under condffions of increasing density sexual reproduction may increase in relation to vegetative reproduction (Piteika et al. 1980; Eriksson 1985). Other studies have found that sexual reproduction may increase with respect to vegetative reproduction under more favorable conditions such as  9  diminished density (Jurik 1985) or increased water, light or nutrient availability (Loehie 1987). Increased light was also found to increase the amount of sexual reproduction in comparison to vegetative reproduction by Cid-Benevento (1987), though vegetative reproduction was also found to increase. Environmental heterogeneity has been inferred from variation within-individuals or plasticity (Primack 1985; Rathcke and Lacey 1985; Schlichting 1986; Kephart 1987). Morphological patterns within individuals have been linked with this environmental heterogeneity (Oberbauer and Strain 1986; Svenssen and Callaghan 1988; Schlichting 1989). Svennson and Callaghan (1988) found that small scale changes in vegetation and microenvironment were associated with small scale changes in the morphology of Lycopodium annotinum . Canopy position and light have also been correlated with leaf thickness and weight in Pentaclethra macroloba (Oberbauer and Strain 1986). Finally Schlichting (1989) demonstrated changes in the correlation structure of weight, number and sizes of leaf, flower, root and stem parts within Phlox individuals due to drfferent nutrient, moisture and clipping treatments. Demographic studies within-individual plants have focused on stem ramets within clones (Sakai and Sluak 1985). Earlier workers assumed that stem ramets experienced constant risks of mortality regardless of age or size (Harper 1978). Mae recent studies, howeva, have questioned this assumption and suggested that ramet mortality is altered by clonal integration (Cook 1979; Cook 1985; Eriksson 1988), positional effects (Eriksson 1988; Douglas 1989) and survival of sibling ramets on the same stolon (Eriksson 1988). Environmental factors have frequently been cited as responsible fa phenological, morphological and demographic variation at all spatial and temporal scales. Climate is considered to maintain phenological variation between years (Rathcke 1988) and within seasons (Kjellson 1985). Annual dffaences in climate also affect pollinator availability which impacts on flowering phenology, whereas seasonal patterns that affect germination determine optimum time of seed dispersal (Rathcke and Lacey 1985). Physiological studies have accentuated drought stress or water potentials as determinants of phenological strategy  10  (Jackson and Bliss 1984; Gill and Manaii 1900) tnougn temperature is Deiieved to De important for woody shrubs (Ueth 1974; Reader 1983). Interactions of photoperiod, temperature and moisture were also necessary to induce flowering (Rathcke and Lacey 1985). Physiological studies have characterized the morphological responses to differing environmental conditions such as humidity, water stress and temperature (Mooney 1980; Roy and Mooney 1982,1989). Light, canopy cover, substrate, soil moisture and the dominant vegetation have also been correlated with size and number of leaves and the size and number of inflorescences in field studies (Piteika et al. 1985; Gawler et al. 1987). Other studies (Givnish 1982; Menges 1987) predicted and subsequently corroborated morphological patterns in differing environmental conditions. Givnish (1982) predicted leaf height and the proportion of the total biomass devoted to leaves in differing light regimes and Menges (1987) constructed a model of leaf thickness, area and height and the percentage of biomass devoted to roots under differing regimes of canopy opening, drainage, soil texture and plant density. Many studies have examined the environmental factors affecting demography though the large number of factors and their interactions do not form many consistent patterns. Descriptive studies have linked soils texture and moisture (Hobbs and Mooney 1987), disease (Cook 1979), environmental patchiness (Cook 1979), fire regime (Piatt et al. 1988) and competition (Groves et al. 1990) with demography. Experimental studies involving manipulations have linked fire regime with demography (Hartnet 1987) as well as nutrient enrichment (Noble et al. 1979), predation (Noble et al. 1979; Bazely and Jefferies 1989) and trampling (Noble et al 1979). Other influences that have been identified include soil nutrients (Beatty 1984; Kelly 1989a), soil texture (Beatty 1984) and water level and sediment depth (Beatty 1984; Huenneke and Sharitz 1986). Cook (1979) also stated that herbivores, pathogens and drought stress are important causes of mortality in many communities. Several studies have accentuated the effects of drffering canopy cover or light conditions on demographic patterns (Huenneke 1983; Sakai and Sluak 1985; Dunn 1986; Balogh and Grigal 1988). In several shrub species abundance is controlled by regeneration which may dfffer under dffering canopies (Balogh and Grigal 1988): under a closed coniferous canopy  11  regeneration was inversely related to site quality and moisture; under an aspen canopy regeneration was inversely related to soil nutrients. Canopy openings allowing greater light penetration into forest stands were also correlated with increased density and survival of Lonicera (Luken 1988). In another study increased survival during cold winters was reported for Plantago growing in the shelter of an overstory canopy (Moot et al. 1989). Kelly (1989b) showed that shading by taller neighbours and density of these neighbours affects recruitment of three annuals and two biennials in the grass chalklands of England. Kelly (1989a) also stated that daylength and temperature were also linked with variation in germination times. Low light and vegetation density were also linked to demography by Cook (1979), Verkaar and Shenkeveld (1984) and Van der Toorn and Pons (1988). 1.5  THIMBLEBERRY  Thimbleberry is a common shrub throughout B.C. Its range extends to 55' N latitude in the interior (Haeussler and Coates 1986), though ft is found as far north as Alaska along the coast (Hulten 1968). The southern limit of its distribution is southern California (Hitchcock and Cronquist 1973).  Thimbleberry is morphologically quite variable and several subspecies have been described (Hulten 1968). The plant is composed of dumps of canes or stems which are connected by a network of rhizomes. Each stem lives from one to three years, bearing fruit and becoming lignified after the first year of growth. The dedduous stems are glandular and hairy before lignrfication occurs. Thimbleberry leaves are palmate, from 5 to 30 cm in length and feel soft like tissue paper. The showy, white flowers are borne in loose cymes (Hitchcock and Cronquist 1973). They produce a thimble-sized, soft, orange-red fruit which is an aggregation of drupes. Thimbleberry is frequently found on sites following dsturbances. It is espedally successful at cdonizing if present before dsturbance (Bs 1981). Halpern (1989) found thimbleberry to be the dominant plant spedes approximately 10 years after logging and to have greater longevity than other early succession cdonizers. Although not plentiful in later  12  successional seres, tnimbieberry is present In natural gaps in the forest, in west central B.C., the plant is widespread, occurring in many forested ecosystems (Coupe et al. 1982). Thimbleberry is perceived by foresters to be a serious competitor for light and nutrients, thus hindering the growth of young trees (Haeussler and Coates 1986). Many reports have focused on forest management of the species (Gratkowski 1971; Stewart 1973; Stewart 1974; Coates and Haeussler 1986). Biomass regression equations have also been prepared to provide inventory information (Alaback 1980; Aiaback 1986). Like many weedy species (Baker 1974; Holzner 1982), thimbleberry may be apomictic. Agamospermy is common in many Rubus species (Grant 1981; Richards 1984), though no information is available on the breeding system for thimbleberry. Richards (1984) states that few members of the genus are known to be fully sexually diploids. Of the thousands of Rubus species most are polyploid and apomictic (Nybom 1987). A subject of other thimbleberry papers is plant/insect interactions, though these are primarily entomological (Briggs et al. 1982; Gilbert and Gutierez 1973; Jones 1983; McNicoi et al. 1983). A comprehensive review of thimbleberry is found in Haeussler and Coates (1986). 1.6  DEVIL'S CLUB  In B.C., devil's club is distributed from Vancouver Island, north along the coast to Alaska and in the interior wet belt (Lyons 1976). Its range also extends into Oregon (Hitchcock and Cronquist 1973). The stems and the underside of the large leaves have many thorns. This large plant is often up to 3 meters high and 5 meter long decumbent stems are common. Leaves are large, to 35 cm wide, maplelike in shape and 7 - 9 lobed. The white flowers are produced on an elongate raceme and yield showy scarlet berries. Devil's club is found in old growth forests and does not persist in logged areas. Klinka et al. (1989) state that it commonly occurs in very moist, water receiving sites with nitrogen rich soils.  13  No information on reproductive biology is available though most members of the Araliaceae are known to be outcrossing and fully sexual and some are dioecious (Flanagan and Moser 1985). Devil's club flowers are hermaphroditic (Hitchcock and Cronquist 1973). Little research has been done on devil's club except for ethnobotanical studies (Turner 1982) and a pharmacological report (Smith 1983). Biomass regression equations, which predict above-ground biomass from stem basal diameter, were calculated by Alaback (1980, 1986) and Yarie and Mead (1989) to assist in field inventories.  14  2.1  II. STUDY AREAS DESCRIPTION OF STUDY AREA  2.1.1 GEOGRAPHIC LOCATION The three study sites are located in west central British Columbia about 8 kilometers east of the townsite of Krtimat along Highway 25. This area is 54*10' latitude and 128' 35' longitude. All three sites are close to the Krtimat River or its estuary. The devil's club site, referred to as DC throughout this document, is located within a B.C. Forest Service reserve on the east bank of the river in an area of old growth forest designated as L6201 and L6202 by the Ministry of Forests (Figure 1). One thimbleberry site, designated as WED throughout this thesis, is situated between the Wedeene and the Little Wedeene rivers close to their confluence with the Krtimat River at L6144 and L6141 (Figure 1). The other thimbleberry site, KIT. abuts a small arm of the Krtimat River at L6235 and L6235 (Figure 1). KIT and WED will be collectively referred to as TB. The study sites were chosen because of the abundance of the study species, their close proximity and the differing canopy cover at each thimbleberry site.  2.1.2 EDAPHIC ENVIRONMENT Much of the bedrock within the area is composed of crystalline rocks. Throughout the valley large deposits of unconsolidated glacial material formed from the breakdown of these hard rocks are common. Along theriverand its tributaries glaciofluvial and fluvial materials form floodpiains and terraces made up of sequential deposition of these materials (Haeussler et al. 1984). The study sites were formed by this sequential deposition and may still experience flooding. Channels of water and standing pools are common throughout the sites. All three sites are edapfucally similar. Soils are generally Orthic Humo-Ferric Podzols with thick Ae horizons. Due to the heavy rainfall and the coarse nature of these soils the Ae horizons lose aluminum and iron to deeper reddish brown Bf horizons. Decaying wood and other organic materials, impregnated with fungal hyphae, blanket the ground forming a surface LFH layer often greater than 10 cm thick (Valentine et al. 1978; Haeussler et al. 1984).  Figure 1.  15  Map of the study area showing the location of each site in relation to the river system and each other. (— « gravel logging roads) KIT - gjdled alder thimbleberry site, WED - nongrdled alder thimbleberry site. DC - devil's dub site  16  2.1.3 DISTURBANCE HISTORY Although one or two trees adjacent to the river were felted, the devil's club site remains relatively undisturbed. The only other signs of human disturbance include a large hole in one of the oldest cedars and a fishermen's path which follows the river at the edge of the site. An area adjacent to the DC site is presently used by the townspeople as a source for firewood and occasionally trees were felled onto the B.C. Forest Service reserve. Within the reserve the DC site encompasses an area of about 60 square meters. Disturbance is more noticeable in the the thimbleberry sites. The KIT thimbleberry site was logged in 1969 and subsequently burned. The WED thimbleberry site was logged in 1969, burned and planted with spruce in 1975. Regeneration of Alnus rubra (alder) proceeded naturally on both sites and a dense canopy of this species rapidly filled Hi both areas. In 1985 alder were girdled leaving the trees standing but effectively dead at the KIT site.  17  2.2 CLIMATE The study area, which is characterized as sub-oceanic or coastal transitional, is located in the Northern Drier Maritime Subzone of the Coastal Hemlock Biogeoclimatic Zone (CWHfl) (Krajina 1965,1969; Haeussler et al. 1984). This zone has wet and mild weather compared to other areas at similar latitudes. This climate is predominantly coastal but is influenced by continental factors. Warm maritime winds deposit rain onto the westward facing slopes of the Coast Mountains and by the time they reach the CWHf zone have lost much of their moisture. As a result this area is drier than the outer coast and may have long dry spells during which fire hazards are common (Haeussler et al. 1984). In the winter the ground is covered by a thick layer of snow and does not usually freeze When cold spells due to the influx of continental air do occur their duration is short (Haeussler al. 1984). Weather patterns for precipitation, hours of sunshine and number of degree days from January to August of 1986 and 1987 are displayed in Figure 2. This number of degree days greater than 0* C for each time period is calculated as: i-n  J(mean T*j > 0*C)  i-1 where n is the number of days in the time period, i is the day number and mean T is the aver temperature for day i greater than 0* C. The summer rainfall was high in both years and there was no two week period without rain. Although spring temperatures dfffered little between years, 1987 had more rain than 1986 from March to mid-June, whereas from mid-June to Augu 1987, was more sunny, warmer and had less rain than 1986.  18  Figure 2.  Weather in the study areas from January to August 31,1986 and 1987 (Environment Canada, 1986 & 7).  a) number of degree days 400  1987 1986  DATE  b) number of hours of sunshine 200  T  1  i  1  1  r  OO rr  150  O UJ  100  X  oo  V ^ *^ ^ <^ ^ j N  i  i  i  i i_  1987 1986  DATE  c) rainfall (mm) 200  1987 1986  ^ 4? # <T  ^ ^ DATE  19  III SAMPLING METHOD & DATA COLLECTION  3.1 DESIGN Transects were established at each site in early May 1986. In the KIT thimbleberry site, transect fines were established perpendicular to an old logging road. The length of each transect line varied to ensure that each line went through standing girdled alder only (Figure 3a). At the WED site (Figure 3b) parallel transects were also positioned at right angles to an o road though these crossed areas of standing live alder. Transect lines were at least 50 meters apart at the KIT site and 75 meters apart at the WED site to minimize the chance of sampling same genet twice. WED is a larger site than KIT so transects were more widely spaced. Two transects, one on either side of a gully which traversed the site, were laid out in the DC site (Figure 3c).  Twenty-one sampling quadrats were systematically positioned along the transects at each site. The distance between quadrats was selected to minimize inlra-incfvictual effects on the data; however, this could not be assurred as genets could not be individually delineated. Thimbleberry quacrats were separated by at least 10 m whereas devil's club quadrats were spaced greater than 15 m apart to accommodate this larger plant. As the study is descriptive and estimates of the standard error of the mean were not required, stringent random sampling was not adhered to. In addition Pimentel (1979, p. 59) states that as biological systems are nonrandom and measures are usually repeatable, ft is safe to assume that nonrandom samples do not differ from those that would be generated by random sampling.  Quadrat size was set to encompass at least five stems of each sampled plant. These circular quacrats had a dameter of 1 m for thimbleberry and 1.5 m for devil's dub. Each quad was within a larger 3 m dameter plot in which sdl samples and spedes presence/absence and frequency data were collected.  20  Figure 3.  Map of quadrats and transects in each study site. (Open circles indicate quadrats which were morphologically sampled in 1986 and 1987. Solid circles indicate quadrats which were morphologically sampled in 1987 only). a) KIT - grcfied alder thimbleberry site Y///A nongrdled alder - closed canopy  KITIMAT  RIVER  b) WED - nongrdled alder thimbleberry site • • i standing water no canopy cover LWED  ROAD  c) DC - devil's club site • • • standing water  F  777777  ! path  no canopy cover - gap in forest  21  3.2  CANOPY COVER To estimate canopy cover high contrast black and white negatives of the canopy over each quadrat were taken with a 28mm lens during the second week of July in both years. The lens chosen showed a field of view wide enough to estimate light effects on the sample plants a all times of day. A smaller field of view would only measure effects due to cover at midday, when the sun is overhead, and a larger field would overestimate the effects of neighbours of the same height as the sample plants. The camera base was aligned in a N-S drectjon with a compass and the camera was always placed on the eastern side of the tripod. The base of the camera was 1.5 meters over the quadrat centre and was levelled with a small bubble level. Each negative was read through a video camera into the Kontron Image Processing System at the University of British Columbia, wherein the analyzer calculated the percent cover of the total area in each picture. This value provides an estimate of the amount of light reaching any one quadrat. The program to run the Kontron was written by Mike Weiss of the Departmen of Botany at the University of British Columbia. The method of cover estimation by analysis of photographs is described by Anderson (1964), Pope and Uoyd (1975), Chan et al. (1986) and Chazdon and Field (1987).  3.3 SOILS. All soil sampling was done outside of the inner quadrat but within the larger surrounding three meter diameter quadrat to minimize disturbance on sample plants. The thickness of the LFH horizon was measured and approximately 250 ml of the mineral soil drectJy underneath tile LFH was collected. As soil properties vary both laterally and vertically (Mader 1963; Beckett & Webster 1971; Coutin et al. 1983; Carter and Lowe 1986), no distinction was made between soil horizons for collecting purposes. Within quadrat variability was not quantified in this study, rather the sampling method was assumed to represent the average condition found i each quadrat. The percentage of coarse fragments. pH, the percentages of sand, silt and clay, total nitrogen, the depth to mineral soil and the percentage organic matter within the mineral soil  22  were measured, me variables were chosen to characterize tne soil's nutrient avajiaomty, structure or texture, biological activity, drainage or water holding properties and buffering ability thereby reflecting the differing envronmental character among quacrats (Singer & Munns 1989). All soils were analyzed using the methods described by Lavkulich (1981) except fa the fine fraction component. The pH was measured by the water measurement method with 50 ml of distilled water added to 20 g of soil as the only change in the method. Total nitrogen was colourmetrically determined with the autoanalyzer. The leco analyzer was used to measure total carbon a the percentage of organic matter. Particle size of the fine fragment was determined by the hydrometer method (Bouyoucos 1962). 3.4  PLANT NEIGHBOURS In 1986 all plants within the three m dameter quadrat were identified. Nomenclature fdlows Coupe et al. (1982). In 1987 each 3 m quadrat was divided into quarters and each pla spedes within the quadrat was identified and was assigned a scae from 1 to 4 dependng on the number of quadrat quarters in which ft was present. This provided a simple measure of loca frequency.  Vegetation is frequently examined to estimate competitive interactions and although surroundng vegetation was considered to potentially impact on plant characters-, it was primarily of interest as a summary of the envronmental condtions within each quadrat (Watkinson 1985; White 1985; Tilman 1986; Tilman 1988; Austin 1990). Vegetation pattern is commonly correlated with environmental factors in community ecological studes (Greig-Smith 1983; Kershaw & Looney 1985; Chang & Gauch 1986; Bradfield & Campbell 1986; Menges 1986; Austin 1987).  23 3.5 MORPHOLOGY In 1986 morphological measures were made during the weeks of May 25, July 6 and August 3 (Table 1). Where possible six stems, which were also phendogjcally sampled, were sampled in each quadrat. Ten quadrats were sampled in each of the three sites (Figure 3). Only devil's club stems originating within the quadrat were measured and the widest range of devil's dub stem sizes and ages were represented in the sample at each quadrat. At the thimbleberry sites, four stems produced in the current year and two produced in 1985 were sampled. Measurements on each stem for both spedes induded the following: a)  number, length, basal diameter of all branches and their position along the main  stem, b) length and basal diameter of all stems, c) number, length, width and position of all leaves. d) number and position of devil's dub inflorescences and e) number of flowers per stem and number of flowers per inflorescence fa thimbleberry. Basal diameter was measured at 1 cm above the ground a immediately above the bas swelling fa each stem. Position of stem leaves and branches referred to their distance up the stem from the basal diameter measurement. Branch basal diameter was measured at the dosest possible point along the branch to the stem and this was also the reference point fa lea branch position. The position of devil's dub inflorescences referred to the particular stem a stem branch terminated by that inflaescence. Leaf length is the distance from the attachment to the stem to the opposite point of the palmate leaf blade. Leaf width is the maximum distance across the leaf blade from point to point. In 1987 all measurements, except fa leaf measurements, were repeated at all 21 quadrats at weekly to biweekly intervals (Table 1). Recads of the number of flowers per stem and pa inflaescence at these same sampling times were also made fa thimbleberry. The number of leaves per stem were counted once during the week of July 5. but measures of leaf length a width wae not recaded.  24  Table 1.  PhenoJogjcal, morphological and demographic sampling in 1986 and 1987. The circles indicate the phonological sampling dates in 1986 and 1987. The squares indicate the morphological and demographic sampling dates in 1986 and 1987.  1986 MAY S M T  W T 1 4 5 6 7 8 11 12 13 14 15 18 19 20  F 2 9 16  S 3 10 17 24 31  T F S 5 liJ hi 11 12 16 17 18 19 20 21 22 23 24 25 26 27 @ © @ 31 1987 MAY S M T  W T  F 1 8  * 5 6 7 10 11 12 18 19 20 21 22 24 25 — — 31 3  0 0 E3 0  O  JULY S M  S 2 9  T  YV T  H £§3 E3 dj d  F 310 17  12 13 AA 15 16 19 20 H 26 31  S 4 11 18 2 5  JUNE S M 1 © 8 9 15 16 22 23 29 @  T © 10 © 24  AUGUST S M T  (a  W © 11 © 25  T © 12 © 26  W T  F 6 13 20 27  S 7 14 21 28  F  S  ® LU m m ill 11 12 13 14 15 16  H  10 17 18 © ® @ 22 23 24 25 26 27 28 29 30 31  JUNE S M 1 7 8 14 15 21 28 29  T 2  \m  g m a:  30  AUGUST S M T  25  3.6  PHENOLOGY  In 1986 phenology was observed biweekly or once every two weeks at each quadrat (Table 1). The observations were recorded using the phenological codes of Dierschke (1972) (Table 2). Vegetative and generative development was coded fa each species. In 1987 phenological codes wae assigned to both species in the same manna as 1986 though sampling was done at weekly to biweekly intervals (Table 1) to more closely monitor changes in growth state. Where possible six individual stems and their branches wae also assigned codes at each observation time to ascertain within quadrat variation fa both species and phenological deferences between branches and one- and two-year-old stems fa thimbleberry.  Table 2. Phenological codes (Dierschke 1972). ygoetative 0 Closed bud 1 buds green tips 1a germination 2 green leaf out but not unfolded 3 leaf unfolding up to 25% 4 leaf unfolding up to 50% 5 leaf unfolding up to 75% 6 full leaf unfolding 7 shoot elongation ceases 6 first leaves turned yellow 9 leaf yellowing up to 50% 10 leaf yellowing ova 50% 11 bare  Generative 0 with blossom buds 1 blossom bud recognizable 2 3 4 5 6 7 8 9 10 11  blossom buds strongly swollen shortly before flowering beginning flowering in bloom up to 25% in bloom up to 50% full bloom facing completely faded bearing frutt seed/fruit dispersal  26  3.7 DEMOGRAPHY Demographic records of all stems within the 1 m diameter quadrats for thimbleberry and the 1.5 m diameter quadrats fa devil's dub wae recaded in 1986,1987 and in July 1988 (Figure 2). Demographic records consisted of the following: a) identifying and counting all stems, b) recading stem births and deaths and c) aging all stems. As thimbleberry stems live only two a three years, become lignified at the end of the first year and branch in the second year, it was possible to determine the age of each stem. Devil's club stems, however, are extremely long lived and age estimations wae based on counts of annual terminal bud scale scars.  27  IV ANALYTICAL TECHNIQUES 4.1 TECHNIQUES The primary techniques used in this study were multivariate in nature though univariate techniques such as analysis of variance (ANOVA) and the Mann-Whitney test were also used. The Mann-Whitney test is comparable to a nonparametric two-sample t-test (Zar 1974). Relationships within each matrix of environmental or plant characters were initially examined with principal components analysis (PCA). PCA also generated summary axis variables which were used in subsequent analyses such as ANOVA and canonical correlation analysis (CANCORR). The latter describes relationships between environmental variables and plant characters whereas the former was used to apportion variation. Weighted multidimensional scaling (WMDS) provided a nonparametric corroboration of the relationships suggested by CANCORR. 4.1.1 PRINCIPAL COMPONENT ANALYSIS (PCA)  Principal component analysis (PCA) is an ordination technique that yields a set of axes which effectively summarize the variation in a data matrix (Pimentel 1979; Greig-Smith 1963). The analysis may proceed via a correlation matrix, thereby removing the effect of differing measurement scales on the analysis, or via a covariance matrix which is used when a single measurement scale is represented in the data. In this study, unless measurement scales dffered for the initial variables, the covariance matrix was used. PCA is a useful technique and has several advantages. All axes are orthogonal or uncorrelated and the primary axes represent most of the variation in the original data set. As a result the dimensionality of the data is reduced. Eigenvectors may also represent more inclusive summaries of biological processes than raw data. Each axis may. therefore, be ascribed with a biological interpretation as a unique response to a set of conditions and further analyzed as a new summary variable. As the sum of the variation expressed by the eigenvectors equals 100% and axes are orthogonal they can also be further repartitioned into component parts and the component parts summed over all the axes (Pimentel 1979; Scagel  28  and Maze 1954). Finally as axes scores approximate multivariate normality, they more closely fulfill the assumptions of parametric analysis (Pimentel 1979). PCA has few assumptions. First it is based on variables which are real numbers. Ideally the sample should be random and variables should vary monotonically in relation to each other or be linearly related (Pimentel 1979; Greig-Smith 1983). If this latter condition is not fulfilled infolding of the data or convolution occurs (Greig-Smith 1983). Although nonlinearity is a common problem with biological data, environmental characters usually fulfill this criterion (Greig-Smith 1983). Regardless the technique is not invalidated by some divergence from these conditions (Pimentel 1979). The primary dsadvantage of the technique is the difficulty of ascribing bidogically meaningful interpretations to the results. If the initial characters measured do not adequately reflect bjdodcal phenomena a meaningful pattern may not emerge. Secondy deddng which axes to interpret and how important those axes are can be quite subjective (Pimentel 1979). Although the first axis will represent more of the total variation of the original data than the second axis, it may not have more biological significance. 4.1.2  ANOVA  While analysis of variance (ANOVA) is mostly used to test for dfferences between group means only, it was only used to partition variance. No hypothesis testing was performed. As sums of square are addtive and independently derived (Sokal and Rohlf 1969). they can be divided into those components of the total variation due to between groups and within groups. Using PCA axis scores variation between and within groups was calculated for the whole data set in the following manner: i«n VariationA=2 (SSA\SStot)tai) M where SS equals the sums of squares due to the ANOVA term A and (Aj) is the percent of variation of the total data set accounted for by eigenvector i of the total n eigenvectors (Scagel and Maze 1984).  29 4.1.3  CANONICAL CORRELATION (CANCORR)  Canonical correlation analysis seeks linear relationships between matrices by orcfinating each matrix while simultaneously maximizing the correlation coefficients between each pair of canonical variates axes (Gittins 1985). In this study, CANCORR was used to seek relationships between plant characters and environmental variables and between pairs of plant characters. CANCORR has many similarities to PCA. Most of the variation of the original data is accounted for by the first few axes so the technique effectively reduces the dimensionality of the data (Gittins 1985). The canonical axes are also uncorrected except for consecutive pairs from each matrix for which correlation is maximized (Gittins 1985). CANCORR generates new axes of canonical variates by muttipiing vectors of original variables by canonical weights. CANCORR is a metric ordination technique implying that some assumptions must be met to achieve accurate results from the data. The sample size must be larger than the sum of the number of variables from each matrix (Pimentel 1979; Gittins 1985). As sample size decreases in relation to the number of variables spurious results are generated and removing or adding samples may substantially alter results (Pimentel 1979). To meet all conditions the data must also be linear and continuous though this assumption can be waived for descriptive analyses (Gittins 1985). Assumptions of multivariate normality can be met by using PCA axes scores of raw data as input data (Pimentel 1979; Gittins 1985). CANCORR has received much criticism as an unwieldy and unreliable technique (Pimentel 1979; Gittins 1985). Spurious correlations may result especially if colltnearity exists between variables of either matrix. Commonly raw data is crthogonally transformed via PCA or some other ordination technique to overcome these problems; however transformation may confound and complicate deciphering of the results. In addrtion to spurious results, it is possible for the canonical correlation to be high while the canonical variates are not strongly correlated with the original data or all multiple correlations are low (SAS Technical Report P-161 1985). Other tools such as redundancy analysis or communalitjes, used with canonical coefficients, may alleviate these shortcomings (Gittins 1985). Perhaps the most difficult problem to overcome  30  is making biological sense ot mathematical relationships. Numerical relationships do not necessarily yield real biological relationships. Another means of seeking relationships between two matrices, whether raw data matrices or axes scores from an ordination, may simply be to correlate axes from each matrix (Jeffers 1978). CANCORR has the advantage of expressing these relationships while also seeking major linear trends in all the axes concurrently. Like other multidimensional techniques, it manipulates all data thereby overcoming the oversimplification of univariate analyses which examine only one variable at a time. The complexity of the analyses may also be an advantage if it forces the user to more closely examine and justify the results than other more straightforward methods. 4.1.4 WEIGHTED MULTIDIMENSIONAL SCALING (WMDS)  Weighted multidimensional scaling (WMDS) is an ordination technique which seeks to maximize the mathematical relationship between similarities (proximities) in symmetric matrices and Euclidean distances between points (Kruskal and Wish 1978). Unlike PCA it does not assume a linear relationship between the variables and is based on distances between points rather than angles between vectors (Schiffman et al. 1961). WMDS can be used to reduce dimensionality and analyze relationships within one matrix or between several matrices using either metric or nonmetric models. Metric models relate proximities directly to distances whereas nonmetric models merely retain the rank order between proximities and distances (Kruskal and Wish 1978). Both models require input data be in the form of symmetric matrices. No other limitations are imposed. INDSCAL or Individual Distances Scaling of Proximity Data is a metric version of WMDS which seeks relationships between several similarities matrices (Carroll 1987). It has two underlying assumptions. First, INDSCAL assumes that a set of dimensions (axes) exist which account fa all the meaningful variation (except fa error) between all similarities between quadrats (Carroll 1987); secondly it assumes that the distances between all quadrats can be simply related to proximities by a unique subject weight fa each matrix (Carroll 1987).  31  Two dimensional WMDS or WMDS of one matrix attempts to maximize the relationship between distances and proximities data so that d ij«f (xjr-xjr) 2  2  where djj is the Euclidean distance between points i and j and xj- and xjr is the similarity or  proximity between quacrats xj and xj on axis r. Multiway WMDS or WMDS between more than  one matrix, such as INDSCAL, stretches or shrinks each axis by a weight factor for each matrix so that  dZjj^-wkKxr-xjr) where dy^ is the distance between points i and j for sample k and wkr is the weighting of each sample k on axis r (Kruskal and Wish 1978). The goodness of fit is assessed by one of many stress measures which assess the dispersion between distances and proximities (Kruskal and Wish 1978). The objective of the procedure is therefore to minimize stress over all values (Kruskal and Wish 1978). Computationally the procedure iterates until the reduction in stress is minimized (Kruskal and Wish 1978). 2  Interpretation is based on graphs which show the linear separation of each quadrat for each matrix and graphs of subject weights. The weight space displays a point for each matrix. The length of the vector from the origin to this point represents the amount of variation of each matrix which is accounted for by the model while the angle between the two vectors is inversely proportional to the relationship between the two original matrices (Young and Lewyckyj 1979; Schiff man et aJ.1981).  As each dimension or axis generated is considered to be a response to a stimulus, the number of axes or dimensions chosen can be critical. More axes diminish stress. However not all dimensions can be meaningfully interpreted. There is also no stringent mathematical criterion for judging dimensionality though the number of quadrats must be almost four times the number of dimensions ("dimensions < 4 ("quacrats -1)). A line graph of the number of dimensions versus the amount of stress for each dimension is used as a subjective aid for deciding dimensionality (Kruskal and Wish 1978).  32  previously stated trie objective is to minimize stress. However stress can De anected by several factors inducing a nonsymmetric matrix, replication at each variable, ties in the data or missing values. The most serious produce local minima fa stress. As the iterative nature of the process terminates when stress reduction ceases it is possible that a global minimum fa the whole data set will not be reached (Kruskal and Wish 1978; Shiftman et al. 1981). As  In this study WMDS was used to provide a nonparametric check on CANCORR. Mae information on WMDS may be obtained from Kruskal (1964a, 1964b) and Carroll and Chang (1970).  33 V DATA ANALYSIS 5.1  GRAPHICAL ANALYSES Initial exploration of the data used graphs. Canopy cover was graphed to illustrate  changing light conditions over the quacrats at each site. Scatters of phenological codes against time also showed how developmental rates differed between sites, species and years and the extent of the variation at each level in this nested hierarchy. Finally graphs of the thimbleberry demographic data were drawn to illustrate the general demographic trends over the three year period of the study. As devil's dub population did not change substantially during the study, graphs of the age dstribution on July 30,1987 only were drawn.  5.2  ENVIRONMENT Table 3 dsplays the analytical methods for environmental variables other than canopy  cover. PCA axes of soils variables, frequency and presence/absence of plant neighbours were generated to produce input data for subsequent CANCORR and to show correlations between raw variables. As PCA requires that samples exceed variables (Pimentel  1979), species lists  used as input data to PCA were modfied prior to ordination. All spedes in greater than 17 quacrats and less than 5 quacrats were removed from the KIT, WED and DC species lists and spedes in greater than 38 quacrats and less than 4 quacrats were removed from the TB species lists. This modification dminished the effects of rare or abundant spedes on the analyses (Greig-Smith  1983).  Distance matrices of these same raw variables were used in WMDS  though sdls variables were first standardized to zero mean and equal variance. Separate Eudidean distance matrices and matrices of PCA axes scores were produced for KIT, WED, TB and DC yieldng 4 matrices for soil variables and 8 matrices for presence/absence and frequency of plant neighbours.  34  Table 3.  Analytical methods for environmental data. Arrows indicate the sequence of analyses from raw variables through summary variables used as input data to CANCORR and WMDS. (When KIT. WED, TB or DC are listed, a separate analysis is performed for each) KIT = girdled alder thimbleberry site, N = 21 WED = nongirdled alder thimbleberry site, N = 21 TB = KIT and WED thimbleberry data, N = 42 DC = devil's club site, N = 21  Soils  variables include: pH. % coarse fragments, %sand, % silt, % clay, %organic matter, depth t mineral soil, total N  PCA ^CANCORR! - correlation matrix among variables -KIT, WED, TB. DC WMDS Euclidean distance matrix among quadrats -KIT, WED, TB, DC  Species Neighbors - 1987 All species in > 17 quadrats and <5 quadrats removed from KIT, WED, DC raw data matrices All species in > 38 quadrats and <4 quadrats removed from TB raw data matrix Frequency PCA variables include lists¥ a i i | j • covariance matrix among »JCANC0RR1 species in quadrats coded :oded fl variables •KIT, WED, TB, DC fromO- 4 based on number of quadrat quarters in Euclidean distance matrix among quadrats -KIT, WED, TB, DC  mm  Presence/Absence PCA JCANCORRI - covariance matrix among variables include lists of al variables species coded 0 or 1 based] |-KIT, WED. TB. DC j l on presence/absence Euclidean distance matrix among quadrats -KIT, WED, TB, DC  mm  35  The influence of plant neighbors was examined at different scales as different environmental attributes may be reflected at each scale. As presence/absence data of plant neighbors represents a coarser scale of measurement than cover or frequency of vegetation, more coarse grained environmental gradients may be reflected at this scale (Allen and Wyleto 1983). Allan and Starr (1982) also indicate that coarse gained presence/absence data may reflect influences due to time scales which are not apparent in finer-scaled frequency data.  5.3 MORPHOLOGY The morphological variables and analyses are displayed in Table 4. Data were divided into groups for analysis. Except for the ANOVA between species, devil's club and thimbleberry were analyzed separately. The two species were sufficiently different that morphological descriptions of each one required different variables. These divisions also reflect differences in annual sample sizes . In 1986,10 quadrats only were sampled whereas 21 were sampled in 1987. Except for the ANOVAs which apportioned variation among quadrats and years, data from 1987 were used.  Input variables used in simple correlation, ANOVA and CANCORR were generated by PCA. All PCAs were performed on correlation matrices as measurement scales differed between variables. Correlations were calculated between PCA scores and the 1986 and 1987 canopy cover data for DC, KIT, WED and TB. ANOVA was used to partition PCA scores among years, sites, species and quadrats. Variation in thimbleberry data was partitioned using a nested ANOVA as years, sites and quadrats could be simultaneously considered. Devil's club was not included in the nested ANOVA as only differences between thimbleberry sites were of interest. ANOVA was also used to partition variation among quadrats fa the number of stem leaves in 1987, the stem lengths in 1987 and leaf length and width in 1986. Most quadrats contained at least 5 replicates of these variables whereas other mophological traits wae not as numerous within quadrate. As sample sizes within quadrats wae unequal these variables wae  36  not summarized with PCA Dirt were  analyzed individually. Finally Euclidean distance  matrices  among quacrats were calculated from the standardized morphological data and analyzed using WMDS. Matrices of PCA axes scores and Euclidean distance matrices were produced fa the KIT, WED, DC and TB data sets. Table 4.  Analytical methods fa morphological data. Arrows indicate the sequence of analyses from raw variables through summary variables to ANOVA, WMDS, CANCORR a simple correlation. Sampling time is the second week of July unless otherwise indicated. (When KIT, WED, TB a DC are listed, a separate analysis is performed fa each) KIT « girdled alder thimbleberry site, N • 21 in 1987,10 in 1986 WED • nondrded alder thimbleberry she, N - 21 in 1987,10 in 198 DC - devil's club ate, N - 21 in 1987,10 in 1986 TB - KIT and WED thimbleberry data, N • 42 in 1987, 20 in 1986  TB variables include: * flowers per branch @ Aug.8, "flowering stems @ Aug. 8," 2-yearold stems @ June 30," 1-year-old PCA stems @ June 30, " leaves/1-year-old-correlation stems, " leaves/2-year-old stems.1- matrix among year-oJd stem length, 2-year-old stem variables length, 1-year-old stem basal diameter 2-year-old stem basal diameter, brandi length, branch basal diameter 1986 & 1987  1986 & 1987 DC variables indude: " stems @ June 30," branches/stem, " leaves/stem, stem length, - correlation stem dameter, branch length, brand matrix amonc diameter variables  TB 8c DC variables indude: " branches per stem, " flowers/stem (TB) a bunches (DC)," stems, PCA " leaves/branch, " leaves, stem - correlation length, stem dameter, branch matrix amonc diameter, branch length variables  ANOVA - to apportion TB variation among years and sites -Y • A + B(A) + E where A - years. B - sites, E erra a within sites 8c Y • PCA axis scaes  AR5VA  - to apportion DC variation among years -Y = A + E where A - years E » erra a within years 8c Y - PCA axis scaes  1987  ANOVA - to apportion variation among spedes - Y - A + E where A - spedes, E » erra 8 Y • PCA axis scaes  37  Table 4 continued DC&TB 1987 *stem leaves 1987 stem length 1986 leaf length 1986 leaf width  ANOVA - to apportion DC & TB variation among quadrats Y - A + E where A - quadrats. E » error & Y - raw data  1987 TB. KIT. WED variables indude: * brandies/stem <§> 30/6 * flower/ PCA inflorescence @ 8/7, * flowering stems- correlation matrix @ 8/7* flowers/stem @ 8/7, # 2-year-among variables old stems @ 30/6, * 1-year-old stems-KIT, WED, TB @ 30/6, # leaves, * branch leaves, * leaves/1-year-old stem, * leaves/ 2-year-old stem, 1-year-old stem length, 2-year-old stem length, 1-year-Eudidean distance old stem diameter, 2-year-old stem matrix among quadrats diameter, branch length, branch KIT, WED, TB diameter  correlation with canopy cover from 1986 & 1987 -KIT, WED, TB  mm  1987 DC correlation with canopy variables indude: cover from 1986 & 1987 » branches/stem, * flower bunches/ PCA stem, * stems, * stem leaves, * branch»- correlation matrix leaves, stem length increment, stem among variables CANCORR length, stem diameter .branch length increment, branch diameter, branch length 4Eudidean distance matrix among quadrats  5.4  PHENOLOGY The sequence of phenological analysis is displayed in Table 5. The slope of a linear  regression line was calculated as input data to all subsequent analyses. The slope of the line was an effective summary variable as it gave an estimate of the rate of change of phenological development with time. Simple linear regression was found to yield higher r values than log2  38  log or log-normal relationships in all cases. Slopes were calculated tor eacn year and Tor Dotn vegetative and generative data at each quadrat for 1986 and 1987 and for stems within each quadrat in 1987. PCA axes scores of covariance matrices and Euclidean distance matrices of slopes of regression lines were used in subsequent CANCORR and WMDS. The slopes of regression lines were also correlated with canopy cover in 1986 and 1987. Finally ANOVA was used to partition generative and vegetative slopes of regression lines between years, sites, species, quadrats and stems within quadrats. Nested ANOVA was used to partition variation among years and sites fa thimbleberry as this model allowed simultaneous comparisons between drfferent levels in the hierarchy. Devil's club was not induded in the nested analysis as site differences fa thimbleberry only were of interest. Separate ANOVAs were used to partition variation fa both generative and vegetative data. Generative data may be less plastic than vegetative data and both vegetative and generative were considered functionally separate. ANOVA was also used to apportion variation among quacrats using both stem and branch phendogies. Variation within quacrats was apportioned separately from other sfata as the number of stems and branches within quacrats were not equal. 5.5  DEMOGRAPHY  The effect of density on thimbleberry longevity and the rate of stem production was examined with regression analysis of the number of stems in a quadrat and the percentage of the total number of stems in a quadrat surviving to each time period. The time periods were those used in PCA analysis (Table 6). The sequence of all further analyses is dsplayed in Table 6. PCA axes scaes of covariance matrices were used as input data in ANOVA and CANCORR; whereas Euclidean dstance matrices were used in WMDS. ANOVA was used to partition variation between years and sites fa thimbleberry and within and between quacrats fa both thimbleberry and devil's dub PCA axes scaes. Comparisons between the two spedes were not possible as devil's dub numbers dd not fluctuate significantly during the course of the study.  39  Table 5.  Analytical methods for phenological data. Arrows indicate the sequence of analyses from raw data through summary variables to CANCORR, ANOVA and WMDS. (When KIT, WED, TB or DC are listed, a separate analysis is performed for each) KIT - girdled alder thimbleberry site, N = 21 WED = nongirdled alder thimbleberry site, N = 21 TB « KIT and WED thimbleberry data, N - 42 DC = devil's club site, N = 21 Euclidean distance matrices among quadrats -KIT, WED.TB, DC  Vegetative and generative Regression codes at each quadrat Y • mX + E where X number of days since Jan 1 when sampling occurred, Y = phenological code. E = error & m = slope slope = summary variable  ANOVA  I  - all are done for vegetative & generative i) - to apportion TB variation among years & sites Y - A + B(A) + E where Y - slope, A - years, B = sites & E = error or within sites -TB ii) - apportion DC variation between years Y = A + E where Y - slope, A - years DC iii) - to apportion variation between species Y = A + E where Y - slope, A - species & E = error KB&DC  mm ICANCORR PCA - slopes of 1986 & 7 vegetative 8c generative data are input variables - covariance matrix among variables -KIT, WED.TB, DC correlation with canopy cover from 1986 8c 1987  40  Table 5 continued SR5VA Vegetative and generative Y m X + E where to apportion variation codes for branches and X» number of days since among quacrats stems in 1987 = A + E where Jan 1 when sampling occurred, Y « phonological A • quadrats and E = error code, E - error & m = slope slope • summary variable The number of stems living fo each age class from cohorts produced in May 1986 and 1987 were used as input data to ANOVAs which apportioned thimbleberry variation between years and sites. This measure incorporates both density effects and life expectancy between quacrats through time. Age dstributions were partitioned among quacrats for both species. Age distributions represented the variation at one time only and allow comparison between devil's club and thimbleberry. The age distributions did not, however, have equal sample sizes within quacrats so devil's dub age data were grouped to satisfy PCA requirements. Table 6.  Analytical methods fa demographic data. Arrows indcate the sequence from raw data through summary variables to ANOVA, CANCORR and WMDS. (When KIT, WED, TB a DC are listed, a separate analysis is performed fa each) KIT • girded alder thimbleberry site, N • 21 WED - nongrded alder thimbleberry she, N - 21 TB - KIT and WED thimbleberry data, N - 42 DC - devil's dub she, N - 21  1986 & 1987 TB variables - * of stems livin< covariance matrix amon to age dass where age variables dasses are: dayO, 1-55 days. 56-105 days, 106-450 days, > 451 days  ANOVA - to partition TB variation among years and sites -Y»A + B(A) + E where A - years, B - sites, E - erra and Y - PCA axis scaes  ^CANCORR 1986 & 7 TB, WED, KIT PCA variables - * of stems livin covariance matrix amon orrelation with canopy to age dass where age variables cover from 1986 & 1987 dasses are: 1986 - 0-55, 56-105, 106Euclidean dstance 360, 361-415. 416-465, 466-810, >811,1987 - 0- matrices among quadrats WMDS 55.56-105,1 OS-450. >451  41  Table 6 continued TB ANOVA - age of all stems in quadrat - to partition TB variation @ 30/6/87 'among quadrats - Y • A + E where A » quadrats, E - error and Y - raw data DC - age of all stems in quadrat @ 30/6/87 ANOVA - to partition DC variation among quadrats - Y • A + E where A • quadrats, E = error and Y - raw data 1 DC PCA JcANCORR - age distribution of stems - covariance matrix among * of stems of 0-5,6-10,11- variables 15, 16-21 and 22-28 years mm old luclidean distance matrix @m i ono quadrats  5.6  CANCORR ANP WMDS  Relationships between all environmental variables and plant characters and between the matrices of plant characters were examined with WMDS and CANCORR (Figure 4). PCA axes scores were used as input data to CANCORR and WMDS was performed on pars of Euclidean distance matrices (Tables 3 - 6). Thefirst10 axes generated by PCAs were used in CANCORR, though interpretation was based on thefirstthree only. Canonical axes were only interpreted when canonical correlation coefficients were greater than .75 and the redundancy value for each matrix was greater than 1 divided by the number of variables in that matrix. All correlations of less than .50 were not considered large enough, either between PCA axes and raw data or canonical axes and PCA axes, to warrant interpretation. The analyses outlined in Figure 4 were performed for KIT, WED, DC and TB. WMDS is used to corroborate CANCORR and WMDS results are reported only where they clarify discrepancies in the CANCORR results.  42  5.7  COMPUTATION All analyses were done using the computing facilities at the University of British  Columbia. The computing packages used included UBC ANOVAR (Greig and Osterlin 1978) for analysis of variance, MIDAS (Fox and Gure 1976) for all simple correlation analyses and some PCA analyses, SYSTAT (Wilkinson 1988) and SYGRAPH (Wilkinson 1988) for all graphical presentation of the data and SAS (SAS Users Guide 1986) for all WMDS and CANCORR. The ALSCAL procedure which is incorporated into SAS was used for all WMDS (Young and Lewyckyj 1979). Some PCA analyses were also performed using a program written by Gary Bradfieid of the Botany Department. All packages, except SYSTAT and SYGRAPH were available on the mainframe. SYSTAT and SYGRAPH were used on the Madntosh Plus personal computer in the Botany department. Other packages and subroutines developed at the University of British Columbia were also used. Figure 4.  WMDS and CANCORR analyses. Arrows indcate all parwise comparisons analyzed with WMDS and CANCORR. Analyses were performed using WED, KIT, TB and DC data sets. KIT - girded alder thimbleberry site, N - 21 WED • nongrded alder thimbleberry site, N - 21 TB - KIT and WED thimbleberry data, N»42 DC - devil's dub site, N - 21  43  VI RESULTS 6.1  ENVIRONMENTAL RESULTS  6.1.1 CANOPY COVER All sites range from little or no cover to a dense canopy overhead (Figure 5). The WED and DC sites have the most dense canopy cover with most quadrats having over 60 percent cover. Due to the alder grdling at the KIT site, most quadrats have less than 30 percent cover. The higher cover values at KIT quadrats 3, 8 and 14 are due to adjacent willows, which were not girdled. Quadrats 18 and 5 are shaded by elderberry. Figure 5.  Percentage canopy cover over 63 quadrats at KIT, WED and DC in July 1987. Numbers refer to the quadrats at each site.  100 *3738 25 3 0 | 2 6  3  3  7  2  3 311 22 33.. o 4x '. 2  90  22* 42 •  3* 80  3  2  8 7  44 46 4 9 ; 45 5 9 : f |  2 9 2 8  39 54*  4 8  55 1 4 7 51*  18« 70  57*  5« 41 •  60-  58 *  53  50« 36«  50  62 •  LU.  40 14*  34«  30 2;  63 < 61 '  1  2  20 21* 16 «  10  2 0  1 • 11  1 tm 9  KIT  35*  60 •  40 • -33-*WED  DC  44 6.1.2  SOILS V A R I A B L E S  Axis I, of the TB soils PCA, may represent a nutrient gradient (Table 7). The soils are more basic with a higher percentage of sand at the positive end of the axis and are acidic, hav more nitrogen, organic material and coarse fragments at the negative end of the axis (Table 7). Axis II suggests a textual gradient or thetimesince the site was flooded and mineral soil was covered with a layer of silt and clay (Table 7). The percentage of clay and silt are negatively correlated with axis II whereas the depth to mineral soil, the percent organic matter and the percentage of sand in the soil are positively correlated with the axis. Axis III may be a gradient of drainage conditions or the amount oftimethat quadrats are submerged (Table 7). The  percentage of clay and the depth to mineral soil are positively correlated with the axis (Table 7) As smaller clay sized particles retain water and limit drainage, biological processes may be inhibited and organic matter may accumulate on the surface. There is some separation between the two sites on both axes (Figure 6). The KIT score are mostly positive for axis I and negative for axis II (Figure 6). This suggests that KIT may hav less rich soils and may have been flooded more frequently than WED (Figure 6). The WED site is further from the river and has a blanket of rich alder leaves covering the ground, which supports this interpretation.  45 TB soils - PCA axes relationships. Correlation coefficients relating soils variables to PCA axes l-lll. Axis 1 II III 47.78 18.33 13.99 % variation soils variables Table 7.  -0863 .3885 .6520  .0539 -.0809 -.0999  -8663  -.6363 -.3349 .3409  -.2357 .8882 .0264  nitrogen  -.9189  .0063  -.1492  depth to mineral soil  -.4372  .4992  .4820  PH %coarse %sand %silt %clay organic matter  .7313 -.7825 .6942 -6311 -.0965  The KIT soils PCA axes are similar to the TB data set (Table 8). Axis I may be a nutrie gradient. Total soil nitrogen, the percentage of organic matter and the percentage of sift are negatively correlated with the axis whereas the percentage of sand and pH are positively correlated with the axis (Table 8). Axis II represents a textual gradient or the time since flooding. The percentage of silt is negatively correlated with the axis and the percentages of coarser fragments and clay and the depth to mineral soil are positively correlated with the axis (Table 8). Axis III suggests a drainage gradient or the duration of flooding. The percentage of clay is positively correlated with the axis, whereas, the percentage of coarse fragments are negatively correlated with the axis (Table 8).  46  Figure 6.  Graph of axes I and II of the soils PCA for TB showing individual quadrats.  • 42  *28 • 4  38»30 • 40 • 35*2 • 41 • 8 20 U  26.36 • 10  • 14 • 16  -0.7263  0768 -0.9019  -0.5610  -0.3757 AXIS 1  -0.0262 -0.2005  0.3253 0.1500  0.6768 0.5006  47  Table 8.  KIT soils - PCA axes relationships. Correlation coefficients relating soils variables to PCA axes Mil.  Axis % variation  1 42.79  II 20.84  III 13.55  %sand %silt  .7250 -.4674 .8479 -.6564  -.0978 .6439 .1526 -.6614  -.4170 -.5359 -.1780 .2186  %clay  -.0914  .5730  .5306  organic matter  -.7784  .3758  -.2135  nitrogen depth to mineral soil  -.9133 .2844  .0064 .5596  -.1095 .4520  soils variables PH %coarse  Axis I of the WED soils PCA suggests a nutrient gradient (Table 9). The percentages of soil nitrogen, organic matter, coarse fragments, sift and clay are negatively correlated with the axis, while the pH and sand content are positively correlated (Table 9). Axis II again suggests a textural gradient or thetimesince the site was flooded. The percent sift and day are negatively correlated with the axis and the percent sand is positively correlated with the axis (Table 9). Axis III suggests a drainage gradient or a gradient of standing water, being positively correlated with the depth to mineral soil (Table 9).  4 8  Table 9.  WED soils - PCA axes relationships. Correlation coefficients relating soils variables to PCA axes Mil. 1  II  III  59.96  18.92  10.47  PH  .8346  -.3089  .2380  %coarse  -.7898  .3767  -.3823  %sand  .8030  .5190  .1158  %silt  -.7886  -.5441  -.1002  %clay  -.5112  -.6580  .3830  organic matter  -.8695  .3231  .0118  nitrogen  -.8867  .0989  -.3449  depth to mineral soil  -.6379  .4050  .5879  Axis % variation soils variables  Axis I of the DC soils PCA may represent a drainage gradient. The depth to mineral soil and the clay content are positively correlated with the axis and the sand content and the percentage of organic fragments are negatively correlated (Table 10). The pH is also postively correlated with axis I (Table 10); however, it is consistently acidic at this site and varies little. Most of the soils samples also contained a high percentage of organic matterfromfallen trees and branches. Axis II is correlated with the soil nitrogen content and the percent coarse fragments and organic matter (Table 10). The underlying biological significance of the correlations to Axis II is not clear. Axis III suggests the time since the time site, which is located adjacent to the river, was flooded. The percentage silt has a strong negative correlation with this axis (Table 10).  49  Table 10.  DC soils - PCA axes relationships. Correlation coefficients relating soils variables to PCA axes l-lll.  Axis % variation  1 32.25  II 22.06  III 18.89  .5778  .2860 .6240  -.4400  -.3483 .0480  %clay  -.8243 .2226 .6350  .2835  -.0486 -.9018 .6330  organic matter  -.6801  .5930  .1095  nitrogen depth to mineral soil  -.3813 .5888  .7139 .4777  -.2689 .0544  soils variables PH %coarse %sand %SJIt  -.3988  .1185  Most quacrats to the northeast of the ditch running between the two transect lines have positive scaes on axis I of the DC soils PCA, suggesting that they may have poorer crainage (Figure 7). Those quadrats closer to theriverhave more negative scaes (Figure 7). These quadrats are somewhat less boggy and may be higher in elevation (Figure 7), though th was not measured. Transects are dissimilar (Figure 7).  AXIS 2  P??P?????P??????????P??P  0  m m m in m m m m ni m oooooooooo  0  0  0  °ooooooooooooooooooooooooooo  51  6.1.3  PLANT NEIGHBOURS-PRESENCE/ABSENCE Although the PCA of the total thimbleberry data set of presence/absence data represents  only 30 percent of the total variation on the first three axes (Table 11), these axes can nonetheless be ascribed with environmental processes. Axis I may be a gradient of disturbance due to girdling and a fluctuating water table. The axis is positively correlated with Sambucus racemosa. Osmorhiza chilensis and Epilobium watsonii (Table 11). Sambucus racemosa is characteristic of disturbed sites whereas Osmorhiza chilensis is frequently indicative of a fluctuating water table (Klinka et al 1989). The axis is negatively correlated with Dryopteris assimilis. Tiarella unifoliata and Cornus canadensis (Table 11), which are usually found in moist woods in the Skeena area (Coupe et al 1982). Axis II is negatively correlated with Equisetum arvense. Thelypteris phegopteris. Galium triflorum and Athyrium filix-femina (Table 11), which are characteristic of moist, nutrient rich areas (Klinka et al. 1989). Axis III may be a disturbance gradient representing changing moisture regimes and nitrogen content. The axis is positively correlated with Epilobium anaustifolium (Table 11), characteristic of disturbed sites (Klinka et al. 1989) and Cornus sericea (Table 11), found in flooded areas (Klinka et al. 1989). Streptopus roseusand Gymnocarpium drvopteris are negatively correlated with the axis (Table 11) and common in stable, moist, rich sites (Klinka et al. 1989). The KIT quacrats are more positive on axis I than the WED quacrats (Figure 8). This acrees with the previous interpretation of the axis as a disturbance gradient. There is no separation between the sites on axis II (Figure 8). Quacrats 21 and 38, at KIT and WED, have most negative scores and both are located in wet, sunken areas. Quacrats 1 and 36, which are most positive, are located in open areas which may be subject to flooding and crying. This suggests that axis II may be interpreted as a disturbance gradient due to moisture. Maianthemum dilatatum and Ribes laxiflorum are positively correlated with this axis (Table 11) and both are characteristic of flooded sites and shaded moist stream banks (Klinka et al. 1989).  52 Table 11.  TB plant neighbour© - PCA axes relotionehipo.  Correlation coefficients relating the presence/absence of plant neighbours to PCA axes I-III. I  II  III  12.19  9.40  8.33  Rubus spectabilis  .0854  .2837  .0800  Sambucus racemosa  .5836  -.0510  -.0843  Galium triflorum  .3215  -.5328  .0414  Stachys mexicana  .2714  .0376  .3529  Osmorhiza chilensis  .5129  .2123  -.0035  Cinna latifolia  .2155  .2725  .1789  Epilobium watsonii  .5024  .2251  .0419  Agrostis capillaris (tenuis)  .3440  -.1157  .1807  Picea sitchensis  -.3586  .2739  -.0516  Aruncus dioicus  .3342  -.1595  -.2461  Epilobium anaustifolium  -1255  .2600  .5834  Chamaecyparis nootkatensis  .3194  -.4551  .0202  Ribes bracteosum  -.3301  -.0844  .1160  Dryopteris assimilis  -.5591  -.0492  .2067  Bromus vulaaris  .3570  -.2046  .0640  Salix sitchensis  .4335  .2583  .4013  Oplopanax horridus  -.0270  .1951  -.4560  Streptopus roseus  -.3283  .2372  -.5260  Athyrium filix-femina  .0394  -.5268  -.0014  Gymnocarpium drvopteris  -.1835  -.0395  -.5340  Equisetum arvense  -.0269  -.7446  -.1356  Smilacina racemosa  -.0019  .1305  -.2269  Tiarella trifoliata  -.0089  -.3162  -.2312  Axis % variation species  53 Table 11 continued  Tiarella unifoliata Viola glabella  -.5379 -.3148  -.4020 -.1537  -.3290 .4333  Tellima orandiflora  .4133  -.1849  -1962  Maianthemum dilatatum  -.4351  .3535  -.2194  Clintonia uniflora Circaea alpina Thelypteris phegopteris  -.3917  .0948  -.3827  -.2330 -.1894  -.3989 -.5918  .0624 .1824  Ribes laxtflorum  -.3474  .4359  .0374  Cornus sericea  -.1820  -.0900  .5035  Alnus rubra Vaccinium ovalifolium Cornus canadensis  .0251 -.4858 -.6626  -.2684 -.2419 -.0859  -.2450 .4546 .3205  Axis I of the KIT presence/absence of plant neighbors PCA may represent a disturbance gradient. It is positively correlated with devil's club, Streptopus roseus. Gymnocarpium dryopteris. Tiarella unifoliata and Tiarella trifoliata (Table 12), which tend to occur in rich, moist, less disturbed areas (Klinka et al. 1989). The axis also has a weak negative correlation with Epilobium watsonii (Table 12), which is an introduced species found in open and disturbed areas. Axis II is positively correlated with Galium triflorum. Athyriumfilix-femina.Bromus vulgaris and Equisetum arvense and negatively correlated with Piceae sitchensis (Table 12). No obvious interpretation emerges due to the ecological dissimilarity between these species (Klinka et al. 1989). This distribution of Picea sitchensis. however, could be due to planting; though no records of this were available. Axis III has no strong positive correlations (Table 12) and was not interpreted.  54  Figure 8.  0. 51490 0. 49485 0. 47480 0. 45476 0. 43469 0. 41464 0. 39459 0. 37454 0. 35449 0. 33444 0. 31438 0. 29433 0. 27428 0. 25423 0. 23418 0 . 21412 0. 19407 0. 17402 0. 15397 0. 13392 0. 11388 0 93813E-01 0 73761E-01 0 53709E-01 0 33657E-01 0 13605E-01 -0 64464E-02 •0 28498E-01 ™ - 0 . 46550E-01 w -0 66602E-01 ~ - 0 . 86654E-01 - - 0 . 10671 - 0 . 12676 - 0 . 14681 - 0 . 16686 - 0 . 18691 • 0 . 20696 • 0 . 22702 - 0 . 24707 - 0 . 26712 • 0 . 28717 - 0 . 30722 - 0 . 32728 - 0 . 34733 - 0 . 36738 - 0 . 38743 - 0 . 40748 - 0 . 42754 - 0 . 44759 - 0 . 46764 - 0 . 48789 - 0 . 50774 - 0 . 52779 - 0 . 54785 - 0 . 56790 • 0 . 58795  Graph of axes I and II of the presence/absence of plant neighbors PCA for TB showing individual quadrats.  • 18 • 33  31 • • i g • 12 • 42 • 28  --I -0.5733  1  -0.4483  1 -0.3232  1  -0.1981 .»,,. .  +29  +37  1 -0.0731  0.0520  0.3021 0.1771  0.5522 0.4272  55  Table 12.  KIT plant neighbours - PCA axes relationships. Correlation coefficients relating the presence/absence of plant neighbours to PCA axes I-III.  Axis % variation species Sambucus racemosa Galium triflorum Osmorhiza chilensis  I 19.67  II 18.63  III 11.83  -.3970 -.1548  .3668 .5626  -.1649 -.5009  -.1832  -.3430  Epilobium watsonii  -.4622  -.2936  -.7068 .2127  Agrostis papillaris (tenuis) Picea sitchensis Epilobium ancrustifolium Bromus vulgaris Oplopanax horridus Streptopus roseus  -.3195 -.0248  .4803 -.5994  .0899  -.1238 .1421 .6937 .5574 .1424 .6788 .2461 .6685 .7725 .0456  .1974 .5891 -.3376 -.4177 .6429 -.0104 .7097 -.0115 .2604 .3212  -.4163 -.4879 -.2420 -.2635 .4820 .2274 .0820 -.0618 .0660 " .0132  Athyrium filix-femina Gymnocarpium drvopteris Equisetum arvense Tiarella trtfoliata Tiarella unifoliata Tellima crandiflora  .3235  56  Only axis I of the WED PCA for the presence/absence of plant neighbours, can be fully interpreted. Axis III has no strong negative correlations while the species both negatively and positively correlated with axis II (Table 13) commonly co-occur and do not differ ecologically (Klinka et al. 1989). Axis I is positively correlated with Galium triflorum and Alnus rubra and negatively correlated with Epilobium anqustifotium (Table 13) and may represent a moisture gradient. Both Cornus canadensis and Epilobium angustifolium are only found in open, drier areas of the site and Equisetum arvense and Alnus rubra are commonly found in areas of standing water. Alnus is also an indicator of a fluctuating water table (Klinka et al. 1989). Table 13.  WED plant neighbours - PCA axes relationships. Correlation coefficients relating the presence/absence of plant neighbours to PCA axes I-III.  Axis % variation species Sambucus racemosa Galium triflorum Aorostis captllaris (tenuis) Picea sitchensis Epilobium angustifolium Dryopertis assimilis Oplopanax horridus Streptopus roseus Athyrium filix-femina Gymnocarpium dryopteris Equjsetum arvense Tiarella  trifoliate  l 19.84  II 15.20  III 13.00  .4287 .6096 .2565 -.1399 -.7787 -.3990 -.0814 -.1938 .4027 .2007 .5369 .2378  .0547 .1469 .1690  -.2309 .4766 .3811  -.4197 .2349 .4933 -.6145 -.4485 .2207 -.6562 .2915 .6460  -.2917 -.1067 .4947 .1860 .6517 -.1371 .1813 .4892 .0546  57  Table 13 continued -.3852  -.0143  -.0494  Clintonia uniflora  .1106  -.5627  .5963  Alnus rubra  .7621  .1135  -.1649  Vaccinium ovalifolium  -.4758  .4834  .2704  Cornus canadensis  -.5467  .2591  .4352  Tsuaa heterophvlla  Although the first three axes of the devil's club presence/absence data summarize over 55 percent of the total variation in the data set, only axis I can be ascribed with any underlying pattern (Table 14). Axes II and III have no strong negative correlations with any species (Table 14). Axis I may be a moisture gradient from damp to very moist to wet conditions. Rubus spectabilis. Maianthemum dilatatum and Cornus sericea are all indicators of very moist to wet sites with either fluctuating groundwater or flood conditions (Klinka et al. 1989). The axis is negatively correlated with Gymnocarpium dryopteris (Table 14), which is indicative of fresh to very moist sites, as used by Klinka et al. (1989). Most quacrats are widely separated on axes I and II (Figure 9). Like the graph of the soils scores (Figure 7), quadrats 60, 61 and 63 are at the wet end of the PCA axis and quadrats 46 and 47 are at the negative crier end (Figures 7 and 9). The first axes of these two PCAs are not equivalent, however, as relationships between all quacrats on both ojaphs are not identical (Figures 7 and 9).  58  Table 14.  DC plant neighbours - PCA axes relationships. Correlation coefficients relating the presence/absence of plant neighbours to PCA axes I-III.  Axis % variation  I 27.23  II 18.13  III 10.16  .7378 .4870 -.0415 .6314  -.4237 .6877 .7604  .4150 -.1108 .0406  .2450  -1358  -.4705 .4376  .3535  .5595  .0990  -.1886  -.7630 -.2106 .6930 -.1866  -.2359 .2546 -.4262 .2869  .6776 -.4406 .4320 -.0090 .4514 .5788  .2762 .0767 -.0638 .1340 .7035 -.4367  -.1837 -.0111 -.1148 .0285 .2235 -.1213 .5034 .6875 -.2773 -.2479  species  Rubus spectabilis Galium triflorum Osmorhiza chilensis Acrostis capillaris (tenuis) Dryopteris assimilis Actaea rubra Gymnocarpium oryopteris Streptopus roseus Tiarella unifoliata  Viola glabella Maianthemum dilatatum Clintonia uniflora Circaea alpina Streptopus amplexifolius Matteuccia struthiopteris  Cornus serjcea  59  Figure 9.  Graph of axes I and II of the presence/absence of plant neighbors PCA for DC showing individual quacrats.  0. 0. 0. 0. 0. 0. 0 0 0. 0. 0 0 0. 0. 0. 0. 0. 0 0 0. 0. 0  35023 33885 32708 3155) 30393 29236 28079 26922 25764 24607 23450 22293 21135 19978 18821 17864 16506 15349 14192 13035 11878 10720 0 95630E-01 8405SE-01 0 0 72486E-01 0 60913E-01 0 49341E-01 0 37768E-01 0 261B8E-01 0 14824E-01 0 30S12E-02 -0 85212E-02 -0 20094E-01 -0 31668E-01 -0 43238E-01 •0 54811E-01 -0 66383E-0I -0 77956E-0! -0 89528E-01 -0 10110 -0 11267 -0 12425 -0 13582 -0 14739 -0 15896 -0 17053 -0 18211 -0 19368 •0 20525 -0 21682 -0 22840 -0 23997 -0 .26154 -0 26311 -0 27469 -0 .28626  •62 •63  '43 -0.0365 AXIS 1  .81  60  6.1.4 PLANT NEIGHBOURS-FREQUENCY Like the presence/absence PCA fa the TB data, the PCA axes fa the frequency data reflect disturbance. Axis I of the TB PCA of the frequency of plant neighbours data, is positively correlated with Sambucus racemosa and Epilobium watsonii and negatively correlated with Tiarella unrfoliata. Clintonia unrflora and Conus canadensis (Table 15) and suggests a disturbance gradient. Axis II may also be a disturbance gradient. Epilobium angustifolium and Cornus canadensis are positively correlated with the axis (Table 15) and are indicative of disturbance and poor nutrient conditions respectively (Klinka et al. 1989). The axis is negatively correlated with Streptopus roseus. and Tiarella trifoliate (Table 15), which are characteristic of rich, moist, somewhat stable areas (Klinka et al. 1989). As axis III has no positive correlations (Table 15), an ecological gradient was not ascribed to the axis.  The graph of axes I and II scores corroborates the interpretation that axis I is a disturbance gradient and may be due to girdling (Figure 10). Most quadrats, numbered from 1 to 21 at the KIT a ungjdled alder site, have positive scores and most quadrats, numbered from 22 to 42 at the WED a ungjdled alder site, have negative scores. Although axis II was also interpreted as a disturbance gradient, it is not a gradient due to girdling. Both KIT and WED quadrats have positive and negative scores on the axis (Figure 10). Table 15. Axis  TB plant neighbours - PCA axes relationships. Correlation coefficients relating the frequency of plant neighbours to PCA axes I III. I II III  % variation species Rubus spectabilis  12.21  8.80  7.91  .4160  -.0378  -.2049  Sambucus racemosa Galium triflorum Stachvs mexicana Osmorhiza chilensis  .5355 .2516 .3035 .3621  -.3650 .0930 .2529 -.3255  .2805 -.5276 -.1424" .2141  61  Table 15 continued  Cinna latifolia Epilobium watsonii Acrostis capillaris (tenuis)  .2682 .5148 .4727  .0118 -.0612 .1477  .1410 .0784 -.4046  Picea sitchensis Aruncus dioicus  -.3167  -.0215  .1513  -.4686  .1197 -.0590  Epilobium anqustifolium  .0755  .6146  .1801  Chamaecyparis nootkatensis  .2411  -.0454  -.0804  Ribes bracteesum  -3318  Dryopteris assimilis  -.4366 .3106 .4977 -.2871 -.3703 -.0591 -.4391 -.2537 .0678  .2678 .0319 .1818 .1439  .0453 .1031 -.0924 .0065  -.3843 -.5361 -.0671 -.4823 -.1628 .0187  .3111 .2861 -.4242 .0907 -.7616 .2891  -.5540 -.2777 -.4121 -.1399 .4145 .1631  .0706 -.2931 -.0419 -.0029 .1135  Maianthemum dilatatum  -.1572 -6904 .2170 .3297 -.3330 -.3267  Clintonia uniflora  -.5065  -.0540  .0890  Circaea alpina  -.1981  -.0642  -.3927  Bromus vulaaris Salix sitchensis Oplopanax horridus Streptopus roseus Athyrium filix-femina Gymnocarpium drvoDteris Equisetum arvense Smilacina racemosa Tiarella trifoliata Tiarella unifoliata Viola glabella Tellima crandiflora  Tsuga heterophylla  .4595  62  Table 15 continued Thelypteris pheqopteris Ribes laxiflorum Cornus sericea Alnus rubra Vaccinium ovalifolium  -.2368 -.1527 .0024 -.2634 -.3666 -.5536  Cornus canadensis Figure 10.  .0071 .3487  -.3634 .4555  .2939 -.2741 .2743  -.1479 -.4263 -.2424 -.0645  .5168  Graph of axes I and II of the frequency of plant neighbors PCA for TB showing individual quadrats.  0. 56028 0. 53530 0. 51032 0. 48534 0. 46036 0. 43638 0. 41040 0. 38542 0. 36044 0. 33546 0. 31048 0. 28550 0. 26052 0. 23554 0. 21056 0. 18557 0. 16059 0. 13561 0. 11063 0. 85654E-01 0. 60674E-01 0. 35693E-01 0. 10713E-01 -0. 14267E-01 -0. 39247E-01 -0. 64228E-01 -0. 89208E-01 <* -0.11419 oi -0. 13917 S-o. 16415 < -0. 18913 -0. 21411 -0. 23909 -0. 28407 -0. 28905 -0. 31403 -0. 33901 -0. 36399 -0. 38897 -0. 41395 -0 43893 -0 46391 -0 48889 -0 51387 -0 53885 -0. 56383 -0 58881 -0 61379 -0. 63877 -0. 66375 -0 68873 -0 71371 -0. -0. 73869 -0 76367 -0 78865 81363  • 33 •42  • 40 • 37 • 17 27.28  OO 18^31 • 38 •6  -0.7354  -0.5905  -0.4456  -0.3007  -0.1558 , A  X  I  S  -0.0109  0.1339  0.2788  0.4237  0.5886  0.7135  63 Axis I  of the KIT frequency PCA may, like axis I of the TB frequency data, represent a  disturbance gradient. Streptopus roseus. devil's club, Gymnocarpium dryopteris. Tiarella trifoliata and Tiarella unifoliata. which are negatively correlated with the axis (Table 16), tend to occur in rich, moist, less disturbed areas (Klinka et al. 1969). Acrostis capillaris (tenuis) is generally found at open, more disturbed quacrats at KIT. Axis II is positively correlated with Galium triflorum. Epilobium anaustifolium and Bromus vulgaris and negatively correlated with Sambucus racemosa (Table 16) and may represent a ligjit gradient of illumination. Bromus is  found in quacrats with less cover at this site and Epilobium is shade intolerant while Sambucus is known to be shade tolerant (Klinka et al. 1989). Axis III has no strong negative correlations (Table 16) and was therefore not interpreted. Table 16.  KIT plant neighbours - PCA axes relationships. Correlation coefficients relating the frequency of plant neighbours to PCA axes scores I-III.  Axis % variation species Sambucus racemosa Galium triflorum Osmorhiza chilensis Epilobium watsonii Acrostis capillaris (tenuis)  I 31.01  II 14.33  III 12.39  .0669 .4897 -.0506 .2904 .7158  Picea sitchensis Epilobium anaustifolium Bromus vulgaris Oplopanax horridus Streptopus roseus  -.3338 .4121 .2747 -.6557 -.7550  -.5211 .6031 .0098 -.4081 .1431 -.0026 .6946 .5470 .4664 .0793  .5119 .2689 -.2953 -.2893 " .1094 -.1899 .4461 .4804 -.3577 -.1237  64  Table 16 continued Athyrium filix-femina Gvmnocarpium dryopteris Equisetum arvense Tiarella trifoliata Tiarella unifoliata Tellima grandiflora  -.0155 -.7266 -.1027 -.7690  -.4442 .1230 -.1935 .0083  .5610 .1250 .4544  -.8198  .1149  .2299  -.4407  .3241 -.1264  .4185  Axis I,of the WED data set, is a moisture gradient. The axis is negatively correlated with Epilobium angustifolium (Table 17), which is characteristic of open moist, disturbed sites and positively correlated wrth Alnus rubra. Equisetum arvense and Galium triflorum (Table 17). Equisetum arvense and Alnus rubra co-occur in areas of standing water at the WED site. Alnus rubra also shades and cools the site reducing surface evaporation and is an indicator of a fluctuating water table (Klinka et al. 1989). Biological significance cannot be readily ascribed to axis II. The axis is positively correlated with devil's dub, Streptopus roseus. Gymnocarpium dryopteris and Clintonia unrflora and negatively correlated with Athvriumfilix-femina(Table 17). All these spedes co-occur. Klinka et al. (1989) also states that Gymnocarpium dryopteris and Streptopus roseus are indicative of damp, very moist soils, devil's dub and Athyrium filix-femina are indicative of very moist to wet soils, and Clintonia uniflora is indicative of moderately dry to damp soils. Axis III may be a light gradient of illumination. It is positively correlated with Aorosti tenuis (tenuis) and Epilobium angustifolium (Table 17), a shade intolerant spedes (Klinka et al. 1989) and negatively correlated with Sambucus racemosa (Table 17), a shade tolerant spedes (Klinka et al. 1989).  65  Table 17.  WED plant neighbour - PCA axes relationships. Correlation coefficients relating the frequency of plant neighbours to PCA axes scores I-III.  Axis % variation  I 24.50  II 15.91  III 13.43  species Sambucus racemosa  .0686  -.0742  -.8424  Galium triflorum Acrostis capillaris (tenuis)  .5780  -.1158  .2318  .4459  -.3469  .6402  Picea sitchensis  -.1382  .3635  .0003  Epilobium anaustifolium Drvopertis assimilis Oplopanax horridus Streptopus roseus Athvrium filix-femina Gymnocarpium crvopteris Equisetum arvense Tiarella trifoliata Tsuaa heterophvlla Clintonia uniflora  -.7949 -.1055  -.0125 -.0024  .5105 -.0629  .3095 .2356 .2786 .3697 .7074 .1820 -.2277 .2526  -.0884 .1514 -.3210 .1419 .4144 -.0250 -.3255 -.1290  Alnus rubra Vaccinium ovalifolium Cornus canadensis  .7595 -.0763 -.4103  .6046 .6007 -.6690 .8120 -.2385 -.4410 .1532 .5182 .1162 -.1215 .0032  .1152 .1048 .1977  The first three axes of the devil's club frequency of plant neighbours data represent almost sixty-five percent of the total variance in the data (Table 18). Axis I is a moisture gradient which is positively correlated with Gymnocarpium dryopteris (Table 18), an indicator of damp to very moist conditions (Klinka et al.1989). Galium triflorum. Actaea rubra. Maianthemum  66  dilatatum. Cornus sericea and Acrostis capillaris (tenuis) are negatively correlated with this axis (Table 18). Both Cornus sericea and Maianthemum dilatatum are tolerant of a fluctuating water table or flood conditions and indicate very moist to wet conditions (Klinka et al. 1989). Axis II is strongly correlated with Dryopteris assimilis (Table 18), an indicator of rich damp to very moist sites (Coupe et al. 1982, Klinka et al. 1989). Circaea alpina is aso correlated with the axis (Tabl 18) and is found on moist, nutrient medium sites (Klinka et al. 1989). As this axis has no negative correlations greater than .5, it was not interpreted further (Table 18). Axis III has positive correlations with Osmorhiza chilensis and Smilacina stellata and is negatively correlated with Rubus spectabilis (Table 18). These species, however, are ecologically similar (Klinka et al. 1989) and a biological interpretation was not ascribed to the axis. Table 18.  DC plant neighbour - PCA axes relationships. Correlation coefficients relating the frequency of plant neighbours to PCA axes I III.  Axis % variation species Rubus spectabilis Galium triflorum Osmorhiza chilensis Acrostis capillaris (tenuis) Dryopteris assimilis  Actaea rubra Gymnocarpium cryopteris Smilacina stellata Tiarella unifoliata Viola dabella Tsuaa heterophylla Maianthemum dilatatum  I  II  III  36.36  14.68  13.43  -.3625 -.7618 -.2362 -.7527 .2197 -.6274 .8485 -.0805  -.1487 .0906 .2648 -.2176 .9365 -.1904 -.2115 -.0302  -.7717 .0848  -.2662 -.2093 .0705 -.8504  -.1623 .0176 .3043 -.1068  -.4137 .3972 .0656 -.2139  .5465 -.3105 .0020 .4649 .0275 .5514  67 Table i s continued  Clintonia uniflcra  .2543  -.1435  .2580  Circaea ajpina  -.2004  .6742  -.3596  Streptopus amplexrfolius  .1202  .3608  .0103  Matteuccia struthiopteris  .1949  -.0015  .0417  Cornus sericea  -.7692  .0764  .4569  Like the DC presence/absence graph (Figure 9), quadrats 61 to 63 and quadrats 46 and 47 are at opposite extremes of the moisture gradient on the graph of the axes scores (Figure 11). The quadrat relationships shown in the graph, however, do not suggest an ecological interpretation of axis II. Figure 11. 0.87137 0.84688 0.82239 0.79790 0.77341 0.74892 0.72443 0.69995 0.67546 0.65097 0.62648 0.60199 0.57750 0.55301 0.52852 0.50403 0.47955 0.45506 0.43057 0.40608 0.38159 0.35710 0.33261 0.30812 0.28364 0.25915 0.23468 0.21017 <u 0.18568 „ 0.16119 S 0.13670 < 0.11221 0.87724E 0.63235E 0.38747E 0.14258E •0.10231E •0.34720E -0.59209E -0.83698E -0.10819 -0.13267 -0.15716 -0.18165 -0.20614 -0.23063 -0.25512 -0.27961 -0.30410 -0.32859 -0.35307 -0.37756 -0.40205 -0.42654 -0.45103 -0.47552  Graph of axes I and II of the frequency of plant neighbors PCA fa DC showing individual quadrats. • 59  •55  •50 • 46  01 01 01 01 01 01 01 01  -13465  -1.1414  -0.9363  -0.7311  -0.5260 xlS 1 A  -0.3208  -0.1157  0.08S5  0.2946  0.4997  0.7049  68  6.2 MORPHOLOGY 6.2.1 UNIVARIATE ANALYSES AND GRAPHS Although the number of thimbleberry flowers per stem was not significantly different between KIT and WED in 1987 (Appendix I), many more quadrats lacked flowering stems at WED as compared to KIT (Figure 12). At those quadrats at WED with flowers, there were fewer flowering stems and therefore fewer flowers and fruit (Appendix 1). The number of flowers per quadrat did not differ between sites in 1986, whereas differences between years were significant (Appendix 1). Figure 12.  Number of thimbleberry flowers per quadrat at WED and KIT on June 22.1987.  22  24  26  28  30  32  34  36  38  40  42  QUADRAT  Annual differences and site differences were also apparent for the number of thimbleberry stems per quadrat. In 1987 more stems were produced at both thimbleberry sites than in 1986 (Appendix 1). Stem density was also higher at KIT than WED fa two-year-old stems in 1987 (Appendix 1). One-year-old stem density in 1987 and the density of all stems in 1986 did not differ significantly between sites (Appendix 1).  69  The number of leaves produced by thimbleberry also varied, with site differences and stem differences observed (Appendix 1). In both 1986 and 1987, the number of leaves produced by one-year-old, two-year-old stems and branches differed (Appendix 1). Two-yearold stems had most leaves and branches had least. In 1987, site differences were also apparent, though these were not observed in 1986 (Appendix 1). KIT had more leaves per stem than WED in 1987 (Appendix 1). The number of leaves per stem did not differ between years (Appendix 1). Differences in stems length were also observed for thimbleberry. One- and two-year-old stems are significantly longer than branches in both 1986 and 1987 (Appendix 1). Stems, however, did not differ in length; neither did they differ between years, though in 1987 two-yearold stems differed between sites (Appendix 1). The number of devil's dub flowers per quadrat dd not differ in 1986 and 1987 (Appendix 1) and little variation is apparent between years (Figure 13). Only 1987 values were used to apportion variation between spedes or to correlate morphology with environment. Unlike thimbleberry, the number of devil's dub stems per quadrat does not annually fluctuate (Figure 14)(Appendx 1). Only six new stems were produced from 1986 to 1987. None of the quacrats with less than five stems increased in stem number (Figure 14). Stems and branches also produced the same amount of new growth at the growing tip in each quadrat, though stems are significantly longer than branches (Appendx 1). There is also no difference between years for the number of leaves per stem or per branch (Appendx 1).  70  Figure 13.  Number of devil's dub racemes per quadrat in 1986 and 1987. All filled cirdes indcating a negative number of inflorescences show quadats which were not sampled in 1986. (o • quacrats sampled in 1987, •« quadrats sampled in 1987, quadrats sampled in both 1986 and 1987)  "T  oo  1  1  1  1  1  1  1  r  LU  O  LU CQ LU UJ DC  co  O O  2  P 2  ©  o  o  o  o  NOT SAMPLED  —i  42  1  44  1  L  46  ©  • ©  ••••  0  O  .  I  48  1  O •  o  O •  O  o ©  o o  o  •••••• I  50  .  I  52  .  I  54  i  '  • © •  • •  56  i  58  •  '  •  « • O  i  60 - 62  1986&1987 1986 1987  64  QUADRAT NUMBER  Figure 14.  Total number of stems per quacrat for devil's dub on June 30,1986 and 1987.  71  6.2.2 PRINCIPAL COMPONENT ANALYSES PCA was used to generate scores to subject to ANOVA. This was done to summarize the data set and to capture some of the interactions among the morphological variables. These  PCAs accounted for at least 70 percent of the variation in the data on the first three axes (Table 19). Table 19.  Percent variance explained by PCA axes of the morphological data used in ANOVA only: 1) 1986 and 1987 Thimbleberry data 2) 1986 and 1967 Devil's club data 3) 1987-bom thimbleberry and devil's club data 19868t7TB  Axis I Axis II Axis III  44.20 14.12 10.91  1986 & 7 D C  1987DC&TB  46.68 17.02 13.40  31.30 30.49 13.00  The PCA of me me 1987 TB data summarized 67 percent of the total variation on me fir three axes (Table 20). Axis I has a positive correlation greater than .5 with all morphological variables except for me number of stems per quadrat and the number of branch leaves (Table 20). This axis represents me size and the number of parts on each stem. Axis U is a gradient o stem number per quadrat. It is strongly positively correlated with the number of one-year-old and two-year-old stems (Table 20). Axis III is positively correlated with me number of branch leaves (Table 20). No other correlations between axis III and me input data are greater than .5 and me axis was not considered to reflect sufficient variation for interpretation (Table 20).  72  Table 20.  Mcrphology - PCA axes relationships for TB data in 1987. Correlation coefficients relating morphological data to PCA axes I - III.  Axis % variation morphological variables  I 47.89  II 9.98  III 9.03  "branches/stem • flowers/inflorescence  .7059 .6628 .6803  -.2951 .1117  -.1066 .1602 -.0279  .6215  -.0580 -.1229 -.0827 .7106  * flowering stems » flowers/quadrat  .1926  .2702  # 2-year-old stems # 1-year-old stems # leaves/quadrat * leaves/branch  .3980 .2420 .7970 .4931  .1072 .6831 .7493 .2666 -.0674  * leaves/1-year-old stem  .7400  -.2955  -.4086  • leaves/2-year-old stem 1-year-old stem length 2-year-old stem length  .6366  .2920  -.0202  .8277 .8502  -.2349 -.0038  -.3735  1-year-old stem diameter  .6948  -.1824  -.4303  2-year-oJd stem diameter  .8910  -.0378  -.0544  branch length branch diameter  .7302 .7808  -.1645 -.2368  .4253 .3571  -.0341  73  l or the KIT P C A Tor 1907 is a vegetative size gradient representing the size OT stems and branches and the number of parts attached to them at each quadrat. The axis is positively correlated with stem and branch lengths and diameters, the number of leaves per quadrat and per one-year-old stem and the number and the number of flowers per inflorescence (Table 21). Axis II has positive correlations with the number of branch leaves and the branch length and h a weak negative correlation with the number of flowering stems per quadrat (Table 21). Axis III is a reproductive axis which is correlated with the number of flowers per inflorescence, the number of flowers per quadrat and the number of two-year-old stems (Table 21). This PCA accounts for almost 65 percent of the raw variation on thefastthree axes (Table 21). AXIS  Table 21.  Mcrphology-PCA axes relationships for KIT data in 1987. Correlation coefficients relating morphological data to PCA axes I - III.  Axis % variation morphological variables * branches/stem ft flowers/inflorescence ft flowering stems ft flowers/quadrat ft 2-year-old stems ft 1-year-old stems ft leaves/quadrat ft branch leaves ft leaves/1 -year-old stem  I 37.45  II 14.57  III 12.35  .6594 .5738 .2951 .4503 -.0911 -.2405 .7400 .2623  -.2730 -.1298 -.5085 .1050 -.3650 -.4614 .1772 .6709  -.3827 .6628 .1207 .6818 .6258 .1539 -.0624 .5274  .7322  -.3853  -.1976  74  Table 21 continued * leaves/2-year-old stem  .4915  .3651  -.1199  1-year-old stem length  .8147  -.3958  -.1954  2-year-old stem length  .8787  -.0617  .2089  1-year-old stem diameter  .7043  -.4507  -.0412  1-year-old stem diameter  .8779  .1167  -.0211  branch length  .5025  .5045  -.1429  branch diameter  .7232  .4795  -.2353  The PCA of the WED morphological data in 1987 summarizes over 70 percent of the total variation on the first three axes (Table 22). Axis I is positively correlated with the length and diameter of one- and two-year-old stems and branches, the number of branches per stem, the number of leaves per branch, per one-year-old stem and per quadrat and the number of flowers per inflorescence and the number of flowering stems (Table 22). Like axis I of the TB data, this axis may summarize the size of stems and branches and the number of their component parts at each quadrat. Axis II is positively correlated with the number of leaves on two-year-old stems and the number of two-year-old stems per quadrat and negatively correlated with the number of branch leaves and branches per stem and branch sizes (Table 22). This axis reflects differing vegetative possibilities of a two-year-old stem. Axis III is a reproductive gradient. It is negatively correlated with the number of flowers per quadrat (Table 22). Together axes II and III summarize the vegetative and reproductive alternatives of the two-year-old stems; to flower or to branch.  75  Table 22.  Morphology-PCA axes relationships for WED data in 1987. Correlation coefficients relating morphological data to PCA axes I - III.  Axis % variation morphological variables » branches/stem # flowers/inflorescence  I 41.70  II 18.56  III 10.90  .6106  -.6583  .1105  .6790  .0702  -.3300  # flowering stems  .6981  .1972  -.3724  # flowers/quadrat * 2-year-old stems » 1-year-old stems # leaves/quadrat tt branch leaves  .3958 .3061 .4146 .6308 .7012  .2258 .5643 .3964 .6315 -.5635  -.7458 .5664 .2956 .3329 -.0841  tt leaves/1-year-old stem  .6058  -.1532  .3810  * leaves/2-year-old stem 1-year-old stem length 2-year-old stem length  .4889  .7406  -.1915  .7864 .8105  -.0882 .1536  .3283 -.1498  1-year-old stem diameter  .6370  .1768  -.2713  1-year-old stem diameter branch length branch diameter  .8526  .1447  .0805  .6616 .7663  -.5760 -.5120  .0356 .0933  76  The first three axes of the 1987 DC PCA account for 73 percent of the total variation in the raw data (Table 23). Axis I is positively correlated with the number and size of branches, stem sizes, the number of branch leaves and the number of flowers per quadrat (Table 23). The axis focuses on older stems and their component vegetative parts. Axis II is positively correlated with the number of stems and negatively correlated with the number of branches per stem and the stem length (Table 23). Larger older stems often have more branches while in quadrats with many stems, the stems were often younger and smaller. Axis III is negatively correlated to the stem length increment and the number of flowers per quadrat (Table 23). Table 23.  Morphology-PCA axes relationships for DC data in 1987. Correlation coefficients relating morphological data to PCA axes I - III. I  II  III  40.82  21.70  10.56  # branches/stem  .6018  -.6517  .1858  # flowers/quadrat  .5872  .3003  -.5590  # stems  -.0890  .6927  .0541  tt leaves/stem  .2897  .3819  .3021  tt leaves/branch  .6986  .4210  .3073  stem length increment  .1224  -.3095  -.7101  stem length  .6816  -.6706  .1710  stem diameter  .7375  -.4927  .0612  branch length increment  .7756  .4145  -.1890  branch diameter  .8842  .1911  -.1689  branch length  .8925  .2697  .1572  Axis % variation morphological variables  77  6.Z.3 PARTITIONING OF VARIATION  For both devil's club and thimbleberry, little variation was accounted for by differences between years suggesting that annual temporal heterogeneity actually influenced the two species very little in 1986 and 1987 (Table 24). Large-scale spatial heterogeneity had little effect upon thimbleberry morphology (Table 24), the percent variation accounted for by site differences being only 15 percent (Table 24). Most of the variation in the total data set was accounted for by within-site influences (error term)(Table 24). Species were not included in the previous comparison because only one site was sampled for devil's club. The percentage of variation due to species differences was only 20 percent (Table 24), suggesting that there was more variation within each species for the parameters measured than between the two species.  Variation within- and among-quadrats was analyzed to further characterize the patterns of observation. Only stem length and the number and size of leaves were analyzed because sample sizes were greater than 3 at all quadrats, and each sample could be obtained from mo than 1 stem, thereby measuring variation within genets rather than within ramets. In most case the majority of variation is within quadrats suggesting environmental or developmental influences were a large component of the morphology.  78  Table 24.  Mophdogical variation apportioned between years, species, sites and within and among quadrats by ANOVA.  Years Sites Error Species  Devil's Club 3.88% n/a 95.81%  Thimbleberry 6.29% 15.74% 75.88% Error  80.66%  KIT  WED  DC  16.84 83.16  21.43 78.57  23.04 76.96  53.24 46.76  48.25 51.75  18.77 81.23  10.57 89.43  9.87 90.13  16.30 83.70_  16.10 83.90  8.63 91.37  18.39 81.61  20.36%  Within and among Quadrats 1987 stem leaves among erra 1987 stem length among erra 1986 leaf length among erra 1986 leaf width among erra  79  0.Z.4  MORPHOLOGICAL-ENVIRONMEr\fTAL RELATIONSHIPS  Mcrpbdogical PCA axes are not strongly correlated with 1986 or 1987 canopy cover (Table 25). Table 25.  PCA Axis I II III  Canopy cover-morphological PCA relationships. Correlations between canopy cover in 1986 and 197 and morphology axes for KIT, LWED, TB and DC (* .P<05). TB 1987 cover -.4059* -.1004 -.1033  KIT 1987 cover 1986 cover .3083 -.4891* -.1262 -.0013 -.0808 -.2709  WED 1986 cover 1987 cover II III  -.0607 -.1015 .4669*  -0229 .0419 .4630*  1986 cover -.1349 -.1429 -.3770 DC  1987 cover .3083 -.1262 -.0808  1986 cover .1349 -.1429 -.3770  Although canonical correlation coefficients are large (Appendix III), CANCORR indicates little if any relationship occurs between the devil's dub and thimbleberry morphdogy and the sdls matrices (Table 26)(Appendix III). Table 26.  Summary of relationships between TB, KIT, WED and DC morphdogical variables and envronmental charaders. (+) positive relationship, (-) no relationship. SOILS CANCORR WMDS  TB KIT WED DC  PLANT NEIGHBOURS FREQUENCY PRESENCE/ABSENCE CANCORR WMDS CANCORR WMDS  80  Both CANCORR and WMDS imply a relationship between the TB and WED morphology andfrequencyof plant neighbours matrices (Table 26). The first TB morphological canonical axis is positively correlated with the first PCA axis (Appendix III), a gradient of stem size and number and size of stem component parts (Table 20). The firstfrequencycanonical axis is positively correlated with the first PCA axis (Appendix III), a disturbance gradient due to girdling (Table 15). The third PCA axis was also correlated with this canonical axis, however, this could not be interpreted (Table 15). When both TB sites are examined together, larger stems with more leaves and flowers and more and longer branches are found at more disturbed sites. Nondisturbed quadrats have smaller stems with fewer smaller vegetative component parts.  At WED the second canonical axis par is correlated with the first PCA axes for both morphology andfrequencyof plant neighbours (Appendix III). The first WED morphology PCA axis is correlated with the length and diameter of one- and two-year-old stems and branches.the number of branches per stem, the number of leaves per branch, per on-year-old stem and per quadrat and the number of flowers per inflorescence and per stem (Table 22). The first plant neighbourfrequencyPCA axis is a moisture gradient (Table 17). CANCORR therefore suggests that in drier quadrats one and two year old stems are larger, have more, larger branches, mor leaves and more flowers than in wetter quadrats. The CANCORR of the morphological and presence/absence of plant neighbours matrices all indicate little relationship between each matrix par, except for the TB data set (Table 26). The size of stems and the number and size of stem components increases as moisture or flooding increases. The morphological canonical variables are most strongly correlated with PCA axis I (Appendix III), which is positively correlated with stem size and the number and size of stem components (Table 20). The presence/absence canonical variables are correlated with the second PCA axis (Appendix III), a disturbance gradient due to moisture (Table 11).  81  6.3  PHENOLOGY  6.3.1  UNIVARIATE ANALYSES AND GRAPHS In 1986 the rate of vegetative development appears similar at both thimbleberry sites  (Figure 15) and the slopes of regression were not significantly different between sites (Appendix II). In 1987, on the other hand, the vegetative development appeared to occur more rapidly at KIT than WED (Figure 15) and the slopes of regression lines for vegetative phenological codes were significantly different (Appendix II). Thimbleberry was not vegetatively different between years (Appendix II) suggesting that annual climatic differences may have little affect on the rate of development. Significant differences were found between the vegetative phenologies of one-year-old and two-year-old stems and branches, however (Appendix II). Figure 15.  Vegetative phenological codes at KIT and WED in 1986 and 1987. The numbers within the circles represent the number of quacrats with the code on the y-axis at the sampling time.  a) KIT-1986 LU  Q O  o < o o o LU X CL  # # *> ^ v  DATE  v  PHENOLOGICAL C O D E  PHENOLOGICAL C O D E  _L  CO  01  -vl  CD  -x  83  Figure 15 continued d) WED-1987 LU Q  O  o _l < o  CD  O _i  o z  LU X  0_  jP-&>^  A ^ A *  ^-Afp'  Unlike thimbleberry, the slopes of regression lines of devil's dub vegetative phenology differed between years (Appendix II) and phenology may have initially proceeded more rapidly in 1986 than 1987 (Figure 16). Devil's dub stems and branches were not vegetatively different (Appendix II).  Figure 16.  Vegetative phenological codes fa devil's dub in 1987 and 1987. The number within the crdes represent the number of quadrats with the code on the y-axis at the sampling time.  a) DC-1986 LU Q  O O _i <  o o o_ i o  LU X CL  jfr  x<y-  DATE  \  Q  ^  84  Figure ie continued b) DC-1987  Reproductive phenologies differed between thimbleberry sites in both years (Appendix II). In 1986 at WED few plants bore fruit (Figure 17), and of those that did many became moldy At KIT most plants flowered and produced fruit (Figure 17). Flowering and fruit ripening occurred at a rapid rate in 1986 (Figure 17). By the end of June, most plants at KIT had begun bloom and within two weeks the same flowers had faded and had developed fruit (Figure 17). During this short period when flowers were opening, variability between quadrats was substantial (Figure 17). In 1987, although KIT again produced most fruit, WED had more flowers than in 1986. Flowers were also seen earlier at KIT than at WED (Figure 17). By June 21, most plants were full bloom (code 7) or fading at KIT while at WED flowering was just beginning (code 4)(Figure 17). These differences between sites continued and although most plants bore fruit at both sites by July 26, at KIT some fruit had already ripened (Figure 17).  85  Reproductive phenologies were also different between years (Appendix II), with  flowering beginning two or three weeks earlier in 1987 (Figure 17). Fruit also matured earlier in 1987 (Figure 17). Few plants had ripe fruit by mid-August in 1986 (Figure 17). Figure 17. Reproductive phenological codes at KIT and WED in 1986 and 1967. The number within the circles represent the number of quacrats with the code on the y-axis at the sampling time. a) KIT-1986 LU Q  o o _l < g o o _i o z  LU X  0_  DATE  b) WED-1986 LU Q  O O  _J <  i  7  o  5  o  3  UJ X Q_  ©  9  g _i  i  11  © ®  (D  ©© •  •  ©  -  1  © <D  © © 1  J  §y  *y DATE  •  86  Figure 17 continued c) KIT-1987 LU Q  o o < g o o _i o  LU X CL  DATE  d) WED-1987 Q  1 1  o < g o Q  7 «  LU X  DATE  Unlike thimbleberry, devil's club branches and stems did not exhibit different reproductive phenological rates (Appendix II). Like thimbleberry though, devil's club reproductive phenology was significantly different between 1986 and 1987 (Appendix II), with  87  development occurring more rapidly in 1986 (Figure 18). In both years floral development was rapid (Figure 18). Figure 18.  Reproductive phenological codes for devil's club in 1986 and 1987. The number within the circles represent the number of quacrats with the code on the y-axis at the sampling time.  a) DC-1986 HI  Q O O _] <  g  o o _i o z  LU X  0_ ^  #  jf** DATE  b) DC-1987 IXI Q  o O  < o (D Oi o LU CL  11'  9 7 5-  3 1  DATE  /  /  88  In all cases of regression of vegetative phenology at each quadrat coefficients of determination were greater than .80 and with the exception of 15 quadrats in both 1986 and 1987 all were greater than .85. Reproductive phenology for each quadrat was not as efficiently summarized by a regression equation. Seven quadrats had r values between .7 and .8 though, in most cases, the simple regression represented greater than 90 percent of the variation in the total data set. 2  89  6.3.2  PRINCIPAL COMPONENTS ANALYSES The PCA of the thimbleberry data reduce the data to three axes which represent over 90  percent of the variation in the total data set. Axis I is highly correlated with reproductive phenology in both 1986 and 1987 and vegetative phenology in 1987 (Table 27). Axis II is strongly correlated with the vegetative phenology in 1986 (Table 27). Axis III is not strongly correlated with any raw variable (Table 27). Table 27.  Phenology - PCA relationships for TB data. Correlation coefficients relating phenological data to PCA axes I - III.  Axis  I  II  III  56.16  25.54  11.24  reproductive 1987  .8962  .1353  .0737  vegetative 1987  .8019  -.3491  .4250  reproductive 1986  .8226  -.1991  .5116  vegetative 1986  .3516  .9174  .0398  % variation phenological variables  As with the TB PCA data set, KIT PCA represents over 90 percent of the variance of the whole data set on the first three axes (Table 28). Axis I is positively correlated with the vegetative and reproductive phenological codes for 1986 and 1987 (Table 28). Axis II is positively correlated with the reproductive phenology and negatively correlated with vegetative phenology in 1986 (Table 28). Axis III is a vegetative axis and is positively correlated with the vegetative phenology in 1986 (Table 28).  90 Table 28.  Phenology - PCA relationships for KIT data. Correlation coefficients relating phenological data to PCA axes I - III.  Axis  I  II  III  55.11  23.11  14.73  reproductive 1987  .8598  -.2103  -.2857  vegetative 1987  .7136  .3461  .6048  reproductive 1986  .7128  .5457  -.3608  vegetative 1986  .6691  -.6803  .1065  % variation phenological variables  Axis I, of the WED PCA, is correlated with all the phenological variables, especially the phenology in 1987 (Table 29). Axis II summarizes the 1986 phenological data being negatively correlated with the 1986 vegetative phenology and positively correlated with the reproductive phenology (Table 29). Axis III is negatively correlated with the 1987 vegetative phenology (Table 29). Table 29.  Phenology - PCA relationships for WED data. Correlation coefficients relating phenological data to PCA axes I - III.  Axis  I  II  III  56.63  20.94  12,70  reproductive 1987  .8584  -.0051  .0444  vegetative 1987  .7947  .1843  -.5454  reproductive 1986  .7563  .4119  .4447  vegetative 1986  .5697  -.7963  .1036  % variation phenological variables  91  Axis I, or the devil's club PCA, Is strongly correlated with all the phenological variables  and summarizes most of the variation in the data (Table 30). Axis II is a vegetative axis and is negatively correlated with the vegetative phenology in 1987 (Table 30). No interpretation of axis III is possible as it is not strongly correlated with any of the phenological variables (Table 30). Table 30.  Phenology - PCA relationships for DC data. Correlation coefficients relating phenological data to PCA axes I - III.  Axis % variation  I  II  III  74.81  15.45  7.05  .9088 .7014 .9381 .8914  .2611 -.7079 .2085 .0715  .2536 .0825 .1148 -.4445  phenological variables reproductive 1987 vegetative 1987 reproductive 1986 vegetative 1986  92  6.3.3  PARTITIONING OF VARIATION Examination of the variation apportioned to years, sites and quadrats for vegetative  development reveals that little variation within the thimbleberry data is accounted for by annual temporal fluctuations (Table 31). Annual fluctuations seem to have a larger impact on devil's club vegetative phenology (Table 31). For thimbleberry most variation (77.18%) is accounted for by site differences, indicating that large-scale environmental patterns affect vegetative developmental rates substantially. Of the remaining variation most is accounted for by the within-site or error term for devil's club and 22.72% is due to wfthin-site variation for thimbleberry (Table 31). For both species the within-quadrat term represents a substantial proportion of the variability within sites (Table 31). This suggests that more variation is due to phenotypic rather than genotypic differences assuming each quadrat represents a single genet. Reproductive patterns are somewhat different from vegetative patterns. Annual climatic fluctuations have a larger effect on the reproductive phenology of thimbleberry; however almost none of the variation in the data is accounted for by differences between years for devil's club (Table 31). The ANOVA suggests that annual temporal fluctuations are important to thimbleberry reproductive developmental rates but not devil's club (Table 31). As the regression equation for nonflowering plants had no slope, 1986 would have more zero values for the input data to ANOVA. These zero values may increase the proportion of the reproductive variation accounted for by annual differences and the annual variation accounted for by those plants with flowers may be larger than 10.77 % indicated by ANOVA (Table 31K The variation accounted for by site differences is not as large f a thimbleberry reproductive data as f a vegetative data (Table 31). Of the remaining variation within sites, both devil's club and thimbleberry are strongly affected by within-quadrat environmental differences as suggested by the large proportion of variation accounted f a by the within-quadrat term and less affected by environmental and genotypic drffaences between quadrats (Table 31). Quadrat diffaences also account f a a substantial proportion of reproductive phenology f a thimbleberry, suggesting that genotypic effects are also important. Most reproductive characters are considered to be conservative and less susceptible to environmental fluctuations and  93  tneretore useTui in taxonomy, pernaps reproductive pnenoiogy is subject to tne same selection pressures and is also less variable in a fluctuating environment than other vegetative characters. Table 31.  Percentage of phenological variation apportioned between years, species, sites and within and among quadrats for devil's club and thimbleberry.  Thimbleberry  Vegetative  Reproductive  between years between sites error  0.00 77.18 22.72  10.77 18.97 70.26  among quacrats error  .12 99.88  20.38 79.62  between years error  18.45 81.55  .41 99.59  among quacrats  7.98 92.02  11.94 88.06  Vegetative 1987 40.01 59.99  Reproductive 1987 3.64 96.36  Devil's dub  error Devil's dub and thimbleberry between spedes error  95  6.3.4  PHENOLOGICAL-ENVIRONMENTAL RELATIONSHIPS The 1987 vegetative phenology for TB and the 1986 reproductive phenology for WED  were both significantly correlated with canopy cover (Table 32). As canopy cover decreases the developmental rate increases in these cases. ANOVA indicates a large proportion of the vegetative phenological variation is accounted for by site differences (Table 31). Perhaps the open canopy at the KIT site let snow melt and soils warm earlier in 1987 than at WED so vegetative development proceeded sooner. In 1986, when spring was warmer and drier (Figure 2), differences in temperature and snow cover may not have been so critical between sites. Certainly the vegetative development is more advanced at KIT from spring through to the end of August, 1987 (Figure 15). Table 32.  Canopy cover - phenology relationships. Correlations between canopy in 1986 and 1987 and reproductive and vegetative phenology summary variables f a KIT, WED, TBandDC.(*.p<.05).  TB reproductive 1987 vegetative 1987 reproductive 1986 vegetative 1986 KIT reproductive 1987 vegetative 1987 reproductive 1986 vegetative 1986 WED reproductive 1987 vegetative 1987 reproductive 1986 vegetative 1986 DC reproductive 1987 vegetative 1987 reproductive 1986 vegetative 1986  1987 CANOPY COVER  1986 CANOPY COVER  -.4275 -.71 07*  -.4443 -.7452* -.4703 -.0485  .1325 -.1985  -.0656 -.1084 .1939 -.1810  -.3050 -.4046  -.1893 -.3341 -.5703* -.1405  -.2545 -.4132  -.2490 -.4874 -.2646 -.5916*  -  96 Devil's club vegetative phenology was also negatively correlated with canopy cover in 1986 (Table 33). In 1986. spring was sunnier and drier than 1987 (Figure 3). This suggests that canopy cover and climate may have jointly affected phenology. Warmer spring temperatures in 1986 would melt snow more rapidly under open canopies than under conifer trees allowing devil's club to develop earlier than in 1987. Phenological codes add credence to these findings as they were greater in early May, 1986 than in 1987 (Figure 16). Little relationship is apparent between phenology and soils for either devil's club or thimbleberry as suggested by CANCORR , though an overlap between the phenology and frequency of plant neighbour matrices for thimbleberry is strongly indicated (Appendix lll)(Tabie 33). Only the CANCORRs for WED and TB were interpretable, however as the PCA axes correlated with the KIT canonical axes were not ascribed with biological significance (Table 15). Table 33.  Summary of relationships between TB, KIT, WED and DC phenological variables and environmental characters. (+) relationship occurs. (-) no relationship, (?) possible relationship indicated by CANCORR.  PLANT NEIGHBOURS SOILS CANCORR TB  FREQUENCY  WMDS  CANCORR  -  KIT WED  -  DC  -  .  CANCORR  WMDS  +  +  +  +  -  • •  WMDS  PRESENCE/ABSENCE  +  -  +  The CANCORR for the TB data set suggested that the rate of reproductive development and vegetative development in 1987 for thimbleberry increased with disturbance, as suggested by the frequency of plant neighbours PCA. The first canonical axes are correlated with the first PCA axis (Appendix III), which are correlated with the reproductive phenology in 1986 and 1987 and the vegetative phenology in 1987 (Table 27). The first PCA axes of the plant frequency  97  axes is correlated witn trie canonical axes (Appendix ill) and represents disturDance gradient due to alder girdling (Table 15). CANCORR also indicated a relationship between TB phenology and disturbance, as suggested by the presence/absence of plant neighbours (Table 33), with developmental rates increasing with increasing disturbance. The first canonical axis is correlated with the first and third phenological PCA axes (Appendix III), which are positively correlated with reproductive phenologies in 1986 and 1987 and the vegetative phenology in 1987 (Table 27). The first plant presence/absence PCA axis, which is correlated with the first canonical axes (Appendix III), may be a disturbance gradient due to aider girdling and a fluctuating water table (Table 11). CANCORR also suggested a relationship between phenology and the frequency of plant neighbours for the KIT data. The first canonical axes were correlated with the first phenological and frequency of plant neighbours PCA axes (Appendix III). The first phenological axis is positively correlated with all phenological variables (Table 28) whereas, the first frequency of plant neighbours PCA axis was a disturbance gradient (Table 16). This relationship suggests that all developmental rates are increased in more open and disturbed sites at KIT. Both the CANCORR for the WED phenology-frequency of plant neighbours and the WED phenology-presence/absence of plant neighbour matrices indicated a relationship between phenology and moisture, with developmental rates decreasing as moisture increases and evaporation decreases due to shading by alder. The first WED phenology canonical axis for the frequency-phenology CANCORR is correlated with thefirstphenology PCA axis-(Appendix III), which summarizes all the phenological variables (Table 29). The first plant frequency canonical axis is negatively correlated with the first PCA axis (Appendix III), a moisture gradient (Table 17). This relationship suggests that as moisture increases and evaporation decreases due to shading by alder, all developmental rates decrease. Redundancies indicate that the strongest relationship between the canonical axes and the PCA axes occurs between the second canonical axis pair for the WED phenologypresence/absence of plant neighbours (Appendix III). The second canonical axes for the WED presence/absence-phenology CANCORR are correlated with the first phenological and plant  98 presence/absence PCA axes (Appendix III).  The first phenological axis summarizes all  phenological variables (Table 29), whereas the first plant presence/absence axis represents a moisture gradient (Table 13).  99 6.4  DEMOGRAPHY  6.4.1  UNIVARIATE ANALYSES AND GRAPHS Though the rate of demise differs for thimbleberry between years, the shape of the  survivorship curve is similar (Figure 19). The curve (Figure 19) indicates that each thimbleberry cohort has a relatively constant risk of mortality throughout its lifespan and suggests a type II demographic curve, which is common for perennial species (Hutchings 1976). There are still changes, however, in theriskof mortality through time. In 1986 the slope of the curve between the two sites was similar though the number of recruits was far larger at KIT (Figure 19). Approximately 10 percent of the stems die within the first month and a half of growth or from May 21 to mid-June (Figure 19). The next deaths occur from the end of August, 1986, to the beginning of the following May, 1987 though approximately 78 p^cent of the stems survived to the second year (Figure 19). From this point on, most stems lived only until the period from August,1987, to August,1988, though almost 10 percent of stems were still alive by the end of the third summer of growth (Figure 19); In many Rubus species canes live for only two years but thimbleberry this is not necessarily the case. Of those 54 stems known to survive from 1985 to 1986, three were still alive at the end of 1988. The longevity of stems produced in 1987 did not appear to differ substantially between sites though the slope of the line appeared more steep than for 1986 stems (Figure 19). Recruitment was obviously greater in 1987 at both sites (Figure 19). Unlike 1986. 65 percent of the stems were dead by the end of August of the second year (Figure 20). Forty-five percent more stems produced in 1987 had died between August of the first summer of growth and August of the second year than those produced in 1986. Most stems die from August ,1987, to August, 1988, regardless of which year they were produced in. Some mortalities may be explained by clearing done by forestry crews at WED early in the summer of 1988. However this effect was not substantial as the slope between KIT and WED does not appear to differ to any great degree during this time (Figure 19).  Figure 19.  100 Survivorship of thimbleberry stems produced in 1986 and 1987 at the WED and KIT sites. a) 1986  o WED KIT  DATE  b) 1987  o WED • KIT  DATE  101  0  As indicated by the survivorship curves, the KIT histograms of the percentage of stems dying within distinct time intervals also indicates that the largest percentage die during the period from August, 1987 to August ,1988 (105-450 days)(Figure 20). These graphs also  suggest that each quadrat, except those at WED for which no stems were produced, contained range of stem ages in each year (Figure 20). Few general patterns are evident among sites though some relationship within quacrats is suggested (Figure 20). Quacrats with a large  percentage of stems which die by the end of June, 1986 produce stems with greatest longevity  in 1987. For example, in 1986 at quadrat 2, almost 60 percent of the stems produced die by th  end of June whereas of those produced in 1987,100 percent live longer than August ,1987 and 25 percent are alive in August, 1988 (Figure 20). Figure 20.  Percentage of stems dying within distinct time periods at KIT and WED sites in 1986 and 1987. a) KIT 1987  " J  QUADRAT  m • • •  >450 D A Y S 105-450 D A Y S 55-105 D A Y S 0-55 D A Y S  102  Figure 20 continued  b) WED 1987 100 80  oc  LU CD  60  Z)  z  40  LU h-  00  m >450  20  1 22  24  26  28  30  32  36  34  38  40  42  DAYS  •  105-450  •  55-105  •  0-55  DAYS DAYS  DAYS  QUADRAT  c) KIT 1986  •I  100 :  80  8  8:  or  LU CO  2 LU  60  40  5  20  E  00 0  ti MI M m r-i 1  3  5  7  9  11  13  15  17  19  S  >810  g  466-810  |  416-465  |  361-415  1  106-360  gj  56-105  |  0-55  S  >810  I  466-810  gj  416-465  |  361-415  I  106-360  i  56-105  I  0-55  21  QUADRAT  d) WED 1986 100  80  cc  LU CO  60  z> z  40  LU r-  00  20 0  I  n m M 22  24  26  28  30  32  34  QUADRAT  36  38  40 42  103  In the devil's club site, only six stems were produced in 1987 and only one stem was s alive by August. This large rate of early mortality and the graph of the age distribution (Figure 21) suggest a Deevey Type III curve (Hutchings 1976). A type III distribution implies that youngest plants have the greatest risks of mortality. This risk of mortality declines with age so that some very old individuals will be found in the population (Hutchings 1976). A type III curve is common for longjived species (Balogh and Grigal 1988). The cumulative distribution, however, shows stem ages at one time only (Figure 21) and is not identical to a Deevey survivorship curve which shows the change in cohort numbers through time (Hutchings 1976). Devil's dub populations do not necessarily have the same age distribution at all times as facto determining longevity may change. It was also impossible to determine the age of this longjived population accurately and it may be hundreds of years old. Indeed aging stems by counting terminal bud scars probably underestimates each stem by at least five years as these scars are obliterated by stem thickening. Figure 21.  Cumulative number of devil's dub stems older than the time indicated on the xaxis. 150  cn LU h-  00  120  LL  90 -  CC  60  O  LU CQ  W  30 10  15  'Si 20  25  30  YEARS 0  104  in comparison with tnimbieberry, the aevirs CIUD population consists or a mixture or ages (Figure 22). The largest overall proportion of stems are less than five years old indicating that although the risks of mortality are high early in life sufficient numbers are produced to ensure that many stems reach reproductive maturity. Few stems were observed to be greater than 22  years old, indicating the maximum time period before stems become stdoniferous and fragment Figure 22.  Percentage of devils' club stems in distinct age classes on June 30,1987.  100  CC LU CO  80  I  60  LU  \— 00  I  / / / / / / /  I  40 h E3 2 2 - 2 8 20  0 43  45  47  49  51  53  55  57  59  61  H  16-21  •  11 -15  •  6-10  •  1-5  63  QUADRAT  There appears to be little relationship between the density of thimbleberry at each quadrat and the percentage of stems surviving to each age dass. As density increases a concomitant change in the longevity at each quadrat does not occur (Table 34). In most cases the slope of the regression is not significantly different from zero (Table 34), indicating no relationship. In those cases where the regression is significant (1986 - all periods from 106 810 days) (Table 34), the variation in the data accounted fa by the regression is less than 30 percent.  105  Table 34.  Regression analysis of thimbleberry stem longevity and stem density. Y=a+bx where y = percentage of the total number of stems in quadrat surviving to the time period, a = intercept, b = slope and x = total number of stems in quadr r.P<05)  Time period (days)  r-square  slope  significance  (1986) 0-55 (1986) 55-105 (1986) 105-360  .07 .07 .14  -.014 -.014 -.033  .10 .10 .01*  (1986) 361-415  .26  -.015  .00*  (1986) 416-465 (1986) 466-810 (1987) 0-55  .26  -.052 -.033 -.013  .00*  (1987) 56-105 (1987) 106-450  .10 .06 .03 .03  -.003 -.004  .04* .10 .26 .25  The regression of the proportion of the total number of devil's dub stems in each dass against the total number of stems in the quadat suggests that density has little or no effect on the proportion of stems of each age dass observed (Table 35). As the density of stems increases the proportion of stems in each age dass does not also increase (Table 35). Table 35. Regression analysis of devil's dub stem age and density. Y=a+bx where Y = ratio of number of stems in age dass to total number of stem in quadat, a = intercept, b = slope and x = total number of stems in quadat. (*, p<.05) Age dass (years)  r-square  slope  1-5 6-10 11-15 16-21 22-28  .33 .12 .02 .01 .05  .035 -.019 -.004 -.003 -.011  significance .00* .12 .60 .61 .32  106  6.4.2 PRINCIPAL COMPONENT ANALYSES The PCA of the number of thimbleberry stems alive in each age class in 1986 and 1987 was used to generate axes scores for use in ANOVA only. The PCA summarized over 99 percent of the variation in the data on the first three axes, (Table 36). No biological interpretation was ascribed to these axes. Table 36.  Eigenvalues  Eigenvalues of 1986 and 1987 PCA for the number of stems surviving to each age class. Axis I  Axis II  Axis III  66.90  31.99  .73  The PCA of the number of stems dying in the age classes in 1986 and 1987 was used subsequent CANCORR analysis, though only 54.81 percent of the variation in the total data were explained by the first three axes (Table 37). Axis I reiterates the relationship observed graphically and groups together those quadrats with the shortest living stems in 1986 and longer lived stems in 1987, all of which are vegetative and not sexually reproductive (Table 37). Axis II implies that quacrats with the longest living stems in 1986 produce shorter living stems 1987 (Table 37). Axis I and II suggest at tradeoff between younger vegetative and older generative stems and indicate that the plant may alternately channel resources~between vegetative and generative stem production. Axis III has no strong positive correlations and is negatively correlated with stem deaths during the period from July to August, 1987 (1986 416465, 1987 56-105 days), the period where least deaths occur whether these stems were produced in 1986 and 1987 (Table 37).  107  Table 37.  Demography - PCA axes for TB data for the number of stems dying during each age class. Correlation coefficients relating demographic data to PCA axis I - III.  Axis I % variation 22.59 demographic variables (days) (1986) 0-55 .7844  II 18.75  III 13.47  -.1013  .1101  (1986) 106-360  -.0299  -.1217  .3179  (1986) 361-415  .7699  -.0014  .0117  (1986) 416-465 (1986) 466-810  .1497 -.0005 .5506  .1669 .7069  -.7688 -.7260  -.3978  .1878  .7417 .1929 .7551  .2930 -.6575 .2875 -.0419  (1986) (1987) (1987) (1987)  >810 0-55 56-105 106-450  (1987) >450  .2144 .1659 .0202 .8065  .0806  PCA of the KIT data represents approximately 60 percent of the variation on the first three axes (Table 38). Axis I of the KIT PCA, like axis I of the TB PCA, groups together those stems dying early in 1966 and 1987, before they can flower and those stems living longest, perhaps because they have also not yet flowered. Axis II is correlated with those stems produced in 1986 and 1987 which die by the end of the summer of 1987 (1986 416-465 days and 1987 56-105 days), the period when stem mortality is at a minimum (Table 38). Axis III is negatively correlated with the stems produced in May, 1986 which die by August 1987 (416-465 days) and positively correlated with those stems from 1987 which die by the end of June, 1987 (0-55) days (Table 38). These opposite correlations suggest that reproductive and vegetative  108  stems are not produced at the same time, assuming two year old stems which die by the end the summer were reproductive. Table 38. Demography - PCA axes for KIT data for the number of stems dying during each age class. Correlation coefficients relating demographic data to PCA axes I - III. Axis %variation  I 29.34  Demographic variables (days) (1986) 0-55 .8451 (1986) 106-360 -.2022  II 15.46  III 13.86 .2632 .1540  (1986) 361-415  .8491  .0327 -.3099 .2917  (1986) 416-465  .0709  .5411  -.6937  (1986) 466-810  -.4316  (1986) (1987) (1987) (1987) (1987)  .6313 .0474 .2247 -.2908 .8547  .3863 -.3334 .4642 .6464 .4180 -.0525  .0465 -.2176 .6203 -.3244 .4921 .0851  >810 0-55 56-105 106-450 >450  .1522  The PCA of the WED summarizes 58 percent of the total variation in the data on the fir three axes (Table 39). It is most strongly correlated with the stems from 1986 which die after August,1987 (466-810 days) perhaps after flowering and producing fruit, and those vegetative stems produced in 1987 which die within two months of their emergence or after June,1987 (055 days)(Table 39). Axis I is also correlated with those stems produced in 1987 which live pas the first summer (106-450 and >450 days). Axis II is correlated with stems produced in spring, 1986, which die in all age categories before July, 1987, or before they can produce fruit. The stems from spring. 1987. are most strongly correlated with axis III. The stems which die during the time period from August, 1987, to August, 1988, (106-450 days) may have died between  109  August  and June, 1980, and can De assumed to De vegetative as Truit did not ripen until mid-July  in 1987. Those stems dying from June to August, 1987, were most probably reproductive as stems which produce fruit common died shortly afterwards. This axis may therefore be a continuum showing the differing longevities of vegetative to reproductive stems. Table 39.  Axis %variation  Demographic - PCA axes for WED data for the number of stems dying during each age class. Correlation coefficients relating demographic data to PCA axes I -III. I 24.24  Demographic variables (days) (1986) 0-55 -.1990 (1986) 106-360 -.3150 (1986) 361-415 .2046 (1986) 416-465 .2634 (1986) 466-810 .8283 (1986) >810 -.2773 (1987) 0-55 .7542 (1987) 56-105 .2234 (1987) 106-450 .6532 (1987) >450 .6048  II 19.36  III 14.82  .6001 .6035 .8998  -.0732 .1233  .1967 -.0041 .4813 .0723 -.2414 .0977 .2414  .0729 .3081 .1637 -.0848 -.4665 .6754 -.5737 .5701_  The devil's club PCA of the number of stems of each age category recorded in quadrats on June 30,1987, explains 80 percent of the total variation on the first three axes (Table 40). Axis I is negatively correlated with the oldest class, those stems aged from 22 to 28 years (Tab 40). Stems greater than 16 years old tend to be stoloniferous and those younger than 10 years old are not reproductive. Axis II may, therefore, be a vegetative gradient for those two vegetative states. Axis III may represent the differences between vegetative and generative  110 stems, certainly that is the most noticeable difference between these two age classes correlated with the axis. Table 40.  Demography - PCA axes for DC data for the number of stems in each age class. Correlation coefficients relating demographic data to PCA axes I - III.  Axis %variation  I  II  III  31.47  30.92  18.33  Demographic variables (years) 0-5  -4856  .7846  .1465  6-10  .3962  .4771  -.6568  11-15  . 5427  .3919  .6739  16-21  .1104  .7329  -.0935  22-28  -.9349  .1087  .0257  Ill  0.4.3  PARTITIONING OF VARIATION  The ANOVA results for thimbleberry indicate that a substantial proportion of the total variation is accounted for by annual differences (Table 41). This agrees with trends suggested by PCA which indicated that vegetative and generative patterns were not maximized every year. The variation accounted fa by differences between sites is less than 10 percent and suggests a relatively small effect due to large scale environmental differences (Table 41). The genetic component in demographic hetaogeneity is also small as suggested by the proportion of variation accounted fa by among-quadrat diffaences (Table 41). Most variation is accounted fa by differences between stems within quadrats (Table 41), indicating that small scale environmental a developmental influences are an important component of longevity, and may be the major impetus determining age structure within populations. Although almost 20 pacent of the variation in devil's dub stem ages on June 30, 1987, is accounted fa among quadrats, most variation is within quadrats a between stems, suggesting that small scale environmental a developmental influences are a large component in population age structure (Table 41). Table 41.  Demographic variation apportioned between years, sites and within and among quadrats by ANOVA. Thimbleberry  Devil's dub  Years Sites  29.10 8.27  n/a  Erra  62.63  Within and among quadrats quadrats erra  7.10 92.09  17.18 82.82  112  6.4.4 DEMOGRAPHIC-ENVIRONMENTAL RELATIONS Correlations between canopy cover and the longevity of thimbleberry stems are all less than .50 and indicate little relationship (Table 42). Table 42. Canopy cover - demographic relationships. Correlations between the number of stems dying in each time period and the canopy cover in 1986 and 1987 for TB, KITandWED.(*,p<.05). Time period (days)  KIT 1986  (1986) 0-55 106-360 361-415 416-465 466-810 >810 (1987) 0-55 56-105 106-450 >450  WED 1987  .11 -.05 -08 -.20 -.22  -.21 -.05 .07  .11  .10  -.41 -.28 -.28 .07  1986 .19 .23 .15 .26 .45* -.35  TB  1987  1986  1987  .27 .21 .45* -.06  -.11 -.05 .14 -.01 -.13 -.23  .15 .01 .04 -.10  .21 -.26 .30 .23  "-.18 -.27 -.06 -.11  113  canopy cover also seems to nave little effect on the longevity or devils cluD stems, as correlations are small (Table 43). Table 43. Canopy cover - age distribution relationships. Correlation between the age distribution of stems and the canopy cover in 1987 for devil's club. (*,p<.05). age class (years)  correlation  1-5  .18  6-10  .06  11-15  .00  16-21  -.07  22-28  -.01  CANCORR suggests little or no relationship exists between all the demography and environmental matrices (Table 44). Although the canonical correlation coefficients are frequently large, redundancies often do not support any overlap between point swarms or correlation structure is not conclusive (Appendix Illl). CANCORR also implies a relationship between the WED demography-soils and the WED and KIT demography-frequency of plant neighbours matrices, but WMDS refutes these relationships (Table 44)(Appendices III and IV). A relationship is also indicated between the phenology and the presence/absence of plant neighbours at KIT; however, the second PCA axis which is correlated with thefirstcanonical axis (Appendix III) could not be interpreted (Table 12).  114  Table 44.  Summary of relationships between TB, KIT, WED and DC demographic variables and environmental characters. (+) relationship occurs, (-) no relationship, (?) possible relationship indicated by CANCORR. PLANT NEIGHBOURS  TB  SOILS  FREQUENCY  PRESENCE/ABSENCE  CANCORR WMDS  CANCORR WMDS  CANCORR WMDS  -  KIT WED  ?  -  ?  -  ?  -  ?  6.5  115  MORPHOLOGY-PHENOLOGY-DEMOGRAPHYINTERACTIONS  Morphology and phenology appear to be related at each level in the nested hierarchy for both thimbleberry and devil's dub, as suggested by CANCORR (Table 45). CANCORR also suggests a relationship between demography and morphology for both devil's club and the thimbleberry data set (Table 45), though WMDS refutes these rdationships at KIT and WED (Appendix IV). In no case, though, is phenology related to demography (Table 45), suggesting that longevity has little relationship to the rate of development. Table 45.  Summary of relationships between TB, KIT, WED and DC morphological, phendogical and demographic variables. (+) relationship occurs, (-) no relationship. PHENOLOGY CANCORR WMDS  DEMOGRAPHY CANCORR  WMDS  IBMORPHOLOGY PHENOLOGY KJIMORPHOLOGY PHENOLOGY WEDMORPHOLOGY PHENOLOGY DCMORPHOLOGY PHENOLOGY CANCORR suggests that both vegetative and reproductive developmental rates in 1966 and vegetative developmental rates in 1967 are more rapid at those quacrats with larger stems with more, larger branches and leaves and more flowers for the TB data set. Thefirstcanonical  116  axes for the TB morphology-phenology CANCORR are correlated with the first morphological and phenological axes (Appendix III). Axis I of the morphological PCA data is correlated with all morphological variables except the number of stems per quadrat and the number of branch leaves (Table 20). The first phenological axis is correlated with the reproductive phenology in 1986 and 1987 and the vegetative phenology in 1986 (Table 27). For the KIT morphology-phenology data, CANCORR indicates that quadrats with more flowers have more rapid developmental rates. In those quadrats, where phenology does not proceed as rapidly, there are fewer flowers. The first canonical axis is correlated with the first phenological PCA axis and the third morphological PCA axis (Appendix III). The first phenological PCA axis is correlated with all the phenological variables (Table 28), whereas the third morphological PCA axis is a reproductive axis and is correlated with the number of flowers per inflorescence and per quadrat and the number of two-year-old stems per quadrat (Table 21). As those quadrats with no flowers have no change in reproductive phenological code, the slope of the regression line between the phenological code and time is zero (Table 5). Phenological summary slopes would be larger than zero at those quadrats with flowers, regardless of how rapidly they matured. Only two-year-old stems produced flowers and they also had the most rapid rate of vegetative development. Quadrats with most two-year-old stems would therefore be expected to have greatest overall developmental rates or slopes of the regression. At WED, the CANCORR indicates a relationship between phenology and vegetative and reproductive morphological features. Those quadrats with more flowers and larger amounts of vegetative tissue have most rapid developmental rates and at those quadrats where there are fewer flowers and smaller vegetative parts, the developmental rates are less rapid. The first canonical axis pair is strongly positively correlated with the first morphological and phenological PCA axes (Appendix III). The first morphological PCA axis is, like axis I of the TB data, a gradient of vegetative tissue size and is also less strongly correlated with the number of flowering stems and the number of flowers per inflorescence (Table 22). Thefirstphenological axis is correlated with all the phenological variables, especially the 1987 phenological variables (Table 29).  117  morpnoiogy-demograpny matrices indicates a reiationsnip between the number of stems in each quadrat and the number of stems in most demographic time periods. The first morphological canonical axis is correlated with the second morphological PCA axis (Appendix III), a gradient of the number of stems per quadrat (Table 20). The first demographic canonical axis is correlated with the first and second PCA axes (Appendix III). These PCA axes are correlated with most stem age classes, except those from July to August i either 1986 or 1987, and from August, 1986, to May, 1987, (Table 37), the time periods when fewest stems die(Figure 20). As the number of stems dying is related to the number of stems in each quadrat, this relationship is not surprising though perhaps it does attest to the validity of th method. It also reiterates the regression analysis between each demographic age dass and the stem density (Table 34). The regression indcates that as the number of stems per quadat increases more stems de but the proportion dying in any one age dass does not change (Ta 34). According to CANCORR (Appendix III), as stem number per quadat increases, no stem age class emerges when stems de. Phendogy is unrelated to demography (Table 30), suggesting that the rate of annual periodic development is uncorrelated to the longevity of stems and may also be subject to dfferent environmental contraints. As suggested for morphology and demography, a relationship between longevity and reproductive phendogy may be expeded as stems usually de after flowering and flowering stems tend to be oldest in the population. Regardless, CANCORR Deiween tne TD  CANCORR does not strongly indicate a relationship (Appendx III). The type II survivorship curve for thimblebery also suggests that the risk of mortality is relatively constant through time fo each cohort (Figure 37). Developmental rates increase in devil's dub quadats where there are more flowers and stems are larger with more vegetative tissue. CANCORR further indcates that plants with smaller stems and branches with fewer leaves may have slower developmental rates. The first canonical axes are correlated with the first morphdogical and phendogical PCA axes (Appendx III). The first morphdogical PCA axis is correlated with stem sizes and the number and size of branches and the number of branch leaves and the number of flowers per quadat  118  (Table 23). The first phenological axis is correlated with the vegetative and reproductive phenological codes in 1986 and 1987 (Table 30). As with the thimbleberry data, quadrats lacking flowers generate a zero slope from the regression of phenological slope against time (Table 5). Reproductive developmental rates will therefore be perceived as more rapid at those quadrats with flowers, regardless of the rate at which flowers and fruit mature. Although, larger devil's club stems mature more rapidly or larger stems may initiate growth earlier than smaller stems, no correlation is apparent for the stem length increment and the rate of development. Branches, though, increase more in length at quadrats with more rapid developmental rates an larger stems (Appendix III). As branches represent the next generation of stems and the means of clonal expansion, their increased growth may indicate that conditions are most favorable for vegetative reproduction at sites where developmental rates are maximized.  CANCORR also indicates a relationship between devil's dub morphology and demography. The first canonical axis is correlated with the second morphological and demographic PCA axes (Appendix III). The second morphological axis is negatively correlated with the number of branches per stems and the stem length and positively correlated with the number of stems per quadrat (Table 23). The second demographic axis is correlated with those stems between 0 and 5 years of age and 16 and 21 years of age (Table 40). This correlation links together those stems, aged 16 to 21 years, which have become stoloniferous and along which branches have rooted to become independent stems estimated to be between 0 and 5 years old. Although the 16 to 21 year old stems would have the greatest overall stem lengths nonetheless, the average stem length per quadrat would be low due to the large number of newer, shorter stems.  119 VII  DISCUSSION  7.1  MORPHOLOGY The environmental characteristics of differing successional niches have been credited with maintaining morphological differences between some species (Lee et al. 1986). Morphological differences are evident between thimbleberry and devil's club with perhaps a palmate leaf shape and a clonal growth habit being the most obvious traits that the two species share. Differences in the successional niche occupied by each species are also apparent. Although thimbleberry is found in clearings in old growth forests with devil's dub, it is most abundant at earlier successional stages. This suggests that differing successional environments may correlate with morphdogical characters unique to devil's dub and thimbleberry. Lee et al. (1986) attributed variation in leaf size and shape and plant architecture between two spedes of Pdygonum to differing successional habitats. Thimbleberry morphologies are correlated with disturbance caused by girding and moisture, whereas devil's dub morphology is uncorrected with any other environmental variables selected (Table 26). Devil's dub may be buffered by large trees in the dd growth forest against fluctuations in dimate and, therefore, may be living in a stable environment. Though few studes have compared vegetative morphdogy between spedes in differing successional environments (Bierzychudek 1982), reproductive effort has frequently been linked to succession (Hancock and Prrtts 1987; Stearns 1977).-Harper (1970) indcates that spedes found earlier in succession devote larger proportions of energy to reprodudion than those species found later in succession. Thimbleberry and devil's dub suggest that those factors which determine reproductive allocation are complex. Both are vegetatively and sexually reproductive, perennial and iteroparous; as well thimbleberry rhizomatous growth is extensive and devil's dub roots are also large and deep (personal observation). However, their flowering responses dffer. Devil's dub doesn't flower in the first year and each stem may flower thereater, whereas thimbleberry stems produce fruits once in their second or third year of growth and then  120  die. This diversity of reproductive possibilities suggests that several differing factors may determine reproductive allocation and that reproductive allocation is a complex speciesspecific phenomena. Both species, however, fit into life history arguments which suggest that earlier successional species flower sooner after germination than later successional species (Stearns, 1977). Differences between the two thimbleberry populations may be due to environmental differences at each site (Jurik 1985; Van Cauteren and Lefebvre 1986; Maddox et al. 1989; Schwaegerie and Levin 1990). Some site differences are apparent, with WED having greater canopy cover (Figure 7), richer soils and showing less sign of flooding than at KIT (Table 6). In 1987, morphological patterns also differed between sites. KIT had greater numbers of flowers, fruits, leaves and stems per quadrat, longer stems and a larger proportion of fruit per flower produced than WED (Appendix I). Disturbance, due to moisture differences and alder girdling, was correlated with morphological differences between sites. These environmental characters were reflected differently between sites, though. At WED drier quadrats contained plants with more flowers, larger stems and more branches (Appendix III); whereas for the overall TB data set more disturbed, moist quadrats contained larger stems with more leaves and branches (Appendix III). At KIT, where disturbance was greater, moist sites produced more flowers and branches on larger stems; whereas at WED, where disturbance is less, moist sites produce fewer flowers and branches on smaller stems. Relationships between morphology and environment were also found to differ between sites by Schwaegerie and Levin (1990) and between and among sites by Gawler et al. (1987) for Phlox drummondii and Peducularis furbishiae respectively. Menges (1987) also found larger leaves and longer stems produced in canopy gaps for Laportea canadensis to differ between upland forests and floodplains. Most variation is accounted for within sites or populations, being greater than between sites for thimbleberry and between years for either thimbleberry or devil's dub. If, as assumed, each quadrat contains a single unique genet then it would appear that  121  morpnoiogy is strongly influenced Dy genetic differences between individuals and Dy small scale environmental heterogeneity. The number and size of leaves are mostly influenced by small scale environmental heterogeneity for both species (Table 24). Variation within other clonal populations for leaf size was also found to be substantial by Barnes (1986) and Herndon (1987). Leaf plasticity has also been observed with such examples as sun versus shade leaves (Schlichting 1986) or different nutrient, moisture and clipping treatments affecting the number and size of Phlox leaves (Schlichting 1989). Plasticity is common for leaf (Oberbauer and Strain 1986) and stem (Schlichting 1986; Piteika et al. 19865) morphology and may even be predictable in differing environments (Givnish 1982; Menges 1987). Morphological plasticity is also commonly observed in weedy species (Holzner 1982) and has been noted for thimbleberry (Hulten 1974). Most variation for stem length is apportioned within individuals for devil's club and equally divided within and between individuals for thimbleberry (Table 24). Thimbleberry stem length is influenced by both genetic differences and small scale environmental heterogeneity. Several factors may account for the differences observed. Devil's dub stems originate individually and are long-lived stoloniferous stems producing roots and vertical branches along their length. Thimbleberry stems are produced in dumps and remain erect. Each thimbleberry stem may therefore encounter less environmental variation than devil's dub, both spatially and temporally. The common origin of each thimbleberry is also assurance that each dump of stems within a quadrat is genetically identical. Devil's dub stems within a quadrat may not be genetically identical and may therefore also be influenced by a combination of genotypic and environmental heterogeneity. The large within-quadrat morphological term suggests that replicate environmental measures made within quadrats might have been informative. If smallscale environmental variability promotes plasticity within individuals as Levin (T986) suggests, this large within-quadrat term (Table 24), indicates that environmental  122 variation within quacrats may also be significant. Certainly soil characteristics are known to be highly variable, perhaps requiring up to 100 samples in a hectare to accurately estimate soils features (Courtin et al. 1983). Although the relative allocations to sexual and vegetative reproduction may be important to maintain or spread clonal plants under different environmental conditions (Pitelka et al. 1980; Abrahamson 1980; Loehle 1987), they are difficult to assess for thimbleberry and devil's club. In addition, both plants are long-lived and do not flower every year. Accounting for vegetative costs for years when no seed is produced is not straightforward. Although thimbleberry was found to increase sexual reproduction at KIT, where light levels are greater than WED, vegetative reproduction was also found to increase. Vegetative and sexual reproduction were also found to increase for Acalypha rhomboidea and Pilea pumila (Cid-Benevento 1987) with increasing light. Physiological integration may be an important strategy for both devil's club and thimbleberry, though devil's club does not maintain the same degree of interconnection between parts as does thimbleberry. Once devil's club stems become stoloniferous, branch and root at stem nodes, the stolon degenerates between nodes, though these connections may be several meters long before degradation occurs. Thimbleberry rhizomes tend to maintain connections between clumps of stems and appear to die at the tip of the growing rhizome (personal observation). Certainly the decree of rhizome branching, primary and secondary root formation and the distance between above ground stems is variable. Architectural differences such as these have been interpreted as a strategy to alleviate stress (Schmid and Bazazz 1987; Hartnett and Bazazz 1985) as stem ramets were found to be longer and fewer through areas of nutrient (Slade and Hutchings 1987a; Slade and Hutchings 1987b) and moisture (Hartnett and Bazazz 1983) stress. Morphological patterns observed for devil's dub and thimbleberry are similar to results reported by Scagel and Maze (1984); more variation is accounted for within individuals rather than between individuals. Environmental correlates reported here do  123  not parallel tnose OT otner studies. Devirs CIUD morpnoiogy was uncorreiated witn tne environmental variables, though thimbleberry showed similar correlations to those reported by Piteika et al. (1985) and Gawler et al. (1987) for other clonal species. Canopy openings (Piteika et al. 1985) and moisture (Gawler et al. 1987) were correlated with the number and size of leaves and the number of flowers. Disturbance and moisture were both positively correlated with these traits for thimbleberry. If disturbance equates to girdling of the alder and therefore canopy openings, the relationship between these studies is more apparent. No interaction, as thimbleberry displayed, between moisture and canopy openings, though was observed by Piteika et al. (1985) for Clintonia borealis or Gawler et al. (1987) for Peducularis furbishiae.  124 7.2  PHENOLOGY The different successional niches occupied by thimbleberry and devil's club may  account for some of the phenological differences observed (Rathcke and Lacey 1985). Devil's club, as a member of a late successional community may be a stronger competitor than thimbleberry, which is found at earlier stages along a successional gradient (Grime 1979). It has a well defined peak of leaf production during the longest days of summer followed by the rapid production of flowers. Grime (1979) indicates that competitive species generally will produce leaves during periods of maximum productivity and then flower. In contrast, thimbleberry produced leaves early in the summer on two-year-old stems, which may then flower for up to two months. One-yearold stems produce leaves throughout the summer. The strategies of devil's club and thimbleberry are quite different and may indicate stronger differences between the two species, especially reproductively, than the ANOVA indicated. Little variation is accounted f a by species differences in the ANOVA of reproductive phenology (Table 31), these results may partly reflect the coding used. Each thimbleberry had several flowering stems which flowered continuously; whereas devil's club had only one a two flowering stems per quadrat with only one floral spike per stem. Thimbleberry was, therefae, potentially mae phenologically varied than devil's dub, although the average quadat value f a the two spedes was similar. Annual dimatic differences appeared to have some effect on thimbleberry reproductive phendogy though not on the vegetative phendogy. Flowering proceeded at a fasta rate in 1987 as compared to 1986 (Figure 17), but in addition far mae thimbleberry plants bore fruit at both sites in 1987 than in 1986 (Appendix I). Annual variation in numbers of fruit and flowers was also reported to be substantial by Duke (1990), and the relationship between variation in dimatic factors, such as frost (Rathcke 1988), temperature (Helenurm and Barrett 1987; Rathcke 1989) and rainfall (Jackson and Bliss 1984; Gill and Mahall 1986; Heideman 1989), on flowering phendogy have been recorded.  125  in contrast to tnimDieDerry, annual cnnerences tor aevii's CIUD were seen tor vegetative phenologies only (Figure 18), with phenological development proceeding more rapidly in 1986 than 1987. Annual differences observed for devil's club phenology may be due to interactions of canopy cover and climate, which would affect the rate of snow melt and the temperature at each quadrat (Table 32. Figure 2). Temperature was also linked to annual differences in leaf and flower phenology of Aralia nudicaulis (Flanagan and Moser 1985). Environmental differences between sites may maintain the phenological differences between two populations (Primack 1985). A large proportion of the phenological variation for thimbleberry is accounted for by site differences (Table 31), with canopy cover negatively correlated with the 1987 vegetative phenology and disturbance due to girdling and fluctuating water conditions positively correlated with all phenological variables (Table 33). Separate populations were also found to exhibit different phenological patterns in different environments by Van Cauteren and Lefebvre (1986) and Nilsen (1986). Differential responses to environmental factors were also observed at each site for thimbleberry. At WED, phenological development was negatively correlated with moisture and canopy cover due to alder; whereas at KIT phenological development was more rapid at open sites with more fluctuations in water conditions. Site differences and differential responses within sites to moisture were also reported by Heideman-(1989). Studies have indicated that phenology may be affected by pollinator availability (Rathcke and Primack 1985; Rathcke 1988) or the availability of seed dispersers (Rathcke and Primack 1985; Primack 1985). Devil's club was observed to have many different pollinators on the large conspicuous floral stems. The short period of flowering or the narrowing of devil's club's floral niche, may as Rathcke (1988) suggests for several other species, be a result of competition for pollinators. The availability of resources or the presence of seed dispersers, though, are equally likely influences on phenology. Devil's club fruit was favored by bears which distributed these large masses  126  of berries widely intheir droppings. No other plant appeared to be a serious competitor for this mode of dispersal as none bore fruits as large, which offered an equivalent reward. Migrating salmon, however, which arrive in August, would certainly distract bears. In this case, the availability of seed dispersers may shape devil's club fruiting phenology. The large clusters of fruit may also be expensive to produce and resource availability may limit devil's club fruiting phenology. Fruits mature immediately following the longest days of summer. Perhaps increasing levels of photosynthate produced in late July are required for fruit to ripen successfully. Flanagan and Moser (1985) inferred that both resource limitation and the lack of pollinators were important factors in the phenology of Aralia nudicaulis c  Thimbleberry reproductive phenology may also be subject to these constraints. Although flowers are produced continuously for almost two months, nonetheless a peak in floral production is apparent. During this peak in late June (Figure 15), flowers are filled with many (>20 per flower) tiny mating beetles, which may be the main pollinator. Many other pollinators were also observed though none were as plentiful as the beetles. In addition, the availability of seed dispersers may determine when thimbleberry fruit ripen. The main period of fruit ripening is August (Figure 15), coinciding with the arrival of migrating birds. From mid-June on. 1987 was warmer and drier than 1986 (Figure 2) and far more fruit ripened that summer (Appendix I). Reproductive development was also more rapid in 1987 (Figure 17). Perhaps flowering proceeds only after a critical number of degree days is achieved, as has been suggested for many temperate woody species (Lieth 1974; Reader 1983; Rathcke 1985).  127 7.3  DEMOGRAPHY  The stem demography for thimbleberry varied greatly over the time of the study, whereas devil's club changed little. The Type II survivorship curve for thimbleberry stems (Figure 19), suggests a constant risk of mortality throughout their lifespan (Hutchings 1976). The devil's dub cumulative distribution (Figure 20) implies that the risks of stem mortality diminish with age increasing the probability that some very old individuals will be found in the population (Hutchings 1976). Indeed devil's dub stems were far older than thimbleberry stems. While the dynamics of ramet production may differ greatly between the two spedes, it is possible that genet dynamics may not be too dissimilar. As the characteristic spedes of an earlier state of forest succession than devil's dub, thimbleberry may be expected to have lower survivorship (Bierzychudek 1982; Whitney 1986). However in areas of recurring disturbance, thimbleberry genets may also have long lifespans. Carlsson and Callaghan (1990) indicate that long term observations are necessary to more fully understand genet dynamics. Annual temporal variation accounts for a larger proportion of the variation in thimbleberry than spatial environmental heterogeneity between populations (Table 41). Given that demography examines changes through time, it is not surprising that temporal influences would be large. Indeed, greater demographic variation between years than between populations was also reported by Weiss (1981) for an annual, and by-Waite (1984) and Mack and Pyke (1983) for Plantago coronopus and Bromus tectorum respectively. Annual dimatic differences may partially explain the demographic fluctuations in stem recruitment for thimbleberry. As climate in 1987 was more condudve to flowering, increased stem mortalities in 1987 for both one- and two-year-old stems may be due to this greater floral production. Two-year-old stems generally die after flowering. Perhaps one-year-old stems have two alternatives; either to augment the resources of flowering stems or to flower in later years. Some vegetative stems produced in 1987 may have  128  transposed resources to flowering stems and subsequently died. This implies that there is a relation between the number of vegetative and flowering stems in a clump, though this was not tested. Alternatively, this pattern may arise from demographic periodicity which is not annual but longer term. In quacrats where most stems produced in 1986 died by the end of that summer, stem survivability appeared greater in 1987 (Figure 19, Table 37); moreover, quacrats where stems produced in 1986 had the longest lifespans, those produced in 1987 had the shortest lifespans (Table 37). Over the period of this study, thimbleberry may alternately maximize vegetative and reproductive strategies and demographies, producing short-lived vegetative stems in one year and longer-lived flowering stems in the next. Most of the demographic variation for thimbleberry was found within rather than between sites, suggesting that environmental influences may be operating on a relatively fine scale (Table 41). Spatial demographic variation was also found to be similarly distributed by Huenneke (1987). Billington et al. (1990), Blom and Lotz (1985), Sarukhan and Harper (1973), Matlock (1987) and Watson and Cook (1987) also found extensive demographic plasticity within clonal individuals. With the exception of the canopy cover at WED for the time interval from 466-810 days, none of the environmental variables were correlated with stem democraphy (Table 44). Several reasons for this can be advanced. As most demographic variation occurs within individuals, the scale of environmental sampling may have been too large. Perhaps replicate samples within quadrats of the environmental correlates would reflect environmental-demographic correlations more strongly. Stem demographics also fluctuate temporally and environmental variables may have failed to encapsulate these fluctuations. If the demographic periodicity was not synchronized with the periodicity of the environmental correlates, a relationship may not be perceived. In addition, if thimbleberry sites within each year and individuals within each site were not sufficiently  129  variable, then, as with Gamier ana Roy (1388), no environmental correlates at tne level of sites or individuals will be perceived. Finally abundance f a thimbleberry and devil's club may, as in other shrub species (Balogh and Grigal 1988), be controlled by shrub regeneration rather than mortality. Recruitment a a compound measure composed of recruitment and mortality may mae strongly reflect the environmental correlates assessed rather than a measure based primarily upon mortality.  130 7.4  NICHE UTILIZATION AND LIFEHISTORY STRATEGY As the niches occupied by devil's dub and thimbleberry differed, so did their life  history strategy. Devil's club is located in an area of old growth forest which may be characterized as having longterm stability; in contrast thimbleberry is found where disturbance has occurred. Perhaps due to the influence of disturbance, thimbleberry's strategy may be more flexible in response to change than devil's club. Most morphological, phenological and demographic variation is apportioned within quadrats (Tables 24, 31 and 41) suggesting that both spedes are somewhat plastic. Unlike thimbleberry. however, this variation is not readily observable for devil's dub. implying that it may be less variable than thimbleberry. If the plasticity observed for thimbleberry is appropriate to the environment, it may, as Levin (1986) suggests for a phenotypically plastic strategy, be a rapid and flexible method of accommodating change in environmental conditions (Levin 1986). Environmental responses also illustrate thimbleberry's flexibility in that disturbance is correlated with both morphology and phenology (Table 26,33, Appendix III).  In contrast devil's dub generally does not survive after disturbance and is shade  tolerant, a trait more characteristic of a competitor of later successional stages (Grime 1979). Thimbleberry has been reported to be more successful under the open canopy of earlier succession (Barber 1976). This flexibility is also structurally apparent. Thimbleberry is rhizomatous-and may, as Abrahamson (1980) suggests for rhizomatous donal spedes, be able to position stem ramets in optimum conditions by growing through less favorable areas. Rhizomes were observed to consist of a complex network with dumps of stems separated by varying rhizome lengths. Devil's dub is stoloniferous, with branches produced on erect stems. Once these stems become horizontal, the branches root and become independent. Their position within the site may therefore, be limited by their placement on the stem. If the stolon grows though an unfavorable area, the branch may root in a less desirable location and be unsuccessful. It does like the thimbleberry stem  131  ramet have the advantage of being physiologically connected to the parent plant, however. Antos and Zobel (1984) also speculate that extensive rhizome systems enable plants to cross unfavorable habitat and provide flexibility in dealing with the heterogeneous forest floor. Other flexibility expressed by thimbleberry stems and their lateral branches, aside from varying the numbers of leaves produced and lengths grown (Appendix I), is their several developmental potentialities. They may, 1) transfer resources to flowering stems and subsequently die, 2) flower in the second year of growth, or 3) if conditions are not suitable they may branch and flower in later years. Annual demographic differences also suggest that clones may alternatively produce vegetative stems in one year and reproductive stems in the next (Table 37, Figure 19). Stems and branches were also observed to develop at different rates, with two-year-old stems developing most rapidly followed by one-year-old stems and branches (Appendix I). These same patterns were not observed for devil's club. Branches and stems were not phenologically different, neither did they differ in annual growth or the number of leaves produced (Appendices I and II). Unlike thimbleberry, devil's dub stems all shared the same fate, being initially vegetative and finally flowering and branching. This flexibility may allow thimbleberry to react to the rapidly changing site conditions that occur in early successional stages. Certainly the amount and size of vegetative and reproductive tissue was correlated with disturbance and mdsture (Appendix lilt. Phenological patterns also suggest somewhat more flexibility for thimbleberry. Devil's dub produces leaves during the longest days of summer and then flowers (personal observation), a competitive strategy as suggested by Grime (1979). Thimbleberry produces leaves all summer on one-year-dd stems and flowers for up to two months (personal observation). While the devil's dub strategy may produce more seed if conditions are optimum and may be advantageous in a temporally stable site, this single pulse of seed and leaf production may be risky in a fluctuationing environment. Thimbleberry, by producing flowers and leaves all summer, has a higher probability of  132  some seed production and some increase in vegetative tissue even if disturbance occurs. A strategy which allows simultaneous and longterm reproductive and vegetative growth may be more suited to an unpredictable growing season (Harper 1977; Rathcke and Lacey 1985). The differences in modular longevity between species may also be a reflection of the character of their differing niches. Both genets and stem ramets of devil's club are long-lived. Thimbleberry stems lived to be four years old (Table 20) and while genets may survive for lengthy periods (Halpern 1989), they are unlikely to live the hundreds of years which may be the case for devil's dub. Devil's club stems flower later in life than thimbleberry and also branch later. This also suggests that environmental fluctuations are perceived in a different time scale than thimbleberry. Although phenological differences were seen between years, perhaps other changes occur at a much larger time scale for devil's dub. For example perhaps flowering or branching are influenced by environmental conditions occurring over several seasons. The scale at which temporal fluctuations affect this spedes may be in the order of decades. Thimbleberry may also be influenced by temporal fluctuations which are not annual, but short-term, such as weather patterns or disturbance events, and longer-term, such as successional patterns. Only long-term monitoring would assess such phenomena, however. Regardess of niche differences certain similarities emerged between both spedes. Phenology and morphology were positively correlated; with larger flowering plants having more rapid developmental rates for both spedes. Slade and Hutchings (1989) found similar relationships between phenology and morphology with Glechoma hederaceae . They suggest that larger stems may initiate growth earlier than smaller stems and may also have a greater probability of flowering. Larger stems were certainly more likely to flower for both devil's club and thimbleberry (Tables 20 and 23). Morphdogy was also linked to demography, however this relationship may be somewhat trivial; number of thimbleberry stems in each quadat was correlated~with the  133 numDer OT  stems dying in eacn time period ana Tor devils CIUD, me number OT stems and  their length were correlated with me number of newly produced stems (Appendix III). The lack of any correlation between floral morphology and demography for thimbleberry was unexpected, as most stems died after flowering whether in their second or third year of growth. In addition, stems which eventually flowered generally had longest lifespans. Bradbury (1981), Hutchings (1983) and Gawler et al. (1987) found that flowering stems had longest lifespans for other clonal species. This has been interpreted as the strong commitment to sexual reproduction by a clone (Slade and Hutchings 1989). There was also no evidence to suggest devil's club stems have longer lives if they flower. The survivorship curve indicated that the risk of mortality declined with age and the rate at which this declines is not altered once stems are mature enough to flower (Figure 21).  134 7.5  MANAGEMENT Both devil's club and thimbleberry live longer than the two year scope of this  study. Together with the annual differences observed, this implies that only a longterm study will achieve a deep understanding of the population dynamics and autecology of these two species. For example, longer-term studies may discern the demographic pattern of devil's club and determine whether the two year pattern observed for thimbleberry repeats. The differing environmental relationships inferred for thimbleberry within and between populations also suggests that stringent management rules may be unattainable. Environmental relationships may also be complex at other levels. Certainly the differing developmental possibilities of thimbleberry stems interacting with environment make any demographic prediction tenuous. The study does suggest, however, that alder girdling may be an effective management practice at some sites.  It appeared to affect thimbleberry growth and  phenology, though not simply through light relations, and also interacted with moisture conditions (Appendix III).  Alder girdling may be a successful management technique for  alder and thimbleberry at drier sites. Where flooding occurs or if the site is moist, however, opening up the canopy by girdling may encourage thimbleberry, especially if it persists at the site after logging (Halpern 1989).  135 FURTHER RESEARCH  7.6  This study was descriptive and hypothesis testing was not performed. Rather the study sought preliminary data to construct a framework for suitable hypothesis generation. Hypotheses based on observation have the advantage of a meaningful relationship to the plant, while not being overly simplistic. Perhaps more importantly, descriptive studies provide criteria to discard unsuitable hypotheses before time and money are spent. Based on this study, future research could explore the following questions more fully: 1.  How do increasing temperatures occurring under an open canopy increase  devil's club's rate of development? 2.  Do longer thimbleberry and devil's club stems begin to develop earlier and do  they develop more rapidly? Does earlier development ensure that maximum stem length is attained in a season for thimbleberry? 3.  What quantities and proportions of light, moisture and temperature interact to  affect thimbleberry's morphology and phenology? 4.  What is the periodicity of the demographic pattern for thimbleberry? Does the  strategy of each thimbleberry dump of stems vary from vegetative to reprodudive in a repeatable pattern? Do thimbleberry vegetative stems adively transport resources to reprodudive stems? Is there a relationship between the number of reprodudive stems and the number of vegetative stems per quadat? Finally as with most studies mae questions are unanswered than answered. The large within quadat term in all ANOVA calculations (Tables 24,31,41) and the extent to which this variation is due to plasticity a development may be of interest to assess by examining within quadat environmental variability and the phenotypic response.  and they all lived happily ever after..  136  LITERATURE CITED Aarssen, L. W. and R. Turkington. 1985. Within-species diversity in natural populations of Holcus lanatus. Lolium perenne and Trifolium repens from four different-aged pastures. Journal of Ecology. 73:869-886. Abrahamson, W. G. 1980. Demography and vegetative reproduction. In: Solbrig, 0. T. ed. Demography and evolution in plant populations. Los Angeles: University of California; p. 89-106. Abrahamson, W. G. and M. Gadgil. 1973. Growth form and reproductive effort in goldenrods (Solidaao . Compositae). American Naturalist. 107:651-661. Alaback, P. B. 1980. Biomass and production of understory vegetation on serai sitka sprucewestern hemlock forests of southeast Alaska. Corvallis: Oregon State University. Dissertation. . 1986. Biomass regression equations for understory plants in coastal Alaska: effects of species and sampling design on estimates. Northwest Science. 60:90-103. Allen, T.F.H. and T.B. Starr. 1982. Hierarchy perspectives for ecological complexity. ChicagoLondon: The University of Chicago Press. Allen, T.F.H. and E.P. Wyleto. 1983. A hierarchical model for the complexity of plant communities. Journal of Theoretical Biology. 101:529-540. Alliende, M.C. and J. L. Harper. 1989. Demographic styudies of a dioecious tree. I. Colonization, sex and age structure of a populationof Salix cinerea . Journal of Ecology. 77:1029-1047. Anderson, M.C. 1964. Studies on the woodland light climate. I. The photographic computation of light conditions. Journal of Ecology. 52:27-41. Angevine, M.W. 1983. Variations in the demography of natural populations of the wild strawberries Fragaria vesca and F. virgianum. Journal of Ecology. 71:959-974. Antos, J. A. and D. B. Zobel. 1984. Ecological implications of belowground morphology of nine coniferous forest herbs. Botanical Gazette. 145:508-517. Austin, M. P. 1987. Models for the analysis of species' response to environmental gradients. Vegetatio 69:35-45. . 1990. Community theory and competition in vegetation. In: Grace, J.B. and Tilman eds. Perspectives in Plant Competition. Academic Press; San Diego; p. 215-239. Baker, H. G. 1974. The ecology of weeds. Annual Review of Ecology and Systematics. 5:1-24. Bakker, J.P., M. Dekkar and Y. De Vries. 1980. The effectof different management practices on a grassland community and the resulting fate of seedlings. Acta Botanica Neerlandica. 29:509-522.  137  Balogh, J. 0. and D. F. Grigal. 1988. Tall shrub dynamics in northern Minnesota aspen and conifer forests. Res. Pap. Nc-283. St. Paul MN: U.S. Department of Agriculture, Forest Service, North Central Forest Experiment Station. 15 p.  Barber, H.W. 1976. An autecological study of salmonberry (Rubus spectabilis. Pursh) in western Washington. Washington: University of Washington. Dissertation. Barnes, P. W. 1986. Variation in the big bluestem (Andropogon gerardii) - sand bluestem (Andropogon hallii) complex along a local dune/meadow gradient in the Nebraska sandhills. American Journal of Botany. 73(2):172-184. Bazely, D.R. and R.L. Jefferies. 1989. Leaf and shoot demography of an arctic stoloniferous grass, Puccinellia phrvganodes in response to grazing. Journal of Ecology. 77:811-822. Bazzaz, F.A. and J.L. Harper. 1977. Demographic analysis of the growth of Linum usitatissimum. New Phytologist. 78:193-208. Beatty, S.W. 1984. Influence of microtopography and canopy species on spatial patterns of forest understory plants. Ecology. 65:1406-1419. Beckett, P.H.T. andR. Webster. 1971. Soil variability: a review. Soil and fertilizers. 34(1):1-15. Bell, A.D. 1984. Dynamic morphology: a contribution to plant population ecology. In: Dirzo, R. and J. M. Sarukhan eds. Perspectives in plant population ecology. Sunderland, Massachusetts: Sinauer Associates; p.48-65. Bierzychudek, P. 1982. Life histories and demography of shade-tolerant temperate forest herbs: a review. New Phytologist. 90:757-776. Billington, H.L., A.M. Mortimer and T. McNeilly. 1990. Survival and reproduction in adjacent grass populations: effects of done, time and environment. Journal of Ecology. 78:1-14. Blom, C.W.P.M. 1988. The realism of models in plant demography. Acta Botanica Neerlandica. 37(4) :421-438. Blom, C.W.P.M. and L.P. Lotz, 1985. Phenotypic plasticity and genetic differentiation of demographic characteristics in some Plantago spedes. In: Haeck, J. and J.W. Woldendorp eds. Structure and functioning of plant populations 2. Amsterdam: North Holland Publishing Company, p. 105-113. Bouyoucos, G.J. 1962. Hydrometer method improved for making particle size analysis of soils. Agronomy Journal. 54:464-465. Bradfield, G. E. and A. Campbell. 1986. Vegetation-elevation correlation in two dyked marshes of northeastern Vancouver Island: a multivariate analysis. Canadian Journal of Botany. 64(11):2487-2494. Briggs, J. B., J. Danek, M. Lyth and E. Keep. 1982. Resistance to the raspberry beetle Byturus tomentosus in Rubus spedes and their hybrid derivatives with Rubus idaeus cultivar Norfolk giant. Journal of Horticultural Sdence. 57(1):73-78.  138  Burdon, J. J. 1980. Intra-specific diversity in a natural population of Trifolium repens. Journal of Ecology. 68:737-757. Burdon, J.J., D.R. and Marshall and A.D.H. Brown. 1983. Demographic and genetic changes in populations of Echium plantagineum . Journal of Ecology. 71:667-679. Carlsson, B.A. and T. V. Callaghan. 1990. Effects of flowering on the shoot dynamics of Carex bigelowii along an altitudinal gradient in Swedish Lapland. Journal of Ecology. .78:152165. Carroll, D. J. 1987. Some multidimensional scaling and related procedures devised at Bell Laboratories with ecological applications. In: Legendre P. and L. Legendre eds. Developments in numerical ecology. Springer-Verlag; Berlin-Heidelberg-New YorkLondon-Paris-Tokyo: p. 65-138. Carroll, D.J. and Jih-Jie Chang. 1970. Analysis of individual differences in multidimensional scaling via an N-way generalization of "Eckart-Young" decomposition. Psychometrika 35(3):283-319. Carter, R. E. and L. E. Lowe. 1986. Lateral variability of forest floor properties under secondgrowth Douglas-fir stands and the usefulnes of composite sampling techniques. Canadian Journal of Forest Resources. 16:1128-1132. Chan, S.S., R. W. McCreight, J.D. Walstad and T. A. Spies. 1986. Evaluating forest vegetation cover with computerized analysis of fisheye photographs. Forest Science 32(4) :108591. Chang, D. H. S. and H. G. Gauch, Jr. 1986. Multivariate analysis of plant communities and environmental factors in Ngari, Tibet. Ecology. 67(6):1568-1575. Chazdon, R. L. and C. B. Field. 1987. Photographic estimation of photosynthetically active radiation: evaluation of computerized technique. Oecologia (Berlin). 73:525-532. Cid-Benevento, C. R. 1987. Relative effects of light, soils moisture availability and vegetative size on sex ratio of two monoecious woodland annual herbs: Acalypha rhomboidea (Euphorbiaceae) and Pilea pumila (Urticaceae) Bulletin of the Torrey Botanical Club. 114(3)293-306. Clark, S.C. 1980. Reproductive and vegetative performance in two winter annual grasses, Catapodium rigidum (L). CE. Hubbard and C. marinum (L.) CE. Hubbard. 2. Leafdemography and its relationship to the production of caryopses. New Phytologist. 84:79-93. Coates, D. and S. Haeussler. 1986. A preliminiary guide to the response of major species of competing vegetation to silvicultural treatments. Land management handbook no. 9, ISSN 02291692. Victoria, B.C.: Ministry of Forests. Cook, R.E. 1985. Growth and development in clonal plant populations. In: Jackson, J.B.C., L.YV. Buss and R.E. Cook eds. Population biology and evolution of clonal organisms. New Haven, Connecticut: Yale University Press, p.259-296.  139 . 1979a. Patterns of juvenile mortality and recruitment in plants. In: Solbrig, O.T., S. Jain, G.B. Johnson and P.H. Raven eds. Topics in plant population biology. New York: Columbia University Press, p.2067-231. . 1979b. Asexual reproduction: further consideration. American Naturalist. 113:767772. Coutin, P.. M. C. Feller and K. Klinka. 1983. Lateral variability in some properties of disturbed forest soils in southwestern British Columbia. Canadian Journai of Soil Science. 63:529-539. Coupe, R., C A . Ray, A. Comeau, M.V. Ketcheson and R.M. Annas. 1982. A guide to some common plants of the Skeena Area, British Columbia. Land Management Report, ISSN 0229-1662; no.4. Ministry of Forests, Victoria, B.C. Crawley, M.J. 1986. Life history and environment. In: Crawley, M.J. ed. Plant Ecology. OxfordLondon-Edinburgh-Boston-Palo Alto-Melbourne: Blackwell Scientific Publications; p. 253-290 Douglas, D. A. 1989. Clonal growth of Salix setchelliana on glacial river gravel bars in Alaska. Journal of Ecology. 77:112-126. Duke, N.C 1990. Phenological trends with latitude in the mangrove tree Avicennia marina. Journal of Ecology. 78d:113-133. Dunn, C P . 1986. Shrub layer response to death of Ulmus americana in southeastern Wisconsin lowland forests. Bulletin of the Torrey Botanical Club. 113:142-148. Eis, S. 1981. Effect of vegetative competition on regeneration of white spruce. Canadian Journal of Forest Resources. 11:1-8. Ennos, RA. 1985. Measuring the effects of genetic variation on plant fitness. In: Haeck, J. and J.W. Woldendorp eds. Structure and functioning of plant populations. Amsterdam: North Holland Publishing Company, p. 51-64. Environment Canada, Atmospheric Environment Service, Monthly Record, Meteorological Observations in Canada. Eriksson, 0. 1988. Patterns of ramet survivorship in clonal fragments of the stoloniferous plant Potentilla anserina. Ecology. 69(3):736-740. . 1985. Reproduction and clonal growth in Potentilla anserina L. (Rosaceae): the relation between growth form and dry weight allocation. Oecologia (Berlin). 66:378-380. Flanagan, L. B. and W. Moser. 1985. Flowering phenology, floral display and reproductive success in dioecious Aralia nudicaulis L. (Araliaceae). Oecologia (Berlin). 68:23-28. Fone, A.L. 1989. A comparative demographic study of annual and perennial Hypochperis (Asteraceae). Journal of Ecology. 77:495-508. »  Fowler, N., J. Zasada and J. L. Harper. 1983. Genetic components of morphological variation in Salix repens. New Phytologist. 95:121-131.  MO  Fox, D.J. and K.E. Guire. 1976. Documentation for MIDAS. 3d ed. University of Michigan, Ann Arbor: Statistical Research Lab. Funk and Wagnalls standard college dictionary, Canadian Edition. 1978. Vancouver, B.C.: Fitzhenry and Whiteside Limited. Gadgil, M. and 0. T. Solbrig. 1972. The concept of r- and K-selection: evidence from wild flowers and some theoretical considerations. American Naturalist. 106:14-31. Gamier, E. and J. Roy. 1988. Modular and demographic analysis of plant leaf area in sward and woodland population of Dactylis glomerta and Bromus erectus. Journal of Ecology. 76:729-743. Gawler, S. C , D. M. Waller and E. S. Menges. 1987. Environmental factors affecting establishment and growth of Peducularis furbishiae: a rare endemic of the St. John river valley, Maine. Bulletin of the Torrey Botanical Club. 114(3):280-292. Gilbert, N. and A.P. Gutierrez. 1973. A plant aphid parasite relationship. Journal of Animal Ecology. 42(2):323-340. Gill, D.S. and B. E. Mahall. 1986. Quantitative phenology and water relations of an evergreen and a deciduous chaparral shrub. Ecological Monographs. 56(2) :127. Gittins, R. 1985. Canonical analysis a review with applications in ecology. Bertin-HeidelbergNew York-Toyko: Springer-Verlag. Grant, V. 1981. Plant Speciation. New York: Columbia University Press. Gratkowski, H. 1971. Mid summer foliage sprays on salmonberry and thimbleberry. United States Forest Service, Pacific Northwest Forest and Range Experimental Station Research Notes. 171:1-15. Greig, M. and D. Osterlin. 1978. UBC ANOVAR analysis of variance and covariance revised. Vancouver: University of British Columbia Computing Centre. Greig-Smith, P. 1983. Quantitative Plant Ecology, 3d ed. Studies in Ecology, vol 9. Berkley: University of California Press. Grime, J.P. 1979. Plant strategies and vegetation processes. Chichester: J. Wiley and Sons. Haeussler, S. and D. Coates. 1986. Autecological characteristics of selected species that compete with conifers in British Columbia: a literature review. Land management report ISSN 0702-9861; no. 33. Victoria, B.C.: Ministry of Forests. Haeussler, S. J. Pojar, B.M. Geisler, D. Yole and R.M. Annas. 1984. A guide to the coastal western hemlock zone, northern drier maritime subzone (CWFf), in the Prince Rupert forest region, British Columbia. Land Management Report. ISSN 0702-9861; no. 21. Victoria, B.C.: Ministry of Forests. Halpern, C. B. 1989. Early successional patterns of forest species: interactions of life history traits and disturbance. Ecology. 70(3)704-720.  H I  Hancock, J. F. and M. P. Pritts. 1987. Does reproductive effort vary across different life forms and serai environments? a review of the literature. Bulletin of the Torrey Botanical Club. 114(1):53-59.  Harper, J.L. 1980. Plant demography and ecological theory. Oikos. 35:244-253. . 1978. The demography of plants with clonal growth. In: Freysen, A.H.J, and J.W. Woldendrop eds. Structure and functioning of plant populations. Amsterdam: NorthHolland Publishing Co. p.27-45. . 1977. Population Biology of Plants. London: Academic Press.  Harper, J.L. and A.D. Bell. 1977. The population dynamics of growth of form in organisms with modular construction. In: Anderson, R.M., B.D. Turner and L.R. Taylor eds. Population dynamics 20th symposium of the British ecological society. Oxford: Blackwell Scientific Publications, p. 29-52. Harper, J.L. and J. White. 1974. The demography of plants. Annual of Ecology and Systematics. 5:419-463.* Harper, J. L., P. J. Lovell and K. G. Moore. 1970. The shapes and sizes of seeds. Annual Review of Ecology and Systematics. 1:327-356. Hardwick, R.C. 1984. Some recent developments in growth analysis - a review. Annals of Botany. 54(6):807-812. Hartgerink, A.P. and F.A. Bazzaz. 1984. Seedling-scale environmental heterogeneity influences individual fitness and population structure. Ecology. 65:198-206. Hartnett, D.C. 1987. Effects of fire on clonal growth and dynamics of Pityopsis graminifolia (Asteraceae). American Journal of Botany. 74(11):1737-1743. Hartnett, D.C. and F. A. Bazzaz. 1985. The genet and ramet population dynamics of Solidago canadensis in an abandoned field. Journal of Ecology. 73:407-413. Heideman, P.D. 1989. Temporal and spatial variation in the phenology of flowering and fruiting in a tropical rainforest. Journal of Ecology. 77:1059-1079. Heinrich, B. 1976. Flowering phenologies: bog, woodland and disturbed habitats. Ecology. 57:890-899. Helenurm, K. and S.C.H. Barret. 1987. The reproductive biology of boreal forest herbs. II. phenology of flowering and fruiting. Canadian Journal of Botany. 65:2047-2056. Herndon, A. 1988. Variation in resource allocation and reproductive effort within a single population of Liatris laevigata Nuttall (Asteraceae). American Midland Naturalist. 118(2):406-413. Hickman, J.C. 1975. Environmental demography of the sand dune annual Caklla edentula growing along an environmental gradient in Nova Scotia. Journal of Ecology. 69:615630.  142  Hitchcock, C. L. and A. Cronquist. 1973. Flora of the pacific northwest an illustrated manual. Seattle, Washington: University of Washington Press. Hobbs, R.J. and H.A. Mooney. 1987. Leaf and shoot demography in Baccharis shrubs of different ages. American Journal of Botany. 74{7):1111-1115. Hobbs, R. J. and H. A. Mooney. 1985. Vegetative regrowth following cutting in the shrub Baccharis pilularis ssp. consanginea (DC). American Journal of Botany. 72(4):514-519. Holzner, W. 1982. Concepts, categories and characteristics of weeds. In: Holzner W. and M Numata eds. Biology and Ecology of weeds. The Hague: Dr. N. Junk; p. 3-20. Huenneke, L.F. 1987. Demography of a clonal shrub, Alnus incana ssp. rugosa (Betulaceae). The American Midland Naturalist. 117(1):43-55. . 1983. Understory response to gaps caused by the death of Ulmus americana in central New York. Bulletin of the Torrey Botanical Club. 110:170-175. Huenneke, L.F. and R. R. Sharitz. 1986. Microsite abundance and distribution of woody seedlings in a South Carolina cypress-tupelo swamp. The American Midland Naturalist. 1145(2):328-335. Hulten, E. 1968. Flora of Alaska and neighbouring territories a manual of vascular plants. Stanford, California: Stanford University Press. Hunt, R. 1978. Demography versus plant growth analysis. New Phytologist. 80:269-272.  . 1982. Plant growth analysis: second derivatives and compounded second derivatives of splined plant growth curves. Annals of Botany. 50:317-327. Hunt, R. and F. A. Bazzaz. 1980. The biology of Ambrosia trifida L. V. response to fertilizer, w growth analysis at the organismal and sub-organismal levels. New Phytologist. 84:113121.  Hunt, R., J.W. Wilson, D. W. Hand and D. G. Sweeney. 1984. Integrated analysis of growth a light interception in winter lettuce. I. analytical methods and environmental influences. Annals of Botany. 54(6)743-757. Hutchings, M.J. 1986. The structure of plant populations. In: Crawley, M. J. ed. Plant ecology. OxforchLcriclcri-Edinburgh-Boston-Palo Alto-Melbourne:Blackwell Scientific Publications; p. 97-136. . 1983. Shoot performance and population structure in pure stands of Mercurialist perennis L. a rhizomatous perennial herb. Oecologia (Berlin). 58:260-264. . 1976. Plant population biology. In: P.D. Moore and S.B. Chapman eds. Methods in plant ecology. Oxford-London-Edinburgh-Boston-Palo Alta-Melbourne: Blackwell Scientific Publications; p. 377-436. Jackson, L.E. and L.C. Bliss. 1984. Phenology and water relations of three plant life forms in a dry tree-line meadow. Ecology. 65(4) :1302-1314.  1-43  Jeffers, J.N.R. 1976. An introduction of systems analysis with ecological applications. London, England: University Park Press. Jolliffe, P.A. and W.H. Courtney. 1984. Plant growth analysis: additive and multiplicative components of growth. Annals of Botany. 54:243-254. Jones, D. 1983. The influence of host density and gall shape on the survivorship of Diastrophus kincaidii Hymenoptera Cynipidae. Canadian Journal of Zoology. 61(9)2138-2142. Jurik, T. W. 1985. Differential costs of sexual and vegetative reproduction in wild strawberry populations. Oecologia tBerlin). 66:394-403. Kaul, R.B. 1985. Reproductive phenology and biology in annual and perennial Alismaticeae. Aquatic Botany. 22:153-164. Kawano, S., S. Hiratsuka and K. Hayashi. 1982. The productive and reproductive biology of flowering plants. V. Life history characteristics and survivorship of Erythronium japonicum . Oikos. 38:129-149. Kelly, D. 1989a. Demography of short-lived plants in chalk grassland. I. Life cycle variation in annuals and strict biennials. Journal of Ecology. 77:747-769. . 1989b. Demography of short-lived plants in chalk grassland. II. Control of mortality and fecundity. Journal of Ecology. 77:770-784. . 1989c. Demography of short-lived plants in chalk grassland. III. Journal of Ecology. 77:785-798.  Population stability.  Kephard, S.R. 1987. Phenological variation in flowering and fruiting of Asclepias. The American Midland Naturalist. 118(1):64-75. Kershaw, K. A. and J. H. Looney. 1985. Quantative and dynamic plant ecology, 3rd ed. London: Edward Arnold. Kirby, K.J. 1980. Experiments on vegetative reproduction in bramble (Rubus vestitus). Journal of Ecology. 68:513-520. Kirkpatrick, M. 1984. Demographic models based on size, not age, for organisms with indeterminate growth. Ecology. 65(6):1874-1884. Kjellson, G. 1985. Seed fall and phenological overlap in a guild of ant-dispersed herbs. Oecologia (Berlin). 68:140-146. Klinka, K., V.J. Krajina, A. Ceska and A.M. Scagel. 1989. Indicator plants of coastal British Columbia. Vancouver, B.C.;University of British Columbia Press. Koehl, M.A.R. 1989. Discussion from individuals to populations. In: Roughbarden, R., R. M. May, S. A. Levin eds. Perspectives in ecological theory. Princeton, N.J.;_Princeton University Press; p.39-53.  144  Krajina, V.J. 1969. Ecology of forest trees in British Columbia. Ecology of Western North America. 2:1-146. Kruskal, J. B. 1964a. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika. 29(1):1-27. . 1964b. Nonmetric multidimensional scaling: a numerical method. Psychometrika. 29(2)1115-129.  Kruskal, J.B. and M. Wish. 1978. Multidimensional Scaling. Beverly Hills, CA.: Sage Publications, Inc. Lau, R. R. and D. R. Young. 1988. Influence of physiological integration on survivorship and water relations in a clonal herb. Ecology. 69(1)215-219. Lavkulich, LM. 1981. Methods manual pedology laboratory. Soil Science, University of British Columbia.  Vancouver, B.C.: Department of  Law, R., D. Bradshawand P.D. Putwain. 1977. Life-history of Poa annua. Evolution. 31:233246. Lee, H. S., A. R. Zangerl, K. Garbutt and F. A. Bazzaz. 1986. Within and between species variation in response to environment gradients in Polygonum pensylvanicum and Polygonum virginianum. Oecologia (Berlin). 68:606-610. Levin, D. A. 1986. Breeding structure and genetic variation. In: Crawley, M. J. ed. Plant Ecology. London, England: Blackwell Scientific Publications; p. 217-252. Lieth, H. ed. 1974. Phenology and seasonality modelling, vol 8. New York: Springer-Verlag. Loehie, C. 1987. Partitioning of reproductive effort in clonal plants: a benefit-cost model. Oikos. 49:199-208. Lovett Doust, J. and G.W. Eaton. 1982. Demographic aspects of flower and fruit production in bean plants, Phaseolus vulgaris L. American Journal of Botany. 69(7) :1156-1164. Lovett Doust, L. 1981. Population dynamics and local specialization in a clonaT perennial (Ranunculus repens) 2. Demography of leaves, and a reciprocal transplant-replant experiment. Journal of Ecology. 69:757-768. Luken, J.O. 1988. Population structure and biomass allocation of the naturalized shrub Lonicera maackii (Rupr.) Maxim, in forest and open habitats. The American Midland Naturalist. 119(2)258-267. Lyons, C. P. 1976. Trees, shrubs and flowers to know in British Columbia. Vancouver: J.M. Dent 8< Sons (Canada) Limited. Mack, R.N. and D. A. Pyke. 1983. The demography of Bromus tectorum variation in time and space. Journal of Ecology. 71:69-93.  145  Mader, D. L. 1963. Soil variability - a serious problem in soil-site studies in the northwest. Soil Science Society of America. 27:707-709. Maddox, 0. D., R. E. Cook, P. H. Wimberger and S. Gardescu. 1989. Clone structure in four Solidago altissima (Asteraceae) populations: rhizome connections within genotypes. American Journal of Botany. 76(2) :318-326. Maillete, L 1982. Structural dynamics of silver birch. 1. The fates of buds. Journal of Applied Ecology. 19:203-218. Matlock, G.R. 1987. Comparative demographies of four adjacent population sof the perennial herb Silene dioica (Caryophvllaceae). Journal of Ecology. 75:113-134. Maurer, B. A. 1987. Scaling of biological community structure: a systems approach to community complexity. Journal of Theoretical Biology. 127:97-110. McGraw, J.B. and J. Antonovics. 1983. Experimental ecology of Dryas octopetala ecotvpes. II. A demographic model of growth, branching and fecundity. Journal of Ecology. 71(3):899912. McNicol, R. J., B. Williamson, D. L. Jennings and J. A. T. Woodford. 1983. Resistance to raspberry cane midge Resseliella theobaldi and its association with wound periderm in Rubus crataegifolium and its red raspberry derivatives. Annals of Applied Biology. 103(3):489-496. Menges, E. S. 1987. Biomass allocation and geometry of the clonal forest herb Laportea canadensis : adaptive responses to the environment or allometric constraints? American Journal of Botany. 74(4).551 -563. . 1986. Environmental correlates of herb species composition in five southern Wisconsin floodplain forests. The American Midland Naturalist. 115(1):106-117. . 1983. Plant strategies in relation to elevation and light in floodplain herbs. The American Naturalist. 122(4):454-473. Moot, J.H., J. Haeck, J. Van der Toorn and P.H. Vantienderen. 1989. Comparative demography of Plantago. I. Observations on eight populations of Plantaao lanceolata. Acta Botanica Neerlandica. 38(1):67-78. Newton, P.N. 1988. The structure and phenology of a moist deciduous forest in the central Indian highlands. Vegetatio. 75:3-16. Nilson, E.T. 1986. Quantitative phenology and leaf survivorship of Rhododendron maximum in contrasting irradiance environments of the southern Appalachian mountains. American Journal of Botany. 73(6):822-831. Noble, J.C.. A.D. Bell and J.L. Harper. 1979. The population biology of plants with clonal growth. I. The morphology and structural demography of Carex arenaria. Journal of Ecology. 67:983-1008.  146 Nybom, H. A. 1987. Demographic study of the apomictic blackberry, Rubus nessensis (Rosaceae). Nordic Journal of Botany. 7:365-373. Oberbauer, S. F. and B. R. Strain. 1986. Effects of canopy position and irradiance on the leaf physiology and morphology of Pentaclethra macroloba (Mimosaceae). American Journal of Botany. 73(3):409-416. Oka, H.I. 1976. Mortality and adaptive mechanisms of Oryza perennis strains. Evolution 30:380-392. O'Neill, R. V. 1989. Perspectives in hierarchy and scale. In: Roughgarden, J., R. M. May and S. Levin eds. Perspectives in ecological theory. Princeton, N.J.: Princeton University Press; p. 140-156. O'Neill, R.V., D. L. DeAngelis, J. B. Waide and T. F. H. Allen. 1986. A hierarchical concept of ecosystems. Princeton, N.J.: Princeton University Press. Pimentel, R. 1979. Morphometries. Dubuque: Randall/Hunt Publ. Co.. Pitelka, L. F., D. S. Stanton and M. 0. Peckenham. 1980. Effects of light and density on resource allocation in a forest herb, Aster acuminatus (Compositae). American Journal of Botany. 67(6):942-948. Piatt, W.J., G.W. Evans and S.L. Rathbun. 1988. The population dynamics of a long-lived conifer Pinus palustris. The American Naturalist. 131(4):491-525. Pojar, J. 1974. Reproductive dynamics of four plant communities of southwestern British Columbia. Canaidan Journal of Botany. 52:1819-1834. Pope, D.J. and P.S. Lloyd. 1975. Hemispherical photography, topography and plant distribution. In: Evans, G.C., R. Bainbridge and 0. Rackham, eds. Light as an ecological factor. 16th Symposium British Ecological Society. Oxford: Blackwell Scientific Publications, p.385-408. Primack, R.B. 1985. Patterns of flowering phenology in communities, populations, individuals and single flowers. In: White, J. ed. Handbook of vegetation science The population structure of vegetation. Dordrecht-Boston-Lancaster: Dr. W. Junk Publishers, p. 571-593. . 1980. Variation in the phenology of natural populations of montane shrubs in New Zealand. Journal of Ecology. 68:849-862. Ralhan, P.K., R.K. Khanna, S.P. Singh and J.S. Singh. 1985. Certain phenological characters of the shrub layer of Kumaun Himalayan forests. Vegetatio. 63:113-119. Rathcke, B. 1989. Flowering phenologies in a shrub community: competition and constraints. Journal of Ecology. 76:975-994. . 1988. Patterns of flowering phenologies: testability and causal inference using a randon model. In: Strong, D.R., D. Simberloff, L.G. Abele and A.B. Thistle eds. Ecological communities: conceptual issues and the evidence. Princeton: Princeton University Press, p. 383-393.  M7 Rathcke, B. and E.P. Lacey. 1985. Phenological patterns of terrestrial plants. Annual Review of Ecology and Systematics. 16:179-214. Reader, R.J. 1983. Using heat sum models to account for geographic variation in the floral phenology of two Ericaceous shrubs. Journal of Biogeography. 10:47-64. Reinartz, J. A. and J. W. Popp. 1987. Structure of clones of northern prickly ash (Xanthoxylum americanum). American Journal of Botany. 74(3):415-428. Richards, A. J. 1984. Plant breeding systems. London, England: George Allen 8c Unwin (Publishers) Ltd. Roy, J. and H. A. Mooney. 1987. Contrasting morphological and physiological traits of Heliotropium curassavicum L. plants from desert and coastal populations. Acta Oecologia (Oecologia Plantarum). 8(22):2:99-112. Sakai, A.K. and J.H. Sluak. 1985. Four decades of secondary succession in two lowland permanent plots in norther lower Michigan. American Midland Naturalist. 113:146-157. Sarukhan, J., M. Martinez-Ramos and D. Pihero. 1984. The analysis of demographic variability at the individual level and its population consequences. In: Dirzo, R. and J. Sarukhan eds. Perspectives on plant population ecology. Sunderland, Massachusetts: Sinauer Associates Inc., Publichers; p. 83-106. Sarukhan, J. and J.L. Harper. 1973. Studies on plant demography: Ranunculus repensL.. R bulbosus L, and R. acris L. I. Population flux and survivorship. Journal of Ecology. 61:675-716.  SAS Users Guide: Statistics, Version 5 ed. 1985. Cary ,NC: SAS Institute Inc. SAS Technical Report P-161. Additional SAS/STAT procedures: CANCORR and ORTHOREG. 1985. Cary.NC: SAS Institute Inc. Scagel, R.K. and J. Maze. 1984. A morphological analysis of local variation in Stipa nelsonnii and S. richardsonii. Canadian Journal of Botany. 62:763-770. Schiffman, S. S., M. L.Reynolds and F.W. Young. 1981. Introduction to multidimensional scaling theory, methods and applications. New York-London-Toronto-Sydney-San Francisco: Academic Press. Schlichting, C. D. 1986. The Evolution of Phenotypic Plasticity in Plants. Annual Review of Ecology and Systematics. 17:667-93. . 1989. Phenotypic plasticity in Phlox II. plasticity of character correlations. Oecologia. 78:496-501. Schwaegerle, K.E. and D. A. Levin. 1990. Environmental effects on growth and fruit production in Phlox drummondii. Journal of Ecology. 78:15-26. Silva, J.F. and M. Ataroff. 1985. Phenology, seed crop and germination of coexisting grass species from a tropical savanna in western Venezuela. Acta Oecdogia(Oecologia Plantarum). 6(20)41-51.  148 Silvertown, J.W. 1985. Survival, fecundity and growth of wild cucumber, Echinovcvstis lobata. Journal do Ecology. 73:841-849. .  1982. Introduction to plant population ecology. New York: Longman Inc.  Singer, M. J. and D. N. Munns. 1987. Soils, an introduction. New York: Macmillan Publishing Company. Slade, A.J. and M.J. Hutchings. 1989. Within- and between-population variation in ramet behaviour in the gynodioecious clonal herb, Glechoma hederacea (Latiatae). Canadian Journal of Botany. 67:633-639. Slade, A. J. and M. J. Hutchings. 1987a. The effects of nutrient availability on foraging in the clonal herb Glechoma hederacea . Journal of Ecology. 75:95-112. Slade, A. J. and M. J. Hutchings. 1987b. An analysis of the costs and benefits of physiological integration between ramets in the clonal perennial herb Glechoma hederacea . Oecologia (Berlin). 73:425-431. Smith, G. W. 1983. Artie pharmacognosia 2. devil's club Oplopanax horridus. Journal of Ethnopharmacology. 7(3) :313-320. Smith, T.M. and D.L. Urban. 1988. Scale and resolution of forest structural pattern. Vegetatio. 74:143-150. Sokal, R.R. and F.J. Rohlf. 1969. Biometry. San Francisco: W.H. Freeman and Co. Somers, G.F. and D. Grant. 1982. Influence of seed source upon phenology of flowering of Spartina alterniflora Loisel and the likelihood of cross pollination. American Journal of Botany. 68:6-9. Southwood, T. R. E. 1988. Tactics, strategies and templets. Oikos. 52:3-18. Stearns, S. C. 1977. The evolution of life history traits: a critique of the theory and review of the data. Annual Review of Ecology and Systematics. 8:145-71. Stewart, R. E. 1974a. Budbreak sprays for site preparation and release from 6 coastal brush species. United States Forest Service Research Paper PNW. 176:1-20. . 1974b. Foliage sprays for site preparation and release from 6 coastal brush species. United States Forest Service Research Paper PNW. 172:1-18. Stiles, E.W. 1980. Patterns of fruit presentation and seed dispersal in bird-disseminated woody plants in the eastern deciduous forest. American Naturalist. 116:670-86. Svensson, B. M. and T. V. Callaghan. 1988. Small-scale vegetation pattern related to the growth of Lycopodium annotinum and variations in its micro-environment. Vegetatio. 76:167-177. Tilman, D. 1988. Plant strategies and the dynamics and structure of plant communities. Princeton, New Jersey: Princeton University Press.  149 . 1986. Resources, competition and the dynamics of plant communities. In Plant ecology, Crawley, M. J. ed. Palo Alto, California: Blackwell Scientific Publications; p. 51-76.  Turner, N. J. 1982. Traditional use of devil's club (Oplopanax horridus: Araliaceae) by native peoples in western North America. Journal of Ethnobotany. 2(1 ):17-38. Valentine, K.W.G., P.N. Sprout, T.E. Baker and L.M. Lavkulich. 1978. The soil landscapes of British Columbia. Victoria, B.C.: Resource Analysis Branch, Ministry of Environment. Van Cauteren, P. and C. Lefebvre. 1986. Morphological, phenological and cemical variationin woodland populations of the daffodil (Narcissus pseudonarcissus L). Acta Oecologia (Oecologia Plantarum). 7(21)327-337. Van der Toorn, J. and T. L. Pons. 1988. Seedling establishment of Plantago lanceolata L. and Plantago major L. between grass; an experimental investigation. II. Seedling survival and selection on germination. Oecologia (Berlin). 76:341-347. Verkaar, H.J. and A.J. Shenkeveld. 1984. On the ecology of short-lived forbs in chalk grassland: seedling development under low phton flux conditions. Flora. 175:135-141. Waite, S. 1984. Changes in the demography of Plantago coronopus at two coastal sites. Journal of Ecology. 72:809-826. Watkinson, A. R. 1985. Plant responses to crowding. In: White, J. ed. Studies on plant demography, a festschrift for John L Harper. London, England: Academic Press Inc. (London) Ltd; p. 275-290. Watkinson, A.R. and C.C. Gibson. 1985. Life-history variation and the demography of plant populations. In: Haeck, J. and J.W. Woldendorp, ed. Structure and functioning of plant populations 2. Amsterdam: North Holland Publishing Company; p. 105-113. Watson, M.A. and G.S. Cook. 1987. Demographic and developmental differences among clones of water hyacinth. Journal of Ecology. 75:439-457. Weiss, P.W. 1981. Spatial distribution and dynamics of the introduced annuarEmex australis in southeastern Australia. Journal of Applied Ecology. 18:849-864. Welter, D. E. 1987. Self-thinning exponent correlated with allometric measures of plant geometry. Ecology. 68(4) :813-821. Werner, P.A. 1985. Phenotypic variation and implications for reproductive success. In: Haeck, J. and J.W. Woldendorp, ed. Structure and functioning of plant populations 2. Amsterdam: North Holland Publishing Company; p. 1-26. Wieder, R.K., C. A. Bennett and G.E. Lang. 1984. Flowering phenology at Big Run Bog, West Virginia. American Journal of Botany. 71(2)203-209. White, J. 1985a. The thinning rule and its application to mixtures of plant populations, in: White, J. ed. Studies on plant demography, a festschrift f a John L. Harper. London, England: Academic Press Inc. (London) Ltd; p. 291-312.  150 . 1981. Demographic factors in populations of plants. In: Solbrig, O.T. ed. Demography and evolution in plant populations. Berkley: University of California Press; p.21-48. . 1981. The allometric interpretation of the self-thinning rule. Journal of Theoretical Biology. 89:475-500. . 1979. The plant as a metapopulation. Annual Review of Ecology and Systematics. 10:109-45. Whitney, G.G. 1986. A demographic analysis of Rubus idaeus and Rubus pubescens. Canadian Journal of Botany. 64:2916-2921. Wilkinson, L. 1988a. SYGRAPH. Evanston, IL: SYSTAT, Inc.. . 1988b. SYSTAT: the system for statistics. Evanston, IL: SYSTAT, Inc.. Yarie, J. and Mead, B, R. 1989. Biomass regression equations for determination of vertical structure of major understory species of southeast Alaska. Northwest Science. 63(5)221-231. Young, F. W. and R.Lewyckyj. 1979. ALSCAL user's guide, 3d. ed. Chapel Hill, N.C. : Psychometric Laboratory, Data Analysis and Theory Associates. Zar, J. H. 1974. Biostatistical analysis. Englewood Cliffs, N. J.: Prentice-Hall, Inc.  151 Appendix 1.  Mann Whitney U tests for differences in morphology between thimbleberry sites in 1986 and 1987, between years (1986 ana 1987) and within plants between stems and branches. THIMBLEBERRY SITES  YEARS  1987  1986  DC  TB  # flowers/quadrat  .0019  1.000  .3609  .0240  # flowers/stem  .1982  1.000  # flowering stems/quadrat  .0004  1.000  # fruit/quadrat  .0004  1.000  .1010  .3253  .0000  .2300  # leaves/branch  .6636  .2378  * leaves/1 -year-old stem  .0030  .1485  .0829  # leaves/2-year-old stem  .0011  .3845  .5159  # leaves/stem  .0004  .0311  1-year-old stem length  .1146  .2568  .8887  2-year-old stem length  .0000  .7622  .3186  branch length  .0029  .6735  .5806  # stems/quadrat # 1-year-old stems/quadrat # 2-year-old stems/quadrat  .0084 .7299 .0000  .5194  .4494  .2480  .8181  1-year-old stems and 2-year-old stems  branches and 1-year-old stems  branches and 2-year-old stems  # stem leaves -1986  .0061  .0000  .0007  * stems leaves -1987  .0003  .0000  _ .0000  leaf length  .0053  .0015  .0000  leaf width  .0045  .0015  .0000  stem length -1987  .0000  .0000  .9923  stem length -1986  .0000  .0000  .4285  Thimbleberry  152  Devil's club  stems and branches  * leaves -1987  .0070  »leaves -1986  .0015  length  .0000  length grown  .8207  153 Appendix II.  Mann-Whitney U tests for differences in generative and vegetative phenologies between thimbleberry sites in 1986 and 1987, between years (1986 and 1987), and within plants between stems and branches. YEARS  THIMBLEBERRY SITES 1986  1987  DC  TB  reproductive phenology  .0062  .0000  .0290  .0099  vegetative phenology  .7914  .0000  .0099  2-year-old stems vs branches  1- year-old stem vs 2-year-old stems  1-year-old stems vs branches  KIT  .0000  .0000  .0000  WED  .0000  .0149  .0000  TB  .0000  .0000  .0000  ^  .8300  stems vs branches  vegetative phenology -  .0653  DC reproductive phenology KIT .0498 WED  .0000  TB  .0000  DC  .7227  154 Appendix III.  CANCORR relationships between morphological, phenological and demographic characters for TB, KIT, WED and DC and soils, frequency and presence/absence of plant neighbour matrices. Morphology  A B C D  -  morphological morphological environmental environmental  variance explained morphological variables variance explained environmental variables variance explained by environmental variables variance explained by morphological variables TB  Canonical axes Correlation coefficient Redundancies A B C D  A B C D Correlation to PCA morphological axis I II III environmental I axis II III  WED  DC  I  II  I  II  I  II  I  II  .9654  .9095  .9654  .9095  .9856  .8614  .9908  .9732  .0915 .0853 .0914 .0851  .1412 .2021 .1011 .0837  .0915 .0853 .0914 .0851  .1412 .1168 .1011 .0837  .1715 .1666 .1116 .1084  .0707 .0626 .1040 .0921  .0920 .0904 .1591 .1562  .2373 .2247 .1885 .1785  Morphology Frequency of plant neighbours Canonical axes Con-elation coefficient Redundancies  KIT  TB  KIT  WED  DC  I  II  I  II  I  II  I  .8589  .7759  .9970  .9876  .9999  .9739  .9996  .9749  .3435 .2534 .1228 .0906 axis  .0725 .0437 .0849 .0511  .1270 .1263 .0613 .0609  .0796 .0776 .1527 .1490  .0858 .0858 .0984 .0984  .1441 .1366 .1282 .1216  .0411 .0411 -.1018 .1017  .1137 .1081 .1136 .1080  .8060 -.3221 .0555  .1485 .3769 -.0637  .1891 .3254 -.5350  .5212 -.0947 -.3798  .5043 -.0887 .6190  .3371 .1790 .0775  -.3507 .1971 -.2998  -.5599 .2821 -.0091  155 Morphology Presence/ absence of plant neighbours Canonical axes Correlation coefficient Redundancies A B C D Correlation to PCA morphological axis I II III environmental axis I II III  TB  KIT  WED  DC  I  II  I  II  I  II  I  .8288  .7872  .9982  .9493  .9998  .9964  .9984  .9915  .3258 .2238 .1321 .0908 axes  .0839 .0520 .0821 .0509  .1252 .1248 .1217 .1213  .0837 .0754 .0729 .0657  .1376 .1372 .0932 .0929  .1044 .1037 .1402 .1392  .1169 .1166 .1051 .1047  .0769 .0756 .0918 .0902  -7924 -.1123 -.0433  .2310 .1677 -.0200  3946 .7307 .1171  .2725 -.3341 .0872  Phenology A B C D  -  phenological variance explained phenological variables phenological variance explained environmental variables environmental variance explained by environmental variables environmental variance explained by phenological variables  Phenology-soils  TB  Canonical axes Correlation coefficient Redundancies A B C D  KIT  WED  DC  .7322  .5154  .9234  .7426  .8279  .6845  .8973  .7993  .1597 .0856 .0678 .0899  .2063 .0548 .1466 .0390  .1353 .1154 .1869 .1594  .4361 .2405 .1644 .0906  .3708 .2336 .0520 .0356  .3578 .1667 .1042 .0488  .1072 .0863 .0576 .0464  .2059 .1315 .2018 .1289  156 Phenology frequency of plant neighbours Canonical axes Correlation coefficient Redundancies A B C D Correlation to PCA phenological axis II II environmental axis II II  TB  A B C D Correlation to PCA phenological axis II II environmental axis II II  WED  DC II  II  .8032  .7372  .9067  .8529  .9246  .7611  .8914  .8533  .4249 .2741 .1470 .0948 axis  .3057 .1661 .1249 .0679  .5138 .4225 .1280 .1052  .1870 .1360 .0846 .0615  .4619 .3949 .1927 .1648  .1899 .1100 .0920 .2181  .3771 .2996 .0611 .0486  .2599 .1892 .1212 .0883  .8218 -.2846 .4286  .5344 .6842 -.4466  .9555 -.1676 .0261  .0610 .7495 -.5647  .8731 .0343 .4854  -.2408 .7338 .4112  .7425 -.1874 .4710  -.0902 .8082 .4863  .5131 .0371 .1581  .0250 -.5304 .1345  -.7720 .1928 .1482  .0840 .2164 .6802  Phenology presence/ absence of plant neighbours Canonical axes Correlation coefficient Redundancies  KIT  TB  KIT  WED  DC  II .8227  .6983  .9150  .8108  .9083  .8928  .9315  .8837  .2728 .1846 .1513 .1024 axis  .4248 .2071 .1131 .0551  .2677 .2241 .1124 .0941  .4368 .2872 .0853 .0561  .1533 .1264 .1020 .0842  .5198 .4143 .1773 .1413  .1388 .1205 .0818 :0710  .1417 .1106 .1641 .1281  .5364 -.4906 .6271  .7560 .6298 -.0839  .1095 .6490 .3237  -.9363 .3099* -.1477  .8281 .1215 -.0517  -.1311 .6354 .4352  -.2885 -.0245 .3362  .8336 .1543 .2228  157 Demograpny  A B C D  -  demographic variance explained phenological variables demographic variance explained environmental variables environmental variance explained by environmental variables environmental variance explained by demographic variables  Demography soils  TB  Canonical axes Correlation coefficient Redundancies A B C D  A B C D  WED  DC  I  II  I  II  I  II  I  II  .8309  .6722  .9912  .9703  .9979  .9841  .9412  .8164  .0743 .0513 .2217 .1530  .1100 .0497 .2267 .1024  .0935 .0919 .0786 .0772  .0812 .0764 .3359 .3163  .0968 .0964 .0948 .0944  .0732 .0709 .0981 .0950  .1066 .0945 .1169 .1035  .2731 .1820 .0827 .0551  Demography frequency of plant neighbours Canonical axes Correlation coefficient Redundancies  KIT  TB  KIT  WED  DC  I  II  I  II  I  II  I  II  .8050  .6747  .9922  .9809  .9733  .9611  .8609  .7381  .1026 .0664 .1238 .0803  .0803 .0366 .1022 .0465  .1061 .1044 .1386 .1365  .0885 .0852 .0858 .0826  .0655 .0621 .0908 .0860  .0796 .0735 .0886 .0819  .2784 .2064 .1175 .0871  .1692 .0922 .1557 .0848  158  Demography presence/ absence of plant neighbours  TB  Canonical axes Correlation coefficient Redundancies A B C D Correlation to PCA demographic axis I II III environmental axis I II III  KIT  WED  DC  I  II  I  II  I  II  I  .8371  .7038  .9995  .9718  .9991  .9799  .8874  .8095  .0896 .0628 .1109 .0777 axis  .0815 .0404 .1074 .0532  .1133 .1132 .1300 .1299  .1164 .1099 .0655 .0619  .0985 .0983 .0802 .0800  .1169 .1123 .1045 .1003  .2252 .1773 .0676 .0532  .1893 .1240 .0732 .0480  -.2530 .3311 .6960  .2961 .2424 .2289  -.0037 .6911 -.2515  -.2249 -.1755 -.0837  Morphology - Phenology A B C D  -  morphological variance explained morphological variables morphological variance explained phenological variables phenological variance explained by phenological variables phenological variance explained by morphological variables  Morphology phenology Canonical axes Correlation coefficient Redundancies A B C D Correlation to PCA morphological axis I II III phenological axis I II III  TB  KIT  WED  DC  I  II  I  II  I  II  I  .9307  .7803  .9542  .8260  .9738  .8463  -.9620  .8623  .3204 .2776 .5290 .4582 axis  .1432 .0872 .1936 .1179  .1156 .1052 .5065 .4612  .0424 .0289 .1128 .0769  .2551 .2419 .4784 .4536  .0647 .0464 .2000 .1432  .2101 .1944 .6190 .5728  .0641 .0476 .0810 .0602  .7647 .2738 .1321  .4362 -.0786 -.0350  .1418 -.4546 .6749  -.0561 -.2646 -.1388  .7071 .1783 -.5371  -.1313 .2182 .3786  .6104 .2845 -.4476  -.0919 -.1627 -.5516  .9660 .0238 .0279  .1114 -.7596 .4974  .9428 -.0665 -.3255  .0842 .4895 .0682  .9007 .0671 .0544  -.3961 .8979 .4883 ".2285 .2805 .3028  -.2622 .1850 -.0602  159 Morphology-demography A B C D  -  morphological variance explained morphological variables morphological variance explained demographic variables demographic variance explained by demographic variables demographic variance explained by morphological variables  Morphologydemography  TB  Canonical axes Correlation coefficient Redundancies A B C D Correlation to PCA morphological axis 1 II III demography 1 axis II III  KIT  WED  DC  I  II  I  II  I  II  I  II  .9825  .8575  .9999  .9863  .9997  .9994  .9877  .9340  .1508 .1456 .1991 .1992 axis  .0672 .0494 .0848 .0626  .0835 .0835 .0991 .0991  .1308 .0173 .1260 .1226  .1520 .1519 .1371 .1370  .1069 .1067 .1650 .1648  .1374 .1340 .2897 .2826  .0791 .0690 .1030 .0898  .3798 .7712 -.0557  -.2201 .1478 .2821  .5076 .0057 .1840  .0134 .5531 .4581  -.1822 .6344 .0155  .0467 .0103 .7049  .6945 .6775 .0343  -.1037 .3332 -.0985  .5790 .2872 -.2509  .7305 .2252 .2110  -.1548 .9251 .1417  .2090 -.1157 .1108  Phenology-demography A B C D  -  phenological variance explained phenological variables phenological variance explained demographic variables demographic variance explained by demographic variables demographic variance explained by phenological variables  Phenology demography  TB  Canonical axes Correlation coefficient Redundancies A B C D  KIT  WED  DC  I  II  I  II  I  II  I  .7147  .6665  .8825  .7804  .8516  .8287  .9551  .7574  .5280 .2697 .1046 .0534  .1616 .0718 .1240 .0551  .2839 .2211 .0672 .0524  .1933 .1178 .1287 .0784  .3886 .2818 .0763 .0554  .1721 .1182 .1698 .1167  .1033 .0943 .0980 .0894  .2823 .1619 .1198 .0687  160 Appendix VI. WMDS relationships between morphological, phenological and demographic characters for TB, KIT, WED and DC and soils, frequency and presence/absence of plant neighbour matrices. TB -morphology-frequency of plant neighbours relationships. Stress Dimension Importance Weights morphology frequency  .195 I .4348  r-square II .2016  .828 III .1047  .9101 .2032  .0403 .6338  .0389 .4559  WED -morphology-frequency of plant neighbours Stress -Dimension Importance Weights morphology frequency  .169 I .3228  II .2300  r-square III .1293  .7894 .1500  .0868 .6726  .1204 .4940  .815 IV .1330 .5154 .0193  TB - phenology-presence/absence of plant neighbours Stress Dimension Importance Weights phenology presence/absence  .161 I .5146  r-square II .2189  .888 III .1543  .9970 .1876  .0126 .6615  .0073 .3555  WED - phenology-presence/absence of plant neighbours  .5576  r-square II .2331  .773 III .0920  .9963 .3502  .0142 .6826  .0035 .4289  .150  Stress Dimension Importance Weights phenology presence/absence WED - demography-soils Stress Dimension Importance Weights demography soils  0.112 I .4604  .2513  r-square III .1994  .0182 .9594  .7086 .0208  .6307 .0308  0.958 IV .0467 .2872 .1042  161  KIT - demography-frequency of plant neighbours Stress Dimension Importance Weights morphology phenology  .245 I .3228  r-square II .8087  .807 III 0.0  .7959 .1102  .0048 .8087  .5607 0.0  .179 I .4836  r-square II .2488  .851 III .1187  .9817 .0580  .0256 .7044  .0277 .4868  .209 I .4658  r-square II .4217  .887  .0514 .9639  .9182 .0169  WED - demography-frequency of plant neighbours Stress Dimension Importance Weights demography frequency KIT - morphology-demography Stress Dimension Importance Weights morphology demography  WED - morphology-demography Stress Dimension Importance Weights morphology demography  .136 I .4810  r-square II .3484  .935 -III .1053  .0736 .9781  .8321 .0671  .4589 0.0  t  

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