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Tree population dynamics of some old sub-boreal spruce stands Kneeshaw, Daniel D. 1992

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Tree Population Dynamics of Some Old Sub-Boreal Spruce Stands By Daniel D. Kneeshaw B.Sc.F University of Toronto 1989 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Faculty of Forestry, Department of Forest Science) We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA July 1992 © Daniel David Kneeshaw In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department of Fores t Sciences The University of British Columbia Vancouver, Canada Date J u l y 3 1 . 1992 DE-6 (2/88) i i ABSTRACT Disturbance at the scale of single trees shapes the development of boreal forest ecosystems, especially in 'overmature' or 'old-growth' stages, despite the acknowledged role of fire as a catastrophic agent of stand reinitiation. This study has reconstructed the population dynamics of fourteen Sub-Boreal Spruce stands (composed of Picea engelmanni x glauca, Abies lasiocarpa and lesser amounts of Pinus contorta and Populus tremuloides) since the last stand destroying wildfire. Identification of the post-disturbance cohort and subsequent recruitment provided a means of assessing the relative role of small-scale (single-tree) disturbance and large-scale, catastrophic disturbance on species composition and stand development. The results suggest that SBS stands are self-perpetuating, and that although Picea may disappear from some stands it is maintained within forests of this zone. Presumably due to its high shade tolerance, Abies recruitment (when present) occurred uniformly throughout the stand's history. Picea ingress was associated with more exacting conditions, and it can return in sufficient numbers to perpetuate itself after long periods of exclusion. Minor disturbances were important in accelerating the reinitiation of Picea below the forest canopy. In many stands Abies was the most abundant species but this study suggests that although Picea regeneration often occurred in lower numbers it is maintained as a dominant overstory species due to poorer Abies recruitment to larger size classes. In many of the stands there was evidence of an earlier Populus component that may have played an important role in conifer establishment after wildfire. The faster growing Populus may ameliorate harsh environmental conditions to the benefit of conifer i i i regeneration. This process may be especially important in the Sub-Boreal Spruce zone where long periods of colonization (on average 75 years but up to 100 years) are common. Another part of this project focused on defining the later stages of forest stand development in terms of tree population biology. Old-growth was recognized as the stage when the rate of tree regeneration and mortality, and thus age structure, are influenced more by single tree processes than by the stand initiating disturbance (Hayward 1991). The tree population structures and old-growth attributes described for SBS stands suggest that this definition was a useful and objective ecological tool for ranking the later stages of stand development. In the SBS structural data was found appropriate for identifying the later stages of stand development through multivariate analysis. Attributes found to be strongly correlated with stand development, such as the number of large downed logs and snags, reflect the importance of single-tree mortalities to the old-growth stage. i v TABLE OF CONTENTS Page ABSTRACT ii TABLE OF CONTENTS iv LIST OF TABLES .vii LIST OF FIGURES viii LIST OF APPENDICES ix ACKNOWLEDGEMENTS x 1. GENERAL INTRODUCTION TO THE RESEARCH AND STAND DEVELOPMENT 1.1 Introduction 1 1.2 Objectives 2 1.3 Literature Review 3 2. DESCRIPTION OF THE SUB-BOREAL SPRUCE ZONE 2.1 Location 8 2.2 Climate 8 2.3 Soils 8 2.4 Flora 8 2.5 Disturbance 10 2.6 The Moist and Cold Sub Boreal Spruce Subzone 13 3. METHODOLOGY 3.1 Methods of studying stand development 15 3.11 Permanent sample plots 15 3.12 Chronosequence 17 3.13 Stand reconstruction 18 3.2 Methods 19 3.21 General approach 19 3.22 Field and Laboratory Techniques 19 3.221 Site selection 19 3.222 Plot layout and mapping 20 3.223 Individual Tree measurements 20 3.224 Tree ageing 21 3.23 Descriptive Data Analysis 22 3.231 Basal Area Conversions 22 3.232 Volume Estimates 23 3.233 Foliage Height Profiles 24 V Page 3.24 Correlative and Multivariate Data Analysis 25 3.241 Histogram construction 25 3.242 Cluster Analysis 25 3.243 Principal Components Analysis 26 3.244 Identifying the Periods of Cohort Recruitment 26 4. DESCRIPTION OF STAND DEVELOPMENT IN THE SBS 4.1 Introduction and Literature Review 28 4.2 Individual Plot Attributes and Stand Histories 30 4.201 Plot 1 30 4.202 Plot 2 31 4.203 Plot 3 41 4.204 Plot 4 41 4.205 Plot 5 43 4.206 Plot 6 46 4.207 Plot 7 48 4.208 Plot 8 48 4.209 Plot 9 51 4.210 Plot 10 53 4.211 Plot 11 53 4.212 Plot 12 56 4.213 Plot 13 56 4.214 Plot 14 59 4.3 Overall Description of Stand Attributes 59 4.31 Age 59 4.32 Understory Diversity 61 4.33 Stand Stocking 61 4.34 Coarse Woody Debris .62 4.34 Tree Regeneration 65 4.4 Trends in SBS Stand Development .66 4.41 Age Class Deficiency 66 4.42 Age Distributions 67 4.43 Initial Colonization 69 4.44 Post-Establishment Stand Development .71 4.45 Conifer Estalishment With Aspen 73 4.5 Summary .74 5. DEFINING THE MATURE AND OLD-GROWTH STAGES OF STAND DEVELOPMENT 5.1 Introduction and Literature Review .76 5.11 Alternative Definitions of Old-Growth 76 5.12 Proposed Population Based Definition of Old-Growth 80 5.2 Using a Subjective Evaluation of Stand History 81 5.21 Summary of Density-Age Distributions 81 5.22 Interpretations 81 v i 5.3 Using Structural Attributes 83 5.31 Summary of Relevant Stand Attributes 83 5.32 Cluster Analysis 83 5.33 Principle Components Analysis .86 5.4 Using Basal Area Cohorts 90 5.41 Summary of Basal Area-Age Distributions 90 5.42 Cohort Basal Area Ratios 90 5.43 Sensitivity to Cohort Identification 93 5.44 Identification of the Old-Growth Threshold 95 5.5 Comparison of Techniques 96 5.51 PCA Ranking 96 5.52 Cluster Analysis Ranking 99 5.53 Basal Area Ratio Ranking 99 5.54 Age Distribution Ranking 100 5.55 Differences in Ranking 101 5.6 Summary 103 6. CONCLUSIONS AND RECOMMENDATIONS 6.1 Observations On Stand Development 105 6.2 Assessing Old-Growth 107 6.3 Recommendations 107 7. REFERENCES 110 v i i LIST OF TABLES Page 2-1. Disturbance due to forest insect infestations in the SBS 11 2-2. Disturbance due to forest diseases in the SBS 12 3-1. Comparison of permanent sample plots, chronosequence and stand reconstruction methods 16 4-1. Summary of SBSmc stands sampled in the Smithers-Houston area, 1991 32-35 4-2. Diameter distributions by species and totals 36-39 5-1. Attributes often cited in old-growth definitions 77-78 5-2. Sub-boreal spruce structural attributes used for multivariate analysis 84 5-3. PCA component loadings 89 5-4 Structural attributes as ordered by the PCA factor 1 axis 91 5-5. Component basal area ratios for sampled SBS plots 92 5-6. Sensitivity of cohort basal area ratios to changes in cohort size 94 5-7. Comparison of stand development rankings 97 5-8. Spearman rank correlation coefficients for the rankings of stand development 102 LIST OF FIGURES Page 2-1. Distribution of the sub-boreal spruce zone in British Columbia 9 2-2. Location of sampled plots in the Smithers and Houston area 14 4-1. Plot 2 age structures 40 4-2. Plot 3 age structures 42 4-3. Plot 4 age structures 44 4-4. Plot 5 age structures 45 4-5. Plot 6 age structures 47 4-6. Plot 7 age structures 49 4-7. Plot 8 age structures 50 4-8. Plot 9 age structures 52 4-9. Plot 10 age structures 54 4-10. Plot 11 age structures 55 4-11. Plot 12 age structures 57 4-12. Plot 13 age structures 58 4-13. Plot 14 age structures 60 4-14. Example of radial increment patterns 63 4-15. Example of height profiles 72 5-1. Example of different age distribution forms 82 5-2. Dendrogram of sampled plots as grouped by cluster analysis 85 5-3. Principal components analysis of SBS structural data 87 5-4. Vector diagram of attribute correlations with the PCA 88 i x LIST OF APPENDICES Page 1. List of understory species observed 124 2. Height profiles 125-137 3. Density by age class distributions 138-144 4. Basal area by age class distributions 145-151 5. Site associations, nutrient and moisture regime for the sampled SBSmc subzone plots ... 152 X Acknowledgements I would like to thank my advisor, Phil Burton, for his endless energy and enthusiasm for this project, his guidance, and all the time that he invested in it. The other members of my committee, Gary Bradfield and Karel Klinka, deserve great thanks for the time they devoted to channelling and directing of this work. There are, also, a number of people who helped me put together the data for this project. First of all my brother, Tim, for the field work - a summer of long weeks, long days, too many bugs and not enough time off; his dedication to the project was wonderful. Then the people who helped me count all the rings especially M. Trevor, S. Lotz and B. Robinson who may all be permanently cross-eyed after staring at all those increment cores. My wife, Valerie, deserves special thanks for the time she devoted to the painstaking task of data entry of the field notes. R. Keenan and C. Prescott, for reading and correcting this manuscript, as well as, preparing me to defend it, were also invaluable. The people at the Ministry of Forests in Smithers and Houston, as well as Houston Forest Products, deserve thanks for providing us maps and cutting plans on short demand. I would also like to state my appreciation to J. Pojar and D. Coates for their helpful suggestions. Importantly, I would like to thank E. Hamilton, A. Nicholson and J. Parminter for arranging the MOF financial support for this project. I would also like to note my heart-felt appreciation to all my family and friends who supported me through this endeavour. Special thanks to my parents - for their time sanding discs, their constant encouragement and their financial support. Tony Peprnik, Deda, thank-you for always inspiring me, this is dedicated to your memory. To my immediate family, Valerie and my son Vincent, words alone can not express my feelings, thank-you for all your many sacrifices. It was the support and love you gave me that carried me through the difficult times. 1. GENERAL INTRODUCTION TO THE RESEARCH AND STAND DEVELOPMENT 1.1 Introduction Disturbance is being increasingly recognized as a pervasive force ubiquitous to all ecosystems (Pickett and White 1985). In the past the importance of disturbance was often obscured by an ecological preoccupation with succession or the ecosystem changes occurring between disturbances. Amongst ecosystems both the temporal and spatial scales at which disturbances act are observed to change. At a stand level, however, one may recognize two distinct disturbance types: 1) catastrophic disturbance in which the dominant trees, if not all members of the stand, are destroyed; and 2) single- or group-tree disturbance in which only one to a few trees are killed. Both types of disturbance are important in shaping stand development. Catastrophic or stand initiating disturbances are responsible for returning the entire stand to an early serai state, although their influences may last hundreds of years. Single- or group-tree disturbances, on the other hand, are dominant when the stand is in equilibrium or a stable, self-perpetuating state. These minor, tree-level disturbances may, however, be equally important in shaping stand development at much earlier stages. Understanding stand development, therefore, requires a knowledge of the disturbance types that have directed successional processes within the stand. In the boreal forest, the role of fire as a catastrophic agent of stand reinitiation has long been acknowledged (Rowe 1961, Heinselman 1973, Rowe and Scotter 1973). Disturbance at the scale of single trees also shapes the development of these ecosystems, especially in 'overmature' or 'old-growth' stages. Single tree replacement or 'gap dynamics' has received less attention in boreal forests than in temperate deciduous or tropical forests (Runkle 1985). Stand development studies may take a number of forms but most describe the history of tree recruitment, growth and mortality within forest stands to try to explain patterns of species change and the important processes responsible for the development of the stands. The results have been used by ecologists for the refinement of successional theory and by silviculturists attempting to mimic patterns of natural forest regeneration. Stand development 2 patterns are also being used by population biologists to define such socially and politically prominent concepts as 'old-growth' in ecological terms. In the Sub-Boreal Spruce (SBS) biogeoclimatic zone of British Columbia fire has prevented most stands from reaching late stages of development (Meidinger et al. 1991). In recent decades a policy of liquidating overmature stands has resulted in a more rapid decrease in the number of remaining old SBS stands. These old stands provide the opportunity to understand ecosystem development in the SBS. Research and conservation of such stands is therefore imperative. This study focuses on the tree population dynamics within some old-growth sub-boreal spruce (SBS) stands. 1.2 Objectives The goals of this study were to characterize the structural attributes and to assess the tree population dynamics within mature and old-growth SBS stands. Population dynamics were to be assessed with respect to the nature and scale of disturbance responsible for recruitment and compositional changes (as determined from inferred mortality, longevity and recruitment) within forest stands. The developmental stage of each of these stands was to be identified and evaluated with respect to other stand characteristics. This study contributes to our understanding of stand development in old-growth SBS forests, and generates data suitable for evaluating various definitions of old-growth forests. Although it is of interest for primarily scientific reasons, an improved understanding of stand dynamics and forest succession within this zone is also an important first step towards reducing the risk of failure of silvicultural interventions in forest management (Weetman 1991). Specific objectives relate to stand development and the associated structural attributes of the old-growth stage. These objectives are: 1) To determine if the stands in these areas form a stable climax association or if they "decline' in the absence of fire (as suggested for the boreal forest by Rowe [1961] and for Engelmann Spruce-Subalpine Fir forests by Bloomberg [1950]). With this knowledge one can predict future changes in these forests in the absence of large scale disturbance (i.e., do 3 old-growth stands maintain themselves in the absence of fire?). These questions have important implications in the design of nature reserves and for fire suppression policy. 2) To determine the relative importance of catastrophic and small scale (single tree) disturbances in SBS stands in the later stages of stand development. In particular, do SBS stands remain dominated by the post-disturbance cohort until the next stand initiating event? Do tree-level disturbances have a role in re-directing stand development? 3) To understand the compositional and structural changes. Do all dominant trees initiate at the same time, as proposed by the "initial floristics" theory of succession [Egler 1954], or do different species establish sequentially as in the "relay floristics" theory? Is the post-disturbance recruitment rapid, or is it slow as Jull [1990] found at higher elevations in the Engelmann Spruce-Subalpine Fir (ESSF) zone? 4) To test whether there are structural characteristics associated with the later developmental stages and whether a population-based definition of old-growth adequately describes old-growth values as perceived by the public (e.g. as illustrated in the definition of old-growth forests as proposed by the B.C. Forest Land Use Liaison Committee 1990)? 1.3 Literature Review In North America, general theories on stand development have been greatly influenced by Clements' (1916) early observations on directional changes in plant species composition. His ideas lead to the classical monoclimax interpretation of succession in which all communities in a region, given enough time, would progress towards a common final composition dictated by climate. This quickly developed into polyclimax theories in which communities within a region governed by distinctive soils, drainage, topography or disturbance regime would proceed towards their own community compositions. These equilibrial theories constitute the basis for many ecosystem classification systems. Autogenic succession, in which each species or group of species alters the environment to its own detriment but makes it suitable for the next group of species (also called relay floristics [Egler 1954] and facilitation [Connell and Slatyer 1977]), was identified by this early work as the dominant process in succession. Subsequently ecologists have recognized the importance of initial floristics (Egler 1954) and tolerance (Connell and Slatyer 4 1977) pathways in which all species are present from the beginning but dominate at different times due to factors such as differing growth rates. A further model is the inhibition pathway (Connell and Slatyer 1977) in which a dominant species may exclude or suppress all others while it is present. Recently, however, many stand development studies (Day 1972, Henry and Swan 1974, Oliver and Stephens 1977, Aplet et al. 1988, Johnson and Fryer 1989, Veblen 1989, Nowacki et al. 1990) have emphasized the importance of disturbance in stand development. Disturbance is now viewed as pervasive and natural (Pickett and White 1985). Many ecosystems are dominated by plants which may not be self-perpetuating or " in balance with their environment1 because of the frequency of disturbance. Disturbance regimes play a central role in vegetation dynamics and are a source of much of the complexity of successional processes. For example, in the Chilean Andes, the composition of a Nothofagus forest was directly related to the intensity and frequency of disturbance (Veblen 1989). In Wisconsin, Nowacki et al. (1990) found that the changing disturbance regime associated with wildfire suppression may be leading to compositional and developmental changes in oak forests. Henry and Swan (1974) found that different disturbance types in the northeastern United States lead to forests with divergent compositions. Following catastrophic disturbance, in all but the most species-poor forests, the next cycle of vegetation is likely to differ in composition from that which previously occupied the site. Franklin (1982) has proposed a general formula for succession or (as he calls it) ecosystem change. In this proposition, succession is a function of the life histories of available species (the silvics of the species and their individual development), the environment (site and climatic factors as well as disturbance regime) and stochastic elements (the probabilities of events occurring). Although this formula encompasses many of the aspects of ecosystem change, its usefulness is limited by its generality. The 'vital1 attributes theory of Noble and Slatyer (1977, 1980) also recognizes the importance of disturbance in directing succession. Their theory is based on three 'vital attributes' of plants: 1) the species' ability to persist or arrive after a disturbance; 2) community relationships or the species' ability to establish and mature; and 3) the timing of the species life stages. The growth rate of species is suggested as a fourth attribute to 5 account for differences in species dominance. This theory is more practical than Franklin's and has been used by both Heinselman (1981) and Rowe (1983) to classify plants from northern ecosystems. An important subset of disturbances in forest ecosystems is the creation of canopy gaps as a result of individual tree mortality. Much work on gap-phase dynamics has been conducted using stand development studies to investigate disturbance regimes. This concept essentially recognizes three stages: gap creation, stand building, and mature forest (Watt 1947, Whitmore 1982, 1989, Martinez-Ramos et al. 1989, Lertzman 1992). Whitmore (1982, 1989) states that the spatial mosaic of structural phases in the forest is a process driven by the creation of gaps. He theorizes that the size of the gap would determine which species (climax or pioneer), would successfully establish and develop. However, this concept, by using average demographic patterns and overlooking demographic variability, does not capture the complexity of forest development. It also de-emphasizes later stages in species' life-cycles which may be equally important due to differential survivorship, growth and reproduction (Martinez-Ramos et al. 1989). It has also been found that tree responses to gaps vary geographically as changes occur in the associated tree and understory species and in the abiotic factors of the particular site (Veblen 1989). For example, in low elevation mesic sites in Chile, Nothofagus spp. were able to invade sites only after large-scale catastrophic disturbances such as landslides, but on xeric or high elevation sites these trees were also recruited after tree-fall gaps (Veblen 1989). There are also examples of shade-tolerant species becoming more gap-dependent during periods of drought (Martinez-Ramos et al. 1989). Oliver (1981a) proposed a framework for forest stand development following catastrophic disturbance which does not necessarily include successional change. These stages have been described as stand initiation, stem exclusion, understory reinitiation and old-growth. Similar patterns have been suggested by Borman and Likens (1979) and Peet and Christensen (1980, 1987). Aplet et al. (1988) found four phases to the development of their spruce-fir forest that closely paralleled those already described: Colonization, spruce exclusion, spruce reinitiation and second generation spruce-fir forests. 6 Composition of the stand initiation stage (gap creation), as defined by Oliver (1981a), is dependent on 1) intensity of the disturbance, 2) area of disturbance, 3) growth rates of invading species, 4) regeneration mechanisms of invading species, 5) coincidence of disturbance, seed crop and favourable weather and 6) the density and multiplication rates of seed predators and competing shrubs (Oliver 1981a, Oliver and Larson 1990). In the majority of forest types this stage is characterized by extensive seedling establishment and rapid growth (Peet and Christensen 1987). The stem exclusion phase subsequently occurs when one or more growth factors, such as light, becomes limiting and seedlings are no longer able to become established. Many of the competitively stressed saplings undergo mortality, so this phase is thus characterized by self-thinning. Understory reinitiation is described as the stage when overstory mortality begins to free resources such that a low stratum of herbs, shrubs and advance regeneration are able to invade the forest floor (Oliver and Larson 1990). Tree seedlings in the understory may be restricted to this stratum for many decades and it is not until these trees begin to join the overstory canopy that the old-growth stage has been reached. The old-growth stage is therefore the point at which overstory mortality allows the understory to be recruited to the canopy (Oliver 1981a). This stage is characterized by single-tree disturbances creating gaps that are large enough to allow understory trees to be released and join the overstory (Runkle 1991). Although defining old-growth in terms of stand development as the point beyond which the dominant overstory trees begin to be replaced by secondary recruitment may be acceptable in conceptual terms, such process-oriented definitions do not facilitate the recognition of old-growth stands. While acknowledging the continuous nature of stand development, one might still ask if there are structural attributes associated with different developmental stages. This is implicit in the works of Franklin et al. (1981) and Thomas et al. (1988), who have defined old-growth coastal forests in precise structural terms. Ecologically, old-growth structure is important in ecosystem functioning and much recent work has been focused on the importance of structural attributes. Resolving these definitions and practical identification problems is important for the inventory and mapping of old-7 growth stands and in planning for the conservation of this increasingly threatened biological resource. 8 2. DESCRIPTION OF THE SUB-BOREAL SPRUCE ZONE 2.1 Location The sub-boreal spruce (SBS) biogeoclimatic zone is located in the north central interior of British Columbia and is bordered by the Interior Cedar Hemlock (ICH), Sub-Boreal Pine-Spruce (SBPS), Interior Douglas-Fir (IDF) and Engelmann Spruce-Subalpine Fir (ESSF) zones. It occurs from valley bottoms up to 1100-1300 m elevations and between latitudes 51°30' and 59° N and longitudes 122° and 128° W (Meidinger et al. 1991) (Figure 2-1). The section of this zone that was studied corresponds to Rowe's (1972) Montane Transition section of his Montane Forest Region. 2.2 Climate Due to its location, the SBS has a moderate boreal climate and is affected by seasonal extremes in temperature and by relatively moderate levels of precipitation. The climate is characterized by moist, short summers and severe, snowy winters. The mean annual air temperature ranges from 1.7°C to 5°C, sub-freezing air temperatures occur for 4-5 months of the year and temperatures above 10°C for 2-5 months. Annual precipitation ranges from 440-900 mm, with 25 to 50 per cent falling as snow (Meidinger et al. 1991). 2.3 Soils The soils of the SBS are usually Luvisols, Podzols or Brunisols, with parent materials of glaciofluvial, fluvial or morainal origin. Soil texture is often loamy to sandy, resulting in imperfect to rapid drainage. The most common humus forms are Hemimors, Humimors, Mormoders and Leptomoders all characterized by having little or no moisture deficiency (see Klinka et al. 1981). The maximum rooting depth is between 50 and 60 cm for most sites. 2.4 Flora The forests of the SBS are dominated by hybrid spruce (Picea engelmanii X glauca), subalpine fir (Abies lasiocarpa) and lodgepole pine (Pinus contorta). Trembling aspen (Populus tremuloides) is a common associate of the dominants in many stands. It is also possible to find Douglas-fir (Pseudotsuga menziesii), paper birch (Betula papyrifera), Distribution of the Sub-Boreal Spruce Biogeociimatic Zone Prepared lor Bmiin Columbia Mmisiry ol Foresis by Canadian Olographic: Lid. Match 1989 Figure 2-1. Distribution of the Sub-Boreal Spruce zone in British Columbia. (From Meidinger et al. 1991) 10 alder (Alnus incana ssp. tenuifolia and A. viridis ssp. sinuata) and black spruce (Picea mariana) in parts of the zone. On wetter sites black cotton wood (Populus balsamifera ssp. trichocarpa), scrub birch (Betula glandulosa), swamp birch (B. pumila), and willows (Salix spp.) may occur. The most commonly occurring shrubs in the moist cool subzone of the SBS are Vaccinium membranaceum, Rubus parviflorus, Ribes lacustre, Oplopanax horridus, Linnaea borealis, Viburnum edule, and Lonicera involucrata. Common ground layer shrubs and herbs include Cornus canadensis, Rubus pedatus, Gymnocarpium dryopterus and Equisetum arvense. The ground layer of established forests is often dominated by feather mosses: Pleurozium schreberi, Hylocomium splendens and Ptilium crista-castrensis. 2.5 Disturbances The SBS, as an extension of the boreal forest, is recognized as being an ecosystem in which fire plays a prevalent role in composition and development. The wildfires that burn in the SBS are combination surface and crown fires that have an average return interval of 100-150 years but which can be as frequent as every 75-100 years, and have a maximum return interval of 150-250 years (MOF 1991a). Historically, these fires have burnt areas ranging from 0.1 to 50, 000 ha but the average area is 50-500 ha. The combined size and frequency of burns is greater than that found in all other biogeoclimatic zones except the Boreal White and Black Spruce (BWBS) zone. Sizes of fires are greater in the Interior Cedar Hemlock (ICH) zone and return intervals are shorter in the Ponderosa Pine (PP) zone (MOF 1991a). Minor disturbances such as windthrow are often due to the effect of pests and pathogens. In the SBS, the spruce bark beetle {Dendroctonus rufipennis) is the most serious insect pest, although other insects can cause significant damage (Table 2-1). The most important disease in this zone is root rot (Armillaria spp.) which causes the greatest damage to Abies. Other diseases can also be noted (Table 2-2), some of which may lead to tree form damage rather than extensive mortality. Table 2-1 Disturbance due to forest insect infestation in the SBS Typical Host Mortality Area Affected Pest Host Average Range Outbreak Interval Min. Max. Avg.Size I | years years ha' s ha' s Spruce Beetle Picea 47 5-90 4-8 100-120 1 6200 1270 {Dendroctonus rufipennis) Spruce budworm spp. Abies 5 0-75 10 4-5 65 8037 2596 (Choristoneura spp.) Picea Mountain Pine Beetle Pinus 40-50 12-80 5-12 40-45 1 900 20-50 (Dendroctonus ponderosae) Douglas-fir beetle Pseudotsuga 25 5-90 3-5 100+ 1 4 1 (Dendroctonus pseudotsugae) Western Blackheaded Picea 5 0-27 2-5 15-20 750 3500 1750 Budworm Abies (Acleris gloverna) Forest tent caterpillar Alnus 5 0-5 3-4 5-7 12 20000 2600 (Malacosoma distria) Betula Populus Salix Source: Ministry of Forests 1991a Table 2-2 Disturbances due to forest diseases in the SBS % area infected, ha % loss Disease Host M m . Max. Aver. M m . Max. Ava. Armillaria root disease Atropellis Canker Brown Cubical Butt Rot Lodgepole pine Dwarf Mistletoe Red Ring Rot Tomentous Root Rot Western Gall and Other Stem Rusts Pinus Abies Picea Pinus Picea Pinus Pinus Pinus Picea Picea Pinus Pinus 10 75 15 5 22 10 0 10 5 0 90 15 10 0 100 35 90 30 15 75 24-28 0 80 7 5 30-40 20 15 60 15-30 5 30-40 20 0 10 5 0 10 5 Source: Ministry of Forests 1991a NJ 13 2.6 The moist and cold Sub-Boreal Spruce subzones The SBS is currently divided into ten subzones which are composed of warmer-drier, moist-cool and wet groups. The subzone in which the research for this thesis took place is the moist and cold (SBSmc) subzone, formerly known as the subalpine fir (SBSe) subzone, located at middle elevations from Blackwater-Ootsa lakes north to Babine Lake and the Babine River. The research was conducted in the vicinity of the towns of Houston and Smithers (Figure 2-2). The study sites were primarily located on the Nechako Plateau, an area dissected by various hills and valleys but of relatively low relief (Pojar et al. 1984). The moist-cold subzone, the SBSmc, receives more moisture during the growing season and has deeper snowpacks, which arrive earlier and last longer, than the other SBS subzones. Tree productivity is also the highest, which is one of the main reasons that the SBSmc is the main timber producing subzone in the SBS zone (Pojar et al. 1984). 14 Figure 2-2. Location of the sampled plots in the Smithers and Houston area. Plot locations are indicated by numbers in circles, the numbers referring to the stand numbers as discussed in the text. 15 3. METHODOLOGY 3.1 Methods of Studying Stand Development There are currently a number of recognized field techniques designed to identify and quantify stand development trends. These can be divided into three broad categories: 1) the study of permanent sample plots (PSP's); 2) chronosequence studies (space-for-time substitutions); and 3) stand reconstruction. These methods are not mutually exclusive but, rather, are often used in combination to provide added insights into stand dynamics. This study combines elements from the stand reconstruction and chronosequence approaches. A discussion of PSP's is provided as a contrast to these methods. 3.11 Permanent Sample Plots Permanent sample plots may be the most preferred of the methods of studying forest population dynamics because the actual development of stands are followed and all changes documented; none need be estimated or inferred. In establishing PSP's, sites must be selected that are representative of the forest type to be studied. All stems should be mapped and identified to facilitate future measurements. Repeated measurements allow accurate information to be collected on mortality events and changes in the composition and abundance of the understory. This information is often unavailable or unreliable from reconstruction techniques due to the rapid rate of decay of small material (Stephenson 1987). It is also possible to experimentally manipulate plots to isolate causal mechanisms involved in development of the stand, a benefit absent from other techniques. The long-term time and financial commitments of PSP's have necessitated the development of other techniques for investigating stand development. These commitments have precluded the use of PSP's from this project, although problems can also arise in long-term projects due to policy changes and funding cutbacks (MOF 1991b). The drawbacks of tree longevity and the retention of size and age structure in terms of following life-cycles in permanent sample plots is, however, beneficial for the other methods of studying population structure (see Table 3-1 for comparison of advantages and disadvantages of the methods). Trees build on their existing structure and thus provide a record of past events in their history, whether it be fire scars, or double leaders as evidence of insect attack, or changes in radial growth pattern evidencing changing climatic or biotic factors. 16 Table 3-1. Comparison of permanent sample plots, chronosequence and stand reconstruction methods. Advantages Disadvantages Permanent Sample Plots Chronosequence Approach Stand Reconstruction Documentation of all data sets Easy to follow understory species Can be experimentally manipulated Comparison of stages over very long time periods Period of study is relatively short Identifies general trends Directly tracks stand development over time Information can be gathered on any stand Relatively short period of study Requires long time period of study Long-term commitment Questions to be answered may change with time Assumption of ecologically analogous sites may be violated Averaging technique obscures variability May miss important mechanisms Availability of data decreases with increasing time into past and for younger, smaller members of the plant community For detailed studies of small area, extrapolations to large areas may be invalid 17 They also provide a continued record of their age through the production of annual rings (in temperate regions) which have been used to date the aforementioned factors. These considerations lead to the development of the retrospective techniques used in this project. 3.12 The Chronosequence Approach Chronosequence studies compare stands of different stages of development, based on the key assumption that they are equivalent in all respects except for their age, and that they represent a typical sequence for that type of forest stand. It has also been called " Space-For-Time' (SFT) substitution (Pickett 1989) as it assumes that the only variable is time and that members of the chronosequence will be the same at the same age. These sorts of studies are advantageous in that long-term effects, over time frames that may not otherwise be reconstructed, can be inferred in a short period of study. Unlike PSP studies, SFT investigations can 'move' backward and forward through time (Oliver 1981b). This allows questions to be answered about early stand development that arise due to interest in some feature in later stand development. A potentially crucial problem with the chronosequence approach is the failure to meet the underlying assumption that all study sites are ecologically analogous and have the same history (Pickett 1989). Over time sequences spanning several centuries it seems unlikely that climatic and biotic conditions will have remained constant. This type of study is also based on an underlying faith in the concept of deterministic monoclimactic succession: that is, that stand development will follow more or less a singular, natural progression to a given end. There is, however, recognition by many researchers that the same site may have multiple successional pathways depending on existing vegetation, disturbance type, disturbance intensity and other factors such as propagule availability or weather events (Franklin 1982). When assumptions on equivalent histories are not met, and conclusions drawn from the chronosequence are accepted, the implication is then that stands will progress to a common end-point despite differing histories and subsequent management practices. 18 3.13 Stand Reconstruction Reconstruction techniques or retrospective analyses of single stands can overcome some of these problems by directly tracking over time the development of trees in a stand. This is achieved by documenting the behaviour of each stem in the stand from establishment to its present condition and thereby reconstructing the stand's history. The advantages of this technique are that stands which currently exhibit some desirable trait (in terms of structure or composition) can be studied to determine the important events which shaped its development. The disadvantages are that the availability and reliability of data, especially for small stems, often decrease, due to decomposition, with increasing time into the past. Events cannot be determined for which all evidence has completely disappeared. Intensive studies of small areas may also be invalid for extrapolation to larger areas. A mixture of techniques is often applied. Destructive techniques are stem analysis to determine height growth patterns (Hibbs 1982a, 1982b, Guildin and Lorimer 1985), and complete removal and analysis of the forest floor for woody debris to uncover past mortality (Henry and Swan 1974, Oliver and Stephens 1977, Johnson and Fryer 1989). Non-destructive techniques include the search for external evidence of past disturbances (Payette et al. 1990, Deal et al. 1991), analysis of age distributions (Lorimer 1980, Harcombe 1986, Sirois and Payette 1989, Begin and Payette 1991, Deal et al. 1991), size distributions (Leak 1964, 1965, Lorimer 1980, Tesch 1981, Stewart 1986, Deal et al. 1991), canopy structure (Stewart 1986), growth increment analysis (Lorimer 1980, Payette et al. 1990, Frelich and Lorimer 1991) and spatial patterns (Duncan and Stewart 1991, Frelich and Lorimer 1991). Observations on tree physiognomy (such as abundance and location of live and dead branches on different trees, stem form and the lean of phototropic trees) can all be used to help infer conditions under which trees developed (Oliver 1981b). Past disturbances or stand initiating events can be identified by evidence such as fire scars (Bergeron and Dubuc 1989, Bergeron 1991) or pit and mound microtopography caused by windthrow events (Lorimer 1985). 19 3.2 Methods 3.21 General Approach The approach in this study was a retrospective analysis of the dynamics involved in the development of a number of old-growth SBS stands. Histories of the development of each stand have been inferred through structural analysis, and compared with each other to determine general trends. Both size and age distributions were used to assess local tree population dynamics. As stands have been selected from across an age range and from similar sites, the chronosequence technique can also be used to infer SBS stand dynamics at a broader scale. 3.22 Field and Laboratory Techniques 3.221 Site Selection The selection of sites was based on five factors. The first being that all sites were located in the SBSmc subzone. Secondly, it was required that there be no evidence of human disturbance because an objective of this project was to investigate stand development stages in relation to the influence of large and small-scale natural disturbance. Thirdly, mixed conifer forests were chosen in which interior spruce was a member of the canopy dominants. Fourthly, sites were to be mesic and zonal (for the subzone) with relatively uniform soils. The study plots located for this project were slightly wetter and slightly richer than a typical mesic, zonal site as defined by plant associations. One site, Plot 14, however, was located in submesic conditions although its nutrient regime was similar to the other sites. Lastly, plots were chosen from across a range of stand ages beyond the stem exclusion stage. These criteria narrowed the study to stands in the later stages of development, in order to gain a better understanding of old SBS forests as well as facilitating a chronosequence and stand reconstruction approach. Forest cover maps (printed in 1988) were used in the initial search for stands. These maps were often inadequate due to the wide range of ages of stands represented by the oldest age classes and the difficulty of finding very old SBS stands that had not been logged or burned despite their appearance on the maps. 20 3.222 Plot Layout and Mapping When a suitable stand was identified, the location for an intensive 30 x 30m sample plot was determined using random numbers. The position of the sample plot was located by pacing out a distance into the stand (perpendicular to the nearest access road) followed by a second transect paced out at right angles to the initial line of entry. The boundary of each plot was marked out by cord. Each plot was further subdivided into four 15 x 15 m plots to facilitate data recording and into 5 x 5 m subplots for mapping purposes. The location of all tree stems were mapped as seedlings, saplings or trees. Seedlings are defined as those trees <_ 7.5 cm dbh and <_ 2m tall, saplings as all trees <. 7.5 cm dbh and > 2 m in height, and trees as those stems > 7.5 cm dbh. These are the categories proposed by the MOF (1991c) for related old-growth studies in other biogeoclimatic zones. All seedlings and sapling stems were recorded according to their location with respect to the canopy (gap, extended-gap or covered), germination substrate (wood, humus or mineral soil) and microtopography (mound, pit, slope or level ground). All trees and snags were numbered and recorded by the position of their stems and relative position of their canopies. Similarly, the locations of all downed logs within the plot boundaries were recorded. Positions were mapped by visually dropping perpendiculars to the nearest 5m x 5m plot line and were considered accurate to the nearest 0.5m. 3.223 Individual Tree Measurements All trees within each plot were numbered with metal tags (to facilitate future remeasurement and stem analysis if desired), and assigned to a canopy class (subjectively, based on neighbours) as dominant, sub-dominant or lower canopy. Species, bole diameter at breast height (dbh, 1.37m), tree height, and height to the base of the crown were recorded for each live and dead tree. For snags (standing dead trees) decay condition as softness, top condition and percent remaining bark (MOF 1991c) were also recorded. All downed coarse woody debris on the site was measured for length (both total and that found within the plot), as well as diameter and elevation at each end. Softness, the per cent of bark remaining, the degree of incorporation into the forest floor, the probable reason 2 1 for falling (if this could be determined), the presence of vegetation on the log and the per cent of the log's length covered by vegetation were recorded for each piece. Understory vegetation was measured in terms of percent cover of each species within six 2m x 2m quadrats randomly located within each plot. A soil pit was dug to observe charcoal horizons, and soil texture, colour, rooting depth, observable horizons and coarse fragment abundance were also characterized. Seedlings and saplings were individually grouped into 1 m height classes and from these a subset was destructively sampled such that the number of seedlings and saplings harvested equalled or exceeded the number of trees measured. Substrate, gap position, microtopography, species, total height, leader length and diameter at root collar were measured for all harvested seedlings and saplings. The ratio of destructively sampled stems to all stems in the plot in that height class was used to convert the recorded data to a per plot average. All data were subsequently converted to per hectare totals. A core from each sound tree or snag was taken at stump height (< 30cm) for ageing and increment analysis. Stem discs were cut and labelled from the base of all harvested seedlings and saplings. The cores were stored in labelled plastic drinking straws until accurate age and increments could be measured under magnification in the laboratory. Plot 1 was an exception to this procedure, in that only a few overstory trees were cored for an estimate of stand age. This stand was commercially harvested soon thereafter and it had been hoped that it would be possible to return and take discs for age and radial increment analysis from all individual tree stumps. Unfortunately, many stumps and most tree tags could not be relocated after harvesting, due to skidder activity on the site. 3.224 Tree Ageing All complete cores were analyzed using the Addo Parker Instrument AGRMM tree ring increment analyzer. Annual rings were counted and all individual ring widths measured to the nearest micrometre, from the core out to the bark. The age data were recorded both manually and on computer disc using the Nutricon AGRMM Ring Width Analyzer Program (written by REA Engineering Services). All seedling and sapling stem discs were counted 22 for total ages under magnification either with a 30x stereomicroscope, or where feasible, with a lOx hand lens. In the determination of tree ages, missing values occurred due to the presence of heart rot in some living trees. Although only a small percentage of trees in each plot did not have complete cores, there was still concern that the shape of the age distribution might be changed without these data, especially if all were from the same age class. The partial cores collected for these trees have therefore been used to estimate the missing ages. The age adjustment procedure required that all partial cores be counted and measured with the tree increment analyzer, and then compared to three to five similar but complete cores from the same plot. The following criteria were used to select cores for comparison: 1) the cores were from the same size class, 2) they were of the same species, 3) they were from trees in close proximity to the tree with the incomplete core. Although these criteria were optimal for finding similar complete cores, they could not be met in all cases. Criterion 3 was especially difficult to meet and therefore was often disregarded. The other criteria were usually easily satisfied with the exception of extremely large diameter trees. The tree with the incomplete core was assumed to be similar to those with complete cores and thus the missing portion was defined as the length of the partial core subtracted from the length of the complete core (as scaled by the DBH ratios of the two trees). A length equivalent to the missing portion was then measured out from the pith on the complete core and all rings counted in this segment to determine the number of missing rings. This process was repeated on three to five complete cores for every incomplete core. The number of missing rings was then calculated as the average of the ring counts (which usually differed by only 3 to 15 years) for the 'missing' portions. The 'missing' total was added to the measured total to give the estimated age for that tree. No effort was made to estimate radial increments for each of the missing years. 3.23 Descriptive Data Analysis 3.231 Basal Area Conversions Basal area at the base of the tree was used to make comparisons between stems of all size classes. The equation used to convert diameter at breast height to diameter at tree base 23 was derived from the general stump to breast height diameter conversion of Demaerschalk and Omule (1978): DBH = DSOB+b*DSOB*ln*(SH+l)/2.3 where DBH = diameter at breast height (cm) DSOB = diameter at stump outside bark (cm) b = parameter for each species SH = stump height (m) Thus the diameter conversion from breast height to stump height is of the form: DSOB = DBH/(l+b(ln((SH+l)/2.3))). The parameter, b, for each of the four species in the Smithers/Houston region was: b = 0.301495 for Abies lasiocarpa; b = 0.415792 for Picea engelmanni x glauca; b = 0.269298 for Pinus contorta; and b = 0.281240 for Populus tremuloides. A stump height (SH) of 0.01 m was used in order to be compatible with the root collar diameter measurements taken for seedlings and saplings. 3.232 Volume Estimates Volume estimates for the trees in each stand were made using equations derived by the B.C. Forest Service (1976). These equations require diameter at breast height and height measurements to make estimates of tree volume. The equation parameters are determined for each species by location of the stems sampled within the province. The equations for the four species studied, Forest Inventory Zone J, which includes the Smithers/Houston area were: Volume = 10-4-291919*DBHL87293*HT°-998274for^/«te/ocfl/77fl; Volume = io-4-3 4 9 5 0 4*DBHL 8 2 2 7 6*HTL 1 0 8 1 2 for Picea engelmanni x glauca; Volume = 10"4-294193*DBH1-85859*HTL00770for/,?>w«conWrra; and Volume = 10"4-419728*DBH1-89476*HT1-05373forPopw/Mj^mw/o^5. 24 3.233 Foliage Height Profiles It has been suggested that one of the structural characteristics of old-growth forests is that they are vertically more diverse than younger forests (Franklin et al. 1981, MOF 1991a). For this project vertical foliage profiles of each stand were crudely quantified by summarizing the number of trees present in each canopy stratum, weighted by individual tree basal area. This was calculated by the formula: F h =ECL i h *pba where F^ = the % foliage in each lm height class, h; CL = 1.0 for each individual, i, with live crown in that height class, h. pba = the basal area of that individual, i, as a % of the total plot basal area Previous research on canopy profiles and bird habitat used the Shannon-Weiner index to determine which stands had more diverse profiles (MacArthur and MacArthur 1960). This project used the same approach although there are many indices which can be used to measure compositional or structural diversity (Burton et al. 1992). Some indices specifically measure richness, others evenness, and some such as the Shannon-Weiner index attempt to measure both. All of these indices have different strengths and weaknesses and it has therefore been suggested that an investigator choose an index that is both easy to calculate and that emphasizes those aspects of diversity one is most interested in measuring (Peet 1974, Magurran 1988). The Shannon-Weiner index, a heterogeneity index, was chosen as it measures both richness and equitability, is easy to calculate and it had been previously used to assess canopy profiles. For each stand a foliage profile diversity index, Wf, was calculated as: Wf = -£p h ln(p h ) where Wf = the measure of diversity using the Shannon-Weiner index p^ = the proportion of the total stand foliage in each height class, h. 25 3.24 Correlative and Multivariate Data Analysis 3.241 Histogram Construction Histograms were constructed to both display and summarize stem densities for different size and age classes. In the construction of histograms, a bin width of ten years was chosen for age distributions and a bin width of five cm was chosen for diameter distributions to present meaningful classes for interpretation. However, as bin width and the starting value for bins alters the shape of a histogram (Scott 1979, Wilkinson 1990) this value was varied to determine whether or not the pre-defined distribution (as above) adequately represents the true distribution. If the bin width is too large then the histogram will be too smooth (large bias) whereas if the bin width is too small then the histogram will be too rough (large variance). While bin widths optimal for the portrayal of a population's mean and variance can be identified mathematically for normally distributed populations (Sturges 1926, Doane 1976, Scott 1979) and approximated for skewed populations (Doane 1976, Scott 1979) there is no objective means for selecting a bin width with the optimal information content for structural analysis of multi-modal populations. The age histograms were used to make inferences on population processes within the stand, especially with respect to tree regeneration patterns. 3.242 Cluster Analysis Hierarchical classification and multivariate ordination were performed using 14 structural attributes of each of the 14 stands to see how the stands could be objectively arranged by these multiple criteria. In choosing values for ordination and cluster analysis, to be compared with the subjective ranking of stand development stage, stand structure, regeneration attributes and mortality data were considered to be important components. In the Pacific Northwest of the U.S.A., these elements of stand structure have been used to define differences between old-growth and mature stand development stages (Franklin et al. 1981, Old-Growth Definitions Task Group 1986). Cluster analysis is used to objectively group similar objects into the same class. This technique was used to determine if any meaningful groupings existed when the plots were classified by structural attributes. This method then required verification by another 2 6 technique, as cluster analysis will produce clusters whether or not natural groupings exist (James and McCulloch 1990). Verification included the subjective interpretation of stand development status, utilizing al of the information available regarding each stand. The cluster analysis was performed in SYSTAT (version 5.01) using the single linkage clustering procedure (Wilkinson 1990). 3.243 Principal Components Analysis Stand development is a continuous process, so multivariate ordination was used to arrange the developmental stages (i.e. the sampled stands) along an objectively defined continuum. The ordination technique used was Principal Components Analysis (PCA) the objective of which is to represent variation in the original X variables by a smaller number of Z variables (components) (Manly 1986). The representation of the original data in only a few dimensions does not result in much loss of information due to redundancy (Pielou 1984). This lower dimensionality suppresses noise in the data, and facilitates trend visualization for the purposes of interpretation (Gauch 1982, Pielou 1984). As the attributes to be tested were not all measured on the same scale, the analysis was performed on a standardized data set using the correlation matrix. Such standardization can sometimes lessen the distortion due to non-linear trends in variation (Kenkel and Orloci 1986). This distortion, often referred to as arching, produced by the analysis of data along non-linear gradients is identified as a potential problem with PCA (Gauch 1982, James and McCulloch 1990). However, Pielou (1984) notes that this is only a problem if these effects are mathematical artefacts devoid of ecological meaning. With linear data or data along short gradients, PCA produces meaningful representations of the interrelationships among the data. The ordination was also conducted in SYSTAT (version 5.01) using principal components analysis (Wilkinson 1990). 3.244 Identifying the Periods of Cohort Recruitment The period of stand initiation must be separated from subsequent recruitment to identify the different stages of stand development. This is important in assessing the time taken for initial colonization, the conditions under which seedlings were recruited (within a 27 forest or within a large open area) and for assessing the relative importance of large-scale and minor disturbances to recruitment. To make this decision as objective as possible, a set of rules in the form of a decision key was used for designating the break between the initial cohort and the replacement cohort based on the visual inspection of histograms portraying multi-species age distributions in SBS stands. The key is as follows: 1) If the age distribution is bimodal 2) If the age distribution is reverse-J or random form 4) 2a) If the distribution has a break between the modes (abundance peaks) then the colonization cohort consists of the oldest age classes (i.e. those to the right of the break between the modes). 2b) If the distribution is continuous (no break between modes) 3) 3a) If Pinus or Picea are present in the age class of minimum recruitment occurring between the modes then include this low recruitment age class with the oldest mode as part of the colonizing cohort. 3b) If neither Picea or Pinus is present in the age class of minimum recruitment occurring between the modes, then do not include this age class as part of the colonizing cohort 4a) If recruitment of Pinus/Picea in the oldest age classes is continuous then the colonizing cohort is represented by this initial time of Pinus/Picea recruitment 4b) If recruitment of Pinus/Picea is sporadic then the colonization cohort is represented by the period of initial Picea or Pinus recruitment in which the gap between any two successive age classes with Picea/Pinus is less than 80 years (in very old stands many stems from this initial cohort may have already died). These decisions can also be verified by a comparison with the basal area by age graphs. A large drop in recruitment of basal area, in the same age class determined by the key, indicates that the individuals recruited in one age class grew well due to open conditions associated with stand initiation and the individuals in the next age class grew poorly due to suppression. 28 4. DESCRIPTION OF STAND DEVELOPMENT IN THE SBS 4.1 Introduction and Literature Review This chapter consists of a description of stand development histories of each of the SBSmc plots sampled to determine developmental patterns. The observed trends address questions on composition changes, the time required for tree re-establishment following a catastrophic disturbance and observed differences in stand development. Studies in areas with similar species mixes to the Sub-Boreal Spruce (SBS) zone (See Section 2.4), often from western Alberta or subalpine areas in Colorado and Wyoming, have generally presented two opposing views on the successional pathways of these species. The two models can be classified as the equilibrium and non-equilibrium theories. In the equilibrium theory, forests of these species are able to form relatively stable, self-perpetuating units, in which pine, if present, is succeeded by spruce and fir, which then form the climax association (Oosting and Reed 1952, Cormack 1953, 1956, Achuff and LaRoi 1977, Schimpf et al. 1980, Knowles and Grant 1983, Veblen 1986). The dissenting opinion states that in the absence of disturbance the forest will not be able to perpetuate itself and will eventually "fall apart" (Bloomberg 1950, Day 1972, Romme and Knight 1981, Johnson and Fryer 1989). Others have suggested that neither theory adequately explains the development of these forests across their range (Whipple and Dix 1979, Alexander 1986, Aplet et al. 1988). Rather, forest development will vary with environmental conditions. Aplet et al. (1988) contend that these forests are a product of both equilibrium and non-equilibrium forces, since most, if not all, stands are subject to recurrent large scale disturbances, but that these are unnecessary for maintaining spruce-fir co-existence. For mixed stands of Pinus contorta, Picea engelmanni X glauca, and Abies lasiocarpa in southern Alberta, Day (1972) proposed a successional pattern for a typical stand. Following fire Pinus dominates, but with time it is invaded by Picea which eventually takes over dominance, similarly Picea is invaded by Abies and eventually in the final stage the stand becomes Abies dominated. This last phase is theoretical, but has Pinus virtually eliminated and the forest exhibiting an uneven-aged Abies-Picea stand, with lower timber volumes and reduced tree vigour. Such stands have rarely existed due to the widespread 29 presence of fire across the landscape at frequencies which destroy most stands at earlier stages. A similar pattern was suggested by Bloomberg (1950) some two decades earlier when he noted that spruce would regenerate below pine but not below itself and therefore, without the return of fire, the forest would go into a state of decline. Although this appears to be a case of a thicker spruce canopy reducing light to the forest floor, Knapp and Smith (1982) found that it was not lower light levels but thicker litter levels that inhibited spruce recruitment with stand development. However, it has also been reported that spruce recruitment changes with stand age, increasing again at extremely old ages, as single-tree disturbances open up the canopy and perhaps provide some suitable seed beds (Aplet et al. 1988). Bloomberg (1950) suggests that most of the recruitment in spruce-fir stands occurs during stand initiation after fire and that only a minor portion of the stand is composed of secondary recruitment. A more recent study in Alberta echoed these ideas by stating that although there was ingress of both lodgepole pine and Engelmann spruce it was not sufficient to offset the losses due to mortality (Johnson and Fryer 1989). This pattern of forest decline in the absence of fire is also a general pattern in the western boreal forest (Rowe 1961, Dix and Swan 1970, Heinselman 1981). However, stand breakup and decline in the absence of fire is more understandable in the west central boreal forest where the presence of extremely shade-tolerant and "climax" species such as Abies lasiocarpa or Abies balsamea is rare. In boreal forests of northern Quebec, recruitment may not compensate volume loss until mortality of the even-aged post-disturbance cohort has reached a peak (Hatcher 1963). As the forest is v opened up1 recruitment becomes more successful, although infilling remains patchy throughout forest gaps and fir is more successful than spruce in establishing itself. If this kind of stand deterioration occurs as a prelude to forest stability then the dynamics of boreal and sub-boreal stands may be more complex than often suggested. In mature, mixed-species forests of the SBS, hybrid Engelmann-white spruce and lodgepole pine often dominate the forest canopy but are poorly represented in the seedling and sapling layers. Subalpine fir, on the other hand, is abundant in the understory and 30 smaller tree size classes, and has been referred to as the climax species for these stands. Such a pattern may seem to support the hypothetical development proposed by Day (1972). Other theories suggest that such forests are in equilibrium and that due to differing life histories, spruce and fir may be able to form a stable, perpetuating community (Veblen 1986). Cormack (1953) proposed an hypothetical four stage development very similar to that of Day's, but in his final stage the spruce-fir forests present "an example of forest growth unmatched in its beauty and stability". In support of this interpretation of stability, a recent study of these species found that, although there were fewer spruce recruits, this was offset by greater mortality of fir, such that the resulting forest was composed of a relatively balanced population (Veblen 1986). The debate on the maintenance of Picea as a canopy dominant is confounded by the differences in environmental conditions between forests of the different studies. By way of example, the Engelmann Spruce - Subalpine Fir zone (ESSF), which is the high elevation spruce-fir zone occurring throughout the central interior of B.C., has shorter, cooler growing seasons and a reduced fire cycle compared to the SBS (Pojar 1991). Stand development within these two zones is therefore likely to differ and needs to be characterized separately. This study also differs from preceding works in that it provides a more complete census of tree ages. Many researchers agree that size cannot be used to replace age in the analysis of population structure and demography, as the two may be weakly correlated (Harper 1977). Sparse sampling of ages may therefore misrepresent or bias interpretations of population dynamics especially in stands with large size or age variations. This age sampling from all stems (seedlings and saplings as well as trees) should more accurately measure past population dynamics. 4.2 Individual Plot Attributes and Stand Histories To identify the post-disturbance cohort and recruitment dynamics of each stand, the history of each individual stand was reconstructed. 4.201 Plot 1 This plot, as previously noted, did not have a complete age census and therefore can not be assessed in a manner similar to the other plots. However from the evidence collected 3 1 it seems likely that Plot 1, after a stand-destroying fire, approximately 180 years ago, initiated as a Pinus stand with smaller components of Picea and Abies. Most of the identifiable mortality in this stand has been Pinus (Table 4-1). The mortality data also show that Picea has never dominated the stand. Although future dynamics, as indicated by the number of stems in the smaller size classes (Table 4-2), are shifting towards Abies, there will still be some representation of Pinus and Picea from existing smaller stems. 4.202 Plot 2 A large proportion of the downed logs were Populus tremuloides (Table 4-1) and although there were no live Populus individuals within the plot a number were observed in the surrounding stand. It seems that after a stand-destroying fire the area was re-populated by Populus, Picea, and to a lesser degree Pinus. As the average life span of Populus is approximately 120 years (Achuff and LaRoi 1977) and stand age of Plot 2 is 135 years, it is not surprising that the Populus overstory has broken up and the majority of stems have died. The stocking of live trees in this stand, 411 trees/ha, is low due both to the loss of the Populus component and to a recent spruce bark beetle attack that has increased mortality of the dominant Picea trees. The age histogram (Figure 4-la) shows a long period of post-disturbance ingress of 60-70 years, which may have occurred beneath the Populus canopy. There is a distinct break between the post-disturbance cohort and subsequent recruitment 60-70 years ago. This is supported by the basal area by age graph (Figure 4-lb) in which the secondary cohort has only nominal basal area. The size class distribution (Table 4-2) also shows a break between the larger and smaller stems, indicating that it will be some time before the secondary regeneration will replace the canopy dominants. There will be a shift in composition from a forest dominated primarily by Picea to a mixed Abies-Picea forest. Some Picea recruitment in all age classes and in the youngest size class supports the model of a mixed Abies-Picea forest. The perturbation caused by spruce-bark beetles has opened the stand up faster than without such an exogenous factor and 32 r- ro vo ro in «* rH o o rH o rH rH f ~ VO O vo in I N CN CM ro «tf •<* CN I D in H oo r~ CN CN CN 00 O rO O rH (N r~ co rH CN rH CO *!• CN O O 0 0 ro *f CN r~ I D * * rH ** •<* O l I D in cn <* <* CN rH rH in r~ I D I D rH H • < * ro ro rH rH o CN CN • * cn r-oo CN rH I D o O <* I D in ** I D in CM r-VO CN I D in >* rH rH n cn 00 CN t> I D ^ CO ro ro rH rH r H r~ I D r H 00 r~ 00 r~ r H rH r~ I D r-I D CN CN o o o o o rH rH 00 10 in 0 0 n CO rH •* ** o o I D in rH CN CN • < * <* rH r H 00 r-** oo t -t -H •* lO O H *^ Ifl O cn ro ro CN CN oo oo vo ro r» rH r~ r~ in m vo r- rn ro ro o in ID rH *!• o o CN r - P I CM O rH VO CO 00 rH vD in cn cn oo r- O CN oo r- vo CN rH 1 0 *^ J^* CTl <* «* oo oo m CN o H ID CO ID ID rH O in ro in in rH o cn *^ ro in ro ro CN rH O CN O CM in co 00 r~ r H o o o ^ r-<tf ID cn ro o ro cn oo n oo r-H ro cn o t> ID cn ID ID Tf in in >* in ro o r~ t> CN o r- rH in io CN o in rH CN in CN ro vo in "* CN CN VO O <* O CN CN in <* CN r~ VO CN cn oo CN CN o o VO in H 00 o r-ID IO r~ 10 CN r> ** o ID •<* ro r-o rH rH O cn cn ro co co ro oo cn 00 r~ 00 r H CN "* o *r ^f ro ro CO ro O O H oo r~ rH I D in ro -* t l O r-VO ro rH CN CN m CN CN I D I D "* <* r~ oo ro ro O O O O ro ro O "* CN CN O O O "* CN CN cn 00 <* r~ o vo I D in rH O t -I D CN O O oo ** ^l-H r-H 10 CN CN r-CN I D »* I D O in O *}• •* 00 r~ vo cn CD r-rH rH rH in r~ I D vo vo in • < * O o o o 00 VO CN cn rH r H ro cn CO <• cn 00 •c* rH rH CN CN CN CN VO in VO rH r H J^-VO in ro CN CN CN VO in in t> t > VO cn rH rH Cn O rH O rH rH 00 rH in H ro vo ro CN O O O in ro CN rH ro O r~ O rH rH rO VO rH VO rH CN CN CN O O CN O «* o CN CN r-cn 0 0 rH rH rH rH rH CO CN <J IB O >J O >* ** in o <* o ** vD ro CN CN rH r-~ r- rH in 00 CN ro H O rH r~ CN ro rH H CTi VO oo i n H rH rH ^J" H H <f ro ro O CN O rH o 01 -p 0 r4 ft. x: 01 0) 0) U H 0) > •rH 1-1 rH rH 00 rH rH ** r H rH rH •-i rd -P 0 EH O O rH 00 t> ro r~ VD rH rd 0) 0 - H ft. rH rH r-o 0 0 r-cn 01 01 •rH S o ro ro r-vO CN 01 3 C -rH ft. 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D •a 0 •d c M C m o o o U l i n O M U ) O o o en in *• o to to o o •tf p -3 C to ui s i *» s i o U l *» o o i n 00 *> t o i n O O U l o ** M *» *» i n P» & p -ID (0 in VO o i n s i *» U l - J M ** U l in en U l CM M to to oo *> U l *» s i - J P* M i n T) P -n (D pi U l VO i n tn en to ^ - j i n 00 *» *. en VO en o in o i n O *> 00 *» i n en M i n i n H 0 rt P> M U l vo U l en en t o U l •f* s i P1 *» U l en vo en o U l o en U l U l o ** - j s i M U l U l D 0) EC X P» X p-3 d 3 n £ L"< H P- 0 < r t t o f ID pi 3 O M pi i Q H i Q 01 H D 0) (D (D (D pi (0 a U l U l O U l O U l P" t o t o M M tO I O M M M tO tO 4* oo cn to * » VO s i t o to U l * » * > ui A o ** I O t o M vi H M (n 00 P> I O VO t o P» U l O * U l in o ** en t o P" M *» en ui ui •^ en ui ui to H 4 * M U l P> 4 » M U l t o O 4 ^ t o t o O 4^ t o t o tO M A U l A CO 4^ U l 4 * VO I O M M 4» I O M P" * i M p H P 1 u i t o t o ui to o to t o M P> t o s i u i 4 * M v l U ) * . P 1 en s i u i 4* - J s i U 6 H 3 a p -< p -a c pi i -w f t (D 3 (0 O H (D pi f t ID Pi 3 O 3 (D O c cr p -o X (D r i -l l (D < 0 P 1 C 3 ID W 3 P> vQ CO M P 1 to • U l 00 U l • o p« 4» en • U l M U l • VO en U l 1 O \-> vO VO M VO s i • M U l s i 4» 4» o • VO M U l M • U l VO to • U l P> t o • en oo o> O U l * U l Q X Q M s i cn • U l M P" • U l U l o U l• s i M U l cn • to s i 4> • 4> M O U l • oo to s ) M • P" M U l U l U l s i i o to to cn • co U l CO 4 * • o M cn s i t VO U l P> 4» 00 M U l M • 0 0 i-3 11 (0 ID 01 i n O O • U l U l p" VO • cn U l 00 cn • CO U l M 00 • cn cn U l CO • to U l o s i • o U l VO • cn 4> s i U l O U l VO P 1 • M 4» VO 00 • U l U l U l t o • cn U l VO o • vO U l 4» to • 4 * o U l • to < 0 P 1 p g ID "3 U l 3" pi s-» H O rt Pi P-1 I 1 I s i to • 4» s i VO • o vO s i • to M U l cn • vO vO s i • t o • CO cn o *> M O • VO i n O t s i M *• t U l VO «* • t o M P 1 cn s i CO vo t P> ?0 ID VQ ID 3 1 1 1 M • i n P> • *. U l • s i o • VO M • cn U l • t o *• cn to • to M • 4^ *> • P" M • cn P> cn o • cn H n (v> ID D3 ID h to ID fa) pi CO to Pi f t m x ID rt o ui U l U l • • s i U l O M • • vo to s i * > s i M • • cn o vo *» U l vo • • ui cn p> ui cn cn to • • o oo vo in U l O • • in IO M O U l vo cn • • CTl U l U l £> U l VO CO U l P 1 O U l CO U l • • s i P" VO CO • • U l VO P" P- U l O CTl • • VO * > t o U l • • CT> s i P 1 p" U l U l U l M O CO * » CO P> t • ui oo CO p 01 PI M > ID Pi 3 to ^« pi G 3 * 0 0 ?r KS 3 0 J-3 M oo VO to s i 00 en M M U l U l CTl t o t o P 1 M *> cn s i I O cn s i M s i CO U l s i oo cn U l U l s i s i co *> s i oo M 00 VO c p" c 01 o U l U l cn o P 1 P* o p« P 1 t o t o O O CO VO o p" M *» *• o s p -ID 0) I O o o o J ^ CO VO *» •^  M U l U l en U l cn U l 00 VO I O U l cn o U l M M M O o co VO t o t o t o o "0 p -3 c 01 *» U l U l o o P 1 cn s i o o p> M o cn s i o to cn s i O to to U l en • 0 p -0 ID Pi £> 4^ *» P> M M P> M M P> P> U l U l U l t o £ • ^ cn s i o o M U l U l cn 00 VO M U l cn i n cn M P 1 P 1 cn t o t o H 0 rt P> M co CT> s i P> O «> 4^ P" to M M CTl CO VO CO s i CO M to IO VO U l cn cn t o t o U l s i CO *> cn s i M M U l en VO U l U l vo s i CO CO en s i O Q 3 o p -3 ID 0 . D (D pi a -^» M O vQ (0 PI 3 a w 3 Pi vQ to 3 " Pi •fl M o r t 0) O P" O t o O U l o *» o U l o CTl o s i o CO o vO M o p> M M to P" U l M *» 1 H pi cr M ro *. i M n 0 3 rt p -3 C ID a ee rH VO rH 00 O CN in in rH 00 O CN CN r~ • * O CN r-l VO rH o 00 rH O O >* (N r~ CN • * CN CN in ID * n O i-t ro n CN CN r~ vo rH O n ID r-CN * oo <-i •<* CN i-l rH CN rH 1" CN r-H rH CN rH CN rH rH «* rH rH CN H CN in ro CN ID n r~ cn ro rH CN ID CN ID in r~ rH rH <• H d\ O H IB Ul <* ID CN «tf rH CN in «* «* in CN CN CN CN 00 00 00 CN 00 CN CN rH CN O r~ oo oo in o o oo in CN r- r-~ co r- vo in r- r-vO CN in CN ro rH ro o o ^ n ^f ro ro ro in in r- oo o CO CO 00 0 1 tn u Id d) r- i i i i >i r- I i I I -~ rH i i | I I I -H Id CD 0) ID id <D Q) 3 3 P O -W 01 CM «2 C rH -rl 3 0* ft 0 CTl - ^ rH 01 01 • r | u 0) ft «* 01 CN +J c 0) VH 01 rH <4H CN VH • r l TJ "«. — CN ro > i -P •rH 01 U 01 " * > CN - r | Q M o •p ID 0) •O C 00 O CN ro CN in r- ro O H * VO rO VO VO 01 01 * ID CN ro ro •** < * rH CTl * rH « * rH * (T> ID <t H n * o> r- vo CN ro CN oo vO O CN ro ro ro 00 CN CTl r- o oo ro O r o CN rH CN ro CN ro in r-n oi I D ro CN ro <f oo h CN CTl CN oo r~ rH CO rH CN ^ io in co IO in rH CN <C CM r i in *# rO rH ^t rH CN CN ro oo CO r- in rH ro CN CTl rH CN * in 00 rH •* r~ r-i • < * CN rH 00 00 rH r^  CN rH O in rH VO CTl CN 00 H CN CN rH VO 00 rH ro CN CTl O CN O "* ID rH C*. r>. m 00 <H o 00 CN a> rH <* CN in CN oo VO 00 ro •* r-CTl CN in r~ ro vo CN O r~ CN r» CTl CN CN CTl CN in CN CTl CN r~ o * ro ro ro rH "* o rH in VO rH o * in <!• CTl oo oo rH o VO rH CTl rH o «* CN rH ** ro O CN ro rH rH vo in rH CTl CN rH O VO ro CN VO VO o *f VO r-rH o CN rH ro ro CN r-ro CTl >* rH ro ro ro ro ** CN rH in VO ro r-o o in CN CN vO rH rH in rH rH r-vO rH r» CTI t r CO lO 00 00 h IN i n H r H r H r H r H C N CN VO H r~ CN rH VO 01 00 01 VO V O C N r H rH rH H rO rH r~ H f - * CTl CN VO O rH r~ H ro vO O rH t~ rH * r- CTI O CN VO ^j- ro CN ro CN in 0) Cn o n o> c 0 Q r-CTi m co co N r- in . . u • • . H n l i H H O O ^ n i n r H r o CM in J H H N N N / N H ' J 1 ^ 1 ft j j v C O C N C ' * 0 0 C N r ~ C T i l V v o r o c N • . . 0 U • • • m r o c N - r l r o r o o O C T i ^ J ' ^ i C N in rH ro -P H H Ul J) o ^" ft O C O C N i i oi I N oi co H cn • • « o C I C l ^ t M U l f l N i n ^ H - r l I r H r H r ^ C C N C N C N r H r H i - H I i n I I I I to 01 VM c o •a w u n) X VM 0 01 01 u tjl 01 Q * 01 01 u .c E-i * u 3 O Cn In rd fn * 0) MH > 0 C s o OH C ft id JX rl C C H M D 0) Cn cd •d c rd -P to -P 0 c TJ C n) 0) c id u 01 -p 01 > to -P c 0) n oi u ft 01 u c 3 o u Cn MH c 3 0 u Cn c 0 - <*H - H o 01 si o id +J -P id •A •P 0) CO 0) Xi u c rd u -p id Vi o Cn ft c u •p o c •rH +J to •rH • H - p CO e u o •rH si o id 01 TJ CD 01 x: o c id u XI c 0 •rH •p id u O Xi ft rd u c 0) u 01 u CO •rH •a 3 O 0 +J T3 id ft -c D W I I OJ o 8£ I I U 01 3 > O -rl (n in 35 "* U) o * * in oo oo VO 00 CN in CM •<* VO en <N rH i-l vO rH vX> VO en CM • n rH rH rH r~ ro CN VO CN rH rH rH r-n rH VO o CN oo oi in (Ti vo rH O I 00 VO CN l/l o ^ r- rH H rH n in t oo co in ix) vo H r-J^1 r- en O in n in ro rH in CN ro O VO t - IX) t f r i H h H N rH r - rH CN rH 10 ro ro ro o r j - rH i n rH cn o 00 o rH in CN • a\ <X) in • o in <* ro in • CN CN o> • r~ CN «* rH ro • oo <x> • rH CN ro VO r~ • o •* en • >x> VO ro CN vO • in in o CN rH r-• ro rH • ro CN O O rH rH in • (N ** 00 • 01 H r» rH r-• rH r~ • rH rH H r~ 00 • in in in • 00 vO r~ ix> ix) rH 00 rH CO r H VO r~ r- vo o o ro iX) o rH VX) r o O ^* ro ro r-o • • • o en oi VO CN • • • rH o r» in CN (N • • • O iO ro ro rH in VO o in O • •^  vO ro • CN ro • ro ro oo • o in • CN CTi • VX) rH • CTl 00 Ch • ro 00 • O rH <Ti • CN rH • o CN • ro O • ** in CN • in *r • r» oo • in CN • 00 ro O • CD CN r H CT> O •*J* o ro O « vO 00 CN • ro x* • o> CN • ro •* • ** VX) • ro rH « r~ en • 00 r~ • o rH • rH CN • CN O O • 01 VO VO • in ro • vO CN • cr> • < * • CN CN • in rH XI Q) 3 C •A •P C 0 o rd CN o 0) •p o +J c OJ u u OJ ft in in o C 0 •A -P C 0 •A 4J 01 O ai c ai ft CP d) a> — c > i ft 0) ft rd +J o t u x 01 C — tt) Q) rd — 05 U +J C 0 1 0) • O CN U vo CO ft O rH O 0 0 ro > i X! ft I I I rd I I I U ~ -CJl - ^ >— 01 0 H 0) B ft Q) ft C 0 > 0 3 4J 0) rH 0 0 rH 01 g o •A X m +J •A ft IT) r j" rH ^ j r o U l rH c U U r7 §3 ' ' ft 0 I I — m i i - . ai +J rd "d <*H rd U 0 >H U Q) •P C 01 - ^ xj e 3 — W 0 3 ? -a Table 4-2. Size Distribution bv Species and Totals Diameter Class(cm) 0-4 .9 5-9.9 10-14.9 15-19.9 20-24.9 25-29.9 30-34.9 35-39 .9 40-44.9 45-49.9 50-54 .9 55-59.9 60-64.9 65-69.9 70-74.9 Plot 1 Abies 0 211 356 356 • 56 11 11 11 0 0 11 11 0 0 0 Picea 0 22 33 56 33 22 11 33 0 0 0 0 0 0 0 Pinus 0 11 22 44 89 67 44 89 33 11 0 11 0 0 o Total 0 244 411 456 178 100 67 133 33 11 11 22 . 0 0 0 Plot 2 Abies 2280 58 0 0 0 0 0 0 0 0 0 0 0 0 Picea 1373 0 0 0 33 22 56 56 78 0 44 44 11 Pinus 0 0 0 0 0 0 0 0 22 0 0 0 11 Total 3653 58 1 1 33 22 56 56 100 1 44 44 22 Table 4-2. Size Distribution by Species and Totals Diameter Class fern) 0-4.9 5-9.9 10-14.9 15-19.9 20-24.9 25-29.9 30-34 .9 35-39.9 40-44 .9 45-49.9 50-54.9 55-59.9 60-64 .9 65-69.9 70-74.9 75-79.9 Plot 3 Abies 3909 463 178 67 100 44 11 44 56 22 22 0 0 0 0 Picea 1156 28 22 33 11 0 0 0 11 0 0 0 11 0 0 Total 5064 491 200 100 111 44 11 44 67 22 22 0 11 0 0 Plot 4 Abies 5611 256 144 44 33 11 22 11 0 11 33 0 0 0 0 0 Picea 133 0 0 0 33 22 11 11 22 22 33 0 22 0 11 0 Pinus 0 0 0 11 22 11 11 > 0 33 0 22 33 44 22 0 0 Populus 0 0 0 0 0 0 0 0 i 0 11 0 0 0 0 0 0 Total 5744 256 144 55 88 44 44 22 55 44 88 33 66 22 11 0 37 Table 4-2. Size Distribution bv SDecies and Totals Diameter Class(cm) 0-4.9 5-9.9 10-14.9 15-19.9 20-24.9 25-29.9 30-34 .9 35-39.9 40-44.9 45-49.9 50-54.9 55-59.9 60-64 .9 65-69.9 70-74.9 75-79.9 Plot 5 Abies 2347 56 122 44 33 33 67 0 " 11 0 0 0 0 0 11 0 Picea 508 11 33 11 33 133 56 100 78 11 11 11 0 0 0 0 Total 2856 233 156 56 67 167 122 100 89 11 11 11 0 0 11 0 Plot 6 Abies 7512 315 156 44 33 67 67 67 67 11 0 0 0 0 Picea 240 11 0 0 0 56 44 33 33 0 0 0 0 0 Total 7752 326 156 44 33 122 111 100 100 11 0 0 0 0 Table 4-2. Size Distribution by Species and Totals Diameter Class fern) 0-4 .9 5-9.9 10-14.9 15-19.9 20-24.9 25-29.9 30-34.9 35-39.9 40-44.9 45-49.9 50-54.9 55-59.9 60-64.9 65-69.9 70-74.9 75-79.9 Plot 7 Abies 8890 533 233 100 89 11 78 56 44 22 0 11 0 0 0 Picea 82 0 0 0 11 0 0 22 33 11 0 0 0 11 0 Pinus 0 0 0 0 0 0 11 11 0 0 0 11 0 0 0 Total 8972 533 233 100 100 11 89 89 78 33 0 22 0 11 0 Plot 8 Abies 6761 634 133 44 11 33 11 11 0 0 0 0 0 0 0 0 Picea 212 22 0 0 11 22 22 33 67 44 22 22 0 0 o 0 Pinus 0 0 0 0 0 0 0 0 44 0 0 0 0 0 0 0 Total 6973 656 133 44 22 56 33 44 111 44 22 22 0 0 0 0 38 T a b l e 4 - 2 . S i z e D i s t r i b u t i o n b y S p e c i e s a n d T o t a l s D i a m e t e r C l a s s ( c m ) 0 - 4 . 9 5 - 9 . 9 1 0 - 1 4 . 9 1 5 - 1 9 . 9 2 0 - 2 4 . 9 2 5 - 2 9 . 9 3 0 - 3 4 . 9 3 5 - 3 9 . 9 4 0 - 4 4 . 9 4 5 - 4 9 . 9 5 0 - 5 4 . 9 5 5 - 5 9 . 9 6 0 - 6 4 . 9 6 5 - 6 9 . 9 P l o t 9 A b i e s 2 3 6 8 6 8 7 1 1 0 0 0 0 0 " 0 0 0 0 0 0 P i c e a 1 0 5 6 7 1 5 6 1 3 3 7 8 7 8 4 4 3 3 0 1 1 0 0 0 0 P i n u s 2 8 0 0 1 1 2 2 1 0 0 1 0 0 5 6 3 3 2 2 0 0 0 0 T o t a l 2 5 0 1 7 5 4 1 6 7 1 4 4 1 0 0 1 7 8 1 4 4 8 9 3 3 3 3 0 0 0 0 P l o t 1 0 A b i e s 1 0 3 3 4 3 3 1 0 0 • 0 1 1 3 3 0 0 0 0 0 0 0 0 P i c e a 5 6 2 2 4 4 1 0 0 7 8 1 1 1 2 2 7 8 5 6 2 2 1 1 0 0 0 P o p u l u s 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 T o t a l 1 0 8 9 4 5 6 1 4 4 1 0 0 8 9 4 4 1 2 2 7 8 5 6 2 2 1 1 0 1 1 0 T a b l e 4 - 2 . S i z e D i s t r i b u t i o n bv S p e c i e s a n d T o t a l s D i a m e t e r C l a s s ( c m ) 0 - 4 . 9 5 - 9 . 9 1 0 - 1 4 . 9 1 5 - 1 9 . 9 2 0 - 2 4 . 9 2 5 - 2 9 . 9 3 0 - 3 4 . 9 3 5 - 3 9 . 9 4 0 - 4 4 . 9 4 5 - 4 9 . 9 5 0 - 5 4 . 9 5 5 - 5 9 . 9 6 0 - 6 4 . 9 6 5 - 6 9 . 9 7 0 - 7 4 . 9 P l o t 11 A b i e s 5 0 9 1 549 56 1 1 44 33 44 22 11 0 0 0 0 0 0 P i c e a 46 58 44 56 78 67 56 22 0 0 0 0 0 0 0 P i n u s 0 22 0 0 0 0 33 100 33 22 0 0 0 0 0 P o p u l u s 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 T o t a l 5 1 3 7 629 100 67 122 100 133 144 56 22 0 0 0 0 0 P l o t 12 A b i e s 3152 17 2 133 56 67 22 11 11 0 11 0 0 0 0 P i c e a 229 170 11 89 100 111 11 67 44 0 0 0 0 0 P i n u s 0 0 0 0 0 0 0 0 11 0 0 0 0 0 P o p u l u s 0 0 11 0 11 0 0 0 0 0 0 0 0 0 T o t a l 3381 342 156 144 178 133 22 78 56 11 0 0 0 0 1 39 Table 4-2. Size Distribution by Species and Totals Diameter Class(cm) 0-4.9 5-9.9 10-14 .9 15-19.9 20-24.9 25-29.9 30-34.9 35-39.9 40-44.9 45-49.9 50-54.9 55-59.9 60-64.9 65-69.9 70-74.9 Plot 13 Abies 6844 377 178 89 100 ..• 56 33 11 11 11 0 0 0 0 0 Picea 578 11 11 11 22 22 22 22 44 33 22 11 11 0 0 Pinus 0 0 0 0 0 0 11 11 0 11 0 0 0 0 0 Total 7422 388 189 100 122 78 67 44 56 55 22 11 11 0 0 Plot 14 Abies 516 30 11 0 0 0 0 0 0 0 0 0 0 0 Picea 1154 22 67 100 111 44 22 0 44 22 11 11 0 0 Pinus 607 0 0 0 0 11 11 22 22 22 11 0 0 0 Total 2278 53 78 100 111 56 33 22 67 44 22 11 0 0 A. 40 0 Picea S Pinus • Abies 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 Age Class, years CO _c \ C\J E cd CD < "cO w CO m B2 Picea 0 Pinus • Abies 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 Age Class, years Figure 4-1. Plot 2 age structures. (A) Density x age class curve is bimodal demonstrating almost continuous Picea recruitment. (B) Basal area x age class demonstrates the dominance of the initial cohort. 41 has accelerated stand development, allowing secondary Picea ingress in large numbers at a young stand age. 4.203 Plot 3 Representing the oldest stand sampled in this study, Plot 3's age distribution was of the classic reverse-J form, indicating a stable self-perpetuating population (Figure 4-2a). Although it can be difficult to define the initial cohort in a reverse-J shaped distribution the initial presence of Picea (Figure 4-2a) indicated that this cohort finished recruiting 280 years ago. After the initial colonizing period ended, Abies continued recruiting but Picea regeneration was excluded for 160 years. Recent Picea ingress during the past century shows that Picea will be retained in this stand and may now be in a self-perpetuating state. Picea ingress in the last 40 years was particularly notable (Figure 4-2a). This may be related to increasing mortality of Abies, which was observed to be succumbing to root rot as it approaches it's maximum age. The basal area graph (Figure 4-2b) demonstrates that recruitment occurring since the initial cohort accounts for much of the current stand dynamics and supports the idea of a self-perpetuating population. The recruitment of Picea basal area in the last 120 years has been greater than that of Abies, supporting the theory that Picea maintains itself. The size distribution was of a decreasing monotonic form (Table 4-2), also corroborating evidence of self-perpetuation. This stand is currently dominated by Abies and has been for a long time, as supported by evidence in this stand and knowledge of the silvics of the trees. Picea is longer-lived and less susceptible to disease than Abies (Schimpf et al. 1980). As well, identifiable logs and snags, of which 489/ha were Abies and 111/ha were Picea (Table 4-1), demonstrates that there was not an earlier larger Picea cohort that has succumbed to mortality. Thus increasing Picea ingress may result in a shift towards a Picea-Abies forest. 4.204 Plot 4 The tree community of Plot 4, although approaching a self-perpetuating state, appears to have also been subject to a number of minor disturbances that resulted in peaks of seedling 2000 I ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 42 1500 CO x: \ E o <» 1000 -w c CD Q 500 •Pn-Pf-i i i i | i i i i | 50 100 150 200 250 300 350 Age Class, years Picea Abies Picea Abies 100 150 200 250 300 350 Age Class, years Figure 4-2. Plot 3 age structures. (A) Density x age class curve is reverse-J with 160 year exclusion of Picea and fairly continuous Abies recruitment. (B) Basal area x age curve demonstrates the success of secondary recruitment, especially Picea. 43 and basal area recruitment (Figure 4-3a). The evidence of minor disturbance is especially strong during the second forty years of the initial colonization period (140-100 years ago). These minor disturbances are responsible for the extended but patchy ingress of Pinus and Picea until as recently as 30 years ago. Peaks in the basal area graph (Figure 4-3b) indicate recruitment of dominants and release of advance regeneration. These waves of recruitment are perhaps the result of insect or disease outbreaks affecting a portion of the overstory population (see Tables 2-1, 2-2). There is some evidence of Populus in both the live and dead stem components of the stand, suggesting that the conifers may have recruited in conjunction with Populus. The period of Populus mortality in this stand may have accounted for one of the surges in conifer recruitment. The stand is exhibiting a shift in composition from a Pinus-Picea to Abies-Pinus-Picea stand. Although there is greater Abies ingress during the past century (Figure 4-3a) Pinus recruitment of basal area is also strong over this period (Figure 4-3b) and Pinus may therefore be able to perpetuate itself as a component of the future stand without the need for catastrophic disturbance. However, there are few stems of any species in the youngest age classes. 4.205 Plot 5 Plot 5 is currently dominated by an initial post-disturbance Picea cohort that colonized the site over a 100 year period beginning 180 years ago (Figure 4-4a). Secondary Picea regeneration was then delayed, perhaps until suitable microsites associated with the opening up of the stand became available. Both Picea and Abies are being recruited in the younger age and smaller size classes. The relative proportion of Abies to Picea appeared to be shifting in favour of Abies after the initial colonizing period (Figure 4-4b) but recent abundant recruitment of Picea to the smallest size (Table 4-2) and youngest age (Figure 4-4a) classes indicates that it will remain a viable part of the population. 2500 i i i i i i i i i i i i i i i i i i 44 [1 Populus 0 Picea S Pinus • Abies 0 10 20 30 10 50 80 70 80 90 100 110 120 130 140 150 ISO 170 1B0 190 Age Class, years i i i i i i i i i i i i i i i i i i POPULUS PICEA PINUS ABIES 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 Age Class, years Figure 4-3. Plot 4 age structures. (A) Density x age class curve approximates reverse-J with initial cohort still evident and large deficiency in youngest age classes. (B) Basal area x age curve in which peaks and troughs are suggestive of minor disturbances releasing and/or removing trees. 600 500 03 r. \ CO b CD CO >. •*—• co <_ <X> Q 400 300 200 100 0 45 Picea Abies 0 10 20 00 40 50 60 70 80 00 100 110 120 130 140 150 180 170 1B0 180 200 Age Class, years J i i i i i i i _ i i i i i Picea Abies 0 10 20 00 40 60 60 70 80 00 100 110 120 100 140 150 160 170 180 100 200 Age Class, years Figure 4-4 Plot 5 age structure. (A) Density x age class curve shows that Abies has reverse-J form and Picea is bimodal. (B) Basal area x age class shows dominance of the stand by Picea. 46 Although most of the downed logs were of recent origin and therefore few exhibited signs of charring, there was charcoal present in two distinct bands within the soil profile. This indicates the consistent importance of fire in the past history of stands on this site. There are also a large number of pits and mounds present on this site. As many of the downed logs are recent (having little rot and minimal incorporation into the forest floor [Table 4-1]) and not of catastrophic or pre-catastrophic disturbance origin, windthrow may also be an important agent of mortality. Mortality due to windthrow may account for the recent Picea ingress. 4.206 Plot 6 This plot also shows consistent evidence of fire exhibiting charcoal bands at two points in the soil profile. The surface band of charcoal is only nominally present, so the most recent fire may not have been very severe, allowing for quick regeneration of Abies. It has often been noted that Abies is not as well adapted to reproduce after intense fires as other species (Oosting and Reed 1952, Rowe and Scotter 1973). The age distribution (Figure 4-5a) demonstrates that Abies ingress occurs simultaneously with Picea in the stand colonization period, although many of the other stands show a delay in Abies regeneration during stand initiation. The deeper charcoal band may indicate a more severe fire (having left a lot of charcoal) and the stand that developed under such conditions may instead have been dominated by species better adapted to regeneration after fire, such as Pinus or Populus. Currently the age distribution curve for Plot 6 suggests a self-perpetuating community as it exhibits a reverse-J shape (Figure 4-5a). However, there is evidence that this stand is still dominated by the post-disturbance cohort, as the basal area by age graph (Figure 4-5b) shows that there is little recruitment of younger stems to larger size classes. The size distribution (Table 4-2) also shows a break between the smallest and largest size classes, suggesting that this stand may not yet be self-perpetuating. The post-disturbance cohort can be isolated as having established in a 100 year period beginning 230 years ago (using the age distribution graph for Picea alone in comparison with 2500 47 Picea Abies 50 100 150 Age Class, years 20 15 -CO JZ \ CM S 10 < CO ro CD 0 -l I l I L_l I I I I I I I I I I I I I I I L 0 50 100 150 Age Class, years 200 Picea Abies Figure 4-5. Plot 6 age structures. (A) Density x age class curve has reverse-J form with recent reinitiation of Picea. (B) Basal area x age class shows that most of the basal area is concentrated in trees from the initial cohort 48 the basal area by age graph). In contrast to this peak in Picea regeneration, Abies recruitment has been steady over this time period. Recent Abies mortality, as evidenced by the large proportion of Abies snags (Table 4-1), may be responsible for the Picea regeneration of the last 30 years. These two factors support a prediction of continued maintenance of Picea in this stand despite the current dominance by Abies. 4.207 Plot 7 The Plot 7 Abies population appears to be a self-perpetuating population, as demonstrated by its reverse-J shaped age distribution (Figure 4-6a). This stand seems to be shifting from a Picea-Pinus mix to an almost exclusively Abies dominated stand, in contrast to the patterns in other stands. Neither Picea or Pinus show evidence of continued maintenance as dominant members of this stand. It is speculated that following a light fire in approximately 1730 (this plot has only a light presence of charcoal at the duff mineral soil interface), Picea, Pinus and Abies all recruited simultaneously. This light fire may have promoted early Abies regeneration in this stand. Picea and Pinus ingress occurred almost exclusively in the initial colonizing period of 180 to 260 years ago (Figure 4-6). The basal area by age graph (Figure 4-6b) is almost a J-shaped curve, showing a continuous recruitment of basal area per age class. This is perhaps indicative of a self-perpetuating population. The basal area distribution indicates that although the stand is dominated by the disturbance cohort, subsequent regeneration is beginning to account for current dominance and dynamics. A number of peaks in both this graph and the age distribution show evidence of the continued importance of small scale disturbance. 4.208 Plot 8 Plot 8 was established during a fifty year period beginning 170 years ago (Figure 4-7). A secondary peak of ingress occurred 70-110 years ago, associated with the arrival of Abies regeneration. This secondary regeneration has occurred in a patchy episodic fashion which is unusual, because Abies recruitment is usually not as sporadic as that of Picea and Pinus. 2500 i i i i i i i i i i i i i i i i i i i i i i i i i i 49 »i q i 1 100 150 200 250 Age Class, years 0 Picea H Pinus • Abies 15 i i i i i i i i i i i i i i i i i i i i i CO _ C \ CM E CO CD < "CO OT cc CD 10 -0 100 150 200 Age Glass, years 0 Picea 0 Pinus • Abies Figure 4-6. Plot 7 age structures. (A) Density x age class curve is a reverse-J and it suggests the probable loss of Picea from the stand. (B) Basal area x age class curve has J form and shows the increasing dominance oi Abies that has occurred over the stand's history. 2000 1500 -CO xz \ OT E CD w 1000 -CO c CD Q 5 0 0 0 50 0 Picea S Pinus • Abies 0 10 20 00 40 60 00 70 BO 90 100 110 120 100 140 150 160 170 1B0 190 Age Class, years 40 I I I I I I I I I I I I I l _ J 1 1 L 30 -CO _c \ CM £ S 20 < "co CO CO 00 10 -0 0 Picea 0 Pinus • Abies 0 10 20 30 40 60 60 70 80 90 100 110 120 130 140 160 160 170 160 190 Age Class, years Figure 4-7. Plot 8 age structures. (A) Density x age class curve is multi-modal or sporadic showing random recruitment and recent Picea reinitiation. (B) Basal area x age class curve shows that the stand is dominated by the initial cohort. 5 1 Despite high variation in the numbers of stems per age class, basal area recruitment has not been as sporadic (Figure 4-7b). Perhaps the conditions that allowed large numbers of individuals to regenerate did not persist long enough to allow many individuals to be successfully recruited to the canopy, resulting in a smoother basal area distribution. A peak in the basal area graph in the 80-90 year age class suggests that at least one disturbance event allowed individuals to be released. Although there is an overall compositional shift from a Picea-Pinus forest to one that is dominated by Abies, the last twenty years show a return to the frequent and large changes in Picea regeneration. This suggests that the minor disturbances during the past two decades are freeing more resources than did earlier disturbances. 4.209 Plot 9 Following a fire approximately 175 years ago, Plot 9 was colonized during an eighty year period by Picea and Pinus (Figure 4-8). After the initial stand colonization period, successful ingress of these two species occurred in a patchy fashion, probably associated with minor disturbance. Abies regeneration was delayed until after the initial colonization period, but in the last 100 years it has been quite successful. This suggests a shift in species dominance of the stand. There was a relative paucity of downed logs at this site, especially logs fully incorporated into the forest floor. The few that were present were of small size and probably died as a result of competition within the current stand, although a small percentage showed evidence of charring. This stand has further evidence of a history of frequent fires, and the amounts of charcoal present in the soil profile suggest that this area may have been burned a number of times and on relatively short intervals two centuries ago. This would account for the relatively minimal presence of coarse woody debris (CWD), as the stands would have been quite young and many of the smaller stems would have been completely consumed by the fire. Frequent burning of young stands may also have consumed logs of previous stands, or the elapsed time may have been sufficient for decomposition to completely eliminate them. 600 500 i i i i i i i i i i i i i i i i i i 52 CO .r \ V> b. CD CO > w en c CD Q 400 300 200 100 0 0 Picea S Pinus • Abies 0 10 20 30 40 60 60 70 80 90 100 110 120 100 140 160 160 170 1B0 190 Age Class, years l__l L_J I I I I I I I I I I I I I L E3 Picea 0 Pinus • Abies 0 10 20 30 40 60 60 70 80 00 100 110 120 130 140 160 160 170 180 190 Age Class, years Figure 4-8. Plot 9 age structures. (A) Density x age class curve is bimodal showing no Abies in the initial cohort. (B) Basal area x age class curve shows both that the stand is dominated by the initial cohort and by Pinus and Picea. 53 4.210 Plot 10 Plot 10 is a relatively young stand of 120-130 years old, with one large, old Populus veteran (Table 4-1). If this veteran and downed Populus logs are used as an indication of the initial stand composition, then the post-disturbance Picea-Abies cohort regenerated over a 60-70 year period beneath a Populus canopy . Picea regeneration occurred primarily during the stand initiation period, and although there are some small stems present, there has been no Picea ingress during the last forty years (Figure 4-9a). This may be due to the relatively young age of the stand, such that canopy openings due to overstory mortality are not sufficient to allow for successful recent Picea regeneration. Although Abies and Picea ingress occurred in nearly equal numbers during the stand initiation period, most Abies stems were suppressed. Size distributions (Table 4-2), show that the majority of the larger trees are Picea. Basal area by age distributions also show that, even when there was less Picea regeneration than Abies, Picea recruitment to the overstory was more successful, as it accounts for the majority of the dominance by basal area (Figure 4-9b). This supports the equilibrium theories based on different life history strategies, in that lower numbers of Picea to Abies regeneration can still result in equal representation in the canopy. The smaller size classes will be dominated by Abies in sheer numbers due to their ability to persist in the understory, but few will join the overstory. 4.211 Plot 11 Plot 11 may also have established as a mixedwood stand, as suggested by the small number of Populus stems in older age classes. Initial conifer establishment was dominated by Pinus followed by Picea and then Abies and lasted for a period of 100 years (Figure 4-10b). Recent ingress (Figure 4-10a) suggests that Abies will completely dominate this stand unless future release of resources by the death of overstory trees allows for greater successful establishment of Picea seedlings. 300 i i i i i i i i i i i i i i i i i i i 03 r. \ co fc <p w >. -*—' CO ( 0) Q 200 100 0 2 5 20 -03 ™ 15 03 CD < CO 03 CD 5 -54 "i—r—T—r 0 10 20 00 40 50 BO 70 80 90 100 110 120 130 1-40 150 IBO 170 IBO 190 200 Populus Picea Abies Age Class, years i i i i i i i i i i i i i i i i i i 0 I i i ! m i m "i i—i r 0 10 20 00 40 60 60 70 80 00 100 110 120 100 140 160 160 170 180 180 200 Populus Picea Abies Age Class, years Figure 4-9. Plot 10 age structures. (A) Density x age class curve is bimodal with very old Populus veterans. (B) Basal area x age class curve is dominated by Picea despite greater numbers of Abies present in most age classes. 1500 CO \ w E CO c CD Q 1000 -500 0 55 100 150 Age Class, years ^ Populus E3 Picea S Pinus • Abies CO JZ \ C\J E cd 0) < "cO CO CO CO H Populus 0 Picea 0 Pinus • Abies 50 100 150 Age Class, years 2 0 0 Figure 4-10. Plot 1 1 age structures. (A) Density x age class curve approximates rcvcrsc-J with initial cohort still evident. (B) Basal area x age curve demonstrates the dominance of the initial cohort, with most vigorous growth by second decade colonizers. 56 Peaks in basal area recruitment of the secondary cohort may be associated with minor disturbances such as windthrow. The pit and mound microtopography discovered on this plot may be evidence of windthrow. Again, there is a deficiency in stems in the youngest age classes, a trend that has appeared in many of the other stands. A possible reason for this recent shift: Is it due to climatic change, or is it due to poor Abies cone crops, forest floor buildup, an artefact of sampling, or some other unexplained environmental parameter? 4.212 Plot 12 Plot 12 is the youngest stand studied in this project with most stems less than 100 years old. One large Pinus individual is twice the stand age, and appears to be a veteran from a previous stand. This is supported by Plot 13 and Plot 12's close proximity to each other and the Plot 13 stand age is similar to that of the Plot 12 Pinus veteran. This stand shows has a pit and mound microtopography as well as a large proportion of highly incorporated logs. Incorporated logs from the previous stand make up a large proportion of the downed biomass in this young stand. The pits and mounds probably also originated from mortality events in the prior stand. The presence of Populus in the oldest age classes indicates that this stand may have initiated as a mixed-stand. The initial 50 year colonizing period was characterized by Picea and Abies ingress with a minor Pinus component (Figure 4-1 la). Despite greater numbers of Abies regeneration Picea has again recruited greater basal area (Figure 4-1 lb). 4.213 Plot 13 The initial colonizing period in Plot 13 lasted 60 years and was characterized by Picea, Pinus and Abies regeneration but dominated by Picea. (Figure 4-12b). The secondary cohort is made up primarily of Abies, with some Picea recruitment during the past twenty years (Figure 4-12a). A surge in Abies ingress 50-60 years ago perhaps occurred in response to minor disturbance. This is also reflected in the basal area by age graph (Figure 4-12b) which show 1000 800 1_J I I I I I I I I I I I I I I I I I I I I L CO XI \ w E CD CO W c CD O 600 400 200 -F^r 50 100 150 Age Class, years •57 -r-f=l ^ Populus H Picea S Pinus • Abies 200 I I I I I I I I I I I I I I I I I I I I L 1. m Popuius 83 Picea 0 Pinus • Abies i—r**f—i—|—i—i—i—i—|—i—r 50 100 150 200 Age Class, years Figure 4-11. Plot 12 age structures. (A) Density x age class curve is almost unimodai, perhaps suggesting stratification into a bimodal form. (B) Basal area x age class curves indicates dominance by Picea of the colonizing cohort. 4000 i i ' i i i i i i i i i i i i i i i i i i i i 3000 -CO sz \ co E o w 2000 -CO C CD Q 1000 0 58 0 Picea H Pinus • Abies 50 100 150 Age Class, years 30 i i i i i i i i i i i i CO sz. \ (M E CO CD _^ < CO CO m 20 10 0 0 Picea 0 Pinus • Abies 0 50 100 150 Age uiass. years 200 Figure 4-12. Plot 13 age structures. (A) Density x age class curve approximates a reverse-J form and shows a 130 year exclusion of Picea. (B) Basal area x age class curve shows continuing dominance by the initial cohort although secondary recruitment is becoming more successful. 59 greater basal area recruitment in the 90-100 and 110-120 year age classes as some of the advanced regeneration were probably released by minor disturbance. The many windthrown trees may have been caused by the disturbance to which the younger trees responded. The basal area by age graph shows significant recruitment of basal area in the younger age classes, indicating the success of members of the secondary cohort. This also suggests (in conjunction with the reverse J-shape that the density age distribution is approaching [Figure 4-12a]) that this stand may soon be self-perpetuating. 4.214 Plot 14 The initial colonizing period lasted 60 years and was dominated by Picea with some Pinus (Figure 4-13b). This stand continues to be dominated by the initial cohort. Following a break in recruitment Abies began to establish on the site but unlike the other plots, Abies did not continue this trend but was replaced by Picea and Pinus regeneration (Figure 4-13a). This site was highly heterogeneous, ranging from almost xeric to hygric conditions and was the most atypical of all stands investigated. The appropriateness of its use in a chronosequence is therefore questionable and it is not included in generalizations about circum-zonal stands. The Pinus ingress occurring under the canopy of a mixed species forest may be the result of the unusual site conditions, as all Pinus seedlings were found on the driest part of the site. Furthermore, it may have been due to the openness of the canopy, which may also have been responsible for the success of Picea ingress. There was also relatively little competition from understory shrubs that may limit seedling establishment on other sites. 4.3 Overall Description of Stand Attributes 4.31 Age The stands sampled had an age range of over 220 years, with the maximum overstory age of the youngest stand being 120 years and the oldest stand being 343 years. The two youngest stands (Plot 10 and Plot 12) also had veterans that greatly exceeded the age of the rest of the stand. Stand ages did not always represent the developmental stage of each stand. J I I 1 I I I I I I I I I I I I I I 60 F^r-r 0 10 20 00 40 60 BO 70 BO 90 100 110 120 130 140 160 160 170 180 180 Age Class, years l l l i l l l I I I I I I I I I L 0 Picea H Pinus • Abies CO w 03 10 CD W i—i—i—i—i—i—i—i—i—i—i—i i i—r 0 10 20 30 40 50 60 70 80 90 100 1 10 120 130 140 150 160 170 180 190 Picea Pinus Abies Age Class, years Figure 4-13. Plot 14 age structures. (A) Density x age class curve is bimodal, demonstrating successful reinitiation of Picea and Pinus at the expense of Abies. (B) Basal area x age class curve shows that recruitment in the second and third decade was most successful. 6 1 For example, both Plot 4 and Plot 9 were almost 180 years old, but Plot 4 had a reverse-J shaped age distribution, suggesting that it is much closer to a self-perpetuating state than is Plot 9 with its bimodal age density distribution. 4.32 Understory Diversity These SBS stands are not floristically diverse but rather uniform and simple. In the plots studied, only four tree species were identified: Picea engelmanni x glauca, Pinus contorta, Abies lasiocarpa and Populus tremuloides. Understory diversity, based solely on species presence, varied between 16 and 32 species per plot with a mean of 23.8 (see species list in Appendix 1). This value is lower than the mean species richness of 26.6 species reported for the SBSmc zonal plant association (MOF 1991a) and may due to the sampling methods used. Species richness in the SBS is similar to that reported in subalpine spruce-fir forests in the Medicine Bow Mountains of Wyoming, where Oosting and Reed (1952) identified an average of 22 understory species per stand. 4.33 Stand Stocking These stands are not be as structurally diverse as coastal old-growth stands but they do exhibit a wide range of variation in most attributes measured (Table 4-1). Densities ranged from a low of 411 trees/ha to a high of 1411 trees/ha. There was also a large variation in seedling stocking, from a low of 1256 stems/ha to a high of 8744 stems/ha. Although the majority of these stems were Abies, Picea was present in high numbers in many stands and the overall totals support the development and application of silvicultural prescriptions requiring advanced regeneration. Saplings in many plots exhibited poor growth (as evidenced by small radial increment and small leader extension), but the ability of both Picea and Abies to release after extended periods of suppression has been noted elsewhere (Schimpf et al. 1980, Veblen 1986). In fact, Veblen (1986) reported that 46% of Abies and 56% of Picea stems examined had been released at least once, and many individuals of both species had been released at ages greater than 200 years. Periods of good growth after two hundred years of small radial increment were also observed. These were not analyzed for patterns of suppression and release as 62 described by Lorimer (1980, 1983, 1985), Lorimer et al. (1988) and Payette et al. (1990) during the age counting of trees sampled in this study (e.g. Figure 4-14). 4.34 Coarse Woody Debris The numbers of dead stems, including both standing and downed, exceeded the number of live trees in all stands (Table 4-1). The numbers of downed logs were comparable to the numbers of live trees, ranging mostly from 500 logs/ha to 1000 logs/ha. Snag totals were lower but more variable, ranging from 78 snags/ha to 511 snags/ha. Volume comparisons, however, show that the live tree component accounts for more of the biomass than the dead component. Volumes of live trees ranged from 319.6 m^/ha to 658.2 irr/ha whereas log volumes ranged from 37.0 nvVha to 384.0 m-Vha. Volume and density estimates of downed logs were biased by the degree of incorporation of the logs. As the forest floor was not excavated to search for obscure logs, some mortality was undoubtedly missed (Henry and Swan 1974, Oliver and Stephens 1977). The degree of decay of such buried pieces would have resulted in such logs being part of the forest floor rather than identifiable structural entities. Forest floor litter, although sufficient to influence seedling establishment (Knapp and Smith 1982), does not tend to accumulate deeply enough to suggest that rapid burial of downed logs is a methodological concern in reconstructing the current stand history of these ecosystems (Oosting and Reed 1952). The importance of dead wood to forest ecosystem functioning is becoming increasingly well recognized (Lambert et al. 1980, Maser and Trappe 1984, Harmon et al. 1986), but there is little information on both its role and its presence in various ecosystems. This research suggests that the numbers of dead stems in old stands in the SBS zone is comparable to that of live stems. As reported above, the number of dead pieces exceeded live stems but the converse was true for volume. This is contrasted with basal area values from old-growth white pine (Pinus strobus) forests of Ontario where the dead component made up a significantly larger portion of the total biomass than the live portion (Quinby 1991). This may, in part, be due to the greater size attained by white pine individuals, and the long periods of time since the last catastrophic disturbance, allowing greater accumulation of downed biomass than in the SBS. 63 100 200 Age. years 000 100 200 Aoe. years 000 Figure 4-14. Examples of release in radial increments taken from two trees in Plot 7: (A) From a Picea tree and (B) from an Abies tree. Both show instances of a dramatic increase in growth after extended periods of slow growth. 6 4 In other ecosystems, such as the Douglas-fir forests of the Pacific Northwest, researchers have typically measured coarse woody debris (CWD) and trees in different units, which does not facilitate comparison. For example, Franklin et al. (1981) reported logs in terms of mass (tonnes/ha) and live trees in volume (m^/ha). In SBS ecosystems, the loss of logs and snags to decay is not expected to be great, due to the relatively cool climate. Johnson and Greene (1991) have reported that decomposition of snags in subalpine Pinus and Picea forests in southwestern Alberta is very slow to non-existent due to the drying out of the boles. They also could not find differential decomposition by size of bole in either standing or downed dead stems, although they found that downed logs decomposed faster in younger stands than older stands, probably due to different micro-environments associated with the different stages of stand development. The greatest loss of such dead wood from these stands has in the past been associated with rapid mineralization of biomass after severe fires (Lang 1985). An attempt was made to identify all dead stems to species using the bark or any other physical characteristics present. Since many pieces were in advanced states of decay, and wood samples were not extracted for laboratory analysis, complete identification was not possible. The state of decay and degree of incorporation of logs varied greatly from stand to stand. In stands where most observable mortality was recent, up to eighty per cent of the dead stems could be identified; where the observable mortality was partially incorporated, identification was as poor as 17 percent (Table 4-1). Although the literature consistently mentions the higher mortality of Abies stems over Picea stems (Oosting and Reed 1952, Achuff and LaRoi 1977, Schimpf et al. 1980), these data from the SBS did not demonstrate that mortality was consistently greater for mature individuals of any one species than any other species. However, any such trend may have been obscured by the number of unidentified stems. Since Abies is reported as being more susceptible to rot (Schimpf et al. 1980), it may possibly make up a large portion of this unidentified component. Information was also recorded about the mode of mortality of all logs. The origin of each log was noted as having been due to snapping of the bole, due to uprooting (tipping) or due to unknown causes. In almost all plots, the majority of trees were downed due to snapping of the main bole (Table 4-1). Only in Plot 4 were there more trees that were 65 known to have tipped than to have snapped. The greater ratio of windsnapped trees to uprooted trees in most plots is indicative of most trees being dead before they fell (Veblen 1986). The differences in modes of falling can be important to regeneration. Peterson and Pickett (1991) state that, with the exception of snapped trees that have the ability to sucker, uprooting has a greater impact on regeneration. This is due to the exposure of mineral soil, inversion of soil profiles, movement of rocks, creation of long lasting pit-and-mound microtopography, and exposure of buried seeds (Peterson and Pickett 1991). 4.35 Tree Regeneration In general, few seedlings or saplings were noted to have regenerated in pits that may have been associated with exposed mineral soil behind uprooted trees (Table 4-1). However, mineral soil was rarely directly observed except in the most recently uprooted trees. In accordance with other reports (Oosting and Reed 1952), lichen and bryophytes were observed in abundance throughout the forest such that the soil was almost always completely covered. Most seedlings were found in gaps or extended gaps (Table 4-1). It is suspected that light conditions did not limit seedling establishment, however, due to the open nature of the canopy, the low sun angle in these high latitude forests, and the shade tolerance of Abies and Picea (Alexander et al. 1990, Alexander and Shepperd 1990, Nienstaedt and Zasada 1990) . Competition from shrubs and herbs, snow press, or the duration of the snow pack are more likely to be important factors in determining the location of seedlings with respect to gaps, although none of these factors were assessed. The type of substrate did not appear to be a determining factor in seedling recruitment (Table 4-1). Regeneration occurred both in large and small numbers on both mineral and organic substrates from plot to plot. The vast majority of seedlings were Abies and it has been observed that Abies lasiocarpa will germinate and survive on a wide variety of seedbeds and is less exacting in its seedbed requirements than Picea glauca, Picea engelmannii and Pinus contorta (Alexander et al. 1990, Alexander and Shepperd 1990, Nienstaedt and Zasada 1990, Lotan and Critchfield 1990). 66 4.4 Trends in SBS Stand Development The composition of most of the stands supports the theory that Picea will remain a component of old-growth SBS forest stands. Only Plots 7 and 11 indicate that Abies will completely dominate some stands. This study also suggests that those plots initially colonized primarily by Picea and/or Pinus will, with time proceed to either a primarily Abies dominated stand (Plot 8 and 11) or to an Abies/Picea mixture (Plots 2, 5, and 9). Stands that are initially colonized by Picea and Abies concurrently, tend to become increasingly dominated by Abies (Plots 6, 7, and perhaps 10). On the otherhand Plot 3, which was a mixture initially dominated by Abies, is shifting towards dominance by Picea. In the absence of disturbance, cycles of Abies followed by Picea dominance may occur, but at this present time such speculation should be used primarily as the impetus for further research. 4.41 Age Class Deficiency Most stands (except Plot 13 and perhaps Plot 10) have a deficiency in the youngest age class, and often the second youngest class as well. This may be an artefact of sampling if stems were missed due to their small size, the difficulty in counting regeneration from layering, or the combination of patchy distribution and small plot size. However, because regeneration was sampled as a sub-plot of the larger tree level plot and each 5m x 5m area was carefully scrutinized for seedlings before moving to the next, this can not entirely account for the deficiency. This deficiency may be explained by changing climate or other environmental factors. Studies in other spruce-fir forests may not have noted a similar trend for a number of reasons: 1) they were from different geographic regions, 2) they were conducted before this trend became manifested, or 3) they did not conduct a complete age census of all individuals. In eastern Canada, an analysis of the age structures of balsam fir (Abies balsamea) and eastern hemlock (Tsuga canadensis) by Hett and Loucks (1976) determined that there were cyclic oscillations in plant populations. They theorized that these population changes were due to the competition for moisture among recent regeneration and slightly older recruits. In Picea-Abies forests, a similar cyclic pattern of Picea recruitment has been 6 7 suggested, in which Picea recruitment may be of the reverse-J form, changing to a decreasing pattern which after a delay in recruitment forms a bimodal curve and then finally returns to a reverse-J form (Whipple and Dix 1979). Although no mechanism was discovered to cause this cycle, it was speculated that changes in spruce recruitment may be due to large fluctuations in overstory shading and understory competition. It seems unlikely that these theories completely explain the observed deficiencies, because 1) they occur in most plots primarily in the youngest age classes, and 2) they occur in both Abies and Picea, and Abies has been observed elsewhere to recruit continuously (Aplet et al. 1988). Oliver and Larson (1990) also provide a list of biotic factors, including crown expansion and densification, pathogens moving from old to young hosts, and competition for below-ground resources and browsing, that may influence the recruitment and survival of seedlings in old-growth stands. Any combination of these factors may have contributed to reduced conifer ingress over this period. However, in this study, no explanatory mechanism could be identified and further study is warranted. The use of spatial analysis to determine patterns of seedling and sapling ingress with respect to the position of overstory trees and their crowns is suggested as a means of generating hypotheses regarding this phenomenon. 4.42 Age Distributions The age distributions in this study fall primarily into two forms, reverse-J and bimodal (see Whipple and Dix 1979), although the gradation between these phases makes some plots difficult to classify. The appearance of these curves is also affected by deficiencies in the youngest age classes of many of the stands. Plot 3 represents the best example of a reverse-J shaped curve in which the post-disturbance cohort can only be recognized by a change in the species composition. The return of Picea also signifies an important ecological shift in the secondary recruitment. Plot 9 represents a bimodal curve, where the post-disturbance cohort is readily identifiable as the initial hump of recruitment. Curves for young stands such as Plot 12 suggest that the stand may just have begun to differentiate between cohorts. Plot 8 demonstrates an age distribution that was probably affected by random disturbance events. 68 Abies lasiocarpa age distributions are fairly smooth and usually of a reverse-J shape (Whipple and Dix 1979). In four stands, Plots 6, 7, 10 and 12, Abies ingress has occurred simultaneously with Picea in the stand initiation period and continued steadily (assuming constant age specific mortality) to the present. In these examples, as well as in plots where Abies establishment began later, there does not appear to be a stem exclusion stage associated with Abies development. This finding is similar to that of Aplet et al. (1988) who noted that only Picea underwent a period of stem exclusion. In this study Picea was heavily recruited in the post-disturbance cohort, such that it compromised many of the dominant stems, and then underwent a period of stem exclusion or sporadic ingress before again recruiting regularly. In both this study and in that of Aplet et al. (1988), the period of stem exclusion was variable and long (100 to 200 years). Picea reinitiation in the understory is probably associated with the opening up of the canopy. It has been suggested that this secondary regeneration is not associated with random gap-phase events but rather due to increasing mortality of Abies as it approaches its maximum age (Aplet et al. 1988, 1989). This may be true for Plot 3; after 300 years many Abies are succumbing to root rot, and the stand is opening up, allowing Picea to regenerate. However, this theory does not hold for all stands. Minor disturbances that open up the canopy at earlier ages can have similar effects. In Plot 2, canopy openings due to a spruce bark beetle attack has resulted in substantial Picea regeneration at a younger stand age. Plot 5, which shows much evidence of windthrow, also has successful Picea ingress in the secondary cohort. Signs of minor disturbance leading to the opening of the stand are evident in many of the stands where secondary Picea regeneration is establishing well. Pinus usually recruits over a shorter period than either Picea or Abies, but it is not restricted to the initial period of stand establishment. A number of stands, Plots 4, 9, and 13, exhibited minimal and sporadic recruitment of pine throughout the stand's history. Pine ingress is probably associated with the creation of large gaps and/or microsites with sufficient light for Pinus. Occurrences of Pinus regeneration in the understory are rare and previous work has reported that except in the case of monoculture stands, Pinus will not maintain it's population beneath a canopy (Whipple and Dix 1979, Despain 1983, Stuart et al. 1986). 69 4.43 Initial Colonization The density-age and basal area-age distributions demonstrated that all plots are still influenced by the last stand-initiating disturbance. Without exception this event was a wildfire, as all plots exhibited some evidence of fire in the form of charred logs or charcoal deposits in soil horizons. The extent of this evidence varied from plot to plot. Plots 4, 6, 7, 10, 12 had very little evidence of fire, with light charring discovered on only a few downed logs and only scattered evidence of charcoal in the soil profiles. Other plots showed ample charring and an easily identifiable charcoal layer between the duff layer and mineral soil. Plots 5 and 6 showed signs of a recent fire and an earlier fire as evidenced by a second charcoal layer 30-40 cm deep in the mineral soil. These fires must have been intense to have left so much charcoal over a long time period. Fire has long been recognized as the prevalent agent of stand reinitiation in boreal ecosystems (Rowe 1961, Rowe and Scotter 1973). For spruce-fir forests of central B.C., Heinselman (1981, 1985) reported return times of 50-200 years but occasionally as long as 300-400 years and Arno (1980) estimated fire return times of 150 years or more. This generally agrees with the SBS fire return intervals of 100-150 years (See Section 2.5). In all cases the initial period of ingress was long, varying from 60 to 100 years (with a mean of 77 years and a standard deviation of 14 years). Long establishment periods have also been noted for other boreal and subalpine ecosystems. Hatcher (1963) found the stand initiation period after fire to last 15 to 25 years in central Quebec; Sirois and Payette (1990), also in Quebec, found this period to be less than thirty years; Aplet et al. (1988) found that the initial colonization period could last for 100 years in subalpine spruce-fir forests of Colorado; Day (1972) suggested initial recruitment lasted for approximately 30 years in Alberta; Agee and Smith (1984) noted that initial stand colonization lasted a minimum of 50 to 70 years on all sites in Washington's Olympic Mountains; and Vanka (1983) and Jull (1990) found regeneration for periods exceeding 50 years in the ESSF zone of B.C. Although most of the preceding examples are recent, it has been suggested that the natural restoration of burned areas is a slow process (Ives 1941, Stahelin 1943). This may be explained in part by the decomposition and compaction of the charred layer, which with time improves the germination potential (Sirois and Payette 1990). But this process probably 70 operates on the scale of years rather than decades. Further explanation is that harsh environmental conditions at these high latitude and high altitude sites may limit the number ofv safe sites' (Harper 1977) available for seedling establishment each year, resulting in long periods before the site becomes completely occupied. On particularly exposed sites some amelioration of conditions may be required before seedling recruitment will be successful (Ronco 1970, Whipple and Dix 1979). The presence of seed sources and adequate seed crops is also important to successful regeneration of the site (Stahelin 1943); seed availability is a limiting factor after large and intense wildfires. Post-fire site conditions favour Picea over Abies ingress (Oosting and Reed 1952). Both Abies lasiocarpa and Abies balsamea regenerate poorly immediately after fire due to their production of small seed crops, retention of little seed in their crowns and their relatively large seeds which do not disperse as far as competing species (Rowe and Scotter 1973). In addition, Abies seeds are palatable to a number of small mammals and their seedlings are often outgrown by other species in open conditions. In the plots surveyed for this project, Abies regeneration usually began several decades after the initial colonization of the site by Picea, Pinus or Populus. This pattern, in which Abies is a late successional species arriving after its other associates, is in accordance with previously developed models (Bloomberg 1950, Day 1972). Only Plots 6, 7, 10 and 12 had Abies ingress at the time of stand initiation. On these sites the fire may have been less severe, or burned a smaller area, so that some seed-producing Abies remained in the immediate area, allowing for quick regeneration by this species. Some authors have suggested that Picea does not reproduce well in open, exposed environments but rather requires site amelioration, often by Pinus or Populus before it re-establishes (Achuff and LaRoi 1977, Whipple and Dix 1979). Perhaps this occurred in Plot 10, with Picea and Abies recruiting beneath a Populus canopy. However, Picea was more commonly a member of the initiating cohort in this study, with evidence of ingress simultaneously with Pinus. Regeneration immediately after fire has also been found for Picea-Pinus mixes in Alberta (Johnson and Fryer 1989) and Colorado (Aplet et al. 1988). 7 1 4.44 Post-Establishment Stand Development In most cases, Abies dominated the plots in sheer numbers of individuals present but was relatively poorly represented in terms of basal area. Even in instances where Abies and Picea ingress occurred simultaneously in the post-disturbance cohort, Picea dominates the basal area for those age classes (even when established in fewer numbers). These results support those of Oosting and Reed (1952) who found that Picea always dominated the total stand basal area, with 2.5 to 13.1 times the basal area of Abies, despite greater Abies stocking. A number of stands show significant Abies recruitment in the older age classes but only minimal representation of Abies in any but the smaller size classes. The basal area by age distributions also indicate that Abies basal area representation in the older age classes is less than that of Picea, even when density-age histograms show greater numbers of Abies in those same age classes. Similar results were found in comparisons of different species of Picea and Abies at Candle Lake, Saskatchewan, where graphs of basal area vs age for individual trees showed a large number of old Abies contributing minimal basal area (Dix and Swan 1971). Therefore, despite vastly superior regeneration, Abies recruitment to the canopy is much inferior to Picea or Pinus. Height profiles for trees of thirteen plots also demonstrate the relative positions of each of the species in the canopy (e.g. Figure 4-15 or see Appendix 2). Pinus is always among the canopy dominants, Picea also contributes many stems to the dominant overstory although it is also found in other layers of the canopy, and Abies is found primarily in the lower strata with only a few individuals in the overstory (except where few individuals of other species are in the plot). Contrary to these findings, Jull (1990) found that there were no distinct height differences between Abies and Picea and that there was no differential stratification of the canopy by species. The different conclusions of these studies may be due to the different developmental stages assessed in each study. Jull's (1990) findings were based on data from immature stands (50-100 years old) in which stratification may not yet have occurred. Performance of these two species may also be more equivalent at the higher elevations of that study. 40 ~T~1—I—I I T T I — r - T ~ 72 a; X S P 30 (f s ^ 20 10 0 i i » • i S S ff Ift Jf J 0 20 L * t i l l 1 I » I I I I I I I I I ^ I f I 1 I I 40 60 80 Tree, number 0 PICEA 0 PINUS • ABIES Percent of Foliage Figure 4-15. Examples of foliage height profiles. In (A) individual tree crowns are shown, species are represented by the first letter of the common name located at the top of the tree and the hne represents crown length. In (B) foliage for the entire plot is portrayed by species as it occurs through the canopy. Both figures illustrate Pinus in the upper strata although Pkea is also present and dominates in terms of total foliage. 73 In most stands Picea is present to a limited extent in the younger age and size classes, indicating that it should remain as a future member of the canopy, especially considering the poor Abies recruitment to larger size classes. Therefore despite vastly superior Abies regeneration, the superior recruitment of Picea to the canopy will ensure its continued dominance. These findings should be verified with stem analyses or permanent sample plot data. A number of earlier authors have instead suggested that, although Abies recruits in greater numbers in forested situations, Picea will be maintained in these stands due to greater mortality of Abies (Oosting and Reed 1952, Schimpf et al. 1980, Veblen 1986). A reconstruction of the mortality of Picea and Abies, showed that although Abies accounted for only 37% of the overstory trees it made up 76% of the downed logs (Veblen 1986). As reported earlier, species-specific mortality was difficult to reconstruct in this study and no trend was identified. If a large difference in species-specific mortality is a factor, it would be further evidence for the maintenance of Picea in SBS stands. 4.45 Conifer Establishment With Aspen The presence of Populus in SBS stands has been previously noted. Evidence from a number of stands (Plots 2,4,6,7,10,12, and 13) supports a successional pathway for mixed deciduous/conifer sites following a catastrophic, stand-initiating event. In this pathway, Populus tremuloides is recruited concurrently with conifers in the stand initiation stage and then dies out 80-100 years later creating openings in the stand. This is supported by evidence from Plot 2, where many downed Populus stems were noted, and may explain the recruitment surge in Plot 13. In other cases, Populus tremuloides is the initial species to establish and conifer regeneration occurs below it's canopy or as it begins to breakup. This may have occurred in Plot 10, explaining the fact that the remaining Populus veteran is significantly older than the rest of the stand. The role of Populus in the succession of Picea-Abies forests has been long established (Stahelin 1943). It has been found that where Populus was present in the pre-disturbance stand it will be present in the post-disturbance stand as well. Achuff and LaRoi (1977) suggest that Populus may become locally extinct with long intervals between fires. Schimpf 74 et al. (1980) found that conifer recruitment occurred under Populus twenty years after Populus invasion of meadows. In their study, Picea or Abies assumed dominance after 100 to 150 years of Populus presence, with the transition period being relatively short. Pinus, where present, also has the ability to invade and replace Populus (Stahelin 1943). Both Pinus and Populus are considered pioneer species in these ecosystems and often dominate the site for fifty years after a fire due to greater growth rates than Picea and Abies (Alexander 1974). It may be that these successional patterns which start with Populus are important in the other stands, as well, but that due to the rapid decomposition of Populus logs the evidence has disappeared. Downed and identified Populus logs were present in Plots 2, 4, 6, 7, 10, 12 and 13. In all but Plots 2 and 10, two of the youngest plots, Populus logs were present in very limited numbers. 4.5 Summary In Sub-Boreal Spruce stands Picea and Abies are the late successional or climax species. Picea is often able to maintain itself, regenerating well in the later stages of stand development in association with the opening up of the stand. The successful reinitiation of Picea may occur at younger stand ages if small scale disturbances such as insects or windthrow speed up development. However the maintenance of Picea is not guaranteed and Abies can almost completely take over some stands. Although Abies ingress in the understory is greater than that of Picea, its recruitment to the overstory occurs in limited numbers. This indicates that despite lower numbers in the understory, Picea is maintained itself in the overstory by virtue of its greater success recruiting into larger size classes. In the absence of catastrophic disturbance SBS are maintained although compositional shifts occur during stand development. There is no evidence that these forests will " degenerate' in the absence of fire. In most stands the population was dominated by individuals from the post-disturbance cohort, but in stands in the later stages of development the secondary recruitment was beginning to replace the original dominants. These stands may reach a state in which their dynamics are entirely dominated by internal disturbances given increased fire protection in recent decades. This trend will continue in areas preserved 75 from logging and fire, however, no stands were found that had achieved such a state. Given the naturally occurring fire intervals for the SBS (see Section 2.5) it is unlikely that any stands have yet reached a state where tree-level disturbances account for all of their population dynamics. Post-disturbance ingress in the SBS is a long process lasting 50 to 100 years, followed by an equally long period of Picea exclusion. Abies recruitment does not usually show a period of exclusion from recruitment. Although Abies ingress can occur immediately after the disturbance (Plots 6,7,10, and 12), it is often delayed until the area is occupied by other species. Populus seems to play an important role in the initial colonization of many of the stands. However, much of the evidence with respect to initial conditions no longer exists and it would therefore be prudent to direct a study specifically at the role of Populus in post-fire stand development. The use of basal area by age graphs is not often used but can provide added insights into stand development. It is especially useful in identifying the post-disturbance cohort and periods of successful recruitment either during initial colonization or associated with small-scale disturbances later in stand development. 7 6 5. DISTINGUISHING MATURE AND OLD-GROWTH STAGES OF SBS STAND DEVELOPMENT 5.1 Introduction and Literature Review Understanding stand development processes is important for both ecological understanding and silvicultural innovation. The initial colonization stage has long been recognized as important by the forest industry in its attempts to regenerate cutovers. Many studies have focused on conditions influencing seedling survival. Similarly, the stem exclusion phase is important for understanding natural self-thinning, and in using mechanical thinning operations to optimize the production of sawlogs. Mature forests have been traditionally regarded as the economically optimal stage for forest harvesting. However, the final stage of forest stand development, the "overmature1 or "old-growth1 stage, has traditionally been ignored in forestry. It has increasingly come under scrutiny due to public interest in the conservation of natural forests and because of its rapid liquidation from the forest land base. Furthermore, the probable fate of old-growth stands is a little appreciated (and often ignored) aspect of regional timber supply analysis. Each stage of forest stand development is associated with different dynamic processes that are reflected in ecosystem structure and functioning. Although it has been recognized that structural attributes and functional processes change during stand development (Borman and Likens 1979, Franklin et al. 1981), little work has been done to establish the links between structure, function and population dynamics in many forest types. This work will focus on the relation of structural attributes in the mature and old-growth stages of development in the SBS zone. 5.11 Alternative Definitions of Old-Growth In a general review of the literature, one encounters many different definitions of old-growth developed for forests ranging from eastern North America to Alaska (Wallmo and Schoen 1980, Franklin et al. 1981, Alaback 1982, Bradey and Hanley 1982, Old-Growth Definitions Task Force 1986, Thomas et al. 1988, Parker 1989, Juday 1988, MOF 1991a, see Table 5-1). The approach tentatively adopted in B.C. (MOF 1991c) to describe and Table 5-1. Attributes Often Cited in Old-Growth Characteristic Climax forest Undisturbed by humans Relatively old Net annual growth close to zero Older than natural disturbance interval Final development stage Complex structurally (and compositionally) Wider than average tree spacing Large, old trees Multi-layered canopy Numerous large snags 77 Old-Growth Definitions Citation Barnes 1989, Brisson and Bergeron 1992, Habeck 1988, Hunter 1989, Parker 1989, Whitney 1987 Achuff 1989, Barnes 1989, Brisson and Bergeron 1992, FLULC 1989, Hunter 1989, Peterken 1992, Smith 1989, Whitney 1987 Achuff 1989, Alaback 1984, Brisson and Bergeron 1992, Fahey 1990, Franklin et al. 1981, Habeck 1988, Hunter 1989, MOF 1991, OGDTF 1986, Parker 1989, Quinby 1991, SAF 1984, Spies and Franklin 1988, Roemer et al. 1988, Wallmo and Schoen 1980 DeBell and Franklin 1987, Franklin et al. 1981, Hunter 1989 Achuff 1989, Hunter 1989 Hayward 1991, OGDTF 1986, Oliver 1981, Oliver and Larson 1990, Thomas 1979 Franklin and Spies 1983, Franklin et al. 1981, OGDTF 1986, SAF 1984, Spies and Franklin 1988, Thomas et al. 1988, Whitney 1987 Alaback 1984, MOF 1991 Alaback 1984, Barnes 1989, Bergeron and Brisson 1992, Franklin and Spies 1983, Franklin et al. 1981, Habeck 1988, MOF 1991, OGDTF 1986, Quinby 1991, SAF 1984, Spies and Franklin 1988, Thomas et al. 1988, Whitney 1987. Franklin and Spies 1983, Franklin et al. 1981, Habeck 1988, MOF 1991, OGDTF 1986, Parker 1989, SAF 1984, Spies and Franklin 1988, Thomas et al. 1988, Whitney 1987 Brisson and Bergeron 1992, Franklin and Spies 1983, Franklin et al. 1981, Habeck 1988, MOF 1991, OGDTF 1986, Runkle 1991, SAF 1984, Spies and Franklin 1988, Thomas et al. 1988,Whitney 1987. Table 5-1. (continued) 78 Numerous large logs Two or more species Replacement of overstory by secondary tree recruitment Patchiness, heterogeneity Some minimum area Steady state condition Nutrient retentive All aged structure Snags and logs in various states of decay Pit and mound microtopography Past rotation age Increased understory productivity Ages that have exceeded the average life span for the species Brisson and Bergeron 1992, Franklin and Spies 1983, Franklin et al. 1981, Habeck 1988, MOF 1991, OGDTF 1986, Runkle 1991, SAF 1984, Spies and Franklin 1988, Thomas et al. 1988, Whitney 1987. Brisson and Bergeron 1992, Franklin and Spies 1983, Franklin et al. 1981, Habeck 1988, MOF 1991, OGDTF 1986, Peterken 1992, SAF 1984, Spies and Franklin 1988, Thomas et al. 1988, Whitney 1987. Brisson and Bergeron 1992, Hayward 1991, Juday 1978, 1985, Oliver 1981a, 1981b, Oliver and Larson 1990, Runkle 1991, Wallmo and Schoen 1980, Whitney 1987. Franklin et al. 1981, Peterken 1992, SAF 1984, Stewart 1986, Thomas et al. 1988, Wallmo and Schoen 1980, Whitney 1987 Smith 1989, Spies and Franklin 1988, Thomas et al. 1988. Borman and Likens 1979, Brisson and Bergeron 1992, Hunter 1989, Peterken 1992, Whitney 1987 Borman and Likens 1979, Franklin and Spies 1984, Franklin et al. 1981, Sollins et al. 1980. Hayward 1991, Leak 1973, MOF 1991, Parker 1989 Franklin et al. 1981, Harmon et al. 1986 Maser et al. 1988, MOF 1991 Whitney 1987 Brady and Hanley 1982, SAF 1984, Thomas 1979. Alaback 1984, MOF 1991 Hunter 1989, MOF 1991 Cathedral-like, humbling scale Rolston 1989 Revered for heritage value and scarcity 79 characterize old-growth is primarily based on work conducted in the Pacific Northwest of the U.S.A. (Franklin et al. 1981, Old-Growth Definitions Task Force 1986). These definitions emphasize regional or zonal standards for structural attributes such as the numbers of large trees, snags and downed logs, age distributions, canopy layers, etc. Such definitions are very site-specific and provide useful information to forest managers for planning efforts, for restructuring inventory procedures and for clarifying issues. However, the development of such definitions requires that the old-growth forests can be identified in the field and distinguished from mature forests. The ability to recognize old-growth forests must also be based on a preconceived definition and not just on the arbitrary designations of those measuring these structural attributes. The procedure should be both repeatable within the forest type being defined and applicable in other locations. Defining old-growth as a stage of forest stand development in terms of population dynamics has been advocated by a number of authors (Borman and Likens 1979, Oliver 1981a, Oliver and Larson 1990, Hayward 1991). The sequence of development can be summarized as young forest, aggradation stage forest, mature forest and old-growth (Hayward 1991); or, similarly, stand initiation, stem exclusion, understory reinitiation, and old-growth (Oliver (1981a). This approach is also implicit in the works of Franklin et al. (1981) and Thomas et al. (1988), who recognize young, mature and old-growth or immature, mature and over-mature stages. Such population based definitions of old-growth assume that the stand becomes shaped less by the stand initiating disturbance and more by single-tree or small group-tree disturbances. This implies that both mortality and regeneration are crucial in stand development and that many of their associated characteristics would change during stand development. These definitions may not be apporpriate for some boreal forest ecosystems in which secondary recruitment is unimportant and stands stagnate in the absence of fire (Bloomberg 1950, Day 1972, Johnson and Fryer 1989). However, in mixed species SBS stands where development occurs for long periods, secondary recruitment begins to replace the post-disturbance cohort (as demonstrated in Chapter 4) and therefore old-growth definitions may be applicable. 8 0 5.12 Proposed Population- Based Definitions of Old-Growth This research has proceeded on the precept that the old-growth stage is achieved when regeneration and stand age structures are based more on single-tree replacement (gap processes) than on the influence of past stand-level disturbance events. However, to apply this concept to the identification of old-growth stands requires that it be defined in more concrete, measurable terms. To assess the importance of single-tree disturbance, the most direct method would be to tabulate the mortality in mature canopy-forming individuals of the initial cohort. But this mortality history is usually elusive and its effect on the overall stand structure depends primarily on the regeneration that it triggers. Therefore, an alternative (and perhaps more relevant) measure of old-growth status is to characterize the success of individuals that have initiated as a result of these processes. As these individuals are replacing already established stems they will hereafter be collectively known as the replacement cohort. Stem basal area is a standard measurement of dominance by forest trees (Mueller-Dombois and Ellenberg 1974). It is therefore hypothesized that a good measurement of replacement cohort success and of old-growth status is the ratio of the basal area of the replacement cohort to the basal area of the initial (colonizing or post-disturbance) cohort. Other definitions of old-growth may also reflect the importance of single-tree replacement processes. The first corollary of the above population based definition is that age distribution curves which approximate an all-age structure (as represented by a reverse-J curve) will identify stands approaching old-growth status (Leak 1973, Parker 1989, Hay ward 1991). The all-aged structure indicates that recruitment is continuous from one age class to the next. These distributions provide information explicitly on the entire history of regeneration and not just on the shift from a colonizing cohort to a replacement cohort. The use of old-growth definitions based on structural attributes also reflects the population processes associated with the final stage of stand development. Structural attributes typically used to identify old-growth, such as numbers of large snags and logs in various states of decay, and multi-layered canopies (Franklin et al. 1981, OGDTF 1986), are related to long periods of development and single-tree replacement processes. Although this relationship is intuitive, it has not often been demonstrated that stands will order themselves 8 1 along a developmental gradient in terms of such structural attributes. Multivariate methods can be used to explore this relationship and provide an objective means of determining which attributes are the most relevant within SBS stands. 5.2 Using a Subjective Evaluation of Stand History 5.21 Summary of Density Age Distributions The preceding chapter presented age structure diagrams for the SBS stands investigated, as well as data on size structure and canopy structure. Focusing on the density x age curves for these stands in the SBSmc as a whole, a number of different curve types are apparent, but most of the curves either have a reverse-J or a bimodal form. In the bimodal form, the last stand-level disturbance still has a large influence on the stand as represented by the presence of this initial cohort. As the age structure becomes dominated more by processes within the stand, it approaches a reverse-J shape (Whipple and Dix 1979). Plots 3, 6, 7, and 13 are of the reverse-J form (e.g. Figure 5-1) and Plots 2, 9, 10, and 14 are of a distinctly bimodal form. The other plots represent more transitional stages. Plots 4, 5 and 11 are approaching the reverse-J form, although a small discernable peak still exists due to the presence of initial colonizers. Plot 8 has a multi-modal age structure with many discontinuities due to intermediate disturbances but evidence of the initial cohort is also easily identifiable. Plot 12 is almost unimodal and may just be beginning to stratify into a bimodal curve. 5.22 Interpretations The form of these curves emphasizes that development occurs along a continuum and that although there are natural groupings (such as mature and old-growth), each stand goes through transitional phases. It has been argued that although stages can be identified, arbitrary definitions of 'mature' and 'old-growth' stages may be better represented by an index of 'old-growthness' (Spies and Franklin 1988). It is suggested from these groupings that Stands 3, 6, 7, and 13 are in the latest stages of stand development, and stands 4, 5 and 11 are entering this phase. Stands 2, 8, 9, 10, 12 82 Plot 3 Plot 9 2000 I M i i i i 1500 2 « 1000 £ 500 I I"l 1 I T T I r i T T T"l I I I I I M I I I I I I I ^TfbTfT>fn, I I • ' • M • Age Class years 600 600-3 400 S 300 -a 200 100 -o - I J I I I I L _ ffl Age Class, years Plot 8 Plot 12 2000 1500 -E CD •o 1000 500 1000 8 0 0 I CO > • p 600 400 2 0 0 - i — i — i — i i i Age Class years Age Cass rears Figure 5-1. Examples of different age distribution forms. Plot 3 is reverse-T, Plot 9 is bimodal, Plot 8 is random or multi-modal and Plot 12 is unimodal that may be just beginning to stratify into a bimodal form. 83 and 14 are in the mature stage, although Plot 12 may be a transitional stand from the aggradation to mature phase of development. If one ranks the density x age class distributions according to their overall slope and evenness, a representation of these stands along the development continuum generates the following ranking (starting with the "youngest" transitional mature stage): Plot 12, 10, 2, 14, 9, 8, 11, 5, 4, 13, 6, 7, 3. Although this ranking is similar to one produced by age alone the position of some plots (e.g. 11, 4, 9) changes significantly. This ranking, is also speculative, however, as some stands are similar in their age distributions and could easily be interchanged. More confidence can be asserted in stating that the stands in the middle represent a later state of development than those listed first and a younger state than those listed later. 5.3 Using Structural Attributes 5.31 Summary of Relevant Stand Attributes Stand structure has been considered an important feature that characterizes old-growth forests. Easily quantifiable attributes include the density and size of live trees, snags and logs (Franklin et al. 1981, OGDTF 1986, MOF 1991a). The attributes that were assessed included densities of trees, snags, logs and regeneration (seedlings and saplings combined); total volumes of trees, snags and logs, number of understory species, stand age, stand basal area (at breast height, 1.37 m), structural diversity of the canopy (as represented by the Shannon-Weiner index) and numbers of large (> 1.0 mJ) trees, snags and logs (Table 5-2). 5.32 Cluster Analysis A cluster analysis of the structural data produced two initial groupings of Plots 2, 10, 5, 14, 9, 1, 12, and Plots 11, 3, 4, 8, 13, 6, 7 (Figure 5-2). This is similar to the population-based breakdowns suggested above for mature and old-growth stages. The plots at the extremes of the continuum from mature to old-growth separate into old and young ranks, but Plot 5 (subjectively ranked as old-growth but perhaps a transitional plot) is ranked as a mature stand using this method. 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X Q) V a •r| U OJ c -rl OJ £ 1 c 0 c c id x: CO M-l 0 tn 0 rH | •rH -P C rd w X CO •D C r-i +J x: Di -rt 0) K ID 00 CN r-iD CN CN rt CN rt r-CN rt • rt CN H • 00 CN rt • r~ CN CN 00 CN o • ID CN t-• in CN 00 • f-CN in • V0 CN 00 # m CN CN • ID CN 07 cm 06 13 04 un 03 12 [Wj 09 14 05 m m Figure 5-2. Dendrogram of sampled plots as grouped by cluster analysis of structural attributes. The two primary clusters represent old-growth and mature'1 groups. 8 6 This analysis was performed in SYSTAT and uses single linkage clustering (Wilkinson 1990). This method has been criticized as being subject to chaining, where initial clusters grow by accretion of single points resulting in clusters of disparate sizes (Pielou 1984). However, the results of this dendrogram show two equally sized groups, indicating that the points in each group are much closer than to those in the other group. The fact that these two groups, based on structural data, also conform to those suggested by the population-based definitions demonstrates that there are consistent structural differences between mature and old-growth stages. This method also categorizes Plot 1 as a mature stand despite the lack of a complete age census necessary for the cohort basal area ratio definition and the density-age distribution definition. Cluster analysis is more appropriate in classifying categorical rather than continuous data (James and McCulloch 1990). The placement of transitional plots may therefore be obscured due to the algorithm's grouping of the plots presented in the dendrogram. 5.33 Principal Components Analysis The use of an ordination technique, such as Principal Components Analysis (PCA), is better suited than cluster analysis to the handling of continuous data. The PCA of these same data, in confirmation of the cluster analysis, provided a similar breakdown along the Factor 1 axis (which accounts for almost 37% of the total variation). This axis separates the plots into two distinct groups corresponding to mature and old-growth (Figure 5-3). The other components did not order the plots along stand development or other identifiable gradients. The PCA analysis assigned most plots factor scores that coincided with their old-growth status, with the notable exception of Plot 10, which was grouped with clearly old-growth stands, and Plot 4, which was ranked as a mature stand. Plot 1 was also portrayed as a mature stand by this ordination, supporting the classification made by the cluster analysis. A comparison of component loadings (Table 5-3) shows that basal area and total tree volume are the strongest correlates, with the number of large snags and large logs also having a high positive correlation. The vector diagram (Figure 5-4) shows those attributes associated with the positioning of the plots in this two dimensional factor space. The 87 2 CvT (X o r -o < LL 1 -0 -1 --2 -2 -1 0 2 FACTORO) 5-3. Principal components analysis (PCA) of SBS structural data. The factor one axis separates stands by development stage; the stands on the left can be considered mature and the stands on the right old-growth. 88 1.0 0.5 Ht Index Regen/ha TreeVolume 0.0 _Soil_Bichness -0.5 Logs>m3_ Snags>m( Logs/ha yolurfi^. Snags/ha 1.0 -0.5 0.0 0.5 1.0 Figure 5-4. Vector diagram of attribute correlations with PCA axes. The length of the vector represents the intensity or strength of the correlation and the angle represents the direction of correlation for each of the attributes presented. For example logs>m-' has a strong positive correlation with the factor 1 axis. 89 Table 5-3. PCA component loadings showing correlation of first four factor scores with individual attributes Attributes #Snags/ha #trees/ha #logs/ha #Regeneration/ha Age Basal Area, m /ha Tree Volume, m3/ha Snag Volume, m3/ha Log Volume, m /ha #Understory Species #Logs > m3 #Snags > m3 /Trees > m3 Height Index Factor 1 0.523 0.277 0.163 0.432 0.694 0.806 0.803 0.682 0.600 -0.757 0.770 0.729 0.502 0.046 Factor 2 -0.517 0.191 -0.764 0.548 0.030 0.280 0.363 -0.427 -0.194 -0.135 -0.130 -0.312 0.335 0.716 Factor 3 -0.632 -0.100 0.522 0.223 0.330 -0.212 -0.185 -0.419 0.656 0.276 0.505 -0.074 0.056 0.133 Factor4 0.094 0.842 -0.008 0.205 0.263 -0.365 -0.133 0.212 -0.306 0.087 -0.063 0.435 0.259 0.442 90 number of logs greater than one cubic metre and understory species richness contribute only to plot distribution along the Factor 1 axis, whereas the height index contributes little to distribution along the Factor 1 axis but is responsible for distribution along the Factor 2 axis. As shown in Table 5-4 basal area, the number of large logs, and age provide strong gradients for the ordering of stands. Density values are among the poorest correlates of the Component 1 or old-growth axis. 5.4 Using Basal Area Cohorts 5.41 Summary of Basal Area x Age Distributions The use of a basal area ratio to define old-growth status requires accurate identification of the initial colonizing cohort and the replacement cohort. These cohorts were identified primarily by obvious modes in the age distribution curve and by changes in species recruitment (Section 3.244). Basal area curves were also useful in corroborating these cohorts but could not be used in isolation for cohort identification. Although basal area curves often exhibited a sudden decrease in recruitment at the end of the initial colonization period, this decrease may occur earlier if only minor recruitment occurred in the final decades of colonization. The period of initial colonization lasted between 60 and 100 years with an average close to 75 years. The age break and basal area associated with each cohort are shown for each stand in Table 5-5. 5.42 Cohort Basal Area Ratios The use of cohort basal area ratios to define old-growth directly addresses the changing influences of stand level and tree level disturbances. This is accomplished by assessing the change in dominance in the stand from the initial colonizing cohort to the replacement cohort. The application of this definition to the data set from this study demonstrates its practical merit. The ranking of stands produced by this method is comparable to those previously suggested by the use of density age distributions and the PCA Factor 1 axis. Table 5-4. Structural attributes as ordered by the PCA factor 1 axis Rank ] Old-Growth Mature Plot # 6 3 7 13 11 5 10 14 9 8 4 12 2 Logs #>lm3 56 222 133 144 111 189 89 44 22 33 44 22 11 Size Snags #>lm3 44 67 33 22 11 22 44 22 22 11 0 0 11 Trees #>lm3 255 144 244 222 244 278 244 167 200 211 233 133 122 BA m2/ha 79 57 69 80 67 71 63 54 60 57 52 48 40 Logs m3/ha 106 306 271 315 384 274 227 132 37 113 156 168 111 Volume Snags m3/ha 199 147 97 82 92 65 131 106 41 57 16 13 83 Trees m3/ha 706 533 647 687 625 723 630 509 632 530 534 404 402 Age yrs 228 343 252 208 209 158 127 161 164 161 177 124 135 Under-story #spp 16 21 25 27 19 19 24 24 25 26 24 28 32 Height Index 26.0 26.5 28.2 26.7 27.4 25.7 24.4 28.6 28.1 27.4 27.8 24.2 25.8 Logs #/ha 611 944 800 811 956 722 944 633 267 489 611 856 1033 Den Snags #/ha 511 267 144 167 200 156 500 222 122 133 78 78 189 isitv Trees #/ha 844 811 1011 944 911 967 844 567 967 667 722 956 411 Regen #/ha 7912 5378 9266 7622 5600 2199 1611 2434 3186 7989 6844 3578 1434 Table 5-5 Cohort basal area ratios for sampled SBS plots Year of Cohort Basal Area (m—/ha) Plot Breakpoint Initial Cohort Replacement Cohort Ratio 01 02 03 04 05 06 07 08 09 10 11 12 13 14 70 280 100 80 110 180 90 100 50 120 40 150 110 69.61643 1.280771 0.0184 8.512133 69.05763 8.1128 * X + 76.96450 16.50354 0.2144 * + 129.1876 6.816273 0.0528 * x 88.40813 6.640357 0.0751 * X + 79.23181 30.40747 0.3838 * X + 102.6534 3.936875 0.0384 + 101.5637 7.144181 0.0703 86.25899 0.278065 0.0032 X 99.64104 10.81053 0.1085 * X + 91.13571 1.475306 0.0162 91.58296 23.54785 0.2571 * X + 82.88690 5.614166 0.0677 * - stands identified as old-growth by form of their age distribution x - stands identified as old-growth by PCA Factor 1 + - stands identified as old-growth by cluster analysis N3 93 Stands previously identified as being mature have very low cohort basal area ratios (e.g. less than 0.02). Stands in the later stages of development have ratios many times greater than most other stands. For example, Plots 3, 4, 7, and 13 all have ratios greater than 0.2. Plot 3 is an example of a stand in which replacement recruitment now dominates. It was initially hypothesized (Burton and Kneeshaw 1991) that all true old-growth stands would similarly have a replacement to initial cohort basal area greater than 1.0. In most stands, however, the ratio is much lower than 1.0, reflecting the continued dominance of the post-disturbance cohort. In Plots 2, 10, and 12 this cohort almost exclusively dominates the stand and it may be assumed that most if not all the overstory trees established in the stand initiation period; these stands are clearly mature. The large ratios of Plots 4, 7, and 13 (>0.2) indicate that members of the replacement cohort are entering the overstory; these stands are old-growth. Surprisingly, therefore we conclude that most SBS stands take on old-growth attributes when their basal area ratios reach 0.2 rather than 1.0. 5.43 Sensitivity to Cohort Identification One difficulty with this measure is that it is very sensitive to the distinction between the initial and replacement cohorts. The sensitivity of this ratio to the distinction of cohorts was tested by varying the breakpoint by 10 year age classes to a maximum of 30 years in any one direction. The results indicated that this ratio is very sensitive in this regard (Table 5-6). Shifting the breakpoint by one ten year age class in either direction easily results in a 100% change in the calculated ratio. Changes that resulted in a smaller replacement cohort were less sensitive than those that resulted in a larger replacement cohort. The loss of one age class of basal area from the replacement cohort resulted in a change from 4% to 97%, whereas the gain of one age class of basal area changed the ratio from 3% to over 1200%. The accuracy of this ratio is therefore very dependent on the confidence placed on the cohort distinctions. On the other hand a change in the bin width used for histogram analysis would affect the precision of this model. A decrease in the bin width would only lower the sensitivity of this model if it decreased the range of uncertainty regarding the cohort breakpoint. Table 5-6. Sensitivity of cohort basal area ratios to changes in cohort size Plots _02 03 04 05 06 07 08 09 10 11 12 13 14 Age Class Change Basal Area Ratio (years) 0 0.0184 8.1128 0.2144 0.0528 0.0751 0.3838 0.0384 0.0703 0.0047 0.1085 0.0162 0.2571 0.0677 -10 0.0184 8.1128 0.1898 0.0129 0.0225 0.2628 0.0091 0.0641 0.0016 0.1034 0.0005 0.2469 0.0677 -20 0.0171 8.1128 0.1556 0.0083 0.0129 0.1928 0.0072 0.0522 0.0011 0.0990 0.0001 0.2278 0.0183 -30 0.0117 5.1921 0.0968 0.0037 0.0089 0.1285 0.0063 0.0318 0.0005 0.0725 0.0000 0.2113 0.0101 +10 0.2396 8.5566 0.4732 0.1057 0.2188 0.5422 0.0610 0.1229 0.0072 0.1112 0.0339 0.5453 0.1379 +20 0.3122 9.1841 0.5356 0.1729 0.4068 0.6622 0.0691 0.2403 0.0122 0.1553 0.2524 1.5001 0.2719 +30 1.0450 13.397 0.6552 0.3179 0.6054 1.0265 0.0946 0.4505 0.0478 0.2764 1.0753 2.8195 0.6430 Percent Change -10 0 0 -11.469 -75.478 -70.059 -31.536 -76.253 -8.8068 -71.128 -4.2801 -96.877 -3.9561 0 -20 -7.1037 0 -27.457 -84.231 -82.814 -49.770 -81.117 -25.809 -80.823 -8.2838 -99.404 -11.395 -72.915 -30 -36.567 -36.001 -54.863 -92.956 -88.159 -66.511 -83.616 -54.820 -91.015 -32.877 -99.964 -17.810 -85.003 +10 1202.3 5.4699 120.68 100.39 191.37 41.278 59.204 74.731 45.159 2.9845 109.76 112.09 103.57 +20 1597.1 13.204 149.79 227.64 441.63 72.559 80.265 244.08 90.461 43.798 1459.4 483.43 301.44 +30 5579.9 65.140 205.58 502.44 705.97 167.47 146.80 475.96 783.82 155.92 6542.5 996.59 849.39 4> 9 5 The data in this study generally indicate a clear separation between the initial and replacement cohorts. Plot 11 provided the most difficult distinction, as the break in the age distribution and the change in species recruitment did not coincide. Fortunately this plot was insensitive to a shift in breakpoint (<5% for one age class change in either direction). A further limitation of this method is that in some locations within a stand, especially large stands, initial infilling may not yet have occurred, while in other locations, secondary recruitment may be actively occurring. This is especially plausible given long periods of establishment on heterogeneous sites or where the size of the disturbance is quite large and infilling occurs from the edges and slowly proceeds to the centre. 5.44 Identification of the Old-Growth Threshold A one to one basal area ratio would indicate that 50% of the stand is composed of each of the replacement and initial cohorts. Such a state may take very long periods to develop, especially in stands composed of long-lived trees. If subsequent regeneration does not completely account for the loss of biomass due to overstory mortality, then this ratio for the old-growth stage may be expected to be lower than in ecosystems where replacement matches mortality. Only Plot 3, with a value of 8.11, has a cohort basal area ratio greater than one. The other methods of old-growth ranking, however, suggest that up to half of the plots sampled were of old-growth status. The plots considered by the other methods to be representative of old-growth can be used to determine a threshold value of the cohort basal area ratio (Table 5-5). The plot with the lowest basal area ratio that was distinguished as old-growth by all three of the other methods was Plot 6 (replacement cohort basal area to initial cohort basal area ratio = 0.0751). If an absolute threshold value is required, then it is suggested that there is greatest support for this value. However such an absolute threshold may not be desirable. Instead this technique may be of greater value if used as an index of the latter stages of stand development or of vold-growthness' as suggested by Spies and Franklin (1988). 9 6 5.5 Comparison of Techniques 5.51 PC A Ranking Most techniques produced similar groupings with respect to the stands at the obvious extremes of the stand development continuum represented here (Table 5-7). The ordering of the transition stands was slightly variable and Plots 4, 5, 8, and 9 were classified differently by one method or another. Only in one case was a stand that appeared to be strongly of one group classified as belonging to another. In this case Plot 10 was ordered as an old-growth stand using the PCA factor 1 axis, probably due to the lasting influence of dead stems from the stand occupying that site prior to wildfire 125 years ago. It is suggested that this deficiency in the PCA ordination might be overcome if one were to use only logs from mortality of the present stand. A combination of techniques probably provides the best interpretations of stand development state. The PCA use of structural attributes is subject to the variation of these attributes due to minor disturbances that may advance the rate at which the stand develops. Similarly, the retention of attributes from earlier stands on the site may adversely affect the use of this index and should be taken into account. This is reflected by Plots 10 and 4, which appear to be misrepresented by the PCA classification. Plot 4 may have been misclassified due to many small disturbances (see Section 4.204) which inhibited the development of those attributes, such as large individual logs, associated with the Factor 1 axis. The presence of downed logs in old plots is expected to be high, but the presence of downed logs may also be high in much younger plots where the logs from previous stands have not disappeared. Spies and Franklin (1988) suggest that a number of attributes, including the amount of coarse woody debris (CWD), number of large snags, percentage of total biomass that is represented by CWD, heterogeneity of understory, plant species diversity and mammal diversity will be high in both young and old stands, and are only low in the intermediate developmental stages. In Plot 6 the volume of downed logs is significantly lower than that of many of the other plots in the later stages of stand development due to the high proportion of dead individuals still standing in this plot. The 97 Table 5-7. Plots ranked along a stand development continuum, comparing four different methods Stand Development Continuum Mature Old-Growth Form of Age Distribution 12 10 2 14 8 9 11 5 4 13 6 7 3 Cluster Analysis 2 10 5 14 9 12 3 11 4 13 6 8 7 PCA Factor 1 2 12 4 8 9 14 10 5 11 13 7 3 6 Cohort Basal Area Ratio 10 12 2 8 5 14 9 6 11 4 13 7 3 9 8 use of total dead volume rather than the density of either snags or logs alone may provide a better structural indicator of stand development. Minimum values can be suggested to be representative of important structural attributes in the SBSmc. For example, before stands are considered to be old-growth they should have a minimum of 55 logs per hectare that are greater than one cubic metre in volume. Although trends exist, they are not uniform through all stands and a number of attributes should be used to identify old-growth by structural attributes. Franklin et al. (1981) also caution that not just one attribute but rather a number of different attributes should be used to define old-growth. Before the adoption of critical values of structural attributes can be implemented for the identification of old-growth, research is also needed into their role in ecosystem functioning in the SBS. Previous assessments of the applicability of old-growth structural criteria to the forests of B.C. has been mostly descriptive. Forests from each biogeoclimatic zones were evaluated primarily on whether or not stands existed that exceeded arbitrary minimum values based on research from coastal forests (Hamilton and Pojar 1990, Pojar 1991, MOF 1991a). This approach fails to recognize that arbitrary definitions without the support of research may provide classifications that are not ecologically meaningful. Rescaled definitions from coastal areas may not recognize unique attributes of the different geographic regions (Hayward 1991). However, these assessments of B.C. forests did identify gaps in our knowledge on the structure of forests in each biogeoclimatic zone. In the SBS there was insufficient data to evaluate densities of snags, large trees or amount of coarse woody debris (MOF 1991a). This research now fills that gap, at least for the moist-cold subzone. It has also been suggested that age may be an important indicator of old-growth status, if for example a stand is older than the natural disturbance interval (Achuff 1989, Hunter 1989) it could be considerd to be old-gorwth. However the fire return interval has been reported as increasing in many parts of the boreal forest due primarily to fire suppression programs (Heinselman 1973, Van Wagner 1978, Bergeron 1991). With increased fire suppression it is highly likely that more and more stands that are not scheduled for harvesting will be able to exceed these ages. If we use 125 years as an average 9 9 disturbance interval for the SBS one can see that almost all stands surveyed, except Plots 10 and 12, have trees of an age greater than the natural average return cycle. This does not seem to confer any exceptional developmental state. Stands exceeding the average maximum disturbance interval of 200 years (Stands 3, 6, 7, 11, 13) begin to show old-growth characteristics, greater influence of gap-phase processes and greater dead biomass. The longer the passage of time since the last stand destroying disturbance, the greater the influence of minor tree-level disturbance, and the lesser the influence of the last catastrophic disturbance. 5.52 Cluster Analysis Ranking The use of cluster analysis does not provide an obvious means of evaluating which structural attributes are important to the clustering of stands. Neither does it provide a portrayal of stands that may be transitional. Cluster analysis of structural attributes can, however, be used to help quickly classify stands, or to confirm classifications by other methods. 5.53 Basal Area Ratio Ranking Basal area ratios are sensitive to the identification of cohorts but potentially provide the best means of assessing the relative influence of disturbance on changes in tree population dominance. In stands where all individuals have been recruited through continual replacement, the index will be undefined. It is possible that cases could occur where secondary minor disturbance could create modes that are interpreted as the post-disturbance cohort and would hence complicate the determination of this ratio. The use of such a ratio is compatible with Borman and Likens' (1979) theory on the shifting mosaic steady state conditions of biomass. According to this theory, stand biomass after catastrophic disturbance will progress to a peak (before overstory mortality reaches a maximum) and will then decline to a steady-state condition in which overstory mortality is balanced by recruitment from below. The use of such an index is an attempt to locate the point at which the stand is entering this steady-state condition. In the initial stages of development the ratio will be zero, or very close to zero, as stand biomass is accounted for 100 almost entirely by the initial colonizing cohort. Even in the understory reinitiation period (Oliver 1981a; see Chapter 1), this ratio will be negligible, because new ingress will not account for much basal area (Aplet et al. 1989). Only when some individuals from the replacement cohort are recruited to the canopy will this ratio begin to increase noticeably. This initial increase even at relatively low percentages indicates the beginning of the transition to the old-growth stage. The cohort basal area ratio also reflects mortality processes within the stand. As large individuals from the colonizing cohort die the ratio will increase even without a subsequent response from the replacement cohort due to the decrease in the value of the ratio's denominator. Basal area measurement is also well correlated to a number of other stand factors including stand volume (Smith 1986), total stand biomass and measures of canopy coverage (Forcella and Weaver 1987). This information can not be inferred from stand age structures alone. 5.54 Age Distribution Ranking Age distributions have long been used to assess stand development, and can provide an adequate measure of old-growth status in many stands. The presentation of species rather than total age structures can provide added insights into plot dynamics. However, they lack the ability of the cohort basal area ratio to assess actual changes in dominance, providing information only on individuals present per age class. Plot 6 provides a good example of this limitation in that the age distribution suggests an all aged structure implying replacement of one age class by the next. The basal area ratio, on the other hand, suggests that this stand is still very much dominated by the initial colonizing cohort. A clear correspondence between age distributions and disturbance can not always be made (Lorimer 1985). In some cases, single-tree disturbance may lead to successful seedling establishment, in others to release of suppressed individuals, and in still other instances to gap filling by crown expansion. Oliver and Stephens (1977) found that a series of large and small disturbances could result in a Gaussian age distribution. Hett and Loucks (1976) and Whipple and Dix (1979) also suggest that the form of the age distribution may oscillate due to changing environmental factors. Interpretations of disturbance made from age distributions should therefore, also be supported by other lines of evidence. Age distributions are also a logical progression from the use of stand age alone to define old-growth forests. Previous reviews of some B.C. forests, have used only minimum age and height classes (age class 8 and 9 [greater than 150 years] and height class 3 to 4 [over 30 m tall]) to isolate potential old-growth forests (Roemer et al. 1988). For the SBS zone, an age of 120 years was suggested as a minimum for the occurrence of old-growth attributes (MOF 1991c). The age distributions from this study, however, suggest that many stands would not be long past the stand initiation state by this age; few therefore, could be considered old-growth in a functional sense. 5.55 Differences in Ranking Differences in the ordering of plots by the different techniques can be explained by a number of factors: 1) Each method is using different criteria to evaluate the plots. The criteria are similar and strongly related but define slightly different conditions, any one of which may be valid depending on the objectives of the investigator. 2) The composition of all stands is not the same and the different species respond differently to similar conditions. Pinus, Picea and Abies all have different regeneration " strategies' (Oosting and Reed 1952, Aplet et al. 1988, Burns and Honkala 1991) and it has also been demonstrated that Picea and Abies have different patterns of biomass production (Aplet et al. 1989). 3) Each stand has an unique history. Disturbance, micro-environment and stochastic events all differ from plot to plot, shaping slightly different developmental paths. A comparison of the different techniques by their ordering of stands shows that the cluster analysis is the most poorly correlated with the other techniques and the more subjective age distribution interpretation is the best (Table 5-8). Each technique has inherent weaknesses: age distributions require subjective interpretations; basal area ratios are sensitive to distinguishing cohorts; and the multivariate Table 5-8. Spearman rank correlation coefficients Ranking Method PCA Component 1 Age Structure Cohort BA Ratio Cluster Analysis PCA 1 1.00 Age Structure 0.745 1.000 BA Ratio 0.618 0.916 1.000 Cluster Analysis 0.415 0.631 0.618 1.000 103 analyses are based on static attributes. Overall, the basal area ratio is the best approach because it not only presents a reasonable ranking of stands but it best addresses the conceptual definition of old-growth. Structural evaluations of stand development are useful for operational purposes and the data are easier to obtain and prepare than that required for basal area ratio calculation. However, assessments made on the basis of structural attributes should be confirmed by a technique such as the basal area ratio that can directly assess the stand dynamics. Age distributions are also easier to prepare than the cohort basal area ratio and their graphic presentation provides an intuitive understanding of a stand's old-growth status. 5.6 Summary The use of PCA or other ordination techniques can define stands along structural gradients and aid in the identification of important structural attributes. Cluster analysis is very limited in that it does not identify the important attributes, but it can provide an easily executable and interpretable confirmatory method. Age structure graphs should be used when interested in defining old-growth based on the change in regeneration cohorts. Basal area definitions are more applicable when population changes in terms of dominance are more of interest. Overall, the cohort basal area ratio best addresses the proposed definition of old-growth as being the stage at which the stand is influenced more by single-tree processes than stand level processes. The most important old-growth attributes in the SBS are total basal area, number of snags and logs greater than one cubic metre in volume, and stand age. These attributes are indicative both of increasing biomass and stature of the dominant trees, and of the increasing importance of canopy mortality. In terms of understanding the role of minor disturbances in SBS old-growth and mature forests, further consideration should be given to the role of canopy gaps in promoting within-stand regeneration. Runkle (1991) has stated that regeneration of woody species is more closely associated with gaps in old-growth forests than in younger forests. But gaps are not only altered light regimes. Stuart et al. (1989) note that, in self-perpetuating lodgepole pine stands, shading was not limiting regeneration as much as was soil moisture. An 104 assessment of the mechanistic role of gaps is therefore necessary to understanding SBS old-growth stand dynamics with respect to limitations to regeneration. Regeneration in gaps may be constrained by competition from shrubs, by a deeper snowpack (and hence a shorter growing season) and by the thick organic layers typical of old-growth forest floors. A dendro-ecological investigation into past release and suppression events through radial increment analysis may also elucidate the importance of singe-tree disturbance in individual tree development. Using population biology to define old-growth and other development states reflects the dynamics of regeneration and mortality and is an improvement on definitions that emphasize static characteristics (Hay ward 1991). An understanding that gap-phase processes have a role in the natural development of these late successional SBS stands provides new stimulation for consideration of alternative silviculture techniques and further study into such processes. Indeed, the rarity of old-growth in such fire-dominated biomes makes all such examples valuable for their floristic and ecological information. 1 0 5 6. SUMMARY AND RECOMMENDATIONS 6.1 Observations on Stand Development This study is primarily a pilot study of the dynamics of tree populations in the SBS. Many trends and patterns have been noted and often processes were inferred, but more research is needed to understand: 1) the role of gaps in tree recruitment; 2) the timing and causation of tree mortality; 3) the functioning of coarse woody debris; and 4) the relative role of competition for resources versus amelioration of harsh environmental conditions by neighbouring individuals during tree establishment. This study described information on stand development processes and evaluated a number of potential means to define old-growth in the SBS zone. In assessing the stand development patterns of the SBS two questions emerged from other ecosystems with similar species: 1) could the stand as a community maintain itself or would it decline in the absence of fire; and 2) would there be a shift in species composition? Age, size and basal area distributions were all used to support the conclusions made concerning the dynamics of these stands. In response to the first question, the data indicate that these stands are able to maintain themselves in the absence of large-scale disturbances. Age distributions progress from bimodal to reverse-J forms, indicating that after an initial surge in recruitment results in the first cohort, subsequent recruitment is relatively steady and will lead to a self-perpetuating population. The oldest stand (>350 years), showed both a reverse-J density-age distribution and a J-shaped basal area-age distribution. Both of these curves support the idea that this stand was self-perpetuating, with continuous recruitment of both individuals and basal area from one age class to the next. Compositional changes were obvious as Pinus and Populus were absent from the youngest age and smallest size classes of most plots. All evidence indicates that, without disturbance, these species would not be maintained in these mixed species stands. Abies was typically the last species to recruit, often after the delay of a number of decades. But once it became established on the site recruitment remained fairly continuous. Picea was the most variable of all species in terms of its recruitment and maintenance in the forest. 1 0 6 The results from this study indicate that Picea is able to maintain itself in late successional SBS stands; however, it may not do so in all stands. In particular, Plot 7 and Plot 11 demonstrate that Picea regeneration may, in some cases, be too sporadic to result in future dominance in the stand. In most of the plots Picea recruited strongly during the initial colonizing period and then was excluded from the stand. This period was quite variable in length but typically lasted 100 to 200 years, followed by Picea reinitiation. It may take long periods for conditions to become suitable within the stand, through the mortality of dominant individuals, before Picea can again successfully establish. In stands with evidence of minor disturbance, Plot 2 and Plot 4, Picea reinitiation often occurred after shorter intervals and in greater numbers than other stands. Many plots surveyed had greater densities of Abies than either Picea or Pinus, yet Abies rarely dominated the overstory or larger size classes. Basal area distributions and species height profiles indicate that Abies is often inferior to Picea in growth. Even during decades where Abies established in greater numbers, Picea often dominated in terms of basal area recruited. Populus appears to have an important role in the development of many SBS stands. Although evidence of its presence was usually minor in old SBS stands, it is postulated that Populus may be important at the time of stand initiation. Conifer ingress may establish either below or in conjunction with Populus. The presence of Populus may aid in successful conifer establishment through the amelioration of environmental conditions. This relationship needs further study. All plots showed evidence of a past fire history, although the intensity and size of the fire probably varied among stands. These differences among fires may be responsible for some of the differential species recruitment patterns observed. It is speculated that due to the silvics of the different species, Abies would be most successful after a small fire that is not too intense whereas larger more severe fires may promote Pinus recruitment. In all cases periods of initial colonization were noted to be quite long, 75 years on average. 107 6.2 Assessing Old-Growth This study also explored the use of a population-based definition of old-growth and the degree to which it concurred with structural definitions. A population-based definition is needed to provide a common method of distinguishing old-growth stands. A common definition of old-growth is based on the precept that this stage occurs when the rate of tree regeneration and mortality, and thus the age structure, are more the result of single tree processes than a stand-initiating disturbance (Hayward 1991). The ratio of the basal area of the replacement cohort to the initial cohort was found to be the best measurement technique for assessing old-growth as defined above. It provides a useful index that is able to assess the relative old-growthness of forest stands as well as support structural measures of old-growth. Initial and replacement cohorts can be distinguished by changes in distribution of stem density or species composition in the age structure. The form of the age distributions were also used to assess the population processes within each stand. They provide an intuitive description that is strongly correlated to the basal area ratio. Analysis of structural attributes demonstrated that SBS stands can readily be ordered along a gradient of stand development. This supports the use of structural attributes in identifying old-growth stands, although the use of a conceptually based definition is encouraged to verify the results of an old-growth ordination. In the SBS it is suggested that the number of logs and snags greater than one cubic metre, as well as stand basal area (or volume) and age are the most positively correlated with stand development (in the mature to old-growth section of the continuum). 6.3 Recommendations The results of this study suggest further areas that need research, changes that need to be made to existing techniques and some possible practical applications. Methods of evaluating and describing decay classes of both snags and logs are inadequate. Among other possibilities, logs and snags may be rotten on the inside and sound on the outside or vice-versa. Yet descriptive techniques often focus on the decay of the outside of the stem ignoring the state of the interior. Furthermore, there is a need to 1 0 8 calibrate decay states for different species of wood in different climates in terms of absolute years. Density-age histograms are often used in assessing population processes in stands but they do not necessarily reflect changes in dominance. The use of basal area-age distributions can provide this additional information. They are also useful in assessing the response of trees to disturbance. This study noted a period in which Picea regeneration is excluded from the forest, often followed by a period of Picea reinitiation. The factors influencing this process need to be explicitly identified. Studies should be specifically directed at the role of canopy gaps in regeneration. A number of potentially limiting factors that need to be investigated include the impact of snowpress, the effect of competition and the effect of forest floor build up. Understanding and possibly manipulating these factors has obvious implications to the use of silvicultural alternatives to clear-cutting and even-aged management. Silvicultural experiments should be conducted growing conifers under the protection of Populus. This may be an important natural pathway that could be particularly useful in conifer regeneration in harsh environments. However, the stocking or basal area of Populus that is most beneficial needs to be determined. Forestry operations in the SBS should also set realistic rotation periods. The results from this study indicate that the natural period of stand initiation can be quite long and that artificial means of regeneration may therefore not be immediately successful, necessitating longer initial establishment periods. Many of the factors limiting natural regeneration can be expected to inhibit planted seedlings as well. 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WilMnson, L. 1990. SYSTAT: The system for statistics. Evanston, Illinois. Systat Inc. 677p. Wright Jr., H.E. 1974. Landscape development, forest fires and wilderness management. Science 186: 487-495. Appendix 1. Understory Species List Abies lasiocarpa (Hook.) Nutt. Pinus contorta Dougl. Picea glauca x engelmanni Parry ex Engelm. Alnus incana (L.) Moench Alnus viridis (Chaix) DC. Actaea rubra (Ait.) Willd. Amelanchier alnifolia Nutt. Arctostaphylos uva-ursi (L.) Spreng Arnica cordifolia Hook. Aster citiolatus Lindl. Aster conspicuus Lindl. Calamagrostis canadensis (Michx.) Beauv. Clintonia uniflora (Schult.) Kunth. Cornus canadensis L. Cornus sericea L. Deschampsia spp. Beauv. Dryopteris spinulosa (Jacq.) Woynar Epilobium angustifolium L. Equisiteum arvense L. Galium triflorum Michx. Gymnocarpium dryopteris (L.) Newm. Lathyrus nevadensis (Wats). Linnea borealis L. Lonicera involucrata (Rich.) Banks Oplopanax horridus (Smith) Miq. Orthilia secunda L. Osmorhiza chilensis H. and A. Pachistima mrysinites (Pursh) Raf. Petasites palmatus (Ait.) Cronq. Ribes lacustre (Pers.) Poir. Rosa acicularis Lindl. Rosa nutkana Presl Rubus parviflorus Nutt. Rubus pedatus J.E. Smith Rubus pebescens Raf. Sheperdia canadensis (L.) Nutt. Smilacina racemosa (L.) Desf. Sorbus sitchensis Roemer Spiraea betulifolia Pall. Streptopus amplexifolius (L.) DC. Thalictrum occidentale Gray Tiarella trifoliata L. Vaccinium membranaceum Dougl. Viburnum edule (Michx.) Raf. Vicia americana Muhl. Viola orbiculata Geyer Lycopodium annotinum L. Cladonia spp. Peltigera spp. Dicranum fuscescens Dicranum scoparium Lophosia spp. Pleurozium schreberi Ptilium crista-castrensis Hylocomium splendens Mnium spp. Brachythecium spp. 125 40 30 & 2 0 '(D X 10 0 ' ' I ' 1 1 1 ' 11 I ' I ' I ' P S s ?s ? f $ f s I ?? f t ? ?<= f ? ? ' ? ? ? s - ! I I I l i M 1 t 1 1 1 1 | t ' T 1 T T 1 1 1 f f f F l : s ? 1 0 10 20 30 40 Tree, number 50 60 CO CD a! E w CO S3 O sz u> CD I 0 PICEA 0 PINUS • ABIES Percent of Foliage Appendix 2. Plot 2 height profiles. For each sampled plot the upper diagram shows the crowns of each individual tree (labelled by the first letter of the common name for the tree: F=Fir [Abies], S=Spruce [Picea] and P=Pine [Pinus] which are represented by the length of the vertical lines. The bottom diagram is the distribution of the canopy, by species, for the plot as a whole for each lm height class and includes seedlings and saplings. 40 126 - I 1 1 T -30 6 20 CD X 10 ?lr f 5 If If S f< I ¥ o 0 0 _J I L_ I . • 50 Tree, number j i i i i I L 2 3 4 5 6 Percent of Foliage 100 PICEA ABIES Appendix 2 ( c o n t i n u e d ) . Height p r o f i l e s fo r P l o t 3 . 127 40 30 •£, 20 -CD I 10 - J 1 I ' • -1 1 1 1 I • • ' • 0 I? 5 ff 5W Ff fff fp Ff S $ P F? FfFf Hi 1 > i — i -i 1 1 1 1 1 1 r — i 1 r-0 50 Tree, number 100 35 -30 E 25 W%%. iU V//XA W POPULUS 0 PICEA 0 PINUS • ABIES 25 Percent of Foliage Appendix 2 (continued). Height profiles for Plot 4. 128 4-U Height, m IV) CO o o 10 : U 0 1 j 1 1 j 1 1 1 1 j 1 T — I " ! 1 1 1 s ? s ss i 1 1 f :: 1 s ? c SS S s if s j f f f f S | ? T f n ? ?T 1 U1 i 1 f A if • I f fff F f F T f 1 F s-s ff s .. fir FI 5 0 100 Tree, number J i i i i i i 35 j 30 -" E 25 -CO w CD O 20 -, sz •*fcs7l A • •CK»<X><>X>0<XXX><) I^I^I^H^ I^^^^^Bv • j - i o IJ^I^I^I^H-y 10 - JHP 5 -Bt^ ^ -f ----- 0 PICEA - • ARIFS u r i i i i i i i 0 1 2 3 4 5 6 7 8 Percent of -oliage Appendix 2 (continued). Height profiles for Plot 5. 129 40 -30 -J cS s ' E \ 6 20" CD 1 -T -- T 10 -n -F o 1 1 1 Lt Lt-s Ff 8 ff f If F 1 f„ 5 f ?F T -f r: ? f T ff 1 FT V s 5 s F U 1,1 i f f . r i s Ff s "~ c F S ---If Ff _ <fl I f ; 0 5 0 100 Tree, number 2 3 4 5 6 Percent of Foliage PICE A ABIES 8 Appendix 2 (continued). Height profiles for Plot 6. 130 4 0 i * * ' * ' ' ' ' * i ' * ' * ' * ' ' ' i * * * ' i ' ' ' 30 £ 20 CD I 10 5 F f 0 V l f Fff i\ i FT 1 | I • • i i i i i—l i . . l „ „ i 0 20 40 60 Tree, number J—t_l—I—I—I—I—L. 80 vpyW, 3 4 5 Percent ol Foliage 6 m PICE A : 0 PINUS • ABIES 7 Appendix 2 (continued). Height profiles for Plot 7. 131 40 —i—I—i—i—i—l—l—I—r-s p 30 6 20 (D X 10 0 in fr 5$? - I I i I t i i « I f f n • [ _ 1 I i I1' M! fs . . I 0 20 40 60 Tree, number IT 80 ~r~ 4 2 3  5 Percent of Foliage 0 PICE A : E3 PINUS • ABIES Appendix.2 (continued). Height profiles for Plot 8. 40 132 30 6 20 CD I 10 0 1 1 r 1 1 ? 1 1 1 1 1 r T •• i i i i i if p -- ? T ft ? ? -PP ; ' P P S P ? ? f? ? ? ? f -•TW J ft uf t « I T -- 1 ? i f ? M M -f f f f ? -¥ f - 5 f f ? 1 % ? f f f f f f 1 ;f Nf If f -» • i < i i i i i i i i — i . - i — 0 5 0 Tree, number 100 35 30 fc 25 w co O 2 0 -C •A77J?1 ////zy//A'*&. yyyysyyyysyyyy'/y'y:'yyYYyyyyx. yy/Z/yyy/y-/////Y////.yyyyyy.m&ft. y//Y//y/y///yyy//yyyyy///yyyyyY Ayy^^zzyyyzzzzzz zz^zzxi mm. ^^22^SS22S^22SzS2s2B2?2SS2S8a # # % # 7ZZZZZZZZ yyyyyyy, yyyyyyy'/yyyyyyy/y/y Wmf^^ 'X/yyyyyyyyy/yyy/yyy//y/'^ Y/yy/yyyYyyyy- .yyy/y/y/y'jycyyy>yyyy>'l V/ -'>' /'.' / 6 0 PICEA Q PINUS • ABIES F'ercent of Foliage Appendix 2 (continued). Height profiles for Plot 9. 133 50 100 Tree, number 35 3 0 - *xX:<*X<l Wk yz/ai-yJ., K*X*Xx/^ :^^^Mi^#^ . \ W v V \ V W v ' v V v \ W v V \ , , l 7 / / / / / ; &&&&^>^Mtfm...,.,,,„ _ OCX" 0 0 2 C)04 0 0 6 0 0 8 Percent ut Foliage POPULUS PICEA ABES 0 10 Appendix 2 (continued). Height profiles for Plot 10. 40 30 ft 2 0 CD I 10 0 0 ' 1 — ' — • — ' - • • ' — • -p p -• :? f * u ? ? > $ f s s If ; If Ifffl — i i — i . . i — i i f t p ? T f 1 P ? ? P p P S ] If ff !' k i i i f c \i f f 1 f f 1 F f . . . i i i i — i — • -f s ? H _ !B i If s f 134 50 Tree, number 100 4 6 Percent of Foliage m POPULUS 0 PICEA 0 PINUS • ABIES Appendix 2 (continued). Height profiles for Plot 11. 40 ~ r — i 1 1 -i 1 1 r~ 135 30 CO CD X 100 35 -30 -S Sk P H POPULUS 0 PICEA 0 PINUS • ABES Percent of Foliage Appendix 2 (continued). Height profiles for Plot 12. 40 136 - 1 1 1 -30 ft 20 h CD X 10 I 0 0 fl 7 (f T l 1 <f 50 £ -' 100 0 PICEA 0 PINUS • ABIES Percent ot Foliage Appendix 2 (continued). Height profiles'for Plot 13. -••J 137 • $ CD I 0 s, ? ? 0 50 Tree, number 100 x 3 5 -co w CO O 20 sz CD 15 -10 -:• 5 -y/^y.,^<^''.;>»i; K-.-X x ^ -^ ' < : ^ A ' . y v / . ' SS2S2SSS3 ssar f~T 0 /2222S25L 0 1^ 4 r 5 0 PICEA - 0 PINUS • ABIES Percent of Foliaue Appendix 2 (continued). Height profiles for Plot 14. Appendix 3. Density of stems per hectare x age class (years) for each plot by species and totals Aqe 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 141-150 151-160 161-170 171-180 181-190 191-200 201-210 211-220 221-230 231-240 241-250 251-260 261-270 271-280 281-290 291-300 301-310 311-320 321-330 331-340 341-350 351-360 Plot2 Abies 182 547 608 697 176 44 0 0 60 0 0 0 0 0 0 Picea 210 390 450 186 96 30 0 100 33 100 44 78 0 22 0 Pinus 0 0 0 0 0 0 0 0 0 11 0 0 22 0 0 Total 392 937 1058 882 271 74 1 100 93 111 44 78 22 22 0 Plot3 Abies 574 1368 710 309 335 371 140 22 110 104 151 96 81 74 122 70 44 78 70 0 11 33 22 11 22 11 0 0 0 11 11 0 0 0 11 0 Picea 177 618 221 132 11 0 11 22 0 11 33 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 11 0 0 Total 750 1986 930 441 346 371 151 44 110 115 185 119 81 74 122 70 44 78 70 0 11 33 22 11 22 11 0 0 11 11 11 0 0 11 11 0 Appendix 3. Density of stems per hectare x age class (years) for each plot by species and totals Age 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 141-150 151-160 161-170 171-180 181-190 Plot 4 Abies 44 1199 1999 1220 750 616 182 67 67 44 67 11 66 22 0 11 0 0 0 Picea 0 0 44 0 44 44 0 0 11 0 22 11 0 0 89 22 22 11 0 Pinus 0 0 0 0 11 0 11 22 0 0 11 11 22 11 44 22 22 22 0 Popul us 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 Total 44 1199 2043 1220 805 660 193 89 78 44 100 33 88 33 133 55 44 44 0 Plot 5 Abies 113 433 463 343 52 176 111 122 89 144 67 22 33 0 0 11 0 0 0 Picea 282 151 56 19 0 0 0 0 11 11 67 67 67 122 78 44 11 0 11 Total 395 584 520 362 52 176 111 122 100 156 133 89 100 122 78 56 11 0 11 Appendix 3. Density of stems per hectare x age class (years) for each plot by species and totals Aqe 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 141-150 151-160 161-170 171-180 181-190 191-200 201-210 211-220 221-230 231-240 241-250 251-260 261-270 < Plot 6 Abies 453 1302 2204 1132 1185 389 416 367 175 145 135 122 162 110 111 44 22 11 33 11 11 0 11 0 Picea 57 113 57 0 0 0 0 0 11 0 0 11 11 22 22 33 33 11 11 0 11 0 0 0 Total 509 1415 2260 1132 1185 389 416 367 186 145 135 133 173 132 133 78 56 22 44 11 22 0 11 0 Plot 7 Abies 1241 2127 1950 1241 838 654 226 313 488 198 184 0 109 70 72 148 33 85 89 22 78 111 33 0 33 0 0 Picea 0 0 89 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 11 11 11 11 11 11 11 0 Pinus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 11 11 0 Total 1241 2127 2039 1241 838 654 226 313 488 198 184 0 109 70 72 148 33 85 100 33 89 122 44 22 56 22 0 141 Appendix 3. Density of stems per hectare x age class (years) for each plot by species and totals Aqe 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 141-150 151-160 161-170 171-180 181-190 Plot 8 Abies 868 1612 620 868 186 886 105 783 997 401 430 72 33 11 11 0 0 0 0 Picea 62 62 0 0 0 0 0 0 0 0 11 11 22 70 111 67 0 0 0 Pinus 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 11 22 0 0 Total 930 1674 620 868 186 886 105 783 997 412 441 83 56 81 122 78 22 0 0 Plot 9 Abies 279 522 524 365 481 349 232 106 66 20 0 0 0 0 0 0 0 0 0 Picea 0 70 35 0 0 0 28 11 44 22 0 89 122 167 89 44 11 0 0 Pinus 0 0 0 35 0 0 11 0 0 0 22 33 11 33 122 56 33 22 0 Total 279 592 559 400 481 349 272 117 110 43 22 122 133 200 211 100 44 22 0 Appendix 3. Density of stems per hectare x age class (years) for each plot by species and totals Age 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 141-150 151-160 161-170 171-180 181-190 191-200 201-210 211-220 221-230 Plot 10 Abies 267 278 167 111 122 44 111 144 89 167 67 22 11 0 0 0 0 0 0 Picea 0 0 0 0 24 23 45 45 89 167 144 67 0 0 0 0 0 0 0 Popul us 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 Total 267 278 167 111 146 68 156 190 178 333 211 89 11 0 0 0 0 0 11 0 Plot 11 Abies 285 655 1171 1194 428 546 437 498 174 164 0 50 0 22 56 22 78 11 11 0 0 11 0 Picea 0 57 0 0 0 0 0 0 11 42 0 0 11 11 44 56 67 67 56 11 0 0 0 Pinus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 11 0 22 33 100 33 0 Popul us 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 1 Total 285 712 1171 1194 428 546 437 498 185 205 0 50 11 33 111 78 156 78 89 44 111 44 0 Appendix 3. Density of steins per hectare x age class (years) for each plot by species and totals Age 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 141-150 151-160 161-170 171-180 181-190 191-200 201-210 211-220 221-230 231-240 Plot 12 Abies 568 402 837 934 314 293 198 100 22 11 0 0 0 0 0 0 0 Picea 0 0 0 26 64 263 194 133 94 22 0 0 11 0 0 0 0 Pinus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Popul us 0 0 0 0 0 0 0 11 0 11 0 0 0 0 0 0 0 Total 568 402 837 960 378 556 392 244 117 44 0 0 11 0 0 0 0 Plot 13 Abies 3000 822 1111 300 478 933 433 111 133 122 56 56 56 44 11 22 44 0 22 0 11 0 0 0 Picea 389 133 0 0 0 0 0 0 0 0 0 0 0 0 0 44 67 78 22 22 22 0 0 0 Pinus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 11 0 0 0 0 0 0 Total 3389 956 1113 296 480 931 431 111 133 122 56 56 56 44 11 67 122 89 44 22 33 0 0 0 144 Appendix 3. Density of steins per hectare x age class (years) for each plot by species and totals Aqe 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 141-150 151-160 161-170 171-180 181-190 Plot 14 Abies 30 0 91 60 165 188 33 75 0 22 0 0 0 0 0 0 0 0 0 Picea 393 423 242 61 30 0 0 22 0 11 0 33 78 111 155 67 0 0 0 Pinus 0 121 303 184 0 0 0 0 0 11 0 11 0 11 33 22 11 0 0 Total 423 544 636 305 195 188 33 98 0 44 0 44 78 122 189 89 11 0 0 Appendix 4. Basal area at base of tree (m'/ha) x age class (years) distributions for each species and for all trees in the plot Aqe 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 141-150 151-160 161-170 171-180 181-190 191-200 201-210 211-220 221-230 231-240 241-250 251-260 261-270 271-280 281-290 291-300 301-310 311-320 321-330 331-340 341-350 351-360 Plot 2 Abies 0.003 0.035 0. 161 0.402 0.293 0.067 0 0 0.04 0 0 0 0 0 Pinus 0 0 0 0 0 0 0 0 0 1.569 0 0 4.657 0 Picea 0.004 0. 039 0. 09 0.084 0.08 0. 023 0 12.42 3. 126 17.79 9.363 13. 1 3.428 4. 116 Total 0.007 0. 074 0. 25 0. 487 0.373 0. 089 0 12 .42 3. 166 19.36 9.363 13.1 8.085 4.116 Plot 3 Abies 0.005 0.017 0. 031 0. 04 0.246 0.238 0. 075 0.312 0.348 1.32 2.891 0.786 7.448 2.079 1.901 5.863 6.336 1.916 5.218 0 0.187 5.671 4.23 2.989 3 .473 4.015 0 0 0 0.5 2 . 229 0 0 0 1.483 0 Picea 0 0.01 0.01 0.016 0.015 0 0.202 1.557 0 0.516 8.405 0.691 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ,0 0.395 0 0 0 0 3.904 0 0 Total 0.005 0.027 0.041 0.056 0.261 0.238 0.277 1.869 0.348 1.836 11.3 1.477 7.448 2.079 1.901 5.863 6.336 1.916 5.218 0 0.187 5.671 4.23 2.989 3.473 4.015 0 0 0.395 0.5 2.229 0 0 3.904 1.483 0 146 Appendix 4. Basal area at base (m2/ha) x age class (years) for species and plot totals. Aqe C l a s s ( y e a r s ) 1-10 11 -20 2 1 - 3 0 31 -40 4 1 - 5 0 5 1 - 6 0 61 -70 7 1 - 8 0 8 1 - 9 0 91 -100 101-110 111-120 121 -130 131-140 141 -150 151-160 161-170 171-180 181-190 P l o t 4 A b i e s 0 0 . 0 5 1 0 . 1 3 5 0 . 2 4 6 0 . 8 9 5 1.499 2 . 2 8 7 0 . 7 7 2 1 .441 1 .591 7 . 2 0 3 0 . 2 6 1 1.872 0 . 2 9 7 0 2 . 5 9 5 0 0 Pinus 0 0 0 0 2 . 6 6 3 0 0 .467 3 .562 0 0 2 . 3 0 5 1.665 2 . 5 2 6 1.050 8 .534 7 .519 5 . 107 6 .989 P i c e a 0 0 0 . 0 0 1 0 0 .002 0 . 0 0 2 0 0 0 . 8 8 9 0 4 . O i l 0 . 6 5 3 0 0 1 3 . 5 7 2 . 4 6 8 4 . 9 4 5 3 . 3 9 P o p u l u s 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.81 T o t a l 0 0 . 0 5 1 0 . 1 3 6 0 . 2 4 6 3 .56 1 .501 2 .754 4 .334 2 . 3 3 1 1 .591 13 .52 2 . 5 7 9 4 . 3 9 9 1.347 2 2 . 1 1 1 2 . 5 8 1 0 . 0 5 1 2 . 1 9 P l o t 5 A b i e s 0 0 0 . 1 2 7 0 .212 0 .164 0 .619 0 . 6 1 5 5 . 0 7 9 3 . 1 2 1 6 .798 2 . 3 1 9 4 . 7 5 3 1 .888 0 0 7 . 7 8 2 0 0 0 P i c e a 0 0 0 0 0 0 0 0 3 .067 0 .244 10 .44 9 . 2 8 3 13 .77 2 5 . 9 1 1 4 . 8 1 1 6 . 2 8 6 .784 0 1.938 T o t a l 0 0 0 .127 0 .212 0 .164 0 .619 0 .615 5 .079 6 .188 7 .042 12 .76 14.04 15 .66 2 5 . 9 1 1 4 . 8 1 24 .07 6.784 0 1.938 1 Appendix 4. Basal area at base of tree (m2/na) x age class (years) distributions for each species and for all trees in the plot A q e 1-10 1 1 - 2 0 2 1 - 3 0 3 1 - 4 0 4 1 - 5 0 5 1 - 6 0 6 1 - 7 0 7 1 - 8 0 8 1 - 9 0 9 1 - 1 0 0 1 0 1 - 1 1 0 1 1 1 - 1 2 0 1 2 1 - 1 3 0 1 3 1 - 1 4 0 1 4 1 - 1 5 0 1 5 1 - 1 6 0 1 6 1 - 1 7 0 1 7 1 - 1 8 0 1 8 1 - 1 9 0 1 9 1 - 2 0 0 2 0 1 - 2 1 0 2 1 1 - 2 2 0 2 2 1 - 2 3 0 2 3 1 - 2 4 0 2 4 1 - 2 5 0 2 5 1 - 2 6 0 P l o t 6 A b i e s 0 . 0 0 1 0 . 0 3 0 . 0 9 0 . 117 0 . 17 0 . 094 0 . 1 5 3 0 . 1 8 0 . 2 4 4 0 . 8 7 9 4 . 5 5 6 . 5 2 2 7 . 9 4 9 5 . 1 9 4 9 . 3 3 1 4 . 8 5 3 . 6 3 1 1 . 8 6 7 2 . 9 1 7 5 . 7 9 2 1 . 4 7 5 0 2 . 1 9 1 0 P i c e a 0 . 0 0 0 0 . 0 0 1 0 . 002 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 0 . 1 2 9 0 . 0 0 0 0 . 0 0 0 3 . 9 0 4 2 . 4 7 0 3 . 162 3 . 5 5 7 1 1 . 4 1 6 . 197 1 . 5 8 1 1 . 9 3 8 0 . 0 0 0 2 . 4 7 0 0 . 0 0 0 0 . 0 0 0 0 . 0 0 0 T o t a l 0 . 0 0 1 0 . 0 3 1 0 . 0 9 2 0 . 117 0 . 17 0 . 0 9 4 0 . 1 5 3 0 . 1 8 0 . 3 7 3 0 . 8 7 9 4 . 5 5 1 0 . 4 3 1 0 . 4 2 8 . 3 5 6 1 2 . 8 9 1 6 . 2 6 9 . 8 2 8 3 . 4 4 8 4 . 8 5 5 5 . 7 9 2 3 . 9 4 5 0 2 . 1 9 1 0 P l o t 7 A b i e s 0 . 0 0 7 0 . 0 9 5 0 . 065 0 . 2 1 8 0 . 3 5 2 0 . 7 1 3 0 . 2 6 4 0 . 3 3 3 1 .524 1 . 3 4 8 1 . 2 0 1 0 . 6 4 1 2 . 2 8 8 0 . 2 9 8 3 . 1 3 6 5 . 2 3 3 5 . 0 9 4 7 . 5 9 4 4 . 5 8 2 1 .744 9 . 0 9 5 1 1 . 2 4 8 . 5 8 0 3 . 8 7 3 0 P i n u s 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 .764 4 . 8 4 5 1 . 8 6 7 P i c e a 0 0 0 . 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 . 5 5 7 3 . 3 9 0 2 . 7 6 1 3 . 2 2 7 1 . 0 6 7 3 . 3 9 0 4 . 6 4 6 9 . 6 0 1 T o t a l 0 . 0 0 7 0 . 0 9 5 0 . 0 6 9 0 . 2 1 8 0 . 3 5 2 0 . 7 1 3 0 . 2 6 4 0 . 3 3 3 1 . 5 2 4 1 . 3 4 8 1 . 2 0 1 0 . 6 4 1 2 . 2 8 8 0 . 2 9 8 3 . 136 5 . 2 3 3 5 . 0 9 4 7 . 5 9 4 8 . 1 3 9 5 . 1 3 4 1 1 . 8 6 1 4 . 4 7 9 . 6 4 6 5 . 1 5 4 1 3 . 3 6 1 1 . 4 7 I 148 Appendix 4. Basal area at base of tree (m2/na) x age class (years) distributions for each species and for all trees in the plot Aqe 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 141-150 151-160 161-170 171-180 Plot 8 Abies 0.003 0.017 0.018 0.074 0.329 0.224 0.099 0.198 0.784 2.197 0.557 2. 123 4.856 0 3.476 0 0 Pinus 0 0 0 0 0 0 0 0 2. 191 0 0 0 0 0 0 2.305 5.082 Picea 0 0 0 0 0 0 0 0 0 0 0.202 0.202 3.729 13.17 33.87 30.88 0 Total 0.003 0.017 0.018 0.074 0.329 0.224 0.099 0. 198 2.975 2. 197 0.759 2.324 8.585 13. 17 37. 35 33.18 5.082 0 Plot 9 Abies 0.001 0.006 0.056 0.235 0.465 0.77 0.636 0.347 0.288 0. 137 0 0 0 0 0 0 0 0 Pinus 0 0 0 0.006 0.697 0 0 0 0 0 4.755 4.213 1.764 3.101 19.08 8.636 6.084 3.805 Picea 0 0.002 0.002 0 0 0 0.472 1.696 0.873 0.454 0 5.072 8.395 16.64 11.21 5.899 2.912 0 Total 0.001 0.008 0.058 0.242 1.162 0.77 1. 108 2.043 1.161 0.591 4.755 9.285 10.16 19.74 30.29 14.54 8.996 3.805 149 Appendix 4. Basal area at base of tree (m:/ha) x age class (years) distributions for each species and for all trees in the plot Age 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 141-150 151-160 161-170 171-180 181-190 191-200 201-210 211-220 Plot 12 Abies 0.001 0.008 0.038 0.248 0.798 5.443 5.475 4.163 2.915 3.41 0 0 0 0 0 0 0 0 0 0 0 0 Pinus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Picea 0 0 0 1.18 0.769 10.18 23.84 21.32 2.915 4.447 0 0 2.065 0 0 0 0 0 0 0 0 0 Populu s 0 0 0 0 0 0 0 0,25 0 0.715 0 0 0 0 0 0 0 0 0 0 0 0 1 Total 0 0 0 1.18 0.769 10.18 23.84 21.57 2.915 5.162 0 0 2.065 0 0 0 0 0 0 0 0 0 Plot 13 Abies 0.043 0.072 0.096 0.088 0.752 0.712 1.77 1.699 2.73 7.074 1.331 3.714 1.277 1.438 0.747 3.489 5.514 0 2.2 31, 0 2.854 0 Pinus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.08 0 1.67 2.79 0 Picea 0.002 0.001 0 0 0 0 0 0 0 0 0 0 0 0 0 13.59 20.86 15.91 8.379 7.504 4.721 0 Total 0.045 0.074 0.096 0.088 0.752 0.712 1.77 1.699 2.73 7.074 1.331 3.714 1.277 1.438 0.747 17.08 28.45 15.91 12.27 7.504 10.36 0 1 Appendix 4. Basal area at tree base (m2/ha) x age class (years) distributions for species and stand totals Aqe C l a s s ( y e a r s ) 1-10 11 -20 2 1 - 3 0 31 -40 41 -50 51 -60 61 -70 7 1 - 8 0 81 -90 9 1 - 1 0 0 101-110 111-120 121-130 131-140 141-150 151-160 161-170 171-180 181-190 191-200 201-210 211-220 P l o t 10 A b i e s 0 . 0 1 1 0 . 0 1 4 0 . 0 2 8 0 . 0 2 7 0 . 198 0 . 125 0 . 1 2 5 0 . 6 2 7 1 . 0 9 1 3 . 0 9 3 1 . 4 6 6 0 . 3 0 9 1 .044 0 0 0 0 0 0 0 0 0 P i c e a 0 0 0 0 0 0 0 1.243 3 . 3 2 1 3 . 0 5 2 1 . 6 7 2 1 . 9 1 1 0 . 6 6 0 0 0 0 0 0 0 0 0 P o p u l u s 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 . 5 2 2 0 0 0 T o t a l 0 . 0 1 1 0 . 0 1 4 0 . 0 2 8 0 . 0 2 7 0 . 198 0 . 125 0 . 1 2 5 1.869 4 . 4 1 2 1 6 . 1 4 2 3 . 1 4 2 2 . 2 2 1 1 . 7 0 0 0 0 0 6 . 5 2 2 0 0 0 P l o t 11 A b i e s 0 . 0 0 1 0 . 0 2 2 0 . 1 6 8 0 . 1 6 6 0 . 198 0 . 8 3 1 3 . 7 4 0 . 9 8 5 0 . 5 4 6 0 . 7 3 9 0 . 3 9 4 0 . 4 6 3 0 3 . 0 6 4 3 . 5 4 3 0 . 2 6 1 9 . 3 8 1 1 .125 1 . 5 8 1 0 0 0 . 8 1 7 P i n u s 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 . 3 0 5 0 1.764 0 4 . 3 8 4 5 .573 1 8 . 7 1 6 .633 P i c e a -0 0 . 0 0 1 0 0 0 0 0 0 0 . 8 0 7 1 .75 0 0 0 .244 0 . 7 2 8 3 . 2 2 3 8 . 5 3 9 6 .312 1 0 . 1 7 5 . 8 6 6 2 . 1 9 6 0 0 P o p u l u s 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 .227 0 T o t a l 0 . 0 0 1 0 .023 0. 168 n. 166 n. 198 . O ^ l l — 3.74 n . o 8 5 1 .153 2 .489 0TT94 0 .463 O.T44 3 .792 9 .07 8 .799 17 .46 11 .29 1 1 . 83 7 .769 21 .94 7 .449 151 Appendix 4. Basal area at base of tree (mJ/ha) x age class (years) distributions for each species and for all trees in the plot Acre 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 141-150 151-160 161-170 171-180 Plot 14 Abies 0 0.001 0.053 0.005 0.002 0.083 0.129 0. 123 0.251 0.108 0 0 0 0 0 0 0 Pinus 0 0.035 0.092 0.085 0 0 0 0 0 3.458 0 3.458 0 1.972 2.421 7.563 3.746 Picea 0.04 0.065 0.173 0.001 0.002 0 0 0 0.454 0.454 0 1.652 8.196 13.75 22.61 17.52 0 Total 0.04 0.101 0.319 0.091 0.004 0.083 0.129 0.123 0.704 4.02 0 5.11 8.196 15.72 25.03 25.08 3.746 o 152 Appendix 5. Site associations, nutrient and moisture regimes for the sampled SBSmc subzone plots. Plot Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Soil Nutrient Recrime mesotrophic permeseotrophic mesotrophic mesotrophic permeseotrophic mesotrophic mesotrophic permeseotrophic permeseotrophic permeseotrophic mesotrophic permeseotrophic permeseotrophic mesotrophic Soil Moisture Recrime mesic mesic subhygric mesic mesic subhygric mesic mesic mesic subhygric mesic mesic mesic submesic Site Association 1.1 06 07 1.1 06 07 1.1 06 06 08 1.1 06 06 04 (Site associations from Lewis et al. 1986) 

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