Forestry, Faculty of
DSpace
UBCV
Goberti, Enrico Maria
Master of Science - MSc
University of British Columbia
The specific objectives of this study were to: (i) assess the impact of overstory conditions and disturbance history of interior Douglas-fir dominated stands on the quantity and quality of regeneration present; and (ii) document the impact of mountain pine beetle (Dendroctonus ponderosae Hopkins) on the regeneration in these types of stands. Overstory tree data from permanent sample plots and regeneration data from newly installed permanent subplots on each of the overstory plots were collected and compiled by plot, block, and treatment on a pre-commercial thinning experiment installation. This installation is located in drybelt Douglas-fir stands on the Knife Creek Block of the UBC Alex Fraser Research Forest in the vicinity of Williams Lake, BC. Several methods of assessing regeneration quantity (number of germinants and seedlings) and quality (three-year height growth) were used: mixed models, zero-inflated general linear models, and Classification and Regression Tree. Regeneration quantity and quality differed across the study site. Block B had relatively few seedlings and all were Douglas-fir; the other two blocks (C and D) had more regeneration comprised of several species. The control plots on all blocks had low amounts of Douglas-fir regeneration. Seedlings in the control plots had the lowest predicted three-year height growth, while the best height growth was found in the 5 m clumped spacing. Plot (larger scale) variables were less important than other variable types for determining the number of seedlings in a subplot. Having less than 35% ground coverage of crown litter (dead needles, small twigs, etc.) on a subplot was the best indicator of the presence of germinants. The best variables to predict the presence of seedlings were grass and herb ground coverage of more than 15% and less than 25% ground coverage by crown litter. The number of germinants did not vary significantly among treatments, although fewer were found on the control plots. Plots were compared to determine the effect of lodgepole pine mortality on the quantities of seedlings or germinants present. There was only weak evidence that seedling quality (as reflected by three-year height growth) was better on plots that had higher levels of recent mortality.
Regeneration in Thinned and Unthinned Uneven-Aged Interior Douglas-fir Stands by ENRICO MARIA GOBERTI B. Forestry and Environment, University of Padova, Italy, 2004 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December, 2010 ©Enrico Maria Goberti, 2010 ii Abstract The specific objectives of this study were to: (i) assess the impact of overstory conditions and disturbance history of interior Douglas-fir dominated stands on the quantity and quality of regeneration present; and (ii) document the impact of mountain pine beetle (Dendroctonus ponderosae Hopkins) on the regeneration in these types of stands. Overstory tree data from permanent sample plots and regeneration data from newly installed permanent subplots on each of the overstory plots were collected and compiled by plot, block, and treatment on a pre-commercial thinning experiment installation. This installation is located in drybelt Douglas-fir stands on the Knife Creek Block of the UBC Alex Fraser Research Forest in the vicinity of Williams Lake, BC. Several methods of assessing regeneration quantity (number of germinants and seedlings) and quality (three-year height growth) were used: mixed models, zero-inflated general linear models, and Classification and Regression Tree. Regeneration quantity and quality differed across the study site. Block B had relatively few seedlings and all were Douglas-fir; the other two blocks (C and D) had more regeneration comprised of several species. The control plots on all blocks had low amounts of Douglas-fir regeneration. Seedlings in the control plots had the lowest predicted three-year height growth, while the best height growth was found in the 5 m clumped spacing. Plot (larger scale) variables were less important than other variable types for determining the number of seedlings in a subplot. Having less than 35% ground coverage of crown litter (dead needles, small twigs, etc.) on a subplot was the best indicator of the presence of germinants. The best variables to predict the presence of seedlings were grass and herb ground coverage of more than 15% and less than 25% ground coverage by crown litter. The number of germinants did not vary significantly among treatments, although fewer were found on the control plots. Plots were compared to determine the effect of lodgepole pine mortality on the quantities of seedlings or germinants present. There was only weak evidence that seedling quality (as reflected by three-year height growth) was better on plots that had higher levels of recent mortality. iii Table of Contents ABSTRACT………………………………………………………………………………………………..…….…..ii TABLE OF CONTENTS………………………………………………………………………….………………..iii LIST OF FIGURES………………………………………………………………………….……………………...iv LIST OF TABLES……………………………………………………………………………………………….…..v AKNOWLEDGMENTS…………………………………………………………………………………….………vi 1.0 INTRODUCTION ................................................................................................................................................ 1 1.1 OBJECTIVES AND ORGANIZATION OF THE THESIS .............................................................................................. 2 1.2 STRUCTURE AND DYNAMICS OF UNEVEN-AGED STANDS .................................................................................. 2 1.3 FACTORS AFFECTING REGENERATION ................................................................................................................ 4 1.4 FACTORS AFFECTING HEIGHT GROWTH OF SMALL TREES ................................................................................. 6 2.0 METHODS ........................................................................................................................................................... 9 2.1 STUDY AREA ...................................................................................................................................................... 9 2.2 STUDY DESIGN ................................................................................................................................................. 10 2.3 MEASUREMENTS .............................................................................................................................................. 13 2.4 ANALYSES ........................................................................................................................................................ 14 3.0 RESULTS ........................................................................................................................................................... 17 3.1 OVERSTORY GROWTH ...................................................................................................................................... 17 3.2 GROUND COVER ON THE REGENERATION SUBPLOTS ....................................................................................... 19 3.3 REGENERATION QUANTITY .............................................................................................................................. 20 3.4 PREDICTING THE PRESENCE OF GERMINANTS AND SEEDLINGS ........................................................................ 23 3.5 PREDICTING THE NUMBER OF GERMINANTS AND SEEDLINGS PRESENT ............................................................ 26 3.6 THREE-YEAR HEIGHT GROWTH OF SEEDLINGS ................................................................................................ 29 3.7 POSSIBLE RESPONSE OF REGENERATION TO LODGEPOLE PINE MORTALITY..................................................... 32 4.0 DISCUSSION ..................................................................................................................................................... 34 5.0 CONCLUSIONS ................................................................................................................................................ 39 REFERENCES ......................................................................................................................................................... 41 APPENDIX 1 ............................................................................................................................................................ 50 OVERSTORY CONDITIONS FOLLOWING THE 2003 AND 2007 GROWING SEASONS. ................................................... 50 iv List of Tables Table 1. Change in overstory conditions between the end of the 2003 growing season and the end of the 2007 growing season. ...................................................................... 18 Table 2. Germinants per ha by species and treatment. ............................................................... 21 Table 3. Germinants per ha by treatment and block. .................................................................. 22 Table 4. Seedlings per ha by species and treatment. .................................................................. 22 Table 5. Seedlings per ha by treatment and block. ..................................................................... 22 Table 6. P-values for fixed effect variables and goodness of fit for predicting the average number of germinants in a plot. .................................................................................... 27 Table 7. P-values for fixed effect variables and goodness of fit for predicting the average number of seedlings in a plot. ....................................................................................... 28 Table 8. P-values for the fixed effect variables and goodness of fit of the zero-inflated Poisson models for the number of germinants per subplot........................................... 28 Table 9. P-values levels for the fixed effect variables and model fits of the zero-inflated Poisson models for the number of seedlings per subplot. ............................................ 29 Table 10. Three-year growth (m) for conifer seedlings by treatment......................................... 30 Table 11. Regeneration levels in the pre-commercially thinned plots in Blocks C and D, expressed as deviations from the mean level for the block, ordered from highest to lowest mortality, in terms of stems per ha, by block. ............................................... 32 Table 12. Three-year height growth of conifer seedlings in the pre-commercially thinned plots in blocks C and D, expressed as deviations from the mean level for the block, ordered from highest to lowest mortality in terms of stems per ha, by block. .............. 33 Table 13. Overstory conditions following the 2003 growing season, including lodgepole pine that had been recently killed. ................................................................................ 50 Table 14. Overstory conditions following the 2007 growing season. ........................................ 51 v List of Figures Figure 1. Study site location. ........................................................................................................ 9 Figure 2. Block and plot locations at Knife Creek. B, C and D are the block names. ............... 10 Figure 3. Layout of the 0.001346 ha (2.07 m radius) systematic regeneration subplots on the 0.05 ha overstory plots. ................................................................................................ 13 Figure 4. Average tree basal area by treatment through time. .................................................... 19 Figure 5. Stems/ha in each treatment by 5 cm DBH class (2008 measurements). ..................... 20 Figure 6. Stems/ha in each block by 5 cm DBH class (2008 measurements). ........................... 20 Figure 7. Ground composition in the systematic regeneration plots by treatment. .................... 21 Figure 8. Seedlings per ha height class and treatment. ............................................................... 23 Figure 9. Average ground composition of the selected regeneration subplots by treatment. ..... 24 Figure 10. CART analysis for predicting the presence of germinants 1 . ..................................... 25 Figure 11. CART results for predicting the presence of seedlings 1 . .......................................... 26 Figure 12. Three-year height growth (m) of seedlings by species 1 and treatments 2 for Block C………………………………………………..30 Figure 13. Three-year height growth of seedlings by species 1 and treatments 2 for Block D. .... 31 Figure 14. Three-year height growth of seedlings by treatment 1 . .............................................. 31 vi Acknowledgements It is a myth that a dissertation is a solely work of its author. Many people helped this author kicking and screaming toward his goal. I thank these people for helping me. I am thankful to Peter Marshall, my supervisor and friend, for his help but most important for having confidence in me. He read draft after draft of this thesis and no one should be subjected to the torture of reading my attempts at technical writing (and thanks to Peter, no one will). Peter’s patience and guidance will always amaze me. I thank him for always being available to meet me whenever he had time and for finding funds to support my work for this thesis. I will miss his advices and guidance but what I will mostly miss is our chats about sports in front a cup of coffee and his smile after winning some coffee bets. I am thankful to Valerie LeMay, for the support with the statistical analysis and any sort of statistical issues. Her dedication, willingness to explain over and over statistical topics, always smiling and always finding time to do it, is a fortune that everyone should have the opportunity to encounter. I thank her for letting me be her TA; it was such an amazing experience that I will always bring with me. Dr. Marshall and Dr. LeMay made the biometrics lab a wonderful workplace and home for the last three years and have been part of this home it is such a great pleasure that I cannot put in words. I would like to thank my committee: Dr. Bruce Larson, Dr. Abdel-Azim Zumrawi and Dr. LeMay for their recommendation and guidance throughout the course of this thesis. I am thankful to my friend and big brother Taehee Lee for his help with SAS/STAT and the statistical analysis. In the last three years I became part of a family: Lee’s family. They were always there ready to help me out, smile with me, have fun and ponder with me. They always care about me and for that I am really fortunate to have found them. I would like to thanks Hyo-Jin Kim my fiancée and friend. Her support and encouragement has always been present even during hard times. She was always there ready to remove my insecurities, her light guided me and will always bring joy to my life. My deepest gratitude goes to my parents for their unflagging love and support throughout my life; this dissertation would not be possible without them. I am indebted to my mother and father for their care and love. They worked hard during their life to support me and to provide the best environment for me to grow up. I cannot ask for more from them, they are simply perfect. Their love and their confidence in me continue to amaze me. Mom, Dad I love you. Finally, I would like to thank the Forest Sciences Program of the BC Forest Investment Account for the funding to cover the costs of conducting the fieldwork for this study and supporting my studies for two years. 1.0 INTRODUCTION British Columbia, Canada (BC) can be grouped into four regions ecologically: Boreal, Interior, Taiga and Coastal (Zabel et al. 2003). The central interior region is mainly composed of mountains, foothills, and high plateaus (Bailey 1996, 1998), with elevations between 1300 to 3000 m and peaks at 3400 m. Temperature depends on elevation, with approximately a mean annual temperature during the growing season of 4 degrees C at higher elevations and 13 degrees C on the plateau. The average annual precipitation ranges from 260 to 890 mm (Bailey 1996, 1998). Interior Douglas-fir (Pseudotsuga menziesii var. glauca Franco) is a common tree species of the southern and central interior of BC. It is considered the climax tree species in the Interior Douglas-fir (IDF) biogeoclimatic ecological classification (BEC) zone of the Province (Meidinger and Pojar 1991). This zone ranges from low to mid-elevations and it encompasses the east Kootenay, the Okanagan-Similkameen and Thompson regions, and the southern parts of the Chilcotin and Cariboo regions. The IDF has warm and dry short summers and cool dry winters; it is driest at low elevations in the Okanagan area and wettest close to the Columbia and Coast mountains (BC Ministry of Forests 1995). Usually, interior Douglas-fir grows in pure stands or with mixtures of pinegrass (Calamagrostis rubescens Buckl.), ponderosa pine (Pinus ponderosa Laws.) and other grasses on hotter sites in the south, and in mixtures with interior spruce (Picea englemannii Parry, Picea glauca (Moench) Voss and their crosses) and lodgepole pine (Pinus contorta var. latifolia Dougl.) on cooler, moister sites (BC Ministry of Forests 1995). The much of the area in which interior Douglas-fir is found in southern and central BC is referred to colloquially as ―the Dry-Belt‖. Interior Douglas-fir has been important to the local economy over the last century. High-quality timber associated with a relative easy access and closeness to manufacturing plants make this species locally important commercially (Day 1996). Furthermore, some interior Douglas-fir stands provide important winter habitat for several wildlife species, especially mule deer (Odocoileus hemionus Raf. ) (Armleder et al. 1986). These stands are also used for cattle crazing and public recreation (Day 1996). 2 Prior to the 1970s, harvesting of interior Douglas-fir was primarily done using diameter-limit cutting or some form of selective cutting for railway ties and fencing materials. This contributed to an abundance of poor residual stands afflicted by insects and disease problems. In the 1970s, selection management was adopted to overcome this situation and to improve stand quality (Day 1996). 1.1 Objectives and Organization of the Thesis The purpose of this study is to assess the relationships between overstory conditions (tree density), and seedbed conditions as affected by different precommercial thinning treatments, and regeneration quantity (number of germinants and seedlings by species) and quality (three- year height growth of ―best‖ seedlings) in interior Douglas-fir stands. The stands used in this study were part of a pre-commercial thinning experiment initiated in 1989-1990, spread across three blocks that differ in terms of the amount of moisture present. Consequently, there is a variety of density and site quality (moisture) conditions present. Several variables for trees greater than 1.3 m in height were measured on a series of permanent sample plots as part of this study to provide current overstory conditions. Regeneration quantity and quality were assessed on several small subplots located within each permanent sample plot. The remainder of this chapter includes a summary of relevant background literature. The second chapter describes the methods followed. The results are presented in Chapter 3. This is followed by a discussion (Chapter 4) and general conclusions (Chapter 5). 1.2 Structure and Dynamics of Uneven-Aged Stands Spatial structure and dynamics of forest stands are closely related (Goreaud et al. 1999). They both play a fundamental role in determining the local environment of each tree (i.e., competition), and consequently impact on the ability of trees to grow. Forest structure usually refers to the distribution of biomass in space, in other words, the vertical and horizontal arrangement of the trees, tree sizes, or their age distribution (Goff and Zedler 1968; McEvoy et al. 1980; Crow et al. 1994; Zenner and Hibbs 2000), and other features such as variation in species and age classes and distribution among diameter classes (Smith 1986; Zenner and Hibbs 2000). Forest structure and its characteristics have been used to examine wildlife habitats, 3 spatial heterogeneity, gap dynamics, understory vegetation, and patterns of regeneration and could be an useful tool for understanding future timber production (MacArthur and McArthur 1961; Whittaker 1966, James and Shugart 1970; Bouchon 1979; Forsman et al. 1984; Spies and Franklin 1989; Runkle 1991; Long and Smith 1992; Buongiorno et al. 1994; Chen et al. 1995; Emborg 1998). Forest structure can be quantified using non-spatial and spatial approaches. In non-spatial approaches, the relative position of the tree does not influence the mean stand characteristics, and relative indices are used to measure the vertical and horizontal structure or species diversity. Spatial methods take into consideration tree position (Kint et al. 2003). An inverse J-shaped diameter distribution that is correlated to the age of the trees is a common feature of uneven-aged stands (de Liocourt 1898 cited in Lee 2008; Leak 1964). However, in some single cohort stands such a diameter distribution may exist as a result of the different growth rates of the various species present (Hicks 1998). The three main variables used to describe an uneven-aged stand are: (1) maximum diameter, (2) density (basal area), and (3) the ratio (factor) q 1 (Meyer 1943, 1952; Murphy and Farrar 1982; O‘Hara 1998; Peng 1999). Assessing stand density in uneven-aged stands is more complex than in even-aged stands for several reasons: (1) uneven-aged stands have a wide range of tree sizes so the parameters to describe the tree size may differ (Woodall et al. 2003; Oliver and Larson 1996); (2) some descriptors, such as trees per ha (TPH) or mean tree size, do not help in understanding uneven- aged stand structure since such stands can present the same amount of TPH independently of the mean diameter (Sterba and Monserud 1993; Fiedler and Cully 1995; Shaw 2000; Woodall et al. 2003); and (3) uneven-aged stand do not follow overall size-density formulations because they often have different levels of competition for different sizes of trees (Burton 1993; Woodall et al. 2003). One method of overcoming these problems is to use the summation method proposed by Stage (1968) for Stand Density Index (SDI). In this method, SDI is calculated separately for each diameter class and then summed for the total stand value (Stage 1968; Woodall et al. 2003). 1 q is ―the ratio of trees in a diameter size class to the number of trees in the next larger diameter class‖ (O‘Hara 1998). 4 1.3 Factors Affecting Regeneration Regeneration varies among species and among varieties within species (Oliver and Larson 1996). While regeneration with seeds (with genetic recombination) produces unique individuals, other methods, such as sprouting from roots, produce individuals without genetic recombination (Harper 1977). Various tree species have different regeneration strategies. Some trees produce a small quantity of seeds, but each seed contains a large food reserve, while others produce many seeds with a small food reserve. The first group has a higher probability of surviving because they do not need available growing space immediately (Oliver and Larson 1996). Seeds can travel by wind, animals, and water. Seeds disseminated by air are usually light and contain special features such as ‗wings‘ or other structures that help them to be carried by wind (Schopmeyer 1974; Oliver and Larson 1996). This is the primary dispersal mechanism for Douglas-fir. Seeds distributed by animals may be dispersed by fecal droppings or remain attached to fur, while others will be stored and later forgotten (Phillips 1910; Van Dersal 1938; Ahlgren 1966; Janzen 1969; Smith 1970; Marks 1974; Harper 1977; Thompson and Wilson 1978). Water can play a role in seed dispersal. Running water can carry seeds depositing them often in clusters that accumulate near downed logs when the water recedes (Oliver and Larson 1996). Some species, such as interior Douglas-fir and ponderosa pine (Pinus ponderosa Laws), have low seed production in most years, while in other years, perhaps because of weather conditions and energetics, they have an abundant crop (Lamb et al. 1973). Other species, such as cottonwood (Populus balsamifera Torr. and Gray) and trembling aspen (Populus tremuloides Michx.) disperse their seeds during the spring and these seeds have to immediately find a favourable site or they will die (Oliver and Larson 1996). In other cases, seeds remain dormant after landing and they will be ‗activated‘ in the future by some environmental stimulus (Eyre 1938; Ahlgren 1959). Exposed soils rich in organic matter provide the best sites for interior Douglas-fir seed germination (Day and Duffy 1963). Douglas-fir seeds also germinate well on moss or on burnt organic surfaces (Haig et al. 1941; Geier-Hayes 1987; Klinka et al. 1998). Litter is the worst 5 surface for Douglas-fir regeneration because it does not allow the seeds to contact the moist underlying layer (Graham et al. 1990). Interior Douglas-fir is considered a moderately shade tolerant species, and some studies show that it grows better in large gaps, although the regeneration can be negatively influenced by increasing light (Daniel et al. 1979; Minore 1979; Steele and Geier-Hayes 1987, 1989; Gray et al. 1997). Available light depends on crown height, gap size, and stand density. In a study of coastal Douglas-fir, Van Pelt and Franklin (2000), found that there was a negative correlation between the understory location of trees and the canopy trees. They also found that canopy structure was not correlated with understory trees, but rather with the shrub-herb layer. They believed this happens because of the ability of understory trees to persevere while the canopy situation changes because of disturbance or growth. Aspect is another important factor that influences Douglas-fir regeneration. Douglas-fir regenerates best on north aspects and, although this depends on other site characteristics (Hatch and Lotan 1969; Ferguson and Carlson 1990). Interaction with understory species plays a crucial role in Douglas-fir regeneration. Understory species offer shade protection after germination, but they also provide competition for water and nutrients which could reduce Douglas-fir regeneration (Oliver and Larson 1996). A good example is pinegrass (Calamagostris rubescens Buckl ), which is one of the dominant understory species on IDF zonal sites (Lloyd et al. 1990). Pinegrass can reduce the amount of water in the first 10 cm of soil and it also can create an above-ground environment prone to air stagnation and frost (Nicholson 1989; Stathers 1989). Simard et al. (1997) found that Douglas- fir regeneration increased when pinegrass was removed from the understory. Del Moral and Gates (1971) found that Symphoricarpus, Sorbus, and Vaccinium shrubs interfere with Douglas- fir regeneration. Douglas-fir seed production varies widely from year to year. Usually every two to seven years abundant or medium cone crops occur (Lowry 1996). Air movement influences seed dissemination, causing to Douglas-fir seed to fall at a rate of 50 to 75 m per minute (Isaac 1943). Douglas-fir seeds usually fall within 300 m of the parent tree, but there are instances of seeds 6 found at 1500 m distances (Isaac 1943). Dispersal distance mostly depends on the density of the stand, wind regime, and the position of the tree in the stand; seeds from the trees in the centre of the stand are screened by neighbours while seeds from trees closer to stand edges are dispersed widely over open areas (Isaac 1943). On dry sites, it is important that Douglas-fir seed trees are in proximity to stand edges (i.e., openings) so that seedlings can find better environmental conditions (e.g., shade) for their establishment, while on moist sites the distance from the edge is not that important (Ryker 1975). Natural regeneration usually is clumped in the early stages, but spatial patterns can change over time leading to random or regular spacing (Oliver and Larson 1996; Moeur 1997). Patterns of regeneration in interior Douglas-fir have not been examined often. However, LeMay et al. (2009) noted that clusters of small trees and closer proximity than expected under a random distribution can be seen between large and small trees on sites with limited moisture. 1.4 Factors Affecting Height Growth of Small Trees Height growth of trees is affected by numerous variables that are closely connected to competition and micro-site variability; these operate together generating a relationship between trees and spatial proximity (Matern 1960 as cited by Kingman 1975; Reed and Burkhart 1985; Schoonderwoerd and Mohren 1987; Magnussen 1990, 1993, 1994; Liu and Burkhart 1994). Competition is a negative effect of one tree or group of trees on others reducing or limiting resource availability. Micro-site variation is related to the soil, topographic features and climate factors common to all the trees in the site (Matern 1960; Keddy 1989; Fox et al. 2001). As a general rule, trees grow best in moist, drained, and sandy clay loam soil, but other factors such as sunlight, water, certain mineral nutrients, suitable temperatures, oxygen, and carbon dioxide are important (Oliver and Larson 1996). Tree growth is affected by the location, climate, and soil; in suitable conditions these contribute to increasing the size and numbers of trees until some of the required factors are no longer available (Oliver and Larson 1996). Tree height versus time is represented by a sigmoidal curve, as is the case for growth features of many biological organisms. In the early stages, height growth is slow because a tree is too small to accumulate enough energy for rapid terminal growth. Energy for the terminal shoot becomes 7 more available when size and foliage increase. However, as time continues the growth rate begins to slow, caused by different factors such as extreme height, exposure, or crown size limits on the extension of the terminal (Oliver and Larson1996). Height growth is influenced by shading, tree vigor, elevation, climate change, and most importantly, by site quality. Site quality is related to the amount of resources available at a given location that are used by trees to reach their genetic height potential (Oliver and Larson 1996). Some studies show that height growth culminates sooner on better sites, and after reaching this point, it declines faster compared to poor sites (Kramer 1988, cited by Von Gadow and Hui 1999). Another factor that contributes to height growth rate is establishment time. For example, the pattern of establishment is believed to be the main factor for the stratification of northern hardwoods (Oliver and Larson 1996). Studies show that the pattern of establishment helps explain the differences between or within species height rates in a single cohort stand (Palik and Pregitzer 1991; Cobb et al. 1993). Height growth, especially of young Douglas-fir, also depends on the understory vegetation (Oliver and Larson 1996). Competing vegetation has been found to significantly impact height growth in several studies (e.g., Clinton et al. 1994; Oliver and Larson 1996). Also, overstory competition has an impact on small tree height growth of Douglas-fir; height growth may increase quickly when Douglas-fir is released from overstory competition (Helms and Standiford 1985; Korpela et al. 1992). Helms and Standiford in 1985 found that prediction of post-release is based on three factors: 1) live crown ratio at the time of the release; 2) annual height growth trend for the years prior (whether it was decelerating, constant or accelerating); and 3) the pre-release height growth (cited by Lencar 2002). Different tree species have different height growth patterns (Carmean 1970). Genetics usually define the characteristics of a species, including its height growth potential. Height growth patterns can vary among the ‗social‘ positions in which a tree may be found. Understory height growth can have cyclical patterns, fluctuating from high rates to low rates as the trees are cyclically released and then suppressed in temporary overstory gaps. In fact, in unsuppressed growth periods, the growth rate of a small tree previously suppressed can be the same as a dominant tree (Oliver and Larson 1996). Interior Douglas-fir can persist under suppression and 8 it can resume height growth after release (Krauch 1956). One study of uneven-aged interior Douglas-fir in the Merrit Timber Supply Area, demonstrated that the height development included periods of both suppression and release (J.S. Thrower & Associates Ltd. 1997). Shade-tolerant species, like interior Douglas-fir, can develop different mechanisms such as favouring lateral branches to increase the horizontal display of needles and braches (Parent and Messier 1995; Beaudet and Messier 1998). This strategy allows them to survive in shady conditions compared to shade-intolerant species that normally have less plasticity (Beaudet and Messier 1998; Williams et al. 1999). Some studies suggest using absolute height growth as an indicator of the shade-tolerance of species (Crawford et al. 1982; Parent and Messier 1995; Murphy et al. 1999). Stand composition may also influence height growth. Some species have more growth potential on particular sites than other species and can compete more effectively (Cobb et al. 1993). Some studies (e.g., Kneeshaw and Bergeron 1996; LePage 1997) found that some combinations of species have more positive effects on growth rate than others. 9 2.0 METHODS 2.1 Study Area This research took place in the Knife Creek Block of the University of British Columbia‘s Alex Fraser Research Forest (Figure 1). The Knife Creek Block is situated on the Fraser plateau near Williams Lake, British Columbia, in the Southern Interior Region of the province (BC Ministry of Forests 1995). This block mainly falls within the IDF dk3 (Interior Douglas-fir dry cool) BEC subzone (Hope et al. 1991). The Fraser plateau has an elevation of approximately 1000 m, with a range between 900-1500 m. Interior Douglas-fir is the dominant species present on the study area. Other tree species include lodgepole pine, interior spruce, white (paper) birch (Betula papyrifera Marsh.) and trembling aspen. Figure 1. Study site location. 10 2.2 Study Design The permanent sample plots used to provide overstory information in this study were established in the summer of 1989. The plots are located in three, approximately 40 ha blocks (B, C and D). There are eight plots in each block for a total of 24 plots (Figure 2). Block B is quite dry, Block C is more mesic, and Block D is the moistest of the sites and is transitional to the Sub-Boreal Spruce (SBS) BEC zone. Block B is comprised of more than 90% Douglas-fir by basal area per hectare. Douglas-fir is also dominant on Block C; however, lodgepole pine and white birch are more common than on Block B. On Block D, Douglas-fir is dominant in most plots, but lodgepole pine, interior spruce, white birch, and small components of trembling aspen are also present in varying amounts. All the plots are dominated by small to mid-size trees (< 25 cm diameter at breast height – DBH) with few larger trees present because of removal by diameter limit logging in the 1950s and 1960s. Figure 2. Block and plot locations at Knife Creek. B, C and D are the block names. D C B 11 Three pre-commercial thinning (spacing) treatments and a control were randomly assigned to one-quarter of each block. Within each of these quarters, two 0.05 ha plots were established prior to treatment in locations deemed to be uniformly dense. Every plot has a buffer perimeter of 5 m established around it. Only trees with a DBH (diameter breast height) greater than 10 cm were tagged and measured in this buffer. Inside the plot, all trees taller than 1.3 m (i.e., with a DBH) were tagged and measured and species was recorded. These plots were established in the summer of 1989; in the next summer, the DBH of all trees greater than 1.3 m in height were measured and species was recorded to establish a pre-treatment baseline. The thinning treatments were applied in the fall and winter of 1990/91. In 1993, the plots were re-established, and boundary and plot trees were tagged and stem-mapped. Boundary trees were measured for DBH and total height; plot trees were measured for total height (m), DBH (cm), crown width (m; two directions), crown length (m; for each of four quarters) and vigor (code). Plots were measured again in 1997, 2004 and 2008. The three pre-commercial thinning treatments applied were: (1) a standard uniform spacing treatment common in the late 1980s and early 1990s (S); (2) 3 m clumped spacing (C1); and (3) 5 m clumped spacing (C2). Control areas (U) without thinning were retained to provide a basis for comparison. The three pre-commercial thinning treatments are briefly described below (from Marshall 1996): 1. Standard Spacing (S): the standard spacing treatment of the BC Ministry of Forests in the early 1990s. Instructions were to leave 0.75 m between Douglas-fir and spruce trees less than 12.5 cm DBH, and 2.5 or 2.8 m for smaller trees of other species. Further: - Any healthy Douglas-fir and spruce trees greater than 25 cm DBH were left standing in any treatment area. - Any Douglas-fir and spruce tree was left standing if it was between 12 and 25 cm DBH and at least 0.75 m from its neighbors. - Douglas-fir tree less than 12 cm DBH were left standing if they were at least 0.75 m from their neighbors and no crown competition was present. - Spacing for other tree species less than 12 cm DBH depended on the species. Lodgepole pine was spaced to an average distance of 2.8 m, with an allowed 12 distance of 1.5 m to 4.0 m. Other species were spaced to an average distance of 2.5 m with a variation of 1.5 m to 3.5 m. - Spacing around the edges of openings with a diameter of 5 m or greater was reduced to the minimum spacing. - Douglas-fir and spruce trees less than 1.0 m in height and lodgepole pine less than 0.5 m in height were retained. 2. 3 m (C1) and 5 m (C2) clumped spacings: As the name of these treatments imply, removals were used to create clumps of trees 3 or 5 m apart. Each clump contained a group of three to nine trees of the same height class within a 3 m circle. The height classes considered were: (1) 1-3 m; (2) 3-7 m; (3) 7-15 m; and (4) greater than 15 m. Further rules were: - The height class of the clump was chosen according to the height class of the healthiest trees present before spacing; - Douglas-fir, spruce, and lodgepole pine trees that were shorter than 1.0 m within a clump were left standing, as were trees larger than 25 cm DBH in any location. - In a clump, the preferred distance between trees was 2.1 m but a distance of 0.5 m to 2.5 m was allowed in order to include seven trees on average in each clump. - As long as there was no crown competition, trees of a greater height class than the main clump were left standing. - Any deciduous tree in crown completion with coniferous trees was cut or girdled. - Clumps with a height difference of at least 3 m could be left immediately adjacent to each other. - Coniferous trees with a DBH over 25 cm and any deciduous trees outside of clumps that did not affect the inter-clump distance were left. Two sets of 2.07 m radius permanent regeneration subplots were established in each of the 24 overstory plots in the 2008 field season: a systematic set and a selected set. The systematic set was comprised of four subplots established along the centre axis of each of the overstory plots at 6.3 m intervals (96 subplots in total (Figure 3). The selected set was comprised of two additional regeneration subplots located in areas of each of the overstory plots where regeneration was present or where it was deemed that conditions were apparently good for future regeneration (48 subplots in total). 13 The centre of each regeneration plot was marked with a painted railway spike, for easier relocation. To map the exact location of the regeneration plot centre, distance and bearing from the subplot centre to three surrounding large trees were recorded. The three large trees were recorded (number, species) and painted at the base in the direction of the regeneration plot centre. Figure 3. Layout of the 0.001346 ha (2.07 m radius) systematic regeneration subplots on the 0.05 ha overstory plots. 2.3 Measurements Two different sets of measurements were taken in the spring/summer of 2008: (1) all trees inside the established PSPs (i.e., the overstory plots) were re-measured for DBH, height, crown width and length, and vigor; and (2) counts of germinants and seedlings by species, three-year height growth of the two best seedlings, and ground cover were measured on each of the regeneration subplots. The measurements made on the overstory plots were made using the methods and protocols described in Marshall (1996) and Bugnot (1999) and not presented here. 14 In each regeneration subplot, seedlings 2 less than 2 cm DBH and greater than 15 cm of height were recorded by species and divided in four height classes: (1) > 0.15 to ≤ 0.50 m; (2) > 0.50 m to ≤ 1.00 m; (3) > 1.00 m to ≤ 1.30 m; and (4) > 1.30 m and < 2.0 cm DBH. Additionally, the number of germinants (trees less than 15 cm in height) were recorded by species. Total height and height growth over the last three years were recorded for the two best 3 seedlings on each subplot. Finally, percentage ground cover to the nearest 10% was recorded in each of the following classes: (1) crown litter; (2) bare mineral soil; (3) grass/herbs; (4) moss; (5) rotten wood; (6) woody debris; (7) live wood; and (8) rock. The percentages recorded were constrained to add to 100%. 2.4 Analyses For testing the differences in quantity of regeneration among treatments, analysis of variance (ANOVA) was applied assuming a randomized complete block design with three blocks and four treatments. The model was: where Y is the number of germinants or seedlings, µ is the overall population mean, β is the effect of the blocks and τ is the effects of the treatments, is the interaction between blocks and treatments, ε1 is the error of the experimental unit and ε2 is the error within the experimental unit. The three main assumptions of this analysis are: the variables in each treatment are normally distributed, the variances are homogeneous, and the observations are independent of one another. Bonferroni‘s test was used to perform comparisons among the treatments and the blocks. The function ‗PROC MIXED‘ in SAS/STAT was used to perform this analysis. Several mixed models were used to estimate the average number of seedlings per overstory plot (i.e., the average of four subplots — ) and the average number of germinants in each subplot ( ), with stems per hectare (sph), quadratic mean diameter (qmd), basal area (baha), average % of moss in the subplot (avemoss), average % of grass and herbs in the subplot (avegrassherb), average % of litter in the subplot (avelitter), basal area within a 3 m radius from the centre of the subplot (baha3meters), and basal area within a 5 m radius from the centre of 2 Seedlings were defined as any tree in this size class, whether they originated from seeds or from suckers. 3 Subjectively assessed as healthiest (i.e., most likely to survive) conifer seedlings present. 15 the subplot (baha5meters) as fixed effects, with Blocks (B, C, and D) as a random effect. The thinning treatment was not explicitly used as a variable since sph, qmd, and baha implicitly defined the residual overstory. Three groups of explanatory variables were used: (1) plot scale variables (i.e., stems/ha, quadratic mean diameter and basal area); (2) ground cover variables; and (3) local competition variables (i.e., basal area within 3 and 5 m radius of the subplot centre). Stand density index was not used because of its high correlation with the other plot scale variables. For each response variable, four different models were run: one with all of the variables and three leaving out one set of variables. For each model, the variables that were not significant within each variable group (significance level (α) of 0.05) were removed to obtain a more parsimonious model. All models were fitted using SAS/STAT (PROC MIXED) 9.1. The model fitted was: where sph, qmd, baha, avemoss, avegrassherb, avelitter, baha3meters, and baha5meters are as previously defined. Zero-inflated models are used when count data have an excess of zeros (Lambert 1992; Greene 1994). These models assume that the data are a combination of two different data process: one generates only zeros and the other generates values greater than according to some assumed distribution (Erdman et al. 2008). In this study, a zero-inflated Poisson model was used to predict the number of seedlings and germinants present on each subplot (many of which have values of zero). For counts greater than zero, the probability of an event count yi, given the vector of covariates xi, was assumed to follow the Poisson distribution: ,...3,2,1,0 , ! )(   y y ue XYP i y i u ii ii A Poisson regression model has the same requirements as any other regression model: data equally dispersed and constant variance; however, it differs in other requirements such as the equality of conditional variance and mean and normality is not required. Clearly, if the lack of fit of a model can be attributed to the large amount of zeros, the zero-inflated model can provide a better form of error distribution (Affleck, 2006). The general model is: 16 where g is the link function used to make the function linear, β is a vector of coefficients, and ε1, ε2, and ε3 are error terms. The fixed effect portion of the model was: ) where sph, qmd, baha, avemoss, avegrassherb, avelitter, baha3meters and baha5meters are as previously defined, but are the values for each subplot rather than averaged over each overstory plot, and and are the estimated numbers of seedlings and germinants, respectively, on each subplot. Classification and Regression Trees (CART) is a powerful statistical method for understanding and dealing with incomplete data. CART is based on binary questions about different features at each node of the classification tree. Each node may be a number or a probability density function. CART is based on a ‗greedy‘ algorithm that chooses the best discriminatory feature for each step of the process (Breiman et al. 1984). For this thesis, CART was used to better understand the difference between regeneration subplots with regeneration versus those without regeneration on the basis of ground cover and local density conditions. Lodgepole pine existed in varying amounts in the overstory plots in Blocks C and D at the onset of the precommercial thinning experiment. Many of these trees were killed by mountain pine beetle over the last several years. To determine whether this mortality influenced regeneration quantity and quality, thinned plots in these blocks were ranked from most to least mortality, by block, and the quantity of seedlings and germinants, and the three-year height growth of seedlings on each plot were compared 4 to the average for these variables on the thinned plots in the block. If lodgepole pine mortality had a positive influence on regeneration quantity and quality, one would expect to see the thinned plots with higher levels of mortality showing higher amounts of regeneration and better seedling height growth compared to block averages. 4 The seedlings and germinants in a thinned plot were compared to the average for these values among all of the thinned plots in its block (either C or D) and assessed as to whether they fell above (positive deviation) or below (negative deviation) the average. 17 3.0 RESULTS 3.1 Overstory Growth The 24 plots in the pre-commercial thinning study were measured several times subsequent to the thinning treatments: following the 1992, 1996, 2003, and 2007 growing seasons. Following the 2003 growing season, the control plots had the highest density (Appendix 1, Table 11). This remained the case following the 2007 growing season (Appendix 1, Table 12). The overstory summary following the 2007 growing season does not include lodgepole pine trees that were killed between 2002 and 2007 by mountain pine beetle. Consequently, the change in conditions between the end of the 2003 growing season and the end of the 2007 growing season (Table 1) reflects both normal mortality and growth, as well as the impact of the mountain pine beetle killing much of the lodgepole pine that was present, primarily on Blocks C and D. Lodgepole pine was quite rare in Block B. The control plots showed the greatest mortality from 2003 to 2007, with 547 trees per ha less on average (Table 1). The largest mortality for the control plots was found on Block B, which had by far the largest number of trees at the beginning of this growth period. This was despite the fact that there are quite dry conditions present on this block and, consequently, few lodgepole pine present to contribute to the mortality. For the thinned plots, the largest decrease in the numbers of trees were found in Blocks C and D, primarily due to the mortality of many of the lodgepole pine originally present on these plots. The average basal area per tree by treatment and year combined across the blocks is shown in Figure 4. Only trees that were alive from the first measurement to the final measurement are included in these averages. The 5 m clumped spacing (C2) showed the greatest increase in basal area per tree, while the control (U) had the lowest increase due to its higher initial density, as expected. The 3 m clumped spacing (C1) and the standard spacing (S) started with approximately the same average basal area per tree as the 5 m clumped spacing, but have fallen behind that treatment over time. 18 Table 1. Change in overstory conditions between the end of the 2003 growing season and the end of the 2007 growing season. Treatment Block Plot Density (stems/ha) Basal Area (m 2 /ha) QMD 1 (cm) Control (U) B 3 -1,000 0.4 0.6 4 -660 0.6 0.5 D 15 -640 -3.0 0.3 16 -500 -0.9 0.9 C 19 -220 1.7 0.4 20 -260 0.1 0.4 mean -547 -0.2 0.5 3 m Clumped spacing (C1) B 1 0 3.5 0.8 2 -60 1.9 0.7 D 11 -180 -0.1 0.7 12 -80 2.2 0.7 C 21 -340 -0.9 1.0 22 -800 -3.2 1.6 mean -256 0.6 0.9 5 m Clumped spacing (C2) B 5 -320 1.8 1.7 6 0 2.2 0.6 D 13 -220 -1.0 1.1 14 -560 -13.4 -0.9 C 17 -300 -1.0 1.9 18 -420 1.1 2.8 mean -304 -2.9 1.0 Standard spacing (S) B 7 0 2.8 0.6 8 -100 -2.0 0.0 D 9 -320 -4.7 0.1 10 -120 -0.1 0.5 C 23 -480 -4.1 1.4 24 -180 -3.9 0.0 mean -200 -2.0 0.4 1 QMD is the quadratic mean diameter 19 Figure 4. Average tree basal area by treatment through time. Figures 5 and 6 show stems per ha versus DBH class by treatment and by block, respectively, based on the 2008 measurements. The DBH classes in these figures are in 5 cm intervals (i.e., Class 1 is from 0.1 to 5 cm, Class 2 is from 5.1 to 10.0 cm, etc.). Not surprisingly, the control plots (U) had the largest number of small trees (Figure 5). The numbers of trees in DBH class 4 and higher (> 15 cm DBH) were quite similar among all treatments and blocks. Almost all of the trees removed by the pre-commercial thinning treatment were less than 15 cm DBH; the great majority were less than 10 cm DBH (Marshall and Wang 1996). Block B had the most trees; the majority of those trees are in DBH classes 2 and 3. 3.2 Ground Cover on the Regeneration Subplots The ground cover was different among treatments (Figure 7). The 5 m clumped spacing (C2) was dominated by grass and herbs, while the 3 m clumped spacing (C1) and the standard spacing (S) were dominated by moss. The control plots were dominated by crown litter and moss. The proportion of the regeneration subplots comprised of rotten wood and woody debris was relatively constant among treatments. 0 0.005 0.01 0.015 0.02 0.025 1993 1997 2004 2008 B a s a l A re a p e r tr e e ( m 2 ) Year C1 C2 S U 20 Figure 5. Stems/ha in each treatment by 5 cm DBH class (2008 measurements). Figure 6. Stems/ha in each block by 5 cm DBH class (2008 measurements). 3.3 Regeneration Quantity Douglas-fir was the most prevalent species of germinant, by far, for each treatment (Table 2) and block (Table 3) accounting for 95% of the total count. Block B had the lowest numbers of germinants, and only germinants of Douglas-fir; this is likely due to the quite dry conditions in this block. Block D contained 90% of the lodgepole pine and spruce germinants that were found, likely due to the moister conditions. In general, the fewest germinants were found in the control plots. There was no consistent pattern for the quantity of germinants among the treated plots across the blocks, although the most germinants were found in the 5 m clumped and the standard spacings. There were no significant differences in germinant quantity among treatments, although the control (which had the densest conditions) averaged the lowest number 0 500 1000 1500 2000 2500 1 2 3 4 5 6 7 8 9 10 11 12 S te m s /h a Dbh class U C1 C2 S 0 2000 4000 6000 8000 10000 12000 1 2 3 4 5 6 7 8 9 10 11 12 S te m s /h a Dbh class B C D 21 per ha, and the 5 m clumped spacing (which had the least dense conditions) averaged the largest number per ha. Figure 7. Ground composition in the systematic regeneration plots by treatment. Table 2. Germinants per ha by species and treatment. Species 1 Treatment 2 At Ep Fd Pl Sx Total % C1 0 557 27764 0 371 28692 21 C2 0 371 39093 1764 2602 43830 33 S 0 0 44200 0 650 44850 34 U 0 0 14950 0 929 15879 12 Means 0 232 31502 441 1138 % 0 0.5 95 1.5 3 1 Species codes used are identical to the BC Ministry of Forests and Range forest inventory codes: At = trembling aspen; Ep = white birch; Fd = Douglas-fir; Pl = lodgepole pine; Sx = interior spruce. 2 C1= 3 m clumped spacing; C2= 5m clumped spacing; S= standard spacing; U= unthinned (control) Many fewer seedlings were present than germinants (Tables 4 and 5). Douglas-fir was the most prevalent seedling species followed by aspen, and was mostly present on the clumped spacings. The 3 m clumped spacing had the fewest seedlings present and 5 m clumped spacing (C2) had the most. The 5 m clumped spacing also had the most diversity of species present. There were few apparent differences in the total number of seedlings among treatments, primarily due to the 0.00 10.00 20.00 30.00 40.00 50.00 C1 C2 S U % Treatment crown litter bare mineral soil grass/herb moss rotten wood woody debris 22 relatively large number of deciduous seedlings in the control plots. The great majority of the conifer seedlings were located in plots that had been thinned and the average number of seedlings on the control plots was significantly lower than on the thinned plots. Table 3.Germinants per ha by treatment and block. Treatment 1 B C D Means % C1 2 557 17643 10493 9564 22 C2 464 6036 37329 14610 33 S 1486 10586 32779 14950 34 U 186 2136 13557 5293 12 Means 673 9100 23539 % 2 27 71 1 The ANOVA analysis revealed no difference among treatments (p=0.539). 2 C1= 3 m clumped spacing; C2= 5m clumped spacing; S= standard spacing; U= unthinned (control) Table 4. Seedlings per ha by species and treatment. Species 1 Treatment 2 At Ep Fd Pl Sx Mean % C1 0 186 928 0 0 1114 14 C2 557 557 1021 93 836 3064 38 S 93 279 743 371 0 1486 18 U 1764 557 93 0 0 2414 30 Means 603 395 696 116 209 % 30 20 34 6 10 1 Species codes used are identical to the BC Ministry of Forests and Range forest inventory codes: At = trembling aspen; Ep = white birch; Fd = Douglas-fir; Pl = lodgepole pine; Sx = interior spruce. 2 C1= 3 m clumped spacing; C2= 5m clumped spacing; S= standard spacing; U= unthinned (control) Table 5. Seedlings per ha by treatment and block. Treatment 1 B C D Means % C1 2 186 929 0 372 14 C2 0 1021 2043 1021 38 S 0 1207 279 495 18 U 0 0 2414 805 30 Means 47 789 1184 % 2 39 59 1 C1= 3 m clumped spacing; C2= 5m clumped spacing; S= standard spacing; U= unthinned (control) 2 The ANOVA analysis revealed no difference among treatments (p=0.253). 23 Most seedlings were in height class 1 (0.15 to 0.50 m in height) and numbers of seedlings declined with increasing height class (Figure 8). The 5 m clumped thinning and the control had considerably more height class 1 seedlings than the other two treatments; in the case of the control, most of these seedlings were deciduous. There were relatively even numbers of seedlings among treatments for the other three height classes. Figure 8. Seedlings per ha height class and treatment. 1 H1 is height class 1 (> 0.15 to 0.50 m); H2 is height class 2 (> 0.50 to 1.00 m); H3 is height class 3( > 1.00 to 1.30 m); H4 is height class 4 (> 1.3 m and less than 2.0 cm dbh) 3.4 Predicting the Presence of Germinants and Seedlings There appeared to be no difference in the ground composition of plots with and without germinants in Block B. In the control plots in Block B, germinants were mostly found in subplots with live wood in them. Block C seemed to follow a different pattern; germinants were primarily found in subplots with no live trees or woody debris. In block D, all subplots seemed to have similar ground composition. As expected, the ground composition for the selected regeneration subplots (located where seedlings were present or where conditions looked good if no seedlings were present) followed similar patterns as the systematically located regeneration subplots. Grass and herbs coverage 0 200 400 600 800 1000 1200 1400 1600 1800 H1 H2 H3 H4 S ee d li n g s/ h a Height class1 C1 C2 S U 24 was prevalent in the 5 m clumped treatment and moss was prevalent in all the other treatments (Figure 9). Figure 9. Average ground composition of the selected regeneration subplots by treatment. 1 C1 represents the 3 m clumped spacing; C2 represents the 5 m clumped spacing, S represents the standard spacing and U represents the control. According to the CART analysis, the best ground cover variable for predicting the presence of germinants was grass and herb coverage (Figure 10). If the grass and herb cover was less than 15% and the crown litter cover was less than 65%, no germinants were predicted to be present. If the grass and herb layer was more than 15%, the presence of germinants followed a more complicated predictive structure. 0.00 10.00 20.00 30.00 40.00 50.00 60.00 C1 C2 S U % Treatment1 crown litter grass/herb moss rotten wood woody debris live wood 25 Figure 10. CART analysis for predicting the presence of germinants 1 . 1 G_H is grass and herb; CL is crown litter; Moss is moss. The best ground cover variables for predicting the presence of seedlings on a regeneration subplot were crown litter and grass and herbs coverage (Figure 11). If crown litter covered less than 25% of the subplot and grass and herbs covered more than 15%, seedlings were predicted to be present. Although only ground cover variables were helpful for predicting the presence of germinants, for seedlings, the basal area within 3 m of the subplot centre was also helpful. 26 Figure 11. CART results for predicting the presence of seedlings 1 . 1 G_H is grass and herb; CL is crown litter; BA3A_10 is basal area larger trees (at least 10cm dbh) within a 3 m radius of the subplot centre. 3.5 Predicting the Number of Germinants and Seedlings Present Plot variables such as stems per ha, quadratic mean diameter, and basal area per plot influenced the prediction of the number of germinants per ha (smaller -2 log likelihood, Table 6). The lowest AIC was found for the model with the basal area per ha of the entire plot and the basal area within 3 m radius from the centre of the subplot included. The lowest -2 log likelihood was found for the model containing all of the variables (Model 1). 27 Table 6. P-values for fixed effect variables and goodness of fit for predicting the average number of germinants in a plot. PLOT VARIABLES REGENERATION VARIABLES COMPETITION VARIABLES GOODNESS OF FIT SPH QM D BAHALO G MOS S GRASS/HER B LITTE R BA3ALOG BA5ALOG - 2 LOG L. AIC MODEL 1: 1A 0.21 9 0.16 0.158 0.632 0.919 0.604 0.701 0.4058 195.8 217.8 1B 0.20 3 0.154 0.148 0.275 0.484 0.681 0.409 195.8 215.8 1C 0.16 7 0.116 0.145 0.269 0.475 0.05 195.9 213.9 MODEL 2: 2A 0.851 0.613 0.394 0.779 0.479 201.6 217.6 2B 0.5 0.185 0.793 0.482 201.6 215.6 2C 0.521 0.191 0.324 201.7 213.7 MODEL 3: 3A 0.25 9 0.151 0.149 0.64 0.664 196.8 212.8 3B 0.28 5 0.171 0.118 0.096 197 211 3C 0.345 0.069 0.073 198.2 210.2 0.032 0.096 199 209 MODEL 4: 4A 0.22 8 0.195 0.538 0.907 0.816 0.953 199.7 217.7 4B 0.22 6 0.189 0.538 0.846 0.722 199.7 215.7 4C 0.21 9 0.169 0.539 0.701 199.8 213.8 The best mixed linear models (lowest -2 log likelihoods) for predicting the average number of seedlings per plot included variables for each level (Model 1A-1C) (Table 7). However, the lowest AIC was found for the model using moss cover, crown litter cover, and all the basal area variables. The plot-level variables were generally not helpful for predicting the average number of seedlings in a plot. The inflation probability was not significant (p = 0.08) for the zero-inflated models for predicting the number of germinants per regeneration subplot (Table 8). The models had inflation probabilities that ranged from 0.139 to 0.141. The zero-inflated model did not perform well, likely because there were relatively few zero values. However, the models showed a lower likelihood (i.e., higher -2log likelihood) when the plot variables were not included, meaning that they were important for predicting the number of germinants present. As was noted for the mixed linear models, better likelihood was given by the models containing all the variables. 28 Table 7. P-values for fixed effect variables and goodness of fit for predicting the average number of seedlings in a plot. PLOT VARIABLES REGENERATION VARIABLES COMPETITION VARIABLES GOODNESS OF FIT SPH QM D BAHALO G MOS S GRASS/HER B LITTE R BA3ALOG BA5ALOG -2 LOG L. AIC MODEL 1: 1A 0.53 6 0.356 0.27 0.251 0.325 0.062 0.0035 0.0012 63.8 85.8 1B 0.412 0.217 0.196 0.38 0.061 0.025 0.009 64.2 84.2 1C 0.13 0.232 0.322 0.087 0.002 0.0005 64.9 82.9 MODEL 2: 2A 0.159 0.506 0.116 0.005 0.001 67.2 83.2 2B 0.0017 0.021 0.006 0.001 67.7 81.7 MODEL 3: 3A 0.57 7 0.558 0.617 0.048 0.09 75.5 91.5 3B 0.49 6 0.42 0.046 0.093 75.7 89.7 3C 0.651 0.042 0.093 76.1 88.1 MODEL 4: 4A 0.40 8 0.179 0.91 0.328 0.928 0.189 75.3 93.3 4B 0.41 1 0.17 0.326 0.948 0.183 75.3 91.3 4C 0.156 0.2 0.764 0.182 76 90 Table 8. P-values for the fixed effect variables and goodness of fit of the zero-inflated Poisson models for the number of germinants per subplot. PLOT VARIABLES COMPETITION VARIABLES REGENERATION VARIABLES GOODNESS OF FIT BAH A SPH QM D BA3A BA5A MOS S GRASS/HER B LITTE R -2 LOG AIC MODEL 1: 1A 0.03 0.28 3 0.03 0.0097 0.0099 0.026 0.008 0.09 1381 1403 1B 0.03 0.013 0.0098 0.01 0.022 0.008 0.10 1383 1403 MODEL 2: 2A 0.01 0.052 0.027 0.018 0.04 1571.1 1587.1 2B 0.01 0.029 0.016 0.03 1588.5 1602.5 MODEL 3: 3A 0.031 0.32 2 0.018 0.028 0.01 0.038 1507.4 1524.6 3B 0.029 0.012 0.028 0.01 0.034 1537.4 1551.3 MODEL 4: 4A 0.032 0.33 0.02 0.009 0.009 1446.2 1484 4B 0.027 0.019 0.009 0.009 1467.3 1506 29 The inflation probability for predicting the number of seedlings ranged from 0.64 to 0.66, significant for all models (p = 0.008), with a standard error of 0.06 (Table 9). The models were slightly worse without the ground cover variables, indicating that these variables were important for predicting the number of seedlings present. There were many more subplots with zero seedlings than there were subplots with zero germinants. Table 9. P-values levels for the fixed effect variables and model fits of the zero-inflated Poisson models for the number of seedlings per subplot. PLOT VARIABLES COMPETITION VARIABLES REGENERATION VARIABLES GOODNESS OF FIT BAHA SPH QMD BA3A BA5A MOS S GRASS/HER B LITTE R -2 LOG AIC MODEL 1: 1A 0.48 0.31 0.64 0.41 0.22 0.21 0.91 0.42 200.5 222.5 1B 0.73 0.28 0.88 0.31 0.12 0.18 0.29 220.1 220.1 1C 0.74 0.22 0.31 0.09 0.16 0.29 200.2 218.2 1D 0.08 0.29 0.09 0.11 0.25 200.3 216.3 1E 0.09 0.29 0.08 0.07 202.6 216.6 1F 0.07 0.09 0.05 204.7 216.7 MODEL 2: 2A 0.18 0.14 0.11 0.32 0.25 207.9 229.9 2B 0.18 0.1 0.12 0.44 209.8 231.8 2C 0.15 0.11 0.13 210.7 232.7 MODEL 3: 3A 0.36 0.13 0.24 0.12 0.28 0.57 206.4 228.4 3B 0.36 0.16 0.28 0.06 0.14 206.9 228.9 3C 0.18 0.37 0.05 0.1 207.6 229.6 3D 0.1 0.05 0.12 209.3 231.3 MODEL 4: 4A 0.31 0.88 0.51 0.2 0.12 211.9 233.9 4B 0.21 0.5 0.17 0.11 211.7 233.7 4C 0.18 0.17 0.09 212.4 234.4 4D 0.12 0.09 216.3 238.3 3.6 Three-year Height Growth of Seedlings There were no significant differences in the three-year height growth of conifer seedlings among treatments (Table 10). However, the number of seedlings measured was small, especially in the 30 control plots where there was only one conifer seedling measured for height growth due to the scarcity of conifer seedlings present on the controls. Table 10. Three-year growth (m) for conifer seedlings by treatment. Treatment Mean 1 N U 0.51 1 C2 0.49 9 S 0.48 5 C1 0.29 4 1 The ANOVA analysis revealed no difference among treatments (p=0.251). Figures 12 and 13 show the average three-year height growth by treatment and species in Blocks C and D. Note that only those combinations of species and treatments where seedlings were present are shown. The small number of seedlings present for each species makes any extrapolation from these results tenuous. Figure 14 shows the average three-year height growth for all seedlings by treatment. The control plots had the highest average height growth. This apparently anomalous result is due to the relatively large number of broadleaf seedlings, many of them suckers, present on the control plots in Block D. Figure 12. Three-year height growth (m) of seedlings by species 1 and treatments 2 for Block C. 1 EP is paper birch; Fd is Douglas-fir; Pl is lodgepole pine. 2 C1 is the 3 m clumped spacing; C2 is the 5 m clumped spacing; S is the standard spacing; and U is the control. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Ep Fd Fd Fd Pl C1 C1 C2 S S 3-Year Height Growth (m) Species/treatment 31 Figure 13. Three-year height growth of seedlings by species 1 and treatments 2 for Block D. 1 Ep is paper birch; Fd is Douglas-fir; Sx is interior spruce; At is aspen. 2 C1 is the 3 m clumped spacing; C2 is the 5 m clumped spacing; S is the standard spacing; and U is the control. Figure 14. Three-year height growth of seedlings by treatment 1 . 1 C1 is the 3 m clumped spacing; C2 is the 5 m clumped spacing; S is the standard spacing; and U is the control. 0 0.5 1 1.5 2 2.5 Ep Fd Sx At Ep At Ep Fd C2 C2 C2 S S U U U 3-Year Height Growth (m) Species/treatment 0 0.2 0.4 0.6 0.8 1 1.2 1.4 C1 C2 S U 3-Year Height Growth (m) Treatment 32 3.7 Possible Response of Regeneration to Lodgepole Pine Mortality Precommercially thinned plots in Blocks C and D (i.e., the blocks that had lodgepole pine present) are ordered from most to least mortality, expressed as stems per ha, by block, in Table 11. There does not appear to be any trend between the amount of mortality that occurred and the number of germinants per ha. However, there does appear to be a trend with the number of seedlings per ha. In Block C, the three plots that had the most lodgepole pine mortality also had more than the average number of total number of seedlings for thinned plots in that block and more than the average number of conifer seedlings, while the three plots with the lowest mortality had fewer seedlings than average (p = 0.0156 under the null hypothesis of equal likelihood of a plot being above or below the mean). However, if the plots are ordered on the basis of mortality expressed as basal area per ha, there is not as strong of relationship (the signs for differences for 4 out of the 6 plots are correct). On Block D, the only two plots with conifer seedlings present were among the three plots with the highest lodgepole pine mortality. Table 11. Regeneration levels in the pre-commercially thinned plots in Blocks C and D, expressed as deviations from the mean level for the block, ordered from highest to lowest mortality, in terms of stems per ha, by block. Plo t Bloc k Treat. Mortality of lodgepole pine (and total mortality) (stems/ha) Mortality of lodgepole pine (and total mortality) (BA/ha m2) Total Germinants per ha Conifer Germinants per Ha Difference from Average for Total Germinants Total Seedlings per ha Conifer Seedlings Per ha Difference from Average for Total Seedlings 22 C C1 360 (800) 4.81 (4.88) 2552 2459 +655 186 139 +74 23 C S 360 (480) 4.87 (5.00) 278 209 -1526 162 255 +50 18 C C2 140 (420) 2.45 (2.52) 626 626 -1178 186 139 +74 21 C C1 260 (340) 6.41 (6.53) 1962 1859 +55 46 46 -66 17 C C2 180 (300) 3.11 (3.22) 881 881 -923 70 23 -42 24 C S 180 (180) 3.07 (3.07) 4524 4524 +2720 23 23 -89 14 D C2 560 (560) 12.94 (12.94) 4246 4153 +851 326 139 +229 9 D S 320 (320) 5.26 (5.26) 1183 1183 -2212 23 0 -73 13 D C2 220 (220) 4.38 (4.38) 5081 5081 +1686 186 186 +89 11 D C1 180 (180) 2.95 (2.95) 1183 1183 -2212 0 0 -97 10 D S 120 (120) 3.52 (3.52) 7238 7238 +3843 46 0 -51 12 D C1 0 (80) 0 (1.31) 1438 1438 -1957 0 0 -97 33 A similar analysis was done for the three-year height growth of seedlings (Table 12). There was weak evidence that there was some correlation between higher mortality, expressed as stems per ha, and higher three-year height growth on Block C (p=0.0781). There appeared to be no relationship if mortality levels were ranked according to basal area. Only two plots on Block D had measured conifer height growth and the higher growth rate occurred on the plot with the lower mortality level. Overall, these results do not support the conjecture that higher seedling quality (as reflected by higher height growth) is evident on the thinned plots with higher mortality at this point in time. Table 12 Three-year height growth of conifer seedlings in the pre-commercially thinned plots in blocks C and D, expressed as deviations from the mean level for the block, ordered from highest to lowest mortality in terms of stems per ha, by block. Plot Block Treatment Mortality of lodgepole pine (and total mortality) (trees/ha) Mortality of lodgepole pine (and total mortality) (BA/ha) Three-year height growth (m) Difference from block average three-year height growth (m) 22 C C1 360 (800) 4.81 (4.88) 0.33 -0.11 23 C S 360 (480) 4.87 (5.00) 0.57 +0.13 18 C C2 140 (420) 2.45 (2.52) 0.63 +0.19 21 C C1 260 (340) 6.41 (6.53) 0.21 -0.23 17 C C2 180 (300) 3.11 (3.22) 0.24 -0.20 24 C S 180 (180) 3.07 (3.07) 0.18 -0.78 14 D C2 560 (560) 12.94 (12.94) 0.39 -0.07 9 D S 320 (320) 5.26 (5.26) - - 13 D C2 220 (220) 4.38 (4.38) 0.56 +0.10 11 D C1 180 (180) 2.95 (2.95) - - 10 D S 120 (120) 3.52 (3.52) - - 12 D C1 0 (80) 0 (1.31) - - 34 4.0 DISCUSSION The control plots had the highest average mortality, with almost 550 trees per hectare less than the previous measurement (2003), followed by the 5 m clumped spacing (304 less trees), then the 3 m clumped spacing (256 less trees), and ultimately the standard spacing (200 less trees). Block B, the driest block, had the highest mortality. However, the control plots in Block B also had the highest density at the beginning of this period, and it is normal to have a higher mortality due to the increased competition for growing space, light, nutrients and water at higher densities (Oliver and Larson 1996). Block B contained almost all the plots that showed an increase in basal area over the 2003-2008 period. This is likely due to the high mortality rate of the lodgepole pine present Blocks C and D; lodgepole pine was not found on any of the Block B plots. The presence of lodgepole pine in Blocks C and D is mostly due to their moister conditions that are more suitable for lodgepole pine and for deciduous tree species such aspen and white birch. Regeneration quantity and quality differed across the study site. Block B, the driest of the three blocks, had only Douglas-fir regeneration, while Blocks C and D had several species present. This is probably due to the moister site conditions and to the overall substrate that seems better suited for seedling establishment and survival. Most of the germinants in Blocks C and D were found on rotten wood, sometimes in groups of at least 50, as found in other studies (e.g., Burton et al. 2000). The 5 m clumped spacing and the standard spacing had the highest number of germinants while the control had the lowest, likely due to the higher densities present on the controls (less light). Douglas-fir germinants were mostly present on the 3 m and 5 m clumped spacing and this could be due to more openings caused by the clumped nature of the residual trees. Most of the broadleaf seedlings were found in the controls, while the majority of conifer seedlings were located in thinned plots. This is not surprising since the thinning applications preferentially selected broadleaf trees for removal or girdling. The standard spacing had the largest number of seedlings followed by the 3 m spacing; the ground cover for both these treatments was similar. The 5 m clumped spacing, which was the 35 most open (least dense) of the thinning treatments, was the only treatment that had seedlings of all of the five tree species present in the area; its ground cover was dominated by grass and herbs, likely a reflection of the lower density. The control plots on all blocks had low amounts of Douglas-fir regeneration, probably due to the high crown closure and low amount of light, as pointed out by Burton et al. (2000). Only a few seedlings were found in Block B. In Block C, Douglas-fir seedlings were found on all thinned plots, but not in the control; in Block D, Douglas-fir seedlings were only found in the control (a few present) and in the 5 m clumped spacing. Block D had the highest number of broadleaf seedlings; this was the moistest site and it had the greatest amount of understory vegetation. Douglas-fir seedlings, especially in the first few years, are negatively affected by understory vegetation (Nicholson 1989). The fitted models predicted more seedlings in the control plots than in the thinned ones. This was probably due to the greater amount of broadleaf seedlings found in the control plots, especially in Block D. When run only for Douglas-fir or for all conifer seedlings, the models predicted fewer seedlings in the control than in the thinned plots. This result is confirmed in other studies (e.g., Zaerr et al. 1981; Minore 1986), where it was found that the percentage of Douglas-fir seedlings is greater on seedbeds of mineral soil than on undisturbed ground and litter. Seedlings in the control treatment had the lowest predicted three-year height growth (Figures 13 and 14), while the best height growth among the treatments was for the 5 m clumped spacing, the least dense plots over all. Higher densities (higher canopy closure) can delay seedling establishment and decrease their relative growth (Franklin et al. 2002). The mixed linear models for the average number of seedlings per overstory plot, based on plot, ground and competition variables, showed that plot (larger scale) variables were less important than the other variable types. Local competition variables were important for predicting the number of seedlings. Indeed, the best model was the one that had all the competition variables, moss coverage and litter coverage as predictors. The models for predicting the number of germinants were more complicated. The large scale (plot) variables appeared to influence the amount of germinants. It might be possible to describe a pattern of mortality based on these models; however, it always quite challenging to describe patterns of mortality across site and 36 stand conditions (Glover and Hool 1979; Hawkes 2000; Keane et al. 2001; Affleck 2006). Clearly it would be difficult to assign a particular mechanism based on the three scale variables used because the conditions present today for seedlings are not the same as they were several years ago due to the natural dynamics of these stands. Other analyses were run using zero-inflated models, where the data were not averaged for the entire overstory plot; rather, all regeneration subplot measurements of the number of seedlings or germinants were used. A zero-inflated model was needed because of the large amount of zeros. The variable groups used in these models were the same as in the mixed linear models: large scale, competition and ground variables. For these models, the most important variables seem to be those based on the plot level (stems per ha, quadratic mean diameter and basal area per ha) for germinants and the ground cover variables for seedlings (percentage cover by moss, grass and herbs, and crown litter). The CART analysis suggested than having less than 35% crown litter on a subplot was the best indicator of the presence of germinants. When crown litter cover was high, grass and herb cover was low (since the coverage sums to 100%). Higher crown litter cover can be related to locations which had low light and/or low moisture. The best variables to predict the presence of seedlings were grass and herb coverage of more than 15% and less than 25% crown litter coverage. As pointed out by Zaerr et al. (1981), in less dense stands that are not arid, such as Block C and D in this study, conditions will be favorable for Douglas-fir and other conifers seedlings in terms of light and moisture, while the conditions in more open areas when moisture is limiting (Block B) will make it difficult for seedlings to establish. While there did not appear to be a correlation between number of germinants and the ranking of plots in terms of recent mortality (primarily lodgepole pine) on thinned plots in Blocks C and D, there did appear to be higher numbers of seedlings in plots that had experienced higher levels of mortality (expressed as stems per ha). This correlation, while still positive, was weaker if plots were ranked in terms of mortality expressed as basal area. It is worth noting that the relationship is correlative rather than causal. For example, it could be that growing conditions that were suitable for greater than average lodgepole pine presence on a plot also led to greater than average seedling presence. However, it could also be that the improvements in light conditions 37 due to the lodgepole pine mortality decreased seedling mortality and/or allowed better survival of germinants allowing more germinants to reach the seedling stage. There was only weak evidence (p = 0.0934) that there was a positive correlation between conifer seedling quality (as evidenced by higher than average three year height growth) and the ranking of thinned plots in terms of mortality expressed as stems per ha in Block C. This relationship was weaker still if plots were ranked in terms of the mortality expressed in terms of basal area. Only two of the thinned plots in Block D had conifer seedlings that were measured for height growth. This precluded any meaningful assessment of possible relationships between mortality level and three-year height growth in this block. If the mortality that has occurred is sufficient to reduce competition to the extent that seedling height growth is improved, it should be more evident upon the next measurement of the regeneration subplots in a few years‘ time. One way to improve this study would be to use a better (i.e., more refined) classification of ground conditions. The problem with the methodology used was that it did not allow for a ―multiple cover layer‖ that could possibly go over 100%. Some plots contained layered ground cover, one over another (e.g., dead wood over moss). Adopting such a system could lead to a better understanding of the regeneration surface and its possible impact on germination and seedling growth. Knowing the conditions under which regeneration grows better could be helpful in planning partial cuts in these types of stands. Moreover, measures of soil characteristics, such as infiltration, moisture and structure on each subplot, could help explain the presence of germinants and seedling success. The regeneration subplots were marked with railroad spikes so they could be relocated through time to provide insight on regeneration patterns, ground coverage changes, and changes around the subplots through time. Additional studies could focus on the mortality and relative chance of survival of seedlings and germinants due to site composition, local density, and treatment. Another study could involve the measurement of light levels (photosynthetic active radiation (PAR)) directly above the subplot and relate this to the relative quantity and quality of regeneration. Such information would likely improve the predictive models developed in this study. 38 Interior Douglas-fir stands are generally quite spatially and structurally heterogeneous, and the stands included in this study reflect this condition. Although the overstory plots were originally located in areas that were among the most uniform and dense present in each of the blocks prior to treatment, some spatial heterogeneity was certainly still present (Marshall 1996). The precommerical thinning treatments, particularly the clumped spacings, introduced additional spatial heterogeneity to the treated areas. Natural dynamics, principally mortality, have also enhanced the spatial and structural heterogeneity in the 17 years from the application of the treatments to the latest measurement. The relatively recent death of many of the lodgepole pine trees that were originally present on the plots, caused by the mountain pine beetle, has added to this. Consequently, it is difficult to quantify the conditions present that might reflect regeneration quantity and quality as an average at the plot level. Future analyses that include detailed spatial relationships between regeneration location and larger trees in the vicinity of the regeneration might help in understanding the complex relationships that affect regeneration quantity and quality in complex uneven-aged interior Douglas-fir stands. This is supported by the findings in this study where the basal area of overstory trees in the immediate vicinity of regeneration subplots was important in predicting the number of seedlings present. 39 5.0 CONCLUSIONS This study examined regeneration levels 18 years after pre-commercial thinning in uneven-aged stands of interior Douglas-fir. The information obtained could be used to inform future management of this forest type. Douglas-fir regeneration was mostly present in moist conditions, where the crown cover was open enough to allow sufficient light in for the germinants and seedlings. More germinants were found in the thinned plots across all three blocks, and a greater proportion of them were Douglas-fir. However, the control plots had more total seedlings and greater three-year height growth than the thinned plots. This was due to the greater number of broadleaf seedlings present on the control plots, especially in Block D (the moistest of the blocks). If only Douglas-fir seedlings were considered, the quality and quantity was better on the thinned plots. The 5 m clumped spacing supported more conifer seedlings and more variety of species than the other treatments. The three-year height growth of Douglas-fir was better in the 5 m clumped spacing (0.54 m) than in the other two spacings (0.36 m and 0.27 m for the standard and the 3 m clumped spacing, respectively). Plot (larger scale) variables had a lesser impact on the number of seedlings present on an overstory plot than the proximate ground and competition variables. The best model (smallest standard error of estimate) for predicting the number of seedlings present was the one that included all possible variables. However, a lower AIC was found for the model that did not include the plot variables. Unlike seedlings, the number of germinants present on an overstory plot was impacted most by plot variables. In the zero-inflated model for predicting the number of germinants on a regeneration subplot, plot variables were more important than ground or the competition variables. However, the model did not perform well (the inflation probability was 0.08, not significant with an alpha level of 0.05). The zero-inflated model for predicting the number of seedlings performed better, with a significant inflation probability of 0.008. In both these models, certain ground cover variables were useful predictors. 40 Crown litter cover of less than 35% on a subplot was the best predictor for the presence of germinants. The best predictors for the presence of seedlings were more than 15% cover of grass and herbs and less than 25% cover of crown litter. A higher amount of crown litter is an indicator of relatively open and dry conditions, which are not conducive for successful germination and ultimate survival of germinants to the seedling stage (> 15 cm in height). Higher levels of lodgepole pine mortality on thinned plots in the two blocks that contained lodgepole pine (Blocks C and D) were apparently associated with higher numbers of seedlings, both conifer seedlings and total seedlings. However, there did not appear to be any association between levels of mortality and amount of germinants. There was only weak evidence that there was an association between higher mortality levels and better conifer seedling height growth (i.e., better quality seedlings) in Block C. There were too few thinned plots in Block D in which conifer seedlings were measured for height growth to draw any meaningful conclusions with respect to any relationship between mortality and seedling quality for this block. The thinning treatments did not have a statistically significant impact on total regeneration (both germinant and seedling quantity) overall, primarily due to the high variability present. 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A new method for modeling the heterogeneity of forest structure. Forest Ecology and Management 129:75–87. 50 APPENDIX 1 Overstory conditions following the 2003 and 2007 growing seasons. Table 13. Overstory conditions following the 2003 growing season, including lodgepole pine that had been recently killed. Treatment Block Plot Density (tree/ha) Basal Area (㎡/ha) QMD (cm) Relative Density Species Composition 1 (% Basal Area) Control (U) B 3 8,380 49.4 8.7 16.8 Fd100 4 7,120 47.1 9.2 15.5 Fd97Pl3 D 15 4,960 41.3 10.3 12.9 Fd71Ep11Pl11Sx4At3 16 3,220 36.3 12.0 10.5 Sx58Fd34Ep6At3 C 19 4,960 41.3 10.3 12.9 Fd97Sx3Ep1 20 4,600 40.1 10.5 12.3 Fd82Pl9Ep6At3 Mean 5,540 42.6 10.2 13.5 3m Clumped spacing (C1) B 1 2,260 28.7 12.7 8.0 Fd100 2 1,920 34.1 15.0 8.8 Fd100 D 11 2,840 44.5 13.9 11.9 Fd45Sx43At9Pl4 12 2,240 36.3 14.4 9.6 Fd63Sx37 C 21 2,160 31.5 13.6 8.5 Fd63Ep16Pl15Sx5 22 2,660 31.3 12.2 8.9 Fd68Pl15Ep13Sx4 Mean 2,363 34.4 13.6 9.3 5m Clumped spacing (C2) B 5 1,960 25.2 12.8 7.0 Fd78Pl22 6 1,400 29.3 16.3 7.3 Fd100 D 13 1,400 27.6 15.8 6.9 Fd82Pl18 14 1,240 26.4 16.5 6.5 Pl62Fd28Sx10 C 17 1,280 23.7 15.3 6.0 Fd73Pl14Sx7Ep7 18 1,520 24.0 14.2 6.4 Fd75Pl10Ep8Sx7 Mean 1,467 27.2 15.4 6.9 Standard spacing (S) B 7 1,880 34.4 15.3 8.8 Fd100 8 1,620 30.3 15.4 7.7 Fd86Pl14 D 9 2,060 32.2 14.1 8.6 Fd78Pl22 10 1,860 32.2 14.8 8.4 Fd88Pl12 C 23 1,840 34.7 15.5 8.8 Fd80Pl20 24 1,400 30.5 16.6 7.5 Fd80Pl20 Mean 1,777 32.4 15.3 8.3 1 Fd is Douglas-fir; Pl is lodgepole pine; Ep is paper birch, At is aspen; Sx is interior spruce 51 Table 14. Overstory conditions following the 2007 growing season. Treatment Block Plot Density (tree/ha) Basal Area (㎡/ha) QMD (cm) Relative Density Species Composition 1 (% Basal Area) Control (U) B 3 7,380 49.8 9.3 16.4 Fd100 4 6,460 47.8 9.7 15.3 Fd100 D 15 4,320 38.3 10.6 11.8 Fd81Ep11Sx5Pl2At1 16 2,720 35.4 12.9 9.9 Sx57Fd35Ep5At3 C 19 4,740 43.0 10.8 13.1 Fd96Sx3Ep1 20 4,340 40.2 10.9 12.2 Fd89Ep6At3Pl2 Avg. 4,993 42.4 10.7 13.1 3m Clumped spacing (C1) B 1 2,260 32.2 13.5 8.8 Fd100 2 1,860 36.0 15.7 9.1 Fd100 D 11 2,660 44.4 14.6 11.6 Fd49Sx42At8 12 2,160 38.5 15.1 9.9 Fd66Sx34 C 21 1,820 30.6 14.6 8.0 Fd71Ep18Pl6Sx5 22 1,860 28.1 13.8 7.6 Fd78Ep15Sx5Pl2 Avg. 2,107 35.0 14.5 9.2 5m Clumped spacing (C2) B 5 1,640 27.0 14.5 7.1 Fd100 6 1,400 31.5 16.9 7.7 Fd100 D 13 1,180 26.6 16.9 6.5 Fd97Pl3 14 680 13.0 15.6 3.3 Fd72Sx21Pl7 C 17 980 22.7 17.2 5.5 Fd84Sx9Ep7 18 1,100 25.1 17.0 6.1 Fd83Ep9Sx7Pl1 Avg. 1,163 24.3 16.4 6.0 Standard spacing (S) B 7 1,880 37.2 15.9 9.3 Fd100 8 1,520 28.3 15.4 7.2 Fd100 D 9 1,740 27.5 14.2 7.3 Fd97Pl3 10 1,740 32.1 15.3 8.2 Fd98Pl2 C 23 1,360 30.6 16.9 7.5 Fd98Sx1Pl1 24 1,220 26.6 16.7 6.5 Fd100 Avg. 1,577 30.4 15.7 7.7 1 Fd is Douglas-fir; Pl is lodgepole pine; Ep is paper birch, At is aspen; Sx is interior spruce.
Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution 2.5 Canada
Graduate
Regeneration in thinned and unthinned uneven-aged interior Douglas-fir stands
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