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Bunchberry (Cornus canadensis) growth and reproductive responses to disturbance in a managed boreal forest Martin, René Adrienne 2001

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B U N C H B E R R Y (CORNUS CANADENSIS) G R O W T H A N D REPRODUCTIVE RESPONSES TO DISTURBANCE IN A M A N A G E D B O R E A L FOREST by RENE ADRIENNE M A R T I N B.Sc, Simon Fraser University, 1998 A THESIS SUBMITTED IN PARTIAL F U L F I L M E N T OF THE REQUIREMENTS OF THE DEGREE OF M A S T E R OF SCIENCE in THE F A C U L T Y OF G R A D U A T E STUDIES Department of Forest Sciences Faculty of Forestry We accep>ihis thesis as conforming to the required standard THE UNIVERSITY OF BRITISH C O L U M B I A April 2001 © Rene Adrienne Martin, 2001 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 j^rctsh S<UJZ/I^S The University of British Columbia Vancouver, Canada Date Tpr-i / ^3 y , DE-6 (2/88) A B S T R A C T I compared the short-term effects (i. e., within one year of forest harvesting) of two methods of partial cut harvesting (strip-cutting with 75% forest retention by area, and strip-cutting and thinning with 50% forest retention by area) on the growth and reproduction of bunchberry (Cornus canadensis) located in the boreal coniferous forest of northern Alberta, Canada. Bunchberry growth and reproductive responses were measured in harvested stands and along the edge of adjacent, non-harvested stands, and compared to the interior of adjacent, non-harvested stands. One character of bunchberry growth (total ramets/m2) and six reproductive characters (proportion of ramets that flowered, number of flowers/flowering ramet, pollen deposition, initiated fruit, set fruit, and fruit weight) were measured. Light levels and percent soil moisture per plot were also measured, and general habitat characteristics (i.e., tree characteristics and ground cover of other vegetation) were described. Bunchberry was affected by harvesting overall (i.e., with all variables combined); however, bunchberry growth, number of flowers, pollen deposition, and initiated fruits did not significantly differ between any locations for either retention level. The proportion of ramets that flowered significantly increased in the harvested stands with 75% forest retention, and significantly decreased in the harvested stands with 50% forest retention. Fruit set was higher in the harvested stands for both retention levels; however, this was only statistically significant for the 75% retention level. Fruit weight was also higher in the harvested stands for both retention levels, but was only statistically significantly in the 50% retention stands. Light levels and percent soil moisture were significantly higher in the harvested plot locations for both the 50% and 75% retention levels; however, no differences were found in light and soil moisture levels between retention levels. n Bunchberry responded to harvesting by producing large and numerous fruits in both retention treatments, a response that is likely related to increased light levels and soil moisture found in these locations. Initial reproductive responses (i.e., the proportion of ramets that flowered) differed between retention levels, however. The increases seen in flowering ramets in 75% retention stands were likely a result of increased light levels, allowing for more resources to be allocated to reproduction. However, in the 50% retention stands, mechanical damage due to this more intensive harvesting technique may have resulted in the decreases seen in flowering ramets. ii i T A B L E OF CONTENTS Page A B S T R A C T ii LIST OF T A B L E S v LIST OF FIGURES vi A C K N O W L E D G E M E N T S vii INTRODUCTION 1 METHODS 6 Study Site 6 Forest Harvesting Techniques 6 Study Species 8 Sampling and Experimental Design 8 Microclimatic Measures 10 Bunchberry Growth and Reproductive Responses 11 Habitat Characteristics 12 Statistical Analyses 12 RESULTS 18 Microclimatic Variables 18 Bunchberry Growth and Reproductive Responses 18 Habitat 19 Correlation Analyses 20 Interior plot data 20 A l l data 20 DISCUSSION 31 Soil Moisture 31 . Light Levels 32 Mechanical Damage 37 Implications of Results 38 CONCLUSIONS 40 LITERATURE CITED 42 APPENDIX I: DESCRIPTIVE STATISTICS FOR E A C H V A R I A B L E M E A S U R E D 48 APPENDIX II: G R A P H I C A L REPRESENTATION OF M E A N S A N D S T A N D A R D ERRORS OF MICROCLIMATIC, B U N C H B E R R Y GROWTH, A N D B U N C H B E R R Y REPRODUCTIVE V A R I A B L E S 56 APPENDIX III: PERCENT COVER OF A L L VEGETATION PRESENT IN PLOTS 60 iv LIST OF TABLES Table Page 3.1 Effects of block (z. e., replicated stands) and treatment (/'. e., plot locations [interior, edge, and harvest] by 50% and 75% retention levels) on microclimate, bunchberry growth and reproductive responses, and habitat 22 1.1 Means, maximum values (max), minimum values (min) and standard deviations (SD) for each variable measured 48 III. 1 Percent cover of all vegetation present in plots 60 v LIST OF FIGURES Figure Page 2.1 Study site 15 2.2 Harvesting techniques for the 75% and 50% retention levels 16 2.3 Experimental layout 17 3.1 Means and standard errors of (a) light levels (photosynthetically active radiation, PAR) and (b) percent water content of soil samples taken at each plot location for the 50%) and 75% retention levels 23 3.2 Means and standard errors of (a) total ramets and (b) proportion of total ramets that flowered at each plot location for 50%> and 75%> retention levels 24 3.3 Means and standard errors of number of flowers per flowering ramet at each site location for the 50%> and 75%> retention levels 25 3.4 Means and standard errors of %> coverage of stigma surface by pollen at each plot location for 50% and 75%) retention levels 26 3.5 Means and standard errors of proportion of flowers (a) initiating fruits per flowering ramet and (b) setting fruit per flowering ramet at each plot location for 50% and 75% retention levels 27 3.6 Means and standard errors of average fruit weight at each plot location for 50% and 75% retention levels 28 3.7 Means and standard errors of (a) number of trees per tree plot and (b) density of trees per tree plot at each plot location for the 50%) and 75%o retention levels 29 3.8 Means and standard errors of total ground cover of (a) shrubs, (b) mosses, and (c) herbs (excluding bunchberry) at each plot location for 50%) and 75%) retention levels 30 II. 1 Means and standard errors of microclimate, bunchberry growth, and bunchberry reproductive variables 56 vi A C K N O W L E D G E M E N T S I thank my supervisory committee, Pam Krannitz, Val LeMay, and Judy Myers for their input and support throughout my degree. Financial support was provided by Science Horizons, Career Edge, Canadian Wildlife Services, and the Backman Award in Natural Resources Conservation. The Canadian Wildlife Service and E M E N D provided much needed gear and logistic support. Numerous people helped me in the woods of northern Alberta. I'd like to thank the E M E N D crew, Joanna Murdoch, and Colin Elner for their time and energy in assisting with my project. I also want to thank Lisa Cuthbertson, a truly great gal who can drive a mean quad. My time in the field would not have been the same without the company and assistance of Chris Smit. He brought me much joy throughout the summer, and we had several 'good leg' moments together in the bush and in the hack circle. The gang in C A C B made my stay at U B C much more pleasant, and at times, downright hilarious. I'd particularly like to thank Nancy Mahony, whose chats, advice, and commiseration regarding grad school truly helped get me through. I also thank Howie Harshaw for making me feel like I know something about something, and Rob D'eon for reminding me about priorities. Nancy Mahony, Stephanie Hazlitt, Jo Smith, and Dave Huggard all struggled through various painful versions of my thesis, and made it much clearer both to me and (hopefully!) to the reader. I thank Val LeMay and Tony Kozak for their statistical help during my degree. So many of my friends gave me encouragement and support throughout my degree, and I couldn't have done it without them: so thanks Toby, Laura, Irene, Steph, Jo, Nancy, Michele, Sharilynn, James, Alice, and Brad. Finally, I want to thank my dog, Mayook, and my espresso machine for making sure I got up in the morning. R . M . vii INTRODUCTION Disturbance plays a major role in the creation of spatial and temporal heterogeneity in the dynamics and structure of natural communities (Sousa, 1984). Disturbance is defined as a relatively discrete event in time that can disrupt ecosystem, community, or population structure, and affects substrate availability, resources, or the physical environment (Parminter, 1998). The main sources of disturbance in forested ecosystems are physical (e.g., fire, high winds, landslides, floods), biological (e.g., insect outbreaks, herbivory, predation), and anthropogenic (e.g., harvesting, development, recreation). Disturbance plays a fundamental role in the ecology of forests, and effects are dependent on the spatial scale at which the disturbances occur. For example, an insect outbreak that is wide-spread, but patchy, or a fire that simply underburns understory vegetation and leaves most of the trees are small-scale disturbances. Small-scale disturbance often involves tree death and/or tree fall and subsequent gap formation (Parminter, 1998). Openings in the canopy can increase the amount of light and precipitation that reaches the understory (Collins et al, 1985). The creation of gaps can increase heterogeneity in the forest by allowing new species to establish that could not do so under a closed-canopy (Lertzman and Krebs, 1991). Large-scale disturbance can occur with intense wildfire, and typically returns a stand to an earlier serai stage, thus allowing the establishment of early successional species (Parminter, 1998). In order to minimise the impact of harvesting on the forest community, forest managers have increasingly focused on harvesting techniques that attempt to mimic natural disturbance (Hunter, Jr., 1993; Moola and Mallik, 1998; Parminter, 1998; Niemela, 1999). In the boreal forest, the dominant source of natural disturbance is fire (Johnson et ah, 1995). Fires vary in intensity and spatial scale; however, they rarely destroy all the vegetation in a site. In order to 1 emulate the amount of remnant forest that would be left in areas where fires frequently occur, forest managers may leave variable amounts of forest when harvesting. Two techniques of mimicking small-scale disturbance are partial cutting and thinning. Partial-cutting/thinning retains some or most of the vegetation structure that existed prior to harvesting. Like small-scale disturbance, partial cutting and thinning in a forest increase the resources available to plants located in remnants through increased light levels, increased soil moisture, and decreased competition due to the removal of other vegetation (Bazzaz, 1996; Bormann and Likens, 1979). In the boreal forest, seasonal averages of solar radiation, daily maximum air temperature, air temperature range, and soil temperature have been shown to increase with increasing size of opening created by partial cuts (Carlson and Groot, 1997). Similarly, thinning in a Ponderosa pine (Pinus ponderosa Laws.) forest increased light levels, decreased midday relative humidity, and increased midday air temperatures (Riegel et al., 1992). Effects on the microclimate from harvesting can also be seen along the edges of forests adjacent to harvested stands. Saunders et al. (1991) reported that increased light levels, increased daily temperature range, increased soil moisture, and increased wind can all be seen along edges of forests that are adjacent to a harvested stand. Light, water, and nutrients are resources required by plants for maintenance, growth, and reproduction, and temperature influences plant performance (Bazzaz, 1996); thus, changes in microclimatic variables due to harvesting can consequently affect plants. Plants that are able to persist in frequently disturbed habitats typically utilise strategies that allow them to take advantage of newly available resources (/. e., increased light, soil moisture) created by disturbance. For example, some plants may switch from vegetative growth to sexual reproduction, a luxury that can only be afforded in areas of high resource availability. Sexual reproduction in salal (Gaultheria shallori) increased and vegetative investment decreased with decreasing crown closure (J. e., in more open areas) in the University of British Columbia 2 Malcolm Knapp Research Forest, near Maple Ridge, BC (Bunnell, 1990). This change was attributed to the increasing amounts of light available in areas of decreased crown closure. Another strategy that might allow plants to respond favourably to increased resource availability is a "bet-hedging" strategy (Sutherland, 1986). For this strategy, plants will consistently produce more flowers and/or initiate more fruit than are likely to mature. In sites/years of low resource availability excess flowers and fruits will be aborted. However, when resources become newly available through disturbance, plants that initiate extra fruits may mature all or most of them, thus maximising their seed output (Lee and Bazzaz, 1982). Lee and Bazzaz (1982) found that the hermaphroditic herb Cassia fasciculata initiated far more fruit than typically matured. When this species was exposed to more resources (e.g., more water and fewer competitors), it was able to mature relatively more fruit. Presumably plants that utilise the bet-hedging strategy will respond favourably (i.e., relative increases in fruit set) to harvesting techniques that open up some of the canopy and increase resource availability in the remnants. Increased light levels in openings created through harvesting can affect plant reproduction indirectly by altering the behaviours of pollinators. Many pollinators tend to prefer more open, brightly-lit habitats (personal observation, 1999; Herrera, 1995b; Walters and Stiles, 1996), so pollinator visits to plants in harvested areas may be higher than in areas of increased crown closure. This may lead to increased pollen transfer, pollen deposition, and ultimately higher fruit production in plants located in areas with high light levels. To explore how variable retention harvesting using partial cutting and thinning affects the forest community in the boreal forest of Northern Alberta, a large, multi-stakeholder project, E M E N D (Ecosystem Management by Emulating Natural Disturbance), was developed (see www.biology.ualberta.ca/emend/). Undertaken within the E M E N D setting, the objective of my study was to examine the effects that two levels of retention harvesting (50% and 75% retention) have had on the growth and reproduction of bunchberry (Cornus canadensis). 3 Bunchberry is a common understory species found throughout Canada. It is pollinated by insects, and its fruits are an important food source for small mammals and birds (Hall and Sibley, 1976; Burger, 1987). Bunchberry frequently produces more flowers and initiates more fruits than can mature, and subsequently has high abortion rates (Barrett and Helenurm, 1987). Bunchberry may utilise a bet-hedging strategy in order to respond favourably to variations in resource availability. Assuming light and soil moisture are limiting in undisturbed sites, harvest-induced increases in light levels and soil moisture should result in higher fruit set in bunchberry. As well, sexual reproduction by bunchberry may increase (e.g., increased inflorescence production) in areas of higher light levels. Hall (1955) reported an increase in bunchberry flowering and fruiting with increased openings in the canopy. Bunchberry has been shown to be an effective competitor for resources in the understory (Yarborough and Bhowmik, 1993), and changes in bunchberry vigour and abundance can have consequences on growth and reproduction in other species. For example, in a replacement-series experiment, lowbush blueberry (Vaccinium angustifolium) fruit count and yield decreased with increased bunchberry density (Yarborough and Bhowmik, 1993). As well, if bunchberry increases inflorescence production in response to increased light levels in harvested areas, it may compete successfully for pollinators that would otherwise visit other plants. For example, Cetonia beetles prefer to visit high bush-cranberry (Viburnum opulus) plants with a larger number of inflorescences displayed than those with a small number (Englund, 1993). The aim of this study was to measure short-term responses (within one year of harvesting) in bunchberry growth and reproduction to harvesting in untouched remnants located in partial-cut stands (75% forest retention by area), in thinned remnants located in partial-cut stands (50% forest retention by area), and the forest edges that border them. Harvesting effects for each location were compared in relation to plots located in adjacent, non-harvested stands. 4 Microclimatic variables (light levels and soil moisture) in each location were also measured to explore the relationship between bunchberry responses and resource variation. 5 METHODS 2.1 STUDY SITE The study area was located in the boreal forest of Northern Alberta, Canada, approximately 70 km N W of Peace River (56° N , 118 ° W) (Figure 2.1 :p.l5). The mature boreal forest in this area typically consist of a matrix of closed-canopy stands and large openings created by swamps and edaphic features. The mean annual temperature ranges from 4.8°C to -2.7°C, depending on the region (Johnson et al, 1995). Mean annual precipitation in the western boreal forest is typically <500mm (Johnson et al, 1995), and mean summer precipitation (May-September 1969-1999) at the study area is 275.2mm (R. Hurdle, Canadian Forest Service, personal communication). This area is well drained, and is subsequently comprised primarily of mixedwood stands (Rowe, 1977). Four specific stand types were represented in the E M E N D sites: deciduous dominated (70-95%), deciduous dominated with a coniferous understory, conifer dominated, and mixed (conifer and deciduous composition each 35-50%). At my study sites, the dominant tree species were white spruce (Picea glauca Voss), black spruce (Picea mariana B.S.P.), trembling aspen (Populus tremuloides Michx.), and balsam poplar (Populus balsamifera L.). The dominant shrub species were prickly rose (Rosa acicularis) and low bush-cranberry (Vibernum edule); the dominant herbs were bunchberry, dewberry (Rubus pubescens), and fireweed (Epilobium angustifolium). 2.2 FOREST HARVESTING TECHNIQUES Forest harvesting in the E M E N D study took place over winter 1998/99. Harvest prescriptions for the E M E N D project were based on the amount of forest remaining following harvest (% retention). My study focused on 50% and 75% retention stands. Two techniques were used to harvest: strip-cutting and thinning. Strip-cutting involved complete tree removal in 6 a 5 metre wide area along a straight line. In thinning, removal of every n t h tree within a forested area occurred. For each stand, five metre wide strip-cuts were made every 15 metres, each running in a North/South direction (Figure 2.2:p.l6), leaving 15 metre-wide retention strips in the harvested stand. For the 50% retention level, thinning of trees was undertaken in the 15 metre-wide retention strips, and in the 75% retention level, retention strips remained unharvested. Harvesting treatments were replicated three times in a randomised complete block design. Each block consisted of stands of similar composition (e.g., age, height, density, species). Each harvested stand was 10 ha in size, and was adjacent to a non-harvested stand. The non-harvested stand was used as a buffer zone to separate harvested stands. Each buffer zone was >100m wide. Harvesting effects in the retained strips in the harvested stands and the forest edges bordering them were compared to the interior (> 20m from harvest edge) of the non-harvested stands. Sites were chosen from forest cover maps of the E M E N D project site. The criteria used for site selection were: 1) sites must be located in conifer-dominated stands; 2) harvested stands must be a similar age class, height class, understory type, and tree density as the adjacent, non-harvested stand, and; 3) a portion of the non-harvested stand must have an edge running parallel to the cut strips in the harvested site. Conifer-dominated stands rather than mixedwood stands were chosen because: a) they are economically important in the forest industry; b) they are more homogeneous with fewer gaps, and; c) they are further along in succession and older, thus more influenced by disturbance. The second criterion was necessary in order to minimise variability between harvested and non-harvested stands associated with the harvesting effects. The third criterion enabled me to test if the edge of the non-harvested stands was influenced by disturbance in the harvested stands. 7 2.3 STUDY SPECIES Bunchberry is a member of the Cornaceae, or dogwood family, and is a low (10-20 cm) perennial. It is found throughout Canada in moist, coniferous and mixed forests in both interior and open habitats, along valley bottoms and up to subalpine habitats (Pojar and MacKinnon, 1994). Bunchberry grows laterally from woody, underground (LFH soil layer; i.e., organic layer through to mineral soil) rhizomes (Hall and Sibley, 1976). Ramets grow from rhizomes, and may either remain vegetative or produce flowers. Leaves are evergreen, short-stalked, and typically grow in whorls consisting of 4-7 leaves (Pojar and MacKinnon, 1994). Flowering ramets consist of four white, showy bracts surrounding a cluster of greenish-white to purple flowers. Each flower can produce one drupe that typically contains one seed (Hall and Sibley, 1976). Bunchberry is insect pollinated, and uses an explosive mechanism to release pollen. Each flower contains a tiny 'antenna' that serves as a trigger mechanism. Flowers remain closed until the antenna is triggered, at which time they instantly open, anthers catapult out and shoot their entire pollen load in the air above the inflorescence (Mosquin, 1998). Bunchberry is self-incompatible (Barrett and Helenurm, 1987), and self-pollination may be prevented by the explosive pollination technique. The primary pollinators of bunchberry in the boreal forest are in the orders Hymenoptera (bees and wasps), Diptera (flies), Coleoptera (beetles), and Lepidoptera (butterflies and moths) (Barrett and Helenurm, 1987; Smit, 1999). 2.4 S A M P L I N G A N D E X P E R I M E N T A L DESIGN Plots were located in the 50% and 75%> retention stands relative to a 'sampling edge' that separated the harvested stand from the adjacent non-harvested stand (Figure 2.3:p.l7). The sampling edges were chosen by three criteria: 1) the start and end point must be > 50m away 8 from any other treatment or disturbance to minimise confounding effects; 2) the edge must run parallel to the cut strips so that transect lines would pass through the cut strips, and; 3) the forest on either side of the edge must have been of the same composition prior to harvest. While the third criterion was generally met for retention stands as a whole, specific problems were encountered in stands where small patches of younger forest were present along the edge between the harvested stand and the non-harvested stand. These sections were excluded from the sampling edge. Sections were also excluded if they were proximal to the concurrent silviculture study being done by Canadian Forest Service (CFS) at the E M E N D site. The CFS study involved extensive scarification (upturning of soil), and was considered to be a confounding disturbance. Three transect lines were randomly placed perpendicular to the sampling edge at a distance greater than 10m apart (Figure 2.3:p.l7). The transects ran from 50m into the interior of the harvested stand through to the middle of the third retained strip in the harvested stand, approximately 50m. Eight, lm plots were placed along each transect line. In total, 144 plots were established (i.e., eight plots per transect line, three transect lines per experimental unit, two experimental units per each of three blocks). In the non-harvested stands, plots were placed at distances of 47.5m, 22.5m, 7.5m, and 2.5m from the sampling edge (Figure 2.3:p.l7). The 47.5m and 22.5m plots were considered to be "interior" plots, and the 7.5m and 2.5m plots were considered to be "edge" plots (Figure 2.3:p.l7). Depth of edge-effect varies with the variable being measured, and typically, microclimatic edge-effects range from two to three tree lengths into the forest when edges are highly contrasting (e.g., clear-cuts to forest) (Kremsater, 1997). However, this depth decreases with decreasing contrast of edges, and biological effects tend to be much shorter (i.e., for most plants) (Kremsater, 1997). Thus, plots less than one tree length away from the edge (under 23m) were considered edge plots. In the harvested stands, plots were located within each of the first three retention strips from the sampling edge. These were 9 considered to be "harvested" plots. Two plots were placed in the first retention strip: one at 2.5m into the strip and the other at 7.5m. The second and third retention strips each had single plots located in the centre. Plots were not placed in the cut-strips, as no vegetation was present in cut-strips in the year following harvesting. For each distance category, plots were placed at the closest location to the transect line that contained > 10 bunchberry shoots. A quadrat was used to designate the plot area, with the densest clump of bunchberry shoots located in the centre of the quadrat. Plots were at least 10m apart to ensure individual plants were not measured in multiple plots (Barrett and Helenurm, 1987). Allowing for this, plots at 7.5m were placed East or West of the line at a distance of at least 8.5 metres, resulting in 10 metres of distance from the 2.5m plots. Plots were marked at the north-east and south-west corners with flags. 2.5 MICROCLIMATIC MEASURES Light levels in each l m plot were measured on days with no cloud cover and no wind. Light levels were measured using an AccuPAR linear ceptometer, a device consisting of a datalogger and probe. The probe contains 80 photodiodes along its length that measure PAR (Photosynthetically Active Radiation) in the 400-700nm waveband. P A R is displayed in units of 2 1 micromols per metre squared per second (umol m" s") (Decagon Devices, 1999). Readings were taken in each plot while holding the ceptometer level and at the height of bunchberry. Ten readings were taken at each plot between lOOOhrs and 1200hrs and again between 1200hrs and 1400hrs. These reading were averaged to assess PAR levels per plot. Percent moisture content was determined with soil samples collected at each plot. A l l samples were collected in one day, following five days without any precipitation. Soil samples were taken from just outside the plot using an Oakfield soil-core sampler. Prior to sampling, the surface of the soil was cleared of any moss and/or debris. The mineral (i.e., inorganic) portion of 10 the sample was discarded and the remaining organic L F H soil layers were placed in a pre-weighed soil can and plastic bag. Total weight was taken for each sample, can, and bag together, and then of the bag alone. The bag and can weight were subtracted from the total to obtain the wet-weight of the sample. Samples were dried in a drying oven at a temperature of 82°C until a constant weight (± 0.05 mg) was reached. Can weight was then subtracted from the constant weight to obtain dry weight. Soil moisture was calculated as (wet weight - dry weight)/wet weight. 2.6 B U N C H B E R R Y GROWTH A N D REPRODUCTIVE RESPONSES Bunchberry growth was determined based on the total number of ramets present per plot, and bunchberry reproductive responses were determined based on measurements of six characteristics: proportion of ramets that flowered, number of flowers/flowering ramet, pollen deposition, initiated fruits, set fruits, and fruit weight. A l l bunchberry flowering ramets in each plot were sequentially numbered on two leaves with an indelible felt marker. Total numbers of flowering and non-flowering ramets were recorded. These values were used to assess relative bunchberry growth. Forty percent of the flowering ramets in each plot were randomly selected to assess reproductive responses of bunchberry. Chosen flowering ramets were marked with a circle on the leaves. If there were 10 or fewer flowering ramets within the plot, all flowering ramets were included. The number of flowers per inflorescence was recorded once the bracts were fully opened. Pollen deposition was estimated for 24 of 144 plots. These plots corresponded to plots examined for pollinator visitation rates in a concurrent study (Smit, 1999). The amount of pollen deposited within four flowering ramets found just outside the perimeter of each plot was used as an estimate of overall pollen deposition within the plot. The flowering ramet closest to each side of the plot was selected. Once the majority (> 80%) of the flowers in a plot were opened, the 11 four ramets were removed to estimate pollen deposition. Stigma surfaces of all flowers per ramet were examined using a dissecting microscope and the percent of stigma surface covered by pollen grains was visually estimated. Percent pollen cover was then averaged for each plot. Fruit development in each plot was estimated over two-days at the end of the season. The proportion of fruits initiated and/or set per flowering ramet was recorded as: ^initiated [or set] fruits/Sflowers (2.1) where Z = sum of the total fruits [flowers] found in each flowering ramet. A fruit was considered to have been initiated if the ovary was swollen, and set if the fruit had achieved a uniform red colour (Barrett and Helenurm, 1987). Two months after the first observation of flowering occurred, fruit were collected from three randomly chosen flowering ramets in each plot. Fruits were weighed, and an average weight per fruit for each plot was calculated. 2.7 H A B I T A T CHARACTERISTICS To describe the immediate forest environment of each 1 m 2 plot, tree height, diameter outside bark at breast height (dbh; 1.3m above ground), and distance from plot centre were measured in a 2.5m radius surrounding each plot. Tree heights were measured using a Vertex Forester™ to the nearest 0.5m, and dbh was measured with a dbh tape to the nearest 0.1 cm. A l l live trees located within 2.5m from the centre of each plot were measured. A l l vegetation species were identified and percent cover estimated within each lm plot. 2.8 STATISTICAL A N A L Y S E S Statistical analyses were conducted using SAS 6.11 for Windows (SAS Institute, 1990). Descriptive statistics calculated for all variables were based on data from 144 plots. To avoid pseudoreplication, line transect data were averaged together by block and experimental unit for 12 each distance (Hurlbert, 1984). Non-normal data were arcsine or log transformed for analysis (Zar, 1984). Power analysis was done using SPSS Version 9.0 to determine if sample size was large enough to detect differences of biological significance. It was found that for all variables tested, power was high enough to detect differences (e.g., for proportion of ramets that flowered: noncentrality = 20.273, power = 0.769). A general linear model (GLM) was used to determine treatment effects on microclimatic variation, bunchberry growth and reproductive performance, and habitat measures. Treatments were based on harvest retention (50% and 75% retention) at each of the three plot locations (interior [47.5m and 22.5m], edge [7.5m and 2.5m], and harvested [retention strips]); thus, six treatments were explored. The number of observations per treatment were as follows: interior plot locations had six observations for each retention level, edge plot locations had six observations for each retention level, and harvested plot locations had twelve observations for each retention level, for a total of 48 observations overall. The model used for the G L M was: Yyk = block, + treatmentj + error^ where block represents the three replications for each retention level; treatment represents the six treatments; and error represents any residual variation. No block by treatment interaction was found for any variable measured, so this factor was not included in the G L M . Post-test multiple comparisons were done using a Student-Newman-Keuls range test (Hicks and Turner, 1999). Significance was tested at an alpha level of 0.05 for all analyses. A multiple analysis of variance ( M A N O V A ) was performed to test treatment effects on combined measures of bunchberry growth and reproductive performance. A Pillai's Trace criterion was used to test significance because it was the most robust criterion to use for this type of M A N O V A (Tabachnick and Fidell, 1996). Significance was tested at an alpha level of 0.05. In order to help interpret the results of the G L M , Spearman's rank correlations were used to investigate correlative relationships between microclimatic variables, habitat measures, and 13 growth and reproductive performance (Conover, 1971). An alpha level of 0.05 was used to test the null hypothesis of no correlation. Correlations were tested with data from the interior plot locations only to explore how the measured variables correlate in the absence of disturbance. Multiple regressions were also performed to assess the importance of light and soil moisture in explaining the variation in bunchberry growth and reproductive performance over all six treatments. 14 Figure 2.1: Study site. Study was located at E M E N D site, near Peace River, Alberta. 15 Top view: 75% Retention Level 50% Retention Level c t c r c i c i Non-hdivcbtoJ btanJ Non-thinned retention strip c r c r c r c r Thinned retention strip I Cut strip Side view: 75% Retention Level A A AAAA iff. tttttttt Non-harvested c r c r c r Harvested 50% Retention Level ttttttttTtttt t.tt Tt.t tt,t c r c r c r Non-harvested Harvested Figure 2.2: Harvesting techniques for the 75% and 50% retention levels. Five metre wide cut strips (c) were made in each harvested stand, running in an north/south direction. 15m wide retained strips (r) remained unharvested in the 75% retention level, and were thinned for the 50% retention level. Each harvested stand was located adjacent to a non-harvested stand of similar composition. 16 H A R V E S T E D S T A N D N O N - H A R V E S T E D S T A N D retained cut retained strjp. §ttiB...5t«p. i i i - 15 m ~5 m-Transect tine • • V • cut retained cut .strip,... 5lrirj strj p.... E.D.G.E |7.5m • _EL 2.5m • • ..INTERIOR.. • 7.5m • k • 22.5m Sampling edge 47.5m • Figure 2.3. Experimental Layout. Three transect lines were located in each replication of the 50% and 75% retention levels. Plots were located along transects in the interior of the non-harvested stand at 47.5m, 22.5m, 7.5m, and 2.5m from the edge of the harvested stand. Plots in the retention strips of the harvested stand were located within the first strip (2 plots at 2.5m and 7.5m within the strip), the second strip, and third strip. A l l plots were > 10m from any other plot, and > 50m from any other non-harvested stand or harvest level located in the same block. 17 RESULTS 3.1 MICROCLIMATIC V A R I A B L E S There was a significant difference in light levels among treatments (F 5 40= 5.08, p=0.0011) (Table 3.1:p.22), and this was attributable to differences between interior plot locations and edge and harvested plot locations for both retention levels (Figure 3.1a:p.23). There were no significant differences in light levels between edge and harvested plots for both retention levels, and there were no significant differences between retention levels at any plot location. There was an overall significant effect of treatments on the percent water content of soil (F5,40= 15.67, p=0.0001) (Table 3.1:p.22). Interior plots were significantly drier than plots located in harvested areas for both retention levels (Figure 3.1b:p.23), and significantly drier than edge plots for the 50% retention level (Figure 3.1b:p.23). 3.2 B U N C H B E R R Y GROWTH A N D REPRODUCTIVE RESPONSES Multiple analysis of variance showed that there was a significant effect of treatments on bunchberry growth and reproductive responses overall (/. e., with all variables as response variables) (F30, |9 5 =1.95, p=0.0037). Univariate tests were then used for each response variable. No difference in bunchberry growth (i.e., the total number of ramets) was detected amongst treatments (F5,40= 1.07, p=0.4214) (Table 3.1:p.22, Figure 3.2a:p.24). However, there was an overall significant effect of treatments on the proportion of total ramets that flowered (F 5 4 0 = 5.63, p=0.0005) (Table 3.1:p.22). These effects differed according to retention level. When 75%o of the forest was retained, there were proportionally more flowering ramets in the harvested areas than in the forest interior (Figure 3.2b:p.24). In contrast, when only 50%> of the 18 forest was retained, there were proportionally more flowering ramets in the interior of the forest as compared to the harvested area (Figure 3.2b:p.24). The number of flowers per flowering ramet was virtually invariant amongst treatments ( F 5 , 4 o = 0.31, p= 0.9021) (Table 3.1:p.22, Figure 3.3:p.25). The mean amount of pollen deposited on stigmas did not differ significantly between treatments (F 5, 6= 0.97, p= 0.4648) (Table 3.1 :p.22, Figure 3.4:p.26). The proportion of initiated fruits per flowering ramet did not differ significantly between treatments (F5,40=0.98, p=0.4395) (Table 3.1:p.22, Figure 3.5a:p.27). However, there was a significant treatment effect on fruit set (F 5 40= 2.61, p=0.0391) (Table 3.1 :p.22). In the sample data, fruit set was higher in the harvested area than the forest interior for both retention levels; however, this difference was statistically significant only for the 75% retention level (Figure 3.5b:p.27). Fruit weight varied significantly over treatments (F5 i 4 0= 3.00, p=0.0011) (Table 3.1 :p.22). In the sample data, fruit weight was higher in the harvested plot location than the interior forest for both retention levels; however, this difference was statistically significant only for the 50%o retention level (Figure 3.6:p.28). 3.3 HABITAT The mean number of trees per plot and the mean basal area per plot (2.5m radius) did not vary between treatments (number of trees: F 5 40= 1.3 1, p=0.4317; basal area: F5,40= 1.33, p=0.2721) (Table 3.1:p.22, Figure 3.7:p.29). This is contrary to expectations, as 25% more trees (by area) were removed in the 50% retention stands than in the 75%) retention stands. Plots were placed in areas where there were sufficient numbers of bunchberry ramets for study. Bunchberry may tend to clump where tree densities are high, therefore plots would not represent a random distribution of tree basal area and numbers of trees. Indeed, a positive relationship was found between total ramets and number of trees (see Section 3.4). Therefore, tree measurements may 19 have been misleading with regard to stand composition. As well, tree plot size was small (2.5m radius), and may not be a representative sample of stand composition overall. White spruce accounted for the majority of the trees present in the 2.5m radius plots (71%, n=156). Black spruce (6%, n=T3), trembling aspen (15%, n=32), balsam poplar (4%, n=9), and balsam fir (Abies balsamea Mill.) (3%, n=7) were also present. Understory vegetation was categorised into four classes: mosses, shrubs, herbs (excluding bunchberry), and bunchberry. Overall, mosses provided the greatest coverage of plots (58%), followed by shrubs (15%), herbs (11%), and bunchberry (8%). Shrubs significantly differed between treatments (F 5 4 0 = 2.52, p=0.0449) (Table 3.1 :p.22). However, multiple comparison tests showed no significant differences between pairs of treatments (Figure 3.8a:p.30). No significant differences were detected between treatments for moss ground cover (F 5,4o= 0.34, p=0.8881) (Figure 3.8b:p.30) nor herb ground cover (F 5.«o= 2.11, p=0.0845) (Figure 3.8c:p.30) (Table3.1:p.22). For all plant species present, stair-step moss (Hylocomium splendens) accounted for the most coverage (43%), followed by big-red stem (Pleurosium schreberi) (10%), bunchberry (8%), prickly rose (6%), and low bush-cranberry (5%) (Appendix III). 3.4 C O R R E L A T I O N A N A L Y S I S 3.4.1 Interior plot data (n=12) Less light was available where there were more trees per 2.5m radius plot (rs = -0.52, p = 0.0853). Soil tended to be drier in plots where there was more light (rs = -0.65, p = 0.0220), and slightly drier where tree basal area was lower (rs = 0.54, p = 0.0709). There were more total ramets where there were more trees (rs = 0.61, p = 0.0352), and where moss abundance was higher (rs = 0.60, p = 0.0387). There were slightly less flowering ramets where there were more shrubs (rs = -0.52, p = 0.0849). Increased flowering ramets was 20 associated with a decrease in number of flowers (rs = -0.56, p = 0.0586). More pollen was deposited in areas where there were more herbs (rs = 0.89, p = 0.0188), less trees (rs = -0.94, p = 0.0051), and less total ramets (rs = -0.89, p = 0.0188). Increased initiated fruits was associated with more light (rs = 0.77, p = 0.0034) and less trees (rs = -0.69, p = 0.0123). There were more set fruits where there was less basal area (rs = -0.55, p = 0.0664) and less shrubs (rs = -0.57, p = 0.0534). No significant correlation was found between soil moisture and any bunchberry growth or reproductive variable. 3.4.2 All data (n=48) Light and soil moisture combined were significantly associated with both fruit set (r2 = 0.26, p = 0.0013) and fruit weight (r2 = 0.19, p = 0.0087). Changes in any other bunchberry growth and reproductive performance variable were not significantly associated with changes in light and soil moisture. Although both light and soil moisture contributed to explaining the variation seen in fruit set and weight, soil moisture was the predominant predictor of these two variables (fruit set: p = 0.0041; fruit weight: p = 0.0109). 21 Table 3.1. Effects of block (i.e., replicated stands) and treatment (i.e., plot location [interior, edge, and harvest] by 50% and 75% retention levels) on microclimate, bunchberry growth and reproductive responses, and habitat. Significant p-values (• < 0.05) are in bold. MS represents Mean Square, d.f. represents degrees of freedom, and R 2 is the coefficient of determination. Variable MSblock d.f. P d.f. P MS e r r o r d.f. Overall R2 Microclimate Light availability 8645 2 0.6719 109266 5 0.0011 21525 40 0.3956 Soil moisture 1.933 2 0.9462 546.6 5 0.0001 34.89 40 0.6623 Growth and Reproductive responses Total ramets" 432.6 2 0.0022 60.89 5 0.4214 56.89 40 0.3505 Flowering ramets 0.0035 2 0.6321 0.0422 5 0.0005 0.0075 40 0.4208 Flowersb 0.5310 2 0.0001 0.0050 5 0.9021 0.0160 40 0.6297 Pollen deposition 71.20 2 0.5850 124.6 5 0.4648 128.4 16 0.2715 Initiated fruit 0.0147 2 0.0228 0.0035 5 0.4395 0.0035 40 0.2487 Fruit seta 0.2515 2 0.4832 0.8864 5 0.0391 0.3396 40 0.2665 Fruit weight 0.0002 2 0.0491 0.0003 5 0.0011 0.0001 40 0.4421 Habitat Number of trees 2.500 2 0.0097 0.5644 5 0.2804 0.4317 40 0.2977 Tree density (ba/tree plot) 0.0007 2 0.4757 0.0012 5 0.2721 0.0009 40 0.1693 Shrub ground covera 1775 2 0.0001 123.9 5 0.0449 49.12 40 0.6796 Moss ground cover 21590 2 0.0001 119.6 5 0.8881 355.6 40 0.7546 Herb ground covera 1069 2 0.0010 90.14 5 0.0845 42.77 40 0.6020 Analysis performed on arcsine-transformed values (arsin(((x/100)square-root)*180/7t)). Analysis performed on log-transformed values (log (x+1)). 22 (a) V 600 # 500 E 400 o 300 -\ I . 200 QT 100 ^ 2 o n=6 b n=6 b Interior n=6 a n=6 T 1 Edge Plot Location n=12 n=12 Harvest 150% 175% (b) o 50 § 40 o = 30 o o . c o 20 re 10 0 n=6 n=6 b C Interior n=6 n=6 b 4 ^ Edge Plot Location n=12 n=12 a a Harvest III50% 175% Figure 3.1. Means and standard errors of (a) light levels (photosynthetically active radiation, PAR) and (b) percent water content of soil samples taken at each plot location for the 50% and 75% retention treatments. Means containing the same letter are not significantly different (p>0.05) based on Student-Newman-Keuls multiple comparison tests. 23 (a) CM 80 E w 60 I 40 To 20 o 0 n=6 a a n = 6 n=6 n = 12 n = 12 Interior E d g e Ha rves t Plot Location H 5 0 % • 75% (b) o E i2 E c _ * ° o ^ 0.1 0 n=6 ab n=6 Interior n=6 n = 6 abc r - £ - | T bc Jim Edge Plot Location n=12 C n=12 a -J—| ! H 5 0 % • 75% Harvest Figure 3.2. Means and standard errors of (a) total ramets and (b) proportion of total ramets that flowered at each plot location for 50% and 75% retention treatments. Means containing the same letter are not significantly different (p>0.05) based on Student-Newman-Keuls multiple comparison tests. 24 CD 50 ^ 25 20 15 10 5 0 I I u _ U) o .E E o 3 LL n=6 n=6 Interior n=6 n=6 Edge Plot Location n=12 n=12 Harvest • 50% • 75% Figure 3.3. Means and standard errors of number of flowers per flowering ramet at each site location for the 50% and 75% retention treatments. No significant differences were detected between means (p>0.05). 25 CD C 50 40 >* 30 ^ I 3 Q-co > to -° I » 20 -s I 10 * 8 0 n=3 n=3 n=3 J . < As*" n=6 n=6 Interior E d g e Ha r ve s t Plot Location • 50% m 75% Figure 3.4. Means and standard errors of % coverage of stigma surface by pollen at each plot location for 50% and 75% retention treatments. No significant differences were detected between means (p>0.05). 26 (a) I 5 3 LL •D 0 .25 0.2 0> E re c re o> S 5 c ~ S 0.1 5 0 .05 0 n = 6 a n = 6 a •f-n = 6 a ' n= n=12 a 4 -n=12 a Inter ior E d g e H a r v e s t Plot Loca t i on • 5 0 % • 7 5 % CD E re en u> c CD 3 o 33 u CO '5 u. 0.25 n 0.2 -0.15 -0.1 -n=6 0.05 - ab 0 - • = • n = 6 Interior n=6 n=6 a ab n=12 n=12 a a Edge Plot Location Harves t B 50% • 75% Figure 3.5. Means and standard errors of proportion of flowers (a) initiating fruits per flowering ramet and (b) setting fruit per flowering ramet at each plot location for 50% and 75% retention treatments. Means containing the same letter are not significantly different (p>0.05) based on Student-Newman-Keuls multiple comparison tests. 27 ™ 0.04 £ 0.03 | 0.02 ^ 0.01 n=6 n=6 be n=6 ba n=6 be - E i n=12 Interior E d g e Ha rves t Plot Location • 5 0 % • 7 5 % Figure 3.6. Means and standard errors of average fruit weight at each plot location for 50% and 75%o retention treatments. Means with the same letter are not significantly different (p>0.05) based on Student-Newman-Keuls multiple comparison tests. 28 (a) o- 2.5 1.5 6 » £ = h- -a ° E 1 * «> 0.5 CM ~ 0 n=6 n=fi_ n=6 n=6 n=12 n=12 • 50% • 75% Interior Edge Harvest Plot Locat ion (b) O) O — w flj 3 <« =5 tS 2 a> E 0.5 0.4 0.3 0.2 0.1 0 n=6 n=6 n = 6 n=6 n = 12 n = 12 Interior Edge Harvest Plot Location • 50% • 75% Figure 3.7. Means and standard errors of (a) number of trees per tree plot and (b) density of trees per tree plot at each plot location for the 50% and 75% retention treatments. No significant differences were detected between any means (p>0.05). 29 (a) ^ 30 S 25 o 20 15 10 5 0 T3 C 3 O O n 3 k_ •C CO n=6 n = 6 Interior n=6 n = 6 n = 12 n = 12 E d g e Ha rves t Plot location 0 50% • 75% b 100 80 60 <D > O o •a 3 o k. CD w (A O 40 20 0 n = 6 n = 6 n = 6 n=6 n = 12 n = 12 Interior E dge Ha rves t Plot location • 50% B 7 5 % £ 25 £ 20 3 o n 15 10 5 0 n=6 n = 6 Interior n = 6 n = 6 Edge Plot Location n = 12 n=12 Harves t H 5 0 % • 75% Figure 3.8. Means and standard errors of total ground cover of (a) shrubs, (b) mosses, and (c) herbs (excluding bunchberry) at each plot location for 50% and 75% retention treatments. No significant differences were detected between any means (p>0.05). 30 DISCUSSION This study focused on the short-term effects, within one year of harvesting, on bunchberry growth and reproductive responses. I found that bunchberry increased sexual investment (e.g., proportion of ramets that flowered) in the retention strips in the 75% retention stands, decreased sexual investment in the retention strips in the 50% retention stands, and increased fruit development in the retention strips for both retention levels. This appears to be associated with three main factors: increased soil moisture, increased light levels (i.e., radiant energy), and mechanical damage to vegetation. Changes in bunchberry reproductive responses due to harvesting can have implications for surrounding vegetation in the retained strips, foragers of bunchberry fruits, and succession patterns in the cut-strips. 4.1 SOIL MOISTURE Increased soil moisture levels in the harvested plot locations may have caused the increases seen in fruit set and fruit weight in bunchberry. Variation in soil moisture, in combination with light level variation, was found to be a significant predictor of changes in fruit set and fruit weight. Soil moisture can affect net photosynthesis, as well as facilitate nutrient transport (Begon et al, 1990; Bazzaz, 1996), and thus can affect growth and fruit production in plants. Benoit et al. (1984) found that increases in soil moisture lead to a higher fruit yield and heavier fruits in lowbush blueberries (Vaccinium angustifolium). Increases in soil moisture commonly occur following forest harvesting (Liechty et al., 1992; Walbridge and Lockaby, 1994; Barg and Edmonds, 1999). Increases are typically caused by: a) decreased interception of precipitation due to removal of adjacent canopy trees (throughfall) (Collins et al., 1985), and; b) a reduction in the number of trees taking up water. Post-harvest increases in soil moisture levels may last a minimum of five years (Liechty et al., 31 1992), which can result in long-term effects on plant assimilation, growth, and allocation patterns (Collins et al, 1985). Soil moisture levels can also increase in harvested areas due to soil compaction from the use of heavy logging equipment. It has been shown that compaction causes increased soil bulk density, decreased soil aeration, and soil erosion (Kozlowski, 1999); thus, highly compacted soils are typically poorly drained (Williamson and Neilsen, 2000). These factors can lead to physiological dysfunction in plants by affecting photosynthesis, nutrient absorption, and the amounts and balances of growth hormones in plants (Kozlowski, 1999). In this area, effects from compaction should be minimal, however, as logging took place in the winter, and deep snow pack should buffer the effects that logging equipment had on the soil (pers. comm., Estabrook logging, 1999). Also, sampled areas were in remnant strips where impacts were low. Nevertheless, some compaction may have occurred, and increases in soil moisture in the harvested sites may reflect this. 4.2 LIGHT L E V E L S Increased light levels in the harvested plot locations may have caused an increase in sexual investment in the 75% retention level and increased fruit set and fruit weight in both retention levels. In my study, I found that light levels increased in the edge of the non-harvested stands and in remnant strips in the harvested stands for both the 50% and 75% retention levels. Increases in light cause concomitant increases in maximum daytime surface temperature (Carlson and Groot, 1997). In the study current with my own, Smit (1999) found that temperature increased with increasing light. A plant requires a certain temperature for floral evocation (flower production) (Taiz and Zeiger, 1998), and plants exposed to higher temperatures may begin to flower earlier in the season, and thus be pollinated sooner. As well, studies have shown that plants are more likely to receive insect visitors if they are located in 32 relatively warm patches (e.g., Herrera, 1995a). Smit (1999) found a relationship between warm temperatures (e.g., 16° - 20° C) and insect visits in my plots. However, at extreme temperatures (e.g., > 23°C) visits dropped again. Increases in radiant energy can allow a plant to increase both vegetative and reproductive growth, and increased growth can allow a plant to spread to more favourable sites (e.g., areas with higher nutrients) as well as capture more light through increased leaf area. Many studies have shown increased allocation to growth via total ramet production (i.e., vegetative and flowering ramets) in sites where light levels were highest (Chen et al., 1992; Matlack, 1994; Lopez de Casenave et al, 1995; Atlegrim and Sjoberg, 1996; Moola and Mallik, 1998). Therefore, I predicted the same pattern in bunchberry in my study plots. However, no significant differences in total ramet production were detected. Atlegrim and Sjoberg (1996) studied responses of bilberry (Vaccinium myrtillus) under similar conditions to this study, and also found that growth did not increase with light levels. They suggested that vegetative growth may be limited by nutrients rather than light levels. If so, it may be that nutrient levels did not vary between plot locations. Alternative explanations are: a) the number of ramets in bunchberry is determined in the previous season, as seen in Saskatoon (Amelanchier alnifolia) (Steeves and Steeves, 1990) and California buckeye (Aesculus californica) (Newell, 1991), and responses to tree harvesting will not be seen until a future date, or; b) resource allocation may have been weighted towards another life history trait, such as flower production or fruit set. Although no change in total ramet production was detected, bunchberry increased investment in sexual investment (i.e., production of flowering ramets) in the 75% retention level. Increases in flowering ramets may have been in response to increased light levels. No relationship was found between light levels and flowering ramets; however, in areas where there were more shrubs, and potentially more shading, there were fewer flowering ramets. Responses in sexual reproduction to increased light levels have been shown in other species. For example, 33 salal will switch from primarily vegetative growth to sexual reproduction in areas of increased light levels (Bunnell, 1990). Similarly, Moola and Mallik (1998) found increased sexual investment in velvet leaf blueberry (Vaccinium myrtilloides) in areas with higher light levels following tree harvesting. Because light levels increased in both the 75% and 50%> retention levels, I expected increased production of flowering ramets in both retention levels. This was not the case, however; the proportion of flowering ramets only increased in the harvested area in the 75%> retention level, and decreased in the harvested area in the 50% retention level. The reduction of flowering ramets in the 50% retention level may have been due to mechanical damage to bunchberry rhizomes (see Section 4.3). The remnant strips in the 75%> retention level had minimal direct effects from logging, and thus bunchberry could take advantage of increased resources by increasing the proportion of ramets that flowered. This could have consequences for reproductive output in bunchberry, as increases in flowering ramets can increase attractiveness to pollinators (Stephenson, 1981; Sutherland, 1986; Helenurm and Barrett, 1987; Sih and Baltus, 1987; Englund, 1993; Heard and Exley, 1994; Aizen, 1997). In my plots, Smit (1999) found a weak relationship between increasing number of visits per plot with increasing proportions of flowering ramets per plot. High visitation rates can positively influence fruit set, as increased pollination and pollen loads can lead to higher fruit set in plants that are pollen limited, as is bunchberry (Helenurm and Barrett, 1987). I predicted that pollen deposition would be highest in the edge of the non-harvested stands and in remnant strips in the harvested stands where it is more exposed and bright. Although I found that pollen deposition was higher in areas with fewer trees, no significant differences in pollen deposition were found between treatments. Although no differences were detected in pollen deposition, Smit (1999) found insect visitation rates to bunchberry were higher in the edge and harvested plot locations. Being unable to detect a difference in pollen deposition 34 between plot locations may be an artefact of my sampling methodology. Estimating percent cover of stigma surface by pollen is a coarse measurement, and would only be effective in detecting large differences in pollen load. As well, it is difficult to determine if pollen on a stigma is truly bunchberry pollen without an electron microscope (Kearns and Inouye, 1993). However, I took care to exclude pollen from cover estimations that had a different morphology than bunchberry pollen grains. The initiation of fruits is an expensive trait that can be limited by both pollen and resources. A positive relationship was found between increased light levels and fruit initiation; therefore, I predicted that fruit initiation in bunchberry would be highest in areas where light levels were highest. However, no differences in fruit initiation were detected between treatments. Bunchberry may utilise the "bet-hedging" strategy in initiating fruits (Sutherland, 1986). Over-initiation of fruits and subsequent abortion has been observed in other studies of bunchberry (Barrett and Helenurm, 1987). In a variable environment such as the boreal forest, this strategy might be advantageous for bunchberry despite the fact that resources are lost that were invested in aborted fruits (Bazzaz, 1997); bunchberry can mature relatively more fruit where resources are adequate. If bunchberry utilises a bet-hedging strategy, I should have seen increased fruit set and fruit weight in areas where resources were the highest. In the harvested plot locations where light levels and soil moisture were the highest, I found the highest fruit set (75% retention level) and fruit weight (50% retention level). As well, light, in combination with soil moisture, was found to be a significant predictor of variation in fruit set and fruit weight. The finding that fruit set and fruit weight increased in areas with high light levels is consistent with other studies. For example, Moola and Mallik (1998) found that the number and weight of fruits of velvet leaf blueberry increased with greater light availability. Similarly, in a study of the understory herb Circea lutetiana, fruit number was limited by light availability (Verburg and During, 1998). 35 Increases in fruit set and fruit weight may be very beneficial for a plant. For example, larger, more numerous fruits may be more attractive to frugivores, thus are more likely to disperse successfully (Crawley, 1997). Dispersal success of the shrub Cornus drumondii was positively related to crop size; plants with large numbers of seeds contributed more to the total pool of dispersed seeds than plants with fewer seeds (Willson and Whelan, 1993). Similarly, frugivore visitation rates were highest on balo plants {Plocama penduld) with large fruit crops (Nogales et al., 1999). In contrast, however, Burger (1987) found no relationship between crop size of bunchberry and removal rates by frugivores. Final assessment of fruit set was done on 19 August 1999, and differences in the numbers and weight of set fruits between plot locations could reflect differences the phenology of fruiting: those in the harvested sites may have begun to develop earlier, and thus set more fruits and/or achieved a higher weight sooner. Bunchberry typically reaches full maturity at this latitude by mid- to late-August (personal observation, 1998; Hall and Sibley, 1976), and the majority of fruit removal by foragers typically occurs in early September (Burger, 1987). Fruits that ripen early have an increased chance of being dispersed prior to seasonal drops in temperatures that can arrest fruit development (Fitter and Hay, 1987). It has been shown that the primary dispersers of bunchberry in Newfoundland were migrant birds (Burger, 1987), and early fruit development may maximise overlap between fruit set and bird migration. Also, Wheelwright (1993) showed that the bird dispersed fruits of Ocotea tenera (Lauraceae) that ripened early were more likely to be removed than late-developing fruits. It may be that bunchberry fruits that ripen earlier will have an increased chance of being removed and successfully dispersing. No differences were detected in light levels between the 50% and the 75% retention levels, even though 25% more of the trees (by area) were removed. One explanation may be that no differences were detected in tree basal area nor number of trees between retention levels in 36 the harvested area, and plots may have received equal amounts of overstory shading. Alternatively, it may be that light levels were higher overall in the 50% retention level stands and the light meter used to detect light levels was not sensitive enough to detect differences between retention levels. If there were no detectable differences in light levels between retention levels, it would suggest that the majority of changes to the microclimate were caused by strip-cutting, and not by thinning in the retention strips. Thus, favourable conditions for bunchberry, such as increase light levels, can be created without more intense, and possibly detrimental techniques such as thinning. 4.3 M E C H A N I C A L D A M A G E Although impacts due to mechanical damage from logging equipment were not directly measured, thinning within the 50%> retention strips may have affected the growth of herbs and shrubs as well as the reproductive output of bunchberry. In the thinned retention strips in the 50%> retention stands, I found lower percent ground cover in herbs and shrubs, and a reduction in the proportion of ramets that flowered in bunchberry. The reduction of flowering ramets in the 50% retention level may have been due to mechanical damage to bunchberry rhizomes and/or apical buds. In a similar study, Atlegrim and Sjoberg (1996) found that billberry ground cover was reduced in selected fellings (i.e., partial cut stands) and suggested that this was directly related to mechanical damage from logging equipment. Similarly, Moola and Malik (1998) attributed lower fruit yields found in velvet leaf blueberry to mechanical damage to above-ground biomass. Reductions in the number of flowering ramets may lead to decreased pollinator visits (Aizen, 1997). Indeed, Smit (1999) found a weak positive relationship between the number of flowering ramets and insect visits. 37 4.4 IMPLICATIONS OF RESULTS Bunchberry responded favourably to harvesting with respect to sexual investment (i.e., proportion of ramets that flowered) in the 75% retention stands and fruit development (i.e., fruit set and fruit weight) in both retention levels. There may be several consequences as a result of increased levels of fruit production in bunchberry. Bunchberry can compete with other plant species for limited resources (Hall and Sibley, 1976; Yarborough and Bhowmik, 1993), and over time may come to dominate the understory. For rare species present in the study area (e.g., some orchids), increased competition by more abundant species can eventually lead to extirpation (Tilman, 1986). Further studies on the demographics of bunchberry and other plant species on these study sites would allow managers to better understand how harvesting impacts the understory plant community. Bunchberry fruits are primarily eaten by generalists that tend to feed on all species of fruits in an area (Hall and Sibley, 1976; Burger, 1987). Increases in bunchberry density and fruit production have been shown to limit fruit production in other species (e.g., Vaccinium) (McCully et al, 1991; Yarborough and Bhowmik, 1993). Bunchberry is a low quality fruit relative to other North American fruits, having a high water content (91.3%) and high soluble carbohydrate content (49.1% of dried pulp), but low lipid (1.2%), protein (3.3%), and energy (17 kJ g"1) content (Burger, 1987). If bunchberry is limiting production of higher quality fruits, this may not be beneficial to frugivores in the site. The subsequent impacts that changes in bunchberry reproduction has on higher trophic levels is an area that could be explored in order to further understand the implications that variable retention harvesting has on the flora and fauna in the understory of the boreal forest. It should be noted that this study was undertaken immediately following harvesting, thus only reflect short term effects. Over the long-term, bunchberry may increase in abundance 38 within the retention strips, as well as colonise and dominate the cut-strips. Messier and Kimmins (1991) found that nine years after clear-cutting a conifer-dominated forest on Northern Vancouver Island, the second most prominent vegetative species was bunchberry. Hall and Sibley (1976) reported that bunchberry becomes the dominant vegetation following canopy removal; however, in an earlier study, Hall found that bunchberry abundance remained fairly constant (yet relatively high) following clear-cutting and burning (Hall, 1955). It would be interesting to monitor the succession and spread of bunchberry and other plant species in both the remnant strips and the cut-strips, and compare trends with those seen following small-scale natural disturbance. 39 CONCLUSIONS In general, few studies have been done that examine the effects of harvesting techniques on herbaceous plants. Bunchberry is a common plant that has a wide and cosmopolitan distribution, and is found throughout Canada and as far north as Greenland (Hall and Sibley, 1976). Because it is so common, bunchberry is ideal for studies on how forest harvesting practices influence herbs in the understory; it allows for large sample sizes to be taken within many site types along a large geographic area. While one plant species may not respond to harvesting in a similar fashion as another, general inferences on what may happen to understory plants can be drawn from species-specific studies (Jules, 1998). As well, comparative studies on bunchberry responses to natural disturbances such as fire may serve as a species-specific study on how harvesting practices mimic natural disturbance. This study offers an opportunity to examine what happens to bunchberry under two different harvesting regimes, strip-cutting and thinning for 50% forest retention, and strip-cutting to achieve 75%> forest retention. Overall, the proportion of ramets that flowered in bunchberry increased in the remnant strips in the 75%> retention stands, and decreased in the thinned retention strips in the 50%> retention stands, a result that may have been caused by mechanical damage occurring in the thinned remnants in the 50%> retention stands. Fruit development (i.e., fruit weight and fruit set) increased in the harvested stands for both retention levels, and this increase was positively correlated with increases in light and soil moisture. Light and soil moisture levels did not differ between retention levels; thus, harvesting with low basal area removal (e.g., strip-cutting only for 75%o forest retention) may increase resource availability for understory plants without the increased disturbance caused by thinning in remnant strips. This is a one year study that brings some understanding to the immediate effects following two different harvesting regimes. 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Englewood Cliffs, NJ: Prentice-Hall, Inc. 46 APPENDICES 47 APPENDIX I DESCRIPTIVE STATISTICS FOR E A C H V A R I A B L E M E A S U R E D Table 1.1. Means, maximum values (Max), minimum values (Min), and standard deviations (SD) for each variable measured. Values based on 144 plots, n=9 per row. 2 1 Light Levels (umol m" s" ) Retention level (%) Distance from edge (m) Mean Max Min SD 50 -47.5 215 624 35 199 50 -22.5 129 375 19 122 50 -7.5 356 753 41 252 50 -2.5 253 599 12 194 50 2.5 299 670 65 251 50 7.5 389 816 37 269 50 22.5 372 701 143 189 50 47.5 406 674 20 221 75 -47.5 68 113 32 27 75 -22.5 107 264 32 83 75 -7.5 290 1033 71 301 75 -2.5 523 1192 123 345 75 2.5 366 816 113 220 75 7.5 376 784 79 274 75 22.5 379 934 98 321 75 47.5 353 693 115 191 Soil Moisture (%) Retention level (%) Distance from edge(m) Mean Max Min SD 50 -47.5 28.79 41.45 14.53 10.89 50 -22.5 31.01 47.82 6.65 12.47 50 -7.5 29.96 49.33 10.73 12.76 50 -2.5 28.95 49.44 11.12 11.16 50 2.5 29.28 49.88 9.34 12.99 50 7.5 28.06 45.13 11.30 10.85 50 22.5 24.91 37.52 7.60 11.36 50 47.5 24.57 43.92 10.41 14.40 75 -47.5 31.51 45.06 15.56 12.49 75 -22.5 33.97 50.90 17.94 10.64 75 -7.5 27.38 40.64 17.01 9.41 75 -2.5 32.13 53.26 17.72 10.91 75 2.5 33.01 46.47 11.21 12.09 75 7.5 32.29 42.32 17.54 9.59 75 22.5 28.29 43.78 16.19 10.94 75 47.5 28.79 44.48 14.52 10.22 48 Table 1.1 (con't). Means, maximum values (Max), minimum values (Min), and standard deviations (SD) for each variable measured. Values based on 144 plots, n=9 per row. Total Ramets/m2 Retention level (%) Distance from edge(m) Mean Max Min SD 50 -47.5 33.67 90 7 23.73 50 -22.5 59.11 212 12 66.34 50 -7.5 46.78 81 24 18.32 50 -2.5 37.89 68 23 17.21 50 2.5 34.33 74 5 20.43 50 7.5 35.11 132 8 37.65 50 22.5 33.44 55 14 16.73 50 47.5 60.11 143 10 45.19 75 -47.5 42.44 79 19 22.57 75 -22.5 35.44 68 9 19.04 75 -7.5 33.67 78 10 20.92 75 -2.5 30.00 61 14 14.73 75 2.5 53.11 174 20 48.28 75 7.5 53.00 85 31 18.73 75 22.5 32.56 60 8 16.03 75 47.5 46.11 104 12 31.39 Flowering Ramets/Total Ramets Retention level (%) Distance from edge(m) Mean Max Min SD 50 -47.5 0.31 0.50 0.08 0.14 50 -22.5 0.35 0.50 0.09 0.14 50 -7.5 0.27 0.49 0.12 0.12 50 -2.5 0.23 0.48 0.04 0.16 50 2.5 0.16 0.40 0.02 0.13 50 7.5 0.21 0.35 0.08 0.10 50 22.5 0.23 0.36 0.00 0.13 50 47.5 0.24 0.50 0.00 0.14 75 -47.5 0.31 0.50 0.13 0.11 75 -22.5 0.25 0.42 0.04 0.13 75 -7.5 0.25 0.56 0.00 0.15 75 -2.5 0.33 0.61 0.06 0.19 75 2.5 0.46 0.59 0.29 0.10 75 7.5 0.32 0.47 0.17 0.11 75 22.5 0.42 0.80 0.08 0.21 75 47.5 0.35 0.68 0.08 0.21 49 Table 1.1 (con't). Means, maximum values (Max), minimum values (Min), and standard deviations (SD) for each variable measured. Values based on 144 plots, n=9 per row. Number of Flowers/Flowering Ramet Retention level (%) Distance from edge(m) Mean Max Min SD 50 -47.5 17.81 29 12 5.98 50 -22.5 17.08 28 5 6.40 50 -7.5 16.43 24 8 5.07 50 -2.5 16.54 19 9 3.19 50 2.5 16.48 25 11 4.73 50 7.5 • 18.78 24 14 2.52 50 22.5 14.21 19 0 5.95 50 47.5 14.82 24 0 6.95 75 -47.5 14.05 23 8 5.18 75 -22.5 17.76 . 35 4 8.86 75 -7.5 14.89 22 0 7.17 75 -2.5 15.96 21 13 2.80 75 2.5 17.17 23 11 4.18 75 7.5 • 17.27 22 13 3.77 75 22.5 18.19 24 12 3.80 75 47.5 15.78 22 5 5.48 Pollen deposition (%) Retention level (%) Distance from edge (m) Mean Max Min SD 50 -47.5 20.51 37.26 14.04 8.66 50 -22.5 not measured - - -50 -7.5 not measured - - -50 -2.5 24.37 35.65 13.46 7.47 50 2.5 25.94 43.99 14.36 12.48 50 7.5 not measured - - -50 22.5 not measured - - -50 47.5 32.03 46.96 16.54 12.45 75 -47.5 15.39 31.95 5.92 9.98 75 -22.5 not measured - - -75 -7.5 not measured - - -75 -2.5 32.16 59.20 12.22 16.69 75 2.5 35.45 55.19 15.89 14.84 75 7.5 not measured - - -75 22.5 not measured - - -75 47.5 20.28 40.10 7.00 11.35 50 Table 1.1 (con't). Means, maximum values (Max), minimum values (Min), and standard deviations (SD) for each variable measured. Values based on 144 plots, n=9 per row. Initiated Fruits ((sum of initiated fruit/ sum of flowers)/flowering ramet) Retention level (%) Distance from edge(m) Mean Max Min SD 50 -47.5 0.19 0.41 0.08 0.11 50 -22.5 0.17 0.41 0.03 0.12 50 -7.5 0.19 0.38 0.03 0.11 50 -2.5 0.24 0.50 0.11 0.12 50 2.5 0.20 0.37 0.00 0.13 50 7.5 0.20 0.44 0.05 0.12 50 22.5 0.08 0.19 0.00 0.07 50 47.5 0.15 0.28 0.00 0.10 75 -47.5 0.11 0.33 0.02 0.10 75 -22.5 0.15 0.30 0.01 0.10 75 -7.5 0.14 0.28 0.00 0.10 75 -2.5 0.19 0.48 0.00 0.13 75 2.5 0.15 0.27 0.06 0.08 75 7.5 0.20 0.36 0.13 0.09 75 22.5 0.23 0.47 0.00 0.16 75 47.5 0.11 0.18 0.00 0.06 Fruit Set ((sum of set fruit / sum of flowers)/flowering ramet) Retention level (%) Distance from edge (m) Mean Max M i n SD 50 -47.5 0.00 0.02 0.00 0.01 50 -22.5 0.02 0.10 0.00 0.03 50 -7.5 0.03 0.13 0.00 0.03 50 -2.5 0.04 0.16 0.00 0.06 50 2.5 0.09 0.31 0.00 0.13 50 7.5 0.06 0.27 0.00 0.09 50 22.5 0.02 0.15 0.00 0.05 50 47.5 0.01 0.08 0.00 0.03 75 -47.5 0.00 0.03 0.00 0.01 75 -22.5 0.00 0.01 0.00 0.00 75 -7.5 0.02 0.08 0.00 0.03 75 -2.5 0.04 0.14 0.00 0.05 75 2.5 0.02 0.07 0.00 0.02 75 7.5 0.03 0.14 0.00 0.05 75 22.5 0.07 0.26 0.00 0.08 75 47.5 0.02 0.07 0.00 0.02 51 Table LI (con't). Means, maximum values (Max), minimum values (Min), and standard deviations (SD) for each variable measured. Values based on 144 plots, n=9 per row. Fruit Weight (g/fruit) Retention level (%) Distance from edge(m) Mean Max Min SD 50 -47.5 0.015 0.023 0.008 0.005 50 -22.5 0.014 0.026 0.000 0.007 50 -7.5 0.018 0.029 0.011 0.006 50 -2.5 0.027 0.058 0.004 0.016 50 2.5 0.045 0.146 0.000 0.043 50 7.5 0.026 0.053 0.007 0.017 50 22.5 0.023 0.090 0.000 0.028 50 47.5 0.018 0.044 0.000 0.014 75 -47.5 0.012 0.026 0.000 0.007 75 -22.5 0.010 0.025 0.005 0.009 75 -7.5 0.012 0.039 0.000 0.012 75 -2.5 0.016 0.032 0.000 0.010 75 2.5 0.019 0.032 0.006 0.008 75 7.5 0.023 0.052 0.004 0.015 75 22.5 0.014 0.029 0.000 0.012 75 47.5 0.015 0.027 0.000 0.008 Tree basal area/plot (2.5m radius) Retention level (%) Distance from edge (m) Mean Max Min SD 50 -47.5 0.056 0.189 0.000 0.060 50 -22.5 0.090 0.200 0.000 0.063 50 -7.5 0.142 0.583 0.000 0.179 50 -2.5 0.074 0.223 0.000 0.078 50 2.5 0.097 0.362 0.000 0.106 50 7.5 0.074 0.191 0.000 0.064 50 22.5 0.048 0.159 0.000 0.055 50 47.5 0.073 0.123 0.000 0.040 75 -47.5 0.127 0.358 0.000 0.108 75 -22.5 0.117 0.276 0.000 0.097 75 -7.5 0.066 0.257 0.000 0.093 75 -2.5 0.076 0.186 0.000 0.073 75 2.5 0.139 0.236 0.069 0.050 75 7.5 0.071 0.177 0.000 0.075 75 22.5 0.098 0.233 0.000 0.090 75 47.5 0.092 0.216 0.000 0.083 52 Table 1.1 (con't). Means, maximum values (Max), minimum values (Min), and standard deviations (SD) for each variable measured. Values based on 144 plots, n=9 per row. Number of trees Retention level (%) Distance from edge (m) Mean Max Min SD 50 -47.5 1.78 4.00 1.00 1.32 50 -22.5 2.00 5.00 1.00 1.22 .. 50 -7.5 2.00 3.00 1.00 1.00 50 -2.5 1.56 3.00 1.00 0.73 50 2.5 1.78 3.00 1.00 0.83 50 7.5 2.11 9.00 1.00 2.62 50 22.5 1.44 3.00 1.00 0.73 50 47.5 1.33 2.00 1.00 0.50 75 -47.5 2.22 4.00 1.00 1.20 75 -22.5 2.33 7.00 1.00 1.94 75 -7.5 1.22 3.00 1.00 0.67 75 -2.5 1.78 4.00 1.00 1.09 75 2.5 1.78 4.00 1.00 0.97 75 7.5 1.56 3.00 1.00 0.73 75 22.5 1.33 2.00 1.00 0.50 75 47.5 1.44 3.00 1.00 0.73 Height of trees Retention level (%) Distance from edge(m) Mean Max Min SD 50 -47.5 20.56 35.60 8.50 10.02 50 -22.5 21.93 29.25 15.74 4.95 50 -7.5 24.21 34.63 15.47 7.25 50 -2.5 22.21 35.50 14.10 7.56 50 2.5 23.66 32.60 13.87 8.17 50 7.5 21.75 29.80 14.20 7.12 50 22.5 18.94 25.40 9.90 5.81 50 47.5 24.42 38.30 15.80 7.11 75 -47.5 23.84 27.70 12.15 2.35 75 -22.5 26.20 41.6 18.40 7.49 75 -7.5 20.77 23.90 14.20 3.97 75 -2.5 24.70 30.35 21.10 3.23 75 2.5 25.66 29.50 22.65 2.25 75 7.5 21.92 25.95 13.10 5.00 75 22.5 23.19 31.60 18.10 5.17 75 47.5 24.01 31.10 15.00 4.94 53 Table 1.1 (con't). Means, maximum values (Max), minimum values (Min), and standard deviations (SD) for each variable measured. Values based on 144 plots, n=9 per row. Shrub ground cover (%) Retention level (%) Distance from edge (m) Mean Max Min SD 50 -47.5 9.89 23.00 2.67 11.37 50 -22.5 7.39 15.00 2.17 6.74 50 -7.5 10.33 17.33 1.33 8.18 50 -2.5 23.28 60.67 1.67 32.51 50 2.5 19.00 43.50 0.17 22.21 50 7.5 7.78 11.83 0.83 6.04 50 22.5 7.67 18.33 2.00 9.24 50 47.5 8.94 20.33 2.67 9.88 75 -47.5 17.5 32.17 2.33 14.92 75 -22.5 17.33 29.83 7.83 11.30 75 -7.5 14.89 15.50 13.83 0.92 75 -2.5 16.61 22.00 13.17 4.73 75 2.5 17.72 36.67 6.33 16.52 75 7.5 18.06 42.33 3.33 21.18 75 22.5 19.39 37.33 8.83 15.62 75 47.5 25.22 60.17 3.33 30.58 Moss ground cover (%) Retention level (%) Distance from edge(m) Mean Max M i n SD 50 -47.5 52.72 100.00 0.00 50.22 50 -22.5 57.94 100.00 0.17 51.74 50 -7.5 59.50 90.33 0.83 50.83 50 -2.5 43.11 77.50 0.50 39.15 50 2.5 54.44 97.67 16.33 40.91 50 7.5 62.83 93.33 25.00 34.75 50 22.5 66.72 96.97 39.50 28.68 50 47.5 54.33 81.67 13.83 35.78 75 -47.5 61.28 97.33 8.83 46.47 75 -22.5 67.17 100.00 1.50 56.87 75 -7.5 53.94 91.83 0.33 47.73 75 -2.5 57.72 87.67 2.17 48.16 75 2.5 66.11 87.33 52.83 18.57 75 7.5 64.39 95.83 17.33 41.51 75 22.5 47.67 51.67 40.00 6.64 75 47.5 51.72 87.67 10.83 30.65 54 Table 1.1 (con't). Means, maximum values (Max), minimum values (Min), and standard deviations (SD) for each variable measured. Values based on 144 plots, n=9 per row. Herb ground cover (%) Retention level (%) Distance from edge (m) Mean Max Min SD 50 -47.5 16.61 24.00 5.67 9.67 50 -22.5 11.72 26.83 4.00 13.09 50 -7.5 5.50 8.83 3.17 2.96 50 -2.5 7.83 13.33 1.33 6.06 50 2.5 5.06 7.00 1.33 3.22 50 7.5 3.72 3.83 1.00 1.51 50 22.5 . 9.61 22.50 1.83 11.24 50 47.5 11.50 29.00 2.00 15.17 75 -47.5 4.32 4.67 4.00 0.33 75 -22.5 12.56 25.50 2.50 11.77 75 -7.5 16.72 42.17 3.50 22.04 75 -2.5 13.22 33.83 2.50 17.85 75 2:5 14.44 21.17 4.33 11.64 75 7.5 21.33 51.50 4.83 26.16 75 22.5 10.61 18.00 4.67 6.78 75 47.5 9.50 19.33 4.50 8.52 55 APPENDIX II GRAPHICAL REPRESENTATION OF MEANS AND STANDARD ERRORS OF MICROCLIMATIC, BUNCHBERRY GROWTH, AND BUNCHBERRY REPRODUCTIVE VARIABLES (a) (b) P 800 non-harvested harvested -47.5 -22.5 -7.5 -2.5 2.5 7.5 22.5 47.5 Distance from forest edge (m) 0 50% m 75% LLL 47.5 -22.5 -7.5 -2.5 2.5 7.5 22.5 47.5 Distance from forest edge (m) Figure ILL Means and standard errors of microclimatic, bunchberry growth, and bunchberry reproductive variables. A l l means and standard errors based on 144 plots. 56 (c) (d) (e) 100 -, 80 60 40 20 -\ 0 non-harvested 53 harvested n 50% • 75% -47.5 -22.5 -7.5 -2.5 2.5 7.5 22.5 47.5 Distance from forest edge (m) harvested m 50% • 75% -47.5 -22.5 -7.5 -2.5 2.5 7.5 22.5 47.5 Distance from forest edge (m) -47.5 -22.5 -7.5 -2.5 2.5 7.5 22.5 47.5 Distance from forest edge (m) Figure II. 1 (con't). Means and standard errors of microclimatic, bunchberry growth, and bunchberry reproductive variables. A l l means and standard errors based on 144 plots. 57 (f) (g) o c o a ra = t O u 0. W >, m n E T3 O) £ ' J 0) OT > ^ o 50 40 30 20 10 -I 0 non-harvested harvested -47.5 -2.5 2.5 47.5 •stance from forest edge (m) 0.3 n no n-harvested harvested 150% • 75% -47.5 -22.5 -7.5 -2.5 2.5 7.5 22.5 Distance from forest edge (m) 47.5 (h) 0.16 0.14 0.12 0.1 H 0.08 0.06 0.04 -I 0.02 0 non-harvested harvested -47.5 -22.5 -7.5 -2.5 2.5 7.5 22.5 Distance from forest edge (m) El 5 0 % • 7 5 % 47.5 Figure I I . l (con't). Means and standard errors of microclimatic, bunchberry growth, and bunchberry reproductive variables. A l l means and standard errors based on 144 plots. 58 (i) _ 0.05 | 0.04 "3> r 0.03 f 0.02 -] x 0.01 • £ o non-harvested harvested • 5 0 % • 7 5 % -47.5 -22.5 -7.5 -2.5 2.5 7.5 22.5 47.5 Distance from forest edge (m) Figure I I . l (con't). Means and standard errors of microclimatic, bunchberry growth, and bunchberry reproductive variables. A l l means and standard errors based on 144 plots. 59 APPENDIX III PERCENT COVER OF A L L VEGETATION PRESENT IN PLOTS Table III.l. Percent cover of all vegetation present in plots. Means based on 144 plots. Retention Distance Actaea rubra Alnus spp. A ralia Aster spp. Circaea Level from edge (m) nudicaulis alpina 50 -47.5 2.222 0.000 0.000 0.000 0.000 50 -22.5 1.667 0.000 0.000 4.444 0.000 50 -7.5 0.000 0.056 0.000 0.111 0.000 50 -2.5 0.056 3.444 0.000 0.000 0.000 50 2.5 0.000 0.000 0.000 0.222 0.000 50 7.5 0.000 0.000 0.000 0.000 0.000 50 22.5 0.000 0.000 0.000 0.056 0.000 50 47.5 0.000 0.000 3.889 0.000 0.000 50 AVERAGE 0.493 0.438 0.486 0.604 0.000 75 -47.5 0.000 0.000 0.000 0.000 0.000 75 -22.5 0.000 0.000 0.000 0.000 1.333 75 -7.5 0.000 0.000 4.444 0.000 0.000 75 -2.5 0.000 0.000 0.000 0.000 0.222 75 2.5 0.000 0.000 0.000 0.000 0.000 75 7.5 0.000 0.000 5.000 0.000 0.000 75 22.5 0.000 0.056 3.389 0.000 0.000 75 47.5 1.111 0.000 0.000 2.222 0.056 75 AVERAGE 0.139 0.007 1.604 0.278 0.201 OVERALL AVERAGE 0.316 0.222 1.045 0.441 0.101 Retention Distance Cornus Equisetum Fragaria Galium spp. Gymno-Level from edge (m) canadensis spp. virginiana carpium dryopteris 50 -47.5 5.889 6.833 0.222 0.111 0.000 50 -22.5 16.000 0.000 0.056 0.111 0.000 50 -7.5 8.889 0.278 0.000 0.056 0.000 50 -2.5 6.667 0.333 0.111 0.056 0.000 50 2.5 8.167 0.111 0.056 0.111 0.000 50 7.5 5.111 0.167 0.000 0.056 0.000 50 22.5 4.111 0.833 0.056 0.111 0.000 50 47.5 17.333 0.556 0.000 0.000 0.000 50 AVERAGE 9.021 1.139 0.063 0.076 0.000 75 -47.5 5.889 3.000 0.111 0.056 0.000 75 -22.5 7.667 2.056 0.056 0.389 0.000 75 -7.5 4.333 0.556 0.333 0.167 2.222 75 -2.5 3.778 1.556 0.167 0.111 0.000 75 2.5 12.778 5.944 0.000 0.167 0.000 75 7.5 10.444 1.444 0.389 0.222 0.000 75 22.5 7.056 0.778 0.167 0.167 0.000 75 47.5 10.611 0.944 0.333 0.278 0.000 75 AVERAGE 7.819 2.035 0.194 0.194 0.278 OVERALL AVERAGE 8.420 1.587 0.128 0.135 0.139 60 Table III . l con't. Percent cover of all vegetation present in plots. Means based on 144 plots. Retention Distance Poaceae Hylocomium Lathy rus Linnaea Mertensia Level from edge (m) splendens ochroleucus boreallis peniculata 50 -47.5 0.111 43.889 0.222 1.500 4.000 50 -22.5 0.222 47.611 0.278 0.61.1 2.111 50 -7.5 0.389 42.944 0.556 0.556 2.000 50 -2.5 0.222 32.722 0.722 2.167 1.889 50 2.5 0.444 32.222 0.333 0.444 0.889 50 7.5 0.056 34.111 0.056 0.389 1.000 50 22.5 0.278 41.222 0.111 0.333 6.556 50 47.5 0.500 50.056 0.056 0.444 4.944 50 AVERAGE 0.278 40.597 0.292 0.806 2.924 75 -47.5 0.333 48.667 0.167 1.333 0.333 75 -22.5 1.500 53.833 0.167 0.444 4.667 75 -7.5 0.500 36.056 0.167 0.500 5.056 75 -2.5 4.833 38.889 0.167 0.556 5.556 75 2.5 0.389 58.167 0.556 3.944 7.056 75 7.5 0.333 55.778 0.611 0.722 6.778 75 22.5 0.333 32.667 0.389 0.667 2.222 75 47.5 0.389 32.778 0.444 0.722 1.500 75 AVERAGE 1.076 44.604 0.333 1.111 4.146 OVERALL AVERAGE 0.677 42.601 0.313 0.958 3.535 Retention Distance Mitella nuda Petasites Pleurozium Pt ilium Pyrola spp. Level from edge (m) palmatus schreberi crista-castrensis 50 -47.5 0.167 0.889 8.000 0.833 0.222 50 -22.5 0.222 1.667 8.000 2.333 0.222 50 -7.5 0.167 1.167 15.389 1.167 0.056 50 -2.5 0.333 1.722 10.278 0.111 0.167 50 2.5 0.222 2.000 13.778 8.444 0.000 50 7.5 0.167 0.722 23.778 4.944 0.000 50 22.5 0.389 0.556 22.944 2.556 0.167 50 47.5 0.278 0.722 4.167 0.111 0.000 50 AVERAGE 0.243 1.181 13.292 2.563 0.104 75 -47.5 0.389 0.222 6.722 5.889 0.000 75 -22.5 0.556 0.167 3.556 9.778 0.000 75 -7.5 0.556 0.611 4.556 13.333 0.000 75 -2.5 0.389 0.222 18.389 0.444 0.111 75 2.5 0.389 0.222 3.333 4.611 0.000 75 7.5 0.500 0.556 2.833 5.778 0.056 75 22.5 0.556 0.333 9.444 5.556 0.667 75 47.5 0.833 0.500 11.667 7.278 0.111 75 AVERAGE 0.521 0.354 7.563 6.583 0.118 OVERALL AVERAGE 0.382 0.767 10.427 4.573 0.111 61 Table III . l con't. Percent cover of all vegetation present in plots. Means based on 144 plots. Retention Distance Ribes Ribes triste Rosa Rubus idaeas Rubus Level from edge (m) lacustre acicularis pubescens 50 -47.5 0.333 1.667 2.222 1.111 1.667 50 -22.5 0.000 0.222 1.444 1.778 0.889 50 -7.5 0.278 0.000 4.000 0.111 1.333 50 -2.5 0.000 0.000 2.556 6.889 1.167 50 2.5 0.000 0.111 8.889 0.389 3.778 50 7.5 0.000 0.000 4.611 0.056 1.167 50 22.5 0.056 0.000 4.000 0.000 0.222 50 47.5 0.000 0.000 4.833 0.222 0.556 50 AVERAGE 0.083 0.250 4.069 1.319 1.347 75 -47.5 2.222 0.056 3.000 0.000 1.500 75 -22.5 0.111 0.056 6.167 0.000 1.222 75 -7.5 0.389 0.000 6.222 0.333 3.222 75 -2.5 0.000 0.000 13.222 0.111 0.944 75 2.5 0.111 0.111 8.667 0.111 3.667 75 7.5 0.056 0.222 9.722 0.000 1.167 75 22.5 0.111 0.000 9.667 0.333 2.333 75 47.5 0.000 0.000 12.556 1.722 3.500 75 AVERAGE 0.375 0.056 8.653 0.326 2.194 OVERALL AVERAGE 0.229 0.153 6.361 0.823 1.771 Retention Distance Salix Vaccinium Viburnum Viola Level from edge (m) myrtilifolia caspetosum edule renifolia 50 -47.5 0.000 0.444 2.444 0.111 50 -22.5 0.000 0.722 2.333 0.111 50 -7.5 0.000 0.222 4.333 0.167 50 -2.5 2.222 0.167 6.833 0.056 50 2.5 0.000 0.056 5.778 0.222 50 7.5 0.000 0.000 1.944 0.111 50 22.5 0.000 0.000 3.389 0.167 50 47.5 0.000 0.111 3.222 0.056 50 AVERAGE 0.278 0.215 3.785 0.125 75 -47.5 0.111 0.000 10.611 0.333 75 -22.5 0.000 0.000 9.778 0.278 75 -7.5 0.000 0.000 4.722 0.111 75 -2.5 0.000 0.000 2.333 0.333 75 2.5 0.000 0.000 5.056 0.167 75 7.5 0.000 0.000 6.889 0.222 75 22.5 0.000 0.000 6.889 0.278 75 47.5 0.000 0.000 7.444 0.222 75 AVERAGE 0.014 0.000 6.715 0.243 OVERALL AVERAGE 0.146 0.108 5.250 0.184 62 

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