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The relative effect of clipping, neighbours, and fertilization on the population dynamics of Lupinus… Graham, Stepanie Ann 1994

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THE RELATIVE EFFECT OF CLIPPING, NEIGHBOURS, AND FERTILIZATION ON THE POPULATION DYNAMICS OF Lupinus arcticus (Family Fabaceae). by STEPHANIE ANN GRAHAM B.Sc., The University of Calgary, 1990 A THESIS SUBMITTED TN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES Department of Botany  We accept this thesis as conforming to the required standard  THE UNIVERSITY OF BRITISH COLUMBIA June 1994 © Stephanie Ann Graham, 1994  In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of Bntish 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.  (Signature)  Department of  /O74IV  V  The University of British Columbia Vancouver, Canada  Date  DE-6 (2188)  vZj2  c,2, /‘)  ABSTRACT  A demographic study was conducted in 1991 and 1992 on replicated field populations of Lupinus arcticus, near Kluane Lake, Yukon. The relative effects of herbivory, neighbours, and soil fertility level were assessed using a factorial experiment of +1- clipping, +1- neighbour removal, and +1- fertilizer (NPK). The main population experiment monitored the dynamics of leaves, however, data on reproduction, survival, and size were also collected from the permanent quadrats. Clipping reduced leaf cohort survivorship, total leaf density, and the incidence of disease on leaves, but resulted in an increased standing crop of leaves. Removing neighbours increased the percent cover of L. arcticus and decreased total leaf mortality. Fertilizing increased the incidence of disease on leaves, and reduce the standing crop of leaves. Significant three-way interactions between treatments affected the plasticity of petiole length distributions for L. arcticus. Between-year differences in the responses to the treatments were also detected, particularly for reproductive investment and output in L. arcticus. Although a number of significant responses to treatments were detected, nevertheless, the overall tendency was for a lack of response, especially pertaining to leaf population dynamics. This low response to the treatments imposed is consistent with Grime’s (1979) arguments that plants growing in low productivity, infrequently disturbed habitats (i.e. stressful sensu Grime 1979) should show little response to short-term changes in local environmental conditions.  ii  TABLE OF CONTENTS Page ABSTRACT  ii  TABLE OF CONTENTS  iii  LIST OF FIGURES  vi  LIST OFTABLES  vii  LIST OF ABBREVIATIONS  xi  ACKNOWLEDGMENTS  xii  Dedication  xiii  INTRODUCTION  1  Background  3  Thesis problem  4  CHAPTER 1 LITERATURE REVIEW  7  The effect of herbivory  7  The effect of neighbours  14  The effect of fertilizer  23  CHAPTER 2 DEMOGRAPHY OF LUPINUS LEAVES  33  METHODS  33 Study Area  33  Experimental Design  36  Leaf Demography Study  38  Statistical Analyses  38  Standing crop available  40  RESULTS  41 Leaf Demography 1991  41  Leaf Demography 1992  41 iii  Standing crop available DISCUSSION  .50 53  Leaf demography  54  Standing crop available  57  CHAPTER 3 LEAF COHORT SURVIVORSHIP  59  METHODS  59 Statistical Analysis  60  Parametric Model Fitting  60  Weibull Model Description  61  Nonparametric Statistics  61  RESULTS  62 Weibull Model Analyses  62  Nonparametric Analyses  65  DISCUSSION  67 Effects of clipping  68  Effect of fertilizer and removing neighbours  69  Comparison of cohorts  70  CHAPTER 4 VEGETATIVE GROWTH  74  METHODS  74 Size structure  74  Cover  75  RESULTS  75 Size structure of petioles 1991  75  Size structure of petioles 1992  78  Changes in percent cover  81  Changes in Vegetation Composition  82  DISCUSSION  .86 Petiole length distribution  .86  Percent Cover  89  CHAPTER 5 REPRODUCTION  94  METHODS  95 Statistical Analysis  95  RESULTS  96 Yearly variation  96  Reproduction 1991  97  Reproduction 1992  105  DISCUSSION  111  CHAPTER 6 SUMMARY AND CONCLUSIONS  115  APPENDIX A  119 Characteristics of the Weibull Distribution  BIBLIOGRAPHY  119 121  V  LIST OF FIGURES  Figure 1:  Bud production I m 2 over time by treatment in 1992.  51  Figure 2:  Mean (±SEM) live summer leaf-days (C ) by treatment and year. 1  52  Figure 3:  Leaf survivorship curves for cohort 1 and cohort 2 by treatment in 1992.  64  Figure 4:  Mean (±SEM) change in percent cover for Lupinus arcticus and Festuca altaica from 1991 to 1992.  85  FigureS:  A comparison of total number of racemes, flowers, and pods produced across all treatments in 1991 and 1992.  99  Figure 6:  Snowshoe hare spring densities on CSP treatment grids from 19871994. The control treatment shows the beginning of the crash in  99  1991. Figure 7:  Reproductive effort in 1991 and 1992 as measured by mean (±SEM) raceme production I m 2 for each treatment.  101  Figure 8:  Summary of mean (±SEM) number of flowers initiated / m 2 and mean (±SEM) number of fruits matured (pods) / m 2 by treatment.  102  Figure 9:  Survival curves generated by the Weibull distribution when 2=1 and o is allowed to vary. Redrawn from Lee (1992).  120  vi  LIST OF TABLES  Table 1:  Summary of mean leaf density, bud density, leaf mortality and incidence of disease by treatment in 1991.  42  Table 2:  Source of variation for three-way ANOVA results of bud density, leaf density, incidence of mortality and disease in 1991.  43  Table 3:  Summary of mean leaf density, bud density, leaf mortality and incidence of disease in 1992.  44  Table 4:  Source of variation for three-wayANOVA of leaf density, incidence of disease and mortality in 1992.  45  TableS:  a) Source of variation for one-way analysis of leaf density and incidence of disease by treatment in 1992.  46  b) Tukey comparison of one-way analysis of leaf density by treatment in 1992.  46  c) Tukey comparison of one-way analysis of disease by treatment in 1992.  46  Table 6:  Source of variation for one-way repeated measures MANOVA of bud production in 1992.  47  Table 7:  Summary of multivariate test statistics for one-way and three way repeated measures analysis of bud production/m 2 in 1992.  47  Table 8:  Three-way repeated measures MANOVA of bud production/rn 2  48  Table 9:  Source of variation for the polynomial test of order (quadratic) for three-way repeated measures analysis of bud production in 1992.  49  Table 10:  Source of variation table for a 3-way ANOVA of log transformed live summer leaf days (Ci) by treatment in 1991 and 1992.  52  Table 11:  Scale (?) and shape (ce) parameter estimates for the Weibull Distribution (1992).  63  vii  Table 12:  Combined source of variation for the one-way ANOVAs for cohort 1 and cohort 2 comparing the log transformed (scale) Weibull parameter by treatment.  63  Table 13:  Combined source of variation table of one-way ANOVAs for cohorti and cohort 2 comparing the log transformed shape (x) parameter by treatment.  66  Table 14:  Combined source of variation table for two-way ANOVAs testing for the effect of cohort within treatment on log transformed shape and scale parameters.  66  Table 15:  The nonparametric log rank statistics testing for between treatment effects in cohort 1 and cohort 2 leaf survivorship curves.  66  Table 16:  Chi square test for independence of fertilizer, clipping and neighbour removal on petiole length distribution using log linear analysis.  77  Table 17:  Length distribution of petioles by treatment reported as frequency and percent occurring in each class.  77  a) Survey5, 1991. b)Survey7, 1991.  78  Table 18:  Chi square test for independence of fertilizer, clipping and neighbour removal on petiole size distribution using log linear analysis.  79  Table 19:  Distribution of petiole lengths by treatment reported as size class frequency and percent occurring in each class.  80  a) Survey 4, 1992. b) Survey 6, 1992.  81  Table 20:  Summary of mean changes (± SEM) by treatment of percent cover of species occurring in quadrats from August 1, 1991 to July 21, 1992.  83  Table 21:  Summary of ANOVA results for arcsine transformed changes in percent cover in Lupinus arcticus.  84  Table 22:  a) Univariate source of variation comparing total raceme, flower bud and pod production I m 2 in by treatment 1991 and 1992. vii ±  100  b) Multivariate test statistics comparing reproductive variables by year.  100  a) Univariate source of variation comparing total raceme, flower bud and pod production/rn 2 by treatment in 1991 and 1992.  103  b) Wilks’ Lambda and F statistics from MANOVA of 1991 estimates of raceme and flower bud production.  103  a) Three-way ANOVA of square root transformed pod production per m i 2 n 1991.  104  b) Summary of Kruskal-Wallis non-parametric test results for some reproduction variables.  104  Table 25:  Three way ANOVA test of arcsine transformed reproductive efficiency /m in 1991. 2  105  Table 26:  a) Univariate statistics by treatment of transformed raceme production and flower bud initiation in 1992.  107  b) Wilks’ Lambda and F statistics from MANOVA of 1992 estimates of log transformed raceme and flower bud production in 1992.  108  a) Three-way ANOVA of log transformed pod production / m 2 in 1992.  108  b) One-way ANOVA of pod production / m 2 in 1992 to determine the effect of the intensified clipping treatment on reproductive output.  108  a) Three-way ANOVA of arcsine transformed relative success in 1992.  109  b) One-way ANOVA of arcsine transformed relative success in 1992.  109  c) Post hoc Tukey grouping of relative success by treatment in 1992.  109  Table 29:  Mean size (±SEM) of reproductive structures by treatment for 1992.  109  Table 30:  a) Univariate (three-way) test statistics of length of racemes, and length of peduncles in 1992.  110  b) Multivariate test statistics from three-way MANOVA of length of racemes, and length of peduncles in 1992.  110  Table 23:  Table 24:  Table 27:  Table 28:  ix  Table 31:  c) Univariate (one-way) test statistics of length of racemes and length of peduncles by treatment in 1992.  110  d) Multivariate test statistics from one-way MANOVA of length of racemes and length of peduncles by treatment in 1992.  111  Summary of significant main effects detected in this experiment for 1991 and 1992.  115  x  LIST OF ABBREVIATIONS  To save space, the following abbreviations are used in all tables or figures unless otherwise specified in the table or figure legend. TREATMENT  TABLES  FIGURES  CONTROL  CONT  CONT  FERTILIZER  FERT  F  CLIPPING  CLIP  C  NEIGHBOURS REMOVED  REM  NR  FERTILIZED/CLIPPED  PERT/CLIP  F-C  FERTILIZED/NEIGHBOURS  PERT/REM  F-NR  CLIP/REM  C-NR  PERT/CLIP/REM  F-C-NR  REMOVED CLIPPED/NEIGHBOURS REMOVED FERTILIZED/CLIPPED! NEIGHBOURS REMOVED EXTRA CLIPPING  EXTRA CLIP or CLIP+  STANDARD ERROR  ±SEM  SHAPE PARAMEThR SCALE PARAMETER  xi  ±SEM  ACKNOWLEDGMENTS I wish to extend my sincere appreciation to Dr. Roy Turldngton for his friendship, constant encouragement and support, both emotional and financial, during the supervision of this thesis. Thank you for giving me the opportunity to experience the Yukon as a field botanist. It was the opportunity of a lifetime that few will ever experience, and I will never forget. I am also very grateful for his diligence as an editor, constructive criticism, and amazing ability to pull much needed references out of the air at the appropriate moment. Thanks, boss. I would also like to thank Dr. Elizabeth John, Dr. F.E.R. MacCauley, Dr. Jack Maze, Dr. Gary Bradfield, and Dr. David Hik for their helpful suggestions, observations, theoretical and statistical assistance during the course of this project. I am very grateful for the dedication and perseverance of many field research assistants and fellow graduate students who endured monster mosquitoes, black ffies, and the risk of bear attack while remembering how many hundred lupines they had just counted. They include Elena Klein, Franklin Dlott, Brian Leung, Sue Crites, Shane Kirby, and Meagan Williams. A very special thanks must go to Mr. Ji Yong Yang, an outstanding research associate and true friend, who both inspired and endured a great deal throughout this thesis. Your friendship, patience, unusual dreams, frequent bouts of insanity, romantic woes, and cannibalistic cravings were refreshing. I am grateful beyond words, Ji. You are one of a kind and I am glad to call you friend. I wish to acknowledge the technical assistance of the Collaborative Special Project, headed by Dr. Charles Krebs, who provided field assistants, equipment, and vehicles, when required. I would also like to thank the Arctic Institute of North America, and Andrew and Carol Williams for making my stay in the Yukon truly memorable. I must also thank the senior CSP technicians (Mark O’Donoghue, Sabine Schweiger, Cathy and Frank Doyle), for their patience and goodnatured tolerance of a bunch of lazy botanists. These people must be credited with transforming a bunch of ‘green’ students (myself included) into seasoned field ecologists in a few short weeks every year. You have done an excellent job. I am also grateful to frene Wingate, who has frequently come to the rescue with equipment, and information on very short notice. I cannot say enough about the role my family has played in my education, but this thesis would not have been possible without their love, and life-long support. To my mother, Audrey Graham, who has been a wonderful role model, and has always encouraged me work hard to achieve my best, you are the greatest! To my father and stepmother, Barry and Dar Graham, who taught me to pursue my dreams and let the sky be my limit, they have always been there when I most needed them. Together, they have all instilled in me inner strength, and an appreciation for the value of education. Thank you all, I could not have done this without you.  xii  Dedication  Finally, I have a special thanks for Shane Kirby, whose contribution to the completion of this thesis cannot be easily stated. Over this long, and sometimes rough road, we have climbed mountains, stumbled down unstable rockslides, passed through many valleys, listened to the rain, and drank orange juice in the sunshine. Many moments. Through it all, you encouraged me to persevere, even when obstacles stood in the way. Together, we have overcome and grown. This thesis is dedicated to you, with friendship, respect, and much love.  xiil  INTRODUCTION  A number of abiotic and biotic factors are known to influence plants on an individual, population, and a community scale. A viable seed may be prevented from germinating by the placement of a stone or leaf that blocks its access to sunlight or moisture, whereas such factors may have little impact on a mature plant. Similarly, a seedling, by virtue of its size, may be undetectable by herbivores or insensitive to wind yet these are important mortality factors on fullsized plants. This is significant because the relative importance of different factors on the dynamics of individual plants and populations change over time, over stage, over a season. The effects of these factors may be localized or patchy within a habitat. Although a myriad of factors influence plant dynamics, few are capable of limiting or regulating plant populations on their own. Rarely, if ever, are plants in natural environments influenced by one factor alone to the exclusion of all others. A challenge for ecologists is to untangle and measure the relative strengths of different factors and determine how they interact to produce observed patterns of abundance (Hunter & Price 1993). Experiments that manipulate a number of potential limiting factors concurrently and measure the response of individual plants and populations permit a better understanding of how different limiting or regulatory agents interact in natural communities and give insight at the individual, population, and community levels. The most effective way to achieve this is to conduct replicated multi-factorial field experiments. Historically, three factors have been investigated as important agents determining plant performance and population dynamics: disturbance (including herbivory), competition, and nutrient availability. Larger disturbances, are not considered here because they frequently operate 1  2 on a scale and an intensity that eliminates entire communities or populations as opposed to operating within an existing community. Another potentially important limiting agent that is not considered here is the effect of the soil microbial community. At present, the implications of the microbial community as a limiting agent in plant dynamics can only be inferred from a few studies although this will likely change in the future as more data are collected. Much of the previous research concentrates on how these agents interact to structure communities. This focus on community level responses to these factors has fueled the debate about top-down or bottom-up regulation of communities. Depending on the community being studied, different researchers have come to the conclusion that either top-down forces (herbivory or predation), or bottom-up forces (nutrient availability and primary production) are more important in determining structure of that particular community. A recent study (Hunter & Price 1993), has tried to synthesize the two opposing views with a more realistic model incorporating both bottom-up and top-down forces. This model proposed a compromise such that the relative roles of top-down or bottom-up forces in a community vary within and among the systems in question. The Hunter & Price model proposes that bottom-up abiotic factors such as nutrient status set absolute limits upon which organisms can exist in a certain environment. However, this bottom-up component of the model is also subject to top-down forces generated by consumers in the community. Variability or heterogeneity in the response of consumers in terms of their interactions with other members of the community (predators, competitors, or prey) act as top down feedback mechanisms that act in conjunction with bottom-up forces. The concession this model makes is that if either top-down and bottom-up forces can operate and prevail in a community depending on local conditions, any species in the food web (consumer or producer) can act as a keystone species depending on the outcome of the interactions both between and among levels of the food web. This model is flexible enough to support both top-down and  3 bottom-up regulation of communities on the basis of interactions between and among different levels in the food web as well as due to environmental variability. The Hunter & Price’s model necessitates more experiments to investigate the effect of different limiting or regulatory agents and especially their interactions in communities. However, to interpret the response of communities to these factors, it is necessary that we first understand how these factors operate at the level of the population and the individual. Few studies examine the effect of these factors and their interactions on plant population dynamics, and those that do are commonly performed in pot or common garden experiments. However, the results of such experiments can rarely be extrapolated to natural communities and thus the need for manipulative field studies is evident.  BAC KGROUND As part of an NSERC Collaborative Special Project (CSP), community dynamics in a boreal forest ecosystem are being investigated with emphasis on testing the relative importance of various interactions between top predators, herbivores and the plant community on the structure of the community (Krebs 1988). Specifically, Krebs and co-workers are investigating the linkages between the various trophic levels in the forest ecosystem with particular emphasis on the vertebrate food web. One major aim of the study is to test a number of hypotheses to explain the 10-year cycle of snowshoe hare (Lepus americanus) population densities that have been observed across northern Canada and Alaska. In this work, top-down (predators, herbivores), and bottom up (nutrients) factors are being manipulated at an ecosystem scale with experimental grids as large as 1 km 2 The vegetation component of this study is concerned with dynamics of the understory herbaceous vegetation as a potential summer food resource for hares and other herbivores. Two hypotheses are being tested regarding dynamics in the plant community: i) Vegetation amount and composition is regulated by nutrient availability alone.  4 ii) Vegetation amount and composition is regulated by nutrient availability and herbivory. To test these hypotheses, experiments on the understory herb community are being conducted that focus on fine-scale analysis of changes in composition, and more recently abundance (biomass), under treatments manipulating soil nutrient availability (fertilizer) and herbivore density (fencing). In spite of its importance in structuring some plant communities, there are currently no studies investigating the role of competition on the composition and structure of the plant community.  THESIS PROBLEM Although it is generally acknowledged that there is a need to understand how different factors interact to influence the structure and dynamics of plant communities, there is also a need to understand how these factors interact at the scale of individuals and populations which comprise the communities. Understanding how populations are limited and regulated in natural communities is important in the formulation of conservation strategies that are becoming an increasing part of ecological research world-wide. Surprisingly, few studies are available that examine the effect of different limiting factors and their interactions on the dynamics of natural populations. If we are to gain a better understanding of how populations are regulated, it is imperative that studies are performed on natural populations exposed to the full range of environmental forces and heterogeneity. In this study, three factors commonly influencing the growth of plants were chosen for investigation: herbivory, interference from neighbours, and soil nutrient availability. The species chosen for study was the Arctic Lupine, (Lupinus arcticus Lindi.: Family Fabaceae), a relatively abundant understory herb in the boreal forest. The effect of these three factors on the population dynamics of L. arcticus will be determined by assessing the relative impacts of different  5  combinations of clipping, neighbour removal, and fertilizer in a factorial design. The reasons for choosing this particular species are as follows: 1.  Lupinus arcticus occurs in high enough abundance to obtain adequate densities for  population experiments. 2.  Lupinus arcticus is known to be a summer food resource for many herbivores in the boreal  forest including snowshoe hares and ground squirrels. 3.  Members of the Genus Lupinus are nitrogen fixers and are also capable of producing antiherbivore defense chemicals. This may result in unique responses to the different limiting factors being tested, particularly nutrient availability and herbivory.  Ideally this study would have preferred to examine the effects of natural levels and types of herbivory on plant population dynamics. However, the population density of snowshoe hare, the primary herbivore in the study area (near Kluane Lake, Yukon), was in the decline phase of its cycle in 1990/91. As a result, herbivore densities were not sufficient to measure the effect of natural grazing on L. arcticus populations. Therefore, herbivory was simulated by clipping to a constant height to control the amount and type of herbivory experienced by the replicate populations. The objectives of this thesis are to determine the direct and interactive effects of clipping, neighbour removal, and fertilization on population dynamics of modules of L. arcticus, as well as determine their relative importance in limiting the growth of L. arcticus. The experimental component of this thesis is divided into four sections. The first is composed of leaf demography studies that comprise the main body of data. This experiment was performed for two reasons. First, L. arcticus grows clonally by sending out underground rhizomes. Because of this, it was not possible to identify genets or ramets for a population experiment. Second, in both 1991 and 1992 there was insufficient recruitment from seed to  6 monitor the dynamics of new recruits (genets). Therefore population dynamics in L. arcticus were investigated by counting leaves rather than ramets. The second section presents the results of a leaf cohort survivorship study done in 1992. Section three summarizes the growth data collected as part of the leaf demography study (section 1). The final section analyzes reproductive data also collected as part of the main experiment (section 1). Because of the detailed methods and statistical analyses associated with the different components of this study, each of the four sections is structured to include its own methods, results, and discussion section. The final chapter attempts to synthesize the results into a more cohesive framework.  CHAPTER 1 LITERATURE REVIEW  This review is divided into three parts that examine ecological studies pertaining to the effects of the major factors being investigated in this study: herbivory, neighbours, and soil fertility. The body of literature available for each of these factors is very extensive and cannot be fully addressed in one chapter, therefore this review concentrates primarily on terrestrial field studies that manipulate herbivory by simulation or herbivore exciosure; neighbour density by removal of species; and soil fertility through fertilization. Although the focus is on field population studies, other studies (pot, common garden) are incorporated in some cases if they manipulate similar treatments, or if they demonstrate potential responses not yet reported for field populations. Because the response of populations to manipulations in the field is influenced by both individual plants as well as to community level effects, some studies are included particularly if they show pertinent results that may explain some population level responses observed in this study.  THE EFFECT OF HERBIVORY Herbivores have a broad range of effects on plants originating from both the consumption of plant material, as well as through activities not associated with direct removal of plant biomass. Direct effects incurred as a consequence of herbivores feeding include: (i) reductions in fitness, (ii) subordination of position in the canopy, as well as changes in (iii) seed number and size, (iv) growth rate, and plant architecture, (v) root growth, (vi) flowering time, (vii) the outcome of competition in mixture, (viii) rates and patterns of dispersal, (ix) the rate of population increase, and (xi) the amount of genetic variation within a population, and (xii) the stimulation of anti 7  8 herbivore defense compounds. Indirect effects that are the product of plant herbivore -  coexistence have also been shown to affect plant dynamics. Some of these include trampling, deposition of faeces, urine, or saliva, transmission of disease, pollen and seed dispersal, and plantanimal mutualisms (e.g. ant-acacia, Crawley 1983). The combination of these direct and indirect effects may have positive or negative repercussions for the plant and the dynamics of plant populations. As a result, the question of herbivory as a beneficial process to plants has been raised by some investigators (see Owen 1980, Beisky 1986). Although herbivores have the ability to heavily damage plants, there is still some debate in the literature on the relative importance of herbivory. Belsky (1987), in a review of the effects of grazing at organismic, community, and ecosystem scales, suggested that there is evidence that low to moderate levels of herbivory have no measurable effect on plant dynamics. Crawley (1988) further submits that there is little evidence in support of herbivores as a regulatory agent in spite of their ability to affect plant growth in such major ways.  Herbivore exclusion studies Previous studies designed to measure the effect of herbivores on plant dynamics in the field have commonly used two methods: (i) herbivore exclusion with fencing, pesticides, insect traps, and (ii) simulated herbivory. Studies that use herbivore exclusion usually compare plant dynamics in an area that is inaccessible to herbivores for a period of time relative to a control area where herbivores are permitted unrestricted access (Tansley & Adamson 1925, Andersson & Jonasson 1986, Coppock et al. 1983, Pyke 1986, Crawley 1990). The advantage of this method is that it is the only one that allows the researcher to measure the effect of natural levels of herbivory (i.e. in terms of intensity, frequency, and type of damage) on plant dynamics. The drawbacks are that the amount of herbivory among replicates is not controllable. Some experimental plots that are accessible to herbivores are not necessarily grazed, or they may not all be grazed with the same intensity, or for the same duration. Different species of herbivores may graze replicate plots  9 differently. This can result in a great deal of variability in the measured response within a treatment, and it may make it difficult to quantify and discern the effects of herbivory from other factors affecting plants. This could be particularly important when herbivore densities are low, and the impact of grazing on plants is patchy or infrequent. The level of replication required to compensate for this problem may be more than is manageable in an average study with limited resources. Pacala & Crawley (1992) in a review of the effects of herbivores on plant diversity suggested that previous experiments removing herbivores have had little effect on plant communities. As a result, they suggested that a more appropriate means of studying herbivory would be to combine herbivore exclusion or removal with some form of experimental perturbation. This has frequently involved manipulating the competitive environment in which the experimental defoliation occurs.  Grazing simulation studies In simulation studies, the effects of herbivory on plants is determined by controlled tissue removal experiments where investigators decide the amount and type of herbivory to be imposed on a plant population or community. Baldwin (1990) reviewed this method and presented a number of inadequacies associated with this procedure. He suggested that while simulated herbivory may adequately mimic the effect of biomass loss on plants, it is not possible for researchers to simulate the myriad of other effects that herbivores have such as their selectivity for certain tissues and patterns of damage, the effect of saliva, timing of attack, stimulation of chemical response in plants, or the exact nature of the mechanical damage (tearing, sucking chewing, stripping). Several studies were cited documenting the difference in response of plants to mechanically damaged plants relative to those subject to real herbivory. In Baldwin (1988), a comparison of Nicotiana sylvestris’s response to real and simulated herbivory reported that the alkaloid response to the simulation was significantly different than that observed for actual  10 herbivore attack. Mauricio et al. (1993) also reported that the pattern of leaf damage (concentrated vs. dispersed damage) in an annual, Raphanus sativus L., had different impacts on reproduction and biomass. Therefore, it is evident that simulation studies must be undertaken with caution, with special attention paid to the nature of the question being addressed. It is obvious that studies of the indirect effects of herbivores are not adequately simulated with this technique (Baldwin 1990). In spite of this, simulated herbivory experiments have some advantages over exclusion experiments in that the magnitude, and the type of damage inflicted can be controlled and measured. Pathogen spread that may confound the results due to alterations of host-plant resistance is minimized, and simulated herbivory can distinguish between the uncontrolled effects of non-random selection of tissue and plants (Baldwin 1990).  Community studies One of the earliest experiments that manipulated the abundance of herbivores and measured the response in a plant community was a study by Tansley & Adamson (1925) on the effect of rabbit grazing on British chalk grassland. Within six years, the exclusion of rabbits resulted in the elimination of a number of plant species to produce a community dominated by a single grass species. This new community was itself eventually replaced by a shrub community (Harper 1977). Crawley (1990) in a similar study excluded rabbits from acid grassland in Britain, and reported that palatable grasses increased in abundance when protected from grazing. Species richness was not affected in this experiment, however this study was only maintained for three years. A reduction in species richness may have occurred if the experiment was maintained longer. Other community studies that have manipulated rodent densities and examined the effect on plant communities have reported changes in species diversity, and increased plant biomass in absence of herbivores (Coppock et al. 1983, Andersson & Jonasson 1986). Studies have also examined the effect of the removal of non-rodent herbivores on plant communities. Bakker & Ruyter (1981) found that re-introduction of grazers (cattle) to a salt-  11 marsh changed patterns of succession in the plant community. Areas remaining ungrazed showed progressive succession (increasing species richness and complexity of structure) and canopy closure with an increase in herb biomass and litter cover, whereas areas that were newly grazed occasionally showed retrogressive succession (decreasing species richness and structural complexity) to a more open canopy. Bazely & Jefferies (1990) excluded Lesser Snow Geese from a salt-marsh in LaPerouse Bay, Manitoba. They reported an increase in species diversity, changes in species composition and increased standing crop inside herbivore exciosures.  Population experiments Many studies have examined the effects of herbivory from a population perspective. An experiment that excluded rabbits in the Breckland showed a change in the population age structure of Hieraceum pilosella L. as a result of increased plant lifespan (Davy & Bishop 1984). This same study also reported a reduction in the rate of flowering when rabbits were excluded from the area. Kotanen & Jefferies (1989) excluded Lesser Snow Geese from a tidal flat on Hudson’s Bay and reported that release from grazing decreased leaf production and turnover, and increased leaf lifespan of Carex Xflavicans. Based on these results they also suggested that C. Xflavicans has the ability to modify its leaf demography in response to grazing. Jonsdottir (1991) compared the population dynamics of Carex bigelowii at grazed and ungrazed sites along an altitudinal gradient in the Icelandic Highlands. This study reported that the effect of sheep grazing varied with altitude, and that grazing resulted in an increased tiller density and reduced the survivorship, turnover time, rate of flowering, and age of the population. The response of plant populations to herbivore exclusion is generally variable. Some suggest that this is due to past grazing history (Detling & Painter 1983, Jaramillo & Detling 1988), while other studies have shown that the response to herbivore exclusion depends on extrinsic factors such as soil nutrients and faecal deposition (Berendse 1985; Kotanen & Jefferies  12 1987; Polley & Detling 1989), changes in the light or competitive environment (Polley & Detling 1989; Reader 1992  ), or intrinsic factors such as below ground reserves (root storage), and  growth substances (Kotanen & Jefferies 1987). Some studies have also shown that the impact of defoliation does not always depend on the competitive environment, and the loss of position in the competitive hierarchy due to herbivory does not always occur (Fowler & Rausher 1985). Similarly, plants with defoliated neighbours do not always show competitive release (Lee & Bazzaz 1980). Crawley (1990) and Pacala & Crawley (1992) have also suggested that the effect of release from grazing within a community will be determined by the interaction between a species’ palatability and its competitive ability. Grazing pressure has been shown to affect plant populations in other ways. Comparisons of grazed and ungrazed populations of some species have shown variation in the incidence of disease depending on the amount of herbivory experienced within a population. In some cases grazing reduced the level of infection, while in others it increased the incidence of disease found within a population (Bradshaw 1959; Clay 1988; Wennstron & Ericson 1991). Reproduction in plants is frequently altered by herbivory, however, the effects of herbivory on all aspects of reproduction depends on many factors including the timing, intensity and frequency of the disturbance, the type of tissue removed (vegetative vs. reproductive), and the nature of the damage (defoliation, frugivory, sap-sucking, etc.) (Crawley 1983). The effects of damage or removal of reproductive tissue on reproductive output is usually negative, but not exclusively so. There have been cases reported where herbivore damage (simulated or natural) does not significantly reduce a plant’s reproductive output. In some cases, these species are believed to be selected for over-initiation of reproductive structures beyond what a plant is normally capable of maturing (Stephenson 1980; Lee & Bazzaz 1982). Several reasons have been proposed to explain this apparent over-initiation of reproductive structures. Lee & Bazzaz (1982) suggest that an over-initiation of reproductive structures may be an attribute of certain plants that have been selected to persist in environments where there is a high degree of resource  13 unpredictability and an abundance of fruit predators. In good years (high resource availability), all fruit could be filled, whereas during unfavourable periods, structures lost to frugivores could be replaced. Alternatively, if all flowers weren’t fully pollinated, a plant could mature those fruit that had been fertilized to maximize its reproductive output (Lee & Bazzaz 1982).  Herbivore exclusion A brief summary of some of the effects reported for herbivore exclusion experiments are: increased reproduction (Louda 1984; Andersson & Jonasson 1986; Pyke 1986; Jonsdottir 1991; Karban & Strauss 1993); increased recruitment (Swank & Oechel 1991; Karban & Strauss 1993); increased leaf or tiller survivorship (Rausher & Feeny 1980; Louda 1984; Kotanen & Jefferies 1987, 1989; Jonsdottir 1991; Reader 1992); increased biomass and growth (Rausher & Feeny 1980; Jaramillo & Detling 1988; Jonsdottir 1991; Fox & Morrow 1992); an increase in tissue nitrogen or phosphate (Jaramillo & Detling 1988; Fox & Morrow 1992); decreased initiation of new leaves or turnover of leaves (Louda 1984; Kotanen & Jefferies 1987, 1989); and increased plant height (Detling & Painter 1983; Louda 1984).  Clipping The response of plants to clipping has also been extensively studied. Johnson et al. (1987) in a study of the effect of defoliation (clipping), and nutrients on the growth, and alkaloid production of Lupinus succulentus found that defoliation reduced tissue biomass, total nitrogen content and alkaloid content. The effect of nutrients in this study are discussed later. Other studies have also reported significant changes in biomass, total nitrogen, and/or alkaloid content in response to simulated herbivory (Stephenson 1980, 1984; Ruess et al. 1983; Fowler & Rausher 1985; Polley & Detling 1989; Baldwin 1988; Abul Fatih & Bazzaz 1984; Holland et al. 1992; Doak 1992). Other commonly seen responses to defoliation are increased leaf production (Abul Fatih & Bazzaz 1984; Polley & Detling 1989; Ruess et al. 1983), increased leaf and seedling  14 mortality (Abul Fatih & Bazzaz 1984; Kotanen & Jefferies 1987), reduced levels of reproduction (Abul Fatih & Bazzaz 1984; Doak 1992), and reduced plant height (Lee & Bazzaz 1980). In other cases, clipping was not shown to significantly reduce reproduction (Lee & Bazzaz 1980; Ruess et al. 1983; Fowler & Rausher 1985). In some cases, the lack of effect on reproduction could be attributed to the short duration of the experiment. Frequently, the effects of clipping are not detected until the following growing season (Pyke 1986; Karban & Strauss 1993). Herbivory is a complicated process and the ability of plants to respond to changes in the intensity of herbivory they experience is a function of both physiological and environmental factors. Jaramillo & Detling (1988) list some of the pertinent physiological changes a plant can experience when levels of herbivory change. These include: (i) changes in photosynthetic rates and balance of assimilates, (ii) changes in nutrient allocation patterns, (iii) differential balance in vegetative vs. reproductive tissues, and (iv) changes in nutrient uptake and hormonal balance. Because the range of responses to herbivory is so broad, it is difficult to predict with certainty its effect on plants, populations or communities. As a result, future studies will likely concentrate on determining how herbivory in the field interacts with other ecological forces operating in the environment.  THE EFFECT OF NEIGHBOURS The presence of neighbours is an important factor influencing plants at all levels of organization from the individual to the community. The components and structure of the neighbourhood that a plant inhabits can determine its access to resources, its susceptibility to herbivory and disease, exposure to harsh environmental conditions, and even the nature of the local disturbance regime (fire, wind, water). Because plants are sessile, the presence of neighbours is presumed to have primarily negative consequences on plant performance as a result of neighbours competing to acquire the resources necessary for growth. However, neighbour effects extend beyond competition for resources. Interference among neighbours can occur as a  15 result of non-competitive interactions (Harper 1977). For instance, Reader (1992) demonstrated that neighbouring plants provided the habitat required to maintain herbivore population densities, thus increasing risk of attack on a target species. Christie et al. (1978) also reported that the abundance of soil microbes in a plant’s rhizosphere varied according to the presence and identity of neighbouring plant species. The consequences of neighbours influencing a plant’s rhizosphere could be either positive or negative depending on the nature of the relationship between the plant and soil microbes (i.e. pest or symbiont). Neighbours have been shown to be beneficial to plants (reviewed in Hunter & Aarssen 1988), in some cases by providing shelter from harsh environmental conditions in severe climates (reviewed in Callaghan & Emanuelsson 1985; Callaghan 1987), in others by improving local conditions by increasing moisture and nutrients (i.e. nitrogen fixation) (Bliss & Svoboda 1984). Another study has also reported that close proximity to an unpalatable neighbour reduced the risk of herbivory for some species (Holmes & Jepson-Innes 1989). Similar results have been reported such that the risk of herbivory was reduced when plants were concealed or shaded by neighbours (Huffaker & Kennet 1959; Rausher 1981). Consequently, although competition is an important component of neighbour interactions, the presence of neighbours in a community can be shown to affect plants and populations in ways not related to competition for limited resources. This review demonstrates that the effect of neighbours on plants and populations are numerous, widespread and varied. Another good review on the response of natural and seminatural vegetation to different types neighbour manipulations in the field is provided in Aarssen & Epp (1990). A brief summary of some commonly observed effects attributed to the presence or absence of neighbours include changes in: (i) plant biomass (Mack & Harper 1977; Campbell & Grime 1992; Rees & Brown 1992), (ii) patterns of survivorship and establishment of seedlings (Gurevitch 1986), (iii) branching patterns or tillering rates (Lee & Bazzaz 1982; Fetcher 1985), (iv) individual reproductive output (Paimblad 1968; Lee & Bazzaz 1982; Campbell & Grime 1992), (v) population size hierarchy (Weiner 1985), (vi) rhizome length and growth patterns  16 (Schmid & Bazzaz 1992), (vii) competitive exclusion from optimal habitats (Gurevitch 1986), (viii) physiology (Wilson 1989), and (ix) root: shoot ratio (Gurevitch et al. 1990). Although interference from neighbours (intra- or interspecific) can regulate or limit the growth of plants and populations, the ability of neighbour interactions to determine the structure of plant communities is often uncertain and difficult to measure (Wailer 1981). The evidence available indicates that the intensity of interference in plant populations on both a temporal and spatial scale is subject to variation. Certain investigators have argued that interference is not a significant factor for communities existing in high stress environments (Grime 1979). Other authors suggest that the level of competition does not change across environments, while the limiting resource does change (Tilman 1987). The effects of interference on plants are evidently confounded by other agents operating concurrently or intermittently in communities such as disturbance and herbivory. Depending on the nature and strength of theses forces, they may disrupt or supercede the effect of competitive interactions as structuring agents within the community.  Methods of study Several studies have attempted to measure the effect of interference on plants and populations in the field by manipulating the density of neighbours by thinning, transplanting, or removal (Fowler 1981; references in Connell 1983; Fetcher 1985; Holmes & Jepson-Innes 1989; Keddy 1989; Gurevitch & Unnasch 1989; Aarssen & Epp 1990). Experiments that remove groups of species study the effect of diffuse competition, whereas experiments that manipulate a single species at a time assess specific differences in competitive ability between species. Diffuse competition measures the cumulative effect of competition from all neighbours on a target species and is frequently studied in field experiments. Single species competition experiments often employ pot or common garden experiments. In these experiments, plants are grown in mixture or monoculture at different densities and the effect of neighbours are then assessed on the target  17 species (i.e. de Wit 1960; Fowler 1982; Firbank & Watkinson 1985; Gurevitch et al. 1990). The scale of response tinder investigation determines what measurements are recorded. Studies of individual plant performance frequently measure survivorship, growth, or physiological responses, whereas population experiments tend to measure changes in density, biomass, or cover. Neighbour interactions in communities are measured by changes in relative biomass, species diversity and/or species richness (Goldberg & Barton 1992).  Consequences According to Mack & Harper (1977), interference among coexisting plants can have three possible consequences: (i) failure to germinate, (ii) death, or (iii) survival combined with a plastic growth or reproductive response. The relative importance of neighbours on the performance of a target plant has been correlated to the size, proximity (or density) and spatial arrangement of neighbours (Mack & Harper 1977; Silander & Pacala 1985). Weiner (1982) and Aarssen & Epp (1990) suggested that an additional factor such as plant age, or stage of development (i.e. pre reproductive vs. mature) can also affect response. Gurevitch et al. (1990) have also demonstrated that the identity of neighbours was an important factor, such that some species of neighbours were shown to have greater impact on the performance of a target plant than others.  Individual-level responses  Individuals and populations respond to their local environments by varying patterns of recruitment, mortality, growth, and reproduction (Harper 1967; White & Harper 1970; Grace & Wetzel 1981; Weiner 1985; Snow & Whigham 1989; Lieffers & Titus 1989; Schmid & Bazzaz 1990; Jonsdottir 1991). Most studies of neighbour interactions tend to concentrate on measuring plastic changes in plant growth and reproduction, although changes in survivorship due to the presence of neighbours also occur. A few studies that have sought neighbourhood predictors of plant performance tend to show that at low neighbourhood densities, plants show a plastic  18 response to interference, whereas mortality occurs primarily when crowding is intense, plants are young (Silander & Pacala 1985; Weiner 1982, 1984), or the habitat is productive. Modifying individual plant size through the birth and death of modular units of construction (vegetative or reproductive) is a common response to changes in a plant’s local environment (Harper 1977). Determining how individual plant size varies as a response to neighbours is interesting because it has often been observed that plant performance, as measured by reproductive output and survival of an individual, is correlated to its relative size within a population (Soibrig 1981; Meagher & Antonovics 1982; Wolfe 1983). Plant populations generally show a skewed size distribution comprised of a few large (dominant) individuals that are reproductive, and many smaller individuals that often fail to reproduce and experience high levels of mortality (thinning) due to suppression from neighbours (Yoda et al. 1963; White & Harper 1970; Harper 1977). In these cases, relative size in the hierarchy is an indicator of competitive success and the ability to acquire resources from the environment. Because reproduction is often positively correlated with size in plants, individual size is often used as a fitness correlate (Harper 1977; Grace & Wetzel 1981). Several researchers have reported plastic changes in reproductive output due to the interference of neighbours. (Palmblad 1968; Mack & Harper 1977; Barkham 1980; Lee & Bazzaz 1982; Fowler 1984; Zimmerman & Weis 1984; Whigham 1984; Silander & Pacala 1985). Palmblad (1968) reported that the effect of experimental thinning on reproduction in two species of Senecio varied depending on the timing and intensity of the treatment. Most of the differences observed were in the number of flower heads produced, total seed production and date of flower initiation. Silander & Pacala (1985) determined that local interactions of neighbours accounted for 70% of the variation in reproduction in Arabidopsis thaliana (L.) Schur. A study of population dynamics in daffodil (Narcissus pseudonarcissus) found that sexual reproduction predominated at lower field densities, whereas vegetative growth occurred at higher densities (Barkham 1980). This study also found that the number of flowers produced to be  19  correlated with the amount of growth in the previous year. Similarly, Whigham (1984) in a study of the effect of competition and nutrient availability on growth and reproduction in Ipomea hederacea  0 found that competition had a negative effect on number and weight of flowers and  flower buds, and on seeds and other vegetative characters. In contrast, soil fertility had no effect on number or weight of flower buds. Measuring the plastic growth response to neighbours in clonal plants has been difficult because plasticity in clonal organisms is manifested at two levels: (i) changes in the size of individual ramets, and (ii) alterations in the rate of production of new ramets and the survival of existing ramets. Although Hutchings & Slade (1988) did not study the effect of neighbours on the dynamics of a clonal plant Glechoma hederacea (L.) they did report that patterns of horizontal space acquisition, architecture, and biomass acquisition in G. hederacea varied with the  amount of light and nutrients supplied.  Population-level responses  Population experiments have investigated the effects of neighbours in a variety of ways. Cavers & Harper (1967) followed the fate of seeds and transplants of two species of Rumex placed in different environments. In habitats where neighbours formed a closed canopy, seeds that germinated failed to survive the growing season, and transplants showed poor growth. Both seedlings and transplants did better at more open sites. Although this experiment was not designed to test the effect of neighbours on seedling recruitment and the survival of young plants, it is possible that interference from neighbours at the closed sites was responsible for poor growth and survivorship. Gurevitch et al. (1990) studied the effect of inter- and intraspecific neighbours on individual plants of different species, at different fertility levels, in a pot experiment. This study reported that the presence of neighbours had a greater effect than simple reduction in available space and that the effect of neighbours on the target varied with the neighbour’s identity.  20 Fetcher (1985) studied the effect of removing moss and shrubs on the populations dynamics of cotton sedge, Eriophorum vaginatum in central Alaska. Cotton sedge tussocks in neighbour removal treatments had more daughter tillers and smaller adult tillers than controls. The response was largely attributed to changes in irradiance when neighbours were absent rather than changes in nutrient status. The presence of neighbouring species in a grassland habitat in south-eastern Arizona was shown to limit the distribution of Stipa neomexicana by limiting seedling establishment and survival, as well as reducing the rate of flowering and growth of mature plants (Gurevitch 1986). The act of removing neighbours allowed S. neomexicana to invade th more favourable habitat previously occupied by neighbours. Lee & Bazzaz (1982) investigated the effect of competitor removal, nutrients, and water on reproduction in an annual legume, Cassia fasciculata. In the absence of neighbours, biomass and branching increased relative to the controls, however, when neighbou Lemoval was combined with fertilizer and/or water, plant biomass increased beyond neighbour removal alone. The number of fruit matured per plant also increased in the combined treatment,  Community-level responses Studies at the community level show a variety of responses to species removal. In a 4-year study, Jonasson (1992) examined the effect of removing a dominant shrub on various types of tundra communities. Few changes in species diversity or various estimates of cover were detected in this study unless the removal of the dominant shrub was combined with the addition of fertilizer. The combined effects of removal and fertilizer increased diversity in the stable tundra and decreased diversity on the disturbed (frost-heaved) tundra. In this case, disturbance was shown to increase the soil nutrient pool relative to the stable communities. Fertilizer was reported to increase graminoid cover substantially. Jonasson (1992) concluded on the basis of these results that the dominant shrub species was not likely to be competing with the herbaceous (vascular, bryophyte and lichen) community.  21 Keddy (1989) performed a similar 4-year removal experiment on wetland vegetation occurring along an environmental gradient. In this case, removal of a dominant shrub produced highly significant increases in cover, richness and diversity in the community, although less than 25% of the species showed a significant individual response to the experimental treatments. The  single species that did respond to the removal treatment were reported to comprise a large proportion of the seed bank. In areas of low fertility and high susceptibility to wave disturbance, no competitive release was detected following shrub removal. Gurevitch & Unnasch (1989) studied the combined effect of the removal of a dominant grass, and application of fertilizer on an old field community in Long Island, New York. Fertilizer alone was reported to reduce species diversity and richness, while fertilizer combined with the removal of the dominant maintained species richness. Removal of the dominant species increased diversity particularly at high levels of soil fertility. This study concluded that competition (from the dominant) was more important in structuring the community at high levels of fertility than at low levels. Fowler (1981) investigated the effect of three types of species removal on percent cover of individual species occurring in a North Carolina grassland community. These included (i) graminoids, (ii) dicots, or (iii) removal of a number of single species. Removal of single species had less effect than removing groups of grasses or dicots. This may have been the result of greater biomass removed when groups of species were eliminated. Evidence was presented that some grasses limit some dicots and vice versa. Based on these results, Fowler (1981) suggested that there is indirect support for the hypothesis that this grassland community is characterized by weak and comparatively equal competitive relationships among its component species. The ability of neighbours to have a strong effect on the structure of this community was not clearly shown. Other factors such as herbivory may be more important in this community, although this was not studied.  22 The results of several experiments investigating the effects of removing a dominant species on the community structure in different habitat types indicate that neighbour interactions are varied and habitat-specific to a certain degree. These three experiments provide good evidence that the effect of a dominant neighbour varied with the disturbance regime and soil fertility.  Limitations Although many studies have shown significant responses in plants when neighbours are removed, Waller (1981) and Campbell et al. (1991) urged caution when interpreting these results. Campbell et al. (1991) suggested that the responses reported in some experiments may be an artefact of the disturbance imposed during the treatment application (puffing up roots, residual herbicide effects) rather than a product of growing without neighbours. These disruptions could be important in infertile communities where the disruption associated with removing neighbours alters the nutrient regime. However, Aarssen & Epp (1990) suggested that the additional nutrients released were resources that would have been available if they hadn’t been usurped by neighbours. More specifically, these nutrients were likely to have been the objects of past competition and therefore do not pose a problem when assessing the results of these experiments. Campbell et al. (1991) also suggested that seasonal variability in resource capture and growth may make the timing of removal critical in determining results of the experiment. Waller (1981) suggested that a significant response may not be an important factor in plant communities if the effect of neighbours is subject to temporal or spatial variability. It was suggested that some factors that may prevent neighbours from exerting a significant effect include: (i) recent disturbance, (ii) frequent herbivory, and (iii) general stress that is capable of reducing growth rate (Waller 1981). Wiens (1977, 1984) suggested that the critical structuring agents in a community may operate for only short periods of time and are therefore not easily detected in the time frame of most experiments. It was also argued that competitive release may not be the reason for increased plant growth following neighbour removal in these experiments. In spite of the  23 complications associated with this method, it is unlikely that all significant responses reported to date are the artefacts of treatment application and uncontrolled density manipulations. Pot and garden experiments that examine the effect of neighbours on plant dynamics with density as a variable have shown significant responses in plant growth depending on whether the species were planted in monoculture or mixture (Banyikwa 1988; Holmes & Jepson-Innes 1989; Wilson 1989; Gurevitch et al. 1990). These experiments neither disturb nutrient conditions, nor vary plant density in an uncontrolled fashion. It remains true however, that removing neighbours is one of the few methods that can be used to study neighbour effects in the field in an established plant community.  THE EFFECT OF FERTILIZER Plants require a variety of nutrients to grow and reproduce, and as a result, the availability of nutrients is a key factor in determining the productivity of a habitat. Other factors considered to be important determinants of productivity are soil moisture content and aeration (Trudgill 1979), light and temperature (DiTommaso & Aarssen 1989). Although the ability of fertilizer to enhance growth in plants is not disputed, the effect of fertilizing a natural community is not necessarily predictable. For instance, Grime (1977) suggested that plants adapted to tolerate stressful environments should not necessarily respond to short term changes in resource availability when fertilizer is applied. Mellinger & McNaughton (1975) also suggested that older plant communities may show greater resistance to perturbations (fertilizer) than younger communities. A review of the effects of addition of inorganic fertilizer will be presented with emphasis on studies pertaining to NPK (nitrogen:phosphorus:potassium) additions. Another extensive review on the effect of resource manipulations in natural vegetation is in DiTommaso & Aarssen (1989). Few studies in ecology are interested in studying the effect of fertilizer alone on plant dynamics. Most recent studies focus on determining how changes in soil fertility affect the  24 dynamics of plants and populations when they are subjected to different levels of interference, herbivory, and disturbance. The question of how these factors interact under field conditions is frequently addressed in order to ascertain which factors are most important in limiting populations and determining the patterns and structures of communities. Manipulating resources in field studies is also used to provide data on the mechanisms and consequences of species interactions and other community processes that cannot be obtained through green-house or descriptive studies (DiTommaso & Aarssen 1989). Although there has been much debate in community ecology as to whether top-down factors are more important than bottom-up forces such as nutrient regime, Hunter & Price (1993) proposed that both operate simultaneously to regulate communities. Hence, studies of how soil nutrient availability interacts with other factors limiting plants and populations are of interest. The addition of inorganic fertilizer to a habitat can have a number of potential effects on organisms within the community beyond simply increasing the availability of nutrients. Some of these include: (i) changing ambient soil chemistry that may result in changes in the availability of other required nutrients (Wilson 1987). This may occur through changes in pH or salinity of the soil. (ii) Fertilizer may change the composition or activity of the soil micro- and meso-fauna. For example, Halvorson et al. (1992) state that high levels of soil nitrogen can have inhibitory effects on nitrogen-fixing activity and nodule formation in lupines. It is not clear whether increases in soil nitrogen concentration mediate this symbiosis by suppressing the microbe, or by acting on the host. (iii) Application of fertilizer may change the rate of nutrient cycling through the habitat (Gosz 1987). This may occur through changes in patterns of litter deposition in response to fertilizer. Tanner et al. (1992) reported that fertilization (N+P) of tropical montane forest increased trunk growth and leaf production. This response was detected after an initial lag and was measured as increased trunk diameter and litterfall in response to fertilizer. The increase in litterfall was greatest when both nitrogen and phosphate were added together in contrast to treatments of either nutrient added alone. The quality of the litter after fertilization reportedly  25 changed when phosphate was added to the system such that both treatments that applied phosphate as P, or N+P, showed increased levels of phosphate in litter. In spite of this, changes in the quality of live leaves in response to fertilizer were not detected and the authors postulated that much of the nitrogen and phosphate added as fertilizer was not likely incorporated into above-ground biomass.  Individual-level responses At an individual level, changes in ambient soil fertility can have a variety of effects on plant dynamics. For instance, the addition of fertilizer may change a plant’s nutrient status and hence their palatability to herbivores (Wilson & Stinner 1984; Coley et al. 1985; Mihaliak & Lincoln 1985, 1989; Larsson et al. 1986; Bryant et a!. 1987a, b; Wilcox & Crawley 1988; Loader & Damman 1991). In some cases fertilizer increases plant quality by increasing the concentration of nutrients in the tissue (Fox & Morrow 1992). This change in physiological status, measured as increased nutrient concentration, makes a plant a more attractive food source to herbivores (Mihaliak & Lincoln 1985 & 1989; Bryant et al. 1987a, b; Loader & Damman 1991). The alternative is that fertilizer may stimulate the production of anti-herbivore defences that reduce palatability (McClure 1980). Anti-herbivore defences include a variety of structures and compounds including thorns, spines, resins, digestion inhibitors, and toxins (Crawley 1983). The two commonly discussed groups of chemical defences known to vary with soil fertility are: (i) carbon-based compounds such as resins, tannins, terpenes, sesquiterpenes, and phenolics, and (ii) nitrogen-based compounds including alkaloids and cyanogenic glycosides. Carbon-based defences are considered quantitative defences because their inhibitory properties are more effective in large doses or at high concentration. These compounds often act to physically inhibit or interfere with the digestive activity of enzymes in the herbivore (Crawley 1983). Nitrogenbased defences are generally highly toxic compounds that occur in plants in low concentration  26 relative to carbon based defences. Many specialist herbivores have shown evolutionary adaptations to the presence of these compounds in contrast to carbon-based defences. According to Bryant et al. (1983), nitrogen based defences are more common than carbonbased defences in dicotyledonous herbs growing in nutrient rich sites, or that are nitrogen fixers. The concentration of nitrogen based defences in Lupinus succulentus has been reported to vary with the level of nitrate fertilizer (Johnson et al. 1987). This study suggested that the quantity of alkaloids in nitrogen fixing lupines depended on levels of soil nitrate because defoliated plants had depressed rates of nitrogen fixation due to reduced levels of carbon available to symbionts as a result of herbivory. Wilcox & Crawley (1988) also reported changes in alkaloid concentration, and amino acid concentration in response to fertilizer in Senecio jacobaea L. foliage. Fertilizer (ammonium sulphate) slightly increased alkaloid concentrations and decreased amino acid concentrations. The decrease observed in amino acid concentration following nitrogen fertilization contrasts with other studies that show increased nitrogen or amino acid concentration with nitrogen fertilization (Onufet al. 1977). However, fertilizer may rapidly produce a flush of new growth in plants with no associated increase in nitrogen content of tissues detected (Crawley 1983).  Population-level responses Chapin & Bliss (1989) studied the population-level response of two herbs (Eriogonum pyrolifolium Hook and Polygonuin newberryii Small) to treatments of fertilizer and water. The response to fertilizer was measured as seedling growth and survivorship. Polygonum sp. showed a substantially greater increase in dry mass in response to fertilizer+water relative to Eriogonum sp. The authors concluded that Eriogonum sp. that grew slower under high nutrients was more tolerant to nutrient stress. Another study examined the effect of NPK fertilizer (with micronutrients added) and shading on seedling dynamics and herbivore resistance of Betula pendz4a Q(Rousi et al. 1993). Fertilizer was reported to increase growth rate and palatability of  27 seedlings to voles, but only one out of four families of Betula pendula became more palatable to hares following fertilization. The response of birch to fertilizer in this study did not concur with the results of similar experiments on birch (see refs in Rousi et al. 1993). Lieffers & Titus (1989) also reported larger seedlings when fertilizer was applied to white spruce and lodgepole pine seedlings. Fertilizer in this experiment was also reported to increase differences in growth rate that resulted in size inequalities within the population. When a nutrient in limiting supply is added, it is expected that a plant will increase its growth rate until other factors become limiting. This increase in growth rate can be beneficial to plant’s in some environments, but Sarukhan & Harper’s (1973) study of Ranunculus spp. dynamics also report that the greatest risk of death for plants occurred when they were growing most rapidly. This was also noticed by Sukatschev who reported a 6% death rate for Matricaria inodora L. on unfertilized soil and a 25% death rate on fertilized soil (described in Crawley 1983). Further evidence supporting this hypothesis is found in studies of self-thinning along a productivity gradient (i.e. Lieffers & Titus 1989). Comparison of thinning rates along a gradient of nutrients (NPK) showed that self-thinning was most intense in the high fertility treatments (Yoda et al. 1963). Some authors suggest that increasing the soil fertility in a population experiencing self-thinning likely results in increased growth in some individuals that results in the suppression and death of others. Fertilizer may act by decreasing the time required before a population becomes light limited and self-thinning begins (Harper 1977; Crawley 1983). The effect of fertilizer on tissue longevity has also been studied. Watkinson et al. (1979) reported that fertilization of Carex arenaria L., a sand dune sedge, increased both birth rate and death rate of tillers. This increased turnover reduced the longevity of tillers, resulting in a change in the age structure of the population. Increased turnover of leaves in response to elevated levels of soil nutrients has been reported or proposed as an explanation of elevated turnover rates in other studies (Bryant et al. 1983; Shaver 1983; Kotanen & Jefferies 1987, Diemers et al.1992).  28 Several studies have reported changes in the allocation of carbon to roots and shoots under different fertility regimes. It is generally observed that application of fertilizer results in a decreased allocation to root biomass (Chapin & Bliss 1989; Bastow Wilson 1989; Lieffers & Titus 1989). However, Gurevitch et al. (1990) reported an increased root:shoot ratio in a pot experiment in response to NPK fertilizer in three species of herb (Achillea millefolium L,, Dactylis glomerata L., and Vicia cracca L.). Fertilizer effects on reproduction in plants have been described in some studies. Davy & Bishop (1984) reported that fertilizer resulted in a more rapid initiation of flowering as well as a five fold increase in the number of flowers produced by Hieraceum pilosella. However, flowering in H. pilosella stopped after one year of fertilization. This was likely due to the increased activity of competitors. Stephenson (1984) in a study of maternal investment in Lotus corniculatus noted that NPK fertilizer produced an increase in seed production per ramet, as well as increased the number of flowers and matured fruit produced per plant. Fertilization of a legume, Cassiafasciculata, was also reported to increase fruit production per plant (Lee & Bazzaz 1982). In a study of Ipornea hederacea, an old field perennial reported that fertiliz er had a significant effect on biomass of leaves, stems, roots, seeds and fruit, but no effect on the numbe r or biomass of flowers (Whigham 1984). The effect of fertilizer under different conditions of available space has been investigated by a few authors. In Gurevitch et al. (1990), available space was manipulated by growing plants in either monoculture or mixture in different sizes of pots. They reported that the impact of fertiliz er on various measures of plant performance was greater in large pots compared to small pots. Donald (1963) did not manipulate available space directly, but rather manipulated density of a sward. He reported that nitrogen uptake by swards was reduced at high density, and postulated that high density conditions result in shallower rooting depth and less efficient soil exploration by the population. It was further suggested that as the fertility of an environment improves, a greater density is required to achieve maximum yield. Banyikwa (1988) also reported that defoliated  29 grasses showed an interaction between nitrogen fertilization and density that decreased yield per plant. Competition experiments have frequently manipulated soil fertility and measured the performance of species growing in mixtures. In Wilson (1989), root competition in pairwise mixtures of three species of upland grasses was assessed. Soil fertility was manipulated with application of sodium nitrate. Nitrogen application to different species mixtures was found to depress growth in one species and increase growth in the remaining two species. In this experiment increased nitrogen availability allowed two species to outcompete a third species. Similarly, nuthent additions to tundra communities have sometimes shown that within a season, the response among species varies; this between-species variation in the pattern of response to fertilizer also varies between years although overall community productivity remained stable (Chapin & Shavers 1985). Schmid & Bazzaz (1992) tested the effect of changing patterns of resource availability with NPK fertilizer on genet architecture. They hypothesized that rhizomatous perennials should increase rhizome numbers but decrease their length in fertilized soil compared to unfertilized soil. Several species were tested including graminoids and dicots. They ranged from spreading to compact growth forms. The results were mixed and no clear evidence was found to support their hypothesis. Only one species (Aster lanceolatus) showed an increase in rhizome number in the fertilizer treatment. Changes in rhizome length were not significant for any species. It was suggested that either response to fertilizer was delayed and not seen in this short term experiment, or that rhizome architecture is subject to some constraints in terms of plasticity of response. DiTommaso & Aarssen (1989) suggest that age and/or successional stage may influence the response of some plants to nutrient additions, although the evidence to clearly show this is not available. Nitrogen fixers have been reported to respond differently to nitrogen based fertilizers than non-fixing species depending on whether they are grown in mixture or pure stand. Dennis &  30 Woledge (1984) studied the effect of nitrogenous fertilizer on growth of mixed swards of Trifolium repens L. (white clover), and Loliurn perenne L.(ryegrass). Application of nitrogen  fertilizer when potassium and phosphate were maintained in adequate supply resulted in a substantial increase in leaf area index and yield in the sward. This increase was attributed entirely to the response of ryegrass. Clover was reportedly reduced to one fifth its leaf area index and yield relative to the unfertilized control. No differences were observed in petiole length, lamina area, or specific leaf area of newly expanded clover leaves. The number of live leaves on clover stolons was significantly greater in the absence of nitrogen fertilization. Early in the experiment measures of photosynthesis in clover were higher in the control relative to the nitrogen treatment. This difference was lost as the experiment progressed. Other experiments reporting growth reduction in clover grown in mixed sward have attributed the suppression of clover to gradual shading by neighbouring grasses as the season progressed (described in Donald 1963). In contrast, this study concluded that shading from ryegrass did not reduce photosynthesis in clover by the end of the experiment as would be predicted if ryegrass suppressed clover by shading (Dennis & Woledge 1984). Although clover did show growth reduction in the nitrogen treatment, the authors attribute this to early season effects rather than a progressive increase in shading as ryegrass overtopped clover (Dennis & Woledge 1984).  Community-level responses DiTommaso & Aarssen (1989) in a review of the effect of several types of resource manipulations (water, nutrients, light) on various types of plant communities from agro ecosystems to arctic tundra concluded that the wide range of responses to fertilizer reported in field studies depends on the habitat type, duration of resource enrichment, and the resources added. This review also describes several fertilizer studies of communities containing leguminous species. Thurston (1968), Zarzycki (1983), Traczyk et al. (1984), and Hobbs (1988) reported that while fertilizer stimulated grass growth, leguminous species were significantly reduced or  31 eliminated. Henry et al. (1986) also noted that increased tiller growth in response to increased nutrient availability is common in northern rhizomatous graminoids. At a community level, Jonasson (1992) found that the effect of fertilization (NPK) of tundra communities varied with the disturbance regime. On stable tundra, species diversity increased when fertilized, whereas frost-heaved communities showed a decrease in diversity when fertilizer was applied. Another consequence of fertilization was increased above-ground biomass. In contrast, Gurevitch & Unnasch (1989) reported that fertilization of a stable, old field herbaceous community led to increased productivity associated with a reduction in both species richness and diversity. In this same study, application of fertilizer was combined with the experimental removal of a dominant grass (Daclylis glornerata). The response to fertilizer (NPK) changed so that in the absence of D. glomerata, species diversity increased more in the high fertility treatment than in the low fertility treatment. Hence the response of populations to fertilizer in an old field community was mediated by the response of a dominant species. Some studies have investigated the interaction between fertilizer, defoliation and competition. Banyikwa (1988) in a pot experiment examined the growth response of two perennial grasses in a factorial experiment that manipulated nitrogen level, defoliation, pure or mixed culture, and density. In this experiment, nitrogen fertilizer substantially increased plant yield, and increased the shoot:root ratio. An interaction between defoliation and nitrogen fertilization was reported to vary between the two test species (Sporobolus ioclados() and Digitaria macroblephara  ()) such that Digitaria sp. showed an increased yield when defoliated  and fertilized with nitrogen, whereas Sporobolus sp. showed a decreased plant yield. This experiment provides additional evidence that response to herbivory is modified by local environmental conditions such as nutrient availability and competition. Fowler (1982) in a study of competition and coexistence in a North Carolina grassland species found that the intensity and outcome of competition in a pot experiment varied with soil fertility (NPK fertilizer application). She reported that fertilizer was the most important factor in  32 determining overall yield in species grown in mixture or monoculture. It was noted that manipulating soil fertility in the experiment resulted in reversal of competitive dominance in two of six pairs of species. There are currently opposing views regarding the intensity of competition along productivity gradients. Grime (1979) and Keddy (1989) supports the view that competition along a productivity gradient is not constant, and unproductive habitats are dominated by stress tolerant species that are not subject to competition. In contrast, Tilman (1987), Newman (1973), and Grubb (1985) maintain that competition occurs equally at all positions along a gradient although the limiting resource may change. Regardless of these conflicting predictions soil fertility has been demonstrated to play a key role in many communities (see DiTommaso & Aarssen 1989). Soil fertility plays a key role in many communities through its ability to limit both individuals and populations, and in its ability to modify the response of plants and populations to other environmental variables. It is through these complicated interactions with other limiting agents that makes soil fertility of interest in current ecological research.  CHAPTER 2 DEMOGRAPHY OF LUPINUS LEAVES  Plants, as modular organisms, have several levels of population organization. These include populations of genets (genetic individuals), ramets (clonally produced individuals), or reiterated plant parts (e.g. leaves, buds, flowers). As it is often difficult to measure population dynamics of genets and ramets in rhizomatous perennials, the relative effects of clipping, neighbours and soil fertility level on the demography of Lupinus leaves (modules) were examined to determine how these factors affected the birth and death of plant parts (i.e. leaves), and how these factors interacted at this level of population organization. Data on the amount of leaf damage (by insect or microtine herbivores) and leaf disease were measured as additional information to explain potential differences in leaf mortality. Tagged sub-populations of lupine clumps were also used to calculate an index of standing crop available to herbivores through the season and determine how this differed between treatments. METHODS STUDY AREA The experiments were conducted in the Kluane Game Reserve, located in the southwest corner of the Yukon Territories, approximately 3 km south of the Alaska Highway at Boutelier Summit (138° 22’W, 610 02’N). This area is presently being used as part of a long-term study of a northern boreal forest ecosystem led by Dr. C.J. Krebs. The dominant vegetation in this tract of northern boreal forest consists of a closed canopy forest dominated by white spruce (Picea glauca Voss), as well as open areas of shrub habitat dominated by grey willow (Salix glauca L.), dwarf birch (Betula glandulosa Michx.), and to a 33  34 lesser degree Shepherdia canadensis (L.)Nutt., and Potentillafruticosa L. The understory throughout the region is dominated by the woody vines Arctostaphylos uva-ursi (L.) Spreng. and Linnea borealis L., and by herbaceous species such as Festuca altaica Torr., Lupinus arcticus Lindi., Mertensia paniculata (Don), Achillea millefoliwn L., Anemone pari4flora Michx., Epilobium angustifolium L, Solidago canadensis L., Senecio lugens Rich., and a number of moss species. The primary mammalian herbivores are snowshoe hare (Lepus americanus), and arctic ground squirrels (Spermophiles parryii). A detailed description of the study area is found in Krebs et al. (1986).  Species The target species in this investigation was the Arctic Lupine, Lupinus arcticus Lindil. (Family Fabaceae). It is a nitrogen-fixing perennial with a rhizomatous growth form that has been observed to grow in a variety of habitats in Kluane including closed canopy spruce-moss forest, open birch-willow shrubland, river flood plains, and alpine tundra. Leaves are palmately compound with leaflet number ranging from 6-10. Leaves emerge in the spring following snowmelt from underground stems with only the petioles and lamina breaking the soil surface. Vertical growth is obtained primarily through petiole extension which, in some instances, exceeds 20 cm. Flowers occur on indeterminate racemes, and colour ranges from bluish to pinicish-violet. Racemes are observed to emerge from the soil following spring thaw concurrently with petioles. The fruit are dehiscent legumes capable of producing up to 5-8 seeds. A full description of the morphology of L. arcticus is found in Dunn & Gillet (1966). Lupinus arcticus is distributed throughout the Yukon, Northwest Territories, Alaska, British Columbia and Washington and is capable of hybridizing with other lupines when distributions overlap (Dunn & Gillet 1966). Some members of this genus are known to be produce toxic alkaloids that can deter herbivores (Johnson & Bentley 1988; Dolinger et al. 1973). It is not currently known if L. arcticus produces anti-  35 herbivore defences. In addition to being a constituent of the summer diet of snowshoe hares, ground squirrels have also been observed to feed on L. arcticus in Kluane.  Site Selection Sites for the experimental quadrats were chosen using three criteria: (i) a minimum of 10 clumps of Lupinus arcticus occurring in a 1 m 2 area, (ii) the minimum distance separating quadrats must be greater than 1.5 m, and (iii) avoidance of natural hazards such as stumps, boles, and deadfall. These criteria were used to ensure a minimum population density, as well as to reduce the possibility that individuals of L. arcticus in neighbouring quadrats were connected by rhizomes. This method of selection was biased towards (i) selecting high density lupine sites that were easier to find in early spring, (ii) selecting plants that tended to break bud earlier and were easier to locate as snowmelt occurred, (iii) selecting larger or older clones as opposed to seedlings and recent recruits which do not generally appear until later in the spring and early summer.  Quadrat Construction Following site selection, 32- 1 m 2 areas were each permanently marked with four 20 cm x 5 cm x 5 cm wooden posts inserted into the ground to a depth of 15 cm. The perimeter of the experimental area was marked with cotton string fastened to each corner post. Orientation of the quadrats was such that the maximum number of lupine clumps were included. All 1 m 2 quadrats were then surrounded by a fence, 1.5 mon a side to a height of 60 cm, using 2.5 cm diameter galvanized chicken wire to limit uncontrolled natural grazing by mammalian herbivores that frequent the area. The total fenced area was 2.25 m 2 to include a 25 cm buffer zone between the 1m 2 experimental quadrat and the fence. Treatments were applied over the entire 2.25 m 2 area. The herbivore exclosure was supported with four 1 m steel fence posts pounded 20 30 cm into -  the ground. A 15 cm skirt of chicken wire was folded outward on the ground and secured with 15 cm wire staples to deter animals from penetrating under the fence. Fences were checked  36 regularly to determine if herbivores had penetrated the experimental area and no such evidence was found during the study. The outer perimeter of the fences were spaded to the depth of a spade blade to sever rhizome connections growing beyond the treated area. This was done once at the onset of each growing season.  EXPERIMENTAL DESIGN The primary experimental design used for the duration of this field study was a factorial cross of three treatments with two levels in each treatment: +1- fertilizer (F), +1- neighbour removal (NR), +1- clipping (C). This factorial design allowed the comparison of population responses of L. arcticus to individual treatments as well as how main effects interact to modify population dynamics. In 1992, an additional treatment consisting of an elevated intensity of clipping was imposed, raising the number of treatments from 8 in 1991, to nine treatments in 1992. Each treatment was replicated 4 times at one field site for a total of 32 experimental quadrats in 1991 and 36 in 1992. All quadrats were located in an area less than 1 ha.  Treatments The protocols used for treatment application were as follows: Fertilizer Fertilizer was applied at the start of each growing season following spring thaw with Hutchinson’s NPK (35-10-5 percent by weight) fertilizer mixture. On June 1, 1991, the application rate per m 2 was 9 g nitrogen, 2.5 g phosphorus, and 1.27 g potassium. The chemical components of this mixture were ammonium nitrate ) 3 N 2 ) 4 ((NH O , super phosphate ) 4 P 2 (H O , and potash 2 (K 0 ). In 1992, the amount of fertilizer applied was doubled due to a low response by L. arcticus in the first year. Fertilizer was applied at the same rate as 1991, but was applied twice in the season (May 27 and June 30). Fertilizer was applied dry, sprinlding evenly over the entire area within the fence. The rate of application in 1992 corresponded with other fertilizer  37 udies in the area and is within established rates of forest fertilization projects (Binkley 1986; Nams et al. 1993). Clipping  To control the amount of leaf tissue removed in this experiment and ensure consistency between treatment replicates, a simulated grazing treatment was used in place of natural grazing. Petioles and/or stems were clipped to a constant height of 8 cm using scissors and maintained at this height using a press design such that plants were continually clipped as re-growth occurred (Bender et a!. 1984). This pattern of leaf removal is similar to the pattern commonly attributed to hare grazing in the field. The initial clipping treatment was imposed mid-season (July 4, 1991, and June 29, 1992) when the plants reached a height of approximately 10 cm. Leaf tissue removed was collected and dried at 50 °C for 5 days and then weighed. In the elevated clipping treatment done in 1992, (designated as C+ to distinguish it from the main clipping treatment C) plants were clipped to a height of 4 cm. All other aspects of this clipping treatment were unchanged.  Neighbour Removal  To examine the effects of the presence of neighbours on populations of Lupinus arcticus, all herbaceous neighbours as well as woody ground-growing vines, mosses, and spruce seedlings were removed. Neighbours were removed at the start of the growing season (May 26, 1991), immediately after quadrat erection, by pulling them out of the ground. Care was taken to avoid excessive disturbance of the soil and of neighbouring lupine clones. Neighbours were continually removed throughout the season in 1991 and 1992 as regrowth or reinvasion occurred.  38 LEAF DEMOGRAPHY STUDY Sampling Regime Following quadrat construction and treatment application in May 1991, biweekly surveys of L. arcticus populations commenced for a total of 7 surveys in 1991, and 6 surveys in 1992. At  the height of the growing season, a survey took up to 5 days to complete. To control the length of the sampling interval, quadrats were surveyed in the same order each time. The following variables were recorded at each survey: Population variables leaf density bud density leaf mortality number of diseased leaves number of damaged leaves  Growth variables number of racemes number of flower buds number of fruits (pods) seedling recruitment  petiole length raceme length peduncle length  The category of damaged leaves measured the number of leaves per quadrat showing visible signs of tissue removal or herbivore damage. The disease category measured the number of leaves per quadrat showing signs of infection including discolored leaves, necrotic spots, the presence of and fungi colonies on the leaf surface. The analyses of the above variables are divided into different sections for convenience. This chapter deals with the analyses of leaf and bud density, leaf mortality, and number of diseased leaves. Chapter four presents the results of a separate experiment on leaf cohort survivorship. Chapter five, analyzes vegetative growth data such as petiole length. Chapter six summarizes all of the reproductive data from this experiment including flower and raceme production, and fruit (pod) production, seediing recruitment and size of reproductive structures (raceme and peduncle lengths).  STATISTICAL ANALYSES These experiments were based on a repeated measures design such that the same variables were measured on the same quadrats at bi-weekly intervals throughout the season. To analyze  39 the population data collected, a Pearson Multiple Correlation (Systat 1992) was first done using Systat for Windows 5.0 on individual population variables to determine if these variables showed a serial correlation in time. The variables analyzed included leaf density, bud density, number of missing leaves, and incidence of disease. This analysis was done by generating a Pearson correlation matrix for each of the four variables after they had been pooled within a single survey, across treatments. The matrix generated in this analysis showed the correlation within a variable (e.g. leaf or bud density) between surveys when the effects of treatment removed. After examining the partial correlation matrix, if no correlation in time were detected, time was not used as a factor and all measurements of these variables over time were considered as replicates (White, R. personal communication). Population variables were then analyzed individually with a three-way or a one-way ANOVA by treatment. A three-way analysis was used to test the effect of the main factorial design in 1991 and 1992, and the one-way analysis was used for unbalanced 1992 design to test the effect of the additional clipping treatment. Post hoc Tukey comparisons were performed to ascertain where differences occurred. With the exception of bud density in 1992, correlation in time was not found for any of the population variables. To analyze bud density data in 1992, a repeated measures MANOVA by treatment was performed with survey as the repeated measures variable. The data on incidence of leaf damage were not analyzed and is not presented in this thesis for two reasons: (i) because herbivores were excluded from the experimental quadrats the level of leaf damage detected in the field was minimal, and (ii) t was difficult to distinguish between damage caused by insects and damage from disease. As a result, most damage was classified and analyzed as incidence of disease. Similarly, the category of missing leaves was also not analyzed or presented because little evidence was found for the removal of leaves by insect or microtine herbivores in 1991 or 1992.  40 STANDING CROP AVAILABLE As a supplementary experiment, the response of individual plants to treatments was measured and used to calculate the standing crop of leaves available as a potential food source for herbivores (live summer leaf-days, Ci). Ten lupine clumps were tagged in each quadrat at the beginning of the first growing season so that each of the 8 treatments consisted of 40 tagged clumps of lupines. Clumps were selected to include a range of sizes in each quadrat. Tagged lupines were numbered and measured as individuals for two seasons. In the second season, lupines reappeared at the same locations marked in the previous field season. From 320 tagged clumps, only one cluster died during the study and did not reappear in the second year. Therefore survivorship curves were not generated for these clumps. Reproduction (measured as raceme production, flower initiation, and pod production) in the tagged population was negligible in 1991 and low in 1992. The formula used to estimate the standing crop available (live summer leaf-days Ci), was -  calculated for each treatment as the sum (over all surveys done in one summer) of the mean number of leaves available at during each survey interval multiplied by the length of time that they were available (i.e. alive) to herbivores during that particular time period (i.e. survey interval). For example, the mean number of leaves available during the first survey interval would be calculated as the leaf density at survey 1  +  survey 2 divided by 2. This method of calculating the  number of leaves available during a survey interval uses the mean number of leaves between two surveys in order to reduce any possible bias (over-estimate or under-estimate) that is the result of instantaneous birth or death of leaves at the onset of each survey. This mean number of leaves available during a survey interval would then multiplied by the number of days between survey 1 and 2 to get an estimate of the number of live leaf-days available for one survey interval. This number was calculated, and summed for all surveys done in a summer for each quadrat and treatment to obtain an estimate of standing crop of live leaves available for consumption for the  41 entire season. Simply, live summer leaf-days (C ), takes into account the number of leaves that 1 are available as food, as well as how long they are available for consumption.  Live summer leaf-days  1 C  =  EkEjj [O.5*(Lj+Lj+1)*(At)]jk At  Time available  =  (1) (2)  -t tj 1  L = 1 number of leaves alive at survey i, At=no. of days between survey i and i +1, j=quadrat, k=treatment  Statistical analysis The variable live summer leaf-days, C 1 was log-transformed to make variance ,  homoscedastic, and analyzed using a three-way ANOVA by treatment in 1991 and 1992.  RESULTS  LEAF DEMOGRAPHY 1991 In 1991, all population variables were analyzed using a three-way ANOVA. Population data collected in 1991 are summarized in Table 1. In 1991, clipping significantly reduced leaf density (P=O.015), and incidence of disease (P=O.009) in leaves, while removing neighbours significantly reduced (P=O.OO1) leaf mortality (Table 2). Application of fertilizer resulted in a significant (P=O.047) increase in the incidence of disease (Table 2).  LEAF DEMOGRAPHY 1992 To determine the effect of the additional clipping treatment (C+) added in 1992 one-way ,  ANOVAs were run in conjunction with the original three-way factorial ANOVAs. Data on the population variables analyzed in 1992 are summarized in Table 3. Three-way ANOVA results of leaf density, leaf mortality and incidence of disease are reported in Table 4. As in 1991, clipping  42 reduced leaf density and incidence of disease (P=O.05, Table 4). No other significant treatment effects were detected including the elevated fertilizer treatment. One-way ANOVA indicated significant treatment effects for both leaf density (P=O.004) and disease (P=O.026, Table 5 a). A post hoc Tukey comparison of the leaf density results indicated that intense clipping, and the interaction fertilizer by clipping had significantly lower leaf densities than populations where neighbours were removed (P<O.05, see Table 5 b). Tukey comparison indicated that populations that were intensely clipped (C+) had less disease than those fertilized (F) (P<O.05, Table 5 c). Tukey comparison also indicated a moderately non-significant treatment effect for regular clipping (C) (P<O.067, Table 5 c).  Table 1: Summary of mean leaf density, bud density, leaf mortality and incidence of disease by treatment in 1991. Note that n=32 as replicates were pooled across surveys. Treatment  Mean leaf density 2 /m  ± SEM  Mean bud density /m 2  ± SEM  Mean leaf mortality  ± SEM  Mean incidence  ± SEM  of disease 2 /m  2 /m  Control  454.7  42.0  125.8  26.6  76.2  18.6  208.5  37.7  Fertilizer  591.3  57.5  146.6  25.9  118.1  16.9  393.6  61.3  Clipping  426.4  30.5  128.3  21.6  62.74  11.0  211.5  34.5  Removal  476.9  62.9  94.5  24.4  49.4  14.3  271.1  48.5  Fert/Clip  425.1  47.7  135.4  13.9  69.0  12.8  261.9  49.2  Fert/Rem  568.3  59.8  138.4  26.1  55.0  11.9  262.9  50.9  Clip/Rem  426.4  30.5  128.3  21.6  62.7  11.0  211.5  34.5  Fert/Clip/Rem  464.6  51.4  180.6  30.3  47.4  9.2  239.9  44.9  43 Table 2: Source of variation for three-way ANOVA results of bud density, leaf density, incidence of mortality and disease in 1991.  Variable  Source  dF  MS  F-ratio  Prob.  Bud density  Fertilizer Clipping Removal Fert*Clip Fert*Rem. Clip*Rem. Fert*Clip*Rem Error  1 1 1 1 1 1 1 216  51656.813 17057.054 235.718 210.164 15073.700 26679.233 1325.612 15833.494  3.263 1.077 0.015 0.013 0.952 1.685 0.084  0.072 0.300 0.903 0.908 0.330 0.196 0.773  Leaf density  Fertilizer Clipping Removal Fert*Clip Fert*Rem. Clip*Rem. Fert*Clip*Rem Error  1 1 1 1 1 1 1 216  241630.248 420288.378 5818.714 130540.41 212.396 6260. 122 23863.012 69265.93  3.488 6.068 0.084 1.885 0.003 0.090 0.345  0.063 0.015 0.772 0.171 0.956 0.764 0.558  Dead  Fertilizer Clipping Removal Fert*Clip Fert*Rem Clip*Rem Fert*Clip*Rem Error  1 1 1 1 1 1 1 216  9723.691 17675.961 55487.714 6236.706 6708.858 10204.707 2943.616 5089.071  1.911 3.473 10.903 1.226 1.318 2.005 0.578  0.168 0.064 0.001 0.270 0.252 0.158 0.448  Disease  Fertilizer Clipping Removal Fert*Clip Fert*Rem Clip*Rem Fert*Clip*Rem Error  1 1 1 1 1 1 1 216  236831.97 162944.66 31168.73 30739.12 155601.40 6072.24 108049.761 59388.116  3.988 2.744 0.525 0.518 2.620 0.102 1.819  0.047 0.009 0.470 0.473 0.107 0.749 0.179  44 TaN 3: Summary of mean leaf density, bud density, leaf mortality and incidence of disease in 1992. Note that n=32 as replicates were pooled across surveys. Treatment  Mean leaf density  ± SEM  Mean bud density 2 /m  ± SEM  Mean leaf mortality un 2  ± SEM  Mean incidence of disease 2 /m  ± SEM  2 1m Control  505.13  49.3  98.96  26.01  80.71  16.35  267.08  49.51  Fertilizer  540.50  62.6  115.13  17.77  116.13  23.05  386.67  66.27  Clipping  386.21  47.15  79.38  15.47  68.04  14.32  175.63  38.93  Removal  600.13  100.6  122.38  36.19  119.58  34.31  316.00  73.97  Fert*Clip  313.67  31.6  98.00  12.95  73.5  16.62  215.13  34.31  Fert*Rem  529.04  55.7  103.78  18.58  62.5  14.28  256.63  55.62  Clip*Rem  469.92  49.3  103.25  21.96  69.33  13.54  191.54  37.16  Fert*Clip*Rem  457.58  50.5  121.38  17.39  74.04  15.66  246.58  46.26  Extra Clip  327.92  47.4  90.54  18.29  55.54  13.97  144.38  25.33  45 Table 4: Source of variation for three-way ANOVA of leaf density, incidence of disease and mortality in 1992.  Variable  Source  dF  MS  F-ratio  Leaf density  Fertilizer Clipping Removal Fert*Clip Fert*Rem Clip*Rem Fert*Clip*Rem Error  1 1 1 1 1 1 1 184  20 155.964 693738.713 99072.234 47026.124 11973.008 43076.778 54865.83 85528.818  0.236 8.111 1.158 0.550 0.140 0.504 0.641  0.628 0.005 0.283 0.459 0.709 0.479 0.424  Dead  Fertilizer Clipping Removal Fert*Clip Fert*Rem Clip*Rem Fert*Clip*Rem Error  1 1 1 1 1 1 1 184  186.445 18190.379 756.684 1403.158 20098.605 717.173 28965. 179 9336.539  0.020 1.948 0.08 1 0.150 2.153 0.007 3.102  0.888 0.164 0.776 0.699 0.144 0.782 0.080  Disease  Fertilizer Clipping Removal Fert*Clip Fert*Rem Clip*Rem Fert*Clip*Rem Error  1 1 1 1 1 1 1 184  72734.167 412207.455 114.366 6354.721 81358.365 36692.861 140543.972 65113.83  1.117 6.331 0.002 0.098 1.249 0.564 2.158  0.292 0.013 0.967 0.755 0.265 0.454 0.143  Probability  A  A  ,  d  IUOD  661  U’  TNJD  DkI  IN/D/d  NJ%J]I  +  4  4  4  A  A  A  A  A  A  A  A  A  A  U!  A  3  Al +5j  iuomau Aq sisTp jo sts(jui M-uo Jo uosudmoo ‘ni :(o ç jqj  WJJD/.I  JN/D  1U03  WST.kT  T  JN  Z66T  +  4  A  A  3  4  +3  iuwiiau Aq Xiisup ji Jo srsApui i-uo jo uosuduio3 i(jnJ :(q ç jqj  O!11U-  £1qq0JJ  OO6  P000  c17vz  9W0  ZUflO  SS  IP  W1k.L  OO968T cLoT99oT OO69T 6cL88ccc8T  L9869E6c LO 8EV9ZEET 8 z%ccL66L LO c6c8I76Tz 8 SI’Sl  J01.I  11J  SUS!U  1OJ.I  1!SUpJ’I  166T UI ppp iuwnan &nddijo suut iiwoiiippi qi pwIotho3uI sist(pu sIq 1661 rn iuunaii put I1!SUp JUJ JO sisXtii MM-UO oj UOUJA jo atnog : ( ç ‘[qj  Cq  JO 3UTOW  9I  47 Table 6: Source of variation for one-way repeated measures MANOVA of bud production in 1992. Source  SS  dF  MS  F-ratio  Probability  Between treatments Treatment Error  1855588.8  231948.6 254090.9  0.913  0.521  6860454.0  8 27  Within treatments Time Time*Treatment  7101515.4  5  1420303.1  107.882  0.000  815643.6  40  20391.2  1.549  0.034  Error  1777320.0  135  13165.3  2284348.4  1  205.622  358017.9  8 27  2284349.4 44752.2  0.000 0.003  Polynomial test of order (linear) Time Time*Treatrnent Error  299954.9  4.028  11109.4  Table 7: Summary of multivariate test statistics for one-way and three way repeated measures analysis of bud production/rn 2 in 1992, Variable  Test  Statistic  dF (hyp)  dF (error)  F ratio  Probability  ONE-WAY Time Time*Treatment  Wilks’ Lambda  0.063  5  23  0.000  Wilks Lambda  0.098  40  103  67.927 1.813  t Lambda Wilks  0.060  5  20  63.042  Wilks’ Lambda  0.506  5  20  3.927  0.009  THREE-WAY Time Time*Clip  0.000 0.012  48 Table 8: Three-way repeated measures MANOVA of bud production/rn 2• Source  SS  dF  MS  F-ratio  Probability  Fertilizer Clipping  43621.021  1  43621.021  0.169  0.685  898995.021  1  898995.021  3.474  0.075  Neighbour Removal Fert*Clip  290474.083 7252.083  1  290474.083  0.300  Fert*Removal  6417.187  1 1  7252.083 6417.187  1.123 0.028  Clip*Removal  62280.021  1  Fert*Clip*Removal  83333.33  Error  Between treatments  0.025  0.868 0.876  62280.021  0.241  0.628  1  83333.33  0.322  0.576  6210177.17  24  258757.4  Time Time*Fertilizer Time*Clipping  6971265.9  1394253.2  109268.0  5 5  0.000 0.161  187830.2  5  21853.6 37566.0  103.019 1.615 2.776  Time*Removal  68154.4  5  13630.9  1.007  Time*Fert*Clip  25556.1  5  5111.2  0.378  Time*Fert*Removal Time*Clip*Rernnial Time*Fert*ClipRem  41453.5 63123.5 15232.5  5 5 5  8290.7 12624.7  0.613  0.021 0.417 0.863 0.690 0.462 0.951  Error  1624067.8  120  Within treatments  3046.5 13533.9  0.933 0.225  49 Bud density showed a serial correlation in time (Fig. 1) and was analyzed separately using a repeated measures MANOVA (one-way and three-way). No significant treatment effects were detected in either the one-way or three-way analysis, however, the repeated measures component of these analyses detected a significant within treatment Time effect in both cases (Tables 6, 8). The multivariate test results for both one-way and three-way analyses also detected significant Time, and Time by Treatment interactions (Table 7). In the three-way analysis, the Time by Treatment interaction occurred only for clipping (C) (Table 7). The polynomial test of order (Gurevitch & Chester 1986, Systat 1992) of the within treatment effects for the one-way analysis in 1992 indicated that this effect was significant and linear (P=0.003) (Table 6). In contrast, when the extra clipping treatment was removed and the data were analyzed with the three-way design, the polynomial test of order of the within treatment effects showed that the significant Time and Time by clipping effects were quadratic (P<0.037, Table 9). The discrepancy between the results for the polynomial test of order for the one-way and three-way analysis is unusual. These results indicate that when the intense clipping treatment is incorporated into the experimental design, the within treatment repeated measures effect is best described with a linear model.  Table 9: Source of variation for the polynomial test of order (quadratic) for three-way repeated measures analysis of bud production in 1992. Source  dF  Time 1 Time*Fertilizer 1 Time*Clipping 1 Time*Removal 1 Time*Fert*Clip 1 Time*Fert*Removal 1 Time*Clip*Removal 1 Time*Fert*Clip*Rem 1 Error 24  MS  F-ratio  Probability  3858099.3 41125.1 130510.8 0.595 13626.0 165.0 52417.0 13464.4  143.926 1.534 4.869 0.000 0.508 0.006 1.955 0.502  0.000 0.227 0.037 0.996 0.483 0.938 0.675 0.485  50 STANDING CROP AVAILABLE Standing crop available in 1991 and 1992 calculated as live summer leaf-days (C ) is 1 summarized by treatment in Figure 2. In 1991, clipping and the interaction between fertilizer and neighbour removal had significant effects on live summer leaf-days (C ) (P=0.05, Table 10). 1 Clipping in 1991 increased mean C (±SEM) to 5421.5 ±420.22 relative to the control of 5131.0 ±555.74. The interaction between fertilizer and neighbour removal was such that C 1 was greatest in the control (no fertilizer or neighbour removal). However, the reduction in C 1 as a consequence of treatment was less when fertilizer was combined with neighbour removal, than fertilizer alone, or neighbour removal alone (Fig. 2). In 1992, the only significant effect on standing crop available detected was fertilizer (P=0.005, Table 10). Mean (±SEM) live summer leaf-days for the fertilized treatment was 4516.25 ±734.11 relative to the control that was calculated as 8075.25 ±216.56 (Fig. 2). In 1991, live summer leaf days violated the assumption of normality in this analysis.  51 800  -  •  CONTROL  •  F  5OO-  A  NR  400-  .  C  o  C+  *  F-C-NR  -I-  F-C  700  -  600-  0 300-  200 100  -  -  00  10  20  30  I  I  I  I  40  50  60  70  80  Time (days)  800  700  -  -  C-NR  500400-  *  F-NR  300-  •  CONTROL  200 100  -  -  (1-  0  10  20  I  I  I  30  40  50  I  60  70  80  Time (days)  1: Bud production I m 2 over time by treatment in 1992. Time 0 is the date of the first survey (June 2), which was approximately 7 days after snowmelt and 3 days after treatments (except clipping) were first applied. The control has been shown in both panels for comparative purposes. Figure  52  100000  •  C,,  1991  1992 10000  1  0  L)  9  L) 0  Figure 2: Mean (±SEM) live summer leaf-days (Ci) by treatment and year.  Table 10: Source of variation table for a 3-way ANOVA of log transformed live summer leaf days (Ci) by treatment in 1991 and 1992. Variable  Treatment  dF  MS  F-ratio  Probability  1991  Fertilizer Clip Removal Fert*Clip Fert*Rem Clip*Rem Fert*Clip*Rem Error Fertilizer Clip Removal Fert*Clip Fert*Rem Clip*Rem Fert*Clip*Rem Error  1 1 1 1 1 1 1 24 1 1 1 1 1 1 1 24  0.247 0.3 14 0.263 0.001 0.403 0.050 0.002 0.070 1.943 0.013 0.310 0.210 0.314 0.241 0.069 0.203  3.511 4.476 3.753 0.007 5.738 0.7 17 0.024  0.074 0.045 0.065 0.933 0.025 0.406 0.879  9.593 0.062 1.531  0.005 0.805 0.228  1.037 1.549 1.192 0.34  0.319 0.225 0.286 0.565  1992  53 DISCUSSION  “The growth and development of most plants depends on the accumulation of reiterated elements (e.g. leaves, shoots and flowers)  ...  Because reiterated elements possess demographic  properties like natality and mortality, plants can be considered as metapopulations (sensu White 1979) of parts.” (Maillette 1992). In designing plant population studies, some problems are encountered in determining the unit of census (leaves, stems, shoots, buds), as plant architecture is quite variable both within and between taxonomic groups (reviewed in White 1979). Maillette (1992) suggests a number of criteria for selecting census variables based partly on their “demographic competence”, (i.e. their ability to satisfy the equation N t+1  =  N  t+  B-D, and  produce daughter units). Although leaves, as demographic units, do not directly produce daughters, they are still used because of their value in determining aspects of plant growth (Harper 1989) Although the number of demographic studies done on plants are accumulating rapidly, many of these are comparative or descriptive studies of dynamics in the field or greenhouse (Sarukhán Harper 1973; Sarukhán 1974; Barkham 1980; Ford 1981; Hartnett Bazzaz 1985). ,  ,  Relatively few studies monitor population dynamics in response to experimental manipulations, and even fewer study clonal perennial species (but see Hawthorne, Cavers 1976; Bazzaz Harper ,  1977; Noble et al. 1979; Jonsdottir 1991). The lack of studies available for rhizomatous perennials is partly attributed to the difficulty in identifying genetic individuals, and in the case of some species such as L. arcticus, difficulty in identifying ramets or other modular units without excavation. This study of the response of L. arcticus to treatments by-passed these problems by measuring populations of leaves per unit area. Other studies have measured the response of plants using the demography of leaves, but this new approach to measure the demography of populations is advantageous in situations where the identification of individuals (ramets or genets) is not feasible without destructive sampling. Although this method is not capable of detecting changes in ramet populations, Maillette (1992), in a comparative study of the plasticity between  54 different levels of modular reiteration in Potentilla anserina L., reported that the dynamics of the smallest units of reiteration (leaves) were more sensitive to treatments (fertilizer), than either modules or stolons. The different levels of modular reiteration compared in Maillette’s study are considered to be hierarchical units of reiteration where the smallest units are compounded to form larger units (White 1979). The results of Maillette (1992) suggests that while this method of monitoring leaf demography is not without flaws, it may have merit in some unique cases, as for L. arcticus.  LEAF DEMOGRAPHY Because no correlation in time was detected for most variables in this experiment, the analyses of leaf demography variables in response to the treatments were done in such a way that these data were pooled over time and analyzed globally by treatment. The final result showed that comparing leaf demography as a metapopulation (sensu White 1979) of leaves per unit area was sensitive enough to detect treatment effects in this experiment. In 1991, fertilizer and clipping both significantly affected incidence of disease occurring in the populations. Clipping reduced the incidence, whereas fertilizer increased it. The reduction associated with clipping is quite surprising as it is often reported that herbivore damage increases susceptibility to disease due to their acting as vectors, or by increasing the vulnerability of tissues to infection after herbivore damage (Crawley 1988, Baldwin 1990). The reduction of disease may be the consequence of reduced crowding of leaves in the clipped population decreasing the rate of spread of disease between remaining leaves. However, under conditions of natural herbivory, parts of leaves or stems may be removed and the pattern of thinning may be quite different from the experimental clipping. In addition, incidence of disease may have been greater in the taller leaves so that the clipping of leaves may have purged the population of disease to some extent. In any case, reduction in incidence of disease in clipped plants agrees with Owen’s (1981) supposition that herbivores may benefit plants by selective removal of unproductive tissue.  55 The higher incidence of disease detected in the fertilizer treatment may indicate that pests prefer to infect rapidly growing tissue, or tissue with higher nutrient concentrations. Alternatively, they could be preferentially selecting tissue that has a higher turnover rate. Very little is currently known about the role of disease in plant population dynamics, and it has not been clearly demonstrated that disease is capable of affecting a planCs fitness, or what factors are important in determining rates of infection in hosts (Alexander 1992). It has been suggested that disease only becomes an important mortality factor once the plant is afready under stress from other factors (i.e. herbivory, Crawley 1988). Wennstrom Ericson (1991) have also shown that Pulsatilla ,  pratensis, a perennial dicot occurring in Sweden, has more vigorous vegetative growth and survivorship when it is infected by Puccinia pulsatillae, a sterilizing rust fungus. Diseased plants were also reported to be less susceptible to grazing by cattle or leaf-eating insects. In 1991, total leaf mortality increased when neighbours were present, indicating that competition is occurring in this environment. Fertilizer marginally increased leaf mortality in 1991. This effect has been shown in other fertilizer studies (Noble et al. 1979; Shaver 1983) and has frequently been attributed to higher probability of leaf death (turnover) in rapidly growing tissue (Harper 1977). More specifically, fertilizer may increase leaf mortality by hastening the onset of competition for light or other factors. Shaver (1983) also proposed that the cost of maintaining old leaves may outweigh their advantages for nutrient storage, and old leaves are allowed to die. Maillette (1992) in a greenhouse experiment using Potentilla anserina also found that increased nutrient levels increased leaf mortality as measured by leaf death ratio (number of dead leaves/total number of leaves). In 1992, clipping again reduced the incidence of disease in the population, but no effect was detected for fertilizer despite the increased application rate. As in 1991, no treatment effects were detected for leaf mortality in 1992. This suggests that variability in other environmental factors may interact to determine the relative importance of the factors tested. For instance, Eriksson (1986), in a study of Potentilla anserina on a Baltic seashore, reported that population  56 dynamics were mostly driven by external factors such as poor weather that synchronized birth and death of ramets and modules. Climatic factors may be interacting with the experimental treatments to produce variability between years. In both 1991 and 1992, fertilizer and neighbour removal treatments were shown not to significantly affect the overall population density of leaves. This at first appears surprising because both treatments should make more resources available to the target plants. However, L. arcticus living in this habitat experiences many of the characteristics described by Grime (1979) as being a high stress environment low light below the canopy, low fertility soils, and low annual -  precipitation (35 cm falling mostly as winter snow). Thus, we might predict that L. arcticus is a stress tolerator (sensu Grime 1977) and should show little or no response to short term increases in nutrients or reduced competition. The production of leaf buds which is an estimate of birth rate of new leaves was analyzed differently in 1991 than in 1992, because a correlation in time was detected in bud density in 1992. In spite of the different analytical techniques, no significant treatment effect was detected in the birth of new leaves (buds) in either year. Although clipping did reduce the overall leaf population, and presumably the leaf surface area in both 1991 and 1992, this was not shown to significantly influence the ability of L. arcticus to produce new leaves. That L. arcticus populations are able to compensate for clipping and maintain the same birth rate of new leaves. This may indicate that L. arcticus has evolved to withstand periodic episodes of intense herbivory during hare peak. Without this attribute, L. arcticus could be driven locally extinct. It is generally believed that some plants may be capable of compensating for tissue lost to herbivores if the tissue removed was relatively unproductive (i.e. respiration costs > photosynthesis), or if there were sufficient reserves available to regenerate the lost surface area. Chazdon (1991), in a 3-year clipping experiment on an understory clonal palm (Geonoma congesta) removed both ramets and/or leaves and reported little response to ramet removal or defoliation in terms of mortality, stem and height growth, rate of ramet production or reproduction. This study concluded that  57 Geonoma congesta’s resistance to repeated defoliation and ramet removal was likely due to the mobilization of stored reserves to maintain normal patterns of growth. There is some question as to the reason why bud density in 1992 was found to have a correlation in time when the other variables did not, and why this effect was not detected in 1991. The repeated measures MANOVA performed on bud density in 1992 showed a significant time trend and a significant time by clipping interaction although no significant treatment effect was detected for clipping. This time trend was either linear or quadratic depending on whether the analysis included the elevated clipping treatment. Although the biological significance of these results is uncertain, perhaps this is the first indication of a treatment effect of clipping on bud density that was not previously detected due to some lag in the response of L. arcticus populations.  STANDING CROP AVAILABLE In 1991, live summer leaf days 1 (C ) , an index of the amount of leaf matter available to herbivores through the season, was greatest when populations were clipped, and there was a significant interaction between fertilizer and neighbour removal such that the reduction in standing crop available when fertilizer was applied was partly ameliorated if neighbours were removed. The result obtained for clipping was rather unexpected as clipping reduced overall leaf density in L. arcticus populations. The higher standing crop in this treatment suggests that lower turnover of leaves is occurring when populations are clipped (Louda 1984). If this were true, it may be a mechanism by which the plants maintain leaves longer to compensate for reduction in leaf surface area when clipped. The significant interaction between fertilizer and neighbour removal suggests that the presence of neighbours is mediating the access of L. arcticus to the nutrients applied. Faster growing neighbouring species may use the nutrients before L. arcticus (Noble et al. 1979) so that  58 the removal of neighbours allowed L. arcticus greater access to the nutrients and resulted in  increased standing crop available when neighbours were absent. In 1992, no interaction between fertilizer and neighbours was detected, however fertilizer did reduce standing crop relative to the control. If neighbours are limiting the access of L. arcticus to the nutrients applied, it is not known why an interaction did not occur in 1992 as in 1991. However, the neighbour removal treatment only manipulated the abundance of herbaceous species, and does not account for competition from trees and shrubs. In 1992, trees and shrubs which were slow to respond in 1991 may have usurped nutrients to the detriment of all herbaceous species including L. arcticus. Although it was not possible to assess the effect of treatments on ramet or genet populations of L. arcticus, it may be possible to speculate that factors affecting metapopulations of leaves may be translated to ramet and genet dynamics if the treatments are maintained for a sufficient period. Maillette (1992) reported that compound reiterated units (modules, stolons) that are composed of several smaller units (leaves, buds, roots) were ‘demographically buffered’ and slower to respond to treatments. Therefore it might be expected that populations responding to changes in environmental factors would respond initially with changes in the smallest units of reiteration (leaves) and these changes would then be transmitted to higher levels in the reiteration hierarchy (sensu Maillette 1992). So changes in the birth rate or death rate of leaves should eventually appear as births or deaths in the ramet population.  CHAPTER 3 LEAF COHORT SURVIVORSHIP In 1992, a leaf cohort survivorship study was initiated as it was not possible to assess patterns of leaf or ramet survivorship in the main experiment. This experiment was designed to determine if the treatments had different effects on patterns of leaf survivorship, and if the survivorship curves differed qualitatively or quantitatively. Quantitative differences in survivorship would indicate that the pattern of mortality between treatments was the same whereas the rate of mortality differed. Qualitative differences in leaf survivorship indicate that the pattern of risk of mortality over time is different between treatments (i.e. the shape of the survivorship curves is different). METHODS In 1992, a single subpopulation of lupine leaves was chosen within the main experimental quadrats. Each subpopulation was chosen to incorporate the highest density of lupine leaves that could fit in a 25 cm X 25 cm (625 cm 2) area, to ensure an adequate population size. The subpopulation was permanently marked in each quadrat with a plastic-coated wire quadrat, 25 cm on a side. The first cohort was tagged on June 7, 1992, the second cohort on June 24, 1992. Leaf cohorts were identified by placing one drop of Testor’s model paint on a leaflet. Different coloured paints were used for two cohorts. These cohorts within the subpopulations of leaves were successfully monitored over the course of the 1992 season. Attempts at marking later cohorts were not successful due to poor weather conditions. Prior to using the paint, field tests were performed in Vancouver, B.C. and there was no indication of damage or reduced leaf or plant survival with this method. Paint tags had a high degree of permanency under variable  59  60 Vancouver weather conditions. Cohorts were surveyed five times through the season: June 7, June 24, July 10, July 28, and August 19, 1992.  STATISTICAL ANALYSIS Between Treatment Survivorship Comparison of survivorship between cohorts was performed to determine if survivorship differed depending on birth date. Two methods were used to analyze the survivorship data in this experiment and showed different results. The first involved fitting a parametric model to survivorship data and then comparing model parameters between treatments and cohorts. The second method was the traditional nonparametric rank sum procedure used for ecological survivorship experiments. Two analytical methods are incorporated for comparative purposes. Cohort life tables were not constructed as part of this analysis because the data did not meet life table requirements described in Lee (1980, 1992). According to Lee (1980, 1992), cohort life table analysis cannot contain right-censored data, so that all cohorts must be followed until 100% mortality occurs. Second, Lee (1980, 1992) states that a large number of censuses must be performed so that data can be grouped into intervals. She suggests that a minimum of 10 intervals is adequate for good analysis. Since this data set does not meet either of these criteria another method was sought. Life table analysis is also a non-parametric method and is not as powerful as parametric methods that are available because it is not capable of accounting for variance between replicates in the analysis. Most life table analyses are performed on unreplicated populations; this cohort study was replicated (n=4) so parametric methods were preferred.  PARAMETRIC MODEL FITTING The results from the leaf cohort survivorship study were analyzed using SAS Proc Lifereg and Proc Nun (SAS 1979) to select and fit an appropriate theoretical survival distribution model  61 to the data. The Proc Lifereg module was used initially to select a survival distribution to fit the data. Both the Weibull distribution and the log-logistic distribution had good fit, but the predicted values calculated by quadrat indicated that the Weibull model showed the better fit. The SAS Proc Nun module was then used to calculate the parameters for the Weibull model for each quadrat and cohort individually. A complete description of the Weibull model is in Appendix A, and a brief description follows.  WEIBULL MODEL DESCRIPTION The survival curve generated by the Weibull distribution is modelled with the following equation: a S(t)=lOO * e .(*age) (1) S(t) is the probability of survivorship at time or age (t), lambda (2) is the scale parameter, and alpha (a) is the shape parameter.  The values for ? and a that were calculated by the nonlinear model fitting module in SAS for each cohort and quadrat were used as the test statistics to compare treatment effects on leaf cohort survivorship. A one-way ANOVA of mean parameter estimates for each treatment and cohort was used to determine if the experimental treatments produced significantly (P<O.05) different patterns of leaf survivorship between the first (emerged June7) and the second (emerged June 24) cohorts. A one-way ANOVA was used in place of a factorial design to accommodate the additional clipping treatment in 1992. Both 2 and a were log transformed prior to analysis to make the data homoscedastic.  62 NONPARAMETRIC STATISTICS The nonparametric log rank procedure used for multiple comparisons of the leaf cohort survivorship is described in Pyke Thompson (1986) and amended in Pyke Thompson (1987). ,  ,  The null hypothesis tested in this analysis is that the survival rates of different populations (i.e. treatments) or cohorts do not differ (Hutchings et al. 1991). The log rank test statistic is based upon a chi square distribution with degrees of freedom equal to one less than the number of groups (treatments) compared. The method used here tests for differences between unreplicated populations. Survivorship data were collected on replicated subpopulations of leaves. To calculate the log rank test statistic, the subpopulation data for each cohort were pooled, and the means of each treatment were used to calculate rank in accordance with Pyke (1993, personal communication). RESULTS WEIBULL MODEL ANALYSES The shape (a) and scale  () parameters generated by the Weibull model are summarized by  treatment for each cohort in Table 11. Analysis of variance for cohort 1 detected no significant treatment effects for either 2. or a (Tables 13, 14). Analysis of the cohort 2 parameters showed the scale and the shape parameters to be highly significant (P=O.OO1, Table 12, and P=O.028, Table 13), indicating that survivorship in the younger cohort was highly dependent on the treatment imposed. A post hoc Tukey comparison of the scale parameter in cohort 2 detected a significant difference between the intense clipping treatment (C+) and the control, fertilized, neighbour removal, fertilizer by neighbour removal and clipping by neighbour removal groups. Tukey comparison of the shape parameter in cohort 2 detected significant differences between clipping and fertilizer, and between clipping and fertilizer by neighbour removal treatments. Survivorship in the clipping treatments decreased more rapidly than other treatments (Fig. 3).  63 Table 11: Scale (2k) and shape (ct) parameter estimates for the Weibull Distribution (1992).  Cohort 1 Treatment  ± SEM  Control  0.0 18  0.002  Fertilized  0.020  Removal  Cohort 2 ±SEM  0.002  1.937 0.334 1.9 17 0.5 07  0.013 0.013  0.021  0.006  2.106 0.24 1  0.014  0.003 0.004  Clipping  0.020  0.006  1.369 0.112  0.053  Fert/Clip  0.019 0.020  0.002  1.359 0.137 1.751 0.402 1.728 0.274 1.576 0.220 1.960 0.079  Clip/Rem Fert/Rem  0.018 Fert/Clip/Rem 0.025 Extra clip 0.02 1  0.002 0.002 0.003 0.001  I ± SEM  ± SEM 2.255 2.146  0.458  2.146  0.670  0.02 1  0.377  0.131  0.185  0.152  0.846  0.356  0.017 0.013  0.002 0.00 1  1.101 2.445  0.322 0.517  0.037 0.439  0.007 0.244  1.407 1.059  0.568 0.577  0.003  0.764  Table 12: Combined source of variation for the one-way ANOVAs for cohort 1 and 2 comparing the log transformed ? (scale) Weibull parameter by treatment.  Cohort  Source  SS  dF  MS  F ratio  Probability  1  Treatment  0.347  0.043  0.5 10  0.838  Error  2.297  8 27  Treatment  33.776 23.285  8  4.222  3.896  0.001  27  0.862  2  Error  0.085  64 1  1-.  0  0.1  0.1  -  4 C  a) 0  0.01  0.01  -  10  20  30  40  50  60  70  -  80  10  20  30  40  50  60  70  80  Time (days)  •  Control  •  F  A  1  NR  •  C  C+  D  1-  Cohort2  1  0.1  a) 0  0.01 10  20  30  40  50  60  70  80  0.1 -  20  30  40  70  50  60  X  FCNR  Time (days) •  Control  -I-  FC  *  FNR  CNR  Figure 3: Leaf survivorship curves for cohort 1 and cohort 2 by treatment in 1992. F=fertilized, C=clipped, NR=neighbours removed, C+=extra clipping.  80  65 Theoretical Survival Curves In addition to determining the effect of treatment on survivorship, parameters generated by fitting the Weibull model to survivorship function can convey pertinent biological information by fitting the experimental data to a theoretical model of survivorship (Begon & Mortimer 1986). Certain combinations of  and a can be used to generate Type I, II, and III survivorship curves  (see Appendix A, Figure 9). When ? is constant, an a greater than 1 produces a Type III curve; a equal to 1 is a Type II curve; a less than 1 is a Type I curve. Examination of the mean  and cc  parameter estimates (Table 11) indicates that a Type III survivorship could describe leaf survivorship for both cohorts. A Type III curve is characterized by a high rate of early mortality that declines as survivors age. Not all treatments show an obvious Type ifi curve. Mean a (shape) parameter estimates by treatment and by cohort show variability that could generate all three of the survivorship models. In the first leaf cohort, all a estimates are greater than one, however in cohort 2, the values for different treatments range from 0.377 to 2.445 (Table 11).  Between Cohort Survivorship After the analysis of survivorship curves by treatment within cohort detected no significant difference (Table 12, 13) an a posteriori decision was made to compare survivorship between ,  cohorts within treatment groups. Two-way ANOVA results of the log transformed shape and scale parameters indicated that only the shape of survivorship curves showed a significant between cohort effect (P=O.003, Table 14).  NONPARAMETRIC ANALYSES In contrast to the parametric model, the nonparametric analysis of differences in survivorship between treatments for cohort 1 and cohort 2 detected a significant treatment effect for both cohorts at P<O.000 (Table 15). The nonparametric log rank test does not permit pairwise comparisons to ascertain where the differences between treatments lie. However,  66 inspection of the data suggests that with the exception of the clipping treatments, survivorship of cohort 2 may be slightly greater than cohort 1.  Table 13: Combined source of variation table of one-way ANOVAs for cohort 1 and 2 comparing the log transformed shape (ct) parameter by treatment.  Cohort 1 2  Source Treatment Error Treatment Error  dF 8 27 8 27  SS 0.736 2.412 14.124 18.096  MS 0.092 0.089 1.765 0.670  F ratio 1.030  Probability 0.438  2.634  0.028  Table 14: Combined source of variation table for two-way ANOVAs testing for the effect of cohort within treatment on log transformed shape and scale parameters.  Parameter Lambda (?.,)  Alpha (x)  Source Cohort Treatment Cohort*Treatment Error Cohort Treatment Cohort*Treatment Error  SS 1.556 18.214 15.909 25.583 3.596 9.446 5.414 20.508  dF 1 8 8 54 1 8 8 54  MS 1.556 2.277 1.989 0.474 3.596 1.181 0.677 0.380  F ratio 3.284 4.806 4.198  Probability 0.075 0.000 0.001  9.464 3.109 1.782  0.003 0.006 0.101  Table 15: The nonparametric log rank statistics testing for between treatment effects in cohort 1 and cohort 2 leaf survivorship curves.  Cohort 1 2  Log rank test statistic 257.832 701.716  dF 8 8  Chi square critical value 15.507 15.507  Probability  0.000 0.000  67  DISCUSSION  Little is known about the causes of mortality in plants in the field and why species and populations show different patterns of survivorship at different sites and environmental conditions (Hutchings et al. 1991). As a result, studies investigating how growth, reproduction, and mortality of populations respond to different environmental factors are still necessary until we are able to explain with some degree of certainty how and why populations change over time. Understanding how patterns and rates of mortality are influenced by time, age, and environmental factors allows us to predict population change when combined with birth, immigration and emigration statistics. Most studies of population survivorship focus on two questions. (1) Does the proportion of individuals reaching age x differ in one population relative to other populations? (2) Does the average lifespan of an individual differ between populations? (Pyke & Thompson 1986). This study is primarily concerned with the question of longevity of leaves under different treatments which depends on the shape of the survivorship curve in a leaf population (Pyke, Thompson 1986). Ideally, we would follow cohorts of seedlings. However, in 1992, there were insufficient seedlings (genets) of L. arcticus established and consequently cohorts of leaves were used. According to Harper (1977), “a leaf has a life history, a changing pattern of behaviour from birth  ...  to death from senescence or some environmental hazard” (p. 23). As such, “for some  purposes the population dynamics of plant parts may be more useful than the dynamics of whole plants in a community” (p. 21). This view is partly based on the presumption that predicting population changes in plant communities may not come until we understand how plants respond to changes in their environment at all levels of organization (i.e. leaf, rarnet and genet) because changes at lower organizational levels (i.e. the leaf) ultimately translate to higher levels of organization. From an ecological standpoint, studies of leaf lifespan are important because leaf  68 longevity determines the duration of soil coverage, shading of neighbours, rainfall interception losses (i.e. from transpiration or evaporation before it hits ground), soil moisture depletion, microbial and herbivory life (diversity and abundance) in the canopy, and air pollution sensitivity (Diemers et al. 1992). With a modular framework of plant organization in mind, patterns of survivorship between treatments were assessed at the level of the leaf by comparing survivorship between leaf cohorts of different ages.  EFFECTS OF CLIPPING In this study, clipping had the strongest effects on survivorship. This effect was greatest in cohort 2, the youngest leaf cohort, with little detectable effect in cohort 1. Clipping at the normal or more intense level caused leaf death because leaves were removed as a part of the treatment protocol. However, all differences were not wholly due to clipping because not all treatments experiencing clipping showed the same survivorship pattern. The more intense clipping treatment caused a more rapid rate of decline in survival in cohort 2. When clipping was combined with other treatments there was greater survival than treatments of clipping alone. Several factors may be responsible for higher levels of mortality in the clipping treatments. This could be the result of (1) the increased likelihood or incidence of disease following tissue removal, however incidence of disease was shown to decline with clipping in chapter 2, (2) decreased competitive ability with neighbours due to subordination in the canopy, (3) the reduced ability to defend against subsequent herbivore attack, or (4) reduced ability to fix nitrogen because of reduced carbon fixing ability. Clipping in combination with neighbour removal or fertilizer may have mitigated some of the negative effects associated with the loss of leaf surface area or tissue damage. Louda (1984) studied the effect of herbivory on leaf dynamics and reported that reduced levels of insect damage increased the survival of mature leaves and increased initiation of leaves. Similarly, clipping in my study did produce a notable decrease in leaf survival, but not in all clipped treatments. Chabot & Hicks (1982) in a review of leaf lifespans suggested that the impact  69 of leaf removal depends on the amount, timing, nature of damage and local environmental conditions. Interaction of clipping with fertilizer and/or neighbour removal alleviated the effect of clipping on leaf survivorship in my study. According to Chabot & Hicks (1982), vulnerability to the effects of clipping may vary with the stage of development a leaf has reached when damage occurs. In my experiment, clipping was performed on the same date for both cohorts, but at different ages, and this difference may explain why clipping effects were not seen in both cohorts.  EFFECT OF FERTILIZER AND REMOVING NEIGHBOURS Fertilizer and removing neighbours did not have a significant effect on leaf survivorship. This lack of significant response was surprising because both of these treatments have the potential to strongly influence leaf longevity. For instance, removing neighbours increases the amount of light available for photosynthesis and reduces shading. This could change a leaf from a net drain or a sink to a source of energy. Application of fertilizer in other studies changes growth rates which could in turn change patterns of leaf turnover and mortality. Several studies detail the ability of light and nutrients to affect leaf properties including longevity, dynamics and nutrient content. Bazzaz & Harper (1977) reported that both light and density influenced leaf survivorship in Linum usitatissimum L. They also showed that experimentally withholding nutrients, removal of flower buds, and shading lower leaves all influenced the onset of leaf mortality although the nutrient treatment had the strongest effect. Shaver (1983), reported leaf lifespan decreased in Ledum palustre when fertilizer was applied. Fox & Morrow (1992) reported that fertilizer did change leaf properties and increased foliage nitrogen and phosphorus content in Eucalyptus spp. although no change in growth was observed. Diemers et al. (1992) studied 29 species of forbs and grasses and detected a strong correlation between leaf duration and nitrogen content. They also noted that in general leaf turnover increased with increasing competition for light, and with increasing vigour of growth.  70 COMPARISON OF COHORTS A difference in survivorship between cohorts and within treatments was noted in this set of experiments. In the parametric analyses of survivorship, no treatment effects were observed in the first leaf cohort, including no observable clipping effect. This may be due to morphological differences in leaves between cohorts. Lupinus arcticus was observed to have small scale-like leaves that never grew larger than 2 3 cm long over the growing season. This heterophylly may -  bias the results of this analysis if the appearance of these leaves within cohorts is not random. These smaller leaves would never be at risk of clipping because the treatment is imposed using a height criteria. This may be why no clipping response was seen in the first leaf cohort. A similar type of heterophylly has been reported for Pseudopanax crassfolius, a sub-tropical tree, where the oldest leaf cohort is scale-like and small relative to subsequent leaves. These leaves are believed to be formed in the previous growing season and remain dormant, only to appear at the beginning of the next season (Clearwater & Gould 1994). Even if the differences between cohorts in lupines is not due to these scale leaves, differences between cohorts in the size of leaves, or position in the herbaceous canopy may produce different patterns of survivorship. Alternatively, the second leaf cohort may respond to treatments differently for other reasons. Normally, if no differences in survivorship are observed between cohorts, patterns of leaf mortality are presumed to be determined by leaf age or leaf stage. Sydes (1984), compared between cohort survivorship among several species of herbs in lime grassland and reported that early leaf cohorts in many species grew rapidly and sustained higher rates of mortality than later cohorts. In Sydes (1984), the differences between cohort survivorship may be determined by other environmental factors or seasonal effects, rather than factors related to leaf age or stage. If this is true, the higher mortality reported in the first cohort would be determined more by external factors such as timing rather than internal factors influenced by leaf age. Other authors studying leaf demography in herbs have also compared cohort survivorship among different species. Diemers et al. (1992) followed leaf cohorts of 29 herbs and showed for  71 many species that early leaf cohorts experienced higher mortality than later cohorts. This was also reported for Solidago spp. by Schmid et al. (1988). As mentioned earlier, Diemers et al. (1992) correlated leaf lifespan to nitrogen content in leaves. The authors surmised from their study that leaf longevity was not determined by efficiency of light harvest, but rather environmental factors such as mechanical strength, herbivory, and pathogens. Examination of patterns of variation present in the shape parameter (o) with respect to the theoretical survival curves they generate produced enough variation present in cohort 2 to produce Type I, II or III curves depending on the treatment. These different theoretical survivorship models have radically different implications for the dynamics of populations. This suggests that further investigation is required to determine if these treatment effects on patterns of leaf survivorship are real or merely the result of the highly variable nature of field experiments. In spite of the need to understand patterns and causes of mortality in ecology, differences in survivorship between cohorts and populations are not often analyzed in ecological journals (Pyke & Thompson 1986). Therefore, most methods available to analyze survivorship data cannot cope well with ecological experiments. Rather they were developed for clinical studies, or often for failure time studies for equipment manufacturers (Lee 1980; Hutchings et al. 1991). Until better methods become available for analyzing this type of data, we are left with rather crude methods to assess differences that may or may not be ecologically meaningful. In choosing the appropriate method a number of factors were considered including the nature of the data collected, right censoring of the data set, replication of data, etc. This experiment was designed to generate life table data but was later analyzed by fitting a theoretical survival distribution to the observed data and using the calculated parameter estimates as the test statistics. Gehan (1969) suggested this as an appropriate method if some survival data are available for populations, but the true theoretical distribution is not known for various reasons. This method is not able to generate the same descriptive statistics as the life table method, however it is the only method available that can handle censored and replicated data.  72 Some of the drawbacks of this method with regards to this study in particular are that it is not possible to determine with certainty that the theoretical model selected is the most accurate description of the population’s survival curve if the survivorship data are censored. Specifically, to say with certainty that the theoretical model chosen to describe the data is the best model, all individuals or cohorts must be monitored to determine the exact time of mortality. That is, all treatments must be monitored until 100% mortality occurs. That is relatively rare in biological studies where the resources are not available to wait until all subjects have died. Therefore methods have been developed to deal with censoring in data sets. With regards to analyses in this study, it can oniy be said that the parameter estimates generated, and the model selected, are appropriate for the time frame over which this study occurred. Therefore, it can not be concluded that these parameter values generated for the Weibull model are true population estimates. An additional problem in a study this large is that the number of survivorship curves available for comparison is so large that it is not conducive for fitting a large number of different types of theoretical distributions. Due to time and computing constraints, I was confined to those distributions available in SAS Proc Lifereg. Also because the treatment groups in this study have the potential to have widely different effects on survivorship patterns, using a single theoretical model to describe all treatments and replicates may not have been the best method. Other theoretical distributions may have fit some treatments better than others. However, it is not possible to statistically compare parameters of different distributions. With regards to this study, this may or may not have been a problem because of the selection of the Weibull model in this analysis. Specifically, the Weibull model, as a two parameter model that allows the hazard rate h(t) to vary in simple to complex patterns, is quite flexible in fitting a variety of linear and non linear survival curves described previously. The results of some of these survival analyses were able to detect a clipping effect on survivorship as well as differences between cohorts within treatment in spite of the limitations associated with the methods used. However, further investigation on the effect of these  C  73 treatments as limiting factors on patterns of survivorship is warranted. In the future, it would be advantageous to modify this study to incorporate additional leaf cohorts, as well as monitor leaf cohort mortality to 100% mortality in all cohorts. More cohorts monitored would also help sort out the nature of differences in survivorship between cohorts that this study only alludes to, and would eliminate the problems associated with analyzing survivorship in right censored data. Further studies comparing the effects of different types of limiting factors on patterns of survivorship may wish to consider more sophisticated (and more complex) analyses where the theoretical distribution that best fits individual treatments is determined for all treatment groups. Once a set of theoretical models have been selected, a comparison that explores biological mechanisms/causes that produce qualitatively different survivorship models may be in order. This would likely involve assessing causes of mortality in the field (i.e. predation, disease, shading/competition) throughout the experiment. Traditional non-parametric log rank tests indicated significant treatment differences in survivorship in both cohorts relative to the parametric analysis. The non-parametric methods are not considered to be as powerful as parametric methods, however, this is the most common method to analyze survivorship in ecological studies, and even, the parametric method used in this analysis is also not without problems.  CHAPTER 4 VEGETATIVE GROWTH Individual plants may respond to changes in the environment by altering reproduction and growth. The variable used to measure plasticity in response to the experimental treatments was the length distribution (i.e. size structure) of petioles in the population; petiole length is a measure of vertical growth of petioles in the leaf population. Plasticity of growth was measured at a population level because individuals were not identifiable. An additional experiment was done to measure the relative effect of different treatments on horizontal space acquisition in L. arcticus. This experiment measured changes in horizontal spread in response to treatments by measuring changes in percent cover of L. arcticus (and other species) from 1991 to 1992.  METHODS SIZE STRUCTURE Data were collected throughout the 199 1/1992 growing season using non-destructive methods to examine the plasticity of size response of L. arcticus. Due to the inability to identify genets or individual ramets without destructive sampling (excavation and/or harvest), the size of L. arcticus individuals were measured collectively. This was done by measuring petiole length for  each leaf in all quadrats and then assigning all leaves to one of four size classes based on petiole length: I) 0-5cm, II) 5-10cm, III) 10-15cm, and IV)> 15 cm. Data in size classes ifi and IV were later pooled because of the infrequent number of petioles in the tallest class. Size class data were analyzed in 1991 and 1992 by constructing multi-dimensional contingency tables. Systat’s log linear model fitting program was then used to generate Pearson’s chi square goodness of fit values for the treatment factors and their interactions. The null hypothesis in this analysis was that the effect of fertilizer, neighbour removal and clipping on size distribution were independent. This 74  75 analysis was done once on mid-season data and once on late season data in each year to determine if distribution of petiole lengths changed through the growing season. In 1991, survey dates were July 24 (survey 5), and August 17 (survey 7), in 1992 they were on July 14 (survey 4), and August 13 (survey 6). Petiole length is used as an indicator of plant size and has been shown to be correlated to other plant characters in other species (Evans 1986), and is capable of responding to environmental change (Birch & Hutchings 1992; Evans 1992).  COVER Vegetation composition in the experimental quadrats was measured in 1991 and 1992. This permitted an assessment of how the abundance of L. arcticus changed relative to changes in the abundance of other species in response to the treatments imposed. These supplementary data were gathered to help interpret the results of the main experiments. Composition of the herb community in the quadrats was determined using a percent cover estimate using a grid of 10 x 10 regularly spaced points at intervals of 10 cm. A vertically placed pin was used as the sampling point. Each different species contacting this pin was recorded once at each point. Percent cover of each species was determined by summing the number of times a particular species contacted the pin in the 100 points occurring within the quadrat. Total cover estimates exceed 100% because the sampling pin often contacted more than one species at a sampling point due to layering in the vegetation. Mosses, lichens, and fungi were grouped into their own categories. Sampling was done on August 1, 1991 and July 211992. RESULTS SIZE STRUCTURE OF PETIOLES 1991 In 1991, the middle and late season analyses (Table 16) indicated that the effect of fertilizer, neighbour removal, and clipping were not independent with respect to distributions of petiole lengths (P <0.000 in all cases). Therefore the null hypothesis was rejected. A source of variation table summarizes the results of the test of independence for first order interactions of treatment by  76 size distribution as well as the higher order interactions (Table 16). All interactions from first order to third order were significant at P=O.000 for surveys 5 (middle season), and 7 (late season) (Table 16). According to this test, petiole lengths vary in a complex manner according to the treatment a population receives. Typically, a chi square analysis of independence of categorical variables stops here. Further statistical methods to describe how different variables on a multidimensional table interact are not currently available. However, there are trends in the data (Table 17 a, b), the most obvious of which, in both surveys, is the interaction between the fertilizer and the neighbour removal treatment, Initial predictions in this set of experiments anticipated that fertilizer and neighbour removal would have similar effects on plant populations. This is not the case for petiole length distribution and the pattern of response to these treatments changes between the middle and the end of the growing season. For survey 5, fertilizer addition appears to produce proportionally greater numbers of petioles in the lower size classes when lupines are not clipped (Table 17 a). In survey 7, leaf populations that have not been clipped differ within the fertilized treatment if neighbours are absent such that when neighbours were removed, more petioles fall into the smallest size class. In the unfertilized, unclipped treatment, this difference between treatments in the presence and absence of neighbours is not as distinct (Table 17 b). Comparison of clipped vs. unclipped populations in both surveys showed a pattern where unclipped populations had a greater proportion of petioles in the tallest length class. The complexity of higher order interactions on length distribution of leaves makes it difficult to make any further conclusive observations without further experimentation. The highly plastic nature of the response of petiole length distribution with respect to the three factors  is important to note.  Similarly, the effect of fertilizer on petiole length distribution in 1991 was significant in spite of the low level of fertilizer applied.  77 Table 16 Chi square test for independence of fertilizer, clipping and neighbour removal on petiole length distribution using log linear analysis. (year=1991, F=fertilized, C=cipped, N= neighbour removal, D=size distribution)  dF  Surveys x2  Probability  dF  38.66 46.55  0.000 0.000  2  C*N*D  2 2  F*N*D  2  126.34  F*C*D F*D  2 2  C*D  2 2  Source F*C*N*D  N*D  Survey 7 2  Probability  2  97.58 29.57  0.000 0.000  0.000  2  150.23  0.000  54.0  0.000  2  17.6  0.000  30.3 688.71 51.04  0.000 0.000  2  40.8  2 2  1082.4 81.44  0.000 0.000  0.000  0000  Table 17 a): Length distribution of petioles by treatment reported as frequency and percent occurring in each class (Survey 5, 1991).  Fertilized Clipped  Not fertilized  Not clipped  Clipped  Neh!hbours Length  +  +  I  Not clipped Neighbours +  +  (cm) 0-5  1309  1048  1103  1706  1346  1204  955  1444  %  58.91  51.5  53  48.21  63.10  61.81  38.12  57.90  5-10  883  967  788  1582  775  739  1201  853  %  39.74  47.52  37.87  44.71  36.38  37.94  47.94  34.20  >10  30  20  190  251  12  5  349  197  %  1.35  0.98  9.13  7.08  0.56  0.26  13.93  7.90  Total  2222  2035  2081  3539  2133  1948.  2505  2494  78 Table 17 b): Length distribution of petioles by treatment reported as frequency and percent occurring in each class (Survey 7, 1991).  Fertilized Clipped  Not fertilized  Not clipped  Neighbours Length  I  Clipped  Not clipped  Neighbours  -  +  -  +  -  +  -  +  0-5  1281  906  1155  731  1044  825  611  656  %  55.17  50.39  51.79  28.16  55.21  52.35  29.45  38.66  5-10  999  874  887  1440  826  742  1092  741  %  43.02  48.61  39.78  55.47  43.68  47.08  52.63  43.67  >10  42  18  188  425  21  9  372  300  %  1.81  1  8.43  16.37  1.11  0.57  17.93  17.68  Total  2322  1798  2230  2596  1891  1576  2075  1697  (cm)  SIZE STRUCTURE OF PETIOLES 1992 Length distribution of petioles measured in 1992 was analyzed as in 1991. The chi square test for independence of fertilizer addition, neighbour removal and clipping on size distribution of petioles for survey 4 (middle season July 14, 1992) indicated that there was a significant -  (P<0.000) three-way interaction of the treatments on size distribution. The interaction of clipping and neighbour removal was not significant (P>0.05), however, this may be due to the presence of higher order interactions in the data. All other interactions were significant at P<0.0O1 (Table 18). As in 1991, the pattern of response did change during the growing season. Near the end of the growing season (survey 6 August 13,1992), the interaction of clipping and neighbour removal -  became significant at P<0.05 (Table 18).  79 As in 1991, the complexity of the higher order interactions make it difficult to interpret how different treatments act on distribution of petiole lengths. In 1992, clipping again reduced the proportion of petioles reaching the tallest class for both surveys (Table 19 a, b). The strongest clipping effect was seen in survey 6. The intensified clipping treatment added in 1992 is designated as “Extra clip” on the tables and was not incorporated into the chi square log linear model fitting because it unbalanced the analysis. The intensified clipping treatment showed the least amount of growth into the tallest length class with the greatest proportion of petioles occurring in the middle class. The clipping regime for this treatment was at a height of 4 cm. The large proportion of leaves growing above this clipping level indicates a rapid re-growth into this size class following clipping. The interaction of fertilizer and neighbour removal on distribution of petioles is notable in both surveys in 1992. When fertilizer is applied, petiole growth into the largest class is higher with neighbours and without clipping than in all other treatment combinations. In contrast, the unfertilized treatment block does not show this pattern. Rather, the greatest amount of growth into the upper size class occurs without clipping and without neighbours. Table 18: Chi square test for independence of fertilizer, clipping and neighbour removal on petiole size distribution using log linear analysis. (year 1992, F=fertiized, C=clipped, N= neighbour removal, D=size distribution)  Survey 4 Probability X 2  Source F*C*N*D  2  131.62  C*N*D  2  F*C*D  2  0.7 231.44  F*N*D  2  F*D  dF  dF  I  Survey 6 Probability x 2  2  115.56  0.000  2  6.22  0.05  0.000  2  207.2  0.000  155.4  0.000  2  27.96  0.000  2  88.05  0.000  2  255.56  0.000  C*D  2  518.43  2  604.14  0.000  N*D  2  14.14  0.000 0.001  2  50.11  0.000  0.000  80 Table 19 a): Length distribution of petioles by treatment reported as frequency and percent occurring in each class (Survey 4, 1992).  Fertilized Clipped  Not fertilized  Not clipped  Clipped  Not clipped  Extra clip  Neighbours Length (cm)  -  +  -  +  -  +  -  +  +  0-5  485  377  157  299  260  253  248  161  425  %  17.76  19.68  5.64  9.07  9.75  11.44  7.82  5.73  21.77  5-10  1227  778  1543  1204  1467  1194  1047  1358  1340  %  44.93  39.72  55.46  36.53  55.03  54.00  33.02  48.29  68.65  >10  1019  761  1082  1793  939  764  1876  1293  187  %  37.31  39.72  38.89  54.40  35.22  35.55  59.16  45.98  9.58  Total  2731  1916  2782  3296  2666  2211  3171  2812  1952  81 Table 19 b): Length distribution of petioles by treatment reported as frequency and percent occurring in each class (Survey 6, 1992). Fertilized Clipped  Not fertilized  Not clipped  Clipped  Not clipped  Extra clip  Neighbours Length  -  +  -  +  -  +  -  +  +  0-5  261  285  196  225  109  75  105  37  88  %  10.97  16.66  7.37  9.92  5.72  4.86  4.41  1.99  8.2  5-10  1372  868  1449  720  1176  927  808  876  940  %  57.65  50.73  54.45  31.73  61.70  60.07  33.96  47.12  87.6  >10  747  558  1016  1324  621  543  1466  946  45  %  31.39  32.61  38.18  58.35  32.58  35.19  61.62  50.89  4.19  Total  2380  1711  2661  2269  1906  1543  2379  1859  1073  (cm)  CHANGES IN PERCENT COVER The effect of treatment on the change in percent cover in L. arcticus from 1991 to 1992 is summarized in Table 20. Analysis of these data as another estimate of size change indicates that only neighbour removal had a significant effect (P<0.025) on clonal spread in L. arcticus after two seasons of treatment (Table 21), The increase in percent cover in the neighbour removal treatment was almost five times greater than the control (Fig. 4). Treatments of neighbour removal in combination with fertilizer or clipping also showed a mean increase in percent cover almost as great as neighbour removal alone, however, the response was more variable. Cover in L. arcticus showed a nonsignificant increase when fertilized, or when clipped, but not to the degree that neighbour removal stimulated. The increase in cover in these two treatments was much greater when combined with neighbour removal. Only the fertilizer by clipping treatment  82 showed a net loss in percent cover of lupines over the study. All other treatments stimulated an increase in cover in L. arcticus, although only the neighbour removal treatment was significant (Fig. 4).  CHANGES IN VEGETATION COMPOSITION Changes occur in vegetation composition along with changes in L. arcticus (Table 20). Species observed to have low percent cover estimates (<2%) within the quadrats are not reported, and no significant changes were observed for these species during the study period. The greatest changes in cover were recorded for Festuca altaica, Achillea millefolium and Senecio lugens. The grass, F. altaica, showed a significant increase (29 %) when fertilized (Fig. 4). Neighbour removal (NR), and the interaction between the fertilizer and neighbour removal (F-NR) also had significant (P<0.0 1) effects on Festuca (Table 20). The significant reduction of vegetation cover for some species is an artefact of the removal treatment itself and will not be further discussed. This increase may indicate that clipping of Lupinus arcticus resulted in the competitive release of Festuca. In contrast with Festuca, Achillea millefolium showed reduced abundance under fertilizer. The interaction between fertilizer and neighbour removal seen for Achillea occurred for  the same reason as for Festuca. The abundance of Senecio lugens showed a significant interaction between fertilizer and clipping. The interaction between these treatments was such that when fertilizer was applied, Senecio declined in abundance. However, when lupines were clipped, or clipped and fertilized, Senecio showed a slight increase in abundance (P=0.063, Table 20). Festuca altaica also showed a large, nonsignificant increase (32%, P>0.05) when fertilized and clipped (Fig. 4). Total cover, which is the summation of cover values of all species, did not change significantly except when neighbours were removed. Solidago canadensis showed a nonsignificant response (P<0.09, Table 20) to clipping and clipping by neighbour removal. Cover of Solidago was reduced relative  83  to control in the clipping treatment. The interaction between clipping and neighbour removal is an artefact of this species being removed as part of the treatment.  Table 20: Summary of mean changes (± SEM) by treatment of percent cover of species occurring in quadrats from August 1, 1991 to July 21, 1992. The significance values from a three way ANOVA of arcsine transformed percent cover are indicated with * P<0.05, ***P<0.001.  Species  Control  Fertilizer (F)  Clipped (C)  Removal (NR)  F/NR  F/C  C/NR  F/C/NR  Lupinus arcticus  2.5 ±3.75  7.0 ±7.94  4.5 ±4.47  12.25.* ±2.78  11.5 ±6.12  -5.5 ±0.87  11.0 ±5.87  ±1.89  Festuca altaica  16.0  29.75**  12.0  Ø75***  75**  32.25  ±4.509  ±3.351  ±4.4 16  ±2.689  ±2.562  ±3.97  -0.75 ±0.75  ±2.056  Linnaea borealis  0.75  0.5  0.50  0  0  -0.25  0  ±0.854  ±0.289  ±0.289  0.0 ±0.408  Achillea millefolium  2.0 ±0.913  0.0 * ±0.408  2.50 ±1.555  0  0  0.0  Solidago canadensis  1.75 ±1.75  -0.75 ±0.479  0.50 ±0.289  2.75 ±1.109  5.0 ±2.858  0.75 ±1.11  0.25 ±0.479  -6.25 ±6.588  Senecio lugens  9.75 ±3.568  -2.5  0.250 ** ±3.473  -2.0  ±1.555  1.75 ±3.25  1.75 * ±1.32  -4.25 ±2.323  ±1.601  Epilobium angustfolium  1.75 ±1.436  0.25 ±0.25  1.5 ±2.217  -2.5 ±0.25  0  0.5 ±0.5  -0.25 ±0.25  ±0.25  Cornis canadensis  2.5 ±2.843  4.0 ±3.028  5.75 ±1.436  -2.5 ±0.25  0.25 ±0.25  -2.25 ±2.25  0  0  Total Cover  22.79 ±11.821  27.5 ±18,799  -8.25 ±7.653  6.75 *** ±6.575  -8.25 ±8.38  31.5 ±2.02  -3.25 ±7.653  -8.0 ±4.601  ±0.63 0  *  -0.75  6.25  -2.25  ±0.48  ±0.816  -3.25 -0.25  84 Table 21: Summary of ANOVA results for arcsine transformed changes in percent cover in Lupinus arcticus.  Source  Sum of  dF  Squares  Mean  F-ratio  Probability  Square  Fertilizer  0.006  1  0.006  0.652  0.427  Clipped  0.015  1  0.015  1.568  0.223  Neighbours Removed  0.053  1  0.053  5.744  0.025  Fert*Clip  0.017  1  0.017  1.875  0.184  Fert*Rem  0.000  1  0.000  0.000  0.998  Clip*Rem  0.001  1  0.001  0.085  0.773  Fert*Clip*Rem  0.005  1  0.005  0.583  0.453  Error  0.223  24  0.009  0  “0 0  -I•  -t  -t  CD  ‘D CD  I—  -.  CD  CD D -I.  0)  -1  Fert-Cl-Rem  Clip-Rem  Fert-Rem  Fert-Clip  Removal  ft  Fert-Cl-Rem  Clip-Rem  Fert-Rem  Fert-Clip  Removal  Clip  0  Clip  0  Fert  0  Fert  0  -  Control  0  Control  0  Change in percent cover () r3 0  0  01  II  I  0  -  I  (11 I  -  Change in percent cover  CD  I  0  r\)  11  00  86 DISCUSSION  PETIOLE LENGTH DISTRIBUTION Competitive advantage in plants has been shown to be positively correlated with size (Grace & Wetzel 1981; Soibrig 1981; Meagher & Antonovics 1982; Wolfe 1983). In herbs such as L. arcticus, the advantage conferred by larger size may be due to vertical growth that results in shading and suppression of competitors, or by horizontal spread. In my experiment, patterns of vertical growth within the population were measured by petiole length distributions, whereas changes in percent cover of L. arcticus were used to estimate horizontal spread, or the acquisition of space in response to the treatments imposed. In some species, the ability to penetrate the upper canopy and become a good competitor may be determined by petiole length. Black (1960) compared three varieties of Trifolium subterraneum that differed in petiole length and reported that in mixture, the long-petiole variety dominated. Petiole length could be particularly important for species such as L. arcticus which have no above-ground support structures with which to obtain canopy position in the herb community except through petiole extension. Alternatively, understory perennial herbs that live in shaded habitats may have little use for vertical growth to gain a competitive edge with their neighbours. Instead, competitive ability may be determined by horizontal space acquisition that usurps space and resources from other species (Hutchings & Slade 1988). In this case, changes in percent cover of L. arcticus may be a better indicator of growth response to the treatments imposed in this experiment. It has long been known that plants respond to stress by varying their number of modules (Harper 1977). Harper (1977) has suggested that understanding the dynamics of plant organs (modules) may in fact be more useful than the dynamics of whole plants for interpreting what is happening in a community, because subtle changes in module dynamics may appear long before changes in the abundance of individuals, or other variables are detectable. In spite of this, Birch & Hutchings (1992) have noted that “there has been little research directly determining the ability of plant organs to respond to changes in environment during development”. Most studies of  87 module dynamics in plants concentrate on changes in module abundance, however, some species may have the ability to alter other module characteristics such as size (i.e. petiole length) as a response to stress. Quantifying this type of morphological change at the individual or population level may provide new methods by which to measure the response of a species to changes in it’s local environment. The results of these experiments investigating length structure of petiole populations suggest that L. arcticus does respond to changes in their environment by modifying the size of plant organs such as petiole length. Although plasticity is often measured as an individual response to local environmental conditions, this experiment measured plasticity at the whole population level because individuals could not be defined. Comparison of the size structure of the total petiole population under different combinations of clipping, fertilizer, and neighbour removal showed that it was dependent on treatment. Contingency table methods can not describe how treatments differed with respect to petiole length distribution, however, inspection of the data suggested certain patterns, the most interesting of which were the fertilizer and neighbour removal effects. The complex nature of the interactions between treatments make it difficult to interpret potential patterns, however, there was evidence to suggest that petiole lengths may be longer when neighbours are present. This was seen to vary in some cases depending on the fertilizer and clipping treatment experienced. Fertilizer appeared to ameliorate the response to clipping and allow petioles to grow longer in some cases, although this was not conclusive. Although petiole length distributions did respond to clipping treatments, this was largely due to the method of removal of leaves during treatment which selected leaves for clipping based on petiole length. Variation was observed in the response of petiole distribution to treatments from 1991 to 1992 as well as from early in the season to later in the season. Further investigation is required to determine if petiole length distribution will show a stable pattern after long term exposure to these treatments. Petiole length distribution may show an inherent variability in response due to variability in other factors in the environment, or there may be a lag time in response to the  88 treatments imposed. Long term exposure to treatments of fertilizer, neighbour removal and clipping may eventually show a predictable petiole length distribution. The treatments in this experiment may eventually select clones with specific petiole distributions if different distributions provide an advantage to populations of L. arcticus against their neighbours. Second, certain petiole length distributions may be short term plastic responses to environmental changes. This is supported by Birch & Hutchings (1992) who reported that petiole length in Glechoma hederacea increased over a longer period of time and was partly age dependent as well as sensitive to changes in the environment. Petiole length distribution in this experiment was used as a measure of growth or size differences between populations of L. arcticus experiencing different treatments. The ecological ramifications for differences in petiole length distribution between populations are not yet known. More research is required to determine the implications of a population dominated by longer or shorter petioles Excessive extension may weaken petioles and make them more susceptible to damage in some situations. De Kroon et al. (1992) explain that plant size can increase without a corresponding increase in biomass due to etiolation. Therefore, taller plants may have increased access to light, but be more susceptible to damage due to structural weakening during petiole extension. Other studies that used leaf characteristics such as size, number, and height of leaves to measure plasticity of plant growth have reported changes in leaf height or petiole length under different environmental conditions. De Kroon et al. (1992) measured longest leaf (leaf sheath height) as an indicator of shoot size, and showed that leaf height increased with density for Carex spp. Smaller (shorter) shoots were also showed to have higher mortality at all ranges of density. A study of plasticity of plant size and architecture measured various leaf traits (number of leaves, shoot height, and leaf length) as size indicators and reported that these growth characters varied with the environment in Aster spp., and to a degree in Solidago spp (Schmid & Bazzaz  89 1990). Of the three traits measured, only number of leaves in Solidago spp. did not show a plastic response to changes in environmental conditions. Grace (1985), in an investigation of the trade-off between growth and reproduction in two species of cattails (Typha spp.) examined the relationship between tallest leaf and reproductive output. He reported that the taller species tended to have higher reproductive output and more ramets than a shorter, closely related species. Although leaf length may not have been the reason that one species had greater reproductive output than the other, a comparison of the correlation between reproductive output and size within a species may determine if a trade-off between vegetative growth and reproductive allocation is occurring. Most studies examining the plasticity of plant growth as a response to changes in the environment concentrate on measuring size in terms of biomass accumulated. While this information is useful, a functional perspective that gives us information on how plants allocate biomass is also important. To understand this plasticity, we need to know if allocation of biomass to structures changes because number of modules changes, or if module size changes, as in the case of petiole extension in L. arcticus. Therefore, studies measuring plasticity through biomass changes alone are not sufficient to describe the nature of a plastic response to environmental changes. A plastic response may occur with little or no detectable change in plant biomass due to a re-allocation of existing biomass to new or different structures. A combined approach is therefore recommended that would measure both biomass changes and structural changes within a population under different treatments.  PERCENT COVER Fertilizer and neighbour removal In the second set of experiments comparing plant growth in different treatments in terms of horizontal space acquisition (percent cover), abundance of L. arcticus showed the strongest response to neighbour removal. Hutchings & Slade (1988) studied clonal spread in Glechoma  90 hederacea and showed that shading had a greater affect on architecture than nutrients. Under low light conditions there was greater allocation of biomass to petioles. Although the increase in percent cover of L. arcticus in the absence of neighbours indicated neighbours interfered with the growth of L. arcticus, increasing nutrient availability by application of NPK fertilizer did not have the same effect. Fertilizer treatment did show a nonsignfficant increase in cover of L. arcticus from 1991 to 1992. There are several possibilities for these differing responses. First, the presence of neighbours in this habitat did not interfere with L. arcticus’s access to the nutrients applied (NPK). Therefore, the limiting factor in this habitat may be something else such as various micronutrients, water, or light. Second, the addition of nutrients in the fertilizer may have been usurped by other more rapidly growing species before L. arcticus could utilize them. Senecio lugens increased when fertilized and clipped, but decreased when only fertilizer was applied. This suggests that L. arcticus was competing with this species as Senecio was only able to increase when fertilized if lupines were clipped. Evidence supporting the hypothesis that rapidly growing species are usurping fertilizer at the expense of L. arcticus is demonstrated in the rapid increase in cover of Festuca altaica when fertilized, a response also demonstrated by other work at these sites (Nams et al. 1993; John & Turkington unpublished). Festuca altaica also increased (nonsignificantly) when lupines were clipped and fertilizer was applied. Although this difference was not significant, it was quite large and may indicate that clipping lupines released of Festuca from competition with L. arcticus. Nams et al. (1993) also reported that fertilizer increased growth in other species at these sites as well including: Calamagrostis lapponica, Epilobium angustifolium, and Achillea millefolium. A similar response was observed by Davy & Bishop (1984) where application of fertilizer produced a rapid increase in the graminoids Festuca ovina and Koeleria macrantha to the detriment of Hieraceu,n pilosella. A nitrogen fixing legume, Astragulus danicus, was not reported to respond to fertilizer treatments in these same experiments. Similarly, although L. arcticus did increase in cover this was not significant in the fertilizer treatment. Third, the rate of fertilizer applied was not sufficient  91 to produce a strong increase in cover relative to that made available through the removal of neighbours. Fox & Morrow (1992) also reported that application of fertilizer did not change leaf growth, but did change leaf quality.  Clipping The effect of removal of leaf tissue in the clipping treatment did not significantly change percent cover of L. arcticus in this experiment although a nonsignificant increase in cover was observed. Several explanations are possible for this unexpected result. First, L. arcticus may  compensate for loss of biomass due to herbivores by stimulating new leaf or ramet production. Crawley (1983) suggests that compensation in plant growth following herbivory may occur at an individual or a population level. At the population level, tissue loss in some individuals may be compensated by increased growth in individuals not attacked or attacked to a lesser degree due to a competitive release. On an individual level, loss of shaded or unproductive tissue may result in increased growth or productivity of remaining leaves. Second, insufficient biomass may have been removed to significantly effect growth. In this habitat, L. arcticus is subjected to periodic high levels of herbivory from fluctuating snowshoe hare populations. Because of this they may have developed compensatory mechanisms that obscure the effect of lost tissue at low to moderate levels of herbivory. It is possible that only high levels of damage are capable of inducing mortality in ramets or clones. Integration among ramets may expedite this recovery. Jonsdottir & Callaghan (1989) demonstrated that only at continuous, high levels of grazing of tillers of Carex bigelowii, was support from physiologically integrated neighbours cut off. In addition, the procedure for simulating herbivory was based on a height criteria. This allowed smaller leaves to escape clipping. This “refugi&’ for small leaves may have permitted a rapid replacement of lost leaves and compensation for lost photosynthetic area. Lupinus arcticus, like many grazed species, has underground meristems that are protected  92 from mammalian herbivory. This may also have permitted quick recovery of lost biomass and leaf surface area.  Interactions Although neighbour removal was the only treatment to show a significant change in percent cover of L. arcticus, some trends in the interaction between neighbour removal, and clipping, and fertilizer were shown. When these two treatments were combined with neighbour removal, the increase (nonsignificant) in percent cover was greater than either treatment alone. More data is required to determine if these trends are true responses or not. Other studies have investigated interactions between competition, nutrient availability, and herbivory on plants. In a study of growth and reproduction in Ipomea hederacea it was shown that fertilizer produced a significant increase in biomass of leaves, stems, seeds, roots and fruit. Negative effects due to the competition treatment were partly ameliorated by the addition of nitrogen to the plants (Whigham 1984). In my investigation, the effect of fertilizer on growth of L. arcticus was not as great as removing neighbours. Davy & Bishop (1984b) reported that the response of Hieraceumpilosella to rabbit grazing varied with application of fertilizer. Application of nutrients (NPK in various combinations) reduced the effect of rabbit grazing on these plants. In contrast, application of fertilizer to clipped populations of L. arcticus aggravated the effects of clipping. This treatment was the only one to show a reduction in cover in L. arcticus relative to control. Although nonsignificant, the ameliorating effect of neighbour removal when combined with clipping in my experiment was comparable to the effect of fertilizer on clipped Hieraceum pilosella reported in Davy & Bishop (1984). Both experiments considered the growth response of L. arcticus under different treatments from two different perspectives (petiole length distribution and percent cover) and both detected significant treatment effects on growth. Although the fact that petiole length distribution in L.  93 arcticus is dependent on treatment is interesting, more conclusive results are required to determine how these distributions vary with treatment. If the trend to grow longer petioles in the presence of neighbours is a real pattern, this would suggest a trade-off between vertical growth and horizontal spread because the increase in percent cover of L. arcticus was highest when neighbours were absent. A more detailed experiment of the pattern of response of petiole length to the treatments imposed would allow a comparison of horizontal (percent cover) to vertical (petiole length) growth by L. arcticus. This type of study may give us greater information about the nature of L. arcticus interactions with its neighbours in the field, and how these interactions change when L. arcticus experiences herbivory or changes in nutrient availability. Questions such as ‘does percent cover increase when petiole populations are taller or shorter?’ and ‘are petioles longer when neighbours are present?’ could be addressed.  CHAPTER 5 REPRODUCTION Many components of reproduction respond to changes in a plant’s local environment, all of which must be measured to get an accurate picture of how plant reproduction varies with the environment. These components are (1) reproductive investment (also known as effort), (2) reproductive output, and (3) reproductive efficiency. Reproductive investment is the amount of resources a plant allocates to a reproductive episode under certain conditions. It includes the amount of energy or resources a plant allocates to the production of reproductive structures and their support tissues including reproductive meristems, inflorescences, fruit, flowers, peduncles, pollen, nectar, and seeds. The second component, reproductive output, is limited by the amount of resources a plant invests in reproduction in one season. It is usually considered to be the number of seeds or propagules a plant or population is capable of maturing under a certain environment, and the number of those seeds that successfully recruit and grow to reproductive maturity. This estimate is often used as a fitness correlate. The third measure is reproductive efficiency, and it is a ratio of the amount of effort invested in reproduction relative to the number of offspring (or propagules) produced as a consequence of that investment. The reproductive data that was used to estimate or calculate these three measures of reproduction for L. arcticus were collected as part of the main population experiment described in Chapter Two, and a detailed description of methods is contained in that chapter. A brief overview of methods used to collect data on reproduction follows.  94  95 METHODS During the bi-weekly population surveys in of the quadrats in 1991 and 1992, the reproductive variables measured included: number of racemes produced / m 2, number of flower buds initiated /m , number of pods produced /m 2, and the length of individual racemes and 2 peduncles. At the end of each growing season, this information was used to calculate (per treatment, and per m ) the mean number of racemes produced, mean number of flowers initiated 2 and mean number of fruit matured (pods), and mean size of reproductive structures. Based on this data, reproductive effort in L. arcticus was estimated as the mean number of racemes and flowers initiated /m 2 Reproductive output was estimated as the mean number of mature fruit (pods) produced /m 2 The reproductive efficiency of L. arcticus at converting flowers to mature fruit under different treatments was calculated as a ratio of the number of mature fruit (pods) set relative to the number of flower buds initiated. This estimate of reproductive efficiency does not translate into a fitness correlate because the proportion of fruits set by a population under certain environmental conditions is not a measure of the ability, or number of the seeds to recruit and grow to maturity. However, this measure of reproductive efficiency does give an indication of how plants respond to different environments (or treatments). Measurements of long-term recruiting success were not in the scope of these experiments.  STATISTICAL ANALYSIS Data in 1991 and 1992 were analyzed separately for two reasons. First, the fertilizer treatment was increased in 1992 because of negligible response in the first year. Second, an additional treatment group was added in 1992 to investigate how a more intense clipping treatment would affect L. arcticus dynamics. The addition of a new treatment unbalanced the experimental design in 1992. To accommodate this change in the design, the effect of the intensified clipping treatment added in 1992 was determined by analyzing the 1992 data with a one-way ANOVA by treatment for all reproductive variables measured in addition to the following statistical analyses of the main factorial design. Yearly variation in reproductive  96 characters was analyzed using a three-way MANOVA of number of racemes, flowers, and pods produced per m 2 by year. The three variables were transformed using the square root function for Poisson data (x  +  0.5) 1/2 (Zar 1984). Reproductive effort between treatments was analyzed  using a three-way MANOVA of raceme and flower production in 1991 and 1992. Poisson variables in all analyses were transformed with the square root function mentioned previously unless otherwise specified. Reproductive output between treatments was compared using a three-way ANOVA of pod production per m . Reproductive efficiency in each treatment group 2 ). This was analyzed 2 was calculated as (pods produced per m 2 / flower buds initiated per m using the same methods as for reproductive output except that reproductive success values were transformed using an arcsine transformation for percent data. The effect of the additional clipping treatment in 1992 was determined by re-analyzing the data set using a one-way MANOVA by treatment. If data were found to violate assumptions of normality or homoscedasticity, transformations were performed on the data prior to analysis. If following transformations data still did not conform to the assumptions required for parametric analysis, a non-parametric Kruskal-Wallis analysis was performed. The results were reported and compared to parametric statistics when necessary. The effect of treatments on mean size of reproductive structures (raceme and peduncle length) was analyzed in 1992 to determine if there was plasticity in the size of the structures in response to treatments. Low numbers of reproductive structures formed in 1991 precluded analysis of 1991 data. RESULTS YEARLY VARIATION There was significant (P=O.OO1) yearly variation in both reproductive investment and output across all treatments (Tables 22 a, b). The trend observed in this experiment was relatively low reproductive effort and output in 1991 with only 107 racemes and 399 flowers initiated (Fig. 5).  97 The total number of fruits matured across all treatments was 31(7.8% of the number of flowers initiated). Peak densities of snowshoe hares occurred in the study area in 1990 and were in decline by 1991 (Fig. 6). Reproductive output in L. arcticus increased substantially in 1992 following the start of the decline in hare numbers at Kluane. Reproductive investment increased to 281 racemes and 2858 flower buds produced in the 1992 season, this was an increase of 262% and 716% respectively over the previous year. The number of fruits reaching maturity increased to 625 pods, an increase of 2 1.9% (Fig. 5). In 1991, five experimental quadrats in four different treatments showed no reproductive investment at all. In the following year, all quadrats not reproducing in the previous year showed some reproductive investment ranging from 1 to 13 racemes. In 1992, two quadrats from two treatments showed no reproductive investment. One had no prior reproductive investment in 1991 and the other had produced 1 raceme.  REPRODUCTION 1991 Investment Analysis of reproductive investment by treatment in 1991 indicated that there were no significant differences in the amount of investment made to the formation of reproductive structures (racemes and flower buds) between the treatment groups (Tables 23 a, b). All treatment groups showed some degree of allocation to reproduction in 1991, but investment within treatments was highly variable (Fig. 7 (raceme production), Figure 8 a (flower bud initiation))  Output Reproductive output in 1991 was low. Parametric tests of the effect of treatments on square root transformed pod production , m 2 indicated no significant treatment effects. Non 1 parametric Kruskal-Wallis ANOVA was performed due to violations in the assumptions of  98 normality and homoscedasticity. No significant effects were found in the parametric (Table 24 a), or the non-parametric tests (Table 24 b).  Reproductive efficiency In addition to comparing the effect of treatments on total reproductive output I m 2, the relative efficiency of reproduction in the experimental populations was tested to determine if the proportion of fruits ripening differed between treatments. This was a comparison of reproductive efficiency, or the ability of treated populations to translate the number of flowers initiated into mature fruit. Reproductive success or efficiency was estimated and compared using the arcsine transformed function of total number of pods / m 2 relative to the total number of flower buds initiated / m 2 A three-way ANOVA of reproductive efficiency in 1991 indicated a significant clipping effect (P<O.003) on that reduced the proportion of fruits maturing (Table 25, Figure 8). Non-parametric Kruskal-Wallis was performed due to violations in the assumptions of normality and homoscedasticity. The non-parametric test indicated no significant treatment effect on reproductive efficiency (P<O.098, Table 24 b).  Recruitment Seedling recruitment in the first year of the study was almost nonexistent across all treatment populations as well as in the natural population. Only three seedlings recruited into the experimental quadrats in 1991, and they all recruited in different quadrats and in different treatment groups. All seedlings over-wintered and re-emerged in 1992. Growth was limited in the first two seasons of growth with a maximum of two leaves appearing in the first summer and three in the second.  99  3000  Racemes  2500-  Flowers • Fruit  20000  z  1500-  1  1000500  -  01992  1991 Yearly differences  Figure  5: A  comparison of total number of racemes, flowers and pods  produced across  all treatments in 1991 and 1992.  7  LEI 6  Control Food Fertilizer Predator Exciosure Predator Exclosure  +  Food  Cu  ii ft,Ii 1987 Figure  6:  1988  1989  1990  the beginning  of the crash  1992  CSP treatment in 1991.  Snowshoe hare spring densities on  treatment shows  1991  1993  grids from  1994  1987-1994. The  control  100 Table 22: a) Univariate source of variation for a one-way MANOVA comparing total raceme, flower bud and pod production/rn 2 in 1991 and 1992.  Variable  SS  dF  MS  F ratio  Probability  Racemes  10.503  1  10.503  6.681  0.001  Error  97.479  62  1.572  Flower buds  325.543  1  325.543  16.373  0.000  Error  1270.582  62  20.493  Pods  69.670  1  69.670  13.886  0.000  Error  311.063  62  5.071  b) Multivariate test statistics of racemes, flower buds, and pods /m 2 by year.  Test Statistic  Statistic  dF  Probability  Wilks’ Lambda  0.58 1  3, 60  0.000  F statistic  14.445  101  40  30  • ü 20  1  -E;  ’k 1 -  -  -  .  1)  E  E  r  i  •  Treatment Figure 7: Reproductive effort in 1991 and 1992 as measured by mean (±SEM) raceme production per m 2 for each treatment.  racemes-1992  Q  ‘—  H-  CD  CD  CD  :  D  C)  CD  CD  CM  ‘0  o  -  a.  CM  -  CD  CD  -t  i  0 I  Extra-Clip  Fert-CI-Rem  Clip-Rem  Fert-Rem.  Fert-Clip  Removal  Clipped  -  -  -  -  ±:j—  Fertilized -—  Control  0  u.  i ‘  0 0 I 0 I  u 0 I  o  t’J 0  ‘3 C I  u  )  L’J  0  I \0  CM  -  CD  0  L1•  0 I  c  Frequency of flower buds or pods 0  c  -  Fert-Ci-Rem  Clip-Rem  Fert-Rem.  Fert-Clip  Removal  Clipped  Fertilized  Control  -  -  -  —  V I  JzH  0  0 I  .  I  I  I  C  cM  I  cM  I  I  I  I  M  0  0  -  Cl)  CD  0  L1•  C  Frequency of flower buds or pods 0  C  103 Table 23: a) Univariate statistics from a MANOVA testing the effect of treatment on squareroot transformed raceme production and flower bud initiation in 1991.  Effect Fertilizer  Clipping  Neighbours Removed  Variable Racemes error Flowers error Racemes error Flowers error Racemes error Flowers error  SS 1.421 19. 149 1.379 134.731 0.061 19. 149 6.275 134.731 1.009 19. 149 16.618 134.731  dF 1 24 1 24 1 24 1 24 1 24 1 24  MS 1.421 0.798 1.379 5.614 0.061 0.798 6.275 5.614 1.009 0.798 16.618 5.614  F 1.781  Probability 0.195  0.246  0.625  0.076  0.784  1.118  0.301  1.265  0.272  2.960  0.098  b) Wilks’ Lambda and F statistics from MANOVA of 1991 estimates of raceme and flower bud production in 1991. Effect Fertilizer Clipping Neighbours Removed  Statistic Wilks Lambda F-statistic Wilks’ Lambda F-statistic Wilks’ Lambda F-statistic  dF  Probability  2, 23  0.375  2, 23  0.494  2, 23  0.261  104 Table 24 a: Three way ANOVA test of square root transformed pod production / m 2 in 1991.  Source  SS  dF  MS  F ratio  Probability  Fertilizer  1.392  1  1.392  2.229  0.150  Clipping  1.692  1  1.692  2.710  0.114  Removal  0.026  1  0.026  0.042  0.840  Fert* Clip  1.008  1  1.008  1.614  0.217  Fert* Rem  0.018  1  0.018  0.029  8.650  Clip*Rem  0.114  1  0.114  0.182  0.674  Fert*Clip*Rem  0.002  1  0.002  0.003  0.960  Error  13.728  22  0.624  Table 24 b: Summary of Kruskal-Wallis non-paramethc test results for some reproduction variables.  Variable  Year  Reproductive output  1991  5.591  7  0.588  Reproductive efficiency  1991  12.068  7  0.098  Reproductive output  1992  6.066  8  0.640  Reproductive efficiency  1992  17.5  8  0.025  Raceme length  1992  8.625  7  0.28 1  Peduncle length  1992  5.180  7  0.638  Kruskal-Wallis statistic dF  Probability  105 Table 25: Three-way ANOVA test of arcsine transformed reproductive efficiency in 1991.  Source  dF  MS  F ratio  Probability  Fertilizer  1  0.000  0.009  0.928  Clipping  1  0.252  15.436  0.003  Removal  1  0.007  0.43 1  0.526  Fert* Clip  1  0.001  0.055  0.8 19  Fert* Rem  1  0.00 1  0.039  0.847  Clip*Rem  1  0.004  0.265  0.618  Fert*Clip*Rem  1  0.002  0.116  0.741  Error  10  0.016  REPRODUCTION 1992 Investment In spite of higher overall levels of reproduction in 1992, no significant treatment effects were observed on the amount of effort invested in reproduction measured by the number of racemes produced and the number of flower buds initiated / m 2 Tests for treatment effects on reproductive investment were performed using a three-way MANOVA to assess the effect of the original factorial experimental design. The effect of the intensified clipping treatment added in 1992 indicated no significant treatment effects (Tables 26 a c). Variables used to measure -  reproductive investment in 1992 violated assumptions of bivariate normality. Transformations used on variables to make them more homoscedastic were only moderately successful. Various transformations did improve the data in that although the assumptions were still violated, it was to a lesser degree. Reproductive investment variables were measured as ‘count’ or discrete numbers and normally fall into a Poisson distribution. When sample sizes are large enough, the Poisson  distribution approximates a normal distribution. The highly variable level of reproductive  106 investment observed in the field (Figs. 6, 7 b), suggest that samples sizes may not have been large enough to meet these criteria.  Output In 1992, parametric analysis of the log transformed data on reproductive output detected no significant treatment effects (Tables 27 a, b). Data in this analysis was not normally distributed. Although ANOVA is robust to departures from normality and homoscedasticity, a non-parametric Kruskal-Wallis was done and also failed to detect significant treatment effects (P<0.640, Table 24 b). Two treatment groups were marginally insignificant in their effect on total pod production : fertilizer (P<0.094), and neighbour removal (P<0.080). Further examination of the fertilizer 2 /m and neighbour removal treatments indicates that they have opposing effects on reproductive output such that neighbour removal increased pod production and fertilizer decreased it (Fig. 8 b). Application of fertilizer reduced mean (±SEM) pod production! m 2 to 2.00! m 2 ±1.683 relative to the control of 23.75±20.126, whereas removal of neighbouring species increased pod production to 61.75 pods /m 2 ±33.908.  Reproductive efficiency The reproductive efficiency of L. arcticus in terms of fruit maturation changed from 1991 to 1992. In 1992, a significant fertilizer effect (P<0.001), and a moderately nonsignificant neighbour removal effect on reproductive efficiency (P<0.084) were detected (Table 28 a). No significant clipping effects were observed in the three-way analysis of the original experimental design. One-way ANOVA of all nine treatments in 1992 found a significant treatment effect at P<0.002 (Table 28 b). A post hoc Tukey grouping (Table 28 c) indicated a strong fertilizer effect such that fertilizer, the interaction between fertilizer and neighbour removal, and the fertilizer by clipping treatment were significantly different from neighbour removal and the extra clipping treatment in 1992. A significant difference in clipping on reproductive efficiency was only seen  107 for the intense clipping treatment in 1992. A Kruskal-Wallis analysis by treatment was performed on arcsine transformed reproductive efficiency was significant (P<0.025, Table 24 b). Fertilizer, fertilizer by clipping and fertilizer by neighbour removal have lower mean reproductive efficiency than the intense clipping treatment and neighbour removal (Fig. 8 b).  Size of Reproductive Structures Multivariate analysis of treatment effects on the length of racemes and peduncles (Table 29) indicated no significant treatment effects (Table 30a d). A Kruskal-Wallis analysis confirmed -  the non-significant results (Table 24 b).  Table 26: a) Univariate statistics from a MANOVA testing the effect of treatment on transformed raceme production and flower bud initiation in 1992.  Effect  Variable  SS  dF  MS  F ratio  Probability  Fertilizer  Racemes Error Flowers Error Racemes Error Flowers Error Racemes Error Flowers Error  0.008 4.988 62.286 864.470 0.427 4.988 6.960 864.470 0.250 4.988 106.164 864.470  1 24 1 24 1 24 1 24 1 24 1 24  0.008 0.208 62.286 36.020 0.427 0.208 6.960 36.020 0.250 0.208 106.16 36.020  0.040  0.84  1.729  0.20  2.055  0.16  0.193  0.66  1.204  0.28  2.947  0.09  Clipping  Neighbours removed  108 b) Wilks’ Lambda and F statistics from MANOVA of 1992 estimates of log transformed raceme and flower bud production in 1992.  Effect  Test  Statistic  Fertilizer  Wilks’ Lambda F-statistic Wilks Lambda F-statistic Wilks’ Lambda F-statistic  0.93 1 0.858 0.9 16 1.057 0.848 2.056  Clipping Neighbours Removed  dF  Probability  2, 23  0.210  2, 23  0.164  2, 23  0.474  Table 27: a) Three-way ANOVA of log transformed pod production I m j 2 n 1992. Source Fertilizer Clipping Removal Fert* Clip Fert* Rem Clip*Rem Fert*CIip*Rem Error  dF 1 1 1 1 1 1 1 24  MS 1.341 0.000 1.476 0.228 0.035 0.014 0.045 0.442  F ratio 3.036 0.001 3.342 0.515 0.079 0.032 0.102  Probability 0.094 0.976 0.080 0.480 0.78 1 0.859 0.752  b) One-way ANOVA of pod production I m 2 in 1992 to determine the effect of the intensified clipping treatment on reproductive output.  Source Treat Error  SS 3.140 11.623  dF 8 27  MS 0.393 0.430  F ratio 0.912  Probability 0.522  109 Table 28: a) Three-way ANOVA of arcsine transformed reproductive efficiency in 1992. Source Fertilizer Clipping Removal Fert*Clip Fert*Rem Clip*Rem Fert*Clip*Rem Error  dF 1 1 1 1 1 1 1 24  MS 4.441 0.152 1.095 0.054 0.268 0.933 0.021 0.337  F ratio 13. 170 0.452 3.246 0.161 0.794 2.767 0.062  Probability 0.001 0.508 0.084 0.692 0.382 0.109 0.806  b) One-way ANOVA of arcsine transformed reproductive efficiency in 1992. Source Treat Error  SS 10.210 8.093  dF 8 27  MS 1.276 0.300  F ratio Probability 4.258 0.002  c) Post hoc Tukey comparison of reproductive efficiency by treatment in 1992. Note: C+ is the intensive clipping treatment added in 1992.  C+  NR  C/NR  C  Cont  F/C/NR  F  F/NR  F/C  V  V  V  V  V  V  V  .  Table 29: Mean size (±SEM) of reproductive structures by treatment for 1992. Length (cm) Raceme ±SEM Peduncle ±SEM  Cont 4.46 0.70 4.60 0.70  Fert (F) 8.05 0.39 6.1 0.02  Clip (C) 4.49 0.70 3.98 0.61  Rem (N) 6.75 1.13 5.66 0.84  F/C  F/N  C/N  F/C/N  2.62 0.69 2.77 0.74  3.81 1.02 2.69 0.54  7.17 0.37 5.57 0.27  4.29 0.36 4.99 0.32  Extra Clip 6.23 0.92 4.3 0.62  110 Table 30: a) Univariate (three-way) test statistics of length of racemes, and length of peduncles in 1992. Effect Fertilizer  Clipping  Removal  Variable Raceme length Error Peduncle length Error Racerne length Error Peduncle length Error Raceme length Error Peduncle length Error  SS 3.503 286.899 0.853 207.613 8.527 286.899 0.172 207.613 11.843 286.899 4.309 207.613  dF 1 24 1 24 1 24 1 24 1 24 1 24  MS F ratio Probability 3.503 0.293 0.59 11.954 0.853 0.009 0.75 8.651 8.527 0.7 13 0.40 11.954 0.172 0.02 0.88 8.651 11.843 0.991 0.33 11.954 4.309 0.498 0.48 8.651  b) Multivariate test statistics from three-way MANOVA of length of racemes, and length of peduncles in 1992. Effect Fertilizer Clipping Neighbours Removed  Test statistic Wilk& Lambda F statistic Wilks’ Lambda F statistic Wilks’ Lambda F statistic  Statistic 0.98 8 0.140 0.946 0.652 0.960 0.484  cIF 2, 23  Probability 0.870  2, 23  0.53 1  2, 23  0.662  c) Univariate (one-way) test statistics of length of racemes and length of peduncles by treatment in 1992.  Variable  SS  dF  MS  F ratio  Probability  Raceme length Error Peduncle length Error  93.939 341.414 52.925 239.143  8 27 8 27  11.742 12.645 6.6 16 8.857  0.929  0.50  0.747  0.65  111 d) Multivariate test statistics from one-way MANOVA of length of racemes and length of peduncles by treatment in 1992  Test statistic  Statistic  dF  Probability  Wilks’ Lambda F statistic  0.695 0.648  16, 52  0.829  Recruitment In 1992, seedling recruitment into the experimental quadrats remained low with only five new seedlings appearing in four different treatment groups. All seedlings surviv ed until August 24, 1992. DISCUSSION  Reproductive investment in L. arcticus in the Kluane region was very low in 1991 relative to investment in the following year irrespective of treatment applied. Yearly variati on in reproduction is not uncommon in many species and in some cases has been shown to be correlated to growth in the previous year (Barkham 1980). Although many enviro nmental factors vary yearly, patterns of reproductive investment in L. arcticus may be correlated with the 10-year cycle of abundance of snowshoe hares, a keystone herbivore in the area that is known to feed on L. arcticus. The first year of the experiment followed the year of peak hare density . Lupinus arcticus probably experienced high levels of herbivory at the study site in 1990, and this may have resulted in low investment to reproduction in 1991 in the treatment quadrats. In 1991, the snowshoe hare population was declining, and experimental quadrats were protected from mammalian herbivores by fencing. This reduced level of herbivory across all treatments may have resulted in higher levels of investment in 1992. Investment to reproductive structures in L. arcticus occurs very early in the growing season immediately following snow melt and prior to leaf opening. Racemes break ground concurrently with leaf buds. As a result, it was unlikel y  112 that any treatment effect on reproductive investment would be observed in 1991, particu larly because clipping and neighbour removal did not occur until plants in the experimental quadrats were established. No treatment effect was observed on other measures of reprod uction in 1991. Several explanations are possible. The slow growth rate of L. arcticus in the northe rn boreal forest may produce a lag in response time to treatments. If this is the case, long term experiments would be required to detect treatment effects. Alternatively, Grime (1977) would argue that L. arcticus in this environment is a stress tolerator and is not adapted to respond to short-t erm  environmental changes. Rather, it is adapted to endure adverse conditions and should not respond to any treatments in this experiment unless they represent long term enviro nmental change. If this were the case, no treatment effect should be expected in 1992. Although there were no significant differences in reproductive investment, reprod uctive output, or size of reproductive structures, reproductive efficiency did show some complex treatment effects in 1992. Three-way analysis of reproductive efficiency indicated that only fertilizer had a significant treatment effect such that reproductive efficiency in maturing fruit was significantly lower when L. arcticus was fertilized than in all other treatments and combi nations. A comparison of treatments showed that intense clipping and neighbour removal were differe nt from fertilizer and all two-factor fertilizer interactions. The fertilizer effect on reproduction has been observed with other species and may be due to suppression of L. arcticus by more rapidly growing neighbours that usurp nutrients. This is a reasonable explanation because when fertiliz er was applied in the absence of neighbours no significant effect was detected for reproductive efficiency of reproduction. Also, experiments on growth of L. arcticus found that percen cover t of a graminoid Festuca altaica increased significantly when fertilized. Removal of neighb ours may have reduced interference and allowed L. arcticus to mature a greater proportion of fruits than in other treatments. The rate of fertilized application was low in 1991 and may not have been sufficient to produce a detectable response. Rate of application was doubled in 1992 to  113 ensuse that the level of nutrient applied to the experimental quadrats was sufficiently high enough to constitute a treatment capable of producing detectable responses. It is unclear why the intense clipping treatment had a higher proportion of fruit mature than other treatments. It is possible that this is not a true treatment effect if there is a lag in the response time of L. arcticus to environmental changes. These populations may be responding to environmental changes occurring in the previous season, rather than to treatment effects imposed in 1992. More data are required to test this hypothesis. Although reproductive output in 1992, as measured by pod production / m , showed no 2 significant treatment effects, two treatments were only marginally nonsignificant: fertilizer and neighbour removal. No conclusions can be drawn due to lack of significant statistical results, but this may indicate a trend that would become significant if the experiment were maintained longer. Reproductive output was lower in the fertilizer treatment than in the control, and much lower than mean output when neighbours were removed. This suggests that the presence of neighbours may be mediating the response of L. arcticus to changes in the environment. With the high level of variability in reproductive measures within treatment, it is not possible to determine if the observations regarding the fertilizer and neighbour effect are spurious. Alternatively, the high level of variability in response variables is commonly observed in field experiments and may be masking treatment effects in this experiment. This variability may be due to uncontrolled factors such as spatial heterogeneity in the growing environment, heterogeneity in the past histories of experimental populations (i.e. past flowering history, grazing, neighbour differences, availability of resources, etc.). These uncontrolled variables may be more important in determining the response of L. arcticus populations than the experimental treatments imposed. Therefore, the interpretation of nonsignificant results must be undertaken with caution and must be verified with further experimentation before any conclusions are drawn. Seedling recruitment into experimental quadrats was low in 1991 and 1992. Little is known about patterns of recruitment of L. arcticus in this environment, however patterns of recruitment  114 may also be correlated to the snowshoe hare cycle. No data are available at this time to comment on this possibility. However, field observations at this study site in 1993, one year after the crash in hare populations, recorded increased levels of L. arcticus recruitment not seen in previous years.  115  CHAPTER 6  SUMMARY AND CONCLUSIONS  The aim of this study was to investigate the relative importance of clipping, neighbours, and soil fertility level as potential limiting agents on field populations of Lupinus  arcticus and  determine how they interact to produce observed patterns of abundance and dynamics. Although populations did respond to the treatments imposed in this experiment, none of the main effects strongly influenced their dynamics, but rather had other effects (Table 31).  Lupinus arcticus  populations were influenced by the interactions of those main effects, in particular, the distribution of petiole lengths was strongly influenced by an interaction between treatments.  Table 31:  Summary of significant main effects detected in  Fertilizer  effects  1991 and 1992.  Clipping effects  Neighbour removal  T incidence of disease 1991 incidence of disease 1991/2 L standing crop available 1992 1’ standing crop available 1991 reproductive efficiency 1992  effects  reproductive efficiency 1992  I- total leaf density 1- leaf survivorship  199 1/2 1992  L total leaf mortality IL. arcticus cover  1991 1991/2  The most striking effects detected were the increased percent cover and the reduced leaf mortality in  L. arcticus  when neighbours were removed, indicating that competitive release  occur. Second, there was some evidence to suggest that  L. arcticus  did  was able to compensate for  clipping as percent cover of lupines was not reduced by clipping, rather it showed an  116  nonsignificant increase. The standing crop of lupine leaves was also greater when populations were clipped in spite of a reduction in overall leaf density, which suggests that a compensatory mechanism that reduces the rate of leaf turnover when populations were clipped. In addition, the production of new leaves (buds) did not decline in response to clipping although total leaf density was reduced. This suggests that L. arcticus is able to maintain leaf production in spite of the disturbance. Third, the consequences of increased nutrient availability on L. arcticus populations were primarily negative as fertilization was shown to increase the incidence of disease on leaves, as well as to reduce both the reproductive efficiency and standing crop. These negative effects may be attributed to the inhibitory effect of nutrient addition (NPK) on nitrogen fixation in L. arcticus. However, there was also evidence to suggest that nutrients may have been usurped by more rapidly growing species consequently leading to reduced growth of L. arcticus. Although L. arcticus populations did show some significant responses in this study, they were comparatively weak considering the intensity of the treatments imposed. These unexpected results necessitate the question as to why L. arcticus did not respond more strongly to these factors. To properly address this question, we must take a ‘plant’s-eye-view’ of the habitat that L. arcticus lives in the boreal forest understory. It is generally considered that plant’s growing in -  this habitat have to contend with two major factors. First, they must endure a relatively constant, stressful (sensu Grime 1979) habitat characterized by reduced levels of light filtering through the canopy, prolonged periods of low winter temperatures, brief, cool growing seasons, low soil fertility, and low moisture (Elliott-Fisk 1993). Second, they are subjected to periodic increases in herbivory when snowshoe hare abundance is at its peak during their 10-year cycle. From Grime’s (1979) point of view, this is a stressful environment subject to short, periodic increases low intensity disturbance (herbivory). In this type of environment, where plants have evolved characteristic which permit prolonged, slow vegetative growth it might be expected that plants would not respond, or would respond very slowly to short term increases in nutrients or reduced interference from neighbours. The abiotic conditions required to sustain elevated levels of plant growth to take advantage of short term increases in nutrients or competitive release (neighbour  117  removal) are not available in this environment. As conditions for plant growth in the boreal forest are relatively poor, competition between neighbours may be weak (Grime 1977), or strong (Tilman 1988). Lupinus arcticus populations persist during the peak densities of snowshoe hares, so it is evident that they must be able to resist, or compensate for the periodic increases in the amount of herbivory they experience. This may explain why clipping, even severe clipping, did not affect L. arcticus dynamics to any great degree. The low level of response of L. arcticus populations to these treatments supports Grime’s (1979) premise that plants living in stressful environments have evolved strategies of tolerance that do not permit them to respond to short term increases in local resource abundance. Lupinus arcticus does have some of the properties characteristic of a stress-tolerant species described by Grime (1979) (e.g. long-lived perennial, nitrogen fixer, low rate of flowering, slow growth rate). However, other common attributes are lacking (e.g. evergreenness, lack of morphogenetic plasticity), and therefore it probably falls into Grime’s (1979) stress-tolerant competitor category. This may explain why a low level of response was detected to the treatments imposed. As for how this study fits into the debate regarding top-down and bottom-up regulation of communities, the lack of strong response to short term treatments of clipping, fertilizer, and neighbour removal suggests that neither top-down or bottom-up factors were strongly limiting these natural populations at the time of the survey. It may be that evolutionary characteristics that have permitted L. arcticus to persist in this stressful (sensu Grime 1979) habitat are more influential in determining the patterns of growth and dynamics than short-term changes in ecological factors. In this sense, the long-term adaptations to the abiotic conditions of the boreal forest may have pre-determined the limited responses to short-term changes in herbivory, interference from neighbours, or soil nutrient availability. An alternative explanation to the low level of response in these experiments may be the result of high levels of variation within treatments masking the response to treatments. The original experimental design tried to deal with this problem by replicating and randomly assigning treatments to the experimental quadrats. In addition, attempts were made to choose quadrat sites  118  that were visually similar in terms of density of lupines and vegetation cover. In spite of this, the large error sums of squares in some of these analyses indicated that high amounts of variation were still present in the data. This thesis did not attempt to partition the variation in this error term to test this possibility due to the lack of pre-treatment data, or appropriate variables to use as covariates in the analysis. Future experiments in this area would be advised to address this problem in the experimental design by (i) doing one pre-treatment survey in order to have some measure of pre-treatment variation in the experiment. (ii) Use a randomized block design, (iii) collect additional data to be used as covariates during the analyses.  APPENDIX A  CHARACTERISTICS OF THE WEIBULL DISTRIBUTION The Weibull distribution (Weibull 1931, 1959) is a “generalization of the exponential distribution,” but it does not assume, as does the exponential model, a constant hazard rate over time or age (Lee 1980). This model of survivorship is described by 4 equations: cx _Q,*t) S(t)= e h(t)= ?.  *  a  *  (1)  -1)  (*t)(a  (2)  a  F(t)= 1 -e  *t)  (3) a  f(t)=?* a  *  (2L*t)(a l)*eO*t)  S(t) is the probability of survivorship at time or age (t), lambda  (4)  () is the scale parameter,  and alpha (a) is the shape parameter, h(t) is the hazard function that describes the instantaneous mortality rate or age-specific mortality rate, F(t) and f(t) are the cumulative distribution function and the probability density function respectively (Lee 1980). The relationship between these equations is described by Lee (1980): S(t)  =  1  -  F(t)  (5)  Therefore F(t), the cumulative distribution function is the probability that an individual dies before time (t). The probability density function f(t), is the limit of the probability that an individual dies or fails in a short time interval of t + z\ t per unit width A t. Briefly, the probability of dying in a very short time window. These two functions are used to determine the instantaneous mortality rate: h(t)=f(t)/(1-F(t)) 119  (7)  120  In the Weibull model of survivorship, the instantaneous mortality rate can remain constant, increase, decrease, or vary nonlinearly. According to Lee (1980), the use of theoretical survival distributions to describe observed survivorship data has occurred since the 1940’s beginning with the exponential decay curve. Theoretical distributions are useful because they allow a mathematical description of survival time between groups even when the factors causing this distribution are not known or available for measure (Lee 1980). This is the case with many ecological studies. The parameters generated by the fitting a theoretical model to an observed data set are not necessarily known biological parameters, however they can be used as the basis for comparing different treatments. In this analysis two Weibull model parameters form the basis of the statistical comparison: the scale parameter (?), and the shape parameter (cc). The two Weibull equation parameters  .  and cx  determine both quantitative and qualitative aspects of the predicted curve generated. When  (X=  1, the hazard rate is constant (exponential decay). The hazard rate increases (over time) when cx >1, and decreases as o< 1. Changes in 2 (scale parameter) produce quantitative changes in the survival curve (Lee 1980, 1992). Varying cc (shape parameter) results in qualitative changes to the survival curve that allow equations to be fit to different models of survivorship including Type I, II , III hypothetical survivorship models (Fig. 9).  iti: (J)  -  (I —  lype ]I  —4  4  t  Figure 9: Survival curves generated by the Weibull distribution when ?=1 and a is allowed to  vary. Excerptedfrom Lee (1992).  BIBLIOGRAPHY Aarssen, L.W. G.A. Epp. 1990. Neighbour manipulations in natural vegetation: a review. J. Veg. Sci. 1:13-30. ,  Aarssen, L.W. D.L. Irwin. 1991. What selection: herbivory or competition. Oikos 60(2):261262. ,  Aarssen, L.W. D.R. Taylor. 1992. Fecundity allocation in plants. Oikos 65:225-232. ,  Abul Fatih, H.A. F.A. Bazzaz. 1984. The influence of defoliation on the performance of Ambrosia trifida plants. J.Coll.Sci. King Saud Univ. 15(1):55-62. ,  Alexander, H. 1992. Evolution of disease resistance in natural plant populations. In ed.R.S. Fritz and E.L. Simms Plant Resistance to Herbivores and Pathogens. University of Chicago Press, Chicago Andersson, M. S. Jonasson. 1986. Rodent cycles in relation to food resources on an alpine heath. Oikos 46:93-103. ,  Antonovics, J. 1980. Concepts of resource allocation and partitioning in plants. Pages 1-25 in Limits to Action. The allocation of individual behaviour. J.E.R. Staddon, editor. Academic Press, New York. Bakker, J.P. J.C. Ruyter. 1981. Effects of five years of grazing on a salt-marsh vegetation. Vegetatio 44:81-100. ,  Baldwin, I. 1988. The alkaloidal responses of wild tobacco to real and simulated herbivory. Oecologia 77:378-38 1. Baldwin, I. 1990. Herbivory simulations in ecological research. TREE 5(3): 91-93. Banyikwa, F.F. 1988. The growth response of two East African perennial grasses to defoliation, nitrogen fertilizer and competition. Oikos 51: 25-30. Barkham, J.P. 1980. Population dynamics of the wild daffodil (Narcissus pseudonarcissus). II. Changes in number of shorts and flowers, and the effect of bulb depth on growth and reproduction. J. Ecol. 68:635-664. Wilson, J. B. 1989. Root competition between three upland grasses. Func. Ecol.3:447-451. Bazely, D.R. R.L. Jefferies. 1986. Changes in the composition and standing crop of salt-marsh communities in response to the removal of a grazer. J. Ecol. 74:693-706. ,  121  122 Bazzaz, F.A. J.L. Harper. 1977. Demographic analysis of the growth of Linum usitatissimum. New. Phytol. 78:198-209. ,  Begon, M. M. Mortimer. 1986. Population Eco1ov: A Unified Study of Animals and Plants.. Sinuaer Associates Inc. Publishers. Sunderland Massachusetts. 220 pp. ,  Beisky, A.J. 1986. Does herbivory benefit plants? A review of the evidence. Am. Nat. 127(6)870-892. Beisky, A.J. 1987. The effects of grazing confounding ecosystem, community, and organism scales. Am. Nat. 129(5):777-783. Berendse, F. 1985. The effect of grazing on the outcome of competition between plant speceies with different nutrient requirements. Oilcos 44: 35-39. Binkley, D. 1986. Forest Nutrition Management. John Wiley, Sons. New York. Birch, C.P.D., M.J. Hutchings. 1992. Analysis of ramet development in the stoloniferous herb Glechoma hederacea using a plastochron index. Oikos 63:387-394. Bishop, G.F. A.J. Davy. 1984. Significance of rabbits for population regulation of Hieraceum pilosella in Brecidand. J. Ecol. 72:273-284. ,  Black, J.N. 1960. The significance of petiole length, leaf area, and light interception in competition between strains of subterranean clover. Aust. J. Agric. Res. 11:277-291. Bradshaw, A.D. 1959. Population differentiation in Agrostis tenuis Sibth. II. The incidence and signficance of infection by Epichloe typhina. New Phytol. 58:310-3 15. Bryant, J.P., F.S. Chapin III., D.R. Klein. 1983. Carbon/nuthent balance of boreal plants in relation to vertebrate herbivory. Oikos 40:357-368. ,  Bryant, J.P., T.P. Clausen, P.B. Reichart, M.C. McCarthy, R.A. Werner. 1987a. effect of nitrogen fertilization upon the secondary chemistry and nutritional value of quaking aspen (Populus tremuloides) leaves for the large aspen tortix (Choristoneura conflictanan (Walker)). Oecologia 73:513-517. Bryant, J.P., F.S. Chapin III, P.B. Reichart, T.P. Clausen. 1987b. Response of winter chemical defence in Alaska paper birch and green alder to manipulations of plant carbon/nutrient balance. Oecologia 72:510-5 14. ,  Callaghan, T.V. U. Emanuelsson. 1985. Population structure and processes of tundra plants and vegetation. The population structure of vegetation. In White, J. (ed.) Junk, Dordrecht. pp. 399439. ,  123 Callaghan, T.V. 1987. Growth and population dynamics of Carex bigelowii in an alpine environment. Oilcos. 27:402-413. Campbell, B.D., J.P. Grime, J.M.L. Mackey, A. Jalili. 1991. The quest for mechanistic understanding of resource competition in plant communities: the role of experiments. Funct. Ecol. 5:241-253. Campbell, B.D. J. P. Grime. 1992. An experimental test of plant strategy theory. Ecolog y 73(1): 15-29. ,  Cavers, P.B. J.L Harper 1967. Studies in the dynamics of plant populations. I. The fate of seed and transplants introduced into various habitats. J. Ecol. 55:59-7 1. ,  Chabot, B.F. D.J. Hicks. 1982. The ecology of leaf life spans. Ann. Rev. Ecol. Sys. 13:229259. ,  Chapin, D.M. L.C. Bliss. 1989. Seedling growth, physiology and survivorship in a subalpine, volcanic environment. Ecology 70(5): 1325-1334. ,  Chazdon, R.L. 1991. Effects of leaf and ramet removal on growth and reproduction of Geonoma congesta, a clonal understorey palm. J. Ecol. 79(4): 1137-1146. Chesson, P.L. R.R. Warner. 1981. Environmental variability promotes coexistence in lottery competitive systems. Am. Nat. 1 17(6):923-943. ,  Clay, K. 1988. Fungal endophytes of grasses: a defensive mutualism between plants and fungi. Ecology 69: 10-16. Clearwater, M.J. K.S. Gould, 1993. Comparative leaf development in juvenile and adult Pseudopanax crassifolius. In press. C.J.B. ,  Cody, M.L. 1966. A general theory of clutch size. Evolution 20:174-184. Coley, P.D., J.P. Bryant, F.S. Chapinlll. 1985. Resource availability and plant antiherbivore defence. Science 230(4728) :895-899. ,  Connell, J.H. 1983. On the prevalence and relative importance of interspecific competition: evidence from field experiments. Am. Nat. 122(5):661-696. Coppock, D.L., J.K. Detling, J. Ellis, M.I. Dyer. 1983. Plant-herbivore interations in a North American mixed grass prairie. I. Effects of Black-tailed Prairie Dogs on intra-seasonal aboveground plant biomass and nutrient dynamics and plant species diversity. Oecologia 56: 1-9. ,  Crawley, M.J. 1983. Herbivorv: The Dynamics of Animal-Plant Interactions. Studies in Ecolog y vol. 10. University of California Press, Berkeley. 437 pp.  124 Crawley, M.J. 1988. Herbivores and plant population dynamics. Chapte r 18 In Plant Population Ecology: The 28th Symposium of the British Ecological Society. Sussex 1988. Ed. A.J. Davy, M.J. Crawley, M.J. 1990. Rabbit grazing, plant competition, and seedling recruitment in acid grassland. J. App. Ecol. 27:803-820. Cristie, P., E. Newman, and R. Campbell. 1978. The influence of neighb ouring grassland plants on each others’ endomycorrhizas and root-surface microorganisms. Soil Biol. Biochem. 10:521527. Davy, A.J. G.F. Bishop. 1984. Response of Hieraceum pilosella in Breckland grass heath to inorganic nutrients. 3. Ecol. 72:319-330. ,  Dc Kroon, H., Hara, T. R. Kwant. 1992. Size hierarchies of shoots and clones in clonal and monocultures:do clonal and non-clonal plants compete differently? Oikos 63 :4 10-419. ,  Dennis, W.D. J. Woledge. 1985. The effect of nitrogenous fertiliz er on the photosynthesis and growth of white clover! perennial ryegrass swards. Ann. Bot. 85:171-178. ,  Detling, J.K. E.L. Painter. 1983. Defoliation responses of western wheatg rass populations with diverse histories of prairie dog grazing. Oecologia:57:65-7 1. ,  Diemer, M., Korner, C., S. Procks. 1992. Leaf lifespans in wild perenn ial herbaceous plants: a survey and attempts at functional interpretation. Oecologia 89:10-16. Doak, D.F. 1992. Lifetime impacts of herbivory for a perennial plant. Ecol 73(6):2186-2099. Dolinger, P., P. Ehrlich, W. Fitch, D. Breedlove. 1973. Alkaloid and predati on patterns in Colorado Lupine populations. Oecologia 13:191-204. Donald, C.M. 1963. Competition among crop and pasture plants. Adv. Agron. 15:1-118. Donald, C.M. 1989. Invasive root growth into disturbed soil of two tussock grasses that differ in competitive effectiveness. Funct. Ecol. 3:345-353. Dunn, D.B. J.M. Gillet. 1966. The Lupines of Canada and Alaska. Can. Dept. Agric. Res. Branch Monograph 2. Queen’s Printer, Ottawa. ,  Elliot-Fisk, D. 1993. The boreal forest. Chapter Two in North American Terres trial Vegetation. ed. M.G. Barbour, and W.D. Billings. Cambridge University Press, New York. Eriksson, 0. 1986. Survivorship, reproduction and dynamics of ramets of Potent illa anserina on a Baltic seashore meadow. Oecologia 47:378-380.  125 Evans, J.P. 1992. The effect of local resource availability and clonal integration on ramet funcitonal morphology in Hydrocotyle bonariensis. Oecologia 89:265-276. Evans, R.C. 1986. Morphological variation in a biotically patchy environment: evidence from a pasture population of TriXolium repens. M.Sc. thesis. University of British Columbia. Fetcher, N. 1985. Effects of removal of neighbouring species on growth, nutrients and microclimate of Eriophorum vaginatum. Arctic and Alpine Research 17(1):7-17. Firbank, L.G. A.R.Watkisnon. 1985. On the analysis of competition within 2 species mixtures of plants. J. App. Ecol. 22:503-5 17. ,  Ford, H. 1981. The demography of three populations of dandelion. Biol. J. Linn. Soc. 15:1-11. Fowler, N. 1981. Competition and coexistence in a North Carolina grassland. II. The effects of experimental removal of species. J.Ecol. 69:843-854. Fowler, N. 1982. Competition and coexistence in a North Carolina grassland. Ill. Mixtures of component species. J.Ecol. 70:77-92. Fowler, N.L. 1984. The role of germination date, spatial arrangement and neighbourhood effect in competitive interactions in Linum perenne. J. Ecol. 72:307-318. Fowler, N.L. M.D. Rausher. 1985. Joint effects of competitors and herbivores on growth, and reproduction in Aristolochia reticulata. Ecology 66:1580-1587. ,  Fox, L.R. P.A. Morrow. 1992. Eucalypt responses to fertilization and reduced herbivory. Oecologia 89:217-222. ,  Gehan, E. 1969. Estimating survival functions from the life table. J. Chronic Diseases 21:629644. Goldberg, D.E. A.M. Barton. 1992. Patterns and consequences of interspecific competition in natural communities: a review of field experiments with plants. Am. Nat. 139(4) :771-801. ,  Gosz, J. R. 1984. Biological factors influencing nutrient supply in forest soils. Chapter 5 in Njitrition of Plantation Forests. Academic Press, London. Grace, J.B. 1985. Juvenile vs. adult competitive abilities in plants: size dependence in cattails. Ecology 66(5): 1630-1638. Grace, J.B. R.G. Wetzel. 1981. Effects of size and growth rate on vegetative reproduction in Typha. Oecologia 50:158-161. ,  126 Grace, J.B. R.G. Wetzel. 1982. Variations in growth and reproduction within populations of two rhizomatous plant species: Typha latifolia and Typha angustifolia. Oecologia 53:258-263. ,  Grime, J.P. 1977. Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. Am. Nat. 111:1169-1194. Grime, J.P. 1979. Plant Strategies and Vegetation Processes. John Wiley, Sons, London. Grubb, P.J. 1985. Plant populations and vegetation in relation to habitat, disturbance and competition: problems in generalizations. pp. 595-621 in J. White and J. Beeftink, ed. The population structure of vegetaton. Dr. W. Junk, Dordrecht, The Netherlands. Gurevitch, J. 1986. Competition and the local distributionof the grass Stipa neomexicana. Ecology 67(1 ):46-57. Gurevtich, J. S.T. Chester. 1986. Analysis of repeated measures experiments. Ecology 67(1):251-255. ,  Gurevitch, J. R.S. Unnasch. 1989. Experimental removal of a dominant species at two levels of soil fertility. Can. J. Bot. 67:3470-3477. ,  Gurevitch, J., P. Wilson, J.L. Stone, P. Teese, R.J. Stoutenburgh. 1990. Competition among old field perennials at different levels of soil fertility and available space. J. Ecol.78:727-744. ,  Halvorsen, J.J., J.L. Smith, E.H. Franz. 1991. Lupine influence on soil carbon, nitrogen, and microbial activity in developing ecosystems at Mt. St. Helens. Oecologia 87:162-170. ,  Halvorson, J.J., E.H. Franz, J.L. Smith, R.A. Black. 1992. Nitrogenase activity, nitrogen fixation and nitrogen imputs by lupines at Mt. St. Helens. Ecology 73(1):87-98. ,  Harper, J.L. 1967. A Darwinian approach to plant ecology. J. Ecol. 55:247-270. Harper, J.L. 1977. Population Biology of Plants. Academic Press, New York. 891 pp. Harper, J.L. 1989. The value of a leaf. Oecologia 80:53-55. Hartnett, D.C. F.A. Bazzaz. 1985. The genet and ramet population dynamics of Solidago canadensis. J. Ecol. 73:407-413. ,  Hawthorn, W.R. P.B. Cavers. 1976. Population dynamics of the perennial herbs Plantago majorL. and P. rueglii. J. Ecol. 64:511-527. ,  Henry, G.H.R., D.C. Glenn-Lewin J. Svoboda. 1986. Effects of fertilization on three tundra plant communities of a polar desert oasis. Can. J. Bot. 64:2502-2507. ,  127 Hobbs, RJ., S.L. Gulmon, V.J. Hobbs H.A. Mooney. 1988. Effects of fertiliser addition and subsequent gopher disturbance on a serpentine annual grassland community. Oecologia 75:291295. ,  Holland, E., W.J. Parton, J.K. Detling, D.L. Coppock. 1992. Physiological responses of plant populations to herbivory and their consequences for ecosystem nutrient flow. Am. Nat. 140(4):685-706. ,  Holmes, R. K. Jepson-Innes 1989. A neighbourhood analysis of herbivory in Bouteloua gracilis. Ecology 70(4)971-976. ,  Horvitz, C.C. D.W. Schemske. 1988. Demographic cost of reproduction in a neotropical herb: an experimental study. Ecology 69(6): 1741-1745. ,  Huffaker, C.B. C.E. Kennet. 1959. A 10 year study of vegetational changes associated with biological control of Klamath weed species. J. Range. Man. 12:69-82. ,  Hunter, M.D. L.W. Aarssen. 1988. Plants helping plants. BioScience 38:34-40. ,  Hunter, M.D. P.W. Price. 1992. Playing chutes and ladders:heterogeneity and the relative roles of bottom up and top down forces in natural communities. Ecology 73:1135 ,  Hutchings, M. A.J. Slade. 1988. Morphological plasticity, foraging and integration in clonal perennial herbs. Chpt 6 in Plant Population Ecology: The 28th Symposium of the British Ecological Society. Sussex 1987. Davy, AJ., M. Hutchings, A.R. Watkinson, editors. Blackwell Scientific Publications, Oxford. pp367-392. ,  ,  Hutchings, M.J., Booth, K. S. Waite. 1991. Comparison of survivorship by the Logrank Test:criticisms and alternatives. Ecology 72(6):2290-2293. ,  Jaramillo, V.J. J.K. Detling. 1988. Grazing history, defoliation and competition on short grass production and nitrogen accumulation. Ecology 69(5): 1599-1608. ,  Johnson, N.D. B.L. Bentley. 1988. Effects of dietary protein and lupine alkaloids on growth and survivorship of Spodoptera eridania. J. Chem. Ecol. 14(5): 1391-1403. ,  Johnson, N.D., B.Lin, B.L. Bentley. 1987. The effects of nitrogen fixation, soil nitrate, and defoliation on the growth, alkaloids and nitrogen levels of Lupinus succulentus. Oecologia 74:425-431. ,  Jon sdottir, I.S. 1991. Effects of tiller size and population dynamics in a clonal sedge Carex bigelowii. Oikos 62: 177-188. Jonasson, S. 1992. Plant response to fertilization and species removal in tundra related to community structure and clonality. Oikos 63:420-429.  128 Jonsdottir, I.S. T.V. Callaghan. 1989. Localized defoliation stress and translocation of 14 Cphotoassimilates between tillers of Carex bigelowii. Oikos 52:211-219. ,  Karban, R. S. Strauss. 1993. Effects of herbivores on growth and reproduction of their perennial host, Erigeron glaucus. Ecology 74(1):39-46. ,  Keddy, P.A. 1989. Effects of competition from shrubs on herbaceous wetland plants: a four year field experiment. Can. 3. Bot 67:708-7 16. Keddy, P.A. 1989. Competition. Population and community biology series. Chapman and Hall, London, England. Kotanen, P. R.L. Jefferies. 1987. The leaf and shoot demography of grazed and ungrazed plants of Carex subspathacea. J. Ecol. 75:961-975. ,  Kotanen, P. R.L. Jefferies. 1989. Responses of arctic sedges to release from grazing: leaf demography of Carex Xflavicans. Can. J. Bot. 67:1408-1413. ,  Krebs, C.J. 1986. Population biology of snowshoe hares. I. Demography of food supplemented populations in southern Yukon 1976-1984. J. Anim. Ecol. 55:963-982. Larsson, S., A. Wiren, L.Lundgren, T. Ericsson. 1986. Effects of light and nutrient stress on leaf phenolic chemistry in Salix dasyclados and susceptibility to Galerucella lineola (Coleoptera). Oikos 47:205-2 10. ,  Lee, E.T. 1980. Statistical methods for survival data analysis. Lifetime Learning Publications, Belmont, California. Lee, E.T. 1992. Statistical methods for survival data analysis. Second Edition. Wiley Sons, Inc. New York. ,  Lee, T.D. 1988. Patterns of fruit and seed production. Chpt. 9 in Plant Reproductive Ecology: Patterns and Strategy. Lovett-Doust, J L. Lovett-Doust, editors. Oxford University Press, New York. ,  Lee, T.D. F.A. Bazzaz. 1980. Effects of defoliation, and competition on growth and reproduction in the annual plant Abutilon theophrasti. J. Ecol. 68:813-821. ,  Lee, T.D. F.A. Bazzaz. 1982. Regulation of fruit and seed production in an annual legume Cassiafasciculata. Ecology 63(5): 1363-1372. ,  Lee, T.D. F.A. Bazzaz. 1986. Maternal regulation of fecundity: non-random ovule abortion in Cassiafasciculata. Ecology 68:459-465. ,  129 Lieffers, V.J. S.J. Titus. 1989. The effects of stem density and nutrient status on size inequality and resource allocation in lodgepole pine and white spruce seedlings. Can. J. Bot. 67:2900-2903. ,  Loader, C. H. Damman. 1991. Nitrogen content of food plants and vulnerability of Pieris rapae to natural enemies. Ecology 72(5): 1586-1590. ,  Louda, S. M. 1984. Herbivore effect on stature, fruiting and leaf dynamics of a native crucifer. J.Ecol. 59:767-783. Mack, R.N. 1976. Survivorship of Cerastium atrovirens at Aberffraw, Anglesey. J. Ecol. 64:309312. Mack, R.N. J.L. Harper. 1977. Interference in dune annuals: spatial pattern and neighbourhood effects. J. Ecol. 65:345-363. ,  Maillette, L. 1992. Plasticity of modular reiteration in Potentilla anserina. J. Ecol. 80(2):231239. Mauricio, R., M.D. Bowers, F.A. Bazzaz. 1993. Pattern of leaf damage affects fitness of annual plant Raphanus sativus L.. Ecology 74(7) :2066-2071. ,  McClure, M.S. 1980. Foliar nitrogen: a basis for host suitability for elongate hemlock scale, Fiorina externa (Homoptera: Diaspididae). Ecology 61(l):72-79. Meagher, T. R. 3. Antonovics. 1982. Life history variation in dioecious plant populations: a case study of Chamaelirium luteum. pages 139-154 in H. Dingle J.P. Hegmann eds. Evolution and Genetics of Life Histories. Springer Verlag, New York. ,  ,  Mellinger, M.V. S.J. McNaughton. 1975. Structure and function of successional vascular plant communities in central New York. Ecol. Monogr. 45:161-182. ,  Mihaliak, C.A. D.E. Lincoln. 1985. Growth pattern and carbon allocation to volatile leaf terpenes under nitrogen-limiting conditions in Heterotheca subaxillaris (Asteraceae). Oecologia 66:423-426. ,  Mihaliak, C.A. D.E. Lincoln. 1989. Plant biomass partitioning and chemical defence: response to defoliation and nitrate limitation. Oecologia 80:122-126. ,  Muenchow, G. 1986. Ecological use of failure time analysis. Ecology 67(1):246-250 Nams, V., Foilcard, J.N.M. Smith. 1993. Effects of nitrogen fertilization on several woody and nonwoody boreal forest species. Can. J. Bot. 7 1:93-97. ,  Newman, E.I. 1973. Competition and diversity in herbaceous vegetation. Nature 244:310.  130 Noble, J.C., A.D. Bell, J.L. Harper. 1979. The population biology of plants with clonal growth. I. The morphology and structural demography of Carex arenaria. J. Ecol. 67:983-1008. ,  Onuf, C.P., J.M. Teal, I.Valiela. 1977. Interactions of nutrients: plant growth and herbivory in a mangrove ecosystem. Ecology 58:514-526. ,  Owen, D.F. 1980. How plants may benefit from the animals that eat them. Oikos 35:230-235. Pacala, S.W. M.J. Crawley. 1992. Herbivores and plant diversity. Am. Nat. 140(2):243-260. ,  Paimblad, G.S. 1968. Competition in experimental populations of weeds with emphasis on regulation of population size. Ecology 49:26-34. Perry, D.A., M.P. Amaranthus, J.G. Borchers, S.L. Borchers, R.E. Brainerd. 1989. Bootstrapping in ecosystems. Bioscience 39(4):230-236. ,  Pitelka, L.F. J.W. Ashmun. 1985. Physiology and integration of ramets in clonal plants. Pages 379-435 in Population biology and evolution of clonal organisms. J.B.C. Jackson, L.W. Buss, R.E. Cook, editors. Yale University Press, New Haven. ,  Polley, H.W. J.K. Detling. 1989. Defoliation, nitrogen and competition: effects on plant growth and nitrogen nutrition. Ecology 790(3):721-727. ,  Potvin, C., M.J. Lechowicz, S. Tardif. 1990. The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology 71(4):13891400. ,  Price, P.W. 1991, The plant vigor hypothesis and herbivore attack. Oikos 62:244-25 1. Pyke, D.A. 1986. Demographic responses of Bromus tectorum and seedlings of Agropyron spicatum to grazing by small mammals: occurrence and severity of grazing. J. Ecol. 74:739-754. Pyke, D.A. J.N. Thompson. 1986. Statistical analysis of survival and removal rate experiments. Ecology 67(1) :240-245. ,  Pyke, D.A. J.N. Thompson. 1987. Erratum. Ecology 68:232. ,  Rausher, M.D. 1981. Host plant selection by Battus philenor butterflies: the roles of predation, nutrition, and plant chemistry. Ecological Monographs 51:1-20. Rausher, M.D. P. Feeny. 1980. Herbivory, plant density and plant reproductive success: the effect of Battus philenor on Aristolochia reticulata. Ecology 6 1(4): 905-917. ,  Reader, R. 1992. Herbivory, competition, plant mortality and reproduction on a topographic gradient. Oikos 65:414-418.  131 Reekie, E.G. F.A. Bazzaz 1987a. Carbon allocation to reproduction. Am. Nat. 129(6):876-896. ,  Reekie, E.G. F.A. Bazzaz 1987b. Reproductive effort in plants. U. Does carbon reflect allocation of other resources? Am. Nat. 129(6):897-906. ,  Reekie, E.G. F.A. Bazzaz 1987c. Reproductive effort in plants. Ill. Effect of reproduction on vegetative activity. Am. Nat. 129(6) :907-917. ,  Reekie, E.G. F.A. Bazzaz. Cost of reproduction in genotypes of two congeneric plant species with contrasting life histories, unpublished manuscript. ,  Rees, M. V.K. Brown 1992. Interactions between invertebrate herbivores and plant competition. ,  Rousi, M., J. Tahvanainen, H. Hentonen, I. Votila. 1993. Effects of shading and fertilization on resistance of winter dormant birch (Betula pendula) to voles and hares. Ecology 74(1):30-38. ,  Ruess, R.W, S.V. McNaughton, M.B. Coughenour. 1983. The effects of clipping, nitrogen source and nitrogen concentration on growth responses and nitrogen uptake of an East African sedge. Oecologia 59:253-261. ,  Sarukhan, J. 1974. Studies on plant demography: Ranunculus repens L., R. bulbosus L., and R..acris L. 3. Ecol. 62:151-177. Sarukhan, J. J.L. Harper. 1973. Studies on plant demography: Ranunculus repens L., R. bulbosa L. and R. acris. I. Population flux and survivorship. 3. Ecol. 61:675-7 16. ,  SAS Institute. 1979. SAS Procedures Manual. SAS Institute. Raleigh, N.C. Schmid, B. F.A. Bazzaz. 1987. Clonal integration and population structure in perennial plants: effects of severing rhizome connections. Ecology 68:20 16-2022. ,  Schmid, B. F.A. Bazzaz. 1990. Plasticity in plant size and architecture in rhizome derived vs. seed derived Solidago and Aster. Ecology 71(2):523-535. ,  Schmid, B. F.A. Bazzaz. 1992. Growth responses of rhizomatous plants to fertilizer application and interference. Oikos 65:13-24. ,  Schmid, B., Puttick, G.M., Burgess, K.H. F.A. Bazzaz. 1988. Correlation between genet architecture and some life history features in 3 species of Solidago. Oecologia 75:459-464. ,  Schmid, B., G.M. Puttick, K.H. Burgess, F.A. Bazzaz. 1988. Clonal integration and the effect of simulated herbivory in old field perennials. Oecologia 75:465-47 1. ,  132 Schmid, B., Puttick, G.M., Burgess, K.H. F.A. Bazzaz. 1988. Correlation betwee n genet architecture and some life history features in 3 species of Solidago. Oecologia 75:459 -464. ,  Shaver, G.R. F.S. Chapin. 1980. Response to fertilization by various plant growth forms in an Alaska tundra: nutrient accumulation and growth. Ecology 61(3):662-675. ,  Shaver, G.R. 1983. Mineral nutrition and leaf longevity in Ledum palustre: the role of individual nutrients and the timing of leaf mortality. Oecologia 56:160-165. Silander, J.A.  ,  S.W. Pacala. 1985. Neighbourhood predictors of plant performance.  Smith, J.N.M., C.J. Krebs, A.R.E. Sinclair, R. Boonstra. 1988. Population biolog y of snowshoe hares. II. Interactions with winter food plants. J. Anim. Ecol. 57 :269-286. Snow, A.A. D.F. Whigham. 1989. Cost of flower and fruit production in Tipula ria discolor (Orchidaea). Ecology 70(5): 1286-1293. ,  Soibrig, O.T. 1981. Studies on the population biology of the genus Viola II. The effect of plant size on fitness in Viola soloria. Evolution 35:1080-1093. Stephenson, A.G. 1980. Fruit set, herbivory, fruit reduction and the fruiting strateg y of Catalpa speciosa. Ecology 61(1):57-64. Stephenson, A.G. 1984. The regulation of maternal investment in an indeterminant flower ing plant Lotus corniculatus. Ecology 65(1): 113-121. Swank, S. Oechel, W.C. 1991. Interactions among the effects of herbivory, compe tition and resource limitaiton on chaparral herbs. Ecology 72(1): 104-115. ,  Sydes, C.L. 1984. A comparative study of leaf demography in limestone grassland. J. Ecol. 72:33 1-345. Tanner, E.V.J., V. Kapos, W. Franco. 1992. Nitrogen and phosphorus fertilizer effects on Venezuelan montane forest trunk growth and litterfall. Ecology 73:78-86. ,  Tansley, A.G. R.S. Adamson. 1925. Studies of the vegetation of the English chalk. III. The chalk grasslands of the Hampshire-Sussex border. J. Ecol. 13:177-223. ,  Thurston, J.M. 1968. The effect of liming and fertilizers on the botanical composition of permanent grassland, and on the yield of hay. In Rorison, I.H. ed. Ecological aspects of the mineral nutrition of plants. Blackwell Scientific Pubi., Oxford. Tilman, D. 1987. On the meaning of competition and the mechanisms of competitive superiority. Funct. Ecol. 1:304-3 15.  133 Tilman, D. 1988. Plant strategies and the dynamics and structure of plant communities. Monographs in population biology. Princeton University Press, Princeton, New Jersey. Traczyck, T., Taczyk, H. D. Pasternak-Kusmierska. 1984. Reaction of meadow vegetation after seven years of intense inorganic fertilization. Ekol. Pol. 32:581-596. ,  Trudgill, S. T. 1979. Soil and Vegetation Systems: Contemporary Problems in Geography. Oxford University Press, Oxford. Underwood, A.J. 1981. Techniques of analysis of variance in experimental marine biology and ecology. Oceanogr. Mar. Biol. Ann. Rev. 19:513-605. Wailer, D.M. 1981. Neighbourhood Competition in several violet populations. Oecologia 51:116-122. Watkinson, A.R., Huiskes, A.H.L. J.C. Noble. 1979. The demography of sand dune species with contrasting life cycle. In Ecological processes in Coastal Environments. Ed. by R.L. Jefferies,, A.J. ,  Watson, M.A.1984. Developmental constraints: effect on population growth and patterns of resource allocation in clonal plants. Am. Nat. 123:411-426. Weibull, W. 1931. A statistical theory of the strength of materials. Ingeniors Vetenskaps Akademien Handlingar: the phenomenon of rupture in solids. No. 151: pp 28 1-298. Weibull, W. 1959. A statistical distribution of wide applicability. J. Appl. Mech. 18:293-197. Weiner, J. 1982. A neighbourhood model of annual plant interference. Ecology 63(5):1237-1241. Weiner, J. 1984. Neighbourhood interference amongst Pinus rigida individuals. J. Ecol. 72:183185. Weiner, J. 1985. Size hierarchies in experimental populations of annual plants. Ecology 66(3) :743-752. Wiens, J.A. 1977. On competition and variable environments. Am. Sci. 65:590-597. Wiens, l.A. 1984. On understanding a non-equilibrium world: myth and reality in community patterns and processes. In D.R. Strong, D. Simberloff, L.G. Abele, and A.B. Thistle, ed., Ecological Communities: Conceptual Issues and the Evidence. Princeton University Press, Princeton, N.J. Wennstron, A. L. Ericson. 1991. Variation in disease incidence in grazed and ungrazed sites for the system Pulsatilla pratensis-Puccinia pulsatillae. Oikos 60:35-39. ,  134 Whigham, D.F. 1984. The effect of competition and nutrient availability on the growth and reproduction of Ipomea hederacea in an abandoned old field. J. Ecol. 72:721-730. White, J. 1979. The plant as a metapopulation. Ann. Rev. Ecol. Syst. 10:109-145. White, J. 1985. The census of plants in vegetation. In The Population Structure of Vegetation. ed. J. White. pp. 34-88. Dr W. Junk Publishers, Dordrecht. White, J. J.L. Harper. 1970. Correlated changes in plant size and number in plant populations. J. Ecol. 58:467-477. ,  Wit, C.T. de. 1960. On competition. Versi. Landbouwk. Onderz. 66. 1-82. Wilcox, A., M.J. Crawley. 1988. The effects of host plant defoliation and fertilizer application on larval growth and oviposition behaviour in cinnabar moth. Oecologia 76:283-287. ,  Wilson, S.D. P.A. Keddy. 1986. Diffuse competition along an environmental gradient: results from a shoreline plant community. Am. Nat. 127(6):862-869. ,  Wilson, K.G. R.E. Stinner. 1984. A potential influence of rhizobium activity on the availability of nitrogen to legum herbivores. Oecologia 61:337-341. ,  Wolfe, L.M. 1983. The effect of plant size on reproductive characteristics in Erythronium arnericanum (Liliaceae). Can. J. Bot. 61:3489-3493. Yoda,K., T. Kira, H. Ogawa, K. Hozimu. 1963. Self-thinning in overcrowded pure stands under cultivated and natural conditions. J. Biol. Osaka Cy University. Zar, J.H. 1984. Biostatistical Analysis. Second Edition. Prentice-Hall, Inc., Englewood Cliffs, NJ. Zarzycki, K. 1983. The competitive performance of grassland plants on acid soils as influenced by fertilization and with different plant competitors in semi-natural meadows in the Pieniny National Park, Poland. Verhandi. Ges. Okol. 11:505-509. Zimmerman, J.K., I.M. Weis. 1984. Factors affecting survivorship, growth, and fruit production in a beach population of Xanthium stromarium. Can. J. Bot. 62:2122-2127.  


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