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The effects of ozone air pollution on plant competition Penney, Kathy Coreen 1986

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THE EFFECT OF OZONE AIR POLLUTION ON PLANT COMPETITION by KATHY COREEN PENNEY B.Sc, University of Toronto, 1983 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE DEPARTMENT OF PLANT SCIENCE We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA OCTOBER 1986 © KATHY COREEN PENNEY In presenting t h i s thesis i n p a r t i a l f u l f i l m e n t of the requirements for an advanced degree at the University of B r i t i s h Columbia, I agree that the Library s h a l l make i t f r e e l y available for reference and study. I further agree that permission for extensive copying of t h i s thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. I t i s understood that copying or publication of t h i s thesis for f i n a n c i a l gain s h a l l not be allowed without my written permission. Department of PLANT SCIENCE The University of B r i t i s h Columbia 1956 Main Mall Vancouver, Canada V6T 1Y3 Date OCTOBER 16 r 1986 ABSTRACT Low levels of ozone air pollution have been shown to cause reductions in plant growth. It has been proposed that these reductions could translate into effects on a plant's competitive ability, and hence, on the inter-relationships of plants in a community. Experiments were carried out to determine if there was an effect of low levels of ozone on intra- and inter-specific competition of barnyardgrass (BYG), redroot pigweed (RPW) and green foxtail (GFT). Three ozone treatments (a filtered air control, and 75 ppb/7 hr and 150 ppb/3.5 hr daily) were applied to monocultures and binary mixtures in replacement and additive series. The two ozone treatments represent the same ambient dose. The effects of each on the plants studied were compared and contrasted. The order of competitive ability of the three species was found to be BYG > RPW > GFT. There were significant differences in the interaction of the species between the three ozone treatments. BYG experienced significant intra-specific competition only in the two ozone-added treatments; GFT was significantly affected by its own density only in the control. In contrast, RPW experienced significant intra-specific competition effects in all treatments. BYG benefitted significantly from the presence of GFT in the control. A similar positive effect of GFT density on BYG yield is seen in the 150 ppb/3.5 hr treatment. It is suggested that this localized positive allelopathic effect may be due to the upward transport of a volatile compound released by GFT, or disseminated through the soil. All inter-specific competitive relations of GFT and RPW were significant in all ozone treatments. RPW appeared to experience the most ozone effects of the three species studied. RPW and GFT in replacement series mixtures showed over-yielding in the control treatment, equal replacement in the 75 ppb/7 hr treatment and under-yielding in the 150 ppb/3.5 hr treatment. This interaction of ozone and competition treatments between RPW and GFT was significant for root dry weight per pot. BYG yield was significantly enhanced in the 75 ppb/7 hr ozone treatment over the control and 150 ppb/3.5 hr treatments. This may reflect an acclimation of BYG plants to the low (0.01-0.04 ppm) ambient background levels of ozone. The concept of an appropriate control treatment is discussed. Although the 75 ppb/7 hr and 150 ppb/3.5 hr treatments represent the same ambient dose, the species used in this study reacted quite differently to the two treatments. Overall, the more acute 150 ppb/3.5 hr dose had a more detrimental effect on the yield variables measured for RPW and GFT, and the 75 ppb/7 hr treatment had a significant enhancing effect on BYG yield over that of the control and the 150 ppb/3.5 hr dose. An examination of the size frequency distributions of the three species confirms that BYG is competitively dominant, whereas the GFT size distributions do not appear to be sensitive to competitive suppression. RPW displays a very skewed size frequency distribution under all treatments. It is suggested that RPW has inherent genetic variability for a wide size distribution in the populations studied. There were no ozone effects on the size frequency distributions of any species studied. i v T A B L E OF CONTENTS ABSTRACT II TABLE OF CONTENTS V LIST OF TABLES VI LIST OF FIGURES VII LIST OF ABBREVIATIONS X ACKNOWLEDGEMENTS XI 1. INTRODUCTION 1 2. LITERATURE REVIEW 3 2.1 OZONE EFFECTS ON PLANTS 3 2.2 OZONE EFFECTS ON COMMUNITIES 9 2.3 COMPETITION 13 3. MATERIALS AND METHODS 25 3.1 PLANT MATERIALS . . . 25 3.2 OZONE TREATMENTS 28 3.3 COMPETITION TREATMENTS 29 3.4 HARVESTS 29 3.5 DATA ANALYSIS . . . 32 .1 ANALYSIS OF VARIANCE 32 .2 REGRESSION ANALYSES 32 .3 REPLACEMENT SERIES 33 .4 SIZE FREQUENCY DISTRIBUTIONS . 34 4. RESULTS 35 4.1 VISUAL OBSERVATIONS 35 4.2 ANALYSES OF VARIANCE 35 .1 BYG INDIVIDUAL 35 .2 BYG TOTALS/POT 37 .3 GFT INDIVIDUAL 39 .4 GFT TOTALS/POT 39 .5 RPW INDIVIDUAL 39 .6 RPW TOTALS/POT 43 .7 ROOT TOTALS/POT 43 4.3 REGRESSION ANALYSES 46 .1 MULTIPLE LINEAR REGRESSION 46 .2 SEQUENTIAL SIMPLE REGRESSION 52 .3 SQUARE ROOT REGRESSION 69 4.4 REPLACEMENT SERIES . .71 4.5 SIZE FREQUENCY DISTRIBUTIONS 98 .1 BYG 98 .2 GFT 107 .3 RPW 107 5. DISCUSSION 124 6. SUMMARY 134 BIBLIOGRAPHY APPENDIX 1 LIST OF TABLES TABLE 1: Listing of individual experiments 30 TABLE 2: Density combinations used in experiments . . . . 31 TABLE 3: Significant treatment effects (ANOVA) for barnyardgrass yield variables (per plant) in monoculture and mixtures 36 TABLE 4: Significant treatment effects (ANOVA) for barnyardgrass yield variables (total per pot) in monoculture and mixtures 38 TABLE 5: Significant treatment effects (ANOVA) for green foxtail yield variables (per plant) in monoculture and mixtures 40 TABLE 6: Significant treatment effects (ANOVA) for green foxtail yield variables (total per pot) in monoculture and mixtures 41 TABLE 7: Significant treatment effects (ANOVA) for redroot pigweed yield variables (per plant) in monoculture and mixtures 42 TABLE 8: Significant treatment effects (ANOVA) for redroot pigweed yield variables (total per pot) in monoculture and mixtures 44 TABLE 9: Significant treatment effects (ANOVA) on total root weight per pot for monocultures and mixtures 45 TABLE 10: Results of multiple linear regression model . . . 47 TABLE 11: Results of multiple linear regression model with interaction 49 TABLE 12: Competition indices from multiple linear regression model 51 TABLE 13: Niche differentiation indices from the multiple linear regression analysis 52 TABLE 14: Results of sequential simple regression model . 53 TABLE 15: Competition indices from sequential simple regression model 68 TABLE 16: Niche differentiation indices from the sequential simple regression model 69 v i TABLE 17: Coefficients of determination from square root regression model .70 TABLE 18: Results of the replacement series model analysis .72 TABLE 19: Summary of interpretations of niche differentiation from regression (MLR and SSR) and replacement series analyses 126 LIST OF FIGURES FIGURE 1: Templates used in pricking out seedlings into low density treatments 26 FIGURE 2: Relationships of estimated total yields (g) of BYG in monoculture and in the presence of three densities of GFT, and of GFT at these densities, in three ozone treatments .54 FIGURE 3: Relationships of estimated total yields (g) of BYG in monoculture and in the presence of three densities of RPW, and of RPW at these densities, in three ozone treatments 56 FIGURE 4: Relationships of estimated total yields (g) of GFT in monoculture and in the presence of three densities of BYG, and of BYG at these densities, in three ozone treatments 58 FIGURE 5: Relationships of estimated total yields (g) of GFT in monoculture and in the presence of three densities of RPW, and of RPW at these densities, in three ozone treatments v 61 FIGURE 6: Relationships of estimated total yields (g) of RPW in monoculture and in the presence of three densities of BYG, and of BYG at these densities, in three ozone treatments 63 FIGURE 7: Relationships of estimated total yields (g) of RPW in monoculture and in the presence of three densities of GFT, and of GFT at these densities, in three ozone treatments 65 FIGURE 8: Replacement series diagrams for BYG and GFT at total density of 10 plants/pot for three ozone treatments 73 FIGURE 9: Replacement series diagrams for BYG and GFT at total density of 15 plants/pot for three ozone treatments 75 FIGURE 10: Replacement series diagrams for BYG and GFT at total density of 20 plants/pot for three ozone treatments 77 FIGURE 11: Replacement series diagrams for BYG and GFT at total density of 25 plants/pot for three ozone treatments 79 FIGURE 12: Replacement series diagrams for BYG and RPW at total density of 10 plants/pot for three ozone treatments 81 FIGURE 13: Replacement series diagrams for BYG and RPW at total density of 15 plants/pot for three ozone treatments 83 FIGURE 14: Replacement series diagrams for BYG and RPW at total density of 20 plants/pot for three ozone treatments 86 FIGURE 15: Replacement series diagrams for BYG and RPW at total density of 25 plants/pot for three ozone treatments 88 FIGURE 16: Replacement series diagrams for GFT and RPW at total density of 10 plants/pot for three ozone treatments 90 FIGURE 17: Replacement series diagrams for GFT and RPW at total density of 15 plants/pot for three ozone treatments 92 FIGURE 18: Replacement series diagrams for GFT and RPW at total density of 20 plants/pot for three ozone treatments 94 FIGURE 19: Replacement series diagrams for GFT and RPW at total density of 25 plants/pot for three ozone treatments 96 FIGURE 20: Size frequency diagrams for BYG in monoculture 99 FIGURE 21: Size frequency diagrams for BYG in mixture with GFT 101 FIGURE 22: Size frequency diagrams for BYG in mixture with RPW 104 FIGURE 23: Size frequency diagrams for GFT in monoculture 108 FIGURE 24: Size frequency diagrams for GFT in mixture with BYG 110 FIGURE 25: Size frequency diagrams for GFT in mixture with RPW 113 FIGURE 26: Size frequency diagrams for RPW in monoculture 116 1 X FIGURE 27: Size frequency diagrams for RPW in mixture with BYG 118 FIGURE 28: Size frequency diagrams for RPW in mixture with GFT 121 ABBREVIATIONS ANOVA analysis of variance BYG barnyardgrass, Echinochloa crus-galli (L.) Beauv. GFT green foxtail, Setaria viridis (L.) Beauv. hr hours N O x oxides of nitrogen MLR multiple linear regression °3 ozone ppb parts per billion ppm parts per million RPW redroot pigweed, Amaranthus retroflexus L. SSR sequential simple regression x i I would like to express my sincere thanks to Dr. V.C. Runeckles for his patient guidance and support throughout this program. His willingness to spend long hours working on this project was greatly appreciated. I would also like to thank Andrew Boland, Jeanne Ensminger, Anna Luchinski, Bill Nickerson, Peggy Nickerson, Clifford Penney, Joan Penney, Chris Pike, Sam Roberts, Phillip Ross and Elaine Wright for their help in collecting data. To Chris Pike, a special thanks for introducing me to computer programming. I am very grateful to Peter Garnett, Dereck White, Christia Roberts and Sean Trehearne for their advice and expertise in technical matters. To Dr. G.W. Eaton, many thanks for his invaluable advice on statistical analysis. Finally, I am very grateful to Dr. P.A. Jolliffe, Dr. R. Turkington and Madhukar Potdar for many stimulating discussions of competition. 1 1. INTRODUCTION Plants are rarely found growing in isolation. In natural ecosystems and agricultural systems, plants usually share their immediate environment with individuals either of the same species in monocultures or of a different species in mixtures. The presence of other plants changes the environment of their neighbours and may alter their growth rate and form (Harper, 1977). Specifically, there is an interaction between the proximity of neighbours and resource availability in defining neighbour relations. The detrimental interference in growth due to the unequal allocation of resources is competition (Harper, 1961). Both the immediate and evolutionary success of a plant depends on its ability to compete. The competitive ability of a plant depends on its integral physiological condition. Any factor that will affect plant growth will have a subsequent effect on that plant's competitive ability. Air pollutants are recognized as a source of stress for plants and it has been suggested that this stress could alter the competitive ability of certain species, or modify their physiology, thereby altering their survival potential (Smith, 1974). Growing plants are very susceptible to air pollutants, with reductions in photosynthesis and growth often occurring before visible symptoms are noted (Bennett and Hill, 1974). Ozone, a strong oxidant component of photochemical smog, has been shown to both stimulate (Bennett et al., 1974) and depress (Manning and Feder, 1975) plant growth at low concentrations. Ozone is rapidly being recognized as a serious pollutant problem both in urban and rural areas (Heck et al., 1983). Bennett and Runeckles (1977) studied the effects of low levels of ozone on competition in binary mixtures of annual ryegrass and crimson clover. Using the de Wit (1960) replacement series, they found that those components of yield which were measured were less depressed by ozone when in species mixtures than in monocultures. The present study was undertaken to extend these results by examining the competitive relationships of three annual species exposed to ozone. The competitive 2 relations of barnyardgrass, redroot pigweed and green foxtail have been well documented in the field (Minjas, 1982), allowing a comparison of the ozone effects in controlled environment chambers to those of natural conditions. One objective of this study was to determine experimentally the effects of low levels of ozone on inter- and intra-specific competition, and compare it to the species' response to ozone in monocultures. The second objective of this study was to examine the differences, if any, between the responses to two ozone exposure regimes which represent the same ambient dose, but differ in concentration and duration. 3 2. LITERATURE REVIEW 2.1 OZONE EFFECTS ON PLANTS The growth of urban areas, accompanied by high-density automotive traffic, industrial activity and a rising need for power generation, has led to increased levels of air pollutant emissions. These high concentrations of combustion products, particularly the oxides of nitrogen, and volatile organic components, participate in a complex series of chemical reactions driven by sunlight, producing photochemical smog (Demerjian, 1986). Ozone (Og) is a secondary reaction product of this process on which a great deal of attention has been focused recently. Ozone and its precursors can be transported for hundreds of kilometers, and because non-urban air contains few compounds which react with ozone, it can persist for longer periods in rural areas (Jacobson, 1982). A great deal of research has been done on the effects of ozone on vegetation, particularly with agricultural plants. In order to allow a prediction of a plant's response to air pollutants, many dose response models have been tested. The classic description of pollutant exposure uses the term "dose", which is the product of concentration and time. However, the necessary existence for many species of a threshold concentration below which there is no response, has led to the conclusion that the relative importance of duration of exposure is much less than that of concentration. As well, because the movement of gaseous pollutants is passive (Heath, 1980), many other factors control the availability of the gas to the plant. The velocity of deposition, V g , to the plant can be described by the equation v g = F/X where F is the flux and X the concentration at some reference height (Chamberlain, 1986). The resistance to transfer (r^Q^gj), the reciprocal of V g , is the sum of three resistances rtotal = ra + rs + r m where r a is the resistance of the boundary layer near the stomata; r § is the stomatal resistance; and r m is the resistance encountered from the substomatal chamber to the 4 cellular interior, including cell walls and membranes. The turbulence of the air above a plant, the presence of a canopy, the thickness of the boundary layer and the water relations of the plant all affect the deposition of the gas to the plant. It is important to recognize that the dose expressed in terms of the ambient concentration above a plant is not necessarily the dose received by the plant. The true dose or effective dose, determined as uptake, or flux, is a better approximation of the dose actually received by the plant (Runeckles, 1974). Membranes are the primary site of ozone attack within a plant (Tingey and Taylor, 1982; Evans and Ting, 1983). The phytotoxicity of ozone involves molecular events which result in a perturbed cellular structure and function and culminates in an altered membrane permeability. Whether or not this translates into injury depends on the ability of the plant to repair or compensate for the altered membrane permeabilit}'. Low levels of ozone can stunt growth (Feder, 1971) and cause reductions in photosynthesis (Bennet and Hill, 1974) without producing visible injury. Yang et aZ.(1983) described decreases in net photosynthesis and dark respiratory rates in Eastern White Pine at ozone concentrations of 0.10 ppm. Lolium multiflorum and Trifolium incarnatum were shown to have less growth and yield than controls when treated daily with 0.03 or 0.09 ppm ozone (Bennett and Runeckles, 1977). Reich and Amundson (1985) carried out experiments in which Acer saccharum, Glycine max cv. Hodgson, Pinus strobus, Populus deltoides, Quercus rubra, Trifolium repens cv. Arlington and Triticum aestivum cv. Vona were exposed to ozone concentrations ranging from 0.01 to 0.125 ppm. In all species, exposure to any increase in ozone reduced net photosynthesis. These ozone-induced reductions in photosynthesis were related to subsequent declines in growth and yield. Hybrid poplar, Populus deltoides x tricarpa, exposed to 0.025, 0.05, 0.085 and 0.125 ppm ozone showed inverse linear reductions in leaf, stem, root and total dry mass per plant (Reich and Lassoie,1985). 5 A great deal of research on the effects of ozone on agricultural species has been undertaken recently in the United States, in the form of a nation-wide study called the National Crop Loss Assessment Network (NCLAN) (Heck et al, 1982; Heck et al., 1983; Heck et al 1984a, 1984b). Based upon these studies, preliminary estimates suggest that about $3 billion of direct agricultural crop losses would result if the USA experienced a seasonal 7-hour/day mean ozone concentration of 0.06 ppm (Heck et al, 1983). The exposure regimes in the studies carried out under the NCLAN program were applied in open top chambers to field grown plants. Winter wheat, Triticum aestivum cv. Abe, Arthur-71 and Roland exposed to 0.023, 0.041, 0.068, 0.095 and 0.122 ppm 7-hr/day seasonal means of ozone showed a general decrease in all yield variables in one season, and in the weight yield variables in the second season (Kress et al, 1985). The ozone effects and cultivar effects were significant in both years. Hordeum vulgare L. cv. Poco and CM-72, exposed under the same treatment regime to 0.03-0.09 ppm ozone showed no ozone effects on yield or growth (Temple et al, 1985). The authors suggest that the threshold for an ozone effect on yield of these barley cultivars is above a seasonal 7-hour mean of 0.06 ppm. Lactuca sativa L. cv. Empire, under similar exposure conditions, showed severe foliar injury at 0.104 and 0.128 ppm ozone (Temple et al, 1986). Total head weight was reduced 13 and 35 percent respectively compared to growth in fully charcoal-filtered air. As well, plants in the 0.104 and 0.128 ppm treatments matured 2-3 weeks later than plants in low ozone chambers. Predicted percent root weight losses for four cultivars of Brassica rapa L. exposed to ambient air in which 0.04, 0.06 or 0.08 ppm ozone were added were 7, 24 and 47 percent, respectively (Heagle et al, 1985). Foliar necrosis was seen on all expanded leaves of all cultivars after a 3.5 hour exposure to 0.09 or 0.11 ppm ozone. In field studies of Glycine max L. Merr. cv. Davis, the radiation intercepted by the canopy during the season decreased as the ozone concentration was increased (Unsworth et 6 al, 1984). A lowered efficiency of dry matter production early in the season, and an altered seed and pod development, restricted yield. In direct contrast to all the reports of linearly decreasing yield variables with increasing ozone concentrations, apparent stimulations of plant growth by low levels of ozone have been reported (Bennett et al., 1974). Concentrations of ozone less than 0.1 ppm appear to have a stimulatory effect on various growth parameters of Phaseolus vulgaris, Hordeum vulgare and Polygonum lapathifolium. The authors suggest that the similarity between reported ambient levels of ozone in many locations and those which stimulate plant growth reflects the fact that plants exhibiting such stimulations are adapted to low levels of air pollution. This brings into question the use of a no-ozone control, which would put plants adapted to low levels at a disadvantage. When plant yields are reduced, it is not necessarily a consistent loss over the whole of the plant. When photosynthesis is inhibited, the supply of assimilates is reduced, and because shoots have priority in the utilization of assimilates, root development is often one of the initial sites of inhibition in response to ozone exposure (Oshima et al., 1978). As the storage capacity of the plant is reduced, transport of nutrients and water to developing fruit decreases (Jacobson, 1982). The root dry weight of Trifolium repens L. cv. Tillman exposed to 0.0, 0.05, 0.10 and 0.15 ppm ozone for six days, four hours daily, was reduced more than shoot dry weight as a response to reduced net photosynthesis over the ozone treatments (Blum et al, 1983). Proportionally, the carbon allocated to the roots increased gradually from 35 to 52 percent with increasing ozone levels up to 0.10 ppm, and then rapidly decreased to 21 percent at 0.15 ppm. Carbon allocation to developing leaves decreased from 64 to 48 percent with increasing ozone levels up to 0.10 ppm, and then increased to 79 percent at 0.15 ppm. The amount of labelled C-assimilates of Phaseolus vulgaris, exposed continuously for four days to 0.20 ppm ozone, translocated to the non-photosynthetic organs (stem and root) decreased by 53%, while that translocated to the photosynthetic organs (leaves) was 7 reduced by only 28% (Okano et al, 1984). Whereas the primary leaf, which is the main source of photosynthates for growth of roots, showed a considerable reduction in CO2 fixation (62%) and an inhibition of translocation, the first trifoliate leaf, which mainly nourished the immature growing leaves, did not show such a decrease in exported labelled assimilates because a smaller reduction in CO2 fixation was almost compensated by an acceleration of translocation. The authors suggest that the plants had adapted themselves to a polluted environment so that the reduction of growth efficiency caused by ozone could be minimized. In contrast, a study by Reich and Lassoie (1985) showed no effect of ozone treatment on partitioning of dry matter in Populus deltoides x trichocarpa. Climatic, edaphic and genetic factors all influence the expression of oxidant damage in plants, as do the pathogen interactions of the plant, the time of exposure and the age of the plant (Heck et al., 1965; Heck, 1968). Haas(1970) suggested that crop maturity regulates the time of symptom expression and crop vigor its severity. Plant foliage appears to be generally most sensitive to ozone at the time of full expansion (Haas, 1970; Blum and Heck, 1980). As new leaves appear on older plants, they become progressively less sensitive to ozone (Ting and Dugger, 1968). Ozone has been shown to cause partial stomatal closure after several hours exposure (Hill and Littlefield, 1969). Relative humidity has a large effect on the expression of ozone damage, and it appears to be due to the interaction of humidity and stomatal closure (Dunning and Heck, 1977; Leone and Brennan, 1969; Otto and Daines, 1969; Rich and Turner, 1972) although McLaughlin and Taylor (1981) suggest that it is not stomatal regulation but altered internal leaf resistance to uptake that is important. In contrast, Tingey and Hogsett (1985) showed that drought stress reduces ozone injury and suggested that the effect is the result of the influence of water stress on stomatal aperture rather than through biochemical or anatomical changes. Further to this, Heggestad et al. (1985) found that exposure to enhanced ambient ozone levels caused Glycine max L. plants to lose tolerance to drought stress. In contrast, Reich et al. (1985) found that although drought 8 stress and increasing ozone concentrations, up to 0.13 ppm, decreased leaf conductance in Glycine max L., there was no interaction between these factors. The effect of air velocity on plant injury due to ozone was found to be significant in Phaseolus vulgareh. cv. Ashley (Heagle et al, 1971). The authors suggest that increased ambient air velocity through dense plant stands could be increasing injury by interrupting the boundary layer, and by continually replacing pollutants absorbed by the plants. An increase in growth temperature was shown to increase sensitivity of Phaseolus vulgaris L. and Nicotiana tabacum L. cv. Bel Wg to ozone injury (Dunning and Heck, 1977). In contrast, Miller and Davis (1981) found that Phaseolus vulgaris L. cv. Pinto III was more sensitive to ozone foliar injury at 15°C and 32°C.than at 24°C, an effect which was not related to stomatal conductance of the exposed leaves. Dunning and Heck (1973) demonstrated that ozone injury to Phaseolus vulgaris and Nicotiana tabacum was increased by an increased light intensity before ozone exposure. This response is species-specific (Dunning and Heck, 1977). The translation of chamber or greenhouse studies to actual field conditions is not entirely straightforward. Lewis and Brennan (1977) compared the greenhouse and growth chamber responses of Phaseolus vulgaris to ozone to that in open top chambers. Both the expression and severity of ozone effects varied depending on the exposure apparatus. The severity of ozone injury was greatest in the greenhouse and least in the open top chambers. De Vos et al. (1983) found that 26 Solanum tuberosum cultivars and genotypes responded differently to ozone stress in controlled environment chambers than in the field. Genotypes that were susceptible to ozone in laboratory tests appeared to be resistant to oxidant injury in the field, but susceptible genotypes in the field were also susceptible in the laboratory. Heagle et al (1983) compared the ozone response of Glycine max cv. Davis in soil, sand and a mixture of perlite, peat moss and vermiculite. Their results suggest that the response to ozone is fairly uniform over the range of substrate types. Soil is a major sink for ozone (Turner et al, 1974) and compaction and additional soil moisture 9 increases the resistance to ozone removal, whereas autoclaving decreases the resistance (Turner et al, 1973). 2.2 OZONE EFFECTS ON COMMUNITIES Although the effects of ozone on plants have been well researched, the subsequent effects on plant populations, communities and ecosystems have not received as much attention (Runeckles, 1986). Most of the reports dealing with ozone effects on ecosystems are descriptions of the ecological changes in an area where elevated oxidant levels occur. Many of these studies involve forest ecosystems, where the effects of pollutants on major tree species threaten to have far-reaching successional impact (Skelly, 1980; Smith, 1980; McLaughlin, 1985). One such area is the San Bernadino Mountains of Southern California, where the decline of the ponderosa pine, Pinus ponderosa Laws., population has been well documented. This forest ecosystem has been subject to oxidants from the Los Angeles Metropolitan complex for thirty years (Smith, 1980). Cobb and Stark (1970) predicted that if the pollutant levels continued to rise, there would be a conversion from the then well-stocked forest dominated by ponderosa pine to poorly stocked stands of less susceptible species. They attributed the mortality of ponderosa pine to subsequent bark beetle infestation of air pollutant-stressed trees. Miller(1973) reported that ponderosa pine mortality in the area ranged from 8-10 percent during 1968-1972. An intensive inventory of vegetation present in a 233 ha study block in the San Bernadino National Forest lead the author to conclude that the lower two-thirds of the area will probably shift to a greater proportion of white fir. The rate of community composition change was deemed dependent on the rate of ponderosa pine mortality. McClenahen (1978) quantified community changes in a deciduous forest in the upper Ohio River Valley situated along a gradient of polluted air containing elevated 10 concentrations of chloride, sulfur dioxide and fluoride. Species richness, evenness and diversity were reduced within the overstory, subcanopy and herb strata near point sources. The importance of sugar maple, Acer saccharum Marsh., was reduced in all strata with increasing pollutant dose, whereas the importance of spicebrush, Lindera benzoin L., increased in the understory strata with an increasing pollutant exposure. Plant communities experience two retrogressive processes when under the influence of air pollutants (Guderian and Kueppers, 1980). The first, a change in structure and function of the community, occurs through a series of direct and indirect effects, and can lead to total destruction. The nature of the pollutant load and the condition of the community govern the severity of the response. The second process occurs parallel to this degradation and involves a spontaneous or human-initiated process during which the original adaptive resistant members of the existing community as well as new arrivals or introductions undergo secondary succession. This secondary succession which begins as soon as the original vegetation begins to change leads in time, under constant loading, to the formation of new, less complex stable structures. Typically, this process leads to a change in species composition toward a simplification of the system. The authors differentiated between the effects of high, intermediate and low pollution dosage. Essentially, the difference is the severity of the immediate reaction to the loading, and the time in which the community has to adjust to the stress. Air pollutants may modify community composition even though only a few species are sensitive (Treshow, 1968). Often the community structure and stability depends on relatively few species. Any effect on these dominant species would have dramatic consequences. Two studies have been carried out to quantify the impact of pollutants on entire vegetative communities. In the first, 70 common species indigenous to intermontaine grassland, oak, aspen and conifer communities were exposed to ozone with the use of portable fumigation chambers (Treshow and Stewart, 1973). Injury thresholds were 11 determined, and species found to be most sensitive to ozone in the grassland and aspen communities investigated included some dominants which were considered key to community integrity, including aspen, Populus tremuloides Michx.. Concentrations greater that 0.150 ppm produced injury in most species. The aspen community was further investigated in the second study (Harward and Treshow, 1975). Fourteen understory species were fumigated in greenhouse chambers throughout their growing seasons. Plant sensitivities varied sufficiently to cause the authors to speculate that a year or two of exposures to ozone above 0.07-0.150 ppm would cause major shifts in composition in the aspen community. The response of any given ecosystem to ozone air pollution is as a result of the expression of direct effects to the individual component species within that ecosystem (Skelly, 1980). As an individual plant or species reacts to the pollutant, its inter-relations with other plants may be affected. The effects of ozone on a mixture of tall fescue, Festuca arundinaceae Schreb. cv. Kentucky 31, and ladino clover, Trifolium repens L. cv. Tillman, have been studied in detail. Exposures were carried out in open top chambers in the field, and concentrations were low (0.03 -0.08 ppm). Growth was greater for both species in mixture than in monocultures (Montes et al., 1982), which the authors interpreted as either protocooperation or the escape by both species of some measure of intra-specific interference. The relative yield data suggested that the ozone effects on clover were greater in mixture than in monocultures. The authors suggest that this may be due to a denser monoculture canopy, which would provide more mutual protection against ozone than the canopy associated with the mixture. Ozone concentrations ranging from 0.02 to 0.10 ppm applied to clover-fescue mixtures in open top chambers reduced the yield of clover substantially more than the yield of fescue (Blum et al, 1983). The magnitude of these ozone effects appeared to be determined to a large extent by water availability. The authors suggest that the stability 12 of the fescue-clover forage system would be reduced by the presence of ozone air pollution. They do, however, report that clover is often lost from such forage mixtures after several years due to diseases, insects, competition, drought and poor management. It is possible that the ozone load would accelerate that process and further reduce the quality of the forage. Similar ozone effects on clover were found in a study of the effects of low levels of ozone, 0.03-0.09 ppm, on mixtures of annual ryegrass, Lolium multiflorum Lam., and crimson clover, Trifolium incarnatum L. (Bennett and Runeckles, 1977). The yield and leaf area of clover were more affected by ozone than that of the ryegrass so that the proportion of ryegrass in the mixtures was greater at 0.09 ppm. The total dry weight, leaf area and tiller number were less depressed in species mixtures by ozone than in monocultures. Tillering of ryegrass in mixture was 26% greater in the 0.09 ppm ozone treatment than in the no-ozone control. The crowding coefficient (De Wit, 1960) of ryegrass relative to clover was less than unity in filtered air, but greater than unity in the 0.09 ppm ozone treatment. The authors suggested that this implied that 0.09 ppm ozone impaired clover's ability to compete with ryegrass in mixture. Similar reductions in yield of clover, Trifolium subterraneum L., and perennial ryegrass, Lolium perenne L., in mixtures in the presence of sulfur dioxide were reported (Murray, 1984). The author suggested that the inability of SC»2-sensitive clover plants to compete with grasses would be further aggravated by the presence of other pollutants, namely ozone. The shift of pasture dominance towards grasses would be enhanced by the loading of two pollutant stresses. Controlled SC»2 exposure of a native grassland dominated by Agropyron smithii Rydb. revealed that at low concentrations and with the addition of nitrogen, plant growth was increased (Lauenroth et al., 1983). As decomposition processes are slowed by SC>2 exposure, the authors suggested that on a long term basis, the decreased decomposition 13 rates and a decreased nutrient availability to the plants may produce detrimental effects on nutrient cycling and the productivity of the system would be negatively affected. 2.3 COMPETITION Competition is a word which has been overused and often misunderstood. In defining the competitive relations between plants it is important to clearly define the use of the word. It is essential to recognize the way in which the words competition and interference are often interchanged. Interference was defined by Harper (1961) as "all responses of an individual plant or plant species to its environment as this is modified by the presence and/or growth of another". Bleasdale (1960) defined competition between two plants as when "the growth of either or both of them is reduced or modified as compared with their growth or form in isolation". Silvertown (1982) used Harper's 1961 definition of interference for all interactions between species in mixtures which lead to a reduction in plastic growth or survival in one or both species, while he defined competition as acting in those situations where interference is expressed as a reduction in the number of plants or in the number or their surviving offspring in both or all species in a mixture. In this thesis the word competition will be used as interference was defined by Harper (1961). Competition only occurs when there is a limited supply of a resource, such as light, water or nutrients, which is necessary to the competitors (Trenbath, 1976). In a stand of young, simultaneously-established plants, the leaf area index and root density are low enough to permit all plants to grow as they would in isolation under the same conditions. However, as growth proceeds, the demands of expanding shoot and root systems on the available resources leads to mutual interference in the acquisition of those resources. The plant's growth in the stand falls below that which it would be under an isolated, but similar situation. There is a marked plastic response of many plants to an increasing density of either plants of the same species or different species (Marshall and Jain, 1969; Pemadasa and Lovell, 1974). 14 The degree of interference depends on the density of planting, the availability of the resources and the plant's ability to intercept and absorb these resources in the face of the interference. This competitive ability of a plant is the character which most competition studies seek to measure. Whereas many authors have emphasized competition for either nutrients, light or water, Bleasdale (1960) pointed out that plants can compete for a supply of growth factors either simultaneously or in rapid succession, thereby integrating the situation. He suggested using plant weights as an index of competition. Although plants may start out with inherent genetic differences in plant size, the varying growth response to an uneven distribution of growth factors amplifies the earlier differences, and final differences in plant size will be predominantly the result of competition (Trenbath, 1976). Two forms of competition are defined; that between plants of the same species, intra-specific competition, and that between individuals of different species, inter-specific competition. Monocultures, or stands of the same species, at varying densities, are used to determine the degree of intra-specific interference. Replacement and additive series designs have been used for the determination of inter-specific competition effects. Many competition studies are of an agricultural nature, dealing with the effect of a weed species on a crop's yield, or the yields of intercropped species. The additive series design is often used to study the former type of interaction; the crop, planted at a fixed density, is sown with a weed species planted at a range of densities. Although this design accurately simulates the real situation of a crop invaded by a weed species, it has certain limitations in its interpretation. The effects of total plant density and of weed density on the weed or crop are confounded in that, at high weed density, the total density is also high. Dew (1972) derived a competition index for wild oats in barley, wheat and flax which was based on the additive series approach. He found that the yield of the indicator (crop) 15 species was a simple function of the square root of the density of the accompanying (weed) species: Y = a - b .fx (1) where Y is the yield of the indicator species, x is the density of the accompanying species and a and b are constants. The index of competition (CI) was then calculated by using the regression coefficient (b) and the intercept (a). CI = b / a This index was used to estimate crop loss due to the presence of a weed species. Cousens (1985a) also developed a model to describe crop yield loss as a function of weed density. The model was of the form of a rectangular hyperbola ; Y L = Id /( 1 + Id/A), where Y ^ is the percentage yield lost because of weed competition, I is the percentage yield lost per unit weed density as d tends towards zero, d is the weed density and A is the percentage yield loss as d tends towards infinity. From this equation, the author was able to generalize and predict that the rate at which yield loss increases with increasing density is proportional to the square of the yield loss per weed plant. The model was extended to include the crop density as a further variable (Cousens, 1985b): Y = aC /( 1 + bC + fD), where Y is yield, C is the crop density, D is the weed density and a,b and f are arbitrary parameters. The author cautioned against interpreting the parameters in terms of an individual plant's occupation of space. The replacement series design in which the total density is kept constant and the proportions of the two species are varied (de Wit, 1960) has been widely used in the study of intercropped mixtures. However, it does not accurately imitate the crop-weed situation in which the proportion of the weed rarely surpasses that of the crop. The design allows an interpretation of the relative yield total (RYT, the sum of the relative yields of each 16 species), which can help identify an overyielding or underyielding situation in multicropping situations. Connolly (1986) has pointed out some fundamental difficulties in the interpretation of the replacement series method. Although mixtures are two-dimensional, the replacement series consists of points on a one-dimensional line. The arbitrary selection of a particular replacement line, including both the pure stand densities and mixture proportions, carries the risk of confounding effects of the two densities. As well, the replacement method is particularly prone to problems when species of different sizes are mixed. Thomas (1970) derived a mathematical model to complement the competition model of de Wit (1960) which tests the hypothesis that two species are crowding for the same "space" (sensu De Wit, 1960). The model can be represented by the equation y j = u 1 k 1 2 z 1 / ( k 1 2 z 1 + z 2 ) (2) where y j is the yield, U j is a constant, and and z 2 are the densities of species one and two respectively. The parameter k j 2 is called the relative crowding coefficient of species one with respect to species two. Thomas's four-parameter model tests whether the standard deviations of the two populations are equal. If this test is not significant, a three-parameter model in which the relative crowding coefficients of the two species on each other are reciprocally related can be tested. If the three-parameter model proves to be significant, the species are interpreted to be crowding for "different space", i.e. are not competing for the same resources. A value of the product of k j 2 and k 2 1 (derived from the 4-parameter model) greater than one indicates the species are operating for mutual benefit, whereas if the product is less than one, an inhibitory mechanism is in operation. Based on the spacing formulae first described by de Wit (1960), Khan et al.(1975) proposed models of interaction between two genotypes in mixture. Equations were formulated describing crowding for the same space ° 1 = k 12 z l M l 1 ( k 12 • 1 ) z l + 1 and crowding for partly the same space 17 °1 = kl(2e) z l M l 1 <kl(2e)" 1 ) z l + 1 where Oj is the yield of the genotype in mixture, Z j is the relative input frequency, Mj is the yield per unit area of each genotype and kj^e) represents the relative crowding coefficient where a genotype crowds for space with the other genotype and also crowds for "empty space".. The interpretation of the classic replacement series was questioned by Jolliffe et al (1984) for its use of expected yields for comparative purposes. The authors pointed out that the actual monoculture yields would be better used as a comparison in order to illustrate the effects of inter-specific competition. Two indices were proposed as measures of plant interference in monoculture and mixtures, the relative monoculture response: R m = Y p • Y m 1 Y p where Yp is the projected yield of a species and Y m is the actual monoculture yield, and the relative mixture response R„ — Y M - Y_ / Y__ x m x m where Y x is the actual mixture yield. The predicted yield is that which would occur were there no interference, that is the projection from the initial slope of the monoculture yield density curve. The predictive ability of a competition study is of great value in an agricultural situation. If a model were able to predict the interaction of two species accurately, the yield of a crop might, theoretically, be increased by maintaining the density of a second species at a level where the yield of the crop was highest, or in multicropping, by maintaining the component densities in such a proportion that the total yield was at a maximum. To this end, many authors have introduced models they deem as best able to predict the competitive interactions of plants of the same or different species. The relationship between yield and density can be defined mathematically in two ways; as an asymptotic one where yield rises to a maximum and is then relatively constant at high density or as a parabolic one where yield rises to a maximum and then declines at 18 high densities. As Wiley and Heath (1969) suggest, these two relationships are merely different degrees of expression of a single relationship. Bleasdale and Nelder (1960) introduced a power function equation to describe the relationship between yield and density in crop plants: where w is the yield per plant, p is the number of plants per unit area and A, B, 0 and 0 are constants. The authors suggested that the use of 9, a positive quantity usually less than unity, gave a much better fit to data from spacing experiments. When 9 = 1 , the equation describes the special case where total yield per unit area is reciprocally related to density (Bleasdale, 1966a). This is the form of the yield density relationship previously described by Shinozaki and Kira (1956). Bleasdale (1966b) reported that in practice, it is the ratio of 9 to Sf which is important, and suggested using JK = 1 in fitting the equation. Watkinson (1980) reparamaterized Bleasdale's equation to describe the relationship between individual plant weight and surviving plant density: w = w m (1 + aN)-b, where w m is the dry matter production of the isolated plant, w is the mean weight per plant, N is plant density and a and b are constants. He interpreted a as the area required to achieve a yield of w m and b as the effectiveness with which resources are taken up from that area. Another equation describing mortality during self-thinning was used in conjunction with the first equation in order to assess the relation between total plant yield and the sowing density. Consideration of allometry then enabled the author to create a model of the population dynamics of annual plants with effectively discrete generations. The original model was then extended to a two species interaction in order to determine the effect of a second species on the reproductive performance of the other (Watkinson, 1981). Further manipulation of the model provided Watkinson (1982) with a method to determine the magnitude of the effect of the time of thinning during the vegetative phase of the life cycle on population density and compare it with the effect during the seed phase w -0 19 of the life cycle. Using the constants a and b from the model, Watkinson (1984) devised an equation which related the effects and intensity of crowding to the addition of nutrients. Although the models of Watkinson are very perceptive in that they consider plant survival and seed yield, and hence, the evolutionary consequences of competition, they are very complicated in their interpretation due to the degree of manipulation of the original data and the number of constants which are produced. Silander and Pacala (1985) proposed a much simpler model to predict the effect the inter-individual interference on fecundity of individuals in populations . The equation S = M (1 + CW) includes a variable W defined as the index of crowding, where S is the number of seeds produced per plant, M is the maximum number of seeds produced by a plant with no neighbours and C is a decay constant. Modeling the performance of Arabidopsis thaliana, they found that fecundity predictors were better based on adult plants rather than seedlings. Vandermeer (1984) proposed a yield density model for monocultures based upon a similar approach to that developed by Bleasdale and Nelder (1960) and Watkinson (1980) and claimed a simpler biological interpretation of the parameters: w = W m / (1 + qD b), where D is the density, W m is the biomass of a plant in isolation, w the average biomass of a plant in the population, q is a measure of competition including its intensity and operational area and b is a measure of the rate at which competition decays as a function of distance between plants. The index W m D could be used to describe the carrying capacity of the population. Firbank and Watkinson (1985) presented a model to describe competiton in two-species mixtures, based on the model of Watkinson (1980): W A = w m A (1 + a A <NA + * N B ) ) - b A 20 where is the mean yield per plant of species A, w m ^ is the mean yield of isolated plants of species A, is a parameter representing the area required by a plant to achieve a yield of w m , and Ng are the density of species A and B, respectively, describes the efficiency of resource utilization by the population and *C is the competition coefficient. The authors further manipulated this equation to obtain other equations which could be used to estimate survival and the yield per unit area for each species in a mixture. Wright (1981) extended the simple reciprocal relationship used in the analysis of plant yield-density relationships in monoculture to binary mixture situations w a " 1 = A a + B aa Pa + B a b Pb where w a is the plant weight of component a in mixture with component b, p a and p^ are the densities of species a and b respectively. The author defined B ^ as the coefficient of the effect of increasing density of plants of type b on the weights of plants of type a. The reciprocal of B a a is the yield that the monoculture of a at high densities would tend toward. The ratio of B a i / B a a is representative of the between species to within species component sources. Spitters (1983a) produced an almost identical model using a multiple linear regression (MLR) of yield on density: w"1 = b 1 > 0 + b 1 A N t + b 1 2 N 2 , . . . . (3) where w is the average weight per plant, Nj and N 2 are the densities of species one and two, respectively, in the mixture and bj Q, bj ^ and bj 2 are constants. He more clearly interpreted all the constants in the model; b j j measures the effect of intra-specific competition and bj 2 measures the effect of inter-specific competition on the yield of species one. The constant bj Q represents the virtual biomass of an isolated plant . Like Wright (1981), Spitters manipulated the coefficients to produce an index of competition: b l x / b 1 2 . 21 He interpreted this index as follows: the addition of one plant of species one has an effect on w** equal to adding bj ^  /bj 2 plants of species two. He derived an index of niche differentiation from the coefficients as well: ( b l , l / b l , 2 ) x ( b 2 , 2 / b 2 , l ) * When this index exceeds unity, there is niche differentiation. He further extended the model to study the effects of competition on marketable yield (Spitters, 1983b). The competitive effects and the advantage of mixed cropping could be estimated from the market yield data. Although the Spitters (1983a) multiple linear regression (MLR) approach to analyzing the effects of density on yield is simple, the coefficients representing intra- and inter-specific competition are not entirely independent of one another. A more intuitively correct way of examining the data has been proposed (Jolliffe, personal communication) which has its origins in the work of Jolliffe et al. (1984). The monoculture densities and yields are analyzed in a simple regression according to the equation w l ' e = B 1 , 0 + B M N l t (4) where W j is the yield of species one at density Nj, 6 is a constant and Bj Q and Bj j are coefficients which represent the virtual biomass of an isolated plant and the effect of the addition of a plant of species one on the yield of species one (intra-specific competition), respectively. The variation from the monoculture yields caused by the addition of a second species is then regressed against the density of the second species, with the regression forced through the origin, i.e. wd-e = 0 + B 1 2 N 2 (5) where is the deviation from the monoculture yield, N 2 is the density of the second species and B j 2 is a coefficient representing the effect on the yield of species one caused by the addition of one plant of species two. The coefficients from these sequential simple regressions (SSR) can be combined to give an expression incorporating both the intra- and inter-specific competition: w l = B l , 0 + B 1,1 N 1 + B 1,2 N 2 In this way, the intra- and inter-specific competition effects are effectively compartmentalized. Competitive indices can be computed as in the Spitters (1983a) multiple linear regression analysis, i.e. B^ j /B^ 2 , a n d a n index of niche differentiation can again be calculated, i.e. (Bj J/BJ 2) X (^2 2^B2 l^ - ^ n e interpretation of these indices follows Spitters (1983a) original interpretation. Mithen et al. (1984) did a simple, but elegant, study of the growth and mortality of individual plants as a function of the area available to each plant. The area around even aged Lapsana communis L. plants, as defined by Thiessen polygons, was a good predictor of plant weight. Plants in small polygons were much more likely to die than those in larger areas. The seedling stage appeared to be the most susceptible to available area effects, implying that some of the reduction in growth of survivors due to interference at an earlier age has an effect which persists after those neighbours have died. There was a hierarchy in plant sizes, i.e. many small plants and a few large ones, which developed before self-thinning began and was maintained as thinning occurred. The authors suggest that this hierarchy is continually generated by growth and interference and reduced by mortality of the smallest individuals. Size hierarchies exist in most plant populations, where much of the population's biomass is contained by a few large individuals (Weiner, 1984). Size hierarchies have great ecological and evolutionary significance in that the smallest plants are more often those which suffer density-dependent mortality and are often less fit than larger plants. There are many causes of size inequality caused by differential growth rates, including age differences, genetic variation, heterogeneity of resources, environmental factors and herbivore activity. Many authors have suggested that competition may cause or exaggerate size differences (White and Harper, 1970; Weiner, 1985). A pattern of dominance and suppression is envisaged, where differences in size are exacerbated as large plants monopolize resources at the expense of the growth of smaller plants. Weiner (1985) 23 demonstrated that size inequality always increased with increasing density in a study of the effects of intra- and inter-specific interference in populations of Trifolium incarnatum and Lolium multiflorum. As well, size inequality was increased by the addition of nutrients when interference was occurring. The dominant species, L. multiflorium, showed less size inequality in mixtures than in monocultures, while T. incarnatum, the suppressed species, displayed more inequality in mixture than in monoculture. These results agreed with the author's interpretation of the dominance and suppression model of size hierarchy; that is, a competitive dominant species would have a more equal size distribution in mixture than in monoculture, while a suppressed species would have a more unequal distribution in mixture than in monoculture. In contrast, Turner and Rabinowitz (1983) attributed shifts in size distribution in even^ aged populations of Festuca paradoxa to variance in the exponential growth rates. They found that competition (intra-specific) retarded the appearance of skewing. Based on many studies, the authors point out that where skewness increased with density, the plants were dicots and conifers, whereas in cases where skewness did not increase with increasing density, the populations were grasses. They suggest that plant architecture influences how plants compete and that the vertical, erect form of grass seedlings renders light competition less crucial than belowground resource depletion, whereas the simplest view of dominance and suppression is that light competition is critical. Huston (1986) suggested that increasing plant densities in a randomly spaced population, as compared to a regular distribution of adult plants which occurs more frequently in natural plant populations (Harper, 1977), may produce symmetric competition. This would result in deviations from the dominance and suppression theory of size inequality, where assymetric, or one-sided competition occurs. A study of the competitiveness and growth of Amaranthus retroflexus L., Chenopodium album L., Echinochloa crus-galli L. and Solanum nodiflorum Jacq. under field conditions in a replacement series design showed that E. crus-galli was always the superior competitor 24 when grown in mixture with the other three species (Roush and Radosevich, 1985). E. crus-galli maintained a low canopy area index but had a greater root volume relative to shoot growth early in the season. McGraw (1985) reported that root competition often develops before shoot competition in depressing the growth of less competitive plants. Roush and Radoseveich (1985) suggest that under similar environmental conditions, E. crus-galli would displace the other species from mixtures over time. In a study of competition between E. crus-galli and A. retroflexus, Siriwardana and Zimdahl (1984) reported that E. crus-galli was more competitive than A. retroflexus at all densities, and experienced more intra-specific competition than inter-specific. In replacement series, as the proportion of E. crus-galli was reduced, the proportion of fresh weight of E. crus-galli increased relative to A. retroflexus, and the authors suggested this was due to reduced intra-specific competition. Competition studies on E. crus-galli (BYG), A. retroflexus (RPW) and Setaria viridis (GFT) in the field showed that BYG was a strong competitor against both RPW and GFT, and RPW strongly competed against GFT (Minjas and Runeckles, 1984). RPW responded more to its own increasing density than to the presence of GFT (Minjas, 1983). The relative yield totals of RPW and BYG were consistently greater than one, indicating that the species were not exclusively competing for the same space. Similar non-competitive interactions between BYG and GFT existed in one season although an increasing proportion of BYG depressed the crowding coefficients of GFT. In the following season, the mixture yield of RPW-GFT in replacement series was consistently greater than the monoculture yield of RPW, with RPW contributing proportionately more. There was a substantial non-competitive component to their interaction, and the author suggested that the tap root system of RPW exploited a different soil layer than the fibrous root system of GFT which occupies the top soil horizons. 25 3 . MATERIALS AND METHODS 3.1 Plant Materials The three plant species used in this study were Echinochloa crus-galli (L.) Beauv., barnyardgrass (BYG), Setaria viridis (L.) Beauv., green foxtail (GFT) and Amaranthus retroflexus L., redroot pigweed (RPW). The seeds used were collected in 1982 from plants used in competition studies in the field at the University of British Columbia (Minjas, 1982). Original seeds of BYG and RPW were collected from agricultural fields at Agassiz Research Station, Agriculture Canada, Agassiz, B.C. in the summer of 1979. Those of GFT were obtained from grain screenings. Seeds were germinated in the greenhouse in flats containing finely sieved (5 mm) soil and an appropriate layer of Redi-Earth professional horticultural growing medium. Flats of RPW and GFT were covered with plastic to increase soil temperatures and promote germination. All fiats were drenched with No-Damp (2.5 % oxine benzoate) to prevent damping off. Sowing was staggered to obtain seedlings of approximately the same size for pricking out, immediately after the development of RPW's first true leaves. This was approximately 10-11 days after sowing for RPW, 9-10 days after sowing for GFT and 7-8 days after sowing for BYG. The seedlings were pricked out into 5 inch round pots in experiment 3 and into 4 inch square pots in all other experiments (see Table 1). All pots contained a homogeneous mixture of finely sieved soil and Osmocote slow release fertilizer (14-14-14)). Mithen et al. (1984) stressed the importance of "available" area in competitive studies, so templates were designed to create equidistant spacing between plants and equal numbers of neighbours (Figure 1). These templates were used to to determine the placement of the seedlings in the lower density treatments. It was impossible to use templates for the higher density treatments due to crowding. When pricking out pots containing mixtures, the placement of individuals of each species was determined randomly. All seedlings were FIGURE 1 : Templates used in pricking out seedlings into low density treatments 27 28 watered in with Green Valley Soluble Plant Starter Mix (10-52-17) to encourage root growth. Several days after pricking out was completed, the pots were checked and dead seedlings were replaced. Approximately 11 days after pricking out, when plants were established and new growth had occurred, the pots were moved to growth chambers for ozone treatment. 3.2 OZONE TREATMENTS The exposure chambers used in these experiments were modified growth cabinets (CONVIRON, Controlled Environment Ltd. Model EF7). Charcoal-filtered air was supplied at a flow rate of 2.2 + 0.3 m/sec at the entrance to the cabinets. A 14-hour photoperiod (6 AM - 8 PM) was used in all chambers. Day and night temperatures were 24°C and 16°C, respectively. Photosynthetically active radiation at plant level was 90 uEm'^ s"^ . Relative humidity was not controlled and varied between 55% and 80%. Plants were watered to capacity each day at the end of the exposure period. Three treatments were applied. One chamber received only charcoal-filtered air, and acted as a no-ozone control. The other two ozone treatments were 75 ppb for 7 hr/daily and 150 ppb for 3.5 hr/daily, which represent the same ambient dose. The 75 ppb/7 hr treatment and the 150 ppb/3.5 hr treatments began 3 and 6.5 hours after the beginning of the photoperiod, respectively. The shorter exposure treatment was applied in the afternoon in order to mimic anthropogenically derived diel peaks of ozone (Fowler and Cape, 1982). Ozone was generated by passing a stream of charcoal-filtered air over a bank of germicidal lamps (Sylvania Germicidal Lamp, Type B) contained in an air-tight plexiglass chamber. Previous use of this system indicated that no NO x was generated. Ozone was monitored continuously (Dasibi Environmental Corp. Model 1003-AH). Monitors were calibrated regularly at the Environmental Laboratory, B.C Ministry of the Environment. Inputs to the chambers receiving ozone were sampled every 40 minutes, during which time a feed-back control system maintained the 150 ppb treatment. The 75 J 29 ppb treatment was kept constant by manual^ manipulating the voltage to the germicidal bulbs. During the 3-week exposure period the treatments were rotated through the chambers at least once to eliminate chamber effects. The chamber positions of the pots were also randomly changed at that time to avoid position effects. 3.3 COMPETITION TREATMENTS Seven experiments were carried out over a two year period following the completion of two preliminary experiments which are not reported here (Table 1). Each consisted of a binary combination of the three species, BYG, GFT and RPW. The replication of each binary mixture was treated as a block. Within each binary experiment there were both monocultures and mixtures at different total densities. The experiments were designed to incorporate both additive and replacement series for each series (Table 2). Experiment 3 did not include the 20 plants/pot monocultures due to lack of space. Neither experiment 3 nor 4 included replacement series at total density 25 due to lack of space in the chambers and poor germination, respectively. The number of plants per pot used represent actual densities similar to those found in infested fields at the Agricultural Research Station, Agassiz, B.C. and those used in previous competition experiments with these same species (Minjas,1982). 3.4 HARVESTS At the end of the 3-week exposure period, all plants were harvested. The root mass was cleaned and the plants cut just above the uppermost root in RPW and within 5 mm of the root for BYG and GFT. The inflorescences were separated from each plant and the number of tillers (for BYG and GFT) and leaves were counted. Each individual plant, its reproductive parts and the root mass/pot was dried in paper bags in a forced air drier at TABLE 1: LISTING OF INDIVIDUAL EXPERIMENTS EXPT. # SPECIES MIXTURE DATE OF FIRST SOWING DATE OF HARVEST 3 BYG/GFT 05/11/84 10/12/84 4 BYG/RPW 16/04/85 28/05/85 5 GFT/RPW 16/05/85 27/06/85 6 BYG/GFT 09/06/85 18/07/85 7 BYG/GFT BYG/RPW GFT/RPW 15/07/85 26/08/85 8 BYG/RPW 13/08/85 24/09/85 9 GFT/RPW 20/02/86 08/04/86 31 T A B L E 2 : D E N S I T Y C O M B I N A T I O N S U S E D I N E X P E R I M E N T S d e n s i t y (# p l a n t s / 4 " s q u a r e p o t ) o f c o m p o n e n t s . S P E C I E S A B S P E C I E S 0 5 1 0 15 20 0 5 1 0 1 5 2 0 5 5 5 5 10 5 15 5 20 5 1 0 1 0 5 10 10 10 15 1 0 1 5 1 5 5 15 1 0 15 < 2 0 20 5 2 0 A d d i t i v e s e r i e s r u n h o r i z o n t a l l y a n d v e r t i c a l l y . R e p l a c e m e n t s e r i e s r u n d i a g o n a l l y f r o m l e f t t o r i g h t . 32 80 C for a minimum of 6 days. The plant materials were cooled and weighed using a Mettler AE100 balance. 3.5 DATA ANALYSIS 3.5.1 Analysis of Variance Two sets of ANOVAs were run on the data set. One set was carried out on the individual per plant variables for each species to examine the treatment effects on a per plant basis. Another set of ANOVAs was run on the variables on a total per pot basis for each species, to enable an examination of the population dynamics under each treatment regime. The experiments were treated as blocks, and the ANOVAs were carried out on each species in three different analyses. The monocultures, the total densities less than or equal to 20 plants/pot, and the total densities equal to 25 plants/pot were analyzed separately because of the design of the experiments. 3.5.2 Regression Analyses Based on the results of the ANOVAs, comparisons of the experimental means and standard deviations and the fact that in experiment 3, plants were grown in a different size pot, the data from that experiment were not included in the regression analysis. The total aboveground yields (vegetative + reproductive) of BYG, RPW and GFT in monoculture and in mixture were analyzed by the multiple linear regression analysis (MLR) using Equation 3 (page 20). Using the partial regression coefficients generated from the equations, competitive indices for each species and a measure of niche differentiation for each species mixture were calculated. The analyses were repeated using a regression in which an interaction term was incorporated: 1/wj = b 1 > 0 + b 1 > 1 N 1 + b 1 > 2 N 2 + b 1 2 N , N 2 . . . .(6) 33 Sequential simple regression (SSR) analyses were also performed. Computation of optimal values of 0 consistently produced numbers less than -1, so the straight reciprocal relationship, where G = 1 was used in all regression analyses. The reciprocal of the total aboveground yield of BYG, GFT and RPW in monoculture was regressed against the density of the species according to Equation 4 (page 21). The reciprocal of the deviation of the mixture yields from the monoculture yields was then regressed against the density of the second species in the mixture and forced through the origin (Equation 5, page 21), to yield a coefficient of inter-specific competition, Bj 2 - Competition indices and a measure of niche differentiation were calculated from the regression coefficients, as for the multiple linear regression approach. The yield of the indicator species at different constant densities was analyzed by the square root regression method (Equation 1, page 15) of Dew (1972). The monoculture data were included in the analysis in association with a negligibly small dummy value (1 x o 10" ) of the associated species density. The dummy value is needed because the regression involves the square root of the density of the associated species. Predicted values were used to obtain curves depicting the results of the effects of increasing densities of one species on the yields of the second species at constant density. Replacement series and additive series curves were generated using predicted values from the sequential simple regressions. 3.5.3 Replacement Series The analysis of Thomas (1975) was used to fit parameters to the two-species competition model proposed by de Wit (1960) (Equation 2, page 16), and to determine whether different binary mixtures were competing for the same space. The 4 parameter model tests whether the standard deviations of the two populations are equal. If this hypothesis is rejected, then the 3 parameter model can be tested. If the 3-parameter model proved to be significant, the species were interpreted to be competing for different space. 3.5.4 Size Frequency Distributions Size frequency distributions were calculated by pooling the data from each pot of the same density treatment and ozone treatment and calculating the mean number of individuals in each size class. The data from experiment 3 were not included for the reasons given previously. The size classes chosen were multiples of 5% of the total plant weight per pot in order to allow a more discriminating analysis of the size frequencies. 35 4. RESULTS 4.1 VISUAL OBSERVATIONS Over all the experiments, BYG grew the tallest, tillered very rarely, and produced the sturdiest aboveground and belowground tissue. RPW monopolized the canopy when grown in mixture with GFT. When grown in combination with BYG, RPW grew taller and lost many of its lower leaves. The elongated RPW plants produced in mixture with BYG were very unsturdy, lacking the supportive structure necessary to remain upright. RPW plants had very fine, fragile root systems. GFT tillered readily, producing many more leaves than BYG, but was, in all experiments, a relatively short plant that failed to maintain its share of the canopy. GFT plants flowered in all experiments. The mean yields for each species over experiments 3 - 9 under each density and ozone treatment are in Appendix A. 4.2 ANALYSES OF VARIANCE Each experiment was treated as a block in the analysis of variance. This precluded some of the density treatments from being analyzed together. The density treatments equal to or less than 20 plants per pot were analyzed separately from the treatments of total density equal to 25 plants per pot. These density treatments will be referred to as low and high density, respectively, for convenience. 4.2.1 BYG individual Analysis of variance done on the individual per plant data showed a significant effect of ozone on vegetative aboveground dry weight of BYG in monocultures (Table 3). This effect had a significant deviation from linearity, which represents the fact that the plants in the 75 ppb/7 hr treatment had a higher mean weight than either the 0 or 150 ppb/3.5 hr 3 6 T A B L E 3 : S i g n i f i c a n t t r e a t m e n t e f f e c t s (ANOVA) f o r b a r n y a r d g r a s s y i e l d v a r i a b l e s ( p e r p l a n t ) i n m o n o c u l t u r e a n d m i x t u r e s . BYG B Y G / G F T B Y G / G F T B Y G / R P W B Y G / R P W MONO < 20 = 2 5 < 2 0 = 2 5 V E G E T A T I V E DRY WEIGHT B L O C K OZONE L I N DEV ERR ( A ) COMP OZONE*COMP E R R ( B ) R E P R O D U C T I V E B L O C K DRY WEIGHT OZONE NUMBER OF L E A V E S NUMBER OF T I L L E R S L I N DEV E R R ( A ) COMP 0 Z 0 N E * C 0 M P E R R ( B ) B L O C K OZONE L I N DEV E R R ( A ) COMP 0 Z 0 N E * C 0 M P E R R ( B ) B L O C K OZONE L I N DEV E R R ( A ) COMP 0 Z 0 N E * C 0 M P E R R ( B ) T O T A L D F **** ** ** ** **** **** *** **** **** **** **** **** **** **** ** **** **** ** N / A N / A N / A 1 3 5 1 9 0 3 _ =• n s * P = . 1 ** P = . 0 5 *** P = . 0 1 **** P = . 0 0 1 * **** ** ** **** *** ** 8 3 8 **** - ( . 1 1 3 9 ) * **** **** *** **** **** **** * **** **** **** **** 9 0 0 *** ** **** **** **** 8 4 0 37 treatments. The only other significant effect of ozone on individual plant variables was on reproductive dry weight/individual of BYG in combination with RPW at higher densities. This also had a significant deviation from linearity. There was a significant difference between density treatments in individual vegetative dry weight of BYG in monoculture and in combination with GFT at total density = 25. There were no differences in reproductive dry weight of BYG in any species combination or in monoculture, suggesting that under the conditions used the reproduction of individuals was not governed by density or the presence of other species. There was, however, a significant difference in the number of leaves produced by individuals in the different densities of the monoculture, and when BYG was in combination with GFT at high density and with RPW at the lower densities. The only significant difference in the number of tillers per individual between density treatments was in the monoculture. 4.2.2 BYG totals/pot The significant non-linear ozone effect on BYG individual plant vegetative dry weight in monoculture also showed up as a population effect when the total vegetative dry weight/pot was analyzed (Table 4). However, in mixture with RPW at low density, there was a significant linear ozone effect on vegetative dry weight, which did not show up in the analysis of individuals. As in the per plant analysis, there was a significant ozone effect on BYG reproductive dry weight per pot in mixture with RPW at high densities. This ozone effect at the total/pot level also significantly deviated from linearity, with 75 ppb producing more reproductive tissue than the 0 or 150 ppb treatments. Significant differences between density treatments in total/pot vegetative weight, number of leaves and number of tillers showed up in all monocultures and species mixtures. No significant effect of density treatment on BYG reproductive dry weight was seen. 38 T A B L E 4 : S i g n i f i c a n t t r e a t m e n t e f f e c t s ( A N O V A ) f o r b a r n y a r d g r a s s y i e l d v a r i a b l e s ( t o t a l p e r p o t ) i n m o n o c u l t u r e a n d m i x t u r e BYG B Y G / R P W MONO < = 20 B Y G / R P W - 2 5 B Y G / G F T < 2 0 B Y G / G F T = 25 V E G E T A T I V E DRY WEIGHT B L O C K OZONE L I N DEV E R R ( A ) COMP OZONE*COMP E R R ( B ) **** ** * ** **** *** **** * ** *** **** **** **** *** ** R E P R O D U C T I V E DRY WEIGHT B L O C K NUMBER OF L E A V E S **** OZONE L I N DEV E R R ( A ) * * * * COMP OZONE*COMP -E R R ( B ) * * * * B L O C K * * * * OZONE - ( . 1 7 ) L I N * DEV E R R ( A ) COMP * * * * OZONE*COMP -E R R ( B ) ** ** ** **** **** **** * * * * N / A ** ** ( . 1 1 ) **** ** **** **** N / A **** NUMBER OF T I L L E R S B L O C K * * * * OZONE L I N DEV E R R ( A ) * * COMP * * * * OZONE*COMP -E R R ( B ) **** **** **** N / A **** **** * n s * p - . 1 ** p = . 0 5 *** p = . 0 1 **** p = . 0 0 1 39 4.2.3 GFT individual There were no significant ozone effects on any of the individual per plant variables which were measured (Table 5). There were significant differences in vegetative dry weight between the density treatments of GFT in mixture with RPW at both high and low densities. There were no significant effects of density on reproductive dry weight/plant. GFT density treatments in monoculture and in mixture with RPW at low density differed significantly in the number of leaves/plant and the number of tillers/plant. 4.2.4 GFT pot totals In contrast to the individual plant analysis, the totals/pot revealed one significant ozone effect: GFT in mixture with RPW at low densities showed a significant linear ozone effect on vegetative dry weight (Table 6). There were significant density effects on vegetative dry weight, number of leaves and number of tillers in the monoculture and in all the species combinations analyzed. No density effects on reproductive dry weight were evident. 4.2.5 RPW individual Analysis of variance on RPW in monoculture showed a significant inversely linear effect of ozone on vegetative dry weight per plant (Table 7). A similar non-significant trend existed in combination with BYG at high density, attributable to the significant linear effect of ozone on the number of leaves per plant. RPW did not flower in combination with BYG or GFT at high density nor with GFT at low density. There was also a significant linear effect of ozone on the number of leaves per plant when RPW was in mixture with BYG at high density. There were significant differences between density treatments for vegetative dry weight/plant in monoculture and in mixture with BYG and GFT at low densities. As well, RPW in monoculture and in mixture with GFT at low and 40 T A B L E 5 : S i g n i f i c a n t t r e a t m e n t e f f e c t s (ANOVA) f o r g r e e n f o x t a i l y i e l d v a r i a b l e s ( p e r p l a n t ) i n m o n o c u l t u r e a n d m i x t u r e s . G F T MONO G F T / B Y G G F T / B Y G G F T / R P W G F T / R P W < 20 = 2 5 < 20 = 2 5 V E G E T A T I V E DRY WEIGHT B L O C K * * * * OZONE L I N DEV E R R ( A ) * * COMP OZONE*COMP -E R R ( B ) * * * * R E P R O D U C T I V E B L O C K DRY WEIGHT OZONE **** NUMBER OF L E A V E S NUMBER OF T I L L E R S L I N DEV E R R ( A ) * * COMP OZONE*COMP -E R R ( B ) * * * * B L O C K * * * * OZONE L I N DEV E R R ( A ) * * * * COMP * * * OZONE*COMP -E R R ( B ) * * * * B L O C K OZONE L I N DEV E R R ( A ) COMP OZONE*COMP E R R ( B ) **** **** *** **** **** **** **** **** **** N / A N / A **** **** **** **** **** ** **** **** **** **** **** **** **** *** **** **** *** **** **** ** ** **** **** *** **** **** **** **** **** **** **** T O T A L D F 1 3 4 8 8 8 6 8 7 7 8 9 3 1 3 4 7 - = n s * p = . 1 ** P = . 0 5 *** p = . 0 1 **** p ° . 0 0 1 41 T A B L E 6 : S i g n i f i c a n t t r e a t m e n t e f f e c t s (ANOVA) f o r g r e e n f o x t a i l y i e l d v a r i a b l e s ( t o t a l p e r p o t ) i n m o n o c u l t u r e a n d m i x t u r e s . G F T G F T / R P W G F T / R P W G F T / B Y G G F T / B Y G MONO < 20 = 2 5 < 2 0 - 25 V E G E T A T I V E DRY WEIGHT B L O C K OZONE L I N DEV E R R ( A ) COMP 0 Z 0 N E * C 0 M P -E R R ( B ) ** R E P R O D U C T I V E B L O C K DRY WEIGHT OZONE NUMBER OF L E A V E S NUMBER OF T I L L E R S L I N DEV E R R ( A ) COMP OZONE*COMP E R R ( B ) B L O C K OZONE L I N DEV E R R ( A ) COMP OZONE*COMP E R R ( B ) B L O C K OZONE L I N DEV E R R A ( A ) COMP OZONE*COMP E R R ( B ) **** ** **** **** **** **** *** **** **** **** **** **** * * **** **** N / A * * * * *** **** *** **** *** **** ** **** * **** **** **** **** **** *** **** *** **** **** **** **** ** **** **** **** **** * * N / A **** *** ** **** N / A * * * * **** - n s * p . 1 ** p = . 0 5 *** p . 0 1 **** p . 0 0 1 42 T A B L E 7 : S i g n i f i c a n t t r e a t m e n t e f f e c t s (ANOVA) f o r r e d r o o t p i g w e e d y i e l d v a r i a b l e s ( p e r p l a n t ) i n m o n o c u l t u r e a n d m i x t u r e s . RPW R P W / B Y G R P W / B Y G R P W / G F T R P W / G F T MONO < 2 0 - 25 < 2 0 = 25 V E G E T A T I V E B L O C K DRY WEIGHT OZONE L I N DEV E R R ( A ) COMP OZONE*COMP-E R R ( B ) **** * ** ** **** **** ** * * * * * * * - ( . 1 2 1 1 ) **** **** **** *** **** R E P R O D U C T I V E DRY WEIGHT B L O C K * * * * * * * * N / A N / A N / A OZONE L I N DEV E R R ( A ) COMP 0 Z 0 N E * C 0 M P -E R R ( B ) - * * NUMBER OF B L O C K L E A V E S OZONE L I N DEV E R R ( A ) COMP OZONE*COMP E R R ( B ) **** **** **** *** T O T A L D F : 1 7 7 7 **** **** **** **** * ** *** **** 8 6 8 8 2 5 ** **** ** ** 8 9 7 1 2 9 1 - n s * p = . 1 ** p = . 0 5 *** p - . 0 1 **** p - . 0 0 1 43 high density showed significant differences in the number of leaves/plant between density treatments. 4.2.6 RPW totals/pot As in the ANOVAs on individual plant variables, total vegetative dry weight/pot showed a significant ozone effect on RPW in monoculture (Table 8). A significant ozone effect on vegetative dry weight was also seen when RPW was in mixture with BYG at high density. Both of these effects were linear. There were significant differences between density treatments for total vegetative dry weight and number of leaves in monoculture and all mixture combinations. A significant difference between density treatments for reproductive dry weight was seen for RPW in mixture with BYG at low density. 4.2.7 Root totals/pot There was a strongly significant inverse linear ozone effect on root weight/pot in the RPW monocultures as determined by Analysis of Variance (Table 9). Mixtures of both GFT and BYG with RPW at low densities also showed weakly significant adverse ozone effects on root dry weight/pot. The ANOVA also revealed a significant ozone*competition interaction effect on root dry weight in mixtures of GFT and RPW at high density. In all cases, the ANOVA revealed significant effects of density treatment on the root weight/pot. The results of the BYG/GFT treatment combination at low densities could not be analyzed because there was only one experimental block; root weight was not measured in Experiment 3. 44 T A B L E 8 : S i g n i f i c a n t t r e a t m e n t e f f e c t s (ANOVA) f o r r e d r o o t p i g w e e d y i e l d v a r i a b l e s ( t o t a l p e r p o t ) i n m o n o c u l t u r e a n d m i x t u r e s . RPW R P W / B Y G MONO < 20 R P W / B Y G R P W / G F T R P W / G F T = 2 5 < 2 0 = 2 5 V E G E T A T I V E B L O C K DRY WEIGHT OZONE L I N DEV E R R ( A ) COMP 0 Z 0 N E * C 0 M P E R R ( B ) R E P R O D U C T I V E B L O C K DRY WEIGHT OZONE L I N DEV E R R ( A ) COMP OZONE*COMP E R R ( B ) **** * ** **** **** **** ( . 1 3 ) **** **** **** **** * ( . 1 1 ) * * . ( . 1 0 * ) * * * * * * * * N / A N / A *** *** N / A **** ** NUMBER OF L E A V E S B L O C K OZONE L I N DEV E R R ( A ) COMP OZONE*COMP E R R ( B ) **** *** **** **** **** ** **** •k-frkie **** **** **** **** *** **** *** n s * p = . 1 ** p = . 0 5 *** p - . 0 1 **** p - . 0 0 1 4 5 T A B L E 9 : S i g n i f i c a n t t r e a t m e n t e f f e c t s (ANOVA) o n t o t a l r o o t w e i g h t p e r p o t f o r m o n o c u l t u r e s a n d m i x t u r e s . MONOCULTURES B L O C K OZONE L I N DEV E R R ( A ) COMP OZONE*COMP E R R ( B ) BYG * * * * **** RPW **** *** *** *** GFT * * * * **** D E N S I T Y < 2 0 B L O C K OZONE L I N DEV E R R ( A ) COMP 0 Z 0 N E * C 0 M P E R R ( B ) B Y G / G F T N / A B Y G / R P W * * * * - ( • 1 1 ) **** G F T / R P W **** * B L O C K OZONE L I N DEV E R R ( A ) COMP OZONE*COMP E R R ( B ) D E N S I T Y - 25 B Y G / G F T B Y G / R P W G F T / R P W * * * * ** **** * - n s * p = . 1 ** p = . 0 5 *** p = . 0 1 **** p - . 0 0 1 46 4.3 REGRESSION ANALYSES 4.3.1 Multiple Linear Regressions The multiple regression analysis of Spitters(1983) uses the partial regression coefficients to describe the degree of intra-specific competition, inter-specific competition and niche differentiation of a single species in mixture with another species and in monoculture (Equation 3, page 20). The coefficients bj Q, bj j, and bj 2 a r e presented in Table 10. GFT and RPW in combination with each other and BYG show a decreasing virtual biomass of an isolated plant as the ozone concentration is increased as indicated by an increasing value of the intercept (bj Q). However, BYG in mixture with RPW shows a slight increase of the virtual biomass of an isolated plant in both the 75 and 150 ppb ozone treatments over the control. In contrast, BYG in mixture with GFT shows a larger increase in the virtual biomass of an isolated plant only in the 75 ppb treatment relative to the control treatment. In all but three cases (BYG with RPW in filtered-air and BYG with both RPW and GFT at 150 ppb), the model is significant; however throughout the coefficients of o determination (R ) are low, ranging from 0.04 to 0.19 indicating that the models do not explain a large proportion of the variation. If the partial regression coefficients were significantly different from 0, they were interpreted as representing an effect of either intra- or inter-specific competition on the yield of that species. BYG shows a significant effect of intra-specific competition only in the model with GFT at 75 ppb, which could be explained by a combination of its own enhanced growth in that ozone treatment and GFT's poor competitive ability, as illustrated by the lack of a significant GFT inter-specific effect on BYG in any of the ozone treatments. The negative interspecific coefficients in the BYG in mixture with GFT models in the 0 and 150 ppb ozone treatments indicate that the presence of GFT has a T A B L E 1 0 : R e s u l t s o f m u l t i p l e l i n e a r r e g r e s s i o n m o d e l . S p e c i e s C o m b i n a t i o n 0 1 , 0 1 . 1 1 , 2 M o d e l BYG+RPW +GFT 6 . 7 7 5 . 9 2 . 0 1 1 . 1 1 5 . 1 7 3 * . 0 9 4 . 0 4 . 0 7 n s GFT+BYG +RPW 1 6 . 1 8 1 9 . 7 7 . 4 4 4 * * . 1 6 6 , 5 6 4 * * * , 7 7 1 * * * * . 1 2 . 1 3 *** *** RPW+BYG +GFT 9 . 6 1 8 . 2 6 . 3 9 4 * . 4 1 4 * * . 4 8 5 * * * . 2 2 1 . 0 8 . 0 5 ** * 7 5 p p b / 7 h r s BYG+RPW +GFT 4 . 6 4 3 . 4 4 . 1 4 2 , 2 0 6 * * * . 1 8 8 * * . 0 6 5 . . 0 6 . 1 0 * ** GFT+BYG +RPW 1 6 . 9 5 2 0 . 9 5 , 5 1 9 * * . 2 7 5 , 8 4 2 * * * * , 7 4 7 * * * , 1 9 . 1 0 **** *** RPW+BYG +GFT 1 3 . 1 6 9 . 8 6 . 2 4 4 . 4 6 3 * * 3 8 5 * * 3 3 3 * * . 0 5 . 0 5 * ** 1 5 0 p p b / 3 . 5 h r s BYG+RPW +GFT 5 . 8 6 6 . 3 4 . 1 0 3 . 0 9 8 , 2 1 9 * * . 0 7 4 . 0 5 . 0 6 n s n s GFT+BYG +RPW 2 0 . 6 1 2 1 . 6 4 . 1 8 4 . 0 9 6 . 6 8 5 * * * 1 . 0 4 * * * * . 1 5 18 *** **** RPW+BYG +GFT 1 3 . 1 1 1 3 . 4 7 , 3 1 7 , 2 2 9 ,592*** , 3 5 6 * * . 0 9 . 0 4 *** * * p = . 1 * * p = . 0 5 * * * p = . 0 1 * * * * p = . 0 0 1 48 significant positive inter-specific effect on the yield of BYG in the control, but this trend fails to reach significance in the 150 ppb treatment. RPW has a significant inter-specific effect on BYG in all ozone treatments and vice versa. RPW is not significantly affected by GFT inter-specific competition in the control but is in both the 75 and 150 ppb treatments. Intra-specific competition appears to be more important to the yield of RPW than the inter-specific effect of GFT in the control. The yield of GFT is very significantly affected by inter-specific competition from the other two species in all ozone treatments and only shows a significant effect of intra-specific competition in the models including BYG at the 0 and 75 ppb ozone levels. Expansion of the multiple regression model to include an interaction term (Equation 6, page 32) yields the coefficients presented in Table 11. An examination of the intercepts shows that the same pattern exists over the ozone treatments for all species mixtures as shown in Table 10. The coefficients of determination, R , are improved slightly by the addition of the extra term, but fewer of the models are significant. Two partial regression coefficients for the interaction term are significant. The interaction between the density of RPW and GFT has a significant effect on the yield of GFT in the control ozone treatment, and the interaction between the density of BYG and GFT has a significant effect on the yield of GFT at the 150 ppb ozone level. The presence of the interaction term in the GFT-RPW model in the control treatment causes the intra-specific coefficient to be significant, whereas it was not in the simpler model. The interaction effect is a negative one, meaning that the interaction of the two densities increases the yield of GFT. In the GFT-BYG model at the 150 ppb ozone level, the presence of the significant positive interaction, which indicates a negative effect of the interaction of the two densities on GFT yield, causes both the intra- and inter-specific coefficients to be negative, and the significance of the inter-specific competition, seen in the simpler model, is lost. 49 T A B L E 1 1 : R e s u l t s o f m u l t i p l e l i n e a r r e g r e s s i o n m o d e l w i t h i n t e r a c t i o n . S p e c i e s 2 C o m b i n a t i o n b ^ ^ ^ 1 1 b l 2 ^ 1 2 ^ M o d e l 0 p p b BYG+RPW +GFT 5 . 9 7 6 . 6 8 . 0 8 9 . 0 4 4 . 3 4 9 * . 2 3 8 . 0 2 1 . 0 1 7 . 0 5 , 0 8 n s GFT+BYG +RPW 1 4 . 9 7 1 5 . 2 4 5 5 7 * * , 6 2 0 * * . 8 0 9 * * 1 . 5 8 8 * * * * , 0 2 8 , 0 9 9 * * . 1 3 . 1 7 ** **** RPW+BYG +GFT 9 . 1 1 7 . 0 2 . 4 4 6 * . 5 4 3 * * . 6 1 1 * . 4 7 5 , 0 1 6 . 0 3 2 . 0 8 . 0 5 * 7 5 p p b / 7 h r s BYG+RPW +GFT 4 . 7 6 2 . 7 2 . 1 3 0 , 2 7 8 * * * . 1 6 2 , 2 2 5 * ' . 0 0 3 . 0 2 0 . 0 6 . 1 3 n s * * GFT+BYG +RPW 1 7 . 8 3 2 1 . 8 2 . 4 3 2 . 1 8 7 . 6 4 7 . 5 8 6 . 0 2 3 . 0 2 0 . 1 9 , 1 0 *** *** RPW+BYG +GFT 1 2 . 2 4 8 . 8 5 , 3 3 7 , 5 6 9 * * . 6 1 5 * . 5 5 6 * . 0 2 9 . 0 2 8 . 0 5 . 0 6 n s 1 5 0 p p b / 3 . 5 h r s BYG+RPW +GFT 4 . 8 9 5 . 6 3 . 2 0 0 . 1 7 0 * . 4 3 2 * * . 0 9 1 , 0 2 6 . 0 2 1 . 0 6 . 0 8 n s GFT+BYG +RPW 2 3 . 5 0 2 0 . 7 9 , 1 0 9 . 1 8 4 - . 0 0 7 1 . 2 0 1 * * . 0 8 6 * * . 0 1 9 19 .19 *** **** RPW+BYG +GFT 1 3 . 6 9 1 2 . 6 2 . 2 5 8 . 3 1 7 4 . 4 3 2 . 5 3 9 . 0 2 0 . 0 2 3 . 0 9 . 0 4 * * n s * p = . 1 ** p = . 0 5 *** p = . 0 1 **** p = . 0 0 1 50 Because the interaction term does not appear to shed much light on the data in terms of explaining the variation, the simpler models were used to calculate the competitive indices as described by Spitters (1983a). Competition indices (the ratios of the partial regression coefficients obtained in the , multiple linear regression models) are shown in Table 12. For GFT with BYG, and RPW with BYG or GFT, competitive ability decreases as the ozone concentration is increased. This agrees with the interpretation of the intercepts of the simple multiple regression models for these species. However, GFT in mixture with RPW at 75 ppb appears to have more competitive ability than in the other two ozone treatments. This is also true of BYG in mixture with RPW. BYG has a much stronger competitive ability than GFT in the 75 ppb treatment. The negative signs of the coefficients in the filtered-air or in 150 ppb ozone represent the significant positive effect of GFT inter-specific competition on the yield of BYG in these treatments. The indices indicate that BYG is a much stronger competitor than GFT, as is RPW. BYG is a 3.17 times stronger competitor than GFT in the 75 ppb treatment; RPW is a 1.87 times stronger competitor than GFT in the control treatment. RPW and BYG are shown to be similar in their competitive abilities. GFT has very poor competitive ability in mixture with RPW but slightly better in mixture with BYG. The overall order of decreasing competitive abilities over the three ozone treatments appears to be: 0 ppb RPW-GFT BYG-GFT RPW-BYG GFT-BYG GFT-RPW BYG-RPW 75 ppb BYG-GFT RPW-GFT BYG-RPW RPW-BYG GFT-BYG GFT-RPW 150 ppb BYG-GFT RPW-GFT RPW-BYG BYG-RPW GFT-BYG GFT-RPW T A B L E 1 2 : C o m p e t i t i o n i n d i c e s f f r o m m u l t i p l e l i n e a r r e g r e s s i o n m o d e l . OZONE TREATMENT S P E C I E S C O M B I N A T I O N 75 p p b / 7 h r 1 5 0 p p b / 3 . 5 h r B Y G +RPW B Y G +GFT G F T +BYG G F T +RPW RPW +BYG RPW . 0 6 4 ( 1 ) * • 1 . 2 2 3 ( 0 ) . 7 8 7 ( 2 ) . 2 1 5 ( 1 ) . 8 1 2 ( 2 ) 1 . 8 7 3 ( 1 ) . 7 7 5 ( 1 ) 3 . 1 6 9 ( 1 ) . 6 1 6 ( 2 ) . 3 6 8 ( 1 ) . 6 3 4 ( 1 ) 1 . 3 9 0 ( 2 ) . 4 7 0 ( 1 ) • 1 . 3 2 4 ( 0 ) . 2 6 9 ( 1 ) . 0 9 2 ( 1 ) . 5 3 5 ( 1 ) . 6 4 3 ( 1 ) +GFT t * ( ) - NUMBER OF S I G N I F I C A N T P A R T I A L R E G R E S S I O N C O E F F I C I E N T S MAXIMUM I S ( 2 ) ( Indices of niche differentiation calculated for the species mixtures from their competitive ability indices are presented in Table 13. Only with BYG-GFT in 75 ppb does this double quotient exceed unity, indicating that there is niche differentiation, i.e. the species are competing for different resources. In all other combinations and ozone treatments, the species appear to be competing for the same resources. However, it should be noted that in no case were all the contributing coefficients significantly different from zero. Table 13. The niche differentiation indices from the multiple linear regression analyses. BYG-GFT BYG-RPW GFT-RPW 0 ppb -.9625(2)* .0520(2) .4027(2) 75 ppb 1.9521(3) .4914(3) .5115(3) * number of significant partial regression coefficients - maximum is 4 150 ppb -.3562(1) .2514(2) .0592(2) 4.3.2 Sequential Simple Regressions The results of these analyses are shown in Table 14 and depicted in an additive series form in Figures 2 through 7. As in the multiple linear regression analysis, the virtual yield of an isolated plant of GFT and RPW decreases with increasing ozone concentration, while that of an isolated plant of BYG is higher in the 75 ppb ozone treatment than in the control or 150 ppb treatment. T A B L E 1 4 : R e s u l t s o f s e q u e n t i a l s i m p l e r e g r e s s i o n m o d e l . B l , 0 B l , l o r B i , 2 t R M o d e l BYG MONO +RPW +GFT 6 . 9 7 . 0 1 8 9 . 1 4 9 7 * * . 1 0 4 * * . 0 0 . 0 8 . 0 9 n s * * * G F T MONO +BYG +RPW 1 4 . 7 7 . 5 3 8 * * , 6 0 3 * * * * . 9 0 0 * * * * . 1 3 . 2 8 . 3 6 ** **** **** RPW MONO +BYG +GFT 6 . 5 8 , 5 6 3 * * , 6 1 5 * * * * , 2 4 9 * * . 1 2 . 2 9 . 0 8 ** **** ** 7 5 p p b / 7 h r s BYG MONO +RPW +GFT 3 . 2 4 . 2 4 1 * * , 2 3 3 * * * * . 0 5 6 . 1 8 . 2 0 . 0 4 * * * * * * n s G F T MONO +BYG +RPW 1 7 . 5 6 . 4 1 5 . 8 7 0 * * * * , 9 2 2 * * * * . 0 7 . 4 0 . 3 5 n s * * * * * * * * RPW MONO +BYG +GFT 8 . 6 9 . 5 4 7 * * ,537**** ,368*** . 1 1 . 2 3 . 1 2 ** **** *** 1 5 0 p p b / 3 . 5 h r s BYG MONO +RPW +GFT 5 . 2 8 . 1 9 9 * . 1 9 6 * * . 0 6 3 . 0 9 . 1 0 . 0 4 * * * n s G F T MONO +BYG +RPW 1 9 . 8 7 . 1 5 3 . 7 6 3 * * * * 1 . 1 4 2 * * * * . 0 2 . 3 3 . 4 0 n s * * * * * * * * RPW MONO +BYG +GFT 1 1 . 3 8 . 3 7 7 * . 6 9 2 * * * * . 4 1 7 * * * . 0 6 . 2 9 . 1 4 * **** **** * p - . 1 ** p = . 0 5 *** p - . 0 1 **** p - . 0 0 1 f ^ m o n o c u l t u r e B . . ' m i x t u r e FIGURE 2: Relationships of estimated total yields (g) of BYG in monoculture and in the presence of three densities of GFT, and of GFT at these densities, in three ozone treatments. The additive series curves were derived from Equations 4 and 5. The curves for the indicator species were obatained using Equation 1 (Dew, 1972). 75 PPB/7 HRS 150 PPB/3.5 HRS I T at DENSITY OF BYG 56 FIGURE 3 : Relationships of estimated total yields (g) of BYG in monoculture and in the presence of three densities of RPW, and of RPW at these densities in three ozone treatments. The curves were derived as for Figure 2. 57 75 PPB/7 HRS DENSITY OF BYG 150 PPB/3.5 HRS DENSITY OF BYG FIGURE 4: Relationships of estimated total yields (g) of GFT in monoculture and in the presence of three densities of BYG, and of BYG at these densities, in three ozone treatments. The curves were derived as for Figure 2. CONTROL J.2 T 9 10 DENSITY O f GFT 4 err MONO X 0TT«5 BYG • GTT+10 BYG • cf M i ere « 5 B Y G . . X 10 BYG • I5 8YC 75 PPB/7 HRS A GTT UONO X GFT+5BYG_ O GTT* 10 BYG • GFT* 15 BYG 0 5 BYG X 10 BYG . • 13 BYG 60 However, in comparison to the multiple linear regression analysis the coefficients of determination are for the most part appreciably higher (0-0.40), meaning that more of the variation is explained by the effect of density in this model. In the case of BYG monoculture in the control treatment, none of the variation is explained by the density, agreeing with the multiple linear regression analysis (Table 10) in which BYG in the control treatment did not show any significant intra-specific competition in the presence of either RPW or GFT. This lack of a significant relationship shows up in Figures 2 and 3. The non-significance of the models of BYG in mixture with GFT in the 75 and 150 ppb treatments indicates that GFT does not have any significant inter-specific effect on BYG yield in those treatments. This is apparent in Figure 2. There is a significant effect of intra-specific competition on yield of BYG in the 75 and 150 ppb ozone treatments. In Figures 2 and 3, this effect is demonstrated by the asymptotic nature of the monoculture curve in those ozone treatments, as compared to the control. In contrast, intra-specific competition only significantly effects GFT yield in the control, but not in the 75 or 150 ppb ozone treatments (Figures 4 and 5), which suggests the ozone is affecting yield to the point where density is no longer important in explaining the variation. RPW experiences significant intra-specific competition in all treatments (Figures 6 and 7). GFT again shows a negative effect on yield of BYG in the control and 150 ppb ozone treatments, but only the former is statistically significant. In Figure 2 this negative effect shows up as an enhanced BYG yield with each addition of GFT. RPW, however, significantly effects BYG yield in all treatments. This significant inter-specific effect is depicted in Figure 3 as asymptotic curves at each density of RPW. Conversely, RPW and GFT experience very significant inter-specific competition from BYG in all treatments (Figures 6 and 4, respectively). RPW yield is significantly affected by GFT in all treatments (Figure 7), and GFT is highly significantly affected by RPW inter-specific competition in all treatments (Figure 5). FIGURE 5: Relationships of estimated total yields (g) of GFT in monoculture and in the presence of three densities of RPW, and of RPW at these densities, in three ozone treatments. The curves were derived as for Figure 2. 62 CONTROL 2 - i 75 PPB/7 HRS 2 - i DENSITY OF GFT 150 PPB/3.5 HRS 2 -1 DENSITY OF GFT 63 FIGURE 6: Relationships of estimated total yields (g) of RPW in monoculture and in the presence of three densities of BYG, and of BYG at these densities, in three ozone treatments. The curves were derived as for Figure 2. CONTROL 75 PPB/7 HRS 150 PPB/3.5 HRS 3.2-, X RPW+5 BYC • RPW+10 BYC • W W « B BYC I 5 BYC H K> BYC • *5BYG DENSITY OF RPW FIGURE 7: Relationships of estimated total yields (g) of RPW in monoculture and in the presence of three densities of GFT, and of GFT at these densities, in three ozone treatments. The curves were derived as for Figure 2. 75 PPB/7 HRS 67 The competition indices derived from these regressions are presented in Table 15. The indices show similar trends as for those derived from the multiple linear regression analysis (Table 12). The competition abilities of RPW with GFT, GFT with RPW and GFT with BYG again decrease with increasing ozone concentration. In addition, the index for BYG over GFT is larger in the 75 ppb treatment than in the other two. However, there are a few discrepancies between the competition indices from the two forms of analysis. BYG in mixture with GFT in the control shows a much lower competitive ability than in the previous analysis (Table 12), although the index is still negative. RPW in mixture with BYG shows somewhat more competitive ability in the 75 ppb treatment than in the other two treatments in the sequential simple regression (Table 15), rather than showing a trend of decreasing ability as the ozone concentration increased in the previous analysis (Table 12). The converse is true of GFT in mixture with RPW, which now suggests a trend of decreasing competitive ability as the ozone concentration is increased, rather than more competitive ability in the 75 ppb treatment (Table 12). From this analysis, the ranking of the competitive abilities of the species for each ozone treatment is: 0 ppb 75 ppb 150 ppb RPW-GFT BYG-GFT BYG-GFT RPW-BYG RPW-GFT BYG-RPW GFT-BYG BYG-RPW RPW-GFT GFT-RPW RPW-BYG RPW-BYG BYG-GFT GFT-BYG GFT-BYG BYG-RPW GFT-RPW GFT-RPW The indices for niche differentiation, are shown in Table 16. Unlike the indices derived from the multiple linear regression model, which indicated that all species were competing for the same resources in all treatments except BYG and GFT at 75 ppb, this analysis 68 T A B L E 1 5 : C o m p e t i t i o n i n d i c e s f f r o m s e q u e n t i a l s i m p l e r e g r e s s i o n m o d e l . S p e c i e s C o m b i n a t i o n 0 OZONE TREATMENT 75 p p b / 7 h r s 1 5 0 p p b / 3 . 5 h r s BYG +RPW BYG +GFT G F T +BYG G F T +RPW RPW +BYG RPW . 1 2 6 ( 1 ) * - . 1 8 2 ( 1 ) . 8 9 2 ( 2 ) . 5 9 8 ( 2 ) . 9 1 5 ( 2 ) 2 . 2 6 1 ( 2 ) 1 . 0 3 4 ( 2 ) 4 . 3 0 4 ( 1 ) • 4 7 7 ( 1 ) . 4 5 0 ( 1 ) 1 . 0 1 9 ( 2 ) 1 . 4 8 6 ( 2 ) 1 . 0 1 5 ( 2 ) - 3 . 1 5 9 ( 1 ) . 2 0 0 ( 1 ) . 1 3 4 ( 1 ) . 5 4 5 ( 2 ) . 9 0 4 ( 2 ) +GFT t b l . l / b 1 . 2 * ( ) - NUMBER OF S I G N I F I C A N T P A R T I A L R E G R E S S I O N C O E F F I C I E N T S MAXIMUM I S ( 2 ) 69 suggests that BYG and RPW at 75 ppb and GFT and RPW at 0 ppb also show niche differentiation. Furthermore, in these latter situations the double quotient is based on coefficients each of which was significant. Table 16. Niche differentiation indices from the sequential simple regressions. 0 ppb 75 ppb 150 ppb BYG-GFT -.1621(3)* 2.0528(2) -.6333(2) BYG-RPW .1155(3) 1.0535(4) .5531(4) GFT-RPW 1.3516(4) .6690(3) .1211(3) * number of significant partial regression coefficients - maximum is 4 4.3.3 Square-root Regressions The method of analysis of Dew (1972) uses a simple square root linear regression to describe the crop loss due to the addition of known densities of a weed (Equation 1, page 15). The coefficients of determination and significance levels are in Table 17; the curves based on values predicted by the regressions are shown in Figures 2 through 7. The regression of GFT density on BYG yield is only significant at density 5 in the control treatment and at density 15 in the 150 ppb treatment. The positive nature of the coefficients reflects the enhancing effect of GFT on BYG yield which was evident, but not significant, in the SSR model. Figure 4 demonstrates this beneficial relationship. The 70 T A B L E 1 7 : C o e f f i c i e n t s o f d e t e r m i n a t i o n f r o m s q u a r e - r o o t r e g r e s s i o n . OZONE TREATMENT S p e c i e s C o m b i n a t i o n 0 7 5 n o b / 7 h r s 1 5 0 Dob/ 3 . 5 D E N S I T Y RPW 5 . 1 4 * * t . 1 8 * * . 1 3 * * +BYG 10 . 1 0 * . 0 8 . 1 5 * * 15 . 1 8 * * . 0 1 . 0 7 2 0 . 1 2 • 0 1 (+) . 1 5 BYG 5 . 0 1 . 1 3 * * . 0 4 +RPW 10 . 0 4 . 0 7 . 0 1 15 • 0 0 (+) . 0 1 . 0 5 (+) 20 ,55*** . 0 4 . 0 2 BYG 5 . 1 5 * (+) . 0 6 . 1 0 (+) +GFT 10 . 0 0 (+) • 0 2 (+) . 0 0 (+) 15 . 0 8 (+) . 0 5 (+) . 2 9 * * (+) 2 0 . 1 3 . 0 2 • 0 2 (+) G F T 5 . 2 0 * * .35*** . 0 6 +BYG 10 . 0 7 . 1 9 * * .35*** 1 5 . 0 2 (+) . 1 3 . 0 2 2 0 . 1 8 . 1 8 . 1 2 RPW +GFT 5 . 0 2 . 1 3 * * . 0 9 * 1 0 . 0 1 . 0 1 . 0 0 (+) 1 5 . 0 4 . 0 6 (+) . 0 1 2 0 . 0 0 . 1 4 . 0 1 G F T 5 . 3 3 * * * * , 2 8 * * * . 3 3 * * * * +RPW 10 . 1 5 * * , 2 0 * * . 3 0 * * * 15 • 0 1 (+) . 0 1 . 0 6 2 0 . 0 5 . 0 4 . 2 0 * * P - . 1 0 ** P - . 0 5 *** P = . 0 1 **** P = . 0 0 1 f s i g n i f i c a n c e l e v e l o f m o d e l 71 regression of BYG density on GFT yield is only significant in the 0 and 75 ppb treatments at density 5 and the 75 and 150 ppb treatments at density 10. Figure 2 reflects this. The regression of BYG density on RPW yield is significant in all ozone treatments at density 5, in the control and 150 ppb treatment at density 10 and only in the control at density 15. This would suggest that as the density of RPW increases, the presence of ozone in some way prevents BYG from significantly affecting RPW yield. These trends are seen in Figure 3. The regression for RPW on BYG is only significant in the 75 ppb ozone treatment at density 5 and in the control treatment at density 20 (Figure 6). The regression of GFT density on RPW yield is only significant at the lowest density of RPW under the two ozone-added treatments (Figure 5). However, the model regressing RPW density against GFT yield is very significant in all ozone treatments at densities 5 and 10, and slightly significant at density 20 in the 150 ppb treatment. These effects are seen in Figure 7. 4.4 REPLACEMENT SERIES The method of Thomas (1970) detected no significant heterogeneity between species in the three mixtures using the 4-parameter model. As this satisfied one of the assumptions underlying the three-parameter model, it could be tested. The results of testing the 4-parameter and 3-parameter models are presented in Table 18. The 3-parameter model of BYG-GFT replacement series mixtures at total density 10 and 15 indicates that in all ozone treatments the species are competing for the same space, except at the highest density (20) in both the filtered air control and 150 ppb treatments. The replacement series diagrams for BYG-GFT in mixture at total density 10, 15, 20 and 25 plants per pot are shown in Figures 8, 9, 10 and 11 respectively. In all total densities and ozone treatments, the yield total shows a beneficial effect of the two species growing together. At total density 10 and 15 (Figures 8 and 9) BYG does better in the 75 ppb T A B L E 1 8 : R e s u l t s o f r e p l a c e m e n t s e r i e s m o d e l a n a l y s i s S p e c i e s M i x t u r e T o t a l D e n s i t y K 12 2 1 b e t w e e n m o d e l s K 1 2 X K 2 1 BYG : G F T 1 0 1 5 2 0 0 . 8 4 0 1 . 7 7 6 3 . 2 4 9 0 . 7 3 9 0 . 5 9 0 1 . 7 5 8 n s n s * * * 0 . 6 2 1 . 0 5 5 . 7 1 BYG : RPW 1 0 15 2 0 3 . 3 2 1 2 . 9 0 5 1 . 7 3 0 0 . 2 1 0 0 . 3 5 4 0 . 9 5 7 n s n s n s 0 . 7 0 1 . 0 3 1 . 6 6 G F T : RPW 10 1 5 2 0 0 . 9 2 1 0 . 6 9 9 1 . 1 8 1 0 . 5 6 1 0 . 9 0 3 9 . 3 5 5 n s n s * * * 0 . 5 2 0 . 6 3 1 1 . 0 5 7 5 p p b / B Y G : G F T 7 h r s 10 2 . 9 2 0 0 . 5 1 2 n s 1 . 5 0 15 2 . 7 4 4 0 . 7 1 8 n s 1 . 9 7 2 0 2 . 1 0 6 0 . 8 3 5 n s 1 . 7 6 B Y G : RPW 10 1 . 4 5 9 0 . 1 9 7 n s 0 . 2 9 15 2 . 5 2 2 2 . 2 4 6 * * * 5 . 6 6 2 0 1 . 5 7 0 0 . 6 8 0 n s 1 . 0 7 G F T : RPW 1 0 0 . 6 8 9 0 . 7 0 8 n s 0 . 5 0 1 5 0 . 9 0 3 0 . 3 6 1 * * 0 . 3 2 2 0 0 . 6 7 9 3 . 4 2 7 n s 2 . 3 3 1 5 0 p p b / BYG : G F T 3 . 5 h r s 1 0 0 . 9 4 3 0 . 3 4 9 n s 0 . 3 3 1 5 2 . 0 1 5 0 . 7 0 3 n s 1 . 4 2 2 0 4 . 5 2 8 0 . 9 5 0 * * * 4 . 3 0 BYG : RPW 10 1 . 9 9 0 0 . 2 6 9 n s 0 . 5 4 1 5 1 . 7 1 4 0 . 6 1 1 n s 1 . 0 5 20 1 . 7 2 5 0 . 5 3 3 n s 0 . 9 2 G F T : RPW 10 1 . 2 0 9 0 . 5 4 7 n s 0 . 6 6 1 5 0 . 6 1 4 0 . 4 4 7 * * * 0 . 2 7 2 0 0 . 5 1 1 2 . 8 2 7 n s 1 . 4 4 * * p = . 0 5 * * * p = . 0 1 73 FIGURE 8: Replacement series diagrams for BYG and GFT at total density of 10 plants/pot for three ozone treatments. The diagrams were constructed by connecting points which were obtained from Equations 4 and 5. REPLACEMENT SERIES, BYG:GFT CONTROL 1.5-1 — a 2.8-10:0 5:5 0:10 DENSITY COMBINATIONS, BYG:GFT REPLACEMENT SERIES, BYG:GFT 75 PPB/7 HRS 3.5-1 °> 2.8-a 10:0 5:5 0:10 OENSITY COMBINATIONS. BYG:GFT REPLACEMENT SERIES, BYG:GfT 150 PPB/3.5 HRS 3.5 | 9 2.8-10:0 5:5 0:10 DENSITY COMBINATIONS, BYG:GFT 75 FIGURE 9: Replacement series diagrams for BYG and GFT at total density of 15 plants/pot for three ozone treatments. The diagrams were constructed as for Figure 8. REPLACEMENT SERIES, BYG:GFT CONTROL <* 2.8 15:0 10:5 5:10 0:15 DENSITY COMBINATIONS, BYG:GFT REPLACEMENT SERIES, BYG:GFT 150 PPB/3.5 HRS 3.5-1 o> Ze-is' >-15:0 10:5 5:10 0:15 DENSITY COMBINATIONS, BYGrGTT FIGURE 10: Replacement series diagrams for BYG and GFT at total density of 20 plants/pot for three ozone treatments. The curves were derived from Equations 4 and 5. 78 REPLACEMENT SERIES, BYG:GFT CONTROL 3.5-1—• 20:0 15:5 10:10 5:15 0:20 DENSITY COMBINATIONS, BYG:GFT REPLACEMENT SERIES, BYGrGFT 75 PPB/7 HRS 3.5-1 w 2.8-O 20:0 15:5 10:10 5:15 0:20 DENSITY COMBINATIONS. BYG:GFT REPLACEMENT SERIES, BYG:GFT 150 PPB/3.5 HRS 3.3 I 20:0 15:5 10:10 5:15 0:20 DENSITY COMBINATIONS, BYG:GFT 79 FIGURE 11: Replacement series diagrams for BYG and GFT at total density of 25 plants/pot for three ozone treatments. The curves were derived as for Figure 10. REPLACEMENT SERIES, BYG:GFT CONTROL J.5 0+—11 1 1 1 1 * 25:0 20:5 15:10 10:15 5:20 0:25 DENSITY COMBINATIONS, BYG:GFT REPLACEMENT SERIES, BYG:GFT 75 PPB/7 HRS J.5-1 25:0 20:5 15:10 10:15 5:20 0:25 DENSITY COMBINATIONS, BYG:GFT REPLACEMENT SERIES, BYG:GFT 150 PPB/3.5 HRS 25:0 20:5 15:10 10:15 5:20 0:25 DENSITY COMBINATIONS, BYG:GFT FIGURE 12: Replacement series diagrams for BYG and RPW at total density of 10 plants/pot for three ozone treatments. The diagrams were constructed as for Figure 8. REPLACEMENT SERIES. BYG:RPW CONTROL 10:0 5:5 0:10 DENSITY COMBNATIONS, BYG.-RPW REPLACEMENT SERES, BYG:RPW 75 PPB/7 HRS K>:0 5:5 0:10 DENSITY COMBINATIONS, B YG:RPW REPLACEMENT SERIES. BYG:RPW 150 PPB/3.5 HRS J.5-] °> It » : 0 5:5 0:10 DENSITY COMBINATIONS, BYG:RPW FIGURE 13: Replacement series diagrams for BYG and RPW at total density of 15 plants/pot for three ozone treatments. The diagrams were constructed as for Figure 8. REPLACEMENT SERIES, BYG:RPW CONTROL J.S-i ;  °> 2» 15:0 10:5 5:10 0:15 OENSfTY COMBINATIONS, BYG:RPW REPLACEMENT SERIES, BYG:RPW 75 PPB/7 HRS St I 15:0 10:5 5:10 0:15 DENSITY COMBINATIONS, BYG:RPW REPUCEMENT SERES, BYG:RPW 150 PPB/3.5 HRS J.S-i . <" 2.»-15:0 10:5 5:10 0:15 DENSITY COMBINATIONS, BYG:RPW 85 treatment than in the control or 150 ppb treatment. In the higher total densities (Figures 10 and 11) the yield of BYG decreases as the ozone concentration increases. GFT appears to be behaving in a linear fashion in all cases, whereas the yield of BYG is enhanced by the presence of GFT. The convex nature of the total yield curves is due to this enhanced yield of BYG, which is an indication of BYG's more vigorous and aggressive nature. A test of the 3-parameter model on the replacement series mixtures of BYG and RPW indicates the species are crowding for the same space in all cases except at total density of 15 under the 75 ppb ozone treatment. The replacement series diagrams for BYG-RPW mixtures at total densities 10, 15, 20 and 25 are shown in Figures 12, 13, 14 and 15, respectively. At the lower total densities (Figures 12 and 13), the yield total curves are linear in all ozone treatments, representing an even replacement of biomass as each species is replaced by the other. At the higher densities (Figures 14 and 15), the 75 and 150 ppb treatments show a linear replacement of BYG with RPW, however the control ozone treatment shows concave total yield curves. This means that in the control treatment at high density, the mixture yields less than would be expected if the species simply replaced each other. This disadvantageous effect of inter-specific competition is relieved by the addition of ozone. The curves for RPW are linear in both cases, with the BYG yield appearing to show the most decrease in yield. Results of the 3-parameter model for the mixture of GFT and RPW indicate that the two species are competing for the same space in all ozone treatments at density =10. Both the 75 and 150 ppb treatments at density = 15 are crowding for different space, in contrast to the control treatment where they crowd for similar space. Exactly the opposite situation exists at total density of 20, where under the control treatment the species are crowding for different space, and under both the 75 and 150 ppb treatments the species are crowding for the same space. The replacement series diagrams for RPW and GFT in mixture at total densities of 10, 15, 20 and 25 are shown in Figures 16, 17, 18 and 19, respectively. In all total densities, FIGURE 14: Replacement series diagrams for BYG and RPW at total density of 20 plants/pot for three ozone treatments. The curves were derived as for Figure 10. REPLACEMENT SERIES, BYG:RPW CONTROL 3.3 • 20:0 15:5 10:10 5:15 0:20 DENSITY COMBINATIONS, BYG:RPW REPLACEMENT SERIES, BYG:RPW 75 PPB/7 HRS 3.5-1 : 0 1 2.8-20:0 15:5 10:10 5:15 0:20 DENSITY COMBINATIONS, BYG:RPW REPLACEMENT SERIES, BYG:RPW 150 PPB/3.5 HRS 3.3 I <* 2.8-Q 20:0 15:5 10:10 5:15 0:20 DENSITY COMBINATIONS, BYG:RPW FIGURE 15: Replacement series diagrams for BYG and RPW at total density of 25 plants/pot for three ozone treatments. The curves were derived as for Figure 10. REPLACEMENT SERIES, BYG:RPW CONTROL J.S 25:0 20:5 15:10 10:15 5:20 0:25 DENSITY COMBNATIONS. BYG:RPW REPLACEMENT SERIES, BYG:RPW 75 PPB/7 HRS 25:0 20:5 15:10 10:15 5:20 0:25 DENSITY COMBINATIONS. BYG:RPW REPLACEMENT SERES, BYG:RPW 150 PPB/3.5 HRS 3.3-] 25:0 20:5 15:10 10:15 5:20 0:25 DENSITY COMBINATIONS, BYG:RPW FIGURE 16: Replacement series diagrams for GFT and RPW at total density of 10 plants/pot for three ozone treatments. The diagrams were constructed as for Figure 8. REPLACEMENT SERIES; RPW:GFT 75 PPB/7 HRS FIGURE 17: Replacement series diagrams for GFT and RPW at total density of 15 plants/pot for three ozone treatments. The diagrams were constructed as for Figure 8. REPLACEMENT SERIES; RPW:GFT 75 PPB/7 HRS US i CO 15:0 10:5 5:10 0:15 DENSITY COMBINATIONS; RPW:GFT REPLACEMENT SERIES; RPW:GFT 150 PPB/3.5 HRS ui-i g r i i x 15:0 10:5 5:10 0:15 DENSITY COMBINATIONS; RPW:GFT FIGURE 18: Replacement series diagrams for GFT and RPW at total density of 20 plants/pot for three ozone treatments. The curves were derived as for Figure 10. REPLACEMENT SERIES; RPW:GFT CONTROL 1.35-1 — — 20:0 15:5 10:10 5:15 0:20 DENSITY COMBINATIONS; RPW:GFT REPLACEMENT SERIES; RPW:GFT 75 PPB/7 HRS t33 I 20:0 15:5 10:10 5:15 0:20 DENSITY COMBINATIONS; RPW:GFT REPLACEMENT SERES; RPW:GFT 150 PPB/3.5 HRS 135-1 20:0 15:5 10:10 5:15 0:20 DENSITY COMBINATIONS; RPW:GFT FIGURE 19: Replacement series diagrams for GFT and RPW at total density of 25 plants/pot for three ozone treatments. The curves were derived as for Figure 10. 97 REPLACEMENT SERIES; RPW:GFT CONTROL A RPW • TOT>I 25 :0 2 0 : 5 15:10 10:15 5 :20 0 : 2 5 DENSITY COMBINATIONS: RPW:GFT REPLACEMENT SERIES; RPW:GFT 75 PPB/7 HRS A BPW p X GFT  • T O U l 2 5 : 0 2 0 : 5 15:10 10:15 5 :20 0 : 2 5 DENSITY COMBINATIONS; RPWiGTT REPLACEMENT SERIES; RPW:GFT 150 PPB/3.5 HRS 2 5 : 0 2 0 : 5 15:10 10:15 5 :20 0 : 2 5 DENSITY COMBINATIONS; RPW:GFT the control ozone treatment displays an over-yielding effect of the mixture on the total yield, the 75 ppb ozone treatment shows an apparently even replacement of the species with the total yield behaving in a linear fashion and the 150 ppb ozone treatment displays an under-yielding situation where the total yield is less than would be expected if the species replaced each other without negative inter-specific competition effects. This implies that with the addition of an increasing concentration of ozone, the inter-specific relation of GFT-RPW changes from one of mutual benefit to one of mutual inhibition. From the data, it is clear that GFT is contributing the most to the under-yielding situation; its yield curves are concave in all total densitites in the 150 ppb treatment. 4.5 SIZE F R E Q U E N C Y DISTRIBUTIONS 4.5.1 BYG The frequency diagrams for BYG in monoculture, in mixture with GFT and in mixture with RPW are in Figures 20, 21 and 22 respectively. As the total density per pot increases, the frequency diagrams become more skewed, displaying a hierarchy of plant sizes consisting of many small plants and a few large plants. At any of the three densities of BYG (clearly in the case of the higher densities), the addition of either RPW or GFT causes a more skewed distribution (for example, Figures 20b, 22d and 22e for 15 BYG with 0,5 or 10 RPW). However, comparing the distributions at constant total density, the competitive dominance of BYG is shown by the decreased skewness as the density of the second species increases (for example, compare 20c, for 20 BYG, with Figures 22d (15 BYG + 5 RPW) and Figure 22b (10 BYG +10 RPW)). No clear effect of ozone is observed in these figures. FIGURE 20: Size frequency diagrams for BYG in monoculture. 100 BYG MONO DENSITY=10 2 Hi m C O N T M X o no wu * H 20 25 30 33 40 43 30 35 60 65 70 75 p e r c e n t o f t o t a l w e i g h t BYG MONO DENSITY=15 1— 1 1 1 1 t 1 I t 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 p e r c e n t o f t o t a l w e i g h t BYG MONO DENS!TY=20 3 m CWTML HI nfrvjm • MffiyuM FIGURE 21: Size frequency diagrams for BYG in mixture with GFT. 102 10 BYG IN MIXTURE WITH 5 GFT m cctnau Q W f f V U M H 10 15 20 25 30 35 40 45 50 55 60 <5 70 75 PERCENT OF TOTAL WEIGHT 10 BYG IN MIXTURE WITH 10 GFT o m CONTROL CD nowo/UMM 20 25 50 55 40 45 50 55 40 55 70 75 PERCENT OF TOTAL WEIGHT 10 BYG IN MIXTURE WITH 15 GFT 3 »• m coMTioi K) 15 20 25 50 55 40 45 50 55 60 65 70 75 PERCENT OF TOTAL WEIGHT 103 15 BYG IN MIXTURE WITH 5 GFT 10 15 20 29 30 35 40 49 SO 95 6 0 6 3 70 75 PERCENT OF TOTAL WEIGHT 15 BYG IN MIXTURE WITH 10 GFT MCOMTKOl 10 15 20 25 30 33 40 43 50 55 60 PERCENT OF TOTAL WEIGHT 20 BYG IN MIXTURE WITH 5 GFT i h u. O m count*. (0 15 20 23 30 33 40 45 30 35 50 65 70 75 PERCENT OF TOTAL WEIGHT FIGURE 22: Size frequency diagrams for BYG in mixture with RPW. 10 BYG IN MIXTURE WITH 5 RPW 1 H H CONTtO. CD OO •** 10 IS JO 25 3 0 3 3 4 0 43 3 0 33 60 63 70 75 PERCENT OF TOTAL WEIGHT 10 BYG IN MIXTURE WITH 10 RPW 3 ° T Q i M COMTRQi o oowvum 15 20 23 30 35 40 45 50 55 60 65 70 75 PERCENT OF TOTAL WEIGHT 10 BYG IN MIXTURE WITH 15 RPW 10 15 20 25 30 33 40 45 50 55 60 PERCENT OF TOTAL WEIGHT 106 15 BYG IN MIXTURE WITH 5 RPW 5 0 9 10 IS 20 23 30 33 40 43 50 SS 60 <S 70 75 PERCENT O f TOTAL WEIGHT 15 BYG IN MIXTURE WITH 10 RPW 3 «A i ' i " ' i " 'i-" — r — i — i — i — i — i — i — i 0 9 10 IS 20 23 30 33 40 49 SO SS 60 69 70 79 PERCENT O f TOTAL WEIGHT 20 BYG IN MIXTURE WITH 5 RPW 1 1 1 1 1 1 1 1 1 0 5 10 15 20 25 30 33 40 45 SO 39 60 65 70 75 PERCENT OF TOTAL WEIGHT 107 4.5.2 GFT The frequency diagrams for GFT monocultures, in mixture with BYG and in mixture with RPW are shown in Figures 23, 24 and 25, respectively. Unlike BYG, GFT appears much less hierarchical in its size distributions in all densities. There are very few individuals in the higher percentage weight classes in any density treatments. Contrary to Wiener's (1986) suggestion that a suppressed species would display more size inequality in mixture than in monoculture, GFT does not appear to display any size inequality in mixture or in monoculture with the exception of monoculture density 20 under the control treatment. This treatment has quite a large number of small individuals and a few large ones. The same total density mixtures with BYG and RPW have a much more even size distribution. The 75 ppb and 150 ppb ozone treatments do not have this size inequality in monoculture, which suggests that the ozone treatments are providing a similar stress as that of the interspecific competition of BYG and RPW. there are no apparent ozone effects on the size distribution of GFT. 4.5.3 RPW The frequency diagrams for RPW in monoculture, in mixture with BYG and in mixture with GFT are shown in Figures 26, 27 and 28, respectively. At all densities and in all ozone treatments, RPW displays more size inequality than either BYG or GFT. There are quite a number of individuals in the large percentage weight classes, even one plant in the 20:5 density treatment with GFT under the control ozone treatment which fell in the 70-75% weight class. RPW has an equally skewed distribution both in monoculture and in the mixtures, which would suggest, by the interpretation of the dominance-suppression model of Weiner (1986) that it is neither suppressed nor competitively superior. However, size differences can also be due to inherent genetic variation, and it is possible that for a population of RPW, its genetic makeup would naturally produce a hierarchical distribution. FIGURE 23: Size frequency diagrams for GFT in monoculture. 109 GFT MONO DENSITY=10 m CONTKOL <tm - i 1 1 i i 1 r~—i i ' -0 5 10 15 20 25 30 35 40 43 SO 55 60 65 70 75 PERCENT OF TOTAL WEIGHT GFT MONO DENSITY=15 3 • i CONTROL • nm / T « i tS 20 25 JO 35 40 45 50 35 *0 65 70 75 PERCENT OF TOTAL WEIGHT GFT MONO DENSITY=20 | « i ^ '[— r _ _ | , , , 1 , , , , , 0 5 10 15 20 25 30 35 40 45 SO 55 60 65 70 75 PERCENT OF TOTAL WEIGHT FIGURE 24: Size frequency diagrams for GFT in mixture with BYG. 10 GFT IN MIXTURE WITH 5 BYG m CONITCC 20 29 30 3 5 4 0 43 SO 35 60 65 70 75 PERCENT O f TOTAL WEIGHT 10 GFT N MIXTURE WITH 10 BYG 3 12 T1 < 3 T " 1 1 1 1 1 1 1 1 1 1 0 5 10 15 20 23 30 33 40 45 50 55 60 65 70 75 PERCENT O f TOTAL WEIGHT m CONTtCK a T l f f t / T Mi 10 GFT IN MIXTURE WITH 15 BYG Q > < 3 m COHTtOL IS 20 25 30 35 40 45 50 55 60 65 70 75 PERCENT OF TOTAL WEIGHT 112 15 GFT IN MIXTURE WITH 5 BYG 3 n r . i I , — i , . i . , — , — , — , — , — , — , — , — , — , — , — , 0 S 10 O 20 29 SO 13 40 45 50 SS iO 65 70 75 PERCENT OF TOTAL WEIGHT 15 GFT IN MIXTURE WITH 10 BYG m CONTROL m n W t / T H R S O U R V U M IS 20 25 30 35 40 43 SO S3 60 PERCENT OF TOTAL WEIGHT 20 GFT IN MIXTURE WITH 5 BYG PERCENT OF TOTAL WEIGHT FIGURE 25: Size frequency diagrams for GFT in mixture with RPW. 114 10 GFT IN MIXTURE WITH 5 RPW 3 UH 5 § H Li-o Hi CQM1KX • HPfVLIM U 20 23 30 39 40 43 80 S3 30 83 70 73 PERCENT OF TOTAL WEIGHT 10 GFT IN MIXTURE WITH 10 RPW "J 12H • 1 COMTMX 20 23 30 35 40 45 50 55 60 65 70 75 PERCENT OF TOTAL WEIGHT 10 GFT IN MIXTURE WITH 15 RPW 3 UH Q 20 25 30 35 40 45 50 55 60 65 70 75 PERCENT OF TOTAL WEIGHT 115 15 GFT IN MIXTURE WITH 5 RPW mm CONTROL mnfrm/imt CJ BOWt/UHH 15 20 25 30 33 40 45 50 55 50 65 70 75 PERCENT OF TOTAL WEIGHT 15 GFT IN MIXTURE WITH 10 RPW RB CONTROL O CM * * / ! . » HRS 15 20 25 30 33 40 45 50 35 60 65 70 75 PERCENT OF TOTAL WEIGHT 20 GFT IN MIXTURE WITH 5 RPW Hi nm/iwi IS 20 25 30 35 4 0 45 SO 55 60 65 70 75 PERCENT OF TOTAL WEIGHT 116 FIGURE 26: Size frequency diagrams for RPW in monoculture. RPW MONO DENSITY=10 pn i,--. i , - . i , - . i, n- | | - , , , , , , 0 5 K> 13 20 25 10 » 40 45 50 55 M 65 70 75 PERCENT OF TOTAL WEIGHT RPW MONO DENSITY=15 9 -m C0MTM1 •I n i w r n a »M/UM 20 25 10 15 40 45 30 55 60 65 70 75 PERCENT OF TOTAL WEIGHT RPW MONO DENSITY=20 0 5 10 15 20 25 30 33 40 43 30 35 60 65 70 75 PERCENT OF TOTAL WEIGHT FIGURE 27: Size frequency diagrams for RPW in mixture with BYG. 10 RPW IN MIXTURE WITH 5 BYG to is PERCENT OF TOTAL WEIGHT • CONTtOL i u. O 10 RPW IN MIXTURE WITH 10 BYG mm CONTROL m TSRfR/THRS 0 3 10 15 20 25 30 35 40 43 50 55 60 63 70 73 PERCENT OF TOTAL WEIGHT 10 RPW M MIXTURE WITH 15 BYG 3 "H i H o m CONTMX • M f r v u w 120 15 RPW IN MIXTURE WITH 5 BYG • J COMTIOl B nwwi 10 15 20 25 10 35 40 45 50 55 50 65 70 75 PERCENT 0F TOTAL WEIGHT 15 RPW IN MIXTURE WITH 10 BYG o e B comm. B nrrw* • Hmyuw 20 25 50 55 40 45 50 55 60 65 70 75 PERCENT OF TOTAL WEIGHT 20 RPW M MIXTURE WITH 5 BYG g > z < 2 B CONtKX B nmynn • B0NVUM1 20 25 30 35 40 43 50 35 60 65 70 73 PERCENT OF TOTAL WEIGHT FIGURE 28: Size frequency diagrams for RPW in mixture with GFT. 122 10 RPW IN MIXTURE WITH 5 GFT 3 uH Q • CdMTMl B nfnj/TMB 20 25 30 35 40 49 90 99 <0 «3 70 73 PERCENT OF TOTAL WEIGHT 10 RPW IN MIXTURE WITH 10 GFT 5 VOL ' ' 1 ' "—'i" l~" 'i " 'i- i 1 1 1 , , , 0 5 10 13 20 23 30 33 40 43 SO 5S 60 63 70 75 PERCENT OF TOTAL WEIGHT m CONTROL mm nm /THM 10 RPW IN MIXTURE WITH 15 GFT Q > 1 O 3 3 mm COMTML ma JS-*V7HW 15 RPW IN MIXTURE WITH 5 GFT 15 20 25 10 U 40 45 to U W M PERCENT OF TOTAL WEIGHT 15 RPW IN MIXTURE WITH 10 GFT IS-, I I " T ™ 1"' 1— I - I I I I - I - - I - I 1 1 0 5 U IS 20 25 JO J S 40 45 50 55 60 65 70 75 PERCENT OF TOTAL WEIGHT 20 RPW IN MIXTURE WITH 5 GFT m COWTOOL 0 S 10 IS 20 25 30 35 40 45 SO 55 60 65 70 73 PERCENT OF TOTAL WEIGHT 124 5. DISCUSSION One of the first points which is apparent in reviewing the results of the various experiments is the large amount of inherent variation. The analyses of variance consistently show significant block effects; i.e. the experiments differ significantly. The method of using experimental chambers as blocks was recommended by Hammer and Urquhart (1979) in order to remove the unwanted variation due to a lack of uniformity of plant responses to controlled environments. Many studies have shown that although controlled environment chambers supposedly offer a uniform environment to plants over treatments, there is a lack of uniformity among plants grown within a chamber, among plants in different chambers and among plants grown in the same chamber at different times (Hammer and Urquhart, 1979). It has been suggested that the between trials (runs over time) component of variance is more important than the between chamber variation. Differences in environmental conditions have been measured within and between chambers (Hammer and Langhans, 1972; Tibbitts et al., 1976) and they may be responsible for much of the unwanted variability and lack of reproducibility in plant growth within and between chambers (Hammer and Urquhart, 1979). Vibrations and handling of plants (Mitchell et al., 1975) and contaminants (such as mercury, freon and volatile paint components) which are ubiquitous in chambers (Tibbetts et al., 1977) may also contribute unwanted sources of variation. Despite the large amount of variation in plant growth over the treatments and between experiments, the extremely conservative ANOVA test (Hammer and Urquhart, 1979) revealed many significant treatment effects after the variation due to blocks was removed. In order to get a more general picture of the treatment (ozone and competition) effects over the range of experimental conditions experienced by the plants, the experiments were pooled for the regression analyses. There was no a priori reason to believe that the experiments would not be combinable. Specific analyses of each experiment for treatment effects would produce results which would be difficult to interpret because the causal 125 differences between the experiments could not be quantified, since the environment was, as far as was possible, controlled. The differences between the experiments are abundantly clear in the regression analyses in that the coefficients of determination (R ) are, for the most part, low. However, despite these low R s, many of the Multiple Linear Regression (MLR) and Sequential Simple Regression (SSR) models were significant. In essence, the trends discussed here are more conservatively significant and general in their application than they would be if the block effects were not significant. Three differing types of competition analysis were used in this thesis; the multiple linear regression (MLR) analysis of Spitters (1983), the sequential simple regression (SSR) analyses of Jolliffe (personal communication) and the analysis of Thomas (1970) based on the replacement series approach to defining competitive interactions, which, when interpreted, describes which species are "crowding" for the same space at each replacement series density. The MLR and the SSR generated competitive indices and models which showed similar trends (Tables 10, 12, 14 and 15), with minor differences in magnitude. It is evident that the SSR model explains proportionally more of the variation than does the MLR (Tables 10 and 14). The interpretation of the regression coefficients in the SSR is much simpler and elegant; the monoculture data are entirely compartmentalized and do not contribute to the mixture coefficients, as is the case in the MLR analysis. This makes the differentiation between intra- and inter-specific competitive effects much clearer. Three measures of niche differentiation were generated in this study; from the MLR, the SSR and from the replacement series analyses. A comparison of the results from the three analyses can be made (Table 19). There are discrepancies in the results. For example, the MLR suggests that GFT and RPW in the control treatment are competing for the same resources, whereas the SSR suggests there is niche differentiation. The replacement series model has GFT and RPW under the same control treatment competing for the same "space" at total density 10 and 15 but for different "space" at total density 126 T A B L E 1 9 : S u m m a r y o f i n t e r p r e t a t i o n s o f n i c h e d i f f e r e n t i a t i o n f r o m r e g r e s s i o n a n a l y s e s (MLR a n d S S R ) a n d r e p l a c e m e n t s e r i e s . Same r e f e r s t o s p e c i e s c o m p e t i n g f o r t h e s a m e s p a c e / r e s o u r c e s ; d i f f e r e n t r e f e r s t o s p e c i e s n o t c o m p e t i n g f o r t h e same s p a c e / r e s o u r c e s . S p e c i e s M i x t u r e R e p l a c e m e n t T o t a l D e n s i t y MLR S S R M o d e l 0 B Y G : G F T s a m e ( 2 ) s a m e ( 3 ) 10 same 1 5 same 2 0 d i f f e r e n t B Y G : R P W s a m e ( 2 ) s a m e ( 3 ) 10 same 1 5 same 2 0 same G F T : R P W s a m e ( 2 ) d i f f e r e n t ( 4 ) 1 0 s a m e 15 s a m e 20 d i f f e r e n t 7 5 p p b / B Y G : G F T d i f f e r e n t ( 3 ) d i f f e r e n t ( 2 ) 7 h r s 1 0 s a m e 15 s a m e 2 0 s a m e B Y G : R P W s a m e ( 3 ) d i f f e r e n t ( 4 ) 1 0 s a m e 1 5 d i f f e r e n t 20 s a m e G F T : R P W s a m e ( 3 ) s a m e ( 3 ) 10 same 15 d i f f e r e n t 2 0 s a m e 1 5 0 p p b / B Y G : G F T s a m e ( l ) s a m e ( 2 ) 3 . 5 h r s 10 same 15 s a m e 2 0 d i f f e r e n t B Y G : R P W s a m e ( 2 ) s a m e ( 4 ) 10 same 15 s a m e 2 0 same G F T : R P W s a m e ( 2 ) s a m e ( 3 ) 1 0 s a m e 1 5 d i f f e r e n t 2 0 s a m e ( ) NUMBER OF S I G N I F I C A N T R E G R E S S I O N C O E F F I C I E N T S MAXIMUM I S ( 4 ) 127 20. The niche differentiation coefficients calculated from the MLR and SSR are based onpartial regression coefficients and regression coefficients, respectively. The statistical value of these indices rests solely on the significance of the coefficients which were used. Because the interpretation of the SSR coefficients is much more biologically meaningful, and because more of the coefficients were significant in the SSR analysis, the niche differentiation indices from those analyses are more representative of the actual data. In the case of GFT and RPW in the control treatment, the SSR index is supported by four significant coefficients, while the MLR has only two. As well, the three-parameter replacement series model uses a much smaller database than that of the SSR. Each replacement series for each species mixture is analyzed separately, consequently its interpretation depends on the total density, pure stand and mixture components chosen (Connolly, 1986). In contrast, the SSR looks first at all the monoculture data and then at all the mixture data for one species in the mixture. Given this, it may be suggested that the analyses represent different ways of looking at the data; if a more general picture is preferred, the SSR should be used. Hence, the interpretation of this example would suggest that GFT and RPW in the control treatment appear to be competing for different resources in the general sense; in a more specific interpretation, this niche differentiation effect is evident only in the total density of 20. Taking into account the more general nature of the MLR and SSR analyses as compared to the more specific nature of the replacement series analyses, and the number of significant coefficients which go into each index of the MLR and SSR, every species combination can be subjected to an interpretation analogous to that used for the GFT/RPW control treatment, except for BYG/GFT at 75 ppb where both the MLR and the SSR suggest that BYG and GFT are competing for different resources. However, the replacement series model indicates that they are competing for the same space in all total densities. A possible explanation for this difference of interpretation may lie in the 128 enhancing effect of the 75 ppb ozone treatment on BYG which complicates the straight interpretation of the indices. Overall, the competitive abilities of the three species agree with those found in competition experiments carried out in the field (Minjas, 1982). BYG is the strongest competitor and GFT is the least competitive of the three species. The effects of inter-specific competition of both RPW and BYG on GFT yield is very significant under all three ozone treatments (Table 14). GFT appears to experience some intra-specific competition in the control, but not in either of the other two ozone treatments. As tested by the ANOVAs, GFT only experiences a weakly significant inversely linear effect of ozone on vegetative dry weight per pot when in combination with RPW at low density (Table 6). One of the major objectives of this work was to determine if the presence of an air pollutant would change a plant's ability to compete. The data presented here indicate that although the nature and magnitude of the effect appears to be species-specific, there are differences between the ways the species studied competed in the presence of ozone as compared to the filtered-air control. The competitive abilities of RPW over GFT, GFT over RPW and GFT over BYG all decreased with increasing ozone concentration (Table 15). In contrast, BYG's competitive ability over both GFT and RPW was higher in the ozone-added treatments than in the control (Table 15). There were also differences between ozone treatments in the way plants of the same species related to each other. BYG experienced no significant intra-specific competition in the filtered air control, in contrast to both ozone-added treatments (Table 14). On the other hand, there was a significant effect of intra-specific competition on GFT yield in the control, but no significant intra-specific effects in the 75 and 150 ppb treatments(Table 14). The effect of ozone on BYG was extremely interesting, in that, in the prolonged low concentration of ozone, 75 ppb, BYG yield was greater than in the charcoal-filtered air control (Tables 3 and 4, and Appendix 1). The significant effect of intra-specific 129 competition under the 75 ppb ozone treatment, but not under the control (Table 14), could be due to its enhanced yield under this treatment. However this would not account for the effect on intra-specific competition under 150 ppb ozone. Apparently the physiology of BYG is such that its growth is enhanced by a low level of ozone as compared to charcoal filtered-air under the experimental conditions employed in these studies. Growth enhancements caused by low levels of air pollutants have been reported in the literature, and Bennett et al. (1974) pointed out that this situation casts doubt on whether or not a filtered-air treatment is an appropriate control. A control treatment should provide an appropriate baseline to which one can compare elevated levels of the treatment. Considering the widespread nature of low-level ambient ozone concentrations (Fowler and Cape, 1982), Bennett et al. (1974) suggested that it would be more appropriate to use a concentration more representative of the real background situation as a control level. Hence, the BYG plants used in this study may have adapted to a low level of ozone. In that case, supplying those plants with filtered air would put them at a disadvantage. BYG yield also appeared to be enhanced by the presence of GFT in mixtures (Table 14, Figure 2) under the 0 and 150 ppb ozone treatments. Competition studies on these two species carried out in the field did not show this type of enhancement (Minjas, 1982). The enhancing effect of GFT's presence on BYG yield may not have showed up in the 75 ppb ozone treatment because of the added enhancement of the BYG yield by the ozone treatment alone. The air velocity dynamics and light quality characteristics of a controlled-environment chamber are very different from the situation in the field. In most chambers, as in those used in the present studies, the air is circulated up through the canopy. This is in contrast to the field situation where wind direction and velocity may change very rapidly, but the transport of a pollutant is usually downward through the canopy. Because the enhancement was not evident in the BYG monocultures treated in the same chambers simultaneously with the BYG/GFT mixtures with 0 or 150 ppb ozone, the effect appears to be a localized one. One possible explanation of this enhancement by 130 GFT is allelopathic, i.e. the shorter GFT plants released some volatile compound beneficial to BYG which was circulated up towards the taller BYG plants by the air flow of the chamber system. Alternatively, the GFT plants may have released a soluble compound through their root systems which was disseminated through the soil. RPW's competitive ability over GFT declined as the ozone concentration was increased (Table 15). RPW, of the three species studied, appears to be the most severely affected by ozone. There were inversely linear ozone effects on RPW vegetative yield both on a per plant and a per pot basis in monocultures and in mixture with BYG at high densities, and on a per pot basis in mixture with GFT at low densities (Tables 7 and 8). There was a significant inversely linear effect on the number of leaves per RPW plant in mixture with BYG at high densities. There was also a very significant inversely linear ozone effect on RPW root weight per pot in monocultures (Table 9). The weakly significant ozone effects on root weight per pot of the RPW/BYG and RPW/GFT mixtures at low densities is probably due to the effect on the RPW root component. However, there was no way to test this because the root masses could not be separated on a per species basis. The results of the replacement series of the RPW/GFT mixtures depicted in Figures 16-19 also could be explained by this extremely negative effect of ozone on RPW yield variables. The yield totals of RPW/GFT change from an over-yielding situation in the no-ozone control to an under-yielding situation under the highest ozone treatment. The significant interaction of the ozone and competition treatments on RPW/GFT total dry root weight/pot (Table 9) supports the replacement series trend. RPW experiences significant intra-specific competition under all three ozone treatments (Table 14), but the effect is less significant in the 150 ppb treatment than in the other two. This could possibly be due to the significant effects of ozone on RPW vegetative dry weight, number of leaves and root mass, which would cause RPW to be less vigorous in the high ozone treatment. 131 In order to better explain the "unwanted" plant variation evident from the ANOVA and regression analyses, size frequency distributions were examined. Although the analysis of the size frequency distributions did not reveal any ozone effects, definite population features of the three species were seen. RPW had a very uneven size distribution over all density and ozone treatments (Figures 25-27). By the interpretation given by Weiner (1985) of the dominance and suppression theory of size distribution, this would suggest that RPW was neither suppressed nor competitively superior. From the regression analyses done in this study, we know this is not true; RPW is competitively superior to GFT and experiences significant inter-specific competition from BYG. As was suggested earlier, there are many reasons other than competition to explain an uneven size distribution, of which inherent genetic variation is one. Turner and Rabinowitz (1983) proposed that assymetry in size distributions reflected variance in exponential growth rates. The RPW populations examined in these studies show a great deal of inherent genetic variation for plant size, and it is possible that the stresses inflicted by being grown in controlled environment chambers (lower light intensity and air velocity, for example) may have exacerbated the variation in exponential growth rates, which was reflected in the very skewed nature of the size distributions. In contrast, GFT, which the MLR and SSR analyses suggest was suppressed by the other two species in mixtures, did not show the expected increase in skewness with an increase in density. Turner and Rabinowitz (1983) in reviewing the literature on size frequency distributions, found that when skewness increased with density, the plants were dicots or conifers; in cases where skewness did not increase with density, the plants were grasses (Festuca and Zed). They suggest that light competition may be crucial in causing dominance and suppression, and that the erect "vertical" form of grass seedlings may render light competition less crucial than belowground resource depletion. Another major objective of this study was to examine the differences, if any, between the effects of two ozone doses which differed in their concentration and exposure terms. 132 Although the two ozone treatments represent the same actual dose in terms of exposure, there were very obvious differences in the way each affected the plant yield variables and competitive interactions. It should be pointed out that although the analysis of variance ozone effects were broken down into linear and deviation from linearity contrasts, the ozone variable involved was concentration during the exposure period. Overall, the 150 ppb/3.5 hr exposure had a much more inhibitory effect on all measured yield variables than did the 75 ppb/7 hr treatment. This confirms the general observation that concentration is more important than duration of exposure in determining response, and that the simple product of concentration and duration of exposure (while defining the ambient dose) does not define the "effective" dose received by the plant (Runeckles, 1974; Fowler and Cape, 1982). In the present studies, the low level concentration, given over a longer exposure period, may have allowed the plants to acclimate themselves to the ozone treatment, thereby reducing the amount of injury or growth impairment. A low level of ozone used as a pre-treatment has been shown to protect plants from acute injury (Rosen,1979). The implications of these findings are far-reaching. If a plant's competitive ability is altered by the presence of low levels of ozone which are commonly experienced in many areas of North America, the dynamics of many plant communities may slowl}' be changing. As the more dramatic example of ponderosa pine in the San Bernadino valley makes very clear, many species may lose their dominant stature in ecosystems and will be slowly replaced by other, more resistant, species. The development of resistance to air pollutants has been shown to be quite rapid in some species (Taylor and Murdy, 1975; Taylor, 1978). However, if the genetic basis for that resistance is not inherent, the gradual weakening of long-lived plants would have serious effects. Effects on short-lived plants such as the annuals used in these studies, would have their most serious evolutionary implications if the reproductive capacity of the plant was decreased. A significant effect of ozone on the reproductive yield of BYG was described (Tables 3 and 4). 133 If that decrease in reproductive tissue translated into a decrease in its reproductive capacity, in terms of viable seed and number of offspring, even though BYG did prove to be the most competitive of the three species, and its growth was even enhanced by the low level dose of ozone, its success in an evolutionary sense would be limited by its inability to contribute as many progeny to later generations as the other, less successfully competitive species, GFT and RPW. The extrapolation of the results discussed here to the field situation must be done with caution. As was pointed out by De Vos et aZ.(1983) and Lewis and Brennan (1977), there are differences in a plant's susceptibility to air pollutants in the field and in controlled-environment chambers. It is possible that the findings of this study would not translate directly to a field situation; however the doses used were low and are characteristic of those found in many areas, including the lower mainland of British Columbia. Even if the effects in the field were not as dramatic as in this study, subtle changes in a plant's competitive ability and reproductive success could translate into long term successional changes. It is evident that as Treshow (1968) and Smith (1974) postulated, the presence of an air pollutant changes the way plants interact with each other. 6. SUMMARY 1. The competition among binary mixtures of barnyardgrass (BYG), redroot pigweed (RPW) and greenfoxtail (GFT), subjected to different treatments with the air pollutant ozone during the early stages of growth was investigated using additive and replacement series designs. 2. Three approaches were used to investigate competitive behaviour; the multiple linear regression (MLR), sequential simple regression (SSR) and the replacement series analyses. The interpretation of the SSR method of analyzing competitive systems has more biological meaning than the MLR analyses, and the SSR explains much more of the biological variation than does the MLR. The replacement series model specifically analyzes at each total density, while the regression analyses utilize all appropriate monoculture and mixture data. Hence, the regression analyses give a more general interpretation of the data set. 3. The order of competitive ability of the three species was shown to be BYG > RPW > GFT, regardless of ozone treatment. 4. There were significant intra-specific effects on RPW in all ozone treatments. BYG only experienced significant intra-specific competition in the two ozone-added treatments. The intra-specific competitive relationships of GFT was significant only in the control treatment. 5. There were significant inter-specific competitive effects of BYG and RPW on each other and GFT in all ozone treatments. BYG yield was significantly enhanced by GFT inter-specific competition in the control treatment. GFT did not effect BYG yield in either the 75 ppb (for 7 hr) or 150 ppb (for 3.5 hr) treatments. There were weakly significant effects of GFT inter-specific competition on RPW yield under all treatments. 6. BYG yield was significantly enhanced by the low level of ozone, 75 ppb/7 hr, over the control and 150 ppb/3.5 hr treatment. 135 7. RPW was most severely affected by the presence of ozone. RPW plants showed significant declines in dry weight per plant and per pot, number of leaves per plant and on root dry weight per pot in monocultures. 8. The replacement series for RPW-GFT showed an over-yielding in the control, an equal replacement of biomass in the 75 ppb/7 hr treatment and an under-yielding in the 150 ppb/3.5 hr. This interaction between ozone treatment and density combination of RPW and GFT was significant for root dry weight. 9. The two ozone treatments, 75 ppb/7 hr and 150 ppb/3.5 hr, which represent the same ambient dose, had different effects on the species studied. Except for BYG yield which was enhanced in the 75 ppb/7 hr treatment, the other species in monoculture and in mixture were more severely affected by the acute 150 ppb/3.5 hr dose than the chronic 75 ppb/7 hr dose. 10. Size frequency diagrams confirmed that BYG is competitively superior to the other two species. 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Technol. 17: 371-373 144 APPENDIX 1 Mean yield variables per pot for a l l density-species combinations for experiments 3 - 9. 145 V E G E T A T I V E DRY WEIGHT ( g ) , B Y G : G F T OZONE TREATMENT 0 7 5 p p b / 7 h r s 1 5 0 p p b / 3 . 5 h r s S p e c i e s M i x t u r e B Y G : G F T D e n s i t y B Y G GFT BYG GFT B Y G GFT 0 5 0 . . 3 3 5 5 0 . 3 0 0 1 0 . 2 4 4 3 0 1 0 0 , . 5 9 7 1 0 . 5 1 3 6 0 . 4 9 6 9 0 1 5 0 . . 6 3 0 4 0 . 6 1 7 9 0 . 6 7 0 7 0 2 0 0 . . 9 6 3 9 0 . 9 0 9 1 1 . 0 0 0 5 0 0 . . 9 0 4 3 1 . . 4 1 7 0 . . 9 2 5 2 5 5 0 . . 4 8 0 4 0 . . 2 0 0 3 0 , . 7 2 6 5 0 . 1 8 2 5 0 . . 4 5 2 9 0 . 1 4 7 0 5 10 0 . . 6 1 2 6 0 . . 3 7 2 9 0 , . 8 0 7 6 0 . 3 4 0 5 0 . . 5 3 9 5 0 . 3 5 5 3 5 1 5 0 . . 8 8 4 1 0 . . 5 4 7 8 0 . . 7 8 5 3 0 , . 5 2 9 7 0 . . 7 9 2 0 0 . 5 7 6 7 5 2 0 1 , . 6 1 5 8 0 . . 6 8 0 6 1 . . 0 1 6 6 0 . , 6 1 7 6 1 . . 5 0 3 1 0 . 7 7 2 0 1 0 0 1 , . 8 5 5 1 , . 7 3 5 1 . . 5 9 0 1 0 5 1 . . 4 6 8 0 . . 1 5 2 9 1 . . 5 0 2 0 , . 1 4 5 8 1 , . 4 8 4 0 . 1 3 5 2 1 0 10 1 . . 1 6 6 0 . . 5 5 5 9 1 , . 1 0 4 0 . , 3 5 1 1 1 . , 0 2 3 0 . 3 0 0 5 1 0 1 5 1 . . 9 1 9 1 0 . . 6 8 6 8 1 , . 9 7 1 8 0 , . 4 7 3 4 1 , . 4 7 3 8 0 . 5 1 0 8 1 5 0 2 . . 1 7 8 2 . . 4 3 9 2 , . 0 4 5 1 5 5 2 . . 1 2 1 0 . . 2 1 3 3 1 , . 2 7 4 0 . . 1 9 9 1 1 . . 6 6 1 0 . 2 0 6 3 1 5 1 0 2 , . 4 5 0 4 0 , . 3 0 3 8 3 , . 2 1 8 8 0 . 3 3 6 4 3 , . 2 4 4 5 0 . 2 9 4 2 20 0 3 . . 2 8 5 3 . . 1 3 7 2 . . 6 1 5 2 0 5 2 . . 7 8 6 4 0 . , 1 8 7 7 2 . , 8 8 0 2 0 , , 1 5 4 5 2 , . 9 3 2 4 0 . 1 9 3 5 M i x t u r e B Y G : G F T D e n s i t y BYG R E P R O D U C T I V E DRY WEIGHT ( g ) , B Y G : G F T OZONE TREATMENT 7 5 p p b / 7 h r s 1 5 0 p p b / 3 . 5 h r s S p e c i e s G F T B Y G G F T BYG GFT 0 0 0 0 5 5 5 5 5 1 0 10 1 0 1 0 1 5 1 5 1 5 5 10 1 5 20 0 5 10 1 5 2 0 0 5 1 0 15 0 5 1 0 0 . 0 1 1 6 0 . 0 0 . 0 0 . 0 0 . 0 1 5 4 0 . 0 0 8 1 0 . 0 1 4 2 0 . 0 0 9 8 0 . 0 0 9 9 0 . 0 0 . 0 0 . 0 0 1 0 0 . 0 0 1 7 0 . 0 0 . 0 4 0 7 0 . 0 0 . 0 0 . 0 0 0 5 0 . 0 0 . 0 0 1 4 0 . 0 0 . 0 0 . 0 0 0 9 0 . 0 0 . 0 0 . 0 0 1 1 0 . 0 0 . 0 0 . 0 1 9 6 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 2 6 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 8 1 0 . 0 0 3 7 0 . 0 0 9 9 0 . 0 0 7 6 0 . 0 0 6 9 0 . 0 0 . 0 0 0 4 0 . 0 0 2 4 0 . 0 0 6 0 0 . 0 0 . 0 0 . 0 0 2 3 0 . 0 0 2 4 0 . 0 0 . 0 0 . 0 0 0 9 0 . 0 0 0 5 0 . 0 1 2 5 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 9 9 0 . 0 0 . 0 0 . 0 0 . 0 0 9 8 0 . 0 0 . 0 0 . 0 0 9 0 0 . 0 0 9 9 0 . 0 1 1 7 0 . 0 1 3 1 0 . 0 0 . 0 0 0 3 0 . 0 0 0 8 0 . 0 0 0 4 0 . 0 0 0 8 0 . 0 0 . 0 0 0 3 0 . 0 0 1 5 0 . 0 0 . 0 0 . 0 0 0 4 0 . 0 2 0 0 2 0 5 0 . 0 3 2 6 0 . 0 0 . 0 0 . 0 0 . 0 0 9 4 0 . 0 0 . 0 0 . 0 0 . 0 0 9 8 0 . 0 0 . 0 0 . 0 147 NUMBER OF L E A V E S , B Y G : G F T OZONE TREATMENT 0 75 p p b / 7 h r s 1 5 0 p p b / 3 . 5 h r s S p e c i e s M i x t u r e B Y G : G F T B Y G G F T BYG G F T BYG GFT D e n s i t y  0 5 4 6 , . 9 5 0 , . 3 0 1 0 8 5 , , 8 9 4 , . 3 0 1 5 1 0 8 , ,1 1 1 4 . .1 0 2 0 1 4 4 , , 3 1 4 8 , ,6 5 0 3 4 . .8 3 5 , 8 3 7 , , 6 5 20 3 1 . . 3 1 0 6 . ,7 2 9 . ,7 1 0 6 , ,8 3 1 , , 5 1 0 0 6 1 , ,1 6 1 , , 0 6 4 . . 8 1 0 15 5 5 , .8 9 2 , ,8 6 1 , ,2 8 4 . .8 5 4 , , 5 1 5 0 8 8 . , 5 8 7 . ,6 9 0 , , 1 1 5 10 8 5 , . 3 5 0 , .8 8 4 , ,3 5 5 . . 3 8 5 , , 8 2 0 0 1 1 5 . . 5 1 2 1 . .0 1 2 5 . , 8 2 0 5 1 0 4 , . 4 2 9 , . 4 1 1 3 , . 4 2 8 , . 0 I l l , . 4 1 2 0 . 7 7 3 . 2 4 9 . 3 2 7 . 4 NUMBER OF T I L L E R S , B Y G : G F T 148 OZONE TREATMENT 0 7 5 p p b / 7 h r s 1 5 0 p p b / 3 . 5 h r s S p e c i e s M i x t u r e B Y G : G F T B Y G G F T BYG G F T BYG GFT D e n s i t y  0 5 1 1 . 4 1 2 . 0 1 1 . 9 0 1 0 2 1 . 7 2 3 . 2 2 4 . 3 0 15 2 3 . 7 2 4 . 8 2 9 . 4 0 2 0 3 1 . 9 3 4 . 0 4 0 . 4 5 0 6 . 9 6 . 4 7 . 9 5 2 0 5 . 2 2 3 . 3 5 . 2 2 5 . 5 5 . 0 2 6 . 7 1 1 . 0 1 1 . 6 1 2 . 8 1 0 1 5 1 0 . 2 2 1 . 3 1 0 . 0 2 0 . 0 1 0 . 0 1 9 . 5 15 0 1 6 . 0 1 6 . 0 1 6 . 2 15 10 1 5 . 5 1 2 . 0 1 5 . 0 1 2 . 3 1 5 . 0 1 0 . 3 2 0 0 2 0 . 9 2 1 . 6 2 1 . 7 2 0 5 2 0 . 0 6 . 8 2 0 . 6 6 . 4 2 0 . 0 6 . 4 V E G E T A T I V E DRY WEIGHT ( g ) , B Y G : R P W 149 OZONE TREATMENT 0 75 p p b / 7 h r s 1 5 0 p p b / 3 . 5 h r S p e c i e s M i x t u r e B Y G : RPW BYG RPW BYG RPW BYG RPW D e n s i t y  0 5 0 . . 8 7 2 8 0 , , 9 8 5 4 0 . , 5 8 2 0 0 1 0 1 , . 2 6 7 1 , . 0 3 6 0 . . 8 7 7 1 0 1 5 1 . . 6 3 6 1 . . 1 2 1 1 . . 2 1 8 0 2 0 1 , . 3 0 3 1 . . 2 1 3 1 . . 3 2 3 5 0 0 , . 9 0 4 3 1 , . 4 1 7 0 , . 9 2 5 2 5 5 0 , . 9 8 1 3 0 . , 3 1 8 8 0 , . 7 6 0 0 0 , . 2 5 5 2 0 , , 8 1 6 0 0 , . 2 5 1 8 5 1 0 1 . . 3 6 6 0 , , 7 6 8 6 1 , . 3 4 7 0 , , 7 6 3 4 1 . . 0 8 0 0 . . 5 5 5 9 5 15 0 , . 8 2 9 5 0 , , 7 9 3 9 0 , . 9 3 3 0 1 , . 0 0 2 0 , , 9 3 9 3 0 , , 8 5 3 7 5 2 0 0 , . 4 8 5 3 0 . . 9 3 5 9 0 . . 7 5 1 9 1 , , 1 8 3 3 0 , . 4 7 8 6 0 . , 7 8 7 1 1 0 0 1 . . 8 5 5 1 , , 7 3 5 1 , . 5 9 0 1 0 5 2 , . 0 9 1 0 , , 2 5 8 9 ' 2 , , 1 4 6 0 , . 2 6 7 3 2 , . 2 3 0 0 . . 3 3 4 2 1 0 1 0 1 . . 9 0 7 0 . . 3 7 5 1 1 , , 4 2 4 0 , . 4 8 0 5 1 , . 5 9 8 0 , , 2 9 2 4 1 0 15 1 . . 3 4 5 5 0 . . 9 7 4 5 1 , . 4 9 4 0 . . 9 7 7 4 1 , . 1 8 2 0 , . 8 8 8 2 1 5 0 2 . . 1 7 8 2 , . 4 3 9 2 . . 0 4 5 15 5 2 . . 7 9 6 , 3 3 0 1 2 , . 7 0 7 , 2 1 1 0 2 . . 5 6 2 . 2 1 3 2 1 5 1 0 1 . . 7 6 0 3 9 6 1 0 1 , , 9 6 9 9 7 4 4 4 2 . , 2 8 4 3 6 4 6 3 2 0 0 3 . , 2 8 5 3 . , 1 3 7 2 . , 6 1 5 20 5 2 . , 0 3 6 8 5 9 9 3 2 . , 6 1 4 5 5 6 4 1 2 . , 4 4 3 2 3 8 4 1 150 R E P R O D U C T I V E DRY WEIGHT ( g ) , B Y G : R P W OZONE TREATMENT 0 7 5 p p b / 7 h r s 1 5 0 p p b / 3 . 5 h r s S p e c i e s M i x t u r e B Y G : RPW BYG RPW BYG RPW BYG RPW D e n s i t y  0 5 0 . . 0 0 3 6 0 . . 0 0 0 7 0 . . 0 0 2 0 0 1 0 0 , . 0 0 1 7 0 , . 0 0 2 2 0 . . 0 0 3 1 0 1 5 0 . . 0 0 3 2 0 , . 0 0 3 8 0 . . 0 0 2 6 0 2 0 0 . . 0 0 7 1 0 . . 0 0 4 8 0 . . 0 0 6 0 . 5 0 0 , . 0 1 1 6 0 , . 0 1 9 6 0 . . 0 1 2 5 5 5 0 . . 0 0 . . 0 0 3 7 0 . . 0 0 5 0 0 , . 0 0 3 2 0 . . 0 0 4 8 0 . . 0 0 2 0 5 1 0 0 . . 0 2 3 2 0 . . 0 0 6 4 0 . . 0 4 7 2 0 , . 0 0 8 0 0 , . 0 0 5 8 0 . . 0 0 5 4 5 15 0 , . 0 0 , . 0 0 9 2 0 , . 0 0 , . 0 0 7 4 0 , . 0 0 3 3 0 . . 0 0 7 9 5 2 0 0 . 0 0 . . 0 0 , . 0 0 , . 0 0 . . 0 0 . . 0 10 0 0 , . 0 4 0 7 0 , . 0 0 2 6 0 , . 0 0 9 9 1 0 5 0 , . 0 4 9 8 0 . . 0 0 2 0 0 , . 0 2 7 5 0 , . 0 0 3 6 0 . . 0 1 3 7 0 . 0 0 3 7 10 1 0 0 . . 0 0 4 5 0 . . 0 0 1 9 0 . . 0 0 . . 0 0 1 0 0 . . 0 1 2 0 0 . . 0 0 2 1 10 15 0 , . 0 0 8 8 0 . . 0 0 . . 0 0 , . 0 0 . . 0 0 7 4 0 , . 0 1 5 0 0 . . 0 0 0 9 0 . . 0 0 . . 0 0 9 8 1 5 5 0 , . 0 0 . . 0 0 1 4 0 . . 0 0 . . 0 0 4 4 0 . . 0 1 2 4 0 . . 0 0 2 0 1 5 10 0 . . 0 3 3 2 0 . . 0 0 . . 0 5 2 4 0 . . 0 0 . . 0 4 5 3 0 . . 0 20 0 0 . . 0 3 2 6 0 . . 0 0 9 4 0 . . 0 0 9 8 2 0 5 0 . . 0 2 4 9 0 . .0 0 . , 0 4 6 3 0 . . 0 0 . ,0 0 . , 0 NUMBER OF L E A V E S , B Y G : R P W 151 OZONE TREATMENT 0 75 p p b / 7 h r s 1 5 0 p p b / 3 . 5 h r s S p e c i e s M i x t u r e BYG : RPW BYG RPW BYG RPW B Y G RPW D e n s i t y  0 5 3 8 , , 2 3 9 . 1 3 7 , .8 0 10 6 5 , , 0 6 2 . 4 6 8 , , 5 0 1 5 9 2 , . 3 8 8 . 1 9 4 . .8 0 2 0 1 0 2 , .9 1 0 8 . 0 1 1 9 . . 3 5 0 3 4 . .8 3 5 , ,8 3 7 , .6 5 5 3 0 , . 1 7 2 9 , .7 3 1 , . 5 0 2 8 . 8 3 6 , . 3 2 9 , . 2 5 1 0 3 3 , . 3 6 0 , , 5 3 1 , .7 6 0 . 2 3 5 , ,7 5 9 , ,7 5 15 2 8 , . 3 7 1 , . 0 3 0 . , 2 8 4 . 2 3 1 . , 8 8 4 . , 3 5 2 0 2 5 , . 8 5 5 , . 6 2 7 , , 5 7 5 . 5 2 5 , . 2 5 7 , . 8 1 0 0 6 1 , . 1 6 1 . , 0 6 4 , .8 1 0 5 6 0 , . 1 7 2 5 , . 8 6 0 , .7 2 6 . 5 6 7 , . 0 3 2 , . 7 1 0 1 0 6 0 , . 8 3 4 9 , , 5 5 4 , . 3 4 6 . 7 6 1 . . 2 5 0 , . 0 1 0 1 5 5 3 , , 6 7 6 6 . , 3 4 9 , . 2 4 8 . 7 5 5 , . 0 5 8 . . 5 1 5 0 8 8 . . 5 8 7 , .6 9 0 . .1 15 5 8 7 , . 0 2 7 , ,8 8 5 . , 0 2 8 . 8 9 4 . . 5 2 9 . . 8 1 5 1 0 7 9 , . 5 4 6 , , 0 7 9 , . 2 4 0 . 2 8 4 . . 3 3 8 , , 2 20 0 1 1 5 . . 5 1 2 1 . . 0 1 2 5 . ,8 2 0 5 1 0 2 , . 2 2 1 , . 0 1 1 3 . , 0 2 7 . 2 1 1 5 . .8 2 1 , .6 NUMBER OF T I L L E R S , B Y G : R P W OZONE TREATMENT 0 75 p p b / 7 h r s 1 5 0 p p b / 3 . 5 h r s S p e c i e s M i x t u r e BYG : RPW BYG RPW BYG RPW BYG RPW D e n s i t y  5 0 6, . 9 6 , . 4 7 . . 9 5 5 6 , . 2 N / A 6 , . 3 N / A 9 , . 0 5 1 0 5 . . 3 5 . . 7 7 . .7 5 15 5 , . 0 7 , . 2 6 , . 2 5 2 0 5 . , 2 5 , , 0 5 , . 0 1 0 0 1 1 , . 0 1 1 . ,6 1 2 , . 8 1 0 5 1 0 , . 8 1 1 , , 3 1 1 . . 5 1 0 1 0 1 1 . . 5 1 0 . , 5 1 3 . . 0 10 1 5 1 0 . , 0 1 0 , . 2 1 0 , . 0 15 0 1 6 . . 0 1 6 . , 0 1 6 , . 2 1 5 5 16 1 4 , ,8 2 1 , . 2 1 5 1 0 1 5 . . 0 1 4 . 8 1 5 . , 0 2 0 0 2 0 . .9 2 1 , ,6 2 1 , .7 2 0 5 2 0 . . 2 2 0 . ,0 2 0 . , 0 153 V E G E T A T I V E DRY WEIGHT ( g ) , G F T : R P W M i x t u r e G F T : RPW G F T RPW OZONE TREATMENT 7 5 p p b / 7 h r s S p e c i e s G F T RPW 1 5 0 p p b / 3 . 5 h r s G F T RPW 0 5 0 , . 8 7 2 8 0 , , 9 8 5 4 0 , . 5 8 2 0 0 1 0 1 , . 2 6 7 1 . . 0 3 6 0 , . 8 7 7 1 0 1 5 1 , . 6 3 6 1 . , 1 2 1 1 , . 2 1 8 0 2 0 1 , , 3 0 3 1 , , 2 1 3 1 , , 3 2 3 5 0 0 , . 3 3 5 5 0 , . 3 0 0 1 0 , . 2 4 4 3 5 5 0 . 2 7 8 1 0 , , 6 1 8 8 0 , , 2 2 6 0 0 , . 5 9 5 2 0 , . 2 6 2 3 0 . . 3 9 3 7 5 1 0 0 . 1 5 7 3 0 , , 6 3 1 3 0 , , 1 7 4 8 0 , . 5 8 2 0 0 , , 1 5 7 9 0 , , 6 4 6 9 5 1 5 0 . . 1 8 3 3 1 . , 6 6 4 0 . , 1 5 5 9 1 . . 8 1 3 0 , . 1 3 9 2 1 . , 4 4 8 5 2 0 0 , . 1 4 9 2 1 , . 2 8 5 0 , , 1 7 4 4 0 . . 8 3 6 5 0 , . 1 4 7 8 1 , , 2 8 1 1 0 0 0 . . 5 9 7 1 0 , , 5 1 3 6 0 . . 4 9 6 9 1 0 5 0 , . 4 4 8 7 0 , . 5 1 0 1 0 . . 3 9 8 2 0 . . 1 8 5 4 0 , , 3 3 0 2 0 . , 2 0 8 8 1 0 1 0 0 , . 5 5 6 9 1 . , 6 3 2 0 . , 3 9 7 7 1 . . 4 8 5 0 . , 3 1 6 2 1 , , 1 3 2 1 0 15 0 , . 3 4 4 0 1 , . 1 3 8 0 . . 2 7 2 8 1 , . 1 4 6 0 , . 2 9 7 9 0 , . 9 6 9 4 1 5 0 0 . , 6 3 0 4 0 . , 6 1 7 9 0 , . 6 7 0 7 1 5 5 0 . . 4 5 5 7 0 . , 8 1 4 9 0 . , 3 8 2 0 0 , . 6 0 9 2 0 . . 4 5 8 3 0 . , 5 0 0 8 15 1 0 0 . . 6 9 6 7 0 . 9 6 2 8 0 . 6 2 0 5 0 . , 8 7 9 2 0 . . 5 7 9 5 0 . , 8 0 1 6 2 0 0 0 . , 9 6 3 9 0 . 9 0 9 1 1 . , 0 0 0 5 2 0 5 0 . 8 2 0 5 0 . 6 5 8 5 0 . 7 7 8 5 0 . 5 1 3 2 0 , 7 2 2 8 0 . 3 3 8 5 154 Mixture GFT : RPW REPRODUCTIVE DRY WEIGHT (g), GFT:RPW OZONE TREATMENT 0 75 ppb/7 hrs 150 ppb/3.5 hrs Species GFT RPW GFT RPW GFT RPW 0 5 0. .0036 0, . 0007 0. .0020 0 10 0, .0017 0. .0022 0, .0031 0 15 0. .0032 0. .0038 0, .0026 0 20 0. .0071 0. . 004.8 0. .0060 5 0 0, ,0081 0, .0 0. .0037 0, .0 0. .0090 0. .0 5 5 0. .0124 0, .0 0, .0017 0. .0 0, .0123 0, .0 5 10 0. .0056 0. .0 0. .0 0. .0 0. .0057 0. .0 5 15 0. .0090 0. .0 0. .0064 0. .0 0. ,0024 0. .0 5 20 0, .0050 0, .0 0. .0021 0, .0 0. .0032 0. .0 10 0 0. .0142 0. .0 0. .0099 0, .0 0. ,0099 0, .0 10 5 0. .010 0. .0 0. .0102 0. .0 0. .0092 0. .0 10 10 0, .0158 0, .0 0, .0094 0, .0 0. .0078 0, .0 10 15 0. .0184 0, .0 0. .0103 0, .0 0. .0103 0, .0 15 0 0. .0098 0. .0 0. .0076 0. .0 0. ,0117 0, ,0 15 5 0. .0144 0. .0 0, .0135 0, .0 0. ,0183 0, ,0 15 10 0. .0190 0, .0 0. .0187 0. .0 0. .0122 0. .0 20 0 0. ,0099 0. .0 0, .0069 0. .0 0. ,0131 0, .0 20 5 0. .0067 0. .0 0. .0147 0. .0 0. .0046 0. ,0 NUMBER OF L E A V E S , G F T : R P W 155 OZONE TREATMENT 0 7 5 p p b / 7 h r s 1 5 0 p p b / 3 . 5 h r s S p e c i e s M i x t u r e G F T : RPW G F T RPW G F T RPW G F T RPW D e n s i t y  0 5 3 8 , . 2 3 9 , . 1 3 7 . 8 0 1 0 6 5 , . 0 6 2 . . 4 6 8 . 5 0 15 9 2 , . 3 8 8 , .1 9 4 . 8 0 20 1 0 2 . .9 1 0 8 , . 0 1 1 9 . 3 5 0 4 6 , . 9 5 0 , . 3 5.0. . 8 5 5 3 8 , . 3 3 4 , . 2 3 8 , . 5 3 6 , . 4 4 8 , . 8 3 3 . 0 5 10 3 1 , . 3 5 3 , . 2 3 2 . . 7 5 6 , . 0 3 7 , . 5 6 0 . 2 5 15 2 9 , , 2 9 0 . .7 3 2 , . 7 9 0 . . 2 3 2 , . 0 9 2 . 7 5 2 0 2 8 , ,7 8 9 , .9 2 9 . . 6 8 1 , .8 3 1 . .1 9 9 . 3 10 0 8 5 , . 8 9 4 , . 3 9 3 , . 0 1 0 5 7 0 3 1 , .8 7 4 , . 2 2 4 . . 8 7 3 , .8 2 7 . 2 1 0 1 0 6 8 . .8 6 9 . .8 6 7 , . 2 7 1 , . 2 7 0 . .7 6 8 . 0 1 0 1 5 6 4 . ,6 7 7 . .7 5 4 , . 4 7 0 . . 2 6 6 , ,6 7 4 . 9 1 5 0 1 0 8 , .1 1 1 4 , . 1 1 2 0 , . 2 1 5 5 9 1 , . 0 3 6 . . 2 8 7 , . 5 3 4 , .8 1 0 5 , .7 3 3 . 0 1 5 1 0 9 7 . .1 5 9 . . 0 1 0 2 , .8 5 1 . . 2 1 0 1 , . 0 5 1 . 9 2 0 0 1 4 4 . ,3 1 4 8 , .6 1 6 6 . 9 20 5 1 2 6 . , 4 3 0 . 3 1 2 9 , . 0 2 7 . 8 1 2 9 . ,7 2 5 . 3 156 NUMBER OF T I L L E R S , G F T : R P W OZONE TREATMENT 0 75 p p b / 7 h r s 1 5 0 p p b / 3 . 5 h r s S p e c i e s M i x t u r e G F T : RPW G F T RPW G F T RPW G F T RPW 5 0 1 1 . 4 1 2 , . 0 1 1 . . 9 5 5 8 . 2 N / A 8 , . 3 N / A 1 2 , . 0 5 1 0 5 . 8 5 , ,8 9 . . 0 5 15 6 . 2 6 , .7 6 . . 2 5 2 0 5 . 4 6 , . 4 6 . .6 10 0 2 1 . 7 2 3 , . 2 2 4 . . 3 1 0 5 1 3 . 3 1 5 , . 3 1 5 . . 5 1 0 1 0 1 2 . 8 1 3 , ,7 1 6 . . 2 1 0 1 5 1 4 . 1 1 2 . . 4 1 4 , .8 1 5 0 2 3 . 7 2 4 , . 8 2 9 , . 4 15 5 1 8 . 2 1 8 , . 8 2 4 . . 2 1 5 10 2 1 . 9 2 4 , , 0 2 2 , .8 20 0 3 1 . 9 3 4 . , 0 4 0 . , 4 20 5 2 6 . 3 2 6 , ,3 2 8 , . 3 ) 157 MEAN T O T A L ROOT DRY WEIGHT ( g ) , B Y G : R P W M i x t u r e B Y G : RPW D e n s i t y OZONE TREATMENT 7 5 p p b / 7 h r s 1 5 0 p p b / 3 . 5 h r s 5 5 5 5 5 1 0 1 0 1 0 10 15 15 1 5 2 0 2 0 0 5 10 1 5 2 0 0 5 1 0 1 5 0 5 1 0 0 5 0 . 2 1 1 5 0 . 2 4 4 9 0 . 3 6 8 4 0 . 3 2 7 3 0 . 1 9 6 7 0 . 3 2 1 6 0 . 3 4 9 8 0 . 4 5 8 4 0 . 2 9 8 9 0 . 4 6 0 9 0 . 5 2 9 6 0 . 3 4 3 7 0 . 6 2 7 0 0 . 3 7 6 1 0 . 2 3 8 5 0 . 2 0 8 8 0 . 3 3 7 3 0 . 3 4 0 1 0 . 2 4 7 1 0 . 2 9 4 6 0 . 3 6 6 1 0 . 3 5 1 4 0 . 2 5 0 9 0 . 4 5 9 2 0 . 5 6 8 7 0 . 3 2 6 7 0 . 6 0 5 4 0 . 4 2 7 3 0 . 1 6 4 6 0 . 1 9 6 4 0 . 2 3 2 6 0 . 3 3 3 2 0 . 1 5 0 1 0 . 2 7 6 1 0 . 3 7 6 5 0 . 4 0 5 5 0 . 2 7 9 3 0 . 4 4 0 8 0 . 4 7 1 8 0 . 4 0 6 6 0 . 5 2 0 7 0 . 3 6 8 4 MEAN T O T A L ROOT DRY WEIGHT ( g ) , G F T : R P W OZONE TREATMENT M i x t u r e G F T : RPW D e n s i t y 0 75 p p b / 7 h r s 1 5 0 p p b / 3 . 5 h r s 5 5 5 5 5 0 5 1 0 1 5 2 0 0 . 0 9 1 0 0 . 1 5 8 6 0 . 1 5 6 7 0 . 1 6 9 6 0 . 2 6 1 1 0 . 1 0 3 4 0 . 1 2 7 6 , 1 0 7 8 1 1 7 3 0 . 0 . 0 . 1 8 6 5 0 . 9 1 1 0 . 1 2 3 1 0 . 1 4 0 0 0 . 0 9 8 5 0 . 2 7 4 5 10 10 10 10 0 5 1 0 1 5 0 . 1 4 4 2 0 . 2 7 6 5 0 . 3 4 1 4 0 . 2 5 3 9 0 . 1 5 1 4 0 . 3 2 5 1 0 . 3 3 6 0 0 . 3 0 2 0 0 . 1 2 7 3 0 . 2 5 0 8 0 . 2 2 8 5 0 . 2 3 6 2 1 5 1 5 15 0 5 1 0 0 . 1 6 6 4 0 . 2 4 8 6 0 . 3 5 7 1 0 . 1 8 6 2 0 . 2 0 0 0 0 . 3 4 0 5 0 . 2 0 0 1 0 . 1 8 3 5 0 . 3 5 8 0 2 0 0 2 0 5 0 . 2 4 4 1 0 . 3 0 5 8 0 . 2 4 0 5 0 . 3 0 7 0 0 . 2 6 0 2 0 . 2 2 6 3 MEAN T O T A L ROOT Y I E L D P E R POT ( g ) , B Y G : G F T 158 OZONE TREATMENT M i x t u r e B Y G : G F T D e n s i t y 0 75 ppb/ 7 hrs 150 ppb/3.5 hrs 0 0 0 0 5 10 15 20 0.0910 0.1442 0.1664 0.2441 0.1034 0.1514 0.1862 0.2405 0.0911 0.1273 0.2001 0.2602 0 20 0.2115 0.3925 0.2385 0.2667 0.1646 0.3834 10 10 0 15 0.3216 0.4414 0.2946 0.4638 0.2761 0.3895 15 0 0.4609 15 10 0.5776 20 0 0.6270 20 5 0.5438 0.4592 0.4408 0.7210 0.5212 0.6054 0.5207 0.5118 0.5635 

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