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Population differences in water-use effeciency for Pinus contorta dougl as indicated by stable carbon.. Holowachuk, Diane L. 1993

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POPULATION DIFFERENCES IN WATER-USE EFFICIENCY FOR PINUS CONTORTA DOUGL. AS INDICATED BY STABLE CARBON ISOTOPIC COMPOSITION by DIANE LYNDA HOLOWACHUK B.Sc.N. The University of Alberta, 1973 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (FACULTY OF FORESTRY, DEPARTMENT OF FOREST SCIENCES) We accept this thesis as conforming to the required standard  THE UNIVERSITY OF BRITISH COLUMBIA September 1993 © Diane Lynda Holowachuk, 1993  In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission.  (Signature)  Department of N./t-zzt,  r  The University of British Columbia Vancouver, Canada  Date^27, /  DE-6 (2/88)  Abstract Stable carbon isotopic composition (813C), an index of water-use efficiency (WUE), was used to test genetic and environmental responses of Pinus contorta Dougl. populations to moisture gradients. To gain insight into patterns of natural selection for high or low WUE, trends in 813C were measured in genotypes from different habitats. Correlations between 513C and productivity were tested. Sixteen-year-old P. contorta provenance trials at three climatically different sites in British Columbia were sampled. To determine whether early selection for WUE was possible in P. contorta, greenhouse grown seedling shoots from the same seedlots as the saplings were analyzed. Correlations among seedling 813C, growth variables and yield were tested as well. A sampling technique to accurately reflect field 813C was determined by assessing 813C variation in foliage and wood for five open-grown P. contorta saplings at one site (Juliet Creek, latitude 121° 00' N, longitude 49° 43' W, elevation 1010-1067 m). Variation in 813C within and among trees was smaller in wood than needles. Paired north and south aspects (wood) appeared to accurately track 813C year to year variation. Therefore, field 513C was determined for whole wood stem cores spanning ten years growth from the north and south sides of trees. The cores were taken at stump level to avoid missing years. Depending on the site, ten or eleven populations representing a wide range of habitats (from 49° 26' to 59° 59' N latitude and 114° 25' to 132° 58' W longitude) were sampled. There were genetic differences in 813C among populations. Differences were related to provenance (habitat of origin), temperature and precipitation. A particular population from the wet, maritime Pacific coast (P. contorta var. contorta) stood out from the others, which were all from the continental interior (P. contorta var. latifolia). There were no genetic differences in plasticity among populations. Indicated relative WUE increased progressively from the wettest to the driest trial site. The magnitude and direction of this increase was similar in all populations.  The relationship between 813C and biomass increment in controlled environments as well as in nature, was not clear. High relative WUE was related to low or high yield, depending on the population, its growing conditions, and its physiological and morphological attributes. However, correlating mean population yield at each trial site with mean 513C showed a positive correlation between high productivity and high WUE. To understand differences within and among populations, it was apparent that the physiological and morphological bases for high productivity must be measured. Of the seedling provenances, only the coastal one had a different relative WUE. Favorable growth conditions may have nullified expression of genetic differences. In P. contorta, it appeared that early selection for WUE may be possible. Mean isotopic  compositions of saplings and greenhouse grown first year seedling shoots from the same seedlots were positively correlated.  Table of Contents Abstract Table of Contents^  iv  List of Tables^  vi  List of Figures^  vii  List of Abbreviations^  viii  Acknowledgements 1.0 Introduction^  1  2.0 Literature Review^  4  2.1 Definition of Water-Use Efficiency ^  4  2.2 Physiology of Pinus contorta ^  7  2.3 Intraspecific Variation in WUE for Forest Tree Species ^ 9 2.4 Intraspecific Variation and Ecotypic Differentiation ^ 11 2.5 The Relationship of WUE to the Ecology of Pinus contorta ^ .11 2.6 Stable Carbon Isotope Discrimination and WUE ^  12  Introduction ^  12  History ^  12  Units of Measurement ^  13  Theory ^  14  2.7 Carbon Isotope Discrimination and Plant Growth Characteristics ^ 16 2.8 Environmental Effects on Carbon Isotope Discrimination ^ 17 Light ^  17  Water ^  18  Nutrients ^  19  2.9 Carbon Isotope Analysis in Woody Plants ^  19  2.10 Variation in $513C Within and Among Trees ^  22  iv  3.0 Materials and Methods ^  24  3.1 Determination of Sampling Methods for Field Trials ^24 3.2 Determination of Genetic Variation in WUE Among Sapling Provenances. . ^26 3.3 Seedling Experiment ^  33  3.4 Data Analysis ^  35  Determination of Sampling Methods for Field Trials ^ 35 Determination of Genetic Variation in WUE Among Sapling Provenances. ^ 35 Seedling Experiment ^ 4.0 Results^  36 37  4.1 Determination of Sampling Methods for Field Trials ^ 37 4.2 Determination of Genetic Variation in WUE Among Sapling Provenances. . ^ 44 4.3 Seedling Experiment ^ 5.0 Discussion^  69 80  5.1 Determination of Sampling Methods for Field Trials ^ 80 5.2 Determination of Genetic Variation in WUE Among Sapling Provenances. ^ 84 5.3 Seedling Experiment ^  92  6.0 Conclusions^  95  7.0 Recommendations for Further Research^  96  References^  98  Appendices^  106  List of Tables 1^Test site characteristics for 70 Mile House, Holden Lake and Salmon Lake ^ 27 2^Provenance characteristics and available climate data ^  31  3^ANOVA for five Pinus contorta saplings at Juliet Creek (discs at stump level) . . . ^ 38 4^ANOVA for 813C comparisons of wood and needles at stump level in five Pinus contorta saplings at Juliet Creek ^  43  5^ANOVA for sample comparisons in one Pinus contorta sapling at Juliet Creek. . . ^ 48 6^Summary statistics for 813C in Pinus contorta sapling provenances planted at 70 Mile House, Holden Lake and Salmon Lake ^  50  7^ANOVA for provenance trials at 70 Mile House, Holden Lake and Salmon Lake. . ^52 8^Test results for differences in 813C among nine provenances over all sites ^ 55 9^ANOVA for provenance trials at 70 Mile House and Holden Lake ^ 57 10 Test results for differences in 813C among ten provenances over two sites (70 Mile House, Holden Lake) ^  60  11 ANOVA for provenance trials at Holden Lake and Salmon Lake ^ 62 12 Test results for differences in 813C among ten provenances over two sites (Holden Lake, Salmon Lake) ^  65  13 Summary statistics for growth parameters of Pinus contorta seedling provenances. ^ 70 14 One-way ANOVAs on growth parameters for Pinus contorta seedling data ^ 71 15 Means differences among seedling shoot weights (g) ^  72  16 Means differences among seedling root weights (g) ^  74  17 Means differences among seedling shoot/root ratios (natural logarithms) ^75 18 Means differences among seedling 813C values ^  vi  76  List of Figures 1^Map showing locations of provenances and test sites ^  30  2^Schematised test block layout ^  32  3^Regression of alpha cellulose 513C on whole wood 513C ^ 34 4^Stump wood 513C values for years 1983, 1984, 1987 and 1989 in five Pinus contorta saplings ^  39  5^Aspect 813C values (stump wood) for years 1983, 1984, 1987 and 1989 ^ 40 6^The 513C values in stump wood as portrayed by averaging north and south aspects in Pinus contorta saplings ^  41  7^The 813C values in stump wood as portrayed by averaging east and west aspects in Pinus contorta saplings ^  42  8^Aspect x material interactions (1989) in Pinus contorta saplings ^45 9^Needle 513C trends with aspect (1989) in Pinus contorta saplings ^ 46 10 Wood 813C trends with aspect (1989) in Pinus contorta saplings ^47 11 Needle 813C trends with aspect and position in one Pinus contorta sapling ^49 12 Changes in provenance 813C least squares means among three sites ^ 54 13 Changes in provenance 513C least squares means between two sites — 70 Mile House and Holden Lake ^  59  14 Changes in provenance 813C least squares means between two sites — Holden Lake and Salmon Lake ^  64  15 Pinus contorta greenhouse seedling 513C means plotted against sapling least squares means averaged over three sites ^  78  16 Pinus contorta greenhouse seedling 513C means plotted against sapling least squares means at Holden Lake ^  79  17 Illustration of geographical trends for 813C in Pinus contorta ^ 85  vii  ^ ^  List of Abbreviations NE^photosynthetic carbon assimilation rate/transpiration rate (i.e., instantaneous water-use efficiency) Ng^photosynthetic carbon assimilation rate/stomatal conductance (i.e., intrinsic water-use efficiency) C3^plant species for which the first product of photosynthesis is a 3-carbon  molecule  De^diffusivity of CO2 De^diffusivity of water vapor ei^water vapor pressure inside the leaf ea^ambient water vapor pressure outside the leaf stomatal conductance to CO2 g^ c gw  stomatal conductance to water vapor  kPa^kilopascal LSD^least significant difference LSM^least squares mean map^mean annual precipitation mat^mean annual temperature mpdm^mean precipitation of the driest month msp^mean summer precipitation mtwm^mean temperature of the warmest month MPa^megapascal PAR^photosynthetically active radiation PDB^fossil Pee Dee belemnite Pi/Pa^intercellular partial pressure of CO2/ambient partial pressure of CO2 PPFD^photosynthetic photon flux density R^molar abundance ratio '3C/'2C ra^boundary layer resistance to diffusion viii  rm^mesophyll resistance to diffusion rs^stomatal resistance to diffusion RuBisCO^ribulose-1,5-bisphosphate carboxylase/oxygenase WUE^water-use efficiency A^isotope discrimination Ac^leaf-to-air concentration difference for CO2 Ae^leaf-to-air concentration difference for water vapor 513c^stable carbon isotope abundance variable 43c^portion of carbon fixed that is lost to respiration Ow^non-productive water loss due to incomplete stomatal closure, cuticular openings and evaporation from the soil v^water vapor pressure difference between the inside and outside of the leaf oho^per mil (parts per thousand)  ix  Acknowledgements This thesis is dedicated to my parents, Pauline and Emmett Holowachuk, who have supported me generously in my educational endeavours. The funding for this study was provided by an NSERC Operating Grant to Dr. Robert Guy and by Challenge 90 and Challenge 91 Grants. The cooperation and assistance of Dr. C. Ying and Ms. Leslie McKnight, research staff for the British Columbia Ministry of Forests, were crucial to the establishment of my project. Frank Kohlberger's conscientious efforts in the field and lab promoted accurate results. Dr. N. Livingston (University of Victoria) generously provided technical help. Dr. A. Kozak gave statistical guidance. My supervisor, Dr. R. Guy and committee members, Drs. C. Ying, P. Burton and D. Lester offered constructive advice.  1 1.0 Introduction The term, water-use efficiency (WUE), relates plant production to water consumption. An indicator of plant performance, WUE is used to study genetic differences in mechanisms that maximize performance in arid environments. The concept of water use economy is not new. Drought limits vegetation growth, distribution and yield more than any other environmental factor (Kramer 1983). Plant breeders, concerned with improved harvests under arid conditions, focus on breeding for drought resistance rather than increased WUE, i.e., gaining higher yields from given amounts of water (Tesar 1984). In agriculture, WUE is desired because it is perhaps the foremost yield component (Martin and Thorstenson 1988). Although short-term yield usually declines with increased WUE, total yield per given land tract will increase if water supply limits growth, i.e., growth is maintained for a longer time (Fisher and Turner 1978). In cases where water is non-limiting, however, high WUE may restrict yield (Meinzer et al. 1990, Condon et al. 1990).  Compared with agricultural crops, we know little about genetic variation in WUE or its physiological basis in woody plants. We do not know the circumstances under which efficient water use confers competitive advantage or promotes acceptable growth rates in trees. We have yet to examine the effect of drought and the magnitude of genetic, and genotype x environment interactions as sources of variation in WUE. Water-use efficiency varies intra-specifically in Populus L. (Blake et al. 1984), Pinus ponderosa Laws. (Monson and Grant 1989) and Pseudotsuga menziesii (Mirb.) Franco (Srnit  and van den Driessche 1992). Populus studies (Dickmann et al. 1992) show that the relationship between dry-weight increment and high WUE is not clear. Monson and Grant (1989) and Eickmeier et al. (1975) have used WUE in Pinus ponderosa and Tsuga canadensis (L.) Carr, respectively, as measures of differential adaptation to moisture-limited  habitats. DeLucia et al. (1988, 1989) show that high WUE in Pinus ponderosa and Pinus  2 jeffreyi Grey. & Balf. may be a competitive disadvantage in water-limited environments. In  desert soils, Pinus seedlings cannot effectively compete with the shrub species. The shrubs' relatively high water-use rates deplete the soil water, thus restricting the pines to areas with greater water availability. In agriculture, stable carbon isotope analysis effectively screens crops for variation in WUE (Farquhar et al. 1989b). Recently the application of this technique to shrubs and trees has burgeoned (Read and Farquhar 1991; Fu et al. 1992a, b; Jackson et al. 1992; Leavitt and Danzer 1992; Livingston 1992; Newberry 1992; Zhang et al. 1993). The isotopic composition of a plant is typically expressed as the abundance ratio 13C/12C relative to a standard and is called 513C. The 813C of a plant changes due to discrimination against 13CO2 over 12CO2 during photosynthetic carboxylation and gaseous diffusion. Isotopic composition or 513C of plant tissue is positively related to WUE. The reasons for this are well understood. Stomatal closure causes increased instantaneous WUE since the photosynthetic rate is reduced proportionally less than the transpiration rate. At the same time, stomatal closure decreases the partial pressure of CO2 in the intercellular air spaces (pi). Discrimination against 13CO2 decreases with low pi, thereby increasing 513C. Thus both seasonal WUE and 513C increase  as a result of stomatal closure in response to drought. The relationship  between 513C and WUE would be similar also if CO2 fixation rate and pi were affected by photosynthetic disturbances of the mesophyll rather than by stomatal behavior. This study uses 813C (an integrated index of WUE) to determine ecotypic variation in WUE for Pinus contorta Dougl. The species has broad edaphic and climatic tolerances, and grows in a wide range of moisture-limited habitats. Based on Rehfeldt's research (1986, 1987) supporting "specialization" as the species' mode for survival, I expect P. contorta populations to be specialized in WUE along moisture gradients. Specialization will be evident in "ecotypes" arising in response to local conditions. To accept that such ecotypes exist, differences in WUE must be demonstrated as heritable by appearing when plants from across the species' range are grown in "common gardens" (Clausen et al. 1948).  3 Research objectives: 1. To develop sampling methods for field trials in this project, as well as for future projects; 2. To use 513C to test for genetic variation in WUE among seedling and sapling P. contorta provenances;  3. To correlate 813C of seedlings with shoot, root and total weight, as well as shoot/root ratio; and to correlate 813C of saplings with diameter, height and volume; 4. To correlate 513C of seedlings with that of saplings grown from the same seed source; 5.  To correlate 513C of saplings with provenance climate variables: mean  temperature of the warmest month, mean annual temperature, mean summer precipitation, mean precipitation of the driest month, mean annual precipitation; and 6. To assess the plasticity of populations by examining their ranked 513C values among field sites distinct in available moisture.  4 2.0 Literature Review 2.1 Definition of Water-Use Efficiency In agriculture or ecosystem science, WUE is defined as the ratio of yield or net primary production to evapotranspiration (Fisher and Turner 1978; Gardner et al. 1985). Yield is expressed as economic yield (e.g., grain or seed, forage in grasses) or dry weight of the whole plant, including roots. The plant parts used depend on the experimenter's objectives. If roots or other vegetative organs are not included, the WUE will be lower than if the entire plant is the basis for measurement. Use of evapotranspiration gives increased variation in WUE, since evaporation is influenced by leaf cover and frequency of soil wetting, independently of transpiration (Tanner 1981). The definition which follows is the most useful in agronomy (Kramer 1983): WUE =  Dry matter or Crop Yield (g) Water used in evapotranspiration (kg)  ^(1)  Physiologists often discuss WUE in terms of gas exchange, expressing it as net CO 2 uptake per unit of transpired water (Fisher and Turner 1978). Simply defined, at the level of a single leaf: WUE =  Net carbon dioxide uptake (mmol) Water used in transpiration (mol)  ^(2)  More recently, Hubick et al. (1988) have used the term "transpiration efficiency" as well as water-use efficiency to discuss dry matter production and water use. They distinguish the terms according to type of water loss. In the single plant, transpiration efficiency is the ratio of moles of carbon in the plant to moles of water used to accumulate that carbon. Water-use efficiency, assigned to determinations in the field, is the ratio of dry matter to total water use including losses such as soil evaporation and runoff.  5 Water-use efficiency is determined by biophysical processes such as CO2 and water vapor gradients between leaf and air, stomatal opening, boundary layer resistance and mesophyll resistance. These effects are accounted for in different ways by Fisher and Turner (1978), Nobel (1980), Osmond  et al.  (1980) and Farquhar et a/. (1989b). Water loss  accompanies CO2 uptake since water vapor diffuses out of a leaf by the same route that CO2 diffuses in. For a given stomatal resistance (rs), more water will be lost than CO2 acquired. The concentration gradient of water vapor is usually greater than that of CO2; water vapor has a higher diffusion coefficient than CO2; and CO2 diffusion is hindered by an added resistance (rm or mesophyll resistance) not encountered by water vapor (Nobel 1983). The two gas-exchange processes are: (Conc.0O2ext. - Conc.0O21m.) x diffusivity  Assimilation =  (3)  ra + rs + rm  Transpiration —  (Conc.H2Oint. - Conc.H20ext) x diffusivity ra + rs  ^(4)  The equations can be combined to give (Fisher and Turner 1978): WUE =  Ac x De (ra + rs) Ae x De (ra + rs + rm)  (5)  where Ac and Ae are the leaf-to-air concentration differences for CO2 and water vapor, respectively; De and De are the diffusivities of CO2 and water vapor, respectively; and ra, rs and rm are the boundary layer, stomatal and internal resistances to diffusion, respectively. Gas exchange through the cuticle is ignored. Researchers interpret mesophyll resistance differently. For many (Nobel 1983), mesophyll resistance represents the diffusion pathway from the substomatal cavity to the chloroplast, and from inside the chloroplast to the site of fixation at ribulose-1,5bisphosphate carboxylase/oxygenase (RuBisC0). For others (Jones 1976; Farquhar  et al.  6 1982b), mesophyll resistance comprises several components, of which the diffusion pathway is only minor since the physical distance between the cell surface and a chloroplast is very small. Carboxylation resistance is more important. Kramer (1983) states that in practice, rm includes biochemical limitations on CO2 fixation as well as physical limitations on diffusion since rm for CO2 is calculated as the difference between (ra + rs) and the total resistance to CO2 uptake. Farquhar et al. (1989b) use the terms "conductance" and "partial pressure" to define WUE. [Conductance, or the reciprocal of resistance, refers to ease of flow (m s-1) of gaseous species through a medium or material]. They begin with an approximate expression for the ratio of instantaneous carbon assimilation rate (A) and transpiration rate (E): A/E = gc(Pa -^/ gw(ei - ea)  (6)  = (pa - pi) / 1.6v  (7)  = Pa(' - Pi/Pa)/ 1.6v  (8)  where ge and gw are conductances to CO2 and water vapor, respectively; pa and pi are partial pressures of CO2 in the atmosphere and inside the leaf, respectively; and ei and ea are the water vapor pressures inside and outside the leaf, v being the difference between them. The ratio of the diffusivities of water vapor and CO2 in air equals 1.6. Using the ratio, moles of carbon in the plant to moles of water transpired during the growth period, water-use efficiency is defined as: WUE = (1 - •:1:1c) pa(1 - pi/pa) / 1.6v (9) where 4:Ic is the portion of carbon fixed during the day that is lost to respiration by the leaf at night and by other plant parts over the whole period. Equations (5) and (9) summarize the important environmental and genotypic influences on WUE. Through its effect on v, vapor pressure deficit (air) alters WUE. In single leaves VVUE decreases with increasing v, although the opposite may occur in whole plants (Rawson et al. 1977). Vapor pressure deficit changes daily, seasonally and regionally,  7 depending on temperature and absolute humidity. Air temperature acts on WUE via its effect on v. Since v is usually closely coupled to air temperature in the field (Schulze et al. 1973), increased air temperature reduces WUE unless leaf temperature is suboptimal for photosynthesis (Miller and Mooney 1974). Incident irradiance is important, due to the non-linear relationship between photosynthesis and transpiration. Transpiration is always positive, showing an upward linear or curvilinear response with increasing light due to rising leaf temperature. Stomatal conductance may or may not increase. Net photosynthesis plateaus with increased light (reflecting saturation kinetics). Accordingly, there is an optimum irradiance for maximum WUE (Bierhuizen and Slatyer 1965; Downes 1970; Jones 1976). The leaf traits — boundary layer, stomatal conductance and biochemical capacity for fixation of carbon dioxide — influence WUE (see Fisher's and Turner's 1978 review). Comparisons between and within genotypes must take place under the same ambient conditions, and genotype x environment interactions must be considered. 2.2 Physiology of Pinus contorta Pinus contorta is one of the most researched conifers in the Pacific northwest, yet  relatively little is known of its basic physiology (Bassman 1985). Few studies concern its tolerances to crucial, limiting environmental factors such as air and soil temperature, soil moisture, relative humidity and nutrients. On the other hand, the species has been a frequent subject for ecophysiological studies trying to explain distributional patterns of overstory conifers along elevational and geographic gradients in montane forests. Its physiological characteristics have been compared with those of other conifers. Having reviewed earlier work, Lopushinsky (1975) and Smith (1985) characterize the water relations for the species: P. contorta shows moderate to relatively high transpiration rates, largely decreasing with soil drought; stomatal closure occurs at a relatively high leaf water potential; the sapwood layer is relatively thick,  8 conferring large stem water storage capacity; drought resistance is moderate — less than that for P. ponderosa; the species shows high tolerance to saturated soils. Photosynthetic data are hard to interpret, since they are measured under various circumstances and expressed in different units. Brix (1979) compared photosynthesis in Pseudotsuga menziesii, Picea glauca (Moench) Voss, Tsuga heterophylla (Rafn.) Sarg. and Pinus contorta. He measured gas exchange on 4-month-old seedlings (greenhouse grown),  following 2 months acclimation in a growth room. Pinus contorta had relatively high photosynthesis per unit leaf area but the lowest rate per unit leaf dry weight. As well, photosynthesis in P. contorta declined most steeply with drying soil and at a higher threshold soil water potential than the other species. Dykstra (1974) observed that 2-year-old P. contorta seedlings (acclimated for 2 months in a greenhouse) attained maximum net  photosynthetic values of about 6.3 larnol CO2 m-2 s-1. Light saturation occurred at close to 400 W m-2 of PAR. The temperature optimum for photosynthesis was near 20°C, with a four-fold reduction occurring at about 2°C. Photosynthesis decreased almost linearly from maximum values to zero when xylem pressure potential decreased from -0.3 MPa to -1.5 MPa. Mesophyll resistance to CO2 uptake was greater than rs under all conditions, especially drought. Dykstra included carboxylation resistance in rm; by implication, WUE would have decreased with drought. Photosynthetic rate increased proportionally with decreases in rm and rs. Sweet and Wareing (1968) reported intraspecific variation in net photosynthesis for 12 and 19-week-old P. contorta. Photosynthetic rates for the 12-week-old seedlings were in the order: California-Oregon coast (var. contorta)> Rocky Mountains, Alberta (var. latifolia) > Cascade Mountains, Oregon (var. murrayanallatifolia)> Washington coast (var. contorta). Ranking changed in the 19-week-old seedlings with different leaf morphology. The authors cautioned that large differences in photosynthetic rates might not reflect field performance, since seedling photosynthetic rate, provenance and growth conditions often interacted. Variation among provenance photosynthetic rates could not be related to climate or location  9 of the seed source (a finding common to studies using non-stressed growing conditions). Correlations with climate/geography have been shown under extreme conditions (Bourdeau 1963). Changes in leaf-to-air water vapor pressure difference (v) account for much of the stomatal regulation in P. contorta (Kaufmann 1982); as well, stomatal response is controlled by light intensity. Sandford and Jarvis (1986) observed large variation in stomatal response to v in current year needles of 3-year-old seedlings of Picea sitchensis (Bong.) Carr., Larix x eurolepis Mill., Pinus sylvestris L. and Pinus contorta. All species' stomates partly closed as  v was increased from 0.4 to 2.0 kPa; P. contorta ranked second in degree of stomata! closure. Only the stomates of P. contorta and L. x eurolepis behaved optimally (Cowan 1986), maximizing the exchange rate of CO2 for water during transpiration as v increased, i.e., change in assimilation/change in transpiration remained constant as v increased. Murdiyaro et al. (1985) found no significant differences in transpiration and stomatal conductance  among seven provenances of non-stressed 2-year-old P. contorta. After drought stressing newly grown seedlings from three of the provenances, they found significant provenance x treatment interactions in stomatal response. On exposed early-successional sites, Pinus contorta saplings had photosynthetic gains similar to Abies lasiocarpa (Hook.) Nutt. and Picea engelmannii Parry, but with more efficient water use (Carter and Smith 1988). The pines' transpiration decreased greatly with progression from shaded to open habitats. The authors noted that all three species had remarkable stomatal and photosynthetic acclimation responses to habitat exposure. 2.3 Intraspecific Variation in WUE for Forest Tree Species Although considerable work has been done on genetically based, intraspecific variation in photosynthetic traits and water loss rate, little has been done on variation in WUE. Blake et al. (1984) found genetic variation in WUE for Populus genotypes from various countries. Some clones showed high dry matter production combined with high  10 WUE. Differences in WUE were related to stomatal resistance (accounting for 40% of the variation), foliar adaptations such as cuticular ledges or hairs above pore openings, time of stomatal opening in the morning, and stomatal size and frequency. There was no evidence of a particular physiological or morphological variable which would consistently explain improved WUE in every case. Large population differences may occur where dissimilar environments exert intense selection pressure (Turesson 1922a, 1923). Eickmeier et al. (1975) suggested that Tsuga canadensis has at least two physiological races correlated with environment. Carbon dioxide  and water vapor exchange, stomatal resistance, WUE and morphology varied in ecologically meaningful ways in first year seedlings from two Wisconsin seed sources. Water-use efficiency was measured as carbon dioxide to water vapor flux ratio. Monson and Grant (1989) found definitive heritable differences in stomatal conductance, maximum photosynthetic rate and intrinsic WUE (measured as pupa) between Pinus ponderosa progeny from two genetic lines. The higher WUEs in families derived  from a coastal x interior cross were manifested in lower pipa values and lower stomatal conductances for any given photosynthetic rate. Results suggested that P. ponderosa had achieved improved WUE and lower transpiration rates in drier habitats, at the expense of reduced maximum photosynthetic rates. Smit and van den Driessche (1992) compared WUE in Pseudotsuga menziesii and Pinus contorta seedlings. Four provenances were tested — one from a wet and one from a  dry site for each species. One-year-old seedlings were planted in wet and dry soil treatments and grown outside for 20 weeks. Pseudotsuga menziesii had higher WUE than Pinus contorta. Pseudotsuga menziesii from the dry site was more water-use efficient than P. menziesii from the wet, but there was little difference between Pinus contorta provenances.  Reasons given for P. contorta's lack of response were that origin sites were not sufficiently different or the dry soil treatment was not dry enough. Root exploitation of soil water, not WUE, determined productivity differences between the species. Pinus contorta, with lower  11 WUE, produced more dry matter than Pseudotsuga menziesii. Pinus contorta seedlings used water in their root environment more completely than the Pseudotsuga menziesii. 2.4 Intraspecific Variation and Ecotypic Differentiation The concept of ecotypic differentiation developed after Turesson's classic studies (1922a, b) on Atriplex L. species. In some cases, diverse forms among groups of individuals in different environments were directly induced by environment, i.e., there was phenotypic plasticity. But in most cases, they were the result of genetic variation among populations. The inherited characteristics were related to distinct native habitats. Believing that "habitat types" arose from genotypic responses to particular environments, Turesson called them "ecotypes". The underlying evolutionary process became known as "ecotypic differentiation". Subsequently, investigators have established that ecotypic differentiation must be mainly responsible for the ability of some species to occupy a wide range of habitats. Ecotypic differences exist with changes in altitude, latitude and precipitation and as a result of soil type, nutrient availability and metal toxicity. As major habitat factors differ spatially in a random or non-random manner, so can ecotypic variation. 2.5 The Relationship of WUE to the Ecology of Pinus contorta Pinus contorta's features make it an ideal species for studying "adaptedness" — the  extent to which individuals are physiologically attuned to their environment (Rehfeldt 1987). It is the most widely distributed conifer in western North America, growing in a variety of sites and in all but the most adverse conditions. Its range, centered in British Columbia, spans 33 degrees latitude, 35 degrees longitude and more than 3900 m elevation (Wheeler and Critchfield 1985). Three distinct, interfertile, geographic races have evolved, each characterized by morphological, physiological and biochemical traits different from the others. These  12 geographic races and affiliated regions are: 1. P. contorta var. contorta: Pacific coast, from Alaska to northern California; 2. P. contorta var. murrayana: southern Cascades, Sierra Nevada range and the  mountains of southern Baja California; 3. P. contorta var. latifolia: Rocky Mountain and Intermountain regions, northern Cascades, Washington; eastern outliers from northern Alberta to South Dakota. The basis for the species' large distribution is the existence of specialized populations for small parts of the species' range. In seedling studies, Rehfeldt (1983, 1986, 1987, 1989) demonstrated that through natural selection, traits have evolved to overcome ecological problems such as changeable frost-free periods, insect infestations and disease epidemics. Research has established within- and among-population variance in drought resistance; however genetic variation in WUE has hardly been tested. 2.6 Stable Carbon Isotope Discrimination and WUE Introduction Plant adaptation and acclimation responses to water shortage have received increasing attention in recent years. During the last decade, agricultural research on WUE has increased greatly with the development of stable carbon isotope analysis as an effective screening tool for variation in WUE (Hubick et al. 1988). The goal of this research has been to select for improved growth in water-limited environments. History The fundamental terms, processes, effects and theory of carbon isotope fractionation or discrimination by plants are discussed by Hayes (1982), Farquhar et al. (1982b, 1984, 1989a, b), O'Leary (1981, 1988) and O'Leary et al. (1992). Stable carbon isotope composition was initially used to indicate photosynthetic pathways in plants (Bender 1968).  13 Isotopic surveys have provided information on the distribution of photosynthetic pathways among various phylogenetic groups and ecological zones (O'Leary 1981, 1988). Early studies have shown substantial variation in isotopic composition at interspecific and intraspecific levels, as well as variation associated with different environmental growth conditions and dry-matter composition (reviewed by Farquhar et al. 1989a). Units of Measurement There are four isotopes of carbon — 14C, 13C, 12C and 11C. The stable isotopes are 13C and 12C; 14C and 11C are unstable, i.e., "radioactive". Terrestrial carbon is 98.9% 12C and 1.1% 13C. Green plants contain relatively less 13C than does air, showing their preferential use of 12CO2 over 13CO2. Variation in the 13C/12C ratio (R) of plants is due to "isotope effects" or fractionations, expressed during the formation and destruction of carbon bonds or as a result of processes affected by mass, such as gaseous diffusion. Isotope effects are not observable but isotopic compositions are altered by them, and from this we infer they exist. Isotopic compositions (513C), expressed as "del" values (5), are conveyed in per mil (oho), i.e., parts per thousand. Carbon isotopic composition is calculated relative to the historic  standard, fossil Pee Dee belemnite (PDB): [R(Sample) - R(Standard)] 813C - ^ [R(Standard)]  x 103  ^  (10)  Isotope effects, expressed as isotope discrimination (A), are calculated from the difference in the 513C value between source and product: A=  513Cair - 513Coant 1 + 513Coant/1000  ^  (11)  The advantage of reporting A rather than 8 is that it directly expresses the consequences of biological processes. It is important where isotopic composition of the  14 source air changes due to antlIropogenic factors or when interpreting results from canopies where an isotopic gradient exists with height (Vogel 1978). Since A values are usually positive while those for 5 are negative (when PDB is the reference), confusion can result. Therefore some authors use molar abundance ratios (R) and compositional deviations (5) as intermediates in determining final isotope effects. Carbon isotopic composition (5), rather than A is reported throughout the thesis. The 513C values are the primary data which can be converted to A, knowing the 613C of source air. I did not measure 813Cair for the field sites tested or for the greenhouse where the seedlings were grown. Measuring 513Cak introduces its own sampling error into the calculation of A. Substituting 513Cair reported in the literature (-8.0 Woo) for 5 13Cair in my study would have given inaccurate measures of A for seedlings grown in the greenhouse. Theory  Plants discriminate against 13C during photosynthesis. In 1982(b), Farquhar et al. predicted this discrimination would be least in plants (C3) which fixed the most carbon per unit of water transpired, i.e., in those that maintained the greatest WUE. This theory was confirmed, in part, from 513C values and short term gas exchange measurements of leaves from contrasting species (Farquhar et al. 1982a). In 1984, Farquhar and Richards confirmed the theory fully by isotope analysis of Triticum aestivum L. genotypes, for which dry matter accumulation and water use had been measured over the long term. Plants with high WUE had large ratios of 13C to 12C, meaning that isotope effects were smaller or that the plants discriminated less against the heavier isotope, 13C. These results suggested that carbon isotope analysis could be a tool for seeking differences in WUE and for genotype selection in breeding programs for increased yield under drought. Current theory states that variation in WUE occurs because WUE is partly dependent on the ratio of CO2 partial pressure inside the leaf to CO2 partial pressure in the atmosphere (pi/pa). This ratio varies and the variation can be seen in the isotopic composition of a plant.  15 Since the pi/pa ratio varies, we expect a large degree of genetic variation in the physiological component of whole plant VVUE. The nature of inheritance in pi/pa is largely unknown, except that it is not under simple genetic control (Farquhar et al. 1989a). Isotopic discrimination is likely a polygenic trait as well (Farquhar et al. 1989a). The genetic control of A appears strong in Arachis hypogaea (Hubick et al. 1988) and in Triticum aestivum (Condon et al. 1987). To increase WUE, a leaf must decrease pi/pa through increased photosynthetic capacity or decreased stomatal conductance. By rewriting equation (7) to give equation (8), Farquhar et al. (1989a) emphasize that a smaller value of pi/pa leads to an increase in A/E (transpiration efficiency). A simple model for discrimination in leaves of A = a + (b - a)pi/pa d, -  C3 plants is:  (12)  where "a" is the fractionation of carbon isotopes due to diffusion in air (4.4 Woo) and "b" the net fractionation due to carboxylation (27.0 oho). The "d" represents minor isotopic effects caused by dissolution of CO2, liquid phase diffusion, respiration and other processes not yet quantified (Masle and Farquhar 1988). Since "a", "b" and "d" are largely constant, A may be a surrogate measure for pi/pa• In experiments relating gas exchange properties to short and long term A (with v kept constant), pi/pa is positively correlated with A (Evans et al. 1986). During whole plant growth, carbon and water losses must be accounted for, requiring another equation. A portion (0c) of the carbon fixed via the stomates during the day is lost in respiration. Some water is lost from the plant independently of CO2 uptake, due to incomplete stomatal closure and cuticular openings. Unavoidable evaporative soil water loss occurs as well. If this "nonproductive" water loss is a portion, Ow, of "productive" water loss, the A/E equation can be changed to describe the molar ratio of carbon gain by a plant, to water lost: Pa (1 - pupa) (1 - 0c) WUE 1.6v(1 + Ow)  =  (13)  16  This equation, combined with equation (12), predicts a negative linear dependence of WUE on A:  WUE =  (b - d - A) Pa ^ [(1 (b - a) 1.6v (1 + Ow)  ^  (14)  Results from pot experiments, using various watering treatments and genotypes, fit the theory well for a number of species — Triticum aestivum (Farquhar and Richards 1984; Masle and Farquhar 1988), Arachis hypogaea L. (Hubick et al. 1986, 1988; Wright et al. 1988), Lycopersicon esculentum Mill. (Martin and Thorstenson 1988), Coffea arabica L. (Meinzer eta!. 1990) and Vigna unguiculata (L.) Walp. (Hall eta!. 1990). 2.7 Carbon Isotope Discrimination and Plant Growth Characteristics Although Hubick et al. (1988) observed a negative relationship between dry matter production and A of field grown Arachis hypogaea cultivars, Condon et al. (1987) found a positive relationship in Triticum aestivum cultivars receiving greater than usual rainfall. Clearly, associations between A and carbon partitioning are important. If strains with larger pi/pa partition more carbon to the shoot, then selecting for large A may result in increased yield when water is not limiting. Masle and Farquhar (1988) examined relations between individual growth components and A in T. aestivum. The grain was grown in a soil resistant to root penetration. Resistance was increased by augmenting soil density or by lowering soil water content. Stomatal conductance and shoot growth responses were similar regardless of which method was used. Masle and Farquhar (1988) found a negative relationship between "partitioning ratio" (total plant carbon/leaf area) as well as root/shoot ratio and A. Although the relationship was not stable across environments, the authors suggested that it might have contributed to the positive relationship between plant mass and A reported by Condon et al. (1987). Relative growth rate and A correlated positively with decreasing soil resistance (as A increased, there was a relatively greater decrease in partitioning ratio than in CO2  17 assimilation rate). Assimilate partitioning, as well as leaf gas-exchange properties must be considered when interpreting the relationship between plant growth and A. 2.8 Environmental Effects on Carbon Isotope Discrimination Light  Field studies have reported an increase in 8 13C1,1ant with increased irradiance (Farquhar et al. 1989a). It is debatable whether, in the field, carbon isotopic composition of leaves in different light levels can be interpreted. It is hard to separate effects of light on A from correlated effects of source air (813Cthr), both of which affect leaf carbon isotopic composition (Farquhar et al. 1989a). Vogel (1978) found a pattern of isotopic variation in canopy leaves, where light varied substantially. Foliar 813C decreased by 3.0 Woo between the top (19 m) and bottom (1 m) of the canopy. The 813C of soil CO2 was approximately -19.0 oho while 813Cair above the canopy was only -7.0 oho. [The CO2 released by decomposing plant litter in soil is more depleted in 13C (ca. -27.0 Woo) than that of atmospheric CO2 (ca. -7.0 Woo), since plants discriminate against 13CO2 in air]. Without measuring isotopic composition of CO2 within the canopy and assuming constant physiological discrimination throughout, Vogel attributed the decrease in 813C of leaves at lower layers to the use of recycled soil CO2. Medina and Minchin (1980) reported 813Coant gradients of 4.7 and 5.6 Woo between upper and lower canopy leaves for two tropical forest types. Results were also attributed to the photosynthetic assimilation of recycled soil CO2 in the lower leaves.  In a moist tropical forest (Panama), Jackson et al. (1992) compared shade tolerant and light demanding species in understory and gap environments. They concluded that different 813C values between plants of the same species growing in gaps and understory were physiologically based and not solely due to differences in 813C of source air (CO2). Francey et al. (1985) examined 813C as well as 513C1an1 in temperate coniferous forest canopies. They noted a decrease in 5 13Coant with canopy depth but no corresponding  18 decrease in 813Cair, thus indicating a physiological effect. Deeper in the canopy 813Coaat and pi were negatively correlated. While soil CO2 contributes to the decrease in leaf isotope composition, most of the change can be attributed to stomatal and photosynthetic effects (Farquhar et al. 1989a). Fu et al. (1992a) reported on leaf 813C variation in open-grown Quercus virginiana Mill. canopies where fixation of respiratory CO2 was likely minimal. Foliar A increased significantly from the upper to lower canopy, attributed to reduced photosynthetic photon flux density (PPFD) in the lower canopy. Cardinal directions showed significant differences in A values. North facing leaves experiencing lowest PPFD showed highest A. South facing leaves with highest PPFD showed lowest A. Foliar A values were significantly higher during the wet sampling period than the dry, suggesting greater WUE during the dry period. There were no significant interactions between canopy height, aspect or sample period (wet, dry). Differences in foliar A were attributed to PPFD changes and perhaps to water vapor pressure differences between leaves and air. It was suggested that leaves subject to low PPFD had higher pi/pa ratios, which might increase quantum yield and give higher photosynthetic carbon gain under light-limiting conditions. The effect of vapor pressure deficit on A would have been largely due to decreased stomatal conductance, resulting in lower pi/pa ratios. Water A common physiological response to drought is decreased photosynthesis, transpiration and leaf conductance concurrently (Farquhar and Sharkey 1982). Partial pressure of CO2 inside the leaf will decline if leaf conductance decreases more than photosynthetic demand for CO2. This effect will be manifested as an increase in 813Coant or a decrease in A. Carbon isotope analysis reveals that, over the long term, plants under water stress (induced by low soil moisture) produce leaves with lower pi values. Over the short term, without new growth, reduction in pi as a measure of stress can be detected in leaf soluble sugars (Guy and Wample 1984; Brugnoli et al. 1988; Lauteri et al. 1992) or by using  19 the "on-line approach to measure variation in 513C of CO2 in air as it flows by a leaf inside a stirred cuvette (Hubick et al. 1988). An increase in leaf-to-air vapor pressure difference will cause reduction of pi and A in the short term (Brugnoli et al. 1988; Madhaven et al. 1991) and long term (Farquhar and Richards 1984). Nutrients Water-use efficiency in any genotype increases with more available nitrogen. The patterns that occur presumably reflect available water and nitrogen (or phosphorus) in the habitat (reviewed by Farquhar and Richards 1984). Fu et al. (1992b) reported the effect of soil water and nutrients on A, in relation to leaf developmental stage in two Phaseolus species. In all treatments, the species native to warmer, drier environments had significantly lower A (suggesting greater long term WUE). Both species were more water-use efficient in high nitrogen. 2.9 Carbon Isotope Analysis in Woody Plants Craig (1953) was among the first to document carbon isotope compositions of plant materials. He analyzed wood from 22 trees world-wide. Wyoming trees showed a "slight local" pattern, with samples from the northwest of the state [Abies lasiocarpa, -27.9 0/oo; Pinus contorta, -27.1 Woo; Artemesia tridentata Nutt., -26.6 Voo] being lighter than those  further south [Abies lasiocarpa, -23.8 Voo; Artemesia tridentata, -23.7 Woo; Salix bebbiana Sarg., -25.0 Woo]. Craig suggested that local environments produced notable 513C variation among trees of the same species. He supported this idea by referring to his early work on 513C differences in one tree, for which he gave no specific causes. He found no correlations  between 513C and altitude, or collection date. In P. contorta, 513C values of wood and leaves were equal, a result unique from the 0.5 to 2.0 Woo difference for the 13 other plants he compared.  20 In 1987, Smith et al. reported differences in photosynthetic CO2 fixation and carbon isotope fractionation among subspecies of Artemesia tridentata. The differences were associated with varying ploidy levels, growth habit and habitat. De Lucia et al. (1988) used carbon isotope analysis in their study of edaphic communities of Pinus ponderosa and Pinus jeffreyi in Great Basin sagebrush and pinyonjuniper communities. They examined the water available to, and the water use of the species as potential factors restricting distribution. Intraspecific comparisons were not made since insufficient numbers occupied each soil type. In September, samples of current year foliage, taken at several points around each of five plants per species per site, were analyzed for 513C. Although lighter by 1.5 ± 0.3 13/00, whole leaf 513C correlated positively with that for cellulose. Carbon isotope ratios (cellulose) were inversely related to maximum stomatal conductances. Results suggested that Pinus monophylla T. & F. and Juniperus osteosperma (Toff.) Little, followed by Pinus ponderosa and Pinus jeffreyi, functioned at lower internal CO2 concentrations and higher WUEs than the shrubs. Read and Farquhar (1991) reported the first study relating genetic differences in A of individual species to their origin climates. They measured leaf A in 22 southern species of Nothofagus, grown in controlled environments. Variation in A was correlated with  temperature range (highest mean monthly maximum temperature minus lowest mean monthly maximum temperature), mean annual temperature, mean annual precipitation, growing season precipitation (December to March) and latitude (p<0.05). The positive correlation between latitude and A for some species, grown under common conditions, was not maintained in field samples. Leavitt and Danzer (1992) examined the influence of light, soil moisture, relative humidity, temperature and CO2 concentration on 813C of Pinus resinosa Ait. seedling growth rings. Treatment and control groups were grown in artificial conditions. Soil moisture and CO2 showed the greatest isotopic effects; however, the true influence of CO2 was masked by inability to regulate the 513C of chamber air. Surprisingly, relative humidity affected 513C to  21 a minor degree, perhaps due to varying humidities at values only >50%. Control plants showed seasonal isotopic changes, suggesting that growth and carbon allocation effects and/or genetic effects might contribute to tree ring 813C changes. Newberry (1992) studied annual climatic variation effects on WUE of Pinus edulis Engelm. at two elevations (1500 m, 2300 m) with similar water stress. Water-use efficiency was determined by 813C values of different-aged leaf cohorts, collected from the southeast aspect of each tree at 1 to 2 m height. Lower elevation trees showed similar trends in annual WUE changes. High elevation trees had randomly fluctuating and more variable 813C values. Despite site differences in annual 813C patterns and foliage variation, average site 813Cs were similar. Possible explanations for this were that competition, low light and herbivory confounded a simple increased drought — increased WUE relationship at the more heavily vegetated, higher elevation site. Influence of soil CO2 was not questioned. At the lower elevation site with open canopy, WUE was constrained only by water availability. Zhang et al. (1993) found genetic differences in A among 25 Pseudotsuga menziesii populations in a 15-year-old field trial. Carbon isotope discrimination was related to geographic location of the seed source and was positively correlated with altitude (r=0.76, p=0.01). Intrinsic water-use efficiency (A/g or assimilation/conductance) was negatively correlated with altitude (r=-0.63, p=0.0007). Fifteen year heights and diameters were not correlated with photosynthetic rate but were negatively correlated with A, pi/pa and g, and positively correlated with A/g (all at p<0.01). Efficient water use for photosynthesis appeared to be more important than absolute photosynthetic rate in determining growth rates. Results from the physiological and genetic studies on photosynthetic traits and water loss rate for P. contorta suggest that populations will differ in relative WUE, as indicated by 813C. The relationship between 813C and WUE is positive (while that between A and WUE is negative). It is expected that populations from dry habitats will maintain greater WUEs (higher 813C) than those from wet habitats over a range of test sites. On dry test sites all  22  populations will likely increase in WUE or 813C, while on wet test sites populations will decrease in WUE. 2.10 Variation in 513C Within and Among Trees Researchers recognize that inherent spatial variation and varying proportions of organic constituents in trees present sampling problems for 513C analysis. Several studies have examined 513C variation within and among trees. Tans and Mook (1980) found "good" correlations between 513C values of whole wood and acid-alkali-acid treated latewood of mature Quercus rubra L. Treated samples from east and west aspects of wood within a bole correlated poorly, with the largest 513C difference for a single year being 4.0 Woo. North and south aspects were not discussed. Within the same radial direction, a single ring sampled over a 40 cm vertical distance showed a 613C fluctuation of up to 0.8 oho. When sampled along the fiber direction, at an angle to the vertical, 513C was nearly constant. Latewood was judged no more reliable than earlywood in providing consistent circumferential 513C trends. Leavitt and Long (1982) compared Juniperus monosperma (Engelm.) Sarg. leaf and wood 813C trends over one growing season. The sampled wood was from a south-facing trunk branching near the ground (noted in Leavitt and Long 1986). Leaves were lighter than wood for both whole tissue and cellulose by approximately 2.5 Woo and 3.5 Woo, respectively. Leaves followed the same 513C trends as the wood. Although rings and leaves showed intraannual isotopic changes, the changes were not significant. Leavitt and Long (1984) studied 513C variability in tree rings at one site by examining 513C trends in one Pinus edulis, as well as differences among several trees at the site. Cellulose was analyzed. The circumferential range within one tree was about 1.0 to 1.5 0/oo, while the difference among individuals was 2.0 to 3.0 0/00. The authors concluded that pooling four orthogonal cores from four trees accurately represented site 513C trends and absolute values. Also, two cores showed a marked improvement over single cores in correlations between 513C of radii and 513C of the full circumference (taken as the "true"  23 813C value). Orthogonal cores yielded more representive samples than cores taken from one side of the tree only. In 1986, Leavitt and Long compared 813C values of leaf, twig and wood cellulose in P. edulis from different locations in the southwestern USA. In most cases, P. edulis rings  were heavier than leaves by 1.0 to 2.0 Woo. Leaves were isotopically heavier than their twigs and over all, followed the same directional 813C pattern as the twigs. The authors found no longitudinal 813C gradients in tree rings, but like previous researchers, they used only single radii at different levels. Single radii do not accurately represent the trend or absolute 813C values in a ring series representing all radii at a certain level aboveground (Leavitt and Long 1986). The range of circumferential variation for ring cellulose was 0.5 to 1.5 0/00; for leaves it was 1.0 to 2.0 0/oo. The authors found a north trunk of f. monosperma yielding a different 813C trend over a growing season than a south trunk from the same tree. They suggested that the trend in the south trunk might reflect greater sun exposure of its leaves in the early part of the season, whereas the north trunk would not receive high exposure until mid season. Yoder et al. (1992) measured 813C of whole wood and needles (taken 1.37 m above ground level) in Pinus ponderosa spanning different age and size classes. Early wood was sampled from the most recent growth ring. Wood and foliage appeared to be enriched with 13C at the rate of 1.0 0/oo for each 20 m increase in tree height. Foliage remained between 1.0 to 2.0 Woo more depleted than wood. There was no change statistically in 813C from the bottom to top of individual large trees (actual size of trees not given).  24 3.0 Materials and Methods This study involved three experiments: 1. development of sampling methods for provenance field trials in British Columbia, as well as for future trials; 2. 513C analysis of wood samples from provenance trials in British Columbia; 3. 513C analysis of seedlings grown from the seed used to establish the provenance trials. 3.1 Determination of Sampling Methods for Field Trials To decide sampling method, 813C variation in foliage and wood was assessed for five open-grown Pinus contorta saplings located at one site (Juliet Creek, lat. 1210 00' N, long. 490 43' W, elev. 1010-1067 m). The site had gravelly soil and vegetation suggesting low moisture availability (Achillea millefolium, Amelanchier alnifolia, Calamagrostis rubescens, Lonicera utahensis, Lupinus spp., Pachistima myrsinites). The saplings were moderately  infected with needle cast. It was assumed that each was infected to the same degree and that each responded similarly to the disease. The sampling was done on June 11, 1989. Saplings were felled and discs of the bole and needles collected accordingly: Tree No. 1 (Age 21.5 yrs., Ht. 5.3 m)  Discs: No. 1 - leader No. 2 - at midsection of 1988-89 stem No. 3 - at 1.2 m above ground No. 4 - within 10 cm of ground Needles: 1. Top of tree a) needles growing on leader b) 1989 needles growing on stem and on north, east, south and westfacing branches 2. Mid-tree - 1989 needles at four cardinal directions around the crown circumference at 1.2 m  25 3. Lower tree - 1989 needles at four cardinal directions on bottom branches Trees No. 2, 3, 4 and 5 (Ages 15, 24.5, 31.5 and 23.5 yrs., respectively; Heights 2.42,  3.95, 5.45 and 9.4 m, respectively) Discs: No. 1 - leader No. 3 - at 1.2 m above ground No. 4 - within 10 cm of ground Needles: 1. growing on leader 2. 1989 needles on bottom N, E, S and W-facing branches Four, 5 mm wide "cores" were cut from the air-dried discs, each core taken from the north, east, south or west aspect of the sapling. Within each core, rings representing years 1983, 1984, 1987 and 1989 were analyzed. Data from six climate stations around Juliet Creek showed that 1983 and 1984 had low growing season temperatures with high precipitation, relative to other years in the 1980s, while 1987 and 1989 had relatively high growing season temperatures and low precipitation. Wood was hand filed for homogeneous well-mixed particles. Weighed samples (0.5 mg) were combusted in a fully automatic elemental analyzer (Howarth 1977). The samples were introduced into the combustion zone at 1050 °C to coincide with oxygen enrichment of the helium gas. This eased combustion which was completed when the combustion products passed through the combustion tube. The carbon was converted quantitatively to carbon dioxide, the hydrogen to water and the nitrogen to oxides of nitrogen. A secondary reaction tube removed excess oxygen and reduced the nitrogen oxides to nitrogen before the gases entered the chromatographic column for separation of the combustion products. For measurement of 1513C, the CO2 was transferred to a VG Isotech Prism triple-collecting mass spectrometer (Middlewich, England) (Nier 1947). Internal precision of the spectrometer was better than ± 0.02 Woo. Samples were corrected for 170 contribution to mass 45 abundance.  26 Needles: After freeze-drying and interim refrigeration, the needles were cleaned, ground in a Wiley Mill (40 mesh), pulverized in a Planetary Micro Mill "Pulverizette 7" (Fritsh GmbH) and analyzed as above. 3.2 Determination of Genetic Variation in WUE Among Sapling Provenances I obtained permission from Dr. Cheng C. Ying (Provenance Forester, British Columbia Ministry of Forests) to sample field trials in British Columbia, set up in 1974 to assess genotype x environment effects in Pinus contorta (Illingworth 1978). From 60 test sites, I chose three: 70 Mile House, Holden Lake and Salmon Lake. They formed a northsouth transect through the B.C. interior, representing contrasting soil moistures and climates (Table 1, Fig. 1). Of 140 provenances tested, I studied eleven (Table 2, Fig. 1). Each test site comprised two blocks, with nine saplings per provenance per block, planted 2.5 m apart in square plots (Fig. 2). Wood was sampled from every living tree, extracted by coring full diameter (north to south) near ground level. Ground level, rather than 1.2 m above ground, was used to avoid missing years. Sapling heights and diameters (at 15 years) were obtained from Ministry growth surveys (1988). Volumes were calculated from a formula by Kovats (1977). Features of each sapling and surrounding neighbors which might influence carbon assimilation and partitioning were noted (e.g., damaged leaders, physical erectness, sexual precociousness, canopy closure). The sampling extended over two field seasons. During July 1990, Holden Lake was sampled. In July 1991, 70 Mile House and Salmon Lake were sampled. Air-dried cores were microscopically magnified and cleaned of extraneous carbon sources (e.g., black smudges from the increment borer, clinging bark particles) with a razor blade. Ten year segments (1980 - 1989) from each side were combined and ground in a Wiley Mill (20 mesh). Following 2 hours freeze-drying, the samples were pulverized for one-half hour in a Planetary Micro Mill and stored in plastic vials.  Table 1.^Test site characteristics for 70 Mile House, Holden Lake and Salmon Lake.  MSP5 MAP6 MTWM7 (mm) (mm)^(°C)  MAT8 Years9 (°C)  16.6  167.9  327.0^14.0  2.6^7  51 39 121 16 1059.2  15.0  196.0  386.0^14.4c  3.0"^10  54 58 124 10 969.3  23.0  280.4  697.1^12.5  0.2^7  Lat  70 Mile House  51 17 121 20 1070^3.0^D^70 Mile House  51 18 121 24 1079.9  Holden Lake  51 37 121 31^1160^3.5^C^100 Mile House  Salmon Lake  54 51 123 55^950^5.0^B/C^Kalder Lalce  Long Elev SMR1 SNR2 Climate Station3 (m)  Lat  Long  Elev (m)  Test Site  MPDM4 (mm)  'Soil moisture regime (Classes: 0 very xeric; 1 xeric; 2 subxeric; 3 submesic; 4 mesic; 5 subhygric; 6 hygric; 7 subhydric) 2Soil nutrient regime (Classes: A very poor; B poor; C medium; D rich; E very rich) 3Normalized data from Climatic Data Summaries for the Biogeoclimatic Zones of British Columbia, Version 3, 1991, compiled by D. Meidinger and G. Reynolds (unpublished) 4Mean precipitation dry month 5Mean summer precipitation (May-Sept. inclusive) 6Mean annual precipitation 7Mean temperature warmest month 8Mean annual temperature 9Years station operating c,c1Temperature adjusted downward 1°C to account for elevation difference between climate station and site  aiiu3sigt plui (unpublished, compiled by Inselberg, MOF 1988/89) 70 Mile House Block 51 Slope: 5%, with undulating shape Aspect: variable Soil Depth: 30+ cm^Rooting Depth: 30+ cm Texture: loam with 5% coarse fragments to 20 cm depth loam with 40% coarse fragments from 20 to 30+ cm Horizons: Ae (2 cm)^Humus mor (2 cm) Base Status: high  Water: No groundwater table, gleyed horizons or flooding. Indicator Species: Arctostaphylos uva-ursi, Astragalus miser, Calamagrostis rubescens, Galium boreale, Rosa acicularis, Shepherdia canadensis. Block 51 shows low productivity, despite its fine-textured rich soil. Soil depth is shallow and precipitation low. Considerable height variation, even within provenances, occurs.  Site descriptions (cont'd)  70 Mile House Block 52 Slope: 12%, with some mounds^Aspect: 50° Soil Depth: 60 cm^Rooting Depth: 60 cm Texture: loam with 5% coarse fragments to 25 cm loam with 35% coarse fragments from 25 to 60 cm Horizons: Ae (2 cm)^Humus mor (2 cm) Base Status: high  Water: No groundwater table, gleyed horizons or flooding. Indicator Species: Achillea millefolium, Arctostaphylos uva-ursi, Arnica cordifolia, Astragalus miser, Calamagrostis rubescens, Galium boreale, Rosa acicularis, Shepherdia canadensis. Block 52 has deeper soil and a slightly cooler exposure than Block 51. Vegetation is only slightly more productive.  Holden Lake Block 53 Slope: 0 - 5%, with mounds^Aspect: flat Soil Depth: 50+ cm^Rooting Depth: 50+ cm Texture: silty loam with 5% coarse fragments to 50 cm silty loam with 35% coarse fragments to 50+ cm Horizons: Ae (3 cm)^Humus mor (3 cm) Base Status: medium  Water: No groundwater table, gleyed horizons or flooding. Indicator Species: Arctostaphylos uva-ursi, Calamagrostis rubescens, Rosa acicularis, Shepherdia canadensis, Vaccinium scoparium. Block 53 has uniform soil characteristics throughout, with most provenances growing well. Exceptions are stunted, multiple leader samples showing poor vigor.  Site descriptions (cont'd)  Holden Lake Block 54 Slope: 0 - 5%, with undulating shape^Aspect: flat Soil Depth: 40+ cm^Rooting Depth: 40+ cm Texture: silty loam with 10% coarse fragments to 20 cm silty loam with 40% coarse fragments from 20 to 40 cm Horizons: Ae (2 cm)^Humus mor (2 cm) Base Status: medium  Water: No groundwater table, gleyed horizons or flooding. Indicator Species: Same as for Block 53. Provenance performance is similar to that on Block 53.  Salmon Lake Blocks 99 and 100 Slope: 0%, with a straight shape^Aspect: flat Soil Depth: 30+ cm^Rooting Depth: 30+ cm Texture: loamy-sand with 15% coarse fragments to 15 cm sandy-loam with 15% coarse fragments from 16 to 30+ cm Horizons: Ae (10 cm - variable)^Humus mor (5 cm) Base Status: medium  Water: No groundwater table or flooding. Gleyed horizons (light mottles) to 8 cm. Indicator Species: Arnica cordifolia, Aulacomnium palustre, Calamagrostis rubescens, Cornus canadensis, Galium boreale, Lonicera involucrata, Peltigera apthosa, Petasites palmatus, Polytrichum juniperinum, Rosa acicularis, Ribes lacustre, Rubus pubescens, Spirea betultfolia, Spirea douglasii, Vaccinium caespitosum, Veratrum viride. The blocks have uniform, fine-textured soils which become dense at 30 cm. Mottles at 10 cm suggest a fluctuating water table due to poor drainage. Several provenances have reddish older foliage. Some provenances are growing slowly; as well, some have broken or forked leaders suggesting minor snow damage. Most of the original forest floor was burned away by a slash fire but mineral soil was not exposed.  30  STAWYE 100^ KILOynE7RES^100^  1 CO^200^yArLES 100^203^300 xltOmFIRES  Dray,b by P. J. Jonce. 1975  ell olden Lk 70 Mile  cr 4  71 e  ( 1 57 1  Fig. 1. Map of British Columbia and Alberta showing locations of provenances and test sites (Salmon Lake, Holden Lake, 70 Mile House). Provenance locations are represented by the provenance numbers on the map.  Table 2. Provenance characteristics and available climate data. Prov Location  ^  Lat^Long Elev Mtwm1 Mat^Msp Map Mpdm Climate Station2 Lat^Long Elev Years3 (111)^(°C) (°C)^(mm) (mm)^(mm)^ (m)  1  Trapping Creek  49 35  119 01 1006  13  Horne Lake  51 46  124 44  914  13.2  1.6  133.3  334.2  13.8  Kleena Kleene  51 59  124 56  899  17b  30  Lower Post  59 59  128 33  640  15.5  -2.9  222.5  460.8  13.9  Lower Post  59 56  128 30  583  8b  44  Marl Creek  51 31  117 11  945  57  Inonoaklin Valley  49 54  118 12  579  61  Purden Lake  53 52  121 44  838  69  Link Creek (Ab)  49 26  114 25 1379  71  Fly Hills  50 43  119 27 1524  95  Petersburg  56 47  132 58  100  Nina Creek  55 48  104  Nechako River  54 01  17.8a  6.4a  254.5  621.8  34.5  Fauquier  49 52  118 04  472  30  14.3  3.0  325.1  852.0  39.0  Castle R. S.  49 24  114 20 1364  14  23  13.2  5.2  885.8  Petersburg  56 82  132 95  15  24  124 49  762  13.6  0.3  223.5  493.7  23.7  Germansen Ld. 55 47  124 42  747  27  124 32  732  13.9  2.0  258.4  525.2  22.2  Fort Fraser 13S 53 53  124 35  701  2671.3 134.0  5b  'Refer to Table 1 for the meaning of Mtwm, Mat, Msp, Map and Mpdm. 2Sources: Canadian Climate Normals 1951-1980; Climatic Data Summaries for the Biogeoclimatic Zones of British Columbia, Version 3, 1991, compiled by D. Meidinger and G. Reynolds (unpublished); personal communication, staff at Atmospheric Environment Service (AES), Vancouver, B.C. 3Years climate station in service. a Temperature adjusted downward 1 °C to account for elevation difference between climate station and provenance location. b Adjusted normals based on 5-19 years, including those from 1951-1980 and any other data from 1931-1950.  32 Schematised Test Block Layout CHILCOTIN REGION 70 Mile House, Site 1: Block 52 Note: NOT PLANTED = • ; DEAD = X  Fig. 2. Schematised test block layout for one field site. The squares represent nine-tree plots, the positioning of individuals designated by open circles. The numbers inside the squares show the provenances allocated to those plots.  33 Whole wood was analyzed, with reproducible analyses to 0.05 0/013 (8 replicates). The decision to use whole wood rather than alpha cellulose was based on previous studies and an experiment on 10 cores representing a range of whole wood 513C values from -23.358 to -27.312 oho. Alpha cellulose was isolated from the wood in Dr. N. Livingston's laboratory, University of Victoria, British Columbia. Alpha cellulose 513C values were regressed on 813C values of the wood from which it was isolated. The regression r2 of 0.981 was highly significant (p<<0.0001) (Fig. 3). 3.3 Seedling Experiment One gram of seed per provenance, cold-stored approximately 20 years, was procured. Germination trials showed 60 to 95% viability. In September 1990, the seed was sown in 164 ml super cell cone-tainers (Stuewe and Sons, Inc.) containing peat and vermiculite 2:1. To each 113 litre-bag of peat was added 662.25 g Dolomite (WesGrow) and 112.5 g trace elements (WesGrow). Soil pH at planting was 4.7. Seeds were covered with No. 2 Granite Grit (Imasco). The seedlings were grown in a greenhouse. After emergence they were watered to field capacity as necessary and after 4 weeks, fertilized every 4 days with 100 ppm 20-8-20 (Plant Prod). The 18 hour photoperiod was supplemented by high intensity discharge sodium lamps. Irradiance ranged from 94 to 513 limo' quanta m-2 s-1 PAR, depending on seedling location and cloud cover. Therefore, seedling positions were rotated weekly. In full sunlight (which seldom occurred) seedlings were exposed to 1400 lamol quanta m-2 s-1 PAR. Relative humidity varied from 40 to 45%, while air temperature above seedlings was 20 ± 2°C on average (Vaisala RH/temperature meter, Cole-Parmer). Air above the seedlings was well mixed by fans. Near the end of December 1990, sulfur deficiency was corrected by supplementing seedling nutrition with K2SO4 (10 ppm) and a commercial (STEM) micronutrient mix (1 ppm). On February 18, 1991, thirty randomly selected seedlings per provenance were harvested. Most had set bud and had mature as well as primary needles. Shoots were frozen  34  -27 —26 —25 —24 —23 —22  WHOLE WOOD 6'3C C/00)  Fig. 3. Regression of alpha cellulose 813C on whole wood 813C: 813C (cellulose) = -1.357 + 0.903 813C (wood). Regression r2=0.981 (p<<0.0001).  35 in liquid nitrogen, freeze-dried and weighed. Roots were washed, frozen, freeze-dried and weighed. An additional five seedlings were prepared for SIT analysis of primary needles, mature needles and stem parts. Since ANOVA showed no significant differences among parts, total shoots were analyzed for 813C following grinding in a Wiley Mill (40 mesh) and pulverization. Analyses were reproducible to 0.1 0/00 (5 replicates). 3.4 Data Analysis Determination of Sampling Methods for Field Trials  Three ANOVAs were used to test effects. Analyses were done with SAS/STAT software, 6.03 Edition (SAS Institute 1988). The first ANOVA was a three-way analysis of 813C values in wood for year, aspect and tree effects as well as interactions in Pinus contorta  saplings. The second ANOVA was a three-way analysis, specifically for material effects (needle vs. wood) and material x aspect interactions. The third ANOVA was a three-way analysis on one sapling, specifically for needle x position effects. The models were mixed models, with aspect fixed and the other factors random. It could be argued that year samples were repeated measures and therefore dependent. They were analyzed as if they were independent, however, since repeated measures analysis produced similar results to factorial ANOVA. Hicks (1982) shows that a repeated measures design is but a special case of factorial and nested-factorial experiments. Numerical results are the same. Determination of Genetic Variation in WUE Among Sapling Provenances  Three-way ANOVA (using SAS/STAT software) was applied to the replicated randomized complete block design of the field trials. All effects were random. The data set had missing cases (or cells) as well as missing observations. Provenance 57 was not tested at Salmon Lake. Provenance 95 at 70 Mile House was deleted from statistical analyses since only four saplings survived from a possible 18. As well, saplings which were dead, too small to core or girdled from top to bottom were removed from the data set. Other outliers with no  36 basis for removal were treated as very unusual measurements and not deleted. To simplify results and test all provenances, balanced data sets were analyzed in three ANOVAs. The data sets were extracted from the following site-combinations: 1. 70 Mile House, Holden Lake and Salmon Lake (provenances 57 and 95 excluded); 2. 70 Mile House and Holden Lake (provenance 95 excluded); and 3. Holden Lake and Salmon Lake (provenance 57 excluded). The design posed a pseudoreplication problem, since sites were not replicated. Pseudoreplication was avoided by not interpreting significant differences among sites as site treatment effects (Hurlbert 1984). Inferences were made on provenances within sites. Correlation and regression analyses for 813C and climate variables were done with SYSTAT software, Version 5.01 (Wilkinson 1990). Multiple regression modelled site mean 813C values as a function of provenance mean temperature of the warmest month (mtwm), mean annual temperature (mat), mean summer precipitation (msp), mean precipitation of the driest month (mpdm) and mean annual precipitation (map). Only provenances 13, 30, 57, 69, 95, 100 and 104 were used, since nearby climate stations did not exist for the others. Seedling Experiment  Statistical analyses were done with SYSTAT software. The seedling design was completely randomized. One-way ANOVAs compared provenance means for 813C values, shoot weights, root weights, total weights and shoot/root ratios. Correlations were determined among growth variables and 813C.  37 4.0 Results 4.1 Determination of Sampling Methods for Field Trials Table 3 shows the effects of tree, aspect and year on 513C values of stump wood in Pinus contorta saplings. Analysis of variance on the mixed model (trees and years random,  aspects fixed) showed significant tree, year and tree x aspect effects (a=0.05). The aspect, aspect x year and tree x year effects were not significant, although the tree x year effect was nearly so (p=0.057). The tree x aspect x year effect (error variance) could not be tested and was assumed non-significant. Multiple range tests (Ryan-Einot-Gabriel-Welsch, Tukey) defined two homogeneous tree subsets (trees 1 and 5; trees 2, 3 and 4). As well, two homogeneous subsets for years were 1987; and 1983, 1984 and 1989. Figures 4 and 5 depict the lack of interaction between tree x year and aspect x year effects. Fig. 4 shows that ranking in 513C changed little among saplings for the years tested. Fig. 5 shows that north, south and west aspects maintained ranking over the years tested. During 1987 (a dry year), 513C was greater in all directions. Sampling allowed two cores per sapling in the provenance field trials. For the saplings at Juliet Creek, it appeared that certain paired aspects would accurately track 813C year to year variation among the years tested. The 513C values in stump wood portrayed by averaging north and south, and east and west aspect 513C values are shown in Figs. 6 and 7. Ranking in 513C among saplings appears to be more stable over the years tested when north and south aspects are used as combined samples rather than east and west aspects. Table 4 shows the effects of tree, aspect and sampling material (needles vs. wood) on 513C values near ground level in 1989. Analysis of variance on the mixed model (trees and materials random, aspects fixed) showed non-significant tree and aspect effects, as well as tree x aspect and tree x material interactions. (Tree 3 was missing an easterly 513C value).  Table 3.^ANOVA for five Pinus contorta saplings at Juliet Creek (discs at stump level). Source  df  SS  MS  EMS  Tree Aspect Year T*A T*Y A*Y T*A*Y  4 3 3 12 12 9 36  11.7332 1.1313 3.8856 2.0315 1.5819 0.6262 2.4042  2.9333 0.3771 1.2952 0.1693 0.1318 0.0696 0.0668  ± 4(32,1y^160.2T (y2^5(y2Ay^402TA 4. OA 42,ry (y2  + 4452TA + 402,ry  cy2^50.2Ay  + 20(52y  F Value  P>F  22.250 2.192 9.820 2.530 1.970 1.040  0.0001 0.1539 0.0015 0.0155 0.0573 0.4273  (52  Satterthwaite Approximation to Test Aspect: Source: Aspect Error: MS (T*A) + MS(A*Y) - MS(Error) DF^MS^Denominator DF^Denominator MS^F Value^P>F 3^0.3771^9.71^0.1721^2.192^0.1539  39  —24  —25  —26 • • • • —27  1983  ^  1984^1987  ^  A  TREE5 TREE4 TREE3 TREE2 TREE!  1989  YEAR  Fig. 4. Stump wood 813C values for years 1983, 1984, 1987 and 1989 in five  Pinus contorta saplings. Isotopic compositions are the means of four observations taken circumferentially per year tested per tree, e.g. sum of north, east, south and west aspect 513C values for 1983/4. Ranking in 813C among  saplings is relatively stable over the years tested when the average circumferential 813C per tree is the measure for comparison.  40  —24  —25  —26  —27  1983  ^  1984^1987  ^  • ^ • •  WEST SOUTH EAST NORTH  1989  YEAR  Fig. 5. Aspect 813C values (stump wood) for years 1983, 1984, 1987 and 1989.  Isotopic compositions are means of five saplings, each sapling contributing one 613C value per year per aspect. Ranking in 613C among aspects remains relatively stable over the years tested, when aspect 813C is a measure of several saplings.  41  —24  —25  'i N c,-)  7.0 —26  —27  • • • • •  TREE5 TREE4 TREE3 TREE2 TREE1  1983^1984^1987^1989 YEAR  Fig. 6. Stump wood 813C values for years 1983, 1984, 1987 and 1989 as portrayed by averaging north and south aspects in five Pinus contorta saplings. Isotopic compositions are means of two samples per tree. Ranking in 813C among trees remains quite stable over the years tested when north and south aspects are used as combined samples.  42  -24  —25  —26 • • • • —27  1983  ^  1984^1987  ^  A  TREE5 TREE4 TREE3 TREE2 TREE1  1989  YEAR  Fig. 7. Stump wood 813C values for years 1983, 1984, 1987 and 1989 as portrayed by averaging east and west aspects in five Pinus contorta saplings. Isotopic compositions are means of two samples per tree. Ranking in 813C among trees is not as stable over the years tested when east and west aspects are used as combined samples rather than north and south aspects.  Table 4. ANOVA for 513C comparisons of wood and needles at stump level in five Pinus contorta saplings at Juliet Creek. Source  df  Tree Aspect Material T*A T*M A*M T*A*M  4 3 1 12 4 3 11  SS 4.4530 1.8463 18.8254 2.2291 1.7528 3.1490 2.1659  EMS  MS 1.1132 0.6154 18.8254 0.1858 0.4382 1.0497 0.1969  F Value  2.540 c52 + 3752TM + 1.75a2T G2 + 4.666702Am + 1.8667(52Th + 0A 0.592 '32 + 3752TM + 18.462a2m 42.960 G2 ÷ 1.9167GTA 0.940 2.230 c:F2 + 3752TM 5.330 02+ 4.66670'2Am G2  Satterthwaite Approximation to Test Aspect: Source: Aspect Error: 0.9739*MS (T*A) + MS(A*M) - 0.9739*MS(Error) DF^MS^Denominator DF^Denominator MS^F Value^P>F 3^0.6154^2.89^1.0388^0.592^0.6624  P> F 0.1943 0.6624 0.0028 0.5419 0.1327 0.0164  44  Material and aspect x material effects were significant. The influence of aspect on 813C for needles and wood is shown in Fig. 8. Wood at 1.2 m height is included for interest; it could not be compared to wood near ground level in this ANOVA. It appeared that the influence of aspect was prominent for needles but not wood. Needles had more negative 813C values than wood. The aspect x position effect in wood appeared non-significant (standard error bars overlap). Figures 9 and 10 depict tree 813C trends with aspect, in needles and wood at stump level. The tree x aspect x material effect (error variance) could not be tested and was assumed non-significant. Overall, wood (base) 813C values were less variable from tree to tree than the needles. In 1989, isotope values among trees ranged from -25.267 to -26.840 Woo in stump wood (a difference of 1.573 Woo) and from -25.750 to -28.989 Woo in lower branch needles (a difference of 3.239 cloo). Analysis of variance (Table 5) shows the effects of aspect, position (base, 1.2 m, top) and material (needles, wood) on 813C values in one sapling during 1989. Materials were significantly different; other effects were not. Figure 11 depicts the lack of response of needle 813C to aspect and position in the sapling. 4.2 Determination of Genetic Variation in WUE Among Sapling Provenances Table 6 gives summary statistics for provenance 813C values at 70 Mile House, Holden Lake and Salmon Lake. Block designs with missing observations require unbiased estimates of treatment means, therefore least squares means (LSMs) are reported. All provenances, but 71, showed progressively decreasing 813C values from the driest to wettest site. Provenance 71 broke the 813C trend from 70 Mile House to Holden Lake by a small increase (0.069 0/oo), rather than a decrease. Standard error of the LSM was 0.1 oho for all but two provenance-site pairs.  45  - 25  —26 -  —27 "co —28 -  —29  o 1.2 M WOOD 0 BASE WOOD A BASE NEEDLES North East South West ASPECT  Fig. 8. Aspect x material interactions (1989) near ground level and at 1.2 m height (wood) in Pinus contorta saplings (±SE). Isotopic compositions are means of five trees (one sample per tree). Needle 613C is influenced by aspect whereas wood 613C is not. Needle 613C is more negative than wood 613C.  46  - 25  —26  • • • • •  —28  —29  North East South  TREE5 TREE4 TREE3 TREE2 TREE1  West  ASPECT  Fig. 9. Needle 513C trends with aspect (1989) in five Pinus contorta saplings. Needles are sampled from the lowest branches. Isotopic compositions represent one sample per tree. Sapling 3 is missing its easterly 513C value.  47  -25  —26 '-'-' ' s......  —27  cP 'Colo —28  —29  • • • • •  TREE5 TREE4 TREE3 TREE2 TREE1  North East South West ASPECT  Fig. 10. Wood 813C trends with aspect (1989) in five Pinus contorta saplings. Wood is cored at 10 cm above ground level. Isotopic compositions represent one sample per tree.  Table 5. ANOVA for sample comparisons in one Pinus contorta sapling at Juliet Creek. Source  df  SS  MS  Aspect Position Material A*P A*M P*M A*P*M  3 2 1 6 3 2 6  0.2092 0.4006 7.4783 0.5233 0.3307 0.7859 1.0651  0.0697 0.2003 7.4783 0.0872 0.1102 0.3930 0.1775  EMS  02  ± 302Am + 202Ap + OA  02  + 402pm + 802p  02  ± 402pm + 12a2m  02 4. 202Ap 02  + 302Am  02  ± 402pm  F Value  P>F  3.496 0.510 19.030 0.491 0.621 2.214  0.9097 0.6624 0.0487 0.7958 0.6267 0.1905  02  Satterthwaite Approximation to Test Aspect: Source: Aspect Error: MS (A*P) + MS(A*M) - MS(Error) DF^MS^Denominator DF^Denominator MS^F Value^P>F 3^0.0697^0.04^0.0199^3.496^0.9097  49  -25  —26  —28  —29  • TOP NEEDLES • 1.2 M NEEDLES A BASE NEEDLES North East South West ASPECT  Fig. 11. Needle 813C trends with aspect and position in one juvenile Pinus contorta sapling. Isotopic compositions represent one sample. Needle 813C responds little to aspect and position in the tree.  Table 6. Summary statistics for 813C in Pinus contorta sapling provenances planted at 70 Mile House, Holden Lake and Salmon Lake.  Provenance 1  13  30  44  57  61  69  71  -24.754 0.130 13  -24.621 0.137 12  -24.867 0.130 14  -24.438 0.118 16  -25.144 0.148 10  -24.264 0.110 18  -24.787 0.113 17  -24.846 0.113 17  -24.942 0.137 15  -25.139 0.127 18  -24.960 0.132 16  -25.415 0.128 17  -24.533 0.128 17  -25.365 0.125 18  -24.777 0.125 18  -25.682 0.152 11  -26.120 0.118 18  -25.592 0.132 15  -25.213 0.132 15  -25.912 -25.764 0.122 0.122 17 17  95  100  104  70 Mile House LSMa S Eb Tic  -24.518 0.113 17  -24.292 0.110 18  Holden Lake LSM SE n  -24.966 0.128 17  -24.133 -24.837 -24.726 0.132 0.128 0.209 17 10 16  Salmon Lake LSM SE n  -25.748 0.132 15  aLeast squares mean^ bStandard error of the LSM^ cMaximum=18  -24.884 -25.467 0.127 0.136 14 16  -25.488 0.130 15  vl 0  51 Table 7 summarizes ANOVA results (all factors random) for the data from 70 Mile House, Holden Lake and Salmon Lake, where all but provenances 57 and 95 were tested. Site, block (within site) and provenance variances were homogeneous (Bartlett's test). The analysis used 431 observations from a total of 486, since 55 trees were missing or dead. The model r2 was 0.569. Site and provenance effects were significant (a=0.05), while block (within site), site x provenance and block x provenance (within site) effects were not. Provenance 813C trends across sites are graphed in Figure 12. The literature does not suggest specific tests to distinguish dependent (least squares) means, other than the Bryant-Paulson-Tukey test and conditional Tukey-Kramer test for adjusted means in one-way ANCOVAs (Day and Quinn 1989). I used Fisher's LSD test with an applied Bonferroni inequality to compare provenance LSMs (Miller 1981). The a level was decreased to 0.001 because 36 means comparisons were made for the analysis including all test sites; and 45, for the analyses including two sites. Lowering the comparisonwise error rate to 0.001 was an attempt to maintain the overall experimentwise error rate near 0.05, e.g., 45 x 0.001 = 0.045. Bonferroni's procedure tested pairwise differences between sites, each site compared at a reduced a level of 0.01, again to keep the overall experimentwise error rate near 0.05. Holden Lake and 70 Mile House were different from Salmon Lake, but not different from each other. Variance components derived according to the format in Table 7 showed that site and sampling error contributed most to the variation in 813C on a per sapling basis. Since the site x provenance component contributed nothing to the variation in 813C (in fact, it was negative), the test term used for means differences among populations was the block x provenance (within site) mean square (MS) rather than the site x provenance MS. The least significant difference (LSD) using block x provenance (within site) as the test term was 0.440 Voo. Table 8 shows provenance means test results. Provenances 61 and 104 were significantly different from provenances 69 and 30. As well, provenance 61 was different from provenances 71 and 1.  Table 7.  ANOVA for provenance trials at 70 Mile House, Holden Lake and Salmon Lake. Variance components (VC) are presented as a percentage of total components.  Source  df  SS  MS  Site  2  80.5247  40.2623  Bl(S)  3  2.2591  0.7530  452+ 7.776402131.pm + 69.9877a2ms)  Prov  8  20.6984  2.5873  a2+ 7.8192a2m.p(s)+ 15.6384a2s.p + 46.915102p  S*P  16  3.3536  0.2096  a2+ 7.8517a2131p(s) + 15.70340-2s.p  Bl*P(S) 24  8.9348  0.3723 0-2±7.8828a2B1w)  Error  94.3909  0.2504  377  EMS a2+ 7.76324:Y2wpm + 15.5264cr2s.p + 69.868702w) + 139.737402s  a2  Legend: Bl-Block SSite F'=-Provenance  Satterthwaite Approximations Used to Derive F Values: Source: Site Error: 0.9983*MS[B1(S)] + 0.9887*MS(S*P) - 0.9848*MS[B1*P(S)] - 0.0022*MS(Error) DF^MS^Denominator DF Denominator MS^F Value P > F 2^40.2623^1.78^0.5918^68.034 0.0208  F Value  P>F  VC(%)  68.034  0.0208  47  2.032  0.1375  1  12.334  0.0001  8  0.564  0.8809  0  1.487  0.0673  3 41  Table 7. (con't)  Satterthwaite Approximations Used to Derive F Values: Source: Block(Site) Error: 0.9865*MS[Bl*P(S)] + 0.0135*MS(Error) DF^MS^Denominator DF Denominator MS^F Value P> F 3^0.7530^24.44^0.3706^2.032^0.1357 Source: Prov Error: 0.9959*MS(S*P) + 0.0041*MS(Error) DF^MS^Denominator DF Denominator MS^F Value P> F 8^2.5873^16.16^0.2098^12.334^0.0001 Source: Site*Prov Error: 0.9961*MS[Bl*P(S)] + 0.0039*MS(Error) DF^MS^Denominator DF Denominator MS^F Value P > F 16^0.2096^24.13^0.3718^0.564 0.8809  54  —24  —25 0  _ _ PROV104 PROV100 ^ PRO V71 ^ PROV69 ^ PRO V61 ^ PRO V44 PRO V30 — — — PROV13 — — - PROV1  --26  —27  °  10e^xkoV`el SITE  Fig. 12. Changes in provenance 513C least squares means among three sites — 70 Mile House, Holden Lake and Salmon Lake. Provenances respond similarly in degree and direction to changes in site moisture.  Table 8. Summary of test results for differences in 513C among nine provenances over all sites (70 Mile House, Holden Lake, Salmon Lake).  Provenance^61 LSM^-24.670  104 -24.835  100 -24.941  44 -24.997  13 -25.082  71 -25.129  Note: Underscores show homogeneous subsets (comparisonwise error rate=0.001). Fishers Least Significant Difference=0.440 0/00.  1 -25.156  69 -25.355  30 -25.375  56 The provenance effect in the ANOVA (Table 7) remained significant at a=0.05 when the block x provenance (within site) MS was used rather than the site x provenance MS to derive the F value. This was the case for the other analyses as well (Tables 9 and 11), with changes to test terms. Least squares means were also compared using the Tukey test since the Fisher's LSD with a comparisonwise error rate of 0.001 might miss real differences among provenances. Tests based on the studentized range (Tukey) assume that the means are independent. In unbalanced designs this assumption is violated to some degree. The Tukey test (experimentwise error rate=0.05) found a few more significant differences than the Fisher's LSD test. The Tukey test determined that provenances 61, 104 and 100 were different from provenances 69 and 30. As well, provenance 61 was different from provenances 13, 71 and 1. Table 9 summarizes ANOVA results (all factors random) for the data from 70 Mile House and Holden Lake, where provenance 57 and all other provenances except 95 were tested. The variances of main effects were homogeneous (Bardett's test). The analysis used 321 observations from a total of 360 (model r2 = 0.369). Provenance and block x provenance (within site) effects were significant (a=0.05) while site, block (within site) and site x provenance effects were not. Provenance 813C trends with site are graphed in Figure 13. Variance components (Table 9) showed that site and provenance contributed 11 and 15% respectively to the variation in 813C on a per sapling basis. Sampling error contributed 65%. The site x provenance component contributed nothing to the variation, therefore the test term used for means differences among populations was block x provenance (within site). The least significant difference using block x provenance (within site) as the test term was 0.625 No. Table 10 shows means test results. Provenances 61 and 104 were different from provenance 57. As well, provenance 61 was different from provenance 69. The Tukey test showed a few more differences among provenances. Provenances 61, 104, 100 and 44 were different from provenance 57. As well, provenances 61 and 104 were different from  Table 9. ANOVA for provenance trials at 70 Mile House and Holden Lake. Variance components are presented as a percentage of total components. Source df^SS^MS^  EMS^  F Value^P > F^VC(%)  Site^1^7.5938^7.5938 cy2+ 7.768202B1*p(s)+ 15.536002s*p + 77.6820a2B1m+ 155.360002s^9.263^0.1534^11 BI(S)^2^2.0048^1.0024 (524.7.81332Bi*P(s) + 78.1330G2ms) ^ a  2.208^0.1383^2  Prov^9 19.1918^2.1324 (T2+ 7.8634G2wp(s) + 15.7270G2s*p + 31.4540a2p ^7.760^0.0027^15 SP^9^2.4371^0.2748 0-2+ 7.86340.2wpm + 15.7270a2s*p^  0.604^0.7783^0  Bl*P(S) 18^8.2361^0.4576 G2 + 7.9526G2Bpp(s) ^  1.816^0.0232^7  Error^281 70.7883^0.2519 G2^ Legend: B1-=B1ock SSite  R--Provenance  Satterthwaite Approximations Used to Derive F Values: Source: Site Error: 0.9942*MS[B1(S)] + 0.9879*MS(S*P) - 0.9768*MS[B1*P(S)] - 0.0053*MS(Error) DF^MS^Denominator DF Denominator MS^F Value P > F 1^7.5938^1.30^0.8198^9.263 0.1534  65  Table 9. (con't)  Satterthwaite Approximations Used to Derive F Values: Source: Block(Site) Error: 0.9825*MS[Bl*P(S).] + 0.0175*MS(Error) DF^MS^Denominator DF 2^1.0024^18.35  Denominator MS 0.4540  F Value 2.208  P> F 0.1383  Denominator MS 0.2748  F Value 7.760  P> F 0.0027  F Value 0.604  P>F 0.7783  Source: Prov Error: MS(Site*Prov) DF^MS^Denominator DF 9^2.1324^9  Source: Site*Prov Error: 0.9888*MS[B1*P(S)] + 0.0112*MS(Error) DF^MS^Denominator DF 9^0.2748^18.23  Denominator MS 0.4553  59  —24  —25 PRO V104 PRO V100 PRO V71 PRO V69 PRO V61 PRO V57 PRO V44 PRO V30 PRO V13 PRO Vi  0  7,10  —26  —27  70 Mile House Holden Lake SITE  Fig. 13. Changes in provenance 513C least squares means between two sites — 70 Mile House and Holden Lake.  Table 10. Summary of test results for differences in 513C among ten provenances over two sites (70 Mile House, Holden Lake).  Provenance^61 LSM^-24.398  104 -24.509  100 -24.678  44 -24.699  13 -24.782  71 -24.812  Note: Underscores show homogeneous subsets (comparisonwise error rate=0.001). Fisher's Least Significant Difference=0.625 0/00.  1 -24.860  30 -25.003  69 -25.076  57 -25.280  61 provenance 69. Provenance 61 was also different from provenance 30. Table 11 summarizes ANOVA results for the data from Holden Lake and Salmon Lake (all factors random), where provenance 95 and all other provenances except 57 were tested. The variances of main effects were homogeneous (Bartlett's test). The analysis used 315 observations from a total of 360 (model r2 = 0.523). Site, block (within site) and provenance effects were significant (a=0.05); block x provenance (within site) and site x provenance effects were not. Provenance 513C trends with site are graphed in Figure 14. The Bonferroni procedure was applied to pairwise comparisons between blocks (within sites). Although the F test found blocks different, pairwise comparisons did not. Huitema (1980) explains this apparent inconsistency. Frequency of false rejections of the null hypothesis is less with multiple comparison procedures than with the F test (Zar 1984). Variance components showed that site and sampling error contributed most to the variation in 813C per sapling (43.5 and 41.5% respectively). Provenance contributed 14%. Since the site x provenance and block x provenance (within site) components contributed nothing to the variation, sampling error became the test term for means differences among provenances. Table 12 depicts means test results. The LSD was 0.399 Woo. Provenance 95 was different from all provenances except provenance 61. Provenances 61, 104 and 100 were different from provenances 30 and 69. As well, provenance 61 was different from provenances 1, 13 and 44. The Tukey test gave nearly the same result as Fisher's LSD test, the only difference being that provenance 61 was different from provenance 71 as well as provenances 1, 13 and 44. Correlations between growth parameters (diameter, height, volume) and 813C were site- and provenance-dependent. (Growth measurements were not available for provenance 95). Significant correlations (a=0.05) are discussed below. If a reflected the number of correlations tested (87), none would be significant (the "p" value required for a significant pairwise correlation would be 0.0006). All correlations are listed in Appendix 1.  Table 11. ANOVA for provenance trials at Holden Lake and Salmon Lake. Variance components are presented as a percentage of total components. Source df^SS^MS^  EMS^  F Value^P > F^VC(%)  Site^1 41.0215 41.0215 a2+ 7.3049,52wpm + 14.6100a2s*p + 73.0490a2m(s)+ 14612s 63.954 ^0.0175^43.5 Bl(S)^2^1.2993^0.6497 a2+ 7.30580281.m + 73.058002ms) ^  3.469^0.0501^1  Prov^9 26.3096^2.9233 a2+ 7.5547a2E1*p(S) + 15.1090a2s.p + 30.2190a2p ^16.594^0.0001^14 S*P^9^1.5855^0.1762 a2+ 7.5547a2wp(s)+ 15.1090a2s*p^  0.954^0.5047^0  B1*P(S) 18^3.2983^0.1832 a2 + 7.6880a2Eop(S)^  0.692^0.8185^0  Error^275 72.8135^0.2648 a2^ Legend: BlaBlock SaSite 1)---Provenance  Satterthwaite Approximations Used to Derive F Values: Source: Site Error: 0.9999*MS[B1(S)] + 0.9669*MS(S*P) - 0.9502*MS[B1*P(S)] - 0.0166*MS(Error) DF^MS^Denominator DF Denominator MS^F Value P > F 1^41.0215^1.91^0.6414^63.954 0.0175  41.5  Table 11. (con't)  Satterthwaite Approximations Used to Derive F Values: Source: Block(Site) Error: 0.9503*MS[Bl*P(S)] + 0.0497*MS(Error) DF^MS^Denominator DF Denominator MS^F Value P> F 2^0.6497^20.82^0.1873^3.469 0.0501 Source: Prov Error: MS(Site*Prov) DF^MS^Denominator DF Denominator MS^F Value P > F 9^2.9233^9^0.1762^16.594 0.0001 Source: Site*Prov Error: 0.9827*MS[Bl*P(S)] + 0.0173*MS(Error) DF^MS^Denominator DF Denominator MS^F Value P> F 9^0.1762^18.93^0.1846^0.954 0.5047  64  -24  -  -  -  -  -27  PROV104 PROV100 PRO V95 PRO V71 PROV69 PRO V61 PRO V44 PRO V30 PRO V13 PRO Vi  Holden Lake Salmon Lake SITE  Fig. 14. Changes in provenance 813C least squares means between two sites — Holden Lake and Salmon Lake.  Table 12. Summary of test results for differences in 813C among ten provenances over two sites (Holden Lake, Salmon Lake).  Provenance^95 LSM^-24.508  61 -24.873  104 -25.107  100 -25.152  71 -25.271  44 -25.276  Note: Underscores show homogeneous subsets (comparisonwise error rate=0.001). Fishers Least Significant Difference=0.399 o/oo•  13 -25.312  1 -25.357  30 -25.629  69 -25.638  66 At 70 Mile House, all growth variables and 813C were negatively correlated in provenance 71 (diameter: r=-0.645, p=0.007; height: r=-0.720, p=0.002; volume: r=-0.679, p=0.004). Provenance 104 showed positive correlations between all growth variables and 813C (diameter: r=0.574, p=0.013; height: r=0.540, p=0.021; volume: r=0.547, p=0.019). It appeared that in provenance 71, decreased WUE (measured relatively by 813C) was accompanied by increased growth, while the opposite occurred in provenance 104. Interpretations had to be site specific, however. Provenance 71 showed positive correlations between growth and 813C at Holden Lake and Salmon Lake (non-significant). At Holden Lake, height and 813C were positively correlated in provenance 30 (r=0.463, p=0.053). All growth parameters correlated positively with 813C in provenance 44 (diameter: r=0.512, p=0.043; height: r=0.681, p=0.004; volume: r=0.554, p=0.026), as well as in provenance 57 (diameter: r=0.745, p=0.001; height: r=0.475, p=0.054; volume: 1=0.671, p=0.003). At Holden Lake, it appeared that growth improved with increased WUE in these provenances. At Salmon Lake, diameter and volume correlated negatively with 813C in provenance 61 (diameter: r=-0.652, p=0.008; volume: r=-0.596, p=0.019). All growth parameters correlated positively with  513C  in provenance 30 (diameter: r=0.547, p=0.019; height:  r=0.650, p=0.004; volume: r=0.556, p=0.017), as well as in provenance 44 (diameter: r=0.600, p=0.018; height: r=0.699, p=0.004; volume: r=0.672, p=0.006). Decreased WUE was accompanied by increased growth in provenance 61, while the opposite occurred in provenances 30 and 44. Provenances 13, 69 and 100 demonstrated no significant relationships between growth factors and 813C at any site. Only one provenance had significant relationships between all growth variables and 813C on more than one site. In provenance 44, diameter, height and volume were positively correlated with 813C at Holden Lake and Salmon Lake.  67 Of interest was whether provenances with more positive 813C values (assumed to be more water-use efficient) produced higher volumes. Per site, correlations between provenance 813C LSMs and volume LSMs were positive: 1. 70 Mile House (r=0.608, p=0.062, n=10); 2. Holden Lake (r=0.759, p=0.011, n=10); 3. Salmon Lake (r=0.689, p=0.040, n=9). Considering average performance over three sites, the correlation between 813C LSMs and volume LSMs was 0.727 (p=0.027, n=9).  At the provenance level there were significant correlations (a=0.1) between 813C and annual climate variables at all sites. At 70 Mile House, 813C correlated with mean temperature of the warmest month (mtwm) (r=-0.794). At Holden Lake, 813C correlated with mtwm (r=-0.667), mean summer precipitation (msp) (r=0.737), mean precipitation of the driest month (mpdm) (r.715) and mean annual precipitation (map) (r=0.730). The coastal provenance (95) had large influence at this site. Removing provenance 95 eliminated the significant correlations between isotope ratios and precipitation variables. At Salmon Lake, 813C correlated  with mtwm (r=-0.786), mean annual temperature (mat) (r=0.739), msp  (r=0.750), mpdm (r=0.783) and map (r=0.753). Removing provenance 95 eliminated the significant correlations between 813C and the precipitation variables. At the provenance level, isotopic composition was related to geographic variables at two sites. At Holden Lake, the correlation between 813C and longitude was 0.822 (p=0.023) while that between 813C and elevation was -0.713 (p=0.072). At Salmon Lake 813C correlated with elevation (r=-0.711, p=0.114). These correlations were high (a=0.1) due to the influence of provenance 95. Since isotope ratios were influenced by several climate variables, regression modelling determined the factors contributing most to the prediction of 813C. The geographic variables were not applied to the regression equations, since longitude and elevation were highly correlated with precipitation. Latitude was highly correlated with mean annual temperature. As well, longitude, latitude and elevation were highly correlated with each other. Initially a common model was sought for all sites. One model including  68 mtwm and msp was appropriate for sites with provenance 95, while another including mat and msp was suited for 70 Mile House and the other sites without provenance 95. The comparison was necessary, since provenance 95 had excessive influence. Collinearity problems were small for the climate factors. Per site, mtwm and msp (at the provenance level) had a correlation of about 0.318 (p<0.05). The correlation became negative when provenance 95 was included. The r value for mat and msp was about 0.311 (p<0.05) without provenance 95. Including 95 increased the correlation to 0.477 at Holden Lake and 0.696 at Salmon Lake (both at p<0.05). To find models suitable for the complete set of data points (with and without provenance 95), indicator variables representing site (Si, S2) were included: Si=0, S2=0 for 70 Mile House; S1=1, S2=0 for Holden Lake; and S1=0, 52=1 for Salmon Lake. The advantage of having replication with regression was the opportunity to test for lack of fit of the models (Ott 1988; Bergerud 1990). The best fitting regression model for the data set including provenance 95 was 813C = -22.607 - 0.365S1 - 1.133S2 - 0.158MTWM + 0.001MSP (r2=0.450; p<<0.0001; df=5, 284). The lack-of-fit test was done by separating the residual sums of squares into two parts: a lack-of-fit sums of squares (df=14) and a within group sums of squares called the pure error (df=271) (see Appendix 3). The test for lack-offit was significant (a=0.05); the model was inadequate for predictive purposes. However the model was useful in describing a relationship between 513C, temperature and precipitation. The best fitting model for the data set without provenance 95 was 513C = -27.970 - 0.380S1 1.103S2 + 1.756MAT + 0.015MSP - 0.007MAT*MSP (r2=0.476; p<<0.0001; df=5, 258). The test for lack-of-fit was not significant (Appendix 3), indicating that the model was adequate for predictive purposes. Standardized coefficients from the regression output showed that the mat variable had six times the influence on 513C as the msp variable and that the interaction between the two had the greatest influence. The equation showed that the slope of the relationship between 813C and mat depends on msp and vice versa. The two variables interact to negatively affect isotopic composition. For example, depending on what  69 the msp is, WUE can increase or decrease with increasing temperature. The equation appropriate to 70 Mile House shows that the change in mean 813C per °C increase in mat is given by 1.756 - 0.007MSP. For 300 mm msp, the estimated change in 513C per °C increase in mat is -0.344 Woo. For 150 mm msp, the estimated change in 4513C per °C increase in mat is +0.706 0/00. Under dry conditions 813C is more positive, indicating higher intrinsic WUE. It is apparent also that as mat decreases, WUE will increase under conditions of increasing moisture. 4.3 Seedling Experiment Summary statistics (Table 13) show little variation among provenance biomass means other than shoot/root ratio. The range for shoot weight was 0.186 g; for root weight, 0.076 g; for shoot+root weight, 0.207 g; and for shoot/root ratio, 0.448. The range for 813C was 1.648 woo. Shoot variances were heterogeneous (Bartlett x2=27.985, 1)=0.002; Cochran's C statistic 0.1947 > Cochran's critical value 0.1678). These heterogeneities were not remedied by transformations (log, square root, 1/3,2, y2). Residuals analysis showed nine outliers. Removing one (1.316 g, prov. 95) decreased variance heterogeneity (Bartlett p=0.01; Cochran's C statistic 0.1663 < Cochran's critical value 0.1678). Since non-parametric (Kruskal-Wallis) analysis of the complete data set gave the same result as ANOVA on the data set, minus the outlier, no further changes were made to shoot data prior to the F test. As well, the outlier was removed from the remaining data sets. Table 14 shows ANOVA results for seedling data. Shoot means were significantly different (a=0.05, p=0.001). Tukey's test (Table 15) found provenance 30, with lowest shoot weight, different from provenances 61, 1, 13, 44 and 57. Provenances 69, 71, 104, 95 and 100 formed subsets with provenances 61, 1, 13, 44 and 57, as well as provenance 30. Root variances were heterogeneous (Bartlett x2=19.170, p=0.038). The Cochran test, less sensitive to non-normality than Bartlett's test, showed homogeneity (Cochran's C  Table 13. Summary statistics for growth parameters of eleven Pinus contorta seedling provenances.  Provenance 1  13  30  44  57^61  69  71  95a  100  104  0.753 0.216  0.575 0.172  0.741 0.249  0.740 0.141  0.761 0.189  0.673 0.158  0.654 0.174  0.622 0.276  0.620 0.180  0.654 0.224  0.257 0.078  0.247 0.052  0.228 0.076  0.257 0.061  0.235 0.085  0.257 0.067  0.253 0.075  0.186 0.096  0.224 0.080  0.209 0.089  1.010 0.274  0.821 0.205  0.969 0.314  0.997 0.174  0.995 0.254  0.930 0.199  0.907 0.225  0.808 0.358  0.844 0.246  0.863 0.298  3.056 0.748  2.348 0.596  3.280 0.655  2.984 0.637  3.494 0.978  2.727 0.760  2.756 0.841  3.703 1.066  2.936 0.678  3.391 0.935  -31.861 0.627  -31.939 0.628  -31.660 0.848  -31.870 0.559  -31.630 0.806  -31.966 0.647  -31.882 0.905  -30.318 0.943  -31.698 0.809  -31.803 0.852  Shoot Weight (g)  Mean^0.753 SD^0.189 Root Weight (g)  Mean^0.262 S D^0.058  Shoot + Root Weight (g)  Mean^1.015 S D^0.220 Shoot I Root Ratio  Mean^2.964 S D^0.736 &BC  Mean SD  -31.628 0.868  an=29 from a maximum of 30  Table 14. One-way ANOVAs on growth parameters for Pinus contorta seedling data.  Shoot Weight  Source  df  SS  MS  F Value  P>F  Between Prov  10  1.304  0.130  3.246  0.0010  318  12.776  0.040  10  0.172  0.017  3.044  0.0010  318  1.800  0.006  10  1.860  0.186  2.824  0.0020  318  20.949  0.066  10  4.895  0.490  7.525  <<0.0001  318  20.688  0.065  Between Prov  10  62.058  6.206  10.168  <<0.0001  Within Prov  318  194.093  0.610  Within Prov Root Weight  Between Prov Within Prov  Shoot + Root Weight  Between Prov Within Prov Shoot/Root Ratio (Natural Logarithms)  Between Prov Within Prov 813C  Table 15. Summary of Tukey test results for means differences among seedling shoot weights (g).  Prov Mean  61 0.761  1 0.753  13 0.753  44 0.741  57 0.740  69 0.673  Note: Underscores show homogeneous subsets (experimentwise error rate=0.05).  71 0.654  104 0.654  95 0.645  100 0.620  30 0.575  73 statistic 0.1429 < Cochran's critical value 0.1678). Therefore, data were not changed prior to ANOVA, which gave a significant result for root means (p=0.001). Provenance 95, with lowest root weight, was different from provenances 1, 13, 57, 69 and 71 (Table 16). Shoot+root variances were heterogeneous (Bartlett x2=26.703, p=0.003; Cochran's C statistic 0.1763 > Cochran's critical value 0.1678). Transforming data or removing two extreme outliers did not correct the problem. Since non-parametric analysis produced the same result as ANOVA, the data were not changed. Although ANOVA gave a significant result for shoot+root growth (p4).002), Tukey's test revealed no means differences. Shoot/root ratio variances were heterogeneous (Bartlett x2=20.784, p=0.023). Cochran's C statistic, 0.1623, was less than Cochran's critical value, 0.1678, but the difference was too small to discount a heterogeneity problem. Homogeneity was achieved by natural log transformation of the data (Bartlett p=0.858). The F test was significant (p«0.0001). The Tukey test (Table 17) showed that provenances 95, 61 and 104 were different from provenances 71, 69 and 30. As well, provenance 95 was different from provenances 1 and 100. Isotope variances were homogeneous (Bartlett x2=17.225, p=0.070) and significantly different (p<<0.0001). Tukey's test (Table 18) found two subsets: (provenance 95) and (provenances 1, 13, 30, 44, 57, 61, 69, 71, 100, 104). Correlations between growth variables of all provenances' pooled seedlings (n=329) and 813C were poor: 1. Shoot mass (r=0.014, p=0.807); 2. Root mass (r=0.041, p=0.464); 3. Shoot+Root (r=0.023, p=0.684); 4. Shoot/Root ratio (r4.016, p=0.772). Individually, few provenances showed significant correlations (a.05) between growth variables and 813C (see Appendix 2). If a reflected the number of correlations tested (44), none would be significant (the "p" value required for a significant correlation would be 0.001). Shoot weight, as well as shoot/root ratio and 613C correlated significantly in provenance 57 (r= -0.364, p=0.048 and r=-0.369, p=0.045 respectively). The shoot/root ratio and 813C correlation in provenance 69 was r=-0.467 (p=0.009). Root weight, as well as  Table 16. Summary of Tukey test results for means differences among seedling root weights (g).  Pray Mean  1 0.262  13 0.257  57 0.257  69 0.257  71 0.253  30 0.247  Note: Underscores show homogeneous subsets (experimentwise error rate=0.05).  61 0.235  44 0.228  100 0.224  104 0.209  95 0.189  Table 17. Summary of Tukey test results for means differences among seedling shoot/root ratios (natural logarithms).  Prov Mean  95 1.270  61 1.214  104 1.185  44 1.168  13 1.087  57 1.070  Note: Underscores show homogeneous subsets (experimentwise error rate=0.05).  1 1.057  100 1.051  71 0.974  69 0.969  30 0.822  Table 18. Summary of Tukey test results for means differences among seedling 813C values.  Prov Mean  95 -30.318  1 -31.628  61 -31.630  44 -31.660  100 -31.698  104 -31.803  Note: Underscores show homogeneous subsets (experimentwise error rate=0.05).  13 -31.861  57 -31.870  71 -31.882  30 -31.939  69 -31.966  77 shoot/root ratio, and 813C correlations were significant in provenance 71 (r=0.405, p=0.026 and r=-0.410, p=0.024 respectively). Correlations were positive as well as negative, depending on provenance and growth variable tested. All provenances showed positive correlations between root weight and isotope ratio. All but provenances 61 and 104 showed negative correlations between shoot/root ratio and 513C. Correlations between population averages of 513C and root weight, as well as shoot/root ratio were significant (r=-0.754, p=0.007 and r=0.680, p=0.021 respectively). Interestingly, the correlations between 513C and biomass variables (root, shoot/root) for seedlings within populations were opposite in sign to correlations using seedling population means. Correlations between population averages of 513C and shoot weight, as well as total weight were insignificant (r=-0.188, p=0.580 and r=-0.386, p=0.241 respectively). Of interest was the correlation between seedling and sapling 513C. Over all sites (minus provenances 57 and 95), seedling 513C means and sapling 513C LSMs correlated by 0.690 (p=0.040, n=9) (Fig. 15). At Holden Lake, where all provenances were tested, r equalled 0.781 (p=0.005, n=11) (Fig. 16). At Salmon Lake and 70 Mile House the correlations were 0.812 (p=0.004, n=10) and 0.591 (p=0.072, n=10), respectively. Spearman's rank correlations did not improve any relationships other than the finding for 70 •  Mile House, where the correlation coefficient became 0.673 (significant at a=0.05).  78  -31.0  0  —31.2  c0 —31.4  —31.6  •  PT-1 V.T4 —31.8  ••  •  •  • •  —32.0 ^ ^ —24.5 —25.3^—25.1^—24.9^—24.7 —25.5  SAPLING 8'3C raj Fig. 15. Pinus contorta greenhouse seedling 513C means plotted against sapling least squares means averaged over three sites — 70 Mile House, Holden Lake and Salmon Lake. Provenances 95 and 57 are not included. Correlation coefficient r=0.690 (p=0.040).  ▪  79  -30.0  1  • —30.5  "c4.0  —31.0  :11 —31.5 4.1 —32.0  —32.5 —26.0  • •  —25.5^—25.0^—24.5^—24.0  SAPLING  —23.5  4513C (°/00)  Fig. 16. Pinus contorta greenhouse seedling 813C means plotted against sapling least squares means at Holden Lake (where all provenances were tested). Correlation coefficient r=0.781 (p=0.005). The outlier is provenance 95.  80 5.0 Discussion This study is based on the assumption that 513C correlates with WUE in trees. Traditional measures of WUE in agricultural crops have been significantly correlated with 513C, but these calibrations have not been done for trees. Recently, however, instantaneous WUE (A/E) and intrinsic WUE (A/g) have been positively correlated with 513C in Pinus seedlings and tropical forest shrubs (Elsik eta!. 1992; Jackson eta!. 1992). 5.1 Determination of Sampling Methods for Field Trials Intra- and inter-sapling 513C variation has not been reported. My data show that carbon isotope trends in saplings generally follow those for mature trees. This suggests that sampling methods developed for mature trees may be applied to younger trees. However, saplings usually show less 513C variation within and among individuals. The results of Freyer and Belacy (1983) and Francey (1981) suggest there is little circumferential variation in the early decades of growth, prior to crown enlargement and the development of associated microenvironmental differences. Around the trunk, the mean stump wood variance within saplings is 0.09 Woo (1989 samples, n=5 trees). At 1.2 m, the variance is 0.09 Woo. For needles, the mean intra-sapling variance (lowest branches) is 0.68 Woo (1989 samples, n=5 trees. The circumferential variance for 513C in wood of mature trees has not been reported, although a range of 0.5 to 1.2 Woo at 1.5 to 2.5 m height is given for Pinus edulis (Leavitt and Long 1986). In the Pinus contorta saplings, at about the same height, the range is 0.03 to 1.10 Woo. For needles  of mature trees, Leavitt and Long (1986) cite studies showing variances of 0.38 to 0.46 Woo in Juniperus species and 0.38 Woo in Pinus edulis.  For P. contorta saplings, the intra-site variance in stump wood (1989) is 0.12 Woo (four observations per tree, n=5 trees). The variance in needles (lowest branch) is 0.26 Woo (four observations per tree, n=5 trees). Intra-site variance in wood of mature trees has not been reported. However, the range for 513C among eight P. edulis has been given as 2.0 to  81 3.0 Woo (Leavitt and Long 1984). Each tree was represented by a pooled sample of four orthogonal cores comprised of seventeen 5-year ring groups. The range of 513C among saplings (16 data points per tree representing four orthogonal cores) is 0.34 to 0.98 Woo. The variance in needles for mature Juniperus species (two to four trees per site) is given as 0.38 Voo (Arnold 1979, cited in Leavitt and Long 1986). To find a sampling technique to accurately reflect field 513C, I used a statistical method lacking rigor. There were no replicates within years, making the error term irretrievable in all analyses of variance. However, sufficient information was gained about 513C variation within and among the saplings at Juliet Creek. The information was adequate to select a technique lending itself to less sampling error than alternative ones (e.g., taking east-west cores or using needles instead of wood as the sampling material). The chosen field sampling technique was to obtain north-south wood stem cores. In Table 3, the assumption of non-significance for the three-way interaction used as the error term is questionable. However, figures 4 and 5 validate the F tests determined by that error term. In Tables 4 and 5, the three-way interactions are probably insignificant. The two-way interactions comprising them are insignificant in all but one case; therefore, the F tests are considered valid. In the saplings, the tree 513C effect (stump wood) as well as tree x aspect interactions were significant. This finding supported Leavitt's and Long's (1984) recommendation that at least four orthogonal cores from each of four trees be sampled to accurately represent site 513C values. To determine what influenced the tree 513C differences, I tested correlations between tree 513C and features which might contribute to the variation. Isotopic compositions for wood based on total observations per tree (n=16) had no significant correlations with height, age, percentage canopy cover, number of neighbors within a 3 m radius or average proximity to neighbors. The same lack of correlation was found for 513C means from north and south aspects combined (n=8). Isotopic compositions in both cases were positively correlated with  82 diameter (for all data points, r=0.891, p=0.043, n=16; for north and south cores, r=0.873, p=0.053, n=8). Needle 513C, derived from averaging 1989 north and south values at stump level correlated positively with diameter (r=0.937, p=0.019), height (r=0.941, p=0.017) and average proximity to neighbors (r=0.918, p=0.028). From the average of four circumferential values (1989), needle 513C correlated positively with proximity to neighbors (r=0.983, p=0.003). Needle 513C at stump level is related to certain features, whereas wood 513C is not. The difference may be artificial since wood 513C is estimated from more data points than needle 513C. Using wood samples from 1989 only, as was done for needles, shows a significant negative correlation between circumferential wood 513C and percentage canopy cover (1..41932, p=0.021). The correlations between needle 513C and tree height, as well as proximity to neighbors, and that between wood 513C and percentage canopy cover may reflect a shading effect. In 1984, Leavitt and Long compared 8 13C trends over 5 year intervals in mature P. edulis. Using four orthogonal cores per tree, they found no relationship between absolute 513C among trees and age, height, diameter, crown shapes or proximity to neighbors. Tree x aspect effects may have been due to variable shading from neighbors. Saplings had neighbors of different crowns and heights as well as at varying distances and directions. Measuring duration or intensity of irradiance at each aspect over the growing season may have illuminated the "variable shading - variable 513C" hypothesis. However, the assumption would have had to be a relatively constant irradiance difference among aspects (within and among trees) over many years. Trees 3 and 4 contributed most to the interaction effect. They had very suppressed growth relative to the others. Many of their lower branches had been browsed. Trees 3 and 4 had taller neighbors to the east-south-east than the others and therefore, less estimated open canopy. Their south sides were more negative in 513C than their north sides, a trend opposite to that in the others. Stomatal and photosynthetic effects influence the relationship between 513C and plant irradiance. The sunny side of a plant is likely to be more drought-stressed than the shady  83 side. Leaf stomates will remain more closed in the sun than in the shade relative to photosynthetic rate. Intercellular pi/pa in the sunlit leaves will be less than that for shaded leaves. With a lower pi/pa, discrimination against 13C by photosynthetic enzymes will be less than that occurring at a higher pi/pa. Increased 813C, positively related to intrinsic WUE, will be found in leaf samples from the sunlit side. Also, decreased illumination of a plant will result in decreased photosynthetic capacity. If photosynthetic capacity is relatively higher on the illuminated side (stomatal control being constant), pi/pa will decrease in the intercellular spaces of sunlit leaves. Leaf samples from more illuminated sides of the plant will record higher 513C values than those from less illuminated areas. The saplings at Juliet Creek were relatively open-growing. There appeared to be no soil respiratory CO2 effect on needle 813C at stump level (Table 5). It was likely, therefore, that wood 513C accurately reflects the physiological functioning of the trees rather than within-canopy gradients in 13CO2. This assumption was applied to the field trials, where saplings were also relatively open-grown. It appears that circumferential 813C in sapling stump wood follows no particular trend with aspect whereas 513C in leaves changes more obviously with aspect. If photosynthates are laid down spirally in tree trunks, they may reflect 813C values of carbon fixed in leaves at different sides of the tree. Some degree of spiral grain is a normal feature of wood formation in trees (Harris 1989). A spiral consistent in direction and degree throughout a stem is seldom seen. Leavitt's and Long's finding (1982) that needles and wood followed similar 813C trends on a monthly basis during one growing season were based on samples from one aspect only. An explanation for the different needle and wood 513C trends might be Schleser's (1992) finding that large contributions of carbon lower in the tree may originate from the top of the tree. Since a 813C gradient in leaves with height is not reflected in trunk wood (Schleser 1992), it is expected that circumferential values of 813C for needles and wood at nearly the same height will not correlate well.  84 The accuracy of the F test for the aspect effect (wood, Table 3) can be questioned since one of the test terms (tree x aspect) is significant. Figure 8 supports the finding of a non-significant aspect effect by showing similar trends for 813C values in wood over aspects. Analyses of variance on the tree, aspect and material effects (Table 4) and on the aspect, position and material effects in one year (Table 5) support the finding of a non-significant aspect effect for wood 513C as well. Leavitt and Long (1984) recommend a reliable sampling technique for tree-ring isotope studies reconstructing climate and past atmospheric CO2. In studies like this one, however, extensive sampling may not be possible or necessary. The sampling protocol I established for P. contorta saplings may not apply to all species and environments but is reasonable for population studies on young pines. Wood is a more appropriate sampling material than needles since 813C variation within and among trees is smaller in wood than in needles. Some studies have used needles from tree tops but in this study the inter-sapling 513C variance shown by top needles (1989) was 0.83 Woo (one data point per sapling), compared with a variance of 0.13 oho for 1989 stump wood (north and south aspects averaged per sapling). 5.2 Determination of Genetic Variation in WUE Among Sapling Provenances There are genetic differences in relative WUE (determined by 513C) among Pinus contorta populations. This genetic variation validates Rehfeldt's premise that P. contorta  populations are adapted to small parts of the species' range. Geography appears to have little influence on WUE (Fig. 17). Differences in WUE appear related to climate of origin. Leavitt and Long (1983) related 513C in Pinta edulis to temperature. Read and Farquhar (1991) found a significant negative correlation between A and precipitation, as well as temperature for Nothofagus species. Since temperature and precipitation were highly correlated (>0.6), they could not regress the two against A. However, they found the negative relationship between A and summer rainfall ecologically  Fig. 17. Geographical trends for 813C in Pinus contorta in British Columbia and Alberta. The 813C values are the least squares means obtained for Holden Lake, where all provenances were tested. The positions of the 813C values on the map correspond to provenance locations.  86 meaningful since, for some species, the rainfall totals during the dry months exceeded the evaporative load and much soil water remained. This led Read and Farquhar (1991) to suggest that the high discrimination of species in drier climates with marked seasonal temperatures resulted from adaptations allowing continued photosynthesis during warm but relatively dry periods. The regression model for the complete data set in this study shows a similar trend between A and summer rainfall as found by Read and Farquhar (1991) (temperature remaining constant). But higher seasonal temperatures (rainfall constant) give lower 813C values or higher A, the opposite trend found by Read and Farquhar (1991). Read and Farquhar (1991) used a wider temperature range than was used in this study. If both temperature and summer rainfall are varied to estimate 813C, the model for this study indicates that increased temperatures with lower rainfall give lower 813C values. This model supports Read's and Farquhar's (1991) suggestion that the relationship between A and temperature is likely influenced by rainfall regimes. As well, indications are that populations have adapted to continue photosynthesis during warm but relatively dry periods. The data are poorly fitted to this model, however, and interpretations are inconclusive. Modelling the relationship between climate and 813C without the coastal influence gives a model with adequate fit. The model predicts that under low summer moisture conditions WUE improves with increasing annual temperature, as expected in dry environments. In this case increased WUE may be due to selection for lower stomatal conductance in response to increasing vapor pressure deficit. Less water will be transpired relative to CO2 assimilated. Under cool annual temperatures WUE also improves (to a lesser extent) with increasing summer precipitation. Increased WUE may be due to selection for lower stomatal conductance in normally humid environments to avoid dessication in unexpected drought. This model implies that the evolution of improved intrinsic WUE in P. contorta var. latifolia populations in British Columbia is related to complex interactions  87 between temperature and precipitation and their effect on biophysical and physiological processes in trees. It appears that populations within the two varieties of the species have evolved high WUEs under very different conditions. The coastal provenance (var. contorta) maintains a high relative WUE similar to provenance 61 (var. latifolia) from the wet cool Sub-boreal Spruce subzone. The subzone has less than half the precipitation of the coastal location and a slightly higher summer temperature (msp=345 mm, mtwm=14.3 °C, mat=2.6 °C) (Meidinger and Reynolds 1991). To better understand the relationship between climate and 813C for the species we would require a class size spanning the species' range, as well as more appropriate climatic and atmospheric estimates. Coastal provenances in areas of summer drought should be sampled and the relative WUEs of interior populations (e.g., 61, 104 and 100) should be compared with coastal populations on very wet maritime test sites. Crucial origin site characteristics which would affect WUE (e.g., soil moisture, nutrients, vapor pressure deficit) were not available for regression analyses. Potential evapotranspiration (PE) was not estimated with climate parameters since Thornthwaite's method (1948) applies only to regional analyses. Complex formulas requiring more data have been devised for local studies. Ideal estimates of PE would require weighing lysimeters (Muller and Oberlander 1984). Atmospheric data such as solar radiation, humidity and wind velocity would have allowed rigorous estimates of PE. The most notable test site factor to elicit changes in 813C was moisture. The test sites were in biogeoclimatic subzones distinguished mainly by precipitation (Table 1). Holden Lake and 70 Mile House were in the dry cool Interior Douglas-fir subzone while Salmon Lake was in the moist cool Sub-boreal Spruce subzone. The drier subzone had higher temperatures than the wetter subzone. Factors such as tree leader damage, number of cones, degree of shading (particularly to the south), disease, girdling or browsing showed no obvious trends with 813C. More than one factor acted on most trees. Signs of severe disease, girdling or browsing were most useful in determining whether to eliminate outliers. Site  88 nutrient status may have been influential. Although mean 813C was not significantly different between 70 Mile House and Holden Lake, the slightly more positive values at 70 Mile House may have been related to its richer soil as well as decreased moisture. Differences in WUE among some populations can be readily linked to climate and geography while others cannot. Provenance collections 100 and 104 have similar WUEs, differing from that of provenance collection 69. Provenances 100 and 104 are in close proximity and have similar mean temperatures of the warmest month and summer rainfall. [As seedlings, both populations had 36 stomates mm-2 leaf area (Illingworth 1973). Stomates are functional and likely to indicate an adaptive response]. Provenance 69 is located further south and has a warmer climate with more precipitation. This population had 35 stomates mm-2 in the seedling stage. [Mingworth (1973) measured stomatal densities on the provenance seedlings when they were two years old. Stomatal counts were grouped and averaged by broad geoclimatic region; e.g., the stomatal density for provenance 69 (in the 'southern foothills') was the average of three provenances in that region. An important point is that average stomatal counts for the provenance collections are likely to have changed over time in response to environment as well as tree age]. In the context of scant site and climate information, provenance 30 appears to be an anomaly. Provenances 30 and 100 have almost identical summer rainfalls whereas provenances 30 and 69 do not, yet provenances 30 and 69 have almost identical WUEs. However provenances 30 and 69 have more similar temperatures (warmest month) than provenances 30 and 100. Provenances 30 and 69 likely grow in harsh conditions since provenance 69 is a high elevation source while provenance 30 is a far northern source. Some research indicates that provenances from the Yukon and northern British Columbia are genetically very different from the rest of the var. latifolia (Ying pers. comm. 1993). Morphologically, as seedlings, provenances 30 and 69 had 34 and 35 stomates mm-2 leaf area, respectively (Mingworth 1973). The stomatal count for provenance 100 was 36 mm-2.  89 In the field trials and seedling study, provenance 95's performance stands out. This population originates in a very wet coastal environment and yet is the most water-use efficient of the provenances. In the seedling stage, provenance 95 had high stomatal density (45 mm-2) Illingworth (1973) found a significant difference between stomatal densities of coastal and inland regions. One explanation for provenance 95's high WUE in different environments may be that this provenance has very responsive stomates to moisture deficits. Relating leaf A to climate parameters in Nothofagus, Read and Farquhar (1991) found the lowest A values (or highest WUE) in species from regions without soil or air moisture deficits. The reason for this was unclear; they offered KOrner's and Bannister's (1985) suggestion that stomatal sensitivity to humidity would be the most protective mechanism against sudden dessication in a normally humid environment. High WUE in wet climates, therefore, might reflect the costs associated with alternative mechanisms for avoidance of water stress. Provenance 95 appears to exemplify this. With adequate water, such as in the greenhouse, provenance 95's high WUE may have resulted from decreased stomatal aperture instead of increased photosynthetic capacity. In this case, high WUE restricted yield. On the other hand, provenance 61's high WUE may have resulted from increased photosynthetic capacity at a given stomatal conductance. In this case, high WUE in adequate water led to relatively high yield. This is conjecture, since physiological measurements which might have helped explain the differences in yield associated with high WUE were not done. In the field, increased 813C is not a reliable predictor of increased growth or yield within P. contorta provenances. But correlating productivity at particular sites with population averages (813C) shows significant correlations between high productivity and high WUE. Carbon isotopic composition may have practical use as a predictor of site yield. Selecting the most water-use efficient individual may not be as important as selecting the most water-use efficient population where long term productivity is concerned. In a P. contorta plantation water is shared among individuals. It is assumed that the more water-use  90 efficient trees will leave water for those requiring more moisture. Overall growth will be promoted by the faster growing less water-use efficient individuals. One assumption underlying this premise is that reproduction on the plantation prior to harvest will not occur, so the genetic composition of the population will not change. Pinus contorta seedlings do not establish under the canopy. The other assumption is that there will not be an abundance of other vegetation to compete for the water left by the more water-use efficient pines. In the case where only the most water-use efficient individuals of several populations are selected, the plantation may or may not ultimately produce a higher yield since high WUE is not always correlated with high yield in individuals. Stronger relationships between 813C and yield in saplings may have been gained by using test sites close to the seed sources studied or sites favorable to the provenances' phenological and photoperiodic traits. Some populations were stunted or growing erratically on the interior test sites. Provenance 13 from the coast-interior transition region (Ying et al. 1989) was very susceptible to frost and foliage disease in the interior environment (Ying pers. comm. 1993). The fast growing populations 44 and 57 (Ying et al. 1985) showed significant positive correlations between 8 1 3 C and yield at Holden Lake but different results at 70 Mile House. Repeated frost injury to trees in Block 51 at 70 Mile House resulted in a negative correlation between 813C and yield in population 57 (insignificant) while a small positive correlation (insignificant) remained in population 44. Fast growing populations are particularly susceptible to frost injury (Ying pers. comm. 1993). Pest problems may have contributed to the variable correlations between 8 13C and yield. These factors could have increased sampling errors in growth measurements. At Salmon Lake about 23% of the trees were damaged by porcupines and small mammals. The very different correlations between 813C and yield for populations 61 and 104 at this site may have been largely due to the severe porcupine damage to population 104 (Ying pers. comm. 1993). Although growth in population 44 was slowed by porcupine damage, there was a rather high and significant correlation between 8 1 3 C and yield in this population. It appears  91 that significant, positive correlations can exist despite adverse circumstances. Explanations remain elusive in these cases. Testing higher numbers of individuals per provenance may have given stronger correlations. Alternating provenance individuals systematically in lines or randomizing them throughout blocks may have prevented the rather selective destruction to certain plots. Growth under contrasting field temperature and moisture regimes has led to changes in ecotypic 813C. Evidence of a plastic reponse is shown by a significant site effect (Schlichting's definition 1986). Variation in 813C is similar among ecotypes; i.e., ranking is maintained from site to site. The similarity in degree and direction of response has implications for studies trying to predict the species' response to rapid global warming. Woodwell (1989) suggests that by the middle of the next century, temperatures will rise by 0.5 to 1.0 °C per decade in middle and high latitudes. In this study, populations 61, 104, 100 and 44 maintained relatively high WUEs as well as yields compared with other populations on climatically different field sites. This pattern could hold over all climates. This prediction would require validation by research involving a wide range of climates on replicated sites. To most accurately assess the species' plasticity in unpredictable conditions, rapidity of response would have to be measured. Response time may differ between seedlings and saplings. In the field, especially, short term variation in WUE may be more easily determined by gas exchange measurements or by measuring 813C of the leaf soluble sugar fraction in needles. Reciprocal transplants could offer useful information about plasticity but an acclimation period following planting would have to be considered in interpreting the outcome of physiological assessments. Previous site effects, especially drought, could determine the metabolism of trees for several years following transplantation, thus confounding assessments for plasticity of response to rapid changes in environment. In P. contorta, the relationship between high WUE and survival is not clear. The relationship is confounded by many factors not measured in this study, e.g., herbivory. Over  92 all sites, provenances 69 and 30 have the lowest relative WUEs and also the highest and third highest survival rates, respectively. Their performance is in contrast to Populus species where high WUE has been shown to promote survival in moisture-stressed environments (Dickmann et al. 1992). Provenance 61 follows the trend in poplars. With the highest WUE over all sites, it has the highest survival as well. One aspect of the experimental design that could hinder finding true population differences is that provenances were planted in plots rather than randomized throughout blocks. Certain microsites may be associated with certain plots, making 4513C a measure of microsite rather than provenance performance. This may have resulted in the significant block x provenance interaction shown in the analysis of variance for 70 Mile House and Holden Lake (Table 9). Yield data supported this as well. Fewer population differences were found in this analysis than in the other two. It is also known that block 51 at 70 Mile House was on a flat site prone to frost (Ying pers. comm. 1993) and would perhaps be best analyzed as a separate site. Due to the block x provenance effect, the finding of a nonsignificant site x provenance effect may not be valid. However, Figure 13 supports a nonsignificant site x provenance effect. The non-significant block effect can also be questioned. Examining block means with associated standard errors reveals that blocks are different at 70 Mile House but not at Holden Lake. 5.3 Seedling Experiment Of 11 seedling populations representing all provenance collections tested, only the coastal one is different in relative WUE. Favorable growth conditions may have nullified expression of other genetic differences. Alternatively, measuring performance at 5 months may have not allowed time for full expression of genetic differences. Plant development may influence variation in 813C but little research has been done on trees. Read and Farquhar (1991) hypothesized that species in dry summer climates (Nothofagus) have evolved physiological and/or morphological mechanisms to allow  93 continued water uptake and high stomatal conductance during mild water deficits. Nothofagus seedlings with low WUE had lower shoot/root ratios than the more water-use  efficient ones. Pinus contorta seedlings showed the opposite trend in nine out of eleven provenances, similar to that for Aestivum triticum (Masle and Farquhar 1988). The more water-use efficient seedlings partitioned more assimilate to roots than the less water-use efficient ones. The positive correlation between root growth and 813C for all provenances supported this as well. However, provenances 61 and 104, relatively high in WUE, demonstrated the trend found by Read and Farquhar (1991). To accurately interpret the results of 813C analyses, assimilate partitioning as well as background physiology must be measured. Seedling stomatal conductance and photosynthetic measurements were begun to help explain these differences but structural and mechanical problems with facilities and equipment precluded their completion. The correlation between 813C and root mass as well as shoot/root ratio for seedling populations followed an opposite trend to that found within populations. For the most part, correlations within populations were very low and not statistically significant, meaning that individuals with high or low WUE performed essentially the same. Regardless, an interpretation to explain the different trends among and within populations might be that the within population correlations between high root growth and high WUE largely reflect response plasticity to microenvironment while the correlation between high root growth and low WUE among populations mainly reflects an evolved response to origin habitat. The seedlings in the greenhouse were not competing for soil water but may have been affected by humidity changes. The high elevation populations 69 and 71 had the highest withinpopulation correlations between increased root growth and increased WUE. High WUE as well as increased root growth would provide flexibility in adjusting to possible future drought. Either mechanism or a combination of the two could be developed as necessary. On a population basis, it seems reasonable to expect that population 95 would grow proportionally the least root mass to shoot mass, having originated in a very wet coastal  94 climate. Population 61 from a wet, cool environment in the Sub-boreal Spruce zone performs similarly. Both have high intrinsic WUEs. The less water-use efficient populations 69, 71 and 30 originating in harsh environments (high elevation or northern interior) allocate proportionally more biomass to roots than shoots. Accessing more soil water would be a means to avoid dessication while achieving growth during a short growing season. Indications are that in seedlings, 513C may detect differences in allocation patterns more readily than in total biomass. More conclusive relationships between 813C and yield in seedlings may have arisen if the seedlings had been grown over two or three seasons out of doors. The illumination in the greenhouse was far from adequate for the establishment of P. contorta seedlings in their natural environment. They set bud earlier than expected, about one month before they were harvested. This may have been due, in part, to a long photoperiod (18 hours) which has been reported to sometimes cause early bud set in greenhouse-grown pines (Charleson, pers. comm. 1990). The good correlations between seedling 513C and that of saplings suggest that nursery performance may be a useful predictor of field performance. Although the correlations were significant, seedling rankings may have been random since only one provenance was clearly different in 513C. Studies with older seedlings over several years under well-defined drought conditions may give more sound results. Nursery shoot weights as well as total biomass were very poor (and insignificant) predictors of sapling volumes. Indirectly, 513C of seedlings shows more potential for predicting sapling yield than seedling growth variables. If seedlings offer a means to predict relative WUE of saplings, the seed may do so as well. However, the seed would have to be collected from one site in one year. An important point is that the 513C of the seed would give a more accurate measure of the mother plant's relative WUE than seedling or sapling WUE.  95 6.0 Conclusions Stable carbon isotope analysis has detected genetic variation in WUE among populations of Pinus contorta saplings. The genetic differences are related to provenance (habitat of origin), temperature and precipitation. There are no genetic differences in plasticity among populations. These results suggest that P. contorta provenances may respond to environmental changes in British Columbia by maintaining their ranking in relative WUE. The relationship of WUE to biomass increment in controlled environments as well as in nature, is not clear. High WUE is associated with low or high productivity, depending on the population, its growing conditions, and its physiological and morphological attributes. However, correlating yield at particular sites with mean population 813C shows a positive correlation between high productivity and high WUE. To understand differences within and among populations, the physiological and morphological bases for high productivity must be measured (Dickmann et al. 1992). By testing the WUE response of provenances in controlled multiple factor experiments, we may improve our understanding of the adaptive nature of high or low WUE in P. contorta seedlings. Early selection for improved WUE as indicated by 813C may be possible in P. contorta. However we do not know whether the significant correlation between 813C in  seedlings and juveniles is repeatable. Stable carbon isotope analysis offers a simple means to assess genetic differences in WUE among populations of large trees, as well as genotype x environment interactions. From a yield aspect, it is not clear whether high WUE is more advantageous than low WUE in promoting survival or productivity. It will be important for future investigators to examine the relevance of using 813C analysis as a selection tool for practical purposes in woody species. Stable carbon isotope analysis of plant tissue may be more valuable as a predictor of site yield than as a means to understand functional differences among tree populations.  96 7.0 Recommendations for Further Research Several goals could be pursued in future studies: 1.  Improving the models relating environmental factors to the evolution of  differences in WUE among wild populations of P. contorta . Precise climatic, atmospheric and site characteristics of origin habitats would be required, the most important being those relating to air and soil moisture. Populations from the climatic range of the species should be tested. The provenance trials in British Columbia were not set up to assist in modelling the evolution of WUE according to climate. Seed sources were not chosen with particular attention to their vicinity to climate stations, nor were detailed micrometeorological data collected. Air moisture, soil moisture carrying capacity and nutrients were not documented. Using these trials for further research would require sampling the highest number of populations tested (total= 140) that could be matched with acceptable climate station data. Biogeoclimatic subzone climate data could be used but would introduce more error. Costs could be reduced by pooling cores within populations in each block, giving two samples per population per site for carbon isotope analysis. 2. Pursuing the global warming problem. The ability of seedling populations to adjust their WUEs to rapid climate changes in ecologically sensitive areas in British Columbia could be assessed. This would require test sites in the boreal forest and the transition zones, e.g., Boreal White and Black Spruce-Alpine Tundra, Mountain HemlockParkland. Micrometerological data should be recorded. Physiological responses and assimilate partitioning should be measured on reciprocal transplants. 3. Determining more conclusively the relationship between improved WUE and yield as well as fitness (surviving to produce viable seed). Stronger correlations between 813C and yield for mature trees may be found on sites with optimal growth conditions for the populations tested. Individuals from certain populations will show stronger relationships between 813C and yield than individuals from others. For example, populations selected for  97 plantation renewal in tree improvement programs may show stronger positive relationships between 813C and yield than wild populations originating from marginal sites in British Columbia. An important distinction must be made between working to understand the variation in WUE within populations vs. the variation in WUE among populations. The variation in WUE within populations is often not addressed in the literature. If seedling establishment and productivity as related to WUE should be the focus, 813C vs. yield relationships can be measured in multi-factor experiments with regulated moisture, temperature and nutrients. For seedlings, changes in assimilate partitioning in response to environment may be more informative than overall growth responses. Improved WUE may be more strongly related to changes in allocation patterns rather than biomass. 4. Continuing to test the correlation between seedling and sapling 813C in other populations and families, as well as in other tree species. At some level, seedling 813C may be a reliable indicator of long-term performance.  98 References Bassman, J. H. 1985. Selected physiological characteristics of lodgepole pine. In Lodgepole Pine — The Species and Its Management. Proceedings of the Cooperative Extension Service, ed. D. M. Baumgartner, R. G. Krebill, J. T. Arnott, and G. F. Weetman, 27-43. Pullman: Washington State University. Bender, M. M. 1968. Mass spectrometric studies of carbon-13 variations in corn and other grasses. Radiocarbon 10:468-472. Bergerud, W. 1990. Biometrics Information. Pamphlet No. 28. Victoria: Ministry of Forests Research Branch. 4 p. Bierhuizen, J. F., and R. 0. Slatyer. 1965. Effect of atmospheric concentration of water vapor and CO2 in determining transpiration-photosynthesis relationships of cotton leaves. Agric. Meteorol. 2:259-270. Blake, T. J., T. J. 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Correlations between 813C and yield and growth variables at each test site (provenance 95 excluded). 70 Mile House  Provenance  Holden Lake  Diameter^Height^Volume 1^0.239^0.231^0.126 13^-0.449^-0.493^-0.326 30^-0.212^-0.342^-0.231 44^0.125^-0.045^0.131 57^-0.456^-0.596^-0.456 61^0.038^0.025^0.063 69^0.330^0.386^0.302 71^-0.645*^-0.720*^-0.679* 100^-0.002^-0.0001^0.082 104^0.574*^0.540*^0.547* Height  Volume  0.037 -0.092 0.463* 0.681* 0.475* 0.338 0.355 0.068 0.219 0.235  -0.090 -0.011 0.337 0.554* 0.671* 0.151 0.375 0.119 0.098 0.233  Diameter  Height  Volume  Provenance^1^0.319 13^-0.141 30^0.547* 44^0.600* 61^-0.652* 69^-0.128 71^0.233 100^0.292 104^0.267  0.482 0.082 0.650* 0.699* -0.458 -0.118 0.268 0.285 -0.027  0.324 -0.033 0.556* 0.672* -0.596* -0.048 0.276 0.346 0.191  Diameter  Provenance^1^-0.028 13^-0.133 30^0.345 44^0.512* 57^0.745* 61^0.079 69^0.268 71^0.114 100^0.176 104^0.273 Salmon Lake  Note: None of the correlations are truly significant. *Correlations significant at a=0.05 (no correction for number of correlations tested). Units of measurement: diameter, mm; height, cm; volume, m3.  ^ ^ 107 Appendix 2. Correlations between seedling 613C and biomass variables, as well as shoot/root ratio. Shoot^Root^Shoot+Root Shoot/Root Provenance  1^-0.161^0.200^-0.085^-0.350 13^0.021^0.145^0.057^-0.171 30^-0.106^0.171^-0.046^-0.219 44^-0.048^0.002^-0.037^-0.030 57^-0.364*^0.037^-0.281^-0.369* 61^0.253^0.059^0.208^0.120 69^-0.171^0.341^-0.021^-0.467* 71^0.199^0.405*^0.288^-0.410* 95^-0.066^0.045^-0.041^-0.096 100^0.181^0.206^0.200^-0.107 104^0.332^0.306^0.340^0.016  Note: None of the correlations are truly significant. *Correlations significant at a=0.05 (no correction for number of correlations tested). Units of measurement: shoot, g; root, g; shoot+root, g.  Appendix 3. Regression analyses of 513C vs. temperature and precipitation (for provenances 13, 30, 57, 69, 95, 100 and 104), including the tests for lack-of-fit of the models.  Source  df  SS  MS  Calculated F Value  F Value  4  67.725  16.931  58.383  2.37  285  82.698  0.290  9.759  0.697  2.591  1.70  271  72.939  0.269  5  63.816  12.763  46.923  2.21  258  70.242  0.272  2.921  0.266  0.974  1.80  67.321  0.273  Complete Data Set Including Provenance 95  Regression Residual Lack-of-fit Within Groups  14a  Complete Data Set Excluding Provenance 95  Regression Residual Lack-of-fit Within Groups adf=290-4-1-271 (according to Ott 1988, p 369) bdf=264-5-1-247  lib 247  

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