{ LIBRARY ‘ Michigan State A University This is to certify that the thesis entitled GENETIC VARIATION OF MORPHOLOGICAL AND PHYSIOLOGICAL TRAITS IN BLUE AND ENGELMANN SPRUCE presented by George Philip Buchert has been accepted towards fulfillment of the requirements for Doctoral degree in Forestry Immmztww D Major professor Date May 21, 1981 0-7 639 UVtRDUE FINES: __,__.————" 25¢ per day per item RETURNING LIBRARY MATERIALS: Place in book return to remove charge from circulation records 77777 ,fi__—____—————_-——_'_v——___ GENETIC VARIATION OF MORPHOLOGICAL AND PHYSIOLOGICAL TRAITS IN BLUE AND ENGELMANN SPRUCE BY George Philip Buchert A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Forestry 1981 DEDICATION To my Father and Mother, who gave me the opportunity to achieve my full potential. ii ABSTRACT GENETIC VARIATION OF MORPHOLOGICAL AND PHYSIOLOGICAL TRAITS IN BLUE AND ENGELMANN SPRUCE By GEORGE PHILIP BUCHERT Genetic variation in height growth, susceptibility to frost damage, timing of phenological events, foliage color, needle morphology, and physiological response to water stress was studied in an all-range collection of blue spruce open-pollinated families and selected Engelmann and white spruce seedlots, under southern Michigan growing conditions. Seedlings of 238 blue spruce families from 44 pOpulations in eight climatic regions throughout the natural range were raised under accelerated-growth condi- tions and under standard nursery conditions. Regional differences in 6-year height were well-expressed in nursery- reared trees, accounting for 19% of total variation, but were nonexistent in trees reared under accelerated condi— tions. Among-population and between-family components were similar in nursery and accelerated trees, ranging from 17 to 20% of total variation. Utah sources grew slowest, had the least number of blue—needled trees, flushed out latest and sustained the least frost damage, while southern New Mexico and Arizona sources grew fastest, flushed out earliest and suffered the most frost damage. Expression of blue foliage color increased with age, and was most prevalent on sources from Colorado and northern New Mexico. Western Colorado appears to be an area of extreme variability for all traits measured. Seedlots from this area varied as much as 88% in 6-year height and ranged from 0-78% in number of blue— needled trees. A selection index based on family growth rate and foliage color identified those families to be in- cluded in a breeding population which would produce geneti— cally improved blue spruce seed for a variety of horticul- tural and regeneration purposes. Although blue spruce may have been derived from Engelmann spruce ancestors, adaptation to warmer, drier habitats resulted in a recognizably different taxon. Blue spruce seedlots were, on average, taller than Bngelmann spruce after 6 years. Blue and Engelmann seedlots from a common Arizona origin were tallest at 5 years; however, growth losses due to repeated spring frost damage were a major factor in height growth between species. Because of adaptations to high elevation environments, Engelmann spruce flushed out earlier than blue spruce and was more suscepti- ble to spring frosts. However, Engelmann spruce was quite variable in growth-related traits at regional, population and family levels. It appears that morphological characters are under very different selective pressures for blue and Engelmann spruce. The ratio of needle length to resin sac length proved to be a reliable diagnostic character for differentiating the two species; however, other morphologi- cal characters were more variable. Needle width of blue spruce was highly variable among families within populations, but relatively uniform among populations, whereas Engelmann spruce populations were very different from each other. Transpiration curves were determined for selected blue, Engelmann and white spruce seedlots by expressing water loss as g-dm_2-h-l. Three patterns of transpiration response to diminishing soil moisture were apparent among species. Blue spruce transpiration rates were inhibited at field capacity (18-22% soil moisture), reached maximum rates at 10-14% soil moisture, declined gradually as soil moisture declined to a threshold of 4.5%, then dropped rapidly to a minimum at 3% soil moisture. Although some Engelmann spruce reacted like blue spruce, others simply decreased transpira- tion gradually as soil moisture decreased. White spruce transpiration rates were highest at field capacity, reached an intermediate level between 8 and 18% and then decreased gradually to a minimum between 3 and 8% soil moisture. Fast—growing blue spruces are evidently capable of maintain- ing gas exchange at lower soil moisture and more negative xylem pressure potential, thus allowing growth processes to take place in stress conditions. The strong differences in transpiration pattern among and within species indicate adaptation to very different habitats, and suggest that family selection for this trait may be an effective way to screen for fast growth. ACKNOWLEDGMENTS It is with deep gratitude that I acknowledge the patient guidance, support and encouragement of my graduate committee chairman, Jim Hanover. The example he set as a gentleman, scholar, inquisitive and careful researcher and enthusiastic physiological geneticist while maintaining the role of father and husband, is a standard for me as well as his numerous other students. I'm also grateful for the opportunity to share some of Jonathan Wright's deep insights into matters concerning the application of forest genetics principles and theory to practical tree breeding. I count myself fortunate to have been one of those to have received training from him. To the other members of my graduate committee, Grant Vest, Wayne Adams and Don Dickman, I ex— press my heartfelt thanks for manuscript reviews, stimulat- ing discussions and the willingness to share their ideas and knowledge with me. A public expression of thanks to my many friends in Michigan and Ontario who gave continuous encouragement and support over the ten years of my life in- volved in this project is, to me, a woefully inadequate way of acknowledging my gratitude; I will do this privately, over the next ten years. To my associates and colleagues in the OMNR and at PFES who looked on my unfinished iii condition with patience and encouragement, I extend my sincere appreciation, and look forward to unencumbered associations in the future. I extend my appreciation and admiration to Pauline Fisher, who waited patiently for five years so that she could put up with changes and a very busy schedule to type my dissertation. It is with deep emotion that I acknowledge the support of parents, brothers and sisters, aunts, uncles and cousins in my educational goals. Finally, I express my thanks and love to Ellen, Rob, Jennie, Martin, Heidi and Johanna for their long-suffering, their unending support and faith in my efforts and abilities; for us it has been a family affair. iv LIST OF LIST OF CHAPTER 1. TABLE OF CONTENTS TABLES . . . . . . . . . . . . . . FIGURES . . . . . . . . . . . . . . . GEOGRAPHIC VARIATION IN BLUE SPRUCE -- 6-YEAR RESULTS . . . . . . . . INTRODUCTION . . . . . . . . . . MATERIALS AND METHODS . . . . . . . . Height Growth . . . . . . . . . . Relative Frost Damage . . . . . . Bud Phenology . . . . . . . . . . Foliage Color . . . . . . . . . . Selection Index . . . . . . . . . Climatic Regions . . . . . . . . . RESULTS . . . . . . . . . . . . . . . Height Growth . . . . . . . . . . Foliage Color . . . . . . . . . Relative Frost Damage . . . . . . Bud Phenology . . . . . . . . . . Selection Index . . . . . . . . DISCUSSION . . . . . . . . . . . . . NUR vs. ACCEL Growth . . . . . . Time Changes in Genetic Variances Climatic Effects Upon Phenology Ecological Significance of Foliage Selection Index Limitations SPECIES DIFFERENCES BETWEEN BLUE AND ENGELMANN SPRUCE . . . . . . . . . . INTRODUCTION MATERIALS AND METHODS . . . . . . . . Height Growth . . . . . . . . Terminal Shoot Elongation . . . . Frost Damage . . . . . . . . . . Phenology . . . . . . . . . . . Needle Morphology . . . . . . . . V Page vii ix \l\l\lO\UlUl J5 H H H N NNNNH oomtnwtx) but» DO 32 36 42 45 47 47 49 50 52 52 54 55 CHAPTER RESULTS . . . . . . . . . . . . . . . . . . Height Growth . . . . . . . . . . . . . Terminal Shoot Elongation . . . . . . . Frost Damage . . . . . . . . . . . . . . Phenology . . . . . . . . . . . . . . . Variation in Needle Morphology . . . . . DISCUSSION . . . . . . . . . . . . . . . . Phenology Reflects Climate . . . . . . . Frost Damage and Phenology . . . . . . . POpulation Differentiation in Height Growth . . . . . . . . . . . . . . . . Adaptive Value of Needle Morphology . . 3. DIFFERENTIAL RESPONSE TO DROUGHT STRESS BY BLUE, ENGELMANN AND WHITE SPRUCE . . . . . INTRODUCTION . . . . . . . . . . . . MATERIALS AND METHODS . . . . . . . . . . . Xylem Pressure Potential Determinations Soil Moisture Curve . . . . . . . . . . Transpiration Rate Determinations . . . RESULTS . . . . . . . Xylem Pressure Potential Response to Soil Moisture . . . . . . . . . . . . Transpiration Response to Soil Moisture Transpiration Patterns by Species . . . Transpiration Relative to Xylem Pressure Potential . . . . . . . . . . . . . . DISCUSSION . . . . . . . . . . . . . . . . Transpiration Rates Reflect Drought Resistance . . . . . . . . . . . . . . Transpiration in Saturated Soils . . . . Indirect Selection for Growth Rate . . . 4. CONCLUSIONS AND RECOMMENDATIONS . . . . APPENDIX . . . . . . . . . . BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . vi Page 58 58 65 66 69 74 78 78 79 8O 81 83 83 85 85 86 87 88 88 96 97 106 108 108 109 111 114 117 122 LIST OF TABLES Table Page 1. Climatic Summary of Origin of 44 Parental Blue Spruce Populations . . . . . . . . . . 9 2. Analysis of Variance for Blue Spruce Half—sib Progenies . . . . . . . . . . . . 10 3. Mean Squares for Measured Characters of Blue Spruce Half—sib Progenies . . . . . . l3 4. Components of Variance for 16 Blue Spruce Characters . . . . . . . . . . . . . . . . l4 5. Mean Values of 16 Blue Spruce Characters for 8 Climatic Regions . . . . . . . . . . 15 6. Summary of Population Character Means Grouped by Climatic Regions . . . . . . . . l7 7. Simple Correlations Among Growth and Environmental Factors for 44 Blue Spruce Populations . . . . . . . . . . . . 19 8. Correlation Coefficient Between 6-Year NUR Height and Climatic Parameters . . . . . . 21 9. Correlation Coefficient Between 6-Year ACCEL Height and Climatic Parameters . . . . . . 22 10. Selection Index Values of Selected Blue Spruce Families . . . . . . . . . . . . . . 31 11. Origins of Engelmann Spruce Seedlots Included in Rangewide Blue Spruce Seed Source Collection . . . . . . . . . . 51 12. Origins of Blue and Engelmann Spruce Seedlots Included in Nursery Shoot Elongation Experiment . . . . . . . . . . . 53 vii Table l3. 14. 15. l6. 17. 18. 19. 20. 21. 22. 23. Page Blue and Engelmann Spruce Seed Sources Used in Needle Morphology Study . . . . . . 56 Mean Heights of Blue and Engelmann Spruce Seedlots from Greenhouse, Nursery and Subsequent Field Plantings . . . . . . . . 58 Mean Height Growth for Blue (B) and Engelmann (E) Spruce Seedlots from Five Natural Populations . . . . . . . . . S9 Variance Components of Needle Characters Among Blue and Engelmann Spruce Expressed as Percent of Total Variance . . . . . . 75 Mean Values for Needle Morphological Characters of Nine Blue and Eight Engelmann Spruce Populations . . . . . . . 76 Origins of Blue and Engelmann Spruce Seedlots Used in Drought Tolerance Studies . . . . . . . . . . . . . . . 86 Comparison of Regression of Xylem Pressure Potential on Percent Soil Moisture Among and Within Engelmann, Blue and White Spruce Seedlots When Soil Moisture is Less Than Eight Percent . . . . . . . . . . 92 Comparison of Xylem Pressure Potential on Percent Soil Moisture Among and Within Engelmann, Blue and White Spruce Seedlots When Soil Moisture is Greater Than Five Percent . . . . . . . . . . . . . 96 Mean Transpiration Rates (g-h_l-dm-2) for Blue, Engelmann and White Spruce Seedlots When Soil Moisture is Not Limiting . . . . 97 Regression dmEquations for Transpiration Rate (g h"1 ) on Percent Soil Moisture, and F— —Va1ues for Significance of Departure from Linear Regression . . . . . . . . . . 101 Comparison of Transpiration Rates (g-h'l'dm'z) Upon Xylem Pressure Potentials Among and Within Engelmann, Blue and White Spruce Seedlots . . . . . . 107 viii 10. LIST OF FIGURES Cumulative terminal shoot growth of blue (B) and Engelmann (E) spruce seedlots from bud flush to bud set . . . Cumulative terminal shoot growth of blue (B) and Engelmann (E) spruce seedlots from bud flush to bud set . . . Relative frost damage of blue (striped) and Engelmann (solid) spruce seedlots from common populations . . . . . . . . Differences in bud phenology between blue (striped) and Engelmann (solid) spruce seedlots at four assessment dates . . . Pattern of growing—degree day accumulation over three consecutive growing seasons . Response of blue, white and Engelmann spruce xylem pressure potential to changing soil moisture . . . . . . . . . Response of blue, white and Engelmann spruce xylem pressure potential to soil water changes below 8% soil moisture . . Transpiration curves for three blue spruce seedlots . . . . . . . . . . . . . Transpiration curves for three Engelmann spruce seedlots . . . . . . . . . . . . . Transpiration curve for bulk Michigan white spruce seedlot . . . . . . . . . . ix 62 64 68 71 73 9O 94 99 103 105 CHAPTER 1. GEOGRAPHIC VARIATION IN BLUE SPRUCE -- SIX-YEAR RESULTS INTRODUCTION Picea pungens Engelm. is known throughout the world's temperate climatic zones as Colorado blue spruce. E; pungens occurs naturally from southern New Mexico and Arizona to southwest Montana, and eastward from central Utah through the Colorado Rocky Mountains (Hanover 1975). In general, blue spruce is found in riparian habitats and the borders of forest openings. Common tree species associated with blue struce are Populus tremuloides Michx., Pseudotsuga menzesii (Mirb.) Franco, Pinus ponderosa Dougl. ex Laws. in the lower portion of its elevation range and Picea engelmanii Parry ex Engelm. near its upper elevation limits. Because blue spruce is tolerant of both cold and drought conditions, it has been used very successfully in windbreak plantings in low rainfall areas such as the Great Plains. Since its introduction into the horticultural trade in 1862, the striking steel blue foliage and symmetrical form of blue spruce has ensured a continuous demand for ornamentals derived from the dozen or so recognized culti- vars, so it is highly valued by the landscape industry (Rehder 1940). Blue spruce is also characterized by very slow growth, and studies have been initiated to ascertain the amount and pattern of genetic variation in several 2 economically important characters for use in selection and breeding. In a 1908 trial of possible coniferous species suitable for Canadian prairie shelterbelts, blue spruce, white spruce (Picea glauca V053) and Norway spruce (Picea gages L. Karst) proved to be the most successful (Department of Regional Economic Expansion, 1977). However, due to pine needle scale and spider mite mortality of white and Norway spruce during drought years, blue spruce has been found to be the best suited for shelterbelt planting (Cram 1964). Because pest resistance appears to be related to the amount of epicuticular waxes on blue spruce needles, Saskatchewan's Indian Head Nursery has, since 1954, been conducting breed- ing studies to increase the frequency of blue foliage seedlings by crossing selected blue needled parent trees (Cram and Lindquist 1969). Cram (1957) reported that sub- stantial difference in survival, vigor and foliage color were present among progenies of 25 selected parents 8 years after planting. Cram (1956) also reported that self incom- patability differed between selected parent trees. I The first provenance study of P;_pungens, in which seedlings from a total of 12 provenances in four states were outplanted in three Minnesota plantations, was initiated in 1951 by D. P. Duncan of the University of Minnesota. Seedlings from seven of these provenances were outplanted by the U. S. Forest Service in North Dakota, and Dawson and Rudolph (1966) reported on survival, height, susceptibility 3 to frost damage and crown diameter after five years. Their results indicated that trees from the north side of the Uinta Mountains, in the Ashley National Forest, Utah, were the tallest after five years in the field. [This provenance also had the best survival and was least damaged by late spring frosts. The slowest-growing provenance came from the south side of the Uinta Mountains, also in the Ashley National Forest. Scholtenl observed that the two provenances from Arizona (Kaibab and Apache National Forests) were tall- est in three Minnesota plantations after 19 years from seed. In these plantations the shortest provenances were from the Wasatch National Forest, Utah and the Targhee National Forest, Wyoming. Provenances having the bluest or most desirable foliage color were from Chaffee County, Colorado and Apache National Forest, Arizona, with 48% and 44% blue trees, respectively. Although both of the above studies gave some indication of relative adaptability to their planting sites, too few provenances were included to accu- rately estimate the amount and pattern of genetic variabil- ity. The North Dakota planting gave evidence of intra- population variation, as seen in the Ashley National Forest seedlots which included the fastest and slowest growing trees. However, trees from these same two sources were very similar in their resistance to frost damage. The three l Scholten, H. unpublished report on file in Forestry Department, Michigan State University. 4 Minnesota plantings indicated that trees with blue color came mostly from areas in Colorado and Arizona. In 1969 Dr. J. W. Hanover of Michigan State Univer- sity initiated a range-wide collection of open-pollinated cones from individual trees as a first step in the genetic improvements of blue spruce. Through the c00peration of the U. S. Forest Service, more than 400 open-pollinated families of blue and Engelmann spruce from 75 populations were collected and sent to Michigan State University. Sub- sequently, 238 of these families were grown under accelerat- ed growth conditions (Hanover and Reicosky 1972) and also in an East Lansing nursery (Hanover 1975). Seedlings from these families were planted out in permanent genetic test plantations near Battle Creek, Michigan. IPatterns of genetic variation for 15 growth and foliage characters are presented in this report, along with a simple selection index for determining seedlots of greatest value in a tree improvement program. MATERIALS AND METHODS Seedlings of each of 238 open-pollinated families were raised under the accelerated growth conditions describ- ed by Hanover and Reicosky (1972). Four—tree plots of each family were outplanted in three replicates of a randomized complete block design genetic test plantation near Battle Creek, Michigan in September 1970. This experiment is designated the ACCEL plantation in the following report. 5 Progenies from the same families were raised in an East Lansing nursery (Hanover 1975) and were outplanted adjacent to and in the same design as the ACCEL plantation. The nursery-grown progeny experiment is referred to as the NUR plantation in this report. ACCEL progenies were measured at 3 and 6 months of age prior to outplanting and at 2, 5 and 6 years of age in the plantation. NUR progenies were measured at 2 years of age in the nursery, and at 5 and 6 years of age in the plan- tation. All plot trees were measured and the mean plot value was used for analysis. On May 18, 1973, a severe frost (-3°C) occurred over most of southern Michigan and resulting damage to newly emerged buds in the ACCEL plantation was evaluated on June 9, 1973. By this date undamaged buds had flushed completely, while those buds killed by frost had dried and were quite conspicuous. Damage was estimated visually and graded in the following manner: Frost Damage Grade Condition of Tree 1 - less than 1/3 of all new shoots killed 2 - more than 1/3 but less than 2/3 of all new shoots killed 3 — more than 2/3 of all new shoots killed Individual plot trees were graded and a plot mean was calculated. Since relative damage between seedlots was considered most important, a relative frost damage value was calculated for each plot by distributing the mean plot value 6 on a relative damage scale from O to 100%. A regression was calculated using damage grade as the independent vari- able and relative frost damage as the dependent variable, as follows: Independent Variable Dependent Variable Damage Grade 1.0 0% Relative Frost Damage Damage Grade 2.0 50% Relative Frost Damage Damage Grade 3.0 100% Relative Frost Damage The resulting linear regression equation was: 9 = 50x -50 Percent Relative Frost Damage (RFD) was expressed as and RFD was calculated for each mean plot value. For analysis of variance, RFD values were transformed to arcsine /percent (Steel and Torrie 1960). In spring 1974, the ACCEL plantations were evaluated for variability in shoot phenology. On May 1, 15, 21 and 29, all seedlots were scored for stage of bud development using six easily recognizable phenophases which correspond closely to Nienstaedt and King's (1970) phenophases for white spruce. Phenophases were recorded as follows: Bud Stage Condition of Buds 1 winter resting bud 2 bud beginning to swell 3 bud fully swollen, needles inside 4 needles bursting through scales 5 shoot elongating, needles closely appressed 6 shoot elongated, needles free Each individual plot tree was scored for bud stage and plot means were calculated and used as observations in subsequent analysis. Concurrent temperature data were re- corded near the plantation site and were used to calculate growing-degree-days of heat accummulation by summing up mean daily temperature in degrees above a base temperature of 5.5°C. PrOportions of blue and steel blue trees per plot were recorded in May of the fifth and sixth years of growth in the ACCEL plantation, and transformed to arcsine /percent for analysis. The assessment was made soon after flushing, to record maximum color expression since environmental weathering of epicuticular waxes had not yet occurred. A simple selection index (SEI) was developed to identify families which might be included in a blue spruce breeding program. Six-year NUR and ACCEL height and 6—year % blue and % steel blue trees were included in the index because of their obvious economic value. The amount con- tributed by each character was additive, and those 8 open-pollinated families with the highest SEI values were considered for selection. Foliage color values which were expressed as percent of blue and steel blue trees were di- rectly additive, while ACCEL and NUR heights were expressed in a percent of the plantation mean and then added, so that: 1+: 62 85 + c + d SEI = mean family 6-year NUR height where a b = mean family 6-year ACCEL height c = mean family % blue trees at 6 years d = mean family % steel blue trees at 6 years With this equation, fast-growing families would be favored regardless of foliage color, while families with predominately blue progeny would also be selected, irre— spective of growth rate. In order to estimate the extent to which regional climatic and topographic patterns influenced genetic varia- tion, populations were grouped into eight climatic regions (Table 1) as suggested by Baker (1944). An analysis of variance was calculated for each character (Table 2), where 02 estimates the variance attributable to error, or2 estimates the variance among climatic regions, op2 estimates the variance among populations within climatic regions and of2 estimates the variance among families within populations. 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Q: . W J O mocm mc+m vc c44m1v+m mvumswm can: cvuuqum 40400 omm44om .meEmo umOum o>4uo4om uLmNo: 4mm» 0 .m .m 4000< Nam No smm av4 Mcm+No GN4 NM Mcom.44+mom+No mm N Moom.mm.moma.N4+w°m+No N m N wMflm mouoowm cow: nouommmm dem uzmao: one» N mcz u£m4wm you» c .m «:2 neuum mw44NEom mco4uo4smom mc04com mxoo4m co4u044m> uo condom uOuum m64442ma mc04u04smom mCONmom mxoo4m co4uo4um> MO QUHSOM newcomOua n4mum4m: oosumm 034m uou ooca4uu> 00 m4m>4mc< .m 04208 11 after the manner of Kempthorne (1957). An F—test was made for each component by dividing by the appropriate mean square. An approximate F—test was made for climatic region mean squares by reconstructing the error mean square from the estimated variance components and expected mean square coefficients to provide a clean F-ratio, as suggested by Namkoong 33 21. (1972). Estimated variance components were then calculated as a percent of total variance so that rela- tive differences would be more meaningful. Significant differences among mean character values were determined by the LSD method (Steel and Torrie 1960). In order to ascertain relationships between measured characters and environmental variables, simple correlations were calculated between characters and elevation, latitude and longitude of parental populations. Additionally, general climatic parameters were estimated for each popula— tion by interpolation from Baker's (1944) climatic charts. In this way, mean January temperature, mean July temperature, annual precipitation, length of growing season, number of frost-free days and length of spring frost season were cor- related with measured characters. RESULTS Height Growth Analyses of variance for all measured characters are summarized in Table 3, while individual variance com- ponents are presented in Table 4. Most characters showed highly significant differences (F 1 0.01) within the three levels of classification. Significant differences among climatic regions were present in NUR trees at 2, 5 and 6 years of age. However, percent of total variance due to differences among climatic regions (02 R) decreased r NU with increasing plantation age so that by age 6 years 2 r NUR in Table 5, trees from the Uinta Mountains (UINTA) were 0 was only two-thirds that at age 2 years. As seen generally the slowest growing at age 6, while trees from southern New Mexico (SNMEX) were generally tallest. Signif- icant changes in ranking occurred among climatic regions with age; western Wyoming materials were shortest in the nursery, but at age 6 were not significantly different from the fastest-growing climatic region, SNMEX. In marked con- trast to NUR materials, differences among climatic regions were absent in ACCEL height data at all ages. Differences among populations within climatic regions (02p) were highly significant for 2, 5 and 6 year NUR heights. When expressed as a percent of total variance, 2 fluctuated with age. At 5 years, 02 was about 0 p NUR p NUR 12 13 4m>m4 4c.c um accomuflcmNm m24m>|m u «« u:M0404:04m uoc 034m>lm u .m.c mm.c vm.c4 cm.n mc.> ccc uOunm mc.vm44 mm4 .. mc.m4 .. 4h.mm .« vs.mm «« mm.v4 nm4 mmN4NEom «. mm.momv mm .. mm.nm «c c4.nn «. mm.4m «« cm.mv cm m:04uMH:QOE .. 4v.mmmcm N .. mo.>mn .. mc.cvm .« mm.ccm «« mm.mmm n mCONmmm mm.v mv.mM4 mm.mm cv.4c m mx004m 534' .u .46 EmeA «RNA IRS}. «Rim .N 6 Sfimflm> :04u004wm >b04oc0£d cam >mo4ocwcm cam xmo4ocwcd cam >m04ocw£m cam 00 mousom mn.on mm.ccm cm.co4 M4.mnm cm.cmm mm.n mm.mm v0.4a ccv wouum .. cm.cM4 .. mv.cav .. ac.cvm .« mc.hmm .m.: vm.mcm .« mm.mm .« cm.mc e. cm.nc4 mm4 mofi4eemm c. hmévm .3 vo.M4cm .2. mm.4mm4 : modmmm 2. mcémo .2. voém : EUmhm .3 3.43 cm m840m4aom .. cm.vMV4 .. mm.mcmm c. cc.4omv .. 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ENS .. mNSmm N 30391 mm.oc m cm.m mm.cm m mx004m 2?. o: G .e Ema: Emmi .0 .6 8.3ng x42 3; m ~52 34> m E2 3; w 00 855w mo4comONE 44mnu4c: mosuam 0:43 00 mucuomu620 penance: you mouosvm coo: .m m4noe 14 wocm44m> 40440 n o mco4um4smom :4nuN3 m04445mw mCOEm mocm4um> n We mco4oou o4uoE44o 242443 m:044c4saoa mcosm mocm44o> n We mcoflmou UNuoEN4o ocean oocm4um> n We 4 ow mm NN No.4m44 mN.vm¢ m4.omN xocc4 c044oo4om me ON oN N4 om.oo4 4m.ON NN.oN om.4v women 6244 4ooum 4 ~0406 News o NN e4 oN NN N4.NNN vo.mm4 mo.40N ov.mNN memos 6:44 N 40400 4664 G we N4 N4 44 mN.om mv.o4 Nm.m4 G4.m4 moons 6:44 4omum 4 no4oo 460» m N4 «4 4N m4 NN.ooN 4N.SN mN.m44 om.004 moons 6:44 4 uo4oo 4664 m cc m m mv 4m.o cm.4 mv.4 mm.N mN >62 .400402624 62m em $4 44 V4 va.04 as.m oN.N NN.N 4N 462 .moo4ocoga com me NN s4 44 om.N 4N.N 4N.N om.N m4 >62 .Nmo4ozona cam vs N4 o4 44 No.N 4m.4 00.4 vm.m 4 4m: .Nmo4ocoza cam No 4 m NN om.omN 4m.44 04.44 «4.4m ommsmo umoum o>4um4om 4o m4 m4 4 vs.4m vv.mN Nm.mN 44.4 44004 .ucm4o; 46mm 4 so 44 44 o os.Nm No.44 44.44 c 4moo< .4nm4mc Hams m 4m N4 m4 N mo.N 4m.v mm.N Nm.o 4moo< .4zm4o; 4mm» N No m :4 o4 No.44 NN.44 ON.44 mo.m4 mcz .ucm4oc 466» G as ON m4 NN NN.N4 mm.m No.0 4N.N4 mcz .4cm4oz new» m .4 ON N4 N4 Nm.m 44.4 mo.N mN.v «:2 .4zm4o; 486» N Mm My mm mm -IflmWI IIMHI) tumml: IIWMI- zo4ea4mommo mueo 4 .muo4ocuoc0 mooudm 0:44 04 400 ooco4uc> uo mucocoaso0 .v 04nme 15 4 AN 4 G4 4.4 44. 44 N NN 44 44 N 3. N4 «.4 c8: 8408:0444 44 44 .0 ON 4... cm N4 N 44 S 8 N 4.4 2. s4 Em o 44 o 4 44 44. 4.4 N N4 44 44 NN 44. G4 N4 4442 ON 44 N4 N4 4.4 om .44 GN 44 44 N4. NN N4. 4.4 4.4 0822 N4 4.4 4 N 44 4:. 44 N 44 SN N4 NN 44. N 4.4 .408 4: 44 4 N4 44 4.4. 44 N 44 N 8 N I. 44 4.4 48,4 N 44 4 44 44 N4. 44 4N 44 44 N... N N4. N 44 mas o N o N 4.... N4. .4 NN N4 om o... NN 44 N 44 4.4.24: 4 N o 4 44 z. 44 4N N4 44 S N N4. 44 4 9: 493m 4 844 4 84m 4 844 4 N 444: 4N ca: 44 mm: 4 4.4.: may. 4.4 e N m N N N s N 4 N N 64% 4048 48» o 4048 4 546$ 844 4.0.8.4 .5 .2446: .4894 .6 SENS: 452 64424440 4534.444: ”40805540 .mco409m o4umE440 m 400 muouooucz0 mosuam 0:49 04 mo mos4c> coo: .m 04469 16 20% lower than 02p NUR at 2 years. However at age 6, 02p NUR accounted for 17% of total variance. This fluctua- tion may be the result of a fairly uniform post-planting response among populations which served to minimize popula- tion differences immediately after planting, the effects of which are decreasing with age from planting. In the ACCEL materials, variation attributable to populations within climatic regions (02p ACCEL) was in the same range as described for NUR materials (18% of total variation at age 6 years), and had increased somewhat since outplanting. In NUR height measurements, differences among popu- lations were detected in 5 out of 7 climatic regions at age 2 years; but at 6 years of age only western Colorado (WCOL) had significant variability among populations (Table 6). The two tallest populations (27B, 27C) in the NUR planta— tions as well as some of the shortest (16A, 35B 37A) were from this area. Based on 6-year heights, the fastest- growing population was 55% taller than the slowest-growing population. WCOL was also the most variable in ACCEL height measurements. At 6 years of age, one of the tallest (27B) and the shortest (5) populations in the ACCEL planta— tion were from this area, indicating that selection of proper source populations could increase height growth by 48%. Six—year ACCEL heights for seedlots from the Wasatch Mountains of Utah (WAS) and northern New Mexico (NNMEX) indicated significant differences among populations from 1.7 H N .4 N ON ca OH 44 ON mN o-d M GOO 000 ”3000000 VVNI‘OOONInONOMN O‘OOQCD $000044 0000 CW .4 N00 000 MMOU‘OOC 4» o 4» m 4» 0 ma vs mN va 0 v4 mn av mm mo cm ow no mv mm ma vm 5N mN 0v mv 5N 70 N5 h vN 34 mm mm ma an NH o o NN m ha mN m o m m «A h m mN m N o N on ma NN ov oN cm on 5N ON a VN o 0N ma MN ma mN om m Nd o mN mfi v4 mv 44 o 0 ca 0 NA #4 N m m o N N wxim mucuk 0343 49444 4 mvoua 034$ p 44 44 44 44 4N 4N 44 4N 44 N4 44 N4 N4 44 44 N4 44 44 N4 4N N4 44 44 4 44224 44 44 44 44 4N 44 N4 4N 44 44 44 444 44 44 44 N4 NN 4N N4 4N 44 44 44 444 44 N4 44 44 4N 44 44 NN N4 44 N4 444 44 44 N4 44 4N 44 44 4N 44 N4 N4 <44 444< 44 44 44 44 4N 44 N4 4N 44 N4 44 44 44 44 44 44 NN 44 44 4N 44 N4 44 44 44 N4 44 N4 4N 44 44 4N 44 44 44 444 NN 44 44 N4 4N N4 44 NN 44 44 44 <44 N4 44 44 44 4N 4N 44 4N 44 44 44 44 4N 44 44 44 4N N4 44 4N 44 N4 N4 4N x4222 44 44 N4 44 NN NN N4 NN 44 44 44 4N 4N 44 44 44 4N 4N N4 4N 44 44 44 4N N4 44 44 44 4N 4N 44 4N 44 NN N4 444 N4 44 44 44 4N 4N 44 NN N4 44 N4 <44 44 N4 44 44 NN 4N 44 4N 44 NN 44 4 4004 44 44 44 44 4N 44 44 4N 44 44 44 44 4N N4 N4 44 NN N4 44 44 N4 4N 44 4N4 44 44 N4 44 4N NN 44 4N N4 4N 44 2 6033040 meow: uvuoouoco :ONuaHSQom uo NNMEEDm .w wands 18 these regions also, withkmfirflnzdifferences of 23% and 25% between the tallest and shortest populations in each region, respectively. In NUR materials, variance due to families within populations (0 ) doubled from age 2 to age 5 and re- 2 f NUR mained at 20% of total variance through age 6 years. This change may be age-related but also may reflect a differen- tial family response to planting effects and test environ— ment. In ACCEL materials, family—within-population variance (02f ACCEL) decreased by l/2 between 2 and 5 years of age and then increased again from 5 to 6 years. At 6 years of age, error variance was much higher for the ACCEL than for the NUR plantation. Significant differences among families were observed in ll of the 44 populations represented in the NUR planta- tion. Difference in height of the tallest family in rela- tion to the shortest family ranged from 32% to 88% among populations. The tallest family in the experiment was seed- lot #8162 from population 27C (Table 7), in WCOL. Two families were included in this population sample; selection of the taller instead of the shorter family would give a growth differential of 26%. The shortest family was seed- lot #8034 from population 8 in eastern Colorado (ECOL), and was little more than 50% as tall as the tallest seedlot in that population. The 15 tallest families constitute about 10% of the total number of families, and are derived from l2 different populations in 5 different climatic regions. l9 HNNMHNN O I NNNNNHH O l o I N Q I osz wsHm a HOQum w uoHou umuHHom m. v.l v. H.I N.I N.| N. N. w. H. H. N.| H. H. m. H.| o N. v. m. v. m.1 m.1 v.1 v.I v.| 0.: H. H. H. H. H. N. N. h. N. m. n. N. 1w .oHocoza can masses uncum 0>HumHom N. H.| m. o N.| H.I m. o. H. v.1 m.| H.I H. v. o m. m. H. H.| H.| v. N. o m.1 v.l m.: 5.1 H. m H.1 o N. m. N. m. m. H. H. v. N. N. v. v. v. o. N. m. v. a. m. o. u» m wmaw u» m unmfio: x32 H. H.I N.0 H. N. N. N. o H.I m.| N.I H.t H. N. H. H. H. H. H.n N.I N.: N. N. N. H. H. N. N.| o H.1 H.I o N.1 H.I N.I N.I m.| m.l H.I N.I m.| N.| N.I N.| N.I «.1 v.1 v.1 m. H. H. N. N. H. N. H. H. m. v. m. u» o u> m u» h unmmw: Amuuc coHumuHaHomum musumumaswe NHsh muzumuomema NMMSCMh umOum uqu ou mxmo mxmo mmum umOum COmmmm mcH3Ouu coHum>mHm mosuHmcoq mcsuHumq msHm Hmmum w .uoHOU umm» o msHm a .uoHou umw» o mN\m HN\m mH\m me Hmofiocmzm osm mmmEmQ uwoum w>HumHmm .mu> o .mu> m .mu» N .unoflm: maz .mu> o .mu» m .mu» N .uanm: Amuom .mcoducHzmo; monumm oaHm vv you mucuonm chcoE:OuH>cm can zuzouo m:OE¢ mcoHumHouuoo mHQEHm .h mHnme 20 Fourteen of these seedlots originated in the climatic regions of WCOL, NNMEX, SNMEX and ARIZ. The remaining seedlot, #8088 came from population 13B in western Wyoming. Correlations between NUR height measurements and environmental variables at seed origin were low, (Table 7) although several factors correlated significantly with 5— and 6—year height. Latitude of seed source origin was significantly correlated with 5- and 6-year NUR height, while mean January temperature was significantly correlated with NUR height at all three measurement ages. ACCEL height was negatively and significantly correlated with relative frost damage and length of frost season. When 6-year height measurements were correlated with the same parameters within a given climatic region, a somewhat different pattern emerged (Table 8). Highly significant relationships between 6 year NUR and 6 year ACCEL height were present in western Colorado, but not in other regions. Although correlation coefficients were relatively high in eastern Colorado and northern New Mexico, small sample sizes precluded statistical significance at these levels. Environmental variables conditioned by eleva- tion were highly correlated with 6-year NUR and ACCEL height in ECOL and NNMEX and with both latitude and longitude in WCOL (Table 9). 21 Table 8. Correlation Coefficient Between 6-Year NUR Height and Climatic Parameters Climatic Regionl WAS VKIE. EIIH. NNMEX AEEZ Trait or Climatic Parameter Ed; 13 df 4 df 5 df 3 df 6-year ACCEL height .26 .75* -.78 .62 .36 Relative frost damage -.59 -.05 -.36 -.66 .52 Bud stage 5/1 .30 .75* .64 ~.33 .78 Bud stage 5/29 .29 .43 .80 .16 .87 Elevation —.54 .13 .44 -.80* —.51 Latitude .31 -.67* -.79* .08 —.11 Longitude .49 —.22 .11 .38 .33 Mean January temperature .58 .07 -.74 .82* .62 Mean July temperature .57 .17 -.43 .84* .50 Annual precipitation .58 .10 .57 -.78* .43 Growing season length -.17 .14 —.43 .81* .51 Frost free days .47 .19 -.43 .78* .68 Days from January 1 to last spring frost -.40 -.11 .48 -.60 -.51 *significant at P = 0.01 l ‘WASatch, western COLorado, Eastern COLorado, Northern New MEXico, ARIZona. 22 chNHm« .oowxmz zmz :uwcuuoz .oomuoaoo cumummm .oomuoqoo zumummz .coummaz .oam om. mo. «Hm.- ea. NH.- mm.. ..mm. mm\m mmmum 62m oo. mv.u .«Hm.u mm. em.. «a. om.- H\m mmmum 65m H¢.- mh.u ma. mm.- mv.n .4mm.n 4m.- momsmo amoum m>Humamm mmlm .mmlm. .mmlw mmflmmm wmlw ‘Mmlm. .mmlm 660658688 aflumawao no uflmwm N Hm< XQZZZ 400m HOUR mag 3 Hconom oHumEHHU .mumumEmumm oHumEHHU can uLmHm: Amou< Hmo>lw cmmBuwm ucmHonmmoo coHumHmuuoo .m mHnme 23 Foliage Color Foliage color, a function of the amount of epicu- ticular waxes on needle surfaces, was highly variable, and changed over the two years of observations. Highly signif- icant differences were detected at all levels of classifi- cation and during both 1974 and 1975 (Table 3). When blue plus steel blue trees were considered together in the fifth year, czr contributed 18%, 02p contributed 21% and 02f accounted for 14% of total vari- ance (Table 4). However, one year later, these components had changed to 27%, 20% and 16% for ozr, 02 and 02f, re— P spectively. When steel blue trees were considered alone, 02r remained fairly constant at about 12%, while 02p and ozr both increased from 12% to 20% of total variation from the fifth to the sixth year. Generally, longitude was negatively correlated with amount of foliar waxes at 6 years. As Table 5 indicates, northern New Mexico and eastern Colorado respectively, had the greatest percent of blue and steel blue trees for both years. With the excep- tion of seedlots #8065 and #8150 from Utah populations 11 and 25 respectively, the percentage of progeny with steel blue foliage was very low for Wyoming and the Uinta and Wasatch Mountains of Utah. Also, seedlots from Arizona showed almost no steel blue progenies at age 6 years. How- ever, the relative importance of 02r in steel blue foliage color is little better than 1/2 that of 02 and 02f at 6 P years of age. As Table 6 shows, population 29 from eastern 24 Colorado had the highest percent of steel blue trees, 46% at age 6 years, while pOpulation 5 from western Colorado was next with 38% steel blue trees at age 6. Variation among families within these pOpulations is extreme (Appendix A). In population 29, family values for percent steel blue progenies range from 5% for seedlot #8173 to 85% for seedlot #8176. Similar extremes are found in popula- tion 5 (range 0-78% steel blue trees) and population 39A (7-75% steel blue progenies) from northern New Mexico. Appendix A demonstrates the magnitude of change in foliage color between the fifth and sixth year after out- planting. Although a few seedlots remained constant or even decreased in percent of blue—needled progeny, most showed an increase in expression of blue foliage color. Even in the climatic regions with little expression of foliage color, that which was expressed was mostly in the sixth year after outplanting. Besides longitude, the only significant correlation involving foliage color was between percent blue trees and relative frost damage (r = 0.48, significant at 0.01 level). The available climatic data proved to be of little value in determining which environmental variables might act as a selective force for or against foliage color. When color data for each population for a given climatic region was correlated with extrapolated climatic data for that region, there were no significant relationships between foliage color or any environmental factor. 25 Relative Frost Damage Differences in relative frost damage (RFD) were highly significant among climatic regions (Table 3) with 02 r accounting for 26% of total variance (Table 4). The areas of western Wyoming and the Wasatch and Uinta Moun- tains of Utah in general exhibited significantly less RFD than did those climatic regions to the east (Table 5). Uinta populations were the least damaged while southern New Mexico populations suffered the most damage. Highly significant differences between populations within climatic regions accounted for 8% of total variance; differences were limited to populations within climatic regions of western Colorado and northern New Mexico (Table 6). Values ranged from 15 to 45 percent RFD for popula- tions 6 and 37A respectively, in western Colorado and from 29 to 47 percent for populations 28 and 36 respectively, in northern New Mexico. Analysis indicated that within a given population, relative frost damage sustained by individual families was fairly uniform, and accounted for a mere 3% of the total variance (Tables 3, 4). However, significant differences between families were identified in three different popu— lations (population 10C, 29 and 31C from western and east- ern Colorado and southern New Mexico, respectively). Other populations included families with mean RFD values which, although widely separated, were not significantly differ- ent at the 0.01 level. 26 Rangewide, RFD was generally highly positively correlated with elevation of source population, mean January temperature and mean annual precipitation, both of which are strongly influenced by elevation, although in Opposite ways. Height of ACCEL trees was significantly negatively correlated with RFD at all three measurement ages; while 2 year NUR height was significantly positively correlated with RFD, but not 5- or 6-year NUR height. Bud Phenology Differences in bud phenology were highly signifi- cant at all levels of classification and on all dates that phenological development was assessed (Table 3). As in— dicated by Table 4, ozr was relatively high at the begin- ning of bud flush (May 1, 1974), then dropped by more than 50 percent at the two intermediate dates of assessment, and then tripled again at the last assessment date. Con- versely, 02p increased by about 50 percent and 02f just about doubled between the first and second phenological assessments, remained rather high during the third, and then dropped to less than 10 percent of total variance by the time of the last assessment. The relative phenological stage at the beginning and ending of phenological develop- ment was highly influenced by climatic region of seed source, while population and family effects were of lesser importance. However, in the middle of active bud flush, population and family effects were more strongly expressed. 27 Seedlots from the more northerly and westerly climatic regions were the most retarded in phenology all during the period of assessment, while seedlots from the most southerly region (Arizona) were the most advanced (Table 5). Populations from western Colorado (climatic region 23) were highly variable throughout the period of bud flush. Indeed, population 27C was most advanced of all populations included in the ‘test eat all assessment dates, while population 37B had phenological scores similar to materials from Utah and Wyoming (Table 6). Significant differences among pOpulations from UINTA, WAS, ECOL, NNMEX and ARIZ were detectable only during the May 15 and May 21 assessment dates, although generally those populations which had lower initial phenological scores tended to re- main begind the others from the same climatic region. Differences among families within populations were present, not only in initial or final stage of phenological development but also in the pattern of development (Appen- dix A). In general, those seedlots which were more pheno- logically advanced on May 1 were more advanced on May 29. However, some families seemed to accelerate phenological development and, although they were retarded at the begin- ning, equalled or surpassed other families which were further along in development in the early assessment dates. Examples include seedlot #8305 in population 50A from the Uinta Mountains. Although retarded in early development 28 relative to seedlot #8309 from the same population, by the last assessment date, the slower seedlot had over taken the faster seedlot in stage of development. Rela- tive rates of phenological development among families in population 10A from western Colorado were also very dif- ferent, where the most retarded family at the initial assessment (#8047) was the most developed at the last assessment. Rangewide, bud stage increased with increasing elevation but decreased with increasing latitude and longitude (Table 7). Furthermore, these trends were present at all four phenological assessment dates. How- ever, of the environmental variables which are conditioned by elevation, only mean January temperature and mean annual precipitation were significantly correlated with phenological development. In general, the more advanced a seedlot was phenologically, the more relative frost damage it incurred. Selection Index Analysis of variance of family selection index values indicated highly significant differences among climatic regions (Table 3), which accounted for about 27% of the total variance (Table 4). The pattern of regional ranking (Table 5) indicated that, in general, blue spruce from the western part of its range had lower SEI values than material from the east. The climatic regions of 29 northern and southern New Mexico had the highest SEI values. Highly significant differences among populations within climatic regions accounted for about one-third of the total variation. As in most other characters, western Colorado exhibited the most variability among populations, with SEI values ranging from the lowest (SEI = 135, popula- tion 16A) to the highest (SEI = 334, population 278). As well, population 27C, with the second-highest SEI value also came from western Colorado. As Table 6 indicates, significant among-population SEI differences were also found in eastern Colorado, northern New Mexico and southern New Mexico. Selection index values for individual families were quite variable and ranged from SEI = 117 (seedlot #8120, population 16A, WCOL) to SEI = 353 (seedlot #8176, population 29, ECOL). Variation within populations accounted for 40% of the total variance, and was concen— trated in 13 populations (Appendix A), where differences between family SEI values exceeded 74 (lsd 0.01). The greatest variation occurred in population 39B, NNMEX, be- tween seedlots #8268 and #8265, where the difference was 162 SEI units. The most promising open-pollinated fami- lies were chosen by SEI value. Arbitrarily, all families with SEI values exceeding 250 were chosen as outstanding phenotypes suitable for inclusion in future breeding work. 30 These 30 families from 14 populations in 4 climatic regions are summarized in Table 10. It is interesting to note that two-thirds of these selected families come from western Colorado and adjacent northern New Mexico. DISCUSSION Genetic variation in blue spruce is complex. Re- sults revealed geographic differentiation which correspond- ed to regional tOpographic and climatic patterns in the central and southern Rocky Mountains. Analysis of 15 growth and foliage color characters indicated that for 12 of these, Utah and wyoming populations were different from populations to the east and south. In addition, materials from populations in western Colorado exhibited extreme variability, indicating strong selection pressures over relatively short distances. However, estimates of vari- ance components for individual traits indicated that significant genetic variation exists not only among climatic regions, but also among pOpulations within several of these regions, as well as among individual open-pollinated families within many populations. In addition, the relative importance of individual components changed over time for growth characters. Differences in expression of geographic variation were found between ACCEL materials raised under optimal greenhouse conditions, which included long photoperiods, and NUR materials raised in nursery beds under natural Table 10. 31 Selection Index Values of Selected Blue Spruce Families 6-Year Height Climatic Popu— Selection cm. Regionl lation Seedlot Index NUR ACCEL WCOL 5 8016 262 36 53 8019 271 39 72 8021 329 43 68 10A 8051 258 43 94 108 8053 286 44 72 15 8111 251 49 85 27B 8160 291 58 98 8161 337 54 91 27C 8162 311 62 90 8163 262 51 93 ECOL 8 8035 267 38 89 24 8145 259 42 85 29 8172 286 32 78 8176 353 46 79 8177 279 37 72 NNMEX 28 8166 325 49 86 8167 268 48 69 8169 267 50 94 8170 301 48 91 39A 8258 343 44 84 8261 298 53 95 8262 318 57 87 39B 8266 259 41 88 8268 287 54 94 53 8326 333 38 88 8329 289 40 86 8331 289 44 74 SNMEX 9 8044 289 56 90 8045 266 53 88 8046 291 48 86 1. Western COLorado, Southern New MEXico. Eastern COLorado, 64h§uer1bxxa Color - % Blue Steel Blue 83 58 74 53 100 78 65 25 90 45 75 10 70 25 100 50 69 44 67 17 78 33 67 33 80 65 100 85 79 58 95 55 84 32 74 16 89 37 100 75 68 42 100 33 72 28 68 32 100 71 89 42 89 47 85 20 74 16 89 33 Northern New MEXico, 32 photoperiod and temperature regimes. In the ACCEL treat- ment, growth differences among broad climatic regions were not discernable. Furthermore, growth differences among families became attenuated after outplanting. In NUR materials, regional effects decreased over the first 6 years while population differences remained fairly con- stant, whereas ACCEL materials did not indicate any age trends in regional or population differences. Genetic variation among pOpulations within climatic 2 p) years of age in both NUR and ACCEL plantations. This is in regions (0 remained fairly constant between 2 and 6 contrast to the increase in variance among populations of ponderosa pine (Namkoong and Conkle 1976; Wang and Patee 1976) and Douglas-fir (Namkoong et 31. 1972) during early growth. However, in the ponderosa pine and Douglas-fir studies, seed sources from only a portion of the species range were compared within the general climatic region of the collections, while the Michigan blue spruce plantations were quite different from environmental conditions at the seed source origins. Considerable genotype x environmental interaction among populations can be expected when trees are grown outside their natural ranges (Morgenstern and Teich 1960; Morgenstern 1976), and since genetic variances for climatic regions, pOpulations within regions and fami- lies within populations indicated substantial selective forces acting at each level, such interactions could be expressed for climatic regions also. 33 It is possible that blue spruce could have a unique pattern of time—mediated genetic variation which is unlike ponderosa pine or Douglas fir. Namkoong gt 21. (1972) observed that high variances within populations of Douglas fir seemed to be limited to that period in the life cycle when competition for moisture and nutrients with grasses and brush was at a maximum, and once the trees achieved dominance over plant competition, differences among families were reduced. This expression of ecological dominance is illustrated in the silvical characteristics of Douglas fir and ponderosa pine. Both are intolerant of competition and require mineral soil and direct sunlight for seedling survival and stand establishment (Fowells 1965). In comparison, blue spruce is a more tolerant species, which can become established and survive in an understory position (Baker 1949). Perhaps time-mediated changes in components of genetic variance in blue spruce reflect growth patterns at different stages of its life cycle. Further data will determine whether blue spruce conforms to this model. The sharp contrast between time trends in variance components for NUR and ACCEL plantations gives evidence of disequilibrium of natural growth processes due to early ACCEL growth conditions. In the ACCEL plantation, vari- ance among populations (02p) increased steadily over time like ponderosa pine and Douglas fir. Family response of ACCEL materials was likewise more representative of time 34 trends for family variance in ponderosa pine, Douglas fir and eastern white pine (Kriebel gt gt. 1972). It seems likely that NUR time trends more accurately reflect the true pattern of variation in blue spruce, while ACCEL patterns are unnatural and result from early growth differ- ences conditioned by long photoperiods in the ACCEL treat- ment. Early differences were pointed out by Hanover and Reicosky (1972) and Hanover (1975), where many of the same blue spruce populations included in this study were ranked after six months in the greenhouses and two years in the nursery. Although many of the tallest trees in the green- houses were from Utah sources, Colorado and Arizona trees were tallest in the nursery. The clinal pattern of early ACCEL height growth variation differs from growth trends of other, nursery-grown Rocky Mountain tree species, where latitude was negatively correlated with height growth (Kung and Wright 1972; Wright 1976). Upon germination, blue spruce seedlings from Utah and Wyoming grow very rapidly under long photoperiods. The mechanism responsible for this is probably the ability of these sources to differen- tiate needle primordia at least as fast as southern sources when day length is not limiting. Such a mechanism was described in Sitka spruce by Pollard gt gt. (1975), where, for a short period after budset, northern provenances under- went more rapid bud morphogenesis than did southern prove- nances. However, as photOperiod became more limiting, southern provenances produced more needle primordia than 35 northern ones. Lanner (1976) has suggested that this short period of rapid bud morphogenesis is an adaptation to harsh climates with short growing season, a description which fits Utah blue spruce habitats. This would be facilitated by a large apical dome capable of producing needle pri- morida rapidly (Cannell gt gt. 1976). A biological feed- back mechanism is then responsible for subsequent growth performance of the ACCEL seedlings. Larger seedlings tend to produce larger apical buds with more primordial stem units, which in turn produce more photosynthetic tissue for subsequent bud develOpment (Pollard 1974). When out- planted under natural photoperiods, the relative advantage of northern sources should disappear and southern sources, with the ability to produce stem units at photoperiods when northern ones stOp, will in time exhibit faster growth. In contrast, NUR seedlings of southern sources will have had the advantage of prolonged primordial production after cessation of initiation by more northerly sources, with the result being that northern sources will always be shorter. However, other factors such as differential physiological response to temperature, nutrient levels and water stress are also important in growth performance. Young and Hanover (1978) have shown that nitrogen levels and moisture stress may induce dormancy in blue spruce seedlings, even under continuous light. Pollard and Logan (1977) found that temperature strongly affected the rate of needle primordia initiation in white spruce. Perhaps a combination of 36 factors might explain why seedlots from populations 27B and 27C from western Colorado performed so well in both NUR and ACCEL experiments. Results of water stress studies (Chapter 3) indicate that trees from these populations may be able to carry on essential gas exchange (and presumably photosynthesis) at soil moisture levels prohibitive to trees of other populations. Thermal efficiency, or the threshold temperatures at which different seed sources can actively undergo cell division may also vary geographically, as well as within populations. Boyer (1976) reported that such variation existed within stands of loblolly pine, and could account for height growth differences in progeny tests. Such factors may explain why Wyoming and Utah popu- lations were similar in phenological and foliage color traits, but differed in growth rate in the NUR experiment. Also, eastern Colorado populations exhibited phenological and foliage color characters like western Colorado, New Mexico and Arizona populations, but grew as slowly as Utah populations. Such regional trends are reflections of adaptations to selection pressures conditioned by broad regional climatic regimes. Strong east—west differences were apparent in phenology-related traits. Utah and Wyoming pOpulations flushed late and were less susceptible to late spring frosts than populations to the east. However, in contrast to white spruce (Nienstaedt and King 1970; Nienstaedt 1973), the tallest families were also the early flushing ones, and 37 were most damaged by late spring frosts. This fundamental difference between closely-related species is probably due to very basic differences in adaptation to regional cli- matic patterns. After mean daily temperatures exceed freezing in the Lake States and adjacent Canada, there is a period of 6 to 10 weeks when arctic airmasses may bring cold air to the region (Hare and Thomas 1974). A combina- tion of advective and radiative frost, in the form of long- wave re-radiation of energy from the earth's surface then may cause below—freezing temperatures in the region as the cold airmass passes (Findlay 1970). Provenance variation in time of budburst and spring frost damage has been re- ported in white spruce (Logan and Pollard 1975) and balsam fir (Lester gt gt. 1977). Further study of Ontario white spruce has indicated that most of the genetic variance in spring phenology occurred among families within stands, rather than among stands (Pollard and Ying 1979) and was interpreted to mean that long—term population fitness to unpredictable spring temperature regimes was maintained by much individual tree variability within a given climatic region. However, Morgenstern (1969) indicated that in a rangewide collection of black spruce, differences in spring growth were expressed clinally, with very large differences among forest regions, but very little among or within stand variability. Blue spruce, which apparently has adapted to quite different climatic patterns than white or black spruce, 38 reacted much differently when planted in Michigan. The arbitrary division of the entire blue spruce range into regions based upon broad climatic patterns proved useful, and results suggest very real regional differences. Large regional variance components in several characters indicat- ed that such regional differences do exist, especially with respect to early NUR height growth, foliage color and phenology-related traits. In contrast to the general pattern reported in white spruce (Nienstaedt and King 1970), height growth at age 6 was significantly correlated with relative frost damage and phenology. The influence of regional climate on frost damage and phenology may be ex— plained in part by broad-scale patterns of spring airmass movement. The Utah and Wyoming pOpulations break bud dor- mancy later, and subsequently suffer less spring frost damage in Michigan than trees from other areas because they are adapted to a much slower spring warming rate at the seed origin. Utah and Wyoming are subject to spring air- masses of predominately Pacific origin (Trewartha 1966), and average temperatures increase slowly as the airmass source areas (the north and central Pacific Ocean) accumu- late heat prOportional to seasonal changes in solar radia- tion intensity. Furthermore, occasional incursions of arctic air may bring spring frosts to these areas, result— ing in selection for a higher threshold temperature to initiate growth. These airmasses may be substantially altered, both in temperature and trajectory as they pass 39 over the Utah and Wyoming mountains, with a subsequently different effect in the Colorado and New Mexico mountains (Colson 1949). . In contrast, airmasses moving into the southern and central Rocky Mountains during the spring originate in the Gulf of Mexico, and are warmer and more moist than Pacific air (Trewartha 1966; Wardle 1968). Such airmass differ- ences may be at least partly responsible for the "Early Ridge" observed by Caprio (1966), where at a given eleva— tion, phenological events were consistently in advance of similar responses in areas to the east and west of the Continental Divide area in Colorado and New Mexico. Caprio (1966, 1967) reported other anomolous areas in the lee of mountain ranges, where foehn or Chinook patterns modify temperatures, and in areas of high solar radiation intens- ity, which tends to advance phenological activity by heat- ing up plant tissue above ambient temperatures. Because of intensive selection pressures in the mountainous west, differentiation of local populations has been suggested in Douglas fir (Irgens-Moller 1967; Namkoong gt gt. 1972; Rehfeldt 1974a, 1978; White gt gt. 1978), white fir (Wright gt gt. 1971; Hamrick and Libby 1972; Hamrick 1976) and ponderosa pine (Wang and Patee 1974, 1976; Madsen and Blake 1977; Mitton gt gt. 1977). This indicates that, although gene flow between populations is facilitated by pollen dispersal, natural selection, condi- tioned by local climatic regimes, is the overriding factor 40 which determines changes in gene frequencies. In all of the above species, variation is also strongly expressed on a regional (ecotypic) level as well as on an individual family level. This variation must be taken into account in any tree improvement program. The one species which does not fit this pattern is western white pine, although early results seemed to indicate strong local differentia- tion into discrete populations (Squillace and Bingham 1958). However, subsequent studies by Townsend gt gt. (1975) and Steinhoff (1979) showed no evidence of population differ- entiation, although the latter work indicated much genetic variability among trees within any given stand. Rehfeldt (1979) suggested that phenotypic plasticity could account for western white pine's adaptability to the wide range of environments in which it grows. Partitioning the variance into component parts in blue spruce gave values which seem quite reasonable when compared with results from studies in other species. It is quite possible that the variances are somewhat inflated, and had other plantations been established, genotype X environment interactions might have reduced the variance due to genetic components significantly (Rehfeldt 1974b). However, Wright (1973) summarized the genotype X environ- mental interactions in a number of studies involving Rocky Mountain tree species in the north-central United States and concluded that, although some winter—hardiness traits indicated north-south interactions, variances due to 41 genotype and plantation were stronger than interactions. Furthermore, King (1965a, b) reported that genotype X plantation interactions in Scotch pine were small when compared to genetic variance. At present there is no reason to suspect that blue spruce is any different in this respect. Results of this study indicate that blue spruce from western Colorado is genetically the most variable over the range of the species. Significant differences among populations were present in all sixteen measured characters, as well as in the composite selection index (SEI). Popula- tions from northern New Mexico exhibited variability in 10 of the measured characters; however, NUR height differences were not apparent among populations from this region. Popu- lations from the Front Range in Colorado showed variability in many of the same traits as the northern New Mexico popu- lations suggesting, perhaps, the same selective forces acting in the eastern part of the Colorado Rockies as in northern New Mexico. The reason for greater differentiation in western Colorado may lie in the large topographical variation throughout the region. Baker (1944) suggested that the area could be subdivided into several climatic provinces, but in spite of a number of sampling stations, he found that data were still too limited to accurately delineate separate entities. However, the diverse physiographical elements suggest the possibilitycflfsignificantndcroclimatic 42 variation over relatively short geographical distances. For example, there are high mountain ranges in several ranks near the continental crest, interspersed with high valleys. The western part of the area is composed of high plateaus and deep canyons of the Colorado and Grand River drainages. In contrast, the Wasatch Mountains of Utah are a rather narrow range and present a fairly uniform barrier to airmasses approaching from the west. Likewise, the eastern ranges of the Rockies which comprise eastern Colo- rado are similarly more uniform in that they are made up of one or two narrow north-south ranges with a single crest. The large physiographic diversity of western Colo- rado increases environmental complexity, where changes in aspect, elevation, moisture regime and soil type interact to vary habitats in neighboring areas (Geiger 1966). It is possible, of course, that differences within other climatic regions would have been more strongly ex- pressed if more populations had been sampled. The area of western Colorado was the most intensively sampled, with 14 pOpulations of two or more trees included. In contrast, the Wasatch Mountains of Utah provided only 7 populations. Although foliage color was not significantly cor- related with environmental variables, it is interesting that the proposed physiological functions of and the developmental changes in the amounts of epicuticular waxes is compatible with a scheme of ecological adaptability which reflects the time—mediated changes in genetic control 43 of height growth. Jeffree gt gt. (1971) postulated that the increase in foliar transpiration commonly observed when surface wax- es were removed from plant leaves was due to the increased cross-sectional area in the stomatal antechamber available for diffusion of water vapor, and to the decrease in dif- fusive resistance once these waxes were removed. They cal- culated that in Sitka spruce, wax in the stomatal ante- chambers reduced transpiration to one-third the rate possible had no wax been plugging up the orifice. They also postulated that there was a 32% reduction in phyto- synthesis due to obstruction to the CO2 diffusion pathway. However, as Reicosky and Hanover (1976) observed, wax plugs are present in the stomatal antechamber of both glaucous and nonglaucous trees (Reicosky 1974) giving evidence that in blue spruce, surface waxes are not an adaptation primarily for control of water loss. Instead, the function of blue spruce epicuticular waxes seems to be one of protection against certain wavelengths of solar radiation. Reicosky and Hanover (1978) showed that glaucous foliage reflected a significantly higher percent- age of radiation from the UV and blue portions of the electromagnetic spectrum than did nonglaucous foliage. They suggested that this feature might be advantageous in environments where trees are subject to high levels or long periods of solar radiation. However, results of this study support the general observation that the amount of 44 epicuticular wax produced on new needles increases with age (Hanover 1976; Hanover and Reicosky 1978). If foliage color is truly aux adaptive trait, why does maximum expres- sion occur years after the very critical period of seedling establishment is past? If, however, seedlings are adapted to spend the earliest part of their life as somewhat tolerant understory, and are afforded partial protection by the older, existing stand whose production of surface waxes are fully expressed, then there is no immediate need for protection from solar radiation. Engelmann spruce is probably closely related to blue spruce (Wright 1955; Daubenmire 1972) but, in the southern and central Rocky Mountains generally grows at higher elevations than blue spruce (Jones and Bernard 1977). Ronco (1970) reported that high seedling mortality in Engelmann spruce planta— tions above 10,000 feet elevation in the Rocky Mountains was probably due to solarization by high light intensities. Ronco found that seedlings of Englemann spruce responded to increasing light intensities like other shade-tolerant species -- i.e., light saturation of photosynthesis occur- red at about one—third the level of full sunlight, whereas photosynthesis in lodgepole pine was not light-saturated even at full sunlight. In the southern Rocky Mountains where both Engelmann and blue spruce reach their maximum altitudinal distributions, Engelmann spruce commonly has blue foliage color, while blue spruce may or may not (Jones and Bernard 1977). When Engelmann spruce seedlings were 45 growing in the nursery in East Lansing, they could be easily recognized by their intense blue foliage even at 2 years of age. Perhaps this is an adaptation to the high levels of solar radiation this species experiences in the southern Rocky Mountains at the elevations where it is found, even though it is a very tolerant species and naturally regenerates under a forest canopy. Blue spruce, however, is not generally found at elevations over 10,000 feet and may not have had the same selective pressure to develop foliage waxes at such an early stage in its life cycle. Indeed, the level of variability of foliage color among families within populations indicates that this trait may not be under as intensive selection pressure as pheno— logical traits. However, the frequency of occurrence of blue foliage in the central Rocky Mountains as compared to Utah and Wyoming suggests adaptative significance between climatic regions. The selection index used here to determine which blue spruce families are the most desirable for inclusion in a breeding program is necessarily quite arbitrary, and certainly will not be adequate for selecting materials for the various special uses for which genetically improved strains of blue spruce might be needed. Height growth was considered an important character, and both NUR and ACCEL heights were equally weighted. However, for ornamental horticultural use, growth rate may be of little importance. It is conceivable that foliage color, crown shape and pest 46 resistance may be important, and slow growth may be desir- able. Slow-growing trees will take much longer to dominate the aesthetic landscape, and may have a longer useful life- span as a component part in landscape design, both reduc— ing maintenance costs (trimming, pruning) and removal/ replacement costs. If, however, trees are to be selected for their performance in wind protection, then aesthetic values such as color will necessarily become secondary to height growth, resistance to dessication and tolerance to pesticides, road salts, etc. The level of variability contained within populations for selection index values is encouraging, and suggests that progeny testing and family selection is the most efficient method of exploiting this variability. CHAPTER 2. SPECIES DIFFERENCES BETWEEN BLUE AND ENGELMANN SPRUCE INTRODUCTION Engelmann spruce (Picea engelmannii Parry) is a wide-ranging species commonly found in the Rocky Mountains at higher elevations in cold, humid habitats (Pearson 1920; Bates 1924; Alexander 1958; Fowler and Roche 1975). In contrast, blue spruce (P. pungens Engelm.), a very closely related species (Wright 1955) generally inhabits a lower elevational zone in the southern and central Rocky Mountains than Engelmann spruce (Pearson 1931). However, occasional areas of range overlap do occur (Daubenmire 1972; Hanover 1975), and because of close morphological similarity, blue and Engelmann spruce are commonly misidentified (Marco 1931; Reed and Freytag 1949; Jones and Bernard 1977). Despite the sympatry of ranges and presumably close phylogenetic relationships, the two species are quite distinct in physiological response to environmental factors when plant— ed outside their natural habitats (Pearson 1920, 1931). Taxonomic keys have commonly used such characters as cone length, needle sharpness and number of resin canals in needle cross-section to differentiate between the two species (Jones and Bernard 1977). Marco (1931) found that needle cross-sections gave misleading results when used to estimate resin sac numbers because resin sacs were dis- continuous over the length of the needle. Reed and Freytag 47 48 (1949) reported that blue spruce from southwestern Wyoming had three times the number of resin sacs per needle as Engelmann spruce. Furthermore, blue spruce resin sacs were three times as long as those of Engelmann spruce. Habeck and Weaver (1969) attempted to characterize suspected blue-Engelmann hybrid populations by analysis of cortical oleoresins. Daubenmire (1972) and Hanover (1975) used cone morphological characters to distinguish populations which were of intermediate and pure blue and Engelmann spruce composition. Daubenmire (1972) concluded from his evidence that blue and Engelmann spruce could occupy the same site at higher elevations in the blue spruce range and still retain their species identity. Evidence of strong incompatibility between blue and Engelmann spruce was reported by Fechner and Clark (1969) and Kossuth and Fechner (1973). Taylor, gt gt. (1975) analyzed phenolic compounds of blue and Engelmann spruce foliage from several sympatric populations and concluded that although natural hybridization does occur, it is infrequent. Recently, Jones and Bernard (1977) presented aids to distinguish the two species in the southwest by means of gross morphological characters, which, when used in conjunction with one another, make field identification of mature trees easier. However, in the seedling stage of development, species identification can be difficult. Marco (1931) and Jones and Bernard (1977) suggested that seedlings could be differentiated by the bristly appearance 49 of blue spruce as opposed to the soft, drooping look of Engelmann spruce. Marco concluded that the angle of growth of the twigs and arching of needles gave Engelmann spruce a "flattened spray" effect, while in blue spruce the needles "bristle from all sides of the twig". When a rangewide study of geographic variation in blue spruce was initiated at Michigan State University in 1969 (Hanover and Reicosky 1972; Hanover 1975) the inclu- sion of several Engelmann spruce seedlots gave the oppor- tunity to study in more detail materials of both species from different geographic origins. Since the seedlots were growing in uniform environments (i.e., genetic test plantations, common nursery beds and greenhouse) species differences in physiology and morphology could be inves- tigated. The following report is a compilation of several studies on genetically similar plant materials which have been grown and outplanted under uniform environmental conditions, thus allowing maximum expression of species and seedlot characters. MATERIALS AND METHODS Eleven Open—pollinated seedlots of Engelmann spruce from five populations were identified from collection records and morphological appearance in Michigan nursery beds. These were characterized by their drooping branches and soft foliage appearance, in contrast to the straight planar branching and stiff, bristly appearance of blue 50 spruce seedlots. Six Engelmann and three blue spruce seedlots came from population 36 in northern New Mexico (Table 11). One blue and one Engelmann seedlot were col- lected from population 57 in southeastern Arizona, and three seedlots from two populations (47, 58) represented Engelmann spruce from southern New Mexico. The remaining Engelmann seedlot represents population 41 from southeast- ern Utah. Height Growth Seedlings of blue and Engelmann seedlots were grown under accelerated growth conditions described by Hanover and Reicosky (1972). Four-tree plots of each seedlot were outplanted in three replicates of a randomized complete block design genetic test plantation near Battle Creek, Michigan in September 1970. This experiment is designated the ACCEL plantation in the following report. Progenies from the same seedlots were raised in an East Lansing nursery (Hanover 1975) and were outplanted adja— cent to and in the same design as the ACCEL plantation. The nursery—grown experiment is referred to as the NUR plantation in this report. ACCEL seedlings were measured at 3 and 6 months of age prior to outplanting (Hanover and Reicosky 1972) and at 2, 5 and 6 years of age in the plantation. NUR seedlings were measured at 2 years of age in the planta— tion. All plot trees were measured and the mean seedlot height was used for comparison. 51 3 mH.NOH Z hm.mm OhHm OOHXOE EOZ CCMEHmmcm mm vmmm 3 mH.eoa z hm.mm onHm ooexoz zoz ccmsaomom mm mmmm 3 mm.moa Z ho.mm vomm mCONHH< CCMEHOOCm hm mvmw 3 mm.mOH Z h©.mm vowm MCONHH4 OSHm hm hvmw 3 mm.boa Z MH.mm mvom OOHXOZ BOZ CCmEHmmcm NV hmmm 3 om.aoa z mm.em Hamm ate: attsfiomom He Name 3 Nv.mOH Z om.©m ommm OUMHOHOU CCMEHmmcm mm mmmm 3 Nv.mOH Z om.om comm OUGHOHOU CCMEHmmcm mm ommm 3 Nv.mOH Z om.mm wmmm OUMHOHOU CcmEHmmcm om mmmm 3 Nv.moH Z om.©m ammm OUMHOHOU CCGEHOOCM mm vmmm 3 Nv.mOH Z Om.wm wmmm OUMHOHOU CCGEmeCm mm mmmm 3 Nv.mOH Z om.wm mmmm OCQMOHOU CCMEmeCm mm Nmmm mmmummo MWflflflMMfl muoumz ououm meoomm coHumHsmom MMMmMMW opsuHmcoq mpsuHumq coHum>me .coHuowHHOO mousom boom mosudm msHm oonmmcom CH pmpoHocH muoHpmom oosudm :coEHmmcm mo mchHHO .HH mHnme 52 Terminal Shoot Elongation In spring 1973, 16 blue and 6 Engelmann seedlots (Table 12) were selected from the nursery materials and transplanted in an adjacent bed in a replicated complete block design, with four trees per plot in each of four replicates. Prior to the 1975 growing season, a straight pin was inserted in the twig of each tree below the termi- nal bud for an index mark. Shoot extension was measured to the nearest millimeter at approximately weekly inter- vals until elongation ceased. Mean plot values were used to determine seedlot cumulative height growth, and a cumulative height growth curve was plotted from the data. Concurrent temperature data were used to calculate accumu- lation of growing—degree-days above a base temperature of 5.6C (42F) in an effort to discern critical heat units required for leader elongation. Frost Damage On May 18, 1973 the ACCEL plantation at Kellogg Forest was subjected to a killing frost (minimum tempera- ture, -3C). Indications of widespread differences among blue and Engelmann seedlots in frost damage to new growth led to a general damage assessment on June 9, 1973. By this date undamaged buds had flushed completely; those buds killed by the cold had dried and were quite conspicu- ous. Damage was estimated visually and graded in the following manner: 53 MNH 3 mH.NOH z mm.mm omHm OUmez 302 mm vmmm NvH 3 mm.moH z nw.mm vomm mcoNHud hm mvmw mm 3 Nm.NOH z MH.mm mwom OOmeE 3mz be thm NOH 3 Nv.mOH z om.mm mmmN OUonZ 3oz om mmmm vOH 3 Nv.mOH z om.om mmmN OOwaE 3oz mm mmmm HOH 3 Nv.mOH z om.om ommN OOonz 302 mm MMNm mosumm camEHmmcm mNH 3 mm.mOH z om.ov movN opmuoHOO mm 55mm va 3 mm.m0H z oo.ov movm opmuoHou mm mhmm omm 3 mm.moH z mo.mm vomm mcoNHum mm hvmm NHN 3 oo.ooH z oo.om comm 00me2 3oz 4mm Hon MNN 3 00.00H z oo.wm oomN OOmez 3oz 4mm mmmm mom 3 Nv.m0H z om.om mmmm oonoE 3oz mm ova mHN 3 oo.moH z mm.mm moVN oowuoHoo Ohm mon HNN 3 oo.m0H z mn.nm movN OpoHoHou mmN Hon mmH 3 Nm.HHH z mm.mm HmmH so»: mm mva mom 3 mo.ooH z mm.mm ommN opouoHOO «N thm moH 3 m>.oHH z mo.me mmmH mvcHEON3 mH wNHm mma z om.oaa z om.me mama mcfleosz «ma «mom mea z om.oaa z om.me mmma oceeosz «ma atom omH 3 Nm.mOH z mm.mm VHmN oomuoHou m mmom NmH 3 om.NOH z Nv.mm onm OpouoHou o eNom NON 3 om.>OH z >v.mm QHMN opmuoHOO m NNom monumm msHm EE .man momwmoo momumoo mumuwz mumum :oHumH uoprmm coHummcon umpomq mpzuHmcoq opsuHumq coHuo>mHm Ismom m>HumHsEso .ucmEHumdxm coHummcon uoonm Nummuoz CH pmpoHocH muoHpmom oooudm ccmEHmmcm pom oSHm mo mchHuo .NH meoB 54 Frost Damage Grade Condition of Tree l - less than 1/3 of all new shoots killed - more than 1/3 but less than 2/3 of all new shoots killed 3 - more than 2/3 of all new shoots killed Individual plot trees were graded for frost damage and a plot mean was calculated. To make average damage values more meaningful, mean seedlot values were distribut- ed from 0 to 100, giving a relative frost damage index which was continuous over the range of data. A regression was calculated using damage value as the independent vari- able and relative frost damage (RFD) as the dependent variable, as follows: Independent Variable Dependent Variable frost damage 1.0 0% relative frost damage frost damage 2.0 50% relative frost damage frost damage 3.0 100% relative frost damage The resulting linear regression equation was: 9 = sox-so Phenology At four different dates in May 1974 the ACCEL plantation was assessed for stage of phenological develop- ment, or phenophase. For convenience and accuracy, six easily recognizable bud stages were scored, corresponding closely to Nienstaedt and King's (1970) phenophases for 55 white spruce (g. glauca Moench Voss): Bud Stage Condition of Buds 1 winter resting bud 2 bud beginning to swell 3 bud fully swollen, needles inside 4 needles bursting through scales 5 shoot elongating, needles closely appressed 6 shoot elongated, needles free Each individual plot tree was scored and seedlot means were calculated by averaging plot values over all replications. Temperature data were used to calculate growing—degree-days above a base temperature of 5.6C (42F). Needle Morphology Twenty-three blue spruce and nine Engelmann spruce . seedlots were selected for study from the rangewide collec- tion of open—pollinated families and bulk seedlots. Sample trees were 2/1 nursery stock from unreplicated transplant beds. Seedlot numbers and origins are summarized in Table 13. Blue spruce seedlots consisted of two Open-pollinated families from each of 11 populations and one bulk lot from a twelfth pOpulation. For Engelmann spruce, in addition to seven bulk seedlots, two open-pollinated families from a single population were analyzed. Each family was repre- sented by two trees, while all bulk populations included four trees. In this way, population comparisons were based on equal numbers of trees. A lateral branch from 56 Table 13. Blue and Engelmann Spruce Seed Sources Used in Needle Morphology Study. POPULATION Blue Spruce l 10 ll 13 26 30 31 39 Engelmann Spruce 36 47 75 70-55 70-90 70-109 3255 3259 ORIGIN Roosevelt N F, Utah Eagle County, Colorado Manti-LaSal N F, Utah Sublette County, Utah Sanpete County, Utah Pitkin County, Colorado Greenlee County, Arizona Rio Arriba County, New Mexico Carson N F, New Mexico Gila N F, New Mexico Madison County, Montana Kaniksu N F, Idaho Beaverhead N F, Montana Kootenai N F, Montana Cache N F, Idaho Wenatchee N E, Washington 57 the south side of each tree was removed and four needles from the previous year’s growth were taken from the mid- portion. Using a binocular dissecting microscope with calibrated eyepiece, nine characters were assessed on each needle. Included were total needle length, width of four needle sides, number of stomates along a 1.1 mm center portion of each needle surface, number of resin sacs in upper, middle and lower one-third of needle, individual resin sac length. From this data stomates -mm_ and percent of twice needle length covered by resin sacs were calculated. Analysis of variance for each character was calculated after Kempthorne's (1957) method for hierarchal classification with unequal subclasses; percent of total variance contributed by levels of classification were calculated and when possible, tests of significance be- tween levels were made. 58 RESULTS Generally blue spruce seedlots grew taller than Engelmann spruce in the ACCEL plantation. Although mean- ingful statistical analysis is frustrated by large sub- class differences, blue spruce mean six-year height was about 25 percent greater than that of Engelmann spruce (Table 14). Table 14. Mean Heights of Blue and Engelmann Spruce Seedlots from Greenhouse, Nursery and Subsequent Field Plantings. Blue Spruce Engelmann Spruce Age, Treatment Hetght, cm. Height, cm. 3-month, ACCEL 3.4 2.4 6—month, ACCEL 22.5 17.2 2-year, ACCEL 26.3 22.3 5-year, ACCEL 58.5 40.6 6-year, ACCEL 77.0 52.1 2—year, NUR 13.9 15.0 5-year, NUR 31.4 32.0 6-year, NUR 43.7 41.1 However, there was substantial variation among Engelmann seedlots included in the progeny test. Seedlots 8236 and 8348 were the tallest Engelmann progenies, with mean heights equal to the mean blue spruce height (Table 15). Species differences accentuated following outplanting. 59 av Ov NH Ov Hm mH mm mN mH In 1| mH Om mv OH OO mm OH Om ON OH I: II mH mm mm VH mm ON NH Hv mm vH NO mm NH I: I: OH mm ON OH Om ON NH :1 1| VH III II Add meow» O muov> m mb13> VN NV mv Om mm mm Ow Om NO HN NV VN Om HO HO Om NV whoow:m Nm mN NM mm vm OH Ow mm mv MN Ho Wm 0v NH mm HN mm VN mm mm mm HN Om ON mm mm NV NN MG MN 3N mm Om mm mwco> m whoa» M OH NH OH ON NH OH OH HH ON OH ON OH OH OH Hm ON NH OJMNMM N (\JNM'TJ‘ mzuZOZ O 93:02 m OSHm zHm Eoum osHm MOO cuBouO ucmHm: com: Amy .mH OHQMB 60 Whereas early seedling growth in the greenhouse was similar for each species, blue spruce seedlots were substantially taller than Engelmann spruce after the fifth and sixth growing season. This development was strikingly evident in the growth response of seedlots 8347 and 8348, blue and Engelmann progenies from population 57 in eastern Arizona. Although nearly equal in height after the first season in the field, the blue spruce seedlot was about 30 percent taller than the Engelmann seedlot after the fifth growing season. Blue and Engelmann seedlots from population 36 responded similarly, although the blue spruce from this population were not as tall as seedlot 8347. Five of the 11 Engelmann progenies represented in the ACCEL plantation were also included in the NUR planta- tion. In contrast to performance of ACCEL materials, mean Engelmann spruce height was somewhat greater than blue spruce height after two years' growth in the nursery. This height difference persisted up through five years of age in the NUR plantation. By the end of the sixth year, however, blue spruce seedlot mean height had exceeded that of Engelmann spruce. Seedlot 8348 grew extremely well in this plantation, ranking second tallest of all seedlots after five years. However, by the end of the sixth year it had drOpped to sixth rank overall, still quite good in the light of other Engelmann spruce performance. Significantly, blue spruce seedlot 8347, from the same 2-tree population, was tallest in the entire experiment after the fifth and 61 Figure 1. Cumulative terminal shoot growth of blue (B) and Engelmann (E) spruce seedlots from bud flush to bud set. :ZSKWP' 200- i? g 5 120 - 100? Cumulative height growth, mm 20'- Figure l. 62 ‘/'—— 83473 ......80228 .):-' ..._... 81483 / 80243 _.-'--/—-— 80828 l/“-"' 8081B . ’..‘;'7‘,.’.-...._..._ 81283 / / :‘I/ - ...... 8235E ,' :zg, /;;._———-——- 8238E . / g '4 8233 /,;I /... ~ . ggéi- ‘ 1 I l I *r l r I l 13 20 27 37 41 51 55 58 61 Days from April 27 63 Figure 2. Cumulative terminal shoot growth of blue (B) and Engelmann (E) spruce seedlots from bud flush to bud set. 220- 200r E 180- E £160- 3 (D h cn140~ dd .2 .9120- d) .C q, 100’ .2 H '59 EM) 3 E 3 (J) (MD- 4()- 2C)- 0. C) Figure 2. 64 8240. 8268. 8161a 3261. 8163a 8297E 27 Days from April 27 {,4 , x , , ‘_., /I .3" I, ’l .‘ _/ / l.’ I / .I, / x / /_./.‘I / I 1.7." l/ / [Ii/l / // I/ - .//I ,/// _ [I I,’ _-/ -' / ,/ /. I I. . // _/z ,’ .I' x" / /--'/,'-’I / / ~'.-//’ I :I'./ / ../' I 4.5 I /.'-[//I //.l // / :‘V'III I'I/ / / // /‘/‘ /.I / ='7.?/ /* / j; / - / / /' /’ /’ // /’ l I 37 41 I I I I 51 55 58 61 65 sixth growing seasons. The remaining Engelmann spruce progenies grew very slowly, obviously poorly adapted to the test environment. Of the six Engelmann progenies from population 36 included in the ACCEL plantation, only three were outplant- ed in the NUR test. Growth of these was below average to average for Engelmann spruce, and substantially below average for blue spruce. The three blue spruce progenies included in this population (8237, 8239 and 8240) all grew better than average in the ACCEL plantation. However, in the nursery and the subsequent NUR plantation, 8237 and 8239 were taller than the mean blue spruce height, while 8240 was substantially shorter. Terminal Shoot Elongation Seedlots of both species began terminal growth almost simultaneously, and ceased growth about the same time, some 49-52 days later (Figures 1, 2). Mean terminal shoot elongation was much greater for blue spruce than for Engelmann spruce, although that of the tallest Engelmann seedlot exceeded the shortest blue spruce seedlot. In addition to total elongation, the cumulative growth curve of most blue spruce seedlots was strikingly different from that of Engelmann spruce. The log phase of shoot extension in most blue seedlots was prominent in the cumulative growth curves from the fourteenth up to the fifty-first day of growth, while most Engelmann seedlots began to slow 66 down at least ten days earlier. Engelmann seedlot 8348 was an exception to this trend. In contrast to other Engelmann seedlots, 8348 responded similarly to blue spruce seedlots, and only began to slow down around the fifty- first day of growth. Mean shoot elongation of Engelmann spruce from population 36 was almost uniformly less than one—half the length of the tallest blue spruce seedlot, 8347, and about one—half the length of blue spruce seedlot 8240 from the same pOpulation. Seedlot 8297 had the short- est terminal shoot growth, while seedlot 8348 grew about 30 percent taller than the remaining Engelmann spruce. Frost Damage Frost damage was much more severe on Engelmann spruce seedlots than on blue spruce (Figure 3). Whereas the blue spruce seedlots generally had less than 50 per- cent RFD, nine of eleven Engelmann progenies were severely damaged. Of the Engelmann spruce represented, population 36 from northern New Mexico alone included enough seedlots to describe intrapopulation variation. Four of the six Engelmann progenies sustained 75 to 100 percent RFD. Seed- lot 8232 showed surprisingly little damage, less than 25 percent, and less than the three blue spruce progenies in- cluded in that population. Of the two seedlots from central Arizona (population 57), blue spruce seedlot 8347 suffered less than one-half the RFD that Engelmann seedlot 8348 did. The remaining Engelmann spruce incurred 65 to 90 percent 67 Figure 3. Relative frost damage of blue (striped) and Engelmann (solid) spruce seedlots from common populations. 68 .. .. ...... ... ... ..h .. .... . ...»...L. v.........m........................l a ....w..7.hw.v....x~....4.....;. .... .. .... «with....Mu......w..w..uwow..w.w vmmw 4.4.1.? .., ... . .4.“ r..4.....w...... .,..I. ,. «.4. 453m...“ u 444...... .......R.....H.....M....m,.u...m{..~. ..l 414...? NW”@ .. heather.» 4...... ....v... . ...3 a... new? r. ...»-..MermEik 4. trafflrfis Ovmm Frill”! $8 .. .... .. .. .. ..J........... an; 54.5»... i... 4.91.33? «mm... film}: LEE Nme a. run... ...... 1. firm... 47.... ..«4..9.¢..~..4..H.fi. ...r. . .... .. 1mm MONO VI’IIIIII’II’ ovum VI’I’I’I’ .. on «m . . .... ,. . ....4......z...r.....4.., . . ...4... «.4. Cum. ...4.... .( .... 4.454.515 .431? wmww 'l”!”’ 58 4 . .. ., .. .. ...14 .........w%¢u.4.wv.¢,.m.~mfi.flr\n.4... 7 vi ..4 my»... “$.29? 13F!» {liafiv omuw . . 11.71.... .. .4. ...I .... ...I ... .... 4.....4.l... . ... .. . 3... . . .. - bug“ «I.» ,4. v...v......,;.......,... warm... .. ...quz... ...4... ....4.4...... .. 4.....«.4I.,.\.n..u...wb..4w 8N” vmww . .. .. . .. .. .. .... ,. .... ...... .. MONO «ONO 100 - 75- ommEmo «moi 033.3— N. Seed source Figure 3. 69 RFD to their shoots. Phenology Blue and Engelmann spruce seedlots were most easily discerned by their differences in spring phenological develOpment. In the spring of 1974, Engelmann spruce reached bud stage 6 (shoot elongated, needles free from each other) at least one week before blue spruce seedlots from common pOpulations (Figure 4). This striking differ- ence in both nursery and plantation in combination with the drooping foliar appearance, was most useful in dis- tinguishing Engelmann from blue seedlots. Unfailingly, Engelmann seedlots were already completely flushed and shoots were elongated while most blue spruce buds were just bursting. Although some early variation was apparent among the representative Engelmann seedlots, by the third week in April differences had disappeared and all seedlots had reached bud stage 6. Meanwhile, blue spruce seedlots common to the same pOpulations were conspicuously less developed. Figure 5 illustrates the relationship of yearly variation in onset of warming spring temperatures to phenological develOpment and subsequent susceptability to frost damage. The spring warm-up in 1975 occurred later than during the previous two years, so that March and April mean monthly temperatures were 3°C to 5°C cooler than dur- ing the same period in 1973 and 1974. However, once warming 70 Figure 4. Differences in bud phenology between blue (striped) and Engelmann (solid) spruce seedlots at four assessment dates. Seed source Figure 4. 72 Figure 5. Pattern of growing-degree day accumulation over three consecutive growing seasons. 73 GNP P). P sews. Eo... goo moF ow mn . _ _ «Soon cozamcofi .09.... 358.3. mv . on J Gov com OOQF Door . nemwno - BUIMOJB eA sAep eaJBep Figure 5. 74 began, temperature increased very rapidly. As a result, the time interval between stages of phenological develop- ment, defined by cumulative growing-degree-days required to reach a given developmental stage, was shortened by al- most one-third in 1975 as compared to the previous years. This time compression masked phenological differences, and explains why growth began almost simultaneously for both blue and Engelmann spruce in 1975. The need for several seasons of phenological observations is apparent, as well as the vulnerability of both blue and Engelmann spruce to random year-to-year fluctuations in meteorological events. General climatic parameters, such as the probable date of last spring frost are only relevant in the context of current season's phenological processes which are condi— tioned by the current season's weather. Variation in Needle Morphology Percent of twice needle length covered by resin sacs was strikingly different between blue and Engelmann spruce. Between-species differences accounted for 73 per- cent of total variance (Table 16). In all populations examined, this ratio always exceeded 40 percent for blue spruce but was always less than 30 percent for those Engelmann spruce seedlots included (Table 17). This character, which is actually a composite of needle length, number of resin sacs per needle and resin sac length, is of more diagnostive value than any of the component 7S ucocomEoo ucocomEoo :oHuoHomom cflsufi3\wouu mCOEo ll 4.) O :ofluoHomom :quH3\moHHHEmm coozuon H No neocomEoo mmHoon :chH3\mcoHuMHomom mcoeo H AND ucocomsoo Hoouu :HsuH3\moHcooc macaw gov uOuuo u o ucocomEoo mmHowmm coozumn u mmo hm 5H mm mm o m NH mm 0 mm mm prooc 10 pHqu woman so moom :Hmom ¢ mu mm m mm NH NH H mm oH m mm oHUoo: mo canu oprHE co momm chmm x mm vH H 5m om NH o co Om o v oHpom: mo puflzu noon so moom :Hmom w Hm no mm mm mm 0 mm m 0H a mu momm :Hmou >2 cowo>oo oHpooz w mm mm mH mm 0? o rm Hm 7m mm mm sumEoH 0mm CHmom MH 0m Hm co m NH mm mm 9H mm mH momm :Hmom w on mm mm mm vm NH cm mm mm Hm m NIEE . moumfioum MH Hv ow vH Hm mm m m mm m mo nupHB oHpooz mm rv mH m mm m mm 7H rm gm VH zumcmH mHUooz I) o s I) s Hi ml -rl lo)- a w 1301.35 No No No No No No no No no No No :cmEHumdm mCOE< msHm mCOE< moHooaw :oo3uwm .mocmHum> Hmuoe wo usoouom mm commmumxm mooumm camEHmmcm pom osHm mCOE< muwuomuonu oHoowz mo mucocomfiou mocoHum> .wH QHQMB 76 0.H N.H m.H mm >0.N m.m 0m wm.0 m.hH zmmz v.0 N.H v.H 0H mm.m H.m mm mm.0 m.mH 5v 0.0 ©.H m.H 0H m0.m v.m mm mm.0 N.mH 0m OOmez 3oz 0.H N.H r.H Hm 0v.H 0.v gm no.0 m.MH mmmm m.0 H.H H.H mH >©.H H.m mm 0v.0 m.©H mmloh OSMUH m.H m.0 0.m om mH.N 0.? 0m 0v.0 m.hH mmmm coumcflzmmz H.m m.H 0.H hm 50.m H.m vm 0m.0 H.0H mm r.H N.H m.H 0N mm.m ©.q cm mm.0 0.0H 00H|05 r.0 N.H m.H mm mm.m m.m mm mm.0 v.5H 00105 mcmucoz oozumm acoEHomcm w.H m.H v.H 0m m©.m m.v mm no.0 H.0H 24m: 0.m ©.H m.H Hv mm.m n.v mm No.0 m.mH Hm ocomHud 0.H v.H v.H mg 00.m n.v mm 00.0 H.0H mm ooflxmz 3oz m.m m.H m.H 0m rm.m 0.m 0m mo.0 H.nH 0m m.H m.H m.H Hm mm.m 0.7 mm No.0 m.mH HH m.m H.m m.H 00 mm.v 0.m mm m0.0 0.0N H Lou: q.H H.m s.H cm mm.q N.¢ mm No.0 H.mH MH mcHsosz m.H n.H m.H 0m ov.m r.m mm H0.0 H.5H 0m w.H 0.m H.H Ho 00.N ¢.v mm m0.0 0.mH vH m.H m.H N.H 0v rn.m 5.? 0m No.0 m.MH 0H OpmuoHoo monumm onm Uquw -cuH:9 ouH:p_ vow :Hmom H5: vHUooz NIEE . 15: EE :oHumH wuoum Momma oHUUHZ 13304 >3 couo>ou .zuvco; use moon mvuoEOum HfiZHC> zuchH Izmom mHoooz :0 moom :Hmom no a oHcooz “a... oom :Hmom :Hmom t oHUmoz oHoomz .mcoHumHomom mosumm somEmecm unmflm can oon ocHz mo muouomumnu HMOHmoHonmuoz memoz Mom moon> coo: .hH oHnme 77 characters taken separately. Of the three main component characters, the between-species component of resin sac length accounted for the greatest percentage of total vari- ation, 35 percent. Between-species components for needle length and number of resin sacs per needle contributed more modestly to the total variation (14 and 19 percent, respectively). Species differences in needle width were very pronounced, accounting for 65 percent of the total vari- ance in this trait. More interesting, however, were the variance components for needle width within each species. Blue spruce data showed no differences among populations, but over one-half of the total variance could be explained by differences between-families-within-populations. Con- versely, Engelmann spruce population differences accounted for almost one-half of the total variance, while trees- within-populations (which included any family differences) accounted for another 41 percent. Similarly, variance components of resin sac number in upper one—third of needle showed substantial between-species variation (39 percent of total variation). However, distribution of within-species variation was different for each species. In blue spruce, error accounted for 85 percent of the total variation, while in Engelmann spruce, over one-half of the total vari- ation was accounted for by differences between populations. Apparently needle morphological characters are or have been under very different selective pressures for each species. 78 This knowledge may be useful in attempting to incorporate such traits into a breeding program. Other measured traits exhibited little or no appreciable between-species variation. In general, these results confirm Reed and Freytag's (1949) findings. How- ever, when the composite character, percent of twice needle length covered by resin sacs, is calculated, species differences are much more pronounced in needle characters. DISCUSSION Timing of phenological events have been shown to be under strong genetic control in temperate zone conifers (Nienstaedt 1973, 1975; Nienstaedt and King 1970; Lester, et al. 1977). Growth differences between blue and Engelmann spruce in southern Michigan plantations can be explained in part by differential frost damage sustained by each species. Engelmann spruce susceptibility to late spring frost damage resulted from its ability to break bud dormancy at a relatively lower temperature threshold than blue spruce. Pearson (1931) gave an explanation for this phenomenon from his and Bates' (1924) climatic studies of Rocky Mountain forest types. At higher elevations, temperatures begin to warm up much later than at the intermediate elevations where blue spruce occurs. Spring growth is retarded by the cooling effect of substantial snow cover lasting through late spring. When temperatures finally begin to warm, they do so rapidly, with little danger from advective 79 frost. Blue spruce, however, is able to avoid most severe late spring frost damage due to its general tendency of flushing only after a greater number of heat units had been accumulated than required for Engelmann, by which time frost danger had passed. The climate of the Lake States region is characterized by late spring anticyclonic disturbances of Arctic origin, the controlling mechanisms of which are not well understood. Hare and Thomas (1975) have attributed this climatic phenomenon to regular anti— cyclonic tracks which are conditioned by broad seasonal- geographic controls, including ice—covered Hudson Bay, which has a directional effect upon late spring airmasses. Presumably similar conditions are present in the seasonal climatic regimes of blue spruce habitats. Wardle (1968) reported similar situations in the western United States involving airmasses of Pacific origin as opposed to those of Gulf of Mexico origin. Southern airmasses are much warmer than those from the Pacific, even in winter. Generally, threshold temperatures for growth are reached after danger from such cold airmasses is past, although occasionally Engelmann spruce at high elevations are damaged by late spring frosts. Another phenomenon which may help to explain earlier flushing times of the higher elevation Engelmann spruce is the radiant energy heating of needles even though ambient termperatures may be low (Pearson 1931). Such a response has been reported in eleva— tional studies of purple lilac (Syringa vulgaris Linn.; 80 Caprio 1967, 1971). At higher elevations, lilacs flowered at a lower threshold temperature than required at lower elevations. Caprio suggested that because of radiant heat- ing, a given phenological stage of development, or plant wave, progresses toward higher elevations more rapidly than a given mean temperature, or thermal wave. The apparent variation in frost damage among Engelmann spruce seedlots from population 36 is interesting in that it illustrates the importance of bud stage of development in relation to freezing injury. Seedlot 8232 was relatively undamaged by the late frost, and also was slightly behind the other Engelmann seedlots in phenological development during the first three weeks of the phenological measurement period. Perhaps this time lag was just enough for it to miss the full effect of the frost. However, as Clements, et al. (1972) pointed out, damage to unopened buds may have occurred, causing them to remain unOpened, thus giving an erroneous frost damage grade. The exceptional performance of both Engelmann and blue spruce seedlots from population 57 indicates inher- ently high growth potential from this area. These two deedlots were tallest in the NUR plantation until the sixth growing season. Clearly, population differences in height growth are present in Engelmann spruce. One explanation for the different growth rates may be the differential ability for populations to tolerate water stresses which curtail physiological activities. Seedlot 8348 was more 81 drought tolerant than seedlots from population 36; tran- spiration rates were higher in 8348 even when soil moisture became limiting (Chapter 3). The implication here is that selective pressures at the seed origin were favorable for development of a more drought tolerant population in Arizona (population 57) than in northern New Mexico (popu- lation 36). Pearson (1920, 1931) attributed Engelmann's high elevation distribution to lack of sufficient soil moisture at lower elevations. His climatic and soil moisture data show that in the Engelmann spruce type, the snow pack did not melt until late June, thus the summer dry period experienced at lower altitudes was not noticeable, due to the groundwater charging by late snowmelt. The soil moisture regime at the Michigan planting site may more closely approximate blue spruce habitats than Engel- mann spruce, thus inhibiting most Engelmann spruce seedlots from expressing full growth potential. Speculation on the adaptive value of needle mor- phology characters is intriguing. Resin sac coverage of of needles seems to imply some past or present selective pressure on blue spruce that is not exerted on Engelmann spruce. If, as Daubenmire (1972) suggests, blue spruce is a recent derivation of Engelmann spruce, then range dis- tribution must have been very small at one time. Selec- tion in the form of insect defoliation may have resulted in those trees with resistance due to more resin sacs per needle, perhaps a deterrent to intensive feeding. Lack of 82 variation between-seedlots—within-populations may have resulted from such a situation. If such an occurrence was climate-related, Engelmann spruce, due to its cooler habi- tat adaptation, may have been ecologically isolated from such a selective force. CHAPTER 3. DIFFERENTIAL RESPONSE TO DROUGHT STRESS BY BLUE, ENGELMANN AND WHITE SPRUCE INTRODUCTION Tree species differ in their ability to survive conditions of high water stress. Shirley and Meuli (1939) and Pharis (1966) demonstrated differences in drought re- sistance among western conifers, while Glerum and Pier- point (1968) reported differences among three eastern coniferous species. Evidence of genetic variation for drought resistance within tree species has been presented by Meuli and Shirley (1937) for green ash and Squillace and Bingham (1958) for western white pine. Pharis and Ferrel (1966) reported that coastal sources of Douglas fir were less drought resistance than inland sources, but that there was substantial variation in the trait among inland seedlots. Ferrel and Woodard (1966), also studying Douglas fir, concluded that selection for drought resist- ance may occur on a local level as well as on a regional level. They suggested that transpiration control played an important role in drought resistance. Differences in transpiration rates have been re— ported in western conifers by Bates (1923), Sperry (1936) and Lopuskinsky (1969, 1975), with pines and spruces having higher transpiration rates than the firs. Lopuskinsky also reported a more sensitive stomatal control mechanism to water stress, and Pereira and Kozlowski (1976) observed 83 84 differential stomatal control between two eucalypt species. Helkvist and Parsby (1976) reported that growth of four clones of scotch pine was related to the xylem pressure potential of each clone, and surmised that this in turn was regulated by the rate of transpiration. Beasley and Klemmedson (1976) postulated that due in part to its lower xylem pressure potential at a given soil water potential, bristlecone pine was better able to withstand high water stress, and therefore able to live to a much greater age than limber pine, it's most common associate. The following study was initiated to determine whether or not inter— and intraspecific differences in certain plant-water relationships were present among blue, Engelmann and white spruce seedlings. These three species form a complex in the Rocky Mountains, with each species occupying a particular geographical and edaphic habitat. Blue spruce especially is widely recognized for its drought-tolerant characteristics, and is widely planted in the American and Canadian praries for windbreaks and amenity plantings. Meaningful differences among the species would help to explain patterns of distribution and informa— tion (n1 intraspecific variation would enable tree breed- ers to formulate strategies for improving yield through selection for maximum water efficiency. This study was comprised of two separate but related experiments. First, seedlings at various levels of water stress were sampled in order to determine the levels of xylem 85 pressure potentials for each species and family. This was done by the pressure chamber method of Scholander, et al. (1965), using the apparatus described by Waring and Cleary (1967). This method, as pointed out by Kaufmann (1968), does not always reflect the exact leaf water potential, but instead, the dimensional deformation caused by xylem tension. Kaufmann suggests the term "xylem pressure potential", and this terminology is used in this report. Although most other authors refer to uncorrected pressure chamber readings as "leaf/plant water potential", Kaufmann (1968) reported that xylem pressure potential of Engelmann Spruce was somewhat higher than leaf water potential over most of the range of water potential. The second experiment was designed to identify differences in pattern of transpiration as soil moisture deficits increased. Potted seedlings were subjected to a drying cycle and water loss was monitored. Transpiration rates were expressed in three different units to ascertain differences in plant control over water loss. Materials and Methods For Experiment 1, forty 2/0 trees from each of three Engelmann and three blue spruce seedlots were potted in six-inch diameter polyethylene rose pots. The soil medium was a Foxe loamy sand. Origins of seedlots are listed in Table 18. 86 Table 18. Origins of Blue and Engelmann Spruce Seedlots Used in Drought Tolerance Studies. Popu- Species Seedlot lation County State Blue spruce 8082 13 Sublette Wyoming 816l 27 Mineral Colorado 8375 65 Larimer Colorado Engelmann spruce 8233 36 Taos New Mexico 8238 36 Taos New Mexico 8348 57 Greenlee Arizona One hundred and twenty 2/0 white spruce trees from a bulk seed collection from the Huron-Manistee National Forest in Michigan were potted in the same manner as the blue and Engelmann seed sources. All potted seedlings were moved into a greenhouse in the fall following potting, and placed in plastic-lined beds. Prior to the start of the experiment the beds were flooded and the pots contain- ing the seedlings were saturated. Water was withheld for a period of two weeks and each day during the drying period one tree from each seedlot was selected for determination of plant water potential. Potted trees were first weighed, and then four branches from each tree were used to deter- mine the plant water potential by the pressure chamber technique (Scholander, gt gt., 1965). The roots were wash- ed and all soil collected in large galvanized buckets. Percent moisture was indirectly determined gravimetrically by subtracting net dry soil weight from net fresh soil 87 weight and dividing the remainder by fresh soil weight. Percent soil moisture of soil samples were determined at specific soil water potentials in a pressure plate and pressure membrane apparatus (Richards, 1947, 1948), and a soil moisture curve was constructed which is expressed by the following equation: log 9 = 0.671 + 0.231 log 1/X Given the percent soil moisture of any pot, the soil water potential in bars could be estimated from the curve. In Experiment 2, a total of 36 potted trees were grouped into a randomized complete block design of four replications. Each replication consisted of one tree from each seedlot used in experiment 1 plus three white spruce seedlings from the bulk Huron-Manistee National Forest collection. Prior to the drying cycle these pots were placed in a laboratory sink and allowed to saturate. After draining excess water, each individual pot was placed in- side a clear polyethylene bag, and the top was sealed tightly around the stem of the seedling with a wire twister. The four replicates were arranged in a growth cabinet under continuous light, with temperature and relative humidity held constant at 74°F and 70%, respectively. Each indi— vidual pot was weighed every 24 hours, and the net water loss was calculated. Samples of eight needles from each tree were taken and frozen in distilled water for further examination. When an individual tree began drOpping 88 needles, the top was cut at soil level and the roots were washed free of soil in a galvanized bucket. Percent soil moisture was subsequently determined indirectly from gravi- metric measurements. Fresh and dry root and shoot weights were recorded for each tree as was net dry foliage weight. Transpiration rate per unit dry foliage was then calculated for each individual tree. Needle samples were measured microscopically. Total length was measured, as was width of all four sides at the midpoint of the needle. Total number of stomates were counted along a 1.1 mm line on all four sides of each needle. From these measurements total needle surface area, stomatal density and total number of stomates per needle were estimated. The needles were oven dried and then weighed. Estimates of total surface area and number of stomata were then calculated for each individual tree.l Results Experiment 1. Xylem pressure potential response to soil moisture stress. Xylem pressure potential response to decreasing soil moisture is presented in Figure 6. Because soil moisture exceededtfluaoperating parameters of the pressure plate apparatus over much of the experimental ranges, the soil moisture variable is expressed as percent soil 1 Data on file, Forestry Department, Michigan State University. 89 Figure 6. Response of blue, white and Engelmann spruce xylem pressure potential to changing soil moisture. 0°. 0‘ ...: .i z. 90 1 o ‘?‘ ‘50'- ‘40 b 3 -30 )— l 9 l 30 25 20 15 10 SJBq ‘lenueiod eJnsseJd weMx Figure 6. % soil moisture 91 moisture, rather than negative bars soil water potential. Attempts at curve fitting indicated a marked change in response at about five percent soil moisture (-0.7 bar soil water potential). When soil moisture dropped below five percent, seedlings of blue, white and Engelmann spruce began to exhibit symptoms of water stress. For the purpose of statistical comparison the response curve in Figure 6 was divided into two segments, that part which was less than eight percent soil moisture and that part which was greater than five percent. Inclusion of common terminal values at the interface of each segment allowed compari- son over the whole range of the curve. That portion of the curve between zero and five percent soil moisture was best fitted by using the log of the inverse of percent soil moisture as the X variate. Linear regressions were then calculated for each seedlot and species by the equation I = a + b log l/X A comparison of regression lines (Snedecor and Cochran 1969) showed no significant differences in xylem pressure potential among the three Engelmann spruce seedlots or among the three blue spruce seedlots at lower (less than five percent) soil moisture levels. When seedlot values were pooled over each species and specific regression lines were compared in the manner above, no significant differ- ences could be detected between the two southern Rocky 92 Mountain species, but both differed significantly from the Michigan white spruce population. In this case, the slopes of the regression lines were similar among species, but the line elevations were different, indicating somewhat higher (less negative) xylem pressure potential values in white spruce than either Engelmann or blue spruce. F-values of these comparisons are summarized in Table 19 (Figure 7). Table 19. Comparison of Regression of Xylem Pressure Potential on Percent Soil Moisture Among and Within Engelmann, Blue and White Spruce Seedlots when Soil Moisture is Less Than Eight Percent. F-Value F—Value 1/ 2/ Among Among Species Seedlot b- r2- Slopes Elevations Engelmann 8348 -75.56 0.93 8233 —78.29 0.85 8238 -77.44 0.88 0.12 n.s. 1.34 n.s. Blue 8082 -75.46 0.88 8161 -47.52 0.63 8375 —58.84 0.96 0.88 n.s. 0.30 n.s. Pooled Engelmann -75.52 0.91 Blue —66.33 0.82 0.59 n.s. 1.44 n.s. Engelmann -75.52 0.91 White -66.59 0.84 3.26 n.s. 6.42* Blue -66.33 0.83 White -66.59 0.84 0.80 n.s. 4.75* t/ b = regression coefficient 2/ r2 = coefficient of determination * significant at P = 0.05 93 Figure 7. Response of blue, white and Engelmann spruce xylem pressure potential to soil water changes below 8% soil moisture. 94 -50 .- Engelmann spruce '40 ,_ :rn-soss- 67. so log ‘1: . r’ =O.92 -30 >- -20 - -10 r- 1 l 1 1 1 l I m 0 h N .0 - -40 — . a Blue spruce E 9 4047 457610‘ : - . r _ g o -00~ x 8 ‘ r2 . 0.60 O. O -20 - h 3 (a U) Q -10 )- b O. E o 1 1 1 1 1 1 1 2 > X -40 P White spruce . 9. 10.01 49.30 log "x -30 - :3: 0.80 -20 _ -10 _. O l l l l l l #1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 1 L09 /% soil moisture Figure 7. 95 When soil moisture was greater than five percent, the xylem pressure potential in all three tended to in- crease (become more negative), indicating that at saturated soil conditions the trees incurred some water stress. There were no intraspecific differences in xylem pressure potential response to increasing soil moisture. However, comparison of pooled regression lines among species in- dicated significantly higher xylem pressure potential values for blue spruce than for white or Engelmann spruce at soil moisture levels greater than five percent (Table 20). From these results it seems that xylem pressure potential begins to increase rapidly at some point between five and six percent soil moisture (-O.7 to -0.3 bar); relatively small decreases in soil water availability may greatly affect the plant water status. 96 Table 20. Comparison of Xylem Pressure Potential on Percent Soil Moisture Among and Within Engelmann, Blue and White Spruce Seedlots When Soil Moisture is Greater Than Five Percent. F-Value F-Value l/ 2/ Between Between Spgcies m r2-— Slopes Elevations Blue -0.14 0.36 Engelmann -0.03 0.03 0.59 6.24* Blue -0.14 0.36 White -0.11 0.19 0.78 10.81** t/ regression coefficient 2/ coefficient of determination * significant at 0.05 level ** significant at 0.01 level Experiment 2. Transpiration Response to Changes in Soil Moisture. Although mean transpiration rates (goh—l-dm-z) did not differ among species (Table 21), analysis of variance indicated that among blue spruce seedlots, transpiration was significantly greater in seedlot 8161 than seedlots 8082 and 8375. However, no differences were detected among Engelmann spruce seedlots. A very marked "block" or "position" effect was evident from the analysis of vari— ance. Transpiration rates were generally much greater for trees in replicate l in the growth cabinet than for the other replicates, probably due to the presence of air circulation vents over this area. For this reason data 97 from replicate l was not included in regression lines fitted to each seedlot. Table 21. Mean Transpiration Rates (g.h-l.dm-2) for Blue, Engelmann and White Spruce Seedlots, When Soil Moisture is Not Limiting. Species Seedlot Mean Transpiration Rate Blue 8082 0.1471 3 0.0268 8161 0.2344 i 0.0487 8375 .1429 i 0.0246 Engelmann 8348 0.1797 1 0.0454 8233 0.1428 i 0.0309 8238 0.1255 1 0.0543 White bulk .1447 i 0.0586 Three distinct patterns of transpiration response to changing soil moisture were evident (Figures 8-10). Blue spruce seedlots 8082 and 8161 followed a curvilinear response over most of the range of soil moisture, best described by the general quadratic equation: I = a + bx + cx2 Transpiration rates reached maximum values somewhere be— tween ll and 14 percent soil moisture. Very waterlogged soils tended to inhibit transpiration -- as soil moisture dropped from saturated levels transpiration increased. When soil moisture dropped to between three and five per- cent, transpiration began to decrease exponentially, 98 Figure 8. Transpiration curves for three blue spruce seedlots. Blue spruce 8082 99 0.3 '- 0.2 ~ - - ° 0.1 '- §.o.oooos Io‘ 755’ x] o L 1 1 l J.— 1 1 g l ’1 E 0.3 r Blue spruce 8161 , '0 " . ‘ . I: . 5,, 0.2 ~ . 9-01314 + 0.0275 x- 0.0013 x 5 e- 0-1 .. . e ; v:o.000141.“59°° ‘1 h a o 1 1 1 1 1 l 1 1 J— J C a h I'- 0.3[. Blue spruce 8375 0.2 '- ' 0.1 r- . ' . ' ° 2 3 . v :o.2101+ 0.0316 x + 0.0035 x -o.0001 x 13000043 1.1'2577 x1 0 1— 1 J 4 k l L 1 L I 2 4 6 8 10 12 14 16 18 20 22 % Soil moisture 100 following the general equation: 9 = aebx Although a quadratic regression line did not accurately describe transpiration response for blue spruce seedlot 8375, the inhibiting effect of saturated soil was evident. Regression equations and F-values for significance of de- parture from linear regression are present in Table 22. Transpiration in Engelmann spruce seedlot 8348 responded quite similarly to blue spruce seedlots 8082 and 8161, characterized by a quadratic equation over most of the soil moisture range, then dropping exponentially to a minimum value around 4.5 percent soil moisture. Inhibi— tion in waterlogged soils was evident in this seedlot also (Figure 9). Seedlots 8233 and 8238 from northern New Mexico could be characterized by individual quadratic regression lines extending along the entire range of soil moisture. However, there was no sharp drOp in transpira- tion rate when a critical soil moisture deficit was reached. Seedlot 8233 also showed an inhibition of transpiration rate at saturated levels, but not seedlot 8238. The third general pattern of transpiration response to decreasing soil moisture is unique to the bulk white spruce seedlot from Michigan (FigurelJH. In contrast to both blue and Engelmann spruce, white spruce transpiration rates were not depressed at very high soil moisture levels. Instead, most seedlings included in the experiment had high 101 MO mHH.m 88mm.o© MU vm.m «sgb.ma MU mv.m «som.mm MU HN.N «www.mm MU mm.m «ymm.m Mp mH.N *«bw.hm MC MN.N «xvm.va m=Hm>1m mx Nooo.ol.mx msoo.o Nx mooo.o Nx «Hoo.o Nx eaoo.o . mx Hooo.o . Nx mmoo.o + X Ho.o x Nmmo.o + mmMN.OI H x mnao.o + Nvmo.01 H x wmmo.o + ommo.OI H wmovo.o + Navoo.01 H x oamo.o + HOHN.O H Nx maoo.o I X mhmo.o + vama.o H ax oaoo.o l x Nmmo.o + Nmoo.o n coflummwm <>'l (>4 4 (>4 <>' m 06 unmoflewcmem «4 xasm 008:2 mmmm mmmm mvmm ccmEHomcm mnmm Hwam Nmom wsam poacmmm .coflmmoummm ummcwq Eouw muouummwo wo mocmoflmflcmflm MOM modam>lh paw .musumfloz HI. HHom ucooumd :0 Am EU. £.mv mumm :oflumuflmmcmue MOM mcoflumsqm coflmmwwmmm .NN MHQMB 102 Figure 9. Transpiration curves for three Engelmann spruce seedlots. 103 0.31- Engelmann spruce 8233 0.2- . ’ ° 0.1 )- ' . o 9 1 -0.0830+ 0.0380 x - 0.0014 x2 0 ° 1 1 1 1 1 1 1 1 1 1 2 .0 U l 1 TranSpiration , g-h - dm 0 Engelmann spruce 8238 9 M r 2 ° 1? : -0.0342 + 0.0178 x - 0.0003 x 0 "’1. 1 m 1 1 1 1 1 1 J O 0.3 1' Engelmann spruce 8348 02» ’ 01% 1? : - 0.041 + 0.0410 x - 0.0010 x2 9 . 0.0000 1416008 XI 0 1 l l 1 L 1 1 l 1 _l 2 4 6 8 10 12 14 16 18 20 22 % Soil moisture Figure 9. 104 Figure 10. Transpiration curve for bulk Michigan white spruce seedlot. 105 0.3208 :om NN ON mp or VF NF _ 4 14 _ d 4 mx 300.0 + ax 200.0 - x «30.0 + 38.0. . OP _ 53 X museum 0 A o:c>> :0 «AV mAu VAV mAV may Lup-vq-B ‘uoueigdsueu, a. Figure 10. 106 transpiration rates under high soil moisture conditions. Reduction in transpiration was relatively gradual, begin- ning around eight percent soil moisture. The cyclic fluctuation of transpiration rate in white spruce was most unusual. Daily fluctuations in the other species were apparent, but much more moderate than those of the white spruce seedlings. The most marked fluctuations occurred before transpiration rate began to decline rapidly. Xylem pressure potentials were calculated for each tree using regression equations obtained from Experiment 1. These were plotted against transpiration rate and regres- sion equations for each seedlot computed. Covariance analysis of regression lines indicated significant differ— ences among blue spruce progenies, as well as among the three species (Table 23). Trees of blue spruce seedlot 8161 had significantly greater transpiration rates at a given xylem pressure potential than did either seedlot 8082 or 8375. White spruce seedlots had significantly lower transpiration rates at a given xylem pressure poten- tial than either blue or Engelmann spruce. Among Engelmann spruce seedlots only 8348 and 8233 had meaningful regres- sion equations (variability in results of 8238 did not allow a good regression line "fit" -- r2 = 0.17 —- so no comparison was attempted). No differences were detected between these seedlots, but when pooled and compared with blue spruce seedlots 8082 and 8375, transpiration rates of the blue spruce seedlots were significantly higher at a 107 mhmm 0:0 Nmom muoH0mmm mcfl03Hocfl oozuam osab 00HOOQ Amy :OHDO:HEu®uw0 wO ucmfloflwwooo AHV Ho>ma Hm>0a Ho.o D0 uc0oHMH:mHm ex mo.o D0 u:00HwH:mHm * «sOOO.©m «mmm.© muflnz ::0Ea0m:m ««om>.mo ««om>.ma 00.0 xAMIOH x mvmo.©v I hvmo.o m open: wsam Ame 4soom.as .m.: mmm.m camssmmcm ®3 m 0 00m ANV H 0 a «400m.m «mnm.© mhmw Hoam «$00.0 «moo.n Hoaw Nmom .m.: mmmm.o .m.: mmm.c mnmm mmom «400:.m ««mmo.m N0.c XANICH x nwm~.av 1 mmmH.o w mhmm 35.0 xflmlofi x mmmm.mv 1 ovmm.o w Hoam m©.o x“; 0a x Ammo.alv l homa.o y mmom osam on ( .m.: omN.o .m.: oo.N no.0 XAMloH x m00m.nv 1 moma.o w mmmm 35.0 ximuos x cmom.mc . News.o l 5 meme camsammcm ncpo0>v~m ©:DE< moloam 9:054 . mm :oflunzmm :oflmmoummm DOH0wwm mwflowmm 03a0>lm 03a0>11 Adv .mboa0mom ousuzm ones: 0:0 03am .::0anm:m :flzuflz 0:0 $:OE< ma0flu:mgom 003mmoum Ema>x :OL: A E0. 2.0v mmu0m :oHu0uHmm:0uB mo :omflu0mEOU .mm wHQ0B ml a 108 given xylem pressure potential. Discussion Differences in pattern of transpiration response to soil water availability seems to reflect the important ex- tremes in the seasonal water cycle at the place of origin. Blue spruce, native to a rather dry climate is subjected to periods of high water stress during the normal growing season. Undoubtedly, as Major (1967) has pointed out, local seasonal and annual climatic regimes are most im- portant in maintaining selective pressure on a population of trees. Ground water levels are influenced by several variables, aspect, soil type, snow depth and drift levels, summer moisture regime and potential evapotranspiration demand being a few of the most obvious. Characteristics observed in these studies which seem to confer a measure of drought tolerance to blue spruce are: 1. rapid stomatal closure at a critical threshold soil moisture level; 2. ability to carry on gas exchange at levels of low plant water potential. Sensitivity to a certain threshold soil moisture level and subsequent curtailment of transpiration by rapid stomatal closure is quite evident in blue spruce seedlots 8082 and 8161 and Engelmann spruce seedlot 8348. Additionally, Lopushinsky (1969, 1975) and Lopushinsky and Klock (1974) reported that drought-tolerant ponderosa and lodgepole 109 pine, as well as Engelmann spruce, followed a similar pattern, while transpiration rates for the drought- susceptible grand-fir and Douglas fir were reduced more gradually. Drought-tolerant seedlots of loblolly pine reacted in a similar manner to blue spruce, while drought- susceptible seedlots displayed a more gradual stomatal response at higher soil moisture levels (van Buijtenen gt gt. 1976). In contrast, white spruce is a species adapted to more moist soil conditions that are not subject to such frequent depletions of soil water. Intuitively, it does not seem so important for white spruce to be able to carry on gas exchange at low soil moisture levels, therefore early and gradual stomatal closure would be adequate for survival in the more mesic environment where white spruce occurs. A general parallel to this idea is apparent in regard to Douglas fir and grand fir (Lopushin- sky, 1969, 1975; Lopushinsky and Klock, 1974). They are found in more mesic habitats than ponderosa and lodgepole pine. Hence, stomatal closure occurs more gradually than in the pines and Engelmann spruce. Transpiration response at very high soil moisture levels may also reflect soil moisture regimes at seed source origins. All three blue spruce seedlots exhibited reduced transpiration rates under saturated conditions, while at the same time exhibiting an increase in xylem pressure potential indicating that plant water stresses were beginning to develop. Oxygen deficiency (anoxia), in llO roots is a result of waterlogging, and seems to reduce root permeability in many species. Anoxia can affect transpira— tion rates, as well as many other physiological processes (Gill 1970). It may be that soil moisture patterns in the natural blue spruce habitats do not reach such high levels, at least during the periods of greatest transpiration de- mand. Well-drained soils may facilitate quick runoff of excess moisture before the growth season is underway. The white spruce bulk seedlot included in the experiment ex- hibited just the Opposite characteristics, with very high transpiration rates at high soil moisture levels; these dropped to a lower level at intermediate soil moistures. At the same time, however, xylem pressure potentials in- creased with increasing soil moisture levels. Results of Experiment 1 indicate that these plant water deficits were significantly lower in white (and Engelmann) spruce than in blue spruce. Clearly, white spruce is somehow able to endure excessive amounts of water more effectively than blue spruce, an adaptation presumably made necessary by the water regimes of the boreal forest environments. The anatomical and physiological characteristics which allow white spruce to do this are not clear. Per- haps, as Coutts and Armstrong (1976) suggest, white spruce may have more efficient internal gas pathways which allow oxygen transport to the roots under waterlogged soil condi- tions. High transpiration rates at these waterlogged conditions may have the effect of drying out the soil, and 111 at the same time allowing foliar gas exchange to take place at a maximum rate, presumably early in the growing season, when high water tables are most prevalent. Ob— viously, it would be of great interest to compare soil water regimes on sites of each species, and see how these moisture levels coincide with (1) changes in transpiration and plant water potential; and (2) changes in water demand as dormancy is broken and active growth continues. Experimental results give some evidence for expect— ing distinct population differences in response to water stress in blue and Engelmann spruce, as well as direction for further comparative work both among and between species. Pharis and Ferrell (1966) indicated variability in toler- ance to drought among inland Douglas fir, as did Ferrell and Woodard (1966). The significance of Ferrell and Woodard's report, however, was in the evidence for very localized adaptation to very localized conditions. The results of the blue and Engelmann spruce drought studies also contribute evidence to this hypothesis, although due to inclusion of limited numbers of seedlots from sites with no supporting climatic or edaphic data, only broad hypotheses can be formed, and must await further testing. Among the blue spruce seedlots tested, 8161 had a significantly greater tranSpiration rate than either 8082 or 8375. Furthermore, 816l exhibited the ability to carry on more gas exchange (as evidenced by higher transpiration rates) at lower xylem pressure potentials (and presumably 112 at lower plant water potentials). This ability, according to Tyree (1976), should permit the plant to maintain longer periods of net CO assimilation, as the hydroactive closure 2 of stomates is postponed until both lower plant and soil water potentials are reached. Perhaps not so coincidental— ly, seedlot 8161 is one of the fastest growing seedlots in experimental plantations in Michigan, and is from the fastest-growing population in the Michigan State University range—wide progeny test. If Tyree's hypothesis is correct, then seedlot 8161 is best adapted to carry on photosynthesis under situations where soil moisture becomes limiting. Of great interest in this respect would be more comparative water relations studies to determine whether other seedlots from population 27 (which includes 8161) have similar physiological responses to changes in soil moisture levels. One handicap to correlative studies between environmental variables and physiological parameters is the absolute lack of onsite climatic and edaphic records for blue and Engel- mann stands of interest. Although general climatic descrip- tions are available, the variations due to topography and elevation alone make them very inaccurate for describing specific sites. Engelmann spruce results indicate that considerable selection pressure exists among populations -- perhaps more so than in blue spruce due to the much greater latitudinal and elevational range of Engelmann spruce, and the differ— ences in climatic regimes due to these extremes. The 113 seedlots included in the drought experiments exemplify this to some extent. Seedlots 8233 and 8238 came from population 38 in north—central New Mexico, while seedlot 8348 of population 57 originated near the middle of the Arizona-New Mexico border. Other than elevation, no en- vironmental parameters are available. However, it seems likely that the southern seed source was subjected to more frequent and severe drought stress than the northern one, and as a result, the pattern of transpiration response to soil moisture is similar to the drought-tolerant blue spruce. Additionally, seedlot 8348 grew surprisingly well for an Engelmann spruce seedlot in the southern Michigan. plantations. On the other hand, the northern New Mexico seedlots exhibited a more gradual reduction in transpira- tion rate, an indication, perhaps, that drought resistance is less important at the point of origin. Recognizing and defining water use patterns may help to explain the importance and adaptive advantages of other processes influential in growth and dry matter pro- duction. Of prime importance is bud initiation and morpho— genesis -- the processes by which the next year's growth is in large part determined. Presumably selection for pro- longed photosynthetic capacity during the time of initia— tion and differentiation would positively affect these processes. CHAPTER 4. CONCLUSIONS AND RECOMMENDATIONS Rate of wood production is of prime importance in domesticating forest trees, and although relatively easy to measure, it is genetically and physiologically complex. Natural populations must optimize environmental fitness, and in the process of adapting to harsh environments, those component traits which contribute to duration of growth or differentiation period may be affected differently from those component traits which contribute to tgtg of growth or differentiation process. Maximum environmental fitness "wins out" over maximum growth in natural populations be- cause of environmental constraints on either duration period or rate of process. However, the resulting variability in both duration and rate can be utilized through selective breeding to create new combinations of genotypes which are adapted to a range of habitats, and which maximize growth by optimizing duration and rate through complementation of traits. Some of this variability has been defined in blue spruce, and to a lesser extent Engelmann spruce, in previous studies, in this study and in subsequent studies. Growth components, environmental fitness traits and some physio- logical and morphological traits are highly variable, with 114 115 substantial portions of that variance attributable to broad regional selection pressures, to much more localized selec- tion pressures, and to random segregation within panmictic populations. However, before practical tree improvement techniques can capitalize on the existing natural variation, it must be more clearly defined. Adaptive significance of these and other component traits is logically deduced, but largely speculative, and needs to be clarified. As well, inheritance mechanisms and phenotypic plasticity must be better understood through selective breeding and extensive progeny testing. The results I report here have, I feel, answered some basic questions in the physiological genetics of blue and Engelmann spruce, but have, to my mind, stimulated many more. To provide answers to some of these, I offer the following recommendations for future study: 1. That elevational transect collections through the blue and Engelmann spruce zones in the Uinta, Wasatch, Western Colorado and Arizona climatic regions be made to more clearly elucidate elevational effects within and among climatic regions. 2. That blue and Engelmann spruce population collections be made from several diverse sites where detailed site information is available, for correlation with component traits important in growth and fitness. 116 That the role of transpiration control in growth rate be examined in fast and slow growing populations of blue and Engelmann spruce, utilizing rooted cuttings to estimate clonal effects, to determine adaptive signifi- cance, and to evaluate this trait's usefulness in early selection. That population diversity in western Colorado be investigated by comparing gene frequencies of endosperm isoenzyme genetic systems. That a systematic study of genetic variation in Engelmann spruce be undertaken to provide basic information for tree improvement strat— egies in this important western conifer. That comparative studies of populations from very different environments be designed and executed to elucidate whether or not rate and duration component traits of growth will be complementary when recombined through selective breeding. APPENDIX 117 mva mad and Ama CAN med Nod QNN and Mad ona Hmm mva mad wed com ova wVH mma xma can AuscH 8383A 0 o No - A ma om MN A mm 0N o 0 ad o On m cm ma o N? no A AH OH mv AN ca ca ca O m HA 0 o OH OH On Ca ON VN m o o m h n - ~\ 000000 000000 000000 00000 000 000000 00013 000000001150 000500 000000 000000 00000 000 000000 001-0110 0000h0000 commoo DAD—4000 01.000061 u-«a-4 l l I l i Illnl’ A A .. 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NNA NAA NN VN NA NN NV NN NN NN NV NA NA NNA NN NV VA NN NV NN VN NN - - - NNA NN NN NV NN NV NN NN NN NN NV NA VNA NN VN NV NA VN NN NN NN NN NA NA ANA NN AN NA NA VA NN VN AA NV NA NA NNA NAA NAN< mmmm AmmMIIMmezxwmm AmmwaxA N N n N N N +ooNN Amnazz coAwa VNNA .. A:,xanAAxA_ Ame»; 9N4 N4 wm< AN AVNNN :oAumA oAumeAAu A5323. .5 5 ENE: AHGN .5 5 23¢: ~52 £268 1&8 AnmscAucoov .¢ xHszamt. BIBLIOGRAPHY BIBLIOGRAPHY Alexander, R. R. 1958. Silvical characteristics of Engelmann spruce. USDA For. Serv., Rocky Mt. For. Range Exp. Stn. Pap. 31. 20 p. Baker, F. S. 1944. Mountain climates of the Western United States. Ecol. Mon. 14: 223—254. Baker, F. S. 1949. A revised tolerance table. J. For. 47: 179—181. Bates, C. G. 1923. Physiological requirements 'of Rocky Mountain trees. J. Agric. Res. 24: 97-164. Bates, C. G. 1924. Forest types in the central Rocky Moun- tains as affected by climate and soil. USDA For. Serv. Dept. Bull. No. 1233. 152 p. Beasley, R. S. and J. O. Klemmedson. 1976. 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