Genetic diversity and population structure ofboreal white spruce (Picea glauca) in pristineconifer-dominated and mixedwood forest stands

Om P. Rajora, Ishminder K. Mann, and Yong-Zhong Shi

Abstract: White spruce (Picea glauca (Moench) Voss) is a characteristic primary species of the Canadian boreal forestregion, where it occurs in conifer-dominated and mixedwood forest types. Genetic diversity and population structure ofwhite spruce may differ between the conifer-dominated and mixedwood forest types owing to the inherent differences instand structure and dynamics. The objective of our study was to determine genetic diversity and population structure ofpristine white spruce stands as they occur in conifer-dominated and mixedwood forest types at the EMEND (EcosystemManagement Emulating Natural Disturbance) study sites in northern Alberta. Nuclear microsatellite DNA markers wereused to examine genetic diversity and population structure of 16 pristine natural old-growth (‡100 years) white sprucestands (subpopulations) of fire origin; 8 from conifer-dominated and 8 from neighboring mixedwood forest types. Highlevels of genetic diversity were observed, as expected. The genetic diversity and genetic constitution of white spruce weresimilar between the conifer-dominated and mixedwood forest types. Most of the genetic variation resided within subpopu-lations, with only about 2% genetic differentiation detected among 16 subpopulations as well as among 8 subpopulationswithin the same forest type. The mean genetic distances among subpopulations within and between the forest types weresimilar. Our study suggests that white spruce genetic resources are similar in the conifer-dominated and mixedwood foresttypes located in the EMEND study area in northern Alberta, and it provides the benchmarks for determining and monitor-ing the genetic diversity impacts of forest harvesting and forest fires.

Key words: Picea glauca, microsatellite DNA, gene conservation, northern boreal forest, ecosystem management emulatingnatural disturbance, conifer genetic diversity.

Resume : L’epinette blanche (Picea glauca (Moench) Voss) est une espece primaire caracteristique de la region forestiereboreale du Canada, ou on la retrouve dans des types forestiers domines par les coniferes, et des forets mixtes. La diversitegenetique et la structure de population de l’epinette blanche peuvent differer entre les types forestiers domines par les co-niferes et les forets mixtes, decoulant des differences inherentes dans la structure et la dynamique des peuplements. L’ob-jectif de l’etude etait de determiner la diversite genetique et la structure de population de peuplements vierges d’epinetteblanche, tels qu’on les retrouve dans les types forestiers domines par les coniferes et les forets mixtes, sur les sites d’etudeEMEND (amenagement des ecosystemes emulant les perturbations naturelles), dans le nord de l’Alberta. Les auteurs ontutilise des marqueurs d’ADN nucleique microsatellitaire, pour examiner la diversite genetique et la structure de populationdans 16 peuplements (sous-populations) vierges d’epinette blanche ages (‡100 ans), provenant de feux, soit 8 domines parles coniferes et 8 voisinant des forets mixtes. Comme on s’y attendait, on y observe une importante variation genetique. Ladiversite et la constitution genetiques de l’epinette blanche sont comparables chez les types de forets domines par les coni-feres et les forets mixtes. La majeure partie de la variation genetique et de la constitution genetique se retrouve a l’interieurdes sous-populations, avec seulement 2 % de la differenciation genetique decelee parmi les 16 sous-populations, ainsi queparmi 8 sous-populations, au sein d’un meme type forestier. L’etude suggere que les ressources genetiques de l’epinetteblanche sont semblables dans les types de forets domines par les coniferes et les forets mixtes se retrouvant sur l’aired’etude EMEND, en Alberta. Elle fournit egalement les points de repaire, pour determiner et suivre les impacts de la re-colte et des incendies, sur la diversite genetique.

Mots cles : Picea glauca, microsatellite ADN, conservation des genes, foret boreale du nord, amenagement des ecosyste-mes emulant les perturbations naturelles, diversite genetique des coniferes.

[Traduit par la Redaction]

Received 25 February 2005. Published on the NRC Research Press Web site at http://canjbot.nrc.ca on 14 October 2005.

O.P. Rajora,1,2 I.K. Mann, and Y.-Z. Shi.3 Forest Genetics and Biotechnology Group, Department of Biology, Life Sciences Centre,Dalhousie University, Halifax, NS B3H 4J1, Canada.

1Corresponding author (e-mail: Om.Rajora@Dal.ca).2Present address: Canada Research Chair in Forest and Conservation Genomics and Biotechnology, Faculty of Forestry andEnvironmental Management, P.O. Box 44555, 28 Dineen Drive, University of New Brunswick, Fredericton, NB E3B 6C2, Canada(e-mail: orajora@unb.ca).

3Present address: Department of Agricultural, Food and Nutritional Science, 4-10 Agriculture/Forestry Centre, University of Alberta,Edmonton, AB T6G 2P5, Canada.

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Can. J. Bot. 83: 1096–1105 (2005) doi: 10.1139/b05-083 # 2005 NRC Canada

Introduction

Diverse genetic resources are among the planet’s most val-uable biological resources, vitally important for sustainabilityof species and the stable functioning of ecosystems. Geneticdiversity provides raw material for adaptability and evolutionof species and individuals, especially under changed environ-ment, climate, and disease conditions. Trees are normally thekeystone species of forest ecosystems; thus, their genetic di-versity has special significance because their existence, sur-vival, and health are critical for many forest-dependantorganisms and stable structure and functioning of these eco-systems. Reductions in genetic diversity can predispose for-ests to environmentally related decline in health andproductivity (Bergmann et al. 1990; Oleksyn et al. 1994;Raddi et al. 1994). Therefore, conservation of genetic diver-sity could be viewed as the foundation of forest sustain-ability (Mosseler and Rajora 1998; Rajora and Mosseler2001a, 2001b). Forest harvesting practices and forest firesmay significantly impact genetic diversity, populationstructure, and other biological processes affecting thesepopulation genetic parameters in forest trees (Buchert etal. 1997; Rajora 1999; Rajora et al. 2000; Rajora and Plu-har 2003). Response of a species to various harvestingpractices and forest fires in terms of impacts on genetic di-versity can depend upon its biological and silviculturalcharacteristics, its geographic distribution, as well as its in-herent genetic diversity and population structure. To de-velop effective guidelines and strategies for conservation offorest genetic resources and sustainable forest managementpractices, genetic diversity and population structure inherentin pristine natural forest tree populations and genetic impactsof forest management practices and natural disturbance mustbe understood (Rajora and Mosseler 2001a, 2001b). TheEMEND (Ecosystem Management Emulating Natural Dis-turbance) experiment, located in the boreal forest in northernAlberta, and based on control and experimental forest har-vesting and forest fire treatments of various intensities inpristine four forest types (http://www.biology.ualberta.ca/old_site/emend//english/homepage_e.html), provides an idealexperimental design for such studies.

The objective of the present study was to determine andcompare genetic diversity and population structure of whitespruce (Picea glauca (Moench) Voss) inherent in natural pris-tine populations as they occur in the conifer-dominated (CD)and mixedwood (MW) forest types at the EMEND studysites, and to develop benchmark information for determiningand monitoring genetic diversity effects of experimental for-est harvesting and forest fires of different intensities.

White spruce is a predominant and characteristic speciesof the Canadian boreal forest region. It is a transcontinentalwidespread species found in almost all forested regions inCanada, with the exception of the Pacific coast (Hosie1979). White spruce is ecologically as well as economicallyimportant and occurs predominantly in CD and MW foresttypes. CD forest types are typically dominated by whitespruce, whereas MW forest types have white spruce mixedwith trembling aspen (Populus tremuloides Michx.) andother hardwoods, such as white birch (Betula papyriferaMarsh.) and balsam poplar (Populus balsamifera L.)(Lieffers et al. 1996).

Genetic diversity and population genetic structure ofwhite spruce in CD and MW forest types may differ owingto the inherent differences in stand structure and dynamics,which may affect population genetic parameters, such asspatial genetic structure, mating system, and gene flow.However, if these forest types are located in proximitywithin the same general area as at the EMEND site, exten-sive gene flow may minimize genetic differentiation be-tween the two forest types. White spruce in CD and MWforest types may also respond differently to forest harvestingpractices and forest fires in terms of impacts on genetic di-versity and population structure.

Genetic diversity of white spruce was found to be high inpristine old-growth populations in the CD stands in northernboreal Saskatchewan based on RAPD markers (Rajora1999). Furthermore, moderate to high allozyme genetic di-versity was reported for white spruce populations from New-foundland (Innes and Ringius 1990), Quebec (Tremblay andSimon 1989), Ontario (Cheliak et al. 1985), central andsouthern Alberta (King et al. 1984; Rajora and Dancik2000), and Alaska (Alden and Loopstra 1987). However,there is no information on the comparison of genetic diver-sity and population structure of white spruce and any otherconifer between CD and MW forest types. Also, no previousgenetic diversity study has been conducted on white sprucepopulations from boreal forest in northern Alberta.

We have used nuclear microsatellite DNA markers tostudy genetic diversity and population structure of eightwhite spruce stands each from the CD and MW forest typesin the EMEND study area located in northern boreal Alberta.

Materials and methods

White spruce populations and samplingWhite spruce populations were located at the EMEND

project sites, in the Upper Boreal – Cordilleran Ecoregionin northern Alberta, approximately 90 km northwest ofPeace River (approximate coordinates for the project centre:56846’13@N, 118822’28@W) (Fig. 1). The EMEND experi-mental design involves 4 forest types (CD, MWs, aspendo-minated, and aspen-dominated with spruce understory), 3replicates within each forest type, 5 experimental harvestingtreatments (10% residual, 20% residual, 50% residual, 75%residual, and clearcut), and 3 experimental fire treatments(high-intensity burn, medium-intensity burn, and low-intensityburn) in each replicate (http://www.biology.ualberta.ca/old_site/emend//english/homepage_e.html). Our study onwhite spruce genetic diversity was focused on 1 replicateeach of the pristine preharvest or prefire treatment CD andMW forest types. Sixteen white spruce stands (subpopula-tions) were sampled: 8 from the CD and 8 from the neigh-boring MW forest types (Table 1; Fig. 1). Each of thestands was approximately 10 ha. The CD stands had>70% white spruce, whereas the MW stands had <50%white spruce as a stand component. All of the sampledsubpopulations were located within 10 km of each other.Thirty-five individual trees were randomly sampled at aminimum distance of approximately 30 m from each otherfrom individual subpopulations, with a total of 560 individ-uals sampled from the 16 subpopulations. As a part of thefieldwork, we measured diameter of all the sampled trees

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and cored about 60% of them for age determination. Foli-age samples were collected from individual sampled treesfor DNA extraction and analysis. The mean age and DBHof the sampled trees in each subpopulation are presented inTable 1.

Microsatellite DNA analysis of genetic diversityTotal genomic DNA was isolated from the needle samples

of individual trees. Genotypes of white spruce trees and ge-netic variability of 16 subpopulations were determined byusing markers of six microsatellite DNA loci (Table 2).These were the genomic microsatellite DNA loci among theavailable ones that showed consistent and unambiguous sin-gle-locus patterns. However, data from five loci were usedfor determining population genetic diversity and structureparameters as described later in the manuscript. The meth-odology for microsatellite DNA genotyping was developedand optimized by using LI-COR (LI-COR, Inc., Lincoln,Nebraska, USA) or ABI 377 (Applied Biosystems, FosterCity, California, USA) automated DNA sequencers.

Polymerase chain reaction amplification ofmicrosatellites

PCR reaction mixtures contained 10–20 ng of DNA tem-plate in 10 mL of reaction buffer: 10� PCR buffer, 1.5 mmol/LMgCl2, 200 mg/mL of bovine serum albumin (BSA),200 mmol/L of each dNTP, 200 nmol/L of each primer(fluorescent or IRDyeTM labeled), and 0.05 U/mL of ther-

mostable Taq polymerase (MBI Fermentas). For primersPGL12, PGL15, and SPAGGO3, amplification was per-formed using the touch-down protocol (Rajora et al.2001). For PGL14, annealing temperature of 50 8C wasused (Rajora et al. 2001). The PCR conditions for primerUAPgCA91 were as follows: denaturing at 94 8C for5 min followed by 30 cycles of 94 8C for 30 s, 60 8C for30 s, 72 8C for 30 s, and final extension at 72 8C for3 min. The PCR conditions for primer UAPgGT8 includeddenaturing at 94 8C for 5 min followed by 35 cycles of94 8C for 30 s, 58 8C for 30 s, 72 8C for 30 s, and finalextension at 72 8C for 3 min. Five microlitres of the am-plified products was examined on 1.5% agarose gels in0.5� Tris–borate–EDTA (TBE) containing 0.3 mg/mL ofethidium bromide to check for positive amplification. Theremaining 5 mL was used for resolving microsatellites ondenaturing gels.

Detection and scoring of microsatellite fragmentsMicrosatellite PCR reaction products were resolved on de-

naturing gels containing 7% Long Ranger polyacrylamide,7.5 mol/L urea, and 1� TBE, using LI-COR or ABI 377 au-tomated sequencers. Amplification products and the loadingdye were mixed in a 1:1 ratio prior to gel loading for the LI-COR automated sequencer. For ABI 377, fluorescently la-beled amplification products were mixed with the internalsize standard GeneScan 350 TAMRA (Applied Biosystems)and formamide. The mixture was heated at 94 8C for 5 minand then quickly cooled on ice. About 0.7 mL of each sam-ple was used for loading. For genotyping analysis on LI-COR, a IRDye-labeled molecular weight marker was loadedin at least three lanes per gel as a size standard. Electropho-resis was carried out on LI-COR automated sequencers us-ing 1� TBE running buffer, with run parameters of 1400V, 40 mA, 40 W, 50 8C plate temperature, and 16 pixeldepths for collection of the TIFF image files. Scoring ofTIFF images for microsatellite profiles was done manuallywith stringent rigor. Scoring of alleles from ABI 377 auto-mated sequencer data was done by using GeneScan 3.1 soft-ware (Applied Biosystems, Foster City, USA). The alleles ata microsatellite locus were identified by their molecular sizeand were designated alphabetically from smaller to largersize for analysis by PopGene software (Yeh and Boyle1997).

Statistical analysisThe PGL12 locus, after repeated analyses, showed null

phenotypes with a frequency range of 0.06 to 0.34 and amean of 0.14. Since heterozygotes of the null allele couldnot be clearly distinguished from the homozygotes of the al-ternate allele, this locus showed an artificial excess of ho-mozygotes in all of the subpopulations. Therefore, toremove this experimental error, PGL12 locus was excludedfrom the statistical analysis. None of the five loci retainedin the statistical analysis showed any null phenotype among560 white spruce individuals. Thus, there was no evidencefor the presence of a null allele at the retained five SSR loci.

The following conventional genetic diversity parameterswere calculated for individual subpopulations: total numberof alleles, AT; mean number of alleles per locus, A; effectivenumber of alleles per locus, Ae; mean observed heterozygos-

Fig. 1. EMEND project site location and a map of the whitespruce (Picea glauca) stands sampled.

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ity, Ho; and unbiased estimate of expected heterozygosity,He (Nei 1978) using the PopGene program. Chi-square (w2)and likelihood ratio (G) goodness-of-fit tests were performedto examine Hardy–Weinberg equilibrium at each locus ineach subpopulation, using Levene’s (1949) method and Pop-Gene software. The latent genetic potential (LGP) (Berg-mann et al. 1990) as well as genotypic genetic diversityparameters, genotype additivity (GA) (Rajora et al. 2000),and genotype multiplicity (GM) (Bergmann et al. 1990)were also calculated for each of the 16 subpopulations.LGP is the difference between A and Ae summed over allloci (l = 1. . .n):

LGP ¼Xn

l¼1

ðA� AeÞ

GA is the observed (GA observed) or theoretical (GA ex-pected) number of single-locus genotypes (G) summed overall loci (l = 1. . .n):

GA ¼Xn

l¼1

G

GM is the multiplication product of all observed or expectedgenotypes across all loci. Means of genetic diversity para-meters were calculated over all 16 subpopulations, and over8 subpopulations from the same forest type. Heterogeneityof allele frequencies at each locus over all subpopulations,and over 8 subpopulations within each of the CD and MWforest types, was calculated using w2 and G tests. Genetic

distances (Nei 1972, 1978) among subpopulations, and theiraverage among and within subpopulations from the CD andMW forest types were calculated using the PopGene soft-ware. To construct a cluster plot of the subpopulationsbased on genetic distance of the subpopulations with 1000bootstrap iterations, allele frequency data from the PopGeneoutput was used to compute Nei’s original genetic distances(Nei 1972) among subpopulations using the Gendist pro-gram of the PHYLIP package (Felsenstein 1989). A consen-sus neighbor-joining tree (Saitou and Nei 1987) wasconstructed from these genetic distances using the Consenseprogram, with 1000 bootstrap iterations done by using theSeqboot program of the PHYLIP package. Monofactorialanalysis of variance (ANOVA) was performed to test thesignificance of differences in genetic diversity parametersdue to forest types (model), using the ANOVA procedureof the SAS program (SAS Institute Inc. 2001).

Correlations of DBH and age with genetic diversity pa-rameters and fixation index were calculated using the SASprogram (SAS Institute Inc. 2001).

Results

Genetic diversityAll six microsatellite DNA loci were highly polymorphic

(Table 2). The Norway spruce SSR primer pair for theSPAGGO3 locus was completely transferable to whitespruce and amplified microsatellites that showed typical sin-gle-locus patterns (Fig. 2), as was the case with the other 5SSR primers from white spruce. Twenty-one to 44 alleleswere detected in 560 white spruce individuals at a locus(Table 2), with a total of 194 alleles over the 6 loci. Asstated earlier, because of the occurrence of a null allele inPGL12, this locus was removed from the further genetic di-versity and population structure analyses.

Most of the loci conformed to Hardy–Weinberg equili-brium in most populations. Significant (P £ 0.05) departuresfrom Hardy–Weinberg equilibrium were observed for 3 ofthe 80 cases tested, which would be expected by chance.These included the PGL15 locus in subpopulations E915and E936 as well as the SPAGG03 locus in subpopulationE937. These departures were due to a deficiency of hetero-zygotes.

Most of the alleles were well spread over the subpopula-tions, whereas a few of them were found in only a singlesubpopulation. The number of such private alleles ranged

Table 1. White spruce (Picea glauca) subpopulations sampled.

Conifer-dominated Mixedwood

Assigned harvestingor fire treatment

Stand andsubpopulation

Mean DBH(mm)

Mean age(years)

Stand and sub-population

Mean DBH(mm)

Mean age(years)

Low burn (LOB) E915 362 96 E936 461 104Medium burn (MEB) E916 348 113 E937 454 122High burn (HIB) E923 395 119 E938 478 10610% residual (10R) E917 417 116 E913 449 10420% residual (20R) E919 330 108 E910 390 11550% residual (50R) E920 376 100 E911 413 12975% residual (75R) E921 365 112 E912 464 126Clear-cut (CCT) E922 366 114 E914 422 113Mean 369.9 109.8 441.4 114.9

Table 2. Microsatellite DNA loci used and the number and sizeof alleles detected at each microsatellite DNA locus.

Microsatellitelocus* Repeat unit

Totalno. ofalleles

Allelesize range(bp)

PGL12 (AAG)2 (AG)3 G4(AG)9 41 204–269PGL14 (AG)20 24 126–178PGL15 (CT)4N16(CT)11 37 157–345UAPgCA91 (CA)20 44 112–204UAPgGT8 (GT)22 27 194–288SPAGGO3 (AG)n 21 108–148

*PGL12, PGL14, and PGL15 are from Rajora et al. (2001);UAPgCA91 and UAPgGT8 are from Hodgetts et al. (2001);SPAGGO3 is from Norway spruce (provided by Ivan Scotti).

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from 1 to 4 (Table 3). In addition, 6 alleles were found inonly the CD stands and 2 alleles, in only the MW stands.Thus, the CD stands had 18 and the MW stands had 9 pri-

vate alleles. White spruce subpopulations showed high mi-crosatellite DNA genetic diversity. Conventional allelic-based genetic diversity parameters are presented in Table 3,

Fig. 2. Representative microsatellite DNA profiles of 32 white spruce (Picea glauca) trees for the SSR locus SPAGGO3 developed fromNorway spruce (Picea abies).

Table 3. Allelic genetic diversity parameters (SE) for white spruce (Picea glauca) subpopulations.

SubpopulationAssignedtreatment AT A Ae Ho He

No. of privatealleles*

Conifer-dominated standsE915 LOB 78 15.60 (2.84) 10.13 (2.35) 0.657 (0.13) 0.821 (0.11) 0E916 MEB 86 17.20 (2.85) 10.95 (2.41) 0.669 (0.08) 0.888 (0.04) 1E923 HIB 87 17.40 (1.81) 9.36 (2.19) 0.687 (0.09) 0.854 (0.06) 4E917 10R 85 17.00 (1.82) 9.77 (1.90) 0.621 (0.11) 0.885 (0.04) 2E919 20R 79 15.80 (2.39) 9.32 (2.10) 0.691 (0.08) 0.852 (0.07) 1E920 50R 81 16.20 (2.31) 9.38 (2.26) 0.642 (0.09) 0.841 (0.08) 2E921 75R 85 17.00 (2.15) 10.59 (2.54) 0.611 (0.10) 0.858 (0.07) 2E922 CCT 84 16.80 (2.13) 8.78 (1.97) 0.647 (0.08) 0.853 (0.06) 0Mean 83.13 16.63 9.79 0.653 0.857 2.25 (18)

MixedwoodE936 LOB 81 16.20 (2.97) 10.43 (2.50) 0.604 (0.13) 0.859 (0.07) 1E937 MEB 81 16.20 (2.03) 9.73 (2.00) 0.697 (0.06) 0.881 (0.04) 2E938 HIB 79 15.80 (2.29) 9.86 (1.93) 0.654 (0.10) 0.865 (0.06) 1E913 10R 73 14.60 (2.37) 9.37 (2.14) 0.596 (0.12) 0.799 (0.13) 1E910 20R 79 15.80 (3.20) 9.52 (2.44) 0.685 (0.10) 0.800 (0.12) 0E911 50R 88 17.60 (2.46) 10.26 (2.24) 0.668 (0.08) 0.864 (0.06) 1E912 75R 85 17.00 (2.19) 9.82 (2.32) 0.638 (0.13) 0.856 (0.07) 1E914 CCT 79 15.80 (1.88) 9.36 (1.94) 0.623 (0.10) 0.843 (0.08) 0Mean 80.63 16.13 9.79 0.646 0.846 1.13 (9)

Overall mean 81.88 16.38 9.79 0.649 0.851 1.69

Note: AT, total number of alleles; A, mean number of alleles per locus; Ae, effective number of alleles per locus; Ho, mean observedheterozygosity; He, mean expected heterozygosity.*Total is given in parentheses.

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whereas genotype-based genetic diversity measures are pre-sented in Table 4. Although there were some differences ingenetic diversity levels of the sampled white spruce subpo-pulations, their genetic diversity was quite similar.

For most of the parameters, genetic diversity levels ofwhite spruce from the CD stands were similar to those ofwhite spruce from the MW stands (Tables 3 and 4). How-ever, for certain parameters, the CD stands showed slightlyhigher genetic diversity than the MW stands (Tables 3 and4), although the differences were not statistically significant(P >F = 0.22–0.98). The correlation between DBH and ge-netic diversity parameters was not significant (P ‡ 0.05).Also, there was no significant correlation between age andvarious genetic diversity parameters.

Population genetic structureSignificant (P < 0.05) heterogeneity of allele frequencies

was observed for each of four (PGL14, PGL15, UAPgGT8,and UAPgCA91) out of five SSR loci over all 16 subpopula-tions as well as over 8 subpopulations from the MW stands.The allele frequency heterogeneity was significant forPGL14, PGL15, and UAPgCA91 among the 8 subpopula-tions from the CD forest type.

The mean F-statistics parameters indicated deficiency ofheterozygotes for both within populations and the total sam-ple relative to Hardy–Weinberg expectations (Table 5). Themean FIS, FIT, and FST values were similar for the completeset of the subpopulations, for CD stands, and for MW stands(Table 5). Gene flow rates estimated from the FST values

were approximately 11 migrants per generation among thecomplete set of 16 subpopulations as well as among 8 sub-populations within each forest type. Nei’s original (Nei1972) and (‘‘unbiased’’) (Nei 1978) estimates of genetic dis-tances among subpopulations ranged from 0.056 (‘‘0.027’’)between E919 and E913 to 0.336 (‘‘0.296’’) between E917and E910 (not shown), with a mean of 0.133 (‘‘0.093’’)among all subpopulations. On average, genetic distancesamong and within CD and MW stands were quite similar(Table 6). A consensus neighbor-joining tree based on Nei’soriginal (Nei 1972) genetic distances grouped the 16 subpo-pulations in 2 subgroups of 13 populations and 3 popula-tions each clustering individually (Fig. 3). The subgroupingwas not related to the forest type or the proximity of the pop-ulations. Both the subgroups include populations from theMW and CD forest types (Fig. 3). The bootstrap values forthe separation of subgroups were 16 and 14, with an averageof 15. Such low bootstrap values do not really support differ-entiation of the subgroups. Overall, the bootstrap valuesranged from 14 to 100 (Fig. 3), with an average of 42.7.

Table 4. Latent genetic potential (LGP) and genotypic measures of genetic diversity in whitespruce (Picea glauca) subpopulations.

GA GM

SubpopulationAssignedtreatment LGP Observed Expected Observed Expected

Conifer-dominatedE915 LOB 27.37 121 728 4.04�106 1.57�1010

E916 MEB 31.26 130 864 9.49�106 5.64�1010

E923 HIB 40.21 126 833 8.61�106 8.61�1010

E917 10R 36.17 129 798 8.55�106 6.69�1010

E919 20R 32.38 123 721 7.08�106 2.60�1010

E920 50R 34.10 123 750 6.70�106 3.62�1010

E921 75R 32.06 127 811 7.66�106 5.95�1010

E922 CCT 40.12 124 793 6.86�106 5.60�1010

Mean 34.21 125.38 787.25 7.37�106 5.03�1010

MixedwoodE936 LOB 28.85 131 785 8.82�106 2.67�1010

E937 MEB 32.36 123 738 7.29�106 3.93�1010

E938 HIB 29.70 131 716 9.36�106 2.54�1010

E913 10R 26.13 118 626 4.12�106 1.06�1010

E910 20R 31.38 124 766 3.86�106 1.33�1010

E911 50R 36.68 138 879 12.54�106 8.28�1010

E912 75R 35.88 120 813 6.54�106 6.06�1010

E914 CCT 32.21 120 699 5.62�106 2.97�1010

Mean 31.65 125.63 752.75 7.27�106 3.60�1010

Overall mean 32.93 125.5 770 7.32�106 4.32�1010

Note: LGP, latent genetic potential (Bergmann et al. 1990); GA, genotype additivity (Rajora et al. 2000);GM, genotype multiplicity (Bergmann et al. 1990).

Table 5. Mean F-statistic estimates.

No. ofstands FIS FIT FST

All subpopulations 16 0.226 0.243 0.022Conifer-dominated stands 8 0.226 0.241 0.020Mixedwood stands 8 0.225 0.242 0.021

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Discussion

The results from our study suggest that the studied borealwhite spruce stands located at the EMEND sites in northernAlberta have high microsatellite DNA genetic diversity. Thiswas the case with the allelic-based as well as genotypic-based genetic diversity measures. To the best of our knowl-edge, this is the first report for application of nuclear micro-satellite DNA markers for population and conservationgenetic studies in white spruce and probably in any otherspruce species. Since microsatellites are hypervariable DNAmarkers, high levels of genetic diversity were expected, andthe genetic diversity results from this study cannot be di-rectly compared with the earlier white spruce genetic diver-sity studies, which were based on allozyme or RAPDmarkers. Nevertheless, the microsatellite DNA genetic diver-sity was about 5 to 10 times higher than that reported fromallozyme analysis for white spruce populations from centraland southern Alberta (Rajora and Dancik 2000), Ontario(Cheliak et al. 1985), Quebec (Tremblay and Simon 1989),and Alaska (Alden and Loopstra 1987). This magnitude ofdifferences between allozyme and nuclear microsatellite ge-netic diversity is higher than that observed in eastern whitepine (Pinus strobus) (Buchert et al. 1997; Rajora et al. 2000)for the same populations, where microsatellite genetic diver-sity was about fourfolds that of allozyme genetic diversity.Also, the microsatellite DNA genetic diversity observed inwhite spruce populations is higher than what was observedin eastern white pine populations from Ontario (Rajora etal. 2000). The expected heterozygosity for microsatellites inthis study was about two times that observed in boreal Sas-katchewan white spruce populations based on RAPDmarkers (Rajora 1999). Our paper presents the first informa-tion on genotype additivity, genotype multiplicity, and latentgenetic potential measures in white spruce. The genotypeadditivity and genotype multiplicity estimates are the meas-ures of genotypic diversity reflecting diversity of allele com-binations in diploid genotypes, which are important todetermine for conservation genetic purposes along with theconventional allelic genetic diversity measures. The latentgenetic potential provides an estimate of new mutations andrare or low-frequency allele complements that could provideselection potential for adaptation under changed environ-ment and stress conditions (Stebbins and Hartl 1988).

In the present study, observed heterozygosity was consis-tently lower than the expected heterozygosity for all subpo-pulations. This seems to be a common phenomenon in whitespruce, as similar results were reported for white sprucepopulations from Alberta (Rajora and Dancik 2000), Quebec(Tremblay and Simon 1989), and Alaska (Alden and Loop-

stra 1987) based on allozyme studies. The mean FIS value inthe present study (0.226) is lower than that observed insouthern and central Alberta white spruce populations(0.651) (Rajora and Dancik 2000), but higher than that ob-served in white spruce populations from Quebec (0.132)(Tremblay and Simon 1989) based on allozyme analysis.Our results contrast with two allozyme-based white sprucestudies, where a tendency for heterozygote excess was ob-served (Cheliak et al. 1985; Furnier et al. 1991).

The lower observed heterozygosity as compared with ex-pected heterozygosity in the sampled white spruce subpopu-lations might be due to inbreeding, selection againstheterozygotes, and (or) Wahlund’s effect. Natural selectionagainst heterozygotes was evident at five allozyme loci inwhite spruce from central and southern Alberta in a previousstudy (Rajora and Dancik 2000). However, the microsatellitedata in the present study did not provide any evidence forsuch a phenomenon occurring in the studied white sprucesubpopulations. Wahlund’s effect could be due to spatialand (or) temporal factors. Since members of each of the sub-populations had chances of free interbreeding with eachother, they were apparently a part of the same breeding pop-ulation. Therefore, spatial Wahlund’s effect can be dis-counted. The temporal Wahlund’s effect may be producedby a difference in flowering phenology among individualswithin populations both within and between years. We donot have any information on flowering phenology of thesampled populations. Therefore, a temporal Wahlund’s ef-fect in the sampled white spruce subpopulations cannot beruled out. The FIS values of 0.225 and 0.226 suggest thatthe sampled populations may have experienced some sort ofinbreeding events, which could result in a deficiency of ob-served heterozygotes.

The nature of the markers used could also contribute to anartificial excess of homozygotes as a result of nondistinctionof homozygotes from heterozygotes due to nondetection of anull allele. Microsatellite markers are hypervariable andhave shown excess of homozygotes in forest tree popula-tions (e.g., Morand et al. 2002). Putative source of artificialexcess of homozygotes in populations from microsatelliteDNA analysis may be due to the occurrence of undetectablenull allele or short allele dominance (Wattier et al. 1998).An excess of homozygotes was evident at PGL12 as a resultof false scoring of possible heterozygotes with a null alleleas homozygotes. Therefore, this locus was removed fromthe analysis. None of the other five microsatellite DNA lociretained for calculation of genetic diversity and populationgenetic parameters showed occurrence of any null allele.No null phenotype was observed at any of the five lociamong the 560 white spruce individuals sampled. If there

Table 6. Mean (SD) genetic distances (Nei 1972, 1978) among white spruce (Piceaglauca) stands within (on the diagonal, in bold) and between (below the diagonal) foresttypes.

Forest typeNo. ofstands

Genetic distancemeasure

Conifer-dominatedstands

Mixedwoodstands

Conifer-dominated 8 Nei 1972 0.1335 (0.059)Nei 1978 0.0925 (0.056)

Mixedwood 8 Nei 1972 0.1337 (0.067) 0.1306 (0.038)Nei 1978 0.0939 (0.060) 0.0922 (0.037)

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was a null allele present at any of these loci, at least someindividuals would be expected to show null microsatellitephenotypes in such a large sample, until and unless the nullallele was lethal in the homozygous condition. However, mi-crosatellite markers are generally considered to be selec-tively neutral. Short-allele dominance at a microsatellitelocus is due to preferential amplification of shorter allelesby PCR (Wattier et al. 1998), resulting in lower amplifica-tion of the larger alleles. However, we did not observe anyevidence that would suggest the occurrence of such a phe-nomenon in our study.

The FST estimates suggest that only about 2% of the totalgenetic variation resides between subpopulations, and the re-mainder is among individuals within subpopulations. Thisestimate of interpopulation differentiation is consistent withthat generally observed in spruce and other conifers. Also,the FST estimates in this study are comparable to the FST es-timates based on microsatellite DNA for distantly locatedpopulations of black spruce (Picea mariana (Mill.) BSP)and for populations of eastern white pine spaced about1 km apart (O.P. Rajora, unpublished data). However, theseestimates are lower than those reported for white sprucepopulations from Alberta (Rajora and Dancik 2000) andQuebec (Tremblay and Simon 1989) based on allozymeanalysis. Since many of the sampled white spruce subpopu-lations were located adjacent to each other and all subpopu-lations were within 10 km of each other, higher gene flowand lower genetic differentiation between populations wouldbe expected. In the white spruce population genetic studyfrom central and southern Alberta (Rajora and Dancik2000), the sampled populations were separated by long dis-tances or mountain ranges (Rocky Mountains), which wouldnaturally result in lower gene flow and higher interpopula-tion genetic differentiation. Nevertheless, our study provides

the first estimates of interpopulation genetic differentiationbased on microsatellite DNA markers in white spruce andfor adjacent mature natural populations in any conifer.

The results of the study suggest that genetic diversity ofwhite spruce is similar between the CD and MW foresttypes. Therefore, white spruce genetic resources are essen-tially similar and could be conserved in any or both of theforest types. Certain factors associated with each of the CDand MW forest types could promote maintenance of highergenetic diversity. In the absence of strong family structure,higher genetic diversity might be expected in the CD standsbecause of higher white spruce stem density than in MWsand less obstruction for pollen gene flow. Because of lowerwhite spruce stem density and obstruction from hardwoodsfor pollen flow, lower genetic diversity of white sprucemight be expected in the MW stands. Since MW stands con-tain white spruce trees of uneven age and receive seeds fromall directions (Lieffers et al. 1996), a more diverse resultingwhite spruce gene pool is expected in the MW stands. Ahigher incidence of family structure is expected in CD whitespruce stands, resulting in an expectation of lower geneticdiversity. Apparently, some of these factors balanced outeach other, resulting in similar genetic diversity in the CDand MW stands. In our present study, the chances of sam-pling members of the same family were minimized, sincethe sampled trees were at least 30 m apart.

The genetic distance, neighbor-joining population clusterplot, and F-statistics results suggest that the population ge-netic structure and gene pool composition of white spruce isquite similar between the CD and MW forest types. Thiswould be expected, since these two forest types were locatedadjacent to or within 10 km of each other and there was nobarrier to gene flow between the forest types. By studyingadjacent or neighboring CD and MW stands, effect of geo-graphic and site differences confounding the comparison ofgenetic diversity and population structure of white sprucebetween these two forest types was minimized, thus provid-ing robust comparisons between the two forest types. Theseresults further reinforce the notion that white spruce geneticresources could be conserved in either forest type. However,effects of forest harvesting and forest fire on genetic diver-sity may vary between these forest types. This remains to beascertained. After our pretreatment sampling, the studiedsubpopulations have undergone experimental harvesting andforest fire treatments. However, the postharvest or postfiretreatment natural regeneration is not yet ready for sampling.Nevertheless, this paper provides the benchmark informationon genetic diversity and population structure of white sprucein the CD and MW forest types that could be used as a con-trol for determining and monitoring the genetic effects offorest harvesting and forest fires of different intensities.

In conclusion, our present study suggests that whitespruce in northern boreal forest in Alberta as it occurs atthe EMEND project site in the natural pristine CD and MWstands is highly genetically variable. The genetic diversityand genetic composition of white spruce are similar betweenthe CD and MW forest types. The white spruce gene poolcould be conserved in both forest types without an adverseeffect on the diversity of genetic resources. The presentstudy provides the benchmarks for determining and monitor-ing the genetic diversity impacts of forest harvesting and

Fig. 3. A consensus neighbor-joining tree of white spruce (Piceaglauca) subpopulations based on Nei’s (1972) genetic distances and1000 bootstrap iterations. The values on the nodes are the bootstrapvalues. CD, conifer-dominated stands; MW, mixedwood stands.

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forest fires of different intensities. Our study provides thefirst information on genetic diversity and population struc-ture of adjacent white spruce populations from CD and MWforest types and to our knowledge the first application of nu-clear microsatellite DNA markers in a large-scale populationgenetic study in conifers. The results of our study have im-plications for other boreal conifer forest trees that occur inthe CD and MW forest types.

AcknowledgementsThe study was funded by research grants and research

contracts from Canadian Forest Products Ltd. and ManningDiversified Forest Research Trust Fund and a Natural Scien-ces and Engineering Research Council of Canada (NSERC)discovery grant to O.P. Rajora. We thank Daishowa-Maru-beni International Ltd. and Canadian Forest Products Ltd.for providing camp and infrastructure facilities for the field-work; Stephen Pluhar, Jason Kuchar, Dennis Kuchar, Dr. S.Dayanandan, and EMEND core crew members for their as-sistance with the fieldwork; Sarah Keen, Michelle Madriaga,Karen Diepeveen, and Allison Stedford for their assistancewith DNA extraction; and Dr. Rama Singh and Dr. Johnvan der Meer of National Research Council of Canada – In-stitute of Marine Bioscience for providing an access to theirDNA-sequencing lab facilities.

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