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Pollination dynamic in an advanced generation Douglas-fir seed orchard Lai, Ben Shu Kwan 2009

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POLLINATION DYNAMIC IN AN ADVANCED GENERATION DOUGLAS-FIR SEED ORCHARD  by  BEN SHU KWAN LAI B.Sc. University of British Columbia, 2005  A THESIS SUBMITtED EN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE  in  THE FACULTY OF GRADUATE STUDIES (Forestry)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) March 2009  © Ben Shu Kwan Lai, 2009  ABSTRACT  The objective of seed orchard management is to maximize the genetic gain while maintaining sufficient level of genetic diversity in the orchard’s crops; however, balancing these two parameters is a challenging task. Eight polymorphic nuclear microsatellite DNA markers were used to construct the full pedigree of 801 Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) bulk seeds from 49 orchard parents and 4 external supplemental mass pollination (SMP) donors. Parental balance curves indicated that 80% of the gametes were produced by 23, 45 and 37% of females, males and clones, respectively. Contamination was found to be 10.36% with overhead cooling applied. Selfing rate was found as 15.23% due to the over representation of few orchard clones. The aggregate SMP (internal & external) success rate was estimated to be 15.02%. The female, male and clonal effective population size (Ne) was estimated to be 6.49, 26.00 and 13.70, respectively. The reduction of Ne is mainly the result of unequal parental contribution and to a minor extent by the co-ancestory of the orchard’s parents. The seed crop’s genetic worth was estimated to be 10.23 and -1.07 for volume and wood density, respectively. High correlations between visual reproductive assessment methods F2 (r  =  0.90 P  <  0.01) and M3 (r  0.77, P  <  0.05) with DNA analysis give credence to the visualize assessment methods. The result of this study indicated that the seed orchard deviated from the ideal conditions affecting the expected genetic diversity and gain estimates.  TABLE OF CONTENTS  ABSTRACT  ii  TABLE OF CONTENTS  iii  LIST OF TABLES  v  LIST OF FIGURES  vi  ACKNOWLEDGEMENTS  vii  INTRODUCTION Taxonomy and nomenclature Species range and habitat Reproductive cycle  I  Domestication and genetic diversity  2  Tree improvement cycle  3  Role of seed orchards  4  Seed orchard types  5  Seed orchard management  6  Seed orchard gametic contribution assessment  8  Molecular marker evolution  9  OBJECTIVES  12  MATERIALS AND METHODS  14  Seed orchard and research samples  14  DNA extraction  15  PCR protocol  16  Genotyping  17  Orchard-seedlot genetic diversity and pedigree reconstruction  17  Supplementary mass pollination success estimate  17  Effective population size and genetic worth determination  18  Effective population size and genetic worth estimation methods comparison  20 22  RESULTS AND DISCUSSION Identification of mislabeled ramets  22  Assessment of genetic diversity  22  Pedigree reconstruction  24  III  .25  Clonal gametic contribution Effective population size and genetic worth  25  Selfing  30  Supplemental mass pollination  31  Pollen contamination  33  CONCLUSION  35  REFERENCES  36  APPENDIX  47  iv  LIST OF TABLES  Table 1. Estimate of genetic diversity for each microsatellite locus in parental and offspring 23  populations  Table 2. Effective male and female population size estimates from the different reproductive 29  output assessment methods  V  LIST OF FIGURES  3  Figure 1. A diagram showing how the domestication process affects genetic diversity  Figure 2. The distribution of 354 full-sib families from 49 wind-pollinated Douglas-fir seed 24  donors with 4 additional SMP donors and contamination  Figure 3. Parental balance curves representing the maternal, paternal, and clonal gametic contribution. The horizontal line represents the gametic contribution intercept with their contributing parents  25  (%)  Figure 4. Correlation between % seed production based of survey assessments (Fl  —  F5) and  27  DNA analysis. (see Material and Methods for survey description) Figure 5. Correlation between % pollen productions based of survey assessment (Ml  —  M3) and  DNA analysis  28  Figure 6. Correlation between parental gametic contribution and selfing rate  31  Figure 7. Parental balance curve representing the 12 internal SMP pollen domors. The horizontal line represents the male gametic output intercept with their contributing parents  vi  (%)  32  Acknowledgements  I would like to thank the following agencies and people who made this project/thesis possible. At first, I would like to thank Western Forest Product Inc. and funds provided by the Natural Resources Canada - Science and Technology Internship Program, Johnson’s Family Forest Biotechnology Endowment, Forest Genetics Council of British Columbia, Natural Sciences and Engineering Research Council of Canada. Special thanks to Cathy Cook and Annett Van Niejenhuis from WFP for buds sample collection and provided very useful information about the seedlot. To make the extraction for 2,709 samples and run 27,090 PCR reactions possible, I thank Simren Brar, Heather Farnden, John Nixon, my cousins Wanda Doy Ming Ho and Candy Doy Key Ho, my nephews Sam Tin Chu Tsang and Ben Yin Cheung Tsang and my niece Jessica Yin Kwan Tsang for assisting with my lab work. I would also like to thank the lovely Czech couple, Irena Fundova and Tomas Funda. Tomas’ great tutoring allowed me to understand the material better. Many thanks to Irena, a.k.a Iris, who helped me with my lab work till 2am every night. I hope Tomas doesn’t mind ©. I thank you my labmates, Charles Chen, Mohammed Ismail and Nasim Massah, for all the helps in the lab, particularly covering my gel. “COVER ME at 7pm Please!” I would also like to thank Drs. Carol Ritland and Cherdsak Liewlaksaneeyanawin for advising on the extraction technique and PCR training. Great thanks to Dr. Kyu Suk Kang in assisting me with my data analysis and providing me with useful reading materials, especially the “bible” (K.S. Kang’s Ph.D thesis). Special thanks to Dr. Jaroslav Klapste who can compute any number aka “walking cpu.” Great thanks to Drs. Michael Stoehr and Richard Hamelin for being supportive thesis committee members. Lastly, tremendous thanks to my fantastic supervisor Dr. Yousry El-Kassaby for having me as a graduate student and providing endless support, guidance and advice inside and outside school. The knowledge that I gained from Dr. El-Kassaby is not limited to Science but also in business management and the worldwide issue. The benefit from him is priceless. An enormous thanks you to my gorgeous parents who give infinite support and sacrifice. To all my friends, I thank you for all your patient and understanding that I haven’t spent much time with you during the past 2 years, but now I AM READY.  vii  INTRODUCTION Taxonomy and nomenclature Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) is part of the Coniferophyta phylum and belongs to the Pinaceae family with pines, larches, spruces, firs, hemlocks, and other genera. It is a diploid organism with 26 chromosomes (n=13). The genus Pseudotsuga given to Douglas-fir implies it is a false (pseudo) hemlock (tsuga) and also not a true fir. Pseudotsuga consists of six tree species with two native to North America and four native to eastern Asia (Bailey 1955).  Species range and habitat There are two varieties of Douglas-fir in North America: the coastal Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco), which ranges from central maritime climates of the Pacific Northwest in latitude 55°N to 35°N along the coast line, and the rocky mountain Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco), which mainly occurs in the continental climates of the Rocky Mountains and extends into central Mexico (Hermann 1985). Douglas-fir is a pioneer species with best growth performance in the mild, wet winters and cool, dry summers with preference to deep, well-drained acidic loamy soils in elevation between 760 m to 1,250 m in the coastal range (Hermann 1985). It can reach up to 90 m or more in height and can live over five centuries with some reported over 1,300 years old on Vancouver Island. Since Douglas-fir is a shade intolerant species, it takes advantage of periodic disturbances such as forest fires and logging which create vast openings for its regeneration.  Reproductive cycle Douglas-fir is monoecious and reaches its reproductive maturity at 12 to 15 years of age. The 17 months reproductive cycle of Douglas-fir begins with the onset of vegetative bud growth in spring (April). During the early summer (May and June), the vegetative buds burst and the lateral bud primordia enlarge and by mid June period, they are 1  differentiated into either pollen or seed cone primordia. The differentiated primordia are dormant over the winter and continue the meiosis development in March of the following year. Pollination occurs around April and fertilization and embryo development are underway in seed cones. By late September, the cones have matured and begin to shed their seeds (Allen and Owens 1972).  Domestication and genetic diversity To meet the high demand of superior seed for reforestation, tree improvement programs and their production populations (seed orchards) have been developed for many important species to fulfill this goal (Forest Genetics Council of B.C. 2008). The objectives of tree improvement programs are to achieve genetic gains of desirable traits in a reasonably short period, maintain adequate genetic diversity, and conserve the diversity for the non-target traits for future selection and adaptation to changes of environment (Zobel and Talbet 1984). However, it is important to monitor the genetic diversity along the domestication process. Intensive selection of few genotypes or breeding with closely related individuals could result in genetic erosion in the breeding population (Butcher et al. 1996). Studies have shown a slight decrease in genetic diversity at subsequent stages of domestication and loss of rare or localized alleles due to the reductionist approach of these programs (Chaisurisri and El-Kassaby 1994; El Kassaby and Ritland 1996; Stoehr and El-Kassaby 1997; Liewlaksaneeyanawin 2006). Seed orchards represent the last stage of any breeding cycle and the linkage between breeding and silviculture thus levels of genetic diversity in their populations and their seedlots should be monitored (El-Kassaby 1992). Variation in seed production among clones, caused by fertility variation, would lead to reduction of effective population size which could result into higher level of inbreeding and genetic erosion (Kang and Namkoong 1988). The subsequent differences in seed germination rate and initial seedlings growth rate among orchards’ parents could also impact the diversity during seedling production phase (El-Kassaby and Tompson 1995; El-Kassaby 2000a). Natural selection is the last stage that operates on the genetic diversity in the reforested populations (Figure 1). 2  Bilogical constraints: affecting genetic quality  End Product Natixal Population  Penotypic selection  I  -  Plus-frees  -  Selection Infusion Adv&iced Generations Testing  Breeding  Genotypic selection Breeding strategy Reproductive pilenology Parental  Seed Orchards  Seed Production  Contamination Seed handling Seed stage  Gemilnation Nurseries Thinning spacing  R(jenemtion  Seedling Production  I  Figure 1. A diagram showing how the domestication process affects genetic diversity (El Kassaby 2000b). Tree improvement cycle To protect and conserve forest genetic resources for long-term selection, while maintaining economic growth by meeting short-term demand, tree improvement programs play an important role in balancing these two objectives. The improved seed can increase timber values by magnifying the desirable traits. It also affects the AAC (Allowable Annual Cut) calculations by shortening the free growing period. Furthermore, it reduces the rotation period by increased yield which will affect the allowable cut and suits the conservation goal by reducing harvest on natural stands old growth. Plus trees with desirable traits are selected from natural forests and bred for the production of fulland half-sib families for progeny testing to ensure the genetic quality of further selection. While evaluating the genetic superiority of the progeny, first generation seed orchards are 3  established using scion material from the selected plus trees for seed production. Breeding values are assigned to each parent based on their progeny performance. As the breeding cycle advances, first generation seed orchards are rouged by removal of low breeding value parents and improved by planting higher breeding value parents selected either through backward or forward selection (the inclusion of selected genotypes based on the progeny tests). As advanced-generations seed orchards are established, co-ancestry is expected to build up. The forward selected progeny contains greater diversity than the backward parents; however, it is fraught with caveats since the selected individuals are not tested. As mentioned above, reduction in diversity is inevitable along the domestication process, so it is essential to maintain adequate effective population size for maximizing genetic diversity and indirectly reducing selfing.  Role of seed orchards Seed orchards perform as the delivery system for tree improvement programs. They play a central role in breeding and reforestation programs and act as in situ conservation populations. They capture and package the genetic gain and diversity that is attained from breeding programs and deliver them to regenerated forests through the production of superior seed (Smith and Adam 2003). Under ideal conditions, a seed orchard is expected to function as a close, perfect, panmictic population following Hardy-Weinberg equilibrium expectations which include the isolation from outside undesirable pollen (no migration), maximum reproductive synchrony and reproductive output equality, and minimum inbreeding and selection (Eriksson et al. 1973). However, these assumptions are rarely met and substantial amount of research has been conducted to illustrate these violations and to device management practices to overcome or lessen their impact (El Kassaby et al. 1989). The collective impact of these violations leads to changes in genotypic and allelic frequencies in seed orchards’ seedlots, resulting in unpredictable estimates of genetic gain and diversity, and subsequently affects future plantations.  4  Seed orchards types Seed orchard types are determined by the nature of the material used during establishment. Seedling seed orchards (S SO) consist of either half- and/or full-sib families (seedlings) and can also serve as progeny tests for determining the breeding value of parent trees (Barnes 1995). Since they require long period to reach sexual maturity (i.e., pollen- and seed-cone production), they are mainly suitable for species with early reproductive maturity or those with low grafting success (Kang 2001). Clonal seed orchards are established with grafts or rooted cuttings from superior trees (plus trees). The advantage of grafting is the capability to retain its original parental material as well as capitalizing on its physiological age; therefore, it is suitable for late reproductive maturity species. Finally, a third type consists of clonal and seedling material (commonly known as clonal-seedling orchards); however, they are not very common. Seed orchard types dictate the possible forms of consanguineous matings. For example, selfing (mating within a tree and among ramets of the same clone) can occur in clonal orchards while selfing and sib-mating (mating among members of a HS or FS family) take place in seedling orchards. Finally, selfing, sib- and parent-offspring mating can occur in clonal seedling orchards (El-Kassaby et al. 1989). Consanguineous mating could lead to build up of genetic load (inbreeding) which immediately affects seed production (Woods and Heaman 1989) while delayed response affects plantations health and individual tree fitness (Wang and Russell 2006), thus care should be directed towards proper seed orchards’ designs and management.  5  Seed orchard management Seed orchards’ designs influence mating dynamics among and within clones, crop management activities, and ultimately the genetic quality of seed crops. Random single tree is the most common design; however, different arrangements such as clonal-rows or clonal blocks have been used (Faulkner 1975). It has been demonstrated that clonal spatial arrangement has a direct impact on mating system parameters such as selfing and correlated matings (El-Kassaby 2003; El-Kassaby et al. 2007). Generally, random spatial arrangement provides more opportunities for among-clones mating and reducing selfing by physically separating related individuals; therefore, it is the commonly used seed orchard design (Bell and Fletcher 1978). The mating system of a tree species in seed orchards is greatly affected by the difference in reproductive phenology among orchards parents (El-Kassaby et at. 1984, 1988; El Kassaby and Ritland 1986), clonal fecundity (El-Kassaby and Askew 1991), level of pollen contamination (El-Kassaby and Ritland 1986; El-Kassaby et al. 1989), genetic relationship among mates (Lindgren and Mullin 1998), orchards’ spatial layout (Erickson and Adams 1990; Burczyk and Prat 1997; El-Kassaby 2003; El-Kassaby et al. 2006), orchard management such as bloom-delay (El-Kassaby and Davidson 1990) and supplemental mass pollination (El-Kassaby and Ritland 1986). Random mating is the key for maintaining diversity in populations; however, deviation from random mating was observed in both natural and artificial populations. Reproductive phenology is one of the factors that impede panmixia (Fashler and El Kassaby 1987). Reproductive phenology and vegetative development are controlled by both genetics and environment. The rapid change in temperature from winter to spring allows trees to quickly accumulate heat-sum leading to reaching their threshold, thus trees in continental environment show reduced phenological differences compared to coastal locations. The phenological variation among seed orchard trees is caused by differences among the original ecological environment of parents (Chaix et al. 2007); therefore, trees in the seed orchard often show different threshold requirements and are expected to be in reproductive asynchrony (Worrall 1993; 1999). Reproductive 6  phenology differences among orchard’s clones subdivide the orchard population into temporally separated populations with a reduced number of parents, hence increasing selfing probabilities and promoting assortative mating (where mating is restricted among clones of the same reproductive phenology class). Supplemental mass pollination (SMP), the broadcast application of viable pollen to nonisolated receptive strobili (Wakeley et al. 1966), is an artificial pollination management technique that contributes to increasing the gametic contribution of a specific set of parents in the seed crops. It is commonly used to promote parents with high breeding values, facilitates mating among asynchronous parents (El-Kassaby et al. 1988), provides viable competition to outside pollen contamination (Askew 1992; El-Kassaby and Ritland 1986), and increases seed set and outcrossing particularly for reproductively early and late parents where the pollen cloud density is low (El-Kassaby and Ritland 1986). Nevertheless, it is a labor intensive process which requires the collection and processing of the desired pollen, close monitoring of trees receptivity, and the application of multiple treatments during a narrow receptive window of target trees (El-Kassaby et al. 1993). Bloom delay is another cone crop management system that is commonly used in Douglas-fir seed orchards in coastal British Columbia. It involves the application of an over-head fine water spray cooling treatment to delay reproductive development and thereby reduces the chances for pollen contamination (migration) from external sources (Silen and Keane 1969). The system has proven to be effective in delaying treated orchards’ reproductive phenology (Fashler and Devitt 1980), compacting orchards’ reproductive phenology thus improving panmixia (Fashler and El-Kassaby 1987), reducing contamination (El-Kassaby and Ritland 1986), and increasing outcrossing rate (El-Kassaby and Davidson 1990; 1991). The combined effect of SMP and over-head cooling treatments was studied and this combination was proven effective in increasing seed yield and enhancing mating system parameters as well as reducing contamination (El-Kassaby et al. 1990; El-Kassaby and Davison 1990) in Douglas-fir seed orchards. Fertility variation is another challenge to seed orchard management. Plus trees selection often focuses on desirable traits but less emphasis is put on tree’s reproductive propensity. Generally, there is tradeoff between growth and reproduction. Trees which 7  allocate their energy to fast growth often have less reproductive output (El-Kassaby and Barclay 1992; Chaix et al. 2007); therefore, variation in seed and pollen production can occur and should be expected among orchard’s trees. Fertility variation (i.e., the gene pool is over represented by few parents) leads to diversity reduction and genetic erosion as parents with low fecundity contribute minimally to the next generation’s gene pool (Kang 2001). Moreover, the large production of pollen and seed by few parents could increase the chances of selfing, consequently leading to seed yield reduction caused by early embryo abortion (Woods and Heaman 1989). Several stress treatments such as drought, growth hormone application, and over fertilizing can be used to increase the reproductive output of lower fertility parents; however, even with these management techniques, fertility variation still occurs among plus trees (Ross et al. 1982). Another approach to balance seed crops and maintain high effective population size is to constrain the number of seeds harvested from the high productive parents (Kang et al. 2003; Funda et al. 2008b); however, this might lead to an overall reduction of available seed for reforestation.  Seed orchard gametic contribution assessment Correct estimation of parental gametic contribution in seed orchard populations is the key for accurate estimation of crops’ effective population size (diversity) and genetic worth. Estimating each parent’s exact gametic contribution is costly and inefficient; therefore, quick, simplified and economic sampling methods were developed to estimate orchards’ male and female gametic contribution. Woods (2005) proposed seven and three methods for estimating female (Fl  —  F7) and male (Ml  —  M3) gametic contribution, respectively.  These methods offer a range of accuracy that increases with the level of details and effort allocated to each (Woods 2005). Female contribution methods vary between the simplest (F 1: visual estimate of the amount of seed cones produced by every ramet of each parent before harvest) to the elaborated methods (F7: estimate of the amount of filled seeds of each parent), while the male contribution methods vary between Ml (contribution is proportional to the number of ramets in the orchard) and M3 where the contribution is approximated by the volume production of pollen cones by each ramet (100% survey). It 8  should be stated, however, that the Ml method was not developed for Douglas fir seed orchards.  Molecular marker evolution The importance of genetic diversity has been well recognized. Inbreeding is one of the causes of the reduction in genetic diversity which can negatively affect the adaptive potential of trees to the changing environment and reduce the selection power for breeders. In order to maintain and manage genetic diversity, it is necessary to estimate the genetic variation magnitude within and among populations. Before the development of molecular markers, morphological traits were often used to estimate genetic variation with the assumption of the homogeneity in landscape; however, this assumption has been contested and proved to be incorrect (Dutkowski 2002). With the advances in molecular biology and technical innovations, scientists are able to use more accurate techniques to analyze genetic variation at the molecular level (Schiotterer 2004) while reducing environmental bias. For this reason, genetic data are frequently used to answer many biological questions such as paternity test, pedigree reconstruction, phylogenetic grouping, gene mapping, population genetics and the modem forensic applications. These molecular techniques act like markers to detect the inheritance of the proteins or DNA sequences and their functions (Bhalerao et al. 2003). The general criteria for an ideal molecular marker should consist of high polymorphism, co-dominant expression, high reproducibility, inheritability, selective neutrality, low linkage disequilibrium, and simple procedure (Vendramin and Hansen 2005). Microsatellites are the first genetic markers which have their specific designed primers for PCR amplification. The characteristic of its simple tandem repeats gives this marker several names such as simple sequence repeats (SSRs), short tandem repeats (STRs), and the most commonly used term “microsatellites.” Microsatellite is a simple tandem of short sequence motifs one to six base pairs long, with a typical repeat region smaller than a hundred base pairs (Tautz 1989). Studies have shown the highly polymorphic nature of microsatellites and confirmed their distribution throughout many organisms’ genomes in both coding and non-coding regions (Lift and Luty 1989; Weber and May 1989; Tautz 9  1989; Goldstein and Schlotterer 1999), with much higher frequency in the non-coding region (Wang et al. 1994; Metzgar et al. 2000). Bell and Jurka (1997) found the coding region has shorter repeats  ( 3 units) compared with the non-coding region which has  longer length distribution (?5 units); furthermore, mutation would break the long repeat into two or more shorter repeats in the untranslated region to make up a compound tandem arrays of different motifs. Di-nucleotide repeats are found to be the common repeats in many species (Wang et al. 1994; Ellegren 2004; Li et al. 2002) and are often chosen along with tri-nucleotide and tetra-nucleotide repeats for molecular studies. Mono-nucleotide repeats such as poly AlT tracts are also found to be abundant in the genome in the untranslated regions. It has been hypothesized that the long poly AlT tail in the non-coding region resulted from a high tolerance of mutation (Toth et al. 2000; Wang et al. 1994; Dokholyan et al. 2000). Kashi et al. (1997) also suggested that eukaryotes which incorporate more repeats may have an advantage of better adaptability to new environments. Although the poly tails are abundant in the genome, they are not suitable for genetic analyses due to their unstable performance during PCR (Beckmann and Weber 1992; Li et al. 2002). The high variation observed in microsatellites is caused by gaining or losing repeat units by DNA replication slippages (unequal crossing over) (Eisen 1999). The rate of mutation in microsatellites varies in different species. Levinson and Gutman (1987) reported about 10.2 per replication in E. coli whereas Henderson and Petes (1992) found approximately 1 0 to i0 in yeast. To understand the microsatellite mutation process, it is important to know the mutation models. The infinite allele model (lAM; Kimura and Crow 1964) assumes that any mutation would involve a change in the tandem repeats of an allele which never existed in the population previously. The K-allele model (KAM; Crow and Kimura 1970) assumes the mutation rates are equal in all directions among the possible number of alleles K. The stepwise mutation model (SMM; Kimura and Ohta 1978) assumes each mutation event creates one new allele by gaining or losing only one repeat unit. Finally, the two phase model (TPM; Di Rienzo et al. 1994) assumes the change in the number of repeat units through mutations is drawn from a specified distribution that allows for large changes in repeat number. The mutation of microsatellites has been considered to coincide with the JAM, SMM and TPM models. 10  Unlike the highly polymorphic microsatellites, the nucleotides surrounding a microsatellite locus are highly conserved across individuals of the same species and sometimes of different species. These conserved regions are termed flanking regions and are often used to construct primers (oligonucleotides) to amplify the tandem repeats by PCR. The advantage of microsatellites is that each marker locus is a sample of the genome; therefore, multiple microsatellite loci in the genome act as statistical replicates of the genotyping which can provide a more precise and powerful result for population study (Kalinowski 2002; Pearse and Crandall 2004). Although isozymes, RAPD, and AFLP also provide multilocus results, the resolving power of the multilocus microsatellites outcompete all of them (Sunnucks 2000). The high allelic diversity and co-dominancy make microsatellites a better genetic marker than the others because only a few microsatellite loci are adequate to identify unique genotypes (Queller et al. 1993; Schlotterer 2000). Moreover, random distribution of the tandem repeats in the genome also makes it superior for mapping. The short length of the repeat sequence makes the amplification more efficient despite some DNA degradation (Taberlet et al. 1999); furthermore, since microsatellites are species-specific markers, the possibility of crosscontamination by non-target organisms is very low compared with the universal primer. Despite these many advantages, microsatellites have their own challenges. The artifact stutter bands make scoring of the microsatellite alleles more challenging. Size-based identification of alleles has its drawbacks such as homoplasy. Homoplasy may lower the observable allelic diversity in populations or inflate the estimates of gene flow when mutation is high due to alleles being of the same size but not identical by descent (different lineages) (Adam et al. 2004; Curtu et al. 2004). Mutation in the flanking region may lead to occurrence of null alleles causing some alleles fail to amplify during PCR; furthermore, preferential amplification of certain alleles may also show partial null allele characteristic such that heterozygous individuals are assigned as homozygous. Both these phenomena cause an apparent excess of homozygosity in the population. Since microsatellites are species specific markers, they require some knowledge of the species’ genome for constructing the primers. 11  OBJECTIVES  Increasing forest productivity is important for sustainable forest management. Production of high genetic quality (gain and diversity) seed crops is imperative to reaching successful sustainability in forests. To achieve high genetic quality seeds, seed orchards need to function as close, perfect, panmictic populations. To reach this state, the seed orchard populations must be at maximum reproductive synchrony, at reproductive output and mating success equality, isolated from pollen migration, and with minimal inbreeding and favorable mating among specific parents (Eriksson et al. 1973). These biological parameters and the obvious economic importance of seed orchards became the subject of intense studies and, generally, research has proven that most of these assumptions are hardly met; however, the system was also proven to be robust to these violations. This study is designed to take advantage of the superiority of SSRs markers for fingerprinting, the availability of advanced pedigree reconstruction analytical methods, and the unique seed biology of conifers (haploid-diploid seed structure) to test the degree of deviation from the expected ideal scenario. Western Forest Products’ second generation Douglas-fir seed orchard provided the material for this study (random bulk seed sample of unknown parentage). The seed sample used was part of the orchard’s 2005 seed crop which was developed under crop management consisting of an over-head cooling treatment and the application of supplemental mass pollination. Visual assessment of parental male and female gametic contribution following the methods of Woods (2005) was also conducted. The specific objectives of the study are to: 1- Evaluate the extent of genetic diversity in the orchard and its 2005 seed crop, 2- Conduct full pedigree reconstruction on the bulk seed sample, 3- Estimate: abcde-  female and male gametic contribution, selfing rate, supplemental mass pollination success rate, extent of pollen contamination, female, male, and parental effective population size, 12  f- genetic worth of the orchard’s seed crop, 4- Compare parental gametic contribution estimation methods (DNA vs. visual assessment).  13  MATERIALS AND METHODS  Seed orchard and research samples The study orchard (#166) is owned and managed by Western Forest Product Inc. and located on the Sannich Peninsula, southern Vancouver Island, Saanichton, British Columbia (latitude 48°35’N, longitude 123°24’W, elevation 50 m), within the Coastal Douglas Fir (CDF) zone that is characterized by dry cool mesothermal maritime climate. This orchard’s population is a second generation, low elevation (<600m) coastal Douglas-fir and consists of 49 parents selected from the British Columbia Ministry of Forests and Range’s low elevation coastal Douglas-fir breeding program (20 backward and 29 forward selections). The orchard was established in 1990 following the permuted neighborhood design (Bell and Fletcher 1978). On average, each parent (clone) is represented by 5 ramets. The studied seed crop (2005) was managed under an overhead cooling treatment (to prevent reproductive buds frost damage and to reduce pollen contamination) and application of supplemental mass pollination (SMP). The pollen for SMP was collected from 16 parents (12 internal and 4 external) and was applied on 40  clones with the number of visits ranging between 1 and 4. Parental male and female gametic contribution was estimated following Woods (2005) methods. During the fall of 2005, young vegetative bud tissues were sampled from the orchard’s parental population (49 parents) as well as the 4 external pollen donors which were included in the supplemental mass pollination treatment (parents from another seed orchard). A total of 149 bud samples (varies from 1-3 ramets/clone), representing the orchard’s parental population as well as the external SMP pollen donors, were collected, placed on ice upon collection, and shipped to UBC where they were immediately stored at -80°C until DNA extraction. Multiple ramets per clone were sampled to determine the presence/absence of labeling error in the orchard. A random bulk seed sample (unknown parentage) of the 2005 seed crop was supplied by the Ministry of Forests and Range’s Tree Seed Centre and was stored at 4°C until used.  14  DNA extraction Seeds were germinated using the protocol described in Edwards and El-Kassaby (1995). Seed dissection for extracting the diploid embryo and haploid megagametophyte tissues followed the suggestions of Krutovskii et al. (1997). In brief, Krutovskii et al. (1997) cautioned about the balance between embryo and subsequent germinant development and the state of megagametophyte. They recommended the removal of germinants when the cotyledons are extended to approximately 4 mm from seed coat to achieve a maximum DNA yield. Early removal of the megagametophyte would impair embryo growth, because it provides nourishment for seed; however, late removal would sacrifice the quality and yield of DNA obtained. A total of 1,280 diploid embryo and haploid megagametophyte tissues were extracted using dissecting forceps and stored at -80°C until use. DNA was extracted from all 149 bud samples and 1,280 seeds (embryos (2n) and their corresponding megagametophytes (in)) using the CTAB (cetyltrimethylammonium bromide) method by Doyle and Doyle (1987) after a slight modification. Each sample was ground in liquid nitrogen using a 1.5 ml tube and a mini pestle, incubated at 65°C for one hour with 800 jil of CTAB extraction buffer (1.8% CTAB, 0.1M Tris base (pH 8.0), 0.02M EDTA (pH 8.0), i.4M NaC1, and 0.2% 3-mercaptoethanol), and centrifuged for 2 minutes. The supematant was transferred to a new tube and incubated for 45 minutes at 37°C with 0.1 mg of RNase A, followed by 45 minutes of incubation at 37°C with 0.2 mg of Proteinase K. Each sample was mixed with 750 jil of chloroform:isoamylalcohol (24:1) for 30 minutes to remove proteins and centrifuged for 10 minutes to separate supematant DNA from proteins. DNA was precipitated from the supernatant overnight at -20°C using 400 jil of cold 100% isopropanol. The DNA pellet was then washed twice with cold 70% ethanol and speed-vac dried, and finally dissolved in 30 jil, 50 tl and 80 tl of sterilized deionized distilled water for megagametophytes, embryos and bud samples,  respectively.  The DNA  quality  spectrophotometry and gel electrophoresis.  15  of all  samples was  evaluated by  PCR protocol The use of 4-6 polymorphic microsatellite loci was reported to be sufficient for attaining high exclusionary power to infer paternity and estimate gene flow by several researches (Dow and Ashley 1998; Gerber et al. 2000; Lian et al. 2001; Slavov et al. 2004; Slavov et al. 2005; El-Kassaby et al. 2006; Selkoe and Toonen 2006). In this study, eight SSR markers (Appendix 1) were used and the polymerase chain reactions (PCR) were carried out with a final volume of 10 pi using PE Applied Biosystems Gene Amp PCR System 9700 thermal cycler. The PCR condition was based on the modification of those used by Slavov et al. (2004). Each reaction was composed of 75 ng of the genomic DNA, 1 pmol of each primer (19-20 oligonucleotides were added to each primer set as either forward or reverse 1 M 3 tail when primers were ordered), 0.5 mM of each dATP, dCTP, dGTP, and dTTP, lox Buffer (10 mM Tris-HC1, 1.5 mM MgCl , 50 mM KC1, pH 8.3) (Roche, 2 Laval, Que.), 1 U of AmpliTaq DNA® Polymerase (Roche, Laval, Que.), and 0.3 pmol of 13 infrared label to detect microsatellite products (LiCor Inc., Lincoln, NE). Samples M were amplified as: 95°C for 5 mm long denaturation followed by 33 amplification cycles of 95°C for 30 sec, Ta (annealing temperature) for 30 sec (Appendix 1), 72°C for 45 see; followed by a 10 mm long elongation at 72°C. A touchdown PCR was used for one of the primers (Appendix 1), by beginning with 7 cycles of touchdown: 95°C for 30 see, Ta  +  6°C for 30 sec and 72°C for 45 sec. The Ta was decreased by 1°C for each of the 6 subsequent touchdown cycles before the amplification cycles and with 10 mm  long  elongation at 72°C in the last step. 2 jil of stop dye buffer (LiCor Inc., Lincoln, NE) were added to each PCR reaction before running on electrophoresis. Samples were loaded in 25 cm long 0.4 mm thick 6% (Long Ranger ) polyacrylamide gels and DNA fragments TM were separated by LiCor 4200 automated sequencer (LiCor Inc., Lincoln, NE).  16  Genotyping Genotyping errors are the major concern in molecular biology especially in parentage analysis; therefore, eight polymorphic SSR loci from those developed by Slavov et al. (2004) were chosen based on their polymorphism and stability in PCR reaction with my samples. PCR condition was optimized for all 8 SSR markers obtained strong and consistent single locus banding pattern. The strongest banding pattern was scored when we encountered the excessive stuttering bands. All 53 parents (49 orchard and 4 external SMP) and 1,280 seeds (in + 2n) were visually scored (genotyped) using SAGATM software (LiCor Inc., Lincoln, NE) with the assistance of four 50  —  350 bp sizing  standards (LiCor Inc., Lincoln, NE) and orchard parent population reference alleles (a mixture of microsatellite products covering the range of alleles) to determine the microsatellite  product  sizes  across  gels.  Furthermore,  each  seed  with  its  megagametophyte (in) and embryo (2n) pair to assist in identif,iing paternal allele to obtain concurrently scoring.  Orchard-seedlot genetic diversity and pedigree reconstruction Due to insufficient genotyping information (<5 loci) caused by poor amplification (due to a combination of low DNA quantity, quality or null alleles), a total of 801 seeds was used in this study. Additionally, 49 orchard’s parents as well as the 4 external SMP pollen donors were also genotyped. The CERVUS program (Kalinowski et al. 2007) was used to determine the number of alleles, the most common allele frequency, and the observed and expected heterozygosites for each microsatellite locus for the orchard population and the seed samples. The parentage assignment of seeds was determined using cumulative average probability of exclusion from the likelihood equation in the CERVUS program (Kalinowski et al. 2007) with the assistance of the megagametophyte to determine the mother. The benefit of the CERVUS program is its ability to accommodate genotyping errors. Considering/accounting for genotyping errors during data analyses has proven to be advantageous in several genealogical relationship studies (SanCristobal and Chevalet 1997; Wang 2004; Vandeputte et al. 2006; Kalinowski et al. 2007). Genotyping errors are the product of contamination, allelic dropout, microsatellite stutter, null alleles, or simply 17  human error. CERVUS program uses the likelihood equation to assign parentage. The high LOD (likelihood-odds ratio) value indicates the parent has higher probability to be assigned correctly than the others (Kalinowski et al. 2007).  Supplemental mass pollination success estimation Success rates of external and internal supplemental mass pollination treatments were estimated separately. The former was determined directly from the proportion of seed sired by the four external pollen donors while the latter was estimated using a quadratic regression model. This model was developed to predict the ambient pollination success rate (Funda et al. 2009) and was based on pollen production estimates (visual assessment; method M3) and the actual DNA siring success for the 37 parents that were not included in the internal SMP treatment. Using this model, the ambient siring success rate for the remaining 12 parents (internal pollen donors) was predicted and compared to the actual DNA results. The difference between the predicted and actual values, after scaling to exclude seed sired by external gene flow and external SMP sources, provided an estimate of the internal SMP success rate. The combined effect of SMP was then calculated as a sum of the internal and external SMP success rates.  Effective population size and genetic worth determination Genetic diversity is important for forest health; therefore, the Forest Genetics Council of British Columbia has adopted regulations to insure the maintenance of diversity in BC forests while promoting economic growth by planting trees with greater genetic gain. Unfortunately, genetic gain and diversity have a negative relation where increasing genetic gain is often associated with diversity reduction (El-Kassaby 2000b; Kang and Lee 2008); thus striving to achieve a balance is of vital importance. Yanchuk (2001) demonstrated that an effective population size (Ne) of 10 is adequate to capture 95% of the quantitative genetic variance of the original population; therefore, (Snetsinger 2004) a minimum Ne of 10 for reforestation seedlots is required. Effective population size of orchard’s seedlots describes their genetic diversity through estimating the parental 18  population’s proportional gametic contributions to these seedlots. When parents are unrelated and non-inbred, it is calculated as follows:  Ne=  N  (1)  .2  where p, is the parental gametic contribution of the parent i. This quantity represents the average of the proportional parental contribution as female  (f)  and male (m ) and is 1  calculated as follows: =  (fi+mi)  (2)  In this study orchard (# 166) as well as in other advanced-generations ones, the orchard’s parental population consists of a combination of forward and backward selections; therefore, co-ancestry (common ancestry) among the parental population must be taken into account whenever Ne of the orchard’s crop is estimated (Lindgren et al. 1996). Co ancestry is the probability that any two alleles sampled at random from two individuals are identical by descent (Maldcot 1948). Cockerham (1967) introduced a concept of group co-ancestry (@) and defined it as the average co-ancestry of all pairs of individuals in the population including the co-ancestry of each individual with itself (self coancestry). By summing the probability of the first allele (i) and second allele (j) originates from its genotype are r and r 1 respectively. The likelihood that these two allele are identical by descent is c, which is the co-ancestry between the genotypes i and j. The kinship coefficients (co-ancestry) are as follows: i) c full-sibling or parent-offspring, iii)  C/  =  =  0.5 for selfing, ii)  0.125 for half-sibling, and iv) c  Cy  0.25 for  0 when there  is no genetic relationship (i.e., unrelated and non-inbred individuals). 0  (3)  =  Lindgren et al. (1996) introduced the concept of status number (Na), which measures the effective population size considering the relatedness among orchard’s parents and is calculated as half the inverse of the average co-ancestry. The status number is a measure of gene diversity which accounts for relatedness among parents (Lindgren and Mullin 19  1998) and is equivalent to the census number (actual number of parents) only if all parents are unrelated, non-inbred and contribute equally to the offspring gene pool: (4)  2Ns  In addition to genetic diversity, the genetic worth of a seedlot is also important since it represents the captured gain from breeding programs. It depends on the genetic quality of the selected parents, their gametic contribution to seed crops, and the amount and genetic quality of the contaminant pollen (Stoehr et al. 1994; Slavov et al. 2005). It is expressed as the average breeding value weighted by the respective parental contributions where the breeding value describes a particular parent’s genotypic value judged by the mean value of its progeny compared to others (Eriksson and Ekberg 2001). In the present study, breeding values for volume and wood density of all the orchard’s 49 as well as the 4 external parents (53 parents) were obtained from the Western Forest Product Inc. through a Douglas-fir breeder, Dr. M. Stoehr, from the BC Ministry of Forests and Range. The 2005 seedlot’s genetic worth was estimated as p X 1 \G=  (5)  where X, and P denote the breeding value and proportional contribution of parent ito the seed crop, respectively.  Effective population size and genetic worth estimation methods comparison DNA  fingerprinting  and  pedigree  reconstruction  (paternity  analysis)  provide  unprecedented insight into a seed orchard’s mating and gametic contribution dynamics that no other method can provide (Slavov et al. 2005; Funda et al. 2008). However, this approach is very costly and time consuming compared to the traditional systematic observations made to gauge seed orchard’s parental reproductive outputs (Woods 2005). If the goal of reproductive output assessment does not require high resolution answer, then DNA analysis may not be necessary. The traditional visual estimation of cone volume to estimate the gamete contribution of a tree is an inexpensive and fast method 20  compared with those based on DNA fingerprinting; however, the errors associated with the estimates would be largely depending on the sample size, sampling method and sampler experience (Wood 2005). It is sensible to use methods that provide accurate results with minimal work and cost, even if their level of precision is not optimal. The seed orchard’s male and female reproductive output assessment (100% sampling) were conducted by a Western Forest Products’ orchard manager and were provided for comparison. Pearson’s product-moment correlation coefficients were calculated between male and female DNA-based gametic contributions as well as between data obtained from the visual assessment methods proposed by Woods (2005) (Ml, M3, and Fl to F5) and the DNA-based results whereby the accuracy of these methods was evaluated. Furthermore, the impact of these methods on the estimate of effective population size and genetic worth was determined.  21  RESULTS AND DISCUSSION Plant mating system is the vehicle by which population’s genetic diversity is mixed and transmitted between generations (Clegg 1980). The mating system of a seed orchard is greatly affected by parental reproductive phenology differences (El-Kassaby et al. 1984), fecundity variation (El-Kassaby and Askew 1991), level of pollen contamination (Adams et al. 1997), and the genetic relationship among orchard parents (El-Kassaby et al. 2007). Understanding the role of these factors individually and collectively is essential for developing seed orchards effective management practice and ensuring that the genetic gain attained through breeding is packaged and delivered to future forests.  Identification of mislabeled ramets With the power of SSR multilocus genotyping, we identified a single incident of a mislabeled ramet in clone 3324 (1 out of 149 genotyped ramets (0.7%)), indicating that 99.3% of the orchard’s parents are correctly labeled (Appendix 2). The multilocus genotype of the mislabeled ramet was informative and the correct parent (#3 360) was identified. Since this genotype is present in the orchard, no further adjustment was needed for subsequent analyses. Mislabeling of ramets in seed orchards is common (Adams et al. 1988; Slavov et al. 2004) and parental verification of seed orchard trees is recommended specifically if they are used for breeding purposes. Assessment of genetic diversity Monitoring the genetic diversity over generations is important in determining the direction and magnitude of changes and understanding the factors causing them such as fertility variation, reproductive phenology asynchrony, and gene flow. Comparing the genetic diversity parameters between the orchard’s parental population and its resultant seed crop indicated the presence of gain and loss of alleles at some of the studied loci (Table 1) (Appendix 3). Gain of alleles was observed at four loci (3B9, 3F1, 2D4, 2G12), which is indicative of gene flow (see pollen contamination section). Similar observation was reported for an “interior” spruce seed orchard (El-Kassaby et al. 2007). Loss of 22  alleles was observed at four loci (3B9, 3D5, 3F 1, 3G9) indicating that these alleles are rare and belong to only a few parents that contributed minimally to gene pooi, thus they were not adequately represented in the analyzed seed sample (Table 1). The seed orchard and its seed crop populations significantly deviated from HardyWeinberg expectations (Table 1). This observed significant deviation is not surprising since the orchard population is artificially composed from selections of the British Columbia Ministry of Forests and Range’s Douglas-fir breeding program while the seed crop is the product of a population straddled with fertility variation, reproductive phenology asynchrony, and gene flow from outside pollen sources as well as SMP treatment (see below).  Table 1. Estimate of genetic diversity for each microsatellite locus in parental and offspring populations. Parental population Locus  A  0 H  2C2  18  3B9  Seed crop A  0 H  0.736  HE 0.877*  18  0.590  HE 0.848**  19  0.755  0.891*  19  0.763  0.869**  3D5  15  0.533  0.905**  14  0.384  0.829**  3F1  19  0.73 1  0.925**  20  0.666  0.889**  2D4  25  0.551  0.938*  26  0.711  0.910**  2G12  17  0.792  0.895  18  0.811  0.867**  1F9  24  0.451  0.951**  24  0.404  0.864**  3G9  24  0.660  0.919**  23  0.695  0.849**  A = # of alleles, H 0 = observed and HE expected heterozygosities. Arlequin 3.1 by Excoffier et al. (2005) was used to test for Hardy-Weinberg equilibrium. *  P< 0.05 indicating significant deviation from Hardy-Weinberg expectations. P< 0.01 indicating highly significant deviation from Hardy-Weinberg expectations.  **  23  Pedigree reconstruction The eight SSR loci used with their associated large allelic number allowed the unambiguous determination of all possible orchard parental (49 clones) gametic haplotypes, including the 4 external SMP pollen donors. Therefore, the pedigree reconstruction was successful in assigning the maternal and paternal parents of the 801 seeds analyzed (Figure 2). The bulk seed sample assignment to their respective parents allowed the direct estimation of: 1) individual parent’s female and male gametic contribution, 2) selfing rate, 3) external SMP success rate, and 4) pollen contamination.  Parental gametic contribution 35 30  wN  25  2O  E  .  15  •510 U-  Figure 2. The distribution of 354 full-sib families from 49 wind-pollinated Douglas-fir seed orchard clones with 4 additional SMP donors and contamination.  24  Clonal gametic contribution Accurate measure of clonal reproductive output is the prerequisite for estimating genetic gain and effective population size of seed orchards’ crops. A total of 44 and 47 out of the 49 orchard clones participated in seed and pollen production, respectively. Female and male cumulative gametic contribution (parental balance curves) indicated that 80% of the seed and pollen were contributed by 23% and 45% of parents, respectively (Figure 3). Clonal cumulative contribution (37%) is better than the 80/20 rule commonly observed in many conifers seed orchards (Anonymous 1976).  Parental Contribution 100 90 80 70 C  o 60 4-,  2 I-.  50  20 10 0 0  10  20  30  40  50  60  70  80  90  100  % Parents  Figure 3. Parental balance curves representing the maternal, paternal, and clonal gametic contribution. The horizontal line represents the gametic contribution intercept with their contributing parents (%) The simplified reproductive output assessment methods proposed by Woods (2005) assume that reproductive energy equals to reproductive success. These methods were evaluated using the results from the DNA analysis. All female reproductive output assessment methods produced highly significant correlations with the DNA results. 25  Additionally, all methods produced high and meaningful R 2 values of 0.89, 0.96, 0.95, 0.86, and 0.91 for Fl, F2, F3, F4, and F5, respectively (Figure 4); however, comparison between these methods is restricted within two groups (F 1 -F3 and F4-F5) due to sample size differences which was caused by the inability of several clones to yield a cone crop that is equal to or higher than 5 litres, the standard volume for Douglas-fir as determined in Woods (2005). This situation affects both effective population size and genetic worth estimates, thus rendering these two methods unusable in low cone years and/or small seed orchards. The male reproductive assessment methods produced significant correlations with the DNA results for Mi and M3 yielding low R 2 estimates (Ml  =  0.31 and M3  =  0.59)  (Figure 5). These results suggest that Mi is not reliable for measuring pollination success due to the observed large variation in male reproductive output among ramets of the same clone and among different clones. However, since this method was not intended for Douglas-fir seed orchard crops’ assessment (see Woods (2005) for details), the low correlation between the Mi-based and DNA-based results is not surprising. On the other hand, M3 method, with its significant correlation and modest R 2 value (0.59), could be considered as a coarse estimator for this difficult parameter (i.e., pollination success). It should be pointed out that no matter which male and female assessment method is used, difference in reproductive energy and success should not be overlooked as this could lead to unpredictable over/under estimation of effective population size and genetic gain (El Kassaby and Cook 1994).  26  ___________ _________ _  __ ________ ____  40  40 • 41) 4-  41) 4-  30  y  < 25  / O.1594x 0.4444C + 0.83 2 Rz=0.8862 ,,,,/‘  25  z  0  0  20  20 0  0  t  15  15  210  210 a-  aV  a) a)  0.0948x + 0.1105x + 0 4344 V=2 2 = 0.9584; N 49 R  a)  -  5  (1)  +4  U,  U,  0  •I  0  0I  0  20  Seedroductioestimat Fl [%]  40  ic  5  15  20  15  20  Seed production estimates; F2 [%]  40  0’  •;• 35  4-  30 y  2 0 1175x  0 0866x + 0 58  25 0 20  2 0 4849x + 1 66 y = O.1161x 0 2 R  25 0 20 0  t15  15  4-  D  210  2  -D a)5  V  a-  a. a) a)  a)  10 5  U,  U)  0 0  5  I  I  I  10  15  20  Seed production estimates; F3 [%]  0  10  5  Seed production estimates; F4 (%]  40  — 35 41) 4-.  30  2 O.3071x + 1.4417 V = 0.0986x 2 0.9051; N = 32 R  a)  -  < 25  z 0  20  0  tD 15 -a 210 a -a  a) 5 a)  •I  0  I  10  5  •  15  Seed production estimates; F5 [%)  20  Figure 4. Correspondence between % seed production based of survey assessments (Fl F5) and DNA analysis (see Materials and Methods for survey description). 27  —  10  10  . y In 4J  y = 0.071x’ + 0.3389x + 0.5509 R’ = 0.5885, N=37  •0.0219x + 1.3147x 0.5335 2 2 = 0.3127, N=49 R -  1/)  a)  D Id,  cvG I  $  z  04  0  .  0  .4-.  0 D  4-.  0  0  I-)  0 0  I  0.  a)  C  0  0  .  z  0  0  Poller? productidli 6 estimate s ; Ml [%]  0  8  ô..  0  2  .4  .  6  Pollen production estimates; M3 [%]  8  Figure 5. Correspondence between % pollen production based of survey assessment (Ml M3) and DNA analysis (see Materials and Methods for survey description).  —  Effective population size and genetic worth Effective population size is a measure of the average number of individuals in a population who contribute offspring to next generation (Wright 1931, 1938). In an ideal population with random mating and in the absence of mutations, selection, drift and migration, the allele frequencies remain constant over generations and the effective population size will be equal to the actual population size. In the present study, excluding pollen contamination, the female (Nej), male (Nern) and clonal (Nec) effective population size estimates that are based on DNA results were 6.49, 26.00 and 13.70, respectively. These estimates are rather low for a seed orchard that is comprised of 44 receptive females and 51 effective males (including the 4 SMP external pollen donors). The low effective population size is mainly the result of parental unequal contribution (see Figure 3 above). Small effective population size and mating among relatives would result in the build-up of higher levels of co-ancestry and would lead to high inbreeding in the resultant seed crop; moreover, the increase in correlated mating could be a hidden cause of inbreeding in the subsequent generation even when the effective population size is high (Hedrick 2005).  28  The estimated genetic worth based on DNA results of the orchard’s seed crop was 10.23 and -1.07 for volume and wood density, respectively. To simulate the effect of inbreeding depression by eliminating selfed seed (nursery selection and mortality during early stand establishment), the seed crop’s effective population size was estimated to be 14.63, resulting into genetic worth estimates of 10.19 and -1.12 for volume and wood density, respectively, indicating that selfing impact is negligible (see selfing section). Furthermore, different reproductive output assessment methods (Woods 2005) are expected to produce different female and male effective population size estimates reflecting the methods’ accuracy (Table 2). It is noteworthy to highlight the differences between the effective population size estimates among the female and male assessment methods to those based on DNA analysis. Consistent and low female effective population size (Nef) estimates were obtained for all the assessment methods (Table 2), yet even lower N€f was found by the DNA analysis (6.49), affirming significant fertility distortion in seed production among orchard parents. Male assessment method (Ml) produced inflated male effective population size (Nern) estimate of 38.51 that is higher than the DNA estimate of 26.00, which is further supported by the low correlation between Mlbased and DNA-based results. On the other hand, M3 method yielded higher correlation with the DNA-based results and the Nern estimate is similar to the DNA estimate. Clonal effective population size (Nec) using F2 and M3 assessment methods yielded 20.86, a value that is higher than that based on the DNA analysis (13.70), indicating an inflated effective population size; therefore, the reproductive output assessment methods should be viewed as an approximation. Table 2. Effective female and male population size estimates from the different reproductive output assessment methods (Woods 2005). Fl F2 N 15.15 13.74 Ne effective population size, Fl method.  —  F3 F4 F5 14.36 12.07 11.55 F5 = female sampling method, Ml  29  -  Ml M3 25.24 38.51 M3 = male sampling  Selfing Self fertilization (selfing) and mating among relatives reduce fitness and heterozygosity in a population. In seed orchards, selfing commonly results in poor seed set (Woods and Heaman 1989), decreased tree size, reduced vigor and increased susceptibility to pests (Orr-Ewing 1965; Sorensen 1971). High estimate of selfing rate was detected in the present study (15.23%); this value is higher than most selfing rates (s  =  1  —  t)  reported for  this species’ natural and seed orchard populations (Ritland and El-Kassaby 1985; El Kassaby and Davison 1991; Slavov et al. 2005). This high selfing rate is unusual because the study seed crop was developed under crop management practices that included bloom delay, which is known to compact the reproductive phenology (Fashler and El-Kassaby 1987) and promote outcrossing (El-Kassaby and Ritland 1986; El-Kassaby et al. 1988; El-Kassaby and Davisdon 1991), and supplemental mass pollination which was also proven to increase outcrossing rate (Wheeler and Jech 1992; El-Kassaby and Davison 1990). Additionally, when seed orchards are exposed to external gene flow, every successful pollen contamination event is in fact an outcrossing event (El-Kassaby and Ritland 1986), thus under the observed moderate contamination for the study year (see contamination section below) we would expect that selfing rate should be lower than observed. The observed distorted female and male gametic contribution in the study year (Figure 3) is the most likely cause for obtaining this high selfing rate. Under bloom delay, the reproductive phenology is compacted and the timing differences between female receptivity and pollen shedding within clones is minimized, thus increasing the chance for selfing. This scenario is the most plausible for the high selfing rate and was further substantiated by the observed highly significant correlation coefficient between clonal gametic contribution and selfing rate (r = 0.944, P<0.01) (Figure 6). While polyembryony is expected to promote outcrossing by favoring embryos sired by unrelated pollen (Sorensen 1971), the passive pollination mechanism of Douglas-fir, and most conifers, which is characterized by the “first-on, first-in” concept (Webber et al. 1987; Owens and Simpson 1982), could be responsible for the reported high selfing. In Douglas-fir, pollen is entrapped and engulfed by the stigmatic tips and subsequently delivered to the nucelles without any differentiation between self and unrelated pollen 30  (Allen and Owens, 1972; Owens et aL, 1981; Owens and Simpson, 1982). If self pollen is in high frequency or exclusively present among those landed first or delivered to the nucelles, then selfed embryos will develop even after competition and selection. Indeed, high selfing rate was reported for Douglas-fir on both the individual tree (El-Kassaby et al. 1986; Erickson and Adams 1990) and population (multiple clone banks) level (Fast et al. 1986).  30  25  20 01 C  a) C  0 U  10  y = 1.1965x 0.401 2 = 0.8908 R -  5  0 0  5  10  15  20  25  30  Clonal gametic contribution [%]  Figure 6. Correlation between parental gametic contribution and selfing rate.  Supplemental mass pollination Supplemental mass pollination (SMP), the application of viable pollen to unisolated receptive strobili (Wakeley et al. 1966), is a common seed orchard crop management practice. It is practiced to: 1) increase seed yield in young and mature seed orchards, 2) boost the genetic gain through the use of high breeding value pollen (present study), 3) overcome fertility variation by using pollen from low gametic contributing parents, 4) improve panmixia by applying pollen produced from different reproductive phenology periods (El-Kassaby and Reynolds 1990), 5) reduce contamination by saturating receptive strobili with desirable pollen, and 6) increase seed crops’ genetic variability using pollen from unrelated parents. SMP success rate varies and depends on the situation 31  at hand. Seed orchard age (Daniels 1978), number (El-Kassaby et a!. 1993) and timing (Owens et al. 1981) of SMP application, reproductive phenology (El-Kassaby and Ritland 1986), other crop management practices (bloom delay: El-Kassaby and Ritland 1986), intensity of within orchard pollen cloud density (Nakamura and Wheeler 1992; Stoehr et al.. 1994), level of pollen contamination (El-Kassaby and Ritland 1986; Slavov et al. 2005), the quality and quantity of SMP pollen (Webber and Painter 1992), and level of crop production (poor vs. good cone year) (El-Kassaby and Ritland 1992), all are expected to affect its success rate.  Parental Contribution 100 90  6  80  /  70  /  /  0  /  60 .0  / / / // II  40 30  •  20  I,  10  /  /  /  50 0 U  ‘I  —  —  — —‘  Internal SMP tre4ment + ambient pollen  —  Ambient pollen  I  —  0 0  10  20  30  40  50  60  70  80  90  100  % Parents  Figure 7. Paternal balance curve representing the 12 internal SMP pollen donors. The horizontal line represents the male gametic output intercept with their contributing parents (%). El-Kassaby et al. (1993) estimated SMP success rate in an operational setting and determined that it is greatly dependent on the number of applications and reported rates of 8.3%, 17.8%, and 17.9% for one, two, and three applications, respectively. Since the 32  paternity analysis was conducted on bulk seed samples, the SMP success rate could only be estimated on clonal rather than individual ramet level and therefore the impact of multiple SMP applications could not be determined. However, it should be stated that, in this case, the majority of the studied orchard’s trees received only one SMP application. The SMP success rate was definitively estimated to be 5.4% for the four external pollen donors. However, the success rate for the 12 internal SMP donors could not be parsed out from their ambient contribution (i.e. the 12 internal parents contributed to the orchard’s pollen cloud in two ways: ambient and supplemented pollen); therefore, it had to be estimated using a regression model, as described in Materials and Methods section (see Figure 5b for details). After scaling, the internal SMP contribution was found to be 9.6%. The aggregate success rate of the internal and external SMP treatment was calculated as the sum of the two estimates and was determined to be 15.0%. This result is similar with the El-Kassaby et al.’s (1993) estimate of 17.8% for two SMP applications; however, it is lower than the average 25% proposed by Woods (2005). As presented in Figure 7, the SMP application was effective in improving the male gamete contribution balance. When only the 12 SMP donors were considered, 80% of the male gametes were produced by 63% of clones as opposed to the 45% observed in the entire orchard’s male population (Figure 3).  Pollen contamination Gene flow (or pollen contamination) from extraneous pollen sources to production populations (seed orchards) plays a counterproductive role to the main objectives of tree breeding programs; namely, maximizing and packaging the genetic gain in seed crops. However, in some cases when the impact of pollen contamination is neutral (i.e., no detrimental adaptive effect), it has been perceived as a mean for increasing genetic diversity (Lindgren and Mullin 1998). Seed orchard’s location and the genetic quality of the contaminant pollen source(s) determine the degree and extent of the genetic impact of pollen contamination on the resultant seed crops. In most British Columbia’s coastal Douglas-fir seed orchard sites, bloom delay and/or supplemental mass pollination are the most commonly practiced crop management options to ameliorate the impact of pollen 33  contamination. In addition to the neutral impact of local pollen contamination to the studied seed orchard, the seed crop under study was also produced under both bloom delay and SMP, a situation that is unique to this site and crop when compared to other species, orchard sites, or specific years’ crops. In spite of what is stated above, a relatively high pollen contamination rate was detected (10.36%). This value is almost double of that reported by El-Kassaby and Ritland (1986) for another seed orchard crop managed under the same conditions and location. Although pollen contamination is orchard-crop specific, the difference between these two studies could be attributed to the genetic markers used. El-Kassaby and Ritland (1986) used allozyme markers which could have underestimated the effective contamination rate due to their limited allelic number, hence affecting the degree of contamination detection. On the other hand, this estimate could be viewed as a support to the effective crop management applied when compared to the 35.3% reported for a Douglas-fir seed orchard’s crop produced under no bloom delay and supplemental mass pollination using the same genetic markers (Slavov et al. 2005). Irrespective of the reported relatively low or high contamination estimate, it should be stated that this site is located in a spot where potential contamination sources are limited, thus long distance gene flow could be the source of the observed contamination.  34  CONCLUSION The objective of seed orchard management is to maximize the genetic gain in the orchard crops while maintaining sufficient level of genetic diversity; however, balancing these two parameters is a challenging task for seed orchard management. In my thesis, I evaluated a random seed sample of a coastal Douglas-fir seed orchard (2005) using parentage analysis through the use of microsatellite DNA markers. The level of genetic gain and diversity depends on the genetic superiority of the selected parents, their actual gametic contribution to the resultant seed crop, and the level of gene flow (pollen contamination) from extraneous pollen sources and their respective genetic quality (Stoehr et al. 1994; Slavov et al. 2005). The genetic diversity of a seed crop is greatly influenced by the magnitude of parental fertility variation (unequal parental contribution to the resulting seed crops) (Xie et al. 1994; Kang et al. 2003) as well as by the level of kinship among the orchard’s parental population (Lindgren and Mullin 1998). The full pedigree reconstruction allowed unraveling the mating system of the studied orchard, permitting clear estimation of female and male genetic contribution which can be further used to estimate the effective population size and genetic worth, selfing rate, supplemental mass pollination success rate, and the level of pollen contamination. 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Primer sets of 8 P. menziesii (Mirb.) Franco SSRs markers (Slavov et a!. 2004). Locus  Forward Primer (5’-3’)  Reverse Primer (5’-3’)  T. (°C)  A°  Allele size (bp)  Repeat motif  PmO5UIF9  CCTCATGCAHGGACACT C  GGATTCTTGAGCAGGTA GG  57  25  189251  34 (AG)  PmOSU_3D5  GGCATCCTA1TITI’CATTT T  GTGATTACCTAACTUGT GC  49  16  117-  25 (AG) 14 (TG)  CAAGGACTCATATGGGA AA  AACATCAGTAATAACCT  50  17  230284  (AC)1 I ..(AC)1 9...(GCAC)5 ...(GCAC)4(AC)7...(AC)6  PmO5U2D4  TTATTGCACATGAGTATF ATGA  CAGATGHGTTfTHAT ACCAC  50  26  120186  (AT)4..(TG)18(AG)26  PrnO5U3G9  ATfCCTTTTGAGACCTAC VP  CTTCAAAAATTCCTACA ACA  52  24  136218  (TG) 12(AG)28  PmOSU_3F1  GACTAGATCATCGCAACT T  GGTA17CflATGGT1TI TAT  52  20  168238  (TG)6...(TG)7(AG)27...(AC)4  PmOSU_2C2*  TAAATCCGCAGCTCATAG AATC  GGGTGGTGGCTAGGGA AAC  60*  18  146202  (AC)32...(CT)4  PmOSU_3B9  TGTGTAAAAATGTCTAAT CC  ACTACTAHCGAGGTI’T TCT  46  19  137235  (CG)6(CA)6.(AC)6...(AC)5.. AC)6  Forward M13 tail  CACGACGTTGTAAAACGAC  Reverse M13 tail  GGATAACAA1TPCACACAGG  PmO5U_2G1 2  155  Tfl  Modification of PCR condition from Slavov et a!. 2004 Ta = annealing temperature *  Touchdown PCR condition was used for this primer (Ta+7°C with decreased by 1°C for each 6 subsequent touchdown cycles) a  A is the number of alleles detected in 49 orchard clones + 4 SMP clones from another orchard = 53 clones  47  Appendix 2. DNA fingerprinting profile for the multiple of the 8 loci detected the mislabeled ramet of clone 3324.  J[j “2C2 3324 -  —  -  —  —  -  4  -___  —  d_  r—--R  -  —-  —  3B9336Oh33g3324 3360  B33241G9  48  13F1 3360  f3F1 3324  -  Appendix 3. Comparing the genetic diversity parameters between the orchard’s parental population and its resultant seed crop (offsprings).  25.00  Locus 2C2 aflele frequency  T—-—-———---—.--—----—--—--—-—-  20.00  Parents 15.00 G) 10.00  ziiLz  LL -  5.00  0.00  165 167 171 173 175 177 181 183 185 187 189 191 199 207 209 213 215 221  Allele Size Locus 389 allele frequency 30.00  25.00  20.00  1 I  •Parents •Offs nfl  cD a) L1  15.00  10.00  ::  TJJI  I_’JI’JIiJ  .1,.  156 164 184 192 200 206 208 210 212 214 216 218 220 222 224 226 246 250 254 204  Allele Size  49  _  Locus 3D5 allele frequency  30.00  25.00  20.00  t  Parents • Offsprings  —  I  a) a)  15.00  LI a) 10.00 0  4:  5.00  0.00  i&LUIJJlihitt 146  148  152  154  156  158  160  162  164  166  168  170  172  174  136  Allele Size Locus 3F1 allele frequency 30.00  25.00  • Parents • Offspng  >20.0O C)  =  ci -15.00  a) I  Li  -  10.00  ii liii  5.00  0.00  jill_li__ 00  00  -i O  n  Lfl  -  I  I  Lt  rn  r-4  (‘4  i  r (‘.4  Allele Size 50  r  I  (‘.4  (‘4  —I-  fl  Li  (  C  -l  çfl  (‘4  (‘4  (‘4  (‘.4  (‘4  (‘1  (‘  Locus 2D4 allele frequency  30.00  25.00  s Parents • Offsprings  “20.00 C)  =  0 15.00  ci) I-. LL -  10,00  5.00  0.00  ILIILJIIILIII IIIIILLIJI . o  r’  -  _4  l  t.O  CO  r  -  -  LI ,-I  r  .o  Co  o  co  -4  -  _I  -  IJ  -l  ‘.0  CO ‘.0 I  Allele  Locus  r’. -  00  r’. _l  -  CD  00 _1  CO -  00 -  ‘0 00 .-i  0  C’ -  ‘.0  O -4  O -i  00  C -  0 CD 4  ‘.0 CD r’  Size  2G12 allele  frequency  30  25 B  = a) =  20  j  Parents  • Offsprings  L_________  ±0 J 5  250 268 272 274 276 278 280 282 284 286 288 290 292 294 298 300 304 314  Allele Size 51  ‘J -l ‘-  Locus I F9 allele frequency  30  .____ ziiZZ*L  25  -__  >, 20 0 1)  = 15 ci) -  U-  10  1)  5  0  J C  o  Lr) r- n -  rI  -  rJ  o-  r4  •Parents •Offspnngs  ri  r’J rJ  Allele Size Locus 3G9 allele frequency 30  25  • Parents • Offsprings  0  20 0 a)  cr 15 ci) LL -  ci)  10  5  0 —  1r- c’ _l  U, 1  1 -I  r ‘.0 -i  Lfl ‘.0  r- c’‘.o m ‘.0 1  ,-1  -i N -  -l  U,  N  _-I  N N $  ø’ N l  -  00  en  00  Allele Size  52  u 00  r.  00 1  rn  Q) -  N Q, -  U,  O  -  C) rJ  -(  (4  N C)  en  


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