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Genealogical relationship among members of selection and production populatoins of yellow-cedar (Callitropsis… Massah, Nasim 2009

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GENEALOGICAL RELATIONSHIP AMONG MEMBERS OF SELECTION AND PRODUCTION POPULATIONS OF YELLOW-CEDAR (CALLITROPSIS NOOTKATENSIS) IN TIlE ABSENSE OF PARENTAL INFORMATION by NASIM MASSAH B.Sc., University of Tehran, 2004 A THESIS SUBMITTED IN 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) March2009 © NASIM MASSAH, 2009 ABSTRACT The recurrent selection scheme of tree improvement programs follows three main steps; namely, phenotypic selection of candidate tree from natural stands or plantations, followed by breeding and testing, and finally the cycle is completed by the identification of elite genotypes for production population(s) establishment andlor starting a new round of selection. An innovative approach to reduce both efforts and the length of the breeding cycle was attempted for yellow- cedar (Chamaecyparis nootkatensis) through capitalizing on natural matings in the forest, application of high intensity phenotypic selection at very early age (1 -year-old seedlings), followed by vegetative propagation and intense clonal testing over multiple sites and years to identif’ superior genotypes for inclusion into production populations. This approach has proven to be successful and significant gain was captured through this short-cut; however, the genetic relationship among early age selections as well as superior genotypes is unknown. DNA fingerprinting (SSRs), pairwise relative kinship estimates, pedigree reconstruction as well as the STRUCTURE program were used to determine the genetic relationship among 3, 1-year-old greenhouse selections from reforestation seedling crops and highlighted the presence of co ancestry among members of the selection and production populations. Results from the pairwise relative kinship estimates (Mzuuc: Ritland 1996) indicated the presence of full- and half-sib family structure and the family structure was verified using Wang’s (2004) pedigree reconstruction COLONY program and individuals within each seedlot were grouped into multiple full-sib families of various sizes (1-10) nested within several half-sib families (17-21). The STRUCTURE program (Pritchard 2000) provided pictorial classification of the seedlots and grouped their individuals in multiple cohorts (9-10). In spite of violating the STRUCTURE program assumptions, good correspondence was observed between the COLONY and STRUCTURE analyses, indicating that the selected seedlings originated from a limited number of seed donors. The species’ social status and reproductive biology, methods of natural stand seed collection and commercial seedling production, and the high selection intensity applied at the greenhouse stage, all point to limiting the selection to fewer trees in spite of the large number of seed donors constituting these seedlots. Build up of co-ancestry in selection and production populations will cause an incorrect estimation of genetic gain and unintentional reduction in genetic diversity in reforestation material. Table of Contents Abstract.ii Table of Contents iii List of Tables v List of Figures vi Acknowledgements vii Dedication viii 1. Introduction 1 1.1 Yellow-cedar range, taxonomy and silviculture 1 1.2 Yellow-cedar genetic diversity 3 1.2.1 Genecology 3 1.2.2 Isozyme 5 1.2.3 Seed germination and dormancy 5 1.2.4 Frost hardening 6 1.2.5 Terpene production variation and its relevance to deer browsing 6 1.2.6 Microsatellite Marker 6 1.2.7 Yellow-cedar SSRs markers 7 1.3 Data analyses 8 1.3.1 The Mark program 8 1.3.2 The Colony program 8 1.3.3 The STRUCTURE program 8 1.4 Forest trees breeding programs 9 1.4.1 Western Forest Products’ yellow-cedar selection and production program 10 1.4.2 Differences between Western’s and conventional breeding programs 11 1.4.3 Caveats and restricted genetic bases 12 1.5 Study objectives 13 2. Materials and Methods 14 2.1 Plant material 14 2.2 DNA extraction and DNA fingerprinting 14 2.2.1 DNA Extraction 14 2.3 Microsatellite and genotyping 17 2.3.1 Yellow-cedar designed primers 17 III 2.3.2 Japanese cedar primers .17 2.3.3 Primers from EST 18 2.4 Genotyping for LICOR 19 2.4.1 PCR amplification for genotyping 19 2.4.2 Typing error 20 2.5 Data analysis 21 2.5.1 M4RK 21 2.5.2 COLONY 21 2.5.3 STRUCTURE 21 3. Results and Discussion 23 3.1 Kinship estimates 23 3.2 COLONY 25 3.3 STR UCTURE 25 3.4 Genealogy of production populations’ members 31 4. Conclusion 37 Bibliography 39 Appendices 44 Appendix 1 44 Appendix 2 46 Appendix 3 48 iv LIST OF TABLES Table 2.1. Seedlots’ information; collection location, coordination and registered number 14 Table 2.2. Number of samples collected from five yellow-cedar clonal trials representing three nursery selections 15 Table 2.3. PCR protocol developed for LiCor (LiCor Inc., Lincoln, NE) genotyping 19 Table 3.1. The number of full-sib families (size) within each half-sib family in the three studied yellow-cedar seedlots based on the COLONY program analysis 27 Table 3.2. Correspondence between the COLONY and STRUCTURE programs represented by the size of the largest half-sib family within a single genetic cohort (%) 31 Table 3.3. The Colony program classification of elite genotype within half-sib (HS) families (genotypes within a single box belong to the same HS family) 36 V LIST OF FIGURES Figure 1.1 The range of yellow-cedar in the Pacific North West (A) and British Columbia (B) and the location of the three studied seedlots (C) 2 Figure 1.2 Yellow-cedar vegetative propagation hedges (photo: J.H. Russell, BCMoF&R) 12 Figure 3.1. Yellow-cedar aerial seed-cone collection using helicopter and cone rakes (photo: Don Pigott, Yellow Point Propagation) 24 Figure 3.2. Yellow-cedar seed-cone collection from the helicopter deposit site (photo: Don Pigott, Yellow Point Propagation) 24 Figure 3.3. Distribution of pairwise relative kinship estimates among the three selection populations (top: seedlot #1; middle: seedlot #2; bottom: seedlot #5) 26 Figure 3.4. Pictorial presentations of COLONY results for seedlots number 1, 2 and 5 28 Figure 3.5. Graphs of LnP(D) (left panel) and AK (right panel) versus K for the three studied seedlots (Top, middle, and bottom panels represent seedlots 1, 2, and 5, respectively) based on the STRucTuRE program analyses 33 Figure 3.6. STRUCTURE program bar plot of population one showing 10 cohorts 34 Figure 3.7. STRUCTURE program bar plot of population two showing nine cohorts 34 Figure 3.8. STRUCTURE program bar plot of population five showing 10 cohorts 35 vi Acknowledgements I would like to thank the following people and organization who helped through my thesis and without their help it would not be possible. First, I wish to thank Western Forests Products Inc. and special thanks to Annette VanNiejenhuis for her great help and assistance through my field work and providing information I needed. I thank Natural Sciences and Engineering Research Council of Canada, Natural Resources Canada — Science and Technology Internship Program, The Forest Genetics Council of British Columbia and the Johnson’s Family Forest Biotechnology Endowment for funding. I would like to thank Simren Brar, Heather Farnden and John Nixon who helped me with DNA extractions. I thank my dear labmates Tomas Funda, Irena Fundova, Ben Shu Kwan Lai for their great help through sample collection despite of their busy schedule. Especial thanks to Irena for her great help with my lab works. I would also like to thank Mohammed Ismail and Charles Chen who assisted me through my data analysis and software I need for my thesis project. I thank Chen (Klaus) Ding for helping me with maps I needed for my thesis. I wish to thank Cherdsak Liewlaksaneeyanawin for advising and tutoring me through DNA extraction and data analysis. Many thanks to Dr. Carol Ritland for her supervision and advising with my lab work and helping me through the challenges I had with DNA extraction. I would like to thank my committee members, Dr. Kermit Ritland and Dr. John H Russell for their support and advice through my thesis. I thank Don Piggot and David Reid for providing useful information. Many thanks to Dr. Yousry El-Kassaby for accept me as graduate student. I am deeply grateful for your help, supervisions, advice, support and patience through my graduate study and with all experiences I learned from you which will help me through my professional and personal life. Lastly, I wish to thank my lovely parents for their love and support. VII Dedication I wou(é(i&e to éed?cate my thesis to my (ove(y parents, granimother, S/iaônam anéPayam. VIII 1. Introduction 1.1 Yellow-cedar range, taxonomy and silviculture Yellow-cedar (Chamaecyparis nootkatensis (D.Don) Spach), Alaska-cedar, Alaska cypress is an important timber species. The species has a continuous range with disjunct distribution throughout most of its range in Western North America with wide geographical and ecological range spanning up to 20° in latitude and elevation from sea level up to tree-line from Prince William Sound in Alaska down to Siskiyou mountains in Northern California (Harris 1990) (Figure 1.1). The species taxonomy has gained recent attention since the discovery of a new species (Xanthocyparis vietnamensis) in Northern Vietnam (Little et al. 2004). Wang et al. (2003) contested the species’ contemporary taxonomy and stated that the evolutionary pathway of C. nootkatensis is different from other species in Chamaecyparis genus. Gadek et al. (2000) and Wang et al. (2003) have classified C. nootkatensis as a member of the Cupressus genus while Little et al. (2004), based on the species close relatedness to X.vietnamensis, placed it under this new genus. Later on, Little et al. (2004) have suggested Callitropsis as a new genus for C. nootkatensis and X vietnamensis. Little et al. (2004) findings were compatible with Gadek et al. (2000) where C. nootkatensis has close similarity to other species in the Cupressus genus. Molecular markers are informative and have substantially contributed to the recent species’ taxonomy (Little et al. 2004). Bérubé et al. (2003) have designed yellow-cedar microsatellite primers (SSR5) and tested their primers’ amplifications across selected species from the Cupressus, Juniperus and Chamaecyparis genera. Their primers showed polymorphic amplification for Cupressus and Juniperus but not for other members of the Chamaecyparis genus. Based on these results they concluded that yellow-cedar is closely related to the Cupressus and Juniperus genera than species within the Chamaecyparis genus, furthermore supporting the findings of Gadek et al. (2000), Wang et al. (2003), and Little et al. (2004). Due to the fact that the taxonomy of the species is in flux, C. nootkatensis (D.Don) Spach is used as the species’ scientific name throughout this thesis. 1 CFigure 1.1. The range of yellow-cedar in the Pacific North West (A)(Harris 1990), British Columbia (B), and the location of the three studied seedlots (C). In British Columbia, yellow-cedar natural populations are commonly found on high elevation sites that are characterize by cool and humid short growing season (Harris 1990). The species has a 3-year reproductive cycle as a result of its high elevation habitat; however, this reproductive cycle is plastic and can be reduced to two years in its southern range (Owens and Molder 1974, 1975, 1977) and at low elevation in its northern range (El-Kassaby et al. 1991). In high elevation, pollen- and seed-cones are initiated in the summer of year 1, pollination and fertilization occur in the spring and/or early summer of year 2, with embryo and seed-cone development and seed shed completed in year 3 (Owens and Molder 1974, 1975, 1977). Thus, yellow-cedar seed production from high elevation stands requires favorable environmental conditions during year 2 and 3 of its reproductive cycle. Additionally, following dispersal, seeds exhibit prolonged seed coat-imposed dormancy and only a low percentage of seeds germinate 2 during the first year while the remainder require another year of moist chilling to undergo germination (Pawuk 1993). The species pre-disposition to vegetative propagation and seed germination difficulties resulted in the use of rooted cuttings as the main stocktype for yellow- cedar artificial reforestation in British Columbia (Forest Genetics Council of British Columbia 2008). 1.2 Yellow-cedar genetic diversity 1.2.1 Genecology Considering the extensive yellow-cedar geographic range, this species has encountered heterogeneous environments resulting in potentially different selection pressures along its contemporary range. C. nootkatensis is considered to be both a specialist and generalist (Russell 1998). The diverse selection pressure encountered along its range is suited to a specialist strategy, while the species response to the local environment requires a generalist as shown by its adaptive responses in terms of growth, physiology and morphology traits (Russell 1998). As a result, specialists exhibit more genetic differentiation among than within populations similar to Douglas-fir (Pseudotsuga menziesi), western hemlock (Tsuga heterophylla), and Sitka spruce (Picea sitchensis) (Russell 1998). In contrast, generalists show more plasticity in different traits as adaptive responses to various environments; hence most of the genetic variation resides within as opposed to among populations similar to white pine (Pinus strobes) and western redcedar (Thujaplicata) (Russell 1998). Phenotypic plasticity is defined as the capability of a genotype to exhibit different forms of a trait in response to heterogeneous environments (Kroon et al. 2005). The best example of phenotypic plasticity in yellow-cedar is its reproductive phenology (El-Kassaby 1995). Yellow-cedar, in its high elevation habitat, exhibits a three years reproductive cycle which is an adaptation to short growing seasons; however, when it is present in lower elevation or in its southern range, this period is reduced to two years when weather is suitable at crucial times within the reproductive cycle. Cherry and Lester (1992) studied yellow-cedar genetic variation using open-pollinated families collected from British Columbia coastal provenances. They observed variation in shoot growth 3 and concluded that this attribute did not show any genetic base and exhibited plastic response to different environment. Moreover, cold susceptibility observations demonstrated variation at the provenance level, which reflected the inclusion of some interior seed sources in the study material. They concluded that genetic diversity exists at both level of study; namely, the provenance and family level. Additionally as indicated above, the species’ poor germination may have limited their ability to sample the entire range of variation in British Columbia provenances. Russell (1998) studied morphological and physiological traits of seeds and seedling from a broad range of seed sources. He studied seed germination percent, 3-year total shoot height, 2-year root collar diameter, shoot growth initiation at the start of 3td growing season, shoot growth cessation, and cold injury after three growing seasons. He observed genetic variation among and within populations (among families within population) in all studied traits and reported the greater genetic differences among families within population than among populations. They concluded that a significant, but moderate level of genetic variation existed among populations, and observed that variation was associated with seed origin geographic information (latitude and elevation). In other words, differences among populations were significant only when traits (e.g., drought resistance) of two extreme locations (from north and south of the range) were compared. The same applies for comparisons with isolated stands. Indeed there is significant genetic variation within population as well as phenotypic plasticity. Moreover, Cherry and El Kassaby (2002) nursery study results confirmed the species plastic response to environmental conditions. Russell (1998) stated that the reason for the observed mode of genetic variation might be due to the time yellow-cedar recolonized its contemporary habitat after glaciations. Environmental condition as well as low disturbance events such as fire may have consequence on shaping the species mode of variation needed for adaption. Indeed species characteristics such as long living cycle have contributed to this situation. Russell (1998) concluded that cold hardiness susceptibility and growth of yellow-cedar are higher in southern than northern populations. Hence in southern ranges trees are prone to cold but have higher growth and later growth termination. Moreover he observed the effect of environmental conditions on morphological and physiological traits and concluded that of 4 drought and photoperiod have significant impacts on these traits (i.e., populations in more dry range are physiologically and morphologically adapted to water deficiency). 1.2.2 Isozymes Ritland et al. (2001) conducted an isozyme marker genetic structure study on yellow-cedar using populations collected throughout the species range from southern Alaska to Northern California including some isolated locations) and revealed the presence of considerable inbreeding and geographical patchiness. They reported total genetic diversity (HT) of 0.171, while differentiation among populations (GST) was 0.139 and indicated that the observed total genetic diversity was higher than reported for conifers. This distribution of genetic diversity might be due to multiple reasons including the species frost tolerance ability, which might have resulted in multiple large refugia during the last ice age and in turn they became the founders of its contemporary range. Additionally, the species low migration rate has contributed to sustaining variation among populations. Gene flow acts as homogenizing factor among populations and the species poor natural reproduction and ability to layer (clonal propagation) has contributed to reducing gene flow. The contrast between Ritland et al. (2001) (i.e., demonstrating the presence of higher level of genetic variation) and that of Cherry and Lester (1992) and Russell (1998) (i.e., low among populations variation) should be viewed in the light of differences between neutral markers (GST) and quantitative and adaptive attributes (Qsr) (Reed and Frankham 2001; McKay and Latta 2002). 1.2.3 Seed dormancy and germination Limited information is available on the species variation in seed germination and dormancy; however, most references discuss its deep dormancy and low viability of seeds (El-Kassaby et al. 1991; Pawuk 1993; Raimondi and Kermode 2004). El-Kassaby et al. (1991) reported variation in seed germination among different genotypes and concluded that the observed differences among individuals reflected variation in heat sum accumulation among genotypes needed to trigger/initiate germination. 5 1.2.4 Cold hardening Hawkins et a!. (1994) reported the presence of variation in cold hardiness among populations and concluded that cold hardiness has both genetic and environmental basis. Elevation as an environmental factor was found to be influencial on cold hardiness, as populations from the species’ northern range were more cold hardy than ones from its southern range. In another study with more extensive sampling, Hawkins et al. (1998) observed similar variation in cold hardiness but the magnitude of differences was lower than that observed in their 1994 study. Cherry and El-Kassaby (2002) evaluated tolerance to frost damage using seedlings originated from seed developed under 3-year and an abbreviated 2-year reproductive cycles and concluded that the observed differences were not large enough to distinguish between the two sources of seedlings. 1.2.5 Terpene variation and its relevance to deer browsing Vourc’h et al. (2002) reported on correlations between herbivore browsing damage and terpenes, a secondary metabolite produced in needles and implicated as a defense mechanism, in yellow- cedar and western redcedar and attempted to unravel the genetic basis of the observed variation. They also reported the presence of a rare recessive allele that is responsible for low terpene production (or metabolites which is required for terpene production). Although their study was based on a limited number of samples, they concluded that terpene production has a genetic base with substantial variation. In summary, herbivores function as a selection pressure reducing the fitness of genotypes with low and/or no terpenes production and the presence of terpenes are commonly associated with low deer browsing. Presently, the terpene production is serving as a focal point for breeding for herbivores resistance in a number of species in British Columbia (Vourc’h et a!. 2002). 1.2.6 Microsatellite markers Microsatellite markers, also known as Simple Sequence Repeats, (SSRs), are informative tools in studying patterns of gene flow, relatedness, co-ancestry and effective population size. The advantages of using microsatellite loci compared to other markers, is that they are co-dominant 6 (heterozygous genotype can be distinguished from either homozygous genotypes), consist of short sequence tandem repeats (1 to 6 nucleotides), neutral (but see hitchhiking effects), display a Mendelian mode of inheritance, and are reproducible as well as highly variable (Venderamin and Hansen 2005; Selkoe and Toonen 2006). Microsatellite loci can be amplified with Polymerase Chain Reaction, PCR, and observed on high resolution gels. In PCR amplification, primers bind to flanking regions, which are the sequences on the sides of microsatellite locus, in target DNA. During PCR reaction amplification, two strands of DNA separate and primers bind to flanking regions and nucleotide in PCR solution reconstruct microsatellite locus (Selkoe and Toonen 2006). Microsatellite loci have high mutation rates and consequently yield high allelic variation and diverse allele sizes. High polymorphism gives SSR markers capability of distinguishing relatedness among individuals (Wang 2004) and individuals under study can be genotyped by different allele size (bands) on gels. Microsatellite markers have drawbacks as well. Null alleles can result in false genotyping. This can happen when mutations occur in the flanking regions so primers are unable to bind resulting in either no amplification and as a consequence no genotype amplification (null), or heterozygous individual at this locus produces one band and individuals will be incorrectly genotyped as homozygous (Venderamin and Hansen 2005). The greatest disadvantage of SSRs is their species specificity; however, the conservative nature of flanking regions within a species makes it possible to amplify in closely related species (Selkoe and Toonen 2006). 1.2.7 Yellow-cedar SSRs markers As previously mentioned, microsatellite markers have a great power and ability to distinguish among individuals and are commonly used for pedigree reconstruction and kinship analysis, hence I employed microsatellite loci in the present study for sibship reconstruction. Bérubé et al. (2003) developed a set of five SSR loci, which were optimized and tested over a number of populations. This set will be tested and used in the present study. 7 1.3 Data analyses 1.3.1 The M4RK program The MARK program was used to determine the type of genetic relationship that existed among individuals within each seedlot. Ritland (1996) analysis was used for our purposes. Results determined the existence of co-ancestry within each seedlot and supported our hypothesis. 1.3.2 The COLONY program Likelihood approaches have been developed and applied to sibship reconstruction studies to generate full-sib and half-sib families with the application of marker information in the absence of parental data. COLONY has power to assign full-sibs and half-sibs within each family based on their genotype information after considering the presence of typing error and automatically diagnoses the errors associated with all loci (Wang 2004). The COLONY program uses group- likelihood methods to reconstruct all sample relationships as a cohort by applying their genotype data. This way, individuals will be studied as a group and genotype information of all samples will be integrated. However, it is restricted to distinguish first-degree relationships such as full- sib and half-sibs in a cohort. The COLONY program, similar to other programs, has assumptions for marker loci used for genotyping. Loci should be co-dominant, polymorphic, neutral, have Mendelian inheritance, and be at linkage equilibrium (Wang 2004). As mentioned earlier, microsatellite loci have high mutation rates which could cause the exclusion of an individual from its true relationship (Wang 2004). Hence, typing errors is considered in COLONY program to prevent these situations. 1.3.3 The STR UCTURE program The STRUCTURE program is a model based method which utilizes the Bayesian algorithem to cluster individuales within population into genetically similar cohorts based on their multilocus genotypes (Pritchard et al. 2000). The program calculates locus allele frequencies seperately for each population and clusters individuals to each cohort based on their posterior probability (Q) which is calculated from their genotype data. Individuals are classfied based on their highest 8 posterior probabilty (Pritchard et a!. 2000). It is commonly used in population structure related studies to allocate each entity to its genetically related cohort even in cases where there is no prior parental or population structure information or in cases where entities come from more than one population. This program can also determine migrants to a population from previous generation. The program is based on the assumption that populations are in Hardy-Weinberg equilibrium and that each popluation (K, in some cases is not known) has unknown allele frequencies for each locus. Data from different genetic markers can be analyzed by this program (Pritchard et al. 2000) even where they are in linkage disequilibrium (Falush et al. 2003). 1.4 Forest trees breeding programs The main purpose of a species’ breeding program is to increase the performance of the traits under selection in the breeding population while maintaining genetic diversity close to that of the base population (i.e., natural stands). However breeding programs are reductionist in nature, so it is important to maintain a reasonable level of genetic diversity throughout the whole process (White 1987). Classical breeding program consists of two major components, breeding and production (Namkoong et a!. 1988). The breeding cycle starts with phenotypic selection of plus-trees from natural stand or unimproved plantations. The purpose of selection is to choose superior individuals (plus-trees), which have a higher mean genetic value than the population under selection (White 1987) with desired trait(s) for breeding (i.e., the application of a mating design to produce material (half- and full-sib families) for testing and further selection. Plus-trees are selected based on the phenotype or in other words the attributes that can be observed. Seed or scion material are collected from selected plus-trees and planted or grafted to a rootstock, respectively, for breeding. Control crosses based on a pre-determined mating design are performed among the selected parents and the resultant seeds are sown to produce offspring for progeny test trials. In progeny trials, genetic information such as heritability (the degree of genetic control over the target attributes), parental genetic worth, genetic and phenotypic correlations, and genotype X environmental interactions are estimated (El-Kassaby et al. 2005). 9 Two types of selection can be applied, forward and/or backward selection. Forward selection is based on including a selected set of the offspring for the establishment of production populations (i.e., seed orchards for seed production) and their inclusion into the breeding population, while backward selection is restricted to selection among parents after inferring their genetic worth based on their progeny performance. Selected individuals from both schemes can be used in the production populations (El-Kassaby et a!. 2005). 1.4.1 Western Forest Products’ yellow-cedar selection and production program The main goal of British Columbia’s tree breeding programs is to improve quantitative and adaptive traits (e.g., disease resistance, growth rate, and wood quality) while maintaining acceptable levels of genetic variability (El-Kassaby et a!. 2005). Yellow-cedar is ranked for breeding program importance based on economic, silvics and harvesting criteria in the Maritime Seed Planning Zone (SPZ) at the provincial level. Approximately 1 .4M seedlings were planted in 2008 and a total of 0.4 million plantables rooted cuttings is forecasted for production by Western Forest Products for 2008 (Forest Genetic Council Business plan 2007/2008). Western Forest Products applied a novel approach to shorten their yellow-cedar improvement program. Their program is based on the use of multiple bulk seedlots (total of 33) collected from natural stands distributed throughout Vancouver Island. From each seedlot, “superior” individuals (i.e., tallest seedlings) were selected at the end of the first nursery growing season to form the subsequent clonal testing populations. Selected seedlings were vegetatively propagated to produce multiple rooted cuttings for testing on different sites throughout the Maritime SPZ, as well as maintaining their genetic legacy (clone bank) for future use. Donor plants were evaluated based on the performance of their cuttings and selected individuals were included in future production populations. Yellow-cedar production populations consist of vegetative hedges (stool-beds) for mass cutting production, and are located at Western’s Saanich Forestry Centre, Saanichton, southern Vancouver Island, British Columbia (see Fig. 1.2). The use of zygotic seedlings as a mode of production in yellow-cedar is fraught with caveats such as: 1) natural stand and orchard seed production is inconsistent and requires the occurrence of favorable environmental conditions during the pollination, fertilization, and seed and cone 10 development stages (El-Kassaby et al. 1991), 2) bulk cone collections are generated from an unknown number of parents with unknown parental contribution, 3) operational cone collections often contain a mixture of 1st and 2’ year cones resulting in approximately 50% loss of potential seed yield since first year cones are developmentally immature and cannot be distinguished from second year cones (Owens and Molder 1974, 1975, 1977), and 4) “fully developed” seed require an extended stratification period for germination and in most cases standard seed pre-chilling treatments are inadequate for breaking seed dormancy resulting into low seed germination (Pawuk 1993). Thus, vegetative propagation is considered the most reliable source of planting stock production for reforestation (Karlsson and Russell 1990). 1.4.2 Differences between Western’s and conventional breeding programs Classical breeding programs follow three phases; namely, selection, breeding and testing. The expected genetic gain is often dependent on the selection intensity and rigor of testing. Western Forest Products’ strategy attempted to: 1) circumvent the classical phenotypic selection of plus- trees from natural stands and replace it with nursery selections, 2) avoid breeding (i.e., mating designs) and capitalize on natural crosses among individuals within a population, and 3) exploit the species biology by incorporating vegetative propagation into the testing and production phases. If successful, this approach has the potential to substantially reduce the breeding cycle; however, it assumes that natural stand seed collection is adequate to capture the variability present in these populations. 11 1.4.3 Caveats and restricted genetic bases This process indeed has its own drawbacks. In British Columbia, natural stand bulk seedlots are collected from a minimum of 10 trees (Chief Forester Standards 2005). It is assumed that a minimum of 10 randomly collected seed donors from any population would capture at least 95% of the genetic variation present in a population (Yanchuk 2001). When the number of seed trees is unknown, their parental contribution to the seed lot is unknown, and the fact that yellow-cedar seed is characterized by low germination and deep dormancy, then it is safe to assume that the seedling crop originating from these seedlots harbor various levels of co-ancestry with possible high preponderance of half- and full-sibs. Furthermore, selection of seedlings based on their first year height in the nursery is expected to further reduce the genetic base of the selected individuals, which harbors various degrees of family relations. Finally, when superior individuals are identified after clonal testing for operational rooted-cuttings production for planting, the likelihood of genetic relationship is high and their vegetative propagation will act as an amplifying factor for the production of related clonal material, thus effectively reducing the genetic diversity of regeneration stock. Figure 1.2. Yellow-cedar vegetative hedges (photo: J.H. Russell BCMoF&R). 12 1.4 Study objectives As stated above, Western Forest Products’ yellow-cedar selection program is innovative and has the potential to substantially reduce the selection cycle; however, the caveats associated with this approach (reduced selection genetic base and build-up of co-ancestry) are worthy of evaluation. The objectives of this thesis are to determine: 1- The extent of genetic diversity in the selected seedlings from bulk seedlots, 2- The genealogical relationships among the nursery phase selections within seedlots, and 3- The extent of co-ancestry in the selected individuals forming the production populations. DNA fingerprinting using SSR markers and pedigree reconstruction without parental information are employed on three out of Western’s 33 natural stands seedlot selections. The selected seedlots represent the oldest selections and elite individuals that were used to establish the production population for vegetative propagation. 13 2. Materials and Methods 2.1 Plant material The study is based on three out of the 33 seedlot natural stand bulk yellow-cedar used by Western Forest Products for the nursery selection phase (Table 2-1). Selected seedlings were vegetatively propagated and the resulting seedlings were planted in clonal trials on multiple sites on Vancouver Island. Fresh foliage samples were collected from five sites near Port McNeill (3 sites) and Jordan River (2 sites), Vancouver Island, British Columbia. Collected samples were placed in a marked plastic bag, stored on ice and shipped to UBC for further processing. A total of 574 individuals were collected representing the three studied seedlots (Table 2-2). The tips of each branch sample were collected and placed in a vial tube (2ml) and were stored at -80°C for DNA extraction. Table 2.1. Seedlots’ information; registration number and collection location. Seedlot BCSPAR’ Source Latitude Longitude Elevation (m) Seedlot# 1 9751 Port McNeill 50-19’ 127-08’ 550 2 9762 Shawnigan 48-45’ 124-00’ 800 5 7520 Adams River 50-25’ 126-05’ 570 ‘British Columbia Seed Planning and Registry (Tree Improvement Branch) 2.2 DNA extraction and DNA fingerprinting 2.2.1 DNA extraction Prior to DNA extraction, 0.5 grams of each sample were ground in chilled mortar and pestles using liquid nitrogen and transferred to SOml falcon tube. DNA extraction protocol followed that of Doyle and Doyle (1987) after optimizations to remove organic compounds such as terpene, 14 monoterpene, protein, and phenolics (Barton, 1976) and lasted for 2 days. Extraction started by adding 2Oml of pre-heated 2X CTAB buffer to each sample. CTAB buffer for 500 ml contained; 2% CTAB of 5M CTAB, 0.1M Tris from 1M Tris (pH = 8.0), 1.4M NaC1 of 5M NaC1, 0.02M EDTA of 0.5M EDTA (pH = 8.0), 90m1 of autoclaved dH2O, lOml of 3-Mercapthoethanol. Table 2.2. Number of samples collected from five yellow-cedar clonal trials representing three nursery selections. Seedlot #1 Seedlot #2 Seedlot #5 Number of samples 170 271 133 Production population samples 5 17 8 The protocol is as follows: 1) Grind 0.5g of leaf tissue in a chilled (-20° C) mortar and pestle using liquid nitrogen, 2) Transfer the ground tissue to 5Oml falcon tube and add 20m1 of a pre-heated CTAB buffer, 3) Incubate in 65°C water bath for 1 hour, with gentle swirling every 10 minutes, 4) Spin at maximum speed (3,000 rpm) for 20 minutes to remove debris, 5) Transfer the supernatant into new falcon tubes; add 1 5ml chloroform-isoamyl alcohol (24:1) to the extraction. Use a rotating machine (360 degree rotation) to mix the solution for 15 minutes. Invert the tubes several times, and then release the pressure before centrifuging, 6) Spin the extraction at maximum speed (3,000 rpm) for 25 minutes, 7) Transfer the supernatant into another 5 Omi falcon tube, 8) Repeat step 5, 15 9) Precipitate the DNA with cold Isopropanol by adding isopropanol to the 2/3 of the volume of supernatant, mix gently, and spin down the DNA pellets for 25 minutes at 3,000 rpm. 10) Discard isopropanol into a beaker, wash pellets with 70% ethanol (5m1), spin the pellet again and remove the supernatant, and allow the pellet to dry, 11)Redissolve the DNA pellet in 500p1 of autoclaved dH2O and leave overnight at 37°C water bath. Transfer DNA to 1 .5m1 microfuge tubes, 12)Add slowly thawed RNAse to the 500j.il dissolved DNA, vortex, and incubate at 37°C water bath for 30 mm followed by adding Proteinase K using the same steps as the RNAse (a final concentration of 1 Op i/mI is required after adding the RNAse and Proteinase K), 13) Add 250i1 of Phenol and 25OpJ of chloroformlisoamyl, wash in a rotator for 10 minutes, and spin at 8,000 rpm for 20 minutes, 14) Transfer the supernatant to a new tube, add 400 jil of chloroform: isoamyl, wash in rotator for 15 minutes, and spin for 10 minutes, 15) Transfer the supernatant into a new tube, add NaC1 to a final concentration of 0.151.il followed by a quick spin, 16) Precipitate DNA with 2 volumes of cold (-20°C ) 100% ethanol, 17) Spool out the DNA with pastor pipettes (shaped on flame to meet our purpose), 18) Wash DNA with 70% ethanol and leave to dry followed by dissolving the DNA with 200il of autoclaved dH2O. Note: This protocol is suitable only for fresh, young, yellow-cedar foliage. Extracted DNA was qualified on 0.8% agarose gel using a total volume of l3jil containing 5i.il DNA stock, 5pi dH2O and 3p1 loading dye was loaded in each well. Gel was run for 30 minutes and stained in Ethidium bromide for 15 minutes, de-stained in dH2O for 15 minutes and DNA 16 was detected by UV light. DNA quantity and quality was determined using spectrophotometer. Based on the DNA concentration, a dilution to 2Ong/jil was made for each sample and stored at - 20°C freezer for PCR amplification. 2.3 Microsatellite and genotyping 2.3.1 Yellow-cedar designed primers Four (Y1F1O, Y1GO9, Y2C12, and Y2HO1) out of the five microsatellite primers designed for yellow-cedar by Bérubé et a!. (2003) were employed for genotyping. These four primers were selected following amplification using 21 randomly selected samples of trees representing the three seedlots studied). Throughout all the optimization/amplification runs, one primer did not amplify with our test samples and therefore was excluded from the study. The PCR reaction used during testing the four selected primers is as follows: start with 5 mm. at 95°C, followed by 34 cycle of 45 sec. at 95°C, 45 sec at annealing temperature (annealing temperatures of 62, 56, 64, and 60°C was used for Y1F1O, Y1GO9, Y2C12, and Y2HO1, respectively) and 45 sec at 72°C, followed by 5 mm at 72°C and hold on 4°C for 24hr. 2.3.2 Japanese cedar primers The search for additional microsatellite pirmers failed to identify primers from any closely related species, thus those developed for C. Obtusa, a member of the same genus, were tested. Selkoe and Toonen (2006) indicated that primer amplification may not be successful as species get more distant from each other; however, Bérubé et al. (2003) successfully obtained cross species amplification from distantly related species. A total of 19 microsatellite primers designed for C. obtusa by Matsumoto et a!. (2006) and Nakao et a!. (2001) were tested. These primers were selected based on their number of alleles and the number of null alleles present. The testing and optimzation efforts yielded only one primer (Cos2590) (Matsumoto et a!. 2006). Incidentally, Matsumoto et al. (2006) indicated that this particular locus deviated from Hardy Weinberg equilibirum and they concluded that this deviation is caused by presence of null alleles. 17 Cos2590 locus showed the presence of null alleles in PCR amplification on our samples. Although this locus had a high percent of null alleles and in most cases produced homozygous banding pattern, it was used in the present study because it amplified multiple unique alleles which played a significant role in distinguishing among individuals. The conditions for PCR reaction were as follow: PCR reaction starts with 3 mm at 94°C, followed by 34 cycles of 30 sec at 94°C, 30 sec at annealing temperature 55, and 30 sec at 72°C. These steps were followed by 5 mm at 72°C and hold on 4°C for 24hr. Additionally, I searched for the availability of SSR primers in species within the Cupressus and Junipersus and only RAPD dominant markers were identified; however, they were not used due to their uninformative nature for sibship assignments. 2.3.3 Primers from EST More loci were needed to increase the accuracy of pedigree reconstruction, so the available Expressed Sequenced (short DNA sequences) of C. Obtusa (accessed via genebank) were searched for the availability of SSR markers. The SSR fmder program (Robinson et al. 2004) was used to search through EST clones to detect microsatellite sequences. Search conditions were set as: 1) primer minimum, optimum, and maximum temperature were 50, 55, and 65°C, respectively, and maximum temperature difference was set at default, 2) Primer GC% was set at minimum, optimum, and maximum of 40, 50, and 60%, respectively, and 3) the default settings was used for the rest. Since GC has three binding sites, I set the percent of their existence on high in order to reach a better binding and amplification. Venderamin and Hansen (2005) stated that longer sequences have tendency to contain and amplify more microsatellite alleles as well as show more variation. This conclusion was supported by their work on Norway spruce (Picea abies) (Scotti et al. 2002). The microsatellite loci finder search resulted in a total of 487 loci within all EST clones, which were prioritized to select longer tn and di repeats. A total of six were selected, and primers were made and tested for amplification. Only one locus out of six amplified but it was monomorphic as predicated by Selkoe and Toonen (2006) who stated that cross species amplification may result in low allelic variation; however, it was used in the populations’ genotyping. 18 The conditions for PCR reaction were as follow: PCR reaction starts with 5 mm at 95°C, followed by 34 cycles of 45 sec at 95°C, 45 sec at annealing temperature, 55, and 45 sec at 72°C. These steps followed by 5 mm at 72°C and hold on 4°C for 24hr. Process of screening for amplification on 2% agarose gel was the same conducted for previous loci. 2.4 Genotyping for LICOR 2.4.1 PCR amplification for genotyping The protocol for PCR genotyping is summarized in the Table 2-3. Table 2.3. PCR protocol developed for LiCor (LiCor Inc., Lincoln, NE) genotyping. Yellow-cedar loci C. Obtusa locus C. Obtusa EST locus l0xBuffer 1 1 1 1OxdNTP 1 1 1 Autoclaved dH2O 3.3 3.7 3.25 Forward primer 0.5 0.5 0.5 Backward primer 0.5 0.5 0.5 Mg 1.5 1 1.5 M13* 0.1 0.1 0.05 DNA 2 2 2 Taq 0.1 0.2 0.2 Total volume 10 10 10 * Infrared label. Total volume of 3pJ stop dye (95% formamide [vol:vol]; 10mM EDTA; 0.005 mg of Fuschin red) were added to each PCR reaction. PCR plates were vortex and denatured in PCR machine 19 on 95°C for 3 minutes and were put on iced block immediately after taking out from PCR machine. By denaturing PCR product two strand of DNA will separate and individuals can be genotyped by each fragment allele size (base pair). A total volume of l.0il from each PCR reaction was loaded individually on 3.5% Long Ranger polyacrylamide gel 64 wells. While gel is running in Li-Cor 4200 DNA sequencer (Li-Cor Biosciences, Lincoln, NE, USA), PCR products run through the polyacrylamide gel matrix and fragments separate based on their size. LiCor conditions were set on 2000V, 35mA and 70W. Polyacrylamide gel were first preheated by setting the LiCor machine for pre-run mode for 15 minutes and before loading the first channel, the second channel was loaded after 3 minutes. For more accurate genotyping and minimizing scoring errors, a search for allelic variation was done using subset samples of 30 individuals from two seedlots (1 and 2), then the six loci used were amplified, genotyped with the SAGA GTTM (Li-Cor Biosciences, Lincoln, NE, USA) software. Samples that captured allelic variation within each locus was selected, mixed, and were used as a mixed DNA reference with known allele size. Additionally, PCR plates contained samples from two seedlots at a time to aid in allele detection. Standard marker/DNA ladder was loaded on wells number 1, 33, and 64 followed by loading the mixed DNA reference PCR products was loaded on wells number 2, 32, and 63. This was helpful in cases where the polyacrylamide gel matrix was curved. 2.4.2 Typing error Genotyping errors may happen as a result of contamination during DNA extraction or PCR preperation, incomplete amplification during PCR process, misscoring, and finally during genotype entry (Selkoe and Toonen 2006). If these errors are not considered in sibship assignments, they can lead to a wrong interpretation and consequently exclusion from the true relationship (Wang 2004). In order to decrease the rate of typing error, Selkoe and Toonen (2006) have suggested the inclusion of a positive control for every PCR set. Moreover by regenotyping 10-15% of the samples an estimate of typing error can be determined. For our study, mixed DNA and standard marker/DNA ladder were used as references. Additionally, all gels used for genotyping were rescored twice. 20 In pairwise assignment methods, every two individuals are studied at a time, thus mistakes in genotyping affects only that particular relationship. In group likelihood approach, dyad relationship is calculated based on a pair’s genotype and others in the cohort are used as reference, then typing error in a particular individual genotype will lead to wrong family assignment and will influence its siblings family inferences. Using all individual genotypes as a reference in group-likelihood methods makes it feasible to detect typing errors and consider it during sibship assignment (Wang 2004). The COLONY program considers typing error during data analysis. 2.5 Data analysis Three independent analyses were conducted: 1) Ritland’s (1996) MARK program was used as a preliminary analysis to determine if genetic relationship existed within each seedlot, 2) Wang’s (2004) CoLoJiy program was employed to construct the family structure within each seedlot into multiple full-sib families nested within half-sib families, and 3) the STRUCTURE program (Pritchard et al. 2000) was used to provide a pictorial demonstration of cohorts within each seedlot. 2.5.1 M4RK The MARK program (Ritland 1996) was used to demonstrate the genetic relationship among each possible dyad within each seedlot by pairwise kinship analysis and each possible dyad’s kinship coefficient value was calculated. These values were classified into five distinct groups (unrelated, half-sib, full-sib, full-sib with inbred parents and inbred). 2.5.2 COLONY The COLONY program (ver. 1.3: Wang 2004) was used to determine the number of half-sib and their nested full-sib families within each seedlot. 2.5.3 STRUCTURE The STRUCTURE program (ver. 2.2: Pritchard et al. 2000) was used with a length of burn-in period parameter set at 50,000 (burn-in lengths between 10,000-100,000 were recommended by the 21 author) and the same number for MCMC replications after burn-in. Since the expected number of cohorts should be 10 or more (see Yanchuk 2001), I set K (cluster) to range between one and 20 with numbers of iterations set at 40 (i.e., each K is simulated 40 times). The number of most likely cohorts was determined using the ad-hoc statistic known as AK, which is based on the log probability change across successive K values (Evanno et al. 2005). The results from the STRUCTURE and CoLoNY programs were combined to unravel the presence of any family structure in the selected seedlings from each seedlot. 22 3. Results and Discussion The multilocus genotypic data representing individuals (the 1-year-old greenhouse selected seedlings) within each of the three selection populations (seedlots) were independently analyzed using the MARK (Ritland 1996), COLONY (Wang 2004), and STRUCTURE (Pritchard et al. 2000) programs to investigate the presence of co-ancestry among the selected populations’ members, to assign individuals to their respective half- and full-sib families, and to provide a pictorial representation of sampled populations, respectively. In British Columbia, the minimum number of seed donors constituting natural stands’ seedlots is regulated and should be 10 trees (British Columbia’s Chief Forester’s Standards 2004). This regulation was designed to ensure a minimum level of genetic diversity that theoretically minimizes risk to biotic threats (Yanchuck 2001). At high elevations, yellow-cedar exists in mixtures of pure stands, scattered groups mixed with other associate tree species, and also as single tree (Harris 1990). Yellow-cedar natural stand seed collections are made using helicopters equipped with cone-rakes to strip seed- cone bearing branches from tree tops (Figures 1 & 2) and in most cases seed-cones are collected from a large number of non-neighboring trees (D. Pigott, Yellow Point Propagation, personal communication), thus it is safe to assume that seed donors are spatially separated. 3.1 Kinship estimates Ritland’s (1996) MARx program allowed estimating all possible pairwise relative kinship among individuals within each population (n(n-1)/2; where n is the number of individuals within each seedlot) (Figure 3.3). This approach was used to explore the presence of co-ancestry among each seedlot selected individuals and the expected kinship values and their frequencies provided insight into the genetic relationship among these individuals. In cases where no genetic relationship exists among the selected seedlings (r 0.0), I would expect that all or an extremely high percentage of the pairwise combinations to produce a value of zero. However, the pattern obtained from the three seedlots indicated that the selected seedlings belonged to groups of half sib families (r = 0.125) with multiple smaller full-sib families (r 0.250) nested within these half-sib families (Figure 3.3). Additionally, for all seedlots, two very small peaks were 23 Figure 3.1. Yellow-cedar aerial seed-cone collection using helicopter and cone rakes (photo: Don Pigott, Yellow Point Propagation). Figure 3.2. Yellow-cedar seed-cone collection from the helicopter deposit site (photo: Don Pigott, Yellow Point Propagation). 24 observed indicating that some members of the full-sib families resulted from mating between inbred parents (r = 0.3 75) or are the product of selfing (r 0.5) (Figure 3.3). The observed pattern was helpful in confirming the presence of family structure within the selected seedlings and allowed hypothesis formation for the second analysis (i.e., Wang’s (2004) COLoNY program). 3.2. COLONY The pattern obtained from Ritland’s (1996) MARK program analyses allowed setting our main hypotheses that no genetic relationship exists among the nursery stage selected seedlings (selection populations) and among the selected elite genotypes forming the production populations. Naturally, the presence of sibship relationship among these individuals formed our alternate hypotheses. Therefore the CoLoNY program (Wang 2004) was used to determine the genetic membership among the selected seedlings within each population (seedlot). Assuming that the seed trees are the recipient of pollen from multiple pollen donors (polygamous) and self fertilization does not result into viable seed and/or self seedlings are selected against at the nursery stage (either during thinning at the end of germination or during crop development and selection), the COLONY program was used to group the selected seedlings into multiple full-sib families nested within half-sib families. The three analyses resulted into 19, 21 and 17 half-sib families within seedlots number one, two and five, respectively (Table 3-1). The full-sib families within these half-sib families varied in size between 1 and 7 (seedlot #1) and 1 to 10 (seedlots # 2 and 5) (Table 3.1). Figure 3.4 demonstrates pictorial presentation of COLONY result for three seedlots. 3.3 STRUCTURE Since seed donors are spatially separated and may also be temporarily isolated during pollination, and then seed collected from each tree were considered as a representative sample of the local gene pool surrounding these trees. Following the recommendations of Bradbury et a!. (2008), the number of iterations for running the STRUCTURE program was set at 40 iterations to allow reaching stable results. Runs were conducted with the number of clusters ranging between 1 and 20 to ensure encompassing the minimum number of of seed donors set by the Chief Forester’s Standards (10 trees). For each cluster (K), mean LnP(D) (the probability of K in data) and Var LnP(D) (variance of LnP(D)) values were estimated and used to generate a set of graphs 25 Figure 3.3. Distribution of pairwise relative kinship estimates among the three selection populations (top: seedlot #1; middle: seedlot #2; bottom: seedlot #5). 100 30 43 .2 60 C 43 43 20 0 0 0.125 0.25 Relative kinship 0.375 0,5 100 80 0 0.125 0.25 0.375 0.5 Relative kinship 100 1 80 1 0 0.125 025 0,375 0.5 Relative kinship 26 Table 3.1. The number of full-sib families (size) within each half-sib family in the three studied yellow-cedar seedlots based on the CoLoNYprogram analysis. Seedlot No. Half-sib No. ______________________________________________________ 1 2 5 1 4(3,1,2,1) 3(3,3,4) 2(6,2) 2 3(1,7,2) 5(2,4,3,1,2) 3(6,3,1) 3 3(3,2,2) 4(1,4,2,2) 4(8,5,2,1) 4 2(3,2) 3(2,5,2) 2(1,2) 5 3(5,4,3) 3(3,9,2) 4(5,8,4,2) 6 4(1,4,3,2) 3(3,2,1) 2(10,4) 7 1(2) 3(2,2,2) 1(1) 8 5(4,4,4,3,2) 1(7) 2(2,1) 9 3(5,2,3) 3(3,2,1) 2(5,4) 10 2(4,3) 8(4,1,3,4,4,1,3,6) 1(5) 11 3(7,3,5) 5(6,2,1,8,2) 1(7) 12 1(4) 4(4,3,3,3) 4(9,4,2,1) 13 5(1,3,3,2,7) 8(4,3,4,10,2,5,5,7) 1(6) 14 5(4,1,2,5,1) 3(4,1,1) 1(4) 15 4(6,1,1,1) 4(1,3,1,2) 1(5) 16 1(2) 3(9,7,3) 1(1) 17 3(4,2,7) 4(8,4,3,1) 2(5,1) 18 2(4,2) 1(3) 19 3(1,3,1) 1(1) 20 1(1) 21 10(5,2,3,6,5,2,10,2,4,2) 27 18 1 14 14 i0 E ii Ii — T I i ‘ Ii 1 2 3 4 S $ 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 60 50 40 30 :: 1 2 3 4 5 6 7 8 9 1011 12 13 14 15 161718192021 35 .1 ¶1 Ii ‘ 15 1o4I-# ii ‘l 0 -. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 HS fam lily numier Figure 3.4 Pictorial presentations of COLONY results for seedlot number 1, 2 and 5. 28 to determine the number of clusters (see below). Evanno et al. (2005) recommended the use of AK to detect the real number of K; however, they also indicated that in situations where the number of individuals and marker loci used are not enough to give adequate information for population inferences, AK does not demonstrate the true number for K and in these cases determining the number of clusters or subpopulations should not be limited to using AK. In these cases, they recommended the use of LnP(D) in determining the true number of clusters. For each seedlot, LnP(D), AK, and IL(k)-L(k-l)I versus K graphs were generated; IL(k)-L(k-1)I graphs were similar to AK, thus they were not presented (Figures 3.5). These parameters are expected to show a quick increase in their respective values until a “plateau-like condition” is reached which is indicative of the number of clusters. As demonstrated in Fig. 3.5, AK does not show the true K since it has multiple peaks (i.e., no plateau) and all show the highest peak at K = 3 which is questionable since natural stand seedlots are collected from a larger number of trees. Thus the number of possible genetic cohorts are determined using LnP(D) graphs. The LnP(D) graphs for seedloti showed a unique trend which increased to K = 7, then it decreased, immediately followed by another increase and finally reached a plateau at K = 10 (Figure 3.5). For this population, ten was chosen to be the number of cohorts, a case similar to that observed by Bradbury et al. (2008) in their estuarine fish study. Deciding on the number of possible cohorts for population two and five was straightforward and a plateau-like condition was reached at K = 9 and 10, respectively, a situation similar to that observed by Aketarawong et al. (2007) for fruit fly populations. The pictorial classification of cohorts within each seedlot is presented using the STRUCTURE program bar plot (Figures 3.6 to 3.8) (individuals’ ID in plots is given by the STRUCTURE software and is not their true ID). Every cohort is defined with a specific color where individuals are represented by vertical bars where colors demonstrate the posterior probability (Q) of each individual belonging to each cohort (cluster) (Figures 3.6 to 3.8). Individuals were classified based on their highest posterior probability value. Furthermore, the posterior probability of some individuals indicates that they belong to more than one cluster (e.g., individuals are represented by two or more dominant colors). This could be due to the fact that this particular individual is descendent from more than one group or it could be the result of incomplete information caused by missing data (genotype) (Bergel and Vigilant 2007). 29 For each seedlot, the results from the COLONY and STRUCTURE programs were compared to investigate the presence of any correspondence between the two analyses (Table 3.2, Appendices 1, 2 and 3). Good correspondence was observed between the COLONY and STRUCTURE programs as presented by the size of the largest single half-sib family within a specific genetic cohort (Table 3.2). While half-sib family membership was not restricted to a single genetic cohort, some families’ representation was high and ranged between 18 and 61% of the cohort’s size, indicating that seed donors were the recipient of pollen from a limited number of male parents. Combining the COLONY and STRUCTURE programs in analyzing natural populations was made by Vähä et a!. (2007) in studying the natal homing of Atlantic salmon populations. Vähä et a!. (2007) repeatedly used the STRUCTURE program to stratify the populations in sequential manner and finally they used the COLONY program to identify the genealogical relationship among the spawning individuals within each stratum. In our study, the seedlots were already stratified as they originated from different locations and were collected in different years (Figure 1.1). The two programs were used to unravel the genetic similarity among the selected seedlings within each seedlot. However, it should be noted that the STRUCTURE and COLONY programs’ results did not completely match in spite of using the same multilocus genotypic data. These discrepancies could be caused by violation to the STRUCTURE program assumptions, specifically the presence of family structure within the seedling populations (Pritchard and Wen 2004; Camus-Kulandaivelu et al. 2007). Under increasing number of groups, the STRUCTURE program analysis starts by identifying the most distinct groups followed by the identification of the less genetically distinct groups, the presence of related individuals creates sufficient correlation among these individuals forcing them to be identified as a genetic group. This situation was unintentionally encountered by Vähä et al. (2007) when they repeatedly applied the STRUCTURE analysis to stratifying their Atlantic salmon populations into subgroups and finally realized that the final “Structure” represents full- and half-sib families. In essence, I followed Yu et al. (2006) intentional exploitation of the STRUCTURE program assumption violations to identify possible family structure in the analyzed populations. 30 Table 3.2. Correspondence between the COLONY and STRUCTURE programs represented by the size of the largest half-sib family within a single genetic cohort (%). Genetic cohorts K (cluster) Seedlot 1 2 3 4 5 6 7 8 9 10 1 20 23 31 42 55 23 23 47 33 29 2 22 61 44 44 38 50 35 29 27 -- 5 38’ 33 33 22’ 43 182 192 22 21 36 ‘two half-sib families,2three haif-sibs. 3.4 Genealogy of production population clones Western Forest Products’ selection procedure to identify superior genotypes for production population establishment consisted of three steps: 1) collection of wind-pollinated seed from seed donors in natural stands, 2) truncation selection of one-year-old “superior” seedlings in greenhouse, followed by clonal propagation and field testing, and 3) selection of elite genotypes for production population establishment. This process resulted in the selection of five, 17 and eight genotypes from seedlots one, two and five, respectively (Table 3.3). These selected genotypes are used to produce large number of rooted cuttings (clones) for reforestation. If these genotypes are related then the genetic diversity of the deployed material will be lower than that assumed under no relationship, and in fact it is lower than their perceived census number. The genetic classification of the selected genotypes was determined using the COLONY program and is presented in Table 3.3. With the exception of seedlot number one where the selected individuals were unrelated, seedlots 2 and 5 contained multiple clones from five and two half-sib families, respectively (Table 3.3). Several factors should consider when interpreting the COLONY and STRUCTURE program results. These are related to the species social status, reproductive biology, commercial seedling production, and the selection process conducted in the greenhouse. Yellow-cedar seed production is sporadic and sizable crops occur at intervals of 4 or more years (Harris 1974). In high elevation, seed-cones mature in two years (Owens and Molder 1974) and most seed-cones 31 are born on the tips of the upper crown branchiets. Mature and immature cones are identical in size and difficult to differentiate during operational seed-cone collection, thus in most cases the collected seed-cones are a mixture of mature (2-year-old) and immature (1-year-old) (i.e., on average 50% of the seed are 2-year-old). Mature cones average 7.2 seed per cone with only 29% filled seed, approximately yielding only one filled seed per cone (Owens and Molder 1977). Furthermore, the species is characterized by low seed germination (36% germination based on 27 natural stands, range: 1 to 73% (El-Kassaby 1995). Due to the scattered nature of yellow-cedar trees in natural stands, seed-cone collections cover large areas and in most cases are collected from up to 100 trees (D. Piggot, Yellow Point Propagation, personal communication); however, in the case of the studied seedlots, the number of seed donors was 29, 30, and 36 trees, for seedlots # 1, 2, and 5, respectively (SPAR 2009).. British Columbia’s 2003-2008 average yellow-cedar class B (natural stand collections) seedling production was 900K seedlings (BC Ministry of Forests and Range SPAR, D. Reid, personal communications). On average, natural stand seedlots are used to produce seedling crops of size varying between 50 and lOOK seedlings. Factoring in the total number of seedlings selected by Western Forest Products from each seedlot (range: 133 — 271; average: 191), then it is safe to assume that their approximate selection intensity was close to 1:500-1000 (i.e., truncation selection restricted the selection to fewer seed donors). Considering these factors collectively, then it is not surprising that the results from either the C0L0IVY and/or STRUCTURE programs produced these small number of seed donors. Partial and/or full pedigree reconstruction is commonly done with the benefit of parental reference information (Jones and Arden 2003). Pedigree reconstruction without parental reference population, as is the case in the present study, is most challenging and even when it is attempted. It is based on assumptions such as those invoked in the present study; namely, polygamous seed-donor trees and lack of or minimum selfing. These two assumptions are reasonable for forest trees and in particular to the present study. 32 K K 1 -3000.0 -3100.0 -3200 0 -3300.0 -3400.0 -3500.0 300.0 3700.O 8.000 7000 6.000 5000 4.000 3.000 2.000 1,000 0000 0 1 2345678 91112 415 12325 ‘1 K 0.0 . .I..I...s,3 4 3. 1 2345678910 112t3I4516t7I8IQ 1000.0 . -2000 0 4000.0 :::. -6000.0 . ——‘———————---— * K 30.000 25.000 0 1 2 3 4 5 6 7 8 9101112131415161718192021 K 0 -500 -1000 -1500 -2000 -2500 I 234567891 471 K 45.0 <. 4fuv 350 300 250 200 150 10,0 : :.: 5.0 . . . 0.0 33 01 Figure 3.5. Graphs of LnP(D) (left panel) and AK (right panel) versus K for the three studied seedlots (Top, middle, and bottom panels represent seedlots 1, 2, and 5, respectively) based on the Structure program analyses. 33 040 I))). (44) 49U) lIft)) 4244) 24)4) 404) 69)) 42)4) 09)0) 24))) 490)) 624)) ‘90) 149(1) 901) 440) 2441) 143(1) 1257) ) 44))) 5357) 679)4 4230) 127(1) 70)0. :U414) 147))) 169)4) 14404) 40))) 26)4) 14)4)) 044)) 571) 04)1464)4)424)1)7)I)159)I)1640.) 4-70). 0> 79)9) 9)I)194)))l70(l) 44(4) 4)44) 4)6) - •% -•-- —— — —57) iL._-.---------- 4014) 9444) 424)) 46)4) ‘Zr) 4440)) ;i) 140)9) 4904 14))) 6749) 149)9) 1460) Figure 3.6. STRUCTURE program bar plot of population one showing 10 cohorts. 94)) 3(9) 44441)491) 4)57)) 57))) I)))) 410) 40)9) 37)4) 79)5767)fl)20(l) Figure 3.7. STRUCTURE program bar plot of population two showing nine cohorts. 34 Forest trees commonly practice mixed-mating system with high outcrossing rates (O’Connell 2003), often with low levels of correlated matings (Liewlaksaneeyanawin 2006), and are characterized by high genetic load which acts as a built-in anti-selfing mechanism (Sorensen 1969, 1982; Bramlett and Popham 1971; Koski 1971, 1973; Bishir and Pepper 1977; Bishir and Namkoong 1987; Namkoong and Bishir 1987; Namkoong et al. 1988; Savolainen et al. 1992; Williams and Savolainen 1996; Williams et al. 1999). While invoking the polygamous nature of seed-donors is online with the yellow-cedar mating system, minimum or low selfing might be contradictory to the application of Wang’s (2004) COLONY pedigree reconstruction program. Ritland et al. (2001) estimated inbreeding level of 30% in yellow-cedar natural populations. This approach (t = (1 - FST)/( 1 + Fsr)) (Wright, 1965) was used and estimated an inbreeding level of 15% in the three studied populations (FST = 0.08021). The difference between Ritland et al. (2001) and the present study’s selfing estimates may reflect the strict selection (high truncation selection) applied in the greenhouse; however, inbreeding depression for early growth is known to be lower in Cupressaceae spp. (Wang and Russell 2006). The observed pattern from the MARJ program (r 0.5) as well as the inbreeding estimates for the species and that of the present study, could indicate that some members of the classified full-sib families could be the results of selfing 29i 4(l) 24(l) 1)1) .89(1) 81)i 11)1) 730) 63(1) 116’) 12(t 910) 32(I) 69(l)93 32(’4 I?))) IS))) 41)1) 35(5) 99)1) 957) 96(1) 86(1) 159(1) 89(1) 836(1) 93)1) 56(1) 29(l). 43))) 39(1) 42(l) ..ll 41111 2)11 40111 501)) Figure 3.8. STRUCTURE program bar plot of population five showing 10 cohorts. 35 Table 3.3. The Colony program classification of elite genotypes within half-sib (HS) families (genotypes within a single box belong to the same HS family). Seedlot No. 21 5 ID HS ID HS ID HS 1-194 1 2-192 5-098 3 1-302 3 2-327 1 5-025 1-203 4 2-430 2 5-018 5 1-205 3 2-256 7 5-129 9 1-145 17 2-444 5-003 10 2-443 10 5-139 2-016 5-068 12 2-331 13 5-126 2-30 1 2-303 14 2-311 15 2-286 2-221 16 2-302 2-171 17 2-049 2-109 21 36 4. Conclusion Western Forest Products implemented an unconventional approach to tree improvement and instead of following the classical breeding scheme that is characterized by selection of phenotypically superior individuals from natural stands (j:ilus-tree selection), followed by the utilization of a mating design for the production of genetic material for testing (breeding), and finally genotypic selection of elite individuals for forming production populations (selection) (Namkoong 1979), they decided to forfeit the phenotypic selection phase and the mating design step and capitalize on obtaining seed from natural crosses among trees in their native habitats and concentrated the first phase of selection phase at very early age (1-year-old) in a greenhouse environment. This approach, while innovative, is risky for the following reasons: 1) commercial natural stand seedlots are collected without any regard to seed-donor attributes except their reproductive output ability, 2) phenotypic selection of 1 -year-old seedlings in the greenhouse does not guarantee the capture of “superior” phenotypes even if high selection intensity is applied, and 3) the genealogical relationship of the selected seedlings is unresolved since neither the number of seed donors constructing a commercial seedlot nor their proportionate contribution to the seedlot are known. However, the species ability to be vegetatively propagated provided a unique tradeoff opportunity where reduced efforts at the selection and breeding phase could be complemented by increased efforts at the testing and selection phase. Vegetative propagation of greenhouse selected seedlings allowed the production of clonal material for testing over multiple site and years providing increased fidelity for the selection of proven genotypes for inclusion in the production population. This approach was successful in capturing high gain (Niejenhuis 2003 and 2006, unpublished); however, the genetic relationship of selected elite genotypes remained unknown and required the use of DNA fingerprinting and pedigree reconstruction for unraveling it. Finally, even with the limited number of loci used and the available genetic analyses, the phenotypic selection of seedling at early age was demonstrated to limit the genetic base of the tested material. In light of the results presented above, I recommend revising: 1) the genetic gain calculations that were based on assumed independence among tested individuals (i.e., members of the selection populations are unrelated) and 2) the genetic diversity estimates of deployment material as member propagules of half-sib-families are expected to produce lower effective population size. Finally, it should be pointed out that the revealed genealogical 37 relationships were limited to resolving only the first degree relationship (i.e., half-sib and full sib). 38 Bibliography Anon. 2005. Chief Forester’s Standards for seed use. British Columbia, Ministry of Forests. Effective Date April . http :I/www.for.gov.bc.calcode/cfstandards/ Aketarawong, N., Bonizzoni, M., Thanaphum, S., Gomuiski, L.M., Gasperi, G., Malacrida, A.R. and Gugliemino, C.R. 2007. Inferences on the population structure and colonization process of the invasive oriental fruit fly, Bactrocera dorsalis (Handel). Mol. 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HS FS Clone K 239 9 1 256 9 219 1 2 244 9 95 1 194 9 4 204 9 1 123 10 310 10 265 2 269 6 2 2 277 6 303 2 128 6 127 6 3 11 1 140 6 302 9 1 130 9 133 6 2 143 8 207 4 151 4 215 4 312 1 1 197 3 4 203 3 2 315 3 217 3 HS ES Clone K 119 1 103 1 1 242 9 153 9 206 7 58 2 2 228 9 286 10 235 10 248 10 3 198 7 226 1 1 264 3 271 2 2 273 2 274 6 6 — 221 1 288 6 3 308 6 268 1 171 2 223 10 255 8 186 6 HS ES Clone K 40 8 267 8 148 8 157 8 231 8 2 250 4 201 4 212 4 8 237 4 304 8 188 1 202 4 263 7 4 289 1 178 8 131 7 218 7 261 4 313 10 1 182 1 142 6 260 1 2 297 9 187 7 149 9 3 168 6 184 7 HS FS Clone K 259 10 246 5 295 5 10 150 5 230 8 2 152 7 220 10 233 2 247 7 254 7 1 301 7 191 6 216 3 165 7 11 — 309 5 2 144 10 210 7 180 6 196 1 3 126 6 193 3 8 6 183 6 12 1 181 6 225 2 232 2 44 -- z 4 CA (j) 0 i C ) I’. ) - 01 () - C/ ) M F’ 3 C ) i\) F’ J I’ J - - CD - C ) ‘ J - M - - - r\) - C ) - C ) \) - I\) - C ) F’. ) - C) 0 0 0 CD 01 C. ) 01 - — . 1 - 0 ,0 , 0 CD CD C. ) - C. ) F’. ) 0) 0 - - CD F’ J r’. - - — CD C ) F’. ) F’. ) 0) 01 CD - - 01 0’ 0 . 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