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Genetic diversity and spatial population structure of Sitka spruce (Picea sitchensis (Bong.) Carr.) :… Gapare, Washington Jingo 2003

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GENETIC DIVERSITY AND SPATIAL POPULATION STRUCTURE OF SITKA SPRUCE {PICEA SITCHENSIS (BONG.) CARR.): IMPLICATIONS FOR GENE CONSERVATION OF WIDESPREAD SPECIES by WASHINGTON JINGO GAPARE Dip. For., Forestry Commission, Zimbabwe, 1988 B.Sc, Aberdeen University, UK, 1995 M.Sc, North Carolina State University, USA, 1999 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Department of Forest Sciences) We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA September 2003 ©Washington Jingo Gapare, 2003 II ABSTRACT Knowledge of genetic diversity and population structure is of fundamental importance in the development of gene conservation sampling strategies to capture and preserve allelic diversity. Such knowledge is critical if we are to understand how to manage and maintain diversity in species and populations. To assess the effects of sampling strategy on capture of allelic diversity in widespread species, I studied Sitka spruce (Picea sitchensis (Bong.) Carr) populations as a model. Sitka spruce is a conifer that occupies wide geographic and ecological niches from 33° N to 60° N latitude along the Pacific coast of North America. A total of 1600 individual trees were sampled in eight populations classified as core or peripheral based on ecological niche, and continuous or disjunct based on distribution. In each population, 200 trees were spatially mapped and genotyped for eight cDNA-based sequence-tagged-site (STS) co-dominant markers. One important finding of this study is the similarity in genetic diversity as measured by expected heterozygosity between core populations (mean HE = 0.58) and peripheral populations (mean HE = 0.56). Another remarkable result found by this study is strong spatial structure as evidenced by coancestry in peripheral populations, both continuous and disjunct, but not in core populations. For example, trees located within 50 metres of each other in peripheral, disjunct populations had coancestry values greater than 0.20 while in core populations, trees within the same distance class had coancestry values below 0.06. Differences in population structure were attributed to an aggregation of similar multi-locus genotypes, in a structured, isolation by distance manner in peripheral populations, both continuous and peripheral but not in core, continuous populations. Irrespective of population classification, over 75% of the alleles were common and widespread. Only one allele, representing two percent of all alleles was classified as rare and localized on average, and this allele was limited to one core, disjunct and two peripheral, disjunct populations. To capture localized alleles (both common and rare), sampling should cover more populations over the geographic and ecological range of species at a cost of fewer individuals per population. The conservation of peripheral populations may present the best opportunity for preserving rare alleles. TABLE OF CONTENTS Abstract ii Table of Contents iii List of Tables vii List of Figures ix List of Appendices xi Acknowledgements xii Dedication xiv CHAPTER 1 Introduction and Overview 1 1.1 General Introduction 1 1.2 Thesis Overview 6 CHAPTER 2 Literature Review 8 2.1 Introduction 8 2.2 Levels and geographic patterns of genetic variation in conifers 8 2.3 Spatial population genetic structure 12 2.3.1 Effects of pollen and seed dispersal on spatial population structure 13 2.3.2 Effects of selection on spatial population structure 13 2.3.3 Spatial population genetic structure in forest trees 14 2.3.4 Importance of spatial genetic structure for sampling strategies 15 iv 2.4 Genetics/demography dichotomy 15 2.5 Core versus peripheral populations 17 2.5.1 Summary of core and peripheral population hypotheses 21 2.6 Common versus rare alleles 23 CHAPTER 3 Genetic diversity, population structure and evolutionary history of sitka spruce (Picea sitchensis (Bong.) Carr.): Implications for capture of allelic diversity 25 3.1 Introduction 25 3.1.1 Species of interest 27 3.1.2 Choice of molecular marker 28 3.2 Materials and Methods 29 3.2.1 Sampling locations and technique 29 3.2.2 DNA isolation and PCR amplification 32 3.2.3 Data analysis 32 3.3 Results 37 3.3.1 Allele frequency and genetic diversity 37 3.3.2 Population genetic structure and gene flow 41 3.3.3 Genetic distances and relationships among populations 43 3.3.4 Test for bottleneck signature 44 3.4 Discussion 47 3.4.1 Allele frequency distribution 47 3.4.2 Gene diversity as revealed by STS markers in Sitka spruce v 51 3.4.3 Population differentiation, , 54 3.4.4 Genetic variation in Core versus Peripheral Plant Populations.......... 57 3.4.5 Evolutionary history 58 3.5 Conservation genetic implications 62 CHAPTER 4 Spatial population genetic structure in Sitka spruce (P/'cea sitchensis (Bong.) Carr.): Implications for conservation of genetic diversity 67 4.1 Introduction 67 4.2 Materials and Methods 71 4.2.1 Spatial autocorrelation analysis 71 4.3 Results 75 4.3.1 Visualization of spatial distributions 75 4.3.2 Spatial population genetic structure 78 4.4 Discussion 81 4.5 Conservation genetic implications 87 CHAPTER 5 Conservation of forest genetic resources as related to conservation population sample size and area sampled:-an empirical approach 88 5.1 Introduction 88 5.2 Materials and Methods 91 5.2.1 Relationships between population sample size and genetic diversity parameters 92 vi 5.2.2 Capture of diversity at varying area sampled and population sample sizes 92 5.3 Results 95 5.3.1 Observed relationships between population sample sizes and genetic diversity estimates 95 5.3.2 Observed trends in capture of diversity at varying spatial scales 99 5.4 Discussion 103 5.4.1 Relationships between population sample size and genetic parameters 103 5.4.2 Observed trends incapture of diversity at varying area sampled and population sample size 104 5.5 Implications of results for design of ex situ collections and in situ reserves 107 CHAPTER 6 Conclusions 109 6.1 Introduction 109 6.2 Major findings 109 6.3 Recommendations 111 6.4 Future research 113 REFERENCES 116 APPENDICES 142 vii LIST OF TABLES 2.1. Expected heterozygosity (HE) at species level and genetic differentiation (Gsr) in selected conifers using different molecular marker types 10 3.1. Two-way classification of Sitka spruce populations according to location of sampling sites relative to ecological and geographic distribution 30 3.2. The modified Marshall-Brown (1975) two-way classification of allele distribution 34 3.3. Allele frequencies for eight loci studied in eight range-wide natural populations of Sitka spruce 37 3.4. Gene diversity and population structure at eight sequence-tagged-site (STS) polymorphic loci in Sitka spruce populations 40 3.5. Classification of alleles based on frequency and geographic distribution in four population classes defined by ecological and geographical distribution of Sitka spruce 41 3.6. Estimates of within-population genetic diversity parameters for eight natural populations of Sitka spruce 42 3.7. Genetic distances between eight natural populations of Sitka spruce 43 viii 3.8. Cornuet and Luikart (1998) test for recent bottleneck in Sitka spruce populations under both the infinite alleles model and stepwise mutation model 46 3.9. Classification of alleles based on frequency and geographic distribution (common widespread, CW; common localized, CL; rare widespread, RW; and rare localized, RL) in selected forest tree species 49 3.10. Summary of average expected heterozygosity (HE) in core and peripheral populations in various plant taxa 57 4 . 1 . Summary of genetic diversity estimates obtained for subpopulations within each of the four Sitka spruce peripheral populations, both continuous and disjunct 79 ix LIST OF FIGURES 3.1. Native range of Sitka spruce and locations of sampled populations 31 3.2. Panels showing amplification products and allelic polymorphisms at loci Sb16, Sb17 and Sb32 among 24 genotypes of Sitka spruce from Prince Rupert 38 3.3. The distribution of allele frequencies in Sitka spruce populations 39 3.4. UPGMA- derived dendrogram showing the clustering of the eight natural populations of Sitka spruce based on the genetic distance of Nei (1978) 44 4.1 a - d. Spatial distribution of STS loci polymorphisms (Sb16 & Sb17) in 550 ha area of Sitka spruce located on Kodiak Island (a and b) (peripheral, disjunct population) and Port McNeill (c and d) (core, continuous population) 76 4.1 a - d. continued 77 4.2 a -h . Spatial correlograms of coancestry coefficients (p<,) for core and continuous (CC), core and disjunct (CD), peripheral and continuous (PC), and peripheral and disjunct (PD) populations of Sitka spruce 80 5.1 a & b. Relationship between allelic richness and population sample size for: (a) overall allelic richness (AR), and X (b) allelic richness for common alleles (p > 0 .05) (ARC) 96 5.2 a&b. Relationship between genetic parameters and population sample size: (a) expected heterozygosity (HE) and population sample; (b) observed heterozygosity (Ho) and population sample 97 5.3. Relationship between expected heterozygosity for common alleles (HEC) and population sample size 9 8 5.4 a - d. Bivariate response surfaces relating allelic richness (AR) to population sample size and area sampled in core (a & b) and peripheral (c & d) populations, respectively 101 5.5 a - d. Bivariate response surfaces relating expected heterozygosity (HE) to population sample size and area sampled in core (a & b) and peripheral (c & d) populations, respectively 102 xi LIST OF APPENDICES I Glossary of relevant terms used in the thesis.. 142 II Protocol for genomic DNA extraction from needles of Sitka spruce [modified from Doyle and Doyle, 1990)] 147 III Sequence-tagged-site (STS) primers and product ranges 148 XII ACKNOWLEDGEMENTS I first acknowledge, with great appreciation, funding, through the Centre for Forest Gene Conservation, which made this study possible, initially from Forest Renewal British Columbia and subsequently from the Forestry Investment Account, with the support of the Forest Genetics Council of British Columbia, Canada. Fellowships were also made available from Van Dusen Graduate Fellowship and Donald S. McPhee Fellowship through the University of British Columbia. I would like to express gratitude to the following: - My doctoral research supervisor, Dr. Sally Aitken and committee members, Drs. Alvin Yanchuk, Kermit Ritland, Carol Ritland and Eric Taylor for their guidance, constructive ideas and reviews of the initial drafts of this thesis. Most gratitude goes to the most supporting supervisor, Dr. Sally Aitken for sharing her extra ordinary wisdom, encouragement to think and write creatively and challenged me to try new forms of expression and Dr. Carol Ritland who patiently shared her ideas on structure of the thesis, wisdom and wise words of encouragement both in the laboratory and outside the academic environment, Thanks Carol. - Drs. Peter Arcese (Forest Sciences) and Wayne Maddison (Zoology and Botany) who were the university examiners, for their helpful suggestions on various aspects of the thesis, which greatly improved the thesis. - Professor Jean Bousquet at Larval University, Quebec, who was the external examiner for my doctoral thesis for reviewing the whole thesis thoroughly, especially Chapters 3, 4 & 5,.where he gave much scrutiny as if they were submitted as manuscripts for publication by major forestry and genetics journals. - Dr. Sally John, both a friend and colleague for her suggestion in the summer of 1999, that I consider pursuing doctoral studies at UBC, under the supervision of Dr. Sally Aitken. - Mr. Don Pigott, Mr. Jim Herbers and Ms. Lynn Norton for help with collection of samples in the field. Xlll - Joanne Tuytel for helping with DNA isolation and Allyson Miscampbell with useful advice in the Genetic Data Centre Laboratory at UBC. - Colleagues at the Centre for Forest Gene Conservation at UBC for their help, encouragement and advices on various aspects of the project. - Drs. John Barker and Steve Mitchell for giving me the opportunity to be the Teaching Assistant for third-year Silviculture courses in Forest Sciences at UBC and subsequent financial support that came with the experience. - Dr. David Gwaze, my former boss while I worked for the Forestry Commission in Zimbabwe and mentor for encouragement over the years. - My mates, Jodie Kraskowsi, Cherdsak Liewlaksaneeyanawin, Yanik Berube, Mohamed Iddrissu, Dilara Ally, Marissa LeBlanc, Jaclyn Beland, Jennifer Wilkin, Charles Chen, Hugh Wellman, Dr. Tanya Wahbe and Dr. Scott Harrison. - My colleague the late Yaw Bennuah, for his helpful advice and friendship for the brief period I knew him before he tragically lost to a heroic battle with cancer in the Fall of 2000. It goes without saying that in any endeavor such as this, family sacrifices are the hardest to make. I would like to thank my son Danai Victor Gapare, parents, brothers, sisters and extended family members for tolerating me (only just, on some occasions) during the four years it has taken me to complete this project. xiv DEDICATION To my late twin sister, Rosemary, this is for you. "After climbing a great hill, one only finds that there are many more hills to climb" (Nelson Mandela, Former President of South Africa). 1 CHAPTER 1 INTRODUCTION AND OVERVIEW 1.1. General Introduction Genetic diversity, including the inter- and intra-population diversity within species, is an important component of biodiversity. It allows local populations of a species to adapt to a variety of niches. Genetic diversity provides evolutionary flexibility for species to adjust, both spatially and temporally in the long term in response to changing climates and other conditions. For example, Rehfeldt et al. (1999) in their study on genetic responses of Pinus contorta Dougl. to climate change revealed that currently projected changes in climate over the next few centuries may affect the productivity and survival of forest tree populations substantially for several generations, although populations will eventually adapt as they contain high levels of genetic variation. Species' potential range is expected to shift rapidly with climate change but little is known of the processes that control range expansions. Environmental changes are likely to bring about new biotic interactions because species respond differently to new environments and in turn cause the biotic environments themselves to change. For example, unfavorable environmental conditions in recent years showed drawbacks in the conventional in situ conservation system for common ash (Fraxinus excelsior L) , an economically important species in Europe (Pliura and Heuertz, 2003). Genetic resources are being lost due to the combined effects of drought, insect outbreaks, and major disturbance events such as fires and wind storms. Equally important to climate change is the introduction of exotic diseases. Declining genetic diversity in both natural and breeding populations may restrict the potential for genetic evolution to the ever-changing natural, economic and social components of the environment as well (Namkoong, 1986). Major areas contemplated in the near 2 future where new adaptations may be required include viral, fungal, and insect resistance, and cold tolerance. Population genetic theory predicts that a small population size sustained over many generations in a partially or completely isolated population will lead to depletion of genetic diversity (Crow and Kimura, 1970). The reduction of heterozygosity can be rapid if population size (A/) is very small. The expected heterozygosity under random mating declines by a factor of [1 - (2A/)"1] per generation (Wright, 1969). For example, with N = 4, after 10 generations of random drift, only 26% of heterozygous genotypes at a locus are left in the population. Inbreeding accelerates the reduction in heterozygosity because it further reduces the effective population size (Ne), which, in the broad sense, is the number of individuals in a population successfully and equally involved in reproduction in a given generation. Under mixed mating and selfing model, Ne is reduced to 1/4 (1 + t)N, where f is the outcrossing rate (Pollak, 1987). Thus, the effective population size is typically smaller than the actual size unless the outcrossing rate is unity. For example, with N = 4, and t = 0.8, Ne = 3.6. Also, Ne< N even if t = 1 if individuals vary in fecundity. Most conifer species have an outcrossing rate greater than 0.8 (reviewed by O'Connell, 2003). Therefore inbreeding usually has relatively less impact on the decay of genetic diversity than the impact of small population size in forest trees. A decrease in population size under some critical level results in a decrease in genetic variation due to genetic drift, as well as an increase in inbreeding. But what is the critical minimum population size? (20 individuals?, 50?, 500?, 5000?) and how should these populations be managed? These are fundamental questions that need to be precisely addressed in any conservation initiative. Earlier, Franklin (1980) and Soule (1980) recommended a minimum effective population size of Ne = 500. Lande (1995) suggested that the Franklin - Soule number should be increased by a factor of 10, to Ne = 5000 as only those mutations with relatively weak effects, which comprise around 10% of all mutations, will persist and contribute to genetic variance of quantitative traits (e.g., Aitken, 2000; Yanchuk, 2001). Lynch (1996) later on 3 suggested that Ne of 1000 is adequate for the maintenance of adaptive potential in quantitative traits under a balance between mutation and genetic drift. Both Lande (1995) and Lynch (1996) suggested that Ne is a potential predictor of fitness and extinction risk. Peripheral populations are often relatively small and isolated from core populations (Lawton, 1993). As a result, their status in gene conservation is often debated. Often, the question asked is under what conditions it is appropriate to expend resources to conserve peripheral populations (e.g., Lesica and Allendorf, 1995). They may be important for their quantitative characters, as they often inhabit stressful environmental conditions at the limits of the ecological niche of the species (i.e., length of growing season, frost, light, and drought), but on the other hand, their overall genetic variability may be low. If alleles responsible for these adaptations are also present (at low frequency) in core populations, it is tempting to limit sampling for conservation to core populations to minimize both the collection and maintenance costs. However, if adaptations are based on alleles unique to peripheral populations, the conservation value of these populations is much higher. In addition, peripheral or disjunct populations may warrant particular attention as they are more likely to contain novel genotypes and be the most difficult to replace with other populations (e.g., Lesica and Allendorf, 1995). For example, seed collections from disjunct populations of Pinus radiata D. Don on Guadalupe Island and Cedros Island (off Baja California, Mexico) have formed the basis of successful breeding programs in Australia, Chile, New Zealand and South Africa (Moran etal. 1988; Rogers, 2002). The large size of the conifer genome which is reputed to be around 10 1 0 bp long (Wakamiya et al. 1993; Schmidt et al. 2000) does not permit us to understand the effects or function of all the genetic variation and the alleles detected. However, there is an understanding that much of this variation either serves some purpose or has some potential evolutionary significance. Whether to conserve rare or only common alleles remains a major question. Since we cannot understand the effects or function of all the genetic variation and the alleles detected, a conservation 4 approach, in which we assume that some of this variation either serves some purpose or has some potential evolutionary significance, is warranted. For example, there are concerns about future adaptations, hence the need to develop conservation programs that also consider unique features such as rare alleles. Brown and Briggs (1991) suggest that it may be efficient to save extreme or unique alleles rather than more common ones. Few genotypes in current breeding programs or wild populations are expected to have low-frequency alleles (Yanchuk, 2001). Allele frequencies in both natural and breeding populations vary over time with selection and with reproductive success of parents. This can be affected by changes in the environment, silvicultural practices, or random climatic fluctuations from year to year. For example, some rare alleles were lost between the first and second-generation seed orchards of Pseudotsuga menziesii (Mirb.) Franco (El-Kassaby and Ritland, 1995). Also, Williams et al. (1995) reported loss of rare alleles in conventional breeding strategies when compared to the multiple population breeding strategy (MPBS) and the hierarchical open-ended strategy (HOPE) and suggested that MPBS has the potential to increase genetic variability in breeding populations, particularly rare alleles in smaller breeding populations. Alleles of low frequency or those unique to one or a few populations and potentially with major effects on expression of a future trait of interest, are the types of alleles that we should consider valuable (e.g., Yanchuk, 2001). Alleles of intermediate frequency are easy to conserve and any small population is likely to contain and maintain such alleles. Examples of rare but valuable alleles include a recessive lignin mutant in loblolly pine (Pinus taeda L.) (Ralph et al. 1997) and the major gene resistance to the introduced blister rust in sugar pine (Pinus lambertiana Dougl.) (Kinloch, 1992). Knowledge of genetic diversity, population structure and evolutionary history for a species' range is of fundamental importance in the development of gene conservation sampling strategies specifically to capture and preserve allelic diversity. Several authors have addressed the theory and the rationale of optimal 5 sampling strategies (e.g., Marshall and Brown, 1975; Crossa, 1989; Lawrence et al. 1995a; Brown and Hardner, 2000). Much of this literature contains sampling strategies for herbaceous plants. The Food and Agricultural Organization (FAO) (1995) highlights several distinctive features in forest tree species that affect sampling in the field. The most important feature is the central concept of provenance in forest trees (e.g., Matyas, 1996). The provenance concept emphasizes that geographical features, like climate, topography, soils and spatial isolation, shape the patterns of genetic variation in tree species. Many studies of both temperate and tropical species have demonstrated genetic divergence among provenances of the same species (e.g., Mikola, 1982; Morgenstern, 1996; Ying, 1997; Jaramillo-Correa er al. 2001; Hodge and Dvorak, 1999; Gapare et al. 2001). Sampling strategies should therefore recognize the centrality of provenances as units of genetic resources (Brown and Hardner, 2000). Tables have been derived using theoretical genotype and allele frequencies to estimate the number of trees per species or population that should be sampled to capture alleles at a given frequency (e.g., Namkoong et al. 1980; Namkoong, 1988; Gregorius, 1980; Crossa, 1989; Sjogren and Wyoni, 1994). However, these models do not take into account the specific landscape dynamics of the target populations, their proximity to neighboring populations and resulting levels of gene flow, variations in mating system, the degree of fragmentation, and influence of introgression with related taxa. These models assume equilibrium but different populations differ substantially in genetic characteristics so that predictions based on common theory become unreliable. Furthermore, field seed collections in geographically isolated locations in the temperate regions seldom have the luxury of a priori assessments of clinal or ecotypic variation to know where to most effectively capture both within- and among-population variation in samples (e.g., Dvorak et al. 1999). The question of how effective such seed collections are in natural populations of temperate forest tree species remains to be seen. 6 Empirical data upon which to base species sampling strategies for capture of diversity and conservation of rare alleles is limited. Such knowledge is critical if we are to understand how to manage and maintain diversity in species and populations. The major thrust of this research is to use empirical data to develop effective sampling strategies for capture of diversity and conservation of rare alleles in widespread species. Conservation involves several sequential stages, ranging from the initial selection of target taxa and identification of conservation objectives, through field exploration and germplasm collection, to the actual storage and maintenance of that germplasm over extended time periods. In situ and ex situ gene conservation are two broad groups of methods for genetic conservation; each has different scientific, political and managerial objectives and challenges. However, both methods are complimentary in that they both maintain, and in some instances, create genetic diversity. In situ conservation means the conservation of ecosystems and natural habitats and the maintenance and recovery of viable populations of species in their natural surrounds and, in the case of domesticated or cultivated species, in the surrounding where they have developed their distinctive properties. Ex situ conservation refers to any conservation method that entails. removal of individual plants or propagation material (seed, pollen, tissue) from its natural occurrence, i.e., conservation "off-site" in gene banks as seed, tissue or pollen; in plantations; or in other live collections, such as ex situ conservation stands. 1.2. Thesis Overview To set the stage for my study I will first review the literature (Chapter 2) pertaining to levels and geographic patterns of genetic variation in conifers, major factors that contribute to the extent of spatial genetic structure within populations, and the genetics/demography dichotomy. I also review the debate on the value of peripheral versus core populations, and contribution of rare alleles in gene conservation. Investigating patterns of genetic diversity (Chapter 3) is a necessary first step towards developing sampling strategies for capturing diversity and conserving rare 7 alleles in widespread species. Amount and patterns of genetic diversity in a species must be understood in order to manage forest genetic resources for present and future generations. I hypothesize that outlying populations in a species distribution are likely to harbour rare or unique alleles. Strategic sampling of populations based on the ecological and geographical distribution of species gives me the ability to explore the relative roles of history, isolation and ecological marginality in producing the spatial genetic structure observed in peripheral populations of Sitka spruce (Chapter 4). These spatial patterns have the potential to influence the efficacy of sampling strategies in capturing genetic diversity. In Chapter 5, I use empirical data (from Chapters 3 & 4) to examine the relationships between population sample size and genetic diversity parameters (expected heterozygosity and allelic richness). I also investigate the effect of varying area sampled given a fixed population sample size on capture of genetic diversity in core versus peripheral populations. I then discuss the implications of my results for the design of in situ reserves and numbers of individuals sampled for establishment of ex situ conservation populations. Recommendations are provided for addressing the national or regional level conservation of the forest genetic resources. The final chapter (Chapter 6) summarizes and discusses the main findings in this thesis - primarily the levels and spatial distribution of genetic diversity in core versus peripheral populations, and the roles of genetic, ecological and evolutionary information that relates to genetic conservation of widespread species. I provide specific conservation recommendations for ex situ and in situ gene conservation of widespread species. Lastly, I suggest new areas of research in the quest for understanding and conserving forest genetic resources for present and future generations. 8 CHAPTER 2 LITERATURE REVIEW 2.1. Introduction In this section, I review the literature pertaining to levels and geographic patterns of genetic variation in conifers, major factors that contribute to the extent of spatial genetic structure either within or among plant populations. I also review and summarize the arguments (genetics/demography dichotomy) for and against the utility of assessments of population genetic diversity for resource conservation. Because most widespread species occupy diverse geographic and ecological niches, it is imperative to review whether peripheral populations will persist better or worse than core populations. I also provide a detailed review on the importance of conserving rare alleles versus common alleles. 2.2. Levels and geographic patterns of genetic variation in conifers Meta-analyses of genetic diversity have provided valuable insights into the patterns of genetic variation in plants (e.g., Hamrick etal. 1979; Loveless and Hamrick, 1984; Hamrick and Godt, 1989; Hamrick et al. 1992). These analyses have classified species by criteria including geographic range and various life-history characters, to search for trends in the levels and patterns of genetic diversity in plants with similar characteristics. Significant differences in the levels of genetic variability were detected among range categories (gymnosperms versus angiosperms; annuals versus perennials) (Hamrick et al. 1979; Hamrick and Godt, 1989). Many researchers have used results from these analyses as a point of reference for data from their own studies. Assessment of genetic variability and its partitioning remains a major concern of plant breeding, genecology or conservation genetics. Extensive reviews of 9 allozyme-based studies have demonstrated that the vagility of pollen and seeds is highly associated with the development of genetic structure (Hamrick and Godt, 1989; 1996a). Thus breeding system, seed dispersal and geographic range have proved to be associated with the amount of total genetic variation and its partitioning among and within populations. Moreover, mean within-population diversity has proved to be an accurate predictor of the total within-species diversity (Hamrick and Godt, 1989). The present distribution of genetic variability in forest trees differs from that of many other life forms. Meta-analyses of isozyme data, collected by Hamrick et al. (1992) and Hamrick and Godt (1996b) across a large number of species, indicate that trees maintain a significantly higher level of genetic diversity within species (expected heterozygosity, HE = 0.177 on average) and within populations (0.148 on average) than annual plants (averaging 0.154 and 0.101, respectively) for nuclear genes. Allozyme-based studies in forest trees also show a lower level of genetic differentiation among populations (see Table 2.1), measured with G S r (Nei, 1973), a coefficient of gene differentiation equivalent to Wright's (1951) FST- GST averages 0.084 for woody long-lived perennial species compared to 0.355 for annual plants. However, the low differentiation observed with isozymes has recently been confirmed by other types of molecular markers using nuclear DNA for several species (e.g., white spruce (Picea glauca (Moench) Voss), Jaramillo-Correa et al. 2001 using expressed sequenced-tagged-polymorphism (ESTP) markers; western redcedar (Thuja plicata D. Don), O'Connell, 2003 using microsatellites; and lodgepole pine (Pinus contorta var latifolia), Thomas et al. 1999 using simple sequence repeats (SSR)) (Table 2.1). However, estimates of expected heterozygosity (HE) and genetic differentiation (GST) are often biased by small sample size and also depend on the type of marker used. For example, sampling variance has two components; i.e., variation in heterozygosity among individuals and among loci. In addition, theoretical properties of distribution of heterozygosity are complicated. 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However, Nei (1978) and Nei and Chesser (1983) provide formulas for correcting for small sample size. Mutation rates in microsatellite loci are considerably higher than that observed for loci such as protein allozymes (10"5 to 10"3 for microsatellites; 10"8 to 10"7 for isozymes) (e.g., Weber and Wong, 1993; Di Rienzo et al. 1994). For example, O'Connell (2003) used eight microsatellite loci and Yeh (1988) used 19 isozyme loci to investigate genetic variation in western cedar and reported HE values of 0.750 and 0.060, respectively. Similarly, Thomas et al. (1999) and Macdonald et al. (2001) used five SSR loci and eight isozymes loci, respectively, and reported HE values of 0.730 and 0.189, respectively (see Table 2.1). However, HE values are difficult to compare from one study to the next because some studies often use only the polymorphic loci, which often bias the estimates. In addition, some conifer species have more of their genetic diversity partitioned among populations than within-populations (Table 2.1). For example, yellow-cedar (Chamaecyparis nootkatensis (D. Don) Spach; GST = 0.139; Ritland et al. 2001); red pine (Pinus resinosa, Ait; GST = 0.57; Walter and Epperson, 2001) and Torrey pine (Pinus torreyana Parry ex Carr; GST = 1 ; Ledig and Conkle, 1983; Waters and Schaal, 1991). Possible factors that lead to higher GST values in these species include lack of gene flow, increased genetic drift in smaller populations, fragmentation and broad ecological niches. The information summarized in Table 2.1 demonstrates that substantial variation existed in the number of populations studied, the number of individuals sampled from an area to represent a population, and the number of loci surveyed. Wright's (1951) and Nei's (1978) methods of assessing populations' genetic variation rely on estimates of genotypic and allele frequencies. As a rule, the precision of such 12 estimates depends upon the number of individuals sampled, loci assayed per population and the type of marker used. 2.3. Spatial population genetic structure Spatial structure is the distribution of genotypes over two-dimensional space of a stand (Epperson, 1992). It can be characterized through physical locations and the genetic or genealogical relationships between individual trees. Spatial population genetic structure (i.e., nonrandom spatial distribution of alleles and genotypes) is evident within populations when the distribution of genetic variation among individuals grouped at increasing spatial scales is nonrandom (McCauley, 1997). Adults from plant species do not move and plants' gametes and propagules, primarily pollen and seeds, often show moderate to strong spatial restriction in their dispersal. Under restricted dispersal at the population level, genetic drift will lead locally to a pattern of spatial genetic structure, meaning that genetic similarity is higher among neighboring individuals. Spatial variation in the direction or intensity of natural selection should also cause a pattern of spatial genetic structure at selected loci. Any factor affecting the level of dispersal, and the intensity of genetic drift or natural selection, is expected to influence pattern of spatial genetic structure. In this section, I will review two major factors that contribute to the extent of spatial genetic structure within plant populations. These are 1) patterns and distances of pollen and seed dispersal (Ennos, 1994) and 2) the extent of selection (Hedrick, 1986). The predicted effects of various patterns of pollen and seed dispersal are well understood (Slatkin, 1985a; Ennos, 1994), as are the theoretical consequences of local selection (Slatkin, 1985a). I also emphasize how information on spatial population genetic structure may be used to develop optimal sampling strategies for capture of diversity for ex situ gene conservation. 13 2.3.1. Effects of pollen and seed dispersal on spatial population structure Fine-scale spatial population genetic structure (i.e., spatial clustering of like genotypes in small patches) can develop as a result of differential effects of pollen and seed dispersal on allelic correlations within and among individuals within a population. When the variance in seed dispersal is less than the variance in pollen dispersal, fine-scale genetic structure can develop with or without inbreeding. Four alternative dispersal scenarios are possible with these dispersal attributes (Epperson, 1995): (i) When both pollen and seed are highly localized around the maternal parent and have similar variances, then inbreeding and genetic sub-structuring of a population will evolve as described by the isolation-by-distance model (Wright, 1946; Sokal and Warternberg, 1983; Barbujani, 1987). (ii) When both pollen and seed dispersal are random within a population, then neither inbreeding nor spatial genetic structure will develop. (iii) When pollen dispersal is highly localized but seed dispersal is random, then neither inbreeding nor genetic structure will develop (if selfing is prohibited). (iv) When pollen dispersal is random and seed dispersal is highly localized there will be some inbreeding but spatial aggregations of siblings will result in significant fine-scale genetic structure (Hamrick and Nason, 1996). 2.3.2. Effects of selection on spatial population structure Selection can also generate particular spatial genetic patterns and therefore enhance or reduce genetic differentiation due to restricted gene flow (Hedrick, 1986; Sokal et al. 1989). However, selection may also reduce spatial differentiation by eliminating inbred trees resulting from either self-fertilization or consanguineous 14 matings caused by restricted gene flow. Even with restricted gene flow, the resulting genetic structure observed at the adult stage may be weak if inbreeding depression is substantial, as is often observed for forest trees (Williams and Savolainen, 1996). 2.3.3. Spatial population genetic structure in forest trees Coniferous species (in which pollination and seed dispersal occurs mainly by wind, and outcrossing rates are generally high) have often shown random or only weakly spatially autocorrelated distributions of genotypes (Epperson and Allard, 1989; Knowles, 1991; Xie and Knowles, 1991). Studies in such species have generally revealed either weak genetic structure (e.g., Pinus contorta ssp. latifolia; Epperson and Allard, 1989) or a random distribution of genotypes (e.g., Picea mariana, Knowles, 1991). There are exceptions, however, for example, Larix laricina Du Koi exhibited strong genetic structure due to identified historical disturbances (Knowles etal. 1992). The general observation of weak local genetic structure among conifer tree species could be the result of life-history traits including longevity and extensive gene flow via wind pollination, in comparison with other plants (Hamrick et al. 1979; Hamrick et al. 1981). However, populations of species (e.g., angiosperms) in which seed dispersal is limited have often shown stronger genetic clustering than in conifers. Quercus species, in which large seeds are dispersed by gravity, are typical. For example, in a continuous old-growth stand of Quercus laevis studied by Berg and Hamrick (1995), the population showed one of the highest proportions recorded of positively significant autocorrelation, over scales of 10 m or less, presumably because of short distance seed dispersal by gravity. Linked to the effects of pollen and seed dispersal mechanisms as a cause of spatial genetic structure are anthropological disturbances such as fragmentation. Young and Merriam (1994) compared fragmented and continuous Acer saccharum populations. They found positive spatial autocorrelations in fragmented populations, 15 suggesting a reduction in gene flow in the shortest distance classes in fragmented populations, and negative autocorrelations in the longest distance class, suggesting that immigrant pollen pools were contributing substantially to mating events at forest patch edges. 2.3.4. Importance of spatial genetic structure for sampling strategies For conservation purposes, analysis of spatial genetic structure can be used to provide baseline information for sampling strategies. Information on genetic processes derived from knowledge of within-population genetic structure can inform the spatial scale for optimal sampling of genotypes for maximum capture of diversity for ex situ gene conservation. Therefore, understanding the genetic processes operating in natural populations is warranted because such knowledge is essential for enhancing the quality and efficiency of conservation, for management of genetic resources, and for controlling the potential risk of genetic deterioration. 2.4. Genetics/demography dichotomy The role of population genetics in plant conservation biology continues to be the subject of considerable discussion. For example Lande (1988), Schemske et al. (1994) and Caughley (1994) have expressed doubts that genetic diversity plays a decisive role in the survival of populations or species. Their doubts rest on the argument that populations usually go extinct for ecological reasons (e.g., habitat destruction or environmental changes). Others (e.g., Brown and Schoen, 1992; Hamrick and Godt, 1996) have countered that genetic diversity provides evolutionary flexibility for species and populations to adjust in the long term to environmental changes, hence knowledge of a species' genetic composition is essential for any comprehensive long-term genetic conservation planning. Young and Clarke (2000) emphasize the fact that the disassociation between genetics and demography has 16 been perpetuated and popularized in the literature by two very influential papers: those by Martin Brookes (Brookes, 1997) and Graeme Caughley (Caughley, 1994). For example, Brookes states that 'money spent on conservation genetics would be better spent on either good science or good conservation, rather than a halfway house of nothingness'. He goes on to say that 'while the ship is sinking, conservation geneticists are busy counting the deck chairs. Conservation and genetics, like pop and politics, just don't mix. A swift divorce should leave both science, and what's left of life on Earth, in better shape.' Caughley (1994) made the distinction between what he termed the 'small population paradigm' and the 'declining-population paradigm'. His argument was that the 'small population paradigm' sought to determine the risk of extinction inherent in low numbers, whereas the 'declining-population paradigm' dealt with the causes of smallness and its cure, which represents stochastic and deterministic processes, respectively. However, Hedrick al (1996) argued that both deterministic factors that reduce population size and the stochastic factors that lead to the extinction of a small population are critical to consider in preventing extinction. Earlier on, Lande (1988) had argued that for wild populations, demographic factors are usually more importance than genetic factors in assessing the requirements for long-term species persistence. However, he also emphasizes the fact that there is an immediate practical need in biological conservation for understanding the interaction of demographic and genetic factors in the extinction of small populations. Future conservation plans should incorporate both demography and population genetics in assessing the requirements for species persistence. While the arguments by Brookes (1997) and Caughley (1994) seem to promote differences between genetics and demography, essentially they all emphasize the same points, i.e., that both genetics and demography as well as their interactions, are important in the extinction process and that only by the integration of these two fields can we hope to achieve effective conservation management and long-term population and species survival. Likewise, biodiversity is represented by genetic 17 variation within and among species and populations. The key questions addressed by the genetics component relate to the possible negative effects of loss of genetic variation (including inbreeding depression and an inability to adapt to new conditions), 'pollution' of co-adapted genomes by the genetic material from translocated conspecifics (outbreeding depression) and loss of genetic variation through failure to conserve genetically distinct geographical variants, i.e., genetic 'subspecies' or, more importantly, morphologically similar but genetically distinct species. Moreover, individuals in a population differ in age and other demographically criteria. The demographic structure of a population may vary between habitats and from area to area. Undoubtedly, the development of and application of highly variable DNA markers have led to a rapid expansion of the field of molecular ecology in which distinctions between genetics and ecology are lost. The two fields are thus becoming more inclusive and integrative through their advances. This can only benefit the conservation of genetic resources for present and future generations. 2.5. Core versus peripheral populations Physical distance separates peripheral and core populations, while ecologically marginal populations experience different biotic and abiotic environments than those occupying environments in the centre of the species ecological niche (Lesica and Allendorf, 1995). These outlier populations, by definition, are often relatively small and isolated from central populations (Lawton, 1993). As one moves from the core to the periphery of a species' geographic range, populations occupy less favourable habitats and exhibit lower and more variable densities (e.g., Brown, 1984; Gaston, 1990; Lawton, 1995). Populations along the periphery of the range tend to be more isolated and, as a result, are less likely to receive immigrants from other populations (Channell and Lomolino, 2000). 18 Given the consequences of climatic and environmental changes, there is increasing debate about the future value of small, isolated or disjunct populations at the margins of the species geographic range. Lesica and Allendorf (1995) summarized empirical evidence for the genetic distinctness of peripheral populations and the importance of conserving peripheral populations. Disjunct populations can be unique biologically, representing either remnants of a species' former range or its extension to the limit of physiological or ecological maxima. Such populations may contain special genetic adaptations as a result of different or more intense natural selection pressures faced in marginal environments, favoring particular genotypes and alleles. Peripheral and disjunct populations could also be of special conservation interest because they may represent the best-adapted seed sources for range expansion and adaptation to new climates. The relative persistence of core versus peripheral populations is determined by micro-evolutionary mechanisms, depending on the size and spatial patterns of these populations, and their interaction with the environment. Population densities often decline from core to periphery, due to reduction in habitat quality, environmental stability (relative to the best performing and most frequently held strategies or phenotypes found across the species' range) (Brussard, 1984). This may result in increasing patchiness and genetic and demographic isolation of local populations from core to periphery (Lawton, 1993). Core and periphery do not always differ much with respect to densities and spatial patterns. This happens when the distribution is abruptly terminated due to competitors or predators that attain superiority or effectiveness, respectively, under the environmental conditions which prevail over the boundary region (Pielou, 1979). Peripheral populations can lose genetic diversity and become increasingly differentiated due to (1) founder effects at the time of fragmentation resulting in increased genetic drift, (2) increased inbreeding, and (3) reduced interpopulation gene flow relative to core populations (Templeton et al. 1990; van Treren ef al. 1991; Ledig, 1992; Karkkainen ef al. 1996; Savolainen, 1996; Mitka, 1997; Gooding, 1998). 19 Such effects have serious implications for population persistence and the probability of extinction. In the short-term, loss of heterozygosity and potential increases in inbreeding may reduce individual fitness. In the long term, reduced allelic richness limits a species' ability to respond to changing selection pressures (Frankel et al. 1995). Populations at range margins may fail to adapt to their local conditions and not spread further because the species will have reached an intrinsic limit to its evolutionary capacity (Kirkpatrick and Barton, 1997). Often species may lack the appropriate adaptations to tolerate greater environmental extremes. Peripheral populations receive gene flow from the centre of the species range. Genes from the centre of the range will typically be adapted to conditions at the centre of the range and could inhibit adaptation at the periphery. In this way, peripheral populations become demographic sinks, preventing the range from expanding outward (e.g., Kirkpatrick and Barton, 1997). Will peripheral populations have a higher or lower probability of persistence than core populations? And should conservation of core or peripheral populations be a priority? The arguments are as follows: (1). Core will persist longer than peripheral populations. Because the core should be environmentally more favorable than the periphery, it harbours dense and contiguous populations (Lewinton, 1974; Lesica and Allendorf, 1995). Environmental favorableness is expressed in the number of types of exploitable niches, which is likely greater in core than periphery. Populations at the core are relatively heterozygous, thus descendants of the same genotypes can perform better in a variety of niches available in the core, and the load of producing less fit homozygotes is reduced by the large size of these populations and their high degree of outcrossing (e.g., Templeton, 1998). Core populations are expected to undergo balancing selection, and therefore maintain high additive genetic variance, whereas peripheral ones are smaller and more isolated, undergoing primarily directional 20 selection, and therefore are likely to have lower additive variance. I therefore assume that core populations are more likely than peripheral ones to respond to novel selection pressure and to persist. Accordingly, measures against habitat destruction and major conservation efforts should be directed toward core distributional areas of species. (2) . Peripheral populations have a higher probability of persisting than core populations. R. A. Fisher (Fisher, 1930a, b) postulated the maintenance of substantial geographic differentiation by weak selection pressure, when a cline in genotypic frequencies exists. If the environment of core populations is perceived as stable, and hence selection is stable, additive genetic variance and heritability will be low. At the periphery the environment fluctuates more, resulting in fluctuating selection, maintaining higher variance than in the core and a greater capacity for adaptation to new conditions. As habitat quality and stability decrease from core to periphery, selection changes from favoring high average fitness to promoting flexibility (Brussard, 1984). Under this theory, in the periphery many genotypes are maintained, each adapted to cope with a specific environmental state. Even if novel, more extreme states occur, genotypes adapted to somewhat less extreme conditions are likely to be pre-adapted to somewhat more extreme conditions by having a wide enough norm of reaction. At the same time, while outlying populations may be more likely to be maladapted prior to climate change than core populations, they are also more likely to contain alleles at quantitative trait loci for producing phenotypic extremes for adaptive traits. They could also be of special conservation concern and priority because they represent the best-adapted seed sources for species migration and adaptation as climates change. (3) . There will be no differences in persistence between core and peripheral populations. This is predicted to occur when gene flow from the core is stronger than selection in the periphery. Mayr (1970) proposed that near a species border, environmental conditions are marginal and selection is severe, hence, only a limited number of genotypes are able to survive. He suggested that gene flow from core to 21 periphery becomes increasingly one-way, whereas core populations are in the midst of a flux of multidirectional gene flow. Thus, core populations harbor at all times a large store of freshly added immigrant genes, therefore peripheral populations are expected to have lower genetic diversity than core populations. However, there could be cases of peripheral populations replenished by a steady stream of immigrants from a more favorable portion of the species range; peripheral populations are thus merely "sinks" while core ones are the "source" (Lawton, 1993). In addition, though each of the relatively isolated peripheral populations loses variability due to genetic drift, all of them combined retain the same variability as the far less isolated core populations. 2.5.1. Summary of core and peripheral population hypotheses Core populations occupy environmentally favorable and varying types of exploitable niches. They are characterized by large effective population sizes, often high degree of outcrossing, extensive gene flow, likely experience balancing selection, and therefore are relatively highly heterozygous. I therefore assume that core populations are more likely than peripheral ones to respond to the novel selection pressure and to persist. Peripheral populations live under conditions different from those of core populations. Peripheral populations are characterized by variable and unstable conditions, relative to core areas. If I assume that variable conditions in the peripheral induce fluctuating selection that maintains high genetic diversity, then peripheral populations are expected to be genetically more variable than core populations. Alternatively, due to marginal ecological conditions at the periphery, populations are small and isolated; the within-population diversity is low, but the among-population genetic diversity is high due to genetic drift. It is also likely that peripheral populations evolve resistance to extreme conditions. Thus, peripheral populations rather than core ones may be resistant to environmental extremes and changes such as global climate change. I argue that peripheral populations should be treated as biogenetic resources to be used for rehabilitation and restoration of damaged ecosystems. 22 Population differentiation between core and peripheral populations can result from the action of genetic drift or adaptation to environmental conditions. Adaptation can be detected as a function of geographical, climatic or biotic variables, whereas genetic drift is observable with neutral genetic markers in populations isolated for many generations, in those which have suffered bottlenecks or in those which are in migration/drift equilibrium (e.g., Jaramillo-Correa et al. 2001). However, the genetic markers are presumed selectively neutral and their relevance to infer genetic structure and levels of genetic diversity in adaptive traits remains a major issue. One way to distinguish between neutral and selective forces in natural populations is to examine variation from locus to locus, expecting that genetic drift and migration should affect loci similarly, whereas natural selection should affect some loci differently. However, if Ne has been small historically, populations relatively isolated, amount of within and between population variation for both neutral and adaptive traits will be largely due to genetic drift. Therefore, assessing neutral and adaptive traits will lead to similar conclusions regarding genetic similarities of populations. If Ne has been large historically, the opposite is true, i.e. different patterns of genetic variation will be revealed by neutral and adaptive characters. Against this background, several reviews have concluded that testing the assumption of an association between marker diversity and adaptive diversity is a pressing concern (Lynch, 1996; Merila and Crnokrak, 2001; McKay and Latta, 2002). For several marker types and mutation models, population genetic structure is often quantified using FST (Hedrick, 1999) and an analogous measure for quantitative genetic traits is Qsr (Spitze, 1993). Generally, quantitative traits exhibit greater population divergence than do putatively neutral markers (Qsr > FST), consistent with locally adaptive selection acting on traits (Merila and Crnokrak, 2001). One extreme example is the large population divergence in the timing of bud burst (Qsr = 0.80) compared to divergence in genetic markers (FST (0.02) in Pinus sylvestris (Hurme, 1999). Similarly, Jaramillo-Correa et al. (2001) detected strong adaptive response in quantitative traits but none in allozymes and expressed sequence tag polymorphisms (ESTPs) in white pine. 23 2.6. Common versus rare alleles Common alleles represent most of the current operating genetic potential of populations, whereas rare alleles contribute little to overall genetic variation (e.g., Stebbins and Hartl, 1988; Bergmann et al. 1990; Buchert et al. 1997). Since the effects of rare alleles are difficult to detect in conifers, it should not be assumed that they have no function or potential evolutionary significance. Many rare alleles are probably deleterious, found at low frequencies because they have been selected against. However, some selectively neutral or mildly deleterious rare alleles could become adaptively advantageous under changed abiotic or biotic conditions (Muller-Starck, 1985; Finkeldeyand Gregorius, 1994). On the contrary, Brown and Briggs (1991) argue that rare alleles are important to breeding but are probably not important to species preservation. One body of evidence suggests that rare alleles in conifers have no adaptive value (e.g., Lindgren and Gregorius, 1976; Bush and Smouse, 1992; Stoehr and El-Kassaby, 1997) in present environments. An intriguing observation emerging from some studies is the general fitness deficit experienced by individuals carrying low frequency alleles. Strauss and Libby (1987) found a negative correlation between high levels of heterozygosity and growth rates in radiata pine, suggesting that the rare alleles, which occur predominantly in heterozygous condition, or loci tightly linked to these rare alleles, tended to have deleterious effects. In Pinus taeda, deleterious effects of rare alleles on height growth have also been observed. For example, the presence of four rare alleles in a parent resulted in a height growth decrease of nearly 90% in progeny compared to progeny of parents with no rare alleles (Schmidtling et al. (1999). Bongarten et al. (1985) found a negative relationship between rare heterozygotes and growth in Douglas-fir and suggested that rare alleles may be deleterious. Bush and Smouse (1991; 1992) have also associated rare alleles with decreased fitness. Others have suggested that rare alleles contribute little to fitness and usually are the result of deleterious 24 mutations or may be evolutionary relics (e.g., Lindgren and Gregorius, 1976; Stoehr and El-Kassaby, 1997). Hawley ef al. (2000) investigated genetic diversity in eastern hemlock and found an association between rare alleles and defective phenotypes. Althukov ef al. (1987) found Scots pine parent trees with a high degree of heterozygosity produced significantly more inviable seeds than trees with lower heterozygosity. This phenomenon is explained by lethal or sublethal alleles in highly heterozygous trees, which appear in a homozygous state in the next generation. Therefore, at least some, and possibly most, rare alleles confer a negative impact on fitness. However, the large size of the tree genome does not permit us to understand the effects or function of all the genetic variation and the alleles detected. Much of the genetic variation that we are able to detect either serves some purpose or has some potential evolutionary significance. Since we cannot understand the effects or function of all the genetic variation and the alleles detected, a conservation approach, in which we assume that some of this variation either serves some current purpose or has some potential evolutionary significance, is warranted. Many of the rare alleles are currently "selectively neutral" in the current environment but may be useful in the event of climate change. For example, there are concerns about future adaptations, e.g., drought resistance, hence the need to develop conservation programs that also consider unique features such as rare alleles. For identifiable marker alleles at specific loci, I think it is safe to say that their value for conservation is indirect. The interest is not in saving such particular alleles as can be distinguished by electrophoretic mobilities but rather in using such alleles to indicate the levels of variation that exist throughout the genome. Moreover, one of the objectives of conservation is to ensure that functional useful alleles will be available in the future. 25 CHAPTER 3 Genetic diversity, population structure and evolutionary history of Sitka spruce (Picea sitchensis (Bong.) Carr.): implications for capture of allelic diversity 3.1. Introduction Most widespread species of conifers comprise many individuals in many populations and occupy wide geographic and ecological niches, which in turn affect levels and distributions of genetic diversity. Levels of genetic variation are high in conifers and populations show little genetic differentiation (Hamrick et al. 1992; Hamrick and Godt, 1996b) conforming to expectations under models of mutation, genetic drift and migration. However, neutral genetic variation is expected to be lower in peripheral and disjunct populations than in core and continuous ones (e.g., Aitken and Libby, 1994; Ledig, 2000). The most obvious reasons are the greater influence of genetic drift in these typically smaller populations combined with lower levels of gene flow in the former (Nei etal. 1975; Haiti and Clark, 1997). Genetic differentiation among regions following glaciation is reported to have arisen both through primary intergradation and secondary contact (Hewitt, 1993). It has been suggested that rapid postglacial range expansion in many species is likely to have involved long-distance dispersants that were able to establish colonies in advance of the main distributional front. These advance colonies, in turn, may have expanded rapidly and acted as sources for further long-distance dispersal events (e.g., Hewitt, 1996; 1999). Historical migration patterns and changes in species distributions have played an important role in determining the present-day geographic structure of intraspecific genetic variation (Hewitt, 1996; Petit et al. 1997; Gamache et al. 2003). In temperate regions, areas that have remained occupied through long periods, during 26 the last ice age up to the present (i.e., refugia), are expected to harbor higher levels of genetic diversity compared to those that have been colonized more recently (Hewitt, 1996). However, there is limited evidence to support this hypothesis, partly because it is often difficult to identify precisely these refugia with independent (non-genetic) data. Many studies of plants have compared average heterozygosity between core and peripheral populations using molecular markers. In this thesis I have chosen a conifer species, Sitka spruce (P/'cea sitchensis (Bong.) Carr.) for testing of this model. Since the geographic distribution and genetic characteristics of Sitka spruce are typical of many widespread tree species in north America, Sitka spruce might function as a suitable model organism for numerous common, wind-pollinated trees and possibly other plants that have colonized their habitats since the last glaciation. Peripheral populations generally showed reduced genetic variation compared to core populations in conifers such as Pinus rigida Mill (Guries and Ledig, 1982), Pinus contorta Douglas ex Loudon (Aitken and Libby, 1994), and Pseudotsuga menziesii (Mirb.) Franco (Li and Adams, 1989). Nevertheless, some species and populations have managed to survive fragmentation, inbreeding, and loss of genetic diversity during extreme bottlenecks, and expand from glacial refugia to occupy extensive ranges. Possible examples are red pine (Pinus resinosa Ait; Fowler and Morris, 1977; Walter and Epperson, 2001), white pine (Pinus strobus L.; Rajoraefa/. 1998) and western red cedar (Thuja plicata D. Don; Yeh, 1988; O'Connell, 2003). However, in some cases, peripheral populations receive sufficient gene flow to have as much variation as core populations; for example, in P/'cea abies (L.) Karst (Muona et al. 1990), Alnus rubra Bong (Hamann et al. 1998), Pinus strobus L (Beaulieu and Simon, 1994) and P/'cea mariana [Mill.] BSP.) (Gamache et al. 2003). A standard challenge in conservation and population genetics is to reconstruct the evolutionary history of a species or portion thereof on the basis of current allele frequencies. Genetic polymorphisms with allele frequencies of 0.05 to 0.95 have been the mainstay of this effort, but polymorphisms of lesser frequencies, "private 27 polymorphisms" or "rare variants", may also be of some value in meeting this challenge. However, low frequency alleles with the potential for major effects on important phenotypic traits (e.g., recessive lignin mutant in loblolly pine or major gene resistance to blister rust in sugar pine) are attracting much interest (Yanchuk, 2001). 3.1.1. Species of interest Sitka spruce [Picea sitchensis (Bong.) Carr is an economically and ecologically important tree species in North America and parts of Europe (Harris, 1990; Peterson et al. 1997; Lee et al. 2002). Its overall distribution has been well documented by several authors (e.g., Little, 1953; Daubenmire, 1968; Peterson etal. 1997). Sitka spruce occurs naturally throughout a narrow belt along the Pacific coast of North America over 3,000 km from north-west California through Oregon, Washington, British Columbia and up to Alaska (Figure 3.1). In British Columbia and Alaska, the species has both mainland and large and small island populations. In northern California, the range is more attenuated and becomes discontinuous. A disjunct population in Fort Bragg, California, marks the southern tip of the species' current range. On the other hand, Kodiak Island, Alaska, marks the northwestern, advancing front. Sitka spruce has been studied genetically from four perspectives. First, it has been the subject of numerous provenance and progeny trials, which demonstrate considerable variation in growth and adaptive traits (Falkenhagen, 1977; Farr and Harris, 1979; lllingworth, 1978; Morgenstern, 1996; Ying, 1997; Xu et al. 2000). Second, a compliment of population surveys document the genetic variation displayed by neutral markers such as isozymes (Yeh and El-Kassaby, 1980; Chaisurisri and El-Kassaby, 1994). Third, although substantial breeding has occurred in Great Britain, efforts towards tree improvement within the native range have been concerned primarily with developing procedures for control of transfer of seed and plant materials within the introgression zone between Sitka spruce and 28 white spruce (P/'cea glauca (Moench) Voss) (Roche, 1969; Yeh and Arnott, 1986; Sutton et al. 1991, Bennuah and Aitken, 2000). Fourth, there are studies related to the genetic resistance of Sitka spruce to the white pine shoot tip weevil (Pissodes strobi (PECK)), which is the most economically damaging natural pest for this species (Alfaro and Ying, 1990). 3.1.2. Choice of molecular marker In this thesis I used cDNA-based sequence-tagged site (STS) markers to quantify the allelic diversity of Sitka spruce. Sequence-tagged-site (STS) markers are those that reveal co-dominant intron-length polymorphisms in specifically targeted sequences (Perry and Bousquet, 1998a). The targeted sequences are initially identified from libraries of either cDNA (messenger RNA transcripts) or genomic DNA (random pieces of the genome). These markers were originally developed for black spruce and subsequently tested for cross-species amplification in 12 other conifer species including Sitka and white spruce (Perry and Bousquet, 1998b). STS markers of coding genes are often termed "expressed sequence tag polymorphisms" (ESTP), and are becoming the marker of preference for gene mapping because they are derived from sequencing of cDNA clones, and their coding portions can often be matched with existing sequences, hence genes associated with these functions can also be mapped (Ritland and Ritland, 2000). For example, STS markers have been used in the marker-assisted backcross program of the resistant B-genome in oilseed rape (Plieske and Struss, 2001). STS markers are known to reveal co-dominant polymorphisms at 9 loci in Sitka spruce (Bennuah and Aitken, 2000) with no evidence of null alleles (presence of null alleles would limit their value as markers to genotype individuals since this could cause erroneous heterozygote estimates), therefore are much more informative than dominant markers for genotyping individuals to assess levels and distribution of genetic diversity within and among populations. The specific questions addressed are: 29 1. What is the overall level of genetic diversity in Sitka spruce populations? 2. How is genetic diversity distributed within and among populations classified as core or peripheral, based on ecological conditions, and continuous or disjunct populations based on geographic distribution? 3. To what extent has the evolutionary history of the species shaped the present genetic diversity and population structure? 4. Based on 1 to 3, how can widespread species be sampled to capture allelic diversity for in situ and ex situ conservation and base breeding populations? 3.2. Materials and Methods 3.2.1. Sampling locations and technique A two-way classification scheme was devised in which populations of Sitka spruce were classified as either core or peripheral based on ecological conditions (different biotic factors, e.g., species density; and abiotic environments, e.g., temperature, moisture, soils), and continuous or disjunct based on geographic distribution (proximity of populations) for a total of four classes (Table 3.1; Figure 3.1). Peripheral populations are ecologically separated from core populations and are often found at the margins of species' range. The peripheral populations experience different abiotic and biotic environments than those occupying environments in the centre of the species ecological niche. Disjunct populations are physically separated from continuous populations. They may or may not experience similar environments. Sites of collections used in this study are indicated in Figure 3.1. For each population and at each sampling site, several East-West transects, each approximately 100 meters wide, were established and fresh needle tissue (current year's growth) was collected from 200 mature trees. For populations in British Columbia, Oregon and California, the genetic material was collected in the Spring of 30 2001. Alaska populations (Kodiak and Seward) were collected in the Spring of 2002. Table 3.1. Two-way classification of Sitka spruce populations according to location of sampling sites relative to ecological and geographic distribution. CORE POPULATIONS PERIPHERAL POPULATIONS CONTINUOUS Port McNeill, BC Brookings, OR Prince Rupert, BC Seward, AL DISJUNCT Qualicum, BC Fort Bragg, CA Queen Charlotte Islands, Kodiak Island, AL BC BC = British Columbia; OR = Oregon; AL = Alaska; CA = California To avoid sampling closely related individuals, sampled trees were at least 30 m apart and sometimes they were > 50 m apart in Qualicum and Fort Bragg. Each sampled area covered approximately 550 ha (3200 m x 1700 m). The overall area sampled for each population is approximately 550 ha (3200 m x 1700 m) except for the Fort Bragg and Qualicum populations, each covering well over 800 ha due to the lower density and clustered distribution of Sitka spruce in those locations. Individual tree locations were identified by a coordinate grid system using a hand-held Global Positioning System instrument (GPS Garmin Model 12XL). After collection, fresh needles were frozen in liquid nitrogen, then stored at - 8 0 degrees Celsius. 31 60 501 40' 30' 70° 150° 140° 130° 120° 110° 100° 90° 80' Prince Rupert (CC) Queen Charlotte Islands (CD) § i a O / or Port McNeill (CC) Qualicum (CD) i & ( WA N Legend o Sampling locations Native range of Sitka spruce Brookings (PC) Fort Bragg (PD) 70' 60' 50° 40° 30° 130° 120° 110-Figure 3.1. Native range of Sitka spruce and locations of sampled populations. 32 3.2.2. DNA isolation and PCR amplification Total genomic DNA was extracted from 0.3 to 0.5 g of fresh frozen needle tissue following a modified CTAB procedure (Doyle and Doyle, 1990) (see Appendix 2). DNA samples were subjected to polymerase chain reactions (PCR) using specific primers for eight polymorphic STS loci (Sb16, Sb17, Sb21, Sb29, Sb32, Sb49, Sb60 and Sb62) previously characterized in black , white and Sitka spruce (Perry and Bousquet, 1998a,b; Bennuah and Aitken, 2000; Bennuah et al. in press) and harboring exclusively co-dominant alleles indicative of insertion-deletion (indel) polymorphisms (Perry and Bousquet, 1998a, b). Seven of these primers reveal intron-length polymorphisms while Sb29 reveals an exon-length polymorphism (Perry and Bousquet, 1998a). Two additional primers (Sb70 and Sb72) with good amplification in Sitka spruce were found to be monomorphic in preliminary assays and were not used in this thesis. For expected and observed primer products ranges, see Appendix 3. PCR products were resolved by electrophoresis on 2% agarose gel in 1 x TBE buffer at 140 V for 4 to 7 hours depending on product length. DNA gels were stained in ethidium bromide and then photographed under UV light using thermal paper. Molecular size markers were DNA fragments of 100-bp and 1-kb ladders (Invitrogen, Canada). Alleles were numbered in decreasing order from anode to cathode. All alleles were consistently scored when reactions were repeated for all primers, thus I have a high degree of confidence in the accuracy of genotyping. 3.2.3. Data analysis Standard genetic diversity parameters (allele frequencies, average number of alleles per locus/allelic richness (AR), observed heterozygosity (H0), and expected heterozygosity (HE)) were estimated for every population using the software program GDA version 1.0 (Lewis and Zaykin, 2001). Deviation in genotype frequencies from Hardy-Weiberg expectations was examined at each of the variable loci, and the 33 possibility of linkage disequilibrium between pairs of loci was examined. Both tests were performed using the chi-square test of the GDA version 1.0 program (Lewis and Zaykin, 2001). GENEPOP (Raymond and Rousset, 1995) was also used to estimate the P-values from exact tests of departure from Hardy-Weinberg equilibrium using the Markov chain method with 1000 iterations (Guo and Thompson, 1992). Neutrality of the STS markers was checked with the Ewen-Watterson test (Manly, 1985) using an empirical distribution of homozygosities for 1000 random neutral samples with a fixed number of alleles and sample size (e.g., Jaramillo-Correa et al. 2001). Measure of fixation indices were calculated with GDA for each allele and locus following methods of Weir and Cockerham (1984) where f, F and 0 correspond to F/s, F/r and FST, respectively. Differentiation among populations was determined by calculating GST values for each allele and locus with FSTAT version 2.9.3.2 (Goudet, 2002) following the methods of Nei (1977). GENEPOP (Raymond and Rousset, 1995) was used to assess the amount of gene flow (Nm) based on the rare allele approach of Slatkin (1985b), using the frequency and distribution of rare alleles among populations. Nm values were estimated for core and peripheral populations separately. The unbiased genetic distance (D) among populations according to Nei (1978) was generated from allele frequency data. From the genetic distances, a dendrogram was created using Ritland's (1989) method, which uses the unweighted pair group method (UPGMA) (Sneath and Sokal, 1973) for clustering, and finds the standard error of branch length at each step. To test for isolation-by-distance (divergence due to drift and mutation), relationships between unbiased genetic distance measures (Nei, 1978) and geographical distances (estimated from latitudes and longitudes converted to kilometers among sampled populations using the R-software version 4 (Casgrain and Legendre, 2001)) was estimated using Mantel's tests on matrices of genetic and geographical distances. Results of these tests were standardized to obtain a correlation 34 coefficient whose significance was tested using a Monte Carlo simulation (1000 permutations). The ability to sample an allele depends on its presence and frequency in the population (Marshall and Brown, 1975). Allele distribution can be classified by two variables. First, alleles can be divided into those which are common (> 0.05) alleles and those which are rare (< 0.05). Potentially a population can maintain many alleles. Second, alleles can be further categorized as to whether they are widespread over populations or localized over a few populations. Marshall and Brown (1975), Adams (1981) and Brown and Hardner (2000) defined any allele occurring in > 0.25 of populations as a widespread allele; otherwise it is a localized allele (in only one or few (< 0.25) adjacent populations). However, given that I have sampled only eight populations, I define any allele occurring in > 0.50 of populations as a widespread allele; otherwise it is a localized allele (in only one or few (< 0.50) adjacent populations). Four classes of alleles emerge from this classification: common, widespread (CW); common, localized (CL); rare and widespread (RW), and rare, localized (RL) (Table 3.2). Table 3.2. The modified Marshall-Brown (1975) two-way classification of allele distribution. Allele occurrence1 Allele frequency2 Allele frequency2 Common (C) > 0.05 Rare (R) < 0.05 Widespread (W) > 50% (CW) (RW) Localized (L) < 50% (CL) (RL) Allele occurrence1 refers to the percentage of populations that have the allele in question and proximity of populations while Allele frequency2 refers to the frequency of an allele in a population In terms of sampling, the first class of allele (CW) is likely to be captured irrespective of sampling strategy employed. Marshall and Brown (1975) argued that the second class (CL) merit priority in sampling and hence in the devising of sampling strategies. Sampling strategy is critical for the capture of C L alleles in ex situ collections. This second class presumably includes the alleles that confer 35 adaptation to local conditions. The third class (rare and widespread, RW) are found at low frequencies in many adjacent populations. Their capture will depend on the total collecting effort and not on how numbers are deployed among versus within populations. The fourth class (rare and localized, RL) are much more difficult to sample than their common counterparts. This class includes variants that are rare in the species as a whole and if present, they occur in widely adjacent populations. We also used the computer program BOTTLENECK (version 1.2.02) described by Cornuet and Luikart (1996) that uses allelic diversity relative to observed heterozygosity for each population separately to determine whether effective population size has been restricted in the past. The program tests whether the observed number of alleles (/c0) in a population fits the expected heterozygosity (/-/eg) under mutation-drift equilibrium. After a bottleneck, the expected heterozygosity (HE) computed from allele frequencies for a sample of genes should be larger than the heterozygosity expected {Heq) based on the number of alleles in the same sample, assuming the population is at mutation-drift equilibrium (Cornuet and Luikart, 1996). Different levels of heterozygosity are expected at mutation-drift equilibrium depending on whether the loci evolve under the Infinite Allele Model (IAM) or the Stepwise Mutation Model (SMM). Under the IAM each new mutation gives rise to a new allele different from all existing ones (Kimura and Crow, 1964). Under the SMM new mutations are one size larger or smaller than the original allele (Ohta and Kimura, 1973). Thus the SMM allows for mutation to existing states (homoplasy) and thereby results in fewer distinct allelic states than the IAM for a given mutation rate (e.g., Ohta and Kimura, 1973). I would expect STS markers to evolve under the IAM since the alleles are the result of small random insertions or deletions that appear unrelated, more like single nucleotide polymorphisms (SNPs) where the number of possible variants truly is infinite, thus matching the IAM. However, Professor J. Bousquet* (personal communication) suggest that exceptions exist, such as Sb01 (not used in this thesis), where a large number of alleles (ca. 8 to 10) 36 were observed in both P. mariana and P. glauca, and where fragment length variation followed a regular pattern of stepwise differences, presumably from a variable number of large repeats similar to microsatellite loci. However, in this thesis, I tested the populations for bottleneck signature under both the 1AM and SMM as these two models cover the range of possible conditions, though the 1AM is probably closer to reality for most of the loci. I then compared the results from the two mutation models. To test for a deficiency or excess in HE, the Wilcoxon signed-ranks test was used as it has more power than the sign-test and can be used effectively with fewer loci (Piry et al. 1999). Eight polymorphic loci were available for each population. The Wilcoxon signed-ranks test is nonparametric for paired comparisons. Paired comparisons are appropriate when the same homologous loci are examined in closely related populations or in samples from the same population (Nei, 1987; Leberg, 1992). Pairing loci controls for differences in heterozygosity among loci and thereby provides more powerful statistical tests than unpaired tests. Professor J. Bousquet* is Professor and Canada Research Chair in Forest and Environmental Genomics at Larval University in Quebec, Canada. His laboratory at Larval University developed the STS markers that were used in this thesis. 37 3.3. Results 3.3.1. Allele frequency and genetic diversity All samples were genotyped for a total of eight STS marker loci, and all loci were polymorphic and variable in all eight populations of Sitka spruce (see example of allelic polymorphisms for three loci in Figure 3.2). Two to six alleles were detected per polymorphic locus, with a total of 26 alleles across all populations and loci (Table 3.3). Across all eight loci, the linkage disequilibrium test revealed the statistical independence of the loci. Most of the alleles were well spread over the populations, but a few were found in only one or a few populations (Table 3.2). Table 3.3. Allele frequencies for eight loci studied in eight range-wide natural populations of Sitka spruce. Populations Locus Allele F B R G B R K S Q U A L P M C N L QCI P R U P T KDIAK S E W D SB16 1 0.195 0.166 0.493 0.518 0.328 0.298 0.269 0.329 2 0.551 0.456 0.364 0.229 0.232 0.344 0.479 0.421 3 0.191 0.155 0.063 0.063 0.114 0.124 0.100 0.149 4 0.050 0.101 0.060 0.109 0.207 0.115 0.152 0.038 5 0.010 0.074 0.020 0.058 0.100 0.109 0.031 0.018 6 0.003 0.048 — 0.023 0.019 0.010 0.033 0.045 SB17 1 0.373 0.518 0.505 0.460 0.477 0.495 0.376 0.358 2 0.569 0.367 0.275 0.278 0.337 0.295 0.325 0.425 3 0.028 0.044 0.110 0.237 0.146 0.152 0.170 0.153 4 0.030 0.071 0.110 0.025 0.040 0.058 0.129 0.065 SB21 1 0.943 0.877 0.662 0.812 0.468 0.958 0.827 0.785 2 0.157 0.123 0.338 0.188 0.532 0.142 0.173 0.215 SB29 1 0.793 0.883 0.430 0.625 0.530 0.460 0.441 0.171 2 0.207 0.117 0.570 0.375 0.470 0.540 0.559 0.829 SB32 1 0.169 0.339 0.439 0.230 0.084 0.100 0.091 0.105 2 0.767 0.270 0.426 0.525 0.661 0.791 0.711 0.708 3 0.028 0.184 0.084 0.153 0.142 0.088 0.091 0.079 4 0.036 0.206 0.051 0.092 0.113 0.021 0.107 0.108 SB49 1 0.640 0.676 0.687 0.788 0.785 0.554 0.545 0.505 2 0.360 0.324 0.313 0.212 0.215 0.446 0.455 0.495 SB60 1 0.655 0.593 0.596 0.508 0.563 0.598 0.316 0.543 2 0.345 0.407 0.404 0.492 0.437 0.402 0.684 0.457 SB62 1 0.501 0.276 0.238 0.398 0.508 0.455 0.480 0.458 2 0.421 0.563 0.630 0.375 0.312 0.368 0.388 0.449 3 0.038 0.161 0.132 0.227 0.170 0.177 0.107 0.093 4 0.040 — 0.010 . . . . 0.025 . . . . BRKS= Brookings; FBRG= Fort Bragg; KDIAK= Kodiak; PMCNL= Port McNeill; QCI= Queen Charlotte Islands; QUAL= Qualicum; PRUPT= Prince Rupert; SEWD= Seward. Rare alleles (p<0.05) marked in bold. 38 1 p: ft .,: 1200 H n Sb 16 HI | i 1100 . . ^ m m 1000 i * • * * m' mm mm P i f 11 * - * Sb 17 -800 p. m m 700 ~ » 600 — m 4191 ||| mmi 1 « b M l i P H Mill * * Sb 32 300 . « 800 •*» i n 700 ' tmwm m u ... rr *• '#*XW> 4"MaK i Figure 3.2. Panels showing amplification products and allelic polymorphisms at loci Sb16, Sb17 and Sb32 among 24 genotypes of Sitka spruce from Prince Rupert. 39 The distribution of allele frequencies (Figure 3.3) shows the proportion of alleles that lie within a certain frequency class. It shows the probability that a randomly chosen allele at an intermediate frequency (0.41 to 0.50) is higher (19%) than for other allele frequency classes. Rare alleles (frequency class 0.01 to 0.04) had a higher frequency than alleles nearing fixation, suggesting a substantial number of rare alleles were present. 0.20 n 0.18 -0.16 -c 0.14 -o 0.12 -o 0.10 -o 0.08 -Q. 0.06 -0.04 -0.02 -0.00 -^ N Q ^ ^ <§> g> ^ $ # c> C A n cP \ N n> ^ N fcN <3 N <bN A N <bN cs? <&• Q>- Q>- Cr ° Allele frequency class Figure 3.3. The distribution of allele frequencies in Sitka spruce populations. The Ewen-Watterson test for neutrality revealed that no case departed significantly from neutral expectations once Bonferroni correction for multiple tests was applied. No departure from neutrality was observed in any of the populations for any locus. Alleles, whether locally common or rare, tended to be distributed throughout the range of the species. However, only one allele (Sb62-4) was RL and was detected in only three widely separated populations (Fort Bragg, Queen Charlotte Islands and Kodiak) and was also at low frequency (Table 3.3). According to our classification (modified from Marshall-Brown's (1975), allele 4 at Sb62 is a RL allele. Certain alleles were rare in certain populations, particularly disjunct populations but common 40 (frequency > 0.05) in others, for example, at loci Sb16, Sb17, Sb32 and Sb62 (Table 3.3). These loci had at least three alleles per locus. At locus Sb16, allele 6 was rare in all populations and missing in Qualicum (Table 3.3) and is therefore a RW. In most cases, alleles 4, 5 and 6 for loci Sb16, Sb17, Sb32 and Sb62 were rare, irrespective of the population classification. At the single-locus level, allelic richness per locus ranged from as low as two alleles up to six alleles per locus, with a mean of AR = 3.3 alleles per locus (Table 3.4). Locus Sb16 was the most variable marker locus assayed, having average observed and expected heterozygosity of 0.560 and 0.712, respectively, and a total of six alleles (Table 3.4). Intrapopulation fixation indices (F/s) for individual loci were significant (P < 0.05) and ranged from -0.040 to 0.196 within an overall significant value of 0.097, suggesting some inbreeding (Table 3.4). Table 3.4. Gene diversity and population structure at eight sequence-tagged-site (STS) polymorphic loci in Sitka spruce populations. Locus na Ho Hs HT FIS FIT FST GST SB16 6 0.560 0.697 0.712 0.196 0.216 0.024 0.021 SB17 4 0.552 0.634 0.645 0.129 0.147 0.021 0.018 SB21 2 0.483 0.464 0.481 -0.040 0.003 0.042 0.036 SB29 2 0.510 0.598 0.600 0.090 0.100 0.011 0.009 SB32 4 0.533 0.661 0.689 0.206 0.230 0.030 0.026 SB49 2 0.450 0.438 0.457 -0.028 0.021 0.048 0.048 SB60 2 0.455 0.479 0.496 0.049 0.086 0.040 0.040 SB62 4 0.560 0.597 0.607 0.063 0.082 0.020 0.020 Overall 3.3 0.510 0.558 0.580 0.097 0.122 0.030 0.030 SE ±0.05 ± 0.030 ± 0.040 ± 0.040 (0.032; (0.065; (0.022; (0.022; 0.152)* 0.174)* 0.036)* 0.037)* na = observed number of alleles; H0 = observed heterozygosity; Hs = expected heterozygosity within populations; HT = total expected heterozygosity; F,s = Fixation index over the total populations; FiT =Fixation index within population; F s r = Reduction in fixation index due to differences among populations; GST = Gene differentiation among populations (according to Nei, 1978). * Lower and upper limits of bootstrap 95% confidence intervals for the fixation indices and GSr values based on 1000 bootstrap resampling over loci. A summary of allele classification based on frequency and geographic distribution (CW, CL, RW, RL) by population classification is presented in Table 3.5. 41 Irrespective of population classification, over 75% of the alleles were CW. No CL alleles were detected. RW alleles were detected in all population classes and averaged 9% of all alleles (see Table 3.5). The sole RL allele averaged 2% of all alleles and was only detected in three populations, the Queen Charlotte Islands, a CD population and Fort Bragg and Kodiak Island, which are PD populations. Table 3.5. Classification of alleles based on frequency and geographic distribution in four population classes defined by ecological and geographical distribution of Sitka spruce. Class Source # loci CW CL RW RL % % % % CC Port McNeill 8 92 0 8 0 Prince Rupert 8 92 0 8 0 PC Brookings 8 92 0 8 0 Seward 8 96 0 4 0 CD Qualicum 8 96 0 4 0 Queen Charlotte Islands 8 89 0 7 4 PD Fort Bragg 8 69 0 27 4 Kodiak Island 8 88 0 7 5 Overall Mean 8 89 0 9 2 Common widespread, CW; common localized, CL; rare widespread, RW; and rare localized, RL) in eight range-wide natural populations of Sitka spruce by population class: CC = core, percent continuous; PC = peripheral, continuous; CD = core, disjunct; PD = peripheral, disjunct. Numbers indicate percent alleles by class. 3.3.2. Population genetic structure and gene flow Allelic richness (AR), observed heterozygosity (H0) and expected heterozygosity (HE) averaged over all loci and across populations were 3.3 ± 0.05, 0.51 ± 0.03, and 0.58 ± 0.04, respectively (Table 3.6). Hardy-Weinberg equilibrium was rejected for five of the eight populations (P < 0.05), which showed a deficiency of heterozygotes. Observed heterozygosities were found to be slightly lower than the expected values within most populations. This heterozygote deficiency is reflected in the mean Wright's F/s, a measure of the deviation of genotypic proportions from Hardy-Weinberg equilibrium at within populations. F/s values for core, continuous 42 populations (mean F/s = 0.03 averaged over two populations) were not significant (P = 0.065) but were positive (mean F/s = 0.16 averaged over four populations) and significant (P < 0.05) for peripheral populations, both continuous and disjunct (Table 3.6). Positive values suggest a deficiency of heterozygotes relative to Hardy-Weinberg expectations. Differentiation among populations was low (mean single-locus FST = 0.03; Table 3.4). Estimated values of gene flow {Nm) among populations were moderately high. Slatkin's (1985b) method based on frequencies of rare alleles estimated 9.04 migrants per generation in core populations and 3.53 migrants per generation in peripheral populations. Table 3.6. Estimates of within-population genetic diversity parameters for eight natural populations of Sitka spruce. Population AR R Ho HE F/s Port McNeill (CC) 3 . 3 ± 0 . 5 2 0 . 5 2 ± 0 . 0 3 0 . 5 6 ± 0 . 0 5 0 . 0 7 N A Prince Rupert (CC) 3 . 3 ± 0 . 5 2 0 . 5 7 ± 0 . 0 3 0 . 5 6 ± 0 . 0 3 - 0 . 0 2 N S Mean CC 3.3±0.5 2 0.54±0.03 0.5610.04 0.03NS Brookings (PC) 3 . 3 ± 0 . 5 2 0 . 4 8 ± 0 . 0 3 0 . 5 8 ± 0 . 0 4 0.17* Seward (PC) 3 . 3 ± 0 . 5 1 0 . 4 9 ± 0 . 0 3 0 . 5 9 ± 0 . 0 4 0.17* Mean PC 3.3±0.5 2 0.49±0.03 0.59+0.04 0.17* Qualicum (CD) 3 . 1 ± 0 . 4 1 0 . 5 4 ± 0 . 0 3 0 . 5 4 ± 0 . 0 3 Q N S Queen Charlotte Islands (CD) 3 . 4 ± 0 . 5 3 0 . 5 5 ± 0 . 0 4 0 . 6 0 ± 0 . 0 5 0 . 0 8 * Mean CD 3.3±0.5 2 0.55±0.04 0.5710.04 0.04NS Fort Bragg (PD) 3 . 4 ± 0 . 5 8 0 . 4 8 ± 0 . 0 2 0 . 5 3 + 0 . 0 3 0 . 0 9 * Kodiak Island (PD) 3 . 4 ± 0 . 5 3 0 . 4 5 ± 0 . 0 3 0 . 5 9 1 0 . 0 4 0 . 2 4 * Mean PD 3.4±0.5 5 0.47±0.03 0.5510.04 0.17* Overall Mean ±s.e. 3.3±0.5 3 0.5110.03 0.5810.04 0.09* CC = core and continuous population; PC = peripheral and continuous population CD = core and disjunct population; PD = peripheral and disjunct population AR = mean number of alleles per locus; R = number of rare alleles in a population H0 = observed heterozygosity; HE = expected heterozygosity F/s = average inbreeding coefficient; s not significant after sequential Bonferroni correction (Rice, 1989); Exact test of departure from Hardy-Weinberg equilibrium * P < 0.05, 43 3.3.3. Genetic distances and relationships among populations Genetic distances (Nei, 1972) among populations were generally small, averaging 0.03 and ranging from 0.013 between Fort Bragg and Brookings to 0.075 between the Queen Charlotte Islands and Kodiak (Table 3.7). Figure 3.4 depicts the hierarchical structure of genetic relatedness among populations. In this figure, standard errors of the branch lengths are indicated by thicker, shaded bars. If the standard error is less than half the genetic distance, then the two groups of populations connected by this branch are significantly different. Pairs of populations sampled from geographically proximal locations (Seward -Kodiak, Fort Bragg - Brookings, Prince Rupert - Queen Charlotte Islands) generally clustered together. Significant clusters included the most northern populations (Seward & Kodiak are the populations that are the most genetically different from all others) and the most southerly populations (Fort Bragg & Brookings). Although Prince Rupert and Queen Charlotte Islands clustered together, this clustering is not significant. Table 3.7. Genetic distances between eight natural populations of Sitka spruce. Population BRKS QCI KDIAK PMCNL FBRG QUAL PRUPT SEWD BRKS -QCI 0.026 -KDIAK 0.058 0.075 -PMCNL 0.064 0.075 0.062 -FBRG 0.013 0.042 0.054 0.043 -QUAL 0.031 0.028 0.072 0.037 0.042 -PRUPT 0.039 0.019 0.053 0.038 0.051 0.020 -SEWD 0.041 0.056 0.033 0.054 0.047 0.051 0.034 BRKS= Brookings; FBRG= Fort Bragg; KDIAK= Kodiak; PMCNL= Port McNeill; QCI= Queen Charlotte Islands; QUAL= Qualicum; PRUPT= Prince Rupert; SEWD= Seward 44 Possibility of migration-drift equilibrium was suggested by the absence of a significant relationship between pairwise population multilocus FST values and geographical distances between populations (Mantel test r = -0.245, P = 0.09). 0.057 0.034 0.053 0.040 0.014 0.026 0.021 Seward (PC) Kodiak (PD) Port McNeill (CC) Fort Bragg (PD) Brookings (PC) Qualicum (CD) Prince Rupert (CC) Queen Charlotte Islands (CD) Figure 3.4. UPGMA- derived dendrogram showing the clustering of the eight natural populations of Sitka spruce based on the genetic distance of Nei (1978). Thicker line indicates standard error and clusters of populations are significant when branch length is at least twice the error bar. 3.3.4. Test for bottleneck signature Generally, it is difficult to determine if a population has recently experienced a bottleneck because historical population size and levels of genetic variation are unknown. The Cornuet and Luikart (1996) test to detect a recent bottleneck (within 0.2 to 4.0 Ne generations) not only depends on Ne but also on factors such as the mutation rate and mutation model of the loci sampled (Cornuet and Luikart, 1996). Under the infinite alleles model (IAM), the Wilcoxon sign-rank test indicated significantly greater heterozygosity (p < 0.05) in all population classes than that expected for populations at mutation-drift equilibrium (Table 3.8), suggesting that they have been bottlenecked in the past. However, under the SMM, the Wilcoxon sign-rank test indicated non-significance in both core, continuous populations (Port McNeill and Prince Rupert) and in one core, disjunct population (Queen Charlotte Islands). Under the SMM, heterozygosity at mutation drift equilibrium (Heq) was 45 generally higher than for the 1AM (e.g., Cornuet and Luikart, 1996). The average expected: observed ratio of 2:1 for number of loci with HE excess is significantly different from the expected ratio (1:1) for a non-bottlenecked population, equilibrium population. CD T3 O c o cc -*—• CD Q . 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Q CD CD J D SZi O O i— i _ C L C L CD CD » -*—» 0 0 c c o o X X X - Q Ul 0) UJ LU HI O X X X X 47 3.4. Discussion 3.4.1. Allele frequency distribution The overall distribution of alleles was somewhat skewed towards low and intermediate frequencies. This is consistent with predictions of the infinite alleles model with varying mutation rates resulting in an increase in the number of low-frequency alleles (Chakraborty et al. 1980). When sample size is large, rare alleles are likely to be included in the sample (e.g., Ewen, 1972). These data suggest that for large equilibrium polymorphisms, every allele sampled is likely to be distinct. This is also consistent with predictions of the infinite alleles model with varying mutation rates that results in an increase of the number of low frequency alleles (Chakraborty et al. 1980). However, there is a diminishing return in finding new alleles when sample size increases (see also Chapter 5, section 5.3.1.). The alleles for STS markers of coding genes, often termed ESTPs, are mostly the result of indel polymorphisms located in transcribed but untranslated regions of arbitrary genes (Perry and Bousquet, 1998a). Therefore, it was assumed their variation is essentially selectively neutral (e.g., Jaramillo-Correa et al. 2001). All of the polymorphisms for STS markers used iri this thesis occur in noncoding regions regions except Sb29 (Perry and Bousquet, 1998a, b; Perry et al. 1999). Sb29 is an interesting exception because length differences observed in Norway, white, black and Sitka spruce are caused by insertions or deletions (indels) in the protein coding region (Perry and Bousquet, 1998a; Perry et al. 1999). Whether any of these Sb29 polymorphisms affect protein functions is not known, nor has the role of the Sb29 gene product been determined. However, significant differences in F / s among loci could be the result of different selection pressures at individual loci. The presence of null alleles could also cause differences in F/s among loci. However, none of the loci studied deviated significantly from the neutral expectations, as revealed by the Ewen-Watterson test. In addition, based upon amplification trials using reference genomic DNA, the loci investigated in this thesis have been judged to reveal no null 48 alleles (Perry and Bousquet, 1998a). Therefore, these STS markers may be less likely to be visible to selection. In previous studies in white spruce (Jaramillo-Correa ef al. 2001) using ESTPs and other conifers, using isozymes (Isabel ef al. 1995; Yang et al. 1996; Latta and Mitton, 1997), none of these loci studied deviated significantly from neutral expectations. The average number of alleles per locus (AR = 3.3 ± 0.05) was the same across all population classes; this value for STS markers is higher than the average values published for isozymes for species with widespread geographic ranges (2.56 ± 0.31), temperate-zone distributions (1.81 ± 0.06), gymnosperms (1.83 ± 0.58) and wind-pollinated outcrossing mating system (1.84 ± 0.05) (Hamrick ef al. 1992). However, markers such as microsatellites would surpass STS markers in terms of number of alleles per locus and heterozygosity due to higher mutation rates for microsatellites. For example, averages of 5.4 and 13 alleles per locus were reported for the polymorphic SSR markers in 16 white pine and 18 Norway spruce populations, respectively (Echt ef al. 1996; Pfeiffer ef al. 1997). Most alleles, whether locally common or rare, were distributed throughout the range of the species. Common, widespread (CW) alleles exceeded 75% in all population classes while rare, widespread (RW) alleles averaged nine percent of all alleles. Only one allele was rare, localized (RL) was detected in one core, disjunct and two peripheral, disjunct populations and averaged two percent of all alleles. No common, localized (CL) alleles were detected. These are the classes of alleles that presumably confer adaptation to local specific conditions or are new alleles with a strong selective advantage. One likely explanation for not detecting CL alleles in Sitka spruce populations is that they are unlikely to occur in the presence of high gene flow. Previous studies that have used isozymes to detect alleles in different classes following Marshall and Brown's (1975) classification have reported slightly different percentages of classes of alleles based on frequency and geographic distribution as 49 in this thesis where I modified the percentage of populations with the allele in question (Table 3.2). For example, Adams (1981) studied four populations of Pseudotsuga menziesii in Washington State, and four populations in Oregon, USA and detected 70% and 63% for common and widespread (CW) alleles, respectively (Table 3.9). However, he was able to detect common and localized (CL) alleles in both regions (10% & 13%, respectively), and yet I did not detect any common and localized alleles. Also using isozymes, but applying a different cut off level for rare alleles (i.e., p < 0.1), Moran et al. (1988) detected no common and widespread alleles, but 33% and 35%, respectively, for RW and RL alleles in Pinus radiata. In their study, the sample size was 620 trees and number of loci studied was 27 (see Table 3.9). It is worth noting that radiata pine is found only in five disjunct populations and has a high GST (0.162). These disjunct populations may be more likely to harbour rare alleles. However, different types of markers are likely to result in different percentages for different classes of alleles because of differing mutation rates, so it is not valid to compare values derived from isozymes, STS markers or microsatellites unless the distribution is standardized on number of alleles. Table 3.9. Classification of alleles based on frequency and geographic distribution (common widespread, CW; common localized, CL; rare widespread, RW; and rare localized, RL) in selected forest tree species. All studies except Sitka spruce (this study) are based on allozyme variation. Source No. # CW CL RW RL pops loci % % % % Sitka spruce (this study) 8 8 89 0 9 2 Pseudotsuga menziesii, British Columbia 11 18 65 11 13 11 populations1 Pseudotsuga menziesii, Washington 4 10 70 13 10 7 populations2 Pseudotsuga menziesii, Oregon populations2 4 10 63 17 13 7 Pinus radiata3 5 27 0 7 33 35 1 Yeh and O'Malley (1980); 2 Adams (1981); 3Moran et al. (1988). 50 The scattered distribution of rare alleles among geographically separated populations is intriguing. The presence of the same, rare allele in widely separated populations might be a reflection of occasional long-distance dispersal by pollen and or seeds (e.g., Slatkin, 1985b) and this pattern is indicative of high gene flow among populations. However, this pattern is unlikely to result from genetic drift in small populations. In cases where similar disjunct distributions of rare alleles have been observed, it has been hypothesized that the possible cause could be introgression from closely related taxa (e.g., Ledig, 2000). However, it is also unlikely because I found the RL allele in populations least likely to be affected by introgression from P/'cea glauca (e.g., Bennuah et al. in press). Another possible cause is that the same independently derived mutation occurred multiple times in widely separated populations, and these alleles are identical in state but not identical by descend. Throughout these populations of Sitka spruce, there are three alleles with low frequencies: Sb16-5, Sb16-6, and Sb62-4. The last allele is only encountered in three populations while the rest are widespread. Of the three alleles, it is worth investigating which ones were most likely present in the founding population, and which ones could have been introduced to these populations by events such as migration or introgression events or even by recent mutation. If the latter, then it is worth investigating the subsequent pattern of migration events. For instance, Sb62-4 allele is present in Fort Bragg at the southern end of the distribution, as well as in centre of the range (Queen Charlotte Islands), both of which have been suggested as potential glacial refugia for Sitka spruce, and in Kodiak, at the north-western, advancing front of the species' range. However, rare alleles have a higher probability of not being sampled when they are actually there than common alleles. 51 3.4.2. Gene diversity as revealed by STS markers in Sitka spruce Observed heterozygosity (Ho) was highest (mean = 0.55 ± 0.03) in core populations, both continuous and disjunct and lowest (0.47 ± 0.03) in peripheral, disjunct populations. Similar but weaker and non-significant trends were observed for expected heterozygosities (HE) (mean = 0.58 ± 0.04) in continuous populations, both core and peripheral, and 0.56 ± 0.03 in peripheral, disjunct populations. This suggests differences in genetic structure rather than in overall levels of genetic diversity between core and peripheral populations. The proportion of total genetic diversity attributable to population differentiation is low: GST = 0.03, smaller than many widespread temperate conifers that are wind-pollinated and primarily outcrossed (Hamrick and Godt, 1996b). The GST value is smaller than those reported for Sitka spruce in isozyme studies (G Sr = 0.082 ± 0.016 (Yeh and El-Kassaby, 1980); GST = 0.079 ± 0.011 (Chaisurisri and El-Kassaby, 1994) and for other conifers (lodgepole pine, GST = 0.06 (Macdonald et al. 2001); Pacific silver fir, GST = 0.051 (Davidson and El-Kassaby, 1997); mountain hemlock, GST = 0.077 (Ally et al. 2000). This could be due to a higher mutation rates for STS markers than for isozymes. However, these results are also consistent with the typical distribution of genetic variability for nuclear genes in forest trees. Some usual explanations for this pattern refer to life history traits of forest trees: large population sizes, mating systems close to strict allogamy and pollen or seed dispersion over great distances (Hamrick et al. 1992; Le Corre and Kremer, 1998). For example, multi-locus outcrossing rates of approximately 92 - 98% have been reported in seed orchards of Sitka spruce using isozymes (Chaisurisri et al. 1994; Cottrell and White, 1995). More recently, Austerlitz et al. (2000) demonstrated that high levels of pollen flow associated with life cycle characteristics of trees (longevity and length of juvenile phase) allow us to explain this observed structure of genetic diversity. In addition, genetic studies in other conifers suggest that mutation rates may be too low to produce much 52 detectable interpopulation genetic differentiation at the molecular level over geological periods such as interglacials (Mosseler et al. 1991; Deverno and Mosseller, 1997). The hierarchical F statistics and genetic distance measures suggest similar levels of population differentiation for the different population classes. They confirm little differentiation among the sampled populations for the eight loci investigated and yet range-wide studies for quantitative traits indicate considerable variation (e.g., Morgenstern, 1996; Ying, 1997; Xu et al. 2000). The finding that most of the genetic diversity is within populations is supported by evidence of an appreciable amount of interpopulation gene flow (Nm = 9.04 in core populations) suggesting either that periodic gene exchange among sampled populations is high or that little genetic change has occurred since populations were restricted to glacial refugia. The gene flow estimates indicate that pollen (and possibly seed dispersal) is substantial in Sitka spruce (see Ellstrand, 1992 for a review of gene flow in forest tree species). The gene flow estimates are based on simplified models that assume the populations are at equilibrium, there is no extinction/recolonization dynamics and that all migration reflects on-going migration (Slatkin, 1985b; Barton and Slatkin, 1986). Slatkin's (1985b) rare allele method is based on the observation that the logarithm of Nm decreases approximately as a linear function of the average frequencies of rare alleles in subdivided populations. Gene flow is an important force for the maintenance of genetic diversity. In addition, high amounts of current gene flow such as that observed in the core populations will reduce inbreeding. However, current gene flow via seed or pollen has the potential to introduce poorly adapted genes (outbreeding depression) that can reduce population viability (e.g., Wiener and Feldman, 1993; Kirkpatrick and Barton, 1997; Garcia-Ramos and Kirkpatrick, 1997). While it is not clear how likely current gene flow will result in outbreeding depression in Sitka spruce and is also beyond the scope of this thesis, the possibility illustrates the connection between gene flow and 53 local adaptation. I speculate that populations at the periphery (e.g., Kodiak and Seward) are likely to experience different patterns of future gene flow than those experienced over a longer period in the past. The significant inbreeding coefficients in peripheral populations, both continuous and disjunct, but not core populations (Table 3.6) are somewhat surprising for a conifer, given that all populations have similar levels of expected heterozygosity. Heterozygote deficiencies can be caused by: 1) selection against heterozygotes; 2) selection-induced micro-scale differentiation; 3) inbreeding (selfing), and 4) the Wahlund effect, due to the presence of breeding subunits inside the studied populations (Brown, 1978; Epperson, 1990; Knowles, 1991; Bush and Smouse, 1992; Sproule and Dancik, 1996). Apparently high inbreeding can also be an artifact of null alleles. However, the eight STS loci screened in this study reveal co-dominant polymorphisms with no evidence of null alleles (Perry and Bousquet, 1998a). The high inbreeding coefficients suggests that there is non-random mating among individuals within a population or a Wahlund effect due to the presence of breeding subunits inside the studied populations. In this study, I sampled mature trees, and would expect an excess of heterozygotes as a result (e.g., Plessas and Strauss, 1986; Ledig et al. 2000). Often conifer seeds and seedlings show a deficiency of heterozygotes, but mature trees typically either do not deviate significantly from Hardy-Weinberg-equilibrium or show an excess of heterozygotes (e.g., Plessas and Strauss, 1986). For example, studies by Politov et al (1992) and Morgante et al. (1993) have shown deviations from Hardy-Weinberg equilibrium with a lack of heterozygotes due to inbreeding in embryos or young seedlings, but not at later life stages. However, Isabel et al. (1995) compared the genetic diversity in natural populations of black spruce using allozyme and RAPD loci and found heterozygosities and population fixation indices to be in agreement between allozyme loci and RAPD loci, thus rejecting the long standing hypothesis for heterozygote advantage at isozymes loci. 54 The Wahlund effect may either be spatial or temporal. In this study, each population represented a large area (approximately 550 ha) with the 200 sampled trees at least 30 to 50 m from one another. Thus, it is possible that each population may consist of several spatial breeding subpopulations, generating a spatial Wahlund effect. The mean F/s for peripheral populations, both continuous and disjunct, is 0.17 (Table 3.6). If I assume that there are several spatial breeding subpopulations, the F/s value could reflect the differentiation among population subunits (local FST) that are at Hardy-Weinberg equilibrium and in effect be inflated by what should be an Fsr differentiation among subpopulations. The low level of inter-population differentiation that I observed for peripheral populations, both continuous and disjunct (FST = 0.02) is less than the mean F / s. As a consequence, it seems difficult to imagine that, inside the stands, there are subunits that would create an FST eight times greater than the value found among populations. Thus, the existence of a Wahlund effect, either spatial or temporal, seems unlikely here. I therefore conclude that the positive F/s values, in peripheral populations, both continuous and disjunct are a result of inbreeding in these populations. In Chapter 4, I subdivide each of the peripheral populations, both continuous and disjunct into several subpopulations, to investigate if there is a spatial Wahlund effect. 3.4.3. Population differentiation The amount of population differentiation for Sitka spruce in this study (F Sr = 0.03) is low. Divergence is probably underestimated by FST for two reasons: (1) If I assume that STS markers evolve under the stepwise mutation model, the stepwise mutations lead to homoplasy, decreasing the magnitude of FST (Balloux and Lugon-Moulin, 2002). (2) If individuals of a species are isolated into separate refugia during glaciation and undergo separate bottlenecks, most alleles lost in both populations/groups will be rare alleles. Common alleles should be the same in different populations and will probably survive the bottleneck and increase in frequency independently (Latta and Mitton, 1999). If low FST is due to the alleles surviving in both refugia, values of FST should vary among loci. However, individual 55 locus FST for Sitka spruce had a narrow range, between 1.1% to 4.8%, indicating that FST may not have been biased by this effect. In parallel with the F S r estimate, genetic distances among populations were relatively low. The UPGMA based on Nei's unbiased genetic distances (D) between populations partially reflects the geographic relationships between them. One interpretation of the clustering of populations is the historically broad connection and substantial gene flow among the populations during the last glaciation period. Previous studies in Sitka spruce (Yeh and El-Kassaby, 1980) demonstrated similar levels of divergence among populations in British Columbia, Oregon and Alaska. Yeh and El-Kassaby (1980) reiterated that the narrow, attenuated distribution of Sitka spruce and its confinement to maritime habitats leads to the expectation that levels of geographic variation exhibited by molecular markers would predominantly be clinal with respect to latitude. However, this is not the case as demonstrated by the relatively small genetic distances between populations and lack of a significant relationship between genetic distances and geographic distance between populations. Under isolation-by-distance, small neighborhood size will lead to more differentiation than large neighborhood size does. If there is a sink-source population structure (or density-dependence), FST may increase from the core to the peripheral populations, which is also consistent with the concept of migration-drift equilibrium. For example, Fort Bragg and Kodiak populations are clearly geographically^separated from the majority of the populations sampled, and yet the genetic distance between these two populations is low and non-significant. Fort Bragg and Kodiak are small, peripheral populations, and perhaps have had opportunity for divergence due to genetic drift, yet have similar levels of genetic diversity (HE = 0.53) to core, continuous populations, including the Queen Charlotte Islands, which have been identified as a potential Pleistocene refugium for plants, animals, insects (e.g., Byun et al. 1997; Soltis et al. 1997; Stone et al. 2002). Given the above, it is possible that other factors may be involved in generating genetic 56 structure in populations and, migration-drift equilibrium alone may not explain the trends observed. Glacial refugia may harbour higher levels of genetic diversity than do areas colonized after the retreat of glaciers. Comps et al. (2001) suggest that this assumption might be too simplistic. Genetic diversity can be either higher or lower in the recolonized areas, depending on the estimator used to assess genetic diversity. For example, the theory of bottlenecks or transitory reductions in effective population size predicts a strong decrease of allelic richness and a more limited decrease of heterozygosity at neutral loci, since rare alleles are more likely to be lost due to drift than more frequent alleles (Nei et al. 1975). But this theory may not be strictly applicable to the founding events that have taken place during postglacial colonization. Results in this study of similar levels of genetic diversity, as measured by HE in all population classes, are consistent with those reported for white pine [Pinus strobus L) from Quebec, where two peripheral populations showed genetic diversity levels that were comparable to or higher than those of core populations (Beaulieu and Simon, 1994; Rajora et al. 1998). My results demonstrate that despite the geographic location of some populations, effective population sizes of peripheral, disjunct populations of Sitka spruce have not been reduced substantially historically, and are as genetically diverse as populations from the species' core range at a macro-geographical scale, although individuals in these populations are more likely to be homozygous than those from core, continuous populations. A significant remaining anomaly is the higher inbreeding in peripheral, disjunct populations. In Chapter 4, I investigate within-population structure and spatial arrangement of genotypes in different population classes to determine whether these populations are structured differently at a micro-geographical scale. 57 3.4.4. Genetic variation in Core versus Peripheral Plant Populations A comparison of levels of genetic diversity in plant literature for core and peripheral populations was conducted using only those studies where populations were classified in terms of position in the species distribution (Table 3.10). Meta-analyses accumulate knowledge across studies and to ameliorate problems associated with lack of statistical power in individual studies (Hunter and Schmidt, 1990). For meta-analyses to be successful, two things are necessary: the findings must be conceptually comparable and the studies must be configured in similar statistical forms (Lipsey and Wilson, 2000). Although the data available in the literature are somewhat heterogeneous, the results suggest lower levels of genetic diversity in peripheral populations than core populations on average (table 3.10). However, three of ten species, in addition to this study, exhibit similar levels of diversity between core and peripheral populations. Table 3.10. Summary of average expected heterozygosity (HE) in core and peripheral populations in various plant taxa. Core Peripheral Species # loci HE HE Reference Picea sitchensis 8 0.56* 0.55* This study (Chapter 3) Picea mariana 9 0.23* 0.20* Gamache et al. (2003) Picea abies 9 0.23* 0.17* Muona et al. (1990) Pinus rigida 21 0.17 0.12 Guries and Ledig (1982) Pinus radiata 31 0.11 0.08 Moran et al. (1988) Pinus contorta 25 0.18 0.15 Yeh and Layton (1979) Pinus strobus 20 0.16* 0.15* Rajora etal. 1998 Pinus coulter! 32 0.19 0.10 Ledig (2000) Castanea dentata 18 0.19 0.05 Huang etal. (1998) Hordeum jubatum 18 0.16 0.03 Shumakerand Babble (1980) * indicates non-significant (p^ 0.05) differences between core and peripheral populations 58 3.4.5. Evolutionary history A reduction in genetic diversity can occur through a reduction in effective population size, either through a large historical bottleneck or small, reoccurring bottlenecks during colonization of new areas. The bottleneck test (Cornuet and Luikart, 1996; Luikart and Cornuet, 1998) can only reveal dynamics in population growth since the last bottleneck. Nevertheless, it indicates the potential impact of glaciation on a species' level of genetic diversity. I found evidence of bottleneck events in all eight populations under the infinite alleles model (IAM), whereas under the stepwise mutation model (SMM), both core, continuous populations (Port McNeill and Prince Rupert) and one core, disjunct population (Queen Charlotte Islands) did not show evidence of a significant bottleneck signature. A higher heterozygosity at mutation-drift equilibrium (Heq) is expected under the SMM than for the IAM for the same number of alleles (Cornuet and Luikart, 1996). The excess of heterozygosity at Hardy-Weinberg expectations over that in populations at mutation-drift equilibrium, in fact, indicate past reductions in effective population size. However, loci not in Hardy-Weinberg proportions may bias the test, and most of the loci in our samples had a deficiency of heterozygotes. This suggests that the results may be biased, hence detecting a false bottleneck. In addition, the test may require 10 to 20 polymorphic loci with 4 to 5 alleles per locus in order to achieve a power of 0.80 (Cornuet and Luikart, 1996). Excluding data not in Hardy-Weinberg proportions may remove the bias (Luikart and Cornuet, 1998), but this would have left me with fewer loci to conduct the test. In addition, bottleneck-induced heterozygosity excess is said to be transient and likely to be detectable for approximately 0.2 - 4.0 Ne generations until a new equilibrium is reached at the new Ne (Luikart and Cornuet, 1998). The last glacial period lasted approximately 100 000 years. Assuming a generation time of about 25 to 30 years, populations of Sitka spruce would have been isolated from each other for only about 100 to 200 generations, and even fewer in recently colonized areas like Kodiak. It is plausible that Sitka spruce has experienced expansions in range 59 and effective population sizes. It is also possible that Kodiak is expanding from a small effective population and mutations are increasing the number of rare alleles. The window of time during which an excess of heterozygosity is detected will also depend on the mutation model and genetic markers used. The 1AM and SMM represent two extreme models of mutation (Chakraborty and Jin, 1992). Di Rienzo et al. (1994) and Luikart and Cornuet (1998) suggest that most loci probably evolve according to a model intermediate between IAM and SMM, hence the actual expected equilibrium heterozygosity (Heq) for a given locus probably lies between the Heq values calculated by these two models. For example, microsatellite markers may follow the stepwise mutation model (SMM) (e.g., Shriver et al. 1993) which allows for mutation to existing allelic states (homoplasy) and thereby results in fewer distinct allelic states than the infinite alleles model (IAM) for a given mutation rate (e.g., Ohta and Kimura, 1973). This is particularly true in this study, where I detected a significant bottleneck all populations, except core, continuous and one core and disjunct populations, but a significant bottleneck in all populations under the IAM. I would expect STS markers to evolve under the IAM since the alleles are the result of small random insertions or deletions, that appear unrelated, more like single nucleotide polymorphisms (SNPs) where the number of possible variants truly is infinite, thus matching the IAM. However, some loci in STS markers are likely to evolve under the SMM, e.g., Sb01 (not used in this thesis) and Sb16 (used in this thesis) where a large number of alleles was observed in P. mariana and P. glauca (ca. 8 to 10) (Perry and Bousquet, 1998a, b) and Sitka spruce (ca. 5 to 7), where fragment length variation follow a regular pattern of stepwise differences, presumably from a variable number of large repeats. Different levels of genetic variation could also be explained by different mutation rates at the two types of loci (e.g., Hedrick, 1996). For example, if mutation rate (u) = 10"6 for STS markers and u = 10~3 for microsatellite loci, then the equilibrium heterozygosity for an effective population size (Ne = 2000) would be 0.0079 and 0.757 for STS markers and microsatellites, respectively. In addition, most conifers, 60 including Sitka spruce harbour overlapping generations, which may increase effective population size. Therefore, it seems unlikely that the bottleneck signature detected under the IAM is a recent bottleneck, but rather a bottleneck prior to the last glaciation. The bottleneck signature under the SMM, in only peripheral populations may be evidence of populations that have not yet reached equilibrium since the last bottleneck prior to the last glaciation. Typically, bottlenecks are characterized by losses in allelic richness and by concomitantly but weaker reductions of heterozygosity, especially if population size rebounds rapidly (Nei ef al. 1975; Ellstrand and Ellam, 1993). For example, a one-generation bottleneck of Ne = 10 represents an expected loss of heterozygosity of 5% (expected loss is 1/2A/e) per generation. During bottlenecks, populations lose rare alleles more quickly than common alleles, so that the number of alleles per locus is reduced more quickly than expected heterozygosity. Therefore a bottleneck can be detected before more than 5% of population heterozygosity has been lost. One can also infer that bottlenecks can be detected before substantial quantitative genetic variation has been lost because the reduction in quantitative genetic variation occurs roughly proportionally to that of heterozygosity (Franklin, 1980; Lande and Barrowclaugh, 1987). Another evolutionary force known as natural selection (e.g., selective sweeps) could rapidly change allele frequencies and reduce variation. Decrease in heterozygosity (due to selection, for instance) accompanied by more limited losses of allelic richness could also result in a significant bottleneck test. However, selection that reduces diversity is unlikely to occur simultaneously at many unlinked loci. In such a case, the necessary assumption of selective neutrality of loci (Cornuet and Luikart, 1996) would not be met. The existence of ice-free refugia during full glacial advances in the Pacific Northwest has been debated (e.g., Demboski ef al. 1999). During the past glaciation, the Cordilleran Ice Sheet, in combination with portions of the Laurentide Ice Sheet, 61 covered most of British Columbia and Alaska (Cowan, 1989). During the last 600 000 years of the Quaternary (2.4 million years ago to the present), a series of glacial cycles of approximately 100 000 years each, separated by warmer interglacial periods of about 10 000 years, have had a profound impact on the distribution of North American conifers, including Sitka spruce (Critchfield, 1984; Hewitt, 2000). The present-day range of Sitka spruce extends along a narrow belt along the Pacific coast of North America over 3,000 km from northwest California through Oregon, Washington, British Columbia and up to Alaska (Figure 3.1). While southern refugia seem the most likely colonizing sources for Sitka spruce, the Queen Charlotte Islands have also been suggested as a glacial refugium for Sitka spruce (Soltis et al. 1997). The large number of coastal endemic taxa combined with molecular and palaeontological investigations in plants, insects, fish and mammals suggest, however, that portions of Queen Charlotte Islands may have remained devoid of ice (Warner et al. 1982; Cowan, 1989; O'Reilly et al. 1993; Zink and Ditmann, 1993; Byun et al. 1997; Stone et al. 2002). It has been suggested that the high degree of endemism and repeated pattern of intraspecific lineage diversity across taxa of the north Pacific Coast may be the result of the persistence of refugial populations in these ice-free areas (palaeoendemic), or secondary contact of populations that have recently expanded into the region (neoendemic) (e.g., Stone et al. 2002). The strongest evidence for a northern glacial refugium of mesic forests off the coast of Queen Charlotte Islands are 16 000 year old fossils of several plant species (e.g., Warner and Mathewes, 1982; Broyles, 1998). The higher additive genetic variability of Sitka spruce on Queen Charlotte Islands compared with that in more mainland regions (Lines, 1987) also suggest that Sitka spruce may have been present on the islands during the last glaciation. The latter is supported by the fact that there is one rare, localized allele and several rare, widespread alleles present in the Queen Charlotte Islands. It is therefore, possible that the present-day Sitka spruce populations in the northern portion of the range descended largely from refugial populations in the Queen 62 Charlotte Islands (Figure 3.1). However, the results of the UPGMA (Figure 3.4) reveal two large clusters (Seward and Kodiak) versus the rest, including Fort Bragg and Brookings, which would seem to suggest that Sitka spruce had multiple refugia sources, i.e., Queen Charlotte Islands and southern refugia (e.g., Soltis ef al. 1997). The similarity in genetic diversity of several co-occurring species often help identify the location of glacial refugia and post-colonization routes (Brunsfield ef al. 2001). Sitka spruce co-occurs with several species including western red cedar, lodgepole pine and yellow-cedar. For example, isozyme work on yellow cedar (Ritland ef al. 2001), red alder (Hamann ef al. 1998) and microsatellite work on red cedar (O'Connell, 2003) suggested multiple glacial refugia along the Pacific coast. Given the evidence above, Sitka spruce may have had both a southern and Queen Charlotte Islands as glacial refugium. The evidence of multiple refugia in several species co-occurring with Sitka spruce during the last glaciation, combined with lack of population differentiation over the range of Sitka spruce, suggest that if there was a bottleneck in Sitka spruce, it predates the last glaciation. 3.5. Conservation genetic implications The STS marker data generated in this thesis have several implications for the conservation of allelic diversity. First, the data demonstrate similar levels of genetic diversity among populations when classified as core or peripheral, based on ecological conditions and continuous or disjunct, based on geographic distribution. Each population captured almost the same level of genetic diversity with an average of 26 alleles per population summarized across the eight loci. Likewise, allelic richness was similar across all population classes. However, 97% of the total genetic variation is within populations and three percent among populations. Missing alleles from the various populations were those with overall frequencies of < 0.05. Alleles, whether locally common or rare, tended to be distributed throughout the range of the species. Two percent of all alleles were classified as rare and localized alleles on average and were detected in one core, disjunct and two peripheral, disjunct populations. 63 While measures of genetic relationships (or conversely, differentiation) among populations are important for devising sampling strategies for ex situ collections in widespread species, within population genetic variability also may be informative for decision making. For example, my data suggest that 97% of the total genetic variation is within populations and three percent among populations. Generally, this would suggest that selection of large numbers of samples from a few populations would still capture most of the species' available genetic variability. However, such a practice would increase the chance of missing some rare alleles, in both core or peripheral and continuous or disjunct populations. In order to capture most of the allelic diversity in Sitka spruce, it is necessary to sample in both core and peripheral populations and in both continuous and disjunct populations. Evidently, the classical opinion that in many cases "most of the genetic diversity resides within populations and therefore selection of large numbers from a few populations would still capture most of the species' available genetic variability" may be misleading, since it is valid for a particular interpretation of diversity (e.g., heterozygosity) which entirely dismisses any potential importance of rare alleles. It is also likely to be even less true for adaptive (quantitative) traits than for selectively neutral markers. Measures of genetic diversity based on number of alleles (allelic richness) are important, especially in the field of conservation genetics. Indeed, for conservation purposes, it is essential to safeguard the largest possible collection of alleles (e.g., Marshall and Brown, 1975; Asins and Carbonell, 1987; Millar and Westfall, 1992; Schoen and Brown, 1991). For example, results from several studies suggested that allelic richness measures may be more useful than allelic evenness measures when quantifying effects of disturbances on gene pools (Buchert et al. 1997; Leberg, 1992; Marshall and Brown, 1975; Stoehrand El-Kassaby, 1997). Allelic richness is particularly vulnerable to a decrease in population size, such as bottleneck or a founder effect (Nei et al. 1975; Petit et al. 1998), contrary to those measures of diversity that rely mostly on the more frequent alleles, such as Nei's measure of expected heterozygosity. For instance, Nei et al. (1975, their Table 3) 64 showed that for two representative loci, 100 individuals sampled in a source population would have 99.5% of the average heterozygosity of the parental population but only 42 - 53% of its allelic richness. The mean number of alleles per locus for the whole species or averaged over populations, which is often reported (e.g., Hamrick ef al. 1992), is obviously dependent on the sample size. I explore relationships between population sample size and genetic diversity parameters in Chapter 5. Sampling in a single population irrespective of its geographic or ecological distribution will still capture the common, widespread alleles. No common, localized alleles were detected in this study. However, such alleles are of particular interest since these may be responsible for adaptation to local conditions (Brown and Hardner, 2000), and patterns of variation detected for presumably selectively neutral genetic markers would not reveal these. For example, genetic resistance to the white pine shoot tip weevil in British Columbia (Pissodes strobi (Peck)) appears to follow this pattern (King, 1994; King ef al. in press) and yet it does not fit the latitudinal clinal patterns typically observed for allele frequency or quantitative traits (e.g., Hamann ef al. 1998). Provenance material from Qualicum (sampled in this study) and Haney (not sampled here) contains genotypes that are 20% resistant to the spruce weevil (Ying, 1991; King, 1994). In addition, some sources (Texada Island, Campbell River and Oyster Bay on Vancouver Island) bordering the Qualicum resistant populations show moderate levels of resistance indicating a possible regional gradient in the geographic distribution of the resistance, (King etal. in press). The approach of collecting samples from different geographic areas to maximize genetic diversity for ex situ collection despite a low FST estimate is substantiated in this study. Relatively high levels of genetic diversity existing in the Queen Charlotte Island population, potentially one of the refugia and therefore origin of much genetic diversity prior to postglacial migration, suggest that conservation efforts definitely should consider this a focal point for capturing much of the genetic variation for Sitka 65 spruce. The conservation of peripheral populations like Fort Bragg and Kodiak Island may present the best possible solution for preserving rare alleles in Sitka spruce, thus supporting a high priority for in situ protection or extensive sampling for ex situ conservation of outlying populations. For assessment of genetic diversity, none of the molecular markers are more advantageous than others in all respects. Also, determining which marker to use depends on questions to be addressed, financial resources, availability of equipment and skill of personnel. In this thesis, I chose to use cDNA-based sequence-tagged-site (STS) markers for several reasons. They have already been developed and tested for cross amplification and repeatability in some Picea species. This reduces the time and resources required in marker development to launch studies in other species. The "low cost" of sequence-tagged-site (STS) markers, ease of use, and speed with which data can be obtained combine to make them attractive markers for use in conservation genetics. Since measures of allelic richness are more important than measures of heterozygosity for gene resource conservation, polymorphic marker loci that reveal at least three alleles per locus are useful for quantifying geographic patterns of genetic diversity as measured by allelic richness. One might argue that microsatellites markers, which are single-locus co-dominantly inherited, would have been superior since they have the highest number of alleles per locus among all DNA markers currently available. However, given the large number of alleles per locus in microsatellite markers, allele frequency spectra are often dominated by low frequency alleles. This would create a problem of identifying the actual low frequency alleles. In addition, the large sample size in this study would have resulted in high genotyping costs with microsatellites. STS markers proved useful for quantifying geographic patterns of rare alleles and inferring evolutionary dynamics producing these patterns. Because ESTPs were located in transcribed but untranslated, genetic parameters derived from these markers should be 66 representative of neutral or nearly neutral markers, which could be used as a yardstick for detecting selection in STS markers. When population genetic parameters are properly estimated from a robust data set, they can provide insights into the present-day genetic structure of plant species as well as provide information on the appropriate sampling strategies for particular species. Such assessments of population genetic structure, as demonstrated in this thesis, can identify the complex genetic structures in both core or peripheral populations and continuous or disjunct populations of widespread species. 67 CHAPTER 4 Spatial population genetic structure in Sitka spruce (Picea sitchensis (Bong.) Carr.): Implications for conservation of genetic diversity 4.1. Introduction Within plant populations, spatial genetic structure is present when the distribution of genetic variation among individuals is non-random (McCauley, 1997). Many evolutionary and ecological factors can affect the development of genetic structure within populations, including limited seed and pollen dispersal (Wright, 1943; Bacilieri et al. 1994), adult density (Knowles et al. 1992; Hamrick et al. 1993; Hamrick and Nason, 1996), colonization and disturbance history (Boyle et al. 1990; Schnabel et al. 1998; Epperson and Chung, 2001; Parker et al. 2001), spatial and temporal patterns of seedling establishment (Ellstrand, 1992; Schnabel and Hamrick, 1995; Parker et al. 2001), and micro-environmental selection (Linhart et al. 1981; Slatkin and Arter, 1991). For plant species, reviews have focused on correlations between life history traits and spatial genetic differentiation among populations (Loveless and Hamrick, 1984; Hamrick and Godt, 1990) and spatial sub-structuring within populations (Epperson, 1990; 1992; Heywood, 1991). The breeding system, of course, strongly affects spatial structuring. Genetic differentiation is far more extensive in selfing species than in primarily outcrossing species. Ecological factors correlated with self-fertilization, such as short life cycle and clonal growth, are also associated with increased spatial genetic structure (Berg and Hamrick, 1994). Several studies in plant species have revealed weak within-population spatial genetic structure (Epperson and Allard, 1989; Perry and Knowles, 1991), and others 68 have observed random distribution of genotypes (Knowles, 1991; Berg and Hamrick, 1995; Leonardi and Menozzi, 1996; Parker et al. 2001). For example, spatial genetic variation in lodgepole pine {Pinus contorta ssp. latifolia (Douglas ex Loudon); Epperson and Allard, 1989) and black spruce (P/'cea mariana (Mill) B.S.P.; Knowles, 1991) indicated a random distribution of genotypes and weak short-distance genetic structure. However, a study of spatial structure in Larix laricina (Du Roi) by Knowles et al. (1992) revealed strong genetic structure resulting from identified historical disturbances. The general observations of weak genetic structure among tree species could be the result of common life-history traits including greater longevity and high levels of gene flow via wind-born pollen than most other plants (Hamrick et al. 1979; Hamrick etal. 1981). Both simulations and empirical studies have shown that mating system and seed dispersal have considerable influence on within-population genetic structure (Sokal and Wartenberg, 1983). Coniferous species (in which pollination and seed dispersal occur mainly by wind, and outcrossing rates are generally high) have often shown random or only weakly autocorrelated spatial distributions of genotypes (Epperson and Allard, 1989; Knowles, 1991; Xie and Knowles, 1991). In contrast, populations of species in which seed dispersal is limited have often shown genetic clustering. Quercus species, in which large seed are dispersed by gravity, are typical. For example, in a continuous old-growth stand of Quercus laevis (Walt) studied by Berg and Hamrick (1995), the population showed one of the highest proportions recorded of positively significant autocorrelation, over scales of 10 m or less, presumably because of short distance seed dispersal by gravity. However, the degree of autocorrelation observed was not as strong as that predicted by the authors' simulations. The authors suggested that pollen flow and bird-cached seed may have important effects on the genetic structure of the stand, which was less pronounced than predicted given the expected isolation by distance. A number of methods have been employed in an effort to detect and describe the genetic structure within populations using genetic markers. Methods have ranged 69 from a simple visual inspection of maps depicting tree genotypes (Geburek and Knowles, 1994) to an examination of the correlation between complex genetic similarities and geographic proximity (Perry and Knowles, 1991). Heywood (1991) reviewed methods of evaluating the spatial structure of genetic variation of populations including the use of hierarchical F-statistics (Wright, 1978), weighted F-statistics (Weir and Cockerham, 1984) and spatial autocorrelation analysis (Sokal and Oden, 1978; Sokal and Wartenberg, 1983; Streiff et al. 1998; Epperson and Clegg, 1986). While it is difficult to assess subtle differences among evolutionary processes, investigating within-population structure can help quantify the main evolutionary forces affecting a particular population and provide information needed to select natural populations or individuals within them for conservation or as candidates for breeding programs. Family structure should also be taken into account in order to maximize diversity in a sample of fixed size, and to avoid making erroneous estimates of populations diversity (Epperson, 1989). For example, Miyamoto et al. (2000) detected family structure in a population of Alnus trabeculosa Hand.-Mazz. using spatial autocorrelation analysis. They concluded that this structure has implications for both in situ and ex situ conservation. Understanding the evolutionary processes operating in natural populations is one of the most important goals of population and conservation genetics, because such knowledge is essential for enhancing the quality and efficiency of conservation, for management of genetic resources, and for controlling the potential risk of genetic deterioration. Information on genetic processes in natural populations derived from knowledge of within-population genetic structure can inform the spatial scale for optimal sampling of genotypes for maximum capture of diversity for ex situ gene conservation. In Chapter 3, I examined the genetic structure, population diversity and evolutionary history of Sitka spruce at eight sequence-tagged site (STS) loci, by comparing 70 genetic diversity in core versus peripheral populations (in terms of ecological niche), and continuous versus disjunct populations (in terms of species distribution). Given that this species has a narrow, attenuated distribution and confined to maritime habitats, one would expect it would reveal isolation by distance with latitude. Sitka spruce occupies a wide latitudinal range, and yet genetic distances among populations are fairly small. For example, one would expect that peripheral, disjunct populations would have reduced levels of genetic diversity due to lower effective population size (NE), less gene flow and therefore greater genetic drift. However, core populations, both continuous and disjunct, showed significantly higher levels of observed heterozygosity than peripheral populations, both continuous and disjunct, which suggests differences in genetic structure between core and peripheral populations. Peripheral populations also showed significant inbreeding. Furthermore, the Cornuet-Luikart (1996) tests provided stronger evidence of past bottlenecks in peripheral, disjunct populations than in core, continuous populations. It is probable that the present data reflect sampling of micro-geographical variation in a highly variable species. An important question emerged from these noteworthy findings: are these population classes structured differently at a micro spatial scale, and if so, what is the relationship between spatial structure and genetic diversity? In this chapter, I focus on the distribution of the approximately 97% of the genetic variation harboured within populations of Sitka spruce (GST - 0.03 ). The specific objectives were to: 1. determine whether there is a spatial genetic structure, and if so, to compare patterns in core, continuous (CC); core, disjunct (CD); peripheral, continuous (PC); and peripheral, disjunct (PD) populations. 2. investigate if there is a spatial Wahlund effect in each of the peripheral populations, both continuous and disjunct. The information can be utilized to infer evolutionary processes at a micro-geographic scale, contribute to the debate on the value of peripheral and disjunct populations for conservation, and determine the spatial scale at which genotypes should be 71 sampled in natural populations to capture maximum genetic diversity in a sample of fixed size. 4.2. Materials and Methods 4.2.1. Spatial autocorrelation analysis A detailed description of sampling protocols for 200 trees per population can be found in Chapter 3. Individual tree locations were identified by a coordinate grid system using a hand-held Global Positioning System instrument (GPS Garmin Model 12XL) during sampling, then used to map the location of each tree on North-South and East-West (x, y, respectively) axes, and to construct inter-tree distance matrices that were subsequently used in the spatial autocorrelation analysis. To aid visualization of spatial distributions, figures mapping locations of specific alleles and genotypes within populations were constructed for each locus and population (see Figures 4.1a - d). Sb16 and Sb17 were the most variable loci with six and four alleles, respectively, and thus were the most useful for visual inspection of non-random patterns. Randomness of the distribution of genotypes and alleles for all loci was initially assessed visually from these single-locus plots. Analytical methods that combine information over alleles and loci to estimate coancestry coefficient (p,y) provide powerful tests of spatial population genetic structure (Smouse and Peakall, 1999). p,y has been used in a number of studies (e.g., Loiselle et al. 1995; Peakall and Beattie, 1996; Foster and Sork, 1997; Burke et al. 2000; Kalisz et al. 2001; Parker ef al. 2001; Chung ef al. 2002) and I will be using coancestry coefficient (p,y) to test for spatial population structure. The software program Spatial Pattern Analysis of Genetic Diversity (SPAGeDi) 1.1 (Hardy and Vekemans, 2002, http://www.ulb.ac.be/sciences/lagev) provides several statistics designed for pairwise comparisons between individuals (coancestry 72 coefficients, p,y) including those based on Loiselle et al. (1995) and Ritland (1996). The two estimators differ mainly in the way data from different alleles and different loci are combined to provide average estimates per locus or multilocus estimates (Hardy and Vekemans, 2002). Ritland's (1996) estimator weights allele distributions by the inverse of allele frequency, giving more weight to rare alleles. This approach results in lower sampling variance, hence it is more powerful for detecting genetic structure. However, Ritland's (1996) estimator is biased downward by the sampling properties of low frequency alleles (< 0.05). The estimator described by Loiselle et al. (1995) weighs allele frequencies by p, (1 - pi), where p, is the frequency of the allele, and is not biased by the presence of low frequency alleles. Because low frequency alleles were detected in the populations (Chapter 3), I decided to use the estimator by Loiselle et al. (1995): Pij = Um-P)(Pi-P) + 2 (i<j), /cp(1-p) (8/c + 1)° 5 - 1 where the first term is the expected value of p,y ; p, and p ; are the frequencies of homologous alleles at a locus for individuals /' and j; p is the mean frequency for that allele; and k = n{n - 1) / 2, which gives the number of possible pairs between n individuals located in each distance class. The second term in the equation adjusts for bias associated with a finite sample size and results in p,y having an expected value of zero for a population in Hardy-Weinberg equilibrium. Since there are no differences in sample size among loci, no adjustments were made for missing genotypes. However, a combined multilocus estimate of coancestry was obtained by weighting the values for each locus by its polymorphic index, S pi (1 - pi). Under random mating, the coancestry between individuals is a measure of the inbreeding coefficient of their hypothetical offspring with expected values of 0.25 for full-sibs, 0.125 for half-sibs, and 0.0625 for first cousins. Twenty-three distance classes were used for the spatial autocorrelation analysis for all populations except Fort Bragg and Qualicum. These were 0 - 50 m, 50 - 100 m, 73 100 - 300 m, and twenty 300 m intervals up to 3200 m. Fort Bragg and Qualicum populations had 26 distance classes: 0 - 50 m, 50 - 100 m, 100 - 300 m, and twenty three 300 m intervals up to 6100 m. These two populations have more scattered distribution. The analysis compares all pairs of trees within the specified distance interval of one another and asks whether pairs exhibit the same alleles more often than expected by chance under a random spatial arrangement. The process is repeated for all pairs falling within each distance class. The first three distance classes were set at smaller intervals (50 to 200 m) and the rest at 300 m intervals since this provided a good compromise between resolution (having distance classes small enough to detect spatial structure over short distances) and power (having enough pairs within each distance class). This was based on the rule of thumb that the proportion (%) of all individuals represented at least once in the interval be greater than 50% for each distance interval (Hardy and Vekemans, 2002). Coancestry has an expected value of p# = 0 when there is no genetic correlation between the frequencies of alleles in individuals at the spatial scale of interest, p,y > 0 when individuals in a given distance class are more closely related than expected by chance, and p,y < 0 when individuals within a given distance class are less related than expected by chance. Randomization procedures were used to test the significance of the estimated p,y values by constructing a confidence envelope about the null hypothesis of no spatial structure: Ho: p,y = 0 (for a general discussion of this approach, see Slatkin and Arter, 1991). In this procedure, map locations occupied by individuals within a population are randomly reassigned by randomly drawing locations with replacement from the set of observed locations. For a given distance class, the values of p,y from the N - 1 simulation trials are ranked p(i), p(2), , p(N-i), where p<i) is the highest and p(N-i) the lowest simulated values. The null hypothesis that there is no spatial genetic structure of the sample population is rejected if the coancestry coefficient based on the data, Odata (the Mh 74 estimate), is greater than P(i - Q / 2 ) N or less than p(Q/2)N- In this study we conducted N-1 = 499 simulation trials with a = 0.05. Thus p ( 4 8 8) and p (i 2) represent the upper and lower limits, respectively, of a 95% confidence interval on the distribution of simulated genetic structure statistics assuming no spatial genetic structure. A p,y estimate falling outside this confidence limit was considered significant. If genetic structure exists, then we expect a pattern of significant values at shorter distance classes becoming insignificant with increasing distance. In order to investigate if there is a spatial Wahlund effect in each of the peripheral populations, both continuous and disjunct, I subdivided each of the populations into four to six subpopulations of 30 to 40 trees. Since Wahlund effects are a result of micro-differentiation among sub-groups within a population, I estimated differentiation among the subgroups within each population and then tested for any significant differences among subpopulations within a population. 75 4.3. Results 4.3.1. Visualization of spatial distributions The plots depicted in Figures 4.1 (a-d) represent spatial genotypic and allelic maps for the most variable marker loci genotyped (Sb16 & Sb17) for a peripheral, disjunct (PD) population (Kodiak) and a core, continuous (CC) population (Port McNeill). Visual inspection of the genotypic and allelic scores for Kodiak (Figure 4.1 a & b ) reveals clustering of some genotypes more than others. For example, at locus SB17, homozygous genotypes 11, 22 and 33 tended to be clustered whereas heterozygous genotypes 23 indicate a nearly random distribution. The genotypic and allelic scores for Port McNeill (Figure 4.1 c-d) showed a nearly random spatial distribution of genotypes with no clear evidence of clustering. 76 (a) 2000 1750 H 1500 Jr. 1250 1000 • K 5 ti 750 500 250 an A O V g a © § o n o * • % v O V O O Cfe o v n o v v v v o o o o o o o n o o © Kodiak Island (PD) Locus Sb 16 A A V A A A A A ^ n • * & n & A r j A A • o o o v • $ V A A V V V o — I 1 1 1 1 1 1 1 — 250 500 750 1000 1250 1500 1750 2000 North-south distance (m) T Genotype Allele U O 1 O 12 • 2 • 13 © 3 V 15 © 4 A 22 • 5 + 23 0 6 x 24 a 33 V 34 # 35 V 36 V 44 A 45 A 46 A 55 + 2250 2500 2750 3000 3250 (b) 2000 1750 -\ 1500 S. 1250 ~ 1000 tu S V, 750 500 H 250 V + v n c n • • • • • P v v @ n m n • • o y _ o 0 ^ 0 s o s n o _ 0 ° 4x ° B V Q Q o ° n u n • • • • r rn° C D * Kodiak Island (PD) Locus Sb 17 ° D °o 8 o i • Genotype Allele H O i O 12 n 13 © 14 © 22 • 23.0 24 0 33 V 34 V 44 + 2 • 3 V 4 + 1 1 1 1 1 1 1 1 1 1 1 1 0 250 500 750 1000 1250 1500 1750 2000 2250 2500 2750 3000 3250 North-south distance (m) Figure 4.1 a-d. Spatial distribution of STS loci polymorphisms (Sb16 & Sb17) in 550 ha area of Sitka spruce located on Kodiak Island (a and b) (peripheral, disjunct population) and Port McNeill (c and d) (core, continuous population). The x-axis is oriented approximately North-South and the y-axis is approximately East-West. Symbol locations indicate mapped tree location and genotype at locus Sb16 & Sb17. 77 Figure 4.1 a-d continued, (c) 2000 1750 1500 H P Jl 1250 8 M IOOO J Q n n o c o o 750 H 500 H 250 © X n v o Port McNeill (CC) Locus Sb 16 • o CP A • o n o n p o 1 o O o n o , 9 o a o n o o o o o o o o o o o o • o o A d3®0 o V © o A ° n ® A V • o o © o o 250 — i — 500 —I— 750 T T T Genotype Allele H O i O 12 n 13 9 14 © 15 © 16 ® 22 • 23 0 24 S 26 0 33 V 34 $ 35 V 44 A 45 A 46 A 55 + 56 * 66 x 2 • 3 V 4 A 5 + 6 x 1000 1250 1500 1750 2000 North-south distance (m) 2250 2500 2750 3000 3250 (d) 2000 1750 . 1500 H 1250 H | 1000 CD s 750 H 8 500 H ® o o V O P 1 Port McNeill (CC) Locus Sb 17 Oi ^O n o ° _MO 9 O b o 0 YP *, o '9 O O • SO 9 Q v o © 9 O 9 • O 250 -JOO ° O S v © o s o o V o o o 9 o o o © • 9 • - 1 — 250 —I— 500 — I — 750 • 1 o T 1000 —I 1 1250 1500 1750 2000 North-south distance (m) T Genotype Allele n o i O 12 • 13 9 14 © 22 • 23 m 24 H 33 V 34 V 2 • 3 V 4 + 2250 2500 2750 3000 3250 78 4.3.2. Spatial population genetic structure Multilocus estimates of coancestry for all population classes are presented in Figure 4.2 (a - h), in the form of correlograms. The distance classes are given on a natural log scale but easily converted to linear units in metres. Peripheral populations, both continuous and disjunct (Brookings, Seward, and Kodiak) had high coancestry coefficients in first four distance classes. For example, an average coancestry value of 0.197 within 100 m suggest that a high proportion of genotypes at 100 m distance class are more likely to be full-sibs than half sibs or first-cousins. The coefficients were positive and significantly different from zero (alpha = 0.05) for the four shortest distances classes (30 - 50 m, 50 - 100 m, 100 - 300 m, and 300 - 600 m), up to 500 m (In distance 6.10 = 455 m) then became negative and insignificant for the subsequent eight, 300 m classes up to 3300 m (Figure 4.2 a - c). The same pattern was observed in Fort Bragg, a PD population and Qualicum, a continuous, disjunct (CD) population, where coefficients were positive and significantly different from zero (alpha = 0.05) at four distances classes up to 500 m, then became negative and significantly different up to 6100 m (Figure 4.2 d & f). In contrast, coancestry coefficients in core, continuous (CC) populations (Figure 4.2. g & h) (Port McNeill and Prince Rupert), were not significantly different from zero and were generally of much lower magnitude (p,y < 0.05) than in peripheral populations. Average coancestry values <0.05 suggest that genotypes within 50 m of each other are more likely to be distantly related or unrelated. However, the Queen Charlotte Islands (Figure 4.2 e) and Qualicum populations, both continuous, disjunct (CD), had positive, significant correlations similar to the patterns observed in the PC populations. Based on analysis of peripheral populations, both continuous and disjunct, subpopulation differentiation within a population measured by fixation index f was significant and positive, indicating a deficient of heterozygotes within the subpopulations (Table 4.1). Fixation indices among subpopulations within populations did not differ (ANOVAS: P = 0.225; P = 0.858; P = 0.281; P = 0.982, for 79 Brookings, Kodiak, Seward and Fort Bragg, respectively), suggesting no evidence of a spatial Wahlund effect due to the presence of breeding subunits inside the studied populations. Table 4.1. Summary of genetic diversity estimates obtained for subpopulations within each of the four Sitka spruce peripheral populations, both continuous and disjunct. Population/subpopulation number No. individuals Ho HE f Brookings 1 20 0.53 0.54 0.03 2 14 0.50 0.53 0.06 3 29 0.42 0.51 0.19 4 20 0.48 0.53 0.10 5 32 0.48 0.57 0.16 6 30 0.47 0.55 0.15 7 24 0.48 0.55 0.12 8 29 0.44 0.53 0.18 Kodiak 1 40 0.41 0.53 0.23 2 29 0.47 0.48 0.02 3 40 . 0.43 0.53 0.18 4 39 0.46 0.53 0.12 5 35 0.42 0.58 0.28 6 17 0.52 0.51 -0.01 Seward 1 39 0.50 0.49 -0.04 2 39 0.50 0.52 0.05 3 64 0.51 0.57 0.10 4 53 0.51 0.58 0.11 Fort Bragg 1 54 0.54 0.51 -0.05 2 41 0.49 0.57 0.12 3 35 0.53 0.54 0.03 4 29 0.49 0.53 0.08 5 41 0.51 0.55 0.08 H0 = observed heterozygosity; HE = unbiased estimate of expected heterozygosity (Nei, 1978); f = fixation index. ANOVAS for differences among subpopulations within a population: P = 0.225; P = 0.858; P = 0.281; P = 0.982, for Brookings, Kodiak, Seward and Fort Bragg, respectively. 80 (a) Brookings (PC) (b) Seward (PC) 5 6 7 Ln distance (m) 6 7 Ln distance (m) (c) Kodiak (PD) 6 7 Ln distance (m) (d) Fort Bragg (PD) 0.4] '0.3 % 0.2-S 0 .1 - —% £ o-<3 -o i --0.2 J '"~4~" 5 7 8 9 10 Ln distance (m) (e) Queen Charlotte Islands (CD) (f) Qualicum (CD) (g) Prince Rupert (CC) 5 6 Ln distance (m) 7 8 (h) Port McNeill (CC) 0.4 0.3 0.2 -I 0.1 ] 0 -0.1 _| -0.2 -r»-6 7 Ln distance (m) —<— Coancestry —•—• Upper 95% confidence limit —•— Lower 95% confidence limit Figure 4.2 a - h. Spatial correlograms of coancestry coefficients (p,y) for core and continuous (CC), core and disjunct (CD), peripheral and continuous (PC), and peripheral and disjunct (PD) populations of Sitka spruce. Dashed lines represent upper and lower 95% confidence limits for p,y under the null hypothesis that genotypes are randomly distributed. 81 4.4. Discussion Under classical models of isolation by distance, continuous plant populations are likely to develop genetic structuring because gene dispersal through pollen and seed is restricted about the maternal parent (Schaal, 1980; Slatkin, 1985a). If the probabilities of survival and reproduction are independent of mating and seed dispersal patterns, then population genetic structure will be compounded over successive generations (e.g., Turner et al. 1982; Sokal and Wartenberg, 1983). The evolution of structure resulting from this process will thus come to be characterized by significant correlations in the form of positive associations between alleles, both within individuals (inbreeding, F/ s) and among neighboring individuals (population genetic structure, p,,). There was evidence of significant inbreeding (mean F/s = 0.16 in all populations classes except core, continuous (CC) populations (Chapter 3). Regardless of the distribution and scale of seed dispersal, random mating prevents the accumulation of more inbreeding and population genetic structure through time for nuclear genes. Both visual inspection and coancestry estimates revealed striking differences between CC populations and the other population classes in terms of spatial genetic structuring. The distribution of alleles and genotypes within CC populations is almost random with a small amount of genetic patterning among near-neighbours whereas within core, disjunct (CD), peripheral, continuous (PC), and peripheral, disjunct (PD) populations, the distribution of alleles and genotypes is non-random, suggesting limited gene dispersal, resulting in neighborhood structure. Positive and significant correlations indicate that trees with identical or similar genotypes are clustered. A significant autocorrelation at distances up to approximately 500 m, followed by non-significant results at greater distances, indicates that genotypes are clustered at scales up to approximately this distance, but are randomly distributed at greater distances. These data indicate that individuals within up to approximately 500 m of each other in CD, PC, and PD 82 populations are more genetically similar than pairs of genotypes drawn at random from the population. However, in CC populations (Port McNeill and Prince Rupert), individuals within only 50 to 100 m of each other are likely to be genetically unrelated. The core populations are generally older than peripheral populations, probably at equilibrium, and exhibit less micro-spatial structure. The negative but significant coancestry values at distances greater than 1300 m (In 7.2 = 1344 m) in Fort Bragg and Qualicum are perhaps due to the clustered, fragmented (atypical) distribution of Sitka spruce in these marginal populations. The correlograms for CD, PC, and PD populations (Figure 4.2a - f) reflect an atypical pattern of autocorrelation for a plant species. The coancestry coefficients were higher than expected in the first four distance classes (mean = 0.05 at 500 m). Several studies have shown a sharp drop in coancestry values between nearby individuals. For example, mean genetic relatedness in an adult population of mountain hemlock dropped to 0.03 at 5 m distance interval and to -0.0025 at 20 m distance interval (Ally, 2001). Miyamoto et al. (2002) found non-significant coancestry values at distances less than 10 m in four populations of Alnus trabeculosa. Ueno et al. (2000) detected strong genetic structure in Camellia japonica L. within 5 m, but relatedness declined as the distance intervals increased to 10 m. Significant, positive coancestry values for the 50 m distance class may be evidence of clumping of individuals resulting from seed shadows around founding individuals which results in neighborhood structure. On the other hand, it may be evidence of genealogical clusters (Knowles, 1991): distantly related trees (descendants of a common, distant founder) distributed more widely throughout a stand. For example, Kodiak, which has strong evidence of spatial structure, is at the northwestern migration tip of the species' range and is the youngest population, having arrived on Kodiak Island about 400 years ago (Griggs, 1934), and may not have reached equilibrium. The neighborhood structure may be the result of recent demographic events in the newly founded populations. 83 Hewitt (1993) suggested that rapid postglacial range expansion in many species, perhaps, including Sitka spruce, is likely to have involved long-distance dispersants that were able to establish colonies in advance of the main migrational front. These advance colonies, in their turn, may have expanded rapidly and acted as sources for further long-distance dispersal events as well as being recipient populations for long-distance pollen dispersal (e.g., Hewitt, 1996; 1999; Petit et al. 2002). Based on tree macrofossil evidence, average tree migration rates of 0.2-0.4 km / year were obtained for Picea taxa in Canada (Payette et al. 2002). These represent mean annual apparent rates of spread between two widely distant sites (one in a source area south of the late Laurentidian ice border and one at the tree line). Since Sitka spruce trees reach the age of sexual maturity (approximately 25 years old; Morneau and Payette, 1989) before they could disperse seeds, minimal dispersal distances could reach 5-10 km per tree per generation, which is too high for a step-by-step colonization (e.g., Petit et al. 1997; Walter and Epperson, 2001). Sitka spruce seeds could presumably be dispersed by strong winds. Consistent with these ecological data, the genetic patterns documented in this thesis give further support for theoretical simulations (Hewitt, 1996) showing that rare long-distance dispersal events, occurring at frequencies = 10"5 times lower than short-distance dispersal events (e.g., Shigesada et al. 1995), might disproportionately influence many aspects of population dynamics, including rates of geographical spread (Clark et al. 1998) and genetic structure (Hewitt, 1996). Also, a hypothesis involving neutrality may be advanced. It posits that, in addition to the initial inbreeding due to a small number of founders, the density of related individuals increases with limited gene flow within a cluster over time, leading to an increased frequency of crosses between related neighbors. Many theoretical investigations have described such a process of isolation by distance (Wright, 1943; Turner et al. 1982; Slatkin, 1993). The establishment of such structuring is especially easy when gene flow is restricted (Heywood, 1991). In this thesis, there is little evidence for restricted gene flow (Nm for peripheral populations = 3.3 migrants per generation; see Chapter 3). 84 The observed patterns in CC populations (Figure 4.2g - h) are generally consistent with those of species that share the same life history traits as Sitka spruce. Similarly, weak within-population structure has been described for other temperate forest species. For example, Epperson and Allard (1989), presented the results of spatial autocorrelation analysis of genotypes in lodgepole pine by sampling trees in a grid pattern with a separation of 15 m between adjacent trees and found no structure in the distributions of most genotypes. Similarly, Knowles (1991) found no strong evidence of patch structure in black spruce. These studies attributed the lack of weak within-population structure to high levels of gene flow. I also attribute the weak within-population structure in CC populations to high levels of gene flow in Sitka spruce (Nm « 9.04 migrants per generation (see Chapter 3). Thus, this pattern of genetic correlation among near neighbors compared to the population as a whole is observed in species with high outcrossing and associated high pollen flow. This pattern clearly demonstrates the homogenizing effect of migration and illustrates one of the seminal predictions of Wright's FST theoretical model: one or more immigrants per generation (Nm > 1) is sufficient to prevent fixation of alternate alleles in recipient populations (Hartl and Clark, 1997). In addition, mean dispersal distances for Sitka spruce seeds are reported to be 800 m when seed source was on an elevated topography (Harris, 1990). Identifying the causes of fine-scale genetic structuring or lack thereof in populations is often difficult because of the diverse array of influences on this structure and complex variation throughout the species range. Studies by Leonardi and Menozzi (1996) and Parker et al. (2001) have suggested that stand history or homogeneity of seed sources may explain some of the causes of fine-scale genetic structure. For example, adult density influences the distribution of genotypes in young cohorts because the arrangement of adults, along with environmental controls, dictates patterns of pollen flow and seed dispersal (Parker et al. 2001). Young and Merriam (1994) suggest that adults in denser populations have overlapping seed shadows limiting the development of genetic structure. Additionally, non-random associations of alleles within distances up to 500 m could also be attributed to local genetic drift if 85 only a few reproductive dominant trees contributed to the next generation's gene pool. Another possible cause for the weak spatial genetic structure in CC populations of Sitka spruce may be the balance between extensive pollen flow and low seed dispersal, assuming variation for STS markers is selectively neutral. Harris (1990) reported that mean seed dispersal distances of Sitka spruce is approximately 30 m when seed is released from the edge of a clearcut. Low seed dispersal over successive generations will result in clusters of related trees, if they are not eliminated by selection against inbred individuals (inbreeding depression). However, extensive pollen flow will act as a homogenizing factor and diminish the clustering effect generated by low seed dispersal. For example, computer simulations by Berg and Hamrick (1995) predict that limited seed dispersal will result in genetic neighborhoods, but that they become blurred by extensive pollen flow. Meanwhile, the within-population pattern of genetic variation is not independent of previous generations (Hamrick et al. 1993). Instead, the spatial genetic structure of current generations may be influenced by the cumulative effect of the genetic clustering of many preceding generations (Knowles et al. 1992). Knowles et al. (1992) studied two' tamarack {Larix laricina) populations that have markedly different anthropological disturbance histories. One population had regenerated from a site with scattered remnant trees but with no seed source nearby. The second population relied on an off-site seed source and its demographic pattern reflected migration. The authors believed that mixing of seed was extensive since the resulting population appeared to be a random assemblage of genotypes. The former showed spatial autocorrelation, whereas the latter did not. These authors also drew attention to the role that preceding generations could play in the genotypic arrays of the remnant trees. Sometimes it is difficult to determine whether a deficiency of heterozygotes is the result of inbreeding or a Wahlund effect. However, heterozygote frequency at all loci 86 should be affected by inbreeding, whereas only the heterozygote frequency at those loci with allelic frequency variation among subpopulations should be reduced by the Wahlund effect (e.g., Hedrick, 2000). There was no significant allele frequency variation over the subpopulations, suggesting no evidence of Wahlund effect (Table 4.1). Further, when there are multiple alleles, as was the case in this study, all heterozygotes should be reduced by inbreeding whereas some may be decreased and others may remain unaffected or be increased by the Wahlund effect. Patterns of spatial structure in adults represent the culmination of ecological and evolutionary processes in both the past and at present. Moreover, fine-scale genetic structure combined with localized mating and seed dispersal influence intra- and inter-individual genetic correlations in the following generations. To truly understand whether the observed spatial structure in peripheral and disjunct populations is caused by seed (diploid immigrants) or pollen (haploid immigrants) dispersal or patterns of seedling and juvenile survival would require additional study. Due to the predominantly maternal mode of mitochondrial DNA (mtDNA) inheritance in the Pinaceae in general, including in Sitka spruce (Chaisurisri et al. 1994), seed-borne mtDNA markers would be best suited to address the impact of historical factors on observed intraspecific genetic diversity because seeds migrate over shorter distances than pollen. A study that evaluates the mating system parameters and indirect estimation of matings among relatives could explain the probable cause of the spatial structure (e.g., Perry and Bousquet, 2001). In addition, if we incorporate age for each individual, it would be possible to study how spatial genetic structure changes over time. An examination of the spatial structuring of juveniles would, for example, compliment and enhance our understanding of the processes generating fine-scale genetic structure in adults in peripheral and disjunct populations. If restricted seed dispersal is important, then we would expect the estimates of coancestry to be even greater and perhaps over a larger spatial scale in juveniles than in adults. In general, nonrandom patterns of seed dispersal and seedling survival contributing to the development of spatial structure can best be identified by a combination of demographic and population genetic approaches. 8 7 4.5. Conservation genetic implications There were striking differences between core, continuous populations and peripheral populations, both continuous and disjunct. The distribution of alleles and genotypes within core, continuous populations is almost random with a small amount of genetic patterning among near-neighbors whereas within peripheral populations, both continuous and disjunct, the distribution of alleles and genotypes is non-random, suggesting strong spatial structure. Non-significant coancestry values within 50 m of each genotype in core, continuous populations suggest that trees interspaced by at least 50 m of each other are likely to be genetically unrelated. However, in peripheral populations, both continuous and disjunct, genotypes are clustered at scales up to approximately 500 m. The evidence of strong spatial structure indicates that conserving just part of these populations, or ignoring this structure when sampling for ex situ conservation will not be sufficient for maintaining genetic variation. Doing so will risk losing a portion of the current level of genetic variation. Moreover, some of the individuals in the PD populations have rare genetic variants (Chapter 3). Conserving and capture of such individuals via in situ or ex situ conservation is important. Consequently, my data support sampling strategies that consider populations in different geographic and ecological regions and take into account spatial population structure if it exists. 88 CHAPTER 5 Conservation of forest genetic resources as related to conservation population sample size and area sampled:- an empirical approach 5.1. Introduction Plant species, and populations within species, vary greatly in levels and patterns of genetic variation (Namkoong 1984; Hamrick and Godt, 1989; Chapters 3 & 4), which would suggest that they require alternate sampling strategies in order to capture maximum genetic diversity possible. However, the population structure for any target species is largely unknown in advance of sampling. General guidelines for sampling have relied on predictions from theoretical models (e.g., Kimura and Crow, 1964; Gregorius, 1980; Brown and Marshall, 1995), often refined with knowledge of breeding system and population distribution. As noted by Marshall and Brown (1975), the infinite alleles model of Kimura and Crow (1964) has been used extensively for developing sampling strategies. The model assumes that all alleles are selectively neutral, each allele arises as a unique mutation and alleles are ultimately lost through sampling effects due to finite population sizes (Hartl and Clark, 1997). Although this model supports sampling strategies that are widely applicable, it does not take into account specific landscape dynamics of target populations (e.g., core versus peripheral), their proximity to neighboring populations and resulting levels of gene flow. From the standpoint of sampling genetic resources, the basic parameter for each population is allelic richness (number of distinct alleles at a single locus) and the number of loci. A diverse array of different alleles may be needed to produce phenotypes adapted to future environmental changes, both physical and biotic (e.g., pathogen pressures). This parameter is essential for our purposes because later 89 users of genetic resources or natural selection can adjust the frequencies of specific desired alleles (Brown and Marshall, 1995). Breeders might be able to use one or a few copies of an allele for disease resistance, irrespective of its frequency in the original population or in an ex situ sample. However, genetic resource managers need to consider how single-gene and multiple gene systems should be structured in a hierarchy of populations, to cover both qualitative and quantitative genetic variations needed currently or in the future (Yanchuk, 2001). Initial sampling to establish conservation or breeding populations should provide the allele in sufficient numbers to guard against its later losses, and provide it in a variety of genetic backgrounds (Brown and Marshall, 1995). Work by Yanchuk (2001) estimates numbers of trees in an in situ reserve that would be required to capture more than a single copy of an allele of interest. For example, to capture 20 copies of a recessive allele at frequency 0.032 with 95% probability, a population would need to contain 27,875 trees of the target species (Yanchuk, 2001). Such numbers are only likely to be achieved in large in situ conservation reserves. More heterozygous individuals are less likely to suffer from inbreeding depression, thus maintenance of observed heterozygosity may promote fitness in conservation populations. Expected heterozygosity is commonly used as a measure of genetic diversity based on allele frequencies and assuming random mating. It is influenced primarily by alleles of intermediate frequency and is relatively insensitive to changes in frequencies of rare or very common alleles. Low-frequency alleles are more likely to be lost from populations via genetic drift, or from a species as populations decline or are extirpated, but expected (or observed) heterozygosity may not reflect these losses. Thus, heterozygosity, although an important measure of overall genetic diversity, is considered more easily managed or maintained than allelic richness. Many different genotypes, i.e., many different combinations of alleles at all the loci constituting the genome, are needed to provide a range of phenotypes. The primary objective of sampling plant gene pools for ex situ conservation is to capture the maximum amount of useful variation while keeping the number of 90 populations and samples per populations within practical limits (Brown and Hardner, 2000). The challenge is to know whether donor populations, and samples from them, contain a representative sample of the species standing genetic diversity. Several authors have suggested that sampling at least 60 to 100 trees per population will allow alleles occurring with a frequency of 0.05 to be captured 95% of the time (e.g., Wheeler and Guries, 1982; Muller-Starck, 1995). Lawrence et al. (1995a) suggest that a sample of about 172 plants, drawn at random from a population of a target species, is of sufficient size to conserve at a very high probability, all or very nearly all of the polymorphic alleles that are segregating in the population, provided that their frequency is not less than 0.05. This suggestion is potentially misleading. It assumes that all populations are equivalent in both the kinds and the levels of genetic variation they contain. The assumption is at odds with the overwhelming bulk of empirical evidence from plant population genetics that suggest populations within species vary greatly in levels and patterns of genetic variation (e.g., Hamrick and Godt, 1989; Briggs and Walters, 1997; see Table 2.1 in Chapter two). In terms of number of seeds to be collected from a single tree in outcrossing species, Lawrence et al. (1995b) recommend not more than eight seeds be collected from each plant. Their analysis tacitly assumes, however, that pollen alighting on the stigma of a plant is randomly drawn sample from the pollen cloud of the population. Though this assumption is theoretically convenient, it is unlikely to hold in practice, because the pollen arriving on a stigma is unlikely to have originated from more than a small number of plants (e.g., Ellstrand, 1984; Ellstrand and Marshall, 1986; Smouse et al. 2001). Contributions to the substantial body of literature concerning theoretical and applied aspects of sampling plant populations to maximize genetic diversity in ex situ collections (e.g., Marshall and Brown, 1975; Frankel and Soule, 1981; Namkoong, 1988) emphasize the trade-offs between obtaining adequate diversity (e.g., capturing low frequency alleles and diversity distributed among populations) and making efficient use of institutional resources. I am aware, however, of no published 91 reports that have used large empirical data sets to design optimal sampling strategies for capture of diversity and conservation of rare alleles. Cognizant of the above facts, I use genetic diversity parameters and fine-scale spatial genetic structure derived from a range-wide analysis of genetic variation within and among large samples from eight populations of Sitka spruce (Chapters 3 & 4) to develop sampling strategies for capture of diversity and conservation of rare alleles for ex situ conservation. My goal is to design a seed collection strategy for establishment of an ex situ collection of seeds of widespread species using Sitka spruce as a model that would enable us to capture at least 95% of the current genetic diversity (both allelic richness and expected heterozygosity) of a species in conservation populations. This case study provides a model for tree breeders, managers and conservation geneticists concerned with implementing both ex situ and in situ programs that take into account current theoretical concepts as well as practical considerations. The specific objectives were to: (1) use empirical data to examine the relationships between population sample size and genetic diversity parameters, and (2) investigate the effect of varying area sampled given a fixed population sample size on capture of genetic diversity in core versus peripheral populations. I then combine all this information to suggest appropriate sampling strategies for ex situ collections of widespread species with similar geographical and ecological distributions, and partitioning of genetic diversity to Sitka spruce. I also discuss the implications of my results for the design of in situ reserves in terms of area and numbers of individuals sampled. 5.2. Materials and Methods The empirical dataset used as a basis for this study comprise genotypes for 200 individuals in each of the eight populations (see Table 3.1 in Chapter 3). Derived genetic parameters from this dataset were allelic richness {AR), expected 92 heterozygosity {HE), degree of population differentiation {GST), inbreeding coefficient (Fis), gene flow (Nm) which measures the number of migrants per generation using the rare allele method of Slatkin (1985b), percentage of alleles in different classes (common, widespread; CW, common, localized; CL, rare, widespread; RW and rare, localized, RL) according to Marshall and Brown (1975), and coancestry (p,y) describing spatial patterns of population genetic structure. 5.2.1. Relationships between population sample size and genetic diversity parameters I examined the relationship between population genetic parameters allelic richness (AR), allelic richness for common alleles (ARC), expected heterozygosity (HE), expected heterozygosity for common alleles (HEC), and observed heterozygosity (Ho), as dependent variables and population sample size as an independent variable based on least-squares regression using the REG procedure of SAS version 8.1 (SAS Institute Inc, 1999). Random sets of population samples of 25, 50, 100, 150, and 200 individuals were sampled without replacement 50 times from each of the eight populations separately, using a computerized algorithm written in SAS version 8.1 (SAS Institute Inc, 1999). Mean values of Nei's (1978)'s unbiased genetic diversity estimates (AR, ARC, HE, HEC, & H0) for the 50 replicates for each population and each preset level of population sample size were calculated, then averaged across for each population separately and plotted against population sample sizes to observe trends. R2 values were estimated for each population and each preset level of population sample size separately, then averaged across populations. 5.2.2. Capture of diversity at varying area sampled and population sample sizes I used data from two populations that were defined as core (Port McNeill & Prince Rupert) and two populations that were defined as peripheral (Kodiak & Fort Bragg) 93 (see Table 2.1 in Chapter 3). I selected fixed population samples of 20, 60, 120, 150, and 180 as target numbers of individuals to be drawn from each area sampled. Genotypes were then sampled randomly from areas sampled of 64, 100, 144, 225, 324, 400 and 484 ha in each of the core and peripheral populations, using a computerized algorithm written in SAS version 8.1 (SAS Institute Inc, 1999). This procedure was repeated 50 times for each population sample / area sampled combination. However, smaller plots (64, & 100 ha) did not have enough trees for the larger population samples of 120 to 180 individuals. Genetic diversity for each combination of population sample size and area sampled was assessed using Nei's (1978) unbiased estimate of expected heterozygosity (HE) and mean number of alleles per locus (AR). Modeling curvature effects can be very important when the objective is to identify the combination of levels of quantitative factors that leads to an optimum response (Rawlings et al. 1998). In this case, I am interested in the optimum combination of population sample size and area sampled that captures at least 95% of allelic richness and expected heterozygosity in core versus peripheral populations. The response surface of interest can be reasonably approximated by a higher-order polynomial response function and the experimental region is defined by the upper and lower limits of the factor levels (i.e., minimum and maximum allelic richness and unbiased expected heterozygosity). The resulting data (50 data points for each population sample / area sampled combination for each population) were then subjected to the procedure PROC RSREG in SAS (SAS Institute Inc, 1999) by fitting a second-order response surface model with population sample size and area sampled as the explanatory independent variables and allelic richness and expected heterozygosity as dependent variables. The lack of fit test suggested that a second-order polynomial was the best fit for both dependent variables. The second-order response surface model was as follows: 94 E(Y) = 3o + P1X1 + B 2X 2 + PnX 2 ! + (3 2 2 X 2 2 + P i 2 XiX 2 + £|. where E(Y) is the expectation of the dependent variable (allelic richness or expected heterozygosity), Bo is the intercept, B-i, B2, are the linear main effects coefficients, B-n, B 2 2 are the quadratic main effect coefficients, and the coefficient B-|2 is the interaction effect coefficient. Xi and X 2 represent independent variables for population sample size and area sampled, respectively. The usual assumptions are made on the random errors, that is, e's are assumed to be independent N (0, a2) random variables. The derived functions were then used to plot the bivariate response surfaces in order to observe trends. Characteristics of the B matrix called eigenvalues derived from the procedure PROC RSREG in SAS (SAS Institute Inc, 1999) were used to determine the points where Xi and X 2 (population sample and area sampled, respectively) are maximum (a negative eigenvalue indicates that the point is a maximum). 95 5.3. Results 5.3.1. Observed relationships between population sample sizes and genetic diversity estimates Mean allelic richness (AR) across all populations was positively correlated with population sample size up to 200 individuals (mean R 2 = 0.49; p = 0.005) (Figure 5.1a). However, the non-linear curve reaches a maximum level at 150 trees, suggesting that any additional increase in population sample size above 150 is unlikely to result in a significant increase in A R . There was no significant relationship between allelic richness for common alleles (ARC) and population sample size when rare alleles were removed from the analysis (mean R 2 = 0.09; p = 0.62) (Figure 5.1b). Expected heterozygosity (HE) had a significant relationship with population sample size (mean R2 = 0.43; p O.008) (Figure 5.2a). However, when rare alleles were removed from the analysis, there was no significant relationship between expected heterozygosity for common alleles (HEC) and population sample size (R2 = 0.13; p = 0.09) (Figure 5.3a). There was no relationship between observed heterozygosity and population sample size (mean R 2 = 0.02; p = 0.905) (Figure 5.2b). 96 Figure 5.1a & b. Relationship between allelic richness and population sample size for: (a) overall allelic richness ( A R ) , and (b) allelic richness for common alleles (p > 0.05) (ARC). Each data point represents the mean simulation from 50 replicates for each population per level of population sample size. Some data points are masked by others. 97 0.60 0:58 -CO o IM 2 O.GG a>. .*—• a>. • C . 0:54 "o a> o . X 0:52 0.50 -0.48 (a) = 0.43; p = 0.008 20 40 60 80' 100 -120 -1'40 160 180 '200^ 220' Population sample size (b) in-rsl o QJ o> CO; o 0.58 -, 0:56 0.54 s0.52 0.50 0.48 0.46 0.44 •0.42 = 0.02; p = 0.905 20 40 60 80 100 120 140 160 180 200 220 Population sample size Figure 5.2a & b. Relationship between genetic parameters and population sample size: (a) expected heterozygosity (HE) and population sample; (b) observed heterozygosity (H0) and population sample. Each data point represents the mean simulation from 50 replicates for each population per level of population sample size. Some data points are masked by others. 98 0.57 -, 0 56 0.55 0.54 o t i l l 0.53 0 52 0.51 0.50 0:49 R = 0 . 1 3 ; p = 0 . 0 9 20 40 60^ 80 100 120 140 160 180 200 220 Population sample size Figure 5.3. Relationship between expected heterozygosity for common alleles (HEc) and population sample size. Each data point represents the mean simulation from 50 replicates for each population per level of population sample size. Some data points are masked by others. The misalignment for population sample size greater than 150 is due to the fact that individual genotypes carrying rare alleles were not included in the data, hence sample sizes were different for each population. 99 5.3.2. Observed trends in capture of diversity at varying spatial scales The bivariate response surfaces relating allelic richness (AR) to population sample size and area sampled indicate a significant increase in allelic richness (AR) as population sample increases (p < 0.05; mean R2 = 0.73) and also as area sampled increases (p < 0.05; mean R2 = 0.37) in core populations (Port McNeill & Prince Rupert) (Figures 5.4a & b). For example, in Port McNeill, population sample size of 60 trees in a fixed area sampled of 144 ha will capture 60% of allelic richness. However, an increase in population sample size to 150 trees in a fixed area sampled of 144 ha will capture 95% of allelic richness. In these two core populations, the response surface curves reaches a maximum for A R at 150 trees in a fixed area sampled of 144 ha, suggesting that any further increase in population sample size and area sampled would not result in a significant increase in allelic richness. All values are based on the assumption that trees sampled are at least 30 to 50 m apart. The bivariate response surfaces relating allelic richness (AR) to population sample size and area sampled in peripheral populations (Kodiak & Fort Bragg) (Figures 5.4c & d) indicate a significant increase in allelic richness (AR) as population sample size increases (p < 0.0001; mean R 2 = 0.78) and also as the area sampled increases (p < 0.0001; mean R2 = 0.47). For example, in Kodiak, a population sample size of 120 trees in a fixed area of 324 ha will capture 60% of allelic richness. Increasing population sample size to 150 trees in 324 ha will capture 87% of allelic richness. In both of the peripheral populations, the response surface curve reached a maximum for AR level at 180 trees in a fixed area sampled of 324 ha and captures at least 95% of allelic richness. The bivariate response surfaces relating expected heterozygosity to population sample size in a core populations (Port McNeill & Prince Rupert) are depicted in Figures 5.5a & b. The response surface curves revealed a weak relationship of 100 expected heterozygosity (HE) to population sample size and area sampled in core populations. For both populations, the model including population sample size and area sampled had a mean R 2 of 0.39. For a fixed population sample size of 100 trees in a fixed sample area size of 225 ha, at least 95% of HE will be captured. An increase in area sampled to 324 ha with a fixed population sample size of 100 trees will capture the same level of HE as in an area sampled of 225 ha. Similarly, an increase in population sample size to 150 trees in an area sampled of 225 ha will capture the same level of HE as 100 trees in 225 ha. In a peripheral populations (Kodiak and Fort Bragg) (Figures 5.5c & d), the effect of area sampled on HE was significant and nonlinear (p < 0.05; R 2 = 0.59). Although population sample size effect on HE was also significant, the relationship is weaker (p < 0.05; R 2 = 0.28). For example, in Kodiak, a population sample of 100 trees from an area of 225 ha will capture just 85% of HE, while the same population sample size across 324 ha will capture at least 95% of HE. An increase in area sampled to 400 ha and reducing population sample size to 80 trees will also capture at least 95% of HE- The response surface curve reaches a maximum level at a population sample size of 100 trees in an area sampled of 324 ha, suggesting that any further increase in population sample size would not increase HE. 1 0 1 (a) Port McNeill Population sample size (c) Kodiak A t e a i m p l e d ( h a ) Population sample size (b) Prince Rupert Population sample size (d) Fort Bragg A r e a i m p l e d ( h a ) Population sample size • 3.4-36 • 3-3.2 D2.6-28 112.2-24 • 3.2-34 • 2.8-3 D2.4-26 ® 2,0-22 Figure 5.4a - d. Bivariate response surfaces relating allelic richness (AR) to population sample size and area sampled in core (a & b) and peripheral (c & d) populations, respectively. 102 (a) Port McNeill Qb) Prince Rupert Population sample size Population sample size BO,57 -0 .99 •0 .53 -0 ,95 •0 .49 -0 ,51 0 0 , 4 5 - 0 , 4 7 • 0.55 -0.57 •0 .51 -0 .53 10 ,47 -0 ,49 Figure 5.5a - d. Bivariate response surfaces relating expected heterozygosity (HE) to population sample size and area sampled in core (a & b) and peripheral (c & d) populations, respectively. 103 5.4. Discussion 5.4.1. Relationships between population sample size and genetic parameters It is important to conserve both allelic diversity and expected heterozygosity. In general, initial allelic composition determines the limit of response to selection over many generations, whereas immediate selection response is related to expected heterozygosity (Petit et al. 1998). Allelic richness has been advocated by some as a more appropriate measure of genetic diversity than heterozygosity because it is more sensitive to population size and number, and thus will be affected first as populations decline in size or are extirpated (Allendorf, 1986). It is also a measure of future evolutionary potential. Sitka spruce exhibits moderate levels of allelic richness that are positively correlated with population sample size. Examination of the relationship between population sample and allelic richness across all populations shows that, for population samples of 100 or fewer trees, allelic richness is low (Figure 5.1a). However, with population samples greater than 100 individuals, allelic richness is generally high and less variable among populations. The lack of relationship between population sample size and expected heterozygosity after removal of rare alleles is not surprising as overall genetic diversity (as measured by heterozygosity) is little influenced by low frequency alleles (Figure 5.1b). In addition, heterozygosity is not expected to decline substantially until population bottlenecks have persisted for several to many generations (Nei ef al. 1975; Petit et al. 1997). Expected heterozygosity under random mating declines by a factor of [1 - (2/V)"1] per generation. Therefore decline can occur in one generation but will not be substantial unless the bottleneck is severe (e.g., Ne = 2). However, in some cases, population size may have an effect on heterozygosity if there have been shifts toward selfing or bi-parental inbreeding in small populations 104 due, for example, to changes in pollinator service or behavior (Young et al. 1999). Such effects will have little impact on genetic diversity and often show up as increased fixation indices (F/s). In the current study, when population sample size is reduced to 25 individuals, observed heterozygosity (Figure 5.2b) is not affected by the low number of individuals. Frankel and Soule (1981) predicted that, while a transient reduction in sample size could result in substantial loss of alleles, loss of heterozygosity would be significant only when sample sizes are reduced to very small numbers (<10). For example, van Treuren et al. (1991) observed loss of allelic richness but not of expected heterozygosity in small populations of the rare European herbs Salvia pratensis and Scabiosa columbaria, thus providing empirical evidence for this prediction. Heterozygosity is mostly affected by mid-frequency alleles (Taggart et al. 1990), whereas it is rare alleles that small populations are likely to lose. The differences among populations in the number of observed alleles in Sitka spruce were caused by alleles in the < 5% frequency class; peripheral, disjunct populations of Sitka spruce did not differ from the core, continuous populations in the number of alleles of £ 5% frequency (Chapter 3). However, in extreme cases, rare alleles may contribute significantly to expected heterozygosity (Figure 5.2a). Rare alleles as a group can be major contributors to excess heterozygosity (Strauss and Libby, 1987). For example, when a locus would otherwise be invariant (HE - 0), a relatively low frequency allele of, for example, 8% frequency effectively increases expected heterozygosity to 0.15 for that locus. 5.4.2. Observed trends in capture of diversity at varying area sampled and population sample sizes Two key components of a sampling strategy are area sampled, and number of individuals sampled. Our knowledge of genetic patterns for different combinations of 105 these two factors can contribute to these components, although, clearly, other biological and practical factors (e.g., breeding system, life history traits and historical NE) will need to be considered in any general sampling strategy. These data suggest that larger population sample sizes and area sampled are needed to capture comparable amounts of AR in peripheral populations compared to core populations. To capture significant amounts of HE, area sampled is more important than population sample size in peripheral but not core populations. Larger areas are needed in peripheral populations than in core populations because of spatial structure in the former. Sampling over larger areas in peripheral populations is likely to ensure break-up of the neighborhood structure. If the ex situ conservation target is to capture at least 95% of AR and HE in core populations, I recommend at least 150 trees, interspaced at least 30 to 50 m apart, in a sample area size of ranging from 225 to 324 ha. The target number of 150 trees is above what is needed to ensure capture of 95% of HE but appropriate for AR. However, for peripheral populations, I suggest sampling at least 180 trees, distributed over a larger area sampled of approximately 324 to 400 ha. This will ensure that targets of 95% capture of HE and AR are met. These data indicate that area sampled is more important than population sample size in peripheral populations for capture of optimum expected heterozygosity. Although previous work (e.g., Marshall and Brown, 1975; Muller-Starck, 1995 and Lawrence et al. 1995a) suggest numbers ranging from 60 to 172 unrelated individuals, which are close to my recommendations, this work addresses the issue of area sampled, which is often neglected when designing sampling strategies for ex situ collections. I have also quantified significant increases in capture of genetic diversity with increases in population sample size and area sampled. This has direct implications when designing in situ reserves for capture of genetic diversity, and is particularly important for designing sampling strategies for peripheral populations. 106 Sampling germplasm for ex situ collections usually involves collecting seeds. However, in this study, the recommendations are based on genotyped mature trees rather than seed collection. This has an effect on the number of copies of rare alleles. For example, if a rare allele is carried by a maternal parent, then there is an increased probability of capturing adequate copies of a rare allele but it is probably more likely to capture a single copy of a very rare allele from the pollen pool in male contribution. When seed is collected, sampling is for the next generation. With self-fertilization, seeds will closely resemble the parent plant. When individuals mate at random, the offspring raised from seed taken from a single individual are no longer expected to be genetically identical either among themselves or to their maternal parent, for they will segregate not only for the loci for which their maternal parent is heterozygous, but also for those which their paternal or parents are heterozygous. For outcrossing species such as conifers, seeds from several cones from different parts of the crown should be gathered from each individual sampled, if possible, rather than from a single cone, to possibly increase the diversity of alleles through increasing the potential number of pollen parents in the sample as well as promoting adequate amounts of seed for storage. Similar numbers of seeds should be collected from each individual sampled. Lawrence et al. (1995b) suggest no more than eight seeds per plant, even when each has resulted from pollination by a different male parent. In practice, it will of course be necessary to take more than eight seeds from each tree sampled in order to provide enough material for distribution of seed. The actual numbers will depend on the likely demand for material for establishing ex situ conservation stands or for storing in seed banks. Assuming that seeds will have a variety of paternities from a single mother tree, my estimates of number of trees that should be sampled to represent a population sample is an overestimate. Even if I assume at least 20% of the trees are selfed, the numbers of genotypes recommended remain an overestimate. From this viewpoint, it is not possible to recommend a specific proportion of the genetic 107 variation in a population, or in a species as a whole, that must be represented in the ex situ population. Rather than a fixed fraction, the approach I advocate is to have these numbers as basic targets, and exceed these numbers prudently for insurance when resources and material permit and likely needs indicate. I also suppose the optimal numbers for any species of interest will have to be based on knowledge of the biology and life history traits of the target species. These recommendations should be applicable to most widespread species that share similar life history traits as Sitka spruce. 5.5. Implications of results for design of ex situ collections and in situ reserves Ex situ and in situ conservation gene conservation methods are complimentary approaches in that they both maintain, and in some instances, create genetic diversity. For ex situ collections in core and peripheral populations, the results suggest sampling over larger areas for peripheral populations (approximately 400 ha) than core populations (approximately 324 ha). If there is evidence of spatial genetic structure in peripheral populations, both continuous and disjunct, an appropriate strategy for ex situ collections would be to sample three areas separated by 500 m (each with a subpopulation sample of 60 trees) within 400 ha to allow break-up of the neighborhood structure. Therefore, more intensive sampling should be conducted in peripheral than core populations. This will still ensure capture of 95% of both AR & HE and also avoid sampling similar genotypes. Is there a future for small, disjunct, and peripheral populations, or should we conserve only larger populations within core distribution areas to optimize costs and benefits? Based on these data, I recommend ex situ and in situ conservation for disjunct and peripheral populations. These are the populations likely to harbour rare, localized alleles (Chapter 3). If resources are a limiting factor, I suggest more resources be spent in conserving peripheral populations in situ. Often peripheral populations are located in different geopolitical units, e.g., Fort Bragg and Kodiak are 108 in different states in the USA and yet Sitka spruce is of economic importance in British Columbia, Canada. In situ protection is suggested so as to preserve the evolutionary potential of these small, disjunct, and peripheral populations. 109 CHAPTER 6 CONCLUSIONS 6.1. Introduction The primary objective of this thesis was to investigate genetic diversity, population structure and evolutionary history of Sitka spruce as a model species in order to develop appropriate sampling strategies for capture of allelic diversity for ex situ, and to a lesser extent, in situ gene conservation for widespread temperate trees. To provide benchmarks and assist development of sampling strategies, I used sequence-tagged-site (STS) markers, which largely reveal intron length polymorphisms, to assess genetic diversity and population structure of core-continuous, core-disjunct, peripheral-continuous, and peripheral-disjunct populations sampled across the entire range of Sitka spruce. I then used the genetic parameters to model sampling strategies for capture of allelic diversity for ex situ conservation. This chapter summarizes the major results of this study and discusses their implications for forest gene resource management. 6.2. Major findings Generally, whether populations were core or peripheral, continuous or disjunct, they all exhibited similar levels of genetic diversity as measured by expected heterozygosity. However, there are pronounced and unexpected differences in genetic structure, rather than levels of genetic diversity, between populations classified as either core or peripheral based on ecological conditions, and continuous or disjunct based on geographic distribution. An investigation of the observed differences in population structure revealed an aggregation of similar multi-locus genotypes, with structured isolation by distance within peripheral populations, 110 both continuous and disjunct. Such structure was noticeably lacking in core, continuous populations. My observation of levels of genetic diversity in all populations may be explained by the evolutionary history of the species since the last glaciation. One hypothesis to explain the similar levels of genetic diversity among widely separated populations may be a steady post-glacial migration of the species. Kodiak is a peripheral, disjunct population at the northern tip of species' range and is likely the youngest population, yet has the same level of genetic diversity as much older core, continuous populations. An alternative hypothesis may be advanced, suggesting that, in addition to some initial inbreeding due to founder events, the density of related individuals increased with reduced gene flow within spatial clusters over time, leading to an increased frequency of crosses between related neighbors. This would explain the spatial structure observed in peripheral populations. Alleles, whether common or rare, tended to be distributed throughout the range of the species. The only rare and localized allele was detected in disjunct populations only, both core and peripheral and represented an average of two percent of all alleles. Polymorphic marker loci that reveal at least three alleles per locus are more useful for quantifying geographic distributions of rare alleles and inferring evolutionary dynamics than less polymorphic loci. For capture of genetic diversity (allelic richness and expected heterozygosity) in core populations, a conservation population sample of 150 trees, interspaced at least 30 to 50 m apart, sampled from at least 225 ha to 324 ha would be adequate. However, an increase in conservation population sample to 180 trees and larger area sampled (at least 400 ha) are necessary for ex situ collections in peripheral populations. Sampling more than two populations in each class could have possibly revealed geographic differentiation among population classes and provided a better 111 understanding of the history of Sitka spruce and its post-glacial colonization routes. This would have reinforced the conclusions in Chapter 3. For example, including populations from coastal Washington state would have allowed comparison of post glaciation processes with the extensive range-wide study of western red cedar (O'Connell, 2003). However, sampling more populations would have been at the cost of sampling fewer individuals per population class, thus limiting the power of analyses on spatial population structure in Chapter 4 and in Chapter 5 on the efficiency of sampling strategies for gene conservation. 6.3. Recommendations The impact of evolutionary history on patterns of intraspecific variation has clear implications for the development of sampling and conservation strategies. The recommendations provided in this thesis are for sampling of neutral genetic diversity for ex situ conservation programs. First, a sampling strategy based on GST estimates would entail collecting a large number of individuals from a few populations. However, this strategy is likely to miss rare, localized alleles. The number of populations sampled and the number of individuals constituting conservation populations must be large enough to include most of the genetic variation that exists both within and among populations. To capture localized alleles (both common and rare), sampling should cover more populations over the full range of environments at a cost of fewer individuals per population. Ex situ sampling in different populations increases the probability of capturing different classes of alleles. I therefore recommend sampling across the diverse range of the species distribution, paying particular attention to peripheral populations, both continuous and disjunct, if the major objective is to capture rare alleles for ex situ conservation. In particular, the discovery of rare alleles in peripheral, disjunct populations suggests the importance of conservation and sampling these populations. These populations are often located in political jurisdictions outside in areas where the species is of substantial economic importance or the subject of intensive research (e.g., Pinus radiata is native to California, USA and yet it is of great economic importance in 112 Australia, Chile, New Zealand and South Africa), which suggests the need for ex situ seed collections in these populations. Second, evidence of spatial structure in peripheral populations suggests the need for more intensive sampling over larger areas in peripheral than in core populations in order to break-up the neighborhood structure in the former. The sampling of 150 individuals within a breeding zone or conservation unit of approximately 225 to 324 ha would provide a representative sample that would likely harbour both common alleles and latent genetic potential in core populations. However, for peripheral populations, at least 180 trees in a conservation unit of approximately 324 to 400 ha would be adequate for ex situ collections of seed. Molecular approaches continue to be of value to conservation efforts by providing a tool for measuring and managing genetic diversity and for investigating historical and current evolutionary processes that influence it. However, caution is always required when using molecular evidence as a basis for proposing conservation action, as ideally decisions should be based on knowledge of patterns of adaptive variation (quantitative traits) within species. Because ESTPs were located in transcribed but untranslated DNA, genetic parameters derived from these markers should be representative of neutral or nearly neutral markers, which could be used as a yardstick for detecting selection in STS markers. Of course, one of the challenges is to know which characters to measure. Another challenge is which of these characters are most likely to be confronted with future selective challenges. I believe that traits currently most critical to survival and reproduction should be a priority. Choice of genetic markers with different rates of evolution may help to explain the relative roles played by historical and contemporary influences in shaping the observed spatial structuring in peripheral populations, both continuous and disjunct. Although knowledge of population structure and dynamics is being facilitated more efficiently by the use of molecular markers, resource limitations will continue to 113 dictate a need to extrapolate appropriate strategies for the majority of taxa from the results of studies of model species such as the current one. Resources should be used to conserve and protect peripheral populations, which should have the highest priority. This thesis has been carried out to provide current knowledge of the genetic diversity and structure of Sitka spruce, investigated its genetic status in relation to historical and current influences, and provided recommendations for genetic conservation in terms of sampling strategies for ex situ collections of seeds for widespread species. Emphasis has been placed on conserving the native populations, both ex situ and in situ conservation and their essential roles in conserving genetic resources. It is expected that this work will assist those in regulatory, management, education, advocacy, and research communities to make decisions that are better informed by science and more likely to contribute to conservation of biodiversity. 6.4. Future research Better definition of metapopulation structure and dynamics, and the long-term consequences of fragmentation in peripheral populations, are two issues that deserve immediate research consideration. I anticipate that a broader range of tree species will be investigated in the future, including those taxa under more immediate threat of extinction than the more widespread taxa that have received more study to date. Given the very large number of tree species that warrant investigation, the immediate challenge is to prioritize and focus future research efforts, and to relate research closely to the needs of conservation practitioners. In the future, I look forward to the increased integration of demographic, ecological and genetic studies to address issues relating to conservation. This suggests that focus should be on ecosystem integrity. For example, it may be worth considering population density as an effective gauge of genetic variation, if one assumes that 114 populations are at equilibrium, so that population density reflects effective population size. Generally, density reflects ecological population size and is much easier to estimate in the field than effective population size. In addition, large populations that can maintain themselves ecologically are not as vulnerable to loss of genetic diversity as are small populations (e.g., Lande, 1988). This would suggest dense populations may contain more genetic diversity than sparse populations. Population density may also be an indicator of habitat quality. In such a case, natural selection may differentially influence the genotypic composition of populations with different densities. For example, if population density varies among microhabitats, selection may favor different genotypes in these different microhabitats, producing an association between genotypic composition and population density. In most cases, denser populations are likely to receive less gene flow, on average, than less dense populations, thus are likely to be better adapted (e.g., Kirkpatrick and Barton, 1997). If genotypes become variable among populations, a critical component of preserving maximal genetic variation of a species will be sampling populations that harbor different genotypes. So if genetic diversity is associated with population density, then population density may be a reliable ecological indicator of population genetic diversity. This can be tested by canonical correlation with genetic diversity as dependent variable and population size as independent variable. In the face of global climate change and concerns about future adaptations, I also look forward to studies that investigate the dynamics and genetic basis of adaptation (e.g., aspects of QTL mapping and candidate gene discovery of adaptive traits) and integrate genetic and phenotypic analyses, as well as improve the temporal and spatial resolution of evolutionary dynamics. 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Adaptation may be achieved by phenotypic tuning to prevailing environmental conditions, or through evolutionary changes of genetic structure at the population level. adaptedness The state of being adapted that allows a population to survive, reproduce and permanently exist in certain conditions of the environment. allele An alternate form of a gene (i.e., different forms of genes which may occupy the same position on a chromosome. allele frequency The frequency of the occurrence of alternative forms of alleles in relation to the frequency of all alleles at a particular locus in a given population biodiversity Can be understood as an assemblage of several hierarchical components: we can count number of ecosystems, ecological communities, species, populations, or genes in any defined area. Although conservation and classification of biodiversity is undertaken on all five levels, a sensible first step is to define the smallest fundamental units of its calculus. It could be argued that individual genes (sequences of DNA on chromosomes that code for a specific function) are the fundamental currency of biological diversity. After all, species are ultimately defined by differences in their genes. bottleneck A severe reduction in population size that causes the loss of genetic variation. The role of random genetic drift is increased, whereas the power of selection is reduced, by bottlenecks. breeding (forest tree breeding) The application of genetic principles and practices to the development of individual trees, varieties or populations more suited for the human needs collecting The activity of gathering or acquiring genetic materials (seeds, pollen, parts of plants) for addition to a gene conservation unit. 1 cryopreservation distribution area ecosystem ecotype effective population size evolutionary adaptability fitness genebank gene conservation stand or population in situ gene conservation stand or population ex situ The preservation or storage of seeds and tissues at very low temperatures, usually in liquid nitrogen. The geographical occurrence and arrangement of a species, or a population; usually refers to the natural extension of the area occupied by a species. The ecological complex of, e.g., a forest community, including the non living components of the environment and functioning together as a stable system in which exchange of nutrients follows a circular path The product of genetic adaptation within a species, to a particular habitat or environment, as a result of natural selection (also local race) In a broad sense, the number of individuals in a population successfully involved in reproduction in a given generation The potential or ability of a population to adapt to changes in the environmental conditions through changes of its genetic structure The average reproductive success of a genotype in a particular environment. Often expressed relative to another genotype such as the ancestor in evolution experiments. Facility where genetic resources are stored in the form of seeds, pollen or tissue culture Forest stand in which appropriate management is carried out to ensure the conservation of genetic resources of target species Population established with the specific objective of genetic conservation using basic material collected by random sampling in the target gene conservation unit gene flow The exchange of genetic material between populations due to the dispersal of gametes (through pollen) and zygotes (through seeds) 144 gene pool The sum of all genetic information encoded in genes and their alternative forms (alleles) present in a population at a given time. in situ Conservation of genetic resources 'on site' in the natural and original population, on the site formerly occupied by that population, or on the site where genetic resources of a particular population developed their distinctive properties. Although usually applied to stands regenerated naturally, the in situ conservation may include artificial regeneration whenever planting or sowing is done without conscious selection and in the same area where the reproductive material was collected. epistasis Any non-additive interaction between two or more mutations at different loci, such that their combined effect on a phenotype deviates from the sum of their individuals effects ex situ genetic diversity genetic load genetic resources genetic variability genotype grafting heterozygous Conservation of genetic resources that entails removal of individuals or reproductive material from its site of natural (original) occurrence, i.e., conservation 'off-site'. The measure of genetic variation present in a population as a consequence of its evolution The loss of fitness that is caused by producing offspring that carry deleterious mutations, and the resulting decrease in the rate of population growth. The biological material containing useful genetic information of actual or potential value. The ability of a population to produce individuals carrying different genetic variants (alleles, genes or genotypes); the capacity of a population to generate genetic variation. Genetic constitution of an individual tree possessing a particular set of alleles The joining together of parts of plants in such a way that they will unite and continue their growth as one plant. The condition of having unlike alleles at corresponding loci (as opposed to homozygous - having identical alleles). An individual organism may be heterozygous or homozygous for 145 one locus, more than one or all loci. hitchhicking hybridization inbreeding in vitro mating system multiple population strategy phenotype population pleiotropy production population base population The process by which a neutral, or even deleterious, mutation increases in frequency owing to its physical linkage with a beneficial mutation elsewhere in the genome. The formation of a diploid organism, mostly by sexual reproduction between individuals of unlike genetic constitution. The mating system in which mating events occur between individuals that are more closely related than average pairs chosen from a population at random. Biological processes made to occur in isolation from the organism ('in glass'). The system whereby individuals of opposite sexual type are paired to produce progeny. The arrangement when two or more populations of sufficient size, originating from a single large resource population, are established over a broad array of environmental conditions, managed or unmanaged, with the purpose of integrating tree breeding and gene conservation. The observable structural and functional characters of an individual resulting from interaction of the genotype with the environment. A Mendelian population is defined as a unit present under certain (environmental) conditions, composed of biological organisms which are able to reproduce sexually and where every pair of individuals is enabled and allowed to have common ancestry over generations. The side effect of a mutation that affect a primary trait or function on a secondary trait or function A population used strictly to produce seeds or vegetative material for afforestation or reforestation purposes. The production of trees from which selection of reproductive material is made for the next generation of breeding. breeding population A subset of trees from a base population that is selected for their desirable characters to serve as parents for the next 146 generation of breeding. progeny Offspring: descendants of a particular mating or a particular mate. provenance provenance trial random genetic drift regeneration reproduction reproductive age sampling The place in which any stand of trees is growing. The stand may be autochthonous or non- autochthonous A well-designed field common garden experiment aimed at the comparison of growth of population samples from a distribution area of a species, established in two or more environments. The change in frequency of genotypes in a population that is caused by the chance differences in survival and reproduction, as opposed to consistent differences in their fitness. The process of rejuvenation of a gene conservation unit (individual tree, accession stored in a gene bank, live collection, stand or population). In case of a population, regeneration can be natural (regeneration stock originates from matings in the respective population) or artificial. The process of forming new individuals of a species by sexual or asexual ways. The age at which the tree produces its first flowers and seed crop. The selection of populations and trees within the population from which seeds or other material is collected seed zone Wahlund effect Zone defined for seed-collection purposes, occupied by trees with relatively uniform genetic composition as determined by progeny testing various seed sources. The encompassed area is based on geographic bounds, climate and growing conditions (e.g., range of altitude) and usually refers to a defined administrative unit. A deficit in average heterozygosity, relative to HWE, that results from population substructure but when subpopulations join together, average homozygosity decreases and heterozygosity increases 147 Appendix II. Protocol For Genomic DNA extraction from needles of Sitka spruce [modified from Doyle and Doyle (1990)]. 1. 0.1 to .25 grams of fresh tissue is ground under liquid nitrogen and homogenized in approx. 800 to 1000 ul extraction buffer. For 6 samples, the extraction buffer is 0.7 ml 1 M Tris; 1.96 ml 5M NaCl; 1.4 ml 10% CTAB; 0.28 ml B-Mercap; 2.5 ml dH20). 2. Incubate homegenate at 65°C for 60 mins (shake every 10 mins). 3. Spin briefly and transfer supernatant into new vials. 4. Add an equal amount of chloroform:isoamyl (24:1); approximately 750 ul. 5. Mix for 20 mins in rotator. 6. Centrifuge at 13 000 x g for 15 mins. 7. Transfer 700 ul of aqueous solution into fresh vials. 8. Add 2 ul RnaseA and incubate @ 37°C for 30 mins. 9. Centrifuge at 13 000 x g for at least 5 mins. 10. Add 2/3 volume (500 ul) isopropanol (-20°C). Let precipitation occur overnight in -20o C in order to increase the DNA yield. 11 .Centrifuge at 13 000 x g for 15 mins. Remove supernant and wash DNA with 500 ul of 70% Ethanol. Spin for 10 mins. 12. Remove EtOH . 13. Dry the pellets in vacuum-drier for 20 mins. 14. Resolubilize dried-down DNA with 50 -100 ul dH20 and let dissolve for 1 hr. at 65°C Appendix III. Sequence-tagged-site (STS) primers and product ranges Locus Expected Product Observed Product Size (bp) size SB16 1050 980 -1400 SB17 640 640 - 800 SB21 471 -474 470 - 480 SB29 553 - 580 550 - 580 SB32 760 760 - 800 SB41 520 520 SB49 323 320 - 330 SB60 378 370 - 380 SB62 681 - 706 680-710 

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