Effect of Interspecific Hybridization and Soil Metal Content on Genetic Variability in Spruce

Effect of interspecific hybridization and soil metal content on genetic variability in spruce

Ramya Narendrula

Thesis submitted as a partial requirement in the Master of Science (M. Sc.) in Biology

School of Graduate Studies Laurentian University Sudbury, Ontario

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Abstract The effects of many biotic and abiotic factors cannot be easily assessed for long lived species such as conifers. It can however be hypothesized that long exposure to metals can result in reduction of genetic variation. Introduction of new genotypes and interspecific hybridization have been used successfully as an approach to increase genetic variability. The objectives of this study were to determine the effect of interspecific hybridization on genetic variability in P. mariana ? P. rubens hybrid populations and the effect of metal contamination on the level of genetic variation in spruce species. ISSR analysis of P. mariana ? P. rubens hybrids revealed polymorphism ranging from 30% to 52%. The level of polymorphism was higher with RAPD markers, ranging from 57% to 76%. Overall, no significant differences were observed among the hybrid populations analyzed for genetic variation. Metal content in soil was not associated with the level of diversity in the P. glauca populations analyzed. Genetic distance values ranged from 0.02 to 0.07 and 0.04 to 0.21 for ISSR and RAPD analysis, respectively. ISSR primers detected no difference among P. glauca populations from sites with different levels of metal content. RAPD analysis revealed a low level of population differentiation. Analysis of several generations of progeny from the samples analyzed is required to confirm the effect of interspecific hybridization and soil metal content on genetic variation.

m Acknowledgments

I would first like to take this opportunity to thank my supervisor Dr. Kabwe Nkongolo for giving me an opportunity to work in his lab and for all his help and dedication throughout the research. I am also indebted to Dr. Peter Beckett for his assistance in the site selection and sample collection. Next, I thank my family and friends for all their help, love, and encouragement in pursuing and completing my Masters. I would also like to express my gratitude to Paul Michael, Melanie Mehes Smith, Sylvia Dobrezeniecka and Sophie Gervais for all their help, guidance and encouragement in the lab. Finally, I would like to thank my committee members for all their helpful input and for reviewing my work so promptly. All of you have been truly helpful in the completion of my thesis.

IV Table of Contents

Abstract iü Acknowledgments iv

Table of Contents ? List of Figures viri

List of Tables x Chapter 1: Literature Review 1 1.1 Overview 1

1.2 Genetic variation 2 1.3 Factors affecting genetic variation 3 1.4 Assessing genetic variation using molecular markers 4 1.4.1 Inter Simple Sequence Repeat (ISSR) Marker 6 1.4.2 Random Amplified Polymorphic DNA (RAPD) Marker 9 1.5 Metal contamination in terrestrial ecosystems 11 1.6 Metal contamination and its effect on the Sudbury region 14 1 .7 Targeted species 19 1.7.1 Picea mariana (Black spruce) 19 1.7.2 Picea rubens (Red spruce) 22 1.7.3 Picea glauca (White spruce) 24 1.8 Interspecific hybridization in Picea 27 1.9 Objectives 31 Chapter 2: Effect of interspecific hybridization on genetic variation in Picea mañana ? Picea rubens populations 32 2.1 Introduction 32 2.2 Materials and Methods 34

? 2.2.1 Genetic Material 34

2.2.2 DNA Extraction 34 2.2.3 Degradation Analysis and DNA Quantification 35 2.2.4 Amplification with ISSR and RAPD primers 37 2.2.5 Statistical Analysis 38

2.3 Results 39 2.3.1 Degradation Analysis 39 2.3.2 ISSR amplification and polymorphism 39 2.3.3 RAPD amplification and polymorphism 40 2.3.4 Genetic Diversity 42 2.4 Discussion 63 Chapter 3: Metal content in soil from Sudbury region (Ontario, Canada) 66 3.1 Introduction 66

3.2 Materials and Methods 67 3.2.1 Sampling 67 3.2.2 Metal Analysis 67 3.3 Results 70

3.4 Discussion 72 Chapter 4: Genetic analysis oí Picea glauca populations from metal contaminated and uncontaminated areas in the Greater Sudbury region (Ontario, Canada) 76

4.1 Introduction 76

4.2 Materials and Methods 78 4.2.1 Sampling 78 4.2.2 Molecular Analysis 78 4.2.6 Statistical Analysis 79

vi 4.3 Results 81 4.3.1 Degradation Analysis 81 4.3.2 ISSR Analysis 81 4.3.3 RAPD Analysis 82 4.3.4 Genetic Relationship 84 4.4 Discussion 104 Chapter 5: General Conclusion 109 References Ill Appendix 120

vii List of Figures

Figure 1. Geographical distribution of Picea mariana (Black spruce) across Northern America 20

Figure 2. Geographical distribution of Picea rubens (Red spruce) across Northern America .· 23

Figure 3. Geographical distribution oí Picea glauca (White spruce) across Northern America 25

Figure 4. Degradation analysis illustrated on a 1% TAE agarose gel for DNA from P. mariana x P. rubens hybrid samples , 44

Figure 5. ISSR amplification of P. mariana ? P. rubens hybrid samples with primer HB 13 53

Figure 6. ISSR amplification of P. mariana ? P. rubens hybrid samples with primer ISSR 5 54

Figure 7. ISSR amplification of P. mariana ? P. rubens hybrid samples with primer ISSR 9 55

Figure 8. ISSR amplification of P. mariana x P. rubens hybrid samples with primer 17899A 56

Figure 9. ISSR amplification of P. mariana ? P. rubens hybrid samples with primer 17898B 57

Figure 10. ISSR amplification of P. mariana ? P. rubens hybrid samples with primer UBC 841 58

Figure 11. RAPD amplification of P. mariana ? P. rubens hybrid samples with primer OPA 4 59

Figure 12. RAPD amplification of P. mariana ? P rubens hybrid samples with primer OPA 8 60

Figure 13. RAPD amplification of P. mariana ? P rubens hybrid samples with primer P 184 61

viii Figure 14. RAPD amplification of P. mariana ? P. rubens hybrid samples with primer UBC 186 62

Figure 15. Location of the soil sampling area from the Greater Sudbury region 68

Figure 16. Genomic DNA for quality test of DNA samples from P. glauca 85

Figure 17. ISSR amplification oí Picea glauca samples with primer HB13 94

Figure 18. ISSR amplification ?? Picea glauca samples with primer ISSR 5 95

Figure 19. ISSR amplification of Picea glauca samples with primer ISSR 9 96

Figure 20. ISSR amplification of Picea glauca samples with primer 17899A 97

Figure 21. ISSR amplification oí Picea glauca samples with primer 17898B 98

Figure 22. ISSR amplification of Picea glauca samples with primer UBC 841 99

Figure 23. RAPD amplification of Picea glauca samples with primer OPA 4 100

Figure 24. RAPD amplification oí Picea glauca samples with primer P 23 101

Figure 25. RAPD amplification of Picea glauca samples with primer P 184 102

Figure 26. RAPD amplification of Picea glauca samples with primer UBC 186 103

IX List of Tables

Table 1. Plant materials used in the study 36

Table 2. The nucleotide sequence of ISSR primers used to screen DNA from Picea mariana x Picea rubens hybrid samples 45

Table 3. Levels of polymorphisms within Picea mariana x Picea rubens hybrid populations generated with ISSR primers 46

Table 4. Intra-population variation in Picea mariana ? Picea rubens hybrid individuals based on ISSR marker system 47

Table 5. Genetic diversity parameters of Picea mariana ? Picea rubens hybrids based on ISSR data 48

Table 6. The nucleotide sequence of RAPD primers used to screen DNA from Picea mariana ? Picea rubens hybrid samples 49

Table 7. Levels of polymorphisms within Picea mariana ? Picea rubens hybrid populations generated with RAPD primers 50

Table 8. Intra-population variation in Picea mariana x Picea rubens hybrid individuals based on RAPD marker system .51

Table 9. Genetic diversity parameters of Picea mariana ? Picea rubens hybrids based on RAPD data 52

Table 10. Metal concentrations in top layer (0-5 cm) of soil from the Sudbury region sites, concentrations are in mg kg"1, dry weight* 71

Table 11. Metal concentrations in bottom layer (5 - 20 cm) of soil from the Sudbury region sites, concentrations are in mg kg" , dry weight* 72

Table 12.The nucleotide sequences of ISSR primers used to screen DNA from Picea glauca samples 86

Table 13. Levels of polymorphisms within and between Picea glauca populations generated with ISSR primers 87

? Table 14. Genetic diversity parameters of Picea glauca based on ISSR data 88

Table 15. Distance matrix generated from ISSR data using the Jaccard similarity coefficient analysis for Picea glauca populations (Free Tree Program). 89

Table 16. The nucleotide sequence of RAPD primers used to screen DNA from Picea glauca samples 90

Table 17. Levels of polymorphisms within and between Picea glauca populations generated with RAPD primers 91

Table 18. Genetic diversity parameters oí Picea glauca based on RAPD data 92

Table 19. Distance matrix generated from RAPD data using the Jaccard similarity coefficient analysis for Picea glauca populations (Free Tree Program) 93

Table 20. Concentrations guidelines for the soil according to the Ontario Ministry of environment and Energy (OMEE) 120

Xl Chapter 1: Literature Review

1.1 Overview

Adaptation and speciation depend primarily on genetic variation. A high level of variability is a prerequisite for forest ecosystem sustainability. A low level of variability limits species ability to respond to biotic and abiotic stresses in long and short term. Variation may be increased either through mutations or through gene flow (migration) from neighbour populations (Amos and Harwood, 1998). Hybridization increases recombination in the DNA, which further helps increase genetic variation. Decrease in variation occurs either actively through natural selection or passively through genetic drift. Selection can be influenced by natural factors such as drought, salinity, oxidative stress and metal contamination (Blanco et al, 2005). Variation is more of a continuum, particularly with plant species that tend to hybridize more freely compared to animals. If replenishment of variation is slow {denovo mutations) or unlikely (influx from a neighbouring populations or species), most large or rapid changes in variation will be attributed to loss rather than gain (Amos and Harwood, 1998). Plant populations are not homogeneous entities and genetic variation is as important as species diversity. Wide genetic variation will enable the species to inhabit a large number of micro sites, to adapt more readily to environmental changes and may lead to recombination and formation of genotypes that are better suited to the degraded habitat than any of the parents (Alden, 1995). In long-lived plant species such as conifers the effects of many factors cannot be easily assessed. It can be however hypothesized that long exposure to metals can result in population fragmentation and low genetic variation

1 in conifers. To address the loss of genetic variation, interspecific hybridization has been used as an efficient approach of increasing genetic variability.

1.2 Genetic variation Genetic variation is the modification in alleles of genes which occurs among and within population. It is the difference that exists between individuals within populations of organisms which are potentially capable of interbreeding to produce fertile offspring (Blanco et ah, 2005). Variability caused by natural differences between living organisms or by genetic and environmental factors or the combination of both is important for population sustainability and species ability to adapt to changing environmental conditions over time. It provides the base material for natural selection (Rajora et al., 2005). Environmental factors that affect variation include radiation, climatic change, pollution, metal contamination, and hybridization. Genetic variations tend to be stable and stay within the population gene pool and can affect both the phenotype and the genotype of an organism, unless it is erased via evolution (Cruzan, 1998). Environmental differences can be very important when considering variations within a species. For example, plants bred from identical seeds may grow in a stunted way in exposed, dry areas with few nutrients in the soil or may grow large and luxuriant in sheltered, well

watered environments, which are rich in nutrients. It is possible to identify genetic variation from observations of phenotypic variation in either quantitative traits, that vary continuously and are coded for by many genes or discrete traits, that fall into discrete categories and are coded for by one or a few genes (Semagn et al., 2006). It can also be identified by examining variation at the level

2 of enzymes using the process of protein electrophoresis. Polymorphic genes have more than one allele at each locus. Overall, about half of the genes that code for enzymes in insects and plants are believed to be polymorphic, whereas polymorphisms are less common in vertebrates (Schwenk et al., 2008). Ultimately, genetic variation is caused by variation in the order of bases in the nucleotides of the genes. New technology allows researchers to directly sequence DNA to identify even more genetic variation than was previously detected by protein electrophoresis. Examination of DNA has revealed genetic variation in both gene coding and non-coding regions (Semagn et al., 2006).

1.3 Factors affecting genetic variation A vast amount of naturally occurring genetic variation affecting development is found within plants species. Some of these natural factors affecting genetic variation are mutation, migration, selection and random drift (Amos and Harwood, 1998). Environmental stressors, such as anthropogenic factors, can affect the genetic frequencies as well by increasing mutation or selection. Environmental factors include abiotic signals such as light, temperature, wind, humidity, water availability, or nutrient resources as well as, biotic components of the surrounding environment from pathogens to competitors (Tonsor et ai, 2005). These will lead to differences among populations and increase uniformity within a population, thus increasing homozygosity and inbreeding (Dimsoski and Toth, 2001). Studies of genetic variation of impacted and unimpacted (control) populations defined a positive relationship between the exposure to the stressor and diversity. There is also widespread concern on habitat fragmentation because it will always lead to a reduction in population size and an increase in the rate at which

3 variability is lost through genetic drift. In extreme cases, the population may temporarily be reduced to very low numbers and suffer exaggerated loss, a phenomenon defined as genetic bottleneck (Amos and Harwood, 1998). The mutation-drift equilibrium model can be used to test the existence of a severe selection event that caused the decrease of the effective population size (bottleneck) in the recent past. If this effective population size is known, it is possible to estimate the change in allele frequencies due to random genetic drift and moderate selection. This will help identify the number of factors that affected the genetic variation (Dimsoski and Toth, 2001).

1.4 Assessing genetic variation using molecular markers A molecular or genetic marker is a fragment of DNA sequence that is associated with a part of the genome. Molecular markers are used in molecular biology and biotechnology experiments to identify a particular sequence of DNA. A genetic marker can be defined in one of the following ways; (1) a chromosomal landmark or allele that allows for tracing of a specific region of DNA; (2) a specific piece of DNA with known position on the genome or (3) gene whose phenotype expression is usually easily discerned, used to identify an individual or a cell that carries it, or as a probe to mark a nucleus, chromosome, or locus (Agarwal et al., 2008). Since the markers and the genes that mark them are close together on the same chromosome, they tend to stay linked as each generation of plants is produced unless meiotic crossing-over occurs between

marker and genes. Molecular markers are an important and very powerful tool for genetic analyses on crop species and for locating important genes in the plant genome. They are present at

4 specific locations of the genome and can be used to 'flag' the position of a particular gene or the inheritance of a particular characteristic (Semagn et al., 2006). Molecular markers can be used for many diverse purposes ranging from forensic sciences to identifying genes responsible for genetic diseases and inferring evolutionary relationships among species (Karp et al., 1997), genome and comparative mapping, phylogeny and population genetics, parental selection, species identification, and selection of qualitative traits (Blanco et al, 2005; Sharma et al., 2008). Discovered in 1980s, the Polymerase Chain Reaction (PCR) has emerged as a major molecular tool for genetic analyses. At the same time, application of PCR in areas related to plants has increased substantially. Molecular markers such as Random Amplification of Polymorphic DNA (RAPD), Inter-Simple Sequence Repeats (ISSR), Simple Sequence Repeats (SSR), and Amplified Fragment Length Polymorphism (AFLP) have been successfully used to assess the genetic diversity in cultivars of many plant species (Sharma et al., 2008). The difference that distinguishes one plant from another is sequence in the deoxyribonucleic acid (DNA). Molecular markers provide an opportunity to characterize genotypes and to measure genetic relationships more precisely than other markers (Semagn et al., 2006). Each one of these marker systems offers a unique combination of advantages and disadvantages (Sharma et al., 2008), and differ in the type of sequence polymorphism detected (insertion/deletions vs. point mutation), information content, the dominance relationships between alleles (dominant vs. codominant markers), amount of DNA required, the need for DNA sequence information in the species under analysis, development costs, the ease of use, and the extent to which they can be automated. The choice of marker systems is dictated by the specific application and there

5 is probably not a single class of markers that can satisfy all the needs encountered by plant geneticists and breeders. To date ISSR and RAPD are widely used for study of plants and have been useful in genetic fingerprinting and genetic diversity analysis of many species of plants and animals. The former permits detection of polymorphism in inter-microsatellite loci, using a primer designed from . dinucleotide or trinucleotide simple sequence repeats (Zietkiewicz et al, 1994; Semagn et al., 2006) and the latter detects nucleotide sequence polymorphisms using a single primer of arbitrary nucleotide sequence.

A genetic marker is any character which can be traced through families. All germ- line polymorphic DNA markers are genetic markers, but not all genetic markers are DNA based. DNA may also undergo somatic alteration, such that polymorphism which occurs in somatic tissue may not always be inherited: for example in tumors. The great advantage of DNA polymorphisms is that they can be scored easily and inexpensively with small amounts of the material.

1.4.1 Inter Simple Sequence Repeat (ISSR) Marker ISSR is a relatively new technique that is used to differentiate closely related individuals (Godwin et al, 1997). This marker system has been successfully applied to genetic analysis of plants. ISSR marker system accesses variation in the numerous microsatellite regions dispersed throughout the genome (Semagn et al, 2006) and circumvents the challenge of characterizing individual loci that other molecular approaches require. ISSR analysis involved amplification of regions between adjacent, inversely oriented microsatellites, using a simple sequence repeat (SSR) motif containing

6 primers anchored at the 3' or 5' end by two or four arbitrary, often degenerate nucleotides (Zietkiewicz et al, 1994). Microsatellites are very short (usually 10-20 bp) stretches of DNA that are hypervariable, expressed as different variants within populations and among different species. They are characterized by mono-, di- or trinucleotide repeats (AA, AG, CAG respectively) that have 4-10 repeat units side by side. ISSRs specifically target the di- and tri-nucleotide repeat types of microsatellite as these are characteristic of the nuclear genome (mononucleotide are found in chloroplast genome). The ISSR method provides an alternative choice to other system for obtaining highly reproducible markers without any necessity for prior sequence information for various genetic analyses. ISSR method takes advantage of the ubiquitously distributed SSRs in the eukaryotic genomes. Because of these abundant and rapidly evolving SSR regions, ISSR amplification has the potential of revealing much larger numbers of polymorphic fragments per primer than any other marker system used such as RFLP or microsatellite. As the PCR reactions amplify the sequence between two SSRs, the PCR products generated reveal multilocus profiles which could be revealed on agarose or

Polyacrylamide gels. The number of targeted genomic loci can be altered by designing primers of different specificities (Zietkiewicz et al., 1994). ISSR-PCR products can be easily excised from the dried gel and cloned or reamplified to be used as probes. This could be an alternative method of identifying microsatellites in genomic libraries (Zietkiewicz et al, 1994). ISSR analysis is faster and it amplifies and detects a greater number of bands per primer (Godwin et al, 1997). Study by Godwin et al, (1997) found that ISSR

7 markers reveal higher levels of polymorphism, that could be related to the method of detection.

ISSRs are regions that lie within the microsatellite repeats and offer great potential to determine intra-genomic and inter-genomic diversity compared to other arbitrary primers, since they reveal variation within unique regions of the genome at several loci simultaneously. Several properties of microsatellite such as high variability among taxa, ubiquitous occurrence and high copy number in eukaryotic genomes make ISSRs extremely useful markers (Morgante et al., 1996). They exhibit specificity of sequence tagged site markers, but need no sequence information for primer synthesis enjoying the advantage of random markers. The multilocus profiles generated by ISSR primers are highly polymorphic and as such are ideal for studying genetic variation. The basic premise of ISSRs is that primer-annealing sites are distributed evenly throughout the genome such that the primer will anneal to two sites orientated on opposing DNA strands. If these are within an appropriate distance of one another, the region between the two primers will be amplified through PCR. ISSRs are now being applied to natural populations to address issues such as hybridization. These studies have demonstrated the utility of the technique in a wide range of applications and plant families (Asteraceae, Brassicaceae, Hippocastanaceae, Orchidaceae, Poaceae, Scrophulariaceae, Violaceae) (Danilova et al, 2003; Bornet and Branchard, 2004). Compared with other molecular marker such as AFLP and SSR, the ISSR marker has its specific advantages: 1) no prior sequence information required, 2) simple and quick operation, 3) amenable to laboratory level, 4) high stability, 5) abundance of genomic information, 6) use of radioactivity is not required and 7) show high

; 8 polymorphism. ISSR markers access variation in the numerous microsatellite regions dispersed throughout the various genomes (particularly the nuclear genome) and circumvent the challenge of characterizing individual loci that other molecular approaches require (Semagn et al., 2006; Sharma et al., 2008).

1.4.2 Random Amplified Polymorphic DNA (RAPD) Marker RAPDs were first described by Williams et al., (1990). This was the first technique to amplify DNA fragments from any species without any prior sequences information. RAPD is a simple and easy method to determine genetic diversity and taxonomic identity of various species (Mishra et al., 2009). This technique has been widely applied in studies of genetic diversity and genetic structures of woody plants such as Quercus liaotungensis (Liaodong oak), Populus tacamahaca (Balsam poplar), Populus tremuloides (Aspen poplar), Tilia (Lime), etc (Congwen and Manzhu, 2006). The technique involves the use of a single oligonucleotide of arbitrary sequence to prime the amplification of template DNA by the Polymerase Chain Reaction (PCR). An oligonucleotide will prime amplification from a genomic template if binding sites on opposite strands of the template exist within a distance which can be traversed by the DNA polymerase (up to several thousand nucleotides). The amplification with arbitrary primers is mainly driven by the interaction between primer, template annealing sites and enzymes (Semagn et al., 2006). Genomic polymorphisms at one or both priming sites result in the non-amplification of a band. RAPDs are thus dominant markers and appearance of a band implies homology with the primer used. All other alleles at the priming site will be represented by absence of the band. Codominant RAPD markers,

9 resulting from insertions or deletions between priming sites and observed as different sized fragments amplified from the same locus, are detected only rarely (Williams et al., 1990). A primer usually amplifies several bands, each originating from a different genomic location. The nature of the fragments amplified is influenced dramatically by the

sequences of both primer and template.

RAPD usually uses a 10 bp arbitrary primer. Although the sequences are arbitrarily chosen, two basic criteria must be met: 1) at least 50% Guanine-Cytosine content and 2) the absence of palindromic sequences (sequence that reads exactly the same in both directions) (Semagn et al., 2006). Primers as short as 5 nucleotides give more complex banding patterns requiring more sophisticated electrophoretic and staining procedures (acrylamide gels and silver staining). RAPD analysis results in the amplification of one locus and two kinds of polymorphism: 1) the band may be present or absent and 2) the brightness of the band may be different. Band intensity differences could be due to low copy number or relative sequence abundance (Devos and Gale, 1992) and may serve to distinguish homozygote dominant individuals from heterozygotes, as less bright bands are expected for the latter. Ellsworth et al, (1993) indicated that the fact that fainter bands are generally robust, varying degrees of primer mismatch may account for band intensity differences. As the source of the band intensity difference is uncertain most studies disregard scoring differences in band intensity (Semagn et al., 2006).

RAPD technique does not require Southern blotting, development of species- specific probes or radioactive labeling. RAPD analyses can be conducted much more quickly (and with fewer laboratory restrictions) and quantities of DNA required for

10 analysis are 100 times lower (only 15-20 ng) compared to other markers. The dominant nature of the marker results in an inability to distinguish homozygotes and heterozygotes which is a limitation for some applications (Sharma et al, 2008). RAPD markers can be used for mapping in areas of the genome not accessible to other analysis due to the presence of repetitive DNA sequences. As with other genetic markers, some polymorphisms are easy to score, while others are ambiguous and not useful as markers. Ambiguous polymorphisms may result from poor discrimination by a primer between alternative priming sites of slightly different nucleotide sequences.

1.5 Metal contamination in terrestrial ecosystems

Metal contamination is one of the most ubiquitous, persistent, and complex environmental issues, encompassing legacies of the past (e.g. abandoned mines) as well as impending, but poorly studied, threats (e.g. metallo-nanomaterials). Generally, the increase of elements in the environment occurs due to human activities (industrial, agricultural, mining, and waste disposal practices) (Alloway and Ayres, 1993). Metal pollution of soil and wastewater is one of the most important environmental problems of industrialized countries, affecting human health, agriculture, and forest ecosystem. The consequence of metal pollution on forest ecosystem can be widespread and long lasting. Metal contamination is a serious problem as it causes both stress and physiological constraints that affect the vigor and growth of plants, although different species show different responses to metal toxicity (Antonovics et al., 1971). The stress is due to formation of reactive oxygen species (ROS) such as singlet oxygen, hydrogen peroxide, and hydroxyl radicals that cause cellular damage in aerobic organisms. Plants have

11 mechanisms to prevent oxidative stress. Many enzymes and others compounds avoid this damage by inhibiting or quenching free radicals and ROS. Several studies have indicated that elevated concentrations of metal in plants affects seedling mortality, delay vegetation, shoot growth, photosynthesis, metabolic pathways, and enzyme activities

(Jung and Thornton, 1996). The Canadian landscape is unique because of extensive glaciations. The majority of the Canadian landmass is covered with a layer of crushed bedrock (glacial till) broadly distributed by the glaciers. The relatively short geologic time the glacial till has had to equilibrate chemically with the atmosphere and the till distribution can result in large changes in soil chemistry over very short distances. The background levels of metals in soils are determined by the levels existing in the parent material and by redistribution in the profile due to pedogenesis (Mills and Zwarich, 1975). Major areas of complex geology exist in Canada: the Canadian Shield, Cordillera and Appalachians. This has an impact on soil chemistry at any given location. The assessment of soil quality for naturally occurring elements must take into consideration regional variations in background concentrations in Canada. Background concentrations and environmental fate of metals strongly depend on geological and biological characteristics and, therefore, any assessment of potential risks should take into consideration regional differences in metal content in the natural environment (Wang and Chapman, 1999). Relatively high concentrations of metals can occur naturally in Canadian soils, stream sediments, and water, blurring the distinction between anthropogenic pollution versus naturally occurring geological formations and natural bodies of ore. Soils and sediments reflect the composition of parent material, resulting in

12 higher metal concentrations in mineralized areas (Wilson et al., 1998). Mining districts are characterized by naturally occurring metals in soil, sediment, rock, and water at

concentrations that could result in their classification as "contaminated sites" (Painter et al., 1994). In the determination of anthropogenic metal contamination of soils, no single guideline concentration can adequately represent the variance in background concentrations across Canada (Painter et al., 1994; Wang and Chapman, 1999). Smelters in Canada continue to be a major source of pollution to air, land and water. In the 2002 National Pollutant Release Inventory (NPRI), the three largest emitters of CEPA toxics into environment in Canada were Base Metal Smelters (BMS) - Vale Copper Cliff, Vale Thompson, Hudson Bay Mining & Smelting - and the BMS sector in total produced more than 26% of all Canadian Environmental Protection Act (CEPA) toxic releases reported in Canada (The Boreal Below, 2008). Over the years, although many smelters have reduced their emissions by the construction of sulphuric acid plants and the introduction of other technologies in the face of legislated limits, BMS remain the single largest source of sulphur dioxide emissions in Canada as well as mercury, arsenic, cadmium, lead, beryllium, and nickel. Frequently, plant or tissue growth is used as the parameter for monitoring the effects of different stress conditions, assuming plant development to be deficient under such conditions. Plants stressed with metal ions show morphological and biochemical alterations at the cell and tissue levels as well as through decreases in their development (Gawel et al, 2001). Several reports indicated that most of the metals accumulate in the root system with some being translocated to the upper parts of the plants. The distribution of metal ions varies considerably depending on the plant species and metal ions. It is also

13 known that specific peptides termed phytochelatins can chelate metal ions and make them unavailable to the cell metabolism, with immobilization of such ions in the vacuole

(Gawel et al., 2001).

1.6 Metal contamination and its effect on the Sudbury region

The Sudbury region in Ontario, Canada has a history over the past 100 years of logging, mining, and sulphide ore smelting, releasing more than 100 million tonnes of SO2 and tens of thousands of tonnes of cobalt, copper, nickel, and iron ores into the atmosphere from the open roast beds (1888-1929) and smelters (1888-present) (Freedman and Hutchison, 1980). These factors have caused acidification, severe metal contamination of the soils and water at sites within approximately 30 km of the smelters in the Sudbury region. Sudbury area is one of the most ecologically disturbed regions in Canada. The SO2 fumigation, irresponsible logging, roasting beds, and metal ores released, devastated forests ecosystem for kilometers downwind by greatly reducing the diversity of vascular plants. The effects of these smelters have been strikingly widespread: 64 km2 around Sudbury had been rendered completely barren of vegetation, 225 km2 around supported only shrubs and herbaceous cover, and vegetation had been in some way affected within a 2735 km2 area (DeLestard, 1967). The emission of SO2 into the atmosphere has had three major effects in the surrounding ecosystems: 1) decrease in plant productivity and possible plant death, 2) acidification of soil to pH of 2.0 to 4.5 (Winterhaider, 1983) and 3) consequent aluminium toxicity.

There have been numerous studies documenting the effects of SO2 in the Sudbury region (Cox and Hutchinson, 1980; Amiro and Courtin, 1981; Gratton et al, 2000).

14 Nieboer et al., (1972) showed that lichens decreased drastically as the smelters are approached and that only some crustose lichens were present in highly polluted areas. Gorham and Gordon, (1960) detected similar decrease in diversity closer to the smelters for vascular plants and documented the effects of acid precipitation on aquatic systems in the Sudbury region. In addition to SO2 pollution, the elevated levels of copper, nickel, and aluminium have also decreased plant growth and retarded natural recolonization. The total levels of copper and nickel in the soil in this area exceed 1000 mg/kg while available levels approach 100 mg/kg (Winterhaider, 1983). As with the SO2 pollution, Winterhaider (1983) showed that copper and nickel levels in the soil increase with the proximity to a smelter. Elevated concentrations of metal accumulations in soils and vegetation have been documented within short distances of the smelters in Sudbury compared to control sites (Freedman and Hutchinson, 1980; Gratton et al., 2000; Nkongolo et al, 2008). Continued investigation and monitoring of soil and vegetation are essential for the understanding of ecosystem recovery following the reduction of emissions from smelters and the establishment of a reforestation program. Since it is

known that metals have an adverse effect and a strong impact on the stability of the ecosystems, efforts have also been made to limit metal emissions. Studies have detected elevated metal concentrations in soil and vegetation within short distances of the smelters in the Sudbury area. A high correlation between concentrations of metals in plants with distance from the source of pollution has been documented. Erosion has removed a significant portion of the topsoil and has left the remaining soil deficient in phosphorous, nitrogen and possibly calcium, magnesium, and manganese. The pH ranges from 2.0 to 4.5 which results in aluminium toxicity. Areas

15 close to the smelters (24 km radius) are contaminated with high concentrations of copper and nickel. Interest in the mineral nutrition of gymnosperm tree species has risen following reports that acidic precipitation upsets tree mineral relations and is probably a major factor in the die-back observed in the forests of Western Europe and North America (Schulze, 1989; Smith, 1992). In particular, increased soil acidity causes major changes in soil chemistry (Hazlett et ah, 1984), with decreases in available calcium and magnesium and increase in available heavy metals and aluminium. Particular metals are known to cause acute toxic effects causing DNA damage (Hughes et al., 1995). Although molecular genetics studies have shown that certain metal ions cause DNA damage, they give negative effects in most bacterial mutagenicity assays and the results obtained in cytogenetic and gene mutation assays with mammalian cells are controversial (Snow, 1992; Rossman, 1995). Furthermore, the application of bacterial and/or mammalian test systems for the investigation of environmental samples require extraction and concentration procedures and no standardized methods have been developed for the environmental monitoring of the genotoxic effects of metals. A number of studies have shown that plant tests are highly sensitive towards specific environmental toxins such as pesticides and other organic compounds (Smith, 1992). Steinkellner et al., (1998) indicated that plant bioassays can effectively detect the genotoxic effects of metals and these systems can be useful for biomonitoring soils for heavy metals. Mechanisms of uptake by mature and/or juvenile woody plants are poorly understood (Smith and Brennan, 1984). Essential metals such as copper and zinc move into non-woody plant cells by metabolic pathways (Tinker, 1981). However, uptake of nonessential elements such as cadmium and lead may enter cells by metabolic or non-

16 metabolic mechanisms (Lee et al., 1976). The threshold level of metal toxicity in woody plants can be highly variable (Antonovics, 1971). For example, the concentration of lead in tissue that causes a decrease in the growth of mature Picea is highly variable (Burton et al, 1983). Processes that may prevent phytotoxicity include formation of insoluble crystals, formation of vesicles and retention of elements by cell walls and dilution onto non-vital organs (Lee et ah, 1976). Tolerance may be significantly influenced by accumulation of toxic metals in root cells (Tinker, 1981). Dudka et al., (1995) published data on soil contamination by elements in the Sudbury mining and smelting region. The study shows that the Sudbury's soil is contaminated with cadmium, cobalt, copper, chromium, iron, manganese, nickel, sulphur, and zinc. Copper and nickel were the primary contaminants in the area. Their data revealed a decline in the soil concentrations of nickel and copper when compared to previous studies. Leeching, washing and erosion processes could explain the element concentration decline in the studied soil. Also the reduction of atmospheric emission has been a key factor. The Ministry of the Environment has been sampling soil and vegetation in the Sudbury area since the early 1970s. In September of 2001, the ministry released a summary report called Metals in Soil and Vegetation in the Sudbury Area. This report included results of sampling and testing conducted in 2000 as well as previously unreported results dating back to 1971. This sampling along with the previous samplings was initiated to assess the environmental impact of local mining and smelting operations in the area over the past 100 years.

17 The Metals in Soil and Vegetation in the Sudbury Area report confirmed that some metals are elevated in the Sudbury basin. The highest metal concentrations are typically found in the upper soil layers, indicating air emissions as the source. The soil metal and arsenic levels currently known to exist in Sudbury are generally comparable or lower than those found in other Ontario mining communities where elevated soil concentrations are found as a result of historical mining activity. The survey involved the collection of over 10,000 samples from hundreds of different locations. The sampling results reconfirm that emissions from over 100 years of mining, smelting and refining have resulted in elevated levels of metals in the soil over a large area. Levels of nickel, copper, lead, cobalt and arsenic levels are highest in the areas around the three industrial centers of Copper Cliff, Conisten, and Falconbridge. These current findings are consistent with historical ministry results. Nkongolo et al., (2008) determined the level of metal content in soil and various tissues from P. mariana populations in the Sudbury region. Their results revealed concentrations of cadmium, cobalt, copper, iron, nickel and zinc to be within the limits set by Ontario Ministry of Environment and Energy (OMEE) guidelines even in sites within the vicinity of the smelters. The level of these elements in P. mariana tissues were much lower and far below the toxic levels for vegetation. Overall, in the past 30 years, production of nickel, copper and other metals has been maintained at high levels while industrial SO2 emissions have been reduced by approximately 90% through combination of industrial technological developments and legislated controls. This has allowed for some degree of recovery to occur such as improved air quality and natural recovery of damaged ecosystems during this period of

18 reduced emissions at Sudbury. Further, the recovery has been done through reforestation program by planting over 9 million trees mostly conifers in the Sudbury region. Information on landscape degradation, soil toxicity, acidification, plant metal accumulation and forest composition in Northern Ontario is readily available but knowledge of genetic variation within and among forest tree populations is lacking. This genetic diversity information is crucial to ensure sustainability of the forest resource. The impoverished plant communities that are currently found in the Sudbury area are not only structurally and floristically different from plant communities found in uncontaminated areas in the basin, but they appear to have a different genetic make-up.

1.7 Targeted species 1.7.1 Picea mariana (Black spruce) P. mariana is a major component of the boreal forest of North America. It is transcontinental, widespread throughout the boreal region. It is found from Newfoundland to Alaska, in the Great Lakes Region, and Minnesota (Figure 1) (Canadian Biodiversity). This spruce is the most important pulpwood species in Canada, due to its favorable fiber properties and the vastness of the resource. It is normally harvested under clear-cut harvesting system and is regenerated naturally or artificially after harvest (Rajora and Pluhar, 2003). It is often a postfire pioneer species and forest stands are generally of the same age. P. mariana is a small to medium sized slow-growing (20 m tall and 25 cm in diameter) evergreen coniferous tree or dwarf shrub in the extreme north (Vincent, 1965). In general, this species varies in height, distribution and growth rate over its extensive

19 range (Bihun and Carter, 1982). This pattern of variation may be due to natural selection based on major environmental effects since the variance correlated well with climatic

î N SS ß C?

? a f

Figure 1. Geographical distribution of Picea mariana (Black spruce) across Northern America. factors, condition of the site, latitude and longitude. This species also grows in sites such as lowland and upland. Due to the wide spectrum of site conditions, P. mariana is preferred as a plantation species in eastern Canada. This spruce species commonly grows on organic soils and in mixed stands on mineral soils. It is tolerant of nutrient-poor soils, and is commonly found on poorly drained acidic peat lands (Vincent, 1965). It often occurs in pure groups of trees or with Pinus contorta (Lodgepole pine) and Picea glauca (White spruce). It usually grows on very moist soils, acidic bogs, swamps, and other wet areas. Growth varies with site

20 conditions. In swamp and muskeg, it shows progressively slower growth rates from the edges toward the centre. The roots are shallow and wide spreading with fallen trees colloquially called "drunken trees", and are often associated with thawing of permafrost. In the northern part of its range, ice pruned asymmetric P. mariana is often found, with diminished foliage on the windward side. In the southern portion of range it is found primarily on wet organic soils, but farther north its abundance on uplands increases (Vincent, 1965). In the Lake States it is most abundant in peat bogs and swamps, also on transitional sites between peat lands and uplands. P. mariana grows best on well drained loamy soils but because it is less competitive in some respects than other species it cannot always occupy these soils. P. mariana can endure the conditions of these sites; other species cannot or will not. Studies have reported that stands in which P. mariana is dominant or co-dominant, or a leading component of the arboreal vegetation, are mostly of fire origin (Farjon, 2001). Many investigators have reported that bog formation and its development has a direct or indirect effect on P. mariana associations. Recently, the harvested product from P. mariana from natural stands has declined. The wood is made up of long fibers that produce a very high quality pulp. Since P. mariana is primarily a pulp and paper species, improvement of the fiber yield per unit area is the major objective of the tree breeding programs. Compared with other species P. mariana is morphologically very closely related to P. rubens and is very difficult to distinguish the two species in mixed stands (Gordon, 1976).

21 1.7.2 Picea rubens (Red spruce) P. rubens is one of the most important conifers in the northeastern United States and adjacent Canada. It is a valuable timber species and an important component of the late-successional forests of eastern Canada (Mello, 1987). P. rubens is centered in the northeastern United States and Canadian Maritimes, extends west into Ontario and south along the upper elevations of the southern Appalachian mountains (Figure 2) (Canadian Biodiversity). Current occupancy of P. rubens has been estimated at between one tenth and one fifth of its former extent in terms of populations sizes, numbers, and geographical distribution (Gordon, 1996). The area of continuous distribution and the center of the range are now limited to southern portions of New Brunswick, Nova Scotia and the northern New England states. It is often found in pure stands or forests mixed with other species such as Pinus strobus (Eastern white pine), Abies balsamea (Balsam fir) and Picea mariana (Black spruce). The dispersal of P. rubens seeds vary across a gradient from forest interior to adjacent old fields because seed distribution patterns are contingent upon the interactions of the location and fecundity of the parent trees, patterns of prédation and decay, and the heterogeneity of the physical environment (Mello, 1987). Studies have found that because of the dispersal ability of spruce seeds, seed abundance in the harvested land decreased with distance from the forest edge. P. rubens has declined substantially across most of its geographic range to the point where it is becoming increasingly uncommon (Gordon, 1976). This decline has been associated with past forestry practices, such as selective removal and clear-felling, particularly during the 1800s and 1900s, atmospheric pollution, and climate change (Mello, 1987).

22 î N ?®£

P O

Figure 2. Geographical distribution of Picea rubens (Red spruce) across Northern America.

P. rubens is identified by its large broad crown, with right-angled branches, curving upward at the ends. The bark and twigs tend to be lighter than P. mariana. It grows up to 18 - 40 m high and has a trunk diameter of about 60 cm (DeHayes, 1988). Its habitat is moist but well drained sandy loam, often at high altitudes. It grows best in a cool, moist climate and attains maximum development in the higher parts of the southern Appalachian Mountains where the atmosphere is more humid and the rainfall heavier during the growing season than in other parts of its range. Local extension of the range of P. rubens, as along the southern Maine coast, is related to marine exposure, which

23 provides a cool growing season and ample moisture supply (Hawley and DeHayes,

1994). The soils where P. rubens and its associates grow are mostly acid Spodosols, Inceptisols, and sometimes Histosols with a thick humus and a well defined horizon characteristic commonly associated with abundant rainfall, cool climates, and softwood cover. P. rubens occurs on well-drained sites in lowland and upland areas (Hawley and DeHayes, 1994). It is found mainly on shallow till soils that average about 45 cm deep to a compact layer. At higher elevations it often grows in organic soils overlying rocks. On poorly drained soils, lack of aeration limits its growth. P. rubens does best on moist, sandy loam soils but also occurs in bogs and on upper, dry rocky slopes. It grows best in mixed wood stands, often along the sides of streams in deep, rich soil. P. rubens grows in a considerable range of soil pH from acid to neutral (Mello, 1987). P. rubens wood is light, soft, narrow-ringed and faintly tinged with red. It is the most common species for lumber production and for log home building. It is the preferred tree for collecting and making spruce gum. Because of its resonance, it is especially adapted to sounding boards in musical instruments. It makes up a large percentage of spruce pulpwood produced in the northeast.

1.7.3 Picea glauca (White spruce) P. glauca is native to North America and is widely distributed across northern North America and exhibits considerable geographic variation (Figure 3) (Canadian Biodiversity). It has a transcontinental distribution (Beaulieu and Deslauriers, 2002). It is one of the farthest ranging conifers in North America. It is found across Canada and

24 î N ?&£ C?

P O

Figure 3. Geographical distribution of Picea glauca (White spruce) across Northern America. throughout Ontario. It grows from Newfoundland, Labrador, and northern Quebec west across Canada along the northern limit of trees to northwestern Alaska, south to southwestern Alaska, southern British Columbia, southern Alberta, and northwestern

Montana, and east to southern Manitoba, central Minnesota, central Michigan, southern Ontario, northern New York, and Maine (Lafontaine et al, 2010).

P. glauca occupies boreal forest and is a native, coniferous, evergreen tree. It typically grows as a medium-sized upright tree with a long, straight trunk, and narrow, spirelike crown. Throughout much of Canada, its average height is about 24 m. It is largely confined to well-drained uplands or river terraces and floodplains (Rajora et ah, 2005). In interior Alaska and the Northwest Territories, P. glauca forests are usually

25 found on stream bottoms, river terraces and lake margins, and on warm, well-drained, south-facing slopes within 8 km of major river valleys (Natural Resources Conservation Service, 2003). Several stands of P. glauca and Populus (Aspen), and P. glauca and

Betual (Birch), are common on relatively dry slopes with a south or southwest exposure, and on excessively drained outwash or deltaic soils. In eastern Canada, the Lake States, and the northeastern United States, P. glauca occurs in many coniferous and mixed coniferous-hardwood forests. Pure stands or mixed stands where it is dominant are not widespread. In Ontario, P. glauca is rarely found in pure stands, except for those growing in plantations (Rajora et al, 2005). It is commonly found with Populus tremuloides (Trembling aspen), Betula papyrifer (White birch) and Abies balsamea (Balsam fir). It is also found with Pinus resinosa (Red pine), Pinus strobus (White pine) and Betula alleghaniensis (Yellow birch). In many areas, P. glauca is classified as the climax species, which means that it represents the last stage in a succession of changes in plant species over a long period of time (Natural Resources Conservation Service, 2003). At climax, it often codominates or forms a significant part of the vegetation in mixed stands with Picea rubens (Red spruce) and P. mariana (Black spruce). Conifers, including P. glauca, tend to occupy shallow outwash soils on upper slopes and flats, while hardwoods or mixtures of hardwoods and spruce are found on deep glacial till soils of lower slopes (Tremblay and Simon, 1989). P. glauca grows well in a wide variety of sites and soils of glacial, lacustrine, marine, or alluvial origin. It also grows well on loams, silt loams, and clays, but rather poorly on sandy soils. It is somewhat site demanding, and often restricted to sites with well-drained, basic mineral soils (Natural Resources Conservation Service, 2003). It

26 grows poorly on sites with high water tables and is intolerant of permafrost. In the Lake States and northeastern United States, it grows mostly on acid Spodosols, Inceptisols, or Alfisols, with a pH ranging from 4.0 to 5.5. In the Northeast, it grows well on calcareous and well-drained soils but is also found extensively on acidic rocky and sandy sites, and in peatlands in coastal areas. P. glauca can tolerate a considerable range in soil pH, from 4.5 to as high as 7.5. P. glauca is an important commercial tree harvested primarily for pulpwood and general construction. Its long, tough fibers produce superior pulp, which is made into a wide range of papers and fiber moulded products. P. glauca wood is light, straight- grained, and resilient.

1.8 Interspecific hybridization in Picea Hybridization is an evolutionary important phenomenon that generates novel genotypes (Arnold, 1997). It is also a major source of genetic variation for adapting species to new environments (Lewontin and Birch, 1966). Transfer of genetic material between species can lead to stabilization of the hybrid zone, hybrid speciation, introgression or can lead to extinction (Harrison, 1993). Introgression can occur to various degrees depending on the number of generations of backcrossing. Unidirectional interspecific gene flow due to partial reproductive barriers between species has been observed in numerous taxa (Vincent, 1965). Hybridization results in a species hybrid complex that combines the traits of both species in a way that may undermine the specific ecophysiological adaptations of both species and can cause identification and forest management problems for these otherwise ecologically distinct species. Hybridization

27 plays a very important role in evolutionary biology which creates multiple variations across genes or gene combinations simultaneously unlike mutations which only affects one gene (Cruzan, 1998).

Picea rubens (Red spruce) is morphologically and genetically closely related to P. mariana (Black spruce) and hybrids between the two are frequently found in nature where their ranges meet (Manley, 1972). Both species are woody plant taxa characterized by large species geographical ranges, air borne pollen dispersal, outcrossing mating system and low levels of population differentiation (Perron and Bousquet, 1997). P. rubens occupy a broad area of allopatry (geographical ranges are entirely separate), but it shares an extensive zone of contact with P. mariana in northeastern North America. In the western part of the P. rubens range, the transition between P. mariana bogs and P. rubens tolerant hardwood stands is usually quite sharp and intermediate habitats suitable for the hybrids are uncommon. Further east, intermediate habitats are common and hybrid swarms extensive (Babola, 1992). It is often difficult to clearly distinguish between P. mariana and P. rubens, particularly in areas where natural hybridization occurs (Gordon,

1996). Morphological, crossability, and randomly amplified polymorphic DNA (RAPD) data have indicated that P. mariana and P. rubens are genetically more closely related than any other species such as P. glauca (White spruce) (Major et al., 2003). The two species are capable of natural and artificial hybridization and share highly similar molecular marker profiles (Gordon, 1996). A morphological investigation demonstrated introgressive hybridization being particularly evident in Nova Scotia, New Brunswick and Quebec. The degree of introgression seems related mainly to differences in

28 ecological isolation, governed by species distribution. Estimates of the degree of natural and artificial hybridization or introgression between these two species vary from minor to extensive (Bobola et al., 1996). There are distinct trends in introgression in the northern

areas. In Quebec the direction of gene flow is mainly into P. rubens, P. mariana remaining relatively pure. In New Brunswick, genes move in both directions, but the tendency is for stronger gene infiltration into P. mariana (Manley, 1972). Present difficulties in the distinction of the two species are the result of habitat modification, migration, and resulting introgressive hybridization during and after the Pleistocene and of disturbance in recent centuries (Perron and Bousquet, 1997). Interspecific hybrids are bred by mating two species from within the same genus. The offspring displays traits and characteristics of both parents. The offspring of an interspecific cross is often sterile thus hybrid sterility prevents the movement of genes from one species to the other keeping both species distinct. In many cases involving segregating genetic materials from interspecific crosses, there is often a high level of aneuploidy and cytological abnormalities due to lack of pairing between some homologous chromosome. P. mariana and P. rubens have the same number of chromosomes 2n = 24, therefore the level of abnormalities are limited. Nkongolo, (1996)

revealed a distinctive chromosome characterization between the two species. Karyotype for both the species was asymmetrical and the P. rubens karyotype was unique in the number of secondary constrictions on many chromosomes. Although the asymmetric karyotype of P. mariana resembles P. rubens karyotype, the two can still be easily identified by distinctive chromosome features. Overall, the diagrammatic comparison of the chromosomes revealed a high degree of similarity between P. mariana and P. rubens.

29 Further, a genomic P. mariana probe strongly hybridized to dots of genomic from P. rubens indicating that there is a high sequence homology between the two species

(Nkongolo, 1996). Populations of P. mariana and P. rubens have declined substantially over most of its geographic range because of excessive harvesting and adverse environmental and climatic changes. Therefore, genetic variation of spruce forests may be enhanced by hybridization. The two species cross-hybridize in nature and in controlled pollination and the importance of these hybrids has been proven and several breeders and geneticists continue to work toward their commercialization (Nkongolo, 1999). The interspecific hybrids of P. mariana and P. rubens can be used as a biological model system for studying the mechanisms of speciation (hybrid speciation) and the level of genetic variation within and between different populations (Perron and Bousquet, 1997). Introgressive hybridization between P. mariana and P. rubens is of ecological and evolutionary interest and is an important issue in the genetic resource management and improvement of these species.

Other hybrids that occur in various Picea species (Alden, 1995) include P. mariana x P. sitchensis (Black ? Sitka spruce), P. glauca ? P. sitchensis (White x Sitka spruce), P. sitchensis ? P. engelmannii (Sitka ? Engelmann spruce) and P. engelmannii ? P. glauca (Engelmann ? White spruce). These interspecific hybrids may play an important role in the improvement, survival and growth of spruce forests.

30 1.9 Objectives

The objective of this study were 1) to determine the effect of interspecific hybridization on genetic variability in P. mariana, P. rubens hybrid populations and 2) to assess the effect of metal contamination on the level of genetic variation in spruce species in the Greater Sudbury region. The first hypothesis is that interspecific hybridization will increase genetic variation and the second is that the long exposure to metals will reduce the level of genetic variation in spruce.

31 Chapter 2: Effect of interspecific hybridization on genetic variation in Picea mariana ? Picea rubens populations

2.1 Introduction

Hybridization is an important factor affecting the evolution and ecology of plant populations. Lewontin and Birch, (1966) suggested that the introduction of genes from another species could serve as the raw material for an adaptive evolutionary advance compared to original hybridization. Genomic studies provided evidence that hybridization indeed facilitates major ecological transitions (Schwenk et al., 2008) and interspecific hybridizations have led to the development of new models in hybridization, which focus on introgression and adaptation (Arnold, 2007). By combining gene pools, interspecific hybridization results in the origin of new genotypes and significant shifts in allele frequencies. Compared to mutations and recombinations within species, interspecific hybridization can result in rapid and long lasting changes among inbreeding species. The evolutionary change due to hybridization can occur within one generation, thereby exposing new gene combinations to natural selection.

P. mariana and P. rubens are morphologically very closely related, are capable of natural and artificial hybridization and they also share highly similar molecular marker profiles. Estimates of the degree of natural hybridization or introgression between the two species vary from extensive to minor (Majora et al., 2005). Controlled crosses by Manley (1975) and Gordon (1976) indicated reproductive barriers to hybridization; however, no record of the reproductive phenological barrier between these closely related species is reported in the literature. Manley and Legid, (1979) presented data supporting P. mariana and P. rubens to be largely ecologically isolated species, each exhibiting physiological

32 characteristics that favor different ecological niches. Results from Manley, (1975) and Manley and Legid, (1979) indicated that artificially produced P. mariana ? P. rubens hybrid seedlings displayed negative heterosis in growth and photosynthesis. The performances of hybrids were lower than that of either species. Examination of photosynthesis and stomatal conductance of mature trees by Johnsen et al., (1998) showed that hybrids did not have lower performance than either parental species.

P. mariana and P. rubens species cross-hybridize in nature and in controlled pollination. The importance of these hybrids has been proven and several breeders and geneticist contribute to work toward their commercialization. Interspecific hybrids developed from the artificial crosses from these two species have not been characterized in detail. The hybrid index presently represents the most popular method of estimating the degree of hybridity of populations and individual plants. This method does not provide any information on the degree of hybrid genetic stability.

Nkongolo, (1999) reported that interspecific hybridization increases genetic variation. But his sampling size was small and the analysis used only few RAPD primers. The objective of the present study was to confirm the level of genetic variation in populations of P. mariana ? P. rubens hybrids made from artificial crosses using ISSR and RAPD marker system.

33 2.2 Materials and Methods

2.2.1 Genetic Material

Picea mariana, Picea rubens and their hybrid spruce seeds were obtained from the Canadian Forest Service seedbank in Fredericton (Table 1). These hybrids are F2 generation and first backcross populations created through a series of controlled pollinations among P. mariana and P. rubens trees across the hybridization index. The hybrid index represents the most popular method of estimating the degree of hybridity of populations and individual plants. Seven hybrid populations (15 samples from each population) were chosen in duplicates. The samples also include a parental P. mariana and a parental P. rubens lines (Table 1). Seeds were placed in clear polycarbonate

"Petawawa germination boxes" lined with a layer of wet filter paper. The boxes were placed in the fridge at 4 0C for 3 days to break dormancy, which were then transferred to a growth chamber at 24 0C. Leaf tissue were then harvested once they reached 5 cm in length and were frozen in liquid nitrogen and stored at -80 0C until DNA extraction.

2.2.2 DNA Extraction

Genomic DNA from individual seed samples was extracted using the CTAB extraction protocol described by Nkongolo, (1999) with some modifications. Leaf tissues were ground in liquid nitrogen into a fine powder and transferred to extraction tubes. 700 µ? of 2x CTAB (hexadecyltrimethyl-ammonium bromide) (2x buffer: 1.4 M Nacl, 100 mM Tris-Hcl [pH 8.0], 20 mM EDTA [pH 8.0] and 2% CTAB) extraction buffer was added to the tube and the mixture was incubated at 60 0C for 45 minutes. The tubes were mixed by inversion every 15 minutes. Following incubation, three successive

34 chloroform/octanol (24:1) washes were performed to separate the DNA from the unwanted proteins and polysaccharides. Each wash consisted of adding to the DNA/CTAB solution a volume of chloroform/octanol (24:1) mix equal to the volume of CTAB that was initially added. The tubes were balanced, mixed well for 15 minutes, and the emulsion was centrifuged at 13000 rpm for 15 minutes at 25 0C. The supernatant was then transferred into a clean tube using filtered pipette tips which would be used for a new wash and then the rest discarded into a waste bottle. Following the washes, DNA was precipitated by adding to the DNA/CTAB solution an equal volume of isopropanol (40% [v/v] final concentration). The tubes were weighed, balanced, and stored overnight in a freezer at -20 0C. The samples were centrifuged at 7000 rpm for 20 minutes at 4 0C. The supernatant was poured out and an ethanol wash was performed in order to remove any remaining salts on the pellets left over by the CTAB buffer. To each tube, 1 ml 70% ethanol was added and left for 10 minutes. The pellets were vacuumed dry and resuspended in 20 µ? of Ix TE (Tris-EDTA) (1OmM Tris HCl [pH 8.0], 1 mM EDTA [pH 8.0]) until completely dissolved. The tubes were then stored at 4 0C overnight and then placed in the freezer at -20 0C until further analysis.

2.2.3 Degradation Analysis and DNA Quantification Degradation analysis was performed to determine the quality of the extracted genomic DNA. The protocol consisted of loading each individual sample into a 1% agarose gel in Ix TAE (Tris-Acetate-EDTA) buffer pre-stained with 2.5 µ? of ethidium bromide. About 5 µ? of stock DNA were mixed with 3 µ? of 6x loading buffer and loaded

35 Table 1. Plant materials used in the study. Hybrid and parent constitution Seed bank number

P. mariana ? P. rubens 100 ? 100 9430298 and 9430299 0x0 9430308 and 9430310 0 ? 100 9430305 and 9430306 100 ? 0 9430328 and 9430329 75 ? 75 9430332 and 9430333 50x50 9430314 and 9430316 25 ? 25 9430320 and 9430322

0 represents pure Picea mariana and 100 pure Picea rubens. into the wells of an agarose gel. The gel was run at 2.8 V/cm for approximately 1 hour. The agarose gel was then visualized under a UV light source, documented using a Bio-

Rad ChemiDoc XRS system and analyzed with the Discovery Series Quantity One ID Analysis Software.

The concentration of the genomic DNA extracted from the leaf tissue of an individual sample was determined by fluorochrome Hoechst 33258 (bis-bensimide) with the Fluorescent DNA quantification kit from Bio-Rad (catalogue # 170-2480). A series of 96-well microplates were spotted with 200 µ? of Hoechst 33258 dye prepared using a fluorescent DNA quantification kit (Bio-Rad). The dye mixture was composed of 17.99 ml ddH20, 2 ml 1Ox TEN Assay buffer and 4 µ? of 10 mg/ml Hoechst 33258 dye. To determine the concentration of the DNA samples, a standard curve was generated by adding in duplicates of known standard calf thymus DNA to the plate in amounts that

36 varied from a maximum of 17500 ng to a minimum of 25 ng. Extracted genomic DNA samples (2 µ?) were then added to the plate in duplicates. The DNA fluorescence intensity was measured using a BMG LABTECH FLUOstar OPTIMA microplate multi- detection reader in fluorescence detection mode. The average concentrations of the unresolved duplicate samples were calculated and all DNA samples were then standardized to a final concentration of 5 ng/µ?.

2.2.4 Amplification with ISSR and RAPD primers

Several ISSR and RAPD primers synthesized by Invitrogen were chosen for preliminary amplification with DNA samples from each population. The amplification was carried as described by Nkongolo, (1999) with some modifications. Polymerase

Chain Reaction (PCR) amplification was performed in 25 µ? volumes which contained 2.1 µ? of 1Ox buffer (Applied Biosystems), 0.5 µ? of 200 µ? of each dNTP (dTTP, dATP, dCTP and dGTP), 2.5 µ? of 2 mM MgCl2, 0.5 µ? of 0.5 µ? primer, 20 ng of genomic DNA template and 4 µ? of 0.625 units of Taq polymerase (Applied Biosystems).

For each primer a negative control reaction was included where ddtkO was added instead of DNA. To each reaction tube a drop of mineral oil was added to prevent evaporation. The amplification was carried out in a DNA thermal cycler (Perkin Elmer, Foster City, CA). The program was set to a hot start with an initial denaturatiön of 5 minutes at 95 0C followed by 2 minutes at 85 0C during which the Taq mix was added, and then 42 cycles of 1 minute at 95 0C, 2 minutes at 55 0C and 1 minute at 72 0C, with a final extension of 7 minutes at 72 0C and a subsequent cooling at 4 0C. To the PCR products 5 µ? of 6x loading buffer was added to make a final volume of 30 µ?. About half of this volume was loaded into 2% agarose gel in 0.5x TBE (Tris-

37 Borate-EDTA) buffer stained with 4 µ? of ethidium bromide to separate the amplified DNA. The gel was run at 2.8 V/cm for 2 hours and then visualized under UV light source, documented with the Bio-Rad ChemiDoc XRS system and analyzed for band presence or absence with the Discovery Series Quantity One ID Analysis Software.

2.2.5 Statistical Analysis

ISSR and RAPD assays of each population were performed twice. Six ISSR primers and four RAPD primers which yielded the best banding patterns and gave consistent profiles across the populations were chosen for further analysis. Only reproducible amplified fragments were scored. The presence and absence of bands were scored as 1 or 0 respectively for each sample in order to determine the within and among population variation. Such designations were carried out with the Quantity One software by identifying the bands and comparing them to the IKb+ ladder which served as a marker system. The level of genetic variation present was analyzed using Popgene software version 1.32 (Yeh et al, 1997). Popgene is a computer software used for the analysis of genetic variation among and within populations using co-dominant and dominant markers and quantitative traits. The Popgene software was used to determine the intra and inter-population genetic diversity parameters such as percentage of polymorphic loci, observed number of alleles (Na), effective number of alleles (Ne), Nei' s gene diversity (h), and Shannon's information index (I).

38 2.3 Results

2.3.1 Degradation Analysis Extracted genomic DNA was tested for quality. Total genomic DNA was run on a 1% agarose gel stained with ethidium bromide using Ix TAE buffer. The gel was then visualized under UV light (Figure 4). All the samples showed a large molecular weight band at the top of the gel with the absence of smearing. This indicated that all the DNA samples extracted were not degraded and were suitable for PCR amplification. Other low molecular weight bands on the gel were RNA, the clearest of which made up the subunits of the ribosomal RNA.

2.3.2 ISSR amplification and polymorphism Preliminary screening with several ISSR primers (Table 2) was performed on

DNA samples from each population in order to determine which primers were most likely to produce reproducible bands. From that initial screening, six primers HB 13,

ISSR 5, ISSR 9, 17899A, 17898B, and UBC 841 with the most reproducible banding patterns were used to amplify all DNA samples for population analysis. The results of these PCR amplifications are illustrated in Figure 2, Figure 3, Figure 4, Figure 5, Figure

6, and Figure 7.

Table 3 shows the level of polymorphism within and among P. mariana ? P. rubens hybrid populations. Primer HB 13 generated the lowest level of polymorphic loci (17%) and primer ISSR 9 produced the highest polymorphism (58%). The amplification of DNA samples using ISSR primers ISSR 9 and 17898B (Figure 7 and Figure 9) produced a high number of strong bands. Primer ISSR 9 produced a total of 32 bands and 39 primer 17898B produced a total of 28 bands. The size of the amplified fragments ranged from 190 bp to 2200 bp (Table 2). Primer ISSR 9 and 17898B generated 58% and 47% polymorphism, respectively across the 14 populations (Table 3). These two primers generated a total of 60 bands (Table 4) and the level of polymorphism within the population ranged from 30% to 73% (Table 4). The highest level of polymorphism was found in population 9430320 (25 ? 25) and the lowest in populations 9430308 and

9430310(0x0). The level of polymorphism in parental line were 31% for 0 x 0 (P. mariana populations 9430308 and 9430310) and 43% for 100 ? 100 (P. rubens populations 9430298 and 9430299). The 46% level of polymorphic loci in 100 ? 0 hybrid (population 9430328) was similar to that found in 0 ? 100 hybrid (population 9430306) (Table 3). The average level of polymorphism was 50% for 25 ? 25 hybrid populations (9430320 and 9430322) and 45% for 75 ? 75 populations (9430332 and 9430333). It varies between 35% and 48% for 50 ? 50 hybrid populations (9430314 and 9430316).

2.3.3 RAPD amplification and polymorphism Conditions were optimized to allow reproducible amplifications of RAPD bands. RAPD primers were also used to determine genetic variation among and within P. mariana ? P. rubens hybrid populations. Several RAPD primers (Table 6) were screened. Some generated poor or no bands and few produced clear bands across all samples. Based on the initial screening, four primers OPA 4, OPA 8, P 184, and UBC 186 that produced good and clear banding patterns were selected to amplify all DNA samples for population analysis. The results of these PCR amplifications are presented in figures 8,

40 9, 10 and 11. These primers generated bands varying from 210 bp to 2220 bp (Table 6). Table 7 shows the level of polymorphism within and among P. mariana ? P. rubens hybrid populations. Primer OPA 4 generated a low level of polymorphism (55%) and primer UBC 186 produced the highest level of polymorphism (82%). Two primers, P 184 and UBC 186 produced the most number of polymorphic bands. Primer P 184 generated a total of 21 bands ranging from 300 bp to 1880 bp (Table 6). The level of polymorphism across the seven populations for this primer was 66% (Table 7). Primer UBC 186 generated a total of 19 band ranging from 210 bp to 2190 bp (Table 6) of which 82% were polymorphic (Table 7). These two primers P 184 and UBC 186 produced a total of 40 bands (Table 8). The level of polymorphism within population ranged from 63% to 83% (Table 8). The highest level of polymorphism was found in population 9430314 (50 ? 50) and the lowest in populations 9430305 (0 ? 100) and 9430333 (75 ? 75). The results of the amplifications from the four primers are presented in Figure 11, Figure 12,

Figure 13, and Figure 14. The level of polymorphism in parental populations were 74% for 0 ? 0 (P. mariana population 9430308) and 77% for 100 x 100 (P. rubens population 9430298). The level of genetic polymorphism of 74% in 100 ? 0 hybrid (population 9430329) was much higher than in the reciprocal cross 0 ? 100 hybrid averaging 57% (population 9430305) (Table 7). The level of polymorphic loci were 57%, 73% and 58% for 25 ? 25 (population 9430324), 50 ? 50 (population 9430314) and 75 ? 75 (population 9430333) hybrids, respectively.

41 2.3.4 Genetic Diversity Various genetic parameters were calculated using Popgene software version 1.32 (Yet et al, 1997). The analyses generated the percentage of polymorphic loci (%), Nei' s gene diversity (h), Shannon's information index (I), observed number of alleles (Na), and

the effective number of alleles. Table 5 describes the genetic parameters of fourteen P. mariana ? P. rubens hybrid populations generated based on ISSR marker system. The level of polymorphic loci among populations was 58%. For each population, the percentage of polymorphic loci varied between 30% (0 ? 0; population 9430308) to 52% (25 ? 25; population 9430320). Nei's gene diversity (h) ranged from 0.1285 to 0.1955, with a mean of 0.1733. The highest gene diversity was in population 9430320 (25 ? 25) and the lowest in population 9430310 (0 ? 0). A similar pattern was observed for the Shannon's information index (I), with the highest value of 0.2885 observed in population 9430320 (25 ? 25) and the lowest value of 0.1878 observed in population 9430310 (0 ? 0). The observed number of alleles (Na) ranged from 1.3034 (0 ? 0; population 9430308) to 1.5241 (25 ? 25; population 9430320) which is a similar pattern observed in percentage of polymorphic loci (%). The effective number of alleles (Ne) ranged from 1.2250 (0 ? 0; population 9430310) to 1.3411 (25 ? 25; population 9430320) which is a similar pattern observed in Nei's gene diversity (h) and Shannon's information index (I). Table 9 presents the genetic parameters of seven P. mariana ? P. rubens hybrid populations generated based on RAPD marker system. The level of polymorphic loci among populations was 67% which is high compared to the ISSR marker system. In comparison, RAPD primers generated higher values for each parameter compared to

42 ISSR. The percentage of polymorphic loci varied between 57% (0 x 100; population 9430305 and 25 x 25; population 9430324) and 76% (100 ? 100; population 9430298). Nei's gene diversity (h) ranged from 0.2127 (0 ? 100; population 9430305) to 0.3166 (100 ? 100; population 9430298), with a mean of 0.2687. A similar pattern was observed for the Shannon's information index (I), with the lowest value observed in population 9430305 (0.3148) (0 ? 100) and the highest value observed in population 9430298 (0.4582) (100 ? 100). The observed number of alleles (Na) ranged from 1.5714 (0 ? 100; population 9430305 and 25 ? 25; population 9430324) to 1.7662 (100 x 100; population 9430298), with a mean of 1.6716. The effective number of alleles (Ne) ranged from 1.3716 (0 ? 100; population 9430305) to 1.5715 (100 ? 100; population 9430298), with a

mean of 1.4793.

43 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

(t.---.<#i„, #*«# fe*rv 12000bp

jijuyi. 600bp

lOObp

Figure 4. Degradation analysis illustrated on a 1% TAE agarose gel for DNA from P. mariana ? P. rubens hybrid samples. The top band indicates intact genomic DNA. Lack of smearing is indicative of lack of degradation. Lane 1 and 22 contain lkb+ ladder; lanes 2 to 21 contain P. mariana ? P. rubens hybrid samples.

44 Table 2. The nucleotide sequence of ISSR primers used to screen DNA from Picea , mariana ? Picea rubens hybrid samples. ISSR Primer Nucleotide sequence (5'—>3') Number of Fragment size identification fragments range (bp) amplified

ISSR HB 13 GAGGAGGAGGC 26 260-1450 ISSR HB 15 GTGGTGGTGGC 24 150-2160

SC ISSR 5 ACGACGACGACGAC 19 330-1500

SC ISSR 9 GATCGATCGATCGC 32 250-2200 ISSR 17899A CACACACACACAAG 23 170-1600

ISSR 17898B CACACACACACAGT 28 190-2170

ISSR UBC 841 GAAGGAGAGAGAGAGAYC 17 250-1600 VO

o 90 O r- VO ? es V)

V) s? OO CN d VO !-?

V) 00 CN O O vo CO S-I Sv (N (U e ? v> bû Os co d f) C CO O

s? (N co £N ICV) (N Oh CO ?5 90 O ¡S Sv VO Dh v> s? 00 ¦a Iov -O es cN d (N Ov >? CO -C S ?\> (N 190 -O ¡5 90 vo S CO 5; vo Ik <3 (N en JN Ivo (N ß VO 90 vo f> Sv vo 'S· X V)

s? Si •2 (N v> fo IT) F^ vo

V) (N O s? co S* co (N VO (N CO Qh Ö (N ? -3 CO Vh ?3 JN •^ O Oh S F^ ¦* CO M VO -C

00 V) s as (N 00 vvj m (N s? s? d d ß? S & fa "ß (U Ö 00 Ov < CQ S & CU as OO O d tí ? c¿ a? OV ? e S Ü tu

-a ¦e Xl »O oo O CN m CS es X

~~O (N OO O vi co ÇN SO VO X i-H O o\ Wi fi V-) CN fi VO~~

~~O CN OO O IO CO CN VO (U X O O O o ON fi fi r- fi Wi C O o (N OO O X CO ÇN VO (U O es O v3 ?d fi f> Wi -D~~

~~O (N oo O c3 X co CN VO O es O ¡ri VO CS fi fi Wi~~

~~(N OO O C O VO ?? O co CN ? O X co VO OS Wi •c fi O CM Ci VO X> >, O (N OO O X!. CO CN VO Wi O S O X co fi Wi ^) fi O CN fi Wi S V. (N O co oo VO O X CN ss O fi O fi~~

X CN O co oo VO OO O O X CN OV CS fi O tri fi O O co O (N oo O s? X CO (N VO O su O ò CS CS CN fi Wi ? Oh O O CN 00 O e X co CN VO o OO O <>5 O co Wi ON O fi C/3 S cd CS CN fi Wi S "S ?- > Vh a IS ft va (U O s* S^ X) O w PQ B t/J e 1^ .a ? oo s ce F TS C Î-H Oh a 00 Ä" s O C O 'C C/3 C-- O « O Oh Ph H Oh g ? 'c/5 ¦)-<<*> Oh^* (? ?* (U G ¡? * ? ? " tí fi Ä ? Vh (? 33 « ?-· xa (UOh faQ

100 represents pure Picea rubens 0 represents pure Picea mariana Table 6. The nucleotide sequence of RAPD primers used to screen DNA from Picea mariana ? Picea rubens hybrid samples. RAPD Primer Nucleotide sequence Number of Fragment size identification (5'-3») fragments range (bp) amplified

~~OPA 4 aatcgggctg 17 240 - 1980~~

~~OPA 8 gtgacgtagg 20 220 - 2220~~

~~P 184 caaacggcac 21 300-1880 UBC 186 gtgcgtcgct 19 210-2190 Grass 4 gaggcgctgc 6 290 - 600~~

~~Grass 8 gggtaacgcc 4 100 - 850~~

~~OPA 17 gaccgcttgt O 0 OPE 9 cttcacccga O 0 OPA 14 tctgtgctgg O 0 o~~

~~LiOh >äS^ O w- IiS3 ut B O IR «? Ci SO CS •a «? SO SO OO Oh Q CL, < O ON r- (N G- CN CN T-H J- Pi X ri 1?1 IT) Tf t- r*i Tf Wi~~

-o CU ?—» O e- On t- T-H t? IT) O ÇN j- D X O JN P^ P; SO ri C t-? t- (U fi in So öß Cl O C- s\ r- t— O CN fi j- ? OO Ci ? ò Wi 3 T-H Wi Oh Ci So Tf O Cl1 ? •a o C- o On t- X t-? CN ÇN T-H j- S. O ,tí O P; r- Tf ci T-H Wi r-

~~-o S Sv O O On t- O C- CN CN T-H j- Wi t-? %> O X Tf Ci O Tf Wi~~

~~t- e O On ÇN P^ t- Ci O Wi O Ci ^t On O O O On r- (U t— ÇN ÇN ?—I J- S3 S3 <5N SO Oh S 2 ON Wi ?-~~

~~?2 S CU ? ? ^ Oh ko & so L S^ S o OO >H e OO T-H C co eu >, < < OO U £· a "o Dh a* PQ O o « Oh L 3 PU H Ph -o C WH O O 2? ^ O C (/3 _o Ph g 'ce 4HCC Qh^H C K > CU IU 46. X ? o CU~~

~~Accession* 298 308 305 329 333 314 324 number Hybrid Index 100x100 0x0 0x100 100x0 75x75 50x50 25x25~~

~~Primer P 184 14/21 17/21 11/21 14/21 11/21 16/21 14/21~~

~~UBC 186 18/19 13/19 14/19 17/19 15/19 17/19 15/19~~

~~Total 32/40 30/40 25/40 31/40 26/40 33/40 29/40~~

~~Polymorphic 80% 75% 63% 78% 65% 83% 73% bands (%) * Each accession number Starts with prefix 9430 100 represents pure Picea rubens 0 represents pure Picea mariana~~

~~51 Table 9. Genetic diversity parameters of Picea mariana ? Picea rubens hybrids based on RAPD data. Accession Hybrid P(%) Na Ne h I number Index 9430298 100x100 76 1.77 1.57 0.31 0.46~~

~~9430308 0x0 74 1.74 1.53 0.29 0.43~~

~~9430305 0x100 57 1.57 1.37 0.21 0.31~~

~~9430329 100x0 74 1.74 1.53 0.29 0.43~~

~~9430333 75x75 58 1.58 1.39 0.22 0.33~~

~~9430314 50x50 72 1.73 1.56 0.31 0.44~~

9430324 25x25 57 1.57 1.39 0.22 0.33 Mean L67 1.48 0.27 0.39 Genetic diversity descriptive statistics. P: percentage of polymorphic loci; Na: observed number of alleles; Ne: effective number of alleles; h: gene diversity (Nei, 1973); I: Shannons' s information index

~~100 represents pure Picea rubens 0 represents pure Picea mariana~~

~~52 1 2 3 4 5 « 7 S 9 lu 11 12 13 14 IS li 17 18 19 20 21 22 23 24 25 26 27 28 2d 30 31 32 33 34 35 3? 37 WMXWmmm m u f mmm «?»«*#^ mmm&mmm»<^^m:^!ímM0M m~~

~~s 1 ? ? ? m~~

~~1 ?;, ? i I i tí~~

Figure 5. ISSR amplification of P. mariana ? P. rubens hybrid samples with primer HB 13. Lanes 1, 19 and 37 contain lkb+ ladder; lanes 3 to 17 contain P. mariana ? P. rubens hybrid samples from population 9430332; lanes 21 to 35 contain P. mariana ? P. rubens hybrid samples from population 9430333.

~~53 1 2 3 4 5 « 7 8 9 16 11 12 13 14 15 16 17 IS 19 20 21 22 23 24 25 2« 27 28 29 30 31 32 33 34 35 36 37 38~~

Figure 6. ISSR amplification of P. mariana ? P. rubens hybrid samples with primer ISSR 5. Lanes 1, 19 and 37 contain lkb+ ladder; lanes 4 to 17 contain P. mariana x P. rubens hybrid samples from population 9430322; lanes 21 to 35 contain P. mariana ? P. rubens hybrid samples from population 9430324.

~~54 1 23 4 5 ? 7 8 9 10 XlJl 13 14 15 li 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35~~

~~? *t(f *»* : . Wf w~~

~~mmém~~

Figure 7. ISSR amplification of P. mariana ? P. rubens hybrid samples with primer ISSR 9. Lanes 1 and 35 contain lkb+ ladder; lanes 3 to 17 contain P. mariana ? P. rubens hybrid samples from population 9430298; lanes 19 to 33 contain P. mariana ? P. rubens hybrid samples from population 9430299.

~~55 1 2 3 4 5 6 7 8 9 lö 11 12 13 14 15 li 17 18 19 20 21 22 23 24 25 2Ó 27 28 29 30 31 32 33 34 3S 36 37 lt!i!; y ta4 batit ttoal *W mam ss ·*· p ? ri r?~~

~~, .—^a (^ luit lutti ' *"** W^ ^f *T llll(¦«(»^?^·,>·,,'*,",**,~~

~~jigSS««*«**·""'»········*·~~

Figure 8. ISSR amplification of P. mariana ? P. rubens hybrid samples with primer 17899A. Lanes 1, 19 and 37 contain lkb+ ladder; lanes 3 to 17 contain P. mariana x P. rubens hybrid samples from population 9430305; lanes 21 to 35 contain P. mariana x P. rubens hybrid samples from population 9430306.

~~56 1 2 3 4 5 6 7 S 9 10 11 12 13 14 15 16 17 IS 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33.34 35~~

Figure 9. ISSR amplification of P. mariana ? P. rubens hybrid samples with primer 17898B. Lanes 1 and 35 contain lkb+ ladder; lanes 3 to 17 contain P. mariana x P. rubens hybrid samples from population 9430328; lanes 19 to 33 contain P. mariana ? P. rubens hybrid samples from population 9430329.

~~57 1 2 3 4 5 ß 7 » 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 » 30 31 32 33 34 35 36 37~~

~~: t , 4 ¦*.¦*¦ ?* m -.eiMtê Ri "* ? ? :s -3 ~~~

Figure 10. ISSR amplification of P. mariana ? P. rubens hybrid samples with primer UBC 841. Lanes 1, 19 and 37 contain lkb+ ladder; lanes 3 to 17 contain P. mariana ? P. rubens hybrid samples from population 9430305; lanes 21 to 35 contain P. mariana ? P. rubens hybrid samples from population 9430306.

~~58 1 2 3 4 5 « 7 » 9 10 11 12 13 14 Ï5 16 17 H 19 20 21 22 13 24 25 M 27 2» 29 30 31 32 33 34 35 36 37 3S 39 40~~

~~L·-" ^kiiBi^Lrc~JBa&MÌ~~

Figure 11. RAPD amplification of P. mariana x P. rubens hybrid samples with primer OPA 4. Lanes 1, 15, 29 and 40 contain lkb+ ladder; lanes 2 to 14 contain P. mariana x P. rubens hybrid samples from population 9430298; lanes 16 to 28 contain P. mariana x P. rubens hybrid samples from population 9430305; lanes 30 to 39 contain P. mariana ? P. rubens hybrid samples from population 9430308.

~~59 I 2 34 5 6 7 S 9 10 1112 13 14 15 1617 18 19 202122 23 24 25 26 27 28 29 30 3132 33 34 35 36 37 38 39 40 HŒiai~~

~~??????? CTJ~~

Figure 12. RAPD amplification of P. mariana x P. rubens hybrid samples with primer OPA 8. Lanes 1, 15, 29 and 40 contain lkb+ ladder; lanes 2 to 14 contain P. mariana ? P. rubens hybrid samples from population 9430298; lanes 16 to 28 contain P. mariana ? P. rubens hybrid samples from population 9430305; lanes 30 to 39 contain P. mariana ? P. rubens hybrid samples from population 9430308.

~~60 1 2 3 4 5 6 7 g 9 10 11 12 lì 14 13 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33~~

~~wt «it mi ß *· m *** *· ß·~~

Figure 13. RAPD amplification of P. mariana ? P. rubens hybrid samples with primer P 184. Lanes 1, 5, 19 and 33 contain lkb+ ladder; lanes 2 to 4 contain P. mariana ? P. rubens hybrid samples from population 9430308; lanes 6 to 18 contain P. mariana ? P. rubens hybrid samples from population 9430314; lanes 20 to 32 contain P. mariana x P. rubens hybrid samples from population 9430324.

~~61 1 2 3 4 5 6 7 8 9 10 ? 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 2728 29 30 31 32 33 34 35 36 37~~

~~w»·~~

Figure 14. RAPD amplification of P. mariana ? P. rubens hybrid samples with primer UBC 186. Lanes 1, 19 and 37 contain lkb+ ladder; lanes 3 to 17 contain P. mariana ? P. rubens hybrid samples from population 9430298; lanes 21 to 35 contain P. mariana x P. rubens hybrid samples from population 9430308.

62 2.4 Discussion The ISSR analysis revealed various levels of polymorphic loci within and among the 14 populations of P. mariana ? P. rubens hybrid. Populations with a hybrid index 0 ? 0 (parental P. mariana) has the lowest polymorphism of 30% and population with hybrid index 25 ? 25 has the highest polymorphism of 50%. The rest of the populations with hybrid index 100 ? 100, 0 ? 100, 50 ? 50, 100 ? 0 and 75 ? 75 all have polymorphism ranging from 41% to 47%. Only one population with hybrid index 25 ? 25 has a high polymorphism compared to the rest of the hybrids. There were no significant differences between the level of polymorphism detected in P. rubens populations compared to those

~~P. mariana.~~

RAPD analysis revealed high level of polymorphism compared to ISSR data. Overall, the polymorphism ranged from 57% to 74%. The level of polymorphism was similar between P. mariana and P. rubens when RAPD marker system was used. Previous reports by Nkongolo, (1999) revealed that the polymorphism was much higher within spruce hybrid populations than pure species. In fact, Nkongolo, (1999) found a 90% level of polymorphism for the hybrids and 10% for the pure lines in his RAPD analysis study (Nkongolo, 1999). The pure P. mariana and P. rubens lines in Nkongolo, (1999) study were derived from embryonic tissues. Detailed analysis of DNA from 0x0 (P. mariana) and 100 ? 100 (P. rubens) revealed that these samples used in the present study were not true pure spruce species. In fact, Nkongolo and Ranger, (2005, unpublished data) detected P. mariana and P. rubens specific markers in all the samples studied including 0 x 0 and 100 ? 100 populations. This suggests that the parental lines used for this study were contaminated and were indeed hybrids.

63 In other studies of adaptive and morphometric trait variation, P. rubens has comparatively low genetic variability than P. mariana (Fowler et al, 1988). These results were supported by low levels of genetic variation in biochemical and molecular markers (Perron et al, 1995), particularly when P. rubens is compared with sympatric spruce species (Tremblay and Simon, 1989).

The RAPD and ISSR analysis of hybrids indicated that the level of genetic variation was statistically similar among populations regardless of hybrid index. Cytological analysis on these hybrids by Nkongolo, (1999) revealed a normal mitotic behavior at prophase, metaphase, anaphase, and telophase in all hybrid populations. All the trees analyzed from different cross combinations were euploids. This is further indication that P. mariana and P. rubens are closely related species. Jaramillo and Bousquet (2003) in their study on mitochondrial DNA revealed that the genetic variation in both P. mariana and P. rubens were low but, unexpectedly, high diverse mtDNA haplotype structures were observed in and around the zone of contact for both species. Jaramillo and Bousquet (2005) confirmed that these spruce species are found in populations next to or into the zone of contact (Jaramillo and Bousquet, 2005).

Overall, the results of ISSR and RAPD analyses were different. Similar trends were reported in a study conducted by Fang and Roose (1997) in Citrus species. There are however few reports of high level of polymorphism with ISSR system compared to RAPDs in several plants (Nkongolo et al., 2005; Raina et al, 2001). Technically, RAPD and ISSR markers target different areas in the genome. RAPD markers reveal polymorphism in coding and non-coding regions, as well as repeated or single copy sequences covering the entire genome (William et al, 1990). The origin of the ISSR

64 amplification products is known to be from the sequences between two simple-sequence repeat (also known as Microsatellite) primer sites where length variation does not necessarily reflect simple-sequence length polymorphism (Zietkiewicz et al, 1994). Microsatellite loci are dispersed throughout the genome and are hypervariable because of DNA slippage (Semagn et al, 2006). Most often ISSR detects more polymorphism than RAPD primers because of the high levels of variability in microsatellite loci. The discrepancy between variations revealed by RAPD and ISSR result from different targeted genomic areas which undergo a different evolutionary process due to selection forces (Qian et al, 2001). Different genetic information is generated when RAPD and ISSR molecular marker techniques are used to assess the inter-specific and intra-specific variability. The level of variation detected with each system greatly depends on the primer used therefore making comparisons inappropriate regarding level of polymorphism generated with ISSR and RAPD marker systems.

~~65 Chapter 3: Metal content in soil from Sudbury region (Ontario, Canada)~~

3.1 Introduction Metal mining and processing in Sudbury is well known for its large deposits of nickel, copper, cobalt, iron and other element contaminating soil and water (Deng et al, 2007). The practice of open yard mining and then the construction of smelters released large amounts of sulphur dioxide and particulates containing metals into the atmosphere (Gratton et al, 2000). The impact of this on the forests in the Sudbury region was extremely detrimental and the release of phytotoxic gases killed trees and ground flora over an extensive area (>1000 km2, Hutchison and Symington, 1997). The toxic metal pollutants along with physical disturbances in the environment can influence plant survivorship, recruitment, reproductive success, mutation rates, and migration all of which affect the genetic diversity of populations in the area (Deng et al, 2007).

During the last 30 years production of nickel, copper and other metals has remained at high levels but industrial sulphur dioxide emission has been reduced by about 90%. This has allowed for a certain degree of recovery to occur (Backor and Fahselt, 2004). This recovery has been sustained by the Sudbury Regreening/Land Reclamation program that has reached over 9 million trees planted in the Greater Sudbury Region (VETAC, Regreening Greater Sudbury, 2010). Most of the trees planted were conifers including Pinus banksiana (Jack pine), Pinus resinosa (Red pine), Picea mariana (Black spruce) and Picea glauca (White spruce).

66 The main objective of this component of the present study is to determine the current level of metal content in soil collected from spruce populations in the Greater Sudbury region to further assess the ecosystem sustainability.

~~3.2 Materials and Methods~~

3.2.1 Sampling Soil samples were collected from five sites within the Sudbury region (site 1 to 5) (Figure 15). One site located approximately 90 km from Sudbury was used as a reference (site 5). For each area three soil samples were collected. Soil samples were collected from top soil 0 - 5 cm (organic layer) and from bottom soil 5 - 20 cm (mineral layer). These samples were air dried, labeled, and stored prior to analysis.

3.2.2 Metal Analysis Soil samples were analyzed in collaboration with TESTMARK Laboratories Ltd. Sudbury, Ontario, Canada. The laboratory is ISO/IEC 17025 certified, a member of the Canadian Council of Independent Laboratory (CCIL) and the Canadian Association of Environmental Analytical Laboratories (CAEAL), and is accredited by the Standards Council of Canada (SSC). The laboratory employs standard QA/QC procedures, involving blank and replicate analyses and with recovery rate of 98 ± 5% in analyses of spiked samples depending on element selected, in their inductively coupled plasma mass spectrometry (ICPMS) analyses reported here. The minimum detection limits (MDL) following microwave digestion of plant tissue Aqua Regia for elements reported here, were: Al 0.05 µg/g (0.5 µ^), As 0.05 µg/g (0.5 µ^), Cd 0.05 µg/g (0.5 µ^), Co 0.05

~~67 ¦ Milnet îfe -Smelter f Timmïos *|Site4 aud 5 > "? }Weiraßei. v' ? Ceittar Capraoi tâke - Stead /'N V Levacfc Valley East ' Wasîiegamî. eh»,* Süäbury^lVal Cwpn ¦? ^ Copper CWl0 JB .^; f~~

~~Cataire~~

~~Burwash~~

.Defame« 1 Figure 15. Locations of the soil sampling area from the Greater Sudbury region. Site 1: Lively; Site 2: Conisten (close to HW17); Site 3: ~ 40km from Sudbury HW144 towards Timmins; Site 4: ~ 16km from site 3 HW144 towards Timmins; Site 5 (control): ~ 35km from Site 4 HW144 towards Timmins.

µ^ (0.5 µ^d), Cu 0.05 µg/g (0.5 µ&£, Fe 1.0 µg/g (10 µ g/g), Pb 0.05 µg/g (0.5 µ^), Mg 0.2 vzJg (2.0 µ^), Mn 0.05 µß^ (0.5 µ^), Ni 0.05 µg/g (0.5 µg/g) and Zn 0.05 µ§/§ (0.5 µ§/§> These MDLs reseci actual sample weights arc dilutions; :.r.sîn::îT.Gri detection limits were lower. The data for the metal levels in soil and tissue samples were analyzed using SPSS 7.5 for Windows. All the data were transformed using a logio transformation to achieve a normal distribution. Variance-ratio test was done which make certain assumptions about the underlying population distributions of the data on which they are used; for example that they are normal. If the assumptions of the parametric test were violated (if the distributions are too severely skewed) nonparametric test was used in place of parametric

68 test. Kruskal-Wallis test the non-parametric analog of a one-way ANOVA was used to compare independent samples, and tests the hypothesis that several populations have the same continuous distribution. ANOVA followed by Tukey's HSD multiple comparison analysis were performed to determine significant differences (p < 0.05) among the five

~~sites.~~

~~; 3.3 Results~~

~~Recovery and precision for all elements in reference soil samples were within acceptable range. The estimated levels of metal content in different sites are illustrated in~~

and 11. The levels of the metals measured were low in the control sites. Overall, the results indicated that nickel and copper continue to be the main contaminants of top soil (Table 10) in sites near the smelters (site 1 and 2). The values ranged from 30.9 to 1600.0 mg kg"1 and from 52.3 to 1330.3 mg kg"1 for nickel and copper respectively. Arsenic concentration exceeded the OMEE guidelines (Table 20) in site 1 and manganese level exceeded the guideline in site 2. Their concentration ranged from 2.2 to 46.0 mg kg" 1 and 163.6 to 6610.3 mg kg"1 for arsenic and manganese, respectively.

Aluminum, iron and magnesium concentrations were significantly higher in sites 1 to 4 (top layer, Table 10) compared to the control site 5. The values ranged from 1673.3 to 9193.3 mg kg"1, 2193.3 to 31433.3 mg kg"1 and 349.6 to 6866.6 mg kg"1 for aluminum, iron and magnesium, respectively. Cadmium, cobalt, lead and zinc levels were within the OMEE guideline (Table 20). The values for these metals ranged from 0.3 to 2.1 mg kg"1, 1.6 to 37.9 mg kg"1, 18.2 to 176.0 mg kg"1 and 52.0 to 86.8 mg kg"1.

~~The control site 5 was always among the least contaminated for the metals analysis. AU the metal concentrations obtained from the bottom layer (5 - 20 cm) (Table 11) were within the OMEE guideline.~~

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~~• ?«* Cd (50 53 53 S3 3.4 Discussion~~

The Canadian Environmental Protection Act (CEPA) has labeled lead and mercury as toxic substances. Further assessments have deemed inorganic arsenic, cadmium, oxidic, sulphidic and soluble inorganic nickel compounds, and a number of other substances to be toxic (Ogilvie, 2003). According to the CEPA, some of these substances are carcinogenic to humans and may be endocrine disrupters and strong chemical poisons. There are 15 base metal smelters in Canada emitting CEPA substances at various levels. Of these 15, five are in Ontario with the major concentration of activity being in the Sudbury district where Vale (formerly Inco) and Xstrata Nickel (formerly Falconbridge) have operated for years (Dudka et al., 1995). The Vale facility at Sudbury is an integrated complex of mines, mill, smelter, and refineries, that produces finished nickel, copper, gold, silver, sulphuric acid, and intermediates that are refined in other Vale facilities. According to the data compiled by the commission for Environmental cooperation, Vale alone accounts for 20% of all the arsenic emitted in North America, 13% of the lead and 30% of the nickel. Reports from 2004 indicated that in 1998, Vale met its target of a 50% reduction in toxic metal releases, reducing its combined metal emissions of arsenic, copper, lead and nickel by 67% (Ogilvie, 2003). Currently, the Vale smelter fixes approximately 70% of the sulphur contained in the feed to the smelter, or 86% of the sulphur that is contained in the ore which is lower than the fixation required for smelters. Xstrata Nickel also operates an integrated complex of mines, mill, and smelters in Sudbury. Xstrata Nickel reduced its sulphur dioxide levels from the 1998 levels by more than 40% in 1999. Currently Xstrata Nickel has almost a 75% reduction in sulphur

73 dioxide emissions using a continuous improvement philosophy and its emissions in Sudbury of the CEPA substances are reasonable, but the nickel emission is still somewhat high (Ogilvie, 2003). Monitoring of metal content in Sudbury ecosystems is necessary to assess the level of recovery following the abatements procedures by industries and the land reclamation program of Greater Sudbury. For arsenic, the values were below the Ontario Ministry of Environment and Energy (OMEE) guideline (Table 20) except for site 1 (top layer) which exceeded the OMEE limit guideline. Concentrations of copper in the top soil exceeded the OMEE guidelines level of 16 to 100 mg kg"1. For the bottom layer the concentrations fall within the guidelines. The OMEE guideline for nickel concentration should range between 16 to 75 mg kg"1. Just like copper, nickel concentrations in the top soil exceed the OMEE guidelines. For the bottom layer except for site 1 all other sites fall within the guidelines. Cadmium and cobalt concentrations for top and the bottom layer were both within the OMEE guidelines. The OMEE guideline for cadmium concentration range between 0.6 to 10 mg kg"1 and for cobalt it should not exceed 50 mg kg"1. Aluminum concentrations for top soil were six times higher in site 1 to 4 compared to the control site. For the bottom layer it was the opposite where the control site has the higher concentration of aluminum compared to the other sites. Similar results were observed for iron, where the concentration in the top soil were high in site 1 to 4 compared to the control site. For the bottom layer, control site has higher concentration compared to the rest of the sites. Lead, manganese and zinc concentrations were within the guidelines for the top and bottom layer. The guideline concentration range between 31 to 250 mg kg" , 460 to 1 100 mg kg"1, 120 to 360 mg kg"1 for lead, manganese and zinc respectively.

74 The 2001 soil survey analyzed over 10,000 samples from hundreds of different locations within the Sudbury area. The sampling results confirm that emissions from over 100 years of mining, smelting and refining have resulted in elevated levels of metals in the soil over a large area. The highest levels of nickel, copper, lead, cobalt and arsenic were found around the three industrial centers of Copper Cliff, Coniston, and Falconbridge. These current findings are consistent with historical ministry results. The highest metal concentrations were typically found in the upper soil layers, indicating air emissions as the source. Arsenic levels currently known to exist in Sudbury are generally comparable or lower than those found in other Ontario mining communities. In the present study most of the sites showed significant difference between individual soil depths for all the elements tested. Metals such as cadmium, cobalt, lead and zinc had concentrations within the OMEE guidelines. Metal concentrations in top soil were high compared to the bottom layer except for aluminum and iron where the concentrations were high in bottom layer. For aluminum, iron and magnesium concentrations for top soil in site 1, 2, 3 and 4 were extremely high compared to site 5 (control). The concentration of iron measured in this study was high compared to those reported in previous studies (Nkongolo et al, 2008; Gratton et al, 2000). The levels of cobalt, copper, lead, magnesium, manganese, nickel, and zinc in soil were found to be high than previously reported (Nkongolo et al, 2008). Soil content of aluminum, arsenic, and cadmium were found to be at the same level as previously reported by Nkongolo et al, (2008). The level of mercury a harmful substance was not analyzed since it is not released from the Sudbury smelters.

~~75 Chapter 4: Genetic analysis of Picea glauca populations from metal contaminated and uncontaminated areas in the Greater Sudbury region (Ontario, Canada)~~

~~4.1 Introduction~~

Sudbury, Ontario, has one of the world's largest deposits of nickel and copper ore. Until 1972, three huge smelters operated a few miles outside the city. Each year they smelted enormous amounts of high sulfur ore and in the process poured more sulfur dioxide into the atmosphere than any other complex in the world (Dudka et al., 1995). In 1972, more than 3 million tons of sulfur dioxide gas spewed from the stacks of the Sudbury smelters. In additions many thousands of tons of heavy metals such as nickel, copper, lead and zinc were also emitted into the air from the smelting process (Freedman and Hutchinson, 1980). Due to this, large areas of the boreal forests in the region suffered widespread destruction from the pollution. Down-wind from the smelters, 258 km of trees and vegetation were destroyed and the remaining soil eroded leaving nothing but

~~blackened rocks.~~

In an effort to maintain the long-term viability of the forest landbase in Northern Ontario, artificial regeneration of conifer seedlings has been used as a primary means of reforestation. To date, over 7 million trees mostly spruce and pine have been planted. The impoverished plant communities that are currently found in Sudbury area are not only structurally and floristically different from plant communities found in uncontaminated areas in the basin, but they appear to have a different genetic make-up. Several studies on genetic analysis have been done on various plant species growing in Sudbury region. Ranger et al., (2007) studied the genetic diversity in Pinus banksiana (Jack pine) and Pinus resinosa (Red pine) populations growing in metal contaminated and

76 uncontaminated areas. The results indicated low to moderate level of genetic diversity in populations growing in Sudbury. The highest level of genetic variation was found in populations from Nurseries used for land reclamation. No association between metal accumulation and genetic diversity for both species was established. Dobrzeniecka et al, (2010) studied genetic variation on Picea mariana growing in wetland and dryland. The level of genetic variation was higher in populations from wet lands than those from dry lands. Like in pine, genetic variation and long-term exposure to metals (more than 30 years) were not associated. Cytological analysis of P. mariana seeds from metal contaminated and uncontaminated areas showed normal mitotic behavior during prophase, metaphase, anaphase, and telophase.

Several authors have reported differences in genetic structure of plants growing in contaminated areas (Muller-Stark, 1985; Scholz and Bergmann, 1984). Enzymatic studies of Picea abies (Norway spruce) revealed genetic differences between groups of sensitive trees in polluted areas (Scholz and Bergmann, 1984). Higher heterozygosity was seen in tolerant plants of European beech in Pinus sylvestris (Scots pine) in Germany and Great Britain (Müller- Starck, 1985; Geburek et al., 1987), Populus tremuloides (Trembling aspen) and Acer rubrum (Red maple) in the United States (Berrang et al, 1986) have been reported.

The main objective of the present project was to determine if metal contamination affects the level of genetic variation in P. glauca populations growing in the greater Sudbury region using ISSR and RAPD marker systems.

~~77 4.2 Materials and Methods~~

4.2.1 Sampling P. glauca needles from 5 populations were collected at different locations in the Greater Sudbury (site 1 to 5; Figure 15). The first site was in Lively near the sport complex, site 2 was at Conisten close to HWY 17, site 3 was about 40 kilometers from the HWY 144 towards Timmins, site 4 was about 16 kilometers from site 3 HWY 144 towards Timmins and site 5 (control) was about 35 kilometers from site 4 HWY 144 towards Timmins. The population from the nursery is considered as a new introduction. It was provided by the Sudbury re-greening program and used as an additional control. For each sampling site, 10% of the entire populations were analyzed. Needles were collected from individual trees, placed in large bags and identified by site. For each tree, 15 grams of needles were weighed in duplicates, frozen in liquid nitrogen and stored at -25 0C until DNA extraction.

~~4.2.2 Molecular Analysis~~

Genomic DNA from needle samples was extracted using the CTAB extraction protocol described in the materials and methods section in chapter 2 with some modifications. The modification involved addition of PVP (polyvinylpyrrolidone) and ß- mercaptoethanol to the CTAB (hexadecyltrimethyl-ammonium bromide) extraction buffer.

Extracted genomic DNA was treated with RNase. RNase enzyme (1 µ?) was added to the genomic DNA and incubated for 1 hour at 37 0C. Following incubation, organic cleanup using 250 µ? of phenol buffer at pH 8.0 and 250 µ? of chloroform:octanol

78 (24:1) was performed to remove proteins. The tubes were weighed, balanced, mixed thoroughly and centrifuged at 15000 rpm for 10 minutes. The supernatant was transferred to a new tube making sure not to transfer any protein/viscous material and the rest discarded into a waste bottle. About 500 µ? of chloroform:octanol (24:1) was added to the supernatant. The tubes were again weighed, balanced, mixed well and centrifuged at 15000 rpm for 5 minutes. The supernatant was transferred to a new tube to which 20 µ? of 0.5 M Sodium Chloride (NaCl) salt and 500 µ? of Isopropanol were added. The tubes were mixed gently and incubated at -20 0C overnight. They were then balanced and centrifuged at 6500 rpm for 15 minutes. The supernatant was drained and 20 mis of 70% ethanol was added in the tubes and let stand for 10 minutes. Tubes were balanced and centrifuged at 13000 rpm for 15 minutes. Ethanol was removed, the pellet was dried and dissolved in 500 µ? of Ix TE buffer. The tubes were labeled and stored at 4 0C overnight and then stored in a freezer at -20 0C until further analysis. Degradation analysis, DNA quantification, ISSR and RAPD amplification was done as indicated in the materials and methods section in chapter 2.

~~4.2.6 Statistical Analysis~~

Both ISSR and RAPD assays of each population were performed twice. Ten primers (six from ISSR and four from RAPD marker system) that amplified consistent profiles across the populations were selected for the final analysis. The presence and absence of bands were scored as 1 or 0 respectively, in order to determine the within and between population variation. The Quantity one software was used for the allele designation by comparing the alleles to the IKb+ ladder. The Popgene software version

79 1.32 (Yeh et al, 1997) was used to determine genetic diversity parameters such as the percentage of polymorphic loci, Nei' s gene diversity (h), Shannon's information index (I), observed number of alleles (Na) and effective number of alleles (Ne). The mean and the total gene diversities, the variation among populations and gene flow were also calculated. The genetic distances were calculated using Jaccard's similarity coefficient estimated with the RAPDistance program version 1.04 (Armstrong et al, 1994) and Free Tree program (Pavlicek et al, 1999).

~~80 4.3 Results~~

4.3.1 Degradation Analysis Extracted genomic DNA was tested for quality. All the samples showed a large molecular weight band at the top of the gel with the absence of smearing (Figure 16). This indicated that all the DNA samples extracted were not degraded and were suitable for PCR amplifications.

4.3.2 ISSR Analysis Seven primers previously screened for P. mariana and P. rubens study were used for P. glauca DNA amplification (Table 12). Of those, six primers, HB 13, ISSR 5, ISSR 9, 17899A, 17898B, and UBC 841 generated strong amplification products (Figure 17, Figure 18, Figure 19, Figure 20, Figure 21, and Figure 22) and were used to amplify all the DNA samples from six population. ISSR primers ISSR 5, ISSR 9, 17899A, 17898B, and UBC 841 produced the clearest bands (Figure 18, Figure 19, Figure 20, Figure 21, and Figure 22).

Table 13 depicts the level of polymorphism within and between P. glauca populations. Primer HB 13 generated the lowest level of polymorphism (28%) and primer 17898B produced the highest polymorphic loci (71%). Two of the six primers that produced a very high level of polymorphic bands were analyzed in detail. Primers ISSR9 generated a total of 21 bands ranging from 260 bp to 2240 bp (Table 12) across six populations resulting in 58% polymorphism level (Table 13). Primers 17899B generated a total of 16 bands ranging from 280 bp to 1510 bp (Table 12) with 71% polymorphism level across six populations (Table 13). 81 The level of polymorphic loci among populations was found to be 55%. Table 14 shows the genetic variation parameters for the six P. glauca populations calculated using the Popgene software version 1.32 (Yeh et al, 1997). The percentage of polymorphic loci within each population varied between 50% observed in site 5 (control) to 61% in site 1 (Table 14). Nei' gene diversity (h) ranged from 0.17 to 0.21 with a mean of 0.19. The highest gene diversity was in population from site 1 and the lowest in site 5 (control). A similar pattern was observed for the Shannon's information index (I), with the highest value of 0.32 observed in site 1 and the lowest value of 0.26 observed in site 5 (control). The observed number of alleles (Na) and the effective number of alleles (Ne) ranged from 1.50 to 1.61 and 1.29 to 1.37 respectively.

The mean gene diversity between populations (Hs) was 0.23 and the total gene diversity (HT) was 0.19. The variation among populations or gene differentiation (GSt) was 16.8%; hence most of the variation is from within the populations. The observed structure of genetic variability shows that there is a low level of differentiation among the P. glauca populations. The estimated gene flow (N1n) calculated from GST was 2.47.

4.3.3 RAPD Analysis Preliminary screenings using eight RAPD primers (Table 16) were performed on DNA samples from each population. Four out of eight primers were selected based on their amplification and reproducibility and were used to amplify the DNA samples from all six populations. The results of these PCR amplifications using the four RAPD primers; OPA 4, P 23, P 184, and UBC 186 are shown in Figure 23, Figure 24, Figure 25,

~~82 and Figure 26. The number of monomorphic and polymorphic bands generated by primers used for amplification of samples for all six populations was recorded.~~

The level of polymorphism within and between P. glauca populations were calculated (Table 17). Primer P 23 generated low level of polymorphism of 67% and primer UBC 186 produced high level of polymorphism of 89%. Two of the four primers that produce a very high level of polymorphic bands were analyzed in detail. Primer P 184 generated a total of 20 bands ranging from 170 bp to 2000 bp (Table 16) with 82% polymorphism across six populations (Table 17). Primer UBC 186 generated a total of 22 bands ranging from 220 bp to 1930 bp (Table 16) with 89% polymorphism level across

~~six pollutions (Table 17).~~

The level of polymorphic loci among populations was 77%. This is high compared to the ISSR data. Table 18 shows the genetic variation parameters for the six P. glauca populations calculated using the Popgene software version 1.32 (Yeh et al., 1997). The percentage of polymorphic loci within each population varied between 70% observed in Nursery to 82% in site 3 (Table 18). Nei' s gene diversity (h) ranged from 0.25 to 0.31 with a mean of 0.29. The highest gene diversity was in population from Nursery and the lowest in site 3. A similar pattern was observed for the Shannon's information index (I), with the highest value of 0.46 observed in site 3 and the lowest value of 0.37 observed in the populations from the Nursery. The observed number of alleles (Na) and the effective number of alleles (Ne) ranged from 1.70 to 1.82 and 1.4264

~~to 1.56, respectively.~~

83 The mean gene diversity between populations (Hs) was 0.35 and the total gene diversity (Hx) was 0.29. The variation among populations or gene differentiation (GSt) was 15.8%; hence most of the variations are from within the populations. The observed structure of genetic variability shows that there is a low level of differentiation among the P. glauca populations. The estimated gene flow (N111) calculated from the GSt was 2.66.

4.3.4 Genetic Relationship Genetic distance coefficients were calculated according to the Jaccard's Similarity Coefficient. The genetic distance scale runs from 0, indicating no genetic difference to 1, indicating differences at all criteria. In the present study, based on ISSR analysis all the six populations were genetically closely related. The genetic distance values were close to 0 as they varied between 0.02 and 0.07 (Table 15). For RAPD marker system, the genetic distance values were also low but slightly higher compared to values from ISSR analysis. The genetic distance values ranged from 0.04 to 0.21 (Table 19). The most closely related populations were sites 2 and 3, and the most distantly related populations were sites 5 (control) and the Nursery. Dendrogram was not constructed considering the low levels of genetic distances.

~~84 10 11 12 13 14 15 16 17 18 19 20 JifVî'.yfv-f·*»!**·'* ?'?^?^??·^~~

~~Figure 16. Genomic DNA for quality test of DNA samples from P. glauca. Lanes 1 and 20 represent IKb+ ladder. Lanes 2 to 13 represent individual samples of P. glauca.~~

85 Table 12.The nucleotide sequences of ISSR primers used to screen DNA from Picea glauca samples. ISSR Primer Nucleotide sequence (5'—>3') Number of Fragment identification fragments size range amplified (bp)

~~ISSR HB 13 GAGGAGGAGGC 12 170-1360~~

~~ISSR HB 15 GTGGTGGTGGC 8 270-2200~~

~~SC ISSR 5 ACGACGACGACGAC 11 420-1450~~

~~SC ISSR 9 GATCGATCGATCGC 21 260-2240~~

~~ISSR 17899A CACACACACACAAG 18 170-1530~~

~~ISSR 17898B CACACACACACAGT 16 280-1510~~

ISSR UBC 841 GAAGGAGAGAGAGAGAYC 18 240-1730 Table 13. Levels of polymorphisms within and between Picea glauca populations generated with ISSR primers. Sitel Site 2 Site 3 Site 4 Site 5 Nursery Polymorphic (control) bands (%) HB 13 5/12 2/12 4/Ï2 3/12 2/12 4/12 28%

~~ISSR 5 5/11 6/11 4/11 5/11 3/11 4/11 41%~~

~~ISSR 9 16/21 16/21 13/21 14/21 13/21 14/21 68%~~

~~17899A 11/18 6/18 9/18 9/18 11/18 9/18 51%~~

~~17898B 10/16 12/16 12/16 11/16 10/16 12/16 71%~~

~~UBC 841 12/18 9/18 11/18 9/18 9/18 12/18 57%~~

~~Total 59/96 51/96 53/96 51/96 48/96 55/96~~

~~Polymorphic 61% 53% 55% 53% 50% 57% bands (%) Table 14. Genetic diversity parameters of Picea glauca based on ISSR data. Population P(%) Na Ne Site 1 61.5 1.61 1.37 0.22 0.32~~

~~Site 2 53.1 1.53 1.33 0.19 0.29~~

~~Site 3 55.2 1.55 1.32 0.19 0.29~~

~~Site 4 53.1 1.53 1.33 0.19 0.28 Site 5 (control) 50.0 1.50 1.30 0.18 0.26 Nursery 57.3 1.57 1.35 0.20 0.30 Mean 1.55 1.33 0.19 0.29~~

Genetic diversity descriptive statistics. P: percentage of polymorphic loci; Na: observed number of alleles; Ne: effective number of alleles; h: gene diversity (Nei, 1973); I: Shannons's information index.

Site 1: represents P. glauca population from Lively; Site 2: P. glauca population from Conisten (close to HW17); Site 3: P. glauca population ~ 40km from Sudbury HW144 towards Timmins; Site 4: P. glauca population ~ 16km from site 3 HW144 towards Timmins; Site 5 (control): P. glauca population ~ 35km from Site 4 HW144 towards Timmins; Nursery: Newly introduced population.

88 Table 15. Distance matrix generated from ISSR data using the Jaccard similarity coefficient analysis for Picea glauca populations (Free Tree Program). Sitel Site 2 Site 3 Site 4 Site 5 Nursery (control) Sitel 0.0000 0.0520 0.0417 0.0417 0.0626 0.0209 Site 2 0.0000 0.0729 0.0729 0.0737 0.0316 Site 3 0.0000 0.0213 0.0632 0.0417 Site 4 0.0000 0.0632 0.0417 Site 5 0.0000 0.0625 Nursery 0.0000

Site 1: represents P. glauca population from Lively; Site 2: P. glauca population from Conisten (close to HW17); Site 3: P. glauca population ~ 40km from Sudbury HW144 towards Timmins; Site 4: P. glauca population ~ 16km from site 3 HW144 towards Timmins; Site 5 (control): P. glauca population ~ 35km from Site 4 HW 144 towards Timmins; Nursery: Newly introduced population.

89 Table 16. The nucleotide sequence of RAPD primers used to screen DNA from Picea glauca samples. RAPD Primer Nucleotide sequence Number of Fragment size identification (5'-»3') fragments range (bp) amplified

~~OPA4 aatcgggctg 18 300-2170~~

~~P 23 cccgccttcc 25 280-2150~~

~~P 184 caaacggcac 20 170-2000~~

~~UBC 186 GTGCGTCGCT 22 220-1930 OPA 8 gtgacgtagg 4 200-1000~~

~~GRASS 2 gtggtccgca 3 290-500~~

~~GRASS 6 cgtcgcccat 4 100-850~~

UBC 48 ttaacgggga 0 0 Table 17. Levels of polymorphisms within and between Picea glauca populations generated with RAPD primers. Sitel Site 2 Site 3 Site 4 Site 5 Nursery Polymorphic (control) bands (%) OPA 4 13/17 14/17 14/17 9/17 12/17 11/17 71%

~~P 184 16/20 13/20 16/20 17/20 19/20 16/20 82%~~

~~P 23 19/25 18/25 19/25 17/25 16/25 12/25 67%~~

~~UBC 186 20/22 20/22 20/22 19/22 18/22 19/22 89%~~

~~Total 68/84 65/84 69/84 62/84 65/84 59/84~~

Polymorphic 81% 77% 82% 73% 77% 70% bands (%) Table 18. Genetic diversity parameters ?? Picea glauca based on RAPD data. Population P(%) Na Ne I Sitel 81.2 1.81 1.52 0.30 0.45 Site 2 77.6 1.78 1.52 0.29 0.43

~~Site 3 82.3 1.82 1.56 0.32 0.47~~

~~Site 4 72.9 1.73 1.50 0.28 0.41 Site 5 (control) 77.6 1.78 1.56 0.32 0.46 Nursery 70.6 1.71 1.43 0.25 0.37~~

~~Mean 1.77 1.51 0.29 0.43~~

Site 1: represents P. glauca population from Lively; Site 2: P. glauca population from Conisten (close to HW 17); Site 3: P. glauca population ~ 40km from Sudbury HW 144 towards Timmins; Site 4: P. glauca population ~ 16km from site 3 HW144 towards Timmins; Site 5 (control): P. glauca population ~ 35km from Site 4 HW144 towards Timmins; Nursery: Newly introduced population.

92 Table 19. Distance matrix generated from RAPD data using the Jaccard similarity coefficient analysis for Picea glauca populations (Free Tree Program). Sitel Site 2 Site 3 Site 4 Site5 Nursery ______(control) "Sitel ÖÖÖÖÖ 0.0714 0.0714 0.1084 0.1412 0.1905 Site 2 0.0000 0.0482 0.1309 0,1190 0.1905 Site 3 0.0000 0.1529 0.1412 0.1905 Site 4 0.0000 0.1111 0.2073 Site 5 0.0000 0.2169 Nursery 0.0000

~~93 11 UUJ~~

Figure 17. ISSR amplification of Picea glauca samples with primer HB 13. Lanes 1, 12, 23 and 34 contain lkb+ ladder; lanes 2 to 11 contain Picea glauca samples from Site 4; lanes 13 to 22 contain Picea glauca samples from Site 5 (control); lanes 24 to 33 contain Picea glauca samples from Nursery.

~~94 6 ' S 9 10 ? i: 13 14 lì 16 G IS 19 ZD :i 22 23 24 25~~

Figure 18. ISSR amplification ?? Picea glauca samples with primer ISSR 5. Lanes 1, 12, 23 and 34 contain lkb+ ladder; lanes 2 to 11 contain Picea glauca samples from Site 4; lanes 13 to 22 contain Pìcea glauca samples from Site 5 (control); lanes 24 to 33 contain Picea glauca samples from Nursery.

~~95 S 9 10 ? 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 2S 29 30 31 32 33 34~~

Figure 19. ISSR amplification ?? Picea glauca samples with primer ISSR 9. Lanes 1, 12, 23 and 34 contain lkb+ ladder; lanes 2 to 11 contain Picea glauca samples from Site 1; lanes 13 to 22 contain Picea glauca samples from Site 2; lanes 24 to 33 contain Picea glauca samples from Site 3.

~~96 -, ? & :. ** *· **"?~~

Figure 20. ISSR amplification oí Picea glauca samples with primer 17899A. Lanes 1, 12, 23 and 34 contain lkb+ ladder; lanes 2 to 11 contain Picea glauca samples from Site 1; lanes 13 to 22 contain Picea glauca samples from Site 2; lanes 24 to 33 contain Picea glauca samples from Site 3.

~~97 It IQ in "M T> ÏÎ Ji }S 7« 17 28 29 30 31 32 33 34~~

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Figure 21. ISSR amplification of Picea glauca samples with primer 17898B. Lanes 1, 12, 23 and 34 contain lkb+ ladder; lanes 2 to 11 contain Picea glauca samples from Site 1; lanes 13 to 22 contain Picea glauca samples from Site 2; lanes 24 to 33 contain Picea glauca samples from Site 3.

~~98 1 2 J 4 S 9 10 ? 12 U 14 15 16 17 18 19 20 21 22 23 24 25 2« 27 25 29 30 31 32 33 34~~

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Figure 22. ISSR amplification of Picea glauca samples with primer UBC 84 1. Lanes I, 12, 23 and 34 contain lkb+ ladder; lanes 2 to 11 contain Picea glauca samples from Site 4; lanes 13 to 22 contain Picea glauca samples from Site 5 (control); lanes 24 to 33 contain Picea glauca samples from Nursery.

~~99 1 2 i 4 5 6 7 S 9 10 11 12 li 14 IS IS 1? 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40~~

Figure 23. RAPD amplification oí Picea glauca samples with primer OPA 4. Lanes 1, 14, 27 and 40 contain lkb+ ladder; lanes 3 to 12 contain Picea glauca samples from Site 1; lanes 16 to 25 contain Picea glauca samples from Site 2; lanes 29 to 38 contain Picea glauca samples from Site 3.

~~100 12 3 4 5 6 S 9 10 11 12 13 14 15 16 17 18 19 20 21 3 24 25 26 27 28 29 30 31 32 33 34 33 36 37 38 39~~

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~~m m ¦ ¦~~

Figure 24. RAPD amplification of Picea glauca samples with primer P 23. Lanes 1, 13, 26 and 39 contain lkb+ ladder; lanes 2 to 11 contain Picea glauca samples from Site 1; lanes 15 to 24 contain Picea glauca samples from Site 2; lanes 28 to 37 contain Picea glauca samples from Site 3.

~~101 38 39 40~~

Figure 25. RAPD amplification ?? Picea glauca samples with primer P 184. Lanes 1, 14, 27 and 40 contain lkb+ ladder; lanes 3 to 12 contain Picea glauca samples from Site 4; lanes 16 to 25 contain Picea glauca samples from Site 5 (control); lanes 29 to 38 contain Picea glauca samples from Nursery.

~~102 1 2 3 4 5 6 7 S 9 10 li 12 13 14~~

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Figure 26. RAPD amplification of Picea glauca samples with primer UBC 186. Lanes 1, 14, 27 and 40 contain lkb+ ladder; lanes 3 to 12 contain Picea glauca samples from Site 1; lanes 16 to 25 contain Picea glauca samples from Site 2; lanes 29 to 38 contain Picea glauca samples from Site 3.

~~103 4.4 Discussion~~

The main objective of this study was to examine the effect of metal contamination on the genetic variation within and among populations of P. glauca in the Sudbury area, subjected to high metal emissions for over 100 years. Although similar studies have been previously done on other species, no data were available for P. glauca populations.

P. glauca populations from the Greater Sudbury region contaminated with different levels of metals were analyzed using ISSR and RAPD markers. The two marker systems have also been used as an effective tool to evaluate genetic diversity and to throw light on the phylogenetic relationships in different cultivated and uncultivated plants. RAPD primers were more efficient than ISSR assay as they generated a polymorphism level of 77% among populations compared to 55% for ISSR marker. This is in contrast to data reported in other studies for wheat (Nagaoka and Ogihara, 1997) and Vigna (Ajibade et al., 2000). ISSRs offer great potential to determine intra and inter specific genomic diversity as compared to arbitrary RAPD primers (Zietkiewicz et al., 1994). The primers with poly (GA)n and (AG)n motifs produced more polymorphism than any other motifs. While primers with (AT)n, (GT)n and other motifs gave low amplification or no amplification.

Data obtained from each marker system were used to calculate genetic diversity parameters. Observed number of alleles (Na) and the effective number of alleles (Ne) were calculated. Na measures the number of alleles using the data obtained from the molecular markers. Ne measures the expected number of alleles that should be found within the populations calculated using Na. For both parameters the scale ranges from 0 to 2. Value close to 0 indicates low heterozygosity and a value close to 2 indicates the

~~104 presence of high heterozygosity. For ISSR, the mean values for Na and Ne were 1.55 and~~

1.33. For RAPD, the mean values were 1.77 and 1.51. Values from both molecular systems indicate high heterozygosity in P. glauca populations. In both cases, observed heterozygosity was higher than the expected heterozygosity for all populations. Other studies using different allozyme markers have reported differently. The observed heterozygosity was consistently lower than the expected heterozygosity for the P. glauca populations from Alberta (Rajora et al, 2005), Quebec (Tremblay and Simon, 1989), and Alaska (Alden and Loopstra, 1987), based on allozyme analysis. Although allozymes and molecular markers allow the analysis of genetic variability in plant species, fundamental differences exist between these two methods. Allozyme analysis reflects alternation in the DNA sequence of coding regions in the genome leading to changes in the amino acid composition which can go undetected (Hamrick, 1989). ISSR for example, targets microsatellite sequences located throughout the entire eukaryotic genome, most of which are selectively neutral areas. These areas are known to evolve rapidly and have been deemed good tools for studies in genetic variation in many organisms (Bornet and Branchard, 2004).

For each marker locus and over all loci, the genetic diversity was calculated corresponding to the Nei's expected heterozygosity (Nei, 1973). In other words, Nei's gene diversity measures the number of different alleles in a population. This parameter ranges from 0 in case of monomorphic (no variation within a population) allele in all loci to 1 when alleles in equal frequencies occur over all polymorphic loci (high variation within a population). Nei's diversity value obtained with RAPD and ISSR were low. With ISSR the mean genetic diversity value was 0.19 and 0.39 with RAPD marker

~~105 system. These low values indicate the presence of monomorphic allele in all loci and that there is no variation within the populations.~~

~~The phenotypic diversity of marker allele profile was also estimated using~~

Shannon's information index. It measures the distribution and abundance of alleles within a population. Shannon's information index (I) measure explains about gene diversity, the value range is from 0 - 1 . Value close to 0 indicates an uneven distribution of alleles and a value close to 1 indicates an even distribution of alleles. Higher value indicates a more equal distribution of diversity throughout the system and a greater number of distinctly different species (or varieties), which also means that heterozygosity in the populations is high. The average estimate of phenotypic diversity for ISSR was 0.29 and 0.43 for RAPD. Values obtained from both marker systems indicate an uneven distribution of alleles.

RAPD analysis showed high level of polymorphic loci within the six populations studied compared to ISSR. A possible difference in resolution of RAPD and ISSR is that the two marker techniques target different portions of the genome as previously discussed in chapter 2. The ability to resolve genetic variation among different genotype may be more directly related to the number of polymorphism detected with each marker system. The results from the ISSR analysis showed that all the sites have genetic variation close to each other. A similar pattern was observed with RAPD where there was virtually no difference among the populations.

~~Genetic distance values were calculated according to the Jaccard similarity coefficient. Both ISSR and RAPD analysis revealed great variation in regard to the~~

106 genetic relatedness of the populations analyzed. In general, the genetic distance values revealed that the genetic structure of P. glauca is quite similar among the populations and are genetically close. The relative small genetic distance values are consistent with other studies on P. glauca populations in various provinces using various genetic markers and allozymes (Rajora et al, 2005; Tremblay and Simon, 1989; Alden and Loopstra, 1987). In general, the genetic similarity among the six populations suggests that these populations could have originated from a common source. Overall, the genetic distance analysis showed a high level of homogeneity among populations which could be due to the species characteristics. In addition, P. glauca is an anemophilous species and its pollen is transported over great distances. The fact that P. glauca populations are fairly distributed should promote the exchange of genes among populations (Beaulieu and Deslauriers, 2002). Hence, it is rare to find alleles that are unique to a given populations, and the frequencies of the main alleles are generally similar from one population to another (Beaulieu and Deslauriers, 2002).

Rajora et al., (2005) analyzed P. glauca stands that occurred in conifer-dominated and mixedwood forest types in Northern Alberta and found a high level genetic diversity using nuclear microsatellite DNA. Most of the genetic variation resided within subpopulations, with only about 2% genetic differentiation detected among 16 subpopulations as well as among 8 subpopulations within the same forest type. Beaulieu and Deslauriers, (2002) reported similar results. They found that all the populations were genetically similar despite the huge distances that separated them which were over 800 km.

107 Attempts were made in this study to use environmental conditions for appropriately interpreting genetic information as elevated levels of metal accumulations in soil and vegetation have been documented within short distances of the smelters in Sudbury compared to control sites (Nkongolo et al, 2008 and Metals in Soil and Vegetation in the Sudbury Area, 2001). No association between the metal concentration and genetic variation was found when two generations were analyzed. Similar results were reported by Dobrzeniecka et al, (2010) in P. mariana populations growing in the Sudbury region. This is in contrast with data observed in herbaceous species Deschampsia cespitosa (Tuffed hair grass) where the accumulation of metal (mostly cobalt) reduced the level of genetic variation significantly (Nkongolo, 2007).

In conclusion, the present study indicates that P. glauca populations from the Sudbury region, Ontario, are genetically variable. Metal contamination does not appear to have an effect on the genetic variation in P. glauca populations. But the impact of metal effects, if any, may require several generations to be detected.

108 Chapter 5: General Conclusion Genetic marker studies have contributed to evolution and ecology studies by providing methods for detecting genetic differences among individuals. They also have contributed to the study of interspecific hybridization by: 1) allowing unequivocal identification of hybrid individuals, 2) producing an assessment of the proportional contribution of parental genomes and 3) providing means of interfering patterns of selection and mating across regions of hybridization and within hybrid populations.

The purpose of the present study was to determine 1) if interspecific hybridization increases genetic variation in Picea. ISSR produced level of polymorphic loci ranging from 30% to 52%. The level of polymorphism was higher with RAPD markers, ranging from 57% to 76%. Overall, no significant differences were observed among the populations analyzed. Based on previous data, interspecific hybridization may have increased the level of genetic variation in hybrid populations. This finding could not be validated with data in the present study since Manley's parental lines were not pure P. mariana or P. rubens.

The study aimed at 2) determining if metal content in soil affects the level of genetic variation within and among populations of P. glauca from greater Sudbury regions. For some metals, the concentrations were within or below the OMEE guidelines. Copper and nickel concentrations detected in sites 1 and 2 were very high compared to the other sites and exceeded the OMEE guideline limit. Arsenic concentration exceeded the guideline limit for site 1. Overall, concentration of metals in the control site (site 5) was low compared to sites 1 and site 2 which are located near smelters.

109 Data from both ISSR and RAPD analyses indicated that metal content level was not associated with the level of diversity in the P. glauca populations analyzed. This lack of association between the level of genetic variation and metal content can be attributed to the long life span of Picea species. In fact, the populations analyzed were only the second generation of progenies from parent exposed to metal contamination. Previous studies by Nkongolo et al, (2007) reported an association between genetic variation and metal content in Deschampsia cespitosa (a short lived plant) population from the Sudbury region. Genetic distance values for the ISSR analysis ranged from 0.02 to 0.07 and for the RAPD analysis, it ranged from 0.04 to 0.21. ISSR analysis detected no difference among populations while RAPD marker revealed a low level of difference among the six populations.

In both studies, RAPD markers were more efficient than ISSR assay with regards to polymorphism detection. Both markers target different regions within the genome and have different distribution. Despite the great and similar discriminating power of both markers, some differences between the two could be detected: 1) genetic similarity values were lower for ISSR than for RAPDs and 2) total number of polymorphic bands was higher for RAPDs than ISSRs. One possible explanation could be due to the different background on which such differentiation estimates are based. ISSR repeats are regions lying within the microsatellite repeats, have a high capacity to reveal polymorphism and offer great potential to determine intra and inter-specific variation compared to other arbitrary primers like RAPDs (Zietkiewicz et al, 1994). ISSR tends to be found in the high copy repeat regions and detects polymorphism in inter-microsatellite loci using primer designed from di- or trinucleotide simple sequence repeats.

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~~119 Appendix~~

~~Table 20. Concentrations guidelines for the soil according to the Ontario Ministry of environment and Energy (OMEE).~~

~~Substances Ontario Sediment Quality Guidelines (mg/kg) LEL SEL Arsenic 6 33 Cadmium 0.6 10 Cobalt 50 Copper 16 110 Iron 2% 4% Lead 31 250 Manganese 460 1100 Nickel 16 75 Zinc 120 820~~

~~LEL = Lowest effect level~~

~~SEL = Severe effect level~~

~~120~~