1

VYTAUTAS MAGNUS UNIVERSITY

LITHUANIAN RESEARCH CENTRE FOR AGRICULTURE AND FORESTRY

Rita VERBYLAIT

EUROPEAN ASPEN (Populus tremula L.) IN LITHUANIA: GENETIC DIVERSITY OF PLUS TREES AND POPULATIONS

ASSESSED USING MOLECULAR MARKERS

Doctoral dissertation

Area of Biomedical Sciences

Field of Ecology and Environmental Science (03B)

Kaunas, 2015

2

UDK 577.175.1(474.5) Ve-142

The research was carried out at the Lithuanian Research Centre for Agriculture and Forestry in

2006–2015. The right of doctoral studies was granted to Vytautas Magnus University jointly

with Aleksandras Stulginskis University and Lithuanian Research Centre for Agriculture and

Forestry on June 21, 2011, by the decision No. V-1124 of the Minister of Education and Science

of the Republic of Lithuania.

Scientific Supervisors

Prof. habil. dr. Remigijus Ozolin ius – Lithuanian Research Centre for Agriculture and Forestry, Area of Biomedical Sciences, Field of Ecology and Environmental Science (03B), supervised from October of 2006 till March of 2013.

Dr. Virgilijus Baliuckas – Lithuanian Research Centre for Agriculture and Forestry, Area of

Biomedical Sciences, Field of Ecology and Environmental Science (03B), supervised from

March of 2013.

Scientific Consultant

Doc. dr. Sigut Kuusien (Lithuanian Research Centre for Agriculture and Forestry, Area of

Biomedical Sciences, Field of Biology 01B)

ISBN 978-609-467-174-6

3

TABLE OF CONTENTS

INTRODUCTION ....................................................................................................................... 5

1. LITERATURE REVIEW ................................................................................................... 8

1.1. Overview of genus Populus ............................................................................................ 8

1.1.1.Biology, ecology and distribution of European aspen ................................................ 9

1.1.2.Sanitary condition of European aspen stands in Lithuania ....................................... 12

1.1.3.Hybridization of Populus spp. .................................................................................. 13

1.1.4.History of European aspen investigation with emphasis on genetic research .......... 16

1.2. Molecular techniques for tree research ......................................................................... 19

1.2.1.DNA extraction from plant material ......................................................................... 19

1.2.2.Markers used in tree population biology .................................................................. 20

2. MATERIALS AND METHODS ..................................................................................... 26

2.1. Sampling of P. tremula trees for molecular analysis ...................................................... 26

2.2. Assessment of tree and stand characteristics ................................................................... 29

2.3. DNA extraction from plant material................................................................................ 32

2.4. Assessment of genetic diversity of P. tremula plus trees ................................................ 36

2.4.1. RAPD analysis ......................................................................................................... 36

2.4.2. Microsatellite analysis ............................................................................................. 37

2.5. Assessment of incidence of Phellinus tremulae infection in aspen stems ...................... 39

2.6. Identification of possible hybridization between P. tremula and hybrid aspens ............. 40

2.7. Statistical evaluation ........................................................................................................ 41

2.7.1. Assessment of genetic variation .............................................................................. 41

2.7.2. Correlation between occurrence of certain RAPD fragments and presence of DNA

of P. tremulae .................................................................................................................... 46

2.7.3. Analysis of European aspen and hybrid aspen leaf parameters ............................... 47

3. RESULTS AND DISCUSSION ....................................................................................... 48

3.1. General evaluation of investigated P. tremula stands and plus trees .............................. 48

3.2. DNA extraction from P. tremula ..................................................................................... 52

3.3. Genetic diversity of P. tremula plus trees ....................................................................... 55

3.3.1. Nei’s genetic distance between P. tremula plus trees .............................................. 58

3.3.2. Correlation between genetic distances and geographic distribution pattern of P.

tremula plus trees ............................................................................................................... 60

3.4. Population structure of P. tremula in Lithuania .............................................................. 60

3.4.1. Genetic relatedness of P. tremula populations by PCA .......................................... 61

4

3.4.2. Genetic parameters of assessed SSR loci of evaluated P. tremula populations ...... 63

3.4.3. Genetic diversity indices of P. tremula populations ................................................ 64

3.4.4. Nei’s genetic distances between populations of P. tremula .................................... 70

3.4.5. Correlation between genetic distances and geographic distribution pattern of P.

tremula populations ........................................................................................................... 75

3.4.6. P. tremula within- vs. among-population genetic diversity .................................... 75

3.4.7. Clustering of P. tremula populations using Bayesian approach .............................. 77

3.5. Correlation between presence of P. tremulae DNA and certain RAPD fragments......... 81

3.6. Hybridization between European and hybrid aspen ........................................................ 83

CONCLUSIONS ....................................................................................................................... 87

REFFERENCES ....................................................................................................................... 89

LIST OF PUBLICATIONS .................................................................................................... 112

LIST OF ABBREVIATIONS ................................................................................................. 113

ACKNOWLEDGMENTS ...................................................................................................... 115

APPENDIXES ......................................................................................................................... 116

5

INTRODUCTION

The scientific problem. The genus Populus is to date the main model system for genomic,

genetic and physiological research in trees (Luquez et al. 2008). Populus trichocarpa Torr. & A.

Gray was the first tree which genome has been sequenced (Tuskan et al. 2006). This

achievement led to increased number of research projects directed towards deeper understanding

of tree genomes.

The only native poplar species in Lithuania is European aspen (Populus tremula L.). P. tremula

is an ecologically important and widespread species with a broad distribution range growing in

temperate and boreal regions of Eurasia, widely used for hybridization and plantation forestry

(Rytter and Stener, 2005; Wühlisch, 2009). Therefore it is important to acquire knowledge on

distribution, diversity, population genetic structure, gene flow among populations, breeding and

propagation, possible hybridization events under natural conditions, and resistance to pathogens

of this tree species in Lithuania. The present study therefore is aimed to fulfill some of the

above-raised tasks and to provide some useful research tools (methods) for further investigations

of P. tremula in Lithuania.

The main aim of the study was to assess genetic diversity of European aspen plus trees and

populations in Lithuania, and to reveal possibility to identify hybridization between P. tremula

and hybrid aspen using morphological traits of their leaves.

The objectives of the study were:

1. To find the most suitable method for DNA extraction from different European aspen

tissues;

2. To assess genetic diversity of European aspen plus trees in Lithuania;

3. To assess the genetic diversity level and fixation indices among different European

aspen populations;

4. To estimate the association between genetic diversity and geographic distribution of

European aspen populations in Lithuania;

5. To assess the incidence of trunk rot caused by a pathogenic fungus Phellinus tremulae

(Bondartsev) Bondartsev & P.N. Borisov in European aspen plus trees and to test

possible correlation between the presence of trunk rot and tree genetic properties;

6. To investigate possibility to identify the presence of hybridization between P. tremula

and hybrid aspens using morphological leaf trait parameters and flowering phenology.

6

Defended Statements:

1. Genetic differentiation between Lithuanian populations of European aspen correlates with

their geographic pattern;

2. Genetic diversity among local (Lithuanian) European aspen populations is low because of a

high gene migration rate;

3. The relationship between susceptibility of European aspen to trunk rot caused by Phellinus

tremulae and tree genetic properties can be revealed using RAPD analysis;

4. Hybridization between hybrid and European aspens can be revealed by morphological leaf

traits of their progenies at juvenile age.

Scientific novelty and practical importance of the research

In Lithuania, European aspen is regarded as a pioneer tree species of comparably low economic

value, thus its local populations usually are left for self-regeneration. There are three aspen

provenance regions in Lithuania. These regions were distinguished by analyzing an actual

material of forest plot inventory and climatic data, yet research results based on molecular

markers were not available at that time and thus were not considered in delimiting of the

provenance regions. Therefore it is necessary to revise the existing boundaries of those regions

using data acquired from current molecular research of species’ genetic diversity.

Long-term breeding program in Lithuania is being implemented jointly with gene resource

conservation. Therefore, studies of genetic diversity of Lithuanian aspen populations are of the

utmost importance in the breeding program of European aspen. Relatively high susceptibility of

this tree species to trunk rot caused by P. tremulae remains the most serious problem that needs

to be solved before practical breeding implementation gets to a larger scale. Research on

interspecific hybridization is relevant when estimating genetic pollution of native aspen genetic

resources by hybrid aspen.

All above-mentioned research questions have never been thoroughly addressed in Lithuania

before.

Approval of the research work

The main research findings were published in one international publication in the journal

included in the Master Journal List of Institute of Scientific information and two publications –

in peer-reviewed Lithuanian journals (one of them in press). The results of the dissertation were

presented in 5 international conferences.

7

Volume and structure of the work

The dissertation is written in English. It consists of Introduction, Literature Review, Material

and Methods, Results and Discussion, Conclusions, List of References, List of Publications, List

of Abbreviations, Appendixes and Acknowledgements. The dissertation consists of 115 pages,

including 20 tables, 32 figures, 292 references and 2 appendixes.

8

1. LITERATURE REVIEW

1.1. Overview of genus Populus

The genus Populus belongs to family Salicaceae of order Salicales (Wühlisch, 2009). The genus

Populus is taxonomically subdivided into six sections: Turanga, Leucoides, Aigeiros,

Tacamahaca, Abaso and Populus (known synonymously as Leuce). The debates over the

species classification of poplars are still ongoing. A big confusion in the nomenclature of

poplars arises due to the wide distribution of many poplar species across the hemisphere,

frequent introgressive hybridization, long history of cultivation, and ease of vegetative

propagation. Poplar hybrids and cultivated varieties have often been named as species (Zsuffa,

1975). Various authors recognize from 20 up to 80 species in this genus, yet the recently

published taxonomies suggest that the total number of species in the genus range from 29

(Eckenwalder, 1996) to 32 (Dickmann and Kuzovkina, 2008).

The genus Populus is widely distributed throughout the Northern Hemisphere – America,

Europe and Asia, bordering Eastern Africa in the south (Bueno et al. 2003). Poplars are

dioecious, deciduous trees with simple, glabrous leaves, scale-covered buds and flowers

concentrated in hanging catkins. Species are wind pollinated and dispersed, producing vast

amount of seeds inside dry fruits (capsules). Reproduction of poplars occurs naturally via seeds,

root suckers, stump sprouts, and artificial budding, cuttings, layering and grafting. The timber is

light, soft and homogeneous, without well-differentiated heartwood. It is used in many industrial

applications such as raw material for wood pulp, particleboard, plywood, lumber, boxes,

matchsticks, and small woodenware (Farrar, 1995).

Poplars usually play an important role in a primary forest succession, invade and re-colonize

areas disturbed by harvesting, land clearing and fire. Poplars usually are water-demanding trees,

living near water bodies in fertile, moist and even wet soils. Vegetative regeneration after

coppicing is intense. Due to their rapid growth poplars are widely used in short rotation forestry

plantations. Worldwide, poplars are among the most widely used species in breeding activities

after pine, spruce and oak (Kleinschmit, 2000).

The most economically important sections of genus Populus are Populus, Aigeiros and

Tacamahaca (OECD, 2001). Section Populus (syn. Leuce Duby) – aspens, is further subdivided

into two subsections: Albidae (containing white poplars) and Trepidae (containing aspens). P.

tremula belongs to the Trepidae subsection together with P. tremuloides – its sister species from

North America.

9

1.1.1. Biology, ecology and distribution of European aspen

European aspen is widely distributed and considered to be the most widely spread tree species in

the world (Worrell, 1995). In Eurasia, this tree species is common in boreal and temperate forest

ecosystems. Supposedly, P. tremula has colonized Europe after the last glacial period from

several connected refugia near the ice cap (Fussi et al. 2010). In Eurasia, this tree species

currently is confined to Atlantic coast in the west and to the Pacific Ocean in the east, and

restricted in Fennoscandia as the northern limit of its natural range (Figure 1.1). At the southern

range of its distribution, P. tremula reaches high altitudes in mountains (up to 1600–2000 m

above sea level in the Alps, Caucasus and Pyrenees) (Wühlisch, 2009). In Eastern Asia,

European aspen is considered different enough to be classified as separate species P. davidiana

(Dode) Schneider (Wühlisch, 2009).

Fig. 1.1. Distribution map of European aspen (Populus tremula L.). Map: Ozolin ius (2003)

A map showing distribution of European aspen in Lithuania is presented in Figure 1.2.

Currently, the area occupied by P. tremula stands in Lithuania comprises 82.5 thousand ha, what

makes almost 3.8% of forested area. P. tremula is widespread in Lithuania, yet prevails mainly

in the central part of the country, characterized by the most fertile soils. The largest share of

European aspen is in K dainiai and Ukmerg State Forest Enterprises (SFE), comprising 6.0 and

5.9 thousand ha, respectively (Lithuanian Statistical Yearbook of Forestry, 2014).

10

Fig. 1.2. Distribution and abundance of European aspen in Lithuania. Map: Ozolin ius (2003)

European aspen usually occurs in mixed stands with Norway spruce and silver birch, while pure

stands are less common. Although the area of aspen-dominated stands in the country decreased

twofold by the end of the last century, the proportion of aspen in admixture with other tree

species (where aspen is not dominating) remains stable. Species composition in aspen stands

varies little over time. The mean age of aspen-dominated stands in Lithuania is 42 years

(Lithuanian Statistical Yearbook of Forestry, 2014).

Although the economic importance of P. tremula in Europe is limited by generally low

resistance to fungal diseases, its ecological importance as a keystone species in forest

biodiversity as habitat trees remains considerable. Large trees are associated with hundreds of

species of herbivorous and saprophytic invertebrates, fungi and epiphytic lichens (Kouki et al.

2004). Dead trunks of aspen serve as the main habitat to many specific insect and fungal species

(Kouki et al. 2004; Vehmas et al. 2009). P. tremula plays a large role and as a winter forage for

large herbivores (Ozolin ius, 2003), while hollow-bearing aspen trees are also important for

hall-nesting birds (Gjerde et al. 2005).

European aspen is a pronounced pioneer species that colonizes abandoned agricultural lands,

forest clear-cuts or fire-disturbed areas. In favorable growing conditions P. tremula can reach up

to 40 m in height and more than 1 m in diameter. Bark of the young tree is pale silvery-grey to

green, with smooth surface turning to fissured and dark grey with age. Leaves are nearly round,

11

slightly wider than long, 2–8 cm in diameter, with a coarsely toothed margin and flattened 4-8

cm long petiole. Leaves on seedlings and stem sprouts are heart-shaped to nearly triangular,

often much larger than regular leaves, up to 20 cm long; their petiole is also less flattened

(Wühlisch, 2009).

European aspen is a dioecious tree species. Sex determination system is located in a

chromosome 19, although the precise sex determination mechanism remains largely unknown

(Tuskan et al. 2012). Flowers of P. tremula are concentrated in catkins, flowering occurs in

early spring before leaf flushing. Seeds maturate 4–5 weeks after flowering (Latva-Karjanmaa et

al. 2003). Flowering of aspen usually begins at the age of 30–40 years, while single trees in open

areas can start flowering at 20 years, and suckers – even at 10 years of age (Kalela, 1945). P.

tremula produces seed almost every year, but exhibit masting, thus seed production varies every

year considerably (Houle, 1999; Shibata et al. 2002). Seed production is extremely high: one

catkin can hold up to 2000 seeds, and seed yield can attain 400–500 million per hectare

(Johnsson, 1942; Reim, 1929). European aspen seeds are very small and light (weight of 1,000

seeds is just 0.06–0.17 g) (Fystro, 1962). European aspen regenerates by seeds and vegetatively,

although sexual reproduction has been stated to be of minor importance (Worrell, 1995; Latva-

Karjanmaa et al. 2003). However, isoenzyme (Lopez-de-Heredia et al. 2004) and microsatellite

(SSR) studies (Suvanto and Latva-Karjanmaa, 2005) have shown that intra-population genetic

differentiation in this species is relatively high. This suggests the larger proportion of juveniles

is arising from seeds than previously thought (Myking et al. 2011). Vegetative reproduction in

aspen occurs via root suckers, thus forming clones of several ramets. According to high-

resolution molecular data, clone sizes in fact occurred to be smaller than previously recognized

using only morphological characters (Lopez-de-Heredia et al. 2004; Suvanto and Latva-

Karjanmaa, 2005).

The main ecologic delimiting factors of the species’ natural distribution are of the limited

tolerance to prolonged shade and interspecific competition, and susceptibility to fungal diseases

(trunk rot in particular); on the other hand, P. tremula is adaptive to a wide range of

environmental conditions. European aspen is frost-hardy and drought-resistant tree species. It

occurs on a wide range of soil types, although favors fertile and well drained soils (Worrell,

1995). These characteristics reflect wide ecological amplitude, which explains extensive

geographical range of distribution and early post-glacial appearance of the species (Myking et

al. 2011).

European aspen was the first tree species, which various latitudinal populations proved to be

different in critical day length for bud set in summer (Sylvén, 1940). Leaf abscission timing,

12

seasonal height and diameter increment varies in its latitudinal and altitudinal populations (Hall

et al. 2007; Fracheboud et al. 2009). Adaptive genetic variation ascertained for European aspen

is high taken at the population and species level as is characteristic for forest trees (König,

2005). While 60% of phenotypic variation explains the total variation, only 1% of the variation

among populations can be explained by neutral molecular markers (Hall et al. 2007). European

aspen is characterized by extensive gene flow which wipes out spatial genetic structure of P.

tremula populations (Lexer et al. 2005).

1.1.2. Sanitary condition of European aspen stands in Lithuania

Sanitary condition of European aspen is highly dependent on stand age. Age structure of aspen

stands in Lithuania: almost 49% of the aspen stands are mature, 35% of the stands are young

and only 16% could be rated as middle-aged and premature (Lithuanian Statistical Yearbook of

Forestry, 2014). In Lithuanian stands, a large proportion of mature aspen trees are affected by

fungal diseases. According to Lithuanian State Forest Service (personal communication), the

area of aspen stands affected by trunk rot disease was constantly decreasing due to sanitary clear

felling’s during the last decade (6977 ha in 2003, 4290 ha in 2007, 2707 ha in 2011 and 3167 ha

in 2014) (Lithuanian Statistical Yearbook of Forestry, 2014).

Fig. 1.3. Fruiting body (basidiocarp) of Phellinus tremulae

13

Sanitary conditions of aspen in Lithuania are foremost associated with an aspen trunk rot fungus

Phellinus tremulae (Bondartsev) Bondartsev & P. N. Borisov (Hymenochataceae). P. tremulae

is widely distributed in temperate and boreal Eurasia and North America (Niemelä, 1974) and in

many countries this fungus is considered as the most destructive pathogen of Populus spp.,

which often almost totally destroys the timber of aspen and limits timber crop rotation in

managed forest sites to merely 40–50 years (Manion, 1991). As the typical true heart-rot fungus,

P. tremulae is very host-specific species and in Europe occurs predominantly on living P.

tremula (Niemelä, 1974).

Basidiocarps typically are located in the tree crowns and produce huge quantities of spores with

a highly efficient airborne spread capacity (Sunhede and Vasiliauskas, 2002). The presence of

fruiting bodies (Figure 1.3) of P. tremulae usually implies that the larger part or entire trunk of

the tree has already been transformed to cull status (Allen et al. 1996).

Small twigs are considered to be the primary infection route of P. tremulae for stems of aspen,

although fresh wounds may serve as infection courts for this fungus as well (Holmer et al.

1994). Significant differences in amount and position of decay among aspen clones have been

reported decades ago (Wall, 1971). Thus, as the aspen clones seem to differ in susceptibility to

P. tremulae, investigations of genetic diversity and breeding of this tree species against the trunk

rot pathogen are required as this may result in future aspen stands with increased resistance

(Manion, 1991).

1.1.3. Hybridization of Populus spp.

Hybridization is the process of interbreeding between individuals belonging to different species,

or otherwise genetically divergent yet belonging to the same species. Hybridization is a complex

evolutionary phenomenon resulting in admixed offspring (Abbott et al. 2013). When Mayr

(1942) postulated concept of biological species, hybridization was seen as a rare incidence – i.e.

“good” species did not form hybrids. To date, concepts and definitions of hybridization and

introgression are judged differently. Hybridization is the initial cross (F1) between parental

species, and introgression occurs when hybrids backcross with one of their parental species and

only some of the genetic material is maintained in subsequent generations (Roe et al. 2014).

Fitness of newly formed hybrids depends on varying factors: genetic incompatibilities, epistasis,

and disruption of co-adapted gene complexes, deleterious gene interactions, heterosis,

transgressive segregation, or selective filtering of adaptive gene regions (Dobzhansky, 1970;

Burke and Arnold, 2001; Martinsen et al. 2001; Tiffin et al. 2001; Rieseberg et al. 2003).

14

Hybridization role is recognized in evolutionary species diversification, adaptation and

maintenance of biodiversity (Abbott et al. 2013). Hybridization is an important evolutionary

force with highly variable outcomes and it is observed frequently among plants (Arnold, 1997)

and animals (Mallet, 2005). Interspecific hybrids can be different (sometimes superior) over the

parental species, e.g. be characterized by increased vigor or be sterile and become evolutionary

dead end (Schweitzer et al. 2002; Arnold and Martin 2010; Whitney et al. 2010). Hybridization

can also be responsible for the new genetic variation (Butlin and Ritchie, 2013), together with

selective forces only the introgression of adaptive genes while maintaining distinct species

boundaries can be tolerated (Martinsen et al. 2001). Hybridization sometimes can result the

evolution of hybrid offspring into novel hybrid species (Rieseberg, 1997), erase species

boundaries (Rhymer and Simberloff, 1996; Seehausen, 2006), or occur with invasive exotic

species and “pollute” local populations and species (Schierenbeck and Ellstrand, 2009).

In nature, some plant taxa are known to form hybrid systems, such systems are formed in areas

where different species belonging to such taxa meet and form hybrid zones. Forest trees forming

such hybrid systems belong to genera Quercus (Petit et al. 2003a; Lepais and Gerber, 2011),

Eucalyptus (Field et al. 2011), Picea (Perron and Bousquet, 1997), Pinus (Cullingham et al.

2012) and Populus (Eckenwalder, 1984; Floate, 2004; Hamzeh et al. 2007; Thompson et al.

2010; LeBoldus et al. 2013).

Genus Populus has pronounced interspecific hybridization. Hybrids within sections are

produced easily and often are more vigorous compared to their parental species (e.g. P.

tremuloides x P. tremula (Ilstedt and Gullberg, 1993)). Hybridization between the sections of

genus Populus is variable, e.g. Aigeiros and Tacamahaca do cross easily, while hybridization

Populus – Aigeiros, and Populus – Tacamahaca is a rare event, usually resulting in infertile

seeds or dwarfed seedlings (Zsuffa, 1975) (Figure 1.4). Crosses between sections are sometimes

easier achieved using interspecific hybrids, rather than pure species.

Interspecific hybrids in Populus section were subjected to extensive studies (Eckenwalder,

1984; Keim et al. 1989; Stettler et al. 1996; Dickmann et al. 2001; Floate, 2004; Fossati et al.

2004; Vanden Broeck et al. 2005; Lexer et al. 2005; 2007; 2010; Meirmans et al. 2010).

Identification of hybrid poplars is carried out mostly using molecular markers (Smulders et al.

2001; Meirmans et al. 2007; Talbot et al. 2011; Isabel et al. 2013). These markers are used not

only for identification of first generation hybrids (F1), but also to assess the advanced generation

hybrids and the level of introgression (Stölting et al. 2013). For identification of poplar hybrids,

both molecular and morphological characters are usually used. Floate (2004) has successfully

15

used leaf morphological characters to identify interspecific hybrids of P. balsamifera, P.

angustifolia and P. deltoides.

Fig. 1.4. Crossability of species within genus Populus. Fig. by: Zsuffa, 1975

Worldwide, several hybrid zones formed by different Populus species are recognized and

investigated so far. In Canada, hybrid zone is formed by a) P. balsamifera and P. deltoides, and

b) by P. balsamifera, P. angustifolia and P. deltoides; in North America – by P. fremontii and P.

angustifolia, in Europe – by P. tremula and P. alba. Extensive research on these poplar hybrid

zones demonstrated that these species form stable hybrid zones where not only first generation

hybrids, but also advanced generation hybrids may be found, and asymmetric introgression

among Populus species that forms hybrid zones, has been documented (Eckenwalder, 1984;

Floate, 2004; Hamzeh et al. 2007; Thompson et al. 2010; LeBoldus et al. 2013). In these zones,

16

hybridization rates differed greatly among different locations, where natural Populus

hybridization occurs (Meirmans et al. 2010; Thompson et al. 2010; Talbot et al. 2012).

Hybrid zone in central Europe formed by P. alba and P. tremula was extensively studied by C.

Lexer and his colleagues. Early research findings suggested the occurrence of introgression from

P. tremula to P. alba genome via P. tremula pollen (Lexer et al. 2005). DNA microsatellites

were used to assess admixture of P. tremula and P. alba in their hybrid zone and to map loci that

contribute to reproductive isolation and trait differences (Lexer et al. 2007). Joseph and Lexer

(2008) have developed a series of different markers suitable to investigate reproductive barriers

of P. tremula and P. alba. PCR-RFLP analysis of P. alba and P. tremula maternally inherited

chloroplast markers indicated hybridization events in the past, during postglacial migration.

Phylogeographic structure was found for P. alba, but not for P. tremula, which haplotype

diversity was evenly distributed among investigated populations (Fussi et al. 2010).

Reproductive isolation between P. tremula and P. alba was studied in Italian, Austrian and

Hungarian hybrid zones (Lexer et al. 2010; Lindtke et al. 2012). Many loci displayed greatly

increased between species heterozygosity in recombinant hybrids despite striking genetic

differentiation between the parental genomes. However, microsatellite markers are not the best

choice for introgression research because of their limited genomic coverage. Stölting et al.

(2013) have overcome this problem using SNP polymorphism screened using RAD (restriction

site associated DNA) sequencing. Obtained results showed great variation in genetic divergence.

Autocorrelations of genetic divergence were involved in low divergence blocks, thus suggesting

that allele sharing was caused by recent genetic flow and not by shared ancestral

polymorphisms. Further analysis of seedlings from European Populus hybrid zone indicates

strong post-zygotic selection that eliminated many hybrid seedlings (Lindtke et al. 2014). Post-

matting reproductive barriers in BC1 crosses demonstrated extensive segregation distortions,

favoring P. tremula donor genes over P. alba genes in more than 90% cases (Macaya-Sanz et al.

2011).

1.1.4. History of European aspen investigation with emphasis on genetic research

In Lithuania, investigation of European aspen started in 1931. Rauktys (1935), Mejeris (1936)

and other researchers published papers describing sanitary condition of local aspen stands. In

addition, some statistical data of aspen stands were published (Vil inskas, 1931; Jameikis, 1933;

Lietuvos mišk statistika, 1937). More comprehensive data about European aspen were

published in a monograph by J. Rauktys (1938) where he described morphological, biological

and ecological characteristics of this tree species. Four aspen subspecies were described in the

monograph, yet information on their abundance and distribution in Lithuania was not provided.

17

More extensive research on European aspen in Lithuania started only in the 70’s. At that time

Prof. L. Kairi kštis (Kairi kštis, 1962, 1963) described his research findings about species’

growth and ecological characteristics of P. tremula. Dr. V. Mikalaikevi ius investigated causal

agents of aspen trunk rot in Lithuanian forests, their biology and the extent of damage they

cause (Mikalaikevi ius, 1958a). The author distinguished some aspen forms according to a bark

color, bud flushing phenology, at the same time indicating the resistance of these forms to the

trunk rot (Mikalaikevi ius, 1958 a; b; 1959). During 1966–1968, extensive research on aspen

breeding, flowering and fruiting, hybridization and propagation was carried out by Ramanauskas

(1968). The author performed investigation of morphological aspen forms in Lithuania and

Kaliningrad District. Dr. R. Murkait (1974) reported that in Lithuania the major part of aspen

forest stands are composed of trees originating from coppice. She has distinguished aspen

morphological forms, determined pollen distribution in the tree crowns and possibilities to

disperse aspen by seeds. Research on aspen hybridization in Lithuania has been performed by A.

Malinauskas and A. Pli ra (Pli ra, 1993). It was shown that the most important factor for

hybridization is parental genotypes, rather than combination of different species from the section

Populus. Results of investigation carried out in mature European aspen stands in Lithuania

showed that the most of the species’ variation is explained by productivity and resistance

criteria, and that selection to breeding programs should be carried out using stem volume

increment ratio to crown projection area (Pli ra, 1993).

The investigation of polymorphism of P. tremula as a model tree species was carried out in a

large number of studies. P. tremula, P. tremuloides and their interspecies hybrids were

investigated using isozyme method by Gallo and Geburek (1991). Following discovery of PCR,

the research of aspen polymorphism has increased substantially. By utilizing RAPD method, a

total of 89 aspen genotypes were identified in Spain (Sanchez et al. 2000). Later on, genetic

differentiation of P. tremula was investigated using a microsatellite method (Gomez et al. 2003).

Swedish researchers have assessed polymorphism and haplotype structure of this tree species

using five different genes (Ingvarsson, 2005). Ingvarsson et al. (2008b) has investigated

utilization of SNP method fitochrome B2 locus polymorphisms and their effect on bud flushing

phenology. PCR-RFLP and microsatellite (SSR) methods were employed to reveal genetic

differentiation among Italian P. tremula populations (Salvini et al. 2001). Clonal structure of

European aspen in Finland was investigated using morphological traits and microsatellite

markers (Suvanto and Latva-Karjanmaa, 2005). This study demonstrated that clones of P.

tremula consisted on average of 2.3 ramets, and that most of the clones (70%) originated from a

single ramet. P. Ingvarsson (2008a) studied demographic history of P. tremula using nucleotide

18

polymorphism of 124 genes. The obtained results showed substantial nucleotide polymorphism

and the bottleneck in the demographic history of this species. Fussi et al. (2010) demonstrated

that it had several refugia during the last glacial period near the ice cap using PCR-RFLP

markers. The results suggested immigration scenario for this species. Investigation of SNP

polymorphisms confirmed the divergence between section Leuce and a group combining

sections Aigeiros and Tacamahaca (Fladung and Buschbom, 2009). Research carried out in

Scandinavia showed, that adaptive variation in European aspen stems from the standing genetic

variation within species, rather than from newly arising mutations. Adaptive characteristics were

found to be facilitated by an admixture of eastern and western P. tremula lineages during post-

glacial migration (De Carvalho et al. 2010).

Discovery of markers capable to discriminate among different species, hybrids and clones and

the possibility to fingerprint them brought a general breakthrough in tree population studies.

AFLP markers were used to fingerprint 44 species, clones and cultivars of the genus Populus

(Zhou et al. 2005). This was the first report of germplasm identification in all sections of the

genus. Fossati et al. (2005) has fingerprinted a collection of 66 commercial poplar cultivars

using AFLP and SSR markers. Ten microsatellite loci used by Liesebach et al. (2009) showed

good discriminatory power to distinguish among various clones of the genus Populus, even

among siblings. Schroeder and Fladung (2010) have developed molecular markers suitable for

discriminating among poplar species, hybrids and clones. The main challenge the authors had to

face was collection of pure species samples and validation of hybrid material for primer testing.

However, Schroeder et al. (2012) found chloroplast sequences suitable for barcoding of tree

individuals which made the discrimination easier.

Sequencing of P. trichocarpa genome (Tuskan et al. 2006) has shed more light on Populus

genome thus allowing scientists to carry out theoretical research based on database sequence

data. Using sequence database information, Unneberg et al. (2005) have recognized 70,000

possible expressed sequence tags (EST’s) in P. tremula and P. trichocarpa genomes. EST

sequences in databases were used to identify ancient polyploidy of Populus spp. (Sterck et al.

2005). Using SSR and AFLP markers, Pakull et al. (2009) have constructed a genetic linkage

map for P. tremula and P. tremuloides. This was the first genetic linkage map associating SSR

and AFLP markers to the physical genomes of P. tremula and P. tremuloides with direct link to

P. trichocarpa genomic sequence. Pakull et al. (2015) have developed a genetic marker

determining sex in P. tremula. Sex determination marker was developed using earlier obtained

results on sex-linked SSR markers that were mapped in linkage group 19 in Populus spp.

(Markussen et al. 2007; Pakull et al. 2011).

19

Following sequencing of P. trichocarpa genome, the advance in P. tremula research has also

started. Current research on Populus spp. concentrates on unraveling candidate genes for a

specific trait or genetic variation in genes responsible for a certain trait. In Sweden, a collection

of P. tremula trees representing different populations (SwAsp) was established (Luquez et al.

2008). This collection was created to facilitate genetic, genomic and physiological research of

this tree species. First phenotypic evaluation results of this collection were published by Luquez

et al. (2008). Adaptive population phenological differentiation was investigated among

European aspen stands from different longitudes using microsatellite method (Hall et al. 2007).

Ma et al. (2010) were looking for genetic variation in genes that control photoperiodic pathway

using the SwAsp collection. The authors didn’t recognize any SNPs associated with aspen’s

response to varying light regime across latitudinal gradient, but detected a large covariance in

allelic effects across populations for growth cessation. Onge (2006), however, found very little

variation in investigated genes coding flowering time of the species. The assessed variation

hasn’t been associated to any of the recorded morphological traits. Investigating the same

Swedish aspen collection, Bernhardsson and Ingvarsson (2012) found that genes associated with

inducible defense responses showed strong longitudinal clines identified by SNPs. Strømme et

al. (2014) has demonstrated that elevated temperature and UVB radiation does affect spring and

autumn phenology of P. tremula individuals originating from southern and eastern Finland.

1.2. Molecular techniques for tree research

1.2.1. DNA extraction from plant material

DNA extraction is crucial for any molecular investigation. DNA extraction from

microorganisms, human or animal tissue is already a routine procedure, whilst plants proved to

be an exception of the rule. There are plenty of protocols for DNA extraction from various plant

species and tissues published already (Murray and Thompson, 1980; Dellaporta et al. 1983;

Rogers and Bendich, 1985; Doyle and Doyle, 1987; Wagner et al. 1987; Bousquet et al. 1990;

Devey et al. 1991; 1996; Stewart and Via, 1993; Nelson et al. 1994; Dumolin et al. 1995; Jobes

et al. 1995; Kim et al. 1997; Lin and Kuo 1998; Tibbits et al. 2006). Many of these protocols

recommend DNA extraction from needles, leaves or buds. These tissues are the best source for

DNA from mature trees, although much effort is required to collect this type of samples as

mature trees are usually tall and sample collection requires special equipment and skills. Sample

collection from crowns of mature trees often results in a limited number of available samples

and narrows down the investigation which is critical in population studies. Tibbits et al. (2006)

described a method for DNA extraction using cambium tissue, but the collection of it is

destructive, and affects the tree.

20

Another challenge associated with tree DNA extraction is contamination by other molecular

substances. Trees possess high levels of endogenous tannins, phenolics and polysaccharides, and

contamination of extracted DNA by these cellular components can inhibit subsequent molecular

reactions. Removal of these components is crucial in molecular-based techniques. Some authors

(e.g., Murray and Thompson, 1980; Doyle and Doyle, 1987; Wagner et al. 1987) suggest using

DNA extraction buffers containing CTAB (cetyltrimethylammonium bromide), while others

(e.g., Stewart and Via, 1993; Devey et al. 1996; Kim et al. 1997) introduce PVP-40

(polyvinylpyrrollidone; mol wt. 40.000) based method. Other methods based on sodium dodecyl

sulfate (SDS) (Nelson et al. 1994; Jobes et al. 1995) and guanidine detergent (Lin and Kuo,

1998) are also published, but these protocols are rarely used for DNA extraction from tree

species, mostly because of complicated cleaning procedure that requires separating DNA from

contaminants (Tibbits et al. 2006).

1.2.2. Markers used in tree population biology

Forest trees are among the longest living plants on Earth. Such long lived species depend on

genetic diversity in their populations in order to survive and successfully reproduce in a

changing environment. Traditionally genetic diversity of forest trees is studied using classical

progeny tests and provenance trial approaches. Progeny tests and provenance trials are

established on different sites with different conditions and focus on quantitative economically or

biologically important traits such as volume growth, timber characteristics, survival, and

tolerance to environmental stress and to pathogen/pest resistance (Newton, 2003). Expression of

these traits is affected by environment and majority of them are polygenic. Traditional

quantitative genetic methods are used for assessment of the amount of variation and its

segmentation due to phenotypic, genetic, environment and genotypic × environment (G×E)

interactions (Mitchell-Olds and Rutledge, 1986). Progeny tests and provenance trials are

laborious, expensive and time-consuming and not always capable to reveal an accurate

assessment of genetic variation among and within populations (Wang and Szmidt, 2001). In the

1960s, population biologists employed protein electrophoresis, and more recently many more

molecular techniques to study genetic variation were offered (Lewontin, 1991). Later on,

population biologists employed methods that utilize data obtained by extracting, cutting,

amplifying and detecting DNA (Mitton, 1994).

DNA markers became a routine investigation technique in population biology after the

discovery of polymerase chain reaction (PCR) (Mullis and Faloona, 1987). DNA sequence

information is inherited over generations, therefore today DNA is considered as the most

accurate source of genetic variation (Wang and Szmidt, 2001). DNA sequences are particularly

21

versatile for designing various kinds of genetic markers: markers related to fitness or markers

that are not affected by selection; e.g. markers distributed in non-coding sequences and nearly

free of constrains imposed by natural selection (Parker et al. 1998). Currently PCR is a major

tool in the analysis of DNA and RNA. It enabled many new molecular marker systems to be

designed. Its relatively low cost, high speed, simple standard preparation and demand of micro

amounts of source material made PCR-based markers applicable to any species. PCR based

primers can be anonymous and random or sequence-specific. Arbitrary primers usually display

variation in anonymous regions of the genome, while specific primers show variations among

known genomic sequences (Wang and Szmidt, 2001).

1.2.2.1. RAPD

In comparison to other molecular techniques used in tree research, RAPD (Williams et al. 1990)

is considered a technically simple method. The basics of the RAPD method are shown in Figure

1.5. Utilizing this method 10-bp-long nucleotide primers are mostly used with a minimum 50%

G+C amount. 10-bp-long PCR primers usually link to the DNA matrix every single million of

base pairs. RAPD fragment is formed when primers link to the DNA matrix in correct

orientation and proximity to each other (yet no more than 4 kb) (Weising et al. 2005). If DNA

sequences flanked by primers differ in number of tandem repeats, the amplified fragments of the

same population will be of different length. However, fragments of the same length can differ in

sequence, thus this method cannot show the difference (Wang and Szmidt, 2001). In some

individuals, primer linkage sequences are mutated; in such case the RAPD fragment might not

form. Amplification products are usually analyzed on agarose gels, staining DNA with ethidium

bromide. Different matrix DNA quality and concentration, primer annealing, elongation and

DNA denaturation times can influence the length and amount of amplified fragments. RAPDs

are dominant Mendelian inheritance markers and usually detect variation in nuclear DNA

(Carlson et al. 1991; Bucci and Menozzi, 1993; Lu et al. 1995). Due to small chloroplast and

mitochondria genome sizes (Sederof et al. 1987) the chance of RAPD fragment to result from

cytoplasmic DNA is low (Kazan et al. 1993; Aagaard et al. 1998). Dominant nature of RAPD

markers is disadvantageous in population biology and genome mapping as homozygotes cannot

be distinguished from heterozygotes (Wang and Szmidt, 2001).

22

Fig. 1.5. The principle scheme of RAPD. Adapted from Weising et al. (2005)

In the subsequent five years after RAPD discovery over 1,000 studies with various applications

of this method were published (Welsh and McLelland, 1990). RAPD primers are widely used

inferring phylogenetic relationships of many organisms. A broad field of RAPD investigation is

a research of genetic diversity. Genetic diversity assessment is important in inferring species’

genetic status, conserving biological diversity, setting genetic collections, looking for starting

material for breeding purposes. Currently the most important application area of the genetic

markers in population biology is assessment of genetic diversity and population structure of

various organisms. RAPD markers have also been extensively used in genetic studies of forest

trees (Mosseler et al. 1992; Tulsieram et al. 1992; Grattapaglia and Sederoff, 1994; Nelson et al.

1994; Chalmers et al. 1994; Isabel et al. 1995; Nesbitt et al. 1995; Schierenbeck et al. 1997), yet

currently reliability of this method is more and more questioned (Pérez et al. 1998; Rabouam et

al. 1999). RAPD markers linked to important genetic traits are often used in plant breeding and

biotechnology, used for generating gene maps (Weising et al. 2005).

1.2.2.2. Microsatellites (SSR’s)

Microsatellite method is a very powerful technique based on PCR. This technique is also known

as analysis of simple sequence repeats (SSRs). As the name indicates, these markers are DNA

fragments composed of simple sequences and tandemly repeated motifs mostly of 1 to 5 bp in

size. These DNA fragments are amplified using PCR. Tandem repeats composed mainly of

dinucleotide CA or GA repeats are common in many eukaryote genomes. This type of repeats is

found in many different genome locations and constitutes specific, repetitive DNA class.

23

Microsatellite repeats commonly are flanked by unique sequences that are found only once in a

genome.

Microsatellite method originally was developed for human genome research (Weber and May,

1989), and later adapted to plants (Morgante and Olivieri, 1993). These markers are useful

because of high polymorphism rate – many different alleles are found in one microsatellite

locus, where each allele has a different number of tandem repeats. Different alleles in a

microsatellite locus are formed due to high mutation rate which arises because of DNA

synthesis “mistakes”. Longer microsatellite loci having more tandem repeats are more

polymorphic (Beckman and Weber, 1992). High polymorphism and co-dominant nature of these

markers makes them highly informative. For example, 8 microsatellite loci with 10 alleles each

may “generate” 83 trillions of possible genotypes. This makes them best markers of choice for

DNA fingerprinting, in forensic, human paternity analysis, and in many more research

applications. In the research of plants these markers are used for determination of mating system

and paternity, as well as for gene flow structure analyses. The microsatellite markers are

extremely useful in tree breeding programs (e.g. for identification of genetically improved seeds

in seed lot).

Microsatellites are detected in all main classes of living organisms so far, and are found more

frequently than it would be predicted solely on base composition (Tautz and Renz, 1984; Epplen

et al. 1993). SSRs are distributed more frequently in non-coding than in protein coding

sequences of the various organisms’ genomes (Wang et al. 1994; Field and Wills, 1996;

Edwards et al. 1998; Metzgar et al. 2000; Wren et al. 2000; Morgante et al. 2002). However,

many tri-nucleotide repeats associated with diseases are found using SSRs in coding sequences

of human genome (Nadir et al. 1996). Different SSR frequencies in coding and non-coding

regions are found because of the selection against frame shift mutations resulting in length

changes in non-triplet repeats in coding regions (Liu et al. 1999; Dokholyan et al. 2000).

Eukaryotic organisms have three times more repeat sequences in protein coding sequences than

prokaryotes. SSR repeat families in prokaryotes and eukaryotes are clustered in non-

homologous proteins. Eukaryotes incorporating more repeats may have an evolutionary

advantage because of faster adaptation to a changing environment (Marcotte et al. 1999).

SSRs are considered evolutionary neutral, but significant part of them is proven to be

functionally significant (Li et al. 2002a); for example, they play a role in chromatin organization

(Cuadrado and Schwarzacher, 1998; Li et al. 2000a; b; c; 2002b; Röder et al. 1998). SSRs are

also significant in DNA organization, as they allow DNA sequence to form simple and complex

loop folding patterns. These patterns can have important regulation function on gene expression

24

(Catasti et al. 1999; Fabregat et al. 2001). Simple sequence multiply mainly constitutes

centromeric and telomeric regions of chromosomes (Centola and Carbon, 1994; Murphy and

Karpen, 1995; Schmidt and Heslop-Harrison, 1996; Brandes et al. 1997; Cambareri et al. 1998;

Areshchenkova and Ganal, 1999). The different organism’s centromeres are composed of SSRs

indicating strong evolutionary link between centromere structure and function (Eichler, 1999).

SSRs are also affecting gene activity and act as transcription elements in promoter regions of

heat-shock protein gene hsp26 in Drosophila (Sandaltzopoulos et al. 1995), in Aspergillus (Punt

et al. 1990), and Phytophthora (Chen and Roxby, 1997). SSRs are acting like transcription

regulatory elements when they are found in introns (Meloni et al. 1998; Gebhardt et al. 1999;

2000). Young et al. (2000b) noticed that triplet SSRs are preferentially located in regulatory

genes related to transcription and signal transduction but are under-represented in genes of

structural proteins. As confirmed in many studies, gene translation is affected by SSRs (Ivanov

et al. 1992; Sandberg and Schalling, 1997; Henaut et al. 1998; Martin-Farmer and Janssen,

1999; Timchenko et al. 1999).

Various SSR functions and effects, their abundance and distribution are associated with their

mutation rates. SSRs mutation rates depend on species, repeat type, loci and alleles, age and sex

(Brock et al. 1999; Hancock, 1999; Ellegren, 2000; Schlötterer, 2000), but in general SSR

mutation rate is very high (10-2–10-6 events per locus per generation) as compared to point

mutations (Li et al. 2002a). SSR mutations predominantly arise as changes in repeat number.

Such high SSR mutation rates can be explained in two ways: a) DNA slippage during DNA

replication (Tachida and Iizuka, 1992) and b) recombination between DNA strands (Harding et

al. 1992).

Microsatellite sequences are usually isolated from genomic libraries by screening them with

specific repeat motifs as probes. Clones having a repeat motif are thereafter sequenced. Non

repetitive DNA sequences flanking repetitive motif region are used to design primers for PCR

amplification. Microsatellite markers are multiallelic, widely dispersed across the genome and

relatively easily scored (Morgante and Oliveri, 1993; Devey et al. 1996; Powel et al. 1996;

Barreneche et al. 1998). The microsatellite primer development scheme is presented in Figure

1.6.

25

Fig. 1.6. Scheme showing development of microsatellite primers (Figure adapted from Young et al. 2000a)

The first microsatellite primers for tree species were developed for Pinus radiata (Smith and

Devey, 1994). Later the number of available primers increased substantially; those were

developed for Quercus spp. (Dow et al. 1995; Barret et al. 1997; Isagi and Suhandono, 1997),

for Eucalyptus spp. (Byrne et al. 1996), for Pinus strobus (Echt et al. 1996), for Picea abies

(Pfeiffer et al. 1997), and for several tropical tree species (Chase et al. 1996; White and Powell,

1997; Dawson et al. 1997; Steinkellner et al. 1997). Single base pair repeat microsatellites were

discovered in pine chloroplast genomes (Vendramin et al. 1996).

26

2. MATERIALS AND METHODS

P. tremula plus trees represent an important share of national Lithuanian forest genetic

resources. Plus trees are selected by Lithuanian State Forest Service as exceptional (both by

quantitative and qualitative characteristics) representatives of autochthonous tree species; those

trees are later managed by local forest enterprises. In 2007, a total number of 137 P. tremula

plus trees were selected in 19 stands in 16 State Forest Enterprises (SFE) and monitored by

Forest State Service (Table 2.1).

2.1. Sampling of P. tremula trees for molecular analysis

All P. tremula plus trees inventoried in Lithuania in 2007 were included into RAPD, SSR and

PCR-RFLP (Narayanan, 1991) studies (n=137). The main data about those plus trees are given

in Table 2.1.

Table 2.1. Basic data about investigated Populus tremula plus trees: numbers, location (geographical coordinates), age and soil type (typology group, according to Vai ys (2006)). The presented data is provided by Lithuanian State Forest Service

Tree No.

Forest enterprise

Forest district

Block no.

Compart- ment no.

Latitude Longitude Forest soil type

Tree age

130 Anykš iai Trošk nai 364 6 55°34'20.5" 24°50'08.9" Lds 62 131 Anykš iai Trošk nai 364 6 55°34'22.0" 24°50'09.1" Lds 62 132 Anykš iai Trošk nai 364 6 55°34'21.0" 24°50'09.0" Lds 62 133 Anykš iai Trošk nai 364 6 55°34'21.5" 24°50'09.3" Lds 62 134 Anykš iai Trošk nai 364 6 55°34'21.8" 24°50'09.9" Lds 62 135 Anykš iai Trošk nai 364 6 55°34'20.9" 24°50'10.1" Lds 62 136 Anykš iai Trošk nai 364 6 55°34'21.5" 24°50'09.7" Lds 62 137 Anykš iai Trošk nai 364 6 55°34'20.6" 24°50'11.0" Lds 62 138 Anykš iai Trošk nai 364 6 55°34'20.7" 24°50'09.6" Lds 62 139 Anykš iai Trošk nai 364 6 55°34'21.3" 24°50'09.2" Lds 62 140 Anykš iai Trošk nai 364 6 55°34'22.4" 24°50'09.3" Lds 62 141 Anykš iai Trošk nai 364 6 55°34'22.5" 24°50'09.8" Lds 62 142 Anykš iai Trošk nai 364 7 55°34'22.7" 24°50'11.6" Lds 62 143 Anykš iai Trošk nai 364 6 55°34'21.6" 24°50'09.5" Lds 62 144 Anykš iai Trošk nai 364 6 55°34'21.6" 24°50'09.4" Lds 62 042 Biržai Latveliai 64 3 56°21'32.4" 24°52'16.9" Lcp 62 043 Biržai Latveliai 64 3 56°21'25.6" 24°52'17.8" Lcp 67 158 Ignalina Tvere ius 1240 6 55°19'33.3" 26°28'08.8" Ldp 67 159 Ignalina Tvere ius 1240 6 55°19'33.2" 26°28'11.2" Ldp 37 160 Ignalina Tvere ius 1240 6 55°19'33.7" 26°28'11.1" Ldp 37 161 Ignalina Tvere ius 1240 6 55°19'33.8" 26°28'09.9" Ldp 37 162 Ignalina Tvere ius 1240 6 55°19'34.7" 26°28'07.1" Ldp 37 084 Jurbarkas Veliuona 51 9 55°09'15.3" 23°19'16.9" Lfp 37 085 Jurbarkas Veliuona 51 9 55°09'15.7" 23°19'18.2" Lfp 52 086 Jurbarkas Veliuona 51 9 55°09'14.9" 23°19'18.5" Lfp 52 087 Jurbarkas Veliuona 51 9 55°09'14.6" 23°19'16.9" Lfp 52 088 Jurbarkas Veliuona 51 9 55°09'14.8" 23°19'19.1" Lfp 52 089 Jurbarkas Veliuona 51 9 55°09'15.4" 23°19'19.0" Lfp 52 090 Jurbarkas Veliuona 51 9 55°09'13.8" 23°19'17.2" Lfp 52 091 Jurbarkas Veliuona 51 9 55°09'14.1" 23°19'19.2" Lfp 52 092 Jurbarkas Veliuona 51 9 55°09'13.0" 23°19'18.7" Lfp 52

27

Tree No.

Forest enterprise

Forest district

Block no.

Compart-ment no.

Latitude Longitude Forest soil type

Tree age

093 Jurbarkas Veliuona 51 9 55°09'13.4" 23°19'19.5" Lfp 52 074 Kaišiadorys B da 287 5 54°53'35.1" 24°19'57.8" Pdn 52 075 Kaišiadorys B da 287 5 54°53'36.0" 24°20'00.3" Pdn 62 076 Kaišiadorys B da 287 5 54°53'34.5" 24°20'00.8" Pdn 62 077 Kaišiadorys B da 287 5 54°53'34.1" 24°19'58.3" Pdn 62 078 Kaišiadorys B da 287 5 54°53'33.8" 24°20'00.5" Pdn 62 079 Kaišiadorys B da 287 5 54°53'33.6" 24°19'59.1" Pdn 62 080 Kaišiadorys Pravienišk s 89 17 54°55'43.5" 24°13'13.3" Lcp 62 081 Kaišiadorys Pravienišk s 89 17 54°55'43.3" 24°13'12.9" Lcp 57 082 Kaišiadorys Pravienišk s 89 17 54°55'43.5" 24°13'13.3" Lcp 57 083 Kaišiadorys Pravienišk s 89 17 54°55'43.3" 24°13'13.5" Lcp 57 109 K dainiai žuolotas 24 4 55°19'43.6" 23°46'36.3" Lds 57 110 K dainiai žuolotas 24 4 55°19'44.2" 23°46'38.4" Lds 62 111 K dainiai žuolotas 24 4 55°19'42.9" 23°46'37.3" Lds 62 112 K dainiai žuolotas 24 4 55°19'41.4" 23°46'37.2" Lds 62 113 K dainiai žuolotas 24 4 55°19'33.6" 23°46'39.0" Lds 62 114 K dainiai žuolotas 24 4 55°19'37.7" 23°46'38.7" Lds 62 115 K dainiai žuolotas 24 4 55°19'37.5" 23°46'37.8" Lds 62 116 K dainiai žuolotas 24 4 55°19'36.0" 23°46'36.3" Lds 62 117 K dainiai žuolotas 24 4 55°19'36.0" 23°46'39.5" Lds 62 118 K dainiai žuolotas 24 4 55°19'35.1" 23°46'37.8" Lds 62 119 K dainiai žuolotas 24 4 55°19'31.1" 23°46'40.2" Lds 62 163 Kretinga Kartena 2 12 55°55'36.8" 21°35'21.9" Lcl 62 164 Kretinga Kartena 2 12 55°55'37.4" 21°35'24.0" Lcl 67 165 Kretinga Kartena 2 12 55°55'36.4" 21°35'23.0" Lcl 47 166 Kretinga Kartena 2 12 55°55'35.9" 21°35'21.5" Lcl 37 167 Kretinga Kartena 2 12 55°55'36.7" 21°35'26.2" Lcl 47 168 Kretinga Kartena 2 12 55°55'35.6 21°35'25.2" Lcl 47 039 Kurš nai Šauk nai 105 20 55°53'42.1" 22°41'32.2" Ncs 67 040 Kurš nai Šauk nai 105 20 55°53'40.6" 22°41'34.5" Ncs 72 041 Kurš nai Šauk nai 105 20 55°53'46.2" 22°41'31.3" Ncs 67 145 Kurš nai Šauk nai 105 11 55°53'44.2" 22°41'38.8" Lcs 67 146 Kurš nai Šauk nai 105 11 55°53'42.4" 22°41'39.3" Lcs 67 104 Marijampol Sasnava 34 11 54°38'03.2" 23°37'30.9" Lds 67 105 Marijampol Sasnava 34 11 54°38'03.3" 23°37'30.4" Lds 47 106 Marijampol Sasnava 34 11 54°38'04.6" 23°37'32.9" Lds 47 107 Marijampol Sasnava 34 11 54°38'04.3" 23°37'31.3" Lds 47 108 Marijampol Sasnava 34 11 54°38'05.1" 23°37'32.8" Lds 47 044 Pakruojis Linkuva 39 11 56°17'10.6" 24°02'02.1" Nfs 47 045 Pakruojis Linkuva 39 11 56°17'11.1" 24°02'01.9" Nfs 57 046 Pakruojis Linkuva 39 11 56°17'11.3" 24°02'02.2" Nfs 57 047 Pakruojis Linkuva 39 11 56°17'12.0" 24°02'04.0" Nfs 57 048 Pakruojis Linkuva 39 11 56°17'11.3" 24°02'04.2" Nfs 57 049 Pakruojis Linkuva 53 24 56°16'01.4" 24°03'44.5" Lfs 57 050 Pakruojis Linkuva 53 24 56°16'02.0" 24°03'43.3" Lfs 67 051 Pakruojis Linkuva 53 24 56°16'01.4" 24°03'43.8" Lfs 67 052 Pakruojis Linkuva 53 24 56°16'00.7" 24°03'43.0" Lfs 67 053 Pakruojis Linkuva 53 24 56°16'01.1" 24°03'43.3" Lfs 67 054 Pakruojis Linkuva 53 24 56°16'00.2" 24°03'42.2" Lfs 68 055 Pakruojis Linkuva 53 24 56°16'00.8" 24°03'45.5" Lfs 67 056 Pakruojis Linkuva 53 24 56°15'55.8" 24°03'44.1" Lfs 67 057 Pakruojis Linkuva 53 24 56°15'55.2" 24°03'43.7" Lfs 71 058 Pakruojis Linkuva 53 24 56°15'59.0" 24°03'46.7" Lfs 67 059 Pakruojis Linkuva 53 24 56°15'58.5" 24°03'45.3" Lfs 67 060 Pakruojis Linkuva 53 24 56°15'58.0" 24°03'43.0" Lfs 71 061 Pakruojis Linkuva 53 24 56°15'58.0" 24°03'43.8" Lfs 67 062 Pakruojis Linkuva 53 24 56°15'58.4" 24°03'43.2" Lfs 67

28

Tree No.

Forest enterprise

Forest district

Block no.

Compart-ment no.

Latitude Longitude Forest soil type

Tree age

063 Pakruojis Linkuva 53 24 56°15'56.5" 24°03'41.9" Lfs 67 064 Pakruojis Linkuva 53 24 56°15'57.8" 24°03'45.9" Lfs 67 065 Pakruojis Linkuva 53 24 56°15'56.3" 24°03'42.3" Lfs 68 066 Pakruojis Linkuva 53 24 56°15'57.3" 24°03'43.2" Lfs 57 067 Pakruojis Linkuva 53 24 56°15'59.7" 24°03'45.2" Lfs 68 068 Pakruojis Linkuva 53 24 56°15'55.8" 24°03'42.9" Lfs 69 069 Pakruojis Linkuva 53 24 56°15'58.7" 24°03'42.5" Lfs 67 070 Pakruojis Linkuva 53 24 56°15'57.1" 24°03'47.2" Lfs 72 071 Pakruojis Linkuva 53 24 56°15'55.7" 24°03'41.5" Lfs 72 072 Pakruojis Linkuva 53 24 56°15'56.5" 24°03'41.9" Lfs 66 073 Pakruojis Linkuva 53 24 56°15'56.8" 24°03'46.1" Lfs 67 038 Raseiniai Šimkai iai 62 12 55°11'48.5" 22°53'33.6" Udp 72 094 Raseiniai Šimkai iai 3 47 55°14'52.6" 22°59'47.8" Lds 47 095 Raseiniai Šimkai iai 3 47 55°14'51.7" 22°59'47.2" Lds 47 096 Raseiniai Šimkai iai 3 47 55°14'51.3" 22°59'48.6" Lds 47 097 Raseiniai Šimkai iai 3 47 55°14'50.7" 22°59'46.7" Lds 47 098 Raseiniai Šimkai iai 3 47 55°14'50.3" 22°59'48.9" Lds 47 099 Raseiniai Šimkai iai 3 47 55°14'49.2" 22°59'47.5" Lds 47 100 Raseiniai Šimkai iai 3 47 55°14'48.0" 22°59'49.3" Lds 47 101 Raseiniai Šimkai iai 3 47 55°14'47.0" 22°59'47.5" Lds 47 102 Raseiniai Šimkai iai 3 47 55°14'44.3" 22°59'46.8" Lds 47 103 Raseiniai Šimkai iai 3 47 55°14'44.8" 22°59'48.2" Lds 47 020 Rokiškis Kamajai 206 6 55°48'04.4" 25°47'17.8" Ndp 47 021 Rokiškis Kamajai 206 6 55°48'04.4" 25°47'17.7" Ndp 62 022 Rokiškis Kamajai 208 9 55°47'59.9" 25°48'13.1" Nds 62 023 Rokiškis Kamajai 208 9 55°48'01.7" 25°48'13.4" Nds 67 120 Šakiai Plokš iai 47 15 55°01'48.9" 23°05'01.7" Lfs 67 121 Šakiai Plokš iai 47 15 55°01'48.4" 23°05'01.5" Lfs 67 122 Šakiai Plokš iai 47 15 55°01'47.6" 23°05'01.2" Lfs 67 123 Šakiai Plokš iai 47 15 55°01'45.2" 23°05'04.8" Lfs 67 124 Šakiai Plokš iai 47 15 55°01'44.4" 23°05'00.2" Lfs 67 125 Šakiai Plokš iai 47 15 55°01'44.3" 23°05'02.1" Lfs 67 126 Šakiai Plokš iai 47 15 55°01'45.3" 23°05'01.5" Lfs 67 127 Šakiai Plokš iai 47 15 55°01'41.9" 23°0502.3'" Lfs 67 128 Šakiai Plokš iai 47 15 55°01'47.8" 23°05'02.5" Lfs 67 129 Šakiai Plokš iai 47 15 55°01'48.1" 23°05'01.6" Lfs 67 036 Šal ininkai Dievenišk s 19 6 54°16'02.2" 25°44'09.6" Ncp 67 152 Šal ininkai Dievenišk s 19 1 54°16'05.0" 25°43'58.8" Ncp 72 153 Šal ininkai Dievenišk s 19 1 54°16'05.0" 25°43'58.8" Ncp 57 154 Šal ininkai Dievenišk s 19 1 54°16'05.0" 25°43'58.2" Ncp 57 155 Šal ininkai Dievenišk s 19 1 54°16'05.8" 25°43'57.9" Ncp 57 156 Šal ininkai Dievenišk s 19 1 54°16'05.8" 25°43'57.9" Ncp 57 157 Šal ininkai Dievenišk s 19 1 54°16'06.2" 25°43'58.2" Ncp 57 037 Taurag Pagramantis 46 7 55°23'35.4" 22°09'26.7" Ncp 57 147 Utena Dubingiai 11 27 55°07'17.9" 25°32'28.5" Ldp 67 148 Utena Dubingiai 11 27 55°07'17.4" 25°32'28.9" Ldp 62 149 Utena Dubingiai 11 27 55°07'21.7" 25°32'28.7" Ldp 62 150 Utena Dubingiai 11 25 55°07'19.3" 25°32'30.1" Ndp 62 151 Utena Dubingiai 11 25 55°07'21.7" 25°32'30.0" Ndp 62

For all molecular analysis, used in this work (RAPD, SSR and PCR-RFLP), one wood and leaf

sample per each plus tree was collected. Sampling of both wood and leaves was carried out in

May–July, 2007. Wood samples were collected using 8-mm Presser’s increment borer. One

wood core was taken from each plus tree at the breast height inserting the borer to the tree pith.

29

Before each sampling the borer was sterilized with 96% ethanol. Collected wood cores were

placed in sterile plastic containers. Leaf samples for DNA extraction were collected from the

upper part of tree crowns using SherrillTree BigShot® line launcher (SherrillTree Inc.). Only

sound looking, fully developed and mechanically intact leaves were sampled. Fresh leaf samples

were immediately placed into plastic sample bags containing self-indicating silica gel

(approximately 10–15 g of silica gel for 1 g of plant material).

For evaluation of DNA extraction protocols we randomly selected two aspen trees in Dubrava

State Forest Enterprise, Vaišvydava Forest district, 54°51'39.2" N, 24°4' 2305" E. Wood and

leaf samples from these two trees were collected as described above in May of 2007.

For microsatellite analysis (SSR) we sampled 177 P. tremula individuals in addition to

European aspen plus tree samples (n=314) in July–September, 2014 (Table 2.2). Selected trees

originated from the same forest stands as plus trees (Table 2.1). For microsatellite analysis only

leaf samples were collected. Sampling was performed as described above.

Table 2.2. Number of Populus tremula trees sampled for microsatellite analysis

State Forest Enterprise

No. of plus trees (2007)

No. of additionally sampled trees

(2014)a

State Forest Enterprise

No. of plus trees (2007)

No. of additionally sampled trees

(2014)a Anykš iai 15 2 Marijampol 5 12 Biržai 2 16 Pakruojis 30 0 Ignalina 5 14 Raseiniai 11 7 Jurbarkas 10 8 Rokiškis 4 12 Kaišiadorys 10 11 Šakiai 10 10 K dainiai 11 11 Šal ininkai 7 13 Kretinga 6 16 Taurag 1 16 Kurš nai 5 16 Utena 5 13

a Within the State Forest Enterprises the additional trees were sampled in vicinity to the inventoried plus trees (see Table 2.1.)

Wood and leaf samples of additional 7 trees exhibiting clear P. tremulae infection and 2 sound

looking trees were collected in Biržai, Ignalina, Kretinga, Raseiniai, Rokiškis, Šal ininkai,

Taurag and Utena SFE’s in May–July, 2007. These 9 trees have been selected from the same

forest stands as plus trees and were used as reference material in PCR-RFLP study.

2.2. Assessment of tree and stand characteristics

In order to assess and compare quality traits of P. tremula plus trees, 30 additional European

aspen trees (non-plus trees) per each plus trees containing stand (n=19) were subjected for

detailed evaluation in May–July, 2007. In total, 570 such trees were evaluated. These trees were

randomly selected yet always in a close vicinity to the aspen plus trees (Table 2.1.).

30

Table 2.3. Criteria values for Populus tremula phenotypic evaluation. The scoring system is made according to assessment methodology of forest tree breeding value used by Lithuanian State Forest Service Scale, score Criteria

1 2 3 4 5

Stem form Few bends in less than 1/3 of stem length

One bend in less than 1/3 of stem length

Bend in the lower part of the stem, in more than 1/3 of stem length

Bend in the upper 1/3 part of the stem

Straight

Branch thickness

Thick branches, tubers are present

Medium thick branches

Thin branches – –

Stem presence in crown part

Stem is absent in crown part, crown starts in the lower half of the tree height

Stem is absent in crown part, crown starts in the upper ½ of the tree height

Stem is present only in the lower part of the crown

Stem is present, branching only in the upper part of the crown

Stem is present through the whole crown

Occlusion of branch wounds occurring following branch dieback at the sites of their attachment

Occlusion of wounds is very poor; big wounds are not closed. Stem with nodules.

Occlusion of wounds is poor, big scars are visible on the stem. Stem with nodules.

Occlusion of wounds is intermediate. Wounds of smaller branches are occluded, while occlusion of larger branches is not always successful. Stem is smooth, yet in some places has nodules.

Occlusion of wounds is good; the sites of branch attachment are clearly visible. Stem is smooth.

Occlusion of wounds is very good; it is hard to notice the sites of branch attachment. Stem is smooth.

31

For each tree (plus additionally selected), were assessed: (a) age (based on the latest forest

inventory data), (b) height (assessed using Haglof vertex IV height meter, m), (c) diameter

(measured at breast height using diameter calipers, cm), (d) stem straightness (score values

given in Table 2.3), (e) stem slenderness (assessed in scores, where 5 means lowest slenderness,

and 1 – the most expressed slenderness), (f) height to dry branches (assessed using Haglof

vertex IV, m), (g) height to green branches (assessed using Haglof vertex IV, m), (h) mean

crown diameter (assessed as a mean after measuring in two directions perpendicular to each

other, m), (i) crown form (irregular, umbelliferous, oval, spherical, egg-shaped, narrow

spherical), (j) branch thickness (score values are given in Table 2.3), (k) stem presence in crown

part (score values given in Table 2.3), (l) branch wound occlusion (score values given in Table

2.3), (m) presence of epicormic branches, (n) tree sanitary condition (categories given in Table

2.4).

Additionally, the presence of mechanical injuries on the stems was also recorded. The sanitary

condition of the investigated aspen trees was evaluated according to (1991) and is

presented in Table 2.4.

Table 2.4. Scoring system for sanitary condition of Populus tremula trees (according to , 1991)

Score, points

Category descriptor

Main symptoms Additional symptoms

1 Sound looking

Leaves are green, shiny, the crown dense, the annual growth increment typical to a given species according to age, forest habitat and season.

–

2 Slightly weakened

Leaves are green, the crown slightly defoliated, the annual growth may be smaller than typical, and less than ¼ of the crown is dead.

Some localized damages on branches, presence of mechanical wounds and sporadic epicormic shoots.

3 Weakened

Leaves are smaller or lighter green, premature defoliation, the crown is thin, the dead portion of the crown composes ¼ – ½.

Localized damages are more abundant; primary colonization by stem pests, sap bleeding, and occurrence of epicormic shoots on the stem and branches.

4 Withering away

Leaves are small, lighter green or yellowish, premature defoliation or wilting, the crown is thin, the dead portion of the crown composes ½ – ¾.

Abundant and clearly visible activity of stem insect pests (exit holes, wounds, sap bleeding, sawdust on the bark and in timber); abundant presence of epicormic shoots.

5 Recently dead

Leaves are dry, wilted or prematurely defoliated, the dead portion of the crown composes > ¾, the bark is still present.

Abundant and clearly visible activity of stem insect pests and pathogens.

6 Old snags Leaves and some branches are lost, bark disrupted or crumbled away from the stem.

Visible insect exit holes as well as fungal mycelium and fruiting bodies on the stem, branches and roots.

32

2.3. DNA extraction from plant material

As extraction of good quality and clean DNA is a prerequisite for successful genetic studies, one

of our study objectives was to find the best DNA extraction method suitable for wood and leaf

tissue samples of P. tremula. For this task, we have randomly selected wood and leaf samples

from two European aspen trees.

We tested six well known DNA extraction techniques: SDS isolation, protein precipitation,

CTAB isolation, CTAB precipitation, guanidinium isothiocyanate and alkaline isolation (the full

description of these techniques is given by Milligan, 1998), and four commercially available kits

for extraction of plant genomic DNA: DNA isolation reagent for genomic DNA with Plant AC

reagent (AppliChem, Maryland Heights, USA), Nucleospin Plant Mini (Macherey-Nagel,

Düren, Germany), Genomic DNA purification kit (Fermentas, Vilnius, Lithuania) and

innuPREP Plant DNA Kit (Analytik Jena, Jena, Germany).

Before the DNA extraction samples of wood tissue (100 mg) and silica-dried leaf tissue (10 mg)

were ground using a mortar and a pestle in liquid nitrogen. The resulting powder was

immediately used for DNA extraction. The extraction of total DNA was performed in ten

different ways using the following protocols:

1. SDS isolation of total DNA (Edwards et al. 1991; Goodwin and Lee, 1993):

Transfer the ground sample material to tube and add 4 ml extraction buffer (200 mM

Tris pH 7.5, 25 mM EDTA, 250 mM NaCl, 0.5% (w/v) SDS) for each 10 mg of

tissue (e.g. for leaf sample add 400 l).

Vortex the sample for 5 sec.

Centrifuge at 12,000 x g for 1 min to pellet cellular debris.

Transfer 3 ml (300 l) of the supernatant to a new tube. Add 3 ml (300 l) of

isopropanol and incubate at 20–25°C for 2 min.

Centrifuge at 12,000 x g for 5 min.

Dry the DNA pellet at 20–25°C.

Dissolve the DNA in a 100 l TE.

Use 2.5 l of the dissolved DNA for a typical PCR reaction.

The dissolved DNA may be stored at 4°C for over one year.

2. Isolation of total DNA by protein precipitation (Fang et al. 1992; Dellaporta et al. 1983)

Transfer the ground sample material to a tube containing 1.2 ml (600 l) extraction

buffer (100 mM Tris pH 8.0, 50 mM EDTA pH 8.0, 500 mM NaCl, 2% (w/v) SDS,

33

1% (w/v) PVP-360, 0.1% (w/v) -mercaptoethanol (added immediately prior to use

in a fume hood)) and incubate at 65°C for 20 min.

Add one third of the volume potassium acetate. Shake vigorously and incubate on ice

for 5 min. Most proteins and polysaccharides are removed as a complex with the

insoluble potassium dodecyl sulfate precipitate.

Spin at 12,000 x g for 20 min at 4°C.

Pipette the supernatant into a clean micro centrifuge tube. Try to avoid as much of

the particulate material as possible. Add 0.5 vol. of isopropanol. Mix and incubate

the solution for 1 h at 4°C.

Pellet the DNA at 12,000 x g for 15 min at 4°C. Gently pour off the supernatant and

lightly dry the pellets either by inverting the tubes on paper towels for 10 min or as

long as necessary.

Incubate the DNA in 200–500 l TE at 65°C for 30 min to re-suspend it.

Transfer the solution to a micro centrifuge tube and spin for 5 min at 4°C to remove

any insoluble debris.

Transfer the supernatant to another micro centrifuge tube. Add 0.1 vol. sodium

acetate and two-thirds of the volume of cold isopropanol. Mix well, incubate at 4°C

for 1 h, and pellet the DNA for 10 min in a micro centrifuge at 4°C.

Wash the pellet with 200–500 l cold 80% ethanol for 10 min and centrifuge again

for 1 min at 4°C. Dry the pellet for 10 min in a Speed Vac.

Re-dissolve the DNA in TE using small increments (e.g. 10–100 l) depending on

the size of the pellet.

3. CTAB isolation of total DNA (Doyle and Doyle, 1987) with modifications described by

(Murray and Tompson, 1980; Saghai-Maroof et al. 1984; Rogers and Bendich, 1985;

1988; Doyle and Dickson, 1987; Fang et al. 1992; Lodhi et al. 1994; Milligan, 1998)

Heat the extraction buffer (50 mM Tris pH 8.0, 0.7 M NaCl, 10 mM EDTA, 1%

(w/v) CTAB, 0.5% (w/v) -mercaptoethanol (added immediately prior to use in a

fume hood)) to 60°C.

Immediately transfer the ground sample material to tube containing 1 ml (500 l) of

extraction buffer. Mix well.

Incubate at 60°C for 30–120 min with periodic gentle swirling.

Extract once with 1 ml (500 l) of chloroform: isoamyl alcohol. Mix gently but

thoroughly. Spin at 12,000 x g for 30 sec at 20–25°C to separate the phases.

Avoiding the interface, pipette the aqueous (top) phase into new tubes.

34

Add 0.5 vol. of 5 M NaCl. Add cold isopropanol to 40%. Mix gently to precipitate

nucleic acids. If no precipitate is visible, place at -20°C for 20 min or longer.

Spin at 12,000 x g for 1 min at 20–25°C. If no pellet or precipitate is visible, place on

ice for 20 min and spin again. In extreme case, spin for 10 min at 12,000 x g.

Gently pour off as much of the supernatant as possible without losing the nucleic

acid pellet. Add 0.5–1.0 ml of wash buffer (76% (w/v) ethanol, 10 mM ammonium

acetate) and swirl gently to wash the pellet. Let the nucleic acids sit in the wash

buffer for 15–20 min. Generally, nucleic acids will become much whiter (cleaner) at

this step.

Spin at 12,000 x g for 1 min at 20–25°C. If this is not sufficient, spin harder and

longer as before. Pour off wash buffer and allow the pellet to dry briefly (2–4 min)

by inverting the tube on a paper towel. Be careful that the pellet does not slide out.

Re-suspend DNA in re-suspension buffer (10 mM ammonium acetate, 0.25 mM

EDTA pH 8.0) in small increments (e.g. 10–100 l) depending on the size of the

pellet.

4. CTAB precipitation of total DNA (Bellamy and Ralph, 1968; Murray and Tompson,

1980; Rogers and Bendich, 1985; 1988)

Follow steps 1–6 from the protocol 3.

Add 0.1 vol. of 10% CTAB solution and mix.

Perform a second chloroform extraction as in steps 5 and 6 of protocol 3.

Add an equal volume of precipitation buffer (50 mM Tris pH8.0, 10 mM EDTA, 1%

(w/v) CTAB) to reduce the concentration of NaCl to 0.35 M. Mix gently and

incubate at 20–25°C for 30 min. Note that it is important to measure the sample

volume so that the concentration of NaCl is reduced to the proper level.

Recover the precipitated DNA by centrifugation at 12,000 x g at 20–25°C for 10–60

sec.

Re-hydrate the DNA pellet in 200 l re-suspension buffer (10 mM Tris pH 8.0, 1

mM EDTA pH 8.0, 1 mM NaCl).

Add 2 vol. of cold 100% ethanol and mix gently to precipitate the nucleic acids.

Recover the precipitated DNA by centrifugation at 12,000 x g at 4°C for 5–15 min.

Wash the DNA pellet in 200 l cold 80% ethanol and centrifuge at 12,000 x g at 4°C

for 5 min.

Re-suspend the DNA pellet in re-suspension buffer in small increments (e.g. 10–

100 l) depending on the size of the pellet.

35

At this point it may be necessary to purify the DNA further in a cesium chloride

gradient. This is especially true for those tissues that contain tannins or other

secondary compounds, although more than one chloroform extraction or a final

phenol extraction may be sufficient.

5. Guanidinium isothiocyanate isolation of total DNA (Cox, 1968; Bowtel, 1987;

Chomczynski and Sacchi, 1987; Jeanpierre, 1987; Puissant and Houdebine, 1990;

Chomczynski, 1993; Chomczynski and Mackey, 1995)

Transfer the ground sample material to the tube containing 5 ml (500 l) of

extraction buffer (6 M guanidinium isothiocyanate, 100 mM sodium acetate, pH 5.5).

Incubate at 20–25°C for 10 min. A longer incubation over 1 h with mixing may be

necessary.

Centrifuge at 12 000 x g for 10 min at 4°C to pellet the cellular debris.

Precipitate the DNA from the supernatant by adding 10 ml (1 ml) of 100% ethanol at

20–25oC. Mix by inversion and incubate at 20–25°C for 1–3 min The DNA should

become visible as a fibrous or cloudy precipitate.

Collect the DNA by centrifugation at 1000 x g for 1–2 min at 4°C.

Wash the DNA and precipitate twice with 0.5–1.0 ml of 80% ethanol.

Remove the ethanol wash and allow the DNA precipitate to dry for 5–15 min at 20–

25°C.

Dissolve the DNA to a concentration of 0.25 g/ l in TE or 8 mM NaOH; typically

this entails addition of 200 l solvent. The alkaline solvent may dissolve the DNA

faster and more completely.

If necessary, centrifuge the sample at 12,000 x g for 10 min to remove insoluble

material such as polysaccharides.

If NaOH was used to dissolve the DNA, adjust the pH of the solution to a desired pH

by adding Tris-HCl or Hepes (free acid).

6. Alkaline isolation of total DNA (Wang and Cutler 1993)

Transfer the ground sample material to the tube and add 1 ml (100 l) of 0.5 M

NaOH. Mix well.

Transfer 5 l quickly to a new tube containing 495 l storage buffer (100 mM Tris

pH 8.0, 1 mM EDTA pH 8.0). Mix well.

Use 1 l directly in a PCR reaction.

Store the isolated DNA at -20°C.

36

For DNA extraction using DNA isolation reagent for genomic DNA with Plant AC reagent

(AppliChem, Maryland Height, USA), Nucleospin Plant Mini (Macherey-Nagel, Düren,

Germany), Genomic DNA purification kit (Fermentas, Vilnius, Lithuania) and innuPREP Plant

DNA kit (Analytik Jena, Jena, Germany) follow the manufacturer’s protocols.

The concentration of the extracted DNA was measured using Biophotometer (Eppendorf) at 260

nm wave length. The purity of DNA was measured at 260/280 and 260/230 nm wave lengths.

PCR was performed in total 25 l volume, and consisted of 2 l of extracted DNA

(concentration as originally obtained), 13.8 l ddH2O, 2.5 l dNTP mix (2 mM), 2 l MgCl2, 2.5

l PCR buffer (10x), 0.2 l Taq polymerase (5 U/ l) and 1 l of each trnLUAA F (CGA AAT

CGG TAG ACG CTA CG) and trnFGAA (ATT TGA ACT GGT GAC ACG AG) primers (10

M). The chosen primers were originally described in Shaw et al. (2005). Reaction mixture was

covered with mineral oil (10 l). PCR conditions were as follows: initial denaturation step 80°C

for 5 min; denaturation step 94°C for 1 min; primer annealing step 50°C for 1 min; elongation

step 72°C for 2 min; final elongation step 72°C for 5 min. Steps 2 – 4 were repeated 35 times.

2.4. Assessment of genetic diversity of P. tremula plus trees

2.4.1. RAPD analysis

DNA for European aspen plus tree genetic polymorphism analysis was extracted using

Nucleospin Plant Mini Kit (Macherey-Nagel, Düren, Germany). In order to generate RAPD

profiles we used DNA extracted from P. tremula leaf and wood samples simultaneously. For

RAPD analysis we selected 15 most informative primers (Table 2.5) out of the 60 tested

(Appendix 1). We have evaluated primer informativeness using several criteria: (a) overall DNA

amplification quality, (b) amplification of polymorphic fragments, (c) size differences of

polymorphic fragments, and (d) reproducibility of DNA fragments (Pivorien , 2008). PCR

conditions and fragment separation was performed as described in Žvingila et al. (2002). With

each of the 15 selected RAPD primers, PCR amplification was carried out three times using

DNA extracted from leaf and wood samples.

Table 2.5. RAPD primers used in genetic analysis of Populus tremula trees

Primer Primer sequence 5’-3’ Primer Primer sequence 5’-3’ Roth A01 CAGGCCCTTC Roth B12 CCTTGACGCA Roth A03 AGTCAGCCAC Roth B13 TTCCCCCGCT Roth A04 AATCGGGCTG Roth B17 AGGGAACGAG Roth A05 AGGGGTCTTG Roth 17002 CAGGGTCGAA Roth A09 GGGTAACGCC Roth 17005 GAGATCCGCG Roth A19 CAAACGTCGG Roth 17009 TGCAGCACCG Roth B01 GTTTCGCTCC Roth 37001 TCCCTGTGCC Roth B03 CATCCCCCTG

37

PCR results were analyzed using only reproducible DNA bands. DNA fragment of particular

size (locus) was scored as present or absent and marked as 1 or 0 respectively. DNA fragment

sizes were recorded according to molecular ladders MassRuler™ DNA Ladder Mix (Fermentas,

Vilnius, Lithuania) and DNA Ladder 100bp plus (AppliChem, Maryland Heights, USA).

DNA bands of the same molecular weight in 1.5% agarose gel were considered identical. Gel

places with multiple banding patterns were hard to evaluate and were therefore excluded from

further analysis. RAPD fragment was considered polymorphic when its frequency was lower

than 99%.

2.4.2. Microsatellite analysis

The DNA from samples representing additionally sampled trees (Table 2.2) was extracted as

described by Dumolin et al. (1995). Microsatellite analysis was performed using 5 microsatellite

primers: PMGC 2607, GCPM 1532 and GCPM 1608 (information on these SSR primers can be

found at the International Populus Genome Consortium IPGC

(http://www.ornl.gov./sci/ipgc/ssr_resource.htm)), WPMS 14 and WPMS 16 (Tuskan et al.

2004). All five primers have previously been tested for transferability to P. tremula and

heterozygosity by Pakull et al. (2009). For microsatellite analysis we selected SSR primers that

amplify regions in different chromosomes (according to P. tremula female parent (Pakull et al.

2009) and Populus consensus map (Yin et al. 2008)). Primer information is given in Table 2.6,

and their location on physical Populus map is presented in Figure 2.1.

Table 2.6. Primer data used for microsatellite analysis of Populus tremula leaf tissue samples

Primer Primer Sequence Fluorescence label

Annealing temperature

Microsatellite locus position (chromosome no)

PMGC2607a Forward 3’TTAAAGGGTGGTCTGCAAGC5’ PET 47.5°C 8 Reverse 3’CTTCTTGCACCTCGTTTTGAG5’

GCPM1532a Forward 3’ATGCTTTGCTTGCTCTTAAC5’ PET 43°C 16 Reverse3’ACTATTGCTTGTCTTGGCAT5’

GCPM1608a Forward 3’GCTCCTGGTTTTACCACAT5’ VIC 47°C 15 Reverse 3’GAACAGCAGGATCATAGAGC5’

WPMS14b Forward 3’CAGCCGCAGCCACTGAGAAATC5’ NED 56°C 5 Reverse 3’GCCTGCTGAGAAGACTGCCTTGAC5’

WPMS16b Forward 3’CTCGTACTATTTCCGATGATGACC5’ FAM 51°C 7 Reverse 3’AGATTATTAGGTGGGCCAAGGACT5’

a Adapted from the International Populus Genome Consortium IPGC (http://www.ornl.gov./sci/ipgc/ssr_resource.htm) b Adapted from Tuskan et al. (2004)

38

Fig. 2.1. Genetic linkage maps of Populus tremula female parent (Pakull et al. 2009) and Populus consensus map based on a Populus trichocarpa and Populus deltoides background, available on

http://popgenome.ag.utk.edu/cgi-bin/cmap/map_set_info, Yin et al. 2008). Figure adapted from Pakull et al. (2009)

39

Polymerase chain reaction for microsatellite analysis was performed in a total reaction volume

of 15 μl, containing 50 ng of template DNA, 5 pmol of forward fluorescent dye-labeled primer

and 5 pmol of unlabeled reverse primer (Table 2.6.), 0.2 U of Dream Taq DNA Polymerase

(Thermo Fisher Scientific, Vilnius Lithuania), 30 μM of dNTP mix (Thermo Fisher Scientific,

Vilnius Lithuania), 1x DreamTaq buffer (Thermo Fisher Scientific, Vilnius Lithuania). The PCR

was performed on GeneAmp® PCR system 9700 (Applied Biosystems (ABI), Waltham, USA)

under following conditions: 4 min at 94°C, 38 cycles of 94°C for 30 sec, 43–56°C for 45 sec,

72°C for 1 min, final extension of 10 min at 72°C.

All obtained PCR products were mixed together, taking 1 μl of each amplification product. 1 μl

of resulting 5 PCR product’s mix was dissolved in 10 μl of formamide, 0.3 μl of internal

fragment size standard solution (Gene Scan 500 LIZ standard, ABI, Waltham, USA), and

separated using ABI-PRISM® Genetic Analyzer 310 (ABI, Waltham, USA). Data analysis was

carried out using Gene-Mapper software.

2.5. Assessment of incidence of Phellinus tremulae infection in aspen stems

To check for the presence of P. tremulae mycelium in wood samples a PCR-RFLP method was

selected. According to Mallett and Myrholm (1995) and Pollastro et al. (2000) P. tremulae is a

slow growing species on growth media thus it is very likely that other, fast growing fungi may

overgrow it, and in this way false negative results may be obtained. Therefore molecular

detection method was selected.

The presence of P. tremulae in European aspen trees was determined using genomic DNA

extracted from wood samples. For amplification of fungal DNA universal primers ITS1-F

(5‘CTTGGTCATTTAGAGGAAGTAA3’) and ITS4 (5‘TCCTCCGCTTATTGATATGC3’)

were used, which are known to amplify ITS (internal transcribed spacer) region in fungi (White

et al. 1990). PCR conditions were as described by Jasalavich et al. (2000). Amplified DNA

fragments were separated on 3% agarose gel and stained with ethidium bromide. As a reference

(positive control), DNA samples of P. tremulae extracted from a fruiting body were amplified in

the same PCR and loaded on the same gels together with the investigated samples. Fungal DNA

fragments showing the same size as the positive control were cut from the gel, re-amplified with

the same universal ITS1_F and ITS4 primers and without subsequent cleaning steps were used

in the restriction analysis. PCR products were digested with restriction enzymes (Alu I, HaeIII,

TaqI and RsaI) providing identification of samples containing P. tremulae DNA. The restriction

analysis was performed as described by Jasalavich et al. (2000). The identification of other

fungal species obtained from P. tremula wood samples was not performed.

40

2.6. Identification of possible hybridization between P. tremula and hybrid aspens

Two forest stands were studied in Dubrava SFE: one natural spruce stand with an admixture of

P. tremula (75-years-old) and one 31-year-old artificially established hybrid aspen stand (P.

tremula x tremuloides, P. tremula x alba, P. tremuloides x alba). The main characteristics of the

evaluated stands are presented in Figure 2.2.

Figure 2.2. Populus tremula and hybrid aspen stands investigated in Dubrava State Forest Enterprise

The distance between the two investigated stands was approximately 130 meters with about

five-meter-high oak forest plantation in between.

In the European aspen stand leaf flushing phenology was recorded for 30 P. tremula trees, while

in hybrid aspen stand a 5,000 m2 area was defined where flushing phenology of all hybrid aspen

trees (155 in total) was assessed. Leaf flushing phenology was assessed for the whole crown of a

selected aspen trees, based on the most advanced stage and using categorical scale of five

degrees (1–5): (1) buds in winter state (no bud stretching or flushing visible), (2) leaves

emerging on 1–50% of buds, (3) leaves emerging on 51–100% of buds, (4) leaves unfolding and

petiole visible on 51–100% of buds, (5) leaves completely unfolded on 51–100% of buds.

According to this scale, the phenology assessment was carried out four times (on the 22nd, 25th

Dubrava State Forest Enterprise

Vaišvydava forest district

Block No. 49

Compartment No. 2

Compartment area 1.6 hectare

Forest soil type Ncp

Stand height 27.1 m

Stand mean diameter 29 cm

Dubrava State Forest Enterprise

Vaišvydava forest district

Block No. 39

Compartment No. 17

Compartment area 2.3 hectare

Forest soil type Ncp

Stand height 26.2 m

Stand mean diameter 28 cm

41

and 29th of April, and on the 2nd of May, 2014). Subsequently, the four categorical scores were

summarized for each assessed tree, resulting in final flushing score. In relation to the final

flushing score, evaluated trees were grouped into three flowering categories: (a) early, (b)

medium and (c) late flushing. Leaf flushing or bud burst phenology is highly correlated or

synchronized with flowering (Linkosalo, 1999). Further in the text the term ‘flowering’ will be

used instead of ‘leaf flushing’.

Seeds from two P. tremula trees and one hybrid aspen tree per flowering category were sampled

in 2014. Immediately after their collection the seeds were sown and half-sib family seedlings

were raised in a greenhouse during the same year (2014).

Ten leaves from each of 100 randomly selected seedlings in each half-sib family were collected

at the end of the first progeny vegetation season. Leaves were scanned (HP ScanJet scaner) and

measured using WinFolia Pro (S) 2004a computer software. A total of 19 traits were measured

for each leaf (reference to the trait in the text is given in brackets): 1) lamina area, 2) lamina

perimeter (perimeter), 3) vertical length of lamina (v_length), 4) horizontal width of lamina

(h_width), 5) aspect ratio (h_width / v_length), 6) form coefficient (form), 7) blade length, 8)

blade maximum perpendicular width, 9) position of maximal perpendicular width, 10)

perpendicular width at position 50% of blade length (width1), 11) perpendicular width at

position 90% of blade length (width2), 12) lobe angle at position 10% of blade length (angle1),

13) lobe angle at position 25% of blade length (angle2), 14) petiole length, 15) petiole area 16)

number of teeth, 17) average teeth height, 18) average teeth width, 19) envelope area.

2.7. Statistical evaluation

2.7.1. Assessment of genetic variation

RAPD and SSR data analysis was performed using GenAlEx 6.5 computer software (Peakall

and Smouse, 2006; 2012) and main genetic diversity indices were calculated. The number of

polymorphic fragments and their percentage in each investigated population were assessed.

Calculations of allelic frequencies were performed using binary matrix, where the presence of a

fragment (1) means AA or Aa genotypes, and the absence of fragment (0) – aa genotype (Lynch

and Milligan, 1994). Allelic frequency of each RAPD fragment was calculated using a formula:

p = 1 - q (2.1)

here p – the frequency of allele A and q – the frequency of allele a.

42

Allele frequency for codominant (SSR) data for all loci was calculated using a formula:

, (2.2)

here Nxx is the number of homozygotes for allele X (XX), and Nxy is the number of

heterozygotes containing the allele X (Y can be any other allele). N = the number of samples.

Shannon’s information index (I) (Brown and Weir, 1983) for RAPD data was calculated using a

formula:

I = -1(p ln (p) + q ln (q)), (2.3)

here for diploid binary data and assuming Hardy-Weinberg Equilibrium, q = (1 - band

frequency)0.5 and p = 1 - q.

Shannon index (I) for SSR data was calculated using a formula by Brown and Weir (1983):

I = - (pi ln)(pi)), (2.4)

here ln – natural logarithm and pi – frequency of the i-th allele.

For each of the RAPD fragments the number of different alleles (Na), as well as the number of

effective alleles (Ne) was calculated using a formula by Kimura and Crow (1964):

Ne = 1 / (p2 + q2), (2.5)

here p – the frequency of allele A and q – the frequency of allele a. Using obtained results the

mean for each investigated population was calculated.

Number of effective alleles Ne for SSR data was calculated using a formula:

Ne = 1/( pi2) , (2.6)

here pi – frequency of the i-th allele.

Observed heterozygosity (Ho) for codominant data was calculated using a formula:

, (2.7)

here N is a sample size.

Expected heterozygosity (He) for dominant data was calculated using a formula:

He = 2 p q, (2.8)

here p – frequency of allele A and q – frequency of allele a.

43

Later, using the obtained values, unbiased heterozygosity (uHe) for dominant marker data

(RAPD) was calculated using a formula:

uHe = (2N / (2N-1)) He , (2.9)

here N – sample size and He – expected heterozygosity.

For codominant marker (SSR) data expected heterozygosity (He) and later unbiased expected

heterozygosity (uHe) were calculated using the formulas 2.10 and 2.11 respectively:

, (2.10)

here pi is the frequency of the i-th allele for the population.

uHe = (2N / (2N - 1)) He, (2.11)

here N – a sample size and He – expected heterozygosity.

Using expected and observed heterozygosity measures for codominant data a fixation index (F)

was calculated using a formula:

, (2.12)

here He – expected heterozygosity and Ho – observed heterozygosity.

For codominant genetic data at a single locus, the total genetic diversity (heterozygosity) was

divided within and among populations (Hartl and Clark, 1997):

– observed heterozygosity, averaged across subpopulations

– expected heterozygosity, averaged across subpopulations

HT – total expected heterozygosity

, (2.13)

here Ho is observed heterozygosity in a subpopulation i, and k is the total number of tested

subpopulations.

44

, (2.14)

, (2.15)

here He is the expected heterozygosity within a subpopulation s, and pi,s is the frequency of the

i-th allele in a subpopulation s. The summation of the allele frequency squared is over all i-th

alleles to h the max number of alleles.

, (2.16)

here HT is the total expected heterozygosity, and pTi is the frequency of allele i over the total

population. If subpopulation sample sizes are equal then pTI=mean pi, where mean pi is the

frequency of allele i averaged over the subpopulations of equal size.

Using the mean of expected and observed heterozygosity and the total heterozygosity, Wright’s

F statistics (Wright, 1946; 1951; 1965) was calculated using equations:

, (2.17)

here FIS – the inbreeding coefficient within individuals relative to the subpopulation, –

expected heterozygosity averaged across subpopulations and – observed heterozygosity

averaged across subpopulations.

, (2.18)

here FIT –the inbreeding coefficient within individuals relative to the total, HT – total expected

heterozygosity and – observed heterozygosity averaged across subpopulations.

, (2.19)

here FST – the inbreeding coefficient within subpopulations relative to the total, HT – total

expected heterozygosity and – expected heterozygosity averaged across subpopulations.

The deviation of allele frequencies from Hardy-Weinberg’s Equilibrium (HWE) were calculated

using a formula:

45

, (2.20)

here, the sum of i to k genotypes is based on Oi, the observed number of individuals of the i-th

genotype, and Ei, the expected number for the i-th genotype. Ei is calculated as either pi2 for a

homozygous genotype or 2 pq for a heterozygous genotype. Degrees of freedom for the Chi-

Squared test was calculated using a formula DF = [Na(Na-1)]/2, where Na is the number of

alleles at the locus.

Using GenAlEx analysis software the following within-population genetic diversity indices

were calculated:

Nei’s genetic identity (I) (Nei, 1972; 1978):

,

, and , (2.21)

here I is Nei’s genetic identity, and pix and piy are the frequencies of the i-th allele in

populations x and y. For multiple loci, Ixy and Iy are calculated by summing over all loci and

alleles and dividing by the number of loci. These averaged values are then used to calculate I.

Nei’s genetic distance (Nei, 1972; 1978) was calculated using a formula:

, (2.22)

here ln is a natural logarithm and I is Nei’s identity.

Dendrograms were constructed with PAST version 2.17c software (Hammer et al. 2001) using

RAPD and SSR data and UPGMA method (Sneath and Sokal, 1973) (paired group algorithm,

similarity measure – Euclidean distance), also on the basis of Nei’s genetic distance matrices

(Nei, 1978). The significance of clusters was assessed using bootstraps (Suzuki and Shimodaira,

2004). Mantel test between genetic distance matrix and geographic distance matrix was

performed with GenAlEx software (Peakall and Smouse, 2006; 2012) using 9,999 permutations.

PCA (principal component analysis) based on 5 SSR markers was performed with PAST version

2.17c software (Hammer et al. 2001) using two options: within group and among groups.

Broken stick method (Macarthur, 1957) was used to determine how many components are

important. Also, a load of each SSR marker in each of important components was analyzed.

46

Molecular variation in Lithuanian P. tremula populations was assessed by hierarchical AMOVA

(Excoffier et al. 1992; Mengoni and Bazzicalupo, 2002) using GenAlEx v. 6.5 (Peakall and

Smouse, 2006; 2012). To assess genetic distance FST and RST fixation indices were calculated

and total variation was partitioned into among- and within-population effects. The probability P

(rand =data) for the fixation indexes was based on standard permutation across the full data set

with 9,999 permutations.

Bayesian clustering approach was implemented using computer analysis software STRUCTURE

version 2.1 (Pritchard et al. 2000) to estimate the most likely number of clusters (K) into which

the SSR and RAPD multilocus genotypes were assigned with certain likelihoods. The

population priors were not used. A Markov chain with 100,000 and 50,000 iterations for SSR

and RAPD analysis, respectively, following a burn in period of respective 100,000 and 50,000

iterations was used. Each run was replicated 10 times. The most likely number of clusters was

identified by the delta K ( K) criterion. Number of clusters was determined by calculation based

on the second order rate of change of the likelihood ( K) (Evanno et al. 2005).

2.7.2. Correlation between occurrence of certain RAPD fragments and presence of DNA of P. tremulae

For correlation assessment a binary data matrix was used. SAS (SAS 9.4 package, by SAS

Institute Inc., Cary, NC, USA) procedure GENMOD (generalized linear models) with model

options of link function ‘logit’ and the binomial distribution variance function was used for

estimation of locus band presence or absence effect for wood infection with P. tremulae. Only

polymorphic fragments were used for the evaluation of data. In order to check which loci were

the most discriminating the sites or populations, stepwise discriminant analysis was done to

select a subset of the fragments for use in discriminating among the classes. The STEPDISC

SAS procedure was applied to select RAPDs contributing most to the differentiation of

individuals grouped by wood infection with P. tremulae. The least significance level 0.05 was

used for the selection of single RAPD markers. Detrended correspondence analysis performed

with PAST version 2.17c software (Hammer et al. 2001) was applied to detect pattern in wood

infection in the two groups of trees (infected and free of pathogen DNA) by RAPD markers best

discriminating those groups.

47

2.7.3. Analysis of European aspen and hybrid aspen leaf parameters

SAS STEPDISC procedure (slentry=0.05) was used to select leaf traits which could be used in

discriminating European and hybrid aspens. Principal component analysis (PCA) using Pearson

correlation and distance-based biplot was applied for selected leaf traits. The attribution of aspen

individuals to one or another taxa was performed by discriminant procedure in PAST version

2.17c software (Hammer et al. 2001). For analysis of variance PROC MIXED (mixed model

equations) and the REML (restricted maximum likelihood) option in SAS (SAS 9.4 package, by

SAS Institute Inc., Cary, NC, USA) was used. Variance components were calculated for half-sib

families and progeny seedlings using the following model:

ijmijiijm etfy )(

here ijmy is the value of a single observation, is the grand mean, if refers to the random

effect of a family i, )(ijt is the random effect of tree j in family i, and ijme is the random error

term.

48

3. RESULTS AND DISCUSSION

3.1. General evaluation of investigated P. tremula stands and plus trees

Evaluation of sanitary condition of P. tremula stands from which plus trees originate, showed

that stands of the best condition were found in Ignalina, Jurbarkas, Anykš iai, Raseiniai and

Utena SFEs. The highest frequency of trees with P. tremulae basidiocarps was found in

Rokiškis, Marijampol , K dainiai, Biržai and Taurag SFEs (Table 3.1). The only forest stand

in which fruiting bodies of P. tremulae have not been observed was located in Ignalina SFE,

although this might be due to a relatively young age of this stand (37-yrs-old).

Investigated plus trees of P. tremula grow in old and very old aspen stands (Table 3.1.). This old

stand age (today in Lithuania, timber crop rotation of P. tremula is 40 years) is one of the main

reasons for frequent occurrence of damages caused by an aspen trunk rot fungus P. tremulae.

The plus trees are rather old – only six trees haven’t reached the age of 40 years at the time of

our investigations (Table 2.1.). One plus tree had fruiting bodies of P. tremulae in the upper

third of its trunk, while other plus trees showed no external symptoms of the trunk rot disease

and thus were assigned for molecular detection of the infection in their wood samples. Several

wood samples were placed onto nutrient media for fungal isolation according to methodology by

Bakys et al. (2009). Pure cultures of P. tremulae have been isolated from some of the collected

wood samples (data not shown), although the frequency of isolation of this fungus was far lower

compared to the frequency of detection of its DNA in the wood samples. These results indicated

that in many cases false negative results showing incidence of infection may be obtained by the

isolation method (Chakravarty and Hiratsuka, 2007).

Tabl

e 3.

1. M

ean

char

acte

rist

ics

of in

vest

igat

ed P

opul

us tr

emul

a st

ands

. Ass

essm

ent m

ade

in M

ay–J

uly,

200

7. F

or e

xpla

natio

n of

diff

eren

t var

iabl

es

see

sect

ion

2.2.

Sta

nd n

ames

giv

en a

ccor

ding

to n

ames

of r

espe

ctiv

e St

ate

Fore

st E

nter

pris

e

Forest stand name

Stand age, years

Tree height, m

Stem diameter(dbh), cm

Stem straight- ness, score

Stem slender- ness, score

Stem height to dry branches, m

Stem height to green branches, m

Crown diameter, m

Branch thickness, score

Stem presence, score

Branch wound occlusion, score

Sanitary condition, score

No. (%) of trees with fruiting bodies of

Phellinus tremulae

Any

kšia

i 61

31

.3

39.9

4.

0 3.

9 12

.6

18.4

8.

7 3.

3 3.

0 3.

5 1.

1 4

(13.

3 %

)

Birž

ai

66

30.0

41

.0

3.7

3.6

10.8

17

.8

7.1

3.7

3.5

3.6

1.2

10 (3

3.3%

) Ig

nalin

a 37

21

.4

23.9

3.

7 3.

9 3.

5 9.

4 5.

7 3.

5 3.

6 4.

7 1.

2 0

(0%

) Ju

rbar

kas

51

29.6

34

.9

3.7

3.5

13.2

17

.1

5.5

3.8

3.4

4.2

1.2

1 (3

.3%

) K

aiši

ador

ys 1

a 59

27

.7

38.2

3.

9 3.

5 8.

9 14

.2

5.8

3.5

3.3

3.6

1.4

4 (1

3.3%

) K

aiši

ador

ys 2

a 55

26

.5

38.2

3.

9 3.

9 7.

1 15

.1

5.9

3.4

3.3

3.7

1.4

3 (1

0%)

Kda

inia

i 62

27

.5

38.5

3.

6 3.

6 9.

9 15

.0

8.3

3.1

3.0

2.7

1.5

16 (5

3.3%

) K

retin

ga

58

28.7

34

.7

3.9

4.0

8.5

14.9

6.

9 3.

9 3.

8 3.

7 1.

6 3

(10%

) K

urš

nai

67

29.7

40

.2

3.7

3.8

7.1

14.4

5.

9 2.

9 3.

0 3.

4 1.

4 2

(6.7

%)

Mar

ijam

pol

46

29

.5

31.6

4.

0 3.

8 8.

7 19

.2

3.6

3.5

2.9

3.3

1.8

6 (2

0%)

Pakr

uojis

1a

55

25.2

34

.1

3.6

3.3

9.9

15.1

7.

0 3.

6 3.

2 3.

5 1.

5 3

(10%

) Pa

kruo

jis 2

a 67

27

.7

35.2

3.

2 3.

1 13

.2

15.8

7.

2 3.

2 3.

0 3.

8 2.

2 8

(26.

7%)

Ras

eini

ai

47

28.9

31

.6

3.9

3.8

12.1

16

.7

6.1

3.8

3.7

4.6

1.2

2 (6

.7%

) R

okiš

kis 1

a 62

28

.5

37.5

3.

3 3.

6 6.

0 16

.3

5.9

3.1

3.0

2.9

1.8

21 (7

0%)

Rok

iški

s 2a

67

28.5

41

.7

2.8

3.2

8.7

14.7

7.

8 2.

8 3.

0 3.

3 1.

8 21

(70%

) Ša

kiai

65

27

.5

41.6

3.

7 3.

6 8.

8 15

.1

6.7

2.9

3.0

3.3

1.3

5 (1

6.7%

) Ša

lin

inka

i 57

27

.8

38.9

3.

3 3.

2 8.

6 14

.4

8.0

3.2

3.0

3.6

1.6

7 (2

3.3%

) Ta

urag

67

30

.9

42.5

4.

2 4.

3 11

.4

17.4

8.

3 4.

2 4.

0 3.

7 1.

2 13

(43.

3%)

Ute

na

62

28.7

29

.8

3.7

3.7

8.9

16.3

5.

8 3.

7 3.

2 3.

8 1.

2 1

(3.3

%)

Mea

n of

P.

trem

ula

plus

tr

eesb

60

29.5

40

.6

4.5

4.4

13.2

16

.5

7.8

4.0

4.0

4.5

1.0

1

a Stat

e Fo

rest

Ent

erpr

ises

, in

whi

ch tw

o fo

rest

stan

ds w

ere

inve

stig

ated

: Kai

šiad

orys

1, B

da fo

rest

ry d

istri

ct, b

lock

no.

287

; Kai

šiad

orys

2, P

ravi

eniš

ks f

ores

try

dist

rict,

bloc

k no

. 89,

Pak

ruoj

is 1

and

2, L

inku

va fo

rest

ry d

istri

ct, r

espe

ctiv

e bl

ock

nos.

39 a

nd 5

2; a

nd R

okiš

kis 1

and

2, K

amaj

ai fo

rest

ry d

istri

ct, r

espe

ctiv

e bl

ock

nos.

206

and

208.

b se

e A

ppen

dix

2.

50

Fig. 3.1. Fragments of amplified fungal DNA extracted from European aspen wood. M – molecular size marker, 1 to 20 – tested DNA samples, K – PCR control samples (left, sample without DNA, and right,

sample with plant DNA), G – amplification product of Phellinus tremulae DNA (positive control)

The presence of P. tremulae DNA in wood samples of P. tremula plus trees was assessed using

molecular methods – PCR-RFLP (Figure 3.1.). Gel electrophoresis allowed preliminary

identification of plus trees infected with this parasitic fungus. The amplified bands of similar

size to that produced by P. tremulae – positive sample (positive control, Figure 3.1.) were cut

out and re-amplified. The re-amplified PCR products were digested with restriction enzymes

allowing identification of samples containing P. tremulae DNA. The results of identification

based on the RFLP profiling are presented in Table 3.2. The presented method of detection of P.

tremulae DNA in aspen wood proved to be a valuable tool for identification of infected trees at

early stages of disease development. Such screening for infection could be useful in resolving

which trees should be selectively felled before trunk rot disease reaches its advanced stages

(Allen et al. 1996).

The results showed that 73 out of 137 (53.3%) European aspen plus trees were infected with P.

tremulae. The mean age of infected (60 years) and sound-looking (59 years) trees was almost

identical. The highest proportion of infected trees was found in Šakiai (80.0%), Anykš iai

(66.7%), Kaišiadorys (60.0%) and Marijampol (60.0%) stands (see Table 3.2). According to

the results of PCR-RFLP profiling, the healthiest plus trees were found in Kretinga and Rokiškis

(16.7% and 25.0% infected trees, respectively), followed by Ignalina and Kurš nai stands both

with 40% of infected trees.

Quality assessment data in the investigated aspen stands (Table 3.1.) showed that plus trees were

selected in forest stands of good and very good quality of phenotypic traits used in forest

51

selection and breeding: almost all stands could be given high quality indices which are close to

the indices given for the plus trees (see Table 3.1 and Appendix 2). The maximum difference in

quality indices was seen between a stand and a plus trees in K dainiai and Rokiškis 1 stands

(Table 3.1.).

Table 3.2. Presence (+) and absence (–) of Phellinus tremulae DNA in wood samples of Populus tremula plus trees as assessed by RFLP profiling

Plus tree no.a DNA of P.tremulae

Plus tree no.a DNA of P.tremulae

Plus tree no.a DNA of P.tremulae

130 Anykš iai + 113 K dainiai + 067 Pakruojis + 131 Anykš iai + 114 K dainiai – 068 Pakruojis – 132 Anykš iai + 115 K dainiai + 069 Pakruojis – 133 Anykš iai + 116 K dainiai + 070 Pakruojis – 134 Anykš iai + 117 K dainiai + 071 Pakruojis – 135 Anykš iai – 118 K dainiai – 072 Pakruojis + 136 Anykš iai – 119 K dainiai – 073 Pakruojis + 137 Anykš iai + 163 Kretinga – 038 Raseiniai + 138 Anykš iai + 164 Kretinga – 094 Raseiniai – 139 Anykš iai + 165 Kretinga – 095 Raseiniai + 140 Anykš iai + 166 Kretinga – 096 Raseiniai + 141 Anykš iai – 167 Kretinga – 097 Raseiniai + 142 Anykš iai – 168 Kretinga + 098 Raseiniai – 143 Anykš iai + 039 Kurš nai – 099 Raseiniai – 144 Anykš iai – 040 Kurš nai – 100 Raseiniai –

042 Biržai – 041 Kurš nai – 101 Raseiniai – 043 Biržai + 145 Kurš nai + 102 Raseiniai –

158 Ignalina – 146 Kurš nai + 103 Raseiniai + 159 Ignalina – 104 Marijampol + 020 Rokiškis – 160 Ignalina – 105 Marijampol – 021 Rokiškis + 161 Ignalina + 106 Marijampol + 022 Rokiškis – 162 Ignalina + 107 Marijampol + 023 Rokiškis –

084 Jurbarkas + 108 Marijampol – 120 Šakiai + 085 Jurbarkas – 044 Pakruojis + 121Šakiai + 086 Jurbarkas + 045 Pakruojis – 122 Šakiai + 087 Jurbarkas – 046 Pakruojis + 123 Šakiai – 088 Jurbarkas + 047 Pakruojis + 124 Šakiai + 089 Jurbarkas – 048 Pakruojis – 125 Šakiai + 090 Jurbarkas + 049 Pakruojis – 126 Šakiai – 091 Jurbarkas – 050 Pakruojis + 127 Šakiai + 092 Jurbarkas – 051 Pakruojis – 128 Šakiai + 093 Jurbarkas + 052 Pakruojis + 129 Šakiai +

074 Kaišiadorys + 053 Pakruojis + 036 Šal ininkai – 075 Kaišiadorys + 054 Pakruojis + 152 Šal ininkai – 076 Kaišiadorys + 055 Pakruojis – 153 Šal ininkai + 077 Kaišiadorys + 056 Pakruojis – 154 Šal ininkai + 078 Kaišiadorys + 057 Pakruojis – 155 Šal ininkai + 079 Kaišiadorys – 058 Pakruojis + 156 Šal ininkai – 080 Kaišiadorys + 059 Pakruojis + 157 Šal ininkai + 081 Kaišiadorys – 060 Pakruojis + 037 Taurag + 082 Kaišiadorys – 061 Pakruojis – 147 Utena – 083 Kaišiadorys – 062 Pakruojis + 148 Utena + 109 K dainiai – 063 Pakruojis + 149 Utena – 110 K dainiai + 064 Pakruojis – 150 Utena – 111 K dainiai + 065 Pakruojis – 151 Utena + 112 K dainiai – 066 Pakruojis +

asee Table 2.1.

52

3.2. DNA extraction from P. tremula

Many genetic studies of tree species depend on possibilities to take samples. The ease of

sampling is a critical issue in many studies, especially when mature trees are investigated.

Nondestructive sampling from a mature tree generally involves collection of leaves using

firearm or climbing a tree. There is also a possibility to sample cambium tissues and to subject

those for DNA extraction (Tibbits et al. 2006). However, this method is not suitable in certain

cases because of the damage it causes to a tree and for various forensic applications (as it is not

always possible to collect fresh cambium tissue). Forensic investigations in forestry mostly deal

with timber trade control. Timber trade control can be performed by applying chloroplast

sequences using DNA extracted from wood, as described in Deguilloux et al. (2003); however, a

laborious investigation of haplotype variability in multiple geographic locations has to be

assessed prior to analysis of the chloroplast sequences.

Collection of wood samples using a Pressler’s increment borer is a simple, fast and almost

nondestructive method as the inflicted wound is small and causes minimal damage to the tree. In

this case a thin wood core is extracted and could be used for further investigations. The

Pressler’s borer has a supremacy over other types of borers or drills as it doesn’t produce

sawdust, which is the primary cause of cross-contamination among trees (Jasalavich et al. 2000).

Another important issue in any molecular investigation is successful DNA extraction. The

success of DNA extraction is largely dependent on a number of steps involved in the extraction

procedure and the ease and efficiency of DNA purification. Tree tissues often contain large

amounts of polysaccharides and phenolic compounds difficult to separate from DNA (Murray

and Thompson, 1980; Katterman and Shattuck, 1983; Mannerlöf and Tenning, 1997; Ostrowska

et al. 1998). Wood tissue is especially impervious to the DNA extraction; for obtaining high

quality DNA the wood tissues have to be subjected to several cleaning steps (Asif and Cannon,

2005; Rachmayanti et al. 2009).

In our study, ten DNA extraction methods were used to extract DNA from European aspen

wood and leaf samples. The results showing concentration and purity of the extracted DNA

(Table 3.3.) indicated that CTAB precipitation, guanidinium isothiocyanate method and

AppliChem DNA isolation reagent (also based on guanidinium isothiocyanate reagent) were not

suitable for efficient DNA extraction from P. tremula tissues, irrespective of the tissue type

(wood or leaf) used. The simplest (based only on three steps) alkaline isolation technique

produced good results only for wood tissues, but not for leaf tissues (i.e. tree DNA was not

detected). This might be explained by the fact, that leaf contains much more of polyphenolics

53

and tannins as well as other substances that can prevent DNA extraction. Other tested six

methods gave satisfactory results: DNA concentrations varied from 20 to 220 ng/ l in wood

tissues and from 100 to 310 ng/ l in leaf tissues, and DNA was of acceptable purity (Table 3.3).

Table 3.3. DNA concentration and purity. The results of DNA extraction from leaves and wood samples of Populus tremula with replicate

DNA extraction method a Wood samples Leaf samples DNA conc. (ng/ l)

Purity 260/280 nm

Purity 260/230 nm

DNA conc. (ng/ l)

Purity 260/280 nm

Purity 260/230 nm

SDS

219 1.24 0.53 264 1.17 0.5 52 2.60 0.29 206 1.15 0.55

Protein precipitation

26 1.96 0.12 185 2.02 2.02 149 1.11 0.45 96 2.05 1.63

CTAB

63 1.24 0.41 307 1.01 0.35 19 1.48 0.17 203 1.04 0.3

CTAB precipitation

– – – – – – – – – – – –

Guanidinium isothiocyanate

– – – – – – – – – – – –

Alkaline isolation

45 1.15 0.46 – – – 33 1.10 0.44 – – –

DNA isolation reagent for genomic DNA with Plant AC reagent (AppliChem)

– – – – – – – – – – – –

Nucleospin Plant Mini (Macherey-Nagel)

56 1.11 0.44 194 1.62 0.65 50 1.32 0.41 245 1.34 0.72

Genomic DNA purification kit (Fermentas)

38 1.30 0.56 364 1.8 1.48 56 1.27 0.41 506 1.56 1.07

InnuPREP Plant DNA Kit (Analytik Jena)

216 1.14 0.16 132 1.5 0.22 82 1.44 0.1 204 1.49 0.55

a for detailed description of DNA extraction methods see section 2.3.

Subsequently, we tested quality of the extracted DNA by PCR amplification (Figure 3.2).

Chloroplast tRNR L-F intergenic spacer amplification revealed that the most effective extraction

technique was protein precipitation, while CTAB precipitation, guanidinium isothiocyanate

method and AppliChem DNA isolation reagent resulted in no amplification at all thus

confirming DNA quantification results. DNA extracted from wood tissue by alkaline isolation

gave no amplification as well, even though some DNA was present in the template. It is possible

that there were some PCR inhibitory substances left in the DNA solution, or that extracted DNA

was degraded or oxidized. Other methods (CTAB and SDS) gave satisfactory results: DNA

concentration on average varied from 88 ng/ l for wood tissues to 245 ng/ l for leaf tissues. The

same success of DNA extraction as for protein precipitation technique was obtained using

commercial DNA extraction kits (Nucleospin Plant Mini (Macherey-Nagel, Düren, Germany);

Genomic DNA purification kit (Fermentas, Vilnius, Lithuania) and InnuPREP Plant DNA Kit

(Analytik Jena, Jena, Germany)).

54

Fig. 3.2. PCR products obtained after amplification using trnLUAA F - trnFGAA primers. M stands for molecular size marker (MassRuller DNA ladder mix, Fermentas), number 1 to 8 designates plant

samples that corresponds to DNA extracted using protein precipitation method from wood (1–2) and leaf (3–4) as well as alkaline isolation method from wood (5–6) and leaf samples (7–8); K1 is negative DNA

control (fungal DNA sample)

In Table 3.4 we compared six DNA extraction methods that proved to be suitable for DNA

extraction from aspen wood and leaf tissues. Here we describe the specific conditions applied in

the DNA extraction, present the number of cleaning steps, as well as the time required for

extraction. Some methods (protein precipitation and CTAB) require a long time for extraction,

but they involve several steps that are not laborious (e.g. cooling, long centrifugation,

precipitation) and in total amount of effort are comparable to commercial DNA extraction kits.

In our study, we used several DNA extraction techniques with different number of cleaning

steps. We started with the alkaline isolation technique, that hardly requires any cleaning step,

and finally we tested several commercial DNA extraction kits, containing several different

cleaning steps as well as enzymatic RNA and protein digestion. Our results suggested that for

large-scale investigations the best methods for DNA extraction are CTAB and protein

precipitation, although these require more time compared to the expensive commercially

available kits. Despite of being time-consumable these two methods yielded good quality DNA

suitable for PCR (see Table 3.4. and Figure 3.2.).

55

Table 3.4. Comparison of six DNA extraction methods/commercial kits that proved to be suitable for DNA extraction from wood and leaf tissues of Populus tremula

DNA extraction method Time required for DNA isolation

No. of cleaning steps DNA quality

Cost

SDS 25 min per sample one alcohol precipitation step

medium low

Protein precipitation ~ 4–4.5 h (possibility to handle several samples at a time)

three steps, protein and 2x alcohol precipitation

high low

CTAB ~ 2.5 h (possibility to handle several samples at a time)

two steps, chloroform and alcohol precipitation

high low

Nucleospin Plant Mini (Macherey-Nagel)

30 min per sample four steps, precipitation (binding to membrane), 3x wash

high high

Genomic DNA purification kit (Fermentas)

25 min per sample three steps, chloroform precipitation, ethanol precipitation

high medium

InnuPREP Plant DNA Kit (Analytik Jena)

40 min per sample five steps, precipitation (binding to the membrane), protein digestion and 3x wash

high high

The size of the amplification product is one of the quality measurements of the extracted DNA,

and is crucial for some molecular investigations (Deguilloux et al. 2003; Rachmayanti et al.

2009). The amplified P. tremula DNA fragment is 820 bp in size (Figure 3.2.), thus we can

conclude that CTAB or protein precipitation techniques are well suited for DNA extraction from

aspen wood and leaf tissues. The SDS method is relatively fast, yet it results in DNA of

comparably low quality and might not be recommended for the routine use. All of the three

commercially available kits produced good results in our study and proved to be effective in

molecular research including DNA extraction from wood tissue samples. The usefulness of

commercial DNA extraction kits was also confirmed by other authors (Deguilloux et al. 2003;

Rachmayanti et al. 2006; 2009). These kits are easy to handle, simple and fast, yet significantly

more expensive.

3.3. Genetic diversity of P. tremula plus trees

One of the objectives of the present study was to assess genetic diversity of European aspen plus

trees and to establish fingerprints that would allow distinguishing different tree genotypes. After

performing amplification of DNA extracted from all 137 European aspen plus trees with 15

random decamer primers (Table 2.5.) we obtained 292 DNA bands. The amplification patterns

obtained with primer Roth A05 are presented in Figure 3.3.

56

Fig. 3.3. Amplification products obtained using Roth A05 primer. Lanes 1 to 8 show Populus tremula plus tree samples, lines M show molecular size markers (on the left – 1 kb MassRuler™ DNA Ladder

Mix (Fermentas); on the right – DNA Ladder 100bp plus (AppliChem))

Three independent PCR amplifications were performed using separately extracted DNA from

wood and leaf tissues. Independent amplification was performed in order to avoid any

inaccuracies and to make sure that all amplified bands were of the tree origin. The results of the

RAPD amplification are shown in Table 3.5. A total of 282 out of 292 amplified bands (96.6%)

were polymorphic. On average, 19.5 bands were produced with each primer. As presented in

Table 3.5, the most informative primer was Roth A05 (27 bands), followed by primers Roth

A01 and Roth B12, both resulting in 25 bands. The least number of bands was produced using

primers Roth B17 and Roth B13 (12 and 14 bands, respectively). Fragment size varied from 250

to 3500 bp. The widest size range (390–3500 bp) was obtained with the primer Roth 17005

(Table 3.5.). Different number of RAPD fragments was obtained in RAPD profiles of the

examined European aspen plus trees. The amount of DNA fragments characteristic to one

genotype varied from 93 (tree No.155 from Šal ininkai) to 160 (tree No. 094 from Raseiniai).

On average, 132.32 loci per genotype were obtained.

57

Table 3.5. Number of amplified and polymorphic DNA bands produced by 15 RAPD primers, their size range and discrimination power in a study of genetic diversity in Lithuanian Populus tremula populations

Primer used Number of

amplified bands

Number of

polymorphic bands

Amplified band

size range

Discrimination

power

Roth A01 25 23 380–3000 bp 0.9890

Roth A03 23 23 550–3500 bp 0.9878

Roth A04 20 19 470–3500 bp 0.9912

Roth A05 27 27 380–3000 bp 0.9918

Roth A09 22 21 400–3000 bp 0.9875

Roth A19 20 20 400–2500 bp 0.9932

Roth B01 15 15 470–2000 bp 0.9871

Roth B03 18 16 460–3000 bp 0.9928

Roth B12 25 23 480–3000 bp 0.9923

Roth B13 14 14 520–2500 bp 0.9919

Roth B17 12 11 400–1900 bp 0.9827

Roth 17002 20 20 250–3000 bp 0.9921

Roth 17005 18 18 390–3500 bp 0.9910

Roth 17009 15 14 800–3000 bp 0.9811

Roth 37001 18 18 520–3000 bp 0.9920

Using RAPD in this study we established the fingerprints unique to each European aspen plus

tree. These fingerprints could be very useful in future genetic studies, or in breeding

experiments, and will allow using genetic material more efficiently. Distinguishing of individual

trees especially in young age is very difficult or even impossible task using morphological

criteria, thus the genetic fingerprints of individual trees can help to overcome this problem.

Using RAPD data, we assessed the genotype specific RAPD profiles for P. tremula plus trees.

Primer A19 distinguished all European aspen plus trees, while other primers – only in

combination with each other. This result is similar to Liu and Furnier’s (1993), where one

primer distinguished all 110 P. tremuloides individuals. Such primers are valuable in research

activities because only one PCR step is required to identify certain individual. However, primers

able to distinguish large number of individuals are rare and more often identification of

genotypes are achieved combining the results of several primers. The example of such

identification is the study of Sanchez et al. (2000), where identification of 89 P. tremula

genotypes was performed combining four different primers. In our study, 21 combinations of

two primers distinguished all assessed genotypes of European aspen trees (Table 3.6).

58

Table 3.6. Primer combinations valid for distinguishing all investigated European aspen genotypes

A01+B03 A03+37001 A05+B13 B01+B03 B03+17005 B12+17002 B13+17005

A03+B03 A04+B13 A05+17002 B03+B13 B03+37001 B13+B17 B13+17009

A03+B13 A04+17002 A09+B13 B03+17002 B12+B13 B13+17002 B13+37001

The discriminating power of primer or particular set of primers was defined by Kloosterman et

al. (1993) as PD=1- (Pi)2, where Pi represents the frequency of each genotype. In our RAPD

study, PD for RAPD primers ranged from 0.9827 (primer Roth B17) up to 0.9932 (primer Roth

A19). Different banding patterns obtained for all European aspen plus trees also confirmed their

sexual origin. Sanchez et al. (2000) demonstrated that 36% of P. tremula trees, originating from

the same stand had the same RAPD amplification profile, however, such results were obtained

probably due to extensive planting of aspen in Spain decades ago. Meanwhile, in Lithuania P.

tremula has never been artificially propagated, which evidently resulted in more diverse aspen

stands due to sexual reproduction.

3.3.1. Nei’s genetic distance between P. tremula plus trees

Based on RAPD data (binary data matrix, showing presence or absence of RAPD fragment in

amplification profile), genetic distances between investigated European aspen trees were

calculated according to Nei’s method (1978). The maximum genetic distance (0.141) was

assessed between tree No. 096 (Raseiniai) and tree No. 150 (Utena). Genetically most similar

trees No. 104 and 105 (genetic distance 0.012) are both located in Marijampol SFE. The

average genetic distance between European aspen plus trees in Lithuania was 0.0964. RAPD

based dendrogram of genetic distance among European aspen trees was constructed using

UPGMA grouping method (Figure 3.4.). High RAPD polymorphism rate (96.6%) of

investigated European aspen confirms significance of the plus trees, specifically utilized in

forest tree breeding. High genetic diversity ensures genotypic plasticity of trees adapting to

changing environment. According to Leibenguth and Shogi (1998), high polymorphism creates

conditions to reproductive and adaptive heterosis. Constructed 137 European aspen plus trees

RAPD profile library could be used for identification of these plus trees in successive selection

and breeding programs.

Fig.

3.4

. Eur

opea

n as

pen

tree

s clu

ster

ed u

sing

UPG

MA

met

hod,

by

algo

rith

m P

aire

d gr

oup,

sim

ilari

ty m

easu

re E

uclid

ean

dist

ance

(Boo

t N=

1,00

0),

base

d on

RAP

D d

ata

(Cop

hene

tic c

orre

latio

n 0.

4729

)

60

3.3.2. Correlation between genetic distances and geographic distribution pattern of P.tremula plus trees

96.6% of all obtained RAPD amplification products were polymorphic, revealing considerable

genetic diversity among P. tremula plus trees. Nevertheless, the correlation between genetic and

geographic distances obtained using Mantel test was weak (r=0.178, correlation significance

p=0.001). The results of Mantel test are showed in Figure 3.5. Weak genetic differentiation

among P. tremula trees from different geographic origins probably occurs due to biological

characteristics of this species. P. tremula is wind pollinated and dispersed, and that ensures high

gene migration rate (Lexer et al. 2005; Hall et al. 2007). The other possible reason for weak

genetic differentiation could be explained by only a small amount of P. tremula genome used

for this study.

Fig.3.5. Correlation between RAPD genetic distances and geographic location data of European aspen plus trees (Mantel test, 9,999 permutations)

3.4. Population structure of P. tremula in Lithuania

The aim of this study was to infer the number of European aspen populations in Lithuania and to

assess their genetic differences based on DNA marker data. A total of 314 P. tremula trees

(including 137 plus trees) from 16 SFE’s in Lithuania were included in this study.

In this study we used five microsatellite primers that amplifies genetic loci in 5th, 7th, 8th, 15th

and 16th chromosomes (Figure 2.1). In the analysis of the obtained SSR data we also considered

the RAPD results. In this particular case, RAPD analysis data should be treated with caution

because of the variable numbers of trees per population included in our analysis (2 to 30

individuals, see Table 2.1).

61

3.4.1. Genetic relatedness of P. tremula populations by PCA

Genetic relatedness of European aspen populations based on 5 microsatellite loci was analyzed

by principal component analysis. Figures 3.6 and 3.7 show PCA of European aspen genetic

variation within and among populations.

Fig. 3.6. A – PCA (principal component analysis) scatter diagram with arrows indicating SSR marker load based on 5 SSR marker correlation matrixes (within group/population option). B – PCA screen plot

with Broken Stick. C, D – PCA loadings indicating SSR marker load coefficients on each of two most important (as indicated by Broken Stick) components explaining accordingly 24.0% and 17.9% of total

variance

62

Fig. 3.7. A – PCA (principal component analysis) scatter diagram with arrows indicating SSR marker load based on 5 SSR marker correlation matrixes (among group/population options). B – PCA screen

plot with Broken Stick. C, D, E – PCA loadings indicating SSR marker load coefficients on each of first three most important (as indicated by Broken Stick) components explaining accordingly 28.7%, 27.0%

and 17.9% of total variance

63

As demonstrated in Figures 3.6 and 3.7, the first two components are important in within group

analysis and three components are important in between group analysis. GCPM 1608 and

GCPM 1532 SSR markers have the largest load in PCA and are the most important in within

group analysis, while WPMS 14, GCPM 1608 and PMGC 2607 markers are most

discriminating among populations.

3.4.2. Genetic parameters of assessed SSR loci of evaluated P. tremula populations

All five microsatellite loci used in this study were polymorphic and revealed 7 to 14 alleles

(Table 3.7). The locus GCPM 1532 was least polymorphic, while locus WPMS 14 had the

highest number of alleles (Table 3.7). The effective number of alleles was considerably lower

than the observed number of alleles for all the loci investigated. Observed heterozygosity was

higher than expected heterozygosity in all loci except GCPM 1532, where expected

heterozygosity exceeded observed. The Fixation Index (also called the Inbreeding coefficient)

was found to be close to zero at WPMS 16, GCPM 1532 and WPMS 14 loci, thus indicating a

random mating. PMGC 2607 and GCPM 1608 loci had Inbreeding coefficient of -0.41 and -0.59

(Table 3.7), which suggests negative assortative mating or selection for heterozygotes. All loci

used in this study except PMGC 2607 show significant population differentiation based on FST

index (Table 3.7). This indicates that all microsatellite loci selected for this study are suitable for

population genetic structure studies. Based on RST fixation index, populations were significantly

differentiated at all the loci as well, except for PMGC 2607 locus (Table 3.7).

Table 3.7. The mean statistics of microsatellite loci with ± standard error. The differentiation showed in this table is among populations. Na – number of different alleles; Ne – effective number of alleles; I – Shannon's information index; Ho – observed heterozygosity; He – expected heterozygosity; uHe – unbiased expected heterozygosity; F – inbreeding coefficient; Rst – fixation index; Fst – fixation index

Index WPMS 16 PMGC 2607 GCPM 1532 GCPM 1608 WPMS 14 Na 10 9 7 13 14

Ne 4.171±0.333 2.238±0.090 2.809±0.181 2.240±0.071 2.947±0.223

I 1.547±0.080 0.890±0.046 1.175±0.056 0.948±0.041 1.315±0.086 Ho 0.760±0.054 0.759±0.032 0.577±0.050 0.869±0.036 0.747±0.051

He 0.731±0.026 0.544±0.016 0.620±0.026 0.546±0.015 0.627±0.031

uHe 0.751±0.026 0.558±0.016 0.636±0.027 0.563±0.016 0.645±0.032 F -0.034±0.076 -0.406±0.062 0.065±0.073 -0.586±0.043 -0.187±0.073

RST 0.038** 0.003 0.073*** 0.159*** 0.012*

FST 0.046*** 0.010 0.089*** 0.043*** 0.054***

* p 0.05; ** p 0.01; and *** p 0.001

64

Microsatellite data were tested for deviation from Hardy-Weinberg equilibrium (HWE) (Table

3.8). According to the obtained data, locus WPMS 14 is in HWE except for Anykš iai and

Kretinga populations. Locus WPMS 16 is mostly under the influence of evolutionary forces

among all investigated loci, because for 10 out of 16 populations it significantly deviates from

HWE. Three remaining loci are in HWE in 9 out of 16 populations.

Table 3.8. Probabilities that given locus in studied population deviates from Hardy-Weinberg equilibrium

Locus Population

WPMS 16 PMGC 2607 GCPM 1532 GCPM 1608 WPMS 14

Anykš iai 0.000*** 0.000*** 0.006** 0.001** 0.000*** Biržai 0.524 0.022* 0.720 0.055 0.679 Ignalina 0.017* 0.448 0.042* 0.017* 0.271 Jurbarkas 0.325 0.521 0.260 0.009** 0.373 Kaišiadorys 0.001*** 0.352 0.218 0.154 0.286 K dainiai 0.396 0.011* 0.146 0.000*** 0.571 Kretinga 0.000*** 0.267 0.003** 0.394 0.001** Kurš nai 0.006** 0.574 0.322 0.066 0.248 Marijampol 0.838 0.758 0.012* 0.001*** 0.680 Pakruojis 0.000*** 0.000*** 0.000*** 0.169 1.000 Raseiniai 0.004** 0.027* 0.000*** 0.268 0.186 Rokiškis 0.045* 0.036* 0.740 0.978 0.121 Šakiai 0.005** 0.058 0.946 0.003** 0.943 Šal ininkai 0.023* 0.840 0.049* 0.037* 0.119 Taurag 0.889 0.235 0.629 0.770 0.924 Utena 0.148 0.042* 0.311 0.698 0.588

* p 0.05; ** p 0.01; and *** p 0.001

Population Anykš iai significantly deviates from HWE in all investigated loci, which suggests

that this population is under selection pressure (Table 3.8). On the contrary, all investigated loci

in Taurag population is in HWE. Five investigated populations (Biržai, Jurbarkas, Kaišiadorys,

Kurš nai and Utena) are in HWE according to 4 out of 5 investigated loci (Table 3.8). Ignalina,

Kretinga, Pakruojis and Raseiniai populations are affected by evolutionary forces in 3 out of 5

investigated loci. The HWE can be disturbed by a number of forces: mutations, natural

selection, nonrandom mating, genetic drift, and gene flow. It is evident that some of investigated

populations are affected by some disturbances.

3.4.3. Genetic diversity indices of P. tremula populations

We calculated genetic diversity parameters for all investigated European aspen populations

(Table 3.9). Number of effective alleles for different populations varied from 2.201 (in

Anykš iai) up to 3.676 (in Marijampol ). The lowest Shannon’s information index was also

assessed for Anykš iai population (0.816), while the highest – for Biržai population (1.393). The

observed heterozygosity for all investigated European aspen populations varied from 0.538 in

Raseiniai up to 0.965 in Anykš iai (Figure 3.8 and Table 3.9). Mean observed heterozygosity

65

assessed for P. tremula was 0.742, and was higher than mean expected heterozygosity (0.614)

(Table 3.9). Calculated expected heterozygosity varied from 0.484 in Rokiškis up to 0.691 in

Biržai. Expected heterozygosity was lower compared to the observed in all investigated

populations, except Šal ininkai, where expected and observed heterozygosity values were

identical (Table 3.9). Inbreeding, as indicated by Inbreeding coefficient (F), have not been

observed in any of the investigated populations (Table 3.9). According to calculated F, half of

the investigated populations are characterized by random mating, while populations from Biržai,

Jurbarkas, K dainiai, Pakruojis, Šakiai, Taurag and Utena showed signs of increased

heterozygotes number, indicating negative assortative mating. Inbreeding coefficient calculated

for Anykš iai population indicates clear negative assortative mating, or selection against

homozygotes. Mean Inbreeding coefficient value (-0.23) shows, that P. tremula in Lithuania

could be experiencing selection pressure against homozygotes (Table 3.9). This could be

expected from the biology of the species’, as it reproduces not only via seed, but also by root

suckers, and to retain high diversity it needs to favor heterozygotes. High genetic diversity rate

is also crucial for P. tremula as for pioneer species. Petit and Hampe (2006) noticed that

heterozygote advantage is an expected genomic feature of long lived and widely distributed in

different environments forest tree species. As P. tremula is distinguished by longevity (further

enhanced by clonal reproduction) (Wühlisch, 2009) and vast amount of seed production every

year (Reim, 1929), there exists high potential to “filter” fit gene combinations via selection

(Lindtke at al. 2012).

Tabl

e 3.

9. T

he in

tra-

popu

latio

n ge

netic

div

ersi

ty in

dice

s with

stan

dard

err

ors o

ver t

he lo

ci, o

f Eur

opea

n as

pen,

ass

esse

d us

ing

SSR

data

. N –

no.

of

sam

pled

tree

s; N

a –

num

ber o

f diff

eren

t alle

les;

Ne

– ef

fect

ive

num

ber o

f alle

les;

I –

Shan

non ’

s inf

orm

atio

n in

dex;

Ho

– ob

serv

ed h

eter

ozyg

osity

;

He

– ex

pect

ed h

eter

ozyg

osity

; uH

e –

unbi

ased

exp

ecte

d he

tero

zygo

sity

; F –

fixa

tion

inde

x

Popu

latio

n N

N

a N

e I

Ho

He

uHe

F A

nykš

iai

17

2.60

0±0.

400

2.20

1±0.

156

0.81

6±0.

086

0.96

5±0.

024

0.53

8±0.

028

0.55

4±0.

029

-0.8

09±0

.086

B

iržai

18

6.

000±

1.04

9 3.

438±

0.44

2 1.

393±

0.15

0 0.

861±

0.04

5 0.

691±

0.03

5 0.

712±

0.03

6 -0

.266

±0.1

18

Igna

lina

19

5.60

0±0.

812

3.38

8±0.

586

1.31

9±0.

175

0.77

3±0.

080

0.67

1±0.

050

0.69

0±0.

051

-0.1

88±0

.179

Ju

rbar

kas

18

4.60

0±0.

600

2.72

0±0.

302

1.13

8±0.

119

0.83

1±0.

039

0.61

6±0.

036

0.63

4±0.

037

-0.3

57±0

.063

K

aiši

ador

ys

21

5.60

0±0.

927

2.79

2±0.

333

1.19

8±0.

162

0.69

3±0.

059

0.61

8±0.

050

0.63

3±0.

052

-0.1

53±0

.134

K

dain

iai

22

5.20

0±0.

917

3.08

7±0.

541

1.22

2±0.

183

0.85

9±0.

042

0.64

0±0.

054

0.65

5±0.

055

-0.3

73±0

.111

K

retin

ga

22

5.60

0±0.

678

3.55

1±0.

804

1.32

5±0.

181

0.68

0±0.

086

0.67

2±0.

053

0.68

8±0.

054

-0.0

58±0

.185

K

urš

nai

21

5.00

0±0.

447

2.94

1±0.

450

1.22

5±0.

132

0.72

4±0.

046

0.63

2±0.

047

0.64

7±0.

048

-0.1

65±0

.103

M

arija

mpo

l

17

5.60

0±0.

927

3.67

6±0.

621

1.36

4±0.

208

0.77

3±0.

095

0.68

7±0.

061

0.70

8±0.

063

-0.1

60±0

.193

Pa

kruo

jis

30

6.40

0±1.

364

2.59

0±0.

393

1.15

0±0.

154

0.71

9±0.

127

0.58

8±0.

043

0.59

8±0.

044

-0.2

23±0

.209

R

asei

niai

18

3.

600±

0.24

5 2.

224±

0.25

7 0.

927±

0.08

3 0.

538±

0.14

1 0.

529±

0.04

7 0.

550±

0.05

0 0.

002±

0.24

7 R

okiš

kis

16

3.80

0±0.

663

2.24

3±0.

536

0.90

0±0.

175

0.55

3±0.

069

0.48

4±0.

074

0.50

1±0.

076

-0.1

70±0

.104

Ša

kiai

20

6.

000±

1.04

9 3.

588±

0.68

8 1.

383±

0.18

5 0.

829±

0.06

6 0.

685±

0.04

9 0.

703±

0.05

0 -0

.235

±0.1

43

Šal

inin

kai

20

6.00

0±0.

707

2.32

8±0.

176

1.13

4±0.

077

0.56

0±0.

097

0.56

0±0.

037

0.57

4±0.

038

-0.0

07±0

.159

Ta

urag

17

5.

600±

1.03

0 2.

918±

0.28

0 1.

261±

0.15

8 0.

776±

0.06

8 0.

640±

0.04

6 0.

659±

0.04

8 -0

.223

±0.0

88

Ute

na

18

4.60

0±0.

510

2.40

8±0.

245

1.04

5±0.

100

0.74

4±0.

106

0.56

8±0.

042

0.58

4±0.

043

-0.2

91±0

.150

M

ean

19.1

63

5.11

3±0.

216

2.88

1±0.

119

1.17

5±0.

039

0.74

2±0.

022

0.61

4±0.

013

0.63

1±0.

013

-0.2

30±0

.040

67

Fig. 3.8. Observed heterozygosity in investigated European aspen populations assessed using SSR

In addition to SSR the mean genetic diversity indices for all investigated European aspen

populations were also calculated using RAPD data (Table 3.10). The effective number of alleles

exceeded the number of different alleles for populations with low number of assessed individuals (5

or less) (Table 3.10). Shannon’s information index varied from 0.192 in Kurš nai up to 0.401 in

K dainiai closely followed by Pakruojis (0.402) population. The lowest expected heterozygosity

values were calculated for Kurš nai (0.127), Taurag (0.136), Marijampol (0.188) and Anykš iai

(0.196) populations. The number of assessed trees in Kurš nai, Marijampol and Taurag

populations was low (5, 5 and 2 respectively) (Table 3.10), whereas the expected heterozygosity in

Anykš iai population was also among the lowest assessed using SSR data (Table 3.9).

68

Table 3.10. The within population genetic diversity indices with standard error over the loci of European aspen, assessed using RAPD data. N – number of sampled trees; Na – number of different alleles; Ne – effective number of alleles; I – Shannon’s information index; He – expected heterozygosity; uHe – unbiased expected heterozygosity

Population N Na Ne I He uHe Anykš iai 15 1.426±0.047 1.331±0.021 0.298±0.016 0.196±0.012 0.203±0.012 Biržai 4 1.313±0.050 1.344±0.022 0.303±0.017 0.203±0.012 0.231±0.013 Ignalina 6 1.451±0.049 1.400±0.023 0.348±0.017 0.232±0.012 0.253±0.013 Jurbarkas 10 1.504±0.045 1.393±0.022 0.343±0.017 0.229±0.012 0.241±0.012 Kaišiadorys 10 1.613±0.044 1.415±0.021 0.377±0.015 0.248±0.011 0.261±0.011 K dainiai 11 1.722±0.038 1.442±0.021 0.401±0.015 0.264±0.011 0.277±0.011 Kretinga 7 1.521±0.046 1.395±0.022 0.352±0.016 0.233±0.011 0.251±0.012 Kurš nai 5 1.039±0.050 1.217±0.020 0.192±0.016 0.127±0.011 0.141±0.012 Marijampol 5 1.158±0.053 1.330±0.023 0.278±0.018 0.188±0.012 0.209±0.014 Pakruojis 30 1.835±0.030 1.439±0.021 0.402±0.014 0.262±0.011 0.267±0.011 Raseiniai 12 1.549±0.045 1.403±0.021 0.360±0.016 0.239±0.011 0.249±0.012 Rokiškis 5 1.342±0.052 1.353±0.022 0.316±0.016 0.209±0.012 0.232±0.013 Šakiai 10 1.655±0.040 1.424±0.022 0.379±0.015 0.250±0.011 0.264±0.012 Šal ininkai 8 1.577±0.046 1.409±0.022 0.368±0.015 0.242±0.011 0.258±0.012 Taurag 2 0.975±0.049 1.232±0.020 0.198±0.017 0.136±0.012 0.181±0.015 Utena 6 1.454±0.049 1.386±0.022 0.346±0.016 0.229±0.011 0.250±0.012 Mean 9.125 1.446±0.012 1.369±0.005 0.329±0.004 0.218±0.003 0.236±0.003

Obtained allele frequencies differed among assessed P. tremula populations (Figure 3.9). The

private alleles were found in 6 populations (Table 3.11). Private alleles were amplified in 4 loci,

where GCPM 1608 locus had the largest number of 5 private alleles (1 private allele per 5 different

populations). Each of the remaining 3 loci had one private allele in Kaišiadorys population (Table

3.11).

Table 3.11. The private (unique to one of investigated Populus tremula populations) SSR alleles

Population Locus Allele Allele frequency Biržai GCPM 1608 164 0.028 Ignalina GCPM 1608 191 0.026 Kaišiadorys PMGC 2607 139 0.024 Kaišiadorys GCPM 1532 207 0.024 Kaišiadorys WPMS 14 227 0.048 K dainiai GCPM 1608 178 0.023 Kretinga GCPM 1608 172 0.023 Taurag GCPM 1608 176 0.031

Fig.

3.9

. SSR

alle

le fr

eque

ncie

s of d

iffer

ent P

opul

us tr

emul

a po

pula

tions

. Na

= n

umbe

r of d

iffer

ent a

llele

s; N

a (F

req

5%) =

num

ber o

f diff

eren

t al

lele

s with

a fr

eque

ncy

5%; N

e =

num

ber o

f effe

ctiv

e al

lele

s; I

= S

hann

on’s

info

rmat

ion

inde

x; N

o. L

com

m A

llele

s (25

%) =

num

ber o

f loc

ally

co

mm

on a

llele

s (fr

eq.

5%) f

ound

in 2

5% o

r few

er p

opul

atio

ns; N

o. L

com

m A

llele

s (50

%) =

num

ber o

f loc

ally

com

mon

alle

les (

freq

. 5%

) fou

nd in

50

% o

r few

er p

opul

atio

ns; H

e =

exp

ecte

d he

tero

zygo

sity

70

3.4.4. Nei’s genetic distances between populations of P. tremula

In order to assess genetic relatedness of P. tremula populations in Lithuania we calculated Nei’s

genetic distances between them, according to SSR data. Pairwise genetic distances for P. tremula

populations are presented in Table 3.12. Assessed mean genetic distances among European aspen

populations in Lithuania is 0.125. The most similar populations are from Jurbarkas and Kurš nai

(genetic distance was 0.035). Slightly greater genetic distance was assessed between Pakruojis –

Šal ininkai (0.036) and Šal ininkai – Taurag (0.038) populations (Table 3.12), while the most

distinct populations are from Anykš iai and Rokiškis (0.388). Anykš iai – Utena (0.313),

Marijampol – Rokiškis (0.293) and Anykš iai – Biržai (0.258) populations also are among the

most different P. tremula populations (Table 3.12).

Somewhat similar results were obtained and using RAPD data for population genetic distance

assessment. European aspen pairwise Nei’s genetic distance data are showed in Table 3.12. Mean

genetic distance between P. tremula populations in Lithuania according to RAPD is 0.131. The two

most similar populations are from Pakruojis and Šal ininkai (0.031), confirming Nei’s genetic

distance calculated between these two populations using SSR data. The other genetically similar

populations according to RAPD data are Pakruojis – Utena and Pakruojis – Kaišiadorys (0.037).

Genetic distance between these populations according to SSR data is close to the mean genetic

distance and is equal to 0.103 and 0.123 respectively (Table 3.12). The most different populations

according to RAPD are Kurš nai – Taurag (0.292), Marijampol – Taurag (0.252) and Rokiškis –

Taurag (0.245). Compared to SSR data, these populations are quite similar, Nei’s genetic distances

among them are 0.068, 0.098 and 0.162 respectively. However, only two samples from Taurag

population were used in RAPD analysis, possibly influencing the results (Table 2.1).

Tabl

e 3.

12. P

airw

ise

Nei

’s g

enet

ic d

ista

nces

bet

wee

n in

vest

igat

ed P

opul

us tr

emul

a po

pula

tions

. The

upp

er n

umbe

r in

dica

tes

gene

tic d

ista

nce

calc

ulat

ed u

sing

SS

R da

ta (b

old)

and

the

low

er n

umbe

r is c

alcu

late

d us

ing

RAPD

dat

a (I

talic

)

Bir

žai

0.25

8 0.

153

Igna

lina

0.22

0 0.

117

0.05

00.

136

Jurb

arka

s 0.

209

0.11

1 0.

054

0.13

4 0.

040

0.10

9

Kai

šiad

orys

0.

196

0.14

7 0.

093

0.09

2 0.

068

0.10

2 0.

118

0.11

2

Kda

inia

i 0.

238

0.10

1 0.

062

0.11

4 0.

066

0.07

2 0.

118

0.07

1 0.

057

0.08

4

Kre

tinga

0.

212

0.10

8 0.

085

0.10

8 0.

068

0.10

1 0.

133

0.08

4 0.

068

0.09

2 0.

076

0.05

9

Kur

šna

i 0.

229

0.23

5 0.

065

0.19

9 0.

054

0.20

7 0.

035

0.22

4 0.

131

0.16

2 0.

129

0.20

6 0.

106

0.19

4

Mar

ijam

pol

0.

213

0.19

4 0.

078

0.15

4 0.

051

0.13

9 0.

106

0.15

4 0.

089

0.08

8 0.

053

0.11

4 0.

062

0.13

5 0.

114

0.18

8

Pakr

uojis

0.

253

0.14

9 0.

079

0.09

1 0.

077

0.10

5 0.

066

0.11

2 0.

123

0.03

7 0.

126

0.08

1 0.

122

0.08

8 0.

061

0.18

5 0.

163

0.10

0

Ras

eini

ai

0.17

4 0.

133

0.12

20.

147

0.12

20.

111

0.07

90.

096

0.13

40.

143

0.15

70.

077

0.15

60.

108

0.12

0 0.

238

0.16

60.

175

0.09

00.

135

Rok

iški

s 0.

388

0.18

7 0.

191

0.11

5 0.

199

0.12

1 0.

164

0.15

4 0.

241

0.05

8 0.

249

0.12

1 0.

188

0.10

6 0.

117

0.18

7 0.

293

0.11

3 0.

111

0.06

3 0.

238

0.18

2

Šaki

ai

0.25

7 0.

143

0.05

30.

099

0.05

40.

112

0.07

90.

101

0.10

20.

040

0.11

00.

094

0.05

80.

097

0.07

9 0.

151

0.11

10.

102

0.07

40.

044

0.14

40.

137

0.18

10.

080

Šal

inin

kai

0.24

4 0.

168

0.08

20.

081

0.09

50.

117

0.05

10.

131

0.12

40.

053

0.13

70.

091

0.15

60.

096

0.07

0 0.

188

0.17

50.

102

0.03

60.

031

0.06

30.

151

0.12

70.

076

0.10

70.

067

Tau

rag

0.

226

0.20

6 0.

054

0.18

6 0.

064

0.19

7 0.

054

0.18

1 0.

056

0.21

7 0.

070

0.16

1 0.

098

0.15

8 0.

068

0.29

2 0.

098

0.25

2 0.

050

0.20

2 0.

086

0.18

1 0.

162

0.24

5 0.

092

0.21

0 0.

038

0.21

1

Ute

na

0.31

3 0.

168

0.13

10.

091

0.10

40.

121

0.06

50.

136

0.17

50.

059

0.22

90.

103

0.21

00.

116

0.08

8 0.

179

0.23

80.

131

0.10

30.

037

0.15

70.

159

0.13

50.

093

0.13

30.

062

0.09

80.

053

0.11

0 0.

223

Popu

latio

n

Anykšiai

Biržai

Ignalina

Jurbarkas

Kaišiadorys

Kdainiai

Kretinga

Kuršnai

Marijampol

Pakruojis

Raseiniai

Rokiškis

Šakiai

Šalininkai

Taurag

Tabl

e 3.

13. P

airw

ise

F ST v

alue

s (W

righ

t, 19

46; 1

951;

196

5) fo

r all

inve

stig

ated

Pop

ulus

trem

ula

popu

latio

ns c

alcu

late

d us

ing

SSR

data

. Sig

nific

antly

diff

eren

t es

timat

es b

y pr

obab

ility

P (r

and

dat

a) b

ased

on

999

perm

utat

ions

are

show

n in

bol

d

Bir

žai

0.07

1

Ig

nalin

a 0.

064

0.01

1

Jurb

arka

s 0.

066

0.01

5 0.

011

Kai

šiad

orys

0.

062

0.02

4 0.

018

0.03

4

Kda

inia

i 0.

071

0.01

5 0.

015

0.03

1 0.

016

Kre

tinga

0.

062

0.01

9 0.

015

0.03

4 0.

018

0.01

9

Kur

šna

i 0.

069

0.01

7 0.

014

0.01

1 0.

036

0.03

4 0.

026

Mar

ijam

pol

0.

061

0.01

6 0.

011

0.02

7 0.

023

0.01

2 0.

014

0.02

7

Pakr

uojis

0.

081

0.02

3 0.

021

0.02

1 0.

038

0.03

6 0.

034

0.02

0 0.

042

Ras

eini

ai

0.07

4 0.

048

0.04

6 0.

036

0.04

9 0.

062

0.05

5 0.

047

0.06

0 0.

040

R

okiš

kis

0.13

6 0.

066

0.06

7 0.

062

0.08

5 0.

085

0.06

6 0.

048

0.08

9 0.

046

0.08

8

Ša

kiai

0.

071

0.01

2 0.

012

0.02

1 0.

026

0.02

6 0.

013

0.02

0 0.

023

0.02

2 0.

055

0.06

5

Šal

inin

kai

0.08

2 0.

026

0.02

7 0.

017

0.04

0 0.

042

0.04

5 0.

024

0.04

7 0.

012

0.03

2 0.

054

0.03

3

T

aura

g

0.06

8 0.

014

0.01

5 0.

015

0.01

6 0.

019

0.02

4 0.

018

0.02

3 0.

016

0.03

7 0.

060

0.02

2 0.

014

Ute

na

0.09

8 0.

039

0.03

2 0.

022

0.05

3 0.

064

0.05

7 0.

028

0.06

1 0.

035

0.05

7 0.

057

0.03

9 0.

035

0.03

4

Popu

latio

n

Anykšiai

Biržai

Ignalina

Jurbarkas

Kaišiadorys

Kdainiai

Kretinga

Kuršnai

Marijampol

Pakruojis

Raseiniai

Rokiškis

Šakiai

Šalininkai

Taurag

Fig.

3.1

0. C

lust

erin

g of

Pop

ulus

trem

ula

popu

latio

ns a

ccor

ding

to N

eis‘

s gen

etic

dis

tanc

es. C

lust

erin

g ca

lcul

ated

usi

ng U

PGM

A m

etho

d an

d Pa

ired

gro

up

algo

rith

m a

nd E

uclid

ean

dist

ance

sim

ilari

ty m

easu

re (B

oot N

=10

00).

A –

dend

rogr

am g

ener

ated

usi

ng S

SR d

ata

(Cop

hene

tic C

orre

latio

n 0.

9198

); B

—

dend

rogr

am g

ener

ated

usi

ng R

ADP

data

(Cop

hene

tic C

orre

latio

n 0.

9379

)

74

Pairwise FST values (Wright, 1946; 1951; 1965) for aspen populations (SSR data) are shown in

Table 3.13. Regardless of the significance of the many estimates (larger than 0.017 in Table

3.13), genetic differentiation of the assessed populations we consider as small or moderate

(exceeding 0.05). The average FST value between Rokiškis and other populations is 0.072, while

average FST value for Anykš iai population is even greater and reached 0.076, indicating the

reduced gene flow to Anykš iai and Rokiškis populations. In similar studies, comparable FST

results were obtained after studying five isozyme loci in P. tremula populations from France,

Austria, Southern and Northern Sweden. Ingvarsson (2005) found that FST values differed from

0.040 to 0.161, averaging to 0.117. Limited gene flow among populations (FST=0.11) has been

observed in the study encompassing P. tremula populations from numerous European countries

using chloroplast SSR markers (Petit et al. 2003b). In our study average FST value was 0.037

indicating more pronounced gene flow, yet limited geographic range among the analyzed

Lithuanian populations should be considered. Lithuania is a relatively small country with no

geographic barriers interrupting gene flow among European aspen populations.

To reveal the similarities between assessed P. tremula populations according to calculated Nei’s

genetic distances, genetic dendrograms were constructed (Figure 3.10). In the dendrogram based

on SSR data, Lithuanian aspen populations are grouped into three separate clusters. P. tremula

population from Anykš iai is clustered separately. The first dendrogram cluster, composed of 8

populations contains both pairs of populations most similar according to Nei’s genetic distance

(Jurbarkas – Kurš nai and Pakruojis – Šal ininkai, see Table 3.12). These two pairs form a

separate sub-cluster. The second cluster is composed of 4 populations from Kaišiadorys,

K dainiai, Kretinga and Marijampol . The third cluster contains three populations from

Raseiniai, Rokiškis and Utena. These three populations can be characterized by high to

moderate genetic distance, e.g. they differ more from the other investigated populations.

Reliability estimates are showed at the branching points of the dendrogram (Figure 3.10). Three

dendrogram clusters are formed with high reliability measure, as the lowest reliability value is

80% and was obtained separating the first and the second clusters (Figure 3.10).

Genetic dendrogram, constructed using genetic distances assessed by RAPD data, shows

grouping of P. tremula populations into two main clusters (Figure 3.10). Anykš iai, Ignalina,

Jurbarkas, K dainiai, Kretinga and Raseiniai populations form separate cluster 1, while Biržai,

Kaišiadorys, Marijampol , Pakruojis, Rokiškis, Šakiai, Šal ininkai and Utena form the second

clade (Figure 3.10).The distinct clade is formed by Kurš nai and Taurag populations. These

75

two populations can be characterized by low number of assessed individuals (2 for Taurag and

5 for Kurš nai, see Table 2.1 in Materials and methods).

3.4.5. Correlation between genetic distances and geographic distribution pattern of P.tremula populations

Obtained microsatellite data were used in Mantel test to examine correlation between genetic

distances and geographic distances of assessed P. tremula populations. The result of Mantel test

is 0.068 with significance level 0.01, when based on 9,999 permutations (Figure 3.11). Results

of Mantel test for SSR data indicate an even weaker correlation than for RAPD data (Figure 3.5)

between genetic and geographic distances of investigated European aspen populations. The

correlations from Mantel test were much stronger for separate loci (data not shown).

Fig. 3.11. Correlation between genetic distances and geographic locations of Populus tremula populations based on SSR data (Mantel test, 9,999 permutations)

3.4.6. P. tremula within- vs. among-population genetic diversity

Microsatellite data were also used to assess genetic diversity within individuals vs among

populations of P. tremula. The multilocus AMOVA (Excoffier et al. 1992; Mengoni and

Bazzicalupo, 2002) using microsatellite distance matrix revealed higher among population

variance for the RST index than for the FST index (Figure 3.12). As presented in Figure 3.12, 96%

and 95% (FST and RST respectively) of variation is explained by variation within aspen

individuals, while 4% and 5% (FST and RST respectively) – because of the variation among

different populations. According to Lexer et al. (2005) and Hall et al. (2007), variation among

European aspen populations based on neutral markers is approximately 1%. The rest of the

variation is between individuals. This might be because of the aspen biology (as a wind

76

pollinated and dispersed tree), that ensures high gene flow and diminishes spatial genetic

structure among populations.

Fig. 3.12. The partition of the variance based on the multilocus AMOVA carried out for FST (left) and RST (right) estimates with GenAlEx ver. 6.5 (Peakall and Smouse, 2006; 2012)

Fig. 3.13. Distribution of Populus tremula populations in principal coordinate axes using pairwise population matrix of Nei’s unbiased genetic distance based on SSR data

PCA of European aspen population distribution based on SSR data is showed in Figure 3.13.

The first axis explains 39.7%, the second axis explains 24.7% and the third axis explains 13.3%

of the assessed genetic variation. In total, the first three axes expalains 77.7% of genetic

variation. PCA indicates that Anykš iai and Rokiškis populations are more genetically distinct

77

from the other assessed P. tremula populations. This result confirms higher FST values

calculated for these two populations.

Fig. 3.14. Distribution of Populus tremula populations in principal coordinate axes using pairwise population matrix of Nei’s unbiased genetic distance based on RAPD data

PCA of distribution of European aspen populations based on RAPD data is showed in Figure

3.14. The first axis explains 26.9%, the second axis explains 15.8% and the third axis explains

12.4% of the assessed genetic variation. In total, the first three axes explains 55.1% of variation.

All investigated European aspen populations form single cluster, except for Kurš nai and

Taurag populations. However, these two populations are represented by a low number of

investigated individuals and might be excluded from the interpretation of the results.

3.4.7. Clustering of P. tremula populations using Bayesian approach

In order to cluster P. tremula populations based on their genetic differences, we used Bayesian

clustering approach. The projected number of populations was calculated by logarithmic

possibilities for the number of clusters in which the analyzed individuals group together. Using

calculated logarithmic possibilities we employed Delta K method ( K) to infer the true number

of genetic clusters. K calculations were based on the second order rate of change of the

likelihood (Evanno et al. 2005).

78

Fig. 3.15. K calculations of the second order rate of change of likelihood, based on SSR (A) and RAPD data (B)

K method based on SSR data suggests, that P. tremula individuals should be grouped in three

clusters, while RAPD data analysis revealed two clusters (Figure 3.15). Based on K

calculations, we attributed P. tremula individuals to each of the three clusters grouped according

to SSR data (Figure 3.16) and two clusters in relation to RAPD data (Figure 3.17). In Figure

3.18 similar calculations with 3 most discriminating populations SSR markers (as indicated by

PCA among population option, see Figure 3.7) are presented. Results of Bayesian clustering

approach confirms clustering results of P. tremula populations according to Nei’s genetic

distance analysis, where three main clusters of assessed populations were obtained for SSR, and

two – for RAPD data (Figure 3.10).

79

Fig. 3.16. Proportion of Populus tremula trees attributed to 3 clusters (as estimated by K from simulation summary of STRUCTURE program output; 100,000 runs) by 5 SSR markers

Fig. 3.17. Proportion of Populus tremula trees attributed to 2 clusters (as estimated by K from simulation summary of STRUCTURE program output; 50,000 runs) by RAPD markers

80

Fig. 3.18. Proportion of Populus tremula trees attributed to 3 clusters (as estimated by K from simulation summary of STRUCTURE program output; 100,000 runs) by 3 SSR markers most

discriminating P. tremula populations (as indicated in PCA analysis of marker load in 3 components, section 3.4.1)

The results from both clustering methods (UPGMA, based on Nei’s genetic distances (3.4.4.

section) and Bayesian clustering approach) indicate that existing provenance regions could be

revised, as both molecular markers revealed latitudinal trend. Site ecological as well as forest

inventory data may also be used in addition to justify the borders of provenance regions and

well-founded use of forest reproductive material in practical forest selection and breeding.

Referring to the relatively small area of Lithuania for the photoperiodic and temperature

gradients, where no geographic boundaries are present and most of the altitudinal variation is

within 150 meters limit, we do not expect any significant effects of the present-day natural

selection on P. tremula. European aspen field trials until now are absent in Lithuania because of

commercial considerations, revealing additional opportunities in the future in order to improve

provenance transfer and promote breeding of P. tremula.

81

3.5. Correlation between presence of P. tremulae DNA and certain RAPD fragments

Correlation between presence of P. tremulae mycelium in wood of European aspen (Table 3.2)

and RAPD fragments was determined using SAS procedure GENMOD. Only 5 RAPD

fragments were important in differentiation of trees by infection of P. tremulae (A05-850, B13-

1100, B17-850, 17002-1750 and 17009-1600). Only fragments A05-850 (p=0.0278) and B13-

1100 (p=0.0222) were significant in group differentiation according to SAS procedure

GENMOD. The cumulative frequency of A05-850 and B13-1100 fragments in investigated P.

tremula populations is showed in Figure 3.19. Loci B17-850, 17009-1600 and 17002-1750 were

close to 5% of statistical significance.

Fig. 3.19. Distribution of investigated Populus tremula populations in Lithuania and cumulative frequency per category (%) in RAPD loci B13-1100 (on the left) and A05-850 fragments

PCA based on detrended correspondence analysis using 5 important RAPD loci was performed

and revealed the distribution of the infected and non-infected trees in two axes (Figure 3.20).

Only B13-1100 locus was important in individual differentiation by presence of P. tremulae

DNA in aspen wood (explaining 7% of variation). The convex hulls of two sample groups

82

(infected and uninfected by P. tremulae) are overlapping, but the frequencies in two loci

indicate quite good possibilities for using these RAPDs loci in tree breeding at early ontogenesis

stages.

Fig. 3.20. Distribution in two axes of European aspen uninfected (indicated as dashed line and diamond shape) and infected by Phellinus tremulae (indicated as solid line and cross shape) trees based on the

results of detrended correspondence analysis using RAPD loci B13-1100, A5-850, B17-850, 17002-1750 and 17009-1600

Additionally, we calculated the correlation whether tree individual heterozygosity, RAPD

marker polymorphism, and provenance region, population or forest soil type had an effect on

presence of P. tremulae in European aspen. Pearson correlation between tree individual

heterozygosity by 5 SSR loci and presence or absence of P. tremulae DNA in wood samples

was estimated close to zero (-0.04). The same correlation between tree individual RAPD marker

polymorphism and presence of fungal DNA also resulted in similar estimate (-0.03). Percentage

of P. tremulae infected trees was slightly lower on temporarily moisturized or overmoistured

and eutrophic sites.

Provenance region and population influence on the presence of P. tremulae in wood of assessed

P. tremula was tested using SAS procedure MIXED (REML option), but the results showed that

these two factors have no effect on distribution of this fungal pathogen.

RAPD markers are known to amplify anonymous regions of genome, thus allowing to catch

some of the genetic variation underlying the resistance mechanisms only by chance. To increase

our chances in capturing correlation, we have used 15 highly informative primers (Table 3.5).

However, statistically significant correlation between the presence of P. tremulae and RAPD

loci was established for only two of them (B13-1100 p=0.0222 and A05-850, p=0.0278).

Obtained results suggest that search for correlation without prior screening of the resistance and

83

recognition of genetic mechanisms underlying the resistance of P. tremula is unreliable. The

chances of inferring genetic markers associated with European aspen resistance to P. tremulae

using RAPD are negligible.

The subsequent step of this study could be the identification of genome regions, represented by

B13-1100 and A05-850 fragments. The identification of these regions could lead to marker

assisted breeding, or QTL involved in identification of the resistance mechanisms.

We can conclude, that the infection of European aspen plus trees with P. tremulae doesn’t

depend on population, provenance region or individual tree heterozygosity. RAPD loci

correlated to the infection of European aspen by P. tremulae should be investigated further to

confirm or reject their value in assessing the resistance of aspen to this fungal pathogen.

3.6. Hybridization between European and hybrid aspen

The aim of this study was to assess the possible hybridization between European and hybrid

aspen by leaf morphology of P. tremula progenies. Tree flowering phenology was assessed to

reveal if European and hybrid aspen phenologies overlap thus allowing the taxa to hybridize.

The assessment of European and hybrid aspen flowering phenology revealed that hybrid aspens

are characterized by earlier phenology compared to European aspen trees. The percentage of late

flowering category trees in European aspen group was 48% and in hybrid aspen group 9%; the

percentages in medium flowering category was 35% vs 25% in European and hybrid aspens

respectively, while early flowering group was comprised of 17% of European and 66% of

hybrid aspen trees (Figure 3.21). European and hybrid aspen trees attributed to the same

flowering category were flowering at the same time.

Fig. 3.21. The proportion of European and hybrid aspen trees in each of early, medium and late flowering phenology categories

84

Two P. tremula and one hybrid aspen tree per flowering category were sampled (n=9), and leaf

morphological parameters of their half-sib progenies (n=900) were analyzed using SAS

STEPDISC procedure; ten leaves per each of the progeny tree (e.g. 9000 leaves). As the result

of SAS STEPDISC, we selected 7 leaf morphological trait parameters (out of 19) best

discriminating between P. tremula and hybrid aspen groups. Selected leaf trait parameters were:

(1) form, (2) v_length, (3) perimeter, (4) h_width, (5) width2, (6) angle1 and (7) angle2.

PCA based on Pearson correlation and distance-based biplot was applied for 7 selected leaf trait

parameters and revealed that the first two components in diagram explain 97.8% of the variation

(Figure 3.22). Perimeter together with v_length, h_width and width2 forms one group in the

component pattern; the other parameter group is represented by leaf lobe angle traits (angle1

and angle2), while leaf form coefficient (form) is clustered separately.

Fig. 3.22. Principal component pattern of seven selected leaf trait parameters (form, v_length, perimeter, h_width, width2, angle1, and angle2), that best discriminates between European and hybrid aspens

Mother trees of 9 half-sib families were clustered together using Euclidean distances according

to 7 important leaf trait parameters (Figure 3.23). In the resulting dendrogram two separate

clades are formed: in the upper clade P. tremula mother trees of all three flowering categories

together with early flowering hybrid aspen mother tree are grouped; the second clade is

comprised of two hybrid aspen mother trees and early and medium flowering P. tremula trees.

The dendrogram revealed that most closely related mother trees are late flowering P. tremula

trees and early flowering P. tremula with late flowering hybrid aspen tree.

85

Fig. 3.23. Clustering of European and hybrid aspen mother trees using Euclidean distance according to

7 selected leaf trait parameters (form, v_length, perimeter, h_width, width2, angle1, angle2) of their offspring

Multivariate permutation (n=10,000) of the offspring, belonging to both (European and hybrid

aspens) groups, using Euclidean distance measure revealed, that hybrid and European aspen

trees differ significantly in means of seven selected leaf trait parameters. However, discriminant

analysis of European and hybrid aspen groups using seven important trait parameters classified

correctly only 61% of individuals, though F value was 15.4 and p<0.0001 (Hotelling’s

t2=108.3) (Figure 3.24).

Fig. 3.24. Discriminant analysis of European and hybrid aspen group trees, using seven selected leaf trait parameters. Grey color indicates European aspen and white color – hybrid aspen group trees

86

Variance components estimates presented in Table 3.14 show that seedling influence on all the

trait parameters is several times higher than that of the family. Family influence is also

significant. Lamina size parameters (h_width, perimeter, v_length, width2) are highly heritable,

while leaf lamina lobe angles had low heritability estimates. The estimates show that even leaf

parameters of one year old seedlings might be used in such testing.

Table 3.14. Variance components (%) of used leaf trait parameters, their standard errors (%), significance (p-value) and individual heritability. p-values: * <0.05, ** <0.01, *** <0.001

Trait Family variance

component, %

Standard error, %

p-value Seedling variance

component, %

Standard error, %

p-value Heritability

angle1 1.2 0.70 * 4.7 0.83 *** 0.05 angle2 4.0 2.10 * 8.9 0.96 *** 0.16 form 12.3 6.40 * 43.2 2.33 *** 0.49 h_width 26.8 13.70 * 45.8 2.35 *** 1.0 perimeter 30.0 15.30 * 47.9 2.41 *** 1.0 v_length 35.7 18.10 * 48.6 2.41 *** 1.0 width2 19.8 10.10 * 37.3 2.04 *** 0.79

Progenies of two aspen maternal trees had significantly more seedlings classified into hybrid

group (tree No. 9 – 76% and tree No. 28 – 47%) compared with the remaining P. tremula

mother trees. The same results were obtained also by clustering mother trees according to their

progeny leaf trait parameters (both trees were clustered together with hybrid aspen mother trees

in the lower clade of the dendrogram, see Figure 3.23).

The leaf variability of European and hybrid aspens’ one-year-old seedlings is very high,

therefore it was complicated to trace possible hybrid seedlings based only on leaf morphology

analysis. Obviously, to trace possible hybrids in obtained half-sib families, additional

application of molecular methods would be required. Phenological structure of aspen and hybrid

aspen stands is helpful in initial phase of screening to predict possible gene flow, because of the

timing in flowering phenology – European aspen trees had the highest number of progenies

classified to the hybrid aspen group. This was expected, as the hybrid aspen group is

characterized by earlier flowering time than European aspen.

87

CONCLUSIONS

1. The best methods for DNA extraction from various tissues of Populus tremula (giving

the highest amounts of good quality DNA) are CTAB and protein precipitation, and

commercially available kits Nucleospin Plant Mini, Genomic DNA purification kit and

innuPREP Plant DNA Kit;

2. Weak correlations between genetic and geographic distances were obtained using both

RAPD and SSR markers(r=0.178 and r=0.068, respectively), indicating that the largest

part of molecular variance can be attributed to a within-population variation;

3. RAPD data allowed genotype clustering into groups that are geographically closer to

each other compared to SSR-based cluster groups;

4. Microsatellite analysis revealed that individuals of P. tremula should be grouped into

three clusters, while RAPD analysis clearly showed only two clusters. The results

obtained by both clustering methods (UPGMA and Bayesian) indicate that existing P.

tremula provenance regions in Lithuania may be revised as both molecular markers

revealed latitudinal trend;

5. Population, provenance region and individual heterozygosity have little or no influence

on infection of P. tremula plus trees with trunk rot fungus Phellinus tremulae. Two

RAPD loci among the 282 loci tested showed association with the wood infection and

thus could be used for development of valuable molecular markers in tree breeding at

early ontogenesis stages;

6. Phenological structure of P. tremula and hybrid aspen stands is helpful for initial

prediction of possible gene flow among aspen populations: trees with the earliest

flowering phenology produced the highest proportion of hybrid seedlings.

88

Conclusions on the defended statements:

Statement Conclusion Comment

1. Genetic differentiation between Lithuanian populations of European aspen correlates with their geographic distribution

Rejected Weak correlations between genetic and geographic distances using both RAPD and SSR markers’ data (0.178 and 0.068) were obtained indicating that the largest part of molecular variance can be attributed to within population variation

2. Genetic diversity among local (Lithuanian) European aspen populations is low because of a high gene migration rate among them

Accepted Only 4% of an assessed genetic variation is attributed to differentiation among P. tremula populations, while 96% of the variation lies within populations

3. The relationship between susceptibility of European aspen to trunk rot caused by Phellinus tremulae and tree genetic properties can be revealed using RAPD analysis

Partially accepted Five RAPD fragments were important in differentiation of P. tremula trees by presence of P. tremulae infection, and only two of them, A5-850 and B13-1100, were significant (at p 0.05) in tree differentiation into susceptibility categories

4. Hybridization between hybrid and European aspens can be revealed by morphological leaf traits of their progenies at juvenile age

Partially accepted It is complicated to trace possible hybridization between hybrid and European aspens based only on leaf morphology and flowering phenology. To trace possible hybrids in half-sib families of P. tremula, application of molecular methods must be considered

89

REFFERENCES

1. Aagaard J.E., Krutovskii K.V., and Strauss S.H. 1998. RAPD markers of mitochondrial

origin exhibit lower population diversity and higher differentiation than RAPDs of nuclear

origin in Douglas fir. Molecular Ecology 7: 801–812.

2. Abbott R., Albach D., Ansell S., Arntzen J.W., Baird S.J.E., Bierne N. et al. 2013.

Hybridization and speciation. Journal of Evolutionary Biology 26: 229–246.

3. Allen E., Morrison D., and Wallis G. 1996. Common Tree Diseases of British Columbia.

Canadian Forest Service, Pacific Forestry Centre, Victoria, British Columbia, Canada.

4. Lietuvos mišk statistika. 1937. Lietuvos Mišk Departamentas, Kaunas. (In Lithuanian).

5. Lithuanian Statistical Yearbook of Forestry. 2014. State Forest Service, Kaunas.

6. Areshchenkova T., and Ganal M.W. 1999. Long tomato microsatellites are predominantly

associated with centromeric regions. Genome 42: 536–544.

7. Arnold M. 1997. Natural hybridization and evolution. Oxford University Press, New

York, NY.

8. Arnold M., and Martin M. 2010. Hybrid fitness across time and habitats. Trends in

Ecology and Evolution 25: 530–536.

9. Asif M.J., and Cannon C.H. 2005. DNA extraction from processed wood: a case study for

the identification of an endangered timber species (Gonostylus bancanus). Plant

Molecular Biology Reporter 23: 185–192.

10. Bakys R., Vasaitis R., Barklund P., Thomsen I.M., and Stenlid J. 2009. Occurrence and

pathogenicity of fungi in necrotic and non-symptomatic shoots of declining common ash

(Fraxinus excelsior) in Sweden. European Journal of Forest Research 128: 51–60.

11. Barreneche T., Bodenes C., Lexer C., Trontin J.F., Fluch S., Streiff R., Plomion C.,

Roussel G., Steinkellner H., Burg K., Favre J.M., Glössl J., and Kremer A. 1998. A

genetic linkage map of Quercus robus L. (pedunculate oak) based on RAPD, SCAR,

microsatellite, minisatellite, isozyme and 5S rDNA markers. Theoretical and Applied

Genetics 97: 1090–1103.

12. Barret C., Lefort F., and Douglas G.C. 1997. Genetic characterization of oak seedlings,

epicormic, crown and micropropagated shoots from mature trees by RAPD and

microsatellite PCR. Scientia Horticulturae 70: 319–330.

13. Beckman J.S., and Weber J.L. 1992. Survey of human and rat microsatellites. Genomics

12: 627–631.

14. Bellamy A.R., and Ralph R.K. 1968. In: Grossman L. and Moldave K. (editors). Methods

in enzymology, Vol. 12B: Nucleic acids. Academic press, New York, NY.

90

15. Bernhardsson C., and Ingvarsson P.K. 2012. Geographical structure and adaptive

population differentiation in herbivore defense genes in European aspen (Populus tremula

L., Salicaceae). Molecular Ecology 21(9): 2197–2207.

16. Bousquet J., Simpson L., and Lalonde M. 1990. DNA amplification from vegetative and

sexual tissues of trees using polymerase chain reaction. Canadian Journal of Forest

Research 20: 254–257.

17. Bowtell D.L. 1987. Rapid isolation of eukaryotic DNA. Anaytical Biochemistry 162: 463–

468.

18. Brandes A., Thompson H., Dean C., and Heslop-Harrison J.S. 1997. Multiple repetitive

DNA sequences in the paracentromeric regions of Arabidosis thaliana L. Chromosome

Research 5: 238–246.

19. Brock G.J., Anderson N.H., and Monckton D.G. 1999. Cis-acting modifiers of expanded

CAG/CTG triplet repeat expandability: association with flanking GC content and

proximity to CpG islands. Human Molecular Genetics 8: 1061–1067.

20. Brown A.H.D., and Weir B.S. 1983. Measuring genetic variability in plant populations.

219–239. In: Tanksley S.D., Orton T.J. (editors). Isozymes in plant genetics and breeding,

Part A. Elsevier Science Publishing.

21. Bucci G., and Menozzi P. 1993. Segregation analysis of random amplified polymorphic

DNA (RAPD) markers in Picea abies Karst. Molecular Ecology 2: 227–232.

22. Bueno M.A., Gomez A., and Manzanera J.A. 2003. Propagation and DNA Markers

characterization of Populus tremula L. and Populus alba L. 37–74. In: Jain S.M. and Ishii

K. (editors). Micropropagation of woody trees and fruits. Kluwer Academic Publishers.

23. Burke J.M., and Arnold M. 2001. Genetics and the fitness of hybrids. Annual Review of

Genetics 35: 31–52.

24. Butlin R.K., and Ritchie M.G. 2013. Pulling together or pulling apart: hybridization in

theory and practice. Journal of Evoliutionary Biology 26: 294–298.

25. Byrne M., Marquez-Garcia M.I., Uren T., Smith D.S., and Moran G.F. 1996. Conservation

and genetic diversity of microsatellite loci in the genus Eucalyptus. Australian Journal of

Botany 44: 331–341.

26. Cambareri E.B., Aisner R., and Carbon J. 1998. Structure of the chromosome VII

centromere region in Neurospora crassa: degenerate transposons and simple repeats.

Molecular and Cellular Biology 18: 5465–5477.

27. Carlson J.E., Tulsieram L.K., Glaubita J.C., Luk V.W.K., Kauffelddt C., and Rutledge R.

1991. Segregation of random amplified DNA markers in F1 progeny of conifers.

Theoretical and Applied Genetics 83: 194–200.

91

28. Catasti P., Chen X., Mariappan S.V., Bradbury E.M., and Gupta G. 1999. DNA repeats in

the human genome. Genetica 106: 15–36.

29. Centola M., and Carbon J. 1994. Cloning and characterization of centromeric DNA from

Neurospora crassa. Molecular and Cellular Biology 14: 1510–1519.

30. Chakravarty P., and Hiratsuka Y. 2007. Antagonism of two decay fungi, Peniophora

polygonia and Phellinus tremulae associated with Populus tremuloides. European Journal

of Forest Pathology 22(6–7): 354–361.

31. Chalmers K. J., Newton A.C., Waugh R., Wilson J., and Powell W. 1994. Evaluation of

the extent of genetic variation in mahoganies (Meliaceae) using RAPD markers.

Theoretical and Applied Genetics 89: 504–508.

32. Chase M., Kesseli R., and Bawa K. 1996. Microsatellite markers for population and

conservation genetics of tropical trees. American Journal of Botany 83: 51–57.

33. Chen Y., and Roxby R. 1997. Identification of a functional CT-element in the

Phytophthora infestans piypt1 gene promoter. Gene 198: 159–164.

34. Chomczynski P., and Sacchi N. 1987. Single-step method of RNA isolation by acid

guanidinium thiocyanate-phenol-chloroform extraction. Analytical Biochemistry. 162:

156–159.

35. Chomczynski P. 1993. A reagent for the single-step simultaneous isolation of RNA, DNA

and proteins from cell and tissue samples. Biotechniques 15: 532–537.

36. Chomczynski P., and Mackey K. 1995. Short technical reports. Modification of the TRI

reagent procedure for isolation of RNA from polysaccharide- and proteoglycan-rich

sources. Biotechniques 19(6): 942–945.

37. Cox R.A. 1968. In: Grossman L. and Moldave K. (editors). Methods in enzymology, Vol.

12B: Nucleic acids. Academic press, New York, NY.

38. Cuadrado A., and Schwarzacher T. 1998. The chromosomal organization of simple

sequence repeats in wheat and rye genomes. Chromosoma 107: 587–594.

39. Cullingham C.I., James P.M.A., Cooke J.E.K., and Coltman D.W. 2012. Characterizing

the physical and genetic structure of the lodgepole pine x jack pine hybrid zone: mosaic

structure and differential introgression. Evolutionary Applications 5: 879–891.

40. Dawson I.K., Waugh R., Simons A.J., and Powel W. 1997. Simple sequence repeats

provide a direct estimate of pollen-mediated gene dispersal in the tropical tree Gliricidia

sepium. Molecular Ecology 6: 179–183.

41. De Carvalho D., Ingvarsson P.K., Joseph J., Suter L., Sedivy C., Macaya Sanz

D.A.V.I.D., Cottrell J., Heinze B., Schanzer I., and Lexer C. 2010. Admixture facilitates

92

adaptation from standing variation in the European aspen (Populus tremula L.), a

widespread forest tree. Molecular Ecology 19(8): 1638–1650.

42. Deguilloux M.F., Pemonge M.H., Bertel L., Kremer A., and Petit R.J. 2003. Checking the

geographical origin of oak wood: molecular and statistical tools. Molecular Ecology 12:

1629–1636.

43. Dellaporta S.L., Wood J., and Hicks J.B. 1983. A plant DNA minipreparation version II.

Plant Molecular Biology Reporter 1: 19–21.

44. Devey M., Jermstad K.D., Tauer C.G., and Neale D.B. 1991. Inheritance of RFLP loci in a

loblolly pine three-generation pedigree. Theoretical and Applied Genetics 83: 238–242.

45. Devey M., Bell J.C., Smith D.N., Neale D.B., and Moran G.F. 1996. A genetic linkage

map for Pinus radiata based on RFLP, RAPD, and microsatellite markers. Theoretical

and Applied Genetics 92: 673–679.

46. Dickmann D., Isebrands J., Eckenwalder J., and Richardson. 2001. Poplar culture in North

America. NRC Research Press Ottawa, ON, Canada.

47. Dickmann D.I., and Kuzovkina J. 2008. Poplars and willows in the world, with emphasis

on silviculturally important species. FAO, Forestry Department, Working Paper IPC/9-2,

Rome, Italy.

48. Dobzhansky T. 1970. Genetics of the evolutionary process. Columbia University Press,

New York, NY.

49. Dokholyan N.V., Buldyrev S.V., Havlin S., and Stanley H.E. 2000. Distributions of

dimeric tandem repeats in noncoding and coding DNA sequences. Journal of Theoretical

Biology 202: 273–282.

50. Dow B.D., Ashley M.V., and Howe H.F. 1995. Characterization of highly varable

(GA/CT)n microsatellites in the bur oak, Quercus macrocarpa. Theoretical and Applied

Genetics 91: 137–141.

51. Doyle J.J., and Dickson E.E. 1987. Preservation of plant samples for DNA restriction

endonuclease analysis. Taxon 36: 715–722.

52. Doyle J.J., and Doyle J.H. 1987. Isolation of plant DNA from fresh tissue. Focus 12: 13–

15.

53. Dumolin S., Demesure B., and Petit R.J. 1995. Inheritance of chloroplast and

mitochondrial genomes in pedunculate oak investigated with an efficient PCR method.

Theoretical and Applied Genetics 91(8): 1253–1256.

54. Echt C.S., Maymarquardt P., Hseih M., and Zahorchak R. 1996. Characterization of

microsatellite markers in eastern white pine. Genome 39: 1102–1108.

93

55. Eckenwalder J. 1984. Natural intersectional hybridization between North American

species of Populus (Salicaceae) in sections Aigeiros and Tacamahaca. II. Taxonomy.

Canadian Journal of Botany 62: 325–335.

56. Eckenwalder J.E. 1996. Systematics and evolution of Populus. In: Stettler R.F., Bradshaw

H.D., Heilman Jr. P.E. and Hinkley T.M. (editors). Biology of Populus and its

implications for management and conservation. NRC Research Press, National Research

Council Canada, Ottawa, ON.

57. Edwards K., Johnstone C., and Thompson C. 1991. A simple and rapid method for the

preparation of plant genomic DNA for PCR analysis. Nucleic Acids Research 19: 1349.

58. Edwards Y.J., Elgar G., Clark M.S., and Bishop M.J. 1998. The identification and

characterization of microsatellites in the compact genome of the Japanese pufferfish Fugu

rubripes: perspectives in functional and comparative genomic analyses. Journal of

Molecular Biology 278: 843–854.

59. Eichler E.E. 1999. Repetitive conundrums of centromere structure and function. Human

Molecular Genetics 8: 151–155.

60. Ellegren H. 2000. Microsatellite mutations in the germline: implications for evolutionary

inference. Trends in Genetics 16: 551–558.

61. Epplen C., Melmer G., Siedlaczck I., Schwaiger F.W., Mäueler W., and Epplen J.T. 1993.

On the essence of “meaningless” simple repetitive DNA in eukaryote genomes. 39–45. In:

Pena S.D.J., Chakraborty R., Epplen J.T., Jeffreys A.J. (editors). DNA Fingerprinting:

State of the Science Birkh. user Verlag, Basel Switzerland.

62. Evanno G., Regnaut S., and Goudet J. 2005. Detecting the number of clusters of

individuals using the software STRUCTURE: a simulation study. Molecular Ecology,

14(8): 2611–2620.

63. Excoffier L., Smouse P.E., and Quattro J.M. 1992. Analysis of molecular variance inferred

from metric distances among DNA haplotypes: Application to human mitocondrial DNA

restriction sites. Genetics, 131: 479–491.

64. Fabregat I., Koch K.S., Aoki T., Atkinson A.E., Dang H., Amosova O., Fresco J.R.,

Schildkraut C.L., and Leffert H.L. 2001. Functional pleiotropy of an intramolecular

triplex-forming fragment from the 3'-UTR of the rat Pigr gene. Physiological Genomics 5:

53–65.

65. Fang G., Hammar S., and Grumet R. 1992. A quick and inexpensive method for removing

polysaccharides from plant genomic DNA. Biotechniques 13: 52–56.

66. Farrar J.L. 1995. Trees in Canada. Fitzhenry and Whiteside/Canadian Forest Service,

Markham and Ottawa.

94

67. Field D., and Wills C. 1996. Long, polymorphic microsatellite in simple organisms.

Proceedings of Royal Society of London B Biological Sciences 263: 209–215.

68. Field D.L., Ayre D.J., Whelan R.J., and Young A.G. 2011. The importance of pre-mating

barriers and the local demographic context for contemporary mating patterns in hybrid

zones of Eucalyptus aggregata and Eucalyptus rubida. Molecular Ecology 20: 2367–

2379.

69. Fladung M., and Buschbom J. 2009. Identification of single nucleotide polymorphisms in

different Populus species. Trees 23(6): 1199–1212.

70. Floate K. 2004. Extent and patterns of hybridization among the three species of Populus

that constitute the riparian forest of southern Alberta, Canada. Canadian Journal of

Botany 82: 253–264.

71. Fossati T., Patrignani G., Zapelli I., Sabati M., Sala F., and Castiglione S. 2004.

Development of molecular markers to assess the level of introgression of Populus tremula

into P. alba natural populations. Plant breeding 123: 382–385.

72. Fossati T., Zapelli I., Bisoffi S., Micheletti A., Vieto L., Sala F., and Castiglione S. 2005.

Genetic relationships and clonal identity in a collection of commercially relevant poplar

cultivars assessed by AFLP and SSR. Tree Genetics and Genomes 1: 11–19.

73. Fracheboud Y., Luquez V., Björken L., Sjödin A., Tuominen H., and Jansson S. 2009. The

control of autumn senescence in European aspen. Plant Physiology 149: 1982–1991.

74. Fussi B., Lexer C., and Heinze B. 2010. Phylogeography of Populus alba (L.) and

Populus tremula (L.) in Central Europe: secondary contact and hybridization during

recolonization from disconnected refugia. Tree Genetics and Genomes 6(3): 439–450.

75. Fystro I. 1962. Skogbrukets frøforsyning. 299–318. In: Børset O. (editor). Skogbruksboka,

Vol. 2, Skogskjøtsel. Skogforlaget , Oslo, Norway. (In Norwegian).

76. Gallo L.A., and Geburek Th. 1991. Genetics of isozyme variant in Populus tremula, P.

tremuloides and their hybrids. Euphytica 53: 225–233.

77. Gebhardt F., Zanker K.S., and Brandt B. 1999. Modulation of epidermal growth factor

receptor gene transcription by a polymorphic dinucleotide repeat in intron 1. Journal of

Biological Chemistry 274: 13176–13180.

78. Gebhardt F., Burger H., and Brandt B. 2000. Modulation of EGFR gene transcription by a

polymorphic repetitive sequence – a link between genetics and epigenetics. International

Journal of Biological Markers 15: 105–110.

79. Gjerde I., Saetersdal M., and Nilsen T. 2005. Abundance of two threatened woodpecker

species in relation to the proportion of spruce plantations in native pine forests of western

Norway. Biodiversity Conservation 14: 377–393.

95

80. Gomez A., Manzanera J.A., Aguiriano E., Grau J.M., and Bueno M.A. 2003. SSR markers

for monitoring an in vitro core collection of Populus tremula. Silvae genetica 52 (5-6):

224–228.

81. Goodwin D., and Lee S. 1993. Microwave miniprep of total genomic DNA from fungi,

plants, protists and animals for PCR. Biotechniques 15: 441–444.

82. Grattapaglia D., and Sederoff R. 1994. Genetic linkage maps of Eucalyptus grandis and

Eucalyptus urophylla using a pseudo-testcross mapping strategy and RAPD markers.

Genetics 137: 1121–1137.

83. Hall D., Luquez V., Garcia V.M., Onge K.R., Jansson S., and Ingvarsson P.K. 2007

Adaptive population differentiation in phenology across a latitudinal gradient in European

aspen (Populus tremula, L.): a comparison of neutral markers, candidate genes and

phenotypic traits. Evolution 61(12): 2849–2860.

84. Hammer Ø., Harper D.A.T., and Ryan P.D. 2001. PAST: Paleontological Statistics

Software Package for Education and Data Analysis. Palaeontologia Electronica 4(1): 1–9.

85. Hamzeh M., Sawchyn C., Perinet P., and Dayanandan S. 2007. Asymmetrical natural

hybridization between Populus deltoides and P. balsamifera (Salicaceae). Canadian

Journal of Botany 85: 1227–1232.

86. Hancock J.M. 1999. Microsatellites and other simple sequences: genomic context and

mutational mechanisms. 1–9. In: Goldstein D.B., Schlötterer C. (editors). Microsatellites:

Evolution and Applications. Oxford University Press, Oxford.

87. Harding R.M., Boyce A.J., and Clegg J.B. 1992. The evolution of tandemly repetitive

DNA: recombination rules. Genetics 132: 847–859.

88. Hartl D.L., and Clark A.G. 1997. Principles of Population Genetics 3rd Edition,

Sunderland, Massachusetts: Sinauer Associates, Inc.

89. Henaut A., Lisacek F., Nitschke P., Moszer I., and Danchin A. 1998. Global analysis of

genomic texts: the distribution of AGCT tetranucleotides in the Escherichia coli and

Bacillus subtilis genomes predicts translational frameshifting and ribosomal hopping in

several genes. Electrophoresis 19: 515–527.

90. Holmer L., Nitare L., and Stenlid J. 1994. Population structures and decay pattern in

Phellinus tremulae as determined by somatic incompatibility. Canadian Journal of Botany

72: 1391–1396.

91. Houle G. 1999. Mast seeding in Abies balsamea, Acer saccharum and Betula

alleghaniensis in an old growth, cold temperate forest of north-eastern North America.

Journal of Ecology 87: 413–422.

96

92. Ilstedt B., and Gullberg U. 1993. Genetic variation in a 26-year-old hybrid aspen trial in

southern Sweden. Scandinavian Journal of Forest Research 8: 185–192.

93. Ingvarsson P.K. 2005. Nucleotide polymorphism and linkage disequilibrium within and

among natural populations of European aspen (Populus tremula L., Salicaceae). Genetics

169(2): 945–53.

94. Ingvarsson P.K. 2008a. Multilocus patterns of nucleotide polymorphism and the

demographic history of Populus tremula. Genetics 180: 329–340.

95. Ingvarsson P.K. 2008b. Nucleotide polymorphism and phenotypic associations within and

around phytochrome B2 locus in European aspen (Populus tremula, Salicaceae). Genetics

178: 2217–2226.

96. Isabel N., Beaulieu J., and Bousquet J. 1995. Complete congruence between gene diversity

estimates derived from genotypic data at enzyme and RAPD loci in black spruce.

Proceedings of the National Academy of Sciences, USA 92(14): 6369–6373.

97. Isabel N., Lamothe M., and Thompson S. L. 2013. A second-generation diagnostic single

nucleotide polymorphism (SNP) – based assay, optimized to distinguish among eight

poplar (Populus L.) species and their early hybrids. Tree Genetics and Genomes 9: 621–

626.

98. Isagi Y., and Suhandono S. 1997. PCR primers amplifying microsatellite loci of Quercus

myrsinifolia Blume and their conservation between oaks. Molecular Ecology 6: 897–899.

99. Ivanov I., Alexandrova R., Dragulev B., Sarafova A., and AbouHaidar A.G. 1992. Effect

of tandemly repeated AGG triplets on the translation of CAT-mRNA in E. coli. FEBS

Letters 307: 173–176.

100. Jameikis M. 1933: Paskirt kirsti 1929-1934 m. s mat pasiskirstymas veisl mis ir

r šimis. M s girios 6: 478–480. (In Lithuanian).

101. Jasalavich C.A., Ostrofsky A., and Jellison J. 2000. Detection and identification of decay

fungi in spruce wood by restriction fragment length polymorphism analysis at amplified

genes encoding rRNA. Applied and Environmental Microbiology 66 (11): 4725–4734.

102. Jeanpierre M. 1987. A rapid method for the purification of DNA from blood. Nucleic

Acids Research 15: 9611.

103. Jobes D.V., Hurley D.L., and Thien L.B. 1995. Plant DNA isolation: method to efficiently

remove polyphenolics, polysaccharides and RNA. Taxon 44: 379–386.

104. Johnsson H. 1942. Generativ och vegetative förökning av Populus tremula. Svensk

Botanisk Tidskrift 36: 177–199. (In Swedish).

97

105. Joseph J.A., and Lexer C. 2008. A set of novel DNA polymorphisms within candidate

genes potentially involved in ecological divergence between Populus alba and P. tremula,

two hybridizing European forest trees. Molecular Ecology Resources 8: 188–192.

106. Kairi kštis L. 1962. Drebulynai. 231–254. In: Lietuvos TSR miškai. Vilnius. (In

Lithuanian).

107. Kairi kštis L. 1963. Medži augimas vegetacijos metu. Lietuvos mišk kio instituto

mokslini tyrim darbai, VII t. Kaunas: 3–38. (In Lithuanian).

108. Kalela A. 1945. Metsät ja metsien hoito -metsänhoidon alkeita, WSOY. (In Finnish).

109. Katterman F.R.H., and Shattuck V.I. 1983. An effective method of DNA isolation from

the mature leaves of Gossypium species that contain large amounts of phenolic terpenoids

and tannins. Preparative Biochemistry and Biotechnology 13: 347–359.

110. Kazan K., Manners J.M., and Cameron D.F. 1993. Inheritance of random amplified

polymorphic DNA markers in an interspecific cross in the genus Stylosanthes. Genome

36: 50–56.

111. Keim P., Paige N., Whitham G., and Lark K.G. 1989. Genetic analysis of an interspecific

hybrid swarm of Populus: occurrence of unidirectional introgression. Genetics 123: 557–

565.

112. Kim C.S., Lee C.H., Shin J.S., Chung Y.S., and Hyung N.I. 1997. A simple and rapid

method for isolation of high quality genomic DNA from fruit trees and conifers using

PVP. Nucleic Acids Research 25: 1085–1086.

113. Kimura M., and Crow J.F. 1964. The number of alleles that can be maintained in a finite

population. Genetics 49: 725–738.

114. Kleinschmit J. 2000. Tree breeding and plantations in Europe: A regional situation report.

IUFRO WP 2.02.00 and 2.08.00 meeting in Kuala Lumpur, Malaysia.

115. Kloosterman A.D., Budowle B., and Daselaar P. 1993. PCR-amplification and detection of

the human D1S8 VNTR locus. International Journal of Legal Medicine 105: 257–264.

116. König A. 2005. Provenance research: evaluating the spatial pattern of genetic variation.

275–333. In: Geburek T. and Turok J. (editors). Conservation and Management of Forest

Genetic Resources in Europe. Arbora Publishers, Zvolen, Germany.

117. Kouki J., Arnold K., and Martikainen P. 2004. Long-term persistence of aspen – a host for

many threatened species – is endangered in old-growth conservation areas in Finland.

Journal for Nature Conservation 12: 41–52.

118. Latva-Karjanmaa T., Suvanto L., Leinonen K., and Rita H. 2003. Emergence and survival

of Populus tremula seedlings under varying moisture conditions. Canadian Journal of

Forest Research 33: 2081–2088.

98

119. LeBoldus J.M., Isabel N., Floate K.D., Blenis P., and Thomas B.R. 2013. Testing the

‘hybrid susceptibility’ and ‘phenological sink’ hypotheses using the P. balsamifera – P.

deltoides hybrid zone and Septoria leaf spot (Septoria musiva). PLoS ONE. 18(12):

e84437.

120. Leibenguth F., and Shoghi F. 1998. Analysis or random amplified polymorphic DNA

markers in three conifer species. Silvae Genetica 47(2-3): 120–126.

121. Lepais O., and Gerber S. 2011. Reproductive patterns shape introgression dynamics and

species succession within the European white oak species complex. Evolution 65: 156–

170.

122. Lewontin R.C. 1991. Twenty-five years ago in GENETICS. Electrophoresis in the

development of evolutionary genetics: milestone or millstone? Genetics 128: 657–662.

123. Lexer C., Fay M.F., Joseph J.A., Nica M.S., and Heinze B. 2005. Barrier to gene flow

between two ecologically divergent Populus species, P. alba (white poplar) and P.

tremula (European aspen): the role of ecology and life history in gene introgression.

Molecular Ecology 14: 1045–1057.

124. Lexer C., Buerkle C.A., Joseph J.A., Heinze B., and Fay M.F. 2007. Admixture in

European Populus hybrid zones makes feasible the mapping of loci that contribute to

reproductive isolation and trait differences. Heredity 98(2): 74–84.

125. Lexer C., Joseph J.A., van Loo M., Barbara T., Heinze B., Bartha D., Castiglione S., Fay

M.F., and Buerkle C.A. 2010. Genomic admixture analysis in European Populus spp.

reveals unexpected patterns of reproductive isolation and matting. Genetics 186(4): 699–

712.

126. Li Y.C., Fahima T., Peng J.H., Röder M.S., Kirzhner V.M., Beiles A., Korol A.B., and

Nevo E. 2000a. Edaphic microsatellite DNA divergence in wild emmer wheat, Triticum

dicoccoides, at a microsite: Tabigha, Israel. Theoretical and Applied Genetics 101: 1029–

1038.

127. Li Y.C., Röder M.S., Fahima T., Kirzhner V.M., Beiles A., Korol A.B., and Nevo E.

2000b. Natural selection causing microsatellite divergence in wild emmer wheat at the

ecologically variable microsite at Ammiad, Israel. Theoretical and Applied Genetics 100:

985–999.

128. Li Y.C., Fahima T., Korol A.B., Peng J., Röder M.S., Kirzhner V., Beiles A., and Nevo E.

2000c. Microsatellite diversity correlated with ecological-edaphic and genetic factors in

three microsites of wild emmer wheat in North Israel. Molecular Biology and Evolution

17: 851–862.

99

129. Li Y.C., Korol A.B., Fahima T., Beiles A., and Nevo E. 2002a. Microsatellites: genomic

distribution, putative functions and mutational mechanisms: a review. Molecular ecology

11(12): 2453–2465.

130. Li Y.C., Röder M.S., Fahima T., Kirzhner V.M., Beiles A., Korol A.B., and Nevo E.

2002b. Climatic effect on microsatellite diversity in wild emmer wheat, Triticum

dicoccoides, at Yehudiyya microsite, Israel. Heredity 89: 127–132.

131. Liesebach H., Schenck V., and Ewald E. 2009. Clonal fingerprinting in the genus Populus

L. by nuclear microsatellite loci regarding difference between sections, species and

hybrids. Tree Genetics and Genomes 6: 259–269

132. Lin J.J., and Kuo J. 1998. A new reagent for simple isolation of plant genomic DNA.

Focus 20: 46–48.

133. Lindtke D., Buerkle C.A., Barbará T., Heinze B., Castiglione S., Bartha D., and Lexer C.

2012. Recombinant hybrids retain heterozygosity at many loci: new insights into the

genomics of reproductive isolation in Populus. Molecular Ecology 21(20): 5042–5058.

134. Lindtke D., Gompert Z., Lexer C., and Buerkle C.A. 2014. Unexpected ancestry of

Populus seedlings from a hybrid zone implies a large role for postzygotic selection in the

maintenance of species. Molecular ecology 23(17): 4316–4330.

135. Linkosalo T. 1999. Regularities and patterns in the spring phenology of some boreal trees.

Silva Fennica 33(4): 237–245.

136. Liu Z., and Furnier G.R. 1993. Comparison of allozyme, RFLP, and RAPD markers for

revealing genetic variation within and between trembling aspen and bigtooth aspen.

Theoretical and Applied Genetics 87: 97–105.

137. Liu Z., Tan G., Li P., and Dunham R.A. 1999. Transcribed dinucleotide microsatellites

and their associated genes from channel catfish Ictalurus punctatus. Biochemical and

Biophysical Research Communication 259: 190–194.

138. Lodhi M.A., YE G.N., Weeden N.F., and Reisch B.I. 1994. A simple and efficient method

for DNA extraction from grapevine cultivars and Vitis species. Plant Molecular Biology

Reporter 12: 3–13.

139. Lopez-de-Heredia U., Sierra-de-Grado R., Cristobal M.D., Martinez-Zurimendi P., Pando

V., and Martin M.T. 2004. A comparison of isozyme and morphological markers to assess

the within population variation in small populations of European aspen (Populus tremula

L.) in Spain. Silvae Genetica 53: 227–233.

140. Lu M.Z., Szmidt A.E., and Wang X.R. 1995. Inheritance of RAPD fragments in haploid

and diploid tissues of Pinus sylvestris (L.). Heredity 74: 282–589.

100

141. Luquez V., Hall D., Albrectsen B.R., Karlsson J., Ingvarsson P., and Jansson S. 2008.

Natural phenological variation in aspen (Populus tremula): the SwAsp collection. Tree

Genetics and Genomes 4: 279–292.

142. Lynch M., and Milligan B.G. 1994. Analysis of population genetic structure with RAPD

markers. Molecular Ecology 3: 91–99.

143. Ma X.F., Hall D., Onge K.R.S., Jansson S., and Ingvarsson P.K. 2010. Genetic

differentiation, clinal variation and phenotypic associations with growth cessation across

the Populus tremula photoperiodic pathway. Genetics 186(3): 1033–1044.

144. Macarthur R.H. 1957. On the relative abundance of bird species. Proceedings of the

National Academy of Sciences, USA 43(3): 293–295.

145. Macaya-Sanz D., Suter L., Joseph J., Barbara T., Alba N., Gonzalez-Martinez S.C.,

Widmer A., and Lexer C. 2011. Genetic analysis of post-mating reproductive barriers in

hybridizing European Populus species. Heredity 107: 478–486.

146. Mallett K.I., and Myrholm C.L. 1995. The Mating System of Phellinus tremulae.

Mycologia 87(5): 597–603.

147. Mallet J. 2005. Hybridization as an invasion of the genome. Trends in Ecology and

Evolution 20: 229–237.

148. Manion P.D. 1991. Tree Disease Concepts, 2nd edition. Prentice-Hall, Englewood Cliffs,

New Jersey.

149. Mannerlöf M., and Tenning P. 1997. Screening of transgenic plants by multiplex PCR.

Plant Molecular Biology Reporter 15: 38–45.

150. Marcotte E. M., Pellegrini M., Yeates T.O., and Eisenberg D. 1999. A census of protein

repeats. Journal of Molecular Biology 293: 151–160.

151. Markussen T., Pakull B., and Fladung M. 2007. Positioning of sex-correlated markers for

Populus in a AFLP- and SSR- marker based genetic map of Populus tremula x

tremuloides. Silvae Genetica 56: 3–4.

152. Martin-Farmer J., and Janssen G.R. 1999. A downstream CA repeat sequence increases

translation from leadered and unleadered mRNA in Escherichia coli. Molecular

Micobiology 31: 1025–1038.

153. Martinsen G.D., Whitham T.G., Turek R.J., and Keim P. 2001. Hybrid populations

selectively filter gene introgression between species. Evolution 55:1325–1335.

154. Mayr E. 1942. Systematics and the origin of species. Columbia University. Press, New

York, NY.

101

155. Meirmans P.G., Lamothe M., Perinet P., and Isabel N. 2007. Species-specific single

nucleotide polymorphism markers for detecting hybridization and introgression in poplar.

Canadian Journal of Botany 85: 1082–1091.

156. Meirmans P.G., Lamothe M., Gros-Louis M.C., Khasa D., Perinet P., Bousquet J., and

Isabel N. 2010. Complex patterns of hybridization between exotic and native North

American poplar species. American Journal of Botany 97: 1688–1697.

157. Mejeris L. 1936. Kaip išnaikinti drebuli atžalas. M s girios 8-9: 423–424. (In

Lithuanian).

158. Meloni R., Albanese V., Ravassard P., Treilhou F., and Mallet J. 1998. A tetranucleotide

polymorphic microsatellite, located in the first intron of the tyrosine hydroxylase gene,

acts as a transcription regulatory element in vitro. Human Molecular Genetics 7: 423–428.

159. Mengoni A., and Bazzicalupo M. 2002. The statistical treatment of data and the analysis

of molecular variance (AMOVA) in molecular microbial ecology. Annals of microbiology

52: 95-101.

160. Metzgar D., Bytof J., and Wills C. 2000. Selection against frameshift mutations limits

microsatellite expansion in coding DNA. Genome Research 10: 72–80.

161. Mikalaikevi ius V.M. 1958a. Kai kurie drebulin s pinties (Phelinus tremulae Bond et

Boriss.) biologijos klausimai. Lietuvos mišk kio instituto mokslinio tyrimo darbai, 3t.:

187-203. (In Lithuanian).

162. Mikalaikevi ius V.M. 1958b. Centrinis drebul s puvinys. M s girios 9: 6-10. (In

Lithuanian).

163. Milligan B.G. 1998. Total DNA isolation. 29–60. In: Hoelzel A.R. (editor). Molecular

genetic analysis of populations. Oxford University Press, Oxford.

164. Mitchell-Olds T., and Rutledge J.J. 1986. Quantitative genetics in natural plant

populations: a review of the theory. The American Naturalist 127: 379–402.

165. Mitton J.B. 1994. Molecular approaches to population biology. Annual review of Ecology

and Systematics 25: 45–69.

166. Morgante M., and Olivieri A.M. 1993. PCR-amplified microsatellites as markers in plant

genetics. The Plant Journal 3: 175–182.

167. Morgante M., Hanafey M., and Powell W. 2002. Microsatellites are preferentially

associate with nonrepetitive DNA in plant genomes. Nature Genetics 30: 194–200.

168. Mosseler A., Egger K.N., and Hughes G.A. 1992. Low levels of genetic diversity in red

pine confirmed by random amplified polymorphic DNA markers. Canadian Journal of

Forest Research 22: 1322–1327.

102

169. Mullis K.B., and Faloona F.A. 1987. Specific synthesis of DNA in vitro via a polymerase

catalyzed chain reaction. Methods in Enzymology 155: 335–350.

170. Murkait R. 1974. Drebul s (Populus tremula L.) formos, biologija, kryžminimas ir

veisimo s klomis perspektyvos Lietuvoje. Ph.D Dissertation. Kaunas, Girionys. (In

Lithuanian).

171. Murphy T.D., and Karpen G.H. 1995. Localization of centromere function in a Drosophila

minichromosome. Cell 82: 599–609.

172. Murray M.G., and Thompson W.F. 1980. Rapid isolation of high molecular weight plant

DNA. Nucleic Acids Research 8: 4321–4325.

173. Myking T., Bøhler F., Austrheim G., and Solberg E.J. 2011. Life history strategies of

aspen (Populus tremula L.) and browsing effects: a literature review. Forestry 84(1): 61–

71.

174. Nadir E., Hargalit H., Gallily T., and Ben-Sasson S.A. 1996. Microsatellite spreading in

the human genome: Evolutionary mechanisms and structural implications. Proceedings of

the National Academy of Sciences USA 93: 6470–6475.

175. Narayanan S. 1991. Applications of restriction fragment length polymorphism. Annals of

Clinical and Laboratory Science 21(4): 291–296.

176. Nei M. 1972. Genetic distance between populations. American Naturalist 106: 283–392.

177. Nei M. 1978. Estimation of average heterozygosity and genetic distance from a small

number of individuals. Genetics 89: 583–590.

178. Nelson C.D., Kubisiak T.L., Stine M., and Nance W.L. 1994. A genetic linkage map of

longleaf pine (Pinus palustris Mill.) based on random amplified polymorphic DNAs.

Journal of Heredity 85: 433–439.

179. Nesbitt K.A., Potts B.M., Vaillancourt R.E., West A.K., and Reid J.B. 1995. Partitioning

and distribution of RAPD variation in a forest tree species, Eucalyptus globulus

(Myratceae). Heredity 74: 628–637.

180. Newton P.F. 2003. Systematic review of yield responses of four North American conifers

to forest tree improvement practices. Forest Ecology and Management172: 29–51.

181. Niemelä T. 1974. On Fennoscandian polypores. III. Phellinus tremulae (Bond.) Bond. &

Borisov. Annales Botanici Fennici 11: 202–215.

182. OECD 2001. Environmental Health and Safety Publications. Series on Harmonization of

Regulatory Oversight in Biotechnology No. 16. Consensus Document on the Biology of

Populus L. (Poplars).

183. Onge K.R.S. 2006. Natural variation in Populus tremula flowering time gene PtCO2B.

Ph.D. dissertation, University of Umea, Umea, Sweden.

103

184. Ostrowska E., Muralitharan M., Chandler S., Volker P., Hetherington S., and Dunshea F.

1998. Technical review: optimizing conditions for DNA isolation from Pinus radiata. In

Vitro Cellular and Deviation Biology - Plant 34: 108–111.

185. Ozolin ius R. 2003. Populus gentis. In: Navasaitis M., Ozolin ius R., Smaliukas D.,

Balevi ien J. Lietuvos dendroflora. Kaunas. (In Lithuanian).

186. Pakull B., Groppe K., Meyer M., Markussen T., and Fladung M. 2009. Genetic linkage

mapping in aspen (Populus tremula L. and Populus tremuloides Michx.). Tree Genetics

and Genomes 5: 505–515.

187. Pakull B., Groppe K., Mecucci F., Gaudet M., Sabatti M., and Fladung M. 2011. Genetic

mapping of linkage group XIX and identification of sex linked SSR markers in a Populus

tremula x Populus tremuloides cross. Canadian Journal of Forest Research 41: 245–253.

188. Pakull B., Kersten B., Lüneburg J., and Fladung M. 2015. A simple PCR based marker to

determine sex in aspen. Plant Biology 17(1): 256–261.

189. Parker P.G., Show A.A., Schug M.D., Boston G.C., and Fuerst P.A. 1998. What

molecules can tell us about populations: choosing and using a molecular marker. Ecology

79: 361–382.

190. Peakall R., and Smouse P.E. 2006. GenAlEx 6: genetic analysis in Excel. Population

genetic software for teaching and research. Molecular Ecology Notes 6: 288–295.

191. Peakall R., and Smouse P.E. 2012. GenAlEx 6.5: genetic analysis in Excel. Population

genetic software for teaching and research – an update. Bioinformatics 28: 2537–2539.

192. Pérez T., Albornoz J., and Domínguez A. 1998. An evaluation of RAPD fragment

reproducibility and nature. Molecular Ecology 7: 1347-1357.

193. Perron M., and J. Bousquet. 1997. Natural hybridization between black spruce and red

spruce. Molecular Ecology 6:725–734.

194. Petit R., Bodenes C., Ducousso A., Roussel G., and Kremer A. 2003a. Hybridization as a

mechanism of invasion in oaks. New Phytologist 161:151–164.

195. Petit R.J., Aguinagalde I., de Beaulieu J.L., Bittkau C., Brewer S., Cheddadi R., Ennos R.,

Fineschi S., Grivet D., Lascoux M., Mohanty A., Müller-Starck G., Demesure-Musch B.,

Palmé A., Martín J.P., Rendell S., and Vendramin G.G. 2003b. Glacial refugia: hotspots

but not melting pots of genetic diversity. Science 300(5625): 1563–1565.

196. Petit R.J., and Hampe A. 2006. Some evolutionary consequences of being a tree. Annual

Review of Ecology Evolution and Systematics 37: 187–214.

197. Pfeiffer A., Olivieri A.M., and Morgante M. 1997. Identification and characterization of

microsatellites in Norway spruce (Picea abies K.). Genome 40: 411–419.

104

198. Pivorien O. 2008. Paprast j pasikartojan i sek intarp (ISSR) identifikavimas ir

charakterizavimas daugiamet s svidr s genome. Ph.D. dissertation, Lithuanian Institute of

Agriculture, Akademija. (In Lithuanian).

199. Pli ra A. 1993. Drebul s (Populus tremula L.) populiacij genetin s-biologin s ypatyb s,

hibridizacija ir selekcijos galimyb s. Ph.D. dissertation, Lithuanian Forest Research

Institute, Kaunas. (In Lithuanian).

200. Pollastro S., Abbatecola A., Dongiovanni C., and Faretra F. 2000. Usage of molecular

markers (PCR-RAPD) for studying genetic variability in Phellinus (Fomitiporia) sp.

Phytopathologia Mediterranea 39: 107–111.

201. Powell W., Morgante M., McDevitt R., Vendramin G., and Rafalski J. 1996. Polymorphic

simple sequence regions in chloroplast genomes: applications to the population genetics of

pines. Proceedings of the National Academy of Science of the USA 99: 7759–7763.

202. Pritchard J. K., Stephens M., and Donnelly P. 2000. Inference of population structure

using multilocus genotype data. Genetics 155(2): 945–959.

203. Puissant C., and Houdebine L.M. 1990. An improvement of the single-step method of

RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction.

Biotechniques 8(2): 148–149.

204. Punt P.J., Dingemanse M.A., Kuyvenhoven A., Soede R.D., Pouwels P.H., and van den

Hondel C.A. 1990. Functional elements in the promoter region of the Aspergillus nidulans

gpdA gene encoding glyceraldehyde-3-phosphate dehydrogenase. Gene 93: 101–109.

205. Rabouam C., Comes A.M., Bretagnolle V., Humbert J.F., Periquet G., and Biqot Y. 1999.

Features of DNA fragments obtained by random amplified polymorphic DNA (RAPD)

assays. Molecular Ecology 8: 493-503.

206. Rachmayanti Y., Leinemann L., Gailing O., and Finkeldey R. 2006. Extraction,

amplification and characterization of wood DNA from Dipterocarpaceae. Plant Molecular

Biology Reporter 24: 45–55.

207. Rachmayanti Y., Leinemann L., Gailing O., and Finkeldey R. 2009. DNA from processed

and unprocessed wood: factors influencing the isolation success. Forensic Science

International: Genetics 3: 185–192.

208. Rauktys J. 1935. Didelis drebul s puvimas. M s girios 10: 504–505. (In Lithuanian).

209. Rauktys J. 1938. Populus tremula L. Drebul . Apuš . Dendrologija. Spindulys, Kaunas.

(In Lithuanian).

210. Reim P. 1929. Die Vermehrungsbiologie der Aspe auf Grundlage des in Estland und

Finnland gesammelten Untersuchungsmaterials. (The regeneration biology of aspen).

University of Tarto: 60. (In German).

105

211. Rhymer J. M., and D. Simberloff. 1996. Extinction by hybridization and introgression.

Annual Review of Ecology, Evolution and Systematics 27: 83–109.

212. Rieseberg L. 1997. Hybrid origins of plant species. Annual Review of Ecology, Evolution

and Systematics 28: 359–389.

213. Rieseberg L.H., Raymond O., Rosenthal D.M., Livingstone Z.L.K., Nakazato T., Durphy

J.L., Schwarzbach A.E., Donovan L.A., and Lexer C. 2003. Major ecological transitions in

annual sunflowers facilitated by hybridization. Science 301: 1211–1216.

214. Röder M.S., Korzun V., Wendehake K., Plaschke J., Tixier M.H., Philippe Leroy Ph., and

Ganal M.W. 1998. A microsatellite map of wheat. Genetics 149: 2007–2023.

215. Roe A.D., MacQuarrie C.J.K., Gros-Louis M.C., Simpson J.D., Lamarche J., Beardmore

T., Thompson S.L., Tanguay P., and Isabel N. 2014. Fitness dynamics within a poplar

hybrid zone: I. Prezygotic and postzygotic barriers impacting a native poplar hybrid stand.

Ecology and Evolution 4(9): 1629–1647.

216. Rogers S.O., and Bendich A.J. 1985. Extraction of DNA from milligram amounts of fresh,

herbarium and mummified plant tissues. Plant Molecular Biology 5: 69–76.

217. Rogers S.O., and Bendich A.J. 1988. Extraction of DNA from plant tissues. 1–10. In:

Gelvin S.B. and Schilperoort R.A. (editors), Plant Molecular Biology Manual, Kluwer

Academic Publishers, Boston.

218. Rytter L., and Stener L.G. 2005. Productivity and thinning effects in hybrid aspen

(Populus tremula L. × P. tremuloides Michx.) stands in southern Sweden. Forestry 78:

285-295.

219. Saghai-Maroof M.A., Soliman K.M., Jorgensen R.A., and Allard R.W. 1984. Ribosomal

DNA spacer-length polymorphisms in barley: Mendelian inheritance, chromosomal

location, and population dynamics. Proceedings of the National Academy of Sciences of

the USA 81(24): 8014–8018.

220. Salvini D., Fineschi S., Malvolti M.E., Taurchini D., Anzidei M., and Vendramin G.G.

2001. Low genetic differentiation among Italian populations of Populus tremula L.

(Salicaceae) estimated using chloroplast PCR-RFLP and microsatellite markers. Forest

genetics 8(1): 81–87.

221. Sanchez N., Grau J.M., Alba N., Manzanera J.A., and De Los Angeles Bueno M. 2000.

Genetic characterisation of Populus tremula regions of origin in Spain using RAPD

fingerprints. Silvae Genetica 49: 66–71.

222. Sandaltzopoulos R., Mitchelmore C., Bonte E., Wall G., and Becker P.B. 1995. Dual

regulation of the Drosophila hsp26 promoter in vitro. Nucleic Acids Research 23: 2479–

2487.

106

223. Sandberg G., and Schalling M. 1997. Effect of in vitro promoter methylation and CGG

repeat expansion on FMR-1 expression. Nucleic Acids Research 14: 2883–2887.

224. Schierenbeck K.A., Skupski M., Lieberman D., and Lieberman M. 1997. Population

structure and genetic diversity in four tropical tree species in Costa Rica. Molecular

Ecology 6: 137–144.

225. Schierenbeck K.A., and Ellstrand N.C. 2009. Hybridization and the evolution of

invasiveness in plants and other organisms. Biological Invasions 11: 1093–1105.

226. Schlötterer C. 2000. Evolutionary dynamics of microsatellite DNA. Chromosoma 109:

365–371.

227. Schmidt T., and Heslop-Harrison J.S. 1996. The physical and genomic organization of

microsatellites in sugar beet. Proceedings of the National Academy of Sciences USA 93:

8761–8765.

228. Schroeder H., and Fladung M. 2010. SSR and SNP markers for the identification of

clones, hybrids and species within the genus Populus. Silvae Genetica 59(6): 257–263.

229. Schroeder H., Hoeltken A.M., and Fladung M. 2012. Differentiation of Populus species

using chloroplast single nucleotide polymorphism (SNP) markers – essential for

comprehensible and reliable poplar breeding. Plant Biology 14(2): 374–381.

230. Schweitzer J.A., Martinsen G.D., and Whitham T.G. 2002. Cottonwood hybrids gain

fitness traits of both parents: a mechanism for their long-term persistence? American

Journal of Botany 89: 981–990.

231. Sederoff R.R. 1987. Molecular mechanisms of mitochondrial genome evolution in higher

plants. The American Naturalist 130: S30–S45.

232. Seehausen O. 2006. Conservation: Losing biodiversity by reverse speciation. Current

Biology 16: 333–337.

233. Shaw J., Lickey E.B., Beck J.T., Farmer S.B., Liu W., Miller J., Siripun K.C., Winder

C.T., Schilling E.E., and Small R.L. 2005. The tortoise and the hare II: relative utility of

21 noncoding chloroplast DNA sequences for phylogenetic analysis. American Journal of

Botany 92(1): 142–166.

234. Shibata M., Tanaka H., Iida S., Abe S., Masaki T., Niiyama K., and Nakashizuka T. 2002.

Synchronized annual seed production by 16 principal tree species in a temperate

deciduous forests, Japan. Ecology 83: 1727–1742.

235. Smith D.N., and Devey M.E. 1994. Occurrence and inheritance of microsatellites in Pinus

radiata. Genome 37: 977–83.

107

236. Smulders M., Van der Schoot J., Arens P., and Vosman B. 2001. Trinucleotide repeat

microsatellite markers for black poplar (Populus nigra L.). Molecular Ecology Notes 1:

188–190.

237. Sneath P.H.A., and Sokal R.R. 1973. Numerical Taxonomy, Freeman, San Francisko.

238. Steinkellner H., Fluch S., Turetschek E., Lexer C., Streiff R., Kremer A., Burg K., and

Gloss J. 1997. Identification and characterization of (GA/CT)n – microsatellite loci from

Qurecus petraea. Plant Molecular Biology 33: 1093–1096.

239. Sterck L., Rombauts S., Jansson S., Sterky F., Rouzé P., and Van de Peer Y. 2005. EST

data suggest that poplar is an ancient polyploidy. New Phytologist 167: 165–170.

240. Stettler R., Bradshaw H. Jr., Heilman P., and Hinckley T. 1996. Biology of Populus and

its implications for management and conservation. NRC, Ottawa, ON, Canada.

241. Stewart N.C., and Via L.E. 1993. A rapid CTAB DNA isolation technique useful for

RAPD fingerprinting and other PCR applications. Biotechniques 14: 748–749.

242. Stölting K.N., Nipper R., Lindtke D., Caseys C., Waeber S., Castiglione S., and Lexer C.

2013. Genome scan for single nucleotide polymorphisms reveals patterns of divergence

and gene flow between ecologically divergent species. Molecular Ecology. 22(3): 842–

855.

243. Strømme C.B., Julkunen-Tiitto R., Krishna U., Lavola A., Olsen J.E., and Nybakken L.

2014. UV-B and temperature enhancement affect spring and autumn phenology in

Populus tremula. Plant, Cell and Environment 1–11.

244. Sunhede S., and Vasiliauskas R. 2002. Ecology and decay pattern of Phellinus robustus in

old-growth Quercus robur. Karstenia 42: 1–11.

245. Suvanto L.I., and Latva-Karjanmaa T.B. 2005. Clone identification and clonal structure of

the European aspen (Populus tremula). Molecular Ecology 14(9): 2851–2860.

246. Suzuki R., and Shimodaira H. 2004. An application of multiscale bootstrap resampling to

hierarchical clustering of microarray data: How accurate are these clusters? The Fifteenth

International Conference on Genome Informatics 2004.

247. Sylvén N. 1940. Lång- och kortdagstyper av de svenska skogsträden. Sv. Papper. 18: 332–

342. (In Swedish).

248. Tachida H., and Iizuka M. 1992. Persistence of repeated sequences that evolve by

replication slippage. Genetics 131: 471–478.

249. Talbot P., Thompson S.L., Schroeder W., and Isabel N. 2011. An efficient single

nucleotide polymorphism assay to diagnose the genomic identity of poplar species and

hybrids on the Canadian prairies. Canadian Journal of Forest Research 41: 1102–1111.

108

250. Talbot P., Schroeder W.R., Bousquet J., and Isabel N. 2012. When exotic poplars and

native Populus balsamifera L. meet on the Canadian Prairies: spontaneous hybridization

and establishment of interspecific hybrids. Forest Ecology and Management 285: 142–

152.

251. Tautz D., and Renz M. 1984. Simple sequences are ubiquitous repetitive components of

eukaryotic genomes. Nucleic Acids Research 12: 4127–4138.

252. Thompson S.L., Lamothe M., Meirmans P.G., Perinet P., and Isabel N. 2010. Repeated

unidirectional introgression towards Populus balsamifera in contact zones of exotic and

native poplars. Molecular Ecology 19:132–145.

253. Tibbits J.F.G., McManus L.J., Spokevicius A.V., and Bossinger G. 2006. A rapid method

for tissue collection and high-throughput isolation of genomic DNA from mature trees.

Plant Molecular Biology Reporter 24: 81–91.

254. Tiffin P., Olson M.S., and Moyle L.C. 2001. Asymmetrical crossing barriers in

angiosperms. Proceedings of the National Academy B: Biological Sciences 268: 861–867.

255. Timchenko N.A., Welm A.L., Lu X., and Timchenko L.T. 1999. CUG repeat binding

protein (CUGBP1) interacts with the 5 region of C/EBP mRNA and regulates translation

of C/EBP isoforms. Nucleic Acids Research 27: 4517–4525.

256. Tulsieram L.K., Glaubitz J.C., Kiss G., and Carlson J.E. 1992. Single-tree genetic linkage

mapping in conifers using haploid DNA from megagametophytes. Biotechnology 10: 686–

690.

257. Tuskan G.A., Gunter L.E., Yang Z.K., Yin T.M., Sewell M.M., and DiFazio S.P. 2004.

Characterization of microsatellites revealed by genomic sequencing of Populus

trichocarpa. Canadian Journal of Forest Research 34: 85–93.

258. Tuskan G.A., DiFazio S., Jansson S., Bohlmann J., Grigoriev I., Hellsten U. et al. 2006.

The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science 313:

1596–1604.

259. Tuskan G.A., DiFazio S., Faivre-Rampant P., Gaudet M., Harfouche A., Jorge V., Labbe

J.L., Ranjan P., Sabatti M., Slavov G., Street N., Tschaplinski T.J., and Yin T. 2012. The

obscure events contributing to the evolution of an incipient sex chromosome in Populus: a

retrospective working hypothesis. Tree Genetics and Genomes 8: 559–571.

260. Unneberg P., Strömberg M., Lundeberg J., Jansson S., and Sterky F. 2005. Analysis of

70,000 EST sequences to study divergence between two closely related Populus species.

Tree Genetics and Genomes 1: 109–115.

109

261. Vai ys M. 2006. Lietuvos miško dirvožemio tyrimas, dirvožemi ir miško augavie i

klasifikavimas 7–38. In: Ozolin ius R., Vai ys M., Karazija S., Kuliešis A., Rutkauskas

A. (editors). Miško augavie i tipai. Kaunas. (In Lithuanian).

262. Vanden Broeck A., Villar M., Van Bockstaele E., and Van Slycken J. 2005. Natural

hybridization between cultivated poplars and their wild relatives: evidence and

consequences for native poplar populations. Annals of Forest Science 62: 601–613.

263. Vehmas M., Kouki J., and Eerikainen K. 2009 Long-term spatiotemporal dynamics and

historical continuity of European aspen (Populus tremula L.) stands in the Koli National

Park, eastern Finland. Forestry 82: 135–148.

264. Vendramin G.G., Lelli L., Rossi P., and M. Morgante. 1996. A set of primers for the

amplification of 20 chloroplast microsatellites in Pinaceae. Molecular ecology 5: 595–

598.

265. Vil inskas J. 1931. Medienos prieauglis Lietuvos valstybiniuose miškuose. M s girios

5/13: 315-328. (In Lithuanian).

266. Wagner D.B., Furnier G.R., Saghai-Maroof M.A., Williams S.M., Dancik B.P., and Allard

R.W. 1987. Chloroplast DNA polymorphisms in lodgepole and jack pines and their

hybrids. Proceedings of the National Academy of Sciences USA 84: 2097–2100.

267. Wall R.E. 1971. Variation in decay in aspen stands as affected by their clonal growth

pattern. Canadian Journal of Forestry Research 1(3): 141–146.

268. Wang H., Qi M., and Cutler A.J. 1993. A simple method of preparing plant samples for

PCR. Nucleic Acids Research 21: 4153–4154.

269. Wang Z., Weber J.L., Zhong G., and Tanksley S.D. 1994. Survey of plant short tandem

DNA repeats. Theoretical and Applied Genetics 88: 1–6.

270. Wang X.R., and Szmidt A.E. 2001. Molecular markers in population genetics of forest

trees. Scandinavian Journal of Forest Research: 16(3): 199–220.

271. Weber J.L., and May P.E. 1989. Abundant class of human DNA polymorphism which can

be typed using the polymerase chain reaction. American Journal of Human Genetics 44:

388–396.

272. Weising K., Nybom H., Wolff K., and Kahl G. 2005. DNA fingerprinting in plants:

principles, methods, and applications. Taylor and Francis.

273. Welsh J., and McClelland M. 1990. Fingerprinting genomes using PCR with arbitrary

primers. Nucleic Acids Research 18(24): 7213–7218.

274. White T.J., Bruns T., Lee S., and Taylor J. 1990. Amplification and direct sequencing of

fungal ribosomal RNA genes for phylogenetics. 315–322. In: Innis M.A., Gelfand D.H.,

110

Sninsky J.J., and White T.J. (editors). PCR protocols: a guide to methods and applications.

Academic Press, San Diego, California.

275. White G., and Powell W. 1997. Isolation and characterization of microsatellite marker loci

in Swietenia humilis (Meliaciea) – an endangered tropical hardwood species. Molecular

Ecology 6: 851–860.

276. Whitney K.D., Randell R.A., and Rieseberg L. 2010. Adaptive introgression of abiotic

tolerance traits in the sunflower Helianthus annuus. New Phytologist 187: 230–239.

277. Williams J.G., Kubelik A.R., Livak K.J., Rafalski J.A., and Tingey S.V. 1990. DNA

polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic

Acids Research 18(22): 6531–6535.

278. Worrell R. 1995. European aspen (Populus tremula L.): a review with particular reference

to Scotland. Distribution, ecology and genetic variation. Forestry 68(2): 93–105.

279. Wren J.D., Forgacs E., Fondon J.W., Pertsemlidis A., Cheng S.Y., Gallardo T., Williams

R.S., Shohet R.V., Minna J.D., and Garner H.R. 2000. Repeat polymorphisms within gene

regions: Phenotypic and evolutionary implications. American Journal of Human Genetics

67: 345–356.

280. Wright S. 1946. Isolation by distance under diverse systems of mating. Genetics 31: 39–

59.

281. Wright S. 1951. The genetical structure of populations. Annual Eugenics 15: 323–354.

282. Wright S. 1965. The interpretation of population structure by F-Statistics with special

regard to systems of mating. Evolution 19: 395–420.

283. Wühlisch G. 2009. EUFORGEN Technical Guidelines for genetic conservation and use of

Eurasian aspen (Populus tremula) Biodiversity International, Rome, Italy.

284. Yin T.M., DiFazio S.P., Gunter L.E., Zhang X., Sewell M.M., Woolbright S.A., Allan

G.J., Kelleher C.T., Douglas C.J., Wang M., and Tuskan G.A. 2008. Genome structure

and emerging evidence of an incipient sex chromosome in Populus. Genome Research 18:

422–430.

285. Young A., Boshier D., and Boyle T. 2000a. Forest conservation genetics. Principles and

practice. CABI publishing, UK.

286. Young E.T., Sloan J.S., and van Riper K. 2000b. Trinucleotide repeats are clustered in

regulatory genes in Saccharomyces cerevisiae. Genetics 154: 1053–1068.

287. Zhou Ch.J., Song H.Z., Li J.H., Sun J.W., Jin D.M., Zhang Q.W., and Wang B. 2005.

Evaluation of genetic diversity and germplasm identification of 44 species, clones, and

cultivars from 5 sections of the genus Populus based on amplified fragment length

polymorphism analysis. Plant Molecular Biology Reporter 23: 39–51.

111

288. Zsuffa L. 1975. A summary review of interspecific breeding in the genus Populus L. 107–

123. In Proceedings 14th meeting of the Canadian Tree Improvement Association, part 2.

Dept. Environment, Canadian Forestry Service, Ottawa.

289. Žvingila D., Verbylait R., Abraitis R., Kuusien S., and Ozolin ius R. 2002. An

assessment of genetic diversity in plus tree clones of Pinus sylvestris L. using RAPD

markers. Baltic Forestry 8(15): 2–7.

290. . ., . , and . . 1991. . .

. .: . . 304 .

291. . . 1959. .

(Phellinus

tremulae Bond. et Boriss.). Ph.D. dissertation, Vilnius, Lithuania. (In Russian).

292. . . . 1968.

. , , 173 .

112

LIST OF PUBLICATIONS

Journals with impact factor in Web of Knowledge database:

Verbylait R., Beišys P., Rimas V. and Kuusien S. Comparison of ten DNA extraction

protocols from wood of European Aspen (Populus tremula L.). Baltic Forestry, 2010. 16 (1):

35–42.

Journal included in other cited data bases [ISI Proceedings and etc.]

Žvingila D., Verbylait R., Baliuckas V., Pli ra A., Kuusien S. 2006. Genetic diversity

(RAPD) in natural Lithuanian populations of common ash (Fraxinus excelsior L.). Biologija.

3: 46–53.

Verbylait R., Baliuckas V., Kuusien S. European aspen (Populus tremula L.) genetic

diversity assessed by molecular methods correlation with P. tremulae infection incidence.

Biologija, 2015. In Press.

Participation in international scientific conferences:

1. Verbylait R., Kuusien S. and Ozolin ius R. Agrobacterium mediated transformation of

poplar (Populus tremula L.) with rolB (root inducing) gene. International Ph.D. courses

„Applied and fundamental aspects of responses, signaling and development process in the

root-microbe systems“. 2007 07. St. Petersburg, Russia.

2. Verbylait R., Kuusien S., Ozolin ius R., Gotoveckien E. Evaluation of genetic

polymorphism of all Lithuanian Populus tremula selected trees, taking into account their

contagion with steam rotting fungi. IUFRO conference “Adaptation, Breeding and

Conservation in the Era of Forest Tree Genomics and Environmental Change”. 2008 08.

Qvebec, Canada.

3. Pli ra A., Verbylait R. Lithuanian poplar breeding program. International

TREEBREEDEX seminar “Poplar breeding strategies in Europe: lessons from the past and

challenges for the next decade“. 2008 09. Orlean, France.

4. Verbylait R. Genetic diversity assessment of selected European Aspen (Populus tremula

L.) trees in Lithuania using RAPD markers. International conference „Climate Change and

Forest Ecosystems“. 2008 10. Vilnius, Lithuania.

5. Verbylait R. Kuusien S. Gotoveckien E. Genetic diversity of selected European aspen

(Populus tremula L.) trees in Lithuania based on RAPD markers. II International Poplar

Symposium „From genes to Function“. 2009 03. Göttingen, Germany.

113

LIST OF ABBREVIATIONS

ABI – Applied Bio Systems

AFLP – Amplified Fragment Length Polymorphism

AMOVA – Analysis Of Molecular VAriance

BC1 – Back Cross first generation

bp – base pair

CTAB – Cetil Trimetil Amonium Bromide

DNA – DeoxyRibonucleic Acid

dNTP – deoxyNucleotide TriPhosphate

EDTA – Ethylene Diamine Tetraacetic Acid

EST – Expressed Sequence Tag

EUFORGEN - EUropean FORest GENetic resources Programme

F1 – first generation hybrid

FAO – Food and Agriculture Organization of the United Nations

GENMOD – generalized linear models, employed in SAS software

HWE – Hardy-Weinberg Equilibrium

IPGC – International Populus Genome Consortium

ISSR – Inter Simple Sequence Repeat

ITS – Internal Transcribed Spacer situated between the small-subunit ribosomal RNA (rRNA)

and large-subunit rRNA genes

IUFRO - The International Union of Forest Research Organizations

MgCl2 – Magnesium chloride

NaCl – Sodium chloride

NaOH – Sodium hydroxide

OECD – Organization of Economic Co-operation and Development

PCA - Pricipal Coordinate Analysis

PCR – Polymerase Chain Reaction

PCR-RFLP – Restriction Fragment Length Polymorphism Analysis of PCR amplified

fragments

PD – discriminating power

PVP – PoliVinyl Pirolidon

QTL – Quantitative Trait Loci

RAD – Restriction Site Associated DNA

114

RAPD – Random Amplified Polymorphic DNA

RFLP – Restriction Fragment Length Polymorphism

RNA – RiboNucleic Acid

SDS – Sodium Dodecyl Sulphate

SFE – State Forest Enterprise

SNP – Single Nucleotide Polymorphism

SSR – Simple Sequence Repeats

SwAsp – A collection of Populus tremula trees representing different Swedish populations

TBE – Tris-Boric acid-EDTA

TE – Tris-EDTA

Tris – 2-amino-2-(hydroxymethyl)-1,3-propanediol

UPGMA – Unweighted Pair Group Method with Arithmetic mean

UVB – Ultraviolet B radiation 320-290 nm

115

ACKNOWLEDGMENTS

First of all I would like to thank my first supervisor Prof. Habil. Dr. Remigijus Ozolin ius for

inspirational ideas, that led me through this research. I also wish to thank my current supervisor

dr. Virgilijus Baliuckas for huge support and great ideas he provided through these years.

Thanks to all friendly and supportive Institute of Forestry colleagues who made my doctoral

studies an overall enjoyable experience. I would especially like to thank Doc. Dr. Sigut

Kuusien and the Forest biotechnology lab colleagues who through their interest and

engagement have helped me in development of this work.

I wish to thank Dr. Vaidotas Lygis for the enormous amounts of encouragement and help.

Thanks to friends and family for being interested in my work and for keeping me interested in

other things! Thank you from all of my heart.

116

APPENDIXES

Appendix 1.

Evaluation of RAPD primers tested for Populus tremula

Primer Primer sequence 5’-3’

Amplification of polymorphic fragments

Size differences of DNA fragments

Reproducibility of DNA fragments

Overall quality of RAPD profile

Roth A01 CAGGCCCTTC *** +++ Roth A02 TGCCGAGCTG * + Roth A03 AGTCAGCCAC *** +++ Roth A04 AATCGGGCTG *** +++ Roth A05 AGGGGTCTTG *** +++ Roth A06 GGTCCCTGAC * +++ Roth A07 GAAACGGGTG * + Roth A08 GTGACGTAGG ** +++ Roth A09 GGGTAACGCC *** +++ Roth A10 GTGATCGCAG *** ++ Roth A11 CAATCGCCGT * + Roth A12 TCGGCGATAG * + Roth A13 CAGCACCCAC *** ++ Roth A14 TCTGTGCTGG * +++ Roth A15 TTCCGAACCC *** +++ Roth A16 AGCCAGCGAA * ++ Roth A17 GACCGCTTGT ** +++ Roth A18 AGGTGACCGT * +++ Roth A19 CAAACGTCGG *** +++ Roth A20 GTTGCGATCC *** ++ Roth B01 GTTTCGCTCC *** +++ Roth B02 TGATCCCTGG * + Roth B03 CATCCCCCTG *** +++ Roth B04 GGACTGGAGT *** +++ Roth B05 TGCGCCCTTC * + Roth B06 TGCTCTGCCC * +++ Roth B07 GGTGACGCAG * + Roth B08 GTCCACACGG ** +++ Roth B09 TGGGGGACTC * ++ Roth B10 CTGCTGGGAC ** ++ Roth B11 GTAGACCCGT * ++ Roth B12 CCTTGACGCA ** +++ Roth B13 TTCCCCCGCT *** +++ Roth B14 TCCGCTCTGG *** +++ Roth B15 GGAGGGTGTT ** ++ Roth B16 TTTGCCCGGA *** +++ Roth B17 AGGGAACGAG ** +++ Roth B18 CCACAGCAGT *** +++ Roth B19 ACCCCCGAAG * ++ Roth B20 GGACCCTTAC ** +++ Roth 170-01 CATCCCGAAC * +++ Roth 170-02 CAGGGTCGAA *** +++ Roth 170-03 ACGGTGCCTG *** ++ Roth 170-04 CGCATTCCGC * +++ Roth 170-05 GAGATCCGCG *** +++ Roth 170-06 GGACTCCACG ** ++ Roth 170-07 ATCTCCCGGG * +++

117

Primer Primer sequence 5’-3’

Amplification of polymorphic fragments

Size differences of DNA fragments

Reproducibility of DNA fragments

Overall quality of RAPD profile

Roth 170-08 CTGTACCCCC *** ++ Roth 170-09 TGCAGCACCG *** +++ Roth 170-10 CAGACACGGC * + Roth 370-01 TCCCTGTGCC *** +++ Roth 370-02 GCTCTCCGTG * ++ Roth 370-03 GAGACGTCCC ** ++ Roth 370-04 GTATGCCGCG * + Roth 370-05 GCACCGAACG ** +++ Roth 370-06 CCGGCGTATC * +++ Roth 370-07 AGCCTGACGC *** +++ Roth 370-08 GCTCTGGCAG * + Roth 370-09 CGCACTCGTC * + Roth 370-10 CTGTCCGGTC * +

– no polymorphic DNA fragments, – few polymorphic DNA fragments, – clear polymorphic DNA fragments; * – most DNA fragments are similar in size, ** – most DNA fragments are similar in size and scorable at some extent, *** – DNA fragments are easy to score; – DNA fragments are not reproducible, – most of the DNA fragments are reproducible yet indistinct, – DNA fragments are reproducible and easy to score; + – no amplification, ++ – low quality amplification, +++ – good quality amplification. Primer evaluation carried out according to Pivorien (2008)

118

Appendix 2.

Assessment of Populus tremula plus trees N

o. o

f plu

s tre

e

Tre

e ag

e, y

ears

Tre

e he

ight

, m

Stem

dia

met

er

(dbh

), cm

Stem

stra

ight

- ne

ss sc

ore,

scor

e

Stem

slen

der-

ne

ss sc

ore,

scor

e

Stem

hei

ght t

o dr

y br

anch

es, m

Stem

hei

ght t

o gr

een

bran

ches

, m

Cro

wn

diam

eter

, m

Bra

nch

thic

knes

s, sc

ore

Stem

pre

senc

e in

th

e cr

own,

scor

e

Occ

lusi

on o

f bra

nch

wou

nds,

scor

e

Sani

tary

con

ditio

n,

scor

e

130 62 32 52 4 4 14 18 7 4 5 4 1 131 62 32 52 4 4 12 16 9 3 4 4 1 132 62 31 52 4 4 14 18 7 4 5 4 1 133 62 33 52 4 4 12 16 9 3 4 4 1 134 62 32 40 3 3 12 17 7 5 4 4 1 135 62 33 45 4 4 15 19 7 4 4 4 1 136 62 32 49 4 4 10 15 11 3 4 4 1 137 62 30 42 4 4 15 20 5 4 4 3 1 138 62 30 39 4 4 14 19 7 5 5 4 1 139 62 32 37 4 3 12 17 7 4 4 5 1 140 62 32 41 3 3 15 19 8 3 3 4 1 141 62 30 48 4 3 14 17 9 3 4 4 1 142 62 31 51 4 3 15 17 11 3 4 4 1 143 62 32 42 5 4 12 15 7 4 5 5 1 144 62 31 38 3 3 14 16 7 4 4 5 1 042 62 29 50 5 5 15 17 12 5 5 5 1 043 67 29 54 5 5 17 21 12 5 5 5 1 158 67 23 27 4 5 2 9 4 4 4 5 1 159 37 24 23 4 4 2 9 6 3 4 5 1 160 37 25 27 4 5 5 10 7 4 4 5 1 161 37 20 18 4 4 3 12 5 4 4 5 1 162 37 25 31 4 5 2 12 6 3 4 5 1 084 37 29 36 4 4 12 14 8 4 3 5 1 085 52 29 38 5 5 16 17 9 4 4 5 1 086 52 28 32 5 5 17 18 6 5 5 5 1 087 52 29 42 4 4 15 16 8 4 4 5 1 088 52 30 44 5 5 15 17 8 4 4 5 1 089 52 29 44 5 5 15 16 6 5 4 5 1 090 52 28 36 5 5 16 17 8 4 4 5 1 091 52 29 48 5 4 15 16 9 4 4 5 1 092 52 28 40 4 4 14 16 6 5 5 5 1 093 52 29 44 5 5 14 15 6 4 4 5 1 074 52 28 38 5 4 16 17 7 4 4 5 1 075 62 29 49 5 4 16 18 8 4 4 5 1 076 62 30 54 5 5 — 15 8 4 4 5 1 077 62 28 40 5 5 15 16 7 5 5 5 1 078 62 29 37 5 4 17 18 6 5 4 5 1 079 62 30 43 4 4 15 16 8 4 4 5 1 080 62 31 41 4 4 15 17 7 4 4 5 1 081 57 31 45 5 5 16 17 8 3 4 5 1 082 57 30 40 5 4 14 16 8 4 4 5 1 083 57 30 40 5 5 13 16 9 4 4 5 1 109 57 29 37 4 4 16 18 7 4 4 3 1

119

No.

of p

lus t

ree

T

ree

age,

yea

rs

Tre

e he

ight

, m

Stem

dia

met

er

(dbh

), cm

Stem

stra

ight

- ne

ss sc

ore,

scor

e

Stem

slen

der-

ne

ss sc

ore,

scor

e

Stem

hei

ght t

o dr

y br

anch

es, m

Stem

hei

ght t

o gr

een

bran

ches

, m

Cro

wn

diam

eter

, m

Bra

nch

thic

knes

s, sc

ore

Stem

pre

senc

e in

th

e cr

own,

scor

e

Occ

lusi

on o

f bra

nch

wou

nds,

scor

e

Sani

tary

con

ditio

n,

scor

e

110 62 31 39 4 4 14 19 9 4 4 3 1 111 62 30 37 4 4 16 20 10 4 5 3 1 112 62 28 33 4 4 14 18 11 4 5 4 1 113 62 30 54 5 5 12 17 11 3 5 4 1 114 62 30 37 5 4 15 18 9 4 5 5 1 115 62 32 43 4 4 12 18 9 3 3 3 1 116 62 29 40 5 5 11 15 9 4 4 5 1 117 62 31 39 5 4 12 18 7 4 4 4 1 118 62 18 34 3 3 16 20 6 4 4 4 1 119 62 29 40 4 4 12 16 9 4 4 4 1 163 62 30.5 44 5 5 9.5 16 10 5 5 5 1 164 67 29.5 30 5 5 6.5 14 6 5 4 4 1 165 47 26 23 5 5 7 12.5 4 5 5 4 1 166 37 28 32 5 5 9 15 5 5 4 4 1 167 47 28 29 5 5 7.5 17.5 4 5 5 4 1 168 47 30 34 4 5 10 16 7 4 4 4 1 039 67 27 50 5 4 12 14 6 5 5 4 1 040 72 27 47 4 4 10 12 7 4 4 5 1 041 67 28 42 4 5 13 16 5 5 4 4 1 145 67 31 38 4 4 13 18 4 4 3 4 1 146 67 30 42.5 4 4 10 14 5 4 3 4 1 104 67 31 38 4 4 17 19 8 3 4 4 1 105 47 30 35 4 5 16 17 7 4 4 5 1 106 47 28 32 5 5 16 18 6 5 4 4 1 107 47 31 40 5 4 18 20 8 4 4 4 1 108 47 31 48 5 5 18 19 9 4 4 4 1 044 47 32 47 5 4 — 17 7 4 4 4 1 045 57 32 50 5 4 — 18 5 4 3 4 1 046 57 32 37 3 4 — 19 5 4 4 4 1 047 57 30 47 4 4 — 17 6 3 4 3 1 048 57 31 44 5 5 — 16 6 4 4 4 1 049 57 29 38 4 4 13 16 9 4 5 4 1 050 67 29.5 47 4 4 13.5 13.5 9 3 4 4 1 051 67 30.5 38 4 4 14 16 9 4 4 4 1 052 67 30 44 4 4 15 16.5 8 4 3 5 1 053 67 30 40 4 4 13 17 8 4 4 4 1 054 68 29.5 41 4 4 14 16.5 7 4 4 4 1 055 67 28.5 38 4 4 15 17.5 7 4 4 5 1 056 67 27 42 3 4 15 15.5 10 4 4 5 1 057 71 27 31 5 4 14 15 7 5 5 5 1 058 67 31.5 38 4 5 15 19 8 4 4 5 1 059 67 33.5 44 4 5 13.5 17 9 3 4 5 1 060 71 32.5 39 5 5 15 22 7 4 3 5 1 061 67 32.5 39 4 5 15 20.5 7 4 3 4 1 062 67 32.5 43 5 5 16.5 18.5 7 4 3 5 1 063 67 32.5 37 4 5 14 19.5 7 4 4 5 1 064 67 32 45 5 5 13 14 9 4 3 4 1

120

No.

of p

lus t

ree

T

ree

age,

yea

rs

Tre

e he

ight

, m

Stem

dia

met

er

(dbh

), cm

Stem

stra

ight

- ne

ss sc

ore,

scor

e

Stem

slen

der-

ne

ss sc

ore,

scor

e

Stem

hei

ght t

o dr

y br

anch

es, m

Stem

hei

ght t

o gr

een

bran

ches

, m

Cro

wn

diam

eter

, m

Bra

nch

thic

knes

s, sc

ore

Stem

pre

senc

e in

th

e cr

own,

scor

e

Occ

lusi

on o

f bra

nch

wou

nds,

scor

e

Sani

tary

con

ditio

n,

scor

e

065 68 31.5 41 5 5 14 14.5 7 4 3 5 1 066 57 32.5 50 5 5 12 17 9 3 3 4 1 067 68 29 47 5 5 12.5 14.5 11 4 3 5 1 068 69 30 47 5 5 15 16 10 4 3 5 1 069 67 30.5 57 5 5 14 16 12 5 5 5 1 070 72 27 48 5 4 10.5 11 10 3 4 5 1 071 72 30 56 5 5 14.5 15 10 5 4 5 1 072 66 29.5 55 5 5 12 14 10 4 4 5 1 073 67 31 52 5 5 12 14 10 4 4 5 1 038 72 33.4 66 5 5 11.5 17.5 8 4 4 5 1 094 47 28 36 5 5 12 15 9 4 4 5 1 095 47 27 35 5 5 10 13 10 4 4 4 1 096 47 28 35 5 4 15 16 6 3 4 5 1 097 47 27 31 4 4 10 14 9 4 5 4 1 098 47 27 32 4 5 12 17 8 4 4 4 1 099 47 28 35 5 4 14 18.5 7 5 4 5 1 100 47 28 38 5 5 13 16 6 5 5 4 1 101 47 27 31 5 5 16 19 8 4 4 4 1 102 47 27 28 5 5 10 15 9 4 4 5 1 103 47 28 29 5 4 12 14 10 4 4 5 1 020 47 26 44 4 4 17 18 6 4 4 5 1 021 62 26 37 4 4 15 18 4.5 4 4 5 1 022 62 27 30 4 4 17 19 5 4 4 5 1 023 67 26 39 4 4 18 19 6 4 4 5 1 120 67 31 47 5 4 17 19 10 4 4 4 1 121 67 31 48 5 4 15 16 9 4 4 5 1 122 67 30 40 4 4 17 18 9 4 4 4 1 123 67 31 40 5 4 15 16 9 4 4 4 1 124 67 30 46 5 4 15 16 11 5 4 5 1 125 67 29 39 4 4 15 17 10 4 4 5 1 126 67 29 47 5 4 14 15 11 4 4 4 1 127 67 29 42 5 4 13 14 10 5 4 5 1 128 67 31 46 5 4 18 19 11 4 4 5 1 129 67 30 45 5 4 15 16 10 4 4 5 1 036 67 29 46 5 4 11 14 5 4 4 4 1 152 72 30 32 5 4 12 18 7 4 4 5 1 153 57 29 37 5 5 9 20 8 4 4 4 1 154 57 31 34 5 5 10 15 7 4 4 5 1 155 57 32 32 5 4 13 14 6 4 4 5 1 156 57 30 34 5 5 8 15 6 4 4 4 1 157 57 28 34 5 5 8 16 5 4 4 5 1 037 57 33 64 5 5 14 18 8 4 4 4 1 147 67 31 36 4 5 12 19 5 4 4 4 1 148 62 32 42 4 5 12 15 9 3 4 4 1 149 62 30 39 4 5 10 16 8 3 4 4 1 150 62 29 36 5 5 11 14 9 4 4 4 1 151 62 29 32 5 5 12 18 8 4 3 5 1

Rita VERBYLAIT

EUROPEAN ASPEN (Populus tremula L.) IN LITHUANIA:

GENETIC DIVERSITY OF PLUS TREES AND POPULATIONS ASSESSED USING MOLECULAR MARKERS

Doctoral Dissertation

Išleido ir spausdino – Vytauto Didžiojo universiteto bibliotekos Leidybos skyrius

(S. Daukanto g. 27, LT-44249 Kaunas) Užsakymo Nr. K15-126. Tiražas 15 egz. 2015 11 13.

Nemokamai.