Genetic structure and variation of five Shorea leprosula natural populations based on microsatellite marker

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GENETIC STRUCTURE AND VARIATION OF FIVE

Shorea leprosula

NATURAL POPULATIONS BASED ON

MICROSATELLITE MARKER

MAHARDIKA PUTRA PURBA

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

BOGOR

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DECLARATION OF ORIGINALITY

I hereby declare that the thesis entitled “Genetic structure and variation of

five Shorea leprosula natural populations based on microsatellite marker” and the

work reported herein was composed by and originated entirely from my

supervisors and I. I declare that this is a true copy of my thesis, as approved by

my supervisory committee and has not been submitted for a higher degree to any

other University or Institution. Information derived from the published and

unpublished work of others has been acknowledged in the text and references are

given in the list of sources.

Bogor, October 2012

Mahardika Putra Purba

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ABSTRACT

MAHARDIKA PUTRA PURBA.Genetic structure and variation of five Shorea leprosula natural populations based on microsatellite marker. Supervised by

ISKANDAR Z. SIREGAR and SRI WILARSO BUDI

Shorea leprosula is an economically and ecologically important timber

species and one of the most common tropical lowland rain forest tree species in

Southeastern Asia. Eleven microsatellites DNA loci were used to investigate the

genetic structure and diversity among five natural populations from Sumatera (CS

and NS), Peninsular Malaysia (SM) and Borneo (WKa and WKb). Results

indicated that expected mean heterozygosity (He) was high (0.721) with values

ranging from 0.542 to 0.788, with the highest values found in the populations

from west Kalimantan (WKb). Most of the populations showed high and positive

fixation indices indicating excess of homozygotes. This implies a high level of

inbreeding which may be caused by selfing or/and mating among relatives. A

gene diversity analysis showed that 89.9% of the variation was harbored within

populations and 11.1% (FST= 0.111) was due to differences among populations.

STRUCTURE analysis showed that five natural populations were clustered into

two main groups namely: i) Sumatera-Peninsular Malaysia, and ii) Borneo. Mean

gene flow (Nm) was high (2.00) indicating extensive gene flow in the past among

the natural populations. Three populations (WKb, NS and SM) were identified to

be genetically unique and we recommended for further consideration in

formulation of genetic conservation strategies.

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RINGKASAN

MAHARDIKA PUTRA PURBA. Struktur genetika dan variasi lima populasi alami Shorea leprosula berdasarkan penanda mikrosatelit. Dibawah bimbingan

ISKANDAR Z. SIREGAR dan SRI WILARSO BUDI.

Shorea leprosula Miq. atau yang dikenal dengan nama Meranti tembaga

atau Light red meranti merupakan salah satu penghasil kayu berkualitas baik di

wilayah hutan hujan tropis. Jenis ini merupakan pohon yang penting baik secara

ekologi maupun ekonomi. Populasi S. leprosula umumnya tersebar di 3 wilayah

utama (Sumatera, Kalimantan dan Semenanjung Malaysia), yang telah terpisah

antara satu dengan lainnya dalam waktu yang cukup lama, sehingga dapat

diasumsikan bahwa ada perbedaan genetik yang cukup tinggi diantara populasi ini

karena aliran gen yang terbatas. Adapun tujuan dari penelitian ini adalah untuk:

(1) Menduga variasi genetik S. leprosula; (2) Menduga sebaran keragaman

genetik di dalam populasi dan antar populasi, dan (3) Merumuskan arahan terkait

implikasi konservasi S. leprosula berdasarkan informasi genetik yang diperoleh.

Hasil penelitian menunjukkan bahwa S. leprosula memiliki tingkat

keragaman genetik yang masih tinggi (He= 0,721). Nilai koefisien inbreeding

dalam penelitian ini menunjukkan nilai yang positif pada sebagian besar populasi

yang berarti kemunculan homozigot lebih banyak dibandingkan heterozigot, hal

ini menunjukkan bahwa adanya inbreeding yang terjadi disebabkan oleh

penyerbukan sendiri maupun persilangan diantara kerabat dekat. Sebagian besar

keragaman genetik tersimpan didalam populasi (89,9% dari total keragaman),

sedangkan keragaman antar populasi (FST) adalah sebesar 11.1% dan termasuk

dalam kategori sedang berdasarkan nilai differensiasi Wright. Hal ini dikarenakan

tingginya aliran gen yang terjadi dimasa lampau (Nm= 2). Hasil analisis

menggunakan program STRUCTURE menunjukkan bahwa kelima populasi

S. leprosula terpisah menjadi dua bagian yaitu: i) grup Sumatera-Semenanjung

Malaysia, dan ii) Grup Kalimantan, dengan populasi yang berbaur disetiap grup.

Pada beberapa populasi S. leprosula ditemukan private allele yaitu alel

yang memiliki frekuensi yang rendah yang menunjukkan keunikan suatu populasi.

Alel-alel unik ini juga digunakan sebagai kriteria untuk konservasi sehingga

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tentunya adalah dengan cara membatasi kegiatan penebangan dan pengrusakan

hutan. Populasi WKa harus mendapat perhatian khusus dalam kegiatan konservasi

karena memiliki keragaman genetik yang paling rendah (A= 3.6; HE= 0.542; FST=

0.080) dibandingkan dengan populasi lainnya, perhatian khusus ini dapat

dilakukan dengan cara melindungi populasi secara keseluruhan dari kegiatan

penebangan dan eksploitasi yang berlebihan demi keberlangsungan populasi ini

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© Copyright owned by IPB, 2012

All rights reserved

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GENETIC STRUCTURE AND VARIATION OF FIVE

Shorea leprosula

NATURAL POPULATIONS BASED ON

MICROSATELLITE MARKER

MAHARDIKA PUTRA PURBA

Thesis

Submitted in partial fulfillments of the requirements for the degree of Master of Science at the Bogor Agricultural University

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

BOGOR

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Thesis Title : Genetic structure and variation of five Shorea leprosula natural populations based on microsatellite marker

Name : Mahardika Putra Purba Student ID : E451100011

Major : Tropical Silviculture (SVK)

Approved by, Supervisor

Prof. Dr. Ir. Iskandar Z Siregar M.For.Sc Dr. Ir. Sri Wilarso Budi R, MS Head-supervisor Co-supervisor

Endorsed by,

Head of Major Dean of Graduate School Tropical Silviculture

Dr. Ir. Basuki Wasis, MS Dr. Ir. Dahrul Syah, M.Sc. Agr.

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ACKNOWLEDGEMENT

I would like to express my sincere appreciation and gratitude to Professor

Iskandar Zulkarnaen Siregar and Dr. Sri Wilarso Budi, whose valuable

supervision, guidance, enthusiasm and especially great patience have facilitated

the completion of this thesis.

I wish to acknowledge Institute of Plant Biology, University of Zurich

(UZH) for granting me a research fellowship and for financial support during my

stayed in Zurich, Switzerland. Assistance from the staff of Gunung Leuser

National Park, Bukit Tigapuluh National Park and Betung Kerihun National Park

are much appreciated during the field work and sample collection, as well as

Forest Research Institute Malaysia (FRIM) for providing samples from Malaysian

site.

I would like to express my appreciation to the staff of the Institute of Plant

Biology, University of Zurich. Prof. Dr. Kentaro Shimizu and Dr. Kevin Ng Kit

Siong for their introduction to microsatellite analysis, for valuable discussion in

terms of data analysis and interpretation, and for their help and companionship

during laboratory works conducted in Genetic Diversity Center (GDC).

Great appreciation is also given to staff and colleagues from Department

of Silviculture, Faculty of Forestry, Bogor Agricultural University (IPB) for their

contribution and help, and Mr. Ismail for help in secretariat work.

Finally, I would like to express my special thanks to my mother and father,

my sisters: Maharani, Maya Vita and Efra, as well as all my friends for their

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ABOUT THE AUTHOR

Mahardika Putra Purba was born in Pematang Siantar, North Sumatera on

19 October 1987. He is the eldest son of Matias Tuah Purba and Marice Lingga.

He finished his elementary, junior and high schools at public schools in Pematang

Siantar. In 2005 he became a student at University of Sumatera Utara, Faculty of

Agriculture, Department of Forestry. He graduated with a Bachelor of Forestry in

2009.

In 2010 he joined Bogor Agricultural University (IPB) graduate school as

a Master of Science student in Tropical Silviculture. He was awarded a research

fellowship from Institute of Plant Biology, University of Zurich, Switzerland. He

continued his master study and research in Switzerland for one semester. He

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LIST OF TABLES

Page

1 Locations and sample size of five natural populations of S. leprosula in Southeast Asia ... 14

2 Details of eleven microsatellite markers of S. leprosula ... 15

3 Average genetic diversity parameters of five natural populations of

S. leprosula in Southeast Asia based on microsatellite loci ... 18

4 Fixation index (FIS) for five natural populations of S. leprosula in

Southeast Asia ... 20

5 Analysis of molecular variance (AMOVA) based on eleven microsatellite loci and five natural populations of S. leprosula in Southeast Asia ... 21

6 Genetic diversity and gene flow for S. leprosula populations after the removal of one population at a time ... 22

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LIST OF FIGURES

Page

1 Problem tree analysis of this study ... 3

2 Tree, fruits, leaves of S. leprosula and Thrips hidden within corolla ... 5

3 Pollinator of Shorea: (a) Thrips; (b) Bettle ... 6

4 Evolutionary factors and their influence on the phenotypic structure of a population ... 7

5 Location and collection site in five populations of S. leprosula ... 13

6 Dendrogram for IPGMA cluster analysis based on Nei’s genetic distance between the five natural populations of S.leprosula in Southeast Asia ... 22

7 Bayesian cluster analysis using STRUCTURE ... 23

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LIST OF APPENDICES

Page

1 Multiplex PCR Microsatellite analysis ... 41

2 Allele frequencies for polymorphic loci in five natural populations of

Shorea leprosula ... 43

3 Distribution of Allele frequencies for polymorphic loci across five natural populations of Shorea leprosula ... 47

4 Sampling data of Shorea leprosula in Gunung Leuser National Park North Sumatra (Code:NS) ... 53

5 Sampling data of Shorea leprosula in Bukit Tigapuluh National Park Central Sumatra (Code: CS) ... 54

6 Sampling data of Shorea leprosula in Betung Kerihun National Park West Kalimantan (Code: Wka) ... 55

7 Sampling data of Shorea leprosula in Ketapang, West Kalimantan (Code: Wkb) ... 56

8 Documentation of Shorea leprosula ... 57

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INTRODUCTION

Background

Tropical forests in Southeast Asia are a center of global biological

diversity. Many of them are dominated by Dipterocarpaceae, with more than 450

species in 13 genera in Asia (Ashton 1982). Shorea is a genus with approximately

196 species, one of the species is Shorea leprosula Miq., locally known as

Meranti Tembaga or Light red Meranti, which is ecologically and economically

important timber in the tropical forest of Southeast Asia (Ashton 1982). Mostly in

Indonesia, The regions with the highest abundance of S. leprosula are mostly in

Indonesia, in particular on Sumatra and Kalimantan (Borneo). It is common and

even abundant on well drained or swampy sites on deep clay soil in lowland and

hilly dipterocarp forest below 700 m elevation (Symington 1943; Ashton 1982).

Deforestation and forest degradation are large-scale problems in

developing countries. In Indonesia, the situation is particularly bad with the

frequent increases in oil palm industry and the area needed for housing as well as

illegal logging all of which pose a significant threat to the sustainability of

tropical forest ecosystems (Tnah et al. 2010). Between 2000 and 2009 Forest

Watch Indonesia (2011) estimated total deforested area in Indonesia was 15.2

million hectares which is a deforestation rate of 1.5 million hectares per year.

Indonesia has been the largest tropical log producing country, producing 34.2

million m3 (mainly from dipterocarps species) in response to increasing demand from the construction industry (International Tropical Timber Organization 2010).

This condition will give more pressure to the natural forest. S. leprosula is one of

dipterocarps species which is economically important, and it has a stabile price

year by year giving a significant income to Indonesia (International Tropical

Timber Organization 2010). As a consequence this species has suffered from a

massive population reduction and is categorized as endangered species according

to the International Union for Conservation of Nature (IUCN, 2011) red list of

threatened species. Although S. leprosula can still be found abundantly in

Sumatera, Kalimantan and Peninsular Malaysia, with continued exploitation for

its timber and conversion of forest to other land uses, it is unlikely for the species

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specific environments can easily be lost, unless a proper conservation strategy is

developed and put in practice.

Population differentiation is a fundamental process of evolution which

may lead to speciation under certain conditions. Therefore, determining and

measuring population differentiation is becoming one of the central themes of

population genetic studies. Several studies have been conducted on the genetic

aspect of S. leprosula. Lee et al. (2000b) used allozyme markers to study the

population genetic structure of this species in Peninsular Malaysia. Lee et al.

(2000b) found that the species exhibited high levels of genetic diversity and the

majority of the diversity was partitioned within population. In addition, Ng et al.

(2006) also found high levels of gene diversity within populations of S. leprosula

in Peninsular Malaysia based on microsatellite DNA.

Recently, microsatellite markers have become the markers of choice for a

wide spectrum of genetic, population, conservation, and evolutionary studies in

plants, particularly in forest tree species including S. leprosula (Lee et al. 2000a;

2000b; 2006, Ng et al. 2006). The popularity of microsatellite markers stems from

a unique combination of several important advantages, such as codominant

markers, high reproducibility, abundance in the genome in every organism and the

ease of assessing size of variation by polymerase chain reaction (PCR) with pairs

of flanking primers (Weising et al. 2005). Microsatellite markers are one of the

best tools for measuring genetic diversity and population genetic structure because

of highly polymorphic markers due to its high mutation rate (Ng et al. 2009).

The ecological and life history traits of S. leprosula (i.e. regionally

distributed, long-lived, predominantly out crossing, weak pollinators) allow us to

postulate high genetic diversity within populations for this species. Also these

populations are from three different land masses (Peninsular Malaysia, Borneo &

Sumatera), probably evolving independently from each other for a long time,

therefore we would expect relatively strong genetic differentiation among

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Problem Tree Analysis

The problem tree analysis for this study is shown in Figure 1.

Figure 1. Problem tree analysis of this study

Research objectives

The objectives of this study are to:

1. Determine the amount of genetic diversity displayed by Shorea leprosula in

Southeast Asia

2. Describe how genetic diversity is distributed within and among populations;

and

3. Propose the implications based on genetic information for formulation of

genetic conservation program of Shorea leprosula. Tropical Forest in Southeast

Shorea leprosula

Natural populations of S. leprosula

originate from 3 different land masses: Sumatera, Peninsular

Malaysia and Borneo

Ecologically and economically important timber in tropical forests of

Southeast Asia (ITTO, 2010)

Genetic Structure and variation

High rates of exploitation mostly caused by illegal logging and forest

fragmentation

Categorized as endangered species by IUCN red list threatened species

(IUCN, 2011)

Implications for Conservation of Shorea leprosula

Limited of gene flow due to limited pollen, fruit dispersal and predominantly out crossing

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LITERATURE REVIEW

Shorea leprosula Miq. General description

Shorea leprosula Miq. locally known as Meranti Tembaga or Light red

Meranti (Figure 2), belongs to the Dipterocarpaceae, the main timber family in the

tropical forest of Southeast Asia (Angiosperm Phylogeny Group, 2009).

Kingdom : Plantae

Trees up to 60 m high; approximate 100 cm in diameter; bark greyish

brown, shallowly fissured, V-shaped (Figure 2). Outer bark dull purple brown,

rather hard, brittle, inner bark fibrous, dull brown or yellowish brown grading to

pale at the cambium, sapwood pale or cream, resinous, heartwood dark red or

light red brown; leaves elliptic to ovate, 8-14 cm long, 3.5 to 5.5 cm wide, cream

scaly, thinly leathery, base obtuse or broadly cuneate, apex acuminate, up to 8 mm

long, secondary vein 12-15 pairs, slender, curved towards margin, set at 40 to 550,

tertiary veins densely ladder-like, very slender, obscure expect in young tress;

stipules 10 mm long, 35 mm wide, scars short, horizontal, obscure, oblong to

broadly hastate, obtuse, fugacious, falling off early; Fruit pedicel to 2 mm long,

calyx sparsely pubescent, 3 longer lobes up to 10 cm long, approximate 2 cm

wide, spatulate, obtuse, approximate 5 mm broad above the to 8 by 6 mm

thickened elliptic, shallowly saccate base, 2 shorter lobes up to 5.5 cm long,

approximate 0.3 cm wide, unequal, similarly saccate at base (Ashton 1982;

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Figure 2 (a) Trees of S. leprosula, (b) Fruits of S. leprosula, (c) Leaves of

S.leprosula seedling (d) Thrips hidden within corolla (Sakai et al. 1999)

Distribution and ecology

Shorea leprosula is distributed from southern Thailand (Pattani)

throughout Peninsular Malaysia (except for the seasonal areas in Perlis, northwest

Kedah and Langkawi island), Sumatera and North Borneo (Symington 1943). S.

leprosula is one of the fastest growing species of Dipterocarp up to approximately

its twentieth year, but it is later surpassed by other Dipterocarp species. S.

leprosula can grow in a wide variety of site conditions including flat topography,

and hilly areas, and it is frequently found on well-drained soil, deep clay soils or

swampy soil in the mixed Dipterocarp forest of lowlands and hills up to 700 meter

above sea level. However, it is a strongly light-demanding species (Meijer and

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Reproductive biology

Outcrossing is predominant in many tropical forest tree species, and the

average out crossing rate is more than 80% (Nason et al. 1996). Outcrossing rates

maybe strongly influenced by flowering tree density (Murawski and Hamrick

1992) and the types and behaviors of pollinators governing the pollen movement

(Ghazoul et al. 1998). Dipterocarpaceae have an outcrossing habit and some

species have a considerable degree of self-incompatibility (Chan 1981). The

flowers of dipterocarps are hermaphroditic and pollinated by a variety of insects

(Appanah 1981).

Figure 3 Pollinator of Shorea: (a) Thrips; (b) Beetle (Sakai et al. 1999)

Flowering behavior of S. leprosula is like all other dipterocarps species, it

is sporadic throughout the year and gregarious at intervals of two or five years

(Soerianegara and Lemmens 1993). Pollination studies in lowland dipterocarps

species showed that S. leprosula is characterized by limited pollen and fruit

dispersal. S. leprosula is pollinated by Thrips from order Thysanoptera (Figure

2d; Figure 3a), weaker fliers than bees or other insect pollinators (Chan and

Appanah 1980; Appanah and Chan 1981). Appanah and his colleagues noted that

the short generation time and high reproductive rate of thrips permit a quick

response of thrips to an abrupt increase of flowers at the beginning of general

flowering and that thrips provide sufficient pollination service for multiple species

of Shorea (Sakai et al. 1999). Sakai et al (1999) also found beetle pollinators

(Figure 3b) to be the predominant flower visitors of all nine species of Shorea

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Hills National Park, Sarawak. Fruit dispersal in S. leprosula is often by gyration

alone, with seeds largely falling within 50 m from the mother tree, and there is

probably little secondary dispersal by animals once the seed reaches the forest

floor (Chan 1980).

System of genetics

The genetic system includes everything that provides for their creation,

storage, testing, modification and release of genetic variation. It determines the

existence and the adaptability of a population to future environmental change, the

organization and transmission of genetic information and the kind and amount of

genetic combinations produced. The genetic system comprises of several

components, i.e. the recombination system, sexual system, mating system, the

dispersal/gene flow system and the mutation system, sexual system, reproductive

system, adaptive system, incompatibility system and selection system (Hattemer

1991; Finkeldey 1998; Ledig 1998). These components are closely related and

interlinked as shown in Figure 4.

Figure 4 Evolutionary factors and their influence on the genotypic structure of a population (Hattemer and Muller-Starck, 1990)

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Genetic diversity

Genetic diversity serves as a way for populations to adapt to changing

environments. With more variation, it is more likely that some individuals in a

population will possess variations of alleles that are suited for the current

environment. Those individuals are more likely to survive to produce offspring

bearing that allele. The population will continue for more generations because of

the success of these individuals. The status of genetic diversity is an important

scientific aspect to be considered for designing effective and efficient programs of

genetic conservation and tree improvement. Genetic diversity plays a very

important role in survival and adaptability of a species because when a species

environment changes, slight gene variations are necessary to produce changes in

the organism’s

Patterns of genetic variation based on 97 isozyme studies of tropical trees

have been reviewed by Loveless (1992). The results demonstrate that levels of

variation in populations of tropical taxa are as high or higher than in plants in

general. Levels of genetic variation differ significantly among species with

different geographic ranges, life form and taxonomic affinities. Levels of

population differentiation are significantly different only between species with

different dispersal modes. Outcrossing rates in 16 tropical tree species showed a

preponderance of highly outcrossed breeding systems. Genetic evidence suggests

that gene flow among local populations is high, but geographically separated

populations showed moderate levels of genetic differentiation (Siregar 2000) that enables it to adapt and survive. If genetic diversity

becomes low at many genes of a species, that species becomes increasingly at

risk. Otherwise, high levels of genetic diversity enable them to survive spatial and

temporal variations of environmental conditions (Finkeldey 1993).

Populations of a species may have different degrees of genetic diversity

according to their breeding systems and life history traits (Lee et al. 2000c). No

matter how many variants of a gene are present in a population today, only the

variants that survive in the next generation can contribute to species diversity in

the future. Once gene variants are lost, they cannot be recovered (Zhou et al.

2003). Genetic diversity of a population can be assessed by proportion of

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demonstrate variability), and mean number of individuals with polymorphic loci

(Heterozygosity)

Population genetic and gene flow

The primary goal of population genetics is to understand the factors

determining evolutionary change and stasis, and the amount and pattern of genetic

variation within and between populations (Hedrick 2005; Hartl and Clark 2007).

The amount and type of genetic variation in populations is potentially affected by

a number of factors, but primarily by selection, inbreeding, genetic drift, gene

flow, mutation and recombination. These factors may have general or specific

effects; for example, genetic drift and inbreeding can be considered to always

increase the amount of variation. Other factors, such as selection and gene flow

may either increase or reduce genetic variation, depending on the particular

situation. A combination of two or more of these factors can generate many

different levels and patterns of genetic variation. To understand the influence of

these evolutionary factors, one must first be able to describe and quantify the

amount of genetic variation in a population and the pattern of genetic variation

among populations (Hedrick 2005). One of the basic concepts of population

genetics is Hardy-Weinberg principle (often called Hardy-Weinberg Equilibrium).

This principle states that after one generation of random mating, single locus

genotype frequencies can be represented by a binomial (with two alleles) or

multinomial (with multiple alleles) function of allele frequencies. This principle

allows great simplification of the description of a population’s genetic content by

reducing the number of parameters that must be considered. Furthermore in the

absence of factors that change allele frequency (selection, genetic drift, gene flow

and mutation) and in the continued presence of random mating the Hardy

Weinberg genotype proportion will not change over time.

In most species, populations are often subdivided into smaller units

because of geographic, ecological or behavioral factors. When a population is

subdivided, the amount of genetic connectedness among the parts of population

can differ. Genetic connection depends primarily on the amount of gene flow,

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subpopulations or sub groups. When the amount of gene flow between groups is

high, gene flow has the effects of homogenizing genetic variation between the

groups. When the gene flow is low, genetic drift, selection and even mutation in

the separate groups may lead to genetic differentiation. Hierarchical

representation is useful in describing the overall relationships of organism’s

populations and in documenting the spatial genetic variation. Phylogeography, the

joint use of phylogenetic techniques and geographic distributions has been used to

understand spatial relationships and distributions of populations within species or

closely related species (Avise 2000).

Gene flow is central to understanding evolutionary potential and

mechanisms in several areas of applied population genetics. Gene flow from other

populations of the same species can result in genetic rescue or genetic restoration

of these populations by introducing new variation that allows removal of

detrimental variation and restoration of adaptive change. Estimating the amount of

gene flow in most situations is rather difficult. Direct estimates of the amount of

movement can be obtained in organisms where different individual marks are

used. However both the movement of individuals and their incorporation into the

breeding population are necessary for gene flow. Using highly variable genetic

markers, it is now possible to identify parents genetically and thereby determine

the spatial movement of gametes between generations without the direct

movement information of the parents. Or, individuals can be assigned to specific

populations using genetic markers, thereby determining whether they are migrants

or not. Indirect measures of gene flow using genetic markers are useful to confirm

behavioral or other observations or when these observations are inconclusive or

impossible. Theoretically assuming finite subpopulations of size Ne

F

and a

proportion m migrants into each subpopulation each generation, then:

ST =

When Nem is large, the measure FST approaches 0, and when Nem is small, FST

can approaches 1. The value of FST for a group of populations can be estimated

using the amount and pattern of molecular genetic variation over subpopulations

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Microsatellite markers

Highly informative genetic markers are required for assisting various

programs in genetics, such as breeding, genome mapping, tree improvement,

forensics and conservation, and the restoration and sustainable management of

forest genetic resources. Microsatellite DNA or simple sequence repeats (SSRs)

are highly informative genetic markers, consisting of tandem repeats of sequence

units, short DNA sequence motifs; they are widely spread in eukaryotic genomes

and are often highly polymorphic due to variation in the number of repeat units.

Microsatellite markers have become the preferred marker in many studies because

they are shorter, easier to amplify, more abundant, and more evenly distributed

throughout the genome than SSRs (Muller-Starck and Schubert 2001).

Microsatellites composed of tri-, tetra- and pentanucleotide repeats are generally

less common than mono- and dinucleotide repeats. Another way to categorize

microsatellites relates to the degree of perfectness of the arrays. Weber (1990)

recognized three classes, comprising (1) perfect repeats, which consist of a single,

uninterrupted array of a particular motif; (2) imperfect repeats, in which the array

is interrupted by one or several out-of-frame bases; and (3) compound repeats,

with intermingled perfect or imperfect arrays of several motifs.

Microsatellite mutation rates proved to vary considerably depending on

the locus, the length of the repeat motif, the organism and sometimes the allele.

The mutational processes governing microsatellite evolution are very complex

and care needs to be taken when microsatellite markers are used in population

genetics. Microsatellites as a molecular marker can be used as single PCR

primers, as primers in combination with other primer types or as hybridization

probes.

Genetic conservation

Forest geneticists have been calling for conservation of diversity for many

years. Since they were geneticists, they naturally called this gene conservation.

Genes were needed in breeding, as raw material for selection, and to maintain

viable populations of commercial species. Research and experience have made us

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diversity in forest tree populations that are undergoing population changes due to

natural or human induced events is seen to be instrumental for adaptability and

continued evolution (Muller-Starck and Schubert 2001). Immediate losses in

genetic diversity due to logging may be compensated for by good seed or seedling

banks in the logged or nearby undisturbed stands.

Genetic improvement and conservation should also be complementary. In

situ conservation, if properly planned, can contribute significantly to an ex situ

tree improvement program by providing a sustainable source of genetic material,

while the tree improvement program can provide the motivation and resources for

successful in situ conservation (Payn et al. 2008). If a conservation reserve system

is to adequately represent the genetic diversity of an ecosystem, information on

gene frequencies and population size is critical. The proper design and

maintenance of ex situ plantings are necessary to maximize genetic recombination

while minimizing outside contamination (Lim et al. 2000). Maintaining separate

breeding populations based on specific traits of interest is one effective way to

maintain diversity and minimize the loss of low-frequency alleles (genes for

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MATERIAL AND METHODS

Place and time of study

The research was conducted at Laboratory Institute of Plant Biology,

University of Zurich, Switzerland and at Genetic Diversity Center Zurich (GDC).

This researched was completed between July 2011 and March 2012.

Research procedure Sample collection

Sample collections were conducted throughout the distribution range of

S. leprosula in Southeast Asia (Figure 5). Samples from five natural populations

of S. leprosula were collected from five forest reserves i.e. North Sumatera (NS),

Riau (CS), Ketapang (WKa), West Kalimantan (WKb), and Sungai Menyala

(SM). A total of 96 individuals, that were more than 10 cm diameter breast height

(dbh) were sampled. The location and names of the sample populations are

presented in Table 1. The samples were collected in the form of inner bark

(Appendices 9) or leaf tissue. The collected samples were kept in plastic bags

containing silica gel, and maintained at low temperature before being brought

back to the laboratory for enzyme extraction.

(30)

Table 1 Locations and sample size from the five natural populations of

S. leprosula in Southeast Asia included in this study

Population Population

code

Sample size

Location Latitude Longitude

North Sumatera NS 20 Bukit lawang, Gunung

Leuser National Park

3°30'00" N 97°30'00" N

Central Sumatera CS 20 Bukit Granit, Bukit Tigapuluh National Park

1°0'00" S 102°30'00" E

Segari Melintang SM 20 Segari Melintang Forest Reserve, Peninsular Malaysia

4°22'48"N 100°34'48"E

West Kalimantan WKa 18 DAS Mendalam, Betung Kerihun National Park

1°13'15"N 113°21'11"E

West Kalimantan WKb 18 Ketapang 1°52'00" N 109°59'00" E

DNA extraction and genetic analysis

Genomic DNA was extracted from leaf and inner bark tissue using the

procedure by DNeasy Plant Mini Kit (QIAGEN, Valencia, CA, USA), all steps

were performed as described in the DNeasy Plant Mini Kit Manual. DNA quality

and quantity were determined by Nanodrop ND-1000 technologies. The samples

were genotyped for sixteen microsatellite loci developed for S. leprosula

(Lee et al. 2004) i.e sle105, sle111a, sle267, sle280, sle294, sle303a, sle384,

sle392, sle475, sle562, sle605, sle074, sle079, sle118, sle271a, and sle291a.

Details of microsatellite markers appear in Table 2. Multiplex microsatellite

amplification was performed in 8μl reaction volume containing 5ng DNA (1 μl template DNA), 4 μl 2x Type-it multiplex PCR (Qiagen) master mix, 10 x primer mix 0.2 μM of each primer, and 2,2 μl dH2O. The PCR was carried out in a

GeneAmp 9700 thermal cycler (Applied Biosystems, USA), for an initial

denaturing step of 95oC for 5 min, followed by 30 cycles each at 95oC for 30 sec, 52-54oC for 90 sec and 72oC for 30 sec. A final extension step at 60oC for 30 min was performed after the 30 cycles (details of multiplex PCR microsatellite

(31)

against the internal size standard and the individuals were genotyped using

GeneScan Analysis 3.1 and Gene Mapper software (Applied Biosystems, USA).

(32)

Data analysis

Microsatellite polymorphism and genetic variation

The levels of genetic diversity were estimated for the mean number alleles

per locus (A), Private alleles (AP) and proportion of polymorphic loci (P) were

calculated for each locus and averaged for each population over all loci using

program GDA 1.1 (Lewis and Zaykin, 2001). Observed heterozygosity (HO) and

Nei’s (1978) expected heterozygosity (HE) were calculated with the assistance of

FSTAT version 2.9.3.2 (Goudet 2002).

Genetic differentiation and gene flow

The genetic differentiation of populations is a key parameter in population

genetic investigation (Leng and Zhang 2011). Wright’s (1951) F statistic is a

standard measure of differentiation and deviation from hardy Weinberg

equilibrium at each polymorphic locus in each population. The fixation index, FIS

(inbreeding within individual in population; inbreeding coefficient) and FST

(inbreeding due to population sub division, an indicator of the degree of

differentiation among populations) were calculated based on Weir and Cockerham

(1984) estimator f, respectively using program FSTAT (Goudet 2002). Significant

positive or negative FIS was tested using 1100 randomizations for each locus. The

standard error of FST was calculated using unbiased jackknife analysis, and the

probability of the FST

Differentiation among populations was quantified using Nei’s genetic

diversity statistics (Nei 1973; 1975). Total genetic diversity (H

> 0 was determined using bootstrap analysis with a 95%

confidence interval.

T) at polymorphic

loci, distribution of genetic diversity within populations (HS) and among

populations (DST), so that DST= HT-HS. Among population variation was

compared to total genetic variation to give GST= DST/HT. The GST values were

calculated for each polymorphic locus and then averaged over all loci. HT, HS,

and GST together were obtained using GENETIX V4.02 (Belkhir 2001). The level

of gene flow among the studied populations was determined using the relationship

Nm= (1 – GST)/4GST (Wright, 1951). The genetic of uniqueness of a specific

(33)

and El-Kassaby 1998) as follows: (1) removing population xi data from original

dataset (i.e., -xi), (2) estimating the GST, and gene flow (Nm) for the new data set

(i.e., the original five populations data set minus the population xi data) and

(3) comparing the estimates obtained from the original analysis to that of the five

new datasets (i.e., -x1, -x2, …, -x5). The same analysis was repeated five times. A

Bayesian clustering analysis was carried out using STRUCTURE V2.1 to cluster

individual into K groups. Ten independent runs were done for each value of K (1

to 5), with a burn in period of 100,000 iterations and 100,000 replications: The

number of distinct cluster (K) was selected based on ΔK statistic of Evanno et al.

(2005).

Genetic clustering

Genetic distance between populations was estimated using the Nei DA

genetic distance (Nei et al. 1983) as implemented in Powermarker v3.25 (Liu and

Muse 2005). The resulting distance matrix was used to construct a

neighbor-joining (NJ) phenogram that was bootstrapped 10000 times. The above test for

genetic structure required predefined groups of individuals. An alternate approach

was implemented in the STRUCTURE program (Pritchard et al. 2000).

STRUCTURE can estimate the number of genetically homogeneous population

(K) using a Bayesian model-based clustering method that does not require prior

information the number of locations and from which location each individual was

sampled. Admixture among populations and correlated allele frequencies were

assumed for algorithm. A burn in period of 100,000 iterations was followed by

100,000 iterations of the Markov chain. The model was run for a range of K

values from 1 to 5, with ten replications each, and the ΔK values was determined

(34)

RESULTS AND DISCUSSION

Results

Microsatellite polymorphisms and level of genetic diversity

After screening 16 primer pairs against 96 individuals from five

populations, eleven primer pairs that clearly amplified were selected for further

analysis, i.e sle105, sle111a, sle267, sle280, sle294, sle303a, sle384, sle392,

sle475, sle562, and sle605. The numbers of alleles varied widely among the

eleven loci, but all of the eleven microsatellite loci displayed polymorphism

among five populations with a total of 135 alleles identified. The most frequent

allele at each locus differed from population to population. Some alleles were

present only in certain populations. The number of allele per locus ranged from 8

alleles at locus sle294 to a maximum 16 alleles at sle562, at population level, the

average number of alleles per polymorphic locus (A) ranged from 3.6 (WKa) to

8.5 (SM), with an average 6.6 (1.77) alleles per locus. A complete list and

distribution of allele frequencies for all the polymorphic loci in each population is

shown in appendix 2 and appendix 3. Genetic diversity parameters for the five S.

leprosula populations were estimated using different statistics (Table 3).

Table 3 Average genetic diversity parameters of five natural populations of

S. leprosula in Southeast Asia based on eleven microsatellite loci. Values in parentheses are standard deviation.

Populations N A AP P HO HE

A = Average number of alleles per locus (arlequin) AP

P = Proportion of polymorphic loci (95% criterion) (GDA) = Number of private allele (GDA)

HO

H

= Observed heterozygosity (Fstat)

(35)

The mean expected heterozygosity (HE) was 0.721 with the lowest value

found in WKa population (0.542) and the highest value in the WKb population

(0.788). Private alleles (AP) were identified in all populations except WKa

population. The mean proportion of polymorphic loci are 0.98, except in the WKa

population (0.91), the P value are 1.0 in remaining populations, this is caused by

one monomorphic locus found in the WKa population (Table 3). The observed

heterozygosity (HO) within population ranged from 0.581 (WKa) to 0.754 (SM).

Overall, mean HO (0.687) is generally lower than mean HE (0.721) across five

natural populations, postulating an excess of homozygotes.

Fixation indices (FIS

The fixation indices (F )

IS) calculated for all polymorphic loci in each

population (Table 4) showed significant positive or negative deviations from

Hardy-Weinberg equilibrium in eight loci in the CS population, nine loci in the

NS population, ten loci in the SM population, nine loci in the WKa population and

eight loci in the WKb population. At the population level, a significant level of

inbreeding was observed in three population (CS, SM and WKa) which may

postulate that the plant practices some selfing and biparental mating in these three

populations. The mean FIS for all populations ranged between -0.075 (WKa) to

0.160 (WKb), mean negative fixation indices value was found in the WKa

population (-0.075), while the remaining populations showed positive values. For

all loci, negative values are found at each locus in different populations, this

negative value showed an excess of heterozygosity. Overall, the mean fixation

(36)

Table 4 Fixation index (FIS)a for five natural populations of S. leprosula in

Significance level based on 1100 randomisations (* p<0.05) FIS

MONO = Monomorphic loci

was estimated according to Weir and Cockerham (1984).

The differentiation among populations (FST) was 0.1110, which indicated

that 11.10% of the total genetic diversity was distributed among populations,

while the remaining percentage of the total genetic diversity partitioned within

populations. This value of FST indicates a highly significant (P<0.01)

differentiation among populations both in allele size and frequency. The mean

weighted RST are 0.1160, both estimates FST and RST showed almost same value.

Analysis of Molecular Variance (AMOVA) showed that most of the variance in

the sample is attributable to within individual variation (84.84% of the variance),

while the variation among populations was 11.07% (Table 5), and the source of

(37)

Table 5 Analysis of molecular variance (AMOVA) based on 11 microsatellite loci and five natural populations of S. leprosula in Southeast Asia

Source of Variation d.f Sum of

Among individuals within populations 91 378.269 0.183*** 4.09

Within individuals 96 364 3.791*** 84.84

Total 191 834.844 4.469 100

Significance levels were based on 1023 permutations ( * p<0.05; ** p<0.01; *** p<0.001)

Total genetic diversity (HT) and within population genetic diversity (HS)

generated overall loci averages of 0.702 and 0.785 respectively (Table 6). The

mean proportion of total diversity among population (GST) was 0.105 indicating

that about 10.5% of encountered genetic diversity was due to inter-population

gene frequency differences. Hence, the majority of genetic diversity (79.5%)

resided within population. The level of gene flow (Nm) among the five natural

populations is relatively high (2 migrants per generation). The genetic uniqueness

of each population was determined by calculating GST statistics, average genetic

distance (D), and gene flow (Nm) for remaining populations after the removal of

the specific population from the data. For notation, we denote the populations

remaining as “minus” the deleted population, for example “-CS” for all

populations less CS. Low among population genetic diversity (GST) values of

0.081 were obtained for the analysis of –WKa population. This value is lower

than obtained from the analysis of the five populations altogether (0.105) as well

(38)

WKb

S. leprosula populations after the removal of one population at a time.

Population HS HT DST GST FST Nm GST= proportion of total diversity, partition among populations, DST

-CS represents an analysis that contained all populations after the removal of the CS population. Gene pool represents an analysis of the five natural populations.

= distribution of genetic diversity among populations.

Genetic clustering

UPGMA dendrogram based on Da values revealed two major groups of

populations (Fig. 6). First group included populations Central Sumatra (CS),

North Sumatra (NS) and Peninsular Malaysia (SM). The other group contained

populations West Kalimantan (WKa) and Ketapang (WKb). SM was separated

from CS and NS populations.

(39)

To further evaluate population structure, model-based clustering

(STRUCTURE analysis) was performed using multi locus genotype data. The ΔK

values showed a clear peak at K = 2, indicating that the investigated S. leprosula

individuals could be split into two genetically distinct groups, at the upper most

hierarchical level (Fig. 7).

Figure 7 Bayesian cluster analysis using STRUCTURE (Pritchard et al. 2000) showed two main clusters with CS, NS and SM as one cluster and WKa and WKb as another cluster

When K = 2, all individuals from populations CS, NS in Sumatera and SM

in Peninsular Malaysia were assigned to one cluster, and all individuals from

populations WKa and WKb in Borneo were assigned to another cluster (Figure 7).

However, individuals from populations Ketapang (WKb) were assigned to either

of two clusters, suggesting this population is a mixture of both clusters. This

analysis revealed that the investigated S. leprosula individuals could be divided

into Sumatera-Peninsular Malaysia and Borneo groups with one admixed

population in each group. Here after we designate the group consisting of

populations from Peninsular Malaysia and Sumatera as Sumatera-Malay Group

and the group consisting of populations from Borneo as Borneo group.

The genetic distances of the five populations of S.leprosula are given in

Table 7. WKa and WKb populations gave the lowest genetic distance (0.234),

while the genetic distance between WKa and NS populations is the highest

(40)

Table 7 Genetic distance according to Nei (1978 above diagonal) and matrix of average gene pool distances (do

) according to Gregorius (1974, below diagonal) among five S. leprosula populations

Population CS NS SM WKa WKb

Genetic diversity in Shorea leprosula populations

Out of the eleven microsatellite loci studied, 135 alleles were observed,

which is mean of 6.6 alleles per locus (Table 3). This value is higher than those

obtained from allozyme markers for S. leprosula (3.1 ± 0.5; Lee et al. 2000b).

Ng et al. (2006) obtained 9.2 alleles per locus in S. leprosula using microsatellite

markers. Microsatellite markers give a high number of alleles due to the length

mutation that causes differences in repeats units. Meanwhile microsatellite

markers can be found dispersed in the whole eukaryote genome. Therefore,

microsatellite markers may give a clearer picture of genetic diversity of species.

Nagamitsu et al. (2001) detected 25 alleles at one locus (Sch07) in S. leprosula,

and 17 alleles at one locus in Neobalanocarpus heimii (Iwata et al. 2000), this

value is a slightly lower number of alleles that is ever detected in tropical trees. In

general, a large sample size may be needed to represent all the alleles present in

the population and for the all the alleles to be in Hardy-Weinberg equilibrium.

Our analysis detected a high level polymorphism based on eleven

microsatellite loci in all five populations of the S. leprosula in Southeast Asia.

Expected heterozygosity in this study was more than observed value by 0.678,

showing an excess in homozygotes. This may because leaf samples of seedlings

were used in this study. The levels of genetic diversity estimated were high

(41)

dipterocarps. For instance, S. leprosula (Ng et al. 2006; Lee et al. 2000b),

Dryobalanops aromatica (Lim et al. 2002), Intsia palembanica (Lee et al. 2002),

and Shorea lumutensis (Lee et al. 2006). The mean expected heterozygosity

(HE =0.721) within populations for S. leprosula in this study was much higher

than reported for other regionally distributed tropical long lived tree species

(HE= 0.125; Hamrick et al 1992). It was also higher than reported for Shorea

lumutensis, an rare and endemic dipterocarpaceae species in Peninsular Malaysia

(HE= 0.648; Lee et al. 2006). The study of Ng et al. (2006) based on five

microsatellite loci at Sungai Lalang Forest Reserve in Peninsular Malaysia also

encountered exceptionally high level of genetic diversity in S. leprosula

(A= 9.2 and HE

In previous allozyme studies of S. leprosula conducted by Lee et al.

(2000b) in Malaysia, a high genetic diversity was found for allozymes (A=2.5

H

= 0.72).

E=0.410). It seems that the two marker systems produced different genetic

profiles, in that apparently higher genetic diversity was generated by

microsatellites. This discrepancy could be attributed to the different marker

system. As pointed out by many recent studies, microsatellites possessed hyper

variability and higher resolving power among various genetic markers

(Powell et al. 1996). Estimates of expected heterozygosity (HE) from

microsatellite data for tree species are generally based on one or two populations

where the focus is mainly on development of microsatellite primers and their

applicability in related species. According to Hamrick et al. (1979) long-lived

trees have a higher level of heterozygosity. This is important for the survival of

species in changing environments especially when natural selection occurs.

Hiebert and Hamrick (1983) supported this theory with a study on P.longaeva that

showed a high level of HE. High levels of genetic diversity can be associated with

population history, strategy and life history traits of species such as predominantly

outcrossed, long life span, distribution and high fecundity species

(42)

Fixation indices (FIS

All the populations of S. leprosula except WKa showed positive fixation

indices, indicating in a general excess of homozygotes. The excess of

homozygotes that is observed here may be caused by inbreeding and/or biparental

mating, as reported in many other studies (Lee et al. 2000a). A similar observation

has also been shown through a study on Pinus (Muona 1989). According to

Muona (1989), positive fixation indices at seedling level might be due to self

pollination. Seedlings produced through inbreeding usually die off before maturity

since they are weaker than outbreed progenies. In Eucalyptus regnans, higher

levels of inbreeding found in natural stands compared to a seed orchard were

explained by the spatial genetic structure (Moran et al. 1989). This also could be

the case for S. leprosula, in which trees in a natural population tend to occur in

clumps. Additional inbreeding may be due to mating between relatives in these

clumps.

)

This phenomenon has also been described in S. megstophylla

(Murawski et al. 1994). Selection favoring and increasing the proportion of

heterozygotes can occur in predominantly outcrossed population as a result of

heterosis and overdominance or by discriminating against the products of selfing

or biparental mating, which tend to have higher level of homozygosity. As shown

by Manokaran et al. (1992), S. leprosula is a dynamic species with high seedling

mortality (19.7%) and a high percentage of recruitment (10.2%). The high

mortality rate in the seedling stage and gradual decrease in sapling and adult

stages (6.4%) suggest that such selection may be common in S. leprosula.

Excess heterozygosity in locus sle111a shown by a negative value might

mean that this locus may be co-segregating with a trait responding to selection.

The Wahlund effect states that natural populations that are divided into

sub-populations might have genotype frequencies that deviate from Hardy-Weinberg

equilibrium due to natural selection or random genetic drift in the case of small

populations (Wahlund 1928). The effect is the homozygous genotype frequency

of the whole populations becomes more than Hardy-Weinberg equilibrium would

predict. In the case of S. leprosula, it could be said that all the populations of

(43)

a few sub-populations. This would explain the high fixation indices observed in

all the S. leprosula populations. Excess of homozygotes was also detected in

S. leprosula populations in Peninsular Malaysia using Allozyme markers

(Lee et al. 2000a).

The microsatellite markers applied in this study revealed a moderate

degree of genetic differentiation among the five S. leprosula populations with an

overall FST value of 0.111 (11.1%) and GST 0.105 (10.5%). Defined by Wright

(1978), FST has since become a standard measure of differentiation, which has

been somewhat strengthened by Wright’s incidental expression: “We will take

F= 0.25 as an arbitrary value above which there is very great differentiation, the

range 0.05-0.15 as indicating moderately great differentiation. Differentiation is,

however, by no means negligible if F is as small as 0.05 or even less”. For loci

with multiple alleles, Nei (1973) introduced GST as an extension of FST. Thus,

GST is equivalent to FST and can be regarded as a weighted average of FST for all

alleles (Nei 1973). In the present study, genetic differentiation was higher

(FST = 0.111) than the result reported by Lee et al. (2000a) on S. leprosula

(FST= 0.085) in Peninsular Malaysia which was obtained using allozyme markers.

This is because microsatellite loci are more polymorphic than allozyme loci.

Several comparative studies have been conducted in D. aromatica based on

allozyme and microsatellite analysis. Lee et al. (2000b) used allozyme markers to

investigate ten populations of D. aromatica collected from Peninsular Malaysia,

based on eight allozyme loci and found low genetic differentiation (FST= 0.042),

while Lim et al. (2002) detected higher genetic differentiation (FST

Maintenance of moderate variation among populations depends on the

efficiency of gene flow, a factor that usually is supported by effective

cross-pollination and seed dispersal mechanisms. This however is not the case in

S. leprosula as it is characterized by poor pollen and fruit dispersal. S. leprosula is

pollinated by Thrips (Appanah and Chan, 1981), weaker fliers than bees or other = 0.062) on D.

aromatica on the basis of microsatellite analysis. This result is slightly lower than

(44)

insect pollinators. S. leprosula can also be pollinated by beetles (Sakai et al. 1999)

which are not likely to diperse pollen for long distances. This may result in

limited pollen movement and greater population differentiation. Fruit dispersal in

S. leprosula is by wind, or more often by gravitation alone, with seeds often

falling within 50 m of the tree crown and there is no evidence that they can

withstand sea water (Ashton, 1988). These facts suggest that migration between

the two population group must have occurred when their distributions were close

enough to allow for pollen and/or seed exchange.

This population genetics studies used indirect methods to establish levels

of gene flow. Statistics that estimate this parameter generally include variance

among populations in allele frequencies (FST Wright, GST Nei, and RST

STRUCTURE analysis revealed that investigated S. leprosula individuals

could be divided into two genetically distinct groups: Sumatra-Peninsular

Malaysia and Borneo with one admixed population in Borneo (Ketapang/WKb).

Clear separation of the two groups suggests limited genomic exchanges between

them and that the admixture occurred quite recently or reproductive isolation has

developed between the two groups. Analysis of S. leprosula based on AFLP data

(Cao et al. 2006) also supported the result in this study. The analysis also revealed

that S. leprosula individuals are separated into two groups. Furthermore they

found another admixed population located in Central Sumatera, where some of the

individuals are clustered with individuals from Borneo, suggesting that a large

area was involved in the admixture event and that S. leprosula has a complicated Slatkin)

and Nm, the effective number of migrants per generation. Values are interpreted

as indications of relative levels of gene flow among populations (Bossart and

Prowell 1998). Estimates of Nm less than one indicate relatively little gene flow

and one or greater suggest high levels of gene flow (Slatkin, 1985; Slatkin and

Barton, 1989). Indirect estimates of Nm represent historical average levels of gene

flow and may not represent present day levels (Loveless, 1992). Thus, the indirect

estimates of Nm of 2.00 (Table 7) indicate the presence of the substantial gene

flow among populations in the past, which might responsible for the maintenance

of moderate genetic differentiation among populations in S. leprosula in the

(45)

population structure. In addition, the STRUCTURE analysis of S. parvifolia

individuals conducted by Iwanaga et al. (2012) revealed that seven populations

from peninsular Malaysia, Sumatera and Eastern Borneo are divided into two

groups: Sumatera-Malay and Borneo. Furthermore, each group contained one

admixed populations. This STRUCTURE analysis is similar to the result obtained

in our present study.

The moderate level of population differentiation may also be due to

historical population fragmentation. At the last glacial maximum (LGM,

approximately 0.02 MYA), Peninsular Malaysia, Sumatra, Java and Borneo island

were connected to each other by the exposed Sunda Shelf (Voris, 2000). The

evidence to suggest that the islands of Sumatera, Peninsular Malaysia, Java and

Borneo were connected when sea levels were 60-120 m below the present level is

shown by Figure 8 (Heaney, 1991, Voris 2000). Many studies suggested that

during this period, rainforest refugia were present in northern and eastern Borneo,

northern and western Sumatra and the Mentawai islands (Thomas 2000; Cannon

et al. 2009; Wurster et al. 2010). Peninsular Malaysia, Sumatra, Java and Borneo

were connected by the exposed Sunda Shelf when the sea level dropped and

savanna vegetation appeared (Morley, 2000). The exposed Sunda Shelf in the

Pleistocene might be allowed for S. leprosula to spread. This is supported by a

recent study which revealed that South west Sumatra and a substantial region of

the Sunda Shelf and Borneo were covered by rainforests during much of the last

(46)

Figure 8 Map of Southeast Asia showing current land areas and the late Pleistocene land area, as estimated from 120 m bathymetric line (Heaney, 1991)

Population structure and divergence history have been shown in some

lowland rainforest species, for instance in S. parvifolia. However, the divergence

times estimated in S. leprosula are not included in this study due to lack of

sampling and limited analysis. Recently, a study of the demographic history of a

tropical lowland rainforest tree species S. parvifolia was conducted by Iwanaga et

al. (2012). It revealed that the divergence time of the Sumatra-Malay and Borneo

groups is in the range from 2.7 to 0.7 MYA, which corresponds to the late

Pliocene and the middle Pleistocene respectively.

The inferred demographic history of S. parvifolia suggests the presence of

a scarcely forested land bridge on the Sunda shelf during glacial periods in the

Pleistocene and predominance of tropical lowland rainforest in at least in

Sumatera and Borneo. In contrast, population structure and divergence history

similar to S. leprosula have been shown in some lowland rainforest species.

Comparable genetic differences between Sumatera and Borneo populations were

detected in S. parvifolia and S. leprosula (Cao et al. 2006). The deep split

between Borneo and other Sunda lineages was also demonstrated for

(47)

between Peninsular Malaysia and Borneo was found in the present study,

migration would be possible between Peninsular Malaysia and Borneo population

via the exposed Sunda shelf. More studies including wider sampling are required

to address this issue.

Implications for conservation of Shorea leprosula

The present results showed S. leprosula maintained a high level of genetic

diversity. Private alleles are the alleles which have low frequency and can show

the uniqueness of a population and it has been found in all Shorea leprosula

populations except WKa populations. This unique allele can be used as criteria for

conservation so that the genetic diversity can be maintained. Private alleles can be

preserved by limited logging activity and forest destruction. As the species

produce valuable timber, disruption of some these existing populations in future is

unavoidable. The high levels of genetic diversity promise a “massive gene pool”

for selection and manipulations. Thus, it is still not too late for tree breeders to

initiate a tree improvement program that can further improve the species for forest

plantations. All the present planting programs rely on natural stand seed

collections. The information on the mating system as reported by Lee et al.

(2000a; 2000b) and the genetic information of some of its population from the

present study could be used to instruct the establishment of several production

areas. The production areas should be established in the three genetically unique

populations (North Sumatera, Peninsular Malaysia and West Kalimantan). This

approach will grant an opportunity to affiliate conservation and utilization efforts

in constructive way. In addition, the establishment of seed production areas in

genetically unique populations will safeguard their elimination from short term

harvesting plans.

In situ conservation of S. leprosula requires a few populations. This is due

to the higher diversity within populations compared to among populations.

For Ex situ conservation, it is suggested that focus is given to variation among

individuals within populations so that all the variations are sampled. Population

selection should be done based on the highest diversity in terms of allele or

Genetic structure and variation of five Shorea leprosula natural populations based on microsatellite marker

GENETIC STRUCTURE AND VARIATION OF FIVE

Shorea leprosula

NATURAL POPULATIONS BASED ON

MICROSATELLITE MARKER

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

BOGOR

DECLARATION OF ORIGINALITY

ABSTRACT

© Copyright owned by IPB, 2012

All rights reserved

GENETIC STRUCTURE AND VARIATION OF FIVE

Shorea leprosula

NATURAL POPULATIONS BASED ON

MICROSATELLITE MARKER

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

BOGOR

ACKNOWLEDGEMENT

ABOUT THE AUTHOR

TABLE OF CONTENTS

LIST OF TABLES

LIST OF FIGURES

LIST OF APPENDICES

INTRODUCTION

LITERATURE REVIEW

MATERIAL AND METHODS

RESULTS AND DISCUSSION

WKb