Pola Yang Kontras Dan Konektivitas Terbatas Pada Lamun Tropis Enhalus Acoroides (Hydrocharitaceae) Di Bagian Barat Kepulauan Indo-Malay Diungkap Menggunakan Dna Mikrosatelit.
CONTRASTING PATTERN AND LIMITED CONNECTIVITY
IN TROPICAL SEAGRASS Enhalus acoroides
(HYDROCHARITACEAE) AT WESTERN REGION OF THE
INDO-MALAY ARCHIPELAGO REVEALED BY
MICROSATELLITE DNA
I NYOMAN GIRI PUTRA
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2015
DECLARATION OF ORIGINALITY
I hereby declare that the master thesis entitled “Contrasting pattern and limited
connectivity in tropical seagrass Enhalus acoroides (Hydrocharitaceae) at western
region of the Indo-Malay Archipelago revealed by microsatellite DNA” 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 Information derived from the published and unpublished work of others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, July 2015
I Nyoman Giri Putra
NIM C551124041
RINGKASAN
I NYOMAN GIRI PUTRA. Pola yang kontras dan konektivitas terbatas pada
lamun tropis Enhalus acoroides (Hydrocharitaceae) di bagian barat Kepulauan
Indo-Malay diungkap menggunakan DNA mikrosatelit. Dibimbing oleh Hawis H.
Madduppa dan Made Pharmawati.
Bagian barat dari Kepulauan Indo-Malay sering dihubungkan dengan barier
Indo-Pasifik yang memisahkan antara Samudra Hindia dan Pasifik. Berbagai studi
filogeografi menunjukkan bahwa terdapat perbedaan struktur genetik antara biota
di samudra Hindia dan Pasifik. Meskipun cukup banyak penelitian filogeografi di
Hindia dan Pasifik akan tetapi hanya sedikit dari penelitian tersebut yang
memfokuskan penelitiannya di Kepulauan Indo-Malay. Studi terhadap fauna laut
menunjukkan bahwa pola filogeografi biota di kepulauan Indo-Malay cukup
kompleks bahkan tiap spesies cenderung menunjukkan pola yang berbeda.
Sementara itu studi filogeografi menggunakan tumbuhan laut belum pernah
dilaporkan sebelumnya. Oleh sebab itu penelitian ini akan mengkaji pola
filogeografi dari lamun Enhalus acoroides menggunakan delapan lokus
mikrosatelit.
Tujuan dari penelitian ini yaitu: (1) menghitung keragaman genetik E.
acoroides di bagian barat dari Kepulauan Indo-Malay, (2) menghitung perbedaan
genetik antar populasi dan (3) menerangkan pola filogeografi dari E. acoroides.
Dalam penelitian ini, sebanyak 202 spesimen E. acoroides dikumpulkan
dari tujuh lokasi pengambilan sampel (Aceh, Anambas, Batam, Bangka, Tunda,
Pramuka, dan Karimun Jawa). Total DNA diekstraksi menggunakan DNeasy plant
mini kit (Qiagen®) dengan tahapan ekstraksi mengikuti protokol dari perusahaan.
Amplifikasi DNA dilakukan menggunakan delapan lokus mikrosatelit (Eaco_001,
Eaco_009, Eaco_019, Eaco_050, Eaco_051, Eaco_052, Eaco_054, Eaco_055).
Hasil penghitungan nilai genotipe menunjukkan bahwa hanya ada enam
lokus (dari delapan lokus) yang berhasil diamplifikasi dan polimorfik. Penelitian
ini menunjukkan bahwa E. acoroides memiliki keragaman genetik tinggi dengan
nilai heterozigositas pengamatan dan harapan secara berturut-turut berkisar dari
0.434 - 0.615 dan 0.458 - 0.605. Nilai statistic F (pairwise θ berkisar antara 0.127
and 0.359) menunjukkan perbedaan genetik yang signifikan antar lokasi dengan
dengan nilai P < 0.001. Analisis isolation by distance juga menunjukkan hasil
signifikan (P=0.008) yang mengindikasikan aliran gen terbatas pada semua lokasi.
Pohon Neighbour Joining berdasarkan jarak genetik DA dan analisis klaster
Bayesian menunjukkan ada 3 klaster utama dari E. acoroides. Analysis of
Molecular Variance (AMOVA) menunjukkan bahwa ketiga grup ini signifikan
berbeda dengan nilai P < 0.05. Hasil studi ini menunjukkan bahwa pola
filogeografi dari E. acoroides dipengaruhi oleh peristiwa membeku dan
mencairnya es selama Pleistocene. Kondisi fisik oceanografi saat ini seperti arus
selatan Jawa dan arus musiman juga memegang peran penting dalam
pembentukan struktur genetik E. acoroides. Hasil penelitian ini akan menyediakan
data genetik yang dapat digunakan untuk tujuan konservasi lamun dan design
manajemen unit (MUs).
Kata kunci: filogeografi, Enhalus acoroides, mikrosatelit, Kepulauan Indo-Malay
SUMMARY
I NYOMAN GIRI PUTRA. Contrasting pattern and limited connectivity in
tropical seagrass Enhalus acoroides (Hydrocharitaceae) at western region of the IndoMalay Archipelago revealed by microsatellite DNA. Supervised by Hawis H.
Madduppa dan Made Pharmawati
The western region of the Indo-Malay Archipelago is often associated with
the Indo-Pacific barrier (IPB), separating Indian and Pacific oceans. Various
phylogeographic studies of marine biota throughout Indian and Pacific oceans
found genetic partition between these two oceans. Although many
phylogeographic studies in the Indian and Pacific oceans have been reported, only
a few of these studies focus on the Indo-Malay Archipelago. Previous studies
found that genetic structure of marine faunas in Indo-Malay Archipelago is clearly
complex, seemingly each species showed different pattern. Meanwhile
phylogeographic studies using marine plant have not been reported previously.
Therefore, this study will reveal phylogeographic pattern of E. acoroides using
eight microsatellite loci.
This study has three main objectives that include the following: (1) to
examine genetic diversity of E. acoroides in western region of the Indo-Malay
Archipelago, (2) to examine genetic differentiation among all sites and (3) to infer
phylogeographic pattern of of E. acoroides.
A total of 202 E. acoroides specimens from seven localities (Aceh, Anambas,
Batam, Bangka, Tunda, Pramuka, and Karimun Java) were collected. At each
location, 18-42 individuals were taken in a zigzag pattern along the line transect.
Total genomic DNA was extracted using DNeasy plant mini kit (Qiagen®)
following the manufacturer’s protocol. DNA amplification was performed using
eight microsatellite loci (Eaco_001, Eaco_009, Eaco_019, Eaco_050, Eaco_051,
Eaco_052, Eaco_054, Eaco_055).
Genotypic scoring showed only six loci (of eight loci) were successfully
amplified and polymorphic. This study showed that E. acoroides has high genetic
diversity among all sites. The observed and expected heterozygosity ranged from
from 0.434 to 0.615 and from 0.458 to 0.605, respectively. F-statistics (pairwise θ
ranges between 0.127 and 0.359) revealed high genetic differentiation between all
sites (P < 0.001). A pattern of significant isolation by distance (P = 0.008) was
observed among all sites indicating restricted gene flow among all sites.
Neighbour Joining tree based on DA distance revealed three major clusters of
E. acoroides consistent with Bayesian clustering analysis result. Further, Analysis
of Molecular Variance (AMOVA) revealed significant partition of these groups (P
< 0.05). Our result indicated that phylogeographic pattern of E. acoroides possibly
influence by glaciation and deglaciation during Pleistocene. Recent physical
oceanography such as South Java Current and seasonality reversing current also
play a role in shaping genetic pattern of E.acoroides. These results will provide
data for seagrass restoration purposes and management unit (MUs) design.
Keywords: phylogeography, Enhalus acoroides, microsatellite, Indo-Malay
Archipelago
© Copyright owned by IPB, 2015
All rights reserved
No part of this document may be reproduced or transmitted in any form or by any
means, electronic, mechanical, photocopying, recording, or otherwise, without prior
written permission from IPB.
CONTRASTING PATTERN AND LIMITED CONNECTIVITY
IN TROPICAL SEAGRASS Enhalus acoroides
(HYDROCHARITACEAE) AT WESTERN REGION OF THE
INDO-MALAY ARCHIPELAGO REVEALED BY
MICROSATELLITE DNA
I NYOMAN GIRI PUTRA
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
2015
External examiner of the thesis supervisor: Dr. Irma Shita Arlyza, M.Si
Thesis Title : Contrasting pattern and limited connectivity in tropical seagrass
Enhalus acoroides (Hydrocharitaceae) at western region of the IndoMalay Archipelago revealed by microsatellite DNA
Name
: I Nyoman Giri Putra
Student ID : C551124041
Major
: Marine Science
Approved by,
Supervisor
Dr. Hawis H.Madduppa, S.Pi. M.Si
Head-supervisor
Ir. Made Pharmawati, M.Sc. Ph.D
Co-supervisor
Endorsed by,
Head of Major
Marine Science
Dean of Graduate School
Dr. Neviaty P. Zamani, M.Sc.
Dr. Ir. Dahrul Syah, M.Sc. Agr
Date of Examination: July 09 2015
Date of Graduation:
ACKNOWLEDGEMENT
This study is mainly funded by PEER (the Partnerships for Enhanced
Engagement in Research, No: PGA-2000003438) funded by United States
Agency for International Development (USAID) and the National Science
Foundation (NSF) in partnership with NSF-PIRE Program. This was also funded
by the government of Indonesia through the Indonesia Endowment Fund for
Education (LPDP).
I would like to express my appreciation to Indonesian Education
Scholarship (BPI) managed by LPDP for Master program scholarship in Bogor
Agricultural University (IPB). I would like to thank Dr. Hawis H. Madduppa,
M.Si and Ir. Made Pharmawati, M.Sc. Ph.D. as my supervisors for their
supervision and guidance. I also thank Dr. Irma Shita Arlyza, M.Si. as external
examiner for valuable comments. I would like to also thank Beginner Subhan and
Dondy Arafat (Marine Biodiversity and Biosystematics Laboratory, IPB) for
collecting samples in Batam, Ibu Widiastuti (Diponegoro University) for
collecting samples in Karimun Jawa, Khalidin (Syah Kuala University) for sample
collection in Aceh, Dedi, Gugun and Okto for their assistance with sample
collection in Bangka.
I would like to also express my gratitude to Indonesian Biodiversity
Research Center (IBRC) who had facilitated all the research works, provided
research networks, organized sampling collection and provided laboratory tools
and equipments. This specifically goes to Aji Wahyu Anggoro, Dita Cahyani and
Prof. IGN Mahardika for their contribution in assuring the research running well. I
would like to also thank Yuliana Fitri Syamsuni, Samsul Bahri, Astria
Yusmalinda, Rizki Wulandari, Andrianus Sembiring, Masriana, Eka Maya
Kurniasih, Dian Pertiwi and Angka Mahardini from IBRC for their helps in
laboratory and field work.
Bogor, July 2015
I Nyoman Giri Putra
TABLE OF CONTENTS
LIST OF TABLES
vi
LIST OF FIGURES
vi
LIST OF APPENDIX
vi
INTRODUCTION
Background
Problem Description
Research Objectives
1
1
2
3
MATERIALS AND METHODS
Study Area and Sample Collection
DNA Extraction and Amplification
Data Analysis
Genetic Diversity and Hardy-Weinberg Equilibrium (HWE)
3
3
5
6
6
Population Structure
RESULTS AND DISCCUSSION
Results
DNA Isolation and Amplification
6
7
7
7
Genetic Diversity and Hardy-Weinberg Equilibrium
8
Genetic differentiation
9
Phylogenetic and Bayesian Clustering Analysis
10
Analysis of Molecular Variance (AMOVA)
11
Discussion
Genetic Diversity
11
11
Population Differentiation
12
Phylogeographic Pattern of E. acoroides
13
Implication for Restoration and Management
14
CONCLUSION AND FUTURE PERSPECTIVE
Conclusion
Future Perspective
15
15
15
REFERENCES
15
APPENDIX
21
ABOUT THE AUTHOR
27
LIST OF TABLES
1. Sampling location, abbreviation, geographical coordinates and the
number of samples (N) used in this study
2. Eight polymorphic microsatellite loci in Enhalus acoroides with
loci, primer sequence, dyes, fragment size range, and GenBank
accession number.
3. Summary of genetic diversity at six microsatellite loci at seven
locations for E. acoroides
4. Pairwise FST values (below diagonal) and significant FST P values
(above diagonal)
5. Result from hierarchical AMOVA for E. acoroides
4
5
9
10
12
LIST OF FIGURES
1. Research framework
2. Sampling location from which Enhalus acoroides were collected
for present study. NK: Nakuri, BK: Bangka, BM: Batam, ANS:
Anambas, TD: Tunda, PR: Pramuka, KJ: Karimun Jawa, SJC:
South Java Current, SRC: Seasonal Reversing Current.
3. Gel electrophoresis of the DNA extraction product. Samples: lane
1-14, size marker: lane 15.
4. Multiplex PCR. Size marker: lane 1, samples: lane 2-8.
5. Single primer PCR. Size marker: lane 1, samples: lane 2-8.
6. Isolation by distance showed significant correlation between
genetic distance (pairwise FST values) and geographic distance
(see Appendix 5) with P = 0.008.
7. Neighbour Joining (NJ) tree based on DA distance and bar plot of
Structure 2.3.4 revealed congruent results with all populations
were divided into three clusters (NK/TD, PR, BK and BM/ANS
and KJ). Each colour represents one cluster.
3
4
7
7
8
10
11
LIST OF APPENDIX
1. Sample collection and preservation.
2. Research documentation. (a) Sample collection, (b) Sample
identification and preservation, (c) Desiccated sample, (d) Gel
electrophoresis, (e) Enhalus acoroides, (f) DNA amplification.
3. Gel electrophoresis.
4. Allele frequencies and sample size by populations.
5. Geographic distance between all sampling sites (in km).
20
21
22
23
26
INTRODUCTION
Background
The Indo-Malay Archipelago, also known as Indo-Australian Archipelago
(IAA) is one of the most important land barriers (The Indo-Pacific Barrier, IPB)
separating the Indian and Pacific oceans (Crandall et al. 2008). Although the
location of the boundary is debated, this barrier is most often associated with
western region of Indo-Malay Archipelago (Sunda shelf) (Barber et al. 2006;
DiBattista et al. 2012). Various levels of taxa confirmed the existence of these
barriers, ranging from reef fishes of the Pomacanthidae (Thresher & Brothers
1985), marine gastropod Nerita albicilla (Crandall et al. 2008), crown-of-thorns
Acanthaster planci (Vogler et al. 2008; Yasuda et al. 2009) and Snapper fishes
Lutjanidae (Gaither et al. 2009). Although many phylogeographic studies in the
Indian and Pacific ocean have been reported, only a few of these studies focus on
the Indo-Pacific Barrier (Carpenter et al. 2011; Keyse et al. 2014). Though this
barrier was an overlapping region of organisms from both ocean (Gaither &
Rocha 2013).
The western Indo-Malay Archipelago mostly constitutes of large lands mass
such as Sumatra, Thai-Malay Peninsula, Borneo, and the Greater Sundas. During
the ice age period, these islands formed a large land known as Sunda shelf (Voris
2000). Despite their same geological origin, population structures of flora and
fauna in these islands is not simple. Studies of the mantis shrimp Haptosquilla
pulchella revealed sharp genetic break across Java seas, which divided population
into the north (Pasific) and south (Indian) (Barber et al. 2002). Other studies on
giant clams Tridacna crocea (DeBoer et al. 2014), tuna and mackerel (Jackson et
al. 2014) showed that the population of Sumatra in western Indonesia represent
the Indian ocean population while the population of Java represents middle
Indonesian population. Thus, the phylogeographic patterns shown by the two
islands is quite complex.
Gene flow is driven by various factors such as currents and geological
history of a location. In eastern Indonesia, Halmahera Eddy and Indonesian
Throughflow are the main factors shaping biogeographic barrier between eastern
and western Indonesia (Barber et al. 2006; Carpenter et al. 2011). Another study
found that water circulation and eddy located at the southern tip of Sumatra plays
a role in maintaining the genetic structure of mangrove Rhizophora mucronata
Lam. in the Malay Peninsula and Sumatra (Wee et al. 2014). Geological history
such as the emergence of Sunda shelf during the Pleistocene period, are the main
factors that inhibit larval dispersal and genetic exchange between the Indian and
Pacific which then triggers lineage diversification in both oceans (Carpenter et al.
2011)
Most of the phylogeographic studies in western region of Indo-Malay
Archipelago used mitochondrial genes from marine animals such as crustacean
(Barber et al. 2006), reef fishes (Nelson et al. 2000; Ackiss et al. 2013), starfishes
(Vogler et al. 2012), and bivalve (DeBoer et al. 2008) showed genetic structuring
between population in Indian ocean and Java and or South China seas. However,
other marine animals, such as pelagic scads Decapterus macrosoma (Arnaud-
2
Haond et al. 1999) and marine gastropod Nerita plicata (Crandall et al. 2008)
indicated lack genetic structuring. Study of marine plants such as mangroves with
DNA nuclear marker revealed genetic discontinuity of mangrove Rhizophora
mucronata Lam. at the boundary between the Andaman sea and Malacca Strait
(Wee et al. 2014). Meanwhile phylogeographic studies using seagrass has not
been reported previously.
Seagrass is marine angiosperm lives in coastal areas on a substrate of sand,
mud or a mixture of both and the entire life cycle occurs below sea level. Enhalus
acoroides is one of seagrass species that widely spread in the Indo-Pacific from
southern Japan, Southeast Asia, northern Australia, southern India and Sri Lanka
(Short & Waycott 2010). In Indonesia, E. acoroides can be found in Papua, North
Maluku, Ambon, Sulawesi, Bali, Java, Kalimantan, and Sumatra (Kiswara &
Hutomo 1985). This species is easily distinguished from other seagrass because it
has long leaves, black stringy rhizome, and usually form a bed. Fruits of E.
acoroides could floats up to 10.2 days (Lacap et al. 2002). Meanwhile, median
dispersal ability of seeds and fruits could reach 0.1 (max. 3.7) km and 41 (max.
63.5) km, respectively (Lacap et al. 2002), which might limiting the dispersal of
E. acoroides. Species with limited dispersal frequently hypothesized to be more
genetically structured (Bay et al. 2006).
In this study, eight microsatellite markers (Nakajima et al. 2012) were used
to infer phylogeographic pattern of E. acoroides. These DNA marker are widely
used because they are both codominant and highly polymorphic (Beebee & Rowe
2008). Microsatellites have been found to be very useful and broadly used in
phylogeographic studies (Koskinen et al. 2002; Adams et al. 2006; Suárez et al.
2009; Liu et al. 2012; Poortvliet et al. 2013; Madduppa et al. 2014; Wee et al.
2014).
Problem Description
Transition zone between Pacific and Indian oceans such as the western
region of the Indo-Malay Archipelago often showed complex phylogeographic
patterns eventhough the islands originate from the same geologic history.
Previous studies agreed that phylogeographic pattern of marine biota could be
affected by several factor such as current, larval dispersal ability, several events in
the past (e.g. Last Glacial Maxima, LGM) and the characteristics of each species.
E. acoroides is one of the seagrass species that showed limited dispersal ability.
Species with low dispersal range are supposed to be more genetically structured.
Genetic diversity and relationship of E. acoroides are less known because
phylogeographic studies of this species has not been reported previously.
Therefore, this study address to some questions about the genetic diversity,
genetic differentiation among populations and phylogeographic pattern of E.
acoroides. The results of this study could be applied in seagrass conservation and
marine management unit. The framework of this study is summarized in Figure 1.
3
The Western Region of the
Indo-Malay Archipelago
Overlapping region
between Pacific and
Indian oceans
Genetic
diversity
Ocean current
Several events in
the past such as
last glacial
maxima (LGM)
E. acoroides
Genetic
differentiation
Phylogeographic pattern
of E. acoroides
Marine conservation
and management
Figure 1 Research framework
Research Objectives
The aims of this study are:
1. To evaluate the genetic diversity of E. acoroides in the western region of the
Indo-Malay Archipelago using microsatellite DNA
2. To examine genetic differentiation among sites
3. To infer phylogeographic pattern of E. acoroides in the western region of the
Indo-Malay Archipelago
MATERIALS AND METHODS
Study Area and Sample Collection
A total of 202 E. acoroides specimens from seven localities over Java and
Sumatra were collected in 2014 (Figure 2, Table 1). At each location, 18-42
individuals were taken in a zigzag pattern along the line transect. To avoid
4
collection of the same genet, only one shoot was collected within a diameter of 5
m (see Appendix 1). Collected shoots were rinsed with fresh water to remove
epiphytic algae. A young leaf from each shoot was desiccated with silica gel and
preserved at room temperature until use (see Appendix 2). Molecular work was
conducted at Indonesian Biodiversity Research Center (IBRC), Bali from March
2014 – April 2015.
Figure 2 Sampling location from which Enhalus acoroides were collected
for present study. NK: Nakuri, BK: Bangka, BM: Batam, ANS:
Anambas, TD: Tunda, PR: Pramuka, KJ: Karimun Jawa, SJC:
South Java Current, SRC: Seasonal Reversing Current.
Table 1 Sampling location, abbreviation, geographical coordinates and the
number of samples (N) used in this study
Collection site
Abbreviation
Latitude
Longitude
N
Nakuri Island, Aceh
Batam, Riau Archipelago
Bangka Island, Bangka
Belitung
Anambas, Riau
Archipelago
Pramuka Island Seribu
Islands
Tunda Island, Banten
Karimun Java, Central
Java
Total
NK
BM
BK
2.217452°
0.741188°
-2.973500°
97.305873°
104.345091°
106.652020°
30
30
42
ANS
3.118658°
106.336531°
31
PR
-5.746902°
106.616174°
18
TD
KJ
-5.815833°
-5.860513°
106.287194°
110.408530°
27
24
202
5
DNA Extraction and Amplification
Silica gel-dried leaves (5 cm in length) from each shoot were grounded
using mortar. Genomic DNA was extracted using DNeasy plant mini kit
(Qiagen®) following the manufacturer’s protocol. Eight microsatellites loci
(Eaco_001, Eaco_009, Eaco_019, Eaco_050, Eaco_051, Eaco_052, Eaco_054,
Eaco_055) developed by Nakajima et al. (2012) were used to score genotypes
(Table 2). Forward primer labeled with 6FAM, VIC, NED, or PET.
Polymerase Chain Reaction (PCR) was performed into two ways. (i) First,
five loci (Eaco_001, Eaco_009, Eaco_019, Eaco_051, Eaco_054) was amplified
using multiplex PCR Kit (Qiagen®) in a total 10 µl reaction containing 3 µl
ddH2O, 5 µl PCR Master Mix, 1 µl primer mix and 1 µl template DNA. PCR
cycling was carried out for 5 min at 950C, followed by 35 cycles of 30 s at 950C,
1.5 min at 570C and 30 s at 720C with an extension of 30 min at 600C in the final
cycle. (ii) Second, PCR of three loci (Eaco_050, Eaco_052, Eaco_055) was
carried out in total 20 µl reaction containing 7.8 µl ddH2O, 2 µl PCR gold buffer,
2 µl MgCl2, 2 µl dNTP, 1.5 µl of each primer (forward and reverse), 0.2 µl
Amplitaq Gold (Applied Biosystem®) and 3 µl DNA template. PCR cycling was
carried out for 15 min at 950C, followed by 32 cycles of 30 s at 940C, 1.5 min at
580C and 60 s at 720C with an extension of 30 min at 600C in the final cycle. All
PCR cycle was performed on 2720 Thermal cycler (Applied Biosystem®).
Table 2 Eight polymorphic microsatellite loci in Enhalus acoroides with loci,
primer sequence, dyes, fragment size range, and GenBank accession
number.
Loci
Primer sequence (5’-3’)
Dye
Eaco_001
GGCTTGAGTTTGTTTAGAATTCTAG F
U19-TTACATGTGGAATGCATACAC R
CAATCGTCCAATCCAAAGGC F
U19-GGAGAATTGTATTATTTAC R
AGGTATTCCTTACCACCGTTC F
U19-CACGGAGGTCTTTCGAAGTTG R
GAATAAATCAAGTCCCTTGAG F
U19-CAAATAAGATGTGGCTTAC R
CATACAGATGCATGCATACTC F
U19-CTAAGCGCTACGTGGTACTAG R
CAGGCGCACAACGTATGTAC F
U19-GAACCACATCATCAGTGTG R
GCTTCTAATTAGCATTTTGGACTTCAG F
U19-ATTTGGGACGTCCAAAGAG R
CTTTTGCTCCCAAATTGAATG F
U19-ATGCTTAGTGCAGCTTGTTC R
FAM
Eaco_009
Eaco_019
Eaco_050
Eaco_051
Eaco_052
Eaco_054
Eaco_055
FAM
VIC
NED
PET
NED
PET
PET
Size
range
(bp)
232246
142154
195197
243255
206231
147149
267295
165191
Accession
no.
AB689192
AB689194
AB689197
AB689199
AB689200
AB689201
AB689202
AB689203
U19 = 5’-GGTTTTCCCAGTCACGACG-3’.
A quality check of the DNA was performed using agarose gel
electrophoresis with 3 µl DNA template. Gel electrophoresis then visualized
under UV transluminator (see Appendix 3). PCR products were sent to UC
6
Berkeley DNA sequencing Facility, USA for fragment analysis. GeneScan™ 500
LIZ® (Applied Biosystems®) was used as internal line standard. Individual
genotypes were scored using Geneious ver. 7.0.6.
Data Analysis
Genetic Diversity and Hardy-Weinberg Equilibrium (HWE)
The number of alleles (A), observed heterozigosities (HO) and expected
heterozigosities (HE) were calculated using Genalex ver. 6.5 (Peakall & Smouse
2012). Departure from the Hardy–Weinberg equilibrium (HWE) for each locus in
all populations was computed via the Markov Chain method (dememorization =
1000, batch = 100, iterations per batch = 1000) using Genepop on the web
(Raymond & Rousset 1995). Levels of statistical significance were corrected
according to a Holm Bonferroni correction (Holm 1979). Micro-checker (Van
Oosterhout et al. 2004) was used to test existence of null alleles and genotypic
scoring error due to stuttering with 1000 randomizations and 95% confidence
level.
Population Structure
Genetic structure among populations was assessed in multiple ways. First,
Genetic differentiation was estimated between pairs of populations with the
estimator θ (Weir & Cockerham 1984) as implemented in Arlequin ver. 3.5.1
(Excoffier & Lischer 2010). Isolation by distance (IBD) was used to infer
correlation between genetic and geographical distance. Geographical distance
between sampling locations was calculated as the shortest distance by sea between
all pair of locations using the PATH tool implemented in Google Earth (Google
Earth Plus for Windows). Pairwise genetic distance was plotted against the
geographical distance and the correlation between the two distances was tested
using Isolation By Distance Web Service (IBDWS) version 3.23 (Jensen et al.
2005) with 10.000 randomizations.
Second, phylogenetic relationship among population was inferred using
Poptree2 (Takezaki et al. 2010) with the Neighbour Joining method (Saitou & Nei
1987) and Nei’s DA distance (Nei et al. 1983). Bootstrapping was performed at
1.000 replicates. Tree topology was edited using Mega 5 (Tamura et al. 2011).
Third, Structure 2.3.4 (Pritchard et al. 2000) was used to infer population
structure and assign individuals to clusters based on microsatellite genotype. Ten
replicate runs were conducted for each K between 1 and 10 using admixture
model and assuming correlated allele frequencies (Falush et al. 2003). Each run
consisted of 20.000 burn-in and 100.000 Markov Chain Monte Carlo (MCMC).
The best K was determined using the ΔK method (Evanno et al. 2005) as
implemented in Structure Harvester (Earl & VonHoldt 2012). Run data were
merged by Clumpp (Jakobsson & Rosenberg 2007) and population structure then
displayed graphically using Distruct (Rosenberg 2004).
Finally, genetic structure was examined using Hierarchical analysis of
molecular variance (AMOVA) (Excoffier et al. 1992) as implemented in Arlequin
3.5.1 (Excoffier & Lischer 2010) with significance tested using 1000 randomized
replicates. Hierarchical AMOVA analyses were run two times: assuming no
7
regional groupings and enforcing regional partitions according to groups
identified by NJ and Structure 2.3.4.
RESULTS AND DISCCUSSION
Results
DNA Isolation and Amplification
Figure 3 showed total DNA extraction product, extracted using DNeasy
plant mini kit (Qiagen®) following manufacturer’s protocol. This extraction
method produced thick and clear DNA band patterns. The size marker on the edge
of the gel was use to estimating the mass (quantity) of DNA manually.
1
2
3
4
5
6
7
8
9
10 11
12
13 14 15
bp
2.000
1.200
810
410
210
100
Figure 3 Gel electrophoresis of the DNA extraction product. Samples: lane 1-14,
size marker: lane 15.
DNA visualization using 1% agarose gel showed that multiplex PCR had
clearer and thicker bands than standard PCR (Figure 4). Multiplex PCR produced
more than one bands in a single gel lane since this PCR using a group of primers
and each primer would produce different size of fragment length. Meanwhile
standard PCR produced only a single band DNA since this PCR used only a pair
of primer (Figure 5). Single PCR also showed existence of the dimer primer
(residues of the primer).
bp
1
2
3
4
5
6
7
8
2.000
1.200
810
410
PCR product
210
Figure 4 Multiplex PCR. Size marker: lane 1, samples: lane 2-8.
8
bp
1 2
16
3
4
5
6
7
8
9
10
11
12 13
2.000
1.200
810
410
210
PCR product
Dimer primer
Figure 5 Single primer PCR. Size marker: lane 1, samples: lane 2-8.
Genetic Diversity and Hardy-Weinberg Equilibrium
Two loci (Eaco_052 and Eaco_050) were not used for further analysis.
Eaco_052 only had one type of allele in all populations (monomorphic), while the
Eaco_050 was not successfully amplified in most of samples. Thus, only six loci
(Eaco_001, Eaco_009, Eaco_019, Eaco_051, Eaco_054, and Eaco_055) were
used for further analysis (Table 3).
A total of 89 alleles (see Appendix 4, for allele frequency) were detected
across six microsatellites loci ranging from one allele at the locus Eaco_019 in
BM, KJ, BK, and ANS populations to 13 alleles at the locus Eaco_054 in the BM
population. The mean number of alleles per locus ranged from 1.57 to 9.57 and
the mean number of alleles per population ranged from 4.33 to 7.00 (Table 2). The
BM population had the highest average number of alleles, while the lowest was
found in the KJ population. The levels of the observed and expected
heterozygosities varied from 0.434 to 0.615 and from 0.458 to 0.605, respectively.
The TD population had the highest HE value (0.605), while the ANS showed the
lowest HE (0.458).
Five loci consisted of Eaco_001 in TD and NK, Eaco_009 in KJ and ANS,
Eaco_051 in KJ, Eaco_054 in TD, NK, ANS and Eaco_055 in ANS were found to
deviate significantly from Hardy-Weinberg equilibrium (P < 0.05) prior to Holm
Bonferroni correction. Two loci (Eaco_001 in NK and Eaco_054 in TD)
significantly deviated from Hardy-Weinberg equilibrium after Holm Bonferroni
correction (P < 0.05). However, according to Micro-checker, these populations
were possibly in HWE with two loci suggest presence of null alleles. Thus, null
alleles are the most probable explanation for the deviation from Hardy-Weinberg
equilibrium, although Wahlund effect and deviation from panmixia are also
possible. Thus, all populations were possibly in Hardy-Weinberg equilibrium.
14
15
9
Table 3 Summary of genetic diversity at six microsatellite loci at seven
locations for E. acoroides
Loci
Eaco_001
A
HO
HE
P
Eaco_009
A
HO
HE
P
Eaco_019
A
HO
HE
P
Eaco_051
A
HO
HE
P
Eaco_054
A
HO
HE
P
Eaco_055
A
HO
HE
P
All
populations
A
HO
HE
Locations
MeanA/
locus
TD
PR
NK
BM
KJ
BK
ANS
3
0.185
0.427
0.002
2
0.389
0.375
1.000
8
0.400
0.611
0.000
5
0.467
0.417
0.834
2
0.417
0.375
1.000
3
0.429
0.489
0.274
2
0.355
0.331
1.000
3.57
5
0.667
0.658
0.940
5
0.667
0.650
0.952
2
0.033
0.095
0.049
6
0.600
0.660
0.331
6
0.417
0.549
0.017
6
0.786
0.677
0.674
6
0.613
0.592
0.026
5.14
2
0.111
0.105
1.000
2
0.500
0.461
1.000
3
0.467
0.376
0.610
1
0.000
0.000
N.A
1
0.000
0.000
N.A
1
0.000
0.000
N.A
1
0.000
0.000
N.A
1.57
9
0.852
0.811
0.554
4
0.722
0.619
0.861
9
0.933
0.808
0.504
9
0.667
0.757
0.022
7
0.917
0.759
0.009
10
0.905
0.826
0.905
8
0.633
0.724
0.189
8.00
10
0.630
0.858
0.000
8
0.667
0.789
0.171
10
0.800
0.839
0.003
13
0.833
0.792
0.246
6
0.792
0.658
0.725
12
0.810
0.841
0.880
8
0.290
0.388
0.006
9.57
6
0.704
0.771
0.130
6
0.667
0.657
0.427
9
0.700
0.823
0.075
8
0.800
0.699
0.889
4
0.667
0.656
0.143
7
0.762
0.747
0.090
5
0.710
0.710
0.022
6.43
5.83
0.525
0.605
4.50
0.602
0.592
6.83
0.556
0.592
7.00
0.561
0.554
4.33
0.535
0.499
6.50
0.615
0.597
5.00
0.434
0.458
5.71
0.478
0.487
Genetic diversity was inferred from the numbers of alleles (A), the proportion of observed
heterozygosities (HO) and expected heterozygosities (HE). Exact P value associated with the
Hardy-Weinberg equilibrium (P). Numbers in bold indicate significant deviation from HardyWeinberg equilibrium at P < 0.05 after Holm Bonferroni corrections. TD: Tunda, PR: Pramuka,
NK: Nakuri, BM: Batam, KJ: Karimun Java, BK: Bangka, ANS: Anambas.
Genetic differentiation
Pairwise FST values ranged from 0.127 to 0.359. FST was statistically highly
significant between all pairs of samples with P < 0.001 (Table 4). The highest
genetic differentiation was found between samples from NK and ANS (FST =
0.359), while the lowest was found between TD and PR and between BM and BK
(FST = 0.127). These result indicated high levels of genetic differentiation among
10
populations of E. acoroides. Isolation by distance (IBD) revealed significant
correlation between genetic differentiation and geographic distance across all
pairs of samples with P = 0.008 (Figure 6).
Table 4 Pairwise FST values (below diagonal) and significant FST P values
(above diagonal)
TD
PR
NK
BM
KJ
BK
ANS
TD
0.127
0.290
0.235
0.225
0.175
0.298
PR
***
0.302
0.270
0.273
0.203
0.348
NK
***
***
0.301
0.338
0.214
0.359
BM
***
***
***
0.247
0.127
0.293
KJ
***
***
***
***
0.247
0.239
BK
***
***
***
***
***
0.303
ANS
***
***
***
***
***
***
-
*** P < 0.001.
Figure 6 Isolation by distance showed significant correlation between
genetic distance (pairwise FST values) and geographic distance
(see Appendix 5) with P = 0.008.
Phylogenetic and Bayesian Clustering Analysis
The Neighbour Joining method base on DA distance identified three major
clusters (Figure 7). Cluster 1 was the NK population, cluster 2 consists of four
populations (TD, PR, BK and BM) and cluster 3 consists of two populations (KJ
and ANS). NK population was genetically distinct from the other populations.
11
Cluster 2, consists of two populations in Java (TD and PR) and two populations in
Sumatra (BK and BM). The highest bootstrap support was found between TD and
PR. Cluster 3 shows discordance between genetic and geographic distance. These
populations were separated about 1095 km but they were closely related.
The ΔK test in Structure (Pritchard et al. 2010) indicated the optimum value
of ΔK at K = 3 with a secondary peak at K = 7. At K = 3, Structure 2.3.4 analysis
mirror those seen in NJ tree with three major clusters (NK/KJ, ANS/TD, PR, BM
and BK) (Figure 7). With K set to 7, each population becomes different cluster
(not shown).
Figure 7 Neighbour Joining (NJ) tree based on DA distance and bar plot of
Structure 2.3.4 revealed congruent results with all populations were
divided into three clusters (NK/TD, PR, BK and BM/ANS and KJ).
Each colour represents one cluster.
Analysis of Molecular Variance (AMOVA)
AMOVA analysis with no a priori regional structure indicates highly
significant genetic partitioning among populations (25.76%) and among
individuals within population (74.24%) with FST = 0.257 and P < 0.001 (Table 5).
When populations were divided into three regions according to phylogenetic
relationship and clustering analysis (NK/TD, PR, BK, BM/KJ, ANS), 10.17%
variance were resided among groups with FCT = 0.103 (P < 0.05). Variances
among population within group and among individual within group were 17.63%
and 71.6 %, respectively.
Discussion
Genetic Diversity
Observed heterozygosity of E. acoroides in this study ranged from 0.434 to
0.615. Previous study of E.acoroides in Lembongan (Bali) and Waigeo (Papua)
12
found that observed heterozygosities were 0.436 – 0.582, respectively
(Pharmawati et al. 2015). Nakajima et al. (2014) revealed observed
heterozygosities of E. acoroides ranged from 0.165 – 0.575 in three locations
(Japan, China, and Philippines) using nine microsatellite loci. Other study found
heterozygosities of E. acoroides varied from 0.100 to 0.567 in China (Gao et al.
2012).
The genetic diversity studies of other seagrass species showed almost
similar result. Alberto et al. (2008) revealed level of observed heterozygosities of
Cymodocea nodosa ranged from 0.296 to 0.750 across Mediterranean-Atlantic
transition region using eight microsatellites loci. In other regions, heterozygosities
of Zostera marina ranged from 0.491 to 0.563 in San Quintin Bay, Mexico using
eight marker (Muniz-Salazar et al. 2006). Serra et al. (2010) found the
heterozygosities of Posidonia oceanica ranged from 0.212 – 0.569 in
Mediterranean using 12 microsatellites loci. Genetic diversity, like species
diversity, may be most important for enhancing the consistency and reliability of
ecosystems by providing biological insurance against environmental change
(Hughes & Stachowicz 2004). Degradation and loss of seagrass meadows has led
to the general notion of low genetic diversity, extensive clonality and minimal
gene flow among populations (Coyer et al. 2004).
Table 5 Result from hierarchical AMOVA for E. acoroides
Global AMOVA
Source of variation
Among population
Within populations
Total
3 Groups
Among groups
Among populations
within groups
Within populations
Total
d.f
6
397
403
SS
212.3
674.213
886.512
Var.
0.589
1.698
2.287
% var.
25.76
74.24
F-statistic
FST=0.257
P-value
0.000
2
4
111.612
100.688
0.255
0.418
10.77
17.63
FCT=0.103
FSC=0.198
0.01
0.000
397
403
674.213
886.512
1.698
2.372
71.6
FST=0.284
0.000
Hierarchical AMOVA showed d.f: degree of freedom, SS: sum of square, Var: variance
component, % var: percentage of variances, F-statistics among region, among populations within
regions, and within populations, P-value for F-statistics.
Population Differentiation
Pairwise FST and AMOVA showed significant genetic differentiation
between all pair of samples. Moreover, isolation by distance (IBD) indicated
significant correlation between genetic differentiation and geographic distance.
These suggest low gene flow between populations, possibly cause by limited
dispersal of E. acoroides. Lacap et al. (2002) revealed that genetic exchange by
pollen dispersal for E. acoroides may occur over spatial scales in the order of
kilometers (
IN TROPICAL SEAGRASS Enhalus acoroides
(HYDROCHARITACEAE) AT WESTERN REGION OF THE
INDO-MALAY ARCHIPELAGO REVEALED BY
MICROSATELLITE DNA
I NYOMAN GIRI PUTRA
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2015
DECLARATION OF ORIGINALITY
I hereby declare that the master thesis entitled “Contrasting pattern and limited
connectivity in tropical seagrass Enhalus acoroides (Hydrocharitaceae) at western
region of the Indo-Malay Archipelago revealed by microsatellite DNA” 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, July 2015
I Nyoman Giri Putra
NIM C551124041
RINGKASAN
I NYOMAN GIRI PUTRA. Pola yang kontras dan konektivitas terbatas pada
lamun tropis Enhalus acoroides (Hydrocharitaceae) di bagian barat Kepulauan
Indo-Malay diungkap menggunakan DNA mikrosatelit. Dibimbing oleh Hawis H.
Madduppa dan Made Pharmawati.
Bagian barat dari Kepulauan Indo-Malay sering dihubungkan dengan barier
Indo-Pasifik yang memisahkan antara Samudra Hindia dan Pasifik. Berbagai studi
filogeografi menunjukkan bahwa terdapat perbedaan struktur genetik antara biota
di samudra Hindia dan Pasifik. Meskipun cukup banyak penelitian filogeografi di
Hindia dan Pasifik akan tetapi hanya sedikit dari penelitian tersebut yang
memfokuskan penelitiannya di Kepulauan Indo-Malay. Studi terhadap fauna laut
menunjukkan bahwa pola filogeografi biota di kepulauan Indo-Malay cukup
kompleks bahkan tiap spesies cenderung menunjukkan pola yang berbeda.
Sementara itu studi filogeografi menggunakan tumbuhan laut belum pernah
dilaporkan sebelumnya. Oleh sebab itu penelitian ini akan mengkaji pola
filogeografi dari lamun Enhalus acoroides menggunakan delapan lokus
mikrosatelit.
Tujuan dari penelitian ini yaitu: (1) menghitung keragaman genetik E.
acoroides di bagian barat dari Kepulauan Indo-Malay, (2) menghitung perbedaan
genetik antar populasi dan (3) menerangkan pola filogeografi dari E. acoroides.
Dalam penelitian ini, sebanyak 202 spesimen E. acoroides dikumpulkan
dari tujuh lokasi pengambilan sampel (Aceh, Anambas, Batam, Bangka, Tunda,
Pramuka, dan Karimun Jawa). Total DNA diekstraksi menggunakan DNeasy plant
mini kit (Qiagen®) dengan tahapan ekstraksi mengikuti protokol dari perusahaan.
Amplifikasi DNA dilakukan menggunakan delapan lokus mikrosatelit (Eaco_001,
Eaco_009, Eaco_019, Eaco_050, Eaco_051, Eaco_052, Eaco_054, Eaco_055).
Hasil penghitungan nilai genotipe menunjukkan bahwa hanya ada enam
lokus (dari delapan lokus) yang berhasil diamplifikasi dan polimorfik. Penelitian
ini menunjukkan bahwa E. acoroides memiliki keragaman genetik tinggi dengan
nilai heterozigositas pengamatan dan harapan secara berturut-turut berkisar dari
0.434 - 0.615 dan 0.458 - 0.605. Nilai statistic F (pairwise θ berkisar antara 0.127
and 0.359) menunjukkan perbedaan genetik yang signifikan antar lokasi dengan
dengan nilai P < 0.001. Analisis isolation by distance juga menunjukkan hasil
signifikan (P=0.008) yang mengindikasikan aliran gen terbatas pada semua lokasi.
Pohon Neighbour Joining berdasarkan jarak genetik DA dan analisis klaster
Bayesian menunjukkan ada 3 klaster utama dari E. acoroides. Analysis of
Molecular Variance (AMOVA) menunjukkan bahwa ketiga grup ini signifikan
berbeda dengan nilai P < 0.05. Hasil studi ini menunjukkan bahwa pola
filogeografi dari E. acoroides dipengaruhi oleh peristiwa membeku dan
mencairnya es selama Pleistocene. Kondisi fisik oceanografi saat ini seperti arus
selatan Jawa dan arus musiman juga memegang peran penting dalam
pembentukan struktur genetik E. acoroides. Hasil penelitian ini akan menyediakan
data genetik yang dapat digunakan untuk tujuan konservasi lamun dan design
manajemen unit (MUs).
Kata kunci: filogeografi, Enhalus acoroides, mikrosatelit, Kepulauan Indo-Malay
SUMMARY
I NYOMAN GIRI PUTRA. Contrasting pattern and limited connectivity in
tropical seagrass Enhalus acoroides (Hydrocharitaceae) at western region of the IndoMalay Archipelago revealed by microsatellite DNA. Supervised by Hawis H.
Madduppa dan Made Pharmawati
The western region of the Indo-Malay Archipelago is often associated with
the Indo-Pacific barrier (IPB), separating Indian and Pacific oceans. Various
phylogeographic studies of marine biota throughout Indian and Pacific oceans
found genetic partition between these two oceans. Although many
phylogeographic studies in the Indian and Pacific oceans have been reported, only
a few of these studies focus on the Indo-Malay Archipelago. Previous studies
found that genetic structure of marine faunas in Indo-Malay Archipelago is clearly
complex, seemingly each species showed different pattern. Meanwhile
phylogeographic studies using marine plant have not been reported previously.
Therefore, this study will reveal phylogeographic pattern of E. acoroides using
eight microsatellite loci.
This study has three main objectives that include the following: (1) to
examine genetic diversity of E. acoroides in western region of the Indo-Malay
Archipelago, (2) to examine genetic differentiation among all sites and (3) to infer
phylogeographic pattern of of E. acoroides.
A total of 202 E. acoroides specimens from seven localities (Aceh, Anambas,
Batam, Bangka, Tunda, Pramuka, and Karimun Java) were collected. At each
location, 18-42 individuals were taken in a zigzag pattern along the line transect.
Total genomic DNA was extracted using DNeasy plant mini kit (Qiagen®)
following the manufacturer’s protocol. DNA amplification was performed using
eight microsatellite loci (Eaco_001, Eaco_009, Eaco_019, Eaco_050, Eaco_051,
Eaco_052, Eaco_054, Eaco_055).
Genotypic scoring showed only six loci (of eight loci) were successfully
amplified and polymorphic. This study showed that E. acoroides has high genetic
diversity among all sites. The observed and expected heterozygosity ranged from
from 0.434 to 0.615 and from 0.458 to 0.605, respectively. F-statistics (pairwise θ
ranges between 0.127 and 0.359) revealed high genetic differentiation between all
sites (P < 0.001). A pattern of significant isolation by distance (P = 0.008) was
observed among all sites indicating restricted gene flow among all sites.
Neighbour Joining tree based on DA distance revealed three major clusters of
E. acoroides consistent with Bayesian clustering analysis result. Further, Analysis
of Molecular Variance (AMOVA) revealed significant partition of these groups (P
< 0.05). Our result indicated that phylogeographic pattern of E. acoroides possibly
influence by glaciation and deglaciation during Pleistocene. Recent physical
oceanography such as South Java Current and seasonality reversing current also
play a role in shaping genetic pattern of E.acoroides. These results will provide
data for seagrass restoration purposes and management unit (MUs) design.
Keywords: phylogeography, Enhalus acoroides, microsatellite, Indo-Malay
Archipelago
© Copyright owned by IPB, 2015
All rights reserved
No part of this document may be reproduced or transmitted in any form or by any
means, electronic, mechanical, photocopying, recording, or otherwise, without prior
written permission from IPB.
CONTRASTING PATTERN AND LIMITED CONNECTIVITY
IN TROPICAL SEAGRASS Enhalus acoroides
(HYDROCHARITACEAE) AT WESTERN REGION OF THE
INDO-MALAY ARCHIPELAGO REVEALED BY
MICROSATELLITE DNA
I NYOMAN GIRI PUTRA
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
2015
External examiner of the thesis supervisor: Dr. Irma Shita Arlyza, M.Si
Thesis Title : Contrasting pattern and limited connectivity in tropical seagrass
Enhalus acoroides (Hydrocharitaceae) at western region of the IndoMalay Archipelago revealed by microsatellite DNA
Name
: I Nyoman Giri Putra
Student ID : C551124041
Major
: Marine Science
Approved by,
Supervisor
Dr. Hawis H.Madduppa, S.Pi. M.Si
Head-supervisor
Ir. Made Pharmawati, M.Sc. Ph.D
Co-supervisor
Endorsed by,
Head of Major
Marine Science
Dean of Graduate School
Dr. Neviaty P. Zamani, M.Sc.
Dr. Ir. Dahrul Syah, M.Sc. Agr
Date of Examination: July 09 2015
Date of Graduation:
ACKNOWLEDGEMENT
This study is mainly funded by PEER (the Partnerships for Enhanced
Engagement in Research, No: PGA-2000003438) funded by United States
Agency for International Development (USAID) and the National Science
Foundation (NSF) in partnership with NSF-PIRE Program. This was also funded
by the government of Indonesia through the Indonesia Endowment Fund for
Education (LPDP).
I would like to express my appreciation to Indonesian Education
Scholarship (BPI) managed by LPDP for Master program scholarship in Bogor
Agricultural University (IPB). I would like to thank Dr. Hawis H. Madduppa,
M.Si and Ir. Made Pharmawati, M.Sc. Ph.D. as my supervisors for their
supervision and guidance. I also thank Dr. Irma Shita Arlyza, M.Si. as external
examiner for valuable comments. I would like to also thank Beginner Subhan and
Dondy Arafat (Marine Biodiversity and Biosystematics Laboratory, IPB) for
collecting samples in Batam, Ibu Widiastuti (Diponegoro University) for
collecting samples in Karimun Jawa, Khalidin (Syah Kuala University) for sample
collection in Aceh, Dedi, Gugun and Okto for their assistance with sample
collection in Bangka.
I would like to also express my gratitude to Indonesian Biodiversity
Research Center (IBRC) who had facilitated all the research works, provided
research networks, organized sampling collection and provided laboratory tools
and equipments. This specifically goes to Aji Wahyu Anggoro, Dita Cahyani and
Prof. IGN Mahardika for their contribution in assuring the research running well. I
would like to also thank Yuliana Fitri Syamsuni, Samsul Bahri, Astria
Yusmalinda, Rizki Wulandari, Andrianus Sembiring, Masriana, Eka Maya
Kurniasih, Dian Pertiwi and Angka Mahardini from IBRC for their helps in
laboratory and field work.
Bogor, July 2015
I Nyoman Giri Putra
TABLE OF CONTENTS
LIST OF TABLES
vi
LIST OF FIGURES
vi
LIST OF APPENDIX
vi
INTRODUCTION
Background
Problem Description
Research Objectives
1
1
2
3
MATERIALS AND METHODS
Study Area and Sample Collection
DNA Extraction and Amplification
Data Analysis
Genetic Diversity and Hardy-Weinberg Equilibrium (HWE)
3
3
5
6
6
Population Structure
RESULTS AND DISCCUSSION
Results
DNA Isolation and Amplification
6
7
7
7
Genetic Diversity and Hardy-Weinberg Equilibrium
8
Genetic differentiation
9
Phylogenetic and Bayesian Clustering Analysis
10
Analysis of Molecular Variance (AMOVA)
11
Discussion
Genetic Diversity
11
11
Population Differentiation
12
Phylogeographic Pattern of E. acoroides
13
Implication for Restoration and Management
14
CONCLUSION AND FUTURE PERSPECTIVE
Conclusion
Future Perspective
15
15
15
REFERENCES
15
APPENDIX
21
ABOUT THE AUTHOR
27
LIST OF TABLES
1. Sampling location, abbreviation, geographical coordinates and the
number of samples (N) used in this study
2. Eight polymorphic microsatellite loci in Enhalus acoroides with
loci, primer sequence, dyes, fragment size range, and GenBank
accession number.
3. Summary of genetic diversity at six microsatellite loci at seven
locations for E. acoroides
4. Pairwise FST values (below diagonal) and significant FST P values
(above diagonal)
5. Result from hierarchical AMOVA for E. acoroides
4
5
9
10
12
LIST OF FIGURES
1. Research framework
2. Sampling location from which Enhalus acoroides were collected
for present study. NK: Nakuri, BK: Bangka, BM: Batam, ANS:
Anambas, TD: Tunda, PR: Pramuka, KJ: Karimun Jawa, SJC:
South Java Current, SRC: Seasonal Reversing Current.
3. Gel electrophoresis of the DNA extraction product. Samples: lane
1-14, size marker: lane 15.
4. Multiplex PCR. Size marker: lane 1, samples: lane 2-8.
5. Single primer PCR. Size marker: lane 1, samples: lane 2-8.
6. Isolation by distance showed significant correlation between
genetic distance (pairwise FST values) and geographic distance
(see Appendix 5) with P = 0.008.
7. Neighbour Joining (NJ) tree based on DA distance and bar plot of
Structure 2.3.4 revealed congruent results with all populations
were divided into three clusters (NK/TD, PR, BK and BM/ANS
and KJ). Each colour represents one cluster.
3
4
7
7
8
10
11
LIST OF APPENDIX
1. Sample collection and preservation.
2. Research documentation. (a) Sample collection, (b) Sample
identification and preservation, (c) Desiccated sample, (d) Gel
electrophoresis, (e) Enhalus acoroides, (f) DNA amplification.
3. Gel electrophoresis.
4. Allele frequencies and sample size by populations.
5. Geographic distance between all sampling sites (in km).
20
21
22
23
26
INTRODUCTION
Background
The Indo-Malay Archipelago, also known as Indo-Australian Archipelago
(IAA) is one of the most important land barriers (The Indo-Pacific Barrier, IPB)
separating the Indian and Pacific oceans (Crandall et al. 2008). Although the
location of the boundary is debated, this barrier is most often associated with
western region of Indo-Malay Archipelago (Sunda shelf) (Barber et al. 2006;
DiBattista et al. 2012). Various levels of taxa confirmed the existence of these
barriers, ranging from reef fishes of the Pomacanthidae (Thresher & Brothers
1985), marine gastropod Nerita albicilla (Crandall et al. 2008), crown-of-thorns
Acanthaster planci (Vogler et al. 2008; Yasuda et al. 2009) and Snapper fishes
Lutjanidae (Gaither et al. 2009). Although many phylogeographic studies in the
Indian and Pacific ocean have been reported, only a few of these studies focus on
the Indo-Pacific Barrier (Carpenter et al. 2011; Keyse et al. 2014). Though this
barrier was an overlapping region of organisms from both ocean (Gaither &
Rocha 2013).
The western Indo-Malay Archipelago mostly constitutes of large lands mass
such as Sumatra, Thai-Malay Peninsula, Borneo, and the Greater Sundas. During
the ice age period, these islands formed a large land known as Sunda shelf (Voris
2000). Despite their same geological origin, population structures of flora and
fauna in these islands is not simple. Studies of the mantis shrimp Haptosquilla
pulchella revealed sharp genetic break across Java seas, which divided population
into the north (Pasific) and south (Indian) (Barber et al. 2002). Other studies on
giant clams Tridacna crocea (DeBoer et al. 2014), tuna and mackerel (Jackson et
al. 2014) showed that the population of Sumatra in western Indonesia represent
the Indian ocean population while the population of Java represents middle
Indonesian population. Thus, the phylogeographic patterns shown by the two
islands is quite complex.
Gene flow is driven by various factors such as currents and geological
history of a location. In eastern Indonesia, Halmahera Eddy and Indonesian
Throughflow are the main factors shaping biogeographic barrier between eastern
and western Indonesia (Barber et al. 2006; Carpenter et al. 2011). Another study
found that water circulation and eddy located at the southern tip of Sumatra plays
a role in maintaining the genetic structure of mangrove Rhizophora mucronata
Lam. in the Malay Peninsula and Sumatra (Wee et al. 2014). Geological history
such as the emergence of Sunda shelf during the Pleistocene period, are the main
factors that inhibit larval dispersal and genetic exchange between the Indian and
Pacific which then triggers lineage diversification in both oceans (Carpenter et al.
2011)
Most of the phylogeographic studies in western region of Indo-Malay
Archipelago used mitochondrial genes from marine animals such as crustacean
(Barber et al. 2006), reef fishes (Nelson et al. 2000; Ackiss et al. 2013), starfishes
(Vogler et al. 2012), and bivalve (DeBoer et al. 2008) showed genetic structuring
between population in Indian ocean and Java and or South China seas. However,
other marine animals, such as pelagic scads Decapterus macrosoma (Arnaud-
2
Haond et al. 1999) and marine gastropod Nerita plicata (Crandall et al. 2008)
indicated lack genetic structuring. Study of marine plants such as mangroves with
DNA nuclear marker revealed genetic discontinuity of mangrove Rhizophora
mucronata Lam. at the boundary between the Andaman sea and Malacca Strait
(Wee et al. 2014). Meanwhile phylogeographic studies using seagrass has not
been reported previously.
Seagrass is marine angiosperm lives in coastal areas on a substrate of sand,
mud or a mixture of both and the entire life cycle occurs below sea level. Enhalus
acoroides is one of seagrass species that widely spread in the Indo-Pacific from
southern Japan, Southeast Asia, northern Australia, southern India and Sri Lanka
(Short & Waycott 2010). In Indonesia, E. acoroides can be found in Papua, North
Maluku, Ambon, Sulawesi, Bali, Java, Kalimantan, and Sumatra (Kiswara &
Hutomo 1985). This species is easily distinguished from other seagrass because it
has long leaves, black stringy rhizome, and usually form a bed. Fruits of E.
acoroides could floats up to 10.2 days (Lacap et al. 2002). Meanwhile, median
dispersal ability of seeds and fruits could reach 0.1 (max. 3.7) km and 41 (max.
63.5) km, respectively (Lacap et al. 2002), which might limiting the dispersal of
E. acoroides. Species with limited dispersal frequently hypothesized to be more
genetically structured (Bay et al. 2006).
In this study, eight microsatellite markers (Nakajima et al. 2012) were used
to infer phylogeographic pattern of E. acoroides. These DNA marker are widely
used because they are both codominant and highly polymorphic (Beebee & Rowe
2008). Microsatellites have been found to be very useful and broadly used in
phylogeographic studies (Koskinen et al. 2002; Adams et al. 2006; Suárez et al.
2009; Liu et al. 2012; Poortvliet et al. 2013; Madduppa et al. 2014; Wee et al.
2014).
Problem Description
Transition zone between Pacific and Indian oceans such as the western
region of the Indo-Malay Archipelago often showed complex phylogeographic
patterns eventhough the islands originate from the same geologic history.
Previous studies agreed that phylogeographic pattern of marine biota could be
affected by several factor such as current, larval dispersal ability, several events in
the past (e.g. Last Glacial Maxima, LGM) and the characteristics of each species.
E. acoroides is one of the seagrass species that showed limited dispersal ability.
Species with low dispersal range are supposed to be more genetically structured.
Genetic diversity and relationship of E. acoroides are less known because
phylogeographic studies of this species has not been reported previously.
Therefore, this study address to some questions about the genetic diversity,
genetic differentiation among populations and phylogeographic pattern of E.
acoroides. The results of this study could be applied in seagrass conservation and
marine management unit. The framework of this study is summarized in Figure 1.
3
The Western Region of the
Indo-Malay Archipelago
Overlapping region
between Pacific and
Indian oceans
Genetic
diversity
Ocean current
Several events in
the past such as
last glacial
maxima (LGM)
E. acoroides
Genetic
differentiation
Phylogeographic pattern
of E. acoroides
Marine conservation
and management
Figure 1 Research framework
Research Objectives
The aims of this study are:
1. To evaluate the genetic diversity of E. acoroides in the western region of the
Indo-Malay Archipelago using microsatellite DNA
2. To examine genetic differentiation among sites
3. To infer phylogeographic pattern of E. acoroides in the western region of the
Indo-Malay Archipelago
MATERIALS AND METHODS
Study Area and Sample Collection
A total of 202 E. acoroides specimens from seven localities over Java and
Sumatra were collected in 2014 (Figure 2, Table 1). At each location, 18-42
individuals were taken in a zigzag pattern along the line transect. To avoid
4
collection of the same genet, only one shoot was collected within a diameter of 5
m (see Appendix 1). Collected shoots were rinsed with fresh water to remove
epiphytic algae. A young leaf from each shoot was desiccated with silica gel and
preserved at room temperature until use (see Appendix 2). Molecular work was
conducted at Indonesian Biodiversity Research Center (IBRC), Bali from March
2014 – April 2015.
Figure 2 Sampling location from which Enhalus acoroides were collected
for present study. NK: Nakuri, BK: Bangka, BM: Batam, ANS:
Anambas, TD: Tunda, PR: Pramuka, KJ: Karimun Jawa, SJC:
South Java Current, SRC: Seasonal Reversing Current.
Table 1 Sampling location, abbreviation, geographical coordinates and the
number of samples (N) used in this study
Collection site
Abbreviation
Latitude
Longitude
N
Nakuri Island, Aceh
Batam, Riau Archipelago
Bangka Island, Bangka
Belitung
Anambas, Riau
Archipelago
Pramuka Island Seribu
Islands
Tunda Island, Banten
Karimun Java, Central
Java
Total
NK
BM
BK
2.217452°
0.741188°
-2.973500°
97.305873°
104.345091°
106.652020°
30
30
42
ANS
3.118658°
106.336531°
31
PR
-5.746902°
106.616174°
18
TD
KJ
-5.815833°
-5.860513°
106.287194°
110.408530°
27
24
202
5
DNA Extraction and Amplification
Silica gel-dried leaves (5 cm in length) from each shoot were grounded
using mortar. Genomic DNA was extracted using DNeasy plant mini kit
(Qiagen®) following the manufacturer’s protocol. Eight microsatellites loci
(Eaco_001, Eaco_009, Eaco_019, Eaco_050, Eaco_051, Eaco_052, Eaco_054,
Eaco_055) developed by Nakajima et al. (2012) were used to score genotypes
(Table 2). Forward primer labeled with 6FAM, VIC, NED, or PET.
Polymerase Chain Reaction (PCR) was performed into two ways. (i) First,
five loci (Eaco_001, Eaco_009, Eaco_019, Eaco_051, Eaco_054) was amplified
using multiplex PCR Kit (Qiagen®) in a total 10 µl reaction containing 3 µl
ddH2O, 5 µl PCR Master Mix, 1 µl primer mix and 1 µl template DNA. PCR
cycling was carried out for 5 min at 950C, followed by 35 cycles of 30 s at 950C,
1.5 min at 570C and 30 s at 720C with an extension of 30 min at 600C in the final
cycle. (ii) Second, PCR of three loci (Eaco_050, Eaco_052, Eaco_055) was
carried out in total 20 µl reaction containing 7.8 µl ddH2O, 2 µl PCR gold buffer,
2 µl MgCl2, 2 µl dNTP, 1.5 µl of each primer (forward and reverse), 0.2 µl
Amplitaq Gold (Applied Biosystem®) and 3 µl DNA template. PCR cycling was
carried out for 15 min at 950C, followed by 32 cycles of 30 s at 940C, 1.5 min at
580C and 60 s at 720C with an extension of 30 min at 600C in the final cycle. All
PCR cycle was performed on 2720 Thermal cycler (Applied Biosystem®).
Table 2 Eight polymorphic microsatellite loci in Enhalus acoroides with loci,
primer sequence, dyes, fragment size range, and GenBank accession
number.
Loci
Primer sequence (5’-3’)
Dye
Eaco_001
GGCTTGAGTTTGTTTAGAATTCTAG F
U19-TTACATGTGGAATGCATACAC R
CAATCGTCCAATCCAAAGGC F
U19-GGAGAATTGTATTATTTAC R
AGGTATTCCTTACCACCGTTC F
U19-CACGGAGGTCTTTCGAAGTTG R
GAATAAATCAAGTCCCTTGAG F
U19-CAAATAAGATGTGGCTTAC R
CATACAGATGCATGCATACTC F
U19-CTAAGCGCTACGTGGTACTAG R
CAGGCGCACAACGTATGTAC F
U19-GAACCACATCATCAGTGTG R
GCTTCTAATTAGCATTTTGGACTTCAG F
U19-ATTTGGGACGTCCAAAGAG R
CTTTTGCTCCCAAATTGAATG F
U19-ATGCTTAGTGCAGCTTGTTC R
FAM
Eaco_009
Eaco_019
Eaco_050
Eaco_051
Eaco_052
Eaco_054
Eaco_055
FAM
VIC
NED
PET
NED
PET
PET
Size
range
(bp)
232246
142154
195197
243255
206231
147149
267295
165191
Accession
no.
AB689192
AB689194
AB689197
AB689199
AB689200
AB689201
AB689202
AB689203
U19 = 5’-GGTTTTCCCAGTCACGACG-3’.
A quality check of the DNA was performed using agarose gel
electrophoresis with 3 µl DNA template. Gel electrophoresis then visualized
under UV transluminator (see Appendix 3). PCR products were sent to UC
6
Berkeley DNA sequencing Facility, USA for fragment analysis. GeneScan™ 500
LIZ® (Applied Biosystems®) was used as internal line standard. Individual
genotypes were scored using Geneious ver. 7.0.6.
Data Analysis
Genetic Diversity and Hardy-Weinberg Equilibrium (HWE)
The number of alleles (A), observed heterozigosities (HO) and expected
heterozigosities (HE) were calculated using Genalex ver. 6.5 (Peakall & Smouse
2012). Departure from the Hardy–Weinberg equilibrium (HWE) for each locus in
all populations was computed via the Markov Chain method (dememorization =
1000, batch = 100, iterations per batch = 1000) using Genepop on the web
(Raymond & Rousset 1995). Levels of statistical significance were corrected
according to a Holm Bonferroni correction (Holm 1979). Micro-checker (Van
Oosterhout et al. 2004) was used to test existence of null alleles and genotypic
scoring error due to stuttering with 1000 randomizations and 95% confidence
level.
Population Structure
Genetic structure among populations was assessed in multiple ways. First,
Genetic differentiation was estimated between pairs of populations with the
estimator θ (Weir & Cockerham 1984) as implemented in Arlequin ver. 3.5.1
(Excoffier & Lischer 2010). Isolation by distance (IBD) was used to infer
correlation between genetic and geographical distance. Geographical distance
between sampling locations was calculated as the shortest distance by sea between
all pair of locations using the PATH tool implemented in Google Earth (Google
Earth Plus for Windows). Pairwise genetic distance was plotted against the
geographical distance and the correlation between the two distances was tested
using Isolation By Distance Web Service (IBDWS) version 3.23 (Jensen et al.
2005) with 10.000 randomizations.
Second, phylogenetic relationship among population was inferred using
Poptree2 (Takezaki et al. 2010) with the Neighbour Joining method (Saitou & Nei
1987) and Nei’s DA distance (Nei et al. 1983). Bootstrapping was performed at
1.000 replicates. Tree topology was edited using Mega 5 (Tamura et al. 2011).
Third, Structure 2.3.4 (Pritchard et al. 2000) was used to infer population
structure and assign individuals to clusters based on microsatellite genotype. Ten
replicate runs were conducted for each K between 1 and 10 using admixture
model and assuming correlated allele frequencies (Falush et al. 2003). Each run
consisted of 20.000 burn-in and 100.000 Markov Chain Monte Carlo (MCMC).
The best K was determined using the ΔK method (Evanno et al. 2005) as
implemented in Structure Harvester (Earl & VonHoldt 2012). Run data were
merged by Clumpp (Jakobsson & Rosenberg 2007) and population structure then
displayed graphically using Distruct (Rosenberg 2004).
Finally, genetic structure was examined using Hierarchical analysis of
molecular variance (AMOVA) (Excoffier et al. 1992) as implemented in Arlequin
3.5.1 (Excoffier & Lischer 2010) with significance tested using 1000 randomized
replicates. Hierarchical AMOVA analyses were run two times: assuming no
7
regional groupings and enforcing regional partitions according to groups
identified by NJ and Structure 2.3.4.
RESULTS AND DISCCUSSION
Results
DNA Isolation and Amplification
Figure 3 showed total DNA extraction product, extracted using DNeasy
plant mini kit (Qiagen®) following manufacturer’s protocol. This extraction
method produced thick and clear DNA band patterns. The size marker on the edge
of the gel was use to estimating the mass (quantity) of DNA manually.
1
2
3
4
5
6
7
8
9
10 11
12
13 14 15
bp
2.000
1.200
810
410
210
100
Figure 3 Gel electrophoresis of the DNA extraction product. Samples: lane 1-14,
size marker: lane 15.
DNA visualization using 1% agarose gel showed that multiplex PCR had
clearer and thicker bands than standard PCR (Figure 4). Multiplex PCR produced
more than one bands in a single gel lane since this PCR using a group of primers
and each primer would produce different size of fragment length. Meanwhile
standard PCR produced only a single band DNA since this PCR used only a pair
of primer (Figure 5). Single PCR also showed existence of the dimer primer
(residues of the primer).
bp
1
2
3
4
5
6
7
8
2.000
1.200
810
410
PCR product
210
Figure 4 Multiplex PCR. Size marker: lane 1, samples: lane 2-8.
8
bp
1 2
16
3
4
5
6
7
8
9
10
11
12 13
2.000
1.200
810
410
210
PCR product
Dimer primer
Figure 5 Single primer PCR. Size marker: lane 1, samples: lane 2-8.
Genetic Diversity and Hardy-Weinberg Equilibrium
Two loci (Eaco_052 and Eaco_050) were not used for further analysis.
Eaco_052 only had one type of allele in all populations (monomorphic), while the
Eaco_050 was not successfully amplified in most of samples. Thus, only six loci
(Eaco_001, Eaco_009, Eaco_019, Eaco_051, Eaco_054, and Eaco_055) were
used for further analysis (Table 3).
A total of 89 alleles (see Appendix 4, for allele frequency) were detected
across six microsatellites loci ranging from one allele at the locus Eaco_019 in
BM, KJ, BK, and ANS populations to 13 alleles at the locus Eaco_054 in the BM
population. The mean number of alleles per locus ranged from 1.57 to 9.57 and
the mean number of alleles per population ranged from 4.33 to 7.00 (Table 2). The
BM population had the highest average number of alleles, while the lowest was
found in the KJ population. The levels of the observed and expected
heterozygosities varied from 0.434 to 0.615 and from 0.458 to 0.605, respectively.
The TD population had the highest HE value (0.605), while the ANS showed the
lowest HE (0.458).
Five loci consisted of Eaco_001 in TD and NK, Eaco_009 in KJ and ANS,
Eaco_051 in KJ, Eaco_054 in TD, NK, ANS and Eaco_055 in ANS were found to
deviate significantly from Hardy-Weinberg equilibrium (P < 0.05) prior to Holm
Bonferroni correction. Two loci (Eaco_001 in NK and Eaco_054 in TD)
significantly deviated from Hardy-Weinberg equilibrium after Holm Bonferroni
correction (P < 0.05). However, according to Micro-checker, these populations
were possibly in HWE with two loci suggest presence of null alleles. Thus, null
alleles are the most probable explanation for the deviation from Hardy-Weinberg
equilibrium, although Wahlund effect and deviation from panmixia are also
possible. Thus, all populations were possibly in Hardy-Weinberg equilibrium.
14
15
9
Table 3 Summary of genetic diversity at six microsatellite loci at seven
locations for E. acoroides
Loci
Eaco_001
A
HO
HE
P
Eaco_009
A
HO
HE
P
Eaco_019
A
HO
HE
P
Eaco_051
A
HO
HE
P
Eaco_054
A
HO
HE
P
Eaco_055
A
HO
HE
P
All
populations
A
HO
HE
Locations
MeanA/
locus
TD
PR
NK
BM
KJ
BK
ANS
3
0.185
0.427
0.002
2
0.389
0.375
1.000
8
0.400
0.611
0.000
5
0.467
0.417
0.834
2
0.417
0.375
1.000
3
0.429
0.489
0.274
2
0.355
0.331
1.000
3.57
5
0.667
0.658
0.940
5
0.667
0.650
0.952
2
0.033
0.095
0.049
6
0.600
0.660
0.331
6
0.417
0.549
0.017
6
0.786
0.677
0.674
6
0.613
0.592
0.026
5.14
2
0.111
0.105
1.000
2
0.500
0.461
1.000
3
0.467
0.376
0.610
1
0.000
0.000
N.A
1
0.000
0.000
N.A
1
0.000
0.000
N.A
1
0.000
0.000
N.A
1.57
9
0.852
0.811
0.554
4
0.722
0.619
0.861
9
0.933
0.808
0.504
9
0.667
0.757
0.022
7
0.917
0.759
0.009
10
0.905
0.826
0.905
8
0.633
0.724
0.189
8.00
10
0.630
0.858
0.000
8
0.667
0.789
0.171
10
0.800
0.839
0.003
13
0.833
0.792
0.246
6
0.792
0.658
0.725
12
0.810
0.841
0.880
8
0.290
0.388
0.006
9.57
6
0.704
0.771
0.130
6
0.667
0.657
0.427
9
0.700
0.823
0.075
8
0.800
0.699
0.889
4
0.667
0.656
0.143
7
0.762
0.747
0.090
5
0.710
0.710
0.022
6.43
5.83
0.525
0.605
4.50
0.602
0.592
6.83
0.556
0.592
7.00
0.561
0.554
4.33
0.535
0.499
6.50
0.615
0.597
5.00
0.434
0.458
5.71
0.478
0.487
Genetic diversity was inferred from the numbers of alleles (A), the proportion of observed
heterozygosities (HO) and expected heterozygosities (HE). Exact P value associated with the
Hardy-Weinberg equilibrium (P). Numbers in bold indicate significant deviation from HardyWeinberg equilibrium at P < 0.05 after Holm Bonferroni corrections. TD: Tunda, PR: Pramuka,
NK: Nakuri, BM: Batam, KJ: Karimun Java, BK: Bangka, ANS: Anambas.
Genetic differentiation
Pairwise FST values ranged from 0.127 to 0.359. FST was statistically highly
significant between all pairs of samples with P < 0.001 (Table 4). The highest
genetic differentiation was found between samples from NK and ANS (FST =
0.359), while the lowest was found between TD and PR and between BM and BK
(FST = 0.127). These result indicated high levels of genetic differentiation among
10
populations of E. acoroides. Isolation by distance (IBD) revealed significant
correlation between genetic differentiation and geographic distance across all
pairs of samples with P = 0.008 (Figure 6).
Table 4 Pairwise FST values (below diagonal) and significant FST P values
(above diagonal)
TD
PR
NK
BM
KJ
BK
ANS
TD
0.127
0.290
0.235
0.225
0.175
0.298
PR
***
0.302
0.270
0.273
0.203
0.348
NK
***
***
0.301
0.338
0.214
0.359
BM
***
***
***
0.247
0.127
0.293
KJ
***
***
***
***
0.247
0.239
BK
***
***
***
***
***
0.303
ANS
***
***
***
***
***
***
-
*** P < 0.001.
Figure 6 Isolation by distance showed significant correlation between
genetic distance (pairwise FST values) and geographic distance
(see Appendix 5) with P = 0.008.
Phylogenetic and Bayesian Clustering Analysis
The Neighbour Joining method base on DA distance identified three major
clusters (Figure 7). Cluster 1 was the NK population, cluster 2 consists of four
populations (TD, PR, BK and BM) and cluster 3 consists of two populations (KJ
and ANS). NK population was genetically distinct from the other populations.
11
Cluster 2, consists of two populations in Java (TD and PR) and two populations in
Sumatra (BK and BM). The highest bootstrap support was found between TD and
PR. Cluster 3 shows discordance between genetic and geographic distance. These
populations were separated about 1095 km but they were closely related.
The ΔK test in Structure (Pritchard et al. 2010) indicated the optimum value
of ΔK at K = 3 with a secondary peak at K = 7. At K = 3, Structure 2.3.4 analysis
mirror those seen in NJ tree with three major clusters (NK/KJ, ANS/TD, PR, BM
and BK) (Figure 7). With K set to 7, each population becomes different cluster
(not shown).
Figure 7 Neighbour Joining (NJ) tree based on DA distance and bar plot of
Structure 2.3.4 revealed congruent results with all populations were
divided into three clusters (NK/TD, PR, BK and BM/ANS and KJ).
Each colour represents one cluster.
Analysis of Molecular Variance (AMOVA)
AMOVA analysis with no a priori regional structure indicates highly
significant genetic partitioning among populations (25.76%) and among
individuals within population (74.24%) with FST = 0.257 and P < 0.001 (Table 5).
When populations were divided into three regions according to phylogenetic
relationship and clustering analysis (NK/TD, PR, BK, BM/KJ, ANS), 10.17%
variance were resided among groups with FCT = 0.103 (P < 0.05). Variances
among population within group and among individual within group were 17.63%
and 71.6 %, respectively.
Discussion
Genetic Diversity
Observed heterozygosity of E. acoroides in this study ranged from 0.434 to
0.615. Previous study of E.acoroides in Lembongan (Bali) and Waigeo (Papua)
12
found that observed heterozygosities were 0.436 – 0.582, respectively
(Pharmawati et al. 2015). Nakajima et al. (2014) revealed observed
heterozygosities of E. acoroides ranged from 0.165 – 0.575 in three locations
(Japan, China, and Philippines) using nine microsatellite loci. Other study found
heterozygosities of E. acoroides varied from 0.100 to 0.567 in China (Gao et al.
2012).
The genetic diversity studies of other seagrass species showed almost
similar result. Alberto et al. (2008) revealed level of observed heterozygosities of
Cymodocea nodosa ranged from 0.296 to 0.750 across Mediterranean-Atlantic
transition region using eight microsatellites loci. In other regions, heterozygosities
of Zostera marina ranged from 0.491 to 0.563 in San Quintin Bay, Mexico using
eight marker (Muniz-Salazar et al. 2006). Serra et al. (2010) found the
heterozygosities of Posidonia oceanica ranged from 0.212 – 0.569 in
Mediterranean using 12 microsatellites loci. Genetic diversity, like species
diversity, may be most important for enhancing the consistency and reliability of
ecosystems by providing biological insurance against environmental change
(Hughes & Stachowicz 2004). Degradation and loss of seagrass meadows has led
to the general notion of low genetic diversity, extensive clonality and minimal
gene flow among populations (Coyer et al. 2004).
Table 5 Result from hierarchical AMOVA for E. acoroides
Global AMOVA
Source of variation
Among population
Within populations
Total
3 Groups
Among groups
Among populations
within groups
Within populations
Total
d.f
6
397
403
SS
212.3
674.213
886.512
Var.
0.589
1.698
2.287
% var.
25.76
74.24
F-statistic
FST=0.257
P-value
0.000
2
4
111.612
100.688
0.255
0.418
10.77
17.63
FCT=0.103
FSC=0.198
0.01
0.000
397
403
674.213
886.512
1.698
2.372
71.6
FST=0.284
0.000
Hierarchical AMOVA showed d.f: degree of freedom, SS: sum of square, Var: variance
component, % var: percentage of variances, F-statistics among region, among populations within
regions, and within populations, P-value for F-statistics.
Population Differentiation
Pairwise FST and AMOVA showed significant genetic differentiation
between all pair of samples. Moreover, isolation by distance (IBD) indicated
significant correlation between genetic differentiation and geographic distance.
These suggest low gene flow between populations, possibly cause by limited
dispersal of E. acoroides. Lacap et al. (2002) revealed that genetic exchange by
pollen dispersal for E. acoroides may occur over spatial scales in the order of
kilometers (