Connectivity and evolution of giant clams (Tridacnidae): a - E-LIB

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Connectivity and evolution of giant clams (Tridacnidae):
a molecular genetic approach
Tridacna crocea, T. maxima, and T. squamosa (photos by Marc Kochzius)
Min Hui
Submitted as partial fulfilment of the requirements for the degree of
Doctor of Natural Sciences (Dr. rer. Nat.)
Faculty of Biology and Chemistry
University of Bremen
Bremen, September 2012
Examination Committee:
First Examiner:
Prof. Dr. Marc Kochzius
Marine Biology, Vrije Universiteit Brussel, Brussels, Belgium
Second Examiner:
Prof. Dr. Dietmar Blohm
Biotechnology and Molecular Genetics, University of Bremen, Germany
Additional Examiner I: Prof. Dr. Ulrich Saint-Paul
Leibniz Center for Tropical Marine Ecology, ZMT, Bremen, Germany
Additional Examiner II: Dr. Florian Leese
Department of Animal Ecology, Evolution and Biodiversity, Ruhr University Bochum,
Bochum, Germany
Student Member I: Tina Dohna
PhD student, University of Bremen, Germany
Student Member II: Jingwen Zhao
MSc student, University of Bremen, Germany
Date of public defence: Thursday, 25th October 2012
Table of Contents
Acknowledgements ............................................................................................................... i
Summary............................................................................................................................. iii
Zusammenfassung ................................................................................................................vi
Abbreviations .......................................................................................................................ix
List of Figures .................................................................................................................... xii
List of Tables .....................................................................................................................xiv
List of Publications..............................................................................................................xv
1.
Overview of the Thesis ............................................................................................. 2
1.1.
General Introduction ........................................................................................... 2
1.1.1.
Genetic population structure and genetic diversity of marine species ............. 2
1.1.2.
The Indo-West Pacific ................................................................................... 3
1.1.3.
Status and exploitation of coral reefs in Eastern Africa and Indonesia............ 5
1.1.4.
Giant clams ................................................................................................... 6
1.1.5.
Molecular markers......................................................................................... 8
1.2.
Objective of the research ..................................................................................... 9
1.3.
Methods performed in the research.....................................................................10
1.3.1.
Population genetic structures of the three giant clams (Tridacna crocea, T.
maxima and T. squamosa) in Indo-West Pacific revealed by mtDNA............10
1.3.2.
Development of microsatellite markers for T. crocea and cross-species
amplification. ...............................................................................................10
1.3.3.
Analysis of population genetic structure of T. crocea in the Indo-Malay
Archipelago using microsatellite DNA markers and comparison with the
results from mtDNA.....................................................................................12
1.4.
1.4.1.
Framework.........................................................................................................12
Comparative population genetic structures of three endangered giant clams
(Tridacnidae) throughout the Indo-West Pacific: implications for origins of
divergence, connectivity, and conservation. ..................................................12
1.4.2.
Isolation and characterisation of nine microsatellite markers in the boring
giant clam (Tridacna crocea) and cross-amplification in five other tridacnid
species..........................................................................................................13
1.4.3.
Comparison of microsatellite and mitochondrial DNA markers in detecting
genetic structure and connectivity in the boring giant clams, Tridacna
crocea, across the Indo-Malay Archipelago. .................................................13
1.5.
Conclusions and overall discussion ....................................................................13
1.6.
Outlook..............................................................................................................14
1.7.
References .........................................................................................................15
2.
Comparative genetic population structures of three endangered giant clams
(Tridacnidae) throughout the Indo-West Pacific: implications for origins of
divergence, connectivity, and conservation ..............................................................23
2.1.
Abstract..............................................................................................................24
2.2.
Introduction........................................................................................................24
2.3.
Materials and methods........................................................................................27
2.3.1.
Sampling and sequencing .............................................................................27
2.3.2.
Genetic diversity ..........................................................................................29
2.3.3.
Demographic history ....................................................................................29
2.3.4.
Genetic population structure and gene flow ..................................................30
2.4.
Results ...............................................................................................................32
2.4.1.
Genetic diversity ..........................................................................................32
2.4.2.
Historical demography .................................................................................32
2.4.3.
Genetic structure and connectivity................................................................33
2.5.
Discussion..........................................................................................................39
2.5.1.
Genetic endemism in peripheral regions of the IWP .....................................40
2.5.2.
Historical sea-level fluctuation and present oceanographic conditions ..........41
2.5.3.
Origin of the high diversity in the IMA.........................................................42
2.5.4.
Conservation implications ............................................................................43
2.6.
Acknowledgements ............................................................................................45
2.7.
References .........................................................................................................45
3.
Isolation and characterisation of nine microsatellite markers in the boring giant
clam (Tridacna crocea) and cross-amplification in five other tridacnid species ........55
3.1.
Abstract..............................................................................................................56
3.2.
Body text ...........................................................................................................56
3.3.
3.4.
4.
References .........................................................................................................61
Concordance of microsatellite and mitochondrial DNA markers in detecting genetic
population structure and connectivity in the boring giant clam, Tridacna crocea,
across the Indo-Malay Archipelago ..........................................................................64
4.1.
Abstract..............................................................................................................65
4.2.
Introduction........................................................................................................66
4.3.
Material and Methods.........................................................................................67
4.3.1.
Samples collection........................................................................................67
4.3.2.
DNA extraction, PCR amplification and fragment analysis...........................70
4.3.3.
Data analysis ................................................................................................71
4.4.
Results ...............................................................................................................73
4.4.1.
Genetic diversity of different populations .....................................................73
4.4.2.
Genetic population structure based on microsatellites and comparison with
mtDNA ........................................................................................................73
4.5.
5.
Discussion..........................................................................................................76
4.5.1.
Genetic diversity and Hardy-Weinberg proportions (HWP) ..........................76
4.5.2.
Genetic population structure .........................................................................77
4.6.
4.7.
References .........................................................................................................78
Declaration ..............................................................................................................85
i
Acknowledgements
The thesis would not have been possible without the help and support from many individuals
and organisations. I would particularlly thank the following persons.
First of all, I would like to thank Prof. Marc Kochzius for being my supervisor and helping
me in my research with suggestions, topics, discussion points etc. I really appreciate the
revisions you made of my manuscripts and thesis. Also thank you for allowing me to work in
the group (Marine Biology) of Vrije Universiteit Brussel in the last months. I have to say that
your serious attitude to scientific work has great influence on me. When I lost my motivation,
you always encouraged me to have confidence in myself, which made me work harder and
find a way to finish my thesis. I am extremely grateful to you for all your support during this
four years study.
I would like to thank Prof. Dietmar Blohm. Thank you for accepting me as your student
and making it possible for me to study at the University of Bremen. Thank you for letting me
work in your lab (Biotechnology and Molecular Genetics) and for all the suggestions to my
scientific work. Thank you for being a reviewer of my thesis and for the critical evaluation.
When I was going to a difficult time, you always gave useful advice and made this thesis
possible. You are a respected Professor for me.
Thank Dr. Florian Leese for helping me in the isolation microsatellites experiment, for
carefully revising my manuscript and for agreeing to be one of examiners of my defense. A
warm thank is also given to Prof. Ulrich Saint-Paul for joining the committee of my thesis
defense.
Great appreciation is given to Agus Nuryanto for collection giant clam samples and Janne
Timm for support during the collection. Without your sampling, my research about giant
clams would not exist. Thank Wiebke Elsbeth Kraemer for teaching me how to culture
zooxanthellae and offering the cultures to make excluding zooxanthellae contamination
possible.
Thank Tina Dohna for your help and suggestions in my research. I can not imagine
working in the lab without your company. One more thank comes to you for being in my
thesis committee. Thank Reinhard Zelm for your patience to answer my questions, for
translating the summary of the thesis into German and also for helping to adjust the format of
the thesis at the last possible moment. Thank Frank Meyer-Jürgens for helping me order
ii
reagents and making me familiar with the lab when I first came to the university. Thanks to
all my nice colleagues of AG Blohm for every talk and help.
A warm thank to the members and students of the APNA lab, especially Timothy Sierens
and Rosa van der Ven. Thank Tim for showing me around the APNA lab. Thank Rosa for
your correction on my English writing and comments on my thesis.
Thank Prof. Aibin Zhan and Dr. Ming Liu for suggestions to my manuscript and teaching
me how to use some genetic analysis software. Thank Till Czypionka for also translating the
summary. Thank Jie Cheng for your suggestions to my manuscript structure. Thank
Guangchao for your proofreading. Thank all my friends.
I want to say a sincere “thank you very very much” to my mother, father and younger
sister. Thank you for all your love. So many years, I have been living in your careing. You
give me great motivation to complete my work.
There are too many people I want to say “thank you” to……in the past four years in
Europe, especially in Bremen, Germany.
Finally, I would like to thank to the institutions that have made my study possible: German
Federal Ministry of Education and Research (BMBF) (grant no. 03F0390B and 03F0472B),
which funded this study in the framework of the SPICE project (Science for the Protection of
Indonesian Coastal Marine Ecosystems); China Scholarship Council (CSC), Chinese embassy
in Berlin, and University of Bremen for the PhD scholarship to me.
㺧ᗳᝏ䉒ʽVielen danke! Dankjewel! Thank you all!
Min Hui
09. 2012
iii
Summary
The Indo-West Pacific (IWP), specifically the Indo-Malay Archipelago (IMA), is known to be
one of the regions exhibiting the highest marine species diversity in the world. Many
hypotheses try to explain the origin of this biodiversity, mainly including the centre-of-origin,
the region-of-overlap, and the centre-of-accumulation concepts (review in Hoeksema 2007).
The IMA has a complicated geological history. Sinking sea levels during glacial periods in
the Pliocene and Pleistocene exposed the Sunda and Sahul shelves. Therefore, the IMA
provides an excellent study system for detecting the contribution of historical and ongoing
processes to genetic diversity and connectivity. In addition, the Red Sea (RS) and Western
Indian Ocean (WIO) are also important evolutionary centres in Indo-West Pacific, with many
endemic species.
The giant clams Tridacna crocea, T. maxima, and T. squamosa are widely distributed
throughout the IWP. Their high commercial value as fishery resource and marine ornamental
led to their large scale exploitation. Meanwhile, tridacnid species are listed in Appendix II of
the Convention on International Trade in Endangered Species of Wild Fauna and Flora
(CITES) and need urgently measures for conservation. Knowledge on genetic population
structure and connectivity are important baseline data for the protection of these species,
especially for the design of Marine Protected Area (MPA) networks.
To test if concordant barriers exist that prevent gene flow among populations and to relate
this to oceanography and geological history of the IWP, the genetic population structure of the
three giant clam species ranging from the RS and WIO across the Eastern Indian Ocean (EIO)
and IMA to the Western Pacific (WP) and the Central Pacific (CP) were compared by using
the mitochondrial cytochrome c oxidase subunit I gene (COI) as a molecular marker. The
three species showed restricted gene flow and highly significant genetic structures in the area
studied. The Φst-values (P < 0.001) are 0.46, 0.81, and 0.68, for T. crocea (EIO to WP), T.
maxima (WIO to CP), and T. squamosa (WIO to WP), respectively. The populations could be
divided into three to six groups in the different species from West to East: (1) WIO (T.
maxima and T. squamosa), (2) RS (T. maxima and T. squamosa), (3) EIO (and Java Sea in T.
maxima), (4) central IMA, (5) WP, and (6) CP (T. maxima). The populations in the central
IMA showed panmixing.
iv
To detect the reliability of the analysis based on the mitochondrial marker, ten
microsatellites were selected to study the genetic population structure of T. crocea in the IMA
and the results were compared with those revealed by mtDNA. Due to the symbiotic
relationship between giant clams and Symbiodinium spp. (zooxanthellae), it is difficult to
isolate microsatellites. By applying a recently developed method (Leese et al. 2008), nine
novel microsatellite markers were isolated for T. crocea and the giant clam specificity was
confirmed by further test in PCRs with DNA extracts from Symbiodinium. These markers
were highly polymorphic and showed a medium degree of cross-species amplification in the
other five tridacnid species. No significant linkage disequilibrium between pairs of loci was
found and eight of nine loci were in Hardy-Weinberg proportions. Therefore, these
microsatellites potentially provide useful nuclear markers for population genetic studies on
giant clams.
The genetic population structure revealed by mtDNA and nDNA markers was congruent,
with only minor difference. The correlation of genetic divergence revealed by the two marker
systems was positive. Three common groups were divided as follows: (1) EIO, (2) central
IMA, and (3) WP. Populations in the central IMA also showed panmixing, being well
connected by currents. However, the structure revealed by microsatellite was not as strong as
in the mtDNA analysis, and the genetic diversity revealed by the two genetic marker systems
was different in certain populations. These minor differences might be caused by the intrinsic
characteristics of mtDNA and nDNA, such as the different effective population size and
mutation rate. Combination of the two marker systems might provide more information of
population structure and connectivity on different temporal scales.
Overall, the results showed concordant barriers for gene flow in the three species and also
supported that mtDNA is applicable for population genetic analysis and recovery of
connectivity in giant clams. It is suggested that sea-level changes during glacial periods of the
Pliocene and Pleistocene, as well as oceanography are important factors that shape the genetic
population structure of giant clams. The observed deep evolutionary lineages in the peripheral
areas of the IMA might include cryptic species, which supports the centre-of-accumulation
hypothesis that aims to explain the high diversity in the IMA. As a consequence, the
information will facilitate the conservation of these endangered giant clam species. The
distinct groups within each species, potentially separated Evolutionary Significant Unit
(ESU), are proposed to be managed separately for their adaptive diversity; small scales MPA
v
network should be arranged to maintain the connectivity; and restoration should be performed
in areas with low genetic diversity.
vi
Zusammenfassung
Der Indo-West-Pazifik (IWP), insbesondere das Indo-Malayische Archipel (IMA), ist eine der
Regionen mit der höchsten marinen Biodiversität weltweit. Mehrere Hypothesen, u.a. “centreof-origin”, “region-of-overlap”, und “centre-of-accumulation” (Review in Hoeksema 2007),
versuchen dieses Phänomen zu erklären. Das IMA hat eine komplexe geologische
Vergangenheit. Sinkende Meeresspiegel während der Glazialen des Pliozänes und des
Pleistozänes führten dazu, dass der Sunda- und der Sahul-Schelf wiederholt freigelegt
wurden. Das IMA stellt daher ein hervoragendes System dar, um den Einfluß historischer und
kontemporärer Prozesse zur genetischen Diversität und Konnektivität zu studieren. Außerdem
stellen das Rote Meer (RM) und der westliche Indische Ozean (WIO) weitere evolutionäre
Zentren des IWPs dar.
Die Riesenmuscheln Tridacna crocea, T. maxima und T. squamosa sind im IWP weit
verbreitet. Ihr hoher kommerzieller Wert als Fischereiressource und mariner Zierorganismus
führte zu ihrer Ausbeutung im großen Maßstab. Inzwischen sind Riesenmuscheln im Anhang
II der Konvention zum internationalen Handel mit bedrohten Pflanzen- und Tierarten (CITES)
aufgeführt und benötigen dringend Schutzmaßnahmen zur Arthaltung. Wissen über
genetische Populationsstruktur und Konnektivität sind wichtige Grundlagen für den Schutz
dieser Arten, vor allem für die Gestaltung von Netzwerken mariner Schutzgebiete.
Um zu testen, ob zwischen den Population eindeutige Barrieren für den Genfluß bestehen
und um solche in Bezug zur Ozeanografie und geologischen Geschichte des IWP zu setzen,
wurde die genetische Populationsstruktur der drei Riesenmuschelarten aus dem Bereich des
RM, des WIO, des östlichen Indischen Ozeans (ÖIO), des IMA, des westlichen Pazifik (WP)
und des zentralen Pazifiks (ZP) unter Verwendung des mitochondrialen Cytochrom-c-Oxidase
I-Gens (COI) verglichen. Die drei Arten zeigten beschränkt Genfluss und hoch signifikante
genetische Strukturen im untersuchten Gebiet. Die Φst-Werte (P < 0,001) sind 0,46, 0,81 und
0,68 für T. crocea (ÖIO zu WP), T. maxima (WIO zu ZP) und T. squamosa (WIO zu WP).
Die Populationen konnten in drei bis sechs Gruppen innnerhalb der verschiedenen Arten (von
West nach Ost) unterteilt werden: (1) WIO (T. maxima und T. squamosa), (2) RM (T. maxima
und T. squamosa), (3) ÖIO (und Java-See in T. maxima), (4) zentrales IMA (5)WP, und (6)
ZP (T. maxima). Die Populationen in im zentralen IMA sind panmiktisch.
vii
Um die Zuverlässigkeit der Analyse des mitochondrialen Markers zu überprüfen, wurden
zehn Mikrosatelliten ausgewählt, um die genetische Populationsstruktur von T. crocea im
IMA zu untersuchen. Die Ergebnisse dieser Analyse wurden mit denen der auf mtDNA
beruhenden
Analyse
verglichent.
Durch
die
symbiotische
Beziehung
zwischen
Riesenmuscheln und Symbiodinium spp. (Zooxanthellen), ist es schwierig, Mikrosatelliten aus
nukleärer DNA (nDNA) zu isolieren. Durch die Anwendung einer neu entwickelten Methode
(Leese et al. 2008) wurden neun neue Mikrosatelliten-Markern für T. crocea isoliert und ihre
Spezifität wurde durch weitere negative Kontrollen mit Symbiodinium DNA Extrakten
bestätigt. Diese Marker waren hoch polymorph und zeigten einen mittleren Grad von
Anwendbarkeit bei den anderen fünf Riesenmuschelarten. Es wurde kein signifikantes
„linkage disequilibrium“ zwischen Paaren von Loci festgestellt. Acht von neun Loci waren in
Hardy-Weinberg-Gleichgewicht. Diese Mikrosatelliten sind deshalb potenziell nützliche
nukleare Marker für die populationsgenetische Studien von Riesenmuscheln.
Die aus mitochondrialen und nukleären Markern abgeleiteten Populationsstrukturen
stimmten bis auf geringe Unterschied überein. Die Korrelation der aufgrund der beiden
Markersystemen berechneten genetischen Divergenzen war positiv. Drei Gruppen konnten
identifiziert werden: (1) ÖIO (2) zentrales IMA und (3) WP. Die Populationen im IMA
zeigten Signaturen von Panmixie und waren auch durch Strömungen verbunden. Allerdings
war die aus den nukleären Markern abgeleitet Divergenz zwischen den Populationen weniger
stark ausgeprägt als die auf den mitochondrialen Marker beruhende Divergenz. Die aus den
beiden Markersystemen abgeleitet genetische Diversität unterschied sich ebenfalls in einigen
Populationen. Solche Unterschiede können durch die intrinsischen Eigenschaften der mtDNA
und nDNA, sowie der unterschiedlichen effektiven Populationsgröße und unterschiedlichen
Mutationsraten verursacht werden. Durch die Kombination beider Marker-Systeme können
mehr Informationen über Populationsstruktur und Konnektivität über unterschiedliche
Zeiträumen analysiert werden.
Die vorliegenden Resultate zeigten übereinstimmende Barrieren für den Genfluß in allen
drei Arten und unterstreichen auch, daß mitochondriale Marker für populationsgenetische
Analysen zur Untersuchung der Konnektivität zwischen Populationen von Riesenmuscheln
anwendbar sind. Es wird vorgeschlagen, daß Veränderungen des Meeresspiegels während der
Eiszeiten des Pliozän und Pleistozän, sowie ozeanographische Bedingungen wichtige
Faktoren sind, die die genetische Populationsstruktur von Riesenmuscheln gestalten. Die
viii
genetisch sehr divergenten Abstammungslinien in den Randbereichen des IMA könnten
kryptische Arten darstellen, was die „centre-of-accumulation“-Hypothese, welche die hohe
Diversität im IMA erklären soll, unterstützt. Somit bieten die vorliegenden Resultate
grundlegende Information die bei der Ausarbeitung von Maßnahmen zur Erhaltung dieser
gefährdeten Riesenmuschelarten von Nutzen sein können. Es wird vorgeschlagen, die
verschiedenen Gruppen innerhalb der einzelnen Arten, die möglicherweise „Evolutionary
Significant Unit (ESU)“ darstellen, in Schutzprogrammen unabhängig voneinander zu
managen, um so ihre adaptive Diversität zu erhalten. Kleinskalige Netzwerke von marinen
Schutzgebieten sollten eingerichtet werden, um die Konnektivität zwischen diesen zu
gewährleisten. Renaturierungsmaßnahmen sollten sich auf Gebiete mit niedriger genetischer
Diversität konzentrieren.
ix
Abbreviations
Л
nucleotide diversity
μl
microliter
μM
micromolar
bp
basepairs
CITES
Convention on International Trade in Endangered Species
COI
cytochrome c oxidase subunit I gene
CP
Central Pacific
DNA
deoxyribonucleic acid
dNTPs
deoxinucleotide triphosphates
EACC
East African Coast Current
EICC
East Indian Coast Current
EIO
Eastern Indian Ocean
EST
Expressed Sequence Tag
ESU
Evolutionary Significant Units
GW
Great Whirl
h
hours
IMA
Indo-Malay Archipelago
IWP
Indo-West Pacific
HCl
hydrochloric acid
He
expected heterozygosity
HRI
Harpending’s raggedness index
Ho
observed heterozygosity
HWP
Hardy-Weinberg proportions
IBD
isolation-by-distance
ITF
Indonesian Throughflow
JC
South Java Current
K
number of clusters
KCl
potassium chloride
LC
Leeuwin Current
x
LD
Linkage disequilibrium
LH
Laccadive High
LL
Laccadive Low
min
minutes
mM
millimolar
Mg2+
Magnesium2+
MPAs
Marine Protected Areas
mtDNA
Mitochondrial DNA
n
number of sequences
Na
numbers of alleles
NaCl
sodium chloride
nDNA
nuclear DNA
NEMC
Northeast Madagascar Current
ng
nanogram
Nhp
number of haplotypes
NMC
Northeast Monsoon Current
PCR
polymerase chain reaction
PLD
pelagic larval duration
RHJ
Ras al Hadd Jet
RMA
Reduced Major Axis
s
seconds
SC
Somali Current
SE
Socotra Eddy
SEC
South Equatorial Current
SECC
South Equatorial Counter current
SEMC
Southeast Madagascar Current
SG
Southern Gyre
SMC
Southwest Monsoon Current
SNP
single nucleotide polymorphism
SSD
sum of square deviation
Ta
optimal annealing temperature
Tris
tris (hydroxymethyl) aminomethane
xi
U
unit
WICC
West Indian Coast Current
WIO
Western Indian Ocean
WP
Western Pacific
xii
List of Figures
Fig. 1 Currents during the Southwest Monsoon (a) and Northeast Moonsoon (b) in the
Indian Ocean. SEC: the South Equatorial Current; SECC: South Equatorial
Counter current; NEMC and SEMC: Northeast and Southeast Madagascar
Current; EACC: East African Coast Current; SC: Somali Current; SG: Southern
Gyre; GW: Great Whirl; SE: Socotra Eddy; RHJ: Ras al Hadd Jet; WICC: West
Indian Coast Current; LH and LL: Laccadive High and Low; EICC: East Indian
Coast Current; SMC and NMC: Southwest and Northeast Monsoon Current; JC :
South Java Current; and LC: Leeuwin Current (Schott 2001)..................................... 4
Fig. 2 Map of the Indo-Malay Archipelago with surface currents. ITF: Indonesian
Throughflow; Dashed line: seasonal changing currents; Solid line: constant
currents (map modified from Voris (2000) by M. Kochzius)...................................... 5
Fig. 3 Distribution of giant clams (bin Othman et al. 2010). Abbreviation for genera T:
Tridacna; H: Hippopus.............................................................................................. 7
Fig. 4 The genral procedure for the microsatellite isolation in the protocol of Leese et al.
(2008).......................................................................................................................11
Fig. 5 Maps of the Indo-West Pacific (a1, b1, c1) and the Indo-Malay Archipelago (a2,
b2, c2) with sample sites (see Table 2 for abbreviations). a2-3: Tridacna crocea;
b1-3: T. maxima; b1-3: T. squamosa. a1: Major genetic breakes in Indo-West
Pacific (dashed lines in red) for the three giant clams T. crocea (Tc), T. maxima
(Tm) and T. squamosa (Ts). Surface currents with dominant (arrows with solid
lines) and seasonally changing currents (arrows with dashed line) (Wyrtki 1961;
Gordon & Fine 1996; Carpenter 1998; Schott & MacCreary 2001; Gordon
2005), including South Equatorial Current (SEC), Northeast Madagascar
Current (NEMC), East African Coast
Current (EACC), South Equatorial
Counter Current (SECC), Indonesia Throughflow (ITF) and North Equatorial
Counter Current (NECC), East Australian Current (EAC) are shown in the
maps. Pleistocene sea level low standard of 120 m (light grey area) is marked in
maps a2, b2, and c2 (Voris 2000). Pie charts in the maps represent the
proportions of clades defined in the network at different sites in each species
(a2: T. crocea; b1 and b2: T. maxima; c1 and c2: T. squamosa). Networks of
xiii
COI haplotypes of T. crocea, T. maxima and T. squamosa are shown in c1, c2
and c3, respectively. The sizes of the circles are proportional to haplotype
frequencies. Lines between circles represent one mutational step. The hatches
and numbers indicate additional mutational steps......................................................29
Fig. 6 Relationships between genetic and geographic distances of populations in the
central Indo-Malay Archipelago for the three giant clam species analysed by
Reduced Major Axis (RMA) regression without divergent populations. a: T.
crocea; b: T. maxima; c: T. squamosa. ......................................................................39
Fig. 7 (a) Map of the Indo-Malay Archipelago with sample sites (see Table 9 for
abbreviations). Surface currents with dominant (solid lines) and seasonally
changing currents (dashed lines) (Wyrtki 1961; Gordon & Fine 1996; Gordon
2005), including the Indonesia Throughflow (ITF), South Equatorial Current
(SEC), and North Equatorial Counter Current (NECC) are shown on the map.
Pleistocene sea-level low stand of 120 m (light grey area) is shown on the map
(Voris 2000). Pie charts represent the proportions of different genetic groups
(black, grey, and white) in each population defined by STRUCTURE 2.3.1. (b)
Bar plot showing Bayesian assignments of individuals to the three groups (K =
3) by STRUCTURE. Each bar means the estimated admixture coefficient from
each inferred group for each individual. The three groups are (A) Eastern Indian
Ocean, (B) the central Indo-Malay Archipelago, and (C) Western Pacific Ocean. .....68
Fig. 8 Relationships between genetic and geographic distances for Tridacna crocea by
Reduced Major Axis (RMA) regression without populations from Padang and
Biak..........................................................................................................................75
xiv
List of Tables
Table 1 Basic characteristics about types of DNA markers (according to Liu & Cordes
2004). ........................................................................................................................ 9
Table 2 Summary statistics for each population of the three giant clams species, number
of sequences (n), number of haplotypes (Nhp), nucleotide diversity (Л), Tajima’s
D, Fu’s Fs, sum of square deviation (SSD), and Harpending’s raggedness index
(HRI). RS: Red Sea; WIO: Western Indian Ocean; EIO: Eastern Indian Ocean;
IMA: Indo-Malay Archipelago; WP: Western Pacific; CP: Central Pacific. ..............31
Table 3 Pairwise Φst-values between populations of Tridacna crocea in the Indo-West
Pacific. For abbreviations, see Table 2. .....................................................................35
Table 4 Pairwise Φst-values between populations of Tridacna maxima in the Indo-West
Pacific. For abbreviations, see Table 2. .....................................................................36
Table 5 Pairwise Φst-values between populations of Tridacna squamosa in the IndoWest Pacific. For abbreviations, see Table 2. ............................................................37
Table 6 Hierachical analysis of molecular variance (AMOVA) of COI sequences in the
three giant clam species Tridacna crocea, T. maxima, and T. squamosa from
Indo-West Pacific. ....................................................................................................38
Table 7 Characteristics of microsatellite loci isolated in Tridacna crocea. ............................59
Table 8 Cross-amplification in five other tridacnid species. ..................................................60
Table 9 Variation in microsatellite loci of 16 Tridacna crocea populations in the IndoMalay Archipelago. ..................................................................................................69
Table 10 Characteristics of microsatellite loci (DeBor et al. 2010, Hui et al. 2011) for
Tridacna crocea........................................................................................................71
Table 11 Pairwise Fst values between populations of Tridacna crocea in the Indo-Malay
Archipelago. .............................................................................................................74
Table 12 Hierachical analysis of molecular variance (AMOVA) of microsatellite
markers in the Tridacna Crocea from Indo-Malay Archipelago. For
abbrevations of site, see Table 9. ..............................................................................75
xv
List of Publications
Publication 1
Title: Isolation and characterisation of nine microsatellite markers in the boring giant clam
(Tridacna crocea) and cross-amplification in five other tridacnid species
Authors: Min Hui, Marc Kochzius, Florian Leese
Journal: Marine Biodiversity (Published)
Publication 2
Title: Comparative genetic population structures of three endangered giant clams
(Tridacnidae) throughout the Indo-West Pacific: implications for origins of divergence,
connectivity, and conservation
Authors: Min Hui, Wiebke Elsbeth Kraemer, Christian Seidel, Aboli Joshi, Agus Nuryanto,
Marc Kochzius
Journal: Molecular Ecology (Submitted)
Publication 3
Title: Concordance of microsatellite and mitochondrial DNA markers in detecting genetic
population structure and connectivity in the boring giant clam, Tridacna crocea, across the
Indo-Malay Archipelago
Authors: Min Hui, Agus Nuryanto, Marc Kochzius
Journal: manuscript prepared for Molecular Ecology
Overview of the Thesis
2
1. Overview of the Thesis
1.1. General Introduction
1.1.1. Genetic population structure and genetic diversity of marine species
Genetic diversity refers to the gene variation within a population, which is a basic source
of biodiversity. Conservation of genetic diversity is important, because it is essential for the
adaptation of populations in response to a changing environment. Low diversity is linked to
the reduction of population fitness (O'Brien 1994; Reed 2003; Bazin et al. 2006). The genetic
diversity is mainly determined by the effective population size (Bazin et al. 2006). Many
factors also influence the genetic diversity, such as population bottlenecks (O'Brien 1994),
natural selection (Charlesworth 1993), and the population connectivity and structure (Cherry
2003), which are related to certain historical and ongoing process.
In the marine environment, the genetic structure and the connectivity among populations
are affected by several factors, such as the species’ dispersal capability, life history, ocean
currents, and oceanographic as well as geographic barriers. Many marine species have a high
fecundity and long pelagic larval duration (PLD). It is assumed that these features lead to
large scale distribution, large population sizes, and high gene flow among populations
(Palumbi 1994). Oceanographic features like eddies and fronts can disturb the dispersal of
larvae and the exchange among populations (Weersing & Toonen 2009; White et al. 2009).
However, distant areas might be well connected by strong ocean currents (Gilg & Hilbish
2003), while close areas might be separated by an eddy or a front (Mitarai et al 2009;
Kochzius & Nuryanto 2008, 2009). In addition, current patterns in population structure are
also the product of historical events, such as the sea-level lowstands, which induced isolation
of populations and subsequent population expansion with rising sea-level. If the genetic
structure is caused by historical vicariance events, highly divergent, reciprocally
monophyletic lineages on the different sides of the barriers might be observed, with restricted
gene flow between these populations (Avise 2000; Nason et al. 2002). Furthermore, if
concordant genetic population structures are found in multiple species in one area, it indicates
that the taxa examined have seemingly been subjected to the same historical biogeographic
process and environmental conditions (Demboski et al. 1999; Avise 2000). In contrast,
3
inconsistent patterns suggest species examined might have different characteristics and
respond differently to environmental conditions (Cartens et al. 2005; Crandall et al. 2008).
1.1.2. The Indo-West Pacific
The Indo-West Pacific (IWP), covering a huge area more than halfway around the world,
including about 6,570,000 km2 of shelf habitat (Briggs 1999) and 261,200 km2 of reef area
(Spalding 2001), is a biodiversity hotspot. Throughout the IWP, many wide-ranging species
occur, but the species diversity is different in various parts of the region. The highest diversity
is found in the Indo-Malay Archipelago (IMA) (Briggs 1999, 2005).
In the Indian Ocean, the South Equatorial Current (SEC), the Northeast Madagascar
Current (NEMC), the East Coastal Current (EACC) and the South Equatorial Counter current
(SECC) connect the Western Indian Ocean (WIO) and the Eastern Indian Ocean (EIO) (Fig.
1, Schott 2001). Eddies and seasonal changing currents are also widely occurring at the coast
of Eastern Africa and in the northern Indian Ocean (Fig. 1, Schott 2001), while there is only
limited connection between the Red Sea and Indian Ocean through the Straits of Bab el
Mandeb.
4
a
b
Fig. 1 Currents during the Southwest Monsoon (a) and Northeast Moonsoon (b) in the Indian Ocean.
SEC: the South Equatorial Current; SECC: South Equatorial Counter current; NEMC and SEMC:
Northeast and Southeast Madagascar Current; EACC: East African Coast Current; SC: Somali Current;
SG: Southern Gyre; GW: Great Whirl; SE: Socotra Eddy; RHJ: Ras al Hadd Jet; WICC: West Indian
Coast Current; LH and LL: Laccadive High and Low; EICC: East Indian Coast Current; SMC and
NMC: Southwest and Northeast Monsoon Current; JC : South Java Current; and LC: Leeuwin Current
(Schott 2001).
The Indo-Malay Archipelago (IMA) (Fig. 2) connects the Indian and Pacific Ocean and
has the greatest diversity of marine shallow water species. This area includes a number of
islands which divide the regions into different seas, and the seas are connected by straits or
channels (Fig. 2). The current pattern in the IMA is complex, including seasonal changing and
unidirectional currents (Fig. 2). The most notable is the Indonesian Throughflow (ITF), which
transports the waters of the Pacific Ocean to the Indian Ocean through Makassar Strait, Flores
Sea, and Banda Sea. It has great influence on ocean circulation and the climate system
(Gordon & Fine 1996; Gordon 2005). In addition to the complicated oceanography, the IMA
experienced dramatic geological change (Hall 2002). Highly significant is the sinking of the
sea level by up to 120 m during glacial periods in the Pliocene and Pleistocene, which
exposed the Sunda and Sahul shelves (Pillans et al. 1998; Voris 2000). These exposed shelves
acted as an vicariance barrier that has been hypothesised to have caused genetic divergence
between Indian and Pacific Ocean populations of many taxa, such as the anemonefish
5
Amphiprion ocellaris (Timm & Kochzius 2008; Timm et al. 2008, Timm et al. 2012) and the
sponge Leucetta chagosensis (Wörheide et al. 2008).
Fig. 2 Map of the Indo-Malay Archipelago with surface currents. ITF: Indonesian Throughflow;
Dashed line: seasonal changing currents; Solid line: constant currents (map modified from Voris
(2000) by M. Kochzius).
The Western Pacific (WP) and Central Pacific (CP) extend from the east coast of New
Guinea to Polynesia. This broad area is dominated by constant currents throughout the year,
such as the west-ward South Equatorial Current, North Equatorial Current and east-ward
Equatorial Counter Current (Carpenter 1998). In the WP biodiversity is high, such as in New
Guinea, while moving to the East, the diversity decreases gradually (Spalding 2001).
1.1.3. Status and exploitation of coral reefs in Eastern Africa and Indonesia
Coral reefs are among the most productive and highest biodiversity bearing ecosystems on
earth (Connell 1978; Moberg & Folke 1999). The coral reefs of East Africa are mainly
fringing reefs and have a high level of species diversity, which makes them the centre of
diversity in the Western Indian Ocean (Spalding 2001). However, the East African region
experienced severe coral bleaching in 1997/1998 and subsequent mortality, which was
associated with El Niño events (Wilkinson 1999). The rising human population, pollution and
6
over-exploitation brought great threats to coral reef in Eastern Africa and called for much
more conservation efforts (Berg et al. 2002; Obura et al. 2004).
Moving to the East, Indonesia, located in the centre of the Indo-Malay Archipelago (IMA),
has about 18 percent of the world coral reefs and about 17,000 Islands (Spalding 2001). The
coral reef system supplies food resources and income to large groups of inhabitants in
Indonesia (Wabnitz et al. 2003). However, the coral reefs in Indonesia degraded greatly,
because of anthropogenetic actions, such as poison fishing, blast fishing, coral mining,
pollution, and overfishing (Cesar et al. 1997). Too many corals are harvested as building
materials, jewelry and aquarium organisms (Bruckner 2001). In the marine ornamental market
of the world, Indonesia is one of the most important exporters, while the USA, Japan and
Europe are the main importers (Wood 2001). Since this trade is handled under the regulations
of the Convention on International Trade in Endangered Species of Wild Fauna and Flora
(CITES), strategies of co-management with Indonesia’s Marine Protected Areas (MPAs) are
proposed (Clifton 2003). However, so far 51 MPAs with an area of 58,000 km2 are
established, covering only about 1 % of the marine area in Indonesia (Spalding 2001). It has
been suggested that MPAs should be arranged in a network and the distribution should match
the dispersal capabilities of the targeted species (Palumbi 2003), but to be really effictive, a
better understanding of the species dispersal and population connectivity is required.
1.1.4. Giant clams
Giant clams (Tridacnidae), with big size and colourful mantels, are distributed in coral reefs
of the tropical Indo-Pacific region. In the Tridacnidae family, ten species are found (reviewed
by bin Othman et al. 2010): Tridacna gigas (Linnaeus 1758), Hippopus hippopus (Linnaeus
1758), T. maxima (Röding 1798), T. derasa (Röding 1798), T. crocea (Lamarck 1819), T.
squamosa (Lamarck 1819), H. porcellanus (Rosewater 1982), T. teveroa (Lucas, Ledua &
Braley 1991), T. rosewateri (Sirenko & Scarlato 1991) and T. costata (Richter, Roa-Quiaoit,
Jantzen, Al-Zibdah & Kochzius 2008). Giant clams are hermaphrodites and reproduce
sexually. They become sessile juvenile clams after about 10 days PLD, settlement, and a
metamorphosis process (Lucas 1994). Another biological character for giant clams is that they
live in symbiosis with Symbiodinium spp (zooxanthellae), which supply photosynthetic
products for them (Goreau et al. 1973).
7
The distribution range varies in the different giant clam species (Fig. 3, bin Othman et al.
2010). Tridacna crocea (boring giant clam) is the smallest species of giant clams, with
maximum shell length 15 cm and adults burrow into the substrate. It is distributes from
Thailand to New Caledonia (Lucas 1988). Tridacna maxima has the widest range from East
Africa and the Red Sea to Polynesia (Rosewater 1965; Lucas 1988; Knop 1996). Tridacna
squamosa, with a maximum size 40 cm, can also be found from East Africa to the Central
Pacific (Rosewater 1965; Lucas 1988; Knop 1996), but the range is a little bit smaller than
that of T. maxima.
Fig. 3 Distribution of giant clams (bin Othman et al. 2010). The blue triangles represent Reef Check
Records and the species are unknown. Abbreviation for genera T: Tridacna; H: Hippopus.
Giant clams play important economic roles by serving as food and marine ornamental
resources (Lucas, 1994; Mingoa-Licuanan & Gomez 2002). Due to large scale collections
from the wild (Lucas 1994; Wells 1997), eight species of giant clams are listed as “Lower
Risk Conservation Dependent” or “Vulnerable” according to the 2007 World Conservation
Union’s Red List of Threatened Species, prompting for urgent management based on good
understanding of the biology for the endangered species, especially on genetic diversity and
connectivity. By using molecular markers, several population genetic and phylogeographic
studies for giant clams have been conducted (Campbell et al. 1975; Benzie & Williams
8
1992a, b; Macaranas et al. 1992; Benzie & Williams 1995; Benzie & Williams 1997;
Kittiwattanawong 1997; Yu et al. 2000; Kittiwattanawong et al. 2001; Laurent et al. 2002;
Juinio-Meñez et al. 2003; DeBoer et al. 2008; Kochzius & Nuryanto 2008; Nuryanto &
Kochzius 2009), but there are no studies on the population structure of giant clams for the
whole range from the WIO to the CP. Furthermore, there is also no investigation on the
connectivity of giant clam populations in the centre of diversity, the Indo-Malay Archipelago
(IMA), by using high resolution microsatellite DNA markers.
1.1.5. Molecular markers
Molecular markers are now widely used in population genetic studies in order to understand
evolutionary processes and historical demography, such as microsatellite, single nucleotide
polymorphism (SNP), and sequences of mitochondrial DNA (mtDNA). The general
properties of these marker types are summarised in Table 1 (Liu & Cordes 2004), showing
advantages and disadvantages. Whether mitochondrial DNA can be considered a strictly
reliable marker has been debated, since it solely represents one locus, inherits maternally, and
might not be a neutral marker (Lee & Edwards 2008; Zink & Barrowclough 2008;
Barrowclough & Zink 2009; Edwards and Bensch 2009). Therefore, it is more reliable to use
a combination of mtDNA and nDNA markers in the population genetic studies.
9
Table 1 Basic characteristics about types of DNA markers (according to Liu & Cordes 2004).
Markers
Prior molecular
Inheritence
Allele number
Polymorphism degree
Mendelian,
2
High
2-4
High
Maternal
Multiple
High mutation rates
inheritance
haplotypes
information required?
Microsatellite
Yes
codominant
SNP
Yes
Mendelian,
codominant
a
mtDNA
a
No
Primers can be designed from sequences of a related species
1.2. Objective of the research
1. Investigations on the genetic diversity and population structure of three giant clams
species (Tridacna crocea, T. maxima and T. squamosa) in Indo-West Pacific using a
mitochondrial DNA marker.
Are the genetic population structures concordant or not? Do common barriers to gene flow
exist?
Which factors influence the genetic population structure and connectivity?
What is a possible explanation for the high biodiversity in the Indo-Malay Archipelago?
2. Development of microsatellite DNA markers for T. crocea and application of these
markers to analyse the genetic population structure.
Is the genetic popualtion structure pattern concordant with the result illustrated by
mtDNA? Is it reliable to use mtDNA?
What are the differences between the results revealed by the two marker systems?
3. Application of the results for the conservation of giant clams.
More preservation effort, such as restoration, should be paid to the low genetic area.
Divergent population groups should be managed separately and the spatial design of MPA
should
match the dispersal ability of the species to facilitate connectivity among
populations.
10
1.3. Methods performed in the research
1.3.1. Population genetic structures of the three giant clams (Tridacna crocea, T. maxima and
T. squamosa) in Indo-West Pacific revealed by mtDNA.
Partial mitochondrial cytochrome c oxidase subunit I gene (COI) sequences of the three giant
clam species were obtained by PCR amplification and subsequent sequencing. This marker is
widely utilised for population genetic and phylogeographic studies. The M1-M6 partition of
the COI gene (Folmer 1994) is an efficient tool for metazoan species identification, which
make it the core fragment of DNA barcoding (Hebert et al. 2003). After editing the COI
sequences, genetic diversity indices (number of haplotypes, haplotype and nucleotide
diversity) were calculated to detect populations with low and high diversity. In order to
compare demographic histories of mtDNA in the three species, two different approaches
(neutrality tests and mismatch distributions) were used to test each population for departures
from the neutral model due to selection or population growth. An Analysis of Molecular
Variance (AMOVA) was performed to illustrate the overall genetic population structure
(shown by the fixation index: Φst), follwed by calculation of the pairwise Φst-values to reveal
the genetic divergence between pairs of populations. Then, a hierarchical AMOVA was
performed by setting different groups for the populations according to oceanography and
geography. In addition, a haplotype network was constructed and different clades were
defined. The frequencies of different clades in each population were shown on the map by
drawing pie charts. All the calculation above were conducted with the software Arlequin ver.
3.5 (Excoffier & Lischer 2010). To test the influence of the geographic distance, the
correlation between geographic and genetic distance was investigated by a isolation-bydistance analysis for the populations in the central Indo-Malay Archipelago. Finally, the
genetic diversity and structures of the three species were compared to find common barriers
for gene flow and the factors which might induce the genetic population structure and
connectivity patterns.
1.3.2. Development of microsatellite markers for T. crocea and cross-species amplification.
Microsatellite DNA markers are widely used in population genetic studies due to their
codominance and high polymorphism, which is most probably caused by slippage events
during DNA replication (Schlötterer & Tautz 1992). Different methods have been developed
11
for the isolation of microsatellite in the past years (Zane 2002), mainly by screening partial
genomic libraries, constructing microsatellite enrichment libraries, cross-species amplification
and using published sequences (e.g. EST database from NCBI). A microsatellite-enriched
library was constructed in this study following the protocol of Leese et al. (2008), which is
based on the concept that evolutionary distant genomes will show high similarity in low
complexity genomic regions such as microsatellite loci (Levinson et al. 1985). The main steps
include digestion of giant clam genomic DNA, hybridisation with the reporter genome (Mus
musculus domesticus), stringent washing, and construction of the microsatellite library (Fig.
4).
Target genome DNA
Digestion, adaptor ligation,
size selection, nick repair
and PCR
Hybridization with
reporter genome
Clone PCR
Elution
Sequencing
Recovery PCR & Cloning
Fig. 4 The genral procedure for the microsatellite isolation in the protocol of Leese et al. (2008).
Blastn searches were performed with the microsatellite sequences to detect possible
contamination with Symbiodinium spp. Primer Premier 5 (Premier Biosoft International) was
used to design primers. After PCR amplification and fragment analysis (GeneMarker v1.91,
SoftGenetics), marker polymorphism was assessed with POPGENE 1.31 (Yeh and Boyle,
1997), including numbers of alleles (Na), the observed heterozygosity (Ho), and expected
heterozygosity (He). Linkage disequilibrium (LD) and Hardy-Weinberg proportions (HWP)
tests were conducted using GENEPOP 4.0 (Raymond & Rousset 1995; Rousset 2008). Finally,
12
the markers were further tested in PCRs with DNA extracts from zooxanthellae Symbiodinium
to confirm giant clam specificity. Cross species amplification was performed in T. costata, T.
derasa, T. gigas, Tridacna maxima, and T. squamosa.
1.3.3. Analysis of population genetic structure of T. crocea in the Indo-Malay Archipelago
using microsatellite DNA markers and comparison with the results from mtDNA.
Microsatellites were analysed in T. crocea populations. The genetic diversity was calculated
as described above (Na, Ho and He) with the software GeneAlex 6.41 (Peakall & Smouse,
2006) and HWP was tested. Pariwise Fst, overall AMOVA, hierarchical AMOVA and
isolation-by-distance analysis were performed and the correlation between Fst and Φst (from
mtDNA result) was checked by a Mantel test. In order to further reveal the genetic structure, a
Bayesian analysis in the software STRUCTURE ver.2.2 (Pritchard et al. 2000) tests for
different numbers (K) of clusters in the data set, based on individual assignment. The
frequencies of clusters in each population were shown by the pie charts on a map. Then, the
genetic structure by the two marker systems (microsatellite and mtDNA) were compared and
discussed.
1.4. Framework
1.4.1. Comparative population genetic structures of three endangered giant clams
(Tridacnidae) throughout the Indo-West Pacific: implications for origins of divergence,
connectivity, and conservation.
Tridacna crocea, T. maxima and T. squamosa are endanged invertebrate species and
distribute in Indo-West Pacific, with similar life cycles. This study compared the genetic
population structures of three giant clam species ranging from Eastern Africa to Polynesian by
using COI. Congruent patterns might indicate the taxa examined were subjected to the same
historical biogeographic process and environmental conditions, while lack of congruence
suggests that species might experience unique biogeographical process or have speciesspecific biological characteristics and ecological requirements. In addition, this large-scale
population genetic study might facilitate the understanding of the origin of high diversity in
the Indo-Malay Archipelago and the design of MPA networks.
13
1.4.2. Isolation and characterisation of nine microsatellite markers in the boring giant clam
(Tridacna crocea) and cross-amplification in five other tridacnid species.
Several studies on the genetic population structure of T. crocea have been conducted using
allozymes and mtDNA. However, microsatellite markers might provide a much higher
resolution for such studies. Since giant clams host Symbiodinium spp. (zooxanthellae) in the
mantel tissue, this makes it difficult to isolate microsatellites. So far, only nine microsatellite
markers have been characterised for giant clams (DeBoer & Barber 2010). In this study, more
novel microsatellite markers for T. crocea were developed, using a new method. Their crossamplification in other tridacnid species was also tested.
1.4.3. Comparison of microsatellite and mitochondrial DNA markers in detecting genetic
structure and connectivity in the boring giant clams, Tridacna crocea, across the IndoMalay Archipelago.
The accuracy of mtDNA for population genetic analysis has been debated. In this study,
microsatellite markers were used for analysis of the genetic population structure of Tridacna
crocea in the Indo-Malay Archipelago and the results were compared with those results
obtained from mtDNA in the first part of the thesis. By comparison, the reliability of mtDNA
analysis results could be verified and much clear genetic structure pattern could be drawn by
combining the results of the two marker systems.
1.5. Conclusions and overall discussion
The study utilizing mtDNA revealed concordant patterns in the genetic population structure of
the giant clams T. crocea, T. maxima, and T. squamosa. They all showed a strong genetic
structure and the divergent populations might include cryptic species. Populations could be
generally divided into the following groups from West to East: (1) Western Indian Ocean (T.
maxima and T. squamosa), (2) Red Sea (T. maxima and T. squamosa), (3) Eastern Indian
Ocean (all species; including Java Sea for T. maxima), (4) central Indo-Malay Archipelago
(all species; Java Sea, Indonesian Throughflow and seas in the East of Sulawesi), (5) Western
Pacific (all species), and (6) Central Pacific (T. maxima).
Vicariance due to Plio-Pleistocene sea-level fluctuations could be a major factor that
established genetic divergence between populations of the Indian and Pacific Oceans. The
oceanography of the Indo-West Pacific, limited larval dispersal capability, and large
14
geographic distances are also important factors that shape the genetic structure in giant clam
populations. In the central Indo-Malay Archipelago, populations were well connected by the
ITF and seasonal changing currents. Moreover, considering the deeply divergent lineages in
different geographic areas, the theory of accumulation-centre might contribute to the high
species diversity in IMA.
Nine novel and highly polymorphic microsatellite DNA markers were developed for the
boring giant clam (T. crocea). The microsatellite DNA studies for T. crocea illustrated similar
genetic structures as revealed by mtDNA, but the structures were shallower in the
microsatellite analyses. In some populations the genetic diversity was different when the
results of the two maker systems were compared. The inconsistency might be caused by
homoplasy of alleles in microsatellites or a small effective population size of mtDNA, which
is more sensitive to genetic drift. Furthermore, mtDNA markers might illustrate a historic
picture of gene flow, while the microsatellite DNA maker possibly revealed much more
contemporary gene flow due to the high mutation rate. However, the general concordant
genetic structures shown by the two marker systems confirm the reliability of the mtDNA
results and the revealed patterns of connectivity for giant clam populations.
For the conservation of the endangered giant clams, the limited gene flow between groups
in each species, and the concordance between genetic and geographic barriers might indicate
six ESUs as following, WIO, RS, EIO, central IMA, WP and CP. Management should put
more effort on maintaining the adaptive diversity in the separated ESUs, and small scale
MPAs should be designed to preserve the genetic connectivity between populations.
Restoration is proposed in the low genetic diversity area, such as Java Sea.
1.6. Outlook
1. More sampling from sites in the Indian Ocean and the Central Pacific Ocean is desirable
to get a better understanding of the connectivity among populations in the whole Indian
and Pacific Ocean and to give a clear explanation for the high genetic diversity in the
Indo-Malay Archipelago (IMA).
2. Further study should be performed on the very divergent linkages to clarify if they are
cryptic species, including examining their morphological, genetic and ecological
characters.
15
3. Analysis of directional gene flow should be conducted in some populations (e.g.
populations along ITF) to test the influence of surface currents on the connectivity among
populations.
4. Small scale population genetic structure analysis should be performed to get much more
detail genetic connectivity information, in which large population size is required.
5. A method to get purified giant clams DNA without zooxanthellae should be optimized,
which will facilitate the genetic study on giant clams.
6. Ginat clams are the largest living bivalve mollusk, and the mechanism responsible for the
growth of giant clams is interesting, as well as the symbiotic relationship between
zooxanthellae and giant clams.
If possible, the whole genomes of one Tridacna species and Symbiodinium spp should
be sequenced in order to find genes responsible for the growth of giant clams and the
mechanism responsible for their symbiotic relationship.
1.7. References
Avise JC (2000). Phylogeography: the history and formation of species, Harvard Univiversity
Press.
Barrowclough GF, Zink RM (2009) Funds enough, and time: mtDNA, nDNA and the
discovery of divergence. Molecular Ecology 18, 2934–2936.
Bazin E, Glémin S, Galtier N (2006) Population size does not influence mitochondrial genetic
diversity in animals. Science, 312, 570-572.
Benzie JAH, Williams ST (1992a) Genetic structure of giant clam (Tridacna maxima)
populations from reefs in theWestern Coral Sea. Coral Reefs, 11, 135-141.
Benzie JAH, Williams ST (1992b) No genetic differentiation of giant clams (Tridacna gigas)
populations in the Great Barrier Reef, Australia. Marine Biology, 112,1-5.
Benzie JAH, Williams ST (1995) Gene flow among giant clam (Tridacna gigas) populations
in Pacific does not parallel ocean circulation. Marine Biology, 123, 781-787.
Benzie JAH, Williams ST (1997) Genetic structure of giant clams (Tridacna maxima)
populations in the west Pacific is not consist with dispersal by present-day ocean currents.
Evolution, 51, 768-783.
16
Berg H, Francis J, Souter P (2002) Support to marine research for sustainable management of
marine and coastal resources in the Western Indian Ocean. AMBIO: A Journal of the
Human Environment, 31, 597-601.
bin Othman AS, Goh GHS, Todd PA (2010) The distribution and status of giant clams
(Family Tridacnidae)-a short review. The Raffles Bulletin of Zoology, 58, 103-111.
Briggs JC (1999) Coincident biogeographic patterns: Indo-west Pacific ocean. Evolution, 53,
326-335.
Briggs JC (2005) The marine East Indies: diversity and speciation. Journal of Biogeography,
32, 1517-1522.
Bruckner AW (2001) Tracking the trade in ornamental coral reef organisms: the importance
of CITES and its limitations. Aquarium Sciences and Conservation, 3, 79-94.
Campbell CA, Valentine JW, Ayala FJ (1975) High genetic variability in a population of
Tridacna maxima from the Great Barrier Reef. Marine Biology, 33, 341-345.
Carpenter, KE (1998) An introduction to the oceanography, geology, biogeography, and
fisheries of the tropical and sub-tropical Western and Central Pacific. In K. Carpenter and
V. Niem, eds. The Living Marine Resources of the Western Central Pacific. Volume 1:
Seaweeds, Corals, Bivalves and Gastropods. FAO, Rome, 1-19.
Cartens BC, Brunsfeld SJ, Demboski JR, et al. (2005) Investigating the evolutionary history
of the pacific northwest mesic forest ecosystem: hpotehesis testing within a comparative
phylogeographic framework. Evolution, 59, 1639-1652.
Cesar H, Lundin CG, Bettencourt S, Dixon J (1997) Indonesian coral reefs-An Economic
analysis of a precious but threatened resource. Ambio 26, 345-350.
Charlesworth B, Morgan M, Charlesworth D (1993) The effect of deleterious mutations on
neutral molecular variation. Genetics 134, 1289-1303.
Cherry JL, Wakeley J (2003) A diffusion approximation for selection and drift in a
subdivided population. Genetics, 163, 421-428.
Clifton J (2003) Prospects for co-management in Indonesia's marine protected areas. Marine
Policy, 27, 389-395.
Connell JH (1978) Diversity in tropical rain forests and coral reefs. Science, 199, 1302-1310.
Crandall ED, Frey MA, Grosberg RK, Barber PH (2008) Contrasting demographic history
and phylogeographical patterns in two IndoϋPacific gastropods. Molecular Ecology, 17,
611-626.
17
DeBoer TS, Barber PH (2010) Isolation and characterization of 9 polymorphic microsatellite
markers for the endangered boring giant clam (Tridacna crocea) and cross-priming testing
in three other Tridacnid species. Conservation Genetics Resources 2, 353-356.
DeBoer TS, Subia MD, Erdmann MV, et al. (2008) Phylogeography and limited genetic
connectivity in the endangered boring giant clam across the coral triangle. Conservation
Biology, 22, 1255-1266.
Demboski JR, Stone KD, Cook JA (1999) Further perspectives on the Haida Gwaii glacial
refugium. Evolution, 53, 2008-2012.
Edwards SV, Bensch S (2009) Looking forwards or looking backwards in avian
phylogeography? A comment on Zink and Barrowclough 2008. Molecular Ecology, 18,
2930-2933.
Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: A new series of programs to perform
population genetics analyses under Linux and Windows. Molecular Ecology Resources,
10, 564-567.
Folmer O, Black M, Hoeh W, Vrijenhoek R (1994) DNA primers for amplification of
mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates.
Molecular marine biology and biotechnology, 3, 294-299.
Gilg MR, Hilbish TJ (2003) The geography of marine larval dispersal: coupling genetics with
fine-scale physical oceanography. Ecology, 84, 2989-2998.
Gordon AL (2005) Oceanography of the Indonesia seas and their through. Oceabography, 8,
14-27.
Gordon AL, Fine RA (1996) Pathways of water between the Pacific and Indian oceans in the
Indonesian seas. Nature, 379, 146-149.
Goreau T, Goreau N, Yonge C (1973) On the utilization of photosynthetic products from
zooxanthellae and of a dissolved amino acid in Tridacna maxima f. elongata (Mollusca:
Bivalvia). Journal of Zoology, 169, 417-454.
Hall R (2002) Cenozoic geological and plate tectonic evolution of SE Asia and the SW
Pacific: computer-based reconstructions, model and animations. Journal of Asian Earth
Sciences, 20, 353-431.
Hebert PDN, Ratnasingham S, de Waard JR (2003) Barcoding animal life: cytochrome c
oxidase subunit 1 divergences among closely related species. Proceedings of the Royal
Society of London. Series B: Biological Sciences, 270 (Suppl 1), S96-S99.
18
Hoeksema B (2007) Delineation of the Indo-Malayan centre of maximum marine
biodiversity: the Coral Triangle. Biogeography, time, and place: Distributions, Barriers,
and Islands, 117-178.
Juinio-Meñez MA, Magsino RM, Ravago-Gotanco R, Yu ET (2003) Genetic structure of
Linckia laevigata and Tridacna crocea populations in the Palawan shelf and shoal reefs.
Marine Biology, 142, 717-726.
Kittiwattanawong K (1997) Genetic structure of giant clams, Tridacna maxima in the
Andaman Seam Thailand. Phuket Marine Biological Center Speial Publication, 17, 109114.
Kittiwattanawong K, Nugranad J, Sriswat T (2001) High genetic divergence of Tridacna
squamosa living at the west and east coasts of Thailand. Phuket Marine Biological Center
Speial Publication, 25, 343-347.
Knop D (1996) Giant clams: a comprehensive guide to the identification and care of
Tridacnid clams. Ettlingen: Dähne Verlag.
Kochzius M, Nuryanto A (2008) Strong genetic population structure in the boring giant clam,
Tridacna crocea, across the Indo ϋ Malay Archipelago: implications related to
evolutionary processes and connectivity. Molecular Ecology, 17, 3775-3787.
Laurent V, Planes S, Salvat B (2002) High variability of genetic pattern in giant clam
(Tridacna maxima) populations within French Polynesia. Biological Journal of the
Linnaean Society, 77, 221-231.
Lee JY, Edwards SV (2008) Divergence across Australia's Carpentarian barrier: statistical
phylogeography of the red-backed fairy wren (Malurus melanocephalus). Evolution, 62,
3117-3134.
Liu Z, Cordes J (2004) DNA marker technologies and their applications in aquaculture
genetics. Aquaculture, 238, 1-37.
Leese F, Mayer C, Held C (2008) Isolation of microsatellites from unknown genomes using
known genomes as enrichment templates. Limnol Oceanogr: Methods, 6, 412-426.
Levinson, G, Marsh JL, Epplen JT, Gutman GA (1985). Cross-hybridizing snake satellite,
Drosophila, and mouse DNA sequences may have arisen independently. Molecular
biology and evolution, 2, 494-504.
Lucas JS (1988) Giant clams: description, distribution and life history. Giant clams in Asia
and the Pacific. ACIAR Monograph, 9, 21-32.
19
Lucas JS (1994) The biology, exploitation, and mariculture of giant clams (Tridacnidae).
Reviews in Fisheries science, 2, 181-223.
Macranas JM, Ablan CA, Pante MJR, Benzie JAH, Williams STH (1992) Genetic structure of
giant clam (Tridacna derasa) populations from reefs in the Indo-Pacific. Marine Biology,
113, 231-238.
Mitarai S, Siegel D, Watson J, et al. (2009) Quantifying connectivity in the coastal ocean with
application to the Southern California Bight. Journal of Geophysical Research. 114,
C10026.
Mingoa-Licuanan SS & Gomez ED (2002) Giant clam conservation in Southeast Asia.
Tropical Coasts, 9, 24-56.
Moberg F, Folke C (1999) Ecological goods and services of coral reef ecosystems, Ecological
Economics, 29, 215-233.
Nason JD, Hamrick J, Fleming TH (2002) Historical vicariance and postglacial colonization
effects on the evolution of genetic structure in Lophocereus, a Sonoran Desert columnar
cactus. Evolution, 56, 2214-2226.
Nuryanto A, Kochzius M (2009) Highly restricted gene flow and deep evolutionary lineages
in the giant clam Tridacna maxima. Coral reefs, 28, 607-619.
O'Brien SJ (1994) A role for molecular genetics in biological conservation. Proceedings of
the National Academy of Sciences, 91, 5748.
Obura D, Suleiman M, Motta H, Schleyer M (2004) Status of coral reefs in East Africa:
Kenya, Mozambique, South Africa and Tanzania. Status of coral reefs of the World: 2004,
2. edited by Wilkinson C.
Palumbi SR (1994) Genetic divergence, reproductive isolation, and marine speciation. Annual
Review of Ecology and Systematics, 25, 547-572.
Palumbi SR (2003) Population genetics, demographic connectivity, and the design of marine
reserves. Ecological applications, 13, 146-158.
Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic
software for teaching and research. Molecular Ecology Notes, 6, 288-295.
Pillans B, Chappell J, Naish TR (1998) A review of the Milankovitch climatic beat: template
for Plio–Pleistocene sea-level changes and sequence stratigraphy. Sedimentary Geology
122, 5-21.
20
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using
multilocus genotype data. Genetics, 155, 945-959.
Reed DH, Frankham R (2003) Correlation between fitness and genetic diversity.
Conservation Biology, 17, 230-237.
Rosewater J (1965) The family Tridacnidae in the Indo-Pacific. Indo-Pacific Mollusca:
monographs of the marine mollusks of the tropical western Pacific and Indian Oceans, 1,
347-394.
Raymond M, Rousset F (1995) GENEPOP (version 1.2): population genetics software for
exact tests and ecumenicism. Journal of Heredity, 86, 248-249
Rousset F (2008) Genepop'007: a complete reimplementation of the Genepop software for
Windows and Linux. Molocular Ecology Resource, 8, 103-106.
Schott FA, McCreary JP (2001) The moon circulation of the Indian Ocean. Progress in
Oceanography, 51, 1-123.
Schlötterer C, Tautz D (1992) Slippage synthesis of simple sequence DNA. Nucleic Acids
Research, 20, 211-215.
Spalding MD, Ravilious C, Green EP (2001). World atlas of coral reefs. University of
California Press.
Timm J, Kochzius M (2008) Geological history and oceanography of the Indo ϋ Malay
Archipelago shape the genetic population structure in the false clown anemonefish
(Amphiprion ocellaris). Molecular Ecology, 17, 3999-4014.
Timm J, Figiel M, Kochzius M (2008) Contrasting patterns in species boundaries and
evolution of anemonefishes (Amphiprioninae, Pomacentridae) in the centre of marine
biodiversity. Molecular Phylogenetics and Evolution, 49, 268-276.
Timm J, Planes S, Kochzius M (2012) High similarity of genetic population structure in the
False Clown Anemonefish (Amphiprion ocellaris) found in microsatellite and
mitochondrial control region analysis. Conservation Genetics, 13, 693-706.
Voris HK (2000) Maps of Pleistocene sea levels in Southeast Asia: shorelines, river systems
and time durations. Journal of Biogeography, 27, 1153-1167.
Wabnitz C, Taylor M, Green E, Razak T (2003) From Ocean to Aquarium. UNEP-WCMC,
Cambridge, UK.
Weersing K, Toonen RJ (2009) Population genetics, larval dispersal, and connectivity in
marine systems. Marine Ecology Progress Series, 393, 1-12.
21
White C, Selkoe KA, Watson J, et al. (2010) Ocean currents help explain population genetic
structure. Proceedings of the Royal Society B: Biological Sciences, 277, 1685-1694.
Wilkinson C, Lindén O, Cesar H, et al. (1999) Ecological and socioeconomic impacts of 1998
coral mortality in the Indian Ocean: An ENSO impact and a warning of future change?
Ambio, 28, 188-196.
Wilson GA, Rannala B (2003) Bayesian inference of recent migration rates using multilocus
genotypes. Genetics, 163, 1177-1191.
Wood EM (2001) Collection of coral reef fish for aquaria: global trade, conservation issues
and management strategies. Marine Conservation Society, Ross-on-Wye, UK.
Wells S (1997) Giant clam: status, trade and mariculture, and the role of CITES management.
Gland, Switzerland and Cambridge, UK: IUCN
Woodland DJ (1983) Zoogeography of Siganidae (Pisces): an interpretation of distribution
and richness pattern. Bulletin of Marine Science 33, 713-717.
Wörheide G, Epp L, Macis L (2008) Deep genetic divergences among Indo-Pacific
populations of the coral reef sponge Leucetta chagosensis (Leucettidae): Founder effects,
vicariance, or both? BMC evolutionary biology, 8, 24.
Yeh FC, Boyle TJB (1997) Population genetic analysis of co-dominant and dominant markers
and quantitative traits. Belgian Journal of Botany, 129, 157.
Yu ET, Juinio-Meñez MA, Monje VD (2000) Sequence variation in the ribosomal DNA
internal trascribed spacer of Tridacna crocea. Marine Biotechnology, 2, 511-516.
Zane L, Bargelloni L, Patarnello T (2002) Strategies for microsatellite isolation: a review.
Molecular Ecology, 11, 1-16.
Zink RM, Barrowclough G (2008) Mitochondrial DNA undersiege in avian phylogeography.
Comparative genetic population structures of three endangered
giant clams (Tridacnidae) throughout the Indo-West Pacific:
implications for origins of divergence, connectivity, and
conservation
Comparative population genetics of giant clams
23
2. Comparative genetic population structures of three endangered giant
clams (Tridacnidae) throughout the Indo-West Pacific: implications for
origins of divergence, connectivity, and conservation
Min Hui1, Wiebke Elsbeth Kraemer2, Christian Seidel3, Aboli Joshi1, Agus Nuryanto4, Marc
Kochzius5
1
Biotechnology and Molecular Genetics, FB2-UFT, University of Bremen, Leobener Strasse,
28359 Bremen, Germany
2
Laboratorio de Fotobiología, Unidad Académica de Sistemas Arrecifales (Puerto Morelos),
Instituto de Ciencias del Mary Limnología, Universidad Nacional Autónoma de México,
Quintana Roo, Cancún, México
3
Institute of Biochemistry, University of Leipzig, Leipzig, Germany
4
Faculty of Biology, Jenderal Soedirman University, Purwokerto, Indonesia
5
Marine Biology, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Correspondence: Marc Kochzius
E-mail: marc.kochzius@vub.ac.be
Keywords: Coral Triangle, Genetic Diversity, Marine Protected Areas, Mitochondrial DNA,
Southeast Asia
Running title: Comparative population genetics of giant clams
24
2.1. Abstract
Giant clams are amongst the most endangered coral reef species. Information on their genetic
population structures is limited, despite the fact that such information is important for
conservation programmes and the understanding of ecological and evolutionary processes. In
this study, the genetic population structures of three co-distributed and ecologically similar
giant clam species (Tridacna crocea, T. maxima, and T squamosa) have been compared. A
fragment of the cytochrome c oxidase subunit I gene (COI) was sequenced for three giant
clam species sampled throughout the Indo-West Pacific, from the Western Indian Ocean
(WIO) and Red Sea (RS) to the Eastern Indian Ocean (EIO), across the centre of marine
biodiversity in the Indo-Malay Archipelago (IMA) to the Western Pacific (WP) and the
Society Islands in the Central Pacific (CP). All three species showed limited gene flow and
highly significant genetic structures in the area studied. The Φst-values (P < 0.001) are 0.46,
0.81 and 0.68, for T. crocea (EIO to WP), T. maxima (WIO and RS to CP), and T. squamosa
(WIO and RS to WP), respectively. Based on a hierarchical AMOVA analysis, each of the
three species could be divided into three to six groups from West to East, respectively: (1)
WIO (T. maxima and T. squamosa), (2) RS (T. maxima and T. squamosa), (3) EIO (including
Java Sea in T. maxima), (4) central IMA, (5) WP, and (6) CP (T. maxima). The distribution of
the haplotype clades in the populations and the pairwise Φst-values between populations of
three species indicate panmixing in the central IMA. Collectively, the concordant genetic
population structure suggests that geological history, sea-level changes during glacial periods
of the Pliocene and Pleistocene, and oceanography are important factors shaping the genetic
population structures of giant clams. As a consequence, improved management strategies for
giant clams are suggested. The observed deep evolutionary lineages in the peripheral areas of
the IMA might include cryptic species and provide new support to the centre-of-accumulation
hypothesis for the high diversity in the IMA.
2.2. Introduction
Congruent patterns of genetic population structures across multiple co-distributed taxa
indicate that the taxa examined might be subjected to the same historical biogeographic
process and environmental conditions (Avise 1992; Bermingham & Moritz 1998; Walker &
Avise 1998; Demboski et al. 1999; Avise 2000). In contrast, the lack of congruence in genetic
25
structure suggests that the species concerned might experience unique biogeographical
processes or have different biological characteristics and species-specific ecological
requirements (Wares & Cunningham 2001; Cartens et al. 2005; Crandall et al. 2008a).
The Indo-West Pacific (IWP) is a biodiversity hotspot, which comprises the tropical waters
of the Red Sea (RS), Western Indian Ocean (WIO), Eastern Indian Ocean (EIO), seas in IndoMalay Archipelago (IMA), as well as the Western Pacific (WP) and Central Pacific (CP)
Oceans. Many species, such as giant clams, are restricted to this biogeographic region, which
is characterised by an exceptionally high diversity, e.g. 4,000 species of fishes and 6,000
species of molluscs (Briggs 1995). In this region, the East African reefs located in the WIO,
exhibit high levels of species diversity similar to those of the Central Indian Ocean, but
distinctive by abundant endemic species, which have led to the recognition of a WIO center of
diversity (Spalding 2001). Also the RS is considered to be an important secondary centre of
evolution (Klausewitz 1989), because of its special oceanographic characteristics, high
number of endemic species, and a large number of coral taxa (Veron 2000). The Coral
Triangle (CT), located in the IMA, hosts the greatest diversity of marine species (Briggs
2005; Hoeksema 2007). Hypotheses explaining the origin of this rich biodiversity describe
this region as a (1) centre-of-origin (Briggs 1999), (2) region-of-overlap (Woodland 1983), or
(3) centre-of-accumulation (Jokiel & Martinelli 1992). The coral reefs of the Society Islands
in the CP form fringing and barrier reefs, as well as atolls. Due to their isolation, they show a
relatively low species diversity, especially on a unit-area basis (Spalding 2001).
The IWP and especially the IMA provide a excellent study systems for detecting the
contribution of historical and ongoing processes relevant for the high degree of biodiversity.
The IMA experienced a complicated geological history (Hall 2002). In particular, sea-level
lowstands of up to 120 m during glacial periods in the Pliocene and Pleistocene exposed the
Sunda and Sahul shelves (Pillans et al. 1998; Voris 2000). These exposed shelves acted as a
vicariance barrier that has been hypothesised to have caused genetic divergence between
Indian and Pacific Ocean populations of many taxa. However, molecular genetic studies
showed that species in this region exhibit different genetic population structure patterns,
ranging from strong divergence to limited or even no genetic structure. Strong genetic
divergence can be observed e.g. in populations of the anemonefish Amphiprion ocellaris
(Timm & Kochzius 2008; Timm et al. 2008, Timm et al. 2012), and the sponge Leucetta
chagosensis (Wörheide et al. 2008). The persistence of this genetic differentiation across the
26
IWP indicates that factors such as restricted dispersal capabilities, special ecological
requirements, and current patterns might have prevented panmixing in these species. In
contrast, such a genetic break is missing at least in case of two moray eels, Gymnothorax
flavimarginatus and G. undulates (Reece et al. 2010) and the swordfish Xiphias gladius
(Chow et al. 1997). In addition, several comparative population genetic studies carried out in
this region (Lourie et al. 2005; Barber et al. 2006; Crandall et al. 2008a, b) often found
discordant patterns of genetic structures among closely related species. These patterns are
generally ascribed to be based on differences in larval dispersal potential or adult ecology.
Giant clams of the family Tridacnidae are economically and ecologically important coral
reef species. Tridacna maxima and T. squamosa are widely distributed from the RS and WIO
across the IMA to the Society Islands in the CP, while T. crocea occurs from the EIO across
the IMA to the WP (Rosewater 1965; Lucas 1988; Knop 1996; Gilbert et al. 2007). Their
habitat are the shallow waters of coral reefs, they all host Symbiodinium spp. (zooxanthellae)
and have a planktonic larval duration of about 10 days (Lucas 1988). The high commercial
value of the giant clams for food and as marine ornamentals attracts a large scale collection
from the wild and aquaculture of the species (Lucas 1988). Tridacnid species are listed in
Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna
and Flora (CITES). Special concerns have been raised on the over-exploitation of the giant
clams, allowing international trade only with appropriate export permits. To provide
background information for the conservation of the species, it is important to understand their
genetic population structure and connectivity. Most of the previous genetic population studies
on giant clams mainly focused on restricted areas using different molecular markers, such as
allozymes, RFLP, and DNA sequences (Campbell et al. 1975; Benzie & Williams 1992a, b;
Macaranas et al. 1992; Benzie & Williams 1995; Benzie & Williams 1997; Kittiwattanawong
1997; Yu et al. 2000; Kittiwattanawong et al. 2001; Laurent et al. 2002; Juinio-Meñez et al.
2003). Recently, large scale area genetic population studies were performed for giant clams in
the IMA for two species of giant clams and a genetic break between Indian Ocean and Pacific
Ocean has been documented (DeBoer et al. 2008; Kochzius & Nuryanto 2008, Nuryanto &
Kochzius 2009), but there is still a gap in terms of the giant clams populations in the WIO and
the connectivity with the EIO and Pacific.
This study compares the genetic population structures of three co-distributed species of
giant clams across their distribution range throughout the IWP: T. crocea, T. maxima and T.
27
squamosa. It aims to test if concordant barriers exist, preventing gene flow among the
populations, and to relate this to oceanography and geological history of the Indo-West
Pacific. Information on connectivity might also facilitate the design of Marine Protected Area
(MPA) networks.
2.3. Materials and methods
2.3.1. Sampling and sequencing
Small pieces of mantel tissue were collected from Tridacna crocea (n = 344, 20 localities), T.
maxima (n = 317, 20 localities) and T. squamosa (n = 182, 18 localities) while SCUBA diving
across their distribution range in the IWP (Table 2; Fig. 5) from 2004 to 2012. Tissues were
preserved in 96 % ethanol.
Genomic DNA was extracted using the Chelex method (Walsh et al. 1991). A fragment of
mitochondrial cytochrome c oxidase subunit I gene (COI) was amplified using tridacnidspecific primers (Kochzius & Nuryanto 2008). PCRs were carried out in 50 μl volumes
containing 10-100 ng template DNA, 10 mM Tris-HCl (pH 9), 50 mM KCl, 0.2 mM dNTPs,
0.2 μM forward and reverse primers, 2 mM MgCl2, and 1 U Taq DNA polymerase. PCR
amplification was conducted under the following conditions: 5 min initial denaturation at 94
o
C, 35 cycles of 1 min at 94 oC, 1.5 min at 45 oC, 1 min at 72 oC, and a final extension at 72 oC
for 5 min. PCR products were purified with the QIAquick PCR purification kit (Qiagen,
Germany). Sequencing of both strands was conducted with an ABI PRISM 310 automated
sequencer and ABI 3730 XL (Applied Biosystems).
28
29
Fig. 5 Maps of the Indo-West Pacific (a1, b1, c1) and the Indo-Malay Archipelago (a2, b2, c2) with
sample sites (see Table 2 for abbreviations). a2-3: Tridacna crocea; b1-3: T. maxima; b1-3: T.
squamosa. a1: Major genetic breakes in Indo-West Pacific (dashed lines in red) for the three giant
clams T. crocea (Tc), T. maxima (Tm) and T. squamosa (Ts). Surface currents with dominant (arrows
with solid lines) and seasonally changing currents (arrows with dashed line) (Wyrtki 1961; Gordon &
Fine 1996; Carpenter 1998; Schott & MacCreary 2001; Gordon 2005), including South Equatorial
Current (SEC), Northeast Madagascar Current (NEMC), East African Coast Current (EACC), South
Equatorial Counter Current (SECC), Indonesia Throughflow (ITF) and North Equatorial Counter
Current (NECC), East Australian Current (EAC) are shown in the maps. Pleistocene sea level low
standard of 120 m (light grey area) is marked in maps a2, b2, and c2 (Voris 2000). Pie charts in the
maps represent the proportions of clades defined in the network at different sites in each species (a2: T.
crocea; b1 and b2: T. maxima; c1 and c2: T. squamosa). Networks of COI haplotypes of T. crocea, T.
maxima and T. squamosa are shown in c1, c2 and c3, respectively. The sizes of the circles are
proportional to haplotype frequencies. Lines between circles represent one mutational step. The
hatches and numbers indicate additional mutational steps.
Sequences were edited with the program Sequence Navigator (ver.1.0.1; Applied
Biosystem) and aligned by ClustalW using the software BioEdit (ver.7.0). The sequences
were compared with sequences in GenBank using BLASTN to check for orthology to
tridacnids and to exclude contamination from zooxanthellae. Using the program Squint
(http://www.cebl.auckland.ac.nz/index.php), the DNA sequences were translated to amino
acid sequences to confirm that a functional mitochondrial DNA sequence was obtained.
2.3.2. Genetic diversity
Molecular diversity indices, such as the number of haplotypes, haplotype diversity h (Nei
1987) and nucleotide diversity л (Nei & Jin 1989) were obtained using the program Arlequin
3.5 (Excoffier & Lischer 2010).
2.3.3. Demographic history
To compare demographic histories of mtDNA in the three species, two different approaches
were used to test each population for departures from the neutral model due to selection or
population growth. First, the Tajima D (Tajima 1989) and Fu’s FS tests (Fu 1997) were used
to test for neutrality. Significant negative D and FS values can be interpreted as signatures of
selection or population expansion. Historic demographic expansions were further explored
30
based on the distributions of pairwise differences between sequences (mismatch distribution),
which was based on three parameters: θ0, θ1 (θ, before and after the population growth) and τ
(units of mutational time; Rogers & Harpending 1992). The concordance of the observed with
the expected distribution under the sudden-expansion model of Rogers was also tested using
Arlequin. The values of τ were converted to estimate time since expansion with the equation τ
= 2ut (Rogers & Harpending, 1992), where t = number of generations since expansion, τ is
units of mutational time, and u = 2μ number of bases sequenced, where μ is the mutation
rate (1.2% per million years; Marko 2002). Then the time since expansion was calculated by
the T = t generation time, and the generation time is two years in giant clams (Lucas 1988).
2.3.4. Genetic population structure and gene flow
To reveal genetic differentiation between populations, pairwise Φst-values (Excoffier et al.
1992) were calculated, including sequence information, haplotype frequencies (Weir &
Cockerham 1984) and genetic divergence. Significance of pairwise population comparisons
were tested by 10,000 permutations and sequential Bonferroni correction (Rice 1989) was
conducted for the P-values. Hierarchical Analysis of Molecular Variances (AMOVA;
Excoffier et al. 1992) was performed with Arlequin in order to define spatial groups of
populations that were maximally differentiated from each other (Φct). Minimum-spanning
networks of the haplotypes were drawn based on results obtained with Arlequin and the
haplotypes were divided into clades based on the number of mutational steps. Frequencies of
the clades were calculated for each population and reflected by drawing pie diagrams on the
maps.
The correlation between genetic distance (pairwise Φst-values) and geographic distance
was investigated using Isolation-by-distance (IBD) analysis with the Reduced Major Axis
(RMA) regression method among populations in the central Indo-Malay Archipelago (IMA).
The shortest way by sea between populations was measured to represent the geographic
distance using an electronic world atlas. A Mantel test was conducted to test the significance
of the correlation with 30,000 permutations using the web service IBDWS (ver. 3.2.3;
http://ibdws.sdsu.edu; Jensen et al. 2005).
31
Table 2 Summary statistics for each population of the three giant clams species, number of sequences
(n), number of haplotypes (Nhp), nucleotide diversity (Л), Tajima’s D, Fu’s Fs, sum of square deviation
(SSD), and Harpending’s raggedness index (HRI). RS: Red Sea; WIO: Western Indian Ocean; EIO:
Eastern Indian Ocean; IMA: Indo-Malay Archipelago; WP: Western Pacific; CP: Central Pacific.
Sites (Code)
Gulf of Thailand (GT)
Phuket (Ph)
Trang Islands (Tr)
Satun Islands (SI)
Padang (Pa)
Pulau Seribu (PS)
Karimunjava (Ka)
Komodo (Ko)
Kupang (Ku)
Spermonde (Sp)
Bira (Bi)
Sembilan Islands (Se)
Kendari (Ke)
Luwuk (Lu)
Togian Islands (TI)
Manado (Ma)
Sangalaki (Sa)
Kota Kinabalu (KK)
Misool (Mi)
Biak (Bk)
Overall
Region
IMA
EIO
EIO
EIO
EIO
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
WP
Kenya(Ky)
Red Sea (RS)
Phuket (Ph)
Trang Islands (Tr)
Satun Islands (SI)
Padang (Pa)
Pulau Seribu (PS)
Karimunjava (Ka)
Komodo (Ko)
Kupang (Ku)
Spermonde (Sp)
Bira (Bi)
Luwuk (Lu)
Togian Islands (TI)
Manado (Ma)
Sangalaki (Sa)
Misool (Mi)
Biak (Bk)
Society Islands (So)
Overall
WIO
RS
EIO
EIO
EIO
EIO
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
WP
CP
Tridacna crocea
n
Nhp
h
7
3
0.67
7
6
0.95
10
6
0.84
9
7
0.92
7
6
0.95
14
6
0.60
16
9
0.77
26
17
0.89
9
7
0.92
40
23
0.96
12
6
0.82
20
14
0.94
28
16
0.82
23
12
0.78
46
21
0.71
8
8
1.00
16
12
0.96
21
16
0.97
11
8
0.93
14
13
0.99
344
149
0.94
Tridacna maxima
9
7
0.92
13
10
0.95
34
13
0.91
19
6
0.86
24
17
0.97
15
9
0.88
12
4
0.45
20
6
0.52
12
4
0.56
14
10
0.89
21
10
0.68
10
9
0.98
12
8
0.85
16
9
0.86
21
16
0.96
22
15
0.90
7
3
0.52
8
5
0.78
16
13
0.97
12
6
0.68
317
135
0.94
Л(%)
0.48
1.03
0.82
1.04
1.00
0.60
0.70
0.96
0.67
1.04
0.70
1.23
0.78
0.68
0.65
0.75
1.09
1.06
1.08
3.30
1.71
D
1.08 NS
0.27 NS
ˉ0.54 NS
ˉ0.09 NS
ˉ0.79 NS
ˉ0.79 NS
ˉ1.71*
ˉ2.15**
ˉ1.47 NS
ˉ1.56*
ˉ0.48 NS
ˉ1.36 NS
ˉ1.69***
ˉ1.51 NS
ˉ1.85*
ˉ0.92 NS
ˉ0.83 NS
ˉ1.49*
ˉ1.20 NS
0.94NS
ˉ1.90**
Fs
ˉ1.32 NS
ˉ1.44 NS
ˉ0.50 NS
ˉ1.57 NS
ˉ1.49 NS
ˉ0.38 NS
ˉ2.65 NS
ˉ8.55***
ˉ2.76*
ˉ13.4***
ˉ0.35 NS
ˉ4.91*
ˉ8.02***
ˉ4.68*
ˉ12.3***
ˉ5.45***
ˉ4.67*
ˉ8.63***
ˉ1.75 NS
ˉ3.05 NS
ˉ24.34**
SSD
0.111 NS
0.019 NS
0.023 NS
ˉ
ˉ
0.054 NS
0.014 NS
0.017 NS
0.028 NS
0.004 NS
0.017 NS
0.007 NS
0.002 NS
0.006 NS
0.011 NS
ˉ
0.005 NS
ˉ
0.010 NS
ˉ
0.008 NS
HRI
0.283 NS
0.066 NS
0.078 NS
ˉ
ˉ
0.148 NS
0.044 NS
0.038 NS
0.101 NS
0.019 NS
0.055 NS
0.014 NS
0.010 NS
0.021 NS
0.040 NS
ˉ
0.017 NS
ˉ
0.032 NS
ˉ
0.008 NS
0.23
0.69
0.46
0.43
0.69
0.56
0.12
0.59
0.16
0.92
0.62
0.85
0.46
0.44
0.79
0.48
0.62
0.85
4.78
0.79
2.78
0.75 NS
ˉ0.94 NS
ˉ1.36 NS
ˉ0.017 NS
ˉ1.13 NS
ˉ0.03 NS
ˉ1.62*
ˉ1.30 NS
ˉ1.18 NS
ˉ1.37 NS
ˉ2.24**
ˉ1.86*
ˉ2.07**
ˉ1.31 NS
ˉ2.18**
ˉ1.94*
ˉ1.58*
ˉ1.57*
ˉ0.45 NS
ˉ0.82NS
ˉ1.02 NS
ˉ0.17NS
ˉ5.61***
ˉ6.90***
ˉ0.80 NS
ˉ13.3***
ˉ4.12**
ˉ2.12**
ˉ0.06 NS
ˉ1.59*
ˉ4.09*
ˉ3.85*
ˉ5.31***
ˉ4.36**
ˉ4.82 NS
ˉ12.0***
ˉ13.5**
1.60 NS
ˉ0.16 NS
ˉ1.87 NS
ˉ0.36NS
-23.87**
0.070 NS
ˉ
ˉ
ˉ
ˉ
ˉ
ˉ
0.069 NS
ˉ
ˉ
0.007 NS
ˉ
0.002 NS
ˉ
ˉ
ˉ
0.078 NS
0.056 NS
0.043 NS
0.053 NS
0.024 NS
0.073 NS
ˉ
ˉ
ˉ
ˉ
ˉ
ˉ
0.280 NS
ˉ
ˉ
0.028 NS
ˉ
0.046 NS
ˉ
ˉ
ˉ
0.209 NS
0.128 NS
0.057 NS
0.092 NS
0.014 NS
Sites (Code)
Kenya(Ky)
Red Sea (RS)
Batam (Bt)
Pulau Seribu (PS)
Karimunjava (Ka)
Bali (Ba)
Komodo (Ko)
Kupang (Ku)
Spermonde (Sp)
Bira (Bi)
Kendari (Ke)
Togian Islands (TI)
Manado (Ma)
Sangalaki (Sa)
Kota Kinabalu (KK)
Misool (Mi)
Biak (Bk)
Overall
*
Region
WIO
RS
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
IMA
WP
Tridacna squamosa
n
Nhp
h
2
2
1.00
6
3
0.60
2
2
1.00
3
2
0.67
17
9
0.83
6
4
0.87
11
6
0.85
6
3
0.73
49
13
0.75
14
11
0.96
6
4
0.87
13
4
0.52
6
5
0.93
9
4
0.75
9
5
0.81
9
6
0.83
11
3
0.62
3
3
1.00
182
56
0.83
32
Л(%)
0.21
0.25
1.31
0.16
0.53
0.52
0.57
0.32
0.48
0.52
0.60
0.17
0.64
0.28
0.96
0.68
0.17
3.68
1.08
D
0.00 NS
ˉ1.23 NS
0.00 NS
0.00 NS
ˉ1.51 NS
ˉ0.31 NS
ˉ1.27 NS
ˉ0.18 NS
ˉ1.73*
ˉ1.57*
0.37 NS
ˉ0.90 NS
ˉ1.01 NS
0.02 NS
ˉ0.37
ˉ1.64*
0.04 NS
0.00 NS
ˉ2.22***
Fs
2.08 NS
ˉ0.19 NS
1.61 NS
0.20 NS
ˉ3.68*
ˉ0.44 NS
ˉ1.37 NS
0.21 NS
ˉ5.45**
ˉ8.53***
ˉ0.22 NS
ˉ1.31 NS
1.62 NS
ˉ0.82 NS
0.32
ˉ1.47 NS
ˉ0.11 NS
1.39 NS
ˉ25.44***
SSD
ˉ
0.008NS
ˉ
ˉ
0.010 NS
ˉ
ˉ
0.022 NS
ˉ
ˉ
0.075 NS
0.001 NS
ˉ
ˉ
0.049 NS
0.020 NS
ˉ
ˉ
0.012 NS
HRI
ˉ
0.062 NS
ˉ
ˉ
0.057 NS
ˉ
ˉ
0.133 NS
ˉ
ˉ
0.240 NS
0.080 NS
ˉ
ˉ
0.119 NS
0.048 NS
ˉ
ˉ
0.035 NS
0.05 ≥ P ≥ 0.01; ** 0.01 > P > 0.001; *** P < 0.001; NS: not significant.
2.4. Results
2.4.1. Genetic diversity
A 417-bp unambiguous COI alignment was obtained based on 843 specimens of three species.
Sequence comparison of the segment from 344 T. crocea, 317 T. maxima, 182 T. squamosa
individuals resulted in 149, 135, and 56 haplotypes, respectively. Intra-population diversity
indices of the three species are shown in Table 2. All three species revealed a high level of
polymorphism and genetic diversity, with high haplotype diversity and nucleotide diversity
(the overall haplotype diversity and nucleotide diversity were higher than 0.83 and 1.08%,
respectively). However, in the Java Sea, populations from Karimujava and Pulau Seribu
showed a much lower genetic diversity compared with other populations of the three species.
2.4.2. Historical demography
Both neutrality test and mismatch distribution were performed for each population of the three
species. Many populations showed significant negative D and FS values (Table 2), which
indicated significant departure from mutation-drift equilibrium, especially Fu’s Fs. Compared
with Tajima’s D, Fu’s FS had more power to detect population growth and genetic hitchhiking
33
(Fu 1997), indicating departures from neutral expectations of the utilised marker, while the
opposite is true for background selection. The mismatch distribution analysis and Rogers’ test
of sudden population expansion indicated population expansion (Rogers 1995; Table 2). The
estimated time of initiation of expansion (T) for all species was in the range of 93,000 to
66,000 years ago.
2.4.3. Genetic structure and connectivity
Overall, all three giant clam species showed a strong genetic structure and restricted gene
flow. Tridacna maxima and T. squamosa populations exhibited the largest genetic
differentiation, with overall Φst-values of 0.81 (P < 0.001) and 0.68 (P < 0.001), respectively.
If only the region of co-distribution (the Indo-Malay Archipelago) was considered, all three
species revealed a high differentiation, with Φst-values of 0.46 in T. crocea, 0.77 in T.
maxima, and 0.40 in T. squamosa (P < 0.001 in all species).
The haplotypes can be partitioned into three (T. crocea), four (T. squamosa), and seven (T.
maxima) star-like clades (Fig. 5, a3; b3; c3).
In T. crocea (Fig. 5, a), three different clades were separated with 14 and 18 mutations.
Clade 2 was the dominant clades in populations of the EIO (Padang, Phuket, Trang Islands
and Satun Islands), while clade 3 was only observed in the WP (Biak). Clade 1 was
distributed in most of the populations throughout the IMA.
In T. maxima, the results were similar to T. squamosa. Haplotypes in populations of the
WIO, RS, WP, and CP were quite divergent from each other (Fig. 5, b). A minor difference
from T. squamosa was that the haplotypes in the Java Sea (Pulau Seribu and Karimunjava) of
T. maxima were more closely related with those in the EIO. In T. squamosa, haplotypes in the
Java Sea showed panmixing with those in the central IMA.
In T. squamosa, clade 4 was separated by more than 10 mutations from clade 1 and
restricted to the WIO (Kenya). Clade 3 (restricted to the RS) showed a distance of 23
mutations to clade 1, while clade 2 only appeared in the WP, which in turn was separated by
more than 10 mutational steps from clade 1. Clade 1 was distributed along the Indonesian
Throughflow, in the Java Sea and the East of Sulawesi, which showed panmixing in this area
(Fig. 5, c).
The observed genetic structures based on the distribution of clades were further verified by
a hierarchical AMOVA and pairwise Φst-values.
34
In T. crocea, the populations from the EIO were the most divergent populations with
pairwise Φst values from 0.61 to 0.89, followed by the WP, with values ranging from 0.23 to
0.66 (Table 3), while the Φst values were low between most of the populations in the IMA.
Pairwise Φst-values for populations of T. maxima and T. squamosa were high for the
populations in the WIO (Φst = 0.47-0.96), RS (Φst = 0.71-0.98), the WP (Φst = 0.64-0.93), and
CP (Φst = 0.79-0.97) (Table 4 and 5). In T. maxima, populations from the EIO and Java Sea
were also highly divergent (Φst = 0.79-0.91).
Based on geography and oceanography, a hierarchical AMOVA was carried out with
different groupings (Table 6). In all species, AMOVA revealed the highest fixation index (Φct
= 0.728, Φct = 0.864, Φct = 0.935, P < 0.001 for T. crocea, T. maxima, and T. squamosa,
respecitively) among groups when the populations were grouped as follows: (1) WIO (T.
maxima and T. squamosa) (2) RS (T. maxima and T. squamosa), (3) EIO (including Java Sea
in T. maxima), (4) central IMA, (5) WP, and (6) CP (T. maxima).
Isolation-by-distance was verified by a significant positive correlation between genetic and
geographic distances, in which only populations from the central IMA were included for
analysis, excluding highly divergent populations (e.g. Kenya, Red Sea, Padang, Phuket, Trang
Islands, Satun Islands, Biak, and Society Islands). All species showed isolation-by-distance
(Fig. 6; T. crocea: r = 0.36, P = 0.009; T. maxima: r = 0.26, P = 0.050; and T. squamosa: r =
0.35, P = 0.042.
0.86
0.86
0.85
0.84
0.86
0.83
0.86
0.82
0.86
0.87
0.87
0.84
0.82
0.82
0.82
0.61
GT
PS
Ka
Ko
Ku
Sp
Bi
Se
Ke
Lu
TI
Ma
Sa
KK
Mi
Bk
0.66
0.84
0.83
0.84
0.86
0.88
0.88
0.87
0.83
0.87
0.84
0.88
0.85
0.87
0.87
0.88
0.07
0.09
TR
0.65
0.83
0.83
0.83
0.85
0.89
0.87
0.87
0.83
0.86
0.84
0.86
0.84
0.86
0.87
0.87
-0.00
SI
0.60
0.82
0.82
0.82
0.84
0.87
0.87
0.86
0.81
0.85
0.83
0.86
0.84
0.86
0.86
0.86
Pa
0.25
0.12
0.11
0.11
0.14
0.37
0.36
0.34
0.29
0.22
0.11
0.40
0.28
0.07
0.09
GT
0.05
0.30
0.31
0.08
0.06
0.03
0.08
0.11
0.24
0.24
0.21
0.21
0.16
0.07
0.24
0.20
Ka
0.09
0.14
0.27
0.27
0.24
0.21
0.20
0.08
0.30
0.23
-0.05
PS
0.37
0.10
0.05
0.06
0.09
-0.00
-0.02
-0.01
0.06
0.03
0.07
-0.04
Ko
0.07
-0.02
0.29
0.11
0.07
0.10
0.16
0.39
0.07
-0.02
-0.02
-0.03
0.09
0.08
-0.01
0.09
-0.02
-0.01
Sp
0.07
0.06
0.08
Ku
With Sequential Bonferroni correction: P < 0.0005 (significant in bold).
0.06
0.07
SI
0.18
Ph
Pa
TR
0.28
0.11
0.37
0.11
0.05
0.06
-0.00
0.01
0.14
0.04
0.04
0.03
Se
-0.01
0.09
0.06
0.08
0.12
Bi
0.41
0.11
0.06
0.08
0.16
-0.01
-0.02
Ke
0.40
0.12
0.06
0.08
0.16
-0.01
Lu
0.47
0.15
0.08
0.10
0.17
TI
0.23
0.11
-0.00
-0.01
Ma
Table 3 Pairwise Φst-values between populations of Tridacna crocea in the Indo-West Pacific. For abbreviations, see Table 2.
0.31
0.06
-0.02
Sa
0.32
0.02
KK
0.28
Mi
35
0.77
0.72
0.70
0.68
0.70
0.68
0.59
0.51
0.57
0.49
0.56
0.60
0.57
0.62
0.48
0.47
0.66
0.90
Ph
TR
SI
Pa
PS
Ka
Ko
Ku
Sp
Bi
Se
Lu
TI
Ma
Sa
Mi
Bk
So
0.95
0.71
0.81
0.82
0.86
0.82
0.86
0.85
0.81
0.83
0.79
0.88
0.87
0.91
0.88
0.87
0.89
0.90
RS
0.96
0.86
0.87
0.88
0.88
0.86
0.89
0.88
0.86
0.86
0.86
0.90
0.07
0.04
0.10
-0.00
-0.01
Ph
0.96
0.82
0.86
0.88
0.88
0.85
0.89
0.89
0.86
0.85
0.84
0.91
0.06
0.06
0.89
-0.03
Tr
0.95
0.83
0.82
0.83
0.85
0.83
0.85
0.85
0.82
0.82
0.82
0.87
0.05
0.02
0.04
SI
0.96
0.80
0.83
0.84
0.86
0.83
0.87
0.86
0.82
0.82
0.81
0.89
0.03
0.08
Pa
0.97
0.79
0.89
0.91
0.90
0.86
0.92
0.92
0.88
0.87
0.86
0.96
-0.02
PS
0.96
0.81
0.81
0.83
0.85
0.82
0.85
0.84
0.81
0.81
0.80
0.87
Ka
0.96
0.74
0.03
0.02
-0.04
-0.03
-0.04
-0.01
-0.00
-0.02
0.02
Ko
0.57
RS
Ky
0.94
0.73
-0.07
-0.08
0.05
0.02
0.04
0.02
-0.05
0.04
Ku
0.95
0.77
0.02
-0.00
0.03
0.03
0.02
-0.00
0.02
Sp
0.94
0.70
-0.09
-0.08
0.01
-0.01
0.02
0.00
Bi
0.95
0.74
0.02
0.00
0.02
0.00
0.02
Se
0.95
0.76
0.04
0.03
-0.02
-0.02
Lu
0.94
0.76
0.01
-0.02
-0.01
TI
0.95
0.78
0.05
0.018
Ma
Table 4 Pairwise Φst-values between populations of Tridacna maxima in the Indo-West Pacific. For abbreviations, see Table 2.
0.94
0.68
-0.09
Sa
0.94
0.69
Mi
0.79
Bk
36
0.73
Bk
0.91
0.98
0.95
0.93
0.97
0.96
0.00
0.64
0.74
0.49
0.14
0.79
0.14
0.90
-0.03
0.09
0.06
-0.06
0.20
0.04
0.03
0.84
-0.01
0.07
0.04
0.03
0.04
-0.06
0.04
0.01
0.11
-0.04
0.14
-0.00
-0.03
0.24
-0.02
Ba
0.01
0.16
0.01
-0.02
Ka
0.28
0.49
0.67
0.06
0.09
-0.16
0.02
0.52
0.76
0.51
0.55
0.56
0.65
0.00
0.51
0.46
-0.02
PS
0.54
0.56
Bt
0.87
-0.02
0.07
-0.00
0.02
0.06
0.03
0.11
-0.00
-0.01
0.20
Ko
0.86
0.41
0.00
0.23
0.22
0.29
0.49
0.23
0.16
0.20
Ku
0.83
0.92
KK
Mi
0.77
Sa
0.96
0.82
0.90
TI
0.93
Ke
Ma
0.98
0.83
Se
0.96
0.96
0.89
0.87
Sp
0.97
0.95
Bi
0.88
Ku
0.96
0.84
0.85
Ba
Ko
0.87
Ka
0.98
0.95
0.64
0.82
0.96
RS
Bt
0.96
Ky
PS
RS
0.93
-0.03
0.10
0.08
-0.01
0.10
0.01
0.13
-0.01
Sp
0.89
-0.04
0.07
0.06
-0.01
0.03
-0.01
0.11
Bi
0.84
0.23
0.10
0.15
0.09
0.20
0.29
Se
0.92
-0.05
0.24
0.10
0.16
0.08
Ke
0.84
0.09
0.17
0.04
0.17
TI
0.89
0.08
0.03
0.13
Ma
Table 5 Pairwise Φst-values between populations of Tridacna squamosa in the Indo-West Pacific. For abbreviations, see Table 2.
0.82
0.08
0.15
Sa
0.86
0.15
KK
0.91
Mi
37
For abbrevations of site, see Table 2; ***P < 0.001.
Groupings
T. crocea
(Ph, TR, SI, Pa) ( GT, PS, Ka, Ko, Ku, Sp, Bi, Se, Ke, Lu, TI, Ma, Sa, KK, Mi) (Bk)
(Ph, TR, SI, Pa) (PS, Ka) ( GT, Ko, Ku, Sp, Bi, Se, Ke, Lu, TI, Ma, Sa, KK, Mi) (Bk)
(Ph, TR, SI, Pa) ( GT, PS, Ka,) ( Ko, Ku, Sp, Bi, Se, Ke, Lu, TI, Ma, Sa, KK, Mi) (Bk)
T. maxima
(Ky) (RS) ( Ph, TR, SI, Pa, PS, Ka) (Ko, Ku, Sp, Bi, Se, Lu, TI, Ma, Sa, Mi) (Bk)(FP)
(Ky,RS) ( Ph, TR, SI, Pa, PS, Ka) (Ko, Ku, Sp, Bi, Se, Lu, TI, Ma, Sa, Mi) (Bk) (FP)
(Ky)(RS) ( Ph, TR, SI, Pa, PS, Ka) (Ko, Ku, Sp, Bi, Se, Lu, TI, Ma, Sa, Mi) (Bk, FP)
( Ph, TR, SI, Pa, PS, Ka) (Ko, Ku, Sp, Bi, Se, Lu, TI, Ma, Sa, Mi)(Bk)
( Ph, TR, SI, Pa) (PS, Ka) (Ko, Ku, Sp, Bi, Se, Lu, TI, Ma, Sa, Mi) (Bk)
T. squamosa
(Ky) (RS) (Bt, PS, Ka, Ba, Ko, Ku, Sp, Bi, Se, Ke, TI, Ma, Sa, KK, Mi) (Bk)
(Ky,RS) (Bt, PS, Ka, Ba, Ko, Ku, Sp, Bi, Se, Ke, TI, Ma, Sa, KK, Mi) (Bk)
(Bt, PS, Ka, Ba, Ko, Ku, Sp, Bi, Se, Ke, TI, Ma, Sa, KK, Mi) (Bk)
(Bt) ( PS, Ka, Ba, Ko, Ku, Sp, Bi, Se, Ke, TI, Ma, Sa, KK, Mi ) (Bk)
squamosa from Indo-West Pacific.
Percentage of variation (%)
72.80
64.52
63.49
86.38
85.06
72.94
81.65
79.74
93.54
87.89
89.64
85.28
Φct
0.728***
0.645***
0.634***
0.864***
0.850***
0.729***
0.816***
0.797***
0.935***
0.879***
0.896***
0.853***
Table 6 Hierachical analysis of molecular variance (AMOVA) of COI sequences in the three giant clam species Tridacna crocea, T. maxima, and T.
38
39
Fig. 6 Relationships between genetic and geographic distances of populations in the central IndoMalay Archipelago for the three giant clam species analysed by Reduced Major Axis (RMA)
regression without divergent populations. a: T. crocea; b: T. maxima; c: T. squamosa.
2.5. Discussion
The three congeneric species of giant clams have a very similar ecology and therefore
concordant population genetic structures might be expected. This study revealed concordant
patterns in the genetic population structures of the giant clams T. crocea, T. maxima, and T.
squamosa. According to the genetic differentiation, the sample sites in this three species could
be generally divided in to the groups from West to East: (1) WIO (T. maxima and T.
squamosa), (2) RS (T. maxima and T. squamosa), (3) EIO (including Java Sea in T. maxima),
40
(4) central IMA, (5) WP, and (6) CP (T. maxima). The exact boundaries of the different
genetic breaks vary slightly among species (Fig. 5, a1). Vicariance due to Plio-Pleistocene
sea-level fluctuation could be a major factor that established genetic divergence between
populations of the Indian and Pacific Oceans (McMillan & Palumbi 1995; Williams & Benzie
1998; Carpenter et al. 2010). The oceanography of the Indo-West Pacific and limited larval
dispersal capability are also important factors that shape the genetic structures in giant clams
populations.
2.5.1. Genetic endemism in peripheral regions of the IWP
The populations of T. maxima and T. squamosa in the WIO showed indications of isolation.
The coral reefs of the WIO are distinct from other reefs in the IWP, with predominantly
fringing reefs along the east African coast, which are separated by deep ocean from reefs in
the EIO. This might have supported the evolution of a distinct biodiversity and endemic coral
fauna in the WIO (Spalding 2001). Even though there are surface currents (Fig. 5), such as
South Equatorial Currents (SEC), Northeast Madagascar Current (NEMC), East African Coast
Current (EACC) and South Equatorial Counter Currents (SECC), crossing the Indian Ocean,
they obviously do not connect giant clam populations of the western and eastern part (Fig. 5,
b1; c1). This might be due to the long distance and limited larval dispersal capabilities of
giant clams. Studies carried out on samples from the whole species’ range of the tiger prawn
(Penaeus monodon; Benzie et al. 2002) and bullethead parrotfish (Chlorurus sordidus; Bay et
al. 2004) also show a genetic partitioning of populations from the WIO and EIO. More
sampling along the Eastern Africa coast and in the central Indian Ocean will facilitate the
drawing of a clearer picture of connectivity among populations across the Indian Ocean.
The populations of T. maxima and T. squamosa from the Red Sea were highly divergent
from other populations. These populations have specific haplotypes, with nine and 23
mutational steps from the nearest clades in T. maxima and T. squamosa, respectively (Fig. 5,
b; c). In comparison with other populations, both showed high Φst-values. In the hierarchical
AMOVA, highest Φct-values were reached in both species when the populations from the Red
Sea were regarded as a single group (Table 6). This divergence might be caused by the special
oceanographic conditions in the Red Sea (for example, high salinity) and limited connectivity
to the Indian Ocean through the Straits of Bab el Mandeb. A genetic differentiation of the Red
Sea populations was also found in the giant clam Tridacna maxima (Nuryanto & Kochzius
2009), the mud crab Scylla serrata (Fratini & Vannini 2002), the damselfish Chromis viridis
41
(Froukh & Kochzius 2008), and the sponge Leucetta chagosensis (Wörheide et al. 2008).
However, in the lionfish Pterois miles (Kochzius et al. 2003; Kochzius & Blohm 2005), no
genetic differentiation could be detected. These deep evolutionary lineages of giant clams in
the Red Sea might be cryptic species, which supports the perspective that the Red Sea is an
important secondary centre of evolution (Klausewitz 1989). However, an integrated taxonomy
approach, comparing morphology, ecology, and genetics would be needed to verify if they are
cryptic species (Dayrat 2005). With such an approach, a new species of giant clam (T.
costata) was recently discovered in the Red Sea (Richter et al. 2008).
The Society Islands are located in the CP and harbour haplotypes of a unique clade in T.
maxima, with more than 27 mutational steps distance from the next closely related clade.
Even though there are no physical barriers between the CP and WP, limited dispersal
capability and isolation-by-distance could be the explanation for this divergence. Many
studies have shown that even if there are no apparent barriers to dispersal, some reef fish with
a high dispersal capacity demonstrate high genetic divergence among distant populations
(Fauvelot & Planes 2002; Taylor & Hellberg 2003). Moreover, in the Pacific Ocean, the
current around Society Islands mostly flows southward, and to the west, the SEC is mainly
directed along the coast of Australia to be the Eastern Australia Current (EAC) and partially
into the seas of New Guinea (Fig. 5) (Carpenter, 1998), which might be another reason for the
restricted gene flow between CP and WP (Biak).
2.5.2. Historical sea-level fluctuation and present oceanographic conditions
2.5.2.1. Genetic divergence between the EIO and the IMA
In T. crocea, the genetic break was detected between the populations from the EIO and the
central IMA (Fig. 5). In T. maxima, a deep divergence was shown for specimens from the
Java Sea and EIO in comparison to the central IMA (Fig. 5, b2). The crown-of-thorns starfish
Acanthaster plancii (Benzie 1999), the anemonefish Amphiprion ocellaris (Nelson et al.
2000; Timm & Kochzius 2008, Timm et al. 2012) and the seahorse Hippocampus
spinosissimus (Lourie et al. 2005) also showed such a genetic break. It was hypothesised that
this differentiation was caused by sea-level lowstands of up to 120 m during glacials, which
created isolated ocean basins (McManus 1985; Voris 2000). During periods of low sea-level
during the Pleistocene (0.012-2.588 mya), the loss of habitat would have led to local
extinction. When the sea-level rose, re-colonisation and growth of the reduced populations
42
might have happened (Fauvelot et al. 2003). Similar patterns were detected in two IndoPacific gastropods (Nerita albicilla and N. plicata) (Crandall et al. 2008a), which was also
likely associated with cyclical flooding of continental shelves and island lagoons following
low sea-level stands. However, in T. maxima, gene flow could be found between populations
in the Java Sea and Eastern Indian Ocean (Padang) through Sunda Strait, while in T. crocea,
connectivity is restricted. This might be explained by subtle differences in ecological
requirements or dispersal capability.
2.5.2.2. Genetic breaks between the IMA and the WP
Populations of the three species showed panmixing along the ITF in the Sulawesi Sea,
Makassar Strait, Flores Sea, Banda Sea, and Timor Sea. Seasonal changing currents in the
seas around Borneo also connect populations in this area to the populations along the ITF
(Fig. 5, a2; b2; c2). Most pairwise Φst-values were not significant.
All species showed genetic isolation between the IMA and the WP. In the three studied
giant clam species, the major haplotype clades in the populations of the WP were separated by
a minimum of 18 nucleotide substitutions from the other haplotype clades (Fig. 5, a3; b3; c3).
A similar result was detected in the anemonefish Amphiprion ocellaris (Timm & Kochzius
2008, Timm et al. 2008, 2012), three species of mantis shrimps (Haptosquilla pulchella, H.
glyptocerus and Gonodactylellus viridis; Barber et al. 2006), as well as in Nautilus (Wray et
al. 1995), showing also a genetic break between the IMA and WP. The results of these studies
support that there is an important biogeographic barrier between the IMA and the WP, which
was supposed to be located to the edge of the Sahul continental shelf (the Australian-New
Guinea continent), being exposed as dry land when sea level fell during the Pleistocene ice
age (McManus 1985; Voris 2000). Another reason for the restricted genetic exchange
between the IMA and WP might be caused by the Halmahera eddy, which transforms the
westward SEC into eastward the North NECC, and therefore limits the water transport from
New Guinea to the central IMA (Fig. 5; Wyrtki 1961, Gordon & Fine 1996). The populations
of the giant clams in the WP were so divergent that they also might be cryptic species, as
populations in the WIO, RS and CP.
2.5.3. Origin of the high diversity in the IMA
Three main theories are proposed to explain the high marine species diversity in the IMA. The
centre-of-origin hypothesis suggests that the IMA is the source of biodiversity in the IWP due
43
to a high speciation rate in this area (Bringgs 1999), while the centre-of-overlap hypothesis
supposes that the high species diversity in the IMA is caused by the overlap of species from
Indian and Western Oceans (Woodland 1983). Finally, the centre-of-accumulation hypothesis
suggests that speciation happens in isolated peripheral locations and novel taxa disperse into
the IMA (Jokiel & Martinelli 1992), which emphasises the importance of gene flow to the
IMA from peripheral areas. In this study, significant genetic structures were found in several
regions with distinctive, deeply divergent haplotypes, and strong signals of isolation-bydistance even only in the central IMA. This is concordant to allopatric speciation in isolated
peripheral locations, which is consistent with the theory of an accumulation centre. This view
of peripheral speciation is also supported by a phylogeographic study on the damselfish
Pomacentrus moluccensis (Drew & Barber 2009). However, one theory alone might not be
sufficient to explain the high diversity in the IMA (Hoeksema 2007). The high genetic
diversity and genetic differentiation of populations in the IMA also supports the centre-oforigin hypothesis (Timm et al. 2008).
2.5.4. Conservation implications
Giant clams are harvested commercially for food, shells, and aquarium trade, and stocks are
severely exploited (Lucas 1994; Wells & Group 1997), which calls for urgent conservation
management. In this study, the genetically distinct groups within each species might be
defined as separated Evolutionary Significant Units (ESU). It is suggested that ESUs are
important units for conservation (Vogler & DeSalle 1992; Moriz 1994). They are defined as
“populations that are reciprocally monophyletic for mtDNA” (Moritz 1994). However, the
definition is argued to be too restrictive and unique haplotypes, low gene flow or concordance
between phylogenetic divergence and geographic barriers provide new criteria for defining
ESU (Crandall et al. 2000). Therefore, the restricted gene flow between groups in each
species of giant clams, as well as concordance between genetic and geographic barriers might
indicate six ESUs as following, WIO, RS, EIO, central IMA, WP and CP. Management
should put more effort on maintaining the adaptive diversity in these ESUs and preservation
of the genetic connectivity among populations (Crandall et al. 2000). MPAs are widely
considered to be a useful means of conserving vulnerable marine species or habitats, and are
increasingly proposed as fisheries management tools (Palumbi 2001). The passive transport of
larvae by ocean currents is supposed to enhance gene flow and dispel geographic
differentiation (Kyle & Boulding 2000; Fievet et al. 2007). It is suggested that MPAs should
44
be arranged in a network and the distance among them should match the dispersal capabilities
of the targeted species (Palumbi 2003).
The Eastern Africa region in the WIO experienced severe coral bleaching events (Wilkison
1999), pollution, and overexploitation (Obura et al. 2004), while populations of giant clams in
the Red Sea (RS) also suffered from reef-top gathering, which is illustrated by higher
abundances in Marine Protected Areas (MPAs) (Ashworth et al. 2004).
In Indonesia, large amount of coral reef species are traded, which makes Indonesia the
most important exporter in marine ornamental market. In this study, populations of all three
tridacnid species in the Java Sea showed a low genetic diversity, which was also observed in
other studies on giant clams (DeBoer et al. 2008; Kochzius & Nuryanto 2008; Nuryanto &
Kochzius 2009). This might be explained by overexploitation. Java is the home of 60 percent
of the Indonesian population, and over-exploitation could be caused by fishery and marine
ornamental trade (Pasaribu 1988; Wells & Group 1997; Kochzius & Nuryanto 2008,
Nuryanto & Kochzius 2009). Furthermore, the bleaching event in the Java Sea (Wilkinson
2002) might be another reason for low genetic diversity. Photosynthetic symbionts of the
genus Symbiodinium are lost in bleaching events. Clams receive photosynthetic products
from the zooxanthellae due to their symbiotic relationship, and therefore, the bleaching events
reduce the fitness of the giant clams and might cause a population bottleneck (Leggat et al.
2003). In the IMA, all populations along the ITF are very well connected, most probably due
to the strong ITF. These three species have larval-dispersal period of about 10 days and they
should be able to travel about 400-700 km, considering 0.47-0.82 m/second velocities of the
ITF (Susanto & Gordon 2005). However, in a large scale, connectivity cannot be detected,
which suggests that the larval dispersal potential is limited and management efforts should
consider smaller and local scales to maintain and enable the population connectivity. For
example, the 51 MPAs with 58,000 km2 in Indonesia, only cover about 1 % of the marine
area, while in Kenya, 11 MPAs with 1, 585 km2 only account 1.3 % of the marine area, which
are most probably not sufficient for the protection of giant clams. In the RS, only a MPA
network along the coastlines of Egypt, Israel, and Jordan in the Gulf of Aqaba (Kochzius
2002) and along the Egyptian coast of the northern Red Sea enables the connectivity between
populations (Froukh & Kochzius 2007).
Collectively, in order to effectively protect the endangered giant clam species, the potential
distinct ESUs revealed in this study should be managed separately to protect their adaptive
45
diversity; small scales MPAs network should be arranged throughout the Indo-West Pacific
(IWP); and restoration should be attempted for the low genetic diversity areas, which are
probably induced by recent anthropogenic activities, such as habitat degradation and overexploitation.
2.6. Acknowledgements
We would like to thank to the institutions and individuals that have made our study possible:
German Federal Ministry of Education and Research (BMBF) (grant no. 03F0390B and
03F0472B), which funded this study in the framework of the SPICE project (Science for the
Protection of Indonesian Coastal Marine Ecosystems); China Scholarship Council (CSC) for
the PhD scholarship to Min Hui; Janne Timm (University of Bremen, Germany), Hilly RoaQuiaoit (Xavier University, Philippines), and Jelvas Mwaura (Kenya Marine and Fisheries
Research Institute, Mombasa) for support during collection of samples; colleagues from
Hasanuddin University (Indonesia), Marine Science Station Aqaba (Jordan), and Phuket
Marine Biological Center (Thailand) for logistical support during field work; The SPICE
project is conducted and permitted under the governmental agreement between the BMBF and
the Indonesian Ministry for Research and Technology (RISTEK), Indonesian Institute of
Sciences (LIPI), Indonesian Ministry of Maritime Affairs and Fisheries (DKP), and
Indonesian Agency for the Assessment and Application of Technology (BPPT).
2.7. References
Ashworth JS, Ormond RFG, Sturrock HT (2004) Effects of reef-top gathering and fishing on
invertebrate abundance across take and no-take zones, Journal of Experimental Marine
Biology and Ecology, 303, 221-242.
Avise JC (1992) Molecular population structure and the biogeographic history of a regional
fauna: a case history with lessons for conservation biology. Oikos, 63, 62-76.
Avise JC (2000) Phylogeography: the history and formation of species, Harvard Univiversity
Press.
Barber PH, Erdmann MV, Palumbi SR (2006) Comparative phylogeography of three
codistributed stomatopods: origins and timing of regional lineage diversification in the
coral triangle. Evolution, 60, 1825-1839.
46
Bay L, Choat JH, Herwerden L, Robertson DR (2004) High genetic diversities and complex
genetic structure in an Indo-Pacific tropical reef fish (Chlorurus sordidus): evidence of an
unstable evolutionary past? Marine Biology, 144, 757-767.
Benzie JAH (1998) Genetic structure of marine organisms and SE Asian biogeography. In:
Biogeography and Geological Evolution of SE Asia (eds Hall R, Holloway JD), pp.197209. Backhuys Publishers, Leiden, The Netherlands.
Benzie JAH (1999) Major genetic differences between crown-of-thorns starfish (Acanthaster
planci) populations in the Indian and Pacific Oceans. Evolution, 53, 1782-1795.
Benzie JAH, Ballment E, Forbes AT, et al. (2002) Mitochondrial DNA variation in Indoϋ
Pacific populations of the giant tiger prawn, Penaeus monodon. Molecular Ecology, 11,
2553-2569.
populations in the Great Barrier Reef, Australia. Marine Biology, 112,1-5.
Evolution, 51, 768-783.
Bermingham E, Moritz C (1998) Comparative phylogeography: concepts and applications.
Molecular Ecology, 7, 36 7-369.
Briggs JC (1995) Global Biogeography. Elsevier, Amsterdam.
Briggs JC (1999) Coincident biogeographic patterns: Indo-west Pacific ocean. Evolution, 53,
326-335.
Carpenter, KE (1998) An introduction to the oceanography, geology, biogeography, and
fisheries of the tropical and sub-tropical Western and Central Pacific. In K. Carpenter and
V. Niem, eds. The Living Marine Resources of the Western Central Pacific. Volume 1:
Seaweeds, Corals, Bivalves and Gastropods. FAO, Rome, 1-19.
47
Carpenter KE, Barber PH, Crandall ED, et al. (2010) Comparative phylogeography of the
Coral Triangle and implication for marine management. Journal of Marine Biology, 2011,
1-14.
Cartens BC, Brunsfeld SJ, Demboski JR, et al. (2005) Investigating the evolutionary history
of the pacific northwest mesic forest ecosystem: hpotehesis testing within a comparative
phylogeographic framework. Evolution, 59, 1639-1652.
Chow S, Okamoto H, Uozumi Y, et al. (1997) Genetic stock structure of the swordfish
(Xiphias gladius) inferred by PCR-RFLP analysis of the mitochondrial DNA control
region. Marine Biology, 127, 359-367.
Crandall ED, Frey MA, Grosberg RK, Barber PH (2008a) Contrasting demographic history
and phylogeographical patterns in two Indo-Pacific gastropods. Molecular Ecology, 17,
611-626.
Crandall ED, Jones ME, Muñoz MM, et al. (2008b) Comparative phylogeography of two
seastars and their ectosymbionts within the Coral Triangle. Molecular Ecology, 17, 52765290.
Crandall KA, Bininda-Emonds ORP, Mace GM, Wayne RK (2000) Considering evolutionary
processes in conservation biology. Trends in Ecology & Evolution, 15, 290-295.
Dayrat B (2005) Towards integrative taxonomy. Biological Journal of the Linnean Society,
85, 407-415.
Biology, 22, 1255-1266.
Demboski JR, Stone KD, Cook JA (1999) Further perspectives on the Haida Gwaii glacial
refugium. Evolution, 53, 2008-2012.
Drew J, Barber PH (2009) Sequential cladogenesis of the reef fish Pomacentrus moluccensis
(Pomacentridae) supports the peripheral origin of marine biodiversity in the IndoAustralian archipelago. Molecular Phylogenetics and Evolution, 53, 335–339.
10, 564-567.
48
Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from
metric distances among DNA haplotypes: application to human mitochondrial DNA
restriction data. Genetics, 131, 479-491.
Fauvelot C, Bernardi G, Planes S (2003) Reductions in the mitochondrial dna diversity of
coral reef fish provide evidence of population bottlenecks resulting from holocene sealevel change. Evolution, 57, 1571-1583.
Fauvelot C, Planes S (2002) Understanding origins of present-day genetic structure in marine
fish: biologically or historically driven patterns? Marine Biology, 141, 773-788.
Fievet V, Touzet P, Arnaud JF, Cuguen JEL (2007) Spatial analysis of nuclear and
cytoplasmic DNA diversity in wild sea beet (Beta vulgaris ssp. maritima) populations: do
marine currents shape the genetic structure? Molecular Ecology, 16, 1847-1864.
Fratini S, Vannini M (2002) Genetic differentiation in the mud crab Scylla serrata (Decapoda:
Portunidae) within the Indian Ocean. Journal of Experimental Marine Biology and
Ecology, 272, 103-116.
Froukh T, Kochzius M (2007) Genetic population structure of the endemic fourline wrasse
(Larabicus quadrilineatus) suggests limited larval dispersal distances in the Red Sea.
Froukh T, Kochzius M (2008) Species boundaries and evolutionary lineages in the blue green
damselfishes Chromis viridis and C. atripectoralis (Pomacentridae). Journal of Fish
Biology, 72, 451-457.
Fu YX (1997) Statistical tests of neutrality of mutations against population growth,
hitchhiking and background selection. Genetics, 147, 915-925.
Gilbert A, Planes S, Andréfouët S, Friedman K, Remoissenet G (2007) First observation of
the giant clam Tridacna squamosa in French Polynesia: a species range extension. Coral
Reefs, 26, 229.
14-27.
Hall R (2002) Cenozoic geological and plate tectonic evolution of SE Asia and the SW
Pacific: computer-based reconstructions, model and animations. Journal of Asian Earth
Sciences, 20, 353-431.
49
Helfman GS, Collette BB, Facey DE (1997) The diversity of fishes, Wiley-Blackwell.
Hoeksema B (2007) Delineation of the Indo-Malayan centre of maximum marine
biodiversity: the Coral Triangle. Biogeography, time, and place: Distributions, Barriers,
and Islands, 117-178.
Jensen JL, Bohonak AJ, Kelley ST (2005) Isolation by distance web service. BMC Genetics,
6, 13.
Jokiel P, Martinelli FJ (1992) The votex model of coral reef biogeography. Journal of
Biogeography, 19, 449-458.
Speial Publication, 25, 343-347.
Klausewitz W (1989) Evolutionary history and zoogeography of the Red Sea ichthyofauna.
Fauna of Saudi Arabia, 10, 310-337.
Knop D (1996) Giant clams: a comprehensive guide to the identification and care of
Tridacnid clams. Dahne Verlag, Ettlingen, Germany.
Kochzius M (2002) Coral reefs in the Gulf of Aqaba. In: Status of Coral Reefs of the World:
2002 (ed. Wilkinson C), p. 52. Australian Institute of Marine Science, Townsville,
Australia.
Kochzius M, Blohm D (2005) Genetic population structure of the lionfish Pterois miles
(Scorpaenidae, Pteroinae) in the Gulf of Aqaba and northern Red Sea. Gene, 347, 295-301.
Kochzius M, Nuryanto A (2008) Strong genetic population structure in the boring giant clam,
Tridacna crocea, across the Indo-Malay Archipelago: implications related to evolutionary
processes and connectivity. Molecular Ecology, 17, 3775-3787.
Kochzius M, Söller R, Khalaf MA, Blohm D (2003) Molecular phylogeny of the lionfish
genera Dendrochirus and Pterois (Scorpaenidae, Pteroinae) based on mitochondrial DNA
sequences. Molecular Phylogenetics and Evolution, 28, 396-403.
50
Kyle C, Boulding E (2000) Comparative population genetic structure of marine gastropods
(Littorina spp.) with and without pelagic larval dispersal. Marine Biology, 137, 835-845.
Leggat W, Buck BH, Grice A, Yellowlees D (2003) The impact of bleaching on the metabolic
contribution of dinoflagellate symbionts to their giant clam host. Plant, Cell &
Environment, 26, 1951-1961.
Lourie SA, Green DM, Vincent ACJ (2005) Dispersal, habitat differences, and comparative
phylogeography of Southeast Asian seahorses (Syngnathidae: Hippocampus). Molecular
Ecology, 14, 1073-1094.
Lucas J (1988) Giant clams: description, distribution and life history. Giant clams in Asia and
the Pacific. ACIAR Monograph, 9, 21-33.
Lucas JS (1994) The biology, exploitation, and mariculture of giant clams (Tridacnidae).
Reviews in Fisheries science, 2, 181-223.
113, 231-238.
Marko PB (2002) Fossil calibration of molecular clocks and the divergence times of geminate
species pairs separated by the Isthmus of Panama. Molecular Biology and Evolution, 19,
2005-2021.
McManus J (1985). Marine speciation, tectonics and sea-level changes in southeast Asia.
McMillan WO, Palumbi SR (1995) Concordant evolutionary patterns among Indo-West
Pacific butterflyfishes. Proceedings of the Royal Society of London. Series B: Biological
Sciences, 260, 229-236.
Moritz C (1994) Defining 'evolutionarily significant units' for conservation. Trends in
Ecology and Evolution, 9, 373-374.
Mous PJ, Pet J, Arifin Z, et al. (2005) Policy needs to improve marine capture fisheries
management and to define a role for marine protected areas in Indonesia. Fisheries
Management and Ecology, 12, 259-268.
Nei M (1987). Molecular evolutionary genetics, Columbia Univesity Press.
51
Nei M, Jin L (1989) Variances of the average numbers of nucleotide substitutions within and
between populations. Molecular Biology and Evolution, 6, 290-300.
Nelson J, Hoddell R, Chou L, et al. (2000) Phylogeographic structure of false clownfish,
Amphiprion ocellaris, explained by sea level changes on the Sunda shelf. Marine Biology,
137, 727-736.
in the giant clam Tridacna maxima. Coral reefs, 28, 607-619.
Obura D, Suleiman M, Motta H, Schleyer M (2004) Status of coral reefs in East Africa:
Kenya, Mozambique, South Africa and Tanzania. Status of coral reefs of the World: 2004,
2, edited by Wilkinson C.
Palumbi SR (2001) The ecology of marine protected areas. Marine community ecology 9,
509-530.
Palumbi SR (2003) Population genetics, demographic connectivity, and the design of marine
reserves. Ecological applications, 13, 146-158.
Pasaribu B (1988) Status of giant clams in Indonesia. Giant clams in Asia and the Pacific.
Australian Centre for International Agricultural Research, Canberra, 44-46.
for Plio–Pleistocene sea-level changes and sequence stratigraphy. Sedimentary Geology
122, 5-21.
Reece JS, Bowen BW, Joshi K, et al. (2010) Phylogeography of two moray eels indicates
high dispersal throughout the Indo-Pacific. Journal of Heredity, 101, 391-402.
Rice WR (1989) Analyzing tables of statistical tests. Evolution, 43, 223-225.
Richter C, Roa-Quiaoit H, Jantzen C, et al. (2008) Collapse of a new living species of giant
clam in the Red Sea. Current Biology, 18, 1349-1354.
Rogers AR (1995) Genetic evidence for a Pleistocene population explosion. Evolution, 608615.
Rogers AR, Harpending H (1992) Population growth makes waves in the distribution of
pairwise genetic differences. Molecular Biology and Evolution, 9, 552-569.
Rosewater J (1965) The family Tridacnidae in the Indo-Pacific. Indo-Pacific Mollusca:
monographs of the marine mollusks of the tropical western Pacific and Indian Oceans, 1,
347-408.
52
Schott FA, McCreary JP (2001) The moon circulation of the Indian Ocean. Progress in
Oceanography, 51, 1-123
Siliotti A (2002) fishes of the red sea Verona. Geodia ISBN, 88-87177-42-2.
Spalding MD, Ravilious C, Green EP (2001). World atlas of coral reefs. University of
California Press.
Susanto RD, Gordon AL (2005) Velocity and transport of the Makassar Strait Throughflow.
Journal of Geophysical Research, 110, C01005.
Tajima F (1989) Statistical method for testing the neutral mutation hypothesis by DNA
polymorphism. Genetics, 123, 585-595.
Taylor MS, Hellberg ME (2003) Genetic evidence for local retention of pelagic larvae in a
Caribbean reef fish. Science, 299, 107-109.
Timm J, Kochzius M (2008) Geological history and oceanography of the Indo-Malay
Archipelago shape the genetic population structure in the false clown anemonefish
(Amphiprion ocellaris). Molecular Ecology, 17, 3999-4014.
Timm J, Figiel M, Kochzius M (2008) Contrasting patterns in species boundaries and
evolution of anemonefishes (Amphiprioninae, Pomacentridae) in the centre of marine
biodiversity. Molecular Phylogenetics and Evolution, 49, 268-276.
False Clown Anemonefish (Amphiprion ocellaris) found in microsatellite and
mitochondrial control region analysis. Conservation Genetics, 13, 693-706.
Veron JEN (2000) Corals of the world, Vol. 3. Australian Institute of Marine Science.
Townsville, Australia.
Vogler AP, Desalle R (1994) Diagnosing units of conservation management. Conservation
Biology, 8, 354-363.
Walker DE, Avise JC (1998) Principles of phylogeography as illustrated by freshwater and
terrestrial turtles in the southeastern United States. Annual Review of Ecology and
Systematics, 29, 23-58.
Walsh PS, Metzger DA, Higuchi R (1991) Chelex 100 as a medium for simple extraction of
DNA for PCR-based typing from forensic material. Biotechniques, 10, 506.
53
Wares JP, Cunningham CW (2001) Phylogeography and historical ecology of the North
Atalantic intertidal. Evolution, 55, 2455-2469.
Weir B, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure.
Evolution, 38, 1358-1370.
Wells SM, Group UiplcdlnedsrMS (1997) Giant clams: Status, trade and mariculture, and the
role of CITES in management, IUCN-The World Conservation Union.
Wilkinson C, Lindén O, Cesar H, et al. (1999) Ecological and socioeconomic impacts of 1998
coral mortality in the Indian Ocean: An ENSO impact and a warning of future change?
Ambio, 28, 188-196.
Wilkinson C (2002) Status of coral reefs of the World: 2002, Australian Institute of Marine
Science.
Williams S, Benzie J (1998) Evidence of a biogeographic break between populations of a high
dispersal starfish: congruent regions within the Indo-West Pacific defined by color morphs,
mtDNA, and allozyme data. Evolution, 52, 87-99.
Woodland DJ (1983) Zoogeography of Siganidae (Pisces): an interpretation of distribution
and richness pattern. Bulletin of Marine Science, 33, 713-717.
Wörheide G, Epp L, Macis L (2008) Deep genetic divergences among Indo-Pacific
populations of the coral reef sponge Leucetta chagosensis (Leucettidae): Founder effects,
vicariance, or both? BMC evolutionary biology, 8, 24.
Wray CG, Landman NH, Saunders WB, Bonacum J (1995) Genetic divergence and
geographic diversification in Nautilus. Paleobiology, 21, 220-228.
Wyrtki K (1961) Physical oceanography of the southeast Asian waters, University of
California, Scripps Institution of Oceanography.
Isolation and characterisation of nine microsatellite markers in
the boring giant clam (Tridacna crocea) and cross-amplification in
five other tridacnid species
Development of microsatellite markers in Tridacna crocea
55
3. Isolation and characterisation of nine microsatellite markers in the
boring giant clam (Tridacna crocea) and cross-amplification in five other
tridacnid species
Min Hui1*, Marc Kochzius2 and Florian Leese3
1
Biotechnology and Molecular Genetics, FB2-UFT, University of Bremen, Leobener Straße,
2
Marine Biology, Free University of Brussels (VUB), Pleinlaan 2, 1050 Brussels, Belgium
3
Department of Animal Ecology, Evolution and Biodiversity, Ruhr University Bochum,
Universitätsstraße 150, 44801 Bochum, Germany
*
E-mail: minhui@uni-bremen.de
56
3.1. Abstract
Nine novel polymorphic microsatellite markers were isolated in the boring giant clam
(Tridacna crocea). The number of alleles ranged from seven to 21. The observed and
expected heterozygosity varied from 0.400 to 1.000 and 0.727 to 0.932, respectively. No
significant linkage disequilibrium between pairs of loci was found and eight of nine loci were
in Hardy-Weinberg proportions. Cross-amplification in other tridacnid species showed that all
loci could be successfully amplified in Tridacna maxima, five in T. squamosa, three in T.
derasa, three in T. gigas, and three in T. costata. These markers are therefore potentially
useful for studies on the genetic diversity and connectivity of giant clam populations in order
to facilitate the spatial arrangement of marine protected areas (MPAs) and fisheries
management for these species.
Keywords Endangered species. Indonesia. Simple sequence repeats. Symbiodinium.
Tridacnidae
3.2. Body text
Giant clams of the genus Tridacna (Cardiidae: Tridacnidae) are conspicuous and colourful
bivalves that inhabit coral reefs across the Indo-Pacific (Lucas 1994). They live in symbiosis
with photosynthetic dinoflagellate algae (Symbiodinium spp.) that grow in the mantle tissues
(Jantzen et al. 2008). Within the genus Tridacna several species are described, T. gigas, T.
maxima, T. squamosa, T. derasa, T. crocea, T. tevoroa and a new species T. costata (Richter
et al. 2008). Total length of the adult individuals ranges from 15 cm in Tridacna crocea to
150 cm in T. gigas.
Over-exploitation of giant clams for food, shells, and marine ornamental trade has caused
the decline of populations at large geographic scales, especially in Southeast Asia (Lucas
1994; Wells 1997). Therefore, tridacnid species are now listed in Appendix II of the
Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES),
allowing international trade only with appropriate export permits. According to CITES data,
international trade in non-captive bred giant clams increased from about 40,000 specimens in
1993 to about 100,000 in 2001. The European Union and the USA are the main importers,
57
with about 48,000 and 45,000 specimens imported in 2001, respectively (Wabnitz et al. 2003).
Thus, it is crucial to clarify the genetic diversity and connectivity of wild populations to assist
conservation efforts for giant clams.
So far, several studies on the genetic population structure of T. crocea have been conducted
using allozymes (Juinio-Meñez et al. 2003; Ravago-Gotanco et al. 2007) and a fragment of
the mitochondrial cytochrome c oxidase subunit I gene (DeBoer et al. 2008; Kochzius and
Nuryanto 2008). However, microsatellite markers, codominant and highly polymorphic,
might provide a much higher resolution for such studies. So far, nine microsatellite markers
have been characterised for giant clams (DeBoer and Barber 2010). In this study, nine novel
microsatellite markers for the boring giant clam (T. crocea) were developed and their crossamplification in five other tridacnid species was tested.
Total genomic DNA was extracted with the Qiagen DNeasy kit from the adductor muscle
tissue to avoid contamination from symbiotic zooxanthellate algae (Symbiodinium spp.). A
microsatellite-enriched library was constructed following the protocol of Leese et al. (2008).
Positive clones were directly amplified using PCR with M13 universal primers and sequenced
with an ABI 3730 automatic sequencer. Microsatellite-containing sequences were assembled
using the programme Contig Express (Vector NTI Suite 8; InforMax, Inc) and BLAST
searches in NCBI were performed to detect possible contamination caused by Symbiodinium.
Unique sequences were selected for primer design with Primer Premier 5 (Premier Biosoft
International).
The allelic variation of microsatellite loci was evaluated in 20 individuals derived from a
population in Spermonde Archipelago (4°52'S, 119°6'E, Southwest Sulawesi, Indonesia). The
PCR master mix contained about 20 ng of template DNA, 0.2 μM of each primer, 200 μM of
each dNTP, 1.5 mM of MgCl2 and 1 U Taq polymerase with 1 × PCR buffer in a total volume
of 20 μl. The thermal profile was 95°C for 5 min, followed by 35 cycles of 30 s at 95°C; 30 s
at the locus-specific annealing temperature (Table 7), 60 s at 72°C, and a final extension at
72°C for 5 min. Amplified fragments were separated on an ABI 3730 automated sequencer,
and different alleles were scored using GeneMarker v1.91 (SoftGenetics). The observed
heterozygosity (Ho) and expected heterozygosity (He) were assessed with POPGENE 1.31
(Yeh and Boyle, 1997). Linkage disequilibrium (LD) and Hardy-Weinberg proportions
(HWP) tests were performed using GENEPOP 4.0 (Raymond and Rousset 1995; Rousset
2008).
58
Since giant clams host zooxanthellae, the developed microsatellites were furthermore tested
in PCRs with DNA extracts from cultures of the zooxanthellae Symbiodinium to confirm giant
clam specificity. DNA extracts of seven Symbiodinium cultures, one of them originating from
T. crocea and one from T. maxima, were used for these tests. The results showed no
amplification success from any of the Symbiodinium cultures, supporting that these loci are
specific for giant clams. The allele number per locus ranged from seven to 21. The values of
Ho and He varied from 0.400 to 1.000 and 0.727 to 0.932, respectively (Table 7). No
significant linkage disequilibrium (P > 0.05) was detected between any pairs of the nine loci.
Tests for Hardy-Weinberg proportions (HWP) revealed that only one marker (TC4)
significantly departed from HWP (P < 0.01) (Table 7). A further test indicated that
heterozygosity deficiency (P < 0.01) at this locus was responsible for the departure.
To determine the potential for application of the developed microsatellites in other tridacnid
species, cross-species amplification was tested in eight individuals of Tridacna maxima, T.
squamosa, and T. costata, six individuals of T. derasa, and five individuals of T. gigas. The
same PCR and genotyping conditions as described above were used. All nine loci were
successfully amplified in Tridacna maxima and found to be polymorphic, five amplified in T.
squamosa, three amplified in T. derasa (TC9 was monomorphic), three amplified in T. gigas
(TC3 was monomorphic), and three amplified in T. costata as well (TC1 and TC8 were
monomorphic) (Table 8). The decline in successful amplification rate in related Tridacna
species was paralleled by a reduction in allelic diversity most likely due to mutations in the
flanking regions (null alleles).
The results of this study showed that the isolated microsatellite markers exhibit a high level
of polymorphism in T. crocea and also for other tridacnid species. Therefore, these markers
are useful for studies on the genetic structure of giant clam populations and can assist
management efforts, such as spatial MPA arrangement based on genetic connectivity and
forensics identification of populations in the international marine ornamental trade to
differentiate wild-caught from cultured animals.
R:GTATACAGAACCTAAGGTAT
R: TACCGTACCAGAGGCAACTA
F: GGTAGAACATTGTAGGCTGG
R: CCATGTATTTCAACACTCTAGC
F: CAAAGTTCAAAATACACTCG
R: CTACGAAAATCACTGACTCTG
F: CTTTGCTTACATTGGAGACG
R: ATACTATGCAGAGGAAGGAG
F: CATGGTGGACGATGCCAAGT
R: GCCGAAAGTAAAACTCAGAA
F: AGAAATGACGTAACACCCAC
R: AGTCGCGTCACTCTGGATAG
F: CAGAGTTACGAACCGATGCA
R:
F: ACCATACCCCTGCCATACAGT
R: GTCTGTCCCACCCGTCCATA
F: TCATCGTCATCCCAGCACTC
F: GCTTTGTGGCTATTGGAGAA
Primer Sequence (5'-3')
56
50
56
56
56
56
56
62
56
(°C)
Ta
(CTGA)7…(ATGT)6
(ATGG)6…(ATGG)8…(ATGG)8
(AG)25
(AG)18
(CT)24
(GT)7…(GTCT)4
(AG)19
(AG)17…(AG) 6... (AGAC)3
(AG)22…(AG)16…(AG)6
Repeat Motif
FAM
HEX
HEX
FAM
FAM
FAM
HEX
FAM
FAM
Dye
195-227
274-358
168-212
160-206
135-165
442-524
227-249
345-377
275-339
(bp)
Size Range
7
16
10
21
15
12
10
14
12
Na
1.000
0.705
0.777
1.000
1.000
0.400
0.750
1.000
0.875
Ho
0.727
0.911
0.876
0.932
0.911
0.890
0.873
0.887
0882
He
0.137
0.023
0.020
0.697
0.300
0.000a
0.095
0.985
1.000
(HWP)
P
HWP.
Ho: observed heterozygosity; He: expected heterozygosity; P (HWP): P value for deviations from Hardy–Weinberg proportions; a: loci departed from
The GenBank accession numbers for the sequences are HQ845365-HQ845372 and JF461266; Ta: optimal annealing temperature; Na: Number of alleles;
TC9
TC8
TC7
TC6
TC5
TC4
TC3
TC2
TC1
Locus
Table 7 Characteristics of microsatellite loci isolated in Tridacna crocea.
59
4
8
7
7
7
3
8
3
8
TC1
TC2
TC3
TC4
TC5
TC6
TC7
TC8
TC9
175-207
302-330
170-206
154-176
127-147
450-494
221-277
339-363
287-299
Range (bp)
7
3
8
4
9
6
7
7
5
Na
7
5
3
5
8
N
187-199
——
170-188
142-148
——
442-476
——
333-363
——
Range (bp)
T. squamosa (n=8)
3
4
4
6
8
Na
3
5
3
N
191
——
——
138-172
——
——
——
333-361
——
Range (bp)
T. derasa (n=6)
1
3
3
Na
4
1
2
N
175-227
——
——
——
——
——
231
343-361
——
Range (bp)
T. gigas (n=5)
N: number of individuals for which PCR was successful; ——: no amplification; Na: number of alleles.
N
Locus
T. maxima (n=8)
Table 8 Cross-amplification in five other tridacnid species.
4
1
3
Na
5
1
1
N
191-203
322
——
——
——
——
——
——
297
Range (bp)
T. costata (n=8)
3
1
1
Na
60
61
We would like to thank to the institutions and individuals that have made our study possible:
the PhD scholarship to Min Hui; Wiebke Krämer (University of Bremen, Germany) for
providing Symbiodinium cultures; Agus Nuryanto (Jenderal Soedirman University, Indonesia)
and Janne Timm (University of Bremen, Germany) for support during collection of samples;
colleagues from Hasanuddin University (Indonesia) for logistical support during field work;
Dr. Aibin Zhan (University of Windsor, Canada) for help comments on the manuscript; Dr.
Christoph Held (Alfred-Wegener-Institute for Polar and Marine Research, Germany) for
laboratory support. The SPICE project is conducted and permitted under the governmental
agreement between the BMBF and the Indonesian Ministry for Research and Technology
(RISTEK), Indonesian Institute of Sciences (LIPI), Indonesian Ministry of Maritime Affairs
and Fisheries (DKP), and Indonesian Agency for the Assessment and Application of
Technology (BPPT).
3.4. References
in three other tridacnid species. Conserv Genet Resour 2:353-356
DeBoer TS, Subia MD, Ambariyanto A, Erdmann MV, Kovitvongsa K, Barber PH (2008)
Phylogeography and limited genetic connectivity in the endangered boring giant clam,
Tridacna crocea, across the Coral Triangle. Conser Biol 22:1255-1266
Jantzen C, Wild C, El-Zibdah M, Roa-Quiaoit HA, Haacke C, Richter C (2008)
Photosynthetic performance of giant clams, Tridacna maxima and T. squamosa, Red Sea.
Mar Biol 155:211–221
62
Mar Biol 142:717-726
Kochzius M, Nuryanto A (2008) Strong genetic population structure in the boring giant clam
Tridacna crocea across the Indo-Malay Archipelago: implications related to evolutionary
processes and connectivity. Mol Ecol 17:3775-3787
Leese F, Mayer C, Held C (2008) Isolation of microsatellites from unknown genomes using
known genomes as enrichment templates. Limnol Oceanogr: Methods 6:412-426
Lucas JS (1994) The biology, exploitation and mariculture of giant clam (Tridacnidae). Rev in
Fish Sci 2:181-223
Ravago-Gotanco RG, Magsino RM, Juinio-Meñez MA (2007) Influence of the North
Equatorial Current on the population genetic structure of Tridacna crocea (Mollusca:
Tridacnidae) along the eastern Philippine seaboard. Mar Ecol Prog Ser 336:161-168
exact tests and ecumenicism. J Hered 86:248-249
Richter C, Roa-Quiaoit H, Jantzen C, Al-Zibdah M, Kochzius M (2008) Collapse of a new
living
species of giant clam in the Red Sea. Curr Biol 18:1-6
Windows and Linux. Mol Ecol Resour 8:103-106
Wabnitz C, Taylor M, Green E, Razak T (2003) From ocean to aquarium. UNEP-WCMC,
Cambridge, UK
Wells S (1997) Giant clam: status, trade and mariculture, and the role of CITES management.
Gland, Switzerland and Cambridge, UK: IUCN
Yeh FC, Boyle TJB (1997) Population genetic analysis of co-dominant and dominant markers
and quantitative traits. Belg J of Bot 129:157
Concordance of microsatellite and mitochondrial DNA markers in
detecting genetic population structure and connectivity in the
boring giant clam, Tridacna crocea, across the Indo-Malay
Archipelago
Population genetics of Tridacna crocea
64
4. Concordance of microsatellite and mitochondrial DNA markers in
detecting genetic population structure and connectivity in the boring
giant clam, Tridacna crocea, across the Indo-Malay Archipelago
Min Hui1, Agus Nuryanto2, Marc Kochzius3
1
Biotechnology and Molecular Genetics, FB2-UFT, University of Bremen, Leobener Strasse,
2
Faculty of Biology, Jenderal Soedirman University, Purwokerto, Indonesia
3
Marine Biology, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Correspondence: Marc Kochzius
E-mail: marc.kochzius@vub.ac.be
Keywords: Corel reef, Indo-Pacific, Molecular marker, Southeast Asia, SSR
Running title: Population genetics of Tridacna crocea
65
4.1. Abstract
Many studies using mitochondrial DNA (mtDNA) have been conducted to investigate
connectivity among populations. Mitochondrial DNA is a single, non-recombining locus and
therefore its ability to reveal recent gene flow among populations in order to estimate
connectivity is questioned. By using 10 microsatellite markers, the genetic population
structure of 366 individuals of the Boring Giant Clam Tridacna crocea was examined in 16
populations from the Indo-Malay Archipelago (IMA) in order to compare these results with
previous studies using mtDNA. Genetic population structure patterns were mostly congruent,
with only minor difference. The studied populations could be divided by both marker systems
as following: (1) Eastern Indian Ocean, (2) central Indo-Malay Archipelago, and (3) Western
Pacific. Similarly, the microsatellite data showed that the divergence in T. crocea likely dates
back to the global fluctuation of sea level during the Plicocene and Pleistocene. Populations in
the central IMA showed panmixing, being well connected by surface currents, as shown by
pairwise comparison among populations in this area. However, the structure patterns through
the IMA revealed by microsatellites are not as strong as pronounced by the mtDNA analysis,
with an Fst-value of 0.023 (P < 0.001). The highly divergent mtDNA clades suggested there
might be cryptic species, which cannot be revealed by microsatellite data. Additionally, the
genetic divergence of populations in the Java Sea revealed by mtDNA was not detected by
microsatellites. mtDNA might reveal historic rather than recent population connectivity due to
its much lower mutation rate than microsatellite DNA. Also, the differences might be induced
by the smaller effective population size of mtDNA comparing with nuclear DNA (nDNA),
which might show stronger signals of genetic drift. However, the general concordant
structures revealed by the two marker systems supported that the mitochondrial cytochrome c
oxidase subunit I (COI) gene is applicable for population genetic analysis and precise
recovery of connectivity patterns of giant clams. The combination of mtDNA and nDNA
markers in population genetics can supply more information and might facilitate the
understanding of population structure at different time scales.
66
4.2. Introduction
Understanding the current distribution and connectivity among populations of a species is
important to illuminate historic and contemporary processes, as well as providing baseline
data for conservation. This research focuses on the Indo-Malay Archipelago (IMA), one of the
main evolutionary centres in the world, which experienced a complex geological evolution
due to plate tectonic movements and global fluctuations of sea level during multiple Pliocene
and Pleistocene glaciations (Pillans et al. 1998, Voris 2000).
In the past years, different molecular marker systems have been developed for population
genetic studies. Mitochondrial DNA (mtDNA) has many advantages and is widely used. It
evolves faster than nuclear DNA and certain region show high variability (Brown et al. 1982).
Due to the availability of universal primers, mtDNA fragments are relatively easy to amplify
and to sequence. In phylogenetic studies, the maternal inheritance reduces the effective
population size and shortens the amount of time required for lineage sorting to reveal
phylogenetic patterns (Avise 2000). The population genetic structure of many marine species
in the IMA was studied using mtDNA, such as giant clams (Tridacna crocea, DeBoer et al.
2008, Kochzius & Nuryanto, 2008; T. maxima, Nuryanto & Kochzius, 2009), the blue starfish
Linckia laevigata and its ectoparasite Thyca crystallina (Crandall et al. 2008a, Kochzius et al.
2009), gastropods (Nerita albicilla and N. plicata, Crandall et al. 2008b), the tiger prawn
Penaeus monodon (Benzie et al. 2002), the anemonefish Amphiprion ocellaris (Nelson et al.
2000, Timm & Kochzius 2008) and the scad mackerel Decapterus russelli (Perrin & Borsa
2001).
Many coral reef organisms, such as corals, sea anemones, and giant clams harbor
symbiotic algae (zoxanthellae) in their tissue, which needs to be considered during genome
marker isolation and application. Therefore, it is much easier to apply mtDNA sequence
markers, since primers are available which do not amplify zooxanthellae. However, whether
mtDNA can be considered a useful maker for connectivity studies has been debated. It solely
represents one locus, inherits maternally and especially might be under selection (Bazin et al.
2006, Lee & Edwards 2008, Zink & Barrowclough 2008, Barrowclough & Zink 2009,
Edwards & Bensch 2009). Many studies also showed discordant genetic structures in the same
species when applying nuclear DNA (nDNA) and mtDNA (Toews & Brelsford 2012). These
67
findings show that caution should be taken when interpreting results from different molecular
markers, and multi-locus studies should focus on revealing the driver of this discordance.
Nuclear microsatellite markers, which are characterised as codominant and highly
polymorphic, are widely used in studies on genetic population structure for marine species,
such as the shrimp Penaeus monodon (You et al. 2008), the anemonefish Amphiprion
ocellaris (Timm et al. 2012), as well as the butterflyfishes Chaetodon meyeri and Chaetodon
ornatissimus (DiBattista et al. 2012).
The boring giant clam Tridacna crocea has a pelagic larval duration (PLD) of around 10
days (Lucas 1988) and therefore a rather low dispersal capability. It lives in symbiosis with
zooxanthellae and the adults are attached to the substrate. All tridacnid species are listed in
Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna
and Flora (CITES), allowing international trade only with appropriate export permits. Special
concerns have been raised on the conservation of giant clams, prompting for urgent
management based on good understanding of the biology of these species. Population genetic
data are already available for giant clams in some areas (Campbell et al. 1975, Benzie &
Williams 1992a, b, Macaranas et al. 1992, Benzie & Williams 1995, Benzie & Williams
1997, Kittiwattanawong 1997, Yu et al. 2000, Kittiwattanawong et al. 2001, Laurent et al.
2002, Juinio-Meñez et al. 2003, Nuryanto & Kochzius 2009), but no study has utilised high
resolution microsatellites.
This study aims to investigate the connectivity of T. crocea in the IMA by using
microsatellites (DeBor et al. 2010, Hui et al. 2011) and comparing the results with a previous
investigation utilising the mitochondrial COI gene (Kochzius & Nuryanto 2008), in order to
assess if the revealed genetic population structure is congruent in both marker systems.
4.3. Material and Methods
4.3.1. Samples collection
366 small pieces of mantel tissue of Tridacna crocea were collected from 16 sites by SCUBA
diving across IMA (Table 9; Fig. 7). Tissues were preserved in 96 % ethanol.
68
Fig. 7 (a) Map of the Indo-Malay Archipelago with sample sites (see Table 9 for abbreviations).
Surface currents with dominant (solid lines) and seasonally changing currents (dashed lines) (Wyrtki
1961; Gordon & Fine 1996; Gordon 2005), including the Indonesia Throughflow (ITF), South
Equatorial Current (SEC), and North Equatorial Counter Current (NECC) are shown on the map.
Pleistocene sea-level low stand of 120 m (light grey area) is shown on the map (Voris 2000). Pie
charts represent the proportions of different genetic groups (black, grey, and white) in each population
defined by STRUCTURE 2.3.1. (b) Bar plot showing Bayesian assignments of individuals to the three
groups (K = 3) by STRUCTURE. Each bar means the estimated admixture coefficient from each
inferred group for each individual. The three groups are (A) Eastern Indian Ocean, (B) the central
Indo-Malay Archipelago, and (C) Western Pacific Ocean.
69
Table 9 Variation in microsatellite loci of 16 Tridacna crocea populations in the Indo-Malay
Archipelago.
Sites(code)-n
Padang(Pa)-7
Na
Ho
He
P
Pulau Seribu(PS)-17
Na
Ho
He
P
Karimunjava (Ka)-14
Na
Ho
He
P
Komodo (Ko)-27
Na
Ho
He
P
Kupang(Ku)-9
Na
Ho
He
P
Spermonde(Sp)-49
Na
Ho
He
P
Bira(Bi)-13
Na
Ho
He
P
Sembilan
Na
Ho
Sembilan
He
P
Kendari(Ke)-28
Na
Ho
He
P
Locus
TC8
Tc059
TC1
TC3
TC5
TC6
3
0.429
0.582
0.0722
3
1.000
0.625
1.0000
5
1.000
0.778
1.0000
7
0.857
0.827
0.08
4
0.333
0.722
0.0663
5
0.167
0.764
0.0007
14
0.625
0.904
0.0047
15
0.882
0.913
0.0031
16
0.529
0.905
0.0000
9
0.231
0.867
0.0000
8
0.636
0.810
0.0213
13
0.583
0.865
0.0006
16
0.091
0.897
0.0000
11
0.542
0.841
0.0000
9
0.429
0.867
0.0000
Mean
Tc074
Tc092
Tc160
Tc161
3
0.333
0.611
0.1984
3
0.000
0.667
0.0667
4
0.667
0.667
0.6083
3
0.333
0.500
0.1945
5
0.667
0.778
0.1950
4
0.562
0.676
13
0.364
0.913
0.0000
9
1.000
0.834
0.0177
10
0.765
0.848
0.2608
17
0.588
0.907
0.0000
9
0.647
0.846
0.0560
14
0.824
0.901
0.1482
12.2
0.639
0.874
18
0.692
0.929
0.0079
8
0.571
0.857
0.0418
7
0.750
0.778
0.1452
9
0.583
0.816
0.0068
11
0.600
0.875
0.0147
7
0.545
0.831
0.0462
11
0.900
0.885
0.7049
10.1
0.609
0.851
19
0.731
0.920
0.0032
27
0.556
0.954
0.0000
18
0.481
0.914
0.0000
10
0.778
0.802
0.1031
14
0.667
0.894
0.0002
24
0.769
0.933
0.0000
12
0.815
0.885
0.3952
13
0.815
0.901
0.1325
16.4
0.624
0.894
11
0.625
0.883
0.0008
9
1.000
0.858
1.0000
12
0.444
0.907
0.0000
10
0.444
0.833
0.0000
7
0.889
0.772
0.4547
10
0.667
0.858
0.1068
12
0.667
0.895
0.0051
7
0.889
0.846
0.8615
8
0.778
0.815
0.1074
9.5
0.683
0.853
21
0.118
0.939
0.0000
14
0.653
0.911
0.0000
22
0.625
0.928
0.0027
31
0.510
0.937
0.0000
26
0.487
0.937
0.0000
12
0.776
0.832
0.0199
16
0.652
0.886
0.0025
25
0.326
0.945
0.0000
11
0.625
0.900
0.0000
18
0.958
0.920
0.8890
19.6
0.573
0.913
6
0.000
0.833
0.0002
12
0.750
0.892
0.1006
15
0.923
0.908
0.0456
15
0.462
0.923
0.0000
11
0.273
0.888
0.0000
7
0.818
0.781
0.2662
11
0.583
0.882
0.0000
11
0.444
0.901
0.0000
10
0.700
0.840
0.0982
12
0.500
0.889
0.0000
11
0.545
0.874
11
0.067
17
0.655
13
0.552
28
0.690
17
0.520
12
0.800
12
0.571
21
0.433
11
0.345
18
0.800
16
0.543
0.882
0.0000
0.898
0.0000
0.825
0.0000
0.951
0.0165
0.913
0.0000
0.857
0.1233
0.885
0.0000
0.914
0.0000
0.856
0.0000
0.915
0.0000
0.889
19
0.333
0.931
0.0000
12
0.893
0.866
0.0355
14
0.680
0.874
0.0000
23
0.571
0.934
0.0000
21
0.536
0.943
0.0000
8
0.840
0.816
0.6049
11
0.857
0.855
0.2767
21
0.654
0.927
0.0041
14
0.679
0.882
0.0000
17
0.857
0.880
0.5814
16
0.690
0.891
Sites(code)-n
Luwuk(Lu)-28
Na
Ho
He
P
Togean Islands(To)Na
Ho
He
P
Manado(Ma)-10
Na
Ho
He
P
Sangalaki(Sa)-17
Na
Ho
He
P
Kota Kinabalu (KK)Na
Ho
He
P
Misool (Mi)-11
Na
Ho
He
P
Biak (Bk)-20
Na
Ho
He
P
70
TC1
TC3
TC5
TC6
TC8
Tc059
Tc074
Tc092
Tc160
Tc161
Mean
11
0.214
0.880
0.0000
14
0.704
0.897
0.0143
19
0.538
0.914
0.0000
18
0.357
0.908
0.0000
16
0.240
0.894
0.0000
11
0.679
0.865
0.1790
14
0.821
0.879
0.4611
19
0.750
0.908
0.0122
13
0.680
0.845
0.0195
16
0.815
0.901
0.0040
15.1
0.580
0.889
27
0.538
0.945
0.0000
18
0.726
0.910
0.0009
24
0.695
0.939
0.0000
32
0.435
0.948
0.0000
28
0.500
0.950
0.0000
11
0.762
0.865
0.0000
19
0.754
0.853
0.0168
30
0.635
0.920
0.0000
17
0.444
0.895
0.0000
20
0.903
0.903
0.0007
22.6
0.639
0.913
6
0.125
0.820
0.0000
6
0.500
0.800
0.0160
13
0.800
0.905
0.1545
12
0.300
0.895
0.0000
7
0.429
0.786
0.0043
7
0.800
0.825
0.0061
10
0.889
0.877
0.2589
10
0.444
0.858
0.0000
10
0.800
0.845
0.2453
9
0.900
0.855
0.9079
9
0.599
0.847
10
0.636
0.851
0.0293
12
0.706
0.893
0.0383
15
0.647
0.882
0.0000
19
0.471
0.929
0.0028
16
0.706
0.915
0.0164
8
0.765
0.768
0.6118
10
0.824
0.860
0.5317
19
0.471
0.931
0.0000
10
0.765
0.843
0.1332
12
1.000
0.875
0.5761
13.1
0.699
0.875
7
0.100
0.805
0.0000
13
0.636
0.896
0.0000
13
0.762
0.848
0.0008
23
0.762
0.940
0.0000
3
0.333
0.611
0.1981
9
0.778
0.795
0.3930
12
0.762
0.838
0.5337
18
0.400
0.921
0.0000
10
0.800
0.840
0.0150
15
0.818
0.914
0.0000
12.3
0.615
0.841
9
0.375
0.859
0.0000
8
0.818
0.839
0.0727
10
1.000
0.885
0.2876
17
0.818
0.934
0.0697
8
0.500
0.830
0.0085
8
0.818
0.773
0.9206
8
0.818
0.810
0.9792
13
0.545
0.901
0.0000
9
0.545
0.806
0.0517
10
0.818
0.860
0.2861
10
0.706
0.850
12
0.111
0.887
0.0000
11
0.450
0.860
0.0000
18
0.950
0.925
0.0007
19
0.350
0.935
0.0000
2
0.000
0.500
0.3336
8
0.750
0.850
0.0079
12
0.500
0.850
0.0000
20
0.700
0.935
0.0008
9
0.600
0.808
0.0274
13
0.650
0.883
0.0000
12.4
0.506
0.843
n: Number of individuals; Na: number of alleles; Ho: observed heterozygosity; He: expected
heterozygosity; P (HWP): P value for deviations from Hardy–Weinberg proportions (P < 0.00052,
significant in bold departed HWP after sequential Bonferroni correction).
4.3.2. DNA extraction, PCR amplification and fragment analysis
Genomic DNA was extracted using the Chelex method (Walsh et al. 1991). The population
structure was investigated with 10 polymorphic microsatellite markers (DeBor et al. 2010,
Hui et al. 2011; Table 10), which were labeled by 6-FAM or HEX. The PCRs were performed
in a volume of 15 μl, containing 1 x PCR buffer, 0.5 U Taq polymerase, 1.5 mM of Mg2+, 200
71
μM of each dNTP, 0.2 μM of each primer and about 40 ng of genomic DNA. The
thermoprofile for all loci was as follows: 5 min for an initial denaturation; 95 °C for 30 s,
locus-specific annealing temperature (Table 10) for 45 s, 72 °C for 45 s repeated 35 times, and
ending with 72 °C for 5 min. The fragment analysis was conducted with an ABI 3730
Automated Sequencer (Applied Biosystems) using the Liz size standard. The lengths of the
fragments were analysed and corrected by using the software Genemarker V1.91
(SoftGenetics, LLC).
Table 10 Characteristics of microsatellite loci (DeBor et al. 2010, Hui et al. 2011) for Tridacna
crocea.
Locus
Primer Sequence (5'-3')
Ta (°C)
Repeat Motif
(AG)22 …(AG)16…
Dye
Expected Size
Range (bp)
6-FAM
275-339
F: GCTTTGTGGCTATTGGAGAA
R: GTCTGTCCCACCCGTCCATA
56
TC3
F: ACCATACCCCTGCCATACAGT
R: AGTCGCGTCACTCTGGATAG
56
(AG)19
HEX
227-249
TC5
F: AGAAATGACGTAACACCCAC
R: ATACTATGCAGAGGAAGGAG
56
(CT)24
6-FAM
135-165
TC6
F: CATGGTGGACGATGCCAAGT
R: CTACGAAAATCACTGACTCTG
56
(AG)18
6-FAM
160-206
TC8
F: CAAAGTTCAAAATACACTCG
R: TACCGTACCAGAGGCAACTA
50
HEX
274-358
TC1
F: AGGTGACTTGAAGGTTAATGTTG
(AG)6
(ATGG)6 …(ATGG)8…
(ATGG)8
R: GGGTTTAAAACAACACGGTGA
57.9
(AAC)15
HEX
110-140
Tc074
F: CCAAAAACAGTCTTCCTTGACA
R: AGACTGCTCGCTGACCTTTT
57.9
(AGTG)10
6-FAM
202-266
Tc092
F: GCAACCCACTGAGTTCCCTA
R: TGCAGAGCGATAACATACAGG
50
(TC) 13TTAA(AC)18
HEX
176-234
Tc160
F: TGCTTTATTGTCTTAACTGCCATT
R: CGGAAGGTTGGGTAGGTAGA
47.1
(AGAT)9
HEX
146-204
Tc161
F: TGAAAATATGTTAACCCTCATCCTG
R: TTTGCAGACTGGTTTAGTCAACA
57.9
(AC) 8
6-FAM
92-122
Tc059
Ta: optimal annealing temperature.
4.3.3. Data analysis
The genetic indices, including the number of alleles (Na), the observed heterozygosity (Ho),
and expected heterozygosity (He) for each population were calculated with the programme
GeneAlex (v. 6.41; Peakall & Smouse 2006). The deviations from Hardy-Weinberg
72
proportions (HWP) for each locus in each population was performed using the Markov chain
randomization test (Guo & Thompson 1992) to estimate the exact two-tailed P-values, as
implemented in the programme Genepop web (v. 4.0.10; Raymond & Rousset 1995, Rousset
2008). The significance level was adjusted by applying the sequential Bonferroni correction
(Rice 1989).
Differences and structures among populations in their genetic composition were examined
in several ways. The index of pairwise Fst was estimated to assess the magnitude of
differentiation among sample sites, and an unbiased estimate of the significance of the
probability test was calculated through 1000 iterations employed by Arlequin 3.5 (Excoffier
& Lischer 2010). The significance in pairwise comparison was evaluated after a sequential
Bonferroni adjustment of critical probabilities. To directly compare the results from both
marker systems, correlations beween population pairwise Fst values obtained from
microsatellites and mtDNA were analysed using a Mantel test with 1,000 permutations
(negative Fst values were set to zero). Genetic structure among populations was investigated
by conducting a Bayesian analysis with the software STRUCTURE (v. 2.3.1; Pritchard et al.
2000), testing for different numbers (K) of clusters in the data based on individual assignment.
The admixture model was implemented, with no allele frequencies correlated between
populations and no prior information on population origin. The individuals were assigned to
clusters, even two or more clusters, if their genotypes indicated they were admixed. The
assignment settings were 200,000 iterations and the first 20,000 as burn-in. K values ranged
from one to eight and each test was run three times. The Delta K method (ΔK = m (|L(K+1)−2
L(K˅+L(K−1)|)/s[L˄K˅]) was used to determine the exact K value (Evanno et al. 2005).
When the correct clusters were found, all individuals were assigned to the most probable
corresponding cluster and their frequencies were calculated for each population. These
frequencies were drawn on the map as pie-diagrams to visualise the distribution of the
clusters. Then, an Analysis of Molecular Variance (AMOVA) was conducted with the
programme Arlequin to test for population structure assuming no regional genetic structure. A
hierarchical AMOVA was conducted in order to define spatial groups of populations that
were maximally differentiated from each other.
Isolation-by-distance (IBD) analysis was tested for a correlation between genetic distances
(Fst) and geographic distances by Reduced Major Axis (RMA) regression analysis and a
Mantel test was conducted to check the significance of the correlation, which are all
73
conducted with the IBD web service (IBDWS 3.23, http://ibdws.sdsu.edu), applying 30,000
permutations (Jensen et al. 2005). Geographic distances are the shortest way between two
sample sites by sea, measured with an electronic world atlas.
4.4. Results
4.4.1. Genetic diversity of different populations
All 10 microsatellite loci showed high polymorphism. Across all loci, the mean number of
alleles per population ranged from 4.0 (Padang) to 22.6 (Togian Islands) (Table 9). The high
within-population diversity was also detected at the levels of the expected and observed
heterozygosities (Table 9). The population in Spermonde had the highest He value (0.913),
while the population from Padang, located in the Eastern Indian Ocean, showed the lowest
(0.676).
Most observed genotype distributions generally conformed to HWP, but in 66 cases a
significant deviation from HWP after adjustment of P-values with the sequential Bonferroni
method was detected. The departures were most probably due to an excess of homozygotes
(Table 9).
4.4.2. Genetic population structure based on microsatellites and comparison with mtDNA
The overall AMOVA analysis showed significant genetic structure (Fst = 0.023, P < 0.001).
When a pairwise Fst-analysis (P < 0.00054 after sequential Bonferroni correction) was
performed, the population from the Eastern Indian Ocean (EIO; Padang), showed a significant
differentiation from all other populations (Fst: 0.158-0.190), while the population from the
Western Pacific (WP; Biak) also differed from most of the populations in the central IMA
(Fst: 0.029-0.048) (Table 11). In the central IMA, the population in the Togian Islands (To)
was different from populations in Ka, Ko, Sp and Se (Fst: 0.009-0.027), while the other
populations showed no significant difference.
A mantel test revealed a significant correlation of pairwise Fst-values (r = 0.946, P < 0.001)
based on microsatellites (Table 11) and COI sequences, suggesting that the two datasets have
a similar genetic population structure.
In a Bayesian analysis conducted with STRUCTURE, the Delta K test showed the highest
value when the number of clusters was K = 3, indicating that the populations were divided
74
into three groups. The ancestry of each individual from the three inferred groups is presented
for each site in Fig. 5b. The groups are (1) Eastern Indian Ocean (EIO), (2) central IndoMalay Archipelago (IMA), and (3) Western Pacific (WP). Individual admixture analysis
indicated that individuals in the central IMA consisted of much more admixed individuals in
contrast with individuals from the EIO and the WP. The cluster frequencies in each
population are shown on the Fig 1a. The dominant cluster in the EIO is grey and individuals
were all assigned to this cluster. In the central IMA, the dominant clusters were black and
white, while in WP, the dominant were grey and white.
Table 11 Pairwise Fst values between populations of Tridacna crocea in the Indo-Malay Archipelago.
Sites
Pa
PS
0.165
PS
Ka
Ka
0.178
0.005
Ko
0.173
0.017
0.006
Ku
0.190
0.017
0.023
Ko
Ku
Sp
Bi
Se
Ke
Lu
To
Ma
Sa
KK
Mi
0.006
Sp
0.158
0.009
0.013
0.007
0.007
Bi
0.174
0.009
0.020
-0.003
-0.001
0.001
Se
0.162
0.023
0.014
0.009
0.020
0.010
0.006
Ke
0.180
0.015
0.017
0.003
0.009
0.007
0.012
Lu
0.177
0.020
0.026
0.010
0.014
0.014
0.003
0.018
0.010
To
0.159
0.018
0.027
0.014
0.018
0.009
0.008
0.019
0.010
0.012
Ma
0.188
0.024
0.029
0.017
0.032
0.016
0.023
0.025
0.027
0.019
0.025
Sa
0.174
0.014
0.009
0.007
0.003
0.005
-0.008
0.009
0.010
0.014
0.014
KK
0.186
0.013
0.021
0.011
0.017
0.007
0.015
0.021
0.021
0.018
0.016
0.029
0.014
Mi
0.186
0.018
0.028
0.009
0.019
0.014
0.002
0.018
0.017
0.020
0.021
0.033
0.004
0.010
Bk
0.161
0.039
0.048
0.042
0.048
0.036
0.030
0.040
0.041
0.038
0.029
0.039
0.035
0.039
0.010
0.023
Significant in bold after sequential Bonferroni correction P < 0.00054 for microsatellite data. For
abbrevations of sites, see Table 9.
Based on geography and oceanography, a hierarchical AMOVA was carried out with
different groupings (Table 12) for the 16 sample sites. AMOVA revealed the highest fixation
index (Fct = 0.063, P < 0.05) and variation (6.27 %) among groups when the populations were
grouped into the following regions: (1) EIO (Padang), (2) central IMA (Java Sea, South China
Sea, Indonesian Throughflow (ITF), as well as seas in the east of Sulawesi), and (3) WP
(Biak). Compared with the results of the previous study based on COI (Kochzius & Nuryanto
2008), the structure was highly similar. However, populations in the Java Sea (Ka, PS) were
0.043
75
grouped into one separate cluster in the study using COI sequences (Kochzius & Nuryanto
2008), while this divergence was not detected by microsatellites.
Table 12 Hierachical analysis of molecular variance (AMOVA) of microsatellite markers in the
Tridacna Crocea from Indo-Malay Archipelago. For abbrevations of site, see Table 9.
Groupings
(Pa) ( PS, Ka, Ko, Ku, Sp, Bi, Se, Ke, Lu, TI, Ma, Sa, KK, Mi) (Bk)
(Pa) (PS, Ka, Ko, Ku, Sp, Bi, Se, Ke, Lu, TI, Ma, Sa, KK) (Mi, Bk)
(Pa) ( PS, Ka, Ko, Ku, Sp, Bi, Se, Ke, Lu, TI, KK, Mi) (Ma, Sa) (Bk)
(Pa) (PS, Ka, Ko, Ku, Sp, Bi, Se, Ke, Lu, TI, Ma, Mi) ( Sa, KK) (Bk)
(Pa) (PS, Ka, Ko, Ku)( Sp, Bi, Se, Ke, Lu, TI, Ma, Sa, KK, Mi) (Bk)
(Pa) (PS, Ka) (Ko, Ku, Sp, Bi, Se, Ke, Lu, TI, Ma, Sa, KK, Mi) (Bk)
*
Fct
0.063*
0.041**
0.033**
0.028*
0.023***
0.016*
Percentage of variation (%)
6.27
4.10
3.35
2.82
2.32
1.60
0.05 ≥ P ≥ 0.01; ** 0.01 > P > 0.001; *** P < 0.001.
Isolation-by-distance showed a significant positive correlation between genetic (Fst) and
geographic distance in the central IMA (r = 0.392, P = 0.009) (Fig. 8). Divergent populations
from Padang and Biak were excluded in this analysis. This correlation was also detected by
the study based on COI (r = 0.430, P = 0.004) (Kochzius & Nuryanto 2008).
Fig. 8 Relationships between genetic and geographic distances for Tridacna crocea by Reduced
Major Axis (RMA) regression without populations from Padang and Biak.
76
4.5. Discussion
The results based on 10 microsatellite loci were generally concordant with the results obtained
using COI sequences, supporting the applicability of this mtDNA marker in connectivity
studies.
4.5.1. Genetic diversity and Hardy-Weinberg proportions (HWP)
The genetic diversities in all populations based on microsatellites were very high, with an
average Na ranging from 4 to 22.6 and He from 0.676 to 0.913. Similar results were found by
COI analysis, which was illustrated by high overall haplotype diversity (0.93). Some other
marine bivalves also showed high genetic diversity revealed by molecular markers, such as
Crassostrea plicatula and C. gigas (microsatellites; Yu et al. 2008), Chlamys farreri
(microsatellites; Zhan et al. 2009), and Tridacna maxima (COI; Nuryanto & Kochzius 2009),
which might be caused by the large population size and high nucleotide mutation rate (Launey
& Hedgecock 2001, Hedgecock et al. 2004). Compared with other populations, the one in the
EIO showed a much lower genetic diversity than the others, which might be due to a founder
effect and a subsequent low level of connectivity with other populations in the central IMA.
The genetic diversity of peripheral populations is supposed to be lower when compared to
centrally located populations, because of continual expansion and contraction of smaller
populations, which can lead to multiple genetic bottlenecks and the loss of potentially
significant genetic variation (Lesica & Allendorf 1995). However, populations from the Java
Sea (Ka, PS) showed the lowest genetic diversity in the COI analysis, which might be induced
by over-exploitation, or by the recolonisation of the Sunda Shelf as sea level rose after the last
glacial (Kochzius & Nuryanto, 2008), while the genetic diversity levels were moderate
detected by microsatellite markers. This suggests that different markers might detect different
levels of genetic diversity (Hoffman et al. 2009). Comparing mtDNA and nDNA, the
effective population size of mtDNA is much smaller than that of nDNA (about a quarter,
Avise 1994). Therefore, mtDNA might be more sensitive to the genetic drift, which partly
induced the difference of genetic diversity revealed by the mtDNA and nDNA.
After a sequential Bonferroni correction, most populations were in HWP, but there were still
66 cases deviated from HWP, which might be caused by lack of heterozygosity. However, it
also could be a consequence of the relation between sample size and microsatellite variability:
77
considering the high allele numbers in the different populations, HWP testing could be
influenced by small populations size (Györffy et al. 2004).
4.5.2. Genetic population structure
Three distinct groups were found in the IMA based on microsatellites, corresponding to the
(1) EIO, (2) central IMA, and (3) WP. This result was similar to previous studies using COI as
a genetic marker. However, in one study (Kochzius & Nuryanto 2008), populations in the
Java Sea were assigned to a separate group. In addition, the highly divergent mtDNA clades
in the previous study (Kochzius & Nuryanto 2008) suggest there that there might be cryptic
species, while the genetic structure revealed by microsatellites was much shallower. Given
that T. crocea is hermaphroditic, sex-biased gene flow in mtDNA could not be the reason for
this difference. One reason might be homoplasy of alleles of microsatellite markers. Alleles of
the same size might have variation in the non-repeated flanking sequences (Grimaldi 1997),
which might lead to underestimation of genetic differentiation. Again, random genetic drift
due to severe bottlenecks, especially for mtDNA that is more sensitive to effective population
size changes, may have created divergent genetic groups during the sea level fluctuations.
Moreover, mtDNA markers may illustrate a historic picture of gene flow, while the
microsatellite maker possibly revealed much more contemporary gene flow (Fauvelot et al.
2003, Timm et al. 2012) because of the higher mutation rate in microsatellite DNA (average
1.2 x 10-3 per generation; Weber & Wong 1993 ). After the re-colonisation of the Sunda Shelf
as the sea-level rose, the populations in the Java Sea might have been well connected with
other populations in the central IMA by currents. However, the pairwise Fst-values based on
microsatellite data are highly correlated with the values obtained from the COI data (r =
0.946, P < 0.001), which indicate that the two maker systems generally revealed concordant
genetic structures. In the central IMA, result from microsatellites also revealed that the
populations in this area showed high gene flow (low pairwise Fst values that were not
significant). There was also a positive correlation between genetic and geographic distance,
which was similar to the results revealed by mtDNA.
Similar genetic structures were found in two other giant clam species (T. maxima,
Nuryanto & Kochzius 2009, T. squamosa, unpublished data). Vicariance due to PlioPleistocene sea-level fluctuation is suggested to be a major force driving the genetic
divergence between Indian and Pacific Ocean populations (e.g. McMillan & Palumbi 1995;
Williams & Benzie 1998; Kochzius et al. 2003; Knittweis et al. 2009; Kochzius et al. 2009).
78
The oceanography of the Indo-West Pacific (IWP), such as the Indonesian Throughflow (ITF)
and Halmahera eddy (Fig.1a), as well as limited larval dispersal capability are important
factors to shape the genetic structures and connectivity in giant clams populations.
Collectively, the concordant genetic structures illustrated by the two marker system
confirmed the reliability of the results. Moreover, the mtDNA revealed much stronger
population structure, which might be induced by the characteristics of mtDNA, such as lower
effective population size and short coalescence time. Also, mtDNA markers might reflect
more former isolations of populations than microsatellite DNA markers. This study supports
the use of mtDNA in population genetic studies of giant clams and suggests combining
mtDNA with nDNA to obtain much information concerning connectivity on different time
scales.
We would like to thank the institutions and individuals that have made our study possible:
the PhD scholarship to Min Hui; Janne Timm (University of Bremen, Germany) for support
during collection of samples; colleagues from Hasanuddin University (Indonesia) for
logistical support during field work; The SPICE project is conducted and permitted under the
governmental agreement between the BMBF and the Indonesian Ministry for Research and
Technology (RISTEK), Indonesian Institute of Sciences (LIPI), Indonesian Ministry of
Maritime Affairs and Fisheries (DKP), and Indonesian Agency for the Assessment and
Application of Technology (BPPT).
4.7. References
Avise, JC (1994) Molecular markers, natural history, and evolution. Chapman and Hall, New
York, NY. pp 511.
Avise, JC (2000) Phylogeography: the history and formation of species. Harvard Univiversity
Press, Cambridge, MA.
79
Barrowclough GF, Zink RM (2009) Funds enough, and time: mtDNA, nuDNA and the
discovery of divergence. Molecular Ecology 18, 2934-2936.
Bazin E, Glémin S, Galtier N (2006) Population size does not influence mitochondrial genetic
diversity in animals. Science 312, 570-572.
Benzie JAH, Ballment E, Forbes AT, et al. (2002) Mitochondrial DNA variation in IndoPacific populations of the giant tiger prawn, Penaeus monodon. Molecular Ecology, 11,
2553-2569.
populations in the Great Barrier Reef, Australia. Marine Biology, 112, 1-5.
Evolution, 51, 768-783.
Brown WM (1985) The mitochondrial genome of animals. Molecular evolutionary genetics,
Plenum, New York, pp. 95-130.
Crandall ED, Jones ME, Muñoz MM, et al. (2008a) Comparative phylogeography of two
seastars and their ectosymbionts within the Coral Triangle. Molecular Ecology, 17, 52765290.
Crandall ED, Frey MA, Grosberg RK, Barber PH (2008b) Contrasting demographic history
and phylogeographical patterns in two Indo-Pacific gastropods. Molecular Ecology, 17,
611-626.
in three other Tridacnid species. Conservation Genetics Resources 2, 353-356.
Biology, 22, 1255-1266.
80
DiBattista JD, Rocha LA, Craig MT, et al. (2012) Phylogeography of two closely related
Indo-Pacific Butterflyfishes reveals divergent evolutionary histories and discordant results
from mtDNA and microsatellites. Journal of Heredity, doi: 10.1093/jhered/ess056.
Edwards SV, Bensch S (2009) Looking forwards or looking backwards in avian
phylogeography? A comment on Zink and Barrowclough 2008. Molecular Ecology, 18,
2930-2933.
Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using
the software STRUCTURE: a simulation study. Molecular Ecology, 14, 2611-2620.
10, 564-567.
Fauvelot C, Bernardi G, Planes S (2003) Reductions in the mitochondrial DNA diversity of
coral reef fish provide evidence of population bottlenecks resulting from Holocene sea
level change. Evolution, 57, 1571-1583.
Fraser DJ, Hansen MM, Oestergaard S, et al. (2007) Comparative estimation of effective
population sizes and temporal gene flow in two contrasting population systems. Molecular
Ecology, 16, 3866-3889.
Fraser DJ, Jones MW, McParland TL, Hutchings JA (2007) Loss of historical immigration
and the unsuccessful rehabilitation of extirpated salmon populations. Conservation
Genetics, 8, 527-546.
14-27.
Grimaldi MC, Crouau-Roy B (1997) Microsatellite allelic homoplasy due to variable flanking
sequences. Journal of Molecular Evolution, 44, 336-340.
Guo SW, Thompson EA (1992) Performing the exact test of Hardy-Weinberg proportion for
multiple alleles. Biometrics, 48, 361-372.
Györffy B, Kocsis I, Vasarhelyi B (2004) Biallelic genotype distributions in papers published
in Gut between 1998 and 2003: altered conclusions after recalculating the Hardy-Weinberg
equilibrium. Gut, 53, 614-616.
81
Hedgecock D, Li G, Hubert S, et al. (2004) Widespread null alleles and poor cross-species
amplification of microsatellite DNA loci cloned from the Pacific oyster, Crassostrea gigas.
Journal of Shellfish Research, 23, 379-386.
Hoffman J, Dasmahapatra K, Amos W, et al. (2009) Contrasting patterns of genetic diversity
at three different genetic markers in a marine mammal metapopulation. Molecular
Ecology, 18, 2961-2978.
Hui M, Kochzius M, Leese F (2011) Isolation and characterisation of nine microsatellite
markers in the boring giant clam (Tridacna crocea) and cross-amplification in five other
tridacnid species. Marine Biodiversity, 42, 1-3.
Jensen JL, Bohonak AJ, Kelley ST (2005) Isolation by distance web service. BMC Genetics,
6, 13.
Special Publication, 25, 343-347.
Knittweis L, Krämer WE, Timm J, Kochzius M (2009) Genetic structure of Heliofungia
actiniformis (Scleractinia: Fungiidae) populations in the Indo-Malay Archipelago:
implications for live coral trade management efforts. Conservation Genetics 10, 241-249.
Kochzius M, Söller R, Khalaf MA, Blohm D (2003) Molecular phylogeny of the lionfish
genera Dendrochirus and Pterois (Scorpaenidae, Pteroinae) based on mitochondrial DNA
sequences. Molecular Phylogenetics and Evolution 28, 396-403.
Kochzius M, Nuryanto A (2008) Strong genetic population structure in the boring giant clam
Tridacna crocea across the Indo-Malay Archipelago: implications related to evolutionary
processes and connectivity. Molecular Ecology 17, 3775-3787.
Kochzius M, Seidel C, Hauschild J, Kirchhoff S, Mester P, Meyer-Wachsmuth I, Nuryanto A,
Timm J (2009) Genetic population structures of the blue starfish Linckia laevigata and its
gastropod ectoparasite Thyca crystallina. Marine Ecology Progress Series 396, 211-219.
82
Launey S, Hedgecock D (2001) High genetic load in the Pacific oyster Crassostrea gigas.
Genetics, 159, 255-265.
Lesica P, Allendorf FW (1995) When are peripheral populations valuable for conservation?
Conservation Biology, 9, 753-760.
Lee JY, Edwards SV (2008) Divergence across Australia's Carpentarian barrier: statistical
phylogeography of the red-backed fairy wren (Malurus melanocephalus). Evolution, 62,
3117-3134.
Lucas J (1988) Giant clams: description, distribution and life history. Giant clams in Asia and
the Pacific. ACIAR Monograph, 9, 21-33.
113, 231-238.
Mcmillan WO, Palumbi SR (1995) Concordant evolutionary patterns among Indo-West
Pacific butterflyfishes. Proceedings of the Royal Society of London. Series B: Biological
Sciences, 260, 229-236.
Nelson JS, Hoddell RJ, Chou LM, Chan WK, Phang VPE (2000) Phylogeographic structure
of false clownfish, Amphiprion ocellaris, explained by sea level changes on the Sunda
shelf. Marrine Biology, 137, 727–736.
in the giant clam Tridacna maxima. Coral Reefs, 28, 607-619.
Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic
software for teaching and research. Molecular Ecology Notes, 6, 288-295.
Perrin C, Borsa P (2001) Mitochondrial DNA analysis of the geographic structure of Indian
scad mackerel in the Indo-Malay archipelago. Journal of Fish Biology, 59, 1421-1426.
for Plio-Pleistocene sea-level changes and sequence stratigraphy. Sedimentary Geology,
122, 5-21.
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using
multilocus genotype data. Genetics, 155, 945-959.
83
exact tests and ecumenicism. Journal of Heredity, 86, 248-249.
Rice WR (1989) Analyzing tables of statistical tests. Evolution, 43, 223-225.
Windows and Linux. Molocular Ecology Resource, 8, 103-106.
Spalding MD, Ravilious C, Green EP (2001) World atlas of coral reefs. University of
California Press.
Timm J, Kochzius M (2008) Geological history and oceanography of the Indo-Malay
Archipelago shape the population structure in the false clown anemonefish (Amphiprion
ocellaris). Molecular Ecology, 17, 3999-4014.
false clown anemonefish (Amphiprion ocellaris) found in microsatellite and mitochondrial
control region analysis. Conservation Genetics, 13, 693-706.
Toews DPL, Brelsford A (2012) The biogeography of mitochondrial and nuclear discordance
in animals. Molecular Ecology, 21, 3907-3930.
Walsh PS, Metzger DA, Higuchi R (1991) Chelex 100 as a medium for simple extraction of
DNA for PCR-based typing from forensic material. Biotechniques, 10, 506.
Williams S, Benzie J (1998) Evidence of a biogeographic break between populations of a high
dispersal starfish: congruent regions within the Indo-West Pacific defined by color morphs,
mtDNA, and allozyme data. Evolution, 52, 87-99.
Wyrtki K (1961) Physical oceanography of the southeast Asian waters, University of
California, Scripps Institution of Oceanography.
You EM, Chiu TS, Liu KF, et al. (2008) Microsatellite and mitochondrial haplotype diversity
reveals population differentiation in the tiger shrimp (Penaeus monodon) in the IndoPacific region. Animal Genetics 39, 267-277.
Yu H, Li Q, Yu R (2008) Genetic differentiation between Zhe oyster Crassostrea plicatula
and Pacific oyster Crassostrea gigas populations in China assessed by microsatellite
analysis. Fisheries Science, 74, 88-97.
84
Weber JL, Wong C (1993) Mutation of short tandem repeats. Human Molecular Genetics, 2,
1123-1128.
Wilson GA, Rannala B (2003) Bayesian inference of recent migration rates using multilocus
genotypes. Genetics, 163, 1177-1191.
Zhan A, Hu J, Hu X, et al. (2009) Fine-scale population genetic structure of Zhikong scallop
(Chlamys farreri): Do local marine currents drive geographical differentiation? Marine
Biotechnology, 11, 223-235.
Zink RM, Barrowclough G (2008) Mitochondrial DNA undersiege in avian phylogeography.
85
5. Declaration
Gemäß §6 der Promotionsordnung der Universität Bremen für die mathematischen,
naturwissenschaftlichen und ingenieurwissenschaftlichen Fachbereiche vom 14. März 2007
versichere ich, dass
(1) die Arbeit ohne unerlaubte fremde Hilfe angefertigt wurde
(2) keine anderen als die angegebenen Quellen und Hilfsmittel benutzt wurden
(3) die den benutzten Werken wörtlich oder innhaltlich entnommenen Stellen kenntlich
gemacht wurden.
Min Hui
Bremen, den 20. 09. 2012

Connectivity and evolution of giant clams (Tridacnidae): a - E-LIB

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