Genetic population structure of the coral reef starfish Linckia laevigata in the Indo-West Pacific

By

Levy Michael Otwoma

“Thesis submitted for the degree of Master of Biology – specialisation Human Ecology of the Vrije Universiteit Brussel”

Msc thesis Supervisor: Professor Dr. Marc Kochzius

September 2012

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ACKNOWLEDGEMENT

I am grateful to my Supervisor Professor Dr. Marc Kochzius for giving me an opportunity to work in the field of molecular ecology and for his scientific guidance from laboratory work to thesis preparation. Special thanks also go to the Flemish Inter-university council (VLIR) and Free University of Brussels (VUB) for the scholarship award that facilitated this Msc study. Many thanks, to the management of Kenya Marine and Fisheries Research Institute (KMFRI) for granting me permission to attend this master program. Much gratitude goes to Tim Seriens for his far-reaching assistance during the entire laboratory work. Specials thanks to the entire human ecology team Professor Ludwig Triest, Iris Stiers, Petra, and fellow classmates. Much gratitudes go to my family and friends for love and support, and for all the joy it brings to life.

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ABSTRACT The blue starfish Linckia laevigata is common on shallow water coral reefs of the Indo-West Pacific. Since L. laevigata is sedentary, long distance dispersal is only possible by their planktonic larval stage. This long larval dispersal mechanism can allow interconnection of populations that are separated by several hundred kilometers. However, a growing number of studies report genetic breaks in populations of L. laevigata across the Indo-Malay Archipelago and between the Indian and Pacific Oceans. Only limited information on the genetic connectivity of this species is available for the Indian Ocean, showing genetic structuring between samples collected in SouthAfrica and Thailand. The study investigates the genetic population structure and connectivity of L. laevigata in the Western Indian Ocean and compares it to previous studies in the Indo-Malay Archipelago by mitochondrial cytochrome oxidase I gene. A total of 138 samples were collected from nine locations in the WIO coastline, from Kenya to Madagascar.

AMOVA revealed a low but significant ΦST value of 0.024 in the WIO population, which indicated reduced gene flow. The genetic structure was stronger (ΦST = 0.13) in the comparative analysis of WIO and Indo-Malay Archipelago population. Five clades were detected from the haplotype network analysis which corresponded to different geographical locations that might have been separated during the glacial sea level low stands in the Pleistocene. The strong genetic differentiation suggests that the population of Linckia laevigata can be classified in the following groups: (1) Western Indian Ocean (2) Eastern Indian Ocean (3) Central Indo-Malay Archipelago and (4) Western Pacific.

Keywords: Linckia laevigata, mtDNA, WIO, Indo-Malay Archipelago, Genetic break,

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TABLE OF CONTENTS ACKNOWLEDGEMENT ...... i ABSTRACT ...... ii LIST OF FIGURES ...... v LIST OF TABLES ...... vi LIST OF ABBREVIATIONS ...... vii CHAPTER 1: General Introduction ...... 1 1.1 Introduction ...... 1 1.2 Linckia laevigata ...... 2 1.3 Genetic diversity and gene flow ...... 4 1.4 Connectivity and its implication to management ...... 6 1.5 Status of genetic population structure in the Western Indian Ocean ...... 9 1.6 Status of genetic structure in the Indo-Malay Archipelago ...... 10 1.7 Factors affecting larval dispersal ...... 12 1.8 Oceanographic conditions in the Western Indian Ocean region ...... 13 1.9 Oceanographic condition in the Indo-Malay Archipelago ...... 14 1.10 Assessment of genetic structure ...... 15 1.11 Choice of marker and Mitochondria DNA ...... 16 1.12 Polymerase chain reaction ...... 17 1.13 DNA Sequencing...... 18 1.14 Problem statement ...... 20 1.15 Objective of the study ...... 21 CHAPTER 2: Materials and methods ...... 22 2.1 Study site and sample collections...... 22 2.2 DNA extraction ...... 22 2.3 Gel electrophoresis ...... 23 2.4 Preparation of the Gel ...... 23 2.5 Running the Gel ...... 23 2.6 Amplification and sequencing ...... 25 2.7 Genetic diversity ...... 25 2.8 Historical demography ...... 26

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2.9 Genetic population structure and connectivity ...... 26 CHAPTER 3: Results ...... 27 3.1 Genetic diversity ...... 27 3.2 Historical demography ...... 27 3.3 Genetic population structure and connectivity ...... 28 3.3.1 Western Indian Ocean ...... 28 3.3.2 Indo-West Pacific ...... 29 CHAPTER4: Discussion ...... 32 4.1 Genetic diversity ...... 32 4.2 Historical demography ...... 32 4.3 Genetic population structure and connectivity ...... 33 4.3.1 Western Indian Ocean ...... 33 4.3.2 Indo-West Pacific...... 34 CHAPTER 5: Conclusion and implications for management ...... 38 REFERENCES ...... 40 APPENDICES ...... 51

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LIST OF FIGURES Figure. 1 A schematic representation of ocean currents in the Indian Ocean...... 14 Figure. 2. Map of WIO and Indo-Malay Archipelago sample sites...... 24 Figure. 3. Mismatch distribution graph of pooled WIO population ...... 28

Figure. 4. Plot of pairwise ΦST values against geographic distance (Km) ...... 31

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LIST OF TABLES

Table 1. Genetic diversity, neutrality tests and mismatch distribution...... 27

Table.2. Pairwise ΦST values among populations of Linckia laevigata in the WIO...... 29 Table 3. Hierarchical AMOVA based on mitochondrial control region sequences from Linckia laevigata with alternative groupings of sample sites from the WIO and Indo-Malay Archipelago...... 30

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LIST OF ABBREVIATIONS

AS Andaman Sea Ko Komodo Ku Kupang, Timor Bi Bira, Sulawesi Sp Spermode, Sulawesi PS Pulau Seribu, Java Ka Karimunjava, java Ke Kendari, Sulawesi Do Donggala, Sulawesi Lu Luwuk, Sulawesi Ti Togian Islands, Sulawesi Sa Sangalaki, Borneo KK Kota Kinabalu, Borneo BI Banggi Islands, Borneo Ma Manado, Sulawesi Ls Lembeh Strait, Sulawesi SSP Sebakor/Sanggala/Papisol, New Guinea Pi Pisang, New Guinea Mi Misool, Moluccas NB New Britain, New Guinea Bk Biak, New Guinea Mer Merida, Leyte Mar Almagro, Western Samar Alm Marabut, Western Samar Sj San Jose, nothern Samar Sal Salcedo, eastern Samar Ce Cebu, Visayas SEC South Equatorial Current EACC East African Coastal Current ECC Equatorial Counter Current MC Mozambique Current MGC Madagascar Current NECC Northern Equatorial Counter Current ITF Indonesian Throughflow

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Chapter 1 “General introduction”

CHAPTER 1: General Introduction

1.1 Introduction

Larval dispersal in benthic marine organisms is a critical factor affecting population dynamics, population persistence and range expansion. However, it is often difficult to establish, the dispersal trajectories of larvae and propagules because of their microscopic sizes (Juinio-Menez et al., 2003; Weersing & Toonen, 2009). Since the distance and direction of dispersal are often poorly understood many marine species genetic structure are not known, despite importance of this information in conservation and management of marine populations (Cowen et al., 2006; Fogarty & Botsford, 2007) . This has been emphasized in coral reef conservation, since knowing the locations where degraded or depleted reefs recruits can allow for protection and management of the source population, which could benefit the recipient reefs (Visram et al., 2010). In an attempt to bridge this gap, a wide range of indirect methods have been employed to reveal dispersal that has involved the use of molecular markers to analyse gene flow (Avise, 2004).

Currently only a few studies on connectivity of marine species have been conducted within Western Indian Ocean, despite being an area that experienced a catastrophic coral bleaching event during the 1997-1998 El-Nino (Wilkinson, 1999; Goreau et al., 2000). These studies are varied in their findings from some reporting extensive genetic structure (Fratini &Vaninni, 2002) to a complete lack of structure (Visram et al., 2010), even over oceanic ranges. Furthermore, genetic differences between the Indian and Pacific oceans have been reported in several fish species (McMillan & Palumbi, 1995; Lacson & Clark, 1995) two sea stars (Williams & Benzie, 1998; Benzie, 1999), and two crustaceans (Lavery et al., 1996; Duda & Palumbi, 1999).This provide a challenge to the view that biodiversity in the Indo-Pacific arose from one centre which then migrated into the Indian and Pacific oceans (Palumbi, 1997). The ubiquitous Linckia laevigata is an excellent model that can boost understanding of the connectivity of coral reefs ecosystems in the Indo-West Pacific, especially if the study is conducted at spatial scale small enough to show the nature or geographic position of sharp genetic discontinuities, as this is limited in previous studies (Barber et al., 2002). The long planktonic larval stage of Linckia laevigata can provide mechanism for long distance dispersal, which is supported by the high

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gene flow reported in previous studies (Williams & Benzie 1993 ; 1996), even though some studies report restricted gene flow (Williams & Benzie 1998; Williams, 2000; Kochzius et al., 2009).

The north-flowing East African Coastal Current can transport planktonic larvae to long distance causing homogeneity among population that are isolated by distance in WIO, whereas bifurcation of South Equatorial Current at the tip of Northern Madagascar could connect Madagascar and East Africa population. In the present study we report genetic population structure of Linckia laevigata in the WIO region where nine samples sites were selected in 3 countries; Kenya, Tanzania and Madagascar. Further this study assesses the connectivity of the WIO population to the Indo Malay Archipelago. The sequence data for Indo-Malay Archipelago population was obtained from two previous studies (Kochzius et al., 2009; Alcazar & Kochzius, 2011 unpublished).

1.2 Linckia laevigata The Blue starfish Linckia laevigata (Linnaeus) is a common benthic animal in the Indo-West Pacific, with a wide distribution from the Western Indian Ocean to Southeastern Polynesia (Clark & Rowe, 1971; Yamaguchi, 1977). It abundantly occurs on the shallow reef flat or lagoon patches of the fringing reefs and will be rarely found on the deep terraces of the sea ward side (Yamaguchi, 1977). Linckia laevigata such as other asteroids is a sedentary organism depending on its planktonic larval stage for long distance dispersal. This makes it an ideal model to examine levels of genetic differentiation and structuring over geographical ranges (Williams & Benzie, 1993). It reproduces sexually, with an external fertilization that involves fusion of gametes freely in the water column. The peak breeding period occurs during the summer months (May to August) as in the case for Guam population (Yamaguchi, 1977) and may vary regionally. Metamorphosis takes at least 22 days in the larvae of Linckia laevigata (Yamaguchi, 1973). Clark (1921), reports that early development after metamorphosis in Linckia laevigata probably takes places near the edge of the reef with the juveniles appearing so different from adults. The juvenile closely resemble the genus of Ophidiaster but do not have a papular pore along the oral surface like in Ophidiaster (Yamaguchi, 1977). Only adults and post transformation individuals are found on the shallow reef areas, juveniles are cryptic and occupy different microhabitats.

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The adult blue starfish may grow up to a diameter of 30cm comprising a rough texture, five elongated arms, slightly tubular, and short yellowish tube feet. They appear occasionally with unequal arms indicating that regeneration of damaged arms occurs.

Although Linckia laevigata is renowned for its royal blue colour, different colours have been reported to occur ranging from various shades of blue, brown, apricot, salmon-orange, to gray or purple, sometimes with colours on the aboral surface different from those on the oral surface (Williams & Benzie, 1998). Different colouration is a characteristic of different regions. The royal blue morph has been found to predominate the Pacific and in reefs off north Western Australia. While the royal blue seems to be the common colour morph in West Pacific populations, some localities like Japan, Palau, and the Philippines have dominant starfish that are either apricot all over, or apricot on the oral surface and blue on the aboral surface. In the Indian Ocean different colour morphs that are unique to this region occur, orange colour morphs predominates the royal blue in this region (William & Benzie, 1998; Crandall et al., 2008a). The cooccurence of two carotenoproteins give rise to the royal blue colour in Linckia laevigata (Zagalsky et al., 1989). “Heritable changes to the factors responsible for the quaternary structure of the protein portion of the pigment, or regulation and/or conjugation of these proteins, may be responsible for the different colour morphs in L. laevigata, in which case it is possible that the none uniform geographic range of colour morphs in L. laevigata is evidence that dispersal is not uniform throughout the entire Indo-West Pacific region” (Williams & Benzie, 1998). This difference in colour between the Indian and Pacific is also exhibited in other marine taxa like the shore crab Ocypode cerathopthalma and the crown of thorns starfish Acanthaster planci. Based on the anecdotal observations from aquaria Linckia laevigata is an opportunistic scavenger, perhaps being even saprophytic (preferring to consume dead items as they begin to decay), but has also been observed to apparently feed on algae and microbial films as a non-selective surface feeder. Laxton (1974), reports that the grazing activity of Linckia laevigata might retard coral re-colonization especially at the area where Acanthaster planci had grazed on live corals. However, this is considered insignificant, since Linckia laevigata at their highest density (1.48 per 10m2) can only graze about 0.2% of an area in a feeding cycle (Yamaguchi, 1977).

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The Blue starfish is the most commonly imported sea star in the aquarium trade. It accounted for 3 percent (32,509 individuals) of the total aquarium trade in invertebrates (Wabnitz et al., 2003). The intensive collection and use as ornamental pauses a lot of danger to this species if the turnover rate are low as indicated by paucity of juveniles (Yamaguchi, 1977). Genetic structure in Linckia laevigata has been investigated in various geographical regions using different genetic markers including allozymes (Williams & Benzie 1996), restriction fragment length polymorphism (RFLP) and allozymes (Williams & Benzie 1998), and mitochondrial COI markers (Crandall et al., 2008a, Kochzius et al, 2009). These studies have shown high gene flow in the Great Barrier Reef, Palawan region, between population of Western Australia and Philippines, and between populations of Fiji and Great Barrier Reef (Williams & Benzie, 1993; 1997; 2002; Juinio-Menez et al., 2003). However, some studies reported genetic break across the Indo-Malay Archipelago (Kochzius et al., 2009). This classical divergence between Indian and Pacific Ocean is also reported by Crandall et al., (2008a), but the estimated structuring between the two populations was low. A study by Williams et al., (2002) also revealed a genetic break between the Indian Ocean and Pacific Ocean populations of Linckia laevigata using allozyme and mtDNA. However, more structuring is reported in the Indian Ocean populations compared to those in the Pacific (Williams & Benzie, 1998), the few islands found in the Indian Ocean and the incapability of the present day surface ocean currents to facilitate long distance dispersal were given as the main reason for the structuring in the Indian Ocean. In addition, there has been a variation in the results of previous studies with the marker employed to investigate genetic structuring.

1.3 Genetic diversity and gene flow The genetic variation within and among individuals in a populations refers to its genetic diversity. Genetic diversity has been considered important in organism complexity (complexity of the genome), ecosystem recovery, and species ability to respond to environmental changes (Bazin et al., 2006). The reduction of population size leads to loss of genetic diversity. Species that inhabit unstable and stressed environments on an evolutionary timescale will have a higher genetic diversity than their counterparts from more stable environments. The lost genetic information is unique and cannot be reclaimed (Feral, 2002). “Populations with higher genetic diversity are more likely to have some individuals that can withstand environmental change and

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thereby pass on the genes to the next generations”, (Nevo et al.,1987). Genetic drift in a small finite population will lead to loss of genetic diversity. The number of breeding individuals will determine the intensity of genetic drift in a population. Therefore, the time required to lose alleles by genetic drift is also inversely proportional to the effective population size. Genetic drift may seem to be inconsequential for many marine species given their high population densities and large geographical ranges. However, it is the effective population size (the number of individual that contribute genetically to the next generation) that will affect genetic drift and not the number of individuals in the population (Hellberg et al., 2002).

While drift and selection cause population to diverge, gene flow acts to homogenize populations and to maintain the genetic cohesion of a biological species. Gene flow is the transfer of genes between populations. Molecular techniques are frequently employed to determine population genetic structure, and from this structure, barriers to gene flow are proposed. For example, the genetic break between populations of the mantis shrimp (Haptosquilla pulchella) collected in the North and South of the Flores and Java Seas is attributed to restricted gene flow between the regions during the lowered sea levels of the Pleistocene (Barber et al., 2002). Without any obvious barriers to gene flow, very large geographic distance can cause allopatric conditions for diversification due to isolation by distance. For instance, populations of the echinoderm Strongylocentrotus droebachiensis are genetically homogenous on the scale of hundreds of kilometers, but populations separated on the order of thousands of kilometers show significant levels of genetic divergence (Palumbi & Wilson, 1990; Addison & Hart, 2004). The very large distance provides physical barrier to gene flow. Geological and hydrographical factors can also be interlinked to act as physical barriers to dispersal and hence to gene flow. Theoretically a species with long pelagic larval stage should be associated with high gene flow that would limit population structure and speciation events (Palumbi, 2003). In genes that are selectively neutral, genetic cohesiveness across all subpopulation can be maintained by one reproductively successful immigrant in each subpopulation per generation (Hedgecock, 1994). Ecologists studying connectivity monitor migrants between populations as a means of estimating gene flow among populations each generation. Since standard genetic models do not estimate migration rate directly, it is reported as the product of the proportion of individuals migrating each generation (m) and the effective population size (Ne). Therefore, when genetic techniques make

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inferences on gene flow, only migrants that successfully contributed to gene flow are accounted for, unintentionally excluding migrants that fail to contribute to gene flow by chance or are selected against (Hellberg et al., 2002). The observation of an inverse relationship between larval duration and the level of genetic differentiation suggest that the more extensive the larval dispersal of a species the low the level of genetic structuring (Williams & Benzie, 1993). This relationship between larval duration and gene flow was confirmed in the two starfish Linckia laevigata and Acanthaster planci, with long dispersal larva Linckia laevigata exhibiting a higher gene flow than Acanthaster planci that have a short dispersal larva (Benzie, 1999).

The scenario of allopatric speciation occurs when genetic exchange between separated populations is broken down by extrinsic forces causing genetic divergence between them and ultimately reproductive incompatibility (Palumbi & Wilson, 1990). However, marine species represents a serious challenge to this phenomenon especially in taxa with high fecundity and long dispersal larvae, as they exhibit high population sizes, large geographical scale and high gene flow between distant localities (Palumbi, 1994). This is further complicated by common speciation observed in taxa possessing high dispersal and fecundity in marine ecosystems suggesting this theory of allopatric speciation has a wide variety of exceptions which include some marine species (Palumbi, 1994). Some scientists suggest that the divergence in population that are well connected today by gene flow might have occurred in the recent past during the cycle of sea level rise and fall in the Pleistocene and this has been exacerbated by the steepening of latitudinal thermal gradients (Palumbi, 1994), hence the reason for observed modern times speciation in these species.

1.4 Connectivity and its implication to management Coral reefs are vital ecosystems providing valuable goods and service to coastal communities in maritime tropical and subtropical nations. Unfortunately, reefs are in serious decline, (Wilkinson, 2004) estimated that 30% are severely damaged, and close to 60% may be lost by the year 2030. Marine protected areas (closures) and reserves (allow controlled fishing) are leading tools that have been widely adopted to restore pristine conditions in these areas. Although few studies have objectively and simultaneously examined the types of MPAs that are most effective in conserving reef resources and the socioeconomic factors responsible for effective conservation

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(Pollnac et al., 2001). A global survey on achievements of MPAs showed that only 9% had achieved their management objectives, 71% did not have known objectives and 20% had failed to meet any objectives (Kelleher et al., 1995). A proper management of marine protected areas need to be based on good scientific evidence, clearly identified goals and objectives, and include efforts that can be evaluated in terms of measurable outcomes and deliverables. However, current reef monitoring and assessment approaches are descriptive in nature with most of them documenting changes only few try to document an effect. Using these descriptive approaches alone it is difficult to identify the causes of deterioration or the etiology of the degenerative processes that lead to the visible and often irreversible changes in reef community structure and function. Real forensic data that links biological change to causative agents is required by resource managers and scientist, otherwise they can only be able to say that the “reefs are ill” or the “reefs are dying” but are incapable of rectifying the situation (Downs et al., 2005).

In the WIO region accelerated degradation of marine ecosystems due to increased coastal population has led to establishment of MPAs to protect biodiversity and ensure sustainable use of marine resources. However, the success of these MPAs has varied from country to country. In Kenya for example, an assessment of the marine parks indicates that they are generally meeting their main objective of biodiversity conservation with diversity, abundance and biomass of coral reef finfish recorded in MPAs being considerably higher than in the adjacent open access sites (Muthiga, 2009). The case of Tanzania has been different from Kenya with community based management being more successful as government established marine parks abandoned the goal of no resource extraction in favour of reserves that allow controlled extraction. This was due to past failures by the marine parks (McClanahan, 1999). However, these reserves lack the capacity to fully eliminate destructive fishing such as dynamite fishing or drag nets. Some countries like Madagascar are yet to appreciate the role of marine parks in enhancing conservation of biodiversity and sustainable resource use. Despite having a long coastline only one marine park has officially been established, the Northern Mananara Biosphere Reserve, but several are proposed in Masoala, near Toliana and on the island of Nosy Tany Kely, which has been considered as a marine reserve for many years but does not have legal status. In the wake of realization that individual protected areas, managed on their own, will be less effective and in many cases unsuccessful at achieving their main objectives than if groups of protected areas are

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managed as systems. Many nations in WIO region have strongly advocated and committed themselves under regional and international treaties to establish protected area networks (WWF, 2004). The term network in this context refers to a group of protected areas spread across a country or region. The establishment of this network will require a greater scientific capacity and knowledge on how individual MPA are connected to each other. Connectivity studies in the WIO region aim to bridge this gap and the use of more species in these studies seeks to confirm the existing patterns of connectivity.

Connectivity refers to the extent to which populations in different parts of a species’ range are linked by exchange of larvae, recruits, juveniles or adults (Palumbi, 2003). Many marine reserves are envisioned to play an ecosystem role beyond their boundaries. Low dispersal between the reserve and the surrounding habitats can limit the overall fishery productivity because extra eggs, larvae, or adults are trapped in the protected area. On the other hand infinite movements of adults especially when adults are highly migratory and the protected area are small in this case the animals are only protected when inside the protected area rendering them less effective (Palumbi, 2003). Spatially connectivity information is expected to benefit management strategies in establishment of marine protected areas, gear restriction, and restocking of organisms (Ablan, 2006). In addition complicated life stages of most marine demersal species that favour both isolation and dispersal might render effort unsuccessful if management units are not in harmony (Ablan, 2006). Physical monitoring of marine propagules is also extremely difficult, hence genetic markers are oftenly used to track the dispersal of larvae from source to sink reefs and this provides a basis to evaluate the distant of how reefs are connected. This information is critical for management of depleted or degraded reefs since identification of their recruitment location will potentiate their recovery if the source population is identified and protected (Palumbi, 2003). Connectivity related information is urgently needed in developing countries due to the following reasons; first destruction of critical habitats by coastal development, destructive fishing, and pollution is high in developing countries hence the need of connectivity to identify potential healthy habitats and stakeholders who should manage them, secondly the manpower and financial resources for developing countries are limited hence the need for cooperation among management units where similar efforts identified by connectivity studies is necessary, thirdly strategies to manage marine resource in developing are

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exclusively area based as a result biological and economical losses can be incurred without proper knowledge of spatial structure (Tuck & Possingham, 2000), and finally shrinking management size as a result of devolution by national governments makes their success dependent or independent on other areas (Ablan, 2006).

1.5 Status of genetic population structure in the Western Indian Ocean The Western Indian Ocean is a coherent part of the tropical Indo-Pacific which represents an important biogeographic region of tropical seas (Sheppard, 2000). In spite of this, signs of environmental degradation and overexploitation of natural resources and biodiversity have been reported in Kenya, Mozambique, Somalia, South Africa, Tanzania, Comoros, Madagascar, Mauritius, Reunion, and the Seychelles (Berg et al., 2002). In addition, the WIO region suffered from a catastrophic coral bleaching event during the 1997-1998 El-Nino (Wilkinson, 1999). This resource use problems have been driven by poverty together with rapid population growth and poor management of coastal resources in the WIO region. Therefore, gaining a better understanding of factors and processes that interplay between healthy and degraded states will contribute successfully to the management of marine systems. Most of population genetics studies in the WIO have been conducted in southern Africa while limited information is available for areas outside southern Africa. These investigations have considered three important directions that include: measurement of population genetic structure, study of the influence of historical biogeographical events on present day populations and the use of more realistic models to understand the genetics of natural populations (Grant et al., 1993). These previous studies have used allozymes, mitochondria DNA, intron variability in nuclear DNA, and microsatellite to determine variation in WIO marine populations.

One such important study was by (Williams & Benzie, 1998), which established a genetic break among population of L. laevigata in Indo-West Pacific that suggested divisions within Indian Ocean and between Indian and Pacific oceans populations. This break was further asserted by (Benzie, 1999) in crown-of-thorns starfish, Acanthaster planci, which confirmed similar pattern. However, these studies indicated a higher genetic differentiation in the WIO populations than in Pacific. This signifies that the populations in the Pacific are more connected. More

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comprehensive genetic information that is available for the WIO comes from the black tiger prawn, Penaeus monodon (Duda & Palumbi, 1999; Benzie et al., 2002). Duda & Palumbi, (1999) used intron variability in the black tiger prawn populations from multiple Indo-Pacific sites that included Madagascar, Mauritius, and Tanzania, where as Benzie et al., (2002) used mitochondrial DNA. Despite using different markers both studies showed a genetic break between the WIO populations and their EIO and pacific counterparts. However, they contrasted previous studies on L. laevigata and A. planci in their findings on the genetic diversity of WIO populations which was the lowest among all population studied. However, some species fail to demonstrate this genetic break for instance Swordfish, Xiphius gladius and Mud crab, Scylla serrata that did not show significant structure between Indian and Pacific populations (Chow et al., 1997; Gopurenko et al., 1999) .

There are some studies that purely address WIO questions that also display varied results from a strong genetic differentiation to high gene flow. Panmixing among WIO populations has been reported in Penaeus monodon (Forbes et al., 1999), Neosarmatium meinerti (Ragionieri et al., 2010), Uca annulipes (Silva et al., 2010), and Scarus ghobban (Visram et al., 2010). Connectivity among these populations is principally potentiated by Ocean Currents, which in some cases coincides with peak spawning period of the species. On the other hand restricted gene flow has been observed in Syclla serrata (Fratini & Vaninni, 2002), Perisesarma guttatum (Silva et al., 2009), and Lutjanus fuviflamma (Dorenbosch et al., 2006). On the basis of genetic variability within populations and genetic diversity indices, the southeast African populations lack significant structuring, where as populations off tropical Africa, the Red Sea and in more tropical Indian Ocean locations appear to show reduced gene flow and hence more population structuring (Ridgway & Sampayo, 2005). However, more genetic studies are required to explore this hypothesis further. Ridgway & Sampayo (2005), conclude that the genetic populations’ information available in the WIO region is not comprehensive enough to address major management initiatives in the region.

1.6 Status of genetic structure in the Indo-Malay Archipelago The Indo-Malay Archipelago (IMA) also referred to as the coral triangle has long been regarded to be an area with the highest marine biodiversity, with species richness decreasing longitudinal

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and latitudinal from this centre (Mora et al., 2006, Bellwood et al., 2002). Three main categories of theories have been proposed to explain the high diversity: centre of evolutionary radiation from where new species disperse (Briggs, 1999), centre of overlap between the Indian and Pacific Ocean biota (Woodland, 1983 as cited in Kochzius & Nuryanto, 2008), and centre of peripheral species accumulation (Jokiel & Martinelli, 1992 as cited in Kochzius & Nuryanto, 2008). Findings on butterflyfishes (Chaetodon spp, (McMillan & Palumbi, 1995), lionfishes (Pterois miles and P. volitans, Kochzius et al., 2003), and starfishes (Linckia laevigata and Linckia multifora, Williams, 2000) indicate a phylogenetic break between the Indian and Pacific Ocean, supporting the speciation view of the separated ocean basins. This genetic break between the two ocean basins might have been triggered by two events: the plate tectonic movements that were characterized by the northward movement of Australia, New Guinea and the Bird’s Head Peninsula and the development of Sulawesi by the amalgamation of several fragments that led to the closure of the Indonesian throughflow, which is the major exchange of water masses between the two oceans (Gordon & Fine, 1996). The other event was dropping of sea level up to 120m during the Pliocene and Pleistocene (Siddall et al., 2003), which caused a division in the oceans leading to divergence through allopatric speciation in the marine organisms (Pandolfi, 1992; Randall, 1998 as cited in Kochzius & Nuryanto, 2008).

Population genetic studies covering the Indo-Malay Archipelago show different patterns of connectivity. Observations on false clown fish, Amphiprion ocellaris reveals a significant genetic differentiation with two major lineages from the Indian Ocean and Pacific ocean (Timm & Kochzius, 2008). A similar pattern is also found in the boring giant clam, Tridacna crocea (Kochzius & Nuryanto, 2008), Blue starfish, Linckia laevigata (Crandall et al., 2008a; Kochzius et al., 2009). This pattern of separation between the Indian and Pacific population invokes the vicariance hypothesis, which supports the Indian and pacific basin divide during the Pleistocene. However, a number of species lack this phylogeographic breaks across the Indo-Malay Archipelago. These include the sea urchins Eucidaris, Diadema, and Tripneustus (Lessios et al., 1999; 2001; 2003), Marine snails, Echinolittorina reticulata (Reid et al., 2006), and Thyca crystallina (Kochzius et al., 2009). The reasons for lack of differentiation in these species is attributed to their ability to have successfully dispersed through the coral triangle during the

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glacial maxima, after which they re-established their gene flow quickly after glacial maxima, and lost their divergent lineages (Vogler et al., 2008).

1.7 Factors affecting larval dispersal In general terms, species with greater dispersal duration exhibit high gene flow and low differentiation and speciation than those with limited dispersal and highly structure population (Duda & Palumbi, 1999; Avise, 2004). Studies on the Great Barrier Reef and Ryukyu Islands on Acropora and Stylophora populations respectively, established a high gene flow in the long dispersal broadcast spawners than in the short dispersal brooding species (Ayre & Hughes, 2000; Nishikawa et al., 2003). A similar pattern was also observed across the Indo-Pacific with the long larval duration Linckia laevigata exhibiting a high gene flow than Acanthaster planci (Benzie, 1999). However, some organisms contradict this intuitive pattern, like the mantis shrimp (Haptosquilla pulchella) in central Indo-West Pacific which display a strong structuring pattern despite having a long dispersal mechanism (Barber et al., 2002), whereas the tropical abalone Haliotis asinine with limited larval duration (Imron et al., 2007) shows no sign of genetic structuring among its population. This contradiction proves that gene flow is not only affected by the duration of larva dispersal (Yasuda et al., 2009) but also by ocean current systems (York et al., 2008), larval behavior (Bird et al., 2007), topographic features, local adaptation, interactions with other species (Barber et al., 2002), and historical processes associated with large-scale climatic variations (Imron et al., 2007).

Most adult marine invertebrates are sessile being unable to move to vast geographical distances during their life history, hence possession of a pelagic larval phase gives them an opportunity to be widely distributed and colonize new habitats. The planktonic larval phase usually drifts with the ocean current. Therefore, the current plays a key role in distributing and dispersing marine organisms. Current flow aid the transport of larvae to long distances making them occupy habitats in different locations from their adults. The Western Indian Ocean current system can affect the dispersal of larvae with the seasonal reversal of the Somali current off the Northeast Africa during the Northeast Monsoon slows down the East African Coastal Current making to contribute water to the Mozambique Channel, which has a series of anti-cyclonic eddies probably transporting larvae from the Kenyan and Tanzanian coast to Mozambique and

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Madagascar. The anti-cyclonic eddies of the Mozambique Channel cause a random larval dispersion that could lead to homogenization of marine population along this coast (Silva et al., 2009). During the South-west Monsoon water is said to be diverted from the Mozambique Channel to the East African Coastal Current that would be flowing northwards transporting larvae in the same direction. Eddies are also known to impede dispersal of larvae as they water moves in circular motion around the same location.

1.8 Oceanographic conditions in the Western Indian Ocean region Marine biodiversity and biogeography of the western Indian Ocean is greatly influenced by the oceanic current patterns and seasonal monsoon winds. The South Equatorial Current that permanently flows westward across the Indian Ocean bifurcates to form the Madagascar Current (also called Southeast Madagascar Current), which flows southwards along the eastern Madagascar coast. The main South Equatorial Current also forms the northward flowing Northeast Madagascar Current which splits to form East African Coastal Current (EACC), and the southward flowing Mozambique current. The Mozambique Current flows through the Mozambique channel to join the Madagascar Current to form the Agulhas Current (Gaudian et al., 2003). The EACC is strongly influenced by the Southeast monsoon and the Somali current from April to October, which makes it flow faster (mean velocities 4-5 knots) along the Somali coast. The EACC joins the Somali current beyond Malindi during the Southeast Monsoon and flows northwards to the Horn of Africa. This meeting of Somali and EACC causes an upwelling event that lead to the high fisheries productivity observed in parts of northern Kenya. However, during the Northeast monsoon, which prevail between November to March a southward flowing coastal Somali current meets and opposes the EACC at 2-N to generates the east flowing Equatorial Counter current (Gaudian et al., 2003). The Somali current is observed to seasonally reverse its flow with the monsoon winds. Eddies and internal currents are generated within the Mozambique Channel by the Madagascar and Mozambique currents. The southward flowing Agulhas Current is dominant along the eastern coast of South Africa. Diurnal tides dominate this region with a range of up to 4 meters in some areas. The surface water temperature affects the distribution of both benthic species and fish (Gaudian et al., 2003; Schott & McCreary, 2001).

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Chapter 1 “General introduction”

Figure. 1 A schematic representation of ocean currents in the Indian Ocean. Map is adopted from (Visram et al., 2010).

1.9 Oceanographic condition in the Indo-Malay Archipelago The current system of the Indo-Malay Archipelago involves a complex circulation of seasonal currents. The most dominant flow system is the Indonesian Throughflow (ITF) that is the only low latitude connection between two major oceans (pacific and Indian) making it possible for larval exchange between the two ocean basins. The ITF is mainly comprised of North Pacific water flowing through Makassar Strait (Gordon & Fine, 1996). A little portion of the water

14

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enters the Indian Ocean through Lombok Strait while the bulk of the ITF turns eastwards through the Flores and Banda Seas and enters the Indian Ocean around Timor. The eastern passages provide a passage through which a small amount of deeper water from South Pacific origin flows via the Molucca and Halmahera Seas. Two passages to the Indian Ocean exist from the Banda Sea, the Ombai Strait and the Timor Passage with sills at about 800m and 1200-1300m respectively. Strait of Malaca, Sunda Strait and flow through the passages of the Lesser Sunda Island chain are the connections that exist in the Indo-Malay Archipelago. In addition to ITF, three major currents can have significant influence larval dispersal exist these are North Equatorial current (that flow westwards to the coral triangle), North Equatorial Counter Current (that flow away from the coral triangle), and the New Guinea Coastal Current.

1.10 Assessment of genetic structure The study of genetic structure can be portioned into three categories: traditional/classical population genetics, phylogeography, and hybrid approaches (Hey & Machado, 2003). While these approaches attempt to answer questions regarding genetic structure, they are inherently different. Classical population genetic approaches use allele frequency and sequence polymorphism data to calculate summary statistics that is based on theoretical foundations built more than half a century ago by Sewall Wright and Ronald A. Fisher. Genetic structure assessment is by interpretation of summary statistics in light of the underlying models (Hey & Machado, 2003). Phylogeographic in contrast to classical approach interprets genealogies or haplotype trees in terms of geographic distribution of individuals. Phylogeographic studies have mostly focused on mitochondrial DNA (mtDNA) because of its lack of recombination. Although some studies involving nuclear genes use phylogeographic approach the interpretation of such genealogies becomes difficult if there has been recombination. Gene trees can be interpreted based on the face value since they don’t depend on any model like in classical/summary statistics; however, phylogeographic approaches suffer from large stochastic variance that cannot be reduced by increasing the number or length of sequence. The third approach hybrid begins with a tree estimate before proceeding to estimate model parameters from features of the tree. Linckia laevigata studies can either be based on population genetic or phylogeographic methods (Hey & Machado, 2003).

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Chapter 1 “General introduction”

Existence or nonexistence of genetic structure in summary statistics is determined by whether

FST or ΦST differs considerably from zero through randomizations. FST is by and large reported for allelic data whereas ΦST is reported for sequence data. Unlike allelic data, sequence data contain information on the evolutionary relationships of haplotypes. The FST analogue ΦST is calculated from a matrix of squared Euclidean distances between DNA haplotypes (Excoffier et al., 1992). In markers that are highly polymorphic, variation within population is nearly high as the total variance this result in very low ΦST even if the compared populations have no alleles in common (Meirmans, 2006). This problem is overcome by dividing the obtained ΦST value by the maximum value possible given the present within population variance. Although FST and ΦST are very useful they should be interpreted cautiously (Whitlock & McCauley, 1999). They only provide a measure of the extent of population subdivision, but are not easily translated into measure of gene flow and therefore cannot differentiate between contemporary or prehistorical barriers to gene flow (Pearse & Crandall, 2004) and are biased by demographic history

(Whitlock & McCauley, 1999). The values of FST and ΦST cannot be directly compared to those of different studies as they depend on the markers used.

1.11 Choice of marker and Mitochondria DNA Genetic markers have been used by fisheries managers as a tool to identify species and spatial structure of stocks. Recently, new markers with greater capability in detecting genetic variability and differentiating individuals have been discovered. Plants and animals genetic structure is subject to change overtime; therefore the resolution of the molecular technique should match the timescale of interest (Feral, 2002). Development in molecular techniques has given population biologists an option to examine variation in nucleic acid sequences. Currently, DNA markers are capable of detecting single nucleotide mutations. DNA has highly variable regions which sometimes make each individual unique, and access to this genetic variation provides the framework to use DNA as markers. The advantage of using DNA as a marker is that it is found in nearly all cell of an individual and is easy to store under field conditions unlike allozyme marker that cannot be used in some species. As a result DNA- based makers have replaced allozymes in population genetic studies. Among these, the supposedly non-recombining and evolutionary nearly neutral mitochondria DNA (mtDNA) has been the most widely used marker

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for population history and diversity (Ballard & Kreitman, 1995), this has been due to the fact that mtDNA diversity reflects the population size more accurately than the allozymes (Foltz, 2003). The higher variability of mitochondrial DNA makes it useful for evaluation of variation in a population. Additionally its smaller effective population size gives it a greater degree to show population structure due to genetic drift than nuclear genes (Williams et al., 2002). In case the drift is overcome by gene flow the smaller effective population size of mtDNA will show either increase or decrease of gene flow faster than nuclear genes (Williams et al., 2002). This might have led to mtDNA reaching equilibrium with present day gene flow such that variations in marine organisms reflect gene flow along ocean currents (Williams et al., 2002; Williams & Benzie, 1997). Several phylogenetically conserved primers have been developed over the last decade to amplify various region of mtDNA genome using Polymerase chain reaction. (Folmer et al., 1994) described the universal DNA primers: LC01490 (5’-GGTCAACAAATCATAA AGATATTGG-3’) and HC02198 (5’-TAAACTTCAGGGTGACCAAAAAATCA-3’), which consistently amplified a 710-bp fragment of COI across the broadest array of invertebrates. Therefore, mitochondrial cytochrome c oxidase subunit I (COI) primers which are highly conservative protein coding-genes have been broadly utilized in systematic studies of metazoan invertebrates. Most current studies on population structure of Linckia laevigata use mtDNA for amplification (Crandall et al., 2008a; Kochzius et al., 2009).

1.12 Polymerase chain reaction The field of molecular biology has greatly benefited from polymerase chain reaction (PCR) which has made numerous regions of the genome (coding and noncoding), in both extant and extinct taxa, accessible for detailed analysis. The application of PCR is well suited in systematic biology since a conserved region that flank variable portions of the genome can be used as primer sites for the amplification and sequencing of variable regions from a wide variety of species. PCR has also been successful in examining polymorphism within individuals. The process involves amplification of particular fragments of DNA, which makes them abundant such that they can be visualized without the use of radioactivity or any other type of labeled probe. Amplification takes advantage of the fact that DNA polymerase enzyme that catalyzes the synthesis of a new DNA strand cannot initiate strand synthesis on its own, but requires a short single-stranded primer as a substrate to be elongated. Synthetic oligonucleotide primers (usually

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Chapter 1 “General introduction”

20-30 bases long) that are a complementary to each of the flanking regions are thus used (Parker et al., 1998). The PCR reaction consists of primers, free deoxynucleotides, a reaction buffer, a Taq DNA polymerase. It follows a series of heating and cooling cycles where the DNA is denatured in single-stranded molecules, the two primers anneal to their complementary sequences on either side of target region, and the DNA polymerase replicates the region downstream from each primer (Parker et al., 1998). During each PCR cycle the amount of target DNA doubles, until microgram quantities are present. The Taq polymerase originally obtained from the thermophilic microorganism Thermus aquaticus greatly enhances the efficiency of the PCR and allows it to be completely automated. Before amplification the target sequence accounts for only 0.0001% of the total DNA. When 30 cycles of PCR amplification are completed, the target sequence accounts for 99.9% of the total DNA and the concentration is sufficient for most use, additional purification is not required (Hartl, 2000).

The time required to isolate a desired segment of the genome has significantly been reduced by the PCR technique. It has also allowed analysis of DNA from small tissue samples. However, for most uses of PCR, one must determine the sequences of regions flanking a given locus, and this can entail considerable effort when working with a new species. More advances have been made in PCR technique, one of them being the development of the long PCR which increases the fragment size that can be amplified without compromising the precision of amplification (Parker et al., 1998). While normal PCR allows analysis of target DNA up to 3-5 kb, the advanced techniques may permit amplification of templates as large as 42 kb (Barnes, 1994). This means that the entire mitochondrial or viral genomes can be amplified in one step, a technique that facilitates efficient and extensive analysis of the DNAs. The long PCR eases concerns about limited or partially degraded samples because even if only a small fraction of the DNA is intact, target segments from unbroken DNA can be amplified completely in one bout. This development in PCR has helped answer many questions in population ecology and is also promising a lot in the field (Parker et al., 1998).

1.13 DNA Sequencing Differences in nucleotide sequence occur in all individuals, this include even the most homogenous populations, yet DNA sequencing to detect such differences is often cumbersome in

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Chapter 1 “General introduction”

most population level studies (Parker et al., 1998). Sequencing has become a routine procedure since the development of dideoxy chain termination method. DNA sequencing combined with PCR provides a method of collecting precise data for short DNA sequences, which when combined with the mitochondria DNA proves to be a powerful tool. The technology of sequencing has a diverse history since its development although majority of DNA sequence production to date still rely on the Sanger biochemistry version (Shendure & Ji, 2008). In the last decade at least many levels of incentive for development of entirely new strategies for DNA sequencing have emerged. Among all these strategies the major contribution comes from the human Genome project, which leaves few avenues of optimization, which has significantly reduce the cost of conventional DNA sequencing. The DNA sequencing production is still carried out with the capillary based semi automated method as implemented in Sanger biochemistry (Swerdlow et al., 1990; Hunkapiller et al., 1991). In Sanger sequencing DNA to be sequenced in high-throughput production pipeline is prepared by one of two approaches. In the first approach for shot gun de novo sequencing, Escherichia coli is transformed by randomly fragmented DNA that has been cloned into a high-copy-number plasmid. While in the second approach for targeted re-sequencing, PCR amplification is carried out with primers that flank the target. The two approaches give an output of an amplified template, either as many clonal copies of a single plasmid insert present within a spatially isolated bacterial colony that can be picked or as many PCR amplicons present within a single reaction volume. The template denaturation, primer annealing and primer extension takes place in a cycle sequencing reaction. The fluorescently labeled dideoxynucleotides (ddNTPs) randomly terminates each round of primer extension. The label on the terminating ddNTP of any given fragment corresponds to the nucleotide identity of its terminal position in the end labeled extension products mixture. The determination of the sequence is by high resolution electrophoretic separation of the single- stranded, end labeled extension products in a capillary based polymer gel. As the fragments exit the capillary the laser excitation of fluorescent labels coupled to four-color detection of emission spectra, provides the read out that is represented in a Sanger sequencing trace. The traces are translated into DNA sequences by software which also generates error probabilities for each base-call (Ewing & Green, 1998). For subsequent analysis like in the genome assembly or variant identification, it will depend on precisely what is being sequenced and why. In the last three decades the Sanger biochemistry has been gradually improved and can be applied to

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Chapter 1 “General introduction”

achieve read lengths of up to approximately 1000bp with a per base raw accuracies as high as 99.999% while costing as low as $0.50 per kilobase (Shendure & Ji, 2008).

Alternative strategies of DNA sequencing exist and can be grouped into the following categories: microelectrophoretic methods, sequencing by hybridization, real-time observation of single molecules and cyclic-array sequencing. First is the cyclic-array method which involves sequencing of a dense array of DNA features by iterative cycles of enzymatic manipulation and imaging-based data collection. Second is hybridization where differential hybridization of labeled nucleic acid fragments to an array of oligonucleotide probes are used to precisely identify variant positions. Third approach involves the real time monitoring of DNA polymerase activity. Nucleotide incorporations can potentially be detected through fluorescence resonance energy transfer interactions between a fluorophore-bearing polymerase and gamma phosphate-labeled nucleotides, or with zero-mode waveguides. The illumination can be restricted to a zeptoliter- scale volume around a surface-tethered polymerase such that incorporation of nucleotides can be observed with low background. The last approach of the alternative of Sanger biochemistry is the microchip-based electrophoretic sequencing which is based on carrying out conventional electrophoretic sequencing in a microfabricated device. There is a difference between conventional (sanger) sequencing and these second generation sequencing in terms of costs, limitations and practical aspects of implementation but generally the conventional sequence is more diverse, and for small-scale projects in the range of kilobase-to-megabase its likely to remain the technology of choice in the immediate future (Shendure & Ji, 2008).

1.14 Problem statement The widespread 1998 coral mortality significantly reduced refuge to coral reef fishes and invertebrates in the WIO region, which made them more vulnerable. McClanahan et al., (2002) reports a decline in some coral reef invertebrates and fishes after the 1998 bleaching event especially outside marine parks in Kenya. Although it is possible that replacement of coral by fleshy algae could increase the density of organism that inhabits the reef, which might have a cascading effect upwards the trophic level. Therefore, loss of coral cover can have an effect on marine population especially those that have restricted movement such as Linckia laevigata.

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Chapter 1 “General introduction”

Additionally, the scarcity of juveniles observed in this species by (Yamaguchi, 1977) suggests that it might have a low turnover rate and more effort is required in conservation of this species.

To effectively manage marine populations, managers need information on the stock structure and gene flow among the harvested species. This information will allow managers to balance between conservation of biodiversity and sustainable exploitation. However, genetic information available in WIO and Indo-Malay Archipelago is not comprehensive enough to address major management initiative in these regions. Increasing studies on species population genetics and connectivity will give more realistic patterns on how ocean currents interact to aid larval dispersal. Unlike most previous studies the proposed study will investigate population genetic structure at spatial scale smaller enough to show the nature or geographic position at sharp genetic discontinuities, as this has been limited in previous studies.

1.15 Objective of the study Studies of genetic structure of widespread taxa in the Indo-West Pacific have provided controversial results that contradict the pattern that planktonic larval dispersal in the marine environment is normally adequate to keep genetic homogeneity in vast scales. Furthermore, genetic analyses disseminate new independent perspectives of dispersal mechanisms that affect the evolution and ecology of marine taxa. The wide distribution of blue starfish Linckia laevigata in the Indo-Pacific coral reefs, from the Western Indian Ocean across the Indo-Malay-Philippine Archipelago to south eastern Polynesia made it possible for this study to analyse the connectivity of the WIO to the Indo-Malay-Philippine Archipelago. In this study, I investigated the genetic population structure and connectivity of L. laevigata in the Western Indian Ocean and compared it to previous studies in the Indo-Malay Archipelago. Specifically, the study established genetic diversity of L. laevigata in the WIO sampling sites, the study further determined the connectivity of L. laevigata in the WIO sample sites and finally it assessed the connectivity of the WIO population to the Indo-Malay Archipelago population.”

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Chapter 2 “Materials and methods”

CHAPTER 2: Materials and methods

2.1 Study site and sample collections The tissue samples of Linckia laevigata were collected from nine sites located in three countries coastline in WIO. These included three sites in Kenya (Mombasa, Watamu, and Diani), two sites in Madagascar (Andilana and Sarodrano), and four sites in Tanzania (Jambiani east coast of Zanzibar, Misali west coast of Pemba, Angel reef and Mikindani). The tissue samples were collected between February 2011 and February 2012. In total 138 tissue samples were collected in the nine sites through SCUBA diving, where a small tissue (~1cm) was cut from an adult L. laevigata with minimal lethargy to avoid killing the animal. The tissues were preserved in absolute ethanol and stored at 4oC separately prior to DNA extraction. Ethanol was replaced thrice within the first month of preservation to avoid adulteration from the tissue fluid of the samples

2.2 DNA extraction Total genomic DNA was extracted using the NucleoSpin Tissue extraction kit (Machenery Nagel) according to the manufacture protocol with slight modifications as follows: ~25mg of L. laevigata was cut from main tissue sample and used in each extraction steps. The tissue was digested in 180µl buffer T1 and 25µl proteinase K solution and then incubated at 56oC while being shaken for at least 3-6hours. After incubation 200µl buffer B3 and 210µ ethanol were added in subsequent steps while being shaken before being transferred to the Nucleospin tissue column. The tubes were then centrifuged at 10,000 rpm for 1 min and the flow through poured off. The bound DNA was subjected to two wash cycles. First the silica membrane was re- suspended in 500µl buffer BW (Nucleospin kit), centrifuged for 1 min at 10,000 rpm, and the wash buffer poured off. Second, the silica membrane was re-suspended in 600µl buffer B5 wash buffer, centrifuged at 10,000 rpm for 1min before the wash buffer was poured off. The remaining ethanol was then removed by centrifuging the column without re-suspension in any solution for 1min at 10,000 rpm. Finally, the silica membrane was re-suspended in 100µl of pre-warmed

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elution buffer BE, which was centrifuged at 11,000 rpm for 1minute and the supernatant pipetted into an archival storage tube that was stored in the freezer until needed.

2.3 Gel electrophoresis Electrophoresis technique separates substances based on the charge differences and molecular structure. Two substances with opposite charges when electrophoresed will migrate to different positions. When two substances have the same charge but different mobilities, the faster moving substances will overrun and migrate ahead of the slower one. In this study the DNA extracts and PCR products were checked by Slab electrophoresis to confirm the presence of DNA.

2.4 Preparation of the Gel Approximately 1.5g of Agarose powder was dissolved in 150ml of TBE (Tris Borate EDTA) buffer solution, which keeps DNA deprotonated and dissolved in water. To dissolve the Agarose the mixture was heated in a microwave. After cooling the gel solution was poured in a gel tray and the combs used to create DNA loading slits placed on it and then it was left to harden.

2.5 Running the Gel 5µl of sample DNA extract or PCR product was stained with 3µl of Gel red (makes the DNA visible) and 2µl of DNA loading dye (weighs down the DNA), and this mixture was then loaded in the gel slits. In each row of the gel slits 100bp ladder was loaded in the first slit to estimate the length and strength of DNA fragments. The gel was run at 100V for almost 60 minutes. Visualization of the gel was done using Bio-Rad ChemiDocTM XRS Gel documentation machine.

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Chapter 2 “Materials and methods”

A B

Indian Ocean

N N

C

1 , 2, 5 , 20 , 35 , 41 , 46

Figure. 2. Map of WIO and Indo-Malay Archipelago sample sites.

(A,B) Map of Indo-Malay Archipelago and WIO sample sites (Indo-Malaly Archipelago samples codes see list of abbreviations). Oceanographic patterns with dominant (solid lines) and seasonally changing (dashed lines) currents (Gordon & Fine 1996, Schott & McCreary, 2001) for currents codes see list of abbreviations. The light grey area on (A) indicates the Pleistocene maximum sea level low stand of 120 m (Voris, 2000). Pie charts represent frequency of clades as defined in the network at different sample sites. (C) Network of mitochondria cytochrome c oxidase I. Large circles represent haplotypes and lines represent one mutational step. Numbers represent the mutational steps between haplotypes and and small circles indicate a missing intermediate haplotype.

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2.6 Amplification and sequencing The amplification of the COI region was through polymerase chain reaction (PCR) using the primers described by Folmer et al., (1994): LC01490 (5’-GGTCAACAAATCATAAAGATATTGG- 3’) and HC02198 (5’-TAAACTTCAGGGTGACCAAAAAATCA-3’). PCR was conducted in a 50µl reaction volume containing 4µl DNA template, 10mM Tris-HCl (pH 9), 50mM KCl, 0.2mM dNTPs, 0.4µl BSA (10mg/ml), 0.2µl Taq polymerase (5u/µl), 0.4µM of each primer and 2.0-2.5 mM MgCl. PCR was conducted under the following temperature profile: 94oC for 5min, followed by 35 cycles of 1 min at 94oC, 1.5 min at 45oC and 1 min at 72oC. Final extension was conducted at 72oC for 5 min (Kochzius et al., 2009). Sequencing was done using the DyeDeoxy terminator chemistry (PE Biosystem) and an automated sequencer (ABI PRISM 310 and 3100, Applied Biosysems).

Sequences for the Indo-Malay-Philippine Archipelago region were obtained from two previous studies, (Kochzius et al., 2009) and (Alacazar & Kochzius, 2011 unpublished). Sequence data for 270 specimen from 24 sample sites across the Indo Malay Archipelago were obtained from Kochzius et al., (2009), while 124 specimen from five sites from the Eastern Visayas, Philippines were obtained from (Alcazar & Kochzius, 2011 unpublished).

2.7 Genetic diversity The software Chromaspro (Version 1.5; Technelysium) was used in editing the sequences. Each sequence was verified from published sequence available in NCBI (National centre for biotechnology information) using BLAST (Basic local alignment search tool). To ensure that only functional mitochondrial DNA was used and not nuclear pseudogenes the sequences were translated to amino acids using the software Squint Alignment Editor (Version 1.02). The online services of FaBox 1.4 software was used to collapse the sequences into haplotypes. A multiple sequences alignment was obtained by using CLUSTAL W (Thompson et al., 1994) as implemented in the software BIOEDIT (version 7.0.4.1; Hall, 1999). The haplotype diversity h (Nei, 1987) and nucleotide diversity (Nei & Jin, 1989) were calculated with the programme Arlequin (http://cmpg.unibe.ch/software/arlequin35/, Version 3.5.1.3; Excoffier & Lischer, 2010).

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Chapter 2 “Materials and methods”

2.8 Historical demography The Tajima’s D-test (Tajima, 1989) and Fu’Fs-test (Fu, 1997) were used to test the null hypothesis of neutral evolution of the marker. Negative Tajima’s D values can indicate selection, but also population bottleneck or population expansions (Tajima, 1989). To analyse the historical demography, the mismatch distribution (Schneider & Excoffier, 1999) of the sum of square deviation (Rogers & Harpending, 1992) and Harpending’s raggedeness index were used, which allowed for testing of the model of sudden population expansion (Rogers, 1995). The mismatch distribution is multimodal in populations under a demographic equilibrium and unimodal if a recent and fast demographic expansion of the population has taken place. The time (t) since population expansion was determined using the formula t = τ/2u, where Tau (τ) is the expansion parameter estimate and u is mutation rate of the entire DNA region under study (In our case of mtDNA). Tau (τ) was calculated from Arlequin under demographic and spatial expansion hypothesis whereas the formula u=2µk was used to estimate u, where k is the number of nucleotides assayed (467 in this case) and µ is the mutation rate per nucleotide (The average mutation rate per nucleotide for mtDNA 5.7 x 10-8 was used, Li & Graur, 1991). Benzie et al., (2002) also used this average mutation rate per nucleotide. Tests in Arlequin programme were carried out at 10000 permutations.

2.9 Genetic population structure and connectivity The significance of population structure in Linckia Laevigata was tested using analysis of molecular variance (AMOVA; Excoffier et al., 1992) and pairwise ΦST-values. The software Arlequin (version 3.5.1.3; Excoffier & Lischer., 2010) was used to carry out both statistical calculations, applying the standard AMOVA computations while matrix of distance was computed by the Pairwise difference test with 10000 numbers of permutations and 0 gamma value. A network of haplotypes was calculated with the programme TCS (version 1.21; Clement et al., 2000). Clades were defined by the number of mutational steps.

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Chapter 3 “Results”

CHAPTER 3: Results

3.1 Genetic diversity Sequence data of mitochondrial COI gene were obtained from 138 individuals from nine localities in the Western Indian Ocean (WIO). A sequence alignment of 467bp was obtained without indels and translated without any stop codons into an amino acid sequence. The 138 sequences yielded 61 haplotypes, of which 53 were unique to the sampling sites, showing 156 polymorphic sites (33 %) and 184 substitutions. High values of haplotype (h) and nucleotide (π) diversity were recorded, with the average haplotype diversity being 0.8658 (0.56-1.0) and nucleotide diversity was 1.9 % (0.7-5.4) (Table 1). From the nine sites, two (Diani and Misali) had a haplotype diversity of 1; the sampled specimen did not share any haplotype.

Table 1. Genetic diversity, neutrality tests and mismatch distribution.

Sample sites, number of sequences (n), number of haplotypes (Nhp), haplotype diversity (h) nucleotide diversity ( π), Tajima’s D , Fu’s Fs, sum of square deviation (SSD), and Harpending’s raggedness index (HRI) for Linckia laevigata in WIO. Genetic Mismatch diversity Neutrality tests distribution

Sampling site Code n N hp h π (%) Tajima's D Fu's Fs SSD HRI Andilana An 20 10 0.83 0.9 -0.17ns -0.54ns 0.04ns 0.06ns Sarodrano Sa 15 3 0.56 0.7 1.78ns 4.42ns 0.24* 0.46ns Watamu Wa 15 12 0.96 1.8 -1.42ns -0.57ns 0.02ns 0.03ns Mombasa Mo 16 13 0.96 3.0 -1.48* -1.55ns 0.005ns 0.01ns Diani Di 12 12 1.00 5.4 -0.95ns -0.65ns 0.01ns 0.02ns Angel reef, Dar es Salaam Ar 27 12 0.81 0.9 -0.56ns -2.10ns 0.04ns 0.07ns Misali, West coast Pemba Mi 6 6 1.00 1.9 -1.10ns -1.18ns 0.07ns 0.24ns Jambiani, East coast Zanzibar Ja 17 8 0.77 2.9 -1.69* 3.54ns 0.08ns 0.14ns Mikindani Mk 10 7 0.91 1.9 -0.83ns 1.87ns 0.05ns 0.12ns *0.05 ≥ P ≥ 0.01; **0.01 > P ≥ 0.001; ***P < 0.001; ns= not significant

3.2 Historical demography The null hypothesis of neutral evolution of the COI marker was only rejected for Mombasa and Jambiani based on Tajimas D test, whereas the results of Fu’s Fs test could not reject the null hypothesis for all the sites. The mismatch distribution analysis and Rogers’ test of sudden population expansion indicate population expansion for all sites except Sarodrano. The value for

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Chapter 3 “Results”

τ for pooled WIO populations under assumption of demographic expansion was 8.2 (95 % confidence interval: 2.92-12.5), which indicated population expansion that began around 154,025 years ago, while under spatial expansion assumption the estimated τ was 5.4 (95 % confidence interval: 0.34-15.18) corresponding to expansion around 101,825 years ago. The mismatch distribution frequency showed a bimodal curve.

Mismatch distribution 1800

1600

1400 Observed Simulated 1200 1000 800 600

Frequency ofFrequency occurrence 400 200 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Pairwise differences

Figure. 3. Mismatch distribution graph of pooled WIO population

. 3.3 Genetic population structure and connectivity

3.3.1 Western Indian Ocean The genetic population structure of Linckia laevigata in the WIO region was determined with samples collected from three sites in Kenya, four sites in Tanzania and two sites in Madagascar.

The Bonferroni correction for the 36 pairwise ΦST statistical tests was 0.0014 instead of 0.05.

The pairwise ΦST values were low and insignificant for all sites with the exception of one site in Kenya, Diani, which is significantly differentiated from one site in Tanzania, Angel reef.

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Chapter 3 “Results”

Table.2. Pairwise ΦST values among populations of Linckia laevigata in the WIO. Samples codes are presented in Table 1.

An Sa Mo Wa Di Ar Ja Mi Sa -0.0486ns Mo 0.0101ns 0.0045ns Wa -0.0204ns -0.0171ns -0.0304ns Di 0.1221ns 0.1113ns 0.0009ns 0.0366ns Ar -0.0096ns 0.0114ns 0.0467ns -0.0014ns 0.1519* Ja 0.0044ns -0.0058ns -0.0074ns -0.0087ns 0.0582ns 0.0401ns Mi 0.0622ns 0.0626ns -0.0474ns -0.0132ns -0.0072ns 0.141ns -0.0323ns Mk 0.0004ns -0.0014ns 0.0228ns -0.0238ns 0.0333ns 0.0231ns -0.0088ns -0.013ns *P ≤0.001; ns = not significant

The AMOVA results for the WIO population revealed a low fixation index, but significant genetic population structure (ΦST = 0.024, P = 0.045). A hierarchical AMOVA was carried out for four groupings based on the geographical location, oceanographic conditions, and clade distribution in the WIO sites, only two rejected the hypothesis of panmixing; Kenya (Watamu,

Diani, and Mombasa) and the rest of WIO sites (ΦCT = 0.024, P = 0.034) and Kenya and Tanzania Island population (Watamu, Mombasa, Diani, Misali, and Jambiani) and the rest of

WIO sites (ΦCT = 0.034, P = 0.047) Table 3. The plot of pairwise ΦST values against geographic distance among sample sites did not exhibit a positive correlation (r = 0.093, P = 0.5896, y = 0.0231 + 3.8902E-6x), which confirmed lack of isolation by distance Figure.4.

3.3.2 Indo-West Pacific Since the sequence obtained for this study were shorter than the sequences from the previous studies in the Indo-Malay Archipelago, the 532 sequences from all the 36 sites were reduced to 441bp yielding 194 haplotypes, although the shortening of sequences might have reduced the number of expected haplotypes.

29

Chapter 3 “Results”

Table 3. Hierarchical AMOVA based on mitochondrial control region sequences from Linckia laevigata with alternative groupings of sample sites from the WIO and Indo-Malay Archipelago. For sample codes see Table.1.

Grouping ΦCT P value WIO (Wa,Mo,Di,Ja,Mi)(Ar,Mk,An,Sa) 0.03465 0.04718 (Wa,Mo,Di)(Ja,Mi,Mk,Ar,An,Sa) 0.02446 0.03475 (An, Sa) (Wa, Mo, Di, Ja, Ar, Mi, Mk) -0.005 0.44385 (An, Sa, Mk) (Wa, Mo, Di, Ja, Ar, Mi) -0.0018 0.35862

WIO and Indo Malay Archipelago (WIO)(EIO)(CIM+Vis)(WP) 0.19979 P < 0.001 (WIO,EIO)(CIM+Vis)(WP) 0.19514 P < 0.001 *0.05 ≥ P ≥ 0.01; **0.01 > P ≥ 0.001; ***P < 0.001; ns= not significant.

The evolutionary relationships of 194 Linckia laevigata haplotypes found in the WIO and the Indo-Malay-Philippine Archipelago are presented in the haplotypic network (Figure.2), showing 5 different clades separated by 3-8 mutational steps. The highest number of shared haplotypes was seen in clade 2, which had also the most common haplotypes. Clade 4 and 5 were the most divergent separated from the other three clades each by at least six mutational steps. Clade 4 was the smallest comprising of only four haplotypes. Clade 1, 2, and 3 were characterized by a star like structure with connection to many rare haplotypes. The distribution of different clades across the WIO and Indo Malay Archipelago is presented in (Figure.2). Clade 3, 4 and 5 were restricted to the WIO. Clade 3 was found at all sites in the WIO, while clade 4 and 5 were only in Diani, Watamu, Mombasa, and Misali. Clade 1 was dominant in the Visayas and Western Pacific, although it was also found at lower frequencies in other sites in the Indo Malay Archipelago, and was not observed in the Eastern Indian Ocean (EIO) (Andaman Sea and Kupang) and WIO. Clade 2 could be found in all the sample sites in the four regions WIO, EIO, and Central Indo Malay Archipelago, except two sites, Sebakor/Sanggal/Papisol (SSP) and New Britain, New Guinea.

AMOVA results for the 36 sample site from the WIO and Indo Malay Archipelago populations revealed a strong genetic structure (ΦST = 0.13, P < 0.001). A hierarchical AMOVA based on the

30

Chapter 3 “Results”

geographical location of sites was conducted for two groupings and both rejected the hypothesis panmixing: WIO, EIO (Andaman Sea and Kupang), Western Pacific (New Britain and Biak), and Central Indo Malay Archipelago (ΦCT = 0.199, P < 0.001) and Indian Ocean (WIO and EIO),

Western Pacific, and Central Indo Malay Archipelago (ΦCT = 0.195, P < 0.001).

0.16

0.14

0.12

0.10

0.08

0.06

0.04

Genetic distance (physt)

0.02

0.00

-0.02 -500 0 500 1000 1500 2000 2500 3000 3500 Distance (Km)

Figure. 4. Plot of pairwise ΦST values against geographic distance (Km)

31

Chapter 4 “Discussion”

CHAPTER4: Discussion

4.1 Genetic diversity The estimated haplotype and nucleotide diversity of Linckia laevigata was high and comparable to previous studies in the Indo Malay Archipelago (Crandall et al., 2008a, Kochzius et al., 2009), which used the same genetic marker. However, the average haplotype diversity (0.86) is slightly higher than (0.63) obtained for Linckia laevigata in the Southern Africa population by (Williams & Benzie, 1998) using RFLP data. They are also high in comparison with those obtained by previous studies for other invertebrates in the WIO region such as the giant tiger prawn, Penaeus monodon (Benzie et al., 2002), fiddler crab, Uca annulipes (Silva et al., 2010), Mangrove crab, Perisesarma guttatum (Silva et al., 2009), but are similar for Mangrove crab, Neosarmatium meinerti (Ragionieri et al., 2010).

These high haplotype and nucleotide diversity values might confirm a recent population expansion in Linckia laevigata (Grant & Bowen, 1998). The comparable nucleotide diversity to populations from Indo-Malay Archipelago population could suggest a similar demographic history among these populations (Duda & Palumbi, 1999). The high genetic diversity could be due to a high maternal effective population size, an increased mutation rate or combination of both. Although results obtained for mtDNA are variable indicating that high polymorphism do not necessary translate to a large population size (Bazin et al., 2006).

4.2 Historical demography While the results of Tajima’s D test could not reject the null hypothesis of neutral evolution in two sample site (Mombasa and Jambiani), the Fu’s Fs rejected it in all the WIO region sites Table. 1. However, the assumption of these tests cannot distinguish between selection and changes in population size. The results of mismatch distribution analysis and Rogers’ test (Rogers & Harpending 1992; rogers, 1995) support a sudden expansion from a small initial population. This population expansion is estimated to have begun around 154,025 years ago, a date that falls well within the Pleistocene.

32

Chapter 4 “Discussion”

Sea level decreased up to 120m below present during the Pleistocene, this was inevitably accompanied by strong decline in shelf habitats suitable for Linckia laevigata in the WIO. During this time the population of Linckia laevigata would have undergone dramatic decline owing to shortage of habitats. This decline in fish and invertebrates population due to shortage of habitats has been supported by current reef monitoring studies, which indicate reduced fish and invertebrate population due to reduced refugia usually after a disturbance (Wilson, 2006). The rising temperature in interglacial periods was associated with sea level rise that provided more habitats that could be colonised resulting in a demographic and spatial population expansion. This population expansion after a bottle neck has also been observed in the Indo-Malay Archipelago population of Linckia laevigata by (Crandall et al., 2008a, Kochzius et al., 2009) and in other invertebrates in the WIO region such as mangrove crab, Neosarmatium meinerti (Ragionieri et al., 2010), fiddler crab, Uca annulipes (Silva et al., 2010), mud crab, Scylla serrata (Fratini & Vannini, 2002), giant tiger prawn, Penaeus monodon (Benzie et al., 2002), and the blue barred parrotfish, Scarus ghobban (Visram et al., 2010).

4.3 Genetic population structure and connectivity

4.3.1 Western Indian Ocean Some previous studies have demonstrated that genetic divergence among marine populations can occur even in absence of any apparent barriers to dispersal (Bell et al., 1982; McMilliam & Palumbi 1995; Shulman & Bermingham, 1995; Waters et al., 2000; Riginos & Victor, 2001; Fauvelot & Planes, 2002; Taylor & Hellberg, 2005). Despite the long larval duration of Linckia laevigata that provides a mechanism for long distance dispersal, AMOVA results for WIO population showed a weak , but significant population structure (ΦST = 0.024, P = 0.04574). Similar results of weak genetic structure in Linckia laevigata were also reported in other studies (Williams & Benzie 1998; Crandall et al., 2008a; Kochzius et al., 2009). The negative result on isolation by distance observed in this study indicates that this weak structure cannot simply be attributed to restricted dispersal (Fratini & Vannini, 2002). This reduced gene flow could be caused by local ocean currents, life history features, topographic features (Yasuda et al., 2009), and success of immigrants in mating after settlement (Burton & Feldman, 1982).

33

Chapter 4 “Discussion”

A more pronounced but still weak significant structure in the WIO populations was observed in the hierarchical analysis with the following groupings: Kenya (Watamu, Diani, and Mombasa) and the rest of WIO sites (ΦCT=0.02446, P=0.03475) and Kenya and Tanzanian Island populations (Watamu, Mombasa, Diani, Misali, West coast of Pemba Island and Jambiani, East coast of Tanzania) and the rest of WIO sites (ΦCT=0.03465, P=0.04718). The first grouping indicate a pattern of discontinuity between Kenyan populations and the rest of WIO population, while the second grouping displays an existences of barrier between Kenyan and Tanzania Island population grouped together against the rest of WIO populations. This is not the first study to demonstrate genetic discontinuities even in sites that are close to each other in a long dispersal species, a similar pattern was observed in Mud crab Scylla serrata (Fratini & Vannini, 2002) and Mangrove crab, Perisesarma guttatum (Silva et al., 2009). A plausible explanation to the observed pattern of discontinuity could be caused by the downwelling event that occurs along Southern Kenya and Tanzania coast throughout the year (Bell 1972, McClanahan, 1988). This phenomenon can influence dispersal continuity, since water is transported to the coast where it piles up and sinks. Downwelling is pronounced during the South East Monsoon (April to October), which may coincide with peak spawning period (May to August) of Linckia laevigata according to (Yamaguchi, 1977). However, the population structure estimated by hierarchical AMOVA analysis were weak and further sampling over time within the WIO is recommended to establish the stability of the genetic differentiation. Studies on tiger prawn, Panaeus monodon (Forbes et al., 1999), fiddler crab, Uca annulipes (Silva et al., 2010), and blue barred parrotfish, Scarus ghobban (Visram et al., 2010) failed to demonstrate this population structure supporting hypothesis of panmixing in the WIO.

4.3.2 Indo-West Pacific

The AMOVA result for the combined WIO and Indo Malay Archipelago populations revealed a strong genetic structure (ΦST = 0.13046, P < 0.001). A comparable value of ΦST = 0.146 was also obtained by Williams & Benzie, (1998) in a study on Linckia laevigata with samples from the similar biogeographies. This strong genetic differentiation in the Indo-Pacific was also shown in giant tiger prawn, Penaeus monodon (Duda & Palumbi, 1999; Benzie et al., 2002), Blue starfish, Linckia laevigata (Williams & Benzie, 1998; Williams et al., 2002), Crown-of-thorn,

34

Chapter 4 “Discussion”

Acanthaster planci (Benzie, 1999), Holothurians: Stchopus chloronotus, Holothuria atra and Holothuria nobilis (Uthicke & Benzie, 2001; Uthicke & Benzie, 2003). However, direct comparison of the fixation indices with previous studies on other invertebrates is not feasible, since most of them used allozymes makers or did not have geographical coverage similar to the present study. Studies on smaller geographical scale have also shown this differentiation between the Indian and Pacific Ocean, but with a weak genetic structure in Linckia laevigata (Crandall et al., 2008a; Kochzius et al., 2009). The hierarchical analysis displayed a stronger genetic break with the following groupings: WIO, EIO, Central Indo Malay, and Western Pacific

(ΦCT=0.19979) and WIO and EIO grouped together, Central Indo Malay and Western Pacific

(ΦCT=0.19514).

The genetic break between the Indian and Pacific population in Linckia laevigata is consistent with effect of lowered sea level during the Pleistocene, which limited gene flow between the Pacific and Indian Ocean for tens of thousands of years (Williams & Benzie; 1998, Benzie, 1999; Williams, 2002). Sea level reduced to levels below 120m during the Pleistocene and occasionally raised above the present levels as a result it changed seaways between the Indian and Pacific. The Torres Strait was closed throughout the Pleistocene while the Indonesian throughflow might have been restricted (Galloway & Kemp, 1981). It is unlikely that dispersal could have happened either in the past or present through Southeastern coast of Australia, because the tropical marine larvae will probably be killed by the cold water (Williams & Benzie, 1998). Furthermore, upwelling of cold water at the base of the Indonesian arc blocked the movement of tropical marine larvae through the few opened narrow channels in the eastern Indonesian Island, effectively closing any dispersal routes between Pacific and Indian oceans (Williams & Benzie, 1998). Findings of a weak genetic structure by studies conducted at lesser geographical scale in comparison to broad scale studies could suggest a contemporary gene flow among Linckia laevigata populations in the Indo-West Pacific, which follows isolation by distance model. Conversely, a number of species demonstrate lack of this apparent phylogeographic break, this include Marine snails, Echinolittorina reticulata (Reid et al., 2006) and Nerita plicata (Crandall et al., 2008b), Soldierfish, Myripristis berndi, (Craig et al., 2007), Swordfish, Xiphius gladius (Chow et al., 1997) and Mud crab, Scylla serrata (Gopurenko et al.,

35

Chapter 4 “Discussion”

1999). Although it is possible that the genetic markers or sample sizes used by these studies were inadequate to detect population structure (Carpenter et al., 2011).

The genetic break between Indian and Pacific is also corroborated in geographical distribution of the different clades (Figure.2). The Western Pacific is predominated by clade 1 that is completely missing in EIO and WIO. Clade 2 is found in almost all the sampling sites with the exception of two sites Sebakor/Sanggal/Papisol (SSP) and New Britain, New Guinea. Clade 2 was the only clade sampled in the two EIO sites (Andaman Sea and Kupang) and also dominated the sites in the west part of the Central Indo-Malay an indication that it is likely the ancestral Indian Ocean clade. Moreover, it is also found in the WIO, which could be attributed to long distance dispersal over several generations that allowed it to reach vast geographical scale. While clade 1 developed in the Pacific Ocean the North Equatorial current that splits to form Mindanao current near the Visayas could be responsible for its westward dispersal into the Central Indo Malay archipelago. The appearance of clade 1 in the Visayas (Mer, Alm, Sj, Sal, Mar, and Ce for sample codes see list of abbreviations) in a higher frequency suggests a genetic affinity of this region to the West Pacific. Clade 1 and 2 were also reported by Kochzius et al., (2009). A strong mixing of clade 1 and 2 occurs in the central Indo-Malay despite genetic barriers between the Indian and Pacific at small geographical scale (Crandall et al., 2008a). Clade 3, 4 and 5 have evolved and accumulated in the WIO and their restriction to this region indicates a limited gene flow between WIO population and EIO and Pacific counterparts. This break within Indian Ocean was also observed in crown thorn starfish, Acanthaster planci (Benzie, 1999), blue starfish, L. laevigata (Williams & Benzie, 1998), and black tiger prawn, Penaeus monodon (Duda & Palumbi, 1999; Benzie, 2002). The limited genetic exchange between EIO and WIO supports the notion of (Williams & Benzie, 1998) that there are fewer Islands in the Indian oceans compared to West Pacific to facilitate dispersal to vast distances through “Island hopping”. Moreover, the enclosure of Andaman Sea by land following sea level regression during the Pleistocene isolated Thailand partially from the Indian Ocean and completely from Pacific Ocean. Clade 2 and 3 are found in all sites in the WIO, which could be attributed to the bifurcation of the South Equatorial Current to form north flowing East African Coastal current that seasonally diverts part of its water into the Mozambique Channel. These currents might be responsible for the wide distribution of clade 2 and 3 throughout the WIO, although the frequency of these two clades

36

Chapter 4 “Discussion”

decrease in sites found along the South coast of Kenya and in Island populations of Tanzania. This lower frequency suggests limited mixing between these two clades in this area that experiences downwelling throughout the year. Clades 4 and 5 have probably emerged recently in Watamu, Mombasa, Diani and Misali where they are restricted.

37

Chapter 5 “Conclusion and implication for management”

CHAPTER 5: Conclusion and implications for management

Despite the long distance dispersal mechanism of Linckia laevigata reported in previous studies, a weak but significant genetic structure was found among the WIO populations. The results of hierarchical AMOVA suggest that gene flow among sites located in Kenya and northern Tanzania is obstructed by the local downwelling event, which is pronounced between April and

October. However, there was no correlation between pairwise ΦST values and distance indicating that distance might not be interrupting gene flow among L. laevigata population in WIO. Since few studies are available to test this hypothesis we recommend that future genetic studies in this region to be done at more or less geographical scale with an increased sample size to establish the stability of this genetic differentiation. While time estimates for population expansion need to be interpreted with caution because the techniques used do not cope well with populations in which a high mixture of haplotypes occurs (Benzie et al., 2002). The population expansion within Pleistocene estimated by this study is also reported in (Crandall et al., 2008a). This study corroborates previous studies in the existence of genetic break in the Indo-West Pacific. Lowered sea level during the Pleistocene that was associated with habitat decline in the Indo-West Pacific is implicated in this genetic divergence. This phylogeographic break has led to different lineages that are separated by at least three mutation steps. The five mitochondrial lineages deduced from this study were found to be geographically restricted or dominated certain biogeographies although mixing was seen in the two clades found in Indo-Malay.

Coral reef species have undergone major declines in their population such that their baseline today is very different from 500 years ago, or even 100 years ago (Jackson et al., 2001; Gardner, et al., 2003). Recovery of this overexploited or damaged marine species could take a considerably long time if their populations have fallen below critical thresholds (Roberts, 2003). Over-harvesting of marine invertebrates for aquarium trade can pose a serious threat to the existence of these species, especially when it is not sustainable. This has become a concern to conservation since demand for marine ornamental continues to grow in developed economies, which has caused increase in harvesting and exporting of this specimen from poor developing

38

Chapter 5 “Conclusion and implication for management”

countries. Linckia laevigata accounts for 3 percent of the total traded invertebrates even though hobbyists report that it has few chances of surviving in aquaria. This species is not listed as an endangered species but the paucity of juveniles observed by (Yamaguchi, 1977) signals a need to direct efforts to protect this species. Heavy collection of this species has been reported in the Indo-Malay archipelago countries. In the WIO region information on aquarium trade is limited yet local depletion in ornamental species has been observed an example is the local depletion of butterflyfish and angel fish in open access sites in Kenya. This could suggests unrestricted harvesting of these stocks especially in open access sites, which unlike marine protected areas lack clear management system. Marine protected areas (MPAs) network with corridors that allow dispersal for propagules between them would be more successful than if they are managed independently. In WIO, Kenya, Tanzania and Mozambique have ratified the international conventions that advocate for establishment of MPAs network; therefore recommendations of this study will boost the information required in the establishment of this network in WIO.

The weak genetic structure established in Western Indian Ocean populations suggests that current MPAs are sufficient to protect Linckia laevigata. Additionally, the connectivity reported between Tanzania and Madagascar population in one of the hierarchical analysis indicate that this species might not require special attention and any spatial designation of future MPAs in this region would contribute to its conservation. However, reports of unrestricted harvesting of ornamental species in open access sites in WIO means that emphasis should put on regulation of harvesting of Linckia laevigata population in these areas. In the case of Indo-Malay Archipelago populations the hierarchical AMOVA grouping suggests that the population of Linckia laevigata can be classified into the following groups: (1) Eastern Indian Ocean (2) Central Indo-Malay Archipelago and (3) Western Pacific, which can be managed independently.

39

“References”

REFERENCES

Ablan, M. C. a. (2006). Genetics and the study of fisheries connectivity in Asian developing countries. Fisheries Research, 78(2-3), 158–168.

Addison, J. a., & Hart, M. W. (2004). Analysis of population genetic structure of the green sea urchin ( Strongylocentrotus droebachiensis ) using microsatellites. Marine Biology, 144(2), 243–251.

Avise, J. C. (2004). Molecular Markers, Natural History, and Evolution. The Auk (Vol. 44, p. 684). Sinauer Associates.

Ayre, D. J., & Hughes, T. P. (2000). Genotypic diversity and gene flow in brooding and spawning corals along the Great Barrier Reef, Australia. Evolution: International Journal of Organic Evolution, 54(5), 1590–1605.

Ballard, J. W., & Kreitman, M. (1995). Is mitochondrial DNA a strictly neutral marker? Trends in ecology & evolution, 10(12), 485–8.

Barber, P. H, Palumbi, S.R, Erdmann, M.V and Moosa, M. . (2002). Sharp genetic breaks among populations of Haptosquilla pulchella ( Stomatopoda ) indicate limits to larval transport : patterns , causes , and consequences. Molecular Ecology, 11, 659–674.

Barnes, W. M. (1994). PCR amplification of up to 35-kb DNA with high fidelity and high yield from lambda bacteriophage templates. Proceedings of the National Academy of Sciences of the United States of America, 91(6), 2216–2220.

Bazin, E., Glémin, S., & Galtier, N. (2006). Population size does not influence mitochondrial genetic diversity in animals. Science (New York, N.Y.), 312(5773), 570–2.

Bell, L. J., Moyer, J. T., & Numachi, K. (1982). Morphological and genetic variation in Japanese populations of the anemonefish Amphiprion clarkii . Marine biology Berlin Heidelberg, 72(2), 99–108.

Bellwood, D. R., Wainwright, P. C., Fulton, C. J., & Hoey, a. (2002). Assembly rules and functional groups at global biogeographical scales. Functional Ecology, 16(5), 557–562.

Benzie, J. a H., Ballment, E., Forbes, a T., Demetriades, N. T., Sugama, K., Haryanti, & Moria, S. (2002). Mitochondrial DNA variation in Indo-Pacific populations of the giant tiger prawn, Penaeus monodon. Molecular ecology, 11(12), 2553–69.

Benzie, J. a. H. (1999). Major genetic differences between crown-ofthorns starfish (Acanthaster planci) populations from the Indian and Pacific Oceans. Evolution, 53, 1782–1795.

40

“References”

Berg H., Francis, J. & Souter, P. (2002). (2002). Support to marine research for sustainable management of marine and coastal resources in the Western Indian Ocean. Ambio, 31, 597– 601.

Bird, C. E., Holland, B. S., Bowen, B. W., & Toonen, R. J. (2007). Contrasting phylogeography in three endemic Hawaiian limpets (Cellana spp.) with similar life histories. Molecular Ecology, 16(15), 3173–3186.

Briggs, J. C. (1999). Coincident Biogeographic patterns: Indo-West Pacific Ocean. Evolution, 53(2), 326–335.

Burton, R. S., & Feldman, M. W. (1982). Population genetics of coastal and estuarine invertebrates: does larval behavior influence population structure? In V. S. Kennedy (Ed.), Estuarine Comparisons (pp. 537–551). Academic Press.

Carpenter, K. E., Barber, P. H., Crandall, E. D., Ablan-Lagman, M. C. a., Mahardika, G. N., Manjaji-Matsumoto, B. M., Juinio-Meñez, M. A., et al. (2011). Comparative Phylogeography of the Coral Triangle and Implications for Marine Management. Journal of Marine Biology, 2011, 1–14.

Chow, S., Okamoto, H., Uozumi, Y., Takeuchi, Y., & Takeyama, H. (1997). Genetic stock structure of the swordfish ( Xiphias gladius ) inferred by PCR-RFLP analysis of the mitochondrial DNA control region. Marine Biology, 127(3), 359–367.

Clark, A. M., Rowe, F. W. E. (1971). Shallow-water Indo-West-Pacific echinoderms. Trustess of the British museum (natural history), (238).

Clark., H. L. (1921). The echinoderm fauna of Torres Strait: Its composition and its origin. Carnegie institution of Washington, X, 1–223.

Clement, M., Posada, D., & Crandall, K. A. (2000). TCS: a computer program to estimate gene genealogies. Molecular Ecology, 9(10), 1657–1659.

Cowen, R. K., Paris, C. B., & Srinivasan, A. (2006). Scaling of connectivity in marine populations. Science (New York, N.Y.), 311(5760), 522–7.

Craig, M. T., Eble, J. A., Bowen, B. W., & Robertson, D. R. (2007). High genetic connectivity across the Indian and Pacific Oceans in the reef fish Myripristis berndti (Holocentridae). Marine Ecology Progress Series, 334(3), 245–254.

Crandall, E. D., Frey, M. A., Grosberg, R. K., & Barber, P. H. (2008). Contrasting demographic history and phylogeographical patterns in two Indo-Pacific gastropods. Molecular Ecology, 17(2), 611–626.

41

“References”

Crandall, E. D., Jones, M. E., Muñoz, M. M., Akinronbi, B., Erdmann, M. V., & Barber, P. H. (2008). Comparative phylogeography of two seastars and their ectosymbionts within the Coral Triangle. Molecular ecology, 17(24), 5276–90.

Dorenbosch, M., Pollux, B. J. a., Pustjens, a. Z., Rajagopal, S., Nagelkerken, I., Velde, G., & Staay, S. Y. M.-. (2006). Population structure of the Dory snapper, Lutjanus fulviflamma, in the western Indian Ocean revealed by means of AFLP fingerprinting. Hydrobiologia, 568(1), 43–53.

Downs, C. a, Woodley, C. M., Richmond, R. H., Lanning, L. L., & Owen, R. (2005). Shifting the paradigm of coral-reef “health” assessment. Marine pollution bulletin, 51(5-7),

Duda., T.F. and Palumbi., S. R. (1999). Population structure of the black tiger prawn , Penaeus monodon , among western Indian Ocean and western Paci fi c populations. Marine Biology, (134) 705–710.

Ewing, B., & Green, P. (1998). Base-Calling of Automated Sequencer Traces Using Phred . II . Error Probabilities. Genome Research, 8(3), 186–194.

Excoffier, L, Smouse, P. E., & Quattro, J. M. (1992). Analysis of Molecular Variance Inferred from Metric Distances among DNA Haplotypes: Application to Human Mitochondrial DNA Restriction Data. Genetics, 131(2), 479–491.

Excoffier, Laurent, & Lischer, H. E. L. (2010). Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Molecular ecology resources, 10(3), 564–7.

Fauvelot, C., & Planes, S. (2002). Understanding origins of present-day genetic structure in marine fish: biologically or historically driven patterns? Marine Biology, 141(4), 773–788.

Feral, J. (2002). How useful are the genetic markers in attempts to understand and manage marine biodiversity ? Journal of Experimental Marine Biology and Ecology, 268, 121–145.

Fogarty, M. J., & Botsford, L. W. (2007). Marine Population Connectivity: Population Connectivity and Spatial Management of Marine Fisheries. Oceanography, 20(3), 112–123.

Folmer, O., Black, M., Hoeh, W., Lutz, R., & Vrijenhoek, R. (1994). DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular marine biology and biotechnology, 3(5), 294–9.

Foltz, D. W. (2003). Invertebrate species with nonpelagic larvae have elevated levels of nonsynonymous substitutions and reduced nucleotide diversities. Journal of molecular evolution, 57(6), 607–12.

Forbes, A. T., Demetriades, N. T., Benzie, J. A. H., & Ballment, E. (1999). Allozyme frequencies indicate little geographic variation among stocks of giant tiger prawn Penaeus

42

“References”

monodon in the South-West Indian Ocean. South African Journal of Marine ScienceSuidAfrikaanse Tydskrif Vir Seewetenskap, 21, 271–277.

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(1), 103–116.

Fu, Y. X. (1997). Statistical Tests of Neutrality of Mutations against Population Growth, Hitchhiking and Background Selection. Genetics, 147(2), 915–925.

Galloway, R.W., & Kemp, E. M. (1981). “Late Cainozoic environments in Australia.” Ecological Biogeography of Australia, (Keast, A. (ed.)), 51–80.

Gardner, T. A., Côté, I. M., Gill, J. A., Grant, A., & Watkinson, A. R. (2003). Long-term region- wide declines in Caribbean corals. Science, 301(5635), 958–60.

Gaudian, G., Koyo, A., Wells, S. (2003). A Global Representative System of Marine Protected Areas. Marine Region 12: East Africa. A Report to the World Bank Environment Department.

Gopurenko, D., Hughes, J. M., & Keenan, C. P. (1999). Mitochondrial DNA evidence for rapid colonisation of the Indo-West Pacific by the mudcrab Scylla serrata. Marine Biology, 134, 227–233.

Gordon, A. L., & Fine, R. A. (1996). Pathways of water between the Pacific and Indian oceans in the Indonesian seas. Nature, 379(6561), 146–149.

Goreau, T., McClanahan, T., Hayes, R., & Strong, A. (2000). Conservation of Coral Reefs after the 1998 Global Bleaching Event. Conservation Biology, 14(1), 5–15.

Grant, W. S., & Bowen, B. W. (1998). Shallow population histories in deep evolutionary lineages of marine fishes: insights from sardines and anchovies and lessons for conservation. Journal of Heredity, 89(5), 415–426.

Grant, W.S., Schneider, A.C., Leslie, R.W &Cherry, M. (1993). Population genetics of the brown mussel Perna perna in southerb Africa. Journal of Experimental Marine Biology and Ecology, 165(7), Hall, T. A. (1999). BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symposium Series, 41(41), 95–98.

Hartl, D. L. (2000). A primer of population genetics 3rd edition (pp. 10–20). Sinauer Associates, Inc.

Hedgecock, D. (1994). Temporal and spatial genetic structure of marine animal populations. CalCOFI Rep,(35) 73–81.

43

“References”

Hellberg, M. E., Burton, R. S., Neigel, J. E., & Palumbi, S. R. (2002). Genetic assessment of connectivity among marine populations, Bulletin of marine science. 70(1), 273–290.

Hey, J., & Machado, C. a. (2003). The study of structured populations--new hope for a difficult and divided science. Nature reviews. Genetics, 4(7), 535–43.

Hunkapiller, T., Kaiser, R. J., Koop, B. F., & Hood, L. (1991). Large-scale and automated DNA sequence determination. Science, 254(5028), 59–67.

Imron, Jeffrey, B., Hale, P., Degnan, B. M., & Degnan, S. M. (2007). Pleistocene isolation and recent gene flow in Haliotis asinina, an Indo-Pacific vetigastropod with limited dispersal capacity. Molecular Ecology, 16(2), 289–304.

Jackson, J. B., Kirby, M. X., Berger, W. H., Bjorndal, K. A., Botsford, L. W., Bourque, B. J., Bradbury, R. H., et al. (2001). Historical overfishing and the recent collapse of coastal ecosystems. Science, 293(5530), 629–637.

Jokiel, P., & Martinelli, F. J. (1992). The vortex model of coral reef biogeography. Journal of Biogeography, 19(4), 449–458.

Juinio-Menez, M., Magsino, R., & Ravago-Gotanco, R. (2003). Genetic structure of Linckia laevigata and Tridacna crocea populations in the Palawan shelf and shoal reefs. Marine Biology, 142, 717–726.

Kelleher, G., Bleakley, C. &, & Wells, S. (1995). An Action Plan for High Seas Marine Protected Areas. The World Bank, 219pp.

Kochzius, M, Seidel, C., Hauschild, J., Kirchhoff, S., Mester, P., Meyer-Wachsmuth, I., Nuryanto, A., et al. (2009). Genetic population structures of the blue starfish Linckia laevigata and its gastropod ectoparasite Thyca crystallina. Marine Ecology Progress Series, 396(Elder 1979), 211–219.

Kochzius, Marc, & 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(17), 3775–87.

Kochzius, Marc, Söller, R., Khalaf, M. a., & Blohm, D. (2003). Molecular phylogeny of the lionfish genera Dendrochirus and Pterois (Scorpaenidae, Pteroinae) based on mitochondrial DNA sequences. Molecular Phylogenetics and Evolution, 28(3), 396–403.

Lacson, J. M., & Clark, S. (1995). Genetic divergence of Maldivian and Micronesian demes of the damselfishes Stegastes nigricans, Chrysiptera biocellata, C. glauca and C. leucopoma (Pomacentridae). Marine Biology Berlin, 121(4), 585–590.

Lavery, S., Moritz, C., & Fielder, D. R. (1996). Indo-Pacific population structure and evolutionary history of the coconut crab Birgus latro. Molecular Ecology, 5(4), 557–570.

44

“References”

Lessios, H. A., Kane, J., & Robertson, D. R. (2003). Phylogeography of the pantropical sea urchin Tripneustes: contrasting patterns of population structure between oceans. Evolution: International Journal of Organic Evolution, 57(9), 2026–2036.

Lessios, H. A., Kessing, B. D., & Pearse, J. S. (2001). Population structure and speciation in tropical seas: global phylogeography of the sea urchin Diadema. Evolution: International Journal of Organic Evolution, 55(5), 955–975.

Lessios, H. A., Kessing, B. D., Robertson, D. R., & Paulay, G. (1999). Phylogeography of the pantropical sea urchin Eucidaris in relation to land barriers and ocean currents. Evolution, 53(3), 806–817.

McClanahan, T. R. (1999). Is there a future for coral reef parks in poor tropical countries? Coral Reefs, 18(4), 321–325.

McClanahan, Tim, Maina, J., & Pet-Soede, L. (2002). Effects of the 1998 coral morality event on Kenyan coral reefs and fisheries. Ambio, 31(7-8), 543–550.

McClanahan, Tr. (1988). Seasonality in East Africa’s coastal waters. Marine Ecology Progress Series, 44, 191–199.

McMillan, W. O., & Palumbi, S. R. (1995). Concordant evolutionary patterns among Indo-West Pacific butterflyfishes. Proceedings of the Royal Society B: Biological Sciences, 260(1358), 229–236.

Meirmans, P. G. (2006). Using the AMOVA framework to estimate a standardized genetic differentiation measure. Evolution, 60(11), 2399–2402.

Mora, C., Andrèfouët, S., Costello, M. J., Kranenburg, C., Rollo, A., Veron, J., Gaston, K. J., et al. (2006). Ecology. Coral reefs and the global network of Marine Protected Areas. Science (New York, N.Y.), 312(5781), 1750–1.

Muthiga, N. a. (2009). Evaluating the effectiveness of management of the Malindi–Watamu marine protected area complex in Kenya. Ocean & Coastal Management, 52(8), 417–423.

Nei, M., & Jin, L. (1989). Variances of the average numbers of nucleotide substitutions within and between populations. Molecular Biology and Evolution, 6(3), 290–300.

Nevo, E., Noy, R., Lavie, B., & Muchtar, S. (1987). Levels of genetic diversity and resistance to pollution in marine organisms. FAO fisheries report, no. 352(Su, 175–82.

Nishikawa, A., Katoh, M., & Sakai, K. (2003). Larval settlement rates and gene flow of broadcast- ( Stylophora pistillata ) corals. Marine Ecology Progress Series, 256, 87–97.

Palumbi, S R. (1997). Molecular biogeography of the Pacific. Coral Reefs, 16(December 1996), S47–S52.

45

“References”

Palumbi, S R, & Wilson, A. C. (1990). Mitochondrial DNA diversity in the sea urchins Strongylocentrotus purpuratus and S. droebachiensis. Evolution, 44(2), 403–415.

Palumbi, S.R. (2003). Population genetics, demographic connectivity and the design of marine protected areas. Ecological applications : a publication of the Ecological Society of America, 13, S146–S158.

Palumbi, S.R. (1994). Genetic divergence, reproductive isolation, and marine speciation. annual review ecology and systematics, 25, 547–72.

Pandolfi, J. M. (1992). Successive isolation rather than evolutionary centres for the origination of Indo-Pacific reef corals. Journal of Biogeography, 19(6), 593–609.

Parker, P. G., Snow, A. A., Schug, M. D., Booton, G. C., & Fuerst, P. A. (1998). What Molecules Can Tell Us about Populations: Choosing and Using a Molecular Marker. Ecology, 79(2), 361–382.

Pearse, D. E., & Crandall, K. A. (2004). Beyond F ST : Analysis of population genetic data for conservation, 585–602.

Pollnac, R. B., Crawford, B. R., & Gorospe, M. L. G. (2001). Discovering factors that influence the success of community-based marine protected areas in the Visayas, Philippines. Ocean & Coastal Management, 44(11-12), 683–710.

Tuck, G. N. & Possingham, H. (2000). Marine protected areas for spatially structured exploited stocks. Marine Ecology Progress Series, 192, 89–101.

Ragionieri, L., Cannicci, S., Schubart, C. D., & Fratini, S. (2010). Gene flow and demographic history of the mangrove crab Neosarmatium meinerti: A case study from the western Indian Ocean. Estuarine, Coastal and Shelf Science, 86(2), 179–188.

Randall, J. E. (1998). Zoogeography of shore fishes of the Indo-Pacific region. Zoological Studies. Institute of Zoology, Academia Sinica.

Reid, D. G., Lal, K., Mackenzie-Dodds, J., Kaligis, F., Littlewood, D. T. J., & Williams, S. T. (2006). Comparative phylogeography and species boundaries in Echinolittorina snails in the central Indo-West Pacific. Journal of Biogeography, 33(6), 990–1006.

Ridgway, T. and Sampayo, E. M. (2005). Population genetic status of the Western Indian Ocean: what do we know? Western Indian Ocean J. Mar. Sci., 4, 1–9.

Riginos, C., & Victor, B. C. (2001). Larval spatial distributions and other early life-history characteristics predict genetic differentiation in eastern Pacific blennioid fishes. Proceedings of the Royal Society B: Biological Sciences, 268(1479), 1931–1936.

Roberts, C. M. (2003). Our shifting perspectives on the oceans. Oryx, 37(2), 166–177.

46

“References”

Rogers, A. R. (1995). Genetic evidence for a Pleistocene population explosion. Evolution, 49(4), 608–615.

Rogers, A. R., & Harpending, H. (1992). Population-Growth Makes Waves In The Distribution Of Pairwise Genetic-Differences. Molecular Biology and Evolution, 9(3), 552–569.

Schneider, S., & Excoffier, L. (1999). Estimation of past demographic parameters from the distribution of pairwise differences when the mutation rates vary among sites: application to human mitochondrial DNA. Genetics, 152(3), 1079–1089.

Schott, F. A., & Jr. Julian, P. M. (2001). The monsoon circulation of the Indian Ocean. progress in Oceanography, 51, 1–123.

Shendure, J., & Ji, H. (2008). Next-generation DNA sequencing. Nature biotechnology, 26(10), 1135–45.

Sheppard, C. R. C. (2000). Coral reefs of the Western Indian Ocean: An overview. In: McClanahan, T.R., Sheppard, C.R.C. & Obura, D.O. Oxford University Press, Newyork, 3– 38.

Shulman, M. J., Bermingham, E., & Oct, N. (1995). Early Life Histories , Ocean Currents , and the Population Genetics of Caribbean Reef Fishes. Genetics, 49(5), 897–910.

Siddall, M., Rohling, E., Almogi-Labin, A., Hemleben, C., Meischner, D., Schmelzer, I., & Smeed, D. A. (2003). Sea-level fluctuations during the last glacial cycle. Nature, 423(June), 19–24.

Silva, I. C., Mesquita, N., & Paula, J. (2009). Genetic and morphological differentiation of the mangrove crab Perisesarma guttatum (Brachyura: Sesarmidae) along an East African latitudinal gradient. Biological Journal of the Linnean Society, 99(1), 28–46.

Silva, I. C., Mesquita, N., & Paula, J. (2010). Lack of population structure in the fiddler crab Uca annulipes along an East African latitudinal gradient: genetic and morphometric evidence. Marine Biology, 157(5), 1113–1126.

Swerdlow, H., Wu, S. L., Harke, H., & Dovichi, N. J. (1990). Capillary gel electrophoresis for DNA sequencing. Laser-induced fluorescence detection with the sheath flow cuvette. Journal Of Chromatography, 516(1), 61–67.

Tajima, F. (1989). Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics, 123(3), 585–595.

Taylor, M. S., & Hellberg, M. E. (2005). Marine radiations at small geographic scales: speciation in neotropical reef gobies (Elacatinus). Evolution: International Journal of Organic Evolution, 59(2), 374–385.

47

“References”

Thompson, J. D., Higgins, D. G., & Gibson, T. J. (1994). CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research, 22(22), 4673–80.

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(18), 3999–4014.

Uthicke, S., & Benzie, J. (2001). Restricted gene flow between Holothuria scabra (Echinodermata: Holothuroidea) populations along the north-east coast of Australia and the Solomon Islands. Marine Ecology Progress Series, 216, 109–117.

Uthicke, S., & Benzie, J. a. H. (2003). Gene flow and population history in high dispersal marine invertebrates: mitochondrial DNA analysis of Holothuria nobilis (Echinodermata: Holothuroidea) populations from the Indo-Pacific. Molecular Ecology, 12(10), 2635–2648.

Visram, S., Yang, M.-C., Pillay, R. M., Said, S., Henriksson, O., Grahn, M., & Chen, C. A. (2010). Genetic connectivity and historical demography of the blue barred parrotfish (Scarus ghobban) in the western Indian Ocean. Marine Biology, 157(7), 1475–1487.

Vogler, C., Benzie, J., Lessios, H., Barber, P., & Wörheide, G. (2008). A threat to coral reefs multiplied? Four species of crown-of-thorns starfish. Biology Letters, 4(6), 696–699.

Voris, H. K. (2000). Maps of Pleistocene sea levels in Southeast Asia: shorelines, river systems and time durations. Journal of Biogeography, 27(5), 1153–1167.

WWF Eastern African Marine, E. (2004). Towards the Establishment of an Ecologically Representative Network of Marine Protected Areas in Kenya , Tanzania and Mozambique Publication Credits :, WWF, 74pp.

Wabnitz, C., Taylor, M., Green, E., & Razak, T. (2003). From Ocean to Aquarium: The global trade in marine ornamental species. UNEPWCMC Cambridge UK (Vol. 64, p. 65). UNEP/Earthprint.

Waters, J. M., Dijkstra, L. H., & Wallis, G. P. (2000). Biogeography of a southern hemisphere freshwater fish: how important is marine dispersal? Molecular Ecology, 9(11), 1815–1821.

Weersing, K., & Toonen, R. (2009). Population genetics, larval dispersal, and connectivity in marine systems. Marine Ecology Progress Series, 393(1), 1–12.

Whitlock, M. C., & McCauley, D. E. (1999). Indirect measures of gene flow and migration: FST not equal to 1/(4Nm + 1). Heredity, 82 ( Pt 2)(November 1998), 117–25.

Wilkinson, C. (2004). Status of coral reefs of the world. (Clive Wilkinson, Ed.)Status of Coral Reefs of the World (Vol. 1, p. 2 Volumes). Australian Institute of Marine Science.

48

“References”

Wilkinson, C. R. (1999). Global and local threats to coral reef functioning and existence: review and predictions. Marine And Freshwater Research, 50(8), 867–878.

Williams, S T. (2000). Species boundaries in the starfish genus Linckia. Marine Biology, 136(1), 137–148.

Williams, S T, & Benzie, J. A. H. (1996). Genetic uniformity of widely separated populations of the coral reef starfish Linckia laevigata from the East Indian and West Pacific Oceans, revealed by allozyme electrophoresis. Marine Biology, 126(1), 99–107.

Williams, S T, Jara, J., Gomez, E., & Knowlton, N. (2002). The marine indo-west pacific break: contrasting the resolving power of mitochondrial and nuclear genes. Integrative and comparative biology, 42(5), 941–52.

Williams, S. T., & Benzie, J. a. H. (1993). Genetic consequences of long larval life in the starfish Linckia laevigata (Echinodermata: Asteroidea) on the Great Barrier Reef. Marine Biology, 117(1), 71–77.

Williams, S. T., & Benzie, J. a. H. (1997). Indo-West Pacific patterns of genetic differentiation in the high-dispersal starfish Linckia laevigata. Molecular Ecology, 6(6), 559–573.

Williams, S. T. and Benzie, A. 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, Volume 52(Issue 1), 87– 99.

Woodland, D. J. (1983). Zoogeography of the Siganidae (Pisces): an interpretation of distribution and richness pattern. Bulletin of marine science, 33(3), 713–717.

Yamaguchi, M. (1977). Population structure, spawning, and growth of the coral reef asteroid Linckia laevigata (Linnaeus), 31(1), 13–30.

Yasuda, N., Nagai, S., Hamaguchi, M., Okaji, K., Gérard, K., & Nadaoka, K. (2009). Gene flow of Acanthaster planci (L.) in relation to ocean currents revealed by microsatellite analysis. Molecular ecology, 18(8), 1574–90.

York, K. L., Blacket, M. J., & Appleton, B. R. (2008). The Bassian Isthmus and the major ocean currents of southeast Australia influence the phylogeography and population structure of a southern Australian intertidal barnacle Catomerus polymerus (Darwin). Molecular Ecology, 17(8), 1948–1961.

Zagalsky, P.F. Haxo .F, Hertzberg .S, and L.-J. (1989). Studies on blue carotenoprotein, linckiacyanin, isolated from the starfish Linckia laevigata. comparative Biochemistry and physiology, 93B, 339–353.

Li W-H, Graur D. (1991). Fundamentals of Molecular Evolution.Sinauer, Sunderland MA

49

“References”

. Nei M .(1987). Molecular evolutionary genetics. Columbia University Press, New York

Laxton, J. H. (1974). A preliminary study of the biology and ecology of the blue starfish Linckia laevigata (L.) on the Australian Great Barrier Reef and an interpretation of its role in the coral reef ecosystem. BioI. J. Linn. Soc.6: 47-64.

Bell, B E. (1972). Marine fisheries. In: Morgan, W. T. W. (ed.) East Africa: its people and resources. Oxford University Press, London, p. 243-254 Blackbum. M., Laurs, R. M., Owen

Alcazar R. Diana & Kochzius M. (2011). Genetic population structure of blue sea star Linckia laevigata in the Visayas, Phillipines. Master’s thesis. Vrije Universiteit Brussel.

Yamaguchi, M. (1973). Early life histories of coral reef asteroids, with special reference to Acanthaster planci (L.). Pages 369-387 in O. A. Jones and R. Endean, eds. Biology and geology of coral reefs. Vol. 2, bioI. 1. Academic Press, New York.

Wilson SK, Graham NAJ, Pratchett MS, Jones GP, Polunin NVC (2006). Multiple disturbances and the global degradation of coral reefs: are reef fishes at risk or resilient? Global Chang Biol12:2220–2234

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“Appendices”

APPENDICES

Appendix 1. Mitochondrial cytochrome c oxidase I Haplotypes of WIO

Hap_1 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_2 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTTATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATGG TCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTGCTCGCTGGAGCAATAACAATGCTCCTAACGGAT CGTAA

Hap_3 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCCC TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_4 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGAGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACACCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATGG TCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGAT CGTAA

51

“Appendices”

Hap_5 ACAGCACACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_6 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCCC TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTYTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_7 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTAGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_8 ACAGCGCACGCCCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCCC TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_9 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATTCC TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATG

52

“Appendices”

GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_10 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAGAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCATCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_11 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTTATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATGG TCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGAT CGTAA

Hap_12 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTYTAATGATAGGGGCRCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTKGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTAGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_13 ACASSGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGAATGATCCC TCTMATGATAGGGGCACCAAATATGGCGTTCCCACGAMYGAACAAAATGAGCTTTKGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCRSTCATGCTGGGGGATCAGTTSATCTTGCCATATTTTCACTCCATCTAGCTGGTGCWTCCTCTATTCWASCA TCAATAAAATTCATTACCACCATTATAAAWATGCGTASGCCAGGAATTTCATTCAACCGGTTACCTCTGTTTGTATG GGCAGTGTTCCTAACAACCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_14 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG

53

“Appendices”

GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACACCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATGG TCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGAT CGTAA

Hap_15 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCCC TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCYGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCCTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATGG TCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGAT CGTAA

Hap_16 ACAGCGCMCGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTYTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACARCCTTTCTCCTTCTTCTTTCCCTTCCAGTGCTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_17 ACAGCGCACGCTCTTGTRATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCCC TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTGG CCTCGCTCACGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTACATCTAGCGGGTGCATCCTCTATTCTAGCAT CAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATGG TCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGAT CGTAA

Hap_18 ACAGCGCACGCTCTTGTGATGATTTTYTTCATGGTAATGCCTATAAWGATAGGAGGATTTGGAAACTGACTTATCC CTCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_19 ACAGCGCACGCTCTTKTGAAGATTTTCTTCTTGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCCC

54

“Appendices”

TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_20 ACAGCGCACGCTCTTGTGAWGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCC CTCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_21 CCGGCCCAGGCTTTKWTTATGATTTTTTTCTTGGGTATGCCCATTAAGATAGGGGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCMSATATGGCGTTCCCACCAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCCTTATCTAGTG GCCTCGCTCACGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTTGCGGGTGCATCCTCTATTCTWGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_22 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCGTTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_23 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAGAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGAGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCATCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAACATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTGCTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

55

“Appendices”

Hap_24 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTAGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCCGTACTCGCYGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_25 CCGGCCCATGTTTTTTTTATGTTTTTTTTCTTGGTAATGCCTATTAAGATAGGAGGATTTGGAAACTGACTTATCCCT CTTATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCCT TCTACTTGTAGCCTCATCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACAATTTACCCACCCTTATCTAGTGGC CTCACTCACGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCATC AATAAATTTCATTACCACCGTAATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATGGG CAGTGTTCGTAACACCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGATC GTAA

Hap_26 ACAGCGCACGCTCTTGTGATGATTTTTTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCCC TCTAATGATAGGGGGGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCCGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTTGTATGG TCAGTGTTCGTAACAGCCTTTTTCCTTCTTCTTTCCCTTCCCGTACTCGCCGGAGCCATAACAATGCTCCTAACGGAT CGTAA

Hap_27 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCCC TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATG GTCTGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTAACTGGAGCAATAACAATGCTCCTACAGGA TCGTAA

Hap_28 ACAGSGCMCGCTCTTGTGATGATTTTATTCATGGTAAKGCCKATAATGATAGGAGGATTTGAAAGCYRAYTTATCC CTYYMMYGAKAGGGGKGCCAGATAMGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCAT TCCTYMTAGTTGTAGCCTCAGMYGGGGTAGAAAGAGGRGSKGGRMCRGGGTKAASKATTKACCCAYCATKATCTA GTGGCSTCGGTCATGCTGGGGGMTCMGWTSATCTTGCCAWATTTTCMCTCCMTYTGGCGGGKGCATYCTSKMT TCRAGSRTCAAWAAATTTCMTTACCACCRWTATWAATAKGSGTASGCCWGGAAWTTCAYTTGACCGGTTACCTC

56

“Appendices”

TGTTTGTATGGTCAGTGTTCGWAAAAGCCTTTCTCCTTCTTYTTTCCCTTSCAGYACTMGCYGGAGCAAKAACAMT GCTCMTAACGGATCCTAA

Hap_29 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCCC TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTGG CCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCAGGTGCATCCTCTATTCTAGCATC AATAAATTTTATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATGGTC AGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTGCTCGCTGGAGCAATAACAATGCTCCTAACGGATCG TAA

Hap_30 ACAGCGCACGCTCTTGTCATGATTTTTTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAAATGACTTATTCC TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_31 CCGSCACCTGCTTTTTTTATGATTTTTTTCTTGGGTTTGCCCATTAAGATAGGRGGGGTTGGAAAATGACTTATCCCT CTAATGATAGGGGCGCCCCATATGGCGTTCCCACCAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCCT TCTACTTGTAGCCTCACCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCCTTATCTAGTGGC CTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCATC AATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATGGG CAGTGTTCGTAACACCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGATC GTAA

Hap_32 CCGSCACATGCTTTTTTTATGATTTTTTTCTTGGGTTTGCCCATTAAGATAAGAGGAGTTGGAAACTGGGTGATCCC CCTAATGATAGGGGCACCCCAAATGGGGTTCCCCCCAATGAACAAAATGAACTTTTTACTTGTCCCTCCCTCATTCC TTCTACTTGTAACCTCACCTGGGGTAGAAAGAGGAGCTGGAACCCGATGAACAATTTACCCACCCTTATCTAGTGG CCTCGCTCACGCTGGGGGATGGGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCAT CAATAAATTTTATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATGG GCAGTGTTCGTAACACCCTTTCTCCTTCTTCTTTCCCTTCCAGTGCTCGCTGGAGCAATAACAATGCTCCTAACGGAT CGTAA

Hap_33 CCGSCCCATGCTTTTTTTATGTTTTTCTTCTTGGTAATGCCTATTAAGATAGGAGGAGTTGGAAACTGACTTATTCCT CTAATGATAGGGGCGCCCCATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCCT TCTACTTGTAGCCTCATCTGGGGGAARRWGAAGAGCTGGAACAGGGTGAACAATTTACCCACCCYTATCTAGAGG

57

“Appendices”

CCTCACTCACGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCCCTATTCTAGCAT CAATAAATTTCATTACCACCGTAATAAATATGCGTACGCCAGGAATTTCATTTGAACGGTTACCTCTGTTCGTATGG GCAGTGTTCGTAACAACCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGAT CGTAA

Hap_34 CMGGCACCTGCTCTTGTGATGATTTTTTTCATGGTAATGCCCATTAAGATAGGAGGAGTTGGAAACTGACTTATCC CTCTAATGATAGGGGCACCAGATATGGGGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGKCCCTCCTTCATT CCTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCCTTATCTAGT GGCCTCGCTCACGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGC ATCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_35 ACAGCGCACGCTCTTGTGATGATTTTTTTCATGGTAATGCCCATWWAGATAGGAGGATKTGGAAAAWGAGTTAT CCCTCTAATGATAGGGGCGCCAGATACGGGGCTCCCACGAAGGAACAAAATGAGCTTTTGACGTGTCCGTCTCTC ATTCTTTTTATATGTAGCGTCAGCTGGGGTAGAAAGAGGAGAGGGAACAGGATGAACTATTTACCCACCATTATCT AGTGGCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCAGGTGCATCCTTTATTCT AGCATCAATAAATTTTATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTTGT ATGGTCTGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTCCCGTTACTAAGTGGAGCAATAACAATGCTCCGAAA GGATTGTAA

Hap_36 CCGCCACCTGTTTTTTTAATGTTTTTTTTCTTGGTWATGCCCATTAAGATAGGAGGATTTGGAAACTGACTGATCCC TCTAATGATAGGGGCACCCCATATGGCGTTCCCACCAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTGG CCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCAT CAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATGG TCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGAT CATAA

Hap_37 ACAGCGCACGCTCTTGTGATGATTTTTTTYATGGTAATGCCTATAATGATAGGAGGATTTGGAAAMTGACTTATCC CTCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_38 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCCC

58

“Appendices”

TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCACGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATGA TCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGAT CGTAA

Hap_39 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCCC TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGCAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_40 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTMGCTGGAGCAATAACAATGCTCCTAACGG ATCGTAA

Hap_41 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTGCTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_42 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGGTTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTATTCGTAACAGCCTTTCTTCTTCTTCTTTCCCTTCCAGTGCTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

59

“Appendices”

Hap_43 ACAGCGCCCGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCCC TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCATCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTGG CCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCAT CAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATGG TCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGAT CGTAA

Hap_44 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTAGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_45 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCATCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_46 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCCC TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCCGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_47 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCTGGTGCATCCTCTATTCTAGCAT CAATAAATTTCATTACCACCATTATAAATATGCGTACACCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATGGT

60

“Appendices”

CAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGATC GTAA

Hap_48 ACAGCGCCCGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGGCTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTGCTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_49 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCCC TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTGG CCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCAT CAATAAATTTCATTACCACCGTTATAAATATGCGTACACCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATGAT CAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTGCTAGCTGGAGCAATAACAATGCTCCTAACGGATC GTAA

Hap_50 ACAGCGCACGCTCTTGTGATGATTTTTTTCATTGTAATGCCCCAAATGATAGGAGGGTTTGGAAAAAGAGTTATCC TTCTAATGATAGGGGCGCCAGATACGGGGCTTCCCCGAATGAACAAAATGAGATTTTGACGTGTCCCTCTCTCATT CCTTTTACTTGTAGCGTCACCGGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTGCCCACCATTATCTAGT GGCCTCGCTCATGCTGGGGGATCAGTTGATCCAGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGC ATCAATAAATTTCATTACCACCATTATAAATATGCGTACCCCAGGAATTTCACTTGACCGGTTACCTCAGTTTGTTTG GTCTGGGTTCCTAACCACCTTTCTCCTCCTAATTTCCCTTCCAGTACTAACTGGAAGGATTACAATACTCCGACAGG ATGGTAA

Hap_51 ACAGCGCACGCTCTTGTGATGATTTTYTTYTTKGTAATGCSTMWAATGATAGGAGGATKTGGAAAATGACTTATCC CTCTAMTKATAGGGGCGCCAGATAKGGCGTTCCCACCAATGAACAAAATGAGGKTTTGACTTGTCCCTCCYTCATT CCTTCTACWTGTAGCCTCASCYGGGGTAGAAAGAGGAGCKGGAACAGGGTGAACTATTTACCCACCATTATCTAG TGGCCYMGCTCATGCTGGGGGATCAGTTGATCCAGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTA GCATCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTTGTA TGGTCAGGGTTACAAACCGCCTTTCTCCTCCTAATTTCCCTTCCAATAATAGCTGGAGGAATAGCAATGSTCYKACA AGATGWTAA

Hap_52 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAGAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG

61

“Appendices”

GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_53 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GGCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGG ATCGTAA

Hap_54 ACAGCGCACGCTCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCCC TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTGG CCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCTGGTGCATCCTCTATTCTAGCATC AATAAATTTCATTACCACCGTTATAAATATGCGTACACCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATGGTC AGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGATCG TAA

Hap_55 WCRGCSCMNGYTTTTTTAATGATTTTYTTCWTGGTAATGCCYATWATGATAGGAGGATTTGGAAACTGACTTATC CCTCTAATGATAGGGGCGCCMGATATGGCGTTCCCCCCAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCAT TCCTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCCCCCTTATCTAG TGGCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGGGCATCCTCTATTCTAG CATCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTAT GGGCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACG GATCGTAA

Hap_56 ACGAAGCACGATCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCC CTCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_57 ACAGCGCACGCTCTTGTGATGATTTTTTTCATGGTAATGCCCCTAATGATAGGAGGATTTGGAAACTGACTTATCCC

62

“Appendices”

TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTTCCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATGG TCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGAT CGTAA

Hap_58 ACAGCGCACGCTCTTGTGATGATTTTYTTCATGGTAATGCCYATAATGATAGGAGGATTTGGAAACTGACTTATCCC TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_59 CCGACGCACAATCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCACCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_60 ACGACGCACAATCTTGTGATGATTTTCTTCATGGTAATGCCTATAATGATAGGAGGATTTGGAAACTGACTTATCCC TCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTCC TTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGCTGGAACAGGGTGAACTATTTACCCACCATTATCTAGTG GCCTCGCTCATGCTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTGGCGGGTGCATCCTCTATTCTAGCA TCAATAAATTTCATTACCACCGTTATAAATATGCGTACGCCAGGAATTTCATTTGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTCTCCTTCTTCTTTCCCTTCCAGTACTCGCTGGAGCAATAACAATGCTCCTAACGGA TCGTAA

Hap_61 ACAGCGCCCGCTCTTGTGATGATTTTCTTCTTGGTAATGCCGATAATGATAGGAGGATTTGGAAACTGACTGATCC CTCTAATGATAGGGGCGCCAGATATGGCGTTCCCACGAATGAACAAAATGAGCTTTTGACTTGTCCCTCCTTCATTC CTTCTACTTGTAGCCTCAGCTGGGGTAGAAAGAGGAGGTGGAACAGGATGAACTATTTACCCACCATTATCTAGTG GCCTCGGTCATGGTGGGGGATCAGTTGATCTTGCCATATTTTCACTCCATCTAGCGGGGGCATCCTTTATTCTAGG GTCAATAAATTTTATTACCACCATTATAAATATGCGTACGCCAGGAATTTCATTCGACCGGTTACCTCTGTTCGTATG GTCAGTGTTCGTAACAGCCTTTTTCCTTCTTCTTTCCCTTCCCGGACTCACTGGAGCTATAACAATTCTCCTAACGGA TCGTAA

63

“Appendices”

Appendix 2. Pairwise ΦST-values among populations of Linckia laevigata in WIO and Indo- Malay Archipelago. For sample site codes see list of abbreviations and Table.1.

64

“Appendices”

Mer Mar Sal Sj Alm Ce Bk Ke Ko KK Ls Lu Ma Mi Pi PS SA Ka SSP TI SP Do Bi Mk As Ku Mi(PE) An Sa Wa Mo Di Ar Ja BI Mer 0.00 Mar -0.03 0.00 Sal 0.08 0.09 0.00 Sj -0.03 -0.02 0.06 0.00 Alm -0.02 0.01 0.02 -0.01 0.00 Ce -0.03 0.00 0.02 -0.03 -0.04 0.00 Bk -0.01 0.02 -0.04 -0.01 -0.04 -0.06 0.00 Ke 0.03 0.05 0.00 0.01 -0.03 -0.04 -0.04 0.00 Ko -0.04 -0.03 0.16 -0.01 0.02 0.03 0.07 0.14 0.00 KK -0.03 -0.05 0.06 -0.03 -0.02 0.00 -0.01 0.04 -0.03 0.00 LS 0.01 -0.01 0.20 0.02 0.08 0.09 0.13 0.18 -0.05 -0.01 0.00 Lu -0.06 -0.05 0.07 -0.05 -0.02 -0.03 -0.02 0.03 -0.06 -0.08 -0.04 0.00 Ma -0.02 -0.03 0.01 -0.03 -0.03 -0.03 -0.04 -0.01 0.01 -0.08 0.03 -0.05 0.00 Mi 0.03 0.01 0.10 0.02 0.04 0.05 0.04 0.10 0.01 -0.06 0.03 -0.01 -0.02 0.00 Pi -0.05 -0.05 0.05 -0.04 -0.03 -0.04 -0.03 0.01 -0.03 -0.05 0.00 -0.07 -0.05 0.01 0.00 PS 0.05 0.07 0.02 0.05 -0.03 0.01 -0.02 -0.04 0.12 0.00 0.18 0.04 -0.01 0.07 0.04 0.00 SA -0.05 -0.05 0.01 -0.04 -0.05 -0.05 -0.05 -0.04 -0.01 -0.08 0.03 -0.07 -0.07 0.00 -0.07 -0.02 0.00 Ka -0.04 -0.02 0.10 -0.01 0.00 0.01 0.01 0.07 -0.05 -0.08 -0.01 -0.05 -0.03 -0.03 -0.03 0.03 -0.04 0.00 SSP 0.16 0.11 0.36 0.13 0.21 0.29 0.32 0.37 0.07 0.13 0.05 0.09 0.18 0.13 0.16 0.34 0.18 0.12 0.00 TI 0.03 0.02 0.17 0.02 0.07 0.06 0.08 0.15 0.00 -0.01 0.01 -0.01 0.02 -0.03 0.02 0.14 0.03 0.00 0.09 0.00 SP -0.02 -0.02 0.11 -0.01 0.02 0.01 0.04 0.07 -0.03 -0.03 -0.01 -0.04 -0.01 0.02 -0.03 0.09 -0.02 -0.02 0.09 0.02 0.00 Do 0.09 0.04 0.27 0.06 0.14 0.18 0.22 0.26 0.04 0.02 -0.02 0.02 0.07 0.04 0.07 0.25 0.09 0.06 0.01 0.03 0.03 0.00 Bi -0.03 -0.03 0.12 -0.03 0.01 0.00 0.04 0.08 -0.05 -0.04 -0.04 -0.06 -0.03 -0.02 -0.05 0.10 -0.03 -0.03 0.10 -0.03 -0.03 0.01 0.00 Mk 0.22 0.22 0.36 0.23 0.25 0.26 0.28 0.32 0.14 0.16 0.16 0.16 0.22 0.22 0.22 0.30 0.21 0.17 0.22 0.25 0.22 0.20 0.20 0.00 As 0.04 0.02 0.27 0.05 0.11 0.15 0.18 0.24 -0.04 -0.02 -0.08 -0.03 0.07 0.05 0.05 0.19 0.05 -0.04 0.11 0.07 0.01 0.07 0.02 0.01 0.00 Ku 0.12 0.10 0.35 0.12 0.19 0.25 0.28 0.36 0.01 0.10 0.02 0.08 0.16 0.08 0.15 0.30 0.16 0.05 0.00 0.05 0.08 0.04 0.07 0.19 0.06 0.00 Mi(PE) 0.15 0.14 0.31 0.15 0.19 0.21 0.22 0.28 0.07 0.06 0.07 0.07 0.15 0.13 0.14 0.24 0.14 0.08 0.13 0.16 0.13 0.11 0.12 -0.01 -0.09 0.11 0.00 An 0.26 0.23 0.40 0.24 0.30 0.35 0.37 0.40 0.20 0.24 0.19 0.22 0.28 0.27 0.27 0.39 0.27 0.22 0.26 0.28 0.21 0.25 0.25 0.00 0.11 0.25 0.06 0.00 Sa 0.27 0.23 0.41 0.24 0.30 0.37 0.39 0.43 0.22 0.27 0.21 0.23 0.29 0.28 0.28 0.41 0.29 0.24 0.31 0.29 0.21 0.29 0.27 0.00 0.13 0.31 0.06 -0.05 0.00 Wa 0.21 0.21 0.36 0.23 0.25 0.27 0.28 0.32 0.14 0.16 0.14 0.15 0.22 0.22 0.21 0.31 0.21 0.16 0.18 0.23 0.21 0.17 0.19 -0.02 0.00 0.16 -0.01 -0.02 -0.02 0.00 Mo 0.16 0.17 0.30 0.19 0.20 0.19 0.21 0.24 0.08 0.08 0.09 0.10 0.16 0.16 0.15 0.23 0.15 0.10 0.11 0.17 0.18 0.10 0.13 -0.02 -0.08 0.09 -0.05 0.01 0.00 -0.03 0.00 Di 0.20 0.23 0.31 0.25 0.23 0.19 0.20 0.23 0.12 0.10 0.14 0.12 0.18 0.19 0.18 0.21 0.17 0.14 0.16 0.21 0.27 0.14 0.17 0.03 -0.03 0.13 -0.01 0.12 0.11 0.04 0.00 0.00 Ar 0.33 0.30 0.46 0.31 0.36 0.41 0.43 0.47 0.27 0.33 0.27 0.29 0.35 0.33 0.34 0.46 0.35 0.30 0.32 0.34 0.28 0.32 0.32 0.02 0.22 0.31 0.14 -0.01 0.01 0.00 0.05 0.15 0.00 Ja 0.16 0.17 0.31 0.19 0.21 0.20 0.22 0.26 0.08 0.10 0.09 0.10 0.17 0.17 0.16 0.25 0.16 0.12 0.11 0.18 0.18 0.10 0.14 -0.01 -0.07 0.09 -0.03 0.00 -0.01 -0.01 -0.01 0.06 0.04 0.00 BI -0.04 -0.01 0.04 -0.02 -0.04 -0.04 -0.03 -0.01 0.01 -0.01 0.06 -0.04 -0.02 0.06 -0.04 0.01 -0.05 -0.02 0.24 0.08 0.00 0.16 0.01 0.25 0.10 0.21 0.19 0.30 0.31 0.24 0.18 0.21 0.37 0.20 0.00 NB 0.28 0.28 -0.02 0.20 0.11 0.17 0.01 0.07 0.44 0.21 0.49 0.23 0.15 0.24 0.26 0.03 0.14 0.26 0.74 0.37 0.32 0.61 0.36 0.46 0.64 0.72 0.44 0.65 0.71 0.48 0.32 0.18 0.68 0.36 0.22

65