Epinephelus Quoyanus) in A

Connectivity of the Longfin Grouper (Epinephelus Quoyanus) in a

marine reserve in the Great Keppel Island Group

Thesis by

Manalle Al-Salamah

In Partial Fulfillment of the Requirements

For the Degree of

Master of Science in Marine Science

King Abdullah University of Science and Technology, Thuwal,

Kingdom of Saudi Arabia

December 2014

The thesis of Manalle Al-Salamah is approved by the examination committee.

Committee Chairperson: Dr. Michael Berumen

Committee Member: Dr. Stein Kaartvedt

Committee Member: Dr. Burton Jones

Manalle Al-Salamah

ABSTRACT

Connectivity of the Longfin Grouper (Epinephelus Quoyanus) in a marine reserve in the

Great Keppel Island Group

Manalle Al-Salamah

With a dramatic decrease of biodiversity as a result of the increase in exploitation of

marine ecosystems, the establishment of marine protected areas (MPAs) serves as an

important means of protecting those resources. Although there is support for the effectiveness of these MPAs and MPA networks, there is room for improvement in terms

of MPA management and design. For example, a better understanding of the dispersal dynamics of targeted species across these MPAs will serve as a more accurate means of

reserve as well as fisheries management. While there have been many methods used to

determine the larval dispersal of a certain species, parentage analysis is becoming the

most robust. In this thesis, I attempt to determine the patterns of self-recruitment and

larval dispersal of the Longfin Grouper (Epinephelus quoyanus) in one focal marine reserve within the Great Keppel Island group through the method of parentage. For this, I developed 14 microsatellite markers and with those, genotyped 610 adults as well as 478

juveniles from the study site. These genotypes allowed me to assign offspring to their potential parents, which then allowed me to measure the self-recruitment, local retention

as well as larval dispersal percentages of this species from and within the reserve. My results indicate that there is 32% local retention to the reserve while 68% of the assigned

juveniles were dispersed to other areas (4% of which dispersed to another reserve).

Previous studies conducted in the same area showed higher reserve self-recruitment rates

5 for both Plectropomus maculatus (~30%) and Lutjanus carponotatus (64%) despite their

similar life history traits. The results from this study add to the growing evidence that dispersal patterns cannot be generalized across marine systems or even between species

within a single system.

Keywords: Epinephelus quoyanus, Keppel Island Group, Larval dispersal, Parentage analysis, Self-recruitment

ACKNOWLEDGEMENTS

First, I would like to thank my advisor Dr. Michael Berumen for his support in many aspects during my tenure at KAUST. He continuously supported me in my work, but also supported my rather obsessive passion for reptiles by allowing me to partake in collaborations outside of my field of study, and for this I am eternally grateful. I’d like to thank my committee members, Dr. Stein Kaartvedt (University of Oslo) and Dr. Burton

Jones, and the Red Sea Research Center for their support of my thesis and their valuable feedback. I would also like to thank Dr. Hugo Harrison (James Cook University) for his patience and mentorship throughout the course of this study and Dr. Pablo Saenz-

Agudelo (Universidad Austral de Chile) for his help and important advice concerning lab and research techniques. I’d like to thank my colleagues at the KAUST Bioscience Core

Lab for carrying out fragment analyses on my samples. Furthermore, I want to thank my lab mates Veronica Chaidez, Diego Lozano-Cortez, Remy Gatins, Emily Giles, Marcela

Herrera-Sarrias, Alex Kattan, May Roberts, Vanessa Robitzch, Lu Sun, Casey Zakroff and Jesse Cochran for their support during my endless nights at the lab.

For their input and help in the other projects I pursued while at KAUST, I thank

Sebastian Baumgarten, Jit Ern Chen, YiJin Liew, Noura Zahran, Dr. Manuel Aranda, and

Till Rothig. I would like to extend a very special thank you to my mother, Cindy

Partridge and my brothers Mohammed, Majed, and Tareq Al-Salamah for raising me in a mentally stimulating environment. For inspiring me to begin a career in genetics I’d like to thank Stan Lee, Jack Kirby, Dr. Charles Xavier, Erik Lehnsherr, and Kurt Wagner.

Finally, I would like to thank my brother Talal Al-Salamah for his constant encouragement while writing this thesis and my closest friend Meshael Alomair for her

7 support of my love for biology since grade school; I would not be where I am today without her.

TABLE OF CONTENTS

 Examination Committee Approvals Form………………….……………….p.2  Copyright Page.…………………….………………….…………………….p.3  Abstract……………………………………....…………………...……..…..p.4  Acknowledgments…………………………....……………………..…...…..p.6  Table of Contents……………………………..…………………….…….....p.7  List of Abbreviations………………..…………………………………….....p.9  List of Figures…………………..……………………………….……...... …p.10  List of Tables.………………….....……………………………………….….p.12  1. Introduction…………..……………...…………………………..………...p.13 o 1.1 Marine Protected Areas………………...……………………...... p.13

o 1.2 Connectivity and Larval Dispersal………………………..……….....p.15 o 1.3 Parentage Analysis…………...... …….p.18 o 1.4 Study Organism: Epinephelus quoyanus………………………..p.20 o 1.5 Study Site: The Great Keppel Island Group…………..….….…p.22 o 1.6 Aim of the Study……….…….…………………………...….…p.23  2. Materials and Methods………………………………………….………..p.24 o 2.1 Sample Collection………..…………………………….…...... p.24 o 2.2 Genetic Analysis……………………………...…………..…….p.26 . 2.2a Microsatellite Primer Design and Optimization…....…p.26 . 2.2b Microsatellite Diversity Analysis……………………...p.28 . 2.2c Population Structure and Parentage Analysis…………p.28  3. Results………………………..……………………………………...…...p.30 o 3.1 Genetic Analysis…………………….……………………...... p.30 . 3.1a Microsatellite Optimization……………………………….p.30 . 3.1b Microsatellite Diversity……………………………………p.30 o 3.2 Simulation and Parentage Analysis…….…………………...... p.30  4. Discussion…………………………………………….………………….p.35  5. Conclusion.………….…………………………………………………...p.36

 References………………………………………………………………….p.37  Appendices………………………………………………………..…………p.44

LIST OF ABBREVIATIONS

 1. Introduction o MPAs: Marine Protected Areas o IUCN: International Union for Conservation of Nature o PLD: Pelagic Larval Duration o GBR: Great Barrier Reef  2. Methods o DNA: Deoxyribonucleic acid o PCR: Polymerase chain reaction o K: Cluster genetically homogeneous o N: Number of Individuals o PIC: Polymorphic Information Content o HWE: Hardy-Weinberg Equilibrium o LD: Linkage Disequilibrium

o FIS: Inbreeding coefficient o LOD: Log of the odds

o NA: Number of alleles

o HO: Observed heterozygosity

o HE: Expected heterozygosity

LIST OF FIGURES

1. Equation 1: Estimation of the proportion of total recruitment assuming that

juvenile E. quoyanus sampled were a proper representation of juvenile population

in selected areas at the time of sampling. PRS = Proportion of recruitment provided

by focal reserve. PAS = Proportion of adults sampled in reserve. nA = Number of

assigned juveniles. NJS = Total number of juveniles sampled………………p.47

2. Figure 1: Map displaying the study site (The Great Keppel Island Group). Inset

showing the northeast coast of Australia. Outlined area displaying adult sampling

site (Clam Bay), red stars pin-pointing juvenile collection sites, green shaded

areas displaying no-take marine reserves, yellow shaded areas displaying

conservation park zones, blue shaded areas displaying habitat protection zones

and light blue areas displaying general use

zones………………………………………………..……………………….p.47

3. Figure 2: Population genetic differentiation with Bayesian clustering of 1 to 5

possible clusters (colors) with each vertical line representing an individual and the

probability that they could belong to a specific cluster. This plot demonstrates that

there is no structure to this population (STRUCTURE 2.3.4; burn-in = 200,000;

iterations=150,000)………………………………………………….…..…..p.48

4. Figure 3: Optimal number of clusters in population (K=1) plotting mean estimated

Ln (K) against potential number of theoretical clusters using STRUCTURE

Harvester……………………………………………………………………p.48

5. Figure 4: Distribution of simulated LOD scores from true and false Parent-

Offspring plotted against proportion of assigned individuals. Orange line

represents drop off of false parent-offspring assignments along LOD score axis.

Green line representing the number of true parent-offspring assignments.

………………………………………………………..……………………...p.49

6. Figure 5: Distribution of assigned recruits dispersal distances plotted against the

number of recruits…………………………………………………………..p.49

7. Figure 6: Top: Distribution of assigned juvenile body sizes. Green bars represent

assigned juveniles collected from 2009 and orange bars represent assigned

juveniles collected from 2008. Middle: Overall size distribution of juveniles

collected in 2008. Bottom: Overall size distribution of juveniles collected in

2009………………………………………………………………………p.49

8. Figure 7: Top: Distribution of body sizes for all collected adults. Bottom:

Distribution of body sizes for adults assigned to

juveniles……………………………………..……………………..…….p.49-50

9. Figure 8: Map of study site displaying the dispersal trajectories of juveniles. Thin

arrows represent between 1 and 4 assignments, thick arrows represent between 5

and 8 assignments. Black arrows represent connectivity between reserves as well

as self-recruitment to focal reserve and red arrows represent connectivity between

the focal reserve and fished areas. Pie chart displays percent of assigned juveniles

dispersed to specific area…………………………………………………….p.50

LIST OF TABLES

1. Table 1: Primer sequence, annealing temperature (Ta), repeat motif, reaction

concentration in µM, total allele size range in bp, number of individuals (N), total

number of alleles (Na), observed (Ho) and expected heterozygosities (He) for 14

microsatellite loci tested on 120 individual Epinephelus quoyanus sampled from

the Great Keppel Island

group…………………………………………………………………………..p. 51

2. Table 2: Table displaying Offspring ID, collection site of juvenile, area type

(fished or reserve), total length of individual in mm, and distance traveled to

collection location from original site in km. Table also displays parent ID,

collection site and total length of individual in

mm……………………………………………………………………….…….p.52

INTRODUCTION

1.1 Marine Protected Areas

The exploitation of marine resources has drastically affected marine species populations globally and continues to do so (Pauly 1995; Olden et al. 2007). With 53% of the world’s fisheries being fully exploited, and 32% overexploited or depleted, several commercially important fish populations have declined to dangerously low levels (FAO,

2010). It has even been suggested that stocks of all commercially important fish species will collapse by 2048 if this issue is not resolved or appropriately managed (Worm et al.

2006). A global assessment of fish stock biomass conducted by Myers and Worm (2003) showed that 90% of the world’s large predatory fish species are overexploited. The decline of these top predators could have a major cascading effect on food webs and shift the trophic balance throughout marine systems dramatically (Pauly et al. 1998). Top predators, however, are not alone when it comes to overexploitation as declines in abundances and biodiversity have also been observed for low trophic-level species populations (Pinksy et al. 2011). Not only are these smaller fish species fished for direct human consumption, but they are also used to feed large quantities of fish located in commercial fish farms, such as salmon and tuna which consume more than 20 times their weight in anchovies and herring.

Habitat degradation in coral reefs is also a major factor contributing to the loss of smaller fish species diversity (Wilkinson 2000; Pandolfi et al. 2003; Munday 2004;), and may in fact exacerbate and accelerate the long term effects of over-exploitation. With the human population ever expanding, as well as the reliance of many communities and

15 individuals’ livelihoods on these marine ecosystems, swift and effective management of these resources is key. In company with controlling bycatch, catch limits, setting safe bag size limits, and the strict enforcement of these limits, the establishment of marine protected areas (MPAs) is an effective method of controlling the anthropogenic threats on the world’s fish populations. According to Wood et al (2008), a Marine protected area is defined as a discrete geographic area of the sea established by international, national, territorial, tribal or local laws designated to enhance the long-term conservation of natural resources therein. Today, 0.65% of the world’s oceans and 1.6% of the total marine area within Exclusive Economic Zones are within MPAs with 0.08% and 0.2% fully protected by marine reserves respectively. Marine reserves, defined as a specific type of MPA in which all extractive uses are forbidden, are often imbedded within larger MPAs in order to more strictly limit the level of fishing pressure on a given population or area and promote population recovery (Almany et al. 2013). The potential benefits of closing areas to fishing was first suggested after the observed restoration of fishery stocks in the North

Sea during World War II when fishing there had been interrupted (Gulland 1974). Since then, a number of studies have corroborated the benefits and successes of establishing marine reserves (Palsson and Pacunski 1995; Wallace 1999; Paddack and Estes 2000), but even then the effects of overfishing are long-term as fish populations may take decades to fully recover from exploitation (McClanahan et al. 2011).

The decline of marine fish population abundances and biodiversity that can be directly attributed to anthropogenic impacts is, unfortunately, severely underrepresented in scientific literature (Menge et al. 2009) despite the high value of these marine systems

16 and their services to humans (Costanza et al. 1997). With a greater focus on terrestrial habitat fragmentation due to human related pressures as opposed to marine systems, it becomes increasingly difficult to properly design and manage MPAs as well as fisheries based on scientific research. The knowledge base of how these MPAs successfully replenishing their own stocks as well as adjacent fisheries is growing (Harrison et al.

2012; Roberts et al. 2001). Comparatively, though, there is still a lack of basic knowledge of specific aspects of the life history traits, distribution, and habitat preferences of many marine taxa as well as their dispersal patterns across multiple MPAs which greatly impede proper management of reserves (Sale et al. 2005).

1.2 Connectivity and Larval Dispersal

Dispersal is an important aspect of reef fish life history as it is a mechanism for fragmented marine populations to colonize suitable yet possibly isolated habitats.

Because most reef fishes have a relatively sedentary and benthic adult phase, most of this dispersal takes place during the early stages of the fish’s life called the pelagic larval phase. This phase succeeds hatching from the egg and precedes the settlement of the juvenile recruits to suitable habitats (Leis 2003). During this phase, the larvae are transported by ocean currents into open water where, depending on the species (and the temperature), they will spend anywhere from two to twenty weeks before settling on a suitable coral reef. This transfer of fish larvae from one location to the next insures that genetic material is exchanged between populations, effectively increasing (or at least maintaining) their genetic diversity and persistence through time.

Marine protected areas can theoretically replenish populations both inside and far outside of their boundaries, but this is only facilitated by the ability of these targeted fish to both retain and export their larvae successfully. The efficiency of MPAs in this regard can only be assessed if the dynamics of reef fish metapopulation connectivity and dispersal into fished areas and to other reserves is accurately quantified. Understanding the potential dispersal distance of larvae from natal populations and their general patterns will allow those involved in MPA management to design networks of reserves that are effective in sustaining targeted fish populations (Christie et al. 2010). There is evidence that either export or self-recruitment can be dominating patterns of dispersal for certain fish populations. Assuming that larval dispersal is dictated predominantly by physical properties (such as ocean currents) and to a lesser degree biological properties and life history traits, these dispersal patterns can be highly variable and dependent on the physical dynamics of the specific areas in which these fish are located and thus may not have an inherent pattern that is applicable to most marine systems. In 1997, Roberts et al made that very assumption and concluded that marine populations relied heavily on connectivity and dispersal over long distances, labeling them as “open” systems after studying larval dispersal in reef fish larvae in the Caribbean as a function of oceanic currents. This model of dispersal has since been questioned and antiquated after a study by Jones et al in 1999 that used isotopic labeling of larvae otoliths to track fish dispersal.

They found that 60% of larvae returned to their natal population thus supporting the idea that self-recruitment plays a larger role in sustaining marine populations than previously thought. However, it is likely that both physical oceanographic properties as well as life history traits play equally important roles in dispersal patterns. This can pose some

18 problems when attempting to design and manage MPAs as specific knowledge of species life histories as well as physical oceanographic properties of each targeted site is needed for effective MPA management.

While single, isolated MPAs are still present, networks of MPAs are increasingly becoming the preferred method of fisheries management with the assumption that the growing patterns of high connectivity among fish populations enhance the resilience of those systems (Kaplan et al. 2009). Understanding these patterns of larval connectivity will ultimately lead to more accurate MPA network designs in terms of their optimal locations and the spacing between them. “Open” populations, where oceanographic currents transport larvae great distances, would then be managed by designing MPAs as a network of connected areas that replenish each other, while more “closed” populations could be managed through larger, yet isolated, reserves if a high percentage self- recruitment is observed. .

Larval dispersal is often assessed via the use of model simulations (Paris and Cowen,

2004) as empirical measurements are notoriously difficult to obtain. In some cases, isotopic labeling of larval otoliths (Thorrold et al. 2006) has been used to confirm larval dispersal trajectories; however, connectivity and self-recruitment can also be assessed through the use of population genetics and more specifically assessed using parentage analysis (Saenz-Algudelo et al. 2009). While application of this method is relatively novel in the marine realm, it shows promising results regarding the ability to detect both small and large scale connectivity patterns despite the inherent difficulty in completely sampling most populations of marine organisms (e.g., Planes et al. 2009).

1.3 Parentage Analysis

The use of genetics in assessing the dynamics of populations inhabiting ecological systems is becoming more and more frequent and can accurately provide important information about such systems in order to aid in their conservation. This field of molecular ecology grants the ability to pinpoint areas of decreased genetic diversity and even map out patterns of dispersal in highly variable marine systems in hopes of counteracting the loss of biodiversity and heightened extinction rates of certain species due to certain anthropogenic effects.

One powerful tool within genetics is the use of parentage analysis which aims to clear up uncertainties pertaining to the assignment of juveniles to their potential parents within populations based on previously established ideas of Mendelian segregation and general population genetic polymorphisms. Parentage analysis uses microsatellites

(tandem repeats of one or more nucleotides) found at multiple loci within the genome of a given species to genotype each individual effectively “fingerprinting” their DNA and allowing for the assignment of juveniles to their parents following the assumptions of

Mendelian inheritance. There are multiple methods of carrying out parentage analyses including categorical likelihood, genotype reconstruction, and exclusion, the categorical likelihood method has been the most commonly used (Jones et al. 2010). According to

Meagher and Thompson 1986, this method chooses the single most likely parent from a group of nonexcluded putative parents with the assumption that different parental genotypes differ in their probability of producing the genotype of a particular juvenile.

Not only can this method be used to assign parent pairs, but it also can be used to assign a single parent for a proposed juvenile

Despite the apparent reliability of this method of parentage analysis (Saenz-Agudelo et al. 2009) there are also a few notable limitations. According to Harrison et al (2013) the accuracy of these likelihood based assignments is greatly affected by the number and quality of highly polymorphic microsatellite markers used as well as the proportion of the population sampled within the study site. However, because it is not yet economically feasible to sample large proportions of targeted populations or design and characterize the amount of microsatellite markers needed to reduce the probability of making a type I error, simulations of accuracy can be made using programs like FAMOZ or R. In these simulations, a predetermined number of parent and offspring assignments are assumed to be true within a given dataset before processing the same dataset through a true assignment run. The known assignments and assignments proposed by the program are then compared, allowing the assessment of the program’s assignment accuracy. This is assessed through an LOD (log of the odds) score curve given by the program, illustrating the probability that a given assignment is true. From this, a threshold LOD can be placed on future assignments given by the program on similar datasets, insuring a reduction in making type I and type II errors.

While parentage analysis has been shown to be a robust method of determining dispersal patterns, its accuracy in doing so (when using less than 20 highly polymorphic loci) is still a subject of much debate (Harrison et al. 2013). Still, the results of such analyses are a far better gauge of connectivity patterns and can be more helpful than

21 previous “best guess” estimates used to determine the size, location and spacing of MPA networks (McCook et al. 2009).

1.4 Study Organism: (Epinephelus quoyanus)

Epinephelus quoyanus (Valenciennes, 1830) is a sedentary shallow-water coral reef grouper that is characterized by rounded caudal fins and close-set dark brown spots.

These groupers are sometimes referred to as ‘reticulated groupers’ (9 members, often confused in literature). This species favors silty, inshore waters and can grow to 400 mm total length (Zeng and Yang 2006; Froese and Pauly 2008c) with a reported average of

140 mm standard length (SL) in adults, and 59mm SL in juveniles (Heemstra and

Randall, 1993). Their native range occurs from southern Japan to the northeast coast of

Australia, including Taiwan, China, Hong Kong, Philippines, Viet Nam, Thailand,

Indonesia and, New Guinea (Heemstra and Randall, 1993). It is of some economic importance to Hong Kong fisheries and is also a common species for catch and release in the GBR (Diggles and Ernst, 1997). They have been observed to feed on shrimps, worms, small fishes (Apogonidae and Blenniidae), and crabs (Connell, 1998, Sadovy, 2000).

Historically, this species was one of the most common groupers in shallow and inter- tidal, eastern, waters of Hong Kong and though it is still observed to be in high abundance, a decrease in catch rate and population size across the areas it is fished (Hong

Kong and Taiwan) is cause for some concern (Sadovy and Cornish 2000). Categorized as

“least concern” according to the IUCN criteria due to its location in well protected marine reserves in Hong Kong, Queensland, Western Australia, and the Northern Territory, an

22 increase in the exploitation of this species throughout much of Southeast Asia could eventually put this species at risk if not monitored. The International Union for

Conservation of Nature (IUCN) has applied a Red List criteria to 163 different grouper species to identify which species are most threatened by fishing pressure. Groupers, in general, are a valuable fishery resource and despite their economic importance, few grouper fisheries are regularly managed at a species level with many species exhibiting population declines (Sadvoy et al. 2012).

Like many groupers, this species is protogynous hermaphroditic, beginning its life as a female and later changing to male as a result of maturation or environmental stimuli.

Sexual maturity for this species is reported at 189mm SL, corresponding to about two years of age, and with this gonochoristic reproductive lifestyle, larger individuals that are the target of heavy fishing pressure could lead to a reduction of males in the population

(Hawkins and Roberts 2003; Alonzo and Mangel 2004). This could lead to potentially negative effects on the reproductive stock and replenishment of these groupers, however, some species of groupers have been observed to adjust sex ratios within the population

(Mackie 2003; Liu and Sadovy 2004) and thus the vulnerability of these species in terms of fishing pressure may be difficult to predict through reproductive traits. A firm understanding of these sexual patterns can be helpful in managing and monitoring threatened populations.

In 1998, Sadovy examined the spawning period of various grouper species in

Hong Kong, including Epinephelus quoyanus. Based on gonadosomatic indices and measured changes in oocyte diameter, spawning of E. quoyanus seems to take place from

May to September. This long spawning season, which is common in groupers (Thresher

1984; Shapiro 1987) implies that this species partakes in ‘batch spawning’, effectively allowing them to spawn multiple times within a spawning season. Generally, groupers spawn on offshore reefs, producing pelagic larvae with a pelagic larval duration (PLD) of

30-40 days that may disperse over hundreds of kilometers. However, recent research suggests that groupers and other reef fishes may in fact retain at least some proportion of their offspring in the natal reef area (Jones et al. 1999; Jones, Planes and Thorrold, 2005).

1.5 Study Site: The Great Keppel Island Group

. The Great Keppel Island Group is a cluster of islands in the Great Barrier Reef

(GBR) that lies 15 kilometres off the Capricorn Coast of Central Queensland and is home to some of the highest cover of coral reefs across the GBR. In 2004, the Great Barrier

Marine Park (GBRMP) was restructured to include a comprehensive network of MPAs with the aim of providing sustainable fisheries that gather fish stock from reserve

“spillover” and protecting the biodiversity of the reef. These MPA networks include no- take reserves which include 33.4% of the GBR, which is considered to be the largest network of no-take marine reserves in the world. The Great Keppel Island Group is one such area that is surrounded by six no-take marine reserves as well as multiple protected yet fished areas. Until recently, there was very little information on the dispersal, self- recruitment and connectivity patterns of coral reef fishes in this area. In 2012, Harrison et al conducted a study on the self-recruitment of coral trout (Plectropomus maculatus) and stripey snapper (Lutjanus carponotatus) within three of the six no-take reserves and their connectivity and larval export to neighbouring reserves as well as neighbouring fisheries.

They findings demonstrate that, at least for this specific study site, these reef fishes disperse relatively short distances despite their lengthy pelagic larval phase as well as retain higher biomass within the reserves as compared with fished areas. Their results also provide compelling evidence that these reserve networks and the populations within them can contribute significantly to replenishment of fish stocks both within the reserves and to fished areas on a scale that is beneficial to the local fishing community.

1.6 Aim of the study

The aim of this study is to assess the patterns of dispersal and connectivity of E. quoyanus within one focal no-take marine reserve around Clam Bay and into adjacent fisheries and reserves within the Great Keppel Island Group through parentage analysis.

Though E. quoyanus is not one of the 20 species of grouper considered at risk for extinction (Epinephelus epistictus, also known as the Strawberry Grouper) or even near- threatened (22 species), studying their patterns of dispersal may be helpful in managing populations of closely related species in other similar systems. this study aims to add to the wealth of knowledge provided by Harrison et al concerning overall dispersal patterns of different species of fish in that particular island group. The ability to assess dispersal patterns of multiple species in a particular site will help determine whether or not connectivity of these reserves can be attributed predominantly to physical phenomena, biological processes and life history traits, or a mixture of the two. A firm grasp of the degree to which these processes affect dispersal patterns in this system will aid in the management and possible restructuring of its no-take marine reserves.

MATERIALS AND METHODS

2.1 Sample Collection

Sampling of the longfin grouper (Epinephelus quoyanus) was conducted at four reefs (Egg Rock, Middle Island, Halfway Island, and Clam Bay) within three no-take marine reserves in the Great Keppel Island group (Figure 1) located in the Great Barrier

Reef (23°10ʹ S, 150°57ʹ E). Within this island group, there are six no-take reserves that had been protected for slightly less than 20 years before the time of this study (Harrison et al. 2012). The available coral reef area at each location was calculated by Harrison et al. in 2012 from satellite imagery in ArcGIS (ESRI, Redlands) by drawing concentric rings from the parameter of each of the no-take reserves with increasing distances of up to 30 km. The total reef area calculated within the study site was 700.83 hectares, 196.04 hectares of which were within the six no-take reserves (~28%). The main reserve from which adults in this thesis were sampled from contains a total of 59.42 hectares of coral reef which accounted for 30.31% of the no-take reserve reefs in the Great Keppel Island group.

A total of 3091 adult longfin groupers (representing a total of 73.06% of the adult population within those reefs with SD±16.35 and SE±8.18) were captured between

November 2007 and February 2008. Once captured, the total length (TL) and fork length

(FL) of each fish was measured to the nearest millimeter. A pectoral fin-clip was then taken from each fish before being tagged with a uniquely-numbered and brightly-colored t-bar tag (Hallprint, Australia). The fish were then released alive and in good condition at

26 the capture site. All fish were captured via barbless hook and line that were baited with

Australian Sardines (Sardinops sagax). For this thesis, however, a subsample of 950 adults were taken from two reefs within one of the three focal no-take marine reserves

(570 from Clam Bay and 380 from Halfway Reef) which represents 80.77% of the adult population within the one reserve around Clam Bay and 22.16% of the adults across the three focal reserves.

During May 2008 and February 2009, 942 juvenile longfin groupers were collected from 17 different locations throughout the island group. As previously stated with the adults, the total length (TL) and fork length (FL) of each juvenile was measured to the nearest millimeter. Juveniles were collected via SCUBA using a variety of methods, including spears, barrier nets, and hand nets. As with the adults, a small clip of the pectoral fin was removed from the juvenile fish and stored in 85% ethanol for genetic analysis. As with the adults, a subsample of 478 juveniles were taken from the overall individuals collected for analysis in this thesis. Juvenile subsampling was taken from 6 different locations including 2 of which that were located within no-take marine reserves

(17 from Middle Island and 300 from Clam Bay) and four located within fished areas (99 from Halfway Reef, 34 from North Keppel Island, 5 from Pumpkin Island and, 23 from

Humpy Island).

The population density estimates were calculated from counts taken through underwater visual census (UVC) surveys at 22 sites within the no-take reserves and fished zones located in the Keppel Island group in December 2007. The following

February, repeated timed swim UVC surveys (a total of 10 replicates) were conducted

27 within the three focal reserves, with the replicate UVC counts averaged to estimate the relative proportion of tagged to untagged individuals and infer population size.

2.2 Genetic Analysis:

2.2a Microsatellite Primer Design and Optimization

Genomic DNA was extracted from the fin clips taken from each individual using

DNeasy kits (Qiagen) following to the manufacturer’s protocol. Extracted DNA quality was then determined using a NanoDrop 8000 spectrophotometer (Thermo Scientific) before selecting one E. quoyanus sample to be sequenced using a Rocher 454 GS FLX

(titanium) sequencer at the King Abdullah University of Science and Technology

(KAUST) Bioscience Core Lab (BCL). Over 200,000 raw reads were generated with an average length of 350 bp. Putative dinucleotide and tetranucleotide repeat motifs were then mined from the raw, unassembled reads generated via 454-pyrosequenceing using the default settings on the software MSATCOMMANDER v 1.0.8 (Faircloth 2008). A total of 11,530 microsatellite loci were obtained through this screening, and subsequently run through the MSATCOMMANDER Primer3 plug-in (Rosen and Skaletsky 2000) in order to design primers for all suitable repeat motifs. Out of the primers designed, 96 were selected to be tested with polymerase chain reaction (PCR) trials. These 96 microsatellite loci were tested for amplification and polymorphism on 12 individuals collected from Halfway Reef.

PCR testing of the 96 selected loci were performed following the standard Qiagen

Multiplex PCR kit protocols. Each 10 μl PCR reaction consisted of 5 μl of the Qiagen

Multiplex PCR Master Mix, 1 μl of primer mix (containing the forward and reverse primers, each at a concentration of 1 μM), 3 μl of nuclease-free water, and 1 μl of genomic DNA (30-150 ng/μl). PCRs were performed using a GeneAmp PCR 9700 system (Applied Biosystems) according to the following Hot Start and Touchdown PCR protocol: Initial denaturation of 95 °C for 15 min followed by 4 cycles of 94 °C for 30 s with annealing temperature stepdowns of 0.5 °C for every cycle (from 61 °C to 59 °C for

90 s) followed by 72 C for 60 s. The annealing temperature for the final 26 cycles was 59

°C with denaturation and extension phases as previously stated. PCR products were then screened for polymorphisms and primer specificity using a QIAxcel genetic analyzer

(Qiagen) paired with a High Resolution gel cartridge kit (1200) (Qiagen). Of the 96 loci tested, 44 successfully amplified and showed polymorphism for every sample. Of these

44 primers, 36 were labeled with ABI fluorescent dyes (6-FAM, NED, PET, and VIC) and formed into 3 multiplex mixes of 12 primers each. For this study, subsets of 10 microsatellite loci were used in 2 mixes for a total of 20 loci. After amplification, PCR products were then diluted with 130 μl Milli-Q water with 1 μl of this dilution mixed into a 10 μl 500-LIZ size standard (GeneScan) and Hi-Di Formamide solution (Applied

Biosystems) (4.5 μl per 1 μl Formamide) and subsequently run on an ABI 3730xl DNA

Analyzer (Applied Biosystems) for fragment analysis. Individual genotypes were then scored using GENEMAPPER v 4.0 (Applied Biosystems) with unique alleles distinguished using marker specific binsets created in MSATALLELE v 1.02 (Alberto

2009).

2.2b Microsatellite Diversity Analysis

Allelic frequencies, number of alleles (Na), Nei’s unbiased observed (H0) and expected heterozygosities (He) (Nei 1987), as well as Weir & Cockerham’s estimator of the inbreeding coefficient FIS (Weir and Cockerham 1984) were estimated for each locus on 120 adult long fin groupers sampled from Clam Bay using GenAlEx v 6.5 (Peakall and Smouse, 2012). Linkage disequilibrium (LD) and deviations from Hardy-Weinberg equilibrium (HWE) were measured using GenePop v 4.3 (Rousset 2008) with 10,000 and

5,000 dememorizations, 5,000 and 1,000 batches, and 10,000 and 10,000 iterations respectively. The significance of multiple P-values (α= 0.05) was determined for multiple comparisons through the use of the strict Bonferroni correction method (P < 0.005; Rice

1989). The program MICROCHECKER v 2.2.3 (van Oosterhaut et al. 2004) was used to identify and verify deviations from HWE that could be explained by the presence of null alleles. Finally, the polymorphic information content (PIC) for each locus in each mix was calculated using CERVUS 3.0 (Kalinowski et al. 2007). Genotyped individuals with a maximum of 2 loci of missing data were considered for the final analysis.

2.2c Population Structure and Parentage Analysis

In order to determine the number of genetically homogenous populations (K) possibly present in the subsampled individuals, a Bayesian analysis was run through the use of the software STRUCTURE v2.3.4 (Pritchard et al. 2000). A likelihood value was assigned to each possible number of populations (K= 1 – 5) after 100,000 iterations that were run at a burn-in of 200,000.

Parent-offspring relationships were then determined based on the maximum likelihood analysis used in the software FAMOZ (Marshall et al. 1998, Gerber et al. 2003).

The program calculates the log of the odds (LOD) scores for assigning individuals to possible parents based on observed allelic frequencies for each locus. Minimum LOD score thresholds for accepting offspring assignments to single parents and parent pairs were determined from the distribution of LOD scores computed for 50,000 known parent-offspring pairs as well as 50,000 known unrelated pairs.

To estimate the proportion of total recruitment (PRT) in the study area supplied by the focal marine reserve, I use the methods of estimating PRT reported in Harrison et al. (2012) (Equation 1). Based on this method, I assumed that all adult Epinephelus quoyanus within the reserve had an equal probability of contributing to local recruitment and that the juveniles sampled represented a random sample of all juveniles in the study area at the time of sampling. Since only a fraction of the reproductive adults within focal reserve were sampled, the observed proportion of assigned juveniles (nA/ NJS) represents only a fraction of the total progeny of the focal reserve (PRS). An estimate of the proportion of missed assignments can be calculated based on the probability that neither

2 parent of unassigned juveniles were sampled, or (1-PAS) , where PAS is the proportion of the population sampled. From the observed number of assigned juveniles (nA) and the total number of juveniles sampled (NJS), I then calculated the proportion of the juveniles that would be expected to be assign from adults in the reserve had I sampled 100% of the population. The dispersal distance for each assigned juvenile was then measured as the distance in kilometers between the reefs where the offspring and parent were sampled.

RESULTS

3.1 Genetic Analysis

3.1a Microsatellite Design and Optimization

Due to difficulties with genotyping, out of the 2 microsatellite multiplex mixes, 4 microsatellite loci were excluded from Mix 1 (Eq13, Eq32, Eq47, and Eq92) and 2 were excluded from Mix 2 (Eq37 and Eq45). This left 2 mixes of 6 and 8 microsatellite loci each that were used to genotype all individuals subsampled (Table 1).

3.1b Microsatellite Diversity

The total number of alleles (Na) found for all loci in both mixes ranged from 11 to

26 with a mean allelic diversity of 18.35 while observed (Ho) and expected (He) heterozygosities ranged from 0.669 – 0.942 (mean = 0.814) and 0.727 – 0.928 (mean =

0.855) respectively. No linkage disequilibrium was observed between any loci while six loci did significantly deviate from HWE (Eq20, Eq28, Eq35, Eq52, Eq60 and, Eq90).

This departure from Hardy Weinberg equilibrium can be explained through the possible presence of null alleles in 3 loci (Eq52, Eq60 and, Eq90) as confirmed through the use of

MICROCHECKER v 2.2.3. The FIS calculated did not significantly deviate from 0 suggesting that there is little inbreeding in the population. Finally, the polymorphic information content for all loci (PIC) ranged from 0.7084 and 0.9206 with a mean PIC of

0.8387 indicating that the markers used for this study are highly informative.

3.2 Simulation and Parentage Analysis

Out of the 950 adult Epinephelus quoyanus subsampled, only 610 individuals were used for the final parentage analysis due to missing data at more than 3 loci for the

340 individuals. All 478 juveniles were genotyped with less than 2 loci of missing data and thus were all used for the final analysis. Population cluster (K) results obtained from

STRUCTURE confirmed that there is a K of 1 indicating a high likelihood that all subsampled individuals, both adults and juveniles, comprise of only one population

(Figures 2-3).

The simulation in FAMOZ estimated the probability of falsely accepting (type I error) or excluding (type II error) parent-offspring pairs associated with the certain LOD thresholds. Minimum LOD score thresholds for single-parent assignments were identified as 5.0 and 20.0 for the assignment to parent-pairs. The resulting probability of assigning a juvenile to a parent that was not its true parent, knowing that the true parent was not sampled was 2.00% (type I error). Conversely, the probability of a true parent-offspring pair not being identified knowing that the true parent was sampled was less than 0.01%

(type II error). All collected juveniles were screened against the total pool of adult samples to identify parent-offspring relationships. Any assigned parent-offspring pairs with over three confirmed mismatches between parent and offspring genotypes were excluded from the final list of assigned pairs. A total of 25 unique parent-offspring assignments were accepted out of the 478 juveniles genotyped. Of the 25 juveniles assigned, 8 were found in the natal reserve. Considering that only 37.6% of the adult population was genotyped, self-recruitment is estimated to be 6.9% assuming 100% of the population could be sampled (Table 2). Out of the other 161 juveniles sampled in fished areas, 16 were assigned accounting for 9.93% (26.43% scaled up to estimated full

33 population) of larval export to adjacent fisheries. One assignment was made from 17 individuals genotypes from Middle Island (one of the other six no-take marine reserves) accounting for 5.88% (scaled up to 15.63%) of export to that reserve. In all, 32% of the assigned juveniles were located within the natal reserve while 4% were found in an adjacent reserve, and 64% were found in surrounding fisheries. The individuals identified as parents of offspring (mean =296.64 ± 28.42 mm) were on average larger than the adults in the subsampled population (mean = 294 ± 33.03 mm) (Figure 7) but not significantly so (p > 0.05). Average dispersal distance and body length of assigned juveniles is 8.29 km and 110.2 mm respectively.

DISCUSSION

The results from this study suggest that ~32% of the offspring generated form adults within the focal no-take marine reserve were locally retained while ~68% dispersed to other areas including four fished areas and one other no-take reserve. Given that only

37.6% of the adults in the population were sampled, it is likely that missing juvenile assignments belong to adults that have no yet been sampled, which could account for the low levels of self-recruitment. When compared with the study conducted by Harrison et al in 2012 where 30% of coral trout and 64% of stripey snapper within reserves was supplied through self-recruitment, there does not seem to be any similarity between recruitment patters on the three species within reserves (Figure 8). However, these results should be interpreted with caution as this study only focuses on one no-take reserve with a sample size of 610 adult E. quoyanus genotyped with a panel of 14 microsatellite loci whereas Harrison et al focuses on three no-take marine reserves with sample sizes of 466 adult P. maculatus and 1,154 adult L. carponotatus with a panel of 11 and 13 microsatellite loci respectively. With the low number of polymorphic microsatellite markers used in this study coupled with the limited sample size, as well as an arbitrarily assigned LOD threshold value (Figure 4) it is possible that some of the juvenile assignments made in this study are false, thus skewing the estimations for self- recruitment and contribution to fisheries.

In terms of dispersal distance, it is evident from my results that assigned juveniles display a bimodal distribution with most individuals either settling close to or within the natal reserve or dispersing up to 18 km. This does not follow the overall trend for coral trout and stripey snapper in the same area which display dispersal distances skewed to the left

35 with the majority of the juveniles settling close to or within their natal areas and decreasing numbers settling farther away (up to 30 km). My results are also inconsistent with other studies that show that fish larvae might disperse short distances despite their lengthy pelagic larval phase (Jones et al 2009). There also seems to be no correlation between juvenile body size (or age) and dispersal distance (Figure 5), suggesting that there is no clear ‘window’ of connectivity or larval dispersal and that connectivity is continuous throughout this system. The lack of a difference between the mean body size of adults with assigned offspring when compared with the overall sampled adult population suggests that all adults can potentially reproduce with no indication of body size dictating reproductive viability (Figure 7).

While the results of this study show relatively low self-recruitment into the focal reserve indicating that the replenishment of the adult stock within the reserve might suffer, is shows some promise of connectivity between reserves for this species. It is also possible that the juveniles found within the reserve are being supplied by other fished areas and no-take reserves. These results can be seen as a proof of concept, in that when added to the wealth of knowledge generated for fish dispersal and connectivity in that area, a trend emerges suggesting that the no-take reserves put into place in the Great Keppel Island

Group are indeed serving their purpose and acting as proper refuges for fish populations both within fished areas and across multiple reserves. It is also evident from the aggregated dispersal patterns that at least for the Great Keppel Island region, a general northern trend can be observed in terms of larval dispersal, with many assigned juveniles found to be dispersed to island clusters in the northern part of the region.

CONCLUSION

It is not unreasonable for self-recruitment to be relatively low in this species considering that they are pelagic spawners with PLDs of up to 30-40 days. Also, with the minimum catch size of groupers in this area just above the maximum observed body length of Epinephelus quoyanus, fishing pressure for this species almost nonexistent in the Great Keppel Island group. We can thus expect this population to be almost completely untouched and natural with nothing to potentially limit their natural dispersal patterns. This study only scratches the surface of the possible information to be gathered from studying the recruitment of this species in this particular area. Future work could include a greater sample size as well as sampling adults from multiple reserves and fished areas in order to properly quantify the level of recruitment to the entire area, as opposed to just one reserve. This, coupled with the use of many more hyper variable microsatellite markers might give us insight into the finer scale population structure of this species.

This can then serve as a baseline of information to then judge the health and sustainability of other, more severely fished, species populations in the region.

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APPENDICES

Figure 1

Figure 2

Figure 3

Figure 4

0.9 FaMoz Simulation

0.8

0.7

0.6

0.5

0.4

0.3 Proportion of Assignments of Proportion 0.2

0.1

0 0 5 10 15 20 25 30 35 LOD Score

Figure 5

12 Recruit Dispersal Distances

Number of Recruits of Number 4

0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Distance (km)

Figure6

6 Distribution of Assigned Juvenile Body Sizes

3 2009

2 2008 Number of Juveniles of Number

0 0 25 50 75 100 125 150 175 200 225 250 275 Total Length (mm)

Juvenile Body Size Distribution (2008) 30

10 Number of Juveniles of Number 5

0 0 25 50 75 100 125 150 175 200 225 250 275 300 Total Length (mm)

Juvenile Body Size Distribution (2009) 16

Number of Juveniles of Number 4

0 0 25 50 75 100 125 150 175 200 225 250 275 300 Total Length (mm)

Figure 7

200 Adult Body Size Distribution 180

160

140

120

100

80 Number of Individuals of Number 60

0 150 175 200 225 250 275 300 325 350 375 Total Length (mm)

Parents Body Size Distribution 12 11 10

9 8 7 6 5 4

Number of Individuals of Number 3 2 1 0 150 200 250 300 350 Total Length (mm)

Figure 8

Table 1

Size Repeat Reaction Locus Primer sequence (5′-3′) T (°C) range N Na Ho He P value a motif con. (µM) (bp) Mix 1 F: GGCAGAGGTTAAATCAGAGCAG Eq22 (AC) 0.02 278-355 120 10 0.802 0.81 0.5036 R: AGTTGGACTGCAATGTGATCAG 59 18 F: CCACAGTAGTTCAGGAGCTTTG Eq24 (AC) 0.02 153-194 120 22 0.711 0.727 0.1663 R: CCACAGTAGTTCAGGAGCTTTG 59 17 F: AACAGATTCTTGCTGACGTCAC Eq30 (AC) 0.02 99-151 113 20 0.833 0.871 0.1834 R: GCTCTGCAGATGACACCTTAAG 59 17 F: CCCATATGTAAAGAACCAGCCG Eq35 (AC) 0.02 129-183 120 17 0.81 0.857 0.0045 R: AGAGCTCCACACATGCACG 59 16 F: ACTGAGATGACTGCAGGGATC Eq46 (AC) 0.02 212-233 120 20 0.785 0.84 0.1579 R: GCTCTTATTATCTCACGCTGCC 59 16 F: CGTCGAACAGCCTCATAAAGAC Eq60 (AC) 0.02 209-261 118 26 0.807 0.928 0.000 R: GTGTGCATGTTGTCCTGTAAG 59 21 F: ACGTTGAGGGCTGCACAC Eq89 (AC) 0.02 99-155 120 23 0.884 0.887 0.1688 R: GAAGGACGAGTTAATGGGCAAG 59 18 F: TCTGGCTGTGATTTAATCGTGC Eq90 (AC) 0.02 88-123 120 13 0.669 0.847 0.0001 R: CTCATATACAGCAGAACCAGGC 59 18 Mix 2 F: TCCTGTGTCACCAGGTCTTAAG Eq20 (AC) 0.02 203-253 120 20 0.917 0.921 0.0108 R: TGAGCAGATAGAGGGACCTTTG 59 18 F: GGCTTTGGATGGATAGCTGATG Eq28 (AC) 0.02 100-150 111 11 0.703 0.761 0.0282 R: TTATTAGGTGAGAGAGGGCAGC 59 17 F: AGGCGAGCACATTCAGGG Eq51 (AC) 0.02 187-252 120 17 0.909 0.883 0.8742 R: TCTGTCCTTGTCTTCGTCCTTC 59 25 F: TTAACACCCTGATTCACACGTC Eq52 (AC) 0.02 273-331 120 19 0.802 0.891 0.0019 R: ACCACATGTATCACTGTCACTC 59 23 F: TGGTGAGAAGAGAGAACGTCAC Eq65 (AC) 0.02 199-241 120 21 0.826 0.854 0.1810 R: CATTGATGAGTTTCCCTGGCAG 59 20 F: CACTCAATCATGCCTCAGGAAG Eq66 (AC) 0.02 107-171 120 18 0.942 0.899 0.7424 R: GAAGAGGAGGAGGTGCATCAAC 59 20 P values of significant deviations from Hardy-Weinberg equilibrium after strict Bonferroni corrections (p < 0.004), shown in bold

Table 2

Offspring ID Collection Site Area Type TL (mm) Approximate Distance Parent ID Collection Site TL (mm) Traveled (km) Juve_13 Humpy Island Fished 224 2.60 Adult_475 Clam Bay 305 Juve_48 Middle Island Reserve 100 8.04 Adult_532 Clam Bay 320 Juve_58 Halfway Island Fished 169 1.30 Adult_16413 Halfway Island 320 Juve_78 North Keppel Island Fished 90 18.80 Adult_26300 Halfway Island 284 Juve_80 North Keppel Island Fished 123 18.80 Adult_26302 Halfway Island 270 Juve_82 North Keppel Island Fished 80 18.80 Adult_26304 Halfway Island 300 Juve_83 North Keppel Island Fished 115 18.80 Adult_26315 Halfway Island 260 Juve_86 North Keppel Island Fished 119 18.80 Adult_26365 Halfway Island 330 Juve_87 North Keppel Island Fished 93 18.80 Adult_26421 Halfway Island 285 Juve_88 Pumpkin Island Fished 88 17.00 Adult_26427 Halfway Island 223 Juve_89 Pumpkin Island Fished 89 17.00 Adult_26428 Halfway Island 300 Juve_93 Pumpkin Island Fished 93 17.00 Adult_26510 Halfway Island 300 Juve_94 North Keppel Island Fished 94 18.80 Adult_26511 Halfway Island 297 Juve_95 North Keppel Island Fished 95 18.80 Adult_16034 Halfway Island 298 Juve_112 Clam Bay Reserve 116 1.30 Adult_19264 Halfway Island 226 Juve_114 Clam Bay Reserve 125 1.30 Adult_19279 Halfway Island 290 Juve_125 Clam Bay Reserve 67 0 Adult_15724 Clam Bay 285 Juve_126 Clam Bay Reserve 125 1.30 Adult_25095 Halfway Island 304 Juve_127 Clam Bay Reserve 127 1.30 Adult_25006 Halfway Island 313 Juve_133 Clam Bay Reserve 138 1.30 Adult_19284 Halfway Island 290 Juve_486 Halfway Island Fished 93 0.50 Adult_16094 Halfway Island 315 Juve_578 Halfway Island Fished 61 1.30 Adult_1584 Clam Bay 320 Juve_649 Clam Bay Reserve 115 1.30 Adult_1830 Halfway Island 325 Juve_738 Halfway Island Fished 68 0.50 Adult_25970 Halfway Island 320 Juve_837 Clam Bay Reserve 148 0 Adult_478 Clam Bay 336